From 61954536c2c0d94654a85ca5dde7958575afc234 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Mon, 8 Mar 2021 14:28:12 -0300
Subject: [PATCH 01/27] added n steps ahead prediction

---
 sysidentpy/polynomial_basis/narmax.py | 45 +++++++++++++++++++++++++--
 1 file changed, 42 insertions(+), 3 deletions(-)

diff --git a/sysidentpy/polynomial_basis/narmax.py b/sysidentpy/polynomial_basis/narmax.py
index d63bac48..2dc5ae2a 100644
--- a/sysidentpy/polynomial_basis/narmax.py
+++ b/sysidentpy/polynomial_basis/narmax.py
@@ -424,7 +424,7 @@ def _unbiased_estimator(
 
         return unbiased_theta[0: len(self.final_model), 0].reshape(-1, 1)
 
-    def predict(self, X, y):
+    def predict(self, X, y, steps_ahead=None):
         """Return the predicted values given an input.
 
         The predict function allows a friendly usage by the user.
@@ -447,8 +447,12 @@ def predict(self, X, y):
             The predicted values of the model.
 
         """
-        yhat = self._model_prediction(self.final_model, X, y, self.theta)
-        return yhat
+        if steps_ahead is None:
+            return self._model_prediction(self.final_model, X, y, self.theta)
+        elif steps_ahead == 1:
+            return self.one_step_ahead_prediction(X, y)
+        else:
+            return self.n_step_ahead_prediction(X, y, steps_ahead=steps_ahead)
 
     def _code2exponents(self, code):
         """
@@ -484,6 +488,41 @@ def _code2exponents(self, code):
 
             return exponents
 
+    def one_step_ahead_prediction(self, X, y):
+        X_base = InformationMatrix().build_information_matrix(
+            X, y, self.xlag, self.ylag, self.non_degree
+        )
+        piv_final_model = self.pivv[:len(self.final_model)]
+        X_base = X_base[:, piv_final_model]
+        yhat = np.dot(X_base, self.theta.flatten())
+        yhat = np.concatenate([y[:self.max_lag].flatten(), yhat])
+        return yhat.reshape(-1, 1)
+
+    def n_step_ahead_prediction(self, X, y, steps_ahead):
+        if len(y) < self.max_lag:
+            raise Exception("Insufficient initial conditions elements!")
+
+        yhat = np.zeros(X.shape[0], dtype=float)
+        yhat.fill(np.nan)
+        yhat[: self.max_lag] = y[: self.max_lag, 0]
+        i = self.max_lag
+        X = X.reshape(-1, self._n_inputs)
+        while i < len(y):
+            k = int(i - self.max_lag)
+            if i + steps_ahead > len(y):
+                steps_ahead = len(y) - i
+
+            yhat[i:i+steps_ahead] = self._model_prediction(
+                self.final_model,
+                X[k:i+steps_ahead],
+                y[k:i+steps_ahead],
+                self.theta)[-steps_ahead:].ravel()
+
+            i += steps_ahead
+
+        yhat = yhat.ravel()
+        return yhat.reshape(-1, 1)
+
     def _model_prediction(self, model_elements, X, y_initial, theta):
         """Perform the infinity steps-ahead simulation of a model.
 

From 83f2a048213bf1207f5717c321fdc03effb3f60f Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Mon, 8 Mar 2021 22:18:58 -0300
Subject: [PATCH 02/27] updated examples

---
 docs/.buildinfo                               |     2 +-
 docs/_config.yml                              |     8 +-
 docs/_images/basic_steps_16_0.svg             |  4770 ++++----
 docs/_images/basic_steps_18_1.svg             |  1416 ++-
 docs/_images/basic_steps_22_1.svg             |  1476 ++-
 docs/_images/f_16_benchmark_14_0.svg          | 10090 ++++++++++++++++
 docs/_images/f_16_benchmark_16_1.svg          |   805 ++
 docs/_images/f_16_benchmark_6_1.svg           |  1386 +++
 docs/_images/f_16_benchmark_7_1.svg           |  1787 +++
 docs/_images/multiple_inputs_example_10_1.svg |  1476 ++-
 docs/_images/multiple_inputs_example_9_1.svg  |  4813 ++++----
 docs/_modules/index.html                      |    76 +-
 docs/_modules/sysidentpy/base.html            |    76 +-
 .../sysidentpy/general_estimators/narx.html   |    76 +-
 .../sysidentpy/metrics/_regression.html       |    76 +-
 .../parameter_estimation/estimators.html      |    76 +-
 .../sysidentpy/polynomial_basis/narmax.html   |   170 +-
 .../residues/residues_correlation.html        |    76 +-
 .../sysidentpy/utils/_check_arrays.html       |    76 +-
 .../sysidentpy/utils/generate_data.html       |    76 +-
 docs/_sources/examples/basic_steps.ipynb.txt  |    70 +-
 .../examples/f_16_benchmark.ipynb.txt         |   313 +-
 .../multiple_inputs_example.ipynb.txt         |    32 +-
 docs/_static/basic.css                        |     2 +-
 ...index.f658d18f9b420779cfdf24aa0a7e2d77.css |     6 +
 docs/_static/doctools.js                      |     9 +-
 docs/_static/js/index.d3f166471bb80abb5163.js |    32 +
 docs/_static/language_data.js                 |     6 +-
 docs/_static/mystnb.css                       |     1 -
 docs/_static/pygments.css                     |     6 +-
 docs/_static/searchtools.js                   |    28 +-
 ...theme.37f24b989f4638ff9c27c22dc7559d4f.css |     5 +
 ...-theme.7d483ff0a819d6edff12ce0b1ead3928.js |    36 +
 docs/_static/underscore-1.12.0.js             |  2027 ++++
 docs/_static/underscore.js                    |    37 +-
 docs/_static/webpack-macros.html              |     6 +-
 docs/changelog/v0.1.3.html                    |    76 +-
 docs/changelog/v0.1.4.html                    |    76 +-
 docs/code.html                                |   196 +-
 docs/dev_guide.html                           |    76 +-
 docs/examples/basic_steps.html                |   105 +-
 docs/examples/basic_steps.ipynb               |  4212 +------
 docs/examples/defining_lags.html              |    74 +-
 docs/examples/defining_lags.ipynb             |    35 +-
 docs/examples/extended_least_squares.html     |    74 +-
 docs/examples/extended_least_squares.ipynb    |  1166 +-
 docs/examples/f_16_benchmark.html             |   100 +-
 docs/examples/f_16_benchmark.ipynb            |   308 +-
 docs/examples/general_estimators.html         |    76 +-
 .../information_criteria_examples.html        |    74 +-
 .../information_criteria_examples.ipynb       |  3403 +-----
 docs/examples/multiple_inputs_example.html    |    86 +-
 docs/examples/multiple_inputs_example.ipynb   |  3455 +-----
 docs/examples/narx_neural_network.html        |    74 +-
 docs/examples/parameter_estimation.html       |    74 +-
 docs/examples/parameter_estimation.ipynb      |    53 +-
 docs/genindex.html                            |    76 +-
 docs/index.html                               |    76 +-
 docs/installation.html                        |    76 +-
 docs/introduction_to_narmax.html              |    74 +-
 docs/notebooks.html                           |    76 +-
 docs/py-modindex.html                         |    76 +-
 docs/search.html                              |    77 +-
 docs/searchindex.js                           |     2 +-
 docs/source/conf.py                           |     4 +-
 docs/source/examples/basic_steps.ipynb        |    70 +-
 docs/source/examples/f_16_benchmark.ipynb     |   313 +-
 .../examples/multiple_inputs_example.ipynb    |    32 +-
 docs/user_guide.html                          |    74 +-
 69 files changed, 24457 insertions(+), 21785 deletions(-)
 create mode 100644 docs/_images/f_16_benchmark_14_0.svg
 create mode 100644 docs/_images/f_16_benchmark_16_1.svg
 create mode 100644 docs/_images/f_16_benchmark_6_1.svg
 create mode 100644 docs/_images/f_16_benchmark_7_1.svg
 create mode 100644 docs/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css
 create mode 100644 docs/_static/js/index.d3f166471bb80abb5163.js
 create mode 100644 docs/_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css
 create mode 100644 docs/_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js
 create mode 100644 docs/_static/underscore-1.12.0.js

diff --git a/docs/.buildinfo b/docs/.buildinfo
index c867b395..8b28a3fe 100644
--- a/docs/.buildinfo
+++ b/docs/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9b48c8eab5647bb95497138fdc22eec8
+config: 27f8835bea0e2daee3a03c91e8d98724
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_config.yml b/docs/_config.yml
index c4192631..84bbafb5 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -1 +1,7 @@
-theme: jekyll-theme-cayman
\ No newline at end of file
+theme: jekyll-theme-cayman
+repository:
+  url                       : https://github.com/wilsonrljr/sysidentpy  # Online location of your book
+  path_to_book              : /docs  # Optional path to your book, relative to the repository root
+  branch                    : step_ahead  # Which branch of the repository should be used when creating links (optional)
+launch_buttons:
+  colab_url: "https://colab.research.google.com"
\ No newline at end of file
diff --git a/docs/_images/basic_steps_16_0.svg b/docs/_images/basic_steps_16_0.svg
index 827a7797..1842c7cc 100644
--- a/docs/_images/basic_steps_16_0.svg
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diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 3f8857ce..c738e78f 100644
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+++ b/docs/_modules/index.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Overview: module code &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
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-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
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+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/index.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
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-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -309,13 +307,13 @@ <h1>All modules for which code is available</h1>
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -334,7 +332,7 @@ <h1>All modules for which code is available</h1>
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/base.html b/docs/_modules/sysidentpy/base.html
index 5e83f8be..e1e0ec69 100644
--- a/docs/_modules/sysidentpy/base.html
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@@ -7,7 +7,7 @@
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     <title>sysidentpy.base &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
     <script src="../../_static/jquery.js"></script>
     <script src="../../_static/underscore.js"></script>
     <script src="../../_static/doctools.js"></script>
-    <script src="../../_static/language_data.js"></script>
     <script src="../../_static/togglebutton.js"></script>
     <script src="../../_static/clipboard.min.js"></script>
     <script src="../../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
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+    <script src="../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/sysidentpy/base.html" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -751,13 +749,13 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -776,7 +774,7 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
     </div>
 
     
-  <script src="../../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/general_estimators/narx.html b/docs/_modules/sysidentpy/general_estimators/narx.html
index 99d4d191..66535ec9 100644
--- a/docs/_modules/sysidentpy/general_estimators/narx.html
+++ b/docs/_modules/sysidentpy/general_estimators/narx.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>sysidentpy.general_estimators.narx &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
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-    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
     <script src="../../../_static/jquery.js"></script>
     <script src="../../../_static/underscore.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/language_data.js"></script>
     <script src="../../../_static/togglebutton.js"></script>
     <script src="../../../_static/clipboard.min.js"></script>
     <script src="../../../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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+    <script src="../../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/sysidentpy/general_estimators/narx.html" />
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -501,13 +499,13 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -526,7 +524,7 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
     </div>
 
     
-  <script src="../../../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../../../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/metrics/_regression.html b/docs/_modules/sysidentpy/metrics/_regression.html
index 374c25ab..7102fa77 100644
--- a/docs/_modules/sysidentpy/metrics/_regression.html
+++ b/docs/_modules/sysidentpy/metrics/_regression.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>sysidentpy.metrics._regression &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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+    <script src="../../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/sysidentpy/metrics/_regression.html" />
     <link rel="index" title="Index" href="../../../genindex.html" />
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@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
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-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
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-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -744,13 +742,13 @@ <h1>Source code for sysidentpy.metrics._regression</h1><div class="highlight"><p
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -769,7 +767,7 @@ <h1>Source code for sysidentpy.metrics._regression</h1><div class="highlight"><p
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-  <script src="../../../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../../../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/parameter_estimation/estimators.html b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
index 36988f43..0513a3ad 100644
--- a/docs/_modules/sysidentpy/parameter_estimation/estimators.html
+++ b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>sysidentpy.parameter_estimation.estimators &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
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-  <link rel="preload" as="script" href="../../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
     <script src="../../../_static/jquery.js"></script>
     <script src="../../../_static/underscore.js"></script>
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     <script src="../../../_static/togglebutton.js"></script>
     <script src="../../../_static/clipboard.min.js"></script>
     <script src="../../../_static/copybutton.js"></script>
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+    <script src="../../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
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     <link rel="index" title="Index" href="../../../genindex.html" />
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@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
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-    <div class="col pl-2 topbar-main">
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-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -1155,13 +1153,13 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -1180,7 +1178,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
     </div>
 
     
-  <script src="../../../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../../../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/polynomial_basis/narmax.html b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
index a0cdf50c..a6b5490d 100644
--- a/docs/_modules/sysidentpy/polynomial_basis/narmax.html
+++ b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>sysidentpy.polynomial_basis.narmax &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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     <script src="../../../_static/togglebutton.js"></script>
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     <script src="../../../_static/copybutton.js"></script>
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+    <script src="../../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/sysidentpy/polynomial_basis/narmax.html" />
     <link rel="index" title="Index" href="../../../genindex.html" />
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@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -723,7 +721,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 
         <span class="k">return</span> <span class="n">unbiased_theta</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">),</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
-<div class="viewcode-block" id="PolynomialNarmax.predict"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<div class="viewcode-block" id="PolynomialNarmax.predict"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Return the predicted values given an input.</span>
 
 <span class="sd">        The predict function allows a friendly usage by the user.</span>
@@ -746,8 +744,13 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 <span class="sd">            The predicted values of the model.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">yhat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">yhat</span></div>
+        <span class="k">if</span> <span class="n">steps_ahead</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_one_step_ahead_prediction</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_positive_int</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;steps_ahead&#39;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_step_ahead_prediction</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="o">=</span><span class="n">steps_ahead</span><span class="p">)</span></div>
 
     <span class="k">def</span> <span class="nf">_code2exponents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">code</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
@@ -783,20 +786,89 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 
             <span class="k">return</span> <span class="n">exponents</span>
 
-    <span class="k">def</span> <span class="nf">_model_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_elements</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y_initial</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_check_positive_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">value</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> must be integer and &gt; zero. Got </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_one_step_ahead_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Perform the 1-step-ahead prediction of a model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : array-like of shape = max_lag</span>
+<span class="sd">            Initial conditions values of the model</span>
+<span class="sd">            to start recursive process.</span>
+<span class="sd">        X : ndarray of floats of shape = n_samples</span>
+<span class="sd">            Vector with entrace values to be used in model simulation.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        yhat : ndarray of floats</span>
+<span class="sd">               The 1-step-ahead predicted values of the model.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">X_base</span> <span class="o">=</span> <span class="n">InformationMatrix</span><span class="p">()</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span>
+            <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span>
+        <span class="p">)</span>
+        <span class="n">piv_final_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">)]</span>
+        <span class="n">X_base</span> <span class="o">=</span> <span class="n">X_base</span><span class="p">[:,</span> <span class="n">piv_final_model</span><span class="p">]</span>
+        <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X_base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+        <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">yhat</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">yhat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_n_step_ahead_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Perform the n-steps-ahead prediction of a model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : array-like of shape = max_lag</span>
+<span class="sd">            Initial conditions values of the model</span>
+<span class="sd">            to start recursive process.</span>
+<span class="sd">        X : ndarray of floats of shape = n_samples</span>
+<span class="sd">            Vector with entrace values to be used in model simulation.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        yhat : ndarray of floats</span>
+<span class="sd">               The n-steps-ahead predicted values of the model.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Insufficient initial conditions elements!&quot;</span><span class="p">)</span>
+
+        <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">yhat</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+        <span class="n">yhat</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_inputs</span><span class="p">)</span>
+        <span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
+            <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
+                <span class="n">steps_ahead</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">i</span>  <span class="c1"># predicts the remaining values</span>
+
+            <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">,</span>
+                <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span>
+                <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">)[</span><span class="o">-</span><span class="n">steps_ahead</span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+
+            <span class="n">i</span> <span class="o">+=</span> <span class="n">steps_ahead</span>
+
+        <span class="n">yhat</span> <span class="o">=</span> <span class="n">yhat</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">yhat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_model_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y_initial</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Perform the infinity steps-ahead simulation of a model.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        model_elements : ndarray of ints</span>
-<span class="sd">            Matrix with regressor codes.</span>
 <span class="sd">        y_initial : array-like of shape = max_lag</span>
 <span class="sd">            Number of initial conditions values of output mensured</span>
 <span class="sd">            to start recursive process.</span>
 <span class="sd">        X : ndarray of floats of shape = n_samples</span>
 <span class="sd">            Vector with entrace values to be used in model simulation.</span>
-<span class="sd">        theta : array-like of shape = number_of_model_elements</span>
-<span class="sd">            Paramters estimated via Least Squares method.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -813,7 +885,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
         <span class="n">y_output</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_initial</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
 
         <span class="n">model_exponents</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_code2exponents</span><span class="p">(</span>
-            <span class="n">model</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">model_elements</span><span class="p">]</span>
+            <span class="n">model</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">]</span>
         <span class="n">raw_regressor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model_exponents</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
 
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
@@ -832,7 +904,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
                     <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">raw_regressor</span><span class="p">,</span> <span class="n">model_exponents</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
                 <span class="p">)</span>
 
-            <span class="n">y_output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">regressor_value</span><span class="p">,</span> <span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+            <span class="n">y_output</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">regressor_value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
         <span class="k">return</span> <span class="n">y_output</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
 <div class="viewcode-block" id="PolynomialNarmax.information_criterion"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.information_criterion">[docs]</a>    <span class="k">def</span> <span class="nf">information_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -1054,13 +1126,13 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -1079,7 +1151,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
     </div>
 
     
-  <script src="../../../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../../../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/_modules/sysidentpy/residues/residues_correlation.html b/docs/_modules/sysidentpy/residues/residues_correlation.html
index c7edb67a..86a0a523 100644
--- a/docs/_modules/sysidentpy/residues/residues_correlation.html
+++ b/docs/_modules/sysidentpy/residues/residues_correlation.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>sysidentpy.residues.residues_correlation &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../../../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../../../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../../../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../../../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
     <script src="../../../_static/jquery.js"></script>
     <script src="../../../_static/underscore.js"></script>
     <script src="../../../_static/doctools.js"></script>
-    <script src="../../../_static/language_data.js"></script>
     <script src="../../../_static/togglebutton.js"></script>
     <script src="../../../_static/clipboard.min.js"></script>
     <script src="../../../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../../../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="../../../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/_modules/sysidentpy/residues/residues_correlation.html" />
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
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+                    title="Fullscreen mode"><i
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+            
+        </div>
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 </div>
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@@ -502,13 +500,13 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
 
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+        <div class='prev-next-bottom'>
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-  <script src="../../../_static/js/index.3da636dd464baa7582d2.js"></script>
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index 00a527b1..8ea88b41 100644
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@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
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+                    title="Fullscreen mode"><i
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+        </div>
 
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+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -448,13 +446,13 @@ <h1>Source code for sysidentpy.utils._check_arrays</h1><div class="highlight"><p
 
               </div>
               
-        </div>
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     <footer class="footer mt-5 mt-md-0">
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index 48c375b3..3f383282 100644
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@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
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+            <!-- Full screen (wrap in <a> to have style consistency -->
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+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
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-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
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+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -432,13 +430,13 @@ <h1>Source code for sysidentpy.utils.generate_data</h1><div class="highlight"><p
 
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-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
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index 58bad144..49fd63d3 100644
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     }
@@ -241,7 +247,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 9
@@ -250,8 +258,8 @@
      "output_type": "display_data",
      "data": {
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iiScs5zl8fHwwe/ZsnDlzxmXhqGs0UT+d9+DeBxG5iNXy8PLq/FsSicRpYejWjIgIhq+PFycLEpHLWC2PqKgofPzxxx3G9u3bh9DQUKeHIvv4+XhjZP9gLpJIRC5j9WqrtLQ0LFq0CGvXrkVERATOnj0LpVKJV155xZX5qIs0KiU2FZ/ClTYj/CXeYschoh7OannI5XK8+eabqKurw/nz59GvXz/07dvXldnIDpooBdYXmXH0zEWMHcTFK4nIuWzO8wgPD0d4eLhTXrypqQmpqalobm6GRCLBq6++itDQUBw5cgQvvfQSvL29odVqMX/+fABAdnY2/vOf/8DHxwfLli3DyJEjodPpsGTJEly5cgV9+vRBZmYmAgICnJK3O1P/dNK8tLqB5UFETifqTSB27dqFIUOGYNu2bYiLi8OmTZsAXL1MOCsrCzt27MDRo0dRUVGBiooKHD58GIWFhVizZg0yMjIAADk5OZg8eTK2b9+O6Oho5Ofni/mWRKMI8sWv+kh50pyIXELU8hgyZIhl0qFer4ePjw/0ej1aW1sRGRkJQRCg1Wpx8OBBlJWVQavVQhAEhIeHw2g0QqfToaysDBMmTAAAxMbG4sCBA2K+JVGNUSlQWt0Ak8ksdhQi6uFsHrb6/PPP8fbbb6O1tdUytnXrVrtfqLCwEFu2bOkwlp6ejs8//xxxcXFobGzEtm3boNfrIZX+vEZTUFAQzpw5Az8/P4SEhHQYb2pqgl6vh0wm6zDmqTRRSuw4fAYnzzdhaJhc7DhE1IPZLI/MzEwsW7YMYWFht/VCCQkJSEhI6DA2f/58PPnkk0hKSsKJEyfwzDPPYMeOHR2WQDEYDJDL5ZBIJDeMy2QySKVSGAwG+Pv7W7b1VBrLzaEaWB5E5FQ2D1v169cP9957LwYNGmT5chS5XG7Za+jVqxcMBgOkUikkEglqampgNptRXFwMjUaDmJgYFBcXw2Qyoa6uDiaTCUqlEjExMSgqKgIA7N+/H2q12mH53E2kMhChMj+e9yAip7O559GrVy+kp6cjOjraslRJYmKiQ1782WefxQsvvIDt27ejvb0dL774IgAgIyMDS5YsgdFohFarxahRowAAGo0GiYmJMJlMSE9PBwDMmzcPaWlpKCgogEKhQFZWlkOyuSNBEKCJUnCyIBE5nc3yiIiIAABcuHDB4S/et29fbNy48Ybxu+++GwUFBTeMP/PMM3jmmWc6jPXu3dtylRZdnSz472PncK7xCsKC/cWOQ0Q9lM3DVvPnz8fw4cPh5+eHoUOHWuZcUPekscz34KErInIem+WRlZWFXbt2QSKR4F//+hdWr17tilx0i6LD5QiQeKO0ioeuiMh5bB62KikpQV5eHgDgT3/6E6ZPn+70UHTrJN5euHtACPc8iMipbO55tLe3w2QyAQDMZrPlpDl1X2NUCnxddwn6lnaxoxBRD2VzzyMuLg4zZszAqFGjUF5ejri4OFfkotugVilhMgNHai5Ce0dvseMQUQ9kszxmzZoFrVaLU6dOIT4+HkOGDHFFLroNMZEh8BKAkiody4OInMJqeRQWFiIhIQFZWVmWQ1Vff/01AGDRokWuSUe3ROYvwdAwOW9LS0ROY7U8ri1Hcv2Mcp7zcA8alQLvlNWi3WiCj7eo618SUQ9k9a/KtZVqv/rqKzz22GOWL09etdadaFRKXG414vhZz10okoicx+qex7Zt27Bu3To0Njbio48+sowPHjzYJcHo9vxysuCIiGCR0xBRT2O1PFJSUpCSkoL169dj7ty5rsxEDhAeEoD+IQEorWrA/4wfKHYcIuphbF5tlZSUhN27d6O9vR1msxnnz5/Hn//8Z1dko9ukjlLg0OkfOT+HiBzOZnksWLAAKpUKJ0+ehJ+fn0feH9xdjVEp8N7ROtQ2NGOAMlDsOETUg3TpMpy//vWvGDhwIN5++200NjY6OxM5iDpKCYCLJBKR43WpPFpaWtDc3AxBEHD58mVnZyIHuTNMBpmfD0q4SCIROZjN8khJScHmzZsxfvx43HfffQ69kyA5l7eXgJgoBcpYHkTkYDbPeTz00EOW/540aRKkUqlTA5FjaaIUyNp7Eo2X2xAcKBE7DhH1EDbLIy8vD3l5eWhtbbWMvf/++04NRY6jUV097/FFTQMeGNpH5DRE1FPYLI+tW7fijTfeQHAwJ5q5o7sHhMDHS0BJlY7lQUQOY7M87rzzTvTr1w/e3t6uyEMOFuDrjbv6B6OUiyQSkQPZLI9x48bhwQcfxIABAyyTzbZu3eqKbOQgmigF/vHfarS0G+Hnww8BRHT7bJZHfn4+XnvtNchkMlfkIScYo1JgU/FpHPv+EtQ/rXlFRHQ7bJZH3759MWLECHh5cVlvd3VtsmBZtY7lQUQOYbM8Wltb8eijj+KOO+6wrI+UlZXl9GDkOKEyP6h6BaKkqgFzYsVOQ0Q9gc3ymDFjBuRyuSuykBOpo5T49JvzXCSRiBzCZnls2rQJO3bscEUWcqIxKgV2flGLUxcMGBzKiZ5EdHtslkdwcDC2bNmCgQMHWs57aLVapwcjx7o2WbCsqoHlQUS3zWZ5KBQKnDhxAidOnLCMsTzcz+DQICgCJSip0mH6mAFixyEiN2ezPDIzM3Hy5El89913GDhwIIYNG+aKXORggiBAHaVEGScLEpED2Lz+Njc3F8uXL8eXX36J5cuXY9OmTa7IRU6gUSlw6oIBF/QtYkchIjdnc89j9+7d2LZtG3x8fNDW1oakpCTMnj3bFdnIwcaors7xKKtuwEN3hYmchojcmc09D7PZDB+fqx0jkUggkXBZb3c1vH8wfH28UFrFOwsS0e2xuecRExODBQsWQK1Wo6ysDKNHj3ZFLnICPx9vjIoI5p0Fiei2Wd3zKCkpAQCkpqZi6tSpaG9vx9SpU5GWluaycOR46iglKuoa0dxqFDsKEbkxq+WxevVqXL58GU8++STGjx+PmTNn4t577+1wUyhyP2NUCrQZzThae1HsKETkxqwetho/fjymTJmCc+fO4eGHHwYAy9IW+/btc1lAcqxrCyOWVTdg3KBeIqchIndltTxSU1ORmpqKtWvX4umnn3ZlJnKikEBf3NFHihKeNCei22DzhPljjz2GjRs3oqXl57kB8+fPd2ooci6NSond5XUwmczw8uIiiURkP5uX6i5cuBB6vR69e/e2fN2OvXv3YvHixZbHR44cQUJCApKSkpCdnW0Zz87ORnx8PJKSklBeXg4A0Ol0mDVrFpKTk7Fw4UI0NzcDAD755BNMmzYNiYmJKCgouK18nkATpUDTlXacPN8kdhQiclM29zyCgoKQmprqkBdbtWoViouLOyxxsmLFCrz++usYMGAA5syZg4qKCgDA4cOHUVhYiLNnz+KZZ57Bzp07kZOTg8mTJ2Pq1Kl44403kJ+fj5SUFGRmZuKdd95BQEAAZsyYgQceeAChoaEOydwTjflpkcTSqgYMDeNy+0RkP5t7HnfccQf27NmDU6dO4fTp0zh9+vQtv1hMTAxWrlxpeazX69Ha2orIyEgIggCtVouDBw+irKwMWq0WgiAgPDwcRqMROp0OZWVlmDBhAgAgNjYWBw4cQGVlJSIjIxEcHAxfX1+o1WqUlpbeckZPMEAZgFCZHycLEtEts7nncfz4cRw/ftzyWBAEbN269aY/U1hYiC1btnQYe/nllxEXF4dDhw5ZxvR6PaTSn5cHDwoKwpkzZ+Dn54eQkJAO401NTdDr9ZZ7qXc2dm1cr9fbelseTRAEjFEpUMpFEonoFtksj9zcXLufNCEhAQkJCTa3k0qlMBgMlscGgwFyuRwSieSGcZlMZtne39/fsm1nz/HLMqHOqaOUeP+rczjXeAVhwf5ixyEiN2O1PBITE63erjQvL88hLy6VSiGRSFBTU4MBAwaguLgY8+fPh7e3N1599VXMnj0b586dg8lkglKpRExMDIqKijB16lTs378farUagwcPRnV1NS5evIjAwECUlpZy4cYuuLZIYmm1DpNHhouchojcjdXyWLNmjUsCZGRkYMmSJTAajdBqtRg1ahQAQKPRIDExESaTCenp6QCAefPmIS0tDQUFBVAoFMjKyoJEIsHSpUsxe/ZsmM1mTJs2DX379nVJdnc2rJ8cARJvlFY1sDyIyG6C2Ww2ix3C2WprazFx4kTs27cPERERYsfpNpI3/heNzW3Ys2CC2FGIqBu62d9Om1dbUc+liVLg+NlL0Le0ix2FiNwMy8ODaVRKmMzAlzW86oqI7MPy8GCjI0PgJVydLEhEZA+WhweT+UswNEyO0mpOFiQi+7A8PNwYlQJf1lxEu9EkdhQiciMsDw+nVilxudWI42e5SCIRdR3Lw8P9crIgEVFXsTw8XL/gAPQPCeBJcyKyC8uDoFEpUFqtgwfMFyUiB2F5EDRRCvxwqQW1Dc1iRyEiN8HyIGiu3RyK5z2IqItYHoQhfWWQ+fughOc9iKiLWB4Eby8BMZEK3lmQiLqM5UEArl6ye/IHPRovt4kdhYjcAMuDAFy9syAAlNVw74OIbGN5EADg7gEh8PESON+DiLqE5UEAgABfb9zVP5jlQURdwvIgizFRChytvYiWdqPYUYiom2N5kIVGpUBLuwnHvr8kdhQi6uZYHmRhOWnOyYJEZAPLgyxCZX5Q9QrkZEEisonlQR1oVEqUVTdwkUQiuimWB3UwRqWAztCKUxcMYkchom6M5UEdWM578NAVEd0Ey4M6GBwaBEWgBCVc54qIboLlQR0IggB1lBKl1dzzICLrWB50gzEqBU5fMOCCvkXsKETUTbE86AYalQIAuFQJEVnF8qAbDO8fDF8fL04WJCKrWB50Az8fb4yKCOZkQSKyiuVBndKolKioa0RzKxdJJKIbsTyoU5ooBdqMZhytvSh2FCLqhlge1Cl11NWT5mW8ZJeIOsHyoE6FBPpiSF8pJwsSUadYHmSVOurqIokmExdJJKKOWB5k1RiVAk1X2nHyfJPYUYiom2F5kFWanxZ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      },
      "metadata": {}
     }
@@ -312,7 +320,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 10
@@ -321,8 +331,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -360,7 +370,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "rrse:  0.001993603325328823\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95556574\n1         y(k-1)     0.1999  0.04107943\n2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+     "text": [
+      "rrse:  0.0018136024659265537\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95739001\n1         y(k-1)     0.2000  0.03917632\n2  x1(k-1)y(k-1)     0.0999  0.00343057\n"
+     ]
     }
    ],
    "source": [
@@ -403,7 +415,23 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "array([[   0,    0],\n       [1001,    0],\n       [1002,    0],\n       [2001,    0],\n       [2002,    0],\n       [1001, 1001],\n       [1002, 1001],\n       [2001, 1001],\n       [2002, 1001],\n       [1002, 1002],\n       [2001, 1002],\n       [2002, 1002],\n       [2001, 2001],\n       [2002, 2001],\n       [2002, 2002]])"
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [1002,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [1002, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [1002, 1002],\n",
+       "       [2001, 1002],\n",
+       "       [2002, 1002],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
      },
      "metadata": {},
      "execution_count": 12
@@ -422,7 +450,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "[0.95556574 0.04107943 0.00335113 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.9000403 ]\n [0.19990693]\n [0.10000669]]\n"
+     "text": [
+      "[0.95739001 0.03917632 0.00343057 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.89995854]\n [0.20004441]\n [0.09991068]]\n"
+     ]
     }
    ],
    "source": [
@@ -440,9 +470,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -454,7 +488,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/examples/f_16_benchmark.ipynb.txt b/docs/_sources/examples/f_16_benchmark.ipynb.txt
index 18a9113b..ad05c646 100644
--- a/docs/_sources/examples/f_16_benchmark.ipynb.txt
+++ b/docs/_sources/examples/f_16_benchmark.ipynb.txt
@@ -55,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_16 = pd.read_csv(r'datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+    "f_16 = pd.read_csv(r'../../../examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
    ]
   },
   {
@@ -64,14 +64,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "(32768, 3)"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
@@ -91,26 +91,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6b0443210>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -123,26 +122,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe697898650>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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332.055062 \r\nL 106.646813 305.13907 \r\nL 106.951329 301.180082 \r\nL 107.255845 300.43657 \r\nL 107.864877 275.597284 \r\nL 108.169393 272.30062 \r\nL 108.473909 270.762319 \r\nL 109.08294 242.166588 \r\nL 109.387456 263.56008 \r\nL 109.691972 276.746736 \r\nL 109.996488 260.04549 \r\nL 110.60552 192.351706 \r\nL 110.910036 194.533102 \r\nL 111.214552 198.0306 \r\nL 111.823583 184.115387 \r\nL 112.432615 141.211749 \r\nL 113.955195 66.281542 \r\nL 114.259711 64.051006 \r\nL 115.173258 83.598109 \r\nL 115.477774 86.138442 \r\nL 115.78229 84.04678 \r\nL 116.086806 88.653137 \r\nL 116.391322 95.641723 \r\nL 117.30487 107.377677 \r\nL 117.913901 138.791062 \r\nL 118.522933 168.595641 \r\nL 119.131965 194.699751 \r\nL 120.654544 284.442514 \r\nL 121.568092 324.525933 \r\nL 121.872608 331.159856 \r\nL 122.177124 335.073977 \r\nL 122.786156 354.155317 \r\nL 123.395187 376.080377 \r\nL 123.699703 383.79752 \r\nL 124.004219 388.237227 \r\nL 124.308735 386.216071 \r\nL 124.613251 380.357708 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -183,14 +181,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
@@ -214,223 +212,15 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.3090560911603647\n"
+      "0.3090559299602927\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Regressors</th>\n",
-       "      <th>Parameters</th>\n",
-       "      <th>ERR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>y(k-1)</td>\n",
-       "      <td>1.8387E+00</td>\n",
-       "      <td>9.43378253E-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>y(k-2)</td>\n",
-       "      <td>-1.8938E+00</td>\n",
-       "      <td>1.95167599E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>y(k-3)</td>\n",
-       "      <td>1.3337E+00</td>\n",
-       "      <td>1.02432261E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>y(k-6)</td>\n",
-       "      <td>-1.6038E+00</td>\n",
-       "      <td>8.03485985E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>y(k-9)</td>\n",
-       "      <td>2.6776E-01</td>\n",
-       "      <td>9.27874557E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>x2(k-7)</td>\n",
-       "      <td>-2.2384E+01</td>\n",
-       "      <td>3.76837313E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>x1(k-1)</td>\n",
-       "      <td>8.2709E+00</td>\n",
-       "      <td>6.81508210E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>x2(k-3)</td>\n",
-       "      <td>1.0587E+02</td>\n",
-       "      <td>1.57459800E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>x1(k-8)</td>\n",
-       "      <td>-3.7975E+00</td>\n",
-       "      <td>7.35086279E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>x2(k-1)</td>\n",
-       "      <td>8.5726E+01</td>\n",
-       "      <td>4.85358786E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>y(k-7)</td>\n",
-       "      <td>1.3955E+00</td>\n",
-       "      <td>2.77245281E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>y(k-5)</td>\n",
-       "      <td>1.3219E+00</td>\n",
-       "      <td>8.64120037E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>y(k-10)</td>\n",
-       "      <td>-2.9306E-01</td>\n",
-       "      <td>8.51717688E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>y(k-4)</td>\n",
-       "      <td>-9.5479E-01</td>\n",
-       "      <td>7.23623116E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>y(k-8)</td>\n",
-       "      <td>-7.1309E-01</td>\n",
-       "      <td>4.44988077E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>y(k-12)</td>\n",
-       "      <td>-3.0437E-01</td>\n",
-       "      <td>1.49743148E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>y(k-11)</td>\n",
-       "      <td>4.8602E-01</td>\n",
-       "      <td>3.34613282E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>y(k-13)</td>\n",
-       "      <td>-8.2442E-02</td>\n",
-       "      <td>1.43738964E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>y(k-15)</td>\n",
-       "      <td>-1.6762E-01</td>\n",
-       "      <td>1.25546584E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>x1(k-2)</td>\n",
-       "      <td>-8.9698E+00</td>\n",
-       "      <td>9.76699739E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>y(k-17)</td>\n",
-       "      <td>2.2036E-02</td>\n",
-       "      <td>4.55983807E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>y(k-14)</td>\n",
-       "      <td>2.4900E-01</td>\n",
-       "      <td>1.10314107E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>y(k-19)</td>\n",
-       "      <td>-6.8239E-03</td>\n",
-       "      <td>1.99734771E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>x2(k-9)</td>\n",
-       "      <td>-9.6265E+01</td>\n",
-       "      <td>2.98523208E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>x2(k-8)</td>\n",
-       "      <td>2.2620E+02</td>\n",
-       "      <td>2.34402543E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>x2(k-2)</td>\n",
-       "      <td>-2.3609E+02</td>\n",
-       "      <td>1.04172323E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>y(k-20)</td>\n",
-       "      <td>-5.4663E-02</td>\n",
-       "      <td>5.37895336E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>x2(k-6)</td>\n",
-       "      <td>-2.3651E+02</td>\n",
-       "      <td>2.11392628E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>x2(k-4)</td>\n",
-       "      <td>1.7378E+02</td>\n",
-       "      <td>2.18396315E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>x1(k-7)</td>\n",
-       "      <td>4.9862E+00</td>\n",
-       "      <td>2.03811842E-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "   Regressors   Parameters             ERR\n",
        "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
@@ -438,11 +228,11 @@
        "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
        "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
        "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
-       "5     x2(k-7)  -2.2384E+01  3.76837313E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
        "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
        "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
        "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
-       "9     x2(k-1)   8.5726E+01  4.85358786E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
        "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
        "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
        "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
@@ -463,11 +253,11 @@
        "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
        "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
        "29    x1(k-7)   4.9862E+00  2.03811842E-05"
-      ]
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
@@ -494,14 +284,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 426.524687 \r\nL 91.073836 425.8022 \r\nL 91.511557 411.152953 \r\nL 91.527769 411.762218 \r\nL 91.657464 414.450677 \r\nL 91.673676 413.929097 \r\nL 91.706099 411.584217 \r\nL 91.738523 416.587624 \r\nL 91.803371 413.657281 \r\nL 91.852006 420.48783 \r\nL 91.88443 417.237727 \r\nL 91.900642 415.724342 \r\nL 91.933066 420.370916 \r\nL 91.949278 421.614602 \r\nL 91.981701 417.132372 \r\nL 91.997913 416.04414 \r\nL 92.030337 420.668488 \r\nL 92.046549 422.442923 \r\nL 92.095185 418.283297 \r\nL 92.14382 421.93661 \r\nL 92.176244 417.362476 \r\nL 92.22488 423.776247 \r\nL 92.257303 421.234152 \r\nL 92.273515 420.18193 \r\nL 92.322151 424.220352 \r\nL 92.354575 421.784311 \r\nL 92.516694 424.020064 \r\nL 92.549117 423.321335 \r\nL 92.711236 418.9788 \r\nL 92.759872 422.615291 \r\nL 92.808508 418.607581 \r\nL 92.840931 420.940681 \r\nL 92.873355 419.416585 \r\nL 92.905779 417.218426 \r\nL 92.970626 420.487016 \r\nL 93.116533 417.101093 \r\nL 93.165169 418.987263 \r\nL 93.246229 417.917766 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 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417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 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305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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587.44086 416.256092 \r\nL 587.570563 416.922889 \r\nL 588.186652 421.561336 \r\nL 588.219078 421.535685 \r\nL 588.348781 421.16788 \r\nL 588.96487 418.872918 \r\nL 589.224277 418.851271 \r\nL 589.710663 418.930773 \r\nL 589.905217 419.607446 \r\nL 590.359178 421.306835 \r\nL 590.456455 421.20103 \r\nL 590.65101 420.493422 \r\nL 591.234673 418.075477 \r\nL 591.33195 418.184357 \r\nL 591.494079 418.948061 \r\nL 591.980465 421.931214 \r\nL 592.012891 421.906036 \r\nL 592.142594 421.379839 \r\nL 592.758684 417.992893 \r\nL 593.082941 417.536084 \r\nL 593.277496 417.305274 \r\nL 593.309921 417.326798 \r\nL 593.439624 417.691438 \r\nL 593.731456 419.624315 \r\nL 593.958436 420.487633 \r\nL 594.023288 420.455164 \r\nL 594.185417 419.885986 \r\nL 594.509674 418.86502 \r\nL 594.606951 418.988603 \r\nL 594.801506 419.68446 \r\nL 595.125763 420.710598 \r\nL 595.255466 420.648394 \r\nL 595.417595 420.226672 \r\nL 595.806704 419.187577 \r\nL 595.968833 419.248109 \r\nL 596.228239 419.388065 \r\nL 596.390368 419.062022 \r\nL 596.811903 417.911863 \r\nL 596.844328 417.930466 \r\nL 596.974031 418.227298 \r\nL 597.36314 420.051954 \r\nL 597.849527 421.979642 \r\nL 597.946804 421.919191 \r\nL 598.108933 421.210917 \r\nL 598.530467 418.948895 \r\nL 598.627745 419.140422 \r\nL 599.016854 420.542051 \r\nL 599.04928 420.51617 \r\nL 599.178983 420.146932 \r\nL 599.568092 418.8608 \r\nL 599.827498 418.586752 \r\nL 600.054478 417.525864 \r\nL 600.313884 416.678037 \r\nL 600.411161 416.920894 \r\nL 600.702993 418.961131 \r\nL 600.865122 419.519132 \r\nL 600.897547 419.505573 \r\nL 601.02725 419.11859 \r\nL 601.319082 418.037737 \r\nL 601.351508 418.05192 \r\nL 601.448785 418.290901 \r\nL 601.675765 419.83455 \r\nL 602.032449 421.907463 \r\nL 602.194578 422.130976 \r\nL 602.227003 422.10867 \r\nL 602.356706 421.782748 \r\nL 602.583687 420.291308 \r\nL 602.94037 418.095237 \r\nL 603.005221 418.124099 \r\nL 603.134924 418.677932 \r\nL 603.491608 420.271844 \r\nL 603.588885 420.057045 \r\nL 603.977994 418.565607 \r\nL 604.042845 418.660573 \r\nL 604.2374 419.574359 \r\nL 604.594083 421.00072 \r\nL 604.821063 421.096825 \r\nL 605.177747 421.140897 \r\nL 605.30745 420.748059 \r\nL 605.599282 418.78732 \r\nL 605.891113 417.292185 \r\nL 606.053242 417.20865 \r\nL 606.345074 417.591401 \r\nL 606.701757 418.221544 \r\nL 607.317846 419.411431 \r\nL 607.836658 419.500934 \r\nL 608.063639 419.030843 \r\nL 608.355471 418.402492 \r\nL 608.387896 418.42175 \r\nL 608.517599 418.779079 \r\nL 608.777005 420.580062 \r\nL 609.068837 422.089535 \r\nL 609.166114 422.014627 \r\nL 609.360669 421.192631 \r\nL 609.684926 419.994662 \r\nL 609.847055 419.969918 \r\nL 610.625273 420.325072 \r\nL 610.754976 419.860589 \r\nL 611.241363 416.737486 \r\nL 611.33864 416.989491 \r\nL 611.533194 418.762311 \r\nL 611.889878 421.935869 \r\nL 611.954729 421.978823 \r\nL 611.987155 421.926451 \r\nL 612.149284 421.028337 \r\nL 612.830224 415.801802 \r\nL 612.86265 415.824233 \r\nL 612.959927 416.193807 \r\nL 613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -522,24 +311,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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ORx99xOLFixk5ciSjR49m2bJlZGRkMGHCBNLS0li3bh3e3t6MHz+e+++/n507d2K1WsnIyCA/P58FCxawZMkSh7yOxgj08aBLkA/fyUWQQogWqN5Tig8dOuSwWYpNJlONT440m81YrVYiIiJQFIW4uDi2bdtGXl4ecXFxKIpCWFgYVVVVFBcXk5eXx5AhQwCIj49n69atHDx4kIiICPz8/PDw8CAmJobc3Nway/bv35+CggKHvAZHGBARIBdBCiFapDuapXjQoEEEBATc8SzFWVlZrFy5ssbY/PnzGTFiBNu3b7ePmc1mDAaD/b6Pjw/Hjx/H09MTf3//GuOlpaWYzWaMRmOdY9fHzWZzrZ+t1WqprKxEp6v5kouKiup8HWVlZbd9vKHCPMootljZtGM3Yb76Bv+cpsrnKJKvcSRf40i+xmloviaZpTgxMZHExMR6lzMYDFgsFvt9i8WCr68ver2+1rjRaLQv7+XlZV/2Vj/jxmWvs9lstQoFqLEr7mZFRUW3fbzB/C/z9jf/4rJHIL+MDm/wj2myfA4i+RpH8jWO5Guc2+XLy8ur83l17v4ym8288MILmM1mAD799FOee+65Gm/UjWUwGNDr9Rw7dgxVVcnJySE2NhaTyUROTg42m41Tp05hs9kIDAzEZDKxefNmALKzs4mJiSEqKoqjR49SUlKC1WolNzeXAQMGYDKZ7NfX5Ofn0717d4flbqxuwQa89Bo5A0wI0eLUuaUyZ84c+vTpY59A8qGHHuLs2bPMmTOHP/7xjw4LMG/ePGbOnElVVRVxcXH069cPgNjYWMaNG4fNZmP27NkATJs2jZSUFDIzMwkICCA9PR29Xs+sWbNITk5GVVUSEhIICQnhgQceYMuWLSQlJaGqqltNL6PTargrzI/dUipCiBamzlI5ffq0/bRfAJ1OR3JyMuPGjWvULxw4cCADBw603+/fvz+ZmZm1lps+fTrTp0+vMRYUFMTy5ctrLTts2DCGDRtWY0yj0fDaa681KmtT6tPBj4xvj1NZZUOnvaPJooUQwu3V+W6m0dz6Ib2+4QeWxU/6hvtxtaKKg+cdtztRCCFcrc5SiYyM5KuvvqoxtnHjRtq1a9fkoVqDvuF+AOw6IfOACSFajjp3f6WkpPD888+zaNEiwsPDOX36NIGBgfznf/6nM/O1WF2CDPh4aNl98hKJsR1dHUcIIRyizlLx9fXlvffe49SpU5w7d4727dsTEhLizGwtmkaj0LuDn5wBJoRoUeq9TiUsLIywsDBnZGl1+ob7sXLbUSqqbOjlYL0QogWQdzIX6hPuj7XSxr6zpa6OIoQQDiGl4kJ9O1QfrJfrVYQQLUW9u7+2bNnC3/72N6xWq31s1apVTRqqtYhs2wajl45dJy+R5OowQgjhAPWWSlpaGi+//DKhoaHOyNOqKIpC33C5sl4I0XLUWyrt27fn3nvvdUaWVqlPB3+W5xyivLIKT53W1XGEEKJR6i2Vtm3bMnv2bHr16mWf+r6xU7WIn/QN96OiSuWH06X06+hf/xOEEMKN1Vsq4eHVU7NfuHChycO0Rn2uHazfdfKSlIoQotmr9+yvZ555ht69e+Pp6UnPnj3tnyEvHCM8wJuANnp2y3QtQogWoN5SSU9P5+OPP0av1/PJJ5/w5ptvOiNXq6EoCn3C/eXKeiFEi1Dv7q9vv/2WtWvXAvDEE08wduzYJg/V2vQL92Px1xe4aq3C20MO1gshmq96t1QqKyux2WwAqKpqP1gvHKdPBz+qbCp7Tl92dRQhhGiUerdURowYwfjx4+nXrx+7du1ixIgRzsjVqvQNrz5Av/tECTGRAS5OI4QQDVdvqUyePJm4uDgOHTrEmDFj3Oqz3luKEF9P2hk92XVSjqsIIZq3OkslKyuLxMRE0tPT7bu89uzZA8Dzzz/vnHSthKIo9O0gV9YLIZq/Okvl+rQsXbp0qTEux1SaRp9wPzbtPYelvBIfz3o3IIUQwi3VeaB+yJAhAOzevZvHHnvM/rV161anhWtN+ob7oapQeEoO1gshmq86/yT+4IMPWLJkCZcuXWLDhg328aioKKcEa216d/jpM+vv7hzo4jRCCNEwdZbKhAkTmDBhAn/961+ZOnWqMzO1SsFGL9r7ebFbDtYLIZqxenfeJyUl8emnn1JZWYmqqpw7d46nn37aGdlanT5ysF4I0czVWyrPPvssnTp1Yt++fXh6euLt7e2MXK1S33A/Nuw5y6WrFfh5610dRwghfrY7+jjh1157jc6dO/O3v/2NS5fkL+mm0ufaRZCFsgtMCNFM3dG5q+Xl5Vy9ehVFUbhy5UqjfuGXX37J559/Tnp6OgD5+fm88cYbaLVa4uLi7LMgT506lZKSEvR6PZ6enrz33nsUFxczc+ZMysrKCA4OJi0tDW9vbzZt2sSiRYvQ6XQkJCQwduxYbDYbc+fOZe/evXh4eJCamkpkZGSjsje1vjdMg39v1yAXpxFCiJ+v3lKZMGEC77//PoMHD2bo0KHExMQ0+JelpqaSk5NDdHS0fWzOnDm8/fbbdOzYkSlTplBYWMhdd93FsWPHWL9+fY3rYhYvXszIkSMZPXo0y5YtIyMjgwkTJpCWlsa6devw9vZm/Pjx3H///ezcuROr1UpGRgb5+fksWLCAJUuWNDi7MwT4eNAx0FuOqwghmq16S+XBBx+033744YcxGAwN/mUmk4nhw4eTkZEBgNlsxmq1EhERAUBcXBzbtm0jJCSEy5cvM3XqVC5fvsyUKVO4//77ycvLs58kEB8fz8KFCxk0aBARERH4+VX/lR8TE0Nubi75+fn2a2369+9PQUFBg3M7U98O/uw6KZ+tIoRonuotlbVr17J27VqsVqt97B//+Mdtn5OVlcXKlStrjM2fP58RI0awfft2+5jZbK5RUj4+Phw/fpyKigomT57MpEmTuHTpEuPHj6dv376YzWaMRqN92dLS0hpj18fNZnOtn63VaqmsrESnq/mSi4qK6nwdZWVlt328KYR4lHO8+Crbdxbg63X7afBdke/nkHyNI/kaR/I1TkPz1Vsqq1atYtmyZfYtgTuRmJhIYmJivcsZDAYsFov9vsViwdfXl6CgIJKSktDpdLRt25bo6GgOHz5sX97Ly8u+7K1+htForDVus9lqFQpQY1fczYqKim77eFMYrr/AirztlPuEEN293W2XdUW+n0PyNY7kaxzJ1zi3y5eXl1fn8+o9+6tHjx60b98eo9Fo/3IUg8GAXq/n2LFjqKpKTk4OsbGxbN26lRkzZgDVJbF//366dOmCyWRi8+bNAGRnZxMTE0NUVBRHjx6lpKQEq9VKbm4uAwYMwGQykZ2dDVSfDNBcZle+69rBerkIUgjRHNW7pTJo0CCGDx9Ox44d7R/StWrVKocFmDdvHjNnzqSqqoq4uDj69esHQE5ODmPHjkWj0fD8888TGBjItGnTSElJITMzk4CAANLT09Hr9cyaNYvk5GRUVSUhIYGQkBAeeOABtmzZQlJSEqqqMn/+fIdlbkp+3no6B/mwSz6zXgjRDNVbKhkZGfz5z3922BbKwIEDGThwoP1+//79yczMrLXcK6+8UmssKCiI5cuX1xofNmwYw4YNqzGm0Wh47bXXHJDY+fp08CP3SLGrYwghxM9Wb6mEhITQp08fNJo7uk5SOEDfcD/+9/tTnC8tp53R09VxhBDijtVbKlarlVGjRtGtWzf7NSPXL1wUTeP6xwv/6at9zB7ZCy/97c8CE0IId1FvqYwfPx5fX19nZBHXxEYG8MQ9kazcdpTcI8UsHNvfPjW+EEK4s3pLZfny5axZs8YZWcQ1Go3CvFG9ub9nMC+t28Vji7cwY3h3no7vgk4ruyGFEO6r3lLx8/Nj5cqVdO7c2X5cJS4ursmDCbivRzBfzIjnD38v4K0v9rKx6CwLx/anU5CPq6MJIcQt1VsqAQEB/PDDD/zwww/2MSkV5wnw8eCd8QP4Va8QXv2kgBF/+Rev/Dqax++OcHU0IYSopd5SSUtLY9++fRw4cIDOnTu79RWgLZWiKIzq34G7OwfyYtYuXvmfAr7ac5ZfReqI6mbDQye7xIQQ7qHeUlm9ejWffvopffv2ZcWKFTz88MMkJyc7I5u4SXs/b1ZNvptV247w5ud7+efeKlK/PsfgrkHc1yOY+3q0I8xfPkRNCOE69ZbKp59+ygcffIBOp6OiooKkpCQpFRfSaBSeHNyZxNiOZG7O54DFk6/3nmfDnrMA9Agxcl+PdtzXI5iBnQPRaJR6fqIQQjhOvaWiqqp9Ika9Xo9eLx9z6w58PHUM6ujD/4uORlVVDpwz8/Xe83y97xwrthxmafYhenfwZdZD0cR1kw/8EkI4R72lYjKZePbZZ4mJiSEvL48BAwY4I5f4GRRFoVuIkW4hRp6K74K5vJLPC87wpy/38dvl2xnSLYhZD/fkrjC51kUI0bTqPML77bffAvDcc88xevRoKisrGT16NCkpKU4LJxrG4KljTEw4G18Yyh9+Hc3uk5f49V9ymLF2J8eLG/dx0EIIcTt1lsqbb77JlStX+N3vfsfgwYOZOHEi9957b40P6xLuzUuv5XdDurD5xfuZdl8UnxWc4Zfpm3nt//ZQbJH/jkIIx6tz99fgwYN59NFHOXPmDA899BCAfer7jRs3Oi2gaDw/bz0pD/XkiXs68acv9/H+1sNk5R7n3SdiGdSlravjCSFakDq3VJ577jk2bNjA008/zcaNG9m4cSObNm2SQmnGQv28eHNMX76YEU87oyfT1+zkgrnc1bGEEC1IvQfqH3vsMd59913Ky39683nmmWeaNJRoWt1CjCyaYGLUoi08n/k97z/5Czn1WAjhEPVeij1jxgzMZjNBQUH2L9H8Rbf3ZfbIXmTvO8+yfx1ydRwhRAtR75aKj48Pzz33nDOyCCebMDCCbQcv8tYXe/lFp0BiIgNcHUkI0czVu6XSrVs31q9fz6FDhzh8+DCHDx92Ri7hBIqikJbQhzB/L55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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -550,20 +338,17 @@
     "\n",
     "# You can use the plot below to choose the \"n_terms\" and run the model again with the most adequate value of terms."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -575,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/examples/multiple_inputs_example.ipynb.txt b/docs/_sources/examples/multiple_inputs_example.ipynb.txt
index f81ee4d8..edad040d 100644
--- a/docs/_sources/examples/multiple_inputs_example.ipynb.txt
+++ b/docs/_sources/examples/multiple_inputs_example.ipynb.txt
@@ -98,7 +98,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fe5cb870a90>"
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2191c4db6d0>"
+      ]
      },
      "metadata": {},
      "execution_count": 4
@@ -123,14 +125,16 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.0026126773911553398\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.90482955\n1  x2(k-2)x1(k-1)    -0.3000  0.05072675\n2        y(k-1)^2     0.3999  0.04410386\n3   x1(k-1)y(k-1)     0.1000  0.00033239\n"
+     "text": [
+      "0.002440520762176125\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.91070733\n1  x2(k-2)x1(k-1)    -0.2997  0.04434814\n2        y(k-1)^2     0.3999  0.04463687\n3   x1(k-1)y(k-1)     0.0996  0.00030042\n"
+     ]
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 720x576 with 3 Axes>",
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      },
      "metadata": {}
     }
@@ -158,7 +162,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 6
@@ -167,8 +173,8 @@
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {}
     }
@@ -190,9 +196,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -204,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index fb51eb71..ef9b6ec5 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css b/docs/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css
new file mode 100644
index 00000000..7fd19a77
--- /dev/null
+++ b/docs/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v4.5.0 (https://getbootstrap.com/)
+ * Copyright 2011-2020 The Bootstrap Authors
+ * Copyright 2011-2020 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index 7d88f807..61ac9d26 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -29,9 +29,14 @@ if (!window.console || !console.firebug) {
 
 /**
  * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
  */
 jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
 };
 
 /**
diff --git a/docs/_static/js/index.d3f166471bb80abb5163.js b/docs/_static/js/index.d3f166471bb80abb5163.js
new file mode 100644
index 00000000..083f2a2c
--- /dev/null
+++ b/docs/_static/js/index.d3f166471bb80abb5163.js
@@ -0,0 +1,32 @@
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+/**!
+ * @fileOverview Kickass library to create and place poppers near their reference elements.
+ * @version 1.16.1
+ * @license
+ * Copyright (c) 2016 Federico Zivolo and contributors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
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n={relatedTarget:this._element},i=e.Event("hide.bs.dropdown",n),o=t._getParentFromElement(this._element);e(o).trigger(i),i.isDefaultPrevented()||(this._popper&&this._popper.destroy(),e(this._menu).toggleClass("show"),e(o).toggleClass("show").trigger(e.Event("hidden.bs.dropdown",n)))}},i.dispose=function(){e.removeData(this._element,"bs.dropdown"),e(this._element).off(".bs.dropdown"),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},i.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},i._addEventListeners=function(){var t=this;e(this._element).on("click.bs.dropdown",(function(e){e.preventDefault(),e.stopPropagation(),t.toggle()}))},i._getConfig=function(t){return t=a(a(a({},this.constructor.Default),e(this._element).data()),t),c.typeCheckConfig(k,t,this.constructor.DefaultType),t},i._getMenuElement=function(){if(!this._menu){var 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diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
index d2b4ee91..863704b3 100644
--- a/docs/_static/language_data.js
+++ b/docs/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,8 @@
 var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
-/* Non-minified version JS is _stemmer.js if file is provided */ 
+/* Non-minified version is copied as a separate JS file, is available */
+
 /**
  * Porter Stemmer
  */
@@ -199,7 +200,6 @@ var Stemmer = function() {
 
 
 
-
 var splitChars = (function() {
     var result = {};
     var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
diff --git a/docs/_static/mystnb.css b/docs/_static/mystnb.css
index 37134eed..51eb87e4 100644
--- a/docs/_static/mystnb.css
+++ b/docs/_static/mystnb.css
@@ -7,7 +7,6 @@ div.container.cell {
 /* Removing all background formatting so we can control at the div level */
 .cell_input div.highlight, .cell_input pre, .cell_output .output * {
   border: none;
-  background-color: transparent;
   box-shadow: none;
 }
 
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index e5cf7364..e76de2a6 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -1,7 +1,7 @@
 pre { line-height: 125%; }
-td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
-td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
 .highlight { background: #f8f8f8; }
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
index 261ecaa9..1a90152e 100644
--- a/docs/_static/searchtools.js
+++ b/docs/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -248,7 +248,7 @@ var Search = {
       // results left, load the summary and display it
       if (results.length) {
         var item = results.pop();
-        var listItem = $('<li style="display:none"></li>');
+        var listItem = $('<li></li>');
         var requestUrl = "";
         var linkUrl = "";
         if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
@@ -273,9 +273,9 @@ var Search = {
         if (item[3]) {
           listItem.append($('<span> (' + item[3] + ')</span>'));
           Search.output.append(listItem);
-          listItem.slideDown(5, function() {
+          setTimeout(function() {
             displayNextItem();
-          });
+          }, 5);
         } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
           $.ajax({url: requestUrl,
                   dataType: "text",
@@ -285,16 +285,16 @@ var Search = {
                       listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
                     }
                     Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
+                    setTimeout(function() {
                       displayNextItem();
-                    });
+                    }, 5);
                   }});
         } else {
           // no source available, just display title
           Search.output.append(listItem);
-          listItem.slideDown(5, function() {
+          setTimeout(function() {
             displayNextItem();
-          });
+          }, 5);
         }
       }
       // search finished, update title and status message
@@ -379,6 +379,13 @@ var Search = {
     return results;
   },
 
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
   /**
    * search for full-text terms in the index
    */
@@ -402,13 +409,14 @@ var Search = {
       ];
       // add support for partial matches
       if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
         for (var w in terms) {
-          if (w.match(word) && !terms[word]) {
+          if (w.match(word_regex) && !terms[word]) {
             _o.push({files: terms[w], score: Scorer.partialTerm})
           }
         }
         for (var w in titleterms) {
-          if (w.match(word) && !titleterms[word]) {
+          if (w.match(word_regex) && !titleterms[word]) {
               _o.push({files: titleterms[w], score: Scorer.partialTitle})
           }
         }
diff --git a/docs/_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css b/docs/_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css
new file mode 100644
index 00000000..ffdbba9d
--- /dev/null
+++ b/docs/_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css
@@ -0,0 +1,5 @@
+/*! sphinx-book-theme CSS
+ * BSD 3-Clause License
+ * Copyright (c) 2020, EBP
+ * All rights reserved.
+ */:root{--color-primary: 0, 123, 255;--color-info: 255, 193, 7;--color-warning: 253, 126, 20;--color-danger: 220, 53, 69}body{padding-top:0px !important}body img{max-width:100%}code{font-size:87.5% !important}main.bd-content{padding-top:3em !important;padding-bottom:0px !important}main.bd-content #main-content a.headerlink{opacity:0;margin-left:.2em}main.bd-content #main-content a.headerlink:hover{background-color:transparent;color:#0071bc;opacity:1 !important}main.bd-content #main-content a,main.bd-content #main-content a:visited{color:#0071bc}main.bd-content #main-content h1,main.bd-content #main-content h2,main.bd-content #main-content h3,main.bd-content #main-content h4,main.bd-content #main-content h5{color:black}main.bd-content #main-content h1:hover a.headerlink,main.bd-content #main-content h2:hover a.headerlink,main.bd-content #main-content h3:hover a.headerlink,main.bd-content #main-content h4:hover a.headerlink,main.bd-content #main-content h5:hover 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print{.tag_popout,div.margin{float:right;clear:right;width:50%;margin-right:-56%;margin-top:0;margin-bottom:0;padding-right:1em;font-size:0.9rem;line-height:1.3;vertical-align:baseline;position:relative;border-left:none;padding-left:0}.bd-content div#main-content>div{flex:0 0 75%;max-width:75%}h1,h2,h3,h4{break-after:avoid}table{break-inside:avoid}pre{word-wrap:break-word}a.copybtn,a.headerlink{display:none}.tag-fullwidth{width:145%;clear:both}div.toggle-hidden{visibility:inherit;opacity:1;height:auto}button.toggle-button{display:none}blockquote.epigraph{border:none}div.container{min-width:50% !important}div.bd-sidebar,div.prev-next-bottom{display:none}div.topbar{height:0;padding:0;position:inherit}div.topbar div.topbar-main{opacity:0}div.topbar div.bd-toc{flex:0 0 25%;max-width:25%;height:auto !important}div.topbar div.bd-toc nav,div.topbar div.bd-toc nav>ul.nav,div.topbar div.bd-toc nav>ul.nav>li>ul.nav{opacity:1;display:block}div.topbar div.bd-toc .nav-link.active{font-weight:inherit;color:inherit;background-color:inherit;border-left:inherit}}
diff --git a/docs/_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js b/docs/_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js
new file mode 100644
index 00000000..3127a34f
--- /dev/null
+++ b/docs/_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js
@@ -0,0 +1,36 @@
+var initTriggerNavBar=()=>{if($(window).width()<768){$("#navbar-toggler").trigger("click")}}
+var scrollToActive=()=>{var navbar=document.getElementById('site-navigation')
+var active_pages=navbar.querySelectorAll(".active")
+var active_page=active_pages[active_pages.length-1]
+if(active_page!==undefined&&active_page.offsetTop>($(window).height()*.5)){navbar.scrollTop=active_page.offsetTop-($(window).height()*.2)}}
+var sbRunWhenDOMLoaded=cb=>{if(document.readyState!='loading'){cb()}else if(document.addEventListener){document.addEventListener('DOMContentLoaded',cb)}else{document.attachEvent('onreadystatechange',function(){if(document.readyState=='complete')cb()})}}
+function toggleFullScreen(){var navToggler=$("#navbar-toggler");if(!document.fullscreenElement){document.documentElement.requestFullscreen();if(!navToggler.hasClass("collapsed")){navToggler.click();}}else{if(document.exitFullscreen){document.exitFullscreen();if(navToggler.hasClass("collapsed")){navToggler.click();}}}}
+var initTooltips=()=>{$(document).ready(function(){$('[data-toggle="tooltip"]').tooltip();});}
+var initTocHide=()=>{var scrollTimeout;var throttle=200;var tocHeight=$("#bd-toc-nav").outerHeight(true)+$(".bd-toc").outerHeight(true);var hideTocAfter=tocHeight+200;var checkTocScroll=function(){var margin_content=$(".margin, .tag_margin, .full-width, .full_width, .tag_full-width, .tag_full_width, .sidebar, .tag_sidebar, .popout, .tag_popout");margin_content.each((index,item)=>{var topOffset=$(item).offset().top-$(window).scrollTop();var bottomOffset=topOffset+$(item).outerHeight(true);var topOverlaps=((topOffset>=0)&&(topOffset<hideTocAfter));var bottomOverlaps=((bottomOffset>=0)&&(bottomOffset<hideTocAfter));var removeToc=(topOverlaps||bottomOverlaps);if(removeToc&&window.pageYOffset>20){$("div.bd-toc").removeClass("show")
+return false}else{$("div.bd-toc").addClass("show")};})};var manageScrolledClassOnBody=function(){if(window.scrollY>0){document.body.classList.add("scrolled");}else{document.body.classList.remove("scrolled");}}
+$(window).on('scroll',function(){if(!scrollTimeout){scrollTimeout=setTimeout(function(){checkTocScroll();manageScrolledClassOnBody();scrollTimeout=null;},throttle);}});}
+var collapsibleListener=()=>{let expandUl=($ul)=>{if($ul.hasClass("collapse-ul")){$ul.removeClass("collapse-ul")
+$ul.next("i").removeClass("fa-chevron-down").addClass("fa-chevron-up")}}
+$elem=$("li.active")
+for(el of $elem){$ul=$(el).closest("ul")
+expandUl($ul)
+$ulChild=$(el).children("ul")
+expandUl($ulChild)
+$p=$ul.prev()
+if($p.is(".caption, .collapsible-parent")){$p.find("i").removeClass("fa-chevron-down").addClass("fa-chevron-up")}}
+$(".collapsible-parent>i, .caption.collapsible-parent").on("click",function(e){e.stopPropagation()
+$i=$(this)
+$collapsibleParent=$i.closest(".collapsible-parent")
+if($collapsibleParent.prop("tagName")=="P"){$ul=$collapsibleParent.next("ul")}else{$ul=$collapsibleParent.find("ul:first")}
+$ul.toggle(0,function(){if(!$i.is(".fas")){$i=$i.find("i")}
+if($i.hasClass("fa-chevron-up")){$i.removeClass("fa-chevron-up")
+$i.addClass("fa-chevron-down")}else{$i.removeClass("fa-chevron-down")
+$i.addClass("fa-chevron-up")}})})}
+var initThebeSBT=()=>{var title=$("div.section h1")[0]
+if(!$(title).next().hasClass("thebe-launch-button")){$("<button class='thebe-launch-button'></button>").insertAfter($(title))}
+initThebe();}
+sbRunWhenDOMLoaded(initTooltips)
+sbRunWhenDOMLoaded(initTriggerNavBar)
+sbRunWhenDOMLoaded(scrollToActive)
+sbRunWhenDOMLoaded(initTocHide)
+sbRunWhenDOMLoaded(collapsibleListener)
diff --git a/docs/_static/underscore-1.12.0.js b/docs/_static/underscore-1.12.0.js
new file mode 100644
index 00000000..3af6352e
--- /dev/null
+++ b/docs/_static/underscore-1.12.0.js
@@ -0,0 +1,2027 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.12.0
+  //     https://underscorejs.org
+  //     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.12.0';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _(obj) {
+    if (obj instanceof _) return obj;
+    if (!(this instanceof _)) return new _(obj);
+    this._wrapped = obj;
+  }
+
+  _.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
+
+  _.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _) a = a._wrapped;
+    if (b instanceof _) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath(path) {
+    return isArray(path) ? path : [path];
+  }
+  _.toPath = toPath;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath$1(path) {
+    return _.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath$1(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has$1(obj, path) {
+    path = toPath$1(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath$1(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_.iteratee !== iteratee) return _.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    // If a variable is not specified, place data values in local scope.
+    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(settings.variable || 'obj', '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    var argument = settings.variable || 'obj';
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath$1(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath$1(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten$1(array, depth) {
+    return flatten(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _[name] = obj[name];
+      _.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_, args));
+      };
+    });
+    return _;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has$1,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten$1,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _$1 = mixin(allExports);
+  // Legacy Node.js API.
+  _$1._ = _$1;
+
+  return _$1;
+
+})));
+//# sourceMappingURL=underscore.js.map
diff --git a/docs/_static/underscore.js b/docs/_static/underscore.js
index 5b55f32b..166240ef 100644
--- a/docs/_static/underscore.js
+++ b/docs/_static/underscore.js
@@ -1,31 +1,6 @@
-// Underscore.js 1.3.1
-// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-// Underscore is freely distributable under the MIT license.
-// Portions of Underscore are inspired or borrowed from Prototype,
-// Oliver Steele's Functional, and John Resig's Micro-Templating.
-// For all details and documentation:
-// http://documentcloud.github.com/underscore
-(function(){function q(a,c,d){if(a===c)return a!==0||1/a==1/c;if(a==null||c==null)return a===c;if(a._chain)a=a._wrapped;if(c._chain)c=c._wrapped;if(a.isEqual&&b.isFunction(a.isEqual))return a.isEqual(c);if(c.isEqual&&b.isFunction(c.isEqual))return c.isEqual(a);var e=l.call(a);if(e!=l.call(c))return false;switch(e){case "[object String]":return a==String(c);case "[object Number]":return a!=+a?c!=+c:a==0?1/a==1/c:a==+c;case "[object Date]":case "[object Boolean]":return+a==+c;case "[object RegExp]":return a.source==
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diff --git a/docs/_static/webpack-macros.html b/docs/_static/webpack-macros.html
index 50acc79f..60432fa3 100644
--- a/docs/_static/webpack-macros.html
+++ b/docs/_static/webpack-macros.html
@@ -16,13 +16,13 @@
 {% endmacro %}
 
 {% macro head_pre_bootstrap() %}
-  <link rel="stylesheet" href="{{ pathto('_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css', 1) }}">
+  <link rel="stylesheet" href="{{ pathto('_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css', 1) }}">
 {% endmacro %}
 
 {% macro head_js_preload() %}
-  <link rel="preload" as="script" href="{{ pathto('_static/js/index.3da636dd464baa7582d2.js', 1) }}">
+  <link rel="preload" as="script" href="{{ pathto('_static/js/index.d3f166471bb80abb5163.js', 1) }}">
 {% endmacro %}
 
 {% macro body_post() %}
-  <script src="{{ pathto('_static/js/index.3da636dd464baa7582d2.js', 1) }}"></script>
+  <script src="{{ pathto('_static/js/index.d3f166471bb80abb5163.js', 1) }}"></script>
 {% endmacro %}
\ No newline at end of file
diff --git a/docs/changelog/v0.1.3.html b/docs/changelog/v0.1.3.html
index bf1d4563..39133553 100644
--- a/docs/changelog/v0.1.3.html
+++ b/docs/changelog/v0.1.3.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Changes in SysIdentPy &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/changelog/v0.1.3.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -242,21 +239,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -274,7 +271,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -293,19 +290,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -354,6 +351,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -441,13 +439,13 @@ <h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -466,7 +464,7 @@ <h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/changelog/v0.1.4.html b/docs/changelog/v0.1.4.html
index c29b53c9..060a150e 100644
--- a/docs/changelog/v0.1.4.html
+++ b/docs/changelog/v0.1.4.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Changes in SysIdentPy &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/changelog/v0.1.4.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -244,21 +241,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +273,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +292,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -356,6 +353,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -443,15 +441,15 @@ <h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="../examples/general_estimators.html" title="previous page">Building NARX models using general estimators</a>
     <a class='right-next' id="next-link" href="../code.html" title="next page">Codes</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -470,7 +468,7 @@ <h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/code.html b/docs/code.html
index 5e6c5799..8f779115 100644
--- a/docs/code.html
+++ b/docs/code.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Codes &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/code.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -243,21 +240,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -275,7 +272,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -294,19 +291,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -371,6 +368,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -388,13 +386,13 @@ <h1>Codes<a class="headerlink" href="#codes" title="Permalink to this headline">
 <p>Base classes for NARX estimator.</p>
 <dl class="py class">
 <dt id="sysidentpy.base.GenerateRegressors">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.base.</code><code class="sig-name descname">GenerateRegressors</code><a class="reference internal" href="_modules/sysidentpy/base.html#GenerateRegressors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.GenerateRegressors" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.base.</span></code><code class="sig-name descname"><span class="pre">GenerateRegressors</span></code><a class="reference internal" href="_modules/sysidentpy/base.html#GenerateRegressors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.GenerateRegressors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Polynomial NARMAX model</p>
 <p>Provides the main functions to generate the regressor dictionary
 and regressor codes for polynomial basis.</p>
 <dl class="py method">
 <dt id="sysidentpy.base.GenerateRegressors.regressor_space">
-<code class="sig-name descname">regressor_space</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">non_degree</span></em>, <em class="sig-param"><span class="n">xlag</span></em>, <em class="sig-param"><span class="n">ylag</span></em>, <em class="sig-param"><span class="n">n_inputs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#GenerateRegressors.regressor_space"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.GenerateRegressors.regressor_space" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">regressor_space</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">non_degree</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#GenerateRegressors.regressor_space"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.GenerateRegressors.regressor_space" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create the code representation of the regressors.</p>
 <p>This function generates a codification from all possibles
 regressors given the maximum lag of the input and output.
@@ -438,17 +436,17 @@ <h1>Codes<a class="headerlink" href="#codes" title="Permalink to this headline">
 
 <dl class="py class">
 <dt id="sysidentpy.base.HouseHolder">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.base.</code><code class="sig-name descname">HouseHolder</code><a class="reference internal" href="_modules/sysidentpy/base.html#HouseHolder"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.HouseHolder" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.base.</span></code><code class="sig-name descname"><span class="pre">HouseHolder</span></code><a class="reference internal" href="_modules/sysidentpy/base.html#HouseHolder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.HouseHolder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Householder reflection and transformation.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt id="sysidentpy.base.InformationMatrix">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.base.</code><code class="sig-name descname">InformationMatrix</code><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.base.</span></code><code class="sig-name descname"><span class="pre">InformationMatrix</span></code><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class for methods regarding preprocessing of columns</p>
 <dl class="py method">
 <dt id="sysidentpy.base.InformationMatrix.shift_column">
-<code class="sig-name descname">shift_column</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">col_to_shift</span></em>, <em class="sig-param"><span class="n">lag</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.shift_column"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.shift_column" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">shift_column</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_to_shift</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lag</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.shift_column"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.shift_column" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shift values based on a lag.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -474,7 +472,7 @@ <h1>Codes<a class="headerlink" href="#codes" title="Permalink to this headline">
 
 <dl class="py method">
 <dt id="sysidentpy.base.InformationMatrix.initial_lagged_matrix">
-<code class="sig-name descname">initial_lagged_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">xlag</span></em>, <em class="sig-param"><span class="n">ylag</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.initial_lagged_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.initial_lagged_matrix" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">initial_lagged_matrix</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.initial_lagged_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.initial_lagged_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Build a lagged matrix concerning each lag for each column.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -502,7 +500,7 @@ <h1>Codes<a class="headerlink" href="#codes" title="Permalink to this headline">
 
 <dl class="py method">
 <dt id="sysidentpy.base.InformationMatrix.build_information_matrix">
-<code class="sig-name descname">build_information_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">xlag</span></em>, <em class="sig-param"><span class="n">ylag</span></em>, <em class="sig-param"><span class="n">non_degree</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.build_information_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.build_information_matrix" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">build_information_matrix</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_degree</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/base.html#InformationMatrix.build_information_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.base.InformationMatrix.build_information_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p>Build the information matrix.</p>
 <p>Each columns of the information matrix represents a candidate
 regressor. The set of candidate regressors are based on xlag,
@@ -535,7 +533,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 <span class="target" id="module-sysidentpy.polynomial_basis.narmax"></span><p>Build Polynomial NARMAX Models</p>
 <dl class="py class">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.polynomial_basis.narmax.</code><code class="sig-name descname">PolynomialNarmax</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">non_degree</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">ylag</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">xlag</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">order_selection</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">info_criteria</span><span class="o">=</span><span class="default_value">'aic'</span></em>, <em class="sig-param"><span class="n">n_terms</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">n_inputs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">n_info_values</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">estimator</span><span class="o">=</span><span class="default_value">'least_squares'</span></em>, <em class="sig-param"><span class="n">extended_least_squares</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">aux_lag</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">lam</span><span class="o">=</span><span class="default_value">0.98</span></em>, <em class="sig-param"><span class="n">delta</span><span class="o">=</span><span class="default_value">0.01</span></em>, <em class="sig-param"><span class="n">offset_covariance</span><span class="o">=</span><span class="default_value">0.2</span></em>, <em class="sig-param"><span class="n">mu</span><span class="o">=</span><span class="default_value">0.01</span></em>, <em class="sig-param"><span class="n">eps</span><span class="o">=</span><span class="default_value">2.220446049250313e-16</span></em>, <em class="sig-param"><span class="n">gama</span><span class="o">=</span><span class="default_value">0.2</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">0.02</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.polynomial_basis.narmax.</span></code><code class="sig-name descname"><span class="pre">PolynomialNarmax</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">non_degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_criteria</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'aic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_terms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_info_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'least_squares'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_least_squares</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.98</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.220446049250313e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gama</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Polynomial NARXMAX model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -615,7 +613,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 </dl>
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.error_reduction_ratio">
-<code class="sig-name descname">error_reduction_ratio</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">process_term_number</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.error_reduction_ratio"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.error_reduction_ratio" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">error_reduction_ratio</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_term_number</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.error_reduction_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.error_reduction_ratio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform the Error Reduction Ration algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -648,7 +646,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.fit">
-<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.fit" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit polynomial NARMAX model.</p>
 <p>This is an ‘alpha’ version of the ‘fit’ function which allows
 a friendly usage by the user. Given two arguments, X and y, fit
@@ -678,7 +676,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.predict">
-<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.predict" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the predicted values given an input.</p>
 <p>The predict function allows a friendly usage by the user.
 Given a previously trained model, predict values given
@@ -703,7 +701,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.information_criterion">
-<code class="sig-name descname">information_criterion</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.information_criterion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.information_criterion" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">information_criterion</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.information_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.information_criterion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine the model order.</p>
 <p>This function uses a information criterion to determine the model size.
 ‘Akaike’-  Akaike’s Information Criterion with</p>
@@ -734,7 +732,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.results">
-<code class="sig-name descname">results</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">theta_precision</span><span class="o">=</span><span class="default_value">4</span></em>, <em class="sig-param"><span class="n">err_precision</span><span class="o">=</span><span class="default_value">8</span></em>, <em class="sig-param"><span class="n">dtype</span><span class="o">=</span><span class="default_value">'dec'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.results"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.results" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">results</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dec'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the model regressors, parameters and ERR values.</p>
 <p>This function returns the model regressors, its respectives parameter
 and ERR value on a string matrix.</p>
@@ -767,7 +765,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 <dl class="py method">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax.compute_info_value">
-<code class="sig-name descname">compute_info_value</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n_theta</span></em>, <em class="sig-param"><span class="n">n_samples</span></em>, <em class="sig-param"><span class="n">e_var</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.compute_info_value"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.compute_info_value" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">compute_info_value</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e_var</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax.compute_info_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax.compute_info_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the information criteria value.</p>
 <p>This function returns the information criteria concerning each
 number of regressor. The informotion criteria can be AIC, BIC,
@@ -801,7 +799,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <span class="target" id="module-sysidentpy.general_estimators.narx"></span><p>Build NARX Models Using general estimators</p>
 <dl class="py class">
 <dt id="sysidentpy.general_estimators.narx.NARX">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.general_estimators.narx.</code><code class="sig-name descname">NARX</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">non_degree</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">ylag</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">xlag</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">n_inputs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">base_estimator</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">fit_params</span><span class="o">=</span><span class="default_value">{}</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.general_estimators.narx.</span></code><code class="sig-name descname"><span class="pre">NARX</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">non_degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX" title="Permalink to this definition">¶</a></dt>
 <dd><p>NARX model build on top of general estimators</p>
 <p>Currently is possible to use any estimator that have a fit/predict
 as an Autoregressive Model. We use our GenerateRegressors and
@@ -845,7 +843,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 </div>
 <dl class="py method">
 <dt id="sysidentpy.general_estimators.narx.NARX.data_preparation">
-<code class="sig-name descname">data_preparation</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.data_preparation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.data_preparation" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">data_preparation</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.data_preparation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.data_preparation" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the lagged matrix and the y values given the maximum lags.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -866,7 +864,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.general_estimators.narx.NARX.fit">
-<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.fit" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Train a NARX Neural Network model.</p>
 <p>This is an training pipeline that allows a friendly usage
 by the user. All the lagged features are built using the
@@ -890,7 +888,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.general_estimators.narx.NARX.predict">
-<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y_initial</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.predict" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_initial</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/general_estimators/narx.html#NARX.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.general_estimators.narx.NARX.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the predicted given an input and initial values.</p>
 <p>The predict function allows a friendly usage by the user.
 Given a trained model, predict values given
@@ -922,12 +920,12 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <span id="sysidentpy-residues"></span><h1>sysidentpy residues<a class="headerlink" href="#module-sysidentpy.residues.residues_correlation" title="Permalink to this headline">¶</a></h1>
 <dl class="py class">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.residues.residues_correlation.</code><code class="sig-name descname">ResiduesAnalysis</code><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.residues.residues_correlation.</span></code><code class="sig-name descname"><span class="pre">ResiduesAnalysis</span></code><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>Residues analysis for Polynomial NARX model.</p>
 <dl class="py method">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis.residuals">
-<code class="sig-name descname">residuals</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">yhat</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis.residuals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.residuals" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">residuals</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yhat</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis.residuals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.residuals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs the residual analysis of output to validate model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -959,7 +957,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis.plot_result">
-<code class="sig-name descname">plot_result</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">yhat</span></em>, <em class="sig-param"><span class="n">e_acf</span></em>, <em class="sig-param"><span class="n">xe_ccf</span></em>, <em class="sig-param"><span class="n">figsize</span><span class="o">=</span><span class="default_value">(10, 8)</span></em>, <em class="sig-param"><span class="n">n</span><span class="o">=</span><span class="default_value">100</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis.plot_result"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.plot_result" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">plot_result</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yhat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e_acf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xe_ccf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">8)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/residues/residues_correlation.html#ResiduesAnalysis.plot_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.plot_result" title="Permalink to this definition">¶</a></dt>
 <dd><p>Plot the free run simulation and residues analysis.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -979,17 +977,17 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py attribute">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis.__dict__">
-<code class="sig-name descname">__dict__</code><em class="property"> = mappingproxy({'__module__': 'sysidentpy.residues.residues_correlation', '__doc__': 'Residues analysis for Polynomial NARX model.', 'residuals': &lt;function ResiduesAnalysis.residuals&gt;, '_input_ccf': &lt;function ResiduesAnalysis._input_ccf&gt;, '_residuals_acf': &lt;function ResiduesAnalysis._residuals_acf&gt;, '_normalized_correlation': &lt;function ResiduesAnalysis._normalized_correlation&gt;, 'plot_result': &lt;function ResiduesAnalysis.plot_result&gt;, '__dict__': &lt;attribute '__dict__' of 'ResiduesAnalysis' objects&gt;, '__weakref__': &lt;attribute '__weakref__' of 'ResiduesAnalysis' objects&gt;})</em><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__dict__" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">__dict__</span></code><em class="property"> <span class="pre">=</span> <span class="pre">mappingproxy({'__module__':</span> <span class="pre">'sysidentpy.residues.residues_correlation',</span> <span class="pre">'__doc__':</span> <span class="pre">'Residues</span> <span class="pre">analysis</span> <span class="pre">for</span> <span class="pre">Polynomial</span> <span class="pre">NARX</span> <span class="pre">model.',</span> <span class="pre">'residuals':</span> <span class="pre">&lt;function</span> <span class="pre">ResiduesAnalysis.residuals&gt;,</span> <span class="pre">'_input_ccf':</span> <span class="pre">&lt;function</span> <span class="pre">ResiduesAnalysis._input_ccf&gt;,</span> <span class="pre">'_residuals_acf':</span> <span class="pre">&lt;function</span> <span class="pre">ResiduesAnalysis._residuals_acf&gt;,</span> <span class="pre">'_normalized_correlation':</span> <span class="pre">&lt;function</span> <span class="pre">ResiduesAnalysis._normalized_correlation&gt;,</span> <span class="pre">'plot_result':</span> <span class="pre">&lt;function</span> <span class="pre">ResiduesAnalysis.plot_result&gt;,</span> <span class="pre">'__dict__':</span> <span class="pre">&lt;attribute</span> <span class="pre">'__dict__'</span> <span class="pre">of</span> <span class="pre">'ResiduesAnalysis'</span> <span class="pre">objects&gt;,</span> <span class="pre">'__weakref__':</span> <span class="pre">&lt;attribute</span> <span class="pre">'__weakref__'</span> <span class="pre">of</span> <span class="pre">'ResiduesAnalysis'</span> <span class="pre">objects&gt;,</span> <span class="pre">'__annotations__':</span> <span class="pre">{}})</span></em><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__dict__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis.__module__">
-<code class="sig-name descname">__module__</code><em class="property"> = 'sysidentpy.residues.residues_correlation'</em><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__module__" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">__module__</span></code><em class="property"> <span class="pre">=</span> <span class="pre">'sysidentpy.residues.residues_correlation'</span></em><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__module__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
 <dt id="sysidentpy.residues.residues_correlation.ResiduesAnalysis.__weakref__">
-<code class="sig-name descname">__weakref__</code><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__weakref__" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">__weakref__</span></code><a class="headerlink" href="#sysidentpy.residues.residues_correlation.ResiduesAnalysis.__weakref__" title="Permalink to this definition">¶</a></dt>
 <dd><p>list of weak references to the object (if defined)</p>
 </dd></dl>
 
@@ -1001,7 +999,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <p>Common metrics to assess performance on NARX models.</p>
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.forecast_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">forecast_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#forecast_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.forecast_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">forecast_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#forecast_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.forecast_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the forecast error in a regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1034,7 +1032,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.mean_forecast_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">mean_forecast_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_forecast_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_forecast_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">mean_forecast_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_forecast_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_forecast_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the mean of forecast error of a regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1068,7 +1066,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.mean_squared_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">mean_squared_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_squared_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_squared_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">mean_squared_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_squared_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_squared_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Mean Squared Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1101,7 +1099,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.root_mean_squared_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">root_mean_squared_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#root_mean_squared_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.root_mean_squared_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">root_mean_squared_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#root_mean_squared_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.root_mean_squared_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Root Mean Squared Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1134,7 +1132,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.normalized_root_mean_squared_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">normalized_root_mean_squared_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#normalized_root_mean_squared_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.normalized_root_mean_squared_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">normalized_root_mean_squared_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#normalized_root_mean_squared_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.normalized_root_mean_squared_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the normalized Root Mean Squared Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1167,7 +1165,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.root_relative_squared_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">root_relative_squared_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#root_relative_squared_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.root_relative_squared_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">root_relative_squared_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#root_relative_squared_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.root_relative_squared_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Root Relative Mean Squared Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1195,7 +1193,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.mean_absolute_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">mean_absolute_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_absolute_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_absolute_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">mean_absolute_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_absolute_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_absolute_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Mean absolute error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1228,7 +1226,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.mean_squared_log_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">mean_squared_log_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_squared_log_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_squared_log_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">mean_squared_log_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#mean_squared_log_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.mean_squared_log_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Mean Squared Logarithmic Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1256,7 +1254,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.median_absolute_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">median_absolute_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#median_absolute_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.median_absolute_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">median_absolute_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#median_absolute_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.median_absolute_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Median Absolute Error.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1289,7 +1287,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.explained_variance_score">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">explained_variance_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#explained_variance_score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.explained_variance_score" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">explained_variance_score</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#explained_variance_score"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.explained_variance_score" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the Explained Variance Score.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1323,7 +1321,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.r2_score">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">r2_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#r2_score"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.r2_score" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">r2_score</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#r2_score"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.r2_score" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the R2 score.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1359,7 +1357,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.metrics._regression.symmetric_mean_absolute_percentage_error">
-<code class="sig-prename descclassname">sysidentpy.metrics._regression.</code><code class="sig-name descname">symmetric_mean_absolute_percentage_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">y_predicted</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#symmetric_mean_absolute_percentage_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.metrics._regression.symmetric_mean_absolute_percentage_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.metrics._regression.</span></code><code class="sig-name descname"><span class="pre">symmetric_mean_absolute_percentage_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_predicted</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/metrics/_regression.html#symmetric_mean_absolute_percentage_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.metrics._regression.symmetric_mean_absolute_percentage_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the SMAPE score.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1399,11 +1397,11 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <p>Least Squares Methodos for parameter estimation</p>
 <dl class="py class">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators">
-<em class="property">class </em><code class="sig-prename descclassname">sysidentpy.parameter_estimation.estimators.</code><code class="sig-name descname">Estimators</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">aux_lag</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">lam</span><span class="o">=</span><span class="default_value">0.98</span></em>, <em class="sig-param"><span class="n">delta</span><span class="o">=</span><span class="default_value">0.01</span></em>, <em class="sig-param"><span class="n">offset_covariance</span><span class="o">=</span><span class="default_value">0.2</span></em>, <em class="sig-param"><span class="n">mu</span><span class="o">=</span><span class="default_value">0.01</span></em>, <em class="sig-param"><span class="n">eps</span><span class="o">=</span><span class="default_value">2.220446049250313e-16</span></em>, <em class="sig-param"><span class="n">gama</span><span class="o">=</span><span class="default_value">0.2</span></em>, <em class="sig-param"><span class="n">weight</span><span class="o">=</span><span class="default_value">0.02</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.parameter_estimation.estimators.</span></code><code class="sig-name descname"><span class="pre">Estimators</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">aux_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.98</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.220446049250313e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gama</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators" title="Permalink to this definition">¶</a></dt>
 <dd><p>Oridanry Least squares for linear parameter estimation</p>
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_squares">
-<code class="sig-name descname">least_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate the model parameters using Least Squares method.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1438,7 +1436,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.total_least_squares">
-<code class="sig-name descname">total_least_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.total_least_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.total_least_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">total_least_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.total_least_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.total_least_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate the model parameters using Total Least Squares method.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1470,7 +1468,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.recursive_least_squares">
-<code class="sig-name descname">recursive_least_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.recursive_least_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.recursive_least_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">recursive_least_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.recursive_least_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.recursive_least_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate the model parameters using the Recursive Least Squares method.</p>
 <p>The implementation consider the forgeting factor.
 :param psi: The information matrix of the model.
@@ -1499,7 +1497,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.affine_least_mean_squares">
-<code class="sig-name descname">affine_least_mean_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.affine_least_mean_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.affine_least_mean_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">affine_least_mean_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.affine_least_mean_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.affine_least_mean_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate the model parameters using the Affine Least Mean Squares.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1528,7 +1526,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares">
-<code class="sig-name descname">least_mean_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Estimate the model parameters using the Least Mean Squares filter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1562,7 +1560,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_error">
-<code class="sig-name descname">least_mean_squares_sign_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_sign_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Sign-Error  Least Mean Squares filter.</p>
 <p>The sign-error LMS algorithm uses the sign of the error vector
 to change the filter coefficients.</p>
@@ -1598,7 +1596,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.normalized_least_mean_squares">
-<code class="sig-name descname">normalized_least_mean_squares</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.normalized_least_mean_squares"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.normalized_least_mean_squares" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">normalized_least_mean_squares</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.normalized_least_mean_squares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.normalized_least_mean_squares" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Normalized Least Mean Squares filter.</p>
 <p>The normalization is used to avoid numerical instability when updating
 the estimated parameters.</p>
@@ -1634,7 +1632,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_error">
-<code class="sig-name descname">least_mean_squares_normalized_sign_error</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_error"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_error" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_normalized_sign_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_error" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Normalized Sign-Error LMS filter.</p>
 <p>The normalization is used to avoid numerical instability when updating
 the estimated parameters and the sign of the error vector is used to
@@ -1671,7 +1669,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_regressor">
-<code class="sig-name descname">least_mean_squares_sign_regressor</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_regressor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_regressor" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_sign_regressor</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_regressor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_regressor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the  Sign-Regressor LMS filter.</p>
 <p>The sign-regressor LMS algorithm uses the sign of the matrix
 information to change the filter coefficients.</p>
@@ -1707,7 +1705,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_regressor">
-<code class="sig-name descname">least_mean_squares_normalized_sign_regressor</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_regressor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_regressor" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_normalized_sign_regressor</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_regressor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_regressor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Normalized Sign-Regressor LMS filter.</p>
 <p>The normalization is used to avoid numerical instability when updating
 the estimated parameters and the sign of the information matrix is
@@ -1744,7 +1742,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_sign">
-<code class="sig-name descname">least_mean_squares_sign_sign</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_sign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_sign" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_sign_sign</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_sign_sign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_sign_sign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the  Sign-Sign LMS filter.</p>
 <p>The sign-regressor LMS algorithm uses both the sign of the matrix
 information and the sign of the error vector to change the filter
@@ -1781,7 +1779,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_sign">
-<code class="sig-name descname">least_mean_squares_normalized_sign_sign</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_sign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_sign" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_normalized_sign_sign</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_sign_sign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_sign_sign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Normalized Sign-Sign LMS filter.</p>
 <p>The normalization is used to avoid numerical instability when updating
 the estimated parameters and both the sign of the information matrix
@@ -1819,7 +1817,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_leaky">
-<code class="sig-name descname">least_mean_squares_normalized_leaky</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_leaky"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_leaky" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_normalized_leaky</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_normalized_leaky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_normalized_leaky" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the  Normalized Leaky LMS filter.</p>
 <p>When the leakage factor, gama, is set to 0 then there is no
 leakage in the estimation process.</p>
@@ -1855,7 +1853,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_leaky">
-<code class="sig-name descname">least_mean_squares_leaky</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_leaky"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_leaky" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_leaky</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_leaky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_leaky" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the  Leaky LMS filter.</p>
 <p>When the leakage factor, gama, is set to 0 then there is no
 leakage in the estimation process.</p>
@@ -1891,7 +1889,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_fourth">
-<code class="sig-name descname">least_mean_squares_fourth</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_fourth"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_fourth" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_fourth</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_fourth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_fourth" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the  LMS Fourth filter.</p>
 <p>When the leakage factor, gama, is set to 0 then there is no
 leakage in the estimation process.</p>
@@ -1937,7 +1935,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py method">
 <dt id="sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_mixed_norm">
-<code class="sig-name descname">least_mean_squares_mixed_norm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">psi</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_mixed_norm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_mixed_norm" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">least_mean_squares_mixed_norm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/parameter_estimation/estimators.html#Estimators.least_mean_squares_mixed_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.parameter_estimation.estimators.Estimators.least_mean_squares_mixed_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parameter estimation using the Mixed-norm LMS filter.</p>
 <p>The weight factor controls the proportions of the error norms
 and offers an extra degree of freedom within the adaptation.</p>
@@ -1981,7 +1979,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <p>Utilities fo data validation</p>
 <dl class="py function">
 <dt id="sysidentpy.utils._check_arrays.check_infinity">
-<code class="sig-prename descclassname">sysidentpy.utils._check_arrays.</code><code class="sig-name descname">check_infinity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_infinity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_infinity" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils._check_arrays.</span></code><code class="sig-name descname"><span class="pre">check_infinity</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_infinity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_infinity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check that X and y have no NaN or Inf samples.</p>
 <p>If there is any NaN or Inf samples a ValueError is raised.</p>
 <dl class="field-list simple">
@@ -1996,7 +1994,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.utils._check_arrays.check_nan">
-<code class="sig-prename descclassname">sysidentpy.utils._check_arrays.</code><code class="sig-name descname">check_nan</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_nan"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_nan" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils._check_arrays.</span></code><code class="sig-name descname"><span class="pre">check_nan</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_nan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_nan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check that X and y have no NaN or Inf samples.</p>
 <p>If there is any NaN or Inf samples a ValueError is raised.</p>
 <dl class="field-list simple">
@@ -2011,7 +2009,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.utils._check_arrays.check_length">
-<code class="sig-prename descclassname">sysidentpy.utils._check_arrays.</code><code class="sig-name descname">check_length</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_length"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_length" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils._check_arrays.</span></code><code class="sig-name descname"><span class="pre">check_length</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_length" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check that X and y have the same number of samples.</p>
 <p>If the length of X and y are different a ValueError is raised.</p>
 <dl class="field-list simple">
@@ -2026,7 +2024,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.utils._check_arrays.check_dimension">
-<code class="sig-prename descclassname">sysidentpy.utils._check_arrays.</code><code class="sig-name descname">check_dimension</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_dimension"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_dimension" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils._check_arrays.</span></code><code class="sig-name descname"><span class="pre">check_dimension</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_dimension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_dimension" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if X and y have only real values.</p>
 <p>If there is any string or object samples a ValueError is raised.</p>
 <dl class="field-list simple">
@@ -2041,7 +2039,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.utils._check_arrays.check_X_y">
-<code class="sig-prename descclassname">sysidentpy.utils._check_arrays.</code><code class="sig-name descname">check_X_y</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_X_y"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_X_y" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils._check_arrays.</span></code><code class="sig-name descname"><span class="pre">check_X_y</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/_check_arrays.html#check_X_y"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils._check_arrays.check_X_y" title="Permalink to this definition">¶</a></dt>
 <dd><p>Validate input and output data using some crucial tests.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2059,7 +2057,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 <p>Utilities for data generation</p>
 <dl class="py function">
 <dt id="sysidentpy.utils.generate_data.get_siso_data">
-<code class="sig-prename descclassname">sysidentpy.utils.generate_data.</code><code class="sig-name descname">get_siso_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span><span class="o">=</span><span class="default_value">5000</span></em>, <em class="sig-param"><span class="n">colored_noise</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">sigma</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">train_percentage</span><span class="o">=</span><span class="default_value">90</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/generate_data.html#get_siso_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils.generate_data.get_siso_data" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils.generate_data.</span></code><code class="sig-name descname"><span class="pre">get_siso_data</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored_noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">90</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/generate_data.html#get_siso_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils.generate_data.get_siso_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform the Error Reduction Ration algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2085,7 +2083,7 @@ <h1>sysidentpy general_estimators<a class="headerlink" href="#sysidentpy-general
 
 <dl class="py function">
 <dt id="sysidentpy.utils.generate_data.get_miso_data">
-<code class="sig-prename descclassname">sysidentpy.utils.generate_data.</code><code class="sig-name descname">get_miso_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span><span class="o">=</span><span class="default_value">5000</span></em>, <em class="sig-param"><span class="n">colored_noise</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">sigma</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">train_percentage</span><span class="o">=</span><span class="default_value">90</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/generate_data.html#get_miso_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sysidentpy.utils.generate_data.get_miso_data" title="Permalink to this definition">¶</a></dt>
+<code class="sig-prename descclassname"><span class="pre">sysidentpy.utils.generate_data.</span></code><code class="sig-name descname"><span class="pre">get_miso_data</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored_noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">90</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/utils/generate_data.html#get_miso_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.utils.generate_data.get_miso_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform the Error Reduction Ration algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2122,14 +2120,14 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="changelog/v0.1.4.html" title="previous page">Changes in SysIdentPy</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -2148,7 +2146,7 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/dev_guide.html b/docs/dev_guide.html
index 9b9e5a08..549fc83c 100644
--- a/docs/dev_guide.html
+++ b/docs/dev_guide.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Contributing &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/dev_guide.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -244,21 +241,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +273,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +292,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -332,6 +329,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -376,15 +374,15 @@ <h2>Development environment<a class="headerlink" href="#development-environment"
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="user_guide.html" title="previous page">User Guide</a>
     <a class='right-next' id="next-link" href="notebooks.html" title="next page">Jupyter notebooks</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -403,7 +401,7 @@ <h2>Development environment<a class="headerlink" href="#development-environment"
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/basic_steps.html b/docs/examples/basic_steps.html
index 04851f0d..ce8d7ac7 100644
--- a/docs/examples/basic_steps.html
+++ b/docs/examples/basic_steps.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Presenting main functionality &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,23 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
     <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
     <link rel="canonical" href="http://sysidentpy.org/examples/basic_steps.html" />
@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -351,6 +350,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -425,7 +425,7 @@ <h2>Model Structure Selection<a class="headerlink" href="#model-structure-select
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fd019689d50&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1fd33cab3d0&gt;
 </pre></div>
 </div>
 </div>
@@ -453,7 +453,7 @@ <h2>Evaluate the model<a class="headerlink" href="#evaluate-the-model" title="Pe
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.001993603325328823
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0018401326800931033
 </pre></div>
 </div>
 </div>
@@ -470,10 +470,13 @@ <h2>Evaluate the model<a class="headerlink" href="#evaluate-the-model" title="Pe
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Regressors Parameters         ERR
-0        x1(k-2)     0.9000  0.95556574
-1         y(k-1)     0.1999  0.04107943
-2  x1(k-1)y(k-1)     0.1000  0.00335113
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Regressors Parameters         ERR
+0        x1(k-2)     0.8999  0.95739001
+1         y(k-1)     0.2000  0.03917632
+2  x1(k-1)y(k-1)     0.0999  0.00343057
+3      x1(k-2)^2    -0.0002  0.00000002
+4        x1(k-1)     0.0001  0.00000001
+5  x1(k-1)y(k-2)     0.0002  0.00000001
 </pre></div>
 </div>
 </div>
@@ -586,12 +589,12 @@ <h2>Setting the <em>n_terms</em> parameter<a class="headerlink" href="#setting-t
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>rrse:  0.001993603325328823
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>rrse:  0.0018136024659265537
 
        Regressors Parameters         ERR
-0        x1(k-2)     0.9000  0.95556574
-1         y(k-1)     0.1999  0.04107943
-2  x1(k-1)y(k-1)     0.1000  0.00335113
+0        x1(k-2)     0.9000  0.95739001
+1         y(k-1)     0.2000  0.03917632
+2  x1(k-1)y(k-1)     0.0999  0.00343057
 </pre></div>
 </div>
 </div>
@@ -635,14 +638,14 @@ <h2>Extra information<a class="headerlink" href="#extra-information" title="Perm
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.95556574 0.04107943 0.00335113 0.         0.         0.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.95739001 0.03917632 0.00343057 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.        ] 
 
 
-[[0.9000403 ]
- [0.19990693]
- [0.10000669]]
+[[0.89995854]
+ [0.20004441]
+ [0.09991068]]
 </pre></div>
 </div>
 </div>
@@ -653,15 +656,15 @@ <h2>Extra information<a class="headerlink" href="#extra-information" title="Perm
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="../notebooks.html" title="previous page">Jupyter notebooks</a>
     <a class='right-next' id="next-link" href="multiple_inputs_example.html" title="next page">Multiple Inputs usage</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -680,7 +683,7 @@ <h2>Extra information<a class="headerlink" href="#extra-information" title="Perm
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/basic_steps.ipynb b/docs/examples/basic_steps.ipynb
index 1de44ba4..c3253b16 100644
--- a/docs/examples/basic_steps.ipynb
+++ b/docs/examples/basic_steps.ipynb
@@ -106,14 +106,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fd019689d50>"
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x18ccb778580>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
    "source": [
@@ -149,10 +149,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.001993603325328823\n"
+      "0.0019476338038772528\n"
      ]
     }
    ],
@@ -174,13 +174,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9000  0.95556574\n",
-      "1         y(k-1)     0.1999  0.04107943\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95550052\n1         y(k-1)     0.2000  0.04133385\n2  x1(k-1)y(k-1)     0.0998  0.00316200\n"
      ]
     }
    ],
@@ -207,2637 +204,13 @@
    },
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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2HRVB9ouiKBAEoeQxQRCg63qbRhQSEhKycK48bz1OYPaCuDe739gnhtXnISEhFbmY/yp2mC+FRZ0Lhud3vJ3/GgAgGk85q1FKGEFuhCUpkEVRLBPDuq5DkqQ2jSgkJCRk4WzfMgRdSEKhTgDgHu5lYfV5SMg8QqcXh8czMvKQwYDComTG7/gvGUcLEYZBlsTAqNNtHunSZEkK5IGBAYyPlyadT0xMoK+vr00jCgkJCVk44yMv4GRjGI/1nA8AeN2GeCiOQ0LmEDq9zDKYktFLstDB4pfWS3CTdS76SAaDKXlmmzyTAK+FArkRlqRA3rx5M4aHh2fcLiilGB4exkknndTegbWRcEYdErL0eeHu/weWUPS98oMAAFvNtnlEISGdxfm3bcYu9lJczt0NhlBczt2NXeylOP+2ze0e2qJz5XnrcQ0+AJFYeJKuwbXmO/FhfLQkJavIJiAamTaOcumyZATy+Pg4VFUFALzmNa9BsVjE9ddfj+eeew6f/exnkc/nccEFF7R5lO1hdkb9gyN+Rh0SslShto2h3bfgCX4T1m04GTplAS3X7mGFhHQUntOL5Tq9eHm3R6LTy/YtQ/j4S+MAgIO0G0MpGZ99w6aSVSeVT0E2Q4HcCEtGIJ999tm4/fbbAQCxWAxf//rXMTw8jIsvvhiPPvoobrzxRsRisTaPsj3MzqjvOeJn1CEhS5VnHr4bK+hB5I9/MwjDoEAiYPRQIIeEzIVPDbp5t47Ti5d3e6Q6vWyMOQ3S/vairXjgqm1lKVmmmELMDleiGqGjbN7m8vTTT9f8+8QTT8Stt966mEPqWLaqN+Aa7vt4LfsgWEKhUAG/sk7DZ8y34k/tHlxISIgvsg9+GwUq4YRXXg4AUEgEbCiQQ0JKuPK89YjcmoUBFgIs/MJ6yRHt9FKc2AcASA6sqfi8JXUhSXOglIJ49jghvlgyEeSQ6szOqJ2c7CN9Rh0SstRQ8hmcMHU3Hu86F7F40nmMiYA1C20eWUhIZ7F9yxCmz/0iBGIBAO4Szj2inV7stCOQ+wbXVHyeyt2QiIFiIb+Iozo8CAXyYcCV563HMmY2x+hX1qlH9Iw6JGSpseue7yFCNMRe8o6ZxzQmCsEMb2ohIfM5MZqe+ffbjueOWHEMACR3AJNIQpIjFZ9no90AgOzU6GIO67AgFMiHAdu3DGHipf8001zg//izjugZdUjIUkN+4kfYSwZx/OmvmnnM4KIQrTCCHBIyn/yh52f+bWcOtHEk7UdWDmGKrW5xy8d6AACF9HjVbUIqEwrkw4Tj5fTMv1+3xgrFcUjIEuHQ7l04Tn8c+1ZtB8POXpINLgbJLrZxZPXZMTyCsz53L9Ze9Uuc9bl7Q+eckEVBn3gRAJCDDCZ3sM2jaS8xbQx5ob/q82LCEc/FTGMC+Ui2kA0F8mFCcewFAIBFCdjsvjaPJiQkpB7ejcf85oWwKLB35etLnreEGGTauQLZs5fU0wfwI+HT0NMHQ3vJkEWBzezBFJI4xK2ArB5q93DaSq89DjVavd4okuwFAGi5icD7PtKbsoQC+TDBntoNnbLYw62BVDgyDt6QI5ulHNmY610+RCZxiPbg0/enSz4D5WOIdrBA/uKdT0MxLHyU+zFOI0/jg9wtUAwLX7zz6fovDglZAJHCPkzwy1AQ+5HQj9zUASWXRhxF0Phg1W1iXQMAACsfXCAf6U1ZOtbmLSQYQm4fxpk+ZKQV6FZebPdwQkJaiicwr6E/xGmsF9mIAsCSSC86/7bN2M4aM38PkUnswqXQbuOBLc6NjIpxSMSArqkQRKldQ63KPcqbIEmzn+Fy7m5czt0NVeEBBL8Zh4T4pUc/iNHERphiCt35P7d7OG1j4sCLWAmA61pRdZtEt5N+YRemAu/fs5C9gP0DBGJBpTzusE4/YixkwwjyYUJcHcG0sBx6bAj91hiobbd7SCEhLWOpRza8bmAGZQFU7gZGpAQAoJhLt2OIdXmT9N+4wzwN1HGXnPkMl0hfb+/AQg5rNF3DAB2HkVgNGh9EkhSQy6bbPay2kB3bAwCI9K6qug0vyihSEUQJLpA9C1kejqWeAPOIspANBfJhQr95EMXoCiC1CjLRMTVxZBcuhBzebFVvwC/N08vE2VJpN+vdeDhYoLSydznT4QL5na85EymmAEIw8xlUJoJ3veaMdg8t5DBmdN/z4IgNrmcNuC5ntWjq0J42j6o9KBN7AQCpZWtrbpdlEuC0dOD9X3neevQzWYwiBQC4yzrliLKQDQXyYUA+l0YXcrCTqyH1rgEATOx/rr2DCglpIXxqECZYEAKYlFlyzXG8G88EEvizfRRuss4tu/HwEUcgK/l0m0ZZm+1bhnAM63irFiDiVu48nDNEl0SKS8jSZXrkWQBAdNnRiPSuBgBkRo9MgWyl9wMAeqs0CfEoMAnweqbmNpXYvmUIhdd/CwJMAMAvhPOOKAvZUCAfBoztcYpihN41iC8/CgCQH32hnUMK6XCWcoEb4AjMAbc5zn32SRUFZiezfcsQihd/BwwodtHV+HrsA2U3Hk5OAQC0Qro9g6yDpqkQbAUAECMaXnfVTRi84mdtHlXI4U5x1PFA7l5xLFIDTmqBMrm3nUNqG0zuACaQgiTJNbdTuAQkI7hABoCzBym6idOw6G9O6ztixDEQCuTDgsxBJ1ocW34MelccDQDQJ3a3cUQhnczhYN2zfcsQdh9/BQCAh1VRYHY6564V0UNyWHPsiXjgqm1lYxdjTstpo5huw+jq88zD9yBBinhSdPK+c+mwMC+k9dhTu2FQFr3L16J7uRNBtqaXzrWrmUh1moR4aEIKEasxgXzwuT/P/NtSj6zOnqGLxWGANu64VvStPBaJVC+yiIBkQi/kkMrMd1C4nLsbl+PuEgeFpcCxSScBeYWQxwNXbWvzaIIzuvtJxAGI/UdXfF6MpgAARjG7eIMKQG7nL6FTFvmjLgSefAyFzCS6+5ZGikvI0kXI7cUY04chjoPIxZFGDEz+yOymF9dGMSWtrLudJaYQz+Uaeo/Cvr/M/NvWjqzOnmEE+XBgeg+KVESqZxkAYJwdCL2QQ6qyVb0Bd5qnLNkCNw/TjawmreDV2Z1AdsRJjUqt3FDx+Ui8CwBgKZ0pkAfH/w/PSCdC7HaWuYvZpfk7hCwtEsoIpsVZ398pthdicbShfS31VLMeewJapP6k1Ja6kKB5mKYZ+D3IxFMoUhEAQPUjK4IcCuTDALGwD6PsMhDG+Tlz4nIktdDFIqQycqIHp7DPgBDAruKgsBSwFWfJsItmYFtWm0cTHGPcSY1avqaaQE4BAKjaeQJ5/4tPYY29D8XV2yDGuwEAWm6yzaMKOdyhlKLPPAglOhs1zQv9iOljgfe11FPN/DQJ8WAi3WAJRS4d/BxN5J7DXuEo2JQAeuc2LmoFoUA+DEiqB5GRZk8SLTaEvtALOaQC1LZxLfM/6CU55KiEQ7RryRW4zeAKR47YSE8uvXaz7PSLGEUPItF4xecj0TgsSkC1xpZGW8n+P+wAAAye9nrIiR4AgJ4PI8ghrSWTnkY3yYGmVs88pskD6LaOvC5xEwec1MpaTUI82JjTbjo3HSzSTm0bQ/pu5BJHQ4EIoocpFiFLCGrbGLAOQYvNniQ0uRIxoiA7feS24AypzIM//iLOVX+N27svx+04GwJZmgVuAEC02aKTzMTSy0GMF/diXKh+cyMMgwKJgNE6L4Is774bI2QZVhx9IqKuQDY7tJgw5PBhfO8zAAChb93MY3Z8ED3IQFWCRTe9Zj2626xHp+ySSjXLju4GULtJiAcfd87RYjqYJhg/uA9JUgDt2wCFSCBmGEEOWUJMTxxClKglM2qx1zENHw+9kEPm8OQffo1Td30eO+WX4DUf+HesXj6AKNSKDgpLAUafjazml6BA7jNGUIjWvrkVIYMxOivvTynksF75M/b3vQwgBLEuJzplF6fbPLKQw53MQccDObF8trCVTTmTzMmDwbyQ5zbrARw3nKWUaqZMOoX4qeW1m4QAgJxwnC6UTDCBfOj5YQBAdMVGaEQCa4QCOWQJMb7fuWBIc2bU8QHnhMkeCr2Qj3S8IpRHrz0F/be/E6OkD2uu+D4YlgUVopCJDtMw6u+oA+GMHBQqAAC09NLKuc9nptCNLKyu2jc3hYmC6zCB/MwffgmJGIhsPB8AIMkx6JQDUdPtHVjIYY827tzT+lbNpoPJPU4+ctqNqPrFa9bzOHXOQQMsljPpJZNqZk3vg00JepevrrttNNUPANDzwXKQPQeLZcdsgcrIYK1QIIcsIXKuB3JycHZG3bfyGACAFnohH9F4RSiXFr6PLeQ5JJHH+8yP4N4XdQAAEaIAgGKx83Jc/SCYORzknNx7M7u0cpBHdz8BABD7j6m5ncZGwJudlfenPnEHilTEsae/xnmAEORIdEGpIEvdTSBkcWDSe5BDBPHUrPdvrN8RiMWJ/YH25TXr2UmcIlmBWJje+NdLZjWNyR/EFEnWbRICALEeRyDbhWB1AuzELqQRQ0//ShiMDD4UyCFLCWPSSdTvX3nszGOJrn4UqASSOTK7C4U4eEUob+XuBSEAT2z8grtypgiFiDEAgJrvvBxXP0hWHllhGTTKA/ngVeztJDPi5FKmVtSOVmlsDILVOQKZ2jZWTz6Ap6OnQJQiM48XmBi4BlrZAnPdBH6wJN0EQhYPubAP4+wygJCZx3rcNsvGdDCBDDgieUMXMIU4VMpj9dTvmjXUliMVD/pqEgIA8US3U/BbDBZBTuSex0FhDUAIDFYGbykNjHTpEgrkDsVvRIVJ78U04ogmumYeIwyDcbYfYj74BSPk8MErQrGr+B1zkiuQi40Jm3Yj2wWYfBxTJAVWWToNTgBAH3NSo5atOa7mdiYXhWR3jkDe+9QjWIZx6OteVfK4wsQhGI1NtGbdBO5Zkm4CIYtHl3YAWbk0whtLdKMACUyusToEzsghw3Rhl7wFQ6O/wYxBfIcT18eQFwd8bUsYFjkSA6P6rxOgto0hYzdycWd12mRlCLba0FiXKqFA7kCC+DNGCvswwS0rezwjLkdCW1rLziHNxStCIQBMypT5HbOSYy+mFZZmikWUFmAJceS4Loja0hLIXPpFjKIb0Vii5nYWH4Nsd86y5sGH/xcAsPqM7SWPa1wcotlYrrQ3kTOpcztaqo1rQlqLZVkYsEehx8sLWyeZXvDFxuoQeLMAlY1CWfNKDNFDGHl+50KHuij02OPQIuX3/mrkmAR4Le17+9GRFxEnCmi/M4k3uQhEGkaQQ9pMEH/GLv0gclK5UbgaHUKf1Vh3oZDDgyvPW48Bxonq3Wy9vMzvmHcjyLqy9AQytW3EaBG2mERB6EVUX1oevLHCXkzw9XMdbSGOKO0cgZzcfy+eZ9Zi2Yp1JY8bQgIRu7HjyJvIsXB825dq45qQxvGzYjp6YA8kYoB0ryl7Lsv3IaY1lmYlWAXobAyrz7gYwKzHdydTzE0hDgU04T9fusgmIOhp39uPPv9nAEB85SYAgM1FINEwghzSZmaXxp08q2oRFcs00W+PQYuXV7HS5EokUEC2gc45IYcH27cMIfOqfwUhwHN0qMzvmI840UuzQ1sZ16KQz4AlFERKwJB6kLSXlsVYnzGCfLR+9TkV44gQDVYDLWKbyY7hEbz5X76D9dpf8HtrQ5mAMYUkorSxCLLnJnCAOl6tt1pnLc3GNSEN4XfFdHK/k7cfGTiqbB+KvAwpszHff9nKw+SiGFpzLJ5n1iC25+6G9rOYTBzYDQDgu/wLZJVLQrb8X+uV/Y6DxfJjtgAAKB+FHArkkHYzuzROQWu0Ap44tBsCscB0l99ohZ41zjYd6IUcVqwvHi9Z7kyyLjj9+DK/YzHipFgYSmfZiPkhn3Emfoycgh3pQxfNtF1E+qWQnUIPMnUt3gCAiM5vlM+lWzyq6ngC5v3qjWAI0GuNlwkYKiYQpwXYVvDunZ6bwARSAIAfSZcsycY1IY3hd8W0eMi5l3UPHVu2Dyu2HD00DcPQA7+/TIsweec8G132cqzX/oJsurNTtnKjjudzpLf+JNvDEFOIBRDIzMRTmEICXX3uCrVrC7pUrrPNIBTIHYgXUVHBgxDgAfuEihGVqX1OoU+kf13ZPmLLnMc6zQs5SH51yMLxOicJbieluYhRJ4Jsq0svxULJORFjNpIEiQ+AJRTTE0vDC3l09y4AgNh/dJ0tAUZyfiPv87YDT8C8jHUiSudzD5cLGLkLHLGRz6cbeo/tW4bQyzoTtc9cdFRHi+Nwgt9cvBVTq86KqTm5GzYl6FtRft6wySFwxMbkaPDC9AhVYAtOulnXSa8FR2w8+8CO4B9kESlOOAI5uWyN79dYUhcS1P+1Ppl3HSw8PFvQwtJbcWyUUCB3IF5EBXAuGFNMV8WISn70eQBAaqjcS7VnyLmIqOMvtnawAQmSXx2ycNSsEwkRE71lz8nRJADA1pZeBFl1BaMQSUFIOJXcmfGl0U0vPfI0ACAxtKHutqzs/EZKg8KzGdRzQwEAVk4BAPILiLzF3RzmTl7RCCf4zcdbMWXgHGDVVky57F6MM93gxHLfX7Hb6aY3fWh3oPe2DB0RogHuSs2xJ78c00iAPn1HA59k8bDTI76bhHhQuRsRokEp1nfFcRws9iKfmJ2MMKIjkNUlWtTdCKFA7lBet2kAMnGWi7aSx/D6zeXVqpY7o+5fUZ6T1d036HQZS3eWF/JW9Qbca50083dYsd5aDLdzktdJaS5yzBFfVO9cQVIN3TW8F2JdkLqdJcDi1NIQKYZr8bZ8bW2LN2A2T1xro0Cu54YCAFzUsZksZhureTAMHQniFCMaaucej+EEv/lced56LGMyM9bGj9PVFVdM48X9mOLLC9KBOc1CxoPd7wpe6pLoXAtZjsNzyTNxdPYhmA2kaywWTO6A7yYhM6+JdgMAMlP1ixlH9z+PGFGA/tlr1IxAXqKNpRohFMgdireM8SyzDt3IYvcTD5Ztw2b3Ypx0lxj2exCGwRjbDyHfWaKBTw1Cggag+s02pHmYrkCe23nKQxAl6JQFtM7x2fWLUXC8m+VEN+I9zk1TnV4atobM9IsYQzdi8WTdbQU3yq8X2udV7aV8WSC41Tq7zA0FAPiYI5DVXGNuItnp2QIrq4OPRy+aTmtE00OCsX3LECZeei0AwKAsukkB+dd9s2zFtMc8iGK0cupNz/I1AAB9KliKRcFL1ZJn7RbZDecjhTyefvjeQPtaTCTlkO8mIR58zEmzy6d9COR5DhbAHFvQYphiEdJmlLxzQxwdcgz5xx/9Zdk2UWUEk3x1YZkRliGudday85XnrccQcZZhf2G9pOLNNqR5UGUaNiWIp8pTLABAIRKI0bmCpBpWMQ0AiCS6kep3llet3NIQyI7FW+VI2HykWAoAYCjtE8jbtwwh/9obwRGKPXSgzA0FAOSEc/PVGxTI+TkC2VQ793h0oukSCAEsSsIJfpPYGHNE1+M9r8FKMo516l9Kni8U8hjAFMxE5ZSCRPeA01EzFywgpM6pZfA45szXwaAsMjt/EWhfi0lCH/XdJMRDTDiCWknXd/tQRpzvf9B1sABmG0t1cgpUswkFcoeiuhEjqf8oPMMejcTI/WXb9OgHUYhUL2ZRokPoNTurBe/2LUPIs060SYdQ8WY7n7AopnEYZQo5EgHLcRWfVyCDMTrHZ9cvtuqcH7FEN6KxpJNOtETaTfcbI8hFy5sdVEJyI7NWm634XrHGWco9e+O6MjcUAIgknQmYWWismLCYmb1pU71zBfKV563HciYNANhDB8IJfpMoju0GAPS+8u9RhAjl4e+XPD+617F443vLC9IBZ8V0gukGXwg2SdYKaQCAMEcgx1M9eEbahOWj5ffcTqHbnoAWDTYpk906FD1Xv06AnXgaE0gh1Tub2sm7EeSl6JvfKKFA7lA0N8WClRMYH9iKY7QnSwpgdFVBH52Cmah+o7UTK9CFLIr5zmklrGsajradwsGjkrTizXYus0UxPygritkxPIKzPncv1l71S5z1uXtD4VwBVssgR6p3a9MYGazZuYKkKmoWOuUgShEQhsE007Uk2k0Xc9PoQRpWqvKNfj4Rt4W8rbZXIHsuGoxcOS0klnQiyF5kPyheMSkA2HrnTti2bxnC1LlfAgDEiOprgh9SH3PayR1etm4Tnup6OU6Yvhf5wmykMj3i5O3Hl5fX23hk+H5E1GCTZN3N7fdSmTzyq8/FWnsv9r2wK9D+FoOZJiFxf6tQHrEupw7Fq0upRTL/PA6Ja0oeE6OOQLaWoOtRo4QCuUPxZmm8FENi0/lgCcVzD/185vmx/c+BIRSc63dcCd59bnxf53gh7336UYjEAADwVv0b4WxRzD2lRTE7NuPqW3ZCTx/Aj4RPQ08fDKvJKyAYaRTZeNXnNUYGZy299qGMnkWeOOIYAHJsF6Ql0G760O6nAADiQH2LNwCIeoWUbb4pFd3UCS5SWSBH411OYyMl3dD+9dycm3YHR5AB4KxVTrFSN7L47ZXnhOK4CXC5/ZhACoIUQey0tyJBinj83ptnnlfHHbvS3pXVI/WKNIBkwGYhhrsy463UeOzt3QoAsL79+o5btZxtErIy0OsS3Y5ApoXaAtm2LKww9qCQKL1GibJzH+nkFKhmEwrkDsU7cflIEutPeQUyNArz6btmnp8+4MyooxW6CnnEBpwoVfrQ8y0caTAmn/0jAOAQeiH4EMjzi2K8/1sg+DL+Ff/GfxWnkafxQe4WKIaFL975dKuGviSRjSwUrnoxmMHIvn6HToPTcyiS6MzfitCNqNH53fQy+x2BnPRh8QYALMsiT2UQrb0RZK1KpM2DYVnkSARESze0f3vuTbvDU3684AVHbF+OACH1kZWDmOIcAXfMSy7EOOmG8MSPZzeY3g2V8kj1rai6DyO6HH32JGzL8v2+lpvbH4nPCuQdwyO49rcaMjSC1WS046z8sq6VXaQ3mEAW5ZiTiqbUrhM4tO85x/quv9Rlx4sg0yVoC9oooUDuUEw3EV6KxiEIAp6JnYY10w+C2k6nKmXUmVF3ryj3QPaY9ULe3drBBoAeeBQ5yDgYORaiD2HmWUwBjusFBcE91kkQYOIC9o84m31iJrK8W3oL7lHe1OqPsKSI2lkYQqrq8wYXgWAvvQgyb+SgsLGZv3W5Dym7sQKxxUQbc1ZzBtb4E8gAUCQy2DZb8elurqYY7aq6TYHEwOoNCvniFEzKIItox+fE63MiaJmJzhBNS52kPoq85OTUEpbDnsELcKLyJxw66LhSiPn9GGOXzawYVYIkByEQE1MBGgbZbiAqEk/NPOatWiZJEYSg46z8lEknHSW1rH4nzvlkSRysWjuQMP7CMAAgsWpTyeOyK5DtDl/haSYdJZB1XccnPvEJnHbaaTjrrLPwjW98o+q273znO7F+/fqS/+6+u/N7qPvFyzkU3YiNtW4bejGNvbv+5Dw/tRs6ZdHn2ttUomdgJXTKgU53jhdyV+ZJ7BWOgcVFIdL6wswpipkGIcC3zVfjJutcWITH+cx/Y4d5JkzqHMIK5XGreRYukb7e6o+wpIjZOZhiqurzFheBaHe2IKmEYOahzRHIdqQPKZrraO9SAODSL2ACKcQT1YXmfBQmAtZsr0A2i66tXrz6uItMDLzRmEAmahpZEoMCCcRUG9rHYjG3SCk/tTS6N3YytmWj3x6HEZtNVRl62d+AJxaeufe7AICUOoK0VDvnVuh2IqrTB/f4f3MtC5MyiERn09C8VUuNOoXNGuU6ysrPTu93moQM+m8S4lFgE+D12jVJyv4nAACDx5xc8rjkplgsRVvQRukogfyFL3wBw8PD+Na3voXrrrsOX/va1/DLX5bbmwHAc889h3/7t3/D7373u5n/Xvayly3yiFuH5S5jeM0cVp/xegDA6KOO9YyQ24tRZqCqOwHgLHuOMX3gc/taPFp/6JqGNcaLyHdvhMVHIdP6wmz7liFMvOLzAIAXMThTFPOB150NhYmWdF9SmQje9ZozWvoZlhIzzRfk6qLG5KKQaGcLkkpIdh46N3tTI/EBMIQiPd7ZgiVa2IsxvvoycSVUJgq+zQLZcw2J1BDIKpeAZDYmkHl9GnkmDo2RwJqdPWGz5kSQ1fTSsBbsZKYmDkAiBpCcPS+Wrz8Vu7m16HvhVtiWjQHrENRYbeeXmJtykB/3L5CJnkeByCWRaW/VkoeTqsHD7CgrPyZ3EJMkBVGUAr9W4ZKQzNoCmZ18CmPoRrK71GeZYVkUqQgsQVvQRukYgVwsFvHjH/8Y11xzDTZu3IhXvvKVePe7342bbrqpbNt8Po/R0VGceOKJ6Ovrm/lPEIQ2jLxFuLO0qDuzXT60Bs8xaxHf9xsAQFw9gGmh/gk7LSxDTO0M0bD36UcgEgPcypNBhSgiPoXZGUMiAOANZ6yfcb3YvmUI5wwBt7LnYY/dj3GkcM4QDQtm5uA1XyCR7qrbUD6KiI9IfqcRsQsw+dkIspB07IjSHb7k3auPIO/T4s1DZ6MQ2uw0Qt1czViy+rFk8HFIVmNCXtAzKLJJ6ETq+KJRc04EzcyGOcgLZXLEqZERe9eUPD511BtwnPUMHvn9XYgRBaSrdsS0y11N1QI0C2H1HIoobbTlNcb5mXU2AOCP9oaOsvKTlIOY4oI1CfHQ+CQidSaxXfnnMTrPwcJDIVLHp0A1k44RyE899RR0Xccpp5wy89gpp5yCxx9/HKZplmz73HPPQRRFDA4GszlZUuh5KFQAx8+K/tH+s3G09gQK2Sn0mYegROtHopTIIHrN0VaO1Ddegd6yDWcAQhwiMWDoWt3Xqa7lHTfPYmrwip/hr669GRNHX4IBkob+ys80f9BLGK/5AhutIZCFKCJEC1TY0glEaQG2OHs8yF1uu+nJzmqMMxcln0EfpmEl1wR6ncHFINptjtpoOaiUh1AjamUICUTtxgSybGWhcQkYTOcL5Lmd/mg+mGtCSDmFMcf2MzFQmlN79La/hkUJmP/7AgCnJ0AtuvtWOHUqGf+TZM7IQWGiJY9t3zKE4sXfwX9E/w4A8DB7YkdZ+SX0MeSFYE1CPAyxC3FaWSDvGB7B6z7zY6w1X8AutatiUaJKpKVpC9ogHSOQx8fHkUwmIYrizGO9vb0wDANTU6XFN8899xwSiQQ+/OEP4+yzz8Yb3/hG3H9/55p6NwIxClBI6c0osekC8MTC0/f/CCnkYCXrR6KsxEr0IA212P7KU3pgGDnIGFx7PCA60T8/Hs2GWyDERyr7+a4+53IAwL7flq82HMl4zReEeOUuegBABOfmUCwuHW9LQ9cQJRqoOHs8JHrddtMdvOR9aLfjqcoPVC+srYTJxyC3OU+c6DnkSbTmNraYRJzmQT2rmQDErCx0MQWDlcHbnZ3yQ12BrFMWbDEUyAtFd4vOeodKBXBiYBV2CifhFOMRAMB//X60ppMEw3GYIN3gCv5XTHmzAI0tP663bxnC/139GuQg49S+zlqZ7LEnoEeX1d+wAlRKIUHzsC275HGv38DfFL8NllCstA9UdO7QiQzW7OwJbDPpGIGsKEpZioT3t66XFt48//zzKBQK2LZtG/7f//t/OOecc/De974Xjz322KKNt9VwRgEKkUseO/bUbchRGbGd3wEACL31q1i57jUAgPH97fdC7s48ib3CsSAMC0Z0UkeKuXTd1+lupXE1i6ne1cfhOf5YLNv7i4Zuzocrmtt8QU7WEMjuREXNt9dGLAiFrNe0YlYgp/odgWzlOmO1pBLp/Y5ATg4GW6q1+RgiaK9A5owciiRSeyMpBYkY0NTgY43THCyxCxYrQeh0gew2Mhlj+sFr9ZsuhNQhsw9FKiLeVZo2sGN4BD9Qzpz5+xzt/rp2a2m+D7Li/xogWnloXKzq81mSBKd2zm9cyE4hBgU03phgJ5EecMRGLlP6mTznjou5BwAAZ7K7Kjp3aKwMbgnagjZKxwhkURTLhLD3tyyXCsWPfvSjuP/++/H6178eGzZswP/3//1/OOecc/CjH/1o0cbbalizAG2eQBZFCc9ET8GxhuOlGl9We8kJAKL9awAA6UMvNH2MQdA1DauNF5Hr3ggAYGXnoqQV6wsz091GqiKQASB91OtxjP0Cnt/154UP9jDBa74QTfVX3YZxBbJSWEoC2flcjJyaeSwSS6FIRZAObjetjTm5lgNrj6uzZSlUjCNKVVC7fWkwvJGvGGmbC3F/j1w6mKBQlSKiRAPkLlisDKHDi0apm4OZFpYhone+tWCnI+QPYJztL7NwO/+2zfgi99WZvy/j7qtrt1YU+5Ew/Ef1JbsIs4ZAznMpiHrn+KtPHnDSUfjuYB7IHl66XXa69DrpOXcYlAUAKFSo6NxhMDJ4q7PPz2bSMQJ5YGAA2Wy2RCSPj49DEAQkk6XCiGVZJBKly+3r1q3D2Fjn3hyDwltF6Kxc9vgLydkZ9Sd/9WJd83LPJ1lx87zahVegJ6zcAgDg3OifWqifYmG7XcTkeHWBfNQr3g6bEhx6IEyz8PCaL8S6qhd0cK51j5+JSqfgtT3mIqmSx6eZFLgObjfNTjsWb4kahW4VEeNgCEWxjVF+waq8FD0XzvVILmSC/QbZKUfQMNFuWHwEIq1fl9BOiFGEQgWoYi/iVrrdw1nyxLVDyAjlKQN+Rdtc9Mgy9NgTM/0C6iHTIiy+ukBW+C5EjLSvfS0G2VHHoSNokxAPL92ukC7VSp5zBwcLNnVcoSo5d5icvCR98xulYwTycccdB57nMTw8PPPYI488ghNOOAHcPCuzD37wg/jUpz5V8tiuXbuwdm1w4+xOxRHIpTekHcMj+M99TiUvpcCFys/rLjn1LlsNg7Kw2+yFPPnsHwAAAxscGzbeFciGD2FG3S5i0RoWU10Dq/CUtBmrD/zS98XxcIcq0zApg3iiuiDjJUcg60soB1l1BbIwr2lFjuvp6HbT0cJejPPBC4sZyTlX/KQjtQrJKrXVq4T3eyjZYBHkfNpZEudivaBcBHKHR5CJUYRKJJhyL1J2ut3DWfL0WmNQI+WOTJ5oY2FDpXxV0TYXkhhElGjIpv1F9mO0CFusflwbYjfidv0gzmKhTDoCuWt5Y1pHdNPt1ExplP3K89ZjgMnABIOfW2fiJuvcis4dFhuBtARdjxqlYwSyLMvYvn07rrvuOuzcuRP33HMPvvnNb+Ltb387ACearKrOhXPbtm342c9+hp///OfYvXs3vvKVr+CRRx6Z2fZwQLAVmGxpzt/5t23G/ZxTWet0+Lmn7pLTL/4yhlGkcPT+W9rbU/7An5FFxCnQAyC6BXeGUl+YUS0PixKIUu0IVnH9xVhJD+LpP/8u0NB2DI/gdZ/9Kf5w7Uva+x01GUadRo7Eanaf4iNO9MTP79ApeF3dpDndrwC33bTZuUveffoIcpFgFm8AwEbaL5BluwCTry2QpXgPAEDLBfsNvGJSMd4N8DIk6C2Z5DbrPGdMBSpEINqHCNFQyHWOgFpqFAtZdCELO1HuyOTZrd1knYuL9U9XFW1z4bqd/Uz6SCk0NAUiMQCh+nFtyT1I0UxHBF12DI/giZ1/AqXA+24abuj4jSaddDsv/c5j+5YhTJ70fvDExq/s02f6DcwvTrT5CMQOn8A2k44RyABw9dVXY9OmTXjHO96BT37yk/jABz6ACy64AABw9tln4/bbbwcAbN++HR//+Mfxla98Ba997Wtx//3343/+53+walXwm0+nItkKTK5UEHpLTirlAdRfcvIqUzlqowfZtvaU70o/iX3CMSCMs1wmuQ1QTB/CjNHzKJBITaEHAMdueyt0ymL6Dz/wPS7vO7qk8EOcRp5u63fUbDgtjRxTJ+rnRvJNdekIZLOYBgDI8dLIuC71IWV3Tr7gXJR8Fn2YgpkKHvnhXXtDzZ0YtIMoLcIWqi9FA4CUcASyUQj2G+huxFlK9AN8FAyhUJXmWknNnuc/WPB5zloKdEYCE3fERnp86V8r2sX4fkfIct3l927Pbu3rsQ/gKbq6qmibS7TX2U9utP6KqVfsS6TK7kiAU9QmEAvZbLru/lqJd/yeZT8KAHij8uOGjt9Et2MPZxXKV3nW5P8MAPj0B989029gPjYf7fgVnmZSvQ1bG5BlGZ///Ofx+c9/vuy5p59+uuTvt73tbXjb2962WENbdGQosPnSCDKfGkQ+L0OA6WvJ6fzbNmM7a8z8fTl3Ny7H3dBu44Eti7cUrWsq1pgv4NH+S2cek6LORYlqfgRyDkXIqH4Zc0ik+vDn6OlYN3onLMsCy7Ilz+8YHsE3f/Ug/lH5Iv5Z/hje9ZozOuY7agWikUaRrf2tzbQyX0IC2VbSAIDovNQRGu1D12QOuqZBmGMX2W52DI/grl/cjK8CuGePibHhkUC2UZ57i+4jX78V2JaFGFFKbPUqEUs6AtkKKJCNvJcr34txwbnmKYUc5GjtyV0Qmnmes6YjkMWUIzZykweBdcc3baxHElk30hvtW1Pxea8plF+SA04KojZVv3tsMZdGCgAjV69tYWNOSkJ28iCSqYC1A02kWcdvLNkDmxKgWH6Oygcfwh5mBVYvqxFo5COIEK3i/fVwpKMiyCEulCJC1bKITdAlp0aKHFrB3qcegUBM8Ctne7tH3eVx24cwY80CVKaOxZSLecJfYQBTeOoPd5Y87s3ALyvchNPIU/iM8mkM3Xqxk8w9h3Z9R61AMnPQuOoXfwCQ3YmKrbbfJ9svVHVz0hOlOchM3BEs0x3UTc877t6k3wIAONl8LHDkR4ymAAC6GzlfbPJuaketSBsAxFOOmPAmMH6xCk5KRrJ7AKzorJppTU752aregD/b62b+Xsh5ztsqdEZCNOUUlhU72Hu701EmnJza1OC6Olv6o2eZI5CtdP2GQUreLfatIZAFNyWhMNXe39i7l9uUAGj8+GU4DlkSBVFL06As08S64uM4lDq5yitdXN98ZQnVrCyEUCB3ILqmgifWTBMHj6BLTrNFDo49lJ8ih1Yw+ZzbQW/9GTOPiVIUFiWAXn8pVTAL0HwK5OPPeROKVEThkVLLP8/n8TLuPjAE2MTsxmnM02Bh4zfWphmd3K7vqBXErAx0IVVzGznmRvL1pSOQiZpFgUolXSYBgHfbTWfHO6ebnnfcvZx9HABwIffHunUD85FiKQCAqbTHxaKYdW6mjFR7ssUJomO1pwaLdBN1ChrlIUXiYETnPNeb3NiITw0iCedaY1B2Qec5byswGQnxPqfg0sh0rvd2p2NN74VFCfrcNtELRRAlTCIFNl//GqDl085ratiHRtxJkJJu72/s3csJqFOPs4DjN0cS4LR0yWMvPvEQ4kQBu/bsmq8lS9AWdCGEArkDUdylVFIh52/7liE8cNU2vPi5C6vmCXl4Eedf20777rvtLW3pKU9HSgv0AIAwDApEBvEhzBxHD38CORJL4mHpDBwz8Wv88drTnWKcW36APbYTCbDmzcDP1P4TOpHwkO14095indWW76gVxGkelpSquY0oyjAp42ui0ikwehaFCk0rIt1uRG+qcyLIXuRn/nEXJPLjRcrtNglkxS26Y2tE2jzyJApWDyaQGWUaWRIDCAHr3oA1pbkC+cpXH4vlxEnl+LF1jq+Cr2oItgaLk5HqdcSJlTt87EUXGy434nS/E5qTErVjeARjdhzHTt1XtxDTS1kS3QloJaJuzq6Ra2/HRO9eroLHHdbpCzp+FTYOUU+XPDbxl3sBACu3vLLma2dWeAphBDmkTXjtlxlpYTl4XsT5a9K7AQC/Z09vS0/5nswT2CseW1Zkp0AGY9QXZqJdhFHDq3IuO4ZHcFPhdHSRIk4lT+Orysewfef7IEPH/1kbQYCy/O3ixd/Bz7gLAQC3Stvb8h01G01TECUqINfOmyMMgyKRQJaQQOaMHIpM+fEQ73Eq2LU2R3vm4kR+JDCgMCnTUOQn4qYj+cnXbwWaez2qFWnzKDBxcHowIc/rGeQZZyWDc51qDLW5x+NLktOQiAkA0MH7KviqhmirsNgIRCmKLCJgwnbTDRNVDmCKH2jKvrx0pigUJFGoW4hpKq5ArnFcp3rcSVC+vb+xdy9nAOxD/4KOX5VPQjJLJ7HiyEMYIQMYWFG7+Rgreb75R4ZA7qgivRAHzV2+YCV/orAW27cM4cKNb4T5zx/Aa1bqOGORhZ+mKVhtvlhSoOehMjI4s36kSLaLmOBqW7x5nH/bZmznnGIGhgAriVPA0M+kscteg5usc/FD61xcxt6DZe4MfPuWIRxd3AL8Grjm5f04YYmLY8BpvtAHgESqe0d7qJB8TVQ6BcHMQWXLz43uAed366R201eetx4Dt46CEOBLxiUYJJMzx51feF5wUhe09kSQPfcMwc2FroXCxSGYwcYpGWkonCOQeVcgm03OiR/586+xHIBGOWzqAR748LaG9yVChc1JAIAMSYFTOqcV8VIjZYzhQGxjU/YVtJDNcldk5ET1a6QcS0ChAlDB9WGxufCEHvC3GTjpqJV43980fvzqQhf6lNnGYbZlYW3xMTyb2op6dz7OjSDrS8gWdCGEEeQORCs6szuv29xC4XkB46QHXLZ+ZW+z2bvr0bICPQ+NiYAz6/d1j1ClZrejuXhL2vpMYSI/s6RdK387knS6zQX1cO1U8m4rUd6twq6FyshgffwOnYJo5sua6ACAFIkjT+WOaje9fcsQxpadAwDYYW1tOPJTJDIYvT03JcMtDpRqNOrx0LkEZDPYOGUrC413oniC24LebHIEmdnzAMaRwj5uFThjYRMNiWqgvHP85bkuyHr7xdNSxDJN9NoTMOLNCUgELUqnbq58rUZKAJAhCbBq++8LxZli2YWtLFtiCgk6e47uefpRpJAHXX1W3dfyEee9l5Jv/kIII8gdiOEWqPBy82yOpvlliKgHm7Y/v0w953TQW7bhzLLndDYC3qotzKhtI4oiIPoTyJ4VHjfTfcmcWdKuZRkU63KW+cz80rZ28/CaLwhu84ZaaIwMrs7v0EnIdgEZfnXF59JMCpzaWb/hmvww9pHleOhzlze8jyKJgDXaU0hpuRP2iA+BbPJxRIrPBtp/zM5hXEwBmPXltpuZ8kMpVmYfxe7YFsj6FISAAn4utmVBJjqoa8GpCt3oUnY3aaBHFlOj+9BHLDCpxtomz8e79vsuStdy0CkLUZRr7jfPJiFo7RfIhWwaScx21mwUGulGlKhQVQWSJGPs8XuwFsCKk2rnHwOznVebvcLTqYQR5A7EcJd+hEhzIsgAUIgMoltfXKuaHcMjOPT4vTApg/f+6MmyXDCDjUCsI8yKxTxYQmt2O5pLI92XACDR7USQ7UL7L4TNQHObL8jJ+hFkg5HBm0unfWiUFmBVOR5yXDfkDmo3bZsm1hUfw0jq1AXtR2Oi4NskkKvZ6lXCFJOIUf/jpLaNJM3BFp19i25nR1trnkA+9OIT6MMUjJVnQefigSPcc1HdyBnhHVGlS71INNhu+nDt4OmXyQPPAwCk3uY0+PKu/ffbJwIAbrdOq3ntZ/ScrwZURb4LETPdlDEuBNVNdeIWqAtIxImY56adIAq370GMogfLV9dP+5Jcb3KryUW0nUookDsQr2mD10yjGZjxleijkzD0xemCM9P5B38GCxtvKv6orGDC4qJ1+7oHXVZqpPsSAEhy1MnzVBoXyJ10w/OaL0RTffW3ZSMQ7KURQaa2jSgtwK7StEIVezqq3fSeXX9AHEWQNbXtk+qhsVEIVnvyxKmWhUkZyBEf56CUQpwoMAyj/rYA8vksBGLO5MrLrkCmTYwgj/z51wCAgU3nwhQSiNiN39yVgvNa4uZi0kgvUjQPw9AD7edw7uDpl8LYbgBAYlntwjC/eNf+7wlvAgD8SnhVzWs/o+dRrOCGMx9d6ELMSjdljAvBqwXgI/WLZWvhpd3lpkdBbRur83/GvsSWuhMFABBdTWItoaLuhRCmWHQgluZchMUmdpJiu1eD3U8xOvIiBtce17T9VsNPwYTFRyHXEciK61XJBsi7Ctp9ySNL4mDm+UP6xbvhXUN/iNNY74YXnRnPYmMXHYGc6O6vu63JRSGqi5+f3giqUoBMLBCx8k3CkHrRlX9kkUdVnYmddzvLl1tevaD9GFwUUTXdlDEFhdGyKBAZSR83UOJaweUzU+jqre9OkJseQxwAE3FSgSRXhFO9eRM2sucBTCCJtetPwthvkojRxm/umpv+5nnUM/E+MIRicuIQ+pb7j4Qezh08/WJMOk1CeoeaI5AB51p7avwVwE3A20+M4vQa117eyEFl6hd/m3IPkpn2dLGci1Hw3GRSC9qP6KbdFdMT2Pf841iFNF5Y9VJfr/UmyVQLI8ghbYK6+T1et7lmEOlfCwCYGgmWH9goW9UbcLe1ZebvSgUT1IdA1gpeweLCZs1+KLAJCA0KZK8hxOXc3WAIxeXc3YEbQjQTWpyGTlnIPpbjLC5SN5LfKeQzTnSYVDke7Gg/kihAVTojIi6O/B57ySCGVi2sU5jJxyAtQNgtBFbPoUD8uciwbiQ4n/En8gruMi/v3rRZjoNKeRCjSb8fpViRfRQvRreAYRlQKYEoUWHqWkO786r3WbehCZ9wvLcz48Eiv1vVG3CHedpMg6LDqYOnX0h2P7KIIp5sbgvnpNvAxa5TrMtbBagVin3LiPQgSrSZ1YN2YRSdVCfJh91iLSS3O6CeHcfBx+4BACw/8Vxfr51ZRTpCIsihQO5E9DwsSiDLC7d58+gaPBoAoIzvbto+a8GnBp0GFIBTCFGhYIKKcUjEqLk86Qlkvon52NUosgmIZmORgln3DGdRRp3jntEOWHUaWRL3tWxm81HIdHFSbxZK0XUZ4SKpis+zcefin54o76S12CkwtmlibXEnRpKnLHhfFh9DhLZH9LNmHoqPSBsA8FFH7BQz/pwdlKwjpMXEbK68QiSQJuXEH9qzC/10EsZKJ0LGyCkAQCHbWBqO7rpreH7NslvcW5wOVt/BJZZhI/MiCHG63R9OHTwBf+eaVDiACaZ+ClhQYvEUNMoDdbyLRasAo4Jd5HxYNyUhPbn4Re5zsRT/xbK1iLn1NkZ+Esze32MSSaw4+kRfr2U4zrG9W0K2oAshFMgdCNHzKELyJW780je0FhYlsKb2NG2ftXAKJpxoy8eMKyoWy3ltK4u56qLUmzWLiyCQdSGJaIMC2WsFysFpRiC0+YbH6WkUGH9pKVSIIgIV1LZbPKqFo7jChqsSReGTzvedmdduuh05n3u9/OO1Wxe8LyrEEaVKW34jwcxD8yEkAEB0b95azp9A1nOOQI7MEcgaRDBNiiCPDDv5x/2bnAp9ZibC3Zg1m+EWJ3Gic27Fup1opZoOJpD/NvEgVjITyFAZhAA/sl5+2HTw9M61NxduqnmuxfVDyIrLmv7+hGGQJgmwdfypZbvgqwEV506681Pt9Venbm2SvMCV5WSX21W2MImV2WHsiZ0USGsoRGra+dnphAK5A2GMAhRS23omKIIoYZz0gF0kL+TtW4bw3BqnWGInPapisZzXVtbLM66E6VbQ12oH2ixMsStQBf5cvArqR+1jAAC/tTe19YYnGRkUOJ9LcUIMDKFQlc6PCniFKmK0chQl2u0I5OJUqUBuRwrM+ON3AwBWbnnVwncmxcERG1qT/YF9vbVVgOFnKRqA7KZK6IVpX9ubbgMGz2YRADRGAms1KeVn9+8wiSTWbXDSvXj3uFEajCB79la87Hwf3nJ+kHbTj/9lJ944/l94UtiEf2X+BgDwU+mvOrqDZ5DVF+9cewt3X81zrdcahxodbMl4c2wKglZHINMiLKG+QJZTjqAstrlDJ9WysChBxE+xbA2kSBwa5SGO7cQyTMBcUW7BWguVSGCWkG/+QggFcgfCmEWoRGr6fqf4ZYgq5UvPrWKV7BSh/PTD5+OBq7aVXfy9VtpqoXrU1nJz/uRY63OQbakLCZoDta3Ar/UqqB9mnaWq37BntfWGJ5tZ6Jy/qDvjVuR7Lc47GdMVXnKVZcZ4n/N9G5nSiN5W9Qbcb22a+Xsxcj7F/c3JPwYAxnXtyGf9Cc9m4jfSBgAR11bQ9CmQPVtFz2YRAHTSJIE8k398EhjWudWJMee4UfMNCmTXfs5raBJLdEOnHGjBXytiRTNg3/JeMARY9a7v4tJtLwEAXLett6PFcZDVl63qDfil+ZKZvyuda/nsFBIogCZWtGTMRb4bESNdfQNKEaVFUL6+2Ix2OVFuPdPeBkREz6MAeeZYbnxHBFkSx4a806Og/8RgXfk0IoMNI8gh7YIzC9CY+vYzQSnKy9G1iF7IVEkDABKpyl68XqdAtVC9s5W3rBRpYsFiVeRusIQim2ns5rl9yxBO6ndykM87JtLWG17UzsIQ/OWqMW4kv9bv0CmYbtMKOVG5AUpXv9tuOlsa7eFTg1hHnBxCi5KW53xSq3n5xwDAuOeK4toeLiZOpM3fZCuecn4XqvgTyESZQpGK4Oc0azAYCVwTBPLY3qcxQCegrZit0JdcL2c939hEw54RyI6wIgyDaZIEV/QnkP/vu9dhs/0EDrz0U4gNrJuZ0ClTnWvvFnT1hU8Ngs75W4Redq5N7Hc8kPmeyg1/FoomdiNew5pNUwsQiAX4aLqR6HXGbdXJaW41fm3p/JBnnGLVDGJYvSGYR7vOyODspVHUvVBCgdyB8JYCnW1uigUAGAnHC9kM6NnZKERNI09lcIJY8Xmv8M4o1jDu13JOweICl5X8wLoFRoWpxiMFM93O1Pa14qSUIkHzsNyCpHqwkieQO799qO22h40lK4t/UYoiiwhIsfQ3/LuNOlYyTr7rk3S17wYyjeLlH6MJ+ccAwLnep1qNdKRWQG0bMVqE7bNRjxSJQ6csqOJvNYLVMsgypSLFZGUI9sKLRvcP3wUA6N80W6Hv5Tr7jXDPx3bt50R5NuXEWc6vPqn20hN2XnsSXr7/a3hEPhPHvOoKAEBXv9NFzki3twCsFlvVG/BH69iZv+utvlx53noMMU56g0EZHKA9Zeda9tALAIBY/5qWjNmSe5Gi6ao5+95KjJ+udIlkD0zKwC60t6W4Uyy7cIG8Y3gEUyYPANhJ1+G2x4IFzQxWhrCEOq8uhFAgdyCCVfSd8xcEtms1OGJj/MCLTd93xffTMsiT6kuzXuGd1zmwEkTPo0iaW7BYDSHu3DzzmcYjBTMCWWtfNFYp5iETHZD82SdxbqqLXuN36BiUjNu0ovqNLUNS4JVSm7Gjnv4GClTEn+wN4GD5biDTKF7+8apm5B8DEFyB7HXTWixUpQCeWCBVGrOUQQhyJAZW8yeQBT2NQgWBzDdBINPdD2AKCaw7bjaKH0s6EW7bXd0KvE83gizP8agvCj2IGpUFspeecGnh+9hEXoQNgv8v/9fY8Wcn1S2a6odOWSDf3vzWWvCpQSSJ87n9rL5s3zKEFwcvAAD8xDoHy8kUsts+U3KuqRNOsXjX4MLTjyoS7YVEDBSqpI15KzF+BDJhGGR8FP21Gt7IQ/PpJlMN73jshbsSZyuBi5VNVobQrBqBDicUyB2IaCswueanWMiuF/L0yHNN33clOCODQo3qd08gW7UEspFHEc3/LiohufmT6gIEMucWL7B6+zwzs663LFulkG0+ghudN5TOjyATPYt8nfaweb4b8pwCnT3PPYFTsvfgicE3gnatRRejVMyJbyZe/vHgyrXN2Z9bpOq5uiwWhRnfaf8uMkUSBaf7E8iSkYEyL1fe4mSICxXIlGIo8wheiJwEdk7OZjQah0FZQG0w397NvZTmWHDqNZbzvfSEt3L3ghBAJgZ+z14xk55AGAbTTBe4YvPzW5tla3jleesxSJzz6Sm6ytfqyyAdh0IFnPG268ASitX7f1HyvJ3eB52y6F3WmhQLNu7ktGcq2D0CgOqm2HA+m25kmWTNVYLFQLAK0LmFCWTveFzNOMfbqeyzgYuVTS4CYYnYgi6UUCB3IDItwlrgiVCJruVOx6LC2OJEkCUjC5WtvjQruYV3do2uPJyRh9qCfOxKyEnnoupZTzWC1w6YNdsnNvPTzsWPi1XO/Z6PF500l0AEmdOzKNZpWqEIPYjNaTd94JefgwUWR73+KthiEvEGnUr8Qi0TawtO/jEhpCn7lF2BbPlMXWgWhZwjJNgAjXoUNg7e8Hf8R6wsdCFV8pjNyZDQWCMPj/F9z2AZHS/JPwYcQZonEZCGBbIChQpgWHbmIVPuQzdNw7bKl/M9f3S7RkOQLNcDWWtufmszbQ3PXm4hTlSYlEEfyfhafRHz+zHG9mPdhs14gjsBy174aUm6A58fwQTTC3bO99hMhITjipKr4l2suqlKgs+2zUUuBclY/ALZuUh2EcYCdcGsX7/zvTdSrGxzEUihQA5pFzJVQfnmC+S+FetgUwJzkbyQZSsHna8eefI6BdYSyLxVXDSBHJ/jD9koopubxRvtiyCrGa/5QuVCtvnMRPLVzm8fyhl5FJnajgqG3IeU7dzMDux7HqdM3Y7H+l6LnmWrQKUkokSF0WAnNT/s2/UHxFAE1jQn/xiYK5AXdxLjRdr4AN27VC4ByecEMUazMOe1DaecvOAb8L4Z/+PyDmF5EgdnNPY9EqNQ5jBEYn0QiIlsBes4PjUIDTwY4uTjVkpPKAq9iBnNXb5vpq3h7kecXO7Ho2egn6RxzwdPr7v6ElcPICO4nuTrL8FKewQv/Pn+meejykFM8fVbkTdKxHWeUNOVI/Nesa9f+1BV6ELUTDdjaA0j2wVYPt1kqjHr129DpXxDxco2H1kyjaUWSiiQOwxqW5ChgQrNF8iiFMEE6QK3SF7IETsPk69+Y5XkKCxKAK36zVQwCzDYxRHIiVQvbEpAi41HCkS325lotU9sal7zhaS/LlWSm09Za6LSKYhmDlqd/Hwa7UeCFKEqBbz4v18AAxurX3sVAIC4hYv5Bp1K/DDW5PxjAIgmUgBmXV0WC93NeRZ8LkUDgMHHEbHrj9OyLCRpHrZUmgpEhShkosO2gtstemkFKx/9AqZoFI9r5Y0oFDYGXm9MIDOmAhWlRcdcwplYV2o3feV567GWcfKLrzPfUTE9QZf70WU193ic39lTp1zDtobW8/+HPJVhHnsRAGB07zN1X9NrjUKJOiL6hFe+A0UqYvr335p5vssYhSK3rolSvMfZt56tnNtteG44PgWyKXUjSdtrgxmhCmwftnS18Pz6b7LOxcX6pxsrVuajkKFVXDE53AgFcoehFHNgCAXE5rWZnsskvwyR4uJYCsVpHpaUqvo8YRinAK9Gvq7YhGUlv3A8jxyJgCiN36y8dsCi3b4qX8ONgEdT/gRyZCbVpfMbhYhWATpX+ybhtZt+4clHsWXsVuzseiX6V29wnpvppNZ4Gk3dMe5/EHvIIAZXNSf/GHAmtxrlF73403AFshTz397WEpOI+EhjyaWnwBIKIs8rJuWdCbGqBJuwzU0r6EUGORrFP972ZFlagcbGIDaYAsVaCnSmNIIsJR0Rnq+wnL99yxD29r8CAHCndXrF9AQ72o8ukoOiNK/waX5nTx4mVAgN2Roun34Yz0c2IT7oOFmkD9SuYSlkp5FCHnbScehIdnVjZ/IcrJ+4C5qSg6lr6KVTMGOtqwFIudZsdq5y6ortNqDyK5DtSC+SKMBYJAeosve3LMSIAiouTCB7fv1fj30AT9HVjRUrC1GnsdQSWHFcKL4E8rnnnot0Ol32+OjoKM48M1gXlpDaFPPOicv46PDTCAV5EF1G672QVaXoOimkam6nQAZTo6+7bBdgcq35LiqRIwlwWrrh10eoc5OL2O0Tm9RtvuCljNRDkqOwKQHaWFjol4idh1nHckx0RYB256cQIRoGLrhm5jk+5nVSa35F+o7hEWz/zM04Lv8Qhq2jcNufm9uUp0BkMPriRpC9vHQpQCdLW0wiQQt1I0w5d/nbs1f0YARXIAe0HZybVkAIsJoZq5hWoPNxSHZjxzprlgvkSLcjkJUq7aZXas9ijPTgT597S8XiUDbhvH5ydG9DY6qEFyncRVfDogAhwGnM04FtDcdGdmMVHYEydBZ6VzmvVceer/ma8f2OgOZ71sw8Jp16OeJQ8NS9P8TkoT1gCQXTtSrYhwqAFIkhT2Wgij81dQVytIpd5HyYqJOulp5cvD4Ccyl6zbSaEDjbvmUID1y1DS9+7sKGipXJTGOpzq9ZWShctSduv/12/OY3vwEAjIyM4JOf/CREsXRp6cCBA+C4qrsIaQDVtaXxusw1GyO+An2Z+2AaOjheaMl7AEA+PQEJAImkam6nMBGwZg2BDAV2iyYLlSiwCQi1OjDVQFOLEImzLByl7Ysg0+I0VMpDivj73gjDoAAJRO/8CHKUFup68u7KSTgRwBb9YdxPT8J0oR/eLUCKOWJMbbJA9qKXX6L/DxxH0WNP4z23PA4ATXPLKJIImCbltu8YHsE3f/Ug/lH5Iv5Z/hje9ZozKo7TdosCIz7z2QGASClwxEY2n0YiWd1qsJB2xItnrzjzevcGrBaDfdat6g24hvs+zmf/CJGY0CiH262X4DPmW/GnOduZQhLRfGPfI2+rZQI50et8b2a2cr5rf/5pHIqsR7XpqtTltFvOju8H1jTHl3v7liHswHfw/K1XIEGLeAJH4WVkGGwy2Dm+99E70Q+gd+M29PSvgEIF0OnaNSyZg46Ajg3MWrhtOutCHLi3H9zjP8R0z0oMAJB6WyeQASDNJMFVs2bTss41UvTXb4B3V6VyU4fQt6y1465EMZtGDP5s6VqN11hKWwK++QulagT5jDPOAMuyM1WmDMPM/O39t2HDBnz1q19dtMEeCWiujRPbIoHMdq0GTyxMHGxtoZ5386tnNaYxEfBV+rpT20aEqosqkJ0Co8ZmxkXXW3MKCUSIBss0mjgy/7DaNLIk2PGjEKlmJL8TsCwLUaigYvW89h3DI/jvh2cv3AWLL6nel1yhZzTYKKIaXvTyQu6PAICt7F8aLoqqhspEm1L8GcThwIu0xQJ0smTcSXEhU3sSomada4SYLBXIrCuQ9YBLuF5aAT+TVmBVLECyxQTitLFjnbdVmPMEcqrHiQDb+XKBnEmnscreD613Y9V9Rl2BrUw2d8Vh+5YhrJULUIRunP63/wEOFkZ+dk39F87BeuG3yCKCtRtfCsIwGGMHIOZr17Co445LUu+Ko2ceY1kWL6x4PY5ThpF79ncAgJTrqtQqCmwKkl45XY7R8ygE6EonJh2BXJxqj1+14rpusAHsFlsF556f2iJbTraDquHf7u5ufPaznwUADA0N4Z3vfCcikcUpljqS0V0vWr5FneM8L+SpkeewbNUxLXkPYHYJW4hWjyABgM5GwFuVb1ZOmoYF4rOLVzPQhRT6tMYmD0o+gy4AU2wfuq0s8rk0kl3+8oCbCa9nUGCDXUhVIoOtMlHpFPLZaSQJrdke9vzbNmM7NzsxuYD7Ey7ApdBu44EtE4gmF9ZJrRpe9PIi9iFwxIZCBfzKOq0serkQtBrnShDOv20ztrOz39Hl3N24HHfPfEdzIVoWBSohGmClkPM6UmYmAVS/xug5N1d+XjHpjEAOmIN85XnrEbk1iwft43EW+yR2WC+tXIAkJSESA6pSgCQHq28QbBU5rjTqyPIC0oiDqbCcv3fXH7CJUETWnFx1n6k+J1dXTzdXIANA1JhCVhpE94pj8ec1l+PsPd/Cww/cjVPPeqWv1w+lH8YLkc04yf390+JyJNTaNSw0vRcq5dHTv6Lk8ZWveCeY730Dxzz/HQBA71CLmoS4FIVuJNXK3ymr51AkUfhdF4l2OY4baqb5ftV+0NwUCz6A3WKrYGWvsdQRHEGey9/93d+hUCjgj3/8Ix588MGZ/+6//3587Wtfa/UYjyh0d1bGy60RhalBZ1ZfGH2hJfv3UPPOzF2qszRrsJEZa7T5FNxZM5EWL4JsiV1I+KjAr4TXnSknuNGGCrZPi4FkZFAMKJA1pvMFsheRZGu00J5fvT/f5zOe8jqpNVcge9FLFjZsiobsk+phsDGITRDIW9Ub8HPzJTN/1/JCZfRcoEgbMJvnreZqR5CtfOVceU5yRKsRUCB7BUi/Y53P9vXIeyoWIDFuXUS+ToS7EqKtwqzgqpNhUuDV8v1lXngYADB4XPVanWTfkOOek2t+fmvCTkOXnGP++Dd9ClMkCfHef4Jh1ncIObjveaygB6ENzXpJq9GV6DcPVW3hDACC64E8v5nP6qOOx6PMRqSQg0FZvPk/ft1w8xI/GGI3Ena64nOcGcxfP+bmmZtViv5aje4KZCGA3WKr4N0mOUHPz6WIr7DAD3/4Q/zzP/8zLMsCIQSUOq7nhBBs3rwZ73vf+1o6yCMJyz3oxBqtdBdC/wpn1m5ONa8gpBKGe/OLJGo3q7C4CCRauXo7SDvQZmHLXYgRBYaughek+i+Yg+7ma2qRAUCZNaNfbGQri7QcrEOVzsjgq0xUOgXveOBqmPvzqUHk8zI4WBV9PkU5Co3yIGq6qWPzopcjtAdFiHjIPh7Lgton1cHko5DUhQtkPjUIK++kztVrHez4TgeLssoJJ4Ks52pPQmjREZTxVOk1QnCDA2YDtoPbtwzhocdkYDdw2z+cB0kqzzH10r6K2SkgYD6pCA2UK78uFPguyBWW89nRxzGNBLoGqp+PDMdjiiTAFJobnTQNA100AzviROiFaAqjp3wUmx7+BO697RvY9lfvrfn6fY/eheUA+k6cjTbTrtWITyjITI8j2VPZxziuHkRaWI753+yO4RH8Tn8ZTub+Ag6Wm9rjHFut6GppRXqRmsrCtqySxi6AYx9azy5yLin3s9IFeOQvBKOYBgCIHSCQvfPzSBDIviLI3/jGN/C+970PO3fuRE9PD+677z784he/wHHHHYdzzy03Yg9pHK8RgBRtjSgUpSjG0QU221qBbLlL2LFUHYHMx6oKZG9ZiVvEvCsm4tzcvXbNQfCi/3bMERpek4XFJm5nYYipQK8xWRlChwtkb1WCr5HXXs/nkxCCHImCabJdmhe9PIReTNJUY/ZJdbD42IxLykK48rz1GGSc7/IJuqamFypv5qAFFMjepLheGgtR08giCobjS9/TXTEyG50M6HnolIMoVp7gesePkg1u9SdVaeKkCj2ImeWfty//FEbk9Y6NRA3SbDcktbnRyempQ46NXmw2Qr/h/PfjBWYNNuz8PP547Wk1W1DTF3+LLKJYc9zpM4+JfU6AZWxfdS/kXnPWA3ku59+2GV/inJolQrCg5iV+INE+p1i0wrVcsgowArgjcbyADKIgSvVjplmtvSvRiJtMqxC8xlJLwDd/ofgSyGNjY9i+fTsEQcAJJ5yA4eFhHH300fjHf/xH/OQnP2n1GI8obNdqS27hTHGSW4aI0vx8t7lQJQ2gPDpUth0frXrT94oA+BZF0yvBxZzlSK9dcxC87kxsysm9MzxrnkWE2jYSNA8q+fetBQCDi0JsgvhqJboPT14/Pp8FJgZOb/5vs33LELp5HVIs2ZB9Uj1sId4Ud5TtW4awZ91lAIA4ijXFvGgVoAW0WYy657xVp+EOp00jV6GYVHSXcBv15SZ6HgUiV23zLbrHj1Ynwj0f27IgEx2UL1+aN6UeJO3SYyqXz2G1tRdKjQI9j4LQi0iTu+llx51rPJ+cbZZy285RXGe8DYNkCqeSZ6oWaFJKsSLzMF6IngRmTv55YpmTopet4oVcyKXRhSzsxMqy57z0J7VK+lOzmWngMlHuTy3RAsyAXemyJAlerZw218zW3pXwfJujiWDX9VYgu/VR9hHgg+wrxaKnpwdTU1NYsWIF1q1bh127duGCCy5Af38/xsbak7R+uOJ1yorEWyeQ8/Igluf/0rL9AwBRp5GjMuJ1inuoGIdEjIq2c57AXMxlJc9yqpgJHs2x3KKFSK9bdOMKusUkn88gTkxgfvOFOlhcBJLd2QLZdCPyUrz2TWL7lqGa4lRh4hCM1hSYSHYRk61qbCPGIRDTsROUFlYwvYp3brjLyDR+97GXl+WLekhWATlpMNC+Y/Eux1e7ThqLqFfOlRcjCxPIjFGAQmRUO0q8FBAj4PmpKnlEABC+PG2DRvuQJAUoxSJkt5h9z5MPYyOxIK+qXqDnoUl9GFBq+wsHpTDlCEM5NSuQ5xZoMqR6geaBPc9iiI5iZOU7SvbZv8ppFmJMVK5hGd//HKIA+N41Zc956U9ClfSnZiMmnLQI53vYUvJclBZhBSz+LnApiHrlSVWQwtdGmNUFqQXva6FIMeecpUvAN3+h+IogX3DBBfj4xz+ORx55BFu3bsXPfvYz3H777fjKV76C1auD5ToeqfhefjGK0CkHocryYDMw4ivQZ0/AMs2WvQerZ5An9WfoxG2pXciVR/RMd9a8mAJZdl0OtAaWX223ZXai3zknvFn/YpJzlxOZOu4h87H5KCSorRhS07DcHO9o0r8nbyU0Pt6wlV89ZKrAChiZ8gsRnRuTZye4IHKueCI6sjWK1SK0EFhIMCyLPImAaLWj9JKZgcKVn9uSG6GiemPRcs4sQCPV/W0j7vFjFoMV0Squ76vn0zwXr3vj9PjsylzaLdBbdtxLyrafjxUdQDdNwzSaZw2puY1LYj2zAnQ2iuuktViU4DbzzLIo7v7huwAAA5tK3S7iqR6kEQPJVE7Ryxx0IsvxgfIukk1pcxyAqFtYp2ZKrdmobSNKi6ABj2uF70LUTFd8bqt6A35nnTC7bbOj41oOChXAt7B3gV/kiCeQO9sWtBn4Esgf+chH8LrXvQ7pdBovfelLcckll+DTn/40Hn/8cXzyk59s9RiXPEGWX4ieR5G0ThwDANu1CgKxMN5CL2Rez6LI1r8AeQ1RlArpCFYxWDvQZhBNOTc6o04FfkXcWX63W/hjK4svkAuuQObjwUQkFaKIULVmdXq7oapzjMRqNJ/wg8EnIDfYSa0eEaqAtqhNPOPm4itNEMhccVY0TB/cXXW7KC3WbcxSiTyJga0jkKN2FoaQKntc9hrcGI0JZN4sQKvhUBB3BTItBkuz0dzGJd6kfi5C0olWZud4GTOHdiKLKHqHjq27byaxDByxMTVeng7QKGbW+Y2Tc+zWPLcVASZ0yoIlFOuZfeDnRJkBALt/izRiWHXcqWX7HeeWQS7sr/ie6vhuAEDPULm9X1PaHAcg4babNuY1cFGLebCEAgHbNhtSN+J25WOGTw1ChgagfuFrIzTiJtMqWI5zJlg1BHIr87EXE18pFhzHlThVfPjDH8aHP/zhlg3qcCPI8gtjFKBARqqF45H75nghr2yNWbtoZqFw9XOHvYYoWr78wuNFZCOxxYsgx13fYquRamU9jwIVEYunYFIGaHIhmB+8wiOpjntIGUIUHLGhakpgb9hFQ8tAoQLkgO4i8zGFJGK0+QLZ1FWIxABtUQSZcz1QlSYUf8rquLNSRUzkxvYCx59Wto2uaU67eDF4DYDCxMAbtY//mJ2HWaGYlGFZKFRoXCBbxZp505IcdW7wAVvKe41LWLFcqHhpDMXpWau27uwujEjHIlGnQA8A+KQjpNLj+9A/2KRObYUx6JRDPDE7ofTcVm6yzsUPrXPxRf6/sZHZg79N/AGAU3BPKcXKzKPYHduCkxi2bLc5aQh9hWcrviWd3guN8ugZWFHx+XrpT81ktoFLabpcITcFGQAjBbuvWHIPUjQL27LBsKWxxSvPW4/ltzorEgWI2GGd3VQXG9bIQ+kQgQw4jaVIFVtQLyB4Df0hTmOfbrlbSSvxFUEGgPvvvx9f//rX8ZWvfAX//u//XvJfs9B1HZ/4xCdw2mmn4ayzzsI3vvGNqts+9dRTuPTSS7F582a84Q1vwM6dO5s2jmazVb0B/2ueMfN3reUXzixCZfy1v2yU1KAjiotjrfNClq0cdB8C2XOoUCsVtGl52JQgElu8Ir1YPAWdskADPrmMkUORREAYxl1iXnyBrLk+nZF5zRfqQdxuhUq+c7sjMVpzoihUTCJOi7Ct+l6wQSi6aUKkRbaEgutsU2kyGZS4MYEXeceRQJ2qHA0suEVsjXwelYtDNKvneeu6jiQpgMqVM4VVIoE0KJBFuwizjoVXjsQCO5l4jUs8n+a5xPucG7+RcQRyUVGw1tqNQvcJZdtWItLjvL4w0bxIG1ucwBRJleSXz4/ivl/+Eh7nT8Rlo/+Khx+8DwCw74WnMIgxGCvPqrhfPb4KA/YYrArnj5DfjzGmD6SCsF5sOF7ANOJgiqVBKC9FiQ1Y/E0iPeCIjVymPP1u+5Yh6IwEixIkiIpb5Dc0NToe1Le51ahEAlvl/PS6il7O3Q2G0Ja7lbQSXxHkz3/+8/j2t7+NDRs2IBYrnZlXqxRuhC984QsYHh7Gt771LRw6dAgf+9jHMDg4iAsvvLBku2KxiHe/+9244IIL8JnPfAY/+tGPcMUVV+DXv/512fg6AT41CDvvXKTq+o7WWR5sBn1uC1BjsnUpFlE7jzGh/gXIa4iiF8tvpkTPoQgJsUW82BKGQZbEwajBBTLrFgcBQJFEwDShLXBQvOYL0YAd/BjXWkspZNHV15qimYXCGVkUmBgCxsbLIHIKDKHI5tJIpBaWzzyXQm4aCQBsixrbCK49mREwNaASPfYkRrpPByafgZWp7GhTzE6hCwDTQPcujU+gu/hi1ecz02PoA8BEqghkiGDMxopGJVuBWadQsshEwenBBLLn+8pWSKFJucv5Zs5Zzt/91KM4nhgQfRToAUCyz4m4atPNS7EQtUnkuC7MS54oi+Iq0yci95WXYtmd78HImt9g5M93YRWAZZsrd9tjuldDOGji0IHdZSuQMfUgpsXlKPewaA9ZJgleK10N9Pzpg9qHcnHnmpqZPIhkd2lzGyWfwSp6EI/JL8EW9SF88hQTW5oYLeXNYiDf5laj1ei8ulW9AV/mv4atrGME0IquoouFL4H805/+FF/+8pdxwQUXtGwgxWIRP/7xj/Hf//3f2LhxIzZu3Ih3v/vduOmmm8oE8u233w6e53HVVVeBYRhcc801uP/++3HHHXfgkksuadkYG+XK89Zj0F1+2UVX4VH7mKrLL7ytwGBbG0GWIjFMIgU2u69l75Ggedhi/Rur56loVMjXZYw8ikTGYk958kwcfMDlV6B0cqOQKPg2CGTbLTxKzutOVg/WzcfzrPU6Ed7MQw3oyVsJT5Tl0xNNFciebzfbIt9uyU01MpWFCeRCdhpRooGmVmN6Mg4mX1mUKW4EuVZjlmpYfBzRGnnehekJ9AHgYpWnOzojgbUaE8gRKLAr5AnPxXEyCXase41LBLn8iiRFkyhSEaTgRBenn3ekwMD608u2rUTXgJNWYeWaJ5CjxhQKQv3ppNy1DOm/+jYGfrIdj3zjrciaPAzC4gO3vIh3nj9YFgWVXS/kqf3PlAnkXvMQnk1saNpnWCgFrgvSPOcJ3RXIQg0/9UoIrm1cYWq07Lm9u/6I9YTCPPHNMP7wJ2j7HgXwjrLtGkW0C8iKnRO40BkJXJXzk08NghScZnIGZVruVtJKfKVYcByH448/vqUDeeqpp6DrOk455ZSZx0455RQ8/vjjMOe5LTz22GM4+eSTwbhLR4QQnHzyyRgeHm7pGBtl+5Yh7F17KQAgBrWO76gCYxFmihPcAORiaxLn1WLeycWs0RLYQ3IdKiy1PILMGXkobVhWKrJJiEY68OvmdmfS2Ch4c/EFMlGmUKASuIB5upx70+9kgSyaeegBPXkrwS2gUUQt1ILz3bWqsY1XrGousPhzetRxIOCSg5hieiEWy2/4wGyjG74RgSylEKPVi3g8G8VqxaQ6I4FrJIJMKSJUgV0nD1zjYhCtYOen17ikkkAGgAyTBOc2kqAHHkMBEvpW+7tvClIEWUTB5iv/Fo2QsKahi/7WW5ZvfBnuWv0POMMexqvJH8HBwiXFmysWk3e5BXj5Q6W2dMV8Bt3IglbwQG4XqtCF6LwGLrNd6VKB9hXpcgox1Uy5tW3GnRCtPPHl2MuuQmTyieCDrYFsFwP7NrcSnY1U7bx65Xnr0UecSfyd1mktdytpJb4E8uWXX46vfOUrKBZb12lrfHwcyWQSoijOPNbb2wvDMDA1NVW2bX9/aYSsp6cHo6PNu7g0m5W8c8D0MZmaTQREWoRVwYi+2eTlQXTph+pv2AC5tHOTYKrkF87Fi4rZFQQyaxZbnm5SCY1PQm7ABkywizA4Z7w6F4NoLb4NDqtlkGOCuw54qS5GBwtk2cpD54J/tvkIMU8gN7cxg5f6IDQgKP3gNQmgFc6VIGTHnZUjuXsIebEPUb2y57f3eWo1ZqmKlIJMdKhK5XuG6k5OquXK64wEzg5uO6ipBXDEBqnjJGLwScgBBbLny+y12p1Pnu2C6Lab7srswj7xmEC5uNNMN3ilOd30bMtCF83AivpPSHrl3hsAOF3uanW66195DGxKYE3tLnl8bL8jmLmezrF+NaReJO10yWPeBFMO6Ckcc23j5rtiAI5jyQRSGBhcjcnEcVihPt1URyCZFltmH9kIJitDqHJ+bt8yhD0JJ9AZIVrL3UpaSdUUi3POOWcmv5hSitHRUdx1113o6uoCO6+v+W9+85sFD0RRFAhCqcef97eu6762nb9dJ0GyTp5fFCrUQrZqK2mZqrC51otCI74S/dnfVuxTv1AKGW/5tL4dV9S9SNkV2lYKVgE6u/gCWRdSiBWfDPw6yS5iyo1wmlwMsrq7ySOrj6CnUWCCRzDFmVSXzjV/b8STtxJSwolaGnVaIQfFSxMSW+S6IstRGJRdsDuKMukU5cX7VuKQNIAVytMVtzPdlBG5TmOWSnh5y7n0BCS53JXByDuTE7mKQDZZGREjmE8x4BRKikBdgWyKcURzAQWy68ssVnF5KQrdiKmHoGo61pov4Mm+7YH2n+d7ENWbs6qRmR5HF7FK2kzXY6t6A/6J+x5ew/4JArGq5o4KkoxR0g0uW+qFnDngCOTYQGuckRrBjvQihTxMXQMnOMG3GT/1gF3pPFcMq1D+G3XndmFEOhq9DAN74ER0p2/H2IHd6F+xboGfAAClDfk2txKLi0BUqq/w9FHn3F3LT+OBq7Yt1rCaTlWB/KEPfWgRhwGIolgmcL2/ZVn2ta0ktdY/eCEIxdncsvTEASyrIpAjVIEttH6myKRWQjhgYuzQPvQPrWnqvotu4wHBh0CW5CgsSgCtPComWkVk+MVvrWlLKSRoHqDUCaX4xFnadW6elhBDpAltgYMiGhlf9nplr3MFcqVUl04hRguwG7Acm0/UFchmE+zS5mJ5EdcWNbYhDIMCkcHoC/uNzLRzLepatgoHYsvRM52BrqllzYk8IRFpQCCzEefcL2Qm0be8XCBbrkBOdA9UfL3FShC04BFkpZB1Cgul2mKCCknEaQHUtqt2ESx7jRtBlqOV921IPUgUn8KeZx7DeqKBX7Gl4nbVUKU+LM88Fug11ciMj6ALAJeo/P1Wgk8NIpuPgoNdt9PdpLAcsWKp+4k67hRl9rpF4J0AE3UmYOnJQ+hd7ka2G+xKJ0Vibp556cqTrhaxytyLPw68HACQPOpU4GngwK6HmiKQNa0IkViBfZtbicXJEGn18zOqOav5vfbS7rRcVSBffPHFM//+9re/jfPPPx8DA/5PtqAMDAwgm81C1/WZ6PD4+DgEQUAymSzbdny8dClqYmICfX3BKvcXk4g2BoOy4ImF7PgIlq0uL2QwdQ0iMYBFWEqRXC/kyZFnmy6QNddJQfLRrIIwDIqQQIzydASJFjHZjmWlSDdEYkAp5CAHsJibO7mxhTiibRDIUSuLCXl+3Xp9vPahlSL5nYCqFCERA/BR+FmPaMpZdraU9IL3NRcvTSjagKD0S5FEwCy0xWvuIApUQjzRBSY1COwDJg/twfLVpTmCXmOWaKL+RHc+9dJYqDINkzIzK0jzsdgIxAZSLDy7SK6OQIacBEdsFItZRPw2InJtraQqOchWpA9dkxk8/cxDWA9gYH39DnpzMSP96ElPBxLt1ci7DUukAIVR8z2SL2PvqVpMXpSHsCpT6klgp/dApxy6BzonB5lPOhH07OTBGYFM9CwKVES0ga50aSYJVi09pvc99TCOIjaElc6EaNVxp8P+JYG691EAb1nYBwBQyExDRP1J32Jic1HINQRyynSi7HEoyKUnEW9iMfRi4ussvOOOO7Bt2za8/e1vx09+8hNkMgu3GZrPcccdB57nSwrtHnnkEZxwwgnguFIdv3nzZgwPD4NSp1KSUorh4WGcdNJJTR9Xs0gZE9jDOpEUpYqVT8Et8kGLOnHNJbncmeUXRpvvhWy60aGIz5bACpEr3vQjtDgTkV1MmKgz7myFauVqGG6TCLjLYERMQCAmNHVxRXKM5mA2ICJld0WDdqhAzmedSRfTBI/hWMxt5NKA13UtvO8uEm9dYxuVRMAZC4sg88oYJpluEEIgdTn2YpnRCq2D9Rw0ykOQgrvqiHFHVGtVOlKy6hSyJFZVCFq8DNHtTBYE3RPIdQolPScTr17CF4YChQpVU9KYWJ+T//zi/0EFj4F1m/zvGwBiyyASA+nphadZqF6b6W7/AjlIpzszuRq9dLokx1zI7cco09/0lL2FILkdDuc6TxDd8atvhDybgjjPFWPKLdBb5k6IovEk9rFDkCb/0tB7zEdxXTeYJqyeNQ0higgqd161TAM9dBp7iHPcTB54vmybpYIvgXzzzTfjrrvuwjnnnIMf/ehHOPvss/He974XP//5z5tWuCfLMrZv347rrrsOO3fuxD333INvfvObePvb3w7AiSarqjNjec1rXoNisYjrr78ezz33HD772c8in8+31IZuIdimgW46jYm4EzXW05WL47zoB9MiH9W5DKzyvJB3N33fVtG5gMSS/gpEFCYCziyNIFPbdtr2LkK6yXx4VyAX0v4LZjzzeeItg7lCrpAJnkfZKLZlO/Z6UvCInxxxxk1rtA9tJ0VPIEdSC94XwzLIkWjTG7kQPQeFCuAbiEz5RWOj4BdY/CmrY8jxzjEe63Mm7YWJ8mYhjJZDvkEhEamT581pGeRrFJNSLlIzQlUN3S0yFeo0geDc46gY4PwkRgEqqZ7Gx7vpDOtzf8B+4SgQlve9bwDg3WjvdKXJSkBm2kz3BSuM2r5lCA9ctQ0vfu7CmsXkXM8aMIRidN9sR724egBpIfjqVSuJuhMELTN7z2X1xrvSKXwKEaP0mKYHHkOWRjE4ZwVmLHYcBouVc/sDv6crkIM2NmkpfBQMoRWLcKdG94MlFIcSJwIAsgdb15Cs1fhexxkaGsK73vUu/OxnP8Mdd9yB9evX4xOf+ATOOuss/MM//AMeeuihBQ/m6quvxqZNm/COd7wDn/zkJ/GBD3xgRvSeffbZuP322wEAsVgMX//61zE8PIyLL74Yjz76KG688caObBICAOnxEXDEhtXvRBTsXDVbJScyxC7CUooUiWMSSTDZyl20FgJ1l65jPiPIGhMBN890XNMUCMQCbUPelZBwUnWUjH+BXHC7qDFuhbvnhesJ58Ugm51yIlg+3EPmw7Csk1/XoQJZcb9HvgkCGQAKJAZOb+5KGNHzTen0Vwudiy7YHSVhTkARnaXnHnfZ2UiXXwdYI9ewkIi6k2Nvsjwf0UijyNaItPMRSMSAPc/isx6mm+Yi1skD9zxw1bx/gcyYClSIVZ8X3XbTvSSDbCq4Larc7XbTm1y4/SbNOyl9iYANg/wSW+YU4qVHZgVytzkGJdpZTgXzG7gArp96g1aqutCNmFV63ejK7MJe8eiS9tPWwInoxxQmDi2814C3KtIqd5xGIKLz/RUrdMBNH9oNALBXOO3r1RY2JGs1vhqFeIyMjOCOO+7AnXfeiSeffBKnnXYaLrzwQoyNjeFDH/oQXv/61+Pqq69ueDCyLOPzn/88Pv/5z5c99/TTpbOxE088EbfeemvD77WYTB/ag24AUt86TD8bBylWFl5a0cufa73Q3zE8gmPsFNZP3o3XfvZneNdrzmiaDQujppFFFAnO3+FlMDKEeTf9Qi4NCQDTBoEccfOlqi0PV0IrpAHMLu16zRXUJheC1SI3NYYU/LmHVKJI5Iq54J2A7goZoUn5vQoTAx+wk1o9WCMPlbS2yY/BxSCqjU9qqW2jx57C3ogjkJPd/dAoD2TL0754IwelQSERc88h2/WcnY9sZZEXa9S0CG7DHSVfNU+5EpbnJFKlCNpDmkkB8X9+spYCnakeQfZswACAGTrJ93494r3O9VeZqtzZMAhscRzTJIn+FqU79Kw4FgCgjjnRQaWQRy/SeK6DPJABIJ7qdZxfCrP3XMEqNNyVzpJ7kExnQSkFIQSmrmGV8SIeXfbG0vddeyrwLDCy6yH0LlvYd+IJZNFvrvwiwLgCWSuWp+TlJ5xJQc/Rp0P/Cwu7wuR7qeBLwfzP//wP7rjjDjzxxBPYuHEjLrroInz1q18tKYobGhrCddddtyCBfLiSH3dmUPG+lcgwKQhVvC695UG+RY0GPHYMj+DqWx7HHURBEkW8qfADXH2Lc8A3QyQzWgZ5EoPfT6FzUSS00hu0mvfSTRZ/WSmecm7cRt5/LqA2L/dRcCOdqiucF4OC13wh1lhBhEqkqu1D241n7t+QJ28FVC4OsQGv61pwZqHljW0sfmHuKPnsFOLEAIk7kTXCMJhgusEVytO+HCHR2GSdFyRnRUKrHKWPWlmkhWOrvp5xO+GpxVwggewVStYrvJM9J5OC/wgya9YWyH8cZ2daLP+/nQZeuWok0PW0yy1uMzML96cXtElk2S4E66fpn55lq6FTDvb0bgDA2P5nsRoA30EeyICzMjZJEmCLs9dyySqg6KPDYCVopAcy0VHIZxGNJ7H/uT9jDTHArTipZLuVx78EuAso7nkEwMK6+5ozk77OiSB7q9xqhbb3+pQrkIfWYZzpBZ9vTUOyxcCXQP7Zz36GCy+8EP/6r/+KVavKLXsA4Pjjj8cnP/nJpg7ucEGbdg6Q1PI1GOV6IOmVL8qm4lzc6+XPLZTzb9uM7awx8/fl3N24HHdDu40Htiy8QIQ3sigy/m+sFheBNC+XSfXyrlo8WahEvNu5rdCi/5vn/GUwb7ZvVFiCahVaxvntpERjy6oaI3esQDbdiUYk3lh0fD46n0DSaG5jId4qQm+xQLYXaB84dWgv4gC41ODMYxmuDxGt3I5JsgpIC41/33kSBVNBIFNKkaA57JWqT3aIG0GuFKGqxWyhZKrmdlE3/cuqEuGuBG+rVQXyjuERXHvXAVzMAATAmdoDuPqWjQD8Bx2i8S4oVADJL1wgR/UpFPnmnCuVYFgWo0w/hJwjhtIHnsdqANEO8kD2yDIp8NrstVy2CzD5xlYmvdbomclDiMaTmHjmj1gDoO+YUseSRKoH+8lyiOMLL9Sz1MYam7QS1l3l1iucnzR7EDrl0NWzHLv4AUSV5rVPX2x85SDffvvt+MAHPlBVHAPAsccei+3btzdrXIcXmRFolENP3yBUsRtxs7Lwsmby51qbVrBVvQE7zJdCp87ym0J53GqehbPVf2/K/iUjCzVAxzOLj0Gmpabj2ozgXHyBLEkyClQCCSCQvcmNN8v3LmZmhRl2q9DdiHck1Vh0RGdkCGZnpljYnidvA5ZjlTCFJKJ2cx07RKsAg2ux64qQgEx0mHpwhwcAyLtd9KTuFTOPKVIfEkb5qpZsFxru3rVjeARZW8Ix0/+H1372ZyXtilVFQZRoQI1ceXZmCTegY4eeh0r5skZS84klnePIO678wNsqzCoC+fzbNuMJ9i1g3C50b+XurdiFriaEYIrpBqcs3Ds2bk1DE1trrZUWB5FUnd9V8TyQhzrHA9mjyHdBnuM8EUGxbivyavAJJ3iSn3ImMdaBnShQESuPLncsGY2ux7ImFOp5nTNjTQoONAPBFciGUn5+8oWDmGB6wLAMCvIgupociFhMFma2GOILtnAIE6QHLMvAlPvQZVfOezNV54YtB+wRHxQ+NYg8ZHCwAAAizKqG8I0g2znovH9hS/koIvMEsuHeuIQ2LSvlSAyMlva9/Uz7UreLmuymAtjq4glk083TjXc1trCqs1HwdvXuSO2EqlnYlARabq+FLSYQp/mmtoOV7CIMrrX1A8Rd2izmGjuulCknHzDRNyuQjcgy9NhTZd9FlBZgCcEnqF4KV5wU0IW8m8L1+IxIzkw7ApCJVBdwnOQIZEMNNmFj9DyKPvLAeV5wJsFVUkAqIdgqLK7yvr2gg0mdW6pChYaCDjmuG7K2sFU8atvopmmYkcYmyn5RoivQZzlC0Z7eC52y6F5WPYjWLjSxGzHLuedS20KUqqANWqZJKefaqqQd0ZeYfgJ7hXVgK9TbGP0nYpCOIT2xQIGoZWFQFpK8+F1lq8G7rkdmBYEsq6NIc84qphUbQi+danhC325CgbwIRJRRZHjnYkWj/YgQDWqhPP/RWx6UWhxBvvK89ehnsrjDOh0A8Bv7RAxUMYRvhKidhyn4F7ZUiEEiBkxjtjuiNzMV22Rtk2eTEPS07+1tbxnMFcbRZFfJ44uBlxISTzWWYmFxMsQOFchEyyBP5OZ5rMopCMSC1sTW2vIi+HbvKzif//l/f01ZZNYPZsZZ7iwRMonlkImO7BxPYMuyEIXaUHvb82/bjF3spRggGRDipHDNjaYWXIHM1Wgk5DX60CvcgGvBGAUoPgslcyQGNoBAFm0VJltZpHhBBwa0bhe6WhTFPiQM/8XBlchmpiAQM1Cb6UagqTVIooDM9CSE/H6MM30d5YHsYUo9SNnO71zIZ8AQCjToFBV3CzH17Bhsy8Jq/TlkkpUdS2JrTwUA7Nu1MIcvoudRJNKCm8c0E9FtlmNp5RPYpDGOouQce0z3SrCEYuLQ0nSy6Jxv/DAmboyj4NoqsXHn/9PjFW5sXv5crLVRU88Q/qvyewAAv2dPq2oIHxRqO168lui/mIq4jVEK+Vkxabk3RrmFXclqoXAJSGaAKJ3720XjjqAXRRkq5ZvutVsLok4jhwgYLpj/qofFRTtWIDN6DkXSPPHpWeHl0gsTI3NpdZv4HcMjuH+P4w28mbxQFpn1A8kdRJZGEJvTzITvcg39XXsmAMjnHCFBGiiS9aKpGnWiajYF7jRPmYmmKm6uvFCjmJR3I8hWwAgyZxag+RTIRSYGzvB/forQQLnKKRZe0OEm61xcrH8aN1nnNhR0MOR+dNkL807PjDurBFy8dZ1vAUDoWwMAGN/3NGLKAUwLzVmBbDY02osoUaEWcyhm0wAaL/6O97i2cfkJHHjhL4gQDcxg5TSalcc7ecn5Fx9u6L08GD2HIjonegwAkruya6mlAQbHJWcSRsSZSMi9TtHm9BL1Qg4FcquhFL32BIyoc8B4Xpm5SgJZz6NARbCLMAvfvmUIP//4xTAoi3MHzaZZvClKHgIxATnl+zVeC03PEB1wltSB2ZSFxUbjU4gEcTnQ81CoAG5Ok4gCiYAstC1wADgtjRxpfPXB5iKQ0ZkC2Sn8bJ5A9hpFFDILL0oFnDbxEjFa2tjm/Ns24z+5GwAADKFlkVk/CMoYptjSXMZoj+OekBub9WxVcm5jlgaKZL1oKg8LOuVAAGxlH8dREae4UM16ufLVVzqEmQhVsPOHs4rQqkR556OyMQgBuhJKVAWtskIQpAtdTeIDiBOlJFgQlPyks0ogNillrhrxZU6+cebAs+g1D0GJDNZ5RXtg3Uh6euIgFNfWj5Ubu6/E4ynolAMKExh75o8AgO6jT6+4bbJnGQ6SPvBjjzf0Xh6cmYfSxGtfM/DqpOY3lspOj0EiBpBwjoXksnUAgMLoi4s7wCaxIIGcz+dx9dVX41Of+hQefPDBZo3psCKXHncOmLhzwES6nf8XK7SbDrI82AwYjsMk6QZXaF6VaW7aKfZhGhDIWn42Yuulm0TbJJAtMYkY9X/zZPRcWZOIIomC0xfWFtgvO4ZHYKYPIGFnGlp6BwBbiDbUvWwxEMw81AYtxyruz/WKVgJ4Xdei6B67pIW+3VvVG3CvddLM343kuUa0ceS50tzURL8T5VEnZ/1Ki65AZhtozDI3mvp6/Xr8r3UmCChutD6Bb3z3W7j/D46w+OSO6tHvWYEczLFDsIrQfXrc6nwckuXv/LQtCzLRQfnq12e/XehqwSacAMrUaOMNJpRpt810T2u72vW7nePUg7vQizSsZOflHwMA77abzk0ehOp2duQbbLpBGAZpkgCrTMLYPwydcli1/uSq2x+KrMdAYWGFerxZ8D3pWywiXufVeSkWU24qheCuSvWtcASyObXwhintYEEC2bIs7N+/H+9+97tx1113wbKsZo3rsGH6oDNz8pYxE64ZvJEpT9xnzULLGw3MJ833QlabV2VadCNyXNR/xS3v3gy14mzUhOg5FKjUtpw2W+p22jb7PKYZo/y3U5kIOLP1EWSvKOposg9RqA0tvQMAhCgEYkHXOk8ki1YBegBnlLr7c/Nf9QCNImpRdFc/WunbzacGMUGd/RuUbSjPNWFOQJFKI7e9bjc9KzN7vHiTVb6BSNv8aOoXYh/Dr7f9EpNsH97x/EfwVvsXAIDz1durHqeSewO2A3Z2lOwiTM6fmDD4BCI+nUxUN1ed8K0VKmKXE0DJjjUuKMysI5C9e02rSKT6kEUE8YNOjm2neSB7yK6vfXF6FHp+4cXfOTYJQZ9GbOoJ7ObWQhCrd1fU+jZhJT2ATLrxtBnRKvie9C0WnCA6DYbmNZbKjjkCOeq2sI9EE5hGHExuaTYL8eWD/P73vx8XXXQRtm3bBkmazcFKJpP43ve+BwChB3IVvAtdpNc5YLr6nJuZVaHdNGsWoTGLK5CLYj96C881bX9K1u14FqCbm3cT1ucIZK8avW2XhUg3WEKRTU8i0VO/2IU381DneeBqbAziIgjkZvlaEzc9QMlnIIjVGyK0g4idR5pb17z9uT64RoBGEbXwfLs5uXUR5CvPW4/IrQpMSrDDeikUiFgWIM/VyQ+cxu5oaW6qIMmYRgLsHP9dze0AKTTYvWv7lqGyCKp230EIxMIy4uz7Mu4+XIb7Kh6nUtQ5FmnACLIjkP1dNSwhgSj1J8CVQg4RzLbYbRXxXsddRJluvJuenR+DRQlSLY4ggxCMs8twjPYEQIDoQPPOz2YS73buuXp2FKzbgGYhDYeKXAqyPo1l5j481bWt5rbR1acAu4F9Tz6E5EsvaOj9JKuAjLyi/oaLjELEss6rmrsKlRqYnSxNsv0QCwvvDtkOfEWQ161bh3/7t3/DmWeeiY985CO47777YJpmq8d2WKBOOgI5NbAGACCKEqYQB1Ms97rkzSK0FjcamI8eXY5ee6Jpdldee2YpgF+t53U811ORWYSuZLVgo46AyqX9Rdf5CrmPBheFaLW+8YZTFHUmKHX+btRiinGLJZUKDivtJkILsBu0ZqpEbKZRRHMiyKqbM8o1uHTrBy8yO4I+CMQKnOeamx536gNi5RHnKbYHojJ7rHv+3c3qXAg4x+n/mmf4skKTpBhsSgAj2PkjQwH16XFLpSRiVPG1SuQ1LPEamLSKVL+TD26kG09789pMV7IeazY5aRAycdyHeoaOafn7NULSC0plx2C59qELabqhCd1YabyIJAqgy0+sue3Q8WcAAHIvPtLw+0lUgdVi+8hGUCGDnXd+2pkR2JSgZ45LTlZcjqS+8OY37cCXQP7oRz+KX//61/jOd76D/v5+XH/99TjrrLNw7bXX4g9/+AOod2cOKcOaOWBm+7FnmC7wSnlkT7CLMNjFjSAjMYgI0ZDNNCeSZrg5XpEA3dy85hqelzAA8EZ+0ScLcxFcU/ZC2l8EVrCKMOYtgxl8HLLd+sYbfGoQNhgQ0vjSOzDbHUnrMIF86yP7EKMFDI3+puH86vl4ApkG6KRWC6O4OI1ttm8ZQkHoxzFyLnCe69Sos/zJd5UXUxWEPsT02WYhlvt5ok10keFTg8gi6ssKjWEZKBACCWRq24hQFbbgM8orpcAQily2/rVPd6v12RZHkJM9AzAoC+QaFxS8Ooksk2reoGqgxV1BT1n0DHRmDnI0moBCBaA4MdOVbiENh0ypGxHi+Pqmjjqt5rbdAysxjm5wo481/H5RWmypO06jaIwEZl7nVSZ/EFMkCV6YTTvRo4Pos8ab6jm/WATKQT7xxBPx8Y9/HDt27MBll12G2267De94xztwzjnn4Ctf+QqKxc5sU9tOmPxBTJJUSWpKgeuGXKHdtGArsHzmzzULPuXcYKcO7m7K/mw3Ihfr8m9SL8UcUWGrsxFkYRHa9tbCa9esZMs7jFVCtgsw583yqRBDBK0/J648bz1WM844rzHe2bDFFOflggf0nm0lO4ZHcP2Oh8ERiuVksvH86nnwPI8clYEAPri18CZ3i+HbXZT6kazQ+a4eObeLXqSnfLlWlQfQZc1OBj3/7miyed27glqhqaT8BlwLTcmDJRTwWSjpFSAWfFj96W4OstfApFUQhsUUSYGtsMLol4g+iQK/OPaYB+Cknx2gPdj6pf9ryuS12TiFdUmwygTgHdcLKP6mboMbkzJYvaG2QAaAA5Fj0Zd/qqH3skzTEeMtLP5tFI2RwVmlrkeSMoppdl5wLLkCUaIi20RLzcXCt0DO5XK49dZb8Z73vAdnnXUW7r77blxxxRW488478eUvfxm//e1v8b73va+VY12SOAdMqVhUxJ6K7ablAPlzzSLq5kbnxppj5E2VadiUIJ7wf4H2LlZ0jqWTU5TVvsIEz4LKyPmLIEtUgTXPAsoWEojSYstnztu3DOHFgVcBAO61T2nYYopzc8GNwuJ1/6vH+bdtxqPsXwMAmApNJxZCPmCjiFp4AllqMGc3CHpkGXrtycDHlTrl5AEm+1aWPWfHlqMHGWiac8OjWhYWJTPFcs0gqBWaRsRAArngFUr6FBNcxLlGFbP1b9yGsjgRZADIcD2QtOATII/FaDMNOJPX2/c7gZ8UyUNPH2zK5LUV5LguiNoUoOWQp/KCrFQP6G4bdPB401furPt594nHYqW1D3+69rTAK2D5XBpAa91xGsVgJPDzBHJcH0deLBXIQo+jMSZGnl+0sTULX0lK73nPe/Dggw+it7cXF1xwAf7hH/4BGzZsmHl+9erVeNe73oVrrrmmZQNdqsS1MUxJpREbU+5FV7Y891GC2vJOXPNJLvMsnppjw8KoaeRIBMkAFyA5EodFCegcz2CJKphqo0COdzmFTFbe36w3SotlHrhEioMlFIVCtmktkqsxZO5DBjE88tk3A4Q0tA/BLTAz1MXzbq7HVvUGfIH/Ol7O7gTg5K3+yjoNnzHfij8tcN9FJgY+QKOIWngR18giCGSSGIQ4aiCbHkei238zCCvjCORK7YDZpJN2MXlwLwbXrAejZVEgESSa3L2rUvFeNTQigTX9O6qorkMB47NLmujmV6s+rP5Md/IutLAI06Mo9CKhNlbURG0bXXYae+XGumkG4Yt3Pg3J6gU4IIEiPsjdgk8Y78QX73y6ab76zaLIdyGmT0B17TgbTVjYMTyC+/ZZ+CsOiEBzV7Sc+1Slz7xjeAR3jvfjIg44Bc/W3X4+am4aSbTWHadRTDaCqF46keu2JzAaOaXkMa94Mzf6IrDpjEUbXzPwJZCXL1+Ob33rWzj11FOrbnPaaafh1ltvbdrADhe67EkclEsPmNl205mZjjRe/lwrGw1UomdZucXTQmD1LPIkhiALWIRhUIRU0lRDpkVYDbS5bRaJVA9sSkB9FHGZhu42iSgdL5Gcb6GYnW65QI7nX8QhYTWSDYpjYLbFudlBKRZ8ahBywcn30yjXcH51JRQuDrFJAtlb/Yi0+HcGAM61jJw6tDuQQGbyh5BGDKlI+cRTcpuFZMYcgcwajpBo523ZYCSwlv/GNaqbO8/5FchuHqpRSNfd1nQ7+vFS66/PutyHrmJjzSUK+QxiRAeirRfI9yhvgiQ67jleS/HLubuhKjyA5jTgaRa60I248hyyZn5Bxd/n37YZ27l5n7mGY9Dc7b3mPkEchrzmWY02NmklJheBoM6en0oh5xQuxkuvzd2DawEA2sTuxRxeU/AVHrjuuutqimMA6OnpwerVnemD2C60YhZJ5GHHS4tiZs3g5/iOqkVwxAb8Fpg0CUGUMIEU2HxzmoXwegZFJriwVYgMZo5AjlIFts9q9FbAchyyJAqi1hfIhZzbJGLezZN181GLTfLarQalFMuNvcjF1i5oPzPtQwN2L2slV563HivIBBTKY/sCWvhWQucSkKwmfVY9B5XyNT1Rm0Wst7zznR8EZRTTTOWc4ribdlGYcPbJGe3v3lVpCbcWuiuQeZ9pIRG3ENcs1D8/bbchgii3/ppkRwfQhVxDfuTpMeeewiZa22YaAN4k/Td2mC+FSp3W9p4rySXS11v+3kGxIj3oohkIRg7qAo7rreoN+IX5EljUCUTUcwzy2q436jDkrYrwi1DbEBSbkyHNaSw14dYxcanSFfOeviFolAdNL71mIWGr6RYyddDJ6+VSpQJZ8tpNT84uoy1GJ65qTLO9EIvNsWGRzBxULvjJrDIyONO5CWlq0bGjWoR8v1rkSAKcVv/mqbiesfOXwXi3m6Cab61Anhg/hB5kQHuPXdB+pKgzftpBAnn7liHojIgHcSKeomsab+FbAUNIIGo3J1ru+XYvBsl+J0UiaFpURBtHnq9cPNu9bA0AwJh2BBbf5M6FjWCwMgTbv0g0FE8g+7v+xFLOd2Ep6brb2rqTCy1GWv+dMG4AZXIs+Kpe3r2niMkWeyADeOdrzoTCRCDAnHElUZkI3vWaDlxGj/ZBICbixjj0BVim8alBpBEDAeo6sXjb5+FcFyxKAq+A6a7LzkIam7QKi49CprMT2MzobgCAPK8ImGFZjDO94JegF3LrjRKPYNKje7Ecs8uXHpEu5+RQ5rSb9pYHPS/axSQv9iPVYM7bfGQrh7wU/OKsMRGwbkFOMZeBCIA00fe2EQpsAqKerrudN8tn5y3tevZ1egNFbzuGR/DNXz2If1S+iH+WP4Z3veaMqqLw0PM70QcgMnh84PeZS8QTyAG7l7WSqYlRrMUBTBy1HS++/cKm7tsWkoj5bBRRD8YooEgiaJ7nQ3V6lq+CTQnsgGlRSXMSe6KVmznEu/qczlhZ5zogWQUUBP9ONK3AYmXwAVqfzxRK+hQTsXjK8Vr2IZC9lrrNLFqshugGVLLj+7F85VGBXqu4/smR7oWnINVj+5YhHPgjcOvYefif4jl4V+R+nNNvYrDD8o8BgIs5bhvLrUOY4IJ9p3NxmvU4Tiw/tM7FZew9NZv1eNuPIYlDdg8eo+sCNfcx3eZZYjTV8JhbBh+BDA3UtkEYBsqE0yQkOVCeSZAWBhBVlp4XciiQW4gyuRcAkOwvPWASfc4FRE/P6VzlngjcIizhzUePLEN3sXGfxrnEaA4HxeCzXZ2JQHCbaij5DLrgv9imVah8EjG9fp6YWvDa8pYKetF15/A8cv3itY6+hv4Qp7FP1y3syI88CQDoX7sp0PvMh+U4KFQA6SCBvOfx36EbQPyoM5u+byolESEaDF0FLyyscyBnFhbNt1sUZUyQJJi8/xsOtS100zSej1ZeeicMgwmmG5y7kiTbeWT4haXsLBSLi0AMEEG2XJtI0adAZlgWGRIB0Xzkobt+zPIiRJCjPc45XpwM3p5XzzjNXpJ9iyNSB6/4Gf4KwF8BAN6/KO/ZCILbblogJqwFpO5t3zKEHfgOvn7n0ziQVvD12Adw5XnrqwYvvO2Hd/wNjiF78fVo7e3n04zGJi1DiIIjNlRNgSRHYaad47Vn+ZqyTYvyINakH6q6qyABocUkFMgtxFuu7F5eKpC7+gadCNCcdtOaK7I4efGjpnZ8OZITBajF3IIiJNS2EacF2I0IZC6ChObcnJV8+76LuRhCElH1hfrbeQJ53o1Z9nIcAwrkoK2j7bFnoFEevSuODvQ+lVCIVNY+tJ0UX3gINiVYvWlr0/fNuD64uekJdA8srJWrYOahLWLO7vS8znf1SE8cQhexwMSrRxazfB8iquO/2+4iWQCwOQkS/Atk23VfCeIkUvBr9WcoUKgAeQH2YH5Jut309OngdSE05/x+qd7WR5CXEtGu2VVNS1jYfSWIE4u3/R8eXI2B0Z343cdfARKgkNprbCI3sWFPsyBuvZRSyEGSoyC5g8giikQFj2krPoTe6WnomgpBLA1GBA0ILSZhDnILIbkDyNAo4vHSA0YQBKRJDExx1iLFKDrRj8WwEZoPl3TEwcSB3QvaT7GQBU8sECkV+LUWF4VkO1EaL++q3YUJltiFhI8cVWNmabd0vN5FzVaDCeSt6g34tXXyzN/1Cjsi2edxgFsBwi58vqsSGWwTBPKO4RG87rM/xR+ufcmCut9Fx/+MfexKyAF8tf3CBvDBrYdgFaEvYpOfvNCHmO6/mcT0qLOaVamLnkdR7EfSdNrOx2gRdpsFMuUjkKjuf3vNOVejsf+fvfuOj7q+Hzj++t6+7J2QBELYUzZIFRXRKqIWJ1rFtlq7bO3PVq3WUa27to5atdpWa50oKopbxAmC7D0DJBCy9+31/f1xuSMh6y655I7wfj4ePDQ3P8ndfe79/Xzfn/c79HnDpklA5+76M664rTiU3t+ACZCalde8gBL+KWnFVkUdia06mQlISGuR9heF97WakEOCYg/WNQ75fs37QRJ60NiktwTSQQMLWkZbObWa9utv61IHolHUdmOMue9MYId2AQt1y4KVPiJV676nJEDuRQZbOTXajHaPGBs0aRgcLQLk5tODvd2qtj3m5l3xDRU9axbS1NyWWRMXfjDj1ccHd8S6bOFttuktPlMq8YoDt6vzVSyvvf08sYTm02KqI7yNYPqU3OAuaZ9Klxs7Mh3F1McNDus5OuJUzMFc8O4KrAhcYn2Vacqubne/83l9FNh3UJ08rkfj6Yg+PnIBsqmPm/w4zTmkeUMft6W5OkV8O130Ajzx/gYkNpsVg+KFKO8BQB+HUXHj9XhCurnismBTjej0+pCfwqlNwOTpOsVC47HjpGdpOKHS6Q3UKUlorKGfIQgwOKpp6KM208eSVivqUagprG/OK6+rCG9jreJswqYa0epi72S/prlqU6DLZLyrikZDVru3NWcOBqCurO0Z2VmOx/jYM6XblT56kwTIvSjBWUmTvv16lBZdaqt208H8ubi+P1JMyvbvig/kTHdXoGWrLiH8rUqqPoG45h2xgZQEc5Q3Jmji/UfDDbWdr9T5mleujj61q9XpsKomFFd4AfJNZ40kW9Pc9ECB172ndljazGq1MECtwJ02PKzn6IhTa0bv7VmAHKkVgZJ920hVmiC/8xKT3WVM9L++zqa2XS3DZVbtPcptDJcvIYcUmnA6Qlvtd9b6D06Sstp20QtKHIBJcVNeshsATZRTnGhumuSwhVarWulGJRGnPrRSf1qvHaembwLkJRtKqfElMLxmedhnX8yuWqy6vtgqemwxmeNoVP1neLRReF+bUgMbL8MLkDXNjU1iUaAmeGD/VKqnCoep/QA5eYB/c7Ct8kDbx0nJJV1pRFEiX+u+pyRA7kXJnmrs5vY3xTiMGSS0aDcdyJ8zJ/T96Z/05hJPnrqeVbIIdKQydCdANiRgVlx43K4jBwtRPq0UCPStdV0EyM3jba9JhFWJQxPKJqAW5k/Koyh/fvDnrw0nd1jarLRoG1pFxZDT87rAAC5tXI8D5FmOx1jjHRFcEXCp2m6tCJRvXwFA5uiTejSejpiT/AGyKwJl+MyqvU+7YGpT/O+FmsOhnfXxNvjzWdNzOg6QDYEGJAf8TSqi3ZxAY/AHBg5raGUHtR4rjjADZI8+kThfCAGyx45L6f0AOXD2JR4HqVjCPvuS6KnF0Qdtpo9FgZX1aATICc11xu014Z1F07p71tikNwUa8rjtTbhdTtLVeryJ7adwZTY3C/HUtZ2vbjprJEM1h6lRE5jvuieite57SgLkXuJ1u/xvmIT2j4I85kxSffXBnwP5c33Rieto8YnJNBCP0tSzbnrO5gDZlBj+BK005zNZLY3Btr3R3phgTPSXubLVV3V6O6WTJhF2TTw6T/h1hXN1/vv4VIUf5ld1uFmhvsQfzKQV9KyCRYBHG4fRF3pzhvbMjDvMZI1/FdKngh4vFkxhrwj4Dq7BhpH8EVO6vnE3xCf736deW89WkL0eN3GKE/qwhrkpzZ8q0VgZWoCssZZTSyJGY8cBZFxzqpWrfAcA+iiczWpJaa6D7rSHGiDbcGrCC5B9xtBK/el9Dtza3g+QA2df8jQ1wU5t4Zx9SfHV4zZHtzxfrLLoUgDQN2/O7UuB9u6e+vAWofQea48am/QmQ3PFLbfDSk3FQTSKGmxZfzRTXAI1JKNpbBtjTExqIlWx8pJyHjvVgojWuu8pCZB7SV2l/w2jSWr/DaMmZBKnOIMJ7riseFRNp19gvalW0/NmIV6rP9CISw5/glaaj0Yd1vqY2ZhgTvanxzibOi/1prisHZ7adWji0bvDD5A1tioaiadEO5D4yg0d3s5dvgufqpBTGJk8XX9pre4HyCXrP+Uh71+wYGaR9zSe8vwARYHhmrKwVwTS6rZQYhiORhd6Tmk4EpsbRfiaN4V2lzUKTX4Sm5uFhFoKzGCvpL6DDTQBKc2pVoZa/8GNPsopTtpggBxaipK+G6X2Wpb66/SxfQ7cfZBiEei85lb91TLsqj7ksy8OWxPxigO1D9pMH2uWbCilwumfR/7z5e5ubxruroSkNOyqAcIozQhg8FpxaWNzBdnQvCnd62iivtx/oG5M6/gMVY0uG7O9bWWWg9+8CsAVP/kt+x+cx4pbTo+J4BgkQO419eUHADCmt/+G0Sb6Uy/qmrslBfLnFE10XpJGQ1ZYu+Lb47X5T1UHOlSFQ9ucz+SwNMbMxoS4FP8Xjbup881QWrcFewcBslMXj8EbflUIvaOGBiWZquQTGOzYjs/ra/d2hvo9lGuyMJgjs8rg08eHVVqrZbWK6+/5K2nvLKRMyeK9U97jiYTf8qz3PGyqEXtiQViTnt1mo9BTRFPGxG78FqExmeP8X1phVhk5mr2x/U6KvSl9gP+UZaCUZFcSXFU0ddH4Iy3HX44yzbYfAFNCdM/g6JoDZFeIAbLRZws7mAhU3Gmq7/wzbvA58Gh7f/Ei0HlNi//zbsQTcj5mbUVzm+nE3m8zfSwJpK3k4j8TeLrr825tGu4JRaOhVpOK3hbexkuj14oryh0tOxLoKul1WrFW+/cvBQ7c22Mx5pDsanuAkFHyIft0Q8koGN07A+0BCZB7SWDXeGJG+28YU3M3vcZq/4dU47Zh76Nd0u1xmLNJ8XTdFKMzqr0er6oEqzeEQ99c3s5pa0Tj7ru2vZ1JSvNvOPBaO//y1HVyGsyjS8DkCz9ANrpqsehSYeBUUrBwsGhru7dLsx2g2jQ47MfviE8fR1yI3cuOrlbxF88D1BPPNyf+iyvmTGXFLaez+cFL2JQ8m+nWz3FY6kMex76tqzAqHoyDZ3TzNwlNk5KA1lnfo8cINIrpy8Y2ScmpWFVjsPNdV5I9NR1uoAnQG83UkUSe178qHe0Up0DTJI89tM+P0Rt+JRFd8yq5tbHzNBujz4FP1/tz0k1njSRL08jr3lMA2OAbGnI+ZlNzfqshWQLklgJpKyM0/s/KD3TfRqWMWKMuA7Oj83S9o5l9Njx9uPk3HIGOlT6H5UjPh3a66AW4EnLJ8Fah+o4s9hTv28Vo7y5qB5/Tu4PtJgmQe4m71v8lk9pOVxmA+LTW7aa1HiuOMPPnIsmbkEua2tDlqcbOKM56GpUENN0opq8zH2nLrHVbscfAzt34+CRcqg7snW/i6uzUrkefgNkX/qa3RE8tdmM62aNPBqB8+9dtH9vjIc97CEdy91untmFIwKi4cbucXd706GoVJsVDvlLNpat+0Op28TOvIR4HOz79b8jDqN/zLQADe6FBSEs2TTw6V3ibKI9m76CTYm9SNBpqNOnoQ0iL8nk8pKn1eDvootdSnTYdk+JvUhPXC7WnwxHYJe8OsVKHSbXjDXOjpK651J+9i1J/Rpx9EiDPn5SH7YIXeCLh/zjoy6RSyQg5H9Ne538vxHVS6/p4FEhbcar+M5LRKiNmM2aS6AmvpGQcNnyG2AyQ4+KbFwRcFtTGUpyqnuT0TuaY5HziFSf1LapClXz9CgAFs67ozaF2mwTIvcTXeBiHqictvYOyJxn+Cc/d4J/UdB5bn7WqbY82JReNolJb3v1SbzpnI1alex9mY3PNY7e9CZ3HgiMGdu4qGg0NSiJaRxerS14r7g6aRPgMSSFtAjpakq8Bjymd/OGTsGJCPbimzW3KSvZgUtxosiK32zfQHclm7fq09pF8Sf804uggX3Lc9DPYpwwicfvLIY9DX76eKiWN1AG92+7Yrk3EEEId3M4EWokbQmxxHCkN+izinV2nRdVVHUbbyX6Iliwt6pgmRHkFObAJyOsM7fMT141KIsbmNJKuSv2ZVAc+fd/MSfMn5bHiltOpThzFOG1JyKlJrubvkqRMCZBbCqSt6PHiUPVRKyPmicsi3VeLGijv0wXV5yNetaFGuWFPR/QGEy5VCy4bemsF1Zq0TlNEjen+1eWa0qLgZYH0iswYTK8ACZB7jd5aRpUmA422/T9xSuaAVu2m9V4b7iiuIJuak+vryrvfLMTgbsCm7d6H2RhM+G9E7wk/l7C3WDRJ6Fyd56gafXbcuvYPDFRjInGKM+RmBwBut4tUmvCZM9DodBwwjiKtfkub21Xt91+WlD8m5MfuSqA7UiBtoDOBLx4dPnyqgqGDfEmNVsPhoZcwzL2bgztWhzSO3KatHI4fG/4vECanLglTN6qMtORurgNq6OOqD3aTv/NdV+qaGwAZQlhZdMb5A2Sbauy1zZGhMjbn1fucXb8+qs9LvOIIu0taoOKOy9rxWSKf14tZcUEfBcgBzoyxDFQP09gQWpUVX7DNtATILQXSVl7yzuEC15+jV0YsMYd4xUFTY2hlJR12K1pF7dPqOOGyKyY0bitxjgoa9J2ncCVm+xc7Giv8exxKinY2p1fM6/VxdpcEyL3E7KikQdfxphi93kC9khhsN23w9W2r2qMd2RUfXiHzlkyeRhy67n2Yzc0VK7wOC0afDXcfdiXrjF2bhNHdebBoVm0drlwpzRu3LCFOigD1Vf60GyXRP+FYMidR6NmP1dJ6pdNx2F+Oa8DwyOXSBbojOUJYQb7prJHkaBrwovCO93udfvGM/P61OFU9FZ8/0+XjVlaUkk85rpzJXd62p9z6ROJCaCfemUAnRXMfl2j0xOf4V6R83k5vZ22udNFZF70AX3NZSqsS/c9foHW7z9V1ipLd2vzZMIZ3BitQ6s9jre/wNo7mMnNKHwfI5sH+8oYHt4d2UKlYK2kkHpM5NhYXYkUgbeWZhOuiWkZMF+imF+JZWmtTYPNv7AbIDkwoHjvJnirsXexxSM/zpwI6mzf0lazwV68YNOuHvTvIHpAAuZcku6uwmTrP+WvQpKJ3+FeATD473igGhWk5zbvia0MrG9Ues9eCW9+9PMy45gBZdTRh8ln7tG1vR5ZsKKXSqWGwc1enHa3iOumipm0OkG1hdGtrqPFvJjEk+d8/5sIZ6BUv+7esbHU7Te0e6kgiMTVym3J0zeN12bpeQZ4/KY/aqf+HTlFZ7pvU6RdPZtYANiWewsjKD7ss21Wy+RsAkoef2I3fIDxeYzLxas9WkAONYsx9XJZQk5SLXvFSV9X5Rj1n8waaQMfMzgTqmMZCcwJTnD8wUEMIkG3dLLWXkOxvBuSz13d4G3vgYNHQt3NS3mj/+79x37qQbq931FCvpPTiiI5dgbSVaJYRMzY34mmqCu071t4cIGuj0Bo7VA6NGa3bQoavFnd8Tqe3TUnPwaHqocEfIGcUf0iRbhhZMZpeARIg9wrV5yPDV4OnizeMRZ9GnMuftG9UHVENkJNT07GpRminkDe0LufVUbCYoFrwGLoXJJjjEvGqCrgsfd62tz2BCg15VGHG1WFHK6/H02mTCG3zaXd7U33Iz22r9ecSmlP9759B4/072hubN64FJFn2UW7oOugJR6BygMsW2qpqodO/in3TNVd0+cVjmHE1idjYvuzFTh/TcWA1XlVh0Lje6aDXkmpMIQkbvjBSYI4WbDWe2LcBcqDzXSCFoiO+hjJ8qkJ6VtcryIFUK4c2+geoJpPZPye4u85BdjQHyIFykaEyxyX68ygd9R3exmnzH0BpjH170JCRM4hqUtBVbg7p9iZXLRZpMx2zkgLd9OpCC5AD72ldlBv2dMalMZHgLMeouFG62OOgaDRUaTIxWA9zcN8uRsVw9YoACZB7QVNdBQbFA128YRyGdBI9/qPEONWO2sen8FpSNBqqO9gVf3Q5r/aCRdXnI1G14DOmdPv5bZjAZSFetUd9526gQsNwzeFOO1pZm0uXKR2c2g10I3N2cgr3aM56/2sQqHSSkpVHqZKDseLISpKqquS4S7AkRnYTm6G5EoMnxNqzmtJ11JDMwMJRXd72hO+dQ4mSi3nLS53eLqF6Iwd1BcEyQr3KnAKApQftphWnBaeq7/MmP3HNX7iWqs7TojTWCmqVZAwGQ5ePud3iD4yzXAc7PWvSFxSNBgdGlBBWkAM581pzeCvIikaDRYnvtB28y+EPkAONS/pSqXkEGU07Q7ptgqcWu1EC5FgVqDPuqW/bLKM9ge+MaHe07IxbY2aA2x/wG1K7PgCvN+SQ4Cin5JtA9YrYTa+AGAqQVVXl0UcfZebMmUybNo2HHnoIr7fj3Lo77riDkSNHtvr33//+t+8G3InaMv+Kjj6l89M4nrhMUnz1+JpXIaO9W7VBn0Wco+2u+KPLebUXLFqa6tEpvmDA0R12xYzW2YBRcUOUA+RAhQZXFx2t7IEAuYPTYIFd8p1tAjqaN7DZpsVu9PLEceRbtwd3QFdXHiaNJtT0ESE/biiMzaV7PI7Q0g6yGrdQYh4dUoMbjVbD2rRzGeXaxuY7J7YbgHm9PgodO6lJiUzr7K5oAwFyXfdrgCuuJmxK39cwT8keDICzi256JnsF9dqu278v2VDKI6v9B0bJ2Do8a9KX7Io/x7ErgTMehm6U2rMqCZ1uxHXZoxcg29PHMdB7ELu1689jiq8et0naTMeqhKRU/1naptCahQQ2/xr7uDpOONzaOJIU/wFsfGbXZzMdcQNI81SSfgykV0AMBcj//e9/eeutt3j88cf5xz/+wXvvvcd//vOfDm+/d+9ebr75Zr755pvgvwULFvThiDvWVOUPkBO6esPEZxGnOKkN5CRFYQJuyWHKIsXTtpD50eW83Kq2TbBoqfffTxvX/dJQDo0Zc3OArhijm3d1pEKD/yCto45Wjqbm02AdbKQwNjciCEx2oVCtVbhUHQlJR1aDvLlTyKaG8oP+EjkV+/wVLOJyI1fBAo6U2/M6ul5BttRXM9BXii1zUkiPvWRDKX+tmIJXhfHK/nYDsAO7N5OsWNEMnN69XyBMuuYDGFtD9wNkrduKLQp1u9Oz8/GoGnydNAtZsqEUk62UdE95lyvCc9+ZwCrttQCdnjXpS07FiDaEANndvFGyO6ttdm0CenfH73d3c4CsM/X9/GwcNBmd4qN4Z9syjy25HHaSsEqb6VimKNRq0tDbQwuQPc37QEwJKb04qJ5p2V0ycMDeGW9iPpnUMcq7i5qC2E6vgBgKkF944QWuv/56pk+fzowZM7jxxht5+eWO66bu27ePcePGkZmZGfxnNke/+xqAs7kSRFIXbxht8yas6mJ/HqcmzB3YkeZOGEC6rxbfUSv3Lct5AejwYsHUKli0NfhzqfU9aE/rVOJIcPsDlWjv3A2UBlrqnQnACt/Ydis0OJpPgwUanRwtvrmWrDeETW8BWls1dUpyq1XZ9JH+fNzSrf4NbJZD2wDIGhLZldbARrNQSmsVb/Y3L4kfGlq3u7nvTGCl7udolY4DsMqdKwDIGt37+ccAhgT/yqrTEvomyqNpPVYcUQiQ9Xo9NUoKWkv7p2wDqVEDqSQFa5crwm3PmkSnoUJLLsWE1tt1ikUgJShQ+SIcDl0iRk/HAbKn+bNgCDN9IxIGjPQfKNYXre30dnWBjqzSZjqmNenTiQuxm57PEZ3qOOHwNe+b8qoK6TkDu7z9Id+RRZ+ni3OienYqFDERIFdUVFBWVsbUqVODl02ZMoXy8nLKytpO/lVVVdTX11NY2LtNBLrL23AYj6ohs4s3jDHFvwmrqWwvANooB4VKYFd8des37U1njWSQphpFgQO+LBQFhmnKWwWL9qZAgNz9HDinNo40n/9xdFH4MmopUBro0bjrAdikHdNuhQZXoAZuB1/McUkpAKiO0FeQja4amrQprS4bNHYGTlWPq9hf8kmt2oVdNZCZPyzkxw1FoDuS6up6Y5Rl3yp8qkJBiN3uAgGYo7mjlarCNl8BpzseDt5GPbgWKyZyh/XNqqWpeZXe1YMA2eCxRq1ud70uA5Oj/RWpQGqUWXGHtCLc8kA4mg0VWnJpTGi9XXf39DZ/vkzdqCTi1iVi9nV8QOhp7uSnD3MDYCRkDxxOA/EoZZs6vM2SDaXc+9xbALy3rTrmg47jmc2YSVIItcvBX9EJjiyyxCKf3r8oWaOkou2ibvqSDaW8U3xk7j/dsTzqKVxdiYkAuarKf0SVlXWkjl5Ghj+Xqry87aaxvXv3otPpePzxx5k1axbnn38+b731Vt8MNgRay2GqlVQMhs7fMPFp/hxTb7U/QO7oNH1fMQSahZQdaHX5/El5lOeeCcDN7l/gUrXUJo5sFSy6mwMMc2LXuY4dceviSMK/WhQLO3fnT8rjiz+eQwVpnJjc0G6FBo+98yYR5rhEPKoGtZNNQG3u46rDpm99oKE3mDhgGEZKrf+LMq5xH4d1+Sia8Nt6d0ZvMOJU9eDqegXZXLmBEk0eqWmh5T0GAjAD3mDb17GaYhYZ7+Wrt/7J4jUlpFSvxaNqOP8v7/TJxBnfHCB7wsgRP5rBZ8UVpQo0VkMWSa72V6RmOR7jM+/E4M9drQjHTEOFFtwaM3pv1ykWanPOfHdK7XkMScR3EiD7mjv5Gc19HyArGg0HjSNIbWx/o17gLMF89/sAfM/9XcwHHcczd1w2qb5aVJ+v6xu7mnCpWoym2Dgz3h61ufRhfSc9HwLmvjOBF7X3ALGTwtWVPguQnU4nxcXF7f5zOPwrBC13WQf+3+VytXmsffv2ATBq1Cj+9a9/cfHFF3PnnXfy4Ycf9sFv0jWTvSKkN0xKpj/gMjT6c5ajvWoayJm2VrUtZD604VsqlAxev/937Eo8kWnWz3G53MHr3c1VAOKSu79JpGWZO0Nc7NR+rNHnkmBrv1JAIEA2NecaH03RaLAqZjTO0JtRJHnrcJraHmjUp01giGs3TqeDTEcxDfGDQ37McNgUExp356e1VZ+PgbYdVCSNC/lxWwZg81338KL3DNb6RuLQJnLK5j8weOlFjFRKSOrDDWIJKf73q89W3+3HMHltUavb7YrLJrX5rMvR9Cm5JDYfcDpVXZcrwrHSUKElt9aM3tf1CrLa/PmK70aA7DMmkahaOwxaAo1KjHHRSYGzpI1hsGc/LqezzXWBswRztBv9P+vWxHzQcTxTEnKIV5w0hVD2U3E2YVXiUBSl9wfWXc0NsqzGzpuEwJEziIHFkVhI4eqKrq+eaMuWLVxxxRXtXnfTTTcB/mBYr9cH/x9oN6/4hz/8IfPmzSMlJQXwB8rFxcW8+uqrzJ07txdGH7olG0qZYD9MElbOe+BNrjn7xA6/YFIycvCpCol2f/AV7aAwUIbGeVSzEIfDzkjbenZmfp9sRUE54WKyVt7A+pXvM/m0+QB4bf4AORBwdIe3RTc6YwcBZzQ0xQ9iaP3Kdq/z2ruugWtV4tF0sgmoJdXnI1Vt4IC57d/RMHgGporX2LLuC8aqVRxMHR7SY4bLgb99aGcqSnaRQyO+3CkhP+78SXks4QWe+XgXh+vtPJNwHTedNZIpJ2TjvieLqZrdwdsu1C1jIctwvqOHSd3fQNeV+Phk3KoW1RF6jvjRzKoNTwetxnubLzGXpGobdktDm9XTm84ayaC3q7CoRi5x/YnLtcvJ6WJFeP6kvKgGxEfz6kwYHF0HyIrLglU1Ea/txhkVUwoGxYPDYcPUThCsNq8gBxqX9DV93iQMZS+zd/d6ho2f2eq6WY7H+KPuZc7VrkKn+LCrBj7yTuN+zxV0vq1PRIOu+eC0rryYpOTO0xG1bgt2JY7YTbAApbmwgCuu854P0HwG0WJGjzdmUri60mcryFOnTmXXrl3t/jvvvPMAqK4+8kUYSLvIzGy7K1dRlGBwHDBkyBAqKkLbHdpbAqe7cqkijaYuV8F0egN1ShLZHv8udGM3NphEUlpWHi5Vi9rQelf8nrWfkaDYMYz6PgAjZl2KFRPODYuO3Mheh0fVkNCDDQUt2zV3Z7NNb/ElDyaDeuyWdtIkQmgS4VDi0LlDK5vW2FjvL7rezm70vHH+hiGW715Co6gYcrquPdwdDk0cOk/nAXLZNv8GvfQR3wvrsdvraKVodZzk+DvveWb0+eqCRquhSYlH00mjiK7ERbFut665lGTNUWlRAD+YkINJcfOJeiI71cExsSIcLq/WjFENIUB2W7Ep3TsVrQmU+qtvfyWe5rMp5iitIGeNmAZA7Z62G/X0Kbl40KLFh0fVHBNBx/HMlOavFRxKNz2txxoTHS07smRDKV/v8qfAbqzydnm2LxZTuLoSEznI2dnZ5Obmsm7dkUYI69atIysriwED2n7QH3zwQX7+85+3umzHjh0MGTKk18famcDpLqPiDTnHpkGTGsy7NUZ5BVmr1VKtpKO1tt4Y2bT1Q1yqlmEz5gFgMCewK+VUxtZ/gS3QZcrZQJOSEFI93A61CDLiYmhjgi7T30O+ornaSEuq04JL1WE0dTyRObQJGDyhBcgNzROnNqntKaus/KFUkcq4umUApBWEnt4QqiUbSrF4NBRa1ndaFsxdvAa7amDw2MiUY9On5FJPQlRWF6xKAtpO6uB2JthJMUoBcly6/wu3oaJtWlTJjrWkKBZyJ34/qi12e0LVx2GibWrB0bRuC45uBsja5rNV1o5K/bnt2FUDmu6sTkdA3tDxWFUTvrKNba676ayRTNDsQ1HgF+7/OyaCjuNZUqb/82qv7Tp1zOCx4NREv6NlewKLgTO8GwAY693dZUpcLKZwdSUmAmSAyy+/nEceeYRvv/2W7777jkceeYSrrroqeH1tbS1Wq39V6/TTT+err77if//7HyUlJbz88sssWbKEa665JlrDB1ru0veniYSyCmbVHwkEu7PBJNIa9BnEHbUrPrvyG/aYxrUKWs1TLidJsbH1yzcB0LkasCg9CxJadqOL7+O2vZ1JzvN/2dSX7m5zncZtwdrFF7NbF4/R23VVCABrc5tpY3I75ZoUhdL4MSQqdryqQs6QsSE9ZqgCk146DV02ikip28Q+w/CQurOFIpqrCzZtAgZ36JsoW7I2t4NVOmg13tsSs/z7Bhy1bVekKjd/AsDAyWf16ZgiyaeLwxTCCrLeY8Wh6V6AvLfB/zVoefUn7R4UKm4rDsXYrceOBI1Wy0HDEJLrt7e57vzx2cRrXHzrG8tnvqnHRNBxPEvN8X9ePQ0d1y4PMHqtuKO0t6ErgcXAWdqtAJys3RZS7nt7ZxBjWcwEyNdccw3nnnsu119/Pb/5zW+YN29eq4D34osv5rnnngNg+vTp/O1vf+P1119n3rx5vPzyyzzyyCOtysRFw5Fd+p6QV8EcxiO5pvEJ0U8rsBqzSXId6aZXUbqPob4DNOWf1up2I2eeSy3JaLYsBkDvbsSu7VmArGkOMuyqAZ0+MoFXJGQX+FMZnJVFba7Tupqwd1ED16NPwOQLLUC21/kD5LjU9tuUl5j9QbEbHRc/+lFEN7EFJr1cTW2nZ0DcTjuDXUU0pEVuI1A0Vxec2qRO6+B2JtBJMVp1u9MHDAbAXd/2fWA4uIKDygByCyJbCrBP6c0YFC9ed9vN2q1u5rXh6sbp6CUbSnlzu/+zOVopbvegUOOx46TvOyW21JAyhkGuIrweT6vLt371JrlUoZtxzTETdBzPEhL93fSUprbVuY5m8tlwR2lvQ1cCi4F21f89fSxsuOuOPtuk1xWtVsstt9zCLbfc0u71y5cvb/XzOeecwznnxFYnlpvOGknc2/5VsFe9c7hc+1mXm2I85gxoBIeqxxQDQaErPof0phX+QoWKQvGqd8kGsqec1+p2Gp2eoqzvc0LFEhrqajB5mrDrerbqGwgyrEocsVTYJjktk3oSUOr2t7nO3ySi89F69InEqV03OwBwNfpX75Mz2h5ULdlQyhvlOZyvAyPu5i9z/wpDJL4UAxt+5mlXo1f8qQ4feqe32fBTsv07hioedIOm9fg5W4rWBjGXPpFUV9crOu1xBALkbrQ4joSExGQa1XiUptZpUT6PmyG2jWxLPYOBsbwLvgtKcxkpu81CQiebmgxeO02G8LvIzX1nAvN1/mo8GqX9zaFarx2nJroBsiZvIvFViyku2krByInBy9U1z1FNCiec8cPoDU6ETlGo0aSjs1d2eVOzasMbpdStrgQ23BlxHzMb7rojZlaQ+4NurYIl+HNNu7vBJNKUxAGYFRdNza2jdfs/o5I0Bo9qW60g9cQrMCpudn7+MnHeRtz6ngUJgTJ33c0l7E2VulziLMVtLjd4rDi7aBKhGhJJCDFAVi3+iTOlnQB57jsTeFn3Z6B36kgGzoBo8aGqdDjp1ez2V/TIHxdag5BY5zEkk6CGliN+NEdzioU+SgEyQI02HaOtdYC8f+u3JGJHKTwlSqOKjECA7OiiVbvRZ8OjC38FeZbjMT7wHMmjb28lTOux41KiGyBnDPePsXL3d8HLykt2M962mt15F2A0xt6cKdoXaje9eNWGTx+bAfKxuOGuO2JmBbm/CHcVTJfoL48SK0GhLtW/iaC2bD/m+GSGW9awPXUOWe1svhs68VRKl+YQt+stElQLXmPPVpD1zc02HDG4c7fRnE+uZVubyw0+GzZdSud3NiZhUDw4HbZON/MBKLZq6kkgxdA25zGwwnu2dg0mxR3xkk4tz4D4VA0/1n/CSE1pm0lPe3gdVaQyYODQCDxr9PlMySSqFlSfL+xNpi6bPzVDH8UNtk36TOKcrb9wa7YuYyhQMOXYzT8G0Br8nxenrfMUGLNqx9uNYEKfkkutJRFVBR9KuweFep8Dtza6AfLA4RNxqno8hzYELzvwyVNkAYPP/GX0BibCZjdmkm1pm0/eksvpwKS4IUp7G7rSUcnO/pbeIwFylBlT/QGyM0YC5PgMfy3kxooSHE21jMSObsT3272totFwMO8cph18HgXw9TBANjbXGe1qRTYaXEkFZDV+gdvlRN8ieDX6bDTqOp8UlObUEUtjXZcBst5eTYMmhZT2rms+rRVOjns4Wk569fV1zNOtxqTXMH9i63zo7MatHIwbQ2ZPKpbEElOKv4asrSnsjbIeez0Q3brdDnM2A+pbp//Ela7ggJLP4LyCKI0qMjSm5jqr9s5z+M2qvVWZyFAFDgoPqel40fKV74Q2aXF6n6Prg+BepjMY2a8fTEKdP7ByuxwMP/Q2m+NmMHFw/1q16+/ccdmkNa7o9IDc1lSPgeht/g1FrNVM7w395Bvu2BWf5n+DObWxESAnZx9pFtKw+UPcqpZhJ57X4e1zZ12FVlHRKCq5B9/vtDRYVwKNUtza2Nu5q8sYik7xUXlwT6vLzT5bqwYn7dGY/UGXvbHrdsZmVx1WXfsl7vritFZgl/G2By9i98hfcoJ3G9u/PtLGvbG2gny1DEfWpIg9Z7Rpms9cWDoq89UJT3OjGFN89KqueOKzSVPr8Hr8ubQel5Mhti2UpUWmBF806Uz+VWGXvZNW0M2l9lRD+MFEIC3ufeU0BipVPB93TZu0OIPPgScG5ue6pNEMcu1B9fnYvOxV0qlHmfaTaA9LhElJzCFOcdLYyfeBrbnTXrT2Ngg/CZCjbF2Nv7Zmjqu4R8FlpKTnDMSnKngbSsmo+JpdhjEkp3a8OWa9LYutvsEADFQqe9QieNUhf73TfNv2mPhbtJSQ468EUHtwZ6vL/U0iOv9iDqSOBCoedCbeU4fD0P7fu68rPUy58P8oJRvTV/ei+rwAFG/2NwhJGDqjV54zGnRx/r93h3VwO+FtbjUezRKNmuQ8tIpKXYW/1Nv+zV8TpzjRDT01amOKFF1zpy63o+MA2Wb1vwYty0SGY/6kPGacdDpaReXp7xvbfJ6MPgc+XfQDZAZMIBkrh4v3YNr4PGVKJuNOuTjaoxJhCjT3qStvu6clwGH1723QSYAcVRIgR9GSDaXc9VkFPpUu6872FZPJRI2SQnz1JoZ49lGf1/mX7Nx3JjBOcwA4sgu8OxvHlmwo5YHl/i/4dBpj4m/RUsagMQDYKo6UevN5vcQrDtQudhrr41IAcFq7XkFOUetwm9I7vL4v60iaTGZKJvyWIZ59bP74vwDY9q3CpyoUjD+51563rxkS/AGyo7GDTmqdcfoDt/iklAiOKDzGtIEA1Fb4v3Brt34GQOGU9lOjjiUGsz9A9nQSIAcOPLsbIAPkjPSvtjfsa9utzogzJgLk1KH+MqbFX77AWNcmigdfilYnWZLHGnOaP2Wtqepgh7dxWuuBI4srIjokQI6iue9MYLv2h2iU3qlK0F212gxGW/3bvtInzuv0toFd4B7V/1bqbj3Eue9MYIP2x0Bs/S0CMnIGYlONqLX7gpcFmkR0tZHC2Ly66LJ23q3N5XSQjBU1LvxyVb1l2rk/o0hTQMaah/G6XcRVbuCAdhDJKR2fVTjWmBL9v4uzqesDmKOprqYuOyn2toQMf4BsrfJ300soW0mRppCM7PZraR9LDM2VbbzOjqvA2Js/h9oe1KLOGTjMX8qxbHOb60yqA58++vsiNrvz8KgKM/Y/hVvVcHDwRdEekuiGxEz/59VR23FpycB3hUEC5KiSADmKjnTe868CxEKx7SUbSilzx6FVVCrUFHZ4BnZ6e31KLrUkokHt0caxwN/CFUN/i5Y0Wg0V2hxMTUda+ga+mDVdBMjm5g6EXlvnAXJdtb9UlyaxbZvpaNHp9dTOuIU8Xxmb3n2CQY6dVCaNj/awIsqc5F+xd1trw76vxtV1J8XeljLA353LXXcIl8PGUMc2KtMjW6M6WgzNG3d9zo5XkF3NKRY9OR2taDQcNA4nrbF1O3mf14tZcUGUA+QlG0q5/b0iGvHPzaVqBncuq4qZM2widGnZ/u/Uzrrpue3+7wpTQkpfDEl0QALkKDrSec8bE8W2A62Gc/Cfam5Uzdz2zrZOJ+FIbRwL/C10MfK3aE+9KY9Ux5HTYvbmjRRac+cBcqBFdyBftSNN1f4JU99em+komnLGZWxWRjFi819IxsI39Wn96os5IcXfzdJrC38FWeO2YI9ygJyekYtL1eJrLGP/hi8wKW4Mw0+L6pgixRwMkDuuYuFsPvDUx/Vsx781dQyDPAdwu460tnYENgdGOUAOdLlMU/x/h8Gaypg6wyZCF5+UilU1ddpNz9tc9zuwuCKiQwLkKIq1YtuBSXiUxp8LPFxT1uUkHKmNY7H2t2iPI7GAbG95cMNa4Iu5q5Wr+OZJTnV2voJsbW4zbU7J6elQI+rdzWU86F5AguIPHEZ6dsVUfnhPfb7fjk+FvJ3/DXtzqM5twdFFq/HeptFqqVHS0FnLqN+xHK+qMGTqsV3/OMAU15xX7O44xSJQScTYw1rUuvxJGBQvJTuP1Bq2W/2PHWhYEi2BM2zOGD3DJsJTq0lD30k3PdXpD5DjElP6aESiPZLhH0WxVmy7u40oIlEPMdb+Fu3RpA3BVOGmqqyEzLzCkPPEDCYzTlUPzs6bHbjq/W2mE9NjZ9UcWrfjBThPt5rzWNCqHe+xasmGUm57ZwfzNFrylJqw23frvbaYqNvdoM/A7KhEYy+jSDeMEWkZ0R5SRBiNJjyqBtXVSYDs8AcTxh6W2ssaOR3WQu3e7xh6wkwAnDb/CrLGGN3XOFADXR/DZ9hE6Br1GW2a+7Ti8H9XxMVLFYtokgA5ymKp2HZvN6LoSiz9LdpjzhkGO6C6ZCeZeYW4m1MmDCGsXFmUODSuzgNkT5M/QE7OjK3NVYEDp7O0azErroh38Iumue9MYL72SPC/ULeMhSwLOfg3eG3YddHfSGM1ZpNv2UKqWs/aAZczItoDihBFo8GOEaWTFWRv8wqyuYf5mvlDxmFVTfgObwpe5mqunqGJ8gpyyy6Xr3rncLn2szYNTcSxw27KYkDTlo5v4GrCoppJ0Gr7blCiDUmxEEHHQppDNKXl+/8OlrLdAHiCeWIpXd7XrsShdXW80QgAaxVOVU98jG3MCOSHG3H3u9WrwKlrr6oA4Z+6NvmseHTRb2zjissmmxoMihfziNOiPZyIcipGNB57h9erzkCA3LMDFY1WS4lhKMkNRzbqBRqU6EzRfY37uga66F2euCzSfbWoPl+712tcFmwx0l33eCYryCLoWEhziKbsQcNxq1q8Nf5Sbz5n6F3U7Jp49J7OV5C19hrqlBRyYqyFc39evQqcNVGafzbiCiv4N/nsXXZS7AvlPn+pOp8K937rZGFqab/53DoUUxcBsgWvqhDXw016AA0pYzih8l28Hg9anQ53jATIEPtn2EQYEnMwlbtpqK8luZ10KJ3bgl0T/ffc8S62volF1PVlI4pjjV5voEKTib7R35DB15z7GJ/U9U5jlzYeg6fjnfgARmcNTbqUHo8z0vrz6lXLsybbfAXYMDJAUx9y8B+v2rrspNjblmwo5cty/1qHAsy3v92vNlG6FBNaT8cpForLgg0TGm3Pv840uROIU5yU7tsKgKe5vJze1P0mJEIcTZ/iT6Orq2jbTW/JhlLcTZWkeatirqPs8UZWkIUIQ60xjyR7c6k3pwW3qsVo7PpUmEuXQIqj84ku3l2LzdBxF71o6q+rVy3PmoxoXMnzhoexDzk7pN811E6Kva3lJspAk51w8qhjnUtjRufteAVZ47ZiU8xE4jAlfdh02ASVu1YzaMREPA7/Qa2hi1KOQoTDnJYPQFPVIRg9JXh5oNTqV5rDJOAIe9OwiCxZQRYiDLaEQWR7mht6uJqwKSaUEFIiPPoETL7OV5ATvfU4jf2j+sCxJHDW5Nk/38oeZTCTS/6L6vV0eb9AJ0Wli0YxvW2W4zHe88zodh51rHNrjOh9jg6v13qsOCOUrzlo5EScqh5v6UbgSP1lo1lWkEXkJGX6A2RHbetFk0Cp1UylMSY7yh5vJEAWIhwphSRhpaGmwr9yRWjln3yGROLp+DSx6vORqjbgjZMAOVr0Oi2Hx/+SPO8h9nz1Wpe3t8dIgKxPyfW3SYZ+t4kSwKM1dxog6z1WHJrIlGHTG4yU6AYTX7cdAF9zeTlDnATIInLScpq76TW27qY3y/EYX3hPCP7c3w52jzUSIAsRBkPWUAAqineiC+OLWTUmEq/aOty13Fhfi0HxoMRnRmysInwz5l1NCTkYVj4Gqtrpbe2WeqDrToq9rb9Xn/HqzBi6CJBdEaxFXZs8mkHOPag+H2rzCrI5AhsAhQiIS0ihSTW36aanT8llhHIIVe2fB7vHGgmQhQhDanOpt6bDu9F7LDhDXbkyJqFVVGzW9ttNN9T4T7XpkrIiMk7RPSajgaKR1zLYvYcDq5d2eltH82up72EHt57qz5soAbzaOIxqxwGywWfDHcEAWc2ZQBJWykt2Bzv4mWUFWURYnTYNg72i1WW/HnyQXE0ta30j+uXB7rFGNukJEYacglEAuKv2kui14dCFFhyV2v0ftV0Pz+FP5j9yzdkntgpgLNX+U23G5NhqM308mnL+Lyl/+CncXz4MJ57f4e3c1noA9OboNwrpr5soAVS9GRMdB8hGnz2itahTh06B7VC+6ztw27GrBszSsEFEWJMuA7PzyCZan9fL1D1/5zCZ3Gi+h5JGr5RajTJZQRYiDOb4RCpJQ9tQjNFnw6PreuVqyYZSvi5xATBRKWremdy6DJejwb+SEJ8mAXK0JcXHs33wjxhu38yhTcs7vJ3L5q9r3dMWx6JjSzaUsreyiXjV3mHJK7Nqx6uP3ArvoFHT8KganAfXo7itOBRjxB5biAC7KZNkT03w5/UfPc9w714OT7qBL/94tpRajQESIAsRpmp9LvHWg5h9Njy6rr+Y574zgcd0TwCgUdR2dya7GyoBSMyIrTbTx6sJ5/+GWjWRg2/dweo7Z7QbnHnt/k16ph52cBPtC5S8Guvbg0aBy6wvtVvfOU61o0awFbQ5PoES7UDiarah8dhxYorYYwsR4InLDnbTczmd5Kx9mP2awUya9/NoD000kwBZiDBZ4weS6S7FrNrxhVADN5SdyT6LP0BOSZcV5FjwdbGd571zmalsZZqyq91Vf6+jucWxrCD3ikDJq0naIgCu0C1vc2Dp9bgxKe6I16KuSRxFrn03Wq8dp0ZWkEUvSMzBqLhprK9mw5LHyFfLaTr5j2h1kvkaKyRAFiJMnuTBZFJHomIP6YtZn5JLqZqBqoJXVdrdmayxVVFPIjq9oTeHLkI0950J/F73OtDxqr/a3Go8LoROiiJ8sxyPscTzPZyqP2Bwqro2B5bW5koikS6158k6gQzqSbKX4lJkBVlEnj7Vf7awbN9Whu14ku2G8Yw/7ZIoj0q0JAGyEGHSN5d6g9C+mG86aySZmib2qrnYMfCK9/Q2O5P1jlrqNSm9MVzRDYHgzKP6p8h265E6m0LupCjCp0/JxYIZPV5UFfR42hxY2pvqgcgHyElD/N3NCj37cGvl9RWRt8fmX1zJXfpD0mlg86gbQmo6JfqOvBpChClpwIjg/4fyxRwow/UP3Y9IUJysMpzYpgyX2VWDVScrkbEiEJxp8Netbm/VX3FZsCpm+VLrJS3rO+9XcyhT09ocWDqbS+1pTZFNsRg4Zob/cRUVt0ZWkEVkLdlQyr83+tunJyl2SnyZ3L0hvt1NqCJ6ZGYXIkzZzaXeALSm0Fau5k/K4+Gbr8eCmSuTN7fZmRzvqcNhTI/oOEX3BYKzN7yn4lNhs6+wTXCmdVuwI6uLvaVlfedvfONJVmzYzv93q8+O0+rfKKmPcLOWpOQ0Dir+U+BeWUEWETb3nQks0/1f8OdBmippKR2DJEAWIkzJ6dk04N81r4sLfYOWwRTH7qSZjKj7Eo/b3eq6FLUBj0kC5FgRCM7+nvB/bFGH4NXo26z6a92Ra3Es2jd/Uh4rbjmdIVPmkKA4mB5X1up6l633mrVUxPsPhrw6CZBFZAVSuHzNzTqlpXRskgA5BEs2lHLSg8spvOV9TnpweUROg6xevZqZM2eycOFCrrzySi677DI++OCDDm9/+PBhli/vuCar6DtLNpRS6vMHsy+v2BPW+0Ez+jzSaGTnd58GL3PYbSRhRY3LiPhYRfcFgrOm3FlMYA9nD2sdDOu9VpzayJUXEx3LPeF0ACq2fdnqcndzgNwbtagPGYcBMKhudYc1mIXojkAKFyjSUjqGSYDchUAtztJ6OypQWm9vtxZnd5x44om8+OKLvPTSS/znP//h3//+Nzt27Gj3tqtWrWL9+vU9fk7RM4H3g7m5s9dp7q/Dej+MPPlCnKqepo1vBS+rr/avimkSsyM/YNFjCWPORKf42Lvmw1aXG71WXBFscSw6NrhwBIfJQHNwdavLPY7eadayZEMpSyoyAchS6tst8ydEd7XMr5eW0rHruC+49+a6Q7y+9mCH128oqcfl9bW6zO72cvPizbz6XUm797l06kAumpIf1jji4+NZsGABH3zwAS+99BLl5eXU1dVxyimn8Jvf/IZnn30Wh8PBpEmTSExM5B//+AcADoeDhx56iMLCwrCeT3TP3HcmMF97JD1inu475rEA5zt6mFTdyT39zIkpbIqfSmHV5/i8PjRaDU01ZeQAhmQJkGPRyKlzsH5mxL5jGZz+w+DlRp8Ni05WfPqCRqNQEj+eYU0bQVVBUQDwOiwAmBIim2Ix950JzNf5P+eKAgt1y1jIspA/50J0Zv6kPJbwAs98vIvD9XZpKR2jZAW5C0cHx11d3hPp6els376diRMn8p///IdXX32VV199Fa1Wy89+9jPOPfdc5syZw549e3j44Yf53//+x+mnn85HH30U8bGI9h1dm7U7uWOekfPIoZrdm74GwFbrX0E2p0qAHIvMcXHsMU9gQM23rS/32fCG0ElRRIYrdwYZai11pXuClwVrUcenRPS5IvE5F6IzgRQuaSkdu477FeSLpuR3utp70oPLKa23t7k8L8XMop/PjOhYDh8+zKRJk9iyZQurVq0iISEBl8vV5nbZ2dncd999xMXFUVFRweTJkyM6DtExfUouFou/Nmt3c8eGz7oUz/o7qVnzJkw+FWdDBQCJ6TJBxipb/izy9/6NqkN7yMwfDoBZteONcAc30bH00afAngc5uHk5qfnNpRadTXhUDXFxkc0Fj8TnXAhxbJMV5C7cdNZIzHptq8vMem3Ec4UsFgtvvPEGiYmJJCYm8re//Y2rr74ah8OBqqpoNBp8Pv+q9e23387999/Pgw8+SFZWFqqqRnQsomORyB1LSstml2kC+eWfoaoq3kCb6Uz58o1VWZPOAaD4u/cB8Hl9xOMACZD7zLBx02hU4/DsXxW8THFZsBL5WtSSIyqEiLkVZFVVueaaa5g7dy6XXNJx28XS0lLuuOMO1q9fz4ABA7jllls49dRTIz6ewGmPh5tzhXJTzBHLFVq1ahULFy5Eo9Hg9Xr5zW9+Q2FhIb/73e9Yt24dZrOZgoICKisrGTFiBE8//TRjx47lBz/4AZdeeilJSUlkZGRQWVnZ47GI0EQqd8w29BzGbr+PfTvXg6USu2ogrhd24ovIGDJqMpWkod3/OfB/2GyNJCgqGCVA7itGg4FtxjFk1B7ZrKx1W7ErZiL9yZEcUSFETAXIPp+P++67jxUrVjB37twOb6eqKr/61a8YOnQoixcvZvny5Vx//fW89957DBw4MOLjmj8pL+IT44wZM/j222/bvW7p0qVtLsvOzubjjz8GYN68edx6660RHY8IXSTeD0NOvhS230fZqjcwOGqo06Rgbt54JGKPRqvhQPJ0RjR8g8/jwd7UQAKgmCJff1d0rClrKpMP/RN7QzXm5Ay0HiuOXup01xvzvhDi2BEzKRYVFRX86Ec/Yvny5SQldf6ls2rVKvbv38+f//xnhg0bxs9+9jMmTZrE4sWL+2i0QvRMeu5gdutHkXXoE4zOWizalGgPSXRl6GxSsLB/60psTXVA6J0URWQkDD8ZgOJN/prwOo8VpzRrEUL0gpgJkLdv386gQYN48803SUzs/Etn06ZNjBkzhoSEI6c3p0yZwsaNG3t5lEJETl3B2Qz3FpHn2INdnxbt4YguFM6YB0D1po9wWv0NKsLppCh6bujEWbhULdY93wBg8NpwSYAshOgFMRMgz549m/vuu4+0tK4DhaqqKrKyslpdlp6eTnl5eW8NT4iIG/S9SwFIp4EB1h3SrSvGZWYPpEhbSELp17hsDQAYzJJi0ZdSklPYqxtGQuU6wB8gu3XSzVAIEXl9loPsdDo7DGDT09NbrQZ3xW63o9frW11mMBhwu90d3EOI2LO6IYWRvkGM1pSQqTQ0d+vyf9lL7mNsqsr8HpPLXmNz3WEADPESIPe16rTJnFj5Bl6XHZNqwysBshCiF/RZgLxlyxauuOKKdq974IEHuPDCC0N+LKPRiMViaXWZy+XCZOqdzRpC9Ia570zAqJFuXceShDHfx1D+Muz2b5g1RbhBheiaoXAmhqpX2b91JamqA69eAmQhROT1WYA8depUdu3aFZHHys7OZufOna0uq66uJjMzMyKPL0RfmOV4jAf0/+J0zUYUxd+t6yPvNO73XMGaaA9OtGvY1DNwfKZnVONKUMCcKDnIfW3gCbPhO6jd8SV5qh2f1KIWQvSCmMlBDseECRPYuXMnNpsteNm6deuYOHFi7z1pUzk8PxeaKiLycKtXr2bkyJF88MEHrS4/77zzuOWWW7q8v9Pp5PTTT+/08W+44YYej1P0Hn1KLmVqOiqKdOs6RpjiEthjHk+C4u+uGZ+YGuURHX9y8wZygDzMpSswKB5p1iKE6BXHTIBcW1uL1WoFYPr06eTm5nLLLbewZ88enn32WTZt2tRpY5Ee+/IvULIKvnwoYg85ZMgQ3nvvveDPu3btwm5v29Za9E/SrevYtC9xOgA+FS557EPZWNnHFEWhNGkCQ22b/D9LsxYhRC+IqUYhnbn44ou54IIL+M1vfoNWq+Wpp57itttu48ILL2TQoEH84x//ID8/P/wH3vgqbHip4+tLVkDLVs5r/+P/pygw6KT27zPpSph4eZdPPWrUKA4cOEBjYyNJSUm8++67nHfeeZSVlfHuu+/ywgsvYDAYGDx4MH/+859xuVzceOONNDY2MmjQoODj7Nq1i3vvvReAlJQU7r///pB+dRFd0q3r2LNkQynPlRXyAx0owKW217j1LX9ZSnnd+o46cAbGbf6zbxqj1KIWQkReTAbIy5cv7/KygoICXnqpk8A2UnKnQd1+sNeA6gNFA3HpkFoYkYc/88wz+fTTT7nwwgvZvHkz1157LTt27OCJJ57g7bffJiEhgfvvv59FixYBMGLECG644QY2bdrE6tWrAbjjjju4//77GTZsGG+88Qb//ve/+d73vheR8YneJd26ji1z35nAfJ1srIy2rLGnwTb//2vNEiALISIvJgPkPjXx8q5Xe5feAOv/CzoTeF0w+nw495GIPP15553HXXfdxcCBA5k6dSrgb7k9bNiwYOm7adOm8c03/sL4s2bNAvx52Dqd/+UrKiri7rvvBsDtdlNYGJngXQjR2izHY/xR9zLnalehU3yysTJKttrSSFcTSFcsvLnmAIcGlMqBphAioiRADoW1Eqb8BKb+BNY+D5bIbNQDGDhwIDabjRdffJHf/e53HDx4EEVRKCoqwmazERcXx3fffUdhYSGKorBx40bOOOMMtm/fjsfjAaCwsJCHHnqI3Nxc1q1bR1VVVcTGJ4Q4Qp+Si8ViRoMqGyujZMmGUm57ZzsfKWbSFQunuldww1v+M2YSJAshIkUC5FBc9vKR/4/QynFL55xzDu+88w6FhYUcPHiQ1NRUzj33XK666io0Gg2DBg3ixhtvRKvVcuutt3L55ZczZMiQYLOUu+66iz/84Q94vV4A7rvvPiorKyM+TiGOdzedNZK4t/0bK1/1zuFy7WfkyMbKPjX3nQnM1x5pCnW2bi1ns0DSXIQQEaWoassdaP3LoUOHmDNnDp999ln3NvAJIcRRlmwo5eHmjZW5KWbZWNnHpt/yEn/UvcxZ2rWYFVfrNJcH229GJYQQ4ZIVZCGECINsrIyuQJqLEbekuQghes0xUwdZCCGEkPrhQoi+IAGyEEKIY8b8SXnYLniBZxKuY6dawDMJ12G74AVZ1RdCRJSkWAghhDimSJqLEKK3yQqyEEIIIYQQLUiALIQQQgghRAsSIAshhBBCCNGCBMhCCCGEEEK00K836QU6y5WXl0d5JEIIIYQQItbk5OSg07UNh/t1gFxVVQXAFVdIdyUhhBBCCNFaR92W+3WraYfDwdatW8nMzESr1UZ7OEIIIYQQIoZ0tILcrwNkIYQQQgghwiWb9IQQQgghhGhBAmQhhBBCCCFakAA5glwuF3fccQfTpk3jpJNO4l//+le0hyQioKSkhF/84hdMmzaNU045hQcffBCn0wlAaWkpV199NRMnTmTu3Ll8+eWXUR6t6KnbbruNhQsXBn+W17j/cLvdPPDAA8yYMYMZM2bwpz/9CZfLBcjr3J80NDRw4403Mn36dGbNmsVf//rXYFUreZ2PfS6Xi3PPPZeVK1cGL+vqdV21ahXnnXceEyZMYOHChRQXF3f5PBIgR9Bf/vIXNmzYwPPPP8/dd9/N008/zfvvvx/tYYkecLlc/OIXv8BgMPDaa6/x17/+lWXLlvHoo4+iqiq/+tWvSElJYfHixVxwwQVcf/31HDx4MNrDFt307bffsnjx4uDP8hr3L3/5y1/49NNPeeqpp3j66af5+uuvefLJJ+V17mfuvvtuKioqeOmll3j44YdZsmQJzz//vLzO/YDT6eR3v/sde/bsCV7W1etaVlbGL3/5S84//3zefPNNMjIy+NWvfoXP5+v8yVQREVarVR0/fry6YsWK4GVPPvmketlll0VxVKKn1qxZo44dO1a1WCzBy9599131e9/7nrpy5Up1/PjxalNTU/C6H/3oR+ojjzwSjaGKHrJareqcOXPUyy67TL3yyitVVVXlNe5HGhoa1LFjx6rffPNN8LI333xTveaaa+R17mcmT56sfvrpp8GfH3jgAXmd+4E9e/ao559/vnreeeepI0aMCMZbXb2ujz32WKtYzGazqZMmTWoVr7VHVpAjZOfOnbhcLqZMmRK8bMqUKWzZsgWPxxPFkYmeGDJkCM8++yzx8fHByxRFweVysWnTJsaMGUNCQkLwuilTprBx48YojFT01KOPPsr06dOZPn168DJ5jfuPdevWYTKZ+N73vhe87MILL+Tf//63vM79TEpKCu+++y52u52Kigq+/vprxo4dK6/zMW7t2rWcdNJJLFq0qNXlXb2umzZtYtq0acHrzGYzY8eOZcOGDZ0+nwTIEVJVVUVycjJGozF4WUZGBm63m9ra2iiOTPREWlpaqy9Un8/HSy+9xJQpU6iqqiIrK6vV7dPT06Vz4zFow4YNfPTRR/zhD39odbm8xv1HSUkJeXl5vPfee8ybN4/Zs2fz0EMP4XK55HXuZ/70pz/x3XffMXnyZE455RQyMjL4zW9+I6/zMe6yyy7j5ptvxmw2t7q8q9e1o+srKio6fb5+3UmvL9ntdgwGQ6vLAj8HNoGIY98DDzzAjh07WLx4Mc8//zx6vb7V9QaDAbfbHaXRie5wuVzcdttt/PGPfyQ5ObnVdXa7XV7jfsJqtXLo0CFeeukl7r77bqxWK3fffTcej0de536mpKSEMWPGcN1112GxWLjnnnt46KGH5HXup7p6XTuKz7qKzSRAjhCj0djmjx34+eijHXHsUVWV++67j1dffZXHH3+c4cOHYzQasVgsrW7ncrkwmUxRGqXojieffJKCggLmzp3b5jp5jfsPnU6HxWLh4YcfZtCgQQDcfPPN3HzzzVxwwQXyOvcTJSUl3H///SxfvpycnBzA/zm++uqrueSSS+R17oe6mqc7is9SUlI6fVwJkCMkOzubxsZGXC5X8EilqqoKg8HQZlVKHFt8Ph+33XYbS5cu5dFHH+WMM84A/K/5zp07W922urqazMzMaAxTdNPSpUupqqpi0qRJgL8UmNfrZdKkSfz85z+X17ifyMrKQqfTBYNjgMLCQpxOJ5mZmezevbvV7eV1PjZt3bqV+Pj4YHAMMG7cOLxer7zO/VRX38XZ2dlUVVW1uX748OGdPq7kIEfI6NGj0ev1rZK+161bx9ixY9vt8S2OHQ8++CBLly7liSee4Pvf/37w8gkTJrBz505sNlvwsnXr1jFx4sQojFJ014svvsh7773HkiVLWLJkCZdccgnjxo1jyZIl8hr3IxMnTsTj8bBr167gZUVFRcTHxzNx4kR5nfuJrKwsGhsbKSsrC15WVFQE+Dddy+vc/3Q1T0+YMIH169cHr7Pb7Wzfvr3L110C5Agxm83Mnz+fu+++m82bN/PZZ5/x3HPPcdVVV0V7aKIHNm7cyAsvvMD111/PuHHjqKqqCv6bPn06ubm53HLLLezZs4dnn32WTZs2cckll0R72CIMeXl5FBQUBP8lJSVhMpkoKCiQ17gfGTx4MHPmzOHWW29l69atrF27lr/+9a9ceumlzJw5U17nfmLixImMHj2aW2+9lZ07d7Jx40buuOMOfvCDH3DWWWfJ69wPdTVPX3TRRWzatImnn36avXv3ctttt5Gbm8vMmTM7fVxFVVW1L36B44Hdbueuu+7ik08+IT4+nquvvpqrr7462sMSPfDQQw/x3HPPtXvdtm3bKC0t5bbbbmPTpk0MGjSIW2+9lZNPPrmPRyki6dFHH2X9+vW8+OKLABQXF8tr3E9YLBbuu+8+PvnkE3Q6HfPnz+fGG29Er9fL69yPVFRUcP/997Nq1Sr0ej1nn302N954IyaTSV7nfmLkyJE8//zzwSpTXb2uX375JQ888ABlZWVMmDCBe++9t1W6VXskQBZCCCGEEKIFSbEQQgghhBCiBQmQhRBCCCGEaEECZCGEEEIIIVqQAFkIIYQQQogWJEAWQgghhBCiBQmQhRBCCCGEaEECZCGEiAEej4ennnqKM888k3HjxjFr1izuuOMOampq+nwsCxcu5NFHH+3z5xVCiFghAbIQQsSAv/3tb7z//vvcddddfPzxxzz66KPs3r2ba6+9FilXL4QQfUsCZCGEiAFvvfUWv/nNbzjppJPIy8tj6tSp/PWvf2Xbtm1s2rQp2sMTQojjigTIQggRI1atWoXX6w3+PHDgQN5//31GjRqFxWLhtttuY+bMmYwbN46zzjqLjz/+OHjbkSNH8v777zN37lwmTJjA73//ew4ePMjChQuZMGECV155JZWVlQA88cQTXH/99fzxj39kwoQJnHXWWSxbtqzDcS1atIg5c+YwadIkLr/8cjZv3hy8bvXq1Vx44YWccMIJnHbaaTzzzDO98JcRQoi+JQGyEELEgKuuuopXX32V2bNnc/vtt/P+++/T2NjIsGHDMJlMPPDAAxQVFfHcc8/x3nvvMW3aNO644w5cLlfwMf7+97/zwAMP8M9//pOPPvqIyy+/nCuvvJJXXnmF0tJSnnvuueBtly9fjtfr5a233uLiiy/m+uuvZ9euXW3GtXz5ch5//HFuvfVW3n77bU455RR+9KMfUVlZidfr5frrr2f27Nl88MEH3HnnnTz55JN8/fXXffI3E0KI3qKL9gCEEELAddddR2FhIa+88gpvvfUWb7zxBkajkeuvv56f/vSnTJkyhauuuoqRI0cCcPXVV/PGG29QUVHBwIEDAX+QPXHiRMC/ojx8+HDOOussAObMmcO+ffuCz5eUlMQ999yDwWBg6NChfPnllyxevJjbbrut1bj+/e9/87Of/YwzzjgDgF/+8pesXLmSN954gyuuuIL6+nrS09PJz88nPz+f//73v8HxCCHEsUoCZCGEiBHnnHMO55xzDo2NjaxcuZJFixbx8MMPM3jwYObPn8+yZct444032LdvH9u2bQPA5/MF798yMDUajeTm5gZ/NplMrVabx4wZg8FgCP48btw49uzZ02ZMRUVFPPLIIzz++OPBy1wuFzk5OaSkpPCzn/2Mu+++m6effprZs2dz/vnnk5mZGZk/iBBCRIkEyEIIEWU7d+5k8eLF3H777YB/dffss8/mrLPO4uKLL2blypV89NFHrF+/nh/84AdcfvnlZGZmsmDBglaPo9O1ntI1mo6z6I6+rdfrRVGUNrfzer384Q9/4OSTT251eVxcHAC///3vueCCC/jss8/4/PPPWbhwIffeey8XXXRR6H8AIYSIMZKDLIQQUeb1ennxxRfZuHFjq8sVRSExMZG0tDTee+89/va3v/Hb3/6WM888k4aGBoBul4DbvXt3q9XnrVu3BtM3WiosLKS8vJyCgoLgv+eee47vvvuOqqoq7rrrLvLy8rj22mt55ZVXuPDCC/nwww+7NSYhhIgVEiALIUSUjR07ltmzZ/PrX/+at99+m4MHD7JlyxYeffRRduzYwUUXXYTZbOaTTz7h0KFDfPPNN/z5z38GaJU2EY7S0lIefPBB9u3bxz//+U+2bt3KJZdc0uZ2P/nJT3jxxRd5++23KSkp4R//+AdvvvkmQ4YMITk5mWXLlnHfffdRXFzM5s2bWbt2LWPHju3R30MIIaJNUiyEECIGPPbYYzz77LM888wz/OlPf8JgMDBt2jRefvllBgwYwMMPP8xDDz3Eyy+/TH5+Pr/4xS944okn2L59OyNGjAj7+caNG0dTUxMXXHABBQUFPPvsswwePLjN7c455xxqamr4xz/+QWVlJUOGDOHJJ59k9OjRADz99NPcf//9zJ8/H6PRyDnnnMN1113X0z+HEEJElaJKiyYhhDiuPPHEE6xcuZJXX3012kMRQoiYJCkWQgghhBBCtCABshBCCCGEEC1IioUQQgghhBAtyAqyEEIIIYQQLUiALIQQQgghRAsSIAshhBBCCNGCBMhCCCGEEEK0IAGyEEIIIYQQLUiALIQQQgghRAsSIAshhBBCCNGCBMhCCCGEEEK0IAGyEEIIIYQQLUiALIQQQgghRAsxFSBv2rSJhQsXtrl8+fLlXHTRRSxYsIDXX38dAJ/Px5133smCBQtYuHAhxcXFfT1cIYToN2T+FUKII3TRHkDAv/71L959913MZnOry91uNw888ACLFy/GbDZz+eWXM3v2bDZs2IDL5WLRokVs3LiRBx98kKeffrrVfT0eD+Xl5eTk5KDTxcyvKoQQMUXmXyGEaC1mVpAHDRrEE0880ebyoqIiBg0aRHJyMgaDgSlTprB27VrWrVvHrFmzAJg4cSJbt25tc9/y8nLmzJlDeXl5yOPw+lSG3Po+T36+t/u/jBBCHENiZf4VQohYETMB8llnndXuKoPFYiExMTH4c3x8PBaLBYvFQkJCQvByrVaLx+Pp8Tg0iv+/dpe3x48lhBDHgliZf4UQIlbETIDckYSEBKxWa/Bnq9VKYmJim8t9Pl9ETuMpioJJr8XpkQBZCHF86+v5VwghYkXMB8hDhw6luLiY+vp6XC4Xa9euZdKkSUyePJmvvvoKgI0bNzJixIiIPadRp8Hp8UXs8YQQ4lgUjflXCCFiQcwe8i9duhSbzcaCBQu45ZZbuOaaa1BVlYsuuojs7GzOPPNMVqxYwWWXXYaqqtx///0Re26jTovTLQGyEOL4FM35VwghYoGiqqoa7UH0lkOHDjFnzhw+++wz8vPzQ77fqQ9/zsSBKTx+2aReHJ0QQvRf3Z1/hRAiFsR8ikU0GHUaWUEWQgghhDhOSYDcDqNONukJIYQQQhyvJEBuh0kvm/SEEEIIIY5XEiC3w7+CLAGyEEIIIcTxSALkdvjLvEmKhRBCCCHE8UgC5HYY9RocsklPCCGEEOK4JAFyO2STnhBCCCHE8UsC5HaY9FLmTQghhBDieCUBcjtkk54QQgghxPFLAuR2yCY9IYQQQojjlwTI7fAHyD76cRduIYQQQgjRAQmQ22HUa1FVcHklzUIIIYQQ4ngjAXI7jDr/n0XykIUQQgghjj8SILfDqNcCSCULIYQQQojjkATI7Tiygiwb9YQQQgghjjcSILdDUiyEEEIIIY5fEiC3w6iTFAshhBBCiOOVBMjtMOr9fxaHpFgIIYQQQhx3JEBuRzDFQlaQhRBCCCGOOxIgt8MUqGIhK8hCCCGEEMcdXbQHAODz+bjrrrvYtWsXBoOBe++9l4KCAgCqqqr43e9+F7ztjh07+P3vf8/ll1/O/PnzSUxMBCA/P58HHnggIuORTXpCiONFrM2/QggRC2IiQF62bBkul4tFixaxceNGHnzwQZ5++mkAMjMzefHFFwHYsGEDjz76KJdeeilOpxMgeF0kBTfpSYAshOjnYm3+FUKIWBATKRbr1q1j1qxZAEycOJGtW7e2uY2qqtxzzz3cddddaLVadu7cid1u5+qrr+aqq65i48aNERvPkRxkSbEQQvRvsTb/CiFELIiJFWSLxUJCQkLwZ61Wi8fjQac7Mrzly5czfPhwhgwZAoDJZOKaa67hkksu4cCBA1x77bV89NFHre7TXUeqWMgKshCif4u1+VcIIWJBTMxmCQkJWK3W4M8+n6/NRPvuu+9y1VVXBX8uLCykoKAARVEoLCwkJSWFqqoqBgwY0OPxHKmDLCvIQoj+LdbmXyGEiAUxkWIxefJkvvrqKwA2btzIiBEj2txm27ZtTJ48Ofjz4sWLefDBBwGoqKjAYrGQmZkZkfGY9LJJTwhxfIi1+VcIIWJBTKwgn3nmmaxYsYLLLrsMVVW5//77Wbp0KTabjQULFlBbW0t8fDyKogTvc/HFF3Prrbdy+eWXoygK999/f8RO7xm0EiALIY4PsTb/CiFELFBUVVWjPYjecujQIebMmcNnn31Gfn5+WPcdefuH/Pikwdw6d3QvjU4IIfqvnsy/QggRbTGRYhGLjDqNdNITQgghhDgOSYDcAaNeK530hBBCCCGOQxIgd8CklxVkIYQQQojjkQTIHTDqtLJJTwghhBDiOCQBcgeMOo2kWAghhBBCHIckQO6AP0CWFWQhhBBCiOONBMgdMOq0koMshBBCCHEckgC5A0a9BoekWAghhBBCHHckQO6ASVaQhRBCCCGOSxIgd8Col016QgghhBDHIwmQOyCb9IQQQgghjk8SIHdA6iALIYQQQhyfJEDugFGnwemWFAshhBBCiOONBMgd8FexkBVkIYQQQojjjQTIHTDptHh9Kh6vBMlCCCGEEMcTCZA7YNT7/zSShyyEEEIIcXzpUYD8zjvvtPrZ6/Xy17/+tUcDihVGnRaQAFkIEXv689wrhBCxoEcB8jPPPMOf/vQnXC4Xhw4d4rLLLmPPnj2RGltUGXWBFWTZqCeEiC39ee4VQohY0KMAefHixXg8Hi666CKuuOIKLr30Up555plIjS2qgikW0k1PCBFj+vPcK4QQsaBHAbKiKBgMBux2Oz6fD0VRIjWuqAukWDhkBVkIEWP689wrhBCxoEcB8vnnn4/FYuGdd97hpZde4rXXXuMXv/hFpMYWVSZZQRZCxKj+PPcKIUQs0PXkztdddx3z588HID4+ntdee41HHnkk7Mfx+Xzcdddd7Nq1C4PBwL333ktBQUHw+ueff57FixeTlpYGwN13383gwYM7vU9PySY9IUSsitTcC7E5/wohRLT1KECeP38+mzdvZvv27Vx44YVs27aNm2++OezHWbZsGS6Xi0WLFrFx40YefPBBnn766eD127Zt46GHHmLcuHHByz755JNO79NTsklPCBGrIjX3QmzOv0IIEW09SrF46623uPXWW/n3v/9NU1MTv/rVr3j99dfDfpx169Yxa9YsACZOnMjWrVtbXb9t2zaeffZZLr/88uBGlK7u01PBFWRJsRBCxJhIzb0Qm/OvEEJEW48C5BdffJFFixaRkJBAeno6b731Fi+88ELYj2OxWEhISAj+rNVq8Xg8wZ/nzZvHXXfdxQsvvMC6dev4/PPPu7xPT0mjECFErIrU3AuxOf8KIUS09SjFQqPRtJokBwwYgFarDftxEhISsFqtwZ99Ph86nX9oqqryox/9iMTERABOPfVUtm/f3ul9IsEUqGLhlhQLIURsidTcC7E5/wohRLT1aAU5JSWFHTt2BEsMvfvuuyQnJ4f9OJMnT+arr74CYOPGjYwYMSJ4ncVi4dxzz8VqtaKqKqtXr2bcuHGd3icSZAVZCBGrIjX3QmzOv0IIEW09OuT/4x//yG9/+1tKSko4+eSTMRqNPPXUU2E/zplnnsmKFSu47LLLUFWV+++/n6VLl2Kz2ViwYAE33HADV111FQaDgZkzZ3Lqqafi8/na3CeSZJOeECJWRWruhdicf4UQItoUVVXVnjyA1+vlwIEDeL1eCgsL0ev1kRpbjx06dIg5c+bw2WefkZ+fH9Z97S4vo+/8iFvmjuIXpw7tpREKIUT3xPLcCz2bf4UQItp6nDSm1WoZOrT/BZAGnTQKEULErv469wohRCzoUQ5yf6bVKOi1iqRYCCGEEEIcZyRA7oRJp8UhK8hCCCGEEMcVCZA7YdRrZAVZCCGEEOI4E/HClbfffjter5ef/exnFBYWRvrh+5RRp5Uyb0KIY0J/mnuFECLaIr6CPG7cOG6//XZqamoi/dB9zqjTSIAshDgm9Ke5Vwghoi3iK8iXXXYZAFOnTo30Q/c5g06DUzrpCSGOAf1p7hVCiGjr0Qqy1Wrl7rvv5kc/+hH19fXceeedrdqPHuuMekmxEELEnv4+9wohRLT1KEC+9957SUpKoqamBqPRiMVi4c4774zU2KLOpNPgkBVkIUSM6e9zrxBCRFuPAuQdO3Zwww03oNPpMJvN/PWvf2XHjh2RGlvUyQqyECIW9fe5Vwghoq1HAbJG0/ruXq+3zWXHMtmkJ4SIRf197hVCiGjr0Sa9adOm8fDDD+NwOPj66695+eWXmTFjRqTGFnX+AFlSLIQQsaW/z71CCBFtPVpyuPHGG4mLiyMxMZFHH32UkSNHcvPNN0dqbFFn1GlxSic9IUSM6e9zrxBCRFuPVpD1ej3XXXcdCxcuJCkpKVJjihn+TnoSIAshYkt/n3uFECLaerSCvG/fPs455xzmzZtHRUUFc+fOpaioKFJjizqTTit1kIUQMae/z71CCBFtPS7zdtttt5Genk52djZXXnllvyo1JCvIQohY1N/nXiGEiLYeBcj19fWcdNJJwZ+vuOIKLBZLjwcVK4w6DS6vD59PjfZQhBAiqL/PvUIIEW09rgvkdDpRFAWAqqoqfL7+s+Jq1GkBcHn7z+8khOgf+vPcK4QQ0dajAPmHP/wh11xzDTU1Nfztb39jwYIFXH755ZEaW9QZdf4/j1SyEELEkv4+9wohRLT1qIrFxRdfTEFBAV988QUej4d77rmn1Wm/Y51J719B9tdC1kd3MEII0ay/z71CCBFtPQqQwV+wftq0aT16DJ/Px1133cWuXbswGAzce++9FBQUBK9/7733eOGFF9BqtYwYMYK77roLjUbD/PnzSUxMBCA/P58HHnigR+M4WmAF2SEryEKIGBOJuRdid/4VQoho6nGAHAnLli3D5XKxaNEiNm7cyIMPPsjTTz8NgMPh4LHHHmPp0qWYzWZ+97vf8fnnn3PyyScD8OKLL/bauIz65hQL6aYnhOinYnX+FUKIaOrxJr1IWLduHbNmzQJg4sSJbN26NXidwWDgtddew2w2A+DxeDAajezcuRO73c7VV1/NVVddxcaNGyM+rsAmPVlBFkL0V7E6/wohRDTFxAqyxWIhISEh+LNWq8Xj8aDT6dBoNGRkZAD+1QqbzcZJJ53E7t27ueaaa7jkkks4cOAA1157LR999BE6XeR+pYwEAwCVTQ4gOWKPK4QQsSJW518hhIimHq0gL1q0KKTLupKQkIDVag3+7PP5Wk20Pp+Phx56iBUrVvDEE0+gKAqFhYWcf/75wf9PSUmhqqqqe79IBwalxQFQUmuL6OMKIURPRGruhdidf4UQIpp6FCBv2bIlpMu6MnnyZL766isANm7cyIgRI1pdf+edd+J0OnnqqaeCp/oWL17Mgw8+CEBFRQUWi4XMzMywn7szafEGEow6imskQBZCxI5Izb0Qu/Nvf+PzqRTXWPFK4ykhjgmKqqpR/7QGdlHv3r0bVVW5//772b59OzabjXHjxnHRRRcxderUYFH8q666ilNPPZVbb72Vw4cPoygKN954I5MnT271uIcOHWLOnDl89tln5Ofnd2tscx//mtxkE//5cc93iwshRKyJ5fm3P3lr/SF+9/omEo06pg5OZXphOtML0xifl4xBFxPbgYQQLfQ4QP7uu+9oaGig5cN8//vf7/HAIiESE/TPX1zLviorn/7u1AiPThzPvthVyegBSWQnmaI9FHGMiuW5FyRAPtptb2/hnY2HOX9iLt/tr2Vvpb81uEmv4c5zx/LDGYOiPMLIO1xvp9HhZlROUrSHEpJFa0p48vMilv3uVDloET3bpHf77bfz1VdftaqZqShKTE3SPVWQHs8Xu6rw+VQ0GiXawxH9gMPt5ZoX1vLTkwu59ZzR0R6OOAYdD3Nvf7O9rJGxuUncf8F4AKotTtYeqOXpL4p45NNdXDwlv18FZTUWJxc/vZLKJicPXDieS6YOjPaQurRkw2FKam1sL2tk4sCUaA9HRFmPAuRvv/2WDz74oNUO6P5mYFocTo+PKotTVvtERBysteH1qRyqt0d7KOIYdTzMvd1R2eTgyn+v5qazRnHmmOxoDyfI61PZVd7EpS2CxIwEI2ePG4BRp+Un/13Dsh0VnDN+QBRHGTker4/fvLqBGquLCQNTuGnxZg7XO7h+zrBgqk6ssTo9rC2uBWBdcZ0EyKJnm/QGDBjQ7ydoqWQhIi2w6bNMAmTRTcfD3BsuVVW59c0t7K6wsGx7RbSH00pxjRWby8uY3LapBqeMyCQvxcwrq0uiMLLe8dBHO1lZVMN9F4zn1WtP5MLJeTy6bDe3vLkFtzc2+wqs2leD26uiUWB9cV20hyNiQI9WkCdPnswNN9zA7NmzMZmOrK72p9N8gQC5uMbGtMFpUR6N6A+Kmw+2yhocUR6JOFYdD3NvuN5Ye4jPdlZi1mvZUtoQ7eG0sqOsCYAxA9oGyFqNwoJpA3nk090cqLYyOCO+r4cXUe9uOsy/vt7PVTMLuHiKP/f8b5dMIC/FzBPL91Le6ODJKyaTYIytmtlf76nGpNdw6ohM1hbXoqpqzK52i77Ro3fohg0bAHjjjTeCl/W3PLi8FDMaRVaQReQU1/hrzlY0OvB4fei0/SfvUPSN42HuDcehOht/fm87Jw5JY+LAVP799T4cbi8mvTbaQwNge1kDWo3CsKz2V/0XTBvI45/t4bU1B7ll7qg+Hl3k7Chr5A+LNzO1IJXb540JXq4oCr///khyU8zcvmQrC575lhevmUFavCGKo23tq91VzChM56RhGXy8rYLSejv5qXHRHpaIoh4FyC+++CLgbz+qqip6vT4ig4olBp2GAclmDkqAHHO+3lPFY8v28PxPppFkOnbee4EUC58KlU1OclPMUR6RONYcD3NvqHw+lZve2AzAwxdPYNvhBjw+lZ3lTTGTR7qjrIlhmQkdBuzZSSbmjMpi8bqD/O7MEcfkZr0Gm5ufv7iORJOOp66c3O7vcPn0QeQkmfjp/9by9Bd7ua1FEB1NB2tt7Ku2csWJBUwelAr485AlQD6+9ehTWFNTw09/+lMmTpzICSecwFVXXUVFRWzlfkXCoLS44KqfiB0fbCljXXEd//yiKNpDCUtxjZVEk//YtKxB8pBF+I6XuTcUL3x7gG/31XDHuaMZmBbHuLxkgJhKs9h+uJHRAxI7vc0PZwyi2uLik+3lfTSqyPH5VH67aANlDXaevnIKWYkdb2ifPSqLeeMH8Op3B2l0uPtwlB37ao+/C+SpIzIYlZNIvEHLOslDPu71KED+85//zMSJE1m5ciUrV65k6tSp3HXXXREaWuwoSI+jpFYCmViz8aD/C/A/3+zn8DGy4c3j9XGozs6MQn8+++F6yUMW4Tte5t6uFFVZePDDnZw+KitYISIvxUxavIEth+qjO7hmtVYX5Y2OdjfotTRr+LG7We+jbeV8sauKO84dw5SC1C5vf+2sIVicHl77LjZ+1692V5GbbGJoZgI6rYaJg1IkQBY9S7E4cOAAjz/+ePDn66+/nnnz5vV4UJFWWQeaHlRoSzHFUW1xUnTYg9kQWxsLjld2l4fd5U2cPSaP5bvKuHfpbm6fOyHaw+rS4XoHHp/K6Ox0lu2oZNdhO1Pyoj0qEW25GeHd/liZe6Hn829HPD4fv3llE0adlv87bTxlNYENVQrDM5PZUNzI4erIP2+41hY3ApAVl9TFeBTmjRvIs9/sZvUeKwNTj43Neqqq8o/P9pGXEsfsoQUh/c3TTclMHpjOv78+wNkjC6O6D8Pj8/HNnhpOHzkg+B4akZHKC6v3svewhzj5zu/XOpt7e/Su9Hg8OJ3O4M92u71f7vrMTfbnIR1ukDzkWLG7shGvqnL6yAFcMnkwH247xJ7KxmgPq0uH6v2pOiOzkokz6ChvlBVkEb7jZe7tiE9V+fvnO9heXs9NZ4wjI6F1BD4yO4l91U043d4ojfCIPVX+eWlYZtfd5M4dNxCtRuHdzbGxshqKzaV1bC+v57IphWjDaKb1w2lDqGxy8Nmusl4cXde2l9VjdXmYMTgzeNn4vDR8qv+6/s7j9cVs6b1o61GAfM455/DjH/+YN954g8WLF3P11Vdz1llnRWpsMSMQIJdGIUCuszlZtb+qz5+3K4frbfz0pRV8uSc6+XLbmieuMTkpLJwxjASTntuXrueDrYdweqL/pdiR0nr/eygvJY7sRBOVTcdGaoiILcfL3Nseh9vLbe+uY/GGAyyYUsicUbltbjM6JwWvqrK3OvoHzXsqG8mIN5IWb+zythkJJmYNzeb9rYdwxfA81tIra/eRbNYzb1x4nfJOLMykMD2BV9bua9Uuva+t3l+FRoGpBUeWEscOSEEBthzu/2kWf3x3PTe+tSbaw4hJPQqQr7vuOi6++GJWrFjBV199xYUXXsivf/3rSI0tZuSnNK8g1/d9gPz459u58a3vsLk8ff7cnXlncwnby+v54zvreGVNUZ9PcDvK68lJMpMWbyTJpOfueZPQaRTu/WgTFzyznA0Ha/p0PKEqrbdh0GrITDSRlWimsklWkEX4jpe592g1VgfXLfqWr/ZU8NvZY7j+tPZbtY/M9m/U21ke/Y16e6saGZ7V9epxwPknDKLe7uKrvbG/6bK41sI3eyu4cEJB2CX1NIrC5VOHsKeykXUl0ZuvVx+oZnROSqtKSIkmPYUZiWwp7d8Bsk9V2XCwhjXFEtd8XQAATP9JREFU1cfFwUC4upVcY7FYSEhIoL6+njlz5jBnzpzgdQ0NDaSkpERqfDEh0aQnwagLrv71lVqrk+W7yvCpcLDOGpz0o82nqnyyo5Spg9JJMhn4x5c7OVRv46YzxvXZad7tZfWMyUkJ/nxiYSYzBp/CupIa7nxvA29tLGbSwPTg9U63l2qrk7yU6JbtOVRvJTc5Do2ikJVoYndl9L/AxbGjv8+9RVVNfLu/kvG5qYwZkIK+RW5qUVUTN761hga7iwfnT2XWsI5bSWcnmkgxG9hZEd3Pl8vjZX+NhZmFWSHfZ/rgDHKTzSzZVMIZ7ayOx5LX1u5Hr9Vw0aTB3br/90fn8s+vd/HKmn2tVnD7SqPdxY7yen4yc3ib607IS2XZzsP4VBVNDKcv9aShyeEGG9bmxbdX1hTxwA+mRnJox7xuBcgLFy7k7bff5sQTT2z1wgReqB07dkRsgLFAURRyk+P6PAf5nc0leHz+ldniWkvMBMgbD9ZS0eTgl6eM4oxRuQz4yszLa/ZRmJ7AJZMLe/35a61OyhrtbSZlRVGYWpDB1IIMNh1q3Qnp+VV7eG3dft68djbp8b2wYyhEpfW2YG3N7EQzdTYXLo8Xgy42GhqI2Nbf594d5fU8/dVOVMCk0zI+L5XJA9NJjzfy2OfbidNreeqymYzK6XwuVBSFUdnJ7IpygHyg1oLXp4a1gqxRFM4/YRD//HoXK4oqOGloxwcC0VRrdfLhtkPMHZsfUvpIeww6LZdMHswz3+yiqKqJoZmdl8KLtDXF1ajAjMFtg/Pxuaks2VTC/mpLn4+rI16fyr7qJraV1bH1cD1by+qobnLwrytOojAj/DHurvCnIJ04OJOv9lRQUmthUJq0sA/oVoD89ttvA7B9+3Y0mtZZGvX19T0eVCzKS4mjqKqpz57P4/WxZFMJkwemsfFQLcU1lj577q58tP0QcQYdpwzLQaMo/OqUURyosfCPL3cyPjetyy+vntpRXg/AmAEp7V5/Qq7/yL+iyUFOkr8Jx6r9Vbg8Pt7eWMJPTxrRq+PriKqqlNbbgislgVqhlRYH+SnHxo51EV39fe49d/xAThmWzYZDtawrqWH9wRqe+WYXAMMyE3n4gmlkJ4XWWGdkTjJrVlfjdHsxRqmjXmDjcCgb9Fq6eNJgvthdzu1L1/P3S09kfG7XpdP62lsbi3F5fVw2tWeLIhdMGMQLq/by6tp9fV6JaPWBKhKMOka3810yPs//N99yuDbqAbLH5+PhT7fy2c7D2Jo3nqaYDYzLTaW6ycFz3+7hnvMmh/24uysb0CoKN39/PJf95wteXbufP3x/fKSHf8zqUQ7yRRdd1OayK6+8sicPGbMK0xM5VG+lxto3OaNf7a2gyuLgsqlDyE2Oo7g2NhqVON1ePt9dzmnDc4I5Z4qicPvcCaTGGbjjvfVYnb1b/H17WT1aRWFkB6sygYltc2ktAA12F3sqG9FqFN7aWBy1TXw1VicOjzeY0x74oq+UShYiTP157k0yGzh1eA6/mzOWl358Cu//6gwev2QGz1z+vZCDY4BR2cl4VTVYRSIa9lY1YdRpwi7ZFmfQ8dcLp5GZYOLGt9awv7rvFmdC4XB7eXPjAU4emk1BD1cck8wGzh0/kE92lFJl6bu5UFVVVh+oZuqgDHSatqFQXnIcqXGGqOchq6rKw59uZemWg8weOYC75k3kjZ/O5v1fncFfLpjKxZMHs3xXWbfeI7srGinMSCAnycw54/L5cNshaq3Oru94nOhWgPyjH/2IyZMns2vXLiZPnhz8N3HiRFJTY+9INxLOGJWLT4WPtx/uk+d7c8MBBiSZmVmYxaC0BErqWq8gf7bzcJtgffH6A/zm9VW9Oq6viyqwujycPbZ18d5ks4G7502ivMHO37/o3dO828sbGJKR2GFN6qGZiZj12uDEtq6kBhW4euZw6u0uPt3RN6/h0VpWsIAjK8gVUslChOh4nHtT44xMK8gIuwb9qOaUtGimWeypbGRoRlJY5c8C0uKNPHbxDAxaDTe8+R0VjbEzT3yw7RANdjc/nDYkIo+3YEohPlXlma938d2BKjYeqmV7WT1FVU0cqrNSa3VGvKrH/hoLVRYHMwoz271eURTG56ZGffPav1fsZumWg/zkxGHcdvYEvj86j7yUuGCK1WVThmDSa3l+1d6wH3t3ZSMjsvyfk8unDsHt9fHGhgORHP4xrVspFk8++ST19fX88Y9/5IEHHjjyYDodmZntv9mOdYPTExiTk8KH2w5x+dTCXtmMVm9z8U1RBd8UVbDhUC3XnTIKrUahIC2etSXVeH0qWo1CVZODO97bwHnjB3LrWScA/qPMRev3U1pvo97mIiXOAPg31L2yZh9njc4js5P2n6H6aHspWYkmJrfYABcwIT+NCyYOYsmmEn5+8shu56V1RlVVtpfXM3tEToe30Wk0jB2QEpzY1pZUE2fQsXD6UD7bVcbr6/czb1x+n9eNPRIg+1eTshObV5ClkoUI0fE493ZXVpQ36qnNq9enjxjQ7cfITYnjbxdN47rXVvG7N7/j6ctmkmQ2RHCU4fP6VF5bu4+xA1KYkBeZg7K8lDhOH5nLB9sO8cG2Qx3ezqDVEG/UkWDUMzE/Lfj91x3fHfCXT53eyebAE/JS+WpvBbVWZ698n3XlrY3FPL9qL+eNH9hhamBKnIGLJg3m5e+KuHrmMAanh5YOUm1xUGtzBvPjB6bGc+rwHN7acICF04dKgxS6uYKckJBAfn4+//vf/8jLywv+y8rKoqTk2ClwHq65Y/Moqm5idy80pGi0u7jiv19y/8eb2VXRwIIphcFNaAVpCbg8vuAKwqbm1IHPdpUFC+HvKG8IBmBFLWp/FlU18dRXO/n3yt09HqPL4+W7A1WcPmJAh7t6L5w4GI9P5b2tB3v8fDaXp83pnuJaK00Od6sKFu0Zn5vK3qpGrC4Pa4urmZSfhk6rYcGUweytamJ9mGXgbC4PK4oqepSecajeilZRgnnRJr2WJJM+KrWQXR4vi9btD+5gbs/qA1V9llIkQnO8zr3doSgKo3OSo1bqrbLJQZPDzbAe5q+OyErmoflTOVRv4+Yla3FEufnJ13vLOVRv4/KpQyK6yHDbWSfw3JUn88/LZ/L4JTN4+IKp3HveZO6YO4HfzxnLz04ewSWTB3PKsBySTHqWbjnYo7lz9YFqBqXFMyC548pGgdzvaKwif7G7jL8t28rJQ7O46czOK0RdPrXQv4r8beiryIEzKy03//9w2hCanB6Wbun593d/0KMc5Ndee43JkyczevRoRo8ezZgxY7jiiisiNbaYc+aoXPRaDR92coTbXc+v2kuD3cXfL53BWz87nd/OHhPM8Q3keAXSLDYd8gfINpeHr4r8tTI/3XkYbfMHqKhFLlLgQ/DJjlIaHT3LDS6uteLxqR1ujgP/Svvkgem8s6kEr69ntZH/8ukWfvryilaP89Vef2OS6YM7Xy0LdEL6fFcZh1psjPv+qDxSzAbu/XATL64u6jLfqrjWwoOfbOb8p5dx09treW7lnm7/PofqbWQnmVqVrspONFMRhRXkV9bu4/HPt/N5B12s6m0ufrf4O+7/aHOvj0VVVf66bCu/XvQtVT38W7g8Xv66bCu/fHUlnn7cHep4m3u7a2R2MgdqLFEJKgO5zyPCqGDRkcmD0vnTORPZUlrHHUvXR+29veVwHX/7bBu5yXGcOrzjs3jdYdRrGZWTzAl5aUwryOCkodmcPnIAc8fmc9Gkwfz4xOFcd+po/vD98dzSvJHsuwPh9xKvt7l48ssdrC2p5sQuvkdGZCej12rY2scB8oaDNdz1/kbG5qbw53Mnt5sj3VJqnJELJxawbOdhDoS4oT+w0De8xQHcuNxUJuSn8dra/T1+j722dh+PLt/Wo8eIth4FyM8++yzPP/88p556Km+//TbXX389Z5xxRqTGFnOSzAZOGpLFJzsOR3SCOlRv5c0NB5g3biBTB2W0OVIclOY/JR/YqLe5tI6pg9LJTjTx4dZDeH0qn+06zMwhmaSYDa2qbeyubECrUXB6fD0O7Pc2T/hDuygnc+HEAsoa7aw+0P0OgB6fj2/3VVLeaGddyZFJcPmuMsYOSAmuwnZkXK6/E9ILq/1H1FMH+VNCjHot950/mQHJcTz99U7mP/NZ8FRbe/7w9lo+2X6Y00YMYOqgdN7aWIylG5sQ3V4fa4qrGH3UyndWi256PlWl0e4K+7HDVdlk53+rioCO8zPXlvjLH327358P2Jv+t7qItzYWs6m0jqtf+oZtZd37MqpssvOr11YFH+vzKHV57AvH29zbXaNyordRL1DBYkiYFSw6cvrIAdx4xjhW7Kvk/o834+vj5kxLt5Tw60WrMOm1/OWCqd3Kq46UIRmJZCQYw+oy2+hw88w3u7j4X8t5Zc0+5owcwI9PbFv/uCWjTsuo7GQ298FGPa9PZXtZPf9bvZc/LFnLgGQzD18wLeQGLD+cNgSjTst/V4W2iLO7soGBqfHEG/WtLr9y2hAqmuw9bgG+eEMx72wqOabbWPcoQE5JSWHChAmMHj2ampoafvnLX7JmTfgtC30+H3feeScLFixg4cKFFBcXt7p++fLlXHTRRSxYsIDXX389pPv0lnPG5VNvd7GqB8Hf0f751S50Gg3XdpRjZDaQZNJTXGvB4nSzt6qRifnpnDUmj++Kq1i+u4xqi5MzRuUyNDOxVYC8q6KRcQNSGJ+bylsbi3s0qe6rbkKv1TAwrfMd2acMyyY93shbG9u+Jqv2VwXLNnVmR3kDTU7/6f8Pt5cC/gOJ3ZWNzA4hpy/BqGdoZiKl9TZS4wwMaRHUTxqYzlOXzeSVn5yCWa/l893tB1K1VicldVZ+etIIbp87getOHY3V5Wn39+rKN0UVNNjdzBuX3+ryrEQTFU0OXB4vf3h7LfOeWsa9H26ipLb3yvo9+eVOfKrKoLR4dnXQqGRNcTUJRh0Z8Ub++fXOXuuU+PnuMp75ZhffH53LfxfOwqjTct1rq7hh8Xf89o3V/PaN1bywai8H6/wHh063l6KqxjaVUg7UWPjJi99woNbCfedPJj8ljtfX7e+VMceCSM29cGzNv+EKbtSLQprFnspG8lPiiI9gLucFEwu49qQRfLS9lCe+2NEnHUw9Xh+PfLaVBz7ewsT8NP5zxUmt5tNoUBSFGYMzWVNSjcfXeQDmcHt5buUeLv7Xcl5YtZcTC7N46cencNe8ScG9Op0Zn5vKzoqGXmn9XWVx8OaGA/zxnXWc8+Qn/PTlFfzz610MTI3nkYumkxxGvnlqnJGLJvlXkYtD+P7YXdHY7tmNmUOyGJyWwCtrut8CvKzBxuEGGy6vL3igeCzqUYCs0+loaGigoKCAzZv9p2K93vDfRMuWLcPlcrFo0SJ+//vf8+CDDwavc7vdPPDAAzz33HO8+OKLLFq0iKqqqk7v05tOHOxfpf24OWgLh8fna/WGU1WVNcXVLN9dxhXTh5CR0P4mOkVRKEhLoLjGwtbDdajACfmpzB2bj0+Fv366BZNOy6yh2QzNSGRfdRM+VcXrU9lT2ciI7GQunFjAwTora4rDPyUVsLeqicHpCV2e7tFpNZw3fiDf7quk7KjmKm9vKual1UVdrsB/d6AKBZg9Iocvd5djc3lY3nxE29kGvZYC+WNT2lmVBxicnsjITpoJBOotj26u6zwyO5kZgzNZtG5/MPc7VO9tOUhmgolpBa1P6WUnmWlyuLn1nXWs2FfJrGHZLNt1mB8+/yX/iUDe+NE2Harl052H+eG0IcwszGJPZWObLxhVVfnuQDVTBqXzk5nD2Vxax8p9lRF5frfXx8p9lbyypoiHP93Cnz/YyLjcFG496wSGZibynytP4pThOTQ6XNjdHuptLp75ZhcL/vMF8576lNmPf8TCF77m8ue/DK68Oz1e7nxvPaoK/77ie8weMYBLJg9mW1l9l6vRb6zfz8Ofbunwi6DJ4WbV/qo+b6XelUjNvXBszb/hykwwkRoXnY16e6saw65/HIofnziMSyYPZtG6/bz4XVHEH7+lepuL/1u8msUbirlsSiF/u2ha1DcJBswYnEmTw82OLg5+AntwJg9M538/msW9508Oq6nG+LxU3F4fuyIc6Fmcbn78v6/522fb2FHewGkj/CXc3vvlGfznypM7zY3uyOVTh2DQavlvFxUtGh1uyhrt7Taw0SgKP5w2hD1VjWw42L2zhy1bh0e7CkhP9OjQ9tJLL+XnP/85//znP5k/fz6ffvopQ4aEX/Zl3bp1zJo1C4CJEyeydevW4HVFRUUMGjSI5GR/kDJlyhTWrl3Lxo0bO7xPb9JpNcwszOTb/VVhtaA8XG/jyv9+BfhXDU16LQfrrNjdXjLijVw+tfO/W0FaPN/ur2JTaR1aRWFsTgpmg46xA1LYVlbPGaNyMRt0DMtMwuHxcrjehsen4vB4GZmVzOwROTz+uYG3NhYzo4u8q44UVTcydVBo7UB/cMIg/rtqL5/uPMxVM4YFL99d0YhXVSlvsnfaHGP1AX86woIphXy+u5wv95Tzxe5yxuSkhDxxjM9L5e1NJcH0ivaMzE7m9fUH8Hh96LStA/8d5Q1olNabGK6aMZTrFq3iva0HuWjSYOptLjQahSST/uiHDqqyOFh9oIorpw9tc1oyUOrt2/1V/H7OWC6aNJhaq5NHPtvGcyv3MLMwq9Oc73Coqsrjn28nK9HEwulD+XJvOU6Pj5Jaa6sVoUP1Niqa7CycMZTzxg/klbX7eOabXcFuf9mJplbdlqxON796bRWD0xO4asawdovqV1kcvLWxmKWbD1Jr8+d9J5n0nJCXxp/OmYix+bGTzQb+fO6kVvctb7Tzxe4y9lY1kZscR1q8kX98uYOb3lrLU5fP5J9f7WRvVRN/u3BacAf3OeMG8uw3u3l93QHuPrf9nfYvfVfEU1/tBGBqQUabMxONdhfXv7Ga3ZWN/PGsEzh3/MA2j7HxUC23vbuOpub8/uFZSfznypM7eRUiI1JzLxxb82+4Ah31+jpAtro8HKq3MXdsftc3DpOiKPx29hga7C7++fUukkwG5k8YFPHnOVxv4zevr6LG6uSOuRN65XfpiWkFGWgUWL2/qsNGKm6vj092lHLGqNw280qoAo+98WBtRBu2vLBqrz8fesGJTMxPi8iGx7R4fy7ya+v28ZMTh3XYFW93ZdsNei3NGTmAv322lc93lzG5k+/PjqwtqSY1zoBeq2FbWX3Y948VPQqQL774Ys455xzi4uJYtGgRW7ZsCU6a4bBYLCQkHHkhtVotHo8HnU6HxWIhMfHIF258fDwWi6XT+/S2qQUZfLi9lKKqppBbiH68oxSnx8vFkwdTY3Fic3mYkJ9GYXoCJxZmdVlSZVBaAu9tPcTKokqGZyUFa4LOHZvfHCD7v9wDgc7eqkZczau0I7OTMOi0nH/CQF76rogaqyPsdssNdhfVFmfIO7Kzk8wUpMW32tzQYHcFa/4errd1GCA3OtxsL6vnRzOGMT43ldzkOF5cXcSBWgu/PnVUyGOeNSyHy6Y0cvrIjlMyRmQn4/b62F9jafNa7iivpyAtodVrMzE/jXG5KfxrxW5eW+cvq5ebbObVq09rtfmupQ+3HcKnwrnj2gZYQzISUYDrZ48JVi1Jizdy61nj2Xy4lr98uoV/X3lSm1X7/63ey1d7KngkjBWd7eX17Kxo4KYzxmE26BiZdaRObMsAOZCTPa0gA51Wwy9njeL2pev57RurATDqNLzx09nBMx4fbDvEnqpGDtZZ+XTnYeaMHMDtZ08Idi/z+Hz88tWVlDXY+d6QLOZPGMQJeWkkdnJQ0VJOkpnLjjqAzEkyc9Nba/jlqyvZW9XEZVMKmTkkK3h9vEHHueMHsnjDAX7dNLpNicNX1uzjqa92csaoXPZXN/Hklzs5aUhW8CCgoTk4Lq6xMDQjkceWb2PSwPRgDWuAg3VWblmyliSTnvOag+eeNk0IVaTmXjj25t9wjcpOZvWBKhxub8j5nD21rznnOZwW0+HQKAq3nz2BJoebhz/dQpJJ3+k8Fy6L081Nb6/B4nTz1GUzI3aQHknJZgOjc1JYfaCqwxJo3+6vpNHh5uwxee1eH4q0eCNjB6Tw3taDXDF9SMiLYp0pa7Dx+voDnD0mj0ntlEztiR9OG8KbGw/wv9VFHXYmDLSY7mgDqdmgY2ZhFl/uKeeGOWPD+p1VVWV9SQ1TBmXg86m9vsHR7vJQY3VidXkYnpUUkdcnoFuz2fPPP9/hda+88go/+clPwnq8hIQErNYjneJ8Pl9woj36OqvVSmJiYqf36W1Tmo+o1pVUhzQBqqrKJztKmZCfxg2nj+3Wcwa+ePdUNbJgypHWnuePH0h6vJFZQ7OBIwFXUXUTNpcHg1YTvO9JQ7P53+oith6uD3sHciCveUhG6BP+2AGprNxXiaqqKIrSqjze4aNSL1paW1yNT4UZhZkoisLcsXn8p7l6RCj5xwHxBh3Xzx7T6W0C3fh2VTS0ei1VVWVHeQMnDc1qdXtFUfjFyaP4y6dbGJKRyImDM3lzYzGf7ChlXosAuN7mItGkR6PA+1sPMSE/jfx2ummNyErm0+vPanOAFG/U83+zx3L70vW8uaG41Wte2WTnuZV7cHl9/PHd9Tx28XR0Wg01VgeL1h3gggmD2l1lX7rlICadlu+PzgX8B11GnYZdFQ2tVofWFFeTk2QOdvw7feQAXk4/hUaHm0aHm1vfWctra/fz69NG41NVFm8oZlxuCg9fMI1X1uzjxe+KmJifFgz4VxRVcrjBzr3nTY7Yl/iJhZn8/oyx/OXTrYzKTuaXp7Q9cLp40mBeX7ef19bt4zenHXkfvLflIP/4cgenjxjAnedMYF1JDTcs/o7FG4r54bQhlNRauGPpBn8Fk/lT/SvjL3zFPR9u5B8LTkSn0dBgd3HjW2vQKAqPXDy9z1qFR3ruhWNv/g3XqJwUfKp/7uyrls27u9liOhw6rYb7zp/Cb99Yzd0fbGREVlK7c0y4PD4fty9dT0mdlccunh6TwXHAjMGZ/HfVHhrtrnYXCj7aVkpqnIHpg0M789mRy6YUcsd7G/imqIJThvW8esfTX+9Co8DPZ43s8WMdLS3eyNyx/q54/3f6GBKMbRcidlc2kJVoIjWu49rOpw7P4Ys95Wwrqw/rc1Nca6Ha6mTqoHR/auTuMqotjg5TSMO1oqiCl9bso8bioNbqDLbeBnjgB1MiWl2lWzPa7t2RzY2cPHkyn3/+Oeeccw4bN25kxIgjR4NDhw6luLiY+vp64uLiWLt2Lddccw2KonR4n96WlWhmUGo8a0tq2qxstWdPVSPFtdZWQU64ClpsjDuhRXF2nVbT6g1h0mvJT42nqKqJRoeboZmJwdSB4ZlJaBWFHeXhB8h7qwMTfui5W2MHpPDBtkOUNdjJTYkL5vpqFSVYs7k9qw9UEW/QBSfms8fk85+Vexidk9ytvKzO5KfGE6fXNp9yOhLgljfaqbe72lSdAH/JpdeuOQ3wB9KbD9fx0ndFzB2bj0ZR+HZfJTe9vQaTTkthRiIH66xcNWNoh2Po6OzB7BE5zCzM5F/f7OK04TnBNrv/XbUXn6ry85NH8sw3u/jrZ1s5cXAmD326hQa7mwM1TfzlgmmtHsvu8rBsZxmnjxwQ3LWs1SgMz0pqlVvn8flYV1LD6SMHtDrl1zJn74xRuby9qZiFM4ayvayeg3VWrvneRJLNBn55yig2HKrh1bX7mT+hINjeOzvRxCnDszv8G3TH/AkF5CSZGZGV3O7qfV5KHHPH5vPq2v0MzUjinHH5bDrkX5WfVpDBXfMmotNomDE4k5mF/i9aq9PNy2v2YdBpeOiCqcF0pN/PGcfdH2zk1iXrMOq07KpsoKrJwROXzuiz4BgiP/fCsTf/hiuwUW9neXhf9D2xt6qJRJOe7Ag0Z+qMSa/l3vMmM/+Zz/hw2yGuPblnAZeqqjy2fDvfHajm1rPGMyXElLpomVGYyXPf7mFNcTVzRuW2uq7R4WbFvkoumDCoy30zXTl1RA45SWZeXbu/xwHytrI6lu08zI9PHEZWYuit08Nx7riBLNlUwrKdZe2m33S0Qa+lk4ZmodMofLG7LKzPTSD/eMqgDOqa0+m2Hq7jtB40zGnp3yt3U9XkZNLANDISTKTFGUmPN/obmHUjHaQz3QqQW3ZwAmhsbCQpqftHymeeeSYrVqzgsssuQ1VV7r//fpYuXYrNZmPBggXccsstXHPNNaiqykUXXUR2dna79+lLUwal8/H20nZzV4/26Y7DaDVKWKufR8tNjkOnUfD41FYBcnuGZiSyt6qReruLM1pMGia9liEZid3KxyuqaiLZrCc9jG5CgQB3W3k9uSlx7K5sJDvRjFGv6XAF2b9BrIqpBenBSS0vJY5rTxoR3CwXSRqlOUisaL0BI7Dxo6vnVBSFhdOHcud7G/hqTzljc1O558NNFKQlMGlgOtvK6ihMT+jWa68oCr+bM46F//2KGxZ/x+OXzsDp9rJ0y0EumDCIH504DLvbw/9WF/Hu5oOMyk7m9BHJvL2phI2HapmYnxZ8rOW7y7C5PG3yaEdlJ/PB1kPBfPqd5Q1YXR6mddJdauH0YXyy4zBvrD/A9rJ6MuKNrX6/H04dwh/fXc9Xe8sZkpHImuJqfnbyiB5/SbXnxMKsTq+/+cxxVFkcPPDxZmwuD899u4fc5DjuOW9yq8/tr08dzVUvfM3zq/Zy+ogB3DBnTKs0pO+PzmXL4To+2l5KRryR7EQz/zd7DOPz0tp72l4T6bkXjs35NxwZCUbS4oxtPuO9ocnh5kCthc2ltQzPTOyTbp2ZiSamFmTw8Y5SfnrSiB495xsbDvDWxmKumDaE88ZHPq850kbnJJNo1LHqQFWbAPnz3WW4vT7OHtPz3GmdRsOCKYU8/vl2tpfVd3tVXVVVnvhiB2lxRq6Y3vGiSU+NzklmSEYi72892CZAtrs8lNRZujybl2DUM60ggy/3lPPrU0eH/L5aW1LNgCQzeSlxZCYYg3nIkQiQ91c3sauikd/OHtOjBcdQ9eic2P79+7nuuutoampi8eLF/PjHP+Yf//gHQ4eG98JrNBr+/Oc/t7qs5WOcfvrpnH766V3epy9NGZTB25tK2FHR0OnRlU9VWbbzMDMGZ4ZVsuVoOq2GvJQ4vD61y/zhoZmJfNFcAzbQZz1gVE4yX+4pD6Y9dORQvZXPdvqra+g0GoqqmhiakRTW5Ds0MxGjTsO2w3WcOSqX3ZUNjMxOwu31dRggF9daqWhy8KMTh7W6/CczO69X2RMjspNZuvlgsJU3+POP9VpNSKdIZ48YQH7KLv63uoh4ow6H28t9508OueVnZ/KaW83e9NYafvXatwxKjUenUYJ/n5+dPBK310eCUc/C6UPx+FS+KarkyS938OwPvxd8vd7bcpBBqfFtWsOOyEpmsbuYQ3VWBqUlsKa4GgU63Yw5NDORU4dn89rafdjcXq49aUSrFdxZw3LIS4njlTX7GDMgBZ1G4fwofdkadFoe+IH/NPQjy7eRYNTx0AVT22yqLMxI5O5zJ2HWa1vlMgcoisKNZ4zjxjPG9dXQOxWpuReOzfk3HIqiMConOViVJpIqGu289F0R+2uaOFBjDW5ABZg9ovfmrKOdNTqPez/axNbDdd0+aFtRVMHfP9/OKcOy201ZikU6jYZpBZmsPlDV5jvto+2lFKTFMzI7Mmku544fyH9W7ubVtfu457zJ3XqML/aUs7m0jpvPHB/R8n9HUxSFc8fl8/cvdrCvuqnVHpO91U34VBgRwt/ltBE5PPDxFnZXNna4oa8lr09l/cFaTh3mP1to0GkZkZUUsTzkj3eUolEI7rnqbT1a0rnnnnu47bbbSE9PJzs7myuvvJI777wzUmOLaZMHHslD7szm0joqmhzBvM+e+On3RoQ0cbVs5HH05DA6J4VGh5vSTnKAVVXlwY8388w3u3hnUwk+VWVfdVO71Qk6o9NoGJXjr7Jhc3k4WGtlRFYyuclxlNbb2i2dFWijHWq1jEgYmZWMw+MN1toFf4A8LDOxw413LWk1CldMH8rOigbWldTwuzljIxIcB0wamM5jl8yg0e7i2/1VXDq5MHiQpFEUfnPaGH4yczg6rQaTXstPTxrOtrL64EFSca2FTaV1nDt+YJsDnMCkt7OigSaHm/e2HGRkdnKX9UF/NGM4NrcXvVbDD05oHfxqNQqXTSlkW1k9SzaVMHvEANLCOPMQaXEGHX+7cBrfH53Lgz+Y2uFGutNHDmg3OI5Fx/Pc2x0T8tLYX2OJaJDs9ancvnQ9S7cexOX1MXNIJtedMoq/XDCV1685jWu+13cB8qnDszHqNHwUQvlRn6pic3motTo5XG+jqKqJlfsq+dN7GxielcSfzpkY0Y1OvW1GYQbVFif7WnSQLWuwselQLWePyY/YKn68QccPThjEF7vL25QvDYXb6+Opr3ZSmJ7AueN7vyLIWWPy0GoU3t/aum307uYzyEcvnrXn5KHZaBSC3yVd2VPZSJPDHexcC/7ufDsqGnrcMMSnqnyy4zDTCjLDLjLQXT0KkOvr6znppJOCP19xxRVYLL3X4CCWpMQZGJ6Z1KreX3s+3VGKUafh5KE9z7+cMyo3pFP1gVVPrUZpU9A9kDLQWe3I1QeqWH+wlkSTnmdX7GZHeT0Ojzes/OOAsTkp7K5sZHt5PSr+gD03JQ6L0xMsjdXSwVorBq2G3JTI5hp3JnAQESh941NVdlY0tpt/3JG5Y/IoSIvnnLH5bZqBRMK43FSeWHAiPzhhUJen5uaOzacwPYHHlm/n5rfX8oe316Jt3ux4tML0BAxa/0a9Bz7eTKXFwQ1zut5IOionmfPGD/z/9u48IOo6/x/4cw7O4UYOYURBQRRSxKtS0MJSSzNNLNhFN1uPUrayCDPsktU02V+W65Yd5s82N822ttzWlBLUtBJvEk9ELhEBhWE4Zz7fP5BpJodzhrl8Pv6Kz2c+w/sd4+vzmvfn/X698ejwYL3J74ORfeDuZIcmlRozovp2vqM9xM3JHq8+OMzoc9TM5XaOvd0xIyoIHk72+HuW8TbX+CznInJLr2PZxCHYmDgGL00aij+M6o+x/f0g95SZZHpFK5mDHWIG+CPzTGm7icipkipMWv8dJry9C1P+sQczP/gBSZuz8fwXv8DZQYo1D4/UVEiyFq3rBLR3bt11uuWLgjEGprTNHNYPEAHbjlzq8rVfHCtA8XUlkscP6pHpZr/n6dyyeP/b3GKdfQfOXq2GWyfnx3s6OyBK7o2sNjbT+r3WAcNorcocdwR4orFZrdmJt7tOFFfhSnUdJg427t+0PQb/lRoaGjSBoLy8HOoOdrWxJcODvHGyuKrNTSMulNfgf78WI3aAf4dl3IwpwMMZjlIJQrxdNfVlW/Xv5Qp7iRin26hNqBYE/CP7DALcnfHOrNFQNjTjtZ3Hbl7b9UdVEQEeaFKpsfNUyzbXrSPIAPSOYhddr0Wgh7NJRzD6ervA/mY1BwC4XKmAsrG5S3Oe7aUSbPlTLNImD+2xG2OYrztS77+j3ZrLQMvI/TP3RsBeKkZZdR16uThiYcxAvd+6pRIx+vu44qvjl7H33BU8GTOw0wsyXpw4BE+18UTD0U6CeWMG4p4w/w7nzFP33M6xt6tkDnZ44u5QHCmsNMqmN5cqFNi4/yzGhfrhvnDT3bDbM3FQAKpvbmyjT+v8V0epBE/FhuO5uAi8NGkI0qdGY+2Mkdg8O+aWcojWwNfVCcHeLvjpUktyJggC/vdrMaLkXkZf1O3n5oS4gb3x9clCKBpuHeBpS3VdIz46eA6j+vXqcN2EMT14c+ffA1qf+Zapju6dvk+ND/XHpUoFLlXUdPjaw5crEOztolOxIuLmfG1Dp1ns+rUYjlKJUaqIdJZBCXJCQgKeeOIJVFRUICMjA48++igSEhKM1TaLNzzIG40qNY4W3TqKXKVswAv//gUyeykWjxtk0naJb44W6vumJZWIEerr1uZCvT15JThXXo35Y8MQ5uuOR4b1RdF1JURoGW3sqtZ/HD+cKYWnsz16uThoasmW6KlkUVhVa5RSRV0hFYsxoNdvC/V+W6Dn0eX3sRQj+/bC9j/fg81zYrD+0TvbHXUe6OcOZZMKY0J8O1WVpbNmRPXFXx8abtKRtNvF7R57u2PakCAEecqwPut0h9sTt0elFvDX/x2Ho50Ez0+ItJjP9+gOdnndf+EqTpZUYe7dYfjjqP54ZFg/PBjZB/cO7I27Q3zbLfll6Ub388GxokrUNTbj9JUbuFxZa1Dt4/YkjAiBsrEZ/zlxudPXfHe6BDX1TW0OKPSU0cE+6CVz0EyzaFKpcfGaosMKFtpaK151NM2iSaXG8aJKTRncVn5uTvBxccSpkutda7yWxmYVvj9bithQP5MONhp0R4+Pj8fTTz+NqVOnorm5GStWrEBiYqKx2mbxhvXxhrfMAenfntDUCQZaPijL/nMEFcoGvPHwCLN8K0+57w4kjtSfFA3yb9leWaXWfdTYpFJj4/4zCPV101S/eOLuMHg42UPuKevWozdf15Z/HI0qNcJ8W761tjWCrBYEFN9QaurvmtJAPzecvXoDn/x8Af/IzoOrg9Rkmz6Y2z1hvRHdxxtpk4da1dzD29ntHnu7QyoR48nYcBRU1uKbk4UdX9CGz3LykVt6HUviIkw2F7IzpBIxJoT3xv4LZbeMbqrUAt7dl4c+njJM6YEpYOZ2Z7APmlRqHC2qxK7TxbCXiA2qGtWegX7uiO7jpdmBtTPOlVfDw6llWqYpScViTI6Q4+DFclxT1CO/ogZNKjXCOrHgrpWPqyMiAzywt4NpFrmlLVMx9ZUGjAzwMGgE+WB+OWoM3PClOwxKkOfMmYORI0ciJSUFqampOnPibgfO9lK8M+tOiEVA8rZDOFpYgS+PF2D+pwdwvKgSL00aapFF1sP9PFDXpEJBpe6cxb3nrqDkRh3mjwnTJEqujnZYO2MkXpw4pNu/r3UUufVbq7O9FJ7O9rdUsiivqUdjsxp9TDyCDLRUslA0NGNDdh76ebvgzRkjb9kW2laN7NsL6x+906AqK2Rat3vs7a7YAX4YKvfC+wfOoraxucvXF1QqsPHAGcQOsJypFdomDg5Eo0p9SzKz69di5FcosGDswA7LklqjoXIvOEjFOHChDLtPl2Bsf79O79TZHQkjQnC1ph7fny3t1OvPl1ejv4nK/v3eg5FyqG5OO+loB722jA/1x9mr1e3uX5Bz+RrEIujdGTAywBOl1XWoqK3vWuNv2vVry4YvI9opP9oTDPqXUlNTA6Wy66s5bUk/bxdseOwuOEjFWPTZIazZfQqNzWosnzzUIgMooL1Q77rO8S+PFSDA3emWlfyDe3vo1NTtqtYvCdplYlorWWgrvN5SRcLUUyyAlr3n594Vik1JY/H2rDsxxMT1bYm6grG3e0QiEZLHDUKVshGf/nyhS9e2Tq1wkEqQcp/lTK3QNtjfA3IPZ51qFo3NKrx/4CzC/dxxT5jp5m+akoNUgug+3vj6ZCGu1zViYg+PNN4V4osgLxm2Hr7Y4aJPlVpA/jWFTnUpUwrycsHQQE98c6oQZ6/egJOdpMuDUONCW0bjs9qZZnH4cgXCfN31rpGJ1MxDvt6l3wu01BY/cPEqJoQHmHwao0GTOZycnHDPPfdg4MCBcHb+7bH4u+++a3DDrIncU4YNj92FnaeKcHeILwb5d34CvDkEebnA2U6CvCs3NNsjX6pQ4GhRJRbGDDT6Y/Zxof7Yd74Mw/r8lnQGejjjZLHuI5eim2XWTLkzWSsXBzv8eYzl7gZGpI2xt/sG9/bAhPAAfHr4Ih4e2rdTU+AEQcCnv1zEqZLreOWBKIuaWqFNJBJh4uBAfPTjOZRV18HPzQn/Pn4ZZTV1WDZpiEXflww1qp8PDuaXw93JDncG+/To7xKLRJg+tC/W/fArrlTXtbsYsOSG8mYVKNNOr9D2YGQfrNx1AteVjQj1devyPT7Qwxmhvm7Ye64UiSNvXadS19iM3JKqNjfvCPNzh1QswqmSqi7v4tu64cvEQaadXgEYmCDPnDnTWO2wer3dna0mwZKIRRjo546jRZVoVqshFYvx1YnLkIhFmBLZp+M36KI+njK8l3i3zrEAd2fsySvR2YmwqEoJe6kYvla4kprIlBh7DbNw7EBknbuCjQfO4KVJQ9t83aWKGuzJK8WevBJcrqpFzAA/o5cOM7aJgwLx4Y/nsDuvBNOHBmHzofMY2bdXu7tj2oI7g32w7gdgwsCATtWvN1RrtZ+8shvtJsgXbpY3+33JVVO6d2Bv/L/vc1Fd39Tl6RWtxof64/0DZ1GuqIePi+49+nhxFZrVQptTIBykEgz0c8epNqpntWfXr8UI8pT1yE66HTEoQf7yyy+xefNmY7WFTOiBSDn++r8TeHP3KSy5NwL/zS3C+FB/k23oEODuDLUAXKmp04wYF16vRaC7aUu8EVkjxl7DBHg4Y+awvvjX4XwEuDvD2V4KO4kYdhIRpGIxrtbUI/NMCc6X10AEIDrIGwkjQzBxUKDFj8LKPWWI6O2B704Xo75Jhet1jVgYM9Dczepxfb1c8OqDUSbbZGqAjyukYhHyrtxod0HghWstnyFzJsjO9lLEDeyNb04VdWqDEH1aE+Tsc1fwyLB+OudyLl+DVCzCkHZKhEYGeOLL4wU6g2IduVJdh6NFlZhn4Bbq3WVQgtw6D077ER9Zhwcj+6DouhKbD53HpQoFauqbMG2o6bYD1i711pogF5mhxBuRNWLsNdycO0ORfb4M7x84q/f8HQGeePbewbgnrLdOXVdrMGlwIDIyc1FQWYt7w3p3uWSltbrfhI/h7aUS9PdxbbNkaqsL5TWQe8rgaCdp93U97ZFh/fBLwTWdTTy6IriXK/p6ybDxwFmcKK7CsD5eiJJ7o6+XDDmXKxAZ4NlupauI3h74LCcf58trEN7J0eDdp0sAmPbvqu22mIPs6wkE2PbTpW55dVoYGtUN2PpzIYJ7yTB1mDdM9SVNZNfyeVE0KxHQC1CrW0q83Rfhy78VUQesJfYClht/A2CH7BfGo6FZhaZmAY0qNZrVajQ1C3C0t+6pXol39ca6H36FWhCQNjXMIv//24Lovh7478lS9PYW2hzhLKiqQUSAq9n/BgG93PFTRJxB7/F2QhQ+2J+PQxcrsDuvJXnt5eKAitoGPB0X2m4f4yI98fI3QGF1Fe6N7DhBFgQBmWeLMLyvJ0YOMM9AAOcg38ZEIhFWTIuEp7M9RgV7mfQRhp+rI+wlYhRWtqzEL61uKfHWz5sjyEQdYew1DolY1LLxgI1VOPR2ccCfY0LgbC9BiM/tUc/dHO4IdMfWny+jsLIOQd63JnHKxmZcqqjFtCjLnrfeWUP7eOCdhGEQBAH512rxU34lfrpYgTNlCkwZ0n7d6QB3R/i5OeDI5SrMubtfh7/rWOF1nC1TYMXDkUZqfdcZlCBPnz4dxcXF+Pnnn9Hc3IxRo0ahb9++xmobmYBUIsYLk0y7uw8AiMUiBPeS4cjllkoWl661VLDopyfIEJEuxl7qyNLJpo/rt5sh8paR0BPF1/UmyOfKFBAEINzffPOPe4JIJEKIjwtCfFyQMKpzUzNFIhGigzxx9PL1Tr1+04FLcHWQYvow80yvAAysg7xv3z488sgj2LNnDzIzMzFz5kzs2bPHWG0jGxc/Qo5fLlXheOF1XKq4mSD34ggyUUcYe4nML8zPFfYSMU4W6Z+HnHelpYJFuL/5SrxZkmFBHrhcqcQ1RUO7r7tyox7/PVmKWSP7wMXBdFtL/55Bv3ndunX45JNPMGDAAADAuXPnkJKSggkTJhilcWTbHhsVhHWZ57Bx30UEuDvCQSqGv5v1zvsjMhXGXiLzs5eKMai3K060mSDXwMlOgiAvPhkFgOiglioX+89dw8PtjAxvOXQJakHAnzoxFaMnGTSC3NTUpAnQABAaGgqVSmVwo+j24OIgReLoIHx7shQHzlegr7czxLfJ9s5EhmDsJbIMd8jdcarkBtTqW3fUyyutQZi/K+9rN0X18UCYnwvWfncG9U3641Vdowqf/nQZ9w32Qx8zf7EwKEF2dHTEyZMnNT+fPHkSTk5OBjeKbh+P3x0MsUiEX0uruUCPqJMYe4kswx2B7qipb0ZBpe7W74Ig4ExZDcL9bGv+sSGkEjFeeygSRVV1eDdL/1bvXx4rRpWyCXPH6N+Vz5QMmmKRkpKChQsXahaH5OfnY926dUZpGN0e/N0d8VBUAL44Usz5x0SdxNhLZBnuCPQAAJwouo5grXtYuaIBlbWNCO/NBFnbXf29MXVoAP6x9wIeiZbrjBILgoBNB/IxuLcbRgV7mbGVLQxKkEeMGIGdO3fi+PHjUKvViIqKgqdn2zupEOkzPzYEXx0rweDeXMhA1BmMvUSWIdTPBQ7SloV606J+m1ebV1oDABhoYxUsjGHZA+HIPF2G17/5Fe/PHqE5fuB8Bc6WKbA2fqhF7FjZ7SkWBw8exLlz5+Dh4YFx48ahoKAAeXl5xmwb3SbC/d2w74V7MHWobdSKJOpJjL1ElsNOIsbgADecKNZdqHfmSkuCzAoWt+rt7oTke0Ox+9cy/HDmqub4Rwfy0cvFHlOHtl9T2VS6lSBnZmbi+eefx40bv30gnJyckJKSgqysrC6/X319PZKTk5GYmIh58+ahsrLyltd8/PHHiI+PR3x8PNavXw+gZTg+JiYGSUlJSEpKQkZGRne6QxYgwMMJEi5kIGoXYy+R5bkj0B25xboL9U5fqYavqwO8ZDa2C42RzB3bDyG9ZHj961/R0KzCxXIFvs+7ij/e2RcOUvNuy92qW1MsNm7ciA8//BDh4b8VIn/00UcRERGB9PR0jBs3rkvvt3XrVoSFhSE5ORk7d+7Ehg0bkJaWpjlfWFiI//znP9i+fTtEIhESExMxYcIEODk5ISIiwiK3VyUiMjbGXiLLc0egO/7/wQJcvFaLAb4tOxeeuVKDcE4bbJODVIJXHorAnI9+xgf78nG1uh72EjH+MNpyNjzq1ghyQ0ODToBuFRkZCaVSqeeK9uXk5CAmJgYAEBsbi4MHD+qc9/f3xwcffACJRAKxWIzm5mY4ODggNzcXZWVlSEpKwrx583Dx4sXudIeIyCow9hJZniFyDwDAyeLrAIBmlRrnripsbgc9YxsX5oOJEX5Y//15bM8pwtShAfBxdTB3szS6NYKsVqvbPCcIt9YC1LZ9+3Zs3rxZ55i3tzdcXVs+SDKZDDU1NTrn7ezs4OXlBUEQsGbNGgwePBjBwcG4du0a5s+fj8mTJ+Pw4cNISUnBjh07utMlIiKLx9hLZHn6+8jgZCfBiaIbmD5MjksVtWhsVmMgS7x1KO3BwZhwJgsNTWo8PqafuZujo1sJckREBL7++mtMnTpV5/g333yDfv36tXtt61w2bYsXL0ZtbctWw7W1tXBzu/WxRENDA5YtWwaZTIZXXnkFQMuoiUTSMldlxIgRKCsrgyAIFrH6kYjI2Bh7iSyPVCJGRICbZsvpvNYFeizx1qE+Xs54ZWoEzpbVIDLQ3dzN0dGtBPmZZ55BQkICsrOzER0dDbVajWPHjuGXX37Bli1buvx+0dHRyMrKwpAhQ5CdnY3hw4frnBcEAU899RRGjx6N+fPna46vX78eHh4emDdvHvLy8hAQEMAATUQ2i7GXyDJFBrrjs18KoVILyCutgUQs0sxHpvYljg4ydxP0EgkdPZdrQ2VlJT799FOcOnUKYrEYUVFRePTRR+Hu3vVvAHV1dUhNTUV5eTns7OyQkZEBHx8fbNq0CUFBQVCr1ViyZAmioqI01yxZsgQhISFISUmBUqmERCLByy+/jP79+2teU1RUhLi4OGRmZkIul3enm0REFsUaYi/A+Eu3ly+OFGHJtuP47tlYrPnfGRRU1GL3kq4tmiXL0u0E2RowQBMRmQfjL91Ozl+twYS/ZWNt/FC8tecsovp4YH1itLmbRQbo9kYhRERERAQE93KBzF6CgxcqUFRVh0Es8Wb1mCATERERGUAiFiEiwB3fnioFAFawsAFMkImIiIgMdIfcHcpGFQBWsLAFBiXI165dQ2ZmJgDgzTffxJw5c5CXl2eUhhERkX6MvUSWZ4i8ZaGsq4MUgR5OZm4NGcqgBHnp0qUoLCzEwYMHsW/fPkybNg3p6enGahsREenB2Etkee64Wcd3oL8ryx7aAIMS5OvXr+NPf/oTsrOzMWXKFMyYMQN1dXXGahsREenB2Etkefp5y9DLxV6z9TRZN4MS5KamJjQ1NWHfvn24++67UVdXB6VSaay2ERGRHoy9RJZHLBbhP4vH4rn7w8zdFDICgxLkuLg43HXXXfD09ERkZCTi4+MxZcoUY7WNiIj0YOwlskwBHk6QOXRrk2KyMAZvFHLlyhX4+flBJBIhLy8P4eHhxmqbwVionohslSXHXoDxl4ism8FVLHJzcyESifDmm29i1apVXElNRNTDGHuJiHoWq1gQEVkZxl4iop7FKhZERFaGsZeIqGexigURkZVh7CUi6lmsYkFEZGUYe4mIepZRqlj4+/sDgMWtpOYqaiKyVZYcewHGXyKybgYV61Or1fj666+RnZ2N5uZmjBkzBgMGDIBUyhqAREQ9hbGXiKhnGTTFIiMjA4cOHcKcOXPw+OOP4+jRo1izZo2x2kZERHow9hIR9SyDhhv27duHHTt2wM7ODgAwfvx4PPTQQ1i2bJlRGkdERLdi7CUi6lkGjSALgqAJ0ABgb2+v8zMRERkfYy8RUc8yKEEODw/HypUrcfnyZRQWFmLVqlUICwszVtuIiEgPxl4iop5l0BSLV155Benp6XjssccgCALGjh2L5cuXd/l96uvrkZKSgoqKCshkMqxevRpeXl46r0lPT8eRI0cgk8kAABs2bICdnV2H1xER2RrGXiKinmVQgvzee+/hjTfeMLgRW7duRVhYGJKTk7Fz505s2LABaWlpOq/Jzc3FBx98oBOEN23a1OF1RES2hrGXiKhnGTTFYu/evUZpRE5ODmJiYgAAsbGxOHjwoM55tVqNgoICvPzyy3jsscfw+eefd+o6IiJbxNhLRNSzDBpBlsvlmDt3LqKjozWP3wDg8ccfb/Oa7du3Y/PmzTrHvL294erqCgCQyWSoqanROa9UKvHHP/4Rjz/+OFQqFWbPno3IyEgoFIp2ryMiskWMvUREPcugBNnDwwMAUFxc3Olr4uPjER8fr3Ns8eLFqK2tBQDU1tbCzc1N57yTkxNmz54NJycnAMCdd96JvLw8uLi4tHsdEZEtYuwlIupZBiXIq1at0vx3Y2Mj7O3tu/U+0dHRyMrKwpAhQ5CdnY3hw4frnL906RKeffZZ/Pvf/4ZarcaRI0cwffp0VFZWtnsdEZEtYuwlIupZ3UqQGxsbsXz5ckyYMAH33XcfACA5ORleXl5YsWJFl7c7TUhIQGpqKhISEmBnZ4eMjAwALQtBgoKCEBcXh6lTp2LWrFmws7PDtGnTEBoaCrlcrvc6IiJbxNhLRGQaIkEQhK5etHbtWuTn5+P111+Ht7c3AKCsrAyvvfYaQkND8eyzzxq9od1RVFSEuLg4ZGZmQi6Xm7s5REQGsZbYCzD+EpF161YVi7179yIjI0MToAHAz88Pa9aswZ49e4zWOCIi+g1jLxGRaXQrQbazs4Ojo+Mtx11cXLo9F46IiNrH2EtEZBrdSpDFYjEUCsUtxxUKBZqbmw1uFBER3Yqxl4jINLqVIE+ZMgVpaWlQKpWaY0qlEmlpabj//vuN1jgiIvoNYy8RkWl0K0GeM2cOXF1dMWbMGMyaNQszZ87EmDFj4ObmhkWLFhm7jUREBMZeIiJT6VYVi1bFxcXIzc2FWCzGkCFD4Ovra8y2GYyrqInIFll67AUYf4nIuhm0UUhgYCACAwON1RYiIuoExl4iop7VrSkWRERERES2igkyEREREZEWJshERERERFqYIBMRERERaWGCTERERESkhQkyEREREZEWJshERERERFqYIBMRERERaWGCTERERESkhQkyEREREZEWJshERERERFqYIBMRERERaWGCTERERESkhQkyEREREZEWqbkbAAD19fVISUlBRUUFZDIZVq9eDS8vL83506dPY+XKlZqfjx07hr///e+IiYlBbGws+vXrBwCIiorCc889Z+rmExFZJcZeIiL9LCJB3rp1K8LCwpCcnIydO3diw4YNSEtL05wfNGgQtmzZAgD49ttv4evri9jYWBQUFCAiIgLvvvuuuZpORGS1GHuJiPSziCkWOTk5iImJAQDExsbi4MGDel+nVCrxzjvv4KWXXgIA5ObmoqysDElJSZg3bx4uXrxosjYTEVk7xl4iIv1MPoK8fft2bN68WeeYt7c3XF1dAQAymQw1NTV6r/38888xadIkzSNAHx8fzJ8/H5MnT8bhw4eRkpKCHTt29GwHiIisEGMvEVHnmTxBjo+PR3x8vM6xxYsXo7a2FgBQW1sLNzc3vdd+/fXXePvttzU/R0ZGQiKRAABGjBiBsrIyCIIAkUjUQ60nIrJOjL1ERJ1nEVMsoqOjkZWVBQDIzs7G8OHDb3lNTU0NGhsb0bt3b82x9evXa0ZE8vLyEBAQwABNRNRJjL1ERPpZxCK9hIQEpKamIiEhAXZ2dsjIyAAAbNq0CUFBQYiLi0N+fj4CAwN1rps/fz5SUlKQlZUFiUSCVatWmaP5RERWibGXiEg/kSAIgrkb0VOKiooQFxeHzMxMyOVyczeHiOi2wfhLRNbMIqZYEBERERFZCibIRERERERamCATEREREWlhgkxEREREpIUJMhERERGRFibIRERERERamCATEREREWlhgkxEREREpIUJMhERERGRFibIRERERERamCATEREREWlhgkxEREREpIUJMhERERGRFibIRERERERamCATEREREWlhgkxEREREpIUJMhERERGRFibIRERERERamCATEREREWlhgkxEREREpIUJMhERERGRFotKkHfv3o3nnntO77lt27ZhxowZmDVrFn744QcAQH19PZKTk5GYmIh58+ahsrLSlM0lIrIJjL1ERLqk5m5Aq/T0dOzfvx+DBg265Vx5eTm2bNmCHTt2oKGhAYmJiRgzZgy2bt2KsLAwJCcnY+fOndiwYQPS0tI016lUKgDAlStXTNYPIqLu8Pf3h1Rq+pDcE7EXYPwlIuvQVuy1mAQ5OjoaEyZMwGeffXbLuRMnTmDYsGGwt7eHvb09goKCkJeXh5ycHPz5z38GAMTGxmLDhg0615WXlwMA/vCHP/R8B4iIDJCZmQm5XG7y39sTsRdg/CUi69BW7DV5grx9+3Zs3rxZ59jKlSvxwAMP4KefftJ7jUKhgKurq+ZnmUwGhUKhc1wmk6GmpkbnusjISPzzn/+Ej48PJBKJkXtCRGQ8/v7+Pfr+poy9AOMvEVmHtmKvyRPk+Ph4xMfHd+kaFxcX1NbWan6ura2Fq6urzvHa2lq4ubnpXOfo6IgRI0YY3mgiIitnytgLMP4SkXWzqEV6bRkyZAhycnLQ0NCAmpoaXLhwAWFhYYiOjkZWVhYAIDs7G8OHDzdzS4mIbAdjLxHdrixmDrI+mzZtQlBQEOLi4pCUlITExEQIgoBnn30WDg4OSEhIQGpqKhISEmBnZ4eMjAxzN5mIyOox9hLR7U4kCIJg7kZYCrVajVdffRVnzpyBvb090tPT0bdvX3M3q9uampqwbNkyFBcXo7GxEU8++SQGDBiApUuXQiQSITQ0FK+88grEYqt4kKBXRUUFZsyYgY8++ghSqdQm+vbee+/h+++/R1NTExISEjBq1Cib6FdTUxOWLl2K4uJiiMVirFixwib+ZsePH8fatWuxZcsWFBQU6O3Ptm3b8K9//QtSqRRPPvkk7rnnHnM32+LYWvxt1ZnPh7Wx9XuLSqVCWloa8vPzIZFIsGrVKgiCYDP9s8X7ZquHH35Ysz5CLpdj4cKF3e+fQBq7du0SUlNTBUEQhKNHjwoLFy40c4sM8/nnnwvp6emCIAhCZWWlMG7cOGHBggXCoUOHBEEQhOXLlwvfffedOZtokMbGRuGpp54S7r//fuH8+fM20bdDhw4JCxYsEFQqlaBQKIS3337bJvolCIKwe/du4S9/+YsgCIKwf/9+YfHixVbft40bNwpTpkwR4uPjBUEQ9Pbn6tWrwpQpU4SGhgahurpa89+ky9biryB07vNhjWz93rJ7925h6dKlgiC0xOSFCxfaTP9s8b7Zqr6+Xpg2bZrOMUP6Z71fE3pATk4OYmJiAABRUVE4deqUmVtkmEmTJuHpp5/W/CyRSJCbm4tRo0YBaCnP9OOPP5qreQZbvXo1HnvsMfj6+gKATfRt//79CAsLw6JFi7Bw4UKMHz/eJvoFAMHBwVCpVFCr1VAoFJBKpVbft6CgILzzzjuan/X1R7tUmqurq6ZUGumytfgLdO7zYY1s/d4yYcIErFixAgBQUlKCXr162Uz/bPG+2SovLw91dXWYO3cuZs+ejWPHjhnUPybIWhQKBVxcXDQ/SyQSNDc3m7FFhpHJZHBxcYFCocBf/vIXPPPMMxAEASKRSHNeX3kma/DFF1/Ay8tLc0MFYBN9q6qqwqlTp7Bu3Tq89tpreP75522iXwDg7OyM4uJiTJ48GcuXL0dSUpLV923ixIk6Beb19aetUmmky9biL9C5z4c1suV7SyupVIrU1FSsWLECEydOtIn+2ep9s5WjoyOeeOIJfPjhh0a5fzJB1vL7kkZqtdosO1sZU2lpKWbPno1p06Zh6tSpOnNv2irPZA127NiBH3/8EUlJSTh9+jRSU1N1tru11r55eHhg7NixsLe3R0hICBwcHHT+QVtrvwDg448/xtixY7Fr1y589dVXWLp0KZqamjTnrblvrfT9+2qrVBrpssX4+3u2En8B2723aFu9ejV27dqF5cuXo6GhQXPcWvtnq/fNVsHBwXjooYcgEokQHBwMDw8PVFRUaM53tX9MkLVER0cjOzsbAHDs2DGEhYWZuUWGuXbtGubOnYuUlBTMnDkTADB48GDNpgDZ2dlWW6f0n//8Jz755BNs2bIFgwYNwurVqxEbG2v1fRs+fDj27dsHQRBQVlaGuro63HXXXVbfLwBwc3PTJIbu7u5obm62mc9jK339aatUGumytfirj6183m353gIAX375Jd577z0AgJOTE0QiESIjI62+f7Z632z1+eef44033gAAlJWVQaFQYMyYMd3uH6tYaGldRX327FkIgoCVK1eif//+5m5Wt6Wnp+Pbb79FSEiI5thLL72E9PR0NDU1ISQkBOnp6Va/y1VSUhJeffVViMViLF++3Or7tmbNGvz000+aslpyudwm+lVbW4tly5ahvLwcTU1NmD17NiIjI62+b0VFRViyZAm2bduG/Px8vf3Ztm0bPvvsMwiCgAULFmDixInmbrbFsbX426oznw9rY+v3FqVSiRdffBHXrl1Dc3Mz5s2bh/79+9vE366Vrd03AaCxsREvvvgiSkpKIBKJ8Pzzz8PT07Pb/WOCTERERESkhVMsiIiIiIi0MEEmIiIiItLCBJmIiIiISAsTZCIiIiIiLUyQiYiIiIi0MEEm0lJUVIRhw4aZuxlERLcVxl6yNEyQiYiIiIi02NY+nkQ9JD8/H6+//jpqa2tRXl6O8PBwvPXWW3BwcEBWVhbWrl0LsViMQYMG4ccff8Snn34KuVxu7mYTEVk1xl4yF44gE3XCtm3b8PDDD2Pbtm347rvvUFRUhL1796KqqgovvPAC3nzzTXz11VcYPXo0ysrKzN1cIiKbwNhL5sIEmagTUlJS4OXlhffffx+vvvoqrl69CqVSicOHD6N///4IDw8HAEyfPh0uLi5mbi0RkW1g7CVz4RQLok5YsmQJVCoVJk+ejPHjx6O0tBSCIEAikeD3u7WLxfzeSURkDIy9ZC78NBF1wv79+7Fo0SI88MADAIDjx49DpVIhOjoaly5dQl5eHgBg165dqK6uhkgkMmdziYhsAmMvmQtHkIl+R6lU3lJu6JlnnsGiRYvg7OwMFxcXjBw5EpcvX4aHhwf+9re/ITU1FWKxGJGRkZBKpXBycjJT64mIrBNjL1kSkfD7ZxRE1GkKhQIbNmxAcnIynJyckJubiwULFmDfvn0cySAi6iGMvdTTOIJMZAAXFxfY2dlh5syZkEqlkEqleOuttxigiYh6EGMv9TSOIBMRERERaeEiPSIiIiIiLUyQiYiIiIi0MEEmIiIiItLCBJmIiIiISAsTZCIiIiIiLf8HEleVy9C/HPwAAAAASUVORK5CYII=\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -2862,753 +235,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 9
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -3644,788 +287,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -4459,15 +337,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "rrse:  0.001993603325328823\n",
-      "\n",
-      "       Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9000  0.95556574\n",
-      "1         y(k-1)     0.1999  0.04107943\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+      "rrse:  0.0019476338038772528\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95550052\n1         y(k-1)     0.2000  0.04133385\n2  x1(k-1)y(k-1)     0.0998  0.00316200\n"
      ]
     }
    ],
@@ -4507,6 +380,7 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "array([[   0,    0],\n",
@@ -4526,9 +400,8 @@
        "       [2002, 2002]])"
       ]
      },
-     "execution_count": 12,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 12
     }
    ],
    "source": [
@@ -4542,17 +415,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "[0.95556574 0.04107943 0.00335113 0.         0.         0.\n",
-      " 0.         0.         0.         0.         0.         0.\n",
-      " 0.         0.         0.        ] \n",
-      "\n",
-      "\n",
-      "[[0.9000403 ]\n",
-      " [0.19990693]\n",
-      " [0.10000669]]\n"
+      "[0.95550052 0.04133385 0.003162   0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.89995742]\n [0.1999992 ]\n [0.09979902]]\n"
      ]
     }
    ],
@@ -4571,9 +437,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -4585,9 +455,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/examples/defining_lags.html b/docs/examples/defining_lags.html
index 3eaa9492..97fe26e9 100644
--- a/docs/examples/defining_lags.html
+++ b/docs/examples/defining_lags.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Setting specific lags &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,23 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
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     <link rel="canonical" href="http://sysidentpy.org/examples/defining_lags.html" />
@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -327,6 +326,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -416,15 +416,15 @@ <h2>Setting specific lags using lists<a class="headerlink" href="#setting-specif
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="extended_least_squares.html" title="previous page">Important notes and examples of how to use Extended Least Squares</a>
     <a class='right-next' id="next-link" href="parameter_estimation.html" title="next page">Parameter Estimation</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -443,7 +443,7 @@ <h2>Setting specific lags using lists<a class="headerlink" href="#setting-specif
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/defining_lags.ipynb b/docs/examples/defining_lags.ipynb
index fbd93ca8..1ef1c41e 100644
--- a/docs/examples/defining_lags.ipynb
+++ b/docs/examples/defining_lags.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,12 +37,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "array([[   0],\n",
@@ -56,9 +57,8 @@
        "       [2004]])"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 2
     }
    ],
    "source": [
@@ -83,10 +83,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "array([[   0],\n",
@@ -96,9 +97,8 @@
        "       [2004]])"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
@@ -113,20 +113,17 @@
     "\n",
     "model.regressor_code"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -138,9 +135,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/examples/extended_least_squares.html b/docs/examples/extended_least_squares.html
index 5cab4578..cc5b8864 100644
--- a/docs/examples/extended_least_squares.html
+++ b/docs/examples/extended_least_squares.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Important notes and examples of how to use Extended Least Squares &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,23 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
     <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
     <link rel="canonical" href="http://sysidentpy.org/examples/extended_least_squares.html" />
@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -342,6 +341,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -526,15 +526,15 @@ <h2>Note<a class="headerlink" href="#note" title="Permalink to this headline">¶
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="information_criteria_examples.html" title="previous page">Information Criteria - Examples</a>
     <a class='right-next' id="next-link" href="defining_lags.html" title="next page">Setting specific lags</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -553,7 +553,7 @@ <h2>Note<a class="headerlink" href="#note" title="Permalink to this headline">¶
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/extended_least_squares.ipynb b/docs/examples/extended_least_squares.ipynb
index 1db62136..a972be10 100644
--- a/docs/examples/extended_least_squares.ipynb
+++ b/docs/examples/extended_least_squares.ipynb
@@ -91,10 +91,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.5626926249339153\n"
+      "0.36911570958830753\n"
      ]
     }
    ],
@@ -118,13 +118,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9094  0.74535677\n",
-      "1         y(k-1)     0.2794  0.07821222\n",
-      "2  x1(k-1)y(k-1)     0.1188  0.00443378\n"
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9158  0.73478263\n1         y(k-1)     0.2901  0.07894655\n2  x1(k-1)y(k-1)     0.1367  0.00637564\n"
      ]
     }
    ],
@@ -155,488 +152,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -680,659 +202,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -1384,9 +260,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -1398,9 +278,9 @@
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    "nbconvert_exporter": "python",
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+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/examples/f_16_benchmark.html b/docs/examples/f_16_benchmark.html
index 2d5a2770..193cf56c 100644
--- a/docs/examples/f_16_benchmark.html
+++ b/docs/examples/f_16_benchmark.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Example: F-16 Ground Vibration Test benchmark &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
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-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
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+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/examples/f_16_benchmark.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -244,21 +241,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +273,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +292,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -347,6 +344,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -395,7 +393,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;datasets/f-16.txt&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">,</span> <span class="s1">&#39;x2&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">])</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;../../../examples/datasets/f-16.txt&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">,</span> <span class="s1">&#39;x2&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -422,11 +420,10 @@ <h2>Visualizating the data<a class="headerlink" href="#visualizating-the-data" t
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fe6b0443210&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_6_1.png" src="../_images/f_16_benchmark_6_1.png" />
-</div>
+<img alt="../_images/f_16_benchmark_6_1.svg" src="../_images/f_16_benchmark_6_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -435,11 +432,10 @@ <h2>Visualizating the data<a class="headerlink" href="#visualizating-the-data" t
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fe697898650&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_7_1.png" src="../_images/f_16_benchmark_7_1.png" />
-</div>
+<img alt="../_images/f_16_benchmark_7_1.svg" src="../_images/f_16_benchmark_7_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="spliting-the-data">
@@ -497,7 +493,7 @@ <h2>Model training and evalutation<a class="headerlink" href="#model-training-an
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.3090560911603647
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.3090559299602927
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -557,7 +553,7 @@ <h2>Model training and evalutation<a class="headerlink" href="#model-training-an
     <tr>
       <th>5</th>
       <td>x2(k-7)</td>
-      <td>-2.2384E+01</td>
+      <td>-2.2385E+01</td>
       <td>3.76837313E-04</td>
     </tr>
     <tr>
@@ -581,7 +577,7 @@ <h2>Model training and evalutation<a class="headerlink" href="#model-training-an
     <tr>
       <th>9</th>
       <td>x2(k-1)</td>
-      <td>8.5726E+01</td>
+      <td>8.5725E+01</td>
       <td>4.85358786E-04</td>
     </tr>
     <tr>
@@ -716,8 +712,7 @@ <h2>Model training and evalutation<a class="headerlink" href="#model-training-an
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/f_16_benchmark_14_0.png" src="../_images/f_16_benchmark_14_0.png" />
-</div>
+<img alt="../_images/f_16_benchmark_14_0.svg" src="../_images/f_16_benchmark_14_0.svg" /></div>
 </div>
 </div>
 <div class="section" id="information-criteria-plot">
@@ -737,8 +732,7 @@ <h2>Information Criteria plot<a class="headerlink" href="#information-criteria-p
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_16_1.png" src="../_images/f_16_benchmark_16_1.png" />
-</div>
+<img alt="../_images/f_16_benchmark_16_1.svg" src="../_images/f_16_benchmark_16_1.svg" /></div>
 </div>
 </div>
 </div>
@@ -746,15 +740,15 @@ <h2>Information Criteria plot<a class="headerlink" href="#information-criteria-p
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="parameter_estimation.html" title="previous page">Parameter Estimation</a>
     <a class='right-next' id="next-link" href="narx_neural_network.html" title="next page">Building NARX Neural Network using Sysidentpy</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -773,7 +767,7 @@ <h2>Information Criteria plot<a class="headerlink" href="#information-criteria-p
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/f_16_benchmark.ipynb b/docs/examples/f_16_benchmark.ipynb
index 7ee0632d..d37ff59f 100644
--- a/docs/examples/f_16_benchmark.ipynb
+++ b/docs/examples/f_16_benchmark.ipynb
@@ -51,7 +51,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_16 = pd.read_csv(r'datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+    "f_16 = pd.read_csv(r'../../examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
    ]
   },
   {
@@ -60,14 +60,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "(32768, 3)"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
@@ -80,26 +80,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6b0443210>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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xZj6Ia4Za8Udv3Aa29pYiUb+oEcvBef3WQxAbiZmjgMkGdO6+8rnOnVRZJmoCqiwTyvFPyrdgSLQMC5HNuS5Lqjafe2wUX3t2AqFYCv/82Cj+8ZcXqr0kQisksSzFwpWCKssEIZ+ZI8Cmg/kHXHXtFgOtMpnKr4sg1kBimVBGJgMEZkV4vBJahoBEaEOmBFxYDOFzj43iHQc24eH/ehPec6gP9z86ihMz/movjdCCley/Y4NT3v4klglCHukUsHAS6D2Q//nOnUAyCvgnKrosglgPiWVCGRE3kEkCrj5lr2vOiuvAlPZrqjIPPjsBs9GAT75lFxhj+PO37IKjwYR/eYo82huCWACwNJUe7S5hdQGpFSAV13ddBFHv+CZE4kVXHgsGAHTuEtulsxVbEkHkg8QyoYxgdrCIc5Oy10mV6A02mCSZzuDhk/N4/a4utDRaAAAOqxnvvXYAD5+cx4yPMkLrHrnT+ySkiLlYUJ/1EMRGwZ0VwR3b8z8vPU6+ZaLKkFgmlBGYFVsXiWUAeHp0Gb5oEnfu673s8Q/eMATOOb51ZLpKKyM0I+aXn7EMrAprsmIQRHHc58S2vYBYbnCI7w439YAQ1YXEMqGMYFYsOxXaMOytgNkO+DeWePzx8Tk4rSb82rb2yx7vbbbhmuFWPHJ6oUorIzRDcWWZxDJByGLpHOAaABqaCu/TPAgENtb3BlF/kFgmlBGYAUxWIX6VwJjwOW+gDz3OOZ4ZXcat2zvRYDJe8fztu7txYTGMS+5wFVZHaEbMr1IsU4MnQRTFfb6wBUOieUAMLiGIKkJimVBGYEb4ldVkCG8wsTwXiGExGMfVg/kjxd64uxsAqLpc71BlmSC0J5MGli8AnTuK79c8AATngFSiMusiiDyQWCaUEZxVnoQh4erfUJ7llyd9AFBQLPc227Cvz4VHTi9WclmE1sQCgE2FZzlODX4EURApCaNDhlgGX20uJ4gqQGK5Rjk64cXfPXIO//jLC4gmUtVeziqBMsVyxA0kV7RdU5V4adIHm9mIHd2Fx36/ZkcnTsz44Y9SVaQuyWREqoXcjGWAKssEIQf3ebGVJZZBVgyiqpBYrkF+emIed33peTzwxBjuf/Qi3vWF57AUjFV7WUA6CYQXlMfGSeSylme1W1MVeWXKh339LpiMhd9GN29tB+fAs5c8FVwZoRnJCAAOWBWIZbMdMJhILBNEMbxjYts6Unw/EstEDUBiuca4uBjCf33oFRwYaMaxT74BX/3gYYwvR/Cx754Ar/ao6NA8wDPKY+MkpIr0BvAtx5JpnJ4L4uBA8RHI+/qa4Wgw4amL7gqtjNCUeEhsGwrfPbgCxmiKH0GUwjsmIhlLNYs7NwHMSGKZqCoklmuMf/rVRVhNBnzxA4fgsJrxmu2d+OPbt+OJC258/+UqV2SD82KrtrK8gcTyxcUwUhmOvX3FG79MRgNu2NKGJy8sV/9ih1COGrEMkFgmiFJ4x0pXlQExOdPZS2KZqCoklmuIcwtB/PTEPD504zBas9PgAOA3rx/CgYFm/O9HziGeSldvgaGsWHb0qHu9Izu4IzinzXqqyIVFIaK2dpUWUTdt7cCsfwUTHprmV3dIYtlCYpkgNEWuWAYoPo6oOiSWa4h/fXoCNrMRH755+LLHDQaG//b6bVgMxvHDY1UUmqFsBJpasWyyAI0dG0MsL4VgMRow2Govue/1I20AgOfHyLdcd0iJFlRZJgjtSCXEHUYSy0SdQGK5RgjHU/jxiTm8dV8Pmu2WK57/ta3t2NnjxBefuIRMpkq380PzgMGsfCDJWhw9G0IsX1wMY6SjsWhzn8Tmjka0NzXgBRLL9QfZMAhCe/xTov+ldbj0voCw8IXmRTYzQVQBEss1wk9PzCGaSOM3Dg/kfZ4xhg/fNIxL7ghenvJVeHVZQgtC7KoZSCLh3LRq56hjLiyGsE2GBQMQ/3bXjrTi+TEv+ZbrDRLLBKE9cpMwJJq6hLiOUKM0UR1ILNcI3zk6gy2dTTg4UHj4wRt3d8FiNOBnp6o0ES40Dzi6yzuGs/4ry5F4CjO+FWzrapL9mutG2rAQjGHKS77lukKtWG5wklgmiEL4xsVWrliWvndCNA2VqA4klmuAxWAMRyd9ePv+XrAiVVuH1Yxf29aOn52cr06FMrSggVjuBVa8dT2YZHQpDEBec5/EdcPCukK+5TqjHLGcjNJtY4LIh3cMsDSJHhY5SH0yYZqGSlQHEss1wC9Oi6vl2/eUFqJ37OnBXCCG4zNVqFpJNoxykBIx6tiKISVhyLVhAMCWzia02M04OlElCw2hjngQMNkAo1nZ6xqydx0ksU0QxCq+CaBlSL6lr6lLbOv4e4Oob0gs1wA/P72AzR2N2NJZWny9fmcXjAaGX52t8BV2IgLEA9rYMIC6tmJMeCIwGRj6W2yyX8MYw4GBlur5zQl1xMPKq8rA6mtILBPElfinAVe//P1zYlm7771MhuPIhBexJN39IUpDYrnK+KMJPD/mlVVVBgCX3Yy9fS48M7qs88rWUW5snIQ00CRYvxWCSU8UfS02WUkYazk40IxL7gj80YROKyPW8/KUDx9+8ChOzaq8ExMPkVgmCK0JTAPNCsSyyQLY24CwNp7lGV8Ub//8M3j3A8/hMz89o8kxiY0NieUq88yoB+kMx2t3dMl+zY2b23F8JoBQLKnjytaRE8tlVpYlsR2q38rytDeKfhn5yuuRRmO/Mu3XeklEHh49t4h3fuFZ/OfZRXz16XF1BylXLCfC6s5LEBuVFb+wNympLANAU7dmDX4PPHEJ5xdCuHlrO775whTOzAU1OS6xcSGxXGWeHnXDYTVhX4mxyWu5YUsb0hmOF8e9Oq5sHeVO75OwOsU0tDq2YUx6oxhQIZb39TfDwIBXJsmKUQl+emIBLXYL3rK3B784s6judqtqsezMvp6+hAniMgLTYquksgyIQo0GYjmVzuDhkwt4w64u/PN7D8JlM+PvHjlX9nGJjQ2J5SrCOcdTF5dx/Uibolv6Bwda0GAy4JnRCiYraFVZBuo6Pi6wkoQ/mlQllhsbTNje7cTLU1RZrgQvTnhwzVAr3n2oH+F4Ck9eUJHRqlYsW6jBjyDy4s+KZVf+mQIF0UgsP3vJA28kgbfu64XLbsa7ru7DM6MeROKpso9NbFxILFeRSU8UM74V3Ly1XdHrrGYjDg21VDaGLDQPmKxi2EK5OHrqtqt5OpuTPNimXCwDwrd8bNqPdLWmML5KmA+sYNq7gsPDrbhhcxua7WY8fFLF71w8SJ5lgtCScirLkSUgkynr9D8+PgdHgwm3bBOxdbdu70QincFzlyjWkygMieUq8nS2Se/GLcrEMgBcPdiKcwvByl0NR5aBxs7ypvdJODfVbWVZGiqixrMMAFcPtiAcT+HiEokoPZEsStcOt8JsNODa4VacUNPkRw1+BKEt/ilReJGbsSzR1A1kUkC0PFH7wrgXN21th9VsBAAcGmqB3WLE4xeWyjousbEhsVxFXhj3ottpxXB7o+LXHhhoRoYDx2cqdEs/4gYalYv6vDh7xO20OhzYUK5Ylpr8Xp4kK4aevDjuRVODCTt7hHd4pKMJU54okmkFVSnONRDL1OBHEJcRmAZcfcoLL7kpfurvSoZiSUx5o9jd68w91mAy4obN7XjsnLs6w76IuoDEcpXgnOPIuBeHh1uLTu0rxMH+bLJCpfyvkSXllYBCOHsBngbC9XclP+mJosVuhtOqcEhFlsE2O1obLZS3rDPHZ/w4MNAMo0G8t0baG5HKcMz4FEyOTMWBTFKdWDYYAXMjNfgRxHr8U8qTMIBVsVzGFL/zC+JOj3QRLXHLtnbM+oV1iyDyQWK5Ssz4VrAQjOHwUIuq17vsZmzuaMTLlUpWiCwDTRqJ5dwUv/qzYkx7oxhoU34nQIIxhoMDzSSWdYRzjsnl6GV3bEY6xP+PLyuo9OZGXTuL71eIBgfZMAhiPX6FGcsS0p3NiIpG3Sxn58XF63qxvD9bfDqpNo+d2PCQWK4Skqfy8FCr6mMcHGjBK9N+/W8dcZ61YWhVWZam+NVfk9+sfwV9zfIn9+XjwEALxmg4iW74okmE4ikMrrmoGW4X6RRj7oj8A0lVYTWVZUCMvCaxTBCrJKJAdFl5EgYA2CWxrH4g15n5IFw2M3pc1sse39bdBLOR4dQciWUiPySWq8SRCS+cVhO2d6n8IgZwcLAF3kgi56PVjZhfNFZoJpalKX71VVnmnGPOv3LFB61ScsNJKEJOFyY9QhAPrvGVtzZa0Gw3Y2xZiViWKstqxTJVlgniMgIzYtusQiw3OABjgxDbKjkzH8LOHscV1scGkxHbuhzqJ30SGx4Sy1Xi6KQPVw+2wGBQny6xK3srSbq1pBvh7G0vrcSyvR0wmOvOhuGLJhFPZdBTZmV5X78LRgMjK4ZOTBWI9xtub8S4osoyiWWC0JTAlNiqsWEwJqwYKivL6QzH+YXgFRYMiT29LpyaDVCTH5EXEstVIBxP4ZI7nPNJqWV7twMGJq6WdSWisVg2GETWcp1VlucDovmj3Mqy3WLCjm4HiWWdmPTkTywZaW/CeEUry04ad00Qa8kNJFEhlgHA3qZaLM/4ooglM9jRnf/9vGeTE75oEnOBmLq1ERsaEstV4ORMAJwDe/vLG/BhNRsx3N6of2VZa7EM1OUUv3m/+BAtVywDwopxbIqGk+jBhCeCHpc1l6MqMdLRiIVgTH42uSR0pWl8SmlwUBoGQawlMA0woyiWqKGxQ3WDn5R0MdCav0F7zybxfUxWDCIfmohlxtjtjLHzjLFRxtjHi+x3mDGWZoy9S4vz1isnstnI+/qayz7Wrl5XfYrlOpziJ1WWe8u0YQDAwcFmRBJpXFik2/RaM+WJ5h1H3tci/t0WgjIrR1JlWa1YtlCDH0Fchn9a9KwYTepe39iu2rM87ZPuOOX//N7Z4wRjFbA1EnVJ2WKZMWYE8DkAdwDYBeC9jLFdBfb7WwCPlHvOeufEbAB9LTa0NlrKPtbOHgdmfCsIxpIarKwAETcAJm6BaYU0xa+O/GFzgRhMBob2poayj5UbTkJWDM2Z9EbzjiPvyP67uUNxeQeSKssN5VSWQ6p+x//t+Um89h8ez12gEcSGIKAyNk6isQOIqJvgN+2NwmRg6HHlF8tWsxGbmm3KEnOIVw1aVJavATDKOR/jnCcAPATgbXn2+wMA3wNQf5MoNObEjB97+8qzYEhIzQrn9PQtR9yAvVV9NSAfzh4gGQVi9XPLayEQQ5fTmht0UQ4DrXa0NVpokp/GRBMpuEPxy2LjJDocQiwvyRbL2S9Ns8pc7QaHSJFJKfNAji6F8b9+cgZj7gj+8KFjZNUhNg7+afV+ZUAUbJIREUGnkGnfCnqbbUU/vzd3NGFMSRY78apBC7G8CcD0mp9nso/lYIxtAvAOAA9ocL66xhtJYNq7gr0aWDCACiViaJmxLCF51urIijHnX0Fvc/l+ZUAMJzkw0IJXqLKsKXN+UYmVLBdrkcSy7MpyPCyEskHlx2Ru5LWyC9m//PFp2MxG/MkdO/DiuBc/PDar7vwEUUukkyIBqazKcjZrWYUVY9obLWjBkBjpaMSYO0KJGMQVaCGW812mrf9N+ycA93HO0yUPxti9jLGjjLGjbrf6ST21iuRX1qqy3OloQGujRWexvKy9WHZmp/gF60cIzAdi6C5wC08NBwebMbYcgS9Cw0m0YjEohHCX88qLGpfNDLORKbNhqLVgAKuT/xSI5VgyjRfGvHjPoT7c+2sjaGu04OmL6nNlCaJmCM4CPFNeZVn6HlLR5Dfji6K/5Up71lpGOpoQTaTl9zUQrxq0EMszANb+9vcBWB9zcAjAQ4yxCQDvAvB5xtjb8x2Mc/4lzvkhzvmhjg6NBVoNcHImAMaAqzZpI5YZY9jZ49BXLIeXVq/otcJRX1P8MhmOhUAMvRokYUjkhpNMU3VZK5ZC4ksun1hmjKGjqUGZWFbb3AesCm0FYvnkbACJdAaHh1rBGMN1I214bsxDlS6i/pFi48qpLOem+CnzLUcTKSyHE1fESa5nc4ewXF1aIt8ycTlaiOUjALYyxoYZYxYAdwH40dodOOfDnPMhzvkQgO8C+D3O+X9ocO6649SYbSMAACAASURBVPhMACPtjXBYzZodc2e3E+cXQ/p5GyPLQGOntsesMxuGN5pAIp3RJDZOYm9fdjgJ+ZY1Q6osdzryN2F2OK05QV2SRASwqPQrA6psGEcmvACAqwfFhdR1m9swH4jlsqMJom4JSBnLKqb3STRmm8wV2jBmfIXtWWvZ3CEucMm3TKynbLHMOU8B+ChEysVZAN/mnJ9mjH2EMfaRco+/0RDNfdr4lSV29jgRS2aUDVyQSyoOxAPa2zDMVsDWWjdZywvZoPpuDcWy3WLCzp4qDifZgNXKxWAMTQ0mNDbkb0ZVVFmOh9UPJAFUieWXJnwY6WhEWza54/oRIQ6eG1OXAEAQNUNuIEmf+mOotGFMe/MPKlpPp6MBTQ0mXFoisUxcjiY5y5zzhznn2zjnmznnf5V97AHO+RUNfZzzD3LOv6vFeeuNhUAMS6G4Zn5liZ16NvlJ05K0tmEAwrdcJ5VlSWB1OLQTy4CwYhyfrsJwkmQM+Ort4r/wxgmoWQrG0eksHO3X4WjAcliuDSNUZmVZmWc5k+E4OunD4cHW3GObOxrR4WjA8ySWiXonMCXuUJrL+Ay1NAHGBsVT/KTKcinPMmNMNPnpUXh6FRKMJfHlp8bw4QeP4AuPX4JH7mdvDUIT/CrIanOftpXlLZ1NMBuZTmI5K6SaNLZhAHU18nrVC1t+xvJaDg60IJJI4/xChYdX/OyPgenngblXgK+8UYjnDcBSKIauIhc0HY4GeCIJpNKZ0gdLRMr0LGcrywl5/7YTnggCK8mcBQMQX977+ioweIgg9MY/DTSXYcEAAMZE4UahWF4IxmA2MrQ3lZ5tMNTWiAkPieVyicRTeP+XX8BnfnoWZ+dD+Nufn8Ob7n8KJ2fqJy52LSSWK8iJmQCMBobdvU5Nj2sxGbC5o0nnyrIOzZZ1VFmWvLAdBbywaqnKcJKFU8DLDwI3/iHwzi8DvnHg0qOVO7+OLMqoLHMOeOQkkMTLTMOwKGvwk76gN3defs7NnU0YX47IE/gEUauUO5BEwt4KrHgVvWQxGEOnwwrGSmfk97faMOeP0futTP77t47h1GwAX/zA1Xjm46/FT/7gJpgMBvzGl57D6FL9TTYlsVxBjs/4sa3LAavZqPmxd3Q7cGFRB59VbtS1TjaMiBtI1X502lIohma7GQ0mbf/t+lttaG+yVFYsn39YbK/7fWDb7YC1GThT//22nHMsBmN5kzAkOpVkLZdbWTbbAGaULZalJr710we3dDQhmeaY8lKTH1GnZDJAYKa82DgJWysQVSaWS9mz1jLQakc6wzEf2Bh326rBkxfc+MWZRfzx7Ttw2+5uAMCeTS5873dvgM1sxO/9+8uIJlJVXqUySCxXCM45Ts4GsE9jv7LE5o4mzPpXtP8FzIllHSrLUiJGeEH7Y2vMUjBe9Pa+WlaHk1QwEePCz4FNhwBHF2A0AzveApz/mWjmrGOCKynEU5mCSRiAgsEknJcfHcfY6shrGUx6omi0GNHWePmt4i3ZSvMoNR0R9UpkCUgnyrdhAKory3I/vyVf8zRdnKoik+H47M/Ooa/Fhg/dOHTZc90uK/7prv24sBjG5x4brc4CVUJiuUJMe1fgjyY19ytLSF+oms+1Dy8BJlt5oqEQucEktW/FWArJr0wo5eBAC8aXI/BWYjhJaBGYfQnYfvvqY7vfDsSDwNjj+p9fR4plLEt0NMkUy4kIAF6eDQMQTX6yxXIEg22NV9wqlmwZo24Sy0SdkkvCqE5lWdxxkvf5LSVmTPtILKvhqdFlnJkP4n+8YVveO7E3b+3Anft68ZWnx3MpU/UAieUKcVzjyX3rkb5QL2n9hSpN75Ph9VJMLmu59pv8loIxzf3KEgcHxAVURUZfX/yF2G67Y/WxoZsBgwmYfkH/8+tIqYxlAGjPiuXliByxjPLSMABllWVv9AoLBgA4rWZ0ORuoskzUL/5JsdXCs2xrAWJ+Ye2QQSyZRjCWQmeRi+i19LisMBoY2Z5U8sNjs3BYTXjz3p6C+3zstu3IZIB//OWFCq6sPEgsV4gTM35YTAZs7y4jt7UIg212GBi0z4eMuPXxKwN1U1nmnMMdjhetWJbD3r5mmAysMr7lmRfFl03X7tXHzFagYycwf1z/8+vIYrB0ZdlqNsBiMiAQTRY/WCL7PrKU+X5taJIlltMZjhnvCgbyiGVA3Dmi7FeibgloWFm2t4qx2XF5qQpLMi6i12IyGtDbbMW0d0X1El+trCTSeOTUAt60p6dof09/qx3vu3YA33t5pm7sLiSWK8TxmQB29ThhNurzV95gMmKg1Y5LWtswIm59/MqAEG3GhpqvLPuiSSTTXPaHrVJsFiN29jgrM8lv/gTQs+/KOwU9+4C5Y3U9qGQpa60oZpdhjKHZZoZftliuTGV5IRhDIp3BYGv+823paMIld4TGXhP1iX8asLoAqwZJULZsDrlMK8aiDHvWegZa7WTDUMGvzi0ikkjjbQd6S+77O7eMwMAYvvDEpQqsrHxILFeAdIbjlI7NfRKbO5p0sGG4gSadxDJjgLOn5ivLUsWyU4cGP4mDA804PuPXN64onQSWzgDde698rmefGCFbJ1F++fCE47CZjbBb8k/vk2ixW+BfKeEPj2ffR2V7luWJ5cnsEIShApXlzZ1NCMdT8qcPEkQtEZgub8z1WuxZsbwi706cnDtO6+lvsddNxbMkkWXgzA+BTFr3U/3i9CLamyy4drit5L49LhvefagP3zk6jaVg7XuXSSxXgItLIUQTaewf0Ke5T2JzZxPGliPaTYPjXN/KMgA4aj9rWU7FslwODrYgmkjj/KKO+ZPuc6IjvWfflc9Jj9WxFcMbSaC1sfTQAZddSWW5XBuGTLGc/WIuZMPIdehTtYuoR/xTQMugNsdSWFmWbBhKBkr1t9qxHE7UXbzZFVx6FPjctcC37wEeunu1CKADmQzHM6PLuGlLO4wGeT1O/+XmEaQyHP/2/KRu69IKEssV4Pi0uL2+v7+lxJ7lsaWjCYlUBrM+jbxWMT+QSekrlp21P8VPuurVIzpOYnU4iY5WjPkTYpuvsty9BwCra7HsiSTQJmNCV7PNjMBKpWwY8tIwZn0rMBoYely2vM/3t4rHqemIqDs4FzYMLfzKwJrKsnwbhsVkgMtmln2KXCJGPfuWU3HgB78L2NuAW/8UuPgI8Pjf6Ha6cwsheCIJ3LRVvl4Yam/E63Z04d9emEIsqX/luxxILFeAY9N+OK2mgrdYtWJzp/hiH3VrVJ3MTe/TYdS1hKNHVJZr2IspVZb1SsMAgL4WG9qbGvDKpI5NfvPHAbMdaNt85XOWRqB9W12LZbmV5Wa7Gb5ohWwYliYgGSl5C9QdiqO9yVKwItPXsgG+vIlXJ1GveA9okbEMiF4X6bgyWArG0elokDW9T6K/RVyc1rUV48S3xQyDOz4L3HofsOvtwMvf0K26/PSomMlw0xZlgQC/ddMQvJEEfnhsVo9laQaJ5QpwbDqAff3Nit6sahhpz8bHLWnU5BdeElu90jAAkYiRisn2n1WD5XAcTQ0m2CzaT16UYIzh4ECzvokYCyeBrj2AocCfo2M7sHxRv/PrjDeSQFtj6QuaFrtFhg1Dio7TwLMMrFaqC7AUKh5NaDUb0eFoqO8vb+LVSS42TiOxbG0GmEF+ZbnEVM98DGQry3V7JyeTAZ79v0D3VcDIa8Rj135EJIiceEiXUz51cRlbOpvQ7VL2d339SBt29jjx1acnarqBmcSyzkQTKZxfCOJAv75+ZQBoabSgrdGiXZOfntP7JHJZy7XrW14OJ9Au4/Z+uRwcbMGEJwpPWKcmruXzQOfOws+3jgC+iYo0gmgN5xzL4bgsG4bLbkY8lSl+2y9nw9BILJewYrjD8ZINpP0tNvIsE/WHf0pstRLLBoMQzDIry+5QPDeMSC6tjRbYLcb6fb8tnhSf99f8zmryUf81QM9+4KUHNT9dOsPx0qQPN2wu3di3HsYYfuvGIZxfDOHZSx7N16YVJJZ15tRsEBkO7KuAWAY0TsSohFjOZS3Xrm/ZHYrlhlnoydWD4vaiLqOvo14g6gHathTep3UEyCSBwIz259eZaCKNeCojz4ZhE/sUrS7HQ4DJChiLJ2uURK5YlvGF3t9qJxsGUX9IGctaiWVAWDFk3o30RhJoVVjsYIxlEzHq9P02+iux3frG1ccYA3a+FVg4sXrXWCOkEIMDKkMM3rqvF+1NFnzl6XFN16UlJJZ1Rmruq5hY7mzULms54gbARIOAXkiV5RoWy6KyrL9YvmqTS7/hJN4xsS0mliUvs7c+ci/XIo0Kl+tZBlA8Pi4RKb+5DxANfkBRsZzJcCyHEyU98f0tdswHVpDUM16QILTGPwU0uACbht+B9lZZNoxMhsMXTaBNxufCesTFaZ1Wli89CnRdBTi6Ln98c9aSMfaEpqfL6Zw+df/GVrMRd187iEfPLWFM6/hbjSCxrDPHpv255q1KsLmjCd5IIiceyiLiFh9K5VbXilEXNoy4rs19ElazEbt7nfqIZU9WAJeqLAOrwrqO8GR/3+V8KTZnu+J9kSKV5US4fAsGsNogGA8W3MUXTSCd4aXFcqsNGQ7M+2s/k5QgcvintBlzvRZbqywbhn8liQyXdxG9nv5WYXuqZR9tXuJhYOr5VWG8lp79wsIy9rimp1wNMVBfYHj/dYOwGA342rMT2i1MQ0gs68yxaX/FqsqAyFoGoI0VQ++MZQAwWQB7e81WlpPpDPzRZMUudg4MtODETEC7rGwJz6hoimkZKrxPUzdgsgHe2r0VVghvRPi85VWWxT6BkpVlLcSyZMMo/H6Um7aSi7OqVx8l8erEP6WtBQPIVpZLFxWk/o82FZ/fA612RBPp3IV43TD5jLDTbXndlc8ZjMDILcDYY5omUEkhBgaZ+cr56HA04M79vfjO0RkESjVgVwESyzriDsUx61/BfpW3JtSwpUNKxNBCLC/rL5YBkbVco5VlT1h8ULY79G/wA4QVI5pIaz+J0TMqvrBMRf4cBgPQOlyXleXlsFRZLv2lmLNhFPtAToTLj40DZHmWpal8pcap5waT1OutYeLVh5SxrLVYlllZVnLHaT11+36beh4wmID+6/I/P3IrEJzV7HM+mkjhwmII+zUoCn7oxiGsJNN46MiUBivTFhLLOpIbRqLz5L619DbbYDEZMLasgW85vFQhsbypZivLy9nKRKUqy/v6xUj0EzMBbQ/svVTcgiHROlKXYlmyHckaSpLzLBcTy1GRSV0uCsRyqcpyl9MKxoD5ANkwiDphxQckQjqI5RaR3ZwuXoFU0suwntU7OXXW5Dd/DOjcBZgLpOv0HRbbuVc0Od2ZuSDSGa7ar7yW3b0uXDfSigefnUCqxnozSCzryCvTPhgNDLt7nRU7p9HAMNBqx4QWYrlileVNNZvAIAmZSonlkfYmNFqMODGjYSIG58Kz3JpnGMl6WkeEDSNTWx9UpfBGEmgwGWCXkYVtMxthMRpKVJY1avCzyBDLMi/ILCYD2psasEBimagXtI6Nk5CaBVeKf06WVVlurcPBJJwLEdy7v/A+HTsAY4NmYvnsgvhs26WRzvntm0YwF4jhkdOLmhxPK0gs68gLY17s2eSC3aJjg1wehtoaMekp8w2eiosA80qIZVefGK2d0CjFQ0MkIaM0p1MtBgPDnk0ubSvL4UVhK5BTWW4ZAtLxmrXFFMITFh3vcgb/MMbQbDfDX2yKn1YNfkaT8IEXafBzh+JotBjR2FD6c6LHZcV8kMQyUSfoJZat4g4cYsXFsjdrz2pRIZbtFhPaGi2YqaceAf+UqOb3Hii8j9EMdO3WbFrrufkgnFYTehQOIynEa3d0YrDNjq88XVt3OEks60Q0kcLxGT+uG2mt+LmH2uyY9EaQKadJLDfqWsfpfRKuPrEN1N64y5wNo0KeZUDEDJ6ZDyKR0qi665sQ29bh0vu6sl3rwdr7tyiGNxJXlKUqxHIFKsuAsGIUqSwvheSnrfS4rJj319ltYeLVi5Sx7NI4DcMqt7Ich8tmhtmoTur01Vu2uVQt7ilSWQZE5Xn+uCZ3EM8thLCj26nZhGKjgeGDNwzh5Sk/XtFzoq1CSCzrxMuTfiTTHNeN6JhRXIDB9kbEkplcl70qItnQ8qZObRZVjJxYntb/XApZDiVgtxgrenfgqk0uJFIZXFgsPshCNn4FQwFcm8S2Rm0xhfBGEmiV0dwn0WyzwFe0sqylWG4qOu7aHYqVnN4n0eOykQ2DqB/8U8KKZGvR9riSDaNEZdkTUZexLNHXYquvyvLcK4AhWzkuRs9+cbfLV17yEecc5xdC2NHjKOs463n3oX44Gkz48lO1k8xEYlknXhj3wGhgODSo8YeEDIbaRGPChKcMW0OuslwhzzJQk9XM5XC8Yn5lCalRQjMrhn9SbKWLkmI461Ms+6JJtGYb9+TgtJkQjKXyP5lJA6mVilWWl8MJWY2JANDtsiIUTyEUq71oJYK4Aik2TqOqYw6ZlWVvOKGquU+iv8WOWf+K9lGeejF/HOjaBZhKfGdJnub5Y2Wdbsa3gnA8hR3d2vZlNTWYcM8Ng/jpyXmcntO42V0lJJZ14vkxD/ZscsFhlf8FrhVSMPhkWWJZGnVdARuGsxcAq1kbRiUGkqylv9WGZrtZuyY//5TIspYj/qwuUQmqwQuXYviiiVx+shwcVnNhwZnMVpK0EssWR9GcZX80IdtTKfkCF8m3LJ+Jp4Ev3bo6ApioHHpkLAOyK8vijlN5leVkmmMpVCfvN/c5oLNEVRkAOnaKCvTCybJOdz7b3Le9W9vKMgDc+2ub4bKZ8b9/fl7zY6uBxLIOrCTSODZdHb8yIL5QzUaGiXKa/HJiuQI2DKMZcHTXZDVzORwv6zaeGhhjuErLJr/AtPwJWowJK0YN/lsUIpXOIBRLwWWTf2HqsJoQKlRZlhpNNbVh5K8sc87hjybRIrMq3u0UYpni42Qydwz45l1i++/vBs79tNor0pWnLy7jmr/6T/zZD07mknyqih4Zy4Aiz7Lcuzb5yMXH1YNvORYQjdkd20rva7IAbZsB94WyTnluQTQu6yGWXTYzfv81m/HEBTeeu+TR/PhKIbGsAy9P+armVwYAk9GA/hZ7eZXl8JLo4tdKMJTCuQkI1p5A80YSqqY/lcvePhfOL4YQS6bLP5jS6o5zU11VlgPZvGS5ghMQYjkcT+UfZZsTyxqkYQBZG0b+ynI4nkIqw9Fsk1tZFnFWJJZl8p9/IT7Dfv9FkQbz5N9Ve0W6cWYuiN/5xlEYGMO3j07jD/7fy9Vd0IpfJCrpIZZNFpGDXqSynMlw+KJJWYOKCtHXIt5vdeFbloRvxw55+7dvE5XoMriwGEZfiw1NMpJ81HDP9UPocVnx2Z+fq/rY8foXy0tngW+9H/iX1wEXHqn2agAIC0a1/MoSg212jC+XU1nOZixr7TUrhGtTzdkwVj9sK1tZBoC9fc1IZzhOzxWOHJMF56JKrKQbvc4qy9JwEaU2jHSGYyXfxYjUjKeZDaOpoGdZSuRolin0O53ii5+a/GQQWQbGnwQO3C2qbQfvEQ1QyxervTJd+Nufn4PNYsIPP3oj/vRNO/H8mBfPji5Xb0G52DiNkzAkrM1FxXJgJYl0hpdlw9jULGUt10FlWRK+7TIqy4AQ1b5xEROrktGlMLZ0alRUyIPVbMR/f/02HJ/24ycnqhtnWt9iOREFvvUBYPwpYRv49j3A5HPVXlVV/coSA6328q6GI+7K+JUlXP1CoFX56nEtWnzYqmVvn8gRPVmubzm8BKRiQPOg/Ne4+sW/fxkfopVEqeAERGUZQH4rRiL7vtFigh9QNA1DSuRokSn0rWYj2hotVFmWw5kfAjwN7H6H+HnPOwEw4MS3q7osPZjxRfHkRTfed+0AupxWvPeaAfS4rPg/vyzvNntZSGJZ69g4CVtzURuGN6p+ep+E1WxEp6OhPirLy+fFsJGWIXn7d2wHeAbwjKo6XSbDMbYcxuYO/cQyALzz6j5ctcmFT/3odC7KtRrUt1h+9NOA5yLwngeB//KYaBT74e9XdfpYtf3KEn0tdoRiqdwtasVE3JWJjZNwbhIJBFFv5c5ZAk9EvDHL8byppdtpRXtTA07OlllZluL4lFR3ajidJB/ScBGllWUA+Zv8tLZhWBzigiV9pTBXI/R7mq1YCNRBpavanP4B0LYV6Nojfnb2ACO3AKe+W9116cB3joo7Qe85JBJvrGYjfuvGYRyd9GkzzVUNUiyZnHx3NVhdwqdbgNXPhfKKVv2tdkzXg1h2nxdVZUPpKaYAVivQbnUNdLP+FcSSGV0ry4DIXf6H9+xDOJ7CH3/3RHnzI8qgfsVyPAS89DVg3/uAkVuBxjbgNX8GeC8Bo7+s2rKOTHir6leWKNtrVenKsiTmAlOVO2cJPOHyKxNqYYxhV68TZ+fLFMtSbJwS32Aua7lexLIKz3LWY5c3Pk5rG0ZDtvklT5OfT4XQ73baqLJcikQEmHwW2PnWy61kW94AeMeAUG2N0i0Hzjm+9/IMbtrSjr6W1bsht+/pBgD88kyV/qzecZGvrHXGsoS1eGV59XOhvM9vkbVcBxen7vPymvsk2rcCYKrF8qhbfE7qLZYBYFuXA5948048em4Jf/vz8nzWaqlfsXzmRyLi6eoPrj62622Aoxd4/vNVW9aj55bQYDLguuFqi2XxoanqTc55VixXIGNZQrIJ+DUUy5kM8NQ/AM99Dogo76b1RqonlgFgZ48Do0vh8ib5+VVM0HJKQ2Lqw7ecE5wym+SAUjYMHdIwgLy+5cCKisqyy0piuRRzx4QFo//ayx/vOyS2s0crvyadmPGtYMa3gjfs6rrs8f5WO3b1OPGLMwvVWZh3DGjRqaoMCBtGEc+ymrs2+ehvsWM+EEMqXb071iVJRMV3p9zmPgAw24CWQWHfUMGlpaxY1tmGIfGB6wbx/usG8MUnx/DAE5cqcs611K9YPv7/gNYRoP+a1ceMZuDwbwFjj6+O+K0wj59fwvWb22CzyLwVohOrlWUVYjnmBzKpCovlbOXTN6nN8TIZ4Md/APzqfwGP/CnwwI1FB0Pkw5MVy5UeSiKxq8eJRDqDS+7CGb0lCc4BDU7AqiA03tkrtqE59eetIIGVJAxsVQDLobI2DEksX/nv6Itkv9AVxN51u6wIrCQRTRSIviNWxbAkjiV69gEGEzBzpPJr0okXx4V17ZrhK61/b9zdhaOTvup4Pb1j4jtaL0pUltVcROejr8WGdIbX9gWqbxwAF3FwSmjfrjo+7pI7jNZGi+yM+HJhjOEv3robb93Xi8/+7By+WGHBXJ9iOTgHTDwF7L3ryrSG3b8utlVIxhhfjmDCE8Vrd1TQ61uAZrsZjRajOhtGbnpfBf8ctmbhQfNrJJZPfgd45d+AW+4DPvAfIn/y+QcUHUKqLJd7G08tu3qEwC3LihGaFxnWSrDYhcCuk1vVvmgCLpsZBoP85JaileWkJJa1avDLXqjkafLzRRNwNJhgMsr/KJYGk1AiRhFmjoq7VeutZGYb0H2VeH6DcGTCC5fNjG2dV2bdvn5nFzgHnrroruyiUgnRL6GnWLY1C2tTnl4AQN1FdD5yWcu17Fv2jomt0r/vti3itSoa60eXwhWrKkuYjAb843v24S17e/A3FRbM9SmWx58U2+13XPlc22bR1HHh55VdE4QFAwBes736Ypkxhr4Wu7rKclj8OSrqWQbEl5sWNgzOgWf+CejcBdzycWDza4DtbwaevV9RA6E3koDDaoLFVJ23yXB7IxpMBpwpJz4utKBcLAPiNeEq3b5ViBjqoeyCZlUsF6gsMwNgsmqxvDU2jCv/HQMrSTQ3KrtN3E1iuTSzL11ZVZbYdAiYfVmMNd8AvDjuxaHBlrwXizt7nHA0mHB0wlfZRQWmRdKCXs19wOpgkjzvK0DdRXQ+yrpLWym82WZKpbaX1mHRWB9S/lk/5o5gc2eF5jCswWQ04J9+Y39OMH/mJ2cqYpGpX7Fsa1ntcl7PttvEiFOFt93L5ZHTC9jW1ZS7Eq02fS02THvVVJal6X0VtGEAwj+lhQ3j4i+ApTPAjX8IGLK/4rd8THyonv2x7MN4IomqZCxLmIwGbO924OxCuWK5R/nrmrpUfYBWA380CZdCX2KjxQTGiniWLU3aZYwXs2FEE4qFPg0mKUFwXiS5bCoglvsOi7sHS2cruy4dcIfiGFuO5LVgACJJYP9AM16arLBYVlvpVII08nol/5/NH00qapwtRI/LBgMDZtR8l1YK7xhgb1v9O5GLdDEjJZfIJBhLwhNJYKit8mIZEN+N/99dB3DP9YP48tPjeNcDz+HUrEYTbwtQp2L5KWDoplUhtJ7tdwDpBHDpsYotyR2K48iEF7fvUSFMdKKvxYZZ34ryyTfVEstSZbncrOWj/yoE4p53rj7Ws19EoilISvGE41Vr7pPY2e3E2XmVF32cq7NhAOI19SKWV5QLToOBoamhwMjrRFjbyZVSZTmvDSOpaEw3sDryeiFIYjkvc6+I7aaD+Z/v2Su2i6crsx4dOTYtPLtXFxmAdWiwFecXQ+pjRNWgttKpBKmyXKDJT4jl8mcdWEwGdDutNV5ZVtlMKV3MSBc3MpnyiAuHwSqJZUBcCP7lnbtx/3sPYNITwVv+79P4bw+9gnBcn16O+hPLvgkRLzZ8S+F9+q8FzI3C11whHjm9AM6BN12lQpjoRH+rHaF4CsEVhb88kWUATFypVpLmQXFLKFKGvy4WBC79SnjXjWs+KBkDtrweGHsCSMv70vBGEmgtY1SqFmzrdsAbSahr0Il6gUxSXWXZ0Q2EF2tqSEwhfJGkogY5CafVXKSyrKVYznqW81SWAyoqyzaLEc12M+Ypazk/0iSzzp35n2/dDDCj6hSAWkLqZ9jZU7iB99BQCzgHXpmqYHXZOya+g/XM6s9VrAxnrgAAIABJREFUlguI5ZWEqs+FfPTVetayb1xdFd/VL94LCsXyZE4sV/cuOmMMd+7rxRN//Br8wWu34EfH5/COzz0Dd0j7htb6E8vjWQE8dHPhfYxm4Vebqtw0v5+fWsBweyO2d13ZZFEtJK+V4jd5ZAmwtwJGfea9F6QlGx9XjhXjwiPirsKut1353JbXCyvG9IuyDuWJJNBehYEka9nWJaqSFxdVJGKEsuNB1VSWm7rFII0i0Uy1QmBF3e1WUVnO51mOaje9D1hjw8iXs6yu+tXjspFnuRDLF8QFotWV/3mTRdx+VpkvW0ucWwhisM2OxobCn9X7+5thNLDKWjG8l8TfsVZWpnxI/75FKstaNWfXdNZyKi5iPtWIZaNZJFF5ldkwJjyiCbraYlnCaTXjj964Hd/47Wsx7Yvid75xFLGktj0J9SeWZ4+K2y8d24vvN3C9uM1WZMKPViwGY3j20jLedFU3mJ4fDgpRnbVc6YxlCSk+rpxEjDP/Ib4o+w5f+dzIrSI2SoYVg3MOXyRRdRvG1myH+8UlFVYMyUbh6FX+Wklg13giRiKVQTieUiU4HdZiNgwNu7xNFsBouWIoSTrDEYypE/o9Livm/CSW8+I+V/r7oX27ENV1ztn5EHZ2F4+FbGwwYXuXI2fZqAjLF7NDL3TEWqKyrKKXoRD9LXYsBGOIp2qwKdQ/VV4zZeuIispyBB2OBtgtFS6oleDGLe34P+/Zj5en/Pibh7XtSag/sTz3CtC7v/QV68B14heoAnma//HKLDIceOfBPt3PpQTVU/wiy9UVywqbDXIkY8Dor4Adb8nvZ7c6Rc6qjNio4EoKqQyvuljucjbAYTXhwqIasVxGZVl6TY0nYkg+TCXT+yQcVhNC8QJpGFraMAAxxW+dDSO4kgTnyjKWJbpdVvIs54NzkRvbXkIsd2wTAkGmJasWiSZSmPBEsKOn9N3MPZucODMXVN6/ooZkTBQ82hVMk1ODrbBnOZnOXkSXmbEs0ddiA+eozQtUqSqstpmydVgcQ8HvxqQniqEaqSqv501X9eCDNwzhwecmcWRCfvpVKepLLKfiwOIZ0axVir7Dwoszqa8Vg3OO7740g6sHWzBS4czBUrhsZjQ1mOqnsmxpFFVQj7Kr3BzTLwjP85bXF96nZx8wf6LkB4MnIjxPbVW2YTDGsK3LodKGIVWWVdow1h6jRvFnBw+4VFRnHZXyLAOiUr2uwc8vCX2F0XEA0OO0whtJaH6rse4JzIikCzmV5UxK8e3nWuL8QgicF/crS+zZ5IInkqjMBZZ3TBSq9BbLZhtgbMhbWc6Nulbx3sqH1Mg2VYuJGFJVWG0zZesIEA8UTBXJx6QnioHW6jX3leJjt21HX4sNf/L9k5rFytWXWF46IxqWemWI5YYmET4/9byuS3p5yo+LS2G86+raqioDUtayCq9VuEpiGRA52Z6L6l479ri4QBq6sfA+3XvFB0OJCY+ro66r2+AHAFs7m3BxSaVn2dYKmFT8GRzZ0bm1LpZXlE/Akyhsw4hoa8MAspXly+8OSFVxpWkYwGrW8iJVly9HatorJZY7tl2+fx1ybkH8PpWyYQDA7l7h7z01W0YMpVykz2+9bRhAwZHXgZXsRbRGDX5SFXUy69WtKfxTosdC7VyEliGxlXnhGEumsRCM1WxlGRDWoz9/yy6MLoXx/ZdnNTmmJmKZMXY7Y+w8Y2yUMfbxPM/fzRg7kf3vWcbYPlUnkiKBeg/I27/vEDB/XIw+1ol/eXIMLpsZd+5T4QutAEIsK7gaTsWFmKyaWN4CeEbVvXb8CXFHoaHIbcme7K/ewomih1oOiw/bauYsS2ztUpmIoTZjGRB/h5YmkYihgKMTXtz95ecxVs6IbgXkKkiqK8vJK29NJ8LaTe+TsDRdIZaDWbHstKoXy9Tktw6paa9jR/H9pKpnHTf5nZsPotFizNntirGzxwEDg+5ZtABWveBtW/Q/l7U5b1+S9LmgRc4yAHQ4GmAzGzGxXIOVZf+kSJJS2y/l6hfbwLSs3aXq+kANi2UAeOOuLuzvb8Y//ucFTe7AlS2WGWNGAJ8DcAeAXQDeyxjbtW63cQC3cM73Avg0gC+pOtncMfHmaB6Ut3/PftFU49VnJOIldxiPnFnAPdcPFu1GriZ9LXZlWcvSqOumKonl9q3idpCCSXsAxGvmXgFGikQKAmKqn8EkLqKKIFWWq23DAMpIxFCbsSzR1LXqe5bBjC+Ke7/xEp4Z9eDDDx5FIKq/H9SXtWGobfBLpjniqXUX08moPp7ldTYMqartUCOWKWs5P+7z4m5KqSpbg0Pkrqu9MK8BLrkj2NLZJGtCnd1iwkhHE07PVUIsXwScfdq/h/Jha85rw/BF1fcy5IMxhsE2O6a8NVpZlvp91NCsUCxnY+MGamT4WiEYY/ifb9yO+UAMPzo+V/bxtKgsXwNglHM+xjlPAHgIwGW5XZzzZznnkiHmeQDqPAvzx+Q190lIdo25Y6pOV4ovPnEJZqMBv3nDkC7H14K+FpuyrOVqDSSRkKoRSr/EJp4WPrmRW4vvZ7aKqlNJsSyquNVu8AOQ88JPKL0FWE5lGRCvVZCG8dcPn0UylcHfvvMqTHmj+Pzj+guRQK6CpCZnWVzgBtfGx6USInpQc7Gcp7KcPa/TpvxCmyrLBfCOya9otgyVtGPVMuPLEQy3y/893dPrrIwNoxJJGBLW/DYMqZdBqwY/QIjDCU8tVpbLFMvWZsDiAPzyxPKsX9g6a2VScTFu3NKGbV1NePDZibKbW7UQy5sArP1bnsk+VojfBvAzxWfJpEXVoHO3/Nd07ABMViGyNWZiOYLvvTyLu68dQHtT9X2thVCctVwrYnlZoW957HERgl9oxO1apCa/IngiCTQ1mNBgMipbhw70OK1oMBmUWRsyaWGhKKuy3CF7QEwilcET59146/5e/MbhAVwz3IonLpQxXEYmvmgCpuw0PqU0ZcVyeK1vOZm9INHas2xpuiINQ8p4VlNZdljNaLQYqbK8Ht/kqgezFC1D5WW6V5GVRBqz/hVFTeW7ep1YCMbgy9410wXOs2JZ5+Y+iQKVZcmGoVV0HAAMtTdiyhtFJlNDg5piAXGxUI5YZkxUl2VWlmd8UVjNhpqwKJaCMYZ7rh/C6bkgXp4qLzpRC7Gcr8yb97eJMfYaCLF8X8GDMXYvY+woY+yo273my9Y3IYYkFJrKlA+jGejas+p11pD7f3URZiPD7966WfNja4nirOWcWFbZLFAuzQPCJqG0sjz2uGjsM8l4A3fuFINXinT/emsgY1nCYGAYbm/E+LKCynJkGeDp8sRyY4f4e5LB0UkvIok0bt0mLrJu3tqBcwshLOks5vwrYqiHmnzzxmxGaCS+xs+WkMSy1pVl5xU2jOBKCgYGNFrUXZB1u6xUWV5LKgEEZ+TnzTYPAqE5EXVWZ0ifBSMd8n9Pt2UHZqmKoZRLcFZYHzsqJJatrryV5WAsCcYAh4b2yME2OxKpTG1doPqnxLYcsQwI37JssbyCTc22mpopUYx3HNiEpgYTvn1E3p+vEFqI5RkA/Wt+7gNwhUGEMbYXwJcBvI1z7il0MM75lzjnhzjnhzo61lQ3S40wLUTvAVFF1LDJ79RsAD84NovfvH4InQ6rZsfVg/6cWFZaWdZxTGkxjGZR8VEilv3TYv+RW+Xtn7N6FPay15JYBoDh9kaMKRHLuYzlMmwYjZ2icpEqXYl6/LwbZiPDDVvERdbNW8X26dFl9eeXgT+aUN3xLlWjw/E1lWVJLGs5wQ8QNoxE+LLPoVAsCYdVndAHNk7WcmAliX/4xXm88wvP4t9fKKPSG5gWViwllWVgVXDUETmx3C6/sry9uwJiefG02HZdpd851mJtBmLBK77fgytJOBpMsvzcchnMRqVN1pIVQzOx3CfbhjHjW8kV4eqBxgYTXrezE784s1BWjJwWYvkIgK2MsWHGmAXAXQB+tHYHxtgAgO8D+ADnXN3YpKUzYlsqEmg9vVKTn8rs3nVwzvHpn5xBi92C33tNBbp9y8RpMynLWo64AZOtMs0ZhWjfpmy61vgTYjtyq7z9ZfiiPeFETd1mGm5vxJQnKv/NnstYLkcsZ+8uyLBiPH5+CYeHWnMCdFePE22NFjx1UW+xrH6krdSUG8knlvWwYQCXVZeDsZQqv7JEl7P+K8updAa/842j+Nxjo1gOx/FnPziFv39EZUKFFH0lN2+2JdsoXs7E0CohWbKG2uWLlm6nFQ6rCed1FcunxFZpUUsttmYAXCQ4rSEUS6myNxVjsBbj43JiWWboQSGa+0WFPl76d2PWvyIrgaWWuGNPN3zRJF4cVz+kpGyxzDlPAfgogEcAnAXwbc75acbYRxhjH8nu9kkAbQA+zxg7xhgrPUJtPUvnANdA8ViwfHRmgzmWTis+ZT4eOb2AF8a9+B9v2KZZhqOeMMaU3a6VMpareYula7fwvSVlCvyxx8WaO9eHsBSgeVDkMRcTy5F4TSRhSAy3NyKV4fIvesqZ3ifRlL27UMKKEYgmcWExjBu3rFp3DAaG6ze34fmxgjeRNMEXTapq7gPWiOVEPrGsQ4MfcJlYDsWScDSo/wzpcVmxFIojXUseSoX8/S8u4PkxL/7+3fvw6B/dil8/sAlfeOISJpTcRZGQJn8qrSwrbPLjnOOJC248fXEZK4nqDIUZW46g12VVNG6YMYbtXQ5cWNAx1nHxtKhyWktnP2tCgZHXwVgKDqu2CVW9zTZYjAZldji98U+JXh17a3nHkeLjSlSXI/EUvJFEXVWWAeCWbZ2wmY14+JT8dKf1aJKzzDl/mHO+jXO+mXP+V9nHHuCcP5D9/w9zzls45/uz/8nowlqH+xzQWSI7Mx8dOwAwMfmvTOKpNP764XPY3uXAXYf7S7+gRuhxWTEv93ZtxF292DiJ7quE33ZJxmx3zoVYHrlVvsA3WURVqYBY5pxnbRi107gpeRNlf1CHFgCwVcGrBsmKEyleHT63IDrsd/Ve/gW5s8eJ+UAs18gmi2QMeOyvgV9+qmRiCQAEognVWapFbRh6eJaBy5r8givlVZa7nVakMxwepfnbNcK0N4ovPzWG9xzqw68f7IPRwPDxN+2A2chw/69UDCbyTYiGbrkXiE1dYn+FYvnRc0v4za++iPd/5QX85r++WJkR0usYW46omhi7rduB84sh/da8eFr0CVWKAiOvg7GkqvzyYhgNDEPtdlxy15hYbh4ov7gl2TgCM0V3k5IwNtVZZdlmMeKWbR345ZlF1b/79THB7/9n782jG8vv687Pw74DJAiCS7HI6tqrurt6U0tqdbdk7bKlyJacyBrLOo6TOPaM48mR7Ykn25yTZE7sceIcO3HGIzlOjmPLlmRZlizJkm2p5W51S71XV3V3da0sVnEBNxD7Drz54/d+IEhix3sogNQ9R4dqEARekQ/v3d/93e+95ZLYlm8VNF8PNpeoc9RBWf4fT9/kVjTDv/zgaSzm4fjVgUaWYx3YMO5UEoaEvNjKLb1mWHtdHPNd7+jsPYLHYKM+WU7mSxTL6oDZMMSNsW3fcnJF/B3NPdwwpA0j1VxZltu6pyZ27vocGxfH3NHN5ZXPwt/+Ojzz2/C1X2r59K1Msav2PgC3XQzW7bRhaGTWMBvG9jZnQvMsd4sJv7hhrQypFeO3vnUVs0nhl967ba0b9zr45Fvn+NL5JZbbvWZJbN0UanG7xEFRxC5TB2S5UlH5jW9eZjbo4lPvOcFz81G+04fUl92YX091FBsncTLsJZ4tspY0YIFVzIkdwXAHiVW9QirLub02jF4Woo1wNOTRtXBpJZ7dGV3ZKWK3tnOSe0G1mKS5f1/OPg2bDQPg8RMhVhP5zmZ/ajAcjG9rXmSfduuDCp/pWVleT+b5z9++xrtOjfPY8TtMJjvEpN/JeipPsR2/a3rjziVhSIwcEeQicrH1c29ofuUjLcpIdiN4TJTV1Bn8jKZk1fXgkOURlxW/08r8RpsX6mSkNwsG1NgwmpOBNyJJfA5LtShD4vi4LFNp0yOpqvD874udhff8G1h8vmnDWq5YJlssM9Ll30mmYaTqpmEYMOAHOzyByVypJ/VrmItJbkcz/NlLi/zUW2YJ7zpvPvamGVRVWN46QnS+fb+yRIfxcV9/dYU3Ikk+9Z4T/Pw7jnJ41MV//KvLfVWX49kiiVyJmdHOCYtMxLgcMcC3vHFZ7Aj2kyw769swkj0uRBvh2LiHhWiGwu4iow6hqiq/99QN3v7/fIfHfv2J7gdb44tiOK9XeMJgsra0YSxpNsBhJMuPHA0C8EyXQ+fDQZZl5m632Y3jZ8WAX6H7Kdbf/GtRmfjPf6RPgws6YtLvQFVhtdVNVVUHQ1k2mYS63BZZ/o4gvp2uroPHRFNbnYa6TS2HdHSAPMvbDVLtepaXexvuA2FFsLpakuXLkSSnJnx7Uh0Oj7qwmU1ca1eJWXwBVi/CQz8D935MRAi+/IcNnx7X6qK7nR0wmRRcNvNOZbmoXSOMaPCDXTaMYk++yrBf2IRafq4HEJ/TYpx+5tG95PZoyMPxcU9nZFlVt5XlTjDSmbL8jVcjjHvtfOjeKaxmEz/7+F28upTg6lp/6t2hlrB0vqCTbaCGJGJE5HDfnVCWd9kwevxsNcLRkIdyRe15yO+zz93i333tEo+fCHFm0se/+NKrPNvpfEchA9moaKLsFSYT+KdbxsctbmWxW0yEBrhbohFmgy6mA06eud7dHM1wkGXpLQ12mWkcPgOo2/FzHeLGeorPPX+LT7xllqNd+MTuNCYDYhXYcsgvF4NK8c7FxtVi4m5x8W0W+VfKi+a+TlVlqEnE2OuNrFZdD5CyDKIx6Xa0zQWfHsoyaFnLjcmyqqpciSSrsVS1sJhNHBlzc63dmu6Lnxfk/J6/K1Tt4++DC58TRKgOYtVK2+7/Tm67pb4Nw6ozWd6VhlGpqKQKJXw9DAmPue1YTMrQ2TBK5QpfePE2bz8RYipQX6F639kJnpuPVj+LLZGJikKZTiO0/IeENSbXutmuUlF55vomjx4fq0aSveu0uFb+7eX+WTGqvtEGv7tmCHrsjHnsxijLSy+KJrh2GxT1QB1lWVVVUvnedm0aQd7/r/dgxYjEc/za19/grXcF+cwnH+T3f/pNTAec/Osvv9be7q9EYkl89es0P+WfaaksL8dzTPodQ5OxXAtFEUPn37ux2VWxzJCQ5avgGgPnSHc/L1e6a91ZMX77W1exW8z8wjsHPyquHia1atzlVjdVOch1p5VlEFvxheT2hHs9LDwtbpDH39v56zfJWJVV18EBWz0fHnWxuJVpnX5QLgqC26uyDOJcaOJZXoplSeZLdckywLGwpwNl+XmYfnBbhT32TtFC2EDtqFba9tDS5bFb9g74mW3tldt0gqqyLEhKMl9CVbcrt7uByaQQ9jlYHTKy/MTldVYTeX7i4cbE9n1nJ6io8K1Lbdaty3Ok0x0mqcpJ4tEElyIJoukCj9akvkz6nZwIe3jyav/Icq++0ZMTHmOU5aUXYfp+oVL2C1aX2IGqUZbThTIVFUOUZTlo3cuQ33/8q8sUyhV+7aP3oCgKTpuZf/XBM1xeTfK1Cx2kNchhPL8OyjKIhWaLAb/VeG6PbWqY8MjRILFMkUuRzmvfh4QsX++ta370iMgO7sK3fH09xVdeWeaTj8wOdK11M0iyHIm32MK/0+19tTj8VvH15ncbP+fKN8U0+5HHO3993xQoprpkeSM1mMry4VEXxbLa2qOa0giGHsqyZ7xpGsaVBsN9EsdCHm5FM+SKLSK2ijmxk3CoJihn4pz42iAVY0tTlnsly3tylo3IGLft9CzLhJBe1a8Jv2PolOU/ee4WIa+dd55qvIN197SPgMvKCzcbt2zugCTLnapsHZDlpzWvY21EIsDjx0M8Ox/tW4zc0lYWp9Xc9UzFibCXK6spfWubizkxkD39oH6v2Q4URVgxapTlXmrkW8FttzDld3CtS9vNejLPl88v8/cemmE2uH2dee+ZMFN+B3/xyp4+t8aQ56weNgwQn53kStMSqkgix4R/eMnym+ZExF431dfDQZY3rnZvwQAwmUWZSReJGH/wzE0sJhP/6LG7un//Owyvw4rHbmE51opkaQriICjLYyfE0MHNp+p/X1XhyjcEUe5mGMtsFReZOttO0XQBl82Mw9pdDbFRODwq/p23WjVI6VFIIuEea5qzLNusGk3mHxv3iHS/VkpM5IKwANXebMNnxYJm5ULdH4lnpbLciw3DvLfuWm8LBoDVKbK9NRtGIisIeq/q14TPMVSe5ZV4licur/H3HjqEtUmikKIonDsU4PztNm9qsW7J8pT4mmhNUr57bZNj4549ytrjJ0IUShW+P29sprjE4laW6ZHu64ZPhr1ki+X2M9vbQeQiVEr9J8sgrBg1yrL8bBmRhgFwdNzTNVn+7LO3KJQr/PTb5nY8bjIpfPDcFE9eXa/umLVEfBFQts/hXhGYAdSGC0dVFULN7kHuYcKhESdjHhsv32pzEV6DwSfLubi4WQd7UJZB3Hg7VJbT+RJffGmJH7l3cmhVZYnJdopJpLLcSzavXlAUmHsU5p+q71nduCIGc068r/v3CBxuYMMYrKprCUmWW/qW9SgkkXBrynID7/jiVhaH1dTw9zWnqSe3Wh3z0ovi63SNsmxziUVTpD5ZrirLPfh+69owjFCWFUUkYmgDflVlucdio7BPVF7fiazfbvD55xepqPCxh1p7i++bCXBlLbnz79MI8UWxJd9pOYN3ElAg3lxZVlWVC4sx3jS31wr4prlRTAq83IVa1Q0WY5me0gikZUrXJr/q5/cOkGVHYEd0nJHKMojFxpXVZMfVyeWKyh89u8A7Tobqzj596N4pimW1/cHW+KK4V1t04ibV+LhGtrcihVJlqG0YiqJw38wI5/elsiyH+3qxYYBod0uvtSxYqMWfn18ilS/xibf0WCU5ABDbta1sGBuAAs4e24D0wtxjkIrULw959YuAAic+0P3r+2fqXhg204NVdS0x6XdgNimtiaeeyrJnXMRBZeuvxJe2skwHGqtcMt5K+iwbYvEFofT7dh3zxL0NleWtTAGb2YTL1v0OgNtu2dvgZ1TVu81btWEkcvooy5N+B5lCufp6g4xyReXzL9zm0WNjHA623g26/3AAVYULi23c2OK3xOe5U7XVYhPneAsbxloyTyxT5NTE3mY6p83M0ZCH15fjdX5Sf8jPXLc4rsXH6epbXnpBXG/0Ujk7gcO/iyzr89lqhDNTPvKlCjc7TMR45voGa8k8H3uo/u7H3dM+Do+6+OvX2/TpJ5b0s2DAdgRdgyE/af8bZhsGiOvKjY00W+0OD2sYfLIsiyN6nbANa1XIq+1bMb744iKnJrw8cDjQ23sPAKb8zjYG/NaFMmM25iLTMaQX+cZ3dj5eqcD5PxZFJL0MNwQOiwtOeWcofDSdH0hl2WI2MR1wtkGWV8SWvx52Gulfb2DFWIplm0ZY+Z1WvHZLazV86UWYfmDv45P3ihi8OovcuFZ13ctk9t40DAPJst1bLSXRy7Mc1m5cw2DFeOrqOkuxLD/xcHtWiftmxHW3LStGL3mzvqmWZPnSihgIauTNPzvl49WlzoeGOkU6X2IrU+ypbthjtzAdcOqXiKGqsPAMzLxZn9frFLvIckKnz1YjyKbS15Y7+3t/+fwyXruFH2rg1VcUhTcfGeXFha32dor0yliWkK/VYMhPkuVhVpZBkGWA8+0swmsw+GR585rwLXYaNr8bMhGjTbK8uJXhpVsxPnRuaihjUnZjwu9gI5VvHqaeXhsMv7LE6F2itfH8H+18/OZTQkm6/xO9vX5gBtTKHr9iNDVYVde1ODzqYqEdZdk7oc9Uurt5McniVqZp9amiKBwadXG7mT+ymBWWmno1uRP3iq91rBhbmUJPw31Qz4aR0r+9T6LGhpHIyq3i3pVlGI4Wvz957jajbhvvORNu6/kBl40jY+727A2x2903mfmmW3qW34jIQda9yjLA3dN+IokcGwZXj+tVN3wirGMiRvSGWGwceUyf1+sUzp0DfnKXpZekmWY4GvJgM5t4faV9spwrlvnGqxHef/dE01mYh+ZG2MoUW7fMqaqwDulJli128Ew0bPGTqTvDriyfOxToyjY1+GR5a16cEL1GOXnGwRVse8hPRrh86N47sK1kAKYCbRSTpDcGiywrCjz0D2D5ZVh6afvx5z4Ndj+c+pHeXl9mstb4llVVZTNdYGyACklq0VbWcnJFH78ybJ8PdeLjpMrVakv40IizuQ0jegNQ6+8eSfvV5vU934plij0N94Fo8csVK9v+w0Ja//Y+CZunOuC3vVXcYxqGpvIMenzcWiLH31xa5ccfPITd0r5t5u5pP6+3UvAKGchsdJ8365tu6Vm+HEky6Xfgb7A461Zt7BR61Q2fCHu5sZ7u2HdbF3IIu5u8ez0glWVNjTXas2w1mzgx4Wl9XtbgW5fWSOVL/Oj9zXdCH5wVnvgXF1oMoGW3RGyqnjYMEAvOFjaMYSwkqYXbbunKNjX4ZDl6o3dVGQTxGm+/9vqrF1Y4d8jflrduGDDh14pJmpLlAWjv241zHxODO8/+rvjva38Db3wV3vZPRMJAL6gz0JAulMmXKgNpwwBxk4ymC81jqpIRffzKUFN5vdcGIVWuVjfumREXt6PZxluL1YbOOnMJngkw2yG2tw42lin2NNwHIg0DxN8dEA1+htkwPDtylu0WEzZLb5fgcZ+4cQ165fUffG+BsqryvzTJVq6HE+MelmLZnVaZ3ei1nME/3bKY5NJKoqEFA+DslB+A1wz2LS/qVDd8POylUK603qVqB/NPis9pP8tIauHwiySdovjdJLIlrGYFh9U4enNm0sfry4m2B2u/fH6Jca+dt9wVbPq8u8Y8BFxWXmwVmVg953VUlqHhHA8IoW3MY+v5mjU5DvIxAAAgAElEQVQIOD3p49JKZzsrg/+vjs6L7Xg9MH4G1i83b4UDlmNZLi7F+cA9OhGOAcCULCaJNdkOH0Sy7PCL+uMLn4Mv/Rx8+Z+Ii/Ijv9j7a/sPAcoOZTmqZSwPKlmWKu5Ss7+jnsqyIyD8z3U8y0tt3rhnRp1ki+VqjfgeyBbF0TrxkCaT2AHY2kuWtzKFntr7QNgwgG0yVkgbaMPw1aRhlHRRvuwWM0G3baBtGJlCiT98doH3ngkz1yBisBHkMFrTOmn5+e3FhgENrRjFcoXr6ylOTda3YIDw5s+MOrtTlovt/+2WtrLYLCbGerSJydrrq71aMVRVa1F9rPPhSr1QrbwWC5VkrojX0dssQyucmfSxmS6wlmxtu4lninzn8jofOjeF2dT8mEwmhQcOj/Biq2izuFFkWbMk1VkERIa8kKQWpyd9LMWyxDPF1k/WMNhkWS2L7vNRHZRlgPHTYuuiRf/5E5cFMXhXk9D8YcNEtZikwYW5lBcXG8+AkWWA9/wbuO8T8MofCyvNRz6tT1yOxS5IZc2202a1vW8wybKsB2646CnmxBadXmTZZGrY4rdYrd1tvvsyM9Ii8m7jGninhPJaDyOzwtNcA1VViWWLBNy9Kss1ZFlVNc+yUWkYnuqAXypfwmPXJ8c7POBZy194YZFYpsg/7CKrvi1S12s5Q5Us1x9smt9IUyyrnAw3VpYBToZ9XOlkaK5cgq/9Mvz7afj+7zasda/F4laWQwFntW67WxwbF7/XK+1W0TdC5IIoQbrrHb29Ti9wCFVfZi2LhaixQ+r3HBIEvZ283r98dYVCucKP3tfe+fnA4QDX1lJVO0ldSA6jtw3DNw2lXN30o0giP9QZy7U4PSk+y500+Q02WS5pqzY9bBggyDLA2qWmT3vijTUOjTirF5T9AK9DpBI0VKAGqep6N0xm+PB/gV98GX7uKX2zPH3TOybho2mpLA+mL2sqIC5WDZXllI6xcRKeUF0bxuJWBqtZYdzb/Hc1I/OhGw35bV6DsSZbuCNze2wY2WKZQqlCwKmPspzKl8RNQq0Yb8NQVdL5Eh6dbuiTfkfzHSMdcGExxj/70wv8yhde4U9fXKTYptc1nS/xn799jYfnRnlodm9GcSvMBt3YLKbmynJ8iZ7KGVoUk9zUhq0aFe9IHBv3cHOzAx/w138Znv+M6BD4xj+D53+v5Y8sxrI9D/cBuGwWZkadvQ/5vfE1MYB/4v09H1PXqJJloSwnckXDkjAk7pn247CaeG6+NVn+8/NL3DXm5u7pxjsTtTit7WA0/dsklsBkFcVdekJ+FuokYqwmctX0nWHHGe13fKmDIc0BJ8vatq1eNozQKfF1vTFZzhXLPH1tk3eeGt8XKRi1aJq1XK26HkCyDGKLb/Qu/bf6fJM7bpLSKjCIOcsgBrpMShNluZqxrJOyDOKcaGDDmGpD5ZI2jbrKsqoKG0az0qHArLgR1qgdMW37bKTHNIxtZbksLBhgTIMfiOg4tQLFLKlcqUrUe8VUwGmoDeN3nrjG3/kvT/P1iys8cXmNX/7CK3zgt55ioY2c2f/23Xk2Unl+9YdPdXU9NZsUjoZaJDcklgRpMHd5LrQoJpFRjbMt5leOjXsoltXW0Y4gFp/n/wge/Gn4+Wdg5i3wvd9paRFc2uqtkKQWJ8a9XO1VWX7j6+LY3WOtn2sU9tgwjFeWbRYT980EeP5mtOnzVuJZnp2P8uH7pts+/09ViVyTcz6+JO5deiQe1cKn2Tp2LRzzpTLRdGHfKMshr52g27aPyHJZKstz+ryeMyC2e5soy8/OR8kWyw2zEIcZk81uqoNOlo2Cb3q78Y5tZXlQbRgWs4kJn6PqF96Danufjsqye7xudNxqm9WnbruFUbetfr1uekPc5JqVDsnPf41veSsjq671GfBL5YvbZNlIGwZAIUUyX8Jj10f9mgo4iWeL7TXddYgnLq/xG9+8zI/cO8kz/+c7ef5fvJv/76ceZCOV5yP/9ZmmGcjX1lL8zhPXeP/ZCR443LmqLHEi7GlO6hLLvZVhtCgmWdjM4HNYWiavyJ3ItqqQz/8RlAvw5p8ThOfhfySSn258u+GPZAtlNlKFngpJanE87OXGRqrtXYI92LoJqxd7TyXqFU6NLGelDaNoOFkGeHhulNeW400/d3/xyjKqCh++r/3zc8rvwOuw8EYzi0B8cZvY6onqLsvOz8JaQnCx/UKWFUXpeMhvsMlyKS9u1I28jN1g/DSsNU7E+N71TSwmEQ6+3zDpc/yALO+GdxLyiWpKQTRdwGE14bINSDFLHUyPOBvbMPRs75Nwj0FqfY+nci2Zb3vgYyrQYFdjs43SoRGtQbPGiiGV5V6j47waYU3VKstGlpIA5JOk8vrd0KU1Z0VnK0YqX+KXP/8Kpya8/IcfP1cdmnrf2Qm++POP4LKb+YlPf48vn1/akwqQK5b51OfP47KZ+TcfPtvTcZwIe1mKZRt7OBNLvTfH+aYa2jAWohlmg63PibtC4jnX1luQZVWFF/47HH5k2xp4+kPi2vv87zf8se30GX0Smk6EhRLezg5BXbz+ZfH11A/rcjxdY7cNI1sy3IYB8KYjo1RUeKlJzNufv7zMuZlAR4OtiqJwesLHG82IXELnQhIJzziYLHvIcrWQZJ/YMEAUDF1ZTVKutJdoMthkuVzQb7hPYvw0rF+BSv3orWfnNzk3ExhostQtJgNNikkOKlmurqSFIruZKhAcUL+yxFSgGVleAbMNnN0reXvgGYdSdptMIgbsVhO5ln5liSm/s751RBLgZnMJdZTlbRtGjznLMjouX6ohywbNKsjXzSd1tWG0lZDSBX7/u/Nspgv82kfvxbmrUvxoyMOf/fzbOD3p43//k/N88vef4+lrG+SKZRY203zyvz3HxaU4//4j9zDeoxp1VCOhNzca2BsSy70Th12zC7W4tZluK0LU57AS9tlbK8trl4SKfN/Htx+z2OHsR+D6t7dndXZBr4xliRPV2usurBiqCuc/C4ce1s8m2S12kWWZhmE0Hjg8gtmk8PT1vfMcIHz+r68k+OgDnQ/hnZr08kYkWT+arlIW96te2msbwWQWQsuuhaMMBtgvyjLA8bCHfKnSeJd2FwabLJfy+n8Qx08Le0d0fs+30vkSFxbjvOWu/acqgxgEalhMkl4Hi9M4VW1QIclyUlwcNge06roW0wEnkXiu/opYtvfp6e2WC6ga33IyXyJXrHSgLDtZjtU572TsVzOy4/ALX2JNIoZ+NoyaAT+tMMRoZVnNJ0Uahm7KskxI0c+3HMsU+MyTN3jvmXC1dno3Ql47X/jHb+Vff/AMry8n+Mnfe5ZT/+obvP03vsPLt7f47Z+4n/ff3fsOx+FR8fdYiNZRQHMJsTPUs7Jcv8WvVK6wuJVldrQ9NffYuIfrrchyoxKPI4+LRenSi3V/TK/2PomjIQ+K0mKQrBGWX4L1N+D+n9TlWHqC2SrmDHIxyhWVdKHcFxuG227h0WNjfPWVFSp1rsV/+P0FXDYzP9aiiKQeTk34SOVL9a1rqTWRK613EoaEb2rPgJ/kDPuJLB8bl7GU7Z3/g02Wy0X9kjAk5LZXnSG/Fxe2KFdU3nykeXD4sKJpMUlKy1jeZ0ONLSHtCtqNMpouDDxZngo4KVVU1pJ1/o7JFX0tGFBTeb2toKxp55AsxWiF6YCTVL5EYvdWemxBFBpYW1yEAzM7lL+4Vhft77GUxG4xYVKEH9R4G4ZQlovZJMWyqpuyPO61YzYpjYd3u8AXXlgkmS/xT999ounzLGYTP/PoEZ7+1XfyX3/yAT71nhP82w+f5VufegcfOqdP+6lUdRc26yjLkuD2Shz804J07yomWYnnKFXUlsN9EsdCHq6vp5uXVcw/KbLDpb1IYu5tgALzT9X9scWtrJY+ow9hcdrMHB51dTfk9/IfCXHl7I/pciw9w+GHXIyUrLru8brQLj583xRLsSwv7YqQi2eLfOWVZT5831RXKvcpLdrsjXpRhL2W8LRCnYVjJJ7DYTXhc+6fHXc5Y9A0aacGg02WUfW3YYydFF/rDPl9/4bwKz/YRcTRMCCsERtp1t+B9NpgZiwbjV2xUcKGMdhkubrtXk91kMqynpCT7jVZy/IcavfG3TAfOnZ7u3a8GbyTOwYxt9IFnFYzDmtvWcWKouC2WcgUyqK9Dwwc8BM3wHxabBfrpX5Vhz51smGoqsrnXrjN/YcD1RrnVnBYzfzwPZP84ruO81NvndO1+dRjtzDmsXOrLlnWFLBeyXKDYpKbmp9XqtutcHTcQypfYrXeNRZE2sXC0zD3+N7vOUdg8l5BputgaSvLpN/ZstiiExwf93auLGdj8MqfwNkf3bZA3GloldeJatV1f0jde89O4LCa+PPzOy08n3nyBrlihU+8ZbbBTzbH0ZAgctfr+d9lxrIRNgzY9u/XLPgi2jD3fkoI8zutjHvbsE1pGHCyjP42DLtHRFHVIcsvLGxxdtpf3Zrdb5DEpq4NI7W+rSAeJFid4iaVXEFVVTbTecba9OHeKUxqA111dwj0rLqWqFZeb5Pl1WRnyrI85r1k+VabZHlie3gR2MoUe46Nk3DZzWQKpeqQp3ENfoIsFzKCLLt1nIvQM2v5pVsxrq2l+NhDBilXXWA26Kpvw6gqyzoM+MGeYhKpZrerLM9pg4ANh+ZWXxURiEcea/ACj8Hic9Xq5los6hgbJ3Ei7GF+I11/jqURXvoDUe71lp/X9Vh6wi6y7OsTWfbYLbz/7AR/9tJSNRpzKZblM0/d4MP3TVVr0DuF32llzGNjfr3OeSQjDo2yYfgPCTtQTVTnamL/tPfV4njYs1+UZfS3YYCWiLGTLJfKFS4uxrm/gT9vP2DEZcVqVupXdB5UZRlEnGBimUyhTK5YGXhlWfrG9rQx5lNiK1lvZdmlKcs7bBhSWW7fhgGwVOurrZSFN65dZTm1JlrPgHi20HMShoRLKst9smEUMmKrXy/PMjTxhHeBL760iNNq5oM62Sj0wOyoq4GyvAwovS8QGyjLt7cy2DTlvh1IsnyzEVm+9X3xdfZt9b8/+zYx2L7yyp5vLW5lDSDLXkoVtfHx7ka5CM99GmYfhclzuh5LT3AGIBsjKW0YfRjwk/iV95/CrCj88hdeYWEzzS/+8csA/B/vP9XT6941JhYye5BYEh5tPYe4a1EnPi6SyFVbgPcTjoXEjEFT25SGwSbLJjO4DBi2Gz8tihBk6QliIjhbLDccZtkPUBThd1vbrUhWKoIIHURlGarFJJspmbE82Mqy32nFbjHt3SFIrYqveivLFpsYsKvJWl5N5HHZzG37bkMeO1azslP9TEbEoEq7yjJqVd3eyhR7Hu6TcNk0ZblaSqKfhWAHtLKTclaQZa+OO1iimCRbd9CoE5TKFb75aoR3nwnr5qnWA4eDLlYSOXLFXSlG8UWx82HpceEki0l2keXlWI7JgKPteumpgAOrWeFmPWIPEHkFXMHGA62SgK5c2PFwvlRmLZlvWS3fKY6HZe11m1aMi38qbACP/IKux9EzpLKclTaM/pHl6YCT/+vvnOXZ+Shv/43vcHExzn/62H0952EfGXNzY6OBDcM/bdx8kVw4agq2SD7aP1XXtTgW9pLKl+rv0u7C4FwN68FsN+aECJ2GSgmi16sDfzJc//7D+5csg9g236MsZ6Oglre32w8afFMQuchGWvxeBrWQREJRFCb8DiK7fZHyRq+3sgxi+LPWs5wUsXHtethMJnHMO8iyTMJoV1kG4Vv2TbGVKXB6oj0/bSu4bGatwS8lCK3erVgSJhPYPFSygpjoqSxPBxwUyyrrqfazr+vhuZtRNtMFfvhuA86hHjAbdKGqwoogp9gBrZBEh+1oWUyyKwVgOZbtiPRYzCZmRlyNbRgrF2Di3sb3Nd+UINORncqy3DXQW1k+GvJgUtqMj6uU4bu/CeG772y9dT1oZFkqy/3yLEv8+IOHODXh5TuX13jk2FhPJTwSR0JuNl4oEM8Wdw4yx5eMs2BAzS6LIMtbmSKFUvvJR8OEY6HtIqFJf/PP1mAryxaDFD6ZiFFjxTh/e4tRt43DbUYEDSvGvfY6iqRGgg5axrKEtsUfTQg1aGzAc5YBwj4Hq7ttGEYUkkh4xvfYMDrNz92TtVwly20MwcgFgPZvjGeK+HVTli1kipoNw+joRLuXiuaN1lO5PaRdt+pWineAv7wYwWk1846Tg7VwrsbH7VZs9Sgkkdg1RArble6dYDboYr5eJnSpIO45k/c2/mFFEWR6l7Ish3n1io2TcFjNzAbdXG1HWb70F7BxBR77pcFLTXIEIJ8gmRUCQr/SMGpx97SfX3jncV2IMsBdYzJffNfCK7FkTCGJhGccFHNVfKlmLO9DG8aRRr/jOhhssmw2SOEbOwGKaRdZjnHukH9fTXvWQ9jn2Kssy8Gtg6ose8KASioqiNigK8sgfMt7to6qVddGKMtjO2wYUlnuBHt8te1kLEvUKMuqqhLL6jjgZzOTkTnLeraF1oPNU81z1lNZlov8Wz2Q5UpF5RuvRXjHydCeEpI7jblG8XF6Kcuwp8WvWK6wmsx1TJbnxtwsbNaJj1t/Q9iOJpqQZYCJe8Rza2yCeheS1OL4uKe1DUNV4bv/CUaPwpkP634MPcPhB7VCLq1ZnPqsLBsB2Qi5w4pRygu7nZFkuVpMIpRlKa7tR2U57LPjsJrqL253YbDJslHKstUhUja0rOV0vsTVtRTn9rFfWWLcayeeLe70/qVke99BJsuQjwmyOeg5y4Bmw8jtvCEnI8Jva9fHnrAD7lB1USU9bJ1ePCf9DlYTuW1fbfyWOOdaZSzL91dMkIyQzJcoV9Se2/skdgz4Ga4sezBpZFlWbeuBQyNOFKU3svzacoL1ZJ73nAnrdlx6YdRtw2k174zHk4UkekVo7SLLkXgOVRUWl04wF3STKZRZT+0SJSKaWtxqMG7ynBjy27hcfWhxK4vZpBjiGz0R9nJzM0O+VL/VFoD5v4WV8/DIPxFkatCgRdgV01EcVhNW82BTm3ZweNSNSWFnIoY8P40kyyA+UxpZlqLMflSWFUVhLuhua8B1sM8oq/6r6CpCp6rK8qWVBKoKd3cZ8zJMkFvn67XqclVZPqA2DI0slxIRvHZLz9m9/UDY56BQqlRrn4HtQhIjdkfc4yJKqFwkXSiTLZYJdagsT/odlCpq1RsuaorbJDoms/g7JVeIpfUpJJFwy+i4Qsq42DgJuxdTMYXZpOCw6nf5tVvMTPoc9RMj2sQTl9dQFHj8xOBdBxRFYSqwy/OuVyGJhHcScrFqbJt8r06H6mTM3J567pULwhM/erT5C0jlucaKsRTLMuFzYDGABB4PeyhX1PrJCxJP/5a4Bpz7eOPn3EloZLmSifd1uM9I2CwmZkZdXK/9u8R1yhVvBd9UdcAvEs+hKO0nHw0b9glZNtA/PH4GojegmOP1FbF1c3baAEVuwCBP+B2+5dSasLw49r+yXhdykZBaGwoLBtTEx9X+HY3IWJZwb8fHRbXUkE4V+PDuyLtOt9C1rGVZda2Xsuy0mfunLNu8WEpp3Daz7pavmVFXT8ryE5fXuPdQgLEBTYMRNp5asqxz3uyugqJlrRFxqkNlueqD3H0DXn0VwmdaD5AGj4LFAauvVR8yImNZ4kRYDEw2HPJbeQWuf1vkKrezC3Qn4BT3rkp2q28Zy/3A4VEXi7Wf6Wp7n8HKsmzxU0VTbNBt3xdqfT3Mjbm5Hc1QKjfPGt+f//p2MH4a1ApsXOG1pQSjbtu+jEbZDUlYdviW0we06lpCs59YMmsDHxsnMeEXx7mTLK8Y41eG7eHP9DqbMjWkQ7Isp41XqmS5w+Es7yQkI8S0eKgRt07Kss1CvlRBzfdDWfZgLaUNUb9mgw3I8vpluPY31YzqeoimC5y/HeOHTg6eqiwxHXDuzOmukmWdBvx2k2XtvTr1LE8HnFhMyt5EjPXLYkezFUxmCB4Xw3Qalrayug/3SdwVcmM2KY2H/J7+LdE++dDPGPL+ukBTlpXc/lGWQZDlHZ/pvinL09Vikkg8V73f7EccGXNRLKstc+oPNlkGWH+D11binJ3y7fvhPmiiLB/UJAwAm/D52nMbA19IIiEXPdVEDFU1pupaotrit15VdjtVlqXnLRLPiQKVXLxDsjwhbBja+/udenmWhe1G7YcNw+bBVs4YkmF8eNTFWjJPtlDjPV16EX7vPfCHH4XfOgdbC3V/9ulrG6gqvH0ALRgSUwEnG6n8trdWr0ISCa92LmqDskuxLEG3rWNblsVs4tCIc6cNIxOFzAaETrb3IqETVc9ysVwhkshxaMSYnVa7xcxs0MXlSB2yHF+E174ED/10Vb0dSEiyXEjsi+E+iZlRF1uZIkmtmZD4IjhHxT3LSNQUk0T2acayxKxWJDTfwopxcMny6FEwWSlHXuNKJMWZyf1vwQCxdb2nxS+9dnCTMCQ847iLm0OjLMvq8qqynIsLJcAwG0aNsizLWzqM2Au6xbkXSeS2kzs6UUg8E5DZJJ4SJES/NAzt5prvT3Sco5LRNQlDYkbGx2nJCeSTgiQ7A/Bjnxae86//slhY7cIz1zfxOizcMz24cxtS4a3aePQqJJHwaZ8dTVnuRc2d3e2DXNeG9cbaJMtjJyF2GwoZIvEcFRUO9Vhy0QynJ31ciiT2fuP8Z8UO7EP/wLD31gWahdCST9yR2DijcLgaCanZjxJL+g20NoO0eSSWWU3kOo4JHSZI21TDbHQNB5csW2wQPEZm8VUK5Qpnpg4GWTaZFEKeXVnLqfWDm4ShQXWP4ytHh0ZZtllMjHls23/Hasay8TaMaFrzDHdogzCZRINkJJ7rbgtd803nEiK9Rc8BPwCl2J80DCtF/LbemvbqQSok1SG/C58XBPmj/w3OfQze+S/h6l/Bpa/s+dnvXd/gzUeChgyQ6QXpHa4mYiSW9bNgANi9wm5QtWFkmewyAeDImJuFzcx2Wo1MtgidaO8Fxo4DKmxeqy5+jPIsA5yZ9HE7miWerRkYrlTg5f8JRx6H0SOGvbcusPsABWsxse88y1CTchNfBJ/BfmWofq6KW4tE04V9rSyPe+24bObmA64cZLIMED6Daf11AM4eELIMIhGjmoahqsKzfFCTMDQUnSHGiA/NgB8IK0ZVZatmLBukLNu9olEzvU40U8BmNnVlJZj0O1iJZ2uSDDohy+IcLSfW8DosuhE7p9WMlRJKpdiXAT+AMWuhxRM7h7yx3txMi8/1C/9dZPYeekg84c3/WOyoff93d/zccizLzc0Mbz0a1P2Y9IRs0qt6CxMGNJn5piApzs3VRK5lq1cjzAZdpPIlNrRdGNavgMUJ/jbaKmHbrrFxhUWDCklqIe9/l1Zq1OWbT4os9Ps/adj76gaTCew+HOXkvvIsz4zUlA2pqthtCMwY/8aeMChmMhvCtrWfybKiKMyMuFoWOh1ssjxxL+7sMiFzhrmgwTfJAcKOFr/slgjKP+DKcsYWJKTEhsaGAbKYRFv0GK0sK4pWeb1ONFVg1G3ryuM/4d+lLHs7J8tqel23JAwAt92CG02ttHubP7lXaK8/asm3eGLnGHFZCbisXF9Pw9JLsHoRHvz724O7JjM88FNw6xnYuFb9ue9d3wTgkQEny9LzvrxDWdabLE9CYoVsoUwiV2Lc1931QN5Pqlu7G5dh7Fj7VeqjR0Wu+MYVlrayKApdE/d2IHdWX1+uIcsX/1Qs7k5/0LD31ROqw4dbTe8rZdnvsuJzWMTuQi4OhST4+0CWtWKSYlQMFIb3YcZyLWZGXdtWlwY42GRZqx1990hkoLcf9caOFj/ZynbAPcsJyyg+Jcu4vXl8zCAhrJV8AMa290l4QlUbxkiXdpVJWaYSXwZXsLMoKo0smzIbBHTyK4OIjnOj/R77YMMA8Jv1J8uKonAs5OH6WgqufENU1t790Z1POvdx8fjL/7P60NPXNxhxWTkZNnih0CPsFjNjHrsgy7KQRE8bBojFW3KFtaTWWubtjiTMVePjNLVq/Ur7fmUQn4vAbFVZnvA5sFmMu0eNex2EvHZek2S5XIQ3vgon329s34GOKNv9+DAmaeZOohoJGb8tHuiHsgzgm0LVRI39rCwDzIw6t2c9GuDgMMR6mBBNSm9xLt7hA+kvxr12YhmtxS+lFZIc5DQMYEsZASBkqjPkMqCY8DmIpgsiHSAZAbvfWLLnDmnRcYWuvd1hn4NcsUIpttg50dE8y9bcBgE9lWWbBZeikVeDybKqpW34TfqTZYBj4x6urafg5lMwdd/eBAPvBBx/D1z8Aqgqqqry9LUNHjk2hsk0+GlA0wGH8CwblTfrm4JkhNWYuHF2W/E7HXBilvFxhYxoq2w3CUNi7ASsX2EplqlaUIzEmUlftXOAm98Vu46DWG3dAEWrD5+S2VdpGFATHxfTyHK7Vp5e4Z/GmhYizL4nyyMukbXfBAeaLCfNPpbUIKeV+Tt9KH1FuLbFr9red7CV5XUEqQiydYePpH3IC9haIm9sxrKERpa3MoWuK8HlVnI51oXf1OEHkxVHIUpAx4l31w5l2djouIJZeBB9puaZnt3i2LiHTDqJuvgCzD1a/0mnPyTIZuQi19ZSrCbyPHZszJDj0RvVYpK4UWR5EtQysXXx+t3aMGwWE9MBpxga2tLuL8EWzX27ETwKW/MsRo0rJKnF2SkfV1eTYvH9+pdF2+Cxdxv+vnqhYPHiI41vnynLh0ddLG5lqcRuiQf6pixP48qt4rAq+Jz7awGyG3LeoxkONFm+spritcoc09krrZ+8jxDSbgBryZxIwoAD71leKYvILG8peoePpH1IH9lKPGdsxrKERpajqXzXZFn6Tk2pLpIMNN+0qxjVLTYONLKsaH41g8lyVhEXZa+puT+uWxwd9/Cg6YoYVpx7vP6Tjr8XUODKN3jq6gYAjx4fJrKcQ5Vb0vg54wUAACAASURBVHp7ljUPfWZTvH63NgwQVoyFzYxoigUYvauzFxg5AsUMpUTE0OE+iXum/ZQqKm8sJ4SN59i7hsaCAZAze/elsnxoxEmhVCG3flMMWbv69Fn1TWOr5DjuLe37DoqZH5Dl5riymuS1yhyu5E1RknBAIG8AawlNWVbM4By5w0d1Z7FUFCTJLJX2IcCOymsjq64l3CEoF1DyiR6UZQd2CtjyW135TVX3GL5yXF8bht3SN89yGkE+qu+nM46FPLzFdImKYobDb67/JM84TD8Il/+S717bYC7oMqzwQm9MBZxki2Vym7fEAJze57x2TpZiS9gtpp4Utbmgi5ubadRNjSyPdBi/psW1HVIjffn7nJsRu2sLb7wgdqqOv8fw99QTGZMb/z70LMuFUn7zlthJaXdItFdon4VTrgbNjvsI7ezcHGiyfDmS5Kr5KAoqrL56pw+nb5Bbi6uJ3HZ7X78+gAOKWzk3FZRtD/cQQJLl1Vi2fzYMIKh0T5ZDXjsTimZ16UIVLDqCBJW4rgN+dosJd588yynE36yavqEzpgNOHjZfYcV5onmyx4n3w/JLXLtxfWhUZRCeZYDcxi1RUmPWWUWsaS4L+xw9KWqzQTfJXIn82lUxzNppA55GrmeVtb54lif9DsY8drj2LfHA0XcZ/p56Im3y4FFydOmcGVhMB7SFUrxPsXES2vX5mCPev/e8Q3DbLYy1iI3VhSEpivJ+RVEuK4pyTVGUX63zfUVRlN/Wvn9BUZQH9HjfXnF1LUlu7G7xHysX7uzB9BGjLhsWk9bi94OMZQDWMyWSJj+kVu/0obQNn9OCw2oiHl0V8X9GK8vaeRIk3vWAn9Vs2lYqulCW8/ZRxpSErmRZURRGLFoersE2jJQqyJ6zYgxZNilwxrTAZVOLLf9jggjdV7rAo8eG5/MvW/wq8UX9/cogtrhNVqzpVcI9sq4jY4LkFNavd27BAAgcRsXEYdNqXzzLiqJw34yf6c1nIHSqP01xOiKJNg+gGPPZulOQyrI9vdSf2DgNqnZ9PmyJ9e097yRa7d70TJYVRTEDvwN8ADgDfFxRlDO7nvYB4Lj2v58F/t9e31cPXI6kCE7Migvkyit3+nD6BpNJIeS1C7KcWjvwfmWAzVSBpGV0qJRlRVGY9DspxLQ0F2/Y2DfUlOUxJdF1dBzASac2cd+Fspy2jBAkwaiONgwAv1mSZYOV5aJCVrXhUJu3RXWN+CJeNc2zmant9rh6mDxHzuzhEfPrA19GUgtJli2pZWPInMkE3gmcudWeK35lo6IlNt8dWbbYSNjDzCqrffEsAzww6eDu0uvk597Rl/fTEzFVLHTdlf1lqfTYLYQcKq7CJgT6lIQBbJlGKakmJpXhmePpBa18y3ooyw8D11RVvaGqagH4E2B33syHgT9QBb4PBBRFMVgGa47NVJ6NVJ6Tkz6YPAeRg0OWQbT4rSZymrL8A7K8kcqTtweHSlkGCPvsIrMY9B922g1tUTWmdK8sA8zZNKWiCyU8bgrgVAqE7KWu378e/Oa8sOFYjfWGZvIlUjiwG6QsE7kIwPPZ6e1a6HowmblgPsvbrW/oVhveDwTdNmwWBVd21bjz3TeFr7jR03AfiDgqh1LAmY10R5aBVfMkxyzr2C3mno6lXbzNMY9dKXLd81Bf3k9PxCpiQWHO7z/bwH0+bQHQR2U5kiyyRoCxynrf3vNO4r6Z5jYpPcjyNHC75r8Xtcc6fU5fcWVVnHwnwl5RTrJ2CUrGZJ8OIsa9dtZrPcsHGIVShUSuRNEZGiplGYRv2ZrW2vuMJssuoUAG6d6zDDBt2iKBu1rQ0QmiiNSSoKJvHrbPlKegOAz37qcLZVKqE1vJIGV59VVUFC6rM5y/3Xj7NJEr8leZ40xWViA+PDnziqJwxl/EquaNsWEARfcEIXWz69g4CZvFxEP+hJiJ6ZIs31LDHFb6d006mbtARVX4bv5Y395TL2yWNPU9t//I8mmX9m8y6Jyvh9VEjhU1iK9wMMjyP3i0+QCuHneGehMQu/f/2nmOeKKi/KyiKC8oivLC+rpxf6Sra8I3eXLCK5TlSkkQ5gOCca+dTHILyvkDryxH02ILvuIOC2W52fb1gCHsc+DKraIqJvAYbMMwW8ha/IyZekujCBFluTJKKt+5OryhiqG1gKqvj85jypNVjN/qTudLpHBiLRo0YR65CCNHKFrcnL/V+Hf0zLUNnilrbrmb3zXmWAzCGVf3Np52kLGHmFCihL29W30e8GjDrF2S5cuFIL5KXDQW9gGOlWe5bj7CdxeLfXk/PbFR3r9k+ahNnEdqH8lyJJFjRR3FmYv07T0HGXqQ5UWgdm/gELDcxXMAUFX106qqPqSq6kOhkHGK5+VIEr/TyrjXDhOi9vog+ZbHPHYsOZGxetA9yxspsaNg8oXF4mGILrYhr51xdRPVHdY/GaAOEuYRJs1JzD20vY2U1omoo0TincenrZQEWbbnNrt+/3rwKHmyivEtVelCiaTqwlIyyFcZuYgycTd3T/maKsvffG2VFcdRVOcIzD9lzLEYhOMOY1W2mGUct5JnytG71eeUTVxj1U5j44B8qcxrOc1PvnWz52NpiVIBbj/P+ugDvLSwRbkyPKIBwHpR+/zm9t9A2oxpg7KqkLAaLIjUIBLPEVGDWFIrQyUgGQU9yPLzwHFFUY4oimIDfgL4yq7nfAX4pJaK8RYgrqrqig7v3TWurCY5EfaIaKCRI6IdbOnFO3lIfcWY105Q1W46BzwNY1NTlm1+LXptiKwYIootSsFtcGychrgpQNjcmyrqzq+yoo4Kz3yHWClonuKMvkMnbrKkVePJciZfJqW4UPIGKIX5lGiLm7iH+2ZGeHU5TrFc2fO0QqnC31xa5d1nJlFm3wY3n9T/WAzErFWQoaKn8zSVdrCujAIwaer9HJtTVomrrurwWSdY2sqyUNHI0VYfWmZXXoFSFsuRR0nlS1xa6Y+arRciBc02M0RiR7sYr6yxygiLyf4p/quJHAnbOEoxsy8XIJ2iZ7KsqmoJ+AXgm8Al4POqqr6mKMrPKYryc9rTvg7cAK4BnwH+117ftxeoqsrlSFL4lUH4FA+9CRafv5OH1VeEPDZCivYBMHr7fsCxqSnLrlHt5jtExSQhj51JJUra3p+/4YbqI0gPN6NSAWt2g4g6KpoHO8TtvCTL+irLLnKkMZ4sp/IlsiY3St4AG0b0uvg6doKH5kbIFSt11eXv3dgkmSvx/rsn4MjjELsFWwv6H49BmGSDvGohUjIm5m+lIgqaxtTez7FwaZmb6gQ3o5mOf/ZWNMOCqu36RftAlm99D4DD94lYwefmhysFYT1noYxpX5LlQHGVJXWMpa3+xeJFEjnyLk2EkfXyBxi6TLOoqvp1VVVPqKp6VFXV/1t77HdVVf1d7f+rqqr+b9r371FV9QU93rdbbKQKJHIljo3XXGwPPSw8y/vwg1YPYx4741Wy3B9VclCxmRLKsndM80AOUSKGVJZj1v7sDqxWfPjVHj4jqQgKKiuMEol3fuFfTpsoYoWsvjdyJzlSFePbDDKFEjmzxxgP6uY18TV4jMdPhLBZTPzlxb1+w69dWMZtM/O2Y2Mw95h48ObwWDGCZc3GkywY8voLRTFE6sz2fh3wZW+zoIa5udn5QOftrSwpXFScwf4oy4vPw8gcE9OHmQ44h44sJ/MlcmYfZPefCurMLLOsjnUlMHSLSDyH6pUlPXVdswcKB7K27ca68AveFaohyzMPAyos3lEe3zcENbJcUSzgGr3Th3NHsZHKY7OYtpXlYbJh2Ap4lSwbpv60sEVKHpFj2m1yjKZQpG2hri780UyRjMWnuw3DUcmS7ANZThfKFMxuyCegstci0RM2NWU5eBSP3cLjx8f4y1dXqNR4T6PpAl8+v8yHzk3hsJpF+YQrOFRDfp78KisEu/K8t4PrObHjqCR7HGwqFbAkNbK80bmyvBjNYLOYUEaP9EdZXnpJ1KADjxwN8r0bm0PjW1ZVlUSuRMHq2X+CV6WMKblMhBDLXQgM3WI1kcMc0OYCEsOTmGMUDiRZnt8Qq/y7xmoKCKYfBJQDY8UY89gIK1ukbWPQQ6XrfsBaMs+4147iHAGTdaiUZX9REPuIavyCp1JRuV3QFpjpLpNqEoIsl7zTHXuWVVUlmi6QtwZ0J8t2jSwbTQ7S+RJFixdQoaDzkN/GVZHDahWpAB+4e5KVeI5XFreVts8+u0C+VOFnZEySySTU5fknh2aIx5FZYUkNduV5bwdLKZWEyV89V7tG/DaKWiHunGGhC2X5VjTDoRGnIMtGK8vJiCBE0yJf+bETIeLZIheXhoN4ZotlyhWVotW3/8hyMoJSKZF0TBq2QNyNXLHMVqaIOzgNivkHyjIHmCzbLKZqGxQADh+Ez8LtZ+/cgfURHruFsBIjYTnYqjKIFfS41y4WDZ4wJIeHLCvaDf12acTw94pni6xVxBZ11wsK7XhN/umOleVkvkShXKFkH9HdhmGrZEjjIFss6/q6u5HJlylbtQWH3kN+m9cgeLT6n+8+HcZmMfGZp24AEMsU+B/PLPDY8bHteQ2AI4+Jv0v0hr7HYwQqZZTkCuvKmGHEYS2RI2Edg2SPM+ja77McOML8ZufK8u2tDDMjLjGAHl8UaRVGQQ63a8ry27RWx+9eHY6M3WROJJeUbf79R5bjoqKi6JlmJdYfsryWEDuH4wE3eCd+QJY5oGT5xkaauaBrb/zVzJvh9vNQ1rcdbBChKAqT5jhR5QdkeS2ZJyyrbT2hoRrwkxex+YLf8LeKZgqsqVrLUbcLivgS2H0ERjonO1HNW15xjuirLKsq1nKWNA4yXWQ/d4J0oUTZphFVPX3LqipsGMHj1Yf8Liu/+M5jfP1ihM8+e4t/+rnzxLMFfuV9J3f+7JG3i6/zQ5CKkVpFUcukHRNEDFCWVVVlLZkn6wj3ThA0smwNHetOWd7McHjUBaNHQK1USZMhWHpRKIiTIkY16LFz97SPJ69uGPeeOiKZEykRFYd//yU3yNKgwEzfbBjyszXhc4BvaqiKi4zCgSTL8xtpjtRaMCTmHoVCElbO9/+g7gBCxFhVm1c8HgRUlWUQmdND5FkmsUwFhasZY5IBahFN15DlXpRl3zSTfgeb6QL5UvtKroz4U1yj+irL5QImtURGdZAuGKssp/MlKnaf+A89leX0BuTjENzZvPazjx/l1ISXf/6li3zn8jr/+kNnuffQrs988JioHh8Gsqx53gvuSUNsGKl8iUyhTMk9qY+ybPMQCh8ilikSy7SvDMezRRK5EjOjTqEsg7G+5cUXxM6qdXu39dFjIV5a2OqqPKjfiGfFMSqOfagsx24BYB+bZTWR2zGDYBQkWQ77HKL85yAoy8XmC5EDR5bLFZWFzTRHxuqQCzkZPgw3jV5RKuBTEyyXjVckBxnZQplkrsT4DmV5OLYeAUgskbKMsJLWeVisDjZTBTbwo6J0T5bji+CbYsIvft9yu68dyKZFi2dMKMt6eWwLQvVL4yBTMFpZLoMky3oqy5tXxdddZNlmMfGFn3sr//3vv4kv/vwjfOLNh/f+rKKICLmbTw2+b1mqq/4ZVjs4d9pF9TV9k+I60O0gKwiyPHKEWU2YudmBFeO2FjU3M6Ipy2Ccb1lVhUA0df+Oh3/oZIhSReWpK4N/PZTKsskZ2H9keWseXGOMjY5SLKtspPU/73djNV6rLE8LkWPQrw294tv/rum3DxxZXtrKUiyrO4f7JDwhCJ0eqhilrqGRnVtF3x0+kDuLtaS4KGwryxpZHpYLQ2KZjD3MZipv+HBaNF2ghIWKc1QMBHWDxBL4hbIMdORbjmo3CbtvDNSyfjdFLfNYkGWjPcsloX6BvspyTRLGbngdVn7o5DgPzo6IEqZ6OPK4OO/X39DvmIyA5nm3jc4QSeRQdf6crmmKmm1Ei5HsJRFj8zoE76ruYnZixaiS5VGXmKOwuoxTlmO3xGdp8tyOhx+cHcHvtPI3lwZ/p016li3uESjloNi/iDXDEZ2H0SNM+oXq348hv0gih8Nqwue0gH8aDkIxSYuWzANHlm9syNi4OmQZxLDLre8bO0wxCNBuAvM5T1+2dQYVa0lBwKqeZfc4VEqQ3bqDR9UBEssU3BNU1G3l1ShsadvIineiO2W5lBeEzHdIKBbASgcePGnDcPm1oga9rBhSWVaNJcuVikq6UMbk1MiyngrY1k3hOfXPdPfzRx4XXwd9Vy2+BDYPgZExCqUKsYy+jWar2uLZPTYrHujWilEuQWwBRo8yM+pCUegoPu72Vg1ZVhQIzIrXMwIrr4ivk/fteNhiNvFDJ0M8cXlt4CPkEpqybHVrg877SV3eugkjR6oCw3IfhvwiiRwTPodYXPsOSNbyD8jyTtxYFzfGup5lEDeNYgYWn+vjUd0BpARZjlQCVRJ0EFGd+vVpyrJHI2LDYsVILFHRguPXk8Zuz22mCrhtZkzecHeKm4zi8k9XbRidqCQbSfH+Np9WwKLXkJ9GljMGD/jJpA2r2wBlObYA/kNgtnT384HDMDI3+GQ5sQi+aSakyqazb1naMHxhza7SLUGI3xKL7uBRHFYzkz5HR8Ukt6NZ/E4rfqdVPDAy1/Jm3jUiF8RCK3xmz7fedTpMNF3g/O3BFg+ksmz37DOyXMoL69roNlnupsypU6wlctsCkk/bZdnPLX6q+gOyvBvzG2l8Dgujblv9Jxx5O5gscPWv+3tg/YZGdtbUABupg0uW5ZBQ2CuVZY2IDcOQXz4FuTgWLTh+PWUsWY6m84x6bKLxsRtlWV5sfdN4HVY8dktHZGctmRPecqeW4KIbWRa7TWnVbqiynNb80DaHR5ATPT3LWzdhZLa315C+5YqxVpSesLUAgcNM+MXiVn+ynMNjt+AKyjKGLsmyjOEbvQuAuTF3R2T5VjQjhvskJFk2wh628gqETu4Y7pN4+8kQNrOJr17ocdjRYCRzRcwmBbtXuzbsF7IcuwWoMHKEUbcNm8XUlxa/SCJXFTSqZLnX3PFBRmazZe79gSTLR0Kext49hw8Ov3X/k+XUKqpiYhM/GwaTrEHGWjKPzWwi4NIUnKqyPARkWdsidgTF1rvRynI0U2TUZQNvWJDlThvoJPHwCyIy4Xd0pCyvJfOEvPbtxkndbBjiIpkxeMAvnRck1O2wgN1b9Urrgq0FsVXfC468XZCMyAV9jskIxBZgZLaqeq3qTBzWEnmxy+QICJ9wtzaMTUmWhYd8NuhmoZMBP5mxLDEyJ87TzGZ3x9MMKxdg4t663/I5rLz7zDhfOb9MsWz8EHG3SGRLeB0WFIeW9LJf/LXSpz56RMS9+h0sG0yWVVVlNZGvWuXwhEEx7W8bRhu7NgeSLNcd7qvF8ffC2mv7O1swGaHsDFHBdLDJciJHyGvfXjy5NbKcGgIbhrbSd4fElrHRf8doOi92ZDwT3fm6ZWWqplRM+BwdqSTrWtNilSzrRRw0G0YKp6HRcWnN4uGyWcSiXC8bRiEjFne9Kstzj4qvg2rFyMYEmQ/MMq7tBOmtLK8lc2KXSXo1u1XTtNg4ufieC7qIpgvEs6091uWKyuJWVviVJUbmxFe9rRjJVWHJ2zXcV4uP3H+IzXSBJwc4FSOZK+J1WMBhwDzAnYRMQNHiAyf9DsNtGFuZIoVSZduGYbaIaMn9rCwPO1nO6dymlSuWWYplG/uVJY6/V3y9+le6vv9AIbUqFEKMVyQHGWvJ/LZfGcA5IrbIh8GzrK30ncEZ3Daz8cpyqsCo2149b6TvvW3El8Tv1yZIQMfKciInSJLdL5QOnW0YWYPTMORre+wW8W/Qy4ah5bASmOvtdbwTMH52cHfV5IDbyCw2i4mg26Z71vJqIk9YXg/8hyDWZRFI9LqIfNMW4XMdJGIsx7IUSpWdoo5RZHn1VfF14u6GT3n7yRCjbhtffGlwxaNkroTPYa0hy/tIWba6q4uuSb/T8AE/eU2u2jCgt4XjMKCNWMaBJstZnW9c0jPWkiyHToqL06W/0PX9BwrJFcy+SaxmpZoycBCxo5AEwGQC99hw2DDkxcs7RchrN5Qsq6rKZrpAUHqWofMhv8QS+A5V/3PS72A9lafUxvZuOl8iXSiLhY3JJEi3zmkYqtVl6IDftrJs1ldZrpLIud5f68T7YOGZwUyD2dL+nZrdJOzrbLHVCmL7uWawKXB4eyHSKTavV/3KAHPB9rOWr62LxdvR8ZougIA2cKh31vLqa+JruDFZtppN/Nj90/z166sDuwuZ2K0sZ/cJWd6aF59rbdE16XcYXkyyWltIIuGb2t8Dfls3hd2kCQaaLOd19kjNa0kYDWPjJBQFzvwo3PhbfWt1BwnJVRRvmKDbzsYBVpYj8Vw1v7IK9/iQ2DCWwRUEq8NwspwtlsmXKoy4bNu+7k6H/OIiY1liwu+gXFHbGjCVEX/VhY0rqLMNQ0GxusjovJtVCzng57ZbRDGJXsry1rbi2jNOfkBkWF/7Vu+vpTdiO/+dE36HrsUkiWyJfKmyXVAUOCwWzS2avfagJjZO4rBmqVjYaK0sX1/TyHKohizbXGKRqruy/JrYYpfWpgb4+MMzFMsqX3xxMNXlZK6E12EFqwMsjjtjw0gs67/IjN7YLqVBkOVSRTV00VKtut6hLB8S/75h6R/oFFsL202ZDTDQZLlQ0pcs39AuVHKV3xRnf1TcNN74qq7HMBAol4TNwDPBmNc2sGqB0UjmiiTzpWokTxWe0HAoy/GlagbmmMduaBrGpkZog26b2K6HzoeftNgviU6ylmVZhPSq4hzVbyGbT4HNjcthNVRZzsgBP7v0LOt0Q9+6KYbRZJJLL5h+EFxjcPkve38tvbG1IBYZ2hBX2OfQ1YYhM5a3bRiamtvp7IqMjatRlp02M5N+B/Nt2DCur6cZcVn3JjaNzG0vjPTC6mui5roFjo17eXhulD9+7pbuRTB6oGrDAKEu95ssP/Hv4TdPw6/Pwdd+SR9SWS4KG8bY8epDUtgxMhEjEs+hKOzccfVNQTG9f7zgu9FGmtBAk+W83mR5Pc2EzyFuVq0weZ+4OL32JV2PYSCQXgdU8IYZ89gPbHScvOBMBoZYWdZsDUYry7LwZNRtA5tb3JASHZDlQkaoLjLgHjrKWq4qy5LIuEb1U3EKKbB5cFrNhg74pTQi7raZ9VWWYyJOjUYJP53AZIYT7xfzGoPWghbTEj+0f+eEz8FmukC+pM/fbHX3gkxaHzotA5GxcbvaFGeDLubbUZbXUztVZQm9s5bLRdHY2AZZBvj4m2e4uZnhe9cNSOToEYmsZsMAsZjqp2f5hd+Hv/01uPujcP9PwfO/B0/+Ru+vu7UAlSKMnag+NBnovMypU6wmcgTddqzmGnpYLSbZh1aMYk7Lsr6r6dMGmiwXShVdV7E3NlKt/coSiiJO/hvf6YwUDAPkYJZ3UtgwDqiyvBwTF5ypRsryACooO5BYrF7EQh478WxRN+KwG1GtuGZEql2+Q51dOKuFJLWe5faLJfbaMHRUlgtpsLlx2y26z0nUQsbS7UjD0OMci9/uvrmvHu75cXFsVwZMXd5a2KH+yKzlNZ2sGNLSUVWWq2S5wyG/zZ0ZyxKnJnxcjiRb+k1vNCPL8UX92mU3rgoy1sSvXIsP3D2J32nls8916eM2CJWKSqpQwifJsjPQP89yKS9U5dlH4SOfgb/zn+Gevwvf+bXtRVO32Lgivo6drD4kr5lGDvmJjGX7zgf9PeaODzK25gEVgseaPm2gyXJFVas3yV6hqirX11IcG69zEWqE+34S1Apc+BNdjmFgkNS8ppoNYzNVGMitNaPRWFkOQSnXMqT8jiKfFMqqdkMPaSTSqF2CaK0NAzqfjo7vjI0DGHFZsVlMbSrLOWwW03ajmXNUeJb1OG81suyymau+YiOQLpSxmU3YLCahzKsVfbKW44s7FiE948jjwsf6yuf0e81eoapi2K4mS7qatayTFWMtuUtZ9k6IgqpOh/yiN7QEg50DQ2cmfWQKZRaijYf8YpkCG6kCR8friDojc4AqFkd6oDrc156y7LCa+cgD03zztQibAySwpAolVBV88trQT2X51S8KYeWxT4ldGUWB9/47MFvhyf/Q22tXyfI2iRtxWbFbTLpHJtYiEs9tZyxLSGV5P8bpbl4XX4dZWQa42ca2VTvYSBVI5EocbTXcV4vgUVFQ8vIfDr7K2Amk19QbJuSxUyhXSGSNIwmDipVYFpMCYe+uVXQ1a3mAfctS7dpNlg2yYlRtGB6NLPunO5uOrhaSbJNlRVHazlpeT+QJeWrysF2jUM6LavpeodkwXDazocpyOl/CZTeL/9CrPKGQEYsGPcmyySzUsWt/DekN/V63FyQjUMruSPyo2nj0IsuJPD6HBadN+xuZzFp8XKdkWUvC2GWLOTPlA+D15cb2m+vaEHpDZRn0s2KsvgomKwSPt36uho8/fJhiWeVLLw/Odrysuq7aMJwj/Utz+f5/hdBpOPrO7ce8E/Cmfwiv/HFv6vLGVTHUKRM+YLuYJGasDSO8myx7JvZvMcnmNfF1l21qNwafLHdQEdoM1+vF8bSD+z8hfpkLz+hyHAMBmWLgHmfMI0iW0VXJg4jluMjttZh3fQw82qDUIGctV7N1hdImybJRvuVopoDVrOCVfn/fNGQ22ve1JrarrmvRbtby3jxsHSuvCymwe3DbLMYqy/kyblvNVjH0vl1ctbfoaMMAsatWKQn/5SAgqqk/NTc0WVGvV3xcXZLgn+lcyY3egOBelep42IPFpPD6SuMhqUsrgkgfH/fu/aa0oOhGll8TMakWW+vnajgR9nLPtJ8vnx8c0pTQil68csDPOdIfG8bmdYhchAd/eu+8wFt/AVDgpT/o/vU3Lu8Y7pOY9Dt1jUysRa5YZitT3Kssmy2CMO9Hshy9LnaTaxYl9TDQZFkB5jd0UI6oIcv1VuzNLWIppAAAIABJREFUcPYjQgV69nd1OY6BQDIiorcstipZPoi+5ZV4tjowsQNDoSxLsrxTWTZq0RNNFRhx2baVXUl6k21ePOOL4oJk2aniT/odbXqWd+Vhu4Liqx7xcZoNw2mwspwplHDvUZZ7nC6X26J6KssA46fg5A+L615+AOxIVfVne0s6oNl49LJh1CXLgdnOlOVySZDZOlu6douZY+Oepsryy7diBN02Zkade7/pmQCzXV+y3KYFoxYfvm+Ki0txbqwPwHnBtrLsqyXL+YT4WxgJWd5z4n17v+ebhOPvgfN/3N1xqKqwYdQM90lM+jtrPu0E0v8f3j3HA5r1bp/aMEabq8ow4GTZZjHpZsO4tpbCZTPvXTG1PAiXWDm+8dXuA+oHDanVarHEmFeoCgeSLMdyTO3OWIbtHOFBjo+LLYDFKQpUgKDbWGV5M13YGWVVnY5ukywnlvaoyrCtLDfzzKuqynIsx1Stt1zmwupRTFIz4JfOG5uG4dqtLPdqw6iS5b2/257x6KfEdvbzn9H/tTvF5nVBFGsWBdLGo1fW8mpi1+4FCDU3GWl/ByV+W4uNq3/zPTPl4/WVJmT59hb3Hw5sL0prYTKJ49GDLGeiYqHbBVn+4L1TKAp85ZXBUBmTOaks7/5sGRxzdu2vxeJttEE+7/0/JYbpr3XRiJleF8dfjywHhMBQNqCYpJqxXI8n+af3p7K8eb3lcB8MA1nWzYaR5q6QG5Opi3ilh/8RoMBzn9blWO44kpFqZbFUljcPWHycqqosx7N7M5ZB5MyiDHZ8XOwWBGaq2382i4kRl7U6pKQ3oum8aO+TkKSlXd9yfKmu+jnhc1AoV6qe6Lo/mi2Sypc4NOLaflBvG4YWHZctlg1rx8oUynuV5V63i+OLgALeqZZP7Rgzb4ITHxCT/euX6z9HVeH6E/Bn/xj+y8PwmXfBt/9/9s47PK7zOvPvnd4BTEFvLCBBUiRFimqWJVldlmxJduK4xnLKKs5uNmXzWJvE9mYdbzbZTew4TpxkHTuxEu86LrHlKlHdkiWrUJREsYIEAaJ3YHqfu3+c+QZtyq1ToO/3PHwuCdy5cwnMzD33fO953z/VzhaPsZiPjzYY13253SNtZaISoihiLlyks+zdDkCUnpy3VH5YaG+HB7OhZNGb2pVYChfnozjU21L6+FrZx82dpq2CYrm9yYart3nx4xP14RIV2lQs539+euqWUzFg5Dlg522l99l1B11L3viG/OOz4cvWwU3f6miy58OctG+MFA0kYXjycypbaX4rGaEbmiKyqY3UdbFsNRlxaTGmiVPD8FwJOx4pNHUDe+8h/VFKm+K9pkRmadodQIvDAoNQ/53lTDaHH7wxhc8/PoShWfUOAiuxNBLp3GYnDID0WQ5vnXeWx1atrfL4XVYshPW56VmOpSm9jyHHd1MUS3aW2c1KuWXFiWUaZuluWdtZZjIMlcWyKBZCSVghG9cpxS+azGzWLGvRWXa3y9KdyuLdf02BJ9/+2HoLTVEEhp8C/ulO4F/vI19m73aS2Tz7v4EvXb06Za4FS8W7P21N2gSTLMfSSGfF9VIfYLXoZTKQSiwW91hmHOmnm7wXL26WD70+Tq+FQ73NpY/PimW110QJMdfluHVPG87PRTBextmjWhRkGPY1MgxA32L50gs0YDxwa+l9jGZg773A0GPypUwzJ2jbfmDTt9hnph5DfrPBIlHXDE/X1gsmKcxCbIHOcjydVb3MFk9lMbkSV14sA8DVv0kvEiV3ifVELpuXYVBn2WgQ4HXWf4rfnz1yFr/9jdfwxSfP40P/+CLGFtV9SE8FS3gsM5yB+h/w21AsB9z6eWYvRpKrtnFAPpikWVqxHF8mDWGRhKSuZuoWTyyX/n2y73WtvbEpXBBVFsuZJCV1WpwFiYReQ37RVGY1EMnipuly1QN+GtvGbcTdBvzCV8jj+P/cADz2aeCJzwD/8HbgX99DsoO7Pwf8/lngQ/8G/MpPgF97ggqJh+7RZtk2l83H/m7u/rR7rBVlPFKYC5coEljRK7Xwnz8LWJs22cYx9nc1wW0z4fkLm11GXhtbgUEADnZXKJaTIfWF4OxJuuEscZ6VuHmQpGpPn6t9Q6GoGwagb7E8/hK9f3uuKb/fZe8lF5ehR+Udf+ZN8rIvEkNe8KfXQbc8E0rAbjauelavRa70rhFYOE/bRi+WrSY6PSmpR+W4uKBwuG8tPVcBnYeAF/8ByGmbLFhVInOkqVujcfQ5rXUtw3j+wgK++rMRfPjqXhz93RuQyYn4+NdfVXWBZB2RHq+j+A7OQP3KMJIRKhKLFMt6DPilszmEEhl4nRu6bh6JGral/BJ2y2ZtX7+ffv7lBnlZZ7lnrQzDaKKiRG1nmXlpWz1w5C3DYjrplmPJbOE5YDDkU/w06CwX6dhrys5bgF9/nJaEX/x74IUvUtfsnr8Bfvs1sslaO7jZcyXwy9+j1+gPf1d9FzQ4AWRTxTvLHhuSmRyCeUcEpWwKJGHYmuizYElGsdw6WDJN0WgQ8LYdPjx3fmHT59dz5+ext9NTPmG2YB8nURZSitlTQOtexamP2wMubPM78eSZ2hfLoXgaFpMBVpPGtozlmDwGtO4DrBVqit5raT5IbhLw9AmgY3NXGVjTWdapWG7zWItr5tnnzFZK8Zs/CwjGxi+WLXlLr0sqdcvMu1JWIMlGBAG45j8Bi+eBC0+oOp+awoqbNRdYn8uCxTKa0VrzF0fPoc/nwKfu3ovd7W588q49OD0dwgsqYlfHKhXLrtb6lWEE13ssM/wufSKvl2MbPJYZTV3SbLXYhb3IIIzbZkbAbcXIQullyonlONxWEzz2DUWEw6veDSOZ19Za3avFsk6OGNFUBq61hZC9Wd2SpihqH0hSirZ9wP0/BP5wAvjkDPDAM8Dhj25yNynQcRC4+VPA+aPAm99R99xFnDAYWnktb4q6Xot3h7zOcmB32V2u2+nH5Eq88BkEAGOLMRwfW8Hd+ytoz1l3fUlFsZzLArOngfb9yo8B4Kbdrfj5xcVCMmWtCCUyq04YgP6d5VwOmHgV6L6i8r4GI7DvPnLOkKrjT8WozigiwQDIBcZmNmBGh8jrmWCi0LneRNMWLZaZfKwC9V0smwywGA0YUVssz0VgEIA+X4nCSCr77qNBmp//rbrj1JKC3+3qh7LXaSk7YFVLhucjeH18BR++urcQFvDug53wOS345+dHFR93bCmGJrt5NRFuI87W+u0sswGf5vWyhoDbilgqi2hS24tXIZDEsaFYbu6j5flKncNCZ7m/6Le3+Z1lV48mlmPoarFv7nY4vOplGCxBz+ouyDDiae0v/plsDol0btUNA6AOmBoZRmyRkia19lguh9lGXWUpXP1xoONy4In/ri6imSWZFSuWPdp4Lc+xYnljZxkgKYaUYjkyT7+TwJ6yu123kxxsnljTlX34dfpcvvfyCsVyyzYAgnQNdTEWh0kaoFCvzLhxdwCpTA6vjFYpAKQE4UR6vWyA+eXqVSwvXgCSQaDriLT9972XZEnnfiJt/7nTlO5Z4maGgknsunSWp1dK2KkCq8EkcsKo6p35cxVvbhl1XSwDQI/Xrto+7sJ8BD1eB2xmY+Wdy2E0A1c/AIz8FJg5qe5YtaLQWV7tRvld+mld1fLwa5MwCMC9l692wm1mIz50dS+ePDtbVutajrGlOHpLdZUBCiZJhYG0fklJimGpUBs0nHp5ZrOo63XWcez5k6HKUojlERooNRfvWGz3O3FxvlyxHF/vhMGwe9XLMNYVy/p1lmP5ocGCGwaQ7yyrKJZZV18P2zgtMBiBWz5NuurXv678OLOnyFWAWTquQavI69lQMh8lXOQa4dtBE/OVhrTmz9C2iIPBWrb7nTjS14K/f2YYkWQG6WwO33ttEldv8663RyyG2UYrCWqGJ2ffpG27umL5yv4WWIwGvFBEf11NQonMql4ZyEu0PPoVy5PHaNt9pbT9u6+k661UKcb0G7QtIcMA8l7LGg/4ZXMiZsPJ4g5RwNYLJsmk6H0UKP9+ZdR9sbzN78SoymASVU4YG7niYzQd/uLfaXO8ahOaIL/SNYMDXqcF4UQGyYx+HrNKEEWKVb1up3/T4M0vHO6GKAJPnJ5VdOzxpRh6y6001HMwydIIdU/s6y2m9ErxW8rLMHwbZRhMVlFJP7k0UlSvzNjmd2IxmkIwtll3KopivlguUkQ4fNp1li2uwsqFHl7LTAe9To+qtrPMOjzVkGEoZcctQPdVwLOfA7IKdcWzp4C24vpa1gmeCap7zRcNJGEwz+RKumVmr1ehsywIAj559x4sRJL44++fwie+/QZGFqL42Nv6pZ2sd7t0DXUxZk4CBpPkIqEUDosJh3qb8bMaF8vhRHrVCYNhV/neKsfEK1SMF/FALorBQKvSF56UVsBPvEI3h2VWjPRI8ZsPJ5HNiaVlGEA+mGSLdJaXhmm4e6sUy30+J0YXo4q9T7M5ESMLUewIOLU5IXsLRcG++W0grKxQqymhKXrBr7nwsCJoOapuSEZrLi3GMLEcx52XtW/6Xr/fie1+J546J18qkc2JmFiOle8sO1nkdW0vBEVZukjF54biIaBXZzkvw2jZKMNgBXAl/eTySGnjflCxDKCo3GrVY7lYsaxlZ9mjqwwjkpfGFAb8AA06yyyQpIoyDLkIAnD9f6GbdKnL0GvJZfNDc8X9gK0mI7xOC2ZV+ovPhpOFm81NMPkHm5wvxdwZuol1b/682sih3hZ89No+/PvxCTz8+hR+99YBvHN/h7ST9e0kKYDSwcnZk1ToSdBpVuK6nX6cng5huYYyvvDGzjKQj7zWqbM88ybpiQ0yyqfL3gvk0sDZH1fed/R5oO9tZYcvO5psmM0Xt1oxnddAl+wsA1urWJ4/S9utIsPYHnAimckVrL7kMrUSRzKT066zDADX/CZ1SY59VbtjVovQ1KbpeZb+Vm9SjNfG6cPucAmT/psGW/Hi8KJsje5MKIF0VqwswwDqc8hveaSojRZLY9S6s8ycUlocG7o3zAquXGc5HQfC02U7y9vz781iQ35sCKposWz3kptFRsX/d40Mw6mnDCM/BOUspllWWvQExwGTbdVzul4ZuJ0K+mP/JP+xy6NAOlY2PKPNYyv4wyplrlxn2T9AnVjmTVzyIGeoSyXRYeJP7r0Mzz14E/71167C79wyIP1kfTtoMFTpjeLMSdXDfYzrdvogisDPi/hGV4tQPA23dWNnWadiOZej37PcMJfOwzTjcfK75fdbvgQEx4D+t5fdraPZhmxO1PSznnWqiwaSMJp66CZ9KwSTzJ8jDbZf2nuv7ovlnfkL6XAZTWM5LuTz63eoccLYiG8H5b4f/xf98+e1Jji5SePIOsv1NuT32tgKnBYjdrW5i37/lsFWpLK5op6l5WAezeU7y3Uqw8hmyGO5SKfW57TCIADzGtsALkVTaHaYYTJu+Lgw22nglWmoi8GGEct0lnu9DhgEYKTIe/zsDBWzRV8DDg2m3tcUy0yGEdehWI4Wk2HYm6nbpFQXz2zjFNp/VQ2DEbjifuDiM/K1toXwjL0ld2n3WFW5YeTyRccm2ziGyUpF8MybZQ6SpSCJjoOynrvH68D1A4HiVl2lkCoLKUYh5lqdXplxoLsZdrMRL9WwWA4nMpudcmwqV21KsXKJbtDLvB6LIgjAvvfQe6DcTc6l52lbqVgu2Mdpp1tmA4Od5WQYzb1081qPK65ymT1FQ+clZmk2UvfFMityh+dkJuDkOZ9Pe9upZWcZAA7fTx2z849pe1w9yeXog9KzfuKahU0sRuusszy2goM9zTCWiCg/0u+Fy2qSbYw/tkRFmTQZRp0Vy8Fx8sku0llmATN6aJY3DfcxvNvLyzBYcVSms2wxGdDvd+LMzOZkxrPTYdjNRvT5isiotIi8TobJZ9NsXw0l0UGzzFY/1g34sal9pRf1UPEI8brk0EepOyu3uzx3GoBQVgfcrjLFbymWQiYnlu4sA9SJnS0z1L1wnoqozsOKz0MycoNS1jKjzXAfw2w04PKeZhy7VBtHjHQ2h3g6C7etSp1lFhNeQhZUlsveSxrZMz8ovc/o83TuFXTvegSTzATjsJoMaN64grgWtpq4MqbZ89YMJqeRSN0Xyz6nBU12c6FDLJez02G0eaxoKXWxV8quO2gy9NWvaXtcPYnOU6FVQoZRT8Ek8VQWZ6ZDZaNfLSYDrh/w4+mz87ICSsaWYjAahPLaLLONQi/qzT6OdXFLFJ96eC0vRVKbbeMY3v7yMoy5vENABV3Yga4mnJjYXDSemQ5hV7u7+A1TIfJaRVcrGaZgAUGA0SDAYjIgpoNmmaUCbrKOA5QPIgUn6luvvBZ3GzD4LuC1r8vrpM+8SasSltI3tm0eGxYiKaQyysKiVj2Wy2h42y6j5kipz4Op47TtqkKx3NxHy8dK7ONYwd+mjQwDIFeMM9Ohgi6/mkQ2pvcxWLGstVyArXS0li9mi9J+gLTir5VwhsnlqPPcd11FPbQekddTwQQ6m4tYdK6FefuvjGr2vDUhEaTrlgw5Ut0Xy4IgYGerS3Fn+cxMGIPtHo3PCmQjd/mHgAuP199SfSmKeCwDgMdugskg1FUwyZuTQWRyYkm9MuOmwVbMhBI4PS3R8B3A8FwUfT7HZlnBRlyB+ussFwI+NneWAX0ir5eiZTrLLdsoPj1VQiY1d4ou7hWSrg72NGM2lFzXIRRFEWdnQtjTXlyGU3B0UeOIkQzTZHsep8WoiwyD6aA3hZIAyjrLmRQQnqlf27hiHPlV+r+eelja/qIITL5asVvLvJbnFA75sddce7nlZ3ZRnS0hxZg8DlhckpLAVGOy0PuO+U/LYeYkRVyzmQwNuKLfi5wIvD6mY2JeCUIJGkr3FOss5zKrMiutYEv3lZL7iiEI9B6YeAWYem3z98d+ToOwe++reKgmuxl2sxHTmnaWE4X3Ukmat0hnmd30bKXOMgDsCDgxrKCznM7mMDwXwWBHiYutWg68n8zDK4n264VCsbz+AisIAqX41dGA36kpSjbb39VUdr937KYP/afPSi9qz8+FpclynIH602YtjQAme8mJ+4AeneVYarNtHIMtCZdyCpA4DHOgmwrHN8ZXL7hz4SSWY2ns6Shxs6uJDCMEWFc/HxwWky4DfkyG4Vgnw1DRWQ5PARAbR4YBANtuAHwD0gejg+PUze29puxubCBJaeHAHld2pYkVy6X89aeOUwCLQaWXv1QCg6tWdXKYfVMzvTLjUG8zBAE4dkmlM40CwqU6y2zVSa215EbmTqv7+R38IGB2Ai9/ZfP3TnyTvjd4V8XDCIKAjmabpjKMsoEkDKuLfrbLlzR73prA5EhlvKw30hDF8s5WFxYiKazE5HU+RxaiSGVz2KNHZxkg8/n2/cCb39Ln+FpTJOqa4XVa62rA7/xcBM0Oc2k7pzytbhsOdDfhKYnFciqTw6XFGAbaJBbL9bZqsHCeulcllsoCbivmI0lZspRyiKKI5Whqs20cgy3nFtNzZpJ0vq2Vh2H2dXpgMgh4Y40Ug60WDOrZWU5F1hXLdotRl/hepoN2rA1GKpy/Am1lI3gsb0QQgCO/Qp21csNyjLGXaNtzddndWJCH0iXpmWACRoNQCPUpisNLwRLTr2/+XiZF/5+uQ4qeXxGtgzTgJycZMZumAlsjvTLDYzNjd5sbr9ZAt8w6y5s0y+y9pUaitZFMkqQvSiQYDHszcPD9VDOsHYxOJ2jFZc+7AYs0m9uOJptmA34VA0nW0tzb+J3l6RN0fXe1SX5IQxTLOxQ6YpxhF1u9OssAsP+XaKlQTaJStQhNAkZLUaspv8uChTrSLF+YjWCg1SVpSvym3a14bXxFUrF/aTGKTE7ETinuKK7W+pNhLAyVtboJuK1IZXIIa6QfDMUzyOTE8gN+Fhd9+BQ7VzEr6eJiMxuxu92NExPBwtcK799SN7tmO3XZ1Q74WVZfCw6LUTfrOJvZsF76o0Zz3Qgey8U4+EEKRTr2z5X3HX+JfjcVbrY6NOgst7qtJQeJC/ReA4w8t1kHO3kMyKYofKVaBAZJZiDHEWNhiM5TQ70y41BvM05MBDW7SZdKKF6hsxzTsIBfHKaVZJVhLrjhQboOP/pHq197/q8pQvvQRyQfRstgkoWIhEASRnMfuYI0MjMnqNEpw4WmIYplVtgwZwupnJ0Jw2wUsN2vsRPGWva9h7Znfqjfc2hFcJL0ykWGB3xOS924YYiiiKG5MHa2SrvJuWmwFaII/HSocmF7Ia99H5BybGcrdf2UJo9pTTpBH1JlkqNYd0wrKUbJ9D6GwUDLkjNFiuXZ/OS4RE/Sgz3NeH1sBYl8NPQTp2cx2O5GU7npbIdXvXXcOhmGPsVyJJlZ77EMUCFotCgslvNR10VWieoah5dcAU58q3J89PhLQPcRitktg9tmhsdmUtVZLusty9hxM908b/RbHjpKTh/bb1T0/IpgA7MsWEEKTEKikcfyWvZ2NiEYT2NS4wjmSoTzneWmTQl+OnSWF/NSM7W6dE8HcOODwNAjwJOfBc49Cjz7F8D+9wHbrpd8mI68C0wmq2ywdS3svSO9szxOA4mNSDpB8kAZemWgQYrlnhYHHBZjwXNVKmemQ9gRcMFi0vG/2dxD3ppSknlqTZFAEobXacVSnXSWSXKTxoBEb+wDXU3wuyx4+mxl54rz+WJ5u5REx0IwSZ04YixdpM5Ghc4yACxoVSznb6C8zjJL1B0HaBl644fn3GnAYJZ8cXnX/g6Ekxk8cnIalxajOD62gvsOVSgGHV4N3DDWa5b1GvBbp1cGqKvh8Ckvlh2+si4RdcsVvwKkwsDJ75TeJ75C0p4KEgxGZ7NdcbE8HYxLKxJ23ETb4afWf/38Y0DvtatWgNXANwBAkKdbnjlBITY6DCHu66TVn1NT0gettaC0ZlkDidZG2EClFj+/a/4jcPlHgOf+EvjG+2kV887/JesQHU125ERgXoNZo5mCbl9CZ7mlD8gmabC7EZl+g/ztu6+U9bCGKJYNBgG7292FZVmpnJoKYW+nTnrltQy+i3R44Rn9n0sNoclNThgMn8uCaCpb6OrVkvNzZYIoimAwCLhxVyt+OjRf8S77wlwE3S329RZepai3YJKF/IVRSmdZo2FNZidY0joOoE5VKrLZQm7iGJn3G8t0htdw7Q4f+n0O/L+XxvC91yYhCMA9B4u/XgvYVUZeb3DDsFuMBZs3LYkW6ywDKorlBrKN20jPVeRT+8pXS1t7DT1KN4Y7b5N0SCqW5S9Ji6KI6WAC7R4JRYKnk5bgLz69+rWVcbopHLhd9nOrwuIgVwY5neXJ49RNq9CpV8Kedg8MAnBqMlh5Zw1hmuV1LjMADc8KBm07ywvnSbeuxAljI0YzcN+XgA99G3j/14HfegVwykviXLWPUy/FmJIy5MooOGI0qBRj4mXa9siTTTVEsQwAezo8ODMdkqyJmgslMB9O4rLOKtztD94NQATOPaL/cykll6PJ8lLFciGYpPbd5YJUQsoQXp6bB1sRjKfx2nh5Z4HzcxFpemVgTTBJnXSWF84DEMp2NlhnWTMZRv714C0lwwBWl7PWSjFSUVpG3yZ9aVoQBHzwql68MrqMv3nqAq7Z5isMb5XE4VXePcplNw34Ocz6WMdFU5n16X2FJ1TYGV8Zb6zhvrWwQb+ZE1TAFeP0D2gVrOsKSYfsbFY27BROZhBLZaUVCQBJMUafX/VbZgETu+6Q/dyqCQwCcxKL5WyGhhMl/jzlYrcYsSPgqkln2WkxbrYBNRjIPk7TYrn8vIgidt0ua6hvLcy5YlqDIb+ZYBw2c4VAEgbz+C8XRlXPjL9MBb+rVdbDVBXLgiB4BUF4XBCE8/ntJlNcQRB6BEF4WhCEM4IgnBIE4XeUPNeedjdCiYzkIY6TzHqsuwrFcute+uEPPar/cykltkDDHZ7iF1ifiwWT1F63PDQbhttmKh8SsIG3D/hhNAhlLeQS6SzOz4ZLW5FtpN5kGAtDJPsps/TebDfDZBA081pmmuWyneXWPTS0Nfbi6tfGfk5LXTJ1nB+4qhfvu6Ibv3HDdvzlL0mIDlbTWU7lNbNW/Qf8osksHJYitmIOv/wLuihSZ5kFBDQiB95PoT/PfW7z95IRYPhJKiIqhDMwOpvtWImlCxZ9UmHLz5I0ywD55OYytHyeCNL5911XdrVHN9r2ko5WSsjL/FmKKdapWAZIilH9Yjm92QmDoXbVaS2iSM2KWvyeS6Blit9UMIGOpgqBJIzmXuralwujqldEkVQAMrvKgPrO8h8AeFIUxQEAT+b/vZEMgN8XRXEPgGsA/CdBEGQGqwOD+QLn7Iy0N+PJyRAEAdILIzUIArDzFmD0Z/KsfKpJiUASBhvgqocUv4vzUewISHPCYDTZzTjS11LWQk5q0EmBupNhDFX8sDYYyDNbs85yJAW72Qh7sUKPYbICO2+lbiDTLV98hobXeq+V9XxNdjP+4n0H8eCdg+iq1FUGqDObWFE2bMIGzNZ2lq16aZYzm5eKAWUyjPgykI42bmcZAGwe4G3/GTj3Y3ITWsvZHwGZBLDnHsmHY68VuV22abnFsn8AOPzLJCH5tw9TMXbH/5Q1Va8ZHZdT4b5x4LAY7GesY8LgZV1NmAklNA9FKkcontmsV2YolTgVIzxDN9dad5ZV4LGZ4LAYNZFhSAokYZgs9Nmz1vquUQhO0Aq7AucatcXyvQAeyv/9IQCbomdEUZwWRfF4/u9hAGcAyB7h3p33Wj0zLW3I7+RkENv8zuIXKD3YcQu9mZgept4oeCzXvwxjbCmGPp/8waWbB1txdiZcctCH+YCWi9Beh9UFmB310VnOZSV3NgJu7YJJyqb3rWXffRSUMXmM/n3xpzScpWB5URZ2L2lblaTgsXSvDTKMVDaHtAYT5muhznKJYjm+QsvkUmFOGI1cLAPANR+n399h70KuAAAgAElEQVTRT67+/zNJ4On/SQ4rMm60Vr2W5RUOM/niWnKhAAA3/gF1dedOA9f/PtB5uazn1IzOvK9zsTS4jUy+SjreEsmfWsAsHodkulapIZxMw7PRCYPh8KlzylkLG+6ro2JZEAR0NNk0kWFICiRZi3d7Y8ow2OpnDTrLbaIoTgNUFAMoKwIRBKEfwCEAL8l9Io/NjO4Wu+RY41NToerolRnbbgAEI3Dhyeo9pxyCxdP7GPUiw0hnc5haiaPPK79YvmmQXn5PnyveCT5+aRn9Pkf58IGN1EswyfIoLaNKsGELuKyaeWaXTe9by647qJN86mFg+GnSo1Zj6Kngp6pgubVQLK8f8AOguRSDNMvFZBg+AKK8Yn+FFcsNOuDHsLqBO/+MJDtP/DGtDjz3eRocuv2zkiUYwNphJ2Wd5TY5xbKnA/iNZ4EHLwK3fFrW82lKUzfJeKaKBKVsZPJVKq517ICzGZPzs/LTdpUSTpTrLGuoWV68QNtqxJnLoLPZXhjOU4qsQBJGy7bG7CxffIa07ArsEyt+GgmC8IQgCCeL/LlXzhMJguAC8O8AflcUxZIVryAIDwiCcEwQhGPz8+s7ege6m9bF4ZZiMZLE5Eocl3VVQYLBsHnobmW4Tovl0CTZeLGhtQ04LUZYTIaap/hNLseRE4EeBcXyQKsLfT4HfvLm9KbviaKI42Mr0iUYjHoJJmFLrRLS8PwaRl5L7izbmqg4fuUfge8+QJ2Hq/6DJudQFjUWUcn8x9AG6zgAmksxoslMic6yAj/YRg0kKcbBD5CV3M//FviLHcBP/5y0yjtulnWYNo8NBgGyfX6nVuLwu6z62ovqhSBQV7tYquBaYkv0+SFTEiWXVrcVHpup4GZUDULxMpplh4/+71oEpSxdJNs9dwV3nirT3eLA5HJM1TFkBZIwvNvpMzeuYEWvVogiOdlsu0FRLH3FTwhRFG8VRfGyIn++D2BWEIQOAMhvi1YVgiCYQYXy/xVF8bsVnu/LoigeEUXxSCCwvrA71NOCieV4xULgtTH6BV7eI7MwUsv2myjJTKulHy0JTVJHpES3RhAE+J21T/EbW6I3fq+CYlkQBLznUBdeGF7c1GEaX4pjIZLEoT6ZrwlnYHXyvZbMnqKhCgnpUQG3FYvRJHI59ReJxUiq/HDfWu75GyqYYwvA3Z+nhD29KYQPqOgsr0nwY91fLSOvU5kc0lkRrpKdZcgslsfpwu30a3OCtebuzwHv/UdKyLvrL4H3PVT5MRswGw3oaLJjYllesTy5Ekd3SxVep3rReYgCFsoN+V16AYAoK/BCCYIgYKDNjaEqd5Y9pTrLdi/5AafkJf8WZekidVNlrHZUg+4WOxYiKVU397ICSRjevCNGIw35LV6gOmj7OxQ9XO1v/gcA7s///X4A39+4g0BTWl8FcEYUxc+reTKmNX29Qnf5+NgyTAYBB6rhhLGW/usAiMCln1f3eaWwMrbqj1gCn8ta8xS/QrGsQLMMAO891A1RBB5+fXLd13/0Jmm2r98ps8BwBupDszx7EvDukBRC4XdZkc6KCMbVJw9K7iwD1CV9/9eBTwyvBjjojSN/86Oos7xZs2w3ay/DYIV3Sc0yIL9YbuquzVCZHhiMwIFfAj74DVqNUND1AYAer73w+SGVieUGL5Y7LqdI+WJx84zRn1EsvI5OGIyBVlfB+lNvRFHMyzDKdJYBbaQYSxcB3w71x9EY9tqdXFHeXZ6WE0jCYNr3RpJiDOf90bcruzapLZb/HMBtgiCcB3Bb/t8QBKFTEISf5Pe5DsAvA7hZEITX83/uUvJkl3U1wWQQ8NpY+c7tq5eWsa/TA5tZ2YeuYrqOkIXWpeer+7xSWBmraDXldVpqLsMYX4rBYjKgzS3jLncNvT4HruxvwbdeGS8MaYmiiG++Mo6rtnnR75c5cOZqpU5prsZhLbOnaKhIAgWvZZX683gqi3g6W95jeSOCsCotqAaqOst5Gcaa5DVW0GpZLEfydmalNcuQd0FfGd8aEgyN6WlxYFxGsZzLiZhaiaOrkYvl3msBCMDIs6X3GX2OJIImGbMaChloc2MpmqqKI0Yyk0MqmyujWdYoxS+Xo2E21k2tI1ixPC5zRWUtE3kZh6z3QUs/bRtpyO/8Y3TeCn+PqoplURQXRVG8RRTFgfx2Kf/1KVEU78r//WeiKAqiKB4QRfHy/J+flD9ycWxmI/Z0eMp2ltPZHE5MBHFIrjZVC8w2oPsI3cnXE5kk2aVUKJZ9LkvNrePGlmLoabHDYFDeNXvghh0YXYzhoRdGAQAvXlzCpcUYPnClggLD2UpuC1r5dSohGaEBv7bLJO2uVeS1JI/lWmNrosFaJRfERBCAUGLATzsZBiu8S4aSAPI1y43uhKEDvV4H5sJJySmkc+Ek0lkR3S0NGBnOcPoobn5tquBaYku0KqWzBIMx0Fq9IT+W3lfWDQNQ31kOT5GcQ0cnEaWw165c+dFaxpfi8NhMaCr1cyyGxQm42hunsxxfpuG+Pe9WfIj6EuBI4FBvM94YXykZa3x2Oox4OovDcrWpWtF3HTkBJKob+1kWNhBUSYbhtGAxmpSckqgHlxZjivTKa7l1TytuHmzFXz0+hG8dG8cffe9NNNnNeOdlHfIPVggmqeGQ3/xZAKKk4T5Au8jrJRZ1LVWGUQtYJ1vJBTERpEJ5jQ6RdX+1HPBjQRlF467NdsDslH4zlk7Qa7GRA0l0gg0FT0gceGJL191S/Lzrme3voFSyZJECdehofh95A5NK2dVGkqZqDPmFE/S+KqlZVuOUsxZWENZhsRxwWWExGjCpplhejikaqId/IJ8q2wCce4RCsva+R/EhGq5Yvma7D9FUFsfHineXXx6lN8ZhqV66WtN/HXUix+vIb3l5lLYVO8tWJNI5XRLMpCCKIsaX1BfLgiDgM/fsQ6vHhge/cwJL0RS+ev+R8sEapaiHYBLmo9ohIdEO2kVes86yJOu4WqI0qSsRXCfBAACHWXsZRjRJxyqa4AfQRT26IO1gLFyId5Y3wS7440vSCgfWjWtozTJAGsxcOj/It4FT3wOaenUNI1lLm8cKp8WIi/MaDNVVIJSfySgbSgJIf2+Voo6LZYNBQFeLXfINYjHGl2LoUbK64h8g/+kaNtckc+phkq6peB80XLF8/YAfJoOAJ8/OFv3+46dnsKvNVbulta4rAAgUqVgvrIzRtlKx7Kxtil8wnkY4mVF2l7uBHq8Dj//eDfjHjx7BD37rOhzpV6ijZfnxtRzym3qd/FQlFkgemwkWo0F9Zzk/7Ol16q91VIXDq8yBpkixrIcMI5pimuUSF3WnX/rrK7hFPJZ1oMdLRa/UIT9WLDe0Zhkg3bLJBpw/uv7r8WVg+CkKDKrSMKggCOjzOTG6qH+xzAaYS8oH7C2AwaR+VXDpInnIl8goqDXdLfJdYBiiKCofcvXvIn94tTcjehOZo/fB3ntVvQ8arlh228y4ersXT57Z/AZYiqbw8sgSbt/bXoMzy2N1A617Nke41pKVMfrQKJHexyhEXtfIEYOlb3VqtCxqMhpw29429PlUpMgxe66aFsvHZQUKCIKgSYofu2mqa80yoG1nWYdQklilYtndQXG6UljZIul9OhBwWWEzGyQP+U0sx+F1Woq7lDQSZhtpMU98a70U48yPqOO8T/nSsxL6/Q5cWlTn/SuFUF6GUbJYFgRt3IwWh/O2cVU2DJCImmJ5PpJEMpNTLsMAgMU6l2Icf4jeB1d8TNVhGq5YBoBbBttwYS6CSxvuXp84M4ucCNyxr4bFMkDd5clX62d5YuUSXVwrvNl9TpbiV5vOMovtlOX3qDe2Zuoq1EqGkYqSZplF20rE71af4rccS8FkEOCx13kx4WhRPuC3sbOsg3VcJC/DcJaSYbjbgIjEYjk4AUCo2y5XLREEAT0tDsmd5cmVOLoaXa/MuOo3yN3lxL/RvzMp4Gd/Rb7sMj871NLvc2J8KVZyrkgrWGfZU8o6Dsiv2qiVYYzUpQSD0d3iwEIkqWjOgkmW2KqMLPy7aMuiwOuRbAY49s+k61cZVd6QxfJte9sAAN8+NrHu6z86MY3OJlt1k/uK0X2ElsDqZVJUgm0csNpZrpV9HIvt1KqzrAladSeUMvMmaeBlaq0CLot6zXI0hRanBUK9+/myzrLcm9MixbLBIMBuNiIu0VFBCrFkhc6yq50GFDMS3nfBccDdDpjqvNtfI3q8Dsk2WhPLscbXKzO6j5Dn8gt/C0QXgWNfBZaGgds+W3U/7n6fE5mcKDtNUS5Ms1zSDQOgmRM1jQ5RpOt4XRfLzD5OfjefaZ0VaZY93eTfXc9Dfqe+R3MeV6pPk23IYrnH68Bd+9vxtRdGsZIfQnrp4iKeHZrHh6/pq/3FnZm/Txyr7XkwJASSAKud5YUayTCmV+IwGYSCm0Pd4AzUrrNcGO67XNbDtJJh1L0EA6BBHiVJXUWKZYCkGMzBQgui+Y6PvZTvuzu/EhYpPoexjiD3WC5Hn8+BS4vRio4+2ZyIiaW46mHiukEQgNs/SxahX7oKePQPafBv4Laqnwrzsh9Z0Fe3HIqnYTUZyucpOAPqOsvhGSATr0uPZUZ/XmaoRPrCJEuKdPsGA+DbWb/FciYJPPVZoG0/sPudqg/XkMUyAPzOLbsQTWXwl4+dQzSZwZ/+5Aw6mmz41evq4EUd2AOYHfWhW07H6SIsoVi2W4xwWIw1lGEk0OaxwajCY1kXXK21s46bOEaaVo8827uAy4qlaBJZFZHXstL7aomS8IFclpatixTLdotRc+s4h8VY2jtcTrG8Ms71ymXYHnAhlspiJpQou9/kchypbA7bAyrmGeqNbTcAH/kufVbc8AngfV+rScpjfz59VW/dciiRLt9VBlaHZ5VKIuvYCYOxWizLvzmZWI7D71Kh2/cPAAvnlD1Wb17+MklQb/8TTfTmDVss725346PX9OHrL47hyj99AicmgvjDu/YoswfTGqOJdGKTddBZZgNBEn1Za5niN7USR2dzHemVGc5WIFIjGcb4S5S+JRO/24qcqE5SsxBJwu+usy5/MZSk+BVJ72M4LSbNB/xKSjCA1WI5PF3+QLkcLSk2885yKXbku5qVrMsuLtAg3Da/S/dzqir91wEf/xlw8ycBe23sUwNuKxwWo+6d5WA8XTlIw9VKneGUwpAUVizXYdQ1o8lhRrPDrMiBZHQxqs59KjAILF8CUvoPdMpi5iTw5GeBgTuAHdp4jDdssQwA//2effjsvftwsLsZ3/74tbjnYHm3h6rSdQXpTTO1kTQUYLZxLZU7ywB5LVcjqrQY08GEvHz6aqG2O6GU4CQtu/deK/uhAZd6r+WFSAr+evdYBpR1llloUInOckxDzXI0mS093AeQZhmo7IgRmQWyKS7DKMP2ABW/F+fLF0esmN5SneU6gdnHKel0yiEUz5QOJGE4WaiUwmbH0jBgMJM+t47p8ypzIBldiGGbGreotr0ARGD+jPJjaE10Afj2x+hm8d4vaXbYhi6WBUHAL1/bj288cA2uVOqjqxfdR+jCNvNmbc9j5RJtJXaW/TXqLOdyImaCCXTUY2fZ3U7WM0q8fNUw/iJte66W/VDWEVZ645NIZxFJZupPP14MJZ3lMsWyw2IsDOVpAckwylzUnX5AMFQulpdHaFvH+sla0+ahruawhM6y22YqeMtztKXfp799nKTOMguVUqpbXrpIjSZjfTsC0c2JvJ93PC9XYhpzRbBU2dnTyo+hJaFp4KF7qMn0i/+8msCrAQ1dLNc1bMiv1rrllUtkfeaSZqfndVpqolleiCaRyubQWY+dZbfEzp/WjL1E2vf2/bIfqrazzIrsxuosy7iZSTAZxmbnHIfFqG2CXyoDVzkZhsEIuCTYxzWAfrLWCIKAbX5nRQnAyEIU2wOu2g+Db1F6vA5MrsQrDlqqQbJmGVA+oF3nThiMfp8DE8sxpDLS7fqYbGObmmK5ZRtdo+ZqXCyLInD6+8A/XEdNhQ99kyRJGsKLZb3wdFGBWmtHjJUxWrY1SPtV+1xWLEaTun7IFWM6H0hSVx7LDHd+uK6SplRrxl+kmy5jhQtCEQIqO8vMo7kxOssttI0tSn9M2c6ySVvruFQWDmuFWQpXGxCuMOC3OEzhQk3SVoneqmwPuAqa5FJcnI8W9M0c7elusSOZyalOES2HtM6yChmGKNa9xzKj1+dEToQsu77RBQ2KZYOBdMuzp5QfQy1jLwL/dAfwrY8C7k7ggWfIV1ljeLGsF4JAUoxaD/lJ9Fhm+JwWpLMiwhouQ0uBBZLUlccyoxad5USQJDx9b1P0cKfVBLvZqLyzHGad5QYolo1mwNqkrQxDw7jrSDIDZ6VpcykpfksXydWmzpeEa812vxMTy3EkStzwxFIZTAcT6ooETlmY96/SZLlKiKKIUDxdPpAEUFcsR+ZoMLABiuVVBxLpOvGR/L6qZBgA6ZZrUSwvDgPf/AgVysuXgHf/NRXKgd26PB0vlvWk8xBd4OIrtTsHucUyi7yushRjJh9I0l6PnWWXRLcCLbn0AoWRbLtB8SECbqvizg6LPPc1ggwDkJ/iV2nAL6llKEm2EKNdEikpfkvDDXHhrjXbA06IIkq6AzCJBhsG5GhPdz7kQq9iOZLMICeWibpmmCz0HldSLC9eoG0dO2Ew+nzyva1HF6Lwu6zlJWJSaLsMiC1UL4sgnQAe+zTwpauBC08BN30K+O3jFGetYyOBF8t60nGQtrMna/P8qSh9SEh0wgBIhgEAS1UOJpkLJ2EyCPUZgmG20VJ/NTvLI88CJhvQfaXiQ7R5rJit4DdbioaSYQAUTKJEhmEtoVlOZzWTIkWTFazjAOosR+dLu+ewJeEGuHDXmj0d9Ds9PRUq+v1T+a/vbufFsl6wGPEJBalyUgglaOXHY5dQHDlblRXLS8O09e2U/9gq43dZ0GQ348KcdIu80YUYtvk1COVhQ37VMDNYHgW+civwwheBg++nIvnGTwAW/VeJeLGsJ+0HaDt9ojbPX/BYllEs56fDF6rcWZ4LJ+F3WUsHN9Qad0d1O8sjzwK91wAm5cVqq8eGuZCym575cBJuq6l8OlY9ITepKxGkQrmIWb3DYkI2JyKVlT4sUwpRFCsP+AFASz9tmdXjRqLzDbMkXGt2BFywm414czJY9PsnJlbgtpqwfat5LNcRTqsJLQ6zbp3lYIyirit2lgHyWq40D1CMxQs0HN8AVo2CIGCg1YXzMorlkcWoNlIk1hRkabN6MXsK+Ood5HTxoW+RLZxbmnGBFvBiWU/cbXRXWyv7OJm2cUDtZBhz4SRaPXXcxXS3V6+zHF2g1QgVEgwAaHPbMBNKKOqQLkSSjSPBAACHX36xXESCAaAgmdAixS+RziEnonJnmRXBzPFiI4vD6/fjlMRoELC304OTJYrlN8aD2N/dVL835luE7haHfsVynIrlipplIN/omJL/JIvD5PagQfpbNdjZ6sKwxGI5lEhjPpzUJpTH3gx4d+hbLC8OkyWcYAB+9Siw6w79nqsEvFjWm44DwEyNOssKrKZYvHHVZRihBFrrOS1OygCWVlx8hrbbblR1mPYmK2Ip8kuWy2Ik1TgSDEB+cIyEYjmqQbHMfvauSm4YLXnv5KWR4t/ntnGy2N/VhFNToU1x74l0FmemQzjYU5t0u7cS3S12HWUY+WJZSmfZ00H+u3KbBovDDSV72tnqwmI0hUUJcyrnZsIAgMF2tzZP3nUYmDyuzbE2ElsCvv5eACJw/w+B1kF9nqcCvFjWm/YDwPzZ2iT5LQ6TS4DDJ/khVpMRbqup6jKM+XASAXcdDvcx3O00gJVTvzRfkaGj9DvrPKTqMG0e+nnOKpBiLESSDVYsByg4JlG8m7iJ+DJgK14w2fPOFXENHDGi+WK5bCgJQMW+xV26szx/lpaEZawSvZXZ1+lBLJXFyAYLuTPTIWRyIg5282JZb7pb7Jhc1sdrmXWWJckw3J1ANinPhz2Xo/diAxXLA21U+ErRLZ+dZrp9jYrlzsPUvde6oZTLAd99AAhNAR/8JuCvnX6cF8t6074fyGWAuRrEQS5eoDe7TON9n6u6KX7pbA6L0VT9d5ZzGZr61ZNcFrjwOLDzNtXLf61uVizLH/JrOBkGs4iSOuQXX1oNM9l4qHxnWYtgEtZZrijDEATA27+a0reR2ZPkZ6rAc/utyP5uWjXYqFt+Y5yciS7nnWXd6W5xIJnJ6dJ4CcVldpYBKrgkP8EEFdjeximWd7aSpEKKbvnsTBgem0m7XIOuw7TVWorx4pfoenjnnwE9yofdtYAXy3rDxO+1kGIsKVtG8jotBeuwasCCM+peswzoP+Q38Qp1QDTQZDEbPrnFcjqbw3Is3WCd5fzqidSp99hiyWLZrmGxzI4hyZ7Ju710Z3n2FFk0cSSxMz/kd2x0fTfxxYtL6Giy1adF5RZj1WtZeylGKJ6GIABuKe8rdydt5XQ9FxvHCYPR2WSD02KU1lmeCWOw3aNdgmX7AUAwaptYvHQReOpPgd13AUd+TbvjKoQXy3rTso2WV6s95JdOkBuGgje7z2Wt6oAfc2xorWsZBkvx01m3PHSUPnR23Kz6UKxTPyOzWGarCv567vRvRE74gCiSDq6EPIlJJrQIJokWOssSVgm828lcP7ehSI/MA5FZoJ0Xy1IxGQ24aTCAo6dmC7rlaDKDp8/N4fa9bTU+u7cGenothxIZuK0maUOarLMsZ8ivgTyWGYIgYGerC0Oz4bL7iaKIczNhDHZoJMEAAIsDaNsHjL+kzfFEEfjh79BK2t2fk706rge8WNYbg4EuctW2j1seASAqWkbyOS1YrKIMYy7MiuU6Ls7cCpbylHD+MUrts6tfJnZaTXBbTbLt41gnuq2efx8bkVMsJ4KAmAXsxTvLjlrIMAC6sc6lgeDE+q8zn/a2farP563E3fs7sRBJ4qURkuY8dXYOyUwO79zfUeMze2vQpWOKXzCeRpNDoiSJhUqFZKwKLl0EzI7Vz/0GYW9nE05OBpHLldaJTyzHEUlmtNMrM/qvB8Zf1mY+67V/JfvU2z4DeDrVH08DeLFcDdr30wWvGsNhjMIykoJiOa9ZLveG05K5MBVndS3DcLWRbU1oUr/nWBmn18nA7Zodsq3JJluGwQYC2YBgQ+Dw0zYqQbPMkv5Kdpa1K5ajcorlUvZxLEqWyzBkcfNgK+xmI358goqkR05Ow++y4sr+4jdJHG1xFbyW9ZFhSLKNAyjFz+GX31n2yp/3qTWX9zQhlMiUTK8ESIIBAIPtmwOZVNH/diCTUC/FiMwDj30K6LsOOPwxTU5NC3ixXA3aD1CgQKnhHT1QsYzkc1qRzYkFex69mQslIQh1nhZnNFGXIahjsXz+Mdpq6CHZ5rHKlmGwm5eGKpblxNrGKhXLzA1DQ+u4Sm4YABDYTduNiZ+zp6g75vSrPp+3EnaLEbfva8O/H5/A5x47h6OnZvGuAx0wcn/lqqGX13IwnpbmhMFg9nFSaTDbOAazRHxjYqXkPq+PL8NoELBHSxkGAPRdC0AARn+m7jhPfZbSh9/1BVqZrxPq50y2Mh0sye+N6j3n0jDdTZfwki0Hc0Goln3cXDgJr8MCs7HOX45N3ZQepBdDRynJzb9Ls0O2KUjxmy3cvDSQGwaQDyaRUyyXl2FENdAss+60JM2yq5WkGGMvrv/61HFaneLI5lN370Wf14m/eeoCLu9pxifu2F3rU3pLoZfXcigho7MM0JCf1M5yNk2xyg1YLLPB1jfGS1tovnppGfs6PZXtLOVib6HPqdHnlB9j+gRw/F+Aqx4AAtpdB7WgzquTLUJgEDCYqjvktziseJLX56QOrxRzcy2YDycQaAR9rKdLPxlGOk4arYE7NF36a/OQDEOOpGYulIDfZYWp3m9eNuIMSCyW81KNEsWy1WSAIGjTWY4mM7CaDNJ/lr3XUrHMvGmDE+SxvP0dqs/lrUjAbcU3HrgGf3TXIP7pY1dKk8NwNIOKZe29lnXtLK+M0UxDAzlhMExGA/Z3NZXsLKezObwxHsTh3hZ9TmDbDcDYS0BSeux2AVEEHv0D+ly+8UHtz00lDXY1bFBMViCwp7r2cSqWkVhnuVpeyxR13QBL/k1dNOCng8k+Rp4DMnHNYzw7m2zI5MSCPZ8UZkMJtNWzfrwUTr80n2W2T4kBP0EQ4LSYNBvwk1Wg9V5DXt5s5uDCk7Tdeavqc3mr4nVa8MANO+QVVxxN0MtrORTPwGOX8b5yd9L7SsrwGZMwNpDH8loO9lB6ZSqzeUbqzHQI8XQWV/TpVCzvupP8qYeflP/Y098HLj0P3PRJ6lLXGbxYrhbt+4GZk5X304JkmNLmlBbL+cjrhWoVy6FkfTthMDzdNMAgNfhCDkOPAmYnDUloSGdzfiJ9RbpucDaUrG8bv1JI7SzHl8ier4xEyW4xajbgJ0mCwei9lrZjP6fthSfodRfg8gFO46GH13Iqk0M8nZV389PcQ9uNTjPFaECP5bVc0edFKpPD8bHNiYWvXqKvHenXqRjtvZaaEGd+JO9xqRjw2KeB1n3A4fv1OTeV8GK5WrTvpwI2Mqf/c7FpeoV3xi35YnmpCprlXL7r2RDFclMXbbXWLYsiDfftuIlWITSE2TdNySiW58KN2lkO0I3MRp/ijbBAkjJyF4fFqI3PcioLpxxtoH+ALjbDTwKZFHDxGWDnLQ03lc/hAPp4LbPBc0npfYyWbbRdkjBkv3gBsDWXlGnVO28f8MNsFPDU2c21xiujFMrT0WTX58mNJgoRGTpKn19See5zQHAMuOsv6Bh1CC+WqwULFKiGbrnghKHszthsNKDJbq5Kit9SLIVMTmyMYtnDimWNdctzp6kA19AyjsE6y1KL5XSWlkwbsrPsagXEXOXOf5lAEobdrF1nWVJ6H0MQgEMfBk59D/j6e4FkCNj3HtXnweHUAj28luj6LvwAACAASURBVIP5qGtZneWWftpKcaRaPE+rsg16g+qymnDVNu+mYjmWyuDps/N4x+5WfU9g8G4gGaQbfSksXABe+CJw4ANA/3W6npoaeLFcLdqqWSyzzvJ2xYfwuSxVSfErpPc1hGa5m7ZaD/kNHaWtDsWyx2aG22rC1Io0+7j5cAN6LDOkRpJLKJadVpNmA34OuUNlN32KZhxGnwPe/nu04sDhNCAuqwnNDrOsla1KsGJZnhtGO2CykctFJebOAK17lJ1cnXDzYBsuzEUwvrQqf3nq7Bzi6SzuOahzyMfOWwFnK3Dsq5X3FUXgkU/Q7+a2P9H3vFTCi+Vq4fACTT3V6yx7uiiCUiF+p7UqneVCIEkjdJYdfsBolaZ7k8PQUaDj4Gosq8Z0Ntsld3YK6X2NKMOQGkkeX6o4QKKVDCOSzMAlR7MMAGYb8MH/B9z5v4Cb/5vqc+Bwaklnk13TYjkUVyDDEATqLlcqliPzNPfQ2thpmTcPUvf4B2+s2uX98I0ptLqtuGqbzvISkwW44mN0Xaskezn9MDD8FA31ues7hp4Xy9WEJfnpzdKwqq4yQBPkVeksF6KuG6CTaTBQ9KaWxXJsCZh4mSzjdKKrRfrFqiHT+xiSO8uLVZNhxORqlhne7cA1H68rU34ORwmdzXZM6tBZbpLjhgGQbrlS8TaXT8ts8M7yNr8TN+4K4MvPXkQwnsbwfARPn5vHXfurFMpz5FcBgxF48e9L7xOaAn70e0DnIeDKX9f/nFTCP4mrSft+YGGIPHX1ZPGC6kleFnmtN2zZv66jrtfS3EM+nFpx4UnS2e66U7tjbqCz2YapoLTXXENEj5fCle9MlOssi2JehlG+u+K01sg6jsPZYnQ127TtLCdoxUdWZxlY7SyXs/6cO0PbtsbuLAPAg3fuRjCexoPfeQO/+fVX4bKa8Bs3qmuiScbTAVz+IeCVr1AC6UYySeA7v0bb936lbof61sKL5WrSdhkVRnOn9XuO6AIQX1ZfLDstWIqlkJURZqGEuVACbpsJNrPMpepa0dIPrFzS7nhDj5KLQ+ch7Y65gc5mO1ZiaUSTlWUFk8txWEwG+J0NWCwbzfSzLNdZToaBXFqCZtko6edVDlEU5Q/4cThbjM5mO0KJDMJ5Fwu1hJRolgHAuw1IR8vbS86eos8GZ0DFGdYH+zqb8MAN2/HEmTlcmIvgC++/XD8XjGLc+hnA3gz84LfJGo6RTQPf+w1g7AXg3V8E/I1h0ceL5WrCImv11C3Pn6Vt66Cqw/hcVogisBzTt7s8F24Q2zhGSz992CpJKNpINkM+ugO367rc3iXDEWNiOY7uZjsM1Viq0wN3e/nOcmSWtq72sodxWkyq464T6RxyIuCQq1nmcLYQzJFnOihtyLgSoXgaVpNBfoOFOWKUk2LMnQFa9zasE8ZG/uiuPTj1mTvwwh/cght2VfkGwOEF7v48MHUc+Jd7gdHnaSX1n+4kt5/b/wdw4H3VPScV8GK5mjT3AVaPvsUyW0YKqC2Wq5PiR8VyA+lj2QeuFt3liZeBxIouLhhr6ZIRTDKxHCvYPTUk7o7yneVQfuDFXb5YdlhMSKRzqlZWWLHNO8uctzKsWNZKtyw76ppR8Fq+WPz7uRxdP7eABGMtNrMR7U01usbuuw9439eo5vnaXWSHuXgeeN9DwNv+c23OSSH8U7yaGAwkxdAzyW/+HGBxr3oCK8TLUvwiSexqc2txZkWZCyf0y6nXg4Jf56j6D9Who4DBBOy4We1ZlaUQDLBUOUVrYjmO2ztLJ9vVPe52YPqN0t9nXWd3eecRlroXTWXkL/fmYTIORQN+HM4WobOZCjWtdMuhRFq+XhkgGYbRsjrEt5HFCyTTYDavHG3Yey+w/R3AyHNkEdd3LWBx1vqsZMM/xatN+37g9f9Ld7F6LL3Pn6VoXJXLSH4XSSP07CyLotg4UdcM1p2Q4tdZifOPAX1vA2we9ccqQ5vHCrvZiJGF8sVyLJXBYjRViKhtSNwdlJKZzRQfGmFd5wqdZTaUF0tmFRfLEVYs884y5y1Mq9sGo0HQrFhW3Fk2mkliMX2i+PcnXqFt95XKT45THFsTsOddtT4LVXAZRrVpvwxIRaQlCSlh/qxqvTKw2lnW0z4ulMggmck1lgzD3gJYm9QXyytjNOipo2UcQxAE9PkcGF2Mlt1vMu/F3NjFcjsAEYiWiJUPT5MUyuoqexiHZbWzrJRoktw0uAyD81bGaBDQ7rFJDkaqRCiegcem8D3VcQCYOVHcEWPiZSrq/LvUnSBnS8KL5Wqj55BfdJGGz1TqlQGgxWGBIACLEf2CSeYb0aZMEICWPvXFMkvt09Eybi3b/E6MLpQvlicKxbLyMJuaUwgmKaFbDk9X7CoDq9IJNY4Y7LF8wI/zVqdLQ6/lYFyhDAMA2g+QW1RwfPP3xl8Buo5wb3NOUfirotoE9gCCUZ9imTlhBNQbqhsNArwOCxZ1lGGwqOtAI8kwAGlJUJUYOkrBE1Wyzen3OzG2FEMmmyu5z8QyyTR6Gr6zjNKOGOEZacWylRXLyr2W+YAfh0N0aui1vBxLocVhUfbgjoO03SjFSIZppY9LMDglUFUsC4LgFQThcUEQzue3JSe1BEEwCoLwmiAIP1LznA2P2UaaYj2KZebfrIEMA9A/xa+h0vvW0tIPLF8i3bkSUlFg5NmqdZUBYJvPiUxOLNvdmWAey64Gu3lZCxtsDU4W/354uuJwH7A64Kcm8jrKNcscDgByxJgJJlT79qezOYQTGeXFcts+QDCQFGMtk68CEIEeXixziqO2s/wHAJ4URXEAwJP5f5fidwCcUfl8W4O2y/SJvZ49CdiaVTthMPRO8WvYtDjvdiCbBEIKY69HnqXH62wZt5Z+P00fj5SRYjS8xzJAYQJmZ/GZAFHMd5YrF8uOvAwjokKGEWGaZe6GwXmL09lsRyYnFhJblbISo0CSFqdCGYbFCfgGgKnX1n/9wpPkTNR1RNX5cbYuaovlewE8lP/7QwDuK7aTIAjdAO4G8BWVz7c1aN8PhCZJY6wlMyepENfIUN3nsmIhqp9meS6UhM1sgLvROm+B3bSdH1L2+KGjgMUF9F2n3TlVoN9POuRyuuXRxSi6vQ2sVwbymvL+4sEDsSUgm5LZWVYhw+CaZQ4HwKrXu1rdMgvJUtxZBoAdNwEXf0rSC4Buok8/TPZm9mZV58fZuqgtlttEUZwGgPy2tcR+XwDwIACF69ZbDDbkN6uhFCOXJRlGu3Yekb4qyDBa3TYIjZaW5M8Xywvn5D9WFMkybsdNgEnFB75MAi4rnBYjRheL28dlcyIuzEWwq7W8S0RDUEpTLtE2DlirWVYhw0hlYDEZYDby0RDOW5tOGSmi5ViOalAs73sPreyde5T+PfUauRPtLdrr43AASCiWBUF4QhCEk0X+3CvlCQRBeBeAOVEUX5W4/wOCIBwTBOHY/HyZDPdGRg9HjKURIB3T1FDd57QiGE8jXWYoTA1z4URjeSwznD7A4V8dqJTD7ElaVaiCZdxaBEHA9oALF+aKx3SPLcWQzOSwq12/AJqq4d1GxfJGe6hCsSxBhpGP0lU14JfM8OE+DgfaBZMsq5VhAED3VYC7kyKXAdoaTMDg3arOjbO1qVgsi6J4qyiKlxX5830As4IgdABAflvM3PQ6APcIgjAK4N8A3CwIwtfLPN+XRVE8IorikUCgylnm1cLppzdrKXN0JTANtIadZW8+8npZJ93yXDjZeHplRmA3pSXKhVnGVVGvzNjT4cbp6RDEIh6j52ZoSXK3jmmNVaOlH8jENztiSIy6BgCT0QCryaBywC9bkHNwOG9l3DYz3DaTBsWyBp1lg4FimC88Drz0f4CXv0zD1g6vqnPjbG3Urg/+AMD9+b/fD+D7G3cQRfEPRVHsFkWxH8AHADwliuJHVD5v49NxsHwsr1xmT5IlnQa2cQw/CybRqVieDyUbzwmDwYrlYub25Tj3CNB5GHC36XNeZdjX2YSlaAozoc3hAEOzVCwPtG0BGYaXpSxu0C0vXgCMVskDsC6rSVUoSSSZ4VHXHE4e8lpWF0yiSbEMANf+Fg1qP/Ig4GoF3v1FdcfjbHnUFst/DuA2QRDOA7gt/28IgtApCMJP1J7clqbzELAwtDpkoJaZNwH/AFnTaYQvbyGmh245nsoinMw0nscyw78bSKxQtLJUQtPA5LGaLfft66RY7VOToU3fG5oNo9frKLhANDQsknzjkN/8OcC3s3gMdhEcVqMqGUYsleG2cRxOns5muyaaZZvZALtF5YpNUxfw608AN/5X4CPfJWkdh1MGVcWyKIqLoijeIoriQH67lP/6lCiKdxXZ/xlRFBs7IFwrOg8BELWTYky9DnRcrs2x8hQir3VwxCjYxjVqsVxwxJChWz6Xv38crM1bYE+HB4IAnJoqXizv2gpdZQBo7iUv1Y2d5YVzq783CTgtJlUDfpFklhfLHE6ezmYbpoLqNcuqu8oMqxu46Y+oycThVICPadeKznxhu9HvUQmhaSAyky/AtcOf1yzr0VkuBJJ4GlSG0ZqXu7AgGCmc/THg3SGrYNMSp9WEbT4nTk0F1309lcnh4nwUu7aCXhkAjGagqXu9I0Y6TkEyMn72DotRtXWci2uWORwA1FleiaVV3YAuR1Wk93E4KuDFcq1wtQKebm2KZXYMjYtlj80Mo0HQp7McYul9DdpZdreTq8LkcWn7J4IURjJ4t2Y+2ErY2+nZ1Fk+ORVEJidiX2dTjc5KBwKD5DvOWDgPQJTXWbaaVIWSRLlmmcMpwLyWp1V0l5djKXVOGByOQnixXEs6L9euWBYMq5Z0GmEwCPA69Unxa3gZBgB0XZGPSZXA+ceBXLpmEgzGwe5mTK7E12kHnxtagCAAb9uxhXR73VcB82eA+Ar9mzmXBKRHwTstJtVx11yGweEQnYVgEuVDfprKMDgcGfBiuZZ0HQaWhoH4srrjTL1GLhgW7dPXfE4LFnSSYZgMQmN/8LHfX2yp8r5nfwQ4W4Hu2sap3ryHcoMeO7Vqq/bc+Xkc6GpCi7OBfxcb6b2athOv0HbhHLnFeHdIPoSaAT9RFBFNces4DofBiuVpFUN+yzEuw+DUBl4s15LuK2k7cUz5MUSRimWNJRgMn8uCxYg+MoyA2wqDocHS+9bSdQVtK60OpBPUWd79TsBQ2+JpR8CFgVYXjp6aBQCEEmm8Nr6Ctw/4a3pemtN1BRXH4y/Rv+fOkFWUjNRENdZxyUwO2ZzIO8scTp42txUGQXkwSTYnIhhPb62bek7DwIvlWtJ5mOQT4y8rP8byKBBbALp0KpadVt1kGA0twQBWb1Aq6ZZHfgqkIsCed+t/ThK4Y187XhpZxFI0hefPLyCbE3H9wBYLALI4SZY09iKQjAAXn1ntNkvEYTEhprCzzLTOPMGPwyFMRgPaPTbFMoxgPA1RBFocXLPMqT68WK4lVhfQtm+1+6UE9tjea7U5pw14nRZd3DDmw0kEGjWQhGFrAvy7gIkKNzsnvgnYmoFtN1TnvCpw52XtyInAZ354Cv/jx2fQ1WzH4d6WWp+W9vRcTZryY1+lm5XD91d+zBqcFiNS2RxSGflx72zinw/4cTirqPFa1iyQhMNRAC+Waw27oOcUWlSNvQhYmzRN7luL32VBOJlBMqPcQqsYDR11vZZtNwCjPyNrsmIkgmQZt/8XAVN9/H8v62rCb98ygO+/PoW5cAJ/9+HDsJi24EfBgV8Csmng8f9G7w8me5KII98VVjLkx7TOXLPM4azS2WxX7LXMVji5DINTC7bgFbLB6L6Kul5zZ5Q9fuxFoOdKyrvXAZbip6UUI5XJYSmaanwZBgDseieQjpEtXDFOfQ/IJICDH6rueVXgv9y2C3/5voP4+w9fgYM9zbU+HX3oPgK872uAwQxc83HZln3MIzmqwGuZaZ25ZpnDWaWz2Y7plQRyOVH2Yxfy3vzM/5/DqSa8WK41PVfRduzn8h8bXyZ7rJ5rtD2nNRRS/DSUYixEmMdyg8swAGDb9YDFBZx7ZPP3RBE49s8Ujd11uPrnVoFfvKIbt+5tq/Vp6MuedwH/dRS44mOyH8qiv5WEKDDNMi+WOZxVupptSGVzWFDg3b+Qb9gEXFugycJpOHixXGta+imcZPQ5+Y8dz9ti9epXLBdS/DTsLBfS+7ZCZ9lkBXbcBAw9CuQ2aFtHfgpMvw5c+x9rGkTylseqLMbbZaNCV0kwSZQP+HE4m2D2cVMKhvwWwkkIwmoDh8OpJrxYrjWCQLrXkec2F1uVGPkpYLSuWpjpgM9JBa2W9nFzoXwgyVbQLAPA3vuA8DRw7ifrv/6zvwJc7cDBD9bmvDiqYIVuJCG/WI4VNMu8WOZwGKvFsnzd8kIkiRaHBSYjL1s41Ye/6uqBbdcD8SVg7rS8xw0/TV1lHcJIGN58Z1lLzfJqZ3kLyDAAKpZ9O4Gn/3T1hufN75Bd2dt+q24G+zjyYMWyKhmGhQ/4cTiMziZ1xbKPd5U5NYIXy/VA//W0LTUkVozwDDB3Cthxsz7nlMdtNcFiNGia4jeXX07bMoMaRhPwjj+km50nPwMMPwX86PdoePPqj9f67DgKYcVyWIUMg3eWOZxVPHYTnBYjJhUVyyn4uV6ZUyN4sVwPNPcALduoEykVtu+Om/Q4owKCIOS9lrWTYcyHE/A5t9hy2r73ktzi+S8A//oewOwAfuErgJEb6DcqbptyGUYklYHFZIB5K73GORyVCIKg2Gt5IZKEfyvMuXAaEt72qBcGbgeOPwSkopQ+VokLTwIOP9C2X/dTC7itBQcLLaCo6y0iwWAYDMB7/oH8lGNLlNZnttf6rDgqYF1hpQN+fLiPw9kMFcvyB/wWI6mtsxrJaTh426NeGLyL/HildJczKeD8UWDgNt38ldcScFsxr2WxHE5uDSeMYuy8lcIweKHc8JiNBtjMBkXFciSRKXSmORzOKko6y4l0FpFkhsswODWDF8v1Qt91lMR39ieV9734DCXD7b1P99MCSFs8H9ayWE5s3WKZs6VwWc2KiuVwgneWOZxidDXbsBhNIZGWHvbDrj/cY5lTK3ixXC8YzdQpHnoEyFa4OJ9+GLB6dNcrM0iGkVKUurSRbE7EQiS1dWzjOFsal9WoSLMcTvLOModTDCX2cUwG6OMyDE6N4MVyPbHvPiC2CFx4ovQ+mSRw9kfA7ruqZkkWcFmRzYlYiadVH2spmkI2J24d2zjOlsZlM6noLPPhTg5nI0qCSZgbE5dhcGoFL5briV13As5WGvQrxcnvkgTjwC9V7bTYMJ4WUoy5cD6QhMswOA2Ay2pS1llOpOHhnWUOZxNdCjrLzI2Ju2FwagUvlusJoxm4/EPA0FEgNLX5+6IIvPT3gH+37v7Ka2ETyNoUy/lAEi7D4DQALqtZkc9yJJkpxGVzOJxV2jw2CAJkeS0XZBg8lIRTI3ixXG8c/igg5oAX/mbz9y49D0y/AVzzcYrJrhKB/N38fES+3c9G5kNbLL2Ps6Vx20yyE/xEUUSYu2FwOEWxmAxodVtldZbnw0l4bCbYzDwRk1MbeLFcb/h2UMH88peB+aHVr2dSwE8+Abg7gQMfqOopFYplDWUYAb6cxmkAXFb5muVEOodsToTbxjXLHE4xOpvtmApKL5angwl0NHE7Tk7t4MVyPXLzpykB7uGPA/FlIJcDnvhjilN+9xcAi6Oqp+OymmAzaxN5Pcc7BJwGwqlAsxxO0CAst47jcIrT2WzH5LK8Yrm9ia9GcmoHL5brEVcAuO/vgOkTwD/cAHz5RuDFvwOO/Cqw646qn44gCPC7rNp0lkNJtHr4hx6nMXDbTEhlc0hmpHvCMo0zl2FwOMXp9TowuRJHJpuTtP90MIHOZn7d4NQOXizXK3veDXzk3wHfdkAwAO/6AnD352t2OgG3RsVyOIE2PtzHaRBYd1hOdzmc4MUyh1OOfp8D6ayI6WDlOZhkJouFSBLtHi7D4NQO/mlez2y/kf7UAQGXFZcWY6qPMxdO4sp+rwZnxOHoDyuWo8ksfC5pj2EyDK5Z5nCK0+dzAgBGF6Po8ZaXFc7lh8I7uAyDU0N4Z5kjiYDbivmIus6yKIqYCye5xzKnYWD2b+Gk9EAe1oXmmmUOpzj9hWK5cgOGdZ87uAyDU0N4scyRRKvbhqVoCqmMNI1ZMULxDFKZHHfC4DQMbi7D4HA0p9Vthc1swOhCtOK+03nXDN5Z5tQSXixzJNHeRAXubEi51/JM/rFtfMCP0yA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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -112,26 +111,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe697898650>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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VBSLpQ0E0Qew+krUQbdv28oTFnQioqbBwCfB8Dp/HK6MLVoIg+szWuM3DIgiiAVyPT8k5TIMthQRCvKM238utD5zFFW/4ML78zXNLIZEhiJ1CMjht27Y32bGQMRaOtWRxt+MRnoxCznHhvh4A4MT5UWvHRBBEMzh+orDQMGrxaW0aoT/0OZ+xu2qK99/6EADgZW+7Ga/+y9taOQaC2I0sWiY60ERP3xYE0c0eBwXRNSAmTCHnuHh/HwDw6PqwtWMiCKIZAjnH8lnciQnr/3r/V/D9f/CpVo5BHkNvuvdUra/13s8/iOu+eqLW1yCIncJMJrpl295kJhoIiribzkSTO0cNxB3LgiC6Z5s4uNrBo5SJJoilx/E4LCkTbZlG6xNOFcjT1f2nt1s5hnMNFWcPJi7+6wfuBAA88Lv/rpHXJIhFJinjGrTcQI4nLO6AIBNNmuglQGj1ZF3kgdUOzg+psJAglh3Pn85E20ticZekDe/75KRZF988O2jmhQhih5AMorcnXqv1EX6isBBop/6EgugaEN2BZF3k/r5NFncEsQtw/enCwuWRc0xPWIMWCouSk2ZdnNumsZogZFQZ3lOb7Zkl+P7smGRRJno5EHKOjhxEr9iNbUUSBNEejufDNqblHM4SWdwJ2iiWbCiGxrlBvGs4abmpBEEsAnKG9/CeLoCgG2JbJDsWAkEmOlkAWTcURNeAo5Bz7F/p4PyA5BwEsewkLe6sJbO4E7TxnpKZp7qQPf23WtZ+EsQiwBFf75ceCMwSHitR5+V6Ph6qQC6lknNYhkGZ6GXAUcg51roWNmkwJoilx/EThYWGsZRyjjYayDSUiMb5YbxruE3jNkFAvtz39GwAwKhE18Lfvu5ufM+bPomTG/MZLfg8bvUtIE30kqCSc9hLooskCCIb1/Nhy4WFJlson2jX83Fys/gElgxg22hl3lRh4ViScGy37IdLEIuA3Kl0xTYBxPVfRfjMscCa8uycO/NpPtGUiV4CVJloyzSWovUvQRDZeKrCwgWSc7zq3bfieb/1CawXnMSSE1YbSYGmCgvHbpxha7szG0EsAnJ/pX4nDKJL1AuwcDn+lhuO4Y//9RtzHY/anYM6Fu54VJpo2zTgeO11+iIIohkcz4dlTMs5FikT/elvnAYAfObY6YKPnJ6w2iiWbCqIloMDknMQxHQmWuyylwqiw0v4X+44gT/613vnOp7kzlQb9ScURNdAstkKgGh7d5EyUgRBVE/S4m5RpVxFi4KS8WsrxZItyDnaaG9814mNVny4CSINOYgW41sZOUdVxcHkE73EOO50228AUaHRIk6mBEFUh+vx6Uz0Akm5fD/WERb1eJ3VRLeRiW7mdeQM2+ao+SD6JX/8afzkn9/S+OsSRBpybCoK+srJOSo6HvKJXl7Uco7yKzeCIHYOjudH1zvQzsCexubYjbSNJwsE0ZuKrOhya6J9HAm9cE9vtWNNeucjG628LkGoia93zoMkYZvxjKrtt9FCJtpq9NV2CY6vkHNEmWgKoglimZnpWGgY4DyQPyQtmZpmQ7Ju0y0sPLk5wvN+6xMzt7eRXW9OE+3hwEoHg4mHx+e04ipK080iCEIH+bTk4LBNVioTbVSUuvUVhYWkiV4SVHIOEUS3YQtFEERzzBQWhgH1IhQXTvkfT/RcJ06sq4PINsaypjoWjl0fXdvAkb3dxlsbL8quBbGclDU3kBd3ng90LGMud4558TmfCcjbsLijTHQNRBZ31rTNlfw3giCWE8/nsIxZKdciBEciE73WtbRdJ9Iyzm3IOfyG3I0mro+uZWCl0208E70M3S2JxcXngFkijp3KRHNePoiuaCHsc5Um2sCwRAOYeaBMdA1EmmhpmdSJMtEURBPEMhO0/Z62uAtub//aF44PF+3r6QfRKcFyG3KOpl5y7ProWAaO7u0V0o5XgUc2qESNlF2kyRlszoNMdJl4pqrNpDRNNBUWLgGqjoXWAmWjCIKoD8efLiy0o12o9q99Iee4eH8fW5pNRNIm3TYy0U0FmGPXQ8cUQfSoUX9/bwHOE2J5KbubIw8DHufomAbGZRIDNVrcWdRsZTlQyTlsykQTxNLj+RycY8biDmgnc5skDqJ72v7HaQv/Nt5PU8Gs63HYpoEje7oYOT42hs3Z3C3CeUIsL2Uz0XLw7fPg+mjV4i61Y2FFL6AJBdE1oJJzLFI2ahk5uz3BFW/4MG4u3IWNIKpDBECyO4dw5FgEj/it0PP4yJ4eBhNPa0JNu08bY1lTeuHAptDAhft6AIDHGtRFk5yDqJOy5xdP/NItqYmuyqDI53wmqU2Z6CUh7lg4bXMFLIYuchn5ykPrAIBrP3285SMhdjMiyDMXtLBw6Hjo2yZ6tglAr1lC2u5ZG2NZUx+hsCm8KAyiT5wfNvPCoMJCol7KWijKmWhvrsLCaqJonpKJJk30EiCaLcgni5BzLEKzlcfOj/Ann/hGozq/uuHhOrldF15ityMCINmdY5EW0EPHQ79jomOF45HGJJiqiW4h2GvKnUN0nTyyJwiimywupCCaqJMqCgv9BSgs9BWFhdT2e0kQW4EyUTZqAbZ0f+49X8QffvxeHD+93fahAADueHgdf/W5B9o+DIKYGzGAG2xBM9ETH33bRDcMosdefnFhuiZ6eYNokQhZ6QQZ+6Gmp3YVUBBN1ElZOYeskuBCE10miK5QzqHMRDccY5FPdA043rRPLLBYhYWPnBsAaK77Vx4/8pbPAgD+4/OvKPX4L33zHO55bAtAQrdFEA0TZaKnNNEiE93+2TlyPPRso5pMdAtjWVMvKeQc/TCIHjXoPbsIiy1ieSkrGfaTFnelCwurcudQ+USzxhbagkoy0YyxFzPG7mGMHWOMvUHx9xcxxs4zxm4P//2G7mN3Io7nR5OUwFqgwkJRoV/mAqiTsguMH33bzfi96++u+GgWg4fODnDFGz4cab6JxUaViY7tLdu/3oSco1sgiE7LNrUxljUlQRNdJ3tWmIluMIimtt9EnZTORIcPe/rFe/Hr//7q0proqvQcvq+Scxg7TxPNGDMBvBXASwBcDeAVjLGrFXf9NOf82eG/NxZ87I5CJedYpGYrYvIbu8129slDOAfMw2Lk1qvjluNnAADv+uz9LR8JoYOYoKY10Ysk5wgKC8V4NNYJolPu08aioCmpQ2Bxx2AYDF2r2S5oi3CeEMtL2UWaWMC++X+7Bhfu66FjGVrjR5LkHF26DXmqT/QOC6IBPA/AMc75cc75BMD7ALy0gccuLE7oMSqzSF6xgkXLRG9WEEQvG3v7NgDgzPak5SMhdBCSDUNZWNh+cDR0PPTsYoWF6e4cy6uJ9vy462S/Y2JEmmhiSShdWBj+LwLXbtnCwkQUXd63enqcBYQmeudZ3F0C4CHp94fD25I8nzH2FcbYRxhjTy/42B3FxPOnNJHA4vhEPyAVE5ZZRdaJaElMxAgt5vqAPpudgK/KRC+QnGMUWtx1Q5mCTmFQ2hzXRsa0Kbmj4/uww++wb5uNZqIpiCbqpLycQ0jVgt9LFxYmctFlxhGeOBaBabDGbDAFVQTRqh305Nv4EoAncM6vAfCnAD5Y4LHBHRl7DWPsNsbYbadOnSp9sE3gev5Uy29gMQoLT22O8aI33xj9vmhyDspEz7Idtmb+6iPn8ap3faHloyHyEBOC7BNtLpKcw/GwUtDiLm27tZXCwgai6KjrpMhE2yaGTnPvdRHOE2J5Ke8THfwvivnKFhYaiajz9hL1PuJYVHKOppMVVQTRDwO4TPr9UgCPynfgnG9wzrfCn68DYDPGDuk8VnqOaznnz+WcP/fw4cMVHHZ9qOQc4vc2t3STmd5xgxODDlVUwC/AjnmlbI/jhcUn7zm1VN7ey4iy2Uo4a3gLcHIOJ9M+0ToL6TQJhbOkFndRx9lwB6Fnm2RxRywNpTsWJrK/pZutJHKnL7/2lsLPkcyKC3aqT/StAK5ijF3JGOsAeDmAD8l3YIxdyMLlC2PseeHrntF57E7EUcg5YneO9gJXM7FqWzQ5R5nMePKCWYSGFlWyNZ7Ozo8WbOFDTKNqthJnotv/7oaTUBNt6meiVXNS1zLa6VjYwEuKTLBY/AQFVBREE8tBeQ3ytPNQxwqcMIpmtqtw1hXHorK4a3onZ26faM65yxh7HYCPAjAB/AXn/GuMsdeGf38HgB8H8POMMRfAEMDLebCsUT523mNqG2WzFUPIOdobIJMr0EWQc8gXYJkAMRkELELxVpUMJkEQvda1sDV2sTV2I+9aYvFY+GYroSY6zkTrBNGzx71/xV7awkI3kYnumOUKqMrw6nffihvuPtnIaxH5+D7H1sTF3p7d9qGUQrVzWSaIHk483H866C/BJE00ENRV9Ixm5yTxtpJyDsNg4Dy0v0umqWuikmYroUTjusRt75B+fguAt+g+dqfjeBx9e/qkshcgE5187UXIRA8kCUeZoD75GGcBsn1VsjX2cGiti199ydPw+r/7ShhUd9s+LCIFdbOVxehW6ng+XJ9PdSzU00TP3rbSsVpZFDSRpRWJDqGJtkzWmJMRBdCLxbWfPo7f/cjd+MTrX4gnHV5r+3AKo7pcylxDr/nr2/Dpb5wGMO3OAYRBtK0fRCezx2VIk3OIHUCPcxgNGd5S2+8aEC1jZeKJtMUg2k1kohdAGiB7Q5cJ6pOPWQQf7ioZTFysdk2sdoNBShQaEouJq8xEG1N/awvhMDHVbEXjelFls9oo4AHSnUKqRLwv4c5hmUYr+m+ifd73hW8CAD5972KbGaShCpjL7OaIABqIM9FiN8spOG9XEdqmFRaK7rBNSqIoiK6BietHWQwBYyzYFmxxME5OmIsg55A1v2UKC5ddzrE9drHasbDaDTaNtifkYLLIxBZ38fW/CAtoAJHXsewTrbOQVg1Zlmm0Ik1LBgB1FNq6iUy03YL3LLEYCAnkfae2c+65mKgC5nlPZRG4ynIOXcauV8niO9ZET9/eRmOrSuQcxDSuz2cs7oBgW7Doqq1KkgHnIjRbkd0nymTGlz0TvTV2sda1sNKxot+JxSVuthLftigdC6NMtNxsRcsneva4bbOdwHI2iK6mUElGjCFiN9FuUBNNLBbCdvW+U1stH0k51EH0fOOQIVncAbM73Fk88799rJK4g/vTxxIdm5BzNLjAX8pM9InzQ/zZp+5rzQ5MJecAgsG4zYl0ETXRU0F0ieNJrmrbbmZTNYOJh5WuGWnsF0GCQ6QTu3PEQ6vIaLbtuiDLOeZ152ijCh6YnRzrKDSM3DkkTfSy7XAR+XDOoyD62MmdGURXJeeQiZqtRAtx/R3kqhJ3eZroJqVmSxlE/59/82X8zkfubm0LxlHIOYAgs1Gmw09VLGIQPa+cQ0xub/qxZ+Elz7hw6bZdt8YuVrsWura+ry/RHmL7V9Vspe1spvA67tsmLNOAwYo3W3n1C67E5371+2EZ7WRnk4F7HXG8+EzEhGybxtIVLBP5DB0Pns+x1rVwcnNcSR+DplGdtmUW83KwyhKZ6DbiiCiIVrT9BpppyiRYyiBaBGZtnfQTRbMVIMxEL1QQ3f6gIGt8y1yMYkA4tKeD/Sv20hUADcYeVqXM4SIsfIh0vHDWmmq2Eu5KLUomWlTSdy1TawyQ56OL9/dw0b4+LLP5pgbA7GdYayY6zLRZBmWidyMiC33FoRUAwLnBpM3DKYUqmCwTYMqyCfGjKE5upzZCHMusTzRAhYVzI77cQYNdpmRcP13O0abcYJJ47UWQBgh3DstgpYL6uM2ysZTaxe2ZTPRyvb9lQ5x+6mYr7QZiI0nOAeh3HJMDVTGZtlVYmNymrSPhFGmiQ0mObS3fuELksxl2+H3CBasAgDNbOzCIVsk5SmWi2czPdgFJWNUkuycK2rATXcogWny554dOzj3rwU3NRLcs53AXUc4RTOwXrHZKNVuRO8RZhrFUGSPOObYngTtH1xKa6PZ3D4h0RCZ6yuIuDMbaPjeHk+DYhL6+YxmahYXxz5EesgWLO9/n8Dnw0991BX7iuZeFx1b9ZzpTWGiwpau1IPLZCBM8Tzi4czPRVTVbkRO+cttvoB2ZWrrFXfOZ6KV05xBf7kZbQbTvT2WiBLZptOrOsZByjrELgwUd0Eplor14+95o1ngAACAASURBVNw22VJljEaOD58jyEQX6DBHtEeUiZZ2ogyDgbH2237L7hxAoGnUOZ/kiZhFmejmJQ4ik39orRNJUuoJopPNVtqV4ZXh8Y0R1rqxNSZRnEjOcTDIRJ/d3nlBtEq6UeaaYQpNtFhktpGJTissJE10RbSdifZ8PqWJFJA7xyxboQ9y3zZLunPEmehlk3MIbf9q16QgeofgKjLRQHtuFjLDhJyjW0rOEfxvtXCteZJ0S0zkdXykItHRkdw52qq1KOsw9R2//Qn8r2/9bMVHs7sQco7Lw0z0jgyiFedtmct2Ws4R/F/EJhOoNmMd+0Qnx1lqtlIJYoXUmpzD5ymZ6HYzpclJfBE00SPHCzuomaUKQeM2ywYsk8Hn5TRfi8ggLLpc7VhBsx7LWIjdAyKduNnK7ODedmGhaLYia6J1FmXyYUeZaKP5wsKok6DJoom8lmYr4euI3YROi4vzK3/1Otx/upzL1Dd2qC3boiAy0Zce6MNgOzOIVrpzlMlESz/PtP3WTOwMK5QiireQJucgTfSciInhjz/xjcYHet/n4DxuPyljm3qZn7pIfhRt6rMFQxFE23oTepJkJhpYjPdVBWIRKLZku5axEAsfIh0xeCd3oiyjfalR5M4RTn66bkFyJlq8r8DirtmxNc5Es2jyrMXizkv4RBsGOG/PXeWjX3us0P3b6o+wbIhM9L6+jQMrHZzZgUG0Us7RUmFhlXOXuBbJJ7omhpIrx833nc64Z/VEQV2qO0eLQXTi4mlbowkE31XfDjLRZS4y2VJsTbTGXpKufjfcfRIAcOWhQJPXtcylWSAsK3KgJ9OWJZzM0PHQMQ1J66snMZHnYVPSQzY9fsgLZvHx1mJxlygsFGN5W2P3ma1xofuT5KsaNkcuGAt2Ag+sdnBugYLof7njUXz14fO591PLOebVRAf/Fy0srHJxF2uiZ8fZ4JgoEz0XsrVd052G0iZRQMg52ptIk6vStt0CgGBi79lBJno0h8WdZbAoY7s9Xg7Jw6PrQxzd28VTL9wDgDLROwEvRc5htpC5TTKceOjZ8ZBvazZMkSc/0dygjcJCtSa6TncOkbFv16Lw7HYxWWJb1q7LxubIxVrXgmEw7OlZU43B2sT3OV73N1/GD7/lM7n3VbpzlPGJlsazKIguuPObdfkUDbAjd47EOCuu2SYLgZcyiJa1tWVs0+Yh0tOluXO0mElMrkDb3l4GQk20bZYOEOXtc5GJ/t7f/2Srspmq2Bi62Ne3o98DyQtNkPPCOa+tEVO0zTgzuLNo16QthhMv0kMD+oGwPGwI5842OhZOZ6KDz7cen+jp3UQxMbfprFSEwWQxgr2dzsbIwd5eMP6udRcniB4UGLuqknOoNNF2QU101oK3aHY8zSc6lnNQJnouho6HH/vWSwE037UwMxPdomn/cOLNDK5tuwUAsSa6N7c7hxEF0UCsZ9vJnB8600G0Ve4zIqZ5x6eO49lv/BjueWyz8ueWfctlzAXoejcMF6wCyzS0XCeUzVbaKCyU7CzrlHOIMbojWdwBaK31t2IqSeX01hi3PXCuvoPZRWyOXOzpBXPKWteKGoO1TRG5olLOMWfHQvFz0Ux01qsWjUXSfKKja7XBOGvpTCQdz8dg4mG1a2r7oFaJmzKJAu2a9l/9m9fPZG3antQBWRNtlGok4kmV9Gu9+HTeHns4uFbZYbbCxsjBhXt70e9dTTcFIps///RxjBwfn7j78UgqUxVpmehFsbjrSUG0bTDNwsL456iwUDMArxK53sTz6yssdBM+0XYLFf8yyUAhixf/vzfh9A7srLeIbI6cKIhe7VoLU2tTJCOudOcopYlWWNxFOzR6z5eVAS96TGk+0ZH0ijTR5Ti7PcFV//UjOD90IseH9jLRaneOtjLRyQC6uyCtbEeOH2iiS2ZZ5UXLVCZ6vPMz0RsjB3unMtHlFhpEzMjxsB52Hrv7RHOZaMtcAIs7p5ycQ9YrThUWtuYTzSJdZh12lpOZwsJ2O04WybZTAF0dQSY6lnNsLkgQLQfzeVpiVdZ5/sLC4BfDYLAMhomnNydlHWrR7HieTzS5c5Tk8Y1R9HPfFhKBZoOOzEy01X5xkWBv3249MwYE+r1+x0DPDloQF50U5YlVtGcFlqO4cCssbBF0S0peiJiHzg6i7OWj68PKn19cU6pmK20vWsWujyDIJusUFsY/R4WFhtG4J3uUITaMWjXR4nVEu/aoM1tL31/b581uZUbOMXYXwj5Qntvy5gNVwPy2G+8r/JppkqKOZsMmAOAZgg6vYFyU5hPdscS1SpnoUsgn1DzFavPgpfjEAu2a9ifZ27NaPxbOebTa71rB5F40SHSkidU2Dbzjp74NALC1BJnowcTDipQ5bEOetGyc2gzswo7u7eLkZjHrMB381Ez0Yljc9WfkHMU00SITHVlJNZjxkbP8YqOvLk20abBowRBV/LekiZ4n8dL2ObeTkeUc/Y4JzhfDPlDOROfttKuC/lMlxr00SVGwu64p56ghEz1bWEjuHHMhtmkBYKVroWebpWzT5iHZ7Uqm7Y6FMnv7duua6LHrw/U59vQsqa11se8r8okOP+8nHwmE0JsLUgRSFs/nGLv+1PZ71zZw14kN/MVn7m/xyHY2p0LP3adfvA8nN0eVZ5ZcX72INo3mNcRJho6HXieRiS6piW5De+hK17pRp8Wd708tgqyWNdHzZMCrnm/ODxz84cfvxemC3tU7DTnBAyBafDYtD1WxPZGD6OKZ6DKkBdG6XU+BbOlJ0fM0vbCQNNGlOT9w8NPvujX6/YqDK+jZRuMWd1nuHLqTVhNcuLfXerOVjdBBY0+44AGKr/ST8hnhg7vT/ZRFdzk5Ey0WGm/8l6+3ckzLgMjCPO3CPRg5fuWeup7PYbBZrZ5ttG9xN0rIOXSzSCqfaDPSHjY3WcmZaFZjx0LH5VHRFCBZ3O1AOUfV3887broPf/KJb+ADX3qk0uddNEZOnOABEM1PTccTKuQEUV4r7bQMb1XBdZHd9ayXLDoOx5ro6dvtFpx0liaI3khYml11ZE9YrLZAmuhw0mpTV8UY8Jevfh6uOrLW+rEIy6BAzhGcikVX+p43/Xm3cRHVgbAj7HdiTbStKFYlinFuMIFpMFxyoA+g+h0Lj/NoS1HGbNGZRzAj59DsOqiSc8SZ6OauM0eSyonhtY7xy/X9qZ3EOIhu5/ubJ6tWtbf1Nx4PmpcdP91sE7OmERapIhMtkjNtZ6J/9q9uw6998M7o97zjSbu8i8ZFaVNPIU204lr901c8B8B0l+kiz6WqPQEoE10K+fv5y1c/Dxfu67WciZ79aDstd74CgqzvC59yOK44b/FYhFXPWtdCV2SQS2aizWQQPefk4fkcj9RQeKbLICweWZUy0U1Lk5aR9YGD/X07aqJQtZ+453PlhLMwmmhZzmEYhZutiPdmF/SIrQK5k6BRZyba86P3B8hbxPW+17QiTW0fXkWQUnX2/KGzAwDAw+eaHxtPb43xwOntRl5rI1xc7xWa6HDxmZf5rZuPf/3xqd/zgmjZOePpF++VHlfsvGBIkXOYRQoLZ9m/EozDRT/XRfKJXpogWmRUfvOHr8YLn3IYANCzzMZXjnmZaKDaL/jDd5zA9//BjdoTdOzz2q7OD4izgGs9Ky4sLHhxez4PLa8SGbI5Z9drbzqOF/zuDbjvVDsZF7G9Jcs5CvRcABB0kvqn2x9pPXuySKyHDWzENu1G1ZloX52Jtgyj1QWr73OMHH8mE63V9lv6WWSiuwW7lVXBubDm5cCKXXOzFT4VRNtREWW931/a+aE7X6iC7aoXOeI72Bg2X7j9b/7oJrzozTc28lpxJjop51issTQvGBbz6V+9+nn4+9d+l/S4gpnolMnHtvTrvFTXqpjfCss5IhekxPG0kKhcmiBaBJGH93Sj24I2yU1nouOuWkmsgubkOvzS+7+M46e2tTNqUctOo33Zw2Yk57Di7bKC2VbH96c+66oyZLc/FHT9qsNLWIehMyvn+C//9lsAxAN7Hv/fZ+7HL77vdvz15x6s/gB3KOcHDvat2NE2bS2ZaMWEY2k2NqkLMQ7O+EQX7FgorjWx6G00iN4WQXRH0kTX485hS3KOpir+096LdhCt+C6qlKBwzrEeBs9tFG6fCb//JpqebEpSQwDRTukiaKJl8uZLMQ9ecqA/de0XjYtSCwtNQ3uulUONrmXgn1/33ejbwVw2LNiqXgxbqT7RlIkujioD3Eom2kvPRHdq8BsVg2RWFyN5m9Bosbo+SbTa79rRSv+9txQL+DyPRx3FAFnOMd/7OrgWLMbObLdTha7KRB/Z28OPXHMxDq52tJ7jrhMbAIBbjp+p/gB3KOeHgZxDLEQq10T7PFosy7Qt5xDbpVM+0UbQACZPV8wVQXTHKie/moezAweMAftXOrX7RFstFBamZqI1xzJ1EF3dMQ8dL3qNZA1Skzxwpn5Jh5zgARYnE51Mzo1yMrji+uwkxqSi7yOtaaZdSM4Rn8fPvGQfnnnpviiwL5qJ5ikWd7Gne7mB4bYHzuLhc4NCj1meIDoqOonfUtc2F8qdo06/0awgWg7aY5/XdivOgfiY9/QsXHPpfgBq3VQWbijnEIhuZvN+xiJQvfGeU/jC/Wfneq4yiEFFDnrE77rntCj+ufuxdrLpi8j6cIL9K53agmjX58qsjWW068yjCqIjmULOhCNfSnEmupwl5Tyc3R5jf9+eKiysIxM9SWiidT+neUlrODGPnKPKc259EATO+1dsbAzbsxA9u11/V8ZkYeGiWNzJSRUgPxMtgmiRSY8eV1jOkW5xp6/Zn32+0nIOoYlOxFmMsdK7fue2J/jxd3wOP/RHN+EjXz2h/bjlCaKFP7OcibaN9tw5lD7RxbKkf3fbQ/ihP/qU1n2ztrimgujw8xGd8LKC77qRNdEdy8CTj6wVHvRVmT+7wBZTGiLzcMPdJ/Ef/uxzcz1XGYQ7R3LQ7HdM7SIMMek9sj5sffBfFNYHQhNdj5zD97lyF6r1THR4PsmTqaW5qJcDVSOhiW7SSvLctoMD4eK2zsJCNynnaKjZirAj+96nHMavvuRp0e26kru65Rxijrl4Xx8Tz290TJF3U88N6s+Cp2aiWy7uFjHET33n5QDy5SXinOiaieC7aGFhWsfCQhZ30rkYPl+vZJO1tGYrgL5MLck3TgZJp8HEw8+/90vaj1uaINpTBK9dy2y+Y2GGO4dtFdPr/srf34F7H9/SsnHKyqjJThXiYtjXD4KI8y0UiAi2xi56thENDGU6TCYz0YB+J7Ys2vbzFivz1e60/rlrG9pB9MbQiYKBNrdfFwXX87E5crF/xcZqx4TB6slEq3ah2ra4247cXuLzSQT7uZloRbOVrvB1b/A6Obs9iXaIWI2Z6NTCwpq/PxGk/+DVR3Hhvl58PJpJF1UgUuU4JsYkcWxNjily8Co3VauLzVEgHVrriCA6OB+Gk7bnBRev+d4n4vU/+FQAGu4cQs5hJTLRRS3usjLR2hZ38vOF/4vOowWD3tgnWpGsLLnrV3aRvDRBtKpTWM8OMpJNZoAy3TmiSavYl/WWG47hg1/ONrcXk6QK+f2Lz2dvGES3UWUt2By5WOva0e/dAltDAtfzZz5r25p/6zz5+KozlnkI38x+IhOtu/L3fY7NsYtLD6wAQKvbr4uCcOLY37fBGMNa16p8J8ZLFLoKLKPdTLTIIsqLskhelns+KTTRZrFMNOc8Kgwsy9ntCQ6sTGei6/CJdhJjSlOaaDGHWwabClrmKiysULMuguije8MgusExRfYRbkLOcS7csRJygUWQc3iSw46YF3Qz0ckgelzR+yjW9ju+n7DME+NJ0bbf4u5K6ZxZLomWjKF1x5alCaLjblbxWxJbME1WkGe5c8STVrEv+A8+fi9+6f23Z95nkFHdOuXzGp50+/rBZNpmJnpz5EQ+nEC5nQNPlYkucGGnkXz8v971eMo96yEqLExooi3DAOf5Hae2Ji44By7ZHzQVoUx0fK7vC71J9/Tsyj8Xj6sX0JbZrsXddng+rUlBtKVpB6UaP2Jfd73J+D23PIjn/I+Pz2UZeXYwwcG1+uUcjudPBR1NNXBwpblDDg50EwuqTHSVRexijrlwb/OZaHn3bb0BOce5QbxgAxZDziE+g9Wuqd2cbOx6sAwWzZG//bJnho+r5rwolImWfhZhmjjPiyYYxP3Vcg6jVFY5+Rjd8XppgmhVJrpsF7wqjkOZiS4o5yhC1ramvMoTq6tFyERvjV2syUF0CQ27609vvQJBxr/qTPRdDVvdDSYeOqYxo/e2TL3dDCFTuDTszNfm93zfqS381Ds/jzNb7TidCMQ28P5+MDnu6Vk1uHP4M8UuQGhx16KdZJyJlgoLDT2vZ7XFXTGf6Ou/9hgA4JGSTTpEJjvORIfHVkMU7SZ07U0VYUdSQMamggPd11WNnVVKUOJMdOBc1KTNnTyHn2tAzhEE0dO7pEC+G0adyF1sGWPoWoaWnENeEL7wqUEPjaLzbFp8UaT+iGdkosvKOVSZaLukdC75HnWvu6UJoj1lYWHzq8dsd45yco405JMya9Ukn6DiR9Gxrd1MtDuVGetaxX29lZnoGuQcJ86P5nq+oowcb6aiGtA3kxct1S/aFwTRbX7Pb/7oPfjMsdP4QI4kqW7WE5nota5Vueesl1ZYaBip7gtNsKWSc1jFM9HC3aeoxd1QYdlYhI2RC9fnuCDSRNeXiZ640+4cnaiwsGZ3DqmuR9Z66mbAVbt4Vfrliu/wqNBENzimDJqWc2w7U5loxljQAbnhvhMyw8TuZM/Ot/CdJHZVelFisWghn/r2boFMtPwc4vQW40lROUdax0IgzESX0UQnrjPdWoSlCaJji7tpTTTQbAV57BOtavtdbZcveQLL2g6RV1ji554dbAlV3bGtCFsjd6pxSNcyS7T9ntVEW1UUFiYuoMfON9vmduL5UfZDRtdMXmQtxPZ30wW2MiJj1VSr4OvvfAw/85e3ziyEzguLrnAXpt8xC1sr5eGlWdyZrNXGRipNtO65pNIGdgtW1Q/D80/loa2D0FNfsDqdia5DE53c3Yp2f2oOoOQEjDyPuT7XytSpCo7rkHMc3RME0U1mokUA2TGNRuQcp7fG0bkm0Ala6yQuNhdBtKHVsVCeR8r6XadnovV3fVUWd0bJTHTkE60YToJjKj4uJGMo3WtnaYLoNHcOoKVMdJbFXUUDm3whZAWN8rkhn8j7+nYUWLTB5siZKizsWEbhggfXU2ui5508ZgsLm11sOIlsmEDXKUBMOiKb0qSLQpJH1oPguals+Gvf80X8610n8a9fn9axR3KO8DNZ6ZhTBUtVEFguLm5h4UoZn2hZDhYqG4v6RIuxyiu5kDgrWn6LINqoLxM927Ew/JwaavttMjZzDukswORz+fvCbftK5Rzhdyi6AjfROVAgFggX7e/VLufwfI7TW+MphxQgsGOrerzI4rPHTuPOR85HSTdZzgGEQb1Gx0I5E90tm4lOOfeLaKKnCgul09s0WOlMtJCFyNgFbPdkksegK79bmiBa2bGwhVadmZroyoNovUy0/Dc5c7PWtfD+2x7CFx9svpnIxPVxcnMc6euAcnKOpH4RCD7nebcxk0G4rq1cVTieOojW9awVWYv9oXShyeJaGc45Hg2D6CasqU5txrrrzxw7PfU3IecQxawrHQsDp6lmK0F2pI7MqQ7bEy+w9ZO1vobeuST/WRx+0V01EQCUDeqEPEl8d3U2W0l2LJyngUMR5Ex0ckzT+dzEGPWF//ID+J0ffVb4uGrlHAaLdwM2mwyiw/Hs4n39uV1e8jizNYbPgw6xMv2O2Zic4/ipLbzynZ/Hv//Tz+A3P3QngNkutn0dOYfrT3UrtEwDlsEKJxbTwouOaWrvlKj85oFg0Vj0NBUBr9IJqaRPtLj+/s3TjwLYjXKOqLJZ1v+EW44NBkBZ7hxVt8qVA7usk4ZPZZJiRHbnV/7+jkqOpwj3n96G63M85eie6LYycg51s5X5PXnlyefKQ6uNZiCC1+dT2TBBlBXLucC3w6BF+IG3FUSPXT/6TptokiDcHzqWgZvvm253fn7oYE/Xis6Xfg2ZaJ+rNdFiXGorGb09dmc8xy3NTLTcrlf8ZBgMHVN/0Ss+57LZ+LiZVvA5xpro6j/QZMdCoHx2qwjTQfT06+tISURA1euY2gXIRdgee1jpWDANhpWO2WgmWrjLXHZBH9sTr9bx7KGw7fPFiUy0TiFfFfg+x0v++NPR73/7hYcAzHax7dpmJJNKY+z60Y68oIwsJe26FXUVWju/siZautkwil/HYkGbVn9SKhMdvsdnXLwPwC6Uc8RaZEnOITLRDQYQWZlosZVS1RabfCFkbZN6Ck00EGeTRPe2JrnleBDgPPuy/dFt5dw5Zn15q5jwHI/jykOr+KOfuAYvfMrhxjPRqokckHYzcrKHwwXJRMvFR03IOR4LC0D/3TMvwv2nt6eC5PMDJ3KlAQJpQ9WaaJW8CJDt5Nr5HrYUQbSuT7Q8f14gFVvpNkc6P3Si2ouy12Wy5iX2iS71dDmv5c8sYK0KFua5rysF0cnX15FziPmgb5vRd1vldT903MifeLWGotwsxGtdsj/wva9zV0s4MT31wj1TtxcpopuHs4MJxq6P/Ss2nnxkDR0zCN4ji85wDOtpBPVj15vxiA6C6GLvI20HrVNgd326sDCZiS4YRGd2hi5XEyWeU+jGd6E7R4YmutFMdJY7R7UD20gzEy2foPLdhDOG7NXcFF9/dAOH1rq44tBqdFvXCvydixQZqNwQqgmig0HsZc+5FCud5gtKkl61Al2v8YHkC2wZDBOvnYIYETzt69uNyDmEi8q3Xh4szh7biF1Vzg+daFEBhJpox6tUYqFyiwGa8xpOI8hEJz3H9dw5OOd4/hMP4q43vjhyNgFCPaTGefXGf/569HP5THTwOHH+1ynnSHYsFK9b9wJIvBfLMGbOIV05h2Uw2KZRi6PIYOJFUoI6GhVlIXbWhGVnnbtad53YwN6eFXnsC4rof+fh8XDM+t0ffSZ++Yeegonn46Nfewz/KdwxFnKanm3m7rInLe4AoN8xMMzoK6EiTbPcKWB1maaJNkrUi2QZOFhGuWvVj4LoYrLbpQmi1R0Lmw+i40y0wp3DKvbl5KGriZbPf/nnJx1ZC46rZMX8PJwdTHBobbr6WSx6ihQFKtt+l9REycia5L5twvF4o63A0zXRetu0InPe75iNDf4qREOGJxxcwfmhU4uvr8xj54fY27Nw5aG18Pc4iF4fOlEWBwgKdDivtmbC42mZ6GZs0tLYHntTLb8Bff9jnwdbrsnumbqZ6DPbsU69bDY3Ob7X3WwlmeGqwvEnD/H8hoGZa19HzjGc+NGcZ9fgKDKYeJGUYLXbsJxj7MJgwEWhxKJOm7u7TmzgaRftnWkp3SnRUbcMJ8O6jiN7e7jsgiDz/uefPh79XSxkdNw5VC5PK7ZVeAcuXROtv7su30P+ZE2DFV4MR5bGKsljyV2jXZ2Jfuenj0cn2XTHwuDn+06W75JVFB2f6Moy0W7xTLQ8Lrz5x68BgKlscFOc257MWAhFFf8FghrXm81EW+b8QaPj8WiAiFusNrcYc1y1JlrXJ3owcWGGutVOiYLNqhByjssOrMDn9bucPHp+hIv29XHhvqBg9fGcTDSQ3e2zKPmZ6Ha+h+3JtCc7IJ1LORNOmm1f1y5nSVkGMWmKYxaHU4fjieP5M4mFKhx/8sjKRP/dFx/KffzI9aI5zzQYGKu+sFBIghrPRI+D1xbOOnXuaj1wZoAnHV6bub1Twbyiw+kwiD681o2CaHncFMF9X8OdI2lxB4S1IEUt7nyOZ1yyFze8/oVTtxfZXU915ygh53AU8l35mEplosPjE/O9biC+FEH0//Phu/DgmaAYYLpjYfBh/MkNx/Dgme1GjkWlzRZEWx8VDWzyVk5Whm+6Kja+fd+Kjf0rdqMZVsHZwSSyqxIUbSUMiEz09GncqUjOISZssTJtUhedponW94n2sGKbYIw1NvirEIO/mAzWh/VKOh47P8KF+3o4GlbWy3KO9UEiE22LILq67zW12YpZrr1tVWyPXawkCwt13TlSsutBYWE1BUp5OKma6Go/T8/n8PnsTmJZnWURsjTRb/3kfbmPDwKm4JxmjME2DEwq7VjoJuQczY2Hg4mL1Y6FA6vB9VuXnGMwcXF2exLJRmSa2tETY+aenoW9PRsd08CJ9WAc+4GnHYnu17PzC6OTFndAsItQPBPN8a2XH8ATE4uLQjFNyqlolMpEC/mu2gZW11lDRlzfwpBC97teiiBaRp7A5LadX/7meiOv7/k+GIOy9a9um11d5K2czI6FinabgioCzqKMHA+Prg8j035B0QYOQPB5q4qA5t02l7uWiWBrNGlWzqGS2eg6KgwnHlZCDewiyDkuu6CZzolntyc4uNrBnp6N1Y4ZyTk459gYOtjXjxduIuNQ5eIotdlKQ17DaWyNPawlNNG6PtGez6POYjJdW++8kj+PsnIOMWnGmuh65BxiLBSuAwKrAU207OyUDOL/l285mvv4setNZR2LNMLQYVrO0XRhoYfVrhn53tflFf1oGKxevL8387eOZTYi5xBBtNg52tOzotd9+099W3Q/rY6F7uw80i8h50gb18pmouWYuVQmWtGhWmCZRqnGVuIYxLypG4csXRAtZ0zkVcrprbHq7pWj8i0WGGGGoaoLcdqdIyuIjn9OXgd2AZuqqrjj4fMYOT5e8OSDU7cXbeAApGmijbm1gI7nw7am5RxNZqLTNNFRYWHOILE9CeyogLCJTUsygo3hdJezuq0Ct8ZxF8yj+3qRnGPk+Jh4/lQmOpZzVJyJVshwxG5JW62/t8duaU2053NlUkDX193x/MgurGyzFbHzIq51cWlUXVgYBdGJIFb4fNeJzr9+1QAAIABJREFU+BosqWMhY0HhtyozmmScKCKzrWoTJHJhYeNB9CRwl+nZJvq2WZtXtNBaH1rrzvytqR29rbGDvm1G1+daOJ4ldyi6Gm3Ig4XV9OI5aDJV7LvjfDZ2AOI5WyemSQtRTKOET3SWnKNk/YIonhSLF135ZiVBNGPsxYyxexhjxxhjb1D8/ZWMsTvCfzczxq6R/vYAY+yrjLHbGWO3zXssaQFsU4FimiZSUEWAJxBfMmPZQdWUJjrxt07oiNEkYvBN00QXKfRKdeeYu7BQ0kS3IOdwPB4F8TK6Lg/DiRsdd7tyDgeWwSLpTp2fIec8DKKDQPnCvb1IziFkJNOFhfVootUZm/Ys7jyfY+h4Cos7vXPJ5+pMtK7Wfuz4kZRk3sJCKwou6/GJFp9FcnerU3FAqkIsMAzGosUy5yLIyH+fY9dHd6ojZbXX/WDiRd3ymtRE/+L7vowb7zkVBfAHVuza5BxCa31gpTPzt6ZqSzZHcSIAiIO6fijPE/QsExPXz5Ryqtw5Vjrl5ByqMaBIA7k06VUZn2hHkj4lsUo2WxPXmPi8deequYNoxpgJ4K0AXgLgagCvYIxdnbjb/QBeyDl/FoD/AeDaxN+/j3P+bM75c+c9nuSHeuMvvwjAtH545Hi1uQQEmej0j7XKCl+xCl3rWDmZaFnQr5BzNBxgyc4RU8dSohlN4MurarZSgya6wYYrgZyk/KAlZ42a8jdVsTEKvJlXGijOHDoePJ9HmZsL9/bweCjnEDKS6cLCcLCsMhOd2mxFryC0DsQiYbawUM9zPC0x0A0n8TzGbtAtUTxXGaJak4Sco2qHO3FdJbWWTbhzOFIAL+9m6LZFniTkHEf3dnHL8TO5uvHvedMN+G8f+lru8w8nbvQ9rnUtjF2/9oWF73P80+2PRq8JBK3f65JzrCe8mGWCcbT+OWBz5EZjGIAooO7Z0/Ol+D1rvlTZNZZpGuNx9W5UMYs79e1l5Bye78My2Ew8A4Tz/xwdC8V59t5bHtR6XBWZ6OcBOMY5P845nwB4H4CXynfgnN/MOT8X/noLgEsreF0lyQ/1ikOrwbZjeLEPJi6e9uvX4w8+fk8tr6+Via5o4BGT/2rXyhzg5TlyRs5hVScv0SXqrJXYZoo10UXkHH5K2+/5M9GRJroNd44cTXS+O4cXZf/a1ERvjgJXiCay+VsJLeHRfT2c3BzD8znWB7OTYx1yDtWiDpALQpsPorfDArCVNJ/o3Ey0usYjkHPkf3Zj148WLKWbrSQy0XX5RIvJN3ntWQ3UjkRdGcPWzALTYFpJn6A7XXzcdz6ygQfODPCZY6dTH3Nma4yHzg7x7psfyHxuzjkGzrScA0Dtkg55bhLn0IGV+oLo84PZxbagqUy0KKIUrHWDY+l3ktrm4PesMdVV1AyZhlG4lsDnyNxh04kh0jPReotEmbSmVkDoE13iWhVjjDi3v/Lwea3HVRFEXwJA9t95OLwtjf8DwEek3zmAjzHGvsgYe03agxhjr2GM3cYYu+3UqVOFDlD2MxVbUKKVZtWogjqZIq1y8xg5QebBtrJXcmnWMkAz7WyTCLlGcmUdu3MUlHMoCgvnXRgkfaKBxdBEi2As7zsbOV40yDblb6piGGbEI115jcWZm+O4qh0ALt7fh+tznN4az3T7AqTvtWJNtMp2PW5s0vz3IMa8ZCa6iCZasSmiL+dw/WhiKpuJ9xKFRLUVFrpq/9kqvOdzX1vSecqZcJPpvbbszgEAP/fCJwIAHj43TH3M8dN6rlVj1wfnkOQcwevULemQxy0RNO1fsaNFcdWsDycwDTZzrQChLM7zK3eESZJcDIlmaP2UTHRWckcVbJpG8R0h3+dQhTVFMtFpr2gyvUWijOvPZtgFVkknHfEe5bhE57uuIohWRYzKV2aMfR+CIPo/Sze/gHP+rQjkIL/AGPte1WM559dyzp/LOX/u4cOHCx1gx4r9TMXJU1fg6HrqbQ9Bt0IN8jDMDAQdejJ8oqcs7mblHE1nKSM5RzKILuMTrdBEV21xV0ewlf/66kFC19w+0MK1r4kWE0IRm8APfPlhvPrdtxbOciUz0ZeEFfaPrA+jDJMcREfboRWOBUGzlfI7CHUgPsdkYaGu53hWYaGWnMOJnUGqsrhjNWWi3ciPetYnuvZMdCTnmM5EG9qZaC9KRADAL/3AUwBkNyY5uREU3K8mpHVJxDk0m4mud0yUzy8RUF+w2qmt2YrQI6tkAh3LAOf1X8NJHbOQdiQD+7wgmnMezI+Jc7lM5jfL5hLQbfutfk1dzb+M6/mpmWix2CmKkOLKz6vzXVcRRD8M4DLp90sBPJq8E2PsWQDeCeClnPMz4nbO+aPh/ycBfACBPKRS5MFe/F/Xtupg4mUOSEGxRzUDj7AcyjsJ5dWUqrCwLTmHPOAD5eQcnmL73DKCwW4eT145E9zr5G+bVY3K3xOQgrGc72wsWRu1KecYOR66YUU9AK2q8Dddfw9uuPskPnVvsR2nZMb14rBt76PrQ6UmOtr5qNjiTp2JblPOEQbRaT7RuR0LUyzuLL1mK2PXjzKYZd+/kMmJ4KYun+iJqy4stIz6C7BdqQtbUs6hE/QkM5j9jok9XQunNtOdqU5uBjUDyXMjiZA89RNBdO2ZaOn8eval+wEA+1c62Bg5tXiuJ4v6ZIpkXech+T2K40l+R6KxTlohfuSlnGxGVjBo5TzwTk9bWACamWjpJeVXN1hxn+ggE50i5yi5a6RaKOiMb1UE0bcCuIoxdiVjrAPg5QA+JN+BMXY5gH8E8L9zzu+Vbl9ljO0RPwP4IQB3VnBMU3TtWLsnvuy6sgqikCqNKt0who6HfscMil4y3Tnin5MXQhMFM0lGjgfGgGQnpW6JQcpVyDmEx2vZ75hzPq2Jbrh9fPD6PjopLU2BfL9hOQhvyt9UxSicEGwzsO3SWYiIrdqvP7pR6LUif9XebBB9enuMjmlMZXNiS8UKM9EphcVmi3KO7TAASutYqOUTnVJUpLMAGbt+dA0VzYAJnIRMrqyc475TW/ift6ZL+dIz0az2bpNRYWEiG1bInSMxpu7t26ldQj9//Az++z9/HQCUC3YZcd2KTPSehoPoV37H5fiZ77kSQODOwTlw2wNnK3+9zZGDPV31/F1mfirDTCY6PJ5kMCycWNK6FkZ1BElNdMFCPnHJzuvOkeoTXSoTna2J9nxeeIGtek6d8W3uIJpz7gJ4HYCPArgLwP/knH+NMfZaxthrw7v9BoCDAN6WsLI7CuAzjLGvAPgCgA9zzq+f95iSyBkT8X9dWzIbQwd7e+lBtG2y6pqtTIIgOu8kzLK4a6JgJsnI8dCzzJmAvowm2vVnt3WKbDGpEJOZGMhEQUvdLasFwQAwO5EDsX9t3oQ+kSbUVuUcjodeaM3Ut81cTfT22I0m7AcKdhkVE7qYBPf2bOzpWnh0fYSTG2Mc3tOdOufEeVJ1JjqrACfrOn3LDd/AFx+sPjCItuIThYWMBQub/I6F6iyUrk/0xPXRtY1wnCp3HnoeTwTR4tiKjeM/8Aefwn/6hztSJ9jIJ1pRWFj3Nr64pi0zzrg/8fCqdtAzdmY9gbOcit7yyWPRz3kJApGJFpKgpgsLn/+kg9FnIqxRf+LaW/DZjKLJMiSdMWSq7jicRtLbWRxP8gwQhfmjFJlhshhXIHZudXXIYuE7tyY65eUCeYnWoURkuaDpJgeSeFIs8eoXBAs2nfEtew9HE875dQCuS9z2DunnnwHwM4rHHQdwTfL2qpEH+7qrazdHbtRyWEWVW+tCzjFx/cwBfmrCmCksrL9gJsnI8WekHEC5joWuz2ErtquA8p60YtdCXIymwbC3Z9XebU8QZaQy5Rw6muhYztF0Qx3B2PUj7V6/Y+ZmouU23Y+upxdEqdgMuyPKk+AlB/p4+NwAQ8fDkb3TDRQYY9qBoC7pzVayv7fBxMWbP3YvXvkdl+PbnnBBZccDpBcWAno7UcE25+ztXcuE6/NMRyLf55h4wYIuCAaLHz+AGW0nm7OwUD4vZaLiPkVhYd0L0WTm8B9+/vm44uAqXvnOz5fORGfNN8dObgEAnnbhHnzz7CDzuYVNYl+yuAPqz0SL+hjZLWW/5OF892ObeMGTD1X2epsjV9mtUD6GJjLRqsLC5EInco1Ky0SLRVki2BQfpetzdDLqtwRioarswiw+E425lk8tA+KfTaYf0Atc31eOs0BcMO36PjoF8sSeJOd41qX7ADQn51h4utK2Y90XwMYoLxOtp0HOO6n+5Y5H8bnjZ2CbRn4mOkMTXdYOZh7SvHSLdiz0w4ztjE+0pZetTeO+U0EG9PILVqLbDtRYzJJkkpINk2/L8/adSBZ5VfmbTly/8Gc6dmLv2r5Gm1rRYfDSA/2oBa8uycJCIPgOHzwzwOMb45k284B+NlWXvPa4aQvW+0OXhDrOsTRNtDiusm2/dbJQYuG5p2eXaqogSLoeiR/LaqLTioRF1jZpcWcb9bf9TnZL/LYnXICDa10tzSjnXBlEpxVEnjg/xInzI/zmD1+NH7z6KIaOl/lZis+reYu74HVlecMBqa5hq+Ldwc2xEzVrSlKmj0EZkp0nv/2KYFG9vz/dACZPE50m5xDBsO61KO6mGteKSFzky2duOUdGZ+iySTQ5GVAkFtkVQbTcbarurZitjO0gQL+iPU87eMvxoDbzu550MAyEsyzu4p9f/IwLp/5mmfW3s03CuTrQECfuW284pqU/ThskxCRU9rv+v99/OwDg6Rfvi27bX6M3aZJ4IldoojW8fb0wOyhnoqs479/wD3fgW37j+kIT58j1o8E+kHNkf69iUnzi4TWc2R4XCpK2Jx46pjE1AV1xaBUPnh3g8fMjHN0728q3a5uFClnzSLO4jDPR6u9BLNzObNUXRK8oMq9BEU6OnCPDnQPInmgeCXcTLtnfL9VUQZDMdkea6JLPl7YjkmzqIihrm1UE1wsstpKftU6hlLi+u4nvOO3av/uxTQDBGNcNXSey5oHtmSA6+L/uIFoErLK8Qe4mWPWYLHztVTSliQ4WQ/H7vXh/H3/zs9+B33rZM6buF8k58s5lRQt7QL/wXtxPFbOW1kRLtxusuFtI0IwsTc5RLokmJwvionPKRAOIsy3vueVB/M51d9X2OpzzyHYu+1jK28EIXI/jyJ4uXvf9V+Wu5MRE8w8//3y8/gefOn08DWRYkqRl68QW7fbEyzX/F88DzHapjAsLi096p7fGkXfqpQf60e0XrNiNZaLTdJlAfjAGxIN8FERXpIn+xy8/AsfjuPsx/YK/kaTT7GnIOcT28KUH+nA8XqgRynDiznTBfMLBFUxcH5tjF0cUMquebRSyVMxCVLGrpA15dnLHTwVb62e2050UyrIROg6oAuHAdSLHJzrNnUOjhkEE0Rfv7wXaxzks7uTrYV6f6LTzMN4FSso56q8dcTx/JngHwiAjL4iOgs3ZTLTq2j8eLtqedHhVS+s7jOQcVvg6JjqmEXmz10VyLAOCXUHBQMPtRxfOObZ03DlqPg9Urbq/60mHcHBtOgkQW9ylZaKFnCORiWbF7DZFLKIa10wjqKso6hMtJ0d0mwnJjMJaGxVl7UTdqUy0vrR0lwTRQXHFr33wzmgFriEFKszbP3UffD7bRERGNyuoimvlE2994ER2XXnZJHERHFrrqrMcDWei0wINGVW3viROyiCha92lQmQCD611poqpLrtgBd88M6jdaB8AnMhma/YzYIzltjUVA5p4fMcKOlTNI9uRsx33n87WT8qMpzLRRn4mOpyULzsQSGmKZJqGjjfjPX7FwdXo58N7FJloTZs2HdIWdcFtsU5PRZSJrmGhtj6YKDuwAcFuR1l3Dp0slPDnvmC1E0yWJa+f5DGw8NIo8nzytZt2HspezTJ2Azt2jseV457O5zZOCaK7KfPNifUh+raJC1Y7ccCQscCNCwvj62u1a9Yv51C8L/kYquw2OnKC2qJUOYdpTh1THcg1BHn0cjoWuin6fnEd6QauYshSFRcD+n0ZeEomWtfCUWbsxPNKkkinXfB78qV6FvH56+yI75IgenYgSavsnIc3XR+0Ek9O5DId04i6YmWhOqnkc/7cYBJppHTdOdK0mk27c/icz3ROTHJIEfAk8Tx10BIXO5QIosNM4J+84jlTtz/x0Co2xy5ObVWfKUwSZcNSBlLLyD6HxgkdYRVWbnKHsAc0u5w5ng/P59G2Y98ulolOvm4eQ8efyURfdXQt+vnJR9aSD9FuXa2Dl5GxyZPhiEz0+sCpvEbh/NCZ0VNGx2Xm10T4XC3n0Gn8sz0RTTqsueQcSSvL2Cda/znk40ybHLPdOeouLFQXS+l8birZA5A+vq8PHRxYscEY08tEh5+XnCBa7Vr1N1sR0jZpLGSM4Z9+4QW4ZH+/0gZYojC5TZ9o1ftNI6/ZipuyqI+7pxbMRKfM2bbJtOYW+eXk69Yo0bFw5KZnonWbSCVxJTmH+Px1YqNdEUR3zFnNcFplZxUkJ3IZWzMTrRo0n/XfPorbH1oHEEyMwo868InOcucI/ldv5zbvzuGnyDmAQDsJ6BUMxZroZEW6noOFijgTPR3ECylAE5KOk2FxnUoTDeRrJKPsjZSJlm8vg5wRvr9Aq2Ag3vbXcefYGrmwTYYj4SKqUBA9cWcWsEf29HDJ/j4MFrgQJKmysDArEy3Gm7Rg6MEzg2ixc7Zinef60Jnq1Jg8rjzP8bTCQp1M9EDS0s6TiXY9dWFhkeeTF0tp56F4L8ndLdsIMtF17kQ5XrrHeG4QndLAKk3KtT5wsC/UFut0ihV/kzOka12rMZ/oZIb+msv24+L9vWiRVgUbYT1GvpyjvoVD2o6Ciq5lgLH0HYQ0z/OihYWRxV1ah0DL1MxESz9Lt5fJRI8Uu46CIjvRD58bRIsnudlKEa33rgiiVavxPDnBPORlovU8FWdPqu2Jh7ffGHh7jhwvKu7QdedQvWVLscComyw5x9/+7HcC0NMzp3dkKu8TvRFeUEmHFTGw1u0VffOx0/jJd34egFrOAeRvnyV1hCI7NY+WTwSzHdPAg2f1guhRInvV0yksHLtY7VqR7rGwnEOxgL3uF78HN7z+RZHft0zXyncM0SXtfATi810VsHLOsTV2cVnoBlN1cWEQMKmDaDvHnUfovJUL8HBhkDWeDSceDBZM+OYcjZ0CveJ8mmj5ONPdOYInTGYCxUK9ji55AtfzlV3YtILoNE10SnOv88MJ9ocLK51MtHD7kbf09/SsKACpC5UmWtDvWJVmooWTTFqztCYs7tK07SoYY+iY6UmAuLBQnYnWPZcji7tUOYeeJnq62Ur8s1HC+nLkqC0qgWI+0d/9e5/Ey952M4DpZiti7iVNdIilMJxPs0ep6vVS/6aZ+c07wWX9p2VkNwKIt2PUBU95dmlVkyXn0G1rDcQr7TQ5RxkNY9ogJnRydU8atz+8Hv2cFkTn6didhK6zo5FpyuP8MAjsnnbRHjy+oSdpidq7S8F83qC0NQ6q44WGd72AN/dwos5O7OvbuOLQquIRoptptZlopWwqDAA9xXktCoOEhOVchbsdnHOcOD/ERSne9XnnkhhWlBZ3mpnolY4FxlipKnxBMsBkpTLRUhCdk72byUTPMaboouq+Cug1o0iTc2RmosNgMVpkZ0nEHEU3xJ6NjWEzzVZUQfRqx6xUEy2uu4OraulTExZ3Yrck+T2m0cmwzE21uGMFg2h/+nEzx2BpaqJTbjeN4i47Q8dL1URbOfUnSYRfulx3oSNVE+yKIFpVoZz0Fq6SQYZOTDfzmzbZiJuHk1gTpOvOoSoMsIzA3qjODMvM8aRY3AH6ba2B9MxfvBItPtilZT6aykQzyck7NYg2jMyFT7QlnSiSmGcbUmSirzqyB2e2xlqLHDHZxJno/DbRW6HFlNDwrhcIKAcTdSY6i8BDvtogWqlrzdDpiYBOSJmqlHOsDxyMHB8X7e8r/27lbKXGEpXZv+V5XwOBe4JwKypThS+QK+cBWRNdLohO232IinJnXC7EuFRfAOV4frTYktFpRiGuq+S4lVbIPph4kddzHBymX5sTz5t57n19u/YGVFGzFWUmutogWkj1ZAs9mSYs7iYJCVweWc10vGhBOP1cZulMtPrvur0vpgoLpZcuKufwfY5Tm+PUhYalmYlOjh1ys5Vdq4kWXWaSdBRbWhrmD4WQv5CsogDdzG/aXcSrjKQiKiundW+UTcrYkm2yuND31ZktQL+tNRBfJOmZ6PJBdDKA3dNtJoiW34rQdifJ0/EmG0aI8zHNCkkHkRF+ytE1+FzPRSLORJvR/7qZ6I5lYLVj4lwBTXSWTi6N4JiqlXNkZaJVAaew6bp4f/WZ6PvCgkXZrlEmdwGe2a0sHDsyvtMgEy0t9kuu1V2PTwWYdck5xPeTDGZj79k65Rxp3S7zW44nd58EaVvtY9eLAjWdwmNlJrpvY6PmIHqSGMtkehV7vIsx7eBadia6Tos78R3ouFOJY0qb55wUOUcURGsGrlljgDgGvWYrsjvHtJyjyOL6g7c/AgD4/P1nlX/X9YlOnu9yJrpIIm7HB9Hiw3/Zcy7B+1/zfOV9bIWco+rEq3i+Sw/08SPXXJx6P5H5zTtpsrYpXc/HxPOjgCHI8KQ/V7ZZerlK1nnQk3PoZ6JnK+nL+0Sn+cTGco6aM9HSy6ZlovsdE6OMDExyG69bweC/PnDQMQ08IbSMO6kh6UgWFvbsIGORFbRtj+NmRfv6diH5jMriLo8qCwvdlJ0RINvfWyw2RLvhKm3ubrr3FEyD4TufeFD59/xGTRkLA01XB7ETYWhkVNOYbbYyfXw6yEVww5QFpeOqr/95Fua6BI16VIWF+Z9bZPeZOO60IEsOinVkChNv1rt4X9/G5titdRczK6jsWsZciYEk5wYTdC0jdQwRx1DVzpWK5JiZR5oPOCDvjM2Zic6RcwSZ6Pznkl9OjleKZqLFrui3KArFAX33keRC2pO6INoFdh12fhAdfvhPPLSaupVrm7Mr+bKDeRrihHz5t1+WumIDZLlC9peTdoJzHm//igyPrk90WrMFYD4P4aJkyTl021oD8YSWzETraDXTEJNFUvoSt1it19JJR86xkrONGTsMVKeJXh9MsG/Fjrr+ndzMb8kdFRZKmWgge2DaHMcdw1YLVv8PS8g5gmxWA+4cGQO7+C73dG3s6VqFHEnyuP3h83jK0T2p7hxmzi5W9J4yNdHZC35x/pVp7xs9T8L+jZXIRMtZ01R3jjRbMI0iynkJGsqkFBbmBBnC7lOVQVdmoj1f2iHKDxiCoHv62hLnVJ11IhM3KGhUzV2VZ6K3Jji42kn3Q24gEx27kWhqolMKR4H0+dEsqomOrDvTj2Gi8T1MdyyMfy5qfSmukf/8kqep/675PSXHAFdyDdtVmug8+xVAHYyULXBJI2/LQ6DTtll+vln4jGen7pZsWmEhUG/BzOzxpLtzRKtkjeOJtl4rzBpNXD+yhpMRfqp1FpUErxP/nJ6JtjDICOZjhwGRia7GnWN/346s/nSKC1WZaCB7IbI9TxCd4s6RRaCJrlbOoTq3DYPBYOqJS7aBW+nmO5gU4d7HNlMzNkC+XWKUhSopBZO1zHMVFkpZIoHBimmiZf1ulk900oUCiCfVOnfsgoWCKhNt5CZ90oqsO1aQQJraSud8qiuejpwjLRMNoFZdtKp7n6BnmXA8Xlkm/Oz2GBekSDmAZnyio8LCApnotO/NS5kfi2aivYzdKEC4RelkotWaaKNgrYTYfUhNmkaJwZxMdGIMkJutFGnYsuOD6MgDOaN7h2p1rxOkFSHPBkZgmXpfcNYJPpqEJ5GmO4eY41THFh1Pgw4dQSZa/TcxUeoVFoqJY/o01mlHnEbWoF1lY4405Mk7TRe3YpsYZASXbiITHfvAlj/2s9sTHFjt4PCafiZ6nJKJzvpeBuM4EN7T0w+iHc+H4/Hico4q3Tkymq0AosV2emFhv2NitWNV4n17/NQW3n7jfXhsY4RLQ+s89THp2WOqDId0FquuFwe/ljlHYaHHZ65zgxXznd6QMqbpHQvVDU+akHM47uxCAQg+e31NtDqhIC+gk3Z4OjtEqnGxkSBaUdAo6Fa8O3h24KQWFQLBtcJYM4WFRTTRacmRtJqhokE0zwuidTXR0svJr2wWXFwndziT6Dp8JccAORMt2pnvCk10lt5X0EwmOvg/rWAuPhY9OUfa+S3LOfpywY6OrlHxbetmxqvE83nqlhljLCiULFBYaCe+fBFIlcnoTVw/NQNc5dZ/GvI7sVMKC/PlHNNa8So00Y9vjnB0bw8dy8AFqx2c3MzPRIuMQZFMtNyJarVjabcVFs9ZprDQ9XklcqasTDQQDO6e4roX52k/zERX4Tjwthvvw+9dfzcA4NIUZ47gWLM10VnvSWfLM9AZhnIOVr6xk+vPeigHQbT+c4gW5IfWuhnNVrjy+m9EzuGrF/CGhgwmsuZLHLu49p2MIFrHnWPsesrCQqCBTHTaeFyx5dzpzfFMky0ZxlhqG/WqKKqJ7pgstbA3rWaoaGFhVhIueH69YNNLiaKD81vrUAAEc4TB1MlRcTxAfiIuOQ/5fHoRq/u+dn4QnZP9AVKC6Jo00XntrHVXgWkZFg4piLZld46sLdmM4qAGMixJeIacA8jfYhakFU6IxUVedzwVqm1LQVDIUncmOv45q7Aw673FrYun5RxlNdGcc5zcGONo2EXwyJ5u1FUxCzEpi6A4LxPt+RyOF7cJX+tZ2NIs5JQD0SJU0RJdkNVsBUCY2VBkooWcw7awUmDhkMVAymZfeVjtkQ3kZ6Kz3Tk0MtGSltmYp2NhorAQCK6VIs/38LkhDqzYOLTWwWePnVa+byel4Ukjcg4vLROd/7mlNdZQbUvHkgF9TXRrmegsOUdO2+sijF0Pj54f4vKMXRtAv1laWarMRKdJfArLOXISlR1MQkPGAAAgAElEQVTLLNxAbkoTbRS7jkWjlbREnG6dV/KY5WYrgL51344Ponn4HtM+UEB9Qvqc48Z7TuL6O09UcxwawTwQ63XygtasE1xMkPqa6OB/VZY82vpo3Cc6/e+2oWfenlVYaLBymWgnJ4iuOxMtfw3ZhYUZco5EVmregpjzQwdDx8PRUA99ZG+vUCZaZIx6dnbGK+5wGNxvtWNqyzmSC0td6giis4pmlZpo8b47RmUNJOQmGFdftDf1fmZOUXLcfGj2c9WxgZJdNeQCok/efRJ/d9tDGe9g9nlmNdEMRWLyr504j2dcsg/PuXw/Tpwf4aZ7T83cJwiiZ6+7RuQcnloTHexg5GWieXRfmdjvNn58sqGUjtZ37Kos7oLahTobrmQmNSqUczx0dgjOgScczAmiNWw65yHZ5TUPVTdmgZNSbFq0sDCyTE35HmyTac0t8l2mfKILFhaOJMcfFboOX8mYJ+kAlNcZWLDjg+gszZ5AtS3u+Rw//a5b8dr3fKma48iZQAW6X3Dahco5j7euJTlHZoU9T8+Sz9MiuyxZcg4gv4uawE3RATLGsNKxSgUjWduHXcus1d4ImF49px1Hv2Nh5Pip+tKkPnJeTbTwGn7SkSCjeWito9WaejbjJTJH6s9wtjmLiZHmhJWUOOkiji0tsN8YOfjo1x7TKmDLarYCpF+no6iw0MJKtxpN9OmtMZ516T68+1XfHjXVUJGXiY47qCkCS43gSy4IlGUJr3r3rfiVv78j/42I5/H4TIA5dDxce9NxHDu5qfUcj50f4dIDK3j1C64EoLYSTPNqjoooc87HwcTFFW/4MP7y5ge0jmnqtX21O4ehEWQk6yAE6kx0IojWaHEcZISnry1xXmUt6Odl7GTJOfJrLHQRO2sX7UuXPgHBZ1ZnJrpoMiArMx7VDKUUFurWJ6R1wxTofibT7hwxZQoLsz4fXYevpJzF43zq+rFNA46bf1w7Poj2C8o5/vA/XIOf/Z4ra/OJznXn0CzkS9vS5QCGicJC0wi0gWknIg8zv6rA1dYM6quE82ztuGUaWoWOsS/v7Gncs7MlD2mMswoL7foLC+XFTJrmayVHrhLLOarJRN/zWBBEX3UkcHk4tNbF6a1xbmCZzER3C2aiu3awTagTwA7mlXOkBPa//eG78HN//UV86Zvncp/LzVlIB1p/RSZ6Ek+cq51q3DnObk9w9UV78aKnHsm8n5kjBYt07aogOlqAZ2ui5Uy0z3mp4sLAQ1n9uX786ydzH885x/rAwYEVGwfCts6q4G+Sl4nOOXaxuLz2puO5x5TE9dJ8ovMLr9IKC1XX/jjxnVqmAdNQN2WJHqPIRK92giB6O6ND77xkZaKrlHOc2gp21g5luHMA2fKJKojGTN1MtEZhYbJmKPKs1w6ip8flJLoZ22l3DknOUbSwUGoUpEK3zitZR+b5fCp+021nvvOD6EiLnBGUSQPTvr6Nta7aM3Wu48hpjSkQJ3SeJUyWLnLGJzqnUMDzs9pst+TOkXHm2Sna0SRuSoMBIPhshiUyJHmWSnXLOeT3nbYwFN97WqY9qY/MCxTzuO3Bszi42om63h1c7WDs+tjOCfZGjgfTYNE51svJRCe3MnsFXFZGk7Jyjuxs1jdOBguIW46ru2P9/+y9abguV1kmfNf0Dns6c05O5gQSSIAwyqRBGtMIgg2t7SeKn3MjCra27QDO7aetF3zqhTYNbdOoqNigoiLQ8iFG5iFhHgIhCUlOpjOfs8/e71jD96NqVa1atZ5nPVXvu0NO6Pu6cp2dvd+3alXVqrWeda/7uR8dCbOoA+ht+dE8Ri/MA5llaKLTNMPJ7RlZea3WJicTTW/lSuQcsZ5YWJyri3SMYogBYM+Kezw/O40Rpxn2rPTK4M/2/piVERXKJEpHXyxZvg7a7zlxjRImmkoA7VkSIqvdhUD7HE8Q2MiFwPcwiPydZaIdbknAYpVYFY4Xix8usRBQzO/OLRrU3G5btNrQFxRbMfuE74gXTFSLLroOxyJMdFv/+DmzWwxUO2ROyazJRKfNxMLp10UQrfS+TPSql0/OV9070Q4lK+GjaKmonwqQdHeOShPts8dMM5ohlwb1y0TCFFsB5Ns7VDINkAdTneQcCSPniOqewodPjpZetEdfzFALQ5f7SMlEa0yT73Xf9vzKkS1cc8FG2Z59xURzYovXRU/jtGShAQkTXWfIqqDb/Ry7aqJdOm1VSOKuEyPnsZzuHL5vZTInWmns1cKdo43/sYnNyRxxmmHfKh8Q5G3l7TFtAVf13dzyi0vg0Zlo38/LfuuT6e3HtkSLBj0Yt7XDBeXMsXslwiDy4Xmw2kTOk9Qq/5N4YgPVtXUJouM0tQbwoWA8VFvXjbLfloDC1EQD+bvJJxYm1nFxWZaM9HmbDLiCS4rVBie2pgh9jyxKpCC1c+uK6Tx3QXHtaCvwmmi7Y0tJugnnfJdjCFfwRQeliW6bcMwVawNkO2RAc+feJBtzOcfXQRCdCBhgfWCJAk/cQVu1Q6iJliaoUANTmmUV62Yw0dRkyPoyC32rdXzx3k380xePiD/fbA9/n0LB9iXAyznWBmGnEt3SxML333IM173qBrzufbe1PgcHyXNYUUza3H59toSSfhh03oY8fGqEi7WsdcVwHnfoos0EECcTbWqoIznTpBcsaQMXE62u8fCpZQTRdou70awqV77SCxGn2UJbxqrNy2CiZ8wE6nmes+SvLsNQwaA+Tj3rd9+Hn37Lp53tjJOUvK+SRdapUX5Pdq/kFelWiEX2jJBUSOQcN9+3ic/efQZAu0qKChTb7pLcqO8CTUJBVb6z+UTr41yPKdqhvmPrAyv9AKOdlHNwFnctxgcXjm9NsXe154wNctuznSOc2haM4oJ6qtiKmnvFTDSTF5EfP5eUuBb+GSfnaPHCJCm9KwXIfaJtiYX6+yOVc9AZJ+cIOPs2hXoQ7TvZ4i4oi744NdEyPRI1MM3itBz8FcvnqvKXpBl5zdIy5Dp+/m8+g8/fs4knXboHf/6jTxHrtxQyJqgH8nso2fItk2ksL9S+1R7uFLCHJpyJhcWAdbxgYW85IktqkkIN0L/1bx9NfsYt52jel17Hynzb0xinR/NSygEA+1flTLTJduW/d2iii8C2CxPdti+WbbJMxHGSlsGXpBS3y26TtLjTJs7y2U4TcvvUhROlvlPCRPNeqGYSmgmXHjJJ6hUL47RZYe7z95xxtjO2TJyv+s5r8fN/81nRjtOp4vkp6cewF1rlSHGSWd//SJBY+NzXfKD8uctOAuUMImHq1Dtv9r3IKudobs9zTHSW5Ys6WyXX1V67qqJtMUvswTugL4CXwUTPRO/LTjPRk3lCFhGh2kMF9TGxqF92YqHuANMjahsA9Z3yRmJhlvczTparEKc8Ey0t2Ka/U9/5ug/j/s1JbYyJhJUYz3kmWlIpsBFEG51qGZ7RkqIvgNwNg2KiZ0mK8TzfWlMschWY24/JbX9IS2Tq+Pw9mwCAm+48hXd/4X7x9xQ4jTZQJCBJgmjGDWHfWh8ntt02bCacFQuLYE3dr2Uvx5I0xa5hhBc/5VLyM6UPtkPOoa+quxYJUIlSB7QJRjGcNncDHW2Z6MqdQ1niybPvTccaKSqLu+a9PLk9KxfHerU7Cgnhy6pAWdyNNSa61OsukCylnssyNdE0C8UH4XrwG/j5xG2+2xc7vHkBu8Xdv3viRQDoxaSO0xoTDSjZjF3OsYyKhZ2YaMs1AjKmTn23Ua7cllhoKS3dC3xS/zlPMmRZtUOkw1X4aVFImOhlOCYd35pi/7o7iJZ6B3fFeJ62GsM4PXJs2ZEE3DvXJqYOnXYpD3EGrdXPpsWd+Xf+OE3PeB3Sgm16zPOJO/PEcX0ns/f14BN996kR3vjBrwJwaKK1lzD0vUYAt4zsXolLCFAFfK4Od3xrit2WhJl5khbBSXVNLp11amSd2trTpmKbfvukCRC19jAabUCeaEDZOgF5lvXJ7VlrzTIbRGslotsw922QOAYIwM1EzwurLH1CzfXc7dusmNi9q1VApn6WMNG9Fkz0lEgsFDHROyDnUBn7528MsCkoKKFeIb7YSvM843mliV7pKya6O7unnotEE5074TBBdDmBUiV2HUy07s5RyLTM87l07FnxHfM994vENpcLT5pmeO0NtwKomGjKlit/d7q7c+htbgvKJzrQmDoKNqYesFvcWTXRjH2nCiRsweywF+xoASpRsZUlMNHHt2bYv+pedO60xd1k3qwMyUG5hdj6Rpym8LzmXKv+X6pDdmmiK7cPWb5A8/v5v22Kv7jmSEkMYZOz6GNR9PXgzvGyv/gk/vQjdwLggzJzW9t8AMtwXFAdxLUdIbVfuff0BBdayvXO4yxnrrRgwa2J5tixYruv4+qaKgjCwSnnEFToAngm+vxdA6QZcL+gsp4OyuIKyJnUicFE/92n78UvtPC7dSFJ3bp6NalQC5/Ycg0c08ThZBFE79EmmEEUYL0fttZEu7LpTXunNhZWlcSpGxNtO4e6visOrOLsNHYuyBQTzVncWd05ZgmGBQOtmOhFtsiPb83geTLXikWZ6NytwBHcFcGvX+wwme+2aweXqwSZ+8Hz9+o9Nx/BLUdylxWVOEYlZM1je8VCiZxDR1smOsuyouS4XRMN8EHGPLEnJfJlvzXWjdmp4nTxg7CblagUrgqywOIkWJZlrZjonaypwCVS2tArE15tQbTdaUbKHCuUGnpiXpQz0bqco/q5bVDPyVMVJEG0bTwf1Jho3vZR4ZwOovWEFmliYeg3EwuXw0Tn/7oernRb8N7TY6vxu5JzqOQywO3OkXCJhS3kHLcd28KL3/DR2lZMlyDaJeeQVOgC9MTC5rEedmANQFUoRAqbH6pCjYnWnt9bWlRecyFNM6d7jEvzNbeUD+5aKOZUIQ3Ys1Jnafat9Zxyjuk8re2YeJ6Xa7OlPtFqkpTIOQr3gLZJw1zy4vGiKuMVB1aRZblNGoeSiSaSXqhy9pN5gmHRjhWHVEeCE9tT7FnpWVlNE2qyoVjOsgwxI+dgCz1pEgWKiXah1HZa7uswCkrffAo6SaLuSUQk0sXpcioWtnXnUEGQbewpg2iOiSaSEq3FVor3rLZLxORMqPfVFkQNdpiJns6TMjmycW4l91pQzrE1jTGNU+wTMNE77RMdp/bdCK49gL1fUsm4au5t4xOtW5WaCEqbXBfJUP1dHzLaVlAUMdEC72lbe/WFpXTBdE4H0TrTwgWv+stvSyxcxvaMWBMtTCzcmsZlWVUdszgPonWGz8VEZ4wm2qWn1vHPNx/Fh249UfudxF7KRO4TTX9PJSC5wMk5yiD6aLsgmnfnCBCnGeIk3bEy6UkmWWWrRRNtbWQGAoOOhWJObjflHEChOXfKOZrJcYOQlpVMDOlAGyZ6OqcTkDioc9m8X08XEo6L9uSaXRfjGTuZaN+6ezCaVYtiVQVuESb6xNZMFBDkbeInsHIrn8nM5zXRaRn8Km1vW4mVetdsrNqwF2BMuNQo2BYIVELk3FIZEaiCUel731pGxtxn1Z+4ITpOs3Jc0GELsmy7CxIm2ta2fHduB5lZZjxW+U2LyjlOCD2i1TmluxFdYCNAXO0B7DEMJfFpm1gYEzskCp000Zb2SN1CJEG0xCbXttDVx4qvi8RCnR3jZBS672cU+I1OuoxCI2WCo0sTLUwspBIq5klaJCLZNNG01Q2ZWFgyLHRn2Z7G+IuP3Wl9kboEk1nGSxYCoW8kx0TvX+thYxDi1pZMNO/OUSXpmMHQsvyiOf26gksSZAuihx0r4R3fmiEKPGwM6gu6vau9MsCmMDGYaCBPTiKZ6EZiodJQu9/PfAehvZtFlVho0SoXQbMKSF33j5MdAEyxlVm1KK5KKS/ARG/JCq0AFbtLvcdzx1ZuRMg57j09xq/+/ecxmVcWd2pia5vIrVyHbBOnJLHtTLEY+jePvaBqd2h3SsnfHVrO4bITU2g7HHDa84okYRYrZLstTHRLTTTnzjDs+TvGRJ8ezTBPMlaWNAh9506EC8ppSSLn2GkmmiNxqPYAdjlmTATkruJsjeNY8hF0SCsg6nOkHqxWi0RhEC3IG1J5BOxxLB/Q5wFpEZlzOojuWwJJG/SAoh/6jdXZMlhFNb659KzSVRtld6TkHMvSRFcBGd1ZXvG2z+GX/vbz+KDBQgN04M6Bk5cAct/IOMmPYws6Pc/Dw89bwy33y4PoOEmRZjTrplf+M71xXVv9UrgyjwH3boZta3cYhc5gY3saN1i7E1tT7FvtNxap6wIfbisTHfm0O0dZJrzwiW5hcZefqwsTTQfR27MkX0AUOlqX9lPiE20L3CZaYuFq8e9CmujtqSipULUJ4Jloz2NyKojkm1e/+8t4U5GvohhS5f9u9lsX71YVErHLOVz9WtkT/u7/9diq3SQTbdcWq4x/ivww+0abMsaAg4kumUP6+xTrWAVZVXtmcdrYnufce9xM9M4E0coF6lEX7CI/szGMRM45HEpfdYmcQxhYdQUV+FJwMdG23QlfGINUbbI71ihIi7dQxJj67acPnxa1J0ndO+C+534HXUH010fZb+0eSDXR/chvBKdt7N0oVB6x/OcqNwxXEJ1ZB615nDPRegJVuRIkjpmmGZm8Ewq2KW/8al7y2G4J1f7epQ7Jgu/zE0Z5bod+7DEX7sLn7z0jHizUJEImFmrZ4OaiQ+LeIEHiYOkBTcdOsYcWhwFXFv2HbzuOR/3au3H5K9+FV/3jl8rfU+WjNwbuycvKRIccE13XNVeaR/ckPSOKQbgQFjtTtjYp67mhUFYiqVho9sUsyzCaxVWxlZKJ7h5Eb03sUjAbAkdfUhU8qZ2+iAgsdfmPzkQnlsRCF6r72ny+KwJN7vY0xsAY9ymWKU4ya8VC9R3qPi0aSE7nTXZYQcUvrCaaSCLrWYKsscUBos/kKnDFNnI5zWIVNil86LbjCH0P115MB9G7hlG509AViok+8CBhottoovsME51ohY50tNUgz8VMNH9f9P57cGNQ/vyZInj+tbd/QdSeJE2dkkeVxMwfp/n3i7V6CD2HfWd5LucnHsTQbwG3Ba7blvQCv8FoLCPbVuJXDehWSW45hy2YmycZtqYx1rXtdZdtHsduRkx2r8Lpcb5SP27RwHbx2E5TXn4T+r6IyUkcq/bL9q9iNEvEg6wriUovzGFOpssqOJCmPEsPuHcPbA4Dw8hnGbuP3lbtMvzxh+4oJ8Xj27OyzLeOjUFeZIEbqCYEE81povVAuE1Fsq5yDkAlVVmY6GmM1X6o+XLLJgnS4i7wGu/9rNj9KIutRIqJ7h6U6RprF5xMNCNvAmhG9wLNWai0uCt2mI5utvNvt/meK+TuHPy9svUNShNNVSwE8nGWYiHNY83iFDfecZJtl3lewD72SNw5qCQymyba1j+4IiK2CocKgyhAmnUjUyhkWYbffMcX8ZYbD+PKg+vYGNByjo1htDCBoTTRZt6HDT2hTrYr5gn/vpngEl4pxjZwxAsmKKmQQugwNlDIMuCSvSt4zYseh9e86HHl77/l6vMAAI+6YEPUnmVZ3Jlz+B98z+NL73ng60TOYZaOpKB3gDCwMNFLkXPIgmiJxZ2qEGUbtGZJijPjOda1gaVMNCMCT47dLBNmmIWEV2y42izNuixAuDLkQIuKhSkfRKvB96xwu8+VRKV7CpvXvSzbI9EA4RgE81LL9WtY6YWsHOG+MxPsWYnwK8+/BuN5grtPjQEoOUdzclkfRMgyuigQ0HTnAPJ7SCUC5cx1UPts/nuJnKOdNVStTVFglXOMCtmUWoRL5RykJ7tlYDf9rX3fy3W+HRdlWZZhexaXshAXAteCLEkRMfc1Iiqm6c9dBQWB7+HUaI7v+58fE7VNodSaWybyQeTW+pt+5QBdvjkmdgABvjrj3KIL/67XfwRfEVY0rZjo5nNzuS8BdEJk4HvwvToTPZrFWO0biwomiGblHMJ3ow1Obs/whg9+FSe3Z052eGOwOBN9ajTDej8UOU2pgknLKNBmAyXLoWDbaVDIE/ib32nLRFPOL+XxhJropCCIXvC4C8uiR0D+/+dvDMTVZjl5qoIvcOcwCaCnXbGvRu6tCHdZzu0gWvuZC15N1tMcbJbDROf/OvWsju1T/W89ouOeGc9r27UuNokLWiUlMtXtsyWStRlMpnGC195wK6axnTVRCDxZkgFlR6Wg2HqXdleh9EN1JBZO46SxCFpaEM04qShELjlHkjUCH1ewcf/mBJfsXcFTLt8LALjuVTdgNItzOYc1iM7v7SZxb7MswyROGgMjxfoCuWxDD4SjIA8AJNn35nfbgNrKHk1jrPZCcaDgTCz0/Ua/UcfUd8uGUXfv3ck8RZZVshAXXPkU89heBluB2vL0NKWzCr5c/ZoCVb4YUImFEm1+/RooveM8sW+BA4WcgyA/qC3+Y2dlrPssadrOKZTFKFg5B80Wmtc60ipkKuTvgCux0BZEq9255QXRd5zYLn/e70iQ3bUEJvrspL6zy4GzlFsGck10CyaaaQ/lp9zWDcOVWNjGJ5oiGPat9crcBXd7+PgByK/RdXnm9Td2b3sh0syd2H5OB9E6WvQ7RKY7xzI00UUHcs0TkgqBLlkBAIOJdmuiqQlMUiKTu6Q29+4NH/gqXv3uL+Pk9oyVc8grFvKsrbpHm5M5siyvWnaUKb7ilHNoTLSp/1pGwR5A+US7BwjAwR4ax1jpBVZXEYVTo1y2cfWhDTz6wnxb7VN3ncZolmCvZSJbd7D8ZalgSzBPMtFG0O15HgaRzN96GqfWssQS9EN7smO+7R2UUouJ0J2DY6LNfqOkCHqi8CDqbhumdgakTLQiFLjEQkojDNByDr2YQlQy0fZjuApUleWLLQdY7YfYdkhfbFXvbO1WlRGphXnIaCSp3//1J+7Gmz92F9s+gNdES9wLuB2snuGJPZ4ljcqePSaItlU4VFC5Octkou89XY3RBxy2c8vQRG9N6zu7HFSgtazx3kS+A92eiba1JyGC1jKIFs7dMaGtLo8ntO1NGYJo90qE0yPe7ak8TupekPueO6g3/26Sq2q3ZtuxK3hOB9H6QqINy2GydMuwuFOUv0vwLvGJVgMyx7LqOjHXStAlEaCKQCjYJjlVTbGNFEZnhFk5h7BiYV7hS8ZEf+6eM3j1u7+Mn2WqC7rkHHoZanMreFk6OYl9j4s9tG2/lW0nBv/Ncc7GBL6H//a9TwQAvOeLRwAAFxc+yTrUTgjF8qtAuR0T3ZR/cEF37buLyDmIZMcyiF4WE22xuHvpn30CQJ2Jzh1MugUlo6mShyyHiVaJhRQoL1X9cCrwaFsIR0GNz7b3Yq2fLw457eLUouu26R3nZbBOB6MU40wF0W/71D34xb/9HNm2so0LaqIpVxF1TL1927O4tFJU6IcBKVPgLe6U5Gp5QaX+nh3aNWA+mY9D27NmoncbtGGibRUgl4mY0eTbUDHjlneQYqKVnEPIRFNSIYUq/uDvSR782v82bEEc6AWcKPi+W87RCKIbxJPMbvTcDqK1n9sU/TBv1jICIPXAXBNFJPCJtrlEfPeTLsaPffMV5f/rcg5XdmyaOXy0/falTJ//2EPsOW3QB7pFgnqFJOWtdypNdFy+oGNm61dNqlRgXjLR8yajuyzbI1c1RyDvY77H+ERbZC5lAgrRzrOTeXm/Ltg9QOB7eP9XjgHIK/aZKFl+ggWqCqe0ZKItxVlkiYULyDkiOws3msVY6YfiIJqTHQB2i7uvFMWA9OApZ6K7BdElE91vp4mmJkAqwVmBSrbTdYTq2iiCwTVyq35umzhVMMixRTPLLkXPouWOU/795+QciwZVHNsr0ZxyutW+UZp7TMg59HbY2mbXROe/WyYTrUtDLrIs4HWoMu6UrEyCNkE0ZylnIsuy1vOCS39sgtNEU+SZ73vwhJLJvE18YqFrJ7xsD8NE94VkCZC/B644K+jgzmGOL2o3j8v7Ac71ILpm2C3/njlILiNJQB1i0QAIsA9av/HCR+Gnv+Wq8v91ixhXdmzuzkG3KQw8tj22S1IBVxs5hz4JcPdJYk8D5DpuLhhf6StXhZitblgez5VYqBX/MCfgZQXRmSBpAij0taQmuhn4cKb8WZZhU7NFCwMfB9f7uP3YNqLAK6s/6nDpzcvt6RZMtJlYqL4vrVjYpkiBpE2jWYKVKCgDDBcjUVqxkdVBmxZ3CvqW9CKaaKUPXhYT7Sr+QCXb6e+vmujb+N/qKIsqEXIOgHfHmcVpI88hskgzVHIgxbpRco733XIM7/niUeYK3OB0x2WFOYZZm6c0W7hvrVc6UAD5vTKZaPWMbTsynMVdm6qiUuiL5qsOrrOfVUH0IpKOsxO5nKPHLDZMvP0z9+KqX/7fuOHL8r7h2vmh2mN9B5mgNRBWBQbcCfxydw66PQOm2I8JVzwDyCSh5t/NeXelXKA/lJnornKOnUgsLN053J/lAqC8PSqxsGpn4Hm1re7ztKxlF1Ph8mUOA59nlIvDvuQZV+BpV+wDUAVR5vfuPzPBP33xiDWjNc1kQXQg2IoBck0XtYUJVFvko1lSBo/cKr+0chIkFu6kO4eoDwUeyR7aDPs5xmI0S5CkWU0idKiQ61x9aMOaNV0F0fbJa0rIOTiWdTJPLG4eUiZ6EYs7Ws6x2g/h+55IYiErtlK/lidcshsAcP3VB8vfLcREFwN+WyaaY1hdcg7buFOXcxSJhV2DaMbibk0QRE/jpOEhrtqtB/vKfpBK6I4IOccPvPHjeP37bnNcBY8ZI5mQuClQnsBAXs5atyc9tT2rVfvVz2vbkZG4cyw3iM6PddMvX49L9gmZ6IWC6BhrLZloyXj/TzfnwfMX7jkjbktbdw6ukiYn45TIHRTmDolJG3cOqj1tJGwSyUsuCeWPY16/uVuvmGhX4vI5HUTrAZyLvXvyZXvLhClza2IZiYUqQBSxiHVgM7AAACAASURBVAGfyGdjRAPfqz3k89Y1JtpheZakmUPOYbd7UpjGKV76zQ/DL37b1WWQUzLRxjn/41s+jR990034wr2bluuS7RyIKxY6snT1IFoFYtyKul1i4c4w0RJ3DiDvD9Qzm1lsySqmqdlOxSbrbIzSItpYaKB6/tQ26oRIlOJcACZzm680XZxFx2wBOceAkXMozedQYKWmFkDUu2ZjR/au9nHNoY0aMziIAow7akxbM9GOscMl54gC3yoR0o8WOeQcLnBac5Gcw7IQsPn1l37UxPX2GDnHolB9nKtYyPtE02zh/rVeGURP5gm2Z0mjgJJIzmG5L9JCRG0wiRMEvof9jqRCAGU10YWY6Gl7dw5JYuEX782DZ5s1rA1poUnvoom2FlvJHHabwr6cOAJ7uTsHPe+3IQ7y47h2/PmdG8DdXjX2u5hoWc95kEK/Ca6b+taXPq38uekTnXfAN3zgdhzZnOCXnndN57ZIAqDQ4YOsBq3Q93Bwo48jm9PGxKxn87tWgi6JQBj4ZFCfprlntQqe1XHUoGtOKh+5PS/accJqh1edg2OlfF8o50gyVqulGMTxPCknWS5BgtMlAmZioaGJXhITnaa0T62OiJEGxEnWcOfoMQyKqjyo6+xV4YFL9tqZoH7ow/foVTrFRCtP5ixrLuwm82bVQa44S/183SoWAoqJbmrc50lWshESiUWSOaygCvZTv/bYouvPr3lBJloYRLsrFmZY6TFBdOiREiGFnoOJdr3plZyj+f2VXrVQpmCT+lTvQwa1fuFcQNT5uy6Wbf1dBzf2uCQ3gF3CpXD+xgDHzk4xi9NyXDZtK3k5R7NMuELFRC8v0W4yTzEQLogXlXNM4wSzOGULuujgxlEdZydz3HYst+qT2hxy5e1d7bH6RKcZqEMFAh/lsl1JhiEzBkiZaM7irh/6ZNK7CVdQD8iIOFd7V8vEQp6JPqeDaKnG1oT5ABSj95vvvBkAOgXRUk00QNtCKajrigIff/sT31irKf83P/40rPXrL7wrO9YlEQgDj/SJPlnYzqjtP3V96jKpjmjb5tcvmbtPrkVGdTw+axgoiozMkvJF4AYoV9nvqvhH2lg8LJOJluxmBBa7NIWY00Rb2qm2QvWJRDEzjy/kBiY8z8MqUy2Osuzqa0yOGWDbfjeIAqs/uY4syxavWGgED+PSei6/D4OeIIhO7cUNFHTGRmeAzee9iCZaJcGsCOUckoqFHBNNaqJriYX5Oah+7WKMKneOZjskQc0saTq3lJVa4xToV5/T/2YiCnxsG/1dyp7Nk4y1LuOqAkrYXk4GcOm+VaQZcPepEY6etZe4VvfHFgxzOl1p0q3CZJ4gzTJ2pySXdcn6b5VY2C2Irnbh2jHRrvH+juOj8mdpEK36gPTaXe3h5BNBICOqgMKDnLO4k7pzcJroKHeH4RaDCtLke+e44lgIqTHUlQtzTgfRNSa6BQnVYKKXWfZb0A6b1ZWORLN0umD3sFZC94mX7m18Xm1pUmblXOcFcncO6h4cKXyVD270yzblbVTb0/bvbY6bq7caE828A5IXAMgnTlfAOYwCjGYJbj+eswLcAOVKLBxEPnqBj9PjWSOAXZYmmvP01hH5dDLozGJJxG37qYlEbY0CwI8/8+F44qV78M1XHSDbMOwFpaWaCcriTv3/1JJEOJ3b3Dnc23zqmhZy5zCCh9G87rc8jAKRTzTPRFeMjbrM2KLrX4omWsxEK020vf/OHb61oe8jzZoTtk0TTck5nIwR484hCWqsPtGWhCwXE22TrkiDt/E8YXeYWMlEWXaeCaIZz3ylKz58aoyb7jiJwPfwDZfX5xFufJjOm5pyBX13ToKn/fZ7cWo0xx2/8zzyM7YEYwpq4d+ViW4bREfaDgaHO0/m883Dz1ur6dE5qN1SM+lT1h6bnGNJiYUOxxDJTgmQW9xRY4Dej5xBtCSxUMJEO56hlIk+pzXResdpo7czJ62llP0W+kQD+cTDvYTlpCHc1tm32sP6IMTtx7atf3cF0bo7x8duP4EbvlRlE6sg+rzCDeSVz70a/+oRB/DMRxzIbWS0y9BXtjYmWrpzINdE83IOIJ+AxvMYN91xqtFGEy5NtOd5OLirj7+66W7cWBzP/O4i+ORdp3DP6bGMiWZsAG3Vy7htPxUI6BPJWj/Esx55kN2CXu2HGJFJgopVoZjo5vfyYivG5yN3YiHnbCBB3xKoq2B02EbO4drxsUw2ViZawHpTGM1ieF7zvrdpk46ZxWNZhyrEYk7i+iLYlVjoWi9zcg4u+FOw7VKUVmXa90pNNNFOm6OH9L13yXOmhQ7YtrM2FEhWcrbQ/pxUoLk1iXH45AiHdg0a8gXdvtMEx0S3Lft9SlCVbmJJBKWgiI2uQfRWEUSbu7sUqv7GX++dJ3Im+gmX7BYz0eo7ZiEcUXvaMtEMCWNCmljodMPIMrIYnVQWlGVZcV2OxEKBOwdXpRmodlkeEHcOz/Oe43nelz3Pu9XzvFdY/u55nvcHxd8/63neE6Tf5aDfJFfVKx2r/QCPvnADL/qGiwEsySe6aIukHbkPslvOIU0w8DwPl+1bxV0nR9a/pwJNtOpQ3/1HH8UP/cmN+ffSDD/8JzcBAC7fl3sFX7JvBX/8Q0/Gaj9seE3qE5KNoanvHDjcOYRBtOtlWukFOL41wxeKJA8uIaSsHsewIOdvDKzygmUw0d/x3z6M41szoa6edniJLUVoopZyDgmGUYARkdBV2WLZmWhzsMy38rImc004Z9TORdjpSWFLdhyVlf/Cst0Sdw6+KEH+Nz2hx6qJLhxJbA43LqiSztLx0DUBurZXKTmF3vTKJ9p+DKl20TYecotDBRsT3bMwimWRK2IxZnMikQbRruCAW6ysRHkffNmbP4lbC19xExxbqFdeu39zgvM3mgVMKvtOiybakqtQfi/04XnL1US7Fm46PM/DxjCy7nxKsDVt56teuWHwffauEyPsX+vj0n2rODuNnUnJAPCiP/po3hbhLhJQLfjIst/EPEuVvbfB5Rii3ktXUM5V4y1lQY77pF4/F1npe4LFueP6fd8rdrJ3mIn2PC8A8FoAzwVwDYDv8TzPFBU/F8CVxX8vAfC6Ft8loQ9orYqtBD7e8ZPX4Tde8GgAbi2PBOqByTx++VUgx7xQ2DWMSLux3J2D/m4uDWjeA7Ulf2jXAHtWm6WfA0N2oQciNv/gOhNNt0diTwMU2l/H/R5EAQ6fHJXH4ya9bcGAetAyAQFVxbFlQDJ/hIyUZpY0A7OehXlT2Gy5pamw2g9IdqwtE00XZ5Ew0bSPrQQqiNaDVnVdihXKWUh3sOcqIgTUHSFsE92gR9uNuTCZNwtpcFABclefaGp7W7+X+1cLKRjRsY9vTXGYIACAanxm5RwsE910brFtg5cVC4mFua3KoSuh+Bee80gAbqaWS4zVk8j/hfAc5nTEKijbnsU4ujnFQUsVQG4xMmWYaM/zChvK5blz2HI6OGwMw84WdypAWhNKKPqC/gYAd50c4ZK9wzKB86SwpDXQTs7heV5esp3IS+CCaOmclTu/MEy0w+GnPA4zPpbFuxzyKEU+OhMLBRZ+EvJ0tR808iBMLIOJfjKAW7Msuz3LshmA/wXgBcZnXgDgTVmOjwLY7XneIeF3SeiBaBcL0jK5ZIlMtKQdzsRCx7aiDWv9kCx8kTmS1WzFVj5863Fc86vvBgD8++uusH7P97xasqA+ANsGNambioup14/neplWekGt0AAXmGxNE0SBxyaoUX+bO5iJNljU4i62lADmLKw2J3P0Qr9VQguQJ92R7hzzdky0moTb+EqX51pUzlGcU58YS6u4YkKTMDdJmrIMiY31tRUyGJQJrO0Dk3GLpKx6m+zXNhVULASa/UqfT3et5BMkdW++cnQL173qBvIcqp/bxjAXE50WOxwNTbSl3WrcpRMLm3IOl3PMVQdzi0inrp9jorUgmirvPJonpX7fhG4DeHYaY8OyWB5ohaSsbWPGxOECGn4b2nol7xpGneUc2+ViuaUm2rHAPbE9xXnrA+wuEvJPOZKjdUilWGWbCFIuSen6ED3LgpCCTR6oQ6qJji2VdBU2HMW7ymMw+RE6fIEkVEKeDgTWpssIoi8EcFj7/7uL30k+I/kuCX1Aa+POoeB5Huty0AZlYqEwAGI9P1N60qCwPqCDaJf3cBT4mKdp7X7+53/4YvkzxUT5Xl37qAchLiaau7a8YiH55xIurRaQT0A23aMN25ZKXrbP2HD41Ai/+LefW8pkIilKQVncJWmGNGsmR/HuHHFrKQeQJ92RTHSZaS5joqfM502W2ARXqEIC3TFEoclEuycdlWxLIbLYyVGaaKBbKeU8YVM+rEsqFnKLE3VNJuujxoUP/sK/Kn/XglysQfVz2wTsSixU7z6VWKiPDVVBphZyDsfCSlqMhGWiIz2Itu8YZlm14DPRC31EgYftWWL1Ygf4Yiu5ppx+eJJAow3mFhKAw0JBdEs5h2TnAwBObs+xZ7WHPcUCkkr6t6EtqUdZ1OZuQUuQczgSC6XuHJyXuWKiqd10/RgAb1Wr2uR2/XHf515oL7CkYxlBtO2umK2jPiP5bn4Az3uJ53k3eZ5307FjxwDUO3KbgFOHS1ohReXOIdFE+6yo3ZUlbsP6ICKrdiWpwFIuyWqTtq6vJoNoo6PWmGjHy8CtNQK/OSnbIGEsdFZuEPEDx/Y0durRXvzUS6y/f88Xj+DNH7ur9MluCz1IlCSn5kx081qqghGGnIN155jXPKKlGHJBNMFE2wJW/fM2X2mq3QolE93VJ1pzDFFQriMqiO4R1ep0JI7iPzYnDNtCcChMsrFhPE9q2/8uuDTRLos7yt1DHe6iPZXPeBeiQz+27d66KshNiQVWz8IoqnGXYoRt7hyuhVXpOuD4HMdE63OK7TorRxb6ua/2Q2xPY0znqbV/cIuRWcw7iwyjQOzxK4EraDOxnCBaaHEnsFTMsgynRjPsWYkqJlog59hdBNxPIGxFKeSVNC07FEyl4jZMtIuskjLRXH6FkhJylUcBuZe2xJxA4u4luU/LCKLvBnCx9v8XAbhX+BnJdwEAWZb9UZZlT8qy7EkHDuS2W4sy0YCSVixDzpH/KwmAKA2yQpzSkwaF9UGIrWls7Th5zXr6u2EhL9EXE3pATbEQuTsHEURbEj30QHEp7hyMrZOCvhW6axixz3prGju1cdddeQA/9S1Xkn+/7/SE/T4FvV1SXb3tHlUe4/VjcMHG5qQrE83IOeJ8C7Ch9yWYuarCoT3o5gLKUjrSkeq0seNm5T+RnCPjtxl1i7vyOzZNdEvbMB0Ti00gBy4piNrVqH3fck2AKi5S/6y6zkddsIH/5wWPsp7PBqpP678jmWjCccem5aYWoPq5zIWUa4JV/dnF1Mape1eNOp8kEFzt5TuVsyS19g/ONcfFRPc7MNFsxV6B/7+OPSu9VnIJHeWOk1ACxSVoK2xO8nl4r85EC4L8vas9PO/aQ62uHcj7JcVEc5pouZyDZpABuTsHR3qtC+Uc6hyuOTIn+NiPIE4ypxOKZNxfRhB9I4ArPc+73PO8HoAXAXi78Zm3A/j+wqXjqQDOZFl2n/C7JHQdahufaB1S/a0LlZxDeE4mmCu3L1tc1AqzBcy9TEDxEhZG5zZwSSU2TfSelQinx/ygxmtH8/O5zOA5WycFXeuWB9H0sx7NElGRCo7xPHyKTpDi0HZBGBIWd4opayPn2JrMWycVAnmfo5IuqGCuT2gvK19pQ86hWGLGoWNhJtrCjm93knOk7E6ULWC1TSxtbcN0tGaimaQgl286oDmONILoZj9WC6GrD23g8ZfsaRyLWpBxEyeXWAXQSadVPoy2K8DIRtTvzXdO9ZnXf98T8C8/+8zGd4Y9d/8F3FIgBds7vy0o9d6PKhs4m9yHK2c9cwTRw6h9YiG3qyNJGNdxYL0vdsAwsT2N0Q99ceDKJWgrqHygXcOKiT4tCPJncdqJCKDmgjSjd8YlMgWFWOg65NREMwmKknEe0Bx0XBZ3njt+mKeZc+f5AWGisyyLAbwcwLsB3AzgrVmWfcHzvJd6nvfS4mPvAnA7gFsB/A8AP8F9V3ruZTDRNs9maSWf2ndayDnywZgbRIpJo8WWFrca5MptAoVdWpKSnYWaRAO/zi4r78xL963i2NlpI2DVbbdccg7A/VJKEgt1eYAriJY6G3BM39FNmSeoiZrnucidw66Dm5fZy0YQHdCTpLJFa4uVXohZnFrbQWk8qaQ5Sv6hyv9yCVzUlr0UNo/c8SyB71XBl2THKnEwNhVrW53H9h2pjtaGybxd5UZuK3ZaLsgkEpWmJtq8FU++fC++6eH78TP/+irre0v5sc7LZCL7i9ELfDKxlyqnbfWJdlyvykPQx1j1/Yeft47L9q/iedceqlUDlD5LlxSo+lzzOhWbyul6B2FQSgqscg7OncNRDXRtEOGsYxveBLez1FbOcV5xv6V+zDq2Z+7dRx0SS0W1WNkYRuiFPlZ7gcgf22bFKEHk29nSPLGQ+E7g47N3n8HRTffOae6WsjgTPWeOo0g1V1wvraEhceeIk9RJmkkY+2Uw0ciy7F1Zll2VZdnDsiz7reJ3r8+y7PXFz1mWZS8r/v6YLMtu4r4rRU0T3TmIbm6FdCm+ogJvqZ6VO0fliyq/JvVZ2wIgtbBCte8W9l1UmyhmxsyAVZPuFfvzMrP3n6FfUK49KuB3JQbMmUQFhTZyDte2pQLlfrDSC3D07AMk5yCqXla6TkMTzQz+43nSyuBfQX3HVnCFTGAimOgpZYknYqIXtLizeORuz3J9vFr4SZgbVzna0BJw2rzOJWWeKUw6a6JpfT13X6sgvKmJNr2qH37eGv78R5+CC3YPre8tndPByyzyZ9O8V3cc38bbPnlP/hljDLPtPpQsFzHehRb22gzSX/u9T8Af/d9PLP+uFoEuljRhWEMdNvJlSyDnGEQ+zhSBnI0ECAMfoe9Z3zNXcLcxCJ0JYSa4vj1P01aSBrVoObbVfuzdnsp2HxV830NI5KMoqHwgJZHbvdLDaYEmeuawk6RAuX1xiYUqTvm+//kx9thpIekSaaIFFqDUO6wO704G5JN/y+N57rLmceJmoiNBLsxSguivFZbCRFuCkbbyjjd95A78euFmIS2UwVYsZHxRKQQMo5QnGNDfjfycGSflHKQ7R113pCYUVWaWCyjZBKxyVepeSbpeJj043BhGrBZvGsvswaig4sqD653YEMBkomWaaFsfqqqu1dvIDf6jWYJhC4N/BTX52AKErkx0s9iKRBO9KBPdPMd4Vg9Ge4GHmcMlxJXoantHeSa6vcwsl9G0d+dg+1JXTTRzXts4SQVWFRNNa5VtE/izf//9+K833AqgKfVRThZjTUKi5BxksG7JK7BprvUgvHyWDjYrdexi/IdnPRwAwUQLSr0PooqJpmRPvdC3Vyx0FD9ZH8iKnaRGv6cw7yDnADoy0YJkchMuaZdZSnzPaiRKLGxTZEYHJQ/lEgu/cvQsAODuU2P22HOBL7Pve/A8tzsHl6AonvOFBKPIJzpNRZroB4SJ/lpBH/i7rOAAZe9W36Jrm2j4q39fKVAkMmYqEUAhdmxf2lDqiC0dx8mQFS9h6yDah7XYyv61fFDbZJIEuHegZMcEFjWul0mXKfTDwJrFrJBvhbvv+cV780XC+iDEtz/2gvL3l+1baWVlpEO/95LnTtkkzpntLmpAGHeWc1SV0ExMO2ui7e4cO6uJbp5je5bUmD31DnA7SO7cg6Z+OLZkiEurd9mwTHeOUlYm0Hmb308dtpq2Pk697uq+UlUYqeqdOoPUC+r3ZLXsu9U9VnIOKpCx5RWoPqN/Rx8vpXKO2OEx/jPPfgQO7RpYg6VKE83IOaJKUkC9631it2XqKMMtZaL1Y7MWr5aKqxxUEH20QxA9Mt5zCfJkM7r9uiYaAHYPe6LEwq5yDsrtixuPlPvWlQfX2WNLfZlDx+66OhYp55AmJwrbIzEniJPM+ewlO5Dt6acHCZQ27ceecQWed+2hVhOHDiXn0IMYVzlIDrKkMLpkMyCvyqOD0xFnjq1CtR1EDQzUpGK6c6iJQmnUuExbriyxeqHciYVu7ZzJJnLbcHllM3c/evwlu3HF/lX84rddjeuvOYh/+ExuKHNgre+09qOg33vJDkRI6OrLZDDCU9ccELIsw2gWd5Rz5MOHzeZuQvgV90km2i7nEDHRS6hYmB9HZ6LjWrARaXIYaoLnihsAdjs4OxNdXLMjycYGrnKdDVzFQvVuy67JllhIn9e2TqQWzXOBdaBrzDaDQJsXt2ur2GYTaWOi1fMM/dydphe4q26mqZuAoRbOI6Gco/rZ3j+6MtEbwwjTOHWOn/r7xREk86SdO8e+1T58r1s+ytY0bp1UHQU+W7RLkUeVnCPCPad5xjdNczllN020vf9zuVB5nJLhEFGBV6GNL7ObRaZ3jj0vZ7PdEk5ecqUfT1L22xVE9x8gi7uvCdTN3LUS4dqL2vkq6giLhCHpKtkFcdlvLrFQaONSPy/taJEz0a720Ew0FZyYmmgVGB0og+h6QKk3QeSnyzyHLMuKAEQu54gIU3qFaSwrVBEFPv75Z5+J6685WPv9xjDCaJaITex11OQcgsUTtfLnPMZtmcbTOEWa2ZONXCg10VY5h31CDXwPUeBZNNEFE21a3AmY6EWLrdiqtW1Pk1qilsuPGJC54AD1fj1Ps8bzVmW/2zLRWZYVi5flaKLLrVOuWhnh7uHMw7C8t9S4mzhyHyh3Ah1mENgrNMD6LopLNmKr+snJOdS/fYF7hcTijnrnt4WJheXPJBMd0BZ3zLgotSfT7xtfbIxPZDMR+B4u27+Kz997BkCe2PfKt30Wn7jzpPO7o1l7OUffscWvmOg1JedY6TnlHFRRIAk4TTS1AH7bTzxddGwVp7ieB7UbpJBleeVQTqYjYY8TwZgE5At4TnoH5GOvy9rQVV0aOIeD6BnDuLVBVFjc6Sb6XCEUFyTSbFeBl0S4ZaGDY6JTZ9lvv6Oco77aU5P+ecXq1hxU9ZaxLJXnZqKl2qhhVA2QasFEYTJPyqCtDS4tNOBq+85W8twF/d5LrA1zdw56C9s2yNi0fGPDyq0NKia6OXlSTDSQT+gNJrqsWGiW/Za7c3SVdFXuHJpP9LyuE5dUKnMF0UFpBeVgopnqcRzU59tIc9Qkax03it9JkiXnjcTCpk907byW+0RNei7ZFuWZrsMMAj3Pw4pRLGgyT+B5NGlg2sD97F99Br/7nlvgefVxSH1O/SspXS9JLKTYvu1pjLBgvCno4xrVP3phk2FNFEMa0H1KMa6uca+NnKONlBEAnnL5Xnzm8GkAwIduPY6//Phh/Pa7vuT83qlR+0JTtvLvOs5OcscP1cf3rOTFYCRVitvY2iooYwAT3Hj0qAt24ZpDG878r2qeXYyJroJf+ji+RMcskJgBKFht9iOIBYmcD2lNNOWH2xYqoNU74SJyDok7hyuYU0F8FybaxijlExrPkOVlvwk5B1P2u8ZEaz7Rge+xOjmJnIN7oaTbTI3ksMSeHJZlmdidw8Q//tQz8Nlff3Y5GHNacApdiq3YBsCYWVzaNI/KWWMhd44WTDSQBzRUxULz/pfyD4c7h9o67wKbTns0jWsV4CTWVolrsWpIH6jdFFWkpi0TrT7fpuy373uN91hBvX98AFuMOw05B2+rabtPdLEVPoE4cCRqA/b3YbVfLxY0miVYiQJybFJ9UfWBv/7E3eWxa/adxb9qbhoKgmhXYiGg2D57YvBKj243YMo5iEWChXWjitXoWDYTzVmhUbhozwpOjeaYzBPcfmwLQO7Z/52v+zDe+MGvWr9zejTDsbNTPOzAWqtzuQKrzckcG5pEZPdKD1nGLzISYXBoA2WZ69oNiojgW0eVqC7RRLt319nEa4mjRiqL+3zPQ2Yvfl1iLtDeP7SD6A6lsW0IfaUH1jTRCzDREk20Yr8pJGmKkEmksUF1clv8n2Z8cB/6PrKszsTpIMvRGppopUkbhAHWB3mFrPffcgx3FwVIanIOR8XC/FrcunHXYKsHh2oiphLysoze6uQw7AXYGEQlI9OlBG2NiRZMIAFhcVe+F5ZJzzYgKHeCLu4cqx000UAejNg00b3QbwRepV7Z4c7R5bnp7QGMINpw54hC2sVCwcVEm04WJTtjfMfzPAxCv3WxFbXQaJskSm3FqmBfEgyb308zsO4c1iCaWDQ776vvOZ0BbEHgaj+suUqMZnHp2sEdw5Q8mMdWx/iOJ1wIIA9a/+7T9+LWwhHBhtiR/A0wmuhZ7JRj6e8Hq4kmyppz5MKG2oFz5IPoYw+nfZXkupg4v9j9vP/MBLcf2wYAHNmc4hN3nsJvvOOLePPH7mp85y03HgaA1nJQ1xb/5nhe3hOgKufNJRdKXDDo9th3tvP3hv5eWPiec2jly+xYGAE8054fgz2NOLHQ89wVCyWJnMMowHiesNKQczaIlqyQJVB6Op2hcwnS3/HZe3HXCXtlOonXZ+j7DeZGR1uzeaAK3m3BuVMTXZyLmrR5i7uKVXvjh/IVv+97ZRD9/W/8OF742g83v8s8NkmmrsQ5AKhYkqsPbZQLrh99002NY08WTE4DFpNz6Ctw0zfYhojQR3LMgS2xsG3ZWx3Dkom2yTnoBDeKibZZs0ncDSZxstBzo8p+61pJtZ3t0kS7ZAdAFXCWuQ+Wd10iATBRJWe2e5bUBChJLKQ00Rlkntm17xCve7wETbRtV+SSvSv46vHt8v+3p0lt98EEVbDI7Htr/RCf+bVn4xXPeSSA6h37gTfeSB47dQQ8AH2d43nKVisEZJpo2yJ7WvhvP1BMtDTXxcShXXkQ/aX7z+Jtn7qn8ffXv++2xu/e+6WjuObQBp56xd5W53I5Npyd1JMV9xRVCzldtLSctQ0h4WWcCHaDXHpfqS+zS6IqwinQKQAAIABJREFUCcZ9QWKhVF7i0kRnWVb6cv/ZjzwZ7/u5Z1o/tzYIkWZ89dhzNogezd22PhKEgY9To5mR2cs/yJe/+VN43h98oPF7af8PC/kEhbjDIFIVW2n+LU35l0kxn9T2MV2xsFrtfeyr9SSO9X6Ee4uM5ONbzaxpboKVBNFUZT4Th3YN8boXPwFv/bGnltf5L18+1vCwrryGu78SUkbGBv1SRUw0oYnmspdtiYWjBTTRKpHJLuegpTGDMGjselAe3TbnjMa5hNaEFGxst9oiV1DPhJVzuKwkDckVxUQDKohuJyur5BxtmWj7BMgF+fp388+ammiZZEsHmVjotMvjJ3DAPoY94vx13H58q5SdufzSKXtGW4C+axiVY666Li45VpJYSC128sJG/Hclcg6b3EsyLq4TmuiJweDpx6aCpWqHuSUTXQTRP/PWTwPISROFn/vWR+Cuk6PGYv+WI2fx+Et2t9rxzdsmkXNYmGgmiF5EEx0R/T91uAVFgsVn+Txcen1iZ7Q8jmC+Fjl8MDk/OlyaaNUX+6GP6648gEv3rVo/p6pZbnF2vWxLHsRQE3cXf1sdw8jH7ce28T3/46Pl7yRqDluZU6kIxJlY6Ni+tCEgJjOAN13P25N3A1swBHByjmqCeNEffbT2t7VBWLI86hnpA+eiFQu5AMTEcx9zCOuDqBxogaYlV2WT1r0/LSLn0LeyJc+ekgRxMie7nKN4jzoE0YrdGlnehfZMtN0BwFYIxUTuHtD9uXmeh17ol7sRSZrr43V2T8ljOAZKWmxFPaPKhcfGwPutLe4qr+12w3o+AVrGDdU+wdjRtLjjd7/ayDlSZ8Jmc/I1WShbEHj91edhnmR4/y3HAajdBzcTbb5Drt1QtbDlxrxUkFhIzRuSKpUiOYclOJS4RmxYmOhT2zM88lf+Ef/9/bfjxjtOYjxLDCbafiwp82lCje2jWYILdw/xh9/z+PJvlxS+/odPVjZz41mC06M5Ltg9bHUegPbTVtic1OUcJRO9vTOa6JBwnXLnaPCOGoAsIVByrFgQjMsKpMgWWb7nsUx0qWRwXJfaUaCqqQLncBA9WWDy17FL6+wK3HPkhO8uGYiC8ommHnKXxAqOvXVt60QOOQe1Uvf9Ss7xiMK0/d9fdzmAfGBV5vdqJa5fLstEl5po8iNibZSOy/dXq02TFVJBWteCHYAu52ifWKj3K8k1BcSEWjHRzWP0LZrHiolur4n2/abDAeBO0uxb/GgnRHEWrhyxwnRBOYfZppGleEVfWdwxDFQe7NHtMIutxIz0ZhA12XoXJh2JhdD3yGINVPsUAkLOkaayd1wHNR7mCVLkoayJVaZ23TZZXnNoFwDgq8fzRLTtWcJqom1WiNSxdaiFOXc/XFIggGbqJMWS9IUVlUdklXMIqoGu9kJ4Xn0H7vaCQHnrjYfxXa//CH70TTeK3DnmcftxHcjHLzX+PuOqA7iiGOs3BiEu2pMHyio3BwDu38x3Is93+CTb4NZEt5dzdKkNUWuPZRGZORILVY0MDnMh8+tikSVGALJS3WrMdCcWcoeS7niUTPRDMYheZBtah81sm8vqdK2UJFCrMWrl1oWJDpkg2pWlqzq2YhJUxUEXdE30RXuGePSFG/il510DALXtLDW41Zlo+rjqPRNZArVgLK7SqjOZzOYymOhB5CMKvE5yjrbe5Moz1gw8WDmHZZJUAWPXHZ2VXlD61CpUFQTtxxxEQYNl5Zhrl7Shq6uKjtwjVwXRxdii+0QLmWhuTDaLrXA6yEGR0NIGVNVHFwLfs+ZoqPZxC3BT563gsrizHZO6tRJtp/n+mEG17fvDXoAD633cWeS3HN2cYM9Kk1RRULr4tky0YiVd1S5diYWUO8eEkELpkPQJm9ZX3/am4Pse1vthjYk+M56VxwSAD916QqSJngvdF2xQC4WHHViF73v4Xy95Kt71U9fh0K48iFaBM1BJDFVNgzawMfYKWZbhrCHnWB+E8D1+h3IRTbTNck9yPKpcuA6plazLnWMuMAKQFWyR3SdX4ZYqp45/Lx7Scg41wSwq57DdHC7Re5FCLAoUe6Mw7+CTaervdKSOxEI1+Cht4Bt+4El47qPPd54zt6TJf54l9apWuibNGkSzkyLtoqGgApE2g04U+HjTDz8ZQDNRbdHS0UDO2G8Mom6Jhdq9kfQxtfAxP8rLOYLGJKnuQ9cdnZVeWDp8KLh0lHYmmnPz8Hkmer6YnMM8hyrAsWKxuHMXW+GYaHtioZ2Jdle5MzGe5Z9v+yyprVhFGEhs+0w5SAa3U9Edv/M8vOQZV1TnI/q9S9sZ+k2rLpd1l8KBtX6eE3N2gvvOTFinBlvZb/33FF78lEsAgK2M53JSANR2d/P3MiZaEETb5BzCBP51bdx7x2fvxV989K7GeWtBNOUJvoDr1sntPHC/4kDOQj/1in24aM8K9q/14Hu5c4eCauuGZSfaBa7s9/YsQZqh5j3t+x52DSMHE92Ngc+/08yPEb27hDWejrmQ+ZUy0Vx7fM8t55D2D99RsVDar7mqvOW52CM8iLGIllOHevF0sEz0EoJolTxATchJmrbe1iknM0vPcRVbUdvoaqV89aF1vO77nug8p+dV55sadjGPv6SajFTiif6+8omF+b/3naFLpXZNQFGDusnyqWDSJilog13DqJMmWg+iJRaLlb7W3MZ2yDmM6150R8fORPOMqJWJZti0QRTwiYXLkHNoOm2bxEUv+03BFQiZJbI5vaGtIM3hkyN86Nbj5PG79uGQ0ERLWJ+wHMvyz/79p+/BZa94J45sTkSFp/TxlJJzuC3umlZd0qqhq/0A29OkDLCUftZ6nsC+YHDJOb7vqZfi6Q/bV7JaNuT6VbcDgu05SapUipnoRkVTmWvR+iAs/fFf/uZP4b1fOlq2WWGWVP2Z2raXygdseNE35IuVJ1++r/b7MPBxYL1fC6LPlqW528vYuMRCFZyvD+rBeV61UMJEd0gstDDRqps45RxCizt3xUL+WJJgPPDlPtHOxEI4mGiB64x+Hm6xcc4G0ZVusf1LoONnnn0Vrr1oV+133ArG7Ciu0pI2lA+GWM3OO8g5FLNra58rU16VGVbBn7QKZOBX4v3cc7EaqPXJSH1GKudQL/5L/uwTZMGWroOOYjubco7FmWgAWB9GnYqt6GOgZKEWEbsZZRBN+UQTFneLyDlMVxeX1RrFRJPFWUK+bDL3XSlyxxAziNaY6MInesYlBDsCoTIJT8JE95pB9HWvugEvfsPHyOOrheGg1zKxkJgAJYmF5q7aW2/KvXe/dP9ZkWe+3n/JxEKHO0dgSbJ1bVMrrPTygitq7NvNyDko6YrEZnW1Hwp2MfhjUHkQuRTK4c4haKMtiJYydhuDyDpW64vff/z8/eXPdGGdbuQIAPzat1+Dz//nb7UuVs7fGNTkHEpy15WJpmRdVXBeP+7ulUjkztGFiY6C5k5SxUTT36Oq3taOI0wslFYsdMo5HK9t5RbiYKJ9nomeChMLVXu5na1zNojesmy5dsGjLtiFt7/8m2oJBm0SC7sUZlEdkrK5S5KstdVNyUQ3suTzf7mJUAVQm+M5eqEvtvzxtVr307gu59C1ZirpQS7nqP52052nrJ/pak5P+Q4vw+IOyJmNReUcMiba7orADTK9oJlYOC4mYIm/uQ0rvRDbppzDwV7ZmOgpEwjYikCY51t08ZMz0XmbbAv00id6ASbaZDLVv1ZNdEjrwLeJJJeSie6QWGhNSBYx0fXAUjH203kisvyUyJgkxVbMd2auyb1+7duvIb+72s93Uk4XLKEt0VzB3ElQkATRkcMCTOKNHBLHGM/dco5dzOJAwRYcluSCY5G62m8mGKu2Kbz7C0dw3ZX7AXByDpl8wIYw8Em2/+DGwMpEcxIbCr3Ao5noMjivH3fPSo9352DGAhdUv9BjkzKfwWlxJ5VzCPT6nE+0wHXF93jjBkC7TyKLO44Zz/+myBEKFfHxEGSij2xOsXslWqhSmY7ffOGjy585OYc5WEsZDx0RMRjr5+hqcWcOTur/eU10fg8/c/eZWpWxZ1x1ABfvpS2AdC/GmbGl7nke3v7yb8TDDqyWA6N+66SZ+9RgVa5s2zLRIRFEd0zKMrFruLgm2jWQALq+1mTgGDmHxVpuNIsX2s1ZHhPtSizk3DkWTyzUXUZsTHRUMtF8EM1N/moiMi3ubBPUWj+oMXu6JvzY2abvOqDp21snFhKaaEEQXbkC1RPCpnEqWozr/Z6a85LMvehuLibz9vzudz0WP/SNl5PfHUYhxrOkZKK5IFpdWxcmmrrHCpLEwsAiW8myjH13FK7Y7y5t3S+KdtS8naXa0X5zMQ3Uaw9cuHuIX/y2qwFwco7uTDSHQ7vqQfSZ8Rz90O+0g5Vronk5R5OJ7rEyP3XdXd05gDoplwrfXXFi4cIVC9V8vVhiYXmfHPGRWBMd8M8/fCgz0Uc2Jzi43t6ehsL11xzEG3/wSQB4n2hzdcMVTaHgKiYSp8uzuFPt5SYhfdLVg6w3/fCT8YGffxZ7zntPj3HLkbNl9R8d1160GwfW++WLKnfnqP5IJ192W7mXcg7SymlBJnoYLezO0UYTbWPgPM9+X/qFlk+fJEeCpCQOuSa6GxOtt2MSuxILXe4ci/rFh40getWiiXZtyXOBkOd5hXQiPwaXbLNntYfNSVwuiu47XQUAR8kgOkXge62TskgmukVioeqHpZdy0Q9dkMiYUofrSWQJLqXuPTkTHYuCaNNdRUGyBR853Askcg4b4z5LUqSZOzeoF/r4wadfhte86HHsZ7KsPqZINdGrvQCjaXOhqy9+1wchSfYolIxlByaaw4V7hjg7rZ7zye0Z9q72Oh2L1URPlCa6TkzsXuETCys7yQ4MvIWUk7y7Lqs+QJ5YGDpYbanF3bISC12aaC53SEe5cGaC6MUExV9DHNmc4LyN9vY0HBRzwumcl8JEOybkZVrcSRIMugZRvufh6Nkpnv3778fBjb5VXxQFPrbiPMjSOzWrs9SunbpH0oQHE8rFwVYxD1jM4g5A4c4RI8uyVpWw9Mcm0dlT+sxZIQWynVstcuZJVm5jjY3KfG2x0g87MdFZVm8H5ROtjsPpCaeCim0u5Ix63k9L27+aJtqdWBgLEoJ1BwSO5VET/KnRHEc2J3j+H36w/NvW1L5Io0qnu0BpoiVMdLkwMN5Hl0etQm0LeiFNtPEexDK510ovxGiaYDSL4Xt8sEi9c5LrdLF+ksRCG1PXptT7r/+bR7F/1/u4mUgrcTGwMdF6EP0tV5/nJJAWYWQ5XLQnz9G559QYu4YRTmxNsW+tWxDdC/NdBVsl4FITPTQTCyOMZkmRBN18VlLrNhts8YR6r1yJhe4KgbJ51slEC+SXksRCtePluk2+57HF79SOoqScOcCTJ+cwEz3FwQ5G6RzUc2GZ6EYQ3Z6JrjI+aZa1tcWdRwTRggSDthXOzHMCKrGweRy9ypa+UOUCTH1g4nSSwAJMdMOdI639vSs2hiFmSdrankz1q6ddsQ8/ct0Vjk9rJaSTZn+kBjz1fHRpgFneui1WotzdQEelzaWZaKDyNXZtSYuY6AWfm9LGAhoTrflE6wwrhTR190c9eavSQTbbrhdouOmOk7W/mfdbwbYbJAHl+lC+Y06ZQR7E/uXH78IJze1I8momNTkH/a67vKqbxVZkTNNqL7d9PDuJMYwCdlxSCwZzTJKslVWBLQpyJtpuUbnouAXYHWimwiBaaaLNZzgq2veWlzwV//H6q7RCWrwmuovFHQez4MqJ7Rn2rXYj4SJmLKjcOUwmOn+fTxMOHUnJwHcJopuSAwkTHQT2Iks6pBUkXe4cVcVC3p2DY6K3pzHe/PG7EAWek6DyHZpo1R5XYmEo2IE8Z4PoY1vTTtWGOFRBoZyJdnVCGypbKJqJbm9xZ9frVZpo+nhclS4O+vtJBtHalpHeqbnJTWckuYVGfpx2XVi9NP/v/3cLTmxV2+LLYqLLqoUtJR3qOf3edz+W3VJWKLValuCBGvDUtemT5HjmLhnMYaUfYjxPaotLVzKSYvuUhGae5A4yrPyD0ERX1REXl3MoRl2VMdf7Ycn2xNzYkDoDTj15i6u6qVwiTm3PcG+h5fzt73hM3j4L4wfQ76ALgcVnGZAvVEPfw5HNCV75ts/hA1+pLPhETHQtsZD+jKv0uLmYlDK0auw7sT0TvQdKwnZUc3pgKa+yjXR1uCzLnB7j6txm8Dle0F1HR8lEJ80g2rXTs9ILkaRZY7GrHu+l+1YRBr6cie6Y6EyhZKJP57apJ7ZmnZnovuU+KWxOYqvW2lW1UOKjTEGN9/oCS7IAjnx7ufBau8rco8WY6DJhlJn3daMCG37rXTfj+NZM5AHvFZpoamEuaQ/Q9Pa34ZwMouMkH3SWL+fI/+Xi4ob2rgsTLRhIWicWFg/bZMol2zpr/RAv+oaLW50PMGUXmTWgjbSt1pqcg7k+PZPcxUS3XWzoK9i/+sTd5c95ItTiCS0qoaRtcmG5YyCUgJRMtE3OwZT1zT9T9dnRfPHEQqCehT92sGN9wyHFVWmPY6LV7xdl4vLEwlyGo9h5nf0MfK8INulAKM0ETLTmkMKV1VbPZDxPcHJ7hgt2DcoCSJTxv+nVLgXl+tAmiOYSPzk8+5qqqBPFQjndOSxyDmn1xtWi/57cmokWYpHv4W2fugdP/i/vLX8nqSxpa6OC+rXr3bexfeo6lxJEW5joKgHLrYkG6PLI6v1U7xTFEs6FzGdb7FmJsNILcPjkGFmW4cT2FPsW0EQDdqees5O51TZPVcKkmejuMharJlrN+473Js34RHZpcE8VbFJQZKPL4o5jj3XSywX1KlGHk7QH0MjJhxoTrQZbCWPXBh4qPR957kYiVwcm2pHxmaR0IERBDcDN8rvF3x0vwe9857X4xC9fj4//4reIz6kH5nMiGTLUVrt607jr2609V5KJZgIQKfRnOY1TDEJ+O1cCNYC2LbiiBjLp+aliK3GSokfJOSyT5KKJhWry1AM7l0tEyUQX7ZDIP6ggTWrB5cKwFyDN8uNtExKXKPBIOYc04OyHMk30UFtoqN2CoeVe65gZNpNSkO4cgi1hQO02Nb8vYaKf8+jzyzGHlHNkAncOU+YgZGgrJnoqZqJNcNXMFHRZm4mq77jO3WTcFRO9DJeqnvFeqp8ltqerxX2kchfU+1nJOezH6Zrr4oLnebh8/yq+cvQsRrMEk3mKfWvdSDgbGaGwOY6tBVwqOQfBRC8wn9nawy3QFSi3GR1V8Zv2uyQ6JNaFgYOJbiNxVWMPdTSpleJD1idaBRzLWH3r8MvVC79ly/2/BFXGJ5E01yGxkLS4K5lo9zH2rfVxXguJjD6xZQQLFwYeJvMUb73xcO16uRW3HsAkZGLh4lnc+rb4ZL641zBgl3PcfN8m3nvzEfZ7bTXe1G4GK+eImpPkonKOYVkWtX4v8/PRiYL650pnlE5MtMw9wAV9MTAmbP9sZZEVpAFnXROtvtNs+4rWHmVD2At8hL5H+kRPjYJHUkQuTbRgK9c2DkpfTTUednXn4Bhap5yj+PvJ7ZloN8P2bsmY6KaDiEKVtyJxQLDLVpYxdvXD5iJ7FqfoCxZmKog+SXghq2OrS3T5RC9bEw0Ajzx/A1+8d7O0iFyciW5ew+Zk3qhWCGjyLAcT3aViYcWWVu2RuHJVDk9csrSUsZW6c3C5UPW8qcY5Wiys1GVTzLakPUDTUcl6LnGrHkRQN2bRaoUNCOQc5r3s4s5BZXlXx0xbr0jLINoIOtWCYFGG1QazjVY5h+/j/s0Jfv5vPos7TozI7+rQ20reowW2vxT0BK3pfHGvYaAqI/vDf3IT/vv7bgMAPPc1H8CP/OlN7PdUNxLLOQitVi6racdEL5JYaGOiXRILmokmgmiLv3V5riVZE65oiwGKieYqlUkDTv0YagFge15DTSaj2uN5Xs3P2sQs6daHKecIaWIhleUvYaKBaqLvXmwld3u57BXvxMvf/EkAwHiW32M3E53//fjWTETK2MYt052G+h5lhxqLmejmfVaT+zKCTrsmWkYuqPfFxrT2gqqYk+pLNgnB4ZMjvKx4fjsRRH/jw/fhxPYM77vlGABg/8JMdPO5b05iawEXpyZ6ASa62tnWmej8Xz6XwK33lZJVge81dkl0zAX91JVY2IZYVDGESzYk6WdRwDvrnKNBdP7vIgyaDdUWgJyJdvks2uDK+MwTC1vKOUomuv57qZyjC8wX3nYO6t1zvZSveO4jAXALDTXoLIeJnsbuggUS6Hq43/7fXxJ/r1zsCC/Hxj4AeZ9yaaKXyUSr7XD9Xo5nebU6SlqgtnZVEFkG3VRiYRggSTPr+1KVa19czqHaTtn+RYFPViyUbJ8CdTmHcrKwuQRQ7Vnth0xiYbIzmmgXCxXYteLyyqf5v9T86bK40xfS7/jsfQDkrhW6F7jk/bc93x98+mWC7+WBvi14lFSXU+c25x/pdyVQhSdMJloiEVJMtLJ406Ev7KgEeAD4w3/+Sjlf2SQRi+IpV+wDANzw5aMA0N3iruhvMwsTPZ7FtT6lMOwF6Ie+252jU7GVZjAskQhJPJCliZ6c5l8/B3cc3+OP0aawWhnLEYeTtEc/70NPzlEy0csNotXtZMt+G3/sVPbblViYtmeiq2Mag6ygYmFXmIG+rc3U4O4aLH64qDJGs1PdBx2F7ZqOd1lMNK3TdxXqALow0c1FHRVEm8FrnKSYJSlWou4TlmKidVZf2dVRQZQKbBSLLGGi83bbguglyTn6FaO+NY3LoEAHV6lMGszoJcxPbuVB9J7VZp9RrOhYk3MA+WS8zWiiuzHRdqmBnIm2y1ykb6arAIfTJ9oy7vyXd90MwB0Y66yhJIg2FxS/9W8fje984kXO71FuOoB8AZYnXtUD8VQoI5Kg8pHXgmihbaKai22uRPoCV8VBtsXEXm0xaUvOWxQX7BpgYxDiw7eeKM7X3ScasGuiuZ29PSs9pyZ6aT7RAlcuqniQjryAFC8LUcdiNdFlH+eZaC7J0bWY11GZRBAxllDrnX+GTigHztEgWrF2S9dE++7EQqq8bBu4EgvjJOss52gkFi6RqTBhbkNbmWgqiHbp/ywZxzoWsUJ69b+7FkBlZQaANMFvC33CMScfG0ujINXUKlD3J04ZOYeheVRazoV8om2aaAerbwbzLjsyU0OtY7IkOcewWEhsz2KcncytW7JRQMs5pPIiXVd9YnuG9X5o7XdR4CMKPIznSW1iXu2FtX6rY9oxsZDyeJX2ycC3J1xKX03K475sh1PO0fybuh5Xv7hwz7CccG3P3ITJhknHH87arY0LClBfbJTuCctgoi2a6Fzm5h4fFPu6OeaZaG7BpI+Xy9gVNOF5Hi7as1L21UV9om1z/4jZ2curFtqZ6EV2Vm2WuZI+JbFv40gZsw3ccco4hDmUO7GwvSaaZKKFWm9Aebw/xIJodZ93ionmbFbMl3+RioXUg4kXkHOYKznV3J0Ios0X3s5EE991dF7f9+B5TWZdQWoCb8N3PeliPOXyvdieJbjvzBivveHWpTHRAPAdj78QQD4Z6c/jLOMd3fY5UZOyXm3MhJk4VHrMLqKJ7jeZ6PEsZRe4VcGbvB0qAKfeZ1NDrWNZ/t4rmnzi7CTGer/JhOXlfunkN6BdYuGZ8Ry7LSy0wjAKsD2NsTmeY60I8FhN9AI+0dbgTmxv5ZW7CjrEmmhHpdg0c1h1CfMrbOiHAS7YlRfi4HaRFMx7IU0EqyqfNa9Rmlio/p7sFBMdNN8zKROtdm5srkS6pIZbMLW1Be2CQ7vyxPlVze2mLWy5JQr5rhEdRJ+hguiFmOgmoVLuQDtyCczvmZA+/2Uw0b7D4q6LOwedWCg3Joh8u49+eS5xqx5EUIbpy9ZEl96CzGfMjkL5YnJwsaydEgsJi7vqZWrbSjfMVZwtoKUmMYm+KWJWt4skYgCVtvRH/uQmvPrdX8bN928urT/93nc/rtR065n7NpZGoY2LCkDbE/FyjvokqYKxnWCiuWSksvR6XK8QSLWDY6KX5ROtyznOEslBnJyjnASdco6gZMJGhH5SYdgL8KcfuRPbswQX7h4W7QzZxMKuFQttC/qSiXbJOQjrv/YLQvvfE4c7R2D0dzVBvvBxF4jOf7CoNyCREEgkbNbvMUy0NLHQlghWJpAtIYguZVaxniSciMgF5T5xYrvp5asvcCmyB2hfoKoLzi+C6Iv3rnQ+RkTIOdI0w2SekoYH64MIZ4l4YZGKhbZiK6lgPOIkRgpSTbzLnSMVxCGuMuRtpJtVYqH97yooljDRAZEzUrZL3KoHGXwP7ATUBZ6DEQGag+Dn7jnT+jxccoX6fVutr2JuG8VWBNqorjBXuW3kHBJ9E7e6bZMYYMNKL8BomuD249sAchP8Ze5sqGO9v8gEB+zZ3AptLe5sJbyBdnKOZQTRJROtu3PMk1q1PxMqgbBiolU77O+zWeFQR+XOsWhiYbEdPZljPE+sNlW9wKMt7qRMtCbnGM9TNiHyyGYVkKhJP9dEMxULO/lE29+zNM3gifSQhCZaLOcozrdA2W8dalfkMRftFp1fLZ4lyWySZGrr9xhbU6nkrnJgajKOyzCzsPmQS3c3osDHrmFU2sfp0Be4pU+05VlvFaXX//rHn9a67VJcvn8VAKw5D1JQTLRLHrfeD7E13TkmWt8lk4xHVMEuHfMlMdGS/Arf4c6h3r2fvv5KZ3vUWciKhWmKwHeXDwfcMpNzkokGcvNy1+DeFtWNpz9j3szbj22VW0RSlElhpAdye000kD9sMzDPdjCINie9VnIOwfWFzDaKunddGZi1ftgIRpZpmajY0x//i0+Wv6OkAED+nDxP7mig5BImKzmLaZ9okzUez/N/hwtcd16gpq4vn8xTltVvMtGFnKNPJRYWTHRsY6KVJ/ViQ5linu8tdrnaaqLyO/wTAAAgAElEQVTbWNzp1n5DYbuffNleAPk29Gi6XDkH9Z7FKV9uW//+IkG05xUEAJNYKLHqUtgq+tMa0Z9MqJ0bCRNtPt+2TLSNOJHq6W3JxMvMeVEJxqZdpbRP7Vvt4f4zk8bv9QVuWbHQch/mSYorD67hURfsatXuNrjq4DoA4EmX7ul8jL4lARNwkxJrgxBbRF5MsgApZJOHJoJ532aNZ4KTB9aOVYwhl73infi7T93T+LtkfAw8PrEwyTL0Qh8/ff1VzvY4NdEtYixXcH/OBtHLrmgEyCzuzCD6yOa09dZQtY1Cr5K6aH1tq0H1vzsSRBvnslvcEXIOwfXl2yj2F3yeZugF7kpaFFZ6IUbTpNaPlikPsg2krDuHw4GAOr7pUctt6avAcLMYyJfBRPu+h5Wo7hgxniesvIJkol0VDm1MdFmxcLGhbK0XwveAu0/xQTS18JUm4eUWdyqhkq8W+ZoXPQ4A8NcvfRr2FE4C64MIm5O5lWHJ3TnaP0vSnSPjGeDq+55Vr96mP9sIgLIdrsRCYy5QuQdStvE5j8pLj6uFCgdJMrUNFRNNJxaKmWirldni47sa/3TZ1LhFRdPdK1G5s6fDXOBSiazzjuRRG1x35X688QefhJ/71kd0PkZEMNGKDKBIibV+iK1pbH13F2GibfLQVCDzcclKgfyZyJjo6jN/9YnDjb+nApLI5ROdtAx81XltyOspyPMZuOD+nA2iO5hiOFHaojDH1jWuWZbh6OYEFxV6RSkin97aA4ot+Y4vkzkZttXatoHZr2wifWpikLSHGmwBYB6nC9nbrfYDbM3i2jUs0+3Fdize4q5dhn3FKteD6Ok8JaUUgyhAL/RL7aH67qLXvWJ4F08cco4w8IvAKz//9ixGL/TJhePAYK51LKvst+97WB9EuKcIom2sZBTQOyOtyn4nKZI0w2fvPsMu3F7wuAtxx+88D0/SgrvzNwYYzZJyIaRj2lUTHRCaaOGkFREylzZBNLdAcSUWmgl5yotXKvd70ZMvwa2/9VxcVmz1c2gw0cIxqGKiLXKOzJ10VT+GJYFsSe4coe/V3mUqP8CGb3r4fuvvzbGAYvZmQieIReB5Hp71yIOdSCoFmxUgIGOi50lmXXAmaf6udSGFbG4hEplPSOTV6JA+E/09sPXFRLCr5XseG3vFqTyIdmmic6JSGJA7/KvP2SB6J4JCV711oM78pVluU7V/vZ1VTmjJplVI0qwood3+0fiWoFNS/rMrzIDdrom2f1ekReI00al8JWnDSi9EltWvYZmaaD2Yuf7qgwD4IDrLslbJn4PIh+fl5v46Jg4WeGMQlQmOy3DnAHKJQcMn2nHMYRSUVeVG06T0m7ahbzDXOqbz5cg5AGBjGDqZaJdPtGuQV+4cf/7ROwEAH/jK8VZtVIlR5rZ5lmULyTkoJlqysPM9e2Jhm3iAW6DkPtHMd005R7HAaLM7IQ2qzEBX7M7B2ImVNnWOQ1ndOZbIRAP5e6kvzDcnc5FrCQBcQJBJ5rtJbdvHHReBDzRoJtqtiQbsZgSxY7dF0h79/VFFobg5Ur03nE+0dEzRA2cyiHbmVvD67DZF6Fya6HzXQ3Ysl3/1g7/HEuhaslMCzmZFH2CSNMMsScvt6WdcdUB0fC6xsDIB78ZEm23fSYs7s4PaJDaLnJfznsxXyIsx0SaWKefQBy8VkM2YbbPc1F5+PZ7nNSY8IGdmOVZ2YxA2mOhFFw8rPZOJptnw6jtB+Z3cB5lmu0xfaR3LknMA+QLj/s1J+bOJ3C/UxUTz7egFPtIMOHo2Pw/nHW7DwY08iD6yWQ+iVRDb5T5QOz5pmokSgOndJnl/1l1LTLhYLHNyVkHKTgRk5rjcWhPNWNxJKhYCdiZ6aUF0LygX13GSYjRLxIVPyJ0kYyzIyZH858/fcwbbxfNqs8X+tURVbKX+LMeOBGklL7LpopMOBdYUbFp59S5x7wBVW0LHPEnLCo1sG3w9iG7+XRZE83KONgsNFyEat4gfXO06J9051vshfu+7H7v046oxbBanZdU1E7qcY5akyLK8o37qV/41mRhlgkssVJNxlwAxsEyGOynnMDtWGyZaAqocMaBegsWYaBO7h90qWNmgD14qiKZKRgNy1k/HSi/AyLB9czHR68OoDNzKhL4FKhYC+YKkWbGQfzZKH6jawQXyZoVDHdN5As+jS4y3gV7BzM5E0+4cUpsy1S/2rHTra8pKzPTjVe3q5s5RlaTWd6ykiYXUxNamO/cCj3w/zHaZoDTROxFEm4GuvDhSM/lLodzFcIz5lSZ6ZxILgboPuRonpHIOKgg0mWjfyxcO41mC5//hB/GsR56Hn/vWR2BzMm+dpP+1AOXOse3wu1/bKSbatzDRiXs8EMk54lS0y+fXgmgLEy2Y33xXYmGLhYbL8aeNA5rvKgIjOsqDDJftX8Ujz99Y+nHVw//Jv/wUAOCO33le4zOm9hPIB+s9LUqIlsb77NZex8RCY3W8s3KO+v/btke6Jv4B9kWBwjxpbwOoQ5cP/PxzHoG//eQ9eM6jz+98PBM9CxPNyznaPyOdNQLyhUWcZmy1r41BWBY1WJacY6UX1srZuhLmgNyJY3uqMdFMEhjHRE8KW7dF+pnCAU2WZbO4y33LXe4cDibaCOx+9fnXtGrjrqEjiO6oiQbyiaWn9cFUmFhIfaSVJporqe6YgM17rrx4HwhWU1yxkJNzCANhu0/0spnoyodc7VjZ3gVr+4jx2NwdCXwPnzp8GqfH+ZjxwVuP45+/dBQA8OgLd86ZY1mwVXYE3OOpKphk232Kk/YF1hSisEnKTSVyDodLGJDPWWstrR9tY7FrIQwImOikPRNN+0SnoloVgH2Hv/Z30VG+TiAZ8+uV2YogumXn9zwv30K1dF41SXdhokPfb3TCr7k7h3Hepz9sH55/rawIAnWPAHk5Ugp60Pbt116An3jmwzsfywZ94lgrqt9RbixA/tzbzoN7Vno4uV0FrxJpw8YgKosVjeYJQt9bmLFb7Qe453T+LmRZhkmcOsv2rvbC0tFjNItJZw6gWeFQx2ROl9lti/PWcxasH/rYs2IJokNBYqGz2Ep+LWocee5j2i3cyCBasH1Lgap+KbWBohYwbfpzrjfv5s5BaaJ3gok2WyENfCLGk1eaWFhuv9d8oou/LWl8H0Z+SQ6pBE3bu2AD1X5TXhYnGT5z+DRe8F8/BKAejC5jR2mnEfgefI9OLKQSWlUVVJOJzrIMm5N554WQrez3XCDvCi0MtomZUGJTZ6Kbf08ydwDsYnzbJRbm/1LMdtyChPN9j3UwefD32AcQkkBTZ/7UYBN1ZH+46lVtSlwq+BZhfqW3a304Jxo+0VZNdP3/n3DJHnzvUy4RHT9gNNFtVpI26Ez0gZaJoRLoE/iaIee480TTBioWWgnpOLDWrxU3UP2RZaKHYcmEjGfLCUBzu0DlPZ0gSTMne7HWD2tMtE2jrsAx0W0suFy4YHceRPuePUs+9N2JhZJiK0C1o9X2PS8dVpYo56CcI6S2i4skDytEgY93fu4+vPaGWxt/czHipCb6wcREq91HG3GSqN1Cx7mC5mKnlHMs6VL1/IZTxe7SbmEQTRE/5rygKuQdtRRm2Qnr2p1AL2x6xlcWdzwTbRZcedNH7sTff/pea6EaCdQ904PhmYCJtvUnE7M4Ec1LdU20Rc6RusdGVwJfq8TCsnCe/e9xKifhAo9nov9PEK2Be8RZluFlf/FJ/N2nKyNxxYx1m7jszEuZWNjJ4q7p9yop/9kVIk208bs2l8WVAY2TrNzG6gLdQ9bFmnZBTRPdr+Qc//CZe/HNr/4XfOjWuitDF3un8zb6tYloIiiBvT6IanKOZTiSrPYqn2jFkO5yJCOtaOWrR7OELfgSBTnzY2WiBay3FE9/2D4AwBMutVe664U+yUi0KbYCVPrJLkHDrmHUKJFc7kJ0cCmhmOhUqBtcxMZSPxcAvPrdX278zZVYaAYtXdw5pDCbIWUPIyZgURO0a4wOLAnpO5FYqN5LxUTvFur3qQDHvC6umNki1nMPJKKgWaVz7LK4IxIL3/W5+xZqi+d5hfRRSywUyLs420WFaZyiL6npoPU/W19M0lTQv3M5x9HNCV797i81Auq4gyaaqvkxbyGfcVVjPDd67AME7hmf2J7hnZ+7r+bxONY00W1BerMKk0xs8D0bE53/uwzNqIn/8KwrccWBylvVXrGw/rs27eA00bMk7cTWKyy7ZLwJShOtgueb79usfX6eZK0XY/tW+zi5PS1dUkq7N4c7xzROMY0TjOa8K4YUuk90OfE6gui1flBLLOQs7jzPQz8MSCZ6WUH0w89bx9+/7Bvxhu//Buvf86pcixZbyduqJtIukiTTUhBYDhNtLuqliYUUS+yxtEQdVClzoPCJZg5lFlV5UGqiGXcO6ZgfWhY70kItUqz0gnJeU0y0NAmWuhe7DCabC6LPBTkHUPm969ie5cW7qH6n5oGzhpxjGc8uCuqSAzVO8Zpot5xDKpfTxwnb5UiYaOUT/cq3fQ6vveE2fPyOk8YxlqeJjtNUXIsj9zVn/i46ytcJuADvtqNbjd9NyqBlmUy0GlC7HdMMzLMdlHNctn8V//Dybyr/3xa8medtw5hETMXCuEPQqUMN7C98nEyf3Ra6xEdN8rMkK4Od41uz2udncdJ60h/2AqRZxUKOBXIO1ZbRNMF4Fi9FCrHaCzAvrk3KRK/2NDnH1D1QDyLfWqRgGrudQNrgsRfvJtsSFRZ3Nu9R1U9d/VuxVKpYSpfFsp78pTBdILHQ5j8MtEksJJjoFk3ZtjgWALpcgW6HuSDeSXcO89HLmWjaCaFtxUJb2e+d8IlWC+KNBd05Du2q+0dzW+PnipwjCvyGm8x4FjusOvNiNiYTvQwpTuTXg/pKzuGWQXGJhSPhbqU+jlkTCwV1EAI/JyNU7tDEcJ7qpInmfKJbFEriZCb/J7FQA3dLT2gJXLlFz2JMdF5cgGGiOwyKuv9mebwddOdQ51SwvWzmxNCmGQEj6J8vaMy/axjhvf/pm3HZPneVsi7QA/xBFJTPWwVP958Z1z4/7yBPUfdb2TG6thOBqjrheJ4UMorlaKKBnFFWE6/JPjW+U8g5kjTD1ix2OgD0w6AxqALL1US7oGsPe8azUv3U9d4q7beS1HTR9a/2Aozn9Yl4KlhAUaC2daWJhcvQRJvMukIikDqYevqd9Ik2g+A2BRsA3tZUWrEwsck5lpVYqDn+nB7NsDEI5YVoiM898vz12v+zTgfnCBNt10TzAafneVgbhI3EwmUw0aHBRN9bFGPi3kGuABCQE3BjgdMSYBZbaf5dUrEwKBILlS+1KZdp485RaaJpi1xzB4vCQ7Zi4U6A68x6wLuvKPRSWtwF7SeuXF/ZHFAX0UTnQXT9mDvpzgHUt4tsAZl52jYTK1dsZb5gxUIAeNiBtaUxOCb0IHqtH6AX+Hjdv9yGf7r5CIBmUk0XtxE9IAa0Mt5cEN2rvrO9LE10EcRszxKcGatkJH4LeK34zvGtKbKs0o1T6BNM9CRenpzDhYpNpEs3u/rTsPDkPjOew/e6LW6HFjnHIkVnqASjVJxYSGmi5ddWuosY70DSgYnemsTwvG5jqAvms5cUowE0OzHO4s7J1DWPIWHq20AVQcqyDKdG81bWrSbredm+FXzklc/CxXtXar9n4pFzBrbqpSOB9GGtHzaY6GVILfNCUFV7/voTdzu/oxbwFBM9jfM6GFy+Snl+XRNN+ES7+qj6e1kRsmhXmmb4oT/+OD5463HxorXURJNyDjmrHfi0ywewYBDted5ez/Pe43neV4p/91g+c7HneTd4nnez53lf8Dzvp7S//brnefd4nvfp4r9vW6Q9i4Lry/qqSE34k4WY6OZKFtDcOToWWzGJ251051DnVJAx0cvRRM/jxSoW7jT0AWNjEDUCPTOInsUdgmgVEBfBswqmOeZAtWM8S7A1mYsLKXBQK/qzk7lczlF8576CMXG1Y/AgYKIVSzaP6UDIFUSrBceZ8bzzInC1F9ZcggCZMwsFqnJZ8gAmFirsWa33G8nixJyct6YxoiV5h5swJXjyiXjxBZgKIPRaAG2CAQlWeiHSLA9gTo1m4qRCvX0Kvu81pBwPFfSIxEIXKbHWDxua6GU8vbw9eb/gAj4dnHc5oJEyLYutUD7Rrn6qgm91LLVImcQJbvjyMQDyuMjtE90ysXAH3TleAeC9WZZdCeC9xf+biAH8pyzLrgbwVAAv8zxPrzDw+1mWPa74710Ltmch8Ex0dRNVZcLSnaNDEK13eh2KSe6SNGdlopeceMLBVuq5qYmWHy+0XI9CG4uarzXWB1FjoNL9nQFVXrW93RlQBc8SOceKxkRvTePSu3QRqEqPp0dznB7NEfoemygIVOzh/UUQ7bLEI5no+fLcOVxQ24xzJiHYrYnOr/P0AkH0Si9oJOItxEQTmug4XdDirkUbLt5rD7ak/tsAsH8t74eb43hHnDmAJmsnr1goSCwUWIABTXeOZUr11IJ0NE1wZjx3JgjrWAapcWBt+XajO4Eo9Btlv7envCYayMmCs4azzjIen25UwCXp6ogsbi86yoq2LZlo2/VIxhLVj9Wx1CJFH5fkiYX5v2TFwhZlv12VFBcdaV4A4E+Ln/8UwAvND2RZdl+WZZ8sfj4L4GYAFy543gcc+taNmvwqOUcHJtqiqcrPsyATTbhz7JRsQYdtMDfP25qJJjXR3Ss8PdAYaAUMFMwtvXkHn2g14U0MOQc36A1rTHQsqkblgvKRPT2a4fR4jl3DyMkCKib6yGYRRDvkHBQTLSkxviyUTDSja3UFe2oRM4vTzhU3zUqVQOWhvRATbal2Khk3liHneMfLr8P1Vx9ssHtqveIKFG/65evxDz+ZJznPknTHdieammhhEM2wftLEQsonepn2pap/jv7/9s48WpKrvu/fXy3d/dZ5s2s0o12jQcIIbZYshISEkFgDgjgEHIOOcYzBxgSwTyzHScDZTEjs5OQkhxiOFXBsY5sYDsTxgnBMHOMFxCqxKCySBdIwEpoZzfLe6+6quvmj6lbdrq56fW9VdVdXvd/nnDnTr193dXW9qlu/+7vf3/cXTbJNxocq7jGH9y+X3sYs6NoWBim3oI2hXiY6LcWqRBOt3Cul5vpn77hs6/dM6FgYr25pSP7U6zMr3gw0nDXk7+X53M8IovVXXWQmOieIDoR2otKZciZ6vxDiKBAGywD2bfViIroQwNUA/kZ5+i1E9GUiujdLDqK8941EdD8R3f/kk0+W3O28z8j/3UgQbY12UEsXGenQzajuBdQCpWJWVXnNVmaQiM4kHUyZNmDI82cMm63Mr5xDhYhGsqivuuYgBn4wYtk2NJgZSxI5R7jt2OxfQ85xtu/h7MCvRM4hdZMn10M5x6SiQiAJvL9zfB3A5NbCeZlo3cKXKog10VtkEydropN9LSzn6Dpj2SY5FhXLRGdronULefKGKpNrfceiiwt3L479jZPCua3fv2e5G6+IANPxfgfG5Rz6mejJ7hy6QYYqCdGxDjNBdnLdGHjYNJRKpc9n07166+2HcfPhvYbvqoeOM+6upeNksdxzxzLR8jop4zalarRlM60L92xdNJ9nbSmR95WeYbOVrNVjnY6FMniWr5NJIXX/7Io00UNfP4lRumMhEX2SiB7M+PcKrT1ItrMM4PcBvE0IIU1y3wvgEgBXATgK4Ffy3i+EeJ8Q4johxHV7907nQtOVc8iDv1GisDBsIZzf9rtoJjo9SM9SzpFFWXeOLYPoOc9E33HF/rHs8u+/+UZcdV7YzOPoyc0481ak2Uq89BoFVDJzoFNY+OSZUJM9KQOsg2wLfGJ9iKfX9ZaAd0WB9yNPySB6gpzDscearQghYmeSWSAnOVkrSLpev5ZF8XctOglccMNjoV4bckLWLZKJtseDMyDKRGuMG1W0/QYix4N0JtrAXUi91qaWiU797U2yWVnvB/SD6KzJTlj8qbULWizGY4ofNTLSH5PS575p/eDLrjxg+I76cG3K1ETLwuE8VjLdOcL/b7h4V4n9GQ+iJ42psklL3j02LvbVCKLVcWKYM1HUlXPIl8kgukgmWn5WbmGhL7SdkSZ1LJx4BxVCvCDvd0R0jIgOCCGOEtEBAE/kvM5FGED/lhDiI8q2jymveT+AP5i0P9Mk72/8xw8exXs/lbSjddJyjoKFhac3x7VLcsZTxPoqq23mLOQc9739ltyWpemPNQnmHYsy9adAZL81x4WFAPD+11839tze5R6Wu2HgeOu//xTe+vxL8Y47jxTSRMvzTg526wMfjkVbno8yU/LYidBiz6RwKI+FqBX1yfUBnt4YxtrUrdgdBdGPHg9boE8Mol1rrNnKwA8QiK0nDVWyVSZat7AQCL/76U2vsBxJFiduDP14EiQnGDpZozR5mWg/EOi5k79PXqBtOnHvOKEbj9pUwSQJYFtJUDAtiU86SaGdid6iY6Fuo55MTbRBAwod5Phwtu8bF+2WaX4FNKfRCiAz0elmK96Y3WKala4T25xK5Ljy3h+9tvD+hJro8LyQnus6CZKt7rFDkyBazURnjI860jB5O5cJy/Xoe6jHWdcNR56K+XIO/Uz0tDsWfhzA3dHjuwF8LP0CCtMUvw7ga0KIX039Tp16vhLAgyX3pxR5A/WbfvPzIye+LDAqE0RnVfcCZjfjNHaGn+G03TkA4PD+FTzn0j2ZvxvLRBvsiG1R5gUJFMvczgP7Vrsjg9tHv/gY1gcevnN8YyxDMYl0sZKO77O8KX416ph40Z7FrV6uBRFhbcHFifUBTm7oVfSv9lw4FsWZaB1NdD+ViS4jYShCsvxZ3OsXSLLwxTXRiS+3pO/5sC0qFJjb8fcaD6JLFRYafj3ZrEkdF03bWstzYVqrE5cfGPU81tZEW/nd4fQz0aMFoL/zmUfxwGNPT0XOsT7wjKVSZQsL3Rldx1WQ1fZbZ/xd6TkYeKNSvr4X4PIDq6VWBV0rCep1uhVKVC11mjiINmz7nSdZ0p0kymNzNiMTrbt6RxM00SZuWJa1dSa67Fn7bgB3ENE3ANwR/QwiOpeIpNPGTQBeB+D5GVZ27yGiB4joywBuA/D2kvtTCp0/z2uvPw8vvyqsiyzVbCWnsFAuqRYZkLILC6Umup6sbfpjTcZ7dXadxvPFXFvc5dFzbexZSSrQbSJ89pETAIBHI32wLmk/zU2Nm54MLr4WB9HVFPLsXOzgROTOMcneDggHpp1LHQy8AEST27BnZaJ15CtVIm/y2TIs/WBv11L49y+aeVtUHBQk/WFQKAsN5Ltz6OgYgfyxxXTMiVdW1CDawJ1D3ca0guh/98PPxq+++tnxz7pJgSQTXbwoNZ2JvucjD+CL3zk5lUy0bJpkIg8qux9Ny0Sr928/CDu2Lk6Qc8hAWS0sD1chyx07tdmKiTmBs0XdUdL10DATnXOOTzq/5bUkkyUbUZJAjQF0NdFJx8Ls3w/8AF3N1aqsWrOR32ttJQchxFMAbs94/nEAL4ke/wVy4lMhxOvKfH7VTBr0V7oOfvlVV+KTXw1VKGXcOboZZu2AUlhYpO23nR9Ez48m2jAT3WBNtMr7X39dHLherBR8fP/MIF62euffeabRNtPyAp3Clq5jgSj83NWeE+uZy7K26OKpM32c3vS0gmgglDU8ebqP5Y4zMRjpOlZGJjqqHs+wVpwG7pbZRP0mSbtLZqLlkrHa+nvT8wvpoYHsgjXAoLCwIk20zCL3fR9AeA7punOktzEtOcdS18EPXmiuXZUBRLlMdBSIF9Rl6yAn4dKCc5aFhY0KolOZ6MQOblImOjyvz/S9uGlbFZIc17ZiGUeSiJt8PPM6JwPF5RxZt2w/EOhOMGCQ18hmKhOtjku6hykZk8Z3RgiBvhegq5uJ5o6F+kyK72QmSo5ZG8PiWeOs5SBAabZS4KKyKCOIjj6iShskE8Yt7vTfm9exUAgRWtQ0aNC944r9eOvthwGEOmSpGz7T93B8PbxhHdpp1pggaUOdaKIndZciojiTedGepcpWKHYuduJMunYQHR2DVY3X91w7HlwlG7PORG9hCWWUiY6+t25WJU2mnGMYFJa15GmidQsL875yEU00gJHJUiLn0NvGeTtDedI0HVuK+DJbFsGiHE20rjuHYpOntjOusjZEBoFPySDa4NpK37NMCwun0aZ9WqQz0Rsa3WKBxA9frYfyDezW8giDYRFvD9Abi7qOHY+jaaQPtmkmOuvv7mk0bkpnou/76jF86DOPjshN0v0Vcre1RbOVoS8ghP4qS1at2chnaW1lmzDplJO/l3+gzaGPjlOsM1aWRQ6Q3KCLDIxZfob1W9yN/mxaWJh105HHrewSWJ3c/0/vwL+66wcAAN85Hhb5mdrNpX2LN4aeVhtvOdAfNAzat2Lnkovvn5Etv/WCaClr2L86ucFCN7pe1PMhvnHNuGNhpjuHrz/5lZnofs7NaxKykc3ZkUx08aYzeR7GfiC0CnnybtZFM9HpZfJwW3obO3JOqFnWncgVoWhCwrGt7EY92h0Lk8nO6BJ3deOg9G8/fjYsFDe5tnT349+88lmZzzcpiHZTFrXyWtQpLATGg+iyf8Mw4SQ10frmBHtWurmmADLJZ6qJFhn64aE/eaKQ9ocGgF/4yAMj53qWGUMWcneygl85vugmHRyLMu1V48/S2so2YdJAnc42bQ597SWBNOmLMP0ZRWamWX6GJhZR06CUnEPpwqSS2AA2+/SVBWbSK3nJsLCkEwfR4d9Yt5peBlt7KuwOphYT6gbRB9fCIH7/am/ia+U+q7rouLBwRs1WOin5jIqRO0fcWW844ZXZyGY6GyOZaL9EJjpHE63ZyKMqTXQ3QxMtDAsL90b1BkWlLToUTRo6OYXS5j7RYmQJvsq231LudfxseG6ayGLGegLkvO5Hbjgf7/o7V4w9P4uGYAEQsoEAACAASURBVFWRzkQnHv2TLO4SOYfEC4LyQbSiiTZJxO1f6eKJU9lBdFygqNEHQz0Hs4rwhn4wsZ9GurBQomqs06uRuWyhie4b1rLdfHgvyzl0mTTmp7MiMhNdBNch9DM10frayjRORhVpbHE3L5pow7bfWQHL0NNfZppn4oYjJ9bRcy3j75OWF+hUhwPJYFVli11VW62bBZQBj85kqJux1B8XFs4sEz0qn1EJnSz0AsfV6Eaat4w6CZntUjufbXpBBZroYoWFVblzxHKODHcO3cn3syMP9meeu2r24QZ0C/QFAKLxbCs5x6SOhcpkR51oVKmJlnIvmYmeVoFm0xMgoS9zIqvZiLvF6so5kgm0L6opypRBvYkkdP9qD48/vZGTPdbPRKvXZ5ZjnucHE8/TWM6RSi6qK/ZZXWu32h+RIS6R29dNOtx06W789k/ckPv78p0WWsSkG6DMgMo/0MbQLxzIycJCIcTI58YXQFXNVmZgcbcVZXyi7UgTnT5GwxIOJvOEmole7povPyfWZFLOMbmwEEiCr70rFWailW5xOxb0vKfvvGI//uiBo/jHLzwy8bXyZr45koku3uq6CPJaz2wmIPR1jdLKbauK761YzNREl8lES5lAqltg6cJCQ020nWFxZyjnuO3IPnzi7bfg8L7ptY8umjhxbCtzZU03Ey1//YG/fAS/9n++pWy32nFwoeMUKiw0oeljtyo96jp2XOQ7ubAwcufoq3KOoPRqgmoqkGSiJ5+nl52zgtObHh47uYFDO0ftTmN3Do3zXU2cZGeiJ2ui5SQyXSumjpPphlt5bNVsJZapaF7HRITnXJJt4QtwJnoE/Ux0+PN3jm/gvF3FdKWubUGI8exPGTlHpsVdULfFXXE5hxxY0rGGiQ/mPLMrkkCcWB8War9NROjYVhzU6bhzAEmwdN6u8h7Rkr2KrnmfhsZZfv7/ePNztPYjKxO9MeNMdFzImSHDMtE1yiXygjF0kolWNNH9EproOBOd9onWLSysShPtjss54sJog41dtn9lquNd0QAwb2VNdh2ctM9EBMciHD87GPnbVy2DWOracWHhtCaoRYtq54VuatUkCaL1LO5Uba/ni9JyS1spwjdJxD0jqiH41pNnx343MMhE717u4i23XQogr7AwmKjRlh+TDqK9oEgmOvw/K6BPMtHVnNvNPpMrZlKAJ/8e6qD13EuLtSCXs6D00nC5wsJxz8dZdCzcijKZ6KTgadx6C6hWC1gHqo54UkFKHo5NcVC3OdBrgS3dMM7bWV0QfWgtmUyuVNBKPE12JjoY+d20iS0Fs3T6vtA+H+X+Fs1E9xwbRElHLyCqzyicIU0K1lT8QK9DWGXuHHHhZvI3NnXnmAVFpQh5cg7PYAKW9boi3W23YsG14yCvjPPNzYfz742Nz0TL8SgK6nQt7nqujU6qW3Eg9MeOPMIJmrmcYyH1PVRMZZM/98IjeM4luzOlITodhuV4kS7cVt2QdCVwW/lES811VU26WM6hMOmUy2pcsmelWNvkeGnYE4CyiWGJADHT4q5mOUc5i7vsm3s8Q25QNXcWHcfCctfBmb5XuFuVayeSl3VNOcevve5a/O5nv2NsqbcVqtPHNLKA85CJljeBQY5PtG5LWvldigbRlhXqVtVs5MArbnGXq4kOAk2LuzxRtNl+ZFrcGco55hnHtjLtEQODILpjW2Oa0aqTuuoYUvTa+oufvw3nbFEw3CRP6Cx6qXNVV84BhLpoVRNtMonKQ21MltRVTT7GvYzVH8nA92GRWQLOIsqxlZvc1yEtT5So49JP3Xqp1n5QLOcI33tyPemkayrnmAQH0Qq647R6Ukm7KlPiG4bSWABIloWLBCJZzVb8muUc6ZufyX7IJb8xyUs80Wj2QAwAe5Y7URBdzJLLtQkDP8DAD+AHYuJyIgA845xV48Yuk1jsOPiPf/+qqVmLyaU3NYCQ2ZPZu3Nku+qYZqKzMtq6LHadEU305lBvFSILeR2lv5euRKWqTHSWxV1g6M4xz2yZidY8VktdB6f7ejZfRVHHkKLXVlpfm6bpCZBeKoOr6xMNhLpoVRNtMonKQ5UKmSTiZB1CloWbFwhjySRRtoRCZ3yUkpYxOUf0ff7kbbfEFpaTUDXR9331GH7iN+7H7/3kjbj+ol0s55gm6UE/vSwh4tclz0mvW1PS9mSScMZW7ILKykTLr1DXPSh9bzAZLORFl9YRJpro5t9Y90XZmiKaaCDKRPvBzD2Ts7jr6oO47Rn7prJtmTFRlx03hz6IqluWm0Tal1vFRBPdLamJBkKvaNWdoz+VTHS5tt+mY06mO4dh2+95JivJAUTOLpoHK2ucKHMeZVFFJnoSTa9nSYJoM000EOqi1bbf1WSiEymndArSOaey6hAkvoFETZKXiQ7lHHo+0en3J5a2JhlxRNsS+OwjxwEAX3j0BIDq5RzNPpOnTHpClQSkyR9TZ/kmC+m9mD55+15QeKkrzHSMbq/uTM5YJtrgvXntiNtSWAgkdmdFM7hO1Klq1t37Zk1WJlr6Ys9qlSXpEJmXaTFz5yjDYqfKTHSOJlrT4i6/2YqhJjojiK7b575KbCu7wZaJJnY5K4iuOIpW72nTqjdofiY6mtR7iSa661ha18tKz8noWFhBJlo2WwkCbd1+UiA5rjUuEtwT5TRbCSYnB/M+q0gNFCEJyNNJgqrlHM0+kysmPegHQmSeEOrrimr15DJKOqsVmpIXzygFYvQkDjIC/1mSDnTTHRW3Iu/mHndkakEQ/bIrD2CxY+NV1xws9H438gc10eQ1kcxMtFc8cCyCLOAqm4k2aWCRx1K3ukx0XsfCINCbfOf7RJvKOcYt7uT8uQ2aaNemMRtBwCxYyaqdyFo+L4NsK99xzL3rdWn62J2Wc+g6IwHActcdkeSEY0e54yHv/UEg4PsCrq7GPmPiKvEMgnGJRTSWfPSDsM32RJ9o5Rq/8eLdOGe1h46TZNhN9iUpLBRjcYSpT/Qkmn0mV0x6nP6jB7+Hx05ujL1OHfCKZnjlrCydiR6UyETbGUFn3W2/012KspaN8siz3irjYDJv3HX1QXz1X7wIVx5aK/R+15ofOcc0yc5EBzP9vpZFoRd7biZavzisLGom2vMDeIEonOHO61jolSwsNL06szoWzqM7R1GyfPwBM03s+mA8YzgtOUfRYmcd5N9anjo/+byLp/ZZ06DnjMs5dKQcALCaKiwMg+hy+5M4BwmjSZkci5441cdXHz818rsi7citDE30UPN+rX7W6oKDH772EPxAxH0hTDLRqiZaPpbXXj/udFvNvYMLCxXSN4Of+dAXMiuM1b9l0SC6k1FEI38umok++vQmAOBPvnIML73yAIBkqa8uTWEn1d3LJIiWA0OeO0fTsxlV4Dqjcg7dgbxp5GWiZ1VUKHEsyslEB9qSAyLCP7r9MG49UsweEwgz0Y+dDI+FnFgUzXDL3VYDPCEEAqEno8jXRBvKOezxpWVp39iGaz2c8JYrLHzB5fvx8PfPxs1QgOzl8zIsRUF0UdtNHeTf07UtfOWXXtg4u9L0eLQx9PQz0anCQhMpWB6q9FHHCUPi2KEE5d5PP4x7P/0wHnn3S5P9MshoSyhDEy3HlclyDvUxxX0v5DVj5BISbUsomWgZC8lO0VU5xDR/ZKqQrD/R905tjj1nVZCJjgsLszLRhYPoMGv+qYeeiJ+rW86R/i7pScNW5BU8eb7eRbkdcKyw8+V6XB3ezks6050j0kTPko6drWs18YkGgLffcRmuPn9n4f1Y7DixT3TZ5UnZxEOVGsRLqDqa6Fx3DrP9sCwK3WaUv3Fb7CwBmYnOtrjTnYC9+dZL8Pl/dsfo+yvOREsf+SLmMT987SG8+rpDE18nx26LwkC6LveoopSRc0hNtJz8VOXOAYT3Sj+Y7Mmsoo4bqkOPHwht206JlaGJ9jSTXmmZrPxOclJt4ocutxWIxOs+yURX6+rUzrRVQXSvY/WPXTTDm5uJ9vRnkWn+zSufhee8+3/jwj1L8XN+zXKOdKBrkonO00R7QXuyU2XpRO3jN6Kl/QW3nZd0dw400YAs5MwIhDSL8KpiqWPH2awq2p+npQa+QUFybsfCAsej69jxpEAIgc88HFbWz5uv8Cff8TzjSYJjEza9DHeOks02qtZE716O/HQNEh6Sf//3nq31Onn/a6rWPR6PZMfCvq9d1L3cdeEHApvDAAsduxp3Dnmv9IXRygYQ/i1kEubswMeOhUQaYpohJ1CGnENP06x+lmNR/HopmTEJ6OUrRzXR4XZYEz1FdGfD6gladBUmbrZSoZzjwI4eHItwVlkqEpotZafFWCa6gCZ6/BhxJlri2KFGt+2Fhek2u0DizjFLHNvK7lhYQYW9CasLLs70PQSBSG4KJTIrjkXwlQy7SbvtvLGlyJDTdaw483T/357AB/7yEQDzl4m+dN8yLt67bPSevLbfnkEmOguTMVWHnVFTir5md7giyElRU4NoOWGVx+jswMOSppRO2hRKXXQR7XEaW8YTQWC0sgGMBpNnxgoeDTPR1rirWZz0mugTrT4ez0QbuXNITTTG+03E7hws56gP9Y9dWhNdYWEhEWGxY48E0YEQtQ5U6ZvfNRfoF9DltSPWXR7aDrgyEz1sdxBNROgoARYAbAyDSpwuTOjYFgZZ2cQKboQmrPZcBCK8ectj0ithnZfORMsbn15hYfbzZFxaGAYnMvN0aiMpvpq3ILoIHcfKDHgDw8xhmr0rxXoV5CHtNg/sqK6jaRo5djc0hlYKC8Nr7/Smp+31HwfR0X267EoEkASofiCM7/lqBvjsiFY7MN6vUBOd0xxNs2MhEI478mc5HpgVFob/CyFiuZnqztFxqpMQtXPtd0rcecV+ACk5R2F3DinnGC+aK1MVvdx1RtoB+0G9s311QvD1f/kioyXnvI6FSbVv82+sZXEjja505+i1NIgGwla7akvofglv5KKE7XUzMtF++eIgE2Sg8/TGML7JlMpEpzLssbWclsVd9muKBAZhJjr8cPWy79rNP697rh1PdlXKLOd/4Md+EFccWC27ayNcce4qXnv9edotlosgz6t5k+no4toEi5IA7/TmECs9Pa//JBMd6qLD5iglM9GKk5UvzOISVT+d9q82d+cYt7jTbY6mTiQdm+L9khMV0/bjQJhElOOIHwj8zmcexecfPYFuhecdB9Ga/NLLn4nXXH8egPEZUxG2LCxcLP4HXuo6Y3KOOmf7agbJNOBRZ9cqQ5ZzxLiRRjeWc7TU4g6QWcokCFmvQc4hM/9pZp6JXgiH7qc3hskEqqQm2s/IROsVFlaoiVb+xur+tCETvZA6fyVlCst+6OLdlU8ku46NX37VlZVuM81qz8GbnncJ7rr63Kl+zrQgong8EkLg9KaX2Qgni+VuGGyf2fSMCni3QrW4CyIJpy7quTfuGmJeWDiWiY6/44TCQuWzrFQm2rHIKHMc+0QHyTjiBwL3fOQBAMCeSPdfBRxEa3JwbSF2CFD/lpVb3JVw5wDC5XzVS3TWBU9pymQaJnYsnGHmb15xbQteEGqiO7bV6uz8ctcZaVJwtq9/46qK0OIuS9caoDvDok7poHBqw8PGMDwmZewN03pdWVioZ3GXv01TRjPR7Qqi05NASZnl/KZK2ogI97z4GXXvRil6ro1Nz0ffC33aTeUcZ/rDpIC3ZEIo6RERaaKN5BzJa9WkXpHEAGHcLUZKmCb6RKdW+OX9ve/5hTLiQKiJlkG8ekiq6Bor4SBaE1cZxEcLC4sOftlFc2U6FgLjGaVA1CvnKBPU5Wuio0x0C26sZXEiS7DNoX51eFNR2+UGgcCZgYeVKTaEyKLjzEcmWpVzyOujTFbesVPjhiwsLNFspcjY2HOtONBUg+g6EwFV0XWt2M1BxS9RWNiG49JUeo6FzWGAU1GBoK6cQ8o1TymZ6LJ9HGQgPPSFceJMzRCrY1sRiZqVpYnW9okeDaLlz3/w5aNG+wCMdizM6hJa5aScoxBN1BOgUou7CgsL5b6pJ7Ef1CvnKEOsic7QjQPll8DaQCfSsq4PvNZ2K5SsLrhxRfv60IcQqCUTXbZjYRWs9mQmelhJUaljWSO1B7pdxoB8h6JimWg7djxIT56bzoJrY+AFY9/LL1lYyNRDzw3tGOXEflVzLJLX7ulNT9v+bRLy/X4g4AdmblzqNT5MSbqMM9EZmui4w7CBnMMmKlXjkXQsFIorR7JjVdnbARxEa6MGtlU0W0kKC8ct7spkWK1UJlrULOeQXHeBeWMJeaKni3GSZit8+oa+xcLI7L+pqJloqfuX+sJZ4dpWpn/uzDPRi1EQvTmMPcKr1ETLIFpnQp+XiS4SGObJOdpAbIvmpcazGZ87TDV0XRsbAz8ek3QNAZZ7DojCVaTkOisr5wj/9wIR3fNN3pt89ls/9AW8/Xe/GG/LpGkLkN1sJZkomMk5yiQTk0y06sqRXHccRNeAGrCp4135joWjJ1y/ZCbaTs0E65ZzAMBn/snt+O8/foPx+6Tp/1NKi1sgvMFbxEuZQFLoti3kHF03tj2TN65ptibOwrWtka5eklkHQssdB1Z0I5aTzDJ/fyfVTS9Zgi0eRBeTcyS64SId8+aZnjPaPEJSRcc6ZvYsd22sD7x4dUxXzmFbhB0LLk6uD7Tt3yYhr0E/EPCNLe5GX/vRLzwWb6uIFvn4+gB/9vWka7Juc7S0dXC3RFIgyUQnY5laj8ByjhpQT4Aq3DmkmfjAH81KDLyg1CzJspKiIADRBVV4c5Wwb7VX6Aa/KzL9f+pMf+T5YVC8q2PbkEH0dstEJzeu2co5XDu7sDC84czunLQswkrPxdMbw6Tle4WZaN1iIKBaiztHOb5yHHvDTRcZb2cekWNgemXNL7Ba+LYXHMazD+2obN8Yc5a7Ds70vXhMMhmL1hZcnFwfKvZv5cYOef783ff+JT710JNGQXTeuRdqog2DaCucJP7YBz6Lk+uDeDvA5PFgOploEY9r6nVXZWEhRyKadJxsTXSZTlPS41elbGFhWhMtam62UgbHtrC26OKpM6lMtCc4iI5wFTnHrD2TZ83qgouNoY+hH+DpKCO9Y6E6qyIdnC0s7mat0d+x4Maa6I5jlcpmOmPNVsLHenKOnOcL7I+tjF9BtA//8OZ2BNHy+kw7dBTJ+L3tBZfhY295bmX7xpiz3HNxetPDGUM5BwDsWOzg5MZQ20N5Eul7vMnplHcv9Qu0/YbSYElOxHUnCuo1YFWpiY5irI0ByzlqRT2ZqgpK0x2sPD9AIMrNSi2i+OYDhEuiTQ2igTAbfWJ9NIj2goA9oiN6jg0/EDi1OdwWmWgg9Fc9uR4G0WuLs9VEd+bEJxoIvaKlT3TZv32eJlpnLMorYioyqVD3Q+5OW6QOXWeLILrBY/R2Ra6MSXeOVU05ByAz0YNEL1xyFSt9jze5ZnIz0UFgbL2nbkpev7rSMHXS7ZTMRCfNVhC7c2woMiqWc9SAWuxX1ZieLlKSj8ta3KnF3/Mg5yhDunkMEN7g2+yHbMJSlP14/OSG0SDeRKTm8NTmMF4q3Lk460w0jXXQBIq1yC3LjgU3DqLLOrM41ujkYGgg58i7CReZvFtWkomWco62xJeyRX1aE13HBIwpz0rXwenNIZ7eGMIiMznHzsW0nKNsYeHo+43cOXLOvSKTO/Wal1poXacfte+DZVGpbLH8pD976InYcaQ/nE4mmn2iNVFP8qoGvK5jjWQlZFa63Axs1BoqEMU9SOeBpa6Ns/3RzM3QF3E3w+2OXELcHAbYtTTbgHLWrCrtck9uDEeemxWubY11GQXqCYR2LLg4dqqP9QqKSu1UYeHQqLAw+/lCmWhSMtEVeejOCwsVyjmY+lnuOuh7AZ441ceOBdfoPru22Iky0VVpolM/V6CJLlJno25KZtljN60J2XY1eWhbVEq3LIP5Tz30ZDwOsSa6ZtTA1mSWtxXLqSxrHERXqolutpxDFm+oDEvaALaJJUWHtzbjrOysWVG8kZ86M8COBXfmKxKuTSNeqpJZ+0QD4fLx0xtDbFaRiU5l2IcGE/pci7vScg5ReDvzSK4mek5sSBkzZOb50ePr2GmYwNix4OLUphevSpQNotMxiYk6JE9KcnrTMy7cVvdDThDk5HyixV1azqHc47/8rjvN9kP5SnJc22B3jnqZRiFbOss6MPBmzSOr2UqTx+eljCC6SNVwW1Et3nYttVvOIfXPJzeGeOL0Jvavdme+D26eJtqfrTsHAOxa6uDE2QHO9L3KNdG6Nz4gX25RZAUsHL/Cx3J/qkpa1E0SRIfHVgiBm9/zv/HtJ89yEN1AlqNJ/aPH141lZTujseyps6HzlKkfc5p05tkocZbx0qEfYGPoa9v2xZtStpUUFur5RKt0HWtEcmEqVcz6/mr92WKF1qgcRGsynSB6NECsIhM93va7ue4cQLYmeuCzxZ1ErQg/uLZY455Mn91LiW/4sVN97F/tzXwfHMvK71g442LXc9cW4AUCjx5fL+3Mkv5eA4OGRlVa3NkWWpuJlqsFMiPW9wJ85/gGgPZIVrYTMkv72MkN4yB613KYAPje05sAqtdEm9zz081RAMSOI6aZaCsrEy0lKwZJho5jxe4cRS6NrLeoQXSV9UMciWgyDTeIMTlHBYWFljXabEWIcjZ8dZMl5/A4iI7ZEw3GtkW4/qJdNe/NdJFLpifODnD06Q2cU0MQ7To0Fx0LAeDg2gIA4OjTm6Uz0U7ancNAzlF1YaEsKIzdOVoSYCaFhWEQrcpn2jJR2E6sKAmMnYYuQXujcfvxkzKILivnGP3Z5HxKt6EHgPv/9gQA/QYyEisjE10kruk4FhY7Dv7pSy/Hn/3srUb7EO7H+PdXr7cq+wtwYaEGjkVTWVJMZ1ll98JyFnejzVaChrtzdJ3x5XMvEGxxF3HhniV8/C034dDOxUp1XvOIa1tY7Tl49Pg6jp3q46K9S7PfByuvY+Hs3TnOjYJooFyjFQCw7XTHwgrkHAUOh61YdCZyDvPtzCPdlCZaPY84iG4ey0ogZlrUvXclDKLv/fTDAMpb3I1novXfmxFD4yd+434AoXbbhNFMtPRnDs9zk9UyGQP9w5svNvp8yaQxg4PoGZNlaVUFSx17VM5RhcWdookeeAG+/r3Tldq5zBrHsiKvxyTTN/DY4k7lykNrde/CzNi/2sP//caTAIBL9i7P/PNde/x8DAKBoIYC3nPXkkz87uVy+vB0JroKOUcRbDUTHbRLzpHORKsrGk1eLdyuqFla06Lufal6DrWZWxHKaKKz5Bw918I5qz3cemSv0X5kFRZuej46tlkzqDJ1YcDk77/SZTlHK1hb7OB034szElV4RhIRZELpZz/8JTz8/bNxW+AmIjNhajbaC0Tpi4xpJhfsXsSxU2ExzlXnzX7ykHU+yqBv1plo9SZ+eF+5CYVtVe/OUQSLQjmaEKJ1co6ObcGipLBQ7VbLhdLNQ2aTAfOi7rQmt3SzlRKa6Cw5x+YwwO2X7zdeFR+Rc0Rj5MbAN+4+WHZVddLX31lhET5nogvyibffgieim3lR9ix3IARwYn2IvSvd+MZcJkC0raQg539+6XEAo4L6piGPxdAP4uWgoR9UuhzDNIeL9oQSjr0r3VoKC7POR3kTMu3uVQWykPjq83eW2k46Ey2L33qd4j7RRdbvZLbKD0Trmq0QEXquHWeiVb/xJhd/b1fUou6L9phPYnuuFU+olgxahmcx1vbbIITI++widRbqbsh4pu+ZW3CWDaK3up5+7s7LcNV55cbLkc+qbEvbjMv2r+C5h/eU2saupXAmK21uqjBet4jGZpbTkqPMApn5U50Dhr7gwsJtygsu3w8AuPHi3bV8ftb5KK+3OrKJv/njN+Bnnn8pjpyzUmo7tmWNjBNPnRlguetoNSWoUooQB9FCIIjsOdticQeEutBNLwqildUMzkQ3E1lQeGS/+fX30Z+6KX6sZrWLkF6tyVBo5LJvJTsZUaSBU5Y7x8bAN3YPKi/nyP/dXVcfrFQixum8Gtm9HFl2nQlbGA+qKCy0Rn2iAYz93CSk9nmodlPzAy4s3KbccPFu/MHPPBcX11BUCCTXZlpeBGDmPtEAcOMlu3HjJeUnFI5FI4VuJ9YH2kueeVmfIsOOmokOWtiEZMG140KrARcWNp4P/Nj12Bj62GHozgEAlx9YxZfeeWemnMKU9NBjcu1dc8Ea7v30+POLBYqVRzTRUTyzOQxmnoneauJdVjoztr1Kt8YYIZeDZHFhLOcoUWRg0Xi1bZabQFPoxBpUpRGEH1R+ITDN4QcO7qjts+XkTe1aWGcmuirSmujjZwfYpVkslS/nMA8OZEZNyjnalIUGgK5rKZno5PhwYWEzeXbJugxT94s80hNZk2vvZVeei30rPbzmfX81EjssFpCYqKexTN5tDP24qHYSHcfCwAum6jRV9YSVg+gJfPIdz8OpzeFUtp2u1q5CzmFnyDmqmOnWhQyW1YkAyzmYupDnneeP28E1OZuY1kSfWB9o23ZVWlgYO56E7hxtKSqU9Bwb/SHLOZhqKTv2XH/RrnDCqqSwi2ii1bFA1jRsDvXlHHuWOnj86c2pJsmqHqc5EpnApfuWcU3Jop08pN6wP0y7c1Qr52iDJno4oolmOQdTD06GnKMNmWjHtkpkorO/90HFx1oXeVn7kTtHkycmWSx07LhokwsLmaoYy0QXuOWnk209jXqINOpeyM2ZBNHPOiRXGacXs3AmukXIE0su70lv1lIdC5VmBZIma6I7GUHLkDsWMjWRLS9qvp9xOhN9/Owg7hA5iXTS6O4bL8Cbb70U5+wwd08ZcecIRGucOSSqI4OqieakAFOG9NhTxR1fp9FSGlWWJOMQE030r776Knzya8dw6b5yhdJbUXWyo1QkQkS7iOg+IvpG9H9mypaIHiGiB4joi0R0v+n724qUc8hMtLSiqvhsGgAAHXlJREFUKyXnsGhcE93oTLRcPh8NWopc4AxTFrnMmJmJbvA5Ka3yhBDYHPpYH/iF5RxEVCiABhQ5h2hnYWHPUSzulDGNkwJMGdKXSRV5syLXnjoUyHHRRBO91HXwiqsOGn+uCfMm57gHwJ8KIQ4D+NPo5zxuE0JcJYS4ruD7W0cv1Qa2Cp9oSrX9Lru9uonlHKo7RxA0+jsxzcV1ZBCtTOpqdOeoCkfJAJ9cD2tA1jQdB6q8J6mFhYFooSbaVeQcI5no5p47TP2MF6aWj6KLBJvqhDoooImukl98yeWZz89bEP0KAB+MHn8QwF0zfn+jcaNWmHG1dpyJLv5HtonG2ng2ue13LOfwRgsLm5z1Y5qLa2V0LJRBdIMDPtkoxgtEHOTpFhalHTTKjF/WiJyjXR7RQBhEp2tgAJZzMOUo4xOdR5HiPlUqMerOMfsg+iduuTizk2vV43TZ6Gq/EOIoAET/78t5nQDwCSL6HBG9scD7W0vPsZQ2sAGIys2UspqtdGs4gatCXpQy2xdEWknO3DB1IDPRI/KilrhzAGHwKlfGdAuL0jel255RfBiX2wqiZittu8xDTXR4fNVAh8czpgzTKEwtMp7ZI0F0+H9/GNQSRANZUrPq7SQnFhYS0ScBnJPxq180+JybhBCPE9E+APcR0deFEH9u8H5EwfcbAeD88883eetco7aBHUTWbWWyL1akiVaz0U3ORCfL59FEIyivG2eYojgZmWipNGqyO4eUoniBQD9a9elq6hjVG9XHfvqmUt65Y81WWpiJluO9WvDN4xlThrFmKxVss8h4pr5HFgcPfPNmK1WRHj6mMUZPDKKFEC/I+x0RHSOiA0KIo0R0AMATOdt4PPr/CSL6KIDrAfw5AK33R+99H4D3AcB1113X3Eq5FOGgmmSiq2p3qWY5Gh1EW6MaVJkBbHLAwjSX7I6FUSa6wUvy8YqPH8Q+xjotv4HRG1XZ5hNqYWEbm60sRJpoEVn4SViexpQhPdks0rJ7bJuFMtFJrCGLlAFoFxZWTToTPY3VwrLf7OMA7o4e3w3gY+kXENESEa3IxwDuBPCg7vvbzmgHq/L+x3FhjhJFX7J3XBfUFOTNRTa3qMJLm2GKkgTR7etYCITfRWaidW98VS6PJoWFUbOVBh/TLHquhUCE549qRcqF0kwZ0tfJSoFug5O2qcNoJhpxfUUVQX0R0hn6aaxslb1y3w3gDiL6BoA7op9BROcS0R9Gr9kP4C+I6EsAPgPgfwkh/nir928n0h2sygaHaiZnqWPj0M4F/Mqrn116P+tCHo9BHESL6Pl23VyZZuDGBXhqJrodPtFA+F02DTPRlbpzRMNfKOdo9jHNQu0NwHIOpirSKzZLFQTRRZICIz7RIpmQ1zVJnEUmutSRFkI8BeD2jOcfB/CS6PG3AWRGcXnv306MmO975Qvm5EkTBGE2+qXPOoCVnp5V1TwSBy1R8MyZaKZO4kmdN+7OMc1WtdMmKxOtKwOrMrtjKYWFoZyjsk3PBXEQPfBZzsFMjeUCQfRHfuo5+NDfPIoPf+67AMpnogMh4hXkuu7X6SDamcJ+cMfCmlELTYZ+UKpbIZBkcgIRivqbnsmJm61Emb9YE81BNFMDbnw+ZvlEN/daU79XIucws7irYnVopLAwaGdhIRB2cVMz0U2egDHzxwW7F43fc835O7F3uVsqiLZThYXD+H5dz3Wc/grTcDHhK7dmeq5dqSbaUjTRbQii5fGQLdETd45mfy+mmcTNf0Z8osPH7dBEB4qcQ9edI/y/imyTHL8E0MqOha7SPEp1UEo3yGKYovznH7kar7y6WNc/9Roun4lOkl91TRLHMtHzJudgyqPKOSrRRMdyjnZoCqU7BxcWMvNAVmGhXB1p8rWmaqITOYdpJrr8NSnveeFKWvuarYxa+CXP+4rGnmHK8LIrzy38XjVjXCTwtcfkHHVnouffnYMpSc8Z94kugzxHHnlqHUCzu6gBqjsHW9wx9eNmZqLrvVFUgby5eL5IJqqO3vfxKvRujzPRIvKJbtkdSm1rrjbFUpv3MExduFbJTLQyBgaBMpbUNDamwx8OoltIV/WJ9sr7RMuT5K7/8mkA1XfnmTVpd45BfIPnU5eZPbF22M9w52jwhFXe/NTgTlc/uNINC5ffeMtFpfcjKSxEK5utpJvJSJrcVZZpD6rXfZGAUx0zfCHisZHlHMzU6LnWiMWdbpewPNLLn03P2CZBy2gm2uVCHKYG5PU0yPCJbrKcQ+1YGBh+n4WOjUfe/dJK9kN+ZBAF822Vc/zVt57CfV89BgC458X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332.055062 \r\nL 106.646813 305.13907 \r\nL 106.951329 301.180082 \r\nL 107.255845 300.43657 \r\nL 107.864877 275.597284 \r\nL 108.169393 272.30062 \r\nL 108.473909 270.762319 \r\nL 109.08294 242.166588 \r\nL 109.387456 263.56008 \r\nL 109.691972 276.746736 \r\nL 109.996488 260.04549 \r\nL 110.60552 192.351706 \r\nL 110.910036 194.533102 \r\nL 111.214552 198.0306 \r\nL 111.823583 184.115387 \r\nL 112.432615 141.211749 \r\nL 113.955195 66.281542 \r\nL 114.259711 64.051006 \r\nL 115.173258 83.598109 \r\nL 115.477774 86.138442 \r\nL 115.78229 84.04678 \r\nL 116.086806 88.653137 \r\nL 116.391322 95.641723 \r\nL 117.30487 107.377677 \r\nL 117.913901 138.791062 \r\nL 118.522933 168.595641 \r\nL 119.131965 194.699751 \r\nL 120.654544 284.442514 \r\nL 121.568092 324.525933 \r\nL 121.872608 331.159856 \r\nL 122.177124 335.073977 \r\nL 122.786156 354.155317 \r\nL 123.395187 376.080377 \r\nL 123.699703 383.79752 \r\nL 124.004219 388.237227 \r\nL 124.308735 386.216071 \r\nL 124.613251 380.357708 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -158,14 +156,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
@@ -182,223 +180,15 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.3090560911603647\n"
+      "0.3090559299602927\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Regressors</th>\n",
-       "      <th>Parameters</th>\n",
-       "      <th>ERR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>y(k-1)</td>\n",
-       "      <td>1.8387E+00</td>\n",
-       "      <td>9.43378253E-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>y(k-2)</td>\n",
-       "      <td>-1.8938E+00</td>\n",
-       "      <td>1.95167599E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>y(k-3)</td>\n",
-       "      <td>1.3337E+00</td>\n",
-       "      <td>1.02432261E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>y(k-6)</td>\n",
-       "      <td>-1.6038E+00</td>\n",
-       "      <td>8.03485985E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>y(k-9)</td>\n",
-       "      <td>2.6776E-01</td>\n",
-       "      <td>9.27874557E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>x2(k-7)</td>\n",
-       "      <td>-2.2384E+01</td>\n",
-       "      <td>3.76837313E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>x1(k-1)</td>\n",
-       "      <td>8.2709E+00</td>\n",
-       "      <td>6.81508210E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>x2(k-3)</td>\n",
-       "      <td>1.0587E+02</td>\n",
-       "      <td>1.57459800E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>x1(k-8)</td>\n",
-       "      <td>-3.7975E+00</td>\n",
-       "      <td>7.35086279E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>x2(k-1)</td>\n",
-       "      <td>8.5726E+01</td>\n",
-       "      <td>4.85358786E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>y(k-7)</td>\n",
-       "      <td>1.3955E+00</td>\n",
-       "      <td>2.77245281E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>y(k-5)</td>\n",
-       "      <td>1.3219E+00</td>\n",
-       "      <td>8.64120037E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>y(k-10)</td>\n",
-       "      <td>-2.9306E-01</td>\n",
-       "      <td>8.51717688E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>y(k-4)</td>\n",
-       "      <td>-9.5479E-01</td>\n",
-       "      <td>7.23623116E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>y(k-8)</td>\n",
-       "      <td>-7.1309E-01</td>\n",
-       "      <td>4.44988077E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>y(k-12)</td>\n",
-       "      <td>-3.0437E-01</td>\n",
-       "      <td>1.49743148E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>y(k-11)</td>\n",
-       "      <td>4.8602E-01</td>\n",
-       "      <td>3.34613282E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>y(k-13)</td>\n",
-       "      <td>-8.2442E-02</td>\n",
-       "      <td>1.43738964E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>y(k-15)</td>\n",
-       "      <td>-1.6762E-01</td>\n",
-       "      <td>1.25546584E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>x1(k-2)</td>\n",
-       "      <td>-8.9698E+00</td>\n",
-       "      <td>9.76699739E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>y(k-17)</td>\n",
-       "      <td>2.2036E-02</td>\n",
-       "      <td>4.55983807E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>y(k-14)</td>\n",
-       "      <td>2.4900E-01</td>\n",
-       "      <td>1.10314107E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>y(k-19)</td>\n",
-       "      <td>-6.8239E-03</td>\n",
-       "      <td>1.99734771E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>x2(k-9)</td>\n",
-       "      <td>-9.6265E+01</td>\n",
-       "      <td>2.98523208E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>x2(k-8)</td>\n",
-       "      <td>2.2620E+02</td>\n",
-       "      <td>2.34402543E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>x2(k-2)</td>\n",
-       "      <td>-2.3609E+02</td>\n",
-       "      <td>1.04172323E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>y(k-20)</td>\n",
-       "      <td>-5.4663E-02</td>\n",
-       "      <td>5.37895336E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>x2(k-6)</td>\n",
-       "      <td>-2.3651E+02</td>\n",
-       "      <td>2.11392628E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>x2(k-4)</td>\n",
-       "      <td>1.7378E+02</td>\n",
-       "      <td>2.18396315E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>x1(k-7)</td>\n",
-       "      <td>4.9862E+00</td>\n",
-       "      <td>2.03811842E-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "   Regressors   Parameters             ERR\n",
        "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
@@ -406,11 +196,11 @@
        "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
        "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
        "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
-       "5     x2(k-7)  -2.2384E+01  3.76837313E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
        "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
        "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
        "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
-       "9     x2(k-1)   8.5726E+01  4.85358786E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
        "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
        "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
        "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
@@ -431,11 +221,11 @@
        "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
        "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
        "29    x1(k-7)   4.9862E+00  2.03811842E-05"
-      ]
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
@@ -462,14 +252,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 426.524687 \r\nL 91.073836 425.8022 \r\nL 91.511557 411.152953 \r\nL 91.527769 411.762218 \r\nL 91.657464 414.450677 \r\nL 91.673676 413.929097 \r\nL 91.706099 411.584217 \r\nL 91.738523 416.587624 \r\nL 91.803371 413.657281 \r\nL 91.852006 420.48783 \r\nL 91.88443 417.237727 \r\nL 91.900642 415.724342 \r\nL 91.933066 420.370916 \r\nL 91.949278 421.614602 \r\nL 91.981701 417.132372 \r\nL 91.997913 416.04414 \r\nL 92.030337 420.668488 \r\nL 92.046549 422.442923 \r\nL 92.095185 418.283297 \r\nL 92.14382 421.93661 \r\nL 92.176244 417.362476 \r\nL 92.22488 423.776247 \r\nL 92.257303 421.234152 \r\nL 92.273515 420.18193 \r\nL 92.322151 424.220352 \r\nL 92.354575 421.784311 \r\nL 92.516694 424.020064 \r\nL 92.549117 423.321335 \r\nL 92.711236 418.9788 \r\nL 92.759872 422.615291 \r\nL 92.808508 418.607581 \r\nL 92.840931 420.940681 \r\nL 92.873355 419.416585 \r\nL 92.905779 417.218426 \r\nL 92.970626 420.487016 \r\nL 93.116533 417.101093 \r\nL 93.165169 418.987263 \r\nL 93.246229 417.917766 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 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417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 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305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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587.44086 416.256092 \r\nL 587.570563 416.922889 \r\nL 588.186652 421.561336 \r\nL 588.219078 421.535685 \r\nL 588.348781 421.16788 \r\nL 588.96487 418.872918 \r\nL 589.224277 418.851271 \r\nL 589.710663 418.930773 \r\nL 589.905217 419.607446 \r\nL 590.359178 421.306835 \r\nL 590.456455 421.20103 \r\nL 590.65101 420.493422 \r\nL 591.234673 418.075477 \r\nL 591.33195 418.184357 \r\nL 591.494079 418.948061 \r\nL 591.980465 421.931214 \r\nL 592.012891 421.906036 \r\nL 592.142594 421.379839 \r\nL 592.758684 417.992893 \r\nL 593.082941 417.536084 \r\nL 593.277496 417.305274 \r\nL 593.309921 417.326798 \r\nL 593.439624 417.691438 \r\nL 593.731456 419.624315 \r\nL 593.958436 420.487633 \r\nL 594.023288 420.455164 \r\nL 594.185417 419.885986 \r\nL 594.509674 418.86502 \r\nL 594.606951 418.988603 \r\nL 594.801506 419.68446 \r\nL 595.125763 420.710598 \r\nL 595.255466 420.648394 \r\nL 595.417595 420.226672 \r\nL 595.806704 419.187577 \r\nL 595.968833 419.248109 \r\nL 596.228239 419.388065 \r\nL 596.390368 419.062022 \r\nL 596.811903 417.911863 \r\nL 596.844328 417.930466 \r\nL 596.974031 418.227298 \r\nL 597.36314 420.051954 \r\nL 597.849527 421.979642 \r\nL 597.946804 421.919191 \r\nL 598.108933 421.210917 \r\nL 598.530467 418.948895 \r\nL 598.627745 419.140422 \r\nL 599.016854 420.542051 \r\nL 599.04928 420.51617 \r\nL 599.178983 420.146932 \r\nL 599.568092 418.8608 \r\nL 599.827498 418.586752 \r\nL 600.054478 417.525864 \r\nL 600.313884 416.678037 \r\nL 600.411161 416.920894 \r\nL 600.702993 418.961131 \r\nL 600.865122 419.519132 \r\nL 600.897547 419.505573 \r\nL 601.02725 419.11859 \r\nL 601.319082 418.037737 \r\nL 601.351508 418.05192 \r\nL 601.448785 418.290901 \r\nL 601.675765 419.83455 \r\nL 602.032449 421.907463 \r\nL 602.194578 422.130976 \r\nL 602.227003 422.10867 \r\nL 602.356706 421.782748 \r\nL 602.583687 420.291308 \r\nL 602.94037 418.095237 \r\nL 603.005221 418.124099 \r\nL 603.134924 418.677932 \r\nL 603.491608 420.271844 \r\nL 603.588885 420.057045 \r\nL 603.977994 418.565607 \r\nL 604.042845 418.660573 \r\nL 604.2374 419.574359 \r\nL 604.594083 421.00072 \r\nL 604.821063 421.096825 \r\nL 605.177747 421.140897 \r\nL 605.30745 420.748059 \r\nL 605.599282 418.78732 \r\nL 605.891113 417.292185 \r\nL 606.053242 417.20865 \r\nL 606.345074 417.591401 \r\nL 606.701757 418.221544 \r\nL 607.317846 419.411431 \r\nL 607.836658 419.500934 \r\nL 608.063639 419.030843 \r\nL 608.355471 418.402492 \r\nL 608.387896 418.42175 \r\nL 608.517599 418.779079 \r\nL 608.777005 420.580062 \r\nL 609.068837 422.089535 \r\nL 609.166114 422.014627 \r\nL 609.360669 421.192631 \r\nL 609.684926 419.994662 \r\nL 609.847055 419.969918 \r\nL 610.625273 420.325072 \r\nL 610.754976 419.860589 \r\nL 611.241363 416.737486 \r\nL 611.33864 416.989491 \r\nL 611.533194 418.762311 \r\nL 611.889878 421.935869 \r\nL 611.954729 421.978823 \r\nL 611.987155 421.926451 \r\nL 612.149284 421.028337 \r\nL 612.830224 415.801802 \r\nL 612.86265 415.824233 \r\nL 612.959927 416.193807 \r\nL 613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -483,24 +272,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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      },
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-     "output_type": "display_data"
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@@ -522,9 +310,13 @@
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-   "language": "python",
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+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
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@@ -536,9 +328,9 @@
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diff --git a/docs/examples/general_estimators.html b/docs/examples/general_estimators.html
index 7c6b2d07..b3f689c5 100644
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@@ -7,7 +7,7 @@
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     <title>Building NARX models using general estimators &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
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-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
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     <link rel="canonical" href="http://sysidentpy.org/examples/general_estimators.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -244,21 +241,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
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-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
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+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +273,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +292,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
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-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -349,6 +346,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
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@@ -611,7 +609,7 @@ <h3>NARX with Bayesian Rigde<a class="headerlink" href="#narx-with-bayesian-rigd
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diff --git a/docs/examples/information_criteria_examples.html b/docs/examples/information_criteria_examples.html
index d47f4ede..9970d140 100644
--- a/docs/examples/information_criteria_examples.html
+++ b/docs/examples/information_criteria_examples.html
@@ -7,7 +7,7 @@
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@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
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@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
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@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
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@@ -352,6 +351,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
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@@ -652,15 +652,15 @@ <h2>Importante Note<a class="headerlink" href="#importante-note" title="Permalin
 
               </div>
               
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@@ -679,7 +679,7 @@ <h2>Importante Note<a class="headerlink" href="#importante-note" title="Permalin
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diff --git a/docs/examples/information_criteria_examples.ipynb b/docs/examples/information_criteria_examples.ipynb
index 554f5bf8..c958867f 100644
--- a/docs/examples/information_criteria_examples.ipynb
+++ b/docs/examples/information_criteria_examples.ipynb
@@ -94,851 +94,32 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
+      "0.3934473153088806\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8805  0.82870279\n1         y(k-1)     0.1990  0.04043061\n2  x1(k-1)y(k-1)     0.0854  0.00229570\n3  x1(k-2)y(k-1)    -0.0274  0.00023702\n4      x1(k-2)^2     0.0192  0.00022685\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     },
     {
+     "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -974,17 +155,17 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([-2694.09936284, -2920.43235501, -2948.99729043, -2951.5656718 ,\n",
-       "       -2953.60017972, -2952.10447799, -2950.4899516 , -2948.64474158,\n",
-       "       -2946.7871786 , -2944.89120846, -2942.95165921, -2940.98557817,\n",
-       "       -2939.02706707, -2937.03646143, -2935.03708992])"
+       "array([-2645.25644153, -2884.57180297, -2898.46898376, -2898.12279253,\n",
+       "       -2898.63304969, -2897.72014153, -2896.42865166, -2894.8323514 ,\n",
+       "       -2892.96893425, -2891.06015084, -2889.14733108, -2887.20019762,\n",
+       "       -2885.22702095, -2883.24160575, -2881.25140789])"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
    "source": [
@@ -1013,834 +194,32 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.38121187399853695\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9235  0.84287328\n",
-      "1         y(k-1)     0.1864  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1188  0.00403228\n"
+      "0.3929140482842041\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8804  0.82870279\n1         y(k-1)     0.1985  0.04043061\n2  x1(k-1)y(k-1)     0.0853  0.00229570\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -1876,17 +255,17 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([-2689.29919277, -2910.83201488, -2934.59678022, -2932.36499152,\n",
-       "       -2929.59932938, -2923.30345758, -2916.88876112, -2910.24338103,\n",
-       "       -2903.58564799, -2896.88950777, -2890.14978846, -2883.38353735,\n",
-       "       -2876.62485618, -2869.83408047, -2863.03453889])"
+       "array([-2640.45627146, -2874.97146283, -2884.06847355, -2878.92211226,\n",
+       "       -2874.63219935, -2868.91912112, -2862.82746118, -2856.43099085,\n",
+       "       -2849.76740364, -2843.05845016, -2836.34546033, -2829.5981568 ,\n",
+       "       -2822.82481007, -2816.03922479, -2809.24885687])"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
@@ -1913,851 +292,32 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
+      "0.3929140482842041\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8804  0.82870279\n1         y(k-1)     0.1985  0.04043061\n2  x1(k-1)y(k-1)     0.0853  0.00229570\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -2789,9 +349,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([-2643.42254629, -2880.90401248, -2892.96729803, -2890.78721156,\n",
+       "       -2889.46357347, -2886.71677006, -2883.59138495, -2880.16118945,\n",
+       "       -2876.46387706, -2872.7211984 , -2868.9744834 , -2865.19345469,\n",
+       "       -2861.38638278, -2857.56707234, -2853.74297923])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
    "source": [
     "model.info_values"
    ]
@@ -2812,855 +386,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
+      "0.3934473153088806\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8805  0.82870279\n1         y(k-1)     0.1990  0.04043061\n2  x1(k-1)y(k-1)     0.0854  0.00229570\n3  x1(k-2)y(k-1)    -0.0274  0.00023702\n4      x1(k-2)^2     0.0192  0.00022685\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 9
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -3692,21 +447,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([-2694.09936201, -2920.4323484 , -2948.99726811, -2951.56561889,\n",
-       "       -2953.60007638, -2952.10429942, -2950.48966802, -2948.64431828,\n",
-       "       -2946.78657589, -2944.89038168, -2942.95055875, -2940.98414946,\n",
-       "       -2939.02525054, -2937.03419259, -2935.03429929])"
+       "array([-2645.2564407 , -2884.57179636, -2898.46896144, -2898.12273962,\n",
+       "       -2898.63294635, -2897.71996295, -2896.42836809, -2894.8319281 ,\n",
+       "       -2892.96833154, -2891.05932406, -2889.14623062, -2887.1987689 ,\n",
+       "       -2885.22520443, -2883.23933691, -2881.24861726])"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     }
    ],
    "source": [
@@ -3739,9 +494,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -3753,9 +512,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/examples/multiple_inputs_example.html b/docs/examples/multiple_inputs_example.html
index a137bf95..4dfa6cef 100644
--- a/docs/examples/multiple_inputs_example.html
+++ b/docs/examples/multiple_inputs_example.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Multiple Inputs usage &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,23 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
     <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
     <link rel="canonical" href="http://sysidentpy.org/examples/multiple_inputs_example.html" />
@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -332,6 +331,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -403,7 +403,7 @@ <h2>Build the model<a class="headerlink" href="#build-the-model" title="Permalin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fe5cb870a90&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2191c4db6d0&gt;
 </pre></div>
 </div>
 </div>
@@ -429,12 +429,12 @@ <h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permal
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0026126773911553398
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.002440520762176125
        Regressors Parameters         ERR
-0         x2(k-1)     0.6000  0.90482955
-1  x2(k-2)x1(k-1)    -0.3000  0.05072675
-2        y(k-1)^2     0.3999  0.04410386
-3   x1(k-1)y(k-1)     0.1000  0.00033239
+0         x2(k-1)     0.6000  0.91070733
+1  x2(k-2)x1(k-1)    -0.2997  0.04434814
+2        y(k-1)^2     0.3999  0.04463687
+3   x1(k-1)y(k-1)     0.0996  0.00030042
 </pre></div>
 </div>
 <img alt="../_images/multiple_inputs_example_9_1.svg" src="../_images/multiple_inputs_example_9_1.svg" /></div>
@@ -460,15 +460,15 @@ <h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permal
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="basic_steps.html" title="previous page">Presenting main functionality</a>
     <a class='right-next' id="next-link" href="information_criteria_examples.html" title="next page">Information Criteria - Examples</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -487,7 +487,7 @@ <h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permal
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/multiple_inputs_example.ipynb b/docs/examples/multiple_inputs_example.ipynb
index d9733224..b00d894b 100644
--- a/docs/examples/multiple_inputs_example.ipynb
+++ b/docs/examples/multiple_inputs_example.ipynb
@@ -84,14 +84,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fe5cb870a90>"
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x25b995a4640>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
    "source": [
@@ -104,2662 +104,20 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.0026126773911553398\n",
-      "       Regressors Parameters         ERR\n",
-      "0         x2(k-1)     0.6000  0.90482955\n",
-      "1  x2(k-2)x1(k-1)    -0.3000  0.05072675\n",
-      "2        y(k-1)^2     0.3999  0.04410386\n",
-      "3   x1(k-1)y(k-1)     0.1000  0.00033239\n"
+      "0.0026749477922297683\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6001  0.91077359\n1  x2(k-2)x1(k-1)    -0.3000  0.04639348\n2        y(k-1)^2     0.4000  0.04252990\n3   x1(k-1)y(k-1)     0.0998  0.00029576\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n",
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cJZBpPpTfRmgW29pOwtLepzEUT6ApNj5NQxjTiiBTwWYAQHKwF0IwMi7vVQ8aJoI8bdo0JJPJApHc19cHjuOKOry0t7dj/vz5BdvmzZuH3bt3T8hYfXx8fA4kaF2GRnEI2AJZyzS+RdNUYqV0B9Zqx0I1rTSEnMnhCe04HC99f5JHNka2/hGDCOOgw44qu5vJCODhR5DrgZwXyIGC7dumn4oQkdD3n8eMWwoPY6pWPnmoFQCQHuqt+3vUk4YRyIceeihYli1wonjttdewePFiMExhoLurqwtvvPFGwbZ3330XnZ2F3ok+Pj4+PmOHNqwIcihmPdj0bHxyB3SAwcY6kIYIBjoAlGzH7JWGyWU2TcxKvoatwWWgyuQfA4DBiOD9CHJdUHNWWgMjDEeQ127owdf+3gTJZHAw6Rm3FB7WVGBSHLhIGwAgl/AFsidEUUR3dzduuukmbNy4Ec899xzuvfdeXHrppQCsaLIkWVWsF154IbZu3Yrbb78dO3bswE9/+lO89NJLuPDCCyfzI/j4+PhMSZzcwXCkyWoTux90wZpKXLN6IdqpJN4xOwAAT+pHF7Vj9kIj5TLv3vomppn9UGYdV3FfwgpgiQ5d9UXyWNFlK4LMisMC+fR1R+B1+mIIRAMhVgrPm/SFOH3dEXV9b85UYdA8AjFLIMvJxk4PahiBDADXXXcdli5dissuuww33HADrrrqKpxxxhkAgOOPPx7r168HAHR0dOC+++7DK6+8gg996EN45JFH8IMf/ACHHXbYZA7fx8fnQCG1F7jvdCC1b7JHMiEwpgKNFkDTNNIIgJIb38N0KtHd1YnsOT/DW7BSC3/LnVrUjtkLp687Am/SF+IS5llQxBw3IeSF3X+3/I+nHV4+/xhAvqmFZKcH+NSOJlmdMPkRAtlJ4VHyKTzsuKTwsFBg0jzCTe3WWNKNXcvQMEV6gBVFvvXWW3HrrbcW/W7z5s0FPy9btgyPPfbYRA3Nx8fHB4AVhTN+80V0ay9h7R2fA3Xmf1YtVPY3OEOGQVmuQmkqBNoXyBNOd1cnNvzOBLLAZYeLOLqGc26ldAfWMD/H6fRfwBMNsslgvX40vq1djFfHYczlINtfRD9imLvwyIr7UqyVLyvlMghGmsd7aFMaXc4CALhAOL+NjXUgnR6ZwqPVnMJTDs5UYTI8Is2WQDYyjS2QGyqC7OPj49PI6De3o3vdYThXfwoUMXGu/hS61x0G/eb2yR7auMKaKgxaAABkqDBYtfFN/qcitN0sQ0/Wlrvp5DKzsJpvseMkhCphGgZmJ17DtlAXKLqyDCGcFUFWcpma3q9h8q4bAEO2/obCiAiyk8LzvLEMAPCUflRNKTzlMA0DAlFBaB48LyBliiC5xraL9AWyj0+V+DfbA5dz2B9hrXYsNNO6dTpuAt3snZM8svGFgwyDsZo4SXQYvNbY/qVTFVbPAQBIprbUHkcI/dOcCwDYZM6tuxDywu4tr6MNg9BmV84/BgDKbmqhSNUL5EbKu24ETMUWyCMiyE4Kz8+5CwAA67nTakrhKYeqWC4kpn0fSVIRMHJj1zI0VIqFj0+j49xs15gPYQXt3Gwtw/WpvszuA2xKiEgzImgYMM1hN4HXE8JkD21c4UwVph1BVtkwYrm+SR7RgQlrd5NjcrUVN3V3dWItfoZtT/wblmIbtmEWzHPumvB71+6/P4NOADOWneZpf8eSTK1BIE9VD+maUawUCyEQLNjc3dWJo4LHAb8ALjuyGSvqfE7Icg4cAMI4K1ERcEq8ru9Rb/wIso9PFTRSkYvPxNMRE9FKkhhECCkIeFA/GW0kgY6YWPnF+zE8lHzkR+OiCBp+sdRkwNkpFoJUu7Dr7urEfC4OAFjAD07KxJ7e/if0ohmzFyz1tD/jCGQ7f7YaJqoAbX+BqBlkTR6EKrbWC9j53Vq2/pFdVbK+O8JaAjnHRiFojV3L4AtkH58qGH2zlQ7wm+2BxjWrF+IL+BJyEMBDw9e1y/EFfGnCl6gnEl1TwRIdYKxJgM5HETJrywX1GRu8aQnkoDa23M0mzcphblYn3oXFNAzMSW3A9nAXCOVNgjgCWatBIBd7SE9O3nWjQLQcJMKX/F0oajUCMnPxur+vKtvpQfZEW2FjCOm+QPbxmTKMvtlyB/jNtp7sD7nd3V2duOXcpYgiA55omBVlcMu5S6d0eo1sL2s7DzYIMfBErblgyqd2nG5yMaP2CJ+sKGg3B6CZFNrMQUjyxHWoW7uhB1d+6wdoQRwvpDo9X+OsI5Cl6gWyk3f9hjkHAPBX45BJybtuFCgtB4mUTgnjhAAUkwGk+hfhKo5AtiPImtCEsNnYtQy+QPbxqQLnZrvRsPxIn9aXH9A323qxPxXSnLm4FWFi3eyf/PTyKS2OAUDJWZ/V8aKlAlEAQDpxAOZv1kA9J36iaQtkMwVFqa1pRv+eHWCIgR3cQWCIgd5dW2oeTzU41/jlyi8AAAdp73m+xlnBypc1lFzV7+sUoP2DLAIAbKFm170AbX+C1nJQXCLIIARpEhwXn3PN/u5ozhLIptiMEHLQFKnu71UvfIHs41MFzs32r9ThAIDfcB/cL2+2jRat3Z9yu1OJYe9OOdPYEZB6INu2UJQd+aEDVp5iOu4L5ErUc+KnqSp4omIAMVDExFDf7prGNLhnKwBgqPUoAEB8z3s1HadanGv8aNrqaXAO86Lna5wTrAiyodS2atHd1YlDmixPgsOFffvd/bqe0HoOCuVeM5GhQqCV+keQHSHs3EeIfR9JDDVuu2lfIPv4VEl3VyeWTrOaJvzb0dP2u5ttI0ZrV0p34CX90PzPjn1aI+Z2p0fc0KXs1PcDViUr8kPZXrRssAkAkEs1todpI1DPiV8ua03G+lkrnSvRX9v1mu3dBgAIHLzS/nlrTcepFqd+wzCtn6u5xp2ub7VEkB0o3UrPmCZvr/kYUwFWz0Gl3F13clQInFr/iX8+gmwLZCbUCgDIDDZuN1JfIPv41AClWRe7Lu1/EcRGjNaysY588wLVpPP2abXkdo93dDybHI6cytmp7+bgOAc4kR8+bBXyyKnG7oLVCIxFFI5Gss+1dGAmACA7WFsEWRvaAQCYvWwVAEAf3FHTcarFqd8gADSTquoa50XbkkytPgfZgdatCGYLEkg2cNRyvOGMHDTaPYIsMyHwev3va5qd607bE20+0gYAyCYa1zLSF8g+PjVA7Jvt/iiQT5T+E+8aww+lRojWXrN6IaaROADgf7Uz8aB+ck253RMRHZdGRE7V/fD7rxan+pzmrQebYwWlphvb5L8RGCkKAcsur9aJn5KzRIsWsYrN5PjemsZEkruRRgDB5hnoRxPo1K6ajlMtTv2GCeBR/YSqrnEhYEWQTbX2fFVGH44+73n3HzUfZ3+HNeWyAlllIxDHQSAbqvX3Z2yBLEYtgSw1cC2DL5B9fGqA1qwbtSnvXxHExNAgbuHuxQJqD0wTMEY0u5hMJ47urk4McNb7ZyHirtBVNeV2T0R0XB0ROVVz+9f3XwuOcwBtdzML2FZQRtZPsajENasXYgY1BGIr5PfMjpqLemXJOtfY1nkAAD1Z29K0kN2NAdpa3h5kpyGYm5jUqu6uTqQ++ENQBNhqzqjqGqdpGrLJAmrtKRasLmEnse4xyR2v13yc/R3ekKAzAdff61wYAXMcBLKdYsHyVoFgMGYJZC3duCtRvkD28akB2u5oBaWxBdLIdINLv/m/2Pf9D2Al+QfeMOZgqzkNe8zmmqO19aZTtyJZH5jN4sVrV9WU271SugNPau+DWYclbTe0zHDkVJca+/uvB5rdecuJ/ESaLHFl5Brbw7QR6O7qxL73fxUAkDYFtJAkEh+6u6ZzW7FTLJhIu9XoIVNbmkBY3oskN90ak9iBJmXickCPn23Vbpy87OCqr3GZcCBa7QKZMyQMCHORMzkYfZtrPs7+jgAJJuOeg2xwUYTNDEznJupCtalsuh39d1Isoi3TrO0ZP4Ls4zOlYOwUC9RYVT0RjE43uEu9HtPNXqxdfAfePue3eI0sBkOMmqO19UTKJNBmWhFJSqldeLGxDoRJFoQAuknGJzqeGxbIWg2tb/c3dLv63GnWwHM80qYIIvkC2QuHBa1z5N3OD6OZpDEn/feajuOcawwfQpxqAlNju+8WvQ9ysMM6Zngm2s0+qJpW07GqJZe0rnE6EKv6tRL4fGpbLXCmBI0JooeZCTExMc4djYhoyjDZYJkdohCICinnnu9dSyqboVpBJceRJBAMI2dyQAOvRPkC2cenBhi75SutNm4EcXS6gUgUREgOZ73xH+ju6sSs9haIRKk5WltP9m59I/9vZgwWQ9esXoiDyB4AwOvGvHGJjhNpWCDXaju1PzG8NDqct2h5pcYnaUT7F/LgTgBA2wlXIAce8qZ1NR3HEcicGEKSaYYgVy8skqkEmkkKZsQSyFTTHHBER9/uiXF2kFLWtcOFmqp+rUI4UFrtApk3JRiMgKHAfLRK22o+TqNRTSRX1zTwRAVY9xQLIsQAAOmEe+pDLalspv3dOfcRQgiSJAxaatxaBl8g+/jUAGunWFBa7VXV401xBX1hW2yTFSGYE9dFqxzxnf8EAKRNEaxau0A+6/Dp4IkVDYuQzLhExyk5gSGEAex/Oei14BTXOM0aAMsrlR0Hr9SpiBa3BEvbnMPwTuT9WDj0AhS1+oitZvtRs2IQEteCsFZ97mZ/j9UUhGmeDQAQW+cCAAZ6JiaiqtjpSVwwVv1riZB3oqgFHjIMJgC1aQE6zF5Imf3//K02kpu1PzPh3AUyY383mYT7BMx5tiim5S0tm0zlVDY7xYIbOdGmImCVuPtrJhlfIPuMG43WjKKecLawZLXGjSAW2yqNaovNBsARHaoy+SJZ3vs2DJNgK3cweK120fnu3/+IVpLAPrRgBhnEH6/5QN2j45ySwCBl5eFCadwJUr1wBDInDD/YJDoMTpv6Dh71gErvQQIhcGII9GFno50M4fVXflf1cQx5OIKsiK2IGvGqj5HYa3keB9vmAgCiHQsAANneiemmp2TiAAAx3Fz1azWKG05tqwHBVGCyIvgZlt96z3sbaz5Wo1BtJDdvS8m5p1iwdvpLrkzxnPNsYWxrTnb0s6UEpuakWAzfR3JMFIIad33NZOMLZJ9xoRGbUdQTRyAzDRxBHllB/zPttKJ0A2K3Ds7lJl/ks0PvYS9phcS3QtRrF14Df3/SEtqzuiEQFUMDe+o4SgteSyLHRiGZ7Jh8WfcXHGstbkQEWWHCEMbwPR1I8Nl9GKIt54+Djv8XKCaD7N8fr/o4hj0ZEwJhmME2NCEFSapOMEr9ludx03TLCaO1cz4AQJsgL2Q9FwcABCPVC2SFEoaLo6tEVSSwRAfYAFrmLgUAxLfv/04WTiRXNy2blEpFyZLdbIbm3SPIfNhKf1FScdd9HMu+jYZ1/vyfcXjlVDZNhmEScOxwm2uJiyGgN24k3xfIPuNCIzajqCc8FOv/RuMKpO6uTvQe+3UAwCZzflG6gbPMpjRAs4tIdjv6+VnQ+QiCZu2CvWXPH/A2twj8zGUAgMHd2+ozwBGIegoyG0WOCCDq5E8uxh1bIPMjIj8KF0HA8AWyF0JKH1KsZWklhJrwdugozO9/HrpuVHUc0xHIYgh02HIAGOyrLuCgx3fBMAmaZ8y1jhWMYAgR0MmJEchm1irsDMVaqn6tTvFgjdoiyNmMdY8jXAAd8xdDMymo+96q6ViNhBPJpWDC9GDZ6Xhp07x7BNmJ7isZ99zg7q5OZM/5GV6nDgEAPMucUDmVTZeggAGhhmWnxscQNnyB7AlFUXD99ddjxYoVOO6443DPPfdUfE08Hsexxx6Lxx+vfkbuM36slO7ADqMt/3MjNKOoJ7zpCOTabYcmgqUR66F6xRnHFBXjUbZAlifby9c0MU3bhUxoHnQ+ipCZASpYDJWif98uHKy+g3jHiQi3Wc0UUn3b6jxYIGSkoHFRSBDyHRWnNJpkRX64YWsow/mefCrSpPdBCkzP/6wvPAsd6MMbf/u/6g6kOhHkINiodbxUf3Xd9JhUDwZJDPSI77KfmQYxW1tXvqqRk1BMBrzgHsF0Q6eFfO1HtSh25JRwQXC8gN3UDPBD79Z0rEbCieRKYCGBrViUrOTsCLIQcj1m0PY517Pli+e6uzpxaFQFAJx9WFPFVDaiyVAIV7DNEJoQNtMwDb3sayeLhhLIt912GzZs2ID77rsPN910E+688048+eSTZV/z7W9/GwMDjWs0vb9Rr7zhY8UdmE1ZNkRjbR3caJi6XQkMQDQbWyDlBqzvL9I2q+h3ThRBnmQv36HeXQgjB7PlIBAhBpbokLLVRxW2vLQOFDHReuRZaJphLSErAzvrOlbTMBA20zD4GGRKaOgUm7qhSZDBFkR+DCGGICQYqjKJA2t8JElCi5mAGRq+7y1YeQE0k0L8r7+s7mBqFjmTA6FoBFssF4pq200HcnswxLYXbEsLMxCbIC9kSk4gRYIgTueUKtBpAWyNRcVSPnJqCfMBcS6ac9tqOlYj0d3VifTZ94KDBpGouCf4qbKRXDXnFHqWE8hWBNmLzzlnF9gZXhq46AoUsAWbSKAFNDGRijemhmsYgZzNZvHII49gzZo1WLJkCU455RRcccUVePDBB11f88ILL2Djxo1obq4+n8mnmHrlDff37sVXjbuQMq0l2V9oqxqmGUU9kO3OYoZJIJi1F41MBFrceoA2T59T9DvH11ad5Bzk3m1WLmBgxiJQYhQAkK7hhkm99zv0I4aDlh6DpvZOqCYNI1HfnPdsJgmO6ECgCQolgtYbe4JUD4gmQR4V+SGC/T2VsYLyAQb27gRFTFDRjvy2YFM73g4sw+zeZ2Ea3tMsiJaFTKz8zbAtkKttNx1Ve5ERphdsU0Iz0W70wqgy5aMWaDWFLCnjwVsGgxbA1yiQnTQyyg4KSLEF6NB3N0SB8lg5cTYDmlgrbr/+5LKykVxNsiLIbJkIMi+EoJg0IFUOUgiaLaKVyvdBSi+OINMhq9g5NVhb2/TxpmEE8ltvvQVFUbB8+fL8tuXLl2PTpk3QSpiYp9Np3HjjjfjGN74BlmWLfu9TPfXIGzZNE+/cfxViSOGpo34C2WSQI0JDNKOoF5J9sx0iYQSIDFOfGJP9mkjtRdbkEYoU+446EWR1kiPIyR4rF7B17mLQQWuym0lU111JUxUckvoLtsSOBaFoUDSNfqoZbLq+S8fpIWtVhA40QaUEsAeAQKY0GQpGPdgC1vmUjjduF6xGILHP8hfmmwtXcOQFH8Icczfe3vSq52NRag4SrNSIWLt1HzVS3iO/um6g3eiDEiq8B5Om2RCJgv7eCpPJ1F7gvtOBKt5zNKyaQo52F2flMBkRHGpbsVDzTVasex4z7VCwRMfuLW+Ue9l+wUhxmU2VT4vQ7Tx2rkwEGYRYPuceGjYFnQI7DytplC5DI4VajQtb6RyZeG1Nb8abhhHIfX19iEaj4PnhCsfW1laoqorBwWI/vttvvx0rV67EihUrJnKYU5phb0MaACCN8s31wkvrH8Ax6Wexcd4ncP5ZH0KWiOiaRjdEM4p6Ids32yQVAwDkakgHmCjY3D4MUs1AiSVNx9dWlyc3gmz0vQPZZDF95kH5BgJSqromCO/87XlEkAGzaHV+W5xpR0Cq/DCvJq3IiZgywWaodKDmoqH9CWIUR37YkDWRyVX5PR1opG3XiHD77ILt81deCMMk6Hv1Uc/HovUcFMp6PvJi2LJwrKLd9ED/HohEAYkW3oeFvBfyO66vXbuhB4/f8XkY217C43d8rubUO15LQ65ZIAs1+7ZrdhCAsSOnsTlLAACD2/d/q7f04PA9LlvhenSsAvlA+e8gS0Kg5crPtahTYOchxYI2FKijJtp8xKpTkpKNKZCZyR6AQy6XA8cV/vGcnxWlcNb4l7/8Bc8//3zF/GSf6mBjHUinRTCwEuY5D96GI9mzdzcOfvV6bGPmY9nF3wQA5IjY0M00akGxUxIydAwwdiCXSiJQg6/nRBCQepFkW0v+zslD06TJjYLyia3YTXdgHsuCt4WXnK6uu1L8H09CNWkseP9Z+W1ZcTqmp8pbOTlpRWvMh7CCdtKKrIlDqQldzr6Rc+EWSIwIfpL/dhMBrUvQRglk53uSUn6KRTmcJiEtMwpTnKLts7CZX4wZu38H0zQ95eTSugSZGi6ui1NNYHPeI/gDu7egHYDQUijWozMsq670vq0lX6ff3I5upziOAOfqTwHrnoL+ax70170LdAAQjDTSfHvlHUth+7brmgqaqW7VWMt7SFvXdueCwwEA0p43axtLAyEnhgWynI6X3dcRyKIYKbtfjg5V9DlXZAlBYgUIvBQrU4YCjSq8jwRjlkBWko25EtUwEWSe54uEsPOzKI4wqJckfO1rX8P111+PcDg8oWOc6oz2NnxKP8pT3rAVgXsU7J1HI2Ym8bcjvw3a9jqUSQDMFLPCUu0cZImzo2iZyktRk0VEG0CObyv5O86Opkx2u+RmaTuGBOuhHbD9UdUqBfK0fS/gbX4xIiPso7TgDLQZAzB09wrpatOKVFsQipFWGLQIvsFz0OsBrctQKb5gm2B/T0rajyCXJbkbsskiFCsWhW81fwAHGdvwjxuO9FQQzeo5aCMEcpppgqB4n6Bk7HSP0LR5BdvbZh4MAFAGSrebPof9EZ7Rl+eNZRxHom72Ts/v7RAwMtC48uLMFdb67LW47jgRZCf3NhCKYi/awA66R833F9TU8CRFrfQscqwCg+W1k8yEwFcQyMkRkWviQSAzhlwkkCMtVj68nmnMiXbDCORp06YhmUwWiOS+vj5wHIdoNJrftnHjRmzfvh1f/vKX0dXVha6uLvT29uKGG27A17/+9ckY+pTB8TZ8jbKWn37DnV4xb9iJwH0h+wO0kiTeMOfgqy+R/M1epkWw+hQTyPYsXBUsMSZnG1Mgm4aBZmMQWnBayd/zASuaYkxiNzhdUzFD3wspZk3K8hZDOe8CuXfXFszXtyE566SC7STaCY5oGOhzz0N20opGP/zd0opU2xs0GGuFyQYg4AAQyIZcFEEORq1VCb2MV6oPwGb2YoBqKXAAAaz75h27FgEADidbPBVEs4YElRrRhYxrRVj1PkFxBHBLx/yC7YFIM5IIgkqUdnzZlBARRhaEoMBr9/WEUHL/coTMDAyutsCW09hIylb/PHE8pEemFvQKcxDLlI6a708Y6eHoq5qNl93XVDPQTAosx5fdT2EjEPTyE5H00LAw99ICnDYU6KMFcqQZqkkD2cacaDdMisWhhx4KlmWxYcMGHH300QCA1157DYsXLwbDDA/z8MMPxzPPPFPw2osvvhiXXXYZzj333Akd81Sku6sTf/kzC/QBnzpmOo6okDd8+roj0E2r+Z+PoLbiTVwIeR0LdPVDoQNjah3ciGiOi0WgFRhojEYbpUgmhhAlMhCaXvL3vJ1iYU6iQN63/W10EB1MqxXFCtsC2axwox/JtpfXoh3AtOVnFWzn7KXkoT3b0Da92OYOsNKKlDQDQgDDg9G+kbFu5OFYKww2ALHGnMj9CcZQoNCFvrXhmC2Q7c5oPqUJyPuQYNvQMWr76euOQDdj3TcpAlzCPItL8Gz+vlkK1pCQoYcnu6rYiljqr57HYiZ6IJssws3F94N+uh1CpvREsiMmYk52L3ImCxnWf20kjo6YWHJ/N1RFgkgUmHy08s4lcHzbFbn6+5WzSiaMKE7LRg7Cgn1rYeg6KJquaUyNAJUbjr4aufJ5w0TNIQceYap8bFRnwxUbNmXj1QlkxlSQHSWQKZrCEAmBkhpTIDdMBFkURXR3d+Omm27Cxo0b8dxzz+Hee+/FpZdeCsCKJkuSBEEQMGfOnIL/KIpCS0sLWlqq787jUwyxE+41D8VbK6U78Ed9Sf7n0RE4jQ42dLe5WtBsQUmFrNQF1YNf5GQwZC+pMrHSkxzRzsczPVj0jBf9drvX8EwrmsZxHNKmCMje/6bs1mexB22Yt2h5wfZQmyWQ073bXF97zeqFmGP7dacgVrQjNKU4FJNGIBgB2CBYokORp3YUmTFlaHRhxCkQCFqttn2BXJaY1o+cUJxe4axcaKb1CPbSSIk3ZWjMsCg1g+2IkgxyWW/3Vy7bg366FYQqFoMpfgaiSum27F869WCwRMdTxvvwLe1jmEYSeME8smrLznTCEkGOlWO1UJz12eUxRJBHphZQ7QsRIDL27ty/G4Yw0iB6YWkfQyp/36S0LCRSOfLvNAIyyzRsku16jKQZAONJIKswqOLIdYqEwcjxiq+fDBpGIAPAddddh6VLl+Kyyy7DDTfcgKuuugpnnHEGAOD444/H+vXrJ3mEBwaOt6shVW4ly8Y6oMK64SomUxSB09kAhAbvNlctuh3BYCPWg0/LNWbL3VSftWQaaCktkBmOtxxL1MmbwGT3bAYATJs3PMlKkyAoDxXUAKBIOSxM/xXbW44rWsZutpuFyIO7XF/f3dWJ7XOslacoyeHHgX8rm1ZESXEkSdh6L86aYOQyjfn91wvWVIoebIQQpEgQVBUTmQMNQzfQagxCDRZHbJ0WwTQMTy2CAYA3JZgjBLLTbnrIY7vpkLQXCbZ0upUUmol2vbekL/PywF60kST+Th+BX+on4FVjIb5MP4ST51a3AJ1NWpFOWoxV9ToHp8mHWoPrjqnmYJgEPD/894vMsu45fVv2bycLXh7EENsO1aRhyuXvRZSWg0LKp1cAAIQoRKJALlOErKat73OAbvPk5sOaCgyaK9qeZWLg7YYjjUZDCWRRFHHrrbdiw4YN+NOf/oTLL788/7vNmze7plD83//9n59eUUdo23XC8HAjumb1QrQQK8XgM+pniiJwBhNEAFNLIDs5u0LMevAZk+wj7Ea+i1576fQCAJAI76nAYrygBt9FHCE0tQ6LiAwVAqtUFshrN/Tg67feigCR8ZvBmUX5m01tHdYEIOkukAFgpjG8tPzIJQvK5tyzShwZylqmdXyka+n6tz/BGQoMujjqlCEhMB68Ug9UBvv3gicqSKT4fHIKov9pzoEKxlMjJcGUYTDDqS5ck3XNJDwK5CatD7lA6XQrEpuFEMlhaLDYbmvvP6yUxk9e9q/Y8p2zEPmXHyKELN558D88va9DLmnlqzPB2iLItJ1ioUnVC2SiZpEDXzCJnrHAKsTN7d6/vZCDWhw5tglpEgBV4b5Ja9kCJxQ3iB3lT5VpBGTYAjnJtXtqAc6VmGgDgMRGIWqNeR9pKIHs0xg4yyWmB3eD7q5OvD3rPADAP435RQ1BDC4E0ZSq6hjV6DgCOdTsCOTGjCBqCWvJtHnabNd9ZPCeLHrGi2BqG/YyswpsriQPFkNOceil+mMwTWCx+s+iIidC0einWis2C+HjW/L/TvSWF9OcmkSOtqrwna5ccrYxv/96wUGGwRQ/2HJ0GJw6tT/7WBjaa6U4sU3FAtkpiH6eej84ouGewCfLrlyYhgEBMsAOR0CDTVZmc26odGrESCRJQps5CD1U+vhcy1wAQP/OYlcHbsefsItMR+c8S7wvPOJovDztQhw58Gtsee3Ziu/t4Fg3cqHaLDEd33ZNrv5+RbQcpFGR02jLdAwiAmrg7ZrG0yiEjQRUvglZEgCjlL8eGT0HhVTOHWfsRkDZZJnc4NwgciYHlYuC8+Dmw0KFWeI+onIxhIzGDDI0TJGez9hYu6EHtz+9GbvjOXTERFyzemHNjTlYJyXC49L7zKCVp/Tba05HpGVUvh0fAkt0SHIOglhbi9FGw8nZjbTMsH9uzAgySe9BGiJCIfeIjUJ4UNrk5dC2KTuxLfq+gm0yE0FMKS9qRxeHXsQ8j4vwfFGRU4JtR0Aq79Uay+3AAGJoQRzZwfLROEFLIc1ZuedOBFlpAIFcz+t/NJypwiwRQZaYCMJaY9ozNQJOk5BQa+kVnO6uTrzy1mxgs9UiONZaOv0BAGQ5B4GYADscQY60Wd+vEq8skPt2b8csYoJuKj2W8HQrHSm1byuA4/PbdU3FQdkNeKPpFMwcsf/hF38be//rGZi/+SK6n16D66T/wjfFL+MTH3y/63mn2I4nfKi4q6cXGDvFwkttzGhoNQu5RO7tXm4OwuktJV6xf2DoBqJmErrYAikZrGipyuoS1BLXctF+wRiA8gKZloeQIBHotAjOrNzh0LqPFAtkXWhC1ExZFike/MAnEj+CPAVwomlKfDf+H3czlPieipZB5eDsfCLiNTdVtgRiKW9FwlnL0dlUvKaxNCKmXcQYisSQM7n85280uGyv1UWvDAoR8jnnE00mFUc7BmE0HVSwXeMiCBjlb/QrpTuwXhvuoulW5JQVpiGmuQtkQ9cxQ9+N7eFlAAAlvtd1X8BqraraPq6MaJ3vag2+rPWk3tf/aHgopSM/bBgBffInB42Kk/semzHXdR9KsM6lbIUGD7mMdY4Rblggx9qsCLKeqtysI77XsjMT2+aU/H1bp+OFvK1g+5aNLyKMHKiDPlCwPRptwnNzv4iDzO34Wu67WEE2V7Sq02xnmkC4NoHMCdZn12tw3aF0qWTubTo8HzPUHfvtCmcqMQCO6CDBNsh0EFwFxyjWkKDRlSPIziRGLuNzzspxZOgITEYEj/IpFqZhgIcKlLiPINACjmjIpRsvzcIXyFOA25/ejJyq43PME1hBNuNzzOPIqTpuf3pzTcfjbOsqymODD1PNQDVpcHzxzJQSLBEhTaVCJk2CYtJgWRZZInr+O000AaUPKZcueg4KxYOZJIG8Z8s/AQDc9EMKtutcGEGzwo0+1gHDvn0pJu1a5KSFOtBqDEB3aRayr2cLAkSG3mlFsfVkeYEcMtPQ+Zg1BrvpgDLJOej1vv5HomsqWKIDTPFDVeeiCFX4ng5kjMRuGCZBc5kaAMa5P1YQyLJdCEyNEMgsH7D8iz20m870bQMAxKbPK/n7cHM7siYPjPJCHtj0OwDAvBUfLHrNedtuAAAcRb/jqcmObrv9OB7a1eKkWBg12Lwxeg5Kidxbs/UQxJBGv8c87kYjMWCtHjDhNihMCHwF72LelGCUuJZHI9idYdUyPueiGkeOicBkxYotwDVNAUVMoEQEmQ5aDhwjG480Cr5AngI8l7sA24SPFnQD2yZ8FM/lLqjpeE53MNpji2hKzSBHhJLLI06UTWrgbnPVQtQsZHAghEAiQsMK5Ig2AKmExdRIVFoE46ECeTyI77KKY5pmHVqw3RRiCCMHQ9NcX3vN6oWYTsUBAF9TL3ctciLRmeCIjoF9pR+AfdusMYRmHY4hREBn3cWGqioIkxxMIQYA4GxPVX2SBXK9r/+RyHZBFCkR+TGEKEJmFqbh3qnwQIZO78EgiYJmiyv3HRi75a9codBTtr3WKT5UsD1BxcBKldv06oOW8G3pKC2QCUWhj24Hny68TkK7X8QWai5ap80ses1K6Q48ow9bK1a0qsslYJgEoUis4nhL4bSJNtTqJ/SMLkEtIZBDnYsBAPve3T+dLDJDlqjkIm3QmBBEs/wzmzcl6B4EcjBiN2wq0wQroCchcU0AK4IjOjTFXSTn3TDY4u+ADVvvlY5X17Z8IvAF8hTgAuF/sVY7FrppCVTnRnW+cFdNxxNtgew1smhVCJfOa2LtB4AylQSyJkG2l+skEgDjcSIxkTgWU1rAPa8RADRK8FSBPB6o+96BYRLMmLe4YDuxBWg65b68193ViS3zLgIA/M08pKg41IFvsR7sQ3tK5xk6NnNt8xYjTjWBzRVX8TukhiwhQkRr+ZEPWOe2PskpNhcI/4sX9KX5n71c/2s39OC47/we8659Esd95/euy+JKznmwFT9UiRADRUzkKkQ/D1TE3D4MMaXbvDtwdn1ApfujYqfxOHm4DimmBaJSWSBTqR4kEAJv55aWIsHPQEQezv2XpQwWSK+jt/XokvuzsQ70mjGYJqCZVEWrOqIkLWu7GptyCHYXPFOtfkLPGTmoJVIL2g86HACQ3vXPmsY02eTilkAWY9Ogc2EEKjT3EEypwAnFjWDUiiAbZRo2hc0kND423OGwjLuIahdWUiUm2nzYWlGQEpXP44nGF8hTgMs/eAxyVAAUzLynpkQF8IkPvr/qY1nV0lbCvVeBzGhZSFTpWSlrR5CVCh1+9icoPZcXyDIdaMhW2vHBXstiKlza1slBpwVPFcjjARt/D/uo1vyDz4GyH+LpePkCsDm8JRoe/Y8P48VrV5UsDgq3z7WO1bej5DHMgXeRNXm0TJ+LDNuMYBmxkUlY4pkJWg8Pp22tFzvE8eTyDx4DnbAAANVONyl3/VeTsyzbn40qEfmhAjEAQDruPqk4kAmrfchw5Vdw+IAlkLUK90dHINNC4bUi8y0Ia5XbfQvZ3Riky4t1KdiJVn04ivfua89DICqEQ04qub9jVZdEAC8aiyta1dFyEllSWZy5fgY7xQI15CCzplzSqrB1xjxkTAHoH3s60mSg2s06wi3TYfJhBM2caz61aRgQIcNkK38HQiBstYB28TnXNQ0RMwMIzfm8eKlMR1mn+yEpcR8JNFlBHKfxSCPhC+QpQHdXJ07sBDLgQQiwjj4FJ3aaNVWxK3LWyhWCNev2Aq1lobgIZD5oRdkatZlGLdC6nC/4UGkR7CTl8JZjaJ8lCEtZTI3EYERwkxRBjma2o48vtqBzLIZyyfIRBTPTB9WkEW1yz2l0lpSVwdICWUxuxR6mE4SikBPaENHco9YZO8LB2jZVon1um5MskLu7OnFwyJrkPKSdhCeY1WWvfydn+YvMYxVzllV7adTpYjYSZ6KQKeOVeiDTrPdDqbCCI4YcgVz+/ug4Nzh5uA6q2IYmY6hsxzMACCu9SPHlx2JGZyGGNBJx6xpIvvkcNJPCQStWl9zfsap715wFFrrrKo4Do6aQpUIlf+cFhmWhmAxQgy0lb0jQ6GJhuO4fe7DNnIZFvetx1i2/rFth60RhpC1RGWudAfBRsERHzqVoWFEkMMQAYT24SRFiN2wqLZBTQ32WTgg0D7cAz7lPXFTJLvxnSqS5NFkTNy3tR5B9xolpVzwKFlYu4LFX/Cc6PvXLmo4zspjOq0Dm9CxUV4EcAwBoDeoVXAuULkHNC+TGbKWd6rdyDsWW4tzBkRgeKpDHA9MwMF3bhVy4OCfSqaCWyqRYAACd7UecRECVWbKNNE+HbLJAovSDr0XeiUTAquzXA21oNuOuERjZtjzi7fw8Mb/kO/nff8+CjwIAFsxsx79c/3DZ69/JWb6Ieb5izrJqR35KRZAdP1upnFfqAUo6nUSUZGCEO8ruJ9iODoZUPoKs28vXrDhKYAbbECY5ZDLu0TvTNNGm90IOlh8L63gh77JaLzftfQnvsocgHHV3wunu6gQJtaGDTbuu4jhwWhoSXbtABgAJXE2NjXjIMEelCTkrKQIURJGt6MLRkGT7kTV58GIIlJ3OmE2UXlGQnBbdnLcofoYEQbs0Hknauc90qHVYIJepxVAVZ6JdfB+JNlmrLGa28e4jvkCeIgwN9oMnVlFTZgwPLMn2dNVNAt5j8ZZbfhcwIso2hQQyo0vQ7I5AOhsA34CttGXbzzdapoIeAEymcgXyeDCwbxfCJAfSclDR7wRbgCrp8kvHrDyIBF3eMopQFPqpFrCZYncKScphhrEPaswW6aHp4ImKZLx0JMNprepU4VM0jazJg3hoqDPeOM1qqAqNAgArZ/lJbTivtFzOspM7SPPF17doV7orZSrdD1QGdm8DALCx8is4QcejvEIeu25HkPlRXvJUxEqhive6i7pEYghRkoFZoqPfSMLTrOsguXcLUolBLFA3Y2ha5TQ9VWhG2KhcYyLoKchMsRVoNSiEA6nBt10s4d5w+roj8CZ9IQ6i9oAQVHThaEQYaRAJyjqHHMvATMpNIFtil/IokHN0CKxa+rzM2mlVfLh1uAV4mRxk3bmPlJpocxwSZhAk13j3EV8gTxES/cM3SClV+5KnnLNO8jiJQIR3gay5JP4HwtbFazaAV/DaDT04+5bH8MrXjx7TchpjyNDsfDaDCTVkK20930WvvEAGK0IkCgwXG7TxYu9WqyhG7FhU9LuALby0CsIroAwgy1b2VE1w7QjKxQJ5z7a3wBADTJtlM8dGreKiIZdues54RtpUSYQH1QBFmqZsCWPa5YE2kss/eAxUyspZdoqr3HKWNcn6bHSJIr1A1JrIqCW8Uut1re2vpHqtLnpCS/nrj2E5y0u9wsTG6d7Ji4UC02l3nxxw//v291geyFyze0dNAGieuQAAIPdvxXt/fQYMMRA+9OSyrwEAXWxBzExWvIeIRgYaOzaBLNfQ2MjQdQhELWiyAlguHGu1Y620DQCSyZZ34WhAeGUIKToGAODs1VrJJbDgdPx0OoBWQmbC4F06mkp2PYYQbQNjH0/NuQtkzY4g0yUiyACQpCJgJD+C7DNOZAaGuylViryVQ7Fz4RJUDILH4q1ylbG8EIBi0pPebc5ZTjs/85AnU/tycKYMzbYMMrmg5fpRIQdwoqHSe62q9dFLsqOxHxrlKpDHg8zutwAAbXMWF/0uFLOEl5ErH5UK6XHIXOW2tVlhOmJqsYXQ4A7L4i0607KZE5osgZzuL31OmDnrBh5pGi52smz+Jl8gE1sgs5q39vDLm61VgxeNxWVzlnX7wTbaPQEYnigY2cL7TT2vtf2V7IA1yYqWafOe35eIoCrcH017lYIfVdAaaLbbTQ+6d9NL7rMEcrC9tMWbQ3NbJ2SThTG0E9Lm5yGbLBYcuari+EmwDTQxkRoqb9MVNDPQubEJZJXwoPXqBLJkP9PIqEkeG+tAGiIYWCuvHLSyLhyNSEAdgsTGAABs0HFEiZfcN1/o6VEgK0wYouGSz2znCwdj08AIlTscaoptHVsiggwAWToCTi097snEF8hTBGlEu1G3C8QLTlewDNNkextWvhkJpgS9TOK/lwfAeOMsp430iq11OY01ZOhORTQXAkMMKNLki6SRcLlexKnK0VViF19JZXIYxwOj723IJot2O2o1klAoBs2kgApLbjEjAU2s3HRAD3Wg1RyCpqoF2+W9bwMAps07DAAQbrPytXNDpYUckeJImSKYEb62k9mJcCTEjhxzHgQyACTffw0AIBYUyuYsa3mBXBxBDkes78mUCicy9bzW9le0uHUOtZTpoucgERF0BS91044gC4FCgRl12k0n3BvcyP1WNLupwlgIRaOXagOf2YW2/lfwjrC4yGGmFEzYmjAmBtzHYBo6gmYWBh+peLxyKJQAqkqBnO9COEoYOi4cT+lWR87njSPKunA0ImE9DoW3ggSCXbuhungX560CBW+TFL1MR1MjY61SR1rawdnOKnoZgayrTqpW6UBajolCUBvPCtYXyFMELTnchUYv411Y8Th2UY7EWRdbNlt+6c80DAQgAZy7QJYw+d3mnOU0Y5RXdC3Labwp51vvEtu4P9dgPs8BpR8prrytEwBQdgRZnuB2yUJyK3bTHSUL7CiaQooEQWT3wqVsOo4AkYGgh88Y7QRLdPTvK0ydoIfewxAiCNtFIrF2SyBridIdnWg5gTQZZbNFiQ0hkJ3UCr5Ci24Hx3eXq2BR6DRlYEs82Gj7e6KkeMH2el5r+ytUajeSCORdKsqRo4JgKrQIhpqFatJguUIfWafdtFGm3bSR2AXdJGieVrrN9Eji/HRMz7yFg4ytSM84tuL+AMBHresnM+QukLPpBGhiggiV/x7l0AgPtsrGRk7aIBmVe+u4cNwnfAwA8CxzQlkXjkbDNAzEzCQ00VpxE0IxAO7NPbR8oae3CLLBRxBy8VU2M4NQTRqhcAyc7axSrgW4YQfaWJcUC4WLIWg0nhWsL5CnCGZ6+AZp5OI1H8dxm9AEKzInV2gRLUk5MMQAWPdIg0SJnrvyjRdsrAMxkgZFhr2ia11O4yDnCz4owRHIjXVxR7V+SIIH8WhHVZQJFsgt0g7ERffl5zQJgVbcJx3xPmvFhA6X95kFAN7OAx3au61geyi9DfvYYZePcKTZcrxIl37Qs0oCGbowAqZSjWHzx9gTUNGjo4pmP0T5SgLZjiCPthdzKPU9sTFLtDl2kTyU/W7peqxw2X0Yolo87at48FInag45UqJNL8sjjhCoMh0gmVQPBqhmUGU6+jnkAp3oNK0JYvPSUyvuDwCBmGUfJ8XdBXImaa0GUWLM0zHdUGkBTJW2lKqdYlEqtaC7qxN3XmGlkXQvCu834hgA0qm45XUfsM6zQMSu3XDx1HYaGnGixzQXIYoAkSFJxfc3WhpEgoRBKGq4o6jsfh807OYuDF9aIOtCE6JmYz1DAV8gTxmobD8GYC15kgqWQeVw7ITMoN3dpoLwy6WthyPlsnQCADIV8JQbOZ78xykLsJx6B4DVEfuX+sqal9MEU4Fp+znSQuO10tZ1HS3mEPRg+SYhwPBDQ5mgHOS1G3rQ/a2HMdPowT/TQde81BwVAqu6T85SA1bHLyd6VY7wtLkAgEzf9oLt7eoupENz8z8TisIA1QQmW9qwXtCSkEZV4WuM6NkOcTxxIsGBCq1mHXR7El1JUDsPNk4o7VKTLfE9XbN6IQ6jrL+1ZDJ41+zY75aux0pQ6UXSwwoOAKh0ALxe/nsgWg4yigUyACSoZrA5dw/ZoLQXQ0zl6wQA9hLbk9YkuObpPk9545EWa0Kklmn0kE1aS/J0YGwRZJ3iq+786USQ3XJvQ7awNEathDQ6STulhQpZ323ItgyEy/PfiSBzLpPd0TgdTVMlfM5ZJY40ZQULnDqXcnaXhmp9Z2wJP3UAMMVmBCDnXXMaBV8gTxF4uR9JuglpEgRx8S70grNMQoWsG6VcIcXCsYUjvPusVKFFcBUeAONN556nESY5rDeOAQD8hv9QTctppqGDJypMuyjRyedSGiiCPNi3BxzRQSKVI3bOQ6OcRU+9cIq3/jX3U1AEmGfscO/gxoRcK6gBQLKXc8Wm8t6uANAywypOUgd35rclhgbRhiHoTYU2cym6GYJcWmyIehIKWxhB1mnRsx2iV2pxgHAEcghZGHppH+eROLZwlQS1aadYuD1UJSYCXis897u7OgGGxwbjYDxtvA8tJIXsWT/er6JzY6VJ64ckVJ6gAoDGhiBUmKjQWi7fnGg0GbYJAcXduSiq9iIrVr4XrN3Qgxf22RN/mDgv+4in4spoqxVBNso0enCcFbhg5bqIchiMAK5KW0pNcnJvS5/Dghi0nCzGEFiqJ16v/9SgdQ/kI9azmmYYZEzB9fnvdPwcncfuBm27YmQTxe4SvJJAlrEmO0LQ6XBYOYLMlqhlAADKbjqUHCxf6DnR+AJ5ihBQB5Fhm5AhQTBjEMhOG082at3c1QodniR7qXZ0C9SRaMzkNtOQFRnT//Zf2E7PxoLuNQCAq98XqOmB7UQjYFfjcgFLMCnZxri5AsNd9LhYZfHoLJ1rEyCQneKtbubPAIDj6Ddci7cUJgJRdz/3ZDtPONJa+cEfbmqHZLJAcvhBs3ebZTMnTD+kYN8s34qQWlpsBI00VC5WsE1nRPB1bNVdqwOEI7AYYiBTYVILALDzu4PIlRfU9oONd4kgq0wYol6YntO37Q0cYmxBasGZmHb0BWghKSzW/1l5TFMEVVWsFZyQN4GsM0EIZvnIGaXnIFOll6clvhVhvXRBq67raDf6oYYq3wtOX3cEvsf8CACq8gXmeQFJMwiScxfpiu3Ny4XGKJDp6gWyM/ln3dwbCEGaBECVqXmYKKq5/qW4JSbF2HCHxAwJuHqh551Qgt4EMmdH+3MlGjaJegIya/2eY3noJgHKpFGamrMSVXqlmQ5ZK9bpodL1H5NFQwlkRVFw/fXXY8WKFTjuuONwzz33uO67fv16nHnmmVi2bBnOPvts/P73v5/AkTYeEW0IMt8KiS6/NF0J5yISo9ZFp5bpjgMActaZnVcSyJO3dPLKurswx+xB+tivoLnTihi6tR6uhCOQneI21hbIjdQpMN1vFaMFK3TRA0YI5Alol7xSugO/0Y6GYTvilSve0rgIAi4WQ8CoFqsVsJqFtIHNDDu9JHZaFm9Nsw4r2FcR2hAzisWGaRiImGmY9rJjfhxM0LMdohdqdYAQzWy+KC6bild8H8cWjiEGsuXyzzUJhknAuRTXaFwEQaPw3N/xxwcBALOO/ygOPeFcZE0eqb89VnFMU4WBvTtBExNU1NsE3ORCCFQQyKyeg+oikDWXdtNrN/Tg0lvuA09UvLJbqzjJWindgXXaMVBNq3C2muLKOBUFK7kLZNUuHBfDYxPIJiOAh1LVaxx3Ba5M5DRDQqDH8NysF9Vc/0rSEsjB5uGJWI4KgnH7HHbwKxD05iTidMqUSwjksJGEylvfJaEoSOAB1f0cNjVrUsO5RJD5sCWQW3/3WSDVOCK5oQTybbfdhg0bNuC+++7DTTfdhDvvvBNPPvlk0X5//etf8eUvfxmXXnop1q1bh/POOw+f/exn8cYbb0zCqCcf0zDQZMahByyBXG5puiJqDrLJQrAbfOgVIouaHWFmy9x8dDYIcZKaaaSzOcz/5w+xlV2Aw076KJpbZ0AyWZiJ3TUdz/ELduzRnE6BeoVI+0Qi2zZlXjxYObuiWZfHP8LPxjpwMLULBIBiMmULJXU+irBLBTUAkGwfkmYAgseK7CTXhpA0fOPV+t6BYRJMn3towX5msB1NSEEelQuXzaTAEQ0YJZBNNgARsmt76mqptXlBwMxhiFhjy3kQyPSIKFPZ/TUJMlgQqvSjQuejVqX7CHHWsn09/kkfinnzD0E0EsXrwaMxp/f3MHWt4rimAkP7rPxrvrnyBBUATC6MAJGhqe7CjzEkqFRpcYFQO0JEQio1XAfhRCIvlh4GACzR36i4EsHGOpBCADQMSCZbVSFzhomBV9wbPThFoYEybau9YDAieLNGgVwmiCPRwTEFlurFsAOM9XO5SYqeDxIMC2SJCrh2vzPtZzvNMJ7G4kxmlFGuWKZhIGqmoAvDkx2rYVKZZ7wtkHmXIr3Xh6wxiYNv4fE7PtcwnumeBPLJJ5+MeDxetH3fvn045phj6jKQbDaLRx55BGvWrMGSJUtwyimn4IorrsCDDz5YtO8TTzyB0047DRdccAHmzJmDSy+9FEcffTTWr19fl7HsbyQScYhEgRlsh8qGIXi0eioFpWWRIzx40YmMlo8gOykY5SpjTbZyhGS8ePnx72MmemGc9FUQigJFU+inWsFmahPITrcgp10n7wjkBugU6GA4XfTaKz+gnQILo4xFT724av4+LKR68KY5Gx9WvoEH9ZPdi7eEKHiiuvpLM7l+JKiY5/fOCtMR04aLiNj4FvRSrfkJgoPTundwlCVcym6t6uTKORAuaPlgl8m/q4ZamhfIioIgkTHEWFGYXDpe+X1GiIFyEWeiSZBJGfcDMQaO6HkXlL6tmzBX24r+OR/K76IuPAstiGPrhucqjmsqkO23ct3DbZUnqAAA2yoyk3Jf4ucMKd+9czRMxFrtGxrRbtqJRJ7B/AUAcBK9seJKhOML/KB+Ms5Rbi5/fY4ixzYhUK7Rg10AF454c/ZwhRHBExWG5n2yZbg0WRmJTIfAV7LamwDYWAcyEEAAGJXclrL9kEwWAdveDQAUJuRq3UipGUjlruVRBOzvSh/V0TSVioMlet49A7A6HJZrAU40GbJZeqKt39yOj/7tImuMBDhXfwrd6w6DfrO3wtLxxHUqsX79evzhD38AAPT09OCGG24AzxcWCezevRuMx9lIJd566y0oioLly5fnty1fvhw/+tGPoGlawftccsklRe9LCIEsV5ebNFVI9O9CDAAbmQZtMIJAuvYLnahZSBAg2HlKlVpE67aAHt0CtQA+aD1EZQmcywxyPBiIJ7D4nbvxnnAYDjrmnPz2BNuGoFTbMo6cs1vv2hHkoNNKu4FSLKjMXgwhgiaX5ayRCE4F8jgLZCmXwfFv3owetOEzwnewVQHuCl2Fa1YvLJkL7thBpRL9aBGKhYYgDyLNxDy/vxbuRGv8d1BVFSzLIprdgX5+NkZnifL2gyjZ14MZsw/Ob8/EreVjdnSRkT1RkjIp8B6rw8txzeqFCDyRxEZjPpbRW/CCsbSiSMmmEuABZIRpQPqdvMdxOUbaipVzYKE0GQrcH6r57ynej5ZAGDv/+HO0mASzV16U32fRyn+B9Lc1iP/1UeCo1RXHtr+jDlqTq0qNORxowZpkZ9NDiDaXbnzDGhJ02mV52ukAObAbWGB1plwp3YHrmQfwIfplUMSKRD6lr8C3tYvxqss4urs6sRY/w11Pb8bueK7s9TkalW9BJPu66+9NKWlFpV1y2T3jNDaSMgh48JgGRjZZcRfIChtGWHV34Zgorlm9EM1PDIAQQDYZPKKfiOku1z+dG0ScRDGdkPw2lQkhopR+thEtBwkCvPqIuHU0TQ3sQwQAFRwWyArhy/rBE12CTNiSPiznsD/CFZkf4yz6JZAR5+q9wU/g1x7HOl64RpDf//73g6Zp0LaRP0VR+Z+d/xYtWoQf/ehHdRlIX18fotFogQhvbW2FqqoYHCxculm0aBEWLBjuwPXOO+/gpZdewooVK+oylv2NlN1mmo9Nh8GFXc29vUDZ1dKiLZCd2bcbhi2QhTJ5TcRuL+olN7IeOFXAqf88CjPIAN5c9Fmr6sTGaj1c283QydV1OgKJQsDK2avwd5pI+Fwv4rS3pUzerkAuZ9FTD/72wBrMNncjvup2/H7Nh7D1Ox/Ci9eucn340gG7UU3cpWBOG0KO9R6NoqOdYIiB/j3bYRoGZmi7kA0Xt94NtljjyQwWLvHlbAsrLlwoYBwfaamCX7hXnOYFG6lFAIDnmeMruq0415UasAtrXRoFjITX08jZjyulTMSZGDKUMlEnxp4wZJOWg0HrjvV4nTkM8+YN359bmluwUXwfZu97FqhTKkojY6Z2QzEZxFq8FenR9mqdlHb/3gRTynuvjyaYbzc9vCrGxjowl9oLilROZxpJd1cnXrx2VcXrczR6oAVRMwXT0Ev+3mqyEwQZcR+uBWL/DfLF0l6wBbJYNg0w7NlDfDzp7urE7iM+AwAQiIZHxQtcr39OHkSaLpS7Ohty/Ry0lnUt9CwFL4atjqajOmWm7eLAkfdClQjlW4DriutEe1NCRBIBmCAFqT2vJyYumOaGa/i3ubkZt9xyCwCgs7MTl19+OQIBd6/bsZLL5cBxhX9A52dFcc85GhgYwGc+8xksX74cp5xyyriNr5GRhiyBHGzuQJqPIkhkSJIEQaj+BGPsamlBCFpFP5UEst1CWgi5C2RaGO42NzJfajxwcu++bj6IOXQv9hpNuOZvTdDm9uRvMmqoA62JZ6Frmud8LAfH7YGxBTKhKGQhgExyK+2RhJR+pNnKLZgBgONE+3sevxSYLZtewvt67serTadjxQnnVH4BANZeNsymSgvkqBHHPvFIz2MYbhayHRTDYRrJAa3Fba4jdrtpZVTTAzVtjUMIF4pyyl4el7w4R3iku6sTr75gAHHgQ4cEcXQFkSLZAteMdAC9gJ6rLJBFI4N+qhWzjB6oZfandQlaGYHM2oU82eQg+t/7O2Zr27HloC8X7ScffCZaN72InRv/gFnLVlUc3/4Mk96LfqoZHS5520X726tvcplIPj+iOdFonHbT6ogOkB9fqOCwv2/De8Z0fEb9PC6in3ONRNYDEmgFQwwkh/oRaZlW9HtaTSNLxr7C4vjtK1UIZFPNQjZZ8GXu9Trn3jVuolnAx/P//vZKAUtdrv+AGkeWLVzRKvc5GD0HhXjXBISikCZBUHLheSklrGCBEBn2+VYpHkwZgUxpMlSwJX/XERPRmrFSex7ST8ZF9HNoJwl0xMa42lAHPKmDz3zmM+jr68Prr78OXR+eISqKgjfeeAOf/vSnxzwQnueLhLDzsyiW/kPt3bsXl19+OSiKwg9+8ANQHm9IUw01b3nVgT5nyTM5CEGobO0zGkbPQaVEUDSFNASQSpFFO6IqBtwFMiVUjpDUi9PXHYFuWs3/PJ0M4U18BPI6FuiyolxUrBPMbgP9+3aitbM4ilgOzbHBG2EZlCMiqEluhDKSmD6AHZGDK+8I6yaYAweUK7CogbUbenDvUy/hq7nbECVZxBHGwR/z3mpYsIVXqeimrqmImikYordJAABEps8FAGT6t8NQZUwDEJxRLBaa2u3WvcnRAtnKwwvFCt/TmfzVuxMha1s1mh7ErmRbDLJNlrj3IpADZhZ7+AWA1FM24kzrMlSqtP8uAAjh4Ur3nW89hyaTYM6I9AqHhSecD3nj9ej/yyNVCeTh8+h2fFP8Mj7xwfc3vJ9yQN6HBNMGr3dfLmhFAZUy34NoyjDZ0gGqaMsMGCaBkbaeA7KqYfnr30SGBPE5/tt4SxGqSpeoBcYWS4n+3SUFMqsmkaPrIJDtFAu5CltKSrXrasrtZHeNU2QZHF92z3FnpF97ds9m1/1CRhxJrrB9uMlH8gWfzKjOiYyeg+JW6OlChgRBj7KNVVLWczQ44l6o0QIEzT2HnjJkaKS0QL5m9UJ84fEvIadZ2vLr2uUQWRq3NEBjIU8C+aGHHsI3v/lN6LoOQkjeToYQgiOOOKIuAnnatGlIJpNQFCUfOe7r6wPHcYhGi7Nmdu7cicsuuwyiKOL+++9HU9PY7GP2Z8x0LwyTINY6A3QgBsCK6KC9eoHMGhJyjC1oCQ+iVrgRqRnkTA5imdk560RIJsAreKV0B9YwP8fZ9EugiFky905otnJaB/duq1ogO+00R7belSgRdKW/0wShKCqazTi2efRgBYAcEcpXIFeJE8VfYz6EFfRmUAT4rPZ5nLxTQ7dHTetEapV0cWV8fGAvWogJEvLWqQwY0SxkYCf0TBwA0Db3sKL9WE7AECKgMoWG9XrWGke4ufA9nfOg3gKZcx42UmWxq9qfR7Rt/Uy5fDTbNE0EzSxy4nRAsvJD3aANuWwE2fmetMwg2revxyZ2KZbNnV+0X3tbG17ll2Punt9ZaRYeghmjzyPLE9b6ezeySI6q/dgXWuR5f94WyG4tglVVsRxUXAQyxbAYJGFQdgfIPzz2I6zWN+Hto27Ek2f+S5Wjrw3OtgXNDJVuN81raUi0xxbHZaDtnP9qGhsRTXLtQpjfxw7ipBMDaK7huVlPSHIXkgiAMXWY/e+47hc1EtghFq5oEbtxVSYVR7S5sMiNNSRITHWdDHMlbGN1uyFMeIS9nE4LYBX3tEVKV6C63Eeca/l2O/e9IyaO62SuGjyFXO+55x58+tOfxsaNG9HS0oLnn38ev/nNb3DooYfi5JNPrstADj30ULAsiw0bNuS3vfbaa1i8eHFRQV48HsfHP/5xhMNhPPDAA2ht9R5JmopQ2T4kSBgUw+ZzAnNJd8udcnCGBN2eZUpEBF1BOFFqBrkKyzaOV7Dq8gCoJ44LAIEJzaRK5t6F2q1Zd6Z3W9XHd3KyR3YWkykRTBmT9IlkoG83GGKA8tBFz0EmPKg6jr/Qy9Pa9kPm+xW9fEcStO2g9GyxJ3Gq38q1ZKPFkSo3wrE2ZE0eJNUDo/9dKCaD1o7iFAsAiFNN4KRRN/tcHKpJF62UsHYEWa9zkaZoWzXSLqb/I3GuKzHaZlWKVxDIuVwGPNGg280jjDJONYyhQCsTQQ5FrXsvs/MlzNR3YnDuGa77ZhaciTazH3vefLHs+Bxq9YSeTEzDQIsxkM8H94LgCGSXNJ2c7TVPWPfoX5JuAi/1Y0fPbix/67vYxi/CIWd8roqRj42g3awiFy/dCU0w0lDYOghkvnqBTGtZyBWeUfnAUqp0w5WJRMjuQT/djt3MTIipbSX3yWZSCBIZCIxK+RKtcymTLP4c5ZxQ3JDoMPhRDZvM7CAMkyA8IoKs0wLYMg1caEOBRrlPtGvNfR9vPAnk3t5edHd3g+M4LF68GBs2bMCCBQvw1a9+FY8++mhdBiKKIrq7u3HTTTdh48aNeO6553Dvvffi0ksvBWBFkyXJynH5r//6LwwNDeE73/kOdF1HX18f+vr6kEo1jpPARMJJA3nLK96u7JXTtV3ovClBdwohiAC6gnCiPNx8nG5z2gREkK9ZvRCzqH4QAtylnVnSqqilw4pwySOWsryi27m6nDgczVGoANhJbqXtELe76PFN3m8wCuFBl7HoqZZhL1+rwNerl+9InJuvMcqDEwAydotVIVZFPjsh6KdbwWX2QEhuxR66A5TLqkeGbUZQKWybS0lxJEmoyKbIaT5Q706ETvMNRq18zei2QBZCTcgQ0bWTloPz8CShNus7KrM/Y8rQaXeBHI5ZE5mD+56BbhLMXflR130PPuF8KCaNfS8/XHZ8Ds555DSukE2m6vNookkM9kEkChDxHoUMhGMAAMMlkq84AtmtExyANNsCURnA2w99GU1IIvQvPwQo2vvAx0i4xZqQq8nSAjlgZKDXQSA7tR+a7H3Fi9YlKBWK0xhbIJfqGjfRhOW9SHLTkQjMQatcuqFVvN+qO6JHraKxdsFnroTQ50Y8272issWdMok0iCQJFtTv6LQIvqxALr8S1ah4EsgtLS15J4n58+fjzTffBAC0t7ejt7d+vbOvu+46LF26FJdddhluuOEGXHXVVTjjDCsicfzxx+d9jp966imk02l0d3fj+OOPz/9300031W0s+xOiMoAMaz2ohJA1o1QztQlkzpShM3YhBC2CKWPdAgC0moVEyl90+QhJBU/letDd1Ykdiz4BAPizuQR3ha4qqgKONbchZ3IFrYe9YtrdgkZ6aqp0YFJbaY/E8WANtnprUgAAKiWUteiplmEvXyunzIuX72gEMWB9R3JxikHOLqALNVdX8Jli2xGUe9Es7cCQ6O5RmxPaENEKH5SMEkeaKn7A887kr84+2E6hDevBm9Wwo9diKIocCYB2aRTgkLUFMi1GkSUiqDIRZ9ZUoJeJIPMch7QpQISMfzKHYf6cOa77dk6fgX9wXZjR80xBYxHX967DeTTRDO7dBgDgmrxff45AdkuNcQpAHe/10azd0IPdOQaHqJuxKvVrvNh8DloPeZ/3QdcBp/jazJReZg+ZGeictw5u5Rju/On9fms1WSmfYuHYN9YaWAKG3ZNe+frROOuWX9bc7KJF74Mc7IAam4/pRi+kXPFnTQ9a+eZspDCNgrHdf2Q77WoklhNKdUYLGhtBYFRfBUaKI0UKv0uTEcHDXSAzhgK9TAS5UfGUg3zGGWfgK1/5Cr75zW9i5cqV+NKXvoRDDz0Uzz//POaUuSFWiyiKuPXWW3HrrbcW/W7z5uFk9VdeeaVu7zkVCOtD2Bew/C/FiCWUNQ9eqKUQTQmmvZSnUiJ4F9NxB1bPVkz8dxwujAnyCp5rWjemO6++CJH2YiFEKAp9lBVNrBrHU3NEioXGBsFLk9dKeyRy3Eo/iHnoouegUgIYo34RZMfL901zDuZjNx4t4+VZDquCujiqpieth0O0tbpluJw4HXPiLyFiprEv6l4opgfa0RyPwzSMfMSYU5PIlcihdCZKZh1bdSuyhACxHjaCh66YjrAKhmLYR4fAVMiHd3yP2aAlqKkygpozFBhlIshrN/TgFFhiV9YNrN3QU3Z5dHPzSVix73ZsumEZ1ohfL1t055xHe80mzCBDMAHMpvrGzYmhHqT6rIhfoIoJKssJVudEFyccJ7/dSS8YiZOn/RjZC5oykTU5fLH/THytwvdQb3heQMIMgmSLXWdUOQeBqIBQB4Fs/w30Kmw1WT0HmXH3QAas1RcAUGoUyPXKl8+k4oghDTPcCbZ5JuidJnZuexNzD11esF8ubt0DhVFpZrzt/qOUFMgyzCojyAYfQXCUK4agDiEzKpfZZAUI5SLIplp2ot2oeIog/8d//AfOPvtsxONxHHvssTj//PNx8803Y9OmTbjhhhvGe4w+ZTBNE01GHJpd0R+KOube8eqPZRgQoAD2LFOjRXBGeeHH6jmoLgb2DkE77aNS8VC9oAbeRhoiIm2zXPdJcu21NQvRJKgmDXaEJaHBBCBMUqfA0ZiJPXbBpvebskYJYI36NdlxvHzfJPORQKhkFN8LGSoEVime6JmZPqgmjUisutoDPTwDbYiDJxqYdneXDxKaBp6oSAwNp1mIWhIyU/yAD9j+3/XsRJgcGo7CiR66YhI5BclkQbEcFDoITi8fQXaiZGwghlyZ1rQAwEKBwZRentZvbkf3usMQssX8CrxZtgPW2g09+EHPQugmsIRss0WEe/tj5zzSCY1XjUMAEPRHlzRMfmIpZLtJSHRadYGjDAm4psY4jg00XyzynDztxZQtzImCv9KXT0qedoKKgpGKBXI6Ya3GELG6ArFScHZjI72KFAvOkKFXSLHIB5Y8eIiXol758v09WwAATPMsRGdaRcSDO94o2k9OWCv3oeZCgSyEnHz2ws9hGgYCRIbJVeckYgrDtrEO1r1w1HfJBsARHZpS+jnCmgp0ev+LIHsSyAzD4NOf/nS+IO8LX/gCXn75ZTz11FM48kjvXqQ+9SeTSSFIJCBoPZTEUNTytbVbe1aDLGVBEROmvZSnMwHwZvnIIm/koFZYtuGFgGU4PkFewaHUVuxlZxU0BxlNVpiOJq2G9CA1BxlcgeG9MYmttEdDZfZhiERBsd5vRjotgK1jBBmwxM38sA6JDtVcdFGqghoA6OyA9Rnp6mwd6djwhCnc4e4ywEStJfx433C76YCRglpiiVgQLb/wevpgZxKWyEggiIAHb1ZKSSNL7OIlOgi+Qj6843rBh2KQqWBBV73R8KYC06Ww5xz2R1irHWtd27A6YD2hHYdu9s6S+5++7gj8hbkCNLEuTS8i4oOHNmMGBmDOPRGbw+/DyuR6pLONca2VQrdXcFqmVyeQc0QE5RL5d7qVsiU6NTp52jnTstByvoPJyNPOMDHwSnEObzZpnc+0bUE6Fli7E181jY04M1cx99YpCq4lsARY38NT2lH5zKFav4fE3q3WeNrnYto8a1VY3lds9aanrUl0pLUw3ShgC/3RVo+S4xvt4oTihtMp05nkAEBQT0LhYwX7OQWkkkstBmsqMKdqigUAvPDCC3jrrbcgy3Le5s3h85//fN0H5uONeG8PQgCosDWTJBSNNBErVrKXQsqkIGA41033EBnlzBx0uvxFRygKWSKCTEC3OdM0MV3dgZ6m8jl4eqgDLfGhkn6R5aB0CRLhURDL4YIQiApDU0Expb0eJwpB6kWcaYH3HnOWQC5XYFErnJqCRJVf2iyHTIcRUosjUpw8gCTdhNJxSneEluG0E+fhUwqx2XropPp2AYusAEDISEMf9VAARvhI17ETYc7uStdLT8c8bWtBqkcpaM0SyM0ANDYEMVv+OtNGFPWlmSDEEqLGgYMCkym9NLopISLNiKBgeuqA5Vgwfoh+BSzRPbU/3rfzHcwhJpjWeeAOeR/an/03vPC7h3Dihy8v+xkng7UbekDv+CcU0PiX/3yyKs9miRLBuHipq5LtvS4UX0tsrAPptAgeWsF3MBl52jm2CU3SruLtdqdHpxBuLAh2BLmaxka86d5kxSEUtlIsylkeloONdYDLaCAE0F3ck7yQG7BWAppmzEc41oIBxEAPvle0n5nph2LSCEcKO6aGIo7QLxTIuUwSItzz2N2gA44rxgBap1mFp1EziW1C4fsS+7hSNpMfw0hYs3yqVqPiKQRz66234sorr8RTTz2FV199FX/961/z/7322mvjPUafMjiWV3xseKklXcLc2wtyzhLVxF6GMdlA2bwiwEr819nKyzZZuEdIHOpR5DA4OIBpGITRXL5RBhWbCZqYGNhXukrY9XWaVNx6l3c6BY6/S0clQkofMlx1qQcGI4IbB4HM6+mKuX/lUNkIRKM4Miuqg0UdpLzwZsYu8DEpfOyel1zPr5CdPyrZ0UBVVREhWZhC6ffMErFyQ50qkO1K+qTQAYYYkLLlo9OMmoFM2ZNaNoQAyo/FiZKFwk1QmZBrgamuaeCIDriIi46YiFZidcA6R7kZD+ono61MByyn6I62i+68iIihXZYPbHD6Asw/5hz0kVaENv2sKEgz2Tg5qEeab4GFXjF9ZDQyFQTrIpB1uwCUFYuvpWtWL0Q7VfgdjHbtmShUvhkRozhFQba9zJ382LEgiPazSfUukAVTrlicRjMM0qYIUqIo2AvXrF6IDmJN5nearTV/D+bQTugmQesMawViHzcLocz24vHmBpAgkaKJMy8ELNeXUQEyyYMTSinYUe4euUzacmkRC++FhC3f4ZCFul8KZE8R5Mceewzf+9738o4SPo1DLm4VmgWahh8yOSoE1oM91GgkO/JEORcRG7DylgwdxMUyyCrqqzwrlSkRdJluc/Uqctjz3ka0ABA6Di27nxNNHNq9DdNmlvbDLQWlS1BI4YVO8cOttIPRamK39afJGEBCdI+OlsJgxIoToVoQ9TQSZdwiKqHzEQRTxedMWB9CIji3qmOt3dCD217O4HwaoGHgguxDuO5x63sbfX41tVsCWbULYVLxfjQDIC5LxDLhK9ohVoNqCwolNBPIWM0LxDKt3Dk9A9nuUmbyYQTNbNmosxMlC0abLEFtlh67LGUQAEBcIsjVdsByiu7W6+/Dmcxf8H/G0ooiIrfPip61zVoIQrPYs+BCLH/nf/D663/HkqVdrq+baEZ38LyEeRaX4NmCDp7lUJkgRKV0gZhhF4COdM5x6O7qxFr8DHfZTRbGu2NeOTSxFdHBZNHzQrEdlfhwcWSxWliOtwSgx8ZGVl2NnC88L4dVFFxbnUx3Vyd+9/RhWCTtxGzSh5+K/4rPnXFk1d8DnepBP2nGNHtVMx2agwWD/1e0HycPIkVHMbpVktUeujifXbWDX3SVApkPFbp7xAf3WZHoYOFzzikgVVycqjhTBaZyDvJhhxV3nfKZfBS7zXS0bfhClOkQOA/V76NxLiLGyXWzI8luESxd06xqew8R5ErNNOpV5JDaZRU0tM5dUna/sO1ukekvnp2Xg9FzUEcLZLt7kZSe3AiyJEloNpMwq+iiBwBgReshYhh1HU/AzEDnavc+NfgowmYGpjHc3h6miZiRgCZUFyU/fd0ReJW27P8q5b+GIs1Ww420ZSeXjlv5fnSw9ANe8dBQpxqc5iikyYoiZSt4s/JGZrgOgA+DIzqyJayh8thFfTTLw+Tc8+cV5xgu4qK7qxO3nLsUnTERBEBnTMQt5y51FQVO0d1/ilYDi3/QSyoWb5pD2yCbLJqmWfnjC1Z/GppJoff5/3X/fJOAkwtcaw6qygRdI/mGbWkmlIggA43TZIEKtoIhBlLxwgmBZnuZB+ogkAFAAgeo3momFEUCQwwQD8+oHBWsKbDk0KHuhAIGFDFx67F6Td9DILcHQ+zwarDRfDCakURisLBeRlTjyDKlV7RyJFDUYEi2nVCYEnns5XBayau2QM4MWeNgw4XSvFIDFx4KTJdi30bGk0C+5JJL8IMf/ADZbGN4vfoMY6SsEzY2IllfYcIQKtizlUK1Z3+MPcskFVIHss72EtXVo1HoALgyxUPWA+aYMRc56L2boZo02meXjyC3dBwEoLDvvRdoXYY6qiKasQWyXKO1Xr0Y2LcLFDGr6qIHAGCDYIgBRa1jFNk0ETIzMMbgfUqEGGhiIpse/rtm03FriS/ovc00MCxgZNNaNCt3fhGKwiDVBCZnCeNswnrgs+HSqwMKJdS1UYyTAiG0Wu2xcyW6Yo1EMLLQ7FQWyrbSypTpCEYrKWSI/aDkwwgQGXKJ6nPZjlxSrPuDrVpx1t3ViefWnIk+M4aVzYmK+/PpHdhHT8tHJAOts7A5dgK6Bp7EUGLyU5oc2FgHZLAgBFBryEHV2SBEl0i+YafvCMGxN9oYT+iwNWlNDhTaZxq2o0IwUp/VNavzp7cJqbMqCq5yBFmiQ558x0uh6wY6te14J3o8ACC71S2rvjxRtRcZYTjAIUw7BACwZ8vrBfuF9DhkzkUgU0Ewoz6HmnN3QilHwF4R1exJe852z+DDhQEKp4GLWiLFQlMV0MQEXFaiGhnXFIsTTzwxX6lvmib27duHZ555Bk1NTaDpwuX2P/zhD+M6SB93SLYPSQQR4YdvACoXRiDr3sPdDWf2x9iRCso+6aWMi/1QJoUwRqRklDs2HUBQjbv+no11QEmzYy5yEBPvYQ89A7MrFN5FYs3ImELVzUKsfvaFNxmnlbYyAZ0Cy5Ho3YFOVNdFD0D+4SFl0uD5yg8SL0i5DASij8n7lLLz39LxfgTtwo943x4EANDh6kr0nGImFrqnYqYk0wxBsoSxkxMsuETAVLq+PtJEiiNjChBi1iRAqdD0RzRz0LlCgZxLxYHppW0OadUq6msBQHhLdGVTCfAthX9T1fb2pjyIi2oghGAfOxPB9LaK+0alHsT5ToxM1Amv/CSafvNR/P7p+7Hqgs/UdWy1cs3qhWh/wspB/Z52ATpIf1Xe32YZJxxiWwjyQnUFVhMNb3vyZgb3AFiW325KCegmQSgydps3wOr8SXRv15ucb7JS+RmlMKGiDppe2bN7J2aSDLZ0Ho3dqc0Q+/5e9TEMXUeb0Y9doeF7Usucw4AXgdSut4Cjhr3bo0YcPULp+5FEB8GNEsianTrCitVNskL2pMaZ5Mh2AXEwVhigYPINXIoFsixlLaG5H0aQXQXy1VdfPYHD8KkVLtePOBXDSBlicNEic28vOHZCvH2y03ZkVMmVFsg55+bjYVaqM0HwLhESwHrAtD1hCYG/GAvxjtlZU3OJFmkbhoLzUCnzlVAU+ulW8FU2C2FMGRpVOHt2WmmruckVyJkBS+yHyvg/l8KpbLaW4aqLzLqRTgxAgHverhcYu7tVNjmcYpAatItSRxnkV8LJf31QPxkP6SfjIvq5sudXjmtBNGetLqhpS/gEIqXTOlQ6gECZyV+10HICKRKC6CxvljD9dzBN08o5Zq1rkLW7Vkpp99ewWgqSnbNM8oJ6CE2jBbK9tF8uglwryeAcLEr8qew+pmmiXduLt5oLrURnH3k6dv+2E61vPQjDuAoU5W7nOFF0d3Xi4Tf+FXjnS/iLsQj7okdUlQtscmEEiQRd14sCUFCzyJo8AhPYOroWAnbr91y8MB2AyEmkEUB09OeqEYVwoL0K5Fz5LoQjUdkwRGlbTWPq3fIPzAQQ6jwMvbsXY1Z8A0zTLLADrcRg3260EhUkOnz/nj53ETSTgtr3dn6bLGURJjmYAZf7ERNCWC70+Nfs4BcvVpliEYxAN0m+o6lm3wtDTYX3X852WNFL+MErUg5BuNcyNDKuAvmcc87J//unP/0pTj/9dEybVt1DyWf8EZUBpJnCmaTJRxBCDpqmgWE8O/lBt2d/nD3LZOyT3pmFj0a2I6aOkC57bDYIsYyncndXJ37x7lU47p9XgiE67gpWX2wiSRI6jb3ojZ3qaf8k247gqBtJJVhDgTHKFzbfbngCWmmXQ7FdF5x8Ta9QeYue+jVyySQG0YqxWTtxtkDOjRDIuSErLzhQZZvpaouZFLEdscxGAIBmF82FmkpHrXU6AK6CX3g1MEoCWSqESKRwebMUsixDICpMOxLM2X9vpUy6D69lINPWtc3Y526uhKBW7WYMdJ1WFUaiN81Hc+JJSKlB18j84EAvWkgWZtMoT2GKwj+mdeP03f+DjTd24avi9VVZqo0Xc1nre7rrs+egrWNuVa910tky6QQi0cK/B6VlIRMejR0/BkIt1jWpJgvbTTspPfWJHwMq4T0L5HwXwhIWeaPRueKucV7J9Fi1L9MOOgLv7fonZsSfxY4dWzF7znzPxxjcvQWtAPiW4fOd5QTsoqaBT2zJb4sP7MU0AFSodDBDY0IQc1sKthmjnu1eIRSFFAmCkqz7iZGxBHJk1GTa8ejWS7QAd+4j4zHRHm88qaff/va3uP3227F8+XKcddZZOO200xCN1ut09xkLIW0I/cFCFwYiRkERE4lkHE3N3ouZDKWwWtrJRVZdhJ9iC6pS/pxFxy5TDOSwMKQAAKbRGbx4rXsrYDd2bXkDC4gOdpq3qHNOnI7p8Zeqeg/elKCPEsiC3U1Nd4m0TxjJPdBNgkhLR1UvcyqbFZcCi1qQbFHJjkEgO1XvSmZYIKt2UWq4ys8IWCLZq4gyAu1oQgqSlIORs4RP2MWhxGBFCHUUyLyWQo4JY4adVmKWWZnIpOKWd7k9SeXtCLJSpiMYb2SQ4637gvP9yCXqDDTbf5f24ABQLVz7wcA2YN+2f2LO0pUl9+nbsdlypGk/qGD72g09+M7OZTiVIlhKttbseFNvjKGdUEwaLVVOUIHh7y+bihcJZKJZ3uuNTlOrJZCNdGEEmVFTyFHVRS7LoVICGI8C2cmJZUq06R6NwUcQquAA4wbpfxsZCIhOm4umg48B3gT2vPFiVQI53bsNABCeNrdg+6AwC7HssB1pyhbIowvlHDQuXNRgyBHIQgknlEpkSChvG0vlBpGGiBA3KkiU73BYQiDbntVkP0yx8HQWPPzww3jmmWdw4okn4v/9v/+H448/HldeeSV+/etf+4V7k0zMiEMVCy8Up/tNNlHcZKEchn0iOxcRHywfGXVcL1gvFx0XAk9U11aUAKDaBYdhs7ZUhaHtViFDdJY3mzM93IkWMw5V8S5uuBLVuKItSowJ6hRYirUbehDftRk6KJx927qqPKSdCmStjgLZsQXiw9X7FTs4RT3aiBxcvURR6nhA2ykcQ727QElxpE0RDFdapBhMoOzqSLWIWgoKE4EgBqCYTH55sxRO5JfirWtVsL1mHeeAUgSMDHQ7JYPLC+ri/XX7fuBFXFRL0yzLFSm+803XfVJ7rDqKWEehp/np647Ay/S/gSGm5458EwGb7kEf1QqqhlQCSrS+v1KpMbSWhUIaX1wIgoiEGQTJFj53eC0Fia7dD300Gs2DMbwVFKu2MPQSxCF8BAwxhovPqyCcei/fvXXWYUdDMyko2/9a1TGUQUsEt3YWTgizkfmYoe+GoVtuPtkhK0ggxEqvaJlcGEEzV+BKZCq1F3rmqCBY2xWLloeQIsXH4PP+1GUiyFzjn8Oj8TxN6uzsxCc+8Qn88pe/xG9/+1ssXLgQ119/PY477jh88YtfxMsvvzye4/QpgZTLIkIyMEdV9DOjzL09Y0eQxYC9XOvMCqXSkVFnOyd6KMTinCVE95uPkbYKAKJmGpqquu7nhrLXetjOOOhwT/vTsU5QxET/Hu9Wb4IpFwnkQNj+/PLkCGTHQ3oZNtfUpIDNrxTUTyCrtuASQ2MQyPkK6nh+G8n2I2kGIYjju+DMxawIdaKvB5ScQLpMR0CTDUCso02e1dY6mvc0JbL7NZOzHT6cVIlAOAYAMMp0BAua2bz9nmALZLVExFnLC+T6R5BnzDsUhkmg7nvbdR+131ombp9duCI02pFEMtlJa688kpC0B3G2tjRE1r6HlnLCYQwJCrV/iIskFQEjjRLIegrKGBoGjUanBLAeBbIz6fdib0bybZWrCyyZpokZ6nYkQ/Pz77WDnYfw4D+qOg4Su5A1eUSaRgW8Wg9GgMjo3W21oVaSVpAg2OSSZiZEwRJ9uL00hoWr4GGiMBqZCYO3BTKnxJGmi7MHhID99y3R4VBzUrX2wxSLqtYRenp68OMf/xhf+MIX8OMf/xjLli3DmjVrcNBBB+Hqq6/GLbfcMl7j9CnBUJ8lgEZX9HN2FEmqViCrWUgmC4a12iULdptJvURlKjAcWXZycMtB572C4677kJx1Y6KIiUS8+mpiZug99KEZoseopdMsJL53m6f9TUOHQNSifvYswyBj8kAdI8jVdBV0PKQ7qYGaImpsvgLZfTWo2i6Hul1YJo7B2ikUbYZhEsC2PQMAVhpAnBr/9K5gs7VUnx3sAaskkKHKRF64AChiQpbqs5oWNtPQebvFKwmBKdMVU7H/zo7ACkSclrmlJ7WapiFEcvmcZdEW1HqJNA7D7lbGjkMEORwKYQ9pAx3f6roPFd+OQUQghgq/b6cjH2t35OOgTVp75ZE0qfuQFatP/QHKO+Eweq7IWrJRSdFNEEa1LheNDFS2djeb0ei04Lnzp16mycpoGHuymCtjkViKgYF+tGMIesvwRG4wthTz5Lehaprn43Dp3eij24rSO4IdiwAAfdv+CQDQUlaOd7SltEB20nUyI2o3iJJB1uRB0dWljgCObaz1bBPVBCSm+P7LcYJVzFei14Ez0aanagT5Jz/5Cc477zyccsopePrpp3HmmWfiD3/4A37605/i/PPPx1VXXYVrr70WjzzyyHiP12cEyX7LgYGNFF4oQsjKYZPLVL+XgqjZglw33l6OMV1SLEw7Yupl2SbfTKNM8dDIyENqoLriOQCIZbail59TeUebiJ3rlenzFkHOC6ASM+EcEUGp9RHITkT4/MxDWEE2V4wIj7VJASs6BRalJ0LVjgcAjJz1PYditTcHYBgGaRS2f+XlAWRcDPLrSdTupqfE90LQEsgx7g94wjl+4WPPQVcV2Wq+I9huFHQQrOp+XCfy66RKsJwIpUSrWYd0Km6N2T6+E+EvJaidlKvxEMgA0MfNRKREG12HQGYX+tli0eu0V/6NfjQA4I/Gkklrr+ygKjJazUHo4dpyoJ3iylKRfNaQoe0nAlnimoocXUJmBjpfP4FsMN4F8nBxWmWBzAasa0FKVhdY2vOuVcwrjujeSs9ajijJYNvbmzwfJyTvRYIrXoFon2ulDGZ2bwYAmJl+6CZB2KVomBatazs7QugTLVdzHrvGRRAwrGdb0EhA4WJF+xCKggS+ZAMXzU5hrLdd5ETgSSD/8pe/xEknnYSnn34ajz76KC677DK0tRUuAxx22GG44YYbxmWQPqXJDVmuBYHmwoeIaBf3lKt+LwWl5SBh+EYccARyCesWYLioLxCqHNFj7RtUOYEsyMPjzSZ6XfcrORbdQKe2E9mI96KIlg6rEYM2tMvT/rK9ZEVKtNbOERF0ifyrWqi2q6ATUQMArQYPaefhYbhEkGvqcignoJo0RA8PpnJkSBD0iBSDoDaELFefjlzlaGqzIoFGci9EPVU2AuYUOUp18MFODlnRISLaD2s6BE53n3g51oK8vWoEQpAlARCltEDO2k1HHL/kgF1ngBIpGYb9sOPGKZ0lG56Ladou5Gd2o2hRdyMlziza7nTkuztwBQDgj/T7K3bkG2/6dm8DTUxQTbW1Vnei5FqJSD5rSNCY/UNcKHwzwsbwPd40dCsfdgwdNUdj0gJ4eBTI9rNL9ODe4FxD1QaWkrusyG7b/OHUvvZFxwEA+jf/2fNxmrVeSGLxPbt1xhxkTR7ot3LyqdwA4iTimuvOBIqtHiktC6nGPHareNF69oWNFDQ+VnI/ifAgJRq46LZAZtjGLzQdjSeBvH79elx11VWYPdv94j/kkEPQ3d1dr3H5eMBpMx1uLVzWc3I3jTKFOqWgtSzkERcRy7KQTBZwE35yBoZJPBnYO0vAas79YR/Q4uiDJX6kKgXy3t3bECI5oM17FCkcbUbKFEE8NguR7LGTEjNhmRJBa/XJ4V0p3YEX9eHW7pUiwtesXogOagiEAD/VTsOD+slVRdQc32vDZSLkRKg1k/I0HgAgUtLKn62yGnw0GToMZkT715gRhyrUpyNXORhOQBxhUJlehOycYDecRjlKHWzyMnbXPscDWmXCEMsIZCc1YmQaQoYEQbusZjjLx85DlGIYq2FOCUFtjmOKBQAYzQsQQg7J/uLrT1EUTDP6oEZKrwh1d3Vi7X98CACwag476RZvQ7vfAwAE2ubW9Hoxnzte/D3wpgSD3j8Esi62IGom8+3hs6kEKGICQqxu72EyAnhT8baz02QlWHmiLtidMtUKjXmKxtP3FhSTQdus4fttx4IjkAUPc9drno4hS1m0Il5yBYJQFHYzMyGmrHQkVhpEqkyaGReMWccc0YGU0XIFz/aqEKIIEhnpdAoRkoUplr7/unU4dCba41HsO96M7enlM6noKUsgN7UVXlQhOwfXzFXX+pjWJahU4SwvRwRQamnhR9QMshA8iSBnCbhcM42wkcinSKip6gol+rZaS1mhzvItpkczQLeB89gsRMnZtlclTOdlSgSre2t/Wgk21oEosf7mXiLC3V2d6D3+GwCAtzELd4WuqiqiJtpRxFIVyM540hBBwypC8xKhZtQkMmTshTkSHQJnF4jomooYUjAD9WlmUokhqhm81IuImYYhuKd1OB6rcpnJn1eySeu8Z4P2KhAXhmi4T7wcQRUYUQwpUQGwLgLZiY6NtN/LkgAopcR72K3Hx6sgUpxutdHt3frPot/17noPDDHAtMxzfT3DB6zImlSdoBkPsrZFV3SG+3jLEQhb90ezRGoMb8rQ95MIMgm2giEG0vZEz8mDpcT61Q2YbAACUfOuDmX3VbPQTQLOQ/5r0H5u6lU+NwOJd7GH6QSh2fw2QjPYwR+C5vjrZV45TL9dgEc3lbYITARmo1W2XC5EdQgZJuZ6LN6ZXI9YQbae7bUJZCcda+92K8WDBEsLZMufukQE2RbILL9/pAmNxBfI+zEk3Ye0KRZ5G1Ishyz4stXvpWD0HBSq8EYsQQBVIvEeACg1g5zHWakQcLyCS4/JNAzEzEQ+RUJPV1ekl3WM2ud7c7BwSHDTEPLYLESxUyxK5VKpdBCcXp8I8jWrF2ImsZbaf68v8xQR7mq3OjZdfOIRePHaVVVF1PLdlVwiyE7O517TuvE+rh9XcTz18j5VmAhEWyAP2Tn3bgb59SbDtSAq9YAjWtkImFMh79ZxshqcttaOB7TODS9vurwAABAIDaeAKHQQrMu56BT1OYW8AJCjAqC1EoJay8HwKC5qoWWOtUqSsnMrRzK4y3K3CEw7qOh3I0lRIdBSvO5jqxZtyMqlbussP143eD4A1aTzdR0jEUwJGAcv6vGAsa9Npz4mawc6GDuqWRfsv4WXoliiZpHzGMQJxeyV1xFFwV5ok7ZjKFA8MUo1H4F52nvI5SoHTuJ7tgEAAm2lV0zU6HxMN3oh5bIIanHInPuEPV94OyKfvdSz3Su0nZud6LGuUzZUOsVNcWngko8gT9Uc5IlCURRcf/31WLFiBY477jjcc889rvu+9dZbuPDCC3HEEUfg3HPPxcaNGydwpI0BI1ltpkuRRghUmer3UrCGBHVUEwyZEkC7CGRay0Ii3k563n6A6y5WaMnEEDiiw4jNgWyyeUcLr5D+t5GGiKZp1eUAyuI0NOnexLhmR9mcnNOC39EB8EZ9IsgnzWERhm2uToiniLBkRx6dZcJqoGi6bCqNk/M5ZDc0f0LorjgeXktDroO108gCkVS/lXPPRCamo6fEt2KmbjvFBN0fSE76UD18pFW7wUre/YOPIkBkV/9wIqeQNXkQerjnk8IEwbsIZNWuARBGCGSZCoAtKZBlyGDHnCbjxozZB0M2GWh97xT9Ltv7LgCgZdYhZY+RpcJgleoifuMBlezBAKI1NWIAABCCDBFBjUp1MXQDIhSYzP6xPM3FrGszPWgFHWQ7pYeto0AmjkDOVl6xqaY4jRds33EXB5hSpNIpdJj7oDYfXPQ7bu4K8ETDtjdeqXicbN82AEBseukaGnbaIaCJib3b30LETJRNMwvaNUj6iLoCzsgVNbjySr59fa+VRsRHShcHujVwMZ1aBsEXyGPitttuw4YNG3Dffffhpptuwp133oknn3yyaL9sNosrrrgCRxxxBB5//HEsX74cn/rUp5BOT26r34lGkAeQcqnoz1JBMC6FOm5whgRtVK6blVtbWvjRWhayx1lp0J7VujliJGzxQ4dakSBhUFUumwZTW7CHmVX1w1wPd6IVcU/RCNXep1Qulc4GIdRJIL/36lOgiYmsyWOmIHmKCCuOsHLp9lYJtwILh+6uTjRTluhac2J7xfEIehoKM/bCHIOLIGxa50zGbjMtxiZGIGuBdit6jOGc4FIIeb/wsd9/dHtZNBizOt0Re2k6nShdWU+pGWRI4fmoMSHXtAxn+XhkSobssvpBtBxkwlX5CbzDcxz2UNPBjWij62AMbINi0mirkLKQY6LgtfERyNVYG4q53RhkSgsHr1iFvoXfgyxlrRzeEmldjUjAvjZzcetazTcMGoMf+micFTzZxXVnJLTdptsLju84VcWEq+e910ETE9z04tS+zsVWod7gO5UFsjbkNAkpfb5HZ1qrLf1bNyKGtGseMDD8rB1ZeMuZxc92r3D2vY8MbQMAiFGXDn60ULKBi5lPsdg/zuGRjEkgp9NpXHfddbjxxhvx0kvVtewdTTabxSOPPII1a9ZgyZIlOOWUU3DFFVfgwQcfLNp3/fr1YFkW1157LQ466CCsWbMG4XAYv/3tb8c0hv2NkDaIHFf6QhmZu+kVzpRgjMp1UykRjIvwY/Ws52UbUQxZPokuot0RP3x0GtJ0BJxcnUBuV3YgEaw+/49usqrk+3dXtnpz2miWEsgGG4CA+ghk5Z3nkTV5vBPogqh7WwXQ0lYEORD13lp8JDJ4UFr5bnAR0/ru5FTliHvASENj6yCQheEIqmw/dIPN7rnP9YSEhoU4F3L/u3IBxwVk7BFkwxbIkZj1ECpl2TQSWk1DogrPR50LIWC6OM/YD81gZFiwqEwQgl68P9FlKBg/gQwAA8JsxHI7irbzqR3YR00DxTAlXjWMzEYQ0Ovf4r1aa8Oosg8pYWznpUQCRQI5Zxd+kv1EIIeaLctRNWmliOUbBoXr5zxD2X8LJedBIOtSVV0ILd9x7+dT3O7e2jx3SdHvWjsXYBBRMHv+VnmcKXsFQiydljZtnmX1pm61dBYpcz9iWNbKzR+RYskbEvQaVyEEOyIdTFvPyGBTaYGs0wK4UgJZs7bxB1oEWdd17Nq1C1dccQWeeeYZ6B6S5t146623oCgKli9fnt+2fPlybNq0Cdoos+1//OMfOPLII0HZ0UJCCI488khs2LCh5vffH4mWqegfae7tFcGUoNOjolG0CN6l+MxKyfB20hOKsgr6XJpp5OLWkpwYa0eOiUHQ4p7HnYgPYhoGobcUL3NVQmyxiiLi+7ZV3NdpvcuV6MpkciEETMnVsqoaZgz+Be+Kh0MWWxE0vH2HZs6KMIZdbl6VUAgPqkyRoapIlj8vAM1DfrjlfTr2whxi5/6mE4NQ7Q5SsbaJcSxgosOCRywz8XDy6416dFKUEsiaPDjeino5uZtO8V7RGLVMkUDOt5otdS7KSegmgRAczlnW2RCEEhFnSpehjGMEGQCk8DzM0HbD1Avv8eFcD4b4yk03NC6GkFF/gVyNtaFpGGjXe6EEa2sS4mBF8gvPIdkWyNR+IpCb7BbwRsYSyE4ebCBSzwiyI5Arr/rReq6qLoSVfMdHo+17C4ZJMGNesUAGIdgVOBTtyeIi1NEI2T1lVyDCsRYMIIbmQUvjsOHyqxVpEgQ9QugLKA5+eSVgp+01KdbkMNpcegVPpwWwZokgiy2Quamag/zv//7vWL9+PSSp8MNHo1E88MADmDlzJm644QbQNfSgd+jr60M0GgXPDy+HtLa2QlVVDA4OFu3b3l54grS0tGDfvuqbS+yvqIqMJqRgBF3ygdgwRI/iykH4/+y9eZgb1ZX+/9auvdWrd7ywB4NtwDDsQ5zEAQIxOx4CmS9MNkg8v0xMAoEQtmAYwhZCCJkEwgAhDGDMlgBhSSAsIRgbDNgGDN7a3e3etJeqVMvvj6pSS2ptLam7pe7zeR4e3FK1dFuluvXec895j6nAzCkG0XgPxAIRZMmQR7QqlRk32AKFYCm7O5C/ZSoUsRneEWybdm+x8s+laSNzsACAwBQr6iyX0SzEaaQh5rO1E73gGBNqlXmoPZ2fYraxE/LMo2FIzQiYsbJaGJtyGKrJQyrD7zMfKusCXySC7PjzAoARL26kr6VUS0xL1UeQndzfeKQPiPdCNTn4K0wjGSmZ/uLeIu/ptv3CjXxOECOEU8KIZrh/ONubSiz/Zy7qcag5i1pIfkhMCkpy+HXLKFHEc+z3dMEHrzn8WFZXoI2yQGba94LIaOjflZ1m0aF3Qfbmr+jPxHAFy75GRsIxydvwtj604C5mbTjQ1w03o4IJlh5vMVKcB2JOJN/xXmfz1D3UI263G2HTCyZhLaJNu+CtltesU+yVKmPHRtCTI2qyYu28ln/flEIfo5udUrCVtdyxELONnQgPFp8zA2oPolKB1tE2u8WZmJuycvOlpuICWWY9WVaPLlOFmce/vxycZk9Tjd1ImgJcnvzzusG5IOVr4KIpUE2+oi5+401ZI543bx5uvfVWHHHEEfjBD36Al19+eVhUt1pkWYYoZk/Gzs+qqpZ1bO5xE5lQuqI//4WiiwF4i1W/52AaBlxQh7VR1jm3VUWdhxEL5ELV8gCMmN0+s20aNCkIv1n+Kj68w1qht87Os4ovQbud85Ua3FHyWMcXVsrT/IKxxWDC7lRWKdv+aaUJtR20FHA3Q2JSkMuwD+OUkCWsGKai902xroKpNAAQGxyKGjNy8cnesXZiauB9KtgCMREZAJfoQ4hpGrOJ1tc6FKkuFpmXXB47faj6RjGCGkYio621ZFtPqQUa7Lj0OFJc9veRsZuAxCKhYcdzahSJnJxlU/LDAxmGni0y+Ty2j7XGO81yQund9mH6sfBAn5Vn2Tyn5O8z9jUSK6OL4Uhyiqf7WRzIWqJdN5mi1oYDtgey2Fp+F898pHgvRCP7O5Sy89r5BhHIABBhA+CdImslAtkUIdXQ4ssRo3oZwYiR7HICtu/4CAJLrfJW9LnnFHzeN+9wsIyJbe//veAxpmGgXe8tuQMR9c2FyFi79N7m4nUYSdYL3r7X6poGiUkBQmXfIbe3ybLKY3REmEDBe4zBu/M2cGF0q9i3ESnrTrNy5Ur85S9/wX333YeOjg5ce+21OOqoo3DllVfiH//4R/6tvBEiSdIwgev87Ha7yzrW5Wo8n71KCfdak7vQlH/VaUoB+M34sJteIZRkHCxjDltlmoIXEvILZDdkGCMQyCrjBl/AEYOJ9yNhSnB5/DBcLWgyY9BSqbJeV9+9GSmTw9Q5I48ge3xNCMMLNrqr5LFOIw0hT54YKznthqsrGGI++xsGEcDcAw4Da9vrRAZ6S/wWIKghxNjKI7YaK4HXC3enkiNDY+BLFFDGnW5tNfA+dYp7lOgARKUfYW7020w7BKdYEcGUycHtLfy3MCxrWUnVoJOiqEUgZxQ3euzcTa1A8wLJlKEJ2QKZs31L5YxOWg6CNtx+j5H84BkDiZyFGGeMfgS5fY5VfBTftSn9WO8Oy05Kai/dFZP1WJ9PtMQ1MpKc4lQqhav1O8DCRKfRgjhcRa0Wo92Wh61/SmUeyA4674MnVyDLjSeQY1wQkmrPAUoEMcYLpsKFez6cGpCUWrrmQzSTI3JvKOU7nomiKpihd0IJ7lXwmFnzjwYAxD4tXKgXCfXDyySBpuKpY2bzkIWgv7V4tFnhfZBsgSzbHT7zNbgqB4blEGOs71+MK3yPMQU3XHkiyMwYpGqNFiMKxRx00EH40Y9+hDVr1mD58uV44okn8PWvfx3HHXccfvGLXyCRqPwGMWXKFEQikSzh29vbC1EU0dTUNOzY3t7sCbGvr29Y++uJTGLAiiC7mwtcKK4m8IyBeLy8Iq9k3LqYWDF7IjYET8HcWreZhCGWP3ErnAdCnmIgAOCS/QizVuSL8baCZUyEB0sLQwBwhT7BLm4aBLGyaNcA2w4xUUazELsaN1/7ZM6O2illRLIKYRoGZof/ic98i8ByXNpvMh4qnfMrpqKQi0xepdA4NwSjcIqFYufApkwOghoq+lpOvmwtvE+dAhE1NghPagAJYewE8gtbZCgmDxPAKTc+XjTimGSkgn7hI8GtRaFkCGSvbfdWqHmBx0zAyIkM8XZOdDKvQI4jmUcgA4Ccs/vBGyq0UY4gT502GzHTBbP/k/RjkS7L9i0wrbDwcBDs/MhS18hIcorffOBKLDA+xFOzf4zfs6chwMh4xH1mQWtDpb86D2QHQ/TCnZPqkrKjpHyB4q16RBZb4NUsgcylokgwtR274HT+VEpfb6KhjGiX0yjlO57Brs82QWI0cB37FTwm0NKBncw0SD3vFjym39mBaCluUeqaOmR5GCwhkFP8UF1B0rknjeBenUvcPodykQYlEDwQGR1aKjt4yeoKUhM5ggwA0WgUjz/+OL75zW/iqKOOwgsvvIBvfetbeO6553DzzTfj1VdfxXe+852KB7L//vtDEISsQru1a9figAMOAJ9TybxgwQKsW7cuHbk2TRPr1q3DwoULK37/RkMJWxX9/tb82zKs3Uo2Fim+He6QtJscsDkODYzgAc8YSKnZ4klVktZ2zwguuhTvGbaF6CCpg4hxQQAA77cWOtGB8tpNtyS3YcBd+fZmROqAXynjvVIJaCYLQRy+GuZt0awk8guZNes6cdQNL2Hupc/gqBteyiu2tn/8HjowAG32sQAA0W8VhmVGbwvh1sJQhUDJ4wqhcy6I+fLHbFTbJaOLnQJXifzwpNPsooCh/Ejw2AJRSwzCr4egSmOTf7xmXSd+vOYDpMBBgF7SxSDJFPYLHwkeIwYto621r6kZhskAyeGfuWma8JgyDDF7wSbY177TNS8TSY8hxWdfs5zt45zrlMGbCnRudAUyy7Ho4mbAHdmafkzttVIb2vco3SpdsgWyHCl+/R6TvA2v6kMpWJrJYo125LCc4g/f/hv+ZevdWB84Hqf9v5U4/cSlAIBrD0dha8PwDiRMCYECxUvlYop+eJHM2vUbqnuo3lN8rFClFvh16/sqpiKQudqO3SmS1stIaXIhCZMvP4Js2r7jqQK+45n0b7UcLJr2KJ7a95m0L2bENxRM7YnYBeK+EjsQmzTr+5UyWSy76cmiC3Zd8MFt32udQk+uCoEss/b9TQwWPMbxp07muIuwuoLURI4gf/Ob38SRRx6JX/ziF9h7773xyCOP4Omnn8ZFF12E2bNnY/HixbjwwguxYcOGigfidruxbNkyXH311Xjvvffw4osv4p577sH5558PwIomO0WCX/7yl5FIJHDttdfik08+wapVqxCLxXDiiSdW/P6Nhh6xChKDHfknbd7enpcLVL/n4piuM7kXUTp1IDsyKsedbZvyJz+d98JVQCB7UoOQ7eigaN/0EuHSojWlKpiudyHZVDraVIikZxpa9TKi1VoSSUh5twtFW5SkEsMjyM72rhrahT+K10ANdeUVW13rngUAzDjkBABDzglqGW23PUYMqSKTVykM3g2pQK45MOTPOyDNgk8vLpBT9iJBymhGUSlOcY+RCCFohKC5K7OxGylOxNHHKGAYFI04AoDKuMAW8ZEuF78Zy3L/4DgOcbiyLJsckrIMidHSEWAHp617vrxltxFHKsd+j7e/u8lY9vGCqUIf5QgyAIQ8e6AlOVQDwIS2YRB+BIKlF0ND10jxQIAQnA4D1nWrmwx4xsBidhMC/qHPIhINw/vMdzDIBrHn//sNwDCYud9iAIC8o7BDkhTfhT6uvfqGKpIPLGMikRg6106erZhn16pe0V0taDIjMA0dohaDWmOB7HT+NMpIsXCZCowRFKcx9mIxXsB3PBOly8qbn7ZX/jkBsOb+v8X3wFQmVDC1R+mzdiBaphVOKVqzrhPXvS7DMAEeRskFe2YHTsVO0+EKFBKWQ9Ju+qRJwYLHMPbnnMxp4MIa6qinao0WZV3R06ZNw7333ouXX34Zl1xyCfbbb/iWwuLFi/H4449XNZjLLrsMBx54IL7+9a/jpz/9KS6++OK06D366KPxpz/9CQDg8/lw9913Y926dTj11FPxzjvv4De/+Q18vsaZRKom3ouEKcHry58b6RQ3JQv4p+aSznXLiVQ41dO5ubWOQM7XVa4QOj98C9HBp4eh2u0z3XaFbrIMgdy1dSMERgffUTraVAjTNw3NiAxb+ebCaMmCjRNEu7JXy9NK+6bnNkNO6fgB/wgWM5uxgl8NOaXjpueyW+yK219FF9ox3c6ldppFpEq4RgBAwIxBLzJ5laJQgUWaxCB0k0HSPwt+s3jajpMv60R/q8Hj9UM1ObCRnXAxKcA7NmlUxyRvwxrtSKvDIIq7GACAyrkLpg+VS0pV4GWSMHOKG+OMF1yerphx+9rOFchue2Gi5dnN8JgJ6Dk5y87iTs2JOIuGCmOUI8gAoDbNwxSjB5pizQ3u+A708uV5Cnttv+hUvPgi8pKl+6KdsT7D89VL8bq+P6YxA/gf9Ue47+kXccqqRyHfdBBmm5145XPXwt9szUG+QLO1Rd73YcHX9iW7ERKLb3mXA2unaSUiQ+fNqXuQKu3QNx5428EzBmLhPriNONQa+KFnItoC2SyR869rmjVnjEAgs+4gACBeYsEFAPzAx+hFCzyBwjtlJzyxAFdw/2u9doHUHiO0E6rJoaVAsMt5nQ3c18AyKGvBDskPD6NA11LpQk+uil2IlJ32ZbgK/61OjnOuPzWrK0ixjSmQi7uw21x99dUlj2ltbUVra3U3RLfbjRtvvBE33njjsOc2b84WEwcddFDVgryR4WWrzbSnQPGDE71zOhmVQrUFspBjYeYUn+W29VScCPIILjorxy5PK0rDQNAMY6sdHfTbW5WpMhpS9G99H3sACJTY5ioG1zwL2Ab0dn6GWXsVfh1Wkws2TnDZUTstTze1F+Wz4HINFRyex7+A8/gXkJQFANbfqGsa9kqsw+bgv2KaHYkKBK2bdClbtWQyCR8jw3RVXhRnCh64i6RYMMlBRBgfTHcL/JChqQr4Ajnfut0cwFvkxlEuVncrL7xRqxCKK+H/WSuE4HTEYm6I0JA0haIuBoDVUCfXomukREJ9aMXQTdpBZn0Q8ghk2Y74su7s1Bq33UnLSGb/jmma8JkJGGL28ZKdK57KEdQCVBgj2J6uFK59L3CdJjq3b8aMvReiRdmFHl/hvM5MHHcRM1H8Glm2aAaefOVf8bnB+/BPc39c4j0Ml+zXi+PW/wBf/ed5WGh2YAobwgfGHvjJe63g9+pMp1Ts9u6DjvhHBV+7Te/BJ03ljbcYrMvOBY8NArBSxtICuUL7xvHASZGL9HXBY8ShVZH6lQ9XWiAXjyAn5Ri8GFmTFSc9SS4jNTEY/xS7pdkotmQ/JnkbruDvx8ncm2AYa6H9rL4Y12vn4p/2MWK8E71sG2YUsck9Jnkbfsw/iBO5f0Bk9Lyvk4VkO9lEQ0jZglWoIoKcss+hUxSbD84pnsy5B/KGCp2Z4DnIRH3hUvoQKVLR73QuShWofs9FU/JHkJ2f1ZzUASeviXeNYOIWfXAzKnQt250iHo9YK32PtcBycvn0ElGhNes68fbrfwEAXP7n7UVzsorhbrOKI8J2NXohOD0JtcCWs9N4wVSGp1ic5fo11hlDBTxJU8Dj2lE403V3+rEt772GAOJg9jxu6DU9PqgmD8jFz2HELlBiikxeJeHdEBkNqZwCi/TTahgxxg/GYy1iwoNFovu2MPPVqDlAjPGhTbW24KWmsWkzfcnSfdHBRvCAvgSnqtcUdTEArCJHsUiRYznEQ1aaD+fJ/txkzgchjz2ik2PM5whkp0terkBOygmrdbYr+3iXvZhO5ex+SKYKcwwiyE0zLHE5sP1D6JqGKcZuqP7iBUsOkssHxRQA22+3GB1sFGH48NENX7Xaty87G17ICDJxLGCta/8AdvuwyJza9jnMNLsQybPtnohH0YIIjEB1HsgAIHicQt+M82B31nM3UATZ8eiNDfbAZ8Zh1MAPPRNBEKGZLJgSAtlJC2RGEEEW7bqJZInAkqEbmKHtRLypeGGmEJyOCIaEab6FtkfuRkgoPq8JwemIwQ0eRlkL9nQHzshAOmgjVBhBXrOuEx8OWoG4Jz9OFrzPOrvJub0AOFOF1qARZBLIDYpXG4QsFhZE3nTuZnm2Y4Vy3ZxVpypnCz/VvpkKIxDITr5yPCfXMdxrFRxyduTB7Q1AMQUwicIC2cnr/bzxD5gm8FV5TdGcrGI0TZkDAJD7inshc3oSKSa/YPA6Ubs83dQu+PIRcEGFaVpmIBJSUFgXLvzyv6SP6d/wPABgzuKhPHqGZRFhfGCToaLjitkC2UmrqQgxf/6Yg6CGIXM+cD7bNWCwSFMeJYyo6QZXok1wuSQ5H6aY1t/oaR6bNtPLFs1A4tT7cLfvYmwyZ+Nu38UFXQwAQOfdkAqkD5WL4/4h5BQ3FuqK6eQMO8LKQXL5oJkskLNYi9nbxqwrf8TZzBHUItQRFThVitNGV+7ajN5dn0JgdLCtZVqmMUxZ1wgACMl+hNnsXZajk7fjae1wpEwrepcvlcYzayEAYOemt4e9Zu9Oy4FAKOFAUA6CvdBRM+ZsMyVDNbmCuzX1iDtopZsk+7ZZ/rtV7Gzlg2FZKBCBIo2NgKGt/tzC82KkF4t5Clwz6d75KXyMDKa9eGqfs9B+WP9XREw3uszmYQvt5tRuJNzF57WRLtgznWwcgVxJHrtzn03abS+iil7wPpu230tm76TxxtjUMowGJJAblIA+CNVVuGDJ12TdZM0yIisAoNnV0rlNMAR7ay83dUCzUzLEAl118sHYYjoZy74RxwYtizUxYG+fMwzCjB9cEb9dp4hqT7arvJysIjjNQrRQCYFsKEgV6MrkcrmgmhyQRyAvWzQDU9kI+uHHddq5YBjgOGFjltjy7XoNn7Gz0TY1OxIVZ/0Q1BKuEWFLPIq+ylOcnPyxZIGmJC4tAplvgitgfeecaGc+ODWatgWqBckM2zN/29gIZMA6b69d+nl8dsNJVsSxkIsBAJ33FC1yLAfFcf/wZwvklODL682aLob0BLOfYBjEGA8YNaew1van5nL8qT1+62czOXS8rmmWS80YCOTm1g4MIABm8FP077BSGbxTyrdMK+caAQCXOoA4n72IFILTEYIPXJHI3LT9DgMARD5bO+w1nV0nT8ecssdbiHRxZYZAZlMJJAssyusVX4slkNXdlnUf46ptigXg2CoWX5AqjjPTCArJ3XbdRMpOEyvE7s+s7q3+mQcUPc5ZaN/h+0/8QjsNM9gBDBzw7+m5RNdSaDP7ofmKeyCPdMEu2HNCMjYIw7m3j+Be7eDUzxzMWtaLS9m389bPAEMNXLScCDJvqtAbNIJcmxAPMaYYmoagGYHhLpz9JEgeKKYAM0/1ez5Mu02umLOVJxYQyLqzKvWUP/lx6Ry7nIK/kBWNdGW0z4xxTRCUwgLZycn6Kvd6wdyucnF5/BiEv2SzEEFPDm/ra8MwDBJwg0nlL/RTwWNn8FD85Pu/wju/SuDg3Y/j7Zcew6GfPx1JOY69k+9j/ZRTkRs3kzk/xFQpWzUr8igFKnd4YO0IslrAy9yjRxFx74Fgk/WdS0YK54fzqQgSbO22hFV+6DvW3D52AnkkGEL+/PqRkLLbSbtzzqPVFXP4wsUpCJXyFOrKjAecmv07znXnuFY4SJIHqsllpQepyQTcAJgxEMgMw6CbnwlfdCti3ZaoaplZftGtzAcgldGa3qsNYtCb7RRwydJ94Xncisw9pC/Bcu5FTM2JzLVNm4MQ/GB6Phj+3r1bAQDNRRwIysVtn0ctY7eO0WQk4ULtJebo0dxuCWQ+ZNcN5OTU1wIVIpgSEWTHQzq3rqYYTt2EUUIgJzqtos2p8w4q+ZrLFs3AskUzkJQPR/eNz+GQj2+DafwbGJZFX/c2TGEMsMGZZb9OOYh2gyU1Fkrnsbu9IxfIufUzJ/L/xFb+37LqZxwEu15JV7PvgYKpwuQaUyBTBLkBCfV3gWNMMP7iFf2xAtXv+XAsc3L7rEv2RaXnCmQ7UjqS6mpHIOd6Bau2z6+vZUj8yHwTXFqo4GsJwelIQALDWJ6mpXKyirFmXSd6zQD26ftL0Ra0vKlAK9KVSWbcYFPDhUxosA9T0A+t1TJ6n///folt3B6Y/cp/obd7Bz5Z+yJcTAqufZYM+92k0AS3VvwcOh7FTlpNJTgemWqeIkMA8JlRaFITvHZ1v1akgFJMRaFwtYsg63ZRWRheSFJl3aBGG0bwWGk0RnmdK/PhWOnlnkdTCsBnJoa9tm5HfJ0UiUxk1pNuNeug2nmVQo5ABsMgwXjAZkScFacSXRibzzvq3QPt6g7oA58hZXLomFF+VzqljGsEAJrMMLScKvxyInMMy6JT2hPNkU25Lwl9cDt0k0HbtDllj7cQLlsgGxmRfE6ToTZYBNnj9iBseuGLbwcACDVoGJSLykrg9FIpFrb/r1T+Pcqpm8hNN8qF7fsIYfgQ7CgtbB1cbi+2L/j/sLf2MdY9dx8AYHCXtYhwt1XXpnzYe3kd29EwYAtkVwUC+SzXr7FGOxKyaQlcJwUps37GQXRbCxE9p4GLYKbGxA1nNCCB3IBE+qxIZ6E20w5x1gu+TIEMe9XnyRHIjmDOza11fi7WgjcXJ9qcWy1vxCyB3NQ29PcoYjO8RaJClyzdF3uwlki7TTutZE5WIZwcK7eZRBDxov6SoqEUbVuaZN15m0V0fWx1UXLNsLbjRLcP7Jn3IGDG8elv/x3r//IgDBO47h+pYe+bEpvgMYp35zNsYeVrrtwCzckfyy2wAABD1xEw4zBdzWhqLV1AKenxrG5w1eL4AoeZYM1es9aYohcsY5a0CiyGcx4DuefR1QSOMZHI2XlxbuKePBFkhfVC0LLH4uRVunzDc9UTjAdcxuJOtS3XWGH0I8gAoDfviTYMwjO4ET1sO3ih/IiTJjbBV+IaUVUVTWYMhmf4NVJOKk2seX/soW0dVsQqxDrRx7TUJEfY67fFWcauH6fLUAqkddUzYTaAjtROAICQ5/tWLSmmtEB2mqwII+hCyPE8YqYbjFL43rNmXSek/g/AmjpOvmH1iOpeDjn5O/iM3QPtb/03NFVBzN6BCEytrk15Lk4kXJfDQCoOzWQhCiP/jl7w5SMgsx5ISKVTkJKsJ6t+xsFJzxwmkEECmRhDYnabaVewuEBOsj4IqTJbH6fiSJrCsMIqj892Z8jpWmSmBXL5QigtkHPzXON9UEwhy9NZk5oRKOK3u2zRDOw84FsAgLfN/UrmZBXCyWWexfaXzGUWTQVGEYGssJ5hogQAIjusjkvtc4dec9Z+i/Gn6RfjcO1tnIPnwCB/oaEuBRHIs72eielEHqvwHXYqkHPzxwAgGhkAy5hgPEG4PX7LG1gubIPkMWLDmlFUA2PnzMb42t9oa4WTopKbPjQSmGQYCVOCKGV/x5yc4XiO9ZSpxGCYDNze4RvwKd47rLDPcalw5WngIrMe8JkC2f4esOLYRJDFDmt3Za/4egyK+buDFsJwBeE3Y+nOqvkI93eDZUywvsoWkfz0BXAxKez8OLsZllvuwmAJB4JykVxe6CaTnlsBgNdlaA0okONcEE2w7xGjIZBZCZxRvNudVmGTFWvnNf990wqovIf9mG3wQS7ZsCMXjucROuIyzDJ34Z0n7oDWb0XZW6fXViB7AkEA9iI6lYAMqaJGNssWzcBxM4DH+aU4Tb0Gj/NLcdwMM+991hHIuf7UkqkCDSqQKQe5AVFClutDZkpC3uN4H6Q89lD5YFIyZMaF3KlYktxWRXxubq2agGryw27mxZDsG3luMw0uOYAQE8CUjAvYcLcgYMahpVLghfweinuKljC8+T9Owox5nyt7HJk4ucwncf+AUMJfUoJS1BdWZd2Q9OGFI8bujUiaAqbusU/W4yd23QkA4Bnrxn4e/wLOwwtQnhCARXYKgysIL5OEosgF0wvYZAhRuOHnK/eaFOzJLZ9Ajg/2ogkA52kBwzCIMH5wRaznPGYculg7gczajTOKubaMN4y9wCjkAlIOrBJGjPEiN2OSswtuEpEBAEPFa4waRZxxwc8O90/VeB9cyZ1ZjxmyJd69eez3FM6b1egkZUeBxiqC3DRrf+AtwMMoSHhHZplmulvgYRTEE3F4vfnFULi/C+0A+EBlPtqtex0CvAP0bXkbcz93SPrx5lQPuv2Ve7BnwrAs4owHbEbuOG8kCxYG1zMJoQWwnQ/c/tpftxrrKuk77kQyR9qmW2a94FP5gzMnPLEAy7gcT/vcObsEC5ecg41v3YF5H9yBz4JHIgIvAk21/Yzcbp/lzKJEwGoJJBkXKp2Rp3/rMZwO4HQAwEUFj3N58vtTi0jB5BtTIFMEuQHRIpYHbbC9eLQ0JQTgzmMPlQ9Wk6Fg+JeYYVlr9ZmTeM+mYkgwI5u4nXSMzBw7ABCVAUS5YPb7elrAMibCg4XdEvSB7TBMBm0jyFfMxfGX5KADyO9T6SCZKky+cERN472Q8kzanvAn2MnvATYnOn9M8jb8SVtsLUCQ32KK8VoTZ6TI58DZHsXV4Nj55RZYAEDcdskQ7BbgUbYJghrK+zqmYcBvxmFK5afelGJrwhL+M6LvFc0RH0+cCLwil7ljkwdBDSPODj+PQ10xsyPIrBqFjPzfR130wW3m7PokCwvkFOeFpA+d+5SdYsGNUc739LlDC1w9OLJ8TMc3utg1khiwggruYGXR3pl7LYBq8kh1vpd+TEul0G70IVXCgWAkJODOEsiinoTG1WfefTFUaUjwectoGT5SNM4FvkhjI2Co8HykXQiTnA9inloSwJqzX9GHFkSlOmzmg2FZmF+4Gm0IYdHgn8GZWs3nNavBkgeMEgWnyVBGeK+uBFF0QTcZIEMgaykVPGOMiRvOaEACucFYs64TWz7aAMMEvvbrvxa9qDTBD49ZXk4kpyUK5rrJjBtMjqUOqyWQHBZvLo7btpPKzWd2pwaREIJZjzndmKIDhf12uWgn+pjmqgq3HH/JVwyrGvlP+mF5c5lNw4CbUYGiAtmT1wt3SvIzhLzDRbwQnI4BBMDCLGgxxdsCOR4qHJ0Q1DASXHUCWUpHkIcLfMexQrIFssw3wZUK5X2dRCxsFZDWyNppzbpOPPuJ9Zl2MKERb2mOFU7DnNyGOiNB1CKQ85xHpytmbitoXotDZvPnVxqiH94cgcwoUSRNAbw4/LrVeC9cxtDxTttnboyK9P7ycQTdpiV0X/hUGdH5TXtzF7EeVMKWQPZW6KPNixJ28LPhG9yYfqyvZwdERgcbrN4D2UFh3eAydv1EU4FeZM6pVwy3NW9pJgtfnhz5ql+fc0EskWJhpt0bRiaQFd4HqUBgSQhOB28HUxSTr7g4/CPxAPxFPwQ8Y8IDZVTmNcvJZuwEMsOySELKauDi1DKAIsjEaOMUlB1ubgAD4KzEH4teVIbUZEXziuTmOfC6DLXARaQwLnA5KRaclkCSHdnE7fEEYJjMMK9gnx6GKmZHtUTb6ioRKtyxzS13YYCvrvWwU8V+v3gmAODP4hfz5jIrjnAssuWsC164cwRyLDKIqeiD1jK8eLAc83fREaXhwgLZrYWR5KoTpKJdyGLkiSArtkuGp8k6J4rYBI+eP9c2bnvtMjWydjrhiQW4l18FAGCr9LseTZyOk7ltVkeCW4tCydOW1x3I3xVT0OJQCl2Dkh9uRoWiDBUysUX8qXUhO+LsbE/zI2iyUCnOvGbY09Qh2roRiQVn4ZYsco1oEacQuHKbwMHAvpihfJKeTwd2fQoAcLXXzoEgyXohZBT6SmayaN1D3eK1AhwxeMBxtZcZOueCWDKCbDszjbBNtyb44TbyX8eXLN0XU5lB6CZwmnpVxcXhJzyxAF/kLF/tan38CyGzXvBazLq3j9F3SGHErGCamrT+zZBAJiphzbpOnLLqUfzjysNLbrOMuDmGqwkSkxoSd0Xg9MK5bgrrBpeTW8trMtQRCmSWY5GAC8jxZ20ywtBc2dtw7oDjt1s4KtSk9iDuKl6oWA7LFs3A1cuPBwD8+wJf3gIEx/aqWNtSM48X7q5PLAcLcfrwHOlyLKbcfkuUOl7H+XDrMahidVGadIMYdfh3Rbf9eb22QNakZvgLuAY43eD43OYVFXJM8jY8ox2WFk+VbGmOBU4hkFag0Uo5eIwYtDwC2WPncGpy9qJE0OJQC9npSdbrxCOh9ENcKoYEk//7a0jZEWfNFhf8GKRYOPPadNZaAJzEvzUiseB2Fm5FrhEj3gfdZOCvwunFmDIfrQhjd5dVWBXvsSy6gjV0IFB5D8SMVBcXFBgNGEF2uqLWsmFQJgbvggi16DGMXXjOcsNz9IuhiX54C+y8Lls0A13SPGzFdHxozqu4OPyY5G1Yox0JxbTS7kZjXlNYL0QtBt5Ijlmhp4LsBi5OqtZY+KmPBiSQa8xIBK8TOTkz/hAWM5tLbrM4F5VhWn3RS11UrN26NBoqfONwEIwkUgVy3VTWBT5HIAt6AqkRCmQAkBlXlp1UUo7DyyQBT3ZzBH+LlSuYKuC3axoGOoxeqL6RVbwXoqnNeh09mj9i7QjkYlX9puiDxKRgZFhBhbdbVe9tc/MbypeymPIErRtNqsjN32dGoVUpkF2e/BXIAGAkLIHs2I/prmYEzChMQx92rJMnWyvvUyE4HYPwA2AKpqHUA2mBnKeTYrn4zVja0i6TdFfMHHtEl5FAis+/fczZ134iFko/JmhRJAsJatGKOKuKFZUz7G1SYQwiyM68ljStXPORigWv3bzGabSSD17uQ4hpApOnoLFcAnMWAQB2bXrLer+BbQCAtpnld/0rhcZ5IWWkurjMJMwii/J6RbKbPsk19EPPgnfDVSKCDM0qPB8pptiU13fcYZreiah3dlkdNgvh1L4I0EdtXlN5HyQ9DtGQxyyPXWVdWfZ7qr2DNVbFvrWGBHINGangdSIn5/EvgGXMkhFh56JiYEIvozmGU7ySiBS+cThIhlywCYbGuYdVDIuGjBQ/8olbZtxgM7YQQ32WZR3ryxbIAbtdqRHPL5AHe3dBYlJgmkZW8V4IX6AZqsnDjOePWCu2uwMjFv6bHSeDRHyoAlrv2QjFFDB9zv6VjcsWpY5IzcXQDcviynZ6qBRBlKyq59TwHGomGUbcdKUdSxhPKzjGzLvwcvJkpRpZO5WThlIPSGm/8Mp8kFOqYi0U85xHl9sLxRQAJVcgx6EL+QWIYw0nR0NDY9TiULj8gtppA5+wj3caB42FQHbmNRFaRWIhkL5GCi8ixWQ/IjmFwCNl5v6HAwDk7esBAGxkJyLwpiP8tUATfHDbAjmlKla77wYUyB67GHKa0QNEC9eRVIrJu+Fmijfm4QoUnpfEFQDPGJATw50sDF3HdH0XlMCckb9uBmMxrzkt6iUjCb1AB9hao+b4U2tpNxxKsZj0jFTwOpETs8ztY+eiMgA8qh9T8qJyonhytLRAFkwFRgHBm+LcEI3s1AFLUI/8olNYD/gMr+BYvyWQhRz7JbfX9tstcNPr37XFGkeNOhAxLItBpglcIr8gd9qWFttyZiWnlXYo/Zgn9DF2cjMLWtWVwh9otrxRE/lt1aLRMERGh+muXpAmGTGrwMKBU0KIZrhk8F7bzWJw+I3PyZN18marpZw0lHrA8SKuVCBH7CJMpkBqSpTxgs1pG++BDF3IL3hFu1teZmGfZMSR4vMLataVHXE2Utb17nTHGk2qFQsub5O1uJNDBY9xpwYRr9JH29fUii6mA2Kf1XLaldiFPq66GohcDMELj13HkEjY36Uii/J65Z1+K1LvMeJYfduK2hfV2hHJYumDrFZZkxXWrp+Ih4fPuT07t8DFpMC07TXi181kLOY1XfTBY8YhmckxK/RMsS7wGQI5pY5tw6FaQz7INeSY5G24nH8Ap3BvgGFQ1FMXsCInaowHwwCGyZSMnCxbNAOPJm4F95ejsdncA8/5T8MlS/cteFFJPkukKLHCnrUOLjNZMNfNyOPO4DJl6BVEkBXOkxWNToQskeVuyrnROH67yVDe14nZ+X/+KbXL/4tyQUhqfkE+1Dih8Jah00o7mdEsoj25FTt981HpJizDcogwPjBK/nMYCzkexdULZAXSMLcSAODVMOIZ7gqi3ZAkNjg8HcXJk3X7a9ccYNmiGXUniHNJt3HNk8NdDvFwH1pR+DwmctrGm4YBrynDFAsIZO9wgewx4ugr0MCFt1MynMWdOYYpFssWzcAa3Ie7n9uMXSEZd/suLjqvDYNhEGV8YAvMFQDg0wfR4zmg6rH2ePZGe/xjAEBA6UbUVZsULwdT9MEDGaZhpB1Ris059Yh+TQeW2w4TDAOcpj8LPPEs9KckcFcWLroeCYztrqLI8XR6WC68LiNVQZtuzl5cJiL9QI6FaN+2DzENgHfafiN+3VxGe14zxQC8pgyFEWGM0S6ExrkgaUP1KZpqiWVujBoO1RoSyDVECE5HNGZ9EbUyUiAuWbovOh63BFmX2YwXjYMxtUTk5F86rLzPE/5lPq48+fNFx+P2BwEMtZgthsssXAyi855h+V5uMwmzwPZuMTTOA5c6lMag2p7O3pbh/qQxrgliAWGo2B2I2qbXLv8vIbTAo+Z/Py0dQS480fB2tbRi39iS8TCmmbvxWfM+BX+nHOKMD3yB1qdpj2Jf9RFbhZGGFWMCgCsVhswPFY85+YVKZHi03cmT9TfV3vu0nhFFFzSThZnbUKdMEmFrHhALnMck54WQYf8ly3F4GCNdjJeLc+1nNuXxmgkYBRq4CLYoUOL29yxlXe+uMYggA9WLhRjrB1/AmxsAgkYYu1zVfyfV1s9hVux1RCMhtOu70eddXPVrZiFZ2/sJOYakXfDJjsEipZacKvwKF8R/h1O418FmBIru8V6Ip2r0Hk7nSqVIa3dOT464kBwY8h3PTE9ySHR9BADomFP9Ymu0MaUABEaHALmoPWkt0VkJojF0X3CKfRtVIFOKRQ1xtgr74cc6Y6+SW4XLFs3Atn0vBAAEGLmsbZaonZIgNZXOz/PY9kd6IlT0ONMw4GEUoIDgNQVPln2ZaRhwQ4FZwdafxnuy/VajllgOtA7/exJ8sKDfLsI7ETclNLVUXpWei+JqhV/PL5B1OzLIuwovCoS0F64lMjodB4tp1U2mCc4PMZVfIMtpj+K2vM+PBJVxgdWSwx536xGoGQLZF7QEsprPYUQJQTEFuNyNFfWqlkINdcpFsdOgRF9+EZfk/VmRmbSdniu/4HXa++q2QNZ1HT5GhllAIA+LOGsyDJOBmMczuR5JcAFIBa6RRCJm/e3e6q8R1x4LwTImPn77RfgZGWbTzKpfMxPWtguMR0JQ7a6MXINFkDeE3YjBjdzC2vfDtfsuOcXSSp7Onw5ikbqaYkh2aqISH56aaPZ/goQpoW1a7az9Rgsu04t+jNJ0dN4NIcPJSU9HkCkHuSpM08Stt96KI444AosXL8aNN94IXR9eJe/wxhtv4PTTT8eiRYuwdOlSPPLII2M42vw4eUXrsR8CTKIswTtXDAEA/IyM5797aMkoStJuM+1tKW1v5rM7GBlFcvOAjEmmwCrPFLxwMSkYmtU7NCnHwTImUGB7txiW32pGlDLeh5TJwd80/OaliMGCfrtifBf6uPaK+ssXHJu7FUEznLfwQ7ftakRX4YlG9FgTUsruphbe9j4AoLWAg0W5JPkmuAu0Pk3ZwsodqEF0jHWBN4YLZJ8RhZbhruBvtqL9ep4bCKtEER0la6d6J8m4sgpQR0IqXvw8pngfXMaQGHBSIfgCHq/eQBAAYCat703Mtnsr1MBFsgVyyilM0hQoEGp6fY0mitAEt57/Ggn1WkEFzld9vvDUfayIcfL9pwEAYmtthZKTCy7HwmlPba7IorwemR50o43JzilvZ8KYHqxdFJGzo+qpIgJZMBToFQhkp35Ciw+/97ijn6GLn94Q1wXrGbrWmTFaZBmcC1LGbrNTyyA0aAS5blIsfv/732P16tW4/fbbYZomVq5ciebmZnzzm98cduzWrVvxrW99CxdddBFOOOEEvPvuu7j88svR2tqKz3++eNrBaLNs0Qy89cYcTO95H3//0fFgGKbo8WZoqHghtLsTXl/xhg9q2EpJCLSVzn2T7H7szk2yEMl4FC4ATIEIsnNxyYkovIFmyLEw3ABYaeQXnZHTTIOT+xFm/GjLYyavS80IRPOP3a90IyxW74GchbcDLiaFSDSEQFP2VrfTOKGYQJbsPFTdbqWtdX8IxeQxY95wD+SRkBKb0KLsyP+cLay8weqjY1pOgQUw1Dpal4ZyY/1NLVY6QZ4CSk6NIDFpBbIbXJ4c7nLQ7eLGQm15NSEAb0bzAicVgnPlt/eT3H6ruNMu7EtEB9AEgHXnP96JOBv2XMFoMlRGKNDIuv5IiU3wJj7O+1zUbjMt5tY5VED7zL0RhQfzBl4BAPinzKn6NTMR7FxwJT4kkIUGE8iXLN0X31+9ErJmBbiu1C6AW+CwqoYODU6KhVpEIEtmEnoF9mZp3/E8hdGtyR3Y7a0uZW6sEDKaNTEV3KsrweDdkDL8qXVbII9Fw6HRoG6WQffddx9WrFiBww47DIcffjhWrlyJBx98MO+xf/rTn7D//vvj29/+NmbPno1TTjkFy5Ytw1NP1SrDqTrMwAz4GRnhUGn3CC7elf53tH9X6deO9UA3GTS3lhaHDMsiznjAFshfdSiV68bYjyfjdm6tnWPLVBBBhuiDh1HS0WhBGUSEDeY91HC3IGDGoWX4Cju0aj2QPbUtkHHaW4d7h58HxxdWchX+m112S2DdvrG5Qh9jJzcDoihWNS5NCsJv5m/M4di/+WohkDkXhJwIsizHLDu9jMmW5ViEGT/Y5PAbiKDFCnvtTnAsD9DKBLJh34wDwfwpQ6YUgC+jeYFiF4KK3vwL6vS1bzflkaPW6zsFSLl47IizI5BZXanMImucMKQgAmZ+D2p50Jpj3cHqF9QMy2KnuCemwkptap5enZtBLk6xpJIIp+sehCJzTj2ybNEMrDrtQMwIusEAmBF0Y9VpB9a0II23BZ9jI5YPsUhdTTF8dv2EKWcHZ1KqgqlGD5SmeSN+zfHASZsCxq7Q0xSy/akNO8VCGIOGQ6NBXUSQe3p60NXVhUMPPTT92CGHHILu7m50dXVh2rTs/NQTTjgBxx57bNZjDMNAUUoYh48RYovlzdvf9RmCzcW3vt1yN5KmABeTQmKwq+ixAMAm+hBiAmgt0zYsznjBq/nFlYNTVFYoIuzYlznHKfY2LO+uTCADQCIRgS/QAldqEAk+mPdQxtsKljExONiL1o6hyTWZiKEFERj+2lYAS01W6kBsoAvA/KznTDsHWSiSW+uyxYqpWJ9Tm7wVnd79KnawSL+3Kwi/mYCWSg23i5NDUE0OLk/xnYdysNq3ZgvkyEAvPABYb3ZEPcb6IeQpoJS0KJL8yFq7ThTyNdQpm2QIsinC7SqQ5iQF4GJSUJMyRJcbKfvmLRYQvACQYDxg7Ws/aadkCIUEsnMztXc/GF2BylS3sBtLTHczvEwSyaQMV85nqISdQuDaNGGIBvcHdm+AavJo6ajxHJROdYlCty0DxUrm2XFmtB0aBHsnTy9iq+g2kxW5N7jcHqgmN8x3vHvbJsxiDPDte4/4NccDMcOLfszSdAQPREaDllLBCyJMxy6yQWoZcqmLCHJvr1Xs09ExtAXW1mZFxLq7u4cdP3fuXMyfPyRg+vr68Mwzz2Dx4hpXFFeIt93KS4v2bC15rD+1G9tFa0Wqhob/rbmIyb6CEdd8JDgfBK14ikVKdhwaCtjl2MI5aQtjxY4kcwUKhIrhFBUlo9bk49NCSEr5ra14u3lIdCDbGqi381Pr+eY9Rvz+xfDYN1A5j78v7Avd5Sk80Xi9fhgmA1OJQ5VjmGr0QA1Wvx3HeJrBMiai4eE7EmmP4hKpPOWg825IORHkhOOS4c0+RwmuCWIe1wC3EYNWoLvbRCeVp6FOuXBKGFGm8OfmpEbEbLcLx53C2bXIh8x4wNuuGk7xXWZUKev1OQ4x0w3YgprTFWgNJJBZj7WAiwwMLxx1umMG22uTkrVDsuZrAwxOuXFNTT1+XT7r/GhyKJ3WJTWgQB5tnIikUxuSi2kYcEEBhJFHLhmWRYzxgsnxHe/fvhEA4J9RX02KCuHJsNoUxijFgrH9jpO2pjA1K2gpFElNrGfGTCArioJt27bl/S+ZdFYZQxOy829VLd5vPZFI4Lvf/S46OjpwzjnnjN4fMAKCU+cAAJJ924seZ5om2vR+hJoslwOjjI5DLnUQsREY3iucH6JWvP2taqdYFHJocFafznEpextWLFAgVAze3i6U7RzKgBmGJuW3thIDlkBOhLIFcqjL8kD2dMwZ8fsXI9BqpWyokeHnwUwloJsMxCIdgXiet50MYuja8h5YxoQwrbr8YyCzMcdwD1FBDSPO1uYGanBuSMjehZFtgSzmuGTIQhCePAsvjxFHSqg+mt2I6Hka6pQLr0aQKHIeneYFCTtVwkgL5MIRZIXzQrCdLzTbWcVVxA5QZtzplAxOTyLFNk6KBW//XbFwHmeVeB8UU4CnBt0d16zrxP2fWZ+5hFTJbqkjxTmfuhyDaTuikEAejhNV1wukWCiKDI4xK27TnWC84NXs+S3ZvRkAMHXu/Hy/UndkCeQx+g4x9uedtB1YHIEsFdgZq3fGLMViw4YNOPfcc/M+d8kllwCwxLBgbyE7wtjtLvzBRqNRfOtb38LOnTvxhz/8oeixY0nr1NkwTAZGeGfR4yKhQTQxMszgbIT6fGAT+dscZ+LTBtHtK79tcYr3wSsXbsEKACmluEBOuzPY269OxLmSrT8n6qzEI0ipSQSQgOnJn4biNA9J5tz0kn1bAQDN02qbC9bUZkWQzehwIcpoSSQhwVuiejnBuMGk4hjctgGzATTPPrDqcTkex4k8vsNCKoIEVxtBagpuSGb2glSNWe/pznEZ0aQgvImNw17DZ8ZhFPDmnehovGdYikq5SFoEMld4wTnkzWrtIuh2Go8vUFj0qZwXLtv6TLcFsqdIA5cE6wFvL6Y5Q22oCLJjj+fseGTCy30IsU2YUoNdlhOeWIBlfAqAtWlzHv8CzsMLUJ4QgEX5u3COBI/tX20qEcC03HRcXhLIuYj2vcrI0/kTAJREDC4MFfONlATrg5DKTk1kB7YgBB+CbTUuDh8lvBlzg1DBbm8lME7xpONPbduGig2agzxmEeRDDz0UmzdvzvvfySefDMBKlXBw0i7a2/MXrQwMDOD888/Hjh078L//+7/YY4/abrdXAyeI6GeawUWLF931d1ntkoXmGQixzRCTpSfYoBmC5iq/IEsTs6vf86HbxSBigY5ETpGIZkeQDfvmLFaQ9+pUaatyBOF+K6WE9eY/x46dWCqW/blogztgmAzapteuix4AiJILEXjB5FmoMJrVkagUMuMGp8WR6voQqslh5p7VRxsk2/pLiQxf6Li1CJRaRWx5t1VAqQ/Z3KXsLoyeHIGsu5oRNCNZlnhqUoaLSRVsXjHRMXgPXBUKZJcWLXoendQIxc4lZpQodJOBVCS3UBOGrOGc4jvH/i0fCuuBYLeB5w0FWgNFkF1Nha8RSR1AlAvW5H2OSd6GNdqRVo4qrCYYj2tH4ejk7TV5fbcnAMNkgGQs3ZWx2DmerDjBGbNA50rZjmBWam+m8l6IevZ90xPbhm6+tr7Xo4kgCIib1jUsFbi315oh+z37s9OSUE0OLMeNyfvXmrrIQZ4yZQqmT5+OtWvXph9bu3YtOjo6hhXoAVZ0+dvf/jYGBwfx4IMPYt68+qsqHRTa4U4WT5mI7rZSMDzteyDOt8BdoM2xQzIRgw8yzAKCMh+6GIDXLFzIAAwVOkgFVpmSXXxm2Mc5Lg3uAhX0xUhHoxMRRG2BzAfy/z0Bu7ueEc8WyFy0E31MM0Sp9jfwMNMEITn8PLBasqyqfoV1g9fikAY/wk52Btzu6osTvE3W56PGhi+gfEYUmlh4m31EON2pkkM3Hd12yQg055wjdwtERoOcGNqGdPJjMx0vJhMm7x7WcbJcvEa0aGqKy7aeSsWs88GoMcQZT1E/Vk3wwW1a59JUItBNBu4COciAFXEWHYFsqtC5xhHIXrt5jRYffu16UoOQheo7TQJWt9QY3OBhZDXBKNQtdaQwLIsE4wJSMSCVQMKUGsJzd6xJd3hM5V+QqrLTpruyCLLC++HKEcjtyg5EvfXfICSTuG25KXnGJoLM2bnOqn0PYXQVKhpnJyqXurnyli9fjltuuQVvvPEG3nrrLdxyyy04//zz088PDAwgHrcm79///vf44IMPsGrVKrjdbvT29qK3txehUGicRj+cmDQVTWrxvvPJfsvbNjhlDpJSK/xa/i5uDuE+KyLN+Ufg5+lqgpdRkMpjleZgqE4EOf9q2+XJ9vc17ZQMVwnP5nw4YjslR5Gwi+FcTcPbTAOA2+tH0hSAHL9dj7wLA3z+36mWqNACl5qnGE6XoZYRUbOicAm0yp+h3z2nJmPy2dZfemz498NnxqBLtRHIjF3Q4tj+AQASg1BNHu6cCZa1u5JFMgoo4xHrc+OLOCtMZEzRa0fgCzc4KoTPjBc9jx6neYHdFZNVY0ig+M3fFH3w2AKZVSIlBXWK90GyI86CqUBvoAiy37lG8jSv8eshKAXqHEaK0y01swlGsW6plZCAG5waBavJSDKNcw7GElF0WT7fBXzHnS1+rsLiNF3ww5PVmCeCKeiHFqztruVoI7PWHDFWEWQ+p4ELoyWhMuU5btUjdWHzBgAXXnghBgYGsGLFCrAsi9NOOw0XXnhh+vkzzjgDp556Kr73ve/h2WefhaZp+Pd///es1zj44IPx0EMPjfHI86N5p6Et+iZMwyh4U9LtJiGt02Zjm7sNQbvbVSEi/V2YAkBoKj8HyumcFQsPoLlA7pRpR4YLRYRdnpztLFtQ54qmcnCKUIxkFMmw9bl4mwuIXYZBmAmAk7OFYVDtQY9vvxG/dzkkxRa0JD4b9jirK0iVcbPSOA8CqV2Ypnfj0+aTajImvx29dTyP02NVFPgZGXBVX3wEDEVblAyBzCohRBkfWnO+w4Ldxjw2uBvYw3LqSNoCnvfUZjyNhlOgIiei8Nq5pOWQSqnwMTLgKvw7nianK6aVS8xrMSTZ4nl9phiwF8cpcKkYEowHxZa0muCDK27bGRoqjAaKIHv8QegmA+TMFaZhoNkMY5u7+k6TgGVftgb34e7nNmNXSMbdvotxydJ9a2pplmQ94LQ4DFOHQgI5LwzLQoEIpkAO8lCTlcqEoS42ZfmOd239EPMACB2N0STEwblneYwEgNosEovhCGTHw5vRVaRAArlqOI7DpZdeiksvvTTv8y+99FL636tXrx6rYVVO0wx4exQMDPSipS2/AORiu9CPIFpFF0zvFPgYGYl4BJ4CQlUesHySPYUEZb738AQBAPFwf0GBDHuSKSR4nfE4VdVQ44ibErwV5BW5bYFsKjFodoWrv0jTkzgXyLITM3Qd7UYfdnpr2yTEISW1IhBbN+xx3iivqj/FezBT3gmWMcFPKb+YshgcLyAKN9hkKOvxaKgPLgCokSBlbZs/JTEkkHklhBjrR668cNlpMZkFlIrtsCDWwC2gEWEkx6FlZAI5OtiHFhRPTfH5miwLwaQlkAUtXrIhi2OpmIiGwKeikEt0ODREH7x2l0sRKgy+cbxLGZZDhPGDyblGIuEQmpgUMIK0tFKMtsdvkvWA1+LQTBMK0zjnYKxRGAlMgQiyE8EsVHheEpcfHkaBpirgRQnhHVZBcnBWbeb0sWDNuk4cpg3CBPDkL38A9iu3jOr3FhhakOi2VrACS5RiQeQgOM1Cdg2PRjq45G4M8NbEzQWstInBnsKFfUrYSknwt5YvDp1onlP9npdUHElTAFdA8HI8D9kUAdtTlU3Fkaxw4vZmCGQz3gfDZBBsKSz4E3wQrlQo/fNA7y5ITAoIjk5RpulpQ9CMDUtJ4fXyipZ03guWMQEAzXMOqtm4YowfnBLKfixk5STz3tpEBpwCi3QFMgq7K3jsLW0lw1lDtdslF/Pmncg4jXYyFxjlELOdFzhv4YUFy3GIMZ60N6uox5HiiqdYsPbuUTw6CEGLQylxvCH64UUChm5AMlWYDRRBBqzmNXzONRKyu5PyI0lLG2dUzgtJj4PXZaRYEsiFUCCB1fLnIFfdhdDezYnbi36lx2pjPnVu9badY4F+TQeWPfE5TGcGwDDAafqzWPbE56BfM7rXgWg30nLs91hDJYFMDMdnNwuJ9W4reExA7UVcsr6wkt0GNdpf2E9zyPC+fIEs2dE8JU/+qgOTkiGXELwy4wKbsr/0mlyxQOY4zqqsVWNg5X6EGR84vvBGhiIG4dWHOhoN7LKahEhto1MswfrbwTImwn3ZTVsEU4HGlbaqMQRrgkiZHGbuVTu/zATrh5DK7uyU9igu4m07EpyGMKmM7lQuLYqkMDw31mfvYmgZhYO6nR/rpANMNtIFKjktaksh284LQomFTozxgrW9WSUjgVSJhiy8XRCbjIUg6TGoJY5nRB84xkQiEYUIFWYDRZABQOb8kHKukbhdCCwVqHOoRzTeA1FPgNeTJJCLoLIiWD2/QE53Iaww95azG/PE7eZMfGgLetAC3wh2hsaTU4VfYY12JFI5bivLhLtG9X0dz27DTsfkDAUaSwKZyKF5mpXMn+wv3CykVe+F4rGqnz3N1v+LtpuO9yJmuuHxlp/767KLe9QiApnVZCRLODQkMwQyr8WhMJX7GsqMG2wqBiE5gDBbvKBLk5oRMIduerHdVkQ+MGV0iiWEgHUjDfdlnwfRSJaXk2nbCu1kp8HnqV33IJkPwJXKMa6PWsLKFaiNIOVd2fljAODVI0jlEchNLVYE2czIi9bt/FhvYPRz3eoRzo5WORX05ZK0d3ckf/HPTWa8EFK2g4yRgMYX3z4W7PQqJRaC24gjJRSfN1jbgjEW6oPI6ECDCeQk3wR3TvMa2e5O6mluDO9awCqWdJkyRCNZ1qJ8sqIyLnAFBLLjuOSqsEEG7w0CABL2temPb0Ov2DgWbxvCbsTgBpfjtvJ+eHSv6SGBbKW+WH7qjZuDTAJ5lGjpmAXNZGGE8keEo+EBBJgETL8VDW5qty6+VJF200LSMrwfCW67MYBT/Z4PTktALRGpUBgXOM0WyLoMtYqJO8m4waXicKUGkSjhT2q6W+A3E9DslAe134rIt83Ys+L3L4bLvpHGcxYqgqlCL+NvFjjrkgpJtc31UsUmuPVs4ZWyBbKngE3eSBHsfL1Mgewv4JIhCKLlGS1npO4kw9BNJp1GM9lwtnPVRHFbxVwc6zZ3iYVOkvdBtDvjecwEDLH4zd/xTlbjYXjMBHSh+PFOSkbMrnVgGkwgp8QmeHKvEbsrZqCtNjZsY4HjPiKYJJCLobES+AKdK50IZqURZNEWyM7idUpqJ+K+ORW91ngwPehGG5PtttLOhDE9OLrfJ5fthGWmnAiyCp0iyEQuLM+jn22BEMufU9zfZQk9odkSUk7ahBEr7J0sKQOIjqDNNAB4A9nV7/ngdBlqiZQJlXWD061VoaAnoJbIZyyGVYSSgFcLQRaL/z2Mt9VKeRi0i8HCOxE3XQgER2cb32cLZCWcvVCRoJTccl6zrhPM9r8DAHh5d83azwKAJjbBZ2bf/B2PYl9z+Y1jipHuTmUXWKiKYrkruPOfowjjB68M7UwwSgQxxtOwpvDVItpFrloyTwQ52g3cewKQp528Zudue5uKn0eV98OlR2HoBrxIAmLxiLBk3+TVRBg+MwFDLG7LKNj2fPEB+7svNJZA1qUg/DnXiBGz5o2mBhLIhuCFx5QhGUkYXGOdg7EkxUrgjQK+43a9zEh2WzNJ+44nwogM7EYQURjNoxOUGQ0uWbovvo+VuFK7ABvN2bhSuwDfx8qa2hHmQxRdVqMbu/CfN9SGsovMhQTyKDLId8CTzB8Rjuy2BLK33So2E0QJIfjAxAu3m/amBpAcoeG91x+0qt+LCGQr1634yjLFuiHqdmcnQ4ZWhUBWWTcEPYGAEUZKKi50eZ8VHY0NWMJCiu9CL9c+aub5TR3WgkWPZHtYS6YKky/8GTlFEYdiEwDgQOazmhZFmK5mBMxYVpc7Uw4BAHwlhFW5OGbyuh19iYSs7yJTwCUjzjVlOYxwSjhtTD8Zcbp76Up2BHnNuk6svu0/YWx9A6tvWzFs4WQmLIHsL7HQ0QQ/3EYciUTEKgSVikfHnLbFWmQ3REYr2eFQtAWyUwzMCo0VvTTdQQSYBBR1SDQxiT7E4IYg1S7dadSR/BAZ3dq94Rto3GPImnWdCMs6Zqmf4uRVjw0PRqgyNJOFIFQmztx2XYcWD6Hnsw8AANLUxrF4W7ZoBladdiBmBN1gAMwIurHqtANH3cWCYVkkM+z3BLOxI8h1Y/M2EUm4pqA9tinvc8k+q0lIZi5tmG2GUKTddMAIYbdr4YjGwHIcIowHjFJYIAtGEjJffFs8xbnhdgqETBl6EbFYCpX3IqDuRsCMwSzhTyoGLNEQt8WaL9mNsDh6+YT+QCtUk4MZG1qomIYBD6MUzck8VfgVLoj/Dl/m/gkXk0LSFPBn/TDc470QT9ViYO5mCIyOSCyEQJM1eTPyIGJww8fXJsfLydcz7QrkeKgPbQB4b/5zlBSa4EkNpVjwWgwyOzaG9PWIs8AwMgSyfk0HljlRLruaHE88C/0pCdyV9iIsGULSFOAqYUlliH54zTgSkTB8GEqJKIQjkM2IJR7YAp0yHZyUDN2OcjMNFkFmPNZ1ERnsQ/sUSwjwcj/CTBMa6VvJStZ58jEy0GCLlLFgzbpOXLZ6A/7MdMMDBWfF/4DLVlvXjiMAGS0BmZHgZ5iK3sNrz7G6HEKk07qHt8xqDAcLh9G2IyxEMsN+jzcby089F4ogjyIp33S0G31ZUT8HLbQTANA2bciNIca3wFOg3bSuaQiaERiekUcL44wHnFq4sl405JL5tTrvhmTYfslmEkYVkQ2N86BN321FwXzF82fdTdl+u636biRHyQMZsFbAIaYJnDy0UFEU22uziGBwiiJEaEiaAkRoNS2K4GyHg9jgkHDn1AiiTO1u/VJO/liihEuGKgbhy3AYEbUoklwjSZHa4vI4LdmHbN6canLNtKbapCkMqyZnlXBZ59GQmuAzZSTs3HOnqK4QHts7WYhbaV5siQ6HLse/2t7F4sTGEmeO3WE8NHTtutQBxEaYljbeZC5kTIEiyLmc8MQCbOTOxhx2NxgGOI9/ARu5s3HCEwvSxzApGUqJwvNi+AL2d0YOI7X7I+gmg6lzRjc9YaJg2e85EeQUjAaOIJNAHkWYphlwMSkM9A1Ps2BjXRhAAKJraAIs1m463N8NjjHBlBCU+UiwPvCpwpX1oqmUjAjrvBeSaRVEuMxk2s6sEnTBiwBsI3d/8b/Hb9uJpWK9kOMxtCAC0z+61cQRrhmiMhQZTfvaFrlZjXZRhOAbfvMX1TAS7MjbfRdCdLah7e0xJWq9VyGXDF1qRsAY+l65tdJWYhMZt53vmG6oA2vhxMEAB2uRnK+aXFAjSJQReWdcTWAZE7Fea/dJKCGQGZZDnHHBk+yxjy8ukD1+63neXhxyUmMJZNFnfU+dhR0AeLVByGJjuapktWongTyMY5K35ViYWYvOo5O3p4/hdLmqJiu8ICBmugElAjH8GbrYKXC5Gut6GC9UdshdRESq4ewiM6EUi1FEspuFDHR9irYp2VFPV6ILg1x7VvNHrUi76XDfLrRgyIZsJCjcUPV7PlxmsqRANgQP3EhC11JwMam0nVklZIprqal4jm7A7rJnxPrQ2/kJ9gDA25/raJEQmuHOSB1QklZElSmy3XnJ0n3x/dUrIWs6AOBK7QK4BQ6ralQUIdmtnZPRjOiYFkaSr6wIJR8MyyJhSoAdQU7ZFdzepvyLGNPdAi+ThJqUIbrccBtx9Aq1E+yNhii5rJu2/fkB1sLp8MRGqODwhn4AjmHfwyxmd9bCSSzQjCUX1ha4iT7LOlIoEREGgAQ8aE5ZEWHBLtorhJOSIdmLQ67BtvdddjqWknGNBIwQ+l21a9gzFmQufNgGi+KPBUJwOmIx97BFpxgcKsTktNKF56Vwdl6bEtvRL81C45i8jS8qIw0JZFOFyVEEmciDr2MOACC6e3izkIC6G1EpWxya3o50u+lcErb1kqt5ZNXYa9Z1oi/JYI7yUf5iBgAuUylagAZYW31uM4lEzNpSZ0pYTBUlw26qVNtst9ePpCkA8gDC3ZYHsqd9TuXvXQaq1IpARiRfsTvLcUUKfUa7KMLT5Nz8h1Jw3HoUah6P4mrIzB9Lu2QE8wtkxs5NjgxaEUovYjBKOCtMdDIb6gDAinm70MGGcbN2Fi5LfQM6OOxCe1Y1uUuPQuFLLywcb1Zj0IogS97S515mPWgzre+MVKLDoeTyQDV5eO3FId9IhW0AvPb3VLWvEU3TEDSjMNy1KWIdK4SM88pUEYiYqFyydF90sBE8qH8eYdONz8xpmMKGs64pzkiWtC4tRYL1QUhFME3biYR/TpWjnjxorAQ+M4LcwDnIFEEeRZqnzQEAqAM7hj3XYvShz7Mo6zHOjg4P9uyCZ172DdMxvPe1lF+g5hQzPM30FixmMA3dKkArkTLBiF5wjInowG74gZIV9EXJ+N1AS2nBH2YC4ORByHZXwuC0eZW/dxno7lYEQ2GYhgGGZZGyfYFLRXNGsyjCG7QWU1psKLLtM6PolYI1fR+FGWrfaiYGYZgMfE35t6gFe0s7OrAbLR2z4DWTMPN4Jk8mkpDA2ALZ0HUcuvkW7EI7nvd+Fd0RA6vxeZzBvoT+lqE8Za8eRUjcu+RrC7abCBuzFrmuMvymFdYL0bB2Ndy+0rm4ccYDvx4CAPANlmLha7YEsmEv7EIDvWhjjIrS0sYTV4ZALrYon6wsWzQDa3Affv3cZgTjcRzObkL85HtxasbcK+gyUlVa5CU5L1rlrdb9sbVxLN7GmxTngqTFoGsahAZsOJQJRZBHkeb2GZYjQjg7ahuPRRBELN0kxEEs0m5aS7eZLl+AOcUMe7JdBYsZnPQBUywxEdsCOmaPjStRcV8MxjUkkJtaS9ugxbgmiOog9NAO6CaD9ulzKn7vsvC2wc2oiNuRfDXpRJDHL5rjD1pRMKdznaEblguIq7aC1OpOZUWQGSWEKOMBW6AVuGRvacvhXsSig2AZE0yJPNeJjpLx+a3702+wp74FnQevxF9/fAI+u+EkHP+NGwAA25/4Wfp3fAWaseTitI13JazFsrtERBiwHGMcPP7SAjnBuBG0O1c2lDUaAF+gxbK0tG3zIv1WcaLgr43V4ljh9mUK5Mmb01+MZYtm4LVLP4+pi05EBxPCgVL2PVMwlKqbrCi8H7MM63U9U6lAr1x01gXRUKDaxe0M37gRZBLIowjDcuhnW4c1C+nv2goA4Juzc2m9xdpNx3ZDNTn4R+B56xQzqEWKGeS4lZvMligGcQSxbI+tmombs+2pwqYXklR6dZngm+BOhcBFOtHHtEAQR/eCY/1WJD+02zpvmm3bxZdaRIwiotsL2RQB2br5R2MRqx1wgSYelaKyQ/ljXDKEGFM4ZcLdZH1OSqQX8bDjrDDJBTLrBq8lkJTjmLH2ZnzC7YlDTvpG+vkpM/fC+vZTsKj/aezauhmpVAp+Ri4r8u62K+sDqpXS4uQMFyOVUTTpbSr9XUmyXut7hcYTyAzHI8p4wSSta8RpeCIFR163MZ64M84r72qsczDWzDn8ZABA7/o/Zz0uGTL0KiPIqYz6jtbZB1T1WpMJnXdDMJNQ7eAbSCAThQgJw5uFhLutVAF3W7ZAbrKjw/naTXOJfoSYJrBc+adMCE5HDG7wRYoZkglbIJeIjrK2IFZCTkV85bmmnB1BDpfZNlsVg/DoEXjkLgwKo3+zk2zh57Tc1W1fYME9vvmAUcYHVgkBGLJ7Yws08aiUFOuCYEdAxVQYiSLFY74Wa+s6FeuDHLVECe8J1nQ8jUaKdYHXZax/5AZMRS+Sx189rLPg3FN/AgDY/sR1iNquJEwZn5snYKW6tOl9UE0OUhkpEE576aQpQBBLCwaFG/qOi+7GE2cxxgfevkaSFaSl1QOejBQL3kUR5GK0z5iHbewseHe+kvW4aColrUtLodv1FIopYOosSrEoF4NzQTRVpBQr0NJofuqZkEAeZRKuqQhq2d3xEv1WFXrTlNlZjxdrNy0qfYhwIxNDTjHD/+nHAgDWGXsNK2ZQZSsXspRAdtILzFj1Atmp0o7zwbKO16RmBMwwgqkexF2jf7Pz2O2mnUh+WiCPc05mnPVDUK3t74QdsXW8X2uFzrogGLadnxZBskjxWFOLtZAwYgNIRqzUD7GEU8JEJ8V50JTqxec+/S3Wuw/H/KNPHnZM+4w98W7HKTh44Bls+/AtAABXxkLHyQX3MTLijKesbpLOTb7cDoepjA6ZjRZBBoAEF4CYsq4Rze6GGWgdPd/00YDlOMtNBoDkJoFciu72I7FvcgMS8SGnJheSVXtIG3bnyU5uOvgCaWbEcAzeDReGUixYEshEITTfdLQb/dB1Pf2YMWg1CWm1i/gcBNFltZtODG837U4NIj7CNtPLFs1A4tT7cIfv/8MuswWdzDQkTr0vq5DMEch8CYEs2F3CnLFJnsrtvET7d2fqO4Ho8MVALoa7FU2Io8PoRco3+p2BAm3WDTVl32B1+0IXx/lmleQDkFJWXrQctc+Dv7YV+hrngmB3fvOUcMlwub3WjVwegBoP2eNpLM/ZWrJmXSf65RRmml3wmglsmr+y4LFzl1lRZM8r1wAY8rkuhiS5rTQbADJT5mLNFsgJpjyxoGU4zLgaMIKc5ANwadY1YtoNTwItjZViAVjFkgAgVhGImCx49v8SJCaFj956Pv2Yy1SramZlvYg194Vco2srOuEQ3HCZClKKY49KKRZEAZimmRAZDQO7h4oImNguhOCDyzN88guzzRAyurg5BLRBqNLIxYdTzNDn3hPz+R3DXBZSjkAuUXQn2N2dRNkRyJVP3O90pwAAPj2C1betyGs9lwlr24mJjAYmOPqTVbDNSkHR7cJIw3YlEKsoTKwFihCAW7du/krEiiB7moq36h4pBueGaDeE8ZpR6CVcMiKMH5wyCM0ujHJPUoHsOMbsYVoLvi3mdFz9plHwu902Y0/83X8C9tU/AQD8/rVPS14HABCzI8HJMgUv7Os2yZX33XUEsmEyEMtIyag3UmITPLoVSWQS/QjBD7ZGrdjHkqS9AKIIcmn2PmwpVJOHvNESyFoqBYlJFfWtL8WadZ34x6fWrtjGuLesa5OwMHk3REZLB98azU89ExLIo4zUatmL93d9NvRYogcDXP7IX75206ZhIGiGoFXh5xlv3h8z9R1IqUrW4ymnAK3EROwIYrdqTRoub2URZP2aDiz/5xkAAIYBTtOfxbInPgf9msKV5rxv6O+W2mYXPK5WiC4PIvCATVgLFUN1IsjjK5BTYhBeu3OdI0g9IyjaLAedd0EyFRi6jiYzBsMVLHp8jGuCqAxCT4Ss8ZThlDARyXSMAYB92F3DHGMyWbOuE9eEvwzdtH4+Xn0Fl63eUPJGnGCt76DClSeQnYJYpcwW4KYtkBUIZaVw1BuaFITfvkZEpR9hNji+A6oQ5/xKXhLIpXB5/PjYdSCm9L0BAJDtuhpUWFTtLHYP0dcDAOYa28u6NgkLxrZDTdp+5BylWFSPaZq49dZbccQRR2Dx4sW48cYbs9ISCqGqKk466STccccdYzDKkeO3m4XEM5qF+NXdiIn5BWG+dtPxWMjqXuet3M9TnH4AREbHzk/ey3pcty3MSok/yWNN1H7NEsgeX2UC+VThV3hS+xeYtjCQTRGPa0dhmXBX4bEHhkRgYMrcit53pISZIPikdYEzdutl1zgLZMMVtKzdTDNtZeVvrq3Hq8m7IZkKopGQ1dq8hEuGzAfgSoVhJq3Itq/GEe1GwXGMSadA2N/rTMeYTE54YgH+yn0XHGP9fAr/ZlFB7SDbLanVMiPCnO0qkuLLO96UrIWwyjRe1BUATFczAogjpWlwqQOI8423YFuzrhNh1bo1n/vLv5AwK4P4rGMxz9iGrp2fQbEjl0yFAtlZ7B7LvQ8AOJLbWNa1SVgwdu6307CHa+BukHUjkH//+99j9erVuP322/HLX/4STz/9NH73u9+V/L277roLn3zyyRiMsDJap1uCLrNZSIvei6Qnf4OMlLsNQSOU9ZhjN8b7K8+la5lnNSXp/3Rd1uO6Yk0mkqu44HXbecNBIwTNZMuqoM/HhrAbEXhhgkHSFNLOGu+HC68y3RntqFtnjE01cZwPwmW33DU1uT62nN3NcDMq5EQMphxCyuTgqiIXPB+m4IELKqKDVnoJ5y0uMBSxGR49AiTDSJgSRKlx882qwXGMkZDK+l5nOsZkMlJB7aDatm0pobzIIm9/PzShvJQoxok4ozHPI+NpBsuYiIb64dNCUCpISxtPnOhlBwZgmsAZif+j6GUZTFl0IgBg21tPQ0nYhecVCuRKr03CwlmYpOJWEIcbZVvW0aRuBPJ9992HFStW4LDDDsPhhx+OlStX4sEHHyz6O5s2bcIjjzyCefNGt7NaNQRapiJpCulmIXIijhZEYPgLVFbnaTcdG7AEsliFn+eMvRYgZXJI7dqQ9bih2vm13uIRJpcdMfYwChJwVbz9Oj3oRhsTwQP6EpyqXoMH9CVoZ8KYHiwsuN/ptcJsmsnga3f9dUxuFrLYAq8dLUdKRhLiuG85O5ZukcE+cMlBRBmfladSSwQPBEZHpM9usuArHhHWpCD8ZgScEknnx05GHMeYzO91rmNMJiMV1A6qLXSNMiPCH/ZbWzUzB94s2Go+E9YWyKkGjSBzdr1CNNSLoBFCytVYOxpO9HIW21ewuRMxnD32X4x+BMF9+hJU2UqxKFV4XohKr03Cwun+aNqe/TxFkKujp6cHXV1dOPTQQ9OPHXLIIeju7kZXV56mGQB0XcePf/xjrFy5EsFgcIxGOnIYlkUf2wYxbv0dfV1WqgUfzO/GkNlu2iExaBX+eMtoy1wIUXJhJzcLnsHNWY+bdg6yu0TRnSS5kHIajjCVR1IvWbovvo+VuFK7ABvN2bhSuwDfx8qCQmLNuk5c/zc7mgkTZyUeGpOISsrViiY7ks9qMhRm/FfBvC1W46Fe8GoYMXYUKtztgop4n2VFKAWKCwzT1YKAGQefCqXzYycjjmPM3b6Lscmcjbt9Fw9zjMlkpILaQRcsAevYtxVjzbpOPLDOuklNZQbtVvPFrx3HvjFVB9/3SnDcQCK7d6KJiQOe2ubojzbDmztR9LIcGJbD1qbDsGfsn0g6za8qLKqu9NokLJyGWowcAjD+9qjVUBfmfr29ljNCR8fQVnpbmzWxdXd3Y9q04cLwd7/7HZqbm7Fs2TI8/PDDYzPQComI7fAqlml9uOczzALgbt0j77FZ7abn7QcA0CLW71br59nv2wszI+uzH7QjyKUEMsMwkOGCgDiUci2m8uAIhpue24xdIRnTg25csnTfgkLihCcWYBmXssdgRVTOwwtQnhCARcPdPmqF4WlHsC8KLZUCoyl1seUs2gJZjvRBTEUgF2niUSnOtmTKtiJ0B4rnODOeFrCMiUCyC0l2chcULVs0o+D3ON+xa3Af7ravg7t9Fxe9DhwMO0cYYunP+oQnFmAZP7Jrh/fYOcuMWNbfUW+47XqF6C4rEMD6apujP9oIwemIxazmThS9HBnMXp9Hy9rnsXHTqwAAocJur5Vem4QFb0eQWbujJQnkMlAUBd3dwzvEAUAyadlKieLQpOz8W1XVYcd/9tln+N3vfofHHntsFEZaexKuaZgRXgsAkPusXOTAlPxuDPnaTeu2521ze3WTZKptf0yNvIDwYB+amu3IipaAbIpw53T7yofMSAggDoWt7gs/EiFxTPI2/Jh/ECdzb4BjTMimiGf1xbheOxf/rGoUxWF8HWAZE/0D3eB0GSo7/oLBbTtWKJF+BPQIZKn20TGnIQwi1g6Gr7n4e3B+S7RP0buwxTW/5uOZyIzkOkhje7MyrtKLo2OSt+Fy/gGcxP0DPGOUde2ItkDW2PFfEFaCJ2h9X43ejwAAfKCwO049csnSfeF53IpePqQvwXLuRUyl6GVZzDvsK8DaS9G83bJ7q6bzaUXXJgEA4OzIvdPUigRyGWzYsAHnnntu3ucuueQSAJYYFgQh/W8AcLuzP1zTNHH55ZfjO9/5DmbOnDmKI64dum8a2kMD0FKpdGSubXp+N4ZAm91uOjy0mGATvQjBh2CVye6emQcBnwK7PnobTYd/GYDl0JBkJJTzFVYYF2ACqSoF8khwIioMMKYRFdG+sUb7usDpCtQq0kpqhc+++WvxAXj1KCJC7QsWnQiykxIUCBaPwIl+63kf5LILwYjK6ZKt+bFj8x9w8qr5uPDL/1LwRi4EpyMa84CFWfa14/IFATSuQPYHrevWE9kCAHAHG6vNNEUvKyc4ZRY+5eZiv9RGgBn/xk6TFcFujy7aDXtEEsilOfTQQ7F58+a8z/X09OCmm25CX18fvHaxmJN20d6efYPetWsX1q5diw8//BC3327lZSWTSWzYsAHvvvsufvvb347iX1EZTHAW+E4D3T07wUZ3IQIvAr78HcqaO+x20xkd5oRkH8JsEMEqxzFl70OAV4DI1ncBWyCzWqLs9AGFdQM6kOLH7gs/XhEVl10QGR/oAm/IdSEYnJu/Hh+Az4yVbOJRCU7DGK/Sg4QpwVNicstMwdDE2jpqENmsWdeJv+/UcBoPzGG67Zxi63zlE1CVXDtu28da58b/+14JziKyLWnVevhaG0sgAxS9rIa+KUdh3i6r5wAJ5PHBsYx1a7YfuavxOnI61EUO8pQpUzB9+nSsXbsWs2dbqQdr165FR0fHsPzjKVOm4Pnnn8967Pvf/z4OPvhgfOMb3xizMY8EV6vV/W2w+1OIiW70s20oJCXytZt2qQOI1cDPc8qMuYjAC2b3B+nHOE2GwpYXHU3ZAlkrs0lBLRiviIpTEJkM9cBvKNDK/IxGE5c3gJTJwYz3IsAkYLqDNX8PJ38smOpFlPGh1Jn2Boe2sA0SyKNKZk4xW0ZOcSXXzivbkjgDwKz4+zh51WNFI9T1CMsLiMKN6UY3wABNbY0zdqJ6tgX/BYftegAA8IMHXsdZJ/oa6vs7EXC6PzpNrSiCXAOWL1+OW265BdOmTQPHcbjllltw/vnnp58fGBiAJEnwer1pEe0gSRKampowZUrlNmijScBuFpLo3Q6/shvRAk1CHMJsM0R5qJueTxtEr2fvqsfBsCx2CvMQiHycfozT5bLTB1KcG0gBWrU97kfIeERUgm1WJF+L7IZgKEgKwTF9/3wwLIsI44MUtfLY2RJNPCrB2R5rNQewky/d1jvQmvFdLuGlTVTHMcnbcA1/L77ErQVbZj7+SK6dNes6ceWfP8XpLNCCaMkIdb0SY/zwQ0bK5OANNJYPMlE5a9Z14toNAZzKsuAZAyfLT+Cy1Vago5G+v42Oy96F9JsxpMBB4OtGZo6Yuhn5hRdeiIGBAaxYsQIsy+K0007DhRdemH7+jDPOwKmnnorvfe974zjKymiZYTcL6d+BFr0XnzbtV/T4GN8MtzoUEQoaIXTVyM8z0rQ35vf+GaZhgGFZCLpcdk6xxlnHmcLEt/PyN7elo7WCoUCvgxQLAEiwPgSTlkDmvLW/+YtuOweZ0SFzpQWvzx+EanIQGR3MKES0iSGE4HT0xazUrNHIx890jAHGzjGm1sS5AKDtRpgJoK0B22UTlZH7/V3Ov4zleLnhvr+NjmSnyXoYBXHThcZ0VLeoG4HMcRwuvfRSXHrppXmff+mllwr+7kMPPTRaw6oJ/qY2JEwJzOBnaDHD+MRX/IaWlNowJbYRAKAqSQQQh1FFm+kspsyHr281unZ8jGmz9wVvKJD5/PnQueh25NiYBAKZYTmEmAA4uQ+iqcAYw7zrYiS4AGaoWwGmdBOPSnAiyACQEkoLZCuq7UcbQuA9wZqPhxhitPPxHceYE7l/WAukMXKMqTVJPgBoQIQLorFckIlqcL6/J3BvQWK0hv3+Njqi6IZhMmAZEyojopHVAi2vxwCGZdHHtaE5tAEsY4Ir0CTEIbPddKjXsttifbWxKwrOsToy7f7Ysp0TDRk6V574M+we64zYyF/58olyQUjJfkhQYHDjn4MMAIoQgI+RAQAuf+0jyFJGYUtKDJb1O1HWEtICCeRRZaTNSEaK00Gs0T14VcFa8CeE2qcgEfWL8/0VoDf097fRYVgWSVi2qKn6icFWRGOPvoGICB3YN/kewBRuEpLG2w4fI0OORxHp34UOAEJTbfKrZ+xzMPAMIO+0Wk5LZhJ6mdFRc5IJ5LjQDHdqAKKpwuTrQyCnMnKhHc/XWuLyDglkwxUsfGAGCb4JUAHJT4JktBnNfPyJ4sGrSUEgCihSY7WZJqpjonx/JwJJRoIHCtQGbTjkQBHkMSLpngqB0QEA/ilzih7L+e1207s7ER+w/Gg9zbWxK/I3taCTmQKx30rhkEaQPuDkHjMVdihqNFSxFX59EG6o6RbM443uGkqH8Qdr3wTBlZFigTKKANes60SvYjWZueXZD0a9BTgxeox2hHqsMFy2VV2N6jaIxmCifH8nAo51rNbgApkiyGOE5p8OhKx/t06fU/RY0e6mF+nrhBq2/JD9VbaZzmS3e0+0xj8BALhGIJCdyDHnmhwCWfO0oS3cD5YxYdaJQDZdQ6LV11R7AcDyPBRTgMSkwHqKC+Q16zpx2eoNeILZDTDAl5S/4LLVVrSGbkqNSaN78K5Z14munRH8C4B3e5LYta6zof8eYmQ0+vd3oqCyLsAANKaRS/QogjxmcEGr618MbvhKWA9ltpvWI5ZADrbXTiDLzftiht6JZCIGD6MAZRTdrVnXiTc+2Q0AePztrZMiUmh62iHaUX+mTgQy47G+OzG4wQqjszpP2qt+3lf8e3rCEwuwkTsb+7BWnvwZ/KvYyJ2NE55YMCrjIohiOAu2g/QPAQAH6Jtx2eoNk2KuIoh6ImXfQ+qhwVY1kEAeIz5VgwAA1tRx8qrHik7aWe2m471WR7MCnfcqQZhxEHjGwLYP3rAeEIv7Gjs3nsP0dQCAY1OvTYobD+cfSmFg60QgO6I1xoxeFN/ZHhNLuGQck7wNa7QjkTStKIFsinhcOwpHJ28ftbERRCGcBdtRnCWQj+Q20oKNIMaBlN1YS2MbO8WCBPIYsGZdJ/73Aw0A4IZqG/AXFphD7aZ3g0v2I8Q2gWGYmo2nbd4iAEDoo9cBAEwJgezceI7mrA58S/m1k+LGIzVlCGSpPtplOqI1wfpH7T0Uu3GMu6l4EaBTNS5Co6pxYtxxFmxyesEm0IKNIMaBlO36pJNAJkpxwhML8Ax/CQCAsVvEFhOYQ+2md0NS+hHlausOMHPPA5A0BYhdbwMA2BKuFEM3HuvLPlkihZ7mIaHH1UkE+cNB65LtUt046oaXRiWKn7K3xXwlXDIuWbovOliravxU9Ro8oC/BFKoaJ8YJZ8EmpRdsGi3YCGIc0O0IskECmSiFIzB104oClyMwnXbT3tQgZLG2freCIGIHvwdmxKyIMCsVF8hDN57UpIoU+tuG/j7WNf7WdmvWdeJ/3g4BAPZjt0MNddU81WXNuk7E7GZUF//vW0Vfm6rGiXqCFmwEUR841rH10oG2UsjFYgwQgtMRi7nBoPwWsU676YA+iAFp/5qPacC3N/YOPwsA4EuIv8nqLxnMEMh8iTSUseCm5zYjpnkAHmhBFCv41fhJ6gLc9NzmmohSJ9f8RSYE0wROkVfjstVWFLnQ61PVOFEvLFs0A2twH+5+bjN2hWTc7bsYlyzdl76fBDHGOI21DI4EMlGCSgSm1W76QwTNCDRPjdpMZ6C3fQ5wBHKJCPJkvfFIbp/lWoUVNwAAemZJREFUOgIZQh1EkF+Uz4LLZYV3nVSd8/gXkJQFAH1Vv/4JTyzAMi6V/vk8/kWchxehPCEAi6p/fYIYbWjBRhDjj9NYy+QaO8WCBPIYUInATLnbMC3SA44xwfpqL5C9sw4Etlj/FtylHREm640nzDTBZ8oQXOMfQT7L9WtcEP8dTuDegsRokE0Rz+qLcY/3QjxVg9c/Jnkbfsw/iK9wb4JnjPTrX6+di3/W4PUJgiCIiY/TddekCDJRDiMWmN52cIwJAOACtWkzncm0fQ4F/mr9W3SPniNCoxPjm4FUN4QSUfax4IIvHwH58YcgQE+n6iRZDy788r/U5PWdVCAW5qTKNScIgiBqh2nnIJt8YwtkKtKrU5x20wDgaqq9QH6tm0W/aUWOr15dvBhrsrJmXSe6VWuL6LI/vjHun9GyRTNw3AzgcX4pTlOvweP8Uhw3w6xZZJ+KnAiCIIhqYUTb9clOtWhUKIJcp2RG7Xw1bDMNWMLvx4+/jxcZAWCAE5N/xmWr5wGgFsEOQ22UewEG+EryKVy2ehaA8f2Mpn/rMZwO4HQAwEU1fe3JmmtOEARB1A7GTrFgKMWCGA28LUMCuamttgI5txjrLP4VnIVXqBgrg9zP6Gz+bzgbf5vwn9FkzTUnCIIgakO6+ZjQ2AKZUizqFKfdtG4yaGqpbYoFtQguDX1GBEEQBDFyOMlKsWAaPMWCBHKd8vcu6/86WHz1v9fUNP+VWgSXhj4jgiAIghg5nwzqAIC293+Hk1c9Nu71O5VSNwLZNE3ceuutOOKII7B48WLceOON0HW94PE9PT246KKLsHDhQvzrv/4rHnzwwTEc7eiyZl0nLn/yIyRNHgJ0nBX/Q007plExVmnoMyIIgiCIkbFmXSf+tDEMAJjLdNVcv4wljGma5ngPAgDuvfde3HPPPbj55pthmiZWrlyJ8847D9/85jeHHWsYBs444ww0Nzfjxz/+MTZu3IhLL70Ud999N4466qj0cTt37sSSJUvw4osvYubMmWP551SFclUbJKSGPw4B0lW1yX9ds64TN9nFWNODbirGygN9RgRBEARRPmOhX8aKuinSu++++7BixQocdthhAICVK1filltuySuQX331VWzduhX33HMPgsEg9txzT7z11ltYt25dlkBuVJyGDbkNIWrZsIGKsUpDnxFBEARBlM8xydtwJf+/+DL3z4ZvOFUXArmnpwddXV049NBD048dcsgh6O7uRldXF6ZNy877fPPNN3H44YcjGAymH7vmmmvGarijjtOwIbMhBOW/EgRBEARRzwjB6QjFfBOi4VRd5CD39vYCADo6OtKPtbW1AQC6u7uHHb99+3ZMnz4dt956K4477jicdNJJeOSRR8ZmsGMA5b8SBEEQBNFoTCT9MmYRZEVR8opdAEgmkwAAURTTjzn/VlV12PHxeBxPPPEEvvSlL+HOO+/Ehx9+iGuuuQbNzc34whe+MAqjH1uoYQNBEARBEI3GRNIvYyaQN2zYgHPPPTfvc5dccgkASwwLgpD+NwC43e5hx3Mch0AggGuvvRYcx2H+/PnYtGkTHnrooQkhkAHKfyUIgiAIovGYKPplzATyoYceis2bN+d9rqenBzfddBP6+vrg9XoBDKVdtLe3Dzu+o6MDhmGA47j0Y3PnzsUbb7wxCiMnCIIgCIIgJhN1kYM8ZcoUTJ8+HWvXrk0/tnbtWnR0dAwr0AOARYsW4aOPPkIqNWQl8sknn2DGjMZfsRAEQRAEQRDjS10IZABYvnw5brnlFrzxxht46623cMstt+D8889PPz8wMIB4PA4AOPHEE8HzPK644gp89tlneOKJJ7B69Wr827/923gNnyAIgiAIgpgg1IXNGwBceOGFGBgYwIoVK8CyLE477TRceOGF6efPOOMMnHrqqfje974Hn8+He++9F9dccw1OOeUUtLe348orr8TnP//5cfwLCIIgCIIgiIlA3XTSGw22bduGL33pS3jwwQcxderU8R4OQRAEQRAEUUdMnToVPD88Xlw3EeTRwCn0K+SeQRAEQRAEQUxeXnzxRcycOXPY4xM6gpxMJvH++++jvb09y/GCIAiCIAiCIApFkCe0QCYIgiAIgiCIkVI3LhYEQRAEQRAEUQ+QQCYIgiAIgiCIDEgg1xBVVfGTn/wEixcvxlFHHYX/+Z//Ge8hETVg+/bt+Pa3v43Fixfj2GOPxQ033ABFUQAAnZ2duOCCC7Bw4UKccMIJ+Nvf/jbOoyWq5fLLL8d5552X/pnO8cQhlUph1apVOPzww3H44Yfjpz/9KVRVBUDneSIRDoexcuVKHHbYYTjmmGPw85//HLquA6DzPBFQVRVf+cpX8Prrr6cfK3Ve33zzTZx88slYsGABzjvvPGzbtq3k+5BAriH//d//jXXr1uHee+/F1VdfjbvuugvPPPPMeA+LqAJVVfHtb38boijij3/8I37+85/jhRdewK233grTNHHRRRchGAzi0UcfxamnnooVK1Zgx44d4z1sokLeeOMNPProo+mf6RxPLP77v/8bf/nLX/CrX/0Kd911F1599VXceeeddJ4nGFdffTV6enrwwAMP4KabbsKaNWtw77330nmeACiKgv/6r//Cxx9/nH6s1Hnt6urCd77zHZxyyil47LHH0NbWhosuugiGYRR/M5OoCfF43DzwwAPN1157Lf3YnXfeaZ5zzjnjOCqiWv75z3+aBxxwgBmLxdKPPfnkk+aRRx5pvv766+aBBx5oRqPR9HNf//rXzVtuuWU8hkpUSTweN5csWWKec8455te+9jXTNE06xxOIcDhsHnDAAebf//739GOPPfaYeeGFF9J5nmAcfPDB5l/+8pf0z6tWraLzPAH4+OOPzVNOOcU8+eSTzX322Sett0qd19tuuy1LiyUSCXPRokVZei0fFEGuEZs2bYKqqjjkkEPSjx1yyCHYsGEDNE0bx5ER1TBv3jz85je/gdfrTT/GMAxUVcW7776Lz33uc/D5fOnnDjnkEKxfv34cRkpUy6233orDDjsMhx12WPoxOscTh7Vr18LlcuHII49MP3baaafht7/9LZ3nCUYwGMSTTz4JWZbR09ODV199FQcccACd5wbn7bffxlFHHYWHH3446/FS5/Xdd9/F4sWL08+53W4ccMABWLduXdH3I4FcI3p7e9HU1ARJktKPtbW1IZVKYWBgYBxHRlRDS0tL1g3VMAw88MADOOSQQ9Db24uOjo6s41tbW9Hd3T3WwySqZN26dXj22Wfxox/9KOtxOscTh+3bt2PGjBl4+umncdJJJ+H444/HjTfeCFVV6TxPMH7605/irbfewsEHH4xjjz0WbW1t+N73vkfnucE555xz8MMf/hButzvr8VLntdDzPT09Rd9vQnfSG0tkWYYoilmPOT87RSBE47Nq1Sps3LgRjz76KO69914IgpD1vCiKSKVS4zQ6ohJUVcXll1+OH//4x2hqasp6TpZlOscThHg8jp07d+KBBx7A1VdfjXg8jquvvhqaptF5nmBs374dn/vc53DxxRcjFovh2muvxY033kjneYJS6rwW0meltBkJ5BohSdKwD9v5OXe1QzQepmniZz/7GR566CHcfvvt2HvvvSFJEmKxWNZxqqrC5XKN0yiJSrjzzjsxe/ZsnHDCCcOeo3M8ceB5HrFYDDfddBP22GMPAMAPf/hD/PCHP8Spp55K53mCsH37dlx//fV46aWXMHXqVADWdXzBBRfgzDPPpPM8ASk1TxfSZ8FgsOjrkkCuEVOmTEEkEoGqqumVSm9vL0RRHBaVIhoLwzBw+eWX46mnnsKtt96KL3zhCwCsc75p06asY/v6+tDe3j4ewyQq5KmnnkJvby8WLVoEwLIC03UdixYtwre+9S06xxOEjo4O8DyfFscAMHfuXCiKgvb2dnz00UdZx9N5bkzef/99eL3etDgGgPnz50PXdTrPE5RS9+IpU6agt7d32PN777130delHOQasf/++0MQhKyk77Vr1+KAAw7I2+ObaBxuuOEGPPXUU7jjjjvwpS99Kf34ggULsGnTJiQSifRja9euxcKFC8dhlESl3H///Xj66aexZs0arFmzBmeeeSbmz5+PNWvW0DmeQCxcuBCapmHz5s3px7Zs2QKv14uFCxfSeZ4gdHR0IBKJoKurK/3Yli1bAFhF13SeJx6l5ukFCxbgnXfeST8nyzI+/PDDkuedBHKNcLvdWLZsGa6++mq89957ePHFF3HPPffg/PPPH++hEVWwfv163HfffVixYgXmz5+P3t7e9H+HHXYYpk+fjksvvRQff/wxfvOb3+Ddd9/FmWeeOd7DJkbAjBkzMHv27PR/gUAALpcLs2fPpnM8gZgzZw6WLFmCyy67DO+//z7efvtt/PznP8dZZ52FI444gs7zBGHhwoXYf//9cdlll2HTpk1Yv349fvKTn+CrX/0qli5dSud5AlJqnj799NPx7rvv4q677sInn3yCyy+/HNOnT8cRRxxR9HUZ0zTNsfgDJgOyLOOqq67C888/D6/XiwsuuAAXXHDBeA+LqIIbb7wR99xzT97nPvjgA3R2duLyyy/Hu+++iz322AOXXXYZjj766DEeJVFLbr31Vrzzzju4//77AQDbtm2jczxBiMVi+NnPfobnn38ePM9j2bJlWLlyJQRBoPM8gejp6cH111+PN998E4Ig4Mtf/jJWrlwJl8tF53mCsO++++Lee+9Nu0yVOq9/+9vfsGrVKnR1dWHBggW47rrrstKt8kECmSAIgiAIgiAyoBQLgiAIgiAIgsiABDJBEARBEARBZEACmSAIgiAIgiAyIIFMEARBEARBEBmQQCYIgiAIgiCIDEggEwRBEARBEEQGJJAJgiDqAE3T8Ktf/Qpf/OIXMX/+fBxzzDH4yU9+gv7+/jEfy3nnnYdbb711zN+XIAiiXiCBTBAEUQfcfPPNeOaZZ3DVVVfhueeew6233oqPPvoI3/jGN0B29QRBEGMLCWSCIIg6YPXq1fje976Ho446CjNmzMChhx6Kn//85/jggw/w7rvvjvfwCIIgJhUkkAmCIOqEN998E7qup3+eNWsWnnnmGey3336IxWK4/PLLccQRR2D+/PlYunQpnnvuufSx++67L5555hmccMIJWLBgAX7wgx9gx44dOO+887BgwQJ87Wtfw+7duwEAd9xxB1asWIEf//jHWLBgAZYuXYoXXnih4LgefvhhLFmyBIsWLcLy5cvx3nvvpZ/7xz/+gdNOOw0HHXQQ/vVf/xV33333KHwyBEEQYwsJZIIgiDrg/PPPx0MPPYTjjz8eV1xxBZ555hlEIhHstddecLlcWLVqFbZs2YJ77rkHTz/9NBYvXoyf/OQnUFU1/Rq/+MUvsGrVKvz617/Gs88+i+XLl+NrX/sa/vCHP6CzsxP33HNP+tiXXnoJuq5j9erVOOOMM7BixQps3rx52Lheeukl3H777bjsssvw+OOP49hjj8XXv/517N69G7quY8WKFTj++OPxpz/9CVdeeSXuvPNOvPrqq2PymREEQYwW/HgPgCAIggAuvvhizJ07F3/4wx+wevVqPPLII5AkCStWrMB//Md/4JBDDsH555+PfffdFwBwwQUX4JFHHkFPTw9mzZoFwBLZCxcuBGBFlPfee28sXboUALBkyRJ8+umn6fcLBAK49tprIYoi9txzT/ztb3/Do48+issvvzxrXL/97W/xzW9+E1/4whcAAN/5znfw+uuv45FHHsG5556LUCiE1tZWzJw5EzNnzsTvf//79HgIgiAaFRLIBEEQdcKJJ56IE088EZFIBK+//joefvhh3HTTTZgzZw6WLVuGF154AY888gg+/fRTfPDBBwAAwzDSv58pTCVJwvTp09M/u1yurGjz5z73OYiimP55/vz5+Pjjj4eNacuWLbjllltw++23px9TVRVTp05FMBjEN7/5TVx99dW46667cPzxx+OUU05Be3t7bT4QgiCIcYIEMkEQxDizadMmPProo7jiiisAWNHdL3/5y1i6dCnOOOMMvP7663j22Wfxzjvv4Ktf/SqWL1+O9vZ2nH322Vmvw/PZUzrLFs6iyz1W13UwDDPsOF3X8aMf/QhHH3101uMejwcA8IMf/ACnnnoqXnzxRbz88ss477zzcN111+H0008v/wMgCIKoMygHmSAIYpzRdR33338/1q9fn/U4wzDw+/1oaWnB008/jZtvvhn/+Z//iS9+8YsIh8MAULEF3EcffZQVfX7//ffT6RuZzJ07F93d3Zg9e3b6v3vuuQdvvfUWent7cdVVV2HGjBn4xje+gT/84Q847bTT8Oc//7miMREEQdQLJJAJgiDGmQMOOADHH388vvvd7+Lxxx/Hjh07sGHDBtx6663YuHEjTj/9dLjdbjz//PPYuXMn/v73v+Oaa64BgKy0iZHQ2dmJG264AZ9++il+/etf4/3338eZZ5457Lj/9//+H+6//348/vjj2L59O375y1/isccew7x589DU1IQXXngBP/vZz7Bt2za89957ePvtt3HAAQdU9XkQBEGMN5RiQRAEUQfcdttt+M1vfoO7774bP/3pTyGKIhYvXowHH3wQ06ZNw0033YQbb7wRDz74IGbOnIlvf/vbuOOOO/Dhhx9in332GfH7zZ8/H9FoFKeeeipmz56N3/zmN5gzZ86w40488UT09/fjl7/8JXbv3o158+bhzjvvxP777w8AuOuuu3D99ddj2bJlkCQJJ554Ii6++OJqPw6CIIhxhTGpRRNBEMSk4o477sDrr7+Ohx56aLyHQhAEUZdQigVBEARBEARBZEACmSAIgiAIgiAyoBQLgiAIgiAIgsiAIsgEQRAEQRAEkQEJZIIgCIIgCILIgAQyQRAEQRAEQWRAApkgCIIgCIIgMiCBTBAEQRAEQRAZkEAmCIIgCIIgiAxIIBMEQRAEQRBEBiSQCYIgCIIgCCIDEsgEQRAEQRAEkQEJZIIgCIIgCILIoK4E8rvvvovzzjtv2OMvvfQSTj/9dJx99tn4v//7PwCAYRi48sorcfbZZ+O8887Dtm3bxnq4BEEQEwaafwmCIIbgx3sADv/zP/+DJ598Em63O+vxVCqFVatW4dFHH4Xb7cby5ctx/PHHY926dVBVFQ8//DDWr1+PG264AXfddVfW72qahu7ubkydOhU8Xzd/KkEQRF1B8y9BEEQ2dRNB3mOPPXDHHXcMe3zLli3YY4890NTUBFEUccghh+Dtt9/G2rVrccwxxwAAFi5ciPfff3/Y73Z3d2PJkiXo7u4uexy6YWLeZc/gzpc/qfyPIQiCaCDqZf4lCIKoF+pGIC9dujRvlCEWi8Hv96d/9nq9iMViiMVi8Pl86cc5joOmaVWPg2Ws/8uqXvVrEQRBNAL1Mv8SBEHUC3UjkAvh8/kQj8fTP8fjcfj9/mGPG4ZRk208hmEg8RwUjQQyQRCTm7GefwmCIOqFuhfIe+65J7Zt24ZQKARVVfH2229j0aJFOPjgg/HKK68AANavX4999tmnZu/pElgomlGz1yMIgmhExmP+JQiCqAfqdsn/1FNPIZFI4Oyzz8all16KCy+8EKZp4vTTT8eUKVPwxS9+Ea+99hrOOeccmKaJ66+/vmbvLfEclBQJZIIgJifjOf8SBEHUA4xpmuZ4D2K02LlzJ5YsWYIXX3wRM2fOLPv3jrvpZSyaFcRt5ywaxdERBEFMXCqdfwmCIOqBuk+xGA8knlIsCIIgCIIgJiskkPNgFemRQCYIgiAIgpiMkEDOgxVBJhcLgiAIgiCIyQgJ5DxIAktFegRBEARBEJMUEsh5kHgOSYogEwRBEARBTEpIIOdB4imCTBAEQRAEMVkhgZwHcrEgCIIgCIKYvJBAzgO1miYIgiAIgpi8kEDOg0StpgmCIAiCICYtJJDz4BKo1TRBEARBEMRkhQRyHhwf5AnchZsgCIIgCIIoAAnkPEg8C8MENIMEMkEQBEEQxGSDBHIeJJ4DAMpDJgiCIAiCmISQQM6DJFgfi5IiJwuCIAiCIIjJBgnkPEi8LZApgkwQBEEQBDHpIIGcB0qxIAiCIAiCmLyQQM7DUASZUiwIgiAIgiAmGySQ8+DkICfJC5kgCIIgCGLSQQI5D+kUCyrSIwiCIAiCmHSQQM4DFekRBEEQBEFMXvjxHgAAGIaBq666Cps3b4Yoirjuuuswe/ZsAEBvby/+67/+K33sxo0b8YMf/ADLly/HsmXL4Pf7AQAzZ87EqlWrajIeKtIjCGKyUG/zL0EQRD1QFwL5hRdegKqqePjhh7F+/XrccMMNuOuuuwAA7e3tuP/++wEA69atw6233oqzzjoLiqIAQPq5WuISqEiPIIjJQb3NvwRBEPVAXaRYrF27FscccwwAYOHChXj//feHHWOaJq699lpcddVV4DgOmzZtgizLuOCCC3D++edj/fr1NRvPUA4yRZAJgpjY1Nv8SxAEUQ/URQQ5FovB5/Olf+Y4DpqmgeeHhvfSSy9h7733xrx58wAALpcLF154Ic4880xs3boV3/jGN/Dss89m/U6lpDvpUYoFQRATnHqbfwmCIOqBupjNfD4f4vF4+mfDMIZNtE8++STOP//89M9z587F7NmzwTAM5s6di2AwiN7eXkybNq3q8ZAPMkEQk4V6m38JgiDqgbpIsTj44IPxyiuvAADWr1+PffbZZ9gxH3zwAQ4++OD0z48++ihuuOEGAEBPTw9isRja29trMh4q0iMIYrJQb/MvQRBEPVAXEeQvfvGLeO2113DOOefANE1cf/31eOqpp5BIJHD22WdjYGAAXq8XDMOkf+eMM87AZZddhuXLl4NhGFx//fU1294TnQgy5SATBDHBqbf5lyAIoh5gTNM0x3sQo8XOnTuxZMkSvPjii5g5c+aIfnfvy/+EbxwzDz/88n6jNDqCIIiJSzXzL0EQxHhTFykW9YjEc5RiQRAEQRAEMQkhgVwAiWepSI8gCIIgCGISQgK5ABLPIkk5yARBEARBEJMOEsgFkARKsSAIgiAIgpiMkEAugMSzUFKUYkEQBEEQBDHZIIFcAIogEwRBEARBTE5IIBeAivQIgiAIgiAmJySQC2AJZIogEwRBEARBTDZIIBdA4jnqpEcQBEEQBDEJIYFcAEmgFAuCIAiCIIjJCAnkAlCKBUEQBEEQxOSEBHIBqNU0QRAEQRDE5IQEcgHIB5kgCIIgCGJyQgK5AFYOMkWQCYIgCIIgJhskkAvgpFiYpjneQyEIgiAIgiDGEBLIBZB466OhKDJBEARBEMTkoiqB/MQTT2T9rOs6fv7zn1c1oHrBJXAASCATBFF/TOS5lyAIoh6oSiDffffd+OlPfwpVVbFz506cc845+Pjjj2s1tnFlKIJMhXoEQdQXE3nuJQiCqAeqEsiPPvooNE3D6aefjnPPPRdnnXUW7r777lqNbVxJC2TqpkcQRJ0xkedegiCIeqAqgcwwDERRhCzLMAwDDMPUalzjjkQpFgRB1CkTee4lCIKoB6oSyKeccgpisRieeOIJPPDAA/jjH/+Ib3/727Ua27hCKRYEQdQrE3nuJQiCqAf4an754osvxrJlywAAXq8Xf/zjH3HLLbeM+HUMw8BVV12FzZs3QxRFXHfddZg9e3b6+XvvvRePPvooWlpaAABXX3015syZU/R3qoVcLAiCqFdqNfcC9Tn/EgRBjDdVCeRly5bhvffew4cffojTTjsNH3zwAX74wx+O+HVeeOEFqKqKhx9+GOvXr8cNN9yAu+66K/38Bx98gBtvvBHz589PP/b8888X/Z1qkXg7xYJykAmCqDNqNfcC9Tn/EgRBjDdVpVisXr0al112GX77298iGo3ioosuwv/93/+N+HXWrl2LY445BgCwcOFCvP/++1nPf/DBB/jNb36D5cuXpwtRSv1OtUgCpVgQBFGf1GruBepz/iUIghhvqhLI999/Px5++GH4fD60trZi9erVuO+++0b8OrFYDD6fL/0zx3HQNC3980knnYSrrroK9913H9auXYuXX3655O9UC6VYEARRr9Rq7gXqc/4lCIIYb6pKsWBZNmuSnDZtGjiOG/Hr+Hw+xOPx9M+GYYDnraGZpomvf/3r8Pv9AIDjjjsOH374YdHfqQXpFAsSyARB1Bm1mnuB+px/CYIgxpuqIsjBYBAbN25MWww9+eSTaGpqGvHrHHzwwXjllVcAAOvXr8c+++yTfi4Wi+ErX/kK4vE4TNPEP/7xD8yfP7/o79SCIR9kSrEgCKK+qNXcC9Tn/EsQBDHeVLXk//GPf4z//M//xPbt23H00UdDkiT86le/GvHrfPGLX8Rrr72Gc845B6Zp4vrrr8dTTz2FRCKBs88+G9///vdx/vnnQxRFHHHEETjuuONgGMaw36klQznIFEEmCKK+qNXcC9Tn/EsQBDHeMKZpmtW8gK7r2Lp1K3Rdx9y5cyEIQq3GVjU7d+7EkiVL8OKLL2LmzJkj+t1IMoWDrnoeV5y0P/7jmHmjNEKCIIjKqOe5F6hu/iUIghhvqk4a4zgOe+65Zy3GUldQkR5BEPXMRJ17CYIg6oGqcpAnMiJHApkgCIIgCGIyQgK5AAzDQOJZ8kEmCIIgCIKYZJBALoLEs9RJjyAIgiAIYpJRc+PKK664Arqu45vf/Cbmzp1b65cfUySBoxQLgiAagok09xIEQYw3NY8gz58/H1dccQX6+/tr/dJjDqVYEATRKEykuZcgCGK8qXkE+ZxzzgEAHHroobV+6THHEsgUQSYIov6ZSHMvQRDEeFNVBDkej+Pqq6/G17/+dYRCIVx55ZVZ7UcbHYnnKAeZIIi6Y6LPvQRBEONNVQL5uuuuQyAQQH9/PyRJQiwWw5VXXlmrsY07kkApFgRB1B8Tfe4lCIIYb6oSyBs3bsT3v/998DwPt9uNn//859i4cWOtxjbuUIoFQRD1yESfewmCIMabqgQyy2b/uq7rwx5rZCSeXCwIgqg/JvrcSxAEMd5UVaS3ePFi3HTTTUgmk3j11Vfx4IMP4vDDD6/V2MYdl8BCiVCKBUEQ9cVEn3sJgiDGm6pCDitXroTH44Hf78ett96KfffdFz/84Q9rNbZxhyLIBEHUIxN97iUIghhvqoogC4KAiy++GOeddx4CgUCtxlQ3WJ30KIJMEER9MdHnXoIgiPGmqgjyp59+ihNPPBEnnXQSenp6cMIJJ2DLli21Gtu4Y7lYUASZIIj6YqLPvQRBEONN1TZvl19+OVpbWzFlyhR87Wtfm1BWQ5RiQRBEPTLR516CIIjxpiqBHAqFcNRRR6V/PvfccxGLxaoeVL1AraYJgqhHJvrcSxAEMd5U7QukKAoYhgEA9Pb2wjAmTsRV4jmkdBO6YY73UAiCILKYyHMvQRDEeFOVQP63f/s3XHjhhejv78fNN9+Ms88+G8uXL6/V2MYdSbA+HpXSLAiCqCMm+txLEAQx3lTlYnHGGWdg9uzZ+Otf/wpN03Dttddmbfs1OhJvCWRF0+EWuXEeDUEQhMVEn3sJgiDGm6oEMmAZ1i9evLiq1zAMA1dddRU2b94MURRx3XXXYfbs2ennn376adx3333gOA777LMPrrrqKrAsi2XLlsHv9wMAZs6ciVWrVlU1jlwk3hLFVKhHEES9UYu5F6jf+ZcgCGI8qVog14IXXngBqqri4Ycfxvr163HDDTfgrrvuAgAkk0ncdttteOqpp+B2u/Ff//VfePnll3H00UcDAO6///5RG1c6gpya2AL54gffwb/Ma8F5R8wZ76EQBDHG1Ov8SxAEMZ5UXaRXC9auXYtjjjkGALBw4UK8//776edEUcQf//hHuN1uAICmaZAkCZs2bYIsy7jgggtw/vnnY/369TUfl5ODPJGdLMKJFJ7Z0IX739w23kMhCGIcqNf5lyAIYjypiwhyLBaDz+dL/8xxHDRNA8/zYFkWbW1tAKxoRSKRwFFHHYWPPvoIF154Ic4880xs3boV3/jGN/Dss8+C52v3J7nsFAt5AnfTe68zBAD4qCeGnYMJzGz2jO+ACIIYU+p1/iUIghhPqoogP/zww2U9Vgqfz4d4PJ7+2TCMrInWMAzceOONeO2113DHHXeAYRjMnTsXp5xySvrfwWAQvb29lf0hBZja5AIAdA7KNX3deuLdHaH0v1/eXNvPjyCI0aFWcy9Qv/MvQRDEeFKVQN6wYUNZj5Xi4IMPxiuvvAIAWL9+PfbZZ5+s56+88kooioJf/epX6a2+Rx99FDfccAMAoKenB7FYDO3t7SN+72LMa/cCAD7ePXEN+NfvCGNeuxezWtz466bd4z0cgiDKoFZzL1C/8y9BEMR4wpimOe5dMJwq6o8++gimaeL666/Hhx9+iEQigfnz5+P000/HoYcemjbFP//883Hcccfhsssuw65du8AwDFauXImDDz4463V37tyJJUuW4MUXX8TMmTMrGtvRN76Eg/doxi+WL6r676w3TNPE4p+9iGP3aYNf4vHw2zuw/sovwSWQpR1BTBbqef4lCIIYL6pOGHvrrbcQDoeRqbO/9KUvjeg1WJbFNddck/XYnnvumf73pk2b8v7ezTffPKL3qYS9OnwTNoLcFU6iL6Zg4awgZrV4cN8b2/Dmp/341307xntoBEGUoBZzL1Df8y9BEMR4UZVAvuKKK/DKK69keWYyDFPRJF2v7N3hwxtb+qEbJjiWGe/h1BQn/3jBzCD2neqHS2Dx1829JJAJos6ZDHMvQRDEeFKVQH7jjTfwpz/9KasCeqKxV4cPimZg52ACs1u94z2cquiLKfi/t3fgP46eB5FnsX5nCCLHYr9pfkg8hyP3bMNLm3bjpyd/Lr2d2sgMxFVctvo9XPPV+ZgScI33cAiiZkyGuZcgCGI8qapIb9q0aRN+gt6rw+oU9ckESLN4cv0u/Pezm/H71z8DYEWQ958eSHcMPH6/DmwfSGBLb7zYyzQM/9w6gOc+6MFDb20f76EQRE2ZDHMvQRDEeFKVQD744IPx/e9/H08++SSef/759H8Tib06rJvQRBDITi717S98jO5wEht2hrFwZlP6+SX7dYBlgEfW7qjp+37WF8dXf/l39MeUmr5uKbrDSQDAE+t3oQ5qUQmiZkyGuZcgCGI8qSrFYt26dQCARx55JP3YRMuDa3IL6PBLE6JQ75PdUcxu9aArlMR3HlyLuKpjwaxg+vnpQTdOPHAa/vDmdnz3+L3gdwk1ed+3tw7g3Z1hvL1tEEsPmFqT1yyHXWHLv/qzvjje2xlO/639MQUsw6DZK47ZWAiilkyGuZcgCGI8qUog33///QCs9qOmaUIQaiOo6o29OnwNH0E2TRMf9cRw0kHT0OwRcOfLWwAgSyADwLeO3RNPv9eFh97ajm8eu2eeVxo5u6NW5PiT3TEsPaAmL1kW3eEk2nwiIrKGx9d1YsGsIGKKhlN++RqmNrnw2HeOHLvBEEQNmSxzL0EQxHhRVYpFf38//uM//gMLFy7EQQcdhPPPPx89PT21GlvdsLctkBt5m74vpiIsp7B3hw8XH78XpjW54HfxmJtTeHjgzCYcuWcr7vn7VqiaUZP33h2xUh22jPEioyuUxLx2Hz6/Xweefm8XNN3ADX/eiM6QjLXbBrGxKzKm4yGIWjFZ5l6CIIjxoiqBfM0112DhwoV4/fXX8frrr+PQQw/FVVddVaOh1Q97dfgQUzT0RGqTQ6sbZjo/NpOeSBLJlF6T98jl491RAMDeHX54RB6//tohuPnMBWDzWNd967g90R1J4sl3d9XkvdMR5N6xFci7wjKmN7mwbNF09MVU3PyXj/DAm9txxiEzIXIs/kjFe0SDMlnmXoIgLBo5QNeoVJVisXXrVtx+++3pn1esWIGTTjqp6kHVmt2DAFuFy1ezZDlZvPlxFIfNqd4u7Mn3duCWlz7Ak99agoDbyoPVDRNfuesVnL5wDv7jqH1KvMLIWfupJU79gg+7+oB2dxDtbmBX3/Bj92puw17tftz50hYcsceMqi3fdgxYi4FPemLo7DXHxELOMK1FiE90Y9+2DvgkHnf9dQtmBj246Oj5iCQMrH6nE18/bP8J2TlQ1XR83BvBAdOax3soRBlMbxvZ8Y0y9wLVz78THSWlQ+BZsDWeFzXDAMcwE8Kyc7KS0g28tbUXL3/UjVc/6cbn952OH33pwPEe1oSi2NxbVQRZ0zQoylBUVZblCXkxzmm1nCy2DsSQ0g3c8Px7WPPutopfb2N3GKpmYEtfNP1YVziBsJzCR7tHZ9v/074ofBKPNq9U8liGYXD6ojn4rD+Gz/qrj/paRXFAIqWjNzY8cj4aDCYUaIaJKX4XJJ7D8ftMAwBctvQguAQOpxy4B6KKhpc/6hqT8Yw1z37YiW88+Dpe/3T3eA+FGAUmy9w70QnLKs6552+49s/v1vy1f/DYP/H1/30VnaFEzV+bGD1M08RrW3pw7Z/W46Rf/QWXPP42XvmkGx1+N57esANdYTqfY0VVAvnEE0/Ev//7v+ORRx7Bo48+igsuuABLly6t1djqhmaPiIBLwJbeKH769Do8+d4OvPxRd8Wvt3XAEp2fZgjkT/utf+8cHB0P4q39Mcxp9ZV9Ez10D2tZtX7nQFXva5om+uMK9uloSo9jLNgdtYR4h98NALjo2P1w59n/gkWzWgEAi2a1YFazF0++NzHTLD7ptRZat7/8IVRtdNJ2iPFjssy9ExnTNPHff9mAnqiM5z7sxKbucM1e+7O+KP65rQ9beqO48IG/4+3tebYKG5jnN3biulFYVOTDME384Z9b0nPqaHPvG5/gksffxt+39ODYvabi56ctxtPf+QJuPn0xwAAPvf3ZmIyDqFIgX3zxxTjjjDPw2muv4ZVXXsFpp52G7373u7UaW93AMAzmtPrw9IYd+OvH3WhyCxiIV56PvNUWw1kC2f53ZzgB3ah9rtFn/THMbfWXffz0Jjc6/C6s29Ff1ftGFQ2qbuDQ2ZbgdhYHo82QQLb2dpvcYlocA9Y5PeXAWXi3cxAfdA2OyZjGkq39MfgkHjsG4/i/d7aO93CIGjNZ5t6JzHMbO/HyR904//A90eQW8Ou/b6rZa//pg53gWAa/Xn4EWjwSvv/IW3j0na01yWN95ZNuPPdhZw1GWTlPvrcdf/pgJ3qjo78j+dvXPsIv/7YJj62rfNe4XD7aHca9b36ML+w3HU9f9EVcccICHDmvAyLPocPvxtL9Z+CpDdsRSqijPhaiwhzkWCwGn8+HUCiEJUuWYMmSJennwuEwgsFgrcZXN8xt9eO9zkF897j9sX0whlc/qaxifDChICynACArfeGzPuvfKd1AT0TG9KCn+kFnvGdIVjG3tfzOWwzDYOHMFqzd3g/TrDxvuM9Oqdi7IwC/xGPbmEWQLQ9kRyDn44QDZuL3b36Cbzz4Og78/9u77/Aoy+zh498pqZPeG4EkEAKEllClSBMEsaAComJHdK2rsrgqVlbUlX11Lb9d1xVdbDQ7KkoHKVIDhIQQSE9ID8mkJ/O8f0wyZkhPJpXzuS6vS6blfiaTM+e5n3Of28+VOeEBXDu0j8VrAbtCYq6eyf19KCyr4OP9Z5k12B9PBykE7ekux9jbG10oLGX11miG+buyZMJAnGyteXdXDEeTc4kIdG/+BZpQbVDYEpPG+CAvhvq78Z/bruClH4/zj+3RnM0u5KkZ4Vhp2jY3Vl5ZzWtbTlJlMDA11Adrbeev36isNnAqowCAIyk5XD04oMN+1k/RqXx8IB4VkNzBkzuV1QZW/hSFi501T00f0uDv6LbRwfwYncqm44nce4Xl1yoJc236K1m8eDEA48aNY/z48ab/av/dG90xNoQ35o3i1tHBuNnbcLG0ok0zvbUlBt6OdiTkFpnO6BNyi3C0MZ6vpBRYtsyi9mf2a8UMMsCIAHdyi8tJbUcNW27NTLuHzoa+7g6dWmJhrVHjYtf4ZiBuOhs+u3syD04Ko6isktd/OWnRy5xdpaiskpzicoLcHXh06mCqDArv7Yrp6mEJC7gcY29vY1AUVv4UhaIorJg9Ao1axU0j+uLpYMv/7Ylt9yzvoaRscvTlzBniD4DOxorXbhjFHWND+P5kCo+uP0BpRVWbXntLTBoFpRXoy6s4nNy+q4ttFZt50dSC9Egbx1BZbWDTscQm4/3x1DxWbTlBRB93Zg72JymvY8ofa31yIJ747CL+ctVQ0+L9SwV5ODIxxIuNxxI7rOOV+EObEuSvv/4agNOnTxMTE2P6LzY2lv3791t0gN2Fr7M9E0O8AXCzt8GgGBdYtFZtgjg11IeLpZXkl1RQZTCQlFfMFTWvn2LhP8Tan9maGWSAEQFuAO0qs6idQfZwsKWfm0O9Eovyymp+T8zm4wNn21W2cqnMojI8HW2bnfn2crRj8dgQXr0+AoCUDqoB70y173E/dwcCXHTcMiqIX2LSOZXe+0pJLjeXY+ztbdYfSeBoSi6PTRuMf82VQhsrDfdcMYDojAL2nmvfwtrNp1JxtrNiQs33CYBapeKBSWE8d/VwotLy2Xm29WtoFEVh3ZEEQjwc0Vlru2yB8/EU47qY4f6uHG1Dgnw+p4j7PvuN1duiue+zvfxjWzTF5ZVmj0nNL+av3x7Gz8WeV6+PpL+HI3kl5RSWVTbyqu1zJvMinxyMZ9Zgfyb1927ysbeNDuFiaSU/nEzpkLF0dxVV1W3KvdqiXTXIN910U73bbr/99va8ZI/gVtMJIr+k9QldYp4eOysN44I8AePivLSCEiqrDYwKdMfeSmPxGeSEXD32Vpomyw0a0tdNh5u9TbsW6tXOILvrbOjn7kB+SQWFNR/ut3ecZtZ7v/D4xt/5YG8cXx23XI1XdlEp3q04Xl9ne1RAqoXf++iMfD7ad5bKastsulLrZHo+G442vFijtsa99orBHWP746Gz4a3tpzHUmZ0q74IZiOLySp777qhpjKJtLtfYW1dFVTXpBSVmn+n2MigKXx1PYvXWUxZ93Vrnc4r4154zTAzxYm54H7P7rgkPoI+rjn/vPdPmdSiFZZXsic/kqjD/Bi/Rzx5iLLVqS4ngoaQcEnL1LBoVzMT+3uyJz6TKwnGtJaLS8gh00zF9oB8ZhaWkt/AKp0FR+PLwee5Zu5fsojJenjuSecP7sulYIos+2sX2MxkoikJhWSVPfXUIRYE3543GydaKvjWTSx1RZlFbWuFqZ82fpw5u9vHDA9wY6ufKF4fPU2Xo/Pe/K51Mz+e2j3ez6KNdFHXQyUpdbapBvvPOOzl58iRlZWVERESYbjcYDAwd2vt79LnaGy9/5JVU0NrNmBNz9fR1cyDYw5i8JOQUoa/5RQe5OxLgqrN4J4vE3CL6uTu2uo64tg75WErbE+QcfRn2VhrsrbX0dTNvl7fuSALTQn2ZEx7Ah7/F8XtStsV6QGcWlTG8Zga8JWy0Gryd7NpVTlKXoihsOJbIOztjqDYoJOXpeeGaERarb/7i0Hl2nr1AmI8LQ/3Mex0n5uqx1qrxcTJ28LC31vLApDBW/hzFltNpzBrsz8f741mz/yxvzBvF+GAvi4ypJb6JSmZ7XAb9vRy5q5UlP0Jiby1FUXj2u6P8dj4LnbWWgd5ODPR2ZqC3M4N9XAhw1TX/Ipc4l13Ia7+cJLqmvnX2kAAG+7q0aXxFZZXkFpejKApKzXgNCvzt5yjsrbU8PXNYvXisVatZMiGU5384xq+xaW2qrd0Wm05FtYFrwht+rkqlYlJ/b348lUp5ZTU2regBv+5IAm72NswI88XBVsuW02kcTcllTD/PVo+zrQyKwsm0PKaE+hJZU6t9NCW32TU7mYWlrPw5iiPJuUwM8eLpmcNw09kwI8yP2UMCeOPXkzz3/VHGB3lSVllN+sUS/rlgnOlzVPvdlZxXTLifZXvLr9l/lnM5Rfx93qhGSysudduYYJ7+5gjbz2Qwc5C/RcfTHVVWG/jvvjg+/f0cHg62FJRW8MXh89w/cWCH/tw2JcjvvfceBQUFPPPMM6xateqPF9Nq8fTsvD+WrlI7g9yWkoDEXD2Rge6462xwtLUiIVePvtxYD9bP3YE+rjpiMy1bB5uYq29zEBsR4Mb2uAwyLpbg69z6hYO5xeW41ywOq53RTMjV821UMt6OtqyYPRwbKw3R6fl8cjCeorJKHG2t2jTWWgZFIVtf1qoZZIAAF3uLnJxUVRt4+acotsamMzHEiwGeTqw5EI+LvTWPTx3cohOV2jrExh5b20P7/d2xvL9wnNnjak/CNHV2Sbx6iD8bjyXyf3ti2R1/gV1nM9GoVGw8lmjxBLm4vBKdTf3fYUVVNeuOGGe9z2XLDHJbXO6xt9amY0n8dj6LecMDATiTWcimY0lU1MxoLp04kDvH9W/Ra5VXVrPmwFk+O3QeRxsrnpw+hLe2n2bPucw2JcjlVdXctmYXOY18P6y6PtL0HXKpaQN9Wfv7OT78LY7pA/1avZjux+hUgj0cCfVyavQxk/p789XxJA4n55iVYTQlMVfP/oRs7rsiFGuthrF9PbGz0rAj7kKnJsjnc4ooKq9iRIAb/dwdcLO34XByDnOH9mn0OcdScln+zWGqDQpPzxzKtUP7mMXLwb4ufHj7BDYdS+I/e89QUlnNitnDTSWGAL7OdmjVKpIsPIMce+Eiaw+eY/Zg/xb/LgAmhnjT103HZ7+f56owv17dA/18ThEv/3icuKxCrgkP4PGpg3ntl5OsO5LAzSP7Nfq3ZAltSpAdHBxwcHDgf//7n9ntiqKQlJREv379LDG2bsvNviZBbmWJRXF5Jdn6MlM/4mB3BxJyiigqq8TP2Q57ay19XHXsjLtAVbUBrUZNUVkliXn6erOELVVYs2CrXyvrj2uN7GMMEsdT89qUIOcUl5s2J/FxssNao+bzQ+dJyS82JccAY/p5suZAPEeSc5hSs6lHW+UVl1NtUFrdtSHAVWeRurpfYtLZGpvOkgmh3DmuPyqMm6SsO5JAXnE5IZ6OeDrYMrm/T6MnA89+d5RqReG16yPrBb/SiipS84sJcLEnKjWPfeezzIJrQm79z4tapeLxaYN54Iv97I3P4rGpg8kvKefT38+RXVOvbQn7z2ex/JvDrFk8iRBP8xniX2LSySkux11nY9bi8FKbjiUS6uXEUP+WXwG4XFzusReMM73v7ophfJAnT80IN/19VFUbOJ9bxEf7zvLhvjjGBXky0Nu5ydc6kpzD67+cJLWghDlDAnj4ykG42Fuz/UwGe+IzWdqGGaqtscbP+YOTwvB1tjNdNVKrjGsemkq61SoVD0wcyJNfHeK7E8ncNLJfi39uYq6e6IwCHr5yUJMJU0Qfd3TWWnbHZ7Y4KdtwNAFrjZobak5IbKw0XBHsxa6zF3hqRrjZyXhzMgtL2XwqlRuGB7Y6uYmqKfcbEeCGSqUiItCdo810Wvrgtzh01la8s3AsAS4NX1nQqtUsjAxiWqgvyfl6IgM96t0f4KKzWIKckl/Mj9GpfH8iBVedNY9NG9Kq56tVKm4bHcKrW05wMDHHVLLZ22w4msB7u2Kxt9by2g2RTO7vA8CSCaHsjLvA/w7G83gr37vWaFcN8pdffklERASDBg1i0KBBDB48mNtuu81SY+u2HGy0WGnUrZ5Brrt4CiDYw5HzuXrO5xSZehT3cdVRrSik1+yW8++9Z3jwi30trrO6VFyWcTZ6QBMzCk0J8nDEydaqzQv1cvRlphlkjVpFoJuOlPxiBng6mV0aGuLrgr21lt+T2t/QvrYHsndNiUFLBbjYc7G0sl0LMRRFYd3RBILcHbhrXH/UNVu9PjJlEDeP7MuhpBw+2BvH334+wcPrD9RbHALGmdZ957PYE5/JLzHp9e4/n1uEAjwwKYwAF3v+b88fNYulFVVcKCxt8IRomL8bz109nHcWjmNhZBBzw/tgUIyzTrW+O5Hc5OYp289k8PC6/Q2OG2B7XAZVBoWf6rwmGGf1Pz10jgFeTswd2ofkvOIGNzDJLirjH9uiefb7o+gb+RkNOZSUwwNf7GPBhzuY/d4v3L12j0UXfXY3l2vsLa+s5vkfjuFgY8WzVw83S4q0GjWhXs48M2sYLnbWrPwpqsna//3ns3hsw0EA/jl/LM/NHo5LTfncpP7enM8pavUudIqisOFoIkHuDtw+JpgZYX5MG+jLtIG+TAn1bdGM9LggT0YEuPHxgfhWxaKfolPRqFTMGuzX5OOsNGrGB3ux91xmi2qdC0sr+DE6lZmD/MwS2qmhvhSUVpiS1uaUVVbz331x3PLRTj7cF8fHB8626Hl1HU/Nw8vR1lQ+FhnoTk5xOcmNXPlLztMTlZrHvBGBjSbHdXk62tZLjmv1dW9fglxUVsk3UUnc//lvLPzvTtYejGeAlxOv3zAKpzZcNZ05yA8PBxs+O3SuzWPqSOVV1ZS0sVsKGH93/2/7aSID3fn0rsmm5Bgg0M2BOeEBfB2VzIXCUksMt0HtSpA/+OAD1qxZw5VXXsnXX3/No48+yowZMyw1tm5LpVLhZm9NfiubdZvardXUMwW5OxpniHP1BHn8kSCD8QyzqtrA9jMZGBT4to07vp2paWMz0KvpmZTGqFUqhge4tWmhnqIo5OqNM4a1amu5HroyzGzWQatRExnozsGE7Da1OTIoimnBQkt6IDfEvyaAprVioV5JRZVZInYsJY+zWYUsiAwy+/JWq1Q8MT2cnx+eyY7HrmbV9ZGczyli+TdH6iWKMRcuUlFtwMFGy9s7TtdbsRtfU54w0NuZpRMHcj6niF9ijI37a1sRNXbFYE54gOnSYYCrjpEBbvxwKgWDonA4yTib9sHeuEZ/BwcSsjiaksdrv5ys9xiDonAgIRuAX2LTzL58957LJDmvmNtGBxPi4Ui1ojS4acyOsxkoGLcn/7/dzW+cUFFVzTs7T/PYhoPkFpczyMeFaaG+JOUW85evD5taIRkUhaPJuRS3I2B3J5dr7H1vdwwJuXqemz280dlHJztrls8cyrmcItbsbzgJO5tVyIrvj9Lf04k1d0wybWRUa1LNl/Ge+NZ1eziZnk9cViHzI4LafNlbpVLx8JWDuFhawTPfHmnRAt9qg8JPp1MZF+SJu675uDepvzf5JRWcbsEmSd+cSKa8ysDCyCCz28cHeWKjVTd71c2gKGw5ncYtH+3kv/vOMjHEm0n9vfnhZGqrTgAURSEqLY/h/m6m9zayZvOnxtq9/RidilplrCdvr75uDqQVlLRpYeLhpByu/ddW3vj1FMXlVfxpchhfL53O/7t5DIN8XNo0HmuthoURQRxJzuWDdizstDRFUdgRl8H8D3fwwBf72ty2MCrNmHM8OnVwg3/rd48fANDo37gltCtBdnFxYfjw4QwaNIjc3FwefPBBDh061OrXMRgMPP/88yxcuJDFixeTlGTezWD79u3cdNNNLFy4kPXr17foOR3N1d6m9TPIuXqsNGrTgoIgD2MSo/BHC7a6CfLh5BwKSitw19nw/cmUNm0ZfCarEG9HO9PMSFsM93cltaCk1cdbUlFFWVW1qcQC4KaR/bh/YmiDdWtj+nqQUVja6lkbgBd+OMaDX+yn2qCQWbuLnkNrZ5CN731rFur9Y1s0iz7aadqG9MsjCbjYWXN1EwsnbKw0XDnAh2evHsbRlFxe+vG4WXCrDQyrro+kqLySd3aa9zCOzyrE3lqLr7MdUwf6EubtzAd74yivqiYxz5g8t7Sl39yhfUgrKGH7mQxe3HwcjVpFXkl5o+3u0i+WolGr2HYmg6+jzE/azmYVkltczvggT3L05RxLNX5pGRSFTw7E4+dsx7SBvoTUnAw2VIe8/UwG/T0dWRAZxNdRyU3OTpVXVfPgl/v54nAC84YHsvbOybw0dyTLrhrKC9eMIOZCAS//eJyT6fks/XwfD68/wMPr9je4C1Virp4Hv9zPu5e8192VpWIv9Jz4+9u5TDYeS2JhZFCzl5QnhngzZ0gAaw+e43TNortaWUWlPPXVIRxsrfj7jaPRWdevNPR3sSfEw5Hdrez2sOFoIo42WmYNanoWtzmDfV14piY+vLblRLNJxqGkHHL05S1OBMcHeaJVq5o9vqpqA5uOJTEq0J0QT/OrkHbWWsYHGcssGuv4EZ9dyNLP9/HSj8dxs7fh/24ZzyvXRrBkQihlVdV8G9XyiZ/0i6Xk6MvNFl/7u9jj7WjLkQa20q4yGPjxVCrjg7wssklSXzcHqgx/XN1tjTX7z+JqZ8N/b5/Ap3dN5vYxIRYZ0/yIflwTbtzw6s8bD3b5VbOMiyX85evDPPvdUaqqFeKzizh9oaBNrxWVmo+LnTWBjSy49XGyY97wQDafSrF4bXitdiXIWq2Wixcv0rdvX06cOAFAdXXrk7itW7dSUVHBunXrePLJJ3nttddM91VWVrJq1So++ugj1q5dy7p168jOzm7yOZ3BTWfT6hrkxFw9fVx1aNXGt73u1s+1XS2c7axxtLUiJb+YX2PScbTR8perhlJQWtGm3pVnMi8S5tO28opaw2pqQU+ktW4WuXaRikedQDAiwI27xg1o8PG1SXNDZRYf/hbHvvMN9wfNLS5jZ9wFojMK+DE6heyiMqy1apztWnfZqrYnaWsW6p3NKqSovIrHN/zOgYQsfjuXyQ3DA1u0OvzqwQE8NDmMHXEX2Hvujy+qqNQ8+rk5EBnoYdo5qW6JS3xOESEejqhVKtQqFX+6MozMolI2HUsiMVePRq1q0eVEMF4m1VlreWnzcYorKnnxmpEAjV4xuFBYwtRQX8YHefL2jtOcqbOgdH+C8ffz1Ixw7K21pu1ovzuRQsyFi9wzPhStWk0fVx1WGnW9OuTMwlJOpOUzbaAv908IxdfJjtd+OdFoO7r/HYgn5sJFU1JsW+c9v3KADw9PGcTOsxdY+vk+LhSWcue4/iTk6nlo3X5yi40nUYqi8E1UEnev3cPpjAI+P3yeTccSW/TedSVLxV7oGfE3R1/G334+wQBPJx6c1LK64MdqZp7+9nMU5TWTC8UVVSz76jDFFVW8eePoJpOUSf29OZGW1+JtfbOKStkZd4FrhwZi10DS3VpXDw7gvitC+el0Gmv2xzf52J+iU3G0tWJiSMsW3DrYWBER6M7u+Mwmk+/tcRlk68vqzR7XmhLqQ05xeYM91jMulvDo+oNkXCzl2auH8eHtE0zJbX9PJ0b39WDjscQWz8jWniwPr7M2QaVSEdHHnWMpefWS9IMJ2eQUlze5gK81At2MMbW1G4aczyniWGoeN47oyyAfF4suqLPWanj26uE8M2sYJ9LzuWvtnhaXvFhSlcHA54fOc9ua3RxJzuWRKYP48p4rsdaq+Tm6bduSn0jLY5i/a5Pv1x1j+2Oj1fDhb3FtHXqT2pUgL1iwgKVLlzJlyhTWrVvHjTfeSHBwcKtf58iRI0yaNAmAESNGcOrUKdN9586dIzAwEGdnZ6ytrYmMjOTw4cNNPqczuNlbtz5BztObzey56WxwsbNGxR+lBwCBrjrO5RSxKz6TKwf4MCHECz9n+3ozds0pLq8kJb+Y0DaWV9Qa6O2MtVZNVFrrNpqo3STE3aFlCzECXOzxdbLj98Rss9tzi8v4aP/ZRvskbzmdTrWiEOim4197zpCQW4SXQ/ObhFzK1kqDp4Nti2ewqw0KSfl6JoZ4Y1AUnth0CI1axY0j+rb4Zy4cFYSbvY2pRKLaoHAyPd/0RXL3uAE421nxTc3vXlEUzmUX0r/OArhRgR6M6+fJJwfjOZmWTx8XHdoWrn63tdJw1SA/qhWFJ6eHMzXUBxc76waDbJXBQGZhGX7OdqyYPQJXe2te/vG46Qtu//lswryd8XW2Z2qoDzviLpBxsYT/2x1DRB83Ztfs7KXVqOnn5lBvBrn2Uu30gX7YWWtZdtVQkvKKefDL/fV+J+dzilj7+zmuHuzPVWENz9bdEhnEA5MGcs/4AXx57xSWThzImzeO5kJhKXf9by+3frSLq9/7lTd+PcUwfzc23jeVCcFevLX9NIdrTtIMitJoWUZecTnv7DzNzHe2MPkfPzL5Hz9y76d7W/S+t5elYi907/hrUBROZxTw4uZjlFZW8dLcES3e3tjR1oq/zhpKQq6e/+47S5XBwIrvjnI+p4iV10bQ37PpiYNJ/b0xKDR6Yn6pr6OSUVC4aWTL//6bc/f4/swZEsCH++Lq1fXXyi0uY1f8BWaG+bVq6+dJId6k5Bc3OvtWuzFIoKuu0U43E4K9sNao2RFnPnlTXFHFX74+TLXBwPu3jOOa8D71WlwujAwiW1/G9hYujD6emoujrZXpymutyEAPCkor6p1wbz6VioudNVdYqEtPoKvx57Z2tvKr40lYa9Rca6FEvSFzh/bhP7dOwFar4eF1B/js93Pt3pGxpfTllSz57Dfe3RVDZKA7n909mUWjgnGys2Zyfx9+jU1v9T4AucVlpBaUmCbnGuOms2FhZBDbzmSYTdZYSrtOc2+++WbmzJmDvb0969at4+TJk6ag2Rp6vR4Hhz8+9BqNhqqqKrRaLXq9HkfHP5IBnU6HXq9v8jmdwU1nQ35JBQZFaVFv2/LKajIuljDrkkvvwR4OZBWVmc1+BbjqTLNvVw3yR61SccPwQN7fHcu57KJ63QEaE5dlvOwf1sxK7uZYadQM9nHhZCMJcnllNVn6MlN5SK2620y3hEqlYkw/T36NTTd18QBj4gV/LDisS1EUfoxOZYivC09MH8K9n/7G/oRsImpq01orwMW+xbvpZRaWUlFlYGKIF/deMYCH1x1gepiv2Yx5c7RqNdPDfPk2Kpni8krSL5aiL69imL+xC0VtOcbWmHTKK6vJKylHX15V78v9wclh3PW/PRxLzWPKAJ+GflSj/jQ5jAnBXlwR7GXqfd3QDHJ2URnVioKfsz0u9tY8MX0IT39zhE3Hk5g92J/ojHzT1YFZg/3ZfCqVh9cdoKzKwLKrhpqdsAR7OtbbBWvbmQxCvZxMn6NxQZ68dkMkf/spirvX7uGpGeFMGeCDVqPmtV9OoLPR8siUQY0el0ql4o6x5q2+RgV68Nb8sXy8/yw2Wg0Rge6EeTszJzwAtUrFi9eM4P7P9/Hsd0cIcNWRkKOnrKqaEA9HRvX1wN/ZnvyScjKLyth2xhj4pw30w8/ZWM5T90S3I1kq9kL3i7/5JeX8npjD/oQsfk80lpmpVfD0zGGmVpEtNS7Ii2uH9uHzQ+c4n1PEgcRs/nLV0Bat+g/zdjZuqnHuAnMa6Stcq7ymXGBiiHebuv00RqVSsXzmUC4UlrJqywm8He2ICHSnstrA/oQsfo5OY++5TBSFVs+UTurvzept0eyOz2zwfd1zLpOYCxd5cvqQRr/jdDZWjOnnwc64DB6dYuyeYVAUXtp8nMRcPatvGk1gI38T44I86eum44vDLWtVFpWWz3B/13pjiQj8ow65Ni7mFZez51wmCyL6tbpVXmMcba1w19m0KkEuLq/k5+hUpg30bVeZY0sM8HLio8UTefXnE7y3O5akPD1/nVW/37albY1N50xmIc/PGc6sQf5mP+/qwf5sjU1nf0KW2SK75pyoyTWGBzTfvWvRqGA2HU/ig71nWH3TmNYfQBPaFM3WrFnT6H2ff/45d999d6tez8HBgeLiP5ISg8FgCrSX3ldcXIyjo2OTz+kMrvY2VBsUisoqcW5Bc++kfD0Gpf7iqcemDqG00nyGqk/N5XE3extTondNeAD/+S2Ob08k8cT08BaNsbafcnOtjlpimL8rnx06T2lFVb3Lhx/8doavjifxw5+uMqvny9HX7qLX8oTximAvvj2RzO9JOaYz/9oZnBx9OXnF5WYF+3FZhZzPKeKpGeEM8nFhzpAAfoxObXUP5FoBrjqzcoem1C4y6+vmwEBvZ75ZOq1VjfdrzRzkx4ajiew6m2la9Vu3B+f0gX58dyKFA4nZpoWN/S85SRrg5cSswf78fDqt1S39HGzMt6UdHuDGzrMXyCwsNesEknHRuPixNgGYFOLNuH6epstbBgVT4jEywB1PB1syCku574rQeoljiIcjW06nUVhWiZOtFRkXS4jOKOCBSy6fT+7vQ//FTqz44Sgvbj6OnZWGIHdHTl8oYMXs4bjat74H5lA/10YDqc7GijfmjWblT8fRatRcN6wPTrbWHE/N5evjxj67KoylUNNCfblr/IB6J4YdydKxF7pX/P3hZAqrtpxAAVzsrBkb5Mm4fp6M6efRpt81wKNTBvF7Yg77zmdx+5gQU6uy5qhUKiaGePFTdFqzm2psjU2noLSC+RH92jTGplhp1Ky6PpKln+/jr98eZkaYHzviLlBQWoGrvTU3jTTWoTY3I34pL0c7wryd2ROfWe9EMuNiCX/7+QShNV1nmjI11Je957KIuXCRwb4ufLD3DHvPZfLnaYOb7JGsVqlYGBnMG7+e5HhqHiObmNTILS4jJb+Y6xoYi4+THf4u9hxJzjWVgmyJMS4SvnS3wvbq66YjuRUlFj+fTqOkstqiVxWa4mBjxd+ui+Bfe86w9vdzDPN3s1iJSWN+jUkn0E1XLzkGav5urfk5Oq3VCbKNVt2iq9+OtlbcPjqE/9sTy6n0fItu5NKmiBYXZ9l6j4iICHbs2MGcOXM4fvw4oaF/7KYWEhJCUlISBQUF2Nvbc/jwYe69915UKlWjz+kMdTcLaUmCXHs5uaHE5lJ9amqdpg30MSVErvY2jAvyZN/5bJ6Y3rIxnsm8iKeDrUUaaQ/zd+N/B89x+kKBWRucKoOBLafTKa8ycDwl1yzRyi0uw0arxsGm5R+z8UGeuNpb88PJFK4I9qKy2sDvSTkE1gSmuKxCs9mfH6NTsdKomVFzmf2BSQPZE3/B1BWktQJcdOSXVDS62UVdSZe07Wvu8Y0Z7OOCn7M9v8Sm4WhjZdbGCIy9qF3srNl2JsNUohPcwJfhkgmhRKXmmXaYaqva5DwqLY+ZTn9c8cgoNJY5+NbMlqpqeivf/vFu3t0Vg7OdlWlFtkatYmFkP3bHZ3L7mPqX/v9YqFfIyD7upku00wbW74Ht52LPvxZdwYGEbA4mZvN7YjZXDvDm6sEds4OUv4s9/7foiktuHUB5VTX6cuMJce06gs5m6dgL3Sv+DvJx5sHJYUT0cSfMx9kiO0/qbKx47YZIjiTncsuohmtpGzN5gA9fRyU3ualGbWu3YA/HNl+5ao6jrRWrbxrNks/2sflUKpP6ezN7SABj+nm067M4eYA3H+yNI0dfZrryVVltYMX3xzAYFFZeF4FNM2UbE0K80apV7IjLICW/mP8dPMf1wwK5uQU9nGcP9uffe2P58khCkwlyVGrtjGLDl9wjA93ZfiaDaoOCWmU80Rri69Lm74HGBLo5sK1mS+rmZmaVmm3LB3o7MbiNnSraQqVScf/EgZy+UMDqbacY7OtiWuNkadlFZRxPzeOeKwY0+H5o1WpmDvLnq+NJpsmQlohKzWOIr0uLZ//nR/QjLusi1lrLxuU2Jch1d3ACKCwsxMmp7QvBrrrqKn777TduueUWFEXh1Vdf5fvvv6ekpISFCxfy9NNPc++996IoCjfddBPe3t4NPqcz/bHddDlBNP/hO59ThLVG3aItUMN9XfBxsuO6YeYzHSMC3NgTn0m2vqxFK2DPZF60yOwxQLifKyqMZ3Z1E+TDSbmmWuzDyeYJco6+HHdd62qBtRo1Vw/2Z/3RRPKKyzmfU0RJRRV3jBnCyp+jiMu6aEqQK6sN/BKTxuT+3qY/PA8HWzYumYZ9GxfJBLjWLNQrKGn2vUvM1eNiZ92iE6SmqFQqrhrkx9qD8TjYWDG2n6d5f1e1misH+PBLTBqlFVX4Ods3uPLe19meTfdPa9dYwLiARmet5Xhqnlmv6oyLpagAb8c/kvdANwduGRXMp7+fY2w/T7PWfbeODuHW0Q1vxl5bJnQ+p4gQTye+OHyecD+XRhcXWmnUTOpvbA/VVWy0mmaThY5m6dgL3Sv+hng61euWYAm121C3Vks21aht7faXS8qILM3X2Z7P756MWq3CoY0n45eaFOLDB3vj2HsukxuGG2c539sVw+kLBfztuogWLfZ1srViVKAHm0+lUlJRxcgAN56YPqRF74WNlYZ5w/vyyYF44+ZHjXw/RqXlYaNVN/o7jOjjzncnUojLuohBUUjI1bN8puW3Xu/r5kBRWWXN7H3TE0/HU/NIyNXzTCeUOVxKozaWi93xyR5WfH+UD2+bYJGFo5faeiYdBRpdBwLGMot1RxLYfibd9BlrSklFFWezClk8tuHvjobYWml45dqIFj++pdr1jiUkJPDQQw9RVFTExo0bueuuu3j33XcJCWn5gQGo1Wpefvlls9vqvsa0adOYNm1as8/pTH/spteyFc7x2UX0dXdo0dm+r7M9XzWQ6NSu3j1Zs9K/KSUVVSTnFZtmVtvLydaKYA9HU21QrV9i0nC00RLs6WRa2FQrt7gMjxYu0KtrbngfvjicwJaYNLKKyrDWqJkS6sN/98VxtqauGoylFxdLK+u1NmrPVtUBpl7IzSfISXl6+rZxh8JLzQzz45OajQEamiWZPtCXb08ks+98VocniRq1imH+rvUW6mVcLMHL0bbeWf1d4/qTkFPE9cNadukawNPBFkcbLfHZRZzJPE1BSQVv3jjaIuO/HFgq9kLPjL+dxUqjZlyQp2lTjYZ2jNtwNBFHW6t2t3ZrCad2noxfKtjDAT9ne/bEGxPkHXEZrD+ayIKIfkxtxY6mUwf6cCAxGz9nO/52XWSr6n5vGtmXzw6dZ/3RhEbLB6NS8wj3c230dSPr1CGnFZRgq9Uwo5nvyLboW6eTRXMJ8qZjSTjaWlnsO7i13HW2vHjNSB7fcJB/bI/m2auHN/rYlsyIN2RrbDoDvZ0arTMHCPVyIsjdgZ9Pp7UoQT6dUUC1ojS7QK8ztGs++pVXXuHZZ5/F3d0db29vbr/9dp5//nlLja1bMyXILew7eC6nkP7tvMwR6uWErVZj6pPblLNZhSi0f4FeXcP8XTmZnm+2a9vusxeYUtP261xOkdn7UbutcGsFeTgyxNeFH06msP98FiP7uGNvrSXU28m08BCMi7pc7KwZ06/hnY/awtTqrQWbhSTl6U0Bs72CPBwZUDNzNty/fg3ViJoyCwVaXWvYFsMD3EjI1ZNfp1NLemEpPg0sQLK31vL3G0c3eYn0UiqVimBPJ3advcAPp1K5dXSwxa52XA4u59jb2ZraVKO2tdt1Q/t0yAxdR1OpVEzu783h5Fzisi7y6s8nGOzjwkNXNr74tSHTBvoxb3ggb944utWL0dx1tlwV5tfoxiHF5ZXEZxeatXdr6DWC3B347XwWv8amM22gb5tL3ppSu5YiuZmFetl6Y2eRa4YEmC3A72yj+3pw57j+bD6VWq8LSmlFFRuPJnLzf3bw6IaDre40kZpfTMyFi03OHoPxMzZrsD8n0vJb9L0alZaHWgVD/VxaNZ6O0K4EuaCggAkTJpj+fdttt6HXd0zD5u7Gyc4KjUpllkA05mJpBTn68hZ3n2iMVqNmiJ9Lo30O47MLWXckAX15pUUX6NUa5u9GSUWVqZ3OnnOZlFRWM2uQP6NrdqI6XKdhe46+vFUdHeqaO7QPCbl6kvOLuSLYWFIR6uVMSn4xxRVVlFdW89s5Yxs8S9aD2ltrcdfZNNsLOb+knIullaZdES1hQWQ/wv0arpvTqo2z6FC/jr0j1NYh171icOFiCX6t3L67Kf09HCkorSDQVcc94xvujS0adjnH3s42PsgLTQObavx2LpMHv9iPSkWrWjt2N5P6e1NZbeDhdQdQq1W8cu3IVnd+0NW0ZWxtl5FaCyODGt045GR6PgbFfOFyQyL6uBOVmkdJRRXXNNN1pK28neyw1qqb7WTx3Ylkqg1Kt/hc3HPFAIYHuPHm1lMk5urJKy7nP3vPMO+D7fxjezT21hqOJOfy1vboVr3ur7HpgHEReXNmDfJHBabuXE05kZZPiKdTh5zgtFa7M4vy8nLT1Hx2djYGQ+u3YeyJ1CoVrvbWLZpBrl2gF+zR/pm/4f5uxGcXUlxe/0z7/22L5u0dp7npPzv4JioJd51NmxPUhtS2HttwNIG84nJ+iUnHy9GWEX3cCPVyxtFGayqziMu6SElFlVm9amvMGOiLbU295xXBxpKC0JoFjfFZhRxIzKa0srrZUpO2qG31VlZp3Eu+oX6SzW3p3BbXhPfhg1snNLow6fphgQS5O3TKpacwb2dstGqO1mxQUlltIKuozKItrAb7uhjbd80a1qbuH5e7yzX2djZHWysi+rizp6a7TcbFEpZ/c5hlXx/G1krDPxeMs+jfRWcb6u+Ki501+vIqnp89vEuOZYCXE6MC3flwXxwrf4oya+l5PDUPjVrFEF+XJl+jtswiwMW+2WS6rdQqFYGuuiY3C6mqNvBtVDJj+3m2aM1RR9Oq1bx8zUistWoe3XCAG/+znTUH4hke4Ma/Fo3nf3dO5rbRwXwdlcy3J1q214KiKGyNTWd4gJtZp6PGeDsZ2xP+fDqtyf7MVQYDp9LzGWbBThTt0a5rQosWLeLee+8lNzeX1atXs3nzZu677z5Lja3bc63phdyccznGsgBLzPwN83fFoMCpjALG1mmhk5Sn51hqHtcN60N2URn7E7ItXqvq42TH9IG+/HAqlZ9qPuiLRgUbEzqVsWH7oaRcqg0Kr205iau9NXPbeCavs7Fi7tA+xFwoMJU91LZ8icu6SHRGAc52VozsgEAY4Kpj86lUpr39MwALIvrx+LQhZo9JzP2jxVtnGejtzGd3X9kpP8taq2FEgDuHEo0nPJlFpSj80cHCEmYO8icy0B2vNp5EXc4u99jb2SbX9Ax+e8dpvolKQoWKP00O45bIoBZvytNdadVqHpw8kPJKQ6MLETvD83NG8L+D8Ww+lcqP0amMDHBjQWQQx1LyGOjl3GwJS0Qfd2y1GuaN6Nuhi+L6ujmYrtA2ZHd8JjnF5fxlZtfPHtfydLTlhTkjePnHKGYN8mfRqGCzyZ0HJoURn13E6q2nCHZ3YGgzkzDncopIyNXz1IyWtZwF42K9v/18glPp+Y2+fnxWEaWV1QzroBOc1mpXgjx//nz69evHzp07qaqq4pVXXjG77Nfbudm3bLvp+OwinO2s2lSPe6khfq6oVXAiNc8sQf7+RAoatYolE0Jx19lyNqvQ1GnDUlQqFa9cG8G9V+jZfCqFI8m5ZguzIgPd2Xn2Am/viCY207gFcHsWlfx52mCzf3s4GHcejM4oYO+5LKYP9O2QL6c7x/Wnj6sOFSp2ns1g19kLPDZ1sFnQTcrTY6NVt+jsuaca08+Dd3bGkFlY+kcPZCfLzS5p1CpJjtvoco+9nW1iiDFBXnckgSsHePPY1CFmrRh7umuHtnyBbUfxcLDlienhLJkwkO9PprDhaCJ//fYIAIta0J7Pyc6aTfdPbXdXoeb0dXNgR1wGFVXVDe5c+NXxRHyc7BgfZJkd/CxlXJAXPz50VYP3adQqXpo7kvs+3csz3x3lo9sn4tnEXgK/xqSjUamYGtry3sZTQn15c+spfj6d1miCXLu+qqF1OF2hXQnynXfeySeffMLo0Zfn6nM3e2vTTGJTzucUEeLhaJGzWp21lgFeTmbbPldWG/gxOpWJIV6mTTka6q9sKf3cHRpcxDGqpg5547Ekxgd5tnsV8aXvl0qlItTbiW01/S5b88fZGgEuOlPjfHtrDau3RZN2scSs5VFSrp5ANweL9Gntrsb09QRiOJSUg6HmspglZ5BF213usbezeTvZsXzmUDwdbC22dbFomKOtFbeODmZBZD/2xGeyNTada1q44UdbN5NpjUA3HQbF2Ar00v7C8dmFHE3J48FJYQ12POnOnGyteO2GUdz/2W88/e1h3r9lfINtLWvLK0b1bd3mPTprrXFX2DMZPDZ1cIMnFyfS8vBxsus2Eyftmn4rKiqipKTEUmPpcYzbTZfXq6k5lJTDuiMJABgUxdTr1VKG+bsRnZFPVc2q0z3xmRSUVnBdF88CBLrq8HK0xVar4akZ4R1ymSvUy5lqg4JjTe/NjlZb13bptshJeXqLLtDrjoI9HPDQ2XAwMZv0iyVoVKomZxVE57ncY29XuH5YoCTHnUirVjM11Je/XRfZYRtdtEVtWV1DC/XWHUnARqvm+mEdu3tdRwn2cGTFnBHEXLjI33891WC9cHRGARmFpc12r2jI1YP9KSqr5MdLOmqAMfE+kZZvWuvUHbRrBtnOzo6pU6cycOBA7O3/uPT6r3/9q90D6wlc7W2oqDZQXFFl1rh9zf6zHE/NY5CPM246G0orq007h1nCcH83NhxNJC6rkMG+Lnx7IhlvR7smt/XsDCqVimUzwlGrVB220KN2od6kEO9Oqf3r6+aAu86GI8m5po1byiqruVBY2uJZjZ5KpVIxpp9nzdbbHng52nbZDnLC3OUee4XoKrVby1+aINcuXJ8bHmDxftWd6coBPtwzfgAf7T9LXnE5T0wfYrbY8NfYdKw1aq4c0Pp69dF9PYno487bO04T7udq1rI07WIJucXlTbbz62ztSpBvvvlmS42jR3LT1eymV1xuSpDLq6o5nVEAwFvbT3NHzW4wluxdW3uG9fC6AwS42hOfXcR9V4R2i0s6Hb3IY7i/sR9wR+8vX0ulUhHRx52jKbmmZurJ+XoULNvBorsa08+DH6NTOZiYbVokKbre5R57hegq9tZavB1t63Wy+CYqmcpqA/MjWredeXd0zxUDcLKz4oO9cdz+8W4Wjw3h9jEhaNVqtp/J4Ipgrza1YTPWOo/g7v/t5dnvjvLR7RNMr3Oypmy0O2wQUqtdCfI333zDJ598Yqmx9Dh1d9MLrPmdns4ooKLawIwwP7bGpvOf3+JQAUEWTKY8HGxZeW0EJ9PzSc7TY2el5boeekmntTwdbRtdaNBRIgLd+TU2naS8Yvq5O3A6w7iC2VKbhHRntWUs+vIqqT/uRi732CtEVwp0czDbLKSiqpqvjhvX3vSGiRO1SsWCiCCmhfryzs4Y/rvvLD+fTmPWIH9yi8vbtTugu86Wl+aO5NH1B1m15SSvXDsSlUpFVFoejjZagjy6z/vXrgS5tg6u7iW+y4mbrv5uesdS8lABT80IJ+NiCdEZxjZllt5ladpA3w7pASzqi6zZIe5oSi7ejrZ8fOCscfvMblQX11HcdDaEehl3MPTrwb1ee5vLPfYK0ZX6ujnwY3Sq6ari1tgM8krKWRjZ82eP6/JwMCaz1w7tw5tbT/HR/rPYW2uZ0M5a/JF93Fk6aSDv745l2DFXFkQEcSLV2P6tOy18vyxqkL1cwa8D1nPZ29uiUkFmcSF+HsZkNfpCLoN8nQgLsGLlvMHMe38fQ/wcO+Tni87h626Pr7MtMZm5lFWXkVVUxvu3jSTAs/v8IXekaYM8icsqZJC/nXyOu4meEnuh4+KvEF1lWKCOjceq0NqU4+Vow6aoBEK9Hbg+0oNulN9ZzHUeHswaPon/7UvCxd6KIN/2b+z01NXBxGXn8d6uGIYF2pOYp2fBGP9uFSukBrkdXOytuTLUk/WHU3hs+gCqFYWjyfncOta4mGtkoCtv3DyMUO/eP9PYm6lUKsaHuPPr6Ux+PZ3JvJH+jOrXfeqkOtqsId78Z895Bvt1XOtA0TqXe+wVoiuFeBrLAM5l6TmfXUxMRiGv3Ti0Qzco6Wo2Wg1LJgdb7PXUahWr54/gmnf28MDaowCM7mbfq+1akj5v3jzGjBkDQFVVFREREcybN88iA+spbhvbl8zCcrbFZnEi9SLlVQbGBrmb7l8wqg8j+rh03QCFRYwPdqeorAqtRsXTs8O6ejidamSgKydemEmYjyTI3YXEXiG6jilBzinmo98ScNNZc8NI/y4eVc/jbG/F/90WCYC1Rs1Q/+61ELxdCfKePXu46aab2Lp1K9u2bePmm29m69atlhpbjzB1oCc+TrZ8djCZg+eNvXLHBnWvsyDRfhMHeGCtUfP4jAF4O11+vYB1NpatoRftI7FXiK7j7WSDzlrD9phMtsZkctvYQGyt2l92cDkaGuDMW7eM4MmZod3uPWzXt97bb7/Np59+Sv/+xl3Hzp49y7Jly5gxY4ZFBtcTaDVqbhnTh7e2niW9oJQwH0dcdT23B6JomK+zHfv/Os3U2k+IriSxV4iuo1KpCPZ0YMeZbKw0KhaP69vVQ+rR5gztng0H2jWDXFlZaQrQAAMGDKC6urrdg+ppFo7ug1oF8Vl6mT3uxdwdbHp1jZnoOST2CtG1QjyNbT6vHeaH12V4VfFy0K4E2dbWlpMnT5r+ffLkSezsLr9eqb7OdkwfZNwgY2ywezOPFkKI9pHYK0TXGlCz+P7uCb2rtZv4Q7tKLJYtW8YDDzxA377GywsJCQm8/fbbFhlYT/PglBCyCsuYENKNepQIIXolib1CdK3bx/VlWIAzQwO618IyYTkqRVGU9rxAQUEBUVFRGAwGRowYgaurq6XG1m6pqalMnz6dbdu2ERAQ0NXDEUIIi+nOsRck/goherY2l1js37+fs2fP4uLiwpVXXklSUhKxsbGWHJsQQohLSOwVQoiO16YEedu2bTz11FNcvHjRdJudnR3Lli1j165drX69srIyHnnkEW699VaWLFlCXl5evcd8/PHHzJ8/n/nz5/Puu+8CoCgKkyZNYvHixSxevJjVq1e35XCEEKJHkNgrhBCdRGmDBQsWKDExMfVuP3nypLJw4cJWv95HH32k/POf/1QURVF++OEH5ZVXXjG7Pzk5WZk3b55SVVWlVFdXKwsXLlRiYmKUxMREZenSpY2+bkpKihIaGqqkpKS0ekxCCNHd9JTYqygSf4UQPVubZpDLy8sJC6u/m1h4eDglJSWtfr0jR44wadIkACZPnsz+/fvN7vfx8eHDDz9Eo9GgVqupqqrCxsaG6OhoMjMzWbx4MUuWLOH8+fNtORwhhOgRJPYKIUTnaFMXC4PB0Oh9SjNr/jZs2MAnn3xidpu7uzuOjsaWKTqdjqKiIrP7rayscHNzQ1EU3njjDQYPHkxQUBA5OTncf//9zJ49m8OHD7Ns2TI2bdrUlkMSQohuT2KvEEJ0jjYlyEOGDOH777/n2muvNbv9hx9+oF+/fk0+t7aWra6HH36Y4uJiAIqLi3Fycqr3vPLycp555hl0Oh0vvPACYJw10WiMWxOOGjWKzMxMFEWRzRyEEL2SxF4hhOgcbUqQH3/8cRYtWsTu3buJiIjAYDBw/PhxDh06xNq1a1v9ehEREezatYthw4axe/duIiMjze5XFIU//elPjB07lvvvv990+7vvvouLiwtLliwhNjYWPz8/CdBCiF5LYq8QQnSONvdBzsvL4/PPP+fUqVOo1WpGjBjBwoULcXZufdPs0tJSli9fTnZ2NlZWVqxevRpPT0/WrFlDYGAgBoOBJ554ghEjRpie88QTTxAcHMyyZcsoKSlBo9Hw/PPPExISYnqM9OEUQvQ2PSH2gsRfIUTP1u6NQrozCdBCCNE1JP4KIXqyNm8UIoQQQgghRG8kCbIQQgghhBB1SIIshBBCCCFEHe1KkHNycti2bRsAf//737nzzjuJjY21yMCEEEI0TGKvEEJ0rHYlyE8//TQpKSns37+fPXv2cP3117Ny5UpLjU0IIUQDJPYKIUTHaleCXFBQwF133cXu3buZO3cuN954I6WlpZYamxBCiAZI7BVCiI7VrgS5srKSyspK9uzZwxVXXEFpaSklJSWWGpsQQogGSOwVQoiO1a4Eefr06YwfPx5XV1fCw8OZP38+c+fOtdTYhBBCNEBirxBCdKx2bxRy4cIFvL29UalUxMbGEhYWZqmxtZs0qhdC9FbdOfaCxF8hRM/W7i4W0dHRqFQq/v73v7Nq1SpZSS2EEB1MYq8QQnQs6WIhhBA9jMReIYToWNLFQgghehiJvUII0bGki4UQQvQwEnuFEKJjSRcLIYToYST2CiFEx7JIFwsfHx+AbreSWlZRCyF6q+4ce0HirxCiZ9O258kGg4Hvv/+e3bt3U1VVxYQJE+jfvz9abbteVgghRBMk9gohRMdqV4nF6tWrOXDgAHfeeSd33303x44d44033rDU2IQQQjRAYq8QQnSsdk037Nmzh02bNmFlZQXAlClTuO6663jmmWcsMjghhBD1SewVQoiO1a4ZZEVRTAEawNra2uzfQgghLE9irxBCdKx2JchhYWG8+uqrJCcnk5KSwqpVqwgNDbXU2IQQQjRAYq8QQnSsdpVYvPDCC6xcuZJbbrkFRVGYOHEiK1asaPXrlJWVsWzZMnJzc9HpdLz++uu4ubmZPWblypUcPXoUnU4HwPvvv4+VlVWzzxNCiN5GYq8QQnSsdiXI//73v3nttdfaPYgvvviC0NBQHnnkETZv3sz777/Pc889Z/aY6OhoPvzwQ7MgvGbNmmafJ4QQvY3EXiGE6FjtKrHYuXOnRQZx5MgRJk2aBMDkyZPZv3+/2f0Gg4GkpCSef/55brnlFjZu3Nii5wkhRG8ksVcIITpWu2aQAwICuOeee4iIiDBdfgO4++67G33Ohg0b+OSTT8xuc3d3x9HREQCdTkdRUZHZ/SUlJdx+++3cfffdVFdXc8cddxAeHo5er2/yeUII0RtJ7BVCiI7VrgTZxcUFgLS0tBY/Z/78+cyfP9/stocffpji4mIAiouLcXJyMrvfzs6OO+64Azs7OwDGjRtHbGwsDg4OTT5PCCF6I4m9QgjRsdqVIK9atcr0/xUVFVhbW7fpdSIiIti1axfDhg1j9+7dREZGmt2fmJjIn//8Z77++msMBgNHjx5l3rx55OXlNfk8IYTojST2CiFEx2pTglxRUcGKFSuYMWMGV111FQCPPPIIbm5uvPLKK63e7nTRokUsX76cRYsWYWVlxerVqwHjQpDAwECmT5/Otddey4IFC7CysuL6669nwIABBAQENPg8IYTojST2CiFE51ApiqK09klvvvkmCQkJvPzyy7i7uwOQmZnJSy+9xIABA/jzn/9s8YG2RWpqKtOnT2fbtm0EBAR09XCEEKJdekrsBYm/QoierU1dLHbu3Mnq1atNARrA29ubN954g61bt1pscEIIIf4gsVcIITpHmxJkKysrbG1t693u4ODQ5lo4IYQQTZPYK4QQnaNNCbJarUav19e7Xa/XU1VV1e5BCSGEqE9irxBCdI42Jchz587lueeeo6SkxHRbSUkJzz33HDNnzrTY4IQQQvxBYq8QQnSONiXId955J46OjkyYMIEFCxZw8803M2HCBJycnHjooYcsPUYhhBBI7BVCiM7Spi4WtdLS0oiOjkatVjNs2DC8vLwsObZ2k1XUQojeqLvHXpD4K4To2dq1UYi/vz/+/v6WGosQQogWkNgrhBAdq00lFkIIIYQQQvRWkiALIYQQQghRhyTIQgghhBBC1CEJshBCCCGEEHVIgiyEEEIIIUQdkiALIYQQQghRhyTIQgghhBBC1CEJshBCCCGEEHVIgiyEEEIIIUQdkiALIYQQQghRhyTIQgghhBBC1CEJshBCCCGEEHVIgiyEEEIIIUQdkiALIYQQQghRh7arBwBQVlbGsmXLyM3NRafT8frrr+Pm5ma6PyYmhldffdX07+PHj/Pee+8xadIkJk+eTL9+/QAYMWIETz75ZGcPXwgheiSJvUII0bBukSB/8cUXhIaG8sgjj7B582bef/99nnvuOdP9gwYNYu3atQD89NNPeHl5MXnyZJKSkhgyZAj/+te/umroQgjRY0nsFUKIhnWLEosjR44wadIkACZPnsz+/fsbfFxJSQnvvPMOzz77LADR0dFkZmayePFilixZwvnz5zttzEII0dNJ7BVCiIZ1+gzyhg0b+OSTT8xuc3d3x9HREQCdTkdRUVGDz924cSNXX3216RKgp6cn999/P7Nnz+bw4cMsW7aMTZs2dewBCCFEDySxVwghWq7TE+T58+czf/58s9sefvhhiouLASguLsbJyanB537//ff885//NP07PDwcjUYDwKhRo8jMzERRFFQqVQeNXggheiaJvUII0XLdosQiIiKCXbt2AbB7924iIyPrPaaoqIiKigp8fX1Nt7377rumGZHY2Fj8/PwkQAshRAtJ7BVCiIZ1i0V6ixYtYvny5SxatAgrKytWr14NwJo1awgMDGT69OkkJCTg7+9v9rz777+fZcuWsWvXLjQaDatWreqK4QshRI8ksVcIIRqmUhRF6epBdJTU1FSmT5/Otm3bCAgI6OrhCCHEZUPirxCiJ+sWJRZCCCGEEEJ0F5IgCyGEEEIIUYckyEIIIYQQQtQhCbIQQgghhBB1SIIshBBCCCFEHZIgCyGEEEIIUYckyEIIIYQQQtQhCbIQQgghhBB1SIIshBBCCCFEHZIgCyGEEEIIUYckyEIIIYQQQtQhCbIQQgghhBB1SIIshBBCCCFEHZIgCyGEEEIIUYckyEIIIYQQQtQhCbIQQgghhBB1SIIshBBCCCFEHZIgCyGEEEIIUYckyEIIIYQQQtQhCbIQQgghhBB1SIIshBBCCCFEHd0qQf7111958sknG7xv/fr13HjjjSxYsIAdO3YAUFZWxiOPPMKtt97KkiVLyMvL68zhCiFEryCxVwghzGm7egC1Vq5cyd69exk0aFC9+7Kzs1m7di2bNm2ivLycW2+9lQkTJvDFF18QGhrKI488wubNm3n//fd57rnnTM+rrq4G4MKFC512HEII0RY+Pj5otZ0fkjsi9oLEXyFEz9BY7O02CXJERAQzZsxg3bp19e47ceIEI0eOxNraGmtrawIDA4mNjeXIkSPcd999AEyePJn333/f7HnZ2dkA3HbbbR1/AEII0Q7btm0jICCg039uR8RekPgrhOgZGou9nZ4gb9iwgU8++cTstldffZU5c+Zw8ODBBp+j1+txdHQ0/Vun06HX681u1+l0FBUVmT0vPDyczz77DE9PTzQajYWPRAghLMfHx6dDX78zYy9I/BVC9AyNxd5OT5Dnz5/P/PnzW/UcBwcHiouLTf8uLi7G0dHR7Pbi4mKcnJzMnmdra8uoUaPaP2ghhOjhOjP2gsRfIUTP1q0W6TVm2LBhHDlyhPLycoqKijh37hyhoaFERESwa9cuAHbv3k1kZGQXj1QIIXoPib1CiMtVt6lBbsiaNWsIDAxk+vTpLF68mFtvvRVFUfjzn/+MjY0NixYtYvny5SxatAgrKytWr17d1UMWQogeT2KvEOJyp1IURenqQXQXBoOBF198kTNnzmBtbc3KlSvp27dvVw+rzSorK3nmmWdIS0ujoqKCBx98kP79+/P000+jUqkYMGAAL7zwAmp1j7iQ0KDc3FxuvPFGPvroI7Raba84tn//+99s376dyspKFi1axJgxY3rFcVVWVvL000+TlpaGWq3mlVde6RW/s6ioKN58803Wrl1LUlJSg8ezfv16vvzyS7RaLQ8++CBTp07t6mF3O70t/tZqyeejp+nt3y3V1dU899xzJCQkoNFoWLVqFYqi9Jrj643fm7VuuOEG0/qIgIAAHnjggbYfnyJMtmzZoixfvlxRFEU5duyY8sADD3TxiNpn48aNysqVKxVFUZS8vDzlyiuvVJYuXaocOHBAURRFWbFihfLLL7905RDbpaKiQvnTn/6kzJw5U4mPj+8Vx3bgwAFl6dKlSnV1taLX65V//vOfveK4FEVRfv31V+XRRx9VFEVR9u7dqzz88MM9/tg++OADZe7cucr8+fMVRVEaPJ6srCxl7ty5Snl5uVJYWGj6f2Gut8VfRWnZ56Mn6u3fLb/++qvy9NNPK4pijMkPPPBArzm+3vi9WausrEy5/vrrzW5rz/H13NOEDnDkyBEmTZoEwIgRIzh16lQXj6h9rr76ah577DHTvzUaDdHR0YwZMwYwtmfat29fVw2v3V5//XVuueUWvLy8AHrFse3du5fQ0FAeeughHnjgAaZMmdIrjgsgKCiI6upqDAYDer0erVbb448tMDCQd955x/Tvho6nbqs0R0dHU6s0Ya63xV9o2eejJ+rt3y0zZszglVdeASA9PR0PD49ec3y98XuzVmxsLKWlpdxzzz3ccccdHD9+vF3HJwlyHXq9HgcHB9O/NRoNVVVVXTii9tHpdDg4OKDX63n00Ud5/PHHURQFlUplur+h9kw9wVdffYWbm5vpCxXoFceWn5/PqVOnePvtt3nppZd46qmnesVxAdjb25OWlsbs2bNZsWIFixcv7vHHNmvWLLMG8w0dT2Ot0oS53hZ/oWWfj56oN3+31NJqtSxfvpxXXnmFWbNm9Yrj663fm7VsbW259957+e9//2uR709JkOu4tKWRwWDokp2tLCkjI4M77riD66+/nmuvvdas9qax9kw9waZNm9i3bx+LFy8mJiaG5cuXm21321OPzcXFhYkTJ2JtbU1wcDA2NjZmf9A99bgAPv74YyZOnMiWLVv49ttvefrpp6msrDTd35OPrVZDf1+NtUoT5npj/L1Ub4m/0Hu/W+p6/fXX2bJlCytWrKC8vNx0e089vt76vVkrKCiI6667DpVKRVBQEC4uLuTm5prub+3xSYJcR0REBLt37wbg+PHjhIaGdvGI2icnJ4d77rmHZcuWcfPNNwMwePBg06YAu3fv7rF9Sj/77DM+/fRT1q5dy6BBg3j99deZPHlyjz+2yMhI9uzZg6IoZGZmUlpayvjx43v8cQE4OTmZEkNnZ2eqqqp6zeexVkPH01irNGGut8XfhvSWz3tv/m4B+Oabb/j3v/8NgJ2dHSqVivDw8B5/fL31e7PWxo0bee211wDIzMxEr9czYcKENh+fdLGoo3YVdVxcHIqi8OqrrxISEtLVw2qzlStX8tNPPxEcHGy67dlnn2XlypVUVlYSHBzMypUre/wuV4sXL+bFF19ErVazYsWKHn9sb7zxBgcPHjS11QoICOgVx1VcXMwzzzxDdnY2lZWV3HHHHYSHh/f4Y0tNTeWJJ55g/fr1JCQkNHg869evZ926dSiKwtKlS5k1a1ZXD7vb6W3xt1ZLPh89TW//bikpKeGvf/0rOTk5VFVVsWTJEkJCQnrF765Wb/veBKioqOCvf/0r6enpqFQqnnrqKVxdXdt8fJIgCyGEEEIIUYeUWAghhBBCCFGHJMhCCCGEEELUIQmyEEIIIYQQdUiCLIQQQgghRB2SIAshhBBCCFGHJMhC1JGamsrIkSO7ehhCCHFZkdgruhtJkIUQQgghhKijd+3jKUQHSUhI4OWXX6a4uJjs7GzCwsJ46623sLGxYdeuXbz55puo1WoGDRrEvn37+PzzzwkICOjqYQshRI8msVd0FZlBFqIF1q9fzw033MD69ev55ZdfSE1NZefOneTn5/OXv/yFv//973z77beMHTuWzMzMrh6uEEL0ChJ7RVeRBFmIFli2bBlubm785z//4cUXXyQrK4uSkhIOHz5MSEgIYWFhAMybNw8HB4cuHq0QQvQOEntFV5ESCyFa4IknnqC6uprZs2czZcoUMjIyUBQFjUbDpbu1q9Vy3imEEJYgsVd0Ffk0CdECe/fu5aGHHmLOnDkAREVFUV1dTUREBImJicTGxgKwZcsWCgsLUalUXTlcIYToFST2iq4iM8hCXKKkpKReu6HHH3+chx56CHt7exwcHBg9ejTJycm4uLjwj3/8g+XLl6NWqwkPD0er1WJnZ9dFoxdCiJ5JYq/oTlTKpdcohBAtptfref/993nkkUews7MjOjqapUuXsmfPHpnJEEKIDiKxV3Q0mUEWoh0cHBywsrLi5ptvRqvVotVqeeuttyRACyFEB5LYKzqazCALIYQQQghRhyzSE0IIIYQQog5JkIUQQgghhKhDEmQhhBBCCCHqkARZCCGEEEKIOiRBFkIIIYQQoo7/D1zESEwXsG/RAAAAAElFTkSuQmCC\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -2783,788 +141,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
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index e45abb70..7a3bc01c 100644
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@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
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-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -357,6 +356,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -954,15 +954,15 @@ <h2>Note<a class="headerlink" href="#note" title="Permalink to this headline">¶
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="f_16_benchmark.html" title="previous page">Example: F-16 Ground Vibration Test benchmark</a>
     <a class='right-next' id="next-link" href="general_estimators.html" title="next page">Building NARX models using general estimators</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -981,7 +981,7 @@ <h2>Note<a class="headerlink" href="#note" title="Permalink to this headline">¶
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/parameter_estimation.html b/docs/examples/parameter_estimation.html
index 44f9de4e..e88eb73f 100644
--- a/docs/examples/parameter_estimation.html
+++ b/docs/examples/parameter_estimation.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Parameter Estimation &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="../_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,23 +26,22 @@
 
     
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="../_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="../_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="../_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
     <script src="../_static/jquery.js"></script>
     <script src="../_static/underscore.js"></script>
     <script src="../_static/doctools.js"></script>
-    <script src="../_static/language_data.js"></script>
     <script src="../_static/togglebutton.js"></script>
     <script src="../_static/clipboard.min.js"></script>
     <script src="../_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="../_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
     <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
     <link rel="canonical" href="http://sysidentpy.org/examples/parameter_estimation.html" />
@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
         </div>
@@ -349,6 +348,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -566,15 +566,15 @@ <h2>Affine Least Mean Squares<a class="headerlink" href="#affine-least-mean-squa
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="defining_lags.html" title="previous page">Setting specific lags</a>
     <a class='right-next' id="next-link" href="f_16_benchmark.html" title="next page">Example: F-16 Ground Vibration Test benchmark</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -593,7 +593,7 @@ <h2>Affine Least Mean Squares<a class="headerlink" href="#affine-least-mean-squa
     </div>
 
     
-  <script src="../_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/examples/parameter_estimation.ipynb b/docs/examples/parameter_estimation.ipynb
index 489f30dd..d5638a5c 100644
--- a/docs/examples/parameter_estimation.ipynb
+++ b/docs/examples/parameter_estimation.ipynb
@@ -100,14 +100,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.0018530175563774912\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9000  0.95654752\n",
-      "1         y(k-1)     0.2001  0.04040759\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00304121\n"
+      "0.0019836907066852426\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95686129\n1         y(k-1)     0.2000  0.03981558\n2  x1(k-1)y(k-1)     0.1000  0.00331978\n"
      ]
     }
    ],
@@ -138,14 +134,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.001835947345516444\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9001  0.95654752\n",
-      "1         y(k-1)     0.2001  0.04040759\n",
-      "2  x1(k-1)y(k-1)     0.1005  0.00304121\n"
+      "0.0019719806156492186\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9001  0.95686129\n1         y(k-1)     0.2002  0.03981558\n2  x1(k-1)y(k-1)     0.1002  0.00331978\n"
      ]
     }
    ],
@@ -176,14 +168,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.015706349502363732\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.8969  0.95654752\n",
-      "1         y(k-1)     0.1968  0.04040759\n",
-      "2  x1(k-1)y(k-1)     0.0749  0.00304121\n"
+      "0.007871725507267023\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8986  0.95686129\n1         y(k-1)     0.1974  0.03981558\n2  x1(k-1)y(k-1)     0.0894  0.00331978\n"
      ]
     }
    ],
@@ -214,14 +202,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.0018472762495629913\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.8999  0.95654752\n",
-      "1         y(k-1)     0.2001  0.04040759\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00304121\n"
+      "0.002014978177958598\n      Regressors Parameters         ERR\n0        x1(k-2)     0.8999  0.95686129\n1         y(k-1)     0.2000  0.03981558\n2  x1(k-1)y(k-1)     0.0999  0.00331978\n"
      ]
     }
    ],
@@ -245,20 +229,17 @@
     "\n",
     "print(results)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -270,9 +251,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
index a7866dc8..74e91f92 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Index &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/genindex.html" />
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" />
@@ -242,22 +239,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -276,19 +273,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -648,13 +646,13 @@ <h2 id="T">T</h2>
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -673,7 +671,7 @@ <h2 id="T">T</h2>
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/index.html b/docs/index.html
index cabc7f05..5de3ec5b 100644
--- a/docs/index.html
+++ b/docs/index.html
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+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
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+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
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         </nav>
         
+        </div>
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+            
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+        </div>
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+            <!-- Source interaction buttons -->
 
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-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
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@@ -327,6 +324,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
         </nav>
         
+        </div>
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 </div>
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@@ -382,15 +380,15 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="index.html" title="previous page">Welcome to SysIdentPy’s documentation!</a>
     <a class='right-next' id="next-link" href="introduction_to_narmax.html" title="next page">A brief introduction to NARMAX models.</a>
 
+        </div>
+        
+        </div>
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     <footer class="footer mt-5 mt-md-0">
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index be37271f..a5946977 100644
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-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
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-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
             <i class="fas fa-list"></i> Contents
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+        </div>
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@@ -412,15 +412,15 @@ <h2>Polynomial NARMAX<a class="headerlink" href="#polynomial-narmax" title="Perm
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="installation.html" title="previous page">Install Guide</a>
     <a class='right-next' id="next-link" href="user_guide.html" title="next page">User Guide</a>
 
+        </div>
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     </div>
     <footer class="footer mt-5 mt-md-0">
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@@ -439,7 +439,7 @@ <h2>Polynomial NARMAX<a class="headerlink" href="#polynomial-narmax" title="Perm
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index ab22e5e3..128a8c68 100644
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+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
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-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/notebooks.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -244,21 +241,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +273,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +292,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -376,15 +374,15 @@ <h1>Jupyter notebooks<a class="headerlink" href="#jupyter-notebooks" title="Perm
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
     <a class='left-prev' id="prev-link" href="dev_guide.html" title="previous page">Contributing</a>
     <a class='right-next' id="next-link" href="examples/basic_steps.html" title="next page">Presenting main functionality</a>
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -403,7 +401,7 @@ <h1>Jupyter notebooks<a class="headerlink" href="#jupyter-notebooks" title="Perm
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index f93afbce..9d6c54b7 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Python Module Index &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,25 +26,22 @@
 
     
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/py-modindex.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -245,22 +242,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -279,19 +276,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -361,13 +359,13 @@ <h1>Python Module Index</h1>
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -386,7 +384,7 @@ <h1>Python Module Index</h1>
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/search.html b/docs/search.html
index ec8d5c8f..eb7c2180 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Search &#8212; NARMAX models</title>
     
-  <link rel="stylesheet" href="_static/css/index.73d71520a4ca3b99cfee5594769eaaae.css">
+  <link rel="stylesheet" href="_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
 
     
   <link rel="stylesheet"
@@ -26,27 +26,25 @@
 
     
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-    <link rel="stylesheet" href="_static/sphinx-book-theme.40e2e510f6b7d1648584402491bb10fe.css" type="text/css" />
+    <link rel="stylesheet" href="_static/sphinx-book-theme.37f24b989f4638ff9c27c22dc7559d4f.css" type="text/css" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
     
-  <link rel="preload" as="script" href="_static/js/index.3da636dd464baa7582d2.js">
+  <link rel="preload" as="script" href="_static/js/index.d3f166471bb80abb5163.js">
 
     
     <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
     <script src="_static/doctools.js"></script>
-    <script src="_static/language_data.js"></script>
     <script src="_static/togglebutton.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
     <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
-    <script src="_static/sphinx-book-theme.d31b09fe5c1d09cb49b26a786de4a05d.js"></script>
-    <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-    <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["\\(", "\\)"]], "displayMath": [["\\[", "\\]"]], "processRefs": false, "processEnvironments": false}})</script>
+    <script src="_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
     <script src="_static/searchtools.js"></script>
+    <script src="_static/language_data.js"></script>
     <link rel="canonical" href="http://sysidentpy.org/search.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="#" />
@@ -247,22 +245,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
 <main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
     
-    <div class="row topbar fixed-top container-xl">
-    <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show">
-    </div>
-    <div class="col pl-2 topbar-main">
-        
-        <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
-            data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
-            aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
-            title="Toggle navigation" data-toggle="tooltip" data-placement="left">
-            <i class="fas fa-bars"></i>
-            <i class="fas fa-arrow-left"></i>
-            <i class="fas fa-arrow-up"></i>
-        </button>
-        
-        
-        <!-- Source interaction buttons -->
+    <div class="topbar container-xl fixed-top">
+    <div class="topbar-contents row">
+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
+            
+            <button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
+                data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
+                aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
+                <i class="fas fa-bars"></i>
+                <i class="fas fa-arrow-left"></i>
+                <i class="fas fa-arrow-up"></i>
+            </button>
+            
+            
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -281,19 +279,20 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
-                data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
-                title="Fullscreen mode"><i
-                    class="fas fa-expand"></i></button></a>
+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
+                        class="fas fa-expand"></i></button></a>
 
-        <!-- Launch buttons -->
+            <!-- Launch buttons -->
 
-    </div>
+        </div>
 
-    <!-- Table of contents -->
-    <div class="d-none d-md-block col-md-2 bd-toc show">
-        
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -325,13 +324,13 @@ <h1 id="search-documentation">Search</h1>
 
               </div>
               
-        </div>
-    </div>
-    
-    
-    <div class='prev-next-bottom'>
         
+        <div class='prev-next-bottom'>
+            
 
+        </div>
+        
+        </div>
     </div>
     <footer class="footer mt-5 mt-md-0">
     <div class="container">
@@ -350,7 +349,7 @@ <h1 id="search-documentation">Search</h1>
     </div>
 
     
-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 72e147cd..cd271c2f 100644
--- a/docs/searchindex.js
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in SysIdentPy","Changes in SysIdentPy","Codes","Contributing","Presenting main functionality","Setting specific lags","Important notes and examples of how to use Extended Least Squares","Example: F-16 Ground Vibration Test benchmark","Building NARX models using general estimators","Information Criteria - Examples","Multiple Inputs usage","Building NARX Neural Network using Sysidentpy","Parameter Estimation","Welcome to SysIdentPy\u2019s documentation!","Install Guide","A brief introduction to NARMAX models.","Jupyter notebooks","User 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\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 83af84d4..4127f6c4 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -53,8 +53,8 @@
 }
 
 master_doc = "index"
-myst_admonition_enable = True
-myst_deflist_enable = True
+# myst_admonition_enable = True
+# myst_deflist_enable = True
 myst_url_schemes = ("http", "https", "mailto")
 jupyter_execute_notebooks = "cache"
 
diff --git a/docs/source/examples/basic_steps.ipynb b/docs/source/examples/basic_steps.ipynb
index 58bad144..49fd63d3 100644
--- a/docs/source/examples/basic_steps.ipynb
+++ b/docs/source/examples/basic_steps.ipynb
@@ -112,7 +112,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fd019689d50>"
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1fd33cab3d0>"
+      ]
      },
      "metadata": {},
      "execution_count": 4
@@ -157,7 +159,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.001993603325328823\n"
+     "text": [
+      "0.0018401326800931033\n"
+     ]
     }
    ],
    "source": [
@@ -180,7 +184,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95556574\n1         y(k-1)     0.1999  0.04107943\n2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+     "text": [
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.8999  0.95739001\n1         y(k-1)     0.2000  0.03917632\n2  x1(k-1)y(k-1)     0.0999  0.00343057\n3      x1(k-2)^2    -0.0002  0.00000002\n4        x1(k-1)     0.0001  0.00000001\n5  x1(k-1)y(k-2)     0.0002  0.00000001\n"
+     ]
     }
    ],
    "source": [
@@ -209,8 +215,8 @@
      "output_type": "display_data",
      "data": {
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     }
@@ -241,7 +247,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 9
@@ -250,8 +258,8 @@
      "output_type": "display_data",
      "data": {
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iiScs5zl8fHwwe/ZsnDlzxmXhqGs0UT+d9+DeBxG5iNXy8PLq/FsSicRpYejWjIgIhq+PFycLEpHLWC2PqKgofPzxxx3G9u3bh9DQUKeHIvv4+XhjZP9gLpJIRC5j9WqrtLQ0LFq0CGvXrkVERATOnj0LpVKJV155xZX5qIs0KiU2FZ/ClTYj/CXeYschoh7OannI5XK8+eabqKurw/nz59GvXz/07dvXldnIDpooBdYXmXH0zEWMHcTFK4nIuWzO8wgPD0d4eLhTXrypqQmpqalobm6GRCLBq6++itDQUBw5cgQvvfQSvL29odVqMX/+fABAdnY2/vOf/8DHxwfLli3DyJEjodPpsGTJEly5cgV9+vRBZmYmAgICnJK3O1P/dNK8tLqB5UFETifqTSB27dqFIUOGYNu2bYiLi8OmTZsAXL1MOCsrCzt27MDRo0dRUVGBiooKHD58GIWFhVizZg0yMjIAADk5OZg8eTK2b9+O6Oho5Ofni/mWRKMI8sWv+kh50pyIXELU8hgyZIhl0qFer4ePjw/0ej1aW1sRGRkJQRCg1Wpx8OBBlJWVQavVQhAEhIeHw2g0QqfToaysDBMmTAAAxMbG4sCBA2K+JVGNUSlQWt0Ak8ksdhQi6uFsHrb6/PPP8fbbb6O1tdUytnXrVrtfqLCwEFu2bOkwlp6ejs8//xxxcXFobGzEtm3boNfrIZX+vEZTUFAQzpw5Az8/P4SEhHQYb2pqgl6vh0wm6zDmqTRRSuw4fAYnzzdhaJhc7DhE1IPZLI/MzEwsW7YMYWFht/VCCQkJSEhI6DA2f/58PPnkk0hKSsKJEyfwzDPPYMeOHR2WQDEYDJDL5ZBIJDeMy2QySKVSGAwG+Pv7W7b1VBrLzaEaWB5E5FQ2D1v169cP9957LwYNGmT5chS5XG7Za+jVqxcMBgOkUikkEglqampgNptRXFwMjUaDmJgYFBcXw2Qyoa6uDiaTCUqlEjExMSgqKgIA7N+/H2q12mH53E2kMhChMj+e9yAip7O559GrVy+kp6cjOjraslRJYmKiQ1782WefxQsvvIDt27ejvb0dL774IgAgIyMDS5YsgdFohFarxahRowAAGo0GiYmJMJlMSE9PBwDMmzcPaWlpKCgogEKhQFZWlkOyuSNBEKCJUnCyIBE5nc3yiIiIAABcuHDB4S/et29fbNy48Ybxu+++GwUFBTeMP/PMM3jmmWc6jPXu3dtylRZdnSz472PncK7xCsKC/cWOQ0Q9lM3DVvPnz8fw4cPh5+eHoUOHWuZcUPekscz34KErInIem+WRlZWFXbt2QSKR4F//+hdWr17tilx0i6LD5QiQeKO0ioeuiMh5bB62KikpQV5eHgDgT3/6E6ZPn+70UHTrJN5euHtACPc8iMipbO55tLe3w2QyAQDMZrPlpDl1X2NUCnxddwn6lnaxoxBRD2VzzyMuLg4zZszAqFGjUF5ejri4OFfkotugVilhMgNHai5Ce0dvseMQUQ9kszxmzZoFrVaLU6dOIT4+HkOGDHFFLroNMZEh8BKAkiody4OInMJqeRQWFiIhIQFZWVmWQ1Vff/01AGDRokWuSUe3ROYvwdAwOW9LS0ROY7U8ri1Hcv2Mcp7zcA8alQLvlNWi3WiCj7eo618SUQ9k9a/KtZVqv/rqKzz22GOWL09etdadaFRKXG414vhZz10okoicx+qex7Zt27Bu3To0Njbio48+sowPHjzYJcHo9vxysuCIiGCR0xBRT2O1PFJSUpCSkoL169dj7ty5rsxEDhAeEoD+IQEorWrA/4wfKHYcIuphbF5tlZSUhN27d6O9vR1msxnnz5/Hn//8Z1dko9ukjlLg0OkfOT+HiBzOZnksWLAAKpUKJ0+ehJ+fn0feH9xdjVEp8N7ROtQ2NGOAMlDsOETUg3TpMpy//vWvGDhwIN5++200NjY6OxM5iDpKCYCLJBKR43WpPFpaWtDc3AxBEHD58mVnZyIHuTNMBpmfD0q4SCIROZjN8khJScHmzZsxfvx43HfffQ69kyA5l7eXgJgoBcpYHkTkYDbPeTz00EOW/540aRKkUqlTA5FjaaIUyNp7Eo2X2xAcKBE7DhH1EDbLIy8vD3l5eWhtbbWMvf/++04NRY6jUV097/FFTQMeGNpH5DRE1FPYLI+tW7fijTfeQHAwJ5q5o7sHhMDHS0BJlY7lQUQOY7M87rzzTvTr1w/e3t6uyEMOFuDrjbv6B6OUiyQSkQPZLI9x48bhwQcfxIABAyyTzbZu3eqKbOQgmigF/vHfarS0G+Hnww8BRHT7bJZHfn4+XnvtNchkMlfkIScYo1JgU/FpHPv+EtQ/rXlFRHQ7bJZH3759MWLECHh5cVlvd3VtsmBZtY7lQUQOYbM8Wltb8eijj+KOO+6wrI+UlZXl9GDkOKEyP6h6BaKkqgFzYsVOQ0Q9gc3ymDFjBuRyuSuykBOpo5T49JvzXCSRiBzCZnls2rQJO3bscEUWcqIxKgV2flGLUxcMGBzKiZ5EdHtslkdwcDC2bNmCgQMHWs57aLVapwcjx7o2WbCsqoHlQUS3zWZ5KBQKnDhxAidOnLCMsTzcz+DQICgCJSip0mH6mAFixyEiN2ezPDIzM3Hy5El89913GDhwIIYNG+aKXORggiBAHaVEGScLEpED2Lz+Njc3F8uXL8eXX36J5cuXY9OmTa7IRU6gUSlw6oIBF/QtYkchIjdnc89j9+7d2LZtG3x8fNDW1oakpCTMnj3bFdnIwcaors7xKKtuwEN3hYmchojcmc09D7PZDB+fqx0jkUggkXBZb3c1vH8wfH28UFrFOwsS0e2xuecRExODBQsWQK1Wo6ysDKNHj3ZFLnICPx9vjIoI5p0Fiei2Wd3zKCkpAQCkpqZi6tSpaG9vx9SpU5GWluaycOR46iglKuoa0dxqFDsKEbkxq+WxevVqXL58GU8++STGjx+PmTNn4t577+1wUyhyP2NUCrQZzThae1HsKETkxqwetho/fjymTJmCc+fO4eGHHwYAy9IW+/btc1lAcqxrCyOWVTdg3KBeIqchIndltTxSU1ORmpqKtWvX4umnn3ZlJnKikEBf3NFHihKeNCei22DzhPljjz2GjRs3oqXl57kB8+fPd2ooci6NSond5XUwmczw8uIiiURkP5uX6i5cuBB6vR69e/e2fN2OvXv3YvHixZbHR44cQUJCApKSkpCdnW0Zz87ORnx8PJKSklBeXg4A0Ol0mDVrFpKTk7Fw4UI0NzcDAD755BNMmzYNiYmJKCgouK18nkATpUDTlXacPN8kdhQiclM29zyCgoKQmprqkBdbtWoViouLOyxxsmLFCrz++usYMGAA5syZg4qKCgDA4cOHUVhYiLNnz+KZZ57Bzp07kZOTg8mTJ2Pq1Kl44403kJ+fj5SUFGRmZuKdd95BQEAAZsyYgQceeAChoaEOydwTjflpkcTSqgYMDeNy+0RkP5t7HnfccQf27NmDU6dO4fTp0zh9+vQtv1hMTAxWrlxpeazX69Ha2orIyEgIggCtVouDBw+irKwMWq0WgiAgPDwcRqMROp0OZWVlmDBhAgAgNjYWBw4cQGVlJSIjIxEcHAxfX1+o1WqUlpbeckZPMEAZgFCZHycLEtEts7nncfz4cRw/ftzyWBAEbN269aY/U1hYiC1btnQYe/nllxEXF4dDhw5ZxvR6PaTSn5cHDwoKwpkzZ+Dn54eQkJAO401NTdDr9ZZ7qXc2dm1cr9fbelseTRAEjFEpUMpFEonoFtksj9zcXLufNCEhAQkJCTa3k0qlMBgMlscGgwFyuRwSieSGcZlMZtne39/fsm1nz/HLMqHOqaOUeP+rczjXeAVhwf5ixyEiN2O1PBITE63erjQvL88hLy6VSiGRSFBTU4MBAwaguLgY8+fPh7e3N1599VXMnj0b586dg8lkglKpRExMDIqKijB16lTs378farUagwcPRnV1NS5evIjAwECUlpZy4cYuuLZIYmm1DpNHhouchojcjdXyWLNmjUsCZGRkYMmSJTAajdBqtRg1ahQAQKPRIDExESaTCenp6QCAefPmIS0tDQUFBVAoFMjKyoJEIsHSpUsxe/ZsmM1mTJs2DX379nVJdnc2rJ8cARJvlFY1sDyIyG6C2Ww2ix3C2WprazFx4kTs27cPERERYsfpNpI3/heNzW3Ys2CC2FGIqBu62d9Om1dbUc+liVLg+NlL0Le0ix2FiNwMy8ODaVRKmMzAlzW86oqI7MPy8GCjI0PgJVydLEhEZA+WhweT+UswNEyO0mpOFiQi+7A8PNwYlQJf1lxEu9EkdhQiciMsDw+nVilxudWI42e5SCIRdR3Lw8P9crIgEVFXsTw8XL/gAPQPCeBJcyKyC8uDoFEpUFqtgwfMFyUiB2F5EDRRCvxwqQW1Dc1iRyEiN8HyIGiu3RyK5z2IqItYHoQhfWWQ+fughOc9iKiLWB4Eby8BMZEK3lmQiLqM5UEArl6ye/IHPRovt4kdhYjcAMuDAFy9syAAlNVw74OIbGN5EADg7gEh8PESON+DiLqE5UEAgABfb9zVP5jlQURdwvIgizFRChytvYiWdqPYUYiom2N5kIVGpUBLuwnHvr8kdhQi6uZYHmRhOWnOyYJEZAPLgyxCZX5Q9QrkZEEisonlQR1oVEqUVTdwkUQiuimWB3UwRqWAztCKUxcMYkchom6M5UEdWM578NAVEd0Ey4M6GBwaBEWgBCVc54qIboLlQR0IggB1lBKl1dzzICLrWB50gzEqBU5fMOCCvkXsKETUTbE86AYalQIAuFQJEVnF8qAbDO8fDF8fL04WJCKrWB50Az8fb4yKCOZkQSKyiuVBndKolKioa0RzKxdJJKIbsTyoU5ooBdqMZhytvSh2FCLqhlge1Cl11NWT5mW8ZJeIOsHyoE6FBPpiSF8pJwsSUadYHmSVOurqIokmExdJJKKOWB5k1RiVAk1X2nHyfJPYUYiom2F5kFWanxZ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      },
      "metadata": {}
     }
@@ -312,7 +320,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 10
@@ -321,8 +331,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -360,7 +370,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "rrse:  0.001993603325328823\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95556574\n1         y(k-1)     0.1999  0.04107943\n2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+     "text": [
+      "rrse:  0.0018136024659265537\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95739001\n1         y(k-1)     0.2000  0.03917632\n2  x1(k-1)y(k-1)     0.0999  0.00343057\n"
+     ]
     }
    ],
    "source": [
@@ -403,7 +415,23 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "array([[   0,    0],\n       [1001,    0],\n       [1002,    0],\n       [2001,    0],\n       [2002,    0],\n       [1001, 1001],\n       [1002, 1001],\n       [2001, 1001],\n       [2002, 1001],\n       [1002, 1002],\n       [2001, 1002],\n       [2002, 1002],\n       [2001, 2001],\n       [2002, 2001],\n       [2002, 2002]])"
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [1002,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [1002, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [1002, 1002],\n",
+       "       [2001, 1002],\n",
+       "       [2002, 1002],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
      },
      "metadata": {},
      "execution_count": 12
@@ -422,7 +450,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "[0.95556574 0.04107943 0.00335113 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.9000403 ]\n [0.19990693]\n [0.10000669]]\n"
+     "text": [
+      "[0.95739001 0.03917632 0.00343057 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.89995854]\n [0.20004441]\n [0.09991068]]\n"
+     ]
     }
    ],
    "source": [
@@ -440,9 +470,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -454,7 +488,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/f_16_benchmark.ipynb b/docs/source/examples/f_16_benchmark.ipynb
index 18a9113b..ad05c646 100644
--- a/docs/source/examples/f_16_benchmark.ipynb
+++ b/docs/source/examples/f_16_benchmark.ipynb
@@ -55,7 +55,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_16 = pd.read_csv(r'datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+    "f_16 = pd.read_csv(r'../../../examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
    ]
   },
   {
@@ -64,14 +64,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "(32768, 3)"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
@@ -91,26 +91,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6b0443210>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -123,26 +122,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe697898650>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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332.055062 \r\nL 106.646813 305.13907 \r\nL 106.951329 301.180082 \r\nL 107.255845 300.43657 \r\nL 107.864877 275.597284 \r\nL 108.169393 272.30062 \r\nL 108.473909 270.762319 \r\nL 109.08294 242.166588 \r\nL 109.387456 263.56008 \r\nL 109.691972 276.746736 \r\nL 109.996488 260.04549 \r\nL 110.60552 192.351706 \r\nL 110.910036 194.533102 \r\nL 111.214552 198.0306 \r\nL 111.823583 184.115387 \r\nL 112.432615 141.211749 \r\nL 113.955195 66.281542 \r\nL 114.259711 64.051006 \r\nL 115.173258 83.598109 \r\nL 115.477774 86.138442 \r\nL 115.78229 84.04678 \r\nL 116.086806 88.653137 \r\nL 116.391322 95.641723 \r\nL 117.30487 107.377677 \r\nL 117.913901 138.791062 \r\nL 118.522933 168.595641 \r\nL 119.131965 194.699751 \r\nL 120.654544 284.442514 \r\nL 121.568092 324.525933 \r\nL 121.872608 331.159856 \r\nL 122.177124 335.073977 \r\nL 122.786156 354.155317 \r\nL 123.395187 376.080377 \r\nL 123.699703 383.79752 \r\nL 124.004219 388.237227 \r\nL 124.308735 386.216071 \r\nL 124.613251 380.357708 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -183,14 +181,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
@@ -214,223 +212,15 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.3090560911603647\n"
+      "0.3090559299602927\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Regressors</th>\n",
-       "      <th>Parameters</th>\n",
-       "      <th>ERR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>y(k-1)</td>\n",
-       "      <td>1.8387E+00</td>\n",
-       "      <td>9.43378253E-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>y(k-2)</td>\n",
-       "      <td>-1.8938E+00</td>\n",
-       "      <td>1.95167599E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>y(k-3)</td>\n",
-       "      <td>1.3337E+00</td>\n",
-       "      <td>1.02432261E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>y(k-6)</td>\n",
-       "      <td>-1.6038E+00</td>\n",
-       "      <td>8.03485985E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>y(k-9)</td>\n",
-       "      <td>2.6776E-01</td>\n",
-       "      <td>9.27874557E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>x2(k-7)</td>\n",
-       "      <td>-2.2384E+01</td>\n",
-       "      <td>3.76837313E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>x1(k-1)</td>\n",
-       "      <td>8.2709E+00</td>\n",
-       "      <td>6.81508210E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>x2(k-3)</td>\n",
-       "      <td>1.0587E+02</td>\n",
-       "      <td>1.57459800E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>x1(k-8)</td>\n",
-       "      <td>-3.7975E+00</td>\n",
-       "      <td>7.35086279E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>x2(k-1)</td>\n",
-       "      <td>8.5726E+01</td>\n",
-       "      <td>4.85358786E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>y(k-7)</td>\n",
-       "      <td>1.3955E+00</td>\n",
-       "      <td>2.77245281E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>y(k-5)</td>\n",
-       "      <td>1.3219E+00</td>\n",
-       "      <td>8.64120037E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>y(k-10)</td>\n",
-       "      <td>-2.9306E-01</td>\n",
-       "      <td>8.51717688E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>y(k-4)</td>\n",
-       "      <td>-9.5479E-01</td>\n",
-       "      <td>7.23623116E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>y(k-8)</td>\n",
-       "      <td>-7.1309E-01</td>\n",
-       "      <td>4.44988077E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>y(k-12)</td>\n",
-       "      <td>-3.0437E-01</td>\n",
-       "      <td>1.49743148E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>y(k-11)</td>\n",
-       "      <td>4.8602E-01</td>\n",
-       "      <td>3.34613282E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>y(k-13)</td>\n",
-       "      <td>-8.2442E-02</td>\n",
-       "      <td>1.43738964E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>y(k-15)</td>\n",
-       "      <td>-1.6762E-01</td>\n",
-       "      <td>1.25546584E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>x1(k-2)</td>\n",
-       "      <td>-8.9698E+00</td>\n",
-       "      <td>9.76699739E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>y(k-17)</td>\n",
-       "      <td>2.2036E-02</td>\n",
-       "      <td>4.55983807E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>y(k-14)</td>\n",
-       "      <td>2.4900E-01</td>\n",
-       "      <td>1.10314107E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>y(k-19)</td>\n",
-       "      <td>-6.8239E-03</td>\n",
-       "      <td>1.99734771E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>x2(k-9)</td>\n",
-       "      <td>-9.6265E+01</td>\n",
-       "      <td>2.98523208E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>x2(k-8)</td>\n",
-       "      <td>2.2620E+02</td>\n",
-       "      <td>2.34402543E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>x2(k-2)</td>\n",
-       "      <td>-2.3609E+02</td>\n",
-       "      <td>1.04172323E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>y(k-20)</td>\n",
-       "      <td>-5.4663E-02</td>\n",
-       "      <td>5.37895336E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>x2(k-6)</td>\n",
-       "      <td>-2.3651E+02</td>\n",
-       "      <td>2.11392628E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>x2(k-4)</td>\n",
-       "      <td>1.7378E+02</td>\n",
-       "      <td>2.18396315E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>x1(k-7)</td>\n",
-       "      <td>4.9862E+00</td>\n",
-       "      <td>2.03811842E-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "   Regressors   Parameters             ERR\n",
        "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
@@ -438,11 +228,11 @@
        "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
        "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
        "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
-       "5     x2(k-7)  -2.2384E+01  3.76837313E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
        "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
        "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
        "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
-       "9     x2(k-1)   8.5726E+01  4.85358786E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
        "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
        "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
        "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
@@ -463,11 +253,11 @@
        "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
        "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
        "29    x1(k-7)   4.9862E+00  2.03811842E-05"
-      ]
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
@@ -494,14 +284,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 426.524687 \r\nL 91.073836 425.8022 \r\nL 91.511557 411.152953 \r\nL 91.527769 411.762218 \r\nL 91.657464 414.450677 \r\nL 91.673676 413.929097 \r\nL 91.706099 411.584217 \r\nL 91.738523 416.587624 \r\nL 91.803371 413.657281 \r\nL 91.852006 420.48783 \r\nL 91.88443 417.237727 \r\nL 91.900642 415.724342 \r\nL 91.933066 420.370916 \r\nL 91.949278 421.614602 \r\nL 91.981701 417.132372 \r\nL 91.997913 416.04414 \r\nL 92.030337 420.668488 \r\nL 92.046549 422.442923 \r\nL 92.095185 418.283297 \r\nL 92.14382 421.93661 \r\nL 92.176244 417.362476 \r\nL 92.22488 423.776247 \r\nL 92.257303 421.234152 \r\nL 92.273515 420.18193 \r\nL 92.322151 424.220352 \r\nL 92.354575 421.784311 \r\nL 92.516694 424.020064 \r\nL 92.549117 423.321335 \r\nL 92.711236 418.9788 \r\nL 92.759872 422.615291 \r\nL 92.808508 418.607581 \r\nL 92.840931 420.940681 \r\nL 92.873355 419.416585 \r\nL 92.905779 417.218426 \r\nL 92.970626 420.487016 \r\nL 93.116533 417.101093 \r\nL 93.165169 418.987263 \r\nL 93.246229 417.917766 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 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417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 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305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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587.44086 416.256092 \r\nL 587.570563 416.922889 \r\nL 588.186652 421.561336 \r\nL 588.219078 421.535685 \r\nL 588.348781 421.16788 \r\nL 588.96487 418.872918 \r\nL 589.224277 418.851271 \r\nL 589.710663 418.930773 \r\nL 589.905217 419.607446 \r\nL 590.359178 421.306835 \r\nL 590.456455 421.20103 \r\nL 590.65101 420.493422 \r\nL 591.234673 418.075477 \r\nL 591.33195 418.184357 \r\nL 591.494079 418.948061 \r\nL 591.980465 421.931214 \r\nL 592.012891 421.906036 \r\nL 592.142594 421.379839 \r\nL 592.758684 417.992893 \r\nL 593.082941 417.536084 \r\nL 593.277496 417.305274 \r\nL 593.309921 417.326798 \r\nL 593.439624 417.691438 \r\nL 593.731456 419.624315 \r\nL 593.958436 420.487633 \r\nL 594.023288 420.455164 \r\nL 594.185417 419.885986 \r\nL 594.509674 418.86502 \r\nL 594.606951 418.988603 \r\nL 594.801506 419.68446 \r\nL 595.125763 420.710598 \r\nL 595.255466 420.648394 \r\nL 595.417595 420.226672 \r\nL 595.806704 419.187577 \r\nL 595.968833 419.248109 \r\nL 596.228239 419.388065 \r\nL 596.390368 419.062022 \r\nL 596.811903 417.911863 \r\nL 596.844328 417.930466 \r\nL 596.974031 418.227298 \r\nL 597.36314 420.051954 \r\nL 597.849527 421.979642 \r\nL 597.946804 421.919191 \r\nL 598.108933 421.210917 \r\nL 598.530467 418.948895 \r\nL 598.627745 419.140422 \r\nL 599.016854 420.542051 \r\nL 599.04928 420.51617 \r\nL 599.178983 420.146932 \r\nL 599.568092 418.8608 \r\nL 599.827498 418.586752 \r\nL 600.054478 417.525864 \r\nL 600.313884 416.678037 \r\nL 600.411161 416.920894 \r\nL 600.702993 418.961131 \r\nL 600.865122 419.519132 \r\nL 600.897547 419.505573 \r\nL 601.02725 419.11859 \r\nL 601.319082 418.037737 \r\nL 601.351508 418.05192 \r\nL 601.448785 418.290901 \r\nL 601.675765 419.83455 \r\nL 602.032449 421.907463 \r\nL 602.194578 422.130976 \r\nL 602.227003 422.10867 \r\nL 602.356706 421.782748 \r\nL 602.583687 420.291308 \r\nL 602.94037 418.095237 \r\nL 603.005221 418.124099 \r\nL 603.134924 418.677932 \r\nL 603.491608 420.271844 \r\nL 603.588885 420.057045 \r\nL 603.977994 418.565607 \r\nL 604.042845 418.660573 \r\nL 604.2374 419.574359 \r\nL 604.594083 421.00072 \r\nL 604.821063 421.096825 \r\nL 605.177747 421.140897 \r\nL 605.30745 420.748059 \r\nL 605.599282 418.78732 \r\nL 605.891113 417.292185 \r\nL 606.053242 417.20865 \r\nL 606.345074 417.591401 \r\nL 606.701757 418.221544 \r\nL 607.317846 419.411431 \r\nL 607.836658 419.500934 \r\nL 608.063639 419.030843 \r\nL 608.355471 418.402492 \r\nL 608.387896 418.42175 \r\nL 608.517599 418.779079 \r\nL 608.777005 420.580062 \r\nL 609.068837 422.089535 \r\nL 609.166114 422.014627 \r\nL 609.360669 421.192631 \r\nL 609.684926 419.994662 \r\nL 609.847055 419.969918 \r\nL 610.625273 420.325072 \r\nL 610.754976 419.860589 \r\nL 611.241363 416.737486 \r\nL 611.33864 416.989491 \r\nL 611.533194 418.762311 \r\nL 611.889878 421.935869 \r\nL 611.954729 421.978823 \r\nL 611.987155 421.926451 \r\nL 612.149284 421.028337 \r\nL 612.830224 415.801802 \r\nL 612.86265 415.824233 \r\nL 612.959927 416.193807 \r\nL 613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -522,24 +311,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -550,20 +338,17 @@
     "\n",
     "# You can use the plot below to choose the \"n_terms\" and run the model again with the most adequate value of terms."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -575,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/multiple_inputs_example.ipynb b/docs/source/examples/multiple_inputs_example.ipynb
index f81ee4d8..edad040d 100644
--- a/docs/source/examples/multiple_inputs_example.ipynb
+++ b/docs/source/examples/multiple_inputs_example.ipynb
@@ -98,7 +98,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fe5cb870a90>"
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2191c4db6d0>"
+      ]
      },
      "metadata": {},
      "execution_count": 4
@@ -123,14 +125,16 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.0026126773911553398\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.90482955\n1  x2(k-2)x1(k-1)    -0.3000  0.05072675\n2        y(k-1)^2     0.3999  0.04410386\n3   x1(k-1)y(k-1)     0.1000  0.00033239\n"
+     "text": [
+      "0.002440520762176125\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.91070733\n1  x2(k-2)x1(k-1)    -0.2997  0.04434814\n2        y(k-1)^2     0.3999  0.04463687\n3   x1(k-1)y(k-1)     0.0996  0.00030042\n"
+     ]
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 720x576 with 3 Axes>",
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      },
      "metadata": {}
     }
@@ -158,7 +162,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 6
@@ -167,8 +173,8 @@
      "output_type": "display_data",
      "data": {
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\n"
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diff --git a/docs/user_guide.html b/docs/user_guide.html
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@@ -244,21 +243,21 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
           
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-        
-        
+    <div class="topbar container-xl fixed-top">
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+        <div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
+        <div class="col pl-md-4 topbar-main">
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+                title="Toggle navigation" data-toggle="tooltip" data-placement="left">
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+            
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     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
             class="fas fa-download"></i></button>
@@ -276,7 +275,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
     </div>
 </div>
 
-        <!-- Source interaction buttons -->
+            <!-- Source interaction buttons -->
 
 <div class="dropdown-buttons-trigger">
     <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
@@ -295,19 +294,19 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 </div>
 
 
-        <!-- Full screen (wrap in <a> to have style consistency -->
-        <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
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+            <!-- Full screen (wrap in <a> to have style consistency -->
+            <a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
+                    data-placement="bottom" onclick="toggleFullScreen()" aria-label="Fullscreen mode"
+                    title="Fullscreen mode"><i
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-        <!-- Launch buttons -->
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+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
         <div class="tocsection onthispage pt-5 pb-3">
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@@ -332,6 +331,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
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+        </div>
     </div>
 </div>
     <div id="main-content" class="row">
@@ -550,15 +550,15 @@ <h2>Important Note:<a class="headerlink" href="#important-note" title="Permalink
 
               </div>
               
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-    </div>
-    
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     <a class='left-prev' id="prev-link" href="introduction_to_narmax.html" title="previous page">A brief introduction to NARMAX models.</a>
     <a class='right-next' id="next-link" href="dev_guide.html" title="next page">Contributing</a>
 
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@@ -577,7 +577,7 @@ <h2>Important Note:<a class="headerlink" href="#important-note" title="Permalink
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-  <script src="_static/js/index.3da636dd464baa7582d2.js"></script>
+  <script src="_static/js/index.d3f166471bb80abb5163.js"></script>
 
 
     

From 3b7e612b0c81af8f287bbc814d155c372e0b1d2c Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Mon, 8 Mar 2021 23:25:13 -0300
Subject: [PATCH 03/27] added new examples of usage

---
 ...ation_of_an_electromechanical_system.ipynb | 358 ++++++++++
 .../simulating_a_predefined_model.ipynb       | 613 ++++++++++++++++++
 ...ntification_using_adaptative_filters.ipynb | 422 ++++++++++++
 docs/source/index.rst                         |   2 +-
 docs/source/notebooks.rst                     |  18 +
 5 files changed, 1412 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/examples/identification_of_an_electromechanical_system.ipynb
 create mode 100644 docs/source/examples/simulating_a_predefined_model.ipynb
 create mode 100644 docs/source/examples/system_identification_using_adaptative_filters.ipynb

diff --git a/docs/source/examples/identification_of_an_electromechanical_system.ipynb b/docs/source/examples/identification_of_an_electromechanical_system.ipynb
new file mode 100644
index 00000000..51619d6e
--- /dev/null
+++ b/docs/source/examples/identification_of_an_electromechanical_system.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "daily-tower",
+   "metadata": {},
+   "source": [
+    "# Identification of an electromechanical system\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior\n",
+    "\n",
+    "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "earlier-person",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "peripheral-colors",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('data/x_cc.csv')\n",
+    "df2 = pd.read_csv('data/y_cc.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "legal-weapon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2[5000:80000].plot(figsize=(10, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "competitive-pathology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will decimate the data using d=500 in this example\n",
+    "x_train, x_valid = np.split(df1.iloc[::500].values, 2)\n",
+    "y_train, y_valid = np.split(df2.iloc[::500].values, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-springfield",
+   "metadata": {},
+   "source": [
+    "## Building a Polynomial NARX model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "personal-easter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=40,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=2, xlag=2,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares'\n",
+    "                         )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "plastic-demographic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\polynomial_basis\\narmax.py:333: UserWarning: n_info_values is greater than the maximum number of all regressors space considering the chosen y_lag, u_lag, and non_degree. We set as 15 \n",
+      "  self.info_values = self.information_criterion(X, y)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08011571455094049\n",
+      "       Regressors Parameters         ERR\n",
+      "0          y(k-1)     1.3016  0.98600038\n",
+      "1       x1(k-1)^2   103.9300  0.00794805\n",
+      "2        y(k-2)^2     0.0000  0.00250906\n",
+      "3   x1(k-1)y(k-1)    -0.1257  0.00143301\n",
+      "4          y(k-2)    -0.5078  0.00102781\n",
+      "5   x1(k-1)y(k-2)     0.0560  0.00053520\n",
+      "6         x1(k-2)   349.8627  0.00027965\n",
+      "7   x1(k-2)y(k-1)    -0.0840  0.00011221\n",
+      "8  x1(k-2)x1(k-1)    -7.8186  0.00004547\n",
+      "9   x1(k-2)y(k-2)     0.0341  0.00003253\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train)\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex, n=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "introductory-bench",
+   "metadata": {},
+   "source": [
+    "## Testing different autoregressive models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "binding-shelter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:49 - INFO - Training the model\n",
+      "03-08 22:53:49 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:49 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:49 - INFO - Done! Model is built!\n",
+      "KNeighborsRegressor 1.833\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:50 - INFO - Done! Model is built!\n",
+      "DecisionTreeRegressor 0.346\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\general_estimators\\narx.py:154: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+      "  self.base_estimator.fit(X, y, **self.fit_params)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:51 - INFO - Done! Model is built!\n",
+      "RandomForestRegressor 0.178\n",
+      "03-08 22:54:03 - INFO - Training the model\n",
+      "03-08 22:54:03 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:03 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:54:12 - INFO - Done! Model is built!\n",
+      "Catboost_NARX 0.179\n",
+      "03-08 22:54:13 - INFO - Training the model\n",
+      "03-08 22:54:13 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:13 - INFO - The regressor matrix have 231 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:54:14 - INFO - Done! Model is built!\n",
+      "ARD_NARX 0.074\n",
+      "Polynomial_NARX 0.032\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.linear_model import BayesianRidge, ARDRegression\n",
+    "from sysidentpy.general_estimators import NARX\n",
+    "\n",
+    "\n",
+    "estimators = [\n",
+    "    ('KNeighborsRegressor', NARX(\n",
+    "        base_estimator=KNeighborsRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('DecisionTreeRegressor', NARX(\n",
+    "        base_estimator=DecisionTreeRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('RandomForestRegressor', NARX(\n",
+    "        base_estimator=RandomForestRegressor(\n",
+    "            n_estimators=200),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "    )),\n",
+    "    ('Catboost_NARX', NARX(\n",
+    "        base_estimator=CatBoostRegressor(\n",
+    "        iterations=800,\n",
+    "        learning_rate=0.1,\n",
+    "        depth=8),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=1,\n",
+    "        fit_params={'verbose': False}\n",
+    "    )),\n",
+    "    ('ARD_NARX', NARX(\n",
+    "        base_estimator=ARDRegression(),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=2\n",
+    "    )),\n",
+    "    ('Polynomial_NARX', PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=50,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=10, xlag=10,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares',\n",
+    "                         )),\n",
+    "    \n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "resultados = {}\n",
+    "for nome_do_modelo, modelo in estimators:\n",
+    "    resultados['%s' % (nome_do_modelo)] = []\n",
+    "    modelo.fit(x_train, y_train)\n",
+    "    yhat = modelo.predict(x_valid,  y_valid)\n",
+    "    result = root_relative_squared_error(y_valid, yhat)\n",
+    "    resultados['%s' % (nome_do_modelo)].append(result)\n",
+    "    print(nome_do_modelo, '%.3f' % np.mean(result))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "pursuant-appreciation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial_NARX 0.03208119098997185\n",
+      "ARD_NARX 0.07364659571266981\n",
+      "RandomForestRegressor 0.17752025859574144\n",
+      "Catboost_NARX 0.17923040407121107\n",
+      "DecisionTreeRegressor 0.3457206837080308\n",
+      "KNeighborsRegressor 1.833370478725381\n"
+     ]
+    }
+   ],
+   "source": [
+    "for aux_results, results in sorted(resultados.items(), key=lambda x: np.mean(x[1]), reverse=False):\n",
+    "    print(aux_results, np.mean(results))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/simulating_a_predefined_model.ipynb b/docs/source/examples/simulating_a_predefined_model.ipynb
new file mode 100644
index 00000000..43edc03c
--- /dev/null
+++ b/docs/source/examples/simulating_a_predefined_model.ipynb
@@ -0,0 +1,613 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulate a Predefined Model\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the object so we can access its functions\n",
+    "model = PolynomialNarmax()\n",
+    "\n",
+    "# create any psi matrix that contains the combinations we want\n",
+    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
+    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
+    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
+    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.108434</td>\n",
+       "      <td>-0.610171</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.011758</td>\n",
+       "      <td>-0.066163</td>\n",
+       "      <td>0.372308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.548087</td>\n",
+       "      <td>-0.370476</td>\n",
+       "      <td>0.108434</td>\n",
+       "      <td>0.300400</td>\n",
+       "      <td>0.203053</td>\n",
+       "      <td>-0.059431</td>\n",
+       "      <td>0.137252</td>\n",
+       "      <td>-0.040172</td>\n",
+       "      <td>0.011758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.009140</td>\n",
+       "      <td>0.730998</td>\n",
+       "      <td>-0.370476</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.006681</td>\n",
+       "      <td>-0.003386</td>\n",
+       "      <td>0.534358</td>\n",
+       "      <td>-0.270817</td>\n",
+       "      <td>0.137252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.332688</td>\n",
+       "      <td>0.042290</td>\n",
+       "      <td>0.730998</td>\n",
+       "      <td>0.110682</td>\n",
+       "      <td>-0.014070</td>\n",
+       "      <td>-0.243195</td>\n",
+       "      <td>0.001788</td>\n",
+       "      <td>0.030914</td>\n",
+       "      <td>0.534358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.589887</td>\n",
+       "      <td>0.369362</td>\n",
+       "      <td>0.042290</td>\n",
+       "      <td>0.347967</td>\n",
+       "      <td>0.217882</td>\n",
+       "      <td>0.024947</td>\n",
+       "      <td>0.136428</td>\n",
+       "      <td>0.015620</td>\n",
+       "      <td>0.001788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.741870</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>-0.685155</td>\n",
+       "      <td>0.550371</td>\n",
+       "      <td>-0.362971</td>\n",
+       "      <td>0.508296</td>\n",
+       "      <td>0.239381</td>\n",
+       "      <td>-0.335223</td>\n",
+       "      <td>0.469437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.801757</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>0.642815</td>\n",
+       "      <td>0.006963</td>\n",
+       "      <td>-0.392272</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>-0.004249</td>\n",
+       "      <td>0.239381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.278465</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.077543</td>\n",
+       "      <td>-0.271475</td>\n",
+       "      <td>-0.002418</td>\n",
+       "      <td>0.950426</td>\n",
+       "      <td>0.008466</td>\n",
+       "      <td>0.000075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>896</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.019212</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>0.000369</td>\n",
+       "      <td>-0.000275</td>\n",
+       "      <td>-0.018729</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.013937</td>\n",
+       "      <td>0.950426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>897</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.872305</td>\n",
+       "      <td>0.562238</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>0.760916</td>\n",
+       "      <td>-0.490443</td>\n",
+       "      <td>0.012470</td>\n",
+       "      <td>0.316112</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>0.000204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>898 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0         1         2         3         4         5         6  \\\n",
+       "0    1.0  0.000000  0.108434 -0.610171  0.000000  0.000000 -0.000000   \n",
+       "1    1.0 -0.548087 -0.370476  0.108434  0.300400  0.203053 -0.059431   \n",
+       "2    1.0  0.009140  0.730998 -0.370476  0.000084  0.006681 -0.003386   \n",
+       "3    1.0 -0.332688  0.042290  0.730998  0.110682 -0.014070 -0.243195   \n",
+       "4    1.0  0.589887  0.369362  0.042290  0.347967  0.217882  0.024947   \n",
+       "..   ...       ...       ...       ...       ...       ...       ...   \n",
+       "893  1.0 -0.741870  0.489266 -0.685155  0.550371 -0.362971  0.508296   \n",
+       "894  1.0 -0.801757 -0.008684  0.489266  0.642815  0.006963 -0.392272   \n",
+       "895  1.0  0.278465 -0.974898 -0.008684  0.077543 -0.271475 -0.002418   \n",
+       "896  1.0  0.019212 -0.014295 -0.974898  0.000369 -0.000275 -0.018729   \n",
+       "897  1.0 -0.872305  0.562238 -0.014295  0.760916 -0.490443  0.012470   \n",
+       "\n",
+       "            7         8         9  \n",
+       "0    0.011758 -0.066163  0.372308  \n",
+       "1    0.137252 -0.040172  0.011758  \n",
+       "2    0.534358 -0.270817  0.137252  \n",
+       "3    0.001788  0.030914  0.534358  \n",
+       "4    0.136428  0.015620  0.001788  \n",
+       "..        ...       ...       ...  \n",
+       "893  0.239381 -0.335223  0.469437  \n",
+       "894  0.000075 -0.004249  0.239381  \n",
+       "895  0.950426  0.008466  0.000075  \n",
+       "896  0.000204  0.013937  0.950426  \n",
+       "897  0.316112 -0.008037  0.000204  \n",
+       "\n",
+       "[898 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(psi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
+    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
+    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "\n",
+    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1001,    0],\n",
+       "       [2002,    0],\n",
+       "       [2001, 1001]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.final_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.psi = psi[:, [1, 3, 5]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the parameter estimation method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
+    "from sysidentpy.parameter_estimation import Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.20014451],\n",
+       "       [0.89992074],\n",
+       "       [0.09997088]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
+    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
+    "\n",
+    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
+    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
+    "\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "model.theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0019184421163563367\n"
+     ]
+    }
+   ],
+   "source": [
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2001  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001879953122213705\n",
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2000  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
+    "model.psi = psi[:, [1, 3, 5]]\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/source/examples/system_identification_using_adaptative_filters.ipynb b/docs/source/examples/system_identification_using_adaptative_filters.ipynb
new file mode 100644
index 00000000..1765a962
--- /dev/null
+++ b/docs/source/examples/system_identification_using_adaptative_filters.ipynb
@@ -0,0 +1,422 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "educated-affect",
+   "metadata": {},
+   "source": [
+    "# System Identification Using Adaptative Filters\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "preliminary-announcement",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "exact-trace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-encounter",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the Adaptative Filters available in SysIdentPy individually. If you want to build a regressor matrix and estimate the parameters using only the Adaptative Filter method (not applying FROLS + ERR algorithm), follow the steps bellow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "binary-shame",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>-0.242635</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.239048</td>\n",
+       "      <td>0.118630</td>\n",
+       "      <td>0.058872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.217385</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>0.047256</td>\n",
+       "      <td>-0.210825</td>\n",
+       "      <td>0.106285</td>\n",
+       "      <td>0.940557</td>\n",
+       "      <td>-0.474171</td>\n",
+       "      <td>0.239048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.503772</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>0.253787</td>\n",
+       "      <td>0.316599</td>\n",
+       "      <td>-0.488570</td>\n",
+       "      <td>0.394958</td>\n",
+       "      <td>-0.609492</td>\n",
+       "      <td>0.940557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.804358</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.248526</td>\n",
+       "      <td>-0.505505</td>\n",
+       "      <td>0.095465</td>\n",
+       "      <td>-0.194177</td>\n",
+       "      <td>0.394958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.379308</td>\n",
+       "      <td>-0.442970</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>0.143875</td>\n",
+       "      <td>0.168022</td>\n",
+       "      <td>-0.117197</td>\n",
+       "      <td>0.196222</td>\n",
+       "      <td>-0.136867</td>\n",
+       "      <td>0.095465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0         1         2         3         4         5         6         7  \\\n",
+       "0  1.0  0.000000 -0.488925 -0.242635  0.000000 -0.000000 -0.000000  0.239048   \n",
+       "1  1.0 -0.217385  0.969823 -0.488925  0.047256 -0.210825  0.106285  0.940557   \n",
+       "2  1.0 -0.503772 -0.628457  0.969823  0.253787  0.316599 -0.488570  0.394958   \n",
+       "3  1.0  0.804358  0.308975 -0.628457  0.646992  0.248526 -0.505505  0.095465   \n",
+       "4  1.0 -0.379308 -0.442970  0.308975  0.143875  0.168022 -0.117197  0.196222   \n",
+       "\n",
+       "          8         9  \n",
+       "0  0.118630  0.058872  \n",
+       "1 -0.474171  0.239048  \n",
+       "2 -0.609492  0.940557  \n",
+       "3 -0.194177  0.394958  \n",
+       "4 -0.136867  0.095465  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "pd.DataFrame(psi).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "received-apparatus",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "growing-anniversary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-culture",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electrical-crowd",
+   "metadata": {},
+   "source": [
+    "Here we are using the Affine Least Mean Squares method, but you can use any of the methods available on SysIdentPy\n",
+    "\n",
+    "- Least Mean Squares (LMS)\n",
+    "- Affine LMS\n",
+    "- LMS Sign Error\n",
+    "- Normalized LMS\n",
+    "- Normalized LSM Sign Error\n",
+    "- LSM Sign Regressor\n",
+    "- Normalized LMS Sign Regressor\n",
+    "- LMS Sign-Sign\n",
+    "- Normalized LMS Sign-Sign\n",
+    "- Normalized LMS Leaky\n",
+    "- LMS Leaky\n",
+    "- LMS Mixed Norm\n",
+    "- LMS Fourth\n",
+    "\n",
+    "Also, you can use:\n",
+    "- Least Squares (LS)\n",
+    "- Total LS\n",
+    "- Recursive LS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "august-robertson",
+   "metadata": {},
+   "source": [
+    "## Building the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ahead-element",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-alignment",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "metric-layer",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0024097628318461625\n",
+      "       Regressors Parameters         ERR\n",
+      "0               1    -0.0000  0.00000000\n",
+      "1          y(k-1)     0.1999  0.00000000\n",
+      "2         x1(k-1)    -0.0000  0.00000000\n",
+      "3         x1(k-2)     0.8999  0.00000000\n",
+      "4        y(k-1)^2    -0.0001  0.00000000\n",
+      "5   x1(k-1)y(k-1)     0.1000  0.00000000\n",
+      "6   x1(k-2)y(k-1)    -0.0000  0.00000000\n",
+      "7       x1(k-1)^2     0.0002  0.00000000\n",
+      "8  x1(k-2)x1(k-1)    -0.0000  0.00000000\n",
+      "9       x1(k-2)^2     0.0000  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-dakota",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "union-mortality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input\n",
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi\n",
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1))\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 70b03ba0..9171b9e9 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -288,5 +288,5 @@ Contents
     user_guide
     dev_guide
     notebooks
-    changelog/v0.1.4
+    changelog/v0.1.5
     code
diff --git a/docs/source/notebooks.rst b/docs/source/notebooks.rst
index 98f7a4e3..1eee2b03 100644
--- a/docs/source/notebooks.rst
+++ b/docs/source/notebooks.rst
@@ -54,3 +54,21 @@ Jupyter notebooks
    :caption: General Estimators
 
    /examples/general_estimators.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: Simulate a predefined model
+
+   /examples/simulating_a_predefined_model.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: System Identification Using Adaptative Filters
+
+   /examples/system_identification_using_adaptative_filters.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: Identification of an electromechanical system
+
+   /examples/identification_of_an_electromechanical_system.ipynb

From 96f63a35b569ab2ed581a70c47c47c3160c6c459 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Mon, 8 Mar 2021 23:33:51 -0300
Subject: [PATCH 04/27] added new examples of usage

---
 CHANGELOG                                     |  23 +
 docs/source/changelog/v0.1.5.rst              |  92 +++
 examples/basic_steps.ipynb                    |  66 +-
 examples/extended_least_squares.ipynb         |  28 +-
 examples/f_16_benchmark.ipynb                 | 306 ++-------
 ...ation_of_an_electromechanical_system.ipynb | 358 ++++++++++
 examples/multiple_inputs_example.ipynb        |  46 +-
 examples/simulating_a_predefined_model.ipynb  | 613 ++++++++++++++++++
 ...ntification_using_adaptative_filters.ipynb | 422 ++++++++++++
 sysidentpy/polynomial_basis/narmax.py         |  61 +-
 10 files changed, 1692 insertions(+), 323 deletions(-)
 create mode 100644 docs/source/changelog/v0.1.5.rst
 create mode 100644 examples/identification_of_an_electromechanical_system.ipynb
 create mode 100644 examples/simulating_a_predefined_model.ipynb
 create mode 100644 examples/system_identification_using_adaptative_filters.ipynb

diff --git a/CHANGELOG b/CHANGELOG
index ef5b9a03..67c5e8ca 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -2,6 +2,29 @@
 File for tracking changes in SysIdentPy
 --------------------------------------------------------------------------------------------------------------
 
+v0.1.5
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+
+CHANGES
+~~~~~~~
+
+- MAJOR: n-steps-ahead prediction.
+    - Now you can define the numbers of steps ahead in the predict function.
+
+- Added 4 new notebooks in the example section.
+
+- Added iterative notebooks.
+
+- Fix issues related to html on Jupyter notebooks examples on documentation.
+
+- Updated Readme with examples of how to use.
+
+
 v0.1.4
 ------
 
diff --git a/docs/source/changelog/v0.1.5.rst b/docs/source/changelog/v0.1.5.rst
new file mode 100644
index 00000000..bf162ff1
--- /dev/null
+++ b/docs/source/changelog/v0.1.5.rst
@@ -0,0 +1,92 @@
+Changes in SysIdentPy
+=====================
+
+v0.1.5
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+
+CHANGES
+~~~~~~~
+
+- MAJOR: n-steps-ahead prediction.
+    - Now you can define the numbers of steps ahead in the predict function.
+
+- Added 4 new notebooks in the example section.
+
+- Added iterative notebooks.
+
+- Fix issues related to html on Jupyter notebooks examples on documentation.
+
+- Updated Readme with examples of how to use.
+
+
+v0.1.4
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+
+CHANGES
+~~~~~~~
+
+- MAJOR: Introducing NARX Neural Network in SysIdentPy.
+    - Now you can build NARX Neural Network on SysIdentPy.
+    - This feature is built on top of Pytorch. See the docs for more details and examples of how to use.
+
+- MAJOR: Introducing general estimators in SysIdentPy.
+    - Now you are able to use any estimator that have Fit/Predict methods (estimators from Sklearn and Catboost, for example) and build NARX models based on those estimators.
+    - We use the core functions of SysIdentPy and keep the Fit/Predict approach from those estimators to keep the process easy to use.
+    - More estimators are coming soon like XGboost.
+
+- Added notebooks to show how to build NARX neural Network.
+
+- Added notebooks to show how to build NARX models using general estimators.
+
+- Changed the default parameters of the plot_results function.
+
+- NOTE: We will keeping improving the Polynomial NARX models (new model structure selection algorithms and multiobjective identification
+is on our roadmap). These recent modifications will allow us to introduce new NARX models like PWARX models very soon.
+
+- New template for the documentation site.
+
+- Fix issues related to html on Jupyter notebooks examples on documentation.
+
+- Updated Readme with examples of how to use.
+
+
+v0.1.3
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+- renard162
+
+CHANGES
+~~~~~~~
+
+- Fixed a bug concerning the xlag and ylag in multiple input scenarios.
+- Refactored predict function. Improved performance up to 87% depending on the number of regressors.
+- You can set lags with different size for each input.
+- Added a new function to get the max value of xlag and ylag. Work with int, list, nested lists.
+- Fixed tests for information criteria.
+- Added SysIdentPy logo.
+- Refactored code of all classes following PEP 8 guidelines to improve readability.
+- Added Citation information on Readme.
+- Changes on information Criteria tests.
+- Added workflow to run the tests when merge branch into master.
+- Added new site domain.
+- Updated docs.
+
+
+v0.1.5
+------
+CONTRIBUTORS
+CHANGES
diff --git a/examples/basic_steps.ipynb b/examples/basic_steps.ipynb
index 56737958..03dfe929 100644
--- a/examples/basic_steps.ipynb
+++ b/examples/basic_steps.ipynb
@@ -108,7 +108,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fd019689d50>"
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1c971108280>"
+      ]
      },
      "metadata": {},
      "execution_count": 4
@@ -149,7 +151,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.001993603325328823\n"
+     "text": [
+      "0.0021916740077804796\n"
+     ]
     }
    ],
    "source": [
@@ -172,7 +176,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95556574\n1         y(k-1)     0.1999  0.04107943\n2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+     "text": [
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9001  0.95764084\n1         y(k-1)     0.2001  0.03929227\n2  x1(k-1)y(k-1)     0.1001  0.00306336\n"
+     ]
     }
    ],
    "source": [
@@ -201,8 +207,8 @@
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      },
      "metadata": {}
     }
@@ -231,7 +237,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 9
@@ -240,8 +248,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -281,7 +289,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 10
@@ -290,8 +300,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -329,7 +339,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "rrse:  0.001993603325328823\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95556574\n1         y(k-1)     0.1999  0.04107943\n2  x1(k-1)y(k-1)     0.1000  0.00335113\n"
+     "text": [
+      "rrse:  0.0021916740077804796\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9001  0.95764084\n1         y(k-1)     0.2001  0.03929227\n2  x1(k-1)y(k-1)     0.1001  0.00306336\n"
+     ]
     }
    ],
    "source": [
@@ -370,7 +382,23 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "array([[   0,    0],\n       [1001,    0],\n       [1002,    0],\n       [2001,    0],\n       [2002,    0],\n       [1001, 1001],\n       [1002, 1001],\n       [2001, 1001],\n       [2002, 1001],\n       [1002, 1002],\n       [2001, 1002],\n       [2002, 1002],\n       [2001, 2001],\n       [2002, 2001],\n       [2002, 2002]])"
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [1002,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [1002, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [1002, 1002],\n",
+       "       [2001, 1002],\n",
+       "       [2002, 1002],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
      },
      "metadata": {},
      "execution_count": 12
@@ -389,7 +417,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "[0.95556574 0.04107943 0.00335113 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.9000403 ]\n [0.19990693]\n [0.10000669]]\n"
+     "text": [
+      "[0.95764084 0.03929227 0.00306336 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.90011397]\n [0.20005433]\n [0.10009018]]\n"
+     ]
     }
    ],
    "source": [
@@ -407,9 +437,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -421,7 +451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/examples/extended_least_squares.ipynb b/examples/extended_least_squares.ipynb
index ea0da8a1..1a2298e7 100644
--- a/examples/extended_least_squares.ipynb
+++ b/examples/extended_least_squares.ipynb
@@ -93,7 +93,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.5626926249339153\n"
+     "text": [
+      "0.5043155844895849\n"
+     ]
     }
    ],
    "source": [
@@ -118,7 +120,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Regressors Parameters         ERR\n0        x1(k-2)     0.9094  0.74535677\n1         y(k-1)     0.2794  0.07821222\n2  x1(k-1)y(k-1)     0.1188  0.00443378\n"
+     "text": [
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9055  0.74383792\n1         y(k-1)     0.2902  0.08256432\n2  x1(k-1)y(k-1)     0.0732  0.00171561\n"
+     ]
     }
    ],
    "source": [
@@ -151,8 +155,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -201,8 +205,8 @@
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {}
     }
@@ -256,9 +260,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -270,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/examples/f_16_benchmark.ipynb b/examples/f_16_benchmark.ipynb
index 7ee0632d..421624b7 100644
--- a/examples/f_16_benchmark.ipynb
+++ b/examples/f_16_benchmark.ipynb
@@ -60,14 +60,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "(32768, 3)"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
@@ -80,26 +80,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6b0443210>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -112,26 +111,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe697898650>"
+       "<AxesSubplot:>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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332.055062 \r\nL 106.646813 305.13907 \r\nL 106.951329 301.180082 \r\nL 107.255845 300.43657 \r\nL 107.864877 275.597284 \r\nL 108.169393 272.30062 \r\nL 108.473909 270.762319 \r\nL 109.08294 242.166588 \r\nL 109.387456 263.56008 \r\nL 109.691972 276.746736 \r\nL 109.996488 260.04549 \r\nL 110.60552 192.351706 \r\nL 110.910036 194.533102 \r\nL 111.214552 198.0306 \r\nL 111.823583 184.115387 \r\nL 112.432615 141.211749 \r\nL 113.955195 66.281542 \r\nL 114.259711 64.051006 \r\nL 115.173258 83.598109 \r\nL 115.477774 86.138442 \r\nL 115.78229 84.04678 \r\nL 116.086806 88.653137 \r\nL 116.391322 95.641723 \r\nL 117.30487 107.377677 \r\nL 117.913901 138.791062 \r\nL 118.522933 168.595641 \r\nL 119.131965 194.699751 \r\nL 120.654544 284.442514 \r\nL 121.568092 324.525933 \r\nL 121.872608 331.159856 \r\nL 122.177124 335.073977 \r\nL 122.786156 354.155317 \r\nL 123.395187 376.080377 \r\nL 123.699703 383.79752 \r\nL 124.004219 388.237227 \r\nL 124.308735 386.216071 \r\nL 124.613251 380.357708 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -158,14 +156,14 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
@@ -182,223 +180,15 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "0.3090560911603647\n"
+      "0.3090559299602927\n"
      ]
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Regressors</th>\n",
-       "      <th>Parameters</th>\n",
-       "      <th>ERR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>y(k-1)</td>\n",
-       "      <td>1.8387E+00</td>\n",
-       "      <td>9.43378253E-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>y(k-2)</td>\n",
-       "      <td>-1.8938E+00</td>\n",
-       "      <td>1.95167599E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>y(k-3)</td>\n",
-       "      <td>1.3337E+00</td>\n",
-       "      <td>1.02432261E-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>y(k-6)</td>\n",
-       "      <td>-1.6038E+00</td>\n",
-       "      <td>8.03485985E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>y(k-9)</td>\n",
-       "      <td>2.6776E-01</td>\n",
-       "      <td>9.27874557E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>x2(k-7)</td>\n",
-       "      <td>-2.2384E+01</td>\n",
-       "      <td>3.76837313E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>x1(k-1)</td>\n",
-       "      <td>8.2709E+00</td>\n",
-       "      <td>6.81508210E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>x2(k-3)</td>\n",
-       "      <td>1.0587E+02</td>\n",
-       "      <td>1.57459800E-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>x1(k-8)</td>\n",
-       "      <td>-3.7975E+00</td>\n",
-       "      <td>7.35086279E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>x2(k-1)</td>\n",
-       "      <td>8.5726E+01</td>\n",
-       "      <td>4.85358786E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>y(k-7)</td>\n",
-       "      <td>1.3955E+00</td>\n",
-       "      <td>2.77245281E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>y(k-5)</td>\n",
-       "      <td>1.3219E+00</td>\n",
-       "      <td>8.64120037E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>y(k-10)</td>\n",
-       "      <td>-2.9306E-01</td>\n",
-       "      <td>8.51717688E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>y(k-4)</td>\n",
-       "      <td>-9.5479E-01</td>\n",
-       "      <td>7.23623116E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>y(k-8)</td>\n",
-       "      <td>-7.1309E-01</td>\n",
-       "      <td>4.44988077E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>y(k-12)</td>\n",
-       "      <td>-3.0437E-01</td>\n",
-       "      <td>1.49743148E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>y(k-11)</td>\n",
-       "      <td>4.8602E-01</td>\n",
-       "      <td>3.34613282E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>y(k-13)</td>\n",
-       "      <td>-8.2442E-02</td>\n",
-       "      <td>1.43738964E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>y(k-15)</td>\n",
-       "      <td>-1.6762E-01</td>\n",
-       "      <td>1.25546584E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>x1(k-2)</td>\n",
-       "      <td>-8.9698E+00</td>\n",
-       "      <td>9.76699739E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>y(k-17)</td>\n",
-       "      <td>2.2036E-02</td>\n",
-       "      <td>4.55983807E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>y(k-14)</td>\n",
-       "      <td>2.4900E-01</td>\n",
-       "      <td>1.10314107E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>y(k-19)</td>\n",
-       "      <td>-6.8239E-03</td>\n",
-       "      <td>1.99734771E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>x2(k-9)</td>\n",
-       "      <td>-9.6265E+01</td>\n",
-       "      <td>2.98523208E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>x2(k-8)</td>\n",
-       "      <td>2.2620E+02</td>\n",
-       "      <td>2.34402543E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>x2(k-2)</td>\n",
-       "      <td>-2.3609E+02</td>\n",
-       "      <td>1.04172323E-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>y(k-20)</td>\n",
-       "      <td>-5.4663E-02</td>\n",
-       "      <td>5.37895336E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>x2(k-6)</td>\n",
-       "      <td>-2.3651E+02</td>\n",
-       "      <td>2.11392628E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>x2(k-4)</td>\n",
-       "      <td>1.7378E+02</td>\n",
-       "      <td>2.18396315E-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>x1(k-7)</td>\n",
-       "      <td>4.9862E+00</td>\n",
-       "      <td>2.03811842E-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "   Regressors   Parameters             ERR\n",
        "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
@@ -406,11 +196,11 @@
        "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
        "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
        "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
-       "5     x2(k-7)  -2.2384E+01  3.76837313E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
        "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
        "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
        "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
-       "9     x2(k-1)   8.5726E+01  4.85358786E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
        "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
        "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
        "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
@@ -431,11 +221,11 @@
        "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
        "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
        "29    x1(k-7)   4.9862E+00  2.03811842E-05"
-      ]
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
@@ -462,14 +252,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 426.524687 \r\nL 91.073836 425.8022 \r\nL 91.511557 411.152953 \r\nL 91.527769 411.762218 \r\nL 91.657464 414.450677 \r\nL 91.673676 413.929097 \r\nL 91.706099 411.584217 \r\nL 91.738523 416.587624 \r\nL 91.803371 413.657281 \r\nL 91.852006 420.48783 \r\nL 91.88443 417.237727 \r\nL 91.900642 415.724342 \r\nL 91.933066 420.370916 \r\nL 91.949278 421.614602 \r\nL 91.981701 417.132372 \r\nL 91.997913 416.04414 \r\nL 92.030337 420.668488 \r\nL 92.046549 422.442923 \r\nL 92.095185 418.283297 \r\nL 92.14382 421.93661 \r\nL 92.176244 417.362476 \r\nL 92.22488 423.776247 \r\nL 92.257303 421.234152 \r\nL 92.273515 420.18193 \r\nL 92.322151 424.220352 \r\nL 92.354575 421.784311 \r\nL 92.516694 424.020064 \r\nL 92.549117 423.321335 \r\nL 92.711236 418.9788 \r\nL 92.759872 422.615291 \r\nL 92.808508 418.607581 \r\nL 92.840931 420.940681 \r\nL 92.873355 419.416585 \r\nL 92.905779 417.218426 \r\nL 92.970626 420.487016 \r\nL 93.116533 417.101093 \r\nL 93.165169 418.987263 \r\nL 93.246229 417.917766 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 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417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 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305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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587.44086 416.256092 \r\nL 587.570563 416.922889 \r\nL 588.186652 421.561336 \r\nL 588.219078 421.535685 \r\nL 588.348781 421.16788 \r\nL 588.96487 418.872918 \r\nL 589.224277 418.851271 \r\nL 589.710663 418.930773 \r\nL 589.905217 419.607446 \r\nL 590.359178 421.306835 \r\nL 590.456455 421.20103 \r\nL 590.65101 420.493422 \r\nL 591.234673 418.075477 \r\nL 591.33195 418.184357 \r\nL 591.494079 418.948061 \r\nL 591.980465 421.931214 \r\nL 592.012891 421.906036 \r\nL 592.142594 421.379839 \r\nL 592.758684 417.992893 \r\nL 593.082941 417.536084 \r\nL 593.277496 417.305274 \r\nL 593.309921 417.326798 \r\nL 593.439624 417.691438 \r\nL 593.731456 419.624315 \r\nL 593.958436 420.487633 \r\nL 594.023288 420.455164 \r\nL 594.185417 419.885986 \r\nL 594.509674 418.86502 \r\nL 594.606951 418.988603 \r\nL 594.801506 419.68446 \r\nL 595.125763 420.710598 \r\nL 595.255466 420.648394 \r\nL 595.417595 420.226672 \r\nL 595.806704 419.187577 \r\nL 595.968833 419.248109 \r\nL 596.228239 419.388065 \r\nL 596.390368 419.062022 \r\nL 596.811903 417.911863 \r\nL 596.844328 417.930466 \r\nL 596.974031 418.227298 \r\nL 597.36314 420.051954 \r\nL 597.849527 421.979642 \r\nL 597.946804 421.919191 \r\nL 598.108933 421.210917 \r\nL 598.530467 418.948895 \r\nL 598.627745 419.140422 \r\nL 599.016854 420.542051 \r\nL 599.04928 420.51617 \r\nL 599.178983 420.146932 \r\nL 599.568092 418.8608 \r\nL 599.827498 418.586752 \r\nL 600.054478 417.525864 \r\nL 600.313884 416.678037 \r\nL 600.411161 416.920894 \r\nL 600.702993 418.961131 \r\nL 600.865122 419.519132 \r\nL 600.897547 419.505573 \r\nL 601.02725 419.11859 \r\nL 601.319082 418.037737 \r\nL 601.351508 418.05192 \r\nL 601.448785 418.290901 \r\nL 601.675765 419.83455 \r\nL 602.032449 421.907463 \r\nL 602.194578 422.130976 \r\nL 602.227003 422.10867 \r\nL 602.356706 421.782748 \r\nL 602.583687 420.291308 \r\nL 602.94037 418.095237 \r\nL 603.005221 418.124099 \r\nL 603.134924 418.677932 \r\nL 603.491608 420.271844 \r\nL 603.588885 420.057045 \r\nL 603.977994 418.565607 \r\nL 604.042845 418.660573 \r\nL 604.2374 419.574359 \r\nL 604.594083 421.00072 \r\nL 604.821063 421.096825 \r\nL 605.177747 421.140897 \r\nL 605.30745 420.748059 \r\nL 605.599282 418.78732 \r\nL 605.891113 417.292185 \r\nL 606.053242 417.20865 \r\nL 606.345074 417.591401 \r\nL 606.701757 418.221544 \r\nL 607.317846 419.411431 \r\nL 607.836658 419.500934 \r\nL 608.063639 419.030843 \r\nL 608.355471 418.402492 \r\nL 608.387896 418.42175 \r\nL 608.517599 418.779079 \r\nL 608.777005 420.580062 \r\nL 609.068837 422.089535 \r\nL 609.166114 422.014627 \r\nL 609.360669 421.192631 \r\nL 609.684926 419.994662 \r\nL 609.847055 419.969918 \r\nL 610.625273 420.325072 \r\nL 610.754976 419.860589 \r\nL 611.241363 416.737486 \r\nL 611.33864 416.989491 \r\nL 611.533194 418.762311 \r\nL 611.889878 421.935869 \r\nL 611.954729 421.978823 \r\nL 611.987155 421.926451 \r\nL 612.149284 421.028337 \r\nL 612.830224 415.801802 \r\nL 612.86265 415.824233 \r\nL 612.959927 416.193807 \r\nL 613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -483,24 +272,23 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0, 0.5, 'Information Criteria')"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -522,9 +310,13 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -536,9 +328,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/identification_of_an_electromechanical_system.ipynb b/examples/identification_of_an_electromechanical_system.ipynb
new file mode 100644
index 00000000..51619d6e
--- /dev/null
+++ b/examples/identification_of_an_electromechanical_system.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "daily-tower",
+   "metadata": {},
+   "source": [
+    "# Identification of an electromechanical system\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior\n",
+    "\n",
+    "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "earlier-person",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "peripheral-colors",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('data/x_cc.csv')\n",
+    "df2 = pd.read_csv('data/y_cc.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "legal-weapon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2[5000:80000].plot(figsize=(10, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "competitive-pathology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will decimate the data using d=500 in this example\n",
+    "x_train, x_valid = np.split(df1.iloc[::500].values, 2)\n",
+    "y_train, y_valid = np.split(df2.iloc[::500].values, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-springfield",
+   "metadata": {},
+   "source": [
+    "## Building a Polynomial NARX model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "personal-easter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=40,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=2, xlag=2,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares'\n",
+    "                         )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "plastic-demographic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\polynomial_basis\\narmax.py:333: UserWarning: n_info_values is greater than the maximum number of all regressors space considering the chosen y_lag, u_lag, and non_degree. We set as 15 \n",
+      "  self.info_values = self.information_criterion(X, y)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08011571455094049\n",
+      "       Regressors Parameters         ERR\n",
+      "0          y(k-1)     1.3016  0.98600038\n",
+      "1       x1(k-1)^2   103.9300  0.00794805\n",
+      "2        y(k-2)^2     0.0000  0.00250906\n",
+      "3   x1(k-1)y(k-1)    -0.1257  0.00143301\n",
+      "4          y(k-2)    -0.5078  0.00102781\n",
+      "5   x1(k-1)y(k-2)     0.0560  0.00053520\n",
+      "6         x1(k-2)   349.8627  0.00027965\n",
+      "7   x1(k-2)y(k-1)    -0.0840  0.00011221\n",
+      "8  x1(k-2)x1(k-1)    -7.8186  0.00004547\n",
+      "9   x1(k-2)y(k-2)     0.0341  0.00003253\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train)\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex, n=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "introductory-bench",
+   "metadata": {},
+   "source": [
+    "## Testing different autoregressive models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "binding-shelter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:49 - INFO - Training the model\n",
+      "03-08 22:53:49 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:49 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:49 - INFO - Done! Model is built!\n",
+      "KNeighborsRegressor 1.833\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:50 - INFO - Done! Model is built!\n",
+      "DecisionTreeRegressor 0.346\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\general_estimators\\narx.py:154: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+      "  self.base_estimator.fit(X, y, **self.fit_params)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:51 - INFO - Done! Model is built!\n",
+      "RandomForestRegressor 0.178\n",
+      "03-08 22:54:03 - INFO - Training the model\n",
+      "03-08 22:54:03 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:03 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:54:12 - INFO - Done! Model is built!\n",
+      "Catboost_NARX 0.179\n",
+      "03-08 22:54:13 - INFO - Training the model\n",
+      "03-08 22:54:13 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:13 - INFO - The regressor matrix have 231 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:54:14 - INFO - Done! Model is built!\n",
+      "ARD_NARX 0.074\n",
+      "Polynomial_NARX 0.032\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.linear_model import BayesianRidge, ARDRegression\n",
+    "from sysidentpy.general_estimators import NARX\n",
+    "\n",
+    "\n",
+    "estimators = [\n",
+    "    ('KNeighborsRegressor', NARX(\n",
+    "        base_estimator=KNeighborsRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('DecisionTreeRegressor', NARX(\n",
+    "        base_estimator=DecisionTreeRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('RandomForestRegressor', NARX(\n",
+    "        base_estimator=RandomForestRegressor(\n",
+    "            n_estimators=200),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "    )),\n",
+    "    ('Catboost_NARX', NARX(\n",
+    "        base_estimator=CatBoostRegressor(\n",
+    "        iterations=800,\n",
+    "        learning_rate=0.1,\n",
+    "        depth=8),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=1,\n",
+    "        fit_params={'verbose': False}\n",
+    "    )),\n",
+    "    ('ARD_NARX', NARX(\n",
+    "        base_estimator=ARDRegression(),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=2\n",
+    "    )),\n",
+    "    ('Polynomial_NARX', PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=50,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=10, xlag=10,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares',\n",
+    "                         )),\n",
+    "    \n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "resultados = {}\n",
+    "for nome_do_modelo, modelo in estimators:\n",
+    "    resultados['%s' % (nome_do_modelo)] = []\n",
+    "    modelo.fit(x_train, y_train)\n",
+    "    yhat = modelo.predict(x_valid,  y_valid)\n",
+    "    result = root_relative_squared_error(y_valid, yhat)\n",
+    "    resultados['%s' % (nome_do_modelo)].append(result)\n",
+    "    print(nome_do_modelo, '%.3f' % np.mean(result))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "pursuant-appreciation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial_NARX 0.03208119098997185\n",
+      "ARD_NARX 0.07364659571266981\n",
+      "RandomForestRegressor 0.17752025859574144\n",
+      "Catboost_NARX 0.17923040407121107\n",
+      "DecisionTreeRegressor 0.3457206837080308\n",
+      "KNeighborsRegressor 1.833370478725381\n"
+     ]
+    }
+   ],
+   "source": [
+    "for aux_results, results in sorted(resultados.items(), key=lambda x: np.mean(x[1]), reverse=False):\n",
+    "    print(aux_results, np.mean(results))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/multiple_inputs_example.ipynb b/examples/multiple_inputs_example.ipynb
index 02e4c1d3..896f501c 100644
--- a/examples/multiple_inputs_example.ipynb
+++ b/examples/multiple_inputs_example.ipynb
@@ -86,7 +86,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x7fe5cb870a90>"
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2014449c730>"
+      ]
      },
      "metadata": {},
      "execution_count": 4
@@ -104,14 +106,16 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "0.0026126773911553398\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.90482955\n1  x2(k-2)x1(k-1)    -0.3000  0.05072675\n2        y(k-1)^2     0.3999  0.04410386\n3   x1(k-1)y(k-1)     0.1000  0.00033239\n"
+     "text": [
+      "0.0027888197126054283\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6001  0.91387720\n1  x2(k-2)x1(k-1)    -0.3007  0.04275254\n2        y(k-1)^2     0.3998  0.04309934\n3   x1(k-1)y(k-1)     0.1011  0.00026347\n"
+     ]
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 720x576 with 3 Axes>",
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JHd8D7U+VlZX0hUYqEL+Tl19+mcWLF7N27Vrlua1bt3Laaafx7rvvUl5e3us9S5YsYceOHTz88MNpz4dCIWKxGFarVXl8wgkncP3113PWWWelLbtu3bo0WcdQ0Nra2m9QBMMbEd/iRsS3+BExLm5EfIubXMW3YCQW1dXVvSolra2typVKJv71r38xb968Xs+bTCYlOZYfjxgxgubm5txvuEAgEAgEX2Oa9jfzxHPPMvmm/+Tlf63G7e488JsEggKnYBLkSZMmYTAYWL9+vfLcunXrOPzwwzP62XV0dLBr1y6OOuqotOej0Shz5szhpZdeUp7z+Xzs3LmTMWPGDN4XyAK1XlpQfIj4FjcivsWPiPHA2buviedffJmHP3mZzzt383/fXs5H/15f0EmyiG9xk6v4FowG2WKxcNZZZ3Hrrbfy29/+ltbWVh555BGWLFkCJKrJdrsds9kMwJdffonBYOjlVqHX6znuuOO4++67qaqqwul0cvfdd1NZWclJJ500KNvudneydft2urq6cTodjG1sxOUq7XN5WS/TH2vXruWqq65i3LhxSJJENBrloosu4rTTTsu4/L59+/j8888VqztB/hhIfAXDFxHf4kfEeOA0/vY/icRTOthVTZ+w6tlPMD6vJ3Tv23ncsr4R8S1uchXfgkmQAW644QYWL17MD37wA2w2G1dccYWSEB533HH85je/4eyzzwYSNh4lJSVotb2L4DfffDNLly7l6quvxuv1cvTRR/OnP/0p68kq0WiUXbv34PF4+xxd2O3xsGnzFiwWMyaTiT179/HRuo+ZPGkCjj6MqiUknA4HDSNH9LtN3/zmN7n77ruBRBV84cKFNDY2MmnSpF7Lvv/++2zbtm1QE2S3u5N//fsDfvHWI/xu3mXMnjKj3wuBryvCgL64EfEtfoY6xm53J2s/+5hrVy3jzlN+zOzDpw+bY+vvZvyQ5bvf4b2WzUiAUavn+BFH8OOpp+Z70/pE7MPFTa7iWzBNevmivya9bdt30N3tyfiazKbPtxCJRJTKNkAwGMRgMDB54oSM7/H5/disVhwOO2MaR2dcZu3atfztb39TEmSA5cuXs2fPHjo6Oti/fz9ut5vjjz+eK6+8ktNPP51gMMjNN9+M3W7ngQceULbljjvuOGRfaLe7k5Wvvc4fP3+FN1s2cnLtdP571n/wzW/MGjYH8qHC4/EU9AG4pbWVl99+k9s//Bu/PHoh3zr2eMrLy/K9WcOGQo+v4NAZyhi3tbXzyqo3eOTL11jdvJHTG2fzs+lnMuvI4VGAeOrv/+Shz15idcun6DRa4lKc00YdxeKT/4tZR87I9+ZlROzDxU2u4ltQFeRCw+fzH3AZr9eHw5EeCJPJ1G9iHQ6HsFmtA1q/mvLycl599VW+/e1v873vfY9QKMTxxx/PVVddxY9//GO2bdvGySefzBNPPMFdd91FdXU1Dz30ECtXrmTRokVZfVZPahYvIBxT30b7mFUvfIxxReHeRssXwWCwYA++bncnK1au4tHtq/nSs48/fbwCXVhi/sknDYuTcSFQyPEV5IahirEkSdT96qw0icKL29fy4va1BS1RkJEkidrqajrWJSy8TqufiS8SpNXfxdgCHtYl9uHiJlfxFQlyP9hs1gNWkEtKbIRCobQKcigUoqTkwKMObTbrAZdRs2/fPmbMmMHGjRt5//33KSkpyegRXV1dzW233YbVaqW5uZkjjzwyq8/JxENzfsqfNr3CB61fEJViGLV6jq6ZxBUzvnPI6xYMHT0vdFa3bGR1y0aMb//fgj8ZCwTFxr6m/dw2+Tz+tu9d/t2xFUhIFE4YeQSXTilciYJMNBrFbi/hlyf9gLOfuxV9TMvF4+cz88jhIxERCPqiYFwsCpGGkSNwOBLm330xor6OQCBIMBhEkiSCwSCBQJAR9XV9vkeDBofDTsPIEQPeFq/Xy9NPP43dbsdut7N06VJ+9KMfKZ+r1WoVo+6bbrqJ22+/nd/+9rdUVVX1qZ/OhlEVtZjQEZViaIBIPIoRHY3V9Ye8bsHQ8cjc/+aYqkkYtDogcTI+tmoSD8+9+gDvFAgEucTt7qS1tY1yRylbuvYAoNNoicSj6NEOi2NrODnowq9J/L+ipoLJE/vuvxEIhhOigtwPer2+T42wmqmHT87KxWKgJtbvv/8+CxcuRKvVEovFuPLKK2lsbOS///u/WbduHRaLhVGjRtHS0sL48eN58MEHOfzwwznzzDP5z//8TxwOBxUVFbS0tGTxrTPTMHIE7pCXCfY6/LEwh9lraQ94Cvo2mqA3oyqTFzrxxOSkxIWOnlp770E8AoFgcPD7A+zavQdJknh091sYtQbqLGVUWBxYtEZavZ3D4tgaCScS4/3exOSyYDRxR7PnVDWBYDgiEuQc4HKVMss18GYEm+3A8ovZs2fz3nvvZXzthRde6PVcdXU1r7zyCgCnn346N9xww4C3ZyA4HHbuPfVK/vjBC7yxbwMXj5/P2DH9Xwh8XRlIfPPF6NENuENejnSNZZ17Kw3WStwhDxUVIkEeKIUcX0FuGMwYRyIRtu3YgSRJPPLxy7QHu1l+1k2s2vQBL+xay0+nn8uE8eOGxbE1HEkkxM0+N3qNdtgkyGIfLm5yFV8hscgDBoMh35uQNfG4hMNup7SsFI1By+SJE7DbS/K9WQVJIcfX6XBw76lXcurIWeg0Wox6A/eeeiU6vS7fmzZsKOT4CnLDYMVYkiR27NxFNBLl7V0bWL7pTZbOW0RlaRlzpszELQWYPHECJpNpUD4/10QiiX6GZp+bEY5KJUEudHMssQ8XN7mKr6gg54FQKDTsdlD5gBeMRQhEQwDEYrF8blLBUsjxlaTEhU5dfQ1TmhvZ0r4bg8VINBIlGAxhNg+PE3M+KeT4CnLDYMX4088286/1H3DXhn/QHvFwx4mXUGVzAVBbUk5HoJtgNIyZRBU2k89/IRGJpCQWo0prhk0FWezDxU2u4lvYe1+RYrFY8r0JWSMf8ELRMIHkbTUpXthVgnxRyPGVYxaKRXCabIwvG8GG5m1AYhiN4MAUcnwFuWEwYrxz527eeW8ty7e9zc5AK/WWMrStYXx+Pzq9Dp1WS729kt3diZ6RYDCU823INeGkBrnZ52a0s3rYJMhiHy5uchVfkSDngY6OjnxvQtYoCXIsQjAaRpKkgj8I5otCjq8cs3A0ilGnZ2bteNY1fQGAVyTIA6KQ4yvIDYMR48OWns+iT5bxdusmALZ49rHok2XM/es1WJMn9NHOanZ0NgOJKliho2iQvR2Mcg6fCrLYh4ubXMVXJMiCASFLLELRMBISwdjwOBAK0pHjGI5HMOkMIkEWCIaI30xdyDFVk5THRq2ek0fO4PFTr1M0x6Oc1ezqSibIGTzuCwlJkohGosTicdoC3TQ4q1TnBXF3UTD8EQmyYECoNciAIrMQCfLwQqkgx6IYdAamV49jc9suQtEIkXBkWFStBILhhiRJ1DnLMZJohjVodUTiUewmK43V9ZiTCXKDs5qdXcOjgizrj9sDXZSabNiN1mFTQRYIBoJIkHNEU1cbJ9yziP3d7TlZ39q1a5kwYQIrVqxIe37BggVcf/31B3x/KBRi7ty5/a7/6qsHPhxCrUEGCCYb9cSBcHghId8JiGDS6bEZzYx11bKxVdYhZzf+XCAQHJhwOEx9XS0t/k6MWj1LZlzI3JojaPW5GdvYiNFkBGB0aTU7kglyoWuQw0qDnpsqmwuT3kAwmnguLonzgmD4IxLkHLHk5UdYs/UTfrXi4QMu299kPjVjxozhxRdfVB5v2bKFQCBw0Nt4KKQS5PQKciwmDoQ9GWh884F86zMci2DUJbp8Z9aOZ90+IbMYKIUcX0FuyHWMw5EIDrudHx+1gMaSalxxCz+ZuoAXfrIUl6tUqSCrJRbhApdYRJQGvQ5qSsqw6I3DpoIs9uHiJlfxFTZvh4jlqhOUgwLAg2ue5cE1z2LWGwnc81bG91RUVAxo3RMnTmTHjh10d3fjcDh4/vnnWbBgAU1NTTz//PP8v//3/zAajYwePZpf/epXhMNhrrnmGrq7u2loaFDWs2XLFn79618DUFpayu23357195QPeIrEQq4gi0pBLwYa33ygaJBjUUyqBPmxDa8BIkEeCIUcX0FuyHWMw6HEOaIz5mdc1Ui+MetISkudyjAQo9GIVqvFZbYTk+J0Br2UmkuIRCIFa0cWiaYqyNU2F2ZVglzoDkdiHy5uchVfkSAPgCm3XcBnTduyek8wGkbz06MzvjapahSbbvnbgNYzb948XnvtNc4++2w2bNjApZdeyubNm7n//vt59tlnKSkp4fbbb2f58uUAjB8/nquvvppPPvmEtWvXAnDzzTdz++23M27cOJ5++mn+9Kc/ccwxx2T1feTKYygaxqw34o8IiUVfdHV14XQ6870ZGUlpkCMYdIndf3rNOK57/Y+EYxEIJSpXRqMxn5tZ0BRyfAW5IdcxluUI+7zt1DsSJ++e+5jRaCQYDDIqqUMuNZcQLGC/3nCPCrJ5GFWQxT5c3OQqviJBHgCf/uKJfl9f9Lc7WfbOPzHqDIRjES479iz+cO61fS6fzZShBQsWsHjxYkaOHMmsWbOAxMFn3LhxlJQkJtkdddRRrFmzBoA5c+YAMG3aNPT6RHi3bt3KrbfeCiQaKxobGwf8+T23ORSLUGouSVWQC/xAmA8cDke+N6FP1HEsMSaspexGK6NLq/msdQczag7D5/OLBLkfCjm+gtyQ6xjLcom93W0cVTcBAGOPxNdsNhEMBhmdTJCnVY9NVJ7zMLC0o8PNmo8/4hdvPcJd8y5j9uHTe42+lpv0mn1uplaNwaDVE5NiROMx9FodkiQVrJRB7MPFTa7iKzTIOaDZ08Hlx32X96/5E5cf9132e/r34PN4PANe98iRI/H7/Tz22GN85zvfARL6mq1bt+L3JxqqPvjgAxobGxkzZgwff/wxAJs2bSIaTYwBbWxs5I477uCxxx7jf/7nfzjhhBOy/o6ylCIUDeNSJ8hCg9yLbOI71KgryLIGGWBm7QTF7s3j9eZl24YLhRxfQW7IdYzlBHmfp416e7KCbEq/CDVlcLII5sHJorW1jedXrOSeNU/zWfsO7nv373z07/W43Z1py6mHhFTbXGg0Gsx6o9KnUsjFE7EP54adO3fzp7/9lQk3/AfPrHyp128kX+QqvqKCnAP+celvlX///vv/c8Dls7XvOe2003juuedobGxk9+7duFwuzjjjDC666CK0Wi0NDQ1cc8016HQ6brjhBs477zzGjBmj3JpbvHgx1113nTIa+rbbbqOlpSWrbVBrkEeaSoTNWz8Usj1TmgZZr06QD2P5Z6u5ZMbpQod8AAo5voLckOsYy57Ge71t1MkJsqFHgpy8azPKWc1r29cNynYMhBFLvks4HlUev7zrI17e9RHG5/WE7n1beT5Ng1ySGJctyyxsRjPxeBydTje0Gz9Aim0fbm5p4bX31nDbB3/lrnmXc+y0mb0q/rlmf3MzL7+6iqea3uNLzz7+8MFzmON6jj169qB/9oHIVXxFglygzJ49m9mzZwOwcOFCFi5cCMDxxx/P8ccfDyTkFz256667ej03ZcoUHnvssbTnGhsblfUPBGVEcbSHxEI06Q0rUglyBIM2tfsfWXMYN61+hEg8CiEKujlIIBhOyAM1wrEI7oCXKpvcmJe+f5nMhTEs5Hcz/ou/7V7Dey2fIyFh1Oo5fsRUfjz1NGUZ+TtF4lHcQQ+V1sR3MuuGjw65WGhpbeX5F1fy1z1r2NK1h7vfXo4+LDH7qFmDmqiOuu2ctAup1S0bWb1iI8ZX0i+khjMiQRYMiNSo6XAyQRYTk4Yj6pHhJpXEwmGyUW+vYFPrTqZVj8Xr9eW9CiAQFAOhpINFk7eDKlspeq0OvUGPVpuucEwNC6lid1cLcSlOOBQeci1vbWkFpt06JCS0aIjEo+jjWhqr65VlZMlIq6+LMosdvTZRKU5v1BPnhqFg5JKz0xLVN5o38sbzGzG+NLiJ6r2zL+WxrW+ytnULMSmOUavn6OpJXHHkdwbtM4caoUEWDAhJkohLcSKxGE6TjaBwsRiWqCUWxmQTp6yFFGOnBYLcE4701h+bMoHSJCsAACAASURBVDTB6nQ6dHodVoMZh9nGfq8bSZKG3A+5troad8iLQaNjrL2Wk6qn0uLrZKyquTscSTlYVNvKlOfNemNq3LS4uzgk3Dv7Uo6pmoSWxEWUUavnpBFH8Pi3rxvUz613VWFCRywZ50g8il7SpF1IDXdEgpwHbDZbvjchK9TjiY06PRaDSZFYyLpmQYpCjq+6SU+uINuTbiizVI16IkHum0KOryA35DLGsgfyXo9Kf9yHS4wyMMSRklkMZaNePB7HZrNy1/xFRKQYLYFOLh4/n/tOvTLtjlI0kqhYNvvc1Nhcqe3XG4eFw1Ex7cP1rmpM6IgjoUlW/A2SbtAT1braGtwhL2XGEnRoObFqCu2BbkarZjDki1zFVyTIeUC2XxsuqKfomfQGLHqTSmJRuAfBfFHI8U3ZvEUVH2SH3Q7AjNpxbGjeSjQeIxQMKS4o2eJ2d7JyzZscsfh8Vq55s2A6m3NFIcdXkBtyGWPFA9nTTr29HOg7QZadLEaV1rCjaz8wtA1lsnVbWBen2uYiroWaUXXYbNa07VAqyMkx0zLDxQu5mPbhEfW1SqI6wlrOSdVTafN3p1X8BwOr1cq9p15JicFCnDinjzyKe0+9EovVMqifOxByFV+RIOcB+SA0XFDrj006IxaDkUBSYpGNp/PXhUKOr3rUtFxBNhgMmM1mdBGw662c/eRNvLvh3+zZsy/r9bvdnbz7/gcsfWs5n7Zt5+63n2Lthx8VVZJcyPEV5IZcxljxQFZbvPXRAKskyM4qdnYlnIaCwSFMkJMXxa3+TqpspRxWVs8XHbt7bYf8nfb73NSUZE6QC3maXjHtwzarjXtPvRKXqQRvJMjF4+fzwBk/H/QekmgkisNuxxcPMbGiAW2ZGYfdTjAQHNTPHQi5im9BJcjhcJibb76Zo446imOPPZY//vGPfS77ox/9iAkTJqT9t2rVKuX1FStWMG/ePKZNm8aiRYtob28fiq8wIOSD4HBBToKD0QgmnT5ZQS7822j5opDjq3axkH2QNRoN0WiUTZu3oAV2B9p5Zvs7vPfBB1kntjWLF3DGS79kVdPHSMCre9dz6vM3U7O4t+PKcKWQ4yvIDbmMsewXvM/TRm0fHsgyisRC5WQxlBrkSHJbW/2dVFpLOaxsBF+07wEgEEwlPsqQEG9vDfJw8EEupn04Fo/hsNsJa+J4on4mTjgMq2Vwq7jxeJxYLEYsHqcr6OXImvFsczcB+fHu7kmu4ltQCfKdd97J+vXr+fOf/8ytt97Kgw8+yEsvvZRx2a+++oq7776bNWvWKP/J9mcbNmzg+uuvZ9GiRSxfvhyv18u11/Y92W6ocbvd+d6ErFDGTMfCmPRGIbE4AIUcX7lxJhyLKhVkrVbDzP/9MYs+WcbeQGLIzeqWjZz/r6VZJ7Z/PuX/cEzVJIxJCzmjVs+c6smD3jAylBRyfAW5IZcxDoUTCcNetcTiABXkfA0Lkb2NW31dVFqdjC8fwZcdewEIBAKp5ZIa5P4qyIV8biimfViWwnWHfKDR4A54kSRpUKvk8me6gx7sJhvjy0ewrTORIBeCx3Su4lswCbLf7+epp57ixhtvZMqUKZxyyilccsklPP74472W9Xq9NDc3c8QRR1BZWan8J+u6Hn/8cebPn8/ZZ5/NxIkTufPOO1mzZg07d+4c6q9VFKSm6CVuy6slFrE8TtJzuzt57vVXmXLzubz8r9VFdRt/sFCPmjYmNcgajUZJbA1Ju6aDTWxHlddiQpfwUybR2Wxg8BtGBIJCJB6PE4vGCERCBCJBKixOoD8NcuL5ensFrf5OQtEI0Uh0yJqh5cRXriBPKB/JF+0JiYW6giw7c2R0sYgKF4uhJBaNEZfieMMBRjmrafUnzoNyLDNxqH0ishSnPdBNhdXBWFcd29wJSZ76dzLcKZgE+fPPPyccDjNz5kzluZkzZ7Jx48ZezUJfffUVJpOJurq6jOv65JNPOOqoo5THtbW11NfXs379+sHZ+CInNUUvUUG2Gsx5ryC73Z28suoN7n3nGTa5d7L07eW8tzZ7ScDXjUyjprVaLeNqR2JCRzSeOBEfbGLbMGok7pCXI11jADixagodQQ9jRo/O3ZcQCIYJsgfyPk87tfZyNBoNBqOhT19jjUaD0WREr9VRV1LOnu7WtPUMNlFFg9xFpa2Usa46dnU1E4lFCYfCxGIxJekPxyJ0h/yUWxzK+4UP8tAiXzh5wwEsBhM1JWW0+JIJcjRzBdnt7mTV6je5a/WTfNq2nfveyzxKvD9kF5N2fzflFgejnTXs6GomFo8TCUcK+u5BNhRMK2draytOpzNNO1JRUUEkEqGjo4Oqqirl+a+++gqHw8HVV1/NunXrqKmp4corr+SEE04AoKWlJW15gPLycpqbm4fmyxQZahcLs86QPAjmd5JezeIFhGOpC6fXmz7h9Rc/wfhy8UzxGRSS56xwNNWkp9FoOGzcWNwhLydWTWF1y6ecWDX1oBJbh72Ee0+9kuc3vM069zbOH3si40c14nQ6DvxmgaDIkCutez1t1JYk5BWZPJDVmE0mwqGwIrMYW1ZHKBTCOgTuAPJt+UQF2YlZb6TWXs72zv2MLx9BIBhEn7zz1OLrpMLqRKcaeCIm6Q0t8gVNV8iH02Sj0lqqVJCjfVSQa365IH2U+M6PeHln71Hi/SH/TtoCXZRbHNiMZkrNJTR52xnhqCQYHJrf62BTMAlyIBDoddtJftyzSWHr1q34fD7mzp3LokWLeO2117j88sv529/+xrRp0wgGgxnX1VezQ2tra9pjs9mMzWbD4/HgcDhoa2vr9Z7y8nJ8Ph8Wi4VQKITf70973WKxYLFY8Pl82O32tHXI+piKigo8Hg82m41AIJCm8YKEjYrJZCIQCGCz2TI2GlZUVNDd3Y3dbsfn8xHscXvDZrNhMBgIhUJYLBY6OjrSXtdoNFRUVNDV1YXD4cDj8fTSEEVjMSLRCJ6AF6POQNgfxBcO4u50o9fraWtro6Kigs7OTpxOJ93d3b3+1iUlJeh0OqLRKAaDgc7O9KtVrVZLeXk5brcbl8tFV1dXr3XY7XY0Gg2xWIw/HPsTlm1cwb87txGV4hg1OmaVj+Oq2eco6+js7Oylw3I4HEiShCRJ6HQ6urq60l7X6/W4XC5lHW63u9cdDKfTSSwWQ6PRoNFo6O7uTnvd4/FQWVmprKOjo6PXLVKn00k0GkWn0yFJEh6PJ+11o9GI0+lU1tHe3t7rhFNaWkokEkGv1xOLxfB6vb3W4XA46OrqorS0lLa2Ntrb25GQCMWi+L1erHr592XlrlMuY+vW7bzT+jlnVs+kccRINJqELKO7uxun00lbW1sv55KysjICgQAmkwmv10ssFsVYkrjQdZQ7lLh7vd5B2Z9khmp/kv/WB7s/2Ww29Ho9kUgEk8nUSy8nr2Mo9ye9Xt9rX9DpdJSVleV9fzIYDJSWlg7p/hQKhXqdFzLtTz33BZfLRSgUwmAwEI1GadrXhLvTzZctu6gwJX5TLlcpXV1dfe5Per0Of8BPg6OKre17mOEajV6vI5qsCA7m+am5uYVwJMx+TzsVVic+n49xrno+27+NSp2NvXv2YjQZcXe62dW1n2qrC5/fj8VipqurCykapdPfjbvTjVarobametDPTwezP/VcZrjuT+3t7bg73ezp2I9Nb8KhM7Hf204oFMLj9VJSYuu1P90xbSFP732ftS1biJGYgPeNivH8YMLJym/+QPtTZ2diu/Z1tlBuddLV1UVDSQUb9nyJrU7P3n17GT1qVNbnpwPtT74e/vwmkwm73d7r/KSOr/r8FIlEeq2jsrKSviiYBNlkMvX6wcmPLT06Mq+55hoWLVqEw5GoSk2cOJHPPvtMSZD7WpfZbM742X39gZxOZ7+v25P+sXq9vk9jankb1etQ/1t+vaSkhJLkwIa+PudA22m325Vle2JINoUcaB3y9qhxuzsx6A1IWg1mg5Ga8kpCsQiuUhc6nY6KikRndmlpadq6MiFfuPS1HS6X64DrAGisGoHNYCYqT/GRYph1Rhqr65V1yNvTHwfaDvn/2axDfiy/t6ysrNd7IF2H2NdvU15HeXl5xtcNqmafnvuJjPx3KC8vp7S0NNHAEY9SWVaBVqNVfnc11dVUlJXj+GI5k6ZOosrmwmazodFolHjIse6JvA6j0Yir1EVsT3Kqk8WMzWpFkgZvf8r0+mDuT1arVfl3f+uQJIl9+5t5b8O/uWv931n6rUV8c+oM5f0H2heGcn8ayDrytT/1XMdQ7E8jRozoc/vkv0Nf+4LswWo0GrE7HARDYdxRP2PK67HZbJhMpn73p7a2dqwWK42uWjY0b8VV6sJut/f6uwzG/tS0v4V4PE5H0EuV1YXNbGNC+Uh2eFtwlbqw2mzYrFZcpS7crVuosZdhS/6eXaUuXCWltIQ8uEpdlJaWph07Buv8JJPN/tTX/jDc9qcSux1XqQvJs48yq4ORZTVsat2JyWRCq9ViMpl6rWNs7ShMez8kRhwNCTmdRW9k+mGT0pbtb38KhsL4/AG8sRBVNhdOp5PxlaNoiXTjKnVhs5VgMBiyOj8NZH9SH3vV9Nyfen5n+VxgMBj6XEcmCkaDXF1d3atS0traqlz5q9HpdL12lDFjxtDS0qKsq+dVdVtbW79XCkNJz6vTQkfRICclFrLNmyRJeZuk1zCyHnfIy4SShA59TuXhdAQ9BTHFp1Djqx4zbdDq0Wq0aFW3R3W6RIOezWDGF0lUerJtwpSX94QTFSt5PcV0u3Wg8d20eQtvvrWGxza9zuddu7n33ey1foL8kKt9WD6fNXnbU1P0+nCwkDGZZSeLqpSTxRB4Icv64kA0RCgWxmFKJBLjy0ekGvUCQWVIyH7VFD2dPnHsSPNBLmCP/EI9RmdLLJo4/3aFfDhMNqqsLlpkiUUfg55qa6pwh7w4DVbMOhMnVU2l1deZlZxOXndboJsKayI/G1Naq1i95dvJIlfxLZgEedKkSRgMhrRGunXr1nH44Yf3morys5/9jMWLF6c9t3nzZhqTk2OmTZvGunXrlNeamprYt28f06dPH7wvkAUDufosJBQXi2STnl6rQ6fRKRrgfCQ/drude0+9kukViWawU0fO5N5Tr8zq6nCwKNT4qj2QTSoHCxmdVpUgh5OJbZYac/mCqTuUSJAVt5N48YwkH2h8Zzx0CYs+Wcbqlo1IwCt71jH/2RuLyhO6WMnVPix7ICfGTCct3vrwQJaRNcqjnNXKsJChSDhk14M2fxeV1lLl2DC+bCRfdCS8kIPBoJL0N3s7lCl6sn+zWW8kGCt8DXKhHqOzJXW8TWqQbU5aff27WJjNZu499Ur0Wh3heIQzR30z63NnRGnS61KaNMe4ahWrt3x7IecqvgWTIFssFs466yxuvfVWNmzYwOuvv84jjzzCRRddBCSqybJ+ae7cufz973/nhRdeYMeOHdx3332sW7dOWfa8887jxRdf5KmnnmLLli1cd911HH/88YwukE76npqgQkexBlM1dlkMxrwOC4nFEuboVmfi1ompvASH3V4QE5IKNb4pB4soRn2yQU+rSpB1icNBidGCN5LQ72Z7hyDVVZ1IkP2R4hsoM5D4SpLEbZPP5+jKicpzRq2ebzXOKipP6GIlV/twNlP0ZIxGI1qtlgqLM+kU4SMejw/6sa2nB7JMla2UWDxOu7+beDyOL6lnbva5qUlavJnUCfIwqCAX6jE6W6KxVAXZmawgt/oT+mC5QVSNJElEI1GsNivdkQCz6iYQK9XjsNt79Wz0h/xbaQ90K9aFY0pr2d7ZhCRJhIKhvMY/V/EtmAQZ4IYbbmDq1Kn84Ac/4Je//CVXXHEFp512GgDHHXccK1asAOCss87iuuuu47777mPBggW89dZbPPzwwzQkb6/PmDGDJUuW8OCDD3Luuedit9u544478va9ejKUk5FygWzXE4xFMOkT1Y18T9NT29sYtHr2eRKSGtmUP58UanxTI8MjyiAPdQVZlltYDSb8yb9jPGuJRaqCbNTpUxKLPPpl55qBxDcWi1HvqkInJf6+ek3CG9qo0QtP6AKgo8PNC2+8xtRfnpfRBzYX+3AsFiMWi9Ed8iFJEk5TQs9vOECCDImEU6PRDOnAkGgPD2QZjUaTNnI6lJR7NPvcVCeHhMgJsmWYDAop1GN0tqgryA6TDZelBE/YTzgWIRaN9UpS5e/d4nNTZrEztWoMm9t2AeDPIkGWpR3tgW7KrYkKst1kxWawsN+XaLQcyhHpPclVfAumSQ8SVeQ77rgjYzK7ZcuWtMcXXnghF154YZ/r+u53v8t3v/vdnG/j15GUzVsYmyEh1rfojQQi+fO7lLfJGw4w1lXLPm/CkUC+pSnojXys7OmBLJPSIB96BdkT9lNtc31tR5LH43FG1NfRsT7R/X3BmBPY7W2l1etmbFIKJsgP+5ubWbFyFY/vfIvP2nfwwPv/QBeSmHXkDFyuAzdODRS1B3KdveKAHshqTCYjgUAgKbNoZmrVGELBEPY+Gk9zQVht8WZL/zuMLx/JF+17OHrE4cpzzd4OpYIsSyxMeoPwQR5CYkoF2c/48pFoNVrKLQ7a/N3U2cuJRCJpzXZyjPd52qkrKWdyRQP/+Dxh7TbQCrKsVQ/HIvgjIUWrDkmZhbuJ2pLypDNJ5mbZ4UJBVZAFhYm68mjSyxKLwqgge8J+JpSPZG+yglwslYHBIFMcNfSuIJcYzfgjB6tBTizfHfJTZXMp6ykmDfJAiMXiOOx2rptzHgDRcJSLx8/nsQtu6TcJ29e0n6dWvMDUW849qAlXggPTcNs5XPzhA4o2/KUdHw6KNlyWRKj1xwfyQJaRK7KjnFUpHfIgH9tSHsgpiYWslx5fNkLRIQMEoiH80RAuS0liuEnye5n1RkLDoIJcLKjHTMuJapWtVGnUi/Ro1AvLF23edurs5UysaODzZAU5EBjYBDxFfxzopsxiR6tJpZHqRr1imKgnEmTBAVFXkE06+UBoymsDllpiMb58pCKxEBXkvpGSU0IisWjGCnJKYpFq0svWxSKabNz0hAPU2Fz4FA3y16uaJF9YaI2Jm3T2SieTJ07o024LYPv2nbzw0koe+ugFPuvYyf3v/UO4XgwCd0xbyDFVk5THRq2eeaNn5lwbLsu91PrjgcgrIFWRHeWsYWfn/sT6BltiIU/R86UkFnLFenz5CL5UJcgt3k6qrKVoNVr0Bj3aZC+DGDU9tMR6aJCBxLAQuVGvx/lQriA3edqptVdQYysjGo/R6u8kFosN6DcmH+MTU/TSm+HUjXr5drLIBQUlsfi60Jc/a6ETjKoqyPp8N+mlJBZyBVmSpIKoIBdqfDNrkFOvKxILowVv+OAkFrLW2BPyUV1SRkCxiyueCvJA4iv/reUmRXm6VX+NVhPuvoCI6mJzxc4PWbHzw6wmXAkOTJ2zEtPexG9dp9EmtOGSNk0bnot9WL5Y3+dpo8FZDZA2KbY/5MpthaGELS27+OCjf+NyleIqLc2pDERN+hS9xGc47Hba2zsYU1rHrq4WRZ7V7HMrDhZGg0G5uDYPEw1yoR6jsyXaQ4MMpE3T6zluWo7xPm8706rHotFolCpyZUMpfn/ggL9RWaveFuiiIulgodPriEVjjCmt44Uv3gPyq0HOVXxFBTkPyInIcCGVWIUxJyvIFoNJ0SBLeagOylVrbzhAbUkZOo2OzpCXeDzep//jUFGo8VXbvMkuFmkaZFliYVBJLLI4ycm+2LF4HH80RKXVWZQuFgOJr3yh4I8G0Wm0Smd5X7/NeDye0fXi22OOEq4XOUSSJGprqmkPJrThx1ZO4qTqqbT00IbnYh+WL9YTGuSkxVsWFeRujwfvvk6a/B3cve1Fmrra+HDd4N1RkG/Ht/q7qLQlKoMmkwmjyYhJb6DeXsH2ZDV7v6+DmpKE/lh23YAeLhYFfNeoUI/R2ZKxgmwr7dPqTa7q7lPJfiZWjMqqUU9xsPB3U56U4sh3GuQKsiRJhEL5c7LIVXxFgpwH8p3AZUtKYqGuIOdXgxxXVZDtRiv19nL2eRKNeoOt1TsQhRpf+YQViqXs+jI16SUkFokDZTaxVTdO2gwWbAbLQSXahc5A4it/30AkRJ29PGW91IcEKByOUO+qguT7hOvF4BCJRHDY7dx0wkIgkVhcPH4+y86+Nq0ym4t9OM0DuSRp8XYAD2QZnU5H0/5mykocaDQatnTt4cV9H2EwGNi6ffshb1smMrlYGAx6rMkJaOPLR7IlOTCk2eumOllBNgzDCnKhHqOzRXaq6A6qNMiqCnLP75nepJf4TU6uaGBz205gYI16il92oFupIJtMJnR6HaXmEow6Pa3+rmSSnJ9zca7iKxLkPDBQHVqhkLFJTyWxGGoNslypjEuJSqXVYKbOXqHokHvqroaaQo2vfDWf0CD3bfNWYjTjPQhphHxQ8oT92I0WbAZzUVaQBxJf+e/mj4RocFb3ectTJhKNMKK+jtZgwr/zp5PO4KTqqbR6O4XrRQ6JJu2pwro4Zr2Rbk2IyRMnKNPrZHKxD4fD4cS4cW/7gD2Q1Sx8ayk/fP8+wvEoEvBa08fMffp/OPYvPzvkbeuJ7EzgCweRpMSwIK1Wi06nU8YMjy8bwZcdewFoVleQDQlnDo1GgyEp3YrEo0iSVLBeyIV6jM6GeDyOJEn4IyEMOoPSV1Jpc9KSrCCrJYeSJBEJR5Ka4y5qkhZ92Tbqycf5DpXFm0Gvx5L8nYwprWVb5z4gfzrkXMVXJMh5YLiNuVR8kKOZJRZD3YAlJ1u+SBCL3oROq6XOXqE4WeS7glyo8U3TIGdq0tOlmvT84ewTZLWDhcNkTfgpF6HN20DiKzco+SMhRjqq6Ax4iMXjGb1JIXFR57DbOf+IUwAwRjVcPH4+y3/065xqTnfs2MXjz/2Dw2/6PivfXv21awBU3x6eUjmavZ5WYvE44VA4LS6Hug9Ho1Hi8TgdAQ9mnQGbMZFwZnPi/t8TruSb5eMVpxmjVs/cEdN47j+XHNK2ZUKudieqx040Gg16QyLZla261COn9/vSK8jAsKoiF+oxOhvkRDUhr0hZraVrkFOVVLXGPFHpTcRthL0SbyRIR8AzoEY9Zcy0v0tp0jMYDIp2eYyrTnGyyNdEvaIbNS0oXJRJen1UkIf6IKh2sCgxJm7/1dvL2StLLIqge3YwSGmQUy4WauRR04lJerI0YuAXP/KdhEQF2YrFYErZvBXRoJCBEFM0yAmfUIfZRkeyOpypUU++9dkdS9ziHDVmFJMnTsBhz10z0RdffcXK117nkU9WsrlzF/e+8/evnUtGVGVRVWevoMxsZ7+3I+cNvvJF+j5vG3Wyg4Uxu6rWkeMPx4AOCUlpJjSgY/bh03O2nTLKFD2VB7JBn0yQk5XBw5JWb5Ik0ezt6JUgazI5WRRoglwMyHdD1A16kG7zFlVpkEMZNPGQuIs4sXwkW9oHVkVW27xVJCvIeoMec/IujNrqLTjMrd5Egiw4IHI1TG3zltAg5+cgGFOcEtQJskpiUQDjpguRVBwjmJISi0w2bzZVk1428plYNDVFLzFVqTglFgMh5WIRxKo3Ja2XEjrknt6kkPrN7ve5AfAlB7XkstFpyn0/ZNEnyxT/35V71g2K/28hI3f9dwS6KbM40ibV5fLOUyR88B7IMi5XKZj11FpcnFp9JCfXTkMy6wbFxSKlP+5S9Meyt7HRaESn0yneyK3+Lpp9ncoUPaNx+FWQDxW3u5OVa97kiMXn582vXD42qxv0AEoMFuKShC8cVKQzkPpNykNC1GTTqJe6C9NFmSUlsZClOGqrt3w6WeQCkSALDoi8gwVjEczqQSF5Sn7UDhZyglxIEotCRa4GR+KpCrJag6zTqX2Qs7d5Uw9vsRuTEos8emXnE7XNm8VgotLqTDXORPpOkFt8bkoMZrwHIXE5ELdNPp9jerhkfKtx1tfKJUP+O7cHuim32GlwVrOrO5kg5/DOk3wM2utpz9oDWUaj0XDfaVdyfOM0Dh8zjrvOuILHL7wlZ9uoJpLmgZxIhPX6lAus2ZwYfT2+bASfNG8lEotQaipBq9Uqy8kDI9IT5MLUIB8Kbncnr76+mrtWP8mnbdt54P38+JXH1BVkcypB1mg0aY16snxG8UD2pirIOn3irmE2jXqyTCzRpJf6rcje3WNK69jm3qc4WQxnhA9yHlBX7YYDaS4WutTEpGABSSzqSsrZ7+0gLsXz3qRXqPFVpDLRiNKkl8nFQt2kF89CGqFILJIaZLXEopgqSQOJb8rFIojVYKLCWkpb0skio8Qi+Zvd7+2g0VWnVJBzdWEhSRL1rir0zYltl10yTBrD18olQz52dAQ9TK1qRJJITapTddwf6j6csnhrY0L5SCAxPjob5P1xKBJOtT61Ojk+2mBIpQcWiwWfLzHOeM3ujVTZXGk6Zeijglygw0IOJb41v1xAOJ66yH1px4e8tGPo/cpjsdQdO3UFGRJWby2+TkaX1hCJRjBjSrMdnFqVaPx1Ohx0dLiZWNHAHz56Dui/ghyNRpXGQACrwYROp0Or1SpNnWUWOwDuoIcyi4NQKDRg/+9ckatzcGGeyYuc8vLyAy9UQKTswcI9bN7ycxtNsXiLpBJki8GEzWimzd+d94EhhRpfOU5hVZNeJhcLi95EOBYhluySHmgnuiJ9SVaQ0yQWRaRBHkh81RVkq96cVkHuT2LR7HMz1lV30JMM+yIWizGivo72pA764sPmJfx/fe6vlUuG/Hfu8CckFqOc1ezq6l1BPtR9OKxUkNtUDhbZJcjq/XGwj7VpY6aTHsgGfaribVE5Wby9a2Oag0Vqe4ePBvlQ4rt01o84pmoSWlXz5HHVk/nLKf8nV5s3IKIqD2RHjwQ5zeotecdK/k2qK8hOhwOtVkuDo5qOoIfukI9YNNbn+TPSY0hIz4skkylxpyFNh5yHKnKunwoHLQAAIABJREFUzsEiQc4Dbrc735uQFerKo5wgW/MpsVBVkO3GVPeuWocczqMOuVDjm9ZsqbhYaNKW0el0aDQarHp1g93Aqphyd7PsYmHQ6pGkOJFYYVs+ZctA4qvYvEVDWA0JDXJfFeR4PE4sFiMci9AV8tHgqDoom70DbY/Dbufa484HwCC7ZPxwyaBNZhsI+5ubefrlF5lyy7lDouWUG5sSEgsHDc4qJUFWn8gPdR9O85uVh4RkqUEeyoRTTnzUY6b1PSrIAHVmF+6gh80tO3l3w7/TGrrkhN6kNxJM6lQLNUE+2PhKkkSFxYkRHXGSTc/xKAZJh1WTXXwPldTxNt3FAlIVZEhphlO/SZUvt9GI2WxGp9UyvmyE4nPdV6OevK6ExVtSXqEayqE06uV55HSuzsEiQc4DLpcr35swYOTEJpK8pST7XOZzUEgsLlcqUxVkSNchh/NkUA6FG99UBTmlQZZ1gzKy1ZvNaMEbyW5YSFwVF7vJikajwaKqIhfLuOmBxFe+FS5rkCusTmVYSM8EWbHY8nVRYXXiMFkPalBLf8iVaI0xEd/60fVJlwx7TtZ/MOzavYfnX1zJgx8+z6aOndz37uC7aqhP8GUWB/X2Clr8nURiUSLhiPL3PtR9OBwKE4vHafZ1qJKR7DTI8r45FP0eaS4WcpOeqjpsNicm+/n3JX7DnmiAZ7a/w2ebP1fiNZym6R1sfL0+H3W1NezztWPRGVk04VQMGh0tgU5cpaW0t3fkeEv7JjVFz69UkGWnFPUdq3A4onggJ36TnaopiAbFxm8gjXrKmGl/4gIT0rX1qQQ5oUOG/DTq5eocLDTIeaCrqwun05nvzRgQmaboAVgMRtVtv6E9CGbSIEOigry3ACrIhRrftFHTGQaFQMLqLUIk6YUsJ7YDOymnNHE+pbJvM5jwR4M4sRVsNSlbBhJfWTssa5D1Wl1KYhHpOd0qsR/t93VQY3NhM5rx5Vi7rW5sBVQSjvxdtIy76zzlwhvg5V0f8fKujwZNyylJErFojEg8ijccwGmyodNqqba52ONppbG0lnA4gtlsOqR9OBJJJCSt/k4cJhsmvQGdTpfW9DYQhtIVIhqJJre5S2nSM6TJJ7Sc8tS1RFSa+NUtG1ndshHjmrsJ3ft2ant1hS+xONj4ejxeHHY7x4+Zzp72ZqZbR/Ht+iNxSwHsJSXs3ddESYltSDS36uOtrEG2mM1EwhGqrC4+bt4KJCrN8m+yzd+F02RVfpM6nQ6rxUI7MKmigff2bAL6btSLxmSLt8TFPKQ3cypeyKW1vLnjYyA/CXKuzsGigpwH8qmPzZZMDXqQrCAfwKGgpbWVJ557lkk3fo9/vLIiZ5WhdImFqoJckho3nc+/caHGNz1B7j0oRP040aiXXaOYfPtariADiQpyWK5+FWY1KVsGEl+1BtlmkDXIss1b+sWbXJVp9rmpsrmwGSxKIpurBFY9mh1IJeB5bKK6fcoFHFM1SXls1OqZN3rmoLlqyLek3QEvTnMJuuRvvcFZzS6lUS/xWz2UfTiUQX98MJO9UhrkwfWclxuvPGE/Bp0ei6rxSs09R13KMVWT0CUr2z3jJV9sDwcN8sHGt7vbA8C6li8544jj+MasI7lp/g9p8new4qu1xONxdu3eMyRysphKgywnyLLVWqXNSasssYhEUvIKbxu19pS8AlLymUkVDXyedLLoq4KseCD3UUG2ZLB6y4fEIlfnYJEgC/olpVsNp1eQD9A40tHh5vmXVvLwJy+zpXsPD6z9Jy+89DL7m5sPeZtSt/L9vSrIiga5QJPUfJJJg9yrgixLLNReyANM0lIuFj4cyQqyNVlBVr/+dUDtg2wxmCizOOgMeIjGY72m6cknr2avm5qSMkpUFeRcNulB4uIFyPn6D4b60kpMJPSL8iAMI7pBc9WQL+BkeYVMg6NK8ULORUOR2m9WTpCzdbCAoRu8kdIfd2XUH8tMGzsRk0ZPXIpj0OqIxGOUGMxKvIaTi8XBEIlECAaDeMJ+tnTs5sjawwAw6gwsOfG/WPr+07T43Ph8flrb2gZ9ezINCpET1CqrS7kgD0ci6R7IiiY+2XBvMaPRaGgsrWW/z40vHCQaiWZ021Gm6AVUCbKqgmw0GtFqtZhiOnyhAP/xxM28vf5Dmltacv79hwKRIAv6JR5PJVVmXU+JReJkkklnVrv4O1z60R+UoQSrWzbyg/fvo+G2/6CltfWQrrDTJBaGzBpk4YXcmzQNcvKgpsnQpAcJL2TZi3egOkJ1EmY3JeLydR0WEldN0rPqzei1OpzmEjoCiQqU+uQjX8w1+xLTyWwGS8qHOkcXFWqJhVajOSif61wSi8Wor6ulLZBw1Ti59oiEq4a3c9BcNVL6Yw/llpT2ui8ni4MlFA7T7fHw3lcbeG/nZ7y74d/4/f17y2ZiqFwsZImPPGYa0pMemcmTJhAkyrcbZvHA3Cs487BjaPGmXFDki21LkfogezxeAD7Y+znTqsZi0ZswmU0YjAYmVYzi+4efxM2r/8yaTz7iuN9dxvOvvzaoenpZ7pCpglxhddLm7yIuxYlGosr5sMmbGhKiTEDUaDCbE8eoca46vuhINOplqiIrPuL+LsqTU/RkL2WZYCjE5s+/wKjVs93fzDPb3+H9Dz4alhM7RYIs6Be1xEI9ntiiNxHsJ/F5cM4VHFM1Cb2qCWyycyS3Tb6Affv288WXWw/qpAF9a5BrS8po9XcRjceIJBsTBCnSJBbazBXklMTCkvLiHWASFY8lbOG6Q34cxmRFQ+2FXERWb/2hbmyNxeOK3rsyedKCdKs3uSqz3+emxlbWY9R3bpv0vOEAldbSQRlEkg3RaBSH3c4Nx1+Q2K5ggIvHz+fhc64bNFcN9ZjpcnUF2VnVS2JxKLS2trFp8xbWt23FHfHyzPZ32LxlS9YJQmYNcu6PafLfpUXVoJdJEuJylfLST/8vvzrlR4x11vGLORfwwk+WKvFK295YYUssDoZuT+Li9t09n3HMyMOBhE1aw8gRAHxv7HHs72zjgc0v8pWvifvf/wcfrhu8plP5eNod9OEwJ+7YGY2GRAVXb8BqMNEZ9CFJkqIp3udppzaZIKvvamRq1MvkZCFXrdVDQtR2gADHPfpzFn2yjO5oQCmOnbVyybCc2Cma9PKAfZA6x1tb23j13bf59QdP8tu5l3L8kd845JONfIssGItg0qd2KEPypB+JRTHo9EiSlJZsNZTXYEJHVIqjRYOExFbPfl5qXcfk9vHs2Lubex9+npu+eT7HTpvJiPq6ATc2RNUaZFMqQTbo9JRb7DR73dQ7KgiHw0NuUA6DF99DRbnYUVWQ+9Igqyu/A6lixuNx4vE4oVgEjYaUHaDeVHQV5APFV046g5EwFoNJ2S8qlM7yUWnT9GQXi2ZvB9UlroTEIpzbiwr1RWWVzZUaZZ2ni0h5H47qJTRoCJkkJk+coHTBD85nJv7mPSUWo5zV7OxOHzd9KPvwzGWXpTUfrm7ZyOrXN2J883dZNR8qLhaqfo/BkCwoU/T8XVTakhKLPhoKXa5SZrlmZHxNbUvXEUwkk4W6z2cbX0mS8Hi8SJLEu3s+44IppyTXU4K9pITKygq+8adFRKTUsXJV08es+ufHGF/IfdOpfLwNRsPEkTDrjGg0GnQ6HQajgVAwRJUt4YVcZrHj8/sB2Odt5+TGI4F0X26rxUIHbiZVNLB+/1dA5ka9dJs3WYOc/lv5/TGL+MsXq1jbuoWYFMeo1XNs7WQWTR+6BDlX52CRIOeBnlW7XNDR4ea5F1fw5O41bOnaw33vPoMuJHHs0bMPKUlOeSCH0yQWAGZDwurNoNMTj8eV2/MA9fV1uENeGm1VNNprINkhXWK2svCF3zDSWslXvib+svFV/G0eJk+agNFo5Iu9O7jj30+zdP7lzJ4yI+O2Z6og6w36/8/em0fNUdV545+utbur16effclKVhKWhGVkGUARiCvvjMscdVARmBeE3+gZD8cNgdF3zsgMIryjeNAZ/f1gdFhUHCUsIwRklyVCkARISEK2Z+99raV/f1TdW7e6q/d6koh8z8lJ8izd1VV1637v534WaKqGkXA/DuZmzQZZVY9Ig7wQ19eLIuhTpSkH2byGiuhnhGKtJzk21SnCeFMH34Y2b62uryMkRLTvvwHGvJ+lWNghISkMKXHrnJVM1wUPfZABc8wMK3GGg3zkEGQASJVyWBIbxv7MDICFpUapDILcx1AshpQ+pEt5FNUyAjDPSS9j+IbjL8LdB57G09PbUYUpZjt70fG45NgLOnodogdYeA4ysRlMYVF0EEDnlnRAo6jpo7NB7vT65gsF6LqO3alJoFrF0tgwOI5DSDF3ykaGh/DtEy7CPQeewTPTO2CgagaIjK7D/z7+/Z4fv+N5Kyvw+XzUolMUBJRRxkDQ9EJelZigC3KWg+wQ1wVtod7PXnmYfma2iAuMUTUcuzC1i6ljRiYgv85DrxrwwQfV8ok+nImdXs3B71AsjkAtxKQ0ct2HcOkLt1LO7yNT2/DBzdf1vK3RyOYNMJGNRs1POBTCzZuuwqroOBK8gs+tPA//9v6/x0NvvYikmsfL6T10++Xyl27De+68Gn94aRt+su0hbE++hRt/d2dD3hKryKdJehb3auwo8EI+WhtBm2LR2AeZivQ6tBpzpOjJbIPMUCzeJoKdVteXeiBrJQQFpkFWYnUUC13Xoes6ypqKXKWIvkAYAsdD5AXaZHhxP5FrmFdLGAr1ee6S0Wnp1lZtqpTH6sQEkqUsypoKTdUWrKmiMdM1CDLPcRgL9+OtjB053ct5GbXEh1XYkd5+Xuq4QaAcZIel5sK4WABOD+TabfN2ytUH2aMdit279+Lf7/oZVn/1I/jVww/1TFvo9PoS/vFT+1/BaRPr4PP5EA6HaCPGcRyOW7YKshUgQhpDocotSGPICvQI/5g0qqTxZRfkgPn8ncrPu1Ms/KZQb0CIYG9qCh/56Tfwuxd+j+mZGfozZCGVKRcQFGVIvAhe4Oua0RUrliNZzuGkvmMwEojhnKH1mC2kD2tip1fPtXca5CNQnfphtlO3/MXf1VnwnDW6vmfLJGfMtFOJ7bQfcj4ISXIXH5Sw7phVWLt6FZYtXYIbTvg0ThtcA8kKHOHgg58ToVV1XP7SbbTB/+2hl/C+X19T1+BXq1UzecwwUNRMCy3AFieMhRM4QKzejpAX8kJcXy+KNKjNfJBJwxwU/R155VIHi5p0Q9PFojM/5aO9Wl1fci7MkBA//Xp/oD4sRGUs3gaCUXr+Q6K/46CWpsfEIMhDSuyIu1gQukOqlENfIIwhpQ8Hc5bAdoEWtlRgxKBf5P5fXGP11ssYHhsZQbKcAwcfvn78x80GId+5+PBwBW/QoBqWYuHiYtGqFsq3ed/+A3jwt4/gP3dsweuZA7jlqXvw7PMv9NQkd3p9SYP89P5Xcdq4yT8Oh0KOn1m5cgWS5Rw2xpdjNNjX9XVvp1iBHnGwIIl25LMNKDFq9QaY4R4hKQC/IFEPZFIcx6FULmPn67sgcyLezE/int1P4qlnfk/PM02hLGSQaMA/BoDhwUHcvOkqXLLyvUhVCvjcyvNw86arDmtip1dz8FHVIFcqFVxzzTU4+eSTcfrpp+OHP/xhw5/dvHkzPvCBD+CEE07Ahz70ITzyyCP0e4Zh4LjjjsOqVascfzKZzOH4GC0rnU57/poj0QRkkG0NeGaZRB5wJU2to1iYCU/uD0I3E/NwKIQzTtiIiD9obrtwPKqo4l39q3Fx9AysjIw68u3/cmRdXYPP2mcFBRmcjwPP85AtT8fRI2j1lkymcO/DD+K4az+BzY9tOepUu+1GTQNEpNc+gkxQqGw5X4Mgv/1cLFqNX/I5i7UUCyVaFzdNHASm8vMYstKtADPJ0F6geNEg2ymHQ0rfEfdBJpNtqpxDzB/CRGQA+zMLO27tmOksbZCJBywbOV2uVLp+RlerVShKEDeefwUAYBhhfG7leQ4xW7vFNpxlvULBAa+LTdEbbOJi0aoWypZu+Q1/4wBPHpnahk2/+npPu6OdXF9N01AoFFDSKvjD1E6cMrYagMk/Zmt4cAA3b7oKl606H3OlLC5e8d4FawypQI+JmSbPbkKPGaxBkA+6OFiwdebtX8DlL92GnF6iu7v/68Fv0fNM7pPZYhr9AXf+MWA2p7FoFKesOwFVHzCxbAKhkELniMNRXvVYR1WDfMMNN2Dr1q348Y9/jOuvvx633nor7rvvvrqfe/7553H11Vfjoosuwq9+9St85CMfwVVXXYVXXzVTYPbt24dKpYJHHnkETzzxBP1ztIqnvKjBgQEkyzmEBD+GA3GcM7Qec4VMz6tXW9jVAkGumWjpFlClQBtkXuCxfNkyZNUiPrzidNx2wRdxwcRJSKsFvGv5cRjz96Fq5durhgbRpcG3rcRsD2SO5yBZ20UOisVhbJCTyRQeengLbnny53g9ewA3Pv4zPMmsvo+Gsm3emgSFMD7IncQdG0wDFqlFkI8w3/VwFzkXebVEdziAWg6yOVkQf9KpfBLDih2PqoiMF7IHVm+szduQEj/iSXosBznmD2Es3M/wkBcmWIBM8EmGYqEo5r26KDrEUCy6f396PiUeETmIU0/eiPXHrkVfX3fRtxzHgfNxEDkBZd08fi+b5Gq1atJaqgbmChmajtZLsIksiJ42yDee+FmcNrgGPgY8eff48QsWKFNbBD1+4dDrWNU3gbAUhOyX6/QtoigiHovhlPUnwC+KGFo8glBIWZB5iIyftBvFwkJ1B5QophkE+VB2FqNNfLlvPeOK+l3oEXsXmnWB6Qu6849JiZIIn8+H0XACh7Jm/DbZqfhTqqNmL7hQKOCuu+7CD37wA6xbtw7r1q3DJZdcgjvuuAPvf7+T5P7LX/4S5513Hj72sY8BAC666CI8+uij2Lx5M9auXYudO3didHQUY2OHjxR+JMswDPhlGTdd8Hmcf/dXUNZUfG7leViyeFHPq1fSsJpJevUc5EYJT2RCzjCIIs/xCMUV/PqKG7Fr926k0xn843svxvKlS1GulPG9WzbjtP7VeCm1B6ckVmC+WN/gs0gYaZB5jqeK3LEj5IU8fN0HUdHtFfIjk9vwyH3bID2wMLG5XZW1O1vRtYYiPdbFohMhlyNmugGC/Odiu8eK9AIOkR6TbmU1a4SLPJVLYohpkEOSt2l6ToqF6WJRrVaPmPUeOR7SII9HBnAgazXIC0SxIAKjVCmHeMBE/0KKgpmZWSyKDuK/X3+Kvn8w4G/2Uo3fg1nAk3FAFp3dlI/zAYYNRvgFCYZh1C1suy17oZKHQnilLil67RTr2+ylLd1IrB/yPnO3caG5vW5F7N2eZuzdaukVpCRJQrFYxOLYMPamp9AfjKJULtPUOq/Knl8LlGJBEGRCj6nlIB/MzTdFkNdMLIe84xEKdqmGBt4AlgyMmv8nISEFG0Fu1CDLkoRyqWym2+ZmsTIxjkqlgmAw4PrzR2sdNQjyjh07UKlUsHHjRvq1jRs3Ytu2bXXQ/N/+7d/iiiuucHzN5/PRlf+uXbuw9DASwo90kazzLMqQBQllaFi7ehVV2PZS5AFXa/MG1FAs9FoE2W2Faw7geDyGkzaciPeccxZO2mA6VQxZvKXLVl2AglbBZ1e8F9+94ErEYs48dcK9YgV6PM/RFfFAMIZ0Kb/ggp/a+uHZf4/TBtdA9FnbXJyAM4ePPWwoRzvFIsjEpq92IuQ5y8WiwzQ3YtuVLTvjv/8sEWRCsdDKDpFeXyCCVDnvSNMj6NJkPomhUA2C7KHVGytsjfoVSJwpAvR6237v3n24/d6f49ivfwwPPN6YZuRAkOUQxsMD2GchyG4JXr0WiVNOlfJQpABETgDHcdT/tZaD3G3pDgGxDQx0W268Xi/HEU3RK6Qo/7gb9BhgXCx4b5P0RoaGkCznwIPDgD+Cc4bWYyZ/+ERfDv/j8XUAgEjEfTea2BQujg7RdMaFEIuTHVp2fiUNMqHHDCoxTBecCPJITYoeW2tWr0Sqksc5g+sRExWckliJ+VIWQ0OD1nuyPuJkp6EBgmzdQ6PhBA5STVDj8zA7O4f/uu9XWPu1j+E3jz581Oy8HjUN8szMDKLRqGPbor+/H6qqYn5+3vGzq1evxjHHHEP//8Ybb+Dpp5/GySefDADYuXMn8vk8PvnJT+KMM87ApZdeijfffPPwfJA2iiXHe1F0YZA8iPWDS1HSKlANjXol9lK2i0W9zZtTpOd8HxoaUcojUsORciufz4d4LIbj1q5FSPZjfOk4IuFw3eKIneiJGIznTSWtIArgOQ7DoTgO5axBeZi2dRb1m77PxAdTNTQIBkdX30dDkQAL08XCXaRnR00H7Ma2jS1+iiBXbEQDAIKCHVn9duEgtxq/tkiv5OAgCxyPqKw40vSoxVtuHkOKk4PslUiPNMFlTUUVVci8aHKcPb4uO3e9ifsf+i1+/PKD2J7ah+8+cQ+ef9E9KEFzRZAXbufHLWZaEAVIkukf2x+IoqxXkC2bdl7d7nawO2fkuScIvTfITr2HdzsxLP/YDgnpbmOZWwAOsmEYUJQgvv3evwN8wHw5h8+sOBc3u4AnnVS7c3ChUISu6TiYnTMdV/onHPZutUX6l8XRIexJTQLwJr68tigAVaoX6RG0us8fQaZcoJ7cbEgI64FMKh6P4Scf/xo+t+o8HBuewOrYOG7edBVFiWmDXGA8kBu4nUgOTZA5FzfaGZqbm8e9v9mM2164DzvS+3DTE3fimd8/11OT7FWPddRQLIrFYt02BPl/Mw7P3NwcrrzySmzcuBHnnmuad+/atQuFQgHf+MY3oCgKbrvtNlx00UW4//77XXnIM4yVCWA6IiiKgmw2i0gkglmXXPVEIoF8Po9AIIByuYxCjWdgIBBAIBBAPp9HOByue42ZmRn09/cjm81CURQUi8U6Y+5gMAhZllEsFqEoCubm5uqOo7+/H9Mz0zCqBnZM7cV4MIFXRD/2zxxCTA4hm80iGAyiXC4jEAjULTZ8Ph/6+/uRTqcRiUSQzWYdCMrszAxKpRKKagkRSYGu68hkTbEjZwDzmRRSqRTGx0aRSqUQjUaRTCYxn5xHUStD4ATkszlAMUn6mqZBFEWkUs6bn+M4CKKAZCqJuD+MA/NTqAZVTE5OUoeKcDiMQrGAUqmEdCkHyccjmUrCMHTMzMygVCxCEEQMBmI4kJ1FQlBw6NBBhJjtsEgkQhtFnufryPyCICAejyOZTNK/a5v0aDRKvVJ9Ph8ymQxCShAz+RRWKSN4LX8IpyRWYLaQwtjYKObn5+tQn2g0Ck3TwPO8ZUKfdXxfkiR6LuPxOObm5uomm1gsBlVVIQgCdF1HLpere41IJEKvbSqVQlmvQOTMz835OGiahnK5DFEUoWka0uk0kqkk1FIJ2XIB+XweEd58jWg0itnZ2brmoa+vD9lsFpqmIVXIYkiKIJlKAgC0cgW5chFG1UA6nUa1Wl2Q8QTAs/GUyWQQDoeRz+dRKjnTpBRFQTgcRi6XazieDKOKXC6HglqGUOXouQCAuBzCgdQ04rKCdDqNYrGEZCqJg5lZBKvm/e+DD4rox3wuhSqqSKZS0HXnPRgKhcDzfNPxlEgkkEwm6fFO55JQBD9S6RT8vIhkPoOIGEA2m4OiBOvGAs/z6Ovro/dgKpWqQ3fZ8bT25ougMoupBw+8iAd/+SKk/xYwed1/O8bTzMwMdF1HspiFIsiQORH7MzMoV8pIpVJIxGN08SaKImKxGD2ObsYTWSjvm5tEIhBGOp2G3+/HzMwMCoU8SuUyJsKDeGNmH9YPLwPHcXXzAjueYrGY61jw+TgUS0Uki1kEeRnJVBK6rtHXkmUZ4XAYmUym6XgqFouQZRmVchnJVBICeEwnZxH3+ZHJpCHLkifz0/z8PKqoYu/sQUSEAJKpJKpVo8v5aR7JVBKlUgFF1Tzu/oQ5tzQbT6IoNpyfiBNRspTFcKgPhmHAFxag6SoOHDgAWZahKAoEQYBq+d4nk0nHa5A5jsxPmUwGuq47rm+j8TQ9M4tkKolH3nwe7xpfi1wuh7HRUWSz2bq+JBwOw+cDSqUSxsP9eH7/DvNZqlYgS2LH46nV/AQAc/kUhNg4kqkkFCWAatUwx4KuQdcNxP1h7Jp8C0PBOPalpxGCiGwuC0mSXMeT3y9hZHgQ5/tOxcO7t8LvlzE7NwdR4DE1NY1CsYDJ7BwUn9mb5fI5xOOxuvkpm81A0zT0y2FsPfQGyuUypqam4Ge4z2Q8jVz/YUewziOT2/DIr7dBuk9A/jtbHPNTPp93nI9m44lcX3Y8qapa9xoDAwNoVEdNgyzLct0NR/5PlMa1NTk5iYsvvhgcx+GWW26hq+2f/vSn0HUdwaC5gr/xxhtx1lln4eGHH8aFF15Y9zqNTlA0Gm36fdJsC4IApcGKMhKJ1L0GuTHZ74dCIUcj5/Y+jY5DFCVwvjL25Wdx6tgaPD25A5wsIR6LQ5JkiKJoeyO2+KzkeEiVyhXoRhWqoSMkB81BHu9DtVpFVInAJ/KIxWIwqgZisQT9LPFYHKXcHGJ+BfFYnKI1ZNHjdhzzyRQi4Qj6/GGUfDrisThi8biD7yUIAvx+P4paBX1KFPFYHIlEHwYGBlAolpBMprA4PowD2VmcPrEO4XAE/f0J18/c7HyQ60P+blYDAwOAz4fvffCL+I/n7sNruw7hXYNr8L+OPwvBQKAh0sEuCMkioNFxJBLun8Fh9t5gnMRiMei6jlgshmylAJkXEY/FwfM8BEGgCIEkSfD7/ZiankVAU1DQylAUBYZh0Pujv7/f9T38fj9KpTIKehnD8X7EY+ZxD1dLKBsqOB8HWQmYXpsejye37/cynshnDYfDrgtqdvy6vcaBg4cQCoVQVMuIBsP0XADASDiBjFaEKIiQJBnVahXxWByzpQxWDC9C3EI3Q5Ifms/kXCqK0vAmFdY2AAAgAElEQVRYm40nwLx/yuUyQqEQ5nQzkjYeiyPiV1CBDkmUIMsyJElqORZiseZ6hn9a9yn8cvL3eGp6u3lsTEBG7Xg6cHASRtVAtlJAfygGkROgSH5ktCIGYjHEYrE6wIT8bl9fH9yq2Xgy0ag5lHwa+gIRRKNRRKMRDAwMIJcvIJ3OYHFsCJOlFDYIIjLZLJY12MIn58FtLMzNzSPgD6CoVRALmM/Bvr543bltNZ7IvRsMBlGtAiF/EGJARjwWR1BRwHGcJ/OT3x+AL51FvqpiPDaIeCyOwcEB+pqdjKehoUFMz8xCqgRQNlTEY3FznmgxngA0nJ9y+TxmZuaQh4r+YNRcOOpFrIvFEQ5HEI3a81WrsUCuGws81Fbta6TSGcRjcWydexPvXnIiIuEIIpEw/Uy1ZRhV+P1+LI+P4WBh3pyDZefYanc8NfssiqIgl8ujoFcw0mdet6GhIUSs8xtSQiiVShhUYqjwQCwaw0wxjdUjSxEQZUiSSPnA7HiKRqMoFEo4VTwW//rs3eB5AYZuoK+vDzNz85BlGRm1gPG+IQBAf8K8f2vnp6CiIJcrYHHfCCb/aP6e3+93/Ty3nHoZbt+1xRmwMrYO//u499fNT6Snq63a8VR7fcm9K4piw9dwq6OGYjE0NIRMJuNokmdmZiiSVlv79u3DJz7xCfh8Ptx+++2OkyHLsuMkyLKM8fFxTE1NLeyHaLPaabo6KbKFs3P+AJbHRxGRg0iXzVVSrwp4shorMUEhvLVlyFIsWJ6qbWJeQJhu/7Rei5GBEA+EkbSiStlIXvZ9nBxk59aSw+rtMHkh67qBSDgMJWY+oIyQgEg4vCDba90U5R9rGkQi0OPq04Y4joPP54PMi9ANA6qutcVTtW3eCnU+yEc6tc3rajV+qUhPc9q8AWzctDludV1HUSujqJUR99vNgyIGPHOacBszLMfZC5cMABizAjIAQPBxptWkT6gTU5F7JVcpwi/KEC1P9LHwwIIl6tkx09m6BDCyLb4oOkSt3vz+7sRETvGUJdLrQVBnC98a09l6KZqix3ggd0+xcLpYeMFvJ8//2UIaA8EolsSGsSfdO3WhnTlY13VMTk7jd394Dlv2/AF8Wkcmm0U41NgNi3CQJ6IDOJSbg2poqJQrnguUdYfNW/0cS64hsXqbK2YQEGUERNncrW0grpMkCZIsYVCJIRGI4rW5fQCAXC7PXIsM+ltwkG3b1QQOUrqj+5gmFrUGquCt50agi2AdtrzqsY6aBnnNmjUQRRFbt26lX3vhhRdw7LHH1l3MVCqFz372swiHw7j99tsdq3BN03DmmWc67OHy+Tz27t2LZcuWLfwHaaNqt0N7KcMwUClXoBsG3kpPYVlsBFFZQaZiNcg9Tq6sd67fEumRgWi6WNRzzcgkkS6xHo2tbzXaIPvDlKep6bUNcr3NG22QiTAgxAgDDpOTBXtc40wqVzfvPzc3j5/++l6s+dpH8fMHN3siWHB6ILvzj0lxvNkkd5KmRx/YFbsxAIhIz3KxWICQgyNRrcavm4uFKBHrpRgNCyHb3tM5M2KavR6K5EfOo4UFa/EWEq0G2WMOsmEYGBsdwby1sP2rxafhnKH1mM4lXZxo7BS9GMNXd3gheyxsYmOm6xpkazJfFBnEXkuo50ZbaKcIOJCt2AvFXkILFjosxJmiZzY9bvzUdopQzgSOB28lCAK93V9E2EU40kuiw2bcM3oTU7YzB+/bdwB/3L4DP9n5W1RRxUMHXsTrb+yso5qwRaiCEi9iIBjDwYw5DxEhvVdlB4UUEJGISM+eYykir5hx0weztgdyK0cNsmN7yugq/P6guRuUzmRgGAZUQ0O2nEfMb6YINrq3BcEUwcbkEFRdR65ShGEYrl7IQ4ODSJZzkDkB/3DshThnaL2rg1Un5VWPddQ0yIFAABdeeCGuv/56vPzyy3j44YfxH//xH7jooosAmGgy4S/ddNNNSCaT+Od//mfKJZqZmUE2m4UgCDjjjDNw00034bnnnsPrr7+OL33pSxgYGMA555xzJD8iLS9V2mTg7c/MIBGMIiDKCMtBZMvm5EsiXbstVqRHrMGI6CQgSii6hEDojMI2UuPR2KzIz/QFwkiSBrnm+Fm7KtvmzfJtdPFCXijLqNqyG+QiVsXHqYK50wdjMpnCffc/iB9t3YzX0vvxvWfvxZbHHu+5SSbCnmYeyKRsq7f2UUyapFeDIJviovKChRwciWo1ftkwG+KDTJqwgaAdFpK3GuTJ/DwGFSfiEZIC1Ie6V/SJFbYqknk8po1f0fH9nt7DMHdQvvWeSwAAWkXD51aeh//vE9fUWU3WCvRIjYX7sT+7QAiyRhBkW6RH1P4y4zxAEORanmK7xT4Hwh2AA42KLP4DgtQwlKmXoi4WeVuk102KHimv0/QcLhvBGJbGhrEndQhAb01nO3Pwyu98Epe/dBu2JncDMIMzLn3h1pYBJX7ZxcnC4/tZZ6KmI/76hZgdN23uWB3MzWK0iYMFW3aDvAbPHXwNAJBOm7qjZDGHqD8EnuPoTnKjcnghNxDNE4vaf37vZTAALBX6uw7WYcurHuuoaZAB4Ctf+QrWr1+PT3/607j22mvx+c9/Hu973/sAAGeccQY2b94MAHjggQeQy+Vw4YUX4owzzqB/rr/+egDANddcg7POOgtf/OIXqVfyj370o6M2AriXIouGXcmDWB43HROisuIZxcIOCrEpFuQ8BkU/RZDZSZxut7MhIW2oSqkNnN+mWNTe6OR4zEhjJ4JMmpAxB8Xi8DTI9nEVsLpvgknl6uwhPnzdB/HpZ26hqVFbprfhr3/7Tz2lRgH29anoGr2OxHi/tqjVm4Me0QJBZoJhWARZ5ExnkbKuvq2a5GblSHu0EGSyjd/PeCFrTMw0CQkh93KnPtTNyrmoDNqv72FYCHkNTjaPP5gIY+3qVVBc+H61ISGkxiMDOGBRLLxuKFiLqr6Azc0F7IaGpOmZ9nvdTbDk2mfKedtlpwebN7Kr4Hd4C3s3hmyENm27WPQwT3qdpmcj3GkMKBbFwgMEuZ36zkYzoIRNdz130YaW1p0OJwsP6CC1RZ6jqqGhpFXorhA7x9pWb3FM51M4lJ2nISGtbPxCIXPO3jiyEn+Y2glV1+j4nium6Q5MIwcLUpRm0WRHlwBYSb2AJbEh/MXJJ+H449YhkXDXGRzuOqo6xkAggG9/+9v49re/Xfe91157jf772Wefbfo6wWAQ11xzDa655hrPj/FoK8o/Th7AMqtBjkhBZCwEuRaB7bTsqOkK/Lx5wztQDRdenG79O92mxRspMuD6AmFsnXzDfC29NYJMjPhF0Vyx9gXCKOkq8pUSFPih67rn1nq1xXJwF0cGUVDL5jYrgtA0re3F2W1n/T1ue2Uznpt5HWpVh8QJOHVwFa7aWC8u7aSIH2lFVynnsxGCTK3epICNMrbwM9V1M4TBRE3tB7au6wgKJs3C65CDo7XIYsKkWJiILeEm1pr3A8Bkzo6ZlmUZhULBpFh4FBTioFhILhQLD7xqbZ6z+ZpzFkrutqWqO2KmbT7nRGQAP9/+OwDe7/y4xUwTpFQQBPA8j5g/BJ7jMV/MgqsaqFQqHQc8sM+nsGyNAw9s3vxNUku7rWq1Cl3ToRsGkqUs+oLmtejWBxmoPd7ebekoR9pCuBOBCLSqebxxfxiqqvZ0vM1qONJPubGCRRkJCK25scSTf0lsGK9bHF4vm3mH3kMOwufz1d1jtQhyQJSxPD7iOL5GRYTwgNnkb5vejQ0jKwCYC0w7bbH5nNaOFzI5L7tTk1gcNYV//pqEwiNZb++Z6s+gnAiyOQDCst0g985BNv92Q5D9gkwpFuz7kAGc6ZBiwbsgyLUTrN7ABxkA9UL2+XwYDSWYyOmFF+qxCX9hMWihUVbwQAdo2KLEUJ2fsuhBapRjJ4Cm6Ln/bKdpemxTEBT94DnOjMi1Gu2g6He9T96u5RYUQtXxwRilWJCaztspeqSRDomBjoJampW7SK/93YF2ijRtxPN6rmhuybptdRL+ZD3FYoBSLLze+SF+v24UC8C+Pouig3grQ3Z/Oj8GNw5yL4tz1gfZy3Q6wL4286UMorJi7vYIfENtQjtFw0K8olhoToqFz+fD0tgw9qYsCtsCosjDg4OYL+XAw4evHvdRnDO0HnOF1gElMrMjsYfsJHp4nGS8uoWEkCLN6WAwjplCGgezsxgNmQhyOxzzcJjQLFbjuYM76NfnCvUc/kZFxtRIiBXqOZ8H5PrtTU9iSXQYAOoivI9kvdMgH4GqtVLrpYpWg/xm8iCOiZtNVFRWkCl7I9JzcpAtkR7LQXYT6ekuKT9toIZE/NcXYEV6NQiyCxrGbmE61bOkQV54moV9XAUMRPscfMZyB1y5ifExJMs5rAqb1/LswXWYL2WxbMmSno7PpliokITmHGTHNn8baW6Ud1lmFi2CHVfLOll4GXJwpKrV+LWDQmwXC1ky/+4LhJEq5aAx1KdJlwZZkfyUg9wrTYouYNQSpSWZ/PIFiLJWzShrsghQXRBk0kSaKXq2SG8gGEWhUkJBLZnIpoeLKU013VjcXCwA+7wvipi8UUVROhq3pNxcLLxK0it6nKRHm898mgr0Wm2btyr7eEWUrUVJL4i3pjIUC+sYHUK9LnnIrcawqqoIhRR88z0XQ+YljHExXLr6Avzm899pyY0lCOiS6LCtRfESQWYFeg1cogi6O6BEMZNP4lBujnKQxRYcZMCmWZw8uhrPMg3ybDGDBEWQ26RYhBM25bGOYmEjyEtiZoPsBYLsVY/1ToN8BMoryxfDMKBWVKiGhn2ZGXqDReQgMhVvEGTycHMgyIyLRYlSLOzP5Jaq1p5Ij0WQzdCLeps3eyJmo6ZJSZKETDYLrlzFPz16B556+UVMzzoN/xei7CaxgJAYwCJGoNEJEhUKhXDzpqtwXN8SAMDfLPtL3LzpqobRpu1W1WA4yBRBbuBiwbEUi9YIMrX1q+QdTQFpDAKiHwViB+iRpdiRrFbjl3WxIA2yIPDgBR4CZ27lkwUgYKboDVsUC7K1qYgB6mLRK+fUjZZkUjg8dLEgtJJKGYsigxRBdhMJN+Igm4Kefupk4RXNgiTj5SpFyLwAWRDBcZwDdXMgyOlpVKvdIcism42NIPdi82aO0YCH6XSktBoBHNBawNWqaJoe3/vxGoYBXdfNBZOh03t3sQdWb63GMLn2mWoJi2KDOOWkDTjhuPVtCcdIOuOgEkOuUkSuYqbxudGNuik6vzIuUbVpjYJg7qSGpSA0Q8e+zAyTotf6GodDpkvFhuEV2DH7Ft0BnCuk0U8XmK1FeoBpu9pIpEeElnvTU7R/IaLZXsqrHuudBvkIlFcXj6DH+9IzGFTi1IYtLAeRKZkIsnccZNXmIFMfZJtiwaIE7ACmXqAd2LxF/Qqy5Tx06wHJni+jiQ8yABSKRby6/TUcKMxhppLBPbufxPMvuMfdelm6Zk/CIdGPxVHbMqqT7TVVVREJhyGGzUZpZPEoIuGwZ3Z9FV2FyLfiILsgyE1QINvBghVOcnSyVEQZBQ+bsSNdLRtk6x4tqiVq88ZxHEXnTKs3+350IMgWehKS2kPv2ylyznOVAhX0hJgocS+uCbtwHQ0nkFdLqOiqyU2veX3b5s3ZIAOWUC/rrVDPKdAzJ/dazmad80C12tW2OHkOsLspPdm8MZQF8qz1av5QVRWZbBZPv/4yXjq0E0+9/CLy+ULrX2xSXtrS1Xo0kwX90mjvQr1W51C1Grn9mRmMRcxwi1bcXVI+nw+SLIHzcVgU6Y5ql0ym8MATj+K46z6BB5541DF/sS5RjSgWhJfs8/kwoMSgiH4okt98DrXRIHMch2AwgIAoY3ViAlsndwKwbBJbxEyTIo34SFORXhlG1cDe9JSnHOR3GuQ/4fJKMEa2l3YlD1D+MWBRLAiC3GtQiEH8cyv1CHIDioXmMoDbCQohg1rgeISkAHXiIBMcUe9qho6SVqHoHHs+j/+3i3H5S7dhV858gG6Z3oZP/O7Gnl0gmhWZ8ItaGSIvQhJFh8VPJ3ZE5LWIzR0VW3pFlWE4yC1t3iTbCqwZT9WgHsg255zneXpdgqIfBc2bZu9oqGbjl8TE6oaBiqHRRSXHcRSd6w/YYSH5SgmariEqK+bEaqFPxIqv4oH7hxuCHJS8DXBhOcghKYC+QLghD9kNQSao0XhkAPs8DgtRXRwsaid3wntMCCHsmNqLT/38/+DhZ57saGFtGAaq1SpKegWczwdZMEXD3gSFyCjp3iLI07OzeHX7a3h25jVktCLu2f0kXt3xWk9ggmuD3GWzUqENcgr9QRu5XRKzKRbdIsit5mBy7x3IzmLMcn/oRLDpXHB1RgdJJlN4+NHH8K+P/gyvzO7Gvz39Czz/og3yEIoFq/Fx+zySaO6mSgaPUqWMp15+EcVSYw/n2iLpc6eM2Tzk2UK6TuTaqETR3KmJ+0Mo62qdF3KlUoFhGJjKJxGSAghJgbYb+FblVY/1ToN8BKo2X73bKrpYvAFARFIYF4vetnVowITGivQYBJlQLJjGx+bhsQO4vVvNTtOL1Hkhk4k8XykhKPrB+bi6gfDDc/4epw2ugeizwkM4AX85sq6lNU8v5eDgygHkcjksigwxllHtT/TUr7WUBefzIesRVYa1eSPNV6OiLhaivQ3fnGJBVNU2gkyM4gHzPim4+GX/qVaz8csK9AKCbC76rHuU3NsDii3Um8onMWiFhJAtSY63FyjtnP9Wpbv4IIfEAOOS4Z2LRb5SgiIFkAhEMVewGuSaZxD1QS7nEJPNSZjYwY2HBxgPc294m4RKwAr0areHZdlsJnIHk5gsJrErP4n/2vkYnutg98nJxXc67HRbDg6yx2PohO9fgstfug178ibCuWV6G/7m0Rt6AhMcDXKPDT2lgDAcacB0O5nKz6Oiq1Aralev32oOrrg2yO03bqSZXhwb7phqN3ztB/HRh/8ZD0++jCqA+/Y+h/N++VV6XdoR6QFmENGr219DVi2iXNVwz+4n8dprb7R9P7vxkM2gHcJXbw16US/kUL0XMhXopaaw1GOBnlc91jsN8p9wkQnEbJBtl4OIP0hFel5sz1arVQd3lUz0JsXCRaTHeOJGO+AgA4DAEx5yyPZCtsQezSzeSB0zvAgyeGisCwT4nl0gmpXtYGHzDqN+BSIvYK5oJRC1aVxOFgPJYhajoYR3gS+MzZvcimLB2RSLdrbhKcWiYkeLc5aTBWA2el5u5x/NRe5R0wPZbEaJN6zTeok0yDb/mGxJkskuxIZ59IIgu9q82dfEC144Xbxa4Sj9gQhmi+ZnrL13bQQ5TxHkYMA8rvFIP42bXgiKRSMFPs/zOPeuq/HFV34CA1VUATwytQ3n3/vVthtGx3OgA3vLZkXunYDoPQf5OxtMn18f4/P73sWtfX6ble3b3Pvxkuc+y5EGAJEXMBJK2DsNCxAGRebW/ZkZjIdNikU3CPKS6FDHThY3n3pZnf/yu8ePp9fF3SWq/j475d+vwOUv3YbpsjkOt0xvw6efuaXt+zmkKOA4DusHl2JvehKZct6yeWvPxQJg0m3D9TQLh8UbEeh5wD/2st5pkP+Ey0aQDzkQZD8vwahWUdIqPW3PstvyIs+D83GOLUOJF6BXdaiGRreWqYm5rqGiqa40iGZFmgjWyUKnCLKFhKnFhib8xxyzFMlyDqf3r0GAlzyJrWxVrHKdIEeAM0mp3a1AMikkSzksig55aNdnI8hiC5Ee5SC3GTVNGwPm8/M842LBIMj627xBJmJVVqBHzidBXNiwEIIgA/a9TxcoEptk6A3FgnWx8EoEyL4GaZATQQZBZhaHxHvXqBqOFLAgQZAjA7RB9p5i4e5gQerGEy+uaxjPXXRi2w2jW5pkO9SyZkXuHb8ge+IrzNZQJAEZPKqMz6+fb+3z26xck/S6dLFQGQeLQcUpjlvowBBC7zjIIMjEiaadYtMZ9xI6SJsUi7H4APVf9sEEeXjDR6+L7RJVQNTfmML4/TOuMHdTOXs39T0TJ7R9P/t8PihKEBIv4vjB5Xhy3x9R1lSEpWCdyLVRSdTJor/OC9kW6E1iicU/Ppos3oB3GuQ/2dJ1HWpFRUVXcSA7gyUx8wbjeZOYH5GDPdMsHPQKhktJ/vb5fCY3jkGR7dWtiSa6mZg3K54iyIwXssW5YpFKNwcLABjo78fNm67CZasvQFlX8dkV78V3L7iyp9jKVuWmXAecDXK7/DMi8kmWss4Guccmhkyq5rVsjiD7qLiuPRcLp2uJraqmgTKi7Pn28NFabhZvRGhlI8gMxSJnp+iR79viRj9yau9Wb27C1nYt/Np+D8N+j6DoR38witliPcWCpSEERb/pvcvzFDkaDfVjMj8P3XLo8UJsQ55JSdYD2YU/uWJsMW0YOfisxqR9D3I2QdSLmGnAvncWwsWC+Pxy8OHrx3/c9PntEUzwMihEYzyQSTgFeaawQj2vvZCr1SrUigrN0DGVT2EkbO3wdECxIBZn5hww3RHVbmR4CMlyDhEhAJETcPbgOszkU9Tqkz5vy81F8MctXQk/J0AzdIicuQAKyUpHCyDCQz55dDUe2PV7JIIRmjfQTklMml4txWKhLN68rHca5CNQXkRe2wbbUxgNJSDxIiRZojduRFbs7fkuJ0AbQbYFeqR5IgOyNk3PyY/qfJuRnJu+QBjzNG7afFAajpCQegcLwFz1xqJRrF+zBkHJj0XLJjxxgWhWOhsOIAfpMS1ivZDbeDiSCSFXKcLPi+gPRKjY0isueUVXKQe5JYIsMl68bfkgFxzIPtvoeSkIO9LVbPxSizethIBAHCzM80DGJpumN5lPYihkNsh1FAsGQe62iWVjaVVDp8cUECWU9Qp1ium1WGpJSDIpFiRNj0WQHR7IFr2CeGYLomnBFveHMZVPAvCGZuFKsXBB3NasXolkOYcT40sx7I/hnKH1mM7Nt90wGmxYkAchIQBjmyZItqWmB0l6qqoiHA7hund/BkFBxogvgsvWbMJvrrixJzDBFUHulmLhSNEzG2TCi2WFet0gyM3GMKFsTOWSSATCkHgRgih0JLYURRE8zyMsBxEQJcwUUjAMo637ORgM4uZNV0HkBfjgw3njG3DzpqsQsGhIriJ4l8+z4pjlKEHDpkUn4ZazP48PH3M6ZvPJjhZAYatBPja+CE+8tQ3JQhZPvfwiCoX23E6cuQROigXbw3gdEuJFjwW80yAfkYrH4z2/RqnICPT6zBVhwO+nHF7TC9lygdC7a66cFm9O5wM24YnlIVOFbaWxiXmzItvQcX+YEelZCLILB9ltAiKIdVRW6pwwFqJqbc4iYXMSNq3e2uefkQfffCmLeCCMsBxE1uPAl4qhMS4WDRpkrlOKRT2CzPOsD/LbS6TXbPxSkV4rigVFkOcxpJgIFRHpORYoPS4sWGGrIvnpoojzcSb1RfNm4UIpFpaANhGMUBcLduyR50OynEOsZnuYTKZj4X5PaRYaQ7HoCzZW4I+PjeLmTVfh/1nzQcxWsvjk8rPx3QuupE1Zq3JF9nqMVWeFrm6OQd0WOa/ZagkTsaGOfH6blbOh7+14K6zNm8VBJtfC4YXcRVhIszFMKAAHsrMYpfSKziLHAUao12FgiFoxQ0oyahFnLTkeBaWKSDhMm1JXEbxLGE08HsNvPv8dXH/uxVgRH8XX/vKT+HWHC6BAwI98vgBtMgcffCgbKu7Z/SS273i9LbEf64Vsc5BN+0dN1VBQS0iXclSH0a6VXqvyoscC3mmQj0glk8meX6NEBXo2/1iWZTq5ss1ht+iT4bB4M29cdoIFCDeOxAjbaBTrgdzJNqMbgkxe001s5DYBkYdFRFYYDu/CNWa1Ir1M1mwMyPYa0CaCTJqHYhYxfxgROYhspXeRFvv7Zc32QW4cFGJ+PSQyHNgmW/xuCDLH2yK9oCg7dhn+1KvZ+KWBGWqZivTIeWD59ely3trCtT2QxZqEQ0WynSa6PW+ORaUYcHwvyHot9xxGYnOQQ1IA/YGonabHhP1QD9eS7WBBBEYEPZqIMJHTHgiwCMVjvphBn9/d5g0wFyaDAwM44dh1WNk3jkqMRyQcbtsbWHNxsegVybIbZAkltd4xqNsiPr8Hs3bCWq8hIYC3UdMk/XCWbZAV855ZEh3CntQkql36VTcbw+Se25+dwTj1QO4c2fQzPOQ9VjR2q/tZ08zPnCzmoEh+bBhegVemdwMA8gWnYNuJILvvVMTjMZy04US855yzcNKGEzteAPl8PpzzX1/C51/+EXSY13HL9DZc9NR32xL7sV7Ihyx3GlVVKUq/Nz2FieggeI4zvaN7XFCS8qLHAtpskM8//3xX24zp6WmcccYZnhzIn1N5giAzEdPLY2aDHPD76QM5LPXOQaYpehrjneurQZBZikXVaDB4258keDZNr+ikWNhisOYIskAR5CDSpd5Q9HbK5iAXEZaDFEGeiAziQHbG9MQtV1ryKYmtUbKURV8gbNn1eYOAsxSL2mtZW9S/mPHKbTYpa7qTYgKYqCDH2MW1g0T/qVQ7CHJBLTMUC/M8sD7fZppeBlP5JIYJxUJycbGgC5RuG+R6/jEpRfRGBGgen9PFIhGM0AaZHXtuHsjk+SBRBNlboR4ZV04XC/eGglpbja2m4Qi5fL6t93FP0euVYmE1nB67WJDzejA715XPb6NyNPQ9+CCTgJm8WoLPB7r7EQj4wfPm+JF4EbPFdNvUBbaajWE3D+RuvHllxsmiXQSZzHXThSQGgzGsG1yKV2bsBrlarULXdeiGgXyl5Nl91qxutcR+AmOd2q54lfVCLukq8pUSdF1HvmCOqT0pbwNCSHmFIDfsXB544AE89thjAIC9e/fi+uuvr1tFHThwwJOD+HOrZDLZ8wUkA23n/AFcsfHDAMwBSVaZpkjP2p7vUuBDRXqOcAmf4+9aioW7B3L7g5+2zRkAACAASURBVJcgO/GAi0iPSesi6JN7g2zzsAmK3qtNWrNiEdRlsRFkshlEwhH4BQl9gQgO5eYwHhlApVJpikRQMVEph7g/jLAc8AwB74WDTB7KjcqVg8xz4HSbhkNdLN4GHORm45fc/0WNEekxqIgoiNA1HQPBKHanJmFUq1QVTu5b96CWLikWzK6LUtMghyRvRICAvYDKW/Z2fkHEXDGDarXqiIunHshMg0xdcawmZDwygEf3/gFA7xxkwzBgGIZpaVetIiia3tSNFu0hRcHs7BxWR8dw9xuPm5+pTQTZplgUqOiI7xER8/l8pkc2Zx6vqmsQeQGGYfSEtrE+vyssip4nCLKDg9z9rpHqEoNNaDGyLKNQKFAni4FgDKVyuaMGv9kYJtznA9lZnDmx3nzPLhYPpOFbFB3C84deA9CaakfoHdP5FAaVGFb2jWN/ZpZaMpLrRsSwvOUm0ehZ7kUdv3wVgm88Br1KxH46FCnYtthPlEQYJcNEkXNzOKZvDNlsDgCwJz2JJTEz5MxLBwsveiygCYJ88sknmzwRa9LWNM3xR9d1LFu2DN///vd7Pog/t+oVDSQOFmVNxWR+HouigwDMLR0a1yyzYSHdTX7strxcs/1LHQoYBLlqVOl7sR7InYn06hFkvSYoxMFBdpkkyDHG/CF7kbCQIj0HghpwvNeiyGDb6AFpHuaLGcT9IUQkxQ4K6TURkbF5a5WkRyZliRfh83GoWAuURhOdnaTnzkEOCn4UKYLsjUVVbVWrVby1bz9uv/fnWP2Vj+DHd/8XXtj6B+x6czde/MNL+PE9d2LNVz+Kuzf/pufY8Wbjl3w+k4tbb3FImpCBYAyvTO/GkBWhy3Ji7aCWQM9BIU7hWC2CzHghe8hBDkl+OnnnVHOBxc4jQAME2eIfTrBpej1SLEizQ2KmW7nqKIp5/66JT+CPM3uh6hqKxWJb54ecAxZB9kIsZKPIsmcoMmm0DuUYioXYO4JMRNwyL6FsWVZ21SATD2QmJISAJ4S6sLQHoV6zMUzuuQNMzLTUBTeWNOxLYu1T7chi0myQ4xB5ASv6xvDqzF4AoI2lQwTfgUtUN7V82TIUoOL9S07FD977BXx4xWkdif3cvJBzOYIg2xZvXiLIXmmOGo7eRCKBG264AQAwNjaGyy67jHpVvlNHtkijtSc9iYnIIEReoPwdMrmG5SDNgO928qu6cJDrRHoODrK9oEqX8rRxb7Sd6VZsg5+rFKFZjSHr55yrmIl1QPsIcq9Rzc3K6TNrjhFREqFWVGr1dvrEupZoGJnMk6UcRkJ9CDNWfT0HhTg8rS0OcgORHmByiHVNpyimLIjQdb2uqSbXpaypqFYBmRdpehxFQkU/8tQH2bvroGkaMpksMtks9u8/iFde3Y67Dj2N17MHcPv2R/CxVBET42PYt/8A7jr0NF7L7Metz/83jIKK895zzoJY/9kuFmU7TY05ZwIj1HtlZjeGakJCgM5Fks3KbVFJKsgkJVZ7WLjYYUIqqqjSHQqSpheWglBVDYIg2FHqpRwmyPPBGsNUpBfpx4GsN2Eh9qIzi4Ql0HPjH5MSRdMNKCQFsCg6iFdn9+L4oeXIFwqIhMPN34txoSHPp16T9ABrnOrEOq2MsBzsvUFmfH5HQ95TLHrlIGsuCDJZXNrUBcbqrQuhXqMi99z+7CzGCf2kC4oFaeTHw2byn6prQNl8ZjZCfMl1mS6kMGh97vWDS7FtejdOGl2FTNYEjVoJ9LyseDyG31xxI3bt3o10OoOvnflJLF+6tO3nJ2v1djBn8pDJPbEnNYmLjjsPgO0dfTRVW8vbL3zhC5ifn8f27dvpByO+fq+++iouu+yyBT3Id8pZDgeLuM0/BmxFuIkg94aesi4Wcs22PPm7lmJBfifDxmB2OIB5gQc0ICwrSJfySAQjUFW1rSQ9wMlBnszNA1hgFwuXJL1gIIC01SATq7dWD3HaPBSzWNu/2IytrhRNLniPfaUbB7nZthzP89A1HSGrieoLRFwnOup7XckjLAfMIBme7DKwTifecZCnZ2Zw/+OP4v88+zN8Yd2HsW7pCpx759VQq/ZJ2jK9DVumtwEvOX+XfF16/Dso3/x4z8dSW+QaFtUShqxwA9YthE3T+91bL+P08WMBOJsTm4McoDZ7XTfILsJWUiEp0DOFgz22vFpCyBImAqBpektiw3U0qXSZFemZzyzCV4zJIRiGQZsAVVW74oAC7cVM11ZIUTAF4MThFXjx0Btmg5xv3SCT50CmnEdE6nz3rFG5O1l0v6AhPr/VahWHcvMLJ9LTuz9WNkWPeCCT+4Sm1MWG8fT+PwLwLixE13WTI1spoaSWkbB2HbpZPBDrQgAYUvqwPzuDpbERlMuVholxtrVdEscNLQcArBtciod2PQ/ARl7bEeh5WfF4DCfFT+zqdyXW6s1CkAFTs/RWZnpBOMheVVsN8l133YV//Md/hKZp8Pl8dLL1+XxYv379Ow3yYS47Qe8gllkNst9qkElzYtq89ZbCxvog+xsiyE6RHkWQ2RVuhwOY8DRNJ4sMEsEINE1nGtEiQk3ECayLxWtz+x2fZSHKkaAlBwDD9LJMpzNYFB3CE/u2AWj9ECfnbr6URdwfhsiZvrAFtYyQZFI3up1w7WupQarhuroVpUc4tuHrzyErnIzUXBPWxaKgeaPATyZTeOChh/H/7noEO/OH8G/bfwNxOw8ffIhJCrJqEXrVgMQJOC6+FKP5AN4KZvHH5FtQqzokTsDJ/Stw5cYLezqORkWejayLBbtAJFZvJCykNkUPsMcwiyB3b/NGmlc3kZ6fSTjsvkFmreSCbIPMpOmRRtWNYsE+HyRJQqlUwlhkAPszs1g7oKDSS4PMeCBTB4sWr6VYO6Ubhlfg168/hc/iAuRyOQBDTX/PDjJidrg8QPe8dIYAzCasWq1irphBUPQjIMrgmWCfno7VoyQ9EiYxU0hjmNggijUIcmwIu9PehoVQeoVl8ebz+bqiV5DyyzJyqobF0UHsSU1ZDXK5ZYNsJmxaCPLAMtz4zN2OXVQzhbJzEfyRKEqxCPXjlZk99OtT+SRCUgAhKQCe54/Kz9HW/s8PfvADXHrppXjxxReRSCTw29/+Fvfeey9Wr16N888/f6GP8W1X0Wi0p98vMwK95XGT4E5WX9TFQmYdEHoU6bkgyI1QDRZF6YaDbH4GwkMOIVk0OVearjnQsLDYeAJy8rAPgw+yxkyMUhChUAjBoHl8nVi9keuULJouFgAQ8cCNBACIkFxtE0GmIR9SoKmQi4oy2fQwztkgBwRzl8GoGj0vVIav+yA+/fTN2DK9DVUA2zP78XJmL4yqgRNjS2FUDZoaFeb9OGVgJeKCAs1Cl1VDgwQBi/qGuz6GZuOXBmZo9S4WgD3JE1SM+H+KzORAfj7E2Lx1K9JsZvOmiP6eX5/93bxacggBE8Eo9UImiKCbSI/1SSc+qGzkdC9Wb24x062eR4qiIBQKYcPwCvxhahd0w0ChUGzpxuDmYuEFusc7Qpl6i28G7G18swn0jn8MOH2Qe/Ftpil6+RQGFMJBJiI9CT6fD6OhfiSLWRTVMjRV62gR2WgMV1wcLLqhV5Aizfzi6LC9k9ikmVdZDnLQXDyPhhPQDYOG5wBOBNkra7SFKrLAqEWQ96QmsdTjgBBSvfZYpNo6s9PT0/jrv/5rBINBrF27Ftu2bcPq1avxta99DXfddZcnB/LnVL0KYoqMxdsxcVNJShBk0pxEWf5ql+gQtXnT1ToOMplk/KJs+3OyQSFlJna4g6AQgPVCjmC+ZE6wuqY3CApxEelZX2O9oBdapFetVulxGbqBoJV6NBpOYK6YRkmrQK2oTScL0kQQFwvAXOhQoV4PTYwDQW5h8wY4rcYKlcbor5uDBWkKfD6fyYvnOMiC6An69aNzvoDTBtdAZCyHTh9cg2uW/zVS5TzOG9uIW8/9e3xg2akoQsWH3r8J6UoB5wytx5LQIE6ILUWynMXERPtxq40+s1u5BYWwNCCy5RqEeQ3+/bnf4KmXX6TuMwDDQWaDQrocw25jhhyDFxxn9tjylRIU0Z7o+gOROi9kJ4Jcv0VMtmPHw/3Yl+3d6k3T6ikWbjHTbPn9ptNFIhhB3B/CzuQBVKtVxzVyK13TYVQNS6jYWCPRaTlQWQ8Cd8iC42B2DqMh7+gVgHccZIKksh7I5L71+XwQJRE8x2EiMoC3MiYA0QmK3GgM2xZvjECvB242WfAtjg5hTxuhUWQOmC6kKILs8/koD5lUuswEcR2FyCtbrEiPeCEDlsUbiZj2mH/s1XzfVoPc19dHjZeXLVuG7du3AwCGhoYwNTXlyYH8OVUvliy6rmN+PoktW5/Fgews9u3dj2w2R28wMtmYHrq9CbwId6ykVVyS9MzPEKwR6TnTpAiC3NkKV6BpejaCrGoqo8gvNPdB5m2RXmaBRXp2xG4ZsiBC4HhwvGm9I0rm/0dDCdvXtQkaZuim2ClZyiIeMNG1sBTo2a4PAKpgbd6aB4UArBeybTXmNtHVxmyzvwvYQsCgR1ZvY7FByOChVnX44INqaAjwEs4+5S/w23/4Hr51/uewJDyIb5z9aWy+6iasX7cW//Gxr+BzK8/DgBDBu4bW4OZNVyGktJeO5lbNzptOg0JKDWzeBGSyWczvNyeKQ6Uk7tn9JLb9cTt11+BZikWlN46w7axgN8gELVQYjrNXHGQnguxM06tWq+ZC1zCQrRRdJ3gi1BuPDOBApnehnlvMdDORHilyf2wYWYmtk2+Yn6+J3Rulmagl+AXJfA5wnCf2W47UUuIt3AMHmQr0ct56IAMNfJC74iDXi/TY68bykHenDgEAyh0I9RqK5IhAL2ML9HpJd2PpIK1SVQ3DzBEoqmWoukYRYgBYN2D7IQNkh7bzIK4jUbYXchglrUIpe3vSk1gaWxgE2Svbu7bO7KZNm/DlL38ZW7duxZlnnolf/OIXePDBB/G9730PixYt8uRA/pyql4t3cHIKr25/DT998zFUAfxyz9N4fdcupFImUkOak4gVU9zKw7ZZsc4HxOatNkkvINpbaWQCNKpkAuzO6og2yIEITdNTVQ2GYUA1NFR01dVCy/59O00wVVpYioXrtirvFJMsig5RR5FGD0fSQOQqRfh5kaK8EY+cLBzXskXUNGBf5xDjdOB2H7EIMuEgsw2hW2xyL+jX6OgwkuUclilDOHNwLc4ZWo90JY/1a9eiry/umhqVSPRh7epVWDWxFFI0gEg4TJuEbqrZ+GWDQlw5yKKIc++8Gl9+9Q4AQBWmcPCD911Lk6lYkV5O7c1lwg1BJmhhSLLdRXq5JgbDc1YYDnKCSdMj1qCAOVYUyQ+B4+s8XMl27DgbFtIDv5QiyIUM+oLmrkw7mohQyFygnjh8DF48RBrkxoEh9jiw7fS8cLAAvPMWJkUt3rKzGPFQoAfYxyoLouVsU+2OYmGl6Jkx09G6Y7QbzxHbyaKD+6RVg3zQETPdffPmZygWe1vwpQlqPp1PYSAYdRxjHYJcsgGoTndoj0SJkuluNBLux6GsKZzfk5pcMIHeYW2Qv/SlL2HTpk2Ym5vD6aefjgsvvBBf//rX8cILL+Ab3/iGJwfy51SZTKbr3z3m2x/H5S/dhufnzZSnLdPbcMlz36OTK9nWFnkBIi/2hNrZPsgVyDyhWLhwkK33IAM8Xykh0AOKQmyfTATZbJDJg8vcxg1QKzG3clskeBHP6lY257pAJ8ZSyUTlCCqzmElSarRdTPjH86Us4gFbLe+My+69QVYZikVbCDIr0nNDkA3m87sgyHQhxdwnvVBFgoEAbt50FSZCA5iQE/jcyvPwnxdd19RyiPB+B4MxzBRMlFbtoUFuNn4JLamglhgOsn2efT4fbtxwsUkTscIfJE7ApuWn0GQqtiFSdQ2aoVMrtU7LmaRnOd1YC9Cg2DtCzf5uQS07GuT+oJNiQe7xFBMzXdusEnQ7xgexa+4APvbTa/HIs0917V2tMsLXThBkw/pMG4ZXYOvkGybFoimCzDjZeNy4UMcgRu/Ry/Vieba2g4U3CDJg3r+cj4PECyjrnXshkxS9bKUAkRcQEGXLwtS+V0hD1a0XcqMxXGYt3iLdp+iRkiSTLy3rHPLlIj7yn9fg8Refw+zsXN3Pknt1upDEgCXeJbSSYweWYMfsW9T21KHxOQwuFr2WwwvZsnrbk57EkgXiIPfSY7HVVoMsiiKuvPJKnHvuuQDMhvm5557D//zP/+Ckk07y5EDeqfbqhhM+jdMG18AH86EpcQLeM3GCI/aRDJiIHLR9gLtAUMmEXGKDQkjUNM80PhaqQVC5DMOP6mbw2hxkO02PrLpzldYIDcdxdJEgCSLyaqknJL1Z2cp1G0HmLMSQ0F4cDXIjBNnibieLWcT8doMcloKUg9wTTcTqrcoMxaKpiwVBfiUbxXQ7f+S+ylZsBJndMSDbfyzFoheBUblcRiQcRtZXxpnrN2Lt6lUYHBho+jvk4TygRDHDIJoLUaxIjzSLtef5hFVr4edEaIYGkROgGjr8nOhIpiLIarDHMA/WO7wWQVYkP722PSHIVfs9HBSLgFOkR+7xRgI9wNzOzmSzmHtrGslKDjvzh3Dnrsfx+xde7KpJ7iRmmi2/32zKxsL94DgO+zIz0HUdRctis+59rM+WZRbKXm19e+UMQYqNmaYeyB6J9ACbVtUtD5mm6DEhIUINb5x45jq8kNtokPfu3Ycf3/NfOO1fL8O9Dz/kuKdY+7uDjEivF4qFz+dDqVzC9h2vQ+QEvFmYwj27n8TTv3+u7n5WKzaCPGQ1yEowCEEUEJaDGA7FsSt5EIBTpLeQMdNelcMLOTuHglpCppTHcKjPXED0cI4Xstpe4j7xxBPYsWMHyuVyHZJx5ZVXenIwlUoF3/zmN/HAAw9AkiR85jOfwaWXXur6szt27MC1116LHTt2YPny5bjuuutw3HHH0e9v3rwZN910E6anp3HaaafhW9/6FhKJhCfHeaSqUqlgQIlBqPpQRRWiz1TrK1LAOblyPFSoiFCBV6Ir1I6Nmm5o88ZQLMgANy3enI4GnRR5GLJpekRY4kzRa/zaHM/BMAzKQ+7VJq1RuXNwrehcBkG+741nADT2QiYTebJkO1gABAX3DkFuJ2qa/Qwh0Y8DWae5O1ut4nVtq7feLcsAFuGZwUTEDJpotQVK7qeBYAwz+d4R5GZFqBAFRqRXe8+tW7MapS0aPrT8Xfj4+nfj7j8+iumcM5mK4znouhnUkquUEJEV6LreMV2JOr+o9RzkkBhoauHX9nswvGtWpBcPhJAu5cxdBo29x9kUPee58fl8OPeuq6EyfPst09uw5d5tkH4tdORdTRbFZU1FSat0lG7n8/mgKEFksznTD3nydSyKDiKfzyMQ8Nf9vOFAkBtbUHZTTkvN3nyQSfS3UTUwmZ/HSNgKqvGIYgHYz5VuG2QSt+zkHzuvGUGQ+3gFe5KH8LGfXosvrr8QQwMD6Otzjxjed+Ag7n/ot7jr0NPYlZ/E/33qHsgah7845STE4zFqfzdbTCMoWmmQHtjfnXn7F6Ea9oJ8y/Q2bNm8DdKDzvuZCvTytkBPEASEFAWpVBrrBpZi2/SbWJWYcGh8/hQoFmQuHAklcDA3h73pKUxEB8FzHCTLleRorLaWuDfccAMuvfRS/OpXv8KTTz6Jp556iv55+umnPTuYG264AVu3bsWPf/xjXH/99bj11ltx33331f1coVDAJZdcguOPPx6/+MUvsHHjRvzd3/2d5VUJvPzyy/jyl7+Myy+/HHfeeSdyuRyuvvpqz47zSFU6k8H42Cjeys0gLin45oZP4T0jx2OukHZMrgKDINsitc4RMyfFoonNWw2P0TF4u0GQeRtBJhxk0qy3crCofQ3WyaJbu7tm5co9JIgPw0HemyEUi8YcZMDpYAF442cNmOfPTDvT2kKQyU4Bi2C6TXJu8brshGJTNez7pFs+rWEY0FQNRa2MVClHUZZW6APZUh9gKBa9cJAbFeFb6oZhBrLUxLOTIslUXz/rbzHIh/HVMz+JX19xo4MmQhZ/IZEVSXZ+3ig/mHFWEBkEmVIsPPBBzln0J1IiJyAsK3W7QG4x02z9ywmftXbJzJI4Ae9dstGxS9ZOkUXQfCmDvkCYxky3OxkrRKg3vAJbJ01KW64BD5ldKNb6gfdaXrpY0CbQSjgMCN55ILsebxcNPY1bLqTsmOkamoMgCMgXCtizcw/g82Fn/hDufvMJPOOCzJJabtETiUXkI1Pb8L5fX0PpidTBImPTK7xA1m859TKcNrgGvPVMlTgB7x4/vu5+Jt7PbIqeJEnUMnTd4FK8YvGQ0+UCgyAf3SI9oDZuetaKmLYcLI7CgBBSbS097r77bvzLv/wLPvCBDyzYgRQKBdx11134wQ9+gHXr1mHdunW45JJLcMcdd+D973+/42c3b94MURTx5S9/GRzH4atf/Soee+wx3H///fjoRz+KO+64A+eddx7+6q/+CoDZeJ999tnYu3cvFi9evGCfod3qltOUTmcQCYexYnAC40IccSOAfzjlYzj1pA3OyZW3RWqkOeyGg+sm0qMIMsst1Zy82l63f2wfZJtiQSrraJAbvzYr1Ev3sEhoVSzFgoSXED4V4Z8NBKMoqhVz+xVBaJpW1xjYkbgZxK3mATApFr1ykCn/2NrS53yteeFUKMZsw7cS6dFwBGZRxCLIBa03BJma+GdmMRpKgOc4Kv5oVsTWi1AsCIrWbTUav3bypBmsw/m4houQVslUtpNFAPlKd1ZvBEHVDQNFhvJB0DhFbH5t2y1bmFiCIjnR1X7LC7k/GEXJsqdMl5mQEJcxvHRoFPJ+HlUAvI+zvKt5xy5ZO0V5/cXO+MeAeY391nnaMLwCd2z7HwCNG2RKsVgQBNnmIJNnQbcNMqVX5OY890AmReYGmRe74iDbDhaMxZvLQuqcn32pHpn9zTZI97vvNHznpIvxsz2P45npHTBQhcQJ+IvBVTQ0iOxS7s/OYCxs0ra82PpfMboI8i4eetWgzjtClau7nwlIMp1PYsPwMQDMsUqEq+sHl+LOP25BtVpFltmlPdpt3oB6L+Q9advizWv+MdAbb5yttpYePM9j/fr1nrxho9qxYwcqlQo2btxIv7Zx40Zs27atji/40ksvYcOGDXTy8fl82LBhA7Zu3Uq/f/LJJ9OfHxkZwdjYGP3+kaxD6Vl8+CdfwWSmnqTfrDRNQy6Xh6preHZyB/7m1PNxykkbcNYZp9UJlMiDOdxjyIRjwrdEej4q0iNR03aSHikHB7mrBlmAz+dDRA6iUCk5HoImUtm6QWaFepmSNaksgFCPTfcjE2Nfn7ltSVKYfD4fFkcHqV+nm1CPRZBrKRa9RoazVJl20GOASXMTAyhUGiPIGkMxcYvXJc1rkElt65Y/ScRF+zIsvaL1BMbzplg0IMiQeBGZcsFskrvkIcdi7oJA2ihqJQSFeg/kTopcH7OJ7U5IxzauQUGmDbtAG2S7+e7FNsxh8ybWNMiMFzLxb2dFem6T+9LFi5Es5zDq78MHJk7GOUPrMZNPOXbJ2imyZT1XaD9mmlQsFoMSDMLn82FZfASZcgHT+RQ0VXPVETieAx56IAO1SXo9IsgWSnkwO4sRywPZa/4niyB3E2zi8EC2qAZuDc93T74Epw2uAefQ45zYcKdhNDoAGTwMS5ChGhp4g8NYzHyW2AEqcxjzULy4cuUKJMs5nNx3DAb8Eet+Ttfdz3Z6YIqK9ERJRDBgitKP6RvDodw8pvMpSIIIkRc8sxJc6GLT9A7m5iwEeeEiphs9ozuttp7en/rUp3DLLbd4lnfuVjMzM4hGo47VRH9/P1RVxfz8fN3PDg4OOr6WSCSoJ/P09HTT7x+p2vvWPnz2+9fi8Z1/wGU//GZHopN02hS7PH/oNSyODmNQiUGSJVc+nNPFgYSF9MZBrkWQaVCIYAeFkMp4sLrlBR6cj0PUH0KqlKNfZykW7YjMnBQL7xFkKlJjxDnZrI16O2gWRKjnwkOmDXIx66BYeBEUQvnHmu1ggRbPVIr8OhBkNxeL5vG6LMWiV74roafsy0xjvEMTf0IrGAhGGSeL7u4H4glfWzYXt4xAA4Feu2Uj+HYT2+kCz7ZVY8YMb6PaEi+giioqutq1Swb7PrlK0ba2sxpRNk2PIHROkV59E9nfn8DNm67CqQMroVd0fG7lefjJ33ytqVOJ63FRZxjGA7lNZCmZTJqLqkAAnI/DCcPHNPVDdlAsPN76JvdCQGS8hbu8VmUXGzOv0DZSnEOk597QT01P4877/hvHfv3juP93WxxzoebwQHam6LG1bHiCNrychcw222kYHTEtIgO8hIgQwLHRRUiWs+hLmM0odffIMAiyBw3yyNAgbt50Fa5Y/T4kyzl8ZsW5uPmCKxGLOdPeHBxkxvuZ4zj4/X6InIBViXE8tf+PPYngj0QRL+S+QBgltYzts3upZkX2OCQEaPyM7rQadi/vfve76cqkWq3i4MGDeOihh9Df31/30H/44Yd7PpBisVg32ZH/1xrFN/pZ8nOlUqnp92trZmbG8X+/3w9FUZDNZhGJRDA7O1v3O4lEwhJsBFAul1GoSVkKBAIIBALI5/MIh8Pwf+EsVHSb9/jr3c+i75pN+P/Ze/Nwyar6XPitPdZctU+d+fTpeaa7oYFmaGY0YjtFYmI0aojDBwqa4Qb1Bm6ihO8x4X4fIiR5fDT5HHK9agQFAyJEsRMZFLBpemBobOimpzPUOTXtqtpVe6j9/bFrrb121a5zajrA1X6fx0f6nDq71t5rr7V+67fe3/vKvIjSF3dD0zRomua5RjgchizL0DQN2VwO2VwWPz70JC4c2YBsLovBgQHYto1CoYBYLIZSqYRKpYJ83vmsZHOYL+VhmiYK+TwGUwNNm41AIIDBwUHk83nE43Goqko3QnPz86hUKtCMyeKM2wAAIABJREFUKnhw0CoaarUa0uk0arUasrksqqZOM4NqUUU0GsVcMYe4FHbaIAkQBR7RaBQ8z8M0TYiiiFzOuzngOA6pVArZbBaKoqCiaVCLRSSkMF6dPQkhaSMcjiBfKSLISTBMA/l8HkEm+8HzPDW14XkealGFBB4z+TnallgsSvm4PM8jn8972iEIAhRFoe3IZrNNgXUikYBlWQgEAvW+yWK+mMN4MIlsLot43Fn8s9ksJEnCiVOnYBaq+H8e/Q6E2SouPutcnHvOdpimCZ7nYds2ZmdnUSqXMatmIFpAsVhENBoFZ9SgVsvI5/MolUpQknHajmTSKSwRBAGWZVEOPoEkSYjH43TxSWfnIQY4ZHNZiIKIdDoNRVFQrVYhiiJM06Rar5VKBdlcFrxpo6RrKBaLkIMy5ubmPIvz3NwcIpEoCtUSwryMSqWCTCZDaQ3ZbBalchkhQUauVIANG+l0GnyDBnM740nTKiiVSjhRSGNETiCby0KWRTp+BwcHoaoqIpFI03hSCwVYtRoGwwkcz85gtTKG6ekpqndLMDg42DSeWBAb4mKxiFAo5BlPmlZBLpeDZlYR5ETYsKFpWtP8EolEIAgCDMOALMtNkzmRalSLKiJiEPOqM55DQRlmfRFtZzyFwxEU1IITuAqy87wkCel0GvlCAcFgEGFRRkbNIxVNYHp6ukGBxB1PiqIgl8s1FTfG43GUNQ3VahUlXYOtG05bQyFomoYYL2OunEdBLSAei6OgFjCrZiEMAtlcFrFoBLFYlI6nQCCAfC4HyzIxOTiCo4UZTC4bRyQcpu3IZDJN2fREIuEZT6qqIj3njPuTmVnEeCdwJ2Nkfn6+KWhjx1MwGEQ6nUa16oyDVcEh/PuzP8f/9+QD+MttV+O9b3snLMukYyGdTiOXz6NQKUFOjMEwDei60dT3siwjFouhUCggkUg0jSfAOYHSNA2yLNMEUTaXhVU1kNeKKJXLiMWidM7uZH2anp5GLp/HsdwMtoysRqlUwsTEWFM7gYXHE+BdnyKRCObnnVPRXC4HtVgEbwdQMXUUi0VYpgVVVVGpVDA7m8ZDP92N++afxgu5Y7j90X/DpwoaLrrgfNh2DTMzsyhrZUwV5hCsOe+jpmmw7aRnfVIScaRLOayLjCEZjCAMGbOlLBLxmOd+yBpnmSa+tOuTeNvdN+OS5EZMGTl8bucHkZmfh6YtQ7FYRKVSwYlCGpePb0U2l0UiHkWtZjWtT/l8vimeiMUcnjsppvWuLTZWTS7H8AEFXFxCLBbF7OysJ5ZKz6Yhh4KY1wqI80EYpoFczlnLKhVnnVmfnMATJw4iKjjvc7lUauo7dn0KBAJNkmeiKCKZTHY8nlhIkoREIkGvsdh4ctYnFVVdx3A4iWOFNAaFKGzYqGgaIuGw71hotT4RLDSeyHNpHE+N1xhaQAWpZYD8iU98ouUfLQVkWW564ci/Q6FQW58ldsuL/b4RrR4Q8fNu9ftYzMn0CYJACzoaEY87Ac3/3vVZ3P6L7+FXmZdh2lad/7QRf3X5B8DzTgDZuFgThMNhlEplJBIJ/GLqefzLOz8NJaFg1epVCAQCtJ2xWKw+SDnouonhRAovzZ+AIAiQg0HwPL/ovZL2AsBcJotqpeooZcghhIIhiIKI+JBz31PTs440Ts3hOcaizs+1mo71ieVQkgpGhkeQSg3Qa5KNS6t2KIqzm08mkxAEEYORJEwxACXp/FwzdaTCcYiCiOHhId/rKIoC3UgjFo1hJJ7CvFaAklSQSCSb+E6LtYP8fysIggAlqUCHhZHkIJSkQiX4FEVBJnsEJ06cwpSeRc4s46H0PowfHcKmjRs8GbF4PAFJkqGaFSwfHKfvwvjAMAq60/e80Nx/bPancZy4147j1NQ0QpEwgqIMJalADsr0WiQocgpCnMy/ruuYz+RQEWooGxVEo04QMzg46Ln2yVPTjr2uUUEyFAPPcRgbG3WP/QIBGIaJiBjEXCCHAAJQFMX3uS82nmZm04hEIjhemMWlW7dBSSoYHxtHIuG+s+T9bRxPZa2CbDaH4YgC1aqAC3CIxxOed5OgcTw1IpPJUBoNex9qsYhkNoeXp2YRlUMIIIB4LNbyHVtoLJw8NYVYNIaIFILJ2VCSCpSB5ue20DWKpRLisTgOT80gFoxASSoIh8MYGhrC4Nw8DN1AVHSuLwoiUqmUb0aeHZN+EAURsiyjbFYxogxBSSoYGFCQyWQxnhzCVDGDeMzpl3gsjpJVxbLUiDM/jI40fefo6CgqVR3LBkZwMHsMsWgMlmXRdpBn3+p5Ak6SQzdM6LqJeaOIp069iD8p78LWNRsAoKWiERlPmUwGQ0NDkGQZ2VweI7Uo7s4fRdmq4u6jj2P1M8s8ZjRqsYRAgEPR0DAQSUAURMSiUc+7yYK8Y43jiYC8u6IoYmh4CGqxhEF1ALWAI/3FXqOT9SlfUBEIcJjV8lgWG0IkEnGCnbh/O1uNJ7/vIe0olTUIgohYKIKKqdO/I+Np4tbf89DmHpnah0em9kH6icMdTs9nnI1jtYTVw8sAOP0VCAQ865MoSvind/4F7t33X/jPU/tx7bbfxeaNG7Bu7dqmNtZqNUQiEeiWAd02ce3F78YHfvR3SA0PISwFAQRgw3lvThXnsXFsJZSYgvHxcc+aQd5B8uwXAtsvuXwBpZKj9JM2nWAzGAzR98MwDCSTThFxXA4jGo6A53mMjtaVgXgBtg2cM7ERtz72v7B5cAVtz0IBXmM7WHQ6nha6xmLjCXCcl0+emkKEk8EHOLx8/BhiwQj921ZjwW99akTjeGLnaMA7nlpdw/e7W/3iD/7gD+h/f+tb38KuXbuWVCZtZGQEhUIBuq7Tjkmn03Sn0vjZxl3T3NwcfRFGRkaadtXs718PSBbnSI3ZFgJw+E9hUYZoLM4fUtUibNvG/tkjSASjWJEYgSAKLe1yyQuVYKyWe9FQrZgG5SCzpgcc50ipOVyzKj3GLXgqbLs7AiIDSwm5Um+Ac4xL3HcWknkTKM0kQq1Il0QHmS1Sk8jAc3fBm7/0x9AbC0lmD0D65T94CkmoDnKDigXhkfdiduLHQQ4swrFgHfBaublRe13d4bjydf1plhPnqmEwOshd8ieJCsKJQhqTMYdC1S7FgoyJoXACsyUnY2t0Sblp9R6xLnqhBZwe24HrpsfK43VHsfBTfqEcZynU9fXp99Tc9yAqEq1lp19SoYTHAQxwinhb6SAD7rHxQChOx343dBjTNFFQVTzx6gHMVPL4+599C18d+e/A8OLrAHl20UgEb/63z8Cw3T5/ZGofHrl3H6R/d4vBPI6SdXrZUjjpNSoGdQqPBnKdZ9uLU5wf/Oym2fbevv3D+O7xx/CL2Rdh14vlLhzdhBu2vwsAqBbxXDmPQapi0fyeCKKAeCyGM9duxCPzB7F544aWwQ9VNKlT2GJiEGeNrsXPj+3DrrXnI5PNOvxy00BGUzFc5wD3y0AlGAyiVCpjFXH+W+HMZySyIe83S69gtZ/Jfa2MDKGoa3h+5iie2P8MLjrzHPyfAk2r4PkXDiFfLcOya7jnyOOIBCRs2rC+Y/rUYujXWt/WCL733ntx2WWX4SMf+Qh+8IMfNKXb+4FNmzZBFEVPId2ePXtwxhlnNPFYzzzzTOzdu5cu/LZtY+/evTjrrLPo7/fs2UM/PzU1hVOnTtHfvx4YSqWQqRRxycgZCPEy3jx+FuZKuZYZBha5+lHN7qN7ccVKp/K91Y4fcCfmXiXC3MBK95WsCrQo1PPoIHfJkSIBgqNk4c9BbqdIj+UgL02AzOqfkna57+vXf+cvsXN4EyTGNe3i4c1NhSSWacG2bWQrKpSQEzwEAgHIggg+wKFi6l2bnZACGVYDedEiPVZ9ooXRCmsdHG0RFLCOfL1YTRMRf8MyMVPKuYt7mwVGksRKvRF3t/5KvZENjGa0NglpF2Qj6pFi67RIj7joGc1jxiMj16VKRuP3lIwKwvVgi3DvWTc9gtwCOsiAqzSRCsUpf7kbBZr1t/8RLvvuX2Km4nz/I1P7sOaL70fwzy9t+xo8zzPmTA78ZOc884DUvQa8H1jFoIpFnPS6H0NWrYaZUoYW6fVTAxlooYPMFOmNJlKQwcNmi+UsYMXgGEzToa3kqkWERAlynYPrN9eTzVUqHEeGMaTxA9kMO4YxTgLiLat34OFXfuX8fN6hSk0V5zESSULg+LYUctoFMY1q5fzn1UCuB+dM9jUYlFEqlTH/6izEAI+iVcE9Rx7Hwedf7Npl8rXGOV+5Fp/Y91WcqjjPevfsAfzJL++iMntvRLQ1e3//+9/Hj3/8Y1xwwQX413/9V1x00UW44YYb8OCDDzZx9LpFKBTCu9/9btxyyy3Yv38/HnnkEXzta1/DH//xHwNwssnku9761reiXC7j1ltvxeHDh/F3f/d3KBaLeNvb3gYAeP/7348HHngA3/ve93Do0CF89rOfxaWXXoqVK1f2pa3dYMP6dbh2xZvx4ZVXombX8O7x8/HRyTdhxeTkgn9Xq9VQKKiwbRu7j+7FlSudIH+hwJpMHKxNcTcawCRjWDX9NV29WsgupYXVQe52kaBuesEYnfyAhiK9BTI0JDBPBCPI96DksRhceSeN0QF227V+YgWCAYEeKRo1EyJ4LBsYca9RXxSKuoYgL0LiRfA8T+8hJvcm9eYptqR61gv/DeHBChwPiRPoQsd+P2uz3Ur7lWyiWB3kbu6BSLydKs5jKJyAyAsQRKHtAJQEXcMR1yyk3++DR8VCJDbTXRbpcSSDH0JxARWRheDJIIvewlbqcCi5Osu9yAjatu1RsWAzyPPM+DVrFoq65qt4QiDQDLKrg96NLN8/XPBxXJBaT/9NVA7u+/2/7eg6a8Ym6wEdKzvnlenyK1btRgPeD74Z5C6UYAjtcK6cR1yOOJtvge/6HW2FxXSQR4eHkakUEQAwHlKwNbECmYqKwVSqwUWvOZPKgmSVU6E45uvyjVaLdY4WbGquosnlK8/EnlOHoFbLdGydVOcwEe9fgR6B7GONzZpGGayCRdhfueOK796I6/f/Mz3N2D17AB987PY3dIDJ4mtv+m9NyaJu9M1fS7Q9MiYnJ3Httdfivvvuw/3334+1a9fipptuwoUXXohPf/rT+NWvftVzY/7qr/4KW7duxTXXXIPPfe5zuOGGG2jQe/HFF+PBBx8E4PBJvvKVr2Dv3r24+uqr8cwzz+CrX/0q5Zls374dt956K7785S/jfe97H2KxGG677bae29cLBgYUnHXmVkiShEExhjlDxeZNGxAK+/NGCdRiEbVaDYezJ2HWatiYWg6e5xFrwQcD3MXPIxHWRXaINQpxKRbuK0MW8ZDQmEF2KRbdLhKkalkJRZFhKRY+2TA/sEYhrllK/zPINZ/MEfuM1qxahZxewhUjW7EyOowzk6uQraqYGB+jn2H1WpV6doMV749LYRR0kgXvXs/asExILcwr/MAxWrxFalbhfr+f9mujKoFHxaIHiSqiYHGikMZkon2JNwKPmx4xC9H7nEG2l0LFovsAlijXFH2kxzwUmh6k3khwrFsmuEAAEu9k3UhWcjDsBC8EhWoZUSkEvp4V9MvQEZnHmBRG1TRQNR2Vjc7sig2gbNKNkcg5rqPJSKwtWhuLNatXIVstYmVkGJeNbMEVI1sxW/TKzpEAzNks9tcCmJXU7MaZjoDImJ0qOjriQP/pFUCrALk+BxkGYrEobrnyTxARgrg8tQURKYQ7d30KNmyaSV1MwQLw2rEHAhzKRtUx6vEZJ+S0iLUcj0lhnDu+AbtffZZ+7qQ6h2V1dY9+0SsA90RlZXIUR3JTsG27IYPsaiCzLnos/vHCj2Pn8CYIAee9kjgBb17eWtbujYYNkyuRDEbqevw8jJqFqBjsWN/8tURHGlzT09N46KGH8NBDD+HAgQM4++yz8ba3vQ3pdBo33HAD3vOe9/TkWBcKhXDbbbf5BrOHDh3y/Hvbtm249957W17r6quvxtVXX911W5YCw0ODkCUJZ5xcjUBSRjwWQ7VSbcklBlx5t58dfRZXrDyrXqgQW/DohwysuORmkFvtrFuBKD2YNQs12BDqwbCHX0rtpr1mIWq1hHiwN7F8N4Mc95iFqPUFFlg4O+1uEpaaYmE5RWq6a5LAFgcoShJff+9NOPj8C/jy8z/C2uQEPrzjbR55Ppd/rCJZ5x8LvIBAAKiCyPVpns92Ag8Hub57b+fokNXiLekVDIWdAJ0kNmptZJD9HPm6CfJdDeRZVwO5A/1MssgSsxCg9XHsYmhVoGMxFIteM8isDnWpHxnkhgCZtCsqsXbTnY8PykM3KlTijeM5T5Cr1xz3w5Age+gVC9GveIGHbdhQQlFkKypGowNU9aMdlEplRKMRnCzNISXH8Jmt78HumQNIF7Nt0drYPh4eHMSduz6F//3Mf2Bf+hX86ZZ3YcvmTZQ3SRwUjZoJ3TJaWox3Cz9d4W42M64G8jyVeOs3vQLwtpdIdFKpyXqgWoSOZYkhfOyKd+Nd//Y/IAYlqGqRJrnmGJOQhWToBFGAoRtIhWKY1wqISEEYhtn07ElyZL5uGkO+5y2rd+BHh3+Jd63fCcDZgI8vQYAsSRI4joMSjEHgeGqeYxgGRFGkAfxsKYezx9Y5f9Nw32eu2Yjgy/8Fy7ZogBmRwm/oAJPFmlWrkDfKePe6i/CHW67Avx3cjdlitmN983bQThFlO2grQP7GN76Bhx56CPv378fGjRvxjne8A3feeSdGRtxj4uXLl+OWW275jbB0XiqQyX0yNoRX83UeUgvpOcCZePN1iZbdR/fixgvfCwBILtL5Hg1VQ6OZJNu22+ZU0aDKNCDzEj1yZ8E6PBEt5Iqpw7JtBOt/03WREnHTa8wge7JhC1hNk01CPYu+0PFbt2CDg6AgQeCc48rGQGN4eAibazXs0F/FdDHjbIyqbr+T4+NsRaUmIWzmPS5HaAa5m0I9spjqltFRBpke80tMoViNpVi4HORW7mH0KJ8t0uvieJg8rxOFNCY71EAG3EV2sH7kb9VqgGF2NCYITNP0/W6XYlGlRiG9FulFFtGhXghsgExsuRuL9FhueC+nTCVDQ5RoINfb7gYvCWTKKibissdFz69Aj0AURJiGSXnIo9EBmJaFdrdExVIJyybGserFYSSVKJZHhnDd5l2IhMNtLchsH0uyhHgshh3rt+Cp/GFs3rjBs8Ft3Ij0Mu/5gaWy9ZJBplbK6pzrotfHIJDAq4PckEGuB+nTxQxGowNQgjGcObIG//nqPuxaex7m6lJxjlnG4gGyKIgwdAMDoTjmtTyWJ4brSQTvm+IW6RUwGhmAZVoQBRGXrdiGLzz2LbpxO6nOYcuQ8370k2IBgErirUqM4mhu2nGXrEuY0QC5nMNwWPG97/Xr1qDyHyZ2LT8X71q3Ew+9ugfp0tIEmEsBRUni/utvx8tHjiCfL+CmSz6ANatW9b1AD2g9R3eKtgLk73znO3j729+OL3zhC1i9erXvZzZt2oSbb7655wb9JoM9Ztl9bB8ALGi+UiyVYJkWThbmMFvKYfvIOnAch1isNb2CwD1CDaGoa0gEI1SfsR24NtNugV6gQbvWm9lw7sNRsHAcqHqp4hY9GeTOi/SImkJQkMAhgIqlIxSQUavV+sa5cyvXmaCdoUYQkH5fkRjBkydfAODtd8KFzTAmIYIg0E1Kr3bTHstwykFePCikdsdMIRe7MJOAqsAoeDRlkEkgJrhOet3cA+uid86YwyvtZAHjOM7ldEthZCsqBsMJqiPcCVq9d+S+ykaVHuNyXHdFPiTzHhVDbpFehwFsjaFYNJ66sCoZRVoE2HnQRf6mpDMZ5HrbSfAyGI5jTstjIj64aIEeAWs3T2oQOuEhl0plxGMxnLRy2KlsRqGgYmR4CGdt29rWgsz2MXnPJmKDOKk6ARyb2PCahPRWnOwHMoZkXoRumbBqNfAc1/HmjgRhp9R5bBtx1vHGLGVf2htopliQuUyv16o4AbITpO9acx4eevkp7Fp7HsPFzWNV3UjCT8GCgPxukDGk8XtPTKZI74yhleB4jtIzLli2GT87uhe/t/GSOge5nkHus8NgMOgEyCuTo3glN4VzxzegWqkiFo3SzLpTpOe/MVCUJB644Ys0wLx5xcYlCzCXCoqSxLnK9iX/nn5tUNuKlh5++OFFP7NhwwZs2LCh5wb9JoM4xiyPj+BVQtRfIEA+duwEnn/xEO575QlsS6xAqVTC5LKJ9rJ/Ag/LsupKFiUkghGYZucBsiPx5p919KNYsAV6vXjEU4pFKIYsqVCumTBqFkKC7JvRbgQv8DANE/FgBIVKGaGo3LedJdCoYOFWrjeKnZOTg5X1zAHg7Xdy/JetqFCCrgUv6QPWEbEXioVuuU56HVEsGCkwNoPt2kxrC2SQ6xxkyXXS6+Z4uMpQLKiLXocLmCiIsEzLKdQr5+pHnJ0HyK1czMjPNcMt0uu2SJVuTtjsfaccZCazSeg/LgfZvf5U0akq7yYrSSXejAoiDSc7JMh1iqicMZytFJFsY34gfZIKxekJUrtFlZZlOcYWRhVHCzN4+zmXQhZEnLF5Y9t9zfYxmS+GwwpylSKqdWoO2WybngxyfxUsCFhJzaqlI8wFUavVOgoEyKZ8qjiPt67ZAcB1mOx3WwFA5iVULPdZAS7vf6qYwVjUoaJdvuIs/P3j3/ZsntKlHHaMO/EE4ZH7gdz/APOO+VGnaBKiXMBAKAbbdgrdc7k83rJ6B77/4s9x9YaL6y56ToC8FBlkAK7UG9x1wKTFiVkmQG4eH69VgPl/Orp1mmxEf8tXT2NBkExiSojgRCENq1aDXtV9OzObzeEXTz6FdCmHB6b3YH10DM+/cKjt4IJM0KySRScZKFJ17GSQnYmiMajyqlg4Az1fLbkayD0sEqQAIyqFULEM6JbhOcJsJzvtL/XWnX6oH9gitSijYNEog0ikyMbjKcxpeVRMHYZu0EWD2kxXih6KBWl/zCPX10WRnkfmrX0OMlvI5Vco5mbQSy1pL/SUgZdg1EyH096hrbFt29CrOqxaDafUeSzr0gZWpIV6CaRL3fOQW8lcevi4Qn+K9FiZt26tpv2UX0iWj+U493I6UdIriDRwb0kAnAolMKc5z9vDQW5DpnGgzi0F2tetLtVd4w7MHsGG1CRkQYQkSx1thNg+DgQCECURPMdhNKpgquhkkUmw59VCX5z+1Q0CDG2BKAZ1uqGhRXrqHMaWSAMZWLxIDwCmSxmMRpwAOSIFsXNyCx458gy9Bqvm0ErFAmCpU4wkoA+VzivzFke5XKImQRcv34Ln0kdxtE57TMgRR8GnhwSPHxoL9QBn00IKC8tGBWbNQkxyTmD7/f2/TeiXFPHpAPk1BJHuCgkSksEopouZejVrMw/5uRdehCiK+MHJJ6FZOl4uzyAcDlGO1mIg2RtWyaITWStXwcI9lieLKgErCM9SLKiiQY/HjLzgBMlKMIasVmybXkHAmoW4Shb9k/aiChbVZpUAFhzHQZREiJyA8WgKxwuzAFyZH1+KBS+4fchQLLqRJvPLILelYsE1UyzYTRZ5R1Rdc4+WfTZFhO4SEmT6nnSqSGDbNmbLWcTkMEKiDJ7nO15AyGLKKlkYfVSy8DMKaaQltQsPNcWsombXOt5YWFQHudKkzeu3+ekmg8xqIEcaxgANXhgtZC/FYvGj84EuMsilkjNW9s4cxvYRx1VtoULodkCoCOOxQZxUHRMqoqziKVZtcZLSK8j12Lm2k/7yaiDnaPZ2KYr0qD6+T4BMM8jqPEajbjHzW9echx+//BT9d5ot0lsgg0ypeEyA7Kdvzsq8EfpTKBhENBpBSJBx/uhG3PXzu6EIEbxw6CVofZKvZUEyyKuZDLJWqdBnMlvKYSiiOMGxKPRNg/k0usfpAPk1BuWjJkfpjpVMtCyu/O6N+JNf3oWfzxwEAPxsaj+u+cWduOR//Xlb38NKhJHsaScZKCLiXrEMmkFuRbFwNG5dikWvLnoEZGJUgk4lux+XciGwjoI0g9zHQj1PkVoLDi6Bp99zMwBcXq2bQWYCZFGg9xhjKBZdBTGsnnU9g9wWTccTRBEOshugkXYXqiV6/35BD0cL9YIo650v7oRe4RTodeagx4K66UWYALmPWsjknlijkG7HAKEQ8RzXddaQbGZYHWRapMdSOHrIILtZc43eM1nYRSaDTIIXr4ve4jKNDsWCZAbb66ti0alZeHb61zhr1AmQIx3Yy/qBBDcTTIBsNGaQ25gHuoUfr7fWAVWJzDWz5SySwSgkXuxIR7yjti6gg0w5yKWMJ0C+aPIMHM6cxEwxi5pdq6s8OIHsQhxkMqYHw2wG2fuekAytUTOdehw5igCcDG0ykUBBVbGOG8LPZ55D1ighXcrhpV8f7rsBBzlJHI0OIFcpOhljw6TUFw//eIFNwWm8djgdIL/GcPmoI3g17wRKfoV6/3z5n+GC1Hpwdf8miRNw+cRW/PC9t7b1PTzfTLHoJHvq1UAmWccFKBamS7GI9ylA9hgGaKqvZe5CYJ/BUki9sQFIKw4uAeWfJUapggnln9WD9qzGqFjwAtP+MNQ+OCLqtW45yP48WDcw0Ba012U3UmWzc0UGveoW6C0nIv5dFNBINIOcwGzdLKSfbnrknsomI/MW6H6K9Ui9daGFTJ30fE5eXApHqCeHQ4vJmjduCnrJIAuiW4NAzULaCJBt20a5rMGsWTgwewRn1TPIkR4zyCKTQT5FM8j1DW4bai69wlvv0bkajM4U6E0QBQux/woWQKtg3jkBsUwLFVOHWtUwGI47J0uhECRexBUrt+PhV55GVisiKjk/a+WiRyB4Thr8KRY0AaEVkQhGHQ3u+tqSTCZw4uQpnDO8FgEEUDQr+NHUHsRiUbx8xGuR3vNz4ThIsgSe4zCZGKGJErW+oZstZ6nN9FJk9k+jc/REcikWi7hW0jtdAAAgAElEQVTtttsgyzLe8pa34LzzzutXu35jEQzKEAURKxIuD4l11CFYP7ES4j7e0SAOOCL3QU7C+We0Z5ftCa6qnfNXWYpFK2kwEqSGRBlz9cmJzSD3yqEif+/YTasICXJHFAuXgxxeErMQWqTm4R7yvtlNN4M8gl+degmAuzEiG5cMm0EWeLoAOnrWhCbTPU9UtwzKE21HXYFnAjQS4LRy0lvIXpf8zCP11kUG+Xgh7RbodZFB9lIsHBWZbigrrb7baxRC+LjdB8g87xSZRiXHzGM40tkYtizHvrxsVJqK9FwOssxsfro3oSnqWlOALPgoDDhFeovrIJMMMhv4tJPtL5XLsG0bL82fwGh0AIlgBLzAU6vfdtHYx66SRQqPzL0KwM3KsioWC1GtesFC0mntgGwyl1oDGWiRQbZrlEowU8piJJIEF+AgiAIUJQFN0/DWNTtw51M/wPkTm9riHwPsKUUccy2K9AxKYXNd9EJB510VBAF/8tiXqDsdAPxk6ln8ZOpZSJyA6tmPdv8gfBCUZehVHavqJ8ibh1agWHTm9hkmg3yaf9wb+lWI31MG2bIsvPzyy/jQhz6EBx54YEmMGH7TIMsyotEoViZbZ5BN00QoGIQlc+AQwI3r3oU3jZ0JPiq1LelCsq8svaCTYIDaTC8gDcZmCkiRnpeH19sBBVuok9FUx0VP7ICDTLWQl8Zu2v9olfMVKSeKCyvZDHJ9Y2SZTiCTqxShhNzsmltoGe6q0JKA1bTuJIPs1UGueq7FtkXV3T73451z1G6aMQvp4D78TEKCHZiEEJDF1lGxyNev3XkGubVRiHNPrIpFL0fYrt00wxNuM2vISs5JvEg1ukm/8x4jku5k5Lzf4wbhpL8JhSIVSlA3PU8GeSEdZCaD3Oro3A+Ef/zszGGaPe6Gf9zYx2T8slJvTUV6uuYq+CxVBpml23RyCkNd9Oapi95SaCADrYN5r8RbnQMtilCSzpp27tgGpMs5PH3qReqit5gMnSB4qTi2bTfJvJmeAj0nAaEoCv39d95xEy5IrfdYIL95+dltn9Z2Ao/ldJYkyJw50VGw8NdAPo3O0C+jkJ4imEQigW9/+9tYsWIF/vZv/7bvu+bfRMiSjIJawIqEy0FulHojVdj/1zlvx8bB5fjDy3bhH37/L/HADV9s+3tY/mqhC/4qq4McXISD3IpisdAC2A7IJEEyyCyVoT2jC+czCTmCfF1LuZ+bOLZILSa73OhsNtv0WTaDfDQ/Q61GTdMxqyjqGoK8SI8VnaNFp/0xiVWx6D6DbNTMlgWXfvAW6TUf8WczOTy+fw/my3k899JLKKiq7xxApd6ELjPIhGKR717iDWDc9NgivS5ULPz6F3A3lY5RSG8cZOdvGZm9DnnCCylYsO2KMkYk3ZjQuBnkCiKitz6AlWqb11S6CWxPB9k9PSpUSrDqx/OLFSmWSk4yYO/0r7G9zj+ORjsPkBv7mARqEwzFojGD7GyUlyqD3FoZoh2Qtk4xChZLoYEM+LfVrtkNJiFukC6KIqLRCHiOw+XLtuHrz/wYB6dfwRP7n0G5rC34XTzvbPxCogye41A0NMcUihknJGBmbabL9TUWAK48bydicrhugSzAqFkI8mLbp7WdIMhIvR2pr/8EsyVXuWOp+ua3Ba3m6E7RVoB8/fXX48EHH6Q7ndPoHrIsIRFPYDSqoFAto2xUYJmWZ0CTSeFg+gi2DDsuOeFwqKPvaXSSAzrMIHuc9FpkkCkvzp0IWYpFL0YhgJuFUeoZZLVTFQtPBnkJOMiMvFNccnnXbHaCgLUa5QMcMpqKWq1Gq6Uzmgol5BboedvP0GS6oFh0m0HmFtDizWSyOPj8C7j7yGOwAdx/7Ek8/8IhFArN8josf5IGyB0EY9VqFbZt44Tquuh1o1HKbrgK1TKMmtlW0NUIv/4ldsM1u4aK2XpT2QnYDUrR6EzqzfLwj5uDdbppEYPQjCptf6dwlTsqTRQLwh+VBRFBQUK2oqLMKmosMIYDgQB4gYfA8YhKIeSrzgZ3sTmsVHIoFs9OH6YBciTceYDc2MeiKFJFnWpdctKyLPo/wJkHiNxjr3NfI/ySEX79lc3m8NCju7Htc3+Ehx77T1popjMuekTnt99GGI1tbc4ge130AGZMJpMoqCrW2Clk9CIKpoZ7jjyO5154cdFiOTJfEsdGADCYLLIfxWJgwC0QVJQk5EQY79l4Kf7xiuvxno2XAEF+SQw4iBfCSiaDTDBbZigWi1BLTmNh+M3R3aCtXli9ejXuuOMO3Hzzzbjyyivxjne8A5dccslpnkwXCAQCKGtlhIIhTMaH8Wp+BpsGV6BSrdJKa7K7PZg+ip3LzgDQeRU2maATcqSr7CM5yq0uoGIRYKymXR3kMhLBpeEgx+QwPXprh77heQZLGSDrZTeDLPCYn59HKpVq+jyxGl2RGMHR/DRS4Tg9Es5WVCQZiTfA6wZo2TVnsyKIHbsBujJvrg5ytyoWJPAau+Vd0Jmiz92zB7F79iD+7ODXUL3Ty9tjA71yhwVhpmmiVqsho6mQOAHxukZpN0eQRD4JhomBUAxz5TzGoikYhtHRcbNf/7rGOjok3tHM7VUhwGMZ32kGmdBfDH/ll0Ag4DwPjofI8aiYOkJi506TrPYzoViw0naCKMCyLAyG43glO4WYHKZFUott0gRBgGVaVOptIBRf0NhF0yqwLAvHC2lwHIexaMrJLjK20O2isY+JFrJe1TEeTeGkOocNqUlUq7rHMGipZN7Is2rk9XrbnMFDP3kEX/v1T3Bw/ij+8Rc/AF+1ce7Z2ymP3+EgL3GRXoPzX82ugQPnZrGL89g2sqbeBqcvE4k43vy9z8BgaD67Zw9g9+wBSI9/qWlOYSHwAnToSIVimNPyWJEcgWEaCNbtpl2KhYrBeoBcVFUAY/Qa999wO/3va/H+Xh9BS7CuqifVOZg1C0J9XLIqFqczyL2h1RrcKdqaCW+88Ub85Cc/wTe/+U0MDw/j1ltvxUUXXYS/+Zu/wZNPPtk315LfFpCXfyVTyVplCvXKWj2DPHsEW2kGubMAmQRZXhWLzo1CKoyKReOC5mpzulkNlZH86tVNiq1k70rmrf4MEkGWg7wERXq65rnnVsFfkMkeEP45ORLOVlSPSQgBsUTtRcmCtZruioPM8FTJovzli2/AzuFNEOpUDYkTcOnoGfjWWz/bdB2PigWxm26T70roR8fV3gr0CIh80lA4yZiFdMZL9+tfVgOZ8o97zCJ6KC4dcrdp4Ooj8Uavz1A4il2oZDifb12kB7A85DgOZ0+25aJHwBZgUR7yAko8ZCw9O3MY20fXIRAIIBIJd6Un69fHrOX0qToP2TAMdzPCFKv2Si9rhK+KRYPM2/gtv4sPPvZF/GzmAGzY+NGrT+Mt996E0c+/E6bhmPSky3lq0LGUSglkcy8LInUeJFSpqaJrEkKc/ARBwBfP+Uh9TnHeGYkT8DsrzvadU1gQvnoqnKAFnexJm+mTQe5Wn7xXCILgFI0KEgbDCSoZaNYsZLUCBusJoNMc5N7QzWmYHzqawbdt24bPfvazuO+++/D+978fP/zhD3HNNdfgsssuw1133eXh9ZxGa5AAeUWyWfKrWq3CMi1kNBX5agkrEiPgOK7jKmyyGLIUi44yyDU2g9zCajrgUiyI1XS+WmZULHoMkAnFIhhDVlNR1MsdFun5mKX00SjEb2FcKLNNArsViREqFE8yyB6TECZ4oDQLj1lIZ0EM2cAalstBbmeBIMU2ToBWD2zr79BgOAEZPEy7Bj7AwaiZCItBrBqZaLoO3UiJjukF0L6Gq87wj0mBXjcSbwQeN70+moVQiTdG7qxfGeSIFEKRbFDafG4LSbw1Xb9LfWr28x6lDGbzSnnI4QQOZ066Lnod6Jg7FKt6gGwsECDX15+9065BSK/ybizI+PVKvVVdPXD2JKnPFAvSVwtxkP/f7R/GzuFNICNb5HhcNLwJt237EAAnQzkQikHkhSXTQCbwOP/V20uK0b1Feu5YPnPdZsjgYdkWRI6HUbMQEUO+cwoLwWcjxdYWsEV6JAkhvo6n336OehmtgEQwCpETKK/6NF5/tN0Lqqri3nvvxbXXXouLLroIP/3pT3Hdddfh4Ycfxu23345HH30Un/jEJ5ayrb8xkBgt5KMNShaEf/xc+ig2D64AF3COCDvNgriBFWM13UFgxeogy3yrIj0vxcKq1VA2Kh3xhNu5B6KF2qrgqBX8rKa7KURqBeqg5VkYW98znRgTbgaZPGfHJKRZH9YttmSoMh2qDXgyyEIXVtMSo6JgOW5uA0kFmYoKIcDjM1t+D1eMbEWuWsKaVauaruNmQmVXxaLNzVqVUbDoRwaZPNvhiNKyUG9ubh7fvv8+bLzp9/Ht+3+ITGbxgg9qEmJWERLqEm89nqCQICsq+utQLwTfIr0WG1xvBrlDO2uiBe7hILvfQ40cQnEczp5qq0Cv8W9TrJveAhtc1c8gJNKbQQgLSWKVLOoBclWnY0Ktap5ahH6CY9zptBYB8lhyEDJ42AA4BGDULBR1DcP1YPSUOudKvC0RvcJtr8tDrlpuMaNt25gpZX2d/LZs2ggNJt4ycQ6+dMl1eNeaC5EuZX3nFBbuOhHHPJF6a8FBJkV6fJ8z/J0gWJeYW8U46nlMQk5nj98waCtAvvbaa7Fz507cddddWLduHe6++2488MADuP7667FixQrs2LEDH/3oR3HgwIGlbu9vBEgGbGVyFK/WBwg5fiJZkOc8BXqdT/LkiCssyjAsA4ZldlSIQ7KOFZPNILc2CimbVah6GREp6HAM+cU5houBXCMiBmFYFua1Qsc6yMTi2KxZqJoGFaxvB9lsDg899p/Y9nlvwQuBZVmo2bV6gdLi7ZJZJYuct4I5Wyn6UixcJYsQ1C551IQBpVsGJK5zq+mwKKNi6tTuWC0WEY1G8NFz34Fl4RRWCoO4dtMu/OiTX/QtbOHpdYIdq1joPi563RToEZAgx8kg1ykWzGKazebw8E9341/2PoiXCifxL3t/hId+8siihUKuBjIr8dbb++8GsP4qIguBGHgsnEFmtJB9rMTbQc1iMshicyDuKlkk8HL2lMuzbyODx26QXRth/wBZ13WYhomMVsCcVsBaZcKZO3p00GMh+wbIToFj1TIQCACyIFIXxH6ilfkGi7HREWSrRfDg8D/O/EOcrazGc/lj2Fs5hoKq4r9e2IPD6RN4Yv8zS15wTwNkXqTtBVDXs5eoXTz7TipKEg9+6g7831d9FBsGl+N/XPZB3H/97YsWy4nMJsxPEtCbQXYC5NezfoqcBq9KjuIIEyBTa+3TBXpvGLTVE2NjY/j617+Oc889t+VnduzYgXvvvbdvDftNxsjwMHK5AlYkRvBqfpZKfhEXKAA4MHsEv7/pMgDd26RyPAfbtp3sY7WMVDgOy7LamrzZrCOtyA80ZpAZFQujigJDr+hXFbcgCjB0AwOhGI7nZzviIJN22LaNpBxBQS9hSEi29Qyy2Rwe/8WTuHPvvTg4dwR3PnYP/rxSw3nnnA1FSTIczwrCguzZFCST/hM6mRgn40OYLmVgWCbEeiYjo6nYPLjCuWfeh2Ihd3cSALB9abqbnTZk3uhCX3MW5rJRRVQKIZdzAstn5g7jzet34LxznWcyMOBfOeypwO+wSI9KvBVm8d745QB6q75npd5+NeUYtrCL6ejn39lQfFgvFHriTloo5Ne/rO5wuN8UCzHkSrG1+dz8MsgtKRZSyHNC0AlIVrCkVxCWiDkKQ7GgVsAJFHWNcpDb2eCKTGbwROEVz301gpgtPDv9MrYNrwbPcQiFQl33gV8fE77sRNylWGia0y8es5weqWV+8I6hZk1y27YRCYfxP9/ycey65yaMBxL4yy1XA3ERn/35v+DZo4dwuDwN1XLUIQaCMUwum1gSpQbAP6AH6vzjBgULFoqSxLnK9o6+i1xnwCdAtm3bUYmq1ZCvlKjO/OBg7wVc3YLVQr7v0OMA6i56pzWQ+4ZWa3CnaCtAvuWWWxb9TCqV6kvV4G8DbNsGzzvyRRFRxmwph5GogmpVh6Y5Oo7PpY/i85ddAwBdVWEDbhV4XA6joJeQCsdhmlZbA9CVBtMXlXlzjtFM5CrFvhepCLwAAwaUYBQzpWzHHD/3GURQqJQxFE629QwaA6WHTuzBQyf2QLpfQPXOR+kErOplRBtslg3D8L0+x3G08HAkouCEmsaqpFNJnWVd9JgMAnnGniK9DrN8NggH2VWxaDe7z/EcarVavVDPoc/k884i9OjxA/ibS/7YaV8s1voaPsVmbQfIdW5svzLIgugGyIRiQbLUAPCVSz6Ff37+ITw99xKMmgWJE7BjcB1u2P4u+hm//qUayB4Xvd4CJfI+RbvJIDPmFcsTw/X2NDph1lUyuugX9rNVy6irYQgeMxKAddNzMnft2EzTv2UpVjSD7M8XLxL945nDOHt0HYDe6BV+fSxTDnIKJ9V5j97uUipYAP7SaR6t3/p8ZAo2UuE4zt9xDv3ddDGLE/WAHmA2fY/dsaA6RG/tbVbdcNrC8I/7VCRINiSD4XjTSQN5Ls7aFHI4vgIP0zSXzChlMbAc5KO5adi27VAswqcpFv1CqzW4U5xmgr8OEASBOW53DUNy+TzVew0KEobCSfACTz/bKVwnNlfFgQ2ustkc7nvkYWz56/fhxz/f7TlGbqdIj2QYuQAHmRcwU8rSDHK/ilQI3YAcjXXKbxZ8eMjtFOrdfrZT8MK3UGlwpZ20JtWOhRZ/VuaHKJgAQFZjVCyYzQW1DGeK9DrliXoyyLw/XaYVWEMJlgd7Sp1HRlNxxtBKAEAsFm15DRLodWo1bVkWLNOCWi1DrznSbIFAoD8qFpEE0iXCQXbfh6FoEjJ4Kjdl1ExIEDCZGqWf8etf6qJnVhEWenfRA7wZZFfmrb2+r/lRLBpOXUggGxaDHcvIsZ8t6RWaNW8s/iTvcirkVOe7LnptcJBFhoNcqXOQW5yeEGoayz/uxkGPfrdPHwuCswGISWGIPI9s3XwIQF2jvT/qPX7wSGr66CCTgDCjqZRnK0oiIpEwvnDGH+HCoQ2euex3Vp6zqDpEL/AL6AGvSUi/AkGP2kmdNkXmeMNopleIgvi6Uiy8mvgBzGsFLwf5tHxuz+hX/54OkF8HWJblSn4lXMvpbM5ZsA/OHqWBRy8cOlbFgfJX6wtMNpvDgw//FHc9/n08n30Vtz/6b/jlU7+iQbLLQXaL9PyUD2i1sihjppRBvE8ayASuuUMUQoBDkJc64vjRADPYfqFeLpfHQDAO27Jg2a4LnWBzWDk0DqBRwcKb1V4oyKAbI8ZyGgAybAaZOaIl/806InZql83qIIudZpADLn+YHMMDwGPHD2DnsjPAcxwikfCC/e01Cmk/0HPpFWksiw1RLdpeuO0kazUcVigHmVVGGEwNIFstIiU5fXFmciWyVRWTE24lvV//Eg6yEyz2KUBmrL67plgsYM/uUcmgMnKdZ5BLRsXXjARw+ZRyzXkWX3nq3/HE/mcoJWIhkCB6IOivTkBgmiaqlSo0o4rD2VPu3NlDBrnVGJY8Um9uVtaRt1waBQtgcSc9EhCyUmayJGHtmtXYvHItggERNbtG1SGiLRRnlrK9gKOB7Bbo9SeDy1IsMhXVYzdN3he2QE8Q+L7q4XcDmXHUO5qbRpoxCRGXUH7vtwX96t/TAfLrgGKx6AmUSMEW0UI+mO5e/5iFf/bReXFGP/dOfPCx27F79gBsAI9M7cPb7v9rjH7+nQAaOcitC7tYbtxMKedykPtUpELuQQnFEZVCTnDcwQJEAjfWLGShANM0TRw/eRLLJsbxXP44VkVGMBJM4pyBtchUVIyPO5SIGmMOEGs4Wi0Wi/4XB6uF7BbqEQtewo/zU7FgKRadSnGR4/+qZTAZ5HY3GKzdtFvY8+ix/bh0+TanbfH4wtegesqdFenpjILFZKJ3egXgPFuiK121dGhm1Sm2rNVgGAZkScbtV10PzdJxRmwS5w1vwJ27PoVwxHWy9OtfKvNmVhHqEweZZPmjjA51VzrILZRfWIpFp+oizmdJgMxoIDdkTwVBQEFVcfKVEwCA6UoW9xx5HPsOHly08JEGPmGHYkH4pI0olcooqCq++8uHgZqNZ54/iKpe7WmT3moMkwLrcaZQD3AyyPEONJ47BVvv4apYuBxk1k55gCmEDAQC2Hn+edBg4Kpl5+DOy67H767didni4uoQ/WhvUJCbOchEh7lPGWSubsoTFCRInICi7tAUTdOk78u8VsBAneYjiuKCc/RrAVYT/5XcFGZLWQyH6xxk4XSA3Cv61b+nA+TXCS4Pyc0gExycPYItQ91ZTLOgAbIPveCOHR9rohBcNraVHruRyXchmTf2ZyFBwkwxgzjhIPdpkSALbCGbR1Gv4In9z1Dt4HbgcngZu+kFgoxTU9OwTAt75g8jGYric2e9DytDQ9ieWo07d32K9hvL8Yx1cM8yyz+r93tR1xDkRUi8SCd79v4BICZ1ZxkOuMGoYZkdGYUA/m56mlnF3unDuHDZZgBAYpEAmQR6YVGmOsjtBHqsxNtkrHeJNwJBdAKHoXASc1TJwoCqOpPqsco8JpPDuGDNFvAxGfFYrKV6AgGVeTMq/adYMPSW7qym/SkWdPPDcJw72XyRPmQpFo3UnUAggDd/7zO4Yf+/AABsOBzY3/3x39LNeCuQsRASZAgcj5JR8fB+CU6emsLzLxzC/cefQqVm4J4jj+PlV44uGoB3AxK0TzQGyMxJ0lJo2FLNeSbgZJ30yLw+z1AsqI60ksSDn/oSbn3LR7F+YAI3X/qBttQhegHr/Ke14CD3M1PqmkrFMafVT4ZMi5F4UxmJt/5TYDpFYwaZtZleSgOX0+gMp8kurxP8MsiAc5T/UuYENtUVDcKhfgTIYagMf9W2baRC8booO0MhAEeP3fw4yAE0B1WuWYiM6VIWW4dXA+jdJISgVCzh+RcO4aX8SZi2hXuOPI6QLWLDurVtTfBuBpkNMP2DM7VYRCaTRUmv4H8+8V3cevlHcOb4Buw0T2K6mEE8FoNWl0ciC1KnC6NHC7ne7xlNhRJqLtAD3KxfXG4+BWgXtm3Dtm3oltmR1TTQqHTg3PuvTh3CxsHliMlhSLK0qImNaxQid2Q1TbTBTxTS1Jq2U8McP4iCCEM3MBROYLaUw2R8GLph0EKvJ088j/MnNmE4ouCl+eMAWheHEbBOegN0Ie4XB9kJYP2Cw1ag+sQeFQt/HeSwGERR76JIz2IpFq1rA+7Y8TF8+8jP8fTcrx1ONyfgTavOxoc3/s6i3yGIAvSqjoFgjMo8Gobp+Z6zvvwxGLWFlUf6BZJBnogN0ncDqKtYLGmRHhNw+pzCkA1cVitgJNKsEtGNOkQvaMlBLmUwFutvBhlweLt6VUcqnMC8VsCq5BgM0/B10XsjFMGxhXo/ObIHNdumJ6SvJz/6NLx4w2SQbdvGHXfcgQsvvBA7duzAbbfdtuBi8Itf/ALvec97sH37dlx11VW4++67Pb+/6qqrsGHDBs//XnjhhaW+jbbhHtWlMK8V6CRyOHMSE7FBRKQgJFnqabCQINVToGaaKJZKGBsdwUw5Bx4chACPK4a3Yr6cx+qVKwG42YmKaSC4QAaZLLohQcJMKYNEnYPcr0KVTV/6ED6x76t4UT0JwFn8PvLUPyyafSIQPFn01hzeWq2G4yec7/jynn/HjvGNOHd8AwBgfWoZXso4R8Sa5jVU8KNYLARSoJEKxWHWLOQqRWQrqqsP26D+wRZaqnrnZg7URa9mgq8XVHbC4SWfjYhB6ub282MHcMnkVqddC6hXELAqFpRi0cY9pOfm8Pj+PXj48NPIp3MoqGpfMsiij5KFaZgoqE4h2JMnX8AFE5swGhnAdMkxCWGVLvzABsjEKKQdKb2FQLj2Ii+A53hULH/9W9/2dOCkFxVdmbdu3DZLhptB9hsD29duRoiXYNZMyoENcmJbHFgyHgZCcWTrZiGNPOQvnPFH2Dm0kf5b4gRctercJSlCkzxKFizFokxPz5YiQKabTEH2fQ9crV+22Pf1y5T6BcgVU4da1ZAKxZ16gj4Gqq6pTIyahZiG6dFATr0BXPQIWKm359NHMRxOOsHxaQ3kNxTeML3xjW98Az/4wQ9w5513wrZt3HjjjVAUBddee23TZ48ePYrrrrsO119/PXbt2oV9+/bh5ptvRiqVwpVXXgld13H8+HF85zvfweTkJP07RfHXaX2tIUlOoZkkS0BVx3hsEMcLs1g3sAwHGHpFryL3rgtbGIU5l7+ay+URj8Vw+drtGJ5KYs/8y/i9lTuxdfV6WthCZd4svaVRiPMz9+hvrpynUkf92gV/46ob8U97foin534NvZ59unhiCz6+7e1t/T3rpkcyyH4BxvTMLPSqjhfnjuHHLz+Ju9/zefq79QOTeGn+BGzbRqXitVxWdY0WnZDNwmJBnCRJqFQqdQWTGWQrqq9JCPvvmOQWWnZil+05CeiQfwx4j/nL9SPux47txz/u+jMAQDzeXoAcCAQgcgJg21T/2bbtlsF6NpvD3mf34/vHn0ClZuBX6Zew6YUxrFu7ZlFKx2IgC/NQJIl0yTmOVYtFmIaJkl7BocxxnDW6Fsfys5itB8is0oVf/7JOeuE+ybwB9SLYGqFBVBAS5LZ0vGs156SoqGtM8Zi3PRxDsSg3WIm3A1bFgnCQ/fpz08b10H5m4F1rLsR7t1yJe57/z7Y5sCI9OnfNQlgecrVaxXhyCJhy5isnADcRFuSeitBajWHiQDceHcRJdZ7+XNU1rEw6SidLEZiyAadmOAGnzXKQfdziXs9gyy9AnillMRJJggtwlObUL7gBcsLVQrZMevIzz7roCfySFFJ2ApIgi9gSAgggXczhif3P4Lwztr2u7fpNQb8KQN8wAfI3v/lN/Omf/vSpIgMAACAASURBVCnOO+88AMCNN96IL37xi74B8oMPPohNmzbh4x//OABgxYoVePrpp3H//ffjyiuvxCuvvIJAIICtW7e+IY5TGkGKmoKyDL2qO5bTuRmsG1iG59JHmQK97ukVgKsPyRZ4maaJcj0L+uipg/j42e9C5pkywiNxxGMxVHUdsiwzVtOtZd7Yn4VEGTXbplar/TIKWTe2HIlwFAaTfYrJkbYXP75FFp3Fqalp3PfTh/Glg/dBh4WPbdmFgVAMkUgYmlbBQCgGWRAxVcxgPJZCtVqlz0etlrF+YBkAN9u1WNFaMCijUqk4Cia5aZg1i1GwaMggMzQZYjXdiZmDH//YhynTEuwxv1ot43D2JHiOx+rkmCN5FW0t78aC4xzDllCdy5zko7Asq+VGqlGH+on5Q3hi/hD+7ODXej42J3PCMJNBJtrOe6ZewpahVQgJMkYiCqaL9QBZd7OWfv3rGoW4KhZ+qi+dgud5WKZFpd4GwwlYVg0LTWskOK5YOniOh8g7wUhjQML2bZFykO2m67WCVWOK9FqoWADO8f4D19+Ol48cQT5fwE2XfABrVq1qiyJFrsfaTbMZ5EqlimUT45jek4UiRvDZbb+PR6b29VyE1moMsyd/M6UManYNXIBDwaNisfQ6yI2uqK7M2xvDLY61xiY8f48Gcp/XZdex0auFTOh0mQaZt269BfoFjuNQqVZx6NCvIXMCSlYV9xx5HHEh1PbYOI3WWGwNbhdvCIrFzMwMpqamPE5955xzDqanpzE1NdX0+V27duGv//qvPT8LBAKUs/jyyy9j2bJlb8jgGADyeWfCYLWQieQXW6AX6oF/DHizpyQ4LJXKsEwLU8V5HMvPYsfEBiyLD+F4IQ0A0Kve7ETV1BekWLAZZPJdQP+MQtasWoV8tYTfXXcRvvHO/453r9uJuVL7ix8NWpkMMktRyGZz+I+f7sY9Rx7Hr0vTyOklrKgmoKpFTC5bhmDQmUg3pCbxUsbhHFYqVY9BQKzhaJX0byt4+Of1DLLSwkCBDWKqpgGjZtIAqB24JwEG5A75x85n60oKdSmwR48dwMWTWxEIBBCLRdvOApFgMdKmFvLXrvxv2Dm8CWL9FETiBFwysrkvx+bUTS+SpFJvpD9/efJ5nDexCYDzLhuWQTPnZGPl178eikUfM8jkFCgqBVE02lOy8HPR89uweqysjc6tpl2KRdVVsWhxz4qSxLlnb8ebrrgM5569ve0AgMo8huLULIStIShrGuKxGH5n3Q5cMLwRSi2EPzv76p6L0FqNYY7jwAs8goKEuBSmJxBFXaNUq34lB3y/m+Mg8jyqlrNJIH3AFukNvAGoBKyTHmkrq4Hcb5MOkrVPheMuxcI06WbKW7zILzpHvxa49Ft/gU/s+ypKljMf7p49gA89fkfb9MHTaI1+9e8bIkBOp53gbHh4mP5scHAQADA9Pd30+VWrVmHLli3033Nzc/jRj36EHTt2AAAOHz4MnufxsY99DBdddBE++MEPYt++fUt5Cx2B2CASySqSQS7qGqaKGawZGEcgEOipQA9ggkPJtSkmE+ojR57B5SvOhMgJWBYfwgkSIBvuxGvbtsee2C8YIj8jQUE8SILF/rxaipLE/dffjpsv+QBGhBhuuqSzCmzSjoQcQb7STFEY/dw78eEn78Lu2QMAgLxRxg37/xlv/t5nEAzKNNOwfmAZDs07POSypjFOehqllZCFcTGbS4+CSW4a2UqxJcUCcLLggUAAMSkEteoESe0qWZCMoG6aEDtUsADcAC1cL7B77NgBXLK8ff4xvQ4t1AsyRUatg/yJgSFq2BFAAEbNRJCX+qLd6rrpJSiFgoDwjwHnOQ1HFMxQHrIzNvytpkmAXEFYqCs69MhBBvxl9hajQbAKFgsFru61Qx079bGfLXm+p79LChkPKYZiwRZMVupFs8dLc7hg7Vacd+7ZOP/cc3rOwC00huW6pTYr9cZulPuVHGgEaxbSqIVsmRbMmgW1WurIrXCpQDbhMi/Stk4V5xkXvf4GyGwGmXVdtEwLNbvmobGJotg3K+Je8A8XXIedw5sgBNwkwJsmz1pSA5ffFrymVtP9QLVa9Q12AXeSYwcN+e/FimPK5TI++clPYnh4GO973/sAOBnkQqGAG2+8ESMjI/je976Ha665Bg888ACWLVvWdA0SoBMEg0FEIhGoqop4PI65ubmmv0mlUiiVSgiFQqhWqyiXvdJjoVAIoVAIpVIJsVjMc41sNgtFUSAHgyiVSpiMD+N7z+3Gk0cPYk1iDMWCimAwCE3TIMsyNE1DJBLB/Px8YzMwODiIQqGAWCyGUqlEnyXgHDFVKhVEBBn5ShFWrYZCwdlZPfTrp/DBTVcil8thIjaIx47uhw0bmUwGAs8hm8tCtwwIHAe9qkMQBJTLZciyjGzWDSpyuRxyuRxCgtNfMSmMYrGIbDbryVRGo1HwvGPxKYoicjmvBBPHcUilUvTZ5PN5T9+vmFyG2GbHSc2yLOi63rRL5HkeAwMD9Bq5XA6VSgXZXBY1Q0e+UoRu6DBMA5qmged53LbtQ/jOq49iT+4VWHYNEifg/OEN+PCGNwNw3ttsLouJoIL/OnEA2VwW4XAImqahquvIV0qwdRPZXBbZTBIVTYOqqli9ejVtRyaT8QQe5bIGwzQwFlJwJDcNkeOxIjxYv3YQATjvfyKRQDabBc/zyMxnEBaCODE3hUDMxMzMDEZGRmAYhmOnbVlN2o+SJEEUJahF1bEDhtOvQVmm77yiKKhWqxBFEaZpolTyGjhUqlXYsMFbAUyp83hp/gTWhoeRzWUxPDSIdDqNgYEB+q4ahtF0DZKFLxaLCIsyprOzGOBCmJmZoTSixvEUDgaRLuWwOjwMRYoiygeRqagYHhyEbdu+Y3JwcBCqqiISiUDTNFpQSRAOhyHLskORsWuQLA4zxQyyOed9ntMKmNcKWCYPoGbXoGkVpIIxHJ45hiSCmJqawtjYKAqFAqLRKEKhEDKZDABgfn4euqGjWNUQFmUUi0UEAgEUCgV6skUQiUQgCIKju9wwngAnMB8cHEQulwPHcSgWi5ACAmZyc8hGskjPRiAKQsvxVC5ryOfzdc6yMy61crlpnguHw9ANHUItgJLhjBMuwNHP+Y0nNjhNp9MwTBNqtQw5LsAwDWQyWYA53RAEAYqi0Gtks9mmzV0ikYBlWZQGUigU6O/yhQLUooqBYBy/VJ+jzzpUVzM5dWoKuqHjpfnj+IP1l0A3dAQCzXM6O54URcH8/HzTCUYymaTjaXZ2tsnBVJIkxONxVCrOezUUjOPXM69iVWgQOa0Iu2rAsixUKhUIdTvjxrEgyzJisRgKhQISiQTm5uaaToNajad8Lg/DdGoJSkYFgulsUtLpNDLZDDIVFVEpBLVQwIAy0PX6RNDuePJbn/KFArK5LGRBRLFSRs2u4XhuFhuVZcjmsgiFghB4DpFIBKIoolqtesYTARkL+Xwe8Xgcqqr6jqeaXYNW0ZCUo3RMq0UVpmmiUC0jJMioahUE4zIKhQJmZmY8NUm9rk8AEIu565MgCIuuT6uGx8G9UINlWxADde68KGMiOUTXp8Zr9DqeAHeD0Gp9ItcwTUctxrZtqPUCZoJOx1Or9SkejyOfzyOZTPqOhcXWp1bjibQLaD2eCIaGhtAKr1mAfODAAXzgAx/w/d2nP/1pAE4wTHaC5OVbiGagqiquu+46nDhxAt/+9rfpZ2+//XZUq1VE6/zIz3/+83jmmWdw33334ZOf/GTTdVo9oEQiseDvYzGXNxppYWtKuDCN1xgaGoJhGIhEIljNj+GYmsbR8hzOHFsLJakglRqg1yTfs1g7Y7EY/SzgZBdm03NQBB6qroELBKAkFcwUszheTONN63dA5AVMVoYwreUQQACiKCGVSkFJKihUS5AFCcFgEDzP0/aw7bBqNiyrhpAgI8iLCAoSAjEZIyMjvm0lG59W90JeanJPC2Gxa5Bd5PRMGkk7iaplIMBzEDkBwWAQgUAAa8aXI3IqCMuugQ9wMGomolIIW9c4ChZjY6MoFks4GxvxzRd+CiWpwDBNCKII2XacyiZSI1BiCkZGhj2LKjtAWViWhWwuj7VDkzhVnMNQOIFlA6NQkgpGR0c9mVlFUTCbnkcikUAyFAUni1CSCpSBAYii6KER+Y2VUrmMWDSGY9o8QpIMJakgFAp5nh3JNEmS1GRMoxaLOHL0GKbTs9g7cxhbk8sRkkIYHRmhpikA6FgTRdHX3EYU0ohGo4iIQfBBCUpSwUBqwMNhZsfTxMQE/umdf4G/f/TbCNki3rPmImw/axuWL3eKblv1PRlv0WiUtqkRA4qCkyensHZkOeYrKpIJp4L8sdkXsWN8Ay0CjITDmEgMowwTSlJBIpFAOBxGqVSi1ybtmEnPwTRMVGoGwqKMaCQKQeARCrXmwi02FpLJJPIFFdFoFEo4Boi80/eKQvva7xoFVUU2l8cLJ04iUc8mJhKJpu+xbRuSKEERRFQtA/F4AjzHNX2ucTwRFEtl5PMFaJaORDgKURAxMjyMZLJ57JJrtFMozX5/MBRCsVjGgBpDoU4xiUSiGBoaQq1WQ+TUNCRLxvFCGhtHVkISJCSTyQWoHs73p1Ip39+T8STLcst+GRwcxOxsGqsGxpGzNChJBSWzgmWDY+B5HvF4DIIg+I4nAjK/kZPSRviNp7lMFtWKUwSqWwaiyUHUajaSSQVKUkF6voRUOAElqUCUxJ7Wp8bfLzSe/NYnSZZRLJYRyssw4fC05yoFrB2ehJJUMDbmnedo4ewia1wrbinP8wgFQxiOJpHXy/j/2TvvODfqM/9/VGbU21Zvs71r7HVfd8cQU2KwMS3UHARMEpyCSezf/Q7DGRyOcLlQArHjHwdcnARMOwJHOBLgDhJ6by5rg+31urct0kqrLs2MNL8/RjMaraQt3tFqVv6+Xy9eyWrk2e/q0cw888zn+TwuZ/p75vVFUW6ywWa1gaIpOBwOMAyT83eN5PVp/tw54D55Gkvr5uCSCd/Am8e3wxP1Y8rkZun4HmgfQz2ecu2j7/VJRF6wFIsc+fYx0PEE5M/lxPNKvmOhv+uTSK7jSf53D3R9yseIJcjz5s1DW1tbzm1dXV148MEH4fF4pANZrADkC67X68XKlSvh8Xjw1FNPYezYsdK2vomDRqNBU1MTuru7lfpzFIGiKOh0OjhTY5Q/OLoT1047D8DwHSyAtNG+QU9Bq9EglmBg0hvw1uFtOGdsizR2uN5eiRPBlMSCYaQ7QcHirf/GLlGjmuCFscyeiB9jHLkPlmKh0wuNTlaDGcF4FGUmm1QpKC8TRgtbdAZ8f+L52OM7Cl88JGmcTakTw1h7NdwRoSpnQVqmEIpHhjQoBBBO5npKDyOErus9niMya6bsfchHhg913LSkQeZYmQfy4CUW/kAAu/e04eNuwSKRSXDYvacNVVX577pzIW/mjA7C6o1hGNhtNsT0CZx3xjxMbWpGdT93+kNBp9NBq9XCQhuh1WgQYoVhL5+d2IOFKXmF+J0ZY3Glrd768UIW/5aoTIOsxMAIySKPllmxDaATTk95zG/xBqRt5JLJJMx6QUJjM5iRSCQGpZ+WNMhMFBYqv9Z5OIhTxcpTY4SBtERK9CQ/7O9EjbVMmKRmoAs+CMJAp6fpbe9sR5JP9munpxRyXa/oZJHkk1Ic5A4WxbYyy+VikdmkVxiJhTiWXO6Qk9G4qIIhISIulxP/87ONUvPqN86YThr0VIYqNMjV1dWora3F1q1bpde2bt2Kqqoq1NTUZL2fYRjcfPPN8Pl8ePbZZ9HU1JSx/eqrr8bmzZuln5PJJNra2rLepwZomkYgGISLsqK16wCYnjACweCwG/RE0oMmLAjEhOTqzYNbcUHTXOk9FSYHIkwMYSaGRCIhdUQLFm/5G/QA4aQdCAbx3v4dYJMJ3P/2M1mPMIpNLqu3RCIJhmFgNpnwb0t+iASSmGasxQ+bl+FVmcZZq9XCYDRAp9VigqsW7Sk/ZJ7nkUgmEeHiUgf/UBKi9MCQaoTYmMzFIpdWNO2FHGBSVnWDdLIQL55MgpVcLIayzgn3/wNWtW7Gl94DAIC24Emsat2Mln+/adD7ANJ/g1lvSE+F66dJT9T7ngh6UGcTqgK0QbmLqqhDrjILVm88z+Ozk3vwjTphOqArVdWosrjSVm/9JcgpzX6Ui6c1yAokyJnDQkQNcv+xlzfpDeSskLZ6Sw+CGfQwkoSsSe8UjoHBIB4PYuIDpMcqx6LCetu9JzAx5SRjylPpUhJxApw4TS/CxmHU09BrddDpdIral8nR5tEgi5+HNxZUzTCMDFu6lOtGV9iXHjOt8LQ4rVYLnU4Hg56CSW+QCglApsVbsT+Xvpxq8yphZFCNzdt1112HDRs2oKZGeEy1YcMG3HjjjdJ2r9cLg8EAi8WCLVu24Ouvv8Yf/vAHmEwmqdosamvOPfdc/PGPf0RzczMaGhqwZcsW+P1+XHXVVcX68/ISj8exe0+bdMJ7t2MXxmgdmD51iiJWNHqdHhzLpWzCwtCENTjQexIL66ZAo9FAp9eBYznU2StxIujBpPJ6Sccc5wb2zq3/5RVSlzIAvNXRireeXA3DsxRiv31/2OtXAr1ODwZM1shtNiqs+1CkC9OrGrFo/nyYzWaUl2c+cjIZjYjH4phUJgwMmTXmDACCvZWZMkKr0Q75wijcGHWC5oTPde/+dpgmTMlZhRY/e2Hc9NCm6WW6WAy9SS+XD/XCyklYPe+KQe9D/jvN1OA8dxlGuKielCfIBZi8VWl2ojviA8/zMOgo1NsrodPrYLNZ4fH0YIzVhXeP7ACQP0GWnrgkGNBavTCQRTu0gSz5kE8yDA2ykS7XFL18x69OJxz/ZlkCPlirt/SoaVmTnkIDgkT0esGizm4wI8Yxku1kMpmUKsjyBDnfo2AlESvIdbYKnAx6EIinh4QUysEC6Jt0pp1gJKeGSEB6ElXsccryaneMY+CLBWHS0zBRBuj0uoKM49bpdUgkEig32eCJ+qWhVd5oUBXWd4TRh2q+LStXroTX68WaNWug1Wpx5ZVXYuXKldL2q6++GldccQVWr16N119/HRzH4fvf/37GPubMmYPnnnsOq1atQjKZxN133w2v14uWlhZs2bIlQ59bTOS6ofm/vxlMxpjUr/BO91dYvfMPioxJ1ckqyP54GF+e3IfFDTNB6yjYbIIuJ8AGUWerwPGAG5PK66ULjzBmOlVBztOR/+zF6/DQR89jq/cAWD6RSqCaced5Nwx77UqRniiYHjed4BLSaOHWroOYVS0kveKgFDkmkwm9vX7B6k02XjYYj0ryCvmFcTC6sFg8ht172nAyLDS2/OXIZzBxesyYNjWriiCe1OUjw7nBJsiii0WCA92Pn3U+JtaMhZUyyXyoOdiNg/ehFpEqyHKJBZ+7EppIJJBIJKSbGbvBLAzWUbDznaZphMMRaVjIAe9Jyb3CbrNJj4DlFWRRYtE3vmLCKsgrUgMzFPBABgBdKlZWyoiukNDANNAkPcnFgh34sb94XA/FRk4kbW0Xk/kgFybx4VkeLqMNvlgQY6xlYFlWupFv9x7HVZPPBqBcBbm/Y1j8HlZbXeiJBuGLBdPTNBW+QZAjlymJBRU+yef2+i16BTk9GjvGMegooLxCRD5u2hsNYIKrFkBmBVk8l6plaBihMCgVX9UkyDqdDuvWrcO6detybn/77bel///SSy8NuK81a9ZgzZo1iq5RKeLxuHSgbj73/2DzV/+DLzztYE9hUtxA9E2u3jy0FStmXgAAcDoc0tCQBnsljqd0yOKFJ8YxkgY5n27VrjPDSpvB8QkpgXJZ7aDYwjxmPBXkEovemCixSCASEf721s79uLFlKYDc2m9jqmN+Unk9Xtv/qfS6YO2USkBkF0Z5fPMx4//9oM+N0S68070r542RuH4bbUZvPCStfzCIFWQmwYLWCmsaSmVzQmMjgskYLho3HxeNn4//PboVvlhwyEMYxIu7mTLIHuXnTvTiKS/uE0EPam0VwhQ+hR/JivGpNDvgjvRiR+d+XDppEQDAarFIj4Dl46bFYSF94yv3QFZyih4g1yAbByVNATIlFn2nPPblVGzkRKRx1mx6kl4hKoOUngLHcpLV2xhrGbhEQlZBPi4N61GqgtzfMSx9Hxnh+7Ov5zistLL2lrnIV0GWpujFghjnFJqji621Tfvjiwmy3OKtMMl7rmEhgFBZn1ktyCtFbfZgztGE0YtS8SXfkCIgv7ufNn4CLG1GcKc4KW4gJP0qbcZBXwf2eY9jUd004bGl3SZVi+pslTjUKwxliaVsdORT9PIlVS6XA1ENi2X1c3F+XQve696N3ngIDocyk2yUQEqQjVapgsywgtUbm+TwtecwZlYJJ9BcFWTRj3piWT32e08ikUxCp9UKCbIh+8I4mOrNExfcike3/zVDurBozBT8dPZlWe/V69JNescCQqPpoJMY2ahp+hQkFn2noC2YcGqNJOLnY6aM6A73ZqytL6KDzcmgB/V2oTHPoLhvqpggO3GotxPbO9vxr+f+AABgs1mlRj67wQwuIYygttBGqblTTlqLm56ip4QHMiDXIA9eYiF+riFZ81y+yqZcwiEl4IPUtycSCUF3zcZhHmBQyHCQdMiyaXrRSBQJLgF/LIwwE0ONrUzoF1BIpz7QMWygabAMizpbBdp6jkpe6IVMuvomnUBmgqwmrW3fJj35kJBCrU387pWZ0sNCALGynjmptNifD6GwKBVfkiAXAY7jpMd0ExobEUnGcUnTQlwx6Zt45cCnwx6TKken0yEQDCIUDON3+19BS1kj4tEYKlwuwYoo9bir3l6JD47uBCCrlA2iSW9CYyPuXng9LGYzTCYTFkVnIhyJKLZ+JUhX0dMa5EBAuNC295xAjbUcdoMFtIHOeWBRFAWdXgcrTCgz2XAs0I3xzjEpiYUp43cAmfHNx6S6cbB+3Ve6YM55Y5Rr3PSgE2Q+3aQ30M1OPlwuJ+a5Zg/p3/RFTBjNlEGqfuX7Gxg2s4IMFGCwQOqzqLI4saX1dTQ4quE0WkEbaOl3UTSFZCyJamsZusI+NNE1qeZBPmM94mccZePSREmlKomidMcqn3Y34KCQbA1yvsQ1owmQFcdND5wgp51uGFBaHfRanWK6675IDgWyIRDBlKdqu+84JrhqodVoYTAYFPv9Ax3DwrYwam0V2OM5irEOoXJbSO1vRgVZ5mKRa8y0Lkez70iSK0GuGaEKckXfCrJcYpG6MR7MOZowelEqvqpwsTjdkLs8iJPi/uXc72GspXLIk+IGIhgKYfeeNhwOdoLlE4hxDHbvSdvtiV+iensFjgczzeJjg2jSE7twaQMNr88H2kCrrhs3PU0vrUEWjfN3dO1HS/UEAP1b64lVZEGHLDhZ5BozDWBQLh4TGhsRSsRwSeNCPLpkDS6bsAjeaG7pgpgk2WQa5KFLLLhTcrFQirQGOZ3o5RuXnZZY9KDOVqDRtJQegWAQHSe74IkGUE+5EAgGYZX5xYpNgdUWF7rCgv6X5bKN5sVYyCUWSn3GYuU3c9rd0F0s8jWPiQllhsRiEBpkaYoeG4Ml9TuU0l33RfzulJtsUgU5FBJi0N4jc7BQoKlZZKBjmJas3sqxz3s8/TkX8NhKV5DlLha8ZHvnVcmYaSD9vaK0eiT4BI4HPekEuYAaZCBVQY5mVpDL+9i8qc1piaAsSsWXVJBVgBIVuny0/PtNGVrXr/xHsap1M+ivHkd80wfSI8k6WwW6wl5wyQT0qYtyPMEMmCAXev1KIHoLOwwW+OOZE6Vauw7grAZhbHl/CbLRaEQwGBJGTnuPYemEeRkSi6FeGMUbI1G68PNzVuSVLmSODE+5cHCDlVjINMi6oWuQlUKuQY4O4GIhl1icnRprrbTEIhwOY/eeNnzavRcA4I76sXtPG8bKJm1SGQlypg5ZjtgIGeFiiksNJI2wXIOcp7lRJJFIIhAMorPXg3vffRrrZl6DisrynKPBc0ksBkrA5e8JM8r/zX0R5TBlJrskzxG/O/t9J9BcLgyPUTJBHgjx5qnOVoEYx0gSi5GqIMufJnAsB57nhQTZqA6JBQDJY9uop3Gkt1PSICvdTyAiPsWrMKcTZJ7nVWV/RxhdkAS5xPnd2avx+92v43P3PnApl4lF1VPw0zmC1lWr1UqPncqMNnSHfdJjbUGDLCQmxUiqlCLDCzqeeWfZ2nUAt8z7NgDknTYFpCvIk8rr8dJeoYkuOAif2f4Y7I1F2olEZvM2SKeBtM1bcSvIYgzMeiMiAzSbiW4RhZRYjLv3ajCJ9I3jjt5DWNWbvnEE+iTIobSTBdXn8XVmk56yzWrifngmAV8kiO/85924deYVqKmuzvuUxtfbi9172uCOBxBLMHjx0EcY11oDi8mc9W/E762VMkqDOAbzdEIaEsLGYKULY/EmIt4glpls2Os5mrGt3XscF5/xDQAjY/EmQssKCwBkw4IKmSCnfJApQ9oTmhOS4yATgUGnF4ZCpWwGi41GqwGSgEFH43jALXOxKFCCLLuR6okII5pDTFSYnKqnVfO5EEYP5NtS4kwbfwaMGuExl6h1tfXRukq+nvZKHAu4pddP1TtXbYiVBYdMgwwIk52YBIex9irodDrJrSIX4sV3UlkD9qWGhQQzpugV7sIod7EQNcinMijEUOQKciAYxP4jh7Cr8xA+3rkNXl9vzvcycQZJPomOUA9qJYmFshfVp5fdjrNrpkvOHrRWj3NqZ+CZC/9Zeo8ksbCmK8i5JhiKNytRmcRCqc9Y7CE4euAowlwM+8MdeOHgB/hy23b48nx+Zz/zj1jVuhnRBAMegkPKJa/djTG/uDR7/1Jl3yhJOAajQZY8kFNe4MDQJjQOhfQNfHqaHiBU0g/4TmJimXAuG4khISLiOdOuFX7n09vfwMc7tyEYCBXsd2ZM0ktJgc8ZewAAIABJREFULEQ5UobXL6WOupe84g2NBuUmu+AAUqAEWZRYVJgc6ElJcXrkU/RU8rkQRg/kG1MEDIb8iZjSnDGhCVFwWFo3B8vHzcObJ1uzbLoMBgPC4QjqbZU4HnBL43bjHAvjKXjnqo20C0RmBXlH137Mqp4AjUYDs9nUb1JjNAoNQLW2ckTYOHyxoFBBzuF/qnR8pSofbUKEjQmP2BPIGKeaj9wa5JFPkP1+YWT1+51fIc6zePHQRzAkdZg4oSmjqsmyLHiehzvsh402w6Q3CKO5FdZUNo2ph9NsA5tMSO4xDpM148ZRvKBWW8rwzmFhWAjDMHD2cWgRZSwRNg6zXnmbt/NfuB2s7InB21278PZ/7wL9V32WJSDP8/jV1O/i5a4v8LFbkI/QWj2WNM7BDyZfkHP/gOiDPDgbOUBm8cakLd4KJS8QK8jl5nSTHiA8YbAbLLAZzNBTekW/IwMdwxRFIRgMofNQBzQAuuJ+vHjoI9Q6KuByOQvSg5HLxUJsaM1oRMsxrr4YyBPkMRYXtBptweQVQLoQUmYS/LKTfDL1uWTrskfyGkwYeZSKrzqOpNOMkRxY4nI58drPNkha17Mmz8rSuuZr1BM0yP27WIwG5D7I8gryjs4D6Qa9fuQVgFARNBqNiEajmFhWj309xzN9kGXJgdLx1Wg0GU1uISYKu8GCRCIxYFKQOWpaeK9SFmRDofH+azIkDaLv8y3bfpeR5MWZbAeLQlScJjQ2oud//bh84pn4h+nn4fmv3slyjxGbieQaZIZls+IrNelxcZgUbtIDgPtmrMBLHZ/h4+49wrq0epw3bhZWTl2W9V6WZVHnqgI6hZ/F5N+opfp1SLFQJkSG4IMsHxIykFPGcBE1yC5jukkPSDXouQpTPR7MMbzkhdsyblze6d6Fd/62C/Rb2TcuSpDLB5mTOVi4xERQJZVSecVbTFILJa8A0uOmAaHXwR8PZzh7yM+VahkaRigMSsV39GY9o5hAIDDwmxRkoHnv4kmr3l6J4ymfXSDlYqEvXtVRKeQV2DATQyJ1cd/ZfQAtY/JP0OuL2AQ0KZUgB+KyJj2ZS0Ah4ivpqE9h3DQgPA04FR9kpXhy6W04s2pKhqThrKopeHLpbRnvY2RDQupTCbJS3rZyxCbJOxdfjwqtNad7jDQsxOqSpthxLJcVXzFZjMp8kJUcGNFYWQcDhO+wXiPIpGiNLmfCy7As6utq4Yn6YdUZ8cvZN+D82pa81pGSNpw2SBXkwXyv5C4WSuuu+yJNPzNZ4Y+FpOO33XcCE8sLM2J6MMfwpgU/wplVU0BphNjQWj0ubJqfIdNREtElRO5iIeKNBqUKcrHHTItotRoEgkEwsTja3Mfw8c5tiEZjBf2d4lOfcpMD3kgw5xQ9YOSvwYSRRan4quNW8zTD4XAUewkZiA0n9bZKnMioILOCfgzFqToqhbwCa6GFCiyl0+FQbyemlI+FRqPp18FCREyQm8sbsK2zHSG5zZtMYlGI+Op1erBgMxr1uEQCAz1IytQgF08uc0ZNA4xaCmySgwYQkjzoUeusyHif2KB3MuiRGqAK5Vc6UJOkOCzERpvB8UnJV7jv04Z0w5qsSU/B46WxcSx8X4RQTttwUcNcnAj1oDvUmzPh5VgOdpsN359zIV74+j24kib8dOalOPusM3M+9he/t1aZjZwoGekPMUkNMzFYC5wgazQawdeXE9w2/PEwykw2tHuP4/zGOQCUryAP5hieNXEq/nzkE9kU0QRMOlqxIU99Ec9hgg9yPGObmoaEiIgWoz4mhCAXxYuHPkK5yY76utqC2YDqdTrEIVgCeqJ+9MhuHOQJstquwQRlUSq+ozfrGcV4PJ6B3zSCGOj0sJBjAXfa+YBjSqJJD0gb54syi6+6D6O5vAEGPQWj0Tioi7tJ5mTR1nMsb5NeIeKrkztxMOlx2QMhaZCTXFEryBMaGxFgwjivegbmV0zCGdYa+OJBjKmuyngfk0NiUajBAoOBoiloNBqMkcksOjo6M94jlxuYFNYgA0BFeTk2LV+N2eVNYOMcVk5aiqe++/OcSQbLCTcYoSSDCVV1WDBvDma3zMybkMhHWYvuIkPzQY4W3OYNkOtL0zrk9p7jmOgqTAV5MMfw5EkTEQWLS5sW4fFLbsPlE89UdMhTX8SbLhOVq4KcllgUsmF4KHzj8Z9hVetmeBmhcfGd7l249t1f52wWVQpp3LTZAW80kCGxkGuQ1XYNJiiLUvElCXIRyDcgoVhQlGAN5DBYAJ6XKpTxhFxiMbq/KpKXcKpRL2NAyCDkFUC6SjXBVYuj/i70xkKwGbL1l4WIr1wmIg4LGYyThVRB5mQ+yEWQy7hcTjx74y+wctJSNJtrUGVyYtPy1VlDA+QJcqEryINBvKhWyazeWDbTCzktsZC5WCj4GVMUBbvNhua68TCVWzF1cjMs5tyaeXGqWnekF5VmISnu7/OTfJYp05AkFvKquUW0eVNQVtIXMQ7lJht6ogFEuTi6wz6MdVZBo9Eo7oE8mGNYHMN+17krUEM5FR/y1BdR5mbUpV0sROQSC3FKZLF59KxVWRKUZY3zCiZBAdI3UuUmOzyRQF6JhdquwQRlUSq+ozvrISgGTdPQaDSCDjkoWL3FuNJo0gPk0/SECnJr1wHMGsQEvcx96EAbaBj1NGptFYgnuIJ38Mt/NyAk+MHUo/BclmN9ERMZNVj21dbUYOrkZlwy7xwcZ7yw22yIRKMZ74nLhoSICbKBLl7HuZhcjkmNmwayE2R5k57Sk/SAdI9AlcUlDcrIF3txbe5wLyotQqLW31Q18XuViLMIxiL4zn/ejQ+3f5nXQk5ESpCZqHQMFFKGlVlBDuKgrwNjHdWgtHrQBrpo3+mB+juURHKxoOisCnKuccrFZkZjM8x6Q4YExaw3FEyCAmQmyFIF2ayuz4UwehjdWQ9BMcRGKEFmITTqCRrkVFI1ipv0gPSJ026woDcWws7ug5hZNbQKMiBUkQPBIMq0FmgAfLprB8LhyID/brikE+T0uGxRB+rz9eK/33oD0+76B7z67lsZyY14Iy24WBT3aYA4bKXJVYPucC+CTARMnJGSPZ7nwTIs2ASHnmgQ1VYXgCJLLGTJaXrcdGZymmHzRik/NEO8sFeZnXBHhNiKlfa+SAmyrILcX2Kg1WoRDIawv+0AEkhif7gD/3XwQ3yxNb/PMtB31PTISyz29RyXRkyL36tSJ5eLhUiGxEIlNm+TJk5AhGdwWdMiPH7x2oJLUAD5kwZhml5PJLfNG4EwGMg3hgAgnQjU2ypxIiDod+IlVEEW1+8wmLG9sx0uow3lZjsomhrSI/w4wwiNJ/EgkuDx4qGPQHMaTJ3SXNDqkZQg93GxOHb8BN565308degd7PEdxcYPnkcyzGDxmYvgcjllTXqcJJfRoDg3O1qtFkajEbFYDM3l9djjPooFdZMRiUZht9nSI6ZDPag0O6DX6qCn9EX97okX1TEWF75yHwaQo4IsDQqJFaSCLD4yr7Q40S1WsfNVkFOvuyO9qEpVkPvKWPoitysTB4u88/Iu0K/ktyuTT9KTKsgFlFhICbLRBm8siJOhHkwqkIOFWkn7IOfQIMdkEguVVEpFCYpoMXrn4uuzLEaVJmOaXqqCnEtiQSAMBvKNKQJlZWXFXkIW8gryru5DADI1yKO9SU9eQX657SN8IzUMZbDyCpGWf78JTDLbz/enO34vJROFiK847MRmMKMjZTnm8fRg3uafgOX7DJH4312g/yYkNzzEQSEsqJTFWjETTrPZhFgshqmV4/G1+7CQIEeEBDmXvGKg5K7QiIMNqiwudB3aBgAw53CxCASD8EWCuPW1R7B2xpWorR0Do7FSkTXQ0hqc6E5VkFmGzfle8fXucLqCPFDC9OuW7+G/TnyCT7r3ggcPWqvH2Q0z8ePpy/P+m0RCLrEorA8yIKsMmu3Y2XUQJ4MenFU/HQD6nYB5qqjxHK3RaKDRaKDX6oSnLUkOlFaPKBdHIpmEhTJmOPaogYGcYpRGPM9XmO3Ck1CNBmbKmOGRDKgzvgTlUCq+o7ssOEqJ9tFdqgFaGjddgROSBpmFUTf6bd4AobM7EAyip8cLd6QXVpZCIBgckrwCAJ5YeivO6uPne17dzIzGk0LEVxw53NnZhTfaP8fHO7eh1+/HuklXYIzJJb2P1urxzaqp0nrSGmROpkFWfHmDRnwcPq1yPL72HAYARCJCRVxM7k4EPaizC8llITyQh4KYoI+RjZsOhTLHCff6/Ni9pw0hNobDkW68eOgjbNuxc0Ad72AR7easlAk8D4SYKHiez9IhJxIJJJNJxDkWETYOl9EqJFQDVM4m1o2DATrw4KGFBmySgz6p6Vcrmh41HR9RDbIwLCSAdu9xTCwv3IhpNZ6jAeHmVqPRCNP0UlP0vJEgXCabEGuVVI+LhVxicSzgTntD93H2UGt8CcqgVHxP76OpSKhxzKXYCNVgq8LxgJAgxzmmJAaFAEAwIHhy7uk9BgA4EOjA7j1tmNA0ND3cpNpxcOy2pKo3QuOJzWjJSCYKEV/RU3RbzwGEEjE8f/B9vHb4c7zn+Rou2goNhMfjbJIDBR3GltcAkI+aZkHri19BNplTCXLFeDz8xX8DgNSol56i14M6WzmA4jpYAOnqq3yanlyiwjAMvvWntRlV/He6d+Gdv+wC/apyE9X0lB7JZFKoIod7YaVNYFkuI/kVHSzckV5UmB2DTpimTZ4M3wchzHI2wsuGcIZ1zIBaUbnN20i4WKQHQNixz3sc0GhQYXIIjbMF+I6o8RwNpHpBEqIOmYHNYIY3pr4x08VClAqKeuwySX+c2ceg1vgSlIGMmh7FsCyrGjN3EbFSV211oScaBJNgS8rmbcpvV2RIIz73tuNzbzv+z1ePDymJmdDYCD8bwWUTzsRVkxfjpX0fwt0nmShEfCc+eF3G+t937wYA6DVaVNA2TLLXYm/gOGpNZfDFg6irTyXISTFB5oo6KETEbDJBo9GgwVGJEBMVusxhB8uyaQ1y0INzx7UAKOxo2sGg1+ulYSFJnkeQiYBKasFxQnLqDwTwq2nfxRPH3sFe/3HwEKr4Sxrn4AeTL1BsHTRFgYkzqDI70R3xoclVA4ZlMuzNRA/kTP3xwJ9fXV0NNi1fjQNHj+DmTx/FPS3XoWnsONjt+ce1it+rCDNCTXqpfZeb7OgK+zC/pjk1/r0wiY4az9GA3MnCIDXqZQ4JOb0v6XKJidNgkY2ZzvxuqjW+BGVQKr6jO+sZpYTD4WIvIQuNRgOKpqDX6jDG6sLJYA/iHCNJLEa7BvnxJf8keHJq056c59TMGLInpzii+Ofn3IBaowvrc3ifFiK+T1xwa8aoZkqjw1lVU3DvtBuwaflqrJy0FDUGFxZWNmPT8tWwmATpiLyCTIk+yEWMpZDUGKHVaDG1Yhy+dh8BAEQiUTAyiYU0JKTIEgtAqF5qNBqMsbrQHepFNBaVqrW9vX7Uuargi4fAA9JTBaOWUtTOKu2m4UxbvbGZEotM/bEj498NtO+KinLMnjYdzRUN4FyC73Kon+9xIpEAz/OCtZ2+sJP0xDUCgD5VqN/nEUYXx+O53TyGixrP0UBaxmLUp63evNEgXEZxSMjpnSADwvEaCAZh0tLYfmIfPt65LctpSK3xJSiDUvElRxNBwkDTYBkWdbYKHA+6ESuhCnJzw3hYvjaAS4qenBxsRvMpJTEj3XgCAJPqxsGyyyBJO7hkAkYdhbmTp6GpcTzsNhtags1I6vQZDW/JZBI8zyOeYEFr1dFwaTabEI1GMa1yPHa7D2Px2BkIRyKIM0JF7ESGB3LxE2SpemtxoTPsRZmtDgzLQK/XIRyOoHpMFdyxAM4sb8Zl47+BT3z7FLezEhOfSosL7lSC3NfJItPBwpXx7wbCarHAG2cwe8xEbOtsx7zaZoTDETjs9qz3SoNRuDgMOho6rVbSxhYKrVaLUCiMQ+2HoYUGfjaCFw99hCqrC3W1NQV1RlATaas3gzQsxBsNoNxMnBpEIpEIdu9pQ5RjEEiNuC4z2go64ppQmpCjiSAhaPnCqLdX4ai/G4lkUhXOB0owobERYZ7BRePm4+KmhXj9yFb0RP0F9eRUEnH9y8fOw0XjF+B/j3wJHxPC7JaZkh60wV6JrR3tAIB4PC5Vj9kkB71WB10qiSl6gmwyoQfA1Mpx+GvbxwCAUCiMBJdAhI0hxsZRbrILTzVU8BhUPC7EcdNTbXVgGRb+VMV2b/AEmssb8NOplyAWj+Pn565Q3M5KcrIwO3HYL4y67uuFLNrPDbWCDKQSZK8Ps8ecgee/fgdA/iqM+H0LMTFYRFu7AuqPRb71/FrJjg5Iab3f2gX63QcV03qrHVHnbdLTiEkSiyAaUk2tp7vEAgAWPbEabA6nIfrDjafN94SgDORoIkiIzS4N9koc8J2EQU9Bo9GM+uQYyPbknN84teCenErSd/0Lz5gurT8QDAIAxtqr8d97PwQAxONMpgeyCvTHIuaUtd60yvG476P/FB7Vp5wsTgQ9qLFVSJKfYifzQLoqVyVr1GM5Tlrzm4e24cIzFmDq5GZUVVWitmaM4msQm4yqLE58fnIPgOxpepxUQfZjcsVYAIPXcItuLrOqz8DP33kcXDKBSCSKZDKZ9Z0RLd4ibAwWWmi6HInv1aYFP8IzB9/DF552sEmuIFpvtZMxLIRNV5BnVjUBIE16APDoN2/BE3v/ji88+8AmE6C1epw7tgU/nHZhsZdGGGWQo6kIqNXYXnycXWerwFuHtsFY5NHESjNS0ohCxTff+sW4NTgqcUx0IGHiOfXHRZoRkoHRaIBWq8UYi+BV2Rn2osYquFYcD3jSDhYqqB4D6XWMsZZhZ9dB0LQB0WgUoVAYbJLDu4e340dXXAwAcDocBVkDRWdrkJk+XsiMpEH2SRXkwdp+GQwG6Ck9nLCi2uLCvp7jmFo5DuFIBDarNeO9aYu3WEEmB+Zj9qRpeOnop+BS/r+F0HqLqPUcLU+QY3KJhcrGTBeTmY2TYG5/XyanS8BCGTO+J2qNL0EZlIpv8ctJKXiex8aNG7Fo0SLMnz8fDzzwgPQoLxd33XUXmpubM/7bsmWLtP3TTz/FpZdeipaWFqxYsQJHjhwZgb9icFj6DBpQC7RsWMhB30kY9KkGvVFu8TbSjHR8aZqGJmV7FeXiCDIRJLiE1EgWT7CqqiBrNBqYTMJQg2kV47HbnT42hSEhwuPiYlu8iciT066wFyaTEYFAEDzPY2vHPtTZK1FrKwdFUzCbCzP2WPR3rTK70sNCuMwEOe1i4UeV2ZX6d4O/ybCmvrezx0zE9k5BqpNrjLr4ZCLERGGlCm/xJtI88QxEeQaXTzwLT317XUFHF6v1HC0WK0x6uYtFkNi8yZh4xgREwOCSpm/gj8tzj7hWa3wJyqBUfFVzNG3ZsgUvvfQSNm3aBJ7nsXbtWrhcLvz4xz/O+f79+/fj9ttvx2WXXSa9Zk1VOjo6OrBq1SrccsstOO+88/DII4/glltuwSuvvKKKBCEYDMJRoErTcBArkfW2SoTYmGSRo4bPbDQx0vHVaDSgDTTisTga7IKP9ZSKcYjFYgAAhuNAq+xpgMlkQjgcEQaGuA9jSeMcAH0a9FTiVSommWMsZegK9yISjkjnmjcPbsMFjXMBFK56DKS1xOVmO3pjIXDJBMAJhQUxphzLged5uMM+VFpEDfLgT/FWiwW9vX7MqZmItw5tw/Uzzs+pQ07KJBZmuvAOFiKig8xIjC5W6zk6XwVZ9P0lGuTBjbhWa3wJyqBUfFWT+Tz55JNYs2YNFixYgIULF2Lt2rV49tln877/4MGDmD59OiorK6X/TKkpXS+88AImT56MH/3oRzjjjDNw7733oqOjA59++ulI/Tn9Ys/RGa4G9Ho9dDodLLQRLqNNqiCP9il6I00x4ive3Ix1CA2WABAVE+QkC0OqsqSWWIojvqdWjsPX7sPS6ycyxkyrRGKRqiALw0K8MKf0ulwygXeObJeSe4ejcHEXh37otTo4jVb0RAIA0o16HCckxyEmCr1WBzNlhE6nG5I3sVh1mVV9BnZ07QfP8wiHI5JUR0TepJeuII/MeGOXy4l5c2ZjyXnnYN6c2QXrIVDrOTrTB5kBm+QQZqJwGi2Dmpp4ujDQ90St8SUog1LxVcXVsqurCx0dHZg3b5702ty5c9HZ2YmOjo6s97vdbvT29qIxz6O11tZWzJ8/X/rZZDJh2rRp2L59u/KLPwU8Hk+xl5AX8bF2vb0CxhKxeBtpihHfdINlFY4FhAQ5HhcewTIJDpQKK8iA0Ki323MESV6oSp4MelBnV48HMpAeFmJNNaSdcAsuEts721FtcaHeXgk9pZckCoVCatRLDQsB0tZujNwDOTUkZKh6VJNJSKprbeWgtXoc9XcjmUxKkw5FRIlFRKZBLrVzhFrP0X0ryL5oCE6jFVqNNmucMiE/ao0vQRmUiq8qzmput9BYVFVVJb1WUSFcJDs7O7Pev3//fuj1emzatAmLFy/GZZddhpdeeiljf/J9AUB5eTm6uroKsfySgqYpBIJBGDgd9ntO4OOd2xAIBoq9LMIAiJMQG+zpRj2xghznZBVklejJjUYDdDodykx2WGkTjvnd4HkeJ4I9qE016anBA1lEHBZSbXHBHfUDENwrlqTkFbn8gpVGfHyea1iIfIpepVlIkKlTqCaKbhaiHzKQbfeWEDXII+xiQUgfvyY9jSgbT8kriP6YQCgEI3ZExePxnMkuAEkrKW/KEf9/X69PQJBXAMDkyZOxYsUKfP755/iXf/kXmEwmLF++HNFoNKvBh6bpnPsC0gm6iNFohMViQTAYhN1uz3k3Ul5ejnA4DJPJhHg8Llk+iZhMppTOMgybzZaxD6/XC0C4CQgGg7BYLIhGo4j2qdSYzWYYDELHvMViQU9PT9Y6KioqEAgEYLPZEA6Hpc9SxGKxgKIoxONxmEwm6XeLaDQaVFRUwO/3w263w+3x4Isvt6Ir4kM0yeC59ndAcxo01NfBarHAYDDA5/Pl3Edvby8cDgcCgUDWZ221WqHT6cBxHCiKQm9vb8Z2rVaL8vJy+Hw+uFwu+P3+rH3YbDZoNBokEglhzK/fn7Fdp9OhrKxM2kdvb6/kDStit9vB8zx4nodOp8vah16vh8vlkvbh8/my7LQcDgcSiYTkKRwIZN5ABAIBVFZWSvvwer1ZDacOhwMcx0Gn04HneQRTVm0iNE3D4XBI++jp6ZEqdyJOpxMsy0Kv14NP8vD1+uDSmnDIexK+Xh8oPQWL1YLekB+0TvjMGSae8X13uVyIx+OgKAocx2UlQwaDATabDYFAAA6HAx6PJ+uRe1lZGaLRKAwGA1iWzdpHvuMpGo0gHIlgkqMWX7kPQZfUgNLqoE9q4Pf7M75n/R1PIoU8nkLBIMKRCCpNDnQGvQiGQ3jr0DZsPOcn8PX64HDY4Ha7s46nYDAoVfJFLBYL9Ho9WJYd0vEUDAaF36U346TfA5Zj4Q/4YbGY0dnZBV+vD4c8J+CgzPD7/Sgrcw35eIrHY/D1+tDsqMNnx3bjisnfxMmTHaiqrJSOp+7ubvh6ffAGe+EwWxFnGGlUeCGOJ4qi4HQ6R/R4isViWdcFmqZht9vh9/vhdDpzHguFPp58Xh/CkQgMOgqhWBg90QDslAm+Xh+sFou05uFcn0TUeH1S6njq+3tOp+tTMY6nRCKBUCiUtY9CHU/y+A50faqsrEQ+RixB3rVrF66//vqc22677TYAQjIsNqOIXz7xUayc7373u7j44ovhdAqVksmTJ+PIkSN47rnnsHz5chgMhqwvL8Mw0vv7ku8DEkXe+bbbbOnxnvm6JkUtTN99iD+L261Wq9T4k+/3DLROm80mvbcv4uc60D4WP/WPYGQm6x962/Chtw30jt9LJuv59iF+vv2J48Ubl3z7cLlcA+5DZKB95Iv3UPYh/u9w9lFWVpZzu/wmLp8tjbiP8vLynNvFuDocdricLkyjJuDk5164nOl162kaBp1wUrTbbVnrFXWLNE1LHsV9EeMhPtnpi/jdpShqwH2Iv5/lEujudmNW7STs8RzBOEc16uwVMBlNcDldOT/XfMdT3+1KH0+RaAw+Xy9qbOXoYYJoD3Sg3GTHjIaJ0Ol1GD9uXIZ8RdxHf1q4gY6FvsdTIsmD54F6VxW8TAiUnoJeT4GiKNjtdkSjMUR4FvXOKjgcDuliKt9Hf1RWVsJssSAeZ/HNxha82C4c77rU90NcT5xhkUgkkdDysBksMNA0LBYzaJoumePJaDQOGJd8x0IhjyedTo9oLA4zZQSHJLzRIMbYyuByuuByOTPWPJzrU9/tark+KXU8MQyT833k+pRGyeMJyJ3LAYU7nuR/92CuT7kYsedi8+bNQ1tbW87/Lr30UgCZuhHxTjhXcDUaTdaXq6mpSZJQVFdXZ939ezyefu8UCAJ/XPJ/cWbVFNCpCXq0Vo9vNczCMxf+c5FXRugPyerN7ECYiSLMpCs1cZkPslo0yEB2o97xoBu1YoMerY4GPRHxRF9tLYM74sffD23F+TJ5xUh8rmLTYpXFBbc4sCRVgZKm6MklFqfgaGA2maDVatHoGoMgE0V3uBcJLoFoNP19EitFYTYOCz1yPsiEdDOkUU8jyjHwRgOS2xBp0CMQlEUVwrHq6mrU1tZi69at0mtbt25FVVUVampqst5///334yc/+UnGa3v27EFTkzBNqKWlBdu2bZO2RaNR7N69G7NmzSrQXzA08t1tqYHJDY2w0SawSU4yWbfR5oKY8ZcqxYivOHlOq9Gi3l6J48H0DSKjMh9kEZNJSK6mVoxDW89xHPO7US8lyOrRHwPp5LTa4oKPC+OtQ9twQVMqQS6ge4UcsemuypzWIIuJsTRFL9yLKouoQR76TYZGo4HZbIJWo0VL9QTs6NwPIFOHLD6ODTNRWEp91k/CAAAgAElEQVS0SU+t52jRk95ECU163lgQZcTibcioNb4EZVAqvqo5q1133XXYsGEDPvnkE3z++efYsGEDbrzxRmm71+uVTtLf+ta38P777+Opp57C0aNH8eyzz+Lll1/GypUrAQBXXXUVWltb8dhjj2H//v1Yv349amtrsWjRoqL8bX3J5S2qFiY0NiKUiOHSpkXYfOH/xbcnnglPpLcgZvylSrHia5A5WYhWb4BQQVabDzIgJME6vQ42gxlVFic+OLZLqiAbVOJgIUJRQvNqT5cH/9P+GfS8FmU6C3Q6Hex5HhsrvoZUwltpccqGhaSa9FLNehkV5FOswouPI+fIG/VkGlYxQfbFQvjd1lfgifihHYFBISOJWs/RGaOmOQY9EVkFmTTpDRq1xpegDErFVzVntZUrV+KSSy7BmjVrsHr1alx88cVSwgsAV199NR5//HEAwIIFC/Cb3/wGL7zwAi6++GI8++yz2LBhg2QTV19fj4cffhh/+ctfcNVVV8Hj8eDRRx9VTZUjnxZHDYhm/HeduwINpgqsX3w9XrnlNwXzGy1FihVfcbDGWEcVjgbSCTKb4GDQqy9BBgSZRSAYRJXOhl3dB+F39yIQDIKm1JUgh8Jh7N7Thi89+5EED4NWj9172jIGdRQaWjbRzy0myCl7N4YVei7cEXkF+dQSJrmThThRLyhrsEkmeQSCQRzoOYF23wnc//YzCPRp4hntqPUcLfqYm/QGxLg4GTN9iqg1vgRlUCq+qjmidDod1q1bh3Xr1uXc/vbbb2f8fNFFF+Giiy7Ku79zzjkH55xzjqJrVIp4PK5qvZjL5cQ81+xiL2PUUqz4pq3eqrCr+1B6PQkWlFa0eVPHTaIIw7LYvacNPka449/asx8z9tTjjAlNIyZdGAxND3wHTCLdvHo43I1VrZtB73oc8f/3wYisQafTCX7MlAk8nxr1TJvAsiwSXAKJpNC0VW4WPjfqFAetWMxmaDQaTKkYi2MBN4JMBDaYEY5E4PX6MOPh72c08r7V0YqGe6+CQU8h9tv3Fflbi41az9F9fZC9fFpiQSrIg0et8SUog1LxVdfV8jShr+UOobQoVnxzDQsBUhpklVaQW/79Jqxq3Yz9IWEg0Gc9+7CqdTMa77+myCvL5Ollt+Ob1ZnNq9+smoqnlt0+ousQ/ZjlXsjiIA9vLACHwQJKq4dOrzvlWGu1WphMJlA6PaZVjkNr1wEAQHv7AfT0ePHrud9HgzndLU5r9Th/7By8fPW/DvOvUw9qPUenfZANiLJx9MgqyESDPHjUGl+CMigVX5IgEwglgoEWJBYNjvSwEACIJzhJg6y2CvITS2/Nck1ZXD1Vda4pTWPqYTWYheZVjQ5skoPNZMaEmvoRXYfkZCGbpheJCAmyO+xHpdmRet/wJCpWq2ALNrVsHF7Z+QG+85934+Od2/BG22e4d+cLiCbi0ABSI6/LbAXFquvmqxSRV5AjXBy90SBcxrSdG4FAUA5yRBEIJYIosai2uBCIhxFl4zBRBrAJFhZKSJ7VVkGeVDsO1l1y1xQOVhW6pkxobESQjeLi8QtxTuVUvOfZDV8sOOLNqxSV1iFLFeRUtaQ74huWxZsci0XQhjujFP7i3ocAG8Fd258BpdHh2vrF+NC7F7PLJ2DZ2Ln4xLcPXSGfqiQxpYqYIJsoGoF4BHaDGZRueE8MCARCbkiCTCCUCBqNBrSBBhNnUGerwLGAG5PK6/tUkNV1EZ3Q2IhQMoYLG+Zh+fh5eP3ol+hlIqpzTRGbVw8cOoQjR4/h3BnzMaGxccSbV6WBChYX3H0kFu6wX2rQG2410Wqx4PznbwfLpydseRmhUe+xg6/jkdk/Rk1NNWrGVGNRbAbCEfXFrBQRE2TRtrGceCATCAWDHFVFgHTQljbFjK+BFhLkBoegQ55UXg8mwYIWB4VAXQmyy+XEq6nE0+8PYNHEmUVJPAeD2Lw6edLEvFPFCg0l80I+7O8EACQ4IYmVW7wN10dap9PhoTk/wPPHPsLn7n3g+ARorR7zKyZhxYRzcPnSi3Dk2DH09vrhcNgxZXKzKmN2qqj5HK3VaoGkILMQLd5O1bHkdEXN8SUMn5JzsTidIAdnaVPM+OZq1ItzrGo1yMDoc00pZnxFL+QqixOfn9yTsc0d7sXM6qbU+4Z/ap/TPA1/OfEZEnxC0hqX2xyYO2UGKisrUFmZezRsKaDmc7RGq0knyEZxSIi6Jk+qHTXHlzB8lIqv+q6WpwHEpLy0KWZ8RR3yWHuV1KjHJjnpkSzRKQ6fYsaXorM1yCJuWQVZCU/caVMmI6ZJ4JKmhXjikttx+cQz0RPxnxZSCjWfo8Vx0ya9Qaogi68RBoea40sYPkrFl1SQi4BthCZvEYpDMeMrd7L428EvAIiT9NTpgzwaKWZ8xcpwldklTdMTyRwSMvyKYl/5y52Lr1et/EVp1HyO1mq0CASDSHIJvNn+Bc62NWPJN84q9rJGFWqOL2H4KBVfkiAXAY/Hg8rKymIvg1AgihlfaZqerILMyCQWGpU16Y1Gihlf8VF6udmO3lgIXDIBvVaoHnaH5RpkZR65jzb5i1Ko+RwdDIWwe08bIlwMQS6GFw99hGqrC1UVFafFzYsSqDm+hOGjVHxJgkwglBBiYlRtKYMvFkSMYxBPsERiUSJoNBpBPsECTqMVPZEAqq0uxDkWETYOl9EqvIc0bZUsZz25BqxskuE73bvwzlu7QL/7IOKbRmaqI4FwOkCetxIIJYRWqwVFU9BptaizVeBEwAM2qd5BIYShIzXqyYaFuCO9qDA70gk0oWT590U348yqKaA0wpMDWqvHhU3zVTdch0AY7ZCrJYFQYhhkThZHA10pFwt12rwRhg6do1FPaf0xQb20TJgMi94ATuYuYtLRqhuuQyCMdkipgUAoMQSrtzAa7MLIacEHmVSQSwVRPlFlcUkJsqA/To2ZVkh/TFAnkyZOQOTvDL59xlm4Ztq5eHH3u+gO+U4LdxECYSQhCXIRqKgoXf9QQvHjazSmnCzsVWj3nkA8Ibd5K+bKSoNix1caN212Sk4WQgXZBYBMVVOCYse4P+RTHU83dxGlUHN8CcNHqfiSM2kRCAaDsNvtxV4GoUAUO76i1dtYRxXePrwdbIIFrSc2b0pR7PjS0rhpJw6dEIaFyCvIZGjE8Cl2jAfidHUXUQq1x5cwPJSKL7laFgGLxVLsJRAKSLHjS2dokLsRT3CgtcTFQimKHV+xCS9Tg+yXKsg0SZCHTbFjTCgsJL6ljVLxJQlyEYhGo8VeAqGAFDu+4jS9MdYy9EQCiLIxUkFWkGLHl6aE+MqHhXSHfVIFmbhYDJ9ix5hQWEh8Sxul4kuulkWAHJylTbHjq9Vqoaf00Gt1qLGWgeOTpIKsIMWOL9WngszzvFBBNgsVZOJiMXyKHWNCYSHxLW1IgkwgEPJiNKR1yHqtDjqtFhqNhiTIJYBOp4NWq4WFMgIAQmwU7rAPlRZRg0wqyAQCgTBcSIJMIJQgog65wuRAkk/CE/GT5LiEoGgKGo0GVRYnDvg6oNfqYKaM0Ol00Ol0xV4egUAgjHpIgkwglCAGgwGBYBC7Ow4hyfO4/+1nEAyFir0sgkJQohey2Ynd7sOoTA0JIfpjAoFAUAZyNi0CZrO52EsgFBA1xHfcr65CPMFKP7/V0Yq3nv1HGJ6nEPvt+0Vc2ehHDfGVvJAtTnzVfQiVZnGKHjmlK4EaYkwoHCS+pY1S8SUV5CJgSOlDCaWJGuL7/GU/xzfKJ4HSCgkTrdXjzKopePnqfy3yykY/aogvJXkhu/C1+7CUIIvSGsLwUEOMCYWDxLe0USq+qkmQeZ7Hxo0bsWjRIsyfPx8PPPAAEolEzveuW7cOzc3NWf8tWbJEes+yZcuytu/Zs2ek/px+IR20pY0a4mvRGGA3WsAlOVBaHdgkB5fZCoolOuThoob4Sk4WZieOBrpRJUosSAVZEdQQY0LhIPEtbZSKr2rOplu2bMFLL72ETZs2ged5rF27Fi6XCz/+8Y+z3rt+/Xrceuut0s89PT347ne/ix/84AcAAIZhcOzYMTz33HNoaGiQ3udyuQr/hwwCYlJe2qghvk6nHWGewdL6ufjWmBl4r/srBNgYHA4yPWq4qCG+khdyKjGWJBZEg6wIaogxoXCQ+JY2SsVXNWfTJ598EmvWrMGCBQsAAGvXrsWGDRtyJsg2mw02m036+Ze//CVaWlpwww03AAAOHjwIjUaDGTNmqHLsak9PDyorK4u9DEKBUEN8JzQ24u6F18NiNsNkMuGb0VkIRyKY0NhY1HWVAmqIr9iMZ9EIifLT297AuKQTFznOQ2VFMVdWGqghxoTCQeJb2igVX1VILLq6utDR0YF58+ZJr82dOxednZ3o6Ojo999u374db775Ju644w7ptQMHDqC+vl6VyTGBMBK4XE7MmzMbtIGG1+cDbaAxb85suFzOYi+NoACUXo9AMAjfcQ8AoDPuw4uHPsKu3bvh8/UWeXUEAoEw+lFFBdntdgMAqqqqpNcqKoQySGdnJ2pqavL+2//4j//A0qVLMWnSJOm1/fv3Q6fT4Yc//CH27NmDxsZG3HbbbWhpaSnQX0AgqA+Xy4l5rtnFXgahAFAUhfOfvx0sL/Rp8ADe6d6Fd/5nF+g39Ihv+qC4CyQQCIRRzoglyPF4HJ2dnTm3xWIxAJkd2OL/Zxgm7z5PnjyJ999/H3/6058yXj9w4AACgQDWrl2L6upqvPDCC/je976HV199FfX19Vn7ERN0EaPRCIvFgmAwCLvdDo/Hk/VvysvLEQ6HYTKZEI/HEYlEMrabTCaYTCaEw2HYbLaMfXi9XgDCTUAwGITFYkE0Gs0SlpvNZhgMBkSjUVgsFvT09GSto6KiAoFAADabDeFwWPosRSwWCyiKQjweh8lkkn63iEajQUVFBfx+P+x2O4LBIOLxeNY+9Ho9WJaFwWCAz+fLuY/e3l44HA4EAoGsuFmtVuh0OnAcB4qi0NubWeXSarUoLy+Hz+eDy+WC3+/P2ofNZoNGo0EikYBer4ff78/YrtPpUFZWJu2jt7cXLMtmvMdut4PnefA8D51Ol7UPvV4Pl8sl7cPn84HjuIz3OBwOJBIJaTJdIBDI2B4IBFBZWSntw+v1ZjWcOhwOcBwHnU4HnucRDAYzttM0DYfDIe2jp6cHyWQy4z1OpxMsy0Kv1yORSCDUx+eYpmnY7Xb4/X44nU54PB7wPJ/xHpfLhXg8DoqiwHEcwuFwxnaDwQCbzYZAIACHw5FzH2VlZYhGozAYDGBZNmsfhT6eREbqeOI4DqFQqOjH030zVuDFE5/gC287EnwStFaPxXXTce34xejp6SmZ44miKDidzhE9nmKxWNZ1gRxPpXN96vt7TqfrUzGOp5G+PsnjO9Dx1J8UQ8P3XVGB+PLLL3H99dfn3HbbbbfhwQcfxLZt2yRxdSwWQ0tLC/7rv/4LM2fOzPnvHn/8cTz//PN44403Ml5nWRbxeBxWqxWA4JBx2WWXYdmyZfjZz36W8d6tW7di7ty5w/3zhoTb7Sb6pxKGxLe0UUt83/3gQ/zru0/h3a5d0Gt14JJJXD7xTNy5+HrMm0OeHAwHtcSYUBhIfEsbpeI7YhXkefPmoa2tLee2rq4uPPjgg/B4PFKCLN699/dHvv/++1i6dGnW6xRFZeiPNRoNmpqa0N3dPZw/QTFE+QihNCHxLW3UEt+W6dMRe4/DxeMX4jvTzsV/7/sQ3SEfacRUALXEmFAYSHxLG6Xiq4omverqatTW1mLr1q3Sa1u3bkVVVVVe/THP89i5cyfmz5+fte3qq6/G5s2bpZ+TySTa2trQ1NSk/OJPgb6PPAilBYlvaaOW+LpcTrz20w24+1vfQ63RhTsXX49XbvkNacRUALXEmFAYSHxLG6Xiq4omPQC47rrrsGHDBtTU1ECn02HDhg248cYbpe1erxcGg0GqMJ84cQLhcBgTJ07M2te5556LP/7xj2hubkZDQwO2bNkCv9+Pq666asT+nv6QW9QRSg8S39JGTfEljZiFQU0xJigPiW9po1R8VZMgr1y5El6vF2vWrIFWq8WVV16JlStXStuvvvpqXHHFFVi9ejUASA0BDocja1+rVq1CMpnE3XffDa/Xi5aWFmzZskU1B4XYGEEoTUh8SxsS39KHxLi0IfEtbZSK74g16akV0qRHUBoS39KGxLf0ITEubUh8Sxul4qsKDTKBQCAQCAQCgaAWSIJMIBAIBAKBQCDIIAkygUAgEAgEAoEgg2iQZdZyBAKBQCAQCITTh3x9aKd9gkwgEAgEAoFAIMghEgsCgUAgEAgEAkEGSZAJBAKBQCAQCAQZJEEmEAgEAoFAIBBkkAR5BGEYBnfddRfmz5+Ps846C7///e+LvSTCMDh69ChuvvlmzJ8/H2effTbuv/9+xONxAMIo9JtuugmzZs3C8uXL8d577xV5tYThsH79eqxYsUL6mcR39MOyLO677z4sXLgQCxcuxN133w2GYQCQ+JYKfr8fa9euxYIFC7B48WI89NBDSCQSAEiMRzMMw+CSSy7Bxx9/LL02UDw//fRTXHrppWhpacGKFStw5MiRAX8PSZBHkF//+tfYvn07nnjiCdxzzz147LHH8NprrxV7WYRTgGEY3HzzzaBpGn/605/w0EMP4c0338TGjRvB8zxuueUWOJ1OvPjii7jiiiuwZs0aHDt2rNjLJpwCn3zyCV588UXpZxLf0uDXv/41/v73v+PRRx/FY489hg8++ACPPPIIiW8Jcc8996CrqwvPPPMMHnzwQbz88st44oknSIxHMfF4HP/0T/+E9vZ26bWB4tnR0YFVq1bhsssuw5///GdUVFTglltuQTKZ7P+X8YQRIRwO8zNmzOA/+ugj6bVHHnmEv/baa4u4KsKp8sUXX/DTpk3jQ6GQ9Npf//pX/swzz+Q//vhjfsaMGXwwGJS2fe973+M3bNhQjKUShkE4HOaXLFnCX3vttfwNN9zA8zxP4lsC+P1+ftq0afyHH34ovfbnP/+ZX7lyJYlvCTFnzhz+73//u/TzfffdR2I8imlvb+cvu+wy/tJLL+UnTZok5VMDxfO3v/1tRq4ViUT42bNnZ+RjuSAV5BFi7969YBgmw29v7ty52LVrFziOK+LKCKdCU1MTNm/eDIvFIr2m0WjAMAxaW1sxdepUWK1WadvcuXOxY8eOYiyVMAw2btyIBQsWYMGCBdJrJL6jn61bt8JoNOLMM8+UXrvyyivxhz/8gcS3hHA6nfjrX/+KaDSKrq4ufPDBB5g2bRqJ8Sjlyy+/xFlnnYXnn38+4/WB4tna2or58+dL20wmE6ZNm4bt27f3+/tIgjxCuN1uOBwOGAwG6bWKigqwLAuv11vElRFOhbKysoyLazKZxDPPPIO5c+fC7Xajqqoq4/3l5eXo7Owc6WUShsH27dvx+uuv45//+Z8zXifxHf0cPXoUdXV1ePXVV3HxxRfjvPPOwwMPPACGYUh8S4i7774bn3/+OebMmYOzzz4bFRUVWL16NYnxKOXaa6/F7bffDpPJlPH6QPHMt72rq6vf36dXYM2EQRCNRkHTdMZr4s9iYwhh9HLfffdhz549ePHFF/HEE0+AoqiM7TRNg2XZIq2OMFQYhsH69etx5513wuFwZGyLRqMkvqOccDiM48eP45lnnsE999yDcDiMe+65BxzHkfiWEEePHsXUqVPx05/+FKFQCL/85S/xwAMPkBiXGAPFM1/+NVDuRRLkEcJgMGQFQ/y5790QYfTA8zx+9atf4bnnnsOmTZswceJEGAwGhEKhjPcxDAOj0VikVRKGyiOPPIJx48Zh+fLlWdtIfEc/er0eoVAIDz74IMaOHQsAuP3223H77bfjiiuuIPEtAY4ePYp7770Xb7/9NsaMGQNAOHZvuukmXHPNNSTGJcRA5+R8+ZfT6ex3vyRBHiGqq6sRCATAMIx0J+N2u0HTdFaFijA6SCaTWL9+PV555RVs3LgR559/PgAh1nv37s14r8fjQWVlZTGWSTgFXnnlFbjdbsyePRuAYAmWSCQwe/Zs/OQnPyHxHeVUVVVBr9dLyTEANDY2Ih6Po7KyEvv27ct4P4nv6OOrr76CxWKRkmMAmD59OhKJBIlxiTHQNbe6uhputztr+8SJE/vdL9EgjxBTpkwBRVEZovCtW7di2rRp0OvJfcpo5P7778crr7yChx9+GEuXLpVeb2lpwd69exGJRKTXtm7dilmzZhVjmYRT4Omnn8arr76Kl19+GS+//DKuueYaTJ8+HS+//DKJbwkwa9YscByHtrY26bUDBw7AYrFg1qxZJL4lQFVVFQKBADo6OqTXDhw4AEBosiYxLh0GOie3tLRg27Zt0rZoNIrdu3cPGG+SII8QJpMJl19+Oe655x7s3LkTb731Fh5//HHceOONxV4a4RTYsWMHnnzySaxZswbTp0+H2+2W/luwYAFqa2uxbt06tLe3Y/PmzWhtbcU111xT7GUTBkldXR3GjRsn/We322E0GjFu3DgS3xJg/PjxWLJkCe644w589dVX+PLLL/HQQw/hO9/5DhYtWkTiWwLMmjULU6ZMwR133IG9e/dix44duOuuu/Dtb38by5YtIzEuIQY6J1911VVobW3FY489hv3792P9+vWora3FokWL+t2vhud5fiT+AIJw1/KLX/wCf/vb32CxWHDTTTfhpptuKvayCKfAAw88gMcffzzntq+//honTpzA+vXr0drairFjx+KOO+7AN7/5zRFeJUEpNm7ciG3btuHpp58GABw5coTEd5QTCoXwq1/9Cn/729+g1+tx+eWXY+3ataAoisS3ROjq6sK9996LTz/9FBRF4cILL8TatWthNBpJjEc5zc3NeOKJJyQ3qYHi+d577+G+++5DR0cHWlpa8G//9m8ZEqtckASZQCAQCAQCgUCQQSQWBAKBQCAQCASCDJIgEwgEAoFAIBAIMkiCTCAQCAQCgUAgyCAJMoFAIBAIBAKBIIMkyAQCgUAgEAgEggySIBMIBAKBQCAQCDJIgkwgEAgqhOM4PProo7jgggswffp0LF68GHfddRd6enpGfC0rVqzAxo0bR/z3EggEQrEgCTKBQCCokN/85jd47bXX8Itf/AJvvPEGNm7ciH379uFHP/oRiH09gUAgFBaSIBMIBIIKeemll7B69WqcddZZqKurw7x58/DQQw/h66+/Rmtra7GXRyAQCCUNSZAJBAJBpXz66adIJBLSzw0NDXjttdcwefJkhEIhrF+/HosWLcL06dOxbNkyvPHGG9J7m5ub8dprr2H58uVoaWnBrbfeimPHjmHFihVoaWnBDTfcgO7ubgDAww8/jDVr1uDOO+9ES0sLli1bhjfffDPvup5//nksWbIEs2fPxnXXXYedO3dK2z777DNceeWVmDlzJs4991z87ne/K8AnQyAQCIWFJMgEAuH/s3fm8VFVd/9/z57JZGYyWdgh7IuiUFDrhkoVrfhYF1DEFqzaqn30oU/tr1Vba6m1QGt9rFWL2tbWulJR2oI7grIpGCCsAmELYck2+5ZZ7++PYYYZEkhmSzLX8369fEnucu75nHvvd773nO/5HkEPZPbs2bz++utMnjyZhx9+mHfeeQeXy8Xw4cMpKipi/vz57Nu3jxdffJFly5Zx7rnn8otf/IJgMJgo449//CPz58/nueee4/3332fmzJl85zvf4bXXXuPIkSO8+OKLiWNXrFhBJBLh7bffZvr06cyZM4fdu3e3qdeKFSt46qmneOihh1iyZAmXXHIJt912G01NTUQiEebMmcPkyZN59913eeSRR3j22WdZvXp1l7SZQCAQ5Ap1d1dAIBAIBG259957GTJkCK+99hpvv/02b775Jjqdjjlz5vC9732PiRMnMnv2bEaNGgXAHXfcwZtvvkljYyMDBw4EYk72+PHjgViP8ogRI7jqqqsAuPzyy9m/f3/ieiaTiV//+tdotVqGDRvGp59+yuLFi/n5z3+eUq+//OUv3HXXXVxxxRUA/OAHP2DdunW8+eabfPvb38bhcFBeXs6AAQMYMGAAf//73xP1EQgEgkJBOMgCgUDQQ5k6dSpTp07F5XKxbt06Fi1axOOPP87gwYO5/vrrWb58OW+++Sb79+9nx44dAESj0cT5yY6pTqejX79+ib+LiopSepvPOOMMtFpt4u+xY8dSW1vbpk779u3j//7v/3jqqacS24LBIH369KG0tJS77rqLX/3qVyxcuJDJkyfzrW99i8rKytw0iEAgEHQRwkEWCASCHsauXbtYvHgxDz/8MBDr3f3mN7/JVVddxfTp01m3bh3vv/8+mzZt4rrrrmPmzJlUVlYyY8aMlHLU6lQTr1SeOqru5GMjkQgKhaLNcZFIhAceeICLL744ZXtxcTEAP/7xj7nhhhv4+OOPWblyJbNmzeKxxx5j2rRpnW8AgUAg6GZEDLJAIBD0MCKRCC+//DI1NTUp2xUKBUajkbKyMpYtW8YTTzzBD3/4Q6ZMmYLT6QTIOAXcnj17Unqft2/fngjfSGbIkCE0NDRQVVWV+O/FF19kw4YNNDc3M3fuXPr378/3v/99XnvtNW688Ubee++9jOokEAgE3YVwkAUCgaCHceaZZzJ58mTuu+8+lixZQn19Pdu2bePJJ5/kyy+/ZNq0aej1ej788EMOHz7MmjVrePTRRwFSwibS4ciRIyxYsID9+/fz3HPPsX37dm666aY2x91+++28/PLLLFmyhEOHDvHMM8/w1ltvMXToUMxmM8uXL+c3v/kNdXV1bN26lerqas4888ys2kMgEAi6GhFiIRAIBD2QP/zhD7zwwgs8//zz/PKXv0Sr1XLuuefy6quv0rdvXx5//HF++9vf8uqrrzJgwADuuecenn76aXbu3MnIkSPTvt7YsWNxu93ccMMNVFVV8cILLzB48OA2xwTEjtUAACAASURBVE2dOhWr1cozzzxDU1MTQ4cO5dlnn2XMmDEALFy4kHnz5nH99dej0+mYOnUq9957b7bNIRAIBF2KQhJLMgkEAsFXmqeffpp169bx+uuvd3dVBAKBoEcgQiwEAoFAIBAIBIIkhIMsEAgEAoFAIBAkIUIsBAKBQCAQCASCJEQPskAgEAgEAoFAkIRwkAUCgUAgEAgEgiSEgywQCAQCgUAgECQhHGSBQCAQCAQCgSAJ4SALBAKBQCAQCARJCAdZIBAIBAKBQCBIQjjIAoFAIBAIBAJBEsJBFggEAoFAIBAIkhAOskAgEAgEAoFAkIRwkAUCgUAgEAgEgiQKykHesmULs2bNarN9xYoVTJs2jRkzZvDPf/4TgGg0yiOPPMKMGTOYNWsWdXV1XV1dgUAgKEiErRUIBF911N1dgc7y5z//mf/85z/o9fqU7aFQiPnz57N48WL0ej0zZ85k8uTJbN68mWAwyKJFi6ipqWHBggUsXLiwm2ovEAgEhYGwtQKBQFBADvKgQYN4+umn+elPf5qyfd++fQwaNAiz2QzAxIkTqa6upqamhkmTJgEwfvx4tm/f3m65GzduZNCgQSnbioqKMBgMuN1uTCYTLS0tiX2LNjXw+5WH2PrLKSjCAfR6PYFAAJ/Pl1KGXq9Hr9fj9XoxGo0pZcSpqKjA7XZjMBjw+/34/f6U/cXFxeh0Ovx+PwaDAavV2m4ZLpcLo9GI1+ultbU1Zb/BYECj0RAIxOpqs9lS9isUCioqKnA6nRiNRjweD4FAoE0ZarWaUCiETqfDbre3W4bD4cBsNuNyuQgGgynHlJSUoFKpCIfDaDQaHA5Hyn6lUkl5eTl2ux2LxYLT6WxThtFoRKFQEIlEUKvVOJ3OlP0qlYqysrJEGQ6Hg1AohCRJKBQKAEwmE5IkIUkSKpWqTRlqtRqLxZIow263Ew6HU44xm81EIhEUCgUKhQKXy5WyX6PRUFpamijDZrMRiUTalBEOh1GpVEiShNvtTtmv1Woxm82JMqxWK9FotN0y1Go1kUgEj8fTpgyTyYTT6aS0tJSWlhYkSUo5xmKxEAgE0Gg0hMNhvF5vyn6dTofRaMTlcmE2m9sto6ysDL/fj06nIxQKtSnjdO9TnPLycrxeL3q9ntbW1jbvQiG+TyaTCbfbnXif4s9hZ9+n7qKn2No4ZWVl+Hw+Wdlak8nUrp0sdFsbR5IkzGazbGxtaWkpoVAIlUpFNBqVja1Nfp/i9qlQ36dkW5tcRvxdyMTWKqST70AP5vDhw9x///2JoT2A6upqXnnlFf7whz8A8NRTT9GvXz9qamq48sorufTSSwG47LLLWL58OWp16jfBxo0bmThxYqfr8NK6g/zyPzvY+PAVlJfocqCq5xB3tuSG0FVYCF3dT0+wtXEKqd3SQa66QL7a5KoL5KstG10FFYPcHiUlJSlfUfGvnpO3R6PRnNx8rTrWZKFIwXxXdJqTvwLlgtBVWAhdPZOutrVxCr3dToVcdYF8tclVF8hXWza6Ct5BHjZsGHV1dTgcDoLBINXV1Xzta19jwoQJrFq1CoCamhpGjhyZk+tpVLEmC4ajHRxZeBgMhu6uQl4QugoLoatn0tW2Nk6ht9upkKsukK82ueoC+WrLRlfB9qcvXboUn8/HjBkzePDBB7nzzjuRJIlp06bRu3dvpkyZwtq1a7nllluQJIl58+bl5LoaVSyWNRiRn4NstVqprKzs7mrkHKGrsBC6ehbdZWvjFGq7dYRcdYF8tclVF8hXWza6CioGOR+kGxf3/vZj3PPKJt774STG9DXlsWZdT3NzsyxfEKGrsBC65EmmMchybTe56gL5apOrLpCvtmx0FXyIRVcj5xALgUAgEAgEAoFwkNPmxCQ94SALBAKBQCAQyBHhIKeJ6EEWCAQCgUAgkDfCQU6ThIMswx7k7lycIJ8IXYWF0CVIRq7tJlddIF9tctUF8tWWjS7hIKeJTsZ5kE9epUguCF2FhdAlSEau7SZXXSBfbXLVBfLVlo0u4SCniZxDLIxGY3dXIS8IXYWF0CVIRq7tJlddIF9tctUF8tWWjS7hIKeJnCfpnbyuu1wQugoLoUuQjFzbTa66QL7a5KoL5KstG13CQU6TxEIhMuxBbm1t7e4q5AWhq7AQugTJyLXd5KoL5KtNrrpAvtqy0SUc5DTRyniSnkAgEAgEAoFAOMhpI+cQC4FAIBAIBAKBcJDTRs6T9AQCgUAgEAgEwkFOGzn3IBsMhu6uQl4QugoLoUuQjFzbTa66QL7a5KoL5KstG13CQU4TtVK+k/Q0Gk13VyEvCF2FhdAlSEau7SZXXSBfbXLVBfLVlo0u4SCniUKhQKNSEJThQiGBQKC7q5AXhK7CQugSJCPXdpOrLpCvNrnqAvlqy0aXcJAzQKdWyjLEQq/Xd3cV8oLQVVgIXYJk5NpuctUF8tUmV10gX23Z6BIOcgaoFApZhljYbLburkJeELoKC6FLkIxc202uukC+2uSqC+SrLRtdwkHOAK1aIcseZIFAIBAIBAKBcJAzQqOUZw+yQCAQCAQCgUA4yBmhVinFSnoCgUAgEAgEMkU4yBmgVckzxEKhUHR3FfKC0FVYCF2CZOTabnLVBfLVJlddIF9t2ehS57AeeSMajTJ37lx2796NVqvlscceo6qqCoDm5mbuv//+xLFffvklP/7xj5k5cybXX389RqMRgAEDBjB//vyc1Eev08oyxKKioqK7q5AXhK7CQujqXnqavS2UdksXueoC+WqTqy6Qr7ZsdBWEg7x8+XKCwSCLFi2ipqaGBQsWsHDhQgAqKyt5+eWXAdi8eTNPPvkkN998cyL3XXxfLlERJSTDPMhOpxOz2dzd1cg5QldhIXR1Lz3N3hZKu6WLXHWBfLXJVRfIV1s2ugoixGLjxo1MmjQJgPHjx7N9+/Y2x0iSxK9//Wvmzp2LSqVi165d+P1+7rjjDmbPnk1NTU3O6lOk08iyB9lkMnV3FfKC0FVYCF3dS0+zt4XSbukiV10gX21y1QXy1ZaNroLoQfZ4PJSUlCT+VqlUhMNh1OoT1V+xYgUjRoxg6NChABQVFXHnnXdy0003cfDgQb7//e/z/vvvp5wTp7m5OeXvoqIiDAYDbrcbk8lES0tLyv5wMEBYocbtdqPX6wkEAvh8vpRj9Ho9er0er9eL0WhsUwbEuv7dbjcGgwG/34/f70/ZX1xcjE6nw+/3YzAYsFqt7ZbhcrkwGo14vV5aW1tT9hsMBjQaDYFAAL1e3yYnoEKhoKKiAqfTmfj75JVnDAYDarWaUCiETqfDbre3W4bD4cBsNuNyuQgGgynHlJSUJO6bRqPB4XCk7FcqlZSXl2O327FYLDidzjZlGI1GFAoFkUgEtVqdqHMclUpFWVlZogyHw0EoFEp5fkwmE5IkIUkSKpWqTRlqtRqLxZIow263Ew6HU44xm81EIhEUCgUKhQKXy5WyX6PRUFpamijDZrMRiUTalBEOh1GpVEiShNvtTtmv1Woxm82JMqxWK9Fo6keZSqWiqKgItVpNJBLB4/G0KcNkMuF0OiktLaWlpQVJSh35sFgsBAIBNBoN4XAYr9ebsl+n02E0GnG5XJjN5nbLKCsrw+/3o9PpCIVCbcro6H0CKC8vx+v1otfraWlpQaVSpewvxPfJZDLhdrsT71P8Oezs+9Rd5NPepmtrIfY+qVQqWdlak8nEsWPH2iyDW+i2No7H46Ffv36ysbWlpaWEQiF8Ph8Gg0E2tjb5fYq/94X6PiXb2uQy/H4/RUVFmdlaqQCYN2+e9M477yT+njRpUptj5syZI1VXVyf+DgQCkt/vT/w9bdo06ejRo23OSz6ns3zn+TXS1KdWpX1eT6epqam7q5AXhK7CQujqXvJlbzOxtZJUOO2WLnLVJUny1SZXXZIkX23Z6CqIEIsJEyawatUqAGpqahg5cmSbY3bs2MGECRMSfy9evJgFCxYA0NjYiMfjobKyMif10ahEHmSBQCBPepq9FQgEgu6gIEIspkyZwtq1a7nllluQJIl58+axdOlSfD4fM2bMwGazYTAYUtJ5TJ8+nYceeoiZM2eiUCiYN29eu+EVmaBRKWWZ5k0gEAh6mr0VCASC7qAgLJhSqeTRRx9N2TZs2LDEv8vKyvj3v/+dsl+r1fLEE0/kpT6iB1kgEMiVnmZvBQKBoDsoiBCLnkZxkZagDNO8GQyG7q5CXhC6CguhS5CMXNtNrrpAvtrkqgvkqy0bXcJBzgCdWiXLEAu5DokKXYWF0CVIRq7tJlddIF9tctUF8tWWjS7hIGeASiHJMsQiOU2PnBC6CguhS5CMXNtNrrpAvtrkqgvkqy0bXcJBzgC9TivLHmSdTtfdVcgLQldhIXQJkpFru8lVF8hXm1x1gXy1ZaNLOMgZEAkGCEclolF5xSGfnERbLghdhYXQJUhGru0mV10gX21y1QXy1ZaNLuEgZ4BGFUtvFJRhL7JAIBAIBALBVx3hIGdA3EGWY5iFQCAQCAQCwVcd4SBngEYVazY5TtQTCAQCgUAg+KojHOQM0KpjzRaSWS7k5JWx5ITQVVgIXYJk5NpuctUF8tUmV10gX23Z6BIOcgZYzCZAfj3IFRUV3V2FvCB0FRZClyAZubabXHWBfLXJVRfIV1s2uoSDnAHhgB+Q3yQ9h8PR3VXIC0JXYSF0CZKRa7vJVRfIV5tcdYF8tWWjSzjIGWA2lgDym6RnNpu7uwp5QegqLIQuQTJybTe56gL5apOrLpCvtmx0CQc5A0KBVkB+IRYul6u7q5AXhK7CQugSJCPXdpOrLpCvNrnqAvlqy0aXcJAzQClFAPn1IAeDwe6uQl4QugoLoUuQjFzbTa66QL7a5KoL5KstG13CQc4AdXyhEJn1IAsEAoFAIBAIhIOcEdp4HmSZ9SALBAKBQCAQCISDnBEnVtKTVx5kgUAgEAgEAoFwkDPCZDQA8guxKCkp6e4q5AWhq7AQugTJyLXd5KoL5KtNrrpAvtqy0SUc5AzQa9WA/CbpqVSq7q5CXhC6CguhS5CMXNtNrrpAvtrkqgvkqy0bXcJBzgCFFAutkFsPcjgc7u4q5AWhq7AQugTJyLXd5KoL5KtNrrpAvtqy0SUc5AwwFGkB+U3S02g03V2FvCB0FRZClyAZubabXHWBfLXJVRfIV1s2ugrCQY5GozzyyCPMmDGDWbNmUVdXl7L/b3/7G9dccw2zZs1i1qxZ7N+/v8NzssHncQPyC7EQS00WFkJXYVEounqavS2UdksXueoC+WqTqy6Qr7ZsdKlzWI+8sXz5coLBIIsWLaKmpoYFCxawcOHCxP4dO3bw29/+lrFjxya2ffjhh6c9Jxs0Ig+yQCCQKT3N3goEAkF3UBAO8saNG5k0aRIA48ePZ/v27Sn7d+zYwQsvvEBzczOXXXYZd999d4fnZIM2keZNOMgCgUBe9DR7KxAIBN1BQTjIHo8nJVWHSqUiHA6jVseqf80113DrrbdSUlLCfffdx8qVKzs8J5nm5uaUv4uKijAYDLjdbkwmEy0tLSn7nQ47AG5fK+FwmEAggM/nSzlGr9ej1+vxer0YjcY2ZQBUVFTgdrsxGAz4/X78fn/K/uLiYnQ6HX6/H4PBgNVqbbcMl8uF0WjE6/XS2tqast9gMKDRaAgEAuj1emw2W8p+hUJBRUUFTqcTSZJwuVwEAoE2ZajVakKhEDqdDrvd3m4ZDocDs9mMy+Vqs7xjSUlJ4h5oNJo2wx5KpZLy8nLsdjsWiwWn09mmDKPRiEKhIBKJoFarcTqdKftVKhVlZWWJMhwOB6FQKEWzyWRCkiQkSUKlUrUpQ61WY7FYEmXY7fY2Qf5ms5lIJIJCoUChULRZ612j0VBaWpoow2azEYlE2pQRDodRqVRIkoTb7U7Zr9VqMZvNiTKsVivRaOoHWTgcxufzoVariUQieDyeNmWYTCacTielpaW0tLQgSam5uy0WC4FAAI1GQzgcxuv1puzX6XQYjUZcLhdms7ndMsrKyvD7/eh0OkKhUJsyOnqfAMrLy/F6vej1enw+X5t3shDfJ5PJhNvtTrxP8WM7+z51F/m0t+naWoiFfLjdbvR6vWxsrclkavO+xssoZFsbx2azycrWlpaWEgqFCIVC+P1+2dja5Pcp/rwW6vuUbGuTy4i3Uya2ViGdfAd6IPPnz2fcuHFMnToVgEsuuYRVq1YBIEkSHo8Ho9EIwKuvvorD4cDlcp3ynGQ2btzIxIkT06qP1Wrlgic3cMdFQ3jw6tHZSOtRWK1WysvLu7saOUfoKiyEru4lX/Y2E1sLhdNu6SJXXSBfbXLVBfLVlo2ugpikN2HChISxrampYeTIkYl9Ho+H//qv/8Lr9SJJEuvXr2fs2LGnPSdbysvL0aqUsguxkOPLAUJXoSF0dS890d7KEbnqAvlqk6sukK+2bHQVRIjFlClTWLt2LbfccguSJDFv3jyWLl2Kz+djxowZ/OhHP2L27NlotVouuOACLr30UqLRaJtzcoXdbkejUshukl58iEluCF2FhdDVvfREe1sI7ZYuctUF8tUmV10gX23Z6CqIEIt8kumw39fnLWfyqF4smHZ2HmolEAgE8iJTWysQCATdQUGEWPQ0nE4nGpVSdj3IJ0+ikAtCV2EhdAmSkWu7yVUXyFebXHWBfLVlo0s4yBkQDAbRqpWyW0nv5JnMckHoKiyELkEycm03ueoC+WqTqy6Qr7ZsdAkHOUPkOElPIBAIBAKBQCAc5IyRY4iFQCAQCAQCgUA4yBmjVSsJRb7S8xsFAoFAIBAIZIlwkDPAaDTKMs1bPPm/3BC6CguhS5CMXNtNrrpAvtrkqgvkqy0bXcJBzgCFQoFWrZLdJD2FQtHdVcgLQldhIXQJkpFru8lVF8hXm1x1gXy1ZaMrrwuFbNiwAafTmbKe+JVXXpnPS3YJkUgErUohu0l6J69hLxeErsJC6EofudpaEM9DISJXbXLVBfLVlo2uvDnIDz/8MKtWraKqqiqxTaFQyMJoq9VqWU7SU6sLYmHFtBG6CguhKz3kbGtBPA+FiFy1yVUXyFdbNrry1iKfffYZ7777LiUlJfm6RLfhdDqPT9KTl4PsdDqprKzs7mrkHKGrsBC60kPOthbE81CIyFWbXHWBfLVloytvMch9+/aVrcEGkeZNIBD0DORuawUCgaA7yFsP8oQJE/jRj37E5MmTKSoqSmyXy7BfbCU9keZNIBB0L3K3tQKBQNAd5M1B3rx5MwBvvvlmYpuc4uLESnoCgaAnIHdbKxAIBN1B3hzkl19+GYBwOIwkSWg0mnxdqstRqVSyzIOsUqm6uwp5QegqLISu9JCzrQXxPBQictUmV10gX23Z6MpbDLLVauV73/se48eP5+yzz2b27Nk0Njbm63JdSllZmSwn6ZWVlXV3FfKC0FVYCF3pIWdbC+J5KETkqk2uukC+2rLRlTcH+dFHH2X8+PGsW7eOdevWcc455zB37tx8Xa5LsdvtaFRKwlGJaFQ+cch2u727q5AXhK7CQuhKDznbWhDPQyEiV21y1QXy1ZaNrrw5yAcPHuS+++7DZDJhsViYM2cOhw4dytfluhSLxYJWHWs6Oa2mZ7FYursKeUHoKiyErvSQs60F8TwUInLVJlddIF9t2ejKm4McDocJBAKJv/1+v2yWMnQ4HGhVsaaTU5iFw+Ho7irkBaGrsBC60kPOthbE81CIyFWbXHVB12k76vCzr9nTJdeC7HTlbZLe1KlT+e53v8uNN96IQqHgrbfe4qqrrsrX5bqUUCiE5riDLKeJeqFQqLurkBeErsJC6EoPOdtaEM9DISJXbXLVBV2n7VdLd7DzmItVP5ncJR/y2ejKm4N877330qdPH1avXk00GuXGG29k+vTp+bpclxMPsQiJXMgCgaAbkbutFQgE8uGQzU+9zc9Bq48hFYburs5pybmD7PF4KCkpweFwcPnll3P55Zcn9jmdTkpLS9MuMxqNMnfuXHbv3o1Wq+Wxxx6jqqoqsX/ZsmW89NJLqFQqRo4cydy5c1EqlVx//fUYjUYABgwYwPz587MXeBw59iALBILCIR+2FnqmvRUIBPKgwekHYHVt81fPQZ41axZLlizh/PPPT+k+lyQJhULBl19+mXaZy5cvJxgMsmjRImpqaliwYAELFy4EoLW1lT/84Q8sXboUvV7P/fffz8qVK7n44ouBEzlCc40cJ+kJBILCIR+2FnqmvRUIBIVPayiC3RcLeVi1p4XZFwzu3gp1QM4d5CVLlgCwc+dOlMrUOYCZBktv3LiRSZMmATB+/Hi2b9+e2KfVannjjTfQ6/VAbMKKTqdj165d+P1+7rjjDsLhMPfffz/jx4/P6PonYzKZ0DbaAHlN0jOZTN1dhbwgdBUWQlfnyIethZ5pb+WIXHWBfLXJVRd0jbYGZ2vsWkVqPt9vJRSJJkbj80U2uvIWgzxt2rSEAY/zne98h2XLlqVdVnwoMY5KpSIcDqNWq1EqlVRUVACx3gufz8dFF13Enj17uPPOO7nppps4ePAg3//+93n//fdRq9tKbm5uTvm7qKgIg8GA2+3GZDLR0tKSsj8QCKBWxsrxB0J4vV58Pl/KMXq9Hr1ej9frxWg0tikDoKKiArfbjcFgwO/34/f7U/YXFxej0+nw+/0YDAasVmu7ZbhcLoxGI16vl9bW1pT9BoMBjUZDIBBAr9djs9lS9isUCioqKnA6nWi1WlwuV8qM+HgZarWaUCiETqdrk1cwXobD4cBsNuNyuQgGgynHlJSUJO6bRqNp8wOuVCopLy/HbrdjsVhwOp1tyjAajSgUCiKRCGq1GqfTmbJfpVJRVlaWKMPhcBAKhQgEAuh0OiD2skiShCRJqFSqNmWo1WosFkuiDLvdTjgcTjnGbDYTiURQKBQoFApcLlfKfo1GQ2lpaaIMm81GJBJpU0Y4HEalUiFJEm63O2W/VqvFbDYnyrBarUSjqR9jRUVFibaIRCJ4PJ42ZZhMpsRwe0tLC5KUGjNvsVgIBAJoNBrC4TBerzdlv06nw2g04nK5MJvN7ZZRVlaG3+9Hp9MRCoXalNHR+wRQXl6O1+tFr9fj8XjatGkhvk8mkwm32514n+LPYWffp86SS1sL+bW36dpaILFfr9cTCARkYWtNJlObZzxeRiHb2jiBQIDKysoeb2ud/jCLapr56TVj8bicp7S1paWlhEIhwuEw0WhUNrY2+X2K26d8vk+76mP378pRZSze0sSaLw9z6RkDcm5rk8sIh8OJD/l0ba1COvkOZMltt93Gtm3baG1tpaioKLE9Go1y1llnZTQEN3/+fMaNG8fUqVMBuOSSS1i1alVK2Y8//jgHDhzgySefRK/XEwwGiUajiTpMnz6dp59+mr59+6aUvXHjRiZOnJhWffx+P9WHPcz66wYW33MB5wyWxwo0fr8/0TMkJ4SuwkLo6hz5sLWQP3ubia0F8TwUIoWi7Y0Nh3jw7W28cdf5nD+0vMPjC0VXJnSFtiWbD/OjRVtY8t8XMm3hOu6bPJz7rxyV12tmoyvnPcjPPvssDoeDn/3sZymTNNRqNZWVlRmVOWHCBFauXMnUqVOpqalh5MiRKfsfeeQRtFotf/rTnxJDjYsXL2bPnj3MnTuXxsZGPB5Pxtc/GZVKJctJemIt9sJC6Coscq0rH7YWeqa9lSNy1QWFo+2gNdZ7urfJ0ykHuVB0ZUJXaDt2PMRiZG8j4waWsnpvS94d5Gx05dxBLikpoaSkhH/84x8p2yVJoq6ujsGDB6dd5pQpU1i7di233HILkiQxb948li5dis/nY+zYsSxevJhzzjmH2267DYDZs2czffp0HnroIWbOnIlCoWDevHnthldkgtPpRHu8LDlN0nM6nTn7UetJCF2FhdDVOfJha6Fn2lvxPBQWhaLtkC0WorC3qXMLVxSKrkzoCm0NzlZMRWoMOjWTRlTyzIpanL4Q5mJN3q6Zja68xSC/8cYb/O53v0uJTSkrK2Pt2rVpl6VUKnn00UdTtg0bNizx7127drV73hNPPJH2tTrLiZX0RB7k0/H/3tzC5FG9uObsvh0fLBAI0iaXthZ6pr0VdA6bN4jTH+rx6bN6CgdbTvQgC/LPMWcrfc2xcIdLRlTwx49rWbevhavP6pn+Qd6mD77wwgv87W9/49JLL2XJkiXMmTOHK664Il+X63ISad5kFGKRa+ptPhZvPMxHOxu6uyoCgWyRu60VdJ45r29m2sJ14nepE0iSRJ01vR5kQXY0OFvpY47NUxg3sJQSnZrVe9tOAowTjkTbTFTsSvLmIJeWljJu3DjGjBmD1WrlBz/4AV988UW+LtflaBI9yMIQnYpVtbEZ6w2u1g6OFAgEmSJ3WyvoHDX1DtbsbcHmDbL2NE6HIIbVG8QbjNDbpKPB1YqrVb7LSPcUYj3IMQdZo1JywbByVtc2t3tsOBLl5uc/4yeLt3ZlFVPIm4McTw1TVVXF1q0xgSenXylU1Go1GlUsMb+cvtRzFTMYZ9We2IPf6Ap0cGR+ybWunoLQVVjkS5ecbS2I56Gz/GnlXsx6DaYiNUu3HM1p2elSCPcs3nt8+ZjeAOzrRC9yIehKF0mSuPXPn/PW1vYd1VwRDEdp8QQSPcgAk0ZUUG/zJ+5FMi99VsemQw42HbK32ZcO2dyzvDnIN998M3fffTeXXXYZixYt4sYbb2To0KH5ulyXYrFYZLmSnsViyVlZoUiUdXtjuQ8bnK3dOkySS109CaGrsMiXLjnbWhDPQ2fY0+jmw52NfPfCwVw9ti8f7mykNdR9H0n5umfRqMT+Zg//2XKUee9+ya1//pwH39pKNJr+70s8/viKMb0AqO2EgyzHZ/GQzce6fVaWbM3vqEPj8ZHkvikOcmzy3Kra1Gsfdfh54sPdqJQKDll9hLPws7K5Z3n7HJo+fTpTp06luLiYRYsWsW3btsTqTIWO3W5HX/hh2AAAIABJREFUq4tNgpBTiEU82XouqKl34A6EmTColE2HYv82FeVvpmoyraEI//jsILddOBidWpVTXT0JoauwyJcuOdtaEM9DZ1j4yT6KtSq+e+Fgdhx1sai6npW7mrpt8lOu71lrKMKPFtWwurYFTyC2mIhWraSqrJh1+6z0K9Uz5/IRaZVZZ/OhVMCFwyrQqpWd6kGW47O4fn9sAY7djW4OWX0MKi/Oy3XiDnIf84mcxIPLixlg0bN6TzOzzq9KbJ/7nx1EJYn7Jg/nqY9rOWz3MzjDiafZ3LOcO8h/+9vfTrnvtdde4/bbb8/1Jbsci8WCLxh7SeUUYpHLF3/VnmZUSgU3ThjApkMOGp2tXeYgf7CjgXnv7mJYZQmXj+ktO4MWR+gqLHKt66tga0E8Dx1Rb/Pxny1Huf3CwVgMWi4YVk5FiY6lW492m4Oc63u2YlcT721v4MYJ/Tl/aDlj+5kZ0bsEtVLB/f/cwpPL93DWADOTR/XqdJl1Vi99zXqKNCqGVhi+sj3Inx+woteo8IcifLizge9Nys/oUzwHcnIPskKhYNKICpZtOUY4EkWtUvLhjgY+3NnIg1eP5tzBFp76uJYDLd6MHeQe1YO8Z8+eXBfZ47Db7ZSYzIDoQT4Vq/Y0M35gKSN7G4HYRL0Rx/+dbzbWxWKW6o4ngZfjVz8IXYVGrnV9FWwtiOehI55ftQ+VQpFwbFRKBdec1Yc3vqjHEwhTouv6uNlc37MPdjRQZtDyu2lno1alRobOu+EsdjW4+d83alh638Wd7gGts/oYXBE7dnivErYednZwhjyfxfX7bVw2qpLaBhcf7WzMm4Pc4Iz3IBelbJ80opLXN9Sz5bCD0X1MzP3PDkb3MXLnxUNwt8Y6Ive3eJmc4XWzuWc5X2r6ZFwuFyaTKZ+XyIq0lz+teZ3ghhfRaDSsP2Cjf6megZb8DEl0NcFQEK1Gm3U5oWiUjXV2BpTqqTDqqKl3MLTCQC9jUccn54CtRxz4ghH6mIoYXG7Ima6ehtBVAHztOzB+JgDNzc15TcQvO1sLCXsrm+chiVw858FIlM31dipLdAytKElsd7eG2HHMxfDKEipKdNlWNf165fAdjkoSG+vslBm0DKssafeY1nCEbUec6NRKzuxnRqVQdFhudZ2NMoOWoRUlHLb7OOzwc97gMpSnOVdWtolYu9XUOxhcXkxrMEyDO8jEKgsaZe6npx20emlyBzh3sAUFJ9o4HI1SXWenf6meaFTimKuVM/uZMOo0SEhU19mpMGgZUtH+vU8hyd7Gycbu5m2S3oEDB5g6dSrXXHMNjY2NXH311ezbty9fl+tyFChQKKAb5571WJz+WLocc7GmyxdUiUgSvmBsckp3TlIRCLoKudvaXCMh4fSHiMjAeB9z+pEk6FeqT9leUqRGq1LS4uneDEK5wNUau1dlhlM7pkVqFcMrS/AFIxxo8SJx+nsbjkYJRyWKNLFliPXa2P/9X7HfjHgPrbFIQ6k+NtLg8OUn3V0wEkWrVqY4xwBqpRKDTk2zO8AxVyu9jDqMulg4pgLF8fCPbhqpl/LE7bffLq1Zs0a67rrrJEmSpFdeeUW69dZb83W5jKmurk77nKamJkmSJOnMR96XHl26I9dV6jbiurLlwbe2SGfP/UAKR6KSJEnSuF99ID28ZFtOyu6IVXuapKoHlknjfvWBNPn3KyVJyp2unobQVVjkS5ecba0k5b7dVuxqlKoeWCb99ysbpWg0mtOy0yFbXQ5vUDrjF+9Jc17f1O7+37yzUxr20DuS3RvI6jrp4g2EpKMNjTkr78G3tkhn/OI9yR8Md3jskx/tlqoeWCb9Y92B0x63td4hVT2wTHpv2zFJkiRp1zGXVPXAMulfmw+f9jy52ab/988aadyvPpAikajU0Ngonfebj6S7/5HZe9oR1z+7Rvr2nz9vd9/vP9glVT2wTJr4648khzeYsu9Hb2yWLpi3POPrZnPP8taD7HA4uOiiixJ/f/vb38bjkddqNVq1sssm6TW7Azh8wS65VrbsbfIwqo8RlTL2pdjHVNRli4VUH7SjVMDUs/py2ObPKP2PQFBIfBVsba6QJImnlteiVip4Z9sxlmw+0t1VypiXPjuINxjhB5cNa3f/t8b1IxyVeH97161kGghHuOKJT3l+7eGclBeJSny0s5HLRvdK9PaejjnfGME3Rvfi0WU7T7s63sHjeXfjMciDK4pRKRVfuRX11h+wxcJKlAqUCgVXjOnNqtrmvIy+Jq+idzJXjOmNQgG/vPYMzMWpk/mHVBg46mzFH+z63v28OcgAgUAAxfF4nubmZqJReUxoM5tjE/Q0KkWXTNKLRCVufv4zrvnjmkT4wulYu7cFawZDa3FdyWTiYB5o8TI0acZpL1NRIsVLZ/hkdxPX/HF1Ri/pxjo7o/qYOLOfiWAkSoOrtV1dckDoKizyqUuuthZy225r9rZQU+/gl9eewXmDy3jk3zuot/lOeXwoEmXlriYC4dz/OGej65DVx4trD3DFmF6M7tN+3PmZ/UwMqTCwdGvXLRry0c5Gjjpb2VCfG0dzY52dFk+Qb57Zp1PHK5UKHrt+LKGIxKd7Tr3wRXxhikFlMQdZp1ZRVVbcoYMsJ9t01OHnkM3H14eWAzFtU87ojS8YYd2+3OZEDkeiNLkDKRkskhk3sJRND0/h2nH92uyLZ6+os7VdTKQzZHPP8uYgz5w5kzvvvBOr1coTTzzBjBkzmDlzZscnFgDxVao0qq7pQV7+ZSMHWrwccfh58K2tp110440Nh/j2X9bzwqr9aV/n5NW3HL4g4x79kGVpGFhXa4gWTzAlJUsfky4tB/mz/VZ2HHWxqS69FXTCkSibD9k5p8pCVdnxl8rqk9WqYskIXYVFvnTJ2dZC7tot3nvc11zEzecO5Imbx6EAfrSohkg7HQHu1hB3/P0Lbv/7F8x5fXNWixW0R6a6ahvd3PT8OgB++s3RpzxOoVBw7dl9+WyflSZ314zgvbGhHoBdDW7cOVi6+YMdDWhVSi4b1flJVv1K9fQzF512BbY6q49eRh3F2hMZPob3Kukw1ZucbNP6A7GFvL4+pAyIabtgWDklOjUf7WzM6bVaPEEiUemUPcgAllPEmA857kscaM7MQc7mnuXNQb7pppv44Q9/yLXXXks4HObXv/41t956a74u16XEe2q0amWXrKT31zUH6F+q56ffHMV72xt4df2hdo9bvrORny3ZBsCXDe60r6M4afbuzmMu3K1hFn6yr9Mr4R1siT3EQ1Ic5CKa3YFO/8ActvsB+PyArVPHx9nV4MYbjHDOYAtVx1P9HLJ52+jqbpy+EOv2tbCvObtelp6mK1cIXekhZ1sLuWu3z/ZZqa6z89+XDUOnVjGwrJhHrz+T6jo7z32aOqnxqMPPTc99xrp9Vq4b348PdjTyk8WZrdh2KjLRtf2IkxkvfE5UgkV3XZBIo3kqrh3Xj6gE7249lmk1O029zceavS2cN7iMqASbDzmyKk+SJD7Y0cDFIyowpplDf0KV5bQdLHVWH4PLU/PqDu9VwsEW72lHhdu7Z7WNufkY6GrW77dhKlIzpm9sBEKhUKBTq7h0ZCXLv2zK6bN+zBn7TT9VD/LpiPsS+1syc5CzsR95S5B422238dJLL3Huuefm6xLdRsJBVinzHmKx/YiTDQds/HzqGO68eAjr99t4dNlOvj6kLCWv8OZDdu57fRNj+5vpZ9ZTU5++cTr5QYoPN+046mJjnZ1zBpd1WMaB4w9xcohFb3MRUSn2FXm6L8g4R447yOv3WztddziR/3hilYU+piLUSgV1Vl+PcbjW1Lbw8L+2cdB6Ykj3qjN7c9/kEZw1IP1hoEx0SZLUY9rjVPT0+mVKvnTJ2dZC7trtqY9r6W3ScdM5AxPbrh/fn4+/bOLJj/YwaUQFZw8oZfsRJ3f8/Qv8wQh/v/1cJo2oZESvEn7/4R6KtSoeu35sTuqUbhnVB23c/rcvMOk1vPq9r3dq4YQRvY2M7mNk6dZjfPeiIZlWtVMs+qIepQJ+c8NYrvrDKqoP2rhkZOZpDXccdXHY7ud/vjE87XMnDLKwbOsxjjn99DXr2+yvs3kTyxzHGd6rhHBUos7qZXiv9j88Tr5nze4A1/xxDbdfNJiHpo5Ju57dyfoDNs4bUpaYKxTXNuWM3ryz7Rg1hx1MGJSbnM/xHMi9Tek7yAadmt4mXcK3SJds3tW89SC73W58vlPHdhUyLpcL6JpJei+uOYBBq2LGeQNRKhX8/qZxhCJR3tmW2iPw5PJaSvVaXvzuuXxtUCkNrlacaaZrieuKs7fJQ4lOjbFIzd/XHexUGfubvSgUMLDsRG7o3sfzH3d2ol68B3lzvSOtOOSNdXZ6m3T0L9WjVikZYNFTZ/O10dVdvPHFIey+ED/95iheuuM85nxjOOv2Wbn2mTV8uCP9iTRxXct3NnaqN7rZHeD8+R8z/90vU4aUI1Ep58PH2dBV92tjnZ21e3Mba3c68qVLzrYWctNun++3sv6AjXsuHZYy2UuhUPCb68+i0qjjf9+o4Z2tx7j5+c9QKxW8+YMLEk7UvZOHc/elQ3l1/SEWvLer0yNqpyMdXatrm5n11w1UGnW8ec8Faa0qdu24fmyss3PYnr9nJByJ8ubGei4dWcmI3kZGVhZTnWaI3Ml8uKMBpSI2gStdJlTFHLtNdW07inzBMI2uAINPWlBkxHGn+HRxyCffs7c2HSYYiSY6ZwqFRlcrB1q8fH1IeWJbXNvkUb1QKRU5DbM4sYpe24+VzjCkwpCxg5yN/cibg6zX65k8eTKzZ8/mnnvuSfwnJzQqZV7z+za5Wlm69Sg3nTMwsUxzpVHHkHIDu46lhlDsPOpk0ogKKkp0jOwTe9F3N6YfZpFMbaOHEb1LuPmcgby/vaFTccQHrV76l+pTfoTivcadOb81FKHFE2DcADPBcLRTqxvF2Vhn55yqssQX46ByA4esp/9R8Acjp52k0xFNrlbufrmaf35Rf9qJPJIksf6AjUtHVvLflw3n0pGV3H/lKNY9+A16GXX8qyaz2fThSJR7X9vEwk86znv76Z5mGl0Bnl+1n7tf3kiLJ8CfV+3n6/M+5rx5H/OnT/biCYQzqkc+8QbCPP/pvsTy7rlg0yE7t/75c+5+eWOP1JwOXwVbmy1Pr6ilokTHzPMGtdlnLtbwxM3jOGD1cu9rmxhaaWDJvRelTH5TKBQ8+M3RzDq/iudX7eeZFXu7rO7r9rVw59+rqSovZtHdF7TJedwRV4+NTXBbtSd/H4Of7I7ZlluOt++4/kY2H3JkNcL6/o4Gzh1cRnkGC52c0deETq1sNw750HF7X3VSiMWwXrG/axs7F/omSRJvbIiFO24/6uxRnQwd8fnx0dmvD207Kmwu1vD1IWU5dZAbXK1o1UosxemFysQZUlGSsYOcDXkLsZg+fXq+iu4x9DLq2HzIkbdh6/9sOUooInH7RYNTto/ua2Tn0RNfRc3uAC2eYCKWaFTvEw7yeUM6Dos4FbVNHiaPqmT2BVW8uPYAr64/xP1TRp72nAMt3pT4YzgxrNIZB/mII9Z7fP3X+rP1iJP1+62d0uD0hzji8POd86sS26rKiqk5xUSNaFRiyeYj/O6DXTh8ITb87Io26WU6w0dfNvLBjth/j3+4mznfGM6sCwa3Oe5AizfWgzu0PGW7sUjD5WN6sXTLMYLhWCL1dNjf4iUQjiaM/ulYU9tMuUHL/3xjOI8u28l5v2kkKsFFw8tRK5X87v3dPP/pfu68eAjfvWhw4qOsu3mzup757+3C5g3mZBiz3ubjrn9UYyxS0+IJ8vamw8xu554VCl8FW5sN1QdtrN1r5eFrxpwyVdiFwyr42dVj2N3o5lffOhNDO8szKxQKfvWtM/EGwjzx0R4sBm2KvckXCz/ZR0WJljfuOp/S4vRXcRtSYaC0WMPWww5u/XrbD4Rc8MYXh6g06vjG6F4AjOtfwqLNjew86mLcwNK0y9vf7GFPo4dfXntGRvXRqpWcPcDcroNcZ407yKk9yMVaNf1L9ezt5NyQz/ZbOWj1cdmoSj7Z3cyeRg9n9Ou5K1kms/6ADaNOzRl926/vlDN686ulO9v9Pc+EY85W+pqLMvaThlYYsHmDOHzBjN6BTMmbg/yvf/2Ll156KV/F9wguH9Ob97Y3sO2Ik7MHpG8EOuKYs5VirarNl+6o3ibe296ALximWKvmy2MxZ3l035hj3NdchFGnZk8GE/XiOHxBWjwBRvQuoarcwORRvXht/SH+5xvD0ajad+IkSeJAs5cbJvRP2V5u0KJWKhJxSKcjHl4xtr+ZUb2NrD9g4386Ud/4sNjI3ieWo6wqL8bVGsbpD5McbeZqDTH7rxuoqXcwwKInEI6y6ZCdyceNezpsP+LErNfw7K0TeGZlLb/49w76mvVccUbqsODn+2MTDtv7Yv/G6N68vqGeLw7auGh4RVrXj38oHe7AQZYkiTV7rVw0vILvXjSEoZUlvL3pMN85vyoRW76l3sHTK2r5v4/28JfV+7nnsmH84NJh3R4T/O8tsSwqf11zgBsnDGBUn9NPTGqPz/dbOWyP5cV+YfV+guEob//3Rfz4nzW8tO4gs86v6nadmfJVsLXZ8NTHtZQbtB06h9+/ZGiHZSmVCn43/WyOOVv5w/I9zDh34CntYS5o8QRYt8/K3ZcMzdgxUCgUjBtQmtG8lM7Q4Gxlxa4m7rl0WKItxvWLvaPVdfaMHOQPdsR6L6/sZHq39pgwyMKLaw/QGoqkfBjFU7zFMx0lM7xXSadzIb++oR5TkZoHrx7NJ7ub2XLYUTgO8n4r5wy2oD7Fsxt3kD/a2cBdl7SfZzuZT/c0s6nOzo9O0YHW4PTTJ4P44ziJTBYtXr42qOscZBGDnAEaTaxn7fLRvVAqyHlKlDh2b7Dd5TVH9zUiSbDn+FBQ3EEe0+fEbNSRfYzsTtNBjuuCEw5nPC7ruvH9aPEE2gw/rdjVmMi5bPUGcQfCbb44lUoFvYy6TsUgx+Pk+pfqOX9oORvr7J0aptvb5E6pL5zIcdngTR1Cf/7TfdTUO3h8+tl88L+XoFIqqK5LL2NGnG1HnJzV38zFIyp46Y7zOKOviQfe2kqzOzUP9foDVipKdCmTF+NcNLwcrVrJx182pXVtjUaTuPfHXK2njYff1eCmxRPg4hExB/ySkZX84ZavpUy8HDewlL/cdi7L/udiJlRZ+N37u/niYNfH1iU/h4esPjYfcnD3JUMxFql5+F/b0o7//GhnI7e88Dn/780t/PStrdTbfDw3ayLDe5Vw24WD2dfsZU0XxCIn68olcra1kF27bayzs7q2hbsuGZqS0isb1Colt180mBZPkNW1p8612xGd0fXutmNEohLXje/f4bGnY9zAUvY0unMaphTnzep6ohLMOPfE5Mf+ZQYGlumpPpiZXf1gRwNnDzDTP81wkmS+NshCKCKx42hqmN5Bq4/SYk27I4YjepWwr9lzygwO8Xtm8wb5YHtD7IO9txGzXsOWPH2A5Jpmd4B9zd5E/uM4yc/jAEsxY/qaOu3bPLtyL099XIvd2/5iZvEe5EwZUnnCQU6XbOyHiEHOgNLS2BexxaDlvCFlfLgjPw6yzde+gxx3hHcdd452NbjpYypKySM4qo+R3Y3utJyJuC4gkQ9yeK9Yj2w8fGN344nQDrs3yJ0vVfP7D3cDJx7e9iaQ9DYX0eTqePGSI3Y/aqWC3qYivj6kDH8o0qk45NpGD0UaJf0tJwxqvOfdFjzRe9DkbuXFNQf51rh+3HTOQAw6NWP7mTJyBAPhCLsb3IztH8tAoVOreOqW8XgCYX66eEui7SVJYv1+G+cPLWu3l7JYq+bCYeV8vKsx7fu18/gzIEmx1FSnYk1tzAGcNKLjHuqx/c08NeNrKBXkxHEMR6JpGbbk5zC+yMGsC6p48OrRfHHQzlubOh+v7WoN8fC/tjG6j5FPf3IZax6YTPXDV3DhsFg7XHN2X8oNWl7q5CTUbEjWlUvkbGsh83Z7f/sx7vj7F1SU5D4U4rJRvbAUa9J6Fk+mM7r+U3OUUb2NGY2aJDN+oJmoBNuP5HaiaDQqsai6nguHlaeMdJaWlnJOVRlfHLSn/UHb4Gylpt7BVVn0HgNMqIq178kT9Q5ZfW1GZeMM71VCayiaCPU7mfg9e/v45LyZ5w1CoVBw9gAzW9KYL9OdnJz/OM7Jz+OUM3qzsc7e4aJjTn8oMUkxXnYy0ahEo6uVPhlO0AMYaImtdJiJg5yN3c2bgzx9+nQefPBBbrjhBq666qrEf5kQjUZ55JFHmDFjBrNmzaKuri5l/4oVK5g2bRozZszgn//8Z6fOyQa7/YQzNeWMPuxudCfy/+YSuzeIpZ1htQEWPQatil3He4i/POZiTN9UAzqqtxGnP0STu/Mr6iXr2tt03OE8/gU/pMKARqVgd8OJHuSdx1xIEry7rYFgOJpI5N1eL2lnl5s+bPfTt7QIlVKRiD1u76U7mdomD8MqSxIpa+BED/LuwyfOf2bFXkKRaEos9cSqMrbUO9LOSLKnwUMoInFW/xMp2kb0NvKzqWNYubuZlz+PPXN1Vh8NrtY2X+zJXD66F3VWX1q5Hm02GzuPuhKxdPWnmaW+em8Lw3uVdHoWsblYw1n9zazLgYP8i3/vYPLvP+HH/9xyyh6GOI8u3cm3X1iLPxhBkiT+tfkI5w62MMBSzE0TBzJhUCkL3tvV6ZXN5r+7i2Z3gN9OO5uqcgMDLMUpOVV1ahUzzxvEx7uaqM1yUmtHjkDy+5VLcmlroWfb233NHn77/i5+8MpGPtzR0O4CH/5ghIfe3sY9r2yiqryYxfdc2G5McTZo1UquG9+fj3Y2pp0tKE5Hz8MRh5/qOjvfGt92dbF0iYcA5rqXc+2+Fg7b/YnJeXHsdjvnDLbQ4gkkYn47y4c7Yxl9snWQexmLGFimbxOHfNDqbZPBIk68Q6i2qX1bYLfHHP7XNxxiwqDSxIfL+Dz20Oea9fttGLSqRMdOnJOfxyvP6E1Ugo93nX5kc+3elsR7uG5f299qqzdIKCJl1YOsVSsZaNFnlAs5G7ubNwf5hhtu4LzzzgMgHA4zYcIEbrjhhozKWr58OcFgkEWLFvHjH/+YBQsWJPaFQiHmz5/Piy++yMsvv8yiRYtobm4+7TnZYrGcyA145fFY046GIn757+2sOs3Sl+1xqh5kpVLBqD5GdjW4CIaj7G3yMPqkYPt4Avl0wiySddU2eRjeqwTlcYdTo1IyrLKE3Q0neiDiQ1dOf4hVe5o5YPWiUSnaHRbrbSqisRMxyEccfgaUxoxXeYmOEb1K+Kydl+5k9jZ5GNGrJGWbXquil1FHky/m+B6y+nht/SFuOW9gSi/3OYMtBMLRNkNxHbHtSOz4s04yNLMvqOKyUZX85p0v2dvkTjj4559msuE3jqcy+vjLzo9GhNXFWL3BxA9Jva39Xo/WUIQNB6xcnGZ884XDK6ipd+BNyvJQ2+jm2ZV7+d5L1Vz15KoOs4R8ts/K6xsOMX5gKf+uOcIV//cp721rf9GCf20+wotrD7B2v4P/XbSZncdc1DZ5+Nbx4WWlUsEPrxhJiyfA+9s7Tov3+f7Yte+8eMhp4yC/ff4gNEolU55cxbm/Wc73/1Gd1lLtq2ubufi3Kzh//sc8+dGeRFL8k0l+v3JJLm0t9Dx7q9KX8PqGQ9z4p7Vc/sSnPP/pPr44aOOulzdy2e9X8pfV+3H6Y07qzqMurn1mDa9vOMTdlw5l8T0XppUSLR1unNCfYLhtys3O0tHzsPR47P21Z2fvIFeUxNJf1hzOrYP8xoZ6Sos1XHVm6pwLi8XCucfDt9JN97ZyVxNDKgwJZzUbJgyysLHuRC92MBzlqMNPVdnpHeRTxSFbLBa+OGhnX7M35aNg3IBSIlGJHUd7RkrR07H+gJWJg8vaxM6f/Dye2c9E/1J9hyPkK3c1YdZruHh4RbsOcnzuUWfWQDgdgysMGXVEZmN38+Ygr169mmnTprF8+XI+/vhjpk+fzvLlyzMqa+PGjUyaNAmA8ePHs3379sS+ffv2MWjQIMxmM1qtlokTJ1JdXX3ac7LFZjsRVzWwrONYnSZXKy99Vpf2MK7N034PMsCoPiZ2NbjZ2+QhHJUSIRBx4pPV9hzvFWtyt3bYe5Csa2+jm+GVqQZq1ElxzTuOuuhl1GEp1vDvLUc50OxlYFlxu4H/vU1FuAPhFGerPQ7bfQxICpO4fExv1u2zntZh8QTCHHH4UxZOiVNVXsyeBifr9rbwwFtbUasUzPnGiJRjzjmeM7M6zTCLbccn6A0sS/0gUChiE3kMOjX/u6iG1bUtlBu0pzX4/Uv1jO5jTCsOef2e2PDuZaMq0agUp+xB3lRnpzUU7VR4RTIXDasgHJXYcHxFQ7s3yHXPruXxD3azv9nD7kZ3IgSiPVpDER56eytV5cW8/v3zWfo/FzPAoucHr25iwXu7Unr/DrZ4+fmSbZw72ML9k6v4YEcjd/69GrVSwTVn9U0cN2l4BVXlxbz6efurScaRJIm5/9nBoLJi7p8y6rTH9jXr+fd9F/HwNWO4dGQln+xu4tfLdqYcs3xnY5tcp05/iIfe3sasv25Ap1Yyuo+JP66o5YL5Kxjx83cZ8fN3ueL/Pk0cn/x+5ZJc2lroWfb2yY/2cN5jy3no7W24WsM8dPVoPn/ocj5/6HIWfnsCfU16HnvnS86f9zH3vraJ6/+0Fqc/xCt3fp2Hrh6TdlaYdDirv5kRvUp4a9PhjM7v6Hn4T81Rxg8sZdApejvTZfzA0pz2IFs9AT7c2cCNXxuATp2aHcRmszG8sgSzXpNWHHIgHOHz/TYuSdNWnYoJgyw0uQOJkInDdh9RqW2KtzilxVoqSnSnTPVms9l4Y8MhjDo1/3X2Cbt09sBYJ0lPj0O2eYPsafS0Ca+Ats+jQqFgyhmxwefhAAAgAElEQVS9WV3bfMqe8WhU4pM9zVwyspKLR1Swt8nTZlnzbFbRSyaeCzndkJ1s7G7eslg89dRTvPLKKwwfHlsFp7a2lp/85CdcccUVaZfl8XgoKTnhXKhUKsLhMGq1Go/Hg9F4wjEyGAx4PJ7TnnMyzc2pPbtFRUUYDAbcbjcmk4mWltRhZpvNRmlpKV6vNxb/N8LCc6vr2F13lLLjgf96vR69Xo/X66WmPua4fL6/hWONTaiP98pWVFTgdrsxGAz4/X78/hM9T4FwFG8wQqlenTjGaj3xdTagRIHDF+KT3TGHalSvWH1bW088nGXFGrYftuPw+Jnx3OfU2Xw8ft0ILhl24otKoVBQUVGB0+kkHA7jcrmwuX0cdbbSr0RJc3MzBoMBtVrNkDId/65p5cDhY5To1Gw9ZGNUpZ7eRi3LdjZQYdAyqq8Jp9NJMJg6lF6mj/1Q1TU5GNHHjMORakiUSiUl5lIaXQH6W/SJMi6t0vPcpxJvrKvlzkuGo1AoiEQiqNVqnM5YD+6O42EffY7/jtjtdiwWCw6Hg17FSt7Z6ebWv6xHp1bw/6aMwKQFv9+PSqXC6XSiAPqbdazd08D0syxYLJZEGXa7nXA41TiYzWYikQhb6+2c0dfY5vnQaDT0Ki3l4SuHcP+S3Ww/4uLykZaU48xmM+FwGJVKhSRJuN1uLqgq4R8bjvHXlTuZ9rV+lJaWJuphtVqJRlNDQOK9FaMq9fQ1FbH3mD3lWdZqtZhMJpZvP4xaqWC4ue2zbrFYCAQCaDQawuEwXu+JL/QqQxStSsGavS1M6KvjtU3N+IIRXvnOmYzqbWD2Kzt4f+th7rlkCH6/H51ORygUSpTx9Kp6Dlp9/OXbZ6FTK+hXLPHPuy/gZ4s38dyn+9hS18J1Z1VSpFby1w2NqJQKnph+FpLXRoOrN69tbOSiIWYiPice6cT7NPO8QSx4bxeff1nHsIoTzkPy+7Ry51F2Nbj5xVVD8DhteIDi4mJ0Oh1+v7/N+1ShhutGG7nz4iGUFyl4fm093xxdxsT+xSzd3syjHxwAYPLICu68eDDLdzawePMxfMEIs87pw90XDUCnVnLE2Y+19a3YXF50Oh0VekWizW02G5FIJPE+hUIhdDpdmyHA+DvZWXJpayG/9jZdW+v1ernmjHKmTejPucN6EQwG8fnc2ANwTh8150wbzkFnmEWbGlm29RiTRlTwwGX9sRRLKdc6na2F0z8byWW4XC6MRiNer5fW1lauGlXKM6sPs2lPPaMHlKPRaAgEAuj1+tj9jkrMfX8/4/sbmT6+d8LWmkwxOxmJpIYKxZ+N3ccc7Dzm4v7Jg1J0xJ8Nh8OB2WzG5XK1sbUlJSWJ9tdoNAlbO8yi5p1tfvbWNzB8YJ+EbWnPXhuNxnZtbRylUslvltfz/9u788AmyrwP4N/J2TTN1aYHUAq0UK5ytaiLHFVBBEVBBVZY0fWEFXFdlUVcUBd5vfF11WXV1VVfXA/AG3cX8YDCggrlRu67BUrPJE3SnPP+ERKSJiltkmk6D7/PX9BJJvOdzDzzZOY5XB4e0y7tioaGBrhc55ua1NXVQa1WY3CuFj8frYXT6Qxbh0wmCytrfzpaB7vLgwFZClRXVwfKWo7jwHFc2KQPcrk8pJz0n2N+Pc7dN/rxYBWuH9QZB0/79oVO6gzsV4VCAZ1OF1hH93Ql9p0KLUv1ej1cLhfONjTi612nMaG/EVZTPfylpVKhQGd9CjYfqcbdI/NRU1MTVpFrqawFAKVSCY1GA7PZDJ1OF3Ed6enpEctav+bn0/FTVThcY8OhajsOVtuw+7TvWjkwJwVutxsOhyPQwde/74LrLlf3y8K7G49h1ZbDuLLX+Uq1/3w62uBGtcWBkk4p6JHuu8Z/s+0YbizpGjif/BVkuduK6mpXyDqan0/B1Gp1yPmUmcLD5vRg77FTyExThNRdtFotLBYLHA5H2DqamppgtVpjKmsFqyC7XK5AgQ0AvXr1CisMWistLS3kQPB6vYGCt/kyq9UKjUbT4nuay8yMPB2mTqeLulwikQQuFNcN7oqlZcex46wHUy8JfRym1Wqxo8J3l83q9OK0Qx4yfaNW6zuDf6l2IkuTGngc6H8sYdSkBD4neDsu6SUFvj+Oz7ZVQiGToCBLA5lUEnLx6ttZi8M1diz6ej+O1dnQw6jGY6sO44N7LkNJt9BfkDqdDtXV1dBqtThq8lXEBvXIDvnMAV0zABxFrVuJnCwdjtc34fohXTGqlxErd5xFRUMTxg/oFNhvwfLMvpOntolHP7k84j71Pz7JNaQG7XugT85xfHfIhNljQ3+B+tdRc8J3UgzJ9zU18D9S0ev1uLO0N1TyQ7h6YFeM7GUM68nuX8dl+UasO1B9vgPmuXUEP5756w+HUNvoxOPXZ8Lh9uDA2UbcOaJH1OPnpst64qeTNny85SRG9ekc9jqF4vzTgZSUFNw3RovdVU14avVRfHvQjGdvHoBuGb7Pz8gIb798uPYQcg0qZGjVyMtQ46zNHfYZDrcHPxzyTRnarXPkGan854VCoUBqaujdqpJu6dh4uBbzxvXB/23ajJG9jBhR1B0AMG5AF7z83QHU290wnqscyeVypKam4liNFf8sP4NfD+2KMQN8jyL93+mSaZegJP8EnvhyNzYdO3/RfP3WEuQZNajmm7D45hJ0yzqCK3pnITPz/DGt1WoxpUSJl745gH8fsODPfUM7X/nPpw/LzyBDrcBvRvQOG/s20vkU7A/j+mPNgTosXn0Yj13bF/+z5hhG9DRiWEEGXl97GD8c2ALpuTvbs0oLQoZ2yswEBrcwM27wZ/q//2jb0VqJLGsBYcvbtpa1CyZlorq6OvB3uVwOtTr07l9mJnBJz054YcqgFofq8x8baWlpIZX5YBc6NvzbqdFooNFo8JsRafjrhgqsPWZDcWHXwDb61/H3siP4z95aHK51YNaY/iHrSEtLi/o5aw40QMIBtwzrhcwIw2P5y6lIZa1f8+NreB8JXik7iZNWCXrifNnW0jr8mm/nW+uP4NNtlXjo6sKIT+4AX4XvsgIj1v1nPxpd0fdpcFm786cqyCQcrhnSI6SvQLTtaL6O9PRmHc/SM5Ai34vdZ2yYfKkSpy2+Ctqggi7I1CgjrqNvZwM+314Jo9EYcjzJ5XJ8e9AEh9uLO0sLkZkZut8G5eoDNy2iVbpaKmv9/N9HtHWkNStro63j9x9twxfbzz/h06TI0DdHi1mlnXB57y6QSjjIZLKQ8yl4/2q1Wlyq9kKnkuPHCjumXh6677VaLco2HwQATBiaD0OqApqUA9hd7cSt59ap0WhQZa6ETMKhd17nQJPN5ln951Mk/vNpYPccAMdh8qagX+b566F/Hf7zuzmr1RrI2NayVrDnTykpKdi1a1fg/7t27YJKFVsvxuLiYpSVlQEAtm/fjsLC8x2sCgoKcPz4cTQ0NMDpdGLLli0YMmRIi+9JtH6dtMjSKLE+SoemHSdNgWYDkdrT1jY6MOPtn/C/3x4I/K3uXGemdHXkIUr8nQMOnm1EYXZaxGYNvbO12FVpwqfbKvH70b2wYuYwdNKl4M53t2DfmehtpZqPYNH8M/edsWDfGTO8vC97cZ4h0O44Wnu//l20kHDA5qPRH3f4x0Bu3oZ54uAu2HqiIWp710NnG6GQSgKd8oKVdDNg3pjuuKZ/TovDPA3tno5aqxPHonzGJ+UVeGH1fvzjv0fx/b6qiB30Inn8+n54YHQvTGxFR5tMjRIf3zsMT984ALtPmTDr/a0tvv7AWVtgoPdcgyriWMgvrTmAozVW/O7KC49lGcmIXkbsPW3G+z8eR5XZgTtH9AgsG903Czzvm0WruQ9/PgEOwMNjI5930y/Lw0+PjcHqB0fhs/sux3cPl2Jc0flOORIJh5mlBRF772ekKXHtgBx8urUyYpOdYzVWfL//LH5zWV7UiSFakiKX4pkbB6Ci3o77/rkVfXI0eH1GCWZf2RNlf7wSz08eiLWPXIFXpg3pEOOeJrKsBTp+eRtNMsax7qRTYURPIz7dVhk2NNjBKgte+GY/NEoZDp5tbHGUmWA8z+OrHafwq/wMZMUxdmxzRV10kHCIezzkdQeq8fS/9uLaATmYc1ULvwaBQDvk1k7FvP5gDYbk6SNWjmMhl0owKFePbec66h2rtUGtkMKYFn0s3V7ZabA0ucM6uPM8j893VWNgrg79O4eX+4O66nGizha4dieTye7Cqp2nMaZvNt66bSg2zLsSO58Yi+WzhuHR8X1COrO3RC6VYHSfLHy/72zEmQLXHqjGoFwdjGlKSCUcLuuRgU1HQus4Z0xNyNamhFWO2yqeod5iJVgFee7cuZg1axamT5+O6dOn495778UjjzwS07quvvpqKBQK3HLLLXjmmWcwf/58fPXVV/j4448hl8vx6KOP4q677sItt9yCm2++GdnZ2RHfIxSO4zC8pxEbD9WEFZJeL48dFQ0oLcxEnxwNNh4Or0Qv+/E4HOc6D/jV23wnWbQ2yDqVPFCRDJ4SNVjvHF8F9/KCDMy5qhcy0pT4vzsvg0ImwaS//hf/2HA04niPh842Qi7lwjoydNGrfBOQVFkCv5T7d9ZCIuECPa2jzbqjTZFjYK6+xWHDKht8FbzgNsgAAuv+Isp0zIfOWpCfqY466HlrDO3ub4ccXoHfVWHC/M92YVh+BvIz1Xhq1d5Az+gLVZDVShkeurqw1YP8SyQcpl+WhzlX9cTe0+aoF1Wb040T9U2Btudd030d9oIrjFuO1eHNsiOYdmlXXNm77ZOgAL5jBwCe+88+5GeqUdrr/C/w/p21yNYqwzoWOtwerCivwJi+2S1e4NPVCvTO0WBIngEFmW3rkHPrr7rB4nDj023hx8S7G49BJuHiGtrrsvwM3D2iBwqz0/DOHZcg7dwoCAa1AlOHdkXXKJ18kiGRZS3Q8cvbjuam4i6oqLdjc1DZ4fJ48fCKHUhTyvDGjBIAaHUn7d2VZhytseKGQfF3zgumVspQmK3Bjjg66h2tsWLOB1tRmK3Bixe4Yw/4ykeFVNKqdsh1Vid2nzJhZK/4nqg0V9zNgD2nzGhyeXC81opuGeoWt9vf9ya4o15NowPv/PcYDtXYI05XDvjuIAOIaf/yPI9qiwPlx+vx+bZKvPLdQTz22S785duD+HLHKeyuNKHxAv13gm08N7LErNJ8jOmXjVxDasw/IMf2z0aDzRU2FGq91YltJ+pxRdC15fKCDByvtYUMkxfvGMh+nbQpUMokOFrTuolcEkGwJhZDhw7F119/jR07dsDr9WLw4MEx9yaUSCRYtGhRyN8KCs7fEbvqqqtw1VVXXfA9iRLpkdTwnkZ8tq0S+6ssIR3mjtZaYWlyY1BXPZQyKf750/GQmX3sTg/+b5NvSKTgYdBqA3eQo1eseudoUNlgD+ug53dlnyxMLsnFH8f1DvxizMtIxao5IzD/011YtOoXrN5zBq/fWgKDWhHIdeisBT2M4RVO/wQk+85Y4PLw0Knkgcrs7cO6w2R3hTQfaW5ETyP+tu4wLE2uiHcIKurtkHDhvV276FW4tEc6Pt9eifuv6hl2oh8829hiRbU1jxB7ZqZBmyLDT0frMGXo+QHvaxodmLlsCzLTlHht+hDsqjTht+9sxktrDkCbIot41zoRruidhaf/tQ9lB6rDhlACfKOT8EDgDqb/e6iot6N3jgZWhxsPr9iBXIMKf7outulaAd8FTqOUweJw487hPULuAnAch6v6ZOPL7ZUh02R/s6cKdVYnpsU4rW1rvq+SbgYU5+mxeNUv6J6RGrioWppcWFlegesGdIr77tuCCf0SOo18a3LFIpFlLSCO8rYjuaZ/DtSK3fhka0VgKMe/rT2MnRUmLP1NMYYVZKCTLgXrmp3L0XJ9uaMScimH8UWdIi6Px6BcPb755UxMx7W5yYW739sMmVSCv982tMUncv5sKXIpBuTqQn48RPPfQzXg+daN1d4WxXkGuL08dlaYcLzOht5RmoT49TzXwf3bvVXYcqwe3+8/i50VDeB5oCBTjeuj/HAZkKsDx/k66rX2hsSeUya89v0hrDvg698RzJAqR4PdheBmyFkaJaZflocHx7T8hKbsYDU0KTIMbsMshtGOx1GFmVDKJPjmlzMYVnC+aUPZwWp4eV8ncT//8k2HazG5JBeAr17TPwFP2iQSLtBRry3iKT8EuYO8adMmHDx4EHq9HqWlpTh+/Dj27dsnxEclRfNOW4BvNjTAd5IH237C92tycFc9hvfMCExr7PfJ1grUWZ0YkufroOZvlF/figpyn3OPn/tGGUQ+S5OCF6cMQpYmtKKQrU3B27cPxfOTB2LriXr8z7/2BnKdrLNhw6GaqCdWYbZvJItfTpnQr5M2UMjm6FLw9I0DWnykfXnPDHi8vkkzIqmot6OTThVx6tZJg7vgcLUVj322G3e88zNufesnVNTb0OTy4ESdLWQGveYifV/NSSQcrumfg0+3VgTG/nW4PZi1rBy1VifemFGCjDQlruidhTF9s2Gyu1DURSfYY91eWWmBi2ok/glC+gXdQQbOz0T4ZtkRnKiz4YXJgwJ3P2Mhk0owvKcR+lQ5bioOn81rdJ8sWJ2ekLGqP/jpBHINKoxs47Byfq35vjiOw1u3X4IeRjXuem8Lfth3Fqv3nMFd725Bo8Md0hQkHon8fluTq61YL2sBYfZbIqUqZBg/oBP+tesM7E4Pdlea8Mp3BzFxcGdcO6ATOI7DqF6Z2HCoJuQxdaRcXi+PVTtPo7QwM+JMb/Ea2FWHepsr6pCQ0Xi8PB78aDuO19qw9DfFF3yCEpxtaHcDdlWa0ORquV38hoM10KbIAmM2J8qQPN/6Nh+rw8m66JOE+GWmKaFTyfHOf4/h5e98zR4fHF2Ir+4fgS9mXRK1PE1TytArK61VE1vtqjDh7ve24LpXNmDDoRrcXJyLJ6/vh3/8dii+fWgU9j01DtseH4u9i8Zh9YOj8PqtxfjjuN7opFfhjXVHYHdG35c8z6PsQA2GFxjb9FQ12nmWqpBhRE8jvtkTOpHV2v3VSFcrQr6v3tkapKsVgSflPM/jtMmekDvIgO8JdVvHQo6n/Eh4Bfm7777DI488EtJjVaVSYe7cuVi3bl0L7xQPqTS8EthJp0J+pjqsgryjogFpShkKMtNwaY90SCVcoB2yx8vjrfVHMKirHtcP7Ayn24v6c4PO11md4DhfU4poruqThT45GgzIbfsvJI7jMHVoV9w9Mh8ryyvw89E6SCQSLPxiN6QcF/UXap8c3wQkuypNbW5/WZxnQIpcgv9GaGYC+GbR62KI3Hby2gE50Chl+KS8AqdNTdh2oh6z/7kVe89NVtLSEGqRvq9InrihPwoy0zD7g604WWfD/E93Ycvxerw0dXDIoOoLJ/SFUiZBSTdhxrUFfN9PaWEmNhysiTjV9voDNdCkyAJ3jrsazk0WUmcDz/P4ZGsFRvbKxK9amJyktZ6aVIRPfnd5xDtGw3saoQyaJvtIdSM2HanFtEvzYm5z1trvK12twIf3/Ao9M9Nwx7ubMXNZOU7W2/DE9f0SfpFNhNbmaq2LoawFEr/fhHBzcS4aHW58tfMUHlq+HelqBf58Q//A8tLembA0uUPa/0bKtflYHU6bmqLepYyXvxlAW8dDfvnbA/h+31k8cUP/VpUpwdku6ZYOl4dvseLI8zzWH6zG8J7GVrePbS1jmhLdM1Lxr12n4fLwUScJ8eM4Di//ejBenDIIm/80Bl/MHo7fj+mFAbk6yKN09PcbmOsbSi/aUGQ7Kxpw57ubcf1rG/Dz0Vr8YUwhNsy7Ck9NKsJvh/fAVX2y0TNLE7jRlCKXoneOBuOKOuG+K3pi3jW9YXd5AqNXRXK42orKBjtGFbatqUpL59nY/tmobLAHbsx4vTzWHahGaWFmyPclkXD4VX46Nh2uBc/zMNldaHJ545pFL1gPoxonam0R20NHE0/5kfAmFm+++Sbefvtt9OnTJ/C3X//61+jfvz8WL16M0tLSRH9ku4t28A8vMOKTrRVwebyBu6A7TjZgQBcdpBIOmpRzs5MdrsWDXh7/t+kYjtXa8Ndr+sB/o+qMqQnpagXqbU7oVPIWfwEO7Z6O/zw4Kq4sc67qiS+3n8LCz3fjnhF5WLu/Gguu64vOESb7AM531PPyaPNjkxS5FJd0Tw/7EeFXUW+LWvjqUxX46U+joZBKIJNK8J/dZzDr/XL84ePtAHwdK6Jp7biJaUoZ3rxtKG54bQNueG0D6m0uPHR1Ia4bGPqos1uGGt89XApjmjLKmhLjit6Z+GjzSWw70RCYVRDwtcX+z54zmF3aI3CH05imgEouxcl6O8qP16Oi3h4yW2A8MjXKsB7ffiqFFMN7GvHp1grYnL6OLTIJhynnHq/Foi3jXBrUCvzz7svw6veHcFl+Okb3yYqrLbqQ2jp+54VcDGUtkPj9JoTLeqSji16FhZ/vhsPtxTt3XBLS78Bf8Vt3oBpDz3Vci5Tryx2noJJLcXW/yCPOxKt3jgZKmQQ7Tja0uo3zWUsT3lh3BDcO6YIZrWzXH5zNfyNh87G6kHIs2JEaK06ZmjD7qsQ2r/ArzjME+iu0ZlzpK/tEbiJxoWNxUFc9VpZXoKLeHnaXfXelCTcu3QhNigxzr+mN24Z1a3NnxEt7pCNDrcDXu05j/IDITXD8bd1HFbZtX7aUbXTfbHDcLnyzpwr9O+uws9KEOqszpHmF37ACI/616wyO19pgP/fUIJF3kN1eHhX19lZPABRP+ZHwK4nD4QgpsP2KiooC4+2JncUSeXa64T2NsDk9gbsETS4PfjltDpnF6/KCDGw/2YArXvwBf/7qFwzoosO4ohxkn2svecbse/RVZ3UivZUdu+KRqpDhiev7YX+VBfM+3YP+nbX47eXdo74+uP1WpJ68FzK8pxEHqhpxttm00y6PF2fMTWEd9Jpvq7/yM64oBzNL83Gs1gaphEP3Fh6bRfu+IulhVOOVW4agwe7CDYM6R+2lnWtIjWmEhLa4PHBRPX+34GSdDQs+242SbgbcWny+AOQ4DrkGFU7W2fD59kqo5NK4p2ptrdlXFmBQVz2+3XsWa/dXY1xRTlztf9vyfQG+SvLj1/fDNf1zOmzlGGh7rgu5GMpaIPH7TQgSCYcbh3SBw+2N2ClWp5JjcFd9SJOp5rmaXB78a9dpjOmX3WL73njIpRIUddG1aUKL/9t4HC6vF78f3evCLz4nOJvh3CRJLXXUW++v1CW4g57fkKCnfS1dKy7kQsfi4Cgd9TxeHo99tgvpagV+ePgKzL6yZ0wjdcikEoztn4Pv952N2mRl3YFq5GeqkWtoW/+YlrIZ05QY2s2Ab85NiPbDvrOQcJG/L3/H7o2HaxM2i56ffxCAtrRDjqf8SPjVpPlkBsHEcCcgHsPyMyDhzrdD3nvaDJeHD2nPO6ZfNrw8j05aFf72m2J8dt/lkEq4wAF0xuQbWqbOGnmaaSFc3S8bo/tkwcsDT984oMVKhkGtQJZGCYVMgvzMthc0/umOg6ekdHm8+N81B1qc4SiSuWN7Y3jPDBR11iZ0xqwr+2Rh/R+vxP/+enBSho7y06bIUZJnCFxUXR5v4I75y78eHJhwxq9reiqO1lixaudpXN0vG+o42h63RUm3dCy76zKULxiDnx8bjRenDGqXz73YXcxlbUf02+HdMbM0P2qn2NLCTOyqNEWdFXRFeQXqbS5MjzJKQqIMytVj9ylTxKZbzdmcbrz/03GM7Zcd15Tdl3Q3YMvx+qiVug2HatAtI1Ww0WGKz7VDVsgkyEng0HnN9c7RQCGThDUn+edPx7GzwoSFE/rBEOd1/boBnWBzeiIOr9nk8vUHEeKHxth+Odh72oyTdTas3X8Wg7vqI2bJN6qRpVFi05FanD5XQU7kHWQAbW6HHKuEV5D79++Pr776Kuzvq1atQvfu3RP9cR2KLtXXhMJfQfb/Sg+uIBfnGbD7yWuwfNYwjB/QKVAZzdIowXHnR7KoszrjPpFai+M4vDp9CJbN6B9ytzua4jwDhnTVR+xMdyH9OmmhT5Vjw6Ea2JxubD1Rj5v/thFL1x7G5JLcsOYMLZFJJXjvjkvx8cxhbd6OC8k1pCa8LVwsSntnYnelGV/tOIUJr2zAluP1eGpSUcQLSVeDCgfPNqLB5sKkIcK0YWwJx3HI0qYIfmed+FzMZW1HZExTYv74vlE7cZUWZoLnEXGoS7fHizfLDmNInh6/yo/cDCFRBnXVocnlxYGqC99ZW1legQabC/eOyo/rM28Y1AWWJjf+d82BsGVOtxebDtcmfPSKYL2zNUhVSJGXnhr3eLwtUcgk6N9ZG9LW/Ky5CS/8Zz9G9jLi+jZc36L5VX46DKly/Hv36bBlm4/VocnlRWkb2x+3hr/Zz0ebT2BnpSnqSB0cx+HyggxsOlyD0ybfyFSZCWqOmK5WQJsiC0wqJrSE32J68MEHMW3aNJSVlaG4uBherxfbt2/H5s2bsWzZskR/XIdzeU8j3iw7gpHPf49TDU3I0aaEPV6IdGdPLpXAmKZE1blfXPU2Z6BDRXtIVcjQO6t1dwhenDoI3hjvUEkkvpPn060VWFleAQDQp8rx+q3FGBfDsEYyqQQyhutjpYWZeGH1fsz5cBu6pqvwxoySqE0n/JXmdLUi4WOJko7nYi9rxaaoiw6GVDnW7a/GxMGhI8J8ves0TtbZsfC6foI/tfLfsNlZYWqxmZyvE/lRDMnTh8282lbDCjIw7dI8/H39EYwrysGQoOFAt52oh9XpEbTMkkkluH5gZ2hShH+qNihXj483n4Tb44VMKsGiVb/A4fHiqYlFCfluZVIJrumfg1U7T4cMGQv42h8rpBJcJsCPrO5GNXpna/D3sqPg+ejttAHg8gIjPt9+CmUHa5ClSUlY0zeO49AjM63dJgtJ+NGSnZ2NlStX4oMPPuihTpcAACAASURBVMC6desgkUgwePBgLFiwoMOPZ9lawVMEN3dzcRfsONkAY5oSEwelojRCI/ZocrQpOGNuAs/zqLe62u0Osl9LuYLFM2wYANw7qgBpSt8Ywt2NagzLz0CGgB3eWpurI+rXSYvJJbnonpGKu0fmhxSGzXP525xNGNgpprv7HYWYv6+WJDrXxVDWAuwcD1IJh5G9MlF20DehlD8Xz/P429rD6JmVhjF9hemcFywvPRX6VDl2nGyIOukFAKz55QxO1Nkwf3x4O/cLifSdPXZtH6zbfxZzV+7EqjkjAmXZhkM1kEq4kDF2hfDc5IFxr6M1x+Kgrjq8u/EYDlU3osrswKqdp/HQ1YVxNVFpbvyATvho80mUHajG2KAbJmUHanBpj/SY2rC3JtvV/bLx2g+HkKlRBoYYjcT/Xe442dCmsZhbI9+oxs8tzMjbXDzlhyA/p9LT03H//fcLseoOoaWLT88sDT6451cxrTdbm4KKehsaHW44Pd6o00wLpb0uqoO76hN+0rREzJUFiYSL2qa3ea7BXfXIz1RjeowTdHQUYv6+WiJELtbLWoCt46G0MBNf7jiFX06bA0NHrt1fjX1nLHhxyiBBH//7cRyHgbn6C045/WbZEeSlp4ZUwFor0nemSZHjmZsH4vZ//Iy/fHcQ88b5Kt5lB33j7msTNL20kFpzLPqf/P58tA5vbziKfKMaM0vja6LS3OUFGdCp5Pj37jOB7+eMqQn7qyy4uSR8vPrWaE22sf19FeTSwswWj9Wu6anINajOzW2Q2DbfPYxqfLatMuzueTQdbqIQ1tXXt25e+bbK0Slx2tSEeqtvLOR0tbDDiDUnVK5ku1hy5ehS8P3DV0SdelwsLpbvi7QOS/tt5Lmht8oOVgdy/W3tYXTWpSR8aumWDM7V4UCVBTZn5EkUyo/XYeuJBtw1okdMfTGifWelhZn49dCueGPdYew42YAGmxO7KhoCnbc7utYci90z1NCmyPDCf/bjeK0NiycVQZngdoByqQRj+2Xj21+q4HD7Oj6WHfQP7xZbU5XWZBvQRYf7r+yJu0deeCKmYeeGbM1OcKdI/5wHuysvPCELEF/5wfEXeXfn8vJylJSUtPr1Xx7+Ep8d/EyQbalssONknQ39Omvxyykz+uRoQsbSJISIy429bsQNBTckezM6hLaWtYCw5W2y7Ko0QSrh0K+TFpYmN/acMqF7hjphQ2G1Rr3Nif1nLOjfWRexXe6BKgvMTW4U5+khSXCbaI+Xx46KBkglHLroVTh0tjHqdojV3jNmmGwuGNOULU5iFY8Gmwv7zpjRO0cDQ6oCB882wtLkQnGecBNYtUVNowOHzjYiLz016rwKsfB4eZQfr0eWRhnWbCXR5S3dQY6By+USZL2Kc+1GbQ7fL8L2HtNVqFzJRrnEhdVctbW1F34RCcPa8aBTyWFpcqPJ6cQpkx0yqQRZ2vZ9WujvR9LoCL+D3OTyoN7mRLZGGXPluKXvTCrhkG9Mg93pwbEaK2QSLu5+Le2ltceiLkUOuVSCbq2YlCRWOpUcMgmHOqsTPACT3dXizLsXkujzTKeSQyGTIC3BP3ykEg4GtQI1Vidac3s3nnJXsKOypqYGO3bswOjRo/HCCy9g9+7dmD9/fsSB7cXkhoIbMEw7DJmZie9xu+FgDW59+ycMyMrF3hMVePHXV7RpXOB4VVdXC5Ir2SiXuLCaq6Vxi+PBalkLCFveJsumw7WY9vcfMTg3F5v2VuDBMb3w4JjEzHrZFsOf/R5yjwI39slHUWctumeoIZFwWPj5bpRXnMQHt16JLE1sd7Vbcw4/smIHVpZXYGy/bLw5fmhMn9PeWls28TwPh9sr+JCXDy/fgW9+OYP7RlyCqWs34X+mDYm5qY6Yyt3v9lbhrve2YMaooRh9gY6t8ZS7gt2ifPTRR3Hy5Els2rQJ69evx8SJE7F48WKhPo4JOTrfXYS9p33jU7bXRCGEEPGislZcSroZoFZI8cbGCqjkUtw+rHtStuPm4i7Yf8aCBz7chquWrMOAJ1dj6uubsKL8JCYN6Rxz5bi1Fl7XD8V5etwcx7T0HRXHce0yHvy1A3JgaXLj2X/vBccBI0XSljteowozYUiV47Nz04cLRbAKckNDA37729+irKwMEyZMwE033QS73S7UxzHB35h9f5UFcql4HjsRQpKHylpxUcgkuLynEV4emHZpXrsP5+n30Nje2P3na/D1AyPw/M0DMbkkFx6eR4ZaiZmlBYJ/vi5Vjk/vGx51XHdyYSN6GaFRyrD1RAMGdtEl7Vhqb3KpBNcP6ow1v1TB0iRcEyzBKsgulwsulwvr16/H5ZdfDrvdDpvNJtTHMUGTIodaIYXT7YUhVZHUaY4JIeJAZa34XDegE9QKaatGAxCSb+Y3HaZe0hV/nliET353Of776FUoyBSmYxlJLKVMijHnZriLdfQKsZo0pAscbi/+s/uMYJ8hWAV59OjRGDZsGAwGA4qKijBlyhRMmDBBqI9rV3q9cGP4+nsyJ6N5hZC5kolyiQvlahuWy1qAzeNh4uDO+Hn+lQnt3d+RsPidAR0z16QhXcBxiHuSmY6YrSVDuurRLSMVn29vuZlFPLkEe4b/wAMPYOrUqcjO9n1pL774IhOdRgDfHRu5XJhBzXN0KThcbYUhCcO7CZkrmSiXuFCutmG5rAXYPB44jgPHe5K9GYJh8TsDOmau0sJM/DR/NLLiHG+4I2ZrCcdxmDS4C175/iDOmJqiDpMYTy7B7iDX1NRgz5494DgOL7zwAp555hns27dPqI9rVzKZcG2D/e2Q09Pav4IsZK5kolziQrnahuWyFqDjQYxYzdZRc8VbOQY6braWTBrSBTwPfLXjVNTXxJOLRrGIgccj3C//HH8FOQl3kIXMlUyUS1woV9uwXNYCdDyIEavZWM0FiDNbD6Mag7vqWxzNIp5cNIpFDBobGwVbt/8xQTJ6owqZK5kol7hQrrZhuawF6HgQI1azsZoLEG+2G4d0wS+nzdh/xhJxeTy5BLunHtyz+tlnn425Z3VTUxPmzp2L2tpaqNVqPPfcc0hPTw95zbvvvouvv/4aAFBaWor7778fPM9j1KhR6N69OwBg8ODBePjhh+POJbRAE4tU8bQFIoQkT6LKWuDiK28JIeI2YWAnLFr1Cz7fXol54xLb90KwCrK/Z3Xfvn1RVFSECRMmxNSz+sMPP0RhYSHmzJmDr7/+GkuXLsWCBQsCy0+ePIkvv/wSK1asAMdxmD59OsaMGQOVSoX+/fvj9ddfT2QswXU516vZqGnfqUcJIeKUqLIWuPjKW0KIuGWkKTGqlxFfbKvE3LG9IZEkbnhcwUexyMnxDQIea8/q8vJy3H333QCAUaNGYenSpSHLc3Jy8NZbb0Eq9c1a43a7oVQqsWfPHlRVVWHGjBlISUnB/PnzkZ+fH/EzqqurQ/6fkpICtVoNi8UCrVaLmpqakOV1dXXIyMiA1WqFSqWCw+EIu2OjUqmgUqlgtVqh0WjC1gEARqMRFosFarUadrsddrsdWXIei67Nx+BMKaxWK5RKJex2O9RqdcQ5xY1GI8xmMzQaDaxWK5qamkKWq9VqyOVyOBwOqFQq1NXVhSznOA5GoxEmkwk8z8NsNsPhcIStQyaTweVyQalUor6+PuI6GhoaoNPpYDab4XQ6Q16TlpYGqVQKt9sNuVyOhoaGkOUSiQQZGRmor6+HwWCAyWQKW4dGowHHcfB4PJDJZDCZTCHLpVIp0tPTA+toaGiAy+UKyazVasHzPHieh1QqDVuHTCaDwWAIrKO+vh5utzvkNTqdDh6Px9cTneNgNptDlsvlcuj1+sA66urqwtpB6XQ6uN1uSKVS8DwPiyX08ZBCoYBOpwuso7a2NmzKTLfbDZvNBplMBo/HE/YoSaFQQKvVwmQyQa/Xo6amBnyzyesNBgMcDgfkcjncbjesVmvIcqVSCY1GA7PZDJ1OF3Ed6enpsNvtUCqVcLlcYeu40PkEIOR8stlsYedkrOdTsNTU1HY9n7RaLSwWS+B88r+2tedTayWqrAWEL2/bWtYCvqliLRZLwsvaYMk4NiI9+hV7WetXV1fHVFmr1+sDT2rsdjszZW3w+eQ/XsV4Pk0a3AW//3g7vtl+BCVdtSHr8O+nWMpajm/+DSSI1+vF22+/jbKyMrjdbgwfPhyzZs1qsUfhihUr8N5774X8LSMjA48//jgKCgrg9XpxxRVXoKysLOy9PM/j+eefh9VqxaJFi7B582bU1NRg/Pjx2LJlC5555hl88sknYe8rLy9HSUlJm7KZTCbodLo2vUcMKJe4UC5xESpXLGUt0P7lbSxlLUDHgxixmo3VXIC4s9mdHlzyP9/ijuHd8fDY3iHL4skl2B3kJUuWYN++fbj99tvh9Xrx8ccf4/nnn8djjz0W9T1TpkzBlClTQv52//33B34lWa1WaLXasPc5HA489thjUKvVeOKJJwAARUVFgbscQ4cORVVVFXieT8jsdJG2gQWUS1wol7gIlSuWshag8jbZWM0FsJuN1VyAuLOpFFKsmDUMGREGN4gnl2CjWKxfvx6vv/46xowZg7Fjx+Jvf/tbxDsRF1JcXIx169YBAMrKysLuQPA8j/vuuw+9e/fGokWLAoX0a6+9Frg7sm/fPnTu3DlhUzc3f1TECsolLpRLXITKlaiyFqDytj2xmgtgNxuruQDxZ+vbSRtxPOh4cgl2B5nn+ZDZSxQKRUyzmUybNg3z5s3DtGnTIJfLsWTJEgDAO++8g7y8PHi9Xvz8889wOp1Yv349AOChhx7Cvffei7lz52LdunWQSqV45plnEhMM4puSsbUol7hQLnERKleiylqAytv2xGougN1srOYC2M0WTy7B2iDPnTsXBoMBt956KziOw/vvv4+amppAgdtRxNIurqampk2daMSCcokL5RIXoXKxXNYCdDyIEavZWM0FsJstnlyCNbF44oknYDabccstt2Dq1Kmoq6vDwoULhfq4diXQb4qko1ziQrnERahcLJe1AB0PYsRqNlZzAexmiyeXYE0s3njjDTz77LNCrZ4QQgiorCWEECEIdgd57dq1Qq2aEELIOVTWEkJI4gl2Bzk3Nxd33nkniouLoVarA3+/4447hPpIQgi56FBZSwghiSdYBdnfc7CyslKoj0gag8GQ7E0QBOUSF8olLkLlYrmsBeh4ECNWs7GaC2A3Wzy5BKsgBw/z43Q6oVCED+AsVg6H44KzVIkR5RIXyiUuQuViuawF6HgQI1azsZoLYDdbPLkS3gbZ6XRi3rx5WLNmTeBvc+bMwfz588PmWRerWMcY7egol7hQLnFJdK6LoawF6HgQI1azsZoLYDdbPLkSXkF+5ZVX0NjYiOLi4sDfFi1aBJPJhFdffTXRH5cULF18glEucaFc4pLoXBdDWQvQ8SBGrGZjNRfAbrZ4ciW8grx27VosWbIEGRkZgb9lZ2fj+eefx7fffpvoj0sKq9Wa7E0QBOUSF8olLonOdTGUtQAdD2LEajZWcwHsZosnV8IryHK5HCkp4fNhp6WlMdc2jhBCkoXKWkIIEU7CK8gSiQSNjY1hf29sbGT2Fj4hhLQ3KmsJIUQ4Ca8gT5gwAQsWLIDNZgv8zWazYcGCBRg7dmyiP44QQi5KVNYSQohwEl5Bvv3226HRaDB8+HBMnToVkydPxvDhw6HVajF79uxEf1xSKJXKZG+CICiXuFAucUl0rouhrAXoeBAjVrOxmgtgN1s8uTie5/kEbktAZWUl9uzZA4lEgoEDByIrK0uIj4lbeXk5SkpK2vQenufBcZxAW5Q8lEtcKJe4CJWL5bIWoONBjFjNxmougN1s8eQSbFToLl26oEuXLkKtPqnMZjN0Ol2yNyPhKJe4UC5xESoXy2UtQMeDGLGajdVcALvZ4smV8CYWFwMWDyKAcokN5RIXVnMJjdX9xmougN1srOYC2M0WTy6qIMegpqYm2ZsgCMolLpRLXFjNJTRW9xuruQB2s7GaC2A3Wzy5qIIcA4GabScd5RIXyiUurOYSGqv7jdVcALvZWM0FsJstnlxUQSaEEEIIISQIVZAJIYQQQggJItgoFonS1NSEuXPnora2Fmq1Gs899xzS09NDXrN48WJs3boVarUaALB06VLI5fILvo8QQsh5VN4SQohPh7+D/OGHH6KwsBAffPABJk2ahKVLl4a9Zs+ePXjrrbewbNkyLFu2DBqNplXvixWrBT/lEhfKJS5iyEXlbfthNRfAbjZWcwHsZosnV4evIJeXl2PkyJEAgFGjRmHTpk0hy71eL44fP47HH38ct9xyC1auXNmq98XDbrcnbF0dCeUSF8olLmLIReVt+2E1F8BuNlZzAexmiydXh2pisWLFCrz33nshf8vIyIBGowEAqNVqWCyWkOU2mw233nor7rjjDng8Htx2220oKipCY2Nji+8LVl1dHfL/lJSUwHu0Wm3YMCFutxupqamwWq1QqVRwOByw2Wwhr1GpVFCpVLBardBoNBGHGjEajbBYLFCr1bDb7WFfZGpqKpRKJex2O9RqNWprayOuw2w2Q6PRwGq1oqmpKWS5Wq2GXC6Hw+GASqVCXV1dyHKO42A0GmEymaBSqWA2m+FwOMLWIZPJ4HK5oFQqUV9fH3EdDQ0N0Ol0MJvNcDqdIa9JS0uDVCqF2+2GXC5HQ0NDyHKJRIKMjAzU19fDYDDAZDKFrUOj0YDjOHg8HshkMphMppDlUqkU6enpgXU0NDTA5XLB7XYH9q1WqwXP8+B5HlKpNGwdMpkMBoMhsI76+nq43e6Q1+h0Ong8HnAcB47jYDabQ5bL5XLo9frAOurq6uDxeMLW4Xa7IZVKwfN82PGpUCig0+kC66itrYXX6w37Xmw2G2QyGTweDxobG8PWodVqYTKZoNfrUVNTE9aj12AwwOFwQC6Xw+12w2q1hixXKpXQaDSBwdYjrSM9PR12ux1KpRIulytsHRc6nwDfee4/n9xud9g5KcbzSavVwmKxBM4n/3HY2vNJaMkob9ta1gII7EeWylqtVgun0xm2P8Re1vr5P5uVslav18PlcoHjONjtdmbK2uDzyV8+ifV8Ci5rg9fBcRysVmtMZa1gU00nyv333497770XAwcOhMViwbRp07Bq1arAco/HA7vdjrS0NADA888/j8LCQnz77bctvs8vlulPbTYbUlNT4wvWAVEucaFc4iKGXEKWt7FONS2G/RYLVnMB7GZjNRfAbrZ4cnX4JhbFxcVYt24dAKCsrCysgD127BimT58Oj8cDl8uFrVu3on///hd8Xzya/2pjBeUSF8olLmLIReVt+2E1F8BuNlZzAexmiydXh2piEcm0adMwb948TJs2DXK5HEuWLAEAvPPOO8jLy8Po0aNx/fXXY+rUqZDL5Zg4cSJ69eqF3NzciO8jhBASGZW3hBDi0+GbWAgtlsd+1dXVyMzMFGiLkodyiQvlEhdWc7VWrE0sWN1vrOYC2M3Gai6A3Wzx5OrwTSwIIYQQQghpT1RBjkFKSkqyN0EQlEtcKJe4sJpLaKzuN1ZzAexmYzUXwG62eHJRBTkG/hmkWEO5xIVyiQuruYTG6n5jNRfAbjZWcwHsZosnF1WQY9D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\n"
      },
      "metadata": {}
     }
@@ -139,7 +143,9 @@
     {
      "output_type": "execute_result",
      "data": {
-      "text/plain": "Text(0, 0.5, 'Information Criteria')"
+      "text/plain": [
+       "Text(0, 0.5, 'Information Criteria')"
+      ]
      },
      "metadata": {},
      "execution_count": 6
@@ -148,8 +154,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -160,27 +166,17 @@
     "plt.xlabel('n_terms')\n",
     "plt.ylabel('Information Criteria')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.6 64-bit ('p-data-science': conda)",
-   "language": "python",
-   "name": "python37664bitpdatascienceconda518311c1ac6644f6a39b768ea8138c75"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -192,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6-final"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/examples/simulating_a_predefined_model.ipynb b/examples/simulating_a_predefined_model.ipynb
new file mode 100644
index 00000000..43edc03c
--- /dev/null
+++ b/examples/simulating_a_predefined_model.ipynb
@@ -0,0 +1,613 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulate a Predefined Model\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the object so we can access its functions\n",
+    "model = PolynomialNarmax()\n",
+    "\n",
+    "# create any psi matrix that contains the combinations we want\n",
+    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
+    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
+    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
+    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>0.347967</td>\n",
+       "      <td>0.217882</td>\n",
+       "      <td>0.024947</td>\n",
+       "      <td>0.136428</td>\n",
+       "      <td>0.015620</td>\n",
+       "      <td>0.001788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.741870</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>-0.685155</td>\n",
+       "      <td>0.550371</td>\n",
+       "      <td>-0.362971</td>\n",
+       "      <td>0.508296</td>\n",
+       "      <td>0.239381</td>\n",
+       "      <td>-0.335223</td>\n",
+       "      <td>0.469437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.801757</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>0.642815</td>\n",
+       "      <td>0.006963</td>\n",
+       "      <td>-0.392272</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>-0.004249</td>\n",
+       "      <td>0.239381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.278465</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.077543</td>\n",
+       "      <td>-0.271475</td>\n",
+       "      <td>-0.002418</td>\n",
+       "      <td>0.950426</td>\n",
+       "      <td>0.008466</td>\n",
+       "      <td>0.000075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>896</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.019212</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>0.000369</td>\n",
+       "      <td>-0.000275</td>\n",
+       "      <td>-0.018729</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.013937</td>\n",
+       "      <td>0.950426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>897</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.872305</td>\n",
+       "      <td>0.562238</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>0.760916</td>\n",
+       "      <td>-0.490443</td>\n",
+       "      <td>0.012470</td>\n",
+       "      <td>0.316112</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>0.000204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>898 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0         1         2         3         4         5         6  \\\n",
+       "0    1.0  0.000000  0.108434 -0.610171  0.000000  0.000000 -0.000000   \n",
+       "1    1.0 -0.548087 -0.370476  0.108434  0.300400  0.203053 -0.059431   \n",
+       "2    1.0  0.009140  0.730998 -0.370476  0.000084  0.006681 -0.003386   \n",
+       "3    1.0 -0.332688  0.042290  0.730998  0.110682 -0.014070 -0.243195   \n",
+       "4    1.0  0.589887  0.369362  0.042290  0.347967  0.217882  0.024947   \n",
+       "..   ...       ...       ...       ...       ...       ...       ...   \n",
+       "893  1.0 -0.741870  0.489266 -0.685155  0.550371 -0.362971  0.508296   \n",
+       "894  1.0 -0.801757 -0.008684  0.489266  0.642815  0.006963 -0.392272   \n",
+       "895  1.0  0.278465 -0.974898 -0.008684  0.077543 -0.271475 -0.002418   \n",
+       "896  1.0  0.019212 -0.014295 -0.974898  0.000369 -0.000275 -0.018729   \n",
+       "897  1.0 -0.872305  0.562238 -0.014295  0.760916 -0.490443  0.012470   \n",
+       "\n",
+       "            7         8         9  \n",
+       "0    0.011758 -0.066163  0.372308  \n",
+       "1    0.137252 -0.040172  0.011758  \n",
+       "2    0.534358 -0.270817  0.137252  \n",
+       "3    0.001788  0.030914  0.534358  \n",
+       "4    0.136428  0.015620  0.001788  \n",
+       "..        ...       ...       ...  \n",
+       "893  0.239381 -0.335223  0.469437  \n",
+       "894  0.000075 -0.004249  0.239381  \n",
+       "895  0.950426  0.008466  0.000075  \n",
+       "896  0.000204  0.013937  0.950426  \n",
+       "897  0.316112 -0.008037  0.000204  \n",
+       "\n",
+       "[898 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(psi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
+    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
+    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "\n",
+    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1001,    0],\n",
+       "       [2002,    0],\n",
+       "       [2001, 1001]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.final_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.psi = psi[:, [1, 3, 5]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the parameter estimation method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
+    "from sysidentpy.parameter_estimation import Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.20014451],\n",
+       "       [0.89992074],\n",
+       "       [0.09997088]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
+    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
+    "\n",
+    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
+    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
+    "\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "model.theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0019184421163563367\n"
+     ]
+    }
+   ],
+   "source": [
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2001  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001879953122213705\n",
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2000  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
+    "model.psi = psi[:, [1, 3, 5]]\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/system_identification_using_adaptative_filters.ipynb b/examples/system_identification_using_adaptative_filters.ipynb
new file mode 100644
index 00000000..1765a962
--- /dev/null
+++ b/examples/system_identification_using_adaptative_filters.ipynb
@@ -0,0 +1,422 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "educated-affect",
+   "metadata": {},
+   "source": [
+    "# System Identification Using Adaptative Filters\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "preliminary-announcement",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "exact-trace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-encounter",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the Adaptative Filters available in SysIdentPy individually. If you want to build a regressor matrix and estimate the parameters using only the Adaptative Filter method (not applying FROLS + ERR algorithm), follow the steps bellow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "binary-shame",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>-0.242635</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.239048</td>\n",
+       "      <td>0.118630</td>\n",
+       "      <td>0.058872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.217385</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>0.047256</td>\n",
+       "      <td>-0.210825</td>\n",
+       "      <td>0.106285</td>\n",
+       "      <td>0.940557</td>\n",
+       "      <td>-0.474171</td>\n",
+       "      <td>0.239048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.503772</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>0.253787</td>\n",
+       "      <td>0.316599</td>\n",
+       "      <td>-0.488570</td>\n",
+       "      <td>0.394958</td>\n",
+       "      <td>-0.609492</td>\n",
+       "      <td>0.940557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.804358</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.248526</td>\n",
+       "      <td>-0.505505</td>\n",
+       "      <td>0.095465</td>\n",
+       "      <td>-0.194177</td>\n",
+       "      <td>0.394958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.379308</td>\n",
+       "      <td>-0.442970</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>0.143875</td>\n",
+       "      <td>0.168022</td>\n",
+       "      <td>-0.117197</td>\n",
+       "      <td>0.196222</td>\n",
+       "      <td>-0.136867</td>\n",
+       "      <td>0.095465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0         1         2         3         4         5         6         7  \\\n",
+       "0  1.0  0.000000 -0.488925 -0.242635  0.000000 -0.000000 -0.000000  0.239048   \n",
+       "1  1.0 -0.217385  0.969823 -0.488925  0.047256 -0.210825  0.106285  0.940557   \n",
+       "2  1.0 -0.503772 -0.628457  0.969823  0.253787  0.316599 -0.488570  0.394958   \n",
+       "3  1.0  0.804358  0.308975 -0.628457  0.646992  0.248526 -0.505505  0.095465   \n",
+       "4  1.0 -0.379308 -0.442970  0.308975  0.143875  0.168022 -0.117197  0.196222   \n",
+       "\n",
+       "          8         9  \n",
+       "0  0.118630  0.058872  \n",
+       "1 -0.474171  0.239048  \n",
+       "2 -0.609492  0.940557  \n",
+       "3 -0.194177  0.394958  \n",
+       "4 -0.136867  0.095465  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "pd.DataFrame(psi).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "received-apparatus",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "growing-anniversary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-culture",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electrical-crowd",
+   "metadata": {},
+   "source": [
+    "Here we are using the Affine Least Mean Squares method, but you can use any of the methods available on SysIdentPy\n",
+    "\n",
+    "- Least Mean Squares (LMS)\n",
+    "- Affine LMS\n",
+    "- LMS Sign Error\n",
+    "- Normalized LMS\n",
+    "- Normalized LSM Sign Error\n",
+    "- LSM Sign Regressor\n",
+    "- Normalized LMS Sign Regressor\n",
+    "- LMS Sign-Sign\n",
+    "- Normalized LMS Sign-Sign\n",
+    "- Normalized LMS Leaky\n",
+    "- LMS Leaky\n",
+    "- LMS Mixed Norm\n",
+    "- LMS Fourth\n",
+    "\n",
+    "Also, you can use:\n",
+    "- Least Squares (LS)\n",
+    "- Total LS\n",
+    "- Recursive LS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "august-robertson",
+   "metadata": {},
+   "source": [
+    "## Building the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ahead-element",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-alignment",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "metric-layer",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0024097628318461625\n",
+      "       Regressors Parameters         ERR\n",
+      "0               1    -0.0000  0.00000000\n",
+      "1          y(k-1)     0.1999  0.00000000\n",
+      "2         x1(k-1)    -0.0000  0.00000000\n",
+      "3         x1(k-2)     0.8999  0.00000000\n",
+      "4        y(k-1)^2    -0.0001  0.00000000\n",
+      "5   x1(k-1)y(k-1)     0.1000  0.00000000\n",
+      "6   x1(k-2)y(k-1)    -0.0000  0.00000000\n",
+      "7       x1(k-1)^2     0.0002  0.00000000\n",
+      "8  x1(k-2)x1(k-1)    -0.0000  0.00000000\n",
+      "9       x1(k-2)^2     0.0000  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-dakota",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "union-mortality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input\n",
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi\n",
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1))\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sysidentpy/polynomial_basis/narmax.py b/sysidentpy/polynomial_basis/narmax.py
index 2dc5ae2a..fc34494c 100644
--- a/sysidentpy/polynomial_basis/narmax.py
+++ b/sysidentpy/polynomial_basis/narmax.py
@@ -448,11 +448,12 @@ def predict(self, X, y, steps_ahead=None):
 
         """
         if steps_ahead is None:
-            return self._model_prediction(self.final_model, X, y, self.theta)
+            return self._model_prediction(X, y)
         elif steps_ahead == 1:
-            return self.one_step_ahead_prediction(X, y)
+            return self._one_step_ahead_prediction(X, y)
         else:
-            return self.n_step_ahead_prediction(X, y, steps_ahead=steps_ahead)
+            self._check_positive_int(steps_ahead, 'steps_ahead')
+            return self._n_step_ahead_prediction(X, y, steps_ahead=steps_ahead)
 
     def _code2exponents(self, code):
         """
@@ -488,7 +489,29 @@ def _code2exponents(self, code):
 
             return exponents
 
-    def one_step_ahead_prediction(self, X, y):
+    def _check_positive_int(self, value, name):
+        if not isinstance(value, int) or value < 1:
+            raise ValueError(
+                f"{name} must be integer and > zero. Got {value}"
+            )
+
+    def _one_step_ahead_prediction(self, X, y):
+        """Perform the 1-step-ahead prediction of a model.
+
+        Parameters
+        ----------
+        y : array-like of shape = max_lag
+            Initial conditions values of the model
+            to start recursive process.
+        X : ndarray of floats of shape = n_samples
+            Vector with entrace values to be used in model simulation.
+
+        Returns
+        -------
+        yhat : ndarray of floats
+               The 1-step-ahead predicted values of the model.
+
+        """
         X_base = InformationMatrix().build_information_matrix(
             X, y, self.xlag, self.ylag, self.non_degree
         )
@@ -498,7 +521,23 @@ def one_step_ahead_prediction(self, X, y):
         yhat = np.concatenate([y[:self.max_lag].flatten(), yhat])
         return yhat.reshape(-1, 1)
 
-    def n_step_ahead_prediction(self, X, y, steps_ahead):
+    def _n_step_ahead_prediction(self, X, y, steps_ahead):
+        """Perform the n-steps-ahead prediction of a model.
+
+        Parameters
+        ----------
+        y : array-like of shape = max_lag
+            Initial conditions values of the model
+            to start recursive process.
+        X : ndarray of floats of shape = n_samples
+            Vector with entrace values to be used in model simulation.
+
+        Returns
+        -------
+        yhat : ndarray of floats
+               The n-steps-ahead predicted values of the model.
+
+        """
         if len(y) < self.max_lag:
             raise Exception("Insufficient initial conditions elements!")
 
@@ -510,7 +549,7 @@ def n_step_ahead_prediction(self, X, y, steps_ahead):
         while i < len(y):
             k = int(i - self.max_lag)
             if i + steps_ahead > len(y):
-                steps_ahead = len(y) - i
+                steps_ahead = len(y) - i  # predicts the remaining values
 
             yhat[i:i+steps_ahead] = self._model_prediction(
                 self.final_model,
@@ -523,20 +562,16 @@ def n_step_ahead_prediction(self, X, y, steps_ahead):
         yhat = yhat.ravel()
         return yhat.reshape(-1, 1)
 
-    def _model_prediction(self, model_elements, X, y_initial, theta):
+    def _model_prediction(self, X, y_initial):
         """Perform the infinity steps-ahead simulation of a model.
 
         Parameters
         ----------
-        model_elements : ndarray of ints
-            Matrix with regressor codes.
         y_initial : array-like of shape = max_lag
             Number of initial conditions values of output mensured
             to start recursive process.
         X : ndarray of floats of shape = n_samples
             Vector with entrace values to be used in model simulation.
-        theta : array-like of shape = number_of_model_elements
-            Paramters estimated via Least Squares method.
 
         Returns
         -------
@@ -553,7 +588,7 @@ def _model_prediction(self, model_elements, X, y_initial, theta):
         y_output[: self.max_lag] = y_initial[: self.max_lag, 0]
 
         model_exponents = [self._code2exponents(
-            model) for model in model_elements]
+            model) for model in self.final_model]
         raw_regressor = np.zeros(len(model_exponents[0]), dtype=float)
 
         for i in range(self.max_lag, X.shape[0]):
@@ -572,7 +607,7 @@ def _model_prediction(self, model_elements, X, y_initial, theta):
                     np.power(raw_regressor, model_exponents[j])
                 )
 
-            y_output[i] = np.dot(regressor_value, theta.flatten())
+            y_output[i] = np.dot(regressor_value, self.theta.flatten())
         return y_output.reshape(-1, 1)
 
     def information_criterion(self, X, y):

From e579767586decbccd313a645c2f155dda81186d8 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Mon, 8 Mar 2021 23:58:50 -0300
Subject: [PATCH 05/27] added n-steps-ahead prediction example

---
 CHANGELOG                                     |   2 +
 docs/_config.yml                              |   2 +-
 docs/source/changelog/v0.1.5.rst              |   1 +
 .../examples/n_steps_ahead_prediction.ipynb   | 410 ++++++++++++++++++
 docs/source/notebooks.rst                     |   6 +
 examples/n_steps_ahead_prediction.ipynb       | 410 ++++++++++++++++++
 sysidentpy/polynomial_basis/narmax.py         |   4 +-
 7 files changed, 831 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/examples/n_steps_ahead_prediction.ipynb
 create mode 100644 examples/n_steps_ahead_prediction.ipynb

diff --git a/CHANGELOG b/CHANGELOG
index 67c5e8ca..4a0a2beb 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -15,6 +15,8 @@ CHANGES
 
 - MAJOR: n-steps-ahead prediction.
     - Now you can define the numbers of steps ahead in the predict function.
+	- Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+
 
 - Added 4 new notebooks in the example section.
 
diff --git a/docs/_config.yml b/docs/_config.yml
index 84bbafb5..91a58738 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -2,6 +2,6 @@ theme: jekyll-theme-cayman
 repository:
   url                       : https://github.com/wilsonrljr/sysidentpy  # Online location of your book
   path_to_book              : /docs  # Optional path to your book, relative to the repository root
-  branch                    : step_ahead  # Which branch of the repository should be used when creating links (optional)
+  branch                    : master  # Which branch of the repository should be used when creating links (optional)
 launch_buttons:
   colab_url: "https://colab.research.google.com"
\ No newline at end of file
diff --git a/docs/source/changelog/v0.1.5.rst b/docs/source/changelog/v0.1.5.rst
index bf162ff1..14bfbfd6 100644
--- a/docs/source/changelog/v0.1.5.rst
+++ b/docs/source/changelog/v0.1.5.rst
@@ -14,6 +14,7 @@ CHANGES
 
 - MAJOR: n-steps-ahead prediction.
     - Now you can define the numbers of steps ahead in the predict function.
+	- only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
 
 - Added 4 new notebooks in the example section.
 
diff --git a/docs/source/examples/n_steps_ahead_prediction.ipynb b/docs/source/examples/n_steps_ahead_prediction.ipynb
new file mode 100644
index 00000000..6e1ee55e
--- /dev/null
+++ b/docs/source/examples/n_steps_ahead_prediction.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark\n",
+    "\n",
+    "**Note**: The following examples do not try to replicate the results of the cited manuscripts. Even the model parameters such as ylag and xlag and size of identification and validation data are not the same of the cited papers. Moreover, sampling rate adjustment and other different data preparation are not handled here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**The following text was taken from the link http://www.nonlinearbenchmark.org/#F16**. \n",
+    "\n",
+    "**Note**: The reader is reffered to the mentioned website for a complete reference concerning the experiment. For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. A more detailed study of this system will be published in the future.  \n",
+    "\n",
+    "> The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads.\n",
+    "\n",
+    "> The experimental data made available to the Workshop participants were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class, held in September 2014 at the Saffraanberg military basis, Sint-Truiden, Belgium.\n",
+    "\n",
+    "> During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study.\n",
+    "\n",
+    "> A detailed formulation of the identification problem can be found here. All the provided files and information on the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format.\n",
+    "\n",
+    "> Please refer to the F16 benchmark as:\n",
+    "\n",
+    "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "import pandas as pd\n",
+    "pd.set_option('display.max_columns', 1000)"
+   ]
+  },
+  {
+   "source": [
+    "## Preparing the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_16 = pd.read_csv(r'examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(32768, 3)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "f_16.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16[['x1', 'x2']][0:500].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x576 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"465.958125pt\" version=\"1.1\" viewBox=\"0 0 721.645312 465.958125\" width=\"721.645312pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-08T23:42:32.923280</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16['y'][0:2000].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1_id, x1_val = f_16['x1'][0:16384].values.reshape(-1, 1), f_16['x1'][16384::].values.reshape(-1, 1)\n",
+    "x2_id, x2_val = f_16['x2'][0:16384].values.reshape(-1, 1), f_16['x2'][16384::].values.reshape(-1, 1)\n",
+    "x_id = np.concatenate([x1_id, x2_id], axis=1)\n",
+    "x_val = np.concatenate([x1_val, x2_val], axis=1)\n",
+    "\n",
+    "y_id, y_val = f_16['y'][0:16384].values.reshape(-1, 1), f_16['y'][16384::].values.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "x1lag = list(range(1, 10))\n",
+    "x2lag = list(range(1, 10))\n",
+    "x2lag"
+   ]
+  },
+  {
+   "source": [
+    "## Building the model"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2523e7e4850>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax(# n_terms=8,\n",
+    "                         non_degree=1,\n",
+    "                         order_selection=True,\n",
+    "                         ylag=20, xlag=[x1lag, x2lag],\n",
+    "                         n_inputs = 2,\n",
+    "                         n_info_values=39,\n",
+    "                         info_criteria='bic',\n",
+    "                         extended_least_squares=False,\n",
+    "                         estimator='least_squares')\n",
+    "model.fit(x_id, y_id)"
+   ]
+  },
+  {
+   "source": [
+    "## Defining the forecasting horizon\n",
+    "\n",
+    "To perform a n-steps-ahead prediction you just need to set the \"steps_ahead\" argument.\n",
+    "\n",
+    "**Note** The default value for steps_ahead is None and it performs a infinity-steps-ahead prediction"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.09660139833917882\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Regressors   Parameters             ERR\n",
+       "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
+       "1      y(k-2)  -1.8938E+00  1.95167599E-02\n",
+       "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
+       "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
+       "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
+       "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
+       "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
+       "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
+       "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
+       "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
+       "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
+       "13     y(k-4)  -9.5479E-01  7.23623116E-04\n",
+       "14     y(k-8)  -7.1309E-01  4.44988077E-04\n",
+       "15    y(k-12)  -3.0437E-01  1.49743148E-04\n",
+       "16    y(k-11)   4.8602E-01  3.34613282E-04\n",
+       "17    y(k-13)  -8.2442E-02  1.43738964E-04\n",
+       "18    y(k-15)  -1.6762E-01  1.25546584E-04\n",
+       "19    x1(k-2)  -8.9698E+00  9.76699739E-05\n",
+       "20    y(k-17)   2.2036E-02  4.55983807E-05\n",
+       "21    y(k-14)   2.4900E-01  1.10314107E-04\n",
+       "22    y(k-19)  -6.8239E-03  1.99734771E-05\n",
+       "23    x2(k-9)  -9.6265E+01  2.98523208E-05\n",
+       "24    x2(k-8)   2.2620E+02  2.34402543E-04\n",
+       "25    x2(k-2)  -2.3609E+02  1.04172323E-04\n",
+       "26    y(k-20)  -5.4663E-02  5.37895336E-05\n",
+       "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
+       "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
+       "29    x1(k-7)   4.9862E+00  2.03811842E-05"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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418.788988 \r\nL 95.823918 417.14612 \r\nL 95.888766 419.571574 \r\nL 95.969825 422.427663 \r\nL 95.986037 419.935081 \r\nL 96.002249 419.588972 \r\nL 96.018461 416.776156 \r\nL 96.067097 421.126941 \r\nL 96.115732 417.322844 \r\nL 96.131944 417.280816 \r\nL 96.229216 423.726638 \r\nL 96.245428 421.302618 \r\nL 96.358911 417.992977 \r\nL 96.375123 418.91002 \r\nL 96.391335 418.523717 \r\nL 96.407546 421.836762 \r\nL 96.504818 420.989002 \r\nL 96.52103 421.52937 \r\nL 96.537242 420.803017 \r\nL 96.553453 417.185378 \r\nL 96.650725 418.894004 \r\nL 96.764208 421.75883 \r\nL 96.78042 420.207059 \r\nL 96.812844 415.902476 \r\nL 96.845267 420.703498 \r\nL 96.861479 419.295515 \r\nL 96.877691 421.960188 \r\nL 96.893903 418.710054 \r\nL 96.974962 419.82729 \r\nL 97.03981 420.582339 \r\nL 97.088446 417.151598 \r\nL 97.201929 421.431456 \r\nL 97.218141 420.172974 \r\nL 97.250565 418.42234 \r\nL 97.266776 417.507238 \r\nL 97.2992 419.6799 \r\nL 97.315412 418.837208 \r\nL 97.347836 418.064348 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420.101691 \r\nL 103.443505 420.742359 \r\nL 103.492141 416.86154 \r\nL 103.621836 422.846315 \r\nL 103.767743 416.84079 \r\nL 103.881226 422.925947 \r\nL 103.91365 422.181374 \r\nL 104.027133 416.755559 \r\nL 104.059557 420.725891 \r\nL 104.140616 419.958285 \r\nL 104.205464 418.003117 \r\nL 104.189252 421.020615 \r\nL 104.2541 419.795129 \r\nL 104.270311 420.131831 \r\nL 104.302735 419.510922 \r\nL 104.318947 418.64919 \r\nL 104.335159 420.367969 \r\nL 104.383795 419.936804 \r\nL 104.43243 421.007442 \r\nL 104.448642 418.574488 \r\nL 104.464854 417.460861 \r\nL 104.529702 419.911969 \r\nL 104.545914 418.527138 \r\nL 104.594549 421.15305 \r\nL 104.643185 418.057142 \r\nL 104.659397 419.227341 \r\nL 104.756668 418.099954 \r\nL 104.740456 419.591364 \r\nL 104.789092 418.126443 \r\nL 104.837727 421.662223 \r\nL 104.902575 418.477377 \r\nL 104.934999 420.103558 \r\nL 104.999846 417.042155 \r\nL 105.129541 422.298224 \r\nL 105.243025 417.27335 \r\nL 105.259237 417.730767 \r\nL 105.275448 421.598409 \r\nL 105.324084 417.068191 \r\nL 105.37272 420.736869 \r\nL 105.437567 417.576288 \r\nL 105.469991 420.850482 \r\nL 105.486203 421.141337 \r\nL 105.534839 418.621245 \r\nL 105.567262 421.921523 \r\nL 105.583474 419.260443 \r\nL 105.599686 420.927807 \r\nL 105.680746 417.991595 \r\nL 105.696957 417.48019 \r\nL 105.713169 419.43557 \r\nL 105.729381 418.761132 \r\nL 105.794229 421.210393 \r\nL 105.826653 420.900028 \r\nL 105.842864 417.398889 \r\nL 105.859076 421.148724 \r\nL 105.940136 418.166941 \r\nL 106.053619 422.706448 \r\nL 106.069831 422.452197 \r\nL 106.167102 416.770083 \r\nL 106.215738 417.590979 \r\nL 106.280585 421.542635 \r\nL 106.329221 420.638757 \r\nL 106.442704 416.967766 \r\nL 106.458916 417.291967 \r\nL 106.572399 421.289577 \r\nL 106.637247 417.136384 \r\nL 106.604823 423.172004 \r\nL 106.685883 417.690177 \r\nL 106.75073 421.274751 \r\nL 106.799366 419.060147 \r\nL 106.83179 421.66916 \r\nL 106.848001 418.835716 \r\nL 106.864213 417.46113 \r\nL 106.896637 419.304261 \r\nL 106.929061 417.824813 \r\nL 107.042544 421.836748 \r\nL 107.058756 421.241315 \r\nL 107.139815 417.699771 \r\nL 107.172239 418.721248 \r\nL 107.204663 417.853339 \r\nL 107.301934 422.050618 \r\nL 107.415417 416.917136 \r\nL 107.431629 417.459288 \r\nL 107.561324 422.312428 \r\nL 107.577536 421.855897 \r\nL 107.626172 416.545222 \r\nL 107.707231 419.342418 \r\nL 107.755867 417.707671 \r\nL 107.739655 420.10933 \r\nL 107.804503 420.000805 \r\nL 107.820715 421.917179 \r\nL 107.86935 417.888338 \r\nL 107.917986 420.63239 \r\nL 107.982834 422.159306 \r\nL 108.047681 417.80408 \r\nL 108.063893 420.515044 \r\nL 108.144952 416.64582 \r\nL 108.161164 420.151233 \r\nL 108.177376 418.28243 \r\nL 108.193588 422.627712 \r\nL 108.258436 420.587697 \r\nL 108.371919 417.324481 \r\nL 108.355707 420.819372 \r\nL 108.388131 417.506599 \r\nL 108.404343 415.718034 \r\nL 108.46919 420.651351 \r\nL 108.485402 418.724417 \r\nL 108.501614 420.390283 \r\nL 108.582673 417.841909 \r\nL 108.598885 419.984296 \r\nL 108.615097 419.873142 \r\nL 108.631309 417.365505 \r\nL 108.712368 419.039348 \r\nL 108.744792 421.815672 \r\nL 108.793428 418.387855 \r\nL 108.80964 419.568351 \r\nL 108.825852 416.874879 \r\nL 108.842063 420.399703 \r\nL 108.923123 418.485202 \r\nL 108.971759 418.411445 \r\nL 109.036606 422.085311 \r\nL 109.166301 417.826321 \r\nL 109.182513 417.455959 \r\nL 109.231149 422.656108 \r\nL 109.295996 419.95027 \r\nL 109.312208 420.167976 \r\nL 109.344632 417.229085 \r\nL 109.393268 420.2449 \r\nL 109.425691 419.079978 \r\nL 109.441903 418.966646 \r\nL 109.490539 421.690094 \r\nL 109.539175 418.88277 \r\nL 109.555387 420.526498 \r\nL 109.652658 417.746278 \r\nL 109.66887 418.211617 \r\nL 109.766141 422.426901 \r\nL 109.782353 419.689925 \r\nL 109.798565 419.446776 \r\nL 109.814777 422.110914 \r\nL 109.879624 416.849548 \r\nL 109.895836 418.486789 \r\nL 110.009319 423.38414 \r\nL 109.960684 417.831446 \r\nL 110.057955 419.888465 \r\nL 110.139014 417.531059 \r\nL 110.122803 420.531087 \r\nL 110.171438 418.872135 \r\nL 110.18765 418.023242 \r\nL 110.203862 420.778306 \r\nL 110.252498 419.779249 \r\nL 110.26871 421.49687 \r\nL 110.333557 418.4384 \r\nL 110.349769 420.85991 \r\nL 110.382193 416.805081 \r\nL 110.479464 418.52655 \r\nL 110.544312 421.675527 \r\nL 110.576735 419.262477 \r\nL 110.592947 415.839351 \r\nL 110.641583 420.16215 \r\nL 110.674007 416.79728 \r\nL 110.70643 420.908929 \r\nL 110.78749 419.305058 \r\nL 110.803702 419.83822 \r\nL 110.852337 418.332862 \r\nL 110.884761 420.776129 \r\nL 110.933397 416.785408 \r\nL 110.949609 422.471818 \r\nL 111.04688 419.753082 \r\nL 111.095516 416.090603 \r\nL 111.079304 420.800841 \r\nL 111.160363 418.914161 \r\nL 111.273846 422.064862 \r\nL 111.403542 416.82817 \r\nL 111.500813 421.846086 \r\nL 111.533237 421.772468 \r\nL 111.630508 416.731744 \r\nL 111.64672 417.885513 \r\nL 111.727779 421.603716 \r\nL 111.760203 420.780567 \r\nL 111.792627 421.628502 \r\nL 111.857474 417.480024 \r\nL 111.954746 422.584253 \r\nL 111.90611 415.849559 \r\nL 111.970958 421.473004 \r\nL 112.084441 417.267456 \r\nL 112.214136 421.42327 \r\nL 112.230348 421.19605 \r\nL 112.278983 416.848134 \r\nL 112.360043 418.195479 \r\nL 112.376255 417.96604 \r\nL 112.392467 418.248834 \r\nL 112.489738 422.495936 \r\nL 112.50595 419.944542 \r\nL 112.619433 416.620296 \r\nL 112.538374 420.326952 \r\nL 112.635645 417.724542 \r\nL 112.76534 423.351084 \r\nL 112.878823 417.51851 \r\nL 112.959883 422.08408 \r\nL 113.008518 419.896162 \r\nL 113.040942 419.516778 \r\nL 113.057154 417.198351 \r\nL 113.122002 420.053382 \r\nL 113.138213 418.429983 \r\nL 113.186849 420.973675 \r\nL 113.170637 417.379174 \r\nL 113.251697 419.381181 \r\nL 113.267909 419.467128 \r\nL 113.28412 422.023543 \r\nL 113.348968 417.418696 \r\nL 113.381392 420.36723 \r\nL 113.430027 421.096866 \r\nL 113.478663 420.375351 \r\nL 113.494875 420.225657 \r\nL 113.527299 417.216306 \r\nL 113.543511 420.371135 \r\nL 113.608358 418.809337 \r\nL 113.656994 422.258418 \r\nL 113.640782 418.541309 \r\nL 113.738053 421.438566 \r\nL 113.851536 416.363806 \r\nL 113.867748 418.561654 \r\nL 113.916384 421.201479 \r\nL 113.932596 418.174796 \r\nL 113.997443 420.547116 \r\nL 114.062291 417.564504 \r\nL 114.094715 420.547729 \r\nL 114.110927 420.729413 \r\nL 114.191986 417.570328 \r\nL 114.22441 420.022976 \r\nL 114.240622 420.450459 \r\nL 114.256834 419.655052 \r\nL 114.273046 417.105015 \r\nL 114.337893 420.684317 \r\nL 114.354105 418.700668 \r\nL 114.402741 422.509545 \r\nL 114.451376 419.457065 \r\nL 114.467588 416.63188 \r\nL 114.548648 420.170431 \r\nL 114.564859 418.517682 \r\nL 114.629707 421.591673 \r\nL 114.678343 419.831123 \r\nL 114.710766 420.148664 \r\nL 114.74319 418.754165 \r\nL 114.808038 417.149098 \r\nL 114.840462 419.236205 \r\nL 114.872885 419.070159 \r\nL 114.937733 422.149065 \r\nL 115.00258 417.339021 \r\nL 115.051216 419.978754 \r\nL 115.08364 417.519746 \r\nL 115.148487 420.724667 \r\nL 115.164699 422.089882 \r\nL 115.180911 419.327113 \r\nL 115.245759 421.16003 \r\nL 115.310606 418.272673 \r\nL 115.34303 420.494977 \r\nL 115.359242 421.429844 \r\nL 115.407878 419.152026 \r\nL 115.424089 420.017137 \r\nL 115.505149 417.282894 \r\nL 115.472725 420.779357 \r\nL 115.537573 418.141815 \r\nL 115.667268 420.811849 \r\nL 115.764539 417.28623 \r\nL 115.878022 420.87449 \r\nL 115.959082 418.128611 \r\nL 115.94287 420.987667 \r\nL 116.007717 419.213353 \r\nL 116.056353 417.685626 \r\nL 116.121201 422.440899 \r\nL 116.137413 418.531918 \r\nL 116.234684 419.771324 \r\nL 116.250896 419.961304 \r\nL 116.299531 417.950351 \r\nL 116.331955 420.036477 \r\nL 116.348167 419.238848 \r\nL 116.413015 420.966489 \r\nL 116.380591 418.909965 \r\nL 116.445438 419.148542 \r\nL 116.494074 417.941605 \r\nL 116.526498 419.328874 \r\nL 116.54271 421.729168 \r\nL 116.607557 417.528212 \r\nL 116.639981 420.140585 \r\nL 116.737252 418.217599 \r\nL 116.769676 419.419365 \r\nL 116.785888 419.588216 \r\nL 116.8021 419.16319 \r\nL 116.818312 419.167732 \r\nL 116.915583 417.235995 \r\nL 116.850736 422.027642 \r\nL 116.931795 418.615731 \r\nL 116.964219 421.291536 \r\nL 117.012854 418.452236 \r\nL 117.029066 416.94894 \r\nL 117.077702 420.091701 \r\nL 117.093914 419.788373 \r\nL 117.126338 420.495759 \r\nL 117.142549 420.434402 \r\nL 117.158761 421.127621 \r\nL 117.207397 418.936756 \r\nL 117.239821 419.747173 \r\nL 117.272245 418.675541 \r\nL 117.304668 420.860897 \r\nL 117.353304 418.376249 \r\nL 117.385728 420.205171 \r\nL 117.40194 420.203473 \r\nL 117.418152 420.576318 \r\nL 117.450575 419.348983 \r\nL 117.482999 417.456614 \r\nL 117.531635 419.438748 \r\nL 117.547847 419.292606 \r\nL 117.645118 421.339822 \r\nL 117.66133 420.44006 \r\nL 117.726177 417.053093 \r\nL 117.791025 417.741534 \r\nL 117.888296 421.740247 \r\nL 117.936932 420.087486 \r\nL 117.985568 418.005398 \r\nL 118.017991 420.557148 \r\nL 118.050415 418.115087 \r\nL 118.082839 421.211419 \r\nL 118.18011 421.045245 \r\nL 118.196322 421.219949 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420.517664 \r\nL 121.714302 420.305801 \r\nL 121.730513 421.545655 \r\nL 121.746725 417.854448 \r\nL 121.811573 420.590321 \r\nL 121.843997 417.058189 \r\nL 121.860208 420.777253 \r\nL 121.941268 418.118185 \r\nL 121.973692 417.930774 \r\nL 122.054751 421.747758 \r\nL 122.103387 418.120215 \r\nL 122.168234 419.733083 \r\nL 122.200658 420.695978 \r\nL 122.21687 418.489798 \r\nL 122.249294 419.64348 \r\nL 122.281718 418.187183 \r\nL 122.297929 419.296647 \r\nL 122.314141 421.343045 \r\nL 122.362777 417.769077 \r\nL 122.395201 419.561866 \r\nL 122.411413 418.101582 \r\nL 122.47626 420.873015 \r\nL 122.492472 420.313076 \r\nL 122.508684 420.654614 \r\nL 122.524896 420.153021 \r\nL 122.541108 420.377782 \r\nL 122.55732 417.938055 \r\nL 122.605955 421.785986 \r\nL 122.638379 420.388484 \r\nL 122.654591 420.14361 \r\nL 122.670803 416.892364 \r\nL 122.751862 420.346047 \r\nL 122.768074 420.93012 \r\nL 122.881557 417.213932 \r\nL 122.946405 422.00181 \r\nL 122.995041 419.714599 \r\nL 123.043676 418.174141 \r\nL 123.027464 421.019921 \r\nL 123.108524 419.168338 \r\nL 123.124736 419.454312 \r\nL 123.140948 417.396059 \r\nL 123.157159 421.530021 \r\nL 123.222007 420.200229 \r\nL 123.254431 420.95629 \r\nL 123.286855 420.189054 \r\nL 123.33549 417.979301 \r\nL 123.384126 421.013793 \r\nL 123.400338 420.774305 \r\nL 123.41655 420.897434 \r\nL 123.432762 417.556208 \r\nL 123.497609 421.581609 \r\nL 123.530033 418.679385 \r\nL 123.546245 420.239517 \r\nL 123.59488 418.231012 \r\nL 123.643516 418.962434 \r\nL 123.724575 421.547816 \r\nL 123.740787 418.04944 \r\nL 123.773211 419.513625 \r\nL 123.789423 420.490353 \r\nL 123.838059 417.807775 \r\nL 123.854271 418.596492 \r\nL 123.870482 417.175148 \r\nL 123.902906 420.640687 \r\nL 123.93533 419.820142 \r\nL 124.000178 421.696754 \r\nL 123.967754 418.673958 \r\nL 124.032601 419.860227 \r\nL 124.048813 417.974042 \r\nL 124.113661 420.010227 \r\nL 124.146085 419.079171 \r\nL 124.19472 420.964382 \r\nL 124.210932 418.683395 \r\nL 124.227144 416.653371 \r\nL 124.243356 421.744155 \r\nL 124.308203 417.457155 \r\nL 124.373051 421.634512 \r\nL 124.421687 419.527463 \r\nL 124.470322 420.02603 \r\nL 124.486534 417.848526 \r\nL 124.502746 421.644799 \r\nL 124.583805 419.996865 \r\nL 124.600017 415.399664 \r\nL 124.648653 421.278123 \r\nL 124.681077 421.076558 \r\nL 124.745924 417.646243 \r\nL 124.810772 419.629264 \r\nL 124.826984 421.162145 \r\nL 124.859408 418.18476 \r\nL 124.908043 419.277519 \r\nL 124.924255 419.396811 \r\nL 124.972891 419.163304 \r\nL 125.021526 420.558144 \r\nL 125.070162 416.544287 \r\nL 125.118798 420.919544 \r\nL 125.183645 419.60278 \r\nL 125.216069 418.381351 \r\nL 125.248493 419.596468 \r\nL 125.264705 421.610921 \r\nL 125.297128 417.855609 \r\nL 125.345764 420.578483 \r\nL 125.378188 417.580937 \r\nL 125.3944 421.345602 \r\nL 125.443035 419.226366 \r\nL 125.459247 420.972349 \r\nL 125.524095 417.741048 \r\nL 125.556519 420.121568 \r\nL 125.605154 419.216362 \r\nL 125.621366 420.511144 \r\nL 125.686214 417.969459 \r\nL 125.702426 420.983424 \r\nL 125.718638 420.226254 \r\nL 125.734849 420.481308 \r\nL 125.767273 418.074187 \r\nL 125.815909 421.139479 \r\nL 125.848333 419.148219 \r\nL 125.880756 419.920463 \r\nL 125.978028 418.226199 \r\nL 126.042875 422.050121 \r\nL 126.075299 419.905309 \r\nL 126.107723 420.764598 \r\nL 126.204994 417.075569 \r\nL 126.237418 421.443357 \r\nL 126.334689 420.58493 \r\nL 126.367113 421.269664 \r\nL 126.399537 417.990807 \r\nL 126.464384 418.496246 \r\nL 126.496808 421.442856 \r\nL 126.529232 417.914024 \r\nL 126.577868 419.685006 \r\nL 126.642715 418.44055 \r\nL 126.675139 419.439048 \r\nL 126.707563 419.294963 \r\nL 126.723775 419.890976 \r\nL 126.756198 416.999771 \r\nL 126.788622 421.925837 \r\nL 126.837258 418.546562 \r\nL 126.869681 420.215907 \r\nL 126.902105 417.608173 \r\nL 126.934529 418.991607 \r\nL 126.950741 418.354215 \r\nL 126.966953 419.904801 \r\nL 126.983165 419.67162 \r\nL 126.999377 421.953818 \r\nL 127.064224 419.042721 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422.029729 \r\nL 129.042074 420.359808 \r\nL 129.155558 416.945097 \r\nL 129.171769 418.553609 \r\nL 129.204193 421.326994 \r\nL 129.301464 420.98431 \r\nL 129.3501 417.304145 \r\nL 129.414948 419.26065 \r\nL 129.479795 421.861436 \r\nL 129.512219 420.483569 \r\nL 129.560855 418.260568 \r\nL 129.577067 421.342238 \r\nL 129.625702 418.771891 \r\nL 129.739185 422.152607 \r\nL 129.706762 418.22097 \r\nL 129.755397 420.71497 \r\nL 129.852669 417.784823 \r\nL 129.820245 421.311643 \r\nL 129.885092 418.58362 \r\nL 129.901304 418.433261 \r\nL 129.933728 421.537135 \r\nL 129.998576 417.628437 \r\nL 130.014787 420.079526 \r\nL 130.030999 418.025555 \r\nL 130.047211 420.586629 \r\nL 130.112059 418.54972 \r\nL 130.20933 421.055195 \r\nL 130.322813 416.970463 \r\nL 130.484932 422.194487 \r\nL 130.533568 417.137556 \r\nL 130.614627 417.626353 \r\nL 130.711899 420.895058 \r\nL 130.728111 419.58505 \r\nL 130.776746 420.333899 \r\nL 130.80917 418.331336 \r\nL 130.906441 421.951528 \r\nL 130.922653 419.12935 \r\nL 130.938865 419.085849 \r\nL 130.955077 416.926462 \r\nL 131.019924 420.350356 \r\nL 131.036136 418.895481 \r\nL 131.117196 420.72225 \r\nL 131.06856 418.412714 \r\nL 131.14962 419.606788 \r\nL 131.198255 419.978523 \r\nL 131.311738 417.727681 \r\nL 131.425222 420.905198 \r\nL 131.441434 420.743529 \r\nL 131.490069 417.840296 \r\nL 131.522493 420.870808 \r\nL 131.571129 418.492324 \r\nL 131.635976 420.97358 \r\nL 131.684612 419.346356 \r\nL 131.765671 417.223134 \r\nL 131.749459 420.013931 \r\nL 131.781883 419.50956 \r\nL 131.798095 419.367262 \r\nL 131.814307 419.9331 \r\nL 131.846731 419.554185 \r\nL 131.862943 421.09158 \r\nL 131.895366 419.52684 \r\nL 131.944002 419.63635 \r\nL 132.00885 418.058013 \r\nL 131.976426 419.750443 \r\nL 132.057485 418.41127 \r\nL 132.170968 421.152994 \r\nL 132.219604 418.365196 \r\nL 132.26824 418.854634 \r\nL 132.284452 421.45087 \r\nL 132.381723 419.523165 \r\nL 132.397935 419.498129 \r\nL 132.446571 420.656993 \r\nL 132.495206 418.487157 \r\nL 132.624901 420.862242 \r\nL 132.657325 418.200768 \r\nL 132.754596 419.074522 \r\nL 132.770808 420.122293 \r\nL 132.835656 418.451058 \r\nL 132.86808 420.099277 \r\nL 132.900503 417.425377 \r\nL 132.916715 420.207806 \r\nL 132.981563 419.452031 \r\nL 133.030198 417.63331 \r\nL 133.111258 421.167106 \r\nL 133.159894 418.182599 \r\nL 133.257165 418.343552 \r\nL 133.322012 420.882413 \r\nL 133.370648 419.728319 \r\nL 133.419284 418.848177 \r\nL 133.435496 419.392295 \r\nL 133.451707 420.70472 \r\nL 133.516555 418.325949 \r\nL 133.548979 420.563457 \r\nL 133.565191 420.465873 \r\nL 133.581403 416.53537 \r\nL 133.64625 420.978246 \r\nL 133.678674 418.436737 \r\nL 133.72731 420.705263 \r\nL 133.792157 419.115444 \r\nL 133.824581 418.902661 \r\nL 133.857005 419.488818 \r\nL 133.873217 418.211036 \r\nL 133.889428 420.81345 \r\nL 133.954276 418.776862 \r\nL 133.9867 421.763742 \r\nL 134.035335 417.000237 \r\nL 134.083971 421.099054 \r\nL 134.116395 417.026835 \r\nL 134.213666 418.655122 \r\nL 134.278514 420.97398 \r\nL 134.310937 418.634788 \r\nL 134.327149 417.892281 \r\nL 134.359573 419.34807 \r\nL 134.375785 418.903951 \r\nL 134.391997 421.20923 \r\nL 134.408209 418.640201 \r\nL 134.489268 419.721971 \r\nL 134.554116 416.856893 \r\nL 134.537904 421.523223 \r\nL 134.58654 419.125062 \r\nL 134.618963 420.929183 \r\nL 134.635175 418.636105 \r\nL 134.700023 420.766153 \r\nL 134.797294 417.377 \r\nL 134.813506 418.658412 \r\nL 134.862142 420.85329 \r\nL 134.84593 418.228434 \r\nL 134.926989 418.956073 \r\nL 134.943201 418.820265 \r\nL 135.040472 420.889328 \r\nL 135.02426 418.663142 \r\nL 135.056684 419.966077 \r\nL 135.10532 415.625286 \r\nL 135.137744 420.166517 \r\nL 135.153956 423.283798 \r\nL 135.202591 419.053156 \r\nL 135.235015 419.798554 \r\nL 135.299863 417.956592 \r\nL 135.316074 421.50681 \r\nL 135.332286 420.663694 \r\nL 135.348498 420.913394 \r\nL 135.36471 418.548368 \r\nL 135.461981 420.00112 \r\nL 135.510617 418.134147 \r\nL 135.526829 419.140454 \r\nL 135.543041 417.524847 \r\nL 135.591677 420.172991 \r\nL 135.6241 419.4416 \r\nL 135.640312 419.367446 \r\nL 135.656524 421.583207 \r\nL 135.721372 417.895528 \r\nL 135.753795 420.089107 \r\nL 135.770007 419.980833 \r\nL 135.802431 418.150085 \r\nL 135.834855 421.414938 \r\nL 135.88349 419.612999 \r\nL 135.899702 419.461682 \r\nL 135.948338 420.951279 \r\nL 135.980762 418.151968 \r\nL 135.996974 419.292453 \r\nL 136.029397 418.715115 \r\nL 136.045609 417.825557 \r\nL 136.110457 420.019977 \r\nL 136.207728 419.166817 \r\nL 136.142881 420.648358 \r\nL 136.22394 419.801393 \r\nL 136.240152 419.866813 \r\nL 136.305 418.385997 \r\nL 136.353635 418.476623 \r\nL 136.450907 420.648444 \r\nL 136.434695 418.064109 \r\nL 136.467118 419.954991 \r\nL 136.531966 418.276936 \r\nL 136.56439 420.351895 \r\nL 136.580602 419.117862 \r\nL 136.613025 420.312971 \r\nL 136.645449 420.138278 \r\nL 136.661661 415.695416 \r\nL 136.694085 421.064563 \r\nL 136.758932 418.083766 \r\nL 136.807568 421.192398 \r\nL 136.888627 419.680378 \r\nL 136.921051 419.37407 \r\nL 136.937263 420.188729 \r\nL 136.969687 417.497874 \r\nL 137.050746 418.493287 \r\nL 137.148018 422.086108 \r\nL 137.16423 420.973129 \r\nL 137.277713 417.137039 \r\nL 137.391196 421.608919 \r\nL 137.407408 418.628017 \r\nL 137.504679 419.066194 \r\nL 137.569527 417.673442 \r\nL 137.618162 421.155947 \r\nL 137.634374 418.049774 \r\nL 137.731646 420.175503 \r\nL 137.812705 418.963 \r\nL 137.780281 420.468338 \r\nL 137.845129 419.619054 \r\nL 137.861341 422.041684 \r\nL 137.893764 416.736577 \r\nL 137.9424 420.152897 \r\nL 138.007248 418.611286 \r\nL 138.02346 418.670201 \r\nL 138.088307 421.298247 \r\nL 138.120731 418.29286 \r\nL 138.136943 419.811882 \r\nL 138.218002 416.96788 \r\nL 138.250426 417.395539 \r\nL 138.28285 421.170863 \r\nL 138.347697 417.275357 \r\nL 138.380121 420.231323 \r\nL 138.396333 420.641495 \r\nL 138.412545 419.588776 \r\nL 138.444969 417.623944 \r\nL 138.493604 420.151164 \r\nL 138.509816 418.686043 \r\nL 138.623299 420.419929 \r\nL 138.671935 416.543472 \r\nL 138.720571 419.579487 \r\nL 138.769206 418.100231 \r\nL 138.850266 421.454731 \r\nL 138.915113 417.837735 \r\nL 138.963749 419.812097 \r\nL 138.979961 419.700095 \r\nL 138.996173 417.649215 \r\nL 139.028596 421.563369 \r\nL 139.093444 418.723775 \r\nL 139.125868 418.892694 \r\nL 139.14208 417.064811 \r\nL 139.158292 420.038907 \r\nL 139.255563 419.782846 \r\nL 139.287987 422.280971 \r\nL 139.352834 417.828821 \r\nL 139.369046 422.070059 \r\nL 139.417682 417.075243 \r\nL 139.466317 419.346279 \r\nL 139.547377 421.164005 \r\nL 139.596013 420.610896 \r\nL 139.693284 416.796932 \r\nL 139.628436 421.247908 \r\nL 139.725708 417.639395 \r\nL 139.822979 422.587224 \r\nL 139.839191 420.311213 \r\nL 139.936462 417.728131 \r\nL 139.952674 417.822199 \r\nL 139.968886 421.056775 \r\nL 140.049945 420.552639 \r\nL 140.066157 417.559313 \r\nL 140.163429 419.440513 \r\nL 140.228276 417.956061 \r\nL 140.244488 420.632693 \r\nL 140.325547 419.431494 \r\nL 140.341759 421.321563 \r\nL 140.390395 416.550442 \r\nL 140.455243 419.499645 \r\nL 140.52009 417.865191 \r\nL 140.536302 420.922133 \r\nL 140.552514 422.532362 \r\nL 140.568726 419.047493 \r\nL 140.617361 419.452047 \r\nL 140.633573 417.944404 \r\nL 140.649785 419.745636 \r\nL 140.714633 418.222289 \r\nL 140.77948 421.152393 \r\nL 140.811904 419.283497 \r\nL 140.892963 420.467247 \r\nL 140.909175 416.688714 \r\nL 141.006447 420.791474 \r\nL 141.03887 420.592346 \r\nL 141.055082 421.384453 \r\nL 141.103718 419.419927 \r\nL 141.11993 419.916382 \r\nL 141.152354 416.973594 \r\nL 141.249625 418.438114 \r\nL 141.363108 421.58264 \r\nL 141.460379 416.970555 \r\nL 141.476591 418.72015 \r\nL 141.492803 421.257171 \r\nL 141.557651 418.472411 \r\nL 141.573863 420.526712 \r\nL 141.687346 418.529381 \r\nL 141.752193 420.429155 \r\nL 141.768405 418.325924 \r\nL 141.800829 420.130755 \r\nL 141.817041 420.517557 \r\nL 141.833253 418.770725 \r\nL 141.849465 417.965675 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416.994869 \r\nL 146.713031 422.05341 \r\nL 146.745455 418.409989 \r\nL 146.842726 420.822033 \r\nL 146.777878 417.617597 \r\nL 146.858938 419.97501 \r\nL 146.87515 416.59021 \r\nL 146.923785 420.908059 \r\nL 146.956209 419.66641 \r\nL 146.972421 419.815885 \r\nL 146.988633 419.271254 \r\nL 147.004845 419.801734 \r\nL 147.021057 421.345204 \r\nL 147.102116 418.48918 \r\nL 147.118328 417.735566 \r\nL 147.13454 419.54851 \r\nL 147.150752 421.578502 \r\nL 147.199387 417.762497 \r\nL 147.248023 421.150495 \r\nL 147.426354 417.2478 \r\nL 147.458778 421.762177 \r\nL 147.539837 419.204162 \r\nL 147.556049 418.786851 \r\nL 147.572261 418.9902 \r\nL 147.620896 417.889588 \r\nL 147.685744 421.961774 \r\nL 147.73438 417.922779 \r\nL 147.799227 418.958172 \r\nL 147.880287 420.520412 \r\nL 147.864075 418.042401 \r\nL 147.896499 419.200409 \r\nL 147.91271 417.925644 \r\nL 147.928922 420.043514 \r\nL 147.99377 419.749491 \r\nL 148.009982 421.199555 \r\nL 148.042405 418.200831 \r\nL 148.074829 418.387106 \r\nL 148.091041 417.446553 \r\nL 148.123465 420.70415 \r\nL 148.139677 419.604025 \r\nL 148.172101 417.730274 \r\nL 148.220736 420.336814 \r\nL 148.269372 417.429611 \r\nL 148.334219 419.247133 \r\nL 148.399067 420.453087 \r\nL 148.431491 418.726994 \r\nL 148.51255 420.4499 \r\nL 148.496338 418.344714 \r\nL 148.528762 418.720998 \r\nL 148.544974 418.756123 \r\nL 148.609822 421.056164 \r\nL 148.59361 417.703979 \r\nL 148.674669 421.049539 \r\nL 148.739517 421.346251 \r\nL 148.804364 418.298773 \r\nL 148.853 420.930596 \r\nL 148.885424 416.639997 \r\nL 148.917847 419.92374 \r\nL 149.015119 418.608743 \r\nL 148.982695 420.666116 \r\nL 149.031331 419.688751 \r\nL 149.079966 419.575866 \r\nL 149.096178 420.231722 \r\nL 149.11239 418.101254 \r\nL 149.193449 420.476027 \r\nL 149.209661 419.470633 \r\nL 149.225873 419.741136 \r\nL 149.242085 417.838853 \r\nL 149.323145 420.399976 \r\nL 149.339356 418.532542 \r\nL 149.404204 421.007593 \r\nL 149.45284 419.6038 \r\nL 149.550111 418.356716 \r\nL 149.485263 420.16312 \r\nL 149.566323 418.427311 \r\nL 149.614958 420.852546 \r\nL 149.663594 420.419168 \r\nL 149.744654 418.321124 \r\nL 149.777077 419.869237 \r\nL 149.793289 419.8328 \r\nL 149.809501 418.653524 \r\nL 149.874349 420.352439 \r\nL 149.890561 421.430995 \r\nL 149.906772 418.879855 \r\nL 149.955408 421.26332 \r\nL 149.987832 416.967097 \r\nL 150.068891 419.545065 \r\nL 150.101315 419.648969 \r\nL 150.117527 421.421112 \r\nL 150.149951 418.903786 \r\nL 150.214798 420.704616 \r\nL 150.295858 417.937207 \r\nL 150.376917 419.177218 \r\nL 150.393129 421.740074 \r\nL 150.441765 417.480014 \r\nL 150.474188 418.652211 \r\nL 150.4904 418.504412 \r\nL 150.522824 418.445676 \r\nL 150.587672 420.721047 \r\nL 150.603884 418.493046 \r\nL 150.701155 418.817671 \r\nL 150.749791 417.259761 \r\nL 150.814638 420.278135 \r\nL 150.911909 418.562185 \r\nL 150.879486 421.232416 \r\nL 150.928121 420.024069 \r\nL 150.992969 417.280331 \r\nL 151.041605 418.330268 \r\nL 151.074028 420.513948 \r\nL 151.155088 420.195724 \r\nL 151.203723 418.265737 \r\nL 151.252359 420.013438 \r\nL 151.268571 420.702981 \r\nL 151.317207 419.062806 \r\nL 151.333418 419.335256 \r\nL 151.43069 417.601647 \r\nL 151.365842 420.749195 \r\nL 151.446902 419.030262 \r\nL 151.463114 418.929075 \r\nL 151.560385 421.597571 \r\nL 151.511749 417.739158 \r\nL 151.576597 420.185482 \r\nL 151.673868 416.935881 \r\nL 151.69008 419.737522 \r\nL 151.722504 418.171119 \r\nL 151.803563 419.463447 \r\nL 151.819775 421.17752 \r\nL 151.868411 419.22262 \r\nL 151.900835 420.914223 \r\nL 151.965682 416.068199 \r\nL 151.998106 420.987735 \r\nL 152.014318 419.583549 \r\nL 152.046741 419.21119 \r\nL 152.062953 420.169502 \r\nL 152.079165 420.533013 \r\nL 152.095377 418.861226 \r\nL 152.160225 420.282103 \r\nL 152.241284 420.615268 \r\nL 152.28992 418.32721 \r\nL 152.338555 421.25711 \r\nL 152.419615 420.086904 \r\nL 152.435827 420.024982 \r\nL 152.468251 418.328852 \r\nL 152.500674 421.123788 \r\nL 152.54931 419.697751 \r\nL 152.565522 420.033369 \r\nL 152.597946 419.654192 \r\nL 152.630369 420.3528 \r\nL 152.711429 417.929984 \r\nL 152.776276 420.723477 \r\nL 152.824912 420.210535 \r\nL 152.88976 418.147814 \r\nL 152.873548 420.330236 \r\nL 152.938395 419.415211 \r\nL 153.003243 419.296469 \r\nL 153.035667 420.84242 \r\nL 153.100514 420.977969 \r\nL 153.14915 418.196538 \r\nL 153.230209 418.178944 \r\nL 153.262633 422.307388 \r\nL 153.311269 418.370086 \r\nL 153.376116 420.959922 \r\nL 153.489599 417.822581 \r\nL 153.505811 418.465203 \r\nL 153.603083 420.133434 \r\nL 153.619295 419.17432 \r\nL 153.651718 418.550689 \r\nL 153.684142 420.471645 \r\nL 153.700354 419.650433 \r\nL 153.716566 420.256145 \r\nL 153.74899 418.627681 \r\nL 153.765201 419.359007 \r\nL 153.781413 417.372598 \r\nL 153.797625 420.124712 \r\nL 153.862473 419.165981 \r\nL 153.878685 419.157374 \r\nL 153.894897 420.626218 \r\nL 153.943532 418.16082 \r\nL 153.975956 420.112697 \r\nL 154.00838 418.18847 \r\nL 154.024592 420.913406 \r\nL 154.089439 419.251632 \r\nL 154.121863 420.811323 \r\nL 154.170499 420.004655 \r\nL 154.235346 418.155446 \r\nL 154.219134 420.019735 \r\nL 154.283982 419.304922 \r\nL 154.348829 420.520343 \r\nL 154.316406 418.988884 \r\nL 154.365041 420.194107 \r\nL 154.446101 418.333569 \r\nL 154.478525 418.941195 \r\nL 154.494736 420.464248 \r\nL 154.575796 419.624301 \r\nL 154.592008 418.251448 \r\nL 154.640643 421.328979 \r\nL 154.689279 418.455509 \r\nL 154.770338 420.851564 \r\nL 154.754127 418.44244 \r\nL 154.802762 420.297687 \r\nL 154.835186 420.687322 \r\nL 154.932457 418.626259 \r\nL 155.062152 421.975192 \r\nL 155.013517 418.277938 \r\nL 155.078364 421.040185 \r\nL 155.127 417.315506 \r\nL 155.208059 418.064361 \r\nL 155.224271 417.867263 \r\nL 155.240483 418.742395 \r\nL 155.256695 418.48256 \r\nL 155.337754 421.126438 \r\nL 155.353966 419.541775 \r\nL 155.435026 418.350331 \r\nL 155.418814 419.728088 \r\nL 155.483661 418.427222 \r\nL 155.580933 420.647878 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417.088712 \r\nL 157.4453 418.431822 \r\nL 157.542571 422.461448 \r\nL 157.639842 417.908595 \r\nL 157.656054 418.805742 \r\nL 157.785749 420.321754 \r\nL 157.850597 418.190705 \r\nL 157.818173 421.622246 \r\nL 157.915444 418.873257 \r\nL 157.931656 418.734837 \r\nL 157.947868 419.192651 \r\nL 157.996504 421.251664 \r\nL 158.028928 419.409794 \r\nL 158.077563 420.48246 \r\nL 158.126199 417.98319 \r\nL 158.22347 420.926151 \r\nL 158.191047 417.338784 \r\nL 158.239682 420.538584 \r\nL 158.30453 418.470895 \r\nL 158.353165 418.479309 \r\nL 158.434225 421.278098 \r\nL 158.385589 417.876581 \r\nL 158.466649 419.673098 \r\nL 158.482861 419.141157 \r\nL 158.499072 420.4076 \r\nL 158.531496 419.473609 \r\nL 158.547708 420.750062 \r\nL 158.628767 418.749457 \r\nL 158.644979 418.246024 \r\nL 158.677403 420.39016 \r\nL 158.709827 418.831575 \r\nL 158.758463 422.472838 \r\nL 158.790886 417.327092 \r\nL 158.82331 420.093457 \r\nL 158.888158 418.803458 \r\nL 158.936793 419.396498 \r\nL 158.953005 419.426062 \r\nL 159.001641 421.342212 \r\nL 158.985429 418.127748 \r\nL 159.066488 421.136056 \r\nL 159.147548 417.451665 \r\nL 159.16376 418.917174 \r\nL 159.179972 421.431006 \r\nL 159.244819 418.804362 \r\nL 159.261031 419.563008 \r\nL 159.342091 417.435014 \r\nL 159.293455 421.484779 \r\nL 159.358302 419.913117 \r\nL 159.390726 419.348506 \r\nL 159.439362 420.293198 \r\nL 159.455574 417.005527 \r\nL 159.504209 421.861899 \r\nL 159.552845 419.43603 \r\nL 159.569057 417.610219 \r\nL 159.650116 419.587199 \r\nL 159.731176 421.333783 \r\nL 159.68254 418.484198 \r\nL 159.747388 420.413834 \r\nL 159.779811 417.844097 \r\nL 159.812235 420.818377 \r\nL 159.877083 418.435577 \r\nL 159.893295 417.814404 \r\nL 159.925718 420.455626 \r\nL 159.94193 419.725851 \r\nL 159.974354 419.975152 \r\nL 159.990566 419.180453 \r\nL 160.006778 419.809607 \r\nL 160.039202 421.614195 \r\nL 160.136473 417.927314 \r\nL 160.233744 420.318692 \r\nL 160.217532 417.860042 \r\nL 160.249956 419.774418 \r\nL 160.266168 419.653526 \r\nL 160.28238 421.078265 \r\nL 160.314804 418.080705 \r\nL 160.363439 419.608956 \r\nL 160.379651 419.566002 \r\nL 160.428287 420.363633 \r\nL 160.476923 418.29991 \r\nL 160.525558 420.090104 \r\nL 160.509346 417.72636 \r\nL 160.590406 420.088562 \r\nL 160.671465 418.597603 \r\nL 160.687677 418.920306 \r\nL 160.720101 421.471187 \r\nL 160.752525 418.837267 \r\nL 160.80116 419.269756 \r\nL 160.866008 421.344085 \r\nL 160.833584 419.166643 \r\nL 160.914644 420.420953 \r\nL 160.947067 416.759638 \r\nL 161.028127 419.031626 \r\nL 161.044339 420.604288 \r\nL 161.06055 418.289254 \r\nL 161.14161 419.476419 \r\nL 161.157822 419.390198 \r\nL 161.174034 420.800448 \r\nL 161.238881 418.306607 \r\nL 161.255093 420.671998 \r\nL 161.271305 418.389428 \r\nL 161.303729 421.081528 \r\nL 161.368576 419.938295 \r\nL 161.449636 418.457548 \r\nL 161.48206 419.654677 \r\nL 161.546907 421.268797 \r\nL 161.579331 417.641793 \r\nL 161.595543 421.016627 \r\nL 161.676602 417.28474 \r\nL 161.692814 418.538273 \r\nL 161.709026 418.452856 \r\nL 161.822509 420.962093 \r\nL 161.790085 417.728556 \r\nL 161.838721 419.963647 \r\nL 161.854933 420.384118 \r\nL 161.887357 419.044488 \r\nL 161.903569 419.268543 \r\nL 161.952204 418.643565 \r\nL 161.935992 419.972408 \r\nL 161.984628 419.170903 \r\nL 162.049476 420.854871 \r\nL 162.017052 418.569766 \r\nL 162.098111 419.36665 \r\nL 162.114323 417.282157 \r\nL 162.162959 421.626352 \r\nL 162.211594 418.025856 \r\nL 162.292654 420.415871 \r\nL 162.357501 420.297359 \r\nL 162.373713 418.646438 \r\nL 162.389925 421.459923 \r\nL 162.454773 419.746986 \r\nL 162.470985 420.935687 \r\nL 162.503408 417.93048 \r\nL 162.552044 419.66959 \r\nL 162.568256 417.400382 \r\nL 162.60068 420.815522 \r\nL 162.665527 417.996971 \r\nL 162.795222 421.443084 \r\nL 162.876282 417.901783 \r\nL 162.908706 420.251357 \r\nL 162.941129 421.081641 \r\nL 162.957341 417.991457 \r\nL 163.054613 419.876892 \r\nL 163.070824 420.860849 \r\nL 163.103248 418.47194 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418.57396 \r\nL 166.151083 418.710542 \r\nL 166.199719 420.384984 \r\nL 166.264566 419.03818 \r\nL 166.394261 420.744664 \r\nL 166.313202 418.752748 \r\nL 166.410473 420.385256 \r\nL 166.475321 420.567239 \r\nL 166.572592 417.75635 \r\nL 166.702287 421.83599 \r\nL 166.848194 416.8083 \r\nL 166.994101 421.955342 \r\nL 167.107584 418.279826 \r\nL 167.237279 421.177073 \r\nL 167.285915 418.235553 \r\nL 167.350763 420.585723 \r\nL 167.399398 417.534006 \r\nL 167.448034 419.625054 \r\nL 167.464246 421.075088 \r\nL 167.49667 418.283951 \r\nL 167.545305 419.079368 \r\nL 167.561517 418.015198 \r\nL 167.642576 419.934615 \r\nL 167.658788 420.764657 \r\nL 167.723636 419.180956 \r\nL 167.820907 417.75737 \r\nL 167.772272 419.78935 \r\nL 167.837119 418.781365 \r\nL 167.853331 418.720275 \r\nL 167.869543 419.052985 \r\nL 167.918179 421.361942 \r\nL 168.01545 419.996247 \r\nL 168.047874 419.224101 \r\nL 168.064086 416.97671 \r\nL 168.128933 420.10611 \r\nL 168.145145 418.887204 \r\nL 168.177569 418.617807 \r\nL 168.258628 421.048015 \r\nL 168.27484 417.791252 \r\nL 168.372111 420.17014 \r\nL 168.436959 418.926842 \r\nL 168.404535 420.332403 \r\nL 168.453171 420.140859 \r\nL 168.469383 420.838616 \r\nL 168.501806 419.535592 \r\nL 168.518018 417.604396 \r\nL 168.582866 421.11691 \r\nL 168.61529 417.627131 \r\nL 168.696349 421.868475 \r\nL 168.744985 419.41952 \r\nL 168.761197 419.963438 \r\nL 168.79362 419.096833 \r\nL 168.809832 417.638499 \r\nL 168.858468 419.63743 \r\nL 168.87468 419.351363 \r\nL 168.923316 421.298296 \r\nL 168.955739 417.813973 \r\nL 168.971951 419.145453 \r\nL 169.004375 421.213036 \r\nL 169.036799 419.317783 \r\nL 169.053011 418.019733 \r\nL 169.117858 418.951961 \r\nL 169.13407 421.762073 \r\nL 169.231341 420.458039 \r\nL 169.279977 418.207674 \r\nL 169.344825 419.033455 \r\nL 169.425884 423.175431 \r\nL 169.442096 418.59579 \r\nL 169.47452 420.377442 \r\nL 169.490732 416.949893 \r\nL 169.588003 419.058866 \r\nL 169.620427 421.109942 \r\nL 169.701486 419.252865 \r\nL 169.717698 419.351986 \r\nL 169.782546 416.383997 \r\nL 169.766334 421.422244 \r\nL 169.831181 417.713861 \r\nL 169.928453 421.653165 \r\nL 169.944664 420.302476 \r\nL 170.041936 417.272969 \r\nL 170.058148 420.037598 \r\nL 170.074359 420.866168 \r\nL 170.090571 418.813034 \r\nL 170.155419 419.551077 \r\nL 170.171631 419.495438 \r\nL 170.187843 420.528019 \r\nL 170.236478 417.890373 \r\nL 170.268902 419.097638 \r\nL 170.285114 417.363777 \r\nL 170.349962 421.20312 \r\nL 170.366173 419.581967 \r\nL 170.398597 422.615794 \r\nL 170.447233 418.496391 \r\nL 170.463445 417.645053 \r\nL 170.495869 419.697752 \r\nL 170.544504 418.128004 \r\nL 170.609352 422.021654 \r\nL 170.576928 417.910581 \r\nL 170.657987 419.646503 \r\nL 170.674199 419.035267 \r\nL 170.690411 422.629411 \r\nL 170.755259 416.98007 \r\nL 170.771471 417.469964 \r\nL 170.901166 420.572594 \r\nL 170.949801 420.212678 \r\nL 171.079496 417.690178 \r\nL 171.144344 423.567288 \r\nL 171.209192 419.280121 \r\nL 171.274039 417.841055 \r\nL 171.241615 420.282668 \r\nL 171.290251 417.973446 \r\nL 171.322675 421.773332 \r\nL 171.403734 418.905546 \r\nL 171.419946 418.338976 \r\nL 171.436158 420.352982 \r\nL 171.484794 418.863627 \r\nL 171.517217 418.569072 \r\nL 171.582065 421.774858 \r\nL 171.695548 417.76728 \r\nL 171.744184 416.369552 \r\nL 171.825243 420.596755 \r\nL 171.873879 420.145304 \r\nL 171.890091 422.130834 \r\nL 171.987362 417.376839 \r\nL 172.003574 421.334045 \r\nL 172.035998 416.985866 \r\nL 172.084633 422.197034 \r\nL 172.117057 420.298189 \r\nL 172.133269 420.442649 \r\nL 172.198117 417.230359 \r\nL 172.246752 418.820288 \r\nL 172.3116 421.434165 \r\nL 172.360236 420.453654 \r\nL 172.392659 420.677969 \r\nL 172.489931 416.674854 \r\nL 172.587202 421.297565 \r\nL 172.619626 419.670324 \r\nL 172.668261 417.647665 \r\nL 172.733109 418.802557 \r\nL 172.797956 421.724248 \r\nL 172.862804 419.422352 \r\nL 172.943863 416.719686 \r\nL 172.895228 419.637878 \r\nL 172.976287 418.51411 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420.028829 \r\nL 174.921714 418.540511 \r\nL 175.002773 422.059859 \r\nL 174.954137 417.645639 \r\nL 175.035197 421.224073 \r\nL 175.132468 417.229928 \r\nL 175.164892 418.653787 \r\nL 175.181104 418.782265 \r\nL 175.197316 422.148812 \r\nL 175.294587 419.603435 \r\nL 175.40807 417.295412 \r\nL 175.537765 422.647884 \r\nL 175.602613 417.416798 \r\nL 175.66746 417.423038 \r\nL 175.699884 422.095151 \r\nL 175.797155 419.926738 \r\nL 175.813367 420.174795 \r\nL 175.862003 420.391507 \r\nL 175.926851 418.44943 \r\nL 175.943062 418.435992 \r\nL 176.040334 420.64424 \r\nL 176.056546 419.960595 \r\nL 176.105181 417.18284 \r\nL 176.153817 420.58057 \r\nL 176.2673 417.624659 \r\nL 176.283512 418.72334 \r\nL 176.315936 421.081194 \r\nL 176.364572 418.252285 \r\nL 176.396995 420.114239 \r\nL 176.429419 420.495631 \r\nL 176.494267 418.40044 \r\nL 176.510478 420.936308 \r\nL 176.52669 418.023778 \r\nL 176.60775 419.038202 \r\nL 176.672597 420.991278 \r\nL 176.656385 418.705807 \r\nL 176.705021 419.600479 \r\nL 176.721233 417.811248 \r\nL 176.802292 420.917751 \r\nL 176.86714 416.980747 \r\nL 176.915776 418.796845 \r\nL 176.931988 420.183513 \r\nL 176.964411 418.491007 \r\nL 177.029259 419.230518 \r\nL 177.045471 418.790299 \r\nL 177.077895 420.831045 \r\nL 177.094106 418.922762 \r\nL 177.175166 418.042741 \r\nL 177.191378 420.957141 \r\nL 177.256225 418.436642 \r\nL 177.304861 419.502128 \r\nL 177.353497 421.390887 \r\nL 177.369708 419.037784 \r\nL 177.38592 420.342929 \r\nL 177.402132 417.532962 \r\nL 177.434556 421.230365 \r\nL 177.499404 418.775113 \r\nL 177.564251 421.025874 \r\nL 177.629099 420.897119 \r\nL 177.758794 418.531712 \r\nL 177.856065 420.629134 \r\nL 177.791218 418.515237 \r\nL 177.872277 418.851066 \r\nL 177.937125 417.939336 \r\nL 177.98576 421.34892 \r\nL 178.034396 418.423667 \r\nL 178.06682 421.454144 \r\nL 178.099243 418.809671 \r\nL 178.131667 417.945653 \r\nL 178.196515 419.66965 \r\nL 178.228938 417.774744 \r\nL 178.24515 420.935692 \r\nL 178.309998 419.300594 \r\nL 178.32621 421.253129 \r\nL 178.391057 418.190698 \r\nL 178.423481 419.899305 \r\nL 178.455905 418.445889 \r\nL 178.504541 420.249203 \r\nL 178.536964 420.934835 \r\nL 178.553176 419.200275 \r\nL 178.601812 420.248477 \r\nL 178.634236 418.428446 \r\nL 178.715295 420.487186 \r\nL 178.682871 417.562205 \r\nL 178.747719 419.746362 \r\nL 178.763931 419.632341 \r\nL 178.796355 420.926864 \r\nL 178.812566 419.117196 \r\nL 178.84499 419.667132 \r\nL 178.909838 417.213008 \r\nL 178.942262 420.277099 \r\nL 178.958473 418.619855 \r\nL 179.055745 420.68872 \r\nL 179.071957 419.076582 \r\nL 179.10438 418.54278 \r\nL 179.120592 419.061688 \r\nL 179.136804 418.241312 \r\nL 179.18544 420.279781 \r\nL 179.201652 419.191075 \r\nL 179.282711 420.866836 \r\nL 179.315135 420.753029 \r\nL 179.379982 418.037993 \r\nL 179.428618 418.238922 \r\nL 179.493466 421.208224 \r\nL 179.558313 420.090387 \r\nL 179.623161 418.042919 \r\nL 179.671796 419.042406 \r\nL 179.688008 420.764381 \r\nL 179.70422 418.317626 \r\nL 179.78528 419.381897 \r\nL 179.801491 420.226642 \r\nL 179.850127 418.282154 \r\nL 179.866339 418.648757 \r\nL 179.882551 417.718436 \r\nL 179.947398 420.181387 \r\nL 179.979822 420.728099 \r\nL 180.012246 419.219184 \r\nL 180.028458 421.081422 \r\nL 180.109517 418.023594 \r\nL 180.190577 420.314624 \r\nL 180.158153 417.755872 \r\nL 180.206789 419.494613 \r\nL 180.223001 417.851563 \r\nL 180.271636 421.369593 \r\nL 180.30406 418.487405 \r\nL 180.433755 421.03804 \r\nL 180.547238 417.731542 \r\nL 180.579662 418.430187 \r\nL 180.595874 418.124019 \r\nL 180.709357 420.825398 \r\nL 180.725569 420.662284 \r\nL 180.741781 420.717492 \r\nL 180.839052 418.100936 \r\nL 180.855264 418.956884 \r\nL 180.887688 418.944898 \r\nL 180.936324 419.714459 \r\nL 180.952535 421.002754 \r\nL 180.984959 418.893146 \r\nL 181.033595 420.095349 \r\nL 181.082231 417.897264 \r\nL 181.114654 419.22765 \r\nL 181.211926 421.114032 \r\nL 181.147078 419.01207 \r\nL 181.228138 419.661618 \r\nL 181.260561 419.963253 \r\nL 181.341621 417.611606 \r\nL 181.357833 420.310356 \r\nL 181.374045 420.36278 \r\nL 181.390256 420.250639 \r\nL 181.406468 419.138744 \r\nL 181.487528 420.680009 \r\nL 181.519951 418.058535 \r\nL 181.617223 419.66898 \r\nL 181.665858 421.715624 \r\nL 181.714494 419.753775 \r\nL 181.730706 419.596854 \r\nL 181.746918 419.992812 \r\nL 181.779342 416.899208 \r\nL 181.860401 419.218551 \r\nL 181.925249 422.142533 \r\nL 181.990096 420.405944 \r\nL 182.054944 417.612862 \r\nL 182.103579 419.615558 \r\nL 182.119791 419.648618 \r\nL 182.136003 418.859506 \r\nL 182.200851 420.434056 \r\nL 182.217063 420.007814 \r\nL 182.249486 419.033595 \r\nL 182.265698 420.469791 \r\nL 182.28191 417.417133 \r\nL 182.36297 420.034799 \r\nL 182.427817 422.292136 \r\nL 182.411605 418.418534 \r\nL 182.444029 419.637286 \r\nL 182.492665 417.858808 \r\nL 182.573724 417.989811 \r\nL 182.654784 421.497101 \r\nL 182.735843 420.1388 \r\nL 182.752055 417.257084 \r\nL 182.768267 420.230096 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418.639625 \r\nL 184.616422 420.265874 \r\nL 184.567786 418.04783 \r\nL 184.648846 420.067835 \r\nL 184.697481 419.584995 \r\nL 184.746117 417.860026 \r\nL 184.729905 420.323907 \r\nL 184.778541 419.261109 \r\nL 184.843388 420.525288 \r\nL 184.892024 420.181549 \r\nL 185.005507 417.85374 \r\nL 185.021719 419.475991 \r\nL 185.11899 420.980153 \r\nL 185.054143 418.875385 \r\nL 185.151414 420.338991 \r\nL 185.248685 418.234312 \r\nL 185.232474 420.466191 \r\nL 185.281109 418.346706 \r\nL 185.329745 421.122323 \r\nL 185.410804 419.725264 \r\nL 185.427016 419.91862 \r\nL 185.443228 418.346924 \r\nL 185.475652 420.782463 \r\nL 185.524287 418.387482 \r\nL 185.653983 420.636339 \r\nL 185.751254 418.299035 \r\nL 185.864737 420.929198 \r\nL 185.97822 418.444616 \r\nL 185.994432 419.48377 \r\nL 186.043068 418.884783 \r\nL 186.075492 419.367244 \r\nL 186.107915 420.555658 \r\nL 186.124127 418.991444 \r\nL 186.156551 419.705134 \r\nL 186.270034 418.262512 \r\nL 186.221399 420.929185 \r\nL 186.286246 418.385511 \r\nL 186.31867 420.917097 \r\nL 186.383517 417.5732 \r\nL 186.399729 420.149251 \r\nL 186.415941 420.191319 \r\nL 186.513213 418.015018 \r\nL 186.448365 420.263116 \r\nL 186.529424 419.56155 \r\nL 186.594272 418.907402 \r\nL 186.610484 419.846205 \r\nL 186.642908 420.761373 \r\nL 186.723967 417.616556 \r\nL 186.756391 420.757166 \r\nL 186.83745 419.620721 \r\nL 186.886086 419.939075 \r\nL 186.902298 418.995402 \r\nL 186.91851 420.042366 \r\nL 186.999569 418.25606 \r\nL 187.015781 419.192508 \r\nL 187.031993 419.29989 \r\nL 187.048205 418.309976 \r\nL 187.080629 420.63677 \r\nL 187.096841 422.149078 \r\nL 187.129264 418.504192 \r\nL 187.161688 420.377334 \r\nL 187.1779 418.207832 \r\nL 187.194112 421.190882 \r\nL 187.275171 419.941328 \r\nL 187.307595 418.559471 \r\nL 187.372443 419.791087 \r\nL 187.404866 419.418818 \r\nL 187.421078 420.961751 \r\nL 187.469714 418.455652 \r\nL 187.534561 420.462987 \r\nL 187.648045 418.42 \r\nL 187.664257 418.693565 \r\nL 187.69668 421.475427 \r\nL 187.77774 419.387333 \r\nL 187.793952 419.380978 \r\nL 187.842587 417.83734 \r\nL 187.875011 420.116099 \r\nL 187.891223 419.710729 \r\nL 187.907435 419.685032 \r\nL 187.939859 418.571633 \r\nL 187.988494 419.950766 \r\nL 188.004706 420.466989 \r\nL 188.03713 419.163025 \r\nL 188.053342 420.168473 \r\nL 188.085766 418.680985 \r\nL 188.134401 420.405504 \r\nL 188.166825 419.243268 \r\nL 188.215461 420.581695 \r\nL 188.280308 418.36686 \r\nL 188.345156 420.435758 \r\nL 188.393791 418.663955 \r\nL 188.458639 417.910162 \r\nL 188.507275 420.745126 \r\nL 188.63697 418.4514 \r\nL 188.669394 420.851762 \r\nL 188.734241 417.625094 \r\nL 188.750453 419.425743 \r\nL 188.766665 419.584083 \r\nL 188.782877 421.374843 \r\nL 188.799089 419.231274 \r\nL 188.863936 420.518658 \r\nL 188.912572 417.842911 \r\nL 188.993631 418.605327 \r\nL 189.042267 421.056679 \r\nL 189.058479 418.050768 \r\nL 189.123326 420.750736 \r\nL 189.139538 420.553778 \r\nL 189.220598 418.070954 \r\nL 189.253021 420.039655 \r\nL 189.269233 420.132968 \r\nL 189.285445 418.548138 \r\nL 189.317869 422.200264 \r\nL 189.382717 419.799917 \r\nL 189.398928 419.690851 \r\nL 189.41514 417.755183 \r\nL 189.479988 421.059975 \r\nL 189.512412 419.397126 \r\nL 189.528624 417.883067 \r\nL 189.593471 421.379375 \r\nL 189.609683 419.902897 \r\nL 189.625895 420.441348 \r\nL 189.658319 419.353884 \r\nL 189.67453 418.269288 \r\nL 189.75559 418.761924 \r\nL 189.788014 420.664396 \r\nL 189.869073 418.909438 \r\nL 189.933921 419.815624 \r\nL 189.950133 419.395177 \r\nL 189.966344 418.041228 \r\nL 190.031192 420.461814 \r\nL 190.047404 418.640435 \r\nL 190.079828 420.620301 \r\nL 190.160887 419.053706 \r\nL 190.193311 418.149159 \r\nL 190.241947 419.50722 \r\nL 190.27437 419.800404 \r\nL 190.290582 418.23736 \r\nL 190.339218 420.373642 \r\nL 190.387853 419.262155 \r\nL 190.404065 419.208467 \r\nL 190.485125 418.071993 \r\nL 190.501337 418.755262 \r\nL 190.566184 420.776304 \r\nL 190.61482 419.228757 \r\nL 190.647244 418.69179 \r\nL 190.663456 419.567817 \r\nL 190.679667 418.759955 \r\nL 190.776939 420.81623 \r\nL 190.744515 418.51258 \r\nL 190.793151 420.103858 \r\nL 190.809363 419.372411 \r\nL 190.825574 420.305075 \r\nL 190.890422 419.806798 \r\nL 190.906634 420.308861 \r\nL 190.939058 418.755514 \r\nL 190.987693 419.726981 \r\nL 191.036329 419.964681 \r\nL 191.052541 419.166618 \r\nL 191.084965 421.087371 \r\nL 191.101177 418.291976 \r\nL 191.149812 419.508164 \r\nL 191.198448 420.272915 \r\nL 191.230872 417.932452 \r\nL 191.328143 421.089038 \r\nL 191.344355 420.201804 \r\nL 191.39299 418.150751 \r\nL 191.425414 420.269769 \r\nL 191.457838 419.36947 \r\nL 191.522686 420.878086 \r\nL 191.490262 419.368157 \r\nL 191.538897 419.981767 \r\nL 191.555109 418.294141 \r\nL 191.587533 420.438161 \r\nL 191.652381 418.96818 \r\nL 191.717228 420.545132 \r\nL 191.701016 418.149107 \r\nL 191.782076 419.89238 \r\nL 191.798288 420.007146 \r\nL 191.8145 419.759259 \r\nL 191.895559 416.789898 \r\nL 191.863135 419.923656 \r\nL 191.927983 419.366351 \r\nL 191.944195 419.087658 \r\nL 191.960407 419.586357 \r\nL 191.99283 419.219401 \r\nL 192.025254 420.466796 \r\nL 192.07389 418.303306 \r\nL 192.106313 419.517833 \r\nL 192.122525 419.531641 \r\nL 192.171161 418.028428 \r\nL 192.219797 419.654263 \r\nL 192.236009 419.031943 \r\nL 192.25222 419.009778 \r\nL 192.268432 420.979906 \r\nL 192.300856 418.758723 \r\nL 192.365704 419.625044 \r\nL 192.430551 418.457665 \r\nL 192.462975 419.113919 \r\nL 192.495399 420.494323 \r\nL 192.511611 418.665751 \r\nL 192.576458 419.874506 \r\nL 192.689941 418.347335 \r\nL 192.722365 421.246247 \r\nL 192.803425 421.001752 \r\nL 192.835848 418.610732 \r\nL 192.93312 419.232095 \r\nL 192.949332 419.092928 \r\nL 193.014179 420.601337 \r\nL 192.997967 418.791223 \r\nL 193.062815 419.961063 \r\nL 193.127662 419.012578 \r\nL 193.11145 420.158801 \r\nL 193.143874 419.046688 \r\nL 193.160086 420.926925 \r\nL 193.208722 418.725835 \r\nL 193.257357 420.210124 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418.779468 \r\nL 195.040665 420.096957 \r\nL 195.121724 419.103409 \r\nL 195.137936 419.602131 \r\nL 195.202784 421.327043 \r\nL 195.218996 418.980834 \r\nL 195.235208 419.34206 \r\nL 195.267631 420.710488 \r\nL 195.300055 419.014388 \r\nL 195.316267 420.082123 \r\nL 195.397326 418.532068 \r\nL 195.364903 421.172636 \r\nL 195.42975 419.865596 \r\nL 195.478386 418.326674 \r\nL 195.527022 419.209138 \r\nL 195.624293 420.470155 \r\nL 195.559445 418.269903 \r\nL 195.640505 419.540358 \r\nL 195.753988 418.3643 \r\nL 195.7702 418.675005 \r\nL 195.802624 420.321475 \r\nL 195.867471 419.412447 \r\nL 195.883683 418.655882 \r\nL 195.948531 419.69197 \r\nL 195.964743 419.022338 \r\nL 195.980954 420.391529 \r\nL 196.013378 418.096233 \r\nL 196.078226 419.726407 \r\nL 196.126861 420.351079 \r\nL 196.207921 417.583861 \r\nL 196.305192 420.92329 \r\nL 196.353828 419.548242 \r\nL 196.37004 417.484889 \r\nL 196.402463 420.396889 \r\nL 196.467311 418.171925 \r\nL 196.532159 421.743713 \r\nL 196.580794 419.825381 \r\nL 196.613218 420.446148 \r\nL 196.678066 418.213967 \r\nL 196.694277 420.694094 \r\nL 196.791549 419.760229 \r\nL 196.872608 420.387103 \r\nL 196.905032 417.76064 \r\nL 197.034727 421.042068 \r\nL 197.099575 418.915278 \r\nL 197.083363 421.286691 \r\nL 197.14821 419.319075 \r\nL 197.164422 420.623024 \r\nL 197.22927 417.703119 \r\nL 197.245482 420.23969 \r\nL 197.277905 420.57366 \r\nL 197.358965 418.423501 \r\nL 197.375177 420.812431 \r\nL 197.440024 417.893052 \r\nL 197.472448 420.618534 \r\nL 197.521084 416.777954 \r\nL 197.504872 421.132428 \r\nL 197.585931 418.427279 \r\nL 197.602143 418.314365 \r\nL 197.618355 418.349982 \r\nL 197.715626 419.962912 \r\nL 197.731838 419.678705 \r\nL 197.796686 420.515459 \r\nL 197.829109 418.495247 \r\nL 197.845321 419.644438 \r\nL 197.877745 417.123121 \r\nL 197.926381 419.481124 \r\nL 197.942593 418.221822 \r\nL 198.00744 421.069698 \r\nL 198.023652 420.123163 \r\nL 198.072288 419.234584 \r\nL 198.0885 421.321186 \r\nL 198.153347 417.963767 \r\nL 198.201983 419.913835 \r\nL 198.234407 419.290236 \r\nL 198.299254 420.834773 \r\nL 198.283042 418.467526 \r\nL 198.34789 419.857449 \r\nL 198.380314 418.256022 \r\nL 198.428949 420.44316 \r\nL 198.445161 420.412841 \r\nL 198.461373 419.266885 \r\nL 198.542432 420.749072 \r\nL 198.558644 420.594013 \r\nL 198.574856 418.174199 \r\nL 198.623492 420.824541 \r\nL 198.672128 418.811655 \r\nL 198.769399 421.754657 \r\nL 198.785611 420.171909 \r\nL 198.818035 421.044624 \r\nL 198.915306 418.266525 \r\nL 198.980153 421.544763 \r\nL 199.045001 420.237559 \r\nL 199.109849 417.820056 \r\nL 199.174696 418.154181 \r\nL 199.239544 420.69479 \r\nL 199.20712 418.03273 \r\nL 199.320603 419.917792 \r\nL 199.336815 417.766232 \r\nL 199.417874 418.984107 \r\nL 199.482722 420.279825 \r\nL 199.515146 419.355128 \r\nL 199.531358 418.400213 \r\nL 199.579993 419.8322 \r\nL 199.612417 419.159549 \r\nL 199.628629 419.798346 \r\nL 199.644841 418.30651 \r\nL 199.709688 419.383566 \r\nL 199.7259 419.213816 \r\nL 199.742112 420.619442 \r\nL 199.758324 418.492901 \r\nL 199.839383 419.949407 \r\nL 199.904231 417.10981 \r\nL 199.936655 419.809366 \r\nL 199.969079 420.804581 \r\nL 199.98529 418.443039 \r\nL 200.017714 419.608837 \r\nL 200.082562 417.99386 \r\nL 200.050138 420.095059 \r\nL 200.114986 418.966388 \r\nL 200.179833 420.527467 \r\nL 200.244681 420.37036 \r\nL 200.341952 418.204454 \r\nL 200.358164 419.981769 \r\nL 200.390588 418.816547 \r\nL 200.471647 419.861287 \r\nL 200.487859 421.1694 \r\nL 200.520283 418.59164 \r\nL 200.568918 420.089347 \r\nL 200.633766 418.8596 \r\nL 200.649978 420.645692 \r\nL 200.698613 419.669281 \r\nL 200.714825 421.037295 \r\nL 200.763461 419.189927 \r\nL 200.795885 419.709422 \r\nL 200.828309 419.170706 \r\nL 200.84452 421.449262 \r\nL 200.941792 420.697112 \r\nL 200.958004 418.392195 \r\nL 200.974215 420.766305 \r\nL 201.055275 419.093844 \r\nL 201.071487 418.5379 \r\nL 201.103911 420.093583 \r\nL 201.136334 418.773148 \r\nL 201.18497 420.44418 \r\nL 201.233606 418.500913 \r\nL 201.249818 417.65477 \r\nL 201.282241 419.676427 \r\nL 201.314665 418.856631 \r\nL 201.411936 418.794319 \r\nL 201.44436 419.998087 \r\nL 201.557843 418.325197 \r\nL 201.574055 418.685755 \r\nL 201.655115 421.706142 \r\nL 201.70375 420.229149 \r\nL 201.817234 418.076104 \r\nL 201.930717 420.690083 \r\nL 201.898293 417.817666 \r\nL 201.946929 420.089306 \r\nL 201.979352 421.039435 \r\nL 201.995564 417.538807 \r\nL 202.092836 420.363572 \r\nL 202.173895 419.316498 \r\nL 202.222531 419.382703 \r\nL 202.271166 422.326258 \r\nL 202.287378 419.159658 \r\nL 202.319802 420.984958 \r\nL 202.352226 418.924541 \r\nL 202.400862 422.530965 \r\nL 202.433285 420.536046 \r\nL 202.530557 418.76233 \r\nL 202.546769 419.831365 \r\nL 202.611616 420.917392 \r\nL 202.64404 419.460018 \r\nL 202.660252 420.038124 \r\nL 202.773735 417.12878 \r\nL 202.789947 418.729432 \r\nL 202.806159 418.712883 \r\nL 202.822371 417.319103 \r\nL 202.887218 420.509397 \r\nL 202.90343 419.063005 \r\nL 202.952066 419.894897 \r\nL 202.968278 418.409415 \r\nL 202.984489 418.238329 \r\nL 203.033125 420.095066 \r\nL 203.097973 418.72064 \r\nL 203.146608 420.478815 \r\nL 203.24388 420.111195 \r\nL 203.260092 418.574494 \r\nL 203.324939 420.508277 \r\nL 203.357363 419.034838 \r\nL 203.405999 419.52717 \r\nL 203.454634 417.832165 \r\nL 203.551905 419.949713 \r\nL 203.568117 418.754895 \r\nL 203.665389 419.931891 \r\nL 203.600541 418.631107 \r\nL 203.681601 419.150756 \r\nL 203.714024 417.954484 \r\nL 203.730236 419.350741 \r\nL 203.76266 418.573568 \r\nL 203.827508 420.444786 \r\nL 203.859931 419.70841 \r\nL 203.876143 417.82654 \r\nL 203.924779 420.976166 \r\nL 203.973415 419.063442 \r\nL 204.054474 418.135515 \r\nL 204.005838 419.950595 \r\nL 204.070686 418.897084 \r\nL 204.119322 420.933397 \r\nL 204.10311 418.395352 \r\nL 204.184169 419.474467 \r\nL 204.232805 418.360842 \r\nL 204.265228 420.368334 \r\nL 204.297652 420.210373 \r\nL 204.313864 420.979985 \r\nL 204.330076 418.4663 \r\nL 204.427347 420.40706 \r\nL 204.508407 419.660803 \r\nL 204.459771 421.034782 \r\nL 204.540831 419.816901 \r\nL 204.605678 422.365896 \r\nL 204.589466 418.37494 \r\nL 204.638102 420.445356 \r\nL 204.702949 418.976224 \r\nL 204.686738 421.189015 \r\nL 204.751585 419.986985 \r\nL 204.767797 421.052752 \r\nL 204.832645 419.34232 \r\nL 204.848856 419.707096 \r\nL 204.865068 418.530599 \r\nL 204.929916 420.015585 \r\nL 204.946128 419.384732 \r\nL 204.994763 418.884331 \r\nL 205.092035 421.074927 \r\nL 205.205518 418.670385 \r\nL 205.22173 420.832709 \r\nL 205.237942 418.108653 \r\nL 205.319001 419.61272 \r\nL 205.335213 417.267045 \r\nL 205.383849 420.209716 \r\nL 205.416272 419.737681 \r\nL 205.432484 420.708183 \r\nL 205.48112 418.495066 \r\nL 205.545968 417.679163 \r\nL 205.513544 418.729223 \r\nL 205.594603 418.079593 \r\nL 205.610815 418.87319 \r\nL 205.675663 417.314232 \r\nL 205.708086 418.834109 \r\nL 205.756722 417.96251 \r\nL 205.772934 418.583306 \r\nL 205.886417 421.060861 \r\nL 205.902629 420.738941 \r\nL 205.9999 417.911059 \r\nL 206.048536 418.64718 \r\nL 206.162019 421.369136 \r\nL 206.275502 418.067598 \r\nL 206.307926 417.826459 \r\nL 206.405198 421.083149 \r\nL 206.470045 417.613251 \r\nL 206.534893 417.648869 \r\nL 206.583528 421.616734 \r\nL 206.664588 419.869005 \r\nL 206.713223 417.746474 \r\nL 206.794283 419.048305 \r\nL 206.85913 421.641638 \r\nL 206.826707 418.918195 \r\nL 206.923978 420.884245 \r\nL 207.005037 418.089171 \r\nL 207.053673 418.620933 \r\nL 207.102309 420.768463 \r\nL 207.150944 419.87695 \r\nL 207.232004 417.401049 \r\nL 207.280639 418.350272 \r\nL 207.377911 420.865683 \r\nL 207.394123 420.273613 \r\nL 207.491394 417.482299 \r\nL 207.523818 418.069873 \r\nL 207.588665 420.419827 \r\nL 207.637301 419.363261 \r\nL 207.685937 417.082704 \r\nL 207.750784 419.160634 \r\nL 207.831844 422.565653 \r\nL 207.864267 420.121964 \r\nL 207.961539 417.200492 \r\nL 207.993962 417.961834 \r\nL 208.05881 422.89877 \r\nL 208.107446 420.275143 \r\nL 208.123658 420.119063 \r\nL 208.139869 422.579429 \r\nL 208.204717 416.219493 \r\nL 208.220929 418.272818 \r\nL 208.237141 418.74474 \r\nL 208.253353 418.354515 \r\nL 208.350624 423.511737 \r\nL 208.366836 422.840279 \r\nL 208.415471 414.946589 \r\nL 208.496531 417.770938 \r\nL 208.593802 424.11794 \r\nL 208.610014 423.814205 \r\nL 208.642438 415.628897 \r\nL 208.739709 415.815782 \r\nL 208.772133 425.161212 \r\nL 208.836981 415.474231 \r\nL 208.853192 416.706972 \r\nL 208.885616 415.68922 \r\nL 208.901828 418.553674 \r\nL 208.934252 419.200904 \r\nL 208.999099 417.37013 \r\nL 209.015311 420.653512 \r\nL 209.031523 423.363199 \r\nL 209.096371 419.260176 \r\nL 209.128795 423.183752 \r\nL 209.161218 418.300775 \r\nL 209.17743 367.808219 \r\nL 209.242278 423.685422 \r\nL 209.25849 417.702961 \r\nL 209.274701 422.468893 \r\nL 209.371973 418.536689 \r\nL 209.388185 420.098512 \r\nL 209.404397 417.497931 \r\nL 209.453032 418.389349 \r\nL 209.485456 415.838806 \r\nL 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211.252552 420.466556 \r\nL 211.268764 420.482161 \r\nL 211.382247 417.644383 \r\nL 211.414671 418.275527 \r\nL 211.49573 421.691685 \r\nL 211.528154 419.322338 \r\nL 211.576789 419.91874 \r\nL 211.593001 418.715594 \r\nL 211.641637 418.020767 \r\nL 211.657849 419.343064 \r\nL 211.674061 419.522328 \r\nL 211.771332 421.521543 \r\nL 211.787544 420.305925 \r\nL 211.884815 417.591999 \r\nL 211.901027 418.10656 \r\nL 211.949663 420.030985 \r\nL 212.01451 419.463657 \r\nL 212.030722 419.37452 \r\nL 212.079358 418.305672 \r\nL 212.127994 419.926026 \r\nL 212.144205 419.357993 \r\nL 212.192841 421.462283 \r\nL 212.209053 421.730737 \r\nL 212.241477 420.560307 \r\nL 212.322536 418.680355 \r\nL 212.371172 418.95717 \r\nL 212.468443 421.408519 \r\nL 212.484655 419.703612 \r\nL 212.500867 419.635818 \r\nL 212.517079 419.950417 \r\nL 212.533291 419.95007 \r\nL 212.549503 419.998319 \r\nL 212.598138 417.854263 \r\nL 212.711621 418.213089 \r\nL 212.744045 418.41094 \r\nL 212.760257 417.996002 \r\nL 212.776469 417.337913 \r\nL 212.841317 418.274965 \r\nL 212.938588 420.16251 \r\nL 212.987224 420.160343 \r\nL 213.052071 417.711896 \r\nL 213.116919 418.697854 \r\nL 213.133131 420.764028 \r\nL 213.230402 419.672322 \r\nL 213.311461 420.322048 \r\nL 213.279037 419.56423 \r\nL 213.327673 419.925324 \r\nL 213.360097 418.970545 \r\nL 213.392521 420.377085 \r\nL 213.408733 419.232692 \r\nL 213.424944 420.251985 \r\nL 213.489792 418.224717 \r\nL 213.506004 419.544666 \r\nL 213.522216 418.280789 \r\nL 213.587063 420.301806 \r\nL 213.603275 420.275706 \r\nL 213.635699 420.11043 \r\nL 213.651911 420.274261 \r\nL 213.668123 420.834786 \r\nL 213.684335 419.993596 \r\nL 213.716758 420.282028 \r\nL 213.73297 418.940307 \r\nL 213.797818 420.287872 \r\nL 213.830242 419.534296 \r\nL 213.862665 421.469104 \r\nL 213.943725 419.905256 \r\nL 213.976149 419.680307 \r\nL 213.992361 419.527895 \r\nL 214.008572 420.460876 \r\nL 214.024784 418.401991 \r\nL 214.105844 420.348554 \r\nL 214.235539 419.078254 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419.826957 \r\nL 226.459301 418.361883 \r\nL 226.491725 419.428802 \r\nL 226.507937 419.475764 \r\nL 226.524149 418.902422 \r\nL 226.572785 420.119128 \r\nL 226.605208 419.655223 \r\nL 226.637632 419.909124 \r\nL 226.653844 419.436665 \r\nL 226.670056 418.866676 \r\nL 226.734903 420.100572 \r\nL 226.751115 419.724871 \r\nL 226.832175 420.400996 \r\nL 226.799751 418.224602 \r\nL 226.864599 420.189661 \r\nL 226.88081 420.137587 \r\nL 226.929446 418.823681 \r\nL 226.994294 419.708552 \r\nL 227.026717 419.554591 \r\nL 227.042929 419.580768 \r\nL 227.091565 420.11004 \r\nL 227.107777 420.033136 \r\nL 227.123989 418.854874 \r\nL 227.205048 419.873539 \r\nL 227.286108 420.450561 \r\nL 227.237472 418.906037 \r\nL 227.302319 420.2551 \r\nL 227.399591 420.277571 \r\nL 227.432015 418.777395 \r\nL 227.529286 420.744463 \r\nL 227.545498 419.483987 \r\nL 227.610345 419.785359 \r\nL 227.626557 418.961508 \r\nL 227.642769 420.394996 \r\nL 227.707617 418.255 \r\nL 227.74004 419.673498 \r\nL 227.788676 420.220528 \r\nL 227.804888 419.298034 \r\nL 227.8211 419.561373 \r\nL 227.869736 418.252761 \r\nL 227.902159 419.845187 \r\nL 227.934583 419.258616 \r\nL 227.950795 419.684476 \r\nL 228.048066 419.388261 \r\nL 228.145338 418.614107 \r\nL 228.08049 419.914603 \r\nL 228.161549 418.81708 \r\nL 228.275033 420.667994 \r\nL 228.193973 418.716659 \r\nL 228.291245 419.957581 \r\nL 228.356092 418.144621 \r\nL 228.33988 420.119932 \r\nL 228.404728 419.650775 \r\nL 228.485787 418.340481 \r\nL 228.501999 419.162885 \r\nL 228.518211 420.312056 \r\nL 228.583059 418.984347 \r\nL 228.59927 419.530873 \r\nL 228.615482 418.161048 \r\nL 228.696542 419.507082 \r\nL 228.761389 420.853954 \r\nL 228.810025 419.696316 \r\nL 228.826237 418.153509 \r\nL 228.891084 420.180937 \r\nL 228.923508 418.215449 \r\nL 229.036991 421.020992 \r\nL 229.069415 420.216572 \r\nL 229.166686 418.341686 \r\nL 229.101839 420.418176 \r\nL 229.182898 419.513199 \r\nL 229.19911 418.965779 \r\nL 229.215322 420.560565 \r\nL 229.263958 418.990602 \r\nL 229.328805 420.518447 \r\nL 229.361229 418.187348 \r\nL 229.377441 419.652532 \r\nL 229.4585 419.095284 \r\nL 229.409865 420.289383 \r\nL 229.474712 419.853831 \r\nL 229.571984 420.404606 \r\nL 229.555772 419.180342 \r\nL 229.588195 420.016999 \r\nL 229.620619 417.496473 \r\nL 229.685467 420.1879 \r\nL 229.701679 419.184409 \r\nL 229.766526 421.087237 \r\nL 229.782738 418.956336 \r\nL 229.815162 418.429377 \r\nL 229.831374 419.471357 \r\nL 229.847586 419.381506 \r\nL 229.863798 419.743419 \r\nL 229.912433 418.575674 \r\nL 229.961069 419.468954 \r\nL 229.977281 419.486607 \r\nL 230.025916 420.207004 \r\nL 230.05834 419.274936 \r\nL 230.074552 418.11824 \r\nL 230.106976 419.461123 \r\nL 230.171823 419.019691 \r\nL 230.188035 420.678548 \r\nL 230.236671 418.991194 \r\nL 230.285307 419.903601 \r\nL 230.366366 419.912329 \r\nL 230.39879 418.476209 \r\nL 230.479849 420.304222 \r\nL 230.431214 418.311341 \r\nL 230.512273 419.036555 \r\nL 230.528485 419.458634 \r\nL 230.560909 418.39597 \r\nL 230.593332 419.088406 \r\nL 230.609544 418.531964 \r\nL 230.641968 420.668118 \r\nL 230.65818 420.757586 \r\nL 230.690604 418.351699 \r\nL 230.804087 419.214721 \r\nL 230.852723 419.380266 \r\nL 230.868935 418.660326 \r\nL 230.949994 421.113901 \r\nL 230.982418 420.946726 \r\nL 231.063477 418.514935 \r\nL 231.112113 419.062002 \r\nL 231.128325 418.53153 \r\nL 231.160748 420.599493 \r\nL 231.193172 419.647252 \r\nL 231.25802 421.396408 \r\nL 231.225596 419.050215 \r\nL 231.274232 420.335037 \r\nL 231.306655 418.015018 \r\nL 231.387715 419.813054 \r\nL 231.420139 420.268238 \r\nL 231.501198 419.022188 \r\nL 231.582258 420.077207 \r\nL 231.614681 419.171762 \r\nL 231.630893 418.424516 \r\nL 231.695741 419.899629 \r\nL 231.711953 420.923704 \r\nL 231.744376 418.784174 \r\nL 231.793012 419.281199 \r\nL 231.825436 419.593913 \r\nL 231.85786 418.607049 \r\nL 231.874072 420.389895 \r\nL 231.890283 418.558651 \r\nL 231.955131 419.466702 \r\nL 231.987555 417.787443 \r\nL 232.019978 419.909698 \r\nL 232.068614 418.707159 \r\nL 232.084826 418.380907 \r\nL 232.11725 418.972598 \r\nL 232.133462 420.471851 \r\nL 232.214521 418.80095 \r\nL 232.230733 419.746152 \r\nL 232.246945 419.853087 \r\nL 232.263157 419.542615 \r\nL 232.37664 418.793161 \r\nL 232.457699 421.026695 \r\nL 232.490123 419.173952 \r\nL 232.522547 418.891381 \r\nL 232.538759 419.171066 \r\nL 232.571183 420.189259 \r\nL 232.603606 418.676165 \r\nL 232.652242 419.859973 \r\nL 232.71709 418.702386 \r\nL 232.700878 420.445422 \r\nL 232.765725 419.1303 \r\nL 232.781937 420.451738 \r\nL 232.830573 418.787419 \r\nL 232.879208 420.167188 \r\nL 232.927844 420.659291 \r\nL 232.97648 419.297305 \r\nL 233.041327 419.213379 \r\nL 233.089963 420.449978 \r\nL 233.106175 419.238164 \r\nL 233.122387 420.64244 \r\nL 233.203446 419.709785 \r\nL 233.219658 419.786659 \r\nL 233.284506 420.038091 \r\nL 233.316929 419.126637 \r\nL 233.333141 419.969244 \r\nL 233.349353 418.244972 \r\nL 233.430413 419.817589 \r\nL 233.462836 420.044737 \r\nL 233.49526 418.912634 \r\nL 233.57632 418.947425 \r\nL 233.624955 420.103939 \r\nL 233.657379 420.079017 \r\nL 233.673591 418.42552 \r\nL 233.738438 420.12202 \r\nL 233.770862 419.275042 \r\nL 233.787074 418.70397 \r\nL 233.83571 419.634432 \r\nL 233.868134 419.217783 \r\nL 233.916769 420.048142 \r\nL 233.932981 418.56781 \r\nL 233.965405 419.729305 \r\nL 234.030252 417.909723 \r\nL 234.014041 420.430696 \r\nL 234.062676 418.508938 \r\nL 234.159948 418.336101 \r\nL 234.176159 420.112142 \r\nL 234.192371 420.163112 \r\nL 234.273431 420.612281 \r\nL 234.289643 418.355666 \r\nL 234.305855 420.21143 \r\nL 234.322066 417.974618 \r\nL 234.403126 419.868191 \r\nL 234.419338 419.825024 \r\nL 234.43555 419.992058 \r\nL 234.516609 420.139272 \r\nL 234.549033 418.619014 \r\nL 234.630092 419.770236 \r\nL 234.662516 419.217085 \r\nL 234.678728 418.469676 \r\nL 234.69494 420.126365 \r\nL 234.759787 419.047002 \r\nL 234.857059 420.681206 \r\nL 234.840847 418.018236 \r\nL 234.873271 419.475286 \r\nL 234.889482 420.438992 \r\nL 234.970542 419.037933 \r\nL 234.986754 419.769204 \r\nL 235.002966 418.511801 \r\nL 235.067813 420.254491 \r\nL 235.100237 418.603683 \r\nL 235.229932 420.452934 \r\nL 235.359627 419.073765 \r\nL 235.424475 418.534282 \r\nL 235.489322 420.440721 \r\nL 235.619017 418.840432 \r\nL 235.651441 419.040232 \r\nL 235.683865 419.730758 \r\nL 235.732501 418.680763 \r\nL 235.764924 419.526379 \r\nL 235.797348 419.986905 \r\nL 235.81356 419.801159 \r\nL 235.894619 418.274133 \r\nL 235.910831 419.9843 \r\nL 235.927043 420.249887 \r\nL 235.959467 419.081818 \r\nL 235.991891 420.143646 \r\nL 236.07295 418.350307 \r\nL 236.089162 420.651218 \r\nL 236.105374 419.072125 \r\nL 236.15401 419.969334 \r\nL 236.202645 418.930609 \r\nL 236.218857 418.814069 \r\nL 236.251281 420.472248 \r\nL 236.299917 417.934729 \r\nL 236.33234 419.457706 \r\nL 236.348552 418.70377 \r\nL 236.380976 420.678868 \r\nL 236.4134 420.304278 \r\nL 236.429612 420.272016 \r\nL 236.445824 420.335394 \r\nL 236.462035 421.22344 \r\nL 236.494459 418.374896 \r\nL 236.526883 419.400484 \r\nL 236.543095 419.405703 \r\nL 236.575519 418.894149 \r\nL 236.640366 419.683803 \r\nL 236.656578 418.89712 \r\nL 236.721426 420.973698 \r\nL 236.770061 420.703097 \r\nL 236.834909 418.123842 \r\nL 236.883544 418.925565 \r\nL 236.997028 420.43842 \r\nL 237.045663 418.766458 \r\nL 237.126723 418.887089 \r\nL 237.240206 420.622736 \r\nL 237.27263 418.47321 \r\nL 237.353689 419.245224 \r\nL 237.418537 420.030731 \r\nL 237.434749 418.843977 \r\nL 237.450961 419.11753 \r\nL 237.467172 419.126408 \r\nL 237.483384 418.924109 \r\nL 237.515808 419.579023 \r\nL 237.53202 419.396576 \r\nL 237.564444 418.105194 \r\nL 237.645503 420.728321 \r\nL 237.726563 418.206435 \r\nL 237.775198 418.26915 \r\nL 237.87247 419.703234 \r\nL 237.888681 419.033364 \r\nL 237.904893 418.5812 \r\nL 237.921105 419.463629 \r\nL 237.937317 421.163522 \r\nL 238.002165 419.251956 \r\nL 238.018377 419.419858 \r\nL 238.067012 418.157567 \r\nL 238.083224 420.054847 \r\nL 238.13186 418.72031 \r\nL 238.180495 419.977381 \r\nL 238.229131 419.314419 \r\nL 238.245343 418.040639 \r\nL 238.277767 420.424282 \r\nL 238.342614 418.736241 \r\nL 238.375038 420.484549 \r\nL 238.456098 420.105139 \r\nL 238.504733 418.027231 \r\nL 238.569581 419.814311 \r\nL 238.602004 420.226847 \r\nL 238.634428 419.899456 \r\nL 238.7317 418.629376 \r\nL 238.683064 420.346455 \r\nL 238.747911 419.356059 \r\nL 238.877607 421.248891 \r\nL 238.828971 418.480334 \r\nL 238.893818 420.459 \r\nL 238.974878 418.249374 \r\nL 239.023514 418.548852 \r\nL 239.104573 420.500158 \r\nL 239.136997 420.485371 \r\nL 239.25048 417.970036 \r\nL 239.266692 418.107979 \r\nL 239.380175 420.434607 \r\nL 239.477446 418.288474 \r\nL 239.412599 421.049835 \r\nL 239.493658 418.843296 \r\nL 239.574718 420.307339 \r\nL 239.639565 420.077859 \r\nL 239.671989 418.209223 \r\nL 239.76926 418.299236 \r\nL 239.85032 420.663079 \r\nL 239.882744 419.504378 \r\nL 239.898955 418.567286 \r\nL 239.980015 419.250572 \r\nL 239.996227 419.396241 \r\nL 240.012439 418.363543 \r\nL 240.077286 420.471546 \r\nL 240.093498 419.969003 \r\nL 240.142134 418.86079 \r\nL 240.174557 420.795515 \r\nL 240.239405 417.893945 \r\nL 240.288041 420.135677 \r\nL 240.304253 421.031125 \r\nL 240.320464 419.361791 \r\nL 240.385312 420.674216 \r\nL 240.45016 417.417253 \r\nL 240.515007 418.867572 \r\nL 240.62849 420.622499 \r\nL 240.677126 418.371441 \r\nL 240.660914 420.640866 \r\nL 240.741974 419.396334 \r\nL 240.790609 420.523476 \r\nL 240.855457 418.357086 \r\nL 240.871669 420.426395 \r\nL 240.96894 419.450153 \r\nL 240.985152 419.198436 \r\nL 241.001364 420.668604 \r\nL 241.033787 419.776297 \r\nL 241.049999 420.279222 \r\nL 241.114847 419.122165 \r\nL 241.131059 419.395765 \r\nL 241.212118 417.900714 \r\nL 241.163483 419.67686 \r\nL 241.22833 419.446864 \r\nL 241.30939 420.78443 \r\nL 241.260754 418.780818 \r\nL 241.341813 420.057712 \r\nL 241.455297 417.998439 \r\nL 241.552568 420.199992 \r\nL 241.56878 420.198756 \r\nL 241.633627 418.432461 \r\nL 241.698475 418.448574 \r\nL 241.844382 420.504724 \r\nL 241.974077 418.346599 \r\nL 242.038924 421.096853 \r\nL 242.119984 420.322961 \r\nL 242.136196 418.218798 \r\nL 242.233467 419.110314 \r\nL 242.298315 420.800215 \r\nL 242.34695 419.365814 \r\nL 242.379374 419.881889 \r\nL 242.42801 418.770339 \r\nL 242.444222 419.093248 \r\nL 242.460434 418.716176 \r\nL 242.509069 419.862434 \r\nL 242.525281 419.220747 \r\nL 242.573917 420.823798 \r\nL 242.638764 420.387374 \r\nL 242.6874 418.500243 \r\nL 242.768459 418.845624 \r\nL 242.817095 420.680605 \r\nL 242.865731 419.609198 \r\nL 242.881943 418.319413 \r\nL 242.914366 419.934183 \r\nL 242.979214 419.366171 \r\nL 242.995426 418.983775 \r\nL 243.044061 420.128495 \r\nL 243.060273 419.624482 \r\nL 243.076485 420.368225 \r\nL 243.141333 418.564281 \r\nL 243.157545 419.569266 \r\nL 243.173757 419.687239 \r\nL 243.20618 418.199909 \r\nL 243.254816 420.615081 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419.021154 \r\nL 244.600403 420.565126 \r\nL 244.632826 419.783532 \r\nL 244.681462 420.597717 \r\nL 244.66525 418.234039 \r\nL 244.697674 418.71251 \r\nL 244.713886 418.214931 \r\nL 244.74631 420.229532 \r\nL 244.762521 420.707397 \r\nL 244.794945 419.063606 \r\nL 244.811157 419.556076 \r\nL 244.892217 418.421389 \r\nL 244.876005 420.066289 \r\nL 244.92464 418.900909 \r\nL 244.940852 420.796076 \r\nL 245.038123 420.527187 \r\nL 245.086759 417.573785 \r\nL 245.167819 418.752376 \r\nL 245.232666 420.9399 \r\nL 245.200242 418.747333 \r\nL 245.297514 420.151411 \r\nL 245.427209 417.879661 \r\nL 245.362361 420.190008 \r\nL 245.443421 419.196664 \r\nL 245.459633 419.15152 \r\nL 245.573116 421.224897 \r\nL 245.60554 417.72005 \r\nL 245.686599 418.590667 \r\nL 245.767658 420.357667 \r\nL 245.719023 418.552262 \r\nL 245.800082 419.269358 \r\nL 245.913565 420.209621 \r\nL 245.86493 418.484255 \r\nL 245.929777 420.049548 \r\nL 245.978413 418.0628 \r\nL 245.962201 420.195396 \r\nL 246.027049 418.683225 \r\nL 246.091896 420.066871 \r\nL 246.12432 418.143398 \r\nL 246.140532 419.768926 \r\nL 246.156744 418.721911 \r\nL 246.205379 420.431439 \r\nL 246.254015 419.484662 \r\nL 246.270227 419.843634 \r\nL 246.318863 419.016698 \r\nL 246.335074 418.221754 \r\nL 246.351286 419.394997 \r\nL 246.416134 419.307514 \r\nL 246.448558 418.924629 \r\nL 246.545829 420.606521 \r\nL 246.659312 418.441394 \r\nL 246.675524 418.987546 \r\nL 246.72416 420.121934 \r\nL 246.740372 418.415093 \r\nL 246.789007 419.425794 \r\nL 246.805219 419.465371 \r\nL 246.821431 419.430888 \r\nL 246.870067 418.475353 \r\nL 246.90249 419.999465 \r\nL 246.918702 420.235768 \r\nL 246.934914 419.163279 \r\nL 246.967338 419.521961 \r\nL 246.999762 418.252182 \r\nL 247.048397 420.168812 \r\nL 247.097033 418.97534 \r\nL 247.113245 418.983937 \r\nL 247.129457 418.863179 \r\nL 247.226728 420.342065 \r\nL 247.24294 419.696816 \r\nL 247.259152 419.616887 \r\nL 247.324 418.40571 \r\nL 247.356423 420.332587 \r\nL 247.372635 419.317667 \r\nL 247.437483 420.758676 \r\nL 247.421271 418.734438 \r\nL 247.469906 419.949737 \r\nL 247.550966 418.744251 \r\nL 247.599602 419.10219 \r\nL 247.632025 418.540035 \r\nL 247.648237 419.281458 \r\nL 247.696873 420.691467 \r\nL 247.680661 419.089645 \r\nL 247.745509 420.291009 \r\nL 247.810356 418.230299 \r\nL 247.84278 420.347549 \r\nL 247.891416 418.603949 \r\nL 247.988687 419.769767 \r\nL 248.037323 420.355233 \r\nL 248.069746 419.533359 \r\nL 248.10217 418.798437 \r\nL 248.118382 419.721142 \r\nL 248.167018 420.333425 \r\nL 248.18323 419.800204 \r\nL 248.264289 419.108469 \r\nL 248.280501 419.600576 \r\nL 248.377772 420.442624 \r\nL 248.312925 418.732033 \r\nL 248.393984 419.929136 \r\nL 248.410196 419.123028 \r\nL 248.475043 420.255003 \r\nL 248.491255 419.332237 \r\nL 248.507467 419.901472 \r\nL 248.556103 419.138252 \r\nL 248.604739 419.458874 \r\nL 248.62095 419.573917 \r\nL 248.637162 418.857633 \r\nL 248.685798 420.063241 \r\nL 248.718222 419.114366 \r\nL 248.734434 419.912022 \r\nL 248.831705 419.770476 \r\nL 248.928976 420.303152 \r\nL 248.945188 418.51613 \r\nL 249.058671 419.813937 \r\nL 249.107307 418.59747 \r\nL 249.172155 419.314416 \r\nL 249.188366 419.210565 \r\nL 249.204578 419.479734 \r\nL 249.253214 419.961117 \r\nL 249.269426 419.260758 \r\nL 249.30185 418.731113 \r\nL 249.318062 419.399236 \r\nL 249.334273 419.278245 \r\nL 249.350485 420.20603 \r\nL 249.399121 418.518934 \r\nL 249.447757 419.927841 \r\nL 249.496392 418.822053 \r\nL 249.577452 419.148502 \r\nL 249.593664 419.117959 \r\nL 249.674723 420.153998 \r\nL 249.626087 419.065407 \r\nL 249.707147 419.469684 \r\nL 249.723359 418.261425 \r\nL 249.771994 420.506723 \r\nL 249.82063 418.656528 \r\nL 249.836842 418.695847 \r\nL 249.966537 420.245694 \r\nL 250.015173 419.001497 \r\nL 250.08002 419.491228 \r\nL 250.096232 419.500302 \r\nL 250.209715 420.374485 \r\nL 250.177292 419.24538 \r\nL 250.225927 419.912695 \r\nL 250.290775 419.058905 \r\nL 250.355622 419.251612 \r\nL 250.452894 420.261459 \r\nL 250.469106 420.17301 \r\nL 250.533953 418.353134 \r\nL 250.598801 418.474432 \r\nL 250.696072 420.243393 \r\nL 250.744708 419.7938 \r\nL 250.793343 418.735228 \r\nL 250.825767 420.078537 \r\nL 250.858191 418.807085 \r\nL 251.004098 420.281205 \r\nL 251.052733 418.588657 \r\nL 251.133793 419.150639 \r\nL 251.19864 418.944835 \r\nL 251.231064 420.700843 \r\nL 251.312124 418.227873 \r\nL 251.344547 419.648953 \r\nL 251.376971 419.091016 \r\nL 251.425607 420.088374 \r\nL 251.441819 420.23604 \r\nL 251.458031 420.163561 \r\nL 251.490454 418.596476 \r\nL 251.571514 419.606098 \r\nL 251.668785 420.830522 \r\nL 251.603938 419.26288 \r\nL 251.701209 419.858193 \r\nL 251.830904 418.314617 \r\nL 251.766056 420.070969 \r\nL 251.847116 418.699068 \r\nL 251.895752 420.497089 \r\nL 251.960599 419.23717 \r\nL 252.009235 420.21677 \r\nL 252.041659 418.89575 \r\nL 252.05787 418.34421 \r\nL 252.106506 419.903291 \r\nL 252.13893 419.29805 \r\nL 252.203777 420.494479 \r\nL 252.171354 418.787951 \r\nL 252.236201 419.986572 \r\nL 252.252413 418.239245 \r\nL 252.333473 420.829771 \r\nL 252.349684 419.491844 \r\nL 252.365896 419.452853 \r\nL 252.39832 419.513698 \r\nL 252.430744 419.894694 \r\nL 252.446956 419.462979 \r\nL 252.479379 418.735347 \r\nL 252.544227 419.531844 \r\nL 252.560439 419.238325 \r\nL 252.576651 418.911826 \r\nL 252.592863 419.260029 \r\nL 252.609075 418.914009 \r\nL 252.65771 420.903552 \r\nL 252.690134 418.72919 \r\nL 252.722558 419.381741 \r\nL 252.754982 418.280237 \r\nL 252.819829 418.939569 \r\nL 252.836041 420.569059 \r\nL 252.933312 419.717661 \r\nL 252.965736 420.084161 \r\nL 252.981948 419.873693 \r\nL 253.030584 417.90541 \r\nL 253.079219 420.366728 \r\nL 253.095431 419.551604 \r\nL 253.176491 420.3307 \r\nL 253.192702 419.435147 \r\nL 253.241338 418.521818 \r\nL 253.273762 420.066582 \r\nL 253.289974 419.242436 \r\nL 253.322398 420.165347 \r\nL 253.338609 420.041923 \r\nL 253.354821 418.494035 \r\nL 253.419669 420.196338 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420.455446 \r\nL 256.402656 418.653557 \r\nL 256.418868 419.076774 \r\nL 256.516139 421.05281 \r\nL 256.451292 418.945462 \r\nL 256.532351 420.062147 \r\nL 256.662046 418.870079 \r\nL 256.678258 418.935957 \r\nL 256.807953 420.414411 \r\nL 256.856589 418.959689 \r\nL 256.921436 419.332996 \r\nL 256.986284 420.062439 \r\nL 257.018708 419.209513 \r\nL 257.083555 417.899939 \r\nL 257.051132 419.95742 \r\nL 257.115979 418.506516 \r\nL 257.132191 420.367638 \r\nL 257.229462 419.284057 \r\nL 257.310522 420.203767 \r\nL 257.29431 419.02012 \r\nL 257.326734 419.358405 \r\nL 257.407793 417.415614 \r\nL 257.391581 420.203953 \r\nL 257.424005 419.004444 \r\nL 257.456429 420.662358 \r\nL 257.537488 419.199635 \r\nL 257.569912 418.310229 \r\nL 257.634759 419.774506 \r\nL 257.650971 420.208067 \r\nL 257.667183 418.504425 \r\nL 257.715819 419.475612 \r\nL 257.796878 418.716183 \r\nL 257.877938 417.761655 \r\nL 257.926573 420.040072 \r\nL 258.007633 418.810793 \r\nL 257.958997 420.398521 \r\nL 258.023845 419.053879 \r\nL 258.07248 420.694391 \r\nL 258.104904 418.239215 \r\nL 258.137328 420.086052 \r\nL 258.169752 419.970838 \r\nL 258.185964 418.023661 \r\nL 258.202175 420.63934 \r\nL 258.283235 419.402384 \r\nL 258.331871 418.901056 \r\nL 258.364294 420.042636 \r\nL 258.380506 419.982159 \r\nL 258.493989 419.014887 \r\nL 258.510201 420.225671 \r\nL 258.575049 418.98361 \r\nL 258.607473 419.374161 \r\nL 258.623685 419.962166 \r\nL 258.704744 419.050239 \r\nL 258.769592 418.951012 \r\nL 258.850651 420.577641 \r\nL 258.947922 419.067967 \r\nL 258.980346 419.532968 \r\nL 259.093829 418.68677 \r\nL 259.045194 419.984497 \r\nL 259.110041 418.794751 \r\nL 259.174889 419.956345 \r\nL 259.207312 419.123978 \r\nL 259.223524 418.628171 \r\nL 259.27216 419.175884 \r\nL 259.288372 420.201505 \r\nL 259.337008 418.844559 \r\nL 259.369431 419.622431 \r\nL 259.450491 418.898823 \r\nL 259.482915 419.126832 \r\nL 259.580186 420.078662 \r\nL 259.563974 418.589371 \r\nL 259.596398 420.01968 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419.36613 \r\nL 269.469437 417.826402 \r\nL 269.534284 420.021335 \r\nL 269.566708 418.818891 \r\nL 269.615344 420.138481 \r\nL 269.696403 419.671608 \r\nL 269.728827 419.756459 \r\nL 269.761251 418.567055 \r\nL 269.777463 418.078463 \r\nL 269.826098 419.656747 \r\nL 269.84231 419.391696 \r\nL 269.858522 419.11505 \r\nL 269.907158 419.835136 \r\nL 269.92337 419.279363 \r\nL 269.955793 420.883908 \r\nL 270.004429 419.102977 \r\nL 270.020641 419.50308 \r\nL 270.085488 418.660092 \r\nL 270.1017 420.039335 \r\nL 270.117912 418.685951 \r\nL 270.198972 420.436506 \r\nL 270.231395 420.175071 \r\nL 270.280031 419.240679 \r\nL 270.344879 419.514863 \r\nL 270.377302 418.490447 \r\nL 270.425938 420.598142 \r\nL 270.458362 418.803012 \r\nL 270.539421 419.53523 \r\nL 270.571845 419.448985 \r\nL 270.669116 420.008468 \r\nL 270.70154 418.124506 \r\nL 270.7826 419.530397 \r\nL 270.831235 418.999408 \r\nL 270.912295 420.271061 \r\nL 271.025778 418.188042 \r\nL 271.090625 420.028465 \r\nL 271.139261 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272.646967 419.708605 \r\nL 272.695602 418.610066 \r\nL 272.728026 419.77422 \r\nL 272.76045 419.05856 \r\nL 272.809085 420.272487 \r\nL 272.857721 419.982221 \r\nL 272.873933 418.655073 \r\nL 272.971204 419.29243 \r\nL 273.036052 418.692961 \r\nL 273.084687 420.696929 \r\nL 273.149535 420.317768 \r\nL 273.214383 417.968718 \r\nL 273.263018 419.835608 \r\nL 273.27923 419.071938 \r\nL 273.36029 420.388727 \r\nL 273.376501 419.40222 \r\nL 273.392713 419.983423 \r\nL 273.441349 418.767047 \r\nL 273.457561 419.441035 \r\nL 273.489985 418.604084 \r\nL 273.53862 419.678577 \r\nL 273.554832 420.395124 \r\nL 273.635892 419.107221 \r\nL 273.652103 419.081753 \r\nL 273.700739 420.026688 \r\nL 273.716951 418.182909 \r\nL 273.79801 419.485624 \r\nL 273.862858 420.678578 \r\nL 273.87907 419.225463 \r\nL 273.895282 420.425088 \r\nL 273.960129 418.580002 \r\nL 274.008765 419.116677 \r\nL 274.073613 420.131727 \r\nL 274.122248 419.613587 \r\nL 274.170884 420.009283 \r\nL 274.235731 419.174659 \r\nL 274.300579 420.218372 \r\nL 274.268155 418.32131 \r\nL 274.349215 419.915238 \r\nL 274.446486 418.499759 \r\nL 274.47891 419.057637 \r\nL 274.495122 419.047995 \r\nL 274.543757 420.104266 \r\nL 274.608605 419.674749 \r\nL 274.624817 419.663485 \r\nL 274.7383 418.40189 \r\nL 274.835571 420.60694 \r\nL 274.851783 419.316667 \r\nL 274.949054 418.645459 \r\nL 274.884207 419.624071 \r\nL 274.981478 418.971208 \r\nL 275.094961 420.724 \r\nL 275.030114 418.77661 \r\nL 275.111173 419.735766 \r\nL 275.208445 418.824323 \r\nL 275.224656 419.212383 \r\nL 275.305716 419.922096 \r\nL 275.321928 419.095988 \r\nL 275.33814 419.61306 \r\nL 275.419199 418.782382 \r\nL 275.451623 419.446017 \r\nL 275.500259 418.941783 \r\nL 275.51647 419.144707 \r\nL 275.581318 420.258146 \r\nL 275.629954 419.719896 \r\nL 275.694801 418.590776 \r\nL 275.711013 419.746323 \r\nL 275.727225 418.680947 \r\nL 275.775861 420.130173 \r\nL 275.840708 420.051473 \r\nL 275.85692 420.08398 \r\nL 275.954191 418.564299 \r\nL 275.889344 420.290643 \r\nL 275.986615 419.529703 \r\nL 276.019039 419.527725 \r\nL 276.035251 419.842026 \r\nL 276.051463 418.646521 \r\nL 276.083886 420.30562 \r\nL 276.132522 419.211324 \r\nL 276.229793 419.997921 \r\nL 276.164946 418.7607 \r\nL 276.246005 419.286926 \r\nL 276.310853 420.239667 \r\nL 276.327065 419.750029 \r\nL 276.408124 420.512017 \r\nL 276.45676 418.724793 \r\nL 276.554031 419.955163 \r\nL 276.586455 419.288412 \r\nL 276.602667 419.254295 \r\nL 276.699938 418.87033 \r\nL 276.71615 420.160014 \r\nL 276.764786 418.898664 \r\nL 276.829633 419.50247 \r\nL 276.878269 418.638147 \r\nL 276.926905 419.943914 \r\nL 276.943116 419.102982 \r\nL 276.959328 420.712801 \r\nL 276.97554 419.014302 \r\nL 277.0566 420.043336 \r\nL 277.202507 418.361891 \r\nL 277.218719 418.797704 \r\nL 277.283566 420.277501 \r\nL 277.348414 419.686782 \r\nL 277.364626 419.75333 \r\nL 277.380837 417.954981 \r\nL 277.461897 418.911072 \r\nL 277.478109 420.18535 \r\nL 277.542956 418.430021 \r\nL 277.57538 419.950044 \r\nL 277.591592 420.01723 \r\nL 277.640228 419.070122 \r\nL 277.705075 419.520791 \r\nL 277.721287 419.022023 \r\nL 277.753711 420.115206 \r\nL 277.786135 419.646196 \r\nL 277.802346 420.077901 \r\nL 277.867194 419.114053 \r\nL 277.883406 419.411171 \r\nL 277.899618 418.77124 \r\nL 277.91583 420.37984 \r\nL 277.996889 418.978936 \r\nL 278.029313 419.825961 \r\nL 278.077949 418.322534 \r\nL 278.110372 419.774663 \r\nL 278.159008 418.419849 \r\nL 278.207644 420.165443 \r\nL 278.223856 420.457069 \r\nL 278.240067 419.325734 \r\nL 278.256279 419.673453 \r\nL 278.288703 418.864692 \r\nL 278.353551 420.09214 \r\nL 278.369763 419.20275 \r\nL 278.450822 420.64911 \r\nL 278.402186 418.871449 \r\nL 278.467034 419.914103 \r\nL 278.548093 420.496881 \r\nL 278.596729 418.611678 \r\nL 278.645365 420.36298 \r\nL 278.726424 419.726226 \r\nL 278.823695 419.010902 \r\nL 278.839907 419.537337 \r\nL 278.872331 419.226714 \r\nL 278.904755 420.078841 \r\nL 278.920967 418.322331 \r\nL 278.985814 420.508954 \r\nL 279.018238 419.0869 \r\nL 279.03445 418.892162 \r\nL 279.050662 419.067135 \r\nL 279.066874 419.953818 \r\nL 279.115509 418.5931 \r\nL 279.164145 419.316883 \r\nL 279.228993 419.667262 \r\nL 279.245204 419.13646 \r\nL 279.261416 419.05935 \r\nL 279.29384 419.351536 \r\nL 279.326264 420.122168 \r\nL 279.342476 419.52151 \r\nL 279.374899 419.968488 \r\nL 279.455959 418.347529 \r\nL 279.520806 420.854245 \r\nL 279.569442 419.310916 \r\nL 279.585654 418.96111 \r\nL 279.601866 419.597397 \r\nL 279.63429 419.072943 \r\nL 279.731561 420.373113 \r\nL 279.682925 418.740753 \r\nL 279.747773 419.290548 \r\nL 279.763985 419.309029 \r\nL 279.861256 420.2999 \r\nL 279.796409 419.083535 \r\nL 279.877468 419.50547 \r\nL 279.926104 418.654342 \r\nL 279.958527 420.118854 \r\nL 279.974739 419.101165 \r\nL 280.055799 420.156629 \r\nL 280.007163 419.025912 \r\nL 280.072011 419.817675 \r\nL 280.136858 418.872851 \r\nL 280.15307 420.312263 \r\nL 280.185494 419.558425 \r\nL 280.234129 418.254583 \r\nL 280.266553 420.264068 \r\nL 280.282765 419.614917 \r\nL 280.298977 419.725105 \r\nL 280.396248 420.22539 \r\nL 280.428672 418.774756 \r\nL 280.461096 419.454376 \r\nL 280.49352 420.258969 \r\nL 280.542155 418.728491 \r\nL 280.590791 420.241279 \r\nL 280.67185 418.533491 \r\nL 280.704274 419.397548 \r\nL 280.769122 418.755342 \r\nL 280.801546 419.526357 \r\nL 280.817757 420.407315 \r\nL 280.850181 418.778922 \r\nL 280.882605 418.920179 \r\nL 280.898817 418.446378 \r\nL 280.931241 419.998047 \r\nL 280.996088 418.61301 \r\nL 281.028512 419.961788 \r\nL 281.125783 419.575722 \r\nL 281.174419 419.133847 \r\nL 281.190631 419.696865 \r\nL 281.239266 419.499349 \r\nL 281.27169 419.70964 \r\nL 281.287902 419.650838 \r\nL 281.304114 418.931825 \r\nL 281.320326 419.971727 \r\nL 281.368962 419.929542 \r\nL 281.385173 420.502109 \r\nL 281.433809 418.885647 \r\nL 281.466233 419.511142 \r\nL 281.547292 420.161566 \r\nL 281.53108 419.061237 \r\nL 281.563504 419.802211 \r\nL 281.61214 418.430376 \r\nL 281.660776 419.943869 \r\nL 281.693199 418.9864 \r\nL 281.790471 419.949048 \r\nL 281.806682 419.648182 \r\nL 281.887742 419.782428 \r\nL 281.920166 419.145292 \r\nL 282.001225 420.471056 \r\nL 282.033649 419.615874 \r\nL 282.049861 418.659396 \r\nL 282.114708 419.917716 \r\nL 282.147132 419.190971 \r\nL 282.179556 419.974602 \r\nL 282.260615 419.506553 \r\nL 282.357887 418.903419 \r\nL 282.293039 420.215099 \r\nL 282.374099 419.110519 \r\nL 282.422734 420.312108 \r\nL 282.47137 419.991502 \r\nL 282.552429 418.771084 \r\nL 282.584853 419.455388 \r\nL 282.633489 419.756964 \r\nL 282.617277 419.30833 \r\nL 282.649701 419.306248 \r\nL 282.665912 419.02235 \r\nL 282.698336 419.916868 \r\nL 282.73076 419.650077 \r\nL 282.763184 420.571146 \r\nL 282.779396 418.686255 \r\nL 282.795608 418.552892 \r\nL 282.828031 418.505902 \r\nL 282.925303 420.717314 \r\nL 282.957726 418.944212 \r\nL 283.054998 419.237117 \r\nL 283.152269 420.038297 \r\nL 283.103633 418.958924 \r\nL 283.184693 419.586079 \r\nL 283.200905 419.594058 \r\nL 283.233329 419.855776 \r\nL 283.3306 418.651744 \r\nL 283.379235 420.208631 \r\nL 283.444083 419.776494 \r\nL 283.460295 419.826586 \r\nL 283.557566 418.62775 \r\nL 283.58999 418.973599 \r\nL 283.703473 420.438894 \r\nL 283.800745 418.712476 \r\nL 283.816956 418.862889 \r\nL 283.881804 420.130471 \r\nL 283.946652 419.970957 \r\nL 284.10877 419.241296 \r\nL 284.222254 419.975552 \r\nL 284.270889 418.791497 \r\nL 284.335737 419.70758 \r\nL 284.368161 419.976456 \r\nL 284.384372 419.604534 \r\nL 284.481644 418.328761 \r\nL 284.416796 419.705283 \r\nL 284.497856 419.449175 \r\nL 284.595127 418.399202 \r\nL 284.643763 419.97284 \r\nL 284.70861 419.823347 \r\nL 284.773458 418.034545 \r\nL 284.805882 419.817884 \r\nL 284.903153 418.976007 \r\nL 284.870729 419.914393 \r\nL 284.935577 419.595425 \r\nL 284.951789 419.988859 \r\nL 284.984212 419.174378 \r\nL 285.04906 419.846538 \r\nL 285.113907 419.265857 \r\nL 285.146331 420.15082 \r\nL 285.276026 418.572173 \r\nL 285.30845 420.12684 \r\nL 285.389509 419.832128 \r\nL 285.421933 418.974332 \r\nL 285.470569 420.050511 \r\nL 285.519205 419.049025 \r\nL 285.600264 420.213099 \r\nL 285.6489 419.866405 \r\nL 285.697535 419.138171 \r\nL 285.681323 420.060664 \r\nL 285.762383 419.547192 \r\nL 285.811018 419.279749 \r\nL 285.90829 420.284279 \r\nL 285.956925 418.632306 \r\nL 286.037985 419.213451 \r\nL 286.070409 420.839395 \r\nL 286.119044 419.05049 \r\nL 286.16768 419.73566 \r\nL 286.232528 419.990635 \r\nL 286.313587 418.605877 \r\nL 286.362223 419.846217 \r\nL 286.42707 419.248747 \r\nL 286.443282 418.72153 \r\nL 286.50813 419.664204 \r\nL 286.524342 419.764841 \r\nL 286.540553 419.617211 \r\nL 286.589189 418.752615 \r\nL 286.605401 419.7739 \r\nL 286.670248 419.119838 \r\nL 286.751308 418.632379 \r\nL 286.783732 420.309368 \r\nL 286.897215 419.259706 \r\nL 286.913427 419.141891 \r\nL 286.929639 419.593045 \r\nL 286.962062 419.197922 \r\nL 287.107969 419.88947 \r\nL 287.189029 418.914734 \r\nL 287.156605 419.947805 \r\nL 287.221453 419.306751 \r\nL 287.302512 420.118327 \r\nL 287.253876 418.722376 \r\nL 287.334936 419.423473 \r\nL 287.399783 419.502457 \r\nL 287.432207 419.184641 \r\nL 287.464631 419.887017 \r\nL 287.497055 418.741449 \r\nL 287.54569 419.35579 \r\nL 287.561902 419.247336 \r\nL 287.578114 419.860917 \r\nL 287.610538 419.87865 \r\nL 287.707809 419.04486 \r\nL 287.740233 419.411729 \r\nL 287.788869 418.743594 \r\nL 287.869928 420.192152 \r\nL 287.934776 419.011376 \r\nL 288.064471 419.349199 \r\nL 288.14553 420.23962 \r\nL 288.113106 419.069388 \r\nL 288.161742 419.414975 \r\nL 288.177954 418.965477 \r\nL 288.259013 419.102951 \r\nL 288.323861 420.072077 \r\nL 288.291437 419.059151 \r\nL 288.340073 420.010198 \r\nL 288.356285 418.582954 \r\nL 288.388708 420.073224 \r\nL 288.453556 419.259909 \r\nL 288.469768 419.016108 \r\nL 288.502192 419.577271 \r\nL 288.518404 420.074622 \r\nL 288.534615 419.370261 \r\nL 288.615675 419.825046 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419.733822 \r\nL 295.489515 419.268608 \r\nL 295.602998 420.25883 \r\nL 295.667846 418.756081 \r\nL 295.716481 419.96336 \r\nL 295.732693 419.90751 \r\nL 295.846176 418.751454 \r\nL 295.894812 419.73405 \r\nL 295.95966 419.412919 \r\nL 295.992083 419.683446 \r\nL 296.056931 419.789895 \r\nL 296.105567 418.885996 \r\nL 296.170414 419.771609 \r\nL 296.267685 419.729766 \r\nL 296.348745 419.985098 \r\nL 296.397381 418.933997 \r\nL 296.47844 419.92385 \r\nL 296.527076 419.616669 \r\nL 296.559499 419.06029 \r\nL 296.591923 419.672624 \r\nL 296.624347 419.323508 \r\nL 296.672983 419.937306 \r\nL 296.689194 419.004292 \r\nL 296.721618 419.246956 \r\nL 296.73783 419.091959 \r\nL 296.754042 419.570567 \r\nL 296.786466 418.877543 \r\nL 296.851313 420.433959 \r\nL 296.964797 418.50781 \r\nL 296.99722 420.188101 \r\nL 297.07828 419.629602 \r\nL 297.110704 419.588781 \r\nL 297.126915 419.966046 \r\nL 297.159339 420.024537 \r\nL 297.25661 418.6135 \r\nL 297.289034 419.941633 \r\nL 297.386306 419.334373 \r\nL 297.402517 419.357565 \r\nL 297.467365 418.842 \r\nL 297.499789 419.400636 \r\nL 297.532213 419.549763 \r\nL 297.548424 419.290713 \r\nL 297.564636 418.666083 \r\nL 297.629484 420.257179 \r\nL 297.645696 418.99777 \r\nL 297.726755 418.706503 \r\nL 297.759179 419.907881 \r\nL 297.775391 419.141247 \r\nL 297.840238 419.991459 \r\nL 297.872662 419.23305 \r\nL 297.93751 420.297939 \r\nL 297.953722 419.209963 \r\nL 297.969934 420.110321 \r\nL 298.083417 418.739506 \r\nL 298.11584 420.231621 \r\nL 298.1969 419.141185 \r\nL 298.245536 419.135838 \r\nL 298.310383 419.973576 \r\nL 298.342807 418.894547 \r\nL 298.423866 419.594557 \r\nL 298.488714 418.9118 \r\nL 298.45629 419.896046 \r\nL 298.521138 419.215492 \r\nL 298.553561 420.469152 \r\nL 298.618409 418.799312 \r\nL 298.650833 420.258459 \r\nL 298.764316 418.807094 \r\nL 298.780528 419.314888 \r\nL 298.79674 419.114381 \r\nL 298.829164 419.983962 \r\nL 298.861587 419.394279 \r\nL 298.894011 420.267288 \r\nL 298.942647 418.599399 \r\nL 299.007494 420.096907 \r\nL 299.104766 419.46622 \r\nL 299.153401 418.808153 \r\nL 299.137189 420.140406 \r\nL 299.202037 419.587521 \r\nL 299.218249 419.703701 \r\nL 299.234461 419.585602 \r\nL 299.250673 418.562444 \r\nL 299.283096 419.948074 \r\nL 299.347944 419.362072 \r\nL 299.380368 418.309497 \r\nL 299.39658 419.504207 \r\nL 299.429003 419.496532 \r\nL 299.510063 420.289904 \r\nL 299.461427 418.959549 \r\nL 299.526275 419.630986 \r\nL 299.591122 419.697473 \r\nL 299.688393 418.344038 \r\nL 299.801877 420.495149 \r\nL 299.818089 420.099391 \r\nL 299.899148 419.048005 \r\nL 299.931572 419.239694 \r\nL 299.980207 419.426722 \r\nL 300.012631 419.311178 \r\nL 300.028843 418.938235 \r\nL 300.045055 420.446568 \r\nL 300.109903 419.40326 \r\nL 300.142326 419.766748 \r\nL 300.17475 419.2128 \r\nL 300.190962 418.17317 \r\nL 300.272021 419.917778 \r\nL 300.369293 418.924903 \r\nL 300.385505 419.591475 \r\nL 300.401717 419.804428 \r\nL 300.417928 419.11931 \r\nL 300.482776 419.555981 \r\nL 300.5152 419.71968 \r\nL 300.628683 419.058893 \r\nL 300.596259 419.799172 \r\nL 300.644895 419.207722 \r\nL 300.709742 419.886032 \r\nL 300.725954 420.326993 \r\nL 300.790802 419.56235 \r\nL 300.871861 418.298644 \r\nL 300.823226 419.991503 \r\nL 300.920497 418.970021 \r\nL 301.066404 420.066429 \r\nL 301.082616 418.902497 \r\nL 301.163675 419.170801 \r\nL 301.196099 419.00471 \r\nL 301.277158 420.088088 \r\nL 301.37443 418.715354 \r\nL 301.358218 420.373906 \r\nL 301.390642 419.765786 \r\nL 301.406853 419.802609 \r\nL 301.423065 418.513866 \r\nL 301.504125 420.429979 \r\nL 301.617608 418.393437 \r\nL 301.63382 418.944923 \r\nL 301.698667 420.134644 \r\nL 301.747303 419.445039 \r\nL 301.795939 419.088987 \r\nL 301.779727 419.942427 \r\nL 301.860786 419.198092 \r\nL 301.89321 419.448422 \r\nL 301.909422 418.81825 \r\nL 301.941846 420.728685 \r\nL 301.990481 419.797878 \r\nL 302.006693 420.19278 \r\nL 302.022905 419.498979 \r\nL 302.055329 419.571759 \r\nL 302.071541 418.924383 \r\nL 302.1526 419.794789 \r\nL 302.168812 419.191652 \r\nL 302.23366 420.863784 \r\nL 302.282295 420.571887 \r\nL 302.298507 418.295343 \r\nL 302.395779 419.36128 \r\nL 302.444414 420.529721 \r\nL 302.460626 419.012002 \r\nL 302.509262 419.867094 \r\nL 302.590321 418.294236 \r\nL 302.574109 420.082558 \r\nL 302.638957 418.482826 \r\nL 302.736228 420.782853 \r\nL 302.75244 419.924087 \r\nL 302.801076 417.970925 \r\nL 302.849711 418.320288 \r\nL 302.865923 420.00818 \r\nL 302.963195 419.471509 \r\nL 303.01183 419.184074 \r\nL 303.028042 419.423469 \r\nL 303.060466 419.791402 \r\nL 303.141525 418.083045 \r\nL 303.206373 421.373332 \r\nL 303.255009 419.167196 \r\nL 303.319856 418.611686 \r\nL 303.287432 419.318024 \r\nL 303.35228 419.042744 \r\nL 303.417127 420.450502 \r\nL 303.449551 419.106919 \r\nL 303.481975 419.358245 \r\nL 303.498187 420.560621 \r\nL 303.563034 418.011859 \r\nL 303.579246 418.844612 \r\nL 303.708941 420.519998 \r\nL 303.773789 418.26106 \r\nL 303.822425 419.148252 \r\nL 303.903484 418.700417 \r\nL 303.95212 420.722206 \r\nL 304.098027 418.513931 \r\nL 304.13045 420.895959 \r\nL 304.21151 418.836801 \r\nL 304.389841 420.402613 \r\nL 304.503324 419.02669 \r\nL 304.584383 420.263464 \r\nL 304.535748 417.807505 \r\nL 304.600595 419.487715 \r\nL 304.616807 419.120714 \r\nL 304.649231 420.230863 \r\nL 304.681655 419.852697 \r\nL 304.697866 420.144692 \r\nL 304.73029 419.457852 \r\nL 304.746502 418.629198 \r\nL 304.81135 420.363724 \r\nL 304.843773 418.694549 \r\nL 304.892409 420.699896 \r\nL 304.957257 419.0264 \r\nL 304.98968 418.970523 \r\nL 305.135587 420.267121 \r\nL 305.297706 418.203606 \r\nL 305.313918 419.265134 \r\nL 305.394978 420.268789 \r\nL 305.378766 419.167908 \r\nL 305.427401 419.563211 \r\nL 305.443613 419.659125 \r\nL 305.459825 418.537333 \r\nL 305.540885 419.443029 \r\nL 305.621944 420.269151 \r\nL 305.573308 419.388933 \r\nL 305.67058 419.85712 \r\nL 305.735427 418.644615 \r\nL 305.784063 419.390234 \r\nL 305.800275 419.165729 \r\nL 305.84891 419.884996 \r\nL 305.881334 420.661624 \r\nL 305.913758 419.236101 \r\nL 305.92997 419.413667 \r\nL 305.962394 419.701541 \r\nL 305.994817 418.565915 \r\nL 306.059665 419.715717 \r\nL 306.124513 419.188293 \r\nL 306.156936 420.412262 \r\nL 306.205572 418.487834 \r\nL 306.270419 419.206316 \r\nL 306.400115 420.279414 \r\nL 306.44875 418.405992 \r\nL 306.513598 419.374096 \r\nL 306.594657 420.198417 \r\nL 306.546022 418.626433 \r\nL 306.627081 419.504984 \r\nL 306.70814 419.683258 \r\nL 306.724352 418.489169 \r\nL 306.821624 420.684951 \r\nL 306.854047 420.101485 \r\nL 306.983743 418.525957 \r\nL 307.04859 421.096587 \r\nL 307.113438 419.505677 \r\nL 307.129649 420.014131 \r\nL 307.194497 418.884435 \r\nL 307.210709 419.155231 \r\nL 307.243133 419.387936 \r\nL 307.259345 418.592645 \r\nL 307.275556 420.62747 \r\nL 307.324192 419.145632 \r\nL 307.340404 420.222092 \r\nL 307.421463 418.829542 \r\nL 307.437675 418.391882 \r\nL 307.502523 419.496466 \r\nL 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419.896155 \r\nL 310.453086 419.553664 \r\nL 310.48551 419.664707 \r\nL 310.566569 418.647125 \r\nL 310.582781 419.767919 \r\nL 310.598993 419.29556 \r\nL 310.712476 420.574871 \r\nL 310.761112 418.700768 \r\nL 310.842172 419.132236 \r\nL 310.874595 419.943125 \r\nL 310.955655 419.645477 \r\nL 311.036714 418.52007 \r\nL 311.101562 418.895861 \r\nL 311.166409 420.102726 \r\nL 311.231257 419.803454 \r\nL 311.279892 418.968978 \r\nL 311.377164 419.372849 \r\nL 311.458223 419.959353 \r\nL 311.442011 419.014726 \r\nL 311.474435 419.373499 \r\nL 311.490647 419.100328 \r\nL 311.506859 420.119498 \r\nL 311.539283 419.298007 \r\nL 311.587918 420.024225 \r\nL 311.652766 419.726929 \r\nL 311.717613 418.728948 \r\nL 311.68519 419.973246 \r\nL 311.782461 419.116225 \r\nL 311.847309 419.8605 \r\nL 311.895944 419.767535 \r\nL 311.960792 418.803019 \r\nL 311.993215 419.825384 \r\nL 312.041851 419.004724 \r\nL 312.090487 419.81385 \r\nL 312.155334 419.736365 \r\nL 312.20397 419.033027 \r\nL 312.285029 419.5181 \r\nL 312.301241 419.430671 \r\nL 312.317453 419.881697 \r\nL 312.333665 420.206887 \r\nL 312.398513 419.364963 \r\nL 312.447148 419.479802 \r\nL 312.479572 418.596557 \r\nL 312.495784 419.758039 \r\nL 312.593055 419.640032 \r\nL 312.625479 419.834879 \r\nL 312.72275 418.626771 \r\nL 312.836234 420.016842 \r\nL 312.852445 419.375841 \r\nL 312.868657 419.505765 \r\nL 312.884869 419.295517 \r\nL 312.949717 419.363031 \r\nL 312.965929 418.842767 \r\nL 313.014564 419.585642 \r\nL 313.046988 419.160868 \r\nL 313.079412 420.263949 \r\nL 313.144259 419.370034 \r\nL 313.160471 419.013828 \r\nL 313.225319 419.605717 \r\nL 313.257743 419.992126 \r\nL 313.290166 419.304804 \r\nL 313.32259 419.467222 \r\nL 313.338802 419.509984 \r\nL 313.436073 419.689896 \r\nL 313.452285 419.113163 \r\nL 313.565768 420.082718 \r\nL 313.484709 419.072789 \r\nL 313.598192 419.846229 \r\nL 313.646828 418.761378 \r\nL 313.711675 419.436148 \r\nL 313.760311 419.824544 \r\nL 313.776523 419.620874 \r\nL 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420.09396 \r\nL 323.519867 418.773179 \r\nL 323.600926 419.814001 \r\nL 323.681986 419.302503 \r\nL 323.71441 419.474991 \r\nL 323.730621 419.576961 \r\nL 323.746833 418.95692 \r\nL 323.795469 419.586416 \r\nL 323.827893 419.119251 \r\nL 323.89274 420.110978 \r\nL 323.860317 419.000678 \r\nL 323.941376 419.625355 \r\nL 323.9738 419.184642 \r\nL 324.054859 419.521516 \r\nL 324.071071 419.466425 \r\nL 324.087283 419.661254 \r\nL 324.135919 419.924842 \r\nL 324.15213 419.42866 \r\nL 324.168342 419.571427 \r\nL 324.249402 419.115253 \r\nL 324.23319 419.835262 \r\nL 324.265614 419.431661 \r\nL 324.298037 419.198166 \r\nL 324.346673 419.835552 \r\nL 324.362885 419.146192 \r\nL 324.460156 419.35318 \r\nL 324.525004 419.618993 \r\nL 324.508792 419.235639 \r\nL 324.57364 419.587391 \r\nL 324.638487 419.161518 \r\nL 324.606063 419.764157 \r\nL 324.670911 419.562485 \r\nL 324.687123 419.588422 \r\nL 324.703335 418.570658 \r\nL 324.784394 419.837891 \r\nL 324.881665 419.295217 \r\nL 324.849242 419.859062 \r\nL 324.914089 419.390636 \r\nL 325.01136 419.656707 \r\nL 324.978937 419.188392 \r\nL 325.027572 419.60829 \r\nL 325.043784 419.223526 \r\nL 325.076208 419.728137 \r\nL 325.141056 419.424122 \r\nL 325.189691 419.079865 \r\nL 325.254539 419.611119 \r\nL 325.319386 419.138981 \r\nL 325.303174 419.915874 \r\nL 325.35181 419.374648 \r\nL 325.368022 419.769737 \r\nL 325.416658 419.221819 \r\nL 325.449081 419.307508 \r\nL 325.497717 419.181434 \r\nL 325.513929 419.189988 \r\nL 325.546353 419.953318 \r\nL 325.627412 419.621081 \r\nL 325.643624 419.093062 \r\nL 325.708472 419.840416 \r\nL 325.740895 419.436868 \r\nL 325.773319 419.65126 \r\nL 325.805743 419.313798 \r\nL 325.821955 419.487566 \r\nL 325.886802 419.738466 \r\nL 325.95165 419.064314 \r\nL 326.032709 419.792735 \r\nL 326.081345 419.442108 \r\nL 326.129981 419.023255 \r\nL 326.194828 419.152428 \r\nL 326.243464 419.855773 \r\nL 326.308311 419.740324 \r\nL 326.454218 419.261328 \r\nL 326.486642 419.374899 \r\nL 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xi2ClbT7pAMAtgpzvn8vZZw1gTlavxu6iQqFQKBSKBkKFWCgU1XB+6CmyQ+egSwGYhvHC0ECG+h+PGM5u/Ly6ZjvZOblN2VWFQqFQKBT1SLMKsQgEAjz44IN8+OGHOJ1Ofvvb3/L73/++0n7XXXcd//3vfyu1n3XWWbz88ssYhkGfPn3w+/1R27/++msSElRCleLwRHQg9SRK7B40zEUWF0FCaPhwRvZ1E4jEJdd1CUqhUCgUCkXzoFkZyI899hg5OTm8+OKL7N69m3vuuYe0tDQuvvjiqP3+9re/EQyWL31v2rSJG2+8kd/+9rcA7Nixg0AgwMqVK3E6y42Z+Pj4RrkORcvGUrBI4SAX2b5Cp/yHcpa2gXhRnsDnFkGQ0Ul9CoVCoVAoWjbNxkAuKyvjzTff5Nlnn6Vnz5707NmTG2+8kXnz5lUykJOSkiJ/Sym5+eabycrKilRd2bRpE2lpaaSnK4+e4ujJq5Cc14oShBlhgRDQQeTzqWtyZF83pixNWpKn0fupUCgUCoWiYWg2BvKGDRsIBAL07ds30ta3b1+efvppQqEQdnvVXf3ggw/YsmULzz77bKRt8+bNjVZpRXHsscF9PS4qJ+eBGYf8jXEqg2w/AKaBXN8SOAqFQqFQKJqWZpOkl5+fT2JiIi6XK9KWnJxMMBjkwIED1R73z3/+k6uvvprk5ORI26ZNmygtLeWaa65h0KBB/P73v2fLli0N2n/FscPKzOW8Zwykor6L9beTIEHKNSLbxkgeHttLxR8rFAqFQnEM0WwMZK/XGxUvDEReV1VdBeCbb75hy5YtXHfddVHtmzdvprCwkNtuu42nn34al8vF+PHjKS4ubpjOK44pRg7ow8W2LyOhFUBUmEVbyidsdw5pr4xjhUKhUBxbFO+GF0dC8Z46n+qrr76ia9euLF68OKp99OjR3HfffUc83u/3R0Joqzv/5MmTq91eW5qNgexyuSoZwtZrj6fq+M4lS5Zw9tln065du6j21157jXfeeYcBAwZw+umn8/jjjxMKhVixYkXDdF5xzCG7DMMnHVhOZAMNn7SDBD/lqxy6XyXnKRQKheIYY9VjsH0NrHq0Xk7XpUsX3n///cjrjRs34vU27+dns4lBbtu2LUVFRQQCgYjnOD8/H6fTSWJiYpXHfPrpp/zud7+r1F4xTMN63b59e/bsqftMSHF8kHfxKyQ8dRIugkhAw8AtzNKXGbbNkf1CgbIm6qFCoVAoFEfJt69Dzrzqt2//nKj4wm+eN/8JAR0GVn1MxrXQ5+rDvm23bt3Ytm0bRUVFJCQk8O677zJ69Gh27drFu+++y0svvYTT6aRTp07Mnj2bQCDA3XffTVFRER06dIicZ+PGjcyZMwcwBRv+/Oc/1/jSj5Zm40Hu3r07DoeDnJycSNvatWvp0aNHlQl6Bw4cYPv27fTv3z+qPRQKMXjwYD744INIW2lpKb/++itdunRpuAtQHFPsLfbjIkBpwklMcsxki9GWqmpOJv93LswJl5quxyUphUKhUCganbT+EJMCImweCg1iUyC9/+GPqwEXXHABH330EVJK1q1bR0ZGBgUFBfztb3/jpZde4vXXXyc+Pp758+ezcOFCTj31VF599VWuuuqqyDmmTZvGjBkzeOWVVzj33HP597//Xed+VUez8SB7PB6ysrKYNWsWjzzyCPn5+bzwwgs8+OCDgOlNjo+Px+12A/DLL7/gcDgqqVXY7XYGDRrEE088QWpqKomJiTzxxBOkpKQwdOjQRr8uRcsjOyeX2e//wJcIFpT05N2yUznT3pNOtj0IzMm1EOb/3sQuxPz+Q/PAiktSo/7SpNegUCgUCkUl+lx9RG8v702G//0H7G7QA9D9knp5po0ePZqZM2dy4okn0q9fPwAMw+Dkk08mLi4OgP79+7N69WoABg8eDEDv3r0jjtLNmzcza9YsAILBYIMqljUbDzLA1KlT6dWrF9dffz0zZszg1ltv5aKLLgJg0KBBUQHe+/fvJy4uDk2rfAnTpk1jyJAhTJ48mSuuuAKAf//739VKxSkUFlYVvdLSUlwiyG6/GwEkiyI+MXpH9rO8yaUBCU/1gpmJ5jKUNMz/ZyaWe5YVCoVCoWgplO6FvjfAjcvN/0vqZ1X0xBNPpKysjFdeeYVLLrkEACEEmzdvpqzMDFf873//S+fOnenSpQvffvstAD/++COhUAiAzp078+ijj/LKK68wZcoUhgwZUi99q4pmZTF6PB4effRRHn20clD4xo0bo15fdNFFEeP5UGJiYpg2bRrTpk1rkH4qjl3Kq+iVAlBEDBKYGJxMb7GJYbbvEAI+1XtyhvYLIV8x/HEdLH0Avn/LPIndA91HwYiHmu5CFAqFQqGoDVe9Wv53Pa+GXnTRRSxatIjOnTuzY8cOWrVqxahRoxg/fjyaptGhQwfuvvtubDYbU6dO5eqrr6ZLly44HA4AZs6cyb333ouu6wA89NBD7N27t177aCGkrCqy8vhg586dDB8+nBUrVtC+ffum7o6iGdD5vg+QwMliJ8td93B74DbeM84BIJ18PndPAuD50EiGaN+xQZ5IaOyLyPcnM0ZfCoBEIPpNUGEWCoVCoVC0UJpViIVC0dRYJaMTMJd7iogFwCYE+yhXU8mTrfHhJFYLMnXBejyBcm3keaFh/PDLL43Ya4VCoVAoFPWJMpAVigpMyeyKx2EjUZghFoUyFo/DxtVnnYjm8FAoYwDYLdvgxYmbAN6gzi3BSZFzPBS6llF7JpKdk9sk16BQKBQKhaJuNKsYZIWiqbGq4n32zqcAuONb8/BIs5R0v46tKVqUQCJleGLj8PmceDCL2SRTGDmHkyA+XMxdulFV2VMoFAqFogWiPMgKxSFkZaTT1V0AwBu/6xsxcrMy0jkxzqw5/X/dNxMQTmJEEIATxP7I8S7MbNu8guZdJUihUCgUCkXVKA+yQlEFA0JfmX/8958w+ilTsi3kL99h3XyGayCBFAo4VeyIbHJiGs1WPLNCoVAoFIqWhfIgKxQVmZMKMxPpxSbz9dr/mJrGUkLPy8FmSs1g91CCWbTm7+nLuMK2KnIKpwjhcdiYktm1kTuvUCiaHarCpkLRIlEGskJRkUnrCJz2G0Iy/NOwe6DX5XDn9+CKB8PUXiTkJQ4fAjhrfzb9bT9HTvGx64987xiv4o8VCkV0hU2FQtFiUCEWCkVF4tsRsMUSi2HqGet+cCVAfNvy6kLdR8MHd2Ec2GLOMIUNQ+qR2aav4zDclz3bhBehUCianEPDsr553vxnd8EDDVPYQKFQ1B/KQFYoDsEo3ks+iThbdSDppDPLy2xWrC7U+TzEgS1IQEgdUeH4kOY0DWqFQnH8Mmkdua/eSvru5QD4cJLffgQnXqkKCCkULQEVYqFQHML3g/9BsYwhlNDBrIZX0TC2KN3LJkdX0zBO6ogPV2STKNtfeX+FQnFckb1JZ31eMQAhqeGUQVZv95O9KdTEPVMoFDVBGcgKxSEUeYO0EsVosa2r3+mqV/lf3FDz74mf8V9bRmRT3um3NHAPFQpFc2fu0o0ky4MAfGr0Yp4+nFaygLlLNzZxzxQKRU1QIRYKxSEUl/lJpJTSuORq98nOyWXD/gBoMPqJ5UzR/YTQsGOgB3yN2FuFQtEcySvw8ro2jH62n3ESYnpoAgBC6aMrFC0C5UFWKCqQnZPLXxd/g01Inl9bWGW56OycXKYuWE+xbkq+HSwqQtMDlGGWoQ4qA1mhOO5JS/KQLMwKmymiMKpdoVA0f5SBrFCEsQxfu78AgF+9bqYuWF/JSJ67dCPeoI5POgFwE8ApQhRJUxc5pAxkheK4Z0pmV1I10zC2DGWlj65QtByUgaxQhHnhwy/5j5hBZ/IAOEg83qBeKWbQKiHtpYKBTJBiaXqQ9YAfhUJxfJOVkU6PBHMsaCOK6ZDo4OGxvZQ+ukLRQlAxyApFmMtLX6e/bSOldlOR4qCMA8oNYou0JA+5BV58FQxkF0HKRCwARlB5kBUKBSTJgsjfn97aCxJOaLrOKBSKo0J5kBWKcHnp6+zL0YRkmO07AN5yzgQqxwxOyeyKx2ErD7EQQZyEcMYmAqCHlAe5WaJK/ioakeycXGyluzCk+brg+bHq3lMoWhDKQFYoJq2DnpdjCBsAeviBtkQ/q8qYwayMdB4e2wu7ywypaOcxcIkgsfGtAJBBZSA3S1TJX0UjYeUztCc/UkQooeBHtrwzvUn7pVAoao4KsVAo4tuBKx5N6gDYwk+0LPsXZPEFfOCCjOjSsFkZ6bgOdoNP4aZz0nB9GsTvMkMyDOVBbl6okr8tkuycXOYu3UhegZe0JA9TMru2mPjdkYt6k2ULRrVpArpsewNmvqHuPYWiBaA8yAoFQOleiDXLQ8uwBxm7B3pdDpPWV3mI3W3GHPu8pTgJIuxuAtijjTFFk7Nk+DKWaYMJyvAKgeY87PeqaHosD2xugRcJ5BZ4q1SUaa4M9j3J4lD/Su1e6VD3nkLRQmhWBnIgEGDatGn079+fgQMH8q9//avafSdMmEDXrl2j/i1fvjyyffHixVxwwQX07t2bm2++mf37VflfxWG46lVITIu8lAjQ/eBKgPi2VR7iCBvIfm8pTkIIh4sATmUgNyOyc3K5a/Fu9gac2DFXCIQe5Ku8YLXfq6LpsaQUK1KVokxzxZGUhh9TJ12X5qTbkOAixJZim7r3FE2DysM4KpqVgfzYY4+Rk5PDiy++yKxZs3jmmWf44IMPqtx306ZNPPHEE6xevTry79xzzwVg3bp13Hfffdx8883Mnz+fkpIS7rnnnsa8FEULIzsnl+15uwAQApZxFls6XgEl1Q8kLrcZgxzwleEiiGZ3E8QBeqBR+qw4MpahlSyK2CJNBYFPjV4c3JvbYryRxyOWckwKB5nvnE0KBVHtzZ0pmV1pG9ZA/lm2J4TGXpnEPH04v2zeTMbsZXS+7wMGPrJS3YeKRiE7J5cFT07C2PYlC568Q913NaDZGMhlZWW8+eab3H///fTs2ZPzzz+fG2+8kXnz5lXat6SkhD179nD66aeTkpIS+ed0mqoC8+bNY8SIEYwdO5Zu3brx2GOPsXr1an799dfGvixFCyA7J5cpb31HjCyLtK0JnkLmL5eS3fWxao9zekwD2fAWoQmJ5nAREg7T86xoOip4SSyDamJwMjtlCgCLjbOYGJzcYryRxyNJMab39Q77QvqLjdxhXwC0gCp04Xsv62Q7m3veAcCfQ9eySbZnvTyJ6aEJ3BSczMGyYIsMHVG0TPTZqWQtOo2x+odoQjJW/5CsRacRnJXS1F1r1jQbA3nDhg0EAgH69u0baevbty/r168nFApF7btp0yZcLhdpaWmHngaA7777jv79y+O/TjjhBNLT08nJyWmYzitaNHOXbiRoGCRQGmnbJxMI6vKwRpTb5SIobQhfAQA2p5uQ5kAoD3LTUkGtoqJBZZX7TaIEaDneyOON7JxcvtTHsc09LiK9eJ19Odvc4/g0cFVTd+/wVLj3urc2H6+l0s0BGU+SKI7sVtEz3pJCRxQtkzGOp1ka6hvJr9GlYHGoP+d4n1STs8PQbAzk/Px8EhMTcblckbbk5GSCwSAHDhyI2nfTpk0kJCQwefJkBg0axGWXXcaqVasi2/fu3UtqamrUMW3atGHPHhV3o6hMXoE3XC66POZxqPZtZFt1eBw2fDjRAkUA2BxudOFAUwZy0xDWs+ab50Ea8M3zfO4bwwbX9UB5ud9EYU6Emr038jhl7tKNDPY/yWq9R6TNK518wGBsk5tpclsV916/1b8HoAQPB4mjNaaBnMJB3nf9KcozriZrioZkXaGH7tqviLBCk4bkPNt3tKGQExaOpf99r6pwnypoNgay1+uNhEhYWK8DgWiDY/PmzZSWljJs2DD+/e9/M2TIECZOnMh335kFHnw+X5XnOvQ8CgWYhtJ3rt9HtY21f8429zg2uK+v9rgYpx0fDhzBCgay5kQz1H3WJExax470i9HDyrN+HOxoP4one76NhkEbwgYypQhgaDe1vNgcySvwkk8rBKa7S5caLoIcCLnI3hQ6wtFNw5Lhy/jIKF+19EonOcbJAITsMRyU8SSJEja4rudr9620FQVRnvHDjTMKRV3QZ6eyzT2ODtq+SJsQECMCfOieypliA/faX+cvZVN5fMFnykiuQLMxkF0uVyUD1nrt8UR7eu6++25WrVrFpZdeSrdu3bj99tsZMmQIb7zxxmHP5Xa7G/AKFC2VKZlduSE4FSAiBeaVThYZA1mZubza4zwOG36cuEKmZ8juVAZyU5K9SWftjiJsYcPKIYOs3u6n28mn0MntxSbM9iRRigTeWasS9Zojlmc/VRQA8ItMZ54+nBRR2GzjdeesOkCp4Yi8dhMwPcnApJFnUCTiaU0xbhGsdGxIwhM93m60viqOL8Y4niY7dE6kANahCAGX2T/jLG0DH2sTmfnuD43bwWZMszGQ27ZtS1FRUZRhm5+fj9PpJDExMWpfm81GQkJCVFuXLl3Yu3dv5Fz79u2L2r5v3z5SUpTHSFGZrIx0bjzvVABs6PikA5cI0qvLiYwc0Kfa49xODa904dHNmNbv9/rY5wU96FfLVU3A3KUb6cePkddrjO60kgXMXbqREwJbI+1WrLmK/WyeTMnsik0T/M8wf5PtRT5dtZ1MC97QbL+zvAIvKWGD3qKzZob0XXrmqaS2TUMIosJGZFj+bZs8gZfW+dR4oWgQ1hd6KMGDhik1aN13UEHzP4xdSL6Vl6PPTq10nuORZmMgd+/eHYfDEZVIt3btWnr06IHdHl3w74477mDmzJlRbT/99BOdO3cGoHfv3qxduzaybdeuXeTl5dGnT58G67+iZdOtlent2ZV2Ae6bP0brN4Eu7pLDHuO0afhwECfN/V5fuwefYcNJUGWnNzZzUvncN4b2Wrne+Tm2nzhP+5a8Ai832JYAEJC2SAwyqNjP5khWRjqnpsaRLvIBiBO+Zh+vm5bkob/2c+S1EOZKBQCPtEfEtgEgFm/UPkLAydoufrJdychFvRu1z4rjg6QYByeI/QgBv0pTf9uKRbb+r0hICrIczzRiD5svzcZA9ng8ZGVlMWvWLNatW8eKFSt44YUXGD9+PGB6k30+HwDDhg3jnXfe4b333mPbtm389a9/Ze3atZF9r776at5//33efPNNNm7cyL333su5555Lp06dmuryFM2Y7Jxc/r7YnJjdsXcU2btaw6i/mMVDDoMQggAu4sMeyVLdhh8HLsxl1Obq7TommbSOZdpgAuEQGSnhVyMVgWSrexzn28zv1yl0emtbIol7KlGv+ZGdk8sve0s4Rytf6m3u8bpTMrtyr3FzVFtIavixw6T12OOSAeihmVKjnx2SgLgwNJBBvqcar8OK44IHstdjL9tLO3EQgNmh8Xxi9GaL0RajCi+ylLBQH8T3hSocFZqRgQwwdepUevXqxfXXX8+MGTO49dZbueiiiwAYNGgQixcvBiArK4t7772Xv/71r4wePZpVq1bx/PPP06FDBwAyMjJ48MEHeeaZZ7jqqquIj4/n0UcfbbLrUjRfrJK2WsBM4Npe6qix5zc7J5cy6SBWmLrHfhwEsOOkPJGoOXq7jkni23FKh7RItTwAByHeNwYgAUOarpKQ1AhIG4P9T+Fx2JiS2bWJOqyoCvP3uA7d0DGIfnh7pZN3jUGHzQtoKrIy0jn75HaR1yE0bMIghB3i2+JKMA1kBzohKdgjWyOleT+6CFKCB2fSCU3VfcUxSHZOLq+u2c4d9oV0F+bErEDGckPwXr4wegKCUHhctIzl7TKFeOEj0eOo5qzHF/Yj79J4eDweHn300SqN2Y0boz1x1157Lddee2215xozZgxjxoyp9z42JNk5ucx89wcKvOWJHK1iHMwY3YOsjPQm7Nmxi1Vp7QSbuTTvJBDx/B7pM5+7dCMzKVdL8UsHAsmJYi8pFJBPkvJQNiKd3aUcaHU6rQvWAZCmHeA3fAaACCfo2TCwATFOwZ/G9FK/q2bG3KUbydGuw+2unMzmFgF6dm5Pl8PkBTQl7ePKrflP9N501XbSSpTAnFRGhsvPCwF2JJfZP0NKWGucwkZ5Iu20QjVZU9QrIxf1JuuQ39FC10x80sEnRh/m6cM5SeyijSigRHroZ9tEsihiWuAGSo0Q2Tm5x/342Kw8yMczVjW3Am8wSkT+YFmQO+d/ywPZzVT/s4VjeXiH2L5DSphofz+qvSbHWgSwc4rYiYsgd9gXKA9lY3PVq7Q+/UIANsk0pCz3HEsJB4w49st4hICZcQuP+8G/OZJb4GWw/0nW6N0ibVLCTqMNove4I+YFNCXbd5sx07tka2wY/GqkssE4kUe6zkeXlYM9hYD+2s/8M+5Wysa8pO5HRb2RnZPLIN+TlZLwANwiyFDbt/wz7lauDf6JkcG5XB6cSVBqxODnDvuCIxbJOl5oVh7k4xmzmpt5N1csrzotNAGAV9dsp1/H1moQrWc2uK+PxAwDXGdfznX25fhxAPuqPxAzfrVz2a7I6/ddD0Sfh+XwgQsy9tZ7vxVVIz97HAGcouUBRLR0hYDWoty4Glq2DGYmomsubNPV99McyM7JRQD5tCJFFCAlkZAlQ9hgTPNOHMrduw8E7JAptBbFSASFMpZ//c/LKbaBjLWtjiRFhaTGdplKa1HEij8Owe2wNW3nFccUVXmPwZxsZusDcV/8Zz4Pr8T4ZiRHyQ9az0Cf98jPwGMd5UFuSop3w4sjYdd6/lI2lY2u8VWWV93guh4JSp+wAViZuZz3jIERD0+N4xzDqgkna7uimq0Zu2H3QK/LYZLy/DcW2Tm5vM+5BKSNgDTn/kYFD0qRdOOTZmxdQNpYGBrIecG/KqWRZsKs934gObx61kGYk5aP9L7slq1IoKyJe3dk7Lq5opQrk2kjiojFRwludCmJFX5+lukYUhCSGjYMCmUsSaKMjGmLlCykol4Z7HuS7NCAKDk3ayxMa5sSJV96hftZskPnEJSmOWiVob7c/c9G7nXzQxnITcmqx2D7GlhwI/21jbynD2BJqF/UTb3FaMdgv5ndXOANqkG0nhk5oA89u7RHQ6JLgUsE6dml/WH1jwGYtA56Xk6Qcs9PLqlgyeeEfOBKgPi2Ddd5RQQr2bIkCCFskWS9igvbxTIGJyGkDCdLobEjGK+WEpsB2Tm5HCwL8qXrds7SNuAQBkLAKPtXnKAdJIESdKOK9eJmRLwWwCud5Msk2lBErPBSKs0chInByWyVaczThzM68BCv6OejYUpLvu58kEDBLiULqag3gjGpCMyVM+tns1g/izdFJmelRFejnHDhALxaTKTAkobkFNsufnfh2Y3c6+aHMpCbgjmpMDMRvnnerLaUvwEtnLgx0v5NlEZhF203X7tvYYPLlLBTD/P6p7O7FK9w8WPc2TXSPwYgvh1bSzRsslw1IcljozC5LwD7T7kcSvY0VJcVh2AlW7rCS4Uf6GcB0Tqf6doBNCGxzKyztA2AUhppDoxc1Jtt7nHYhRHVLiVsIR0fTgpK/U3Uu5rRyh6kFDcHZDweESCZAs7V1pFCAWAaydNDE/hJdmR6aAIP69cAcLrYyh32BUoWUlEvZOfkUuILcbIwJ1v3B3/HK/r5ODQD16VPVJIvzcpI5yptJZooD0c7Rewka9Fppq1yHKMM5CZgyfBlvKufU201m4pYVW+KZAwpFKiHeUNwxSs4CbE/9qQa6R9DWKt1yxaW6GdG2r4qPYE1iaYs4bYet9XoPIr6wfpduAiQK1OYHbousi2i9xl+rYULNHTQ8putru7xRHZOLpf6ZlU5DgoBncglRgQY8tD7zTYUITsnF4KleKWL/ZhVXp3CoK04GClwUpENrut5wzkHiNZ4XuG9olH7rTj2sPKZhJBICT21rUwPTeBe2z3V5jB9mPlxlaGGS4Yva8yuNzuUgdwEzFl1gCLpOWw1Gwur2lKqVmR6ktXDvP7xHsSOTtBd81Lkc5du5A+ByUwN3RhpmxiYxMptpqEWLCus924qqseS03MTxI+DAuIj2wTgkw4ksE22wx+OT/ZJR7PV1T2emLt0I9fYV0ZeW4ZySAq2ynZsNEx9+3nOh5plKIKl3RyDn1LcPGR/PrJNCKJySdKTPAjgvMBTvBsqX8K2ioWouE9FXVnhvYJt7nGcpu1ACLjWvpJt7nGsMcZVe8ycVQcoNNxomEa1iyCFhps5qw40Ys+bH8pAbgKCBXlcY1tR7XbLa1yVR8VF8Lhf9qhvgoWm4kEotubxwpbHsoi4SFsiJezymklguq+oHnuoOBJTMrvicdhwEcCHkyB2imQMYEq+jQnM5g1jBAkugUOYYTHOmsabKxqOcLLrdfblUQ4DKcGG5GtxOg+FmncoghneYxCDDy8uJgYmR22vaPx+ft8wtj5yMfddcR6lWhxSgh4uFuLTYo6duE8rAb1YhZk1NlbSnWU/1GTylVfgJVkU8a1xEkLA6/pQUkThcb9irQzkJuC+2Pciy72H5p3o0lwKLiAGhPl3RBlB2JQyQgPgPWAayPIoDOSqCoDcYV+II8ZcXjW8yoPcmGRlpPPw2F64RBB/WKlivzS9yCuNDH6SHbk/8FvWhToS6nE5ALuT+jZrXd3jgknrMKi8hCYE6ELwG/kR85wPA803FMEyImKEn95iMy+4/i9qu0cEGG37Isr4zcpIZ0g67CWJH2UHFtozGZIujx0Zz48fMhPQV5lFv7Jzchn4yEo63/dBsw2TOVY4vXtXAtgRAoLSVqPJV1qSh4nByfxHzwTgBX0kE4OTj/tCV8pAbkT02WZy3lj9Q7TwM0ET0R7jj/R+zNPPxy+diKSOFHvaA+Y2IXW2FNuUMkI9E9zzEwBul/MIe5YzJbMrG1zXs81dvmx1nX05L4Smmi/8yoPcKFTwVGVlpNPaJcMa1nAgHAtaKMu9/DeU3YH90r8BsDWhn4oTb2ri27E5tm9UU0hqvGsM4qPMTxjj/jeLQ/0j25pjKIJlRMTg5zOjF++Gzo44NXzSzhajHZsTzqpk/Kbd9A7bEs+ira2Y30ybT9pN7zR21+sfKwH9fy+bCejfPA8zE7kwuzeBgjzecM5ulmEyxwrZObm8szaXE8MyiQ+GrmWePpzTW/kPO/myVuBypVkSPV3sU4WuUAZyo5Gdk8vQ4FNkhwZEtUtZHmcsBFxo/4bx9uW00krJHrKENaUnsE52xgBeDQ1n05bNamCpZ9wbFgJwSl52jY/Jykjn4wuXs0wbHNHWDdnchE692NzBX1zf3VRUxarH4Ncv4Z/nQvEePCIAdhdCwIGwB/lghTCYtCQPmtNNAXE4yvKbqteKMEu+/Ja0EnNFzJBENILbpiQzckAfJlw4gCItERne1hxDEaZkdsVl14jBRwFxFBGLROCTDpzofG70ZELw3iqP9caeSLJxAELNW6Gjxkxax652wyIvvZgTmiz/bN533R8pgNXcwmSOFSw1n4+MfgCsM7owPTSBP/gnH/Y4awWuwGk632baXyLdrlZBVSW9RmLu0o3kBhNIsEcL3n9m9OSATOBC29e4RRCvdPKRPBPHyIfMYwKTudL2MY86/sUzoUvIJYX0pRuPnaW4pmROKoT8xIZfnrj1LZj5Fthd8MCRq6uNHNAH9p0C//scbG7segAjtrW5URnIDUv4u4tQshseP5UTgO9tHbELQZl0AeXlph02EfGIHBCtcPuVgdzUeJc/jAfze5yvD+VlfQRX21bQ4eBuwHxw5/0XyvKcfKafTqkrmSGpIdKa0fiXlZHO1n2lxKz2U2q4SBZFzNOH87o+nKttK0g9TCxnKLED2i6J/nwmtnHzW/zqYPYmndZ5BzhBI5LsNdr2JWPsn0f2UZXaGg7rPhtr+wwp4Te2T/k2dEqNY4nzjFboGnQWu7k++CZTFyQCHLf2hjKQGwnrBj3D9nNUuw2DEjw4CeGTDrNQRef2dBnQh1sWfWAeK9sAkCb2kytTjvvA+Xpj0jpY+gDGD++gSQPD5kY7bTSMeKjm5yjdC31vgH43wDcvopXswZACW0AZyA3JkuHLGPHhedhEdBC/AIaHVhM0JnKS3YwtH277H/ONYcQ67ZGBvtDWmoSAejg3GQ+mgu5nLESquVxt/5gxttV087+ECMHW8K5pN73D1of6kiw0Lrx/fhN1+PCc2bk1Mat9DLd/xyjfHPJJAmB6aAIA6dXEcmqtO5n/7/rWjNcd9ZdG6G3DMeu9H3gbc3KTK9vQThyspG0NpkLJYP9T/Ckn97g1vhqCDe7rzUT+MNfZV3CdfUU47Ozw493IRb3Jsh1Scprl+Bc5IOP4HCtViEUjscFtxqsmiWjjtp/2M8miiIX2TNw3fxxVqMKKbbMM5BPE/qh2RR2JbweueIQ0MKRA6P6jr3531avmQ61dr4iGconwYAs2o+SvYzCjfM6qA2TrA6OUXkJSi0i4bXOPo6f2KwAjbP+rJHNU4mhDfOj4ljBqUnqMjXppSFgYGhipGnroGFfmTCY+tL/Rune0eP0hYvGRygHuci6M2lZtLOecVIZ9YRaAEshIvG5LVSmyqiFaFQITRCkD/H9jUYWwQivXZqE+iHySVJhFPbMycznvGQMjyf+WnnFNpCzN8tTnRGkhLwwNZJDvqYbscrNGGciNxMrM5WSHzsEbjle1uMA/l5uDk7EdYmRBeeC8ZSDfbX+TEx3Fx33gfL1Suhe/oxXfGKfg6/3beql+V0oM9uZkIH/ysBmnG84oPxbIK/ASK/zsk+V6xzYMbBi8yXCyQ+dEjOWqErtC9liS9b3m5EHReFhJXOtej2oWQAke8kmq0qAMuJNJ0g82YkePgjmpnP9WVzOPBLha+yhK9/jhsb2q9JIuGb6M94xoOa6WXJzBqobYWTPD0xKEj6/dt3KxbQ1Qnm/jw0EbYa6w5arV0Hpl5IA+nNQ2Cc1SsDgKKUtHUholeCpoIQc4R/sBgAeyj0/lLGUgNxIjB/ShBA8uQgTCD+6QFGwnFUnVMT5W4LzdFYtP2jlR5HO77RjIdG5GZHd9jP1BOztJZfiG0WR3fazO5/RqsThCzcBAtoyRtf+BRvBQNaaUkyVLVFihIMhHRl9sGHgSU/BqMTjQzbClKhK7OoW2oiF574mblfRUYzJpHXuTz6rUnCvbkCIKaRXjqNKg1GNTaU0hvkCw0rFNzqR17Gh7fvlruwd6XY777h/4/L5h1YYQmMUZTE+5lYDYkoszWB7Iiqs6i0IDyDFOAeANfShgFvPpIbaRQgEC1O+unjkhsA2AA+c8ELUifSSmZHYlWRTxpdEdIcyiSqmigDvsC3h1zfbj8ntSBnIj0t5Zyjx9ONcHzIxmDUkKhdXGpwGMfi+D9eJK3CKEEHAFy8hadJopGaeoE2YFrPUkyBIKZBx5hb56kR/yihgcemk99bIOTFoHPS8vf213N5iOtvVZ5hZ4kZieoYaUcrJWVz4yyiXCPhQDEQKSkxIYkg4L7ZmMDcyO1pgNTxo6e78HYLSxkq3ucazwXs7k+d8et56SxiJ7k84v+eZvo6Ih5UBnWvAGfMHK8aoAucF4HELnhzkDGP3wO83rYR3fjoAwJSKlZocahmpZxRkOEMc3xinM04e36OIMjqQ0ynBFCr0ABLHzb7IAM8YcTC9yW62Ar9238JPrehVmUY9k5+SSW+hFSvjyq8/JTv9jjaUsszLSmRicTD/tFwC6aLsjlSC3uscxclHvhux6s0QZyI1IwegXeFj8njWyOyEpEMBdzoWHDZkY43ia7NA5hKT5VVnLxTfYHz7m4kobm7lLNxIK+okXXg6GtXLrQ35I1xx09v3U9N9NfDu2lmjlRWlC/gbR0c7OyeXPb37Mf8QMuvErC5zTWeCcTlxwPzPf/aFe38vCWl2JtemRtut6mpXzbA4PaTe9w2+mzWfxw7dEa8xOWseO9Isj4RchKZAS3tMHIOG49ZQ0FrPe+4HW0pSPsirnARFPVVW/v+ycXFbuMP/OEJu4ovS1Zqej6/SZSUzB8x4wk3ZrEKplrYKsN07CLYJMD01o0cUZpmR25QStAID1sjMAJ4gDtMHUhLdiWyviFsFmVfSlJaPPTiVr0Wn0ZAtCQJax/KidaelJHob750a1Hc+xyMpAbkSyMtL53jGere5rsQuJEGa8Wtai06pd9l5f6AnHBZmeFRdBSvBwQdlijG1f8ubjtzWrB0VLIq/ASxLm8tPBCkv1dfXgpBr5uKWXBU/e0aTL99k5ufyyZQsHwsb/F/ppbNqymQey13PJw2/z1fSz6uyNy87JZcpb33G7bSH9xUaecv6dDLGJDLGJO+wLKPAGG+zaszLSaRtT/uD9boPp+bC5qjcwsjfprN7hx0kIIPI7vNz+Gdvc45RHqwGxkrg+MfpU2mZ5qqqqkjdyUW+espsPZ6ua3k+2Kxm56PTG6HaNWN3hZgDsbU+LyiM5HNYqSL5MJEWYk4aWXJwhKyOdrWfOAGCD0QGAf+iXkmSYseNLxTmRJD0ww0qaW9GXlswYx9N8FMqoVGI6y/FMjc/xaeAqPnNHayZblSCdSSfUZ3dbBMpAbmRsk8PL3lo4WS8cr1bdsndakodkUcTHhrm8IZARHUlNSBVyUQfSkjwkCdNALpDRxSRqRXj5vp2+CwGM1T8ML99f0SQer7lLN/KHwGR+Dj+svjB68IfAZF5ds53LS1+nv9hYZ2/cyEW9+cV5deR+7KrlRoreWAZPQy3NZefkUlRSyn5MrU5PwFQ5yC2uepkezM+klSxkkW5m1lsPE7+0R1QUWuoSd3PHmngkU16AoFDGVHqgH2owDfY9ydJQeSiNETay3j2k6FJTIv3mOKK54o6wZznWKshBLYlkCklPdFeb0NdS6JlsmhTbpfk8mmX/DyeKvRRLD5oe4BdpXpuUZlJtcyv60pJZX+ihh7bNLNMuRcSZ9n2hu8bnsE1ex3dJF6BXCH/KNdrwuezdYidudUEZyI1NWFoMqZsxoUeIV5uS2ZXb9buYHjT1NCVm+dK6zBIVJlMyu9LObsZDHgh7kOvkwQkv3welDTC/m8Wh/vwoOxIX3N/onknL0LML01uaLvazwXU9W93jIgZtuTfu6I3Y7JxcBvmeZEmoX+R+rBhXqkvBklD/Bluam7t0Iw6ClEkXZdJFctgL983O6g3cvAIvE4OTuTN0GyGpRZb5nYQiKgotdYm7uWPdj9b3BOAgFKk6Zz3QDzWYHElp5JMUube08ATscvtnzUYWTQbDOQfOmKM6LisjnY4dO+MSIVZPOqNFG8cA/lLzu90uzefZSSKPLNtqDsg4JgYns1WmkSNPQQjIl4m011SxnnphTipb3eNI00xvvU1INCEZZ1txdONZfDt6n3QimhAEwmUyEjQvZSOfavH3Zm1oVgZyIBBg2rRp9O/fn4EDB/Kvf/2r2n0XL17MqFGj6NOnD5dccgkrV66MbDMMg9NPP52uXbtG/SsqKmqMyzgyVnGJG5cfMV4tKyOd753j+dw9CTAfDlbCHoCbACG0o5olKoDi3WR9eyO3Z5jGbIGMO6wkU02wlu9tmHGxLoKcpOXRR2zmDvuCRpc0sgbGOMz3vdi2hiz/bLJD5xA8JKa9Nkbs3KUbyacVJ2vl3ueKCToakn0kNtjSXF6BFxdB/DgoIiZieO3za9V6xMsfFuUPAIAAdtLFPgQwtFtKg/T3eMf67FMqGMgeAvws0xgTmB1JUjv09zclsytX21ZGxSxbhKRoHrJo/rCB7Ig9/H5V4EhoB0DJ/rz67FGT4C8xv9u/Ov8OmM8rjwjSUctng2s8NwUn81TI1MBOFkVsCyU3u3jylsiS4ct4Vz8nUjU0Mq6Hnj56h0/pXkS/Cey67D0MCXGUMXL/yw3Q6+ZPszKQH3vsMXJycnjxxReZNWsWzzzzDB988EGl/b755hvuuecexo8fz6JFi7jsssu4/fbb+fHHHwHYsWMHgUCAlStXsnr16si/+Pj4SudqEqooLnE4BvuerDLBAUyD5De2z444S2xMCa4WwarHYPsaeu1ZBMBd5598WEmmmmAt338cjrG0Qg4sT+0297hGDYWxYhzjw8VpEinlGvsKSvBgPySmvTZG7ArvFWxzj+MUbVeU8VLx77ZaYYMtzaUlmRUoA9gpljGkiAIA/DiqfehOyexqFW7jR9kp0u4kRA+xjWQKeGdtrvp9NABTMrvSThTQQ2yLtAkB3bRclrimcoVtFQ/G3l/puKyMdM72/53lekakrWLBiWYhixYsM/93Hr2B/KvfPGbHv65qfgodR0mwzDSQq07IC7HVNY6XnKYee11XsBTlzFl1gCLpQSAjkoElePA6k4/+mXbVq/DtPDq+PdJcrYEWX8SmtjQbA7msrIw333yT+++/n549e3L++edz4403Mm/evEr7Lly4kBEjRnDFFVfQsWNHxo8fz1lnncXixYsB2LRpE2lpaaSnp5OSkhL5J6pyQbQAHElpkaphFZewLTQBn/vGVHvzPpC9nsnzvyVQkMcbztkECnYdv7N2Sxv4m+dBGnj2rAVg+GdX1fnU1vL9wyGzYtt3eucmDYUxk0Kvo70wM+ytuOBr7Ssi+3xinF4rIzY7J5dz/U+yzUitFF7hlQ6CUuNthlE25qUGW5qbktkVJ0ECOCjGE4lt9eOoVo0kKyOda87ugAAyxC+R9orSUznatSpRr4G4w/Y2YHp+jUN+G0NDf632PnQmpbFbto4aAzfKdOKFr1nEjGsRA/noQiyyc3J5c4Op7dxNbG+WCh1Hg+4zC4DsSrugymeVEOXKMRC9gtVSr7k5YEoGFmIA7+iDI6sxhd7a6YabRWwGRlXVa8lFbGpLszGQN2zYQCAQoG/f8mSMvn37sn79ekKhUNS+1113HbfccktUmxACv98PwObNm+ncuXPDd7qRmJLZlVjh5+cKCQ5R2cC26vVts3NyeXXNdiRwh91UGqhOTum44FBt4DACo84zZMuLnyuTAegc1pEEapUwUVeyc3K5xPZ0JKkJIKS5eZ9B7KI1AF9ofWtlxI5c1Jv/um+lk7Y3co1WeIVLhCjGwwV8xfMfrmlQFQuPFsQvHRTLGJzCDG3xh6tVVmc4zcnqxRNX9uFjreokLyU9Vf9YElTj7GaohF1INOt+IUjQHsu9lw2p9j60ihi8op/P50YPiohhm0xrNrJotlDYQD7KEIuRi3qzxP5HwHR0tHSPqvSZYYw2I8Bm2a5K1YpsfZC5rzRDBAdq5nOroSQhjwfSkjzcHZyITcAvsn2dJQPNIjbuClX1WnYRm9rSbAzk/Px8EhMTcblckbbk5GSCwSAHDkR/Kd26dePkk0+OvP7ll1/48ssv6d+/P2B6kEtLS7nmmmsYNGgQv//979myZUvjXEgDkJWRzlTHPWyVaWyXKbyvn8Wv4SxhKUHTo/VtK4ZT/PHN7/jJdT3bDknMqkpO6bjgEG3g+izzaoU05LhuAiBBlBtotUqYqANW4Y7NxTa0Cgsnmu7nYMhFKgUApBj7anX+QeGqWZYXUJeCQunha+NU3jAuwEmIREob1CMmpcQhg+iak2LKPXcezIny4T7rrIx0zj/BHz6Pkp5qaMY4nq5y2V0I0DTBFd2ch52kZWWks7rvE8wITeA7eRIx+GglikihoFnEjNv0MkLYwe48quOs6nPWZyMlbDHatVjNWek3PciXF9zOJnkiP8t0JIKQ1CKqFXHCxyrj9MjEOkUUNbgk5LHOlMyutHOYcfAHZd0Tzq0iNj/JDpThavFFbGpLszGQvV4vTmf04GK9DgQC1R63f/9+brvtNvr27cv555vlPjdv3kxhYSG33XYbTz/9NC6Xi/Hjx1NcXNxwF9DAzBjdg8nczZDAU9wemsQG2ZHvZSdCaMwLDWPTls1k5+RWqmimS8lgv2XMRAfwH49GwAPZ69m0+ZdIHKqo4MWq6wzZkm0a63wmUojCYoXeh/OMZxtNKmfu0o14gzpx+ADYLVsB8L5+Fu3FPuzCjEFux76jXknIzsllH63QMYvd6OGiN+/qAzld28o4bRlxwh8J6Wgoj5g/ZOAkREJcLMWy3Bi+wvbJ4R8OVphNnhleY8nSgZKeaijWF3pYY3QDKk9GuGtDjXSD52T14pqzO7DFSMMhDPqLn7nDvqBZxIzbQ1782tFPfh1JaYyyrcEmzA9FCLOC2dfuW1pkvKcWKKFMxJBb6I+oVszThzM68BCv6OfTShYw1PYt59nWATSaJOSxiuUMmzz/W1I0U2rwAPF1Tji3itgs1fsTK/zMCl3fbFZrGhP7kXdpHFwuVyVD2Hrt8VT9pezevZsJEyagaRp//etf0TTT3n/ttdfQdZ2YGNOr9PjjjzNkyBBWrFhBVlZWw11EA2Ld6HOXbiQ3HOt6vW0psxwv8VToMvaTSHrY0PEG9ahj82lFCWYAv2UMViWndKxjhZtMd30X1S6EqS89zraC6QUT6vQeWR9kkBXywyHOsvNs3/HHSwc3mlSONdOPE+bS72fG6VyhreJV/QJi8TLU9h2GhKFaDnMKdh3Vuecu3YgEztF+RAhYp3dhvexMqihksP9J/mR/lYtsX+EUOj7pYIl+Jn8OXcPX9XyNJf4QToJ0Lf2S3vZy7eNL7V9yKV/CBy7I2Fv5wEnrYOkDsOF9CHkx0NhLK9qxn8/EGQxJk6Qdh5JGDUlakoe4UnOyZk7cTY+idMUfVWXHad8OxeU04yqt1bDrWI5/kQMyarcaUh84DC8Bzc3RpuhNyezKZwt60VHsprPYE5mwL5X9kcP/j5EN0tsGoHg3+/5zDUaxkyLNhSYEupRMDJYXnZgemkB6kgc5pDV88EdG2NZGJgZSwnaZwmWBWfwpJ/e4lBQ7WixnWFxwH+84nyBBloKAzP6n8eKYYXU695TMrkxdsJ5Cw7yjEyjF52h13GkhNxsPctu2bSkqKooykvPz83E6nSQmJlbaf8eOHYwbNw4hBK+88gqtWrWKbHO5XBHj2Hrdvn179uxp2WWZszLS+fy+YRHba0/YK9hWmNqHeQXeapdA0sW+yGx9kX4OZ7QJHHeDkGXYrTfM+PRDk4TO9v+j7jPksBaydW5LSqy01WmN+nkfKvFmxUUnihKuty+N9C0eL1Nj3z2qc1vqFSeENTczbJsZb1/Oedq3OJPSwioZ5iTN0hduCKm3Un8IlwjyfeK5kWqBcPiYfKBci1z3g92NJqBVt3MBMHpeXl6WWlFvTMnsSrY0P+O39CGMDjzE63IEZ6eGjnBkNIN9T7I4dGbkdXMpg+s0ygjaji5BD8wx/S7HA3xh9ATKveudyWs58Z7Fu/H+fRCt963ldLGJEulBryJDz1rVGTmgD8X2VpQHupnPpY5aPl+7b1Fe5BpirRLeYV9IhtjEScJ0dDz734I6r6hYq6FBRwIAJ8XrLb6ITW1oNgZy9+7dcTgc5OTkRNrWrl1Ljx49sNujHd0FBQXccMMNxMfH88orr5CcnBzZFgqFGDx4cJQ8XGlpKb/++itdunRp+AtpBCzjZ69MAiA1LG+VluQhKcZR5THPhC6J/B3seQU9Jr/XoH1sjliGXV/bJoBIbK6bACV4KHG0qfMM2dJCJlz8wC5NQ/HDuKw6nfdoOVTiLRfzN/Kc8wmG2EzD0dLTHqt/eFQJile4nyU7dA6hQ7SUL3f/kymZXUnVilism0ZMbVUyakKJP0Q6+zi98BNahysiAth1H8b6dw7vmTxEi9wRNI83vAfrvZ8K84Hbs41pED0Q+h1Fid2IGfPkUU9GHElpHCAeKYmSs2rqMrhOw0fIVrvJdUFZMKLzbP3rquUeVpmo2TAnFR7visefjyYknbS9nKzlscF1PQA2YYZfHbrkP+gESa5szU6jTZQKzuIGLCx0rGE9z66zL48KE/vENZkLs3vXi5F82cAeADx6cYfjzjiGZmQgezwesrKymDVrFuvWrWPFihW88MILjB8/HjC9yT6fuUT3xBNPcPDgQR555BF0XSc/P5/8/HyKi4ux2+0MGjSIJ554gq+//pqff/6Zu+++m5SUFIYOHdqUl1hvWMaP5UFOFQfxOGwM7ZZCiS9ECgdZ4JzOAud0UsLJWD21rZHjh215DIpNb/rxpI98qGEXkhorQn14Wz+XFFHIb/qm13kQsLSQ5+nDGROYzVu66TXbsqNxP1fLA2B5kHdKM5FpvxFXueqd3XV4j+shTLhwAF4tBhtGpKSpFbeblZFO2ZiX+KfnRgA+t53VYFJvZQGdYjxsNtpFkpx80s4Wox1rtD6HP/gQLXLtajMGVnoLD3+cotYkaV68uPnlkUtrrTluTcD2E0eOcXIkeagpE/Wyc3JxGV62l4hajaFpSR7O9v+dRaEBUUnDHzC4xr/JJmFOKoT8lZoNCYP9T4X/lmx95OJK33faTe8w2v5PPglrxlvXfZKW1+STnZbCFe5nWRLqF1mtBPNzXBLqz2D/U/WiUhWbaDpWig8enxUPm42BDDB16lR69erF9ddfz4wZM7j11lu56KKLABg0aFBE5/jDDz+kpKSErKwsBg0aFPk3a9YsAKZNm8aQIUOYPHkyV1xhKjX8+9//ruSJbqlYxo8eY3oX7nRk85eL2vHxhnyChowsuWSITdxhX0AKB7nL/hZSgi6hTSAXVj1aKaEvt8DbojU4j0RFw86QAg1JHslMCU1kYnAyH2+o+yBgaSFPD03gJ9mR+0OmoegMlhzhyPonKyOd9tp+AF52PAJAG60kSpYNwAgFolRQakIHzYztzdbP4S0xgiHpMvIAzMpI560/misWIzqKBjGOs3NymfjKWhzo+HAiAJ904ETnc6Mn15TdfXQntLvw4UTzKQO5wfAVUabFHXm/w5CVkc6nZzzBV8ZptBFFETmrpkrUy87J5b4F6/Dgp0y6ajWGTsnsSoHWOqLEYk06C3QX2ZuOLgSlMVkyfBlLjLOjJtxSElWh8nAhazNG92i5nvNmwIQLB3BQJEaluwgBJbjJJ6leFCcSWpsTz7KiFhLuU880K4vR4/Hw6KOP8uijj1batnFj+Wzoq6++Oux5YmJimDZtGtOmTav3PjYXsjLS6XNiEr6/2TlB5JO2/2WGel/G7Y4WBr/Ovpzr7Msjr23WH988T9Y3z3Oh5qAbL0W2W/rIx+JySlZGOnn/hfy8RHKN5EhimUV9DChpSZ6oktI6Nkqkm9b2ppHHudhm/lY+1PuSaVuLS5gPXF0KfDiIFQEWh87EuWUz62qQHGNNqv4inQg7tBbF3KLfwcNn9iKrwn5ut4cC4hCl9e95sPrgDYZwuoK4CDJPH87r+nCutq0gVRTWKpa8VMRhCygDuaGwB4oI2OtmIAN8vCGfdNmWC7RvmO+czW2BO8gPJjXJuDV36UZ8QYMYp58yTI3zox1DszLSmfXeDyQHi8iVbfDh5AujB6mikAeb8Vh8/0d7mSv9CK3cA2wALkLcYV/An8XvDxtalZWRzhLfJxhL7uMi8SVCgBcn+9qP4MQr/9I4F9GCycpIZ8WiIqSEXKNNxBlylrYBOPzkpKYktTI9yIGS49NArpEHefjw4RQUFFRq37NnDwMGVC22r2hg5qTS6e9pZhwpwDfP4xZBQpKoJZfq8EoHC0MDGex/ihQOMt85OxKOcSxrHabd9A4HZQJ7aB3xPkW21cOAYoW/VKSQWNq7Ky9FNihhKbN+whwsL7F/FTGODWmKbHweTgx6RR/BHwKTa7QkN3JRb1O2zf4NAENt66qVcSvQWuHw1r+BbCWn2NGxCckifWDEYz89NIHJ3F2rmGevLQ5HsKje+6uAhf/biS1QRK7PWedQrrwCL7/KtjiFESl8ZLU3NtZ7xggfZdJVqb2mFJQFmRiczEdGP9qJg0wP3cDE4ORmOxZn5+RysCxIF5FnrkyiIQTYKsi2/WS7kqwPMg57Hr87hcKw9ocuBS4ZZPV2f7P2nDcn7vVNQBNEjGOADlo+29zj+DRQ9+qwH20xn1tf/bjlmA/BrIpqPciLFy/mk08+ASA3N5cZM2ZEFfEAyMvLO2bCFloaS4YvI7TkfkaKL7ELA690sszoj47GGO2zKo+pmFjsCqsL5JPEg/YXIg+aaaEJx7zWYbwoo0RGZ5zXRVS9IhXl+PIKvCR6HBTrMehlBQx8ZCVTMrs2jkdo0jp8H0zF/lN25P6woeMUOquNnmyT7ThZ5AGQIEpBmiE2ne/7gLQkT7X9HOx7kvvtrzLKtiZy3g/1/lXKuJXaW+MJ7K90jrpiGQ1OzIeon/LE1PTD9P1I+O3xuJogFOZYJzsnl6kL1/OmKGOfTIyEIQC1+p42uK/HxSFSb/bl+KSD7JzvGl0tJrfASwx+ynBFtdfmPNtkO+KEjwXOGdwUuIugp+mLoFSFNZl2hX+DK/XejLCXJ9j7cOLudSmMeOiI55kmC9kjksiXSeQYJ5MqCpq157w5cUpsKejwld6V3toW3CKIVzpZKc7i4snP1+nc2Tm5TH33F0ZodhJEaZ1/ty2Raj3IZ599NjabDZvN9IZpmhZ5bf3r1q0bTz/9dKN1VlFOeSlIU//VTYCA1EjQzETGIumpFBsG4MdOCME8fTjjbCuqrLBXHzPP5oqUkji8JLVqTXqSp8oM67piyfE9cWUf/CGDQmJJrDDANMosPL4dPiveGjOmcb80JXu+kyczPTSB+0K/N3fF1EpO4SBvOGcTKNhVbT8dYRk3M46balUEsnNyyfM56RT4hdEPv1Ov12wZH86wkRQIG8jpSZ5aJ38BBB0JePSWW0youWKFISRQRlE4zrYupe5XZi5nmd4v8tpSURnsf4opb3/XqF6uKZldcdo1YvBygbaWFApqNdmektkVhybYJtsB0Eds5g77AkoDoWbptbMUFNK1/QhBlHEM5vOI74+gJEN5zsZa41Q8BCKres3Vc97cGNnR/P+gjMNJCJ904CJI907pR5VTUhXmSp1BCR7Gap+SQkGdfrctkWrdv61bt+bhhx8GID09nQkTJkRpCyualrwCL8mOIj7U+3OR3fTdnaVt4Ar/dM53f82PekfO1DYgMJe8/qefxPeyM2drP3GqlsuDofH8LTSGme7XuJjPAXPWn3+Mx3/5AqGwgZzM5zfUTUz9SFihAD6Hk37aRlIoaNRYSXnwVwDeDZ1NEbFk2VYDkG+YuuJW9bmEcDGRO+wLy1cSghOq7OeUzK7ELCyikFh+NDqySabR7hAZNytG+B2xGxfBcLlpcxm1Pq7bErF3BcsN5LquAGTn5GIvstFLljDwkRVMyex23HhJGhrL2IkXZRQZsZXaj5aRA/rw5tJWSGnGvLoJYCfE351/5bbAHY0ai5yVkc5PO/fhXGtwAvuZGvsu2qi/HPX7Z2WkM3JR78qe8WZQBKUqrnA/y8TSZyPPnlA46VkTppJMnkwmpt0ptDvCeSzP+X6ZSBvtx6h2xZHp4jZXvBIdOvOCw1kRcxEz0v4baa8L1u/Tjk5rURxZYT6eJi81io+47bbbyM/P5/vvv0fXy6u0BQIBfvzxR26++eYG66CiatKSPJzn/Ra3MAdUIaCDyGeN+3YAHELnff1sLrGvAUyt5OnBCVxr+4g52oskUUKJI5n0tm0hXD/FRYAT2xxdZauWRklJIR4hEe74Bn8vayBpL/KJwd+oA0x2Ti6xuwJcIKCVKOHO4O38xmaG3pyt/cjLRmYka/4B+zxmOl6OHBtZsvY6gOgHc1ZGOtnyP7gX9eF72ZmX4m6sFNIwclFvsmzB6PPV44Peeq9ns1cAZlLuwxfVfgXAMujvlTEk2krJLfA12VJidk5uJDzncKEuLQnTCCojgTKK8US115YEvYAN4kROETsByXm274gN/8bqWg3zqJiTytSw1JlmaYov+tCs4vhAFVUcD8Ng35P8yT6PS2zhhLXDhC81Nad374r2jbl6GZA2HOHCQKaSTIjPjZ48W3Rr2PVSPdZkd5+RSCtRgp0QDofruKvYVltk8W4Azpr6IQOcHkxR3N/Vy7krhjJB+XPBDGlrXhO2hqJGSXqvv/465513HuPHj2fChAnccMMN3HDDDUycOJFVq1Y1dB8VVTAlsysjjL+RHTonogGrS0GOfhIAPcXWiHEMZhD/Nvc4ZthNQ+iU+CC/6ZvO/j07y08qIfe7Fc1ySa++KA3L1dg8lasz1jcb3NezzT2Ok7RdkcSVbe5xbHBf36Dvq89OJWvRaVyAqWAxxLaebe5xxArzQX6R/Wu2ucfxvet3lEkX2WIY2aFzCFZR+KMqhiftwS2C9D61S5UhDYN9T5IdOoeANMOzfOGE0PosAJCVkc6lPc0M6wcu6VMnI9Ly9IfQSKSUFA40yVLisSq7OCWzK4m2AA6hM1r7stZhCBUZavuW7toO7EIiBMQLX8TrutU9rvFkwiatY0fyueWv7Z6j0hSviCMprZLUW3MognIo2Tm5vLM2lxOEOZbeEpzEdpnCdpnCmMDsiDZ1TRwBlmRpqSMJgO4JgeOyYlttyM7JZe/2DYSkxqWPL673cWJl5nLeMwZG7AuvdPKuMYiVmcuPcOSxQ40M5H/961/cfPPNrFu3jjZt2vDxxx/z/vvv0717d4YPH97QfVRUQVZGOneNPZeQIw4RLtmpITlRmO7gq/1/Ijt0Dn5pLhL4cLKj/Si+6P1nAMZnJPDO2lwGyvLYMSEgnb1kLToNffaxqUPpKy4AwOZJaPD3sgaYYNhQbKwBZpzxIPtkfIVKdw52GMn4pCPSjw8YzLvnLaGIGFp5bOho2MPx7BULfxxKdk4u37x8HwD+LV8cNk65octNG8HwA9juOvyOR8B6kPcRmxEC/mh/O6q9sbAM9YqqMsdCzF9WRjpXn25OSNuLfKbGvltnI2hl5nIW62dxaEVjr3SwyBjIkuHL6tLlmhPfjpBu/m6kzWGWL3cl1GoVziqCslW2ZR+JzaIISlVY9+knMgNdCj41ejMk8BRDAk9FlGQmBifXeIUgKyOdkWeZKjhPja57sabjAWsy3U/+gA0jHMZWv5PpkQP60LNLezQhkRJcIkjPLu0ZOaBPvb1Hc6dGBvLevXvJysrC6XTSo0cPcnJyOPnkk/nTn/7EW2+91dB9VFRDVkY6Y+XySMlkISBZM2OP3nTNpgSPufRld+MWIU5s1xbZ2vQwL1+7AW9Q5wp/tFa05T3McjzTqNfSWARKCwBwxCQ1+HtZA4xNmIZiYwwwD2SvZ1RoGW0orlDpLkQZLlP1we7Go4W4uN+pZA3uS7GMIVh6kAHaD5HCIWuMbpzeyl/pQWV5pofyXwDOZW2Vk6nyctNnAfCp0atByk3rwbB0nt1dp/NYnv5+tl8AuMr+SYN7+rNzcrnk4bf5avpZkSRGSz/bjAXfwPvO+0mhIEpXu0VWvpyTyn0/jQXKwxCyFp1WJy/vyAF96H5Sp6g2M2k0RJHhYc6qxtNtdftMKUNxxTyzfHnJnlqdxyqC8o4+hLaigO7adqYFb2iyIijVYU0cTxXbCWInkdJK+xztCsFWr2lMz3z9k5ZzXzchltxmRy0/SlavKrnNutDZXUpB6lkIAQc6X1Ivsc0tiRoZyG3atOHAAXPA6dKlCz/99BMAqamp7N17dHFWivojOyeX8/R/kB06p5InxS4k19pXYAjgxuWRgdsV3wYA4TsIgBTmLWApXVjLet8X1s3oaK4ESs1CEM7Yhg+xAHOAKUwzl2APdLiwQQcYfXYqc74dxHX25ZHKVDYhMYBESlloz4y6F5x2jZNEHhdq/yVNOxg5zzm2nzipoLJ3eIzj6XAoRrlHvKrJlFVu+pkYUyXjc9uZDVJuWlplbm3OOp3H8vRbqy1+aW9QT7/l/bm89HX6i41cUfoaU976jg2u6yuoykBbrYCv3bewwXU9p01bQqf7PuDO+d+2vBCMSevYkXRm+es6hCFUpLO7lO+MLpHXpbiPanm/vlgfP9D846ShZvnyq16t9bk+3pDPz7I9AH3FL9xhX9DsVhEsz3B/7WdcBCM61DYhaqUKlJ2Ty79yzHFxlv1F/lY2hccXfHbY+7pFThTrESuMzap5YI3F9RnGBsBVr1I44F4Atp0wsk73dkukRkl6F110Effeey9z5sxh8ODB3H333XTv3p2PP/6Yjh07NnQfFdUwd+lGcoOJlNjNAcsykq0Ej4/kmThGPsTIdr3MgRtwb9oBQLrTC15oI8zCCHtJpBWlvKGfV+tKZC2BYFkBAJ741o3zhle9ysH/raDVu5+yrcNYks+/osHeaozjaRb6bsQmomdLdiFJohTbqL9Au/TIvQCwxuhON+1XEvBiF0bkHnpPH8D0QxLV1hd6KLGboRPGESZTWRnpXNIrC+PB3zOsvWRAAyybyqAvfIF1C7EYOaAPWze2x7HtCwAchOjZpT1dGsjTX10So0/a0aWo9P25RZD/addFVby0aBGVL+PbEQrHMUqbE1GHMIQoNn1EH1t5AZ544WN8WA+5Mccvj3cPB0Uirep4H4Ipn+Z2VqHxXEXCbFPxif9KHO5A5LXVR11zYZt+9A6zuUs3UhiMAxt0EnvpxF7+YLzF3KWpVd7X5ZU0zZW541Gf15GURkmJKVMaklqDxqvHtz8NgFNz5sCAYcd0Ev+h1MiD/Mc//pFLLrmEgoICzjnnHC6//HJmz57N+vXrmTFjRkP3UVENlpckWRTxs0zHH57vHC5eKD4+iaC0kZFiLoO1wtR9/VjPwClC/Dl0Ta0rkbUEdK85IfDEJTXae8a0MsWOQsX1X1WuIusLPWTrA6NWE0JSY2FoIBeLf1T58CgVHhIowxaOP7Y8z5fbP6u0ZJeW5CFZFFGGk5VGn4i3rjpjRLM7KBAJaGUNc90RD3I9GCad3aVs63g5ISn4XnZi+/Zt9euVKt4NL46E4j0R74/liTckLA71Z7D/ryzR+0fplutSVFvx0qIlyC65A+YKpBj3Zp3CEKKYtI4d6RdHPP/WvX6B8fdGHb/iA3s4YKufnI0r3M+SHRoQ8QxKacZVZ/lnNwsvaXZOLsNDf2W/UV4y3CudfJt0AbbJtVsRWOG9gu/dNwLl48919uV87htTZRjOrPd+IC64jwXO6SxwTo+O1a/wOzuWmZLZlbZaERJ4Ux/CPH14g4SxAazaruOTduLLdrLgyTuaxX3YWNTIQLbb7dx8882RhLzJkyezZs0aPvzwQ84444wG7aCieizDxCpR6iREEBuLxSC0fhOqXM5PjHFSRAz9C5bw+Mi2tA57kLdKc+Z5akLwmM4idpTsAiBGCx5hz/ojrrX52cqShjWQk2IcxAo/AWxm+VepYcMUer/90kFVHnOStgcbkgA29hqJFTKWHewzErjUNzuy79BuKTwRvIxYEWC90aVGZZ0Lba1w+hrmumOsKn3+uoetZHd9jIu3/IadMpVdsg2eUAEPzf+EB7LrFgYQYdVjsH0NrHq0UhKjAE7S8sgniT7a5kgsOJiJt1bFy4o61RVpCas96+PDSg8dB9Y5DCFCfDtObNcWp9AxpMCGgXTF88exgxt1/GoT3EWqsadejLIJFw5glO2rqLwSjwjyoXtqvceX1oaRi3rzqW0ibbTy35xHBOhZsLzWnsUr3M+yJNQvMikAc7LzsXZ2pTAcq8T1HfaFZIhNZIhNkd9DoCCPPf93FsavX7Llnem16ktLISsjndILn0QTsEm2559xtzZIGJs+O5Wx7/XALUKICvkDx2oS/6HUuE70qlWr2LBhA36/H3lIwOukSZPqvWOKI2NpSHqDOgdlPJoAJzpx7XvAqMeqPCbR46AMA09gH8PzX2KvyEcCw884FdbDc5edRLtTj03jODsnl5O3r0AC7//zAbTRTzTKgzQ2PomAtCO89V922SI7J5cSX4iJxp1sdl3Lj7IjdwcncrVtBX3bBOhx6HXOSYWQny4AAlzopIrCiOfSTRC3CPKH2FXALWTn5PLp2u/JdpkGc09tK0KH3/StPus8OyeX5GA8scH8BimzfbY3LDG59iXoPLhO57Iy83c7WjFU+xY7hqmpuyaJfh1b177f4c85wjfP8znPI21EDGEhoKvIZZt7XGQ3a5sExtlWcJ29PB66Ymnlbv6XWsRqjzNwgBJiibPXLV68EqV7Ef0m8P3/Pqcjuxl7ih0aeXLf1thtJkOvejQqfKk2ZGWk8/GCXgzRvosYyRYugub9dJT6yvXJYN+TzLC/xMV2M1HXKgqyTbaltmWXJlw4gIKFryMoXzmxYdCxQ6dKRvfIRb3Jckc7N6zfQ0W6bHsDZr5hri414efVkAxub/o3h53Rjem/aZiiV2McTzOh9HlG2dZgF0ZEm/uF2N/xXoO8Y/OiRgbyo48+yn/+8x+6detGXFxc1DYhRDVHKRoa66E9490fOOAvL3xxaufOVR8wJxVnyI8z/JW5cl7kt3bzIdxpn1lEoqSgecS51Tf67FSyDL/prgPGGkth0Wno79Uubu5oEJrGQZGA3dtwn+3cpRsJGpLWFGMTkjdD5/GT7MhfHDfx7eQRlfZfMnwZ/g+mcpHtK5xhlY0CGUM8XmzIiIE2Vv8QZiZyKZBlKz/+AlsOW23j8H/rgKzK12XFCT6Oh37aT+Hy1WbcYp2N5LDRGfGnff+W+a8OD8O8Ai8bXNdHCu9ABWH8oyhwcmihjweGL2Nk7t/hhwUgdQxh5339bGyGn4tsXyMxlR380sY+mUiaOBDJISiWbuKEj98EZnGH7R0utH2DEBCQdpbrGbTTDnKSu6RFrPa4Awcp0hKJO/KuR0fYE31g/aWkhopIaMwkovB9GDH5v3ne/FdHo+yB2JncVfI4Y22rI7/DkNR4Tx+AO/PPjKxzx2uPIykNvcQ0zILShhPdLAoSd2utDeSsjHTy/gs781IISo0TxV72iGTc/sq/ucG+J5llfzHyWwDTqK7KDNER2OqYCNqcKSvIpw1gj2vTYO9h5Z3YMI6LJP5DqVGIxdtvv83jjz/OwoULeeWVV6L+vfzyy0c+gaLByMpI5/8u781Byg3kmFbVLHWFY/YsfVwLAaTuWglAlyXXNFRXmxRLgSF0SDGMxpKzKxVxdCv8rMFi46w41J5iKwAl0hzACr1Vh5LMWXUgvMxvRNp+NDpyWcDMKZAVYiANGZlXRBGSotqsacsj21HsxkWIO+zv1F82/qR10PNygoQtdru7zqoIG9zRxrFFSFLlNVYl01ZVoY+7Fu9ma4kGMhxOIUNcoq3mYvvXCEHES+hEJ0aYSYfWg8guDDwEuMG1ki7arsh72wlxnu07+ojN/KvTylpfc2MSEyqg2NZwyjFBZyIxRlGDnb8qlgxfxkqjPMTQ0jmvqwbzlMyuxAo/P8v0yO/PCpVqTPm66vqWppl9uD84od5iX/975l85X/8bz+sX4xCSfCOBK3deXine1ZGUxn7K7yNrnCqSnqgxS0pYEBpE9qZQnfrVnPEVmRMIRwMayFbeyVdGN4SABfrAw+adHGvUOAb5tNNOa+i+KGpJuwQ3B2W5b+beD/OqDKTP3qSzeoc/MhusmAxk2MxEp3XdJzdKnxub9YUeSjBnwkdSYGgI4kUZsUaxuQzbAFgD1gT7YgCGad9GtR9KXoGXZFHEPL280E9nsZsfZScgevlfq+Cpsf6XEhbqg6rNml7hvYJt7nH00LaHk25WsM09jhXeelDxiG8HrvhIDC96oM6qCCszl4cnUGG1hfA1bpMnVLrGqmTapi5Yz6z3fqiy0MeWbVv5XpwCwM9GOnlGawKRxDIR8YC1EmWRJCVNSFqLEoSAK/iIrlpu5DvRBMSIAJqQ4aXkxFppCjemVFacXkCZPanBzq+7koiVpWDoDfYehzJn1QEChvmlBKQNF0EKDXedjdisjHQmBiezVaaRJ1uzWabxin5+1fJ1jZyUlpWRzg/txgDwX3lavcW+zl26EX/I4BfDPE9vsZm3xb08/+GaqP2mZHalnVYQNT4JAQnCG+VF3ijTiRe+ZiWPV98ES0wD2Z3YcIVkpmR2ZTJ386x+CQCv6ecf00n8h1IjA/m6667jr3/9K2VlZQ3dH0UtyNlxgAMVPMg/F7uq1Eedu3QjrWQhEhEZWCIPXd2Mk/T5AxyLWDPhfSSQY5x8RAWGemNOKsxMJMXYZ3phv3m+1gbN4fg0cBXb3OM4z2Z6US+2f8U29zg+DVxV5f5pSR7O075lfIXYvRO0g2x0/7aSprYuiTYcKX8AVTdQmtn450RpCx+ufPVRU7qXX+iIX7jrRRVh5IA+nNA2BVu4KqX12zhZ21Upm94S6Tf1imVEpP9L3YwhvsO+ICqZ7ne+O1kV7A7A97IzK40M7JieLQ0oxhNOqjQ/Y5+0s8NIjnx2uhSRbVURkuKovZaNXdY63ijC52zVIOcGwJ2EhgRfYcO9xyHkFXhJFQUA3BW8pV41mNOTPEwMTmapcSbpYj8zQr+tujpdheTPxiA7J5cDu38FQEs4od7yCqwQpzddD5rnDuuAv+efEPXby8pIZ/NZcwD4zugcpfYC8LORhhCwSyYzMTi5RSi81Ba9xJyIeRKSG+w9IqXA3aaToJun4JhO4j+Uag3kIUOGcN5553Heeecxf/58Fi9ezJlnnsngwYMj7dY/RdPyxEe/cFCWG8j7ZUKVy9l5BV4mBidzT9As4GANLj5pR7Y+CV2CUXaQYxFrJlwm3ewgtUYKDPXBkuHLeM8YGBXaUR/LsIdim2yGz1jSYVZp8eqkl6ZkdmWE8TeyQ+dElaBeGBrIxnChgkjCjDC1lMu9m3CKyGNicHK1A+WECwfg1WJwoCOlWW4azVZl+epacdWr/CA74bcn1JsqwlkpOttPvJQvREbkgatrzkrhG5ZMm/WdBqSNhaGBCGS40MeKiOG8zT2ODa7ricd8UKeL/SSLIrbIE5DS9BRb3i9L/9iqfOgIe8g1JBoyasXHwvLkH63X0gqBqYg3qDPz3R+O6jxVUckz/b+dJMlCupd902CeTi3GNL4DJY03fqUleXjbOA+Ar42uR11i+XBMyeyKx2Fjs0wjRvhZ4JzBiY7iyHilzzYn3nzzPEijfOI9M6nBPmNrUtXa2E+BjGVroVFvk6q0JA+D/U9WPREM+U0jOewt7x1r3uvFMgYwjWPrqKWGWZCmrThICgXHXihAhRUDWbYfXQrikxrOQAbTSB59bn8AJoZe5fkP1xw3Um/VJundeeedjdgNRW2xZG/AQ0Bq2DFwYnqBD509pyV5yC3w8lOFZfSQ1HCi81/Ri5PZy0m5ixj98Hn87sKzKxk/2Tm5zHz3BwrCca2tYhzMGN2jec8mi3fD2zeQddl/YGwvWmcXc8BIID3JU++qClUxZ9UBbjbc2GxmrK+1DPvsqgOMHFCPbxSWvJK5OhIipcWrCzswr/tc5PsLcYZC+HHgEkHKhIc9shXd2GmuNCArHRuSggH+f5B+mIePlXizcG8mQ0OraUUJF8RtJq4eP+8YWUbAXo9pX1e9Skeg43uTMdZ+C4DNCFYK37BE+rVw/LYDnRI8XGD8jbvkK5HEx4C08YF+Nn8OXcNUx2sA9BGbGBx4in84n6JIxrJdT+VC29eR+GevtPO+fg6DtXXM04czyvYVTgIIJPtkEkmihETKV/J2y1bEC99Re8qs/VM4yD+dTwBwU+Au8r1JZOfk1vp3UbGIQwoH+UvZbGYsnEiWTScxuLdelB6qwhaOwxRvjYfxCxqlmMGUzK5sW2B+rwdIAI6+xHJ1WJ//2+98D5ghB7dr7wCDyc7J5S/Bp7hf/psL7d8A5uS21JVKciC3wT5ja1LVwbEHJ0FSKCA/mFQvhWqmZHZl8nwv2fpAxtpWA+XPp6ViIM/abuCKR2/lGvuXeA6YZosHP7myDV5c7JVJDLT9yOdGT24nm25iO3fYF7Cx28y6XnbzoqJcZOluDATx+kGg4SYC2Tm5/HnFTq7U7Jwo8sMhZbHAsV+YpVoDecyYMZG///Of/zBy5Ejatj1+Kqi0FMq9xIIQdhwEuMOezbTQhEqzZ0sWbmewfMb5z9Ao4kUZqXtzsWkGrUVxlT+A7Jxcprz1HcEKYpUHy4JMefu7qP2aHZ88EhlQsi58GBZ56XhiBz6/qWFkcQ4lr8BLsqOItcYp9LP9wnx9SMOVwi3dSx6pOB0OUnpfeMSwg6yMdNjogLgJuPrdAN+8yNDcXynONT00H+u9GWJbh12YhqDlvVyoD2IfSTxxBEMgLf8zfqOXK4fEleWaXq56kF4K6gZxsoygo951EcjL285/jYFkaatZK3pwQu6vpFXYPiWzKzELi/jWOJkzbJvYIk8gVRSyy0iiRHginl8HOths5IeSiA8btS6C3Gt/jT5iE0v0MyPySRYeEeJy+6f4pIPpoQl0F7/SXduOhmSzTKMzeSSKMr7TO9Hbto0C4pgYnHzYyUpVWJPlO+3vkCE2AWZoyLTQhDoZPJYRlcJB3nf9iVQK+FC7E6A8xKgelB4OZUuxg2GAbe/3LHjyDrRRf2nwMSkrI51Vq4IUHIwlhL3eJ92j38sgy26GvmkCrmAZLDoNPw7uDL5EssMMJ5HS1CL2BHaaBzbQZ2yNWb20bXgIRO6X+hjLsjLSuXP+t5HkxFNFLjKcnJgpP+di/2cRa6V38aeRfizUBzHE9h0fGGdzjvyRN5xm+IUWLjbCt8vR1zW8UlGDU4VcZHfMcDfH5//XIBMii+oqfx6Nuk9LpUYyb0uWLGHu3Ln07duX0aNHM2LECBITGy4jWVFzDidPpQdcQPnAYA3cd72ZQ1BqOIRBe5FPpu2byscf8gOwZMQOJajL5lnqtooBhW+eB0D3NGAs5CGkJXmYWDCZq20r6Gf7hSdDl7GH1kdt0NQEeeU88macTarbU/MBs2JowrfzSAv5I4FXw+3fRjZZX/3P4dhjyZEnRUuGLyO05H4yxRqcQscnHSyTZ+EY/lCdpar8IYN4UUbQkXbknY+C7Jxcpu78PUbQS5Z7NSsCp/Hizt/wcAWvalZGOtm8xJoF93MGm0gRBVwdnEZQSpIdRWyVbekizMlJf7Eh6vcpBFxmNz1ko21fssroTUexm/ZiH06ho0vBu/o5/DlkqsnECy+xmPdxiihgj2xNZ/YSwixL/ZHRt1Zey08DV2Fz+6Pa6qOscVXjURR2D3QfBSMeqtX5q0KfncqNRrkhOVb/EBZ92CgSjq0p4qBIZOsjF9f7uS0N2ktsX6AJM5Rgqd6PGaEbAOiumbHAB2QsxTKWTlr4WhvgMwZT6cVFFTKI1E8Z7PTwWPmsw1zROEEcIFsfyIkiH0OKyHjkl3ZcIsR/z/kn+1e/RzKFnCJyCaKxInQGmba1aEJGafb+7hD5xcZYPaxXJq2DpQ+YcpYVEIIGmxBZDPY9yf32VytpIf85dA1f1/u7NS9qlKQ3f/58li1bxpAhQ3jjjTcYNGgQEydO5L333qvXxL1AIMC0adPo378/AwcO5F//+le1+27YsIErr7yS3r17M3bsWNatWxe1ffHixVxwwQX07t2bm2++mf37G65IQ1NixW4dKmH2AYOrjD/N+iCDLa5rcIS9Vpfav8QtgoSkqCSBVlHeqqKX4NCSt80xEcKK/S2vDOfkE6MPAIanYWO2KmLFEpZK0yCOE956W4Y9lLKATgIl6O5aTgDC8mkhm6ns4ZVOguF74gejI/P089km02rssZyz6gCFhjsiJeckVC9Z/gC+oE48ZeiO+CPvfBRYHlA/ToqkhxRRWK08nUea9308Xm63mQl552nf0kUzjWMhoD17cYtglfHDmoChtu/oIPZix8AnHQigBA+fuSaxzT2ObtrOSOx3L20bZ9s2ANDXtgmbkNxqf7dWSTPLLljGd3qnqDYp4VcjpU6JlNXJ5YEZq22EfGwpttVrCMQYx9MsDvWPvG5MCUenbz8ltqQGObelvGMV0NCQnKTlRe6NWGGG0rXRSiPGsS4FMuSrs6pLVazMXM77xoDIfWzlU6zMXH74A2vIlMyuCMzKsG/p55EgvLwUGkGM8NNbM1c5pARHOMH1ybVB9otW2IXBOdr3ODDoou1ChOP1LaWi9YVu/jz/Y/5SNpVkCho8IbVBiG+H8cM7VW6SkgbJa7GwKn9aIWXW51qdgtGxRI0MZID09HR+97vf8c4777BkyRK6du3KtGnTGDhwIHfddRdr1qw58kmOwGOPPUZOTg4vvvgis2bN4plnnuGDDz6otF9ZWRk33ngjvXv3ZsGCBfTt25ebbrqJkhKz/OW6deu47777uPnmm5k/fz4lJSXcc889de5fc2RKZldKHMnhG1jikw5cBOneKb3KAXLJ8GVV6gFn64MO+wOoGK5xaMnb5pgIYRlnGlbiU5Bg2FgWsY1nIFtZwLjMUID2MUaDZQEfKA3QSpQg3a1rdwJLPs0IRJZtrYlUL9uvjLcv57ywfFxNDHxLSu5d3Qy2/sI4rd7CS0wPshfDWb8GcsW+7ZcJJIvCSu1gLjveYF8KEJaxWx6uhCcpka6IEeHHwY72oyjFlFGsaCj7pIOFoYF8apzOPH04YwKzI7qyo7V/sCTUL6IeAqYXXw8P2dLuoUS62dzq6MsqZ+fkMnnxLjQRbbQLAR21/ErKAUfDyszlfGb0jLyueL23BCcxLzScTVs216txsr7QwwHikdKMWW0sCcfsnFwc3j2khXZEtLDrk7QkD1fbVkYpDnXVcnGLYFQym5Sw3zDHl00yjVdCw/nhl1/qtS9gKr2c2DoWIcwiIS4RpGeX9owc0Kdezp+VkR7JeNgm2wGw3HUPZ2kbSA6Xtq4oO7mpSHCvMOswtBKlkc/H+qz2yQQ6i13Md85miv0N+osNvO+8PyK/2JIk4LJzcvlU7xU1Hlga2RJRb46HqpiS2ZVUrYilej8AioghXexjaLeGk5drLtTYQAbIzc3l3//+N5MnT+bf//43ffr04f777+ekk07izjvv5OGHH651R8rKynjzzTe5//776dmzJ+effz433ngj8+bNq7Tv4sWLcTgc3HfffZx00kncf//9xMfHs2TJEgDmzZvHiBEjGDt2LN26deOxxx5j9erV/Prrr7XuX3PFMsDaO0uZpw/nJtejbOt8JV3cJVXubxWIqGhMl+AhTvj4OOxhXab3rST+PiWzKxtc14ez9JdHZelXJyXWlFjGWSFmLPU7+iBSMY0de3zjGchgfkc3DDWNhmkj2jfY0l5BaYAkiiMZ/bWidC/0vYFJzplsMdpGeeAXhgYy2P8USR5Hja4hLSxVdV/IVE35wuhZb1n+lgfZcCXU+VwVqdi3fSTShqLy9goZ5IN9T5JrlE9ELGN3sP8p3JRLJToxkyVj8VeSVnSLIKNtX3CX/QH+GXcrG2THiK7srZcMokBLwlZBvUKAOYm1uxG6H79wQujoJxtzl27EFzRIoYAggjyj/H7RpVYnb9TIAX1oEx8beS2BvdIMx/ufcSrTQxP4Q2ByvRonpoRjMT4cLNP7NoqEo5WMmMZ+WlMc0cKuTyN5SmZXhhrP8H7orCiv7bdJF7DGMGUDdWlKdu6Q5oQmWRQxPTSBUXsmNoiHtK1heqqDFz+J1m9Ctc+Z2mKtTP3DYa5eHlohT0r40TgRMFdabg7cGbXdK518pptjbbIo5BztB87SNnC5/bOIdNzX7lvY4BpPbjNc+ayOuUs38tvAPZGkaet++FWm1qu8YFVkZaTz6RlPcGfoNnQJiZSSK5N5Z21uy/LC14IaxSA///zzLFmyhB9++IGePXsyatQonn76aVJSymcQ6enpzJo1i6lTp9aqIxs2bCAQCNC3b99IW9++fXn66acJhULY7eVd/e677zjjjDPQNNO+F0JwxhlnkJOTw+WXX853333HhAkTIvufcMIJpKenk5OTQ8eOHWvVv+ZMVkY6ZJjerPEA/K7afa2ksXn6cF7Xh3O1bQWpopCJwcmc6i5iOBPpYd/J95nzo4ygrIx0Fns/IrD4fkbbvsQmJF6c7Gs/ghOvbLgEgdqSFONgYtlkvnbdDMC/9FGM0L6mt20LMY6jmhfWC85Y00gIlDZcta/CogKcQsceX4fKSuGY5GHpuaxZsJpOYkXURKrA1oq5l/So0amspFBv0MVBGccJYn+9hZcEvGW4RAjprF8DubzPOvtkIieJvPI+V8ggtyddTAUxCZyEKMFDIqXYhWSr0ZbOYg9fidM5cec2UtHYapzASSIPm5D4pJ08mcyvtGPmJVUrweT9F/J2pZIT6ozbaWMI/8OR0Nb8jr55EfHN2zgDR38/WQ/SEjw4CfGF0ZPfiM8wMA1wvyF4tA4qK61EceTvdUYXYoWPthRGJqsV+1AfTMnsyuQFfyRb3o0mYHpwAh6HjYcbUMKxMRKXLKWZooVvA2BIgYsgP+yX9BXm9/5E6Dfc7Xib08LxyG1EMTH4KMPdILkh3yYO58KidXhOuxDOHF+v5wbzu7wwuzcuUXUFPCGgGzvwSich7GzkxMi2gLThJsBgm6n8oYkqTwGYKj8bXNeTnfNdi4hFzivw4iKILXxN+0igDUXo2JgeMm2dhshrsZj27VDmuBvgfg+rTHHZfxpFeeZoqZGl8M477zB06FCWLl3KW2+9xfXXXx9lHAOcdtppzJgxo9Ydyc/PJzExEZfLFWlLTk4mGAxy4MCBSvumpkYvAbZp04Y9e8zYv7179x52+/GM5dWbHprAT7JjRLszPcnDXb85D5+0057djNxfuYT4kL69TO+zML1abg4vJdZUZOfkUuILAZIkTA9Ha1HMCNtapIQum+uumXu0uMIGcrCsYQzk7Jxc/vz25wD8J6eozjP7rIx0zmsPb4kRkaX/NHsRcy/rXeMHirW6kZ7kZpdsQ3vbwXoLLwl6C8w/3PVrIFt9bpfgplS66azt5nvHdWQtOi1Kc/YL31jSxAF84WIenxq9aKsVMjV2EWCGZwAsDJzJsG3X4RAGZcKMJ/VJB050vtFOp3DMa9V+Hmk3vUP6zF8YNWcZ509fgmP6HrhzHbTrBaP+wveuDDyho7+fkmIcaBh0ErtpRQn9tQ1slO0xwo+Ds7QNdTJglzjOj/y9Rp7GZ8bpFEsPulUanPoNyzK/s9M5QAKtRRHpSZ4GL2Yw2PckS0Plzpyq8jbqi2QK+dY4CZDskwmcwD42GicSkDbWGuYkwFJOAXjD+SAp4Vjb+iQ7J5fSX3MIScElf/20QbyHWRnpjNb+QXbonEhicKTaa/j/vTKJYkwN5HyZFDl2mX4Gb+uD2SvaUEU+eSXcIsjIRb3rsfcNR1qSJ6Kl7pd2WlOMJuB13VRjaqi8FotDtd/r7X5v5EI3R0uNPMiLFy8+4j6nnnoqp556aq074vV6cTqdUW3W60AgUKN9rf18Pt9htx/PVPSQWXgcNjOr/Z1ySa6qMmMPlAZIFkXoaOhoBHpdQ3wdK5g1BJbiRiw+HMK8Tkv+B6DtL6/BzNcaLOu3KjxxSQCEfMWH37EWWMu9XUIHwQU7vC5eW2AmaNbFSEi76R2uBK4E4JZancN6/wML4zmT77nqwzVAZY3to0UvM8NlhLv+1XSyMtIZ2asdG2b90TQ8el4GhgE/LQI9gE86WKKfSQ9tGwEc9BTbGKj9YN5r4Z9VP5sZAzrH/jwr9QwAHDLAG1xAu2ETGVbyAVeU7IE6fA4BZyIxR+lBzs7J5Ut9HO4K3qCOIj9qnw4in63ucTCndr+P/fv3gwZl0kUcXjwiEOU9boiHeVZGOh+/n8Rpth18fl/DSzg6ktJwlpqfYUDaGyxxae7SjeQGJtNbbGKRazrJFJJrJJNp+xoHOq+7TKWKiqEIvcRW7rAvYHpoQp00rStijTHLxXpsSK4oe52pC8y45/qeiGzyxlFiNyeTRji0yPofII9kEmVpJbWUUXZTUyFkRLuOrVLulqFt6Su/pw9oMUoMUzK78syCbQDkymS6aLsBKCK2UTT9Le13G0ZUAmSt7/fqVKYa8ZlcExp/rbkaXC5XJQPWeu3xeGq0r9vtrtH245lyr545AFneFtvkdRSfkhWZIWL3VKogVlAW5K7gzdgxcIkQuWc+UC8VzOoby/vVSpTHx+UYXSLXZlRxbQ1NbLxpyBkNYCBbygtJ4es9KOObTRKK9WBN5SAx+OstVlP3mgay5qlfDzIAc1JxzWlNb9tW8/W6+aa8km6OKVY4hZsAedIMZ3leXAo9LydgVTKUDgwJL+kj6CjMh9nroWHcH/gt09aIeqn+p7uSiJWlYOhH3jnM3KUbGex/koJwFTIAr3SQa7SOGBBSwj5n+1r/PtxGKboU7JMJxAofiZRQKE0DuSG9u15HK2JDBfV+3qqYktmVDmIfhoQrAg9Ekivr2/C3ZPMWuaYD5fq+qaKwUnyuhZU0utU9rt48pFZ59XRtf+T8P9mubBAPrBlTXsQr+vl8afRgo0znS6MHKw3zvTqJ3ZRQrt5klWS3vJqC6PCKquKYbRiE0FqMEkNWRjoT+pu5M7myPIfm3F4n8fl9wxo8TMRK1PvGOAUh4E393Drd741VYbauNBsDuW3bthQVFUUZtvn5+Tidzkqay23btiU/P9rrsW/fvkjYR9u2bdm3b1+12493sjLS+fy+YWx95OLyH1d8O5yxSRElC3R/JamgA2WBSFY/gLcRy7oeDdbybRIVjFEpsAkDQwpEqPK1NTRxsfGmBJO//g1ka0LQEcubL6PamxLrwXpqOLu8Ph6s2Tm5vPnRZwA8u3Jj/S/1huXuAtYCm80FCenQ1oy9/sQ4nRRRSKzwsVcmoUtBKKSDKz6qtHYQO3YMJtjM5OFhthyg/r4X6U4yVVp8hUfeOcwK7xV87b6VJFEeQO0RQdLEgYghIQQkB3bC46fWSs0iUfgowUMJMcThI0mUUiDjSE/yNOjD3OdsRZxRDHrV8av1SVZGOnrcCfhwkitTI8mV9X1tVcl4VlQeOVQ6sGK7tQReH7+Pwb4neTd0dlSyYEOFlEzJ7Mrt+l1MD03gmuCfuDAwl2uCf+Kvod8A0FqU0EnsJtHjoASzME/FPImz/X+PMpxDUmNFqA+5sg37ZXzkPj9L29CilBgCJeZq0U5Z3ue3fyhulES5rIx0ysa8xNs2U+87251Vp/vdUpmyYaCHY+sbUomjtjQbA7l79+44HA5ycnIibWvXrqVHjx5RCXoAvXv3JicnBymtjE5JTk4Offr0iWxfu3ZtZP9du3aRl5cX2a6oGqdvH2tkOAGr52WVKrEdLA2QTPnD2F9c0Ii9qzmW9nCSKI20JYkS9slEvjO6sEAbQV5u4yqa2O02SvEgAvVvIFsTgkttZgzyFbZPotqbEit2raJntS4PVssjfXFoBQBDAp/Wv6apJXdnxUvoATj1QuhgZq29rw9gYnAycfgoIYYiYmlt80LpXjaTxkHimKcPx0mI39qXcVF46XeY7Tu2ucexwX19vXTT7TTHxeDen2t8zBXuZ/lKL/f6+KSdLUY79sv4KDULKeFjcXatvMht7D6KiaEUF7F4SaSUQmIbNEYSIGjJG3ob5yHbyfs9HgJ8NfCbBjP8o2U8yysuSmkqjgAUSQ9GWG2moqzeQM387upjJcmRlIYDHSHMZLiG1MLNykhn7uW9SfI4Im0xDo1SR/n9mSDKeK7jSlK1oiiJxBRRSD6togxnDUkeySSLIpI1c/wVAjpo+cz5dhD67NpJGjY2X/9sPrMqepD3hTzMfPeHRnn/rIx0rhp+JgCzh7Wp0/1uqUyV4WR5BeWZ5uDUqUizMZA9Hg9ZWVnMmjWLdevWsWLFCl544QXGjzczZfPz8/H5fABceOGFlJWV8eCDD7Jp0yYefvhhSkpKuOiiiwC4+uqref/993nzzTfZuHEj9957L+eeey6dOnVqqstrESzq+hh/D2UB8KefOrAvfzcUlxvJB8sCpFTwIAdKm6cH2QojaaOVh1isld04KOPYTWv+WDae4Tt/3+gSNV7hQQRKj7zjUfJp4Cq2ucdxls18EF5u/6zZyO9ZIvOWsWmFJ9T2wWp5pIfYzIf/KPtXDbPUW7qX/3oGmX+fNNycLHrN+72N3YedEC4RpES6KZSxpLv8BC57hR1GCiXuE3g27hZ+lunsMlpFPFk+6ai3wgrZObnYtqwE4LOXZtT4Xp5w4QA0YVpQ/nCy4OdGT/oF/slK44woA+ss43+R9xr4yErOvG8eObMGsOTLbw/7HjGUoTviKJUeYoSPJFFC0JHY4MvAhies3vLq5VHjVr0zJxVmJuKUpnSftvYFs4R6LbWjD0dWRjq/6ZvO1baVkbABSyrQJgyEgDi8iHClPWs7QIoo4g77gnoxOqZkdqVDuBjJH4J3NVhIiUVWRjrfzhjBtkcuZtsjF/PjgyP50PbHyHYBdNn2BiO0r7nSvioikbi6r1mFL1lUNpwtT7xxiBe8MQrK1JXsnFy0gPk8q2ggFxJLgTfYaM+yhFRTNaRsf93eLy3Jwy3BSbgI8TPtI2IBzcGpU5E6GcglJSVMnTqVmTNn8uWXX9a5M1OnTqVXr15cf/31zJgxg1tvvTVi9A4aNCiSLBgXF8c///lPcnJyGDNmDP/73/947rnniIszkwYyMjJ48MEHeeaZZ7jqqquIj4/n0UebZ5Zkc8HyzO0M//gu9n9A631reeXRWxj4yEoeyF7PX5b9HBViESotaKLeHpmsjHROdZqepP0yjmHaWhJFKcXh2MumiNH1Cg+2YP3qhgLYJq9jR/rFkSVYH052tB9VZSXFxuZQkfmPjd51erAe6pFusKXeq17lw3bhxMSeY8144TLzfhrbPZYUl5kc5PDE47PHkyhKKfAGaCcOIBLS+OK+4RRpSbjt5ZW/nCJUL4UV9NmpZC06jcGYq23D5BqyFp1WI09YVkY6J9hL0CWMCcxkoT2TNLu5dHtoUYoYEYDHT+XC7NMJFOTxnutP9DZ+omDJnMM+kN16KZo7Ed0RywnuEEmiFBHbCOXdY8yxS+76rmGz4ietg1Myy183cE7DxxvyOdv/d5aE+kUVCAlJjSWh/qyxnYHoNwFd2KLCZCrGIteH8f4LJxKSGj8YnXjCeVODhJRUhz47FbusnGRvoOH64w+RcME5Wb1oFeOoUqnJ8ixbSX+NVVCmrljP5jhhTnQqGshF4edZYz3LktuZMrldfvhbnSahUzK7ku4owS4M9oaVSBpaiaM21MlA1nWdnTt3cuONN7Js2TJ0vebJIlXh8Xh49NFHycnJYfXq1VFaxhs3bmTs2LGR16effjoLFy5k/fr1vP322/Ts2TPqXGPGjOHjjz8mJyeHf/zjH7RuXcvqYscJVqLXMqdZcfAc20+RYiCf+8bwQM55FPtDdGR35JhteXlN1d0aMVg3qzt6CNCKElpRHJEHgsaP0fXbYrGH6t+DTHw7TmzXFpswkJgan81Ffs+KXfunywwrWGUfVKcHq+WRtmStGnKp1xlWHsFXQHZOLhu2bgdg3abt9E4xDfRJF2WgOxOxBwoZ9dfVtBMH+TLfRXZOLgF7HC7pJ8/ZiVLc9VZYYYzjabJD5+CT5hJ0QNqPyhO2wdaVg462LH74Nn4zbT7Fl/4HhyY42//3KAPMwi1CfO2+lbaiAE1IrtY+MmXvqjC63vpmB7GU8Uuh4EDISUxwPw5CnOP9tMG9ulf/dCuAWUzhm+cbzKtLfDtyi81JT0OVz65IXoGXfFqxn0S0CoVjbBjsI5G9o+fBqL+wInMFy4z+URJnIanxfeIQxmsP89X0s2pV8c8y0AaJddgwuMO+EF/QOPKB9Yh1z+sVEkmlhA+1IZU+9xmje+Bx2KLaPA4brWIcJIsivjVOQhPwhn5egxeUqQ+sZ7Ml81bRQC4JP88a61m2cnMxfmkj3pvHgifvqLXnOisjnXsGJgGmbF9jSDPWhhoZyLfccguLFy+OhDhYJCYm8sorr9C+fXtmzJiBzWar5gyK5o71AxvsfzKq3QgPRO+FywUPsX0X2bY9d1ej9a9iFbMjEl4CPV1sBkxPmBDgFDq/sy9hQ9hYa+yBMWCLwamXHXnH2lC6l12iHfvs7aDvDZXix5uSrIx0/u/64QBc2SOmToOg5ZH+SXagTDobbKk3O+f/2bvv8KjK7IHj3zt9UkgoCZDQkaKAEIqKyCqiRmwbilIU2AW7K/5Qca2ILisoCpZV1F3bqqsoQhRQUEBFQSwYigqRIi20UNInmXLf3x93ZpKQAEmYSSZwPs/DQ+bOnZk77c657z3vOVm8vyEXXWm89NlP/N+ctUT7jJFWU0kuf+zxlyKyxZCroonyFWDL30VDrYCDHjsPzNtAPtEUmmLYb0ok25IUksoVYLRWDjT5ALD401aqMhI2/+fdOIsPsMPdgH7TlwfLgMU4LGTTkHRfv2NO/CrLq7QKI6bpGVk8kv4LsRSRTxQFup1YZRwQNvbuC/uo7s6mA0svh3FUNz0ji/17dgHwd8/NYWmfXVZgP9VEy2OnSmCh71wW+s5lp0ogQcsNfp8G9e1Bh3bt0PAHkBhpGBuO2Li06FP6aJk1qiITSGtqouWFvYLFsQQ+8yaMz54CMlUypkrOyB2rUtOjV3dhIvcy23cNAGebtvGI568RP1Ev8NscoxXhUWYOYJyNyVPOYO3y2vgt8z2eyOBPumDXjFz0Ib7FVT5zVZkuDYznNf7yvrVSiaMmqhQgt2vXjlmzZtG3b1/uuecevvzyS7ze8M8UFrUnKd7JJvtYfnTcUW65yX+qLpDX2slUumOdor0anhGao6RnZDHv2bvQt39XtaPWu9bjPnNosG992R/89Xob+pc8V+unc9Izsjjk0mnp3lajUZwTGvEuu7WmFFkahSwQC6XYuEa4lRlVeHJdxgIj0pu0duQSE5bqAYERs7xinXycROnGj3CgjF4DrYhk5Q+QdS+7i63EaoXca/kAgO6mrbg8Pva77UT5Cmjk2UeOPXQj3IEyWP/zNwlYr7c79khYmQPLwPNqph1mn2pIVo4rGCzlFBkpI9FaCVtVs3LfmbIjkoGRu/m+C0jfUv43YMaSTIq9OjGaiwLlZLT5i+B1GoR9VPeA1ygl51WmsI7qzliSyVteoxnKOtU+LO2zy5qU2gkNuNUzkQvdz3Gn9y7u9N7Fhe7n+Ef0g+XWbesoJN+ZzEHVAN0RD8BI0xeMtiwNnhGsbnDbv/hZVvhKz9CGs4LFsZQt/Xa1+wne8V3CdpVU4fkHVFapKZDPnamMPNqztB1MsMyL+JbJge91DC4KcOLBQpGyYcNDAjm19ltWOopv/K6ebA530WHjNW+Q0CJk2xhqVQqQ7733Xr744gveeustEhMT+cc//kG/fv2YPHky33//fbCahKi/JqV24mLPc6R7zz/mCJKuKDdb+ndV81qpVRXItxziW4xJU1U7ao1tRok5GjPKKHhW5qxxgTJOx9fm6ZxAYNKSAzhDWAv4aE69ELc1JqT3GSpxUTYO0wCT69BJ31daSjLtY3WKTdFhGXkInNIEyFXRNNCKsOAl1p8D2IAiRlmMKhp7Vr3HEL4kQcsjzbIKgH7m39juGMX12hKiKaKVvgu3PXQpXpNSOzGRe3nYO5585WSNMi5X+iNZplPVjCWZxHoP0VrbSx8tkwRygrn4gR/hWz0T2aJa8rtKDgbDwRFJ/34hUyUTqxVXCAgDI10NcJFPFLO9V5e7Ppy1TtMzsjicvRePMvGx7/ywjuruyXER56+QE6jvHFgeDmkpyVx/XiuOTn6pNDAa8S7Pd5vLF3ovclw+PvL2L7c/r0lwa41PohF5KGV0cQtnWtOxBD7zZfOKj/mZP45H1g7ga/vdQGlN6doeDa8uoyqTiSZaLk5KSCAHhYYdLw9Ef1Jrv2Wlo/iqXLOQmuRwp2dk8cNPRnWf+z8K/W9hqFQrB/nss8/m73//O+np6YwcOZKPP/6YsWPHcuGFF/L8889TVBSm08ci7NJSknE5EijAicJ/GuuofK9irGioYHkhXUG/F38N64c7cNQaUNWjVlVwgEPE4LHGc4jGweXt4021fjoncIqyvWlvWE9RRqkifNbYkN5nqDisZnKIxVIcmhJcVm8+xeboE69YA2UDnVyiiaOQeEpzx/uaf+MSf03jpH3LsWhGPqbnqFJ2r3kvB8CMomHhtpBtX9lTyLlE08RcWPFH0p9mVLY99sriwXxn/xsWDRK0XCZY5gWfb2CUEowg+Q+VhIJyk/YC/zpoe7jVM7FCQJgU78SGB7vmIU852U/pQUGgTm24ap3OWJLJze6JHKAhOqawjuomxTuJ838eynYIDOdp7qlp3Zg1vEeFtIGj92PpGVm88e0fXGf+msZaPsMs35RrlOHAbYxCOqueVjCgcwLtTUY63Rf+klzhrGBRmWOlTVR3Px6Y5Hv0KGhtjoZXV1pKMo9efRbdtG3Y8fCj43aitZJyaQ61cSY3MIr/i2qDFxPv+C6uUQ53YMDofH0NSsHQ4rlhGTAKhSq1mgbIz89n6dKlfPbZZ3z33Xe0bt2aW265hSuvvJIDBw7w1FNPsWbNGt56661wbq8Io5wiD02sebzju4RG5HOl+ftgm85c5aSBf5LA/7wDSLN8S1ttH+6cvTwwz5hdHI6gc0OukwJL6Rewqketmy6cTYs/epEZ15eZ+87mDcs0ABY1Gc+4494y9PoXP8uDlne50vw9Vs2HS9lY7OsT0janJV4fsRSRbYvMABkgzxRHU3doSgPafQXkmUPfZhqMH4Isf/CXp6KJ0wpppxk77xJlxosZKz5smlFnNR8nCVoeljJNQq42fxcMnAE6lPxqBKwhaqUaOGX8+2OxnBHtoctR373PBn6O97MHuVxbjVXzBb/HFn+Jt8CB2mjLUkqwYk85yE87DvPOamMi4kWmteW6kQXoCs4reTH4OpU1KbUT0+YaDVyuM3/N6/4DBIDB7scZaV5GYphqnQbuM1fFlKt/Ho7HmpTaiSPziihUdrz+n9DaOM0deM+PZ8aSTHwKHvCMZ4bt3+Wu0xUcVg1I1g5S6PZWqQ217/FEpuolwbNwV1m+B8Cj2bDWcs5oVZ7/iQRaJgdGQR248WKq1gFDrZuayAhvSeXDmRYnnHkVXPbPsG/GpNROTJx3L9f5PuUx61s85x1GkbUR06r5uR/0cXfSzKUtwkdbljKapZR8bIWUk0vBC7UqjSDffPPNnH/++Tz//PN06NCBDz/8kIULF3L77bfTunVr+vTpw/jx49mwoe7LSomaS4p3BsvjWDSdt32X4MUYFYvTXKWlg6zLiKEEG14mWOaFtWRa4Kg1kE+ch5Nk7eAJj1oLit00Jp/vD5jY4ysNpD7dXFTrR6qBygvmMFZeKCj2EkuR0SEwQrlNUTR3/R6SagYOXyEec3jSSQKNZqB0BDnQDc+FAwu+YF1nh+YhQTMm7wVHWVHlguMAHxUntp0sl6UBuHK4ZtrcclUKSjtVlVYWytbLHzx5lYnP9XOCdZmnpnXj2eE90DRjsu5ib+8KZ5HyiCKb+EoDwrSUZK7rZnzXWmn7g50DS5SFgyourLVOA/eZo6KJK9NiPhyPlZaSTJdGOrlEn9RoZjgEDgg2qPYVrjNp0EjLJ0s1weNTVdpnD7a+xJe+0jNdgRHXIbaXQ7fRtWhSaicStDy+1s8u11EvcMAQke5az5H2fw6mN6L5iyGY7ZV2vA2XwCh+gdWoonFmTCVnrqogMIqv14NR/CoFyM2bN+eNN97gyy+/ZNKkSXTu3LnCOn369GH+/Pkh30BRe8oGBoFAebCn8iPTsnU2tztGscx1Xdi26f+4h73+0jZxFLFfSzjhaI2rIA+75uGAL4b9qrQG62Gfo9brHwcqL3zmM7oQrdC7hfwUZUGRC6fmBkfkBsgt2I9VuUNSzcCpF+IJ02h56elcB6mmn+hgyuJyi9GZM14rxK4ZQWceTrbpTcuVRtuhJzCoZFqF1sBKwTxvxYltJyuPGOyeXK4tfK9clYIsf6eqtboRKB0iloZaQXBbAmXC4hKSytVlTktJZtZ1PcgxNeIgRrAbqBqQSxRO3McOCKcmcs8mozmNSYOLzOsBggfSYAxEhmOkNbDvyiGGeIwAOZyjuvGmIvKIZtu0KyJqBn5gsvVi+/2VXh+YqFfVffb6XCdx/tfTXSb/ONJrBx9LWkoyF5nXBj+bgY56m60jIzcPObYZLi0KDYXSTKB8kNAZblpW6xWL0lKSGXXJuQBMGVCzbnqBASMNhVeZ6iSnvaqqFCA/9thj9O7d+7jrNG7cmNatW4dko0TdqCzP68ZhV1dYb5fepEIO17WOV8K2Tb9Yx9DSlA0YO7Tj1WENcOcb6x+mATnEBLuZ5auo4Onz2hKovDDDZjSeWGvtHvLKC64CI3XB5AhP2sFJ8efDtvFtD1k1g2jCm29tzIIfyBLLn1AYJbOAYLPfgzRgr0pgteoWbDwAYNc87I86w3/GQC9XkqqyiW0n63z3d5xh2lOhSsEm+1hu9UxkreoAGDPgA5NWv/D1DJYJcx3ZV+E+A61+m1vyg1UDPtRSyTU1xK55WZn4FGlnVJKdd9d6djc8p8LisgfSG+1jwxJMBvZdeRgpFuEe1TWX5OIyxaJpleSh1KGqTLZWiirts9MzstCAjqbdKAWf+3oGu9JFeu3g4ykdwTQuKwXb9GYROYIZVHgABeR1Hg69x0PjM6BZtzqpWBTd2Kg6UXK4ZiPuk1I70dyUg6bB295L6iSnvaqqnIMsTg/l8rymJsInJRXWSdYOlutGVGyKYvzl54Vtm9YNWUH3D/sG8ydPlHeVnpHFRyt+ZjBwUDUANA7RgCQOY8ON5l+nNkd90lKSaZ9wMbn/dnKrbTGOMx4J6f0X5xsBsjkqAgPku9bDkofx/jIfC16wOODMq2ueN+ctwY4HZQ9/vvUZpr1ovtIAOTCikKjlkUgeHU07y02CaqblkKFfi8+s8bbvEt7zDQzm3t7qmYgW4oOzt32XMNr8OT7M2Py5xn+oZgx3T8ZpNTPWtAQwmn2AMYJ7meVnipWVziVvobnhj0ruNy0lGVKMMm1jALid916aQusDs4JVMbhqZvkbxTbD7T8QdWPGqnwoNEyaCubdvx49ngUhfQXKb/O8L5rQsKiQlX8fAGEMXm3uXEqskTfiZezTLsL98XyUMtpPm1FoWmkVEoVWpX32oI+7k+YozRW9yvIDYEy4LI7AYKaqrPFJXOVaXa51dzttHz86bqf40bvoXGLMo2oYZeXRq7vU/dmB/H1YirIxaWBOOhv6316nmxPX1AiQfbk1axSWlpJM+sGHYeVg1qoz+Cn2Uialdqr717kSJ9VJT5zi7loPXa8Fs7XcYldUc7aSTA4xzLekcmGyCtuHOz0ji1s/+iMYHCs4bt5VYIas3T8Z7LAyUg6seFEKbrUsQFF7rTnLahhtxYMFe3F2yJsmuP1tv61R8SG935CIbcYfBSZMygjSdG/JSdWo9RblAKDCmW/tH/Xu6DE+J5WFWjvLpFO4lA0oLWV2sXq5QqtbCG1ObHpGFnkqCrO/CQ74f+xNxo/9WvMN/Kh3qnQk0aF52GQfW/XtmZrIyAOz/BeO3aku8L3L6DOTzbRAQwUrWIT7QBoAZzxWPCz4aQv9pi+n7f2Lgg1RQiU9IwurJ4+dRbaQ33copKUkc11nO6Y+47F0ucYIjv3XFWOp8j67f/GzLPKWnhEInC3sX/JcRAYzVTUptRPf6N3KpUf5lCn43AKOFHmYNHdd3b+/Xz9FQq4xd8HZMKlutwVo3CCWQyqGdjvm1Hg+SdeYfADGXN4volKUjiYBsji22GZgjwWfp9ziaNdeztCyaKgVMPSROSTd8lFYHj49I4uZ81Yw2zcZAI8y4VUmtrW+rvK8q/x9tP5kGDGeQ7TWjOvn2h5ju2MUCcEuUMvCmjN9TFMTafFcc5po+WFpmuDxB8i2mMgbQU7PyGLztm18pRs5fp96+5xUjVpXnhGEaeFMJ7lrPVnNBpbLLw50lQzII4pNtKYApxEAlilllmtuVGm721CdRgwcCPY2GQG8t8x2+pQWnPQSq7nYoxqVq2Ps9QcDl+r/qvr23LWe36JK0+yKsbGrxVUVJh2ubvRnAM694BJikjozz3I5Q9yPh/1AOkCLMkrLzfzke7JyXCgo1xDlZAVe9wYU0N+03l/FJwJLVI141xjh133Qezzard+yJ6YLTs3L4Hv/XaV9tjU+Cbtm7PvddVT/OBzSUpL5q+fvrNK7BqtZaCgKcJJNfLl1A5MZ0zOywnbAdUxlSjVq/kMc89yxtVLS7Xg+3bAXDYh2H6pxu2l3lpED3ig+PrQbF2ISIIvjKzwAZ4+EMy4tnT1rcbLf0c74wdUrztYPlRlLMrlJzaWntgWAHaoZVk3nysxBpHd6qty6gW573fWNTLDM4yrTagAW+c4h3Xt+8NRvuHOmj+mu9aiuw4K1ckPZCjc9I4u5K38D4MFFOyPuxzpQo/ZVn5HP/q5+6UnVqC0qMOromp3hC5DTt/j4OksL/oAGGmZkqmQOKqN6hg0jeGii5fGObyCD3Y8HczRzXZ6Q1G09lkBt7fPNG4HS8m1Q/se+sZaHjslo/KFp+DBhRkfZY7lnSP8qb0/6Fh+/5xsTszzKjE15+HZnSYVJh5rbX0HCFk3SLR8x9JE5fDrt9rAeSJdljTFqnj+nPU1ndjDH9ni5higna8aSTLyeEqI0N021I2Gv4nPSAoFys25s6fBXAHxvXFmlkb9JqZ1orxn1j293T4joXNHqSvZXRwpUsziioknWDpLAEebZJjPPNpkEcoDSA6yyB1wT56zl4fQwV+3yn8FVZc5frdD6hKXRTlUFGnc10gpq3G46PSML9ctHKAUZ6c9F3O9VWZKDLI4vMAFgwURAGfmjvhK89igjh8vrAlsYGjZMTWSlt6TcJ/QMk5HzFKWKeGCesXNKS0k2vrS6P1faPyEoYLC/u5lS1O6p3qPFNkOzN8DiPxWOtzgk5XkCI1rDffvABrmF5V+bSBAoPZWvSlumll1eXZ6DRtas0xK+Dp4zlmTyCLnsVAms19sBcLZpG9tVEtGUgFbAfmWMVgbSJwAme40q28nxzpDUbT2WQG3ty80/4vCP8hUrC1a8/KLakKDlAopG5PGafgVttX187z2TMX+bAj+9wZCC/VCNbZuxJJPJ/vftfe9FKE0jUcvh/k9+ZcaSTDw5e3jF+SI++5nGDWx109Fxd7EdgK7adp6z/YsO2h4mWObxiHdcSGoiL3Ndh8OflxvoxDbaspRilxWIrBquR9vsSeBCwLx/PfOenYDpqpkn/HxaNSM17ULzembabomMnNwQmJTaidvmTKQph1htvpOGWgGH9VgW2h8i0R8YL7Q9yFXuJ8gmHpfHRwJH+JftBf7mnkA28by7eie9WzcK3+sR24wduV5aU7qf2+2J4R+f7qPEUbtzaAIGW19iXOFrXGVejUXTqz234Ojf6iH65/DxWfgW2DFPPvn68KEmAbKomsIDRkmZ3n+Fn97A+tsKAJS7EC0cAfJd6/l81o1cpn8TXLRDT6S16QAxmotD/lGbtJTk4Jf2avN3mDWFVxkjfybNGDE+omL40dSNV0ouZ3zU11yY6CWpLnbyhQdYZz6bHr71cPa1ISnPE2iLfKXVKOA/yryM+zwdgq9NJAg03iigfIBck3zc9IwsEpe+Qgvg0Or3SG96SVie554cF7cysdyyTfaxtDaVtnbpb/6F7eZRwQlvAbXRNCIw0ahsveXARLyu7OTP7idoyx5smg+XsnKrdyLJ8U7GBGa+V9OeHBe3cA9rTLdg0uAh/4EAHg9W1wEW2h8iQc/FWlSI22TFdtS8hdrgezyRW/0/viZN0cnf3KVsQ5STDWKvc7zM/xW9wMXmtQC1MvkwFHyPJ3Jj8LUxRv74eDHF6VYGOj9gQOcEvtyUzZ4cF0nxTr4qGU6acgeT70dbljJaX4pvgR1SIi+Qqa60lGQuT+8ePLg0aTDM8k25dZpqOcbEPf/3e4JlPn20zOABlwKmfPJrWOfftNm+ntZlMrWaaUeCZyzqYv8eaNxlRq9Ru+mjf6sj/fsjKRaiasqcquOqmaxvcT0AHlfBCW5YTfn74I1BgEaHVuUnJOT427rGYrQ0D4wIrT+qR7zZf1JKV0bZreJ2l/HnKZ/U6qneyqR3eorZ7lQAxm/qXSFNpCaWua5ju2MUfcy/A3CdZUXd5FgfR6BGbUFgBFlz1SiIDJzeO591AFzGd9U+vVdVlQXv/UuM8lDF/nQdr8kO3a7ly8uXhi2V4ljKTjQKpA/5lMYRFc1q3ahTf5e/9vA5psyTDtoDr8cBFU+ClhNcvsk+lh8dd9BUy8GkKbqadhipJ3WQJznY+hILvedWmJTo8rf+DjREORnjLu8LmvGzGcjLrZMzUtU02PpSudJmLmXjU28fflOtcefs5Z3VO3Hn7OF92+O4c/bSzzWLz7ylOeeB1LQ06+w6egahd53j5eOWwwtwaB62O0aVK6W43TGKTfax5Lg8dJm8OORpAukZWUz6cB36UdODs/z9AMLRIbIqAo27vtc7o2kwz3dBtcr+bSjzW122BnKk1tWWAFnUiLJFAeAtDnGA/PVTwTJSbR2F5a4y+6vQxmqlI5CBWp1NtDwKsaNpsFs1oQQLv9IOU+9xtHOEeBtrIJAKke01gnxvwaGQTO4J7OQDTSnqLMf6OAI1ap0x8QAk2tw1CiIDP/Jufx53OH+0J6V2wnpUr+VsGlKkRRmNQiwOLMoD9gYM6tuDlfdfzB/Tr6y1GdlpKcncbX2YVXpXLBgtrzWgQDnoa9rIdsco0vzpRReYf2WjeThpi1Jq/HiTUjvhsJrIVvEk+gPkTfaxwRG4spQi5B0Dq2JDrpMcyqd2GO2EPXgxUeI4+XbCaSnJdIgy9kt3eO6stcmHJysQmBgDBxp23FxkXkcPbWuwiYsxQrqJhbYHUWjBsxOnQoOQyoy7vC8FODk6Pj56Quun3j585u1d5uDCykG9AeNK7mWO7XGi3IdCXu1i0Mfd2WwbSU/zlnLLg8G5Y2zIHqs6JqV2YiL38h/flQD813cZE7m3ygffSfFOmmuH0DR43Tso4utqS4AsasbqD/RCFSCXmbGL0o3/Ny0st8rT3uGAcYo+MCI2Y0kmCrjV839Y/QH0cPdk/lBJ+KKb1Ukh9coEUiGOYNTubUh+SCb3jLu8Ly5TFGb0WqtLXRNpKcl8dOcA3MrMgDbOGgUUgR95y1Etu8Pxox1olhHvLE0VaBhl5aIWoPUeBzcurfUuVkd79OouJJrKTxAEjVwtBrpeixf/tpvtJz0hNC0lmWmDu5GNMYKcHO/katOLrPB1Da5Tth11bbS+PVpSvJOR5uXlyh8HOn4ONX8TsmoTO5ON4OC5v/+tTs9IVUdg5G+TaonmTz+L0tzlRkSNEVJoajJSCwaafgbgL+5JER/I1ERaSjKjLF9y1HFw8PMT6DZ5iDgs+DBppQdcjbU8/mN/JphyUdXW3VVVWTMTCO3ZkJoIDHa4/AebHaIKqjXYMSm1E/9hMACr1FlM9o6rVoBd2yQHWdRIYATZV5wfmju8az0suqc0KLY42dVsIC13lwbJOVbjS9nc6WHaVcaXcuKctQA0oChYkihOKyRWK8Ic1zg02xYCgVNih5URIDfS8sstr6m0lGT2/AD79zQkWzXgd+uZdZdjfQJxUTZjxKYkr0a3T4p30qQwj99Uazqxi/d8F5MYxh/tyifZXVb6Zw1yeUMpLSWZdN7ilSWZ7Mlx8UrMHTQ0vc1VRfNA0zD7q2zgc4dkQujgni1445NGNNVyWfn3AaSv3YN5XmndWAUUYUdHq/VGPGD8+Pb/8EXuN/2Xq82rywXKJg02modT8rEVUk5yMl1hNm5lIapB5OxfTiQwKe13++hK+6coVbGvSiAg3KxasMrbDafVzLQIDWRqynTPRna9fzfJWYswASVYQLPgwkw8hczz9iNBy6W3ZqSwlX2NonADlU/UTM/IYob/e5kU76x2IwxrfBIFBc5gqqAG/pQELx5LNIPLtIevbWkpyXSOvhj+B+O7OzirGs8rLSWZhF+OwFbIUTEk1+C1qU0ygixqRPPPUvcWF55gzSqKbQb7fwleVN5ivt1ZjF6mvqvXZ4wQ39CjdOZwIDgqmxcZrxXQgEJMYSwDVl2B7cwjCp/SiPcHyKEI7pJu+YhiczTeBq0jekTLYTVTSBSU1OygKnB672e9IwVERfzoQ20wWmIb6R2TUjvRrGgjGrBvw3I2a23wYIHe40I20p1vbYJFeeG1y0g7wxJMtwDYoIzH+0VvWydlz9JSkrnv2osotsSioFwN60A6TijaCVtd2RzR4sLaqS/U0lKSUcBt7gmVXn+sp6Jp8J39b2jA0F7hq8pSZ2Kb0bJZU0yaCSwO7JqOvdf1ZA74DwBplpVcbv6Rhqbj/855FcG0tofTNzBxztqTKgs3KbUTSf5UhO2qGZkqme/1M/mffgkXNA9fadWqatKsBT4FrX57pdrNQrodWoxS8O+zf4/oJiEgI8iihjS7kWKhl4QgxWJqInjLt7TWUFynLcNUpr7rKBYDUOxvigHGjuSBeRtI9JUua6wVEkMxh5zxJ79tIRLYTpcHcoihEfkhrXYQr+ewzx75I1ouLQqTu2afmcCO1JReTCGOiB99qE3lyicBzThIM3XQyK8M4Uh3kb0JeIDdP8LXT/KT3pE4rRALXtbpHbCZfBTirLNJRGkpyZBp56PNlxNVcpBBlp+CqUehanJhKzlMvjme2k8iOTmZjrHYqZgzXlZlI8kWTfGHYxQla62QFtml7GrkqApNFOxnvb055wIo2Kaa0lbbH2zXXfZ/MP7erprT/cyOpGdk8e7qnTQ5qiScgmqVhUtLSebNLRPg13E84b2epXqvYOvrSDg7+M2WI1yGjWjXviqXDAz8zjcA0CAh812Y8i5Y7PBwZFZGkRFkUSMmWyBALgx2GTrn/nfIeKwvn323tnp3dtd61sVfWm7E5ydfB3yU70Q2yvIlAF03/Su4LJAT1dSUG1x2TsN8TJrCEh1fve0Io8B2No9zcETFkmApDFm1A+V1E08BXmeTEGxpeBWbozB7an5QlZaSTPs4KNaiIn70oTYFJjC6lJF3rJSRr7hOnRG6B5mayP0FgRbpRrvpkZYvaUIuuSqaOK2QaIopxI5J0+quAcCIdzFfNROzScOjTMz3XRDSJhfRnsMUWRuFYENr1/LUpSzQ+1XIay0rEPyVvc6rTHzm7ROS0feIdFSFJt/vX3DTyosAI82knak0OKZM2+4ATYMzTHuZuvYCLk/vjqJ0wuOntvuDTUcUVPnMSnpGFht+Nc6oFkcl8+zwHmRMviwi9ne+xxMZ/EkXojV3tZqFfDbwc//nz/iddykbn+gX1GnjkxOJmABZKcWsWbPo27cvffr04cknn8Tn8x1z/e+++46hQ4eSkpJCamoqH374YbnrU1NT6dSpU7l/GzduDPfTOG2Y7EaKxZbd+3hg3gbcOXtYYH+I7vpGcj6bWq0fx/QtPjYc1DGh0JWGUtDZtJO73LcDBLvPuZSNEmVhXaPUcrdPS0mmmyM7eDnBtw8Ae0xk/YilpSRzX2onCnBwnlrPa4tXhySIKMoxnq8edfKz9MPNY47B5ju5tByLt5Bi06kzWSgUNuQ6ucq8Gqc/D1/TwKl56KFtCV3JtbvWsz7q3NLLFidF2MkhmkIcxFNAtFZMkXLgU6pOWzCnpSRTNPgtDtAINI1XYu6gaPBbJx1gpGdkEes9SLOi37l62kcR3QXsaIP69qBruxagaRQrK2jgMkUROGHvUxqFyk4hRrOVQKBsRucgcfW+xXRVBQ42AwM2gf+3quZsVi0osTVkh5bEAT2uXNm8+d5+aKhyEx4TTHn0NG3hO/sdgJFucSKBiketPEZDpN0FdftdOtrRr09VqwlN/fowuboDrUyJt1zdwdSvD9fGZtdIxATIb775JvPmzeO5557jX//6FwsXLuS1116rdN3t27dzyy23cOmll5Kens4dd9zB448/zvLlywFwu93s2rWL9957j2+//Tb4r0OHDrX5lE5pJn+KxS879pJhuqFcLdSRpi9I+/isKv8wz1iSSRMtj3ycrNC74cZMNCWkmVcCxg460AXPjYU/svPoN315uR1GP+/3/rxDcBbuBmDmin0Rs1MBY8f34PxfaEQeDSjiusL/hWTHV3jYaAdriq392rPV5bFGYz/JANnqLcJtjgrRFp0akuKdZeoil5552aY3Dd0ITWwzPFZ/Xr/JAr4SHJTQUCugIfnEaQX+EWSjqkhdt2BOS0mm2BpP26iSkJxtMAKXdTQhlwQtN2Tf39rU1lGIqfc4HLd9idZ7PM4GTYL5t0Zd2/58q5/NTpXAQt+5LPSdy06VQIJ2arSYrooNR9XV1/xjxhM9d3BZyVMMNL3OgOKn+Vw36kSXbZhxQclz5c6EBlg0FaydfKLPS6DiUar5R5SC8ZbP6vy7VNbRr09VqwntyXGRqB1B0+Bt7yXByih1lY5VFRGTg/zWW28xYcIEzjnnHADuvfdeZs6cyc0331xh3U8//ZQzzzyTW2+9FYDWrVvz448/smDBAi6++GK2bduGpml069YNq7X2OzqdDmw2GyXKyk2+D8t18wrwKg1LFctKBbqW/WEfxUXm9cHlqZY1gHFKa7D7cUZZlvNn07fE4iIrx8UD8zZw9YIUzHoJnf23MWtwkWY0kthRZOXjCGq7POjj7qSZS3MAR1uWMpqlJz2z3r3HODPitNtPehvDKT0ji+J8M+21QvpNX17j/GGrXoTHEvnpJLVpUmonbp/3IA/przLKvDyYIxmDi7tD2Jo2xt+kxxhe1IMjLMmmwyRjjAQVqtIfyrr+8Su2xuN05554xSoI1/e3VpUteXnVTHj/ejjjUuj9V7Yv/hcJW7eWa50eEO+0sjYC9qG1IVAtJ0NvT0/zVtb62pNi3sruMk06Aut8pXfnYvM6cnQnV5m/413vQPJw0BBXhTzlnSqBYe7H8Jyg+17ZduYQee3MA8/9B70T55ozmeutWrOQpHgn03NHcZn5Z9apM0j3XQAYzZUiVUSMIO/fv5+9e/fSu3dp555evXqxb98+9u7dW2H9QYMG8cgjj5RbpmkaJSXGJJWtW7fSokULCY7DyGo2UYidRdqfKtRrVAo+N19U5bJSSfFOzPjQNNikt8CjAp2qjJGwIe4pbFStecTzV7aoZM4xbSSBHFweH4OtL6F3HRY8atdV6SmxPBUdUUfegdqWgZQRXWl8GoLcvpiN7wHQas/ik97GcAmcNvToikbk4c7ZW+PRN7vPhdcShvbm9Vggx32keTkmTQV/mBNNeWw0D2fQx91D8jiLu87Cq0xsbjOSb/Szg8u9yhTcBwwzryCBHCA0VVpOhtceT7QvNAFy/+JnWebrEbwcysoYdaZM/m27v7zCt71mcfT4p9NqZso1Xepk8+rCpNRO3Om7m9f8zTB8mIwBH3/99UDZtjt9d/OQ50YA4rUioyyc/VHiqXhQqGnQ2pTNj47bWa2POu5+L9D86ej0jUhp/hSoJjTb92cA3vddXKVqQpNSO9HGYhxE71HGhPJQTlQPh4gIkLOzjfzRxMTSU8RNmhhHa/v27auwftu2benatbRA/cGDB1m0aBF9+vQBYMuWLZjNZm688Ub69evHDTfcwLp168L5FE47douJIhy0aRyFyxRVbqeaqZJpHXvs/PGjTUrtRFNrMQB5KirYMc/q3yFtV6W5b4kcoQFFwe5PG3KdeCwxmFD4lIYGmP2VL3IxTsPX9ShWgDU+iQJK+9hrKNqb9tQ8t8/fXCV+/2oAErZ/bDRbqYM2vycSOG3YTfsDs6a4yzK3xgcvTlWEzyoBcmXOK/lXuXbYxf7GAqEK4hpEWckninU7DlHiP9BzKwtm9GDDhZbaASZY5kXEj5/uaEScysOnHz21qvqs8UnBv8t2lzuVcnOnpnVj1vAetd46PZKkpSQT47CwXRkDPL3NmzGjmGCZjwbBM1+/2MbwneNOoLQhTZR/4lpgGZROeFQKtunN6F/yHP83Zy1t7l9Em/sXkfL45+UC5nGX98VjsmHSCObqRlLzp8DB+GGb8blvpR3AYT1xKJmWkszY9kZ6XTHWevHZqrUAuaSkhB07dlT6r7jYCI5sNltw/cDfbrf7uPdbVFTE3/72NxITExkxYgRgjCDn5eUxatQoXn31Vdq3b8/YsWPZvXt3mJ7d6cdmMVGiLHTO/46ecQUcNIq3UIyV7SqJYYfvqPLoYFpKMg8PMII6O14W+owdgc8fdjsoYZN9LNsdo2hhMmpDlm25qfIPkI+RgxmYYAJGsA11P4oVMCm1U7kRPk2DTqYsVhYPrlFQG5gVXLbNdKTOCl7muo7tjlH0MG8D4AbLcrY7RrHMdV317kgpnLjQbRIgH23GkkyyaWgEbf720za8IQ3iGjis5CsnZk8+jTAavtzluR1V5hDZ5P9+nmx761DQohsTpxWRU1B00vc1KbUTTf11n29y3x3SyhiRpGxt7dO1UkxOkYd5tinBy4HfnD8co4Kf6f7Fz/KZt3el1UDACIj1MmkWmgbtTPv40XE72+zX05kdzLE9jqUom/8rUyc5LSWZ82ONQcNnvMMitp35Dl9jdAX3WD7EUpR9wjOC6RlZNN6+AKXgL45v6kWJzloLkDds2MBll11W6b+1a9cC5YPhwN9O57GDm/z8fG688UZ2797NK6+8Elz3mWeeYfHixVx88cV06dKFKVOm0LJlS9LT08P2/E43VrOJOK0Qm/sIvxbFk60aAuDDzK2eidUeHezayBgtftp3HXd7b8enjIkNSsHfLB9zsec5PtHPx+0fGVMK/lDNWJ66lKzL/00sLi4yrydGK60Fu95xM5vsYyPmBywtJZnPL/+KJab+wTQLr8lR4zbAgVnBZdtMR+qs4MBpw5KjRjarfdrQW4wFHawxYdjK+i1wpqSJVr79dCgnWP22N488oomlkHd9lwKwQbXjH97ry69ocZ50e+tQyCoxfhNSn/i4wsTe6kpLSWZroz+hK43vVNeQVcYQkWeTY2ywM2tZPrTgZ9oan8QhjEmrZUvjlf17j2rENr1pcBBDKchVTkAxz/4ofbRM/m55jzm2x1myej3pGVmkZ2RxoNB47C62bMxXzYy45k8zlmSS67FQhJ1k7SATLPOO+5vvezyRtI/PoivbjNJw6osqlYara7U2Sa93795kZlb+4u3fv58ZM2Zw8OBBoqONkaFA2kVCQuWlqw4fPsz48eM5ePAg//3vf2nVqlXwOqvVWi7/WNM02rVrx4EDkVmMut6ZmkhbbwmBQaMhvsXBQ62yheizclxcM20uD7lmMNV5H+MvPy/4Y3J0K87/a7GZVsBr1qexad7gfQSO3EezFH8ZyuDytuyj7ZIL0ZfaUUCm3oK22j7s/ttv05txk+lxlkXQD9igvj34IzMJ8x/GAYFJL2Fbvpl2NWgDvCfHRRNrHjlEs1FvzRaVRGKEzgoed3lfXPPfC6bN2Gp42tBdlIcN0OwSIB8tKd5JVo6r3CSryd5xIZtglZ6Rxdurd3CJFkUDrYhozficFSoHu1Tp59eHCZO3GC0E7a1PRnpGFl/v8HKlGV6zPcWNOZN4YJ4x8FLToLaJfogjpnh+n/7nUG6qiDDLU5fi/exBrtBWYtZKA97dra6htf8zPSm1E455eexUCSg0WmsHguttV4mYUGwxteVC9VNwIrumQZw/RznQqnqY5RsAvrPfgTfdgqNMYH6V+hI+PgvfAjvmyZETv1R3IuFg60uMK3yNq83fYdYULmVjsa8Pr0ePZ0Etbnd1RUQOctOmTUlKSmLNmjXBZWvWrCExMZHmzSueGnS73dx6660cOXKEd999l3bt2pW7ftiwYbz66qvBy7quk5mZWWE9UUN3rae485DgZLhibBQqI7XBqvkw+4MgDbi28D36aJnlSiIFJmy5c/bwvu1x3Dl7+XHjFgCGlUzmM29vyqYM6pig89Vobf5ESVzb4NE4mgm6Xcv6Kz7GpEFHLSsYHINxOmsZN0dUTm56Rhabt23ja/8Ep0+957Bl29YajWwlxTu51fN/2PCyUbVmsncct3omRkxKSVlpKclcmAyfmv4EwM/aWTU6bejKzwHA5IgN9SbWe5NSO+G0lm+uE8oJVjOWZOL26uQRRSxFxPh/6L+z38l/bM8E1zOjo6HQf3ojJI9bUzOWZHLAZwy4dNO2n3CU60TSM7Lw5uxmt6/hSY9Gi8gWqBlt5AFrKA3yYs+gdXTp3Jq0lGS+7TWLC93PsUm15m3fJVzhnsbbvkvIVK25yP0c+X9+M1h+0XeCNHiLpnBoHvQyI9BVrTFc2wJnBANnQgP51VdqL1a6ftnScD6lVbk0XF2LmDJvI0eOZObMmTRv3hyz2czMmTMZM2ZM8PrDhw9jt9uJjo7mzTff5Ndff+U///kPTqczONpstVqJj4/noosu4rXXXqNTp060bNmSN998k9zcXIYOHVpXT+/UEtsMzdHAqIOI8WF3UTpib8NDhn1cuSPhsiWRLnZ8gMvj40F/t6GFtgdZ4M87PkIsh4hDo7RzkYYOMYkw4h3U/LswrX3TuFOlg8mCyx8TH4xuT3FRIU1VNnbNi44JU7ehcNk/a+d1qYIZSzLJck+kp/Y7A8zr+VC/iK+93UleklntYHFSaieenLeSaK0kWIIoEiZGHUvSLR8RW1SM98nmmNqeT9JfnjnxjY7iKswjDjBLgFxB4PNT9sxMKPP8Amcm8jFGkGO0YtzKTP+SZ5lseZtB5h+waHrEjA4tc12Hw2bsg0yaOqlyWYGD+gXaARpp+f4qLCc3Gi0iW1tHIfQej8XfgjquYH/5MnkYkxoBbls9Mdhhb7J3HBpw/XmtSEtJpt+SKXztGoq5YnnkSpnKrOfU3FxtXsXduXec/BMKoXGX9+Xq9JswlxkZb6cZA1K+x++sMNqdFO8ksdCogfyuZyBokFiF0nB1LWIC5PHjx3P48GEmTJiAyWRiyJAhjB8/Pnj9sGHDGDx4MHfeeSeLFy/G6/Xyl7/8pdx99OzZk/fee4/bbrsNXdd59NFHOXz4MN27d+fNN98kNlZ+VEPFUnSQP1RTmjhMRHW9AstPb6IrDZOmaBNnpn/uszxkebfCKZUnvNfzjSp/eqaplsONpsUoBTdbFtJEM05brdeNEf/u5m1YsnaQBNhLDjJXv5BrzV8bN96xCm/c1QB4L36UVnu/hJ/fBLMDk88NdXya92iBIOOQf1JjY3LLLa+uFPNWwJiQ2DDKyqNXd4noH+wYp519xGMuqFi+sSqKC43Xy+JsEMrNOmWkpSSH7f0PpHDkKWMEORoXhTjJpiG5RGNCBRv6RMLo0HWOl7m98CUu99dTP5nAPVCFpaX9ADZ8TLDM4xHPOGbU4MBW1BNH14w+hqlp3ejdutExD0wnpXbim3ndaK3to7V2ALN/bk2AdozAOTDPZrtqGnGBZFpKMt98fDbJ+l7aaAcwaQqfMvGJr2+l369JqZ144aMRXMbP/KDOZKGvL06rmWkROpgTEDEBstls5v777+f++++v9PpAlzyAefPmnfC+JkyYwIQJE0K6jaKUGv42P0/5M5eyhYKB07H/9DZFOIinkM9u70O/2ZkUFFTstmOLb87VJS+yWL8lWI4twMg3XgYYk7ju9N5lXOGF5CNOVgL6719wrbl0Ih45O+i/YhQA1pgmUHgAev0V/Ef9FOyvjZejygJBxiHlD5C1vODy6giMaL3MIjDDANNaPvVcHPLtDTVN08g1NaTD4a8hf3+1D158R3YCEGWpehlBERqTUjvxwLz15KsoGmguYjUXhThoGGWliceYGPiebyAjzcsiYnRo3OV9yZtvBDknWy4r0ps3iLp1vAPTtJRk0nmPnz6+izZqKT7/QNJOlUgrSvOWywbKSoFCY6XelcfVeGZEYCA5pvg+/mF5jTbmZcGypcc7MO6hbQYgR0XXi8EciJAcZFH/WMwmPJgx625cbi92PBRqRr6f8hYzKbUTiaY8conCiylYEmlA5wQKS7wcJrbcUXRpzpVR3aB/Sfm6rXtyXKRnZDHA8xzp3vPLlTb7w3EWAPYGCeUK33PVzAqnxOpaIE+0ACclykoTLbdGaRGDPu7ORvNwLjQbM6qvsnwf0oYQ4RSnFeLUC+DrJ6t1u/SMLA58Y+S1Zn31luSA1jKj/unZwfKJzbUjlJijefTqLkzkXiZ7xwVz4avSOKA2tveCZKMc5ULfuSdVLus6x8uVNgmJlOYNIrKlpSRzXWc7pj7jMN/2DVrv8SQ6FG/7LmGR79wK62uakRY0yryMaJslIgPJpHgnTbQ8vtB7oWlG34Fk7SBxzvIN2gKDOYP5EoArzasp9lTsvhuJJEAWNebTrJiUB1dxCSZN4TIblQXcxUbN0bu1e/FgwarpvGIdQ9Hgt1i0fi+3mebRxF9DFcqXx7H767ZmE1/usZLincxYkslOT4NyzTbseCjw19F2miP/Sxcosh7vtJFNHE20vCoVWT9a/+Jn2aEnVJjMEdFdvfyNTZrr+4xqJD+9VuXGJoEyQX0xWpGnsrJelAk61aSlJGN2GqWtepp+x2SxBz/TkdhcIumWjzhkbUZCtJWhj8ypcbmscZf3DZ7xCjQJiaTmDaIeOGrwxvnAFmbZbsGi6WSpRuzWGwcnp/uUifnefpxX8iK5rorl5iJBoJvgXZ6/4VMQTyFZqgmFbm+5wYvAYE5PszERf6Tlq3ozmCMBsqgx3WTFrHso8QfEJf4A+ctfd/HAvA0UlHiJx+ic4/DmcOUnPcjQr2W0ZWmwUQaU/r9PNeQd30ASTeVbwwZGWMvWec3Q26Np8J5vAK3UXpSCBa88Um9GFUu8OrkqioGmn6tUZL2cqYn84LiD1qbs4GsXmMwRyV29Ao1NAjOfq9PYZLD1pXJF+SN1dvfpwGc3AmQbHuL1HCCym0sUWRsR7Tl0UveRlpJMe3s+AH9x/z1imzeI+uXRq7twq2ciF7j/xVd6D0CjWFmD6QrZxNd5qtKxBLoJbnL8FbNWWpJ1s3VkueD36IYq9WIwx08CZFFjPs2GSXlxlxgBssdqTIKc/8M2XB4f0RRj1YxcUYc3n/7Fz5brMQ+wUzfqXGsa/M83kEe943ANfqvS0ajAjuJWz0Tm6hcBcL1lOXGayyg+ri+pF6OKgQk/cRQSR2G1y099NvBzPvGVvo7FysI2vRnfqO51flr7eEobmxifieo0NtmQ66S9aQ9AsClKJEwEO+1MTeSRwicAo4xjQ++BiG1vHlBib0wDX85J38/mmN4UY+N/T0w6qdFoIQLSUpJpGGWkJFTW4AeI6H164De9bMrj0cGvNT6JzqZdaBroZUq8RfJgTkDETNIT9Y9usmL2eShxGSO7XlscFEJRUQEA8RQE122oFfCr3taohVhmMsI+1YhWGGX69tGIpHjnMSc8GJOENuDy+MhRRr7zcl8P/mRaH1HlpU7kZCf8TP36MLcp4wBCVxo2fKzUu/KM9ZaQNIQIl0BjkyW+3gyy/MRyvQcJVWlsMjWRPxylEzONj4+Rn/dyTGSVPzrl3bWe9S+N5mzXD4Ax+W2Z+QL0gf9gUB1v2rHoUU1oeORHij0+HEfVia6q9Iwsog79wS4S+MuTX9aLNrmifnj06i5MnLO2QoMfgHinNaI/Z9b4JP9kfCO9sULwOzWRld6S4FCsyZ+mNMq8jNjU5+tik6tFRpBFjekmKyYU3mLj1CN2ozJDgtP4EsRrhcF14ynArGk00fLK3UfgiwVwyNT4uEfLpfm7VnIw0jm8ylyu1XJ9GFU8ush6dSf87Mlx0UQzOuit1s8MjjZEaq5agNHYZCL/8Br1zb/Qe1etscld69mVfGWF1+si/eWIHl05JcU244C/rTwYTUEOuG3c/em+yE1vik6goVbAobzCE69bicAko9bsoYmW46+BXI2UKCGOIy0lmevPa4V21PJQNvkJl8Bk/C/0XgAs13vQ1FTa2j6QVhdoKhbYd8/omh7RgX+ABMiixpTJODXkdRlBr+aMB2BghzicVjNxWukIchNzISPPbclE7uWIKm0T/LtqETw9k2uuvK14WWkpyUwf2o0c/3000XLYrxryq2oTDBQjNWcrYNzlfXGZosqlGlRnwk8g0ASNLSo5ojvolRWo4HGAeHSl0Vw7hAYM6Hz89z19i4+MrHws+IIHQh5LNPcM6V8vdrKnGuU6TIG/c+YG1YYELfekOtSF2063sa8YOuPjGnXAC6REtdH2EV+DlCghTmRqWjdmDe8RkRNdjyctJZmiwW8xw3Y7AJ3Ne/ANejq43YG0uqM76C3YGvkT6kECZHESAgGy8gfIJv/s9k6NbUwb0o3GZUaQB7SyMDWtG79YR9OwTOA8yvJlsE/9oWJVpZGZ+ChbMECep13CfhpyUMVFTHmpEwm0Xf5K6w0YR91HT0w8nkmpnYi3+mioFbDPP5oXyR30AtJSkhnaKxkfFrKJoxmHUcBHa7KO+Z6nZ2Qx6cN1pOi/AbBNNeMd30Di9CO1uOWirJtKJrJRtQZgka9v8NRwTZvdhFN6RhafbjNabf7b9nSNRn+Xua5ju2MUds0XnIi03TGKZa7rwrXZ4jQUyRNdjyctJZnrL+6FS1lJVvtxLZ0W/H7tyXHRTDuMpsGb3tTgIFYk7isqIwGyqDFltgGglxgpFrZoI1jzuYtIS0mmdbQ7uG5Lp1GKLe+mH43b+pe7lI1iZUEpGG/5tEojM/FRpSkWI7rGEK0VU4ij3hx1A/xwzvM85jM6RX6l92B88f9V+Yc7LSWZp1KNSVEHaFivnveXm7JRwF7ViOaaMTnveO/5oI+7s9k2kpamg2ganGHaxxjLUi7U1soIXh2Jc1rY7z8wG2FeTgI5QPWb3dSGGUsy2es1Ur+6aNtrNPorNZCFODbf44n89YseODWPMVnetzg4WT4p3sl73gEArFdt683ZzgAJkEWNpGdksSPHGJlZts5od2yPMX40dbcRDDu8xsjyIRVD852LIH8/Rzylk2SUMsqTOTRvsIteVUZmGkbZKMKOT7PQtZFOjFZC84TG9eqoe8aSTHZ4GlCkbNxhTieBnGr9cPeL3QfA5T3a1KvnHRg5OKJi6WPaFAyujjWiEJgl7Q7mH5c2kqkvoxCnmjObNyBbxQPQWtvPBMu8iD2Dscx1HfPsUwAw1XD0d9zlfUEzfiqlBrIQ5Q22vlRpJYs062wGdE7gJsunAFxoMmrYR+q+ojISIItqC0xacelG0GLxGCkTvx0xEvF1rxEgN/Zlo6OhoXC4D8HXT5J3xKjSkNv0PO6yTWGb3rRCAv+JRmaMTj0aJZY4VNFhHKoYZYs57m0ijRHcaRRjo5l2hAmWeWWWH196RhYb5j8DgPfXBfVqslBg5KCZdhgn7uDzPtaIgjU+iQKcWPFVaCRTX0YhTiXpGVm8mXU1f7UsAUqDzo3m4aQtSqnjrasoMCH26Bqs1Rn9TUtJpkOUkS52h+dOqYEsRBkbcp1GdaqjKll8WHwLU9dewHnmTQAMsaxku2MU6y2j6813RwJkUW2BSSsef5XAGIyg7oNfjBFj3eNCKcVwPseEopFWGOyc1mP+RQDkdb+Ri6+4ju/phgYUK2uVR2YcVjNOqxmXyQkbFxCFC5MtKkzPNjw2Ocay3TGKRlpBubzGTY6xx71doKPcef6Ocpezql7Ufg5Y4R7BdscozvTXxQw87xXuEZWuPym1EwlaHrtVE/5QTYM5bFazVm9GIU4lM5Zk0r/kWT729i1XVWSJ6U9w14Y63rqKAhNiwThj5cANJnO1R3+3NTOK2M1+6C6pgSxEGYGW0yv0swH41HcOCVouF7mfI917foUBsCG2l+tyc6tFAmRRbYFRTg/GD2SMZlzOKtRwKzNdNr+C9lh8uXrHAXsbnWPcJr5JcLLafEsqQ9yPV2tkpmGUFYu3CFyHsWk6mr1+jSAvT13KJ76Kpd4u8jx/3BHhwOmso29XXzrKmScaJdtK8E/wVJAf3RrzxMqDq7SUZFxD3qIIJ1tVCyZ7x/GA9T5mDOteb0YhTiV7clxk05B8ojCjBw9sD7htENu0rjevgsA+Zo3qFOw62ce0qdr3Yyo8QAlWLFHxod1AIeq5SamdmMi9POU1BjkW6ecxkXvZp+IpxG6UglWmelOGtSxpFCKqLSneSVaOC7f/4xPrH0EuUVZKsPFjg4vpk+TA/Ns8zJqitHGextbDxTQH7pq/jaElHUm75SOGAkMBuL1Kj+97PJFVekm5ZSmb/wVT/w0PHzj5J1gLBvXtwQdLojGr0lJvBTjZ64tjyie/HjP425DrpMDixBJMOahnO53YZrTcsxj8Je40DWILd8AzHcFir/T9S+uRhOvjbLbH9mH7PVfW8gaLsgLf/UDXr/d8AxlpXkYrW8GJb1xHmu7/hiSTsb/QNGjBAVp8fBa+BXbMk6u2v7C4ssnRGtJUq+SoX4jTWOC3auZCN/jgIdt7tO5yMfPW7OZq83doGrzkuZp4rZDEelCGtSwZQRbVFqhnG0yx8I8gl2ClBCu7c914LNGYKA2OPVh4T7+UeN0oZ7Yl31qjYvvpGVkM8DxXblIAwO7oLhF5ivd4Gvhy+MB3EbqCIuwka0Z+do7Lc8zXJXA6a7tqym7VpN7Ufi5rX8L5/KGa4fG/fz6l8Yl+AZ8N/LzyGxzYiJNidEfDyq8XtSbw3b/VM5HJ3nFsVK2Zpt1EztWv1/WmHdNg60t87O1bIQ+5OmddnCWHKLDK50+IyqSlJDNuYDd/qbcDJK97gTst84nFmI/UUjvIZO84bvNMrFepcRIgi2oLdLTzKCNAjvaPILuxUowNk7cEVZhNEXY2NriA3/TWmJWPWe4/04AiAHKJrlGx/RlLMtnpaUABTsxluvDtLjRF5Cne4/lH9IPc772ZHGKIppgs1SR43bFel8DprB2qGYeoP7WfyxqadzcrfV2C758G5OoOpn59uNL11ZdPANDR/VttbaI4hsB3vz41NNiQ6yQfIw+5bLOCqp51Sc/IwlFykG2umBo1GhHiVOd7PJG/fF5a6m20ZSmjLUuDaU2D/RP0NtrHRvS+4miSYiFqJC0lmQ/SjR+Y2LIjyMpKtNnL9oGv0H7rGXx3JI58lUgXyw4mWObRUstGKSOoLsJR7VJdgfWbaHn8pHegj3kzAFZf/Sv5tcI9ArOjNFUksFMpVlbOzHmr0tsEdi5x84vIU1EkxzuZlNqpXu109uS4aGLNY6nei8vMa/hC71l58fipieAtCbZgbZ/7HUyJO2YqhqgdaSnJ9erzlhTvpElhHltUEonaEVzKOFtjVMM5PqNiz3q+NR2mEbn+RiNGfff69BoIEU6DrS8xrvA1rjKvxqLpwbM1gQDZq0x84evFS9G3saDuNrPaZARZ1NjZrY0Rz9jgCLIFN1YSnFDsKsSpuTmkR3OHOR0w6hxrmvGl+dFxB5vsY6udGhBY/1bPRN71XRpc/h/76BA8o9plnrieT7mAYv9IvFKwTW9G/5Lnjvu6pKUk09jiIrpBo3pVAzkg0Cp7pvdaANJ9F1RePP4uY0JfYDJoMTZ2tbiq3qXSiLo1KbUTd/ru5h3fpcRpLhK1XLJUEwrd3hOOBs9Ykonb46ERBSRqudJmWohKBEq9Bc8KaqXBMYAZnYPE1bva4RIgixrrlNwYgGjNRbGykhwfBSYTXdwb4KAxsptLDM/4A6GjOTTPMct7HUsgBxLgEA2Cy9PO6VCTp1C3YptxubYKh2Y0XNE0aGfax4+O20/4ujj1Qny22NrYypALvIeHlbH9jbT8SovHp2/x8e2ukuBERpvy8O3OEtK3eGt9m0X9lZaSzC+2MTxmNc7KmDTFaMtSNltHMujj7se97TLXdWx1jA7+4EubaSEqSop3MtK8nMrmsOoKslRjkix59W4wRwJkUWNWm5FiEYMLZbaz8v6LSSAXp55P0i/GBJgcFUO2vy2tKi1ngRcTu1pcdczyXscSyIFsHG3jkCoNkN/86WC9zA00tb+Y/KhWwVFSpWA7zfn80mNMWAOUUsSoQpQjrrY2M6QC76EzzjgD0cxaWGke64wlmTRUufymWuNWFt7xDaShypHRO1Ft/Yuf5VPvOcHLgYl6FxQ/d9zbSZtpIU5sUmon+pb8y1/32FimlPHvI19/LnC/QP6f36zTbawJCZBFjdnsdsAIkB16AUyJowlH0ICk3Z8BMMv6Ei5swdsoBTomLOi0bNa0RhPr0lKSubF/Ww6q0gBxe55Wo6oYde6Gj4h27cZapuxZG/YyaMmFx2z+UeRy4dTcaPU0QAbjPfz6gcspwEmPxnqlIwt7clzc6pnIz3pH8nEy2TuOWz0TpcW0qDZrfBKHiUUpytVktcU3P+7txl3eF59mpEC5lVnaTAtRibSUZA7Q0N9RD7xKQwGZKplYrZh4p7XejR5DBAXISilmzZpF37596dOnD08++SQ+n++Y6z/yyCN06tSp3L8333wzeP3q1au5+uqr6d69O6NHj2bHjh218CxOLzb/CLJd8+LDQlazS4JdcwI1kr/s9QJWh9HEQ9Ngq2rOGMsMtrUZAQX7a/zY//1uB0coTTEowl5vcwNX04NtetNg2Tqf0o5bhirX367bHFV/A+SAAlMDTK7Kq1cEcpJjNBeFylFhuRBVNSm1E4mmPI4QzY96R97xDaSpKfeE1V/SUpLpFGOUqvq75xZpMy3EMST7S5C+7buEq91P8I7vErarJG7zTGTKNV3qevNqJGIC5DfffJN58+bx3HPP8a9//YuFCxfy2muvHXP9LVu2cN999/Htt98G/w0fPhyAvXv3ctttt3HNNdfw0Ucf0aRJE26//XZ0XT/m/YnqsztKAxUzXor2bsSEQldgUUaeaHGDNnRu1Sy43jf62Xxb0Jwrtw4mvdNTNX7sfbnFeLCQo6IBKMIIoOrj6OL1rntZpXcN9rLX4LhlqApyjYDSGl2/67KmZ2RxwBeDJ/9gpeWzArnKMbgoxPisVZarLMSJpKUkUzT4LX5WnWmoFfJKzB0UDX6rSoHu9nYjAXj0tjHSZlqIYwiUIA3URw/UPb7+vFb19oAyYgLkt956iwkTJnDOOedw7rnncu+99/Luu+8ec/1t27bRtWtXEhISgv+cTuNH9IMPPqBz587cdNNNnHHGGTzxxBPs3buX1atX19bTOS2cO+/84N8a0EHLMiazAD/rxqS5GV/t57PMvOB6+f5A52RHe+OjjBJNOSoaXUE8Riev+ji6GGj+sUo3jrI/8fU9bvOPojwjQLbX4wDZKJ+1gUN6DA21fLJyXBVSZAK5yrGmEgpw1IuauyJypaUk423QitambFb+fUCVP0feohwAYuMbh3HrhKjfKquRPmt4D6amdavrTauxiAiQ9+/fz969e+ndu3dwWa9evdi3bx979+6tsH52djY5OTm0bdu20vtbt24dffr0CV52Op106dKFjIyM0G/8aWzzdV+Vu+xSNrbozSnAwXrVDl1BSXEhRWVykAtUadBX09He9IwsCoqNEWqnZtTJnWCZh9Ws1cvRxcCR96u+qwB423fpMZt/pGdk8fKSnwF4ZsW++pdz7TdjSSYujw8Xdjppu0ggp9KDprSUZJrZPWCLrZcl7URkcUW3wEkxvHYZ5FcxxcuVA4DZGR+27RLiVJCWkszK+y/mj+lXnhL764gIkLOzswFITCydlNSkiTHDfd++fRXW37JlCxaLheeee47+/ftzzTXXMG/evHL3V/a+ABo3bsz+/TXPeRUVWRq1Cv7tw+hQ5cKOEzcXmH7xB67zcSl7cL0Cf0crqPlo74wlmWywjmG7YxRNtdxg+aXN1pGkLUqp8fOpK4Ejb19UAgBtHJVXdQiMumolxoj8zqKateuOBIGDo7baXux4mGCZV255WVZfEV5LVIXlQlSXHtcSALX7R/j6yardqDgXF3aw2E68rhDilFFrnfRKSkoqDXYBiouNSRA2W+kOKPC32+2usP62bdsA6Ny5M6NHj+aHH35g8uTJOJ1OBg0ahMvlKndfgfur7L5EzUXZLfgUmDXYEn8Baw47GaF9jkmDjpoRtAW6wwUERpA1qPFo754cF/15locs7zLI/AN2zYtL2Vjs68Pge9446edVF9JSkrmkRSq8eC9/7mDjT5UceQdGXZPMxiQ9i/IGR13r25H6JsdY7HiClwOfkxKswMFy69r1IrzWmFreQnHKmZrIUK/RuVJDwU+vGf9O0JnR4s6j0BRD/UveEkKcjFobQd6wYQOXXXZZpf/Wrl0LlA+GA38H8orLGjVqFCtXrmT06NF07tyZMWPGMHz4cN577z0A7HZ7hWDY7XbjcFQ+6UnUjNNqphhjdLhBUkeiBj/LLO9QAHR/NQuXsvGJt7QkUj5GftLJJO4nxTvJ9peUseKjWFmx40HZY2tUNi5SxDQySk558yo/0xEYXR1k+gGAG/wHHvVxYuLy1KUs0PvhVv4uecrKJ/oFLE9dCvn74I1BwVPgTuVCSYAsTtJnAz9nsV6+FvIn+gV8NvDYNccBrJ48is31symPEKLmai1A7t27N5mZmZX+u/rqqwE4eLB05CiQdpGQkFDhvjRNIz4+vtyydu3aBVMomjZtGrx9wMGDByu9L1FzDn9HOwBTVCPSUpLJxpg4pqHwKSPtIpfoYMDsjIk/6cT9QHWDJloe7/gGMtj9OO+rSzkvsX53WEtff4AjKobdu3dWWtVhk2Ms2x2j6G02uhSOsHzFdscoNjnG1sXmnpRBfXvQtV0LLJpRucOmeejargWD+vaAr5+CnauNU+BKEaVcaHYJkMXJmfr1YQ7qMeVqIefqDqZ+XXmZwQC7N58SiwTIQpxuIiIHuWnTpiQlJbFmzZrgsjVr1pCYmEjz5hULuU+fPp1bbrml3LKNGzfSrl07ALp3787PP/8cvM7lcvHbb7/Ro0eP8DyB05TdYiJaM05ZmvwVFTSbkSuqA9/pZ/GObyAJWh7FmjHS/NK4C086HSCQs/uP6Ad51DuOvLjORA1+tl6XXwrkFx9UcTTRciut6hAYdQ3USw6MgC1PXXqsu41obR2FFHYZhU9BTlwX2u34AKbEGae9lW78/1g8Jk2hOSRAESdnT46LJlo+JVhZ7Ovt3zflHvcMTHpGFnZvPn8UWCo9aBVCnLpqLQf5REaOHMnMmTNp3rw5ZrOZmTNnMmbMmOD1hw8fxm63Ex0dzcUXX8zYsWP573//y0UXXcQ333xDenp6sFHI0KFDee2115g9ezaXXnopL730EklJSfTt27eOnt2pSSvTeN0aY5RA6tEuGbYaecl7VWMme8fhtJrJsN8OJcVgb3Csu6uWtJTkepd3ezyB/OI8m5MLTBtIIIdsT3y5/OJBXZvh+ioTc7GOrjTsmoeubVvQrm+Put34mhrxLt5CN7s2fEGzot0sv3AuhcufIVVbjU0zUmdWqB5cZvoRswTI4iQlxTu5NWciX9gmoWkw2TMOMMpRVSY9I4v7563nc62QTFoED1qBU2rfI4SoXESMIAOMHz+eq666igkTJnDnnXdy5ZVXMn78+OD1w4YN4/XXXwfgnHPO4ZlnnuGDDz7gyiuv5N1332XmzJnBMnEtWrTghRde4OOPP2bo0KEcPHiQl156CZMpYp7uKcfmD5C7tS0d8c8nKli71uH0nyK3S6BTmcAoVmPyiMXFQtuDJJBTOrqVvw9e+ROOkoN4MLMt/jxMvcfRzlFQh1t98mIdxjG63ZNLzjevkqs7gm23bXgp0o161xZnaA6sxOkrkJp1mFgaa/nA8RvPzFiSSbFHJ14roJ/26zFLEQohTk0RM4JsNpu5//77uf/++yu9fvny5eUuX3HFFVxxxRXHvL8LL7yQCy+8MKTbKI7NHmuU5XNElwbAZ7VNZuX4i40Lq6ICK9b2ptULR1d1aKrl8KPjdqOqw1QTBGffg03zcUbud7D25+POvo94UxOxeEto4z9uHeJbDBbQlfE8V+udSdRyALBHS4AsTk5g1PfI/Aa0ZQ/J8U4mpXY65mjwnhwXGjqxuIjVXEywzOMR77h6OSlWCFF9MqQqQsLsz0GOKhPIWJ1xZVawAhoUHX9CzOlqeepSfEqrsNyOJxgcl6XQ4K4NtbFp4XPXeuh6LR7/cXoxVuZ7+5GljLMRa1QnnvcNAeDFlfsl/1OctLSUZGyxTWhqLjhhI4NNjrH84bjB6A7qr7VeXyfFCiGqTwJkUWNlA5ZLX/6F9IwsosqMIFujywTIBdmAqnpx/tPMoL49yGp5NQpQylima2ayml/C5/o56Kr8+j/oHUnfUr+rdhDbDOyxmDGeh015KcROc+0IAC21AyRjVKMpdFWctChETficjYlReaDrx11veepSluk9g5fr+6RYIUT1SIAsaiRQdSEgJzeHB+Zt4NvthcFl9ug4mJpoVCYo8LcM/+k14/LUxKPv8rTXKkYnP6Z98LKmfKzca+KAHouGkXoQCJ5bks2kuevqf8BYeICfbUZt2iW+XrTX9gRLvw00/cx15q8AGGb+RvI/RUhoUY2xoKP7W0gfy6C+PWjWwKid71YWY1JsoBShEOKUJwGyqJFA1YWACZb5uDw+Zq3YE1wWFdsweBods7+zocUJ3a6t/+kBYZDe6SlW5zVihW7UiF7oPY8GviO00fahaWDyn+oFSDIdZrN1JIM+7l6HWxwCI95laeORAPxPv4RCf78ypSBWK+ZcsxEQp1lWsd0ximWu6+psU8WpwdrAqIfv++/gYDOaY2msjJQwbcTbp8SkWCFE1UXMJD1RvyxzXYfDUbFVcHGx1ZhhBcQ0aBg8jY7uBYsDfCVGqbd63PEuXGYsySTLPZHe2iYuNG/gQ/1CVujdedM6HYAsvRGNtXwcmifYWvsJ7/X8WMfbfbIsDiNv/W3b9OAy7ah0bKUg3deP16PHs6A2N06ccna4jIMw8751zHt2AqarZh4zF/mXBn+iWeEmrGcMgDOPPSlcCHHqkRFkUSPXOV4m3Xt+sFWwS9mY7+3HldqLlCijNNed87caKQCFB6DXX+HGpcb/BccftTldBWbH7/d3I3zN+jTbHaO4yLwegGTTYRyaB6WMyXsFOLHFV2ykU5+kZ2TxxTYjLedn/Qw8/iYogVQS5U8rUWgUm6IYf/l5x7orIU7I93giY7beDYBJUwzxLSbt47PwPV55ype9aB85NABr5bWShRCnLgmQRY2Mu7wvLlMUFnSKlRU7Hoo0Jzs9sRRhdM37I89kTKzq9BRcNROadTP+H/FuHW99ZEryNyw4oIwA+VXvFaR7zw92zvMpjW16M653P8g7voEkmnKPWcO1PgjksWeXGOk3PmXCgh4MjgO8mJhvSeXCZCUNGsRJGWx9icXe3sHLgQP7NOvsStePKt7PEUuT2to8IUQEkQBZ1EhaSjIXJsN8SypD3I8z35JKM3MeHp8KBsj5OGViVTVMSu2E1aRRgo1cFUW0VkIBTszo+JSGBqzUu7JKdWWW7RZcg9+q1wFjII+9EGMiVAOtkD9UM0qwkm02RvQKTdHompmhj8yp163ERWTYkOvkIHEoBV5lCp6J2ZDroM39i2hz/yJSHv88OPm1kXsPCXr2CXOVhRCnHslBFjWWdMtHDAWGAnA7be9fBIBL2UEjOOFKCutXTVpKMo8t+JUjRR4OqQb82fwtoOFDI913AUXYSdRyeXZ4j3odGAcEPhclWPEoM8v0Xpyl7SCPKD5rNpEH9tyJWfk4ZGlGUh1vqzg1JMU7aVKYx37iOaJi+VHvREstmzm2x/mbewKg+Jf3Bf5v7l38tKMbD+t7seM9Ya6yEOLUIwGyCJmkeCdZOS7cmPEpjUbkk018MHVAnFhOkTHx0UEJ8RQBxoS131ULXvVdDcD2U+RHOvB5AY1CHETjoqGWTw4NWLnbDSZwUsxu5PMjQmNSaicmzruX+3yvM9z8JXmmaHbqCQw1fcNC24Os0LvRR8vkG8sdWNbqwQnHQ3yL4ePF+BbYMU+ux90rhRBVJikWImQmpXbCaTXTWMvHhGKCZR5Oq7le58nWtk2OsWx3jCLJdCTYwQvgQet7bLKPJfkUOtgIfF4ACnASoxXTiHwOq1iy3fbgetlua/2v9ywiQlpKMkN7JbNRtSJKc3OuaRPXWr7BpEFTU47/bxWsxR1wolxlIcSpRwJkETJpi1LYaB5OUy0n2Jp1o3k4aYtS6nrT6o3lqUv5zNu7wkQ1AIfmYYV7RO1vVJikpSQzbUg3mjVwUKgcRFNMQy2fQyqGgjKjxgXKIXnsImQeWTuAp6z/Pu46SoHbPzm2RFmCucq/5DpqYxOFEBFAAmQROoGmICajzJs0Bam+QX170KipkUKhynTO8yoTa+MvxTzx1Hot01KSmZTakUIcNNTyidGKOayMSii6MobPC3BKHrsImf7Fz+JT2nHX0TSw+iuq/NU9iXd8A0nQciVdTIjTiATIInQCTUGUT5qCnIRzE3wURSWTpTUDQAFmTafHGa1Oyddy5hebKVBOWmjZABwhFoWJAn91i0LlkMBEhIw1Pol0X79yB6Bl/w4IpDj9zz6N68xfM5F7JV1MiNOITNIToRVoCtL7r/DTG9IUpCZGvEs0EP3+9RDT9JR/LffkuCi0OkjWDgFwRMUCkE8UDXDh0pwSmIiQmZTaCee8En5XyXTQ9qCUCszFQ1fGvLyjOzk6NA+/WMdgTpEJekKcLiRAFqFVtgnIVTPrbjtOBafJa5kU76SwsHSEOJhaoZygQbEpuq42TZyC0lKSSectdn/8F773nsl7voG8ap8FSrHO144rzd8HR5M1zUhvWqT6Ykt9gkF1u+lCiFokAbIQok4N6JyA76fSIburzN/xubdPcKLeYa+N2fOM3GupQytCIS0lGVK+AGAMALeTnpGFY95YdqoEFBqttQPoCszo5OlOXv76MIP61uVWCyFqkwTIQog6k56RxcMZA3BYPMFl11hWc41ldXAiVSGOYEdGCZBFuKSlJNNmzkQAXrbOYoV+Nu/5BjLSvIxELVcmigpxmpEAWQhRZwZ93B275qmw3Ks0vtR7cKk5w0i1QDoyivBL9jevudUzMbhssndc8DohxOlDqlgIIepM/+JnSfeej++oagLzfRdwUMUDxggyIJUsRNhNSu2E1VSxBJzVrMlEUSFOMxIgCyHqjDU+iQKcmDBGjRWQqZKJ1Yrx+WsLWJRXOjKKWpGWksyMa7sT77QGlzWMsjJjWHdJ7xHiNBMxKRZKKZ599lk++OADvF4vw4YN495778VsNldY9/7772f+/PkVlrdo0YJly5YBkJqayvbt28tdn56ezplnnhmW7RdCVN+k1E5Ezc/jbd8lwXzPZqZcvu01i1Hr/gIKRjlXc83V4yVAEbUiLSVZPmtCCDSlKmtqW/veeOMNXn/9dZ555hmUUtx7772MHj2am2++ucK6+fn5FBcXBy8fOnSIUaNGcffdd3PDDTfgdrvp0aMH77zzDi1btgyu17BhQyyW0mOC3bt3M3DgQJYtW0aLFi3C+wSFEJVKz8hixpJM9uS4SIp3ssI9ArNeUnFFix0eljq0Qgghwi9iRpDfeustJkyYwDnnnAPAvffey8yZMysNkGNjY4mNjQ1e/sc//kH37t254YYbANi2bRuaptGtWzesVmuF2wshIkeFEbv89bDkYdj0CXhLjJblZ14Fl/2z7jZSCCHEaSUiAuT9+/ezd+9eevfuHVzWq1cv9u3bx969e2nevPkxb5uRkcHSpUtJT08PLtu6dSstWrSQ4FiI+ijQstznkZblQggh6kRETNLLzs4GIDExMbisSZMmAOzbt++4t3355Ze57LLL6NixY3DZli1bMJvN3HjjjfTr148bbriBdevWhWHLhRBhEWhZfuNS4/9TtM22EEKIyFRrI8glJSXHDHYD+cQ2my24LPC32+0+5n3u2bOHFStW8P7775dbvnXrVvLy8rj33ntp2rQpH3zwAWPHjmXhwoWSayxEfXCatNkWQggRmWotQN6wYQPXX399pddNmjQJMILhQFpEIDB2Oo9d+3Tx4sW0atWK7t27l1v+zDPPUFJSQkxMDABTpkzh559/Jj09nb/97W8n/VyEEEIIIcSpq9YC5N69e5OZmVnpdfv372fGjBkcPHiQ6OhooDTtIiEh4Zj3uWLFCi677LIKy61Wa7n8Y03TaNeuHQcOyAx4IYQQQghxfBGRg9y0aVOSkpJYs2ZNcNmaNWtITEw85gQ9pRTr16+nT58+Fa4bNmwYr776avCyrutkZmbSrl270G+8EEIIIYQ4pUREFQuAkSNHMnPmTJo3b47ZbGbmzJmMGTMmeP3hw4ex2+3BEeasrCwKCwvp0KFDhfu66KKLeO211+jUqRMtW7bkzTffJDc3l6FDh9ba8xFCCCGEEPVTxATI48eP5/Dhw0yYMAGTycSQIUMYP3588Pphw4YxePBg7rzzTsBoDgIQFxdX4b5uu+02dF3n0Ucf5fDhw3Tv3p0333yzXO1kIYQQQgghKhMxnfTqwo4dO7jssst49913adasWV1vjhBCCCGECLNmzZqV66xcmYgZQa4LgYmAx6quIYQQQgghTi3Lli07Ydnf03oEubi4mF9++YWEhATMZnNdb44QQgghhAizqowgn9YBshBCCCGEEEeLiDJvQgghhBBCRAoJkIUQQgghhChDAuRa5Ha7eeSRR+jTpw/9+vXj3//+d11v0mnB7XZz1VVXsWrVquCyrKwsxo0bR48ePRg0aBBff/11udusXr2aq6++mu7duzN69Gh27NhR25t9ytm5cye33norffr04U9/+hPTp0+npKQEkPejLmzdupW//OUvpKSkMGDAAP7zn/8Er5P3o2499NBDjB49OnhZ3o+6sWDBAjp16lTu3+233w7Ie1IXPB4P06ZN49xzz+Xcc8/l0Ucfxe12A+F5PyRArkVPPfUUGRkZvPHGGzz22GPMnj2bRYsW1fVmndJKSkq4++672bx5c3CZUorbb7+d+Ph45s6dy+DBg5kwYQK7du0CYO/evdx2221cc801fPTRRzRp0oTbb78dXdfr6mnUe263m1tvvRWbzcb777/P008/zdKlS5k1a5a8H3XA4/Fw00030bx5c9LT05k8eTIvvfQSn3zyibwfdey7775j7ty5wcvyftSdLVu2cOmll/Ltt98G/02fPl3ekzry1FNP8cUXX/DSSy8xe/ZsvvnmG1588cXwvR9K1IrCwkLVrVs3tXLlyuCyF198UY0YMaIOt+rUtnnzZnXNNdeoq6++WnXs2DH42q9atUp169ZN5efnB9cdO3asmjlzplJKqWeffbbc+1JUVKRSUlLKvXeien788UfVpUsXVVBQEFz2ySefqPPPP1/ejzqwa9cudddddymXyxVcdscdd6iHH35Y3o86VFhYqAYOHKhGjBihbrjhBqWU7K/q0h133KGef/75CsvlPal9ubm5qkuXLurbb78NLvvoo4/U+PHjw/Z+yAhyLdm0aRNut5tevXoFl/Xq1YsNGzbg9XrrcMtOXT/99BP9+vVjzpw55ZavW7eOs846i5iYmOCyXr16sXbt2uD1ffr0CV7ndDrp0qULGRkZtbLdp6J27drx6quvBlvFA2iahtvtlvejDrRo0YJnn30Wh8OBUoo1a9bw448/0rdvX3k/6tCsWbM455xzOOecc4LL5P2oO1u2bKFt27YVlst7UvvWrFmDw+Hg/PPPDy4bMmQI//nPf8L2fkiAXEuys7OJi4vDbrcHlzVp0gSPx8Phw4frcMtOXSNGjOC+++7D6XSWW56dnU1iYmK5ZY0bN2bfvn3HvX7//v3h3eBTWKNGjcrt2HRd55133qFXr17yftSxP/3pT4waNYqUlBRSU1Pl/agjGRkZLF68mL///e/llsv7UTfcbje7du3iyy+/5LLLLuOSSy7h6aefxu12y3tSB3bu3ElycjILFy7kyiuvZMCAATz55JNhfT9O6056tcnlcmGz2cotC1wOJJmL2uFyubBareWW2Ww2PB5P8PrK3it5n0Jn2rRpbNy4kblz5/LGG2/I+1GHXnrpJQ4cOMCUKVOYNm2afD/qgNvt5qGHHuLBBx8kLi6u3HXyftSNHTt24PV6iYqK4vnnn2fnzp3885//pLCwkJKSEnlPallhYSG7d+/mnXfe4bHHHqOwsJDHHnsMr9cbtu+IBMi1xG63V3gzApePHuEU4WW32ykoKCi3zO1243A4gtdX9l7Fx8fX1iaespRS/POf/+S9997jueeeo0OHDvJ+1LFu3boBRmfRv//97wwdOlTej1r24osv0rp1awYNGlThOvl+1I0OHTqwevVqGjZsCEDnzp1RSnHPPfdw7bXXyntSyywWCwUFBcyYMYNWrVoBcN9993HfffcxePDgsLwfkmJRS5o2bUpeXl65Nyk7OxubzVZhxECEV9OmTcnOzi637ODBgyQkJFTpelEzuq7z4IMP8v777zNr1iwuueQSQN6PurB//36WLVtWbln79u3xeDwkJCTI+1HLFixYwLfffktKSgopKSm89tpr/PTTT6SkpMj3ow4FguOAwHckMTFR3pNalpiYiMViCQbHAG3btqWkpCRs+ywJkGvJmWeeidVqLZcUvmbNGrp06XLCfuAitLp3786mTZsoKioKLluzZg09evQIXv/zzz8Hr3O5XPz222/B60XNTJ8+nQULFvDCCy9w2WWXBZfL+1H7tm7dyp133smhQ4eCy3799VcaNWpEr1695P2oZW+//TYLFy4kPT2d9PR0rr32Wrp27Up6erp8P+rI559/zvnnn19uUOu3336jQYMG9OjRQ96TWtajRw+8Xi+ZmZnBZVu3biU6Ojp870coy3CI43vkkUfUoEGD1Lp169TSpUtVz5491aJFi+p6s04LZcu8eb1edcUVV6g777xT/f777+qVV15R3bt3V7t27VJKGSWwunXrpl566SW1efNmNXHiRHXllVcqn89Xl0+hXsvIyFAdO3ZUr7zyijpw4EC5f/J+1D63262uuuoqdeONN6otW7ao5cuXq759+6o333xT3o8IMHPmzGCZN3k/6sbhw4fVeeedp/7+97+rbdu2qS+//FL169dPzZ49W96TOnLbbbepwYMHqw0bNqgff/xRDRgwQE2bNi1s74cEyLWoqKhI3XfffapHjx6qX79+6rXXXqvrTTptlA2QlVJq+/bt6vrrr1ddu3ZVV1xxhfrmm2/Krf/VV1+p1NRUdfbZZ6vRo0erHTt21PYmn1KmT5+uOnbsWOk/j8cj70cdyMrKUrfccotKSUlRF1xwgXr55ZeVrutKKfl+1LWyAbJS8n7UlV9//VXdcMMNqkePHuqCCy5QL7zwgnxH6lB+fr66//77Vc+ePdU555yjnnjiCeV2u5VS4Xk/NKWUCuUwuBBCCCGEEPWZ5CALIYQQQghRhgTIQgghhBBClCEBshBCCCGEEGVIgCyEEEIIIUQZEiALIYQQQghRhgTIQgghhBBClCEBshBCRBiv18tLL73EpZdeSteuXenfvz+PPPJIuc53tWX06NHMmjWr1h9XCCHqkgTIQggRYZ555hkWLVrElClTWLJkCbNmzeL333/npptuQkrXCyFE+EmALIQQEWbevHnceeed9OvXj+TkZHr37s3TTz/Nr7/+yrp16+p684QQ4pQnAbIQQkSg1atX4/P5gpdbtmzJokWL6Ny5MwUFBTz00EP07duXrl27kpqaypIlS4LrdurUiUWLFjFo0CC6d+/OPffcw65duxg9ejTdu3fnhhtu4MCBAwC88MILTJgwgQcffJDu3buTmprK0qVLj7ldc+bMYeDAgaSkpDBy5EjWr18fvO77779nyJAhnH322Vx00UW88sorYXhlhBAi/CRAFkKICDNmzBjee+89BgwYwMMPP8yiRYvIy8vjjDPOwOFwMG3aNLZu3crrr7/OwoUL6dOnD4888ghutzt4H88//zzTpk3j5ZdfZvHixYwcOZIbbriB//3vf2RlZfH6668H112+fDk+n4958+YxbNgwJkyYQGZmZoXtWr58Oc899xwPPPAA8+fP509/+hNjx47lwIED+Hw+JkyYwIABA/j000+ZPHkyL774It98802tvGZCCBFKlrreACGEEOXdcccdtG3blv/973/MmzePDz/8ELvdzoQJE7jxxhvp1asXY8aMoVOnTgCMGzeODz/8kP3799OyZUvACLJ79OgBGCPKHTp0IDU1FYCBAweybdu24OM1aNCAf/zjH9hsNtq3b8/XX3/N3Llzeeihh8pt13/+8x9uvvlmLrnkEgBuu+02Vq1axYcffsj1119PTk4OjRs3pkWLFrRo0YI333wzuD1CCFGfSIAshBAR6IorruCKK64gLy+PVatWMWfOHGbMmEGbNm1IS0tj6dKlfPjhh2zbto1ff/0VAF3Xg7cvG5ja7XaSkpKClx0OR7nR5rPOOgubzRa83LVrVzZv3lxhm7Zu3crMmTN57rnngsvcbjfNmjUjPj6em2++mccee4zZs2czYMAArrnmGhISEkLzggghRC2SAFkIISLIpk2bmDt3Lg8//DBgjO5efvnlpKamMmzYMFatWsXixYv5+eef+fOf/8zIkSNJSEhg+PDh5e7HYim/ezeZjp1Rd/S6Pp8PTdMqrOfz+fj73//OBRdcUG55VFQUAPfccw+DBw9m2bJlfPnll4wePZqpU6cydOjQqr8AQggRASQHWQghIojP5+Ptt99m7dq15ZZrmkZsbCyNGjVi4cKFPPPMM9x1111ceuml5ObmAtS4BNzvv/9ebvT5l19+CaZvlNW2bVv27dtH69atg/9ef/11fvjhB7Kzs5kyZQrJycncdNNN/O9//2PIkCF89tlnNdomIYSoSxIgCyFEBOnSpQsDBgzgb3/7G/Pnz2fXrl1s2LCBWbNmsXHjRoYOHYrT6eTzzz9n9+7dfPvttzz++OMA5dImqiMrK4vp06ezbds2Xn75ZX755ReuvfbaCuv99a9/5e2332b+/Pns3LmTf/3rX3z00Ue0a9eOuLg4li5dyj//+U927NjB+vXr+emnn+jSpctJvR5CCFEXJMVCCCEizLPPPsurr77KK6+8wqOPPorNZqNPnz68++67NG/enBkzZvDkk0/y7rvv0qJFC2699VZeeOEFfvvtNzp27Fjtx+vatSv5+fkMHjyY1q1b8+qrr9KmTZsK611xxRUcOnSIf/3rXxw4cIB27drx4osvcuaZZwIwe/ZsnnjiCdLS0rDb7VxxxRXccccdJ/tyCCFErdOUtGUSQojT1gsvvMCqVat477336npThBAiYkiKhRBCCCGEEGVIgCyEEEIIIUQZkmIhhBBCCCFEGTKCLIQQQgghRBkSIAshhBBCCFGGBMhCCCGEEEKUIQGyEEIIIYQQZUiALIQQQgghRBkSIAshhBBCCFGGBMhCCCGEEEKUIQGyEEIIIYQQZUiALIQQQgghRBkSIAshhBBCCFFGvQqQ161bx+jRoyssX758OUOHDmX48OF88MEHAOi6zuTJkxk+fDijR49mx44dtb25QghR78h+VgghwFLXG1BV//73v/nkk09wOp3llns8HqZNm8bcuXNxOp2MHDmSAQMGkJGRgdvtZs6cOaxdu5bp06cze/bscrf1er3s27ePZs2aYbHUm5dCCCHCQvazQghhqDcjyK1ateKFF16osHzr1q20atWKuLg4bDYbvXr14qeffmLNmjX0798fgB49evDLL79UuO2+ffsYOHAg+/btq/J2zPzidzo9/FnNn4gQQkSoSNnPCiFEXas3AXJqamqlow8FBQXExsYGL0dHR1NQUEBBQQExMTHB5WazGa/Xe9Lb4dN1fLo66fsRQohIEyn7WSGEqGv1JkA+lpiYGAoLC4OXCwsLiY2NrbBc13U5vSeEEDUg+1khxOmm3gfI7du3Z8eOHeTk5OB2u/npp59ISUmhZ8+erFixAoC1a9fSsWPHOt5SIYSon2Q/K4Q43dTbQ/0FCxZQVFTE8OHDuf/++xk/fjxKKYYOHUrTpk259NJLWblyJSNGjEApxRNPPFHXmyyEEPWK7GeFEKcrTSl12ibU7t69m4EDB7Js2TJatGhRpdvMWLKJV77expYnrgjz1gkhRP1Xk/2sEELUtXqfYiGEEEIIIUQoSYBcA6ftkLsQQgghxGlAAuRq0tDqehOEEEIIIUQYSYAshBBCCCFEGRIgCyGEEEIIUYYEyEIIIYQQQpQhAbIQQgghhBBlSIAshBBCCCFEGRIgCyGEEEIIUYYEyDVwGjcfFEIIIYQ45UmAXE2alEEWQgghhDilSYAshBBCCCFEGRIgCyGEEEIIUYYEyEIIIYQQQpQhAbIQQgghhBBlSIAshBBCCCFEGRIgCyGEEEIIUYYEyDUgVZCFEEIIIU5dEiBXk5RBFkIIIYQ4tUmALIQQQgghRBmWut6AqtB1nSlTppCZmYnNZmPq1Km0bt0agOzsbO6+++7guhs3buSee+5h5MiRpKWlERsbC0CLFi2YNm1anWy/EEJEOtnPCiFEqXoRIC9duhS3282cOXNYu3Yt06dPZ/bs2QAkJCTw9ttvA5CRkcGsWbO47rrrKCkpAQheJ4QQ4thkPyuEEKXqRYrFmjVr6N+/PwA9evTgl19+qbCOUop//OMfTJkyBbPZzKZNm3C5XIwbN44xY8awdu3aWt5qIYSoP2Q/K4QQperFCHJBQQExMTHBy2azGa/Xi8VSuvnLly+nQ4cOtGvXDgCHw8H48eO59tpr2b59OzfddBOLFy8udxshhBAG2c8KIUSperEXi4mJobCwMHhZ1/UKO+BPPvmEMWPGBC+3bduW1q1bo2kabdu2JT4+nuzsbJo3b37S26OkzpsQ4hQTaftZIYSoS/UixaJnz56sWLECgLVr19KxY8cK6/z666/07NkzeHnu3LlMnz4dgP3791NQUEBCQsLJb4wmhd6EEKeeiNrPCiFEHasXI8iXXnopK1euZMSIESileOKJJ1iwYAFFRUUMHz6cw4cPEx0djVYmeB02bBgPPPAAI0eORNM0nnjiCTntJ4QQxyD7WSGEKKUpdfomDOzevZuBAweybNkyWrRoUaXbzPzid55ftpnt068M89YJIUT9V5P9rBBC1LV6kWIhhBBCCCFEbZEAWQghhBBCiDIkQBZCCCGEEKIMCZCFEEIIIYQoQwJkIYQQQgghypAAuZqkCrIQQgghxKlNAmQhhBBCCCHKkABZCCGEEEKIMiRAFkIIIYQQogwJkIUQQgghhChDAmQhhBBCCCHKkAC5hpRSdb0JQgghhBAiDCRAriZN6rwJIYQQQpzSJEAWQgghhBCiDAmQhRBCCCGEKEMCZCGEEEIIIcoIa4D88ccfl7vs8/l4+umnw/mQQghxWpH9rBBChF5YA+RXXnmFRx99FLfbze7duxkxYgSbN28O50MKIcRpRfazQggRemENkOfOnYvX62Xo0KFcf/31XHfddbzyyivhfEghhDityH5WCCFCL6wBsqZp2Gw2XC4Xuq6jnUI10qQMshAiEpzK+1khhKgrYQ2Qr7nmGgoKCvj444955513eP/997n11lvD+ZBhpyE/PkKIyHEq7meFEKKuWcJ553fccQdpaWkAREdH8/777zNz5sxq34+u60yZMoXMzExsNhtTp06ldevWwevfeOMN5s6dS6NGjQB47LHHaNOmzXFvI4QQpwLZzwohROiFNUBOS0tj/fr1/PbbbwwZMoRff/2V++67r9r3s3TpUtxuN3PmzGHt2rVMnz6d2bNnB6//9ddfefLJJ+natWtw2eeff37c2wghxKlA9rNCCBF6YU2xmDdvHg888AD/+c9/yM/P5/bbb+eDDz6o9v2sWbOG/v37A9CjRw9++eWXctf/+uuvvPrqq4wcOTI4OeVEtxFCiFOB7GeFECL0whogv/3228yZM4eYmBgaN27MvHnzeOutt6p9PwUFBcTExAQvm81mvF5v8PKVV17JlClTeOutt1izZg1ffvnlCW8jhBCnAtnPCiFE6IU1xcJkMpXbeTZv3hyz2Vzt+4mJiaGwsDB4Wdd1LBZj05VSjB07ltjYWAAuvPBCfvvtt+PeRgghThWynxVCiNAL6whyfHw8GzduDJYd+uSTT4iLi6v2/fTs2ZMVK1YAsHbtWjp27Bi8rqCggKuuuorCwkKUUnz//fd07dr1uLcJBanyJoSIBKfyflYIIepKWA/1H3zwQe666y527tzJBRdcgN1u56WXXqr2/Vx66aWsXLmSESNGoJTiiSeeYMGCBRQVFTF8+HAmTpzImDFjsNls9O3blwsvvBBd1yvcJhSkxKgQIpKcivtZIYSoa5pS4W154fP52L59Oz6fj7Zt22K1WsP5cNWye/duBg4cyLJly2jRokWVbvP8ss3M/OJ3tj5xBWaTRMsCVm05SJHbxyVnNa3rTRGnqVNtPyuEEHUt7MliZrOZ9u3bh/thhKgzo/7zPQDbp19Zx1siTleynxVCiNAKaw6yEEIIIYQQ9Y0EyEIIIYQQQpQhAbIQQgghhBBl1HrByocffhifz8fNN99M27Zta/vhhRDilCf7WSGEODm1PoLctWtXHn74YQ4dOlTbDx1SYS7+IYQQNXaq7GeFEKKu1PoI8ogRIwDo3bt3bT90SEhhNyFEpKvv+1khhKhrYR1BLiws5LHHHmPs2LHk5OQwefLkcm1JhRBCnBzZzwohROiFNUCeOnUqDRo04NChQ9jtdgoKCpg8eXI4H1IIIU4rsp8VQojQC2uAvHHjRiZOnIjFYsHpdPL000+zcePGcD6kEEKcVmQ/K4QQoRfWANlkKn/3Pp+vwjIhhBA1J/tZIYQIvbBO0uvTpw8zZsyguLiYb775hnfffZdzzz03vmrJnAAANsxJREFUnA8phBCnFdnPCiFE6IV1mOHee+8lKiqK2NhYZs2aRadOnbjvvvvC+ZBCCHFakf2sEEKEXlhHkK1WK3fccQejR4+mQYMG4XyoWidVkIUQkeBU3s8KIURdCesI8rZt27jiiiu48sor2b9/P4MGDWLr1q3hfMiw06QQshAigpyK+1khhKhrYS/z9tBDD9G4cWOaNm3KDTfcIOWHhBAihGQ/K4QQoRfWADknJ4d+/foFL19//fUUFBSE8yGFEOK0IvtZIYQIvbDXAiopKUHz5yVkZ2ej63q4H1IIIU4rsp8VQojQCuskvVGjRjF+/HgOHTrEM888w6JFi7jxxhvD+ZBCCHFakf2sEEKEXlgD5GHDhtG6dWu++uorvF4v//jHP8qdChRCCHFyZD8rhBChF9YAGYwi9n369Dmp+9B1nSlTppCZmYnNZmPq1Km0bt06eP3ChQt56623MJvNdOzYkSlTpmAymUhLSyM2NhaAFi1aMG3atJPajrKU1HkTQkSIU3U/K4QQdSXsAXIoLF26FLfbzZw5c1i7di3Tp09n9uzZABQXF/Pss8+yYMECnE4nd999N19++SUXXHABAG+//XZIt0WTOm9CiFNQJO1nhRCiroV9kl4orFmzhv79+wPQo0cPfvnll+B1NpuN999/H6fTCYDX68Vut7Np0yZcLhfjxo1jzJgxrF27ti42XQgh6gXZzwohRKl6MYJcUFBATExM8LLZbMbr9WKxWDCZTDRp0gQwRjGKioro168fv//+O+PHj+faa69l+/bt3HTTTSxevBiLpV48ZSGEqFWynxVCiFJhHUGeM2dOlZadSExMDIWFhcHLuq6X2wHrus6TTz7JypUreeGFF9A0jbZt23LNNdcE/46Pjyc7O7tmT0QIISKU7GeFECL0whogb9iwoUrLTqRnz56sWLECgLVr19KxY8dy10+ePJmSkhJeeuml4CnAuXPnMn36dAD2799PQUEBCQkJ1X5sIYSIZLKfFUKI0NOUivx6DIHZ1b///jtKKZ544gl+++03ioqK6Nq1K0OHDqV3797BCXRjxozhwgsv5IEHHmDPnj1omsa9995Lz549y93v7t27GThwIMuWLaNFixZV2pYXv9zCjCWZ/D51EDZLvUjhFmHW5v5FAGyffmUdb4kQNRdJ+1khhKhrYU8U++GHH8jNzaVsHH7ZZZdV6z5MJhOPP/54uWXt27cP/r1p06ZKb/fMM89U63GEEKI+kv2sEEKEVlgD5IcffpgVK1aUq6WpaVq1d9yRSBHxA+9CiNPAqbyfFUKIuhLWAPm7777j008/LTczWgghROjIflYIIUIvrEm0zZs3l522EEKEkexnhRAi9MI6gtyzZ08mTpzIgAEDcDgcweVy6k8IIUJD9rNCCBF6YQ2QMzIyAPjwww+DyyQ3TgghQkf2s0IIEXphDZDffvttwGhLqpTCarWG8+GEEOK0I/tZIYQIvbDmIB86dIgbb7yRHj16cPbZZzNmzBj2798fzocUQojTiuxnhRAi9MIaID/++OP06NGDVatWsWrVKnr37s2UKVPC+ZC1JvLbqwghTgen8n5WCCHqSlgD5O3bt/O3v/2NBg0a0LBhQyZMmMDOnTvD+ZBh528iJYQQEeFU3M8KIURdC2uA7PV6KSkpCV52uVzBNqVCCCFOnuxnhRAi9MI6Se+KK67gL3/5C0OGDEHTND766CNSU1PD+ZBCCHFakf2sEEKEXlgD5DvuuINmzZrxzTffoOs6Q4YMYdiwYeF8SCGEOK3IflYIIUIvLAFyQUEBMTEx5OTkMHDgQAYOHBi8Ljc3l/j4+HA8rBBCnDZkPyuEEOETlgB59OjRzJ8/n/POO69cLpxSCk3T2LhxYzgeVgghThuynxVCiPAJS4A8f/58AH777TdMpvLzAHNycsLxkLVu0fq9DO3Voq43Qwhxmjod9rNCCFFXwlrFYujQoRWW3XDDDeF8yLD7bU8eAPd8uK6Ot0QIIU7N/awQQtS1sIwgjx07lg0bNlBcXEzPnj2Dy3Vdp1u3buF4yFrz7ZaDdb0JQghxSu9nj7b7SBHJ8U4pXyeEqDVhCZBffPFFcnJyePDBB5k2bVrpg1ksJCQkhOMha01OkeeY1xWUeImxh7UwiBBCAKf2frasXYeL6P/Ul9x7WUf+dnEHvv49mxi7hV6tG9b1pgkhTmFhieZiYmKIiYnhv//9b7nlSil27NhBmzZtwvGwtW7trhyWbzpA16QGtG0SzaWzVjBj2Nlc27tlufW2HCjA7dU5K6lBHW2pCJeDBaUNGnYdLqJlo6g63BpxOjkd9rNur86eHBcAS37dz98u7sDY138A4IbzWjE1zRgpL/H6WL87l96tG1Y6ypxb5KGB0yIj0EKIKgtrDvL7779Pz549OfPMMznzzDM566yzuP7668P5kLUq7cWVPL9sMze/vYYF6/YA8FVmdrl1vtmczSUzv+aK57/hj4OFtLl/ESt+z8bt1YPrzM/YzeFCN3tzXWT5fwyOZePePGZ+nonL7ePh9A28+OUWdhwqPOb6m/fnk7HzSPDyD38cps39i9hyIP+Ez+9fyzfz0ldbuG/uOnJdlY+c67oqd/9H+31/PkcK3Sd8rPrq4qe/Cv49cObXdbch4rR1qu5nv8w8QMeHP+MX/7yPo2Pbd1aXttOeu2Y31778HT9ur7gvKvb46P745zy24Lewbq8Q4tQS1nyAV199lTfeeIPZs2fzf//3f3z55Zfs27cvnA9ZZ55fvgWARRv20u3rrUz/bFOFdQb4g6kx/hGQ50b04IF5Gyhy+4iymSly+4z7mHABK7cc5IlPjfu4/txW5BV7g0F42ccDmLEkM/j3qvsvZuPePLYcKKB7y3hGvLoagD5tGtK9RTz/+fYPAC6ZuYL+HZrwzeaD3H5Re85s3oAF6/Zw20XtcVjNDHrum3Lb/sFPu8tdvvSsplx1dnNmf7WVTfvy6dW6IWt2HOG5ET34dvNB2jSJpm/7xgx5aRWJsXZ+eOgSfLpCKYXbp7PjUBHJDZ38sjuX3/fnc0ZiLOe0bYTZpFHs8VFQ4mXh+r0kxTmIslsY+/oPdElqwK/+H8uAzyf+iS0HCujbrjG6UsQ6rNgsJj74aRd92zWmRcPj5y2+ufIPLuiQgFKKlo2icFjNZOeX8NiCX3lq2NnszS2mWQMH0XYLv+3Jo2PTGAAK3T7inFbyir3B+woc9CilUAoOF7kp8eokxzuP+fhCnKxTdT/7rj8A/m7rIQDW786tsF8qLDH2i2+t2g4YAxJRNjO3vL2Gp4adTb8zmrA/rxiAN1dtZ8o1XcgpchNls2CzGONDhwpK2JNTTLcWcRW24buth3BYTaS0Ck06R5HbS5RN0vCEqAldVwx/9TuG92nFsFqoIqYppVS47nzIkCHMmzeP5557jt69e9OvXz+uuOIKPv3002rdj67rTJkyhczMTGw2G1OnTqV169bB65cvX86LL76IxWJh6NChXHfddSe8DcDu3bsZOHAgy5Yto0WLqr3Ybe5fVK1tF5Ghb7vGfLftEOe2bcT3fxwG4MpuzVm0YW/YH3v8BW1pEmPnycWbuPPiM3jl620AuH06ZyTGsOVAAWsevoT9eSWYTRozlmSydON+XhzVk++2HWRwSjIZO3NYuyuHv/Zrw9pduew6XMTeXBdfZhpnI0b0aUl8lI2Xv94afNyXb+hFSqt4msTYKXR7sZpM5LjcfLv5IG6fzsg+rTCZNMa/+SPLNh1g5nXdufLs5ixYt5derRuSFO/gj4OFdEiMDR64dH5kMSP6tMTl8XFRpwQ27y+gS1IcCsWgrs0xmzQO5BXz6548+ndogldX6EoFgwKlFN9tPUTP1g1xWM3Veh11XZHj8uD26iTE2nl+2WYOF7pp4LTw137Ga+zTFd//cYguzeOIi7KG7k2MYKfifhbgz//6lnW7cys9MA746LbzGTp7VaXXXXJmIr1aN2L3kSLe/d4Itrf8cxBnPPQZAP8c3JW0Hsn0e3I5OUUeLu6cyNCeLbjjfz8zYWAH7r60Y3B/v336lQC89u0fuL06t13Unk378vDpii5JpYF1XrGHeWt2c23vlkQfNR9lW3YBFz/zNbOGd2dwSsXX4c2Vf9CmSTQXdUoEwOvTKSjxEh9lq7DuHwcLad0oikK3l1iH8TlfuH4PnZrG0qFp7LFfVL+DBSXEO61YzBVPIv+6J5f2CTHV+n4u37Sf3m0a0cBhZfvBQlZvO0RaSjJeXQXn5fy+P58zEmIwmUKb5lLi9ZFf7KVJjL1K6+e6PDisJg4XumkeF97Bi7xiDx6vTqNo2wnTezw+Y4DFWsl7Upt0XYXkPVq15SAOm5mzk+PQNI3V2w5xfvvGFV4Ht1fns1/2ctXZSZj9jxuo5X60Ma//wIrfjbP0n0/8Ex2r8Fk/GWE9lLVYLOTm5tK6dWvWr19Pv3798Pl81b6fpUuX4na7mTNnDmvXrmX69OnMnj0bAI/Hw7Rp05g7dy5Op5ORI0cyYMAAMjIyjnkbcfr5bpsxChUIjoFaCY7B+FENeKHMyD8Y+ekAvaYurXC7O/73M1D+VPLC9ZVv8/s/7qqw7NZ31hx3ux6a/0u5y3d/sI67Pzhx+cLAY328ds9R12Sc8Lbh8uKXW0+8UjW1axLNtoPHTl+qzMhzWjI4pQV92lSeCxsOp+p+9pA/NWv7cd6DbzZnH/O6lVsOsXTjgXLLpi4qbZ6SnpFFi4ZRwYnXyzcdIM+fSvb8ss389fw2wXXv/2g9j1x1Fv9YaKRpXHV2c8a/+RNZOS5aNHTy5NCzSWkVz61vr2HV1kMcLnRzWZdmfPjTLjy6Ynjvlvz5xZUATJyzjtQuzbCYTLz27R94fDrXn9uKKf4UkO8fHMjKLQeD38VhvVrQsmEU8zJ288CgMwHFre8Y+wabxcTPj1zK3hwXf/tfRvD2c37cRZ7LQ5NYOwvW7WHkOa3YcqCAEee0xONVDJ29isEpyUwf2o23Vm3ni437eeyaLuw8XMS4N38C4I2/9KFbizje/2Env+3NY1DX5lzUKYHvth4iPsrG3lwXfds3ZsQrq9l2sJA2jaM4XOgOnlV77ds/2HyggLsv7cgfBwuZn5HFee0a0a99E1JaNSRj5xHOa98Yi0ljzY4j/LYnj8u6NOPWd9Zw1dnNueVP7Ymym8ncl8+HP+2id5tGbMsupFfrhlzTI4kf/jjEez/s4ovf9gMwqGszLu/ajNe+/YMSj86DV55J8zgH981dzwVnNGFA5wQOF3q46b8/0aKhk91HXLwyuhfbDxYGexqMf/NHdh4u4vKuzbkvtRMbsnJp3TiKBev24PL42JCVx6uje7HlQAEJsXY8Pp0pn/zGocISbu7fjp6tG/LGyu2Mv6At+cUernj+G4o9Oue3b8zT13bnpa+2MPKcViz5ZR9Hijz0at2QKJuZy7o047pXvmPD7lzio2zEOiy0bhzFw1eeRZvGUXy4ZjcZO4/w2948JlzcgWZxDu74389c1DGRn3Yc4VBBCc3jHPy1X1v6tG3Ep+v30rl5LJ2bNeDd73dwVvMG7Dri4qqzm5Nf7OW7rQfp3LwBuUUevtmczXs/7OKDW/uSU+TmL2/8CMCnE/pT5Pbi1RW9Wzfklz15/LYnjx2HC4m1W2jdOJr3f9zJ2S3iOZBXwuHCEn744zBv33gubRtHM+o/3wOQGGsnp8iD26fzyFVnMa5fG+743898umEf1/VuETw77fUprjy7OR+vzeLvH20A4M6Lz+DPPZJo1ySGTfvyg8ExwF3vr2VYrxZc3rVZ2M7ShnUEee7cucydO5eXX36ZtLQ0GjVqRNOmTau9A502bRpnn302V15pHMX379+fb74xTrVt2rSJGTNm8NprrwHwxBNPkJKSwtq1a495m4DAyMZ7c5fRrHnVRjbOf1pGkIUQJzb5iu5cflb1TgMmNan+45yK+9lP1u9k+ucbjnn9mHPb89/vt9ItqSEb9hx7DsSxWEwaXv34P33dkxuyLqv0vlvER7E7p+iY65+d3JD1WVXblnPaNPn/9u48IKqq/x/4eza2GZAdVERAQFEyRTNNIH1wSdPMEhUVzcqlXFKLBzO3glQ0+pWZWfloPloWasuTVlaUol+xUsOUJDURxQUBF5gZtpm5vz+IcUZQROYyLO/XX9x7597zmQPz4TN3zjmDvKIS5Fyt2xswS7NXyFBSUfc3U2RZbZ0dcOEOf1t10drJHpeK7jyXyZpkUgn0tbz2bmX6OlfayKEp18FdaYsCTRn6+Hsg+cledbre3eZZUe/ljxo1Chs2bICzszM+++wzPP/883jrrbfqfB21Wg2VSmXclslk0Ol0xmOOjjdvsyuVSqjV6jueQ0QkttDWDbMMWXPMswHujng4yOu2x/3cKtusS3FsJ785ZKC3v6fZsbn/6mz82f+fa1cVxy/07wxnextjcTywUxvjY6vGMQPAHxeuoaOXE57qHXjHOHycHfDr2QKoy3SY+GAHRATefJ4j7/c1/hzi3QqBHrf/CLmTV/Ux06ZMYwtwd4TSZOzz5N6BaOeivOviuIO7I7r7uBq3Y3t1wIN+9VtK0Me55hV/HC24VGp0mB+iw/yq7beRS41DU5zszIdiTQ0PNtvu5ecOZ3sbdG3rAn83FXr5uZst59rKXoEevm7G61b9XKWm36GvixL/mx6FIZ3bAoBZcSyrYXhDbz8PrHi8x52eKmJ6+gMALhWVYEwPf3g52sHfTYWwdm5mw0/clLYIcHdEOxelcZ/pa+N2fJwdsDE2HPe3rTmvBXk6QX6boRlVrykAxuLYXWl727/h+EH3YfOkSOO26ev81WHd0dbZAQWayhWkRnX3qzX2eyXKEIuNGzfe9tgnn3yCyZMn1+l6KpUKGs3Nd9oGgwFyubzGYxqNBo6Ojnc851aeLvd254aoJejZ3gU6g4CM89fv+RpuShsUasrhprTBztnh6LP8JwR5qrBgaAhkUgm8nOww/J39KNcbMC0yAKN6+GB35mV4ONqiTGfA/50uwO7MPNjIpegX7IHv//lYtTZfPP8QDIIAG5kMF65rMX3LEbw0KBh9A91RqC7H/e2cYSOTwiAIUNnJcVVTDneVbbV/Upqyyo8azxVqEeipgq1cijKdATZyKbIuF+GP3BuI6eV7myjE0ZzzbBt3Fwzq2vO2cz7ub6+scX8V0+EQVT6e8qBxvHJkRxfs//vm39D4h9rg//1U+fjxvdthxbdZ0BkEDL3PGy8M9EPujSLsOJKLmF7tsGzkfdh2yB3v7jmNb1+IgIONHFmXi/Denr8xoXd7tLJX4JvM81gzLgxdfVrBVi7Fc1uOoFSnx+sj70OFzoDfz1/DkNDWxnG+e/66gvziMgzq4g1XRxl+zb6KhMdD0a2dMwrVZfjzUhEigjygLdch63IxurdzhkQiQdJ3WXBT2mBEt7YordDDu5Ud8opKIZNK0MpegXmfHcWM/oG4z6cVzl/VYsW3WUh4PBSuShssQUfj8y//52/5urYcuddKYCOXIrtAgwEhXmbjQk/mqdHO1R4ONnIIgoCiEh1s5FJcLirFloM5iAz2gL+bEn9euoH0vwsxtpcvQlo74b09fyOvqBQvD+2En05cQXdfF3i3sgMA5BRqUFphwNL/ZeLpcH8M7OxlHIOae02LvKJS2Clk8HdXwl4hg0QigSAIePOHkwj0VCH5+5N4ISoImnIdlDZypJ8pxOx/BaFcr0egZ2Vx+tIjHbA7s3Li6oj726KVgwJ6g4DMizdw3z9jZNft/RtBnipEhXhh3uAAbDmYg8e7t61xbHOF3oAynQF/XS5CR28nqGzl0JTpYK+QQSqVYP+pyjdAkcHucLCR45czhQj0VEEikSCvqBSejrZwU9niPb9u2H7YHYGeKnT0csRnv53DyDAftLJXIOm7LPx04go2P9sLno6VfTUi7BGcu6qFRFI5dEEqlWDUewfwbEQAonv4IDzYBQEeSoS0dgLQ2Szm/acKYBAERAbffGNzLPcGNqWfxaJHO+P4xRsoUJeZjQUGKsd4r9+XjSfC2qJ1K3v4e92PX7ILobSV4+FgD6SdLEBUiCfsFDLjJPz/7M/GzqOXoLKV49GurTGxT3tcvFGK0go9/u90AQI9VXiogztKK/So0Btw5Nx1fHf8Mo5duI45UcEY0LnyTeO26X2Qna/BX3nFkEqA0LatMLyrB/645IH/pucAAHoHO6KN8x3TwT0TZYjFyy+/fMfjpova343du3fj559/xooVK5CRkYE1a9Zg/fr1ACrHxj366KNISUmBg4MDxo4di/feew8ZGRm3PacKJ+lVF9LaCZP6tEfKofM4cu76PV1j3IOVY6wKNeXGlTJqUzWJrm+gGwrV5ejgqcKu24y3vVWYrzPOXyvB8/06IMBDhXKdAVP+e8h4PMBdiS9n9kXXpd+bnZeV8Ah0BgFnCzT481IRnOzkuHSjFJsP5mByX38s+vI43oi+H+euarE69RQA4KVBwXjj+5OI7d0emw/m1BiPv7sScwcGo7e/K/539CK+OXYJ8wZ2hIejLfafLsDEPu1rnYihLdfhbIEW6jIdevnfvHNTrjPgwvUSONnJcaW4DO1cHaCylaO0Qg+pRAIbuRSCIODUFfVdT2DQ6Q01TtbRluuM/5Cq/PzXFXRwV8HXzfrrPZ8t0EBTrjNOkiqt0KO0Qo99pwow/P42tZzd9DXnPFsl9UQe3v25cty+aT766cWH8a/kymUVXx8ZajaePm5wR0yNDEDQP5PxAMDZQYH0+VEIWfwdAGDnrHCMWncA3du5YNJDfhjcxQv+L1dOajz1+hBkF2jQxtneeKcw95oW2w/nYmb/QONr5XYTiWo7RkT189/0s1j8VSYA4MyyoRaf+FlF1DHIVYqKiuDkdO9fklE1U/rkyZMQBAHLli3Dn3/+Ca1WizFjxhhnVwuCgCeffBLjx4+v8ZwOHTqYXVfMAjnpyfsQv+MYerZ3waAuXmjvpsS0zYcxobcvgjwdMemfCSC3Xu/A/H9B/88s0tIKPaKS92JIqDd+O3sVBepytHW2R5CXyrjectUMb1elDaZEBOB+n1ZwslcgtG0raMt1+D4zDyO6tamWrPUGARIA69L+Roi3Ezp6O0JvEMy+6KIqtpOJQyBAgLpUh39v/wNtXewxd0Awikor0N5NidNXipFx/gYigtzh5WRXrS+uFJeiqKTC+G5eW165NJMgAI92bW38qOt2Xkw5ih1Hbi4z5+VkC225Hu/H9sCmA2fx7riwagXexeslOJOvQe8AV+Ox4tIK4zvj4lJdjbHezuGcq9j5xyUsfLQzfsq6ggEhnpBIJNV+f1Wz3YkaWnPKs7f6MO0MXv/m5gS7QwsHYMp/D6GopAJrxoWZLf+W+epgKG3lmLD+F+w/XYBds8Ph5WQHd5Ut3vzhJIK9VBjWtU21N4Zr95xG61Z2Na4wQUSNx5XiUvR6PRWAuP9zRS2Qs7OzMWPGDBQXF2P79u146qmnsGbNmmoJ1FosUSD7uTngqxnhkMskuHSjBJoyPe5v5wwASDuZj55+Lndc9/LzI7nIvVaCqZEB+DtfbbZk0K0uXi9Bm39ma/5+7ho8HG3h4+KAfafy8YCfa52XzarNpRslMAiw+jq+FXoDrmrKceJSEQQB6N/Js/aTGsjkjb/iZ5Mvh2GBTA2tOebZW+3OvIxpm2+uynL69SHQVuih1wtwtJNjyf8yEduncnhD1dJd17Xl+PufVQ+IqHn539GLcLSTo39H8eoBUSfpJSQk4JVXXoGbmxu8vLwwYcIELF68WMwmG9SIbm2wJ64/WjkooLSVI9DT0VgcA0BksEeti8I/EeaD2VFBsFPI7lgcAzAWxwDQ3dcFPi6Vd3sjgjwsXhwDQOtW9lYvjoHKdSG9nOzQr6NnoyqOAeC1EaHGn3fNDrdiJNRSNfc8CwADQ7ywbXof47ZcJoWTnQIuShvIZVK8PvI+dPJ2MlvX1tnBhsUxUTP12P1tRC2OAZEL5OvXr6Nv377G7fHjx0OtVovZZINaOryLtUMgK2vn6oBfF0Qh89XBtb7BIRJDc8+zACCVSvCAn2vtDyQishDRv7KlrKzMOP41Pz8fBoNB7CZFtWjYzZmhLsrq33BELY/nP19FTWQtzS3P3k4HDyX8GsEEUSJq/kT9rx4TE4NnnnkGhYWFSE5Oxq5du/Dss8+K2aTo7mvLu4RE1Hg0xzx7O1/O6Gv1r+IlopZB1AI5Ojoafn5+2LNnD3Q6HRISEsw+CmyKuHIPETUmzTHP3k5tK94QEVmKqAXypEmTsGnTJjzwwANiNmMVAe53XqieiKghNOc8S0RkLaJ+VlVcXAyt1jLfL95YVN1A5vhjImoMmmOeJSKyNlHvINvb26N///7o2LEjHBxuTqxYt26dmM2KqquPMwZ29kLc4I61P5iISGTNMc8SEVmbqAXyqFGjxLy8VdjIpfhwYk9rh0FEBKB55lkiImsTtUD+8ssvsWnTJjGbICJq0ZhniYgsj2OQiYiaMOZZIiLL4xhkIqImjHmWiMjyOAaZiKgJY54lIrI8UQvkkSNH4sKFC/j111+h0+nQq1cvtG/fXswmiYhaFOZZIiLLE3UM8r59+/Dkk0/ixx9/RGpqKkaNGoUff/xRzCaJiFoU5lkiIssT9Q7y22+/jS1btiAwMBAAcOrUKcTFxWHAgAFiNktE1GIwzxIRWZ6od5ArKiqMSRsAgoKCoNfrxWySiKhFYZ4lIrI8UQtkOzs7HDt2zLh97Ngx2Nvbi9kkEVGLwjxLRGR5og6xiIuLw/Tp040TRrKzs/H222+L2SQRUYvCPEtEZHmiFsg9e/bErl27cPToURgMBnTr1g0uLi5iNklE1KIwzxIRWZ5oQyzS09Nx6tQpODs74+GHH0ZOTg6ysrLEao6IqMVhniUiEocoBXJqaipeeukl3Lhxw7jP3t4ecXFx2Lt3b52uVVpailmzZmHcuHGYMmUKrl69Wu0xH330EaKjoxEdHY01a9YAAARBQEREBGJjYxEbG4vk5OT6PSkiokaEeZaISESCCEaPHi2cOHGi2v5jx44JY8aMqdO1NmzYIKxevVoQBEHYuXOnkJCQYHb83LlzwsiRIwWdTifo9XphzJgxwokTJ4SzZ88K06ZNu+O1z58/LwQHBwvnz5+vU0xERNbGPEtEJB5R7iCXlZWhU6dO1faHhoZCq9XW6VqHDx9GREQEACAyMhLp6elmx729vbF+/XrIZDJIpVLodDrY2toiMzMTeXl5iI2NxZQpU3DmzJl7f0JERI0M8ywRkXhEmaRnMBhue0wQhNse27ZtGzZt2mS2z83NDY6OjgAApVKJ4uJis+MKhQKurq4QBAErV65E586d4e/vj4KCAkydOhVDhgzBoUOHEBcXhx07dtTjWRERNR7Ms0RE4hGlQO7SpQu+/vprDB8+3Gz/zp074efnd9vzqsa3mZo5cyY0Gg0AQKPRwMnJqdp5ZWVlWLBgAZRKJZYsWQKg8i6KTCYDUDnLOy8vD4IgQCKR1OepERE1CsyzRETiEaVAnjNnDmJiYpCWloawsDAYDAZkZGTgt99+w+bNm+t0rbCwMOzduxddu3ZFWloaevToYXZcEAQ8//zzePDBBzF16lTj/jVr1sDZ2RlTpkxBVlYW2rRpw6RNRM0G8ywRkXgkwp0+i6uHq1ev4pNPPsHx48chlUrRrVs3jBkzBq1atarTdUpKShAfH4/8/HwoFAokJyfDw8MDGzduhK+vLwwGA+bNm4du3boZz5k3bx4CAgIQFxcHrVYLmUyGxYsXo0OHDmbXzs3NRVRUFFJTU+Hj42OJp01E1GCYZ4mIxCFagdwUMHETEYmLeZaImiLRviiEiIiIiKgpYoFMRERERGSCBTIRERERkQlRC+SCggKkpqYCAFatWoVJkyYhKytLzCaJiFoU5lkiIssTtUCeP38+zp8/j/T0dOzbtw8jRoxAYmKimE0SEbUozLNERJYnaoF8/fp1PPXUU0hLS8OwYcPwxBNPoKSkRMwmiYhaFOZZIiLLE7VArqioQEVFBfbt24eHHnoIJSUl0Gq1YjZJRNSiMM8SEVmeqAVyVFQU+vTpAxcXF4SGhiI6OhrDhg0Ts0kiohaFeZaIyPJE/6KQy5cvw8vLCxKJBFlZWejUqZOYzdUJF7AnouaAeZaIyLJEX8UiMzMTEokEq1atwvLlyzm7mojIgphniYgsj6tYEBE1YcyzRESWx1UsiIiaMOZZIiLL4yoWRERNGPMsEZHlcRULIqImjHmWiMjyGmQVC29vbwDg7GoiIhEwzxIRWZZczIsbDAZ8/fXXSEtLg06nQ9++fREYGAi5XNRmiYhaDOZZIiLLE3WIRXJyMg4ePIhJkyZh8uTJ+P3337Fy5UoxmyQialGYZ4mILE/UWwz79u3Djh07oFAoAAD9+vXDY489hgULFojZLBFRi8E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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.22158488532181117\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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416.460444 \r\nL 91.28459 417.205973 \r\nL 91.317014 421.148718 \r\nL 91.398073 417.733503 \r\nL 91.414285 417.838531 \r\nL 91.430497 417.271702 \r\nL 91.462921 415.912017 \r\nL 91.527769 416.496645 \r\nL 91.608828 418.92139 \r\nL 91.641252 417.70473 \r\nL 91.706099 415.14352 \r\nL 91.738523 417.700691 \r\nL 91.770947 421.995213 \r\nL 91.819582 415.891038 \r\nL 91.852006 420.424143 \r\nL 91.868218 420.670712 \r\nL 91.88443 419.376142 \r\nL 91.997913 417.158645 \r\nL 91.933066 419.716206 \r\nL 92.014125 417.685755 \r\nL 92.062761 420.280933 \r\nL 92.127608 420.060063 \r\nL 92.176244 415.836209 \r\nL 92.208668 419.919809 \r\nL 92.305939 422.3915 \r\nL 92.322151 421.498091 \r\nL 92.354575 419.528306 \r\nL 92.40321 421.526897 \r\nL 92.435634 420.457162 \r\nL 92.468058 420.054875 \r\nL 92.48427 420.275628 \r\nL 92.516694 421.708023 \r\nL 92.581541 419.968256 \r\nL 92.613965 419.612914 \r\nL 92.630177 420.492608 \r\nL 92.74366 422.7944 \r\nL 92.711236 418.923104 \r\nL 92.759872 422.350493 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418.801778 \r\nL 96.52103 421.976094 \r\nL 96.634513 418.756395 \r\nL 96.650725 419.301075 \r\nL 96.683148 420.764973 \r\nL 96.764208 420.034942 \r\nL 96.812844 416.09562 \r\nL 96.845267 419.962349 \r\nL 96.877691 421.614997 \r\nL 96.910115 419.679633 \r\nL 96.958751 421.547129 \r\nL 97.007386 416.869534 \r\nL 97.088446 417.438059 \r\nL 97.169505 420.01862 \r\nL 97.201929 422.639706 \r\nL 97.250565 417.73456 \r\nL 97.282988 416.531902 \r\nL 97.331624 418.804023 \r\nL 97.347836 418.85767 \r\nL 97.396471 420.821049 \r\nL 97.428895 418.897073 \r\nL 97.445107 418.197311 \r\nL 97.493743 419.404031 \r\nL 97.509955 419.273926 \r\nL 97.542378 420.503821 \r\nL 97.574802 419.421709 \r\nL 97.607226 416.520127 \r\nL 97.672074 420.434803 \r\nL 97.769345 415.945717 \r\nL 97.801769 419.0945 \r\nL 97.850404 424.713581 \r\nL 97.89904 418.685049 \r\nL 98.012523 417.694878 \r\nL 98.15843 424.177204 \r\nL 98.190854 421.285896 \r\nL 98.23949 416.505467 \r\nL 98.304337 418.712767 \r\nL 98.450244 422.782676 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421.505686 \r\nL 100.249764 420.139066 \r\nL 100.314611 421.277873 \r\nL 100.363247 419.835338 \r\nL 100.428094 421.191168 \r\nL 100.460518 421.567424 \r\nL 100.492942 420.849598 \r\nL 100.541578 416.921107 \r\nL 100.606425 420.402646 \r\nL 100.622637 420.397364 \r\nL 100.655061 419.662162 \r\nL 100.703696 420.093312 \r\nL 100.719908 420.810313 \r\nL 100.768544 418.471933 \r\nL 100.800968 417.861579 \r\nL 100.833391 420.126984 \r\nL 100.865815 419.643112 \r\nL 100.930663 420.857343 \r\nL 100.99551 417.786008 \r\nL 101.07657 418.656443 \r\nL 101.190053 421.20725 \r\nL 101.141417 418.204563 \r\nL 101.206265 420.715963 \r\nL 101.254901 417.150168 \r\nL 101.33596 417.932522 \r\nL 101.368384 419.072117 \r\nL 101.400808 417.19072 \r\nL 101.417019 416.121131 \r\nL 101.449443 419.688428 \r\nL 101.465655 420.439795 \r\nL 101.498079 417.51486 \r\nL 101.514291 416.585744 \r\nL 101.562926 418.850887 \r\nL 101.611562 421.815918 \r\nL 101.67641 419.587216 \r\nL 101.692621 419.73189 \r\nL 101.708833 419.640431 \r\nL 101.741257 417.461178 \r\nL 101.806105 419.520412 \r\nL 101.822317 419.624095 \r\nL 101.870952 418.268159 \r\nL 101.903376 420.39564 \r\nL 101.952012 421.423064 \r\nL 101.968224 420.748559 \r\nL 102.016859 418.40751 \r\nL 102.065495 420.985802 \r\nL 102.097919 421.515221 \r\nL 102.146554 421.243113 \r\nL 102.308673 417.412507 \r\nL 102.389733 423.946529 \r\nL 102.45458 420.138143 \r\nL 102.600487 417.817994 \r\nL 102.665335 422.191724 \r\nL 102.71397 418.899251 \r\nL 102.79503 416.578678 \r\nL 102.811242 417.590455 \r\nL 102.876089 421.675126 \r\nL 102.924725 418.79683 \r\nL 103.05442 414.601038 \r\nL 102.989572 419.500656 \r\nL 103.086844 417.117462 \r\nL 103.119267 422.155857 \r\nL 103.200327 420.916601 \r\nL 103.248963 415.866082 \r\nL 103.31381 419.733698 \r\nL 103.346234 420.251255 \r\nL 103.378658 419.446321 \r\nL 103.492141 416.711911 \r\nL 103.443505 419.50956 \r\nL 103.524565 417.764106 \r\nL 103.5732 421.922545 \r\nL 103.605624 425.678303 \r\nL 103.670472 421.101165 \r\nL 103.767743 415.31144 \r\nL 103.783955 416.951117 \r\nL 103.897438 424.91561 \r\nL 103.946074 422.273025 \r\nL 104.010921 416.605108 \r\nL 104.059557 420.09156 \r\nL 104.091981 421.589198 \r\nL 104.17304 421.183788 \r\nL 104.221676 418.756113 \r\nL 104.302735 419.467671 \r\nL 104.400007 422.454066 \r\nL 104.416218 423.517075 \r\nL 104.43243 421.370709 \r\nL 104.464854 416.322174 \r\nL 104.545914 418.517812 \r\nL 104.594549 420.931938 \r\nL 104.626973 418.871781 \r\nL 104.659397 416.978493 \r\nL 104.740456 417.890732 \r\nL 104.77288 417.529625 \r\nL 104.789092 417.998534 \r\nL 104.837727 422.429287 \r\nL 104.886363 418.980285 \r\nL 104.983634 413.593039 \r\nL 104.999846 415.327328 \r\nL 105.129541 422.856932 \r\nL 105.145753 421.822763 \r\nL 105.243025 416.797577 \r\nL 105.307872 417.384377 \r\nL 105.324084 417.466104 \r\nL 105.469991 422.096172 \r\nL 105.421355 416.896582 \r\nL 105.486203 421.875124 \r\nL 105.680746 415.722916 \r\nL 105.55105 422.44701 \r\nL 105.696957 415.842997 \r\nL 105.794229 423.091157 \r\nL 105.875288 421.535719 \r\nL 105.940136 417.89813 \r\nL 105.988771 420.320171 \r\nL 106.053619 425.116113 \r\nL 106.086043 422.561465 \r\nL 106.183314 415.779324 \r\nL 106.215738 417.909691 \r\nL 106.280585 424.055501 \r\nL 106.345433 421.364725 \r\nL 106.361645 421.679812 \r\nL 106.377857 421.036999 \r\nL 106.442704 414.912909 \r\nL 106.49134 419.335547 \r\nL 106.523764 421.34507 \r\nL 106.604823 421.086374 \r\nL 106.669671 415.267581 \r\nL 106.718306 419.42613 \r\nL 106.766942 421.785714 \r\nL 106.83179 420.216656 \r\nL 106.929061 416.201798 \r\nL 106.961485 417.763779 \r\nL 107.026332 424.163174 \r\nL 107.074968 420.114743 \r\nL 107.156027 417.190052 \r\nL 107.188451 418.164281 \r\nL 107.26951 421.786632 \r\nL 107.318146 419.146977 \r\nL 107.415417 414.243239 \r\nL 107.366782 420.415886 \r\nL 107.447841 417.97586 \r\nL 107.577536 424.734442 \r\nL 107.626172 415.38664 \r\nL 107.707231 419.014474 \r\nL 107.739655 417.946354 \r\nL 107.772079 419.440994 \r\nL 107.820715 422.284756 \r\nL 107.86935 419.455715 \r\nL 107.934198 419.068448 \r\nL 107.901774 419.669124 \r\nL 107.95041 419.497998 \r\nL 107.982834 422.47672 \r\nL 108.031469 418.931457 \r\nL 108.047681 418.979714 \r\nL 108.063893 419.176008 \r\nL 108.096317 418.299521 \r\nL 108.144952 415.309927 \r\nL 108.177376 419.410652 \r\nL 108.258436 421.857875 \r\nL 108.290859 420.571612 \r\nL 108.388131 414.049876 \r\nL 108.436766 418.433478 \r\nL 108.517826 419.990876 \r\nL 108.55025 419.562595 \r\nL 108.647521 416.230953 \r\nL 108.679945 417.830272 \r\nL 108.744792 422.320716 \r\nL 108.793428 419.509834 \r\nL 108.825852 417.401334 \r\nL 108.906911 417.722705 \r\nL 109.036606 422.817083 \r\nL 109.06903 421.280366 \r\nL 109.166301 417.51875 \r\nL 109.182513 418.309565 \r\nL 109.231149 422.560283 \r\nL 109.295996 418.713533 \r\nL 109.393268 419.760097 \r\nL 109.360844 416.711974 \r\nL 109.40948 419.598759 \r\nL 109.441903 418.797755 \r\nL 109.458115 420.108124 \r\nL 109.506751 423.775895 \r\nL 109.555387 420.869352 \r\nL 109.620234 416.009331 \r\nL 109.685082 419.004756 \r\nL 109.766141 423.544402 \r\nL 109.8472 421.630691 \r\nL 109.895836 417.015158 \r\nL 109.960684 418.537996 \r\nL 110.009319 425.216868 \r\nL 110.057955 417.851464 \r\nL 110.074167 416.45309 \r\nL 110.155226 417.448736 \r\nL 110.26871 422.696476 \r\nL 110.317345 419.357833 \r\nL 110.365981 417.527905 \r\nL 110.398405 415.370736 \r\nL 110.44704 419.609291 \r\nL 110.495676 420.872589 \r\nL 110.5281 423.065252 \r\nL 110.560523 419.548346 \r\nL 110.592947 415.873027 \r\nL 110.674007 417.684504 \r\nL 110.722642 421.910999 \r\nL 110.78749 419.537739 \r\nL 110.852337 417.036506 \r\nL 110.917185 417.652322 \r\nL 110.982033 423.257075 \r\nL 111.04688 419.495851 \r\nL 111.111728 414.367375 \r\nL 111.160363 417.859805 \r\nL 111.241423 422.091388 \r\nL 111.290058 419.325126 \r\nL 111.38733 417.104083 \r\nL 111.403542 417.309295 \r\nL 111.484601 422.506694 \r\nL 111.549449 419.654422 \r\nL 111.630508 416.807256 \r\nL 111.662932 418.418891 \r\nL 111.727779 422.739438 \r\nL 111.792627 420.520337 \r\nL 111.90611 416.739505 \r\nL 111.922322 417.627027 \r\nL 111.954746 423.268397 \r\nL 112.035805 419.394486 \r\nL 112.052017 419.353242 \r\nL 112.100653 417.398114 \r\nL 112.133076 419.976347 \r\nL 112.181712 420.615776 \r\nL 112.214136 422.671943 \r\nL 112.262772 418.449577 \r\nL 112.360043 416.116541 \r\nL 112.376255 417.015211 \r\nL 112.473526 423.391422 \r\nL 112.522162 419.884653 \r\nL 112.554586 419.949488 \r\nL 112.570797 418.871278 \r\nL 112.603221 413.451267 \r\nL 112.668069 419.198291 \r\nL 112.732916 422.197204 \r\nL 112.749128 423.498979 \r\nL 112.781552 419.65633 \r\nL 112.895035 417.04087 \r\nL 112.911247 417.108775 \r\nL 112.976095 421.859146 \r\nL 113.02473 419.347457 \r\nL 113.057154 418.313673 \r\nL 113.10579 419.902037 \r\nL 113.170637 420.216823 \r\nL 113.186849 420.21173 \r\nL 113.332756 416.708629 \r\nL 113.28412 420.434678 \r\nL 113.348968 417.322071 \r\nL 113.413816 423.469956 \r\nL 113.478663 421.375151 \r\nL 113.575934 416.464297 \r\nL 113.608358 418.178184 \r\nL 113.738053 422.796035 \r\nL 113.754265 421.517456 \r\nL 113.851536 417.415695 \r\nL 113.88396 420.161685 \r\nL 113.900172 421.955796 \r\nL 113.948808 418.985916 \r\nL 113.981232 420.080251 \r\nL 114.062291 418.265308 \r\nL 114.094715 419.826059 \r\nL 114.110927 420.403687 \r\nL 114.159562 418.873971 \r\nL 114.191986 416.937356 \r\nL 114.240622 419.571655 \r\nL 114.289257 417.125409 \r\nL 114.354105 419.693835 \r\nL 114.402741 422.992689 \r\nL 114.435164 419.945166 \r\nL 114.467588 416.525746 \r\nL 114.548648 417.847748 \r\nL 114.597283 421.616807 \r\nL 114.678343 420.61668 \r\nL 114.791826 414.56095 \r\nL 114.82425 418.381886 \r\nL 114.921521 421.839744 \r\nL 114.937733 420.701964 \r\nL 114.970157 417.279457 \r\nL 115.035004 420.754694 \r\nL 115.067428 417.59197 \r\nL 115.08364 417.034642 \r\nL 115.116064 419.326394 \r\nL 115.197123 422.117058 \r\nL 115.229547 420.672465 \r\nL 115.278183 418.468701 \r\nL 115.326818 420.971661 \r\nL 115.359242 423.458789 \r\nL 115.407878 418.758524 \r\nL 115.424089 418.502422 \r\nL 115.456513 419.605923 \r\nL 115.472725 419.564211 \r\nL 115.521361 416.970252 \r\nL 115.553785 420.610343 \r\nL 115.569996 421.186691 \r\nL 115.618632 419.840471 \r\nL 115.667268 421.096282 \r\nL 115.68348 421.00191 \r\nL 115.764539 417.152295 \r\nL 115.845599 418.342089 \r\nL 115.894234 421.526554 \r\nL 115.959082 419.527327 \r\nL 115.991506 419.601739 \r\nL 116.040141 418.622598 \r\nL 116.056353 418.321164 \r\nL 116.072565 418.683522 \r\nL 116.104989 420.607064 \r\nL 116.169836 418.414999 \r\nL 116.186048 418.868162 \r\nL 116.218472 420.012994 \r\nL 116.267108 418.058787 \r\nL 116.283319 417.563832 \r\nL 116.331955 419.113702 \r\nL 116.348167 419.0706 \r\nL 116.364379 419.314177 \r\nL 116.413015 421.556407 \r\nL 116.445438 418.987232 \r\nL 116.494074 416.644421 \r\nL 116.510286 418.607596 \r\nL 116.54271 421.724719 \r\nL 116.591345 416.42974 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418.177757 \r\nL 118.196322 421.907687 \r\nL 118.244958 417.627903 \r\nL 118.26117 417.631184 \r\nL 118.423289 419.861371 \r\nL 118.488136 416.493721 \r\nL 118.536772 418.609165 \r\nL 118.634043 421.631462 \r\nL 118.650255 420.480065 \r\nL 118.682679 416.40295 \r\nL 118.763738 419.27269 \r\nL 118.77995 418.973251 \r\nL 118.812374 420.106282 \r\nL 118.844798 424.530113 \r\nL 118.909645 420.328038 \r\nL 119.023128 416.674321 \r\nL 119.055552 418.218292 \r\nL 119.087976 421.351601 \r\nL 119.185247 420.759762 \r\nL 119.250095 415.700015 \r\nL 119.29873 419.284617 \r\nL 119.331154 420.953696 \r\nL 119.412214 419.818313 \r\nL 119.477061 417.403918 \r\nL 119.509485 420.017254 \r\nL 119.574332 419.509799 \r\nL 119.622968 421.534406 \r\nL 119.736451 417.904905 \r\nL 119.768875 418.926077 \r\nL 119.833723 422.586734 \r\nL 119.882358 419.678039 \r\nL 119.97963 416.795929 \r\nL 120.012053 418.716578 \r\nL 120.060689 422.437429 \r\nL 120.141749 421.704742 \r\nL 120.222808 414.412316 \r\nL 120.271444 419.223035 \r\nL 120.303867 421.735357 \r\nL 120.384927 420.28897 \r\nL 120.433562 417.460284 \r\nL 120.482198 420.170185 \r\nL 120.514622 420.688899 \r\nL 120.530834 419.914382 \r\nL 120.563258 418.634905 \r\nL 120.611893 421.294449 \r\nL 120.628105 420.875557 \r\nL 120.709165 414.770272 \r\nL 120.741588 418.11197 \r\nL 120.790224 424.17184 \r\nL 120.855072 420.356142 \r\nL 120.887495 417.823917 \r\nL 120.968555 418.896102 \r\nL 121.114462 422.451957 \r\nL 121.130674 421.336242 \r\nL 121.195521 415.906521 \r\nL 121.244157 420.039011 \r\nL 121.35764 421.709645 \r\nL 121.325216 419.277198 \r\nL 121.373852 421.610252 \r\nL 121.422488 416.461483 \r\nL 121.503547 418.919668 \r\nL 121.584606 421.199564 \r\nL 121.61703 419.696424 \r\nL 121.633242 418.721498 \r\nL 121.69809 421.314924 \r\nL 121.714302 421.354304 \r\nL 121.762937 418.256044 \r\nL 121.860208 419.573975 \r\nL 121.87642 419.776797 \r\nL 121.892632 419.392565 \r\nL 121.941268 416.915578 \r\nL 121.973692 419.180529 \r\nL 122.006115 421.097092 \r\nL 122.070963 419.427067 \r\nL 122.103387 418.008855 \r\nL 122.168234 419.363951 \r\nL 122.200658 420.112114 \r\nL 122.249294 418.499234 \r\nL 122.265506 418.038891 \r\nL 122.297929 420.161266 \r\nL 122.330353 421.787567 \r\nL 122.378989 417.860713 \r\nL 122.492472 422.020275 \r\nL 122.524896 420.506084 \r\nL 122.573532 417.054836 \r\nL 122.622167 421.295588 \r\nL 122.670803 417.262659 \r\nL 122.703227 418.490111 \r\nL 122.73565 420.991097 \r\nL 122.800498 418.151247 \r\nL 122.81671 418.922302 \r\nL 122.832922 419.247667 \r\nL 122.849134 418.797669 \r\nL 122.881557 416.597119 \r\nL 122.930193 420.664855 \r\nL 122.946405 421.186545 \r\nL 122.962617 420.266769 \r\nL 122.995041 418.344804 \r\nL 123.0761 419.891344 \r\nL 123.092312 419.783139 \r\nL 123.124736 417.901416 \r\nL 123.189583 420.316195 \r\nL 123.238219 422.623674 \r\nL 123.270643 420.673758 \r\nL 123.319278 417.77431 \r\nL 123.367914 419.579675 \r\nL 123.400338 421.568785 \r\nL 123.448973 418.680854 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418.750388 \r\nL 126.739986 417.932223 \r\nL 126.756198 418.359533 \r\nL 126.788622 423.326777 \r\nL 126.869681 419.677884 \r\nL 126.918317 416.313427 \r\nL 126.966953 419.650155 \r\nL 126.999377 422.226039 \r\nL 127.064224 419.428467 \r\nL 127.193919 417.810763 \r\nL 127.307402 422.63568 \r\nL 127.339826 420.345842 \r\nL 127.37225 416.58991 \r\nL 127.453309 418.74491 \r\nL 127.566793 421.353374 \r\nL 127.583004 420.697427 \r\nL 127.680276 416.784748 \r\nL 127.696488 417.839095 \r\nL 127.761335 422.630429 \r\nL 127.793759 419.055184 \r\nL 127.858607 417.725652 \r\nL 127.907242 418.107501 \r\nL 128.020725 424.568445 \r\nL 128.069361 420.627004 \r\nL 128.134209 415.002572 \r\nL 128.182844 418.756896 \r\nL 128.263904 423.199099 \r\nL 128.312539 420.772687 \r\nL 128.409811 415.211768 \r\nL 128.442234 416.789353 \r\nL 128.49087 423.088353 \r\nL 128.57193 418.926068 \r\nL 128.652989 416.648908 \r\nL 128.669201 417.859274 \r\nL 128.766472 421.23971 \r\nL 128.798896 421.006493 \r\nL 128.815108 420.948705 \r\nL 128.879955 416.33528 \r\nL 128.928591 420.331284 \r\nL 128.977227 422.782309 \r\nL 129.025862 420.732548 \r\nL 129.155558 417.248994 \r\nL 129.171769 417.952538 \r\nL 129.220405 421.870403 \r\nL 129.301464 419.920338 \r\nL 129.3501 415.980082 \r\nL 129.414948 418.825786 \r\nL 129.479795 422.313386 \r\nL 129.528431 419.520981 \r\nL 129.544643 418.463246 \r\nL 129.625702 419.118696 \r\nL 129.739185 422.511882 \r\nL 129.69055 418.965918 \r\nL 129.755397 422.350401 \r\nL 129.868881 418.0475 \r\nL 129.885092 418.346335 \r\nL 129.933728 422.350257 \r\nL 129.998576 418.675014 \r\nL 130.047211 417.89938 \r\nL 130.095847 418.158886 \r\nL 130.160694 421.522671 \r\nL 130.20933 419.779283 \r\nL 130.322813 415.740948 \r\nL 130.339025 417.100453 \r\nL 130.46872 422.080517 \r\nL 130.533568 417.441813 \r\nL 130.630839 418.714522 \r\nL 130.695687 421.2713 \r\nL 130.728111 419.721008 \r\nL 130.776746 417.964116 \r\nL 130.841594 419.020073 \r\nL 130.857806 418.946905 \r\nL 130.906441 421.664331 \r\nL 130.938865 418.722581 \r\nL 130.955077 417.485632 \r\nL 131.003713 420.48279 \r\nL 131.052348 418.286483 \r\nL 131.06856 418.271326 \r\nL 131.133408 420.733649 \r\nL 131.198255 419.978978 \r\nL 131.279315 417.18273 \r\nL 131.311738 418.134413 \r\nL 131.40901 421.887712 \r\nL 131.441434 421.301725 \r\nL 131.490069 417.819174 \r\nL 131.587341 418.700823 \r\nL 131.619764 420.090391 \r\nL 131.652188 421.328883 \r\nL 131.700824 419.525512 \r\nL 131.717036 419.708629 \r\nL 131.733247 419.724466 \r\nL 131.765671 418.596222 \r\nL 131.798095 419.952281 \r\nL 131.862943 421.277995 \r\nL 131.895366 419.921804 \r\nL 131.976426 418.18458 \r\nL 132.025061 418.810518 \r\nL 132.057485 417.885288 \r\nL 132.106121 419.487995 \r\nL 132.170968 421.04059 \r\nL 132.18718 419.740495 \r\nL 132.219604 416.747037 \r\nL 132.284452 420.294224 \r\nL 132.300664 420.619682 \r\nL 132.333087 419.522222 \r\nL 132.349299 419.669292 \r\nL 132.381723 419.144361 \r\nL 132.414147 417.998615 \r\nL 132.446571 419.493361 \r\nL 132.462782 420.373948 \r\nL 132.52763 418.673409 \r\nL 132.543842 418.623806 \r\nL 132.608689 421.252535 \r\nL 132.641113 419.656773 \r\nL 132.722173 416.718412 \r\nL 132.754596 418.499812 \r\nL 132.78702 420.284464 \r\nL 132.835656 417.955151 \r\nL 132.86808 418.898794 \r\nL 132.884291 418.449435 \r\nL 132.949139 419.44384 \r\nL 132.981563 420.172962 \r\nL 133.013987 418.262316 \r\nL 133.030198 417.744915 \r\nL 133.062622 419.000415 \r\nL 133.111258 422.203507 \r\nL 133.143682 419.490163 \r\nL 133.159894 418.04066 \r\nL 133.224741 420.782288 \r\nL 133.240953 420.596049 \r\nL 133.257165 420.388775 \r\nL 133.273377 420.781287 \r\nL 133.3058 421.776303 \r\nL 133.370648 420.376139 \r\nL 133.451707 420.879472 \r\nL 133.516555 417.771591 \r\nL 133.597614 417.478925 \r\nL 133.64625 421.148112 \r\nL 133.743521 421.382542 \r\nL 133.792157 418.816685 \r\nL 133.824581 418.548338 \r\nL 133.840793 418.159245 \r\nL 133.873217 419.111918 \r\nL 133.9867 421.261534 \r\nL 134.002912 420.606824 \r\nL 134.116395 417.858069 \r\nL 134.067759 421.148769 \r\nL 134.132607 418.034274 \r\nL 134.16503 421.180048 \r\nL 134.262302 420.196201 \r\nL 134.294726 419.26105 \r\nL 134.327149 416.708863 \r\nL 134.391997 420.043686 \r\nL 134.473056 419.1898 \r\nL 134.440633 420.129309 \r\nL 134.489268 419.492189 \r\nL 134.521692 420.30738 \r\nL 134.554116 417.48813 \r\nL 134.570328 416.846154 \r\nL 134.602751 419.388178 \r\nL 134.618963 419.385457 \r\nL 134.651387 418.738587 \r\nL 134.683811 420.81973 \r\nL 134.813506 417.25246 \r\nL 134.84593 419.028097 \r\nL 134.975625 421.114954 \r\nL 134.991837 420.880569 \r\nL 135.072896 416.730835 \r\nL 135.089108 415.137296 \r\nL 135.121532 418.254905 \r\nL 135.153956 422.607647 \r\nL 135.235015 419.340103 \r\nL 135.267439 418.193895 \r\nL 135.299863 419.505264 \r\nL 135.332286 421.187516 \r\nL 135.413346 419.99978 \r\nL 135.461981 420.737119 \r\nL 135.478193 420.031048 \r\nL 135.543041 416.187353 \r\nL 135.591677 419.437136 \r\nL 135.607888 419.462297 \r\nL 135.672736 422.515906 \r\nL 135.70516 419.477732 \r\nL 135.802431 417.821813 \r\nL 135.753795 419.568535 \r\nL 135.818643 418.496409 \r\nL 135.932126 421.545189 \r\nL 135.948338 421.26046 \r\nL 136.045609 417.087091 \r\nL 136.078033 418.353075 \r\nL 136.191516 420.89576 \r\nL 136.207728 420.432796 \r\nL 136.305 417.664192 \r\nL 136.353635 417.841778 \r\nL 136.402271 419.788742 \r\nL 136.48333 419.39787 \r\nL 136.515754 418.923743 \r\nL 136.548178 419.604862 \r\nL 136.596813 419.347503 \r\nL 136.629237 420.032225 \r\nL 136.645449 419.272579 \r\nL 136.710297 420.586666 \r\nL 136.758932 417.696951 \r\nL 136.791356 419.129719 \r\nL 136.839992 422.558798 \r\nL 136.904839 420.643901 \r\nL 136.985899 417.967855 \r\nL 137.050746 419.154149 \r\nL 137.099382 420.291074 \r\nL 137.148018 423.121658 \r\nL 137.180441 420.869582 \r\nL 137.277713 418.15433 \r\nL 137.310137 418.909032 \r\nL 137.407408 421.930199 \r\nL 137.439832 420.29075 \r\nL 137.569527 418.05654 \r\nL 137.60195 419.302989 \r\nL 137.68301 418.928339 \r\nL 137.715434 418.124236 \r\nL 137.747857 420.177809 \r\nL 137.764069 420.42676 \r\nL 137.796493 419.601275 \r\nL 137.812705 419.33889 \r\nL 137.845129 420.350866 \r\nL 137.861341 420.569671 \r\nL 137.909976 416.437766 \r\nL 137.974824 419.330121 \r\nL 138.007248 419.047952 \r\nL 138.02346 419.503914 \r\nL 138.088307 422.238184 \r\nL 138.120731 420.102245 \r\nL 138.218002 415.937498 \r\nL 138.250426 417.301418 \r\nL 138.299062 421.578673 \r\nL 138.396333 420.722489 \r\nL 138.444969 416.723632 \r\nL 138.509816 419.244436 \r\nL 138.54224 420.339774 \r\nL 138.623299 419.42832 \r\nL 138.671935 417.731829 \r\nL 138.720571 420.094869 \r\nL 138.785418 418.916893 \r\nL 138.850266 422.015997 \r\nL 138.898901 417.405922 \r\nL 138.996173 418.718831 \r\nL 139.044808 423.205921 \r\nL 139.093444 419.004711 \r\nL 139.125868 416.740538 \r\nL 139.190715 418.214807 \r\nL 139.304199 422.143846 \r\nL 139.352834 421.447864 \r\nL 139.433894 415.762406 \r\nL 139.482529 419.859034 \r\nL 139.547377 422.807124 \r\nL 139.612224 420.86713 \r\nL 139.677072 417.777361 \r\nL 139.709496 416.843056 \r\nL 139.758131 418.923912 \r\nL 139.822979 424.128608 \r\nL 139.855403 419.797614 \r\nL 139.92025 417.455914 \r\nL 139.952674 418.651519 \r\nL 139.985098 420.757778 \r\nL 140.033733 417.910812 \r\nL 140.049945 418.19064 \r\nL 140.114793 419.943001 \r\nL 140.147217 418.235011 \r\nL 140.195852 416.192457 \r\nL 140.228276 417.971462 \r\nL 140.276912 422.993932 \r\nL 140.341759 419.488657 \r\nL 140.390395 414.543707 \r\nL 140.439031 419.706631 \r\nL 140.487666 416.944868 \r\nL 140.52009 420.139948 \r\nL 140.568726 423.316244 \r\nL 140.617361 419.053563 \r\nL 140.682209 417.325026 \r\nL 140.714633 419.039372 \r\nL 140.763268 422.24595 \r\nL 140.811904 419.153511 \r\nL 140.828116 419.164393 \r\nL 140.86054 419.668683 \r\nL 140.892963 418.813992 \r\nL 140.925387 417.464286 \r\nL 140.974023 418.835963 \r\nL 141.055082 423.725509 \r\nL 141.087506 421.068347 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418.650655 \r\nL 146.79409 420.250142 \r\nL 146.810302 420.732338 \r\nL 146.858938 418.872645 \r\nL 146.87515 418.232494 \r\nL 146.923785 420.642759 \r\nL 146.972421 419.615804 \r\nL 146.988633 420.181871 \r\nL 147.021057 422.347349 \r\nL 147.069692 419.301214 \r\nL 147.085904 418.553556 \r\nL 147.13454 420.332183 \r\nL 147.150752 421.497496 \r\nL 147.199387 419.50939 \r\nL 147.248023 421.301096 \r\nL 147.410142 417.298911 \r\nL 147.426354 417.494186 \r\nL 147.458778 421.756704 \r\nL 147.539837 418.385538 \r\nL 147.588473 419.182719 \r\nL 147.604685 418.15115 \r\nL 147.620896 416.549237 \r\nL 147.685744 421.03591 \r\nL 147.73438 418.027212 \r\nL 147.847863 418.141757 \r\nL 147.864075 418.147236 \r\nL 147.945134 418.05778 \r\nL 148.009982 419.958315 \r\nL 148.074829 417.994248 \r\nL 148.107253 419.007516 \r\nL 148.123465 420.512041 \r\nL 148.172101 417.446041 \r\nL 148.220736 419.570476 \r\nL 148.269372 417.283134 \r\nL 148.350431 418.847249 \r\nL 148.382855 420.916866 \r\nL 148.463915 419.544611 \r\nL 148.480126 419.468886 \r\nL 148.496338 419.886456 \r\nL 148.51255 420.0103 \r\nL 148.544974 419.519382 \r\nL 148.561186 419.350261 \r\nL 148.626033 419.764977 \r\nL 148.642245 419.791344 \r\nL 148.674669 421.442781 \r\nL 148.739517 421.018174 \r\nL 148.885424 417.965214 \r\nL 148.982695 420.99197 \r\nL 149.079966 420.452573 \r\nL 149.161026 420.629715 \r\nL 149.242085 417.703573 \r\nL 149.306933 421.448767 \r\nL 149.420416 420.594955 \r\nL 149.550111 417.806965 \r\nL 149.566323 417.850281 \r\nL 149.614958 421.257665 \r\nL 149.679806 419.288422 \r\nL 149.696018 419.396476 \r\nL 149.71223 419.073819 \r\nL 149.744654 417.903029 \r\nL 149.793289 419.07567 \r\nL 149.809501 418.807154 \r\nL 149.890561 421.746439 \r\nL 149.955408 419.684471 \r\nL 150.004044 416.044008 \r\nL 150.052679 418.121534 \r\nL 150.117527 420.930189 \r\nL 150.166163 418.487561 \r\nL 150.214798 421.327915 \r\nL 150.247222 419.51688 \r\nL 150.295858 417.141141 \r\nL 150.344493 420.102767 \r\nL 150.376917 421.72096 \r\nL 150.409341 420.476085 \r\nL 150.457977 415.852971 \r\nL 150.522824 419.302182 \r\nL 150.587672 421.856418 \r\nL 150.636307 420.739764 \r\nL 150.717367 419.20377 \r\nL 150.766002 416.702306 \r\nL 150.798426 419.204059 \r\nL 150.863274 422.553892 \r\nL 150.911909 420.158091 \r\nL 150.944333 419.03317 \r\nL 150.976757 417.135338 \r\nL 151.041605 419.708913 \r\nL 151.122664 421.057751 \r\nL 151.155088 419.839458 \r\nL 151.203723 417.625729 \r\nL 151.252359 419.761312 \r\nL 151.365842 421.022652 \r\nL 151.317207 419.069401 \r\nL 151.382054 420.8657 \r\nL 151.43069 417.526123 \r\nL 151.527961 418.866605 \r\nL 151.560385 421.635237 \r\nL 151.625232 418.157966 \r\nL 151.673868 416.406145 \r\nL 151.754928 416.516783 \r\nL 151.819775 420.867541 \r\nL 151.917046 419.286683 \r\nL 151.94947 415.94361 \r\nL 152.014318 420.295664 \r\nL 152.046741 419.605795 \r\nL 152.079165 420.664434 \r\nL 152.127801 419.76481 \r\nL 152.241284 421.136538 \r\nL 152.257496 420.336081 \r\nL 152.273708 419.540649 \r\nL 152.322344 421.568647 \r\nL 152.338555 421.825468 \r\nL 152.354767 420.544315 \r\nL 152.387191 417.821778 \r\nL 152.468251 418.426537 \r\nL 152.565522 421.662738 \r\nL 152.597946 420.365464 \r\nL 152.711429 418.763482 \r\nL 152.727641 418.785899 \r\nL 152.776276 421.787924 \r\nL 152.841124 419.375124 \r\nL 152.857336 419.429133 \r\nL 152.873548 419.21636 \r\nL 152.88976 418.810524 \r\nL 152.938395 419.794604 \r\nL 153.051878 421.454419 \r\nL 153.003243 419.422018 \r\nL 153.084302 421.193564 \r\nL 153.213997 417.193515 \r\nL 153.230209 418.632737 \r\nL 153.262633 421.375843 \r\nL 153.311269 417.691177 \r\nL 153.343692 419.472296 \r\nL 153.376116 421.121991 \r\nL 153.440964 419.343493 \r\nL 153.489599 417.293547 \r\nL 153.538235 419.088047 \r\nL 153.586871 420.150829 \r\nL 153.619295 418.25813 \r\nL 153.635506 418.095409 \r\nL 153.651718 418.488162 \r\nL 153.700354 421.050529 \r\nL 153.74899 418.551925 \r\nL 153.781413 417.786394 \r\nL 153.813837 419.410302 \r\nL 153.846261 418.686127 \r\nL 153.878685 419.755238 \r\nL 153.911108 418.917576 \r\nL 153.943532 417.392814 \r\nL 154.00838 419.471047 \r\nL 154.073227 418.630228 \r\nL 154.154287 420.679365 \r\nL 154.251558 418.761168 \r\nL 154.316406 419.85284 \r\nL 154.348829 421.194785 \r\nL 154.381253 419.024018 \r\nL 154.397465 418.224255 \r\nL 154.462313 419.631548 \r\nL 154.494736 421.039812 \r\nL 154.559584 419.108585 \r\nL 154.592008 418.314427 \r\nL 154.624431 419.991782 \r\nL 154.640643 420.637657 \r\nL 154.689279 418.358391 \r\nL 154.770338 419.588799 \r\nL 154.818974 421.369794 \r\nL 154.86761 419.305976 \r\nL 154.900034 419.019327 \r\nL 154.916245 418.482876 \r\nL 154.981093 419.939783 \r\nL 155.013517 419.248792 \r\nL 155.045941 420.778068 \r\nL 155.078364 422.731252 \r\nL 155.110788 418.726336 \r\nL 155.175636 419.925747 \r\nL 155.240483 416.418244 \r\nL 155.353966 420.557943 \r\nL 155.370178 420.015662 \r\nL 155.46745 417.129154 \r\nL 155.499873 418.448769 \r\nL 155.580933 420.775966 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157.672266 418.003613 \r\nL 157.801961 421.415206 \r\nL 157.834385 419.700686 \r\nL 157.915444 417.555884 \r\nL 157.947868 418.8278 \r\nL 157.996504 422.132738 \r\nL 158.061351 419.13776 \r\nL 158.126199 417.890757 \r\nL 158.191047 418.351145 \r\nL 158.239682 422.300101 \r\nL 158.288318 419.096756 \r\nL 158.385589 417.546451 \r\nL 158.401801 418.232492 \r\nL 158.547708 421.71895 \r\nL 158.628767 417.691571 \r\nL 158.693615 419.173627 \r\nL 158.742251 424.125247 \r\nL 158.790886 418.553551 \r\nL 158.807098 417.486668 \r\nL 158.871946 419.169846 \r\nL 158.888158 419.125033 \r\nL 158.920581 418.559199 \r\nL 158.969217 419.996531 \r\nL 159.050277 422.332688 \r\nL 159.066488 420.844523 \r\nL 159.131336 416.712475 \r\nL 159.179972 420.570365 \r\nL 159.244819 419.354769 \r\nL 159.293455 421.477668 \r\nL 159.342091 418.305133 \r\nL 159.455574 418.438637 \r\nL 159.504209 421.29799 \r\nL 159.552845 417.877 \r\nL 159.569057 417.893755 \r\nL 159.731176 420.630209 \r\nL 159.747388 420.26092 \r\nL 159.828447 420.646471 \r\nL 159.877083 417.905436 \r\nL 159.958142 420.914095 \r\nL 160.087837 419.684455 \r\nL 160.120261 418.328236 \r\nL 160.20132 419.081131 \r\nL 160.266168 421.527229 \r\nL 160.298592 419.733853 \r\nL 160.314804 418.739395 \r\nL 160.363439 420.644097 \r\nL 160.395863 419.89729 \r\nL 160.428287 419.871517 \r\nL 160.493134 417.722237 \r\nL 160.54177 419.764179 \r\nL 160.574194 420.467201 \r\nL 160.606618 419.265781 \r\nL 160.639041 418.640332 \r\nL 160.687677 419.973551 \r\nL 160.720101 421.756321 \r\nL 160.768737 418.135075 \r\nL 160.784948 417.896882 \r\nL 160.833584 418.579594 \r\nL 160.88222 420.592918 \r\nL 160.914644 419.35168 \r\nL 160.947067 417.26992 \r\nL 160.995703 420.561883 \r\nL 161.011915 419.898411 \r\nL 161.076762 418.177959 \r\nL 161.14161 418.392793 \r\nL 161.190246 420.484372 \r\nL 161.238881 418.266803 \r\nL 161.255093 418.088004 \r\nL 161.287517 418.702202 \r\nL 161.319941 419.499625 \r\nL 161.384788 418.577928 \r\nL 161.401 418.402313 \r\nL 161.417212 418.908486 \r\nL 161.530695 421.257115 \r\nL 161.563119 420.069305 \r\nL 161.579331 419.649406 \r\nL 161.627967 420.68711 \r\nL 161.644178 420.878878 \r\nL 161.790085 417.327722 \r\nL 161.838721 421.37653 \r\nL 161.91978 419.097579 \r\nL 161.935992 419.092983 \r\nL 161.968416 418.743519 \r\nL 162.017052 419.079339 \r\nL 162.146747 421.896059 \r\nL 162.098111 418.474152 \r\nL 162.162959 421.786785 \r\nL 162.211594 418.129952 \r\nL 162.276442 421.340614 \r\nL 162.292654 421.994457 \r\nL 162.34129 420.074815 \r\nL 162.373713 419.602396 \r\nL 162.422349 419.85927 \r\nL 162.454773 421.093509 \r\nL 162.51962 419.398821 \r\nL 162.552044 418.891368 \r\nL 162.584468 420.484289 \r\nL 162.60068 421.102741 \r\nL 162.633104 419.208898 \r\nL 162.649315 418.374801 \r\nL 162.697951 420.144969 \r\nL 162.730375 419.805014 \r\nL 162.746587 419.72981 \r\nL 162.795222 421.953358 \r\nL 162.827646 419.371722 \r\nL 162.86007 416.202748 \r\nL 162.924917 420.920949 \r\nL 162.973553 417.223816 \r\nL 163.038401 420.380587 \r\nL 163.168096 418.206352 \r\nL 163.20052 419.242811 \r\nL 163.216731 419.7443 \r\nL 163.249155 418.44334 \r\nL 163.265367 417.701822 \r\nL 163.330215 419.269873 \r\nL 163.346427 420.161303 \r\nL 163.395062 418.593238 \r\nL 163.443698 419.655214 \r\nL 163.45991 419.593712 \r\nL 163.476122 419.634128 \r\nL 163.524757 420.539214 \r\nL 163.557181 419.885677 \r\nL 163.589605 417.194965 \r\nL 163.654452 420.802021 \r\nL 163.703088 418.696548 \r\nL 163.800359 419.174755 \r\nL 163.832783 421.44465 \r\nL 163.897631 418.371174 \r\nL 163.913843 417.915236 \r\nL 163.930054 419.204888 \r\nL 163.962478 420.668017 \r\nL 164.043538 420.243542 \r\nL 164.140809 418.531508 \r\nL 164.157021 418.988324 \r\nL 164.286716 421.221512 \r\nL 164.31914 420.095025 \r\nL 164.400199 420.160182 \r\nL 164.448835 418.745615 \r\nL 164.465047 418.70364 \r\nL 164.481259 419.201841 \r\nL 164.529894 418.033818 \r\nL 164.562318 419.171909 \r\nL 164.643377 416.978675 \r\nL 164.675801 418.261776 \r\nL 164.724437 421.845052 \r\nL 164.789284 419.141813 \r\nL 164.805496 419.236621 \r\nL 164.83792 417.516327 \r\nL 164.902768 419.06694 \r\nL 165.032463 422.022238 \r\nL 164.967615 418.652321 \r\nL 165.048675 422.004316 \r\nL 165.17837 419.444607 \r\nL 165.340489 417.357499 \r\nL 165.243217 419.970546 \r\nL 165.3567 417.496621 \r\nL 165.486396 421.052549 \r\nL 165.502607 420.436101 \r\nL 165.616091 418.355913 \r\nL 165.761998 421.665174 \r\nL 165.810633 418.352714 \r\nL 165.891693 419.759573 \r\nL 165.940328 420.856194 \r\nL 165.95654 419.509371 \r\nL 165.972752 418.391022 \r\nL 166.021388 421.576315 \r\nL 166.053812 420.217521 \r\nL 166.086235 420.554667 \r\nL 166.102447 419.903624 \r\nL 166.134871 418.407951 \r\nL 166.183507 421.020179 \r\nL 166.199719 421.477259 \r\nL 166.21593 420.707027 \r\nL 166.280778 418.031484 \r\nL 166.329414 419.023943 \r\nL 166.394261 422.2679 \r\nL 166.426685 419.708161 \r\nL 166.55638 417.158606 \r\nL 166.475321 419.772574 \r\nL 166.572592 417.351658 \r\nL 166.702287 422.426478 \r\nL 166.718499 421.288896 \r\nL 166.848194 415.965974 \r\nL 166.864406 416.223939 \r\nL 166.994101 421.942613 \r\nL 167.010313 420.824586 \r\nL 167.07516 417.093532 \r\nL 167.123796 419.706322 \r\nL 167.140008 419.597918 \r\nL 167.172432 418.323744 \r\nL 167.221067 420.026935 \r\nL 167.237279 420.970803 \r\nL 167.285915 418.772714 \r\nL 167.399398 416.345508 \r\nL 167.350763 420.248253 \r\nL 167.41561 417.527274 \r\nL 167.464246 421.581597 \r\nL 167.529093 417.937715 \r\nL 167.642576 421.519683 \r\nL 167.723636 419.705045 \r\nL 167.837119 417.374673 \r\nL 167.853331 417.935272 \r\nL 167.918179 422.477035 \r\nL 167.983026 420.350042 \r\nL 167.999238 420.350633 \r\nL 168.047874 416.877916 \r\nL 168.177569 418.455175 \r\nL 168.226204 420.996707 \r\nL 168.27484 418.914889 \r\nL 168.339688 418.099028 \r\nL 168.355899 418.753634 \r\nL 168.453171 421.075663 \r\nL 168.469383 420.205082 \r\nL 168.518018 417.473361 \r\nL 168.582866 419.821769 \r\nL 168.631502 417.798818 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420.864128 \r\nL 170.204055 419.768388 \r\nL 170.268902 417.584081 \r\nL 170.317538 418.432699 \r\nL 170.366173 422.212049 \r\nL 170.398597 424.64043 \r\nL 170.431021 419.423061 \r\nL 170.479657 416.767527 \r\nL 170.560716 417.364816 \r\nL 170.690411 421.513275 \r\nL 170.706623 420.715676 \r\nL 170.755259 414.821874 \r\nL 170.820106 419.162866 \r\nL 170.884954 420.837705 \r\nL 170.933589 420.116 \r\nL 170.982225 416.697605 \r\nL 171.063285 418.343326 \r\nL 171.079496 418.301389 \r\nL 171.144344 423.852932 \r\nL 171.19298 418.859606 \r\nL 171.274039 417.209612 \r\nL 171.241615 419.071647 \r\nL 171.290251 417.277269 \r\nL 171.355099 421.289957 \r\nL 171.419946 419.873637 \r\nL 171.436158 419.96992 \r\nL 171.45237 419.64243 \r\nL 171.517217 418.561493 \r\nL 171.533429 419.679922 \r\nL 171.630701 421.697605 \r\nL 171.646912 421.406026 \r\nL 171.727972 415.936385 \r\nL 171.760396 418.471851 \r\nL 171.857667 422.750495 \r\nL 171.890091 421.368766 \r\nL 171.954938 416.954443 \r\nL 172.019786 418.636752 \r\nL 172.05221 420.275506 \r\nL 172.100845 423.768329 \r\nL 172.149481 420.183004 \r\nL 172.198117 416.755442 \r\nL 172.262964 419.903709 \r\nL 172.295388 420.390239 \r\nL 172.344024 422.568704 \r\nL 172.376447 420.551592 \r\nL 172.473719 416.695795 \r\nL 172.506142 417.569941 \r\nL 172.57099 421.536769 \r\nL 172.619626 418.84473 \r\nL 172.668261 415.989094 \r\nL 172.733109 417.535621 \r\nL 172.781745 421.888754 \r\nL 172.814168 423.006899 \r\nL 172.862804 420.402132 \r\nL 172.943863 414.856977 \r\nL 172.992499 418.66933 \r\nL 173.073559 424.596536 \r\nL 173.138406 420.720133 \r\nL 173.203254 416.064075 \r\nL 173.251889 419.420528 \r\nL 173.332949 421.836499 \r\nL 173.365372 420.09454 \r\nL 173.397796 417.294829 \r\nL 173.462644 419.233772 \r\nL 173.576127 421.85552 \r\nL 173.592339 421.532061 \r\nL 173.640975 415.542657 \r\nL 173.722034 418.244782 \r\nL 173.738246 418.290057 \r\nL 173.786882 422.725718 \r\nL 173.835517 419.490693 \r\nL 173.867941 417.666851 \r\nL 173.949 418.093573 \r\nL 174.03006 422.330914 \r\nL 174.078695 419.304083 \r\nL 174.175967 416.525962 \r\nL 174.208391 417.994636 \r\nL 174.273238 422.288416 \r\nL 174.338086 420.631176 \r\nL 174.435357 416.406678 \r\nL 174.467781 418.291625 \r\nL 174.532628 421.940424 \r\nL 174.581264 419.969175 \r\nL 174.613688 416.190855 \r\nL 174.694747 419.107098 \r\nL 174.759595 422.028079 \r\nL 174.80823 419.385104 \r\nL 174.88929 417.580533 \r\nL 174.937925 418.104544 \r\nL 174.954137 418.214843 \r\nL 175.018985 424.758102 \r\nL 175.067621 418.503113 \r\nL 175.14868 416.758335 \r\nL 175.164892 417.059837 \r\nL 175.245951 423.28346 \r\nL 175.294587 420.391079 \r\nL 175.40807 417.096008 \r\nL 175.424282 417.704802 \r\nL 175.521553 424.240655 \r\nL 175.553977 421.735298 \r\nL 175.586401 417.225331 \r\nL 175.66746 418.271416 \r\nL 175.716096 422.647709 \r\nL 175.780944 420.304223 \r\nL 175.797155 420.621106 \r\nL 175.829579 419.398592 \r\nL 175.862003 419.349205 \r\nL 175.926851 416.677289 \r\nL 175.959274 419.469603 \r\nL 175.991698 422.328252 \r\nL 176.056546 418.788472 \r\nL 176.088969 416.829159 \r\nL 176.137605 419.980858 \r\nL 176.153817 420.49423 \r\nL 176.186241 418.157039 \r\nL 176.202453 417.705889 \r\nL 176.234876 418.331342 \r\nL 176.2673 417.97447 \r\nL 176.315936 420.841777 \r\nL 176.364572 417.870411 \r\nL 176.380783 417.675502 \r\nL 176.396995 418.282515 \r\nL 176.461843 421.337277 \r\nL 176.510478 419.790886 \r\nL 176.559114 417.236863 \r\nL 176.623962 418.836215 \r\nL 176.640174 418.789096 \r\nL 176.672597 419.597642 \r\nL 176.705021 418.608796 \r\nL 176.721233 417.92814 \r\nL 176.753657 420.1128 \r\nL 176.786081 421.193632 \r\nL 176.834716 418.642336 \r\nL 176.86714 417.920122 \r\nL 176.915776 419.70344 \r\nL 176.964411 417.912702 \r\nL 177.045471 418.104263 \r\nL 177.110318 420.569758 \r\nL 177.142742 418.485195 \r\nL 177.158954 417.89093 \r\nL 177.191378 420.217993 \r\nL 177.20759 420.942928 \r\nL 177.256225 419.085669 \r\nL 177.288649 419.947975 \r\nL 177.402132 417.722996 \r\nL 177.418344 418.61274 \r\nL 177.548039 421.131039 \r\nL 177.596675 418.849163 \r\nL 177.693946 419.674976 \r\nL 177.710158 419.72867 \r\nL 177.742582 419.488206 \r\nL 177.775006 419.053549 \r\nL 177.807429 419.901019 \r\nL 177.839853 420.759286 \r\nL 177.888489 419.563093 \r\nL 177.937125 417.013776 \r\nL 177.953336 419.217531 \r\nL 177.98576 422.343593 \r\nL 178.06682 419.968477 \r\nL 178.115455 417.835371 \r\nL 178.212727 418.513134 \r\nL 178.342422 420.505376 \r\nL 178.358634 419.432653 \r\nL 178.391057 417.688301 \r\nL 178.455905 419.07601 \r\nL 178.504541 422.529452 \r\nL 178.569388 419.185926 \r\nL 178.666659 417.436407 \r\nL 178.682871 418.228551 \r\nL 178.796355 421.285869 \r\nL 178.812566 420.548113 \r\nL 178.909838 416.072353 \r\nL 178.92605 417.577255 \r\nL 179.039533 421.242269 \r\nL 179.055745 420.59251 \r\nL 179.120592 417.924705 \r\nL 179.153016 418.950248 \r\nL 179.282711 422.903965 \r\nL 179.396194 417.729339 \r\nL 179.461042 420.561456 \r\nL 179.493466 422.797494 \r\nL 179.558313 420.544608 \r\nL 179.606949 417.204994 \r\nL 179.671796 419.440776 \r\nL 179.70422 418.621783 \r\nL 179.736644 419.642145 \r\nL 179.769068 420.72163 \r\nL 179.817703 419.145537 \r\nL 179.882551 415.819073 \r\nL 179.931187 418.956035 \r\nL 179.979822 421.902493 \r\nL 180.04467 420.239089 \r\nL 180.125729 417.350848 \r\nL 180.239212 418.587853 \r\nL 180.271636 421.041602 \r\nL 180.336484 417.646367 \r\nL 180.352696 417.664644 \r\nL 180.433755 421.075469 \r\nL 180.482391 418.808626 \r\nL 180.531026 417.796798 \r\nL 180.56345 416.863805 \r\nL 180.628298 418.409924 \r\nL 180.676933 419.295891 \r\nL 180.725569 421.597687 \r\nL 180.774205 420.257715 \r\nL 180.839052 417.796243 \r\nL 180.887688 418.855059 \r\nL 180.936324 421.049844 \r\nL 181.001171 419.074049 \r\nL 181.098442 418.642896 \r\nL 181.195714 422.178405 \r\nL 181.228138 420.945813 \r\nL 181.325409 417.139801 \r\nL 181.357833 418.549411 \r\nL 181.471316 421.531541 \r\nL 181.487528 421.514267 \r\nL 181.536163 418.511322 \r\nL 181.601011 420.460452 \r\nL 181.649647 421.976082 \r\nL 181.665858 423.011245 \r\nL 181.714494 419.878481 \r\nL 181.779342 416.432402 \r\nL 181.844189 418.787953 \r\nL 181.925249 423.665841 \r\nL 181.990096 420.743097 \r\nL 182.071156 415.694738 \r\nL 182.119791 418.982581 \r\nL 182.184639 420.248873 \r\nL 182.233274 419.63006 \r\nL 182.314334 417.114559 \r\nL 182.346758 419.403414 \r\nL 182.427817 420.972993 \r\nL 182.444029 420.221299 \r\nL 182.492665 416.165096 \r\nL 182.573724 417.09444 \r\nL 182.670995 421.783745 \r\nL 182.719631 419.216063 \r\nL 182.800691 417.143526 \r\nL 182.816902 418.205632 \r\nL 182.88175 422.997706 \r\nL 182.930386 419.594075 \r\nL 183.043869 417.215426 \r\nL 183.14114 423.051899 \r\nL 183.205988 421.141387 \r\nL 183.254623 417.818287 \r\nL 183.335683 418.973507 \r\nL 183.368107 419.501854 \r\nL 183.449166 421.303945 \r\nL 183.465378 420.216503 \r\nL 183.530225 417.151893 \r\nL 183.578861 419.926327 \r\nL 183.611285 422.038793 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420.444958 \r\nL 185.816101 418.403405 \r\nL 185.864737 421.786257 \r\nL 185.897161 419.786615 \r\nL 185.97822 418.720687 \r\nL 186.026856 418.904076 \r\nL 186.043068 418.299297 \r\nL 186.091704 419.950983 \r\nL 186.107915 420.503743 \r\nL 186.156551 418.532955 \r\nL 186.172763 418.404032 \r\nL 186.205187 418.921808 \r\nL 186.253822 418.476771 \r\nL 186.270034 418.541614 \r\nL 186.31867 421.534335 \r\nL 186.351094 418.812994 \r\nL 186.367306 417.386465 \r\nL 186.432153 419.662507 \r\nL 186.448365 420.132191 \r\nL 186.497001 418.585517 \r\nL 186.529424 419.296714 \r\nL 186.626696 420.570764 \r\nL 186.642908 420.468565 \r\nL 186.691543 417.055387 \r\nL 186.740179 419.571564 \r\nL 186.772603 421.514547 \r\nL 186.83745 419.415288 \r\nL 186.886086 420.302691 \r\nL 186.91851 419.304934 \r\nL 186.950934 419.460509 \r\nL 187.015781 418.093404 \r\nL 187.048205 419.40239 \r\nL 187.096841 421.702615 \r\nL 187.129264 419.252539 \r\nL 187.145476 418.803056 \r\nL 187.194112 419.800705 \r\nL 187.210324 420.492442 \r\nL 187.258959 418.865125 \r\nL 187.275171 418.869825 \r\nL 187.307595 418.104289 \r\nL 187.340019 419.328581 \r\nL 187.404866 421.133126 \r\nL 187.43729 419.913892 \r\nL 187.51835 419.296378 \r\nL 187.534561 419.779212 \r\nL 187.550773 420.145014 \r\nL 187.583197 419.497305 \r\nL 187.631833 417.052315 \r\nL 187.664257 419.832468 \r\nL 187.69668 421.722195 \r\nL 187.761528 418.889558 \r\nL 187.842587 418.588413 \r\nL 187.793952 418.970991 \r\nL 187.858799 418.768636 \r\nL 187.939859 418.290825 \r\nL 188.004706 421.203394 \r\nL 188.085766 418.100414 \r\nL 188.183037 418.845306 \r\nL 188.215461 420.480416 \r\nL 188.280308 418.828599 \r\nL 188.29652 418.711644 \r\nL 188.312732 419.263805 \r\nL 188.328944 420.439812 \r\nL 188.393791 417.573149 \r\nL 188.507275 420.514107 \r\nL 188.539698 419.461185 \r\nL 188.572122 419.665756 \r\nL 188.588334 419.125284 \r\nL 188.620758 417.862718 \r\nL 188.669394 420.744818 \r\nL 188.734241 417.270422 \r\nL 188.766665 420.035466 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418.166471 \r\nL 192.284644 420.981156 \r\nL 192.365704 420.304051 \r\nL 192.414339 418.612693 \r\nL 192.462975 420.11857 \r\nL 192.479187 421.054438 \r\nL 192.560246 419.411559 \r\nL 192.625094 418.682256 \r\nL 192.689941 418.932077 \r\nL 192.722365 421.266095 \r\nL 192.803425 421.245225 \r\nL 192.85206 418.1036 \r\nL 192.93312 419.258217 \r\nL 193.014179 421.126103 \r\nL 193.062815 419.990581 \r\nL 193.127662 418.903639 \r\nL 193.208722 419.205159 \r\nL 193.257357 422.002403 \r\nL 193.305993 418.546833 \r\nL 193.322205 418.679105 \r\nL 193.338417 418.624597 \r\nL 193.370841 417.058507 \r\nL 193.403264 418.859351 \r\nL 193.500536 420.718603 \r\nL 193.516748 419.621464 \r\nL 193.646443 418.273926 \r\nL 193.695078 420.778429 \r\nL 193.727502 418.57496 \r\nL 193.824773 415.241135 \r\nL 193.776138 420.337649 \r\nL 193.857197 417.519679 \r\nL 193.889621 419.507395 \r\nL 193.97068 418.379043 \r\nL 194.084164 417.44878 \r\nL 194.035528 418.653187 \r\nL 194.116587 417.811336 \r\nL 194.230071 422.333079 \r\nL 194.246283 420.841747 \r\nL 194.294918 416.650856 \r\nL 194.359766 419.492639 \r\nL 194.473249 421.510132 \r\nL 194.489461 421.509397 \r\nL 194.635368 418.585758 \r\nL 194.684003 421.175658 \r\nL 194.748851 420.19332 \r\nL 194.797487 418.65386 \r\nL 194.846122 420.159404 \r\nL 194.894758 421.444192 \r\nL 194.927182 420.720846 \r\nL 194.975817 419.174611 \r\nL 195.040665 419.751219 \r\nL 195.056877 419.382931 \r\nL 195.105513 420.576131 \r\nL 195.121724 420.611108 \r\nL 195.202784 421.642553 \r\nL 195.25142 419.398393 \r\nL 195.267631 419.416312 \r\nL 195.283843 419.284779 \r\nL 195.300055 418.812065 \r\nL 195.332479 420.215452 \r\nL 195.364903 421.685355 \r\nL 195.397326 419.309998 \r\nL 195.42975 419.626554 \r\nL 195.51081 417.200308 \r\nL 195.559445 418.817089 \r\nL 195.591869 419.825437 \r\nL 195.608081 420.660508 \r\nL 195.672929 418.56815 \r\nL 195.68914 418.55994 \r\nL 195.705352 419.010467 \r\nL 195.737776 417.934521 \r\nL 195.753988 416.870677 \r\nL 195.802624 420.081202 \r\nL 195.835047 418.605603 \r\nL 195.851259 418.377431 \r\nL 195.883683 419.046455 \r\nL 195.916107 420.057833 \r\nL 195.980954 419.288231 \r\nL 196.013378 417.529962 \r\nL 196.062014 419.547699 \r\nL 196.094438 418.676264 \r\nL 196.143073 419.719761 \r\nL 196.159285 418.692482 \r\nL 196.191709 416.812454 \r\nL 196.240345 420.171754 \r\nL 196.256556 421.136015 \r\nL 196.337616 419.731403 \r\nL 196.353828 418.87554 \r\nL 196.402463 421.297084 \r\nL 196.418675 421.408727 \r\nL 196.467311 417.641564 \r\nL 196.532159 420.980695 \r\nL 196.580794 421.68354 \r\nL 196.597006 421.174745 \r\nL 196.661854 418.475499 \r\nL 196.742913 419.491596 \r\nL 196.872608 420.477726 \r\nL 196.969879 418.476339 \r\nL 196.986091 418.675527 \r\nL 197.034727 421.393934 \r\nL 197.099575 419.742321 \r\nL 197.131998 419.994724 \r\nL 197.14821 420.151193 \r\nL 197.164422 419.890847 \r\nL 197.213058 418.180561 \r\nL 197.245482 419.934667 \r\nL 197.310329 420.613963 \r\nL 197.326541 419.536525 \r\nL 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211.041797 420.449776 \r\nL 211.090433 414.821934 \r\nL 211.171492 417.316342 \r\nL 211.252552 422.369018 \r\nL 211.284975 419.271499 \r\nL 211.333611 416.165325 \r\nL 211.398459 417.461595 \r\nL 211.49573 423.232903 \r\nL 211.544366 420.250291 \r\nL 211.625425 417.085 \r\nL 211.657849 418.796504 \r\nL 211.738908 422.673137 \r\nL 211.771332 421.617488 \r\nL 211.819968 417.47571 \r\nL 211.901027 417.910344 \r\nL 212.01451 419.804966 \r\nL 212.063146 416.975194 \r\nL 212.127994 419.1634 \r\nL 212.209053 424.314509 \r\nL 212.257689 420.881525 \r\nL 212.322536 417.884946 \r\nL 212.371172 420.027939 \r\nL 212.468443 423.558398 \r\nL 212.419807 419.90693 \r\nL 212.500867 421.357512 \r\nL 212.549503 419.8612 \r\nL 212.598138 416.407953 \r\nL 212.679198 417.406806 \r\nL 212.792681 415.008998 \r\nL 212.808893 416.15598 \r\nL 212.9548 420.128173 \r\nL 212.987224 419.590203 \r\nL 213.035859 417.509451 \r\nL 213.100707 419.298752 \r\nL 213.246614 421.263456 \r\nL 213.506004 417.132563 \r\nL 213.538428 417.9079 \r\nL 213.684335 422.153306 \r\nL 213.700547 421.643011 \r\nL 213.749182 419.07251 \r\nL 213.830242 420.261642 \r\nL 213.878877 422.299825 \r\nL 213.943725 420.718322 \r\nL 214.040996 419.400249 \r\nL 214.057208 419.844099 \r\nL 214.105844 421.177314 \r\nL 214.154479 419.937365 \r\nL 214.267963 417.889674 \r\nL 214.219327 420.067903 \r\nL 214.284174 418.341017 \r\nL 214.430081 420.856572 \r\nL 214.478717 417.039 \r\nL 214.543565 420.59245 \r\nL 214.559777 420.536541 \r\nL 214.738107 417.073015 \r\nL 214.754319 417.094169 \r\nL 214.867802 420.218786 \r\nL 214.900226 419.198574 \r\nL 215.013709 417.956853 \r\nL 215.046133 418.154749 \r\nL 215.175828 419.988173 \r\nL 215.19204 419.475467 \r\nL 215.2731 418.08802 \r\nL 215.289311 418.367362 \r\nL 215.321735 420.050882 \r\nL 215.370371 417.258841 \r\nL 215.386583 417.076985 \r\nL 215.435218 419.396116 \r\nL 215.500066 418.574362 \r\nL 215.548702 416.70261 \r\nL 215.581125 419.215509 \r\nL 215.597337 419.512616 \r\nL 215.629761 418.555332 \r\nL 215.662185 418.19587 \r\nL 215.694609 419.191486 \r\nL 215.79188 421.911867 \r\nL 215.824304 420.255206 \r\nL 215.856727 419.598911 \r\nL 215.905363 420.782821 \r\nL 215.921575 420.733093 \r\nL 216.099906 422.889094 \r\nL 216.116118 422.332035 \r\nL 216.180965 419.141023 \r\nL 216.245813 420.060748 \r\nL 216.278237 419.507283 \r\nL 216.294448 418.796425 \r\nL 216.343084 420.001642 \r\nL 216.39172 419.171816 \r\nL 216.407932 419.067298 \r\nL 216.440355 419.457911 \r\nL 216.456567 419.40334 \r\nL 216.472779 419.523804 \r\nL 216.488991 419.247659 \r\nL 216.537627 417.663371 \r\nL 216.586262 419.729421 \r\nL 216.699746 421.700977 \r\nL 216.65111 419.439944 \r\nL 216.715957 421.406647 \r\nL 216.845653 417.166647 \r\nL 216.861864 417.505617 \r\nL 216.975348 419.403814 \r\nL 216.99156 419.093452 \r\nL 217.105043 417.639331 \r\nL 217.121255 417.919625 \r\nL 217.25095 420.771447 \r\nL 217.267162 419.996245 \r\nL 217.332009 417.994222 \r\nL 217.380645 418.91686 \r\nL 217.494128 421.175639 \r\nL 217.558976 420.476153 \r\nL 217.737306 419.013378 \r\nL 217.867001 421.847679 \r\nL 217.899425 421.086649 \r\nL 218.061544 418.991747 \r\nL 218.077756 419.284128 \r\nL 218.207451 421.589988 \r\nL 218.223663 421.46932 \r\nL 218.337146 417.683421 \r\nL 218.36957 418.536802 \r\nL 218.401994 419.95443 \r\nL 218.483053 419.082355 \r\nL 218.580324 418.008227 \r\nL 218.612748 418.536388 \r\nL 218.645172 418.489279 \r\nL 218.661384 418.388304 \r\nL 218.677596 418.889143 \r\nL 218.71002 420.27916 \r\nL 218.774867 418.798113 \r\nL 218.823503 417.634796 \r\nL 218.872138 419.06668 \r\nL 219.018045 417.543763 \r\nL 219.034257 418.047275 \r\nL 219.066681 419.655533 \r\nL 219.14774 418.585724 \r\nL 219.277436 417.33456 \r\nL 219.293647 417.85073 \r\nL 219.374707 421.624632 \r\nL 219.455766 420.711895 \r\nL 219.536826 418.988765 \r\nL 219.56925 420.304779 \r\nL 219.617885 422.951927 \r\nL 219.682733 421.025311 \r\nL 219.74758 419.38808 \r\nL 219.796216 420.740267 \r\nL 219.861063 420.13625 \r\nL 219.909699 421.111764 \r\nL 220.071818 419.077029 \r\nL 220.136666 420.581656 \r\nL 220.185301 419.621151 \r\nL 220.217725 418.974974 \r\nL 220.282573 419.989954 \r\nL 220.314996 420.28856 \r\nL 220.331208 420.917845 \r\nL 220.396056 419.550355 \r\nL 220.42848 418.82173 \r\nL 220.493327 416.692507 \r\nL 220.541963 418.554061 \r\nL 220.655446 419.950102 \r\nL 220.671658 419.353119 \r\nL 220.817565 416.738854 \r\nL 220.963472 420.11816 \r\nL 220.979684 420.07993 \r\nL 221.012107 418.71428 \r\nL 221.060743 420.635164 \r\nL 221.076955 421.129843 \r\nL 221.109379 419.840437 \r\nL 221.141803 417.757417 \r\nL 221.222862 418.45179 \r\nL 221.401193 422.085826 \r\nL 221.46604 417.762231 \r\nL 221.530888 420.306493 \r\nL 221.579523 421.400074 \r\nL 221.628159 420.91403 \r\nL 221.676795 418.480524 \r\nL 221.757854 419.129749 \r\nL 221.871337 420.387687 \r\nL 221.919973 419.941734 \r\nL 221.984821 416.970785 \r\nL 222.017244 418.880592 \r\nL 222.06588 420.164176 \r\nL 222.114516 418.911496 \r\nL 222.130728 418.126896 \r\nL 222.195575 419.847459 \r\nL 222.227999 420.146567 \r\nL 222.244211 419.695264 \r\nL 222.276635 418.240798 \r\nL 222.309058 419.879301 \r\nL 222.357694 419.524114 \r\nL 222.390118 419.903343 \r\nL 222.40633 419.418522 \r\nL 222.454965 416.806042 \r\nL 222.503601 419.236601 \r\nL 222.536025 420.470628 \r\nL 222.58466 418.392655 \r\nL 222.600872 417.540078 \r\nL 222.66572 418.411217 \r\nL 222.698144 417.952951 \r\nL 222.844051 420.202467 \r\nL 222.795415 417.844457 \r\nL 222.860263 420.023784 \r\nL 222.941322 417.581792 \r\nL 222.973746 419.784967 \r\nL 223.006169 421.660569 \r\nL 223.087229 421.372516 \r\nL 223.103441 421.356296 \r\nL 223.233136 418.451648 \r\nL 223.249348 418.607689 \r\nL 223.314195 420.636359 \r\nL 223.379043 420.017495 \r\nL 223.395255 419.339043 \r\nL 223.44389 421.368439 \r\nL 223.460102 421.240343 \r\nL 223.557374 418.923392 \r\nL 223.589797 420.041819 \r\nL 223.622221 421.153435 \r\nL 223.670857 419.189401 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229.020779 422.360229 \r\nL 229.085627 419.81251 \r\nL 229.166686 418.05172 \r\nL 229.19911 419.321337 \r\nL 229.263958 419.296271 \r\nL 229.328805 421.707261 \r\nL 229.377441 418.660166 \r\nL 229.4585 419.54916 \r\nL 229.490924 420.707697 \r\nL 229.53956 419.262889 \r\nL 229.571984 420.193671 \r\nL 229.620619 418.710956 \r\nL 229.669255 420.37995 \r\nL 229.685467 420.341481 \r\nL 229.815162 417.49377 \r\nL 229.750314 420.446342 \r\nL 229.831374 418.176124 \r\nL 229.961069 420.093566 \r\nL 230.009705 420.546566 \r\nL 230.025916 420.180396 \r\nL 230.074552 417.308907 \r\nL 230.155612 418.760083 \r\nL 230.204247 421.211161 \r\nL 230.269095 419.267781 \r\nL 230.285307 419.390656 \r\nL 230.301519 419.152535 \r\nL 230.415002 417.733488 \r\nL 230.350154 419.279761 \r\nL 230.431214 418.153519 \r\nL 230.479849 420.278554 \r\nL 230.544697 418.324648 \r\nL 230.577121 417.596377 \r\nL 230.609544 418.923396 \r\nL 230.65818 420.982005 \r\nL 230.723028 420.082861 \r\nL 230.787875 417.982207 \r\nL 230.868935 419.106848 \r\nL 230.91757 420.807658 \r\nL 230.949994 422.555831 \r\nL 230.99863 420.321237 \r\nL 231.031053 418.084136 \r\nL 231.112113 418.552944 \r\nL 231.25802 421.762326 \r\nL 231.274232 421.150407 \r\nL 231.355291 417.880806 \r\nL 231.387715 419.725783 \r\nL 231.436351 421.599948 \r\nL 231.484986 419.758941 \r\nL 231.533622 418.421854 \r\nL 231.566046 419.736013 \r\nL 231.582258 420.695692 \r\nL 231.647105 418.063012 \r\nL 231.695741 420.447791 \r\nL 231.7768 418.771681 \r\nL 231.841648 418.121287 \r\nL 231.874072 418.791077 \r\nL 231.922707 420.941091 \r\nL 231.955131 418.66806 \r\nL 232.019978 419.079164 \r\nL 232.084826 416.173411 \r\nL 232.149674 420.972268 \r\nL 232.246945 418.993498 \r\nL 232.279369 418.629716 \r\nL 232.360428 418.947404 \r\nL 232.392852 418.461137 \r\nL 232.425276 419.096989 \r\nL 232.457699 421.037179 \r\nL 232.506335 418.436707 \r\nL 232.554971 420.573929 \r\nL 232.571183 420.58103 \r\nL 232.700878 419.106705 \r\nL 232.733302 418.288874 \r\nL 232.765725 420.863785 \r\nL 232.798149 422.034605 \r\nL 232.846785 419.116747 \r\nL 232.911632 421.604566 \r\nL 232.97648 419.921628 \r\nL 233.041327 419.413495 \r\nL 233.122387 421.508845 \r\nL 233.203446 418.848251 \r\nL 233.284506 419.707459 \r\nL 233.300718 419.783279 \r\nL 233.349353 418.097696 \r\nL 233.397989 419.592615 \r\nL 233.446625 419.940083 \r\nL 233.462836 419.496913 \r\nL 233.543896 419.753499 \r\nL 233.592532 418.871243 \r\nL 233.624955 419.487628 \r\nL 233.641167 419.893925 \r\nL 233.689803 418.9604 \r\nL 233.738438 419.689209 \r\nL 233.787074 418.123539 \r\nL 233.83571 419.622188 \r\nL 233.884345 419.976815 \r\nL 233.900557 419.592132 \r\nL 233.932981 417.81348 \r\nL 233.997829 420.025901 \r\nL 234.030252 419.119944 \r\nL 234.111312 417.934869 \r\nL 234.143736 419.0278 \r\nL 234.176159 419.921862 \r\nL 234.208583 418.911944 \r\nL 234.241007 417.799435 \r\nL 234.273431 419.039513 \r\nL 234.305855 418.873979 \r\nL 234.403126 420.51687 \r\nL 234.43555 419.304233 \r\nL 234.451761 418.880415 \r\nL 234.500397 420.262444 \r\nL 234.532821 419.778958 \r\nL 234.565245 418.999437 \r\nL 234.630092 420.23237 \r\nL 234.678728 419.165718 \r\nL 234.743575 419.826187 \r\nL 234.759787 420.013231 \r\nL 234.808423 419.355413 \r\nL 234.824635 419.067428 \r\nL 234.873271 420.01759 \r\nL 234.889482 419.914445 \r\nL 234.905694 420.099763 \r\nL 234.921906 420.492961 \r\nL 234.95433 419.807847 \r\nL 235.002966 418.146671 \r\nL 235.051601 420.171173 \r\nL 235.067813 420.147943 \r\nL 235.100237 419.293097 \r\nL 235.148873 420.207878 \r\nL 235.181296 421.606273 \r\nL 235.246144 420.001503 \r\nL 235.310991 418.381703 \r\nL 235.359627 419.284728 \r\nL 235.489322 420.976941 \r\nL 235.424475 418.821512 \r\nL 235.505534 420.208252 \r\nL 235.602805 417.697798 \r\nL 235.635229 418.953633 \r\nL 235.683865 420.166859 \r\nL 235.716289 418.954769 \r\nL 235.732501 418.128801 \r\nL 235.797348 420.148937 \r\nL 235.862196 417.79832 \r\nL 235.910831 419.51035 \r\nL 236.008103 420.12737 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241.779534 418.40761 \r\nL 241.876806 419.636588 \r\nL 241.974077 418.422211 \r\nL 241.925441 419.881452 \r\nL 241.990289 418.9008 \r\nL 242.022713 420.101986 \r\nL 242.119984 419.886974 \r\nL 242.152408 418.177504 \r\nL 242.233467 419.684009 \r\nL 242.298315 422.599292 \r\nL 242.34695 420.136402 \r\nL 242.460434 418.408553 \r\nL 242.476645 418.56668 \r\nL 242.525281 419.886057 \r\nL 242.573917 421.567043 \r\nL 242.638764 421.009213 \r\nL 242.6874 417.951864 \r\nL 242.768459 419.149542 \r\nL 242.800883 420.722517 \r\nL 242.849519 418.878569 \r\nL 242.881943 417.848802 \r\nL 242.914366 419.532787 \r\nL 242.94679 418.830647 \r\nL 242.963002 418.775363 \r\nL 242.979214 418.992068 \r\nL 243.044061 420.312309 \r\nL 243.092697 419.844785 \r\nL 243.189968 418.002204 \r\nL 243.222392 418.378569 \r\nL 243.335875 419.80263 \r\nL 243.352087 419.586147 \r\nL 243.400723 417.583462 \r\nL 243.46557 419.311233 \r\nL 243.481782 419.305783 \r\nL 243.530418 421.113964 \r\nL 243.562842 419.093312 \r\nL 243.611477 417.984807 \r\nL 243.676325 418.035588 \r\nL 243.773596 421.52739 \r\nL 243.838444 419.619705 \r\nL 243.919503 416.62685 \r\nL 243.951927 418.545013 \r\nL 244.049198 420.805003 \r\nL 244.081622 420.127842 \r\nL 244.114046 418.918939 \r\nL 244.211317 419.028707 \r\nL 244.227529 419.03757 \r\nL 244.259953 420.124263 \r\nL 244.292377 422.079017 \r\nL 244.341012 419.653036 \r\nL 244.373436 418.867425 \r\nL 244.454496 418.997435 \r\nL 244.519343 421.091935 \r\nL 244.567979 419.16694 \r\nL 244.616614 420.425741 \r\nL 244.681462 419.216713 \r\nL 244.713886 417.954696 \r\nL 244.762521 420.337535 \r\nL 244.778733 419.901002 \r\nL 244.876005 418.119526 \r\nL 244.908428 418.64103 \r\nL 245.038123 421.133935 \r\nL 245.054335 420.684083 \r\nL 245.102971 417.904199 \r\nL 245.18403 418.290328 \r\nL 245.216454 419.446936 \r\nL 245.281302 420.879019 \r\nL 245.329937 419.83181 \r\nL 245.378573 419.175891 \r\nL 245.427209 417.702329 \r\nL 245.475844 419.084371 \r\nL 245.540692 421.124969 \r\nL 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248.831705 419.257572 \r\nL 248.864129 418.275288 \r\nL 248.912764 419.965299 \r\nL 248.945188 418.947394 \r\nL 249.010036 418.473351 \r\nL 249.04246 419.390261 \r\nL 249.107307 418.618187 \r\nL 249.123519 418.265826 \r\nL 249.188366 419.111021 \r\nL 249.237002 419.574529 \r\nL 249.269426 418.863505 \r\nL 249.285638 418.421605 \r\nL 249.318062 419.418093 \r\nL 249.350485 420.612963 \r\nL 249.399121 418.694445 \r\nL 249.415333 418.945946 \r\nL 249.528816 419.86724 \r\nL 249.545028 419.86609 \r\nL 249.593664 418.939417 \r\nL 249.642299 419.702904 \r\nL 249.674723 421.023177 \r\nL 249.723359 418.545217 \r\nL 249.739571 418.434353 \r\nL 249.771994 419.873928 \r\nL 249.836842 418.189951 \r\nL 249.853054 418.60077 \r\nL 249.934113 420.194417 \r\nL 249.998961 419.790018 \r\nL 250.015173 419.846152 \r\nL 250.047596 419.580837 \r\nL 250.08002 419.39401 \r\nL 250.096232 419.798465 \r\nL 250.177292 419.395881 \r\nL 250.225927 420.369066 \r\nL 250.242139 420.477574 \r\nL 250.258351 420.199955 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419.494255 \r\nL 252.122718 419.781244 \r\nL 252.155142 418.859166 \r\nL 252.171354 418.487521 \r\nL 252.219989 419.921062 \r\nL 252.268625 417.794801 \r\nL 252.317261 420.016848 \r\nL 252.349684 421.471544 \r\nL 252.39832 419.668391 \r\nL 252.430744 420.399955 \r\nL 252.511803 418.339561 \r\nL 252.576651 418.555122 \r\nL 252.641498 420.836754 \r\nL 252.65771 421.119759 \r\nL 252.673922 420.430659 \r\nL 252.754982 416.897532 \r\nL 252.803617 419.198151 \r\nL 252.949524 420.9781 \r\nL 252.981948 419.380995 \r\nL 253.014372 416.947125 \r\nL 253.079219 419.972562 \r\nL 253.160279 421.369137 \r\nL 253.176491 420.818629 \r\nL 253.225126 418.014276 \r\nL 253.306186 419.597301 \r\nL 253.322398 419.763293 \r\nL 253.338609 419.276113 \r\nL 253.354821 418.759639 \r\nL 253.403457 420.6525 \r\nL 253.419669 420.781037 \r\nL 253.435881 420.367438 \r\nL 253.500728 417.112832 \r\nL 253.549364 419.70962 \r\nL 253.581788 421.858657 \r\nL 253.662847 420.189507 \r\nL 253.711483 418.223779 \r\nL 253.77633 419.81526 \r\nL 253.889814 421.722089 \r\nL 253.841178 419.35638 \r\nL 253.922237 421.039871 \r\nL 253.987085 417.164472 \r\nL 254.051932 419.651034 \r\nL 254.084356 419.741615 \r\nL 254.230263 417.93727 \r\nL 254.165416 419.788866 \r\nL 254.246475 418.452255 \r\nL 254.37617 420.400373 \r\nL 254.392382 420.229379 \r\nL 254.441018 419.051168 \r\nL 254.489653 420.494221 \r\nL 254.505865 420.641071 \r\nL 254.538289 420.365261 \r\nL 254.586925 418.91611 \r\nL 254.651772 419.93311 \r\nL 254.71662 418.631783 \r\nL 254.765256 419.5329 \r\nL 254.813891 420.677339 \r\nL 254.846315 419.928251 \r\nL 254.927374 417.459773 \r\nL 254.959798 418.785417 \r\nL 254.992222 420.176324 \r\nL 255.073281 419.600768 \r\nL 255.121917 420.704165 \r\nL 255.154341 419.56095 \r\nL 255.186765 418.861461 \r\nL 255.251612 419.471848 \r\nL 255.300248 420.474073 \r\nL 255.348883 419.096194 \r\nL 255.365095 418.497537 \r\nL 255.413731 420.275333 \r\nL 255.429943 420.553966 \r\nL 255.446155 419.548889 \r\nL 255.462367 418.434563 \r\nL 255.527214 420.11898 \r\nL 255.543426 419.947645 \r\nL 255.673121 417.663003 \r\nL 255.705545 418.877547 \r\nL 255.737969 420.649232 \r\nL 255.802816 418.915085 \r\nL 255.851452 419.591517 \r\nL 255.867664 419.794293 \r\nL 255.900088 419.282163 \r\nL 255.932511 418.742368 \r\nL 255.964935 419.760447 \r\nL 256.013571 420.836734 \r\nL 256.078418 420.434053 \r\nL 256.143266 417.805381 \r\nL 256.191902 419.916486 \r\nL 256.256749 419.257106 \r\nL 256.321597 420.615235 \r\nL 256.402656 418.086179 \r\nL 256.451292 419.704169 \r\nL 256.516139 421.060205 \r\nL 256.548563 419.507227 \r\nL 256.662046 418.323949 \r\nL 256.597199 419.82812 \r\nL 256.678258 418.636297 \r\nL 256.807953 420.820688 \r\nL 256.824165 420.121123 \r\nL 256.905225 418.222842 \r\nL 256.937648 419.144984 \r\nL 256.970072 420.597754 \r\nL 257.018708 418.784932 \r\nL 257.03492 418.230046 \r\nL 257.115979 418.828974 \r\nL 257.132191 418.877306 \r\nL 257.148403 418.767203 \r\nL 257.197039 421.389073 \r\nL 257.278098 419.984541 \r\nL 257.29431 419.962378 \r\nL 257.310522 420.202611 \r\nL 257.342945 419.159302 \r\nL 257.407793 418.156388 \r\nL 257.424005 418.592472 \r\nL 257.505064 421.45032 \r\nL 257.537488 419.23809 \r\nL 257.569912 418.226512 \r\nL 257.618548 419.45683 \r\nL 257.634759 419.454008 \r\nL 257.780666 418.573201 \r\nL 257.796878 418.651169 \r\nL 257.942785 421.289644 \r\nL 257.877938 418.250368 \r\nL 257.958997 421.010572 \r\nL 258.007633 418.616809 \r\nL 258.104904 418.686063 \r\nL 258.15354 420.542085 \r\nL 258.250811 420.113924 \r\nL 258.315659 418.467802 \r\nL 258.41293 419.208685 \r\nL 258.526413 420.018305 \r\nL 258.477778 419.090117 \r\nL 258.558837 419.36032 \r\nL 258.575049 419.041859 \r\nL 258.656108 419.584137 \r\nL 258.67232 419.72152 \r\nL 258.704744 419.257759 \r\nL 258.720956 419.129832 \r\nL 258.75338 419.430444 \r\nL 258.769592 419.395081 \r\nL 258.850651 421.154995 \r\nL 258.899287 419.67896 \r\nL 259.093829 418.782616 \r\nL 259.110041 419.000946 \r\nL 259.158677 420.884149 \r\nL 259.207312 419.003683 \r\nL 259.239736 417.19116 \r\nL 259.288372 420.056119 \r\nL 259.304584 420.713118 \r\nL 259.369431 419.264307 \r\nL 259.401855 418.533453 \r\nL 259.466703 419.05016 \r\nL 259.499126 419.557296 \r\nL 259.53155 418.799393 \r\nL 259.547762 418.36366 \r\nL 259.596398 419.917368 \r\nL 259.661245 420.042645 \r\nL 259.645033 419.763206 \r\nL 259.677457 419.842381 \r\nL 259.726093 417.814338 \r\nL 259.774728 419.162155 \r\nL 259.807152 420.713505 \r\nL 259.872 419.097822 \r\nL 259.904424 418.638486 \r\nL 259.953059 419.480984 \r\nL 260.001695 419.69058 \r\nL 260.017907 419.66839 \r\nL 260.13139 421.133621 \r\nL 260.147602 420.358183 \r\nL 260.228661 418.055199 \r\nL 260.261085 418.989747 \r\nL 260.293509 420.78346 \r\nL 260.374568 420.359056 \r\nL 260.488052 418.117861 \r\nL 260.520475 419.098827 \r\nL 260.552899 420.063795 \r\nL 260.601535 418.64943 \r\nL 260.617747 418.154114 \r\nL 260.682594 419.092597 \r\nL 260.698806 418.659286 \r\nL 260.715018 418.691529 \r\nL 260.828501 421.272987 \r\nL 260.860925 420.378921 \r\nL 260.941984 417.502882 \r\nL 260.974408 419.493278 \r\nL 261.055468 421.350738 \r\nL 261.104103 420.004689 \r\nL 261.217586 418.124424 \r\nL 261.25001 418.662435 \r\nL 261.298646 420.936037 \r\nL 261.363493 419.237036 \r\nL 261.444553 417.882129 \r\nL 261.460765 418.252324 \r\nL 261.5094 421.107096 \r\nL 261.59046 420.291422 \r\nL 261.606672 420.333519 \r\nL 261.671519 418.665532 \r\nL 261.736367 419.590478 \r\nL 261.768791 420.577341 \r\nL 261.833638 419.586485 \r\nL 261.963333 418.354494 \r\nL 261.979545 418.804902 \r\nL 262.011969 420.616693 \r\nL 262.076816 418.620356 \r\nL 262.10924 419.048332 \r\nL 262.125452 418.728874 \r\nL 262.157876 417.028267 \r\nL 262.222723 419.475494 \r\nL 262.303783 420.116637 \r\nL 262.319995 419.878158 \r\nL 262.433478 419.019568 \r\nL 262.44969 419.242882 \r\nL 262.482114 419.035134 \r\nL 262.530749 420.088428 \r\nL 262.563173 421.309694 \r\nL 262.611809 419.53907 \r\nL 262.628021 419.607238 \r\nL 262.660444 418.955073 \r\nL 262.70908 420.07539 \r\nL 262.741504 421.03023 \r\nL 262.790139 418.93975 \r\nL 262.822563 418.567865 \r\nL 262.854987 419.46547 \r\nL 262.96847 420.472887 \r\nL 262.919835 419.34212 \r\nL 262.984682 420.270162 \r\nL 263.130589 417.429878 \r\nL 263.146801 418.363686 \r\nL 263.276496 420.759899 \r\nL 263.292708 420.619118 \r\nL 263.341344 417.93031 \r\nL 263.422403 419.074063 \r\nL 263.487251 420.666948 \r\nL 263.535886 419.196561 \r\nL 263.56831 418.508198 \r\nL 263.633158 419.469807 \r\nL 263.665581 419.36128 \r\nL 263.681793 419.461399 \r\nL 263.714217 420.070673 \r\nL 263.746641 418.860119 \r\nL 263.811488 419.685511 \r\nL 263.860124 417.973177 \r\nL 263.941183 421.225171 \r\nL 263.989819 419.51728 \r\nL 264.08709 417.888381 \r\nL 264.119514 418.483768 \r\nL 264.249209 420.810205 \r\nL 264.265421 420.20736 \r\nL 264.297845 418.762085 \r\nL 264.378904 418.913418 \r\nL 264.443752 421.169094 \r\nL 264.524811 420.223529 \r\nL 264.573447 419.578541 \r\nL 264.638294 419.927341 \r\nL 264.68693 421.265775 \r\nL 264.735566 419.86242 \r\nL 264.784201 418.809907 \r\nL 264.849049 419.619719 \r\nL 264.881473 419.257446 \r\nL 264.930108 419.571116 \r\nL 264.962532 420.215726 \r\nL 264.994956 419.327205 \r\nL 265.02738 418.556454 \r\nL 265.092227 418.966927 \r\nL 265.140863 420.313135 \r\nL 265.205711 419.201662 \r\nL 265.319194 418.570453 \r\nL 265.28677 419.248535 \r\nL 265.335406 418.751795 \r\nL 265.400253 419.974052 \r\nL 265.448889 418.984125 \r\nL 265.513736 417.935775 \r\nL 265.562372 418.44259 \r\nL 265.611008 419.238664 \r\nL 265.692067 418.985143 \r\nL 265.708279 418.954258 \r\nL 265.724491 419.096819 \r\nL 265.740703 419.173709 \r\nL 265.756915 418.924725 \r\nL 265.837974 418.369019 \r\nL 265.854186 418.579113 \r\nL 265.902822 420.582986 \r\nL 265.967669 419.248047 \r\nL 265.983881 418.976201 \r\nL 266.048729 419.605208 \r\nL 266.113576 420.7574 \r\nL 266.162212 420.160677 \r\nL 266.210848 418.662504 \r\nL 266.259483 420.353313 \r\nL 266.324331 418.68595 \r\nL 266.437814 421.120623 \r\nL 266.470238 420.21177 \r\nL 266.502661 419.305641 \r\nL 266.583721 419.950603 \r\nL 266.697204 420.378565 \r\nL 266.810687 419.336144 \r\nL 266.875535 420.379209 \r\nL 266.940382 418.458798 \r\nL 266.972806 419.895239 \r\nL 267.070077 419.592215 \r\nL 267.086289 419.709775 \r\nL 267.102501 419.158512 \r\nL 267.118713 418.571842 \r\nL 267.183561 419.837492 \r\nL 267.215984 418.987271 \r\nL 267.232196 418.623885 \r\nL 267.297044 419.561598 \r\nL 267.313256 419.826026 \r\nL 267.361891 418.933541 \r\nL 267.378103 418.666274 \r\nL 267.394315 419.278013 \r\nL 267.410527 420.0218 \r\nL 267.507798 419.822981 \r\nL 267.572646 418.276077 \r\nL 267.653705 419.24852 \r\nL 267.750977 419.013946 \r\nL 267.783401 419.995481 \r\nL 267.799612 420.214815 \r\nL 267.832036 419.645408 \r\nL 267.86446 419.678317 \r\nL 267.913096 417.45598 \r\nL 267.945519 420.097044 \r\nL 267.961731 420.53885 \r\nL 267.994155 419.053693 \r\nL 268.010367 418.622895 \r\nL 268.059003 419.97538 \r\nL 268.075214 419.708809 \r\nL 268.091426 419.651361 \r\nL 268.107638 419.770877 \r\nL 268.140062 420.590165 \r\nL 268.221121 419.939198 \r\nL 268.237333 419.930146 \r\nL 268.253545 420.250099 \r\nL 268.285969 419.650111 \r\nL 268.334605 418.089168 \r\nL 268.38324 419.814166 \r\nL 268.399452 419.87646 \r\nL 268.415664 419.557633 \r\nL 268.431876 419.195879 \r\nL 268.4643 420.794804 \r\nL 268.480512 421.488084 \r\nL 268.512935 419.239009 \r\nL 268.529147 418.453743 \r\nL 268.561571 419.577217 \r\nL 268.626419 418.919032 \r\nL 268.72369 420.624754 \r\nL 268.756114 419.902677 \r\nL 268.853385 418.197094 \r\nL 268.885809 419.125105 \r\nL 268.98308 420.255707 \r\nL 268.999292 420.193576 \r\nL 269.06414 418.118266 \r\nL 269.161411 418.67333 \r\nL 269.210047 419.584085 \r\nL 269.274894 419.369449 \r\nL 269.372165 418.550111 \r\nL 269.388377 418.775849 \r\nL 269.420801 420.17193 \r\nL 269.50186 419.201637 \r\nL 269.663979 420.96976 \r\nL 269.566708 418.618753 \r\nL 269.680191 420.706724 \r\nL 269.777463 417.836949 \r\nL 269.858522 418.910279 \r\nL 269.874734 418.74172 \r\nL 269.907158 419.182806 \r\nL 269.955793 421.356824 \r\nL 270.020641 419.465497 \r\nL 270.036853 419.101019 \r\nL 270.1017 419.704113 \r\nL 270.198972 420.880111 \r\nL 270.215184 420.596549 \r\nL 270.247607 419.164194 \r\nL 270.312455 420.32461 \r\nL 270.377302 419.728013 \r\nL 270.425938 420.599746 \r\nL 270.523209 419.211909 \r\nL 270.555633 419.409528 \r\nL 270.571845 419.349408 \r\nL 270.588057 419.415669 \r\nL 270.652904 420.557818 \r\nL 270.669116 419.905755 \r\nL 270.70154 418.583299 \r\nL 270.7826 418.906868 \r\nL 270.896083 421.054529 \r\nL 270.944718 419.872127 \r\nL 271.025778 418.332887 \r\nL 271.058202 418.885461 \r\nL 271.090625 420.191466 \r\nL 271.139261 418.472012 \r\nL 271.187897 419.882009 \r\nL 271.236532 417.601672 \r\nL 271.317592 418.672809 \r\nL 271.398651 419.796882 \r\nL 271.431075 418.898272 \r\nL 271.463499 417.527277 \r\nL 271.528346 419.31662 \r\nL 271.658041 420.486236 \r\nL 271.576982 419.129423 \r\nL 271.674253 419.888212 \r\nL 271.739101 417.826325 \r\nL 271.787737 419.226693 \r\nL 271.82016 419.866999 \r\nL 271.868796 420.770369 \r\nL 271.90122 419.605335 \r\nL 271.949855 418.245915 \r\nL 272.014703 419.325835 \r\nL 272.128186 421.757831 \r\nL 272.063339 419.27096 \r\nL 272.16061 420.413383 \r\nL 272.209246 418.057728 \r\nL 272.274093 419.595208 \r\nL 272.371364 421.699487 \r\nL 272.403788 420.220573 \r\nL 272.50106 417.832783 \r\nL 272.533483 418.77841 \r\nL 272.614543 421.573461 \r\nL 272.646967 420.514966 \r\nL 272.695602 417.825807 \r\nL 272.76045 419.290469 \r\nL 272.841509 419.916302 \r\nL 272.873933 419.531447 \r\nL 272.906357 419.725669 \r\nL 272.93878 419.237424 \r\nL 273.01984 418.425903 \r\nL 273.036052 419.040103 \r\nL 273.084687 421.575942 \r\nL 273.149535 419.633409 \r\nL 273.181959 416.914261 \r\nL 273.246806 420.203698 \r\nL 273.263018 420.201228 \r\nL 273.295442 418.801576 \r\nL 273.344078 420.72555 \r\nL 273.36029 420.830134 \r\nL 273.376501 420.307125 \r\nL 273.506197 418.357396 \r\nL 273.587256 421.405802 \r\nL 273.635892 418.990418 \r\nL 273.716951 418.646398 \r\nL 273.684527 419.034879 \r\nL 273.733163 418.708245 \r\nL 273.862858 420.62728 \r\nL 273.87907 420.378879 \r\nL 273.976341 418.954403 \r\nL 274.008765 419.344276 \r\nL 274.089824 420.286492 \r\nL 274.122248 419.449705 \r\nL 274.13846 419.133298 \r\nL 274.21952 419.57236 \r\nL 274.316791 421.111477 \r\nL 274.349215 419.920512 \r\nL 274.39785 418.326049 \r\nL 274.462698 419.367208 \r\nL 274.576181 420.341292 \r\nL 274.608605 419.805683 \r\nL 274.673452 418.048132 \r\nL 274.7383 418.367328 \r\nL 274.819359 420.463593 \r\nL 274.851783 419.484578 \r\nL 274.949054 417.733446 \r\nL 274.981478 418.220003 \r\nL 275.094961 420.462872 \r\nL 275.111173 420.340967 \r\nL 275.159809 418.832999 \r\nL 275.240868 419.376088 \r\nL 275.273292 419.530912 \r\nL 275.321928 419.487762 \r\nL 275.386775 417.934657 \r\nL 275.435411 419.133473 \r\nL 275.484047 418.797623 \r\nL 275.51647 420.097069 \r\nL 275.548894 421.087413 \r\nL 275.613742 420.180859 \r\nL 275.678589 417.48979 \r\nL 275.727225 419.051321 \r\nL 275.775861 421.165469 \r\nL 275.85692 420.291045 \r\nL 275.873132 420.452051 \r\nL 275.905556 420.035177 \r\nL 276.035251 419.178769 \r\nL 276.100098 421.219554 \r\nL 276.148734 419.244725 \r\nL 276.181158 418.947599 \r\nL 276.19737 419.719534 \r\nL 276.229793 420.506206 \r\nL 276.310853 420.497687 \r\nL 276.359489 419.168694 \r\nL 276.391912 420.213721 \r\nL 276.408124 421.00969 \r\nL 276.45676 418.92931 \r\nL 276.489184 419.745565 \r\nL 276.521607 420.61523 \r\nL 276.570243 419.355693 \r\nL 276.602667 418.987575 \r\nL 276.635091 420.02566 \r\nL 276.651303 420.224859 \r\nL 276.71615 419.658088 \r\nL 276.894481 418.157105 \r\nL 276.910693 418.475167 \r\nL 276.959328 420.186689 \r\nL 277.024176 418.94627 \r\nL 277.072812 419.473651 \r\nL 277.089023 418.991193 \r\nL 277.153871 418.109672 \r\nL 277.202507 418.712294 \r\nL 277.218719 418.696579 \r\nL 277.251142 419.8067 \r\nL 277.31599 418.606263 \r\nL 277.332202 418.324991 \r\nL 277.397049 418.959917 \r\nL 277.478109 420.199184 \r\nL 277.510533 419.363964 \r\nL 277.542956 418.10557 \r\nL 277.591592 419.696968 \r\nL 277.624016 419.175622 \r\nL 277.640228 418.668301 \r\nL 277.688863 419.975629 \r\nL 277.721287 419.418349 \r\nL 277.753711 420.39504 \r\nL 277.83477 419.671982 \r\nL 277.948253 420.842144 \r\nL 277.964465 420.33922 \r\nL 278.077949 418.683396 \r\nL 278.09416 419.254937 \r\nL 278.207644 422.057001 \r\nL 278.159008 418.552882 \r\nL 278.240067 420.551661 \r\nL 278.288703 418.201445 \r\nL 278.353551 420.009808 \r\nL 278.369763 419.995608 \r\nL 278.402186 418.809096 \r\nL 278.450822 420.545881 \r\nL 278.483246 419.727384 \r\nL 278.499458 419.456644 \r\nL 278.515669 419.941417 \r\nL 278.531881 420.936195 \r\nL 278.580517 418.626304 \r\nL 278.612941 417.434758 \r\nL 278.661576 420.376185 \r\nL 278.677788 420.377393 \r\nL 278.77506 418.768408 \r\nL 278.839907 419.11864 \r\nL 278.856119 419.104078 \r\nL 278.872331 419.246179 \r\nL 278.904755 418.687294 \r\nL 278.937179 417.47813 \r\nL 278.985814 419.840949 \r\nL 279.018238 418.539204 \r\nL 279.03445 418.512496 \r\nL 279.083086 420.192734 \r\nL 279.164145 419.766084 \r\nL 279.212781 419.806335 \r\nL 279.245204 418.750645 \r\nL 279.261416 418.420654 \r\nL 279.310052 419.681994 \r\nL 279.407323 420.815722 \r\nL 279.423535 420.536878 \r\nL 279.455959 418.506891 \r\nL 279.504595 420.895444 \r\nL 279.537018 420.061757 \r\nL 279.585654 418.821376 \r\nL 279.63429 419.900386 \r\nL 279.650502 420.154941 \r\nL 279.699137 419.195507 \r\nL 279.731561 419.98946 \r\nL 279.780197 418.966739 \r\nL 279.796409 419.304648 \r\nL 279.845044 421.523986 \r\nL 279.89368 419.524496 \r\nL 279.909892 418.861334 \r\nL 279.990951 419.882366 \r\nL 280.055799 420.296749 \r\nL 280.088223 419.776907 \r\nL 280.234129 417.954668 \r\nL 280.15307 420.129503 \r\nL 280.250341 418.924886 \r\nL 280.282765 419.94652 \r\nL 280.363825 419.441509 \r\nL 280.380036 419.44266 \r\nL 280.396248 419.613619 \r\nL 280.41246 418.907429 \r\nL 280.428672 418.315934 \r\nL 280.477308 420.080981 \r\nL 280.49352 420.077692 \r\nL 280.542155 418.806262 \r\nL 280.607003 419.644889 \r\nL 280.639427 419.600133 \r\nL 280.769122 418.582023 \r\nL 280.785334 418.692885 \r\nL 280.817757 419.818548 \r\nL 280.866393 418.522405 \r\nL 280.882605 417.745901 \r\nL 280.931241 419.267901 \r\nL 280.979876 417.773377 \r\nL 281.044724 419.370039 \r\nL 281.125783 418.936957 \r\nL 281.141995 418.617192 \r\nL 281.190631 419.37303 \r\nL 281.223055 419.080673 \r\nL 281.368962 420.241576 \r\nL 281.401385 419.755069 \r\nL 281.433809 419.119493 \r\nL 281.482445 420.113464 \r\nL 281.498657 420.224954 \r\nL 281.53108 419.980116 \r\nL 281.547292 420.072134 \r\nL 281.61214 418.783095 \r\nL 281.660776 419.973923 \r\nL 281.709411 419.323258 \r\nL 281.774259 419.809281 \r\nL 281.806682 420.432775 \r\nL 281.887742 419.958303 \r\nL 281.936378 419.261608 \r\nL 281.968801 420.156237 \r\nL 282.001225 420.496925 \r\nL 282.033649 419.690721 \r\nL 282.049861 419.412745 \r\nL 282.082285 420.037929 \r\nL 282.098496 420.557736 \r\nL 282.147132 419.733142 \r\nL 282.195768 420.282068 \r\nL 282.357887 418.205072 \r\nL 282.374099 418.834521 \r\nL 282.422734 420.313975 \r\nL 282.47137 418.704083 \r\nL 282.487582 418.592169 \r\nL 282.536217 418.994916 \r\nL 282.568641 418.784187 \r\nL 282.746972 420.723641 \r\nL 282.763184 420.25192 \r\nL 282.828031 417.160304 \r\nL 282.876667 419.452337 \r\nL 282.909091 420.767611 \r\nL 282.973938 418.889508 \r\nL 282.99015 418.928651 \r\nL 283.022574 419.641852 \r\nL 283.087422 418.634336 \r\nL 283.103633 418.570374 \r\nL 283.119845 418.718753 \r\nL 283.200905 419.938143 \r\nL 283.265752 419.719627 \r\nL 283.314388 417.716611 \r\nL 283.346812 419.514956 \r\nL 283.379235 421.143892 \r\nL 283.460295 420.068355 \r\nL 283.557566 418.281397 \r\nL 283.606202 419.315787 \r\nL 283.703473 421.154063 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419.667768 \r\nL 293.171215 419.752914 \r\nL 293.187427 419.36299 \r\nL 293.236063 418.962511 \r\nL 293.284698 419.404806 \r\nL 293.30091 419.379542 \r\nL 293.333334 418.938066 \r\nL 293.398181 419.647782 \r\nL 293.430605 419.990058 \r\nL 293.479241 419.362207 \r\nL 293.495453 419.29361 \r\nL 293.527877 420.331388 \r\nL 293.576512 419.232196 \r\nL 293.608936 420.010634 \r\nL 293.754843 419.09361 \r\nL 293.787267 419.597486 \r\nL 293.852114 419.251814 \r\nL 293.933174 418.837726 \r\nL 293.949386 418.893161 \r\nL 294.079081 420.209421 \r\nL 294.257411 418.915938 \r\nL 294.273623 418.903951 \r\nL 294.322259 420.381584 \r\nL 294.403318 419.491694 \r\nL 294.581649 418.134601 \r\nL 294.597861 418.494892 \r\nL 294.646497 419.896293 \r\nL 294.711344 419.078754 \r\nL 294.727556 418.987057 \r\nL 294.75998 419.368934 \r\nL 294.808616 419.091266 \r\nL 294.841039 419.501223 \r\nL 294.889675 419.146199 \r\nL 294.905887 418.895786 \r\nL 294.922099 419.498126 \r\nL 294.970734 421.237333 \r\nL 295.01937 419.48399 \r\nL 295.035582 419.361213 \r\nL 295.051794 419.713463 \r\nL 295.084218 420.421433 \r\nL 295.132853 419.07628 \r\nL 295.181489 419.682238 \r\nL 295.27876 420.601213 \r\nL 295.294972 420.25284 \r\nL 295.376032 419.129634 \r\nL 295.424667 419.596783 \r\nL 295.440879 419.570011 \r\nL 295.489515 419.685211 \r\nL 295.521939 419.855257 \r\nL 295.570574 420.720099 \r\nL 295.61921 420.144799 \r\nL 295.765117 418.111491 \r\nL 295.716481 420.448581 \r\nL 295.781329 418.146402 \r\nL 295.894812 419.686958 \r\nL 295.927236 419.347204 \r\nL 296.040719 418.520757 \r\nL 296.056931 418.651216 \r\nL 296.121778 418.4113 \r\nL 296.186626 419.413387 \r\nL 296.202838 419.304897 \r\nL 296.251474 419.692811 \r\nL 296.283897 419.574791 \r\nL 296.300109 419.491443 \r\nL 296.316321 419.860781 \r\nL 296.348745 420.265739 \r\nL 296.397381 419.275679 \r\nL 296.429804 419.673139 \r\nL 296.446016 420.056689 \r\nL 296.494652 419.006536 \r\nL 296.510864 418.949661 \r\nL 296.543287 419.130087 \r\nL 296.640559 419.821429 \r\nL 296.672983 419.319954 \r\nL 296.689194 419.024805 \r\nL 296.770254 419.570184 \r\nL 296.802678 419.183853 \r\nL 296.835101 420.119607 \r\nL 296.851313 420.553221 \r\nL 296.899949 419.46612 \r\nL 296.916161 419.737386 \r\nL 296.932373 419.742227 \r\nL 296.964797 418.998008 \r\nL 297.045856 419.132726 \r\nL 297.094492 420.412272 \r\nL 297.191763 420.095938 \r\nL 297.25661 418.271292 \r\nL 297.33767 418.952003 \r\nL 297.386306 419.605786 \r\nL 297.434941 418.71897 \r\nL 297.483577 418.071071 \r\nL 297.516001 418.911465 \r\nL 297.613272 420.711012 \r\nL 297.564636 418.712062 \r\nL 297.645696 419.681024 \r\nL 297.775391 418.821235 \r\nL 297.791603 418.803399 \r\nL 297.921298 420.746349 \r\nL 297.953722 419.983361 \r\nL 298.067205 418.339639 \r\nL 298.083417 418.771657 \r\nL 298.11584 420.020449 \r\nL 298.164476 418.508548 \r\nL 298.1969 418.951052 \r\nL 298.294171 420.069279 \r\nL 298.326595 419.435787 \r\nL 298.342807 419.426339 \r\nL 298.375231 420.264011 \r\nL 298.45629 419.971347 \r\nL 298.504926 418.539454 \r\nL 298.53735 420.226555 \r\nL 298.553561 421.352348 \r\nL 298.602197 419.651934 \r\nL 298.650833 420.555249 \r\nL 298.780528 418.844257 \r\nL 298.79674 419.16386 \r\nL 298.877799 420.765829 \r\nL 298.910223 420.197389 \r\nL 298.958859 418.59206 \r\nL 298.991282 420.315155 \r\nL 299.039918 419.06865 \r\nL 299.05613 418.97312 \r\nL 299.072342 419.252043 \r\nL 299.202037 420.183721 \r\nL 299.364156 417.537338 \r\nL 299.299308 420.352148 \r\nL 299.380368 417.700975 \r\nL 299.526275 419.72537 \r\nL 299.688393 417.263739 \r\nL 299.704605 417.943379 \r\nL 299.785665 420.682017 \r\nL 299.8343 419.786824 \r\nL 299.947784 419.055078 \r\nL 299.963996 419.170122 \r\nL 300.061267 420.391529 \r\nL 300.109903 419.604415 \r\nL 300.158538 419.168643 \r\nL 300.190962 417.960268 \r\nL 300.25581 419.572359 \r\nL 300.288233 420.041944 \r\nL 300.336869 418.760114 \r\nL 300.482776 420.229709 \r\nL 300.498988 420.21194 \r\nL 300.580047 420.551496 \r\nL 300.661107 418.765614 \r\nL 300.709742 420.086675 \r\nL 300.742166 421.070808 \r\nL 300.790802 419.777763 \r\nL 300.823226 420.410346 \r\nL 300.888073 418.043674 \r\nL 300.985344 418.706821 \r\nL 301.03398 419.773454 \r\nL 301.11504 419.339383 \r\nL 301.147463 418.538876 \r\nL 301.179887 419.613433 \r\nL 301.228523 418.993288 \r\nL 301.277158 420.029717 \r\nL 301.358218 419.647611 \r\nL 301.406853 419.050639 \r\nL 301.439277 419.876988 \r\nL 301.471701 420.38508 \r\nL 301.536549 419.56098 \r\nL 301.63382 418.63275 \r\nL 301.568972 419.585887 \r\nL 301.650032 419.260996 \r\nL 301.714879 419.77936 \r\nL 301.747303 419.302261 \r\nL 301.763515 419.288251 \r\nL 301.779727 419.503512 \r\nL 301.860786 419.103625 \r\nL 301.876998 418.977065 \r\nL 301.909422 419.236125 \r\nL 301.958058 421.183073 \r\nL 302.039117 419.738812 \r\nL 302.087753 418.759662 \r\nL 302.1526 419.305678 \r\nL 302.217448 420.952269 \r\nL 302.266083 419.622876 \r\nL 302.363355 418.326525 \r\nL 302.395779 419.386058 \r\nL 302.444414 421.074261 \r\nL 302.509262 420.055382 \r\nL 302.525474 420.285349 \r\nL 302.557897 419.215239 \r\nL 302.606533 418.131714 \r\nL 302.638957 418.934468 \r\nL 302.736228 420.865302 \r\nL 302.75244 420.491245 \r\nL 302.817288 417.781845 \r\nL 302.865923 419.326255 \r\nL 302.946983 420.352363 \r\nL 302.963195 420.024952 \r\nL 303.09289 417.698773 \r\nL 303.109102 417.743431 \r\nL 303.157737 419.67115 \r\nL 303.206373 422.006868 \r\nL 303.238797 418.932256 \r\nL 303.336068 417.224232 \r\nL 303.35228 417.841682 \r\nL 303.417127 420.689998 \r\nL 303.498187 420.546005 \r\nL 303.530611 419.388686 \r\nL 303.579246 416.786829 \r\nL 303.627882 419.112721 \r\nL 303.725153 420.295594 \r\nL 303.741365 419.987785 \r\nL 303.790001 417.830416 \r\nL 303.854848 419.324196 \r\nL 303.903484 418.72539 \r\nL 303.919696 419.396398 \r\nL 303.95212 420.840068 \r\nL 304.016967 418.849382 \r\nL 304.146662 421.513346 \r\nL 304.162874 421.333906 \r\nL 304.227722 418.775235 \r\nL 304.308781 419.831261 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418.101452 \r\nL 306.286631 419.650485 \r\nL 306.302843 419.646222 \r\nL 306.335267 419.321342 \r\nL 306.367691 420.060437 \r\nL 306.383903 420.392108 \r\nL 306.432538 419.401205 \r\nL 306.464962 418.581932 \r\nL 306.546022 419.137113 \r\nL 306.594657 421.078723 \r\nL 306.643293 418.396936 \r\nL 306.659505 417.964695 \r\nL 306.70814 419.226912 \r\nL 306.724352 419.18918 \r\nL 306.821624 421.037533 \r\nL 306.870259 419.707342 \r\nL 306.983743 417.326336 \r\nL 306.999954 417.946303 \r\nL 307.064802 421.580358 \r\nL 307.129649 419.554562 \r\nL 307.178285 418.893453 \r\nL 307.226921 418.399407 \r\nL 307.259345 419.37073 \r\nL 307.356616 420.081517 \r\nL 307.372828 419.940846 \r\nL 307.437675 418.11476 \r\nL 307.486311 418.865238 \r\nL 307.551159 420.749008 \r\nL 307.599794 419.410097 \r\nL 307.632218 418.604532 \r\nL 307.713277 419.028421 \r\nL 307.810549 421.164673 \r\nL 307.842972 420.374697 \r\nL 307.891608 418.58455 \r\nL 307.956456 419.373315 \r\nL 308.037515 422.217947 \r\nL 308.069939 420.919224 \r\nL 308.199634 417.921379 \r\nL 308.215846 418.106435 \r\nL 308.280693 421.855216 \r\nL 308.361753 419.731551 \r\nL 308.442812 418.079781 \r\nL 308.475236 419.178454 \r\nL 308.523872 420.980298 \r\nL 308.588719 419.719367 \r\nL 308.637355 419.001162 \r\nL 308.653567 418.72995 \r\nL 308.702202 419.513363 \r\nL 308.718414 419.484852 \r\nL 308.734626 419.402025 \r\nL 308.76705 419.800534 \r\nL 308.799474 420.526234 \r\nL 308.831898 419.506825 \r\nL 308.896745 417.908761 \r\nL 308.929169 418.895061 \r\nL 308.961593 419.731371 \r\nL 309.042652 419.277117 \r\nL 309.091288 418.121121 \r\nL 309.172347 418.630225 \r\nL 309.237195 420.169128 \r\nL 309.253407 420.928381 \r\nL 309.302042 418.788089 \r\nL 309.334466 417.515052 \r\nL 309.383102 419.11526 \r\nL 309.480373 420.227277 \r\nL 309.512797 419.560792 \r\nL 309.642492 418.567675 \r\nL 309.593856 419.64393 \r\nL 309.658704 418.598712 \r\nL 309.723551 419.8979 \r\nL 309.788399 419.477259 \r\nL 309.837035 418.064027 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419.850216 \r\nL 313.776523 419.651971 \r\nL 313.857582 419.844898 \r\nL 313.938642 418.863936 \r\nL 313.971066 419.411511 \r\nL 314.035913 421.108394 \r\nL 314.068337 419.660717 \r\nL 314.133185 418.70917 \r\nL 314.165608 419.508502 \r\nL 314.279092 420.349582 \r\nL 314.295303 420.223651 \r\nL 314.424998 418.566847 \r\nL 314.44121 418.759415 \r\nL 314.506058 420.382095 \r\nL 314.554694 419.278443 \r\nL 314.603329 417.686312 \r\nL 314.651965 418.846565 \r\nL 314.797872 420.262774 \r\nL 314.878931 418.26539 \r\nL 314.943779 419.13819 \r\nL 315.008626 420.733582 \r\nL 315.057262 419.250003 \r\nL 315.089686 418.389243 \r\nL 315.154533 419.361664 \r\nL 315.251805 420.722366 \r\nL 315.268017 420.110748 \r\nL 315.3815 418.092331 \r\nL 315.397712 418.363513 \r\nL 315.494983 420.704043 \r\nL 315.543619 419.761701 \r\nL 315.608466 418.26686 \r\nL 315.657102 419.279885 \r\nL 315.721949 420.826763 \r\nL 315.786797 419.883618 \r\nL 315.835433 418.469571 \r\nL 315.916492 419.316588 \r\nL 315.997551 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419.35362 \r\nL 476.674489 419.582465 \r\nL 477.031173 420.072096 \r\nL 477.225727 419.791406 \r\nL 477.387856 419.696358 \r\nL 477.517559 420.012322 \r\nL 477.939094 421.617826 \r\nL 477.971519 421.59614 \r\nL 478.101222 421.204859 \r\nL 478.555183 419.400057 \r\nL 478.684886 419.567857 \r\nL 479.009143 420.080854 \r\nL 479.138846 419.863725 \r\nL 479.72251 418.423907 \r\nL 480.371025 418.616769 \r\nL 480.598005 419.255502 \r\nL 480.922263 420.077235 \r\nL 481.01954 419.985845 \r\nL 481.473501 418.969598 \r\nL 481.538352 419.050453 \r\nL 481.732907 419.732718 \r\nL 482.186867 421.419204 \r\nL 482.284144 421.451015 \r\nL 482.31657 421.411608 \r\nL 482.446273 421.00152 \r\nL 482.673253 419.353309 \r\nL 483.029937 416.947572 \r\nL 483.127214 416.880993 \r\nL 483.15964 416.925773 \r\nL 483.354194 417.560494 \r\nL 483.581175 417.9634 \r\nL 483.873006 417.618354 \r\nL 483.937858 417.724462 \r\nL 484.067561 418.383769 \r\nL 484.586373 422.186848 \r\nL 484.651224 422.115302 \r\nL 484.878205 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420.347732 \r\nL 492.400979 420.819049 \r\nL 492.530682 420.661347 \r\nL 492.952217 419.992538 \r\nL 493.114346 419.785793 \r\nL 493.276475 419.043723 \r\nL 493.633158 417.209778 \r\nL 493.698009 417.285852 \r\nL 493.860138 418.16227 \r\nL 494.184396 419.877749 \r\nL 494.281673 419.764275 \r\nL 494.638356 418.952501 \r\nL 494.670782 418.978959 \r\nL 494.962614 419.344834 \r\nL 494.995039 419.302329 \r\nL 495.157168 418.68754 \r\nL 495.384148 417.797455 \r\nL 495.416574 417.81077 \r\nL 495.513852 418.183126 \r\nL 495.740832 420.529583 \r\nL 496.000238 422.308943 \r\nL 496.065089 422.153543 \r\nL 496.227218 420.840209 \r\nL 496.583901 418.145349 \r\nL 496.681179 418.345689 \r\nL 497.297268 420.638501 \r\nL 497.329694 420.619168 \r\nL 497.524248 420.245932 \r\nL 497.945783 419.374497 \r\nL 498.075486 419.521687 \r\nL 498.367318 420.036439 \r\nL 498.399743 419.995962 \r\nL 498.529447 419.508335 \r\nL 498.950981 416.883124 \r\nL 499.015833 417.036164 \r\nL 499.177962 418.300927 \r\nL 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417.606982 \r\nL 521.778711 418.323377 \r\nL 522.16782 420.138851 \r\nL 522.265097 420.12892 \r\nL 522.427226 419.727591 \r\nL 522.848761 418.387877 \r\nL 522.881187 418.397205 \r\nL 523.01089 418.651049 \r\nL 523.626979 420.432272 \r\nL 524.016088 420.690614 \r\nL 524.145791 420.381939 \r\nL 524.405197 418.782855 \r\nL 524.632177 417.91631 \r\nL 524.729455 418.17571 \r\nL 525.021286 420.426802 \r\nL 525.215841 421.066928 \r\nL 525.313118 420.808145 \r\nL 525.896782 418.430947 \r\nL 525.929207 418.44647 \r\nL 526.188613 418.763129 \r\nL 526.675 419.699803 \r\nL 526.739851 419.645316 \r\nL 526.934406 419.136613 \r\nL 527.12896 418.728975 \r\nL 527.161386 418.745714 \r\nL 527.291089 419.116752 \r\nL 527.680198 420.742342 \r\nL 527.712624 420.715566 \r\nL 527.874753 420.153711 \r\nL 528.134159 419.367672 \r\nL 528.231436 419.502408 \r\nL 528.490842 419.981286 \r\nL 528.620545 419.669604 \r\nL 528.912377 418.702937 \r\nL 528.944802 418.737316 \r\nL 529.074505 419.23249 \r\nL 529.366337 420.493806 \r\nL 529.463614 420.1423 \r\nL 529.852724 417.253203 \r\nL 529.950001 417.460689 \r\nL 530.176981 419.364549 \r\nL 530.371536 420.247841 \r\nL 530.468813 420.064357 \r\nL 530.728219 419.328708 \r\nL 530.825496 419.616741 \r\nL 531.149754 421.096178 \r\nL 531.182179 421.023803 \r\nL 531.311882 420.140967 \r\nL 531.63614 417.521958 \r\nL 531.733417 417.92627 \r\nL 532.122526 421.072192 \r\nL 532.187378 420.873562 \r\nL 532.544061 418.518596 \r\nL 532.608912 418.684597 \r\nL 532.965596 420.824567 \r\nL 533.030447 420.57095 \r\nL 533.225002 418.423453 \r\nL 533.419556 416.790923 \r\nL 533.451982 416.800838 \r\nL 533.549259 417.366861 \r\nL 533.970794 421.574828 \r\nL 534.035646 421.430216 \r\nL 534.359903 419.940423 \r\nL 534.424755 420.091532 \r\nL 534.716586 421.377855 \r\nL 534.781438 421.206996 \r\nL 534.943567 419.654219 \r\nL 535.267824 416.66675 \r\nL 535.332676 416.856688 \r\nL 535.52723 418.675051 \r\nL 535.75421 420.186672 \r\nL 535.819062 420.117133 \r\nL 536.013616 419.062729 \r\nL 536.208171 418.331204 \r\nL 536.240597 418.336986 \r\nL 536.3703 418.652307 \r\nL 536.759409 419.760816 \r\nL 537.051241 420.1756 \r\nL 537.213369 420.356638 \r\nL 537.245795 420.337292 \r\nL 537.375498 419.954046 \r\nL 537.732181 418.414188 \r\nL 537.764607 418.472984 \r\nL 537.926736 419.475185 \r\nL 538.218568 421.311686 \r\nL 538.283419 421.118757 \r\nL 538.445548 419.52953 \r\nL 538.769805 416.649879 \r\nL 538.834657 416.83849 \r\nL 539.029211 418.720127 \r\nL 539.353469 421.523477 \r\nL 539.483172 421.673567 \r\nL 539.515598 421.651394 \r\nL 539.677727 421.369547 \r\nL 539.904707 420.455686 \r\nL 540.391093 417.54812 \r\nL 540.455945 417.711819 \r\nL 540.618073 418.946428 \r\nL 540.942331 421.159787 \r\nL 541.007182 421.110886 \r\nL 541.169311 420.368454 \r\nL 541.525994 418.903845 \r\nL 541.882678 418.464573 \r\nL 542.142084 418.052215 \r\nL 542.239361 418.184569 \r\nL 542.758173 419.67808 \r\nL 542.823025 419.634196 \r\nL 543.147282 419.463484 \r\nL 543.536391 419.594404 \r\nL 543.828223 419.5191 \r\nL 544.022777 419.845695 \r\nL 544.249758 420.110717 \r\nL 544.411886 419.910974 \r\nL 544.638867 419.671517 \r\nL 544.671292 419.691566 \r\nL 545.060401 420.065851 \r\nL 545.092827 420.008235 \r\nL 545.287382 419.285427 \r\nL 545.514362 418.664493 \r\nL 545.611639 418.739278 \r\nL 545.935897 419.483418 \r\nL 546.000748 419.418246 \r\nL 546.162877 418.864313 \r\nL 546.357432 418.400087 \r\nL 546.454709 418.685631 \r\nL 546.746541 420.926785 \r\nL 546.908669 421.450718 \r\nL 547.005947 421.185734 \r\nL 547.427481 419.533678 \r\nL 547.719313 419.662211 \r\nL 547.751739 419.600182 \r\nL 547.946293 418.801653 \r\nL 548.173274 418.181904 \r\nL 548.270551 418.427706 \r\nL 548.562383 419.545944 \r\nL 548.594809 419.529374 \r\nL 548.724512 419.03728 \r\nL 548.983918 417.778894 \r\nL 549.016343 417.791092 \r\nL 549.113621 418.208128 \r\nL 549.50273 421.423652 \r\nL 549.600007 421.154394 \r\nL 549.794561 419.269541 \r\nL 550.021542 417.673005 \r\nL 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=5)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.3090559299602927\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 102.016859 418.076669 \r\nL 102.065495 422.140917 \r\nL 102.19519 418.343003 \r\nL 102.146554 422.176544 \r\nL 102.227614 420.867986 \r\nL 102.308673 418.243215 \r\nL 102.357309 423.092104 \r\nL 102.503216 418.831567 \r\nL 102.519428 419.482405 \r\nL 102.551851 421.573841 \r\nL 102.600487 418.114244 \r\nL 102.649123 421.543057 \r\nL 102.665335 421.534197 \r\nL 102.79503 416.853222 \r\nL 102.827454 417.643115 \r\nL 102.876089 418.420414 \r\nL 102.908513 417.295476 \r\nL 102.940937 416.312655 \r\nL 103.005784 417.79695 \r\nL 103.05442 414.12297 \r\nL 103.086844 416.176934 \r\nL 103.200327 420.112725 \r\nL 103.248963 415.443468 \r\nL 103.31381 419.948584 \r\nL 103.362446 418.895474 \r\nL 103.39487 419.45217 \r\nL 103.427293 421.20571 \r\nL 103.475929 418.394362 \r\nL 103.524565 420.677945 \r\nL 103.556988 419.990873 \r\nL 103.5732 421.115549 \r\nL 103.621836 424.544413 \r\nL 103.670472 420.999622 \r\nL 103.767743 418.935856 \r\nL 103.719107 421.522441 \r\nL 103.783955 420.122914 \r\nL 103.897438 424.311263 \r\nL 103.91365 423.412431 \r\nL 104.027133 418.725821 \r\nL 104.043345 419.642524 \r\nL 104.17304 421.271663 \r\nL 104.221676 418.238914 \r\nL 104.302735 420.822883 \r\nL 104.318947 420.821367 \r\nL 104.416218 423.156657 \r\nL 104.43243 422.096437 \r\nL 104.464854 418.498653 \r\nL 104.562125 419.583529 \r\nL 104.594549 421.42263 \r\nL 104.643185 417.971119 \r\nL 104.77288 414.932632 \r\nL 104.708032 418.53113 \r\nL 104.789092 415.678459 \r\nL 104.821516 419.100431 \r\nL 104.886363 415.682828 \r\nL 104.951211 414.541039 \r\nL 104.967423 413.927152 \r\nL 105.016058 416.059553 \r\nL 105.129541 418.941538 \r\nL 105.178177 418.179504 \r\nL 105.194389 418.10923 \r\nL 105.226813 416.15303 \r\nL 105.259237 418.660117 \r\nL 105.324084 417.541416 \r\nL 105.37272 422.465125 \r\nL 105.421355 416.6598 \r\nL 105.469991 423.279049 \r\nL 105.502415 420.260182 \r\nL 105.518627 418.30897 \r\nL 105.567262 423.155176 \r\nL 105.615898 419.076668 \r\nL 105.63211 418.77679 \r\nL 105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 419.701183 \r\nL 196.094438 418.00952 \r\nL 196.143073 420.649234 \r\nL 196.191709 416.199579 \r\nL 196.28898 417.366366 \r\nL 196.37004 416.308742 \r\nL 196.418675 420.426478 \r\nL 196.467311 416.606475 \r\nL 196.515947 420.559165 \r\nL 196.532159 421.148637 \r\nL 196.580794 418.879609 \r\nL 196.613218 420.68436 \r\nL 196.62943 420.609831 \r\nL 196.661854 418.763258 \r\nL 196.710489 420.967438 \r\nL 196.742913 420.44045 \r\nL 196.759125 420.47238 \r\nL 196.856396 422.149606 \r\nL 196.872608 421.599822 \r\nL 196.905032 420.208393 \r\nL 196.953668 422.230145 \r\nL 196.969879 422.162306 \r\nL 196.986091 422.129023 \r\nL 197.067151 423.465547 \r\nL 197.099575 422.612249 \r\nL 197.164422 423.376404 \r\nL 197.22927 420.278935 \r\nL 197.277905 422.495307 \r\nL 197.310329 420.714202 \r\nL 197.440024 415.336633 \r\nL 197.456236 416.381807 \r\nL 197.472448 417.053758 \r\nL 197.504872 414.877169 \r\nL 197.618355 412.860758 \r\nL 197.634567 413.456525 \r\nL 197.845321 417.763333 \r\nL 197.861533 417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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304.114239 420.63193 \r\nL 304.146662 422.477989 \r\nL 304.21151 420.603813 \r\nL 304.260146 421.000483 \r\nL 304.292569 420.601905 \r\nL 304.324993 420.263745 \r\nL 304.357417 420.857399 \r\nL 304.373629 421.051835 \r\nL 304.389841 420.669467 \r\nL 304.535748 417.180814 \r\nL 304.55196 417.233379 \r\nL 304.697866 419.693106 \r\nL 304.746502 418.199841 \r\nL 304.795138 420.016161 \r\nL 304.892409 422.099487 \r\nL 304.843773 419.405684 \r\nL 304.924833 420.842236 \r\nL 304.957257 419.950565 \r\nL 305.038316 420.141007 \r\nL 305.103164 421.07975 \r\nL 305.151799 420.304603 \r\nL 305.200435 420.035808 \r\nL 305.249071 418.202238 \r\nL 305.33013 418.766127 \r\nL 305.394978 419.126866 \r\nL 305.411189 419.004515 \r\nL 305.459825 417.790766 \r\nL 305.540885 418.259238 \r\nL 305.605732 419.8005 \r\nL 305.686792 419.632806 \r\nL 305.735427 418.264432 \r\nL 305.784063 419.919016 \r\nL 305.816487 419.280174 \r\nL 305.84891 420.361139 \r\nL 305.881334 421.338921 \r\nL 305.92997 419.011347 \r\nL 305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val) # inifity-steps-ahead prediction\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/source/notebooks.rst b/docs/source/notebooks.rst
index 1eee2b03..8bed507d 100644
--- a/docs/source/notebooks.rst
+++ b/docs/source/notebooks.rst
@@ -72,3 +72,9 @@ Jupyter notebooks
    :caption: Identification of an electromechanical system
 
    /examples/identification_of_an_electromechanical_system.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: N-steps-ahead simulation
+
+   /examples/n_steps_ahead_prediction.ipynb
diff --git a/examples/n_steps_ahead_prediction.ipynb b/examples/n_steps_ahead_prediction.ipynb
new file mode 100644
index 00000000..6e1ee55e
--- /dev/null
+++ b/examples/n_steps_ahead_prediction.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark\n",
+    "\n",
+    "**Note**: The following examples do not try to replicate the results of the cited manuscripts. Even the model parameters such as ylag and xlag and size of identification and validation data are not the same of the cited papers. Moreover, sampling rate adjustment and other different data preparation are not handled here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**The following text was taken from the link http://www.nonlinearbenchmark.org/#F16**. \n",
+    "\n",
+    "**Note**: The reader is reffered to the mentioned website for a complete reference concerning the experiment. For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. A more detailed study of this system will be published in the future.  \n",
+    "\n",
+    "> The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads.\n",
+    "\n",
+    "> The experimental data made available to the Workshop participants were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class, held in September 2014 at the Saffraanberg military basis, Sint-Truiden, Belgium.\n",
+    "\n",
+    "> During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study.\n",
+    "\n",
+    "> A detailed formulation of the identification problem can be found here. All the provided files and information on the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format.\n",
+    "\n",
+    "> Please refer to the F16 benchmark as:\n",
+    "\n",
+    "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "import pandas as pd\n",
+    "pd.set_option('display.max_columns', 1000)"
+   ]
+  },
+  {
+   "source": [
+    "## Preparing the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_16 = pd.read_csv(r'examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(32768, 3)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "f_16.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16[['x1', 'x2']][0:500].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x576 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"465.958125pt\" version=\"1.1\" viewBox=\"0 0 721.645312 465.958125\" width=\"721.645312pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-08T23:42:32.923280</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16['y'][0:2000].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1_id, x1_val = f_16['x1'][0:16384].values.reshape(-1, 1), f_16['x1'][16384::].values.reshape(-1, 1)\n",
+    "x2_id, x2_val = f_16['x2'][0:16384].values.reshape(-1, 1), f_16['x2'][16384::].values.reshape(-1, 1)\n",
+    "x_id = np.concatenate([x1_id, x2_id], axis=1)\n",
+    "x_val = np.concatenate([x1_val, x2_val], axis=1)\n",
+    "\n",
+    "y_id, y_val = f_16['y'][0:16384].values.reshape(-1, 1), f_16['y'][16384::].values.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "x1lag = list(range(1, 10))\n",
+    "x2lag = list(range(1, 10))\n",
+    "x2lag"
+   ]
+  },
+  {
+   "source": [
+    "## Building the model"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2523e7e4850>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax(# n_terms=8,\n",
+    "                         non_degree=1,\n",
+    "                         order_selection=True,\n",
+    "                         ylag=20, xlag=[x1lag, x2lag],\n",
+    "                         n_inputs = 2,\n",
+    "                         n_info_values=39,\n",
+    "                         info_criteria='bic',\n",
+    "                         extended_least_squares=False,\n",
+    "                         estimator='least_squares')\n",
+    "model.fit(x_id, y_id)"
+   ]
+  },
+  {
+   "source": [
+    "## Defining the forecasting horizon\n",
+    "\n",
+    "To perform a n-steps-ahead prediction you just need to set the \"steps_ahead\" argument.\n",
+    "\n",
+    "**Note** The default value for steps_ahead is None and it performs a infinity-steps-ahead prediction"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.09660139833917882\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Regressors   Parameters             ERR\n",
+       "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
+       "1      y(k-2)  -1.8938E+00  1.95167599E-02\n",
+       "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
+       "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
+       "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
+       "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
+       "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
+       "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
+       "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
+       "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
+       "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
+       "13     y(k-4)  -9.5479E-01  7.23623116E-04\n",
+       "14     y(k-8)  -7.1309E-01  4.44988077E-04\n",
+       "15    y(k-12)  -3.0437E-01  1.49743148E-04\n",
+       "16    y(k-11)   4.8602E-01  3.34613282E-04\n",
+       "17    y(k-13)  -8.2442E-02  1.43738964E-04\n",
+       "18    y(k-15)  -1.6762E-01  1.25546584E-04\n",
+       "19    x1(k-2)  -8.9698E+00  9.76699739E-05\n",
+       "20    y(k-17)   2.2036E-02  4.55983807E-05\n",
+       "21    y(k-14)   2.4900E-01  1.10314107E-04\n",
+       "22    y(k-19)  -6.8239E-03  1.99734771E-05\n",
+       "23    x2(k-9)  -9.6265E+01  2.98523208E-05\n",
+       "24    x2(k-8)   2.2620E+02  2.34402543E-04\n",
+       "25    x2(k-2)  -2.3609E+02  1.04172323E-04\n",
+       "26    y(k-20)  -5.4663E-02  5.37895336E-05\n",
+       "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
+       "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
+       "29    x1(k-7)   4.9862E+00  2.03811842E-05"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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418.788988 \r\nL 95.823918 417.14612 \r\nL 95.888766 419.571574 \r\nL 95.969825 422.427663 \r\nL 95.986037 419.935081 \r\nL 96.002249 419.588972 \r\nL 96.018461 416.776156 \r\nL 96.067097 421.126941 \r\nL 96.115732 417.322844 \r\nL 96.131944 417.280816 \r\nL 96.229216 423.726638 \r\nL 96.245428 421.302618 \r\nL 96.358911 417.992977 \r\nL 96.375123 418.91002 \r\nL 96.391335 418.523717 \r\nL 96.407546 421.836762 \r\nL 96.504818 420.989002 \r\nL 96.52103 421.52937 \r\nL 96.537242 420.803017 \r\nL 96.553453 417.185378 \r\nL 96.650725 418.894004 \r\nL 96.764208 421.75883 \r\nL 96.78042 420.207059 \r\nL 96.812844 415.902476 \r\nL 96.845267 420.703498 \r\nL 96.861479 419.295515 \r\nL 96.877691 421.960188 \r\nL 96.893903 418.710054 \r\nL 96.974962 419.82729 \r\nL 97.03981 420.582339 \r\nL 97.088446 417.151598 \r\nL 97.201929 421.431456 \r\nL 97.218141 420.172974 \r\nL 97.250565 418.42234 \r\nL 97.266776 417.507238 \r\nL 97.2992 419.6799 \r\nL 97.315412 418.837208 \r\nL 97.347836 418.064348 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420.101691 \r\nL 103.443505 420.742359 \r\nL 103.492141 416.86154 \r\nL 103.621836 422.846315 \r\nL 103.767743 416.84079 \r\nL 103.881226 422.925947 \r\nL 103.91365 422.181374 \r\nL 104.027133 416.755559 \r\nL 104.059557 420.725891 \r\nL 104.140616 419.958285 \r\nL 104.205464 418.003117 \r\nL 104.189252 421.020615 \r\nL 104.2541 419.795129 \r\nL 104.270311 420.131831 \r\nL 104.302735 419.510922 \r\nL 104.318947 418.64919 \r\nL 104.335159 420.367969 \r\nL 104.383795 419.936804 \r\nL 104.43243 421.007442 \r\nL 104.448642 418.574488 \r\nL 104.464854 417.460861 \r\nL 104.529702 419.911969 \r\nL 104.545914 418.527138 \r\nL 104.594549 421.15305 \r\nL 104.643185 418.057142 \r\nL 104.659397 419.227341 \r\nL 104.756668 418.099954 \r\nL 104.740456 419.591364 \r\nL 104.789092 418.126443 \r\nL 104.837727 421.662223 \r\nL 104.902575 418.477377 \r\nL 104.934999 420.103558 \r\nL 104.999846 417.042155 \r\nL 105.129541 422.298224 \r\nL 105.243025 417.27335 \r\nL 105.259237 417.730767 \r\nL 105.275448 421.598409 \r\nL 105.324084 417.068191 \r\nL 105.37272 420.736869 \r\nL 105.437567 417.576288 \r\nL 105.469991 420.850482 \r\nL 105.486203 421.141337 \r\nL 105.534839 418.621245 \r\nL 105.567262 421.921523 \r\nL 105.583474 419.260443 \r\nL 105.599686 420.927807 \r\nL 105.680746 417.991595 \r\nL 105.696957 417.48019 \r\nL 105.713169 419.43557 \r\nL 105.729381 418.761132 \r\nL 105.794229 421.210393 \r\nL 105.826653 420.900028 \r\nL 105.842864 417.398889 \r\nL 105.859076 421.148724 \r\nL 105.940136 418.166941 \r\nL 106.053619 422.706448 \r\nL 106.069831 422.452197 \r\nL 106.167102 416.770083 \r\nL 106.215738 417.590979 \r\nL 106.280585 421.542635 \r\nL 106.329221 420.638757 \r\nL 106.442704 416.967766 \r\nL 106.458916 417.291967 \r\nL 106.572399 421.289577 \r\nL 106.637247 417.136384 \r\nL 106.604823 423.172004 \r\nL 106.685883 417.690177 \r\nL 106.75073 421.274751 \r\nL 106.799366 419.060147 \r\nL 106.83179 421.66916 \r\nL 106.848001 418.835716 \r\nL 106.864213 417.46113 \r\nL 106.896637 419.304261 \r\nL 106.929061 417.824813 \r\nL 107.042544 421.836748 \r\nL 107.058756 421.241315 \r\nL 107.139815 417.699771 \r\nL 107.172239 418.721248 \r\nL 107.204663 417.853339 \r\nL 107.301934 422.050618 \r\nL 107.415417 416.917136 \r\nL 107.431629 417.459288 \r\nL 107.561324 422.312428 \r\nL 107.577536 421.855897 \r\nL 107.626172 416.545222 \r\nL 107.707231 419.342418 \r\nL 107.755867 417.707671 \r\nL 107.739655 420.10933 \r\nL 107.804503 420.000805 \r\nL 107.820715 421.917179 \r\nL 107.86935 417.888338 \r\nL 107.917986 420.63239 \r\nL 107.982834 422.159306 \r\nL 108.047681 417.80408 \r\nL 108.063893 420.515044 \r\nL 108.144952 416.64582 \r\nL 108.161164 420.151233 \r\nL 108.177376 418.28243 \r\nL 108.193588 422.627712 \r\nL 108.258436 420.587697 \r\nL 108.371919 417.324481 \r\nL 108.355707 420.819372 \r\nL 108.388131 417.506599 \r\nL 108.404343 415.718034 \r\nL 108.46919 420.651351 \r\nL 108.485402 418.724417 \r\nL 108.501614 420.390283 \r\nL 108.582673 417.841909 \r\nL 108.598885 419.984296 \r\nL 108.615097 419.873142 \r\nL 108.631309 417.365505 \r\nL 108.712368 419.039348 \r\nL 108.744792 421.815672 \r\nL 108.793428 418.387855 \r\nL 108.80964 419.568351 \r\nL 108.825852 416.874879 \r\nL 108.842063 420.399703 \r\nL 108.923123 418.485202 \r\nL 108.971759 418.411445 \r\nL 109.036606 422.085311 \r\nL 109.166301 417.826321 \r\nL 109.182513 417.455959 \r\nL 109.231149 422.656108 \r\nL 109.295996 419.95027 \r\nL 109.312208 420.167976 \r\nL 109.344632 417.229085 \r\nL 109.393268 420.2449 \r\nL 109.425691 419.079978 \r\nL 109.441903 418.966646 \r\nL 109.490539 421.690094 \r\nL 109.539175 418.88277 \r\nL 109.555387 420.526498 \r\nL 109.652658 417.746278 \r\nL 109.66887 418.211617 \r\nL 109.766141 422.426901 \r\nL 109.782353 419.689925 \r\nL 109.798565 419.446776 \r\nL 109.814777 422.110914 \r\nL 109.879624 416.849548 \r\nL 109.895836 418.486789 \r\nL 110.009319 423.38414 \r\nL 109.960684 417.831446 \r\nL 110.057955 419.888465 \r\nL 110.139014 417.531059 \r\nL 110.122803 420.531087 \r\nL 110.171438 418.872135 \r\nL 110.18765 418.023242 \r\nL 110.203862 420.778306 \r\nL 110.252498 419.779249 \r\nL 110.26871 421.49687 \r\nL 110.333557 418.4384 \r\nL 110.349769 420.85991 \r\nL 110.382193 416.805081 \r\nL 110.479464 418.52655 \r\nL 110.544312 421.675527 \r\nL 110.576735 419.262477 \r\nL 110.592947 415.839351 \r\nL 110.641583 420.16215 \r\nL 110.674007 416.79728 \r\nL 110.70643 420.908929 \r\nL 110.78749 419.305058 \r\nL 110.803702 419.83822 \r\nL 110.852337 418.332862 \r\nL 110.884761 420.776129 \r\nL 110.933397 416.785408 \r\nL 110.949609 422.471818 \r\nL 111.04688 419.753082 \r\nL 111.095516 416.090603 \r\nL 111.079304 420.800841 \r\nL 111.160363 418.914161 \r\nL 111.273846 422.064862 \r\nL 111.403542 416.82817 \r\nL 111.500813 421.846086 \r\nL 111.533237 421.772468 \r\nL 111.630508 416.731744 \r\nL 111.64672 417.885513 \r\nL 111.727779 421.603716 \r\nL 111.760203 420.780567 \r\nL 111.792627 421.628502 \r\nL 111.857474 417.480024 \r\nL 111.954746 422.584253 \r\nL 111.90611 415.849559 \r\nL 111.970958 421.473004 \r\nL 112.084441 417.267456 \r\nL 112.214136 421.42327 \r\nL 112.230348 421.19605 \r\nL 112.278983 416.848134 \r\nL 112.360043 418.195479 \r\nL 112.376255 417.96604 \r\nL 112.392467 418.248834 \r\nL 112.489738 422.495936 \r\nL 112.50595 419.944542 \r\nL 112.619433 416.620296 \r\nL 112.538374 420.326952 \r\nL 112.635645 417.724542 \r\nL 112.76534 423.351084 \r\nL 112.878823 417.51851 \r\nL 112.959883 422.08408 \r\nL 113.008518 419.896162 \r\nL 113.040942 419.516778 \r\nL 113.057154 417.198351 \r\nL 113.122002 420.053382 \r\nL 113.138213 418.429983 \r\nL 113.186849 420.973675 \r\nL 113.170637 417.379174 \r\nL 113.251697 419.381181 \r\nL 113.267909 419.467128 \r\nL 113.28412 422.023543 \r\nL 113.348968 417.418696 \r\nL 113.381392 420.36723 \r\nL 113.430027 421.096866 \r\nL 113.478663 420.375351 \r\nL 113.494875 420.225657 \r\nL 113.527299 417.216306 \r\nL 113.543511 420.371135 \r\nL 113.608358 418.809337 \r\nL 113.656994 422.258418 \r\nL 113.640782 418.541309 \r\nL 113.738053 421.438566 \r\nL 113.851536 416.363806 \r\nL 113.867748 418.561654 \r\nL 113.916384 421.201479 \r\nL 113.932596 418.174796 \r\nL 113.997443 420.547116 \r\nL 114.062291 417.564504 \r\nL 114.094715 420.547729 \r\nL 114.110927 420.729413 \r\nL 114.191986 417.570328 \r\nL 114.22441 420.022976 \r\nL 114.240622 420.450459 \r\nL 114.256834 419.655052 \r\nL 114.273046 417.105015 \r\nL 114.337893 420.684317 \r\nL 114.354105 418.700668 \r\nL 114.402741 422.509545 \r\nL 114.451376 419.457065 \r\nL 114.467588 416.63188 \r\nL 114.548648 420.170431 \r\nL 114.564859 418.517682 \r\nL 114.629707 421.591673 \r\nL 114.678343 419.831123 \r\nL 114.710766 420.148664 \r\nL 114.74319 418.754165 \r\nL 114.808038 417.149098 \r\nL 114.840462 419.236205 \r\nL 114.872885 419.070159 \r\nL 114.937733 422.149065 \r\nL 115.00258 417.339021 \r\nL 115.051216 419.978754 \r\nL 115.08364 417.519746 \r\nL 115.148487 420.724667 \r\nL 115.164699 422.089882 \r\nL 115.180911 419.327113 \r\nL 115.245759 421.16003 \r\nL 115.310606 418.272673 \r\nL 115.34303 420.494977 \r\nL 115.359242 421.429844 \r\nL 115.407878 419.152026 \r\nL 115.424089 420.017137 \r\nL 115.505149 417.282894 \r\nL 115.472725 420.779357 \r\nL 115.537573 418.141815 \r\nL 115.667268 420.811849 \r\nL 115.764539 417.28623 \r\nL 115.878022 420.87449 \r\nL 115.959082 418.128611 \r\nL 115.94287 420.987667 \r\nL 116.007717 419.213353 \r\nL 116.056353 417.685626 \r\nL 116.121201 422.440899 \r\nL 116.137413 418.531918 \r\nL 116.234684 419.771324 \r\nL 116.250896 419.961304 \r\nL 116.299531 417.950351 \r\nL 116.331955 420.036477 \r\nL 116.348167 419.238848 \r\nL 116.413015 420.966489 \r\nL 116.380591 418.909965 \r\nL 116.445438 419.148542 \r\nL 116.494074 417.941605 \r\nL 116.526498 419.328874 \r\nL 116.54271 421.729168 \r\nL 116.607557 417.528212 \r\nL 116.639981 420.140585 \r\nL 116.737252 418.217599 \r\nL 116.769676 419.419365 \r\nL 116.785888 419.588216 \r\nL 116.8021 419.16319 \r\nL 116.818312 419.167732 \r\nL 116.915583 417.235995 \r\nL 116.850736 422.027642 \r\nL 116.931795 418.615731 \r\nL 116.964219 421.291536 \r\nL 117.012854 418.452236 \r\nL 117.029066 416.94894 \r\nL 117.077702 420.091701 \r\nL 117.093914 419.788373 \r\nL 117.126338 420.495759 \r\nL 117.142549 420.434402 \r\nL 117.158761 421.127621 \r\nL 117.207397 418.936756 \r\nL 117.239821 419.747173 \r\nL 117.272245 418.675541 \r\nL 117.304668 420.860897 \r\nL 117.353304 418.376249 \r\nL 117.385728 420.205171 \r\nL 117.40194 420.203473 \r\nL 117.418152 420.576318 \r\nL 117.450575 419.348983 \r\nL 117.482999 417.456614 \r\nL 117.531635 419.438748 \r\nL 117.547847 419.292606 \r\nL 117.645118 421.339822 \r\nL 117.66133 420.44006 \r\nL 117.726177 417.053093 \r\nL 117.791025 417.741534 \r\nL 117.888296 421.740247 \r\nL 117.936932 420.087486 \r\nL 117.985568 418.005398 \r\nL 118.017991 420.557148 \r\nL 118.050415 418.115087 \r\nL 118.082839 421.211419 \r\nL 118.18011 421.045245 \r\nL 118.196322 421.219949 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420.517664 \r\nL 121.714302 420.305801 \r\nL 121.730513 421.545655 \r\nL 121.746725 417.854448 \r\nL 121.811573 420.590321 \r\nL 121.843997 417.058189 \r\nL 121.860208 420.777253 \r\nL 121.941268 418.118185 \r\nL 121.973692 417.930774 \r\nL 122.054751 421.747758 \r\nL 122.103387 418.120215 \r\nL 122.168234 419.733083 \r\nL 122.200658 420.695978 \r\nL 122.21687 418.489798 \r\nL 122.249294 419.64348 \r\nL 122.281718 418.187183 \r\nL 122.297929 419.296647 \r\nL 122.314141 421.343045 \r\nL 122.362777 417.769077 \r\nL 122.395201 419.561866 \r\nL 122.411413 418.101582 \r\nL 122.47626 420.873015 \r\nL 122.492472 420.313076 \r\nL 122.508684 420.654614 \r\nL 122.524896 420.153021 \r\nL 122.541108 420.377782 \r\nL 122.55732 417.938055 \r\nL 122.605955 421.785986 \r\nL 122.638379 420.388484 \r\nL 122.654591 420.14361 \r\nL 122.670803 416.892364 \r\nL 122.751862 420.346047 \r\nL 122.768074 420.93012 \r\nL 122.881557 417.213932 \r\nL 122.946405 422.00181 \r\nL 122.995041 419.714599 \r\nL 123.043676 418.174141 \r\nL 123.027464 421.019921 \r\nL 123.108524 419.168338 \r\nL 123.124736 419.454312 \r\nL 123.140948 417.396059 \r\nL 123.157159 421.530021 \r\nL 123.222007 420.200229 \r\nL 123.254431 420.95629 \r\nL 123.286855 420.189054 \r\nL 123.33549 417.979301 \r\nL 123.384126 421.013793 \r\nL 123.400338 420.774305 \r\nL 123.41655 420.897434 \r\nL 123.432762 417.556208 \r\nL 123.497609 421.581609 \r\nL 123.530033 418.679385 \r\nL 123.546245 420.239517 \r\nL 123.59488 418.231012 \r\nL 123.643516 418.962434 \r\nL 123.724575 421.547816 \r\nL 123.740787 418.04944 \r\nL 123.773211 419.513625 \r\nL 123.789423 420.490353 \r\nL 123.838059 417.807775 \r\nL 123.854271 418.596492 \r\nL 123.870482 417.175148 \r\nL 123.902906 420.640687 \r\nL 123.93533 419.820142 \r\nL 124.000178 421.696754 \r\nL 123.967754 418.673958 \r\nL 124.032601 419.860227 \r\nL 124.048813 417.974042 \r\nL 124.113661 420.010227 \r\nL 124.146085 419.079171 \r\nL 124.19472 420.964382 \r\nL 124.210932 418.683395 \r\nL 124.227144 416.653371 \r\nL 124.243356 421.744155 \r\nL 124.308203 417.457155 \r\nL 124.373051 421.634512 \r\nL 124.421687 419.527463 \r\nL 124.470322 420.02603 \r\nL 124.486534 417.848526 \r\nL 124.502746 421.644799 \r\nL 124.583805 419.996865 \r\nL 124.600017 415.399664 \r\nL 124.648653 421.278123 \r\nL 124.681077 421.076558 \r\nL 124.745924 417.646243 \r\nL 124.810772 419.629264 \r\nL 124.826984 421.162145 \r\nL 124.859408 418.18476 \r\nL 124.908043 419.277519 \r\nL 124.924255 419.396811 \r\nL 124.972891 419.163304 \r\nL 125.021526 420.558144 \r\nL 125.070162 416.544287 \r\nL 125.118798 420.919544 \r\nL 125.183645 419.60278 \r\nL 125.216069 418.381351 \r\nL 125.248493 419.596468 \r\nL 125.264705 421.610921 \r\nL 125.297128 417.855609 \r\nL 125.345764 420.578483 \r\nL 125.378188 417.580937 \r\nL 125.3944 421.345602 \r\nL 125.443035 419.226366 \r\nL 125.459247 420.972349 \r\nL 125.524095 417.741048 \r\nL 125.556519 420.121568 \r\nL 125.605154 419.216362 \r\nL 125.621366 420.511144 \r\nL 125.686214 417.969459 \r\nL 125.702426 420.983424 \r\nL 125.718638 420.226254 \r\nL 125.734849 420.481308 \r\nL 125.767273 418.074187 \r\nL 125.815909 421.139479 \r\nL 125.848333 419.148219 \r\nL 125.880756 419.920463 \r\nL 125.978028 418.226199 \r\nL 126.042875 422.050121 \r\nL 126.075299 419.905309 \r\nL 126.107723 420.764598 \r\nL 126.204994 417.075569 \r\nL 126.237418 421.443357 \r\nL 126.334689 420.58493 \r\nL 126.367113 421.269664 \r\nL 126.399537 417.990807 \r\nL 126.464384 418.496246 \r\nL 126.496808 421.442856 \r\nL 126.529232 417.914024 \r\nL 126.577868 419.685006 \r\nL 126.642715 418.44055 \r\nL 126.675139 419.439048 \r\nL 126.707563 419.294963 \r\nL 126.723775 419.890976 \r\nL 126.756198 416.999771 \r\nL 126.788622 421.925837 \r\nL 126.837258 418.546562 \r\nL 126.869681 420.215907 \r\nL 126.902105 417.608173 \r\nL 126.934529 418.991607 \r\nL 126.950741 418.354215 \r\nL 126.966953 419.904801 \r\nL 126.983165 419.67162 \r\nL 126.999377 421.953818 \r\nL 127.064224 419.042721 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422.029729 \r\nL 129.042074 420.359808 \r\nL 129.155558 416.945097 \r\nL 129.171769 418.553609 \r\nL 129.204193 421.326994 \r\nL 129.301464 420.98431 \r\nL 129.3501 417.304145 \r\nL 129.414948 419.26065 \r\nL 129.479795 421.861436 \r\nL 129.512219 420.483569 \r\nL 129.560855 418.260568 \r\nL 129.577067 421.342238 \r\nL 129.625702 418.771891 \r\nL 129.739185 422.152607 \r\nL 129.706762 418.22097 \r\nL 129.755397 420.71497 \r\nL 129.852669 417.784823 \r\nL 129.820245 421.311643 \r\nL 129.885092 418.58362 \r\nL 129.901304 418.433261 \r\nL 129.933728 421.537135 \r\nL 129.998576 417.628437 \r\nL 130.014787 420.079526 \r\nL 130.030999 418.025555 \r\nL 130.047211 420.586629 \r\nL 130.112059 418.54972 \r\nL 130.20933 421.055195 \r\nL 130.322813 416.970463 \r\nL 130.484932 422.194487 \r\nL 130.533568 417.137556 \r\nL 130.614627 417.626353 \r\nL 130.711899 420.895058 \r\nL 130.728111 419.58505 \r\nL 130.776746 420.333899 \r\nL 130.80917 418.331336 \r\nL 130.906441 421.951528 \r\nL 130.922653 419.12935 \r\nL 130.938865 419.085849 \r\nL 130.955077 416.926462 \r\nL 131.019924 420.350356 \r\nL 131.036136 418.895481 \r\nL 131.117196 420.72225 \r\nL 131.06856 418.412714 \r\nL 131.14962 419.606788 \r\nL 131.198255 419.978523 \r\nL 131.311738 417.727681 \r\nL 131.425222 420.905198 \r\nL 131.441434 420.743529 \r\nL 131.490069 417.840296 \r\nL 131.522493 420.870808 \r\nL 131.571129 418.492324 \r\nL 131.635976 420.97358 \r\nL 131.684612 419.346356 \r\nL 131.765671 417.223134 \r\nL 131.749459 420.013931 \r\nL 131.781883 419.50956 \r\nL 131.798095 419.367262 \r\nL 131.814307 419.9331 \r\nL 131.846731 419.554185 \r\nL 131.862943 421.09158 \r\nL 131.895366 419.52684 \r\nL 131.944002 419.63635 \r\nL 132.00885 418.058013 \r\nL 131.976426 419.750443 \r\nL 132.057485 418.41127 \r\nL 132.170968 421.152994 \r\nL 132.219604 418.365196 \r\nL 132.26824 418.854634 \r\nL 132.284452 421.45087 \r\nL 132.381723 419.523165 \r\nL 132.397935 419.498129 \r\nL 132.446571 420.656993 \r\nL 132.495206 418.487157 \r\nL 132.624901 420.862242 \r\nL 132.657325 418.200768 \r\nL 132.754596 419.074522 \r\nL 132.770808 420.122293 \r\nL 132.835656 418.451058 \r\nL 132.86808 420.099277 \r\nL 132.900503 417.425377 \r\nL 132.916715 420.207806 \r\nL 132.981563 419.452031 \r\nL 133.030198 417.63331 \r\nL 133.111258 421.167106 \r\nL 133.159894 418.182599 \r\nL 133.257165 418.343552 \r\nL 133.322012 420.882413 \r\nL 133.370648 419.728319 \r\nL 133.419284 418.848177 \r\nL 133.435496 419.392295 \r\nL 133.451707 420.70472 \r\nL 133.516555 418.325949 \r\nL 133.548979 420.563457 \r\nL 133.565191 420.465873 \r\nL 133.581403 416.53537 \r\nL 133.64625 420.978246 \r\nL 133.678674 418.436737 \r\nL 133.72731 420.705263 \r\nL 133.792157 419.115444 \r\nL 133.824581 418.902661 \r\nL 133.857005 419.488818 \r\nL 133.873217 418.211036 \r\nL 133.889428 420.81345 \r\nL 133.954276 418.776862 \r\nL 133.9867 421.763742 \r\nL 134.035335 417.000237 \r\nL 134.083971 421.099054 \r\nL 134.116395 417.026835 \r\nL 134.213666 418.655122 \r\nL 134.278514 420.97398 \r\nL 134.310937 418.634788 \r\nL 134.327149 417.892281 \r\nL 134.359573 419.34807 \r\nL 134.375785 418.903951 \r\nL 134.391997 421.20923 \r\nL 134.408209 418.640201 \r\nL 134.489268 419.721971 \r\nL 134.554116 416.856893 \r\nL 134.537904 421.523223 \r\nL 134.58654 419.125062 \r\nL 134.618963 420.929183 \r\nL 134.635175 418.636105 \r\nL 134.700023 420.766153 \r\nL 134.797294 417.377 \r\nL 134.813506 418.658412 \r\nL 134.862142 420.85329 \r\nL 134.84593 418.228434 \r\nL 134.926989 418.956073 \r\nL 134.943201 418.820265 \r\nL 135.040472 420.889328 \r\nL 135.02426 418.663142 \r\nL 135.056684 419.966077 \r\nL 135.10532 415.625286 \r\nL 135.137744 420.166517 \r\nL 135.153956 423.283798 \r\nL 135.202591 419.053156 \r\nL 135.235015 419.798554 \r\nL 135.299863 417.956592 \r\nL 135.316074 421.50681 \r\nL 135.332286 420.663694 \r\nL 135.348498 420.913394 \r\nL 135.36471 418.548368 \r\nL 135.461981 420.00112 \r\nL 135.510617 418.134147 \r\nL 135.526829 419.140454 \r\nL 135.543041 417.524847 \r\nL 135.591677 420.172991 \r\nL 135.6241 419.4416 \r\nL 135.640312 419.367446 \r\nL 135.656524 421.583207 \r\nL 135.721372 417.895528 \r\nL 135.753795 420.089107 \r\nL 135.770007 419.980833 \r\nL 135.802431 418.150085 \r\nL 135.834855 421.414938 \r\nL 135.88349 419.612999 \r\nL 135.899702 419.461682 \r\nL 135.948338 420.951279 \r\nL 135.980762 418.151968 \r\nL 135.996974 419.292453 \r\nL 136.029397 418.715115 \r\nL 136.045609 417.825557 \r\nL 136.110457 420.019977 \r\nL 136.207728 419.166817 \r\nL 136.142881 420.648358 \r\nL 136.22394 419.801393 \r\nL 136.240152 419.866813 \r\nL 136.305 418.385997 \r\nL 136.353635 418.476623 \r\nL 136.450907 420.648444 \r\nL 136.434695 418.064109 \r\nL 136.467118 419.954991 \r\nL 136.531966 418.276936 \r\nL 136.56439 420.351895 \r\nL 136.580602 419.117862 \r\nL 136.613025 420.312971 \r\nL 136.645449 420.138278 \r\nL 136.661661 415.695416 \r\nL 136.694085 421.064563 \r\nL 136.758932 418.083766 \r\nL 136.807568 421.192398 \r\nL 136.888627 419.680378 \r\nL 136.921051 419.37407 \r\nL 136.937263 420.188729 \r\nL 136.969687 417.497874 \r\nL 137.050746 418.493287 \r\nL 137.148018 422.086108 \r\nL 137.16423 420.973129 \r\nL 137.277713 417.137039 \r\nL 137.391196 421.608919 \r\nL 137.407408 418.628017 \r\nL 137.504679 419.066194 \r\nL 137.569527 417.673442 \r\nL 137.618162 421.155947 \r\nL 137.634374 418.049774 \r\nL 137.731646 420.175503 \r\nL 137.812705 418.963 \r\nL 137.780281 420.468338 \r\nL 137.845129 419.619054 \r\nL 137.861341 422.041684 \r\nL 137.893764 416.736577 \r\nL 137.9424 420.152897 \r\nL 138.007248 418.611286 \r\nL 138.02346 418.670201 \r\nL 138.088307 421.298247 \r\nL 138.120731 418.29286 \r\nL 138.136943 419.811882 \r\nL 138.218002 416.96788 \r\nL 138.250426 417.395539 \r\nL 138.28285 421.170863 \r\nL 138.347697 417.275357 \r\nL 138.380121 420.231323 \r\nL 138.396333 420.641495 \r\nL 138.412545 419.588776 \r\nL 138.444969 417.623944 \r\nL 138.493604 420.151164 \r\nL 138.509816 418.686043 \r\nL 138.623299 420.419929 \r\nL 138.671935 416.543472 \r\nL 138.720571 419.579487 \r\nL 138.769206 418.100231 \r\nL 138.850266 421.454731 \r\nL 138.915113 417.837735 \r\nL 138.963749 419.812097 \r\nL 138.979961 419.700095 \r\nL 138.996173 417.649215 \r\nL 139.028596 421.563369 \r\nL 139.093444 418.723775 \r\nL 139.125868 418.892694 \r\nL 139.14208 417.064811 \r\nL 139.158292 420.038907 \r\nL 139.255563 419.782846 \r\nL 139.287987 422.280971 \r\nL 139.352834 417.828821 \r\nL 139.369046 422.070059 \r\nL 139.417682 417.075243 \r\nL 139.466317 419.346279 \r\nL 139.547377 421.164005 \r\nL 139.596013 420.610896 \r\nL 139.693284 416.796932 \r\nL 139.628436 421.247908 \r\nL 139.725708 417.639395 \r\nL 139.822979 422.587224 \r\nL 139.839191 420.311213 \r\nL 139.936462 417.728131 \r\nL 139.952674 417.822199 \r\nL 139.968886 421.056775 \r\nL 140.049945 420.552639 \r\nL 140.066157 417.559313 \r\nL 140.163429 419.440513 \r\nL 140.228276 417.956061 \r\nL 140.244488 420.632693 \r\nL 140.325547 419.431494 \r\nL 140.341759 421.321563 \r\nL 140.390395 416.550442 \r\nL 140.455243 419.499645 \r\nL 140.52009 417.865191 \r\nL 140.536302 420.922133 \r\nL 140.552514 422.532362 \r\nL 140.568726 419.047493 \r\nL 140.617361 419.452047 \r\nL 140.633573 417.944404 \r\nL 140.649785 419.745636 \r\nL 140.714633 418.222289 \r\nL 140.77948 421.152393 \r\nL 140.811904 419.283497 \r\nL 140.892963 420.467247 \r\nL 140.909175 416.688714 \r\nL 141.006447 420.791474 \r\nL 141.03887 420.592346 \r\nL 141.055082 421.384453 \r\nL 141.103718 419.419927 \r\nL 141.11993 419.916382 \r\nL 141.152354 416.973594 \r\nL 141.249625 418.438114 \r\nL 141.363108 421.58264 \r\nL 141.460379 416.970555 \r\nL 141.476591 418.72015 \r\nL 141.492803 421.257171 \r\nL 141.557651 418.472411 \r\nL 141.573863 420.526712 \r\nL 141.687346 418.529381 \r\nL 141.752193 420.429155 \r\nL 141.768405 418.325924 \r\nL 141.800829 420.130755 \r\nL 141.817041 420.517557 \r\nL 141.833253 418.770725 \r\nL 141.849465 417.965675 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416.994869 \r\nL 146.713031 422.05341 \r\nL 146.745455 418.409989 \r\nL 146.842726 420.822033 \r\nL 146.777878 417.617597 \r\nL 146.858938 419.97501 \r\nL 146.87515 416.59021 \r\nL 146.923785 420.908059 \r\nL 146.956209 419.66641 \r\nL 146.972421 419.815885 \r\nL 146.988633 419.271254 \r\nL 147.004845 419.801734 \r\nL 147.021057 421.345204 \r\nL 147.102116 418.48918 \r\nL 147.118328 417.735566 \r\nL 147.13454 419.54851 \r\nL 147.150752 421.578502 \r\nL 147.199387 417.762497 \r\nL 147.248023 421.150495 \r\nL 147.426354 417.2478 \r\nL 147.458778 421.762177 \r\nL 147.539837 419.204162 \r\nL 147.556049 418.786851 \r\nL 147.572261 418.9902 \r\nL 147.620896 417.889588 \r\nL 147.685744 421.961774 \r\nL 147.73438 417.922779 \r\nL 147.799227 418.958172 \r\nL 147.880287 420.520412 \r\nL 147.864075 418.042401 \r\nL 147.896499 419.200409 \r\nL 147.91271 417.925644 \r\nL 147.928922 420.043514 \r\nL 147.99377 419.749491 \r\nL 148.009982 421.199555 \r\nL 148.042405 418.200831 \r\nL 148.074829 418.387106 \r\nL 148.091041 417.446553 \r\nL 148.123465 420.70415 \r\nL 148.139677 419.604025 \r\nL 148.172101 417.730274 \r\nL 148.220736 420.336814 \r\nL 148.269372 417.429611 \r\nL 148.334219 419.247133 \r\nL 148.399067 420.453087 \r\nL 148.431491 418.726994 \r\nL 148.51255 420.4499 \r\nL 148.496338 418.344714 \r\nL 148.528762 418.720998 \r\nL 148.544974 418.756123 \r\nL 148.609822 421.056164 \r\nL 148.59361 417.703979 \r\nL 148.674669 421.049539 \r\nL 148.739517 421.346251 \r\nL 148.804364 418.298773 \r\nL 148.853 420.930596 \r\nL 148.885424 416.639997 \r\nL 148.917847 419.92374 \r\nL 149.015119 418.608743 \r\nL 148.982695 420.666116 \r\nL 149.031331 419.688751 \r\nL 149.079966 419.575866 \r\nL 149.096178 420.231722 \r\nL 149.11239 418.101254 \r\nL 149.193449 420.476027 \r\nL 149.209661 419.470633 \r\nL 149.225873 419.741136 \r\nL 149.242085 417.838853 \r\nL 149.323145 420.399976 \r\nL 149.339356 418.532542 \r\nL 149.404204 421.007593 \r\nL 149.45284 419.6038 \r\nL 149.550111 418.356716 \r\nL 149.485263 420.16312 \r\nL 149.566323 418.427311 \r\nL 149.614958 420.852546 \r\nL 149.663594 420.419168 \r\nL 149.744654 418.321124 \r\nL 149.777077 419.869237 \r\nL 149.793289 419.8328 \r\nL 149.809501 418.653524 \r\nL 149.874349 420.352439 \r\nL 149.890561 421.430995 \r\nL 149.906772 418.879855 \r\nL 149.955408 421.26332 \r\nL 149.987832 416.967097 \r\nL 150.068891 419.545065 \r\nL 150.101315 419.648969 \r\nL 150.117527 421.421112 \r\nL 150.149951 418.903786 \r\nL 150.214798 420.704616 \r\nL 150.295858 417.937207 \r\nL 150.376917 419.177218 \r\nL 150.393129 421.740074 \r\nL 150.441765 417.480014 \r\nL 150.474188 418.652211 \r\nL 150.4904 418.504412 \r\nL 150.522824 418.445676 \r\nL 150.587672 420.721047 \r\nL 150.603884 418.493046 \r\nL 150.701155 418.817671 \r\nL 150.749791 417.259761 \r\nL 150.814638 420.278135 \r\nL 150.911909 418.562185 \r\nL 150.879486 421.232416 \r\nL 150.928121 420.024069 \r\nL 150.992969 417.280331 \r\nL 151.041605 418.330268 \r\nL 151.074028 420.513948 \r\nL 151.155088 420.195724 \r\nL 151.203723 418.265737 \r\nL 151.252359 420.013438 \r\nL 151.268571 420.702981 \r\nL 151.317207 419.062806 \r\nL 151.333418 419.335256 \r\nL 151.43069 417.601647 \r\nL 151.365842 420.749195 \r\nL 151.446902 419.030262 \r\nL 151.463114 418.929075 \r\nL 151.560385 421.597571 \r\nL 151.511749 417.739158 \r\nL 151.576597 420.185482 \r\nL 151.673868 416.935881 \r\nL 151.69008 419.737522 \r\nL 151.722504 418.171119 \r\nL 151.803563 419.463447 \r\nL 151.819775 421.17752 \r\nL 151.868411 419.22262 \r\nL 151.900835 420.914223 \r\nL 151.965682 416.068199 \r\nL 151.998106 420.987735 \r\nL 152.014318 419.583549 \r\nL 152.046741 419.21119 \r\nL 152.062953 420.169502 \r\nL 152.079165 420.533013 \r\nL 152.095377 418.861226 \r\nL 152.160225 420.282103 \r\nL 152.241284 420.615268 \r\nL 152.28992 418.32721 \r\nL 152.338555 421.25711 \r\nL 152.419615 420.086904 \r\nL 152.435827 420.024982 \r\nL 152.468251 418.328852 \r\nL 152.500674 421.123788 \r\nL 152.54931 419.697751 \r\nL 152.565522 420.033369 \r\nL 152.597946 419.654192 \r\nL 152.630369 420.3528 \r\nL 152.711429 417.929984 \r\nL 152.776276 420.723477 \r\nL 152.824912 420.210535 \r\nL 152.88976 418.147814 \r\nL 152.873548 420.330236 \r\nL 152.938395 419.415211 \r\nL 153.003243 419.296469 \r\nL 153.035667 420.84242 \r\nL 153.100514 420.977969 \r\nL 153.14915 418.196538 \r\nL 153.230209 418.178944 \r\nL 153.262633 422.307388 \r\nL 153.311269 418.370086 \r\nL 153.376116 420.959922 \r\nL 153.489599 417.822581 \r\nL 153.505811 418.465203 \r\nL 153.603083 420.133434 \r\nL 153.619295 419.17432 \r\nL 153.651718 418.550689 \r\nL 153.684142 420.471645 \r\nL 153.700354 419.650433 \r\nL 153.716566 420.256145 \r\nL 153.74899 418.627681 \r\nL 153.765201 419.359007 \r\nL 153.781413 417.372598 \r\nL 153.797625 420.124712 \r\nL 153.862473 419.165981 \r\nL 153.878685 419.157374 \r\nL 153.894897 420.626218 \r\nL 153.943532 418.16082 \r\nL 153.975956 420.112697 \r\nL 154.00838 418.18847 \r\nL 154.024592 420.913406 \r\nL 154.089439 419.251632 \r\nL 154.121863 420.811323 \r\nL 154.170499 420.004655 \r\nL 154.235346 418.155446 \r\nL 154.219134 420.019735 \r\nL 154.283982 419.304922 \r\nL 154.348829 420.520343 \r\nL 154.316406 418.988884 \r\nL 154.365041 420.194107 \r\nL 154.446101 418.333569 \r\nL 154.478525 418.941195 \r\nL 154.494736 420.464248 \r\nL 154.575796 419.624301 \r\nL 154.592008 418.251448 \r\nL 154.640643 421.328979 \r\nL 154.689279 418.455509 \r\nL 154.770338 420.851564 \r\nL 154.754127 418.44244 \r\nL 154.802762 420.297687 \r\nL 154.835186 420.687322 \r\nL 154.932457 418.626259 \r\nL 155.062152 421.975192 \r\nL 155.013517 418.277938 \r\nL 155.078364 421.040185 \r\nL 155.127 417.315506 \r\nL 155.208059 418.064361 \r\nL 155.224271 417.867263 \r\nL 155.240483 418.742395 \r\nL 155.256695 418.48256 \r\nL 155.337754 421.126438 \r\nL 155.353966 419.541775 \r\nL 155.435026 418.350331 \r\nL 155.418814 419.728088 \r\nL 155.483661 418.427222 \r\nL 155.580933 420.647878 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417.088712 \r\nL 157.4453 418.431822 \r\nL 157.542571 422.461448 \r\nL 157.639842 417.908595 \r\nL 157.656054 418.805742 \r\nL 157.785749 420.321754 \r\nL 157.850597 418.190705 \r\nL 157.818173 421.622246 \r\nL 157.915444 418.873257 \r\nL 157.931656 418.734837 \r\nL 157.947868 419.192651 \r\nL 157.996504 421.251664 \r\nL 158.028928 419.409794 \r\nL 158.077563 420.48246 \r\nL 158.126199 417.98319 \r\nL 158.22347 420.926151 \r\nL 158.191047 417.338784 \r\nL 158.239682 420.538584 \r\nL 158.30453 418.470895 \r\nL 158.353165 418.479309 \r\nL 158.434225 421.278098 \r\nL 158.385589 417.876581 \r\nL 158.466649 419.673098 \r\nL 158.482861 419.141157 \r\nL 158.499072 420.4076 \r\nL 158.531496 419.473609 \r\nL 158.547708 420.750062 \r\nL 158.628767 418.749457 \r\nL 158.644979 418.246024 \r\nL 158.677403 420.39016 \r\nL 158.709827 418.831575 \r\nL 158.758463 422.472838 \r\nL 158.790886 417.327092 \r\nL 158.82331 420.093457 \r\nL 158.888158 418.803458 \r\nL 158.936793 419.396498 \r\nL 158.953005 419.426062 \r\nL 159.001641 421.342212 \r\nL 158.985429 418.127748 \r\nL 159.066488 421.136056 \r\nL 159.147548 417.451665 \r\nL 159.16376 418.917174 \r\nL 159.179972 421.431006 \r\nL 159.244819 418.804362 \r\nL 159.261031 419.563008 \r\nL 159.342091 417.435014 \r\nL 159.293455 421.484779 \r\nL 159.358302 419.913117 \r\nL 159.390726 419.348506 \r\nL 159.439362 420.293198 \r\nL 159.455574 417.005527 \r\nL 159.504209 421.861899 \r\nL 159.552845 419.43603 \r\nL 159.569057 417.610219 \r\nL 159.650116 419.587199 \r\nL 159.731176 421.333783 \r\nL 159.68254 418.484198 \r\nL 159.747388 420.413834 \r\nL 159.779811 417.844097 \r\nL 159.812235 420.818377 \r\nL 159.877083 418.435577 \r\nL 159.893295 417.814404 \r\nL 159.925718 420.455626 \r\nL 159.94193 419.725851 \r\nL 159.974354 419.975152 \r\nL 159.990566 419.180453 \r\nL 160.006778 419.809607 \r\nL 160.039202 421.614195 \r\nL 160.136473 417.927314 \r\nL 160.233744 420.318692 \r\nL 160.217532 417.860042 \r\nL 160.249956 419.774418 \r\nL 160.266168 419.653526 \r\nL 160.28238 421.078265 \r\nL 160.314804 418.080705 \r\nL 160.363439 419.608956 \r\nL 160.379651 419.566002 \r\nL 160.428287 420.363633 \r\nL 160.476923 418.29991 \r\nL 160.525558 420.090104 \r\nL 160.509346 417.72636 \r\nL 160.590406 420.088562 \r\nL 160.671465 418.597603 \r\nL 160.687677 418.920306 \r\nL 160.720101 421.471187 \r\nL 160.752525 418.837267 \r\nL 160.80116 419.269756 \r\nL 160.866008 421.344085 \r\nL 160.833584 419.166643 \r\nL 160.914644 420.420953 \r\nL 160.947067 416.759638 \r\nL 161.028127 419.031626 \r\nL 161.044339 420.604288 \r\nL 161.06055 418.289254 \r\nL 161.14161 419.476419 \r\nL 161.157822 419.390198 \r\nL 161.174034 420.800448 \r\nL 161.238881 418.306607 \r\nL 161.255093 420.671998 \r\nL 161.271305 418.389428 \r\nL 161.303729 421.081528 \r\nL 161.368576 419.938295 \r\nL 161.449636 418.457548 \r\nL 161.48206 419.654677 \r\nL 161.546907 421.268797 \r\nL 161.579331 417.641793 \r\nL 161.595543 421.016627 \r\nL 161.676602 417.28474 \r\nL 161.692814 418.538273 \r\nL 161.709026 418.452856 \r\nL 161.822509 420.962093 \r\nL 161.790085 417.728556 \r\nL 161.838721 419.963647 \r\nL 161.854933 420.384118 \r\nL 161.887357 419.044488 \r\nL 161.903569 419.268543 \r\nL 161.952204 418.643565 \r\nL 161.935992 419.972408 \r\nL 161.984628 419.170903 \r\nL 162.049476 420.854871 \r\nL 162.017052 418.569766 \r\nL 162.098111 419.36665 \r\nL 162.114323 417.282157 \r\nL 162.162959 421.626352 \r\nL 162.211594 418.025856 \r\nL 162.292654 420.415871 \r\nL 162.357501 420.297359 \r\nL 162.373713 418.646438 \r\nL 162.389925 421.459923 \r\nL 162.454773 419.746986 \r\nL 162.470985 420.935687 \r\nL 162.503408 417.93048 \r\nL 162.552044 419.66959 \r\nL 162.568256 417.400382 \r\nL 162.60068 420.815522 \r\nL 162.665527 417.996971 \r\nL 162.795222 421.443084 \r\nL 162.876282 417.901783 \r\nL 162.908706 420.251357 \r\nL 162.941129 421.081641 \r\nL 162.957341 417.991457 \r\nL 163.054613 419.876892 \r\nL 163.070824 420.860849 \r\nL 163.103248 418.47194 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418.57396 \r\nL 166.151083 418.710542 \r\nL 166.199719 420.384984 \r\nL 166.264566 419.03818 \r\nL 166.394261 420.744664 \r\nL 166.313202 418.752748 \r\nL 166.410473 420.385256 \r\nL 166.475321 420.567239 \r\nL 166.572592 417.75635 \r\nL 166.702287 421.83599 \r\nL 166.848194 416.8083 \r\nL 166.994101 421.955342 \r\nL 167.107584 418.279826 \r\nL 167.237279 421.177073 \r\nL 167.285915 418.235553 \r\nL 167.350763 420.585723 \r\nL 167.399398 417.534006 \r\nL 167.448034 419.625054 \r\nL 167.464246 421.075088 \r\nL 167.49667 418.283951 \r\nL 167.545305 419.079368 \r\nL 167.561517 418.015198 \r\nL 167.642576 419.934615 \r\nL 167.658788 420.764657 \r\nL 167.723636 419.180956 \r\nL 167.820907 417.75737 \r\nL 167.772272 419.78935 \r\nL 167.837119 418.781365 \r\nL 167.853331 418.720275 \r\nL 167.869543 419.052985 \r\nL 167.918179 421.361942 \r\nL 168.01545 419.996247 \r\nL 168.047874 419.224101 \r\nL 168.064086 416.97671 \r\nL 168.128933 420.10611 \r\nL 168.145145 418.887204 \r\nL 168.177569 418.617807 \r\nL 168.258628 421.048015 \r\nL 168.27484 417.791252 \r\nL 168.372111 420.17014 \r\nL 168.436959 418.926842 \r\nL 168.404535 420.332403 \r\nL 168.453171 420.140859 \r\nL 168.469383 420.838616 \r\nL 168.501806 419.535592 \r\nL 168.518018 417.604396 \r\nL 168.582866 421.11691 \r\nL 168.61529 417.627131 \r\nL 168.696349 421.868475 \r\nL 168.744985 419.41952 \r\nL 168.761197 419.963438 \r\nL 168.79362 419.096833 \r\nL 168.809832 417.638499 \r\nL 168.858468 419.63743 \r\nL 168.87468 419.351363 \r\nL 168.923316 421.298296 \r\nL 168.955739 417.813973 \r\nL 168.971951 419.145453 \r\nL 169.004375 421.213036 \r\nL 169.036799 419.317783 \r\nL 169.053011 418.019733 \r\nL 169.117858 418.951961 \r\nL 169.13407 421.762073 \r\nL 169.231341 420.458039 \r\nL 169.279977 418.207674 \r\nL 169.344825 419.033455 \r\nL 169.425884 423.175431 \r\nL 169.442096 418.59579 \r\nL 169.47452 420.377442 \r\nL 169.490732 416.949893 \r\nL 169.588003 419.058866 \r\nL 169.620427 421.109942 \r\nL 169.701486 419.252865 \r\nL 169.717698 419.351986 \r\nL 169.782546 416.383997 \r\nL 169.766334 421.422244 \r\nL 169.831181 417.713861 \r\nL 169.928453 421.653165 \r\nL 169.944664 420.302476 \r\nL 170.041936 417.272969 \r\nL 170.058148 420.037598 \r\nL 170.074359 420.866168 \r\nL 170.090571 418.813034 \r\nL 170.155419 419.551077 \r\nL 170.171631 419.495438 \r\nL 170.187843 420.528019 \r\nL 170.236478 417.890373 \r\nL 170.268902 419.097638 \r\nL 170.285114 417.363777 \r\nL 170.349962 421.20312 \r\nL 170.366173 419.581967 \r\nL 170.398597 422.615794 \r\nL 170.447233 418.496391 \r\nL 170.463445 417.645053 \r\nL 170.495869 419.697752 \r\nL 170.544504 418.128004 \r\nL 170.609352 422.021654 \r\nL 170.576928 417.910581 \r\nL 170.657987 419.646503 \r\nL 170.674199 419.035267 \r\nL 170.690411 422.629411 \r\nL 170.755259 416.98007 \r\nL 170.771471 417.469964 \r\nL 170.901166 420.572594 \r\nL 170.949801 420.212678 \r\nL 171.079496 417.690178 \r\nL 171.144344 423.567288 \r\nL 171.209192 419.280121 \r\nL 171.274039 417.841055 \r\nL 171.241615 420.282668 \r\nL 171.290251 417.973446 \r\nL 171.322675 421.773332 \r\nL 171.403734 418.905546 \r\nL 171.419946 418.338976 \r\nL 171.436158 420.352982 \r\nL 171.484794 418.863627 \r\nL 171.517217 418.569072 \r\nL 171.582065 421.774858 \r\nL 171.695548 417.76728 \r\nL 171.744184 416.369552 \r\nL 171.825243 420.596755 \r\nL 171.873879 420.145304 \r\nL 171.890091 422.130834 \r\nL 171.987362 417.376839 \r\nL 172.003574 421.334045 \r\nL 172.035998 416.985866 \r\nL 172.084633 422.197034 \r\nL 172.117057 420.298189 \r\nL 172.133269 420.442649 \r\nL 172.198117 417.230359 \r\nL 172.246752 418.820288 \r\nL 172.3116 421.434165 \r\nL 172.360236 420.453654 \r\nL 172.392659 420.677969 \r\nL 172.489931 416.674854 \r\nL 172.587202 421.297565 \r\nL 172.619626 419.670324 \r\nL 172.668261 417.647665 \r\nL 172.733109 418.802557 \r\nL 172.797956 421.724248 \r\nL 172.862804 419.422352 \r\nL 172.943863 416.719686 \r\nL 172.895228 419.637878 \r\nL 172.976287 418.51411 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420.028829 \r\nL 174.921714 418.540511 \r\nL 175.002773 422.059859 \r\nL 174.954137 417.645639 \r\nL 175.035197 421.224073 \r\nL 175.132468 417.229928 \r\nL 175.164892 418.653787 \r\nL 175.181104 418.782265 \r\nL 175.197316 422.148812 \r\nL 175.294587 419.603435 \r\nL 175.40807 417.295412 \r\nL 175.537765 422.647884 \r\nL 175.602613 417.416798 \r\nL 175.66746 417.423038 \r\nL 175.699884 422.095151 \r\nL 175.797155 419.926738 \r\nL 175.813367 420.174795 \r\nL 175.862003 420.391507 \r\nL 175.926851 418.44943 \r\nL 175.943062 418.435992 \r\nL 176.040334 420.64424 \r\nL 176.056546 419.960595 \r\nL 176.105181 417.18284 \r\nL 176.153817 420.58057 \r\nL 176.2673 417.624659 \r\nL 176.283512 418.72334 \r\nL 176.315936 421.081194 \r\nL 176.364572 418.252285 \r\nL 176.396995 420.114239 \r\nL 176.429419 420.495631 \r\nL 176.494267 418.40044 \r\nL 176.510478 420.936308 \r\nL 176.52669 418.023778 \r\nL 176.60775 419.038202 \r\nL 176.672597 420.991278 \r\nL 176.656385 418.705807 \r\nL 176.705021 419.600479 \r\nL 176.721233 417.811248 \r\nL 176.802292 420.917751 \r\nL 176.86714 416.980747 \r\nL 176.915776 418.796845 \r\nL 176.931988 420.183513 \r\nL 176.964411 418.491007 \r\nL 177.029259 419.230518 \r\nL 177.045471 418.790299 \r\nL 177.077895 420.831045 \r\nL 177.094106 418.922762 \r\nL 177.175166 418.042741 \r\nL 177.191378 420.957141 \r\nL 177.256225 418.436642 \r\nL 177.304861 419.502128 \r\nL 177.353497 421.390887 \r\nL 177.369708 419.037784 \r\nL 177.38592 420.342929 \r\nL 177.402132 417.532962 \r\nL 177.434556 421.230365 \r\nL 177.499404 418.775113 \r\nL 177.564251 421.025874 \r\nL 177.629099 420.897119 \r\nL 177.758794 418.531712 \r\nL 177.856065 420.629134 \r\nL 177.791218 418.515237 \r\nL 177.872277 418.851066 \r\nL 177.937125 417.939336 \r\nL 177.98576 421.34892 \r\nL 178.034396 418.423667 \r\nL 178.06682 421.454144 \r\nL 178.099243 418.809671 \r\nL 178.131667 417.945653 \r\nL 178.196515 419.66965 \r\nL 178.228938 417.774744 \r\nL 178.24515 420.935692 \r\nL 178.309998 419.300594 \r\nL 178.32621 421.253129 \r\nL 178.391057 418.190698 \r\nL 178.423481 419.899305 \r\nL 178.455905 418.445889 \r\nL 178.504541 420.249203 \r\nL 178.536964 420.934835 \r\nL 178.553176 419.200275 \r\nL 178.601812 420.248477 \r\nL 178.634236 418.428446 \r\nL 178.715295 420.487186 \r\nL 178.682871 417.562205 \r\nL 178.747719 419.746362 \r\nL 178.763931 419.632341 \r\nL 178.796355 420.926864 \r\nL 178.812566 419.117196 \r\nL 178.84499 419.667132 \r\nL 178.909838 417.213008 \r\nL 178.942262 420.277099 \r\nL 178.958473 418.619855 \r\nL 179.055745 420.68872 \r\nL 179.071957 419.076582 \r\nL 179.10438 418.54278 \r\nL 179.120592 419.061688 \r\nL 179.136804 418.241312 \r\nL 179.18544 420.279781 \r\nL 179.201652 419.191075 \r\nL 179.282711 420.866836 \r\nL 179.315135 420.753029 \r\nL 179.379982 418.037993 \r\nL 179.428618 418.238922 \r\nL 179.493466 421.208224 \r\nL 179.558313 420.090387 \r\nL 179.623161 418.042919 \r\nL 179.671796 419.042406 \r\nL 179.688008 420.764381 \r\nL 179.70422 418.317626 \r\nL 179.78528 419.381897 \r\nL 179.801491 420.226642 \r\nL 179.850127 418.282154 \r\nL 179.866339 418.648757 \r\nL 179.882551 417.718436 \r\nL 179.947398 420.181387 \r\nL 179.979822 420.728099 \r\nL 180.012246 419.219184 \r\nL 180.028458 421.081422 \r\nL 180.109517 418.023594 \r\nL 180.190577 420.314624 \r\nL 180.158153 417.755872 \r\nL 180.206789 419.494613 \r\nL 180.223001 417.851563 \r\nL 180.271636 421.369593 \r\nL 180.30406 418.487405 \r\nL 180.433755 421.03804 \r\nL 180.547238 417.731542 \r\nL 180.579662 418.430187 \r\nL 180.595874 418.124019 \r\nL 180.709357 420.825398 \r\nL 180.725569 420.662284 \r\nL 180.741781 420.717492 \r\nL 180.839052 418.100936 \r\nL 180.855264 418.956884 \r\nL 180.887688 418.944898 \r\nL 180.936324 419.714459 \r\nL 180.952535 421.002754 \r\nL 180.984959 418.893146 \r\nL 181.033595 420.095349 \r\nL 181.082231 417.897264 \r\nL 181.114654 419.22765 \r\nL 181.211926 421.114032 \r\nL 181.147078 419.01207 \r\nL 181.228138 419.661618 \r\nL 181.260561 419.963253 \r\nL 181.341621 417.611606 \r\nL 181.357833 420.310356 \r\nL 181.374045 420.36278 \r\nL 181.390256 420.250639 \r\nL 181.406468 419.138744 \r\nL 181.487528 420.680009 \r\nL 181.519951 418.058535 \r\nL 181.617223 419.66898 \r\nL 181.665858 421.715624 \r\nL 181.714494 419.753775 \r\nL 181.730706 419.596854 \r\nL 181.746918 419.992812 \r\nL 181.779342 416.899208 \r\nL 181.860401 419.218551 \r\nL 181.925249 422.142533 \r\nL 181.990096 420.405944 \r\nL 182.054944 417.612862 \r\nL 182.103579 419.615558 \r\nL 182.119791 419.648618 \r\nL 182.136003 418.859506 \r\nL 182.200851 420.434056 \r\nL 182.217063 420.007814 \r\nL 182.249486 419.033595 \r\nL 182.265698 420.469791 \r\nL 182.28191 417.417133 \r\nL 182.36297 420.034799 \r\nL 182.427817 422.292136 \r\nL 182.411605 418.418534 \r\nL 182.444029 419.637286 \r\nL 182.492665 417.858808 \r\nL 182.573724 417.989811 \r\nL 182.654784 421.497101 \r\nL 182.735843 420.1388 \r\nL 182.752055 417.257084 \r\nL 182.768267 420.230096 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418.639625 \r\nL 184.616422 420.265874 \r\nL 184.567786 418.04783 \r\nL 184.648846 420.067835 \r\nL 184.697481 419.584995 \r\nL 184.746117 417.860026 \r\nL 184.729905 420.323907 \r\nL 184.778541 419.261109 \r\nL 184.843388 420.525288 \r\nL 184.892024 420.181549 \r\nL 185.005507 417.85374 \r\nL 185.021719 419.475991 \r\nL 185.11899 420.980153 \r\nL 185.054143 418.875385 \r\nL 185.151414 420.338991 \r\nL 185.248685 418.234312 \r\nL 185.232474 420.466191 \r\nL 185.281109 418.346706 \r\nL 185.329745 421.122323 \r\nL 185.410804 419.725264 \r\nL 185.427016 419.91862 \r\nL 185.443228 418.346924 \r\nL 185.475652 420.782463 \r\nL 185.524287 418.387482 \r\nL 185.653983 420.636339 \r\nL 185.751254 418.299035 \r\nL 185.864737 420.929198 \r\nL 185.97822 418.444616 \r\nL 185.994432 419.48377 \r\nL 186.043068 418.884783 \r\nL 186.075492 419.367244 \r\nL 186.107915 420.555658 \r\nL 186.124127 418.991444 \r\nL 186.156551 419.705134 \r\nL 186.270034 418.262512 \r\nL 186.221399 420.929185 \r\nL 186.286246 418.385511 \r\nL 186.31867 420.917097 \r\nL 186.383517 417.5732 \r\nL 186.399729 420.149251 \r\nL 186.415941 420.191319 \r\nL 186.513213 418.015018 \r\nL 186.448365 420.263116 \r\nL 186.529424 419.56155 \r\nL 186.594272 418.907402 \r\nL 186.610484 419.846205 \r\nL 186.642908 420.761373 \r\nL 186.723967 417.616556 \r\nL 186.756391 420.757166 \r\nL 186.83745 419.620721 \r\nL 186.886086 419.939075 \r\nL 186.902298 418.995402 \r\nL 186.91851 420.042366 \r\nL 186.999569 418.25606 \r\nL 187.015781 419.192508 \r\nL 187.031993 419.29989 \r\nL 187.048205 418.309976 \r\nL 187.080629 420.63677 \r\nL 187.096841 422.149078 \r\nL 187.129264 418.504192 \r\nL 187.161688 420.377334 \r\nL 187.1779 418.207832 \r\nL 187.194112 421.190882 \r\nL 187.275171 419.941328 \r\nL 187.307595 418.559471 \r\nL 187.372443 419.791087 \r\nL 187.404866 419.418818 \r\nL 187.421078 420.961751 \r\nL 187.469714 418.455652 \r\nL 187.534561 420.462987 \r\nL 187.648045 418.42 \r\nL 187.664257 418.693565 \r\nL 187.69668 421.475427 \r\nL 187.77774 419.387333 \r\nL 187.793952 419.380978 \r\nL 187.842587 417.83734 \r\nL 187.875011 420.116099 \r\nL 187.891223 419.710729 \r\nL 187.907435 419.685032 \r\nL 187.939859 418.571633 \r\nL 187.988494 419.950766 \r\nL 188.004706 420.466989 \r\nL 188.03713 419.163025 \r\nL 188.053342 420.168473 \r\nL 188.085766 418.680985 \r\nL 188.134401 420.405504 \r\nL 188.166825 419.243268 \r\nL 188.215461 420.581695 \r\nL 188.280308 418.36686 \r\nL 188.345156 420.435758 \r\nL 188.393791 418.663955 \r\nL 188.458639 417.910162 \r\nL 188.507275 420.745126 \r\nL 188.63697 418.4514 \r\nL 188.669394 420.851762 \r\nL 188.734241 417.625094 \r\nL 188.750453 419.425743 \r\nL 188.766665 419.584083 \r\nL 188.782877 421.374843 \r\nL 188.799089 419.231274 \r\nL 188.863936 420.518658 \r\nL 188.912572 417.842911 \r\nL 188.993631 418.605327 \r\nL 189.042267 421.056679 \r\nL 189.058479 418.050768 \r\nL 189.123326 420.750736 \r\nL 189.139538 420.553778 \r\nL 189.220598 418.070954 \r\nL 189.253021 420.039655 \r\nL 189.269233 420.132968 \r\nL 189.285445 418.548138 \r\nL 189.317869 422.200264 \r\nL 189.382717 419.799917 \r\nL 189.398928 419.690851 \r\nL 189.41514 417.755183 \r\nL 189.479988 421.059975 \r\nL 189.512412 419.397126 \r\nL 189.528624 417.883067 \r\nL 189.593471 421.379375 \r\nL 189.609683 419.902897 \r\nL 189.625895 420.441348 \r\nL 189.658319 419.353884 \r\nL 189.67453 418.269288 \r\nL 189.75559 418.761924 \r\nL 189.788014 420.664396 \r\nL 189.869073 418.909438 \r\nL 189.933921 419.815624 \r\nL 189.950133 419.395177 \r\nL 189.966344 418.041228 \r\nL 190.031192 420.461814 \r\nL 190.047404 418.640435 \r\nL 190.079828 420.620301 \r\nL 190.160887 419.053706 \r\nL 190.193311 418.149159 \r\nL 190.241947 419.50722 \r\nL 190.27437 419.800404 \r\nL 190.290582 418.23736 \r\nL 190.339218 420.373642 \r\nL 190.387853 419.262155 \r\nL 190.404065 419.208467 \r\nL 190.485125 418.071993 \r\nL 190.501337 418.755262 \r\nL 190.566184 420.776304 \r\nL 190.61482 419.228757 \r\nL 190.647244 418.69179 \r\nL 190.663456 419.567817 \r\nL 190.679667 418.759955 \r\nL 190.776939 420.81623 \r\nL 190.744515 418.51258 \r\nL 190.793151 420.103858 \r\nL 190.809363 419.372411 \r\nL 190.825574 420.305075 \r\nL 190.890422 419.806798 \r\nL 190.906634 420.308861 \r\nL 190.939058 418.755514 \r\nL 190.987693 419.726981 \r\nL 191.036329 419.964681 \r\nL 191.052541 419.166618 \r\nL 191.084965 421.087371 \r\nL 191.101177 418.291976 \r\nL 191.149812 419.508164 \r\nL 191.198448 420.272915 \r\nL 191.230872 417.932452 \r\nL 191.328143 421.089038 \r\nL 191.344355 420.201804 \r\nL 191.39299 418.150751 \r\nL 191.425414 420.269769 \r\nL 191.457838 419.36947 \r\nL 191.522686 420.878086 \r\nL 191.490262 419.368157 \r\nL 191.538897 419.981767 \r\nL 191.555109 418.294141 \r\nL 191.587533 420.438161 \r\nL 191.652381 418.96818 \r\nL 191.717228 420.545132 \r\nL 191.701016 418.149107 \r\nL 191.782076 419.89238 \r\nL 191.798288 420.007146 \r\nL 191.8145 419.759259 \r\nL 191.895559 416.789898 \r\nL 191.863135 419.923656 \r\nL 191.927983 419.366351 \r\nL 191.944195 419.087658 \r\nL 191.960407 419.586357 \r\nL 191.99283 419.219401 \r\nL 192.025254 420.466796 \r\nL 192.07389 418.303306 \r\nL 192.106313 419.517833 \r\nL 192.122525 419.531641 \r\nL 192.171161 418.028428 \r\nL 192.219797 419.654263 \r\nL 192.236009 419.031943 \r\nL 192.25222 419.009778 \r\nL 192.268432 420.979906 \r\nL 192.300856 418.758723 \r\nL 192.365704 419.625044 \r\nL 192.430551 418.457665 \r\nL 192.462975 419.113919 \r\nL 192.495399 420.494323 \r\nL 192.511611 418.665751 \r\nL 192.576458 419.874506 \r\nL 192.689941 418.347335 \r\nL 192.722365 421.246247 \r\nL 192.803425 421.001752 \r\nL 192.835848 418.610732 \r\nL 192.93312 419.232095 \r\nL 192.949332 419.092928 \r\nL 193.014179 420.601337 \r\nL 192.997967 418.791223 \r\nL 193.062815 419.961063 \r\nL 193.127662 419.012578 \r\nL 193.11145 420.158801 \r\nL 193.143874 419.046688 \r\nL 193.160086 420.926925 \r\nL 193.208722 418.725835 \r\nL 193.257357 420.210124 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418.779468 \r\nL 195.040665 420.096957 \r\nL 195.121724 419.103409 \r\nL 195.137936 419.602131 \r\nL 195.202784 421.327043 \r\nL 195.218996 418.980834 \r\nL 195.235208 419.34206 \r\nL 195.267631 420.710488 \r\nL 195.300055 419.014388 \r\nL 195.316267 420.082123 \r\nL 195.397326 418.532068 \r\nL 195.364903 421.172636 \r\nL 195.42975 419.865596 \r\nL 195.478386 418.326674 \r\nL 195.527022 419.209138 \r\nL 195.624293 420.470155 \r\nL 195.559445 418.269903 \r\nL 195.640505 419.540358 \r\nL 195.753988 418.3643 \r\nL 195.7702 418.675005 \r\nL 195.802624 420.321475 \r\nL 195.867471 419.412447 \r\nL 195.883683 418.655882 \r\nL 195.948531 419.69197 \r\nL 195.964743 419.022338 \r\nL 195.980954 420.391529 \r\nL 196.013378 418.096233 \r\nL 196.078226 419.726407 \r\nL 196.126861 420.351079 \r\nL 196.207921 417.583861 \r\nL 196.305192 420.92329 \r\nL 196.353828 419.548242 \r\nL 196.37004 417.484889 \r\nL 196.402463 420.396889 \r\nL 196.467311 418.171925 \r\nL 196.532159 421.743713 \r\nL 196.580794 419.825381 \r\nL 196.613218 420.446148 \r\nL 196.678066 418.213967 \r\nL 196.694277 420.694094 \r\nL 196.791549 419.760229 \r\nL 196.872608 420.387103 \r\nL 196.905032 417.76064 \r\nL 197.034727 421.042068 \r\nL 197.099575 418.915278 \r\nL 197.083363 421.286691 \r\nL 197.14821 419.319075 \r\nL 197.164422 420.623024 \r\nL 197.22927 417.703119 \r\nL 197.245482 420.23969 \r\nL 197.277905 420.57366 \r\nL 197.358965 418.423501 \r\nL 197.375177 420.812431 \r\nL 197.440024 417.893052 \r\nL 197.472448 420.618534 \r\nL 197.521084 416.777954 \r\nL 197.504872 421.132428 \r\nL 197.585931 418.427279 \r\nL 197.602143 418.314365 \r\nL 197.618355 418.349982 \r\nL 197.715626 419.962912 \r\nL 197.731838 419.678705 \r\nL 197.796686 420.515459 \r\nL 197.829109 418.495247 \r\nL 197.845321 419.644438 \r\nL 197.877745 417.123121 \r\nL 197.926381 419.481124 \r\nL 197.942593 418.221822 \r\nL 198.00744 421.069698 \r\nL 198.023652 420.123163 \r\nL 198.072288 419.234584 \r\nL 198.0885 421.321186 \r\nL 198.153347 417.963767 \r\nL 198.201983 419.913835 \r\nL 198.234407 419.290236 \r\nL 198.299254 420.834773 \r\nL 198.283042 418.467526 \r\nL 198.34789 419.857449 \r\nL 198.380314 418.256022 \r\nL 198.428949 420.44316 \r\nL 198.445161 420.412841 \r\nL 198.461373 419.266885 \r\nL 198.542432 420.749072 \r\nL 198.558644 420.594013 \r\nL 198.574856 418.174199 \r\nL 198.623492 420.824541 \r\nL 198.672128 418.811655 \r\nL 198.769399 421.754657 \r\nL 198.785611 420.171909 \r\nL 198.818035 421.044624 \r\nL 198.915306 418.266525 \r\nL 198.980153 421.544763 \r\nL 199.045001 420.237559 \r\nL 199.109849 417.820056 \r\nL 199.174696 418.154181 \r\nL 199.239544 420.69479 \r\nL 199.20712 418.03273 \r\nL 199.320603 419.917792 \r\nL 199.336815 417.766232 \r\nL 199.417874 418.984107 \r\nL 199.482722 420.279825 \r\nL 199.515146 419.355128 \r\nL 199.531358 418.400213 \r\nL 199.579993 419.8322 \r\nL 199.612417 419.159549 \r\nL 199.628629 419.798346 \r\nL 199.644841 418.30651 \r\nL 199.709688 419.383566 \r\nL 199.7259 419.213816 \r\nL 199.742112 420.619442 \r\nL 199.758324 418.492901 \r\nL 199.839383 419.949407 \r\nL 199.904231 417.10981 \r\nL 199.936655 419.809366 \r\nL 199.969079 420.804581 \r\nL 199.98529 418.443039 \r\nL 200.017714 419.608837 \r\nL 200.082562 417.99386 \r\nL 200.050138 420.095059 \r\nL 200.114986 418.966388 \r\nL 200.179833 420.527467 \r\nL 200.244681 420.37036 \r\nL 200.341952 418.204454 \r\nL 200.358164 419.981769 \r\nL 200.390588 418.816547 \r\nL 200.471647 419.861287 \r\nL 200.487859 421.1694 \r\nL 200.520283 418.59164 \r\nL 200.568918 420.089347 \r\nL 200.633766 418.8596 \r\nL 200.649978 420.645692 \r\nL 200.698613 419.669281 \r\nL 200.714825 421.037295 \r\nL 200.763461 419.189927 \r\nL 200.795885 419.709422 \r\nL 200.828309 419.170706 \r\nL 200.84452 421.449262 \r\nL 200.941792 420.697112 \r\nL 200.958004 418.392195 \r\nL 200.974215 420.766305 \r\nL 201.055275 419.093844 \r\nL 201.071487 418.5379 \r\nL 201.103911 420.093583 \r\nL 201.136334 418.773148 \r\nL 201.18497 420.44418 \r\nL 201.233606 418.500913 \r\nL 201.249818 417.65477 \r\nL 201.282241 419.676427 \r\nL 201.314665 418.856631 \r\nL 201.411936 418.794319 \r\nL 201.44436 419.998087 \r\nL 201.557843 418.325197 \r\nL 201.574055 418.685755 \r\nL 201.655115 421.706142 \r\nL 201.70375 420.229149 \r\nL 201.817234 418.076104 \r\nL 201.930717 420.690083 \r\nL 201.898293 417.817666 \r\nL 201.946929 420.089306 \r\nL 201.979352 421.039435 \r\nL 201.995564 417.538807 \r\nL 202.092836 420.363572 \r\nL 202.173895 419.316498 \r\nL 202.222531 419.382703 \r\nL 202.271166 422.326258 \r\nL 202.287378 419.159658 \r\nL 202.319802 420.984958 \r\nL 202.352226 418.924541 \r\nL 202.400862 422.530965 \r\nL 202.433285 420.536046 \r\nL 202.530557 418.76233 \r\nL 202.546769 419.831365 \r\nL 202.611616 420.917392 \r\nL 202.64404 419.460018 \r\nL 202.660252 420.038124 \r\nL 202.773735 417.12878 \r\nL 202.789947 418.729432 \r\nL 202.806159 418.712883 \r\nL 202.822371 417.319103 \r\nL 202.887218 420.509397 \r\nL 202.90343 419.063005 \r\nL 202.952066 419.894897 \r\nL 202.968278 418.409415 \r\nL 202.984489 418.238329 \r\nL 203.033125 420.095066 \r\nL 203.097973 418.72064 \r\nL 203.146608 420.478815 \r\nL 203.24388 420.111195 \r\nL 203.260092 418.574494 \r\nL 203.324939 420.508277 \r\nL 203.357363 419.034838 \r\nL 203.405999 419.52717 \r\nL 203.454634 417.832165 \r\nL 203.551905 419.949713 \r\nL 203.568117 418.754895 \r\nL 203.665389 419.931891 \r\nL 203.600541 418.631107 \r\nL 203.681601 419.150756 \r\nL 203.714024 417.954484 \r\nL 203.730236 419.350741 \r\nL 203.76266 418.573568 \r\nL 203.827508 420.444786 \r\nL 203.859931 419.70841 \r\nL 203.876143 417.82654 \r\nL 203.924779 420.976166 \r\nL 203.973415 419.063442 \r\nL 204.054474 418.135515 \r\nL 204.005838 419.950595 \r\nL 204.070686 418.897084 \r\nL 204.119322 420.933397 \r\nL 204.10311 418.395352 \r\nL 204.184169 419.474467 \r\nL 204.232805 418.360842 \r\nL 204.265228 420.368334 \r\nL 204.297652 420.210373 \r\nL 204.313864 420.979985 \r\nL 204.330076 418.4663 \r\nL 204.427347 420.40706 \r\nL 204.508407 419.660803 \r\nL 204.459771 421.034782 \r\nL 204.540831 419.816901 \r\nL 204.605678 422.365896 \r\nL 204.589466 418.37494 \r\nL 204.638102 420.445356 \r\nL 204.702949 418.976224 \r\nL 204.686738 421.189015 \r\nL 204.751585 419.986985 \r\nL 204.767797 421.052752 \r\nL 204.832645 419.34232 \r\nL 204.848856 419.707096 \r\nL 204.865068 418.530599 \r\nL 204.929916 420.015585 \r\nL 204.946128 419.384732 \r\nL 204.994763 418.884331 \r\nL 205.092035 421.074927 \r\nL 205.205518 418.670385 \r\nL 205.22173 420.832709 \r\nL 205.237942 418.108653 \r\nL 205.319001 419.61272 \r\nL 205.335213 417.267045 \r\nL 205.383849 420.209716 \r\nL 205.416272 419.737681 \r\nL 205.432484 420.708183 \r\nL 205.48112 418.495066 \r\nL 205.545968 417.679163 \r\nL 205.513544 418.729223 \r\nL 205.594603 418.079593 \r\nL 205.610815 418.87319 \r\nL 205.675663 417.314232 \r\nL 205.708086 418.834109 \r\nL 205.756722 417.96251 \r\nL 205.772934 418.583306 \r\nL 205.886417 421.060861 \r\nL 205.902629 420.738941 \r\nL 205.9999 417.911059 \r\nL 206.048536 418.64718 \r\nL 206.162019 421.369136 \r\nL 206.275502 418.067598 \r\nL 206.307926 417.826459 \r\nL 206.405198 421.083149 \r\nL 206.470045 417.613251 \r\nL 206.534893 417.648869 \r\nL 206.583528 421.616734 \r\nL 206.664588 419.869005 \r\nL 206.713223 417.746474 \r\nL 206.794283 419.048305 \r\nL 206.85913 421.641638 \r\nL 206.826707 418.918195 \r\nL 206.923978 420.884245 \r\nL 207.005037 418.089171 \r\nL 207.053673 418.620933 \r\nL 207.102309 420.768463 \r\nL 207.150944 419.87695 \r\nL 207.232004 417.401049 \r\nL 207.280639 418.350272 \r\nL 207.377911 420.865683 \r\nL 207.394123 420.273613 \r\nL 207.491394 417.482299 \r\nL 207.523818 418.069873 \r\nL 207.588665 420.419827 \r\nL 207.637301 419.363261 \r\nL 207.685937 417.082704 \r\nL 207.750784 419.160634 \r\nL 207.831844 422.565653 \r\nL 207.864267 420.121964 \r\nL 207.961539 417.200492 \r\nL 207.993962 417.961834 \r\nL 208.05881 422.89877 \r\nL 208.107446 420.275143 \r\nL 208.123658 420.119063 \r\nL 208.139869 422.579429 \r\nL 208.204717 416.219493 \r\nL 208.220929 418.272818 \r\nL 208.237141 418.74474 \r\nL 208.253353 418.354515 \r\nL 208.350624 423.511737 \r\nL 208.366836 422.840279 \r\nL 208.415471 414.946589 \r\nL 208.496531 417.770938 \r\nL 208.593802 424.11794 \r\nL 208.610014 423.814205 \r\nL 208.642438 415.628897 \r\nL 208.739709 415.815782 \r\nL 208.772133 425.161212 \r\nL 208.836981 415.474231 \r\nL 208.853192 416.706972 \r\nL 208.885616 415.68922 \r\nL 208.901828 418.553674 \r\nL 208.934252 419.200904 \r\nL 208.999099 417.37013 \r\nL 209.015311 420.653512 \r\nL 209.031523 423.363199 \r\nL 209.096371 419.260176 \r\nL 209.128795 423.183752 \r\nL 209.161218 418.300775 \r\nL 209.17743 367.808219 \r\nL 209.242278 423.685422 \r\nL 209.25849 417.702961 \r\nL 209.274701 422.468893 \r\nL 209.371973 418.536689 \r\nL 209.388185 420.098512 \r\nL 209.404397 417.497931 \r\nL 209.453032 418.389349 \r\nL 209.485456 415.838806 \r\nL 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211.252552 420.466556 \r\nL 211.268764 420.482161 \r\nL 211.382247 417.644383 \r\nL 211.414671 418.275527 \r\nL 211.49573 421.691685 \r\nL 211.528154 419.322338 \r\nL 211.576789 419.91874 \r\nL 211.593001 418.715594 \r\nL 211.641637 418.020767 \r\nL 211.657849 419.343064 \r\nL 211.674061 419.522328 \r\nL 211.771332 421.521543 \r\nL 211.787544 420.305925 \r\nL 211.884815 417.591999 \r\nL 211.901027 418.10656 \r\nL 211.949663 420.030985 \r\nL 212.01451 419.463657 \r\nL 212.030722 419.37452 \r\nL 212.079358 418.305672 \r\nL 212.127994 419.926026 \r\nL 212.144205 419.357993 \r\nL 212.192841 421.462283 \r\nL 212.209053 421.730737 \r\nL 212.241477 420.560307 \r\nL 212.322536 418.680355 \r\nL 212.371172 418.95717 \r\nL 212.468443 421.408519 \r\nL 212.484655 419.703612 \r\nL 212.500867 419.635818 \r\nL 212.517079 419.950417 \r\nL 212.533291 419.95007 \r\nL 212.549503 419.998319 \r\nL 212.598138 417.854263 \r\nL 212.711621 418.213089 \r\nL 212.744045 418.41094 \r\nL 212.760257 417.996002 \r\nL 212.776469 417.337913 \r\nL 212.841317 418.274965 \r\nL 212.938588 420.16251 \r\nL 212.987224 420.160343 \r\nL 213.052071 417.711896 \r\nL 213.116919 418.697854 \r\nL 213.133131 420.764028 \r\nL 213.230402 419.672322 \r\nL 213.311461 420.322048 \r\nL 213.279037 419.56423 \r\nL 213.327673 419.925324 \r\nL 213.360097 418.970545 \r\nL 213.392521 420.377085 \r\nL 213.408733 419.232692 \r\nL 213.424944 420.251985 \r\nL 213.489792 418.224717 \r\nL 213.506004 419.544666 \r\nL 213.522216 418.280789 \r\nL 213.587063 420.301806 \r\nL 213.603275 420.275706 \r\nL 213.635699 420.11043 \r\nL 213.651911 420.274261 \r\nL 213.668123 420.834786 \r\nL 213.684335 419.993596 \r\nL 213.716758 420.282028 \r\nL 213.73297 418.940307 \r\nL 213.797818 420.287872 \r\nL 213.830242 419.534296 \r\nL 213.862665 421.469104 \r\nL 213.943725 419.905256 \r\nL 213.976149 419.680307 \r\nL 213.992361 419.527895 \r\nL 214.008572 420.460876 \r\nL 214.024784 418.401991 \r\nL 214.105844 420.348554 \r\nL 214.235539 419.078254 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419.826957 \r\nL 226.459301 418.361883 \r\nL 226.491725 419.428802 \r\nL 226.507937 419.475764 \r\nL 226.524149 418.902422 \r\nL 226.572785 420.119128 \r\nL 226.605208 419.655223 \r\nL 226.637632 419.909124 \r\nL 226.653844 419.436665 \r\nL 226.670056 418.866676 \r\nL 226.734903 420.100572 \r\nL 226.751115 419.724871 \r\nL 226.832175 420.400996 \r\nL 226.799751 418.224602 \r\nL 226.864599 420.189661 \r\nL 226.88081 420.137587 \r\nL 226.929446 418.823681 \r\nL 226.994294 419.708552 \r\nL 227.026717 419.554591 \r\nL 227.042929 419.580768 \r\nL 227.091565 420.11004 \r\nL 227.107777 420.033136 \r\nL 227.123989 418.854874 \r\nL 227.205048 419.873539 \r\nL 227.286108 420.450561 \r\nL 227.237472 418.906037 \r\nL 227.302319 420.2551 \r\nL 227.399591 420.277571 \r\nL 227.432015 418.777395 \r\nL 227.529286 420.744463 \r\nL 227.545498 419.483987 \r\nL 227.610345 419.785359 \r\nL 227.626557 418.961508 \r\nL 227.642769 420.394996 \r\nL 227.707617 418.255 \r\nL 227.74004 419.673498 \r\nL 227.788676 420.220528 \r\nL 227.804888 419.298034 \r\nL 227.8211 419.561373 \r\nL 227.869736 418.252761 \r\nL 227.902159 419.845187 \r\nL 227.934583 419.258616 \r\nL 227.950795 419.684476 \r\nL 228.048066 419.388261 \r\nL 228.145338 418.614107 \r\nL 228.08049 419.914603 \r\nL 228.161549 418.81708 \r\nL 228.275033 420.667994 \r\nL 228.193973 418.716659 \r\nL 228.291245 419.957581 \r\nL 228.356092 418.144621 \r\nL 228.33988 420.119932 \r\nL 228.404728 419.650775 \r\nL 228.485787 418.340481 \r\nL 228.501999 419.162885 \r\nL 228.518211 420.312056 \r\nL 228.583059 418.984347 \r\nL 228.59927 419.530873 \r\nL 228.615482 418.161048 \r\nL 228.696542 419.507082 \r\nL 228.761389 420.853954 \r\nL 228.810025 419.696316 \r\nL 228.826237 418.153509 \r\nL 228.891084 420.180937 \r\nL 228.923508 418.215449 \r\nL 229.036991 421.020992 \r\nL 229.069415 420.216572 \r\nL 229.166686 418.341686 \r\nL 229.101839 420.418176 \r\nL 229.182898 419.513199 \r\nL 229.19911 418.965779 \r\nL 229.215322 420.560565 \r\nL 229.263958 418.990602 \r\nL 229.328805 420.518447 \r\nL 229.361229 418.187348 \r\nL 229.377441 419.652532 \r\nL 229.4585 419.095284 \r\nL 229.409865 420.289383 \r\nL 229.474712 419.853831 \r\nL 229.571984 420.404606 \r\nL 229.555772 419.180342 \r\nL 229.588195 420.016999 \r\nL 229.620619 417.496473 \r\nL 229.685467 420.1879 \r\nL 229.701679 419.184409 \r\nL 229.766526 421.087237 \r\nL 229.782738 418.956336 \r\nL 229.815162 418.429377 \r\nL 229.831374 419.471357 \r\nL 229.847586 419.381506 \r\nL 229.863798 419.743419 \r\nL 229.912433 418.575674 \r\nL 229.961069 419.468954 \r\nL 229.977281 419.486607 \r\nL 230.025916 420.207004 \r\nL 230.05834 419.274936 \r\nL 230.074552 418.11824 \r\nL 230.106976 419.461123 \r\nL 230.171823 419.019691 \r\nL 230.188035 420.678548 \r\nL 230.236671 418.991194 \r\nL 230.285307 419.903601 \r\nL 230.366366 419.912329 \r\nL 230.39879 418.476209 \r\nL 230.479849 420.304222 \r\nL 230.431214 418.311341 \r\nL 230.512273 419.036555 \r\nL 230.528485 419.458634 \r\nL 230.560909 418.39597 \r\nL 230.593332 419.088406 \r\nL 230.609544 418.531964 \r\nL 230.641968 420.668118 \r\nL 230.65818 420.757586 \r\nL 230.690604 418.351699 \r\nL 230.804087 419.214721 \r\nL 230.852723 419.380266 \r\nL 230.868935 418.660326 \r\nL 230.949994 421.113901 \r\nL 230.982418 420.946726 \r\nL 231.063477 418.514935 \r\nL 231.112113 419.062002 \r\nL 231.128325 418.53153 \r\nL 231.160748 420.599493 \r\nL 231.193172 419.647252 \r\nL 231.25802 421.396408 \r\nL 231.225596 419.050215 \r\nL 231.274232 420.335037 \r\nL 231.306655 418.015018 \r\nL 231.387715 419.813054 \r\nL 231.420139 420.268238 \r\nL 231.501198 419.022188 \r\nL 231.582258 420.077207 \r\nL 231.614681 419.171762 \r\nL 231.630893 418.424516 \r\nL 231.695741 419.899629 \r\nL 231.711953 420.923704 \r\nL 231.744376 418.784174 \r\nL 231.793012 419.281199 \r\nL 231.825436 419.593913 \r\nL 231.85786 418.607049 \r\nL 231.874072 420.389895 \r\nL 231.890283 418.558651 \r\nL 231.955131 419.466702 \r\nL 231.987555 417.787443 \r\nL 232.019978 419.909698 \r\nL 232.068614 418.707159 \r\nL 232.084826 418.380907 \r\nL 232.11725 418.972598 \r\nL 232.133462 420.471851 \r\nL 232.214521 418.80095 \r\nL 232.230733 419.746152 \r\nL 232.246945 419.853087 \r\nL 232.263157 419.542615 \r\nL 232.37664 418.793161 \r\nL 232.457699 421.026695 \r\nL 232.490123 419.173952 \r\nL 232.522547 418.891381 \r\nL 232.538759 419.171066 \r\nL 232.571183 420.189259 \r\nL 232.603606 418.676165 \r\nL 232.652242 419.859973 \r\nL 232.71709 418.702386 \r\nL 232.700878 420.445422 \r\nL 232.765725 419.1303 \r\nL 232.781937 420.451738 \r\nL 232.830573 418.787419 \r\nL 232.879208 420.167188 \r\nL 232.927844 420.659291 \r\nL 232.97648 419.297305 \r\nL 233.041327 419.213379 \r\nL 233.089963 420.449978 \r\nL 233.106175 419.238164 \r\nL 233.122387 420.64244 \r\nL 233.203446 419.709785 \r\nL 233.219658 419.786659 \r\nL 233.284506 420.038091 \r\nL 233.316929 419.126637 \r\nL 233.333141 419.969244 \r\nL 233.349353 418.244972 \r\nL 233.430413 419.817589 \r\nL 233.462836 420.044737 \r\nL 233.49526 418.912634 \r\nL 233.57632 418.947425 \r\nL 233.624955 420.103939 \r\nL 233.657379 420.079017 \r\nL 233.673591 418.42552 \r\nL 233.738438 420.12202 \r\nL 233.770862 419.275042 \r\nL 233.787074 418.70397 \r\nL 233.83571 419.634432 \r\nL 233.868134 419.217783 \r\nL 233.916769 420.048142 \r\nL 233.932981 418.56781 \r\nL 233.965405 419.729305 \r\nL 234.030252 417.909723 \r\nL 234.014041 420.430696 \r\nL 234.062676 418.508938 \r\nL 234.159948 418.336101 \r\nL 234.176159 420.112142 \r\nL 234.192371 420.163112 \r\nL 234.273431 420.612281 \r\nL 234.289643 418.355666 \r\nL 234.305855 420.21143 \r\nL 234.322066 417.974618 \r\nL 234.403126 419.868191 \r\nL 234.419338 419.825024 \r\nL 234.43555 419.992058 \r\nL 234.516609 420.139272 \r\nL 234.549033 418.619014 \r\nL 234.630092 419.770236 \r\nL 234.662516 419.217085 \r\nL 234.678728 418.469676 \r\nL 234.69494 420.126365 \r\nL 234.759787 419.047002 \r\nL 234.857059 420.681206 \r\nL 234.840847 418.018236 \r\nL 234.873271 419.475286 \r\nL 234.889482 420.438992 \r\nL 234.970542 419.037933 \r\nL 234.986754 419.769204 \r\nL 235.002966 418.511801 \r\nL 235.067813 420.254491 \r\nL 235.100237 418.603683 \r\nL 235.229932 420.452934 \r\nL 235.359627 419.073765 \r\nL 235.424475 418.534282 \r\nL 235.489322 420.440721 \r\nL 235.619017 418.840432 \r\nL 235.651441 419.040232 \r\nL 235.683865 419.730758 \r\nL 235.732501 418.680763 \r\nL 235.764924 419.526379 \r\nL 235.797348 419.986905 \r\nL 235.81356 419.801159 \r\nL 235.894619 418.274133 \r\nL 235.910831 419.9843 \r\nL 235.927043 420.249887 \r\nL 235.959467 419.081818 \r\nL 235.991891 420.143646 \r\nL 236.07295 418.350307 \r\nL 236.089162 420.651218 \r\nL 236.105374 419.072125 \r\nL 236.15401 419.969334 \r\nL 236.202645 418.930609 \r\nL 236.218857 418.814069 \r\nL 236.251281 420.472248 \r\nL 236.299917 417.934729 \r\nL 236.33234 419.457706 \r\nL 236.348552 418.70377 \r\nL 236.380976 420.678868 \r\nL 236.4134 420.304278 \r\nL 236.429612 420.272016 \r\nL 236.445824 420.335394 \r\nL 236.462035 421.22344 \r\nL 236.494459 418.374896 \r\nL 236.526883 419.400484 \r\nL 236.543095 419.405703 \r\nL 236.575519 418.894149 \r\nL 236.640366 419.683803 \r\nL 236.656578 418.89712 \r\nL 236.721426 420.973698 \r\nL 236.770061 420.703097 \r\nL 236.834909 418.123842 \r\nL 236.883544 418.925565 \r\nL 236.997028 420.43842 \r\nL 237.045663 418.766458 \r\nL 237.126723 418.887089 \r\nL 237.240206 420.622736 \r\nL 237.27263 418.47321 \r\nL 237.353689 419.245224 \r\nL 237.418537 420.030731 \r\nL 237.434749 418.843977 \r\nL 237.450961 419.11753 \r\nL 237.467172 419.126408 \r\nL 237.483384 418.924109 \r\nL 237.515808 419.579023 \r\nL 237.53202 419.396576 \r\nL 237.564444 418.105194 \r\nL 237.645503 420.728321 \r\nL 237.726563 418.206435 \r\nL 237.775198 418.26915 \r\nL 237.87247 419.703234 \r\nL 237.888681 419.033364 \r\nL 237.904893 418.5812 \r\nL 237.921105 419.463629 \r\nL 237.937317 421.163522 \r\nL 238.002165 419.251956 \r\nL 238.018377 419.419858 \r\nL 238.067012 418.157567 \r\nL 238.083224 420.054847 \r\nL 238.13186 418.72031 \r\nL 238.180495 419.977381 \r\nL 238.229131 419.314419 \r\nL 238.245343 418.040639 \r\nL 238.277767 420.424282 \r\nL 238.342614 418.736241 \r\nL 238.375038 420.484549 \r\nL 238.456098 420.105139 \r\nL 238.504733 418.027231 \r\nL 238.569581 419.814311 \r\nL 238.602004 420.226847 \r\nL 238.634428 419.899456 \r\nL 238.7317 418.629376 \r\nL 238.683064 420.346455 \r\nL 238.747911 419.356059 \r\nL 238.877607 421.248891 \r\nL 238.828971 418.480334 \r\nL 238.893818 420.459 \r\nL 238.974878 418.249374 \r\nL 239.023514 418.548852 \r\nL 239.104573 420.500158 \r\nL 239.136997 420.485371 \r\nL 239.25048 417.970036 \r\nL 239.266692 418.107979 \r\nL 239.380175 420.434607 \r\nL 239.477446 418.288474 \r\nL 239.412599 421.049835 \r\nL 239.493658 418.843296 \r\nL 239.574718 420.307339 \r\nL 239.639565 420.077859 \r\nL 239.671989 418.209223 \r\nL 239.76926 418.299236 \r\nL 239.85032 420.663079 \r\nL 239.882744 419.504378 \r\nL 239.898955 418.567286 \r\nL 239.980015 419.250572 \r\nL 239.996227 419.396241 \r\nL 240.012439 418.363543 \r\nL 240.077286 420.471546 \r\nL 240.093498 419.969003 \r\nL 240.142134 418.86079 \r\nL 240.174557 420.795515 \r\nL 240.239405 417.893945 \r\nL 240.288041 420.135677 \r\nL 240.304253 421.031125 \r\nL 240.320464 419.361791 \r\nL 240.385312 420.674216 \r\nL 240.45016 417.417253 \r\nL 240.515007 418.867572 \r\nL 240.62849 420.622499 \r\nL 240.677126 418.371441 \r\nL 240.660914 420.640866 \r\nL 240.741974 419.396334 \r\nL 240.790609 420.523476 \r\nL 240.855457 418.357086 \r\nL 240.871669 420.426395 \r\nL 240.96894 419.450153 \r\nL 240.985152 419.198436 \r\nL 241.001364 420.668604 \r\nL 241.033787 419.776297 \r\nL 241.049999 420.279222 \r\nL 241.114847 419.122165 \r\nL 241.131059 419.395765 \r\nL 241.212118 417.900714 \r\nL 241.163483 419.67686 \r\nL 241.22833 419.446864 \r\nL 241.30939 420.78443 \r\nL 241.260754 418.780818 \r\nL 241.341813 420.057712 \r\nL 241.455297 417.998439 \r\nL 241.552568 420.199992 \r\nL 241.56878 420.198756 \r\nL 241.633627 418.432461 \r\nL 241.698475 418.448574 \r\nL 241.844382 420.504724 \r\nL 241.974077 418.346599 \r\nL 242.038924 421.096853 \r\nL 242.119984 420.322961 \r\nL 242.136196 418.218798 \r\nL 242.233467 419.110314 \r\nL 242.298315 420.800215 \r\nL 242.34695 419.365814 \r\nL 242.379374 419.881889 \r\nL 242.42801 418.770339 \r\nL 242.444222 419.093248 \r\nL 242.460434 418.716176 \r\nL 242.509069 419.862434 \r\nL 242.525281 419.220747 \r\nL 242.573917 420.823798 \r\nL 242.638764 420.387374 \r\nL 242.6874 418.500243 \r\nL 242.768459 418.845624 \r\nL 242.817095 420.680605 \r\nL 242.865731 419.609198 \r\nL 242.881943 418.319413 \r\nL 242.914366 419.934183 \r\nL 242.979214 419.366171 \r\nL 242.995426 418.983775 \r\nL 243.044061 420.128495 \r\nL 243.060273 419.624482 \r\nL 243.076485 420.368225 \r\nL 243.141333 418.564281 \r\nL 243.157545 419.569266 \r\nL 243.173757 419.687239 \r\nL 243.20618 418.199909 \r\nL 243.254816 420.615081 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419.021154 \r\nL 244.600403 420.565126 \r\nL 244.632826 419.783532 \r\nL 244.681462 420.597717 \r\nL 244.66525 418.234039 \r\nL 244.697674 418.71251 \r\nL 244.713886 418.214931 \r\nL 244.74631 420.229532 \r\nL 244.762521 420.707397 \r\nL 244.794945 419.063606 \r\nL 244.811157 419.556076 \r\nL 244.892217 418.421389 \r\nL 244.876005 420.066289 \r\nL 244.92464 418.900909 \r\nL 244.940852 420.796076 \r\nL 245.038123 420.527187 \r\nL 245.086759 417.573785 \r\nL 245.167819 418.752376 \r\nL 245.232666 420.9399 \r\nL 245.200242 418.747333 \r\nL 245.297514 420.151411 \r\nL 245.427209 417.879661 \r\nL 245.362361 420.190008 \r\nL 245.443421 419.196664 \r\nL 245.459633 419.15152 \r\nL 245.573116 421.224897 \r\nL 245.60554 417.72005 \r\nL 245.686599 418.590667 \r\nL 245.767658 420.357667 \r\nL 245.719023 418.552262 \r\nL 245.800082 419.269358 \r\nL 245.913565 420.209621 \r\nL 245.86493 418.484255 \r\nL 245.929777 420.049548 \r\nL 245.978413 418.0628 \r\nL 245.962201 420.195396 \r\nL 246.027049 418.683225 \r\nL 246.091896 420.066871 \r\nL 246.12432 418.143398 \r\nL 246.140532 419.768926 \r\nL 246.156744 418.721911 \r\nL 246.205379 420.431439 \r\nL 246.254015 419.484662 \r\nL 246.270227 419.843634 \r\nL 246.318863 419.016698 \r\nL 246.335074 418.221754 \r\nL 246.351286 419.394997 \r\nL 246.416134 419.307514 \r\nL 246.448558 418.924629 \r\nL 246.545829 420.606521 \r\nL 246.659312 418.441394 \r\nL 246.675524 418.987546 \r\nL 246.72416 420.121934 \r\nL 246.740372 418.415093 \r\nL 246.789007 419.425794 \r\nL 246.805219 419.465371 \r\nL 246.821431 419.430888 \r\nL 246.870067 418.475353 \r\nL 246.90249 419.999465 \r\nL 246.918702 420.235768 \r\nL 246.934914 419.163279 \r\nL 246.967338 419.521961 \r\nL 246.999762 418.252182 \r\nL 247.048397 420.168812 \r\nL 247.097033 418.97534 \r\nL 247.113245 418.983937 \r\nL 247.129457 418.863179 \r\nL 247.226728 420.342065 \r\nL 247.24294 419.696816 \r\nL 247.259152 419.616887 \r\nL 247.324 418.40571 \r\nL 247.356423 420.332587 \r\nL 247.372635 419.317667 \r\nL 247.437483 420.758676 \r\nL 247.421271 418.734438 \r\nL 247.469906 419.949737 \r\nL 247.550966 418.744251 \r\nL 247.599602 419.10219 \r\nL 247.632025 418.540035 \r\nL 247.648237 419.281458 \r\nL 247.696873 420.691467 \r\nL 247.680661 419.089645 \r\nL 247.745509 420.291009 \r\nL 247.810356 418.230299 \r\nL 247.84278 420.347549 \r\nL 247.891416 418.603949 \r\nL 247.988687 419.769767 \r\nL 248.037323 420.355233 \r\nL 248.069746 419.533359 \r\nL 248.10217 418.798437 \r\nL 248.118382 419.721142 \r\nL 248.167018 420.333425 \r\nL 248.18323 419.800204 \r\nL 248.264289 419.108469 \r\nL 248.280501 419.600576 \r\nL 248.377772 420.442624 \r\nL 248.312925 418.732033 \r\nL 248.393984 419.929136 \r\nL 248.410196 419.123028 \r\nL 248.475043 420.255003 \r\nL 248.491255 419.332237 \r\nL 248.507467 419.901472 \r\nL 248.556103 419.138252 \r\nL 248.604739 419.458874 \r\nL 248.62095 419.573917 \r\nL 248.637162 418.857633 \r\nL 248.685798 420.063241 \r\nL 248.718222 419.114366 \r\nL 248.734434 419.912022 \r\nL 248.831705 419.770476 \r\nL 248.928976 420.303152 \r\nL 248.945188 418.51613 \r\nL 249.058671 419.813937 \r\nL 249.107307 418.59747 \r\nL 249.172155 419.314416 \r\nL 249.188366 419.210565 \r\nL 249.204578 419.479734 \r\nL 249.253214 419.961117 \r\nL 249.269426 419.260758 \r\nL 249.30185 418.731113 \r\nL 249.318062 419.399236 \r\nL 249.334273 419.278245 \r\nL 249.350485 420.20603 \r\nL 249.399121 418.518934 \r\nL 249.447757 419.927841 \r\nL 249.496392 418.822053 \r\nL 249.577452 419.148502 \r\nL 249.593664 419.117959 \r\nL 249.674723 420.153998 \r\nL 249.626087 419.065407 \r\nL 249.707147 419.469684 \r\nL 249.723359 418.261425 \r\nL 249.771994 420.506723 \r\nL 249.82063 418.656528 \r\nL 249.836842 418.695847 \r\nL 249.966537 420.245694 \r\nL 250.015173 419.001497 \r\nL 250.08002 419.491228 \r\nL 250.096232 419.500302 \r\nL 250.209715 420.374485 \r\nL 250.177292 419.24538 \r\nL 250.225927 419.912695 \r\nL 250.290775 419.058905 \r\nL 250.355622 419.251612 \r\nL 250.452894 420.261459 \r\nL 250.469106 420.17301 \r\nL 250.533953 418.353134 \r\nL 250.598801 418.474432 \r\nL 250.696072 420.243393 \r\nL 250.744708 419.7938 \r\nL 250.793343 418.735228 \r\nL 250.825767 420.078537 \r\nL 250.858191 418.807085 \r\nL 251.004098 420.281205 \r\nL 251.052733 418.588657 \r\nL 251.133793 419.150639 \r\nL 251.19864 418.944835 \r\nL 251.231064 420.700843 \r\nL 251.312124 418.227873 \r\nL 251.344547 419.648953 \r\nL 251.376971 419.091016 \r\nL 251.425607 420.088374 \r\nL 251.441819 420.23604 \r\nL 251.458031 420.163561 \r\nL 251.490454 418.596476 \r\nL 251.571514 419.606098 \r\nL 251.668785 420.830522 \r\nL 251.603938 419.26288 \r\nL 251.701209 419.858193 \r\nL 251.830904 418.314617 \r\nL 251.766056 420.070969 \r\nL 251.847116 418.699068 \r\nL 251.895752 420.497089 \r\nL 251.960599 419.23717 \r\nL 252.009235 420.21677 \r\nL 252.041659 418.89575 \r\nL 252.05787 418.34421 \r\nL 252.106506 419.903291 \r\nL 252.13893 419.29805 \r\nL 252.203777 420.494479 \r\nL 252.171354 418.787951 \r\nL 252.236201 419.986572 \r\nL 252.252413 418.239245 \r\nL 252.333473 420.829771 \r\nL 252.349684 419.491844 \r\nL 252.365896 419.452853 \r\nL 252.39832 419.513698 \r\nL 252.430744 419.894694 \r\nL 252.446956 419.462979 \r\nL 252.479379 418.735347 \r\nL 252.544227 419.531844 \r\nL 252.560439 419.238325 \r\nL 252.576651 418.911826 \r\nL 252.592863 419.260029 \r\nL 252.609075 418.914009 \r\nL 252.65771 420.903552 \r\nL 252.690134 418.72919 \r\nL 252.722558 419.381741 \r\nL 252.754982 418.280237 \r\nL 252.819829 418.939569 \r\nL 252.836041 420.569059 \r\nL 252.933312 419.717661 \r\nL 252.965736 420.084161 \r\nL 252.981948 419.873693 \r\nL 253.030584 417.90541 \r\nL 253.079219 420.366728 \r\nL 253.095431 419.551604 \r\nL 253.176491 420.3307 \r\nL 253.192702 419.435147 \r\nL 253.241338 418.521818 \r\nL 253.273762 420.066582 \r\nL 253.289974 419.242436 \r\nL 253.322398 420.165347 \r\nL 253.338609 420.041923 \r\nL 253.354821 418.494035 \r\nL 253.419669 420.196338 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420.455446 \r\nL 256.402656 418.653557 \r\nL 256.418868 419.076774 \r\nL 256.516139 421.05281 \r\nL 256.451292 418.945462 \r\nL 256.532351 420.062147 \r\nL 256.662046 418.870079 \r\nL 256.678258 418.935957 \r\nL 256.807953 420.414411 \r\nL 256.856589 418.959689 \r\nL 256.921436 419.332996 \r\nL 256.986284 420.062439 \r\nL 257.018708 419.209513 \r\nL 257.083555 417.899939 \r\nL 257.051132 419.95742 \r\nL 257.115979 418.506516 \r\nL 257.132191 420.367638 \r\nL 257.229462 419.284057 \r\nL 257.310522 420.203767 \r\nL 257.29431 419.02012 \r\nL 257.326734 419.358405 \r\nL 257.407793 417.415614 \r\nL 257.391581 420.203953 \r\nL 257.424005 419.004444 \r\nL 257.456429 420.662358 \r\nL 257.537488 419.199635 \r\nL 257.569912 418.310229 \r\nL 257.634759 419.774506 \r\nL 257.650971 420.208067 \r\nL 257.667183 418.504425 \r\nL 257.715819 419.475612 \r\nL 257.796878 418.716183 \r\nL 257.877938 417.761655 \r\nL 257.926573 420.040072 \r\nL 258.007633 418.810793 \r\nL 257.958997 420.398521 \r\nL 258.023845 419.053879 \r\nL 258.07248 420.694391 \r\nL 258.104904 418.239215 \r\nL 258.137328 420.086052 \r\nL 258.169752 419.970838 \r\nL 258.185964 418.023661 \r\nL 258.202175 420.63934 \r\nL 258.283235 419.402384 \r\nL 258.331871 418.901056 \r\nL 258.364294 420.042636 \r\nL 258.380506 419.982159 \r\nL 258.493989 419.014887 \r\nL 258.510201 420.225671 \r\nL 258.575049 418.98361 \r\nL 258.607473 419.374161 \r\nL 258.623685 419.962166 \r\nL 258.704744 419.050239 \r\nL 258.769592 418.951012 \r\nL 258.850651 420.577641 \r\nL 258.947922 419.067967 \r\nL 258.980346 419.532968 \r\nL 259.093829 418.68677 \r\nL 259.045194 419.984497 \r\nL 259.110041 418.794751 \r\nL 259.174889 419.956345 \r\nL 259.207312 419.123978 \r\nL 259.223524 418.628171 \r\nL 259.27216 419.175884 \r\nL 259.288372 420.201505 \r\nL 259.337008 418.844559 \r\nL 259.369431 419.622431 \r\nL 259.450491 418.898823 \r\nL 259.482915 419.126832 \r\nL 259.580186 420.078662 \r\nL 259.563974 418.589371 \r\nL 259.596398 420.01968 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419.36613 \r\nL 269.469437 417.826402 \r\nL 269.534284 420.021335 \r\nL 269.566708 418.818891 \r\nL 269.615344 420.138481 \r\nL 269.696403 419.671608 \r\nL 269.728827 419.756459 \r\nL 269.761251 418.567055 \r\nL 269.777463 418.078463 \r\nL 269.826098 419.656747 \r\nL 269.84231 419.391696 \r\nL 269.858522 419.11505 \r\nL 269.907158 419.835136 \r\nL 269.92337 419.279363 \r\nL 269.955793 420.883908 \r\nL 270.004429 419.102977 \r\nL 270.020641 419.50308 \r\nL 270.085488 418.660092 \r\nL 270.1017 420.039335 \r\nL 270.117912 418.685951 \r\nL 270.198972 420.436506 \r\nL 270.231395 420.175071 \r\nL 270.280031 419.240679 \r\nL 270.344879 419.514863 \r\nL 270.377302 418.490447 \r\nL 270.425938 420.598142 \r\nL 270.458362 418.803012 \r\nL 270.539421 419.53523 \r\nL 270.571845 419.448985 \r\nL 270.669116 420.008468 \r\nL 270.70154 418.124506 \r\nL 270.7826 419.530397 \r\nL 270.831235 418.999408 \r\nL 270.912295 420.271061 \r\nL 271.025778 418.188042 \r\nL 271.090625 420.028465 \r\nL 271.139261 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272.646967 419.708605 \r\nL 272.695602 418.610066 \r\nL 272.728026 419.77422 \r\nL 272.76045 419.05856 \r\nL 272.809085 420.272487 \r\nL 272.857721 419.982221 \r\nL 272.873933 418.655073 \r\nL 272.971204 419.29243 \r\nL 273.036052 418.692961 \r\nL 273.084687 420.696929 \r\nL 273.149535 420.317768 \r\nL 273.214383 417.968718 \r\nL 273.263018 419.835608 \r\nL 273.27923 419.071938 \r\nL 273.36029 420.388727 \r\nL 273.376501 419.40222 \r\nL 273.392713 419.983423 \r\nL 273.441349 418.767047 \r\nL 273.457561 419.441035 \r\nL 273.489985 418.604084 \r\nL 273.53862 419.678577 \r\nL 273.554832 420.395124 \r\nL 273.635892 419.107221 \r\nL 273.652103 419.081753 \r\nL 273.700739 420.026688 \r\nL 273.716951 418.182909 \r\nL 273.79801 419.485624 \r\nL 273.862858 420.678578 \r\nL 273.87907 419.225463 \r\nL 273.895282 420.425088 \r\nL 273.960129 418.580002 \r\nL 274.008765 419.116677 \r\nL 274.073613 420.131727 \r\nL 274.122248 419.613587 \r\nL 274.170884 420.009283 \r\nL 274.235731 419.174659 \r\nL 274.300579 420.218372 \r\nL 274.268155 418.32131 \r\nL 274.349215 419.915238 \r\nL 274.446486 418.499759 \r\nL 274.47891 419.057637 \r\nL 274.495122 419.047995 \r\nL 274.543757 420.104266 \r\nL 274.608605 419.674749 \r\nL 274.624817 419.663485 \r\nL 274.7383 418.40189 \r\nL 274.835571 420.60694 \r\nL 274.851783 419.316667 \r\nL 274.949054 418.645459 \r\nL 274.884207 419.624071 \r\nL 274.981478 418.971208 \r\nL 275.094961 420.724 \r\nL 275.030114 418.77661 \r\nL 275.111173 419.735766 \r\nL 275.208445 418.824323 \r\nL 275.224656 419.212383 \r\nL 275.305716 419.922096 \r\nL 275.321928 419.095988 \r\nL 275.33814 419.61306 \r\nL 275.419199 418.782382 \r\nL 275.451623 419.446017 \r\nL 275.500259 418.941783 \r\nL 275.51647 419.144707 \r\nL 275.581318 420.258146 \r\nL 275.629954 419.719896 \r\nL 275.694801 418.590776 \r\nL 275.711013 419.746323 \r\nL 275.727225 418.680947 \r\nL 275.775861 420.130173 \r\nL 275.840708 420.051473 \r\nL 275.85692 420.08398 \r\nL 275.954191 418.564299 \r\nL 275.889344 420.290643 \r\nL 275.986615 419.529703 \r\nL 276.019039 419.527725 \r\nL 276.035251 419.842026 \r\nL 276.051463 418.646521 \r\nL 276.083886 420.30562 \r\nL 276.132522 419.211324 \r\nL 276.229793 419.997921 \r\nL 276.164946 418.7607 \r\nL 276.246005 419.286926 \r\nL 276.310853 420.239667 \r\nL 276.327065 419.750029 \r\nL 276.408124 420.512017 \r\nL 276.45676 418.724793 \r\nL 276.554031 419.955163 \r\nL 276.586455 419.288412 \r\nL 276.602667 419.254295 \r\nL 276.699938 418.87033 \r\nL 276.71615 420.160014 \r\nL 276.764786 418.898664 \r\nL 276.829633 419.50247 \r\nL 276.878269 418.638147 \r\nL 276.926905 419.943914 \r\nL 276.943116 419.102982 \r\nL 276.959328 420.712801 \r\nL 276.97554 419.014302 \r\nL 277.0566 420.043336 \r\nL 277.202507 418.361891 \r\nL 277.218719 418.797704 \r\nL 277.283566 420.277501 \r\nL 277.348414 419.686782 \r\nL 277.364626 419.75333 \r\nL 277.380837 417.954981 \r\nL 277.461897 418.911072 \r\nL 277.478109 420.18535 \r\nL 277.542956 418.430021 \r\nL 277.57538 419.950044 \r\nL 277.591592 420.01723 \r\nL 277.640228 419.070122 \r\nL 277.705075 419.520791 \r\nL 277.721287 419.022023 \r\nL 277.753711 420.115206 \r\nL 277.786135 419.646196 \r\nL 277.802346 420.077901 \r\nL 277.867194 419.114053 \r\nL 277.883406 419.411171 \r\nL 277.899618 418.77124 \r\nL 277.91583 420.37984 \r\nL 277.996889 418.978936 \r\nL 278.029313 419.825961 \r\nL 278.077949 418.322534 \r\nL 278.110372 419.774663 \r\nL 278.159008 418.419849 \r\nL 278.207644 420.165443 \r\nL 278.223856 420.457069 \r\nL 278.240067 419.325734 \r\nL 278.256279 419.673453 \r\nL 278.288703 418.864692 \r\nL 278.353551 420.09214 \r\nL 278.369763 419.20275 \r\nL 278.450822 420.64911 \r\nL 278.402186 418.871449 \r\nL 278.467034 419.914103 \r\nL 278.548093 420.496881 \r\nL 278.596729 418.611678 \r\nL 278.645365 420.36298 \r\nL 278.726424 419.726226 \r\nL 278.823695 419.010902 \r\nL 278.839907 419.537337 \r\nL 278.872331 419.226714 \r\nL 278.904755 420.078841 \r\nL 278.920967 418.322331 \r\nL 278.985814 420.508954 \r\nL 279.018238 419.0869 \r\nL 279.03445 418.892162 \r\nL 279.050662 419.067135 \r\nL 279.066874 419.953818 \r\nL 279.115509 418.5931 \r\nL 279.164145 419.316883 \r\nL 279.228993 419.667262 \r\nL 279.245204 419.13646 \r\nL 279.261416 419.05935 \r\nL 279.29384 419.351536 \r\nL 279.326264 420.122168 \r\nL 279.342476 419.52151 \r\nL 279.374899 419.968488 \r\nL 279.455959 418.347529 \r\nL 279.520806 420.854245 \r\nL 279.569442 419.310916 \r\nL 279.585654 418.96111 \r\nL 279.601866 419.597397 \r\nL 279.63429 419.072943 \r\nL 279.731561 420.373113 \r\nL 279.682925 418.740753 \r\nL 279.747773 419.290548 \r\nL 279.763985 419.309029 \r\nL 279.861256 420.2999 \r\nL 279.796409 419.083535 \r\nL 279.877468 419.50547 \r\nL 279.926104 418.654342 \r\nL 279.958527 420.118854 \r\nL 279.974739 419.101165 \r\nL 280.055799 420.156629 \r\nL 280.007163 419.025912 \r\nL 280.072011 419.817675 \r\nL 280.136858 418.872851 \r\nL 280.15307 420.312263 \r\nL 280.185494 419.558425 \r\nL 280.234129 418.254583 \r\nL 280.266553 420.264068 \r\nL 280.282765 419.614917 \r\nL 280.298977 419.725105 \r\nL 280.396248 420.22539 \r\nL 280.428672 418.774756 \r\nL 280.461096 419.454376 \r\nL 280.49352 420.258969 \r\nL 280.542155 418.728491 \r\nL 280.590791 420.241279 \r\nL 280.67185 418.533491 \r\nL 280.704274 419.397548 \r\nL 280.769122 418.755342 \r\nL 280.801546 419.526357 \r\nL 280.817757 420.407315 \r\nL 280.850181 418.778922 \r\nL 280.882605 418.920179 \r\nL 280.898817 418.446378 \r\nL 280.931241 419.998047 \r\nL 280.996088 418.61301 \r\nL 281.028512 419.961788 \r\nL 281.125783 419.575722 \r\nL 281.174419 419.133847 \r\nL 281.190631 419.696865 \r\nL 281.239266 419.499349 \r\nL 281.27169 419.70964 \r\nL 281.287902 419.650838 \r\nL 281.304114 418.931825 \r\nL 281.320326 419.971727 \r\nL 281.368962 419.929542 \r\nL 281.385173 420.502109 \r\nL 281.433809 418.885647 \r\nL 281.466233 419.511142 \r\nL 281.547292 420.161566 \r\nL 281.53108 419.061237 \r\nL 281.563504 419.802211 \r\nL 281.61214 418.430376 \r\nL 281.660776 419.943869 \r\nL 281.693199 418.9864 \r\nL 281.790471 419.949048 \r\nL 281.806682 419.648182 \r\nL 281.887742 419.782428 \r\nL 281.920166 419.145292 \r\nL 282.001225 420.471056 \r\nL 282.033649 419.615874 \r\nL 282.049861 418.659396 \r\nL 282.114708 419.917716 \r\nL 282.147132 419.190971 \r\nL 282.179556 419.974602 \r\nL 282.260615 419.506553 \r\nL 282.357887 418.903419 \r\nL 282.293039 420.215099 \r\nL 282.374099 419.110519 \r\nL 282.422734 420.312108 \r\nL 282.47137 419.991502 \r\nL 282.552429 418.771084 \r\nL 282.584853 419.455388 \r\nL 282.633489 419.756964 \r\nL 282.617277 419.30833 \r\nL 282.649701 419.306248 \r\nL 282.665912 419.02235 \r\nL 282.698336 419.916868 \r\nL 282.73076 419.650077 \r\nL 282.763184 420.571146 \r\nL 282.779396 418.686255 \r\nL 282.795608 418.552892 \r\nL 282.828031 418.505902 \r\nL 282.925303 420.717314 \r\nL 282.957726 418.944212 \r\nL 283.054998 419.237117 \r\nL 283.152269 420.038297 \r\nL 283.103633 418.958924 \r\nL 283.184693 419.586079 \r\nL 283.200905 419.594058 \r\nL 283.233329 419.855776 \r\nL 283.3306 418.651744 \r\nL 283.379235 420.208631 \r\nL 283.444083 419.776494 \r\nL 283.460295 419.826586 \r\nL 283.557566 418.62775 \r\nL 283.58999 418.973599 \r\nL 283.703473 420.438894 \r\nL 283.800745 418.712476 \r\nL 283.816956 418.862889 \r\nL 283.881804 420.130471 \r\nL 283.946652 419.970957 \r\nL 284.10877 419.241296 \r\nL 284.222254 419.975552 \r\nL 284.270889 418.791497 \r\nL 284.335737 419.70758 \r\nL 284.368161 419.976456 \r\nL 284.384372 419.604534 \r\nL 284.481644 418.328761 \r\nL 284.416796 419.705283 \r\nL 284.497856 419.449175 \r\nL 284.595127 418.399202 \r\nL 284.643763 419.97284 \r\nL 284.70861 419.823347 \r\nL 284.773458 418.034545 \r\nL 284.805882 419.817884 \r\nL 284.903153 418.976007 \r\nL 284.870729 419.914393 \r\nL 284.935577 419.595425 \r\nL 284.951789 419.988859 \r\nL 284.984212 419.174378 \r\nL 285.04906 419.846538 \r\nL 285.113907 419.265857 \r\nL 285.146331 420.15082 \r\nL 285.276026 418.572173 \r\nL 285.30845 420.12684 \r\nL 285.389509 419.832128 \r\nL 285.421933 418.974332 \r\nL 285.470569 420.050511 \r\nL 285.519205 419.049025 \r\nL 285.600264 420.213099 \r\nL 285.6489 419.866405 \r\nL 285.697535 419.138171 \r\nL 285.681323 420.060664 \r\nL 285.762383 419.547192 \r\nL 285.811018 419.279749 \r\nL 285.90829 420.284279 \r\nL 285.956925 418.632306 \r\nL 286.037985 419.213451 \r\nL 286.070409 420.839395 \r\nL 286.119044 419.05049 \r\nL 286.16768 419.73566 \r\nL 286.232528 419.990635 \r\nL 286.313587 418.605877 \r\nL 286.362223 419.846217 \r\nL 286.42707 419.248747 \r\nL 286.443282 418.72153 \r\nL 286.50813 419.664204 \r\nL 286.524342 419.764841 \r\nL 286.540553 419.617211 \r\nL 286.589189 418.752615 \r\nL 286.605401 419.7739 \r\nL 286.670248 419.119838 \r\nL 286.751308 418.632379 \r\nL 286.783732 420.309368 \r\nL 286.897215 419.259706 \r\nL 286.913427 419.141891 \r\nL 286.929639 419.593045 \r\nL 286.962062 419.197922 \r\nL 287.107969 419.88947 \r\nL 287.189029 418.914734 \r\nL 287.156605 419.947805 \r\nL 287.221453 419.306751 \r\nL 287.302512 420.118327 \r\nL 287.253876 418.722376 \r\nL 287.334936 419.423473 \r\nL 287.399783 419.502457 \r\nL 287.432207 419.184641 \r\nL 287.464631 419.887017 \r\nL 287.497055 418.741449 \r\nL 287.54569 419.35579 \r\nL 287.561902 419.247336 \r\nL 287.578114 419.860917 \r\nL 287.610538 419.87865 \r\nL 287.707809 419.04486 \r\nL 287.740233 419.411729 \r\nL 287.788869 418.743594 \r\nL 287.869928 420.192152 \r\nL 287.934776 419.011376 \r\nL 288.064471 419.349199 \r\nL 288.14553 420.23962 \r\nL 288.113106 419.069388 \r\nL 288.161742 419.414975 \r\nL 288.177954 418.965477 \r\nL 288.259013 419.102951 \r\nL 288.323861 420.072077 \r\nL 288.291437 419.059151 \r\nL 288.340073 420.010198 \r\nL 288.356285 418.582954 \r\nL 288.388708 420.073224 \r\nL 288.453556 419.259909 \r\nL 288.469768 419.016108 \r\nL 288.502192 419.577271 \r\nL 288.518404 420.074622 \r\nL 288.534615 419.370261 \r\nL 288.615675 419.825046 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419.733822 \r\nL 295.489515 419.268608 \r\nL 295.602998 420.25883 \r\nL 295.667846 418.756081 \r\nL 295.716481 419.96336 \r\nL 295.732693 419.90751 \r\nL 295.846176 418.751454 \r\nL 295.894812 419.73405 \r\nL 295.95966 419.412919 \r\nL 295.992083 419.683446 \r\nL 296.056931 419.789895 \r\nL 296.105567 418.885996 \r\nL 296.170414 419.771609 \r\nL 296.267685 419.729766 \r\nL 296.348745 419.985098 \r\nL 296.397381 418.933997 \r\nL 296.47844 419.92385 \r\nL 296.527076 419.616669 \r\nL 296.559499 419.06029 \r\nL 296.591923 419.672624 \r\nL 296.624347 419.323508 \r\nL 296.672983 419.937306 \r\nL 296.689194 419.004292 \r\nL 296.721618 419.246956 \r\nL 296.73783 419.091959 \r\nL 296.754042 419.570567 \r\nL 296.786466 418.877543 \r\nL 296.851313 420.433959 \r\nL 296.964797 418.50781 \r\nL 296.99722 420.188101 \r\nL 297.07828 419.629602 \r\nL 297.110704 419.588781 \r\nL 297.126915 419.966046 \r\nL 297.159339 420.024537 \r\nL 297.25661 418.6135 \r\nL 297.289034 419.941633 \r\nL 297.386306 419.334373 \r\nL 297.402517 419.357565 \r\nL 297.467365 418.842 \r\nL 297.499789 419.400636 \r\nL 297.532213 419.549763 \r\nL 297.548424 419.290713 \r\nL 297.564636 418.666083 \r\nL 297.629484 420.257179 \r\nL 297.645696 418.99777 \r\nL 297.726755 418.706503 \r\nL 297.759179 419.907881 \r\nL 297.775391 419.141247 \r\nL 297.840238 419.991459 \r\nL 297.872662 419.23305 \r\nL 297.93751 420.297939 \r\nL 297.953722 419.209963 \r\nL 297.969934 420.110321 \r\nL 298.083417 418.739506 \r\nL 298.11584 420.231621 \r\nL 298.1969 419.141185 \r\nL 298.245536 419.135838 \r\nL 298.310383 419.973576 \r\nL 298.342807 418.894547 \r\nL 298.423866 419.594557 \r\nL 298.488714 418.9118 \r\nL 298.45629 419.896046 \r\nL 298.521138 419.215492 \r\nL 298.553561 420.469152 \r\nL 298.618409 418.799312 \r\nL 298.650833 420.258459 \r\nL 298.764316 418.807094 \r\nL 298.780528 419.314888 \r\nL 298.79674 419.114381 \r\nL 298.829164 419.983962 \r\nL 298.861587 419.394279 \r\nL 298.894011 420.267288 \r\nL 298.942647 418.599399 \r\nL 299.007494 420.096907 \r\nL 299.104766 419.46622 \r\nL 299.153401 418.808153 \r\nL 299.137189 420.140406 \r\nL 299.202037 419.587521 \r\nL 299.218249 419.703701 \r\nL 299.234461 419.585602 \r\nL 299.250673 418.562444 \r\nL 299.283096 419.948074 \r\nL 299.347944 419.362072 \r\nL 299.380368 418.309497 \r\nL 299.39658 419.504207 \r\nL 299.429003 419.496532 \r\nL 299.510063 420.289904 \r\nL 299.461427 418.959549 \r\nL 299.526275 419.630986 \r\nL 299.591122 419.697473 \r\nL 299.688393 418.344038 \r\nL 299.801877 420.495149 \r\nL 299.818089 420.099391 \r\nL 299.899148 419.048005 \r\nL 299.931572 419.239694 \r\nL 299.980207 419.426722 \r\nL 300.012631 419.311178 \r\nL 300.028843 418.938235 \r\nL 300.045055 420.446568 \r\nL 300.109903 419.40326 \r\nL 300.142326 419.766748 \r\nL 300.17475 419.2128 \r\nL 300.190962 418.17317 \r\nL 300.272021 419.917778 \r\nL 300.369293 418.924903 \r\nL 300.385505 419.591475 \r\nL 300.401717 419.804428 \r\nL 300.417928 419.11931 \r\nL 300.482776 419.555981 \r\nL 300.5152 419.71968 \r\nL 300.628683 419.058893 \r\nL 300.596259 419.799172 \r\nL 300.644895 419.207722 \r\nL 300.709742 419.886032 \r\nL 300.725954 420.326993 \r\nL 300.790802 419.56235 \r\nL 300.871861 418.298644 \r\nL 300.823226 419.991503 \r\nL 300.920497 418.970021 \r\nL 301.066404 420.066429 \r\nL 301.082616 418.902497 \r\nL 301.163675 419.170801 \r\nL 301.196099 419.00471 \r\nL 301.277158 420.088088 \r\nL 301.37443 418.715354 \r\nL 301.358218 420.373906 \r\nL 301.390642 419.765786 \r\nL 301.406853 419.802609 \r\nL 301.423065 418.513866 \r\nL 301.504125 420.429979 \r\nL 301.617608 418.393437 \r\nL 301.63382 418.944923 \r\nL 301.698667 420.134644 \r\nL 301.747303 419.445039 \r\nL 301.795939 419.088987 \r\nL 301.779727 419.942427 \r\nL 301.860786 419.198092 \r\nL 301.89321 419.448422 \r\nL 301.909422 418.81825 \r\nL 301.941846 420.728685 \r\nL 301.990481 419.797878 \r\nL 302.006693 420.19278 \r\nL 302.022905 419.498979 \r\nL 302.055329 419.571759 \r\nL 302.071541 418.924383 \r\nL 302.1526 419.794789 \r\nL 302.168812 419.191652 \r\nL 302.23366 420.863784 \r\nL 302.282295 420.571887 \r\nL 302.298507 418.295343 \r\nL 302.395779 419.36128 \r\nL 302.444414 420.529721 \r\nL 302.460626 419.012002 \r\nL 302.509262 419.867094 \r\nL 302.590321 418.294236 \r\nL 302.574109 420.082558 \r\nL 302.638957 418.482826 \r\nL 302.736228 420.782853 \r\nL 302.75244 419.924087 \r\nL 302.801076 417.970925 \r\nL 302.849711 418.320288 \r\nL 302.865923 420.00818 \r\nL 302.963195 419.471509 \r\nL 303.01183 419.184074 \r\nL 303.028042 419.423469 \r\nL 303.060466 419.791402 \r\nL 303.141525 418.083045 \r\nL 303.206373 421.373332 \r\nL 303.255009 419.167196 \r\nL 303.319856 418.611686 \r\nL 303.287432 419.318024 \r\nL 303.35228 419.042744 \r\nL 303.417127 420.450502 \r\nL 303.449551 419.106919 \r\nL 303.481975 419.358245 \r\nL 303.498187 420.560621 \r\nL 303.563034 418.011859 \r\nL 303.579246 418.844612 \r\nL 303.708941 420.519998 \r\nL 303.773789 418.26106 \r\nL 303.822425 419.148252 \r\nL 303.903484 418.700417 \r\nL 303.95212 420.722206 \r\nL 304.098027 418.513931 \r\nL 304.13045 420.895959 \r\nL 304.21151 418.836801 \r\nL 304.389841 420.402613 \r\nL 304.503324 419.02669 \r\nL 304.584383 420.263464 \r\nL 304.535748 417.807505 \r\nL 304.600595 419.487715 \r\nL 304.616807 419.120714 \r\nL 304.649231 420.230863 \r\nL 304.681655 419.852697 \r\nL 304.697866 420.144692 \r\nL 304.73029 419.457852 \r\nL 304.746502 418.629198 \r\nL 304.81135 420.363724 \r\nL 304.843773 418.694549 \r\nL 304.892409 420.699896 \r\nL 304.957257 419.0264 \r\nL 304.98968 418.970523 \r\nL 305.135587 420.267121 \r\nL 305.297706 418.203606 \r\nL 305.313918 419.265134 \r\nL 305.394978 420.268789 \r\nL 305.378766 419.167908 \r\nL 305.427401 419.563211 \r\nL 305.443613 419.659125 \r\nL 305.459825 418.537333 \r\nL 305.540885 419.443029 \r\nL 305.621944 420.269151 \r\nL 305.573308 419.388933 \r\nL 305.67058 419.85712 \r\nL 305.735427 418.644615 \r\nL 305.784063 419.390234 \r\nL 305.800275 419.165729 \r\nL 305.84891 419.884996 \r\nL 305.881334 420.661624 \r\nL 305.913758 419.236101 \r\nL 305.92997 419.413667 \r\nL 305.962394 419.701541 \r\nL 305.994817 418.565915 \r\nL 306.059665 419.715717 \r\nL 306.124513 419.188293 \r\nL 306.156936 420.412262 \r\nL 306.205572 418.487834 \r\nL 306.270419 419.206316 \r\nL 306.400115 420.279414 \r\nL 306.44875 418.405992 \r\nL 306.513598 419.374096 \r\nL 306.594657 420.198417 \r\nL 306.546022 418.626433 \r\nL 306.627081 419.504984 \r\nL 306.70814 419.683258 \r\nL 306.724352 418.489169 \r\nL 306.821624 420.684951 \r\nL 306.854047 420.101485 \r\nL 306.983743 418.525957 \r\nL 307.04859 421.096587 \r\nL 307.113438 419.505677 \r\nL 307.129649 420.014131 \r\nL 307.194497 418.884435 \r\nL 307.210709 419.155231 \r\nL 307.243133 419.387936 \r\nL 307.259345 418.592645 \r\nL 307.275556 420.62747 \r\nL 307.324192 419.145632 \r\nL 307.340404 420.222092 \r\nL 307.421463 418.829542 \r\nL 307.437675 418.391882 \r\nL 307.502523 419.496466 \r\nL 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419.896155 \r\nL 310.453086 419.553664 \r\nL 310.48551 419.664707 \r\nL 310.566569 418.647125 \r\nL 310.582781 419.767919 \r\nL 310.598993 419.29556 \r\nL 310.712476 420.574871 \r\nL 310.761112 418.700768 \r\nL 310.842172 419.132236 \r\nL 310.874595 419.943125 \r\nL 310.955655 419.645477 \r\nL 311.036714 418.52007 \r\nL 311.101562 418.895861 \r\nL 311.166409 420.102726 \r\nL 311.231257 419.803454 \r\nL 311.279892 418.968978 \r\nL 311.377164 419.372849 \r\nL 311.458223 419.959353 \r\nL 311.442011 419.014726 \r\nL 311.474435 419.373499 \r\nL 311.490647 419.100328 \r\nL 311.506859 420.119498 \r\nL 311.539283 419.298007 \r\nL 311.587918 420.024225 \r\nL 311.652766 419.726929 \r\nL 311.717613 418.728948 \r\nL 311.68519 419.973246 \r\nL 311.782461 419.116225 \r\nL 311.847309 419.8605 \r\nL 311.895944 419.767535 \r\nL 311.960792 418.803019 \r\nL 311.993215 419.825384 \r\nL 312.041851 419.004724 \r\nL 312.090487 419.81385 \r\nL 312.155334 419.736365 \r\nL 312.20397 419.033027 \r\nL 312.285029 419.5181 \r\nL 312.301241 419.430671 \r\nL 312.317453 419.881697 \r\nL 312.333665 420.206887 \r\nL 312.398513 419.364963 \r\nL 312.447148 419.479802 \r\nL 312.479572 418.596557 \r\nL 312.495784 419.758039 \r\nL 312.593055 419.640032 \r\nL 312.625479 419.834879 \r\nL 312.72275 418.626771 \r\nL 312.836234 420.016842 \r\nL 312.852445 419.375841 \r\nL 312.868657 419.505765 \r\nL 312.884869 419.295517 \r\nL 312.949717 419.363031 \r\nL 312.965929 418.842767 \r\nL 313.014564 419.585642 \r\nL 313.046988 419.160868 \r\nL 313.079412 420.263949 \r\nL 313.144259 419.370034 \r\nL 313.160471 419.013828 \r\nL 313.225319 419.605717 \r\nL 313.257743 419.992126 \r\nL 313.290166 419.304804 \r\nL 313.32259 419.467222 \r\nL 313.338802 419.509984 \r\nL 313.436073 419.689896 \r\nL 313.452285 419.113163 \r\nL 313.565768 420.082718 \r\nL 313.484709 419.072789 \r\nL 313.598192 419.846229 \r\nL 313.646828 418.761378 \r\nL 313.711675 419.436148 \r\nL 313.760311 419.824544 \r\nL 313.776523 419.620874 \r\nL 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420.09396 \r\nL 323.519867 418.773179 \r\nL 323.600926 419.814001 \r\nL 323.681986 419.302503 \r\nL 323.71441 419.474991 \r\nL 323.730621 419.576961 \r\nL 323.746833 418.95692 \r\nL 323.795469 419.586416 \r\nL 323.827893 419.119251 \r\nL 323.89274 420.110978 \r\nL 323.860317 419.000678 \r\nL 323.941376 419.625355 \r\nL 323.9738 419.184642 \r\nL 324.054859 419.521516 \r\nL 324.071071 419.466425 \r\nL 324.087283 419.661254 \r\nL 324.135919 419.924842 \r\nL 324.15213 419.42866 \r\nL 324.168342 419.571427 \r\nL 324.249402 419.115253 \r\nL 324.23319 419.835262 \r\nL 324.265614 419.431661 \r\nL 324.298037 419.198166 \r\nL 324.346673 419.835552 \r\nL 324.362885 419.146192 \r\nL 324.460156 419.35318 \r\nL 324.525004 419.618993 \r\nL 324.508792 419.235639 \r\nL 324.57364 419.587391 \r\nL 324.638487 419.161518 \r\nL 324.606063 419.764157 \r\nL 324.670911 419.562485 \r\nL 324.687123 419.588422 \r\nL 324.703335 418.570658 \r\nL 324.784394 419.837891 \r\nL 324.881665 419.295217 \r\nL 324.849242 419.859062 \r\nL 324.914089 419.390636 \r\nL 325.01136 419.656707 \r\nL 324.978937 419.188392 \r\nL 325.027572 419.60829 \r\nL 325.043784 419.223526 \r\nL 325.076208 419.728137 \r\nL 325.141056 419.424122 \r\nL 325.189691 419.079865 \r\nL 325.254539 419.611119 \r\nL 325.319386 419.138981 \r\nL 325.303174 419.915874 \r\nL 325.35181 419.374648 \r\nL 325.368022 419.769737 \r\nL 325.416658 419.221819 \r\nL 325.449081 419.307508 \r\nL 325.497717 419.181434 \r\nL 325.513929 419.189988 \r\nL 325.546353 419.953318 \r\nL 325.627412 419.621081 \r\nL 325.643624 419.093062 \r\nL 325.708472 419.840416 \r\nL 325.740895 419.436868 \r\nL 325.773319 419.65126 \r\nL 325.805743 419.313798 \r\nL 325.821955 419.487566 \r\nL 325.886802 419.738466 \r\nL 325.95165 419.064314 \r\nL 326.032709 419.792735 \r\nL 326.081345 419.442108 \r\nL 326.129981 419.023255 \r\nL 326.194828 419.152428 \r\nL 326.243464 419.855773 \r\nL 326.308311 419.740324 \r\nL 326.454218 419.261328 \r\nL 326.486642 419.374899 \r\nL 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.22158488532181117\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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416.460444 \r\nL 91.28459 417.205973 \r\nL 91.317014 421.148718 \r\nL 91.398073 417.733503 \r\nL 91.414285 417.838531 \r\nL 91.430497 417.271702 \r\nL 91.462921 415.912017 \r\nL 91.527769 416.496645 \r\nL 91.608828 418.92139 \r\nL 91.641252 417.70473 \r\nL 91.706099 415.14352 \r\nL 91.738523 417.700691 \r\nL 91.770947 421.995213 \r\nL 91.819582 415.891038 \r\nL 91.852006 420.424143 \r\nL 91.868218 420.670712 \r\nL 91.88443 419.376142 \r\nL 91.997913 417.158645 \r\nL 91.933066 419.716206 \r\nL 92.014125 417.685755 \r\nL 92.062761 420.280933 \r\nL 92.127608 420.060063 \r\nL 92.176244 415.836209 \r\nL 92.208668 419.919809 \r\nL 92.305939 422.3915 \r\nL 92.322151 421.498091 \r\nL 92.354575 419.528306 \r\nL 92.40321 421.526897 \r\nL 92.435634 420.457162 \r\nL 92.468058 420.054875 \r\nL 92.48427 420.275628 \r\nL 92.516694 421.708023 \r\nL 92.581541 419.968256 \r\nL 92.613965 419.612914 \r\nL 92.630177 420.492608 \r\nL 92.74366 422.7944 \r\nL 92.711236 418.923104 \r\nL 92.759872 422.350493 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418.801778 \r\nL 96.52103 421.976094 \r\nL 96.634513 418.756395 \r\nL 96.650725 419.301075 \r\nL 96.683148 420.764973 \r\nL 96.764208 420.034942 \r\nL 96.812844 416.09562 \r\nL 96.845267 419.962349 \r\nL 96.877691 421.614997 \r\nL 96.910115 419.679633 \r\nL 96.958751 421.547129 \r\nL 97.007386 416.869534 \r\nL 97.088446 417.438059 \r\nL 97.169505 420.01862 \r\nL 97.201929 422.639706 \r\nL 97.250565 417.73456 \r\nL 97.282988 416.531902 \r\nL 97.331624 418.804023 \r\nL 97.347836 418.85767 \r\nL 97.396471 420.821049 \r\nL 97.428895 418.897073 \r\nL 97.445107 418.197311 \r\nL 97.493743 419.404031 \r\nL 97.509955 419.273926 \r\nL 97.542378 420.503821 \r\nL 97.574802 419.421709 \r\nL 97.607226 416.520127 \r\nL 97.672074 420.434803 \r\nL 97.769345 415.945717 \r\nL 97.801769 419.0945 \r\nL 97.850404 424.713581 \r\nL 97.89904 418.685049 \r\nL 98.012523 417.694878 \r\nL 98.15843 424.177204 \r\nL 98.190854 421.285896 \r\nL 98.23949 416.505467 \r\nL 98.304337 418.712767 \r\nL 98.450244 422.782676 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421.505686 \r\nL 100.249764 420.139066 \r\nL 100.314611 421.277873 \r\nL 100.363247 419.835338 \r\nL 100.428094 421.191168 \r\nL 100.460518 421.567424 \r\nL 100.492942 420.849598 \r\nL 100.541578 416.921107 \r\nL 100.606425 420.402646 \r\nL 100.622637 420.397364 \r\nL 100.655061 419.662162 \r\nL 100.703696 420.093312 \r\nL 100.719908 420.810313 \r\nL 100.768544 418.471933 \r\nL 100.800968 417.861579 \r\nL 100.833391 420.126984 \r\nL 100.865815 419.643112 \r\nL 100.930663 420.857343 \r\nL 100.99551 417.786008 \r\nL 101.07657 418.656443 \r\nL 101.190053 421.20725 \r\nL 101.141417 418.204563 \r\nL 101.206265 420.715963 \r\nL 101.254901 417.150168 \r\nL 101.33596 417.932522 \r\nL 101.368384 419.072117 \r\nL 101.400808 417.19072 \r\nL 101.417019 416.121131 \r\nL 101.449443 419.688428 \r\nL 101.465655 420.439795 \r\nL 101.498079 417.51486 \r\nL 101.514291 416.585744 \r\nL 101.562926 418.850887 \r\nL 101.611562 421.815918 \r\nL 101.67641 419.587216 \r\nL 101.692621 419.73189 \r\nL 101.708833 419.640431 \r\nL 101.741257 417.461178 \r\nL 101.806105 419.520412 \r\nL 101.822317 419.624095 \r\nL 101.870952 418.268159 \r\nL 101.903376 420.39564 \r\nL 101.952012 421.423064 \r\nL 101.968224 420.748559 \r\nL 102.016859 418.40751 \r\nL 102.065495 420.985802 \r\nL 102.097919 421.515221 \r\nL 102.146554 421.243113 \r\nL 102.308673 417.412507 \r\nL 102.389733 423.946529 \r\nL 102.45458 420.138143 \r\nL 102.600487 417.817994 \r\nL 102.665335 422.191724 \r\nL 102.71397 418.899251 \r\nL 102.79503 416.578678 \r\nL 102.811242 417.590455 \r\nL 102.876089 421.675126 \r\nL 102.924725 418.79683 \r\nL 103.05442 414.601038 \r\nL 102.989572 419.500656 \r\nL 103.086844 417.117462 \r\nL 103.119267 422.155857 \r\nL 103.200327 420.916601 \r\nL 103.248963 415.866082 \r\nL 103.31381 419.733698 \r\nL 103.346234 420.251255 \r\nL 103.378658 419.446321 \r\nL 103.492141 416.711911 \r\nL 103.443505 419.50956 \r\nL 103.524565 417.764106 \r\nL 103.5732 421.922545 \r\nL 103.605624 425.678303 \r\nL 103.670472 421.101165 \r\nL 103.767743 415.31144 \r\nL 103.783955 416.951117 \r\nL 103.897438 424.91561 \r\nL 103.946074 422.273025 \r\nL 104.010921 416.605108 \r\nL 104.059557 420.09156 \r\nL 104.091981 421.589198 \r\nL 104.17304 421.183788 \r\nL 104.221676 418.756113 \r\nL 104.302735 419.467671 \r\nL 104.400007 422.454066 \r\nL 104.416218 423.517075 \r\nL 104.43243 421.370709 \r\nL 104.464854 416.322174 \r\nL 104.545914 418.517812 \r\nL 104.594549 420.931938 \r\nL 104.626973 418.871781 \r\nL 104.659397 416.978493 \r\nL 104.740456 417.890732 \r\nL 104.77288 417.529625 \r\nL 104.789092 417.998534 \r\nL 104.837727 422.429287 \r\nL 104.886363 418.980285 \r\nL 104.983634 413.593039 \r\nL 104.999846 415.327328 \r\nL 105.129541 422.856932 \r\nL 105.145753 421.822763 \r\nL 105.243025 416.797577 \r\nL 105.307872 417.384377 \r\nL 105.324084 417.466104 \r\nL 105.469991 422.096172 \r\nL 105.421355 416.896582 \r\nL 105.486203 421.875124 \r\nL 105.680746 415.722916 \r\nL 105.55105 422.44701 \r\nL 105.696957 415.842997 \r\nL 105.794229 423.091157 \r\nL 105.875288 421.535719 \r\nL 105.940136 417.89813 \r\nL 105.988771 420.320171 \r\nL 106.053619 425.116113 \r\nL 106.086043 422.561465 \r\nL 106.183314 415.779324 \r\nL 106.215738 417.909691 \r\nL 106.280585 424.055501 \r\nL 106.345433 421.364725 \r\nL 106.361645 421.679812 \r\nL 106.377857 421.036999 \r\nL 106.442704 414.912909 \r\nL 106.49134 419.335547 \r\nL 106.523764 421.34507 \r\nL 106.604823 421.086374 \r\nL 106.669671 415.267581 \r\nL 106.718306 419.42613 \r\nL 106.766942 421.785714 \r\nL 106.83179 420.216656 \r\nL 106.929061 416.201798 \r\nL 106.961485 417.763779 \r\nL 107.026332 424.163174 \r\nL 107.074968 420.114743 \r\nL 107.156027 417.190052 \r\nL 107.188451 418.164281 \r\nL 107.26951 421.786632 \r\nL 107.318146 419.146977 \r\nL 107.415417 414.243239 \r\nL 107.366782 420.415886 \r\nL 107.447841 417.97586 \r\nL 107.577536 424.734442 \r\nL 107.626172 415.38664 \r\nL 107.707231 419.014474 \r\nL 107.739655 417.946354 \r\nL 107.772079 419.440994 \r\nL 107.820715 422.284756 \r\nL 107.86935 419.455715 \r\nL 107.934198 419.068448 \r\nL 107.901774 419.669124 \r\nL 107.95041 419.497998 \r\nL 107.982834 422.47672 \r\nL 108.031469 418.931457 \r\nL 108.047681 418.979714 \r\nL 108.063893 419.176008 \r\nL 108.096317 418.299521 \r\nL 108.144952 415.309927 \r\nL 108.177376 419.410652 \r\nL 108.258436 421.857875 \r\nL 108.290859 420.571612 \r\nL 108.388131 414.049876 \r\nL 108.436766 418.433478 \r\nL 108.517826 419.990876 \r\nL 108.55025 419.562595 \r\nL 108.647521 416.230953 \r\nL 108.679945 417.830272 \r\nL 108.744792 422.320716 \r\nL 108.793428 419.509834 \r\nL 108.825852 417.401334 \r\nL 108.906911 417.722705 \r\nL 109.036606 422.817083 \r\nL 109.06903 421.280366 \r\nL 109.166301 417.51875 \r\nL 109.182513 418.309565 \r\nL 109.231149 422.560283 \r\nL 109.295996 418.713533 \r\nL 109.393268 419.760097 \r\nL 109.360844 416.711974 \r\nL 109.40948 419.598759 \r\nL 109.441903 418.797755 \r\nL 109.458115 420.108124 \r\nL 109.506751 423.775895 \r\nL 109.555387 420.869352 \r\nL 109.620234 416.009331 \r\nL 109.685082 419.004756 \r\nL 109.766141 423.544402 \r\nL 109.8472 421.630691 \r\nL 109.895836 417.015158 \r\nL 109.960684 418.537996 \r\nL 110.009319 425.216868 \r\nL 110.057955 417.851464 \r\nL 110.074167 416.45309 \r\nL 110.155226 417.448736 \r\nL 110.26871 422.696476 \r\nL 110.317345 419.357833 \r\nL 110.365981 417.527905 \r\nL 110.398405 415.370736 \r\nL 110.44704 419.609291 \r\nL 110.495676 420.872589 \r\nL 110.5281 423.065252 \r\nL 110.560523 419.548346 \r\nL 110.592947 415.873027 \r\nL 110.674007 417.684504 \r\nL 110.722642 421.910999 \r\nL 110.78749 419.537739 \r\nL 110.852337 417.036506 \r\nL 110.917185 417.652322 \r\nL 110.982033 423.257075 \r\nL 111.04688 419.495851 \r\nL 111.111728 414.367375 \r\nL 111.160363 417.859805 \r\nL 111.241423 422.091388 \r\nL 111.290058 419.325126 \r\nL 111.38733 417.104083 \r\nL 111.403542 417.309295 \r\nL 111.484601 422.506694 \r\nL 111.549449 419.654422 \r\nL 111.630508 416.807256 \r\nL 111.662932 418.418891 \r\nL 111.727779 422.739438 \r\nL 111.792627 420.520337 \r\nL 111.90611 416.739505 \r\nL 111.922322 417.627027 \r\nL 111.954746 423.268397 \r\nL 112.035805 419.394486 \r\nL 112.052017 419.353242 \r\nL 112.100653 417.398114 \r\nL 112.133076 419.976347 \r\nL 112.181712 420.615776 \r\nL 112.214136 422.671943 \r\nL 112.262772 418.449577 \r\nL 112.360043 416.116541 \r\nL 112.376255 417.015211 \r\nL 112.473526 423.391422 \r\nL 112.522162 419.884653 \r\nL 112.554586 419.949488 \r\nL 112.570797 418.871278 \r\nL 112.603221 413.451267 \r\nL 112.668069 419.198291 \r\nL 112.732916 422.197204 \r\nL 112.749128 423.498979 \r\nL 112.781552 419.65633 \r\nL 112.895035 417.04087 \r\nL 112.911247 417.108775 \r\nL 112.976095 421.859146 \r\nL 113.02473 419.347457 \r\nL 113.057154 418.313673 \r\nL 113.10579 419.902037 \r\nL 113.170637 420.216823 \r\nL 113.186849 420.21173 \r\nL 113.332756 416.708629 \r\nL 113.28412 420.434678 \r\nL 113.348968 417.322071 \r\nL 113.413816 423.469956 \r\nL 113.478663 421.375151 \r\nL 113.575934 416.464297 \r\nL 113.608358 418.178184 \r\nL 113.738053 422.796035 \r\nL 113.754265 421.517456 \r\nL 113.851536 417.415695 \r\nL 113.88396 420.161685 \r\nL 113.900172 421.955796 \r\nL 113.948808 418.985916 \r\nL 113.981232 420.080251 \r\nL 114.062291 418.265308 \r\nL 114.094715 419.826059 \r\nL 114.110927 420.403687 \r\nL 114.159562 418.873971 \r\nL 114.191986 416.937356 \r\nL 114.240622 419.571655 \r\nL 114.289257 417.125409 \r\nL 114.354105 419.693835 \r\nL 114.402741 422.992689 \r\nL 114.435164 419.945166 \r\nL 114.467588 416.525746 \r\nL 114.548648 417.847748 \r\nL 114.597283 421.616807 \r\nL 114.678343 420.61668 \r\nL 114.791826 414.56095 \r\nL 114.82425 418.381886 \r\nL 114.921521 421.839744 \r\nL 114.937733 420.701964 \r\nL 114.970157 417.279457 \r\nL 115.035004 420.754694 \r\nL 115.067428 417.59197 \r\nL 115.08364 417.034642 \r\nL 115.116064 419.326394 \r\nL 115.197123 422.117058 \r\nL 115.229547 420.672465 \r\nL 115.278183 418.468701 \r\nL 115.326818 420.971661 \r\nL 115.359242 423.458789 \r\nL 115.407878 418.758524 \r\nL 115.424089 418.502422 \r\nL 115.456513 419.605923 \r\nL 115.472725 419.564211 \r\nL 115.521361 416.970252 \r\nL 115.553785 420.610343 \r\nL 115.569996 421.186691 \r\nL 115.618632 419.840471 \r\nL 115.667268 421.096282 \r\nL 115.68348 421.00191 \r\nL 115.764539 417.152295 \r\nL 115.845599 418.342089 \r\nL 115.894234 421.526554 \r\nL 115.959082 419.527327 \r\nL 115.991506 419.601739 \r\nL 116.040141 418.622598 \r\nL 116.056353 418.321164 \r\nL 116.072565 418.683522 \r\nL 116.104989 420.607064 \r\nL 116.169836 418.414999 \r\nL 116.186048 418.868162 \r\nL 116.218472 420.012994 \r\nL 116.267108 418.058787 \r\nL 116.283319 417.563832 \r\nL 116.331955 419.113702 \r\nL 116.348167 419.0706 \r\nL 116.364379 419.314177 \r\nL 116.413015 421.556407 \r\nL 116.445438 418.987232 \r\nL 116.494074 416.644421 \r\nL 116.510286 418.607596 \r\nL 116.54271 421.724719 \r\nL 116.591345 416.42974 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418.177757 \r\nL 118.196322 421.907687 \r\nL 118.244958 417.627903 \r\nL 118.26117 417.631184 \r\nL 118.423289 419.861371 \r\nL 118.488136 416.493721 \r\nL 118.536772 418.609165 \r\nL 118.634043 421.631462 \r\nL 118.650255 420.480065 \r\nL 118.682679 416.40295 \r\nL 118.763738 419.27269 \r\nL 118.77995 418.973251 \r\nL 118.812374 420.106282 \r\nL 118.844798 424.530113 \r\nL 118.909645 420.328038 \r\nL 119.023128 416.674321 \r\nL 119.055552 418.218292 \r\nL 119.087976 421.351601 \r\nL 119.185247 420.759762 \r\nL 119.250095 415.700015 \r\nL 119.29873 419.284617 \r\nL 119.331154 420.953696 \r\nL 119.412214 419.818313 \r\nL 119.477061 417.403918 \r\nL 119.509485 420.017254 \r\nL 119.574332 419.509799 \r\nL 119.622968 421.534406 \r\nL 119.736451 417.904905 \r\nL 119.768875 418.926077 \r\nL 119.833723 422.586734 \r\nL 119.882358 419.678039 \r\nL 119.97963 416.795929 \r\nL 120.012053 418.716578 \r\nL 120.060689 422.437429 \r\nL 120.141749 421.704742 \r\nL 120.222808 414.412316 \r\nL 120.271444 419.223035 \r\nL 120.303867 421.735357 \r\nL 120.384927 420.28897 \r\nL 120.433562 417.460284 \r\nL 120.482198 420.170185 \r\nL 120.514622 420.688899 \r\nL 120.530834 419.914382 \r\nL 120.563258 418.634905 \r\nL 120.611893 421.294449 \r\nL 120.628105 420.875557 \r\nL 120.709165 414.770272 \r\nL 120.741588 418.11197 \r\nL 120.790224 424.17184 \r\nL 120.855072 420.356142 \r\nL 120.887495 417.823917 \r\nL 120.968555 418.896102 \r\nL 121.114462 422.451957 \r\nL 121.130674 421.336242 \r\nL 121.195521 415.906521 \r\nL 121.244157 420.039011 \r\nL 121.35764 421.709645 \r\nL 121.325216 419.277198 \r\nL 121.373852 421.610252 \r\nL 121.422488 416.461483 \r\nL 121.503547 418.919668 \r\nL 121.584606 421.199564 \r\nL 121.61703 419.696424 \r\nL 121.633242 418.721498 \r\nL 121.69809 421.314924 \r\nL 121.714302 421.354304 \r\nL 121.762937 418.256044 \r\nL 121.860208 419.573975 \r\nL 121.87642 419.776797 \r\nL 121.892632 419.392565 \r\nL 121.941268 416.915578 \r\nL 121.973692 419.180529 \r\nL 122.006115 421.097092 \r\nL 122.070963 419.427067 \r\nL 122.103387 418.008855 \r\nL 122.168234 419.363951 \r\nL 122.200658 420.112114 \r\nL 122.249294 418.499234 \r\nL 122.265506 418.038891 \r\nL 122.297929 420.161266 \r\nL 122.330353 421.787567 \r\nL 122.378989 417.860713 \r\nL 122.492472 422.020275 \r\nL 122.524896 420.506084 \r\nL 122.573532 417.054836 \r\nL 122.622167 421.295588 \r\nL 122.670803 417.262659 \r\nL 122.703227 418.490111 \r\nL 122.73565 420.991097 \r\nL 122.800498 418.151247 \r\nL 122.81671 418.922302 \r\nL 122.832922 419.247667 \r\nL 122.849134 418.797669 \r\nL 122.881557 416.597119 \r\nL 122.930193 420.664855 \r\nL 122.946405 421.186545 \r\nL 122.962617 420.266769 \r\nL 122.995041 418.344804 \r\nL 123.0761 419.891344 \r\nL 123.092312 419.783139 \r\nL 123.124736 417.901416 \r\nL 123.189583 420.316195 \r\nL 123.238219 422.623674 \r\nL 123.270643 420.673758 \r\nL 123.319278 417.77431 \r\nL 123.367914 419.579675 \r\nL 123.400338 421.568785 \r\nL 123.448973 418.680854 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418.750388 \r\nL 126.739986 417.932223 \r\nL 126.756198 418.359533 \r\nL 126.788622 423.326777 \r\nL 126.869681 419.677884 \r\nL 126.918317 416.313427 \r\nL 126.966953 419.650155 \r\nL 126.999377 422.226039 \r\nL 127.064224 419.428467 \r\nL 127.193919 417.810763 \r\nL 127.307402 422.63568 \r\nL 127.339826 420.345842 \r\nL 127.37225 416.58991 \r\nL 127.453309 418.74491 \r\nL 127.566793 421.353374 \r\nL 127.583004 420.697427 \r\nL 127.680276 416.784748 \r\nL 127.696488 417.839095 \r\nL 127.761335 422.630429 \r\nL 127.793759 419.055184 \r\nL 127.858607 417.725652 \r\nL 127.907242 418.107501 \r\nL 128.020725 424.568445 \r\nL 128.069361 420.627004 \r\nL 128.134209 415.002572 \r\nL 128.182844 418.756896 \r\nL 128.263904 423.199099 \r\nL 128.312539 420.772687 \r\nL 128.409811 415.211768 \r\nL 128.442234 416.789353 \r\nL 128.49087 423.088353 \r\nL 128.57193 418.926068 \r\nL 128.652989 416.648908 \r\nL 128.669201 417.859274 \r\nL 128.766472 421.23971 \r\nL 128.798896 421.006493 \r\nL 128.815108 420.948705 \r\nL 128.879955 416.33528 \r\nL 128.928591 420.331284 \r\nL 128.977227 422.782309 \r\nL 129.025862 420.732548 \r\nL 129.155558 417.248994 \r\nL 129.171769 417.952538 \r\nL 129.220405 421.870403 \r\nL 129.301464 419.920338 \r\nL 129.3501 415.980082 \r\nL 129.414948 418.825786 \r\nL 129.479795 422.313386 \r\nL 129.528431 419.520981 \r\nL 129.544643 418.463246 \r\nL 129.625702 419.118696 \r\nL 129.739185 422.511882 \r\nL 129.69055 418.965918 \r\nL 129.755397 422.350401 \r\nL 129.868881 418.0475 \r\nL 129.885092 418.346335 \r\nL 129.933728 422.350257 \r\nL 129.998576 418.675014 \r\nL 130.047211 417.89938 \r\nL 130.095847 418.158886 \r\nL 130.160694 421.522671 \r\nL 130.20933 419.779283 \r\nL 130.322813 415.740948 \r\nL 130.339025 417.100453 \r\nL 130.46872 422.080517 \r\nL 130.533568 417.441813 \r\nL 130.630839 418.714522 \r\nL 130.695687 421.2713 \r\nL 130.728111 419.721008 \r\nL 130.776746 417.964116 \r\nL 130.841594 419.020073 \r\nL 130.857806 418.946905 \r\nL 130.906441 421.664331 \r\nL 130.938865 418.722581 \r\nL 130.955077 417.485632 \r\nL 131.003713 420.48279 \r\nL 131.052348 418.286483 \r\nL 131.06856 418.271326 \r\nL 131.133408 420.733649 \r\nL 131.198255 419.978978 \r\nL 131.279315 417.18273 \r\nL 131.311738 418.134413 \r\nL 131.40901 421.887712 \r\nL 131.441434 421.301725 \r\nL 131.490069 417.819174 \r\nL 131.587341 418.700823 \r\nL 131.619764 420.090391 \r\nL 131.652188 421.328883 \r\nL 131.700824 419.525512 \r\nL 131.717036 419.708629 \r\nL 131.733247 419.724466 \r\nL 131.765671 418.596222 \r\nL 131.798095 419.952281 \r\nL 131.862943 421.277995 \r\nL 131.895366 419.921804 \r\nL 131.976426 418.18458 \r\nL 132.025061 418.810518 \r\nL 132.057485 417.885288 \r\nL 132.106121 419.487995 \r\nL 132.170968 421.04059 \r\nL 132.18718 419.740495 \r\nL 132.219604 416.747037 \r\nL 132.284452 420.294224 \r\nL 132.300664 420.619682 \r\nL 132.333087 419.522222 \r\nL 132.349299 419.669292 \r\nL 132.381723 419.144361 \r\nL 132.414147 417.998615 \r\nL 132.446571 419.493361 \r\nL 132.462782 420.373948 \r\nL 132.52763 418.673409 \r\nL 132.543842 418.623806 \r\nL 132.608689 421.252535 \r\nL 132.641113 419.656773 \r\nL 132.722173 416.718412 \r\nL 132.754596 418.499812 \r\nL 132.78702 420.284464 \r\nL 132.835656 417.955151 \r\nL 132.86808 418.898794 \r\nL 132.884291 418.449435 \r\nL 132.949139 419.44384 \r\nL 132.981563 420.172962 \r\nL 133.013987 418.262316 \r\nL 133.030198 417.744915 \r\nL 133.062622 419.000415 \r\nL 133.111258 422.203507 \r\nL 133.143682 419.490163 \r\nL 133.159894 418.04066 \r\nL 133.224741 420.782288 \r\nL 133.240953 420.596049 \r\nL 133.257165 420.388775 \r\nL 133.273377 420.781287 \r\nL 133.3058 421.776303 \r\nL 133.370648 420.376139 \r\nL 133.451707 420.879472 \r\nL 133.516555 417.771591 \r\nL 133.597614 417.478925 \r\nL 133.64625 421.148112 \r\nL 133.743521 421.382542 \r\nL 133.792157 418.816685 \r\nL 133.824581 418.548338 \r\nL 133.840793 418.159245 \r\nL 133.873217 419.111918 \r\nL 133.9867 421.261534 \r\nL 134.002912 420.606824 \r\nL 134.116395 417.858069 \r\nL 134.067759 421.148769 \r\nL 134.132607 418.034274 \r\nL 134.16503 421.180048 \r\nL 134.262302 420.196201 \r\nL 134.294726 419.26105 \r\nL 134.327149 416.708863 \r\nL 134.391997 420.043686 \r\nL 134.473056 419.1898 \r\nL 134.440633 420.129309 \r\nL 134.489268 419.492189 \r\nL 134.521692 420.30738 \r\nL 134.554116 417.48813 \r\nL 134.570328 416.846154 \r\nL 134.602751 419.388178 \r\nL 134.618963 419.385457 \r\nL 134.651387 418.738587 \r\nL 134.683811 420.81973 \r\nL 134.813506 417.25246 \r\nL 134.84593 419.028097 \r\nL 134.975625 421.114954 \r\nL 134.991837 420.880569 \r\nL 135.072896 416.730835 \r\nL 135.089108 415.137296 \r\nL 135.121532 418.254905 \r\nL 135.153956 422.607647 \r\nL 135.235015 419.340103 \r\nL 135.267439 418.193895 \r\nL 135.299863 419.505264 \r\nL 135.332286 421.187516 \r\nL 135.413346 419.99978 \r\nL 135.461981 420.737119 \r\nL 135.478193 420.031048 \r\nL 135.543041 416.187353 \r\nL 135.591677 419.437136 \r\nL 135.607888 419.462297 \r\nL 135.672736 422.515906 \r\nL 135.70516 419.477732 \r\nL 135.802431 417.821813 \r\nL 135.753795 419.568535 \r\nL 135.818643 418.496409 \r\nL 135.932126 421.545189 \r\nL 135.948338 421.26046 \r\nL 136.045609 417.087091 \r\nL 136.078033 418.353075 \r\nL 136.191516 420.89576 \r\nL 136.207728 420.432796 \r\nL 136.305 417.664192 \r\nL 136.353635 417.841778 \r\nL 136.402271 419.788742 \r\nL 136.48333 419.39787 \r\nL 136.515754 418.923743 \r\nL 136.548178 419.604862 \r\nL 136.596813 419.347503 \r\nL 136.629237 420.032225 \r\nL 136.645449 419.272579 \r\nL 136.710297 420.586666 \r\nL 136.758932 417.696951 \r\nL 136.791356 419.129719 \r\nL 136.839992 422.558798 \r\nL 136.904839 420.643901 \r\nL 136.985899 417.967855 \r\nL 137.050746 419.154149 \r\nL 137.099382 420.291074 \r\nL 137.148018 423.121658 \r\nL 137.180441 420.869582 \r\nL 137.277713 418.15433 \r\nL 137.310137 418.909032 \r\nL 137.407408 421.930199 \r\nL 137.439832 420.29075 \r\nL 137.569527 418.05654 \r\nL 137.60195 419.302989 \r\nL 137.68301 418.928339 \r\nL 137.715434 418.124236 \r\nL 137.747857 420.177809 \r\nL 137.764069 420.42676 \r\nL 137.796493 419.601275 \r\nL 137.812705 419.33889 \r\nL 137.845129 420.350866 \r\nL 137.861341 420.569671 \r\nL 137.909976 416.437766 \r\nL 137.974824 419.330121 \r\nL 138.007248 419.047952 \r\nL 138.02346 419.503914 \r\nL 138.088307 422.238184 \r\nL 138.120731 420.102245 \r\nL 138.218002 415.937498 \r\nL 138.250426 417.301418 \r\nL 138.299062 421.578673 \r\nL 138.396333 420.722489 \r\nL 138.444969 416.723632 \r\nL 138.509816 419.244436 \r\nL 138.54224 420.339774 \r\nL 138.623299 419.42832 \r\nL 138.671935 417.731829 \r\nL 138.720571 420.094869 \r\nL 138.785418 418.916893 \r\nL 138.850266 422.015997 \r\nL 138.898901 417.405922 \r\nL 138.996173 418.718831 \r\nL 139.044808 423.205921 \r\nL 139.093444 419.004711 \r\nL 139.125868 416.740538 \r\nL 139.190715 418.214807 \r\nL 139.304199 422.143846 \r\nL 139.352834 421.447864 \r\nL 139.433894 415.762406 \r\nL 139.482529 419.859034 \r\nL 139.547377 422.807124 \r\nL 139.612224 420.86713 \r\nL 139.677072 417.777361 \r\nL 139.709496 416.843056 \r\nL 139.758131 418.923912 \r\nL 139.822979 424.128608 \r\nL 139.855403 419.797614 \r\nL 139.92025 417.455914 \r\nL 139.952674 418.651519 \r\nL 139.985098 420.757778 \r\nL 140.033733 417.910812 \r\nL 140.049945 418.19064 \r\nL 140.114793 419.943001 \r\nL 140.147217 418.235011 \r\nL 140.195852 416.192457 \r\nL 140.228276 417.971462 \r\nL 140.276912 422.993932 \r\nL 140.341759 419.488657 \r\nL 140.390395 414.543707 \r\nL 140.439031 419.706631 \r\nL 140.487666 416.944868 \r\nL 140.52009 420.139948 \r\nL 140.568726 423.316244 \r\nL 140.617361 419.053563 \r\nL 140.682209 417.325026 \r\nL 140.714633 419.039372 \r\nL 140.763268 422.24595 \r\nL 140.811904 419.153511 \r\nL 140.828116 419.164393 \r\nL 140.86054 419.668683 \r\nL 140.892963 418.813992 \r\nL 140.925387 417.464286 \r\nL 140.974023 418.835963 \r\nL 141.055082 423.725509 \r\nL 141.087506 421.068347 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418.650655 \r\nL 146.79409 420.250142 \r\nL 146.810302 420.732338 \r\nL 146.858938 418.872645 \r\nL 146.87515 418.232494 \r\nL 146.923785 420.642759 \r\nL 146.972421 419.615804 \r\nL 146.988633 420.181871 \r\nL 147.021057 422.347349 \r\nL 147.069692 419.301214 \r\nL 147.085904 418.553556 \r\nL 147.13454 420.332183 \r\nL 147.150752 421.497496 \r\nL 147.199387 419.50939 \r\nL 147.248023 421.301096 \r\nL 147.410142 417.298911 \r\nL 147.426354 417.494186 \r\nL 147.458778 421.756704 \r\nL 147.539837 418.385538 \r\nL 147.588473 419.182719 \r\nL 147.604685 418.15115 \r\nL 147.620896 416.549237 \r\nL 147.685744 421.03591 \r\nL 147.73438 418.027212 \r\nL 147.847863 418.141757 \r\nL 147.864075 418.147236 \r\nL 147.945134 418.05778 \r\nL 148.009982 419.958315 \r\nL 148.074829 417.994248 \r\nL 148.107253 419.007516 \r\nL 148.123465 420.512041 \r\nL 148.172101 417.446041 \r\nL 148.220736 419.570476 \r\nL 148.269372 417.283134 \r\nL 148.350431 418.847249 \r\nL 148.382855 420.916866 \r\nL 148.463915 419.544611 \r\nL 148.480126 419.468886 \r\nL 148.496338 419.886456 \r\nL 148.51255 420.0103 \r\nL 148.544974 419.519382 \r\nL 148.561186 419.350261 \r\nL 148.626033 419.764977 \r\nL 148.642245 419.791344 \r\nL 148.674669 421.442781 \r\nL 148.739517 421.018174 \r\nL 148.885424 417.965214 \r\nL 148.982695 420.99197 \r\nL 149.079966 420.452573 \r\nL 149.161026 420.629715 \r\nL 149.242085 417.703573 \r\nL 149.306933 421.448767 \r\nL 149.420416 420.594955 \r\nL 149.550111 417.806965 \r\nL 149.566323 417.850281 \r\nL 149.614958 421.257665 \r\nL 149.679806 419.288422 \r\nL 149.696018 419.396476 \r\nL 149.71223 419.073819 \r\nL 149.744654 417.903029 \r\nL 149.793289 419.07567 \r\nL 149.809501 418.807154 \r\nL 149.890561 421.746439 \r\nL 149.955408 419.684471 \r\nL 150.004044 416.044008 \r\nL 150.052679 418.121534 \r\nL 150.117527 420.930189 \r\nL 150.166163 418.487561 \r\nL 150.214798 421.327915 \r\nL 150.247222 419.51688 \r\nL 150.295858 417.141141 \r\nL 150.344493 420.102767 \r\nL 150.376917 421.72096 \r\nL 150.409341 420.476085 \r\nL 150.457977 415.852971 \r\nL 150.522824 419.302182 \r\nL 150.587672 421.856418 \r\nL 150.636307 420.739764 \r\nL 150.717367 419.20377 \r\nL 150.766002 416.702306 \r\nL 150.798426 419.204059 \r\nL 150.863274 422.553892 \r\nL 150.911909 420.158091 \r\nL 150.944333 419.03317 \r\nL 150.976757 417.135338 \r\nL 151.041605 419.708913 \r\nL 151.122664 421.057751 \r\nL 151.155088 419.839458 \r\nL 151.203723 417.625729 \r\nL 151.252359 419.761312 \r\nL 151.365842 421.022652 \r\nL 151.317207 419.069401 \r\nL 151.382054 420.8657 \r\nL 151.43069 417.526123 \r\nL 151.527961 418.866605 \r\nL 151.560385 421.635237 \r\nL 151.625232 418.157966 \r\nL 151.673868 416.406145 \r\nL 151.754928 416.516783 \r\nL 151.819775 420.867541 \r\nL 151.917046 419.286683 \r\nL 151.94947 415.94361 \r\nL 152.014318 420.295664 \r\nL 152.046741 419.605795 \r\nL 152.079165 420.664434 \r\nL 152.127801 419.76481 \r\nL 152.241284 421.136538 \r\nL 152.257496 420.336081 \r\nL 152.273708 419.540649 \r\nL 152.322344 421.568647 \r\nL 152.338555 421.825468 \r\nL 152.354767 420.544315 \r\nL 152.387191 417.821778 \r\nL 152.468251 418.426537 \r\nL 152.565522 421.662738 \r\nL 152.597946 420.365464 \r\nL 152.711429 418.763482 \r\nL 152.727641 418.785899 \r\nL 152.776276 421.787924 \r\nL 152.841124 419.375124 \r\nL 152.857336 419.429133 \r\nL 152.873548 419.21636 \r\nL 152.88976 418.810524 \r\nL 152.938395 419.794604 \r\nL 153.051878 421.454419 \r\nL 153.003243 419.422018 \r\nL 153.084302 421.193564 \r\nL 153.213997 417.193515 \r\nL 153.230209 418.632737 \r\nL 153.262633 421.375843 \r\nL 153.311269 417.691177 \r\nL 153.343692 419.472296 \r\nL 153.376116 421.121991 \r\nL 153.440964 419.343493 \r\nL 153.489599 417.293547 \r\nL 153.538235 419.088047 \r\nL 153.586871 420.150829 \r\nL 153.619295 418.25813 \r\nL 153.635506 418.095409 \r\nL 153.651718 418.488162 \r\nL 153.700354 421.050529 \r\nL 153.74899 418.551925 \r\nL 153.781413 417.786394 \r\nL 153.813837 419.410302 \r\nL 153.846261 418.686127 \r\nL 153.878685 419.755238 \r\nL 153.911108 418.917576 \r\nL 153.943532 417.392814 \r\nL 154.00838 419.471047 \r\nL 154.073227 418.630228 \r\nL 154.154287 420.679365 \r\nL 154.251558 418.761168 \r\nL 154.316406 419.85284 \r\nL 154.348829 421.194785 \r\nL 154.381253 419.024018 \r\nL 154.397465 418.224255 \r\nL 154.462313 419.631548 \r\nL 154.494736 421.039812 \r\nL 154.559584 419.108585 \r\nL 154.592008 418.314427 \r\nL 154.624431 419.991782 \r\nL 154.640643 420.637657 \r\nL 154.689279 418.358391 \r\nL 154.770338 419.588799 \r\nL 154.818974 421.369794 \r\nL 154.86761 419.305976 \r\nL 154.900034 419.019327 \r\nL 154.916245 418.482876 \r\nL 154.981093 419.939783 \r\nL 155.013517 419.248792 \r\nL 155.045941 420.778068 \r\nL 155.078364 422.731252 \r\nL 155.110788 418.726336 \r\nL 155.175636 419.925747 \r\nL 155.240483 416.418244 \r\nL 155.353966 420.557943 \r\nL 155.370178 420.015662 \r\nL 155.46745 417.129154 \r\nL 155.499873 418.448769 \r\nL 155.580933 420.775966 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157.672266 418.003613 \r\nL 157.801961 421.415206 \r\nL 157.834385 419.700686 \r\nL 157.915444 417.555884 \r\nL 157.947868 418.8278 \r\nL 157.996504 422.132738 \r\nL 158.061351 419.13776 \r\nL 158.126199 417.890757 \r\nL 158.191047 418.351145 \r\nL 158.239682 422.300101 \r\nL 158.288318 419.096756 \r\nL 158.385589 417.546451 \r\nL 158.401801 418.232492 \r\nL 158.547708 421.71895 \r\nL 158.628767 417.691571 \r\nL 158.693615 419.173627 \r\nL 158.742251 424.125247 \r\nL 158.790886 418.553551 \r\nL 158.807098 417.486668 \r\nL 158.871946 419.169846 \r\nL 158.888158 419.125033 \r\nL 158.920581 418.559199 \r\nL 158.969217 419.996531 \r\nL 159.050277 422.332688 \r\nL 159.066488 420.844523 \r\nL 159.131336 416.712475 \r\nL 159.179972 420.570365 \r\nL 159.244819 419.354769 \r\nL 159.293455 421.477668 \r\nL 159.342091 418.305133 \r\nL 159.455574 418.438637 \r\nL 159.504209 421.29799 \r\nL 159.552845 417.877 \r\nL 159.569057 417.893755 \r\nL 159.731176 420.630209 \r\nL 159.747388 420.26092 \r\nL 159.828447 420.646471 \r\nL 159.877083 417.905436 \r\nL 159.958142 420.914095 \r\nL 160.087837 419.684455 \r\nL 160.120261 418.328236 \r\nL 160.20132 419.081131 \r\nL 160.266168 421.527229 \r\nL 160.298592 419.733853 \r\nL 160.314804 418.739395 \r\nL 160.363439 420.644097 \r\nL 160.395863 419.89729 \r\nL 160.428287 419.871517 \r\nL 160.493134 417.722237 \r\nL 160.54177 419.764179 \r\nL 160.574194 420.467201 \r\nL 160.606618 419.265781 \r\nL 160.639041 418.640332 \r\nL 160.687677 419.973551 \r\nL 160.720101 421.756321 \r\nL 160.768737 418.135075 \r\nL 160.784948 417.896882 \r\nL 160.833584 418.579594 \r\nL 160.88222 420.592918 \r\nL 160.914644 419.35168 \r\nL 160.947067 417.26992 \r\nL 160.995703 420.561883 \r\nL 161.011915 419.898411 \r\nL 161.076762 418.177959 \r\nL 161.14161 418.392793 \r\nL 161.190246 420.484372 \r\nL 161.238881 418.266803 \r\nL 161.255093 418.088004 \r\nL 161.287517 418.702202 \r\nL 161.319941 419.499625 \r\nL 161.384788 418.577928 \r\nL 161.401 418.402313 \r\nL 161.417212 418.908486 \r\nL 161.530695 421.257115 \r\nL 161.563119 420.069305 \r\nL 161.579331 419.649406 \r\nL 161.627967 420.68711 \r\nL 161.644178 420.878878 \r\nL 161.790085 417.327722 \r\nL 161.838721 421.37653 \r\nL 161.91978 419.097579 \r\nL 161.935992 419.092983 \r\nL 161.968416 418.743519 \r\nL 162.017052 419.079339 \r\nL 162.146747 421.896059 \r\nL 162.098111 418.474152 \r\nL 162.162959 421.786785 \r\nL 162.211594 418.129952 \r\nL 162.276442 421.340614 \r\nL 162.292654 421.994457 \r\nL 162.34129 420.074815 \r\nL 162.373713 419.602396 \r\nL 162.422349 419.85927 \r\nL 162.454773 421.093509 \r\nL 162.51962 419.398821 \r\nL 162.552044 418.891368 \r\nL 162.584468 420.484289 \r\nL 162.60068 421.102741 \r\nL 162.633104 419.208898 \r\nL 162.649315 418.374801 \r\nL 162.697951 420.144969 \r\nL 162.730375 419.805014 \r\nL 162.746587 419.72981 \r\nL 162.795222 421.953358 \r\nL 162.827646 419.371722 \r\nL 162.86007 416.202748 \r\nL 162.924917 420.920949 \r\nL 162.973553 417.223816 \r\nL 163.038401 420.380587 \r\nL 163.168096 418.206352 \r\nL 163.20052 419.242811 \r\nL 163.216731 419.7443 \r\nL 163.249155 418.44334 \r\nL 163.265367 417.701822 \r\nL 163.330215 419.269873 \r\nL 163.346427 420.161303 \r\nL 163.395062 418.593238 \r\nL 163.443698 419.655214 \r\nL 163.45991 419.593712 \r\nL 163.476122 419.634128 \r\nL 163.524757 420.539214 \r\nL 163.557181 419.885677 \r\nL 163.589605 417.194965 \r\nL 163.654452 420.802021 \r\nL 163.703088 418.696548 \r\nL 163.800359 419.174755 \r\nL 163.832783 421.44465 \r\nL 163.897631 418.371174 \r\nL 163.913843 417.915236 \r\nL 163.930054 419.204888 \r\nL 163.962478 420.668017 \r\nL 164.043538 420.243542 \r\nL 164.140809 418.531508 \r\nL 164.157021 418.988324 \r\nL 164.286716 421.221512 \r\nL 164.31914 420.095025 \r\nL 164.400199 420.160182 \r\nL 164.448835 418.745615 \r\nL 164.465047 418.70364 \r\nL 164.481259 419.201841 \r\nL 164.529894 418.033818 \r\nL 164.562318 419.171909 \r\nL 164.643377 416.978675 \r\nL 164.675801 418.261776 \r\nL 164.724437 421.845052 \r\nL 164.789284 419.141813 \r\nL 164.805496 419.236621 \r\nL 164.83792 417.516327 \r\nL 164.902768 419.06694 \r\nL 165.032463 422.022238 \r\nL 164.967615 418.652321 \r\nL 165.048675 422.004316 \r\nL 165.17837 419.444607 \r\nL 165.340489 417.357499 \r\nL 165.243217 419.970546 \r\nL 165.3567 417.496621 \r\nL 165.486396 421.052549 \r\nL 165.502607 420.436101 \r\nL 165.616091 418.355913 \r\nL 165.761998 421.665174 \r\nL 165.810633 418.352714 \r\nL 165.891693 419.759573 \r\nL 165.940328 420.856194 \r\nL 165.95654 419.509371 \r\nL 165.972752 418.391022 \r\nL 166.021388 421.576315 \r\nL 166.053812 420.217521 \r\nL 166.086235 420.554667 \r\nL 166.102447 419.903624 \r\nL 166.134871 418.407951 \r\nL 166.183507 421.020179 \r\nL 166.199719 421.477259 \r\nL 166.21593 420.707027 \r\nL 166.280778 418.031484 \r\nL 166.329414 419.023943 \r\nL 166.394261 422.2679 \r\nL 166.426685 419.708161 \r\nL 166.55638 417.158606 \r\nL 166.475321 419.772574 \r\nL 166.572592 417.351658 \r\nL 166.702287 422.426478 \r\nL 166.718499 421.288896 \r\nL 166.848194 415.965974 \r\nL 166.864406 416.223939 \r\nL 166.994101 421.942613 \r\nL 167.010313 420.824586 \r\nL 167.07516 417.093532 \r\nL 167.123796 419.706322 \r\nL 167.140008 419.597918 \r\nL 167.172432 418.323744 \r\nL 167.221067 420.026935 \r\nL 167.237279 420.970803 \r\nL 167.285915 418.772714 \r\nL 167.399398 416.345508 \r\nL 167.350763 420.248253 \r\nL 167.41561 417.527274 \r\nL 167.464246 421.581597 \r\nL 167.529093 417.937715 \r\nL 167.642576 421.519683 \r\nL 167.723636 419.705045 \r\nL 167.837119 417.374673 \r\nL 167.853331 417.935272 \r\nL 167.918179 422.477035 \r\nL 167.983026 420.350042 \r\nL 167.999238 420.350633 \r\nL 168.047874 416.877916 \r\nL 168.177569 418.455175 \r\nL 168.226204 420.996707 \r\nL 168.27484 418.914889 \r\nL 168.339688 418.099028 \r\nL 168.355899 418.753634 \r\nL 168.453171 421.075663 \r\nL 168.469383 420.205082 \r\nL 168.518018 417.473361 \r\nL 168.582866 419.821769 \r\nL 168.631502 417.798818 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420.864128 \r\nL 170.204055 419.768388 \r\nL 170.268902 417.584081 \r\nL 170.317538 418.432699 \r\nL 170.366173 422.212049 \r\nL 170.398597 424.64043 \r\nL 170.431021 419.423061 \r\nL 170.479657 416.767527 \r\nL 170.560716 417.364816 \r\nL 170.690411 421.513275 \r\nL 170.706623 420.715676 \r\nL 170.755259 414.821874 \r\nL 170.820106 419.162866 \r\nL 170.884954 420.837705 \r\nL 170.933589 420.116 \r\nL 170.982225 416.697605 \r\nL 171.063285 418.343326 \r\nL 171.079496 418.301389 \r\nL 171.144344 423.852932 \r\nL 171.19298 418.859606 \r\nL 171.274039 417.209612 \r\nL 171.241615 419.071647 \r\nL 171.290251 417.277269 \r\nL 171.355099 421.289957 \r\nL 171.419946 419.873637 \r\nL 171.436158 419.96992 \r\nL 171.45237 419.64243 \r\nL 171.517217 418.561493 \r\nL 171.533429 419.679922 \r\nL 171.630701 421.697605 \r\nL 171.646912 421.406026 \r\nL 171.727972 415.936385 \r\nL 171.760396 418.471851 \r\nL 171.857667 422.750495 \r\nL 171.890091 421.368766 \r\nL 171.954938 416.954443 \r\nL 172.019786 418.636752 \r\nL 172.05221 420.275506 \r\nL 172.100845 423.768329 \r\nL 172.149481 420.183004 \r\nL 172.198117 416.755442 \r\nL 172.262964 419.903709 \r\nL 172.295388 420.390239 \r\nL 172.344024 422.568704 \r\nL 172.376447 420.551592 \r\nL 172.473719 416.695795 \r\nL 172.506142 417.569941 \r\nL 172.57099 421.536769 \r\nL 172.619626 418.84473 \r\nL 172.668261 415.989094 \r\nL 172.733109 417.535621 \r\nL 172.781745 421.888754 \r\nL 172.814168 423.006899 \r\nL 172.862804 420.402132 \r\nL 172.943863 414.856977 \r\nL 172.992499 418.66933 \r\nL 173.073559 424.596536 \r\nL 173.138406 420.720133 \r\nL 173.203254 416.064075 \r\nL 173.251889 419.420528 \r\nL 173.332949 421.836499 \r\nL 173.365372 420.09454 \r\nL 173.397796 417.294829 \r\nL 173.462644 419.233772 \r\nL 173.576127 421.85552 \r\nL 173.592339 421.532061 \r\nL 173.640975 415.542657 \r\nL 173.722034 418.244782 \r\nL 173.738246 418.290057 \r\nL 173.786882 422.725718 \r\nL 173.835517 419.490693 \r\nL 173.867941 417.666851 \r\nL 173.949 418.093573 \r\nL 174.03006 422.330914 \r\nL 174.078695 419.304083 \r\nL 174.175967 416.525962 \r\nL 174.208391 417.994636 \r\nL 174.273238 422.288416 \r\nL 174.338086 420.631176 \r\nL 174.435357 416.406678 \r\nL 174.467781 418.291625 \r\nL 174.532628 421.940424 \r\nL 174.581264 419.969175 \r\nL 174.613688 416.190855 \r\nL 174.694747 419.107098 \r\nL 174.759595 422.028079 \r\nL 174.80823 419.385104 \r\nL 174.88929 417.580533 \r\nL 174.937925 418.104544 \r\nL 174.954137 418.214843 \r\nL 175.018985 424.758102 \r\nL 175.067621 418.503113 \r\nL 175.14868 416.758335 \r\nL 175.164892 417.059837 \r\nL 175.245951 423.28346 \r\nL 175.294587 420.391079 \r\nL 175.40807 417.096008 \r\nL 175.424282 417.704802 \r\nL 175.521553 424.240655 \r\nL 175.553977 421.735298 \r\nL 175.586401 417.225331 \r\nL 175.66746 418.271416 \r\nL 175.716096 422.647709 \r\nL 175.780944 420.304223 \r\nL 175.797155 420.621106 \r\nL 175.829579 419.398592 \r\nL 175.862003 419.349205 \r\nL 175.926851 416.677289 \r\nL 175.959274 419.469603 \r\nL 175.991698 422.328252 \r\nL 176.056546 418.788472 \r\nL 176.088969 416.829159 \r\nL 176.137605 419.980858 \r\nL 176.153817 420.49423 \r\nL 176.186241 418.157039 \r\nL 176.202453 417.705889 \r\nL 176.234876 418.331342 \r\nL 176.2673 417.97447 \r\nL 176.315936 420.841777 \r\nL 176.364572 417.870411 \r\nL 176.380783 417.675502 \r\nL 176.396995 418.282515 \r\nL 176.461843 421.337277 \r\nL 176.510478 419.790886 \r\nL 176.559114 417.236863 \r\nL 176.623962 418.836215 \r\nL 176.640174 418.789096 \r\nL 176.672597 419.597642 \r\nL 176.705021 418.608796 \r\nL 176.721233 417.92814 \r\nL 176.753657 420.1128 \r\nL 176.786081 421.193632 \r\nL 176.834716 418.642336 \r\nL 176.86714 417.920122 \r\nL 176.915776 419.70344 \r\nL 176.964411 417.912702 \r\nL 177.045471 418.104263 \r\nL 177.110318 420.569758 \r\nL 177.142742 418.485195 \r\nL 177.158954 417.89093 \r\nL 177.191378 420.217993 \r\nL 177.20759 420.942928 \r\nL 177.256225 419.085669 \r\nL 177.288649 419.947975 \r\nL 177.402132 417.722996 \r\nL 177.418344 418.61274 \r\nL 177.548039 421.131039 \r\nL 177.596675 418.849163 \r\nL 177.693946 419.674976 \r\nL 177.710158 419.72867 \r\nL 177.742582 419.488206 \r\nL 177.775006 419.053549 \r\nL 177.807429 419.901019 \r\nL 177.839853 420.759286 \r\nL 177.888489 419.563093 \r\nL 177.937125 417.013776 \r\nL 177.953336 419.217531 \r\nL 177.98576 422.343593 \r\nL 178.06682 419.968477 \r\nL 178.115455 417.835371 \r\nL 178.212727 418.513134 \r\nL 178.342422 420.505376 \r\nL 178.358634 419.432653 \r\nL 178.391057 417.688301 \r\nL 178.455905 419.07601 \r\nL 178.504541 422.529452 \r\nL 178.569388 419.185926 \r\nL 178.666659 417.436407 \r\nL 178.682871 418.228551 \r\nL 178.796355 421.285869 \r\nL 178.812566 420.548113 \r\nL 178.909838 416.072353 \r\nL 178.92605 417.577255 \r\nL 179.039533 421.242269 \r\nL 179.055745 420.59251 \r\nL 179.120592 417.924705 \r\nL 179.153016 418.950248 \r\nL 179.282711 422.903965 \r\nL 179.396194 417.729339 \r\nL 179.461042 420.561456 \r\nL 179.493466 422.797494 \r\nL 179.558313 420.544608 \r\nL 179.606949 417.204994 \r\nL 179.671796 419.440776 \r\nL 179.70422 418.621783 \r\nL 179.736644 419.642145 \r\nL 179.769068 420.72163 \r\nL 179.817703 419.145537 \r\nL 179.882551 415.819073 \r\nL 179.931187 418.956035 \r\nL 179.979822 421.902493 \r\nL 180.04467 420.239089 \r\nL 180.125729 417.350848 \r\nL 180.239212 418.587853 \r\nL 180.271636 421.041602 \r\nL 180.336484 417.646367 \r\nL 180.352696 417.664644 \r\nL 180.433755 421.075469 \r\nL 180.482391 418.808626 \r\nL 180.531026 417.796798 \r\nL 180.56345 416.863805 \r\nL 180.628298 418.409924 \r\nL 180.676933 419.295891 \r\nL 180.725569 421.597687 \r\nL 180.774205 420.257715 \r\nL 180.839052 417.796243 \r\nL 180.887688 418.855059 \r\nL 180.936324 421.049844 \r\nL 181.001171 419.074049 \r\nL 181.098442 418.642896 \r\nL 181.195714 422.178405 \r\nL 181.228138 420.945813 \r\nL 181.325409 417.139801 \r\nL 181.357833 418.549411 \r\nL 181.471316 421.531541 \r\nL 181.487528 421.514267 \r\nL 181.536163 418.511322 \r\nL 181.601011 420.460452 \r\nL 181.649647 421.976082 \r\nL 181.665858 423.011245 \r\nL 181.714494 419.878481 \r\nL 181.779342 416.432402 \r\nL 181.844189 418.787953 \r\nL 181.925249 423.665841 \r\nL 181.990096 420.743097 \r\nL 182.071156 415.694738 \r\nL 182.119791 418.982581 \r\nL 182.184639 420.248873 \r\nL 182.233274 419.63006 \r\nL 182.314334 417.114559 \r\nL 182.346758 419.403414 \r\nL 182.427817 420.972993 \r\nL 182.444029 420.221299 \r\nL 182.492665 416.165096 \r\nL 182.573724 417.09444 \r\nL 182.670995 421.783745 \r\nL 182.719631 419.216063 \r\nL 182.800691 417.143526 \r\nL 182.816902 418.205632 \r\nL 182.88175 422.997706 \r\nL 182.930386 419.594075 \r\nL 183.043869 417.215426 \r\nL 183.14114 423.051899 \r\nL 183.205988 421.141387 \r\nL 183.254623 417.818287 \r\nL 183.335683 418.973507 \r\nL 183.368107 419.501854 \r\nL 183.449166 421.303945 \r\nL 183.465378 420.216503 \r\nL 183.530225 417.151893 \r\nL 183.578861 419.926327 \r\nL 183.611285 422.038793 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420.444958 \r\nL 185.816101 418.403405 \r\nL 185.864737 421.786257 \r\nL 185.897161 419.786615 \r\nL 185.97822 418.720687 \r\nL 186.026856 418.904076 \r\nL 186.043068 418.299297 \r\nL 186.091704 419.950983 \r\nL 186.107915 420.503743 \r\nL 186.156551 418.532955 \r\nL 186.172763 418.404032 \r\nL 186.205187 418.921808 \r\nL 186.253822 418.476771 \r\nL 186.270034 418.541614 \r\nL 186.31867 421.534335 \r\nL 186.351094 418.812994 \r\nL 186.367306 417.386465 \r\nL 186.432153 419.662507 \r\nL 186.448365 420.132191 \r\nL 186.497001 418.585517 \r\nL 186.529424 419.296714 \r\nL 186.626696 420.570764 \r\nL 186.642908 420.468565 \r\nL 186.691543 417.055387 \r\nL 186.740179 419.571564 \r\nL 186.772603 421.514547 \r\nL 186.83745 419.415288 \r\nL 186.886086 420.302691 \r\nL 186.91851 419.304934 \r\nL 186.950934 419.460509 \r\nL 187.015781 418.093404 \r\nL 187.048205 419.40239 \r\nL 187.096841 421.702615 \r\nL 187.129264 419.252539 \r\nL 187.145476 418.803056 \r\nL 187.194112 419.800705 \r\nL 187.210324 420.492442 \r\nL 187.258959 418.865125 \r\nL 187.275171 418.869825 \r\nL 187.307595 418.104289 \r\nL 187.340019 419.328581 \r\nL 187.404866 421.133126 \r\nL 187.43729 419.913892 \r\nL 187.51835 419.296378 \r\nL 187.534561 419.779212 \r\nL 187.550773 420.145014 \r\nL 187.583197 419.497305 \r\nL 187.631833 417.052315 \r\nL 187.664257 419.832468 \r\nL 187.69668 421.722195 \r\nL 187.761528 418.889558 \r\nL 187.842587 418.588413 \r\nL 187.793952 418.970991 \r\nL 187.858799 418.768636 \r\nL 187.939859 418.290825 \r\nL 188.004706 421.203394 \r\nL 188.085766 418.100414 \r\nL 188.183037 418.845306 \r\nL 188.215461 420.480416 \r\nL 188.280308 418.828599 \r\nL 188.29652 418.711644 \r\nL 188.312732 419.263805 \r\nL 188.328944 420.439812 \r\nL 188.393791 417.573149 \r\nL 188.507275 420.514107 \r\nL 188.539698 419.461185 \r\nL 188.572122 419.665756 \r\nL 188.588334 419.125284 \r\nL 188.620758 417.862718 \r\nL 188.669394 420.744818 \r\nL 188.734241 417.270422 \r\nL 188.766665 420.035466 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418.166471 \r\nL 192.284644 420.981156 \r\nL 192.365704 420.304051 \r\nL 192.414339 418.612693 \r\nL 192.462975 420.11857 \r\nL 192.479187 421.054438 \r\nL 192.560246 419.411559 \r\nL 192.625094 418.682256 \r\nL 192.689941 418.932077 \r\nL 192.722365 421.266095 \r\nL 192.803425 421.245225 \r\nL 192.85206 418.1036 \r\nL 192.93312 419.258217 \r\nL 193.014179 421.126103 \r\nL 193.062815 419.990581 \r\nL 193.127662 418.903639 \r\nL 193.208722 419.205159 \r\nL 193.257357 422.002403 \r\nL 193.305993 418.546833 \r\nL 193.322205 418.679105 \r\nL 193.338417 418.624597 \r\nL 193.370841 417.058507 \r\nL 193.403264 418.859351 \r\nL 193.500536 420.718603 \r\nL 193.516748 419.621464 \r\nL 193.646443 418.273926 \r\nL 193.695078 420.778429 \r\nL 193.727502 418.57496 \r\nL 193.824773 415.241135 \r\nL 193.776138 420.337649 \r\nL 193.857197 417.519679 \r\nL 193.889621 419.507395 \r\nL 193.97068 418.379043 \r\nL 194.084164 417.44878 \r\nL 194.035528 418.653187 \r\nL 194.116587 417.811336 \r\nL 194.230071 422.333079 \r\nL 194.246283 420.841747 \r\nL 194.294918 416.650856 \r\nL 194.359766 419.492639 \r\nL 194.473249 421.510132 \r\nL 194.489461 421.509397 \r\nL 194.635368 418.585758 \r\nL 194.684003 421.175658 \r\nL 194.748851 420.19332 \r\nL 194.797487 418.65386 \r\nL 194.846122 420.159404 \r\nL 194.894758 421.444192 \r\nL 194.927182 420.720846 \r\nL 194.975817 419.174611 \r\nL 195.040665 419.751219 \r\nL 195.056877 419.382931 \r\nL 195.105513 420.576131 \r\nL 195.121724 420.611108 \r\nL 195.202784 421.642553 \r\nL 195.25142 419.398393 \r\nL 195.267631 419.416312 \r\nL 195.283843 419.284779 \r\nL 195.300055 418.812065 \r\nL 195.332479 420.215452 \r\nL 195.364903 421.685355 \r\nL 195.397326 419.309998 \r\nL 195.42975 419.626554 \r\nL 195.51081 417.200308 \r\nL 195.559445 418.817089 \r\nL 195.591869 419.825437 \r\nL 195.608081 420.660508 \r\nL 195.672929 418.56815 \r\nL 195.68914 418.55994 \r\nL 195.705352 419.010467 \r\nL 195.737776 417.934521 \r\nL 195.753988 416.870677 \r\nL 195.802624 420.081202 \r\nL 195.835047 418.605603 \r\nL 195.851259 418.377431 \r\nL 195.883683 419.046455 \r\nL 195.916107 420.057833 \r\nL 195.980954 419.288231 \r\nL 196.013378 417.529962 \r\nL 196.062014 419.547699 \r\nL 196.094438 418.676264 \r\nL 196.143073 419.719761 \r\nL 196.159285 418.692482 \r\nL 196.191709 416.812454 \r\nL 196.240345 420.171754 \r\nL 196.256556 421.136015 \r\nL 196.337616 419.731403 \r\nL 196.353828 418.87554 \r\nL 196.402463 421.297084 \r\nL 196.418675 421.408727 \r\nL 196.467311 417.641564 \r\nL 196.532159 420.980695 \r\nL 196.580794 421.68354 \r\nL 196.597006 421.174745 \r\nL 196.661854 418.475499 \r\nL 196.742913 419.491596 \r\nL 196.872608 420.477726 \r\nL 196.969879 418.476339 \r\nL 196.986091 418.675527 \r\nL 197.034727 421.393934 \r\nL 197.099575 419.742321 \r\nL 197.131998 419.994724 \r\nL 197.14821 420.151193 \r\nL 197.164422 419.890847 \r\nL 197.213058 418.180561 \r\nL 197.245482 419.934667 \r\nL 197.310329 420.613963 \r\nL 197.326541 419.536525 \r\nL 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211.041797 420.449776 \r\nL 211.090433 414.821934 \r\nL 211.171492 417.316342 \r\nL 211.252552 422.369018 \r\nL 211.284975 419.271499 \r\nL 211.333611 416.165325 \r\nL 211.398459 417.461595 \r\nL 211.49573 423.232903 \r\nL 211.544366 420.250291 \r\nL 211.625425 417.085 \r\nL 211.657849 418.796504 \r\nL 211.738908 422.673137 \r\nL 211.771332 421.617488 \r\nL 211.819968 417.47571 \r\nL 211.901027 417.910344 \r\nL 212.01451 419.804966 \r\nL 212.063146 416.975194 \r\nL 212.127994 419.1634 \r\nL 212.209053 424.314509 \r\nL 212.257689 420.881525 \r\nL 212.322536 417.884946 \r\nL 212.371172 420.027939 \r\nL 212.468443 423.558398 \r\nL 212.419807 419.90693 \r\nL 212.500867 421.357512 \r\nL 212.549503 419.8612 \r\nL 212.598138 416.407953 \r\nL 212.679198 417.406806 \r\nL 212.792681 415.008998 \r\nL 212.808893 416.15598 \r\nL 212.9548 420.128173 \r\nL 212.987224 419.590203 \r\nL 213.035859 417.509451 \r\nL 213.100707 419.298752 \r\nL 213.246614 421.263456 \r\nL 213.506004 417.132563 \r\nL 213.538428 417.9079 \r\nL 213.684335 422.153306 \r\nL 213.700547 421.643011 \r\nL 213.749182 419.07251 \r\nL 213.830242 420.261642 \r\nL 213.878877 422.299825 \r\nL 213.943725 420.718322 \r\nL 214.040996 419.400249 \r\nL 214.057208 419.844099 \r\nL 214.105844 421.177314 \r\nL 214.154479 419.937365 \r\nL 214.267963 417.889674 \r\nL 214.219327 420.067903 \r\nL 214.284174 418.341017 \r\nL 214.430081 420.856572 \r\nL 214.478717 417.039 \r\nL 214.543565 420.59245 \r\nL 214.559777 420.536541 \r\nL 214.738107 417.073015 \r\nL 214.754319 417.094169 \r\nL 214.867802 420.218786 \r\nL 214.900226 419.198574 \r\nL 215.013709 417.956853 \r\nL 215.046133 418.154749 \r\nL 215.175828 419.988173 \r\nL 215.19204 419.475467 \r\nL 215.2731 418.08802 \r\nL 215.289311 418.367362 \r\nL 215.321735 420.050882 \r\nL 215.370371 417.258841 \r\nL 215.386583 417.076985 \r\nL 215.435218 419.396116 \r\nL 215.500066 418.574362 \r\nL 215.548702 416.70261 \r\nL 215.581125 419.215509 \r\nL 215.597337 419.512616 \r\nL 215.629761 418.555332 \r\nL 215.662185 418.19587 \r\nL 215.694609 419.191486 \r\nL 215.79188 421.911867 \r\nL 215.824304 420.255206 \r\nL 215.856727 419.598911 \r\nL 215.905363 420.782821 \r\nL 215.921575 420.733093 \r\nL 216.099906 422.889094 \r\nL 216.116118 422.332035 \r\nL 216.180965 419.141023 \r\nL 216.245813 420.060748 \r\nL 216.278237 419.507283 \r\nL 216.294448 418.796425 \r\nL 216.343084 420.001642 \r\nL 216.39172 419.171816 \r\nL 216.407932 419.067298 \r\nL 216.440355 419.457911 \r\nL 216.456567 419.40334 \r\nL 216.472779 419.523804 \r\nL 216.488991 419.247659 \r\nL 216.537627 417.663371 \r\nL 216.586262 419.729421 \r\nL 216.699746 421.700977 \r\nL 216.65111 419.439944 \r\nL 216.715957 421.406647 \r\nL 216.845653 417.166647 \r\nL 216.861864 417.505617 \r\nL 216.975348 419.403814 \r\nL 216.99156 419.093452 \r\nL 217.105043 417.639331 \r\nL 217.121255 417.919625 \r\nL 217.25095 420.771447 \r\nL 217.267162 419.996245 \r\nL 217.332009 417.994222 \r\nL 217.380645 418.91686 \r\nL 217.494128 421.175639 \r\nL 217.558976 420.476153 \r\nL 217.737306 419.013378 \r\nL 217.867001 421.847679 \r\nL 217.899425 421.086649 \r\nL 218.061544 418.991747 \r\nL 218.077756 419.284128 \r\nL 218.207451 421.589988 \r\nL 218.223663 421.46932 \r\nL 218.337146 417.683421 \r\nL 218.36957 418.536802 \r\nL 218.401994 419.95443 \r\nL 218.483053 419.082355 \r\nL 218.580324 418.008227 \r\nL 218.612748 418.536388 \r\nL 218.645172 418.489279 \r\nL 218.661384 418.388304 \r\nL 218.677596 418.889143 \r\nL 218.71002 420.27916 \r\nL 218.774867 418.798113 \r\nL 218.823503 417.634796 \r\nL 218.872138 419.06668 \r\nL 219.018045 417.543763 \r\nL 219.034257 418.047275 \r\nL 219.066681 419.655533 \r\nL 219.14774 418.585724 \r\nL 219.277436 417.33456 \r\nL 219.293647 417.85073 \r\nL 219.374707 421.624632 \r\nL 219.455766 420.711895 \r\nL 219.536826 418.988765 \r\nL 219.56925 420.304779 \r\nL 219.617885 422.951927 \r\nL 219.682733 421.025311 \r\nL 219.74758 419.38808 \r\nL 219.796216 420.740267 \r\nL 219.861063 420.13625 \r\nL 219.909699 421.111764 \r\nL 220.071818 419.077029 \r\nL 220.136666 420.581656 \r\nL 220.185301 419.621151 \r\nL 220.217725 418.974974 \r\nL 220.282573 419.989954 \r\nL 220.314996 420.28856 \r\nL 220.331208 420.917845 \r\nL 220.396056 419.550355 \r\nL 220.42848 418.82173 \r\nL 220.493327 416.692507 \r\nL 220.541963 418.554061 \r\nL 220.655446 419.950102 \r\nL 220.671658 419.353119 \r\nL 220.817565 416.738854 \r\nL 220.963472 420.11816 \r\nL 220.979684 420.07993 \r\nL 221.012107 418.71428 \r\nL 221.060743 420.635164 \r\nL 221.076955 421.129843 \r\nL 221.109379 419.840437 \r\nL 221.141803 417.757417 \r\nL 221.222862 418.45179 \r\nL 221.401193 422.085826 \r\nL 221.46604 417.762231 \r\nL 221.530888 420.306493 \r\nL 221.579523 421.400074 \r\nL 221.628159 420.91403 \r\nL 221.676795 418.480524 \r\nL 221.757854 419.129749 \r\nL 221.871337 420.387687 \r\nL 221.919973 419.941734 \r\nL 221.984821 416.970785 \r\nL 222.017244 418.880592 \r\nL 222.06588 420.164176 \r\nL 222.114516 418.911496 \r\nL 222.130728 418.126896 \r\nL 222.195575 419.847459 \r\nL 222.227999 420.146567 \r\nL 222.244211 419.695264 \r\nL 222.276635 418.240798 \r\nL 222.309058 419.879301 \r\nL 222.357694 419.524114 \r\nL 222.390118 419.903343 \r\nL 222.40633 419.418522 \r\nL 222.454965 416.806042 \r\nL 222.503601 419.236601 \r\nL 222.536025 420.470628 \r\nL 222.58466 418.392655 \r\nL 222.600872 417.540078 \r\nL 222.66572 418.411217 \r\nL 222.698144 417.952951 \r\nL 222.844051 420.202467 \r\nL 222.795415 417.844457 \r\nL 222.860263 420.023784 \r\nL 222.941322 417.581792 \r\nL 222.973746 419.784967 \r\nL 223.006169 421.660569 \r\nL 223.087229 421.372516 \r\nL 223.103441 421.356296 \r\nL 223.233136 418.451648 \r\nL 223.249348 418.607689 \r\nL 223.314195 420.636359 \r\nL 223.379043 420.017495 \r\nL 223.395255 419.339043 \r\nL 223.44389 421.368439 \r\nL 223.460102 421.240343 \r\nL 223.557374 418.923392 \r\nL 223.589797 420.041819 \r\nL 223.622221 421.153435 \r\nL 223.670857 419.189401 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229.020779 422.360229 \r\nL 229.085627 419.81251 \r\nL 229.166686 418.05172 \r\nL 229.19911 419.321337 \r\nL 229.263958 419.296271 \r\nL 229.328805 421.707261 \r\nL 229.377441 418.660166 \r\nL 229.4585 419.54916 \r\nL 229.490924 420.707697 \r\nL 229.53956 419.262889 \r\nL 229.571984 420.193671 \r\nL 229.620619 418.710956 \r\nL 229.669255 420.37995 \r\nL 229.685467 420.341481 \r\nL 229.815162 417.49377 \r\nL 229.750314 420.446342 \r\nL 229.831374 418.176124 \r\nL 229.961069 420.093566 \r\nL 230.009705 420.546566 \r\nL 230.025916 420.180396 \r\nL 230.074552 417.308907 \r\nL 230.155612 418.760083 \r\nL 230.204247 421.211161 \r\nL 230.269095 419.267781 \r\nL 230.285307 419.390656 \r\nL 230.301519 419.152535 \r\nL 230.415002 417.733488 \r\nL 230.350154 419.279761 \r\nL 230.431214 418.153519 \r\nL 230.479849 420.278554 \r\nL 230.544697 418.324648 \r\nL 230.577121 417.596377 \r\nL 230.609544 418.923396 \r\nL 230.65818 420.982005 \r\nL 230.723028 420.082861 \r\nL 230.787875 417.982207 \r\nL 230.868935 419.106848 \r\nL 230.91757 420.807658 \r\nL 230.949994 422.555831 \r\nL 230.99863 420.321237 \r\nL 231.031053 418.084136 \r\nL 231.112113 418.552944 \r\nL 231.25802 421.762326 \r\nL 231.274232 421.150407 \r\nL 231.355291 417.880806 \r\nL 231.387715 419.725783 \r\nL 231.436351 421.599948 \r\nL 231.484986 419.758941 \r\nL 231.533622 418.421854 \r\nL 231.566046 419.736013 \r\nL 231.582258 420.695692 \r\nL 231.647105 418.063012 \r\nL 231.695741 420.447791 \r\nL 231.7768 418.771681 \r\nL 231.841648 418.121287 \r\nL 231.874072 418.791077 \r\nL 231.922707 420.941091 \r\nL 231.955131 418.66806 \r\nL 232.019978 419.079164 \r\nL 232.084826 416.173411 \r\nL 232.149674 420.972268 \r\nL 232.246945 418.993498 \r\nL 232.279369 418.629716 \r\nL 232.360428 418.947404 \r\nL 232.392852 418.461137 \r\nL 232.425276 419.096989 \r\nL 232.457699 421.037179 \r\nL 232.506335 418.436707 \r\nL 232.554971 420.573929 \r\nL 232.571183 420.58103 \r\nL 232.700878 419.106705 \r\nL 232.733302 418.288874 \r\nL 232.765725 420.863785 \r\nL 232.798149 422.034605 \r\nL 232.846785 419.116747 \r\nL 232.911632 421.604566 \r\nL 232.97648 419.921628 \r\nL 233.041327 419.413495 \r\nL 233.122387 421.508845 \r\nL 233.203446 418.848251 \r\nL 233.284506 419.707459 \r\nL 233.300718 419.783279 \r\nL 233.349353 418.097696 \r\nL 233.397989 419.592615 \r\nL 233.446625 419.940083 \r\nL 233.462836 419.496913 \r\nL 233.543896 419.753499 \r\nL 233.592532 418.871243 \r\nL 233.624955 419.487628 \r\nL 233.641167 419.893925 \r\nL 233.689803 418.9604 \r\nL 233.738438 419.689209 \r\nL 233.787074 418.123539 \r\nL 233.83571 419.622188 \r\nL 233.884345 419.976815 \r\nL 233.900557 419.592132 \r\nL 233.932981 417.81348 \r\nL 233.997829 420.025901 \r\nL 234.030252 419.119944 \r\nL 234.111312 417.934869 \r\nL 234.143736 419.0278 \r\nL 234.176159 419.921862 \r\nL 234.208583 418.911944 \r\nL 234.241007 417.799435 \r\nL 234.273431 419.039513 \r\nL 234.305855 418.873979 \r\nL 234.403126 420.51687 \r\nL 234.43555 419.304233 \r\nL 234.451761 418.880415 \r\nL 234.500397 420.262444 \r\nL 234.532821 419.778958 \r\nL 234.565245 418.999437 \r\nL 234.630092 420.23237 \r\nL 234.678728 419.165718 \r\nL 234.743575 419.826187 \r\nL 234.759787 420.013231 \r\nL 234.808423 419.355413 \r\nL 234.824635 419.067428 \r\nL 234.873271 420.01759 \r\nL 234.889482 419.914445 \r\nL 234.905694 420.099763 \r\nL 234.921906 420.492961 \r\nL 234.95433 419.807847 \r\nL 235.002966 418.146671 \r\nL 235.051601 420.171173 \r\nL 235.067813 420.147943 \r\nL 235.100237 419.293097 \r\nL 235.148873 420.207878 \r\nL 235.181296 421.606273 \r\nL 235.246144 420.001503 \r\nL 235.310991 418.381703 \r\nL 235.359627 419.284728 \r\nL 235.489322 420.976941 \r\nL 235.424475 418.821512 \r\nL 235.505534 420.208252 \r\nL 235.602805 417.697798 \r\nL 235.635229 418.953633 \r\nL 235.683865 420.166859 \r\nL 235.716289 418.954769 \r\nL 235.732501 418.128801 \r\nL 235.797348 420.148937 \r\nL 235.862196 417.79832 \r\nL 235.910831 419.51035 \r\nL 236.008103 420.12737 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241.779534 418.40761 \r\nL 241.876806 419.636588 \r\nL 241.974077 418.422211 \r\nL 241.925441 419.881452 \r\nL 241.990289 418.9008 \r\nL 242.022713 420.101986 \r\nL 242.119984 419.886974 \r\nL 242.152408 418.177504 \r\nL 242.233467 419.684009 \r\nL 242.298315 422.599292 \r\nL 242.34695 420.136402 \r\nL 242.460434 418.408553 \r\nL 242.476645 418.56668 \r\nL 242.525281 419.886057 \r\nL 242.573917 421.567043 \r\nL 242.638764 421.009213 \r\nL 242.6874 417.951864 \r\nL 242.768459 419.149542 \r\nL 242.800883 420.722517 \r\nL 242.849519 418.878569 \r\nL 242.881943 417.848802 \r\nL 242.914366 419.532787 \r\nL 242.94679 418.830647 \r\nL 242.963002 418.775363 \r\nL 242.979214 418.992068 \r\nL 243.044061 420.312309 \r\nL 243.092697 419.844785 \r\nL 243.189968 418.002204 \r\nL 243.222392 418.378569 \r\nL 243.335875 419.80263 \r\nL 243.352087 419.586147 \r\nL 243.400723 417.583462 \r\nL 243.46557 419.311233 \r\nL 243.481782 419.305783 \r\nL 243.530418 421.113964 \r\nL 243.562842 419.093312 \r\nL 243.611477 417.984807 \r\nL 243.676325 418.035588 \r\nL 243.773596 421.52739 \r\nL 243.838444 419.619705 \r\nL 243.919503 416.62685 \r\nL 243.951927 418.545013 \r\nL 244.049198 420.805003 \r\nL 244.081622 420.127842 \r\nL 244.114046 418.918939 \r\nL 244.211317 419.028707 \r\nL 244.227529 419.03757 \r\nL 244.259953 420.124263 \r\nL 244.292377 422.079017 \r\nL 244.341012 419.653036 \r\nL 244.373436 418.867425 \r\nL 244.454496 418.997435 \r\nL 244.519343 421.091935 \r\nL 244.567979 419.16694 \r\nL 244.616614 420.425741 \r\nL 244.681462 419.216713 \r\nL 244.713886 417.954696 \r\nL 244.762521 420.337535 \r\nL 244.778733 419.901002 \r\nL 244.876005 418.119526 \r\nL 244.908428 418.64103 \r\nL 245.038123 421.133935 \r\nL 245.054335 420.684083 \r\nL 245.102971 417.904199 \r\nL 245.18403 418.290328 \r\nL 245.216454 419.446936 \r\nL 245.281302 420.879019 \r\nL 245.329937 419.83181 \r\nL 245.378573 419.175891 \r\nL 245.427209 417.702329 \r\nL 245.475844 419.084371 \r\nL 245.540692 421.124969 \r\nL 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248.831705 419.257572 \r\nL 248.864129 418.275288 \r\nL 248.912764 419.965299 \r\nL 248.945188 418.947394 \r\nL 249.010036 418.473351 \r\nL 249.04246 419.390261 \r\nL 249.107307 418.618187 \r\nL 249.123519 418.265826 \r\nL 249.188366 419.111021 \r\nL 249.237002 419.574529 \r\nL 249.269426 418.863505 \r\nL 249.285638 418.421605 \r\nL 249.318062 419.418093 \r\nL 249.350485 420.612963 \r\nL 249.399121 418.694445 \r\nL 249.415333 418.945946 \r\nL 249.528816 419.86724 \r\nL 249.545028 419.86609 \r\nL 249.593664 418.939417 \r\nL 249.642299 419.702904 \r\nL 249.674723 421.023177 \r\nL 249.723359 418.545217 \r\nL 249.739571 418.434353 \r\nL 249.771994 419.873928 \r\nL 249.836842 418.189951 \r\nL 249.853054 418.60077 \r\nL 249.934113 420.194417 \r\nL 249.998961 419.790018 \r\nL 250.015173 419.846152 \r\nL 250.047596 419.580837 \r\nL 250.08002 419.39401 \r\nL 250.096232 419.798465 \r\nL 250.177292 419.395881 \r\nL 250.225927 420.369066 \r\nL 250.242139 420.477574 \r\nL 250.258351 420.199955 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419.494255 \r\nL 252.122718 419.781244 \r\nL 252.155142 418.859166 \r\nL 252.171354 418.487521 \r\nL 252.219989 419.921062 \r\nL 252.268625 417.794801 \r\nL 252.317261 420.016848 \r\nL 252.349684 421.471544 \r\nL 252.39832 419.668391 \r\nL 252.430744 420.399955 \r\nL 252.511803 418.339561 \r\nL 252.576651 418.555122 \r\nL 252.641498 420.836754 \r\nL 252.65771 421.119759 \r\nL 252.673922 420.430659 \r\nL 252.754982 416.897532 \r\nL 252.803617 419.198151 \r\nL 252.949524 420.9781 \r\nL 252.981948 419.380995 \r\nL 253.014372 416.947125 \r\nL 253.079219 419.972562 \r\nL 253.160279 421.369137 \r\nL 253.176491 420.818629 \r\nL 253.225126 418.014276 \r\nL 253.306186 419.597301 \r\nL 253.322398 419.763293 \r\nL 253.338609 419.276113 \r\nL 253.354821 418.759639 \r\nL 253.403457 420.6525 \r\nL 253.419669 420.781037 \r\nL 253.435881 420.367438 \r\nL 253.500728 417.112832 \r\nL 253.549364 419.70962 \r\nL 253.581788 421.858657 \r\nL 253.662847 420.189507 \r\nL 253.711483 418.223779 \r\nL 253.77633 419.81526 \r\nL 253.889814 421.722089 \r\nL 253.841178 419.35638 \r\nL 253.922237 421.039871 \r\nL 253.987085 417.164472 \r\nL 254.051932 419.651034 \r\nL 254.084356 419.741615 \r\nL 254.230263 417.93727 \r\nL 254.165416 419.788866 \r\nL 254.246475 418.452255 \r\nL 254.37617 420.400373 \r\nL 254.392382 420.229379 \r\nL 254.441018 419.051168 \r\nL 254.489653 420.494221 \r\nL 254.505865 420.641071 \r\nL 254.538289 420.365261 \r\nL 254.586925 418.91611 \r\nL 254.651772 419.93311 \r\nL 254.71662 418.631783 \r\nL 254.765256 419.5329 \r\nL 254.813891 420.677339 \r\nL 254.846315 419.928251 \r\nL 254.927374 417.459773 \r\nL 254.959798 418.785417 \r\nL 254.992222 420.176324 \r\nL 255.073281 419.600768 \r\nL 255.121917 420.704165 \r\nL 255.154341 419.56095 \r\nL 255.186765 418.861461 \r\nL 255.251612 419.471848 \r\nL 255.300248 420.474073 \r\nL 255.348883 419.096194 \r\nL 255.365095 418.497537 \r\nL 255.413731 420.275333 \r\nL 255.429943 420.553966 \r\nL 255.446155 419.548889 \r\nL 255.462367 418.434563 \r\nL 255.527214 420.11898 \r\nL 255.543426 419.947645 \r\nL 255.673121 417.663003 \r\nL 255.705545 418.877547 \r\nL 255.737969 420.649232 \r\nL 255.802816 418.915085 \r\nL 255.851452 419.591517 \r\nL 255.867664 419.794293 \r\nL 255.900088 419.282163 \r\nL 255.932511 418.742368 \r\nL 255.964935 419.760447 \r\nL 256.013571 420.836734 \r\nL 256.078418 420.434053 \r\nL 256.143266 417.805381 \r\nL 256.191902 419.916486 \r\nL 256.256749 419.257106 \r\nL 256.321597 420.615235 \r\nL 256.402656 418.086179 \r\nL 256.451292 419.704169 \r\nL 256.516139 421.060205 \r\nL 256.548563 419.507227 \r\nL 256.662046 418.323949 \r\nL 256.597199 419.82812 \r\nL 256.678258 418.636297 \r\nL 256.807953 420.820688 \r\nL 256.824165 420.121123 \r\nL 256.905225 418.222842 \r\nL 256.937648 419.144984 \r\nL 256.970072 420.597754 \r\nL 257.018708 418.784932 \r\nL 257.03492 418.230046 \r\nL 257.115979 418.828974 \r\nL 257.132191 418.877306 \r\nL 257.148403 418.767203 \r\nL 257.197039 421.389073 \r\nL 257.278098 419.984541 \r\nL 257.29431 419.962378 \r\nL 257.310522 420.202611 \r\nL 257.342945 419.159302 \r\nL 257.407793 418.156388 \r\nL 257.424005 418.592472 \r\nL 257.505064 421.45032 \r\nL 257.537488 419.23809 \r\nL 257.569912 418.226512 \r\nL 257.618548 419.45683 \r\nL 257.634759 419.454008 \r\nL 257.780666 418.573201 \r\nL 257.796878 418.651169 \r\nL 257.942785 421.289644 \r\nL 257.877938 418.250368 \r\nL 257.958997 421.010572 \r\nL 258.007633 418.616809 \r\nL 258.104904 418.686063 \r\nL 258.15354 420.542085 \r\nL 258.250811 420.113924 \r\nL 258.315659 418.467802 \r\nL 258.41293 419.208685 \r\nL 258.526413 420.018305 \r\nL 258.477778 419.090117 \r\nL 258.558837 419.36032 \r\nL 258.575049 419.041859 \r\nL 258.656108 419.584137 \r\nL 258.67232 419.72152 \r\nL 258.704744 419.257759 \r\nL 258.720956 419.129832 \r\nL 258.75338 419.430444 \r\nL 258.769592 419.395081 \r\nL 258.850651 421.154995 \r\nL 258.899287 419.67896 \r\nL 259.093829 418.782616 \r\nL 259.110041 419.000946 \r\nL 259.158677 420.884149 \r\nL 259.207312 419.003683 \r\nL 259.239736 417.19116 \r\nL 259.288372 420.056119 \r\nL 259.304584 420.713118 \r\nL 259.369431 419.264307 \r\nL 259.401855 418.533453 \r\nL 259.466703 419.05016 \r\nL 259.499126 419.557296 \r\nL 259.53155 418.799393 \r\nL 259.547762 418.36366 \r\nL 259.596398 419.917368 \r\nL 259.661245 420.042645 \r\nL 259.645033 419.763206 \r\nL 259.677457 419.842381 \r\nL 259.726093 417.814338 \r\nL 259.774728 419.162155 \r\nL 259.807152 420.713505 \r\nL 259.872 419.097822 \r\nL 259.904424 418.638486 \r\nL 259.953059 419.480984 \r\nL 260.001695 419.69058 \r\nL 260.017907 419.66839 \r\nL 260.13139 421.133621 \r\nL 260.147602 420.358183 \r\nL 260.228661 418.055199 \r\nL 260.261085 418.989747 \r\nL 260.293509 420.78346 \r\nL 260.374568 420.359056 \r\nL 260.488052 418.117861 \r\nL 260.520475 419.098827 \r\nL 260.552899 420.063795 \r\nL 260.601535 418.64943 \r\nL 260.617747 418.154114 \r\nL 260.682594 419.092597 \r\nL 260.698806 418.659286 \r\nL 260.715018 418.691529 \r\nL 260.828501 421.272987 \r\nL 260.860925 420.378921 \r\nL 260.941984 417.502882 \r\nL 260.974408 419.493278 \r\nL 261.055468 421.350738 \r\nL 261.104103 420.004689 \r\nL 261.217586 418.124424 \r\nL 261.25001 418.662435 \r\nL 261.298646 420.936037 \r\nL 261.363493 419.237036 \r\nL 261.444553 417.882129 \r\nL 261.460765 418.252324 \r\nL 261.5094 421.107096 \r\nL 261.59046 420.291422 \r\nL 261.606672 420.333519 \r\nL 261.671519 418.665532 \r\nL 261.736367 419.590478 \r\nL 261.768791 420.577341 \r\nL 261.833638 419.586485 \r\nL 261.963333 418.354494 \r\nL 261.979545 418.804902 \r\nL 262.011969 420.616693 \r\nL 262.076816 418.620356 \r\nL 262.10924 419.048332 \r\nL 262.125452 418.728874 \r\nL 262.157876 417.028267 \r\nL 262.222723 419.475494 \r\nL 262.303783 420.116637 \r\nL 262.319995 419.878158 \r\nL 262.433478 419.019568 \r\nL 262.44969 419.242882 \r\nL 262.482114 419.035134 \r\nL 262.530749 420.088428 \r\nL 262.563173 421.309694 \r\nL 262.611809 419.53907 \r\nL 262.628021 419.607238 \r\nL 262.660444 418.955073 \r\nL 262.70908 420.07539 \r\nL 262.741504 421.03023 \r\nL 262.790139 418.93975 \r\nL 262.822563 418.567865 \r\nL 262.854987 419.46547 \r\nL 262.96847 420.472887 \r\nL 262.919835 419.34212 \r\nL 262.984682 420.270162 \r\nL 263.130589 417.429878 \r\nL 263.146801 418.363686 \r\nL 263.276496 420.759899 \r\nL 263.292708 420.619118 \r\nL 263.341344 417.93031 \r\nL 263.422403 419.074063 \r\nL 263.487251 420.666948 \r\nL 263.535886 419.196561 \r\nL 263.56831 418.508198 \r\nL 263.633158 419.469807 \r\nL 263.665581 419.36128 \r\nL 263.681793 419.461399 \r\nL 263.714217 420.070673 \r\nL 263.746641 418.860119 \r\nL 263.811488 419.685511 \r\nL 263.860124 417.973177 \r\nL 263.941183 421.225171 \r\nL 263.989819 419.51728 \r\nL 264.08709 417.888381 \r\nL 264.119514 418.483768 \r\nL 264.249209 420.810205 \r\nL 264.265421 420.20736 \r\nL 264.297845 418.762085 \r\nL 264.378904 418.913418 \r\nL 264.443752 421.169094 \r\nL 264.524811 420.223529 \r\nL 264.573447 419.578541 \r\nL 264.638294 419.927341 \r\nL 264.68693 421.265775 \r\nL 264.735566 419.86242 \r\nL 264.784201 418.809907 \r\nL 264.849049 419.619719 \r\nL 264.881473 419.257446 \r\nL 264.930108 419.571116 \r\nL 264.962532 420.215726 \r\nL 264.994956 419.327205 \r\nL 265.02738 418.556454 \r\nL 265.092227 418.966927 \r\nL 265.140863 420.313135 \r\nL 265.205711 419.201662 \r\nL 265.319194 418.570453 \r\nL 265.28677 419.248535 \r\nL 265.335406 418.751795 \r\nL 265.400253 419.974052 \r\nL 265.448889 418.984125 \r\nL 265.513736 417.935775 \r\nL 265.562372 418.44259 \r\nL 265.611008 419.238664 \r\nL 265.692067 418.985143 \r\nL 265.708279 418.954258 \r\nL 265.724491 419.096819 \r\nL 265.740703 419.173709 \r\nL 265.756915 418.924725 \r\nL 265.837974 418.369019 \r\nL 265.854186 418.579113 \r\nL 265.902822 420.582986 \r\nL 265.967669 419.248047 \r\nL 265.983881 418.976201 \r\nL 266.048729 419.605208 \r\nL 266.113576 420.7574 \r\nL 266.162212 420.160677 \r\nL 266.210848 418.662504 \r\nL 266.259483 420.353313 \r\nL 266.324331 418.68595 \r\nL 266.437814 421.120623 \r\nL 266.470238 420.21177 \r\nL 266.502661 419.305641 \r\nL 266.583721 419.950603 \r\nL 266.697204 420.378565 \r\nL 266.810687 419.336144 \r\nL 266.875535 420.379209 \r\nL 266.940382 418.458798 \r\nL 266.972806 419.895239 \r\nL 267.070077 419.592215 \r\nL 267.086289 419.709775 \r\nL 267.102501 419.158512 \r\nL 267.118713 418.571842 \r\nL 267.183561 419.837492 \r\nL 267.215984 418.987271 \r\nL 267.232196 418.623885 \r\nL 267.297044 419.561598 \r\nL 267.313256 419.826026 \r\nL 267.361891 418.933541 \r\nL 267.378103 418.666274 \r\nL 267.394315 419.278013 \r\nL 267.410527 420.0218 \r\nL 267.507798 419.822981 \r\nL 267.572646 418.276077 \r\nL 267.653705 419.24852 \r\nL 267.750977 419.013946 \r\nL 267.783401 419.995481 \r\nL 267.799612 420.214815 \r\nL 267.832036 419.645408 \r\nL 267.86446 419.678317 \r\nL 267.913096 417.45598 \r\nL 267.945519 420.097044 \r\nL 267.961731 420.53885 \r\nL 267.994155 419.053693 \r\nL 268.010367 418.622895 \r\nL 268.059003 419.97538 \r\nL 268.075214 419.708809 \r\nL 268.091426 419.651361 \r\nL 268.107638 419.770877 \r\nL 268.140062 420.590165 \r\nL 268.221121 419.939198 \r\nL 268.237333 419.930146 \r\nL 268.253545 420.250099 \r\nL 268.285969 419.650111 \r\nL 268.334605 418.089168 \r\nL 268.38324 419.814166 \r\nL 268.399452 419.87646 \r\nL 268.415664 419.557633 \r\nL 268.431876 419.195879 \r\nL 268.4643 420.794804 \r\nL 268.480512 421.488084 \r\nL 268.512935 419.239009 \r\nL 268.529147 418.453743 \r\nL 268.561571 419.577217 \r\nL 268.626419 418.919032 \r\nL 268.72369 420.624754 \r\nL 268.756114 419.902677 \r\nL 268.853385 418.197094 \r\nL 268.885809 419.125105 \r\nL 268.98308 420.255707 \r\nL 268.999292 420.193576 \r\nL 269.06414 418.118266 \r\nL 269.161411 418.67333 \r\nL 269.210047 419.584085 \r\nL 269.274894 419.369449 \r\nL 269.372165 418.550111 \r\nL 269.388377 418.775849 \r\nL 269.420801 420.17193 \r\nL 269.50186 419.201637 \r\nL 269.663979 420.96976 \r\nL 269.566708 418.618753 \r\nL 269.680191 420.706724 \r\nL 269.777463 417.836949 \r\nL 269.858522 418.910279 \r\nL 269.874734 418.74172 \r\nL 269.907158 419.182806 \r\nL 269.955793 421.356824 \r\nL 270.020641 419.465497 \r\nL 270.036853 419.101019 \r\nL 270.1017 419.704113 \r\nL 270.198972 420.880111 \r\nL 270.215184 420.596549 \r\nL 270.247607 419.164194 \r\nL 270.312455 420.32461 \r\nL 270.377302 419.728013 \r\nL 270.425938 420.599746 \r\nL 270.523209 419.211909 \r\nL 270.555633 419.409528 \r\nL 270.571845 419.349408 \r\nL 270.588057 419.415669 \r\nL 270.652904 420.557818 \r\nL 270.669116 419.905755 \r\nL 270.70154 418.583299 \r\nL 270.7826 418.906868 \r\nL 270.896083 421.054529 \r\nL 270.944718 419.872127 \r\nL 271.025778 418.332887 \r\nL 271.058202 418.885461 \r\nL 271.090625 420.191466 \r\nL 271.139261 418.472012 \r\nL 271.187897 419.882009 \r\nL 271.236532 417.601672 \r\nL 271.317592 418.672809 \r\nL 271.398651 419.796882 \r\nL 271.431075 418.898272 \r\nL 271.463499 417.527277 \r\nL 271.528346 419.31662 \r\nL 271.658041 420.486236 \r\nL 271.576982 419.129423 \r\nL 271.674253 419.888212 \r\nL 271.739101 417.826325 \r\nL 271.787737 419.226693 \r\nL 271.82016 419.866999 \r\nL 271.868796 420.770369 \r\nL 271.90122 419.605335 \r\nL 271.949855 418.245915 \r\nL 272.014703 419.325835 \r\nL 272.128186 421.757831 \r\nL 272.063339 419.27096 \r\nL 272.16061 420.413383 \r\nL 272.209246 418.057728 \r\nL 272.274093 419.595208 \r\nL 272.371364 421.699487 \r\nL 272.403788 420.220573 \r\nL 272.50106 417.832783 \r\nL 272.533483 418.77841 \r\nL 272.614543 421.573461 \r\nL 272.646967 420.514966 \r\nL 272.695602 417.825807 \r\nL 272.76045 419.290469 \r\nL 272.841509 419.916302 \r\nL 272.873933 419.531447 \r\nL 272.906357 419.725669 \r\nL 272.93878 419.237424 \r\nL 273.01984 418.425903 \r\nL 273.036052 419.040103 \r\nL 273.084687 421.575942 \r\nL 273.149535 419.633409 \r\nL 273.181959 416.914261 \r\nL 273.246806 420.203698 \r\nL 273.263018 420.201228 \r\nL 273.295442 418.801576 \r\nL 273.344078 420.72555 \r\nL 273.36029 420.830134 \r\nL 273.376501 420.307125 \r\nL 273.506197 418.357396 \r\nL 273.587256 421.405802 \r\nL 273.635892 418.990418 \r\nL 273.716951 418.646398 \r\nL 273.684527 419.034879 \r\nL 273.733163 418.708245 \r\nL 273.862858 420.62728 \r\nL 273.87907 420.378879 \r\nL 273.976341 418.954403 \r\nL 274.008765 419.344276 \r\nL 274.089824 420.286492 \r\nL 274.122248 419.449705 \r\nL 274.13846 419.133298 \r\nL 274.21952 419.57236 \r\nL 274.316791 421.111477 \r\nL 274.349215 419.920512 \r\nL 274.39785 418.326049 \r\nL 274.462698 419.367208 \r\nL 274.576181 420.341292 \r\nL 274.608605 419.805683 \r\nL 274.673452 418.048132 \r\nL 274.7383 418.367328 \r\nL 274.819359 420.463593 \r\nL 274.851783 419.484578 \r\nL 274.949054 417.733446 \r\nL 274.981478 418.220003 \r\nL 275.094961 420.462872 \r\nL 275.111173 420.340967 \r\nL 275.159809 418.832999 \r\nL 275.240868 419.376088 \r\nL 275.273292 419.530912 \r\nL 275.321928 419.487762 \r\nL 275.386775 417.934657 \r\nL 275.435411 419.133473 \r\nL 275.484047 418.797623 \r\nL 275.51647 420.097069 \r\nL 275.548894 421.087413 \r\nL 275.613742 420.180859 \r\nL 275.678589 417.48979 \r\nL 275.727225 419.051321 \r\nL 275.775861 421.165469 \r\nL 275.85692 420.291045 \r\nL 275.873132 420.452051 \r\nL 275.905556 420.035177 \r\nL 276.035251 419.178769 \r\nL 276.100098 421.219554 \r\nL 276.148734 419.244725 \r\nL 276.181158 418.947599 \r\nL 276.19737 419.719534 \r\nL 276.229793 420.506206 \r\nL 276.310853 420.497687 \r\nL 276.359489 419.168694 \r\nL 276.391912 420.213721 \r\nL 276.408124 421.00969 \r\nL 276.45676 418.92931 \r\nL 276.489184 419.745565 \r\nL 276.521607 420.61523 \r\nL 276.570243 419.355693 \r\nL 276.602667 418.987575 \r\nL 276.635091 420.02566 \r\nL 276.651303 420.224859 \r\nL 276.71615 419.658088 \r\nL 276.894481 418.157105 \r\nL 276.910693 418.475167 \r\nL 276.959328 420.186689 \r\nL 277.024176 418.94627 \r\nL 277.072812 419.473651 \r\nL 277.089023 418.991193 \r\nL 277.153871 418.109672 \r\nL 277.202507 418.712294 \r\nL 277.218719 418.696579 \r\nL 277.251142 419.8067 \r\nL 277.31599 418.606263 \r\nL 277.332202 418.324991 \r\nL 277.397049 418.959917 \r\nL 277.478109 420.199184 \r\nL 277.510533 419.363964 \r\nL 277.542956 418.10557 \r\nL 277.591592 419.696968 \r\nL 277.624016 419.175622 \r\nL 277.640228 418.668301 \r\nL 277.688863 419.975629 \r\nL 277.721287 419.418349 \r\nL 277.753711 420.39504 \r\nL 277.83477 419.671982 \r\nL 277.948253 420.842144 \r\nL 277.964465 420.33922 \r\nL 278.077949 418.683396 \r\nL 278.09416 419.254937 \r\nL 278.207644 422.057001 \r\nL 278.159008 418.552882 \r\nL 278.240067 420.551661 \r\nL 278.288703 418.201445 \r\nL 278.353551 420.009808 \r\nL 278.369763 419.995608 \r\nL 278.402186 418.809096 \r\nL 278.450822 420.545881 \r\nL 278.483246 419.727384 \r\nL 278.499458 419.456644 \r\nL 278.515669 419.941417 \r\nL 278.531881 420.936195 \r\nL 278.580517 418.626304 \r\nL 278.612941 417.434758 \r\nL 278.661576 420.376185 \r\nL 278.677788 420.377393 \r\nL 278.77506 418.768408 \r\nL 278.839907 419.11864 \r\nL 278.856119 419.104078 \r\nL 278.872331 419.246179 \r\nL 278.904755 418.687294 \r\nL 278.937179 417.47813 \r\nL 278.985814 419.840949 \r\nL 279.018238 418.539204 \r\nL 279.03445 418.512496 \r\nL 279.083086 420.192734 \r\nL 279.164145 419.766084 \r\nL 279.212781 419.806335 \r\nL 279.245204 418.750645 \r\nL 279.261416 418.420654 \r\nL 279.310052 419.681994 \r\nL 279.407323 420.815722 \r\nL 279.423535 420.536878 \r\nL 279.455959 418.506891 \r\nL 279.504595 420.895444 \r\nL 279.537018 420.061757 \r\nL 279.585654 418.821376 \r\nL 279.63429 419.900386 \r\nL 279.650502 420.154941 \r\nL 279.699137 419.195507 \r\nL 279.731561 419.98946 \r\nL 279.780197 418.966739 \r\nL 279.796409 419.304648 \r\nL 279.845044 421.523986 \r\nL 279.89368 419.524496 \r\nL 279.909892 418.861334 \r\nL 279.990951 419.882366 \r\nL 280.055799 420.296749 \r\nL 280.088223 419.776907 \r\nL 280.234129 417.954668 \r\nL 280.15307 420.129503 \r\nL 280.250341 418.924886 \r\nL 280.282765 419.94652 \r\nL 280.363825 419.441509 \r\nL 280.380036 419.44266 \r\nL 280.396248 419.613619 \r\nL 280.41246 418.907429 \r\nL 280.428672 418.315934 \r\nL 280.477308 420.080981 \r\nL 280.49352 420.077692 \r\nL 280.542155 418.806262 \r\nL 280.607003 419.644889 \r\nL 280.639427 419.600133 \r\nL 280.769122 418.582023 \r\nL 280.785334 418.692885 \r\nL 280.817757 419.818548 \r\nL 280.866393 418.522405 \r\nL 280.882605 417.745901 \r\nL 280.931241 419.267901 \r\nL 280.979876 417.773377 \r\nL 281.044724 419.370039 \r\nL 281.125783 418.936957 \r\nL 281.141995 418.617192 \r\nL 281.190631 419.37303 \r\nL 281.223055 419.080673 \r\nL 281.368962 420.241576 \r\nL 281.401385 419.755069 \r\nL 281.433809 419.119493 \r\nL 281.482445 420.113464 \r\nL 281.498657 420.224954 \r\nL 281.53108 419.980116 \r\nL 281.547292 420.072134 \r\nL 281.61214 418.783095 \r\nL 281.660776 419.973923 \r\nL 281.709411 419.323258 \r\nL 281.774259 419.809281 \r\nL 281.806682 420.432775 \r\nL 281.887742 419.958303 \r\nL 281.936378 419.261608 \r\nL 281.968801 420.156237 \r\nL 282.001225 420.496925 \r\nL 282.033649 419.690721 \r\nL 282.049861 419.412745 \r\nL 282.082285 420.037929 \r\nL 282.098496 420.557736 \r\nL 282.147132 419.733142 \r\nL 282.195768 420.282068 \r\nL 282.357887 418.205072 \r\nL 282.374099 418.834521 \r\nL 282.422734 420.313975 \r\nL 282.47137 418.704083 \r\nL 282.487582 418.592169 \r\nL 282.536217 418.994916 \r\nL 282.568641 418.784187 \r\nL 282.746972 420.723641 \r\nL 282.763184 420.25192 \r\nL 282.828031 417.160304 \r\nL 282.876667 419.452337 \r\nL 282.909091 420.767611 \r\nL 282.973938 418.889508 \r\nL 282.99015 418.928651 \r\nL 283.022574 419.641852 \r\nL 283.087422 418.634336 \r\nL 283.103633 418.570374 \r\nL 283.119845 418.718753 \r\nL 283.200905 419.938143 \r\nL 283.265752 419.719627 \r\nL 283.314388 417.716611 \r\nL 283.346812 419.514956 \r\nL 283.379235 421.143892 \r\nL 283.460295 420.068355 \r\nL 283.557566 418.281397 \r\nL 283.606202 419.315787 \r\nL 283.703473 421.154063 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419.667768 \r\nL 293.171215 419.752914 \r\nL 293.187427 419.36299 \r\nL 293.236063 418.962511 \r\nL 293.284698 419.404806 \r\nL 293.30091 419.379542 \r\nL 293.333334 418.938066 \r\nL 293.398181 419.647782 \r\nL 293.430605 419.990058 \r\nL 293.479241 419.362207 \r\nL 293.495453 419.29361 \r\nL 293.527877 420.331388 \r\nL 293.576512 419.232196 \r\nL 293.608936 420.010634 \r\nL 293.754843 419.09361 \r\nL 293.787267 419.597486 \r\nL 293.852114 419.251814 \r\nL 293.933174 418.837726 \r\nL 293.949386 418.893161 \r\nL 294.079081 420.209421 \r\nL 294.257411 418.915938 \r\nL 294.273623 418.903951 \r\nL 294.322259 420.381584 \r\nL 294.403318 419.491694 \r\nL 294.581649 418.134601 \r\nL 294.597861 418.494892 \r\nL 294.646497 419.896293 \r\nL 294.711344 419.078754 \r\nL 294.727556 418.987057 \r\nL 294.75998 419.368934 \r\nL 294.808616 419.091266 \r\nL 294.841039 419.501223 \r\nL 294.889675 419.146199 \r\nL 294.905887 418.895786 \r\nL 294.922099 419.498126 \r\nL 294.970734 421.237333 \r\nL 295.01937 419.48399 \r\nL 295.035582 419.361213 \r\nL 295.051794 419.713463 \r\nL 295.084218 420.421433 \r\nL 295.132853 419.07628 \r\nL 295.181489 419.682238 \r\nL 295.27876 420.601213 \r\nL 295.294972 420.25284 \r\nL 295.376032 419.129634 \r\nL 295.424667 419.596783 \r\nL 295.440879 419.570011 \r\nL 295.489515 419.685211 \r\nL 295.521939 419.855257 \r\nL 295.570574 420.720099 \r\nL 295.61921 420.144799 \r\nL 295.765117 418.111491 \r\nL 295.716481 420.448581 \r\nL 295.781329 418.146402 \r\nL 295.894812 419.686958 \r\nL 295.927236 419.347204 \r\nL 296.040719 418.520757 \r\nL 296.056931 418.651216 \r\nL 296.121778 418.4113 \r\nL 296.186626 419.413387 \r\nL 296.202838 419.304897 \r\nL 296.251474 419.692811 \r\nL 296.283897 419.574791 \r\nL 296.300109 419.491443 \r\nL 296.316321 419.860781 \r\nL 296.348745 420.265739 \r\nL 296.397381 419.275679 \r\nL 296.429804 419.673139 \r\nL 296.446016 420.056689 \r\nL 296.494652 419.006536 \r\nL 296.510864 418.949661 \r\nL 296.543287 419.130087 \r\nL 296.640559 419.821429 \r\nL 296.672983 419.319954 \r\nL 296.689194 419.024805 \r\nL 296.770254 419.570184 \r\nL 296.802678 419.183853 \r\nL 296.835101 420.119607 \r\nL 296.851313 420.553221 \r\nL 296.899949 419.46612 \r\nL 296.916161 419.737386 \r\nL 296.932373 419.742227 \r\nL 296.964797 418.998008 \r\nL 297.045856 419.132726 \r\nL 297.094492 420.412272 \r\nL 297.191763 420.095938 \r\nL 297.25661 418.271292 \r\nL 297.33767 418.952003 \r\nL 297.386306 419.605786 \r\nL 297.434941 418.71897 \r\nL 297.483577 418.071071 \r\nL 297.516001 418.911465 \r\nL 297.613272 420.711012 \r\nL 297.564636 418.712062 \r\nL 297.645696 419.681024 \r\nL 297.775391 418.821235 \r\nL 297.791603 418.803399 \r\nL 297.921298 420.746349 \r\nL 297.953722 419.983361 \r\nL 298.067205 418.339639 \r\nL 298.083417 418.771657 \r\nL 298.11584 420.020449 \r\nL 298.164476 418.508548 \r\nL 298.1969 418.951052 \r\nL 298.294171 420.069279 \r\nL 298.326595 419.435787 \r\nL 298.342807 419.426339 \r\nL 298.375231 420.264011 \r\nL 298.45629 419.971347 \r\nL 298.504926 418.539454 \r\nL 298.53735 420.226555 \r\nL 298.553561 421.352348 \r\nL 298.602197 419.651934 \r\nL 298.650833 420.555249 \r\nL 298.780528 418.844257 \r\nL 298.79674 419.16386 \r\nL 298.877799 420.765829 \r\nL 298.910223 420.197389 \r\nL 298.958859 418.59206 \r\nL 298.991282 420.315155 \r\nL 299.039918 419.06865 \r\nL 299.05613 418.97312 \r\nL 299.072342 419.252043 \r\nL 299.202037 420.183721 \r\nL 299.364156 417.537338 \r\nL 299.299308 420.352148 \r\nL 299.380368 417.700975 \r\nL 299.526275 419.72537 \r\nL 299.688393 417.263739 \r\nL 299.704605 417.943379 \r\nL 299.785665 420.682017 \r\nL 299.8343 419.786824 \r\nL 299.947784 419.055078 \r\nL 299.963996 419.170122 \r\nL 300.061267 420.391529 \r\nL 300.109903 419.604415 \r\nL 300.158538 419.168643 \r\nL 300.190962 417.960268 \r\nL 300.25581 419.572359 \r\nL 300.288233 420.041944 \r\nL 300.336869 418.760114 \r\nL 300.482776 420.229709 \r\nL 300.498988 420.21194 \r\nL 300.580047 420.551496 \r\nL 300.661107 418.765614 \r\nL 300.709742 420.086675 \r\nL 300.742166 421.070808 \r\nL 300.790802 419.777763 \r\nL 300.823226 420.410346 \r\nL 300.888073 418.043674 \r\nL 300.985344 418.706821 \r\nL 301.03398 419.773454 \r\nL 301.11504 419.339383 \r\nL 301.147463 418.538876 \r\nL 301.179887 419.613433 \r\nL 301.228523 418.993288 \r\nL 301.277158 420.029717 \r\nL 301.358218 419.647611 \r\nL 301.406853 419.050639 \r\nL 301.439277 419.876988 \r\nL 301.471701 420.38508 \r\nL 301.536549 419.56098 \r\nL 301.63382 418.63275 \r\nL 301.568972 419.585887 \r\nL 301.650032 419.260996 \r\nL 301.714879 419.77936 \r\nL 301.747303 419.302261 \r\nL 301.763515 419.288251 \r\nL 301.779727 419.503512 \r\nL 301.860786 419.103625 \r\nL 301.876998 418.977065 \r\nL 301.909422 419.236125 \r\nL 301.958058 421.183073 \r\nL 302.039117 419.738812 \r\nL 302.087753 418.759662 \r\nL 302.1526 419.305678 \r\nL 302.217448 420.952269 \r\nL 302.266083 419.622876 \r\nL 302.363355 418.326525 \r\nL 302.395779 419.386058 \r\nL 302.444414 421.074261 \r\nL 302.509262 420.055382 \r\nL 302.525474 420.285349 \r\nL 302.557897 419.215239 \r\nL 302.606533 418.131714 \r\nL 302.638957 418.934468 \r\nL 302.736228 420.865302 \r\nL 302.75244 420.491245 \r\nL 302.817288 417.781845 \r\nL 302.865923 419.326255 \r\nL 302.946983 420.352363 \r\nL 302.963195 420.024952 \r\nL 303.09289 417.698773 \r\nL 303.109102 417.743431 \r\nL 303.157737 419.67115 \r\nL 303.206373 422.006868 \r\nL 303.238797 418.932256 \r\nL 303.336068 417.224232 \r\nL 303.35228 417.841682 \r\nL 303.417127 420.689998 \r\nL 303.498187 420.546005 \r\nL 303.530611 419.388686 \r\nL 303.579246 416.786829 \r\nL 303.627882 419.112721 \r\nL 303.725153 420.295594 \r\nL 303.741365 419.987785 \r\nL 303.790001 417.830416 \r\nL 303.854848 419.324196 \r\nL 303.903484 418.72539 \r\nL 303.919696 419.396398 \r\nL 303.95212 420.840068 \r\nL 304.016967 418.849382 \r\nL 304.146662 421.513346 \r\nL 304.162874 421.333906 \r\nL 304.227722 418.775235 \r\nL 304.308781 419.831261 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418.101452 \r\nL 306.286631 419.650485 \r\nL 306.302843 419.646222 \r\nL 306.335267 419.321342 \r\nL 306.367691 420.060437 \r\nL 306.383903 420.392108 \r\nL 306.432538 419.401205 \r\nL 306.464962 418.581932 \r\nL 306.546022 419.137113 \r\nL 306.594657 421.078723 \r\nL 306.643293 418.396936 \r\nL 306.659505 417.964695 \r\nL 306.70814 419.226912 \r\nL 306.724352 419.18918 \r\nL 306.821624 421.037533 \r\nL 306.870259 419.707342 \r\nL 306.983743 417.326336 \r\nL 306.999954 417.946303 \r\nL 307.064802 421.580358 \r\nL 307.129649 419.554562 \r\nL 307.178285 418.893453 \r\nL 307.226921 418.399407 \r\nL 307.259345 419.37073 \r\nL 307.356616 420.081517 \r\nL 307.372828 419.940846 \r\nL 307.437675 418.11476 \r\nL 307.486311 418.865238 \r\nL 307.551159 420.749008 \r\nL 307.599794 419.410097 \r\nL 307.632218 418.604532 \r\nL 307.713277 419.028421 \r\nL 307.810549 421.164673 \r\nL 307.842972 420.374697 \r\nL 307.891608 418.58455 \r\nL 307.956456 419.373315 \r\nL 308.037515 422.217947 \r\nL 308.069939 420.919224 \r\nL 308.199634 417.921379 \r\nL 308.215846 418.106435 \r\nL 308.280693 421.855216 \r\nL 308.361753 419.731551 \r\nL 308.442812 418.079781 \r\nL 308.475236 419.178454 \r\nL 308.523872 420.980298 \r\nL 308.588719 419.719367 \r\nL 308.637355 419.001162 \r\nL 308.653567 418.72995 \r\nL 308.702202 419.513363 \r\nL 308.718414 419.484852 \r\nL 308.734626 419.402025 \r\nL 308.76705 419.800534 \r\nL 308.799474 420.526234 \r\nL 308.831898 419.506825 \r\nL 308.896745 417.908761 \r\nL 308.929169 418.895061 \r\nL 308.961593 419.731371 \r\nL 309.042652 419.277117 \r\nL 309.091288 418.121121 \r\nL 309.172347 418.630225 \r\nL 309.237195 420.169128 \r\nL 309.253407 420.928381 \r\nL 309.302042 418.788089 \r\nL 309.334466 417.515052 \r\nL 309.383102 419.11526 \r\nL 309.480373 420.227277 \r\nL 309.512797 419.560792 \r\nL 309.642492 418.567675 \r\nL 309.593856 419.64393 \r\nL 309.658704 418.598712 \r\nL 309.723551 419.8979 \r\nL 309.788399 419.477259 \r\nL 309.837035 418.064027 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419.850216 \r\nL 313.776523 419.651971 \r\nL 313.857582 419.844898 \r\nL 313.938642 418.863936 \r\nL 313.971066 419.411511 \r\nL 314.035913 421.108394 \r\nL 314.068337 419.660717 \r\nL 314.133185 418.70917 \r\nL 314.165608 419.508502 \r\nL 314.279092 420.349582 \r\nL 314.295303 420.223651 \r\nL 314.424998 418.566847 \r\nL 314.44121 418.759415 \r\nL 314.506058 420.382095 \r\nL 314.554694 419.278443 \r\nL 314.603329 417.686312 \r\nL 314.651965 418.846565 \r\nL 314.797872 420.262774 \r\nL 314.878931 418.26539 \r\nL 314.943779 419.13819 \r\nL 315.008626 420.733582 \r\nL 315.057262 419.250003 \r\nL 315.089686 418.389243 \r\nL 315.154533 419.361664 \r\nL 315.251805 420.722366 \r\nL 315.268017 420.110748 \r\nL 315.3815 418.092331 \r\nL 315.397712 418.363513 \r\nL 315.494983 420.704043 \r\nL 315.543619 419.761701 \r\nL 315.608466 418.26686 \r\nL 315.657102 419.279885 \r\nL 315.721949 420.826763 \r\nL 315.786797 419.883618 \r\nL 315.835433 418.469571 \r\nL 315.916492 419.316588 \r\nL 315.997551 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419.35362 \r\nL 476.674489 419.582465 \r\nL 477.031173 420.072096 \r\nL 477.225727 419.791406 \r\nL 477.387856 419.696358 \r\nL 477.517559 420.012322 \r\nL 477.939094 421.617826 \r\nL 477.971519 421.59614 \r\nL 478.101222 421.204859 \r\nL 478.555183 419.400057 \r\nL 478.684886 419.567857 \r\nL 479.009143 420.080854 \r\nL 479.138846 419.863725 \r\nL 479.72251 418.423907 \r\nL 480.371025 418.616769 \r\nL 480.598005 419.255502 \r\nL 480.922263 420.077235 \r\nL 481.01954 419.985845 \r\nL 481.473501 418.969598 \r\nL 481.538352 419.050453 \r\nL 481.732907 419.732718 \r\nL 482.186867 421.419204 \r\nL 482.284144 421.451015 \r\nL 482.31657 421.411608 \r\nL 482.446273 421.00152 \r\nL 482.673253 419.353309 \r\nL 483.029937 416.947572 \r\nL 483.127214 416.880993 \r\nL 483.15964 416.925773 \r\nL 483.354194 417.560494 \r\nL 483.581175 417.9634 \r\nL 483.873006 417.618354 \r\nL 483.937858 417.724462 \r\nL 484.067561 418.383769 \r\nL 484.586373 422.186848 \r\nL 484.651224 422.115302 \r\nL 484.878205 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420.347732 \r\nL 492.400979 420.819049 \r\nL 492.530682 420.661347 \r\nL 492.952217 419.992538 \r\nL 493.114346 419.785793 \r\nL 493.276475 419.043723 \r\nL 493.633158 417.209778 \r\nL 493.698009 417.285852 \r\nL 493.860138 418.16227 \r\nL 494.184396 419.877749 \r\nL 494.281673 419.764275 \r\nL 494.638356 418.952501 \r\nL 494.670782 418.978959 \r\nL 494.962614 419.344834 \r\nL 494.995039 419.302329 \r\nL 495.157168 418.68754 \r\nL 495.384148 417.797455 \r\nL 495.416574 417.81077 \r\nL 495.513852 418.183126 \r\nL 495.740832 420.529583 \r\nL 496.000238 422.308943 \r\nL 496.065089 422.153543 \r\nL 496.227218 420.840209 \r\nL 496.583901 418.145349 \r\nL 496.681179 418.345689 \r\nL 497.297268 420.638501 \r\nL 497.329694 420.619168 \r\nL 497.524248 420.245932 \r\nL 497.945783 419.374497 \r\nL 498.075486 419.521687 \r\nL 498.367318 420.036439 \r\nL 498.399743 419.995962 \r\nL 498.529447 419.508335 \r\nL 498.950981 416.883124 \r\nL 499.015833 417.036164 \r\nL 499.177962 418.300927 \r\nL 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417.606982 \r\nL 521.778711 418.323377 \r\nL 522.16782 420.138851 \r\nL 522.265097 420.12892 \r\nL 522.427226 419.727591 \r\nL 522.848761 418.387877 \r\nL 522.881187 418.397205 \r\nL 523.01089 418.651049 \r\nL 523.626979 420.432272 \r\nL 524.016088 420.690614 \r\nL 524.145791 420.381939 \r\nL 524.405197 418.782855 \r\nL 524.632177 417.91631 \r\nL 524.729455 418.17571 \r\nL 525.021286 420.426802 \r\nL 525.215841 421.066928 \r\nL 525.313118 420.808145 \r\nL 525.896782 418.430947 \r\nL 525.929207 418.44647 \r\nL 526.188613 418.763129 \r\nL 526.675 419.699803 \r\nL 526.739851 419.645316 \r\nL 526.934406 419.136613 \r\nL 527.12896 418.728975 \r\nL 527.161386 418.745714 \r\nL 527.291089 419.116752 \r\nL 527.680198 420.742342 \r\nL 527.712624 420.715566 \r\nL 527.874753 420.153711 \r\nL 528.134159 419.367672 \r\nL 528.231436 419.502408 \r\nL 528.490842 419.981286 \r\nL 528.620545 419.669604 \r\nL 528.912377 418.702937 \r\nL 528.944802 418.737316 \r\nL 529.074505 419.23249 \r\nL 529.366337 420.493806 \r\nL 529.463614 420.1423 \r\nL 529.852724 417.253203 \r\nL 529.950001 417.460689 \r\nL 530.176981 419.364549 \r\nL 530.371536 420.247841 \r\nL 530.468813 420.064357 \r\nL 530.728219 419.328708 \r\nL 530.825496 419.616741 \r\nL 531.149754 421.096178 \r\nL 531.182179 421.023803 \r\nL 531.311882 420.140967 \r\nL 531.63614 417.521958 \r\nL 531.733417 417.92627 \r\nL 532.122526 421.072192 \r\nL 532.187378 420.873562 \r\nL 532.544061 418.518596 \r\nL 532.608912 418.684597 \r\nL 532.965596 420.824567 \r\nL 533.030447 420.57095 \r\nL 533.225002 418.423453 \r\nL 533.419556 416.790923 \r\nL 533.451982 416.800838 \r\nL 533.549259 417.366861 \r\nL 533.970794 421.574828 \r\nL 534.035646 421.430216 \r\nL 534.359903 419.940423 \r\nL 534.424755 420.091532 \r\nL 534.716586 421.377855 \r\nL 534.781438 421.206996 \r\nL 534.943567 419.654219 \r\nL 535.267824 416.66675 \r\nL 535.332676 416.856688 \r\nL 535.52723 418.675051 \r\nL 535.75421 420.186672 \r\nL 535.819062 420.117133 \r\nL 536.013616 419.062729 \r\nL 536.208171 418.331204 \r\nL 536.240597 418.336986 \r\nL 536.3703 418.652307 \r\nL 536.759409 419.760816 \r\nL 537.051241 420.1756 \r\nL 537.213369 420.356638 \r\nL 537.245795 420.337292 \r\nL 537.375498 419.954046 \r\nL 537.732181 418.414188 \r\nL 537.764607 418.472984 \r\nL 537.926736 419.475185 \r\nL 538.218568 421.311686 \r\nL 538.283419 421.118757 \r\nL 538.445548 419.52953 \r\nL 538.769805 416.649879 \r\nL 538.834657 416.83849 \r\nL 539.029211 418.720127 \r\nL 539.353469 421.523477 \r\nL 539.483172 421.673567 \r\nL 539.515598 421.651394 \r\nL 539.677727 421.369547 \r\nL 539.904707 420.455686 \r\nL 540.391093 417.54812 \r\nL 540.455945 417.711819 \r\nL 540.618073 418.946428 \r\nL 540.942331 421.159787 \r\nL 541.007182 421.110886 \r\nL 541.169311 420.368454 \r\nL 541.525994 418.903845 \r\nL 541.882678 418.464573 \r\nL 542.142084 418.052215 \r\nL 542.239361 418.184569 \r\nL 542.758173 419.67808 \r\nL 542.823025 419.634196 \r\nL 543.147282 419.463484 \r\nL 543.536391 419.594404 \r\nL 543.828223 419.5191 \r\nL 544.022777 419.845695 \r\nL 544.249758 420.110717 \r\nL 544.411886 419.910974 \r\nL 544.638867 419.671517 \r\nL 544.671292 419.691566 \r\nL 545.060401 420.065851 \r\nL 545.092827 420.008235 \r\nL 545.287382 419.285427 \r\nL 545.514362 418.664493 \r\nL 545.611639 418.739278 \r\nL 545.935897 419.483418 \r\nL 546.000748 419.418246 \r\nL 546.162877 418.864313 \r\nL 546.357432 418.400087 \r\nL 546.454709 418.685631 \r\nL 546.746541 420.926785 \r\nL 546.908669 421.450718 \r\nL 547.005947 421.185734 \r\nL 547.427481 419.533678 \r\nL 547.719313 419.662211 \r\nL 547.751739 419.600182 \r\nL 547.946293 418.801653 \r\nL 548.173274 418.181904 \r\nL 548.270551 418.427706 \r\nL 548.562383 419.545944 \r\nL 548.594809 419.529374 \r\nL 548.724512 419.03728 \r\nL 548.983918 417.778894 \r\nL 549.016343 417.791092 \r\nL 549.113621 418.208128 \r\nL 549.50273 421.423652 \r\nL 549.600007 421.154394 \r\nL 549.794561 419.269541 \r\nL 550.021542 417.673005 \r\nL 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=5)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.3090559299602927\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 102.016859 418.076669 \r\nL 102.065495 422.140917 \r\nL 102.19519 418.343003 \r\nL 102.146554 422.176544 \r\nL 102.227614 420.867986 \r\nL 102.308673 418.243215 \r\nL 102.357309 423.092104 \r\nL 102.503216 418.831567 \r\nL 102.519428 419.482405 \r\nL 102.551851 421.573841 \r\nL 102.600487 418.114244 \r\nL 102.649123 421.543057 \r\nL 102.665335 421.534197 \r\nL 102.79503 416.853222 \r\nL 102.827454 417.643115 \r\nL 102.876089 418.420414 \r\nL 102.908513 417.295476 \r\nL 102.940937 416.312655 \r\nL 103.005784 417.79695 \r\nL 103.05442 414.12297 \r\nL 103.086844 416.176934 \r\nL 103.200327 420.112725 \r\nL 103.248963 415.443468 \r\nL 103.31381 419.948584 \r\nL 103.362446 418.895474 \r\nL 103.39487 419.45217 \r\nL 103.427293 421.20571 \r\nL 103.475929 418.394362 \r\nL 103.524565 420.677945 \r\nL 103.556988 419.990873 \r\nL 103.5732 421.115549 \r\nL 103.621836 424.544413 \r\nL 103.670472 420.999622 \r\nL 103.767743 418.935856 \r\nL 103.719107 421.522441 \r\nL 103.783955 420.122914 \r\nL 103.897438 424.311263 \r\nL 103.91365 423.412431 \r\nL 104.027133 418.725821 \r\nL 104.043345 419.642524 \r\nL 104.17304 421.271663 \r\nL 104.221676 418.238914 \r\nL 104.302735 420.822883 \r\nL 104.318947 420.821367 \r\nL 104.416218 423.156657 \r\nL 104.43243 422.096437 \r\nL 104.464854 418.498653 \r\nL 104.562125 419.583529 \r\nL 104.594549 421.42263 \r\nL 104.643185 417.971119 \r\nL 104.77288 414.932632 \r\nL 104.708032 418.53113 \r\nL 104.789092 415.678459 \r\nL 104.821516 419.100431 \r\nL 104.886363 415.682828 \r\nL 104.951211 414.541039 \r\nL 104.967423 413.927152 \r\nL 105.016058 416.059553 \r\nL 105.129541 418.941538 \r\nL 105.178177 418.179504 \r\nL 105.194389 418.10923 \r\nL 105.226813 416.15303 \r\nL 105.259237 418.660117 \r\nL 105.324084 417.541416 \r\nL 105.37272 422.465125 \r\nL 105.421355 416.6598 \r\nL 105.469991 423.279049 \r\nL 105.502415 420.260182 \r\nL 105.518627 418.30897 \r\nL 105.567262 423.155176 \r\nL 105.615898 419.076668 \r\nL 105.63211 418.77679 \r\nL 105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 419.701183 \r\nL 196.094438 418.00952 \r\nL 196.143073 420.649234 \r\nL 196.191709 416.199579 \r\nL 196.28898 417.366366 \r\nL 196.37004 416.308742 \r\nL 196.418675 420.426478 \r\nL 196.467311 416.606475 \r\nL 196.515947 420.559165 \r\nL 196.532159 421.148637 \r\nL 196.580794 418.879609 \r\nL 196.613218 420.68436 \r\nL 196.62943 420.609831 \r\nL 196.661854 418.763258 \r\nL 196.710489 420.967438 \r\nL 196.742913 420.44045 \r\nL 196.759125 420.47238 \r\nL 196.856396 422.149606 \r\nL 196.872608 421.599822 \r\nL 196.905032 420.208393 \r\nL 196.953668 422.230145 \r\nL 196.969879 422.162306 \r\nL 196.986091 422.129023 \r\nL 197.067151 423.465547 \r\nL 197.099575 422.612249 \r\nL 197.164422 423.376404 \r\nL 197.22927 420.278935 \r\nL 197.277905 422.495307 \r\nL 197.310329 420.714202 \r\nL 197.440024 415.336633 \r\nL 197.456236 416.381807 \r\nL 197.472448 417.053758 \r\nL 197.504872 414.877169 \r\nL 197.618355 412.860758 \r\nL 197.634567 413.456525 \r\nL 197.845321 417.763333 \r\nL 197.861533 417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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304.114239 420.63193 \r\nL 304.146662 422.477989 \r\nL 304.21151 420.603813 \r\nL 304.260146 421.000483 \r\nL 304.292569 420.601905 \r\nL 304.324993 420.263745 \r\nL 304.357417 420.857399 \r\nL 304.373629 421.051835 \r\nL 304.389841 420.669467 \r\nL 304.535748 417.180814 \r\nL 304.55196 417.233379 \r\nL 304.697866 419.693106 \r\nL 304.746502 418.199841 \r\nL 304.795138 420.016161 \r\nL 304.892409 422.099487 \r\nL 304.843773 419.405684 \r\nL 304.924833 420.842236 \r\nL 304.957257 419.950565 \r\nL 305.038316 420.141007 \r\nL 305.103164 421.07975 \r\nL 305.151799 420.304603 \r\nL 305.200435 420.035808 \r\nL 305.249071 418.202238 \r\nL 305.33013 418.766127 \r\nL 305.394978 419.126866 \r\nL 305.411189 419.004515 \r\nL 305.459825 417.790766 \r\nL 305.540885 418.259238 \r\nL 305.605732 419.8005 \r\nL 305.686792 419.632806 \r\nL 305.735427 418.264432 \r\nL 305.784063 419.919016 \r\nL 305.816487 419.280174 \r\nL 305.84891 420.361139 \r\nL 305.881334 421.338921 \r\nL 305.92997 419.011347 \r\nL 305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val) # inifity-steps-ahead prediction\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/sysidentpy/polynomial_basis/narmax.py b/sysidentpy/polynomial_basis/narmax.py
index fc34494c..1d6421e8 100644
--- a/sysidentpy/polynomial_basis/narmax.py
+++ b/sysidentpy/polynomial_basis/narmax.py
@@ -552,10 +552,8 @@ def _n_step_ahead_prediction(self, X, y, steps_ahead):
                 steps_ahead = len(y) - i  # predicts the remaining values
 
             yhat[i:i+steps_ahead] = self._model_prediction(
-                self.final_model,
                 X[k:i+steps_ahead],
-                y[k:i+steps_ahead],
-                self.theta)[-steps_ahead:].ravel()
+                y[k:i+steps_ahead])[-steps_ahead:].ravel()
 
             i += steps_ahead
 

From 832b7bcc039ae0ab1a7b818b38226fbfe0c7fca2 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Tue, 9 Mar 2021 00:01:42 -0300
Subject: [PATCH 06/27] updated documentation for v0.1.5

---
 ...ion_of_an_electromechanical_system_3_1.png |   Bin 0 -> 27188 bytes
 ...ion_of_an_electromechanical_system_7_2.png |   Bin 0 -> 91757 bytes
 .../_images/n_steps_ahead_prediction_14_0.svg | 11121 ++++++++++++++++
 .../_images/n_steps_ahead_prediction_15_1.svg | 10342 ++++++++++++++
 .../_images/n_steps_ahead_prediction_16_1.svg | 10090 ++++++++++++++
 docs/_images/n_steps_ahead_prediction_6_1.svg |  1386 ++
 docs/_images/n_steps_ahead_prediction_7_1.svg |  1787 +++
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diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index c738e78f..c58b5df8 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/base.html b/docs/_modules/sysidentpy/base.html
index e1e0ec69..29ab5d34 100644
--- a/docs/_modules/sysidentpy/base.html
+++ b/docs/_modules/sysidentpy/base.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/general_estimators/narx.html b/docs/_modules/sysidentpy/general_estimators/narx.html
index 66535ec9..71d77b47 100644
--- a/docs/_modules/sysidentpy/general_estimators/narx.html
+++ b/docs/_modules/sysidentpy/general_estimators/narx.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/metrics/_regression.html b/docs/_modules/sysidentpy/metrics/_regression.html
index 7102fa77..5ab17b60 100644
--- a/docs/_modules/sysidentpy/metrics/_regression.html
+++ b/docs/_modules/sysidentpy/metrics/_regression.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/parameter_estimation/estimators.html b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
index 0513a3ad..79bcb419 100644
--- a/docs/_modules/sysidentpy/parameter_estimation/estimators.html
+++ b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/polynomial_basis/narmax.html b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
index a6b5490d..8468f9dc 100644
--- a/docs/_modules/sysidentpy/polynomial_basis/narmax.html
+++ b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -849,10 +869,8 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
                 <span class="n">steps_ahead</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">i</span>  <span class="c1"># predicts the remaining values</span>
 
             <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">,</span>
                 <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">)[</span><span class="o">-</span><span class="n">steps_ahead</span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+                <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">])[</span><span class="o">-</span><span class="n">steps_ahead</span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
 
             <span class="n">i</span> <span class="o">+=</span> <span class="n">steps_ahead</span>
 
diff --git a/docs/_modules/sysidentpy/residues/residues_correlation.html b/docs/_modules/sysidentpy/residues/residues_correlation.html
index 86a0a523..7ce21c10 100644
--- a/docs/_modules/sysidentpy/residues/residues_correlation.html
+++ b/docs/_modules/sysidentpy/residues/residues_correlation.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/utils/_check_arrays.html b/docs/_modules/sysidentpy/utils/_check_arrays.html
index 8ea88b41..831683d0 100644
--- a/docs/_modules/sysidentpy/utils/_check_arrays.html
+++ b/docs/_modules/sysidentpy/utils/_check_arrays.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_modules/sysidentpy/utils/generate_data.html b/docs/_modules/sysidentpy/utils/generate_data.html
index 3f383282..acd1b7ae 100644
--- a/docs/_modules/sysidentpy/utils/generate_data.html
+++ b/docs/_modules/sysidentpy/utils/generate_data.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../../../changelog/v0.1.4.html">
+  <a class="reference internal" href="../../../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/_sources/changelog/v0.1.5.rst.txt b/docs/_sources/changelog/v0.1.5.rst.txt
new file mode 100644
index 00000000..14bfbfd6
--- /dev/null
+++ b/docs/_sources/changelog/v0.1.5.rst.txt
@@ -0,0 +1,93 @@
+Changes in SysIdentPy
+=====================
+
+v0.1.5
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+
+CHANGES
+~~~~~~~
+
+- MAJOR: n-steps-ahead prediction.
+    - Now you can define the numbers of steps ahead in the predict function.
+	- only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+
+- Added 4 new notebooks in the example section.
+
+- Added iterative notebooks.
+
+- Fix issues related to html on Jupyter notebooks examples on documentation.
+
+- Updated Readme with examples of how to use.
+
+
+v0.1.4
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+
+CHANGES
+~~~~~~~
+
+- MAJOR: Introducing NARX Neural Network in SysIdentPy.
+    - Now you can build NARX Neural Network on SysIdentPy.
+    - This feature is built on top of Pytorch. See the docs for more details and examples of how to use.
+
+- MAJOR: Introducing general estimators in SysIdentPy.
+    - Now you are able to use any estimator that have Fit/Predict methods (estimators from Sklearn and Catboost, for example) and build NARX models based on those estimators.
+    - We use the core functions of SysIdentPy and keep the Fit/Predict approach from those estimators to keep the process easy to use.
+    - More estimators are coming soon like XGboost.
+
+- Added notebooks to show how to build NARX neural Network.
+
+- Added notebooks to show how to build NARX models using general estimators.
+
+- Changed the default parameters of the plot_results function.
+
+- NOTE: We will keeping improving the Polynomial NARX models (new model structure selection algorithms and multiobjective identification
+is on our roadmap). These recent modifications will allow us to introduce new NARX models like PWARX models very soon.
+
+- New template for the documentation site.
+
+- Fix issues related to html on Jupyter notebooks examples on documentation.
+
+- Updated Readme with examples of how to use.
+
+
+v0.1.3
+------
+
+CONTRIBUTORS
+~~~~~~~~~~~~
+
+- wilsonrljr
+- renard162
+
+CHANGES
+~~~~~~~
+
+- Fixed a bug concerning the xlag and ylag in multiple input scenarios.
+- Refactored predict function. Improved performance up to 87% depending on the number of regressors.
+- You can set lags with different size for each input.
+- Added a new function to get the max value of xlag and ylag. Work with int, list, nested lists.
+- Fixed tests for information criteria.
+- Added SysIdentPy logo.
+- Refactored code of all classes following PEP 8 guidelines to improve readability.
+- Added Citation information on Readme.
+- Changes on information Criteria tests.
+- Added workflow to run the tests when merge branch into master.
+- Added new site domain.
+- Updated docs.
+
+
+v0.1.5
+------
+CONTRIBUTORS
+CHANGES
diff --git a/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt b/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt
new file mode 100644
index 00000000..51619d6e
--- /dev/null
+++ b/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "daily-tower",
+   "metadata": {},
+   "source": [
+    "# Identification of an electromechanical system\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior\n",
+    "\n",
+    "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "earlier-person",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "peripheral-colors",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('data/x_cc.csv')\n",
+    "df2 = pd.read_csv('data/y_cc.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "legal-weapon",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2[5000:80000].plot(figsize=(10, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "competitive-pathology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will decimate the data using d=500 in this example\n",
+    "x_train, x_valid = np.split(df1.iloc[::500].values, 2)\n",
+    "y_train, y_valid = np.split(df2.iloc[::500].values, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "organized-springfield",
+   "metadata": {},
+   "source": [
+    "## Building a Polynomial NARX model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "personal-easter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=40,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=2, xlag=2,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares'\n",
+    "                         )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "plastic-demographic",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\polynomial_basis\\narmax.py:333: UserWarning: n_info_values is greater than the maximum number of all regressors space considering the chosen y_lag, u_lag, and non_degree. We set as 15 \n",
+      "  self.info_values = self.information_criterion(X, y)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08011571455094049\n",
+      "       Regressors Parameters         ERR\n",
+      "0          y(k-1)     1.3016  0.98600038\n",
+      "1       x1(k-1)^2   103.9300  0.00794805\n",
+      "2        y(k-2)^2     0.0000  0.00250906\n",
+      "3   x1(k-1)y(k-1)    -0.1257  0.00143301\n",
+      "4          y(k-2)    -0.5078  0.00102781\n",
+      "5   x1(k-1)y(k-2)     0.0560  0.00053520\n",
+      "6         x1(k-2)   349.8627  0.00027965\n",
+      "7   x1(k-2)y(k-1)    -0.0840  0.00011221\n",
+      "8  x1(k-2)x1(k-1)    -7.8186  0.00004547\n",
+      "9   x1(k-2)y(k-2)     0.0341  0.00003253\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train)\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex, n=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "introductory-bench",
+   "metadata": {},
+   "source": [
+    "## Testing different autoregressive models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "binding-shelter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:49 - INFO - Training the model\n",
+      "03-08 22:53:49 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:49 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:49 - INFO - Done! Model is built!\n",
+      "KNeighborsRegressor 1.833\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:53:50 - INFO - Done! Model is built!\n",
+      "DecisionTreeRegressor 0.346\n",
+      "03-08 22:53:50 - INFO - Training the model\n",
+      "03-08 22:53:50 - INFO - Creating the regressor matrix\n",
+      "03-08 22:53:50 - INFO - The regressor matrix have 21 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sysidentpy\\general_estimators\\narx.py:154: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+      "  self.base_estimator.fit(X, y, **self.fit_params)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:53:51 - INFO - Done! Model is built!\n",
+      "RandomForestRegressor 0.178\n",
+      "03-08 22:54:03 - INFO - Training the model\n",
+      "03-08 22:54:03 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:03 - INFO - The regressor matrix have 21 features\n",
+      "03-08 22:54:12 - INFO - Done! Model is built!\n",
+      "Catboost_NARX 0.179\n",
+      "03-08 22:54:13 - INFO - Training the model\n",
+      "03-08 22:54:13 - INFO - Creating the regressor matrix\n",
+      "03-08 22:54:13 - INFO - The regressor matrix have 231 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\wilso\\miniconda3\\envs\\v0.1.4\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  return f(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "03-08 22:54:14 - INFO - Done! Model is built!\n",
+      "ARD_NARX 0.074\n",
+      "Polynomial_NARX 0.032\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.linear_model import BayesianRidge, ARDRegression\n",
+    "from sysidentpy.general_estimators import NARX\n",
+    "\n",
+    "\n",
+    "estimators = [\n",
+    "    ('KNeighborsRegressor', NARX(\n",
+    "        base_estimator=KNeighborsRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('DecisionTreeRegressor', NARX(\n",
+    "        base_estimator=DecisionTreeRegressor(),\n",
+    "        xlag=10,\n",
+    "        ylag=10)),\n",
+    "    ('RandomForestRegressor', NARX(\n",
+    "        base_estimator=RandomForestRegressor(\n",
+    "            n_estimators=200),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "    )),\n",
+    "    ('Catboost_NARX', NARX(\n",
+    "        base_estimator=CatBoostRegressor(\n",
+    "        iterations=800,\n",
+    "        learning_rate=0.1,\n",
+    "        depth=8),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=1,\n",
+    "        fit_params={'verbose': False}\n",
+    "    )),\n",
+    "    ('ARD_NARX', NARX(\n",
+    "        base_estimator=ARDRegression(),\n",
+    "        xlag=10,\n",
+    "        ylag=10,\n",
+    "        non_degree=2\n",
+    "    )),\n",
+    "    ('Polynomial_NARX', PolynomialNarmax(non_degree=2,\n",
+    "                         order_selection=True,\n",
+    "                         n_info_values=50,\n",
+    "                         extended_least_squares=False,\n",
+    "                         ylag=10, xlag=10,\n",
+    "                         info_criteria='bic',\n",
+    "                         estimator='recursive_least_squares',\n",
+    "                         )),\n",
+    "    \n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "resultados = {}\n",
+    "for nome_do_modelo, modelo in estimators:\n",
+    "    resultados['%s' % (nome_do_modelo)] = []\n",
+    "    modelo.fit(x_train, y_train)\n",
+    "    yhat = modelo.predict(x_valid,  y_valid)\n",
+    "    result = root_relative_squared_error(y_valid, yhat)\n",
+    "    resultados['%s' % (nome_do_modelo)].append(result)\n",
+    "    print(nome_do_modelo, '%.3f' % np.mean(result))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "pursuant-appreciation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial_NARX 0.03208119098997185\n",
+      "ARD_NARX 0.07364659571266981\n",
+      "RandomForestRegressor 0.17752025859574144\n",
+      "Catboost_NARX 0.17923040407121107\n",
+      "DecisionTreeRegressor 0.3457206837080308\n",
+      "KNeighborsRegressor 1.833370478725381\n"
+     ]
+    }
+   ],
+   "source": [
+    "for aux_results, results in sorted(resultados.items(), key=lambda x: np.mean(x[1]), reverse=False):\n",
+    "    print(aux_results, np.mean(results))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
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+    "name": "ipython",
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diff --git a/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt b/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt
new file mode 100644
index 00000000..6e1ee55e
--- /dev/null
+++ b/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark\n",
+    "\n",
+    "**Note**: The following examples do not try to replicate the results of the cited manuscripts. Even the model parameters such as ylag and xlag and size of identification and validation data are not the same of the cited papers. Moreover, sampling rate adjustment and other different data preparation are not handled here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**The following text was taken from the link http://www.nonlinearbenchmark.org/#F16**. \n",
+    "\n",
+    "**Note**: The reader is reffered to the mentioned website for a complete reference concerning the experiment. For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. A more detailed study of this system will be published in the future.  \n",
+    "\n",
+    "> The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads.\n",
+    "\n",
+    "> The experimental data made available to the Workshop participants were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class, held in September 2014 at the Saffraanberg military basis, Sint-Truiden, Belgium.\n",
+    "\n",
+    "> During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study.\n",
+    "\n",
+    "> A detailed formulation of the identification problem can be found here. All the provided files and information on the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format.\n",
+    "\n",
+    "> Please refer to the F16 benchmark as:\n",
+    "\n",
+    "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "import pandas as pd\n",
+    "pd.set_option('display.max_columns', 1000)"
+   ]
+  },
+  {
+   "source": [
+    "## Preparing the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_16 = pd.read_csv(r'examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(32768, 3)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "f_16.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16[['x1', 'x2']][0:500].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x576 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"465.958125pt\" version=\"1.1\" viewBox=\"0 0 721.645312 465.958125\" width=\"721.645312pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-08T23:42:32.923280</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "f_16['y'][0:2000].plot(figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1_id, x1_val = f_16['x1'][0:16384].values.reshape(-1, 1), f_16['x1'][16384::].values.reshape(-1, 1)\n",
+    "x2_id, x2_val = f_16['x2'][0:16384].values.reshape(-1, 1), f_16['x2'][16384::].values.reshape(-1, 1)\n",
+    "x_id = np.concatenate([x1_id, x2_id], axis=1)\n",
+    "x_val = np.concatenate([x1_val, x2_val], axis=1)\n",
+    "\n",
+    "y_id, y_val = f_16['y'][0:16384].values.reshape(-1, 1), f_16['y'][16384::].values.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "x1lag = list(range(1, 10))\n",
+    "x2lag = list(range(1, 10))\n",
+    "x2lag"
+   ]
+  },
+  {
+   "source": [
+    "## Building the model"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2523e7e4850>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax(# n_terms=8,\n",
+    "                         non_degree=1,\n",
+    "                         order_selection=True,\n",
+    "                         ylag=20, xlag=[x1lag, x2lag],\n",
+    "                         n_inputs = 2,\n",
+    "                         n_info_values=39,\n",
+    "                         info_criteria='bic',\n",
+    "                         extended_least_squares=False,\n",
+    "                         estimator='least_squares')\n",
+    "model.fit(x_id, y_id)"
+   ]
+  },
+  {
+   "source": [
+    "## Defining the forecasting horizon\n",
+    "\n",
+    "To perform a n-steps-ahead prediction you just need to set the \"steps_ahead\" argument.\n",
+    "\n",
+    "**Note** The default value for steps_ahead is None and it performs a infinity-steps-ahead prediction"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.09660139833917882\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Regressors   Parameters             ERR\n",
+       "0      y(k-1)   1.8387E+00  9.43378253E-01\n",
+       "1      y(k-2)  -1.8938E+00  1.95167599E-02\n",
+       "2      y(k-3)   1.3337E+00  1.02432261E-02\n",
+       "3      y(k-6)  -1.6038E+00  8.03485985E-03\n",
+       "4      y(k-9)   2.6776E-01  9.27874557E-04\n",
+       "5     x2(k-7)  -2.2385E+01  3.76837313E-04\n",
+       "6     x1(k-1)   8.2709E+00  6.81508210E-04\n",
+       "7     x2(k-3)   1.0587E+02  1.57459800E-03\n",
+       "8     x1(k-8)  -3.7975E+00  7.35086279E-04\n",
+       "9     x2(k-1)   8.5725E+01  4.85358786E-04\n",
+       "10     y(k-7)   1.3955E+00  2.77245281E-04\n",
+       "11     y(k-5)   1.3219E+00  8.64120037E-04\n",
+       "12    y(k-10)  -2.9306E-01  8.51717688E-04\n",
+       "13     y(k-4)  -9.5479E-01  7.23623116E-04\n",
+       "14     y(k-8)  -7.1309E-01  4.44988077E-04\n",
+       "15    y(k-12)  -3.0437E-01  1.49743148E-04\n",
+       "16    y(k-11)   4.8602E-01  3.34613282E-04\n",
+       "17    y(k-13)  -8.2442E-02  1.43738964E-04\n",
+       "18    y(k-15)  -1.6762E-01  1.25546584E-04\n",
+       "19    x1(k-2)  -8.9698E+00  9.76699739E-05\n",
+       "20    y(k-17)   2.2036E-02  4.55983807E-05\n",
+       "21    y(k-14)   2.4900E-01  1.10314107E-04\n",
+       "22    y(k-19)  -6.8239E-03  1.99734771E-05\n",
+       "23    x2(k-9)  -9.6265E+01  2.98523208E-05\n",
+       "24    x2(k-8)   2.2620E+02  2.34402543E-04\n",
+       "25    x2(k-2)  -2.3609E+02  1.04172323E-04\n",
+       "26    y(k-20)  -5.4663E-02  5.37895336E-05\n",
+       "27    x2(k-6)  -2.3651E+02  2.11392628E-05\n",
+       "28    x2(k-4)   1.7378E+02  2.18396315E-05\n",
+       "29    x1(k-7)   4.9862E+00  2.03811842E-05"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>1.8387E+00</td>\n      <td>9.43378253E-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>y(k-2)</td>\n      <td>-1.8938E+00</td>\n      <td>1.95167599E-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>y(k-3)</td>\n      <td>1.3337E+00</td>\n      <td>1.02432261E-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>y(k-6)</td>\n      <td>-1.6038E+00</td>\n      <td>8.03485985E-03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>y(k-9)</td>\n      <td>2.6776E-01</td>\n      <td>9.27874557E-04</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>x2(k-7)</td>\n      <td>-2.2385E+01</td>\n      <td>3.76837313E-04</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>x1(k-1)</td>\n      <td>8.2709E+00</td>\n      <td>6.81508210E-04</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>x2(k-3)</td>\n      <td>1.0587E+02</td>\n      <td>1.57459800E-03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>x1(k-8)</td>\n      <td>-3.7975E+00</td>\n      <td>7.35086279E-04</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>x2(k-1)</td>\n      <td>8.5725E+01</td>\n      <td>4.85358786E-04</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>y(k-7)</td>\n      <td>1.3955E+00</td>\n      <td>2.77245281E-04</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>y(k-5)</td>\n      <td>1.3219E+00</td>\n      <td>8.64120037E-04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>y(k-10)</td>\n      <td>-2.9306E-01</td>\n      <td>8.51717688E-04</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>y(k-4)</td>\n      <td>-9.5479E-01</td>\n      <td>7.23623116E-04</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>y(k-8)</td>\n      <td>-7.1309E-01</td>\n      <td>4.44988077E-04</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>y(k-12)</td>\n      <td>-3.0437E-01</td>\n      <td>1.49743148E-04</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>y(k-11)</td>\n      <td>4.8602E-01</td>\n      <td>3.34613282E-04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>y(k-13)</td>\n      <td>-8.2442E-02</td>\n      <td>1.43738964E-04</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>y(k-15)</td>\n      <td>-1.6762E-01</td>\n      <td>1.25546584E-04</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>x1(k-2)</td>\n      <td>-8.9698E+00</td>\n      <td>9.76699739E-05</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>y(k-17)</td>\n      <td>2.2036E-02</td>\n      <td>4.55983807E-05</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>y(k-14)</td>\n      <td>2.4900E-01</td>\n      <td>1.10314107E-04</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>y(k-19)</td>\n      <td>-6.8239E-03</td>\n      <td>1.99734771E-05</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>x2(k-9)</td>\n      <td>-9.6265E+01</td>\n      <td>2.98523208E-05</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>x2(k-8)</td>\n      <td>2.2620E+02</td>\n      <td>2.34402543E-04</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>x2(k-2)</td>\n      <td>-2.3609E+02</td>\n      <td>1.04172323E-04</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>y(k-20)</td>\n      <td>-5.4663E-02</td>\n      <td>5.37895336E-05</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>x2(k-6)</td>\n      <td>-2.3651E+02</td>\n      <td>2.11392628E-05</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>x2(k-4)</td>\n      <td>1.7378E+02</td>\n      <td>2.18396315E-05</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>x1(k-7)</td>\n      <td>4.9862E+00</td>\n      <td>2.03811842E-05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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418.788988 \r\nL 95.823918 417.14612 \r\nL 95.888766 419.571574 \r\nL 95.969825 422.427663 \r\nL 95.986037 419.935081 \r\nL 96.002249 419.588972 \r\nL 96.018461 416.776156 \r\nL 96.067097 421.126941 \r\nL 96.115732 417.322844 \r\nL 96.131944 417.280816 \r\nL 96.229216 423.726638 \r\nL 96.245428 421.302618 \r\nL 96.358911 417.992977 \r\nL 96.375123 418.91002 \r\nL 96.391335 418.523717 \r\nL 96.407546 421.836762 \r\nL 96.504818 420.989002 \r\nL 96.52103 421.52937 \r\nL 96.537242 420.803017 \r\nL 96.553453 417.185378 \r\nL 96.650725 418.894004 \r\nL 96.764208 421.75883 \r\nL 96.78042 420.207059 \r\nL 96.812844 415.902476 \r\nL 96.845267 420.703498 \r\nL 96.861479 419.295515 \r\nL 96.877691 421.960188 \r\nL 96.893903 418.710054 \r\nL 96.974962 419.82729 \r\nL 97.03981 420.582339 \r\nL 97.088446 417.151598 \r\nL 97.201929 421.431456 \r\nL 97.218141 420.172974 \r\nL 97.250565 418.42234 \r\nL 97.266776 417.507238 \r\nL 97.2992 419.6799 \r\nL 97.315412 418.837208 \r\nL 97.347836 418.064348 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420.101691 \r\nL 103.443505 420.742359 \r\nL 103.492141 416.86154 \r\nL 103.621836 422.846315 \r\nL 103.767743 416.84079 \r\nL 103.881226 422.925947 \r\nL 103.91365 422.181374 \r\nL 104.027133 416.755559 \r\nL 104.059557 420.725891 \r\nL 104.140616 419.958285 \r\nL 104.205464 418.003117 \r\nL 104.189252 421.020615 \r\nL 104.2541 419.795129 \r\nL 104.270311 420.131831 \r\nL 104.302735 419.510922 \r\nL 104.318947 418.64919 \r\nL 104.335159 420.367969 \r\nL 104.383795 419.936804 \r\nL 104.43243 421.007442 \r\nL 104.448642 418.574488 \r\nL 104.464854 417.460861 \r\nL 104.529702 419.911969 \r\nL 104.545914 418.527138 \r\nL 104.594549 421.15305 \r\nL 104.643185 418.057142 \r\nL 104.659397 419.227341 \r\nL 104.756668 418.099954 \r\nL 104.740456 419.591364 \r\nL 104.789092 418.126443 \r\nL 104.837727 421.662223 \r\nL 104.902575 418.477377 \r\nL 104.934999 420.103558 \r\nL 104.999846 417.042155 \r\nL 105.129541 422.298224 \r\nL 105.243025 417.27335 \r\nL 105.259237 417.730767 \r\nL 105.275448 421.598409 \r\nL 105.324084 417.068191 \r\nL 105.37272 420.736869 \r\nL 105.437567 417.576288 \r\nL 105.469991 420.850482 \r\nL 105.486203 421.141337 \r\nL 105.534839 418.621245 \r\nL 105.567262 421.921523 \r\nL 105.583474 419.260443 \r\nL 105.599686 420.927807 \r\nL 105.680746 417.991595 \r\nL 105.696957 417.48019 \r\nL 105.713169 419.43557 \r\nL 105.729381 418.761132 \r\nL 105.794229 421.210393 \r\nL 105.826653 420.900028 \r\nL 105.842864 417.398889 \r\nL 105.859076 421.148724 \r\nL 105.940136 418.166941 \r\nL 106.053619 422.706448 \r\nL 106.069831 422.452197 \r\nL 106.167102 416.770083 \r\nL 106.215738 417.590979 \r\nL 106.280585 421.542635 \r\nL 106.329221 420.638757 \r\nL 106.442704 416.967766 \r\nL 106.458916 417.291967 \r\nL 106.572399 421.289577 \r\nL 106.637247 417.136384 \r\nL 106.604823 423.172004 \r\nL 106.685883 417.690177 \r\nL 106.75073 421.274751 \r\nL 106.799366 419.060147 \r\nL 106.83179 421.66916 \r\nL 106.848001 418.835716 \r\nL 106.864213 417.46113 \r\nL 106.896637 419.304261 \r\nL 106.929061 417.824813 \r\nL 107.042544 421.836748 \r\nL 107.058756 421.241315 \r\nL 107.139815 417.699771 \r\nL 107.172239 418.721248 \r\nL 107.204663 417.853339 \r\nL 107.301934 422.050618 \r\nL 107.415417 416.917136 \r\nL 107.431629 417.459288 \r\nL 107.561324 422.312428 \r\nL 107.577536 421.855897 \r\nL 107.626172 416.545222 \r\nL 107.707231 419.342418 \r\nL 107.755867 417.707671 \r\nL 107.739655 420.10933 \r\nL 107.804503 420.000805 \r\nL 107.820715 421.917179 \r\nL 107.86935 417.888338 \r\nL 107.917986 420.63239 \r\nL 107.982834 422.159306 \r\nL 108.047681 417.80408 \r\nL 108.063893 420.515044 \r\nL 108.144952 416.64582 \r\nL 108.161164 420.151233 \r\nL 108.177376 418.28243 \r\nL 108.193588 422.627712 \r\nL 108.258436 420.587697 \r\nL 108.371919 417.324481 \r\nL 108.355707 420.819372 \r\nL 108.388131 417.506599 \r\nL 108.404343 415.718034 \r\nL 108.46919 420.651351 \r\nL 108.485402 418.724417 \r\nL 108.501614 420.390283 \r\nL 108.582673 417.841909 \r\nL 108.598885 419.984296 \r\nL 108.615097 419.873142 \r\nL 108.631309 417.365505 \r\nL 108.712368 419.039348 \r\nL 108.744792 421.815672 \r\nL 108.793428 418.387855 \r\nL 108.80964 419.568351 \r\nL 108.825852 416.874879 \r\nL 108.842063 420.399703 \r\nL 108.923123 418.485202 \r\nL 108.971759 418.411445 \r\nL 109.036606 422.085311 \r\nL 109.166301 417.826321 \r\nL 109.182513 417.455959 \r\nL 109.231149 422.656108 \r\nL 109.295996 419.95027 \r\nL 109.312208 420.167976 \r\nL 109.344632 417.229085 \r\nL 109.393268 420.2449 \r\nL 109.425691 419.079978 \r\nL 109.441903 418.966646 \r\nL 109.490539 421.690094 \r\nL 109.539175 418.88277 \r\nL 109.555387 420.526498 \r\nL 109.652658 417.746278 \r\nL 109.66887 418.211617 \r\nL 109.766141 422.426901 \r\nL 109.782353 419.689925 \r\nL 109.798565 419.446776 \r\nL 109.814777 422.110914 \r\nL 109.879624 416.849548 \r\nL 109.895836 418.486789 \r\nL 110.009319 423.38414 \r\nL 109.960684 417.831446 \r\nL 110.057955 419.888465 \r\nL 110.139014 417.531059 \r\nL 110.122803 420.531087 \r\nL 110.171438 418.872135 \r\nL 110.18765 418.023242 \r\nL 110.203862 420.778306 \r\nL 110.252498 419.779249 \r\nL 110.26871 421.49687 \r\nL 110.333557 418.4384 \r\nL 110.349769 420.85991 \r\nL 110.382193 416.805081 \r\nL 110.479464 418.52655 \r\nL 110.544312 421.675527 \r\nL 110.576735 419.262477 \r\nL 110.592947 415.839351 \r\nL 110.641583 420.16215 \r\nL 110.674007 416.79728 \r\nL 110.70643 420.908929 \r\nL 110.78749 419.305058 \r\nL 110.803702 419.83822 \r\nL 110.852337 418.332862 \r\nL 110.884761 420.776129 \r\nL 110.933397 416.785408 \r\nL 110.949609 422.471818 \r\nL 111.04688 419.753082 \r\nL 111.095516 416.090603 \r\nL 111.079304 420.800841 \r\nL 111.160363 418.914161 \r\nL 111.273846 422.064862 \r\nL 111.403542 416.82817 \r\nL 111.500813 421.846086 \r\nL 111.533237 421.772468 \r\nL 111.630508 416.731744 \r\nL 111.64672 417.885513 \r\nL 111.727779 421.603716 \r\nL 111.760203 420.780567 \r\nL 111.792627 421.628502 \r\nL 111.857474 417.480024 \r\nL 111.954746 422.584253 \r\nL 111.90611 415.849559 \r\nL 111.970958 421.473004 \r\nL 112.084441 417.267456 \r\nL 112.214136 421.42327 \r\nL 112.230348 421.19605 \r\nL 112.278983 416.848134 \r\nL 112.360043 418.195479 \r\nL 112.376255 417.96604 \r\nL 112.392467 418.248834 \r\nL 112.489738 422.495936 \r\nL 112.50595 419.944542 \r\nL 112.619433 416.620296 \r\nL 112.538374 420.326952 \r\nL 112.635645 417.724542 \r\nL 112.76534 423.351084 \r\nL 112.878823 417.51851 \r\nL 112.959883 422.08408 \r\nL 113.008518 419.896162 \r\nL 113.040942 419.516778 \r\nL 113.057154 417.198351 \r\nL 113.122002 420.053382 \r\nL 113.138213 418.429983 \r\nL 113.186849 420.973675 \r\nL 113.170637 417.379174 \r\nL 113.251697 419.381181 \r\nL 113.267909 419.467128 \r\nL 113.28412 422.023543 \r\nL 113.348968 417.418696 \r\nL 113.381392 420.36723 \r\nL 113.430027 421.096866 \r\nL 113.478663 420.375351 \r\nL 113.494875 420.225657 \r\nL 113.527299 417.216306 \r\nL 113.543511 420.371135 \r\nL 113.608358 418.809337 \r\nL 113.656994 422.258418 \r\nL 113.640782 418.541309 \r\nL 113.738053 421.438566 \r\nL 113.851536 416.363806 \r\nL 113.867748 418.561654 \r\nL 113.916384 421.201479 \r\nL 113.932596 418.174796 \r\nL 113.997443 420.547116 \r\nL 114.062291 417.564504 \r\nL 114.094715 420.547729 \r\nL 114.110927 420.729413 \r\nL 114.191986 417.570328 \r\nL 114.22441 420.022976 \r\nL 114.240622 420.450459 \r\nL 114.256834 419.655052 \r\nL 114.273046 417.105015 \r\nL 114.337893 420.684317 \r\nL 114.354105 418.700668 \r\nL 114.402741 422.509545 \r\nL 114.451376 419.457065 \r\nL 114.467588 416.63188 \r\nL 114.548648 420.170431 \r\nL 114.564859 418.517682 \r\nL 114.629707 421.591673 \r\nL 114.678343 419.831123 \r\nL 114.710766 420.148664 \r\nL 114.74319 418.754165 \r\nL 114.808038 417.149098 \r\nL 114.840462 419.236205 \r\nL 114.872885 419.070159 \r\nL 114.937733 422.149065 \r\nL 115.00258 417.339021 \r\nL 115.051216 419.978754 \r\nL 115.08364 417.519746 \r\nL 115.148487 420.724667 \r\nL 115.164699 422.089882 \r\nL 115.180911 419.327113 \r\nL 115.245759 421.16003 \r\nL 115.310606 418.272673 \r\nL 115.34303 420.494977 \r\nL 115.359242 421.429844 \r\nL 115.407878 419.152026 \r\nL 115.424089 420.017137 \r\nL 115.505149 417.282894 \r\nL 115.472725 420.779357 \r\nL 115.537573 418.141815 \r\nL 115.667268 420.811849 \r\nL 115.764539 417.28623 \r\nL 115.878022 420.87449 \r\nL 115.959082 418.128611 \r\nL 115.94287 420.987667 \r\nL 116.007717 419.213353 \r\nL 116.056353 417.685626 \r\nL 116.121201 422.440899 \r\nL 116.137413 418.531918 \r\nL 116.234684 419.771324 \r\nL 116.250896 419.961304 \r\nL 116.299531 417.950351 \r\nL 116.331955 420.036477 \r\nL 116.348167 419.238848 \r\nL 116.413015 420.966489 \r\nL 116.380591 418.909965 \r\nL 116.445438 419.148542 \r\nL 116.494074 417.941605 \r\nL 116.526498 419.328874 \r\nL 116.54271 421.729168 \r\nL 116.607557 417.528212 \r\nL 116.639981 420.140585 \r\nL 116.737252 418.217599 \r\nL 116.769676 419.419365 \r\nL 116.785888 419.588216 \r\nL 116.8021 419.16319 \r\nL 116.818312 419.167732 \r\nL 116.915583 417.235995 \r\nL 116.850736 422.027642 \r\nL 116.931795 418.615731 \r\nL 116.964219 421.291536 \r\nL 117.012854 418.452236 \r\nL 117.029066 416.94894 \r\nL 117.077702 420.091701 \r\nL 117.093914 419.788373 \r\nL 117.126338 420.495759 \r\nL 117.142549 420.434402 \r\nL 117.158761 421.127621 \r\nL 117.207397 418.936756 \r\nL 117.239821 419.747173 \r\nL 117.272245 418.675541 \r\nL 117.304668 420.860897 \r\nL 117.353304 418.376249 \r\nL 117.385728 420.205171 \r\nL 117.40194 420.203473 \r\nL 117.418152 420.576318 \r\nL 117.450575 419.348983 \r\nL 117.482999 417.456614 \r\nL 117.531635 419.438748 \r\nL 117.547847 419.292606 \r\nL 117.645118 421.339822 \r\nL 117.66133 420.44006 \r\nL 117.726177 417.053093 \r\nL 117.791025 417.741534 \r\nL 117.888296 421.740247 \r\nL 117.936932 420.087486 \r\nL 117.985568 418.005398 \r\nL 118.017991 420.557148 \r\nL 118.050415 418.115087 \r\nL 118.082839 421.211419 \r\nL 118.18011 421.045245 \r\nL 118.196322 421.219949 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420.517664 \r\nL 121.714302 420.305801 \r\nL 121.730513 421.545655 \r\nL 121.746725 417.854448 \r\nL 121.811573 420.590321 \r\nL 121.843997 417.058189 \r\nL 121.860208 420.777253 \r\nL 121.941268 418.118185 \r\nL 121.973692 417.930774 \r\nL 122.054751 421.747758 \r\nL 122.103387 418.120215 \r\nL 122.168234 419.733083 \r\nL 122.200658 420.695978 \r\nL 122.21687 418.489798 \r\nL 122.249294 419.64348 \r\nL 122.281718 418.187183 \r\nL 122.297929 419.296647 \r\nL 122.314141 421.343045 \r\nL 122.362777 417.769077 \r\nL 122.395201 419.561866 \r\nL 122.411413 418.101582 \r\nL 122.47626 420.873015 \r\nL 122.492472 420.313076 \r\nL 122.508684 420.654614 \r\nL 122.524896 420.153021 \r\nL 122.541108 420.377782 \r\nL 122.55732 417.938055 \r\nL 122.605955 421.785986 \r\nL 122.638379 420.388484 \r\nL 122.654591 420.14361 \r\nL 122.670803 416.892364 \r\nL 122.751862 420.346047 \r\nL 122.768074 420.93012 \r\nL 122.881557 417.213932 \r\nL 122.946405 422.00181 \r\nL 122.995041 419.714599 \r\nL 123.043676 418.174141 \r\nL 123.027464 421.019921 \r\nL 123.108524 419.168338 \r\nL 123.124736 419.454312 \r\nL 123.140948 417.396059 \r\nL 123.157159 421.530021 \r\nL 123.222007 420.200229 \r\nL 123.254431 420.95629 \r\nL 123.286855 420.189054 \r\nL 123.33549 417.979301 \r\nL 123.384126 421.013793 \r\nL 123.400338 420.774305 \r\nL 123.41655 420.897434 \r\nL 123.432762 417.556208 \r\nL 123.497609 421.581609 \r\nL 123.530033 418.679385 \r\nL 123.546245 420.239517 \r\nL 123.59488 418.231012 \r\nL 123.643516 418.962434 \r\nL 123.724575 421.547816 \r\nL 123.740787 418.04944 \r\nL 123.773211 419.513625 \r\nL 123.789423 420.490353 \r\nL 123.838059 417.807775 \r\nL 123.854271 418.596492 \r\nL 123.870482 417.175148 \r\nL 123.902906 420.640687 \r\nL 123.93533 419.820142 \r\nL 124.000178 421.696754 \r\nL 123.967754 418.673958 \r\nL 124.032601 419.860227 \r\nL 124.048813 417.974042 \r\nL 124.113661 420.010227 \r\nL 124.146085 419.079171 \r\nL 124.19472 420.964382 \r\nL 124.210932 418.683395 \r\nL 124.227144 416.653371 \r\nL 124.243356 421.744155 \r\nL 124.308203 417.457155 \r\nL 124.373051 421.634512 \r\nL 124.421687 419.527463 \r\nL 124.470322 420.02603 \r\nL 124.486534 417.848526 \r\nL 124.502746 421.644799 \r\nL 124.583805 419.996865 \r\nL 124.600017 415.399664 \r\nL 124.648653 421.278123 \r\nL 124.681077 421.076558 \r\nL 124.745924 417.646243 \r\nL 124.810772 419.629264 \r\nL 124.826984 421.162145 \r\nL 124.859408 418.18476 \r\nL 124.908043 419.277519 \r\nL 124.924255 419.396811 \r\nL 124.972891 419.163304 \r\nL 125.021526 420.558144 \r\nL 125.070162 416.544287 \r\nL 125.118798 420.919544 \r\nL 125.183645 419.60278 \r\nL 125.216069 418.381351 \r\nL 125.248493 419.596468 \r\nL 125.264705 421.610921 \r\nL 125.297128 417.855609 \r\nL 125.345764 420.578483 \r\nL 125.378188 417.580937 \r\nL 125.3944 421.345602 \r\nL 125.443035 419.226366 \r\nL 125.459247 420.972349 \r\nL 125.524095 417.741048 \r\nL 125.556519 420.121568 \r\nL 125.605154 419.216362 \r\nL 125.621366 420.511144 \r\nL 125.686214 417.969459 \r\nL 125.702426 420.983424 \r\nL 125.718638 420.226254 \r\nL 125.734849 420.481308 \r\nL 125.767273 418.074187 \r\nL 125.815909 421.139479 \r\nL 125.848333 419.148219 \r\nL 125.880756 419.920463 \r\nL 125.978028 418.226199 \r\nL 126.042875 422.050121 \r\nL 126.075299 419.905309 \r\nL 126.107723 420.764598 \r\nL 126.204994 417.075569 \r\nL 126.237418 421.443357 \r\nL 126.334689 420.58493 \r\nL 126.367113 421.269664 \r\nL 126.399537 417.990807 \r\nL 126.464384 418.496246 \r\nL 126.496808 421.442856 \r\nL 126.529232 417.914024 \r\nL 126.577868 419.685006 \r\nL 126.642715 418.44055 \r\nL 126.675139 419.439048 \r\nL 126.707563 419.294963 \r\nL 126.723775 419.890976 \r\nL 126.756198 416.999771 \r\nL 126.788622 421.925837 \r\nL 126.837258 418.546562 \r\nL 126.869681 420.215907 \r\nL 126.902105 417.608173 \r\nL 126.934529 418.991607 \r\nL 126.950741 418.354215 \r\nL 126.966953 419.904801 \r\nL 126.983165 419.67162 \r\nL 126.999377 421.953818 \r\nL 127.064224 419.042721 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422.029729 \r\nL 129.042074 420.359808 \r\nL 129.155558 416.945097 \r\nL 129.171769 418.553609 \r\nL 129.204193 421.326994 \r\nL 129.301464 420.98431 \r\nL 129.3501 417.304145 \r\nL 129.414948 419.26065 \r\nL 129.479795 421.861436 \r\nL 129.512219 420.483569 \r\nL 129.560855 418.260568 \r\nL 129.577067 421.342238 \r\nL 129.625702 418.771891 \r\nL 129.739185 422.152607 \r\nL 129.706762 418.22097 \r\nL 129.755397 420.71497 \r\nL 129.852669 417.784823 \r\nL 129.820245 421.311643 \r\nL 129.885092 418.58362 \r\nL 129.901304 418.433261 \r\nL 129.933728 421.537135 \r\nL 129.998576 417.628437 \r\nL 130.014787 420.079526 \r\nL 130.030999 418.025555 \r\nL 130.047211 420.586629 \r\nL 130.112059 418.54972 \r\nL 130.20933 421.055195 \r\nL 130.322813 416.970463 \r\nL 130.484932 422.194487 \r\nL 130.533568 417.137556 \r\nL 130.614627 417.626353 \r\nL 130.711899 420.895058 \r\nL 130.728111 419.58505 \r\nL 130.776746 420.333899 \r\nL 130.80917 418.331336 \r\nL 130.906441 421.951528 \r\nL 130.922653 419.12935 \r\nL 130.938865 419.085849 \r\nL 130.955077 416.926462 \r\nL 131.019924 420.350356 \r\nL 131.036136 418.895481 \r\nL 131.117196 420.72225 \r\nL 131.06856 418.412714 \r\nL 131.14962 419.606788 \r\nL 131.198255 419.978523 \r\nL 131.311738 417.727681 \r\nL 131.425222 420.905198 \r\nL 131.441434 420.743529 \r\nL 131.490069 417.840296 \r\nL 131.522493 420.870808 \r\nL 131.571129 418.492324 \r\nL 131.635976 420.97358 \r\nL 131.684612 419.346356 \r\nL 131.765671 417.223134 \r\nL 131.749459 420.013931 \r\nL 131.781883 419.50956 \r\nL 131.798095 419.367262 \r\nL 131.814307 419.9331 \r\nL 131.846731 419.554185 \r\nL 131.862943 421.09158 \r\nL 131.895366 419.52684 \r\nL 131.944002 419.63635 \r\nL 132.00885 418.058013 \r\nL 131.976426 419.750443 \r\nL 132.057485 418.41127 \r\nL 132.170968 421.152994 \r\nL 132.219604 418.365196 \r\nL 132.26824 418.854634 \r\nL 132.284452 421.45087 \r\nL 132.381723 419.523165 \r\nL 132.397935 419.498129 \r\nL 132.446571 420.656993 \r\nL 132.495206 418.487157 \r\nL 132.624901 420.862242 \r\nL 132.657325 418.200768 \r\nL 132.754596 419.074522 \r\nL 132.770808 420.122293 \r\nL 132.835656 418.451058 \r\nL 132.86808 420.099277 \r\nL 132.900503 417.425377 \r\nL 132.916715 420.207806 \r\nL 132.981563 419.452031 \r\nL 133.030198 417.63331 \r\nL 133.111258 421.167106 \r\nL 133.159894 418.182599 \r\nL 133.257165 418.343552 \r\nL 133.322012 420.882413 \r\nL 133.370648 419.728319 \r\nL 133.419284 418.848177 \r\nL 133.435496 419.392295 \r\nL 133.451707 420.70472 \r\nL 133.516555 418.325949 \r\nL 133.548979 420.563457 \r\nL 133.565191 420.465873 \r\nL 133.581403 416.53537 \r\nL 133.64625 420.978246 \r\nL 133.678674 418.436737 \r\nL 133.72731 420.705263 \r\nL 133.792157 419.115444 \r\nL 133.824581 418.902661 \r\nL 133.857005 419.488818 \r\nL 133.873217 418.211036 \r\nL 133.889428 420.81345 \r\nL 133.954276 418.776862 \r\nL 133.9867 421.763742 \r\nL 134.035335 417.000237 \r\nL 134.083971 421.099054 \r\nL 134.116395 417.026835 \r\nL 134.213666 418.655122 \r\nL 134.278514 420.97398 \r\nL 134.310937 418.634788 \r\nL 134.327149 417.892281 \r\nL 134.359573 419.34807 \r\nL 134.375785 418.903951 \r\nL 134.391997 421.20923 \r\nL 134.408209 418.640201 \r\nL 134.489268 419.721971 \r\nL 134.554116 416.856893 \r\nL 134.537904 421.523223 \r\nL 134.58654 419.125062 \r\nL 134.618963 420.929183 \r\nL 134.635175 418.636105 \r\nL 134.700023 420.766153 \r\nL 134.797294 417.377 \r\nL 134.813506 418.658412 \r\nL 134.862142 420.85329 \r\nL 134.84593 418.228434 \r\nL 134.926989 418.956073 \r\nL 134.943201 418.820265 \r\nL 135.040472 420.889328 \r\nL 135.02426 418.663142 \r\nL 135.056684 419.966077 \r\nL 135.10532 415.625286 \r\nL 135.137744 420.166517 \r\nL 135.153956 423.283798 \r\nL 135.202591 419.053156 \r\nL 135.235015 419.798554 \r\nL 135.299863 417.956592 \r\nL 135.316074 421.50681 \r\nL 135.332286 420.663694 \r\nL 135.348498 420.913394 \r\nL 135.36471 418.548368 \r\nL 135.461981 420.00112 \r\nL 135.510617 418.134147 \r\nL 135.526829 419.140454 \r\nL 135.543041 417.524847 \r\nL 135.591677 420.172991 \r\nL 135.6241 419.4416 \r\nL 135.640312 419.367446 \r\nL 135.656524 421.583207 \r\nL 135.721372 417.895528 \r\nL 135.753795 420.089107 \r\nL 135.770007 419.980833 \r\nL 135.802431 418.150085 \r\nL 135.834855 421.414938 \r\nL 135.88349 419.612999 \r\nL 135.899702 419.461682 \r\nL 135.948338 420.951279 \r\nL 135.980762 418.151968 \r\nL 135.996974 419.292453 \r\nL 136.029397 418.715115 \r\nL 136.045609 417.825557 \r\nL 136.110457 420.019977 \r\nL 136.207728 419.166817 \r\nL 136.142881 420.648358 \r\nL 136.22394 419.801393 \r\nL 136.240152 419.866813 \r\nL 136.305 418.385997 \r\nL 136.353635 418.476623 \r\nL 136.450907 420.648444 \r\nL 136.434695 418.064109 \r\nL 136.467118 419.954991 \r\nL 136.531966 418.276936 \r\nL 136.56439 420.351895 \r\nL 136.580602 419.117862 \r\nL 136.613025 420.312971 \r\nL 136.645449 420.138278 \r\nL 136.661661 415.695416 \r\nL 136.694085 421.064563 \r\nL 136.758932 418.083766 \r\nL 136.807568 421.192398 \r\nL 136.888627 419.680378 \r\nL 136.921051 419.37407 \r\nL 136.937263 420.188729 \r\nL 136.969687 417.497874 \r\nL 137.050746 418.493287 \r\nL 137.148018 422.086108 \r\nL 137.16423 420.973129 \r\nL 137.277713 417.137039 \r\nL 137.391196 421.608919 \r\nL 137.407408 418.628017 \r\nL 137.504679 419.066194 \r\nL 137.569527 417.673442 \r\nL 137.618162 421.155947 \r\nL 137.634374 418.049774 \r\nL 137.731646 420.175503 \r\nL 137.812705 418.963 \r\nL 137.780281 420.468338 \r\nL 137.845129 419.619054 \r\nL 137.861341 422.041684 \r\nL 137.893764 416.736577 \r\nL 137.9424 420.152897 \r\nL 138.007248 418.611286 \r\nL 138.02346 418.670201 \r\nL 138.088307 421.298247 \r\nL 138.120731 418.29286 \r\nL 138.136943 419.811882 \r\nL 138.218002 416.96788 \r\nL 138.250426 417.395539 \r\nL 138.28285 421.170863 \r\nL 138.347697 417.275357 \r\nL 138.380121 420.231323 \r\nL 138.396333 420.641495 \r\nL 138.412545 419.588776 \r\nL 138.444969 417.623944 \r\nL 138.493604 420.151164 \r\nL 138.509816 418.686043 \r\nL 138.623299 420.419929 \r\nL 138.671935 416.543472 \r\nL 138.720571 419.579487 \r\nL 138.769206 418.100231 \r\nL 138.850266 421.454731 \r\nL 138.915113 417.837735 \r\nL 138.963749 419.812097 \r\nL 138.979961 419.700095 \r\nL 138.996173 417.649215 \r\nL 139.028596 421.563369 \r\nL 139.093444 418.723775 \r\nL 139.125868 418.892694 \r\nL 139.14208 417.064811 \r\nL 139.158292 420.038907 \r\nL 139.255563 419.782846 \r\nL 139.287987 422.280971 \r\nL 139.352834 417.828821 \r\nL 139.369046 422.070059 \r\nL 139.417682 417.075243 \r\nL 139.466317 419.346279 \r\nL 139.547377 421.164005 \r\nL 139.596013 420.610896 \r\nL 139.693284 416.796932 \r\nL 139.628436 421.247908 \r\nL 139.725708 417.639395 \r\nL 139.822979 422.587224 \r\nL 139.839191 420.311213 \r\nL 139.936462 417.728131 \r\nL 139.952674 417.822199 \r\nL 139.968886 421.056775 \r\nL 140.049945 420.552639 \r\nL 140.066157 417.559313 \r\nL 140.163429 419.440513 \r\nL 140.228276 417.956061 \r\nL 140.244488 420.632693 \r\nL 140.325547 419.431494 \r\nL 140.341759 421.321563 \r\nL 140.390395 416.550442 \r\nL 140.455243 419.499645 \r\nL 140.52009 417.865191 \r\nL 140.536302 420.922133 \r\nL 140.552514 422.532362 \r\nL 140.568726 419.047493 \r\nL 140.617361 419.452047 \r\nL 140.633573 417.944404 \r\nL 140.649785 419.745636 \r\nL 140.714633 418.222289 \r\nL 140.77948 421.152393 \r\nL 140.811904 419.283497 \r\nL 140.892963 420.467247 \r\nL 140.909175 416.688714 \r\nL 141.006447 420.791474 \r\nL 141.03887 420.592346 \r\nL 141.055082 421.384453 \r\nL 141.103718 419.419927 \r\nL 141.11993 419.916382 \r\nL 141.152354 416.973594 \r\nL 141.249625 418.438114 \r\nL 141.363108 421.58264 \r\nL 141.460379 416.970555 \r\nL 141.476591 418.72015 \r\nL 141.492803 421.257171 \r\nL 141.557651 418.472411 \r\nL 141.573863 420.526712 \r\nL 141.687346 418.529381 \r\nL 141.752193 420.429155 \r\nL 141.768405 418.325924 \r\nL 141.800829 420.130755 \r\nL 141.817041 420.517557 \r\nL 141.833253 418.770725 \r\nL 141.849465 417.965675 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416.994869 \r\nL 146.713031 422.05341 \r\nL 146.745455 418.409989 \r\nL 146.842726 420.822033 \r\nL 146.777878 417.617597 \r\nL 146.858938 419.97501 \r\nL 146.87515 416.59021 \r\nL 146.923785 420.908059 \r\nL 146.956209 419.66641 \r\nL 146.972421 419.815885 \r\nL 146.988633 419.271254 \r\nL 147.004845 419.801734 \r\nL 147.021057 421.345204 \r\nL 147.102116 418.48918 \r\nL 147.118328 417.735566 \r\nL 147.13454 419.54851 \r\nL 147.150752 421.578502 \r\nL 147.199387 417.762497 \r\nL 147.248023 421.150495 \r\nL 147.426354 417.2478 \r\nL 147.458778 421.762177 \r\nL 147.539837 419.204162 \r\nL 147.556049 418.786851 \r\nL 147.572261 418.9902 \r\nL 147.620896 417.889588 \r\nL 147.685744 421.961774 \r\nL 147.73438 417.922779 \r\nL 147.799227 418.958172 \r\nL 147.880287 420.520412 \r\nL 147.864075 418.042401 \r\nL 147.896499 419.200409 \r\nL 147.91271 417.925644 \r\nL 147.928922 420.043514 \r\nL 147.99377 419.749491 \r\nL 148.009982 421.199555 \r\nL 148.042405 418.200831 \r\nL 148.074829 418.387106 \r\nL 148.091041 417.446553 \r\nL 148.123465 420.70415 \r\nL 148.139677 419.604025 \r\nL 148.172101 417.730274 \r\nL 148.220736 420.336814 \r\nL 148.269372 417.429611 \r\nL 148.334219 419.247133 \r\nL 148.399067 420.453087 \r\nL 148.431491 418.726994 \r\nL 148.51255 420.4499 \r\nL 148.496338 418.344714 \r\nL 148.528762 418.720998 \r\nL 148.544974 418.756123 \r\nL 148.609822 421.056164 \r\nL 148.59361 417.703979 \r\nL 148.674669 421.049539 \r\nL 148.739517 421.346251 \r\nL 148.804364 418.298773 \r\nL 148.853 420.930596 \r\nL 148.885424 416.639997 \r\nL 148.917847 419.92374 \r\nL 149.015119 418.608743 \r\nL 148.982695 420.666116 \r\nL 149.031331 419.688751 \r\nL 149.079966 419.575866 \r\nL 149.096178 420.231722 \r\nL 149.11239 418.101254 \r\nL 149.193449 420.476027 \r\nL 149.209661 419.470633 \r\nL 149.225873 419.741136 \r\nL 149.242085 417.838853 \r\nL 149.323145 420.399976 \r\nL 149.339356 418.532542 \r\nL 149.404204 421.007593 \r\nL 149.45284 419.6038 \r\nL 149.550111 418.356716 \r\nL 149.485263 420.16312 \r\nL 149.566323 418.427311 \r\nL 149.614958 420.852546 \r\nL 149.663594 420.419168 \r\nL 149.744654 418.321124 \r\nL 149.777077 419.869237 \r\nL 149.793289 419.8328 \r\nL 149.809501 418.653524 \r\nL 149.874349 420.352439 \r\nL 149.890561 421.430995 \r\nL 149.906772 418.879855 \r\nL 149.955408 421.26332 \r\nL 149.987832 416.967097 \r\nL 150.068891 419.545065 \r\nL 150.101315 419.648969 \r\nL 150.117527 421.421112 \r\nL 150.149951 418.903786 \r\nL 150.214798 420.704616 \r\nL 150.295858 417.937207 \r\nL 150.376917 419.177218 \r\nL 150.393129 421.740074 \r\nL 150.441765 417.480014 \r\nL 150.474188 418.652211 \r\nL 150.4904 418.504412 \r\nL 150.522824 418.445676 \r\nL 150.587672 420.721047 \r\nL 150.603884 418.493046 \r\nL 150.701155 418.817671 \r\nL 150.749791 417.259761 \r\nL 150.814638 420.278135 \r\nL 150.911909 418.562185 \r\nL 150.879486 421.232416 \r\nL 150.928121 420.024069 \r\nL 150.992969 417.280331 \r\nL 151.041605 418.330268 \r\nL 151.074028 420.513948 \r\nL 151.155088 420.195724 \r\nL 151.203723 418.265737 \r\nL 151.252359 420.013438 \r\nL 151.268571 420.702981 \r\nL 151.317207 419.062806 \r\nL 151.333418 419.335256 \r\nL 151.43069 417.601647 \r\nL 151.365842 420.749195 \r\nL 151.446902 419.030262 \r\nL 151.463114 418.929075 \r\nL 151.560385 421.597571 \r\nL 151.511749 417.739158 \r\nL 151.576597 420.185482 \r\nL 151.673868 416.935881 \r\nL 151.69008 419.737522 \r\nL 151.722504 418.171119 \r\nL 151.803563 419.463447 \r\nL 151.819775 421.17752 \r\nL 151.868411 419.22262 \r\nL 151.900835 420.914223 \r\nL 151.965682 416.068199 \r\nL 151.998106 420.987735 \r\nL 152.014318 419.583549 \r\nL 152.046741 419.21119 \r\nL 152.062953 420.169502 \r\nL 152.079165 420.533013 \r\nL 152.095377 418.861226 \r\nL 152.160225 420.282103 \r\nL 152.241284 420.615268 \r\nL 152.28992 418.32721 \r\nL 152.338555 421.25711 \r\nL 152.419615 420.086904 \r\nL 152.435827 420.024982 \r\nL 152.468251 418.328852 \r\nL 152.500674 421.123788 \r\nL 152.54931 419.697751 \r\nL 152.565522 420.033369 \r\nL 152.597946 419.654192 \r\nL 152.630369 420.3528 \r\nL 152.711429 417.929984 \r\nL 152.776276 420.723477 \r\nL 152.824912 420.210535 \r\nL 152.88976 418.147814 \r\nL 152.873548 420.330236 \r\nL 152.938395 419.415211 \r\nL 153.003243 419.296469 \r\nL 153.035667 420.84242 \r\nL 153.100514 420.977969 \r\nL 153.14915 418.196538 \r\nL 153.230209 418.178944 \r\nL 153.262633 422.307388 \r\nL 153.311269 418.370086 \r\nL 153.376116 420.959922 \r\nL 153.489599 417.822581 \r\nL 153.505811 418.465203 \r\nL 153.603083 420.133434 \r\nL 153.619295 419.17432 \r\nL 153.651718 418.550689 \r\nL 153.684142 420.471645 \r\nL 153.700354 419.650433 \r\nL 153.716566 420.256145 \r\nL 153.74899 418.627681 \r\nL 153.765201 419.359007 \r\nL 153.781413 417.372598 \r\nL 153.797625 420.124712 \r\nL 153.862473 419.165981 \r\nL 153.878685 419.157374 \r\nL 153.894897 420.626218 \r\nL 153.943532 418.16082 \r\nL 153.975956 420.112697 \r\nL 154.00838 418.18847 \r\nL 154.024592 420.913406 \r\nL 154.089439 419.251632 \r\nL 154.121863 420.811323 \r\nL 154.170499 420.004655 \r\nL 154.235346 418.155446 \r\nL 154.219134 420.019735 \r\nL 154.283982 419.304922 \r\nL 154.348829 420.520343 \r\nL 154.316406 418.988884 \r\nL 154.365041 420.194107 \r\nL 154.446101 418.333569 \r\nL 154.478525 418.941195 \r\nL 154.494736 420.464248 \r\nL 154.575796 419.624301 \r\nL 154.592008 418.251448 \r\nL 154.640643 421.328979 \r\nL 154.689279 418.455509 \r\nL 154.770338 420.851564 \r\nL 154.754127 418.44244 \r\nL 154.802762 420.297687 \r\nL 154.835186 420.687322 \r\nL 154.932457 418.626259 \r\nL 155.062152 421.975192 \r\nL 155.013517 418.277938 \r\nL 155.078364 421.040185 \r\nL 155.127 417.315506 \r\nL 155.208059 418.064361 \r\nL 155.224271 417.867263 \r\nL 155.240483 418.742395 \r\nL 155.256695 418.48256 \r\nL 155.337754 421.126438 \r\nL 155.353966 419.541775 \r\nL 155.435026 418.350331 \r\nL 155.418814 419.728088 \r\nL 155.483661 418.427222 \r\nL 155.580933 420.647878 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417.088712 \r\nL 157.4453 418.431822 \r\nL 157.542571 422.461448 \r\nL 157.639842 417.908595 \r\nL 157.656054 418.805742 \r\nL 157.785749 420.321754 \r\nL 157.850597 418.190705 \r\nL 157.818173 421.622246 \r\nL 157.915444 418.873257 \r\nL 157.931656 418.734837 \r\nL 157.947868 419.192651 \r\nL 157.996504 421.251664 \r\nL 158.028928 419.409794 \r\nL 158.077563 420.48246 \r\nL 158.126199 417.98319 \r\nL 158.22347 420.926151 \r\nL 158.191047 417.338784 \r\nL 158.239682 420.538584 \r\nL 158.30453 418.470895 \r\nL 158.353165 418.479309 \r\nL 158.434225 421.278098 \r\nL 158.385589 417.876581 \r\nL 158.466649 419.673098 \r\nL 158.482861 419.141157 \r\nL 158.499072 420.4076 \r\nL 158.531496 419.473609 \r\nL 158.547708 420.750062 \r\nL 158.628767 418.749457 \r\nL 158.644979 418.246024 \r\nL 158.677403 420.39016 \r\nL 158.709827 418.831575 \r\nL 158.758463 422.472838 \r\nL 158.790886 417.327092 \r\nL 158.82331 420.093457 \r\nL 158.888158 418.803458 \r\nL 158.936793 419.396498 \r\nL 158.953005 419.426062 \r\nL 159.001641 421.342212 \r\nL 158.985429 418.127748 \r\nL 159.066488 421.136056 \r\nL 159.147548 417.451665 \r\nL 159.16376 418.917174 \r\nL 159.179972 421.431006 \r\nL 159.244819 418.804362 \r\nL 159.261031 419.563008 \r\nL 159.342091 417.435014 \r\nL 159.293455 421.484779 \r\nL 159.358302 419.913117 \r\nL 159.390726 419.348506 \r\nL 159.439362 420.293198 \r\nL 159.455574 417.005527 \r\nL 159.504209 421.861899 \r\nL 159.552845 419.43603 \r\nL 159.569057 417.610219 \r\nL 159.650116 419.587199 \r\nL 159.731176 421.333783 \r\nL 159.68254 418.484198 \r\nL 159.747388 420.413834 \r\nL 159.779811 417.844097 \r\nL 159.812235 420.818377 \r\nL 159.877083 418.435577 \r\nL 159.893295 417.814404 \r\nL 159.925718 420.455626 \r\nL 159.94193 419.725851 \r\nL 159.974354 419.975152 \r\nL 159.990566 419.180453 \r\nL 160.006778 419.809607 \r\nL 160.039202 421.614195 \r\nL 160.136473 417.927314 \r\nL 160.233744 420.318692 \r\nL 160.217532 417.860042 \r\nL 160.249956 419.774418 \r\nL 160.266168 419.653526 \r\nL 160.28238 421.078265 \r\nL 160.314804 418.080705 \r\nL 160.363439 419.608956 \r\nL 160.379651 419.566002 \r\nL 160.428287 420.363633 \r\nL 160.476923 418.29991 \r\nL 160.525558 420.090104 \r\nL 160.509346 417.72636 \r\nL 160.590406 420.088562 \r\nL 160.671465 418.597603 \r\nL 160.687677 418.920306 \r\nL 160.720101 421.471187 \r\nL 160.752525 418.837267 \r\nL 160.80116 419.269756 \r\nL 160.866008 421.344085 \r\nL 160.833584 419.166643 \r\nL 160.914644 420.420953 \r\nL 160.947067 416.759638 \r\nL 161.028127 419.031626 \r\nL 161.044339 420.604288 \r\nL 161.06055 418.289254 \r\nL 161.14161 419.476419 \r\nL 161.157822 419.390198 \r\nL 161.174034 420.800448 \r\nL 161.238881 418.306607 \r\nL 161.255093 420.671998 \r\nL 161.271305 418.389428 \r\nL 161.303729 421.081528 \r\nL 161.368576 419.938295 \r\nL 161.449636 418.457548 \r\nL 161.48206 419.654677 \r\nL 161.546907 421.268797 \r\nL 161.579331 417.641793 \r\nL 161.595543 421.016627 \r\nL 161.676602 417.28474 \r\nL 161.692814 418.538273 \r\nL 161.709026 418.452856 \r\nL 161.822509 420.962093 \r\nL 161.790085 417.728556 \r\nL 161.838721 419.963647 \r\nL 161.854933 420.384118 \r\nL 161.887357 419.044488 \r\nL 161.903569 419.268543 \r\nL 161.952204 418.643565 \r\nL 161.935992 419.972408 \r\nL 161.984628 419.170903 \r\nL 162.049476 420.854871 \r\nL 162.017052 418.569766 \r\nL 162.098111 419.36665 \r\nL 162.114323 417.282157 \r\nL 162.162959 421.626352 \r\nL 162.211594 418.025856 \r\nL 162.292654 420.415871 \r\nL 162.357501 420.297359 \r\nL 162.373713 418.646438 \r\nL 162.389925 421.459923 \r\nL 162.454773 419.746986 \r\nL 162.470985 420.935687 \r\nL 162.503408 417.93048 \r\nL 162.552044 419.66959 \r\nL 162.568256 417.400382 \r\nL 162.60068 420.815522 \r\nL 162.665527 417.996971 \r\nL 162.795222 421.443084 \r\nL 162.876282 417.901783 \r\nL 162.908706 420.251357 \r\nL 162.941129 421.081641 \r\nL 162.957341 417.991457 \r\nL 163.054613 419.876892 \r\nL 163.070824 420.860849 \r\nL 163.103248 418.47194 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418.57396 \r\nL 166.151083 418.710542 \r\nL 166.199719 420.384984 \r\nL 166.264566 419.03818 \r\nL 166.394261 420.744664 \r\nL 166.313202 418.752748 \r\nL 166.410473 420.385256 \r\nL 166.475321 420.567239 \r\nL 166.572592 417.75635 \r\nL 166.702287 421.83599 \r\nL 166.848194 416.8083 \r\nL 166.994101 421.955342 \r\nL 167.107584 418.279826 \r\nL 167.237279 421.177073 \r\nL 167.285915 418.235553 \r\nL 167.350763 420.585723 \r\nL 167.399398 417.534006 \r\nL 167.448034 419.625054 \r\nL 167.464246 421.075088 \r\nL 167.49667 418.283951 \r\nL 167.545305 419.079368 \r\nL 167.561517 418.015198 \r\nL 167.642576 419.934615 \r\nL 167.658788 420.764657 \r\nL 167.723636 419.180956 \r\nL 167.820907 417.75737 \r\nL 167.772272 419.78935 \r\nL 167.837119 418.781365 \r\nL 167.853331 418.720275 \r\nL 167.869543 419.052985 \r\nL 167.918179 421.361942 \r\nL 168.01545 419.996247 \r\nL 168.047874 419.224101 \r\nL 168.064086 416.97671 \r\nL 168.128933 420.10611 \r\nL 168.145145 418.887204 \r\nL 168.177569 418.617807 \r\nL 168.258628 421.048015 \r\nL 168.27484 417.791252 \r\nL 168.372111 420.17014 \r\nL 168.436959 418.926842 \r\nL 168.404535 420.332403 \r\nL 168.453171 420.140859 \r\nL 168.469383 420.838616 \r\nL 168.501806 419.535592 \r\nL 168.518018 417.604396 \r\nL 168.582866 421.11691 \r\nL 168.61529 417.627131 \r\nL 168.696349 421.868475 \r\nL 168.744985 419.41952 \r\nL 168.761197 419.963438 \r\nL 168.79362 419.096833 \r\nL 168.809832 417.638499 \r\nL 168.858468 419.63743 \r\nL 168.87468 419.351363 \r\nL 168.923316 421.298296 \r\nL 168.955739 417.813973 \r\nL 168.971951 419.145453 \r\nL 169.004375 421.213036 \r\nL 169.036799 419.317783 \r\nL 169.053011 418.019733 \r\nL 169.117858 418.951961 \r\nL 169.13407 421.762073 \r\nL 169.231341 420.458039 \r\nL 169.279977 418.207674 \r\nL 169.344825 419.033455 \r\nL 169.425884 423.175431 \r\nL 169.442096 418.59579 \r\nL 169.47452 420.377442 \r\nL 169.490732 416.949893 \r\nL 169.588003 419.058866 \r\nL 169.620427 421.109942 \r\nL 169.701486 419.252865 \r\nL 169.717698 419.351986 \r\nL 169.782546 416.383997 \r\nL 169.766334 421.422244 \r\nL 169.831181 417.713861 \r\nL 169.928453 421.653165 \r\nL 169.944664 420.302476 \r\nL 170.041936 417.272969 \r\nL 170.058148 420.037598 \r\nL 170.074359 420.866168 \r\nL 170.090571 418.813034 \r\nL 170.155419 419.551077 \r\nL 170.171631 419.495438 \r\nL 170.187843 420.528019 \r\nL 170.236478 417.890373 \r\nL 170.268902 419.097638 \r\nL 170.285114 417.363777 \r\nL 170.349962 421.20312 \r\nL 170.366173 419.581967 \r\nL 170.398597 422.615794 \r\nL 170.447233 418.496391 \r\nL 170.463445 417.645053 \r\nL 170.495869 419.697752 \r\nL 170.544504 418.128004 \r\nL 170.609352 422.021654 \r\nL 170.576928 417.910581 \r\nL 170.657987 419.646503 \r\nL 170.674199 419.035267 \r\nL 170.690411 422.629411 \r\nL 170.755259 416.98007 \r\nL 170.771471 417.469964 \r\nL 170.901166 420.572594 \r\nL 170.949801 420.212678 \r\nL 171.079496 417.690178 \r\nL 171.144344 423.567288 \r\nL 171.209192 419.280121 \r\nL 171.274039 417.841055 \r\nL 171.241615 420.282668 \r\nL 171.290251 417.973446 \r\nL 171.322675 421.773332 \r\nL 171.403734 418.905546 \r\nL 171.419946 418.338976 \r\nL 171.436158 420.352982 \r\nL 171.484794 418.863627 \r\nL 171.517217 418.569072 \r\nL 171.582065 421.774858 \r\nL 171.695548 417.76728 \r\nL 171.744184 416.369552 \r\nL 171.825243 420.596755 \r\nL 171.873879 420.145304 \r\nL 171.890091 422.130834 \r\nL 171.987362 417.376839 \r\nL 172.003574 421.334045 \r\nL 172.035998 416.985866 \r\nL 172.084633 422.197034 \r\nL 172.117057 420.298189 \r\nL 172.133269 420.442649 \r\nL 172.198117 417.230359 \r\nL 172.246752 418.820288 \r\nL 172.3116 421.434165 \r\nL 172.360236 420.453654 \r\nL 172.392659 420.677969 \r\nL 172.489931 416.674854 \r\nL 172.587202 421.297565 \r\nL 172.619626 419.670324 \r\nL 172.668261 417.647665 \r\nL 172.733109 418.802557 \r\nL 172.797956 421.724248 \r\nL 172.862804 419.422352 \r\nL 172.943863 416.719686 \r\nL 172.895228 419.637878 \r\nL 172.976287 418.51411 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420.028829 \r\nL 174.921714 418.540511 \r\nL 175.002773 422.059859 \r\nL 174.954137 417.645639 \r\nL 175.035197 421.224073 \r\nL 175.132468 417.229928 \r\nL 175.164892 418.653787 \r\nL 175.181104 418.782265 \r\nL 175.197316 422.148812 \r\nL 175.294587 419.603435 \r\nL 175.40807 417.295412 \r\nL 175.537765 422.647884 \r\nL 175.602613 417.416798 \r\nL 175.66746 417.423038 \r\nL 175.699884 422.095151 \r\nL 175.797155 419.926738 \r\nL 175.813367 420.174795 \r\nL 175.862003 420.391507 \r\nL 175.926851 418.44943 \r\nL 175.943062 418.435992 \r\nL 176.040334 420.64424 \r\nL 176.056546 419.960595 \r\nL 176.105181 417.18284 \r\nL 176.153817 420.58057 \r\nL 176.2673 417.624659 \r\nL 176.283512 418.72334 \r\nL 176.315936 421.081194 \r\nL 176.364572 418.252285 \r\nL 176.396995 420.114239 \r\nL 176.429419 420.495631 \r\nL 176.494267 418.40044 \r\nL 176.510478 420.936308 \r\nL 176.52669 418.023778 \r\nL 176.60775 419.038202 \r\nL 176.672597 420.991278 \r\nL 176.656385 418.705807 \r\nL 176.705021 419.600479 \r\nL 176.721233 417.811248 \r\nL 176.802292 420.917751 \r\nL 176.86714 416.980747 \r\nL 176.915776 418.796845 \r\nL 176.931988 420.183513 \r\nL 176.964411 418.491007 \r\nL 177.029259 419.230518 \r\nL 177.045471 418.790299 \r\nL 177.077895 420.831045 \r\nL 177.094106 418.922762 \r\nL 177.175166 418.042741 \r\nL 177.191378 420.957141 \r\nL 177.256225 418.436642 \r\nL 177.304861 419.502128 \r\nL 177.353497 421.390887 \r\nL 177.369708 419.037784 \r\nL 177.38592 420.342929 \r\nL 177.402132 417.532962 \r\nL 177.434556 421.230365 \r\nL 177.499404 418.775113 \r\nL 177.564251 421.025874 \r\nL 177.629099 420.897119 \r\nL 177.758794 418.531712 \r\nL 177.856065 420.629134 \r\nL 177.791218 418.515237 \r\nL 177.872277 418.851066 \r\nL 177.937125 417.939336 \r\nL 177.98576 421.34892 \r\nL 178.034396 418.423667 \r\nL 178.06682 421.454144 \r\nL 178.099243 418.809671 \r\nL 178.131667 417.945653 \r\nL 178.196515 419.66965 \r\nL 178.228938 417.774744 \r\nL 178.24515 420.935692 \r\nL 178.309998 419.300594 \r\nL 178.32621 421.253129 \r\nL 178.391057 418.190698 \r\nL 178.423481 419.899305 \r\nL 178.455905 418.445889 \r\nL 178.504541 420.249203 \r\nL 178.536964 420.934835 \r\nL 178.553176 419.200275 \r\nL 178.601812 420.248477 \r\nL 178.634236 418.428446 \r\nL 178.715295 420.487186 \r\nL 178.682871 417.562205 \r\nL 178.747719 419.746362 \r\nL 178.763931 419.632341 \r\nL 178.796355 420.926864 \r\nL 178.812566 419.117196 \r\nL 178.84499 419.667132 \r\nL 178.909838 417.213008 \r\nL 178.942262 420.277099 \r\nL 178.958473 418.619855 \r\nL 179.055745 420.68872 \r\nL 179.071957 419.076582 \r\nL 179.10438 418.54278 \r\nL 179.120592 419.061688 \r\nL 179.136804 418.241312 \r\nL 179.18544 420.279781 \r\nL 179.201652 419.191075 \r\nL 179.282711 420.866836 \r\nL 179.315135 420.753029 \r\nL 179.379982 418.037993 \r\nL 179.428618 418.238922 \r\nL 179.493466 421.208224 \r\nL 179.558313 420.090387 \r\nL 179.623161 418.042919 \r\nL 179.671796 419.042406 \r\nL 179.688008 420.764381 \r\nL 179.70422 418.317626 \r\nL 179.78528 419.381897 \r\nL 179.801491 420.226642 \r\nL 179.850127 418.282154 \r\nL 179.866339 418.648757 \r\nL 179.882551 417.718436 \r\nL 179.947398 420.181387 \r\nL 179.979822 420.728099 \r\nL 180.012246 419.219184 \r\nL 180.028458 421.081422 \r\nL 180.109517 418.023594 \r\nL 180.190577 420.314624 \r\nL 180.158153 417.755872 \r\nL 180.206789 419.494613 \r\nL 180.223001 417.851563 \r\nL 180.271636 421.369593 \r\nL 180.30406 418.487405 \r\nL 180.433755 421.03804 \r\nL 180.547238 417.731542 \r\nL 180.579662 418.430187 \r\nL 180.595874 418.124019 \r\nL 180.709357 420.825398 \r\nL 180.725569 420.662284 \r\nL 180.741781 420.717492 \r\nL 180.839052 418.100936 \r\nL 180.855264 418.956884 \r\nL 180.887688 418.944898 \r\nL 180.936324 419.714459 \r\nL 180.952535 421.002754 \r\nL 180.984959 418.893146 \r\nL 181.033595 420.095349 \r\nL 181.082231 417.897264 \r\nL 181.114654 419.22765 \r\nL 181.211926 421.114032 \r\nL 181.147078 419.01207 \r\nL 181.228138 419.661618 \r\nL 181.260561 419.963253 \r\nL 181.341621 417.611606 \r\nL 181.357833 420.310356 \r\nL 181.374045 420.36278 \r\nL 181.390256 420.250639 \r\nL 181.406468 419.138744 \r\nL 181.487528 420.680009 \r\nL 181.519951 418.058535 \r\nL 181.617223 419.66898 \r\nL 181.665858 421.715624 \r\nL 181.714494 419.753775 \r\nL 181.730706 419.596854 \r\nL 181.746918 419.992812 \r\nL 181.779342 416.899208 \r\nL 181.860401 419.218551 \r\nL 181.925249 422.142533 \r\nL 181.990096 420.405944 \r\nL 182.054944 417.612862 \r\nL 182.103579 419.615558 \r\nL 182.119791 419.648618 \r\nL 182.136003 418.859506 \r\nL 182.200851 420.434056 \r\nL 182.217063 420.007814 \r\nL 182.249486 419.033595 \r\nL 182.265698 420.469791 \r\nL 182.28191 417.417133 \r\nL 182.36297 420.034799 \r\nL 182.427817 422.292136 \r\nL 182.411605 418.418534 \r\nL 182.444029 419.637286 \r\nL 182.492665 417.858808 \r\nL 182.573724 417.989811 \r\nL 182.654784 421.497101 \r\nL 182.735843 420.1388 \r\nL 182.752055 417.257084 \r\nL 182.768267 420.230096 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418.639625 \r\nL 184.616422 420.265874 \r\nL 184.567786 418.04783 \r\nL 184.648846 420.067835 \r\nL 184.697481 419.584995 \r\nL 184.746117 417.860026 \r\nL 184.729905 420.323907 \r\nL 184.778541 419.261109 \r\nL 184.843388 420.525288 \r\nL 184.892024 420.181549 \r\nL 185.005507 417.85374 \r\nL 185.021719 419.475991 \r\nL 185.11899 420.980153 \r\nL 185.054143 418.875385 \r\nL 185.151414 420.338991 \r\nL 185.248685 418.234312 \r\nL 185.232474 420.466191 \r\nL 185.281109 418.346706 \r\nL 185.329745 421.122323 \r\nL 185.410804 419.725264 \r\nL 185.427016 419.91862 \r\nL 185.443228 418.346924 \r\nL 185.475652 420.782463 \r\nL 185.524287 418.387482 \r\nL 185.653983 420.636339 \r\nL 185.751254 418.299035 \r\nL 185.864737 420.929198 \r\nL 185.97822 418.444616 \r\nL 185.994432 419.48377 \r\nL 186.043068 418.884783 \r\nL 186.075492 419.367244 \r\nL 186.107915 420.555658 \r\nL 186.124127 418.991444 \r\nL 186.156551 419.705134 \r\nL 186.270034 418.262512 \r\nL 186.221399 420.929185 \r\nL 186.286246 418.385511 \r\nL 186.31867 420.917097 \r\nL 186.383517 417.5732 \r\nL 186.399729 420.149251 \r\nL 186.415941 420.191319 \r\nL 186.513213 418.015018 \r\nL 186.448365 420.263116 \r\nL 186.529424 419.56155 \r\nL 186.594272 418.907402 \r\nL 186.610484 419.846205 \r\nL 186.642908 420.761373 \r\nL 186.723967 417.616556 \r\nL 186.756391 420.757166 \r\nL 186.83745 419.620721 \r\nL 186.886086 419.939075 \r\nL 186.902298 418.995402 \r\nL 186.91851 420.042366 \r\nL 186.999569 418.25606 \r\nL 187.015781 419.192508 \r\nL 187.031993 419.29989 \r\nL 187.048205 418.309976 \r\nL 187.080629 420.63677 \r\nL 187.096841 422.149078 \r\nL 187.129264 418.504192 \r\nL 187.161688 420.377334 \r\nL 187.1779 418.207832 \r\nL 187.194112 421.190882 \r\nL 187.275171 419.941328 \r\nL 187.307595 418.559471 \r\nL 187.372443 419.791087 \r\nL 187.404866 419.418818 \r\nL 187.421078 420.961751 \r\nL 187.469714 418.455652 \r\nL 187.534561 420.462987 \r\nL 187.648045 418.42 \r\nL 187.664257 418.693565 \r\nL 187.69668 421.475427 \r\nL 187.77774 419.387333 \r\nL 187.793952 419.380978 \r\nL 187.842587 417.83734 \r\nL 187.875011 420.116099 \r\nL 187.891223 419.710729 \r\nL 187.907435 419.685032 \r\nL 187.939859 418.571633 \r\nL 187.988494 419.950766 \r\nL 188.004706 420.466989 \r\nL 188.03713 419.163025 \r\nL 188.053342 420.168473 \r\nL 188.085766 418.680985 \r\nL 188.134401 420.405504 \r\nL 188.166825 419.243268 \r\nL 188.215461 420.581695 \r\nL 188.280308 418.36686 \r\nL 188.345156 420.435758 \r\nL 188.393791 418.663955 \r\nL 188.458639 417.910162 \r\nL 188.507275 420.745126 \r\nL 188.63697 418.4514 \r\nL 188.669394 420.851762 \r\nL 188.734241 417.625094 \r\nL 188.750453 419.425743 \r\nL 188.766665 419.584083 \r\nL 188.782877 421.374843 \r\nL 188.799089 419.231274 \r\nL 188.863936 420.518658 \r\nL 188.912572 417.842911 \r\nL 188.993631 418.605327 \r\nL 189.042267 421.056679 \r\nL 189.058479 418.050768 \r\nL 189.123326 420.750736 \r\nL 189.139538 420.553778 \r\nL 189.220598 418.070954 \r\nL 189.253021 420.039655 \r\nL 189.269233 420.132968 \r\nL 189.285445 418.548138 \r\nL 189.317869 422.200264 \r\nL 189.382717 419.799917 \r\nL 189.398928 419.690851 \r\nL 189.41514 417.755183 \r\nL 189.479988 421.059975 \r\nL 189.512412 419.397126 \r\nL 189.528624 417.883067 \r\nL 189.593471 421.379375 \r\nL 189.609683 419.902897 \r\nL 189.625895 420.441348 \r\nL 189.658319 419.353884 \r\nL 189.67453 418.269288 \r\nL 189.75559 418.761924 \r\nL 189.788014 420.664396 \r\nL 189.869073 418.909438 \r\nL 189.933921 419.815624 \r\nL 189.950133 419.395177 \r\nL 189.966344 418.041228 \r\nL 190.031192 420.461814 \r\nL 190.047404 418.640435 \r\nL 190.079828 420.620301 \r\nL 190.160887 419.053706 \r\nL 190.193311 418.149159 \r\nL 190.241947 419.50722 \r\nL 190.27437 419.800404 \r\nL 190.290582 418.23736 \r\nL 190.339218 420.373642 \r\nL 190.387853 419.262155 \r\nL 190.404065 419.208467 \r\nL 190.485125 418.071993 \r\nL 190.501337 418.755262 \r\nL 190.566184 420.776304 \r\nL 190.61482 419.228757 \r\nL 190.647244 418.69179 \r\nL 190.663456 419.567817 \r\nL 190.679667 418.759955 \r\nL 190.776939 420.81623 \r\nL 190.744515 418.51258 \r\nL 190.793151 420.103858 \r\nL 190.809363 419.372411 \r\nL 190.825574 420.305075 \r\nL 190.890422 419.806798 \r\nL 190.906634 420.308861 \r\nL 190.939058 418.755514 \r\nL 190.987693 419.726981 \r\nL 191.036329 419.964681 \r\nL 191.052541 419.166618 \r\nL 191.084965 421.087371 \r\nL 191.101177 418.291976 \r\nL 191.149812 419.508164 \r\nL 191.198448 420.272915 \r\nL 191.230872 417.932452 \r\nL 191.328143 421.089038 \r\nL 191.344355 420.201804 \r\nL 191.39299 418.150751 \r\nL 191.425414 420.269769 \r\nL 191.457838 419.36947 \r\nL 191.522686 420.878086 \r\nL 191.490262 419.368157 \r\nL 191.538897 419.981767 \r\nL 191.555109 418.294141 \r\nL 191.587533 420.438161 \r\nL 191.652381 418.96818 \r\nL 191.717228 420.545132 \r\nL 191.701016 418.149107 \r\nL 191.782076 419.89238 \r\nL 191.798288 420.007146 \r\nL 191.8145 419.759259 \r\nL 191.895559 416.789898 \r\nL 191.863135 419.923656 \r\nL 191.927983 419.366351 \r\nL 191.944195 419.087658 \r\nL 191.960407 419.586357 \r\nL 191.99283 419.219401 \r\nL 192.025254 420.466796 \r\nL 192.07389 418.303306 \r\nL 192.106313 419.517833 \r\nL 192.122525 419.531641 \r\nL 192.171161 418.028428 \r\nL 192.219797 419.654263 \r\nL 192.236009 419.031943 \r\nL 192.25222 419.009778 \r\nL 192.268432 420.979906 \r\nL 192.300856 418.758723 \r\nL 192.365704 419.625044 \r\nL 192.430551 418.457665 \r\nL 192.462975 419.113919 \r\nL 192.495399 420.494323 \r\nL 192.511611 418.665751 \r\nL 192.576458 419.874506 \r\nL 192.689941 418.347335 \r\nL 192.722365 421.246247 \r\nL 192.803425 421.001752 \r\nL 192.835848 418.610732 \r\nL 192.93312 419.232095 \r\nL 192.949332 419.092928 \r\nL 193.014179 420.601337 \r\nL 192.997967 418.791223 \r\nL 193.062815 419.961063 \r\nL 193.127662 419.012578 \r\nL 193.11145 420.158801 \r\nL 193.143874 419.046688 \r\nL 193.160086 420.926925 \r\nL 193.208722 418.725835 \r\nL 193.257357 420.210124 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418.779468 \r\nL 195.040665 420.096957 \r\nL 195.121724 419.103409 \r\nL 195.137936 419.602131 \r\nL 195.202784 421.327043 \r\nL 195.218996 418.980834 \r\nL 195.235208 419.34206 \r\nL 195.267631 420.710488 \r\nL 195.300055 419.014388 \r\nL 195.316267 420.082123 \r\nL 195.397326 418.532068 \r\nL 195.364903 421.172636 \r\nL 195.42975 419.865596 \r\nL 195.478386 418.326674 \r\nL 195.527022 419.209138 \r\nL 195.624293 420.470155 \r\nL 195.559445 418.269903 \r\nL 195.640505 419.540358 \r\nL 195.753988 418.3643 \r\nL 195.7702 418.675005 \r\nL 195.802624 420.321475 \r\nL 195.867471 419.412447 \r\nL 195.883683 418.655882 \r\nL 195.948531 419.69197 \r\nL 195.964743 419.022338 \r\nL 195.980954 420.391529 \r\nL 196.013378 418.096233 \r\nL 196.078226 419.726407 \r\nL 196.126861 420.351079 \r\nL 196.207921 417.583861 \r\nL 196.305192 420.92329 \r\nL 196.353828 419.548242 \r\nL 196.37004 417.484889 \r\nL 196.402463 420.396889 \r\nL 196.467311 418.171925 \r\nL 196.532159 421.743713 \r\nL 196.580794 419.825381 \r\nL 196.613218 420.446148 \r\nL 196.678066 418.213967 \r\nL 196.694277 420.694094 \r\nL 196.791549 419.760229 \r\nL 196.872608 420.387103 \r\nL 196.905032 417.76064 \r\nL 197.034727 421.042068 \r\nL 197.099575 418.915278 \r\nL 197.083363 421.286691 \r\nL 197.14821 419.319075 \r\nL 197.164422 420.623024 \r\nL 197.22927 417.703119 \r\nL 197.245482 420.23969 \r\nL 197.277905 420.57366 \r\nL 197.358965 418.423501 \r\nL 197.375177 420.812431 \r\nL 197.440024 417.893052 \r\nL 197.472448 420.618534 \r\nL 197.521084 416.777954 \r\nL 197.504872 421.132428 \r\nL 197.585931 418.427279 \r\nL 197.602143 418.314365 \r\nL 197.618355 418.349982 \r\nL 197.715626 419.962912 \r\nL 197.731838 419.678705 \r\nL 197.796686 420.515459 \r\nL 197.829109 418.495247 \r\nL 197.845321 419.644438 \r\nL 197.877745 417.123121 \r\nL 197.926381 419.481124 \r\nL 197.942593 418.221822 \r\nL 198.00744 421.069698 \r\nL 198.023652 420.123163 \r\nL 198.072288 419.234584 \r\nL 198.0885 421.321186 \r\nL 198.153347 417.963767 \r\nL 198.201983 419.913835 \r\nL 198.234407 419.290236 \r\nL 198.299254 420.834773 \r\nL 198.283042 418.467526 \r\nL 198.34789 419.857449 \r\nL 198.380314 418.256022 \r\nL 198.428949 420.44316 \r\nL 198.445161 420.412841 \r\nL 198.461373 419.266885 \r\nL 198.542432 420.749072 \r\nL 198.558644 420.594013 \r\nL 198.574856 418.174199 \r\nL 198.623492 420.824541 \r\nL 198.672128 418.811655 \r\nL 198.769399 421.754657 \r\nL 198.785611 420.171909 \r\nL 198.818035 421.044624 \r\nL 198.915306 418.266525 \r\nL 198.980153 421.544763 \r\nL 199.045001 420.237559 \r\nL 199.109849 417.820056 \r\nL 199.174696 418.154181 \r\nL 199.239544 420.69479 \r\nL 199.20712 418.03273 \r\nL 199.320603 419.917792 \r\nL 199.336815 417.766232 \r\nL 199.417874 418.984107 \r\nL 199.482722 420.279825 \r\nL 199.515146 419.355128 \r\nL 199.531358 418.400213 \r\nL 199.579993 419.8322 \r\nL 199.612417 419.159549 \r\nL 199.628629 419.798346 \r\nL 199.644841 418.30651 \r\nL 199.709688 419.383566 \r\nL 199.7259 419.213816 \r\nL 199.742112 420.619442 \r\nL 199.758324 418.492901 \r\nL 199.839383 419.949407 \r\nL 199.904231 417.10981 \r\nL 199.936655 419.809366 \r\nL 199.969079 420.804581 \r\nL 199.98529 418.443039 \r\nL 200.017714 419.608837 \r\nL 200.082562 417.99386 \r\nL 200.050138 420.095059 \r\nL 200.114986 418.966388 \r\nL 200.179833 420.527467 \r\nL 200.244681 420.37036 \r\nL 200.341952 418.204454 \r\nL 200.358164 419.981769 \r\nL 200.390588 418.816547 \r\nL 200.471647 419.861287 \r\nL 200.487859 421.1694 \r\nL 200.520283 418.59164 \r\nL 200.568918 420.089347 \r\nL 200.633766 418.8596 \r\nL 200.649978 420.645692 \r\nL 200.698613 419.669281 \r\nL 200.714825 421.037295 \r\nL 200.763461 419.189927 \r\nL 200.795885 419.709422 \r\nL 200.828309 419.170706 \r\nL 200.84452 421.449262 \r\nL 200.941792 420.697112 \r\nL 200.958004 418.392195 \r\nL 200.974215 420.766305 \r\nL 201.055275 419.093844 \r\nL 201.071487 418.5379 \r\nL 201.103911 420.093583 \r\nL 201.136334 418.773148 \r\nL 201.18497 420.44418 \r\nL 201.233606 418.500913 \r\nL 201.249818 417.65477 \r\nL 201.282241 419.676427 \r\nL 201.314665 418.856631 \r\nL 201.411936 418.794319 \r\nL 201.44436 419.998087 \r\nL 201.557843 418.325197 \r\nL 201.574055 418.685755 \r\nL 201.655115 421.706142 \r\nL 201.70375 420.229149 \r\nL 201.817234 418.076104 \r\nL 201.930717 420.690083 \r\nL 201.898293 417.817666 \r\nL 201.946929 420.089306 \r\nL 201.979352 421.039435 \r\nL 201.995564 417.538807 \r\nL 202.092836 420.363572 \r\nL 202.173895 419.316498 \r\nL 202.222531 419.382703 \r\nL 202.271166 422.326258 \r\nL 202.287378 419.159658 \r\nL 202.319802 420.984958 \r\nL 202.352226 418.924541 \r\nL 202.400862 422.530965 \r\nL 202.433285 420.536046 \r\nL 202.530557 418.76233 \r\nL 202.546769 419.831365 \r\nL 202.611616 420.917392 \r\nL 202.64404 419.460018 \r\nL 202.660252 420.038124 \r\nL 202.773735 417.12878 \r\nL 202.789947 418.729432 \r\nL 202.806159 418.712883 \r\nL 202.822371 417.319103 \r\nL 202.887218 420.509397 \r\nL 202.90343 419.063005 \r\nL 202.952066 419.894897 \r\nL 202.968278 418.409415 \r\nL 202.984489 418.238329 \r\nL 203.033125 420.095066 \r\nL 203.097973 418.72064 \r\nL 203.146608 420.478815 \r\nL 203.24388 420.111195 \r\nL 203.260092 418.574494 \r\nL 203.324939 420.508277 \r\nL 203.357363 419.034838 \r\nL 203.405999 419.52717 \r\nL 203.454634 417.832165 \r\nL 203.551905 419.949713 \r\nL 203.568117 418.754895 \r\nL 203.665389 419.931891 \r\nL 203.600541 418.631107 \r\nL 203.681601 419.150756 \r\nL 203.714024 417.954484 \r\nL 203.730236 419.350741 \r\nL 203.76266 418.573568 \r\nL 203.827508 420.444786 \r\nL 203.859931 419.70841 \r\nL 203.876143 417.82654 \r\nL 203.924779 420.976166 \r\nL 203.973415 419.063442 \r\nL 204.054474 418.135515 \r\nL 204.005838 419.950595 \r\nL 204.070686 418.897084 \r\nL 204.119322 420.933397 \r\nL 204.10311 418.395352 \r\nL 204.184169 419.474467 \r\nL 204.232805 418.360842 \r\nL 204.265228 420.368334 \r\nL 204.297652 420.210373 \r\nL 204.313864 420.979985 \r\nL 204.330076 418.4663 \r\nL 204.427347 420.40706 \r\nL 204.508407 419.660803 \r\nL 204.459771 421.034782 \r\nL 204.540831 419.816901 \r\nL 204.605678 422.365896 \r\nL 204.589466 418.37494 \r\nL 204.638102 420.445356 \r\nL 204.702949 418.976224 \r\nL 204.686738 421.189015 \r\nL 204.751585 419.986985 \r\nL 204.767797 421.052752 \r\nL 204.832645 419.34232 \r\nL 204.848856 419.707096 \r\nL 204.865068 418.530599 \r\nL 204.929916 420.015585 \r\nL 204.946128 419.384732 \r\nL 204.994763 418.884331 \r\nL 205.092035 421.074927 \r\nL 205.205518 418.670385 \r\nL 205.22173 420.832709 \r\nL 205.237942 418.108653 \r\nL 205.319001 419.61272 \r\nL 205.335213 417.267045 \r\nL 205.383849 420.209716 \r\nL 205.416272 419.737681 \r\nL 205.432484 420.708183 \r\nL 205.48112 418.495066 \r\nL 205.545968 417.679163 \r\nL 205.513544 418.729223 \r\nL 205.594603 418.079593 \r\nL 205.610815 418.87319 \r\nL 205.675663 417.314232 \r\nL 205.708086 418.834109 \r\nL 205.756722 417.96251 \r\nL 205.772934 418.583306 \r\nL 205.886417 421.060861 \r\nL 205.902629 420.738941 \r\nL 205.9999 417.911059 \r\nL 206.048536 418.64718 \r\nL 206.162019 421.369136 \r\nL 206.275502 418.067598 \r\nL 206.307926 417.826459 \r\nL 206.405198 421.083149 \r\nL 206.470045 417.613251 \r\nL 206.534893 417.648869 \r\nL 206.583528 421.616734 \r\nL 206.664588 419.869005 \r\nL 206.713223 417.746474 \r\nL 206.794283 419.048305 \r\nL 206.85913 421.641638 \r\nL 206.826707 418.918195 \r\nL 206.923978 420.884245 \r\nL 207.005037 418.089171 \r\nL 207.053673 418.620933 \r\nL 207.102309 420.768463 \r\nL 207.150944 419.87695 \r\nL 207.232004 417.401049 \r\nL 207.280639 418.350272 \r\nL 207.377911 420.865683 \r\nL 207.394123 420.273613 \r\nL 207.491394 417.482299 \r\nL 207.523818 418.069873 \r\nL 207.588665 420.419827 \r\nL 207.637301 419.363261 \r\nL 207.685937 417.082704 \r\nL 207.750784 419.160634 \r\nL 207.831844 422.565653 \r\nL 207.864267 420.121964 \r\nL 207.961539 417.200492 \r\nL 207.993962 417.961834 \r\nL 208.05881 422.89877 \r\nL 208.107446 420.275143 \r\nL 208.123658 420.119063 \r\nL 208.139869 422.579429 \r\nL 208.204717 416.219493 \r\nL 208.220929 418.272818 \r\nL 208.237141 418.74474 \r\nL 208.253353 418.354515 \r\nL 208.350624 423.511737 \r\nL 208.366836 422.840279 \r\nL 208.415471 414.946589 \r\nL 208.496531 417.770938 \r\nL 208.593802 424.11794 \r\nL 208.610014 423.814205 \r\nL 208.642438 415.628897 \r\nL 208.739709 415.815782 \r\nL 208.772133 425.161212 \r\nL 208.836981 415.474231 \r\nL 208.853192 416.706972 \r\nL 208.885616 415.68922 \r\nL 208.901828 418.553674 \r\nL 208.934252 419.200904 \r\nL 208.999099 417.37013 \r\nL 209.015311 420.653512 \r\nL 209.031523 423.363199 \r\nL 209.096371 419.260176 \r\nL 209.128795 423.183752 \r\nL 209.161218 418.300775 \r\nL 209.17743 367.808219 \r\nL 209.242278 423.685422 \r\nL 209.25849 417.702961 \r\nL 209.274701 422.468893 \r\nL 209.371973 418.536689 \r\nL 209.388185 420.098512 \r\nL 209.404397 417.497931 \r\nL 209.453032 418.389349 \r\nL 209.485456 415.838806 \r\nL 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211.252552 420.466556 \r\nL 211.268764 420.482161 \r\nL 211.382247 417.644383 \r\nL 211.414671 418.275527 \r\nL 211.49573 421.691685 \r\nL 211.528154 419.322338 \r\nL 211.576789 419.91874 \r\nL 211.593001 418.715594 \r\nL 211.641637 418.020767 \r\nL 211.657849 419.343064 \r\nL 211.674061 419.522328 \r\nL 211.771332 421.521543 \r\nL 211.787544 420.305925 \r\nL 211.884815 417.591999 \r\nL 211.901027 418.10656 \r\nL 211.949663 420.030985 \r\nL 212.01451 419.463657 \r\nL 212.030722 419.37452 \r\nL 212.079358 418.305672 \r\nL 212.127994 419.926026 \r\nL 212.144205 419.357993 \r\nL 212.192841 421.462283 \r\nL 212.209053 421.730737 \r\nL 212.241477 420.560307 \r\nL 212.322536 418.680355 \r\nL 212.371172 418.95717 \r\nL 212.468443 421.408519 \r\nL 212.484655 419.703612 \r\nL 212.500867 419.635818 \r\nL 212.517079 419.950417 \r\nL 212.533291 419.95007 \r\nL 212.549503 419.998319 \r\nL 212.598138 417.854263 \r\nL 212.711621 418.213089 \r\nL 212.744045 418.41094 \r\nL 212.760257 417.996002 \r\nL 212.776469 417.337913 \r\nL 212.841317 418.274965 \r\nL 212.938588 420.16251 \r\nL 212.987224 420.160343 \r\nL 213.052071 417.711896 \r\nL 213.116919 418.697854 \r\nL 213.133131 420.764028 \r\nL 213.230402 419.672322 \r\nL 213.311461 420.322048 \r\nL 213.279037 419.56423 \r\nL 213.327673 419.925324 \r\nL 213.360097 418.970545 \r\nL 213.392521 420.377085 \r\nL 213.408733 419.232692 \r\nL 213.424944 420.251985 \r\nL 213.489792 418.224717 \r\nL 213.506004 419.544666 \r\nL 213.522216 418.280789 \r\nL 213.587063 420.301806 \r\nL 213.603275 420.275706 \r\nL 213.635699 420.11043 \r\nL 213.651911 420.274261 \r\nL 213.668123 420.834786 \r\nL 213.684335 419.993596 \r\nL 213.716758 420.282028 \r\nL 213.73297 418.940307 \r\nL 213.797818 420.287872 \r\nL 213.830242 419.534296 \r\nL 213.862665 421.469104 \r\nL 213.943725 419.905256 \r\nL 213.976149 419.680307 \r\nL 213.992361 419.527895 \r\nL 214.008572 420.460876 \r\nL 214.024784 418.401991 \r\nL 214.105844 420.348554 \r\nL 214.235539 419.078254 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419.826957 \r\nL 226.459301 418.361883 \r\nL 226.491725 419.428802 \r\nL 226.507937 419.475764 \r\nL 226.524149 418.902422 \r\nL 226.572785 420.119128 \r\nL 226.605208 419.655223 \r\nL 226.637632 419.909124 \r\nL 226.653844 419.436665 \r\nL 226.670056 418.866676 \r\nL 226.734903 420.100572 \r\nL 226.751115 419.724871 \r\nL 226.832175 420.400996 \r\nL 226.799751 418.224602 \r\nL 226.864599 420.189661 \r\nL 226.88081 420.137587 \r\nL 226.929446 418.823681 \r\nL 226.994294 419.708552 \r\nL 227.026717 419.554591 \r\nL 227.042929 419.580768 \r\nL 227.091565 420.11004 \r\nL 227.107777 420.033136 \r\nL 227.123989 418.854874 \r\nL 227.205048 419.873539 \r\nL 227.286108 420.450561 \r\nL 227.237472 418.906037 \r\nL 227.302319 420.2551 \r\nL 227.399591 420.277571 \r\nL 227.432015 418.777395 \r\nL 227.529286 420.744463 \r\nL 227.545498 419.483987 \r\nL 227.610345 419.785359 \r\nL 227.626557 418.961508 \r\nL 227.642769 420.394996 \r\nL 227.707617 418.255 \r\nL 227.74004 419.673498 \r\nL 227.788676 420.220528 \r\nL 227.804888 419.298034 \r\nL 227.8211 419.561373 \r\nL 227.869736 418.252761 \r\nL 227.902159 419.845187 \r\nL 227.934583 419.258616 \r\nL 227.950795 419.684476 \r\nL 228.048066 419.388261 \r\nL 228.145338 418.614107 \r\nL 228.08049 419.914603 \r\nL 228.161549 418.81708 \r\nL 228.275033 420.667994 \r\nL 228.193973 418.716659 \r\nL 228.291245 419.957581 \r\nL 228.356092 418.144621 \r\nL 228.33988 420.119932 \r\nL 228.404728 419.650775 \r\nL 228.485787 418.340481 \r\nL 228.501999 419.162885 \r\nL 228.518211 420.312056 \r\nL 228.583059 418.984347 \r\nL 228.59927 419.530873 \r\nL 228.615482 418.161048 \r\nL 228.696542 419.507082 \r\nL 228.761389 420.853954 \r\nL 228.810025 419.696316 \r\nL 228.826237 418.153509 \r\nL 228.891084 420.180937 \r\nL 228.923508 418.215449 \r\nL 229.036991 421.020992 \r\nL 229.069415 420.216572 \r\nL 229.166686 418.341686 \r\nL 229.101839 420.418176 \r\nL 229.182898 419.513199 \r\nL 229.19911 418.965779 \r\nL 229.215322 420.560565 \r\nL 229.263958 418.990602 \r\nL 229.328805 420.518447 \r\nL 229.361229 418.187348 \r\nL 229.377441 419.652532 \r\nL 229.4585 419.095284 \r\nL 229.409865 420.289383 \r\nL 229.474712 419.853831 \r\nL 229.571984 420.404606 \r\nL 229.555772 419.180342 \r\nL 229.588195 420.016999 \r\nL 229.620619 417.496473 \r\nL 229.685467 420.1879 \r\nL 229.701679 419.184409 \r\nL 229.766526 421.087237 \r\nL 229.782738 418.956336 \r\nL 229.815162 418.429377 \r\nL 229.831374 419.471357 \r\nL 229.847586 419.381506 \r\nL 229.863798 419.743419 \r\nL 229.912433 418.575674 \r\nL 229.961069 419.468954 \r\nL 229.977281 419.486607 \r\nL 230.025916 420.207004 \r\nL 230.05834 419.274936 \r\nL 230.074552 418.11824 \r\nL 230.106976 419.461123 \r\nL 230.171823 419.019691 \r\nL 230.188035 420.678548 \r\nL 230.236671 418.991194 \r\nL 230.285307 419.903601 \r\nL 230.366366 419.912329 \r\nL 230.39879 418.476209 \r\nL 230.479849 420.304222 \r\nL 230.431214 418.311341 \r\nL 230.512273 419.036555 \r\nL 230.528485 419.458634 \r\nL 230.560909 418.39597 \r\nL 230.593332 419.088406 \r\nL 230.609544 418.531964 \r\nL 230.641968 420.668118 \r\nL 230.65818 420.757586 \r\nL 230.690604 418.351699 \r\nL 230.804087 419.214721 \r\nL 230.852723 419.380266 \r\nL 230.868935 418.660326 \r\nL 230.949994 421.113901 \r\nL 230.982418 420.946726 \r\nL 231.063477 418.514935 \r\nL 231.112113 419.062002 \r\nL 231.128325 418.53153 \r\nL 231.160748 420.599493 \r\nL 231.193172 419.647252 \r\nL 231.25802 421.396408 \r\nL 231.225596 419.050215 \r\nL 231.274232 420.335037 \r\nL 231.306655 418.015018 \r\nL 231.387715 419.813054 \r\nL 231.420139 420.268238 \r\nL 231.501198 419.022188 \r\nL 231.582258 420.077207 \r\nL 231.614681 419.171762 \r\nL 231.630893 418.424516 \r\nL 231.695741 419.899629 \r\nL 231.711953 420.923704 \r\nL 231.744376 418.784174 \r\nL 231.793012 419.281199 \r\nL 231.825436 419.593913 \r\nL 231.85786 418.607049 \r\nL 231.874072 420.389895 \r\nL 231.890283 418.558651 \r\nL 231.955131 419.466702 \r\nL 231.987555 417.787443 \r\nL 232.019978 419.909698 \r\nL 232.068614 418.707159 \r\nL 232.084826 418.380907 \r\nL 232.11725 418.972598 \r\nL 232.133462 420.471851 \r\nL 232.214521 418.80095 \r\nL 232.230733 419.746152 \r\nL 232.246945 419.853087 \r\nL 232.263157 419.542615 \r\nL 232.37664 418.793161 \r\nL 232.457699 421.026695 \r\nL 232.490123 419.173952 \r\nL 232.522547 418.891381 \r\nL 232.538759 419.171066 \r\nL 232.571183 420.189259 \r\nL 232.603606 418.676165 \r\nL 232.652242 419.859973 \r\nL 232.71709 418.702386 \r\nL 232.700878 420.445422 \r\nL 232.765725 419.1303 \r\nL 232.781937 420.451738 \r\nL 232.830573 418.787419 \r\nL 232.879208 420.167188 \r\nL 232.927844 420.659291 \r\nL 232.97648 419.297305 \r\nL 233.041327 419.213379 \r\nL 233.089963 420.449978 \r\nL 233.106175 419.238164 \r\nL 233.122387 420.64244 \r\nL 233.203446 419.709785 \r\nL 233.219658 419.786659 \r\nL 233.284506 420.038091 \r\nL 233.316929 419.126637 \r\nL 233.333141 419.969244 \r\nL 233.349353 418.244972 \r\nL 233.430413 419.817589 \r\nL 233.462836 420.044737 \r\nL 233.49526 418.912634 \r\nL 233.57632 418.947425 \r\nL 233.624955 420.103939 \r\nL 233.657379 420.079017 \r\nL 233.673591 418.42552 \r\nL 233.738438 420.12202 \r\nL 233.770862 419.275042 \r\nL 233.787074 418.70397 \r\nL 233.83571 419.634432 \r\nL 233.868134 419.217783 \r\nL 233.916769 420.048142 \r\nL 233.932981 418.56781 \r\nL 233.965405 419.729305 \r\nL 234.030252 417.909723 \r\nL 234.014041 420.430696 \r\nL 234.062676 418.508938 \r\nL 234.159948 418.336101 \r\nL 234.176159 420.112142 \r\nL 234.192371 420.163112 \r\nL 234.273431 420.612281 \r\nL 234.289643 418.355666 \r\nL 234.305855 420.21143 \r\nL 234.322066 417.974618 \r\nL 234.403126 419.868191 \r\nL 234.419338 419.825024 \r\nL 234.43555 419.992058 \r\nL 234.516609 420.139272 \r\nL 234.549033 418.619014 \r\nL 234.630092 419.770236 \r\nL 234.662516 419.217085 \r\nL 234.678728 418.469676 \r\nL 234.69494 420.126365 \r\nL 234.759787 419.047002 \r\nL 234.857059 420.681206 \r\nL 234.840847 418.018236 \r\nL 234.873271 419.475286 \r\nL 234.889482 420.438992 \r\nL 234.970542 419.037933 \r\nL 234.986754 419.769204 \r\nL 235.002966 418.511801 \r\nL 235.067813 420.254491 \r\nL 235.100237 418.603683 \r\nL 235.229932 420.452934 \r\nL 235.359627 419.073765 \r\nL 235.424475 418.534282 \r\nL 235.489322 420.440721 \r\nL 235.619017 418.840432 \r\nL 235.651441 419.040232 \r\nL 235.683865 419.730758 \r\nL 235.732501 418.680763 \r\nL 235.764924 419.526379 \r\nL 235.797348 419.986905 \r\nL 235.81356 419.801159 \r\nL 235.894619 418.274133 \r\nL 235.910831 419.9843 \r\nL 235.927043 420.249887 \r\nL 235.959467 419.081818 \r\nL 235.991891 420.143646 \r\nL 236.07295 418.350307 \r\nL 236.089162 420.651218 \r\nL 236.105374 419.072125 \r\nL 236.15401 419.969334 \r\nL 236.202645 418.930609 \r\nL 236.218857 418.814069 \r\nL 236.251281 420.472248 \r\nL 236.299917 417.934729 \r\nL 236.33234 419.457706 \r\nL 236.348552 418.70377 \r\nL 236.380976 420.678868 \r\nL 236.4134 420.304278 \r\nL 236.429612 420.272016 \r\nL 236.445824 420.335394 \r\nL 236.462035 421.22344 \r\nL 236.494459 418.374896 \r\nL 236.526883 419.400484 \r\nL 236.543095 419.405703 \r\nL 236.575519 418.894149 \r\nL 236.640366 419.683803 \r\nL 236.656578 418.89712 \r\nL 236.721426 420.973698 \r\nL 236.770061 420.703097 \r\nL 236.834909 418.123842 \r\nL 236.883544 418.925565 \r\nL 236.997028 420.43842 \r\nL 237.045663 418.766458 \r\nL 237.126723 418.887089 \r\nL 237.240206 420.622736 \r\nL 237.27263 418.47321 \r\nL 237.353689 419.245224 \r\nL 237.418537 420.030731 \r\nL 237.434749 418.843977 \r\nL 237.450961 419.11753 \r\nL 237.467172 419.126408 \r\nL 237.483384 418.924109 \r\nL 237.515808 419.579023 \r\nL 237.53202 419.396576 \r\nL 237.564444 418.105194 \r\nL 237.645503 420.728321 \r\nL 237.726563 418.206435 \r\nL 237.775198 418.26915 \r\nL 237.87247 419.703234 \r\nL 237.888681 419.033364 \r\nL 237.904893 418.5812 \r\nL 237.921105 419.463629 \r\nL 237.937317 421.163522 \r\nL 238.002165 419.251956 \r\nL 238.018377 419.419858 \r\nL 238.067012 418.157567 \r\nL 238.083224 420.054847 \r\nL 238.13186 418.72031 \r\nL 238.180495 419.977381 \r\nL 238.229131 419.314419 \r\nL 238.245343 418.040639 \r\nL 238.277767 420.424282 \r\nL 238.342614 418.736241 \r\nL 238.375038 420.484549 \r\nL 238.456098 420.105139 \r\nL 238.504733 418.027231 \r\nL 238.569581 419.814311 \r\nL 238.602004 420.226847 \r\nL 238.634428 419.899456 \r\nL 238.7317 418.629376 \r\nL 238.683064 420.346455 \r\nL 238.747911 419.356059 \r\nL 238.877607 421.248891 \r\nL 238.828971 418.480334 \r\nL 238.893818 420.459 \r\nL 238.974878 418.249374 \r\nL 239.023514 418.548852 \r\nL 239.104573 420.500158 \r\nL 239.136997 420.485371 \r\nL 239.25048 417.970036 \r\nL 239.266692 418.107979 \r\nL 239.380175 420.434607 \r\nL 239.477446 418.288474 \r\nL 239.412599 421.049835 \r\nL 239.493658 418.843296 \r\nL 239.574718 420.307339 \r\nL 239.639565 420.077859 \r\nL 239.671989 418.209223 \r\nL 239.76926 418.299236 \r\nL 239.85032 420.663079 \r\nL 239.882744 419.504378 \r\nL 239.898955 418.567286 \r\nL 239.980015 419.250572 \r\nL 239.996227 419.396241 \r\nL 240.012439 418.363543 \r\nL 240.077286 420.471546 \r\nL 240.093498 419.969003 \r\nL 240.142134 418.86079 \r\nL 240.174557 420.795515 \r\nL 240.239405 417.893945 \r\nL 240.288041 420.135677 \r\nL 240.304253 421.031125 \r\nL 240.320464 419.361791 \r\nL 240.385312 420.674216 \r\nL 240.45016 417.417253 \r\nL 240.515007 418.867572 \r\nL 240.62849 420.622499 \r\nL 240.677126 418.371441 \r\nL 240.660914 420.640866 \r\nL 240.741974 419.396334 \r\nL 240.790609 420.523476 \r\nL 240.855457 418.357086 \r\nL 240.871669 420.426395 \r\nL 240.96894 419.450153 \r\nL 240.985152 419.198436 \r\nL 241.001364 420.668604 \r\nL 241.033787 419.776297 \r\nL 241.049999 420.279222 \r\nL 241.114847 419.122165 \r\nL 241.131059 419.395765 \r\nL 241.212118 417.900714 \r\nL 241.163483 419.67686 \r\nL 241.22833 419.446864 \r\nL 241.30939 420.78443 \r\nL 241.260754 418.780818 \r\nL 241.341813 420.057712 \r\nL 241.455297 417.998439 \r\nL 241.552568 420.199992 \r\nL 241.56878 420.198756 \r\nL 241.633627 418.432461 \r\nL 241.698475 418.448574 \r\nL 241.844382 420.504724 \r\nL 241.974077 418.346599 \r\nL 242.038924 421.096853 \r\nL 242.119984 420.322961 \r\nL 242.136196 418.218798 \r\nL 242.233467 419.110314 \r\nL 242.298315 420.800215 \r\nL 242.34695 419.365814 \r\nL 242.379374 419.881889 \r\nL 242.42801 418.770339 \r\nL 242.444222 419.093248 \r\nL 242.460434 418.716176 \r\nL 242.509069 419.862434 \r\nL 242.525281 419.220747 \r\nL 242.573917 420.823798 \r\nL 242.638764 420.387374 \r\nL 242.6874 418.500243 \r\nL 242.768459 418.845624 \r\nL 242.817095 420.680605 \r\nL 242.865731 419.609198 \r\nL 242.881943 418.319413 \r\nL 242.914366 419.934183 \r\nL 242.979214 419.366171 \r\nL 242.995426 418.983775 \r\nL 243.044061 420.128495 \r\nL 243.060273 419.624482 \r\nL 243.076485 420.368225 \r\nL 243.141333 418.564281 \r\nL 243.157545 419.569266 \r\nL 243.173757 419.687239 \r\nL 243.20618 418.199909 \r\nL 243.254816 420.615081 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419.021154 \r\nL 244.600403 420.565126 \r\nL 244.632826 419.783532 \r\nL 244.681462 420.597717 \r\nL 244.66525 418.234039 \r\nL 244.697674 418.71251 \r\nL 244.713886 418.214931 \r\nL 244.74631 420.229532 \r\nL 244.762521 420.707397 \r\nL 244.794945 419.063606 \r\nL 244.811157 419.556076 \r\nL 244.892217 418.421389 \r\nL 244.876005 420.066289 \r\nL 244.92464 418.900909 \r\nL 244.940852 420.796076 \r\nL 245.038123 420.527187 \r\nL 245.086759 417.573785 \r\nL 245.167819 418.752376 \r\nL 245.232666 420.9399 \r\nL 245.200242 418.747333 \r\nL 245.297514 420.151411 \r\nL 245.427209 417.879661 \r\nL 245.362361 420.190008 \r\nL 245.443421 419.196664 \r\nL 245.459633 419.15152 \r\nL 245.573116 421.224897 \r\nL 245.60554 417.72005 \r\nL 245.686599 418.590667 \r\nL 245.767658 420.357667 \r\nL 245.719023 418.552262 \r\nL 245.800082 419.269358 \r\nL 245.913565 420.209621 \r\nL 245.86493 418.484255 \r\nL 245.929777 420.049548 \r\nL 245.978413 418.0628 \r\nL 245.962201 420.195396 \r\nL 246.027049 418.683225 \r\nL 246.091896 420.066871 \r\nL 246.12432 418.143398 \r\nL 246.140532 419.768926 \r\nL 246.156744 418.721911 \r\nL 246.205379 420.431439 \r\nL 246.254015 419.484662 \r\nL 246.270227 419.843634 \r\nL 246.318863 419.016698 \r\nL 246.335074 418.221754 \r\nL 246.351286 419.394997 \r\nL 246.416134 419.307514 \r\nL 246.448558 418.924629 \r\nL 246.545829 420.606521 \r\nL 246.659312 418.441394 \r\nL 246.675524 418.987546 \r\nL 246.72416 420.121934 \r\nL 246.740372 418.415093 \r\nL 246.789007 419.425794 \r\nL 246.805219 419.465371 \r\nL 246.821431 419.430888 \r\nL 246.870067 418.475353 \r\nL 246.90249 419.999465 \r\nL 246.918702 420.235768 \r\nL 246.934914 419.163279 \r\nL 246.967338 419.521961 \r\nL 246.999762 418.252182 \r\nL 247.048397 420.168812 \r\nL 247.097033 418.97534 \r\nL 247.113245 418.983937 \r\nL 247.129457 418.863179 \r\nL 247.226728 420.342065 \r\nL 247.24294 419.696816 \r\nL 247.259152 419.616887 \r\nL 247.324 418.40571 \r\nL 247.356423 420.332587 \r\nL 247.372635 419.317667 \r\nL 247.437483 420.758676 \r\nL 247.421271 418.734438 \r\nL 247.469906 419.949737 \r\nL 247.550966 418.744251 \r\nL 247.599602 419.10219 \r\nL 247.632025 418.540035 \r\nL 247.648237 419.281458 \r\nL 247.696873 420.691467 \r\nL 247.680661 419.089645 \r\nL 247.745509 420.291009 \r\nL 247.810356 418.230299 \r\nL 247.84278 420.347549 \r\nL 247.891416 418.603949 \r\nL 247.988687 419.769767 \r\nL 248.037323 420.355233 \r\nL 248.069746 419.533359 \r\nL 248.10217 418.798437 \r\nL 248.118382 419.721142 \r\nL 248.167018 420.333425 \r\nL 248.18323 419.800204 \r\nL 248.264289 419.108469 \r\nL 248.280501 419.600576 \r\nL 248.377772 420.442624 \r\nL 248.312925 418.732033 \r\nL 248.393984 419.929136 \r\nL 248.410196 419.123028 \r\nL 248.475043 420.255003 \r\nL 248.491255 419.332237 \r\nL 248.507467 419.901472 \r\nL 248.556103 419.138252 \r\nL 248.604739 419.458874 \r\nL 248.62095 419.573917 \r\nL 248.637162 418.857633 \r\nL 248.685798 420.063241 \r\nL 248.718222 419.114366 \r\nL 248.734434 419.912022 \r\nL 248.831705 419.770476 \r\nL 248.928976 420.303152 \r\nL 248.945188 418.51613 \r\nL 249.058671 419.813937 \r\nL 249.107307 418.59747 \r\nL 249.172155 419.314416 \r\nL 249.188366 419.210565 \r\nL 249.204578 419.479734 \r\nL 249.253214 419.961117 \r\nL 249.269426 419.260758 \r\nL 249.30185 418.731113 \r\nL 249.318062 419.399236 \r\nL 249.334273 419.278245 \r\nL 249.350485 420.20603 \r\nL 249.399121 418.518934 \r\nL 249.447757 419.927841 \r\nL 249.496392 418.822053 \r\nL 249.577452 419.148502 \r\nL 249.593664 419.117959 \r\nL 249.674723 420.153998 \r\nL 249.626087 419.065407 \r\nL 249.707147 419.469684 \r\nL 249.723359 418.261425 \r\nL 249.771994 420.506723 \r\nL 249.82063 418.656528 \r\nL 249.836842 418.695847 \r\nL 249.966537 420.245694 \r\nL 250.015173 419.001497 \r\nL 250.08002 419.491228 \r\nL 250.096232 419.500302 \r\nL 250.209715 420.374485 \r\nL 250.177292 419.24538 \r\nL 250.225927 419.912695 \r\nL 250.290775 419.058905 \r\nL 250.355622 419.251612 \r\nL 250.452894 420.261459 \r\nL 250.469106 420.17301 \r\nL 250.533953 418.353134 \r\nL 250.598801 418.474432 \r\nL 250.696072 420.243393 \r\nL 250.744708 419.7938 \r\nL 250.793343 418.735228 \r\nL 250.825767 420.078537 \r\nL 250.858191 418.807085 \r\nL 251.004098 420.281205 \r\nL 251.052733 418.588657 \r\nL 251.133793 419.150639 \r\nL 251.19864 418.944835 \r\nL 251.231064 420.700843 \r\nL 251.312124 418.227873 \r\nL 251.344547 419.648953 \r\nL 251.376971 419.091016 \r\nL 251.425607 420.088374 \r\nL 251.441819 420.23604 \r\nL 251.458031 420.163561 \r\nL 251.490454 418.596476 \r\nL 251.571514 419.606098 \r\nL 251.668785 420.830522 \r\nL 251.603938 419.26288 \r\nL 251.701209 419.858193 \r\nL 251.830904 418.314617 \r\nL 251.766056 420.070969 \r\nL 251.847116 418.699068 \r\nL 251.895752 420.497089 \r\nL 251.960599 419.23717 \r\nL 252.009235 420.21677 \r\nL 252.041659 418.89575 \r\nL 252.05787 418.34421 \r\nL 252.106506 419.903291 \r\nL 252.13893 419.29805 \r\nL 252.203777 420.494479 \r\nL 252.171354 418.787951 \r\nL 252.236201 419.986572 \r\nL 252.252413 418.239245 \r\nL 252.333473 420.829771 \r\nL 252.349684 419.491844 \r\nL 252.365896 419.452853 \r\nL 252.39832 419.513698 \r\nL 252.430744 419.894694 \r\nL 252.446956 419.462979 \r\nL 252.479379 418.735347 \r\nL 252.544227 419.531844 \r\nL 252.560439 419.238325 \r\nL 252.576651 418.911826 \r\nL 252.592863 419.260029 \r\nL 252.609075 418.914009 \r\nL 252.65771 420.903552 \r\nL 252.690134 418.72919 \r\nL 252.722558 419.381741 \r\nL 252.754982 418.280237 \r\nL 252.819829 418.939569 \r\nL 252.836041 420.569059 \r\nL 252.933312 419.717661 \r\nL 252.965736 420.084161 \r\nL 252.981948 419.873693 \r\nL 253.030584 417.90541 \r\nL 253.079219 420.366728 \r\nL 253.095431 419.551604 \r\nL 253.176491 420.3307 \r\nL 253.192702 419.435147 \r\nL 253.241338 418.521818 \r\nL 253.273762 420.066582 \r\nL 253.289974 419.242436 \r\nL 253.322398 420.165347 \r\nL 253.338609 420.041923 \r\nL 253.354821 418.494035 \r\nL 253.419669 420.196338 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420.455446 \r\nL 256.402656 418.653557 \r\nL 256.418868 419.076774 \r\nL 256.516139 421.05281 \r\nL 256.451292 418.945462 \r\nL 256.532351 420.062147 \r\nL 256.662046 418.870079 \r\nL 256.678258 418.935957 \r\nL 256.807953 420.414411 \r\nL 256.856589 418.959689 \r\nL 256.921436 419.332996 \r\nL 256.986284 420.062439 \r\nL 257.018708 419.209513 \r\nL 257.083555 417.899939 \r\nL 257.051132 419.95742 \r\nL 257.115979 418.506516 \r\nL 257.132191 420.367638 \r\nL 257.229462 419.284057 \r\nL 257.310522 420.203767 \r\nL 257.29431 419.02012 \r\nL 257.326734 419.358405 \r\nL 257.407793 417.415614 \r\nL 257.391581 420.203953 \r\nL 257.424005 419.004444 \r\nL 257.456429 420.662358 \r\nL 257.537488 419.199635 \r\nL 257.569912 418.310229 \r\nL 257.634759 419.774506 \r\nL 257.650971 420.208067 \r\nL 257.667183 418.504425 \r\nL 257.715819 419.475612 \r\nL 257.796878 418.716183 \r\nL 257.877938 417.761655 \r\nL 257.926573 420.040072 \r\nL 258.007633 418.810793 \r\nL 257.958997 420.398521 \r\nL 258.023845 419.053879 \r\nL 258.07248 420.694391 \r\nL 258.104904 418.239215 \r\nL 258.137328 420.086052 \r\nL 258.169752 419.970838 \r\nL 258.185964 418.023661 \r\nL 258.202175 420.63934 \r\nL 258.283235 419.402384 \r\nL 258.331871 418.901056 \r\nL 258.364294 420.042636 \r\nL 258.380506 419.982159 \r\nL 258.493989 419.014887 \r\nL 258.510201 420.225671 \r\nL 258.575049 418.98361 \r\nL 258.607473 419.374161 \r\nL 258.623685 419.962166 \r\nL 258.704744 419.050239 \r\nL 258.769592 418.951012 \r\nL 258.850651 420.577641 \r\nL 258.947922 419.067967 \r\nL 258.980346 419.532968 \r\nL 259.093829 418.68677 \r\nL 259.045194 419.984497 \r\nL 259.110041 418.794751 \r\nL 259.174889 419.956345 \r\nL 259.207312 419.123978 \r\nL 259.223524 418.628171 \r\nL 259.27216 419.175884 \r\nL 259.288372 420.201505 \r\nL 259.337008 418.844559 \r\nL 259.369431 419.622431 \r\nL 259.450491 418.898823 \r\nL 259.482915 419.126832 \r\nL 259.580186 420.078662 \r\nL 259.563974 418.589371 \r\nL 259.596398 420.01968 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419.36613 \r\nL 269.469437 417.826402 \r\nL 269.534284 420.021335 \r\nL 269.566708 418.818891 \r\nL 269.615344 420.138481 \r\nL 269.696403 419.671608 \r\nL 269.728827 419.756459 \r\nL 269.761251 418.567055 \r\nL 269.777463 418.078463 \r\nL 269.826098 419.656747 \r\nL 269.84231 419.391696 \r\nL 269.858522 419.11505 \r\nL 269.907158 419.835136 \r\nL 269.92337 419.279363 \r\nL 269.955793 420.883908 \r\nL 270.004429 419.102977 \r\nL 270.020641 419.50308 \r\nL 270.085488 418.660092 \r\nL 270.1017 420.039335 \r\nL 270.117912 418.685951 \r\nL 270.198972 420.436506 \r\nL 270.231395 420.175071 \r\nL 270.280031 419.240679 \r\nL 270.344879 419.514863 \r\nL 270.377302 418.490447 \r\nL 270.425938 420.598142 \r\nL 270.458362 418.803012 \r\nL 270.539421 419.53523 \r\nL 270.571845 419.448985 \r\nL 270.669116 420.008468 \r\nL 270.70154 418.124506 \r\nL 270.7826 419.530397 \r\nL 270.831235 418.999408 \r\nL 270.912295 420.271061 \r\nL 271.025778 418.188042 \r\nL 271.090625 420.028465 \r\nL 271.139261 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272.646967 419.708605 \r\nL 272.695602 418.610066 \r\nL 272.728026 419.77422 \r\nL 272.76045 419.05856 \r\nL 272.809085 420.272487 \r\nL 272.857721 419.982221 \r\nL 272.873933 418.655073 \r\nL 272.971204 419.29243 \r\nL 273.036052 418.692961 \r\nL 273.084687 420.696929 \r\nL 273.149535 420.317768 \r\nL 273.214383 417.968718 \r\nL 273.263018 419.835608 \r\nL 273.27923 419.071938 \r\nL 273.36029 420.388727 \r\nL 273.376501 419.40222 \r\nL 273.392713 419.983423 \r\nL 273.441349 418.767047 \r\nL 273.457561 419.441035 \r\nL 273.489985 418.604084 \r\nL 273.53862 419.678577 \r\nL 273.554832 420.395124 \r\nL 273.635892 419.107221 \r\nL 273.652103 419.081753 \r\nL 273.700739 420.026688 \r\nL 273.716951 418.182909 \r\nL 273.79801 419.485624 \r\nL 273.862858 420.678578 \r\nL 273.87907 419.225463 \r\nL 273.895282 420.425088 \r\nL 273.960129 418.580002 \r\nL 274.008765 419.116677 \r\nL 274.073613 420.131727 \r\nL 274.122248 419.613587 \r\nL 274.170884 420.009283 \r\nL 274.235731 419.174659 \r\nL 274.300579 420.218372 \r\nL 274.268155 418.32131 \r\nL 274.349215 419.915238 \r\nL 274.446486 418.499759 \r\nL 274.47891 419.057637 \r\nL 274.495122 419.047995 \r\nL 274.543757 420.104266 \r\nL 274.608605 419.674749 \r\nL 274.624817 419.663485 \r\nL 274.7383 418.40189 \r\nL 274.835571 420.60694 \r\nL 274.851783 419.316667 \r\nL 274.949054 418.645459 \r\nL 274.884207 419.624071 \r\nL 274.981478 418.971208 \r\nL 275.094961 420.724 \r\nL 275.030114 418.77661 \r\nL 275.111173 419.735766 \r\nL 275.208445 418.824323 \r\nL 275.224656 419.212383 \r\nL 275.305716 419.922096 \r\nL 275.321928 419.095988 \r\nL 275.33814 419.61306 \r\nL 275.419199 418.782382 \r\nL 275.451623 419.446017 \r\nL 275.500259 418.941783 \r\nL 275.51647 419.144707 \r\nL 275.581318 420.258146 \r\nL 275.629954 419.719896 \r\nL 275.694801 418.590776 \r\nL 275.711013 419.746323 \r\nL 275.727225 418.680947 \r\nL 275.775861 420.130173 \r\nL 275.840708 420.051473 \r\nL 275.85692 420.08398 \r\nL 275.954191 418.564299 \r\nL 275.889344 420.290643 \r\nL 275.986615 419.529703 \r\nL 276.019039 419.527725 \r\nL 276.035251 419.842026 \r\nL 276.051463 418.646521 \r\nL 276.083886 420.30562 \r\nL 276.132522 419.211324 \r\nL 276.229793 419.997921 \r\nL 276.164946 418.7607 \r\nL 276.246005 419.286926 \r\nL 276.310853 420.239667 \r\nL 276.327065 419.750029 \r\nL 276.408124 420.512017 \r\nL 276.45676 418.724793 \r\nL 276.554031 419.955163 \r\nL 276.586455 419.288412 \r\nL 276.602667 419.254295 \r\nL 276.699938 418.87033 \r\nL 276.71615 420.160014 \r\nL 276.764786 418.898664 \r\nL 276.829633 419.50247 \r\nL 276.878269 418.638147 \r\nL 276.926905 419.943914 \r\nL 276.943116 419.102982 \r\nL 276.959328 420.712801 \r\nL 276.97554 419.014302 \r\nL 277.0566 420.043336 \r\nL 277.202507 418.361891 \r\nL 277.218719 418.797704 \r\nL 277.283566 420.277501 \r\nL 277.348414 419.686782 \r\nL 277.364626 419.75333 \r\nL 277.380837 417.954981 \r\nL 277.461897 418.911072 \r\nL 277.478109 420.18535 \r\nL 277.542956 418.430021 \r\nL 277.57538 419.950044 \r\nL 277.591592 420.01723 \r\nL 277.640228 419.070122 \r\nL 277.705075 419.520791 \r\nL 277.721287 419.022023 \r\nL 277.753711 420.115206 \r\nL 277.786135 419.646196 \r\nL 277.802346 420.077901 \r\nL 277.867194 419.114053 \r\nL 277.883406 419.411171 \r\nL 277.899618 418.77124 \r\nL 277.91583 420.37984 \r\nL 277.996889 418.978936 \r\nL 278.029313 419.825961 \r\nL 278.077949 418.322534 \r\nL 278.110372 419.774663 \r\nL 278.159008 418.419849 \r\nL 278.207644 420.165443 \r\nL 278.223856 420.457069 \r\nL 278.240067 419.325734 \r\nL 278.256279 419.673453 \r\nL 278.288703 418.864692 \r\nL 278.353551 420.09214 \r\nL 278.369763 419.20275 \r\nL 278.450822 420.64911 \r\nL 278.402186 418.871449 \r\nL 278.467034 419.914103 \r\nL 278.548093 420.496881 \r\nL 278.596729 418.611678 \r\nL 278.645365 420.36298 \r\nL 278.726424 419.726226 \r\nL 278.823695 419.010902 \r\nL 278.839907 419.537337 \r\nL 278.872331 419.226714 \r\nL 278.904755 420.078841 \r\nL 278.920967 418.322331 \r\nL 278.985814 420.508954 \r\nL 279.018238 419.0869 \r\nL 279.03445 418.892162 \r\nL 279.050662 419.067135 \r\nL 279.066874 419.953818 \r\nL 279.115509 418.5931 \r\nL 279.164145 419.316883 \r\nL 279.228993 419.667262 \r\nL 279.245204 419.13646 \r\nL 279.261416 419.05935 \r\nL 279.29384 419.351536 \r\nL 279.326264 420.122168 \r\nL 279.342476 419.52151 \r\nL 279.374899 419.968488 \r\nL 279.455959 418.347529 \r\nL 279.520806 420.854245 \r\nL 279.569442 419.310916 \r\nL 279.585654 418.96111 \r\nL 279.601866 419.597397 \r\nL 279.63429 419.072943 \r\nL 279.731561 420.373113 \r\nL 279.682925 418.740753 \r\nL 279.747773 419.290548 \r\nL 279.763985 419.309029 \r\nL 279.861256 420.2999 \r\nL 279.796409 419.083535 \r\nL 279.877468 419.50547 \r\nL 279.926104 418.654342 \r\nL 279.958527 420.118854 \r\nL 279.974739 419.101165 \r\nL 280.055799 420.156629 \r\nL 280.007163 419.025912 \r\nL 280.072011 419.817675 \r\nL 280.136858 418.872851 \r\nL 280.15307 420.312263 \r\nL 280.185494 419.558425 \r\nL 280.234129 418.254583 \r\nL 280.266553 420.264068 \r\nL 280.282765 419.614917 \r\nL 280.298977 419.725105 \r\nL 280.396248 420.22539 \r\nL 280.428672 418.774756 \r\nL 280.461096 419.454376 \r\nL 280.49352 420.258969 \r\nL 280.542155 418.728491 \r\nL 280.590791 420.241279 \r\nL 280.67185 418.533491 \r\nL 280.704274 419.397548 \r\nL 280.769122 418.755342 \r\nL 280.801546 419.526357 \r\nL 280.817757 420.407315 \r\nL 280.850181 418.778922 \r\nL 280.882605 418.920179 \r\nL 280.898817 418.446378 \r\nL 280.931241 419.998047 \r\nL 280.996088 418.61301 \r\nL 281.028512 419.961788 \r\nL 281.125783 419.575722 \r\nL 281.174419 419.133847 \r\nL 281.190631 419.696865 \r\nL 281.239266 419.499349 \r\nL 281.27169 419.70964 \r\nL 281.287902 419.650838 \r\nL 281.304114 418.931825 \r\nL 281.320326 419.971727 \r\nL 281.368962 419.929542 \r\nL 281.385173 420.502109 \r\nL 281.433809 418.885647 \r\nL 281.466233 419.511142 \r\nL 281.547292 420.161566 \r\nL 281.53108 419.061237 \r\nL 281.563504 419.802211 \r\nL 281.61214 418.430376 \r\nL 281.660776 419.943869 \r\nL 281.693199 418.9864 \r\nL 281.790471 419.949048 \r\nL 281.806682 419.648182 \r\nL 281.887742 419.782428 \r\nL 281.920166 419.145292 \r\nL 282.001225 420.471056 \r\nL 282.033649 419.615874 \r\nL 282.049861 418.659396 \r\nL 282.114708 419.917716 \r\nL 282.147132 419.190971 \r\nL 282.179556 419.974602 \r\nL 282.260615 419.506553 \r\nL 282.357887 418.903419 \r\nL 282.293039 420.215099 \r\nL 282.374099 419.110519 \r\nL 282.422734 420.312108 \r\nL 282.47137 419.991502 \r\nL 282.552429 418.771084 \r\nL 282.584853 419.455388 \r\nL 282.633489 419.756964 \r\nL 282.617277 419.30833 \r\nL 282.649701 419.306248 \r\nL 282.665912 419.02235 \r\nL 282.698336 419.916868 \r\nL 282.73076 419.650077 \r\nL 282.763184 420.571146 \r\nL 282.779396 418.686255 \r\nL 282.795608 418.552892 \r\nL 282.828031 418.505902 \r\nL 282.925303 420.717314 \r\nL 282.957726 418.944212 \r\nL 283.054998 419.237117 \r\nL 283.152269 420.038297 \r\nL 283.103633 418.958924 \r\nL 283.184693 419.586079 \r\nL 283.200905 419.594058 \r\nL 283.233329 419.855776 \r\nL 283.3306 418.651744 \r\nL 283.379235 420.208631 \r\nL 283.444083 419.776494 \r\nL 283.460295 419.826586 \r\nL 283.557566 418.62775 \r\nL 283.58999 418.973599 \r\nL 283.703473 420.438894 \r\nL 283.800745 418.712476 \r\nL 283.816956 418.862889 \r\nL 283.881804 420.130471 \r\nL 283.946652 419.970957 \r\nL 284.10877 419.241296 \r\nL 284.222254 419.975552 \r\nL 284.270889 418.791497 \r\nL 284.335737 419.70758 \r\nL 284.368161 419.976456 \r\nL 284.384372 419.604534 \r\nL 284.481644 418.328761 \r\nL 284.416796 419.705283 \r\nL 284.497856 419.449175 \r\nL 284.595127 418.399202 \r\nL 284.643763 419.97284 \r\nL 284.70861 419.823347 \r\nL 284.773458 418.034545 \r\nL 284.805882 419.817884 \r\nL 284.903153 418.976007 \r\nL 284.870729 419.914393 \r\nL 284.935577 419.595425 \r\nL 284.951789 419.988859 \r\nL 284.984212 419.174378 \r\nL 285.04906 419.846538 \r\nL 285.113907 419.265857 \r\nL 285.146331 420.15082 \r\nL 285.276026 418.572173 \r\nL 285.30845 420.12684 \r\nL 285.389509 419.832128 \r\nL 285.421933 418.974332 \r\nL 285.470569 420.050511 \r\nL 285.519205 419.049025 \r\nL 285.600264 420.213099 \r\nL 285.6489 419.866405 \r\nL 285.697535 419.138171 \r\nL 285.681323 420.060664 \r\nL 285.762383 419.547192 \r\nL 285.811018 419.279749 \r\nL 285.90829 420.284279 \r\nL 285.956925 418.632306 \r\nL 286.037985 419.213451 \r\nL 286.070409 420.839395 \r\nL 286.119044 419.05049 \r\nL 286.16768 419.73566 \r\nL 286.232528 419.990635 \r\nL 286.313587 418.605877 \r\nL 286.362223 419.846217 \r\nL 286.42707 419.248747 \r\nL 286.443282 418.72153 \r\nL 286.50813 419.664204 \r\nL 286.524342 419.764841 \r\nL 286.540553 419.617211 \r\nL 286.589189 418.752615 \r\nL 286.605401 419.7739 \r\nL 286.670248 419.119838 \r\nL 286.751308 418.632379 \r\nL 286.783732 420.309368 \r\nL 286.897215 419.259706 \r\nL 286.913427 419.141891 \r\nL 286.929639 419.593045 \r\nL 286.962062 419.197922 \r\nL 287.107969 419.88947 \r\nL 287.189029 418.914734 \r\nL 287.156605 419.947805 \r\nL 287.221453 419.306751 \r\nL 287.302512 420.118327 \r\nL 287.253876 418.722376 \r\nL 287.334936 419.423473 \r\nL 287.399783 419.502457 \r\nL 287.432207 419.184641 \r\nL 287.464631 419.887017 \r\nL 287.497055 418.741449 \r\nL 287.54569 419.35579 \r\nL 287.561902 419.247336 \r\nL 287.578114 419.860917 \r\nL 287.610538 419.87865 \r\nL 287.707809 419.04486 \r\nL 287.740233 419.411729 \r\nL 287.788869 418.743594 \r\nL 287.869928 420.192152 \r\nL 287.934776 419.011376 \r\nL 288.064471 419.349199 \r\nL 288.14553 420.23962 \r\nL 288.113106 419.069388 \r\nL 288.161742 419.414975 \r\nL 288.177954 418.965477 \r\nL 288.259013 419.102951 \r\nL 288.323861 420.072077 \r\nL 288.291437 419.059151 \r\nL 288.340073 420.010198 \r\nL 288.356285 418.582954 \r\nL 288.388708 420.073224 \r\nL 288.453556 419.259909 \r\nL 288.469768 419.016108 \r\nL 288.502192 419.577271 \r\nL 288.518404 420.074622 \r\nL 288.534615 419.370261 \r\nL 288.615675 419.825046 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419.733822 \r\nL 295.489515 419.268608 \r\nL 295.602998 420.25883 \r\nL 295.667846 418.756081 \r\nL 295.716481 419.96336 \r\nL 295.732693 419.90751 \r\nL 295.846176 418.751454 \r\nL 295.894812 419.73405 \r\nL 295.95966 419.412919 \r\nL 295.992083 419.683446 \r\nL 296.056931 419.789895 \r\nL 296.105567 418.885996 \r\nL 296.170414 419.771609 \r\nL 296.267685 419.729766 \r\nL 296.348745 419.985098 \r\nL 296.397381 418.933997 \r\nL 296.47844 419.92385 \r\nL 296.527076 419.616669 \r\nL 296.559499 419.06029 \r\nL 296.591923 419.672624 \r\nL 296.624347 419.323508 \r\nL 296.672983 419.937306 \r\nL 296.689194 419.004292 \r\nL 296.721618 419.246956 \r\nL 296.73783 419.091959 \r\nL 296.754042 419.570567 \r\nL 296.786466 418.877543 \r\nL 296.851313 420.433959 \r\nL 296.964797 418.50781 \r\nL 296.99722 420.188101 \r\nL 297.07828 419.629602 \r\nL 297.110704 419.588781 \r\nL 297.126915 419.966046 \r\nL 297.159339 420.024537 \r\nL 297.25661 418.6135 \r\nL 297.289034 419.941633 \r\nL 297.386306 419.334373 \r\nL 297.402517 419.357565 \r\nL 297.467365 418.842 \r\nL 297.499789 419.400636 \r\nL 297.532213 419.549763 \r\nL 297.548424 419.290713 \r\nL 297.564636 418.666083 \r\nL 297.629484 420.257179 \r\nL 297.645696 418.99777 \r\nL 297.726755 418.706503 \r\nL 297.759179 419.907881 \r\nL 297.775391 419.141247 \r\nL 297.840238 419.991459 \r\nL 297.872662 419.23305 \r\nL 297.93751 420.297939 \r\nL 297.953722 419.209963 \r\nL 297.969934 420.110321 \r\nL 298.083417 418.739506 \r\nL 298.11584 420.231621 \r\nL 298.1969 419.141185 \r\nL 298.245536 419.135838 \r\nL 298.310383 419.973576 \r\nL 298.342807 418.894547 \r\nL 298.423866 419.594557 \r\nL 298.488714 418.9118 \r\nL 298.45629 419.896046 \r\nL 298.521138 419.215492 \r\nL 298.553561 420.469152 \r\nL 298.618409 418.799312 \r\nL 298.650833 420.258459 \r\nL 298.764316 418.807094 \r\nL 298.780528 419.314888 \r\nL 298.79674 419.114381 \r\nL 298.829164 419.983962 \r\nL 298.861587 419.394279 \r\nL 298.894011 420.267288 \r\nL 298.942647 418.599399 \r\nL 299.007494 420.096907 \r\nL 299.104766 419.46622 \r\nL 299.153401 418.808153 \r\nL 299.137189 420.140406 \r\nL 299.202037 419.587521 \r\nL 299.218249 419.703701 \r\nL 299.234461 419.585602 \r\nL 299.250673 418.562444 \r\nL 299.283096 419.948074 \r\nL 299.347944 419.362072 \r\nL 299.380368 418.309497 \r\nL 299.39658 419.504207 \r\nL 299.429003 419.496532 \r\nL 299.510063 420.289904 \r\nL 299.461427 418.959549 \r\nL 299.526275 419.630986 \r\nL 299.591122 419.697473 \r\nL 299.688393 418.344038 \r\nL 299.801877 420.495149 \r\nL 299.818089 420.099391 \r\nL 299.899148 419.048005 \r\nL 299.931572 419.239694 \r\nL 299.980207 419.426722 \r\nL 300.012631 419.311178 \r\nL 300.028843 418.938235 \r\nL 300.045055 420.446568 \r\nL 300.109903 419.40326 \r\nL 300.142326 419.766748 \r\nL 300.17475 419.2128 \r\nL 300.190962 418.17317 \r\nL 300.272021 419.917778 \r\nL 300.369293 418.924903 \r\nL 300.385505 419.591475 \r\nL 300.401717 419.804428 \r\nL 300.417928 419.11931 \r\nL 300.482776 419.555981 \r\nL 300.5152 419.71968 \r\nL 300.628683 419.058893 \r\nL 300.596259 419.799172 \r\nL 300.644895 419.207722 \r\nL 300.709742 419.886032 \r\nL 300.725954 420.326993 \r\nL 300.790802 419.56235 \r\nL 300.871861 418.298644 \r\nL 300.823226 419.991503 \r\nL 300.920497 418.970021 \r\nL 301.066404 420.066429 \r\nL 301.082616 418.902497 \r\nL 301.163675 419.170801 \r\nL 301.196099 419.00471 \r\nL 301.277158 420.088088 \r\nL 301.37443 418.715354 \r\nL 301.358218 420.373906 \r\nL 301.390642 419.765786 \r\nL 301.406853 419.802609 \r\nL 301.423065 418.513866 \r\nL 301.504125 420.429979 \r\nL 301.617608 418.393437 \r\nL 301.63382 418.944923 \r\nL 301.698667 420.134644 \r\nL 301.747303 419.445039 \r\nL 301.795939 419.088987 \r\nL 301.779727 419.942427 \r\nL 301.860786 419.198092 \r\nL 301.89321 419.448422 \r\nL 301.909422 418.81825 \r\nL 301.941846 420.728685 \r\nL 301.990481 419.797878 \r\nL 302.006693 420.19278 \r\nL 302.022905 419.498979 \r\nL 302.055329 419.571759 \r\nL 302.071541 418.924383 \r\nL 302.1526 419.794789 \r\nL 302.168812 419.191652 \r\nL 302.23366 420.863784 \r\nL 302.282295 420.571887 \r\nL 302.298507 418.295343 \r\nL 302.395779 419.36128 \r\nL 302.444414 420.529721 \r\nL 302.460626 419.012002 \r\nL 302.509262 419.867094 \r\nL 302.590321 418.294236 \r\nL 302.574109 420.082558 \r\nL 302.638957 418.482826 \r\nL 302.736228 420.782853 \r\nL 302.75244 419.924087 \r\nL 302.801076 417.970925 \r\nL 302.849711 418.320288 \r\nL 302.865923 420.00818 \r\nL 302.963195 419.471509 \r\nL 303.01183 419.184074 \r\nL 303.028042 419.423469 \r\nL 303.060466 419.791402 \r\nL 303.141525 418.083045 \r\nL 303.206373 421.373332 \r\nL 303.255009 419.167196 \r\nL 303.319856 418.611686 \r\nL 303.287432 419.318024 \r\nL 303.35228 419.042744 \r\nL 303.417127 420.450502 \r\nL 303.449551 419.106919 \r\nL 303.481975 419.358245 \r\nL 303.498187 420.560621 \r\nL 303.563034 418.011859 \r\nL 303.579246 418.844612 \r\nL 303.708941 420.519998 \r\nL 303.773789 418.26106 \r\nL 303.822425 419.148252 \r\nL 303.903484 418.700417 \r\nL 303.95212 420.722206 \r\nL 304.098027 418.513931 \r\nL 304.13045 420.895959 \r\nL 304.21151 418.836801 \r\nL 304.389841 420.402613 \r\nL 304.503324 419.02669 \r\nL 304.584383 420.263464 \r\nL 304.535748 417.807505 \r\nL 304.600595 419.487715 \r\nL 304.616807 419.120714 \r\nL 304.649231 420.230863 \r\nL 304.681655 419.852697 \r\nL 304.697866 420.144692 \r\nL 304.73029 419.457852 \r\nL 304.746502 418.629198 \r\nL 304.81135 420.363724 \r\nL 304.843773 418.694549 \r\nL 304.892409 420.699896 \r\nL 304.957257 419.0264 \r\nL 304.98968 418.970523 \r\nL 305.135587 420.267121 \r\nL 305.297706 418.203606 \r\nL 305.313918 419.265134 \r\nL 305.394978 420.268789 \r\nL 305.378766 419.167908 \r\nL 305.427401 419.563211 \r\nL 305.443613 419.659125 \r\nL 305.459825 418.537333 \r\nL 305.540885 419.443029 \r\nL 305.621944 420.269151 \r\nL 305.573308 419.388933 \r\nL 305.67058 419.85712 \r\nL 305.735427 418.644615 \r\nL 305.784063 419.390234 \r\nL 305.800275 419.165729 \r\nL 305.84891 419.884996 \r\nL 305.881334 420.661624 \r\nL 305.913758 419.236101 \r\nL 305.92997 419.413667 \r\nL 305.962394 419.701541 \r\nL 305.994817 418.565915 \r\nL 306.059665 419.715717 \r\nL 306.124513 419.188293 \r\nL 306.156936 420.412262 \r\nL 306.205572 418.487834 \r\nL 306.270419 419.206316 \r\nL 306.400115 420.279414 \r\nL 306.44875 418.405992 \r\nL 306.513598 419.374096 \r\nL 306.594657 420.198417 \r\nL 306.546022 418.626433 \r\nL 306.627081 419.504984 \r\nL 306.70814 419.683258 \r\nL 306.724352 418.489169 \r\nL 306.821624 420.684951 \r\nL 306.854047 420.101485 \r\nL 306.983743 418.525957 \r\nL 307.04859 421.096587 \r\nL 307.113438 419.505677 \r\nL 307.129649 420.014131 \r\nL 307.194497 418.884435 \r\nL 307.210709 419.155231 \r\nL 307.243133 419.387936 \r\nL 307.259345 418.592645 \r\nL 307.275556 420.62747 \r\nL 307.324192 419.145632 \r\nL 307.340404 420.222092 \r\nL 307.421463 418.829542 \r\nL 307.437675 418.391882 \r\nL 307.502523 419.496466 \r\nL 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419.896155 \r\nL 310.453086 419.553664 \r\nL 310.48551 419.664707 \r\nL 310.566569 418.647125 \r\nL 310.582781 419.767919 \r\nL 310.598993 419.29556 \r\nL 310.712476 420.574871 \r\nL 310.761112 418.700768 \r\nL 310.842172 419.132236 \r\nL 310.874595 419.943125 \r\nL 310.955655 419.645477 \r\nL 311.036714 418.52007 \r\nL 311.101562 418.895861 \r\nL 311.166409 420.102726 \r\nL 311.231257 419.803454 \r\nL 311.279892 418.968978 \r\nL 311.377164 419.372849 \r\nL 311.458223 419.959353 \r\nL 311.442011 419.014726 \r\nL 311.474435 419.373499 \r\nL 311.490647 419.100328 \r\nL 311.506859 420.119498 \r\nL 311.539283 419.298007 \r\nL 311.587918 420.024225 \r\nL 311.652766 419.726929 \r\nL 311.717613 418.728948 \r\nL 311.68519 419.973246 \r\nL 311.782461 419.116225 \r\nL 311.847309 419.8605 \r\nL 311.895944 419.767535 \r\nL 311.960792 418.803019 \r\nL 311.993215 419.825384 \r\nL 312.041851 419.004724 \r\nL 312.090487 419.81385 \r\nL 312.155334 419.736365 \r\nL 312.20397 419.033027 \r\nL 312.285029 419.5181 \r\nL 312.301241 419.430671 \r\nL 312.317453 419.881697 \r\nL 312.333665 420.206887 \r\nL 312.398513 419.364963 \r\nL 312.447148 419.479802 \r\nL 312.479572 418.596557 \r\nL 312.495784 419.758039 \r\nL 312.593055 419.640032 \r\nL 312.625479 419.834879 \r\nL 312.72275 418.626771 \r\nL 312.836234 420.016842 \r\nL 312.852445 419.375841 \r\nL 312.868657 419.505765 \r\nL 312.884869 419.295517 \r\nL 312.949717 419.363031 \r\nL 312.965929 418.842767 \r\nL 313.014564 419.585642 \r\nL 313.046988 419.160868 \r\nL 313.079412 420.263949 \r\nL 313.144259 419.370034 \r\nL 313.160471 419.013828 \r\nL 313.225319 419.605717 \r\nL 313.257743 419.992126 \r\nL 313.290166 419.304804 \r\nL 313.32259 419.467222 \r\nL 313.338802 419.509984 \r\nL 313.436073 419.689896 \r\nL 313.452285 419.113163 \r\nL 313.565768 420.082718 \r\nL 313.484709 419.072789 \r\nL 313.598192 419.846229 \r\nL 313.646828 418.761378 \r\nL 313.711675 419.436148 \r\nL 313.760311 419.824544 \r\nL 313.776523 419.620874 \r\nL 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420.09396 \r\nL 323.519867 418.773179 \r\nL 323.600926 419.814001 \r\nL 323.681986 419.302503 \r\nL 323.71441 419.474991 \r\nL 323.730621 419.576961 \r\nL 323.746833 418.95692 \r\nL 323.795469 419.586416 \r\nL 323.827893 419.119251 \r\nL 323.89274 420.110978 \r\nL 323.860317 419.000678 \r\nL 323.941376 419.625355 \r\nL 323.9738 419.184642 \r\nL 324.054859 419.521516 \r\nL 324.071071 419.466425 \r\nL 324.087283 419.661254 \r\nL 324.135919 419.924842 \r\nL 324.15213 419.42866 \r\nL 324.168342 419.571427 \r\nL 324.249402 419.115253 \r\nL 324.23319 419.835262 \r\nL 324.265614 419.431661 \r\nL 324.298037 419.198166 \r\nL 324.346673 419.835552 \r\nL 324.362885 419.146192 \r\nL 324.460156 419.35318 \r\nL 324.525004 419.618993 \r\nL 324.508792 419.235639 \r\nL 324.57364 419.587391 \r\nL 324.638487 419.161518 \r\nL 324.606063 419.764157 \r\nL 324.670911 419.562485 \r\nL 324.687123 419.588422 \r\nL 324.703335 418.570658 \r\nL 324.784394 419.837891 \r\nL 324.881665 419.295217 \r\nL 324.849242 419.859062 \r\nL 324.914089 419.390636 \r\nL 325.01136 419.656707 \r\nL 324.978937 419.188392 \r\nL 325.027572 419.60829 \r\nL 325.043784 419.223526 \r\nL 325.076208 419.728137 \r\nL 325.141056 419.424122 \r\nL 325.189691 419.079865 \r\nL 325.254539 419.611119 \r\nL 325.319386 419.138981 \r\nL 325.303174 419.915874 \r\nL 325.35181 419.374648 \r\nL 325.368022 419.769737 \r\nL 325.416658 419.221819 \r\nL 325.449081 419.307508 \r\nL 325.497717 419.181434 \r\nL 325.513929 419.189988 \r\nL 325.546353 419.953318 \r\nL 325.627412 419.621081 \r\nL 325.643624 419.093062 \r\nL 325.708472 419.840416 \r\nL 325.740895 419.436868 \r\nL 325.773319 419.65126 \r\nL 325.805743 419.313798 \r\nL 325.821955 419.487566 \r\nL 325.886802 419.738466 \r\nL 325.95165 419.064314 \r\nL 326.032709 419.792735 \r\nL 326.081345 419.442108 \r\nL 326.129981 419.023255 \r\nL 326.194828 419.152428 \r\nL 326.243464 419.855773 \r\nL 326.308311 419.740324 \r\nL 326.454218 419.261328 \r\nL 326.486642 419.374899 \r\nL 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.22158488532181117\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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416.460444 \r\nL 91.28459 417.205973 \r\nL 91.317014 421.148718 \r\nL 91.398073 417.733503 \r\nL 91.414285 417.838531 \r\nL 91.430497 417.271702 \r\nL 91.462921 415.912017 \r\nL 91.527769 416.496645 \r\nL 91.608828 418.92139 \r\nL 91.641252 417.70473 \r\nL 91.706099 415.14352 \r\nL 91.738523 417.700691 \r\nL 91.770947 421.995213 \r\nL 91.819582 415.891038 \r\nL 91.852006 420.424143 \r\nL 91.868218 420.670712 \r\nL 91.88443 419.376142 \r\nL 91.997913 417.158645 \r\nL 91.933066 419.716206 \r\nL 92.014125 417.685755 \r\nL 92.062761 420.280933 \r\nL 92.127608 420.060063 \r\nL 92.176244 415.836209 \r\nL 92.208668 419.919809 \r\nL 92.305939 422.3915 \r\nL 92.322151 421.498091 \r\nL 92.354575 419.528306 \r\nL 92.40321 421.526897 \r\nL 92.435634 420.457162 \r\nL 92.468058 420.054875 \r\nL 92.48427 420.275628 \r\nL 92.516694 421.708023 \r\nL 92.581541 419.968256 \r\nL 92.613965 419.612914 \r\nL 92.630177 420.492608 \r\nL 92.74366 422.7944 \r\nL 92.711236 418.923104 \r\nL 92.759872 422.350493 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418.801778 \r\nL 96.52103 421.976094 \r\nL 96.634513 418.756395 \r\nL 96.650725 419.301075 \r\nL 96.683148 420.764973 \r\nL 96.764208 420.034942 \r\nL 96.812844 416.09562 \r\nL 96.845267 419.962349 \r\nL 96.877691 421.614997 \r\nL 96.910115 419.679633 \r\nL 96.958751 421.547129 \r\nL 97.007386 416.869534 \r\nL 97.088446 417.438059 \r\nL 97.169505 420.01862 \r\nL 97.201929 422.639706 \r\nL 97.250565 417.73456 \r\nL 97.282988 416.531902 \r\nL 97.331624 418.804023 \r\nL 97.347836 418.85767 \r\nL 97.396471 420.821049 \r\nL 97.428895 418.897073 \r\nL 97.445107 418.197311 \r\nL 97.493743 419.404031 \r\nL 97.509955 419.273926 \r\nL 97.542378 420.503821 \r\nL 97.574802 419.421709 \r\nL 97.607226 416.520127 \r\nL 97.672074 420.434803 \r\nL 97.769345 415.945717 \r\nL 97.801769 419.0945 \r\nL 97.850404 424.713581 \r\nL 97.89904 418.685049 \r\nL 98.012523 417.694878 \r\nL 98.15843 424.177204 \r\nL 98.190854 421.285896 \r\nL 98.23949 416.505467 \r\nL 98.304337 418.712767 \r\nL 98.450244 422.782676 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421.505686 \r\nL 100.249764 420.139066 \r\nL 100.314611 421.277873 \r\nL 100.363247 419.835338 \r\nL 100.428094 421.191168 \r\nL 100.460518 421.567424 \r\nL 100.492942 420.849598 \r\nL 100.541578 416.921107 \r\nL 100.606425 420.402646 \r\nL 100.622637 420.397364 \r\nL 100.655061 419.662162 \r\nL 100.703696 420.093312 \r\nL 100.719908 420.810313 \r\nL 100.768544 418.471933 \r\nL 100.800968 417.861579 \r\nL 100.833391 420.126984 \r\nL 100.865815 419.643112 \r\nL 100.930663 420.857343 \r\nL 100.99551 417.786008 \r\nL 101.07657 418.656443 \r\nL 101.190053 421.20725 \r\nL 101.141417 418.204563 \r\nL 101.206265 420.715963 \r\nL 101.254901 417.150168 \r\nL 101.33596 417.932522 \r\nL 101.368384 419.072117 \r\nL 101.400808 417.19072 \r\nL 101.417019 416.121131 \r\nL 101.449443 419.688428 \r\nL 101.465655 420.439795 \r\nL 101.498079 417.51486 \r\nL 101.514291 416.585744 \r\nL 101.562926 418.850887 \r\nL 101.611562 421.815918 \r\nL 101.67641 419.587216 \r\nL 101.692621 419.73189 \r\nL 101.708833 419.640431 \r\nL 101.741257 417.461178 \r\nL 101.806105 419.520412 \r\nL 101.822317 419.624095 \r\nL 101.870952 418.268159 \r\nL 101.903376 420.39564 \r\nL 101.952012 421.423064 \r\nL 101.968224 420.748559 \r\nL 102.016859 418.40751 \r\nL 102.065495 420.985802 \r\nL 102.097919 421.515221 \r\nL 102.146554 421.243113 \r\nL 102.308673 417.412507 \r\nL 102.389733 423.946529 \r\nL 102.45458 420.138143 \r\nL 102.600487 417.817994 \r\nL 102.665335 422.191724 \r\nL 102.71397 418.899251 \r\nL 102.79503 416.578678 \r\nL 102.811242 417.590455 \r\nL 102.876089 421.675126 \r\nL 102.924725 418.79683 \r\nL 103.05442 414.601038 \r\nL 102.989572 419.500656 \r\nL 103.086844 417.117462 \r\nL 103.119267 422.155857 \r\nL 103.200327 420.916601 \r\nL 103.248963 415.866082 \r\nL 103.31381 419.733698 \r\nL 103.346234 420.251255 \r\nL 103.378658 419.446321 \r\nL 103.492141 416.711911 \r\nL 103.443505 419.50956 \r\nL 103.524565 417.764106 \r\nL 103.5732 421.922545 \r\nL 103.605624 425.678303 \r\nL 103.670472 421.101165 \r\nL 103.767743 415.31144 \r\nL 103.783955 416.951117 \r\nL 103.897438 424.91561 \r\nL 103.946074 422.273025 \r\nL 104.010921 416.605108 \r\nL 104.059557 420.09156 \r\nL 104.091981 421.589198 \r\nL 104.17304 421.183788 \r\nL 104.221676 418.756113 \r\nL 104.302735 419.467671 \r\nL 104.400007 422.454066 \r\nL 104.416218 423.517075 \r\nL 104.43243 421.370709 \r\nL 104.464854 416.322174 \r\nL 104.545914 418.517812 \r\nL 104.594549 420.931938 \r\nL 104.626973 418.871781 \r\nL 104.659397 416.978493 \r\nL 104.740456 417.890732 \r\nL 104.77288 417.529625 \r\nL 104.789092 417.998534 \r\nL 104.837727 422.429287 \r\nL 104.886363 418.980285 \r\nL 104.983634 413.593039 \r\nL 104.999846 415.327328 \r\nL 105.129541 422.856932 \r\nL 105.145753 421.822763 \r\nL 105.243025 416.797577 \r\nL 105.307872 417.384377 \r\nL 105.324084 417.466104 \r\nL 105.469991 422.096172 \r\nL 105.421355 416.896582 \r\nL 105.486203 421.875124 \r\nL 105.680746 415.722916 \r\nL 105.55105 422.44701 \r\nL 105.696957 415.842997 \r\nL 105.794229 423.091157 \r\nL 105.875288 421.535719 \r\nL 105.940136 417.89813 \r\nL 105.988771 420.320171 \r\nL 106.053619 425.116113 \r\nL 106.086043 422.561465 \r\nL 106.183314 415.779324 \r\nL 106.215738 417.909691 \r\nL 106.280585 424.055501 \r\nL 106.345433 421.364725 \r\nL 106.361645 421.679812 \r\nL 106.377857 421.036999 \r\nL 106.442704 414.912909 \r\nL 106.49134 419.335547 \r\nL 106.523764 421.34507 \r\nL 106.604823 421.086374 \r\nL 106.669671 415.267581 \r\nL 106.718306 419.42613 \r\nL 106.766942 421.785714 \r\nL 106.83179 420.216656 \r\nL 106.929061 416.201798 \r\nL 106.961485 417.763779 \r\nL 107.026332 424.163174 \r\nL 107.074968 420.114743 \r\nL 107.156027 417.190052 \r\nL 107.188451 418.164281 \r\nL 107.26951 421.786632 \r\nL 107.318146 419.146977 \r\nL 107.415417 414.243239 \r\nL 107.366782 420.415886 \r\nL 107.447841 417.97586 \r\nL 107.577536 424.734442 \r\nL 107.626172 415.38664 \r\nL 107.707231 419.014474 \r\nL 107.739655 417.946354 \r\nL 107.772079 419.440994 \r\nL 107.820715 422.284756 \r\nL 107.86935 419.455715 \r\nL 107.934198 419.068448 \r\nL 107.901774 419.669124 \r\nL 107.95041 419.497998 \r\nL 107.982834 422.47672 \r\nL 108.031469 418.931457 \r\nL 108.047681 418.979714 \r\nL 108.063893 419.176008 \r\nL 108.096317 418.299521 \r\nL 108.144952 415.309927 \r\nL 108.177376 419.410652 \r\nL 108.258436 421.857875 \r\nL 108.290859 420.571612 \r\nL 108.388131 414.049876 \r\nL 108.436766 418.433478 \r\nL 108.517826 419.990876 \r\nL 108.55025 419.562595 \r\nL 108.647521 416.230953 \r\nL 108.679945 417.830272 \r\nL 108.744792 422.320716 \r\nL 108.793428 419.509834 \r\nL 108.825852 417.401334 \r\nL 108.906911 417.722705 \r\nL 109.036606 422.817083 \r\nL 109.06903 421.280366 \r\nL 109.166301 417.51875 \r\nL 109.182513 418.309565 \r\nL 109.231149 422.560283 \r\nL 109.295996 418.713533 \r\nL 109.393268 419.760097 \r\nL 109.360844 416.711974 \r\nL 109.40948 419.598759 \r\nL 109.441903 418.797755 \r\nL 109.458115 420.108124 \r\nL 109.506751 423.775895 \r\nL 109.555387 420.869352 \r\nL 109.620234 416.009331 \r\nL 109.685082 419.004756 \r\nL 109.766141 423.544402 \r\nL 109.8472 421.630691 \r\nL 109.895836 417.015158 \r\nL 109.960684 418.537996 \r\nL 110.009319 425.216868 \r\nL 110.057955 417.851464 \r\nL 110.074167 416.45309 \r\nL 110.155226 417.448736 \r\nL 110.26871 422.696476 \r\nL 110.317345 419.357833 \r\nL 110.365981 417.527905 \r\nL 110.398405 415.370736 \r\nL 110.44704 419.609291 \r\nL 110.495676 420.872589 \r\nL 110.5281 423.065252 \r\nL 110.560523 419.548346 \r\nL 110.592947 415.873027 \r\nL 110.674007 417.684504 \r\nL 110.722642 421.910999 \r\nL 110.78749 419.537739 \r\nL 110.852337 417.036506 \r\nL 110.917185 417.652322 \r\nL 110.982033 423.257075 \r\nL 111.04688 419.495851 \r\nL 111.111728 414.367375 \r\nL 111.160363 417.859805 \r\nL 111.241423 422.091388 \r\nL 111.290058 419.325126 \r\nL 111.38733 417.104083 \r\nL 111.403542 417.309295 \r\nL 111.484601 422.506694 \r\nL 111.549449 419.654422 \r\nL 111.630508 416.807256 \r\nL 111.662932 418.418891 \r\nL 111.727779 422.739438 \r\nL 111.792627 420.520337 \r\nL 111.90611 416.739505 \r\nL 111.922322 417.627027 \r\nL 111.954746 423.268397 \r\nL 112.035805 419.394486 \r\nL 112.052017 419.353242 \r\nL 112.100653 417.398114 \r\nL 112.133076 419.976347 \r\nL 112.181712 420.615776 \r\nL 112.214136 422.671943 \r\nL 112.262772 418.449577 \r\nL 112.360043 416.116541 \r\nL 112.376255 417.015211 \r\nL 112.473526 423.391422 \r\nL 112.522162 419.884653 \r\nL 112.554586 419.949488 \r\nL 112.570797 418.871278 \r\nL 112.603221 413.451267 \r\nL 112.668069 419.198291 \r\nL 112.732916 422.197204 \r\nL 112.749128 423.498979 \r\nL 112.781552 419.65633 \r\nL 112.895035 417.04087 \r\nL 112.911247 417.108775 \r\nL 112.976095 421.859146 \r\nL 113.02473 419.347457 \r\nL 113.057154 418.313673 \r\nL 113.10579 419.902037 \r\nL 113.170637 420.216823 \r\nL 113.186849 420.21173 \r\nL 113.332756 416.708629 \r\nL 113.28412 420.434678 \r\nL 113.348968 417.322071 \r\nL 113.413816 423.469956 \r\nL 113.478663 421.375151 \r\nL 113.575934 416.464297 \r\nL 113.608358 418.178184 \r\nL 113.738053 422.796035 \r\nL 113.754265 421.517456 \r\nL 113.851536 417.415695 \r\nL 113.88396 420.161685 \r\nL 113.900172 421.955796 \r\nL 113.948808 418.985916 \r\nL 113.981232 420.080251 \r\nL 114.062291 418.265308 \r\nL 114.094715 419.826059 \r\nL 114.110927 420.403687 \r\nL 114.159562 418.873971 \r\nL 114.191986 416.937356 \r\nL 114.240622 419.571655 \r\nL 114.289257 417.125409 \r\nL 114.354105 419.693835 \r\nL 114.402741 422.992689 \r\nL 114.435164 419.945166 \r\nL 114.467588 416.525746 \r\nL 114.548648 417.847748 \r\nL 114.597283 421.616807 \r\nL 114.678343 420.61668 \r\nL 114.791826 414.56095 \r\nL 114.82425 418.381886 \r\nL 114.921521 421.839744 \r\nL 114.937733 420.701964 \r\nL 114.970157 417.279457 \r\nL 115.035004 420.754694 \r\nL 115.067428 417.59197 \r\nL 115.08364 417.034642 \r\nL 115.116064 419.326394 \r\nL 115.197123 422.117058 \r\nL 115.229547 420.672465 \r\nL 115.278183 418.468701 \r\nL 115.326818 420.971661 \r\nL 115.359242 423.458789 \r\nL 115.407878 418.758524 \r\nL 115.424089 418.502422 \r\nL 115.456513 419.605923 \r\nL 115.472725 419.564211 \r\nL 115.521361 416.970252 \r\nL 115.553785 420.610343 \r\nL 115.569996 421.186691 \r\nL 115.618632 419.840471 \r\nL 115.667268 421.096282 \r\nL 115.68348 421.00191 \r\nL 115.764539 417.152295 \r\nL 115.845599 418.342089 \r\nL 115.894234 421.526554 \r\nL 115.959082 419.527327 \r\nL 115.991506 419.601739 \r\nL 116.040141 418.622598 \r\nL 116.056353 418.321164 \r\nL 116.072565 418.683522 \r\nL 116.104989 420.607064 \r\nL 116.169836 418.414999 \r\nL 116.186048 418.868162 \r\nL 116.218472 420.012994 \r\nL 116.267108 418.058787 \r\nL 116.283319 417.563832 \r\nL 116.331955 419.113702 \r\nL 116.348167 419.0706 \r\nL 116.364379 419.314177 \r\nL 116.413015 421.556407 \r\nL 116.445438 418.987232 \r\nL 116.494074 416.644421 \r\nL 116.510286 418.607596 \r\nL 116.54271 421.724719 \r\nL 116.591345 416.42974 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418.177757 \r\nL 118.196322 421.907687 \r\nL 118.244958 417.627903 \r\nL 118.26117 417.631184 \r\nL 118.423289 419.861371 \r\nL 118.488136 416.493721 \r\nL 118.536772 418.609165 \r\nL 118.634043 421.631462 \r\nL 118.650255 420.480065 \r\nL 118.682679 416.40295 \r\nL 118.763738 419.27269 \r\nL 118.77995 418.973251 \r\nL 118.812374 420.106282 \r\nL 118.844798 424.530113 \r\nL 118.909645 420.328038 \r\nL 119.023128 416.674321 \r\nL 119.055552 418.218292 \r\nL 119.087976 421.351601 \r\nL 119.185247 420.759762 \r\nL 119.250095 415.700015 \r\nL 119.29873 419.284617 \r\nL 119.331154 420.953696 \r\nL 119.412214 419.818313 \r\nL 119.477061 417.403918 \r\nL 119.509485 420.017254 \r\nL 119.574332 419.509799 \r\nL 119.622968 421.534406 \r\nL 119.736451 417.904905 \r\nL 119.768875 418.926077 \r\nL 119.833723 422.586734 \r\nL 119.882358 419.678039 \r\nL 119.97963 416.795929 \r\nL 120.012053 418.716578 \r\nL 120.060689 422.437429 \r\nL 120.141749 421.704742 \r\nL 120.222808 414.412316 \r\nL 120.271444 419.223035 \r\nL 120.303867 421.735357 \r\nL 120.384927 420.28897 \r\nL 120.433562 417.460284 \r\nL 120.482198 420.170185 \r\nL 120.514622 420.688899 \r\nL 120.530834 419.914382 \r\nL 120.563258 418.634905 \r\nL 120.611893 421.294449 \r\nL 120.628105 420.875557 \r\nL 120.709165 414.770272 \r\nL 120.741588 418.11197 \r\nL 120.790224 424.17184 \r\nL 120.855072 420.356142 \r\nL 120.887495 417.823917 \r\nL 120.968555 418.896102 \r\nL 121.114462 422.451957 \r\nL 121.130674 421.336242 \r\nL 121.195521 415.906521 \r\nL 121.244157 420.039011 \r\nL 121.35764 421.709645 \r\nL 121.325216 419.277198 \r\nL 121.373852 421.610252 \r\nL 121.422488 416.461483 \r\nL 121.503547 418.919668 \r\nL 121.584606 421.199564 \r\nL 121.61703 419.696424 \r\nL 121.633242 418.721498 \r\nL 121.69809 421.314924 \r\nL 121.714302 421.354304 \r\nL 121.762937 418.256044 \r\nL 121.860208 419.573975 \r\nL 121.87642 419.776797 \r\nL 121.892632 419.392565 \r\nL 121.941268 416.915578 \r\nL 121.973692 419.180529 \r\nL 122.006115 421.097092 \r\nL 122.070963 419.427067 \r\nL 122.103387 418.008855 \r\nL 122.168234 419.363951 \r\nL 122.200658 420.112114 \r\nL 122.249294 418.499234 \r\nL 122.265506 418.038891 \r\nL 122.297929 420.161266 \r\nL 122.330353 421.787567 \r\nL 122.378989 417.860713 \r\nL 122.492472 422.020275 \r\nL 122.524896 420.506084 \r\nL 122.573532 417.054836 \r\nL 122.622167 421.295588 \r\nL 122.670803 417.262659 \r\nL 122.703227 418.490111 \r\nL 122.73565 420.991097 \r\nL 122.800498 418.151247 \r\nL 122.81671 418.922302 \r\nL 122.832922 419.247667 \r\nL 122.849134 418.797669 \r\nL 122.881557 416.597119 \r\nL 122.930193 420.664855 \r\nL 122.946405 421.186545 \r\nL 122.962617 420.266769 \r\nL 122.995041 418.344804 \r\nL 123.0761 419.891344 \r\nL 123.092312 419.783139 \r\nL 123.124736 417.901416 \r\nL 123.189583 420.316195 \r\nL 123.238219 422.623674 \r\nL 123.270643 420.673758 \r\nL 123.319278 417.77431 \r\nL 123.367914 419.579675 \r\nL 123.400338 421.568785 \r\nL 123.448973 418.680854 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418.750388 \r\nL 126.739986 417.932223 \r\nL 126.756198 418.359533 \r\nL 126.788622 423.326777 \r\nL 126.869681 419.677884 \r\nL 126.918317 416.313427 \r\nL 126.966953 419.650155 \r\nL 126.999377 422.226039 \r\nL 127.064224 419.428467 \r\nL 127.193919 417.810763 \r\nL 127.307402 422.63568 \r\nL 127.339826 420.345842 \r\nL 127.37225 416.58991 \r\nL 127.453309 418.74491 \r\nL 127.566793 421.353374 \r\nL 127.583004 420.697427 \r\nL 127.680276 416.784748 \r\nL 127.696488 417.839095 \r\nL 127.761335 422.630429 \r\nL 127.793759 419.055184 \r\nL 127.858607 417.725652 \r\nL 127.907242 418.107501 \r\nL 128.020725 424.568445 \r\nL 128.069361 420.627004 \r\nL 128.134209 415.002572 \r\nL 128.182844 418.756896 \r\nL 128.263904 423.199099 \r\nL 128.312539 420.772687 \r\nL 128.409811 415.211768 \r\nL 128.442234 416.789353 \r\nL 128.49087 423.088353 \r\nL 128.57193 418.926068 \r\nL 128.652989 416.648908 \r\nL 128.669201 417.859274 \r\nL 128.766472 421.23971 \r\nL 128.798896 421.006493 \r\nL 128.815108 420.948705 \r\nL 128.879955 416.33528 \r\nL 128.928591 420.331284 \r\nL 128.977227 422.782309 \r\nL 129.025862 420.732548 \r\nL 129.155558 417.248994 \r\nL 129.171769 417.952538 \r\nL 129.220405 421.870403 \r\nL 129.301464 419.920338 \r\nL 129.3501 415.980082 \r\nL 129.414948 418.825786 \r\nL 129.479795 422.313386 \r\nL 129.528431 419.520981 \r\nL 129.544643 418.463246 \r\nL 129.625702 419.118696 \r\nL 129.739185 422.511882 \r\nL 129.69055 418.965918 \r\nL 129.755397 422.350401 \r\nL 129.868881 418.0475 \r\nL 129.885092 418.346335 \r\nL 129.933728 422.350257 \r\nL 129.998576 418.675014 \r\nL 130.047211 417.89938 \r\nL 130.095847 418.158886 \r\nL 130.160694 421.522671 \r\nL 130.20933 419.779283 \r\nL 130.322813 415.740948 \r\nL 130.339025 417.100453 \r\nL 130.46872 422.080517 \r\nL 130.533568 417.441813 \r\nL 130.630839 418.714522 \r\nL 130.695687 421.2713 \r\nL 130.728111 419.721008 \r\nL 130.776746 417.964116 \r\nL 130.841594 419.020073 \r\nL 130.857806 418.946905 \r\nL 130.906441 421.664331 \r\nL 130.938865 418.722581 \r\nL 130.955077 417.485632 \r\nL 131.003713 420.48279 \r\nL 131.052348 418.286483 \r\nL 131.06856 418.271326 \r\nL 131.133408 420.733649 \r\nL 131.198255 419.978978 \r\nL 131.279315 417.18273 \r\nL 131.311738 418.134413 \r\nL 131.40901 421.887712 \r\nL 131.441434 421.301725 \r\nL 131.490069 417.819174 \r\nL 131.587341 418.700823 \r\nL 131.619764 420.090391 \r\nL 131.652188 421.328883 \r\nL 131.700824 419.525512 \r\nL 131.717036 419.708629 \r\nL 131.733247 419.724466 \r\nL 131.765671 418.596222 \r\nL 131.798095 419.952281 \r\nL 131.862943 421.277995 \r\nL 131.895366 419.921804 \r\nL 131.976426 418.18458 \r\nL 132.025061 418.810518 \r\nL 132.057485 417.885288 \r\nL 132.106121 419.487995 \r\nL 132.170968 421.04059 \r\nL 132.18718 419.740495 \r\nL 132.219604 416.747037 \r\nL 132.284452 420.294224 \r\nL 132.300664 420.619682 \r\nL 132.333087 419.522222 \r\nL 132.349299 419.669292 \r\nL 132.381723 419.144361 \r\nL 132.414147 417.998615 \r\nL 132.446571 419.493361 \r\nL 132.462782 420.373948 \r\nL 132.52763 418.673409 \r\nL 132.543842 418.623806 \r\nL 132.608689 421.252535 \r\nL 132.641113 419.656773 \r\nL 132.722173 416.718412 \r\nL 132.754596 418.499812 \r\nL 132.78702 420.284464 \r\nL 132.835656 417.955151 \r\nL 132.86808 418.898794 \r\nL 132.884291 418.449435 \r\nL 132.949139 419.44384 \r\nL 132.981563 420.172962 \r\nL 133.013987 418.262316 \r\nL 133.030198 417.744915 \r\nL 133.062622 419.000415 \r\nL 133.111258 422.203507 \r\nL 133.143682 419.490163 \r\nL 133.159894 418.04066 \r\nL 133.224741 420.782288 \r\nL 133.240953 420.596049 \r\nL 133.257165 420.388775 \r\nL 133.273377 420.781287 \r\nL 133.3058 421.776303 \r\nL 133.370648 420.376139 \r\nL 133.451707 420.879472 \r\nL 133.516555 417.771591 \r\nL 133.597614 417.478925 \r\nL 133.64625 421.148112 \r\nL 133.743521 421.382542 \r\nL 133.792157 418.816685 \r\nL 133.824581 418.548338 \r\nL 133.840793 418.159245 \r\nL 133.873217 419.111918 \r\nL 133.9867 421.261534 \r\nL 134.002912 420.606824 \r\nL 134.116395 417.858069 \r\nL 134.067759 421.148769 \r\nL 134.132607 418.034274 \r\nL 134.16503 421.180048 \r\nL 134.262302 420.196201 \r\nL 134.294726 419.26105 \r\nL 134.327149 416.708863 \r\nL 134.391997 420.043686 \r\nL 134.473056 419.1898 \r\nL 134.440633 420.129309 \r\nL 134.489268 419.492189 \r\nL 134.521692 420.30738 \r\nL 134.554116 417.48813 \r\nL 134.570328 416.846154 \r\nL 134.602751 419.388178 \r\nL 134.618963 419.385457 \r\nL 134.651387 418.738587 \r\nL 134.683811 420.81973 \r\nL 134.813506 417.25246 \r\nL 134.84593 419.028097 \r\nL 134.975625 421.114954 \r\nL 134.991837 420.880569 \r\nL 135.072896 416.730835 \r\nL 135.089108 415.137296 \r\nL 135.121532 418.254905 \r\nL 135.153956 422.607647 \r\nL 135.235015 419.340103 \r\nL 135.267439 418.193895 \r\nL 135.299863 419.505264 \r\nL 135.332286 421.187516 \r\nL 135.413346 419.99978 \r\nL 135.461981 420.737119 \r\nL 135.478193 420.031048 \r\nL 135.543041 416.187353 \r\nL 135.591677 419.437136 \r\nL 135.607888 419.462297 \r\nL 135.672736 422.515906 \r\nL 135.70516 419.477732 \r\nL 135.802431 417.821813 \r\nL 135.753795 419.568535 \r\nL 135.818643 418.496409 \r\nL 135.932126 421.545189 \r\nL 135.948338 421.26046 \r\nL 136.045609 417.087091 \r\nL 136.078033 418.353075 \r\nL 136.191516 420.89576 \r\nL 136.207728 420.432796 \r\nL 136.305 417.664192 \r\nL 136.353635 417.841778 \r\nL 136.402271 419.788742 \r\nL 136.48333 419.39787 \r\nL 136.515754 418.923743 \r\nL 136.548178 419.604862 \r\nL 136.596813 419.347503 \r\nL 136.629237 420.032225 \r\nL 136.645449 419.272579 \r\nL 136.710297 420.586666 \r\nL 136.758932 417.696951 \r\nL 136.791356 419.129719 \r\nL 136.839992 422.558798 \r\nL 136.904839 420.643901 \r\nL 136.985899 417.967855 \r\nL 137.050746 419.154149 \r\nL 137.099382 420.291074 \r\nL 137.148018 423.121658 \r\nL 137.180441 420.869582 \r\nL 137.277713 418.15433 \r\nL 137.310137 418.909032 \r\nL 137.407408 421.930199 \r\nL 137.439832 420.29075 \r\nL 137.569527 418.05654 \r\nL 137.60195 419.302989 \r\nL 137.68301 418.928339 \r\nL 137.715434 418.124236 \r\nL 137.747857 420.177809 \r\nL 137.764069 420.42676 \r\nL 137.796493 419.601275 \r\nL 137.812705 419.33889 \r\nL 137.845129 420.350866 \r\nL 137.861341 420.569671 \r\nL 137.909976 416.437766 \r\nL 137.974824 419.330121 \r\nL 138.007248 419.047952 \r\nL 138.02346 419.503914 \r\nL 138.088307 422.238184 \r\nL 138.120731 420.102245 \r\nL 138.218002 415.937498 \r\nL 138.250426 417.301418 \r\nL 138.299062 421.578673 \r\nL 138.396333 420.722489 \r\nL 138.444969 416.723632 \r\nL 138.509816 419.244436 \r\nL 138.54224 420.339774 \r\nL 138.623299 419.42832 \r\nL 138.671935 417.731829 \r\nL 138.720571 420.094869 \r\nL 138.785418 418.916893 \r\nL 138.850266 422.015997 \r\nL 138.898901 417.405922 \r\nL 138.996173 418.718831 \r\nL 139.044808 423.205921 \r\nL 139.093444 419.004711 \r\nL 139.125868 416.740538 \r\nL 139.190715 418.214807 \r\nL 139.304199 422.143846 \r\nL 139.352834 421.447864 \r\nL 139.433894 415.762406 \r\nL 139.482529 419.859034 \r\nL 139.547377 422.807124 \r\nL 139.612224 420.86713 \r\nL 139.677072 417.777361 \r\nL 139.709496 416.843056 \r\nL 139.758131 418.923912 \r\nL 139.822979 424.128608 \r\nL 139.855403 419.797614 \r\nL 139.92025 417.455914 \r\nL 139.952674 418.651519 \r\nL 139.985098 420.757778 \r\nL 140.033733 417.910812 \r\nL 140.049945 418.19064 \r\nL 140.114793 419.943001 \r\nL 140.147217 418.235011 \r\nL 140.195852 416.192457 \r\nL 140.228276 417.971462 \r\nL 140.276912 422.993932 \r\nL 140.341759 419.488657 \r\nL 140.390395 414.543707 \r\nL 140.439031 419.706631 \r\nL 140.487666 416.944868 \r\nL 140.52009 420.139948 \r\nL 140.568726 423.316244 \r\nL 140.617361 419.053563 \r\nL 140.682209 417.325026 \r\nL 140.714633 419.039372 \r\nL 140.763268 422.24595 \r\nL 140.811904 419.153511 \r\nL 140.828116 419.164393 \r\nL 140.86054 419.668683 \r\nL 140.892963 418.813992 \r\nL 140.925387 417.464286 \r\nL 140.974023 418.835963 \r\nL 141.055082 423.725509 \r\nL 141.087506 421.068347 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418.650655 \r\nL 146.79409 420.250142 \r\nL 146.810302 420.732338 \r\nL 146.858938 418.872645 \r\nL 146.87515 418.232494 \r\nL 146.923785 420.642759 \r\nL 146.972421 419.615804 \r\nL 146.988633 420.181871 \r\nL 147.021057 422.347349 \r\nL 147.069692 419.301214 \r\nL 147.085904 418.553556 \r\nL 147.13454 420.332183 \r\nL 147.150752 421.497496 \r\nL 147.199387 419.50939 \r\nL 147.248023 421.301096 \r\nL 147.410142 417.298911 \r\nL 147.426354 417.494186 \r\nL 147.458778 421.756704 \r\nL 147.539837 418.385538 \r\nL 147.588473 419.182719 \r\nL 147.604685 418.15115 \r\nL 147.620896 416.549237 \r\nL 147.685744 421.03591 \r\nL 147.73438 418.027212 \r\nL 147.847863 418.141757 \r\nL 147.864075 418.147236 \r\nL 147.945134 418.05778 \r\nL 148.009982 419.958315 \r\nL 148.074829 417.994248 \r\nL 148.107253 419.007516 \r\nL 148.123465 420.512041 \r\nL 148.172101 417.446041 \r\nL 148.220736 419.570476 \r\nL 148.269372 417.283134 \r\nL 148.350431 418.847249 \r\nL 148.382855 420.916866 \r\nL 148.463915 419.544611 \r\nL 148.480126 419.468886 \r\nL 148.496338 419.886456 \r\nL 148.51255 420.0103 \r\nL 148.544974 419.519382 \r\nL 148.561186 419.350261 \r\nL 148.626033 419.764977 \r\nL 148.642245 419.791344 \r\nL 148.674669 421.442781 \r\nL 148.739517 421.018174 \r\nL 148.885424 417.965214 \r\nL 148.982695 420.99197 \r\nL 149.079966 420.452573 \r\nL 149.161026 420.629715 \r\nL 149.242085 417.703573 \r\nL 149.306933 421.448767 \r\nL 149.420416 420.594955 \r\nL 149.550111 417.806965 \r\nL 149.566323 417.850281 \r\nL 149.614958 421.257665 \r\nL 149.679806 419.288422 \r\nL 149.696018 419.396476 \r\nL 149.71223 419.073819 \r\nL 149.744654 417.903029 \r\nL 149.793289 419.07567 \r\nL 149.809501 418.807154 \r\nL 149.890561 421.746439 \r\nL 149.955408 419.684471 \r\nL 150.004044 416.044008 \r\nL 150.052679 418.121534 \r\nL 150.117527 420.930189 \r\nL 150.166163 418.487561 \r\nL 150.214798 421.327915 \r\nL 150.247222 419.51688 \r\nL 150.295858 417.141141 \r\nL 150.344493 420.102767 \r\nL 150.376917 421.72096 \r\nL 150.409341 420.476085 \r\nL 150.457977 415.852971 \r\nL 150.522824 419.302182 \r\nL 150.587672 421.856418 \r\nL 150.636307 420.739764 \r\nL 150.717367 419.20377 \r\nL 150.766002 416.702306 \r\nL 150.798426 419.204059 \r\nL 150.863274 422.553892 \r\nL 150.911909 420.158091 \r\nL 150.944333 419.03317 \r\nL 150.976757 417.135338 \r\nL 151.041605 419.708913 \r\nL 151.122664 421.057751 \r\nL 151.155088 419.839458 \r\nL 151.203723 417.625729 \r\nL 151.252359 419.761312 \r\nL 151.365842 421.022652 \r\nL 151.317207 419.069401 \r\nL 151.382054 420.8657 \r\nL 151.43069 417.526123 \r\nL 151.527961 418.866605 \r\nL 151.560385 421.635237 \r\nL 151.625232 418.157966 \r\nL 151.673868 416.406145 \r\nL 151.754928 416.516783 \r\nL 151.819775 420.867541 \r\nL 151.917046 419.286683 \r\nL 151.94947 415.94361 \r\nL 152.014318 420.295664 \r\nL 152.046741 419.605795 \r\nL 152.079165 420.664434 \r\nL 152.127801 419.76481 \r\nL 152.241284 421.136538 \r\nL 152.257496 420.336081 \r\nL 152.273708 419.540649 \r\nL 152.322344 421.568647 \r\nL 152.338555 421.825468 \r\nL 152.354767 420.544315 \r\nL 152.387191 417.821778 \r\nL 152.468251 418.426537 \r\nL 152.565522 421.662738 \r\nL 152.597946 420.365464 \r\nL 152.711429 418.763482 \r\nL 152.727641 418.785899 \r\nL 152.776276 421.787924 \r\nL 152.841124 419.375124 \r\nL 152.857336 419.429133 \r\nL 152.873548 419.21636 \r\nL 152.88976 418.810524 \r\nL 152.938395 419.794604 \r\nL 153.051878 421.454419 \r\nL 153.003243 419.422018 \r\nL 153.084302 421.193564 \r\nL 153.213997 417.193515 \r\nL 153.230209 418.632737 \r\nL 153.262633 421.375843 \r\nL 153.311269 417.691177 \r\nL 153.343692 419.472296 \r\nL 153.376116 421.121991 \r\nL 153.440964 419.343493 \r\nL 153.489599 417.293547 \r\nL 153.538235 419.088047 \r\nL 153.586871 420.150829 \r\nL 153.619295 418.25813 \r\nL 153.635506 418.095409 \r\nL 153.651718 418.488162 \r\nL 153.700354 421.050529 \r\nL 153.74899 418.551925 \r\nL 153.781413 417.786394 \r\nL 153.813837 419.410302 \r\nL 153.846261 418.686127 \r\nL 153.878685 419.755238 \r\nL 153.911108 418.917576 \r\nL 153.943532 417.392814 \r\nL 154.00838 419.471047 \r\nL 154.073227 418.630228 \r\nL 154.154287 420.679365 \r\nL 154.251558 418.761168 \r\nL 154.316406 419.85284 \r\nL 154.348829 421.194785 \r\nL 154.381253 419.024018 \r\nL 154.397465 418.224255 \r\nL 154.462313 419.631548 \r\nL 154.494736 421.039812 \r\nL 154.559584 419.108585 \r\nL 154.592008 418.314427 \r\nL 154.624431 419.991782 \r\nL 154.640643 420.637657 \r\nL 154.689279 418.358391 \r\nL 154.770338 419.588799 \r\nL 154.818974 421.369794 \r\nL 154.86761 419.305976 \r\nL 154.900034 419.019327 \r\nL 154.916245 418.482876 \r\nL 154.981093 419.939783 \r\nL 155.013517 419.248792 \r\nL 155.045941 420.778068 \r\nL 155.078364 422.731252 \r\nL 155.110788 418.726336 \r\nL 155.175636 419.925747 \r\nL 155.240483 416.418244 \r\nL 155.353966 420.557943 \r\nL 155.370178 420.015662 \r\nL 155.46745 417.129154 \r\nL 155.499873 418.448769 \r\nL 155.580933 420.775966 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157.672266 418.003613 \r\nL 157.801961 421.415206 \r\nL 157.834385 419.700686 \r\nL 157.915444 417.555884 \r\nL 157.947868 418.8278 \r\nL 157.996504 422.132738 \r\nL 158.061351 419.13776 \r\nL 158.126199 417.890757 \r\nL 158.191047 418.351145 \r\nL 158.239682 422.300101 \r\nL 158.288318 419.096756 \r\nL 158.385589 417.546451 \r\nL 158.401801 418.232492 \r\nL 158.547708 421.71895 \r\nL 158.628767 417.691571 \r\nL 158.693615 419.173627 \r\nL 158.742251 424.125247 \r\nL 158.790886 418.553551 \r\nL 158.807098 417.486668 \r\nL 158.871946 419.169846 \r\nL 158.888158 419.125033 \r\nL 158.920581 418.559199 \r\nL 158.969217 419.996531 \r\nL 159.050277 422.332688 \r\nL 159.066488 420.844523 \r\nL 159.131336 416.712475 \r\nL 159.179972 420.570365 \r\nL 159.244819 419.354769 \r\nL 159.293455 421.477668 \r\nL 159.342091 418.305133 \r\nL 159.455574 418.438637 \r\nL 159.504209 421.29799 \r\nL 159.552845 417.877 \r\nL 159.569057 417.893755 \r\nL 159.731176 420.630209 \r\nL 159.747388 420.26092 \r\nL 159.828447 420.646471 \r\nL 159.877083 417.905436 \r\nL 159.958142 420.914095 \r\nL 160.087837 419.684455 \r\nL 160.120261 418.328236 \r\nL 160.20132 419.081131 \r\nL 160.266168 421.527229 \r\nL 160.298592 419.733853 \r\nL 160.314804 418.739395 \r\nL 160.363439 420.644097 \r\nL 160.395863 419.89729 \r\nL 160.428287 419.871517 \r\nL 160.493134 417.722237 \r\nL 160.54177 419.764179 \r\nL 160.574194 420.467201 \r\nL 160.606618 419.265781 \r\nL 160.639041 418.640332 \r\nL 160.687677 419.973551 \r\nL 160.720101 421.756321 \r\nL 160.768737 418.135075 \r\nL 160.784948 417.896882 \r\nL 160.833584 418.579594 \r\nL 160.88222 420.592918 \r\nL 160.914644 419.35168 \r\nL 160.947067 417.26992 \r\nL 160.995703 420.561883 \r\nL 161.011915 419.898411 \r\nL 161.076762 418.177959 \r\nL 161.14161 418.392793 \r\nL 161.190246 420.484372 \r\nL 161.238881 418.266803 \r\nL 161.255093 418.088004 \r\nL 161.287517 418.702202 \r\nL 161.319941 419.499625 \r\nL 161.384788 418.577928 \r\nL 161.401 418.402313 \r\nL 161.417212 418.908486 \r\nL 161.530695 421.257115 \r\nL 161.563119 420.069305 \r\nL 161.579331 419.649406 \r\nL 161.627967 420.68711 \r\nL 161.644178 420.878878 \r\nL 161.790085 417.327722 \r\nL 161.838721 421.37653 \r\nL 161.91978 419.097579 \r\nL 161.935992 419.092983 \r\nL 161.968416 418.743519 \r\nL 162.017052 419.079339 \r\nL 162.146747 421.896059 \r\nL 162.098111 418.474152 \r\nL 162.162959 421.786785 \r\nL 162.211594 418.129952 \r\nL 162.276442 421.340614 \r\nL 162.292654 421.994457 \r\nL 162.34129 420.074815 \r\nL 162.373713 419.602396 \r\nL 162.422349 419.85927 \r\nL 162.454773 421.093509 \r\nL 162.51962 419.398821 \r\nL 162.552044 418.891368 \r\nL 162.584468 420.484289 \r\nL 162.60068 421.102741 \r\nL 162.633104 419.208898 \r\nL 162.649315 418.374801 \r\nL 162.697951 420.144969 \r\nL 162.730375 419.805014 \r\nL 162.746587 419.72981 \r\nL 162.795222 421.953358 \r\nL 162.827646 419.371722 \r\nL 162.86007 416.202748 \r\nL 162.924917 420.920949 \r\nL 162.973553 417.223816 \r\nL 163.038401 420.380587 \r\nL 163.168096 418.206352 \r\nL 163.20052 419.242811 \r\nL 163.216731 419.7443 \r\nL 163.249155 418.44334 \r\nL 163.265367 417.701822 \r\nL 163.330215 419.269873 \r\nL 163.346427 420.161303 \r\nL 163.395062 418.593238 \r\nL 163.443698 419.655214 \r\nL 163.45991 419.593712 \r\nL 163.476122 419.634128 \r\nL 163.524757 420.539214 \r\nL 163.557181 419.885677 \r\nL 163.589605 417.194965 \r\nL 163.654452 420.802021 \r\nL 163.703088 418.696548 \r\nL 163.800359 419.174755 \r\nL 163.832783 421.44465 \r\nL 163.897631 418.371174 \r\nL 163.913843 417.915236 \r\nL 163.930054 419.204888 \r\nL 163.962478 420.668017 \r\nL 164.043538 420.243542 \r\nL 164.140809 418.531508 \r\nL 164.157021 418.988324 \r\nL 164.286716 421.221512 \r\nL 164.31914 420.095025 \r\nL 164.400199 420.160182 \r\nL 164.448835 418.745615 \r\nL 164.465047 418.70364 \r\nL 164.481259 419.201841 \r\nL 164.529894 418.033818 \r\nL 164.562318 419.171909 \r\nL 164.643377 416.978675 \r\nL 164.675801 418.261776 \r\nL 164.724437 421.845052 \r\nL 164.789284 419.141813 \r\nL 164.805496 419.236621 \r\nL 164.83792 417.516327 \r\nL 164.902768 419.06694 \r\nL 165.032463 422.022238 \r\nL 164.967615 418.652321 \r\nL 165.048675 422.004316 \r\nL 165.17837 419.444607 \r\nL 165.340489 417.357499 \r\nL 165.243217 419.970546 \r\nL 165.3567 417.496621 \r\nL 165.486396 421.052549 \r\nL 165.502607 420.436101 \r\nL 165.616091 418.355913 \r\nL 165.761998 421.665174 \r\nL 165.810633 418.352714 \r\nL 165.891693 419.759573 \r\nL 165.940328 420.856194 \r\nL 165.95654 419.509371 \r\nL 165.972752 418.391022 \r\nL 166.021388 421.576315 \r\nL 166.053812 420.217521 \r\nL 166.086235 420.554667 \r\nL 166.102447 419.903624 \r\nL 166.134871 418.407951 \r\nL 166.183507 421.020179 \r\nL 166.199719 421.477259 \r\nL 166.21593 420.707027 \r\nL 166.280778 418.031484 \r\nL 166.329414 419.023943 \r\nL 166.394261 422.2679 \r\nL 166.426685 419.708161 \r\nL 166.55638 417.158606 \r\nL 166.475321 419.772574 \r\nL 166.572592 417.351658 \r\nL 166.702287 422.426478 \r\nL 166.718499 421.288896 \r\nL 166.848194 415.965974 \r\nL 166.864406 416.223939 \r\nL 166.994101 421.942613 \r\nL 167.010313 420.824586 \r\nL 167.07516 417.093532 \r\nL 167.123796 419.706322 \r\nL 167.140008 419.597918 \r\nL 167.172432 418.323744 \r\nL 167.221067 420.026935 \r\nL 167.237279 420.970803 \r\nL 167.285915 418.772714 \r\nL 167.399398 416.345508 \r\nL 167.350763 420.248253 \r\nL 167.41561 417.527274 \r\nL 167.464246 421.581597 \r\nL 167.529093 417.937715 \r\nL 167.642576 421.519683 \r\nL 167.723636 419.705045 \r\nL 167.837119 417.374673 \r\nL 167.853331 417.935272 \r\nL 167.918179 422.477035 \r\nL 167.983026 420.350042 \r\nL 167.999238 420.350633 \r\nL 168.047874 416.877916 \r\nL 168.177569 418.455175 \r\nL 168.226204 420.996707 \r\nL 168.27484 418.914889 \r\nL 168.339688 418.099028 \r\nL 168.355899 418.753634 \r\nL 168.453171 421.075663 \r\nL 168.469383 420.205082 \r\nL 168.518018 417.473361 \r\nL 168.582866 419.821769 \r\nL 168.631502 417.798818 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420.864128 \r\nL 170.204055 419.768388 \r\nL 170.268902 417.584081 \r\nL 170.317538 418.432699 \r\nL 170.366173 422.212049 \r\nL 170.398597 424.64043 \r\nL 170.431021 419.423061 \r\nL 170.479657 416.767527 \r\nL 170.560716 417.364816 \r\nL 170.690411 421.513275 \r\nL 170.706623 420.715676 \r\nL 170.755259 414.821874 \r\nL 170.820106 419.162866 \r\nL 170.884954 420.837705 \r\nL 170.933589 420.116 \r\nL 170.982225 416.697605 \r\nL 171.063285 418.343326 \r\nL 171.079496 418.301389 \r\nL 171.144344 423.852932 \r\nL 171.19298 418.859606 \r\nL 171.274039 417.209612 \r\nL 171.241615 419.071647 \r\nL 171.290251 417.277269 \r\nL 171.355099 421.289957 \r\nL 171.419946 419.873637 \r\nL 171.436158 419.96992 \r\nL 171.45237 419.64243 \r\nL 171.517217 418.561493 \r\nL 171.533429 419.679922 \r\nL 171.630701 421.697605 \r\nL 171.646912 421.406026 \r\nL 171.727972 415.936385 \r\nL 171.760396 418.471851 \r\nL 171.857667 422.750495 \r\nL 171.890091 421.368766 \r\nL 171.954938 416.954443 \r\nL 172.019786 418.636752 \r\nL 172.05221 420.275506 \r\nL 172.100845 423.768329 \r\nL 172.149481 420.183004 \r\nL 172.198117 416.755442 \r\nL 172.262964 419.903709 \r\nL 172.295388 420.390239 \r\nL 172.344024 422.568704 \r\nL 172.376447 420.551592 \r\nL 172.473719 416.695795 \r\nL 172.506142 417.569941 \r\nL 172.57099 421.536769 \r\nL 172.619626 418.84473 \r\nL 172.668261 415.989094 \r\nL 172.733109 417.535621 \r\nL 172.781745 421.888754 \r\nL 172.814168 423.006899 \r\nL 172.862804 420.402132 \r\nL 172.943863 414.856977 \r\nL 172.992499 418.66933 \r\nL 173.073559 424.596536 \r\nL 173.138406 420.720133 \r\nL 173.203254 416.064075 \r\nL 173.251889 419.420528 \r\nL 173.332949 421.836499 \r\nL 173.365372 420.09454 \r\nL 173.397796 417.294829 \r\nL 173.462644 419.233772 \r\nL 173.576127 421.85552 \r\nL 173.592339 421.532061 \r\nL 173.640975 415.542657 \r\nL 173.722034 418.244782 \r\nL 173.738246 418.290057 \r\nL 173.786882 422.725718 \r\nL 173.835517 419.490693 \r\nL 173.867941 417.666851 \r\nL 173.949 418.093573 \r\nL 174.03006 422.330914 \r\nL 174.078695 419.304083 \r\nL 174.175967 416.525962 \r\nL 174.208391 417.994636 \r\nL 174.273238 422.288416 \r\nL 174.338086 420.631176 \r\nL 174.435357 416.406678 \r\nL 174.467781 418.291625 \r\nL 174.532628 421.940424 \r\nL 174.581264 419.969175 \r\nL 174.613688 416.190855 \r\nL 174.694747 419.107098 \r\nL 174.759595 422.028079 \r\nL 174.80823 419.385104 \r\nL 174.88929 417.580533 \r\nL 174.937925 418.104544 \r\nL 174.954137 418.214843 \r\nL 175.018985 424.758102 \r\nL 175.067621 418.503113 \r\nL 175.14868 416.758335 \r\nL 175.164892 417.059837 \r\nL 175.245951 423.28346 \r\nL 175.294587 420.391079 \r\nL 175.40807 417.096008 \r\nL 175.424282 417.704802 \r\nL 175.521553 424.240655 \r\nL 175.553977 421.735298 \r\nL 175.586401 417.225331 \r\nL 175.66746 418.271416 \r\nL 175.716096 422.647709 \r\nL 175.780944 420.304223 \r\nL 175.797155 420.621106 \r\nL 175.829579 419.398592 \r\nL 175.862003 419.349205 \r\nL 175.926851 416.677289 \r\nL 175.959274 419.469603 \r\nL 175.991698 422.328252 \r\nL 176.056546 418.788472 \r\nL 176.088969 416.829159 \r\nL 176.137605 419.980858 \r\nL 176.153817 420.49423 \r\nL 176.186241 418.157039 \r\nL 176.202453 417.705889 \r\nL 176.234876 418.331342 \r\nL 176.2673 417.97447 \r\nL 176.315936 420.841777 \r\nL 176.364572 417.870411 \r\nL 176.380783 417.675502 \r\nL 176.396995 418.282515 \r\nL 176.461843 421.337277 \r\nL 176.510478 419.790886 \r\nL 176.559114 417.236863 \r\nL 176.623962 418.836215 \r\nL 176.640174 418.789096 \r\nL 176.672597 419.597642 \r\nL 176.705021 418.608796 \r\nL 176.721233 417.92814 \r\nL 176.753657 420.1128 \r\nL 176.786081 421.193632 \r\nL 176.834716 418.642336 \r\nL 176.86714 417.920122 \r\nL 176.915776 419.70344 \r\nL 176.964411 417.912702 \r\nL 177.045471 418.104263 \r\nL 177.110318 420.569758 \r\nL 177.142742 418.485195 \r\nL 177.158954 417.89093 \r\nL 177.191378 420.217993 \r\nL 177.20759 420.942928 \r\nL 177.256225 419.085669 \r\nL 177.288649 419.947975 \r\nL 177.402132 417.722996 \r\nL 177.418344 418.61274 \r\nL 177.548039 421.131039 \r\nL 177.596675 418.849163 \r\nL 177.693946 419.674976 \r\nL 177.710158 419.72867 \r\nL 177.742582 419.488206 \r\nL 177.775006 419.053549 \r\nL 177.807429 419.901019 \r\nL 177.839853 420.759286 \r\nL 177.888489 419.563093 \r\nL 177.937125 417.013776 \r\nL 177.953336 419.217531 \r\nL 177.98576 422.343593 \r\nL 178.06682 419.968477 \r\nL 178.115455 417.835371 \r\nL 178.212727 418.513134 \r\nL 178.342422 420.505376 \r\nL 178.358634 419.432653 \r\nL 178.391057 417.688301 \r\nL 178.455905 419.07601 \r\nL 178.504541 422.529452 \r\nL 178.569388 419.185926 \r\nL 178.666659 417.436407 \r\nL 178.682871 418.228551 \r\nL 178.796355 421.285869 \r\nL 178.812566 420.548113 \r\nL 178.909838 416.072353 \r\nL 178.92605 417.577255 \r\nL 179.039533 421.242269 \r\nL 179.055745 420.59251 \r\nL 179.120592 417.924705 \r\nL 179.153016 418.950248 \r\nL 179.282711 422.903965 \r\nL 179.396194 417.729339 \r\nL 179.461042 420.561456 \r\nL 179.493466 422.797494 \r\nL 179.558313 420.544608 \r\nL 179.606949 417.204994 \r\nL 179.671796 419.440776 \r\nL 179.70422 418.621783 \r\nL 179.736644 419.642145 \r\nL 179.769068 420.72163 \r\nL 179.817703 419.145537 \r\nL 179.882551 415.819073 \r\nL 179.931187 418.956035 \r\nL 179.979822 421.902493 \r\nL 180.04467 420.239089 \r\nL 180.125729 417.350848 \r\nL 180.239212 418.587853 \r\nL 180.271636 421.041602 \r\nL 180.336484 417.646367 \r\nL 180.352696 417.664644 \r\nL 180.433755 421.075469 \r\nL 180.482391 418.808626 \r\nL 180.531026 417.796798 \r\nL 180.56345 416.863805 \r\nL 180.628298 418.409924 \r\nL 180.676933 419.295891 \r\nL 180.725569 421.597687 \r\nL 180.774205 420.257715 \r\nL 180.839052 417.796243 \r\nL 180.887688 418.855059 \r\nL 180.936324 421.049844 \r\nL 181.001171 419.074049 \r\nL 181.098442 418.642896 \r\nL 181.195714 422.178405 \r\nL 181.228138 420.945813 \r\nL 181.325409 417.139801 \r\nL 181.357833 418.549411 \r\nL 181.471316 421.531541 \r\nL 181.487528 421.514267 \r\nL 181.536163 418.511322 \r\nL 181.601011 420.460452 \r\nL 181.649647 421.976082 \r\nL 181.665858 423.011245 \r\nL 181.714494 419.878481 \r\nL 181.779342 416.432402 \r\nL 181.844189 418.787953 \r\nL 181.925249 423.665841 \r\nL 181.990096 420.743097 \r\nL 182.071156 415.694738 \r\nL 182.119791 418.982581 \r\nL 182.184639 420.248873 \r\nL 182.233274 419.63006 \r\nL 182.314334 417.114559 \r\nL 182.346758 419.403414 \r\nL 182.427817 420.972993 \r\nL 182.444029 420.221299 \r\nL 182.492665 416.165096 \r\nL 182.573724 417.09444 \r\nL 182.670995 421.783745 \r\nL 182.719631 419.216063 \r\nL 182.800691 417.143526 \r\nL 182.816902 418.205632 \r\nL 182.88175 422.997706 \r\nL 182.930386 419.594075 \r\nL 183.043869 417.215426 \r\nL 183.14114 423.051899 \r\nL 183.205988 421.141387 \r\nL 183.254623 417.818287 \r\nL 183.335683 418.973507 \r\nL 183.368107 419.501854 \r\nL 183.449166 421.303945 \r\nL 183.465378 420.216503 \r\nL 183.530225 417.151893 \r\nL 183.578861 419.926327 \r\nL 183.611285 422.038793 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420.444958 \r\nL 185.816101 418.403405 \r\nL 185.864737 421.786257 \r\nL 185.897161 419.786615 \r\nL 185.97822 418.720687 \r\nL 186.026856 418.904076 \r\nL 186.043068 418.299297 \r\nL 186.091704 419.950983 \r\nL 186.107915 420.503743 \r\nL 186.156551 418.532955 \r\nL 186.172763 418.404032 \r\nL 186.205187 418.921808 \r\nL 186.253822 418.476771 \r\nL 186.270034 418.541614 \r\nL 186.31867 421.534335 \r\nL 186.351094 418.812994 \r\nL 186.367306 417.386465 \r\nL 186.432153 419.662507 \r\nL 186.448365 420.132191 \r\nL 186.497001 418.585517 \r\nL 186.529424 419.296714 \r\nL 186.626696 420.570764 \r\nL 186.642908 420.468565 \r\nL 186.691543 417.055387 \r\nL 186.740179 419.571564 \r\nL 186.772603 421.514547 \r\nL 186.83745 419.415288 \r\nL 186.886086 420.302691 \r\nL 186.91851 419.304934 \r\nL 186.950934 419.460509 \r\nL 187.015781 418.093404 \r\nL 187.048205 419.40239 \r\nL 187.096841 421.702615 \r\nL 187.129264 419.252539 \r\nL 187.145476 418.803056 \r\nL 187.194112 419.800705 \r\nL 187.210324 420.492442 \r\nL 187.258959 418.865125 \r\nL 187.275171 418.869825 \r\nL 187.307595 418.104289 \r\nL 187.340019 419.328581 \r\nL 187.404866 421.133126 \r\nL 187.43729 419.913892 \r\nL 187.51835 419.296378 \r\nL 187.534561 419.779212 \r\nL 187.550773 420.145014 \r\nL 187.583197 419.497305 \r\nL 187.631833 417.052315 \r\nL 187.664257 419.832468 \r\nL 187.69668 421.722195 \r\nL 187.761528 418.889558 \r\nL 187.842587 418.588413 \r\nL 187.793952 418.970991 \r\nL 187.858799 418.768636 \r\nL 187.939859 418.290825 \r\nL 188.004706 421.203394 \r\nL 188.085766 418.100414 \r\nL 188.183037 418.845306 \r\nL 188.215461 420.480416 \r\nL 188.280308 418.828599 \r\nL 188.29652 418.711644 \r\nL 188.312732 419.263805 \r\nL 188.328944 420.439812 \r\nL 188.393791 417.573149 \r\nL 188.507275 420.514107 \r\nL 188.539698 419.461185 \r\nL 188.572122 419.665756 \r\nL 188.588334 419.125284 \r\nL 188.620758 417.862718 \r\nL 188.669394 420.744818 \r\nL 188.734241 417.270422 \r\nL 188.766665 420.035466 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418.166471 \r\nL 192.284644 420.981156 \r\nL 192.365704 420.304051 \r\nL 192.414339 418.612693 \r\nL 192.462975 420.11857 \r\nL 192.479187 421.054438 \r\nL 192.560246 419.411559 \r\nL 192.625094 418.682256 \r\nL 192.689941 418.932077 \r\nL 192.722365 421.266095 \r\nL 192.803425 421.245225 \r\nL 192.85206 418.1036 \r\nL 192.93312 419.258217 \r\nL 193.014179 421.126103 \r\nL 193.062815 419.990581 \r\nL 193.127662 418.903639 \r\nL 193.208722 419.205159 \r\nL 193.257357 422.002403 \r\nL 193.305993 418.546833 \r\nL 193.322205 418.679105 \r\nL 193.338417 418.624597 \r\nL 193.370841 417.058507 \r\nL 193.403264 418.859351 \r\nL 193.500536 420.718603 \r\nL 193.516748 419.621464 \r\nL 193.646443 418.273926 \r\nL 193.695078 420.778429 \r\nL 193.727502 418.57496 \r\nL 193.824773 415.241135 \r\nL 193.776138 420.337649 \r\nL 193.857197 417.519679 \r\nL 193.889621 419.507395 \r\nL 193.97068 418.379043 \r\nL 194.084164 417.44878 \r\nL 194.035528 418.653187 \r\nL 194.116587 417.811336 \r\nL 194.230071 422.333079 \r\nL 194.246283 420.841747 \r\nL 194.294918 416.650856 \r\nL 194.359766 419.492639 \r\nL 194.473249 421.510132 \r\nL 194.489461 421.509397 \r\nL 194.635368 418.585758 \r\nL 194.684003 421.175658 \r\nL 194.748851 420.19332 \r\nL 194.797487 418.65386 \r\nL 194.846122 420.159404 \r\nL 194.894758 421.444192 \r\nL 194.927182 420.720846 \r\nL 194.975817 419.174611 \r\nL 195.040665 419.751219 \r\nL 195.056877 419.382931 \r\nL 195.105513 420.576131 \r\nL 195.121724 420.611108 \r\nL 195.202784 421.642553 \r\nL 195.25142 419.398393 \r\nL 195.267631 419.416312 \r\nL 195.283843 419.284779 \r\nL 195.300055 418.812065 \r\nL 195.332479 420.215452 \r\nL 195.364903 421.685355 \r\nL 195.397326 419.309998 \r\nL 195.42975 419.626554 \r\nL 195.51081 417.200308 \r\nL 195.559445 418.817089 \r\nL 195.591869 419.825437 \r\nL 195.608081 420.660508 \r\nL 195.672929 418.56815 \r\nL 195.68914 418.55994 \r\nL 195.705352 419.010467 \r\nL 195.737776 417.934521 \r\nL 195.753988 416.870677 \r\nL 195.802624 420.081202 \r\nL 195.835047 418.605603 \r\nL 195.851259 418.377431 \r\nL 195.883683 419.046455 \r\nL 195.916107 420.057833 \r\nL 195.980954 419.288231 \r\nL 196.013378 417.529962 \r\nL 196.062014 419.547699 \r\nL 196.094438 418.676264 \r\nL 196.143073 419.719761 \r\nL 196.159285 418.692482 \r\nL 196.191709 416.812454 \r\nL 196.240345 420.171754 \r\nL 196.256556 421.136015 \r\nL 196.337616 419.731403 \r\nL 196.353828 418.87554 \r\nL 196.402463 421.297084 \r\nL 196.418675 421.408727 \r\nL 196.467311 417.641564 \r\nL 196.532159 420.980695 \r\nL 196.580794 421.68354 \r\nL 196.597006 421.174745 \r\nL 196.661854 418.475499 \r\nL 196.742913 419.491596 \r\nL 196.872608 420.477726 \r\nL 196.969879 418.476339 \r\nL 196.986091 418.675527 \r\nL 197.034727 421.393934 \r\nL 197.099575 419.742321 \r\nL 197.131998 419.994724 \r\nL 197.14821 420.151193 \r\nL 197.164422 419.890847 \r\nL 197.213058 418.180561 \r\nL 197.245482 419.934667 \r\nL 197.310329 420.613963 \r\nL 197.326541 419.536525 \r\nL 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211.041797 420.449776 \r\nL 211.090433 414.821934 \r\nL 211.171492 417.316342 \r\nL 211.252552 422.369018 \r\nL 211.284975 419.271499 \r\nL 211.333611 416.165325 \r\nL 211.398459 417.461595 \r\nL 211.49573 423.232903 \r\nL 211.544366 420.250291 \r\nL 211.625425 417.085 \r\nL 211.657849 418.796504 \r\nL 211.738908 422.673137 \r\nL 211.771332 421.617488 \r\nL 211.819968 417.47571 \r\nL 211.901027 417.910344 \r\nL 212.01451 419.804966 \r\nL 212.063146 416.975194 \r\nL 212.127994 419.1634 \r\nL 212.209053 424.314509 \r\nL 212.257689 420.881525 \r\nL 212.322536 417.884946 \r\nL 212.371172 420.027939 \r\nL 212.468443 423.558398 \r\nL 212.419807 419.90693 \r\nL 212.500867 421.357512 \r\nL 212.549503 419.8612 \r\nL 212.598138 416.407953 \r\nL 212.679198 417.406806 \r\nL 212.792681 415.008998 \r\nL 212.808893 416.15598 \r\nL 212.9548 420.128173 \r\nL 212.987224 419.590203 \r\nL 213.035859 417.509451 \r\nL 213.100707 419.298752 \r\nL 213.246614 421.263456 \r\nL 213.506004 417.132563 \r\nL 213.538428 417.9079 \r\nL 213.684335 422.153306 \r\nL 213.700547 421.643011 \r\nL 213.749182 419.07251 \r\nL 213.830242 420.261642 \r\nL 213.878877 422.299825 \r\nL 213.943725 420.718322 \r\nL 214.040996 419.400249 \r\nL 214.057208 419.844099 \r\nL 214.105844 421.177314 \r\nL 214.154479 419.937365 \r\nL 214.267963 417.889674 \r\nL 214.219327 420.067903 \r\nL 214.284174 418.341017 \r\nL 214.430081 420.856572 \r\nL 214.478717 417.039 \r\nL 214.543565 420.59245 \r\nL 214.559777 420.536541 \r\nL 214.738107 417.073015 \r\nL 214.754319 417.094169 \r\nL 214.867802 420.218786 \r\nL 214.900226 419.198574 \r\nL 215.013709 417.956853 \r\nL 215.046133 418.154749 \r\nL 215.175828 419.988173 \r\nL 215.19204 419.475467 \r\nL 215.2731 418.08802 \r\nL 215.289311 418.367362 \r\nL 215.321735 420.050882 \r\nL 215.370371 417.258841 \r\nL 215.386583 417.076985 \r\nL 215.435218 419.396116 \r\nL 215.500066 418.574362 \r\nL 215.548702 416.70261 \r\nL 215.581125 419.215509 \r\nL 215.597337 419.512616 \r\nL 215.629761 418.555332 \r\nL 215.662185 418.19587 \r\nL 215.694609 419.191486 \r\nL 215.79188 421.911867 \r\nL 215.824304 420.255206 \r\nL 215.856727 419.598911 \r\nL 215.905363 420.782821 \r\nL 215.921575 420.733093 \r\nL 216.099906 422.889094 \r\nL 216.116118 422.332035 \r\nL 216.180965 419.141023 \r\nL 216.245813 420.060748 \r\nL 216.278237 419.507283 \r\nL 216.294448 418.796425 \r\nL 216.343084 420.001642 \r\nL 216.39172 419.171816 \r\nL 216.407932 419.067298 \r\nL 216.440355 419.457911 \r\nL 216.456567 419.40334 \r\nL 216.472779 419.523804 \r\nL 216.488991 419.247659 \r\nL 216.537627 417.663371 \r\nL 216.586262 419.729421 \r\nL 216.699746 421.700977 \r\nL 216.65111 419.439944 \r\nL 216.715957 421.406647 \r\nL 216.845653 417.166647 \r\nL 216.861864 417.505617 \r\nL 216.975348 419.403814 \r\nL 216.99156 419.093452 \r\nL 217.105043 417.639331 \r\nL 217.121255 417.919625 \r\nL 217.25095 420.771447 \r\nL 217.267162 419.996245 \r\nL 217.332009 417.994222 \r\nL 217.380645 418.91686 \r\nL 217.494128 421.175639 \r\nL 217.558976 420.476153 \r\nL 217.737306 419.013378 \r\nL 217.867001 421.847679 \r\nL 217.899425 421.086649 \r\nL 218.061544 418.991747 \r\nL 218.077756 419.284128 \r\nL 218.207451 421.589988 \r\nL 218.223663 421.46932 \r\nL 218.337146 417.683421 \r\nL 218.36957 418.536802 \r\nL 218.401994 419.95443 \r\nL 218.483053 419.082355 \r\nL 218.580324 418.008227 \r\nL 218.612748 418.536388 \r\nL 218.645172 418.489279 \r\nL 218.661384 418.388304 \r\nL 218.677596 418.889143 \r\nL 218.71002 420.27916 \r\nL 218.774867 418.798113 \r\nL 218.823503 417.634796 \r\nL 218.872138 419.06668 \r\nL 219.018045 417.543763 \r\nL 219.034257 418.047275 \r\nL 219.066681 419.655533 \r\nL 219.14774 418.585724 \r\nL 219.277436 417.33456 \r\nL 219.293647 417.85073 \r\nL 219.374707 421.624632 \r\nL 219.455766 420.711895 \r\nL 219.536826 418.988765 \r\nL 219.56925 420.304779 \r\nL 219.617885 422.951927 \r\nL 219.682733 421.025311 \r\nL 219.74758 419.38808 \r\nL 219.796216 420.740267 \r\nL 219.861063 420.13625 \r\nL 219.909699 421.111764 \r\nL 220.071818 419.077029 \r\nL 220.136666 420.581656 \r\nL 220.185301 419.621151 \r\nL 220.217725 418.974974 \r\nL 220.282573 419.989954 \r\nL 220.314996 420.28856 \r\nL 220.331208 420.917845 \r\nL 220.396056 419.550355 \r\nL 220.42848 418.82173 \r\nL 220.493327 416.692507 \r\nL 220.541963 418.554061 \r\nL 220.655446 419.950102 \r\nL 220.671658 419.353119 \r\nL 220.817565 416.738854 \r\nL 220.963472 420.11816 \r\nL 220.979684 420.07993 \r\nL 221.012107 418.71428 \r\nL 221.060743 420.635164 \r\nL 221.076955 421.129843 \r\nL 221.109379 419.840437 \r\nL 221.141803 417.757417 \r\nL 221.222862 418.45179 \r\nL 221.401193 422.085826 \r\nL 221.46604 417.762231 \r\nL 221.530888 420.306493 \r\nL 221.579523 421.400074 \r\nL 221.628159 420.91403 \r\nL 221.676795 418.480524 \r\nL 221.757854 419.129749 \r\nL 221.871337 420.387687 \r\nL 221.919973 419.941734 \r\nL 221.984821 416.970785 \r\nL 222.017244 418.880592 \r\nL 222.06588 420.164176 \r\nL 222.114516 418.911496 \r\nL 222.130728 418.126896 \r\nL 222.195575 419.847459 \r\nL 222.227999 420.146567 \r\nL 222.244211 419.695264 \r\nL 222.276635 418.240798 \r\nL 222.309058 419.879301 \r\nL 222.357694 419.524114 \r\nL 222.390118 419.903343 \r\nL 222.40633 419.418522 \r\nL 222.454965 416.806042 \r\nL 222.503601 419.236601 \r\nL 222.536025 420.470628 \r\nL 222.58466 418.392655 \r\nL 222.600872 417.540078 \r\nL 222.66572 418.411217 \r\nL 222.698144 417.952951 \r\nL 222.844051 420.202467 \r\nL 222.795415 417.844457 \r\nL 222.860263 420.023784 \r\nL 222.941322 417.581792 \r\nL 222.973746 419.784967 \r\nL 223.006169 421.660569 \r\nL 223.087229 421.372516 \r\nL 223.103441 421.356296 \r\nL 223.233136 418.451648 \r\nL 223.249348 418.607689 \r\nL 223.314195 420.636359 \r\nL 223.379043 420.017495 \r\nL 223.395255 419.339043 \r\nL 223.44389 421.368439 \r\nL 223.460102 421.240343 \r\nL 223.557374 418.923392 \r\nL 223.589797 420.041819 \r\nL 223.622221 421.153435 \r\nL 223.670857 419.189401 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229.020779 422.360229 \r\nL 229.085627 419.81251 \r\nL 229.166686 418.05172 \r\nL 229.19911 419.321337 \r\nL 229.263958 419.296271 \r\nL 229.328805 421.707261 \r\nL 229.377441 418.660166 \r\nL 229.4585 419.54916 \r\nL 229.490924 420.707697 \r\nL 229.53956 419.262889 \r\nL 229.571984 420.193671 \r\nL 229.620619 418.710956 \r\nL 229.669255 420.37995 \r\nL 229.685467 420.341481 \r\nL 229.815162 417.49377 \r\nL 229.750314 420.446342 \r\nL 229.831374 418.176124 \r\nL 229.961069 420.093566 \r\nL 230.009705 420.546566 \r\nL 230.025916 420.180396 \r\nL 230.074552 417.308907 \r\nL 230.155612 418.760083 \r\nL 230.204247 421.211161 \r\nL 230.269095 419.267781 \r\nL 230.285307 419.390656 \r\nL 230.301519 419.152535 \r\nL 230.415002 417.733488 \r\nL 230.350154 419.279761 \r\nL 230.431214 418.153519 \r\nL 230.479849 420.278554 \r\nL 230.544697 418.324648 \r\nL 230.577121 417.596377 \r\nL 230.609544 418.923396 \r\nL 230.65818 420.982005 \r\nL 230.723028 420.082861 \r\nL 230.787875 417.982207 \r\nL 230.868935 419.106848 \r\nL 230.91757 420.807658 \r\nL 230.949994 422.555831 \r\nL 230.99863 420.321237 \r\nL 231.031053 418.084136 \r\nL 231.112113 418.552944 \r\nL 231.25802 421.762326 \r\nL 231.274232 421.150407 \r\nL 231.355291 417.880806 \r\nL 231.387715 419.725783 \r\nL 231.436351 421.599948 \r\nL 231.484986 419.758941 \r\nL 231.533622 418.421854 \r\nL 231.566046 419.736013 \r\nL 231.582258 420.695692 \r\nL 231.647105 418.063012 \r\nL 231.695741 420.447791 \r\nL 231.7768 418.771681 \r\nL 231.841648 418.121287 \r\nL 231.874072 418.791077 \r\nL 231.922707 420.941091 \r\nL 231.955131 418.66806 \r\nL 232.019978 419.079164 \r\nL 232.084826 416.173411 \r\nL 232.149674 420.972268 \r\nL 232.246945 418.993498 \r\nL 232.279369 418.629716 \r\nL 232.360428 418.947404 \r\nL 232.392852 418.461137 \r\nL 232.425276 419.096989 \r\nL 232.457699 421.037179 \r\nL 232.506335 418.436707 \r\nL 232.554971 420.573929 \r\nL 232.571183 420.58103 \r\nL 232.700878 419.106705 \r\nL 232.733302 418.288874 \r\nL 232.765725 420.863785 \r\nL 232.798149 422.034605 \r\nL 232.846785 419.116747 \r\nL 232.911632 421.604566 \r\nL 232.97648 419.921628 \r\nL 233.041327 419.413495 \r\nL 233.122387 421.508845 \r\nL 233.203446 418.848251 \r\nL 233.284506 419.707459 \r\nL 233.300718 419.783279 \r\nL 233.349353 418.097696 \r\nL 233.397989 419.592615 \r\nL 233.446625 419.940083 \r\nL 233.462836 419.496913 \r\nL 233.543896 419.753499 \r\nL 233.592532 418.871243 \r\nL 233.624955 419.487628 \r\nL 233.641167 419.893925 \r\nL 233.689803 418.9604 \r\nL 233.738438 419.689209 \r\nL 233.787074 418.123539 \r\nL 233.83571 419.622188 \r\nL 233.884345 419.976815 \r\nL 233.900557 419.592132 \r\nL 233.932981 417.81348 \r\nL 233.997829 420.025901 \r\nL 234.030252 419.119944 \r\nL 234.111312 417.934869 \r\nL 234.143736 419.0278 \r\nL 234.176159 419.921862 \r\nL 234.208583 418.911944 \r\nL 234.241007 417.799435 \r\nL 234.273431 419.039513 \r\nL 234.305855 418.873979 \r\nL 234.403126 420.51687 \r\nL 234.43555 419.304233 \r\nL 234.451761 418.880415 \r\nL 234.500397 420.262444 \r\nL 234.532821 419.778958 \r\nL 234.565245 418.999437 \r\nL 234.630092 420.23237 \r\nL 234.678728 419.165718 \r\nL 234.743575 419.826187 \r\nL 234.759787 420.013231 \r\nL 234.808423 419.355413 \r\nL 234.824635 419.067428 \r\nL 234.873271 420.01759 \r\nL 234.889482 419.914445 \r\nL 234.905694 420.099763 \r\nL 234.921906 420.492961 \r\nL 234.95433 419.807847 \r\nL 235.002966 418.146671 \r\nL 235.051601 420.171173 \r\nL 235.067813 420.147943 \r\nL 235.100237 419.293097 \r\nL 235.148873 420.207878 \r\nL 235.181296 421.606273 \r\nL 235.246144 420.001503 \r\nL 235.310991 418.381703 \r\nL 235.359627 419.284728 \r\nL 235.489322 420.976941 \r\nL 235.424475 418.821512 \r\nL 235.505534 420.208252 \r\nL 235.602805 417.697798 \r\nL 235.635229 418.953633 \r\nL 235.683865 420.166859 \r\nL 235.716289 418.954769 \r\nL 235.732501 418.128801 \r\nL 235.797348 420.148937 \r\nL 235.862196 417.79832 \r\nL 235.910831 419.51035 \r\nL 236.008103 420.12737 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241.779534 418.40761 \r\nL 241.876806 419.636588 \r\nL 241.974077 418.422211 \r\nL 241.925441 419.881452 \r\nL 241.990289 418.9008 \r\nL 242.022713 420.101986 \r\nL 242.119984 419.886974 \r\nL 242.152408 418.177504 \r\nL 242.233467 419.684009 \r\nL 242.298315 422.599292 \r\nL 242.34695 420.136402 \r\nL 242.460434 418.408553 \r\nL 242.476645 418.56668 \r\nL 242.525281 419.886057 \r\nL 242.573917 421.567043 \r\nL 242.638764 421.009213 \r\nL 242.6874 417.951864 \r\nL 242.768459 419.149542 \r\nL 242.800883 420.722517 \r\nL 242.849519 418.878569 \r\nL 242.881943 417.848802 \r\nL 242.914366 419.532787 \r\nL 242.94679 418.830647 \r\nL 242.963002 418.775363 \r\nL 242.979214 418.992068 \r\nL 243.044061 420.312309 \r\nL 243.092697 419.844785 \r\nL 243.189968 418.002204 \r\nL 243.222392 418.378569 \r\nL 243.335875 419.80263 \r\nL 243.352087 419.586147 \r\nL 243.400723 417.583462 \r\nL 243.46557 419.311233 \r\nL 243.481782 419.305783 \r\nL 243.530418 421.113964 \r\nL 243.562842 419.093312 \r\nL 243.611477 417.984807 \r\nL 243.676325 418.035588 \r\nL 243.773596 421.52739 \r\nL 243.838444 419.619705 \r\nL 243.919503 416.62685 \r\nL 243.951927 418.545013 \r\nL 244.049198 420.805003 \r\nL 244.081622 420.127842 \r\nL 244.114046 418.918939 \r\nL 244.211317 419.028707 \r\nL 244.227529 419.03757 \r\nL 244.259953 420.124263 \r\nL 244.292377 422.079017 \r\nL 244.341012 419.653036 \r\nL 244.373436 418.867425 \r\nL 244.454496 418.997435 \r\nL 244.519343 421.091935 \r\nL 244.567979 419.16694 \r\nL 244.616614 420.425741 \r\nL 244.681462 419.216713 \r\nL 244.713886 417.954696 \r\nL 244.762521 420.337535 \r\nL 244.778733 419.901002 \r\nL 244.876005 418.119526 \r\nL 244.908428 418.64103 \r\nL 245.038123 421.133935 \r\nL 245.054335 420.684083 \r\nL 245.102971 417.904199 \r\nL 245.18403 418.290328 \r\nL 245.216454 419.446936 \r\nL 245.281302 420.879019 \r\nL 245.329937 419.83181 \r\nL 245.378573 419.175891 \r\nL 245.427209 417.702329 \r\nL 245.475844 419.084371 \r\nL 245.540692 421.124969 \r\nL 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248.831705 419.257572 \r\nL 248.864129 418.275288 \r\nL 248.912764 419.965299 \r\nL 248.945188 418.947394 \r\nL 249.010036 418.473351 \r\nL 249.04246 419.390261 \r\nL 249.107307 418.618187 \r\nL 249.123519 418.265826 \r\nL 249.188366 419.111021 \r\nL 249.237002 419.574529 \r\nL 249.269426 418.863505 \r\nL 249.285638 418.421605 \r\nL 249.318062 419.418093 \r\nL 249.350485 420.612963 \r\nL 249.399121 418.694445 \r\nL 249.415333 418.945946 \r\nL 249.528816 419.86724 \r\nL 249.545028 419.86609 \r\nL 249.593664 418.939417 \r\nL 249.642299 419.702904 \r\nL 249.674723 421.023177 \r\nL 249.723359 418.545217 \r\nL 249.739571 418.434353 \r\nL 249.771994 419.873928 \r\nL 249.836842 418.189951 \r\nL 249.853054 418.60077 \r\nL 249.934113 420.194417 \r\nL 249.998961 419.790018 \r\nL 250.015173 419.846152 \r\nL 250.047596 419.580837 \r\nL 250.08002 419.39401 \r\nL 250.096232 419.798465 \r\nL 250.177292 419.395881 \r\nL 250.225927 420.369066 \r\nL 250.242139 420.477574 \r\nL 250.258351 420.199955 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419.494255 \r\nL 252.122718 419.781244 \r\nL 252.155142 418.859166 \r\nL 252.171354 418.487521 \r\nL 252.219989 419.921062 \r\nL 252.268625 417.794801 \r\nL 252.317261 420.016848 \r\nL 252.349684 421.471544 \r\nL 252.39832 419.668391 \r\nL 252.430744 420.399955 \r\nL 252.511803 418.339561 \r\nL 252.576651 418.555122 \r\nL 252.641498 420.836754 \r\nL 252.65771 421.119759 \r\nL 252.673922 420.430659 \r\nL 252.754982 416.897532 \r\nL 252.803617 419.198151 \r\nL 252.949524 420.9781 \r\nL 252.981948 419.380995 \r\nL 253.014372 416.947125 \r\nL 253.079219 419.972562 \r\nL 253.160279 421.369137 \r\nL 253.176491 420.818629 \r\nL 253.225126 418.014276 \r\nL 253.306186 419.597301 \r\nL 253.322398 419.763293 \r\nL 253.338609 419.276113 \r\nL 253.354821 418.759639 \r\nL 253.403457 420.6525 \r\nL 253.419669 420.781037 \r\nL 253.435881 420.367438 \r\nL 253.500728 417.112832 \r\nL 253.549364 419.70962 \r\nL 253.581788 421.858657 \r\nL 253.662847 420.189507 \r\nL 253.711483 418.223779 \r\nL 253.77633 419.81526 \r\nL 253.889814 421.722089 \r\nL 253.841178 419.35638 \r\nL 253.922237 421.039871 \r\nL 253.987085 417.164472 \r\nL 254.051932 419.651034 \r\nL 254.084356 419.741615 \r\nL 254.230263 417.93727 \r\nL 254.165416 419.788866 \r\nL 254.246475 418.452255 \r\nL 254.37617 420.400373 \r\nL 254.392382 420.229379 \r\nL 254.441018 419.051168 \r\nL 254.489653 420.494221 \r\nL 254.505865 420.641071 \r\nL 254.538289 420.365261 \r\nL 254.586925 418.91611 \r\nL 254.651772 419.93311 \r\nL 254.71662 418.631783 \r\nL 254.765256 419.5329 \r\nL 254.813891 420.677339 \r\nL 254.846315 419.928251 \r\nL 254.927374 417.459773 \r\nL 254.959798 418.785417 \r\nL 254.992222 420.176324 \r\nL 255.073281 419.600768 \r\nL 255.121917 420.704165 \r\nL 255.154341 419.56095 \r\nL 255.186765 418.861461 \r\nL 255.251612 419.471848 \r\nL 255.300248 420.474073 \r\nL 255.348883 419.096194 \r\nL 255.365095 418.497537 \r\nL 255.413731 420.275333 \r\nL 255.429943 420.553966 \r\nL 255.446155 419.548889 \r\nL 255.462367 418.434563 \r\nL 255.527214 420.11898 \r\nL 255.543426 419.947645 \r\nL 255.673121 417.663003 \r\nL 255.705545 418.877547 \r\nL 255.737969 420.649232 \r\nL 255.802816 418.915085 \r\nL 255.851452 419.591517 \r\nL 255.867664 419.794293 \r\nL 255.900088 419.282163 \r\nL 255.932511 418.742368 \r\nL 255.964935 419.760447 \r\nL 256.013571 420.836734 \r\nL 256.078418 420.434053 \r\nL 256.143266 417.805381 \r\nL 256.191902 419.916486 \r\nL 256.256749 419.257106 \r\nL 256.321597 420.615235 \r\nL 256.402656 418.086179 \r\nL 256.451292 419.704169 \r\nL 256.516139 421.060205 \r\nL 256.548563 419.507227 \r\nL 256.662046 418.323949 \r\nL 256.597199 419.82812 \r\nL 256.678258 418.636297 \r\nL 256.807953 420.820688 \r\nL 256.824165 420.121123 \r\nL 256.905225 418.222842 \r\nL 256.937648 419.144984 \r\nL 256.970072 420.597754 \r\nL 257.018708 418.784932 \r\nL 257.03492 418.230046 \r\nL 257.115979 418.828974 \r\nL 257.132191 418.877306 \r\nL 257.148403 418.767203 \r\nL 257.197039 421.389073 \r\nL 257.278098 419.984541 \r\nL 257.29431 419.962378 \r\nL 257.310522 420.202611 \r\nL 257.342945 419.159302 \r\nL 257.407793 418.156388 \r\nL 257.424005 418.592472 \r\nL 257.505064 421.45032 \r\nL 257.537488 419.23809 \r\nL 257.569912 418.226512 \r\nL 257.618548 419.45683 \r\nL 257.634759 419.454008 \r\nL 257.780666 418.573201 \r\nL 257.796878 418.651169 \r\nL 257.942785 421.289644 \r\nL 257.877938 418.250368 \r\nL 257.958997 421.010572 \r\nL 258.007633 418.616809 \r\nL 258.104904 418.686063 \r\nL 258.15354 420.542085 \r\nL 258.250811 420.113924 \r\nL 258.315659 418.467802 \r\nL 258.41293 419.208685 \r\nL 258.526413 420.018305 \r\nL 258.477778 419.090117 \r\nL 258.558837 419.36032 \r\nL 258.575049 419.041859 \r\nL 258.656108 419.584137 \r\nL 258.67232 419.72152 \r\nL 258.704744 419.257759 \r\nL 258.720956 419.129832 \r\nL 258.75338 419.430444 \r\nL 258.769592 419.395081 \r\nL 258.850651 421.154995 \r\nL 258.899287 419.67896 \r\nL 259.093829 418.782616 \r\nL 259.110041 419.000946 \r\nL 259.158677 420.884149 \r\nL 259.207312 419.003683 \r\nL 259.239736 417.19116 \r\nL 259.288372 420.056119 \r\nL 259.304584 420.713118 \r\nL 259.369431 419.264307 \r\nL 259.401855 418.533453 \r\nL 259.466703 419.05016 \r\nL 259.499126 419.557296 \r\nL 259.53155 418.799393 \r\nL 259.547762 418.36366 \r\nL 259.596398 419.917368 \r\nL 259.661245 420.042645 \r\nL 259.645033 419.763206 \r\nL 259.677457 419.842381 \r\nL 259.726093 417.814338 \r\nL 259.774728 419.162155 \r\nL 259.807152 420.713505 \r\nL 259.872 419.097822 \r\nL 259.904424 418.638486 \r\nL 259.953059 419.480984 \r\nL 260.001695 419.69058 \r\nL 260.017907 419.66839 \r\nL 260.13139 421.133621 \r\nL 260.147602 420.358183 \r\nL 260.228661 418.055199 \r\nL 260.261085 418.989747 \r\nL 260.293509 420.78346 \r\nL 260.374568 420.359056 \r\nL 260.488052 418.117861 \r\nL 260.520475 419.098827 \r\nL 260.552899 420.063795 \r\nL 260.601535 418.64943 \r\nL 260.617747 418.154114 \r\nL 260.682594 419.092597 \r\nL 260.698806 418.659286 \r\nL 260.715018 418.691529 \r\nL 260.828501 421.272987 \r\nL 260.860925 420.378921 \r\nL 260.941984 417.502882 \r\nL 260.974408 419.493278 \r\nL 261.055468 421.350738 \r\nL 261.104103 420.004689 \r\nL 261.217586 418.124424 \r\nL 261.25001 418.662435 \r\nL 261.298646 420.936037 \r\nL 261.363493 419.237036 \r\nL 261.444553 417.882129 \r\nL 261.460765 418.252324 \r\nL 261.5094 421.107096 \r\nL 261.59046 420.291422 \r\nL 261.606672 420.333519 \r\nL 261.671519 418.665532 \r\nL 261.736367 419.590478 \r\nL 261.768791 420.577341 \r\nL 261.833638 419.586485 \r\nL 261.963333 418.354494 \r\nL 261.979545 418.804902 \r\nL 262.011969 420.616693 \r\nL 262.076816 418.620356 \r\nL 262.10924 419.048332 \r\nL 262.125452 418.728874 \r\nL 262.157876 417.028267 \r\nL 262.222723 419.475494 \r\nL 262.303783 420.116637 \r\nL 262.319995 419.878158 \r\nL 262.433478 419.019568 \r\nL 262.44969 419.242882 \r\nL 262.482114 419.035134 \r\nL 262.530749 420.088428 \r\nL 262.563173 421.309694 \r\nL 262.611809 419.53907 \r\nL 262.628021 419.607238 \r\nL 262.660444 418.955073 \r\nL 262.70908 420.07539 \r\nL 262.741504 421.03023 \r\nL 262.790139 418.93975 \r\nL 262.822563 418.567865 \r\nL 262.854987 419.46547 \r\nL 262.96847 420.472887 \r\nL 262.919835 419.34212 \r\nL 262.984682 420.270162 \r\nL 263.130589 417.429878 \r\nL 263.146801 418.363686 \r\nL 263.276496 420.759899 \r\nL 263.292708 420.619118 \r\nL 263.341344 417.93031 \r\nL 263.422403 419.074063 \r\nL 263.487251 420.666948 \r\nL 263.535886 419.196561 \r\nL 263.56831 418.508198 \r\nL 263.633158 419.469807 \r\nL 263.665581 419.36128 \r\nL 263.681793 419.461399 \r\nL 263.714217 420.070673 \r\nL 263.746641 418.860119 \r\nL 263.811488 419.685511 \r\nL 263.860124 417.973177 \r\nL 263.941183 421.225171 \r\nL 263.989819 419.51728 \r\nL 264.08709 417.888381 \r\nL 264.119514 418.483768 \r\nL 264.249209 420.810205 \r\nL 264.265421 420.20736 \r\nL 264.297845 418.762085 \r\nL 264.378904 418.913418 \r\nL 264.443752 421.169094 \r\nL 264.524811 420.223529 \r\nL 264.573447 419.578541 \r\nL 264.638294 419.927341 \r\nL 264.68693 421.265775 \r\nL 264.735566 419.86242 \r\nL 264.784201 418.809907 \r\nL 264.849049 419.619719 \r\nL 264.881473 419.257446 \r\nL 264.930108 419.571116 \r\nL 264.962532 420.215726 \r\nL 264.994956 419.327205 \r\nL 265.02738 418.556454 \r\nL 265.092227 418.966927 \r\nL 265.140863 420.313135 \r\nL 265.205711 419.201662 \r\nL 265.319194 418.570453 \r\nL 265.28677 419.248535 \r\nL 265.335406 418.751795 \r\nL 265.400253 419.974052 \r\nL 265.448889 418.984125 \r\nL 265.513736 417.935775 \r\nL 265.562372 418.44259 \r\nL 265.611008 419.238664 \r\nL 265.692067 418.985143 \r\nL 265.708279 418.954258 \r\nL 265.724491 419.096819 \r\nL 265.740703 419.173709 \r\nL 265.756915 418.924725 \r\nL 265.837974 418.369019 \r\nL 265.854186 418.579113 \r\nL 265.902822 420.582986 \r\nL 265.967669 419.248047 \r\nL 265.983881 418.976201 \r\nL 266.048729 419.605208 \r\nL 266.113576 420.7574 \r\nL 266.162212 420.160677 \r\nL 266.210848 418.662504 \r\nL 266.259483 420.353313 \r\nL 266.324331 418.68595 \r\nL 266.437814 421.120623 \r\nL 266.470238 420.21177 \r\nL 266.502661 419.305641 \r\nL 266.583721 419.950603 \r\nL 266.697204 420.378565 \r\nL 266.810687 419.336144 \r\nL 266.875535 420.379209 \r\nL 266.940382 418.458798 \r\nL 266.972806 419.895239 \r\nL 267.070077 419.592215 \r\nL 267.086289 419.709775 \r\nL 267.102501 419.158512 \r\nL 267.118713 418.571842 \r\nL 267.183561 419.837492 \r\nL 267.215984 418.987271 \r\nL 267.232196 418.623885 \r\nL 267.297044 419.561598 \r\nL 267.313256 419.826026 \r\nL 267.361891 418.933541 \r\nL 267.378103 418.666274 \r\nL 267.394315 419.278013 \r\nL 267.410527 420.0218 \r\nL 267.507798 419.822981 \r\nL 267.572646 418.276077 \r\nL 267.653705 419.24852 \r\nL 267.750977 419.013946 \r\nL 267.783401 419.995481 \r\nL 267.799612 420.214815 \r\nL 267.832036 419.645408 \r\nL 267.86446 419.678317 \r\nL 267.913096 417.45598 \r\nL 267.945519 420.097044 \r\nL 267.961731 420.53885 \r\nL 267.994155 419.053693 \r\nL 268.010367 418.622895 \r\nL 268.059003 419.97538 \r\nL 268.075214 419.708809 \r\nL 268.091426 419.651361 \r\nL 268.107638 419.770877 \r\nL 268.140062 420.590165 \r\nL 268.221121 419.939198 \r\nL 268.237333 419.930146 \r\nL 268.253545 420.250099 \r\nL 268.285969 419.650111 \r\nL 268.334605 418.089168 \r\nL 268.38324 419.814166 \r\nL 268.399452 419.87646 \r\nL 268.415664 419.557633 \r\nL 268.431876 419.195879 \r\nL 268.4643 420.794804 \r\nL 268.480512 421.488084 \r\nL 268.512935 419.239009 \r\nL 268.529147 418.453743 \r\nL 268.561571 419.577217 \r\nL 268.626419 418.919032 \r\nL 268.72369 420.624754 \r\nL 268.756114 419.902677 \r\nL 268.853385 418.197094 \r\nL 268.885809 419.125105 \r\nL 268.98308 420.255707 \r\nL 268.999292 420.193576 \r\nL 269.06414 418.118266 \r\nL 269.161411 418.67333 \r\nL 269.210047 419.584085 \r\nL 269.274894 419.369449 \r\nL 269.372165 418.550111 \r\nL 269.388377 418.775849 \r\nL 269.420801 420.17193 \r\nL 269.50186 419.201637 \r\nL 269.663979 420.96976 \r\nL 269.566708 418.618753 \r\nL 269.680191 420.706724 \r\nL 269.777463 417.836949 \r\nL 269.858522 418.910279 \r\nL 269.874734 418.74172 \r\nL 269.907158 419.182806 \r\nL 269.955793 421.356824 \r\nL 270.020641 419.465497 \r\nL 270.036853 419.101019 \r\nL 270.1017 419.704113 \r\nL 270.198972 420.880111 \r\nL 270.215184 420.596549 \r\nL 270.247607 419.164194 \r\nL 270.312455 420.32461 \r\nL 270.377302 419.728013 \r\nL 270.425938 420.599746 \r\nL 270.523209 419.211909 \r\nL 270.555633 419.409528 \r\nL 270.571845 419.349408 \r\nL 270.588057 419.415669 \r\nL 270.652904 420.557818 \r\nL 270.669116 419.905755 \r\nL 270.70154 418.583299 \r\nL 270.7826 418.906868 \r\nL 270.896083 421.054529 \r\nL 270.944718 419.872127 \r\nL 271.025778 418.332887 \r\nL 271.058202 418.885461 \r\nL 271.090625 420.191466 \r\nL 271.139261 418.472012 \r\nL 271.187897 419.882009 \r\nL 271.236532 417.601672 \r\nL 271.317592 418.672809 \r\nL 271.398651 419.796882 \r\nL 271.431075 418.898272 \r\nL 271.463499 417.527277 \r\nL 271.528346 419.31662 \r\nL 271.658041 420.486236 \r\nL 271.576982 419.129423 \r\nL 271.674253 419.888212 \r\nL 271.739101 417.826325 \r\nL 271.787737 419.226693 \r\nL 271.82016 419.866999 \r\nL 271.868796 420.770369 \r\nL 271.90122 419.605335 \r\nL 271.949855 418.245915 \r\nL 272.014703 419.325835 \r\nL 272.128186 421.757831 \r\nL 272.063339 419.27096 \r\nL 272.16061 420.413383 \r\nL 272.209246 418.057728 \r\nL 272.274093 419.595208 \r\nL 272.371364 421.699487 \r\nL 272.403788 420.220573 \r\nL 272.50106 417.832783 \r\nL 272.533483 418.77841 \r\nL 272.614543 421.573461 \r\nL 272.646967 420.514966 \r\nL 272.695602 417.825807 \r\nL 272.76045 419.290469 \r\nL 272.841509 419.916302 \r\nL 272.873933 419.531447 \r\nL 272.906357 419.725669 \r\nL 272.93878 419.237424 \r\nL 273.01984 418.425903 \r\nL 273.036052 419.040103 \r\nL 273.084687 421.575942 \r\nL 273.149535 419.633409 \r\nL 273.181959 416.914261 \r\nL 273.246806 420.203698 \r\nL 273.263018 420.201228 \r\nL 273.295442 418.801576 \r\nL 273.344078 420.72555 \r\nL 273.36029 420.830134 \r\nL 273.376501 420.307125 \r\nL 273.506197 418.357396 \r\nL 273.587256 421.405802 \r\nL 273.635892 418.990418 \r\nL 273.716951 418.646398 \r\nL 273.684527 419.034879 \r\nL 273.733163 418.708245 \r\nL 273.862858 420.62728 \r\nL 273.87907 420.378879 \r\nL 273.976341 418.954403 \r\nL 274.008765 419.344276 \r\nL 274.089824 420.286492 \r\nL 274.122248 419.449705 \r\nL 274.13846 419.133298 \r\nL 274.21952 419.57236 \r\nL 274.316791 421.111477 \r\nL 274.349215 419.920512 \r\nL 274.39785 418.326049 \r\nL 274.462698 419.367208 \r\nL 274.576181 420.341292 \r\nL 274.608605 419.805683 \r\nL 274.673452 418.048132 \r\nL 274.7383 418.367328 \r\nL 274.819359 420.463593 \r\nL 274.851783 419.484578 \r\nL 274.949054 417.733446 \r\nL 274.981478 418.220003 \r\nL 275.094961 420.462872 \r\nL 275.111173 420.340967 \r\nL 275.159809 418.832999 \r\nL 275.240868 419.376088 \r\nL 275.273292 419.530912 \r\nL 275.321928 419.487762 \r\nL 275.386775 417.934657 \r\nL 275.435411 419.133473 \r\nL 275.484047 418.797623 \r\nL 275.51647 420.097069 \r\nL 275.548894 421.087413 \r\nL 275.613742 420.180859 \r\nL 275.678589 417.48979 \r\nL 275.727225 419.051321 \r\nL 275.775861 421.165469 \r\nL 275.85692 420.291045 \r\nL 275.873132 420.452051 \r\nL 275.905556 420.035177 \r\nL 276.035251 419.178769 \r\nL 276.100098 421.219554 \r\nL 276.148734 419.244725 \r\nL 276.181158 418.947599 \r\nL 276.19737 419.719534 \r\nL 276.229793 420.506206 \r\nL 276.310853 420.497687 \r\nL 276.359489 419.168694 \r\nL 276.391912 420.213721 \r\nL 276.408124 421.00969 \r\nL 276.45676 418.92931 \r\nL 276.489184 419.745565 \r\nL 276.521607 420.61523 \r\nL 276.570243 419.355693 \r\nL 276.602667 418.987575 \r\nL 276.635091 420.02566 \r\nL 276.651303 420.224859 \r\nL 276.71615 419.658088 \r\nL 276.894481 418.157105 \r\nL 276.910693 418.475167 \r\nL 276.959328 420.186689 \r\nL 277.024176 418.94627 \r\nL 277.072812 419.473651 \r\nL 277.089023 418.991193 \r\nL 277.153871 418.109672 \r\nL 277.202507 418.712294 \r\nL 277.218719 418.696579 \r\nL 277.251142 419.8067 \r\nL 277.31599 418.606263 \r\nL 277.332202 418.324991 \r\nL 277.397049 418.959917 \r\nL 277.478109 420.199184 \r\nL 277.510533 419.363964 \r\nL 277.542956 418.10557 \r\nL 277.591592 419.696968 \r\nL 277.624016 419.175622 \r\nL 277.640228 418.668301 \r\nL 277.688863 419.975629 \r\nL 277.721287 419.418349 \r\nL 277.753711 420.39504 \r\nL 277.83477 419.671982 \r\nL 277.948253 420.842144 \r\nL 277.964465 420.33922 \r\nL 278.077949 418.683396 \r\nL 278.09416 419.254937 \r\nL 278.207644 422.057001 \r\nL 278.159008 418.552882 \r\nL 278.240067 420.551661 \r\nL 278.288703 418.201445 \r\nL 278.353551 420.009808 \r\nL 278.369763 419.995608 \r\nL 278.402186 418.809096 \r\nL 278.450822 420.545881 \r\nL 278.483246 419.727384 \r\nL 278.499458 419.456644 \r\nL 278.515669 419.941417 \r\nL 278.531881 420.936195 \r\nL 278.580517 418.626304 \r\nL 278.612941 417.434758 \r\nL 278.661576 420.376185 \r\nL 278.677788 420.377393 \r\nL 278.77506 418.768408 \r\nL 278.839907 419.11864 \r\nL 278.856119 419.104078 \r\nL 278.872331 419.246179 \r\nL 278.904755 418.687294 \r\nL 278.937179 417.47813 \r\nL 278.985814 419.840949 \r\nL 279.018238 418.539204 \r\nL 279.03445 418.512496 \r\nL 279.083086 420.192734 \r\nL 279.164145 419.766084 \r\nL 279.212781 419.806335 \r\nL 279.245204 418.750645 \r\nL 279.261416 418.420654 \r\nL 279.310052 419.681994 \r\nL 279.407323 420.815722 \r\nL 279.423535 420.536878 \r\nL 279.455959 418.506891 \r\nL 279.504595 420.895444 \r\nL 279.537018 420.061757 \r\nL 279.585654 418.821376 \r\nL 279.63429 419.900386 \r\nL 279.650502 420.154941 \r\nL 279.699137 419.195507 \r\nL 279.731561 419.98946 \r\nL 279.780197 418.966739 \r\nL 279.796409 419.304648 \r\nL 279.845044 421.523986 \r\nL 279.89368 419.524496 \r\nL 279.909892 418.861334 \r\nL 279.990951 419.882366 \r\nL 280.055799 420.296749 \r\nL 280.088223 419.776907 \r\nL 280.234129 417.954668 \r\nL 280.15307 420.129503 \r\nL 280.250341 418.924886 \r\nL 280.282765 419.94652 \r\nL 280.363825 419.441509 \r\nL 280.380036 419.44266 \r\nL 280.396248 419.613619 \r\nL 280.41246 418.907429 \r\nL 280.428672 418.315934 \r\nL 280.477308 420.080981 \r\nL 280.49352 420.077692 \r\nL 280.542155 418.806262 \r\nL 280.607003 419.644889 \r\nL 280.639427 419.600133 \r\nL 280.769122 418.582023 \r\nL 280.785334 418.692885 \r\nL 280.817757 419.818548 \r\nL 280.866393 418.522405 \r\nL 280.882605 417.745901 \r\nL 280.931241 419.267901 \r\nL 280.979876 417.773377 \r\nL 281.044724 419.370039 \r\nL 281.125783 418.936957 \r\nL 281.141995 418.617192 \r\nL 281.190631 419.37303 \r\nL 281.223055 419.080673 \r\nL 281.368962 420.241576 \r\nL 281.401385 419.755069 \r\nL 281.433809 419.119493 \r\nL 281.482445 420.113464 \r\nL 281.498657 420.224954 \r\nL 281.53108 419.980116 \r\nL 281.547292 420.072134 \r\nL 281.61214 418.783095 \r\nL 281.660776 419.973923 \r\nL 281.709411 419.323258 \r\nL 281.774259 419.809281 \r\nL 281.806682 420.432775 \r\nL 281.887742 419.958303 \r\nL 281.936378 419.261608 \r\nL 281.968801 420.156237 \r\nL 282.001225 420.496925 \r\nL 282.033649 419.690721 \r\nL 282.049861 419.412745 \r\nL 282.082285 420.037929 \r\nL 282.098496 420.557736 \r\nL 282.147132 419.733142 \r\nL 282.195768 420.282068 \r\nL 282.357887 418.205072 \r\nL 282.374099 418.834521 \r\nL 282.422734 420.313975 \r\nL 282.47137 418.704083 \r\nL 282.487582 418.592169 \r\nL 282.536217 418.994916 \r\nL 282.568641 418.784187 \r\nL 282.746972 420.723641 \r\nL 282.763184 420.25192 \r\nL 282.828031 417.160304 \r\nL 282.876667 419.452337 \r\nL 282.909091 420.767611 \r\nL 282.973938 418.889508 \r\nL 282.99015 418.928651 \r\nL 283.022574 419.641852 \r\nL 283.087422 418.634336 \r\nL 283.103633 418.570374 \r\nL 283.119845 418.718753 \r\nL 283.200905 419.938143 \r\nL 283.265752 419.719627 \r\nL 283.314388 417.716611 \r\nL 283.346812 419.514956 \r\nL 283.379235 421.143892 \r\nL 283.460295 420.068355 \r\nL 283.557566 418.281397 \r\nL 283.606202 419.315787 \r\nL 283.703473 421.154063 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419.667768 \r\nL 293.171215 419.752914 \r\nL 293.187427 419.36299 \r\nL 293.236063 418.962511 \r\nL 293.284698 419.404806 \r\nL 293.30091 419.379542 \r\nL 293.333334 418.938066 \r\nL 293.398181 419.647782 \r\nL 293.430605 419.990058 \r\nL 293.479241 419.362207 \r\nL 293.495453 419.29361 \r\nL 293.527877 420.331388 \r\nL 293.576512 419.232196 \r\nL 293.608936 420.010634 \r\nL 293.754843 419.09361 \r\nL 293.787267 419.597486 \r\nL 293.852114 419.251814 \r\nL 293.933174 418.837726 \r\nL 293.949386 418.893161 \r\nL 294.079081 420.209421 \r\nL 294.257411 418.915938 \r\nL 294.273623 418.903951 \r\nL 294.322259 420.381584 \r\nL 294.403318 419.491694 \r\nL 294.581649 418.134601 \r\nL 294.597861 418.494892 \r\nL 294.646497 419.896293 \r\nL 294.711344 419.078754 \r\nL 294.727556 418.987057 \r\nL 294.75998 419.368934 \r\nL 294.808616 419.091266 \r\nL 294.841039 419.501223 \r\nL 294.889675 419.146199 \r\nL 294.905887 418.895786 \r\nL 294.922099 419.498126 \r\nL 294.970734 421.237333 \r\nL 295.01937 419.48399 \r\nL 295.035582 419.361213 \r\nL 295.051794 419.713463 \r\nL 295.084218 420.421433 \r\nL 295.132853 419.07628 \r\nL 295.181489 419.682238 \r\nL 295.27876 420.601213 \r\nL 295.294972 420.25284 \r\nL 295.376032 419.129634 \r\nL 295.424667 419.596783 \r\nL 295.440879 419.570011 \r\nL 295.489515 419.685211 \r\nL 295.521939 419.855257 \r\nL 295.570574 420.720099 \r\nL 295.61921 420.144799 \r\nL 295.765117 418.111491 \r\nL 295.716481 420.448581 \r\nL 295.781329 418.146402 \r\nL 295.894812 419.686958 \r\nL 295.927236 419.347204 \r\nL 296.040719 418.520757 \r\nL 296.056931 418.651216 \r\nL 296.121778 418.4113 \r\nL 296.186626 419.413387 \r\nL 296.202838 419.304897 \r\nL 296.251474 419.692811 \r\nL 296.283897 419.574791 \r\nL 296.300109 419.491443 \r\nL 296.316321 419.860781 \r\nL 296.348745 420.265739 \r\nL 296.397381 419.275679 \r\nL 296.429804 419.673139 \r\nL 296.446016 420.056689 \r\nL 296.494652 419.006536 \r\nL 296.510864 418.949661 \r\nL 296.543287 419.130087 \r\nL 296.640559 419.821429 \r\nL 296.672983 419.319954 \r\nL 296.689194 419.024805 \r\nL 296.770254 419.570184 \r\nL 296.802678 419.183853 \r\nL 296.835101 420.119607 \r\nL 296.851313 420.553221 \r\nL 296.899949 419.46612 \r\nL 296.916161 419.737386 \r\nL 296.932373 419.742227 \r\nL 296.964797 418.998008 \r\nL 297.045856 419.132726 \r\nL 297.094492 420.412272 \r\nL 297.191763 420.095938 \r\nL 297.25661 418.271292 \r\nL 297.33767 418.952003 \r\nL 297.386306 419.605786 \r\nL 297.434941 418.71897 \r\nL 297.483577 418.071071 \r\nL 297.516001 418.911465 \r\nL 297.613272 420.711012 \r\nL 297.564636 418.712062 \r\nL 297.645696 419.681024 \r\nL 297.775391 418.821235 \r\nL 297.791603 418.803399 \r\nL 297.921298 420.746349 \r\nL 297.953722 419.983361 \r\nL 298.067205 418.339639 \r\nL 298.083417 418.771657 \r\nL 298.11584 420.020449 \r\nL 298.164476 418.508548 \r\nL 298.1969 418.951052 \r\nL 298.294171 420.069279 \r\nL 298.326595 419.435787 \r\nL 298.342807 419.426339 \r\nL 298.375231 420.264011 \r\nL 298.45629 419.971347 \r\nL 298.504926 418.539454 \r\nL 298.53735 420.226555 \r\nL 298.553561 421.352348 \r\nL 298.602197 419.651934 \r\nL 298.650833 420.555249 \r\nL 298.780528 418.844257 \r\nL 298.79674 419.16386 \r\nL 298.877799 420.765829 \r\nL 298.910223 420.197389 \r\nL 298.958859 418.59206 \r\nL 298.991282 420.315155 \r\nL 299.039918 419.06865 \r\nL 299.05613 418.97312 \r\nL 299.072342 419.252043 \r\nL 299.202037 420.183721 \r\nL 299.364156 417.537338 \r\nL 299.299308 420.352148 \r\nL 299.380368 417.700975 \r\nL 299.526275 419.72537 \r\nL 299.688393 417.263739 \r\nL 299.704605 417.943379 \r\nL 299.785665 420.682017 \r\nL 299.8343 419.786824 \r\nL 299.947784 419.055078 \r\nL 299.963996 419.170122 \r\nL 300.061267 420.391529 \r\nL 300.109903 419.604415 \r\nL 300.158538 419.168643 \r\nL 300.190962 417.960268 \r\nL 300.25581 419.572359 \r\nL 300.288233 420.041944 \r\nL 300.336869 418.760114 \r\nL 300.482776 420.229709 \r\nL 300.498988 420.21194 \r\nL 300.580047 420.551496 \r\nL 300.661107 418.765614 \r\nL 300.709742 420.086675 \r\nL 300.742166 421.070808 \r\nL 300.790802 419.777763 \r\nL 300.823226 420.410346 \r\nL 300.888073 418.043674 \r\nL 300.985344 418.706821 \r\nL 301.03398 419.773454 \r\nL 301.11504 419.339383 \r\nL 301.147463 418.538876 \r\nL 301.179887 419.613433 \r\nL 301.228523 418.993288 \r\nL 301.277158 420.029717 \r\nL 301.358218 419.647611 \r\nL 301.406853 419.050639 \r\nL 301.439277 419.876988 \r\nL 301.471701 420.38508 \r\nL 301.536549 419.56098 \r\nL 301.63382 418.63275 \r\nL 301.568972 419.585887 \r\nL 301.650032 419.260996 \r\nL 301.714879 419.77936 \r\nL 301.747303 419.302261 \r\nL 301.763515 419.288251 \r\nL 301.779727 419.503512 \r\nL 301.860786 419.103625 \r\nL 301.876998 418.977065 \r\nL 301.909422 419.236125 \r\nL 301.958058 421.183073 \r\nL 302.039117 419.738812 \r\nL 302.087753 418.759662 \r\nL 302.1526 419.305678 \r\nL 302.217448 420.952269 \r\nL 302.266083 419.622876 \r\nL 302.363355 418.326525 \r\nL 302.395779 419.386058 \r\nL 302.444414 421.074261 \r\nL 302.509262 420.055382 \r\nL 302.525474 420.285349 \r\nL 302.557897 419.215239 \r\nL 302.606533 418.131714 \r\nL 302.638957 418.934468 \r\nL 302.736228 420.865302 \r\nL 302.75244 420.491245 \r\nL 302.817288 417.781845 \r\nL 302.865923 419.326255 \r\nL 302.946983 420.352363 \r\nL 302.963195 420.024952 \r\nL 303.09289 417.698773 \r\nL 303.109102 417.743431 \r\nL 303.157737 419.67115 \r\nL 303.206373 422.006868 \r\nL 303.238797 418.932256 \r\nL 303.336068 417.224232 \r\nL 303.35228 417.841682 \r\nL 303.417127 420.689998 \r\nL 303.498187 420.546005 \r\nL 303.530611 419.388686 \r\nL 303.579246 416.786829 \r\nL 303.627882 419.112721 \r\nL 303.725153 420.295594 \r\nL 303.741365 419.987785 \r\nL 303.790001 417.830416 \r\nL 303.854848 419.324196 \r\nL 303.903484 418.72539 \r\nL 303.919696 419.396398 \r\nL 303.95212 420.840068 \r\nL 304.016967 418.849382 \r\nL 304.146662 421.513346 \r\nL 304.162874 421.333906 \r\nL 304.227722 418.775235 \r\nL 304.308781 419.831261 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418.101452 \r\nL 306.286631 419.650485 \r\nL 306.302843 419.646222 \r\nL 306.335267 419.321342 \r\nL 306.367691 420.060437 \r\nL 306.383903 420.392108 \r\nL 306.432538 419.401205 \r\nL 306.464962 418.581932 \r\nL 306.546022 419.137113 \r\nL 306.594657 421.078723 \r\nL 306.643293 418.396936 \r\nL 306.659505 417.964695 \r\nL 306.70814 419.226912 \r\nL 306.724352 419.18918 \r\nL 306.821624 421.037533 \r\nL 306.870259 419.707342 \r\nL 306.983743 417.326336 \r\nL 306.999954 417.946303 \r\nL 307.064802 421.580358 \r\nL 307.129649 419.554562 \r\nL 307.178285 418.893453 \r\nL 307.226921 418.399407 \r\nL 307.259345 419.37073 \r\nL 307.356616 420.081517 \r\nL 307.372828 419.940846 \r\nL 307.437675 418.11476 \r\nL 307.486311 418.865238 \r\nL 307.551159 420.749008 \r\nL 307.599794 419.410097 \r\nL 307.632218 418.604532 \r\nL 307.713277 419.028421 \r\nL 307.810549 421.164673 \r\nL 307.842972 420.374697 \r\nL 307.891608 418.58455 \r\nL 307.956456 419.373315 \r\nL 308.037515 422.217947 \r\nL 308.069939 420.919224 \r\nL 308.199634 417.921379 \r\nL 308.215846 418.106435 \r\nL 308.280693 421.855216 \r\nL 308.361753 419.731551 \r\nL 308.442812 418.079781 \r\nL 308.475236 419.178454 \r\nL 308.523872 420.980298 \r\nL 308.588719 419.719367 \r\nL 308.637355 419.001162 \r\nL 308.653567 418.72995 \r\nL 308.702202 419.513363 \r\nL 308.718414 419.484852 \r\nL 308.734626 419.402025 \r\nL 308.76705 419.800534 \r\nL 308.799474 420.526234 \r\nL 308.831898 419.506825 \r\nL 308.896745 417.908761 \r\nL 308.929169 418.895061 \r\nL 308.961593 419.731371 \r\nL 309.042652 419.277117 \r\nL 309.091288 418.121121 \r\nL 309.172347 418.630225 \r\nL 309.237195 420.169128 \r\nL 309.253407 420.928381 \r\nL 309.302042 418.788089 \r\nL 309.334466 417.515052 \r\nL 309.383102 419.11526 \r\nL 309.480373 420.227277 \r\nL 309.512797 419.560792 \r\nL 309.642492 418.567675 \r\nL 309.593856 419.64393 \r\nL 309.658704 418.598712 \r\nL 309.723551 419.8979 \r\nL 309.788399 419.477259 \r\nL 309.837035 418.064027 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419.850216 \r\nL 313.776523 419.651971 \r\nL 313.857582 419.844898 \r\nL 313.938642 418.863936 \r\nL 313.971066 419.411511 \r\nL 314.035913 421.108394 \r\nL 314.068337 419.660717 \r\nL 314.133185 418.70917 \r\nL 314.165608 419.508502 \r\nL 314.279092 420.349582 \r\nL 314.295303 420.223651 \r\nL 314.424998 418.566847 \r\nL 314.44121 418.759415 \r\nL 314.506058 420.382095 \r\nL 314.554694 419.278443 \r\nL 314.603329 417.686312 \r\nL 314.651965 418.846565 \r\nL 314.797872 420.262774 \r\nL 314.878931 418.26539 \r\nL 314.943779 419.13819 \r\nL 315.008626 420.733582 \r\nL 315.057262 419.250003 \r\nL 315.089686 418.389243 \r\nL 315.154533 419.361664 \r\nL 315.251805 420.722366 \r\nL 315.268017 420.110748 \r\nL 315.3815 418.092331 \r\nL 315.397712 418.363513 \r\nL 315.494983 420.704043 \r\nL 315.543619 419.761701 \r\nL 315.608466 418.26686 \r\nL 315.657102 419.279885 \r\nL 315.721949 420.826763 \r\nL 315.786797 419.883618 \r\nL 315.835433 418.469571 \r\nL 315.916492 419.316588 \r\nL 315.997551 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419.35362 \r\nL 476.674489 419.582465 \r\nL 477.031173 420.072096 \r\nL 477.225727 419.791406 \r\nL 477.387856 419.696358 \r\nL 477.517559 420.012322 \r\nL 477.939094 421.617826 \r\nL 477.971519 421.59614 \r\nL 478.101222 421.204859 \r\nL 478.555183 419.400057 \r\nL 478.684886 419.567857 \r\nL 479.009143 420.080854 \r\nL 479.138846 419.863725 \r\nL 479.72251 418.423907 \r\nL 480.371025 418.616769 \r\nL 480.598005 419.255502 \r\nL 480.922263 420.077235 \r\nL 481.01954 419.985845 \r\nL 481.473501 418.969598 \r\nL 481.538352 419.050453 \r\nL 481.732907 419.732718 \r\nL 482.186867 421.419204 \r\nL 482.284144 421.451015 \r\nL 482.31657 421.411608 \r\nL 482.446273 421.00152 \r\nL 482.673253 419.353309 \r\nL 483.029937 416.947572 \r\nL 483.127214 416.880993 \r\nL 483.15964 416.925773 \r\nL 483.354194 417.560494 \r\nL 483.581175 417.9634 \r\nL 483.873006 417.618354 \r\nL 483.937858 417.724462 \r\nL 484.067561 418.383769 \r\nL 484.586373 422.186848 \r\nL 484.651224 422.115302 \r\nL 484.878205 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420.347732 \r\nL 492.400979 420.819049 \r\nL 492.530682 420.661347 \r\nL 492.952217 419.992538 \r\nL 493.114346 419.785793 \r\nL 493.276475 419.043723 \r\nL 493.633158 417.209778 \r\nL 493.698009 417.285852 \r\nL 493.860138 418.16227 \r\nL 494.184396 419.877749 \r\nL 494.281673 419.764275 \r\nL 494.638356 418.952501 \r\nL 494.670782 418.978959 \r\nL 494.962614 419.344834 \r\nL 494.995039 419.302329 \r\nL 495.157168 418.68754 \r\nL 495.384148 417.797455 \r\nL 495.416574 417.81077 \r\nL 495.513852 418.183126 \r\nL 495.740832 420.529583 \r\nL 496.000238 422.308943 \r\nL 496.065089 422.153543 \r\nL 496.227218 420.840209 \r\nL 496.583901 418.145349 \r\nL 496.681179 418.345689 \r\nL 497.297268 420.638501 \r\nL 497.329694 420.619168 \r\nL 497.524248 420.245932 \r\nL 497.945783 419.374497 \r\nL 498.075486 419.521687 \r\nL 498.367318 420.036439 \r\nL 498.399743 419.995962 \r\nL 498.529447 419.508335 \r\nL 498.950981 416.883124 \r\nL 499.015833 417.036164 \r\nL 499.177962 418.300927 \r\nL 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417.606982 \r\nL 521.778711 418.323377 \r\nL 522.16782 420.138851 \r\nL 522.265097 420.12892 \r\nL 522.427226 419.727591 \r\nL 522.848761 418.387877 \r\nL 522.881187 418.397205 \r\nL 523.01089 418.651049 \r\nL 523.626979 420.432272 \r\nL 524.016088 420.690614 \r\nL 524.145791 420.381939 \r\nL 524.405197 418.782855 \r\nL 524.632177 417.91631 \r\nL 524.729455 418.17571 \r\nL 525.021286 420.426802 \r\nL 525.215841 421.066928 \r\nL 525.313118 420.808145 \r\nL 525.896782 418.430947 \r\nL 525.929207 418.44647 \r\nL 526.188613 418.763129 \r\nL 526.675 419.699803 \r\nL 526.739851 419.645316 \r\nL 526.934406 419.136613 \r\nL 527.12896 418.728975 \r\nL 527.161386 418.745714 \r\nL 527.291089 419.116752 \r\nL 527.680198 420.742342 \r\nL 527.712624 420.715566 \r\nL 527.874753 420.153711 \r\nL 528.134159 419.367672 \r\nL 528.231436 419.502408 \r\nL 528.490842 419.981286 \r\nL 528.620545 419.669604 \r\nL 528.912377 418.702937 \r\nL 528.944802 418.737316 \r\nL 529.074505 419.23249 \r\nL 529.366337 420.493806 \r\nL 529.463614 420.1423 \r\nL 529.852724 417.253203 \r\nL 529.950001 417.460689 \r\nL 530.176981 419.364549 \r\nL 530.371536 420.247841 \r\nL 530.468813 420.064357 \r\nL 530.728219 419.328708 \r\nL 530.825496 419.616741 \r\nL 531.149754 421.096178 \r\nL 531.182179 421.023803 \r\nL 531.311882 420.140967 \r\nL 531.63614 417.521958 \r\nL 531.733417 417.92627 \r\nL 532.122526 421.072192 \r\nL 532.187378 420.873562 \r\nL 532.544061 418.518596 \r\nL 532.608912 418.684597 \r\nL 532.965596 420.824567 \r\nL 533.030447 420.57095 \r\nL 533.225002 418.423453 \r\nL 533.419556 416.790923 \r\nL 533.451982 416.800838 \r\nL 533.549259 417.366861 \r\nL 533.970794 421.574828 \r\nL 534.035646 421.430216 \r\nL 534.359903 419.940423 \r\nL 534.424755 420.091532 \r\nL 534.716586 421.377855 \r\nL 534.781438 421.206996 \r\nL 534.943567 419.654219 \r\nL 535.267824 416.66675 \r\nL 535.332676 416.856688 \r\nL 535.52723 418.675051 \r\nL 535.75421 420.186672 \r\nL 535.819062 420.117133 \r\nL 536.013616 419.062729 \r\nL 536.208171 418.331204 \r\nL 536.240597 418.336986 \r\nL 536.3703 418.652307 \r\nL 536.759409 419.760816 \r\nL 537.051241 420.1756 \r\nL 537.213369 420.356638 \r\nL 537.245795 420.337292 \r\nL 537.375498 419.954046 \r\nL 537.732181 418.414188 \r\nL 537.764607 418.472984 \r\nL 537.926736 419.475185 \r\nL 538.218568 421.311686 \r\nL 538.283419 421.118757 \r\nL 538.445548 419.52953 \r\nL 538.769805 416.649879 \r\nL 538.834657 416.83849 \r\nL 539.029211 418.720127 \r\nL 539.353469 421.523477 \r\nL 539.483172 421.673567 \r\nL 539.515598 421.651394 \r\nL 539.677727 421.369547 \r\nL 539.904707 420.455686 \r\nL 540.391093 417.54812 \r\nL 540.455945 417.711819 \r\nL 540.618073 418.946428 \r\nL 540.942331 421.159787 \r\nL 541.007182 421.110886 \r\nL 541.169311 420.368454 \r\nL 541.525994 418.903845 \r\nL 541.882678 418.464573 \r\nL 542.142084 418.052215 \r\nL 542.239361 418.184569 \r\nL 542.758173 419.67808 \r\nL 542.823025 419.634196 \r\nL 543.147282 419.463484 \r\nL 543.536391 419.594404 \r\nL 543.828223 419.5191 \r\nL 544.022777 419.845695 \r\nL 544.249758 420.110717 \r\nL 544.411886 419.910974 \r\nL 544.638867 419.671517 \r\nL 544.671292 419.691566 \r\nL 545.060401 420.065851 \r\nL 545.092827 420.008235 \r\nL 545.287382 419.285427 \r\nL 545.514362 418.664493 \r\nL 545.611639 418.739278 \r\nL 545.935897 419.483418 \r\nL 546.000748 419.418246 \r\nL 546.162877 418.864313 \r\nL 546.357432 418.400087 \r\nL 546.454709 418.685631 \r\nL 546.746541 420.926785 \r\nL 546.908669 421.450718 \r\nL 547.005947 421.185734 \r\nL 547.427481 419.533678 \r\nL 547.719313 419.662211 \r\nL 547.751739 419.600182 \r\nL 547.946293 418.801653 \r\nL 548.173274 418.181904 \r\nL 548.270551 418.427706 \r\nL 548.562383 419.545944 \r\nL 548.594809 419.529374 \r\nL 548.724512 419.03728 \r\nL 548.983918 417.778894 \r\nL 549.016343 417.791092 \r\nL 549.113621 418.208128 \r\nL 549.50273 421.423652 \r\nL 549.600007 421.154394 \r\nL 549.794561 419.269541 \r\nL 550.021542 417.673005 \r\nL 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val, steps_ahead=5)\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0.3090559299602927\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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414.131681 \r\nL 89.225681 421.030031 \r\nL 89.355376 416.945805 \r\nL 89.371588 417.894631 \r\nL 89.404011 421.351565 \r\nL 89.452647 417.715495 \r\nL 89.501283 420.249625 \r\nL 89.64719 415.102205 \r\nL 89.793097 411.329811 \r\nL 89.728249 415.789533 \r\nL 89.809309 412.081846 \r\nL 89.922792 414.628556 \r\nL 89.874156 410.744757 \r\nL 89.939004 414.31991 \r\nL 89.971427 411.92643 \r\nL 90.003851 415.651996 \r\nL 90.198394 425.842258 \r\nL 90.214606 425.055052 \r\nL 90.247029 423.231351 \r\nL 90.295665 426.495824 \r\nL 90.311877 426.06355 \r\nL 90.441572 423.078886 \r\nL 90.457784 423.484142 \r\nL 90.473996 423.982862 \r\nL 90.522632 422.287512 \r\nL 90.571267 420.812262 \r\nL 90.587479 420.013135 \r\nL 90.636115 421.564097 \r\nL 90.668539 420.55303 \r\nL 90.700962 421.007672 \r\nL 90.717174 420.461545 \r\nL 90.749598 419.170202 \r\nL 90.782022 421.240167 \r\nL 90.879293 423.566112 \r\nL 90.911717 422.987951 \r\nL 90.927929 422.754586 \r\nL 90.944141 423.644054 \r\nL 90.976564 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 102.016859 418.076669 \r\nL 102.065495 422.140917 \r\nL 102.19519 418.343003 \r\nL 102.146554 422.176544 \r\nL 102.227614 420.867986 \r\nL 102.308673 418.243215 \r\nL 102.357309 423.092104 \r\nL 102.503216 418.831567 \r\nL 102.519428 419.482405 \r\nL 102.551851 421.573841 \r\nL 102.600487 418.114244 \r\nL 102.649123 421.543057 \r\nL 102.665335 421.534197 \r\nL 102.79503 416.853222 \r\nL 102.827454 417.643115 \r\nL 102.876089 418.420414 \r\nL 102.908513 417.295476 \r\nL 102.940937 416.312655 \r\nL 103.005784 417.79695 \r\nL 103.05442 414.12297 \r\nL 103.086844 416.176934 \r\nL 103.200327 420.112725 \r\nL 103.248963 415.443468 \r\nL 103.31381 419.948584 \r\nL 103.362446 418.895474 \r\nL 103.39487 419.45217 \r\nL 103.427293 421.20571 \r\nL 103.475929 418.394362 \r\nL 103.524565 420.677945 \r\nL 103.556988 419.990873 \r\nL 103.5732 421.115549 \r\nL 103.621836 424.544413 \r\nL 103.670472 420.999622 \r\nL 103.767743 418.935856 \r\nL 103.719107 421.522441 \r\nL 103.783955 420.122914 \r\nL 103.897438 424.311263 \r\nL 103.91365 423.412431 \r\nL 104.027133 418.725821 \r\nL 104.043345 419.642524 \r\nL 104.17304 421.271663 \r\nL 104.221676 418.238914 \r\nL 104.302735 420.822883 \r\nL 104.318947 420.821367 \r\nL 104.416218 423.156657 \r\nL 104.43243 422.096437 \r\nL 104.464854 418.498653 \r\nL 104.562125 419.583529 \r\nL 104.594549 421.42263 \r\nL 104.643185 417.971119 \r\nL 104.77288 414.932632 \r\nL 104.708032 418.53113 \r\nL 104.789092 415.678459 \r\nL 104.821516 419.100431 \r\nL 104.886363 415.682828 \r\nL 104.951211 414.541039 \r\nL 104.967423 413.927152 \r\nL 105.016058 416.059553 \r\nL 105.129541 418.941538 \r\nL 105.178177 418.179504 \r\nL 105.194389 418.10923 \r\nL 105.226813 416.15303 \r\nL 105.259237 418.660117 \r\nL 105.324084 417.541416 \r\nL 105.37272 422.465125 \r\nL 105.421355 416.6598 \r\nL 105.469991 423.279049 \r\nL 105.502415 420.260182 \r\nL 105.518627 418.30897 \r\nL 105.567262 423.155176 \r\nL 105.615898 419.076668 \r\nL 105.63211 418.77679 \r\nL 105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 419.701183 \r\nL 196.094438 418.00952 \r\nL 196.143073 420.649234 \r\nL 196.191709 416.199579 \r\nL 196.28898 417.366366 \r\nL 196.37004 416.308742 \r\nL 196.418675 420.426478 \r\nL 196.467311 416.606475 \r\nL 196.515947 420.559165 \r\nL 196.532159 421.148637 \r\nL 196.580794 418.879609 \r\nL 196.613218 420.68436 \r\nL 196.62943 420.609831 \r\nL 196.661854 418.763258 \r\nL 196.710489 420.967438 \r\nL 196.742913 420.44045 \r\nL 196.759125 420.47238 \r\nL 196.856396 422.149606 \r\nL 196.872608 421.599822 \r\nL 196.905032 420.208393 \r\nL 196.953668 422.230145 \r\nL 196.969879 422.162306 \r\nL 196.986091 422.129023 \r\nL 197.067151 423.465547 \r\nL 197.099575 422.612249 \r\nL 197.164422 423.376404 \r\nL 197.22927 420.278935 \r\nL 197.277905 422.495307 \r\nL 197.310329 420.714202 \r\nL 197.440024 415.336633 \r\nL 197.456236 416.381807 \r\nL 197.472448 417.053758 \r\nL 197.504872 414.877169 \r\nL 197.618355 412.860758 \r\nL 197.634567 413.456525 \r\nL 197.845321 417.763333 \r\nL 197.861533 417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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304.114239 420.63193 \r\nL 304.146662 422.477989 \r\nL 304.21151 420.603813 \r\nL 304.260146 421.000483 \r\nL 304.292569 420.601905 \r\nL 304.324993 420.263745 \r\nL 304.357417 420.857399 \r\nL 304.373629 421.051835 \r\nL 304.389841 420.669467 \r\nL 304.535748 417.180814 \r\nL 304.55196 417.233379 \r\nL 304.697866 419.693106 \r\nL 304.746502 418.199841 \r\nL 304.795138 420.016161 \r\nL 304.892409 422.099487 \r\nL 304.843773 419.405684 \r\nL 304.924833 420.842236 \r\nL 304.957257 419.950565 \r\nL 305.038316 420.141007 \r\nL 305.103164 421.07975 \r\nL 305.151799 420.304603 \r\nL 305.200435 420.035808 \r\nL 305.249071 418.202238 \r\nL 305.33013 418.766127 \r\nL 305.394978 419.126866 \r\nL 305.411189 419.004515 \r\nL 305.459825 417.790766 \r\nL 305.540885 418.259238 \r\nL 305.605732 419.8005 \r\nL 305.686792 419.632806 \r\nL 305.735427 418.264432 \r\nL 305.784063 419.919016 \r\nL 305.816487 419.280174 \r\nL 305.84891 420.361139 \r\nL 305.881334 421.338921 \r\nL 305.92997 419.011347 \r\nL 305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "y_hat = model.predict(x_val, y_val) # inifity-steps-ahead prediction\n",
+    "rrse = root_relative_squared_error(y_val, y_hat)\n",
+    "print(rrse)\n",
+    "results = pd.DataFrame(model.results(err_precision=8, dtype='sci'), columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "a, b,c,d = model.residuals(x_val, y_val, y_hat)\n",
+    "model.plot_result(y_val, y_hat, a, b, n=600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
new file mode 100644
index 00000000..43edc03c
--- /dev/null
+++ b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
@@ -0,0 +1,613 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulate a Predefined Model\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the object so we can access its functions\n",
+    "model = PolynomialNarmax()\n",
+    "\n",
+    "# create any psi matrix that contains the combinations we want\n",
+    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
+    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
+    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
+    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.108434</td>\n",
+       "      <td>-0.610171</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.011758</td>\n",
+       "      <td>-0.066163</td>\n",
+       "      <td>0.372308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.548087</td>\n",
+       "      <td>-0.370476</td>\n",
+       "      <td>0.108434</td>\n",
+       "      <td>0.300400</td>\n",
+       "      <td>0.203053</td>\n",
+       "      <td>-0.059431</td>\n",
+       "      <td>0.137252</td>\n",
+       "      <td>-0.040172</td>\n",
+       "      <td>0.011758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.009140</td>\n",
+       "      <td>0.730998</td>\n",
+       "      <td>-0.370476</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>0.006681</td>\n",
+       "      <td>-0.003386</td>\n",
+       "      <td>0.534358</td>\n",
+       "      <td>-0.270817</td>\n",
+       "      <td>0.137252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.332688</td>\n",
+       "      <td>0.042290</td>\n",
+       "      <td>0.730998</td>\n",
+       "      <td>0.110682</td>\n",
+       "      <td>-0.014070</td>\n",
+       "      <td>-0.243195</td>\n",
+       "      <td>0.001788</td>\n",
+       "      <td>0.030914</td>\n",
+       "      <td>0.534358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.589887</td>\n",
+       "      <td>0.369362</td>\n",
+       "      <td>0.042290</td>\n",
+       "      <td>0.347967</td>\n",
+       "      <td>0.217882</td>\n",
+       "      <td>0.024947</td>\n",
+       "      <td>0.136428</td>\n",
+       "      <td>0.015620</td>\n",
+       "      <td>0.001788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>893</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.741870</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>-0.685155</td>\n",
+       "      <td>0.550371</td>\n",
+       "      <td>-0.362971</td>\n",
+       "      <td>0.508296</td>\n",
+       "      <td>0.239381</td>\n",
+       "      <td>-0.335223</td>\n",
+       "      <td>0.469437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>894</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.801757</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.489266</td>\n",
+       "      <td>0.642815</td>\n",
+       "      <td>0.006963</td>\n",
+       "      <td>-0.392272</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>-0.004249</td>\n",
+       "      <td>0.239381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.278465</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>-0.008684</td>\n",
+       "      <td>0.077543</td>\n",
+       "      <td>-0.271475</td>\n",
+       "      <td>-0.002418</td>\n",
+       "      <td>0.950426</td>\n",
+       "      <td>0.008466</td>\n",
+       "      <td>0.000075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>896</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.019212</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>-0.974898</td>\n",
+       "      <td>0.000369</td>\n",
+       "      <td>-0.000275</td>\n",
+       "      <td>-0.018729</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.013937</td>\n",
+       "      <td>0.950426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>897</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.872305</td>\n",
+       "      <td>0.562238</td>\n",
+       "      <td>-0.014295</td>\n",
+       "      <td>0.760916</td>\n",
+       "      <td>-0.490443</td>\n",
+       "      <td>0.012470</td>\n",
+       "      <td>0.316112</td>\n",
+       "      <td>-0.008037</td>\n",
+       "      <td>0.000204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>898 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0         1         2         3         4         5         6  \\\n",
+       "0    1.0  0.000000  0.108434 -0.610171  0.000000  0.000000 -0.000000   \n",
+       "1    1.0 -0.548087 -0.370476  0.108434  0.300400  0.203053 -0.059431   \n",
+       "2    1.0  0.009140  0.730998 -0.370476  0.000084  0.006681 -0.003386   \n",
+       "3    1.0 -0.332688  0.042290  0.730998  0.110682 -0.014070 -0.243195   \n",
+       "4    1.0  0.589887  0.369362  0.042290  0.347967  0.217882  0.024947   \n",
+       "..   ...       ...       ...       ...       ...       ...       ...   \n",
+       "893  1.0 -0.741870  0.489266 -0.685155  0.550371 -0.362971  0.508296   \n",
+       "894  1.0 -0.801757 -0.008684  0.489266  0.642815  0.006963 -0.392272   \n",
+       "895  1.0  0.278465 -0.974898 -0.008684  0.077543 -0.271475 -0.002418   \n",
+       "896  1.0  0.019212 -0.014295 -0.974898  0.000369 -0.000275 -0.018729   \n",
+       "897  1.0 -0.872305  0.562238 -0.014295  0.760916 -0.490443  0.012470   \n",
+       "\n",
+       "            7         8         9  \n",
+       "0    0.011758 -0.066163  0.372308  \n",
+       "1    0.137252 -0.040172  0.011758  \n",
+       "2    0.534358 -0.270817  0.137252  \n",
+       "3    0.001788  0.030914  0.534358  \n",
+       "4    0.136428  0.015620  0.001788  \n",
+       "..        ...       ...       ...  \n",
+       "893  0.239381 -0.335223  0.469437  \n",
+       "894  0.000075 -0.004249  0.239381  \n",
+       "895  0.950426  0.008466  0.000075  \n",
+       "896  0.000204  0.013937  0.950426  \n",
+       "897  0.316112 -0.008037  0.000204  \n",
+       "\n",
+       "[898 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(psi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
+    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
+    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "\n",
+    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1001,    0],\n",
+       "       [2002,    0],\n",
+       "       [2001, 1001]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.final_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.psi = psi[:, [1, 3, 5]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the parameter estimation method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
+    "from sysidentpy.parameter_estimation import Estimators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.20014451],\n",
+       "       [0.89992074],\n",
+       "       [0.09997088]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
+    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
+    "\n",
+    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
+    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
+    "\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "model.theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0019184421163563367\n"
+     ]
+    }
+   ],
+   "source": [
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2001  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001879953122213705\n",
+      "      Regressors Parameters         ERR\n",
+      "0         y(k-1)     0.2000  0.00000000\n",
+      "1        x1(k-2)     0.8999  0.00000000\n",
+      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
+    "model.psi = psi[:, [1, 3, 5]]\n",
+    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.err = [0, 0, 0] # just to use the `results` method\n",
+    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt b/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt
new file mode 100644
index 00000000..1765a962
--- /dev/null
+++ b/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt
@@ -0,0 +1,422 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "educated-affect",
+   "metadata": {},
+   "source": [
+    "# System Identification Using Adaptative Filters\n",
+    "\n",
+    "Example created by Wilson Rocha Lacerda Junior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "preliminary-announcement",
+   "metadata": {},
+   "source": [
+    "## Generating 1 input 1 output sample data  \n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
+    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
+    "\n",
+    "If *colored_noise* is set to True:\n",
+    "\n",
+    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
+    "\n",
+    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
+    "\n",
+    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "exact-trace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "convenient-encounter",
+   "metadata": {},
+   "source": [
+    "One can create a model object and access the Adaptative Filters available in SysIdentPy individually. If you want to build a regressor matrix and estimate the parameters using only the Adaptative Filter method (not applying FROLS + ERR algorithm), follow the steps bellow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "binary-shame",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>-0.242635</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>0.239048</td>\n",
+       "      <td>0.118630</td>\n",
+       "      <td>0.058872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.217385</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>-0.488925</td>\n",
+       "      <td>0.047256</td>\n",
+       "      <td>-0.210825</td>\n",
+       "      <td>0.106285</td>\n",
+       "      <td>0.940557</td>\n",
+       "      <td>-0.474171</td>\n",
+       "      <td>0.239048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.503772</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.969823</td>\n",
+       "      <td>0.253787</td>\n",
+       "      <td>0.316599</td>\n",
+       "      <td>-0.488570</td>\n",
+       "      <td>0.394958</td>\n",
+       "      <td>-0.609492</td>\n",
+       "      <td>0.940557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.804358</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>-0.628457</td>\n",
+       "      <td>0.646992</td>\n",
+       "      <td>0.248526</td>\n",
+       "      <td>-0.505505</td>\n",
+       "      <td>0.095465</td>\n",
+       "      <td>-0.194177</td>\n",
+       "      <td>0.394958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.379308</td>\n",
+       "      <td>-0.442970</td>\n",
+       "      <td>0.308975</td>\n",
+       "      <td>0.143875</td>\n",
+       "      <td>0.168022</td>\n",
+       "      <td>-0.117197</td>\n",
+       "      <td>0.196222</td>\n",
+       "      <td>-0.136867</td>\n",
+       "      <td>0.095465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0         1         2         3         4         5         6         7  \\\n",
+       "0  1.0  0.000000 -0.488925 -0.242635  0.000000 -0.000000 -0.000000  0.239048   \n",
+       "1  1.0 -0.217385  0.969823 -0.488925  0.047256 -0.210825  0.106285  0.940557   \n",
+       "2  1.0 -0.503772 -0.628457  0.969823  0.253787  0.316599 -0.488570  0.394958   \n",
+       "3  1.0  0.804358  0.308975 -0.628457  0.646992  0.248526 -0.505505  0.095465   \n",
+       "4  1.0 -0.379308 -0.442970  0.308975  0.143875  0.168022 -0.117197  0.196222   \n",
+       "\n",
+       "          8         9  \n",
+       "0  0.118630  0.058872  \n",
+       "1 -0.474171  0.239048  \n",
+       "2 -0.609492  0.940557  \n",
+       "3 -0.194177  0.394958  \n",
+       "4 -0.136867  0.095465  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "pd.DataFrame(psi).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "received-apparatus",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    0],\n",
+       "       [1001,    0],\n",
+       "       [2001,    0],\n",
+       "       [2002,    0],\n",
+       "       [1001, 1001],\n",
+       "       [2001, 1001],\n",
+       "       [2002, 1001],\n",
+       "       [2001, 2001],\n",
+       "       [2002, 2001],\n",
+       "       [2002, 2002]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "growing-anniversary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unable-culture",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "electrical-crowd",
+   "metadata": {},
+   "source": [
+    "Here we are using the Affine Least Mean Squares method, but you can use any of the methods available on SysIdentPy\n",
+    "\n",
+    "- Least Mean Squares (LMS)\n",
+    "- Affine LMS\n",
+    "- LMS Sign Error\n",
+    "- Normalized LMS\n",
+    "- Normalized LSM Sign Error\n",
+    "- LSM Sign Regressor\n",
+    "- Normalized LMS Sign Regressor\n",
+    "- LMS Sign-Sign\n",
+    "- Normalized LMS Sign-Sign\n",
+    "- Normalized LMS Leaky\n",
+    "- LMS Leaky\n",
+    "- LMS Mixed Norm\n",
+    "- LMS Fourth\n",
+    "\n",
+    "Also, you can use:\n",
+    "- Least Squares (LS)\n",
+    "- Total LS\n",
+    "- Recursive LS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "august-robertson",
+   "metadata": {},
+   "source": [
+    "## Building the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ahead-element",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-alignment",
+   "metadata": {},
+   "source": [
+    "## Simulating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "metric-layer",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0024097628318461625\n",
+      "       Regressors Parameters         ERR\n",
+      "0               1    -0.0000  0.00000000\n",
+      "1          y(k-1)     0.1999  0.00000000\n",
+      "2         x1(k-1)    -0.0000  0.00000000\n",
+      "3         x1(k-2)     0.8999  0.00000000\n",
+      "4        y(k-1)^2    -0.0001  0.00000000\n",
+      "5   x1(k-1)y(k-1)     0.1000  0.00000000\n",
+      "6   x1(k-2)y(k-1)    -0.0000  0.00000000\n",
+      "7       x1(k-1)^2     0.0002  0.00000000\n",
+      "8  x1(k-2)x1(k-1)    -0.0000  0.00000000\n",
+      "9       x1(k-2)^2     0.0000  0.00000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "derived-dakota",
+   "metadata": {},
+   "source": [
+    "## Final code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "union-mortality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
+    "from sysidentpy.metrics import root_relative_squared_error\n",
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.parameter_estimation import Estimators\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
+    "                                                   colored_noise=False,\n",
+    "                                                   sigma=0.001,\n",
+    "                                                   train_percentage=90)\n",
+    "\n",
+    "model = PolynomialNarmax()\n",
+    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2) # creating the regressor matrix\n",
+    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
+    "regressor_code # the entire regressor space is our input in this case. But you can define specific subsets to use as an input\n",
+    "model.final_model = regressor_code # defines the model representation\n",
+    "model.psi = psi\n",
+    "model.theta = Estimators(mu=0.01).affine_least_mean_squares(model.psi,  y_train[1:, 0].reshape(-1, 1))\n",
+    "yhat = model.predict(x_valid, y_valid)\n",
+    "rrse = root_relative_squared_error(y_valid, yhat)\n",
+    "print(rrse)\n",
+    "model.n_terms = 10 # the number of terms we selected (necessary in the 'results' methods)\n",
+    "model.err = model.n_terms*[0] # just to use the `results` method\n",
+    "results = pd.DataFrame(model.results(err_precision=8,\n",
+    "                                     dtype='dec'),\n",
+    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "\n",
+    "print(results)\n",
+    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
+    "model.plot_result(y_valid, yhat, ee, ex)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:v0.1.4] *",
+   "language": "python",
+   "name": "conda-env-v0.1.4-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index 70b03ba0..9171b9e9 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -288,5 +288,5 @@ Contents
     user_guide
     dev_guide
     notebooks
-    changelog/v0.1.4
+    changelog/v0.1.5
     code
diff --git a/docs/_sources/notebooks.rst.txt b/docs/_sources/notebooks.rst.txt
index 98f7a4e3..8bed507d 100644
--- a/docs/_sources/notebooks.rst.txt
+++ b/docs/_sources/notebooks.rst.txt
@@ -54,3 +54,27 @@ Jupyter notebooks
    :caption: General Estimators
 
    /examples/general_estimators.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: Simulate a predefined model
+
+   /examples/simulating_a_predefined_model.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: System Identification Using Adaptative Filters
+
+   /examples/system_identification_using_adaptative_filters.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: Identification of an electromechanical system
+
+   /examples/identification_of_an_electromechanical_system.ipynb
+   
+.. toctree::
+   :maxdepth: 1
+   :caption: N-steps-ahead simulation
+
+   /examples/n_steps_ahead_prediction.ipynb
diff --git a/docs/changelog/v0.1.3.html b/docs/changelog/v0.1.3.html
index 39133553..6c25920d 100644
--- a/docs/changelog/v0.1.3.html
+++ b/docs/changelog/v0.1.3.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="v0.1.4.html">
+  <a class="reference internal" href="v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/changelog/v0.1.4.html b/docs/changelog/v0.1.4.html
index 060a150e..d058ca5c 100644
--- a/docs/changelog/v0.1.4.html
+++ b/docs/changelog/v0.1.4.html
@@ -45,8 +45,6 @@
     <link rel="canonical" href="http://sysidentpy.org/changelog/v0.1.4.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Codes" href="../code.html" />
-    <link rel="prev" title="Building NARX models using general estimators" href="../examples/general_estimators.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="en" />
@@ -85,7 +83,7 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
   <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" >
 </form>
 <nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
-    <ul class="current nav sidenav_l1">
+    <ul class="nav sidenav_l1">
  <li class="toctree-l1">
   <a class="reference internal" href="../installation.html">
    Install Guide
@@ -156,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
- <li class="toctree-l1 current active">
-  <a class="current reference internal" href="#">
+ <li class="toctree-l1">
+  <a class="reference internal" href="v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -444,8 +462,6 @@ <h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline
         
         <div class='prev-next-bottom'>
             
-    <a class='left-prev' id="prev-link" href="../examples/general_estimators.html" title="previous page">Building NARX models using general estimators</a>
-    <a class='right-next' id="next-link" href="../code.html" title="next page">Codes</a>
 
         </div>
         
diff --git a/docs/changelog/v0.1.5.html b/docs/changelog/v0.1.5.html
new file mode 100644
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@@ -0,0 +1,544 @@
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+   A brief introduction to NARMAX models.
+  </a>
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+     Presenting main functionality
+    </a>
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+    <a class="reference internal" href="../examples/multiple_inputs_example.html">
+     Multiple Inputs usage
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/information_criteria_examples.html">
+     Information Criteria - Examples
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/extended_least_squares.html">
+     Important notes and examples of how to use Extended Least Squares
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/defining_lags.html">
+     Setting specific lags
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/parameter_estimation.html">
+     Parameter Estimation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/f_16_benchmark.html">
+     Example: F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/narx_neural_network.html">
+     Building NARX Neural Network using Sysidentpy
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/general_estimators.html">
+     Building NARX models using general estimators
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+  </ul>
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+  </i>
+ </li>
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+   Changes in SysIdentPy
+  </a>
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+   Codes
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+   sysidentpy base
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+ <li class="toctree-l1">
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+   sysidentpy narmax
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+   sysidentpy generate data
+  </a>
+ </li>
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+  <a class="reference internal nav-link" href="#v0-1-5">
+   v0.1.5
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#contributors">
+     CONTRIBUTORS
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#changes">
+     CHANGES
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#v0-1-4">
+   v0.1.4
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id1">
+     CONTRIBUTORS
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id2">
+     CHANGES
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#v0-1-3">
+   v0.1.3
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id3">
+     CONTRIBUTORS
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id4">
+     CHANGES
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#id5">
+   v0.1.5
+  </a>
+ </li>
+</ul>
+
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+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="changes-in-sysidentpy">
+<h1>Changes in SysIdentPy<a class="headerlink" href="#changes-in-sysidentpy" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="v0-1-5">
+<h2>v0.1.5<a class="headerlink" href="#v0-1-5" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="contributors">
+<h3>CONTRIBUTORS<a class="headerlink" href="#contributors" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p>wilsonrljr</p></li>
+</ul>
+</div>
+<div class="section" id="changes">
+<h3>CHANGES<a class="headerlink" href="#changes" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><dl class="simple">
+<dt>MAJOR: n-steps-ahead prediction.</dt><dd><ul>
+<li><dl class="simple">
+<dt>Now you can define the numbers of steps ahead in the predict function.</dt><dd><ul>
+<li><p>only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.</p></li>
+</ul>
+</dd>
+</dl>
+</li>
+</ul>
+</dd>
+</dl>
+</li>
+<li><p>Added 4 new notebooks in the example section.</p></li>
+<li><p>Added iterative notebooks.</p></li>
+<li><p>Fix issues related to html on Jupyter notebooks examples on documentation.</p></li>
+<li><p>Updated Readme with examples of how to use.</p></li>
+</ul>
+</div>
+</div>
+<div class="section" id="v0-1-4">
+<h2>v0.1.4<a class="headerlink" href="#v0-1-4" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id1">
+<h3>CONTRIBUTORS<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p>wilsonrljr</p></li>
+</ul>
+</div>
+<div class="section" id="id2">
+<h3>CHANGES<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><dl class="simple">
+<dt>MAJOR: Introducing NARX Neural Network in SysIdentPy.</dt><dd><ul>
+<li><p>Now you can build NARX Neural Network on SysIdentPy.</p></li>
+<li><p>This feature is built on top of Pytorch. See the docs for more details and examples of how to use.</p></li>
+</ul>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>MAJOR: Introducing general estimators in SysIdentPy.</dt><dd><ul>
+<li><p>Now you are able to use any estimator that have Fit/Predict methods (estimators from Sklearn and Catboost, for example) and build NARX models based on those estimators.</p></li>
+<li><p>We use the core functions of SysIdentPy and keep the Fit/Predict approach from those estimators to keep the process easy to use.</p></li>
+<li><p>More estimators are coming soon like XGboost.</p></li>
+</ul>
+</dd>
+</dl>
+</li>
+<li><p>Added notebooks to show how to build NARX neural Network.</p></li>
+<li><p>Added notebooks to show how to build NARX models using general estimators.</p></li>
+<li><p>Changed the default parameters of the plot_results function.</p></li>
+<li><p>NOTE: We will keeping improving the Polynomial NARX models (new model structure selection algorithms and multiobjective identification</p></li>
+</ul>
+<p>is on our roadmap). These recent modifications will allow us to introduce new NARX models like PWARX models very soon.</p>
+<ul class="simple">
+<li><p>New template for the documentation site.</p></li>
+<li><p>Fix issues related to html on Jupyter notebooks examples on documentation.</p></li>
+<li><p>Updated Readme with examples of how to use.</p></li>
+</ul>
+</div>
+</div>
+<div class="section" id="v0-1-3">
+<h2>v0.1.3<a class="headerlink" href="#v0-1-3" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id3">
+<h3>CONTRIBUTORS<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p>wilsonrljr</p></li>
+<li><p>renard162</p></li>
+</ul>
+</div>
+<div class="section" id="id4">
+<h3>CHANGES<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p>Fixed a bug concerning the xlag and ylag in multiple input scenarios.</p></li>
+<li><p>Refactored predict function. Improved performance up to 87% depending on the number of regressors.</p></li>
+<li><p>You can set lags with different size for each input.</p></li>
+<li><p>Added a new function to get the max value of xlag and ylag. Work with int, list, nested lists.</p></li>
+<li><p>Fixed tests for information criteria.</p></li>
+<li><p>Added SysIdentPy logo.</p></li>
+<li><p>Refactored code of all classes following PEP 8 guidelines to improve readability.</p></li>
+<li><p>Added Citation information on Readme.</p></li>
+<li><p>Changes on information Criteria tests.</p></li>
+<li><p>Added workflow to run the tests when merge branch into master.</p></li>
+<li><p>Added new site domain.</p></li>
+<li><p>Updated docs.</p></li>
+</ul>
+</div>
+</div>
+<div class="section" id="id5">
+<h2>v0.1.5<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
+<p>CONTRIBUTORS
+CHANGES</p>
+</div>
+</div>
+
+
+              </div>
+              
+        
+        <div class='prev-next-bottom'>
+            
+    <a class='left-prev' id="prev-link" href="../examples/n_steps_ahead_prediction.html" title="previous page">Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark</a>
+    <a class='right-next' id="next-link" href="../code.html" title="next page">Codes</a>
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+      <p>
+        
+          By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
+        
+            &copy; Copyright 2020, Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins.<br/>
+      </p>
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+
+
+      </div>
+    </div>
+
+    
+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
+
+
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/code.html b/docs/code.html
index 8f779115..3d6b64dd 100644
--- a/docs/code.html
+++ b/docs/code.html
@@ -45,7 +45,7 @@
     <link rel="canonical" href="http://sysidentpy.org/code.html" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="prev" title="Changes in SysIdentPy" href="changelog/v0.1.4.html" />
+    <link rel="prev" title="Changes in SysIdentPy" href="changelog/v0.1.5.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="en" />
@@ -155,12 +155,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -2123,7 +2143,7 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
         
         <div class='prev-next-bottom'>
             
-    <a class='left-prev' id="prev-link" href="changelog/v0.1.4.html" title="previous page">Changes in SysIdentPy</a>
+    <a class='left-prev' id="prev-link" href="changelog/v0.1.5.html" title="previous page">Changes in SysIdentPy</a>
 
         </div>
         
diff --git a/docs/dev_guide.html b/docs/dev_guide.html
index 549fc83c..a052a801 100644
--- a/docs/dev_guide.html
+++ b/docs/dev_guide.html
@@ -156,12 +156,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/basic_steps.html b/docs/examples/basic_steps.html
index ce8d7ac7..d361ef66 100644
--- a/docs/examples/basic_steps.html
+++ b/docs/examples/basic_steps.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/defining_lags.html b/docs/examples/defining_lags.html
index 97fe26e9..1d3cbdda 100644
--- a/docs/examples/defining_lags.html
+++ b/docs/examples/defining_lags.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/extended_least_squares.html b/docs/examples/extended_least_squares.html
index cc5b8864..2fa02219 100644
--- a/docs/examples/extended_least_squares.html
+++ b/docs/examples/extended_least_squares.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/f_16_benchmark.html b/docs/examples/f_16_benchmark.html
index 193cf56c..d402a4d2 100644
--- a/docs/examples/f_16_benchmark.html
+++ b/docs/examples/f_16_benchmark.html
@@ -156,12 +156,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/general_estimators.html b/docs/examples/general_estimators.html
index b3f689c5..6f881f81 100644
--- a/docs/examples/general_estimators.html
+++ b/docs/examples/general_estimators.html
@@ -45,7 +45,7 @@
     <link rel="canonical" href="http://sysidentpy.org/examples/general_estimators.html" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Changes in SysIdentPy" href="../changelog/v0.1.4.html" />
+    <link rel="next" title="Simulate a Predefined Model" href="simulating_a_predefined_model.html" />
     <link rel="prev" title="Building NARX Neural Network using Sysidentpy" href="narx_neural_network.html" />
 
     <meta name="viewport" content="width=device-width, initial-scale=1" />
@@ -156,12 +156,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -586,7 +606,7 @@ <h3>NARX with Bayesian Rigde<a class="headerlink" href="#narx-with-bayesian-rigd
         <div class='prev-next-bottom'>
             
     <a class='left-prev' id="prev-link" href="narx_neural_network.html" title="previous page">Building NARX Neural Network using Sysidentpy</a>
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+    <a class='right-next' id="next-link" href="simulating_a_predefined_model.html" title="next page">Simulate a Predefined Model</a>
 
         </div>
         
diff --git a/docs/examples/identification_of_an_electromechanical_system.html b/docs/examples/identification_of_an_electromechanical_system.html
new file mode 100644
index 00000000..4ba2e784
--- /dev/null
+++ b/docs/examples/identification_of_an_electromechanical_system.html
@@ -0,0 +1,628 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+    <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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+    <ul class="current nav sidenav_l1">
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+   Install Guide
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../introduction_to_narmax.html">
+   A brief introduction to NARMAX models.
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../user_guide.html">
+   User Guide
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../dev_guide.html">
+   Contributing
+  </a>
+ </li>
+ <li class="toctree-l1 current active collapsible-parent">
+  <a class="reference internal" href="../notebooks.html">
+   Jupyter notebooks
+  </a>
+  <ul class="current collapse-ul">
+   <li class="toctree-l2">
+    <a class="reference internal" href="basic_steps.html">
+     Presenting main functionality
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="multiple_inputs_example.html">
+     Multiple Inputs usage
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="information_criteria_examples.html">
+     Information Criteria - Examples
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="extended_least_squares.html">
+     Important notes and examples of how to use Extended Least Squares
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="defining_lags.html">
+     Setting specific lags
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter_estimation.html">
+     Parameter Estimation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="f_16_benchmark.html">
+     Example: F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="narx_neural_network.html">
+     Building NARX Neural Network using Sysidentpy
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="general_estimators.html">
+     Building NARX models using general estimators
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+  </ul>
+  <i class="fas fa-chevron-down">
+  </i>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
+   Changes in SysIdentPy
+  </a>
+ </li>
+ <li class="toctree-l1 collapsible-parent">
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+   Codes
+  </a>
+  <ul class="simple collapse-ul">
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.base">
+   sysidentpy base
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-narmax">
+   sysidentpy narmax
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
+   sysidentpy narx_neural_network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-general-estimators">
+   sysidentpy general_estimators
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.residues.residues_correlation">
+   sysidentpy residues
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+  <a class="reference internal" href="../code.html#module-sysidentpy.metrics._regression">
+   sysidentpy metrics
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.parameter_estimation.estimators">
+   sysidentpy estimators
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils._check_arrays">
+   sysidentpy utils
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils.generate_data">
+   sysidentpy generate data
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#building-a-polynomial-narx-model">
+   Building a Polynomial NARX model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#testing-different-autoregressive-models">
+   Testing different autoregressive models
+  </a>
+ </li>
+</ul>
+
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+        
+        </div>
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+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="identification-of-an-electromechanical-system">
+<h1>Identification of an electromechanical system<a class="headerlink" href="#identification-of-an-electromechanical-system" title="Permalink to this headline">¶</a></h1>
+<p>Example created by Wilson Rocha Lacerda Junior</p>
+<p>More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;data/x_cc.csv&#39;</span><span class="p">)</span>
+<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;data/y_cc.csv&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span><span class="p">[</span><span class="mi">5000</span><span class="p">:</span><span class="mi">80000</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
+</pre></div>
+</div>
+<img alt="../_images/identification_of_an_electromechanical_system_3_1.png" src="../_images/identification_of_an_electromechanical_system_3_1.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># we will decimate the data using d=500 in this example</span>
+<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[::</span><span class="mi">500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">df2</span><span class="o">.</span><span class="n">iloc</span><span class="p">[::</span><span class="mi">500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="building-a-polynomial-narx-model">
+<h2>Building a Polynomial NARX model<a class="headerlink" href="#building-a-polynomial-narx-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">(</span><span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                         <span class="n">order_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <span class="n">n_info_values</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+                         <span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">ylag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                         <span class="n">info_criteria</span><span class="o">=</span><span class="s1">&#39;bic&#39;</span><span class="p">,</span>
+                         <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;recursive_least_squares&#39;</span>
+                         <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
+                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+
+<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\wilso\miniconda3\envs\v0.1.4\lib\site-packages\sysidentpy\polynomial_basis\narmax.py:333: UserWarning: n_info_values is greater than the maximum number of all regressors space considering the chosen y_lag, u_lag, and non_degree. We set as 15 
+  self.info_values = self.information_criterion(X, y)
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08011571455094049
+       Regressors Parameters         ERR
+0          y(k-1)     1.3016  0.98600038
+1       x1(k-1)^2   103.9300  0.00794805
+2        y(k-2)^2     0.0000  0.00250906
+3   x1(k-1)y(k-1)    -0.1257  0.00143301
+4          y(k-2)    -0.5078  0.00102781
+5   x1(k-1)y(k-2)     0.0560  0.00053520
+6         x1(k-2)   349.8627  0.00027965
+7   x1(k-2)y(k-1)    -0.0840  0.00011221
+8  x1(k-2)x1(k-1)    -7.8186  0.00004547
+9   x1(k-2)y(k-2)     0.0341  0.00003253
+</pre></div>
+</div>
+<img alt="../_images/identification_of_an_electromechanical_system_7_2.png" src="../_images/identification_of_an_electromechanical_system_7_2.png" />
+</div>
+</div>
+</div>
+<div class="section" id="testing-different-autoregressive-models">
+<h2>Testing different autoregressive models<a class="headerlink" href="#testing-different-autoregressive-models" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span><span class="p">,</span> <span class="n">LinearSVC</span><span class="p">,</span> <span class="n">NuSVC</span>
+<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span><span class="p">,</span> <span class="n">AdaBoostRegressor</span><span class="p">,</span> <span class="n">GradientBoostingRegressor</span>
+<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
+<span class="kn">from</span> <span class="nn">catboost</span> <span class="kn">import</span> <span class="n">CatBoostRegressor</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">BayesianRidge</span><span class="p">,</span> <span class="n">ARDRegression</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.general_estimators</span> <span class="kn">import</span> <span class="n">NARX</span>
+
+
+<span class="n">estimators</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;KNeighborsRegressor&#39;</span><span class="p">,</span> <span class="n">NARX</span><span class="p">(</span>
+        <span class="n">base_estimator</span><span class="o">=</span><span class="n">KNeighborsRegressor</span><span class="p">(),</span>
+        <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s1">&#39;DecisionTreeRegressor&#39;</span><span class="p">,</span> <span class="n">NARX</span><span class="p">(</span>
+        <span class="n">base_estimator</span><span class="o">=</span><span class="n">DecisionTreeRegressor</span><span class="p">(),</span>
+        <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s1">&#39;RandomForestRegressor&#39;</span><span class="p">,</span> <span class="n">NARX</span><span class="p">(</span>
+        <span class="n">base_estimator</span><span class="o">=</span><span class="n">RandomForestRegressor</span><span class="p">(</span>
+            <span class="n">n_estimators</span><span class="o">=</span><span class="mi">200</span><span class="p">),</span>
+        <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="p">)),</span>
+    <span class="p">(</span><span class="s1">&#39;Catboost_NARX&#39;</span><span class="p">,</span> <span class="n">NARX</span><span class="p">(</span>
+        <span class="n">base_estimator</span><span class="o">=</span><span class="n">CatBoostRegressor</span><span class="p">(</span>
+        <span class="n">iterations</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span>
+        <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
+        <span class="n">depth</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
+        <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">non_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">fit_params</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;verbose&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+    <span class="p">)),</span>
+    <span class="p">(</span><span class="s1">&#39;ARD_NARX&#39;</span><span class="p">,</span> <span class="n">NARX</span><span class="p">(</span>
+        <span class="n">base_estimator</span><span class="o">=</span><span class="n">ARDRegression</span><span class="p">(),</span>
+        <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+        <span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span>
+    <span class="p">)),</span>
+    <span class="p">(</span><span class="s1">&#39;Polynomial_NARX&#39;</span><span class="p">,</span> <span class="n">PolynomialNarmax</span><span class="p">(</span><span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                         <span class="n">order_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <span class="n">n_info_values</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+                         <span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">ylag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                         <span class="n">info_criteria</span><span class="o">=</span><span class="s1">&#39;bic&#39;</span><span class="p">,</span>
+                         <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;recursive_least_squares&#39;</span><span class="p">,</span>
+                         <span class="p">)),</span>
+    
+    <span class="p">]</span>
+
+
+
+
+<span class="n">resultados</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">nome_do_modelo</span><span class="p">,</span> <span class="n">modelo</span> <span class="ow">in</span> <span class="n">estimators</span><span class="p">:</span>
+    <span class="n">resultados</span><span class="p">[</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">nome_do_modelo</span><span class="p">)]</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">modelo</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+    <span class="n">yhat</span> <span class="o">=</span> <span class="n">modelo</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span>  <span class="n">y_valid</span><span class="p">)</span>
+    <span class="n">result</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+    <span class="n">resultados</span><span class="p">[</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">nome_do_modelo</span><span class="p">)]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">nome_do_modelo</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>03-08 22:53:49 - INFO - Training the model
+03-08 22:53:49 - INFO - Creating the regressor matrix
+03-08 22:53:49 - INFO - The regressor matrix have 21 features
+03-08 22:53:49 - INFO - Done! Model is built!
+KNeighborsRegressor 1.833
+03-08 22:53:50 - INFO - Training the model
+03-08 22:53:50 - INFO - Creating the regressor matrix
+03-08 22:53:50 - INFO - The regressor matrix have 21 features
+03-08 22:53:50 - INFO - Done! Model is built!
+DecisionTreeRegressor 0.346
+03-08 22:53:50 - INFO - Training the model
+03-08 22:53:50 - INFO - Creating the regressor matrix
+03-08 22:53:50 - INFO - The regressor matrix have 21 features
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\wilso\miniconda3\envs\v0.1.4\lib\site-packages\sysidentpy\general_estimators\narx.py:154: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
+  self.base_estimator.fit(X, y, **self.fit_params)
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>03-08 22:53:51 - INFO - Done! Model is built!
+RandomForestRegressor 0.178
+03-08 22:54:03 - INFO - Training the model
+03-08 22:54:03 - INFO - Creating the regressor matrix
+03-08 22:54:03 - INFO - The regressor matrix have 21 features
+03-08 22:54:12 - INFO - Done! Model is built!
+Catboost_NARX 0.179
+03-08 22:54:13 - INFO - Training the model
+03-08 22:54:13 - INFO - Creating the regressor matrix
+03-08 22:54:13 - INFO - The regressor matrix have 231 features
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\wilso\miniconda3\envs\v0.1.4\lib\site-packages\sklearn\utils\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  return f(*args, **kwargs)
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>03-08 22:54:14 - INFO - Done! Model is built!
+ARD_NARX 0.074
+Polynomial_NARX 0.032
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">aux_results</span><span class="p">,</span> <span class="n">results</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">resultados</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">aux_results</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial_NARX 0.03208119098997185
+ARD_NARX 0.07364659571266981
+RandomForestRegressor 0.17752025859574144
+Catboost_NARX 0.17923040407121107
+DecisionTreeRegressor 0.3457206837080308
+KNeighborsRegressor 1.833370478725381
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+
+
+              </div>
+              
+        
+        <div class='prev-next-bottom'>
+            
+    <a class='left-prev' id="prev-link" href="system_identification_using_adaptative_filters.html" title="previous page">System Identification Using Adaptative Filters</a>
+    <a class='right-next' id="next-link" href="n_steps_ahead_prediction.html" title="next page">Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark</a>
+
+        </div>
+        
+        </div>
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
+        
+            &copy; Copyright 2020, Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins.<br/>
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+</html>
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diff --git a/docs/examples/information_criteria_examples.html b/docs/examples/information_criteria_examples.html
index 9970d140..929eea2f 100644
--- a/docs/examples/information_criteria_examples.html
+++ b/docs/examples/information_criteria_examples.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/multiple_inputs_example.html b/docs/examples/multiple_inputs_example.html
index 4dfa6cef..04e9c5cc 100644
--- a/docs/examples/multiple_inputs_example.html
+++ b/docs/examples/multiple_inputs_example.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/n_steps_ahead_prediction.html b/docs/examples/n_steps_ahead_prediction.html
new file mode 100644
index 00000000..8fcd5f9e
--- /dev/null
+++ b/docs/examples/n_steps_ahead_prediction.html
@@ -0,0 +1,802 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark &#8212; NARMAX models</title>
+    
+  <link rel="stylesheet" href="../_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css">
+
+    
+  <link rel="stylesheet"
+    href="../_static/vendor/fontawesome/5.13.0/css/all.min.css">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
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+  <link rel="preload" as="script" href="../_static/js/index.d3f166471bb80abb5163.js">
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+    <script src="../_static/jquery.js"></script>
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+    <script src="../_static/clipboard.min.js"></script>
+    <script src="../_static/copybutton.js"></script>
+    <script >var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
+    <script src="../_static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js"></script>
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+    <link rel="next" title="Changes in SysIdentPy" href="../changelog/v0.1.5.html" />
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+
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+<meta property="og:type"        content="article" />
+<meta property="og:title"       content="Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark" />
+<meta property="og:description" content="Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark  Note: The following examples do not try to replicate the results of the cited manuscri" />
+<meta property="og:image"       content="http://sysidentpy.org/_static/sysidentpy-logo.png" />
+
+<meta name="twitter:card" content="summary" />
+
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+  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+    
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+      <div class="row">
+          
+<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
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+<a class="navbar-brand text-wrap" href="../index.html">
+  
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+  <a class="reference internal" href="../introduction_to_narmax.html">
+   A brief introduction to NARMAX models.
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../user_guide.html">
+   User Guide
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../dev_guide.html">
+   Contributing
+  </a>
+ </li>
+ <li class="toctree-l1 current active collapsible-parent">
+  <a class="reference internal" href="../notebooks.html">
+   Jupyter notebooks
+  </a>
+  <ul class="current collapse-ul">
+   <li class="toctree-l2">
+    <a class="reference internal" href="basic_steps.html">
+     Presenting main functionality
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="multiple_inputs_example.html">
+     Multiple Inputs usage
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="information_criteria_examples.html">
+     Information Criteria - Examples
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="extended_least_squares.html">
+     Important notes and examples of how to use Extended Least Squares
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="defining_lags.html">
+     Setting specific lags
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter_estimation.html">
+     Parameter Estimation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="f_16_benchmark.html">
+     Example: F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="narx_neural_network.html">
+     Building NARX Neural Network using Sysidentpy
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="general_estimators.html">
+     Building NARX models using general estimators
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+  </ul>
+  <i class="fas fa-chevron-down">
+  </i>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
+   Changes in SysIdentPy
+  </a>
+ </li>
+ <li class="toctree-l1 collapsible-parent">
+  <a class="reference internal" href="../code.html">
+   Codes
+  </a>
+  <ul class="simple collapse-ul">
+  </ul>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.base">
+   sysidentpy base
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-narmax">
+   sysidentpy narmax
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   sysidentpy narx_neural_network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-general-estimators">
+   sysidentpy general_estimators
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.residues.residues_correlation">
+   sysidentpy residues
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.metrics._regression">
+   sysidentpy metrics
+  </a>
+ </li>
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+  <a class="reference internal" href="../code.html#module-sysidentpy.parameter_estimation.estimators">
+   sysidentpy estimators
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils._check_arrays">
+   sysidentpy utils
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils.generate_data">
+   sysidentpy generate data
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#indices-and-tables">
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+            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
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+                    class="fab fa-github"></i>repository</button></a>
+        <a class="issues-button"
+            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/n_steps_ahead_prediction.html&body=Your%20issue%20content%20here."><button
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+
+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show">
+            
+        <div class="tocsection onthispage pt-5 pb-3">
+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#preparing-the-data">
+   Preparing the data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#building-the-model">
+   Building the model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#defining-the-forecasting-horizon">
+   Defining the forecasting horizon
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+        </div>
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="example-n-steps-ahead-prediction-f-16-ground-vibration-test-benchmark">
+<h1>Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark<a class="headerlink" href="#example-n-steps-ahead-prediction-f-16-ground-vibration-test-benchmark" title="Permalink to this headline">¶</a></h1>
+<p><strong>Note</strong>: The following examples do not try to replicate the results of the cited manuscripts. Even the model parameters such as ylag and xlag and size of identification and validation data are not the same of the cited papers. Moreover, sampling rate adjustment and other different data preparation are not handled here.</p>
+<p><strong>The following text was taken from the link http://www.nonlinearbenchmark.org/#F16</strong>.</p>
+<p><strong>Note</strong>: The reader is reffered to the mentioned website for a complete reference concerning the experiment. For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. A more detailed study of this system will be published in the future.</p>
+<blockquote>
+<div><p>The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads.</p>
+</div></blockquote>
+<blockquote>
+<div><p>The experimental data made available to the Workshop participants were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class, held in September 2014 at the Saffraanberg military basis, Sint-Truiden, Belgium.</p>
+</div></blockquote>
+<blockquote>
+<div><p>During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study.</p>
+</div></blockquote>
+<blockquote>
+<div><p>A detailed formulation of the identification problem can be found here. All the provided files and information on the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format.</p>
+</div></blockquote>
+<blockquote>
+<div><p>Please refer to the F16 benchmark as:</p>
+</div></blockquote>
+<blockquote>
+<div><p>J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_columns&#39;</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="preparing-the-data">
+<h2>Preparing the data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;examples/datasets/f-16.txt&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">,</span> <span class="s1">&#39;x2&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(32768, 3)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span><span class="p">[[</span><span class="s1">&#39;x1&#39;</span><span class="p">,</span> <span class="s1">&#39;x2&#39;</span><span class="p">]][</span><span class="mi">0</span><span class="p">:</span><span class="mi">500</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
+</pre></div>
+</div>
+<img alt="../_images/n_steps_ahead_prediction_6_1.svg" src="../_images/n_steps_ahead_prediction_6_1.svg" /></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">2000</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
+</pre></div>
+</div>
+<img alt="../_images/n_steps_ahead_prediction_7_1.svg" src="../_images/n_steps_ahead_prediction_7_1.svg" /></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x1_id</span><span class="p">,</span> <span class="n">x1_val</span> <span class="o">=</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">16384</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">][</span><span class="mi">16384</span><span class="p">::]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">x2_id</span><span class="p">,</span> <span class="n">x2_val</span> <span class="o">=</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;x2&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">16384</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;x2&#39;</span><span class="p">][</span><span class="mi">16384</span><span class="p">::]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">x_id</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x1_id</span><span class="p">,</span> <span class="n">x2_id</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">x_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x1_val</span><span class="p">,</span> <span class="n">x2_val</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">y_id</span><span class="p">,</span> <span class="n">y_val</span> <span class="o">=</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">16384</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">f_16</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">][</span><span class="mi">16384</span><span class="p">::]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x1lag</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">x2lag</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="n">x2lag</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[1, 2, 3, 4, 5, 6, 7, 8, 9]
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="building-the-model">
+<h2>Building the model<a class="headerlink" href="#building-the-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">(</span><span class="c1"># n_terms=8,</span>
+                         <span class="n">non_degree</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                         <span class="n">order_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <span class="n">ylag</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="p">[</span><span class="n">x1lag</span><span class="p">,</span> <span class="n">x2lag</span><span class="p">],</span>
+                         <span class="n">n_inputs</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+                         <span class="n">n_info_values</span><span class="o">=</span><span class="mi">39</span><span class="p">,</span>
+                         <span class="n">info_criteria</span><span class="o">=</span><span class="s1">&#39;bic&#39;</span><span class="p">,</span>
+                         <span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;least_squares&#39;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_id</span><span class="p">,</span> <span class="n">y_id</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2523e7e4850&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="defining-the-forecasting-horizon">
+<h2>Defining the forecasting horizon<a class="headerlink" href="#defining-the-forecasting-horizon" title="Permalink to this headline">¶</a></h2>
+<p>To perform a n-steps-ahead prediction you just need to set the “steps_ahead” argument.</p>
+<p><strong>Note</strong> The default value for steps_ahead is None and it performs a infinity-steps-ahead prediction</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_hat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;sci&#39;</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+<span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09660139833917882
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Regressors</th>
+      <th>Parameters</th>
+      <th>ERR</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>y(k-1)</td>
+      <td>1.8387E+00</td>
+      <td>9.43378253E-01</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>y(k-2)</td>
+      <td>-1.8938E+00</td>
+      <td>1.95167599E-02</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>y(k-3)</td>
+      <td>1.3337E+00</td>
+      <td>1.02432261E-02</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>y(k-6)</td>
+      <td>-1.6038E+00</td>
+      <td>8.03485985E-03</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>y(k-9)</td>
+      <td>2.6776E-01</td>
+      <td>9.27874557E-04</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>x2(k-7)</td>
+      <td>-2.2385E+01</td>
+      <td>3.76837313E-04</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>x1(k-1)</td>
+      <td>8.2709E+00</td>
+      <td>6.81508210E-04</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>x2(k-3)</td>
+      <td>1.0587E+02</td>
+      <td>1.57459800E-03</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>x1(k-8)</td>
+      <td>-3.7975E+00</td>
+      <td>7.35086279E-04</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>x2(k-1)</td>
+      <td>8.5725E+01</td>
+      <td>4.85358786E-04</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>y(k-7)</td>
+      <td>1.3955E+00</td>
+      <td>2.77245281E-04</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>y(k-5)</td>
+      <td>1.3219E+00</td>
+      <td>8.64120037E-04</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>y(k-10)</td>
+      <td>-2.9306E-01</td>
+      <td>8.51717688E-04</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>y(k-4)</td>
+      <td>-9.5479E-01</td>
+      <td>7.23623116E-04</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>y(k-8)</td>
+      <td>-7.1309E-01</td>
+      <td>4.44988077E-04</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>y(k-12)</td>
+      <td>-3.0437E-01</td>
+      <td>1.49743148E-04</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>y(k-11)</td>
+      <td>4.8602E-01</td>
+      <td>3.34613282E-04</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>y(k-13)</td>
+      <td>-8.2442E-02</td>
+      <td>1.43738964E-04</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>y(k-15)</td>
+      <td>-1.6762E-01</td>
+      <td>1.25546584E-04</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>x1(k-2)</td>
+      <td>-8.9698E+00</td>
+      <td>9.76699739E-05</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>y(k-17)</td>
+      <td>2.2036E-02</td>
+      <td>4.55983807E-05</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>y(k-14)</td>
+      <td>2.4900E-01</td>
+      <td>1.10314107E-04</td>
+    </tr>
+    <tr>
+      <th>22</th>
+      <td>y(k-19)</td>
+      <td>-6.8239E-03</td>
+      <td>1.99734771E-05</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>x2(k-9)</td>
+      <td>-9.6265E+01</td>
+      <td>2.98523208E-05</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>x2(k-8)</td>
+      <td>2.2620E+02</td>
+      <td>2.34402543E-04</td>
+    </tr>
+    <tr>
+      <th>25</th>
+      <td>x2(k-2)</td>
+      <td>-2.3609E+02</td>
+      <td>1.04172323E-04</td>
+    </tr>
+    <tr>
+      <th>26</th>
+      <td>y(k-20)</td>
+      <td>-5.4663E-02</td>
+      <td>5.37895336E-05</td>
+    </tr>
+    <tr>
+      <th>27</th>
+      <td>x2(k-6)</td>
+      <td>-2.3651E+02</td>
+      <td>2.11392628E-05</td>
+    </tr>
+    <tr>
+      <th>28</th>
+      <td>x2(k-4)</td>
+      <td>1.7378E+02</td>
+      <td>2.18396315E-05</td>
+    </tr>
+    <tr>
+      <th>29</th>
+      <td>x1(k-7)</td>
+      <td>4.9862E+00</td>
+      <td>2.03811842E-05</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span><span class="n">c</span><span class="p">,</span><span class="n">d</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/n_steps_ahead_prediction_14_0.svg" src="../_images/n_steps_ahead_prediction_14_0.svg" /></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_hat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;sci&#39;</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span><span class="n">c</span><span class="p">,</span><span class="n">d</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.22158488532181117
+</pre></div>
+</div>
+<img alt="../_images/n_steps_ahead_prediction_15_1.svg" src="../_images/n_steps_ahead_prediction_15_1.svg" /></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_hat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">)</span> <span class="c1"># inifity-steps-ahead prediction</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;sci&#39;</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span><span class="n">c</span><span class="p">,</span><span class="n">d</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.3090559299602927
+</pre></div>
+</div>
+<img alt="../_images/n_steps_ahead_prediction_16_1.svg" src="../_images/n_steps_ahead_prediction_16_1.svg" /></div>
+</div>
+</div>
+</div>
+
+
+              </div>
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+        
+        <div class='prev-next-bottom'>
+            
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+        
+          By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
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diff --git a/docs/examples/narx_neural_network.html b/docs/examples/narx_neural_network.html
index 7a3bc01c..288fa371 100644
--- a/docs/examples/narx_neural_network.html
+++ b/docs/examples/narx_neural_network.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
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  </li>
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-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/parameter_estimation.html b/docs/examples/parameter_estimation.html
index e88eb73f..c1ceadbc 100644
--- a/docs/examples/parameter_estimation.html
+++ b/docs/examples/parameter_estimation.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="../changelog/v0.1.4.html">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/examples/simulating_a_predefined_model.html b/docs/examples/simulating_a_predefined_model.html
new file mode 100644
index 00000000..da07f56a
--- /dev/null
+++ b/docs/examples/simulating_a_predefined_model.html
@@ -0,0 +1,849 @@
+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
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+   A brief introduction to NARMAX models.
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+  <ul class="current collapse-ul">
+   <li class="toctree-l2">
+    <a class="reference internal" href="basic_steps.html">
+     Presenting main functionality
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+   <li class="toctree-l2">
+    <a class="reference internal" href="multiple_inputs_example.html">
+     Multiple Inputs usage
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="information_criteria_examples.html">
+     Information Criteria - Examples
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="extended_least_squares.html">
+     Important notes and examples of how to use Extended Least Squares
+    </a>
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+   <li class="toctree-l2">
+    <a class="reference internal" href="defining_lags.html">
+     Setting specific lags
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+     Parameter Estimation
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+   <li class="toctree-l2">
+    <a class="reference internal" href="f_16_benchmark.html">
+     Example: F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="narx_neural_network.html">
+     Building NARX Neural Network using Sysidentpy
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="general_estimators.html">
+     Building NARX models using general estimators
+    </a>
+   </li>
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+  </ul>
+  <i class="fas fa-chevron-down">
+  </i>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../changelog/v0.1.5.html">
+   Changes in SysIdentPy
+  </a>
+ </li>
+ <li class="toctree-l1 collapsible-parent">
+  <a class="reference internal" href="../code.html">
+   Codes
+  </a>
+  <ul class="simple collapse-ul">
+  </ul>
+  <i class="fas fa-chevron-down">
+  </i>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.base">
+   sysidentpy base
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-narmax">
+   sysidentpy narmax
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
+   sysidentpy narx_neural_network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-general-estimators">
+   sysidentpy general_estimators
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.residues.residues_correlation">
+   sysidentpy residues
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.metrics._regression">
+   sysidentpy metrics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.parameter_estimation.estimators">
+   sysidentpy estimators
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils._check_arrays">
+   sysidentpy utils
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.utils.generate_data">
+   sysidentpy generate data
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#indices-and-tables">
+   Indices and tables
+  </a>
+ </li>
+</ul>
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+            
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+            <i class="fas fa-list"></i> Contents
+        </div>
+        <nav id="bd-toc-nav">
+            <ul class="nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#generating-1-input-1-output-sample-data">
+   Generating 1 input 1 output sample data
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#the-data-is-generated-by-simulating-the-following-model">
+     The data is generated by simulating the following model:
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#defining-the-model">
+   Defining the model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#defining-the-parameter-estimation-method">
+   Defining the parameter estimation method
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#simulating-the-model">
+   Simulating the model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#final-code">
+   Final code
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+        </div>
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="simulate-a-predefined-model">
+<h1>Simulate a Predefined Model<a class="headerlink" href="#simulate-a-predefined-model" title="Permalink to this headline">¶</a></h1>
+<p>Example created by Wilson Rocha Lacerda Junior</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="generating-1-input-1-output-sample-data">
+<h2>Generating 1 input 1 output sample data<a class="headerlink" href="#generating-1-input-1-output-sample-data" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="the-data-is-generated-by-simulating-the-following-model">
+<h3>The data is generated by simulating the following model:<a class="headerlink" href="#the-data-is-generated-by-simulating-the-following-model" title="Permalink to this headline">¶</a></h3>
+<p><span class="math notranslate nohighlight">\(y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}\)</span></p>
+<p>If <em>colored_noise</em> is set to True:</p>
+<p><span class="math notranslate nohighlight">\(e_{k} = 0.8\nu_{k-1} + \nu_{k}\)</span></p>
+<p>where <span class="math notranslate nohighlight">\(x\)</span> is a uniformly distributed random variable and <span class="math notranslate nohighlight">\(\nu\)</span> is a gaussian distributed variable with <span class="math notranslate nohighlight">\(\mu=0\)</span> and <span class="math notranslate nohighlight">\(\sigma=0.1\)</span></p>
+<p>In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:</p>
+<p>We already know that the generated data is a result of the model  <span class="math notranslate nohighlight">\(𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘\)</span> . Thus, we can create a model with those regressors and estimate its parameters as follows:</p>
+</div>
+</div>
+<div class="section" id="defining-the-model">
+<h2>Defining the model<a class="headerlink" href="#defining-the-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># build the object so we can access its functions</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
+
+<span class="c1"># create any psi matrix that contains the combinations we want</span>
+<span class="c1"># in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))</span>
+<span class="c1"># so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher</span>
+<span class="c1"># build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)</span>
+<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">psi</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>2</th>
+      <th>3</th>
+      <th>4</th>
+      <th>5</th>
+      <th>6</th>
+      <th>7</th>
+      <th>8</th>
+      <th>9</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+      <td>0.000000</td>
+      <td>0.108434</td>
+      <td>-0.610171</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>-0.000000</td>
+      <td>0.011758</td>
+      <td>-0.066163</td>
+      <td>0.372308</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1.0</td>
+      <td>-0.548087</td>
+      <td>-0.370476</td>
+      <td>0.108434</td>
+      <td>0.300400</td>
+      <td>0.203053</td>
+      <td>-0.059431</td>
+      <td>0.137252</td>
+      <td>-0.040172</td>
+      <td>0.011758</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1.0</td>
+      <td>0.009140</td>
+      <td>0.730998</td>
+      <td>-0.370476</td>
+      <td>0.000084</td>
+      <td>0.006681</td>
+      <td>-0.003386</td>
+      <td>0.534358</td>
+      <td>-0.270817</td>
+      <td>0.137252</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1.0</td>
+      <td>-0.332688</td>
+      <td>0.042290</td>
+      <td>0.730998</td>
+      <td>0.110682</td>
+      <td>-0.014070</td>
+      <td>-0.243195</td>
+      <td>0.001788</td>
+      <td>0.030914</td>
+      <td>0.534358</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.0</td>
+      <td>0.589887</td>
+      <td>0.369362</td>
+      <td>0.042290</td>
+      <td>0.347967</td>
+      <td>0.217882</td>
+      <td>0.024947</td>
+      <td>0.136428</td>
+      <td>0.015620</td>
+      <td>0.001788</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>893</th>
+      <td>1.0</td>
+      <td>-0.741870</td>
+      <td>0.489266</td>
+      <td>-0.685155</td>
+      <td>0.550371</td>
+      <td>-0.362971</td>
+      <td>0.508296</td>
+      <td>0.239381</td>
+      <td>-0.335223</td>
+      <td>0.469437</td>
+    </tr>
+    <tr>
+      <th>894</th>
+      <td>1.0</td>
+      <td>-0.801757</td>
+      <td>-0.008684</td>
+      <td>0.489266</td>
+      <td>0.642815</td>
+      <td>0.006963</td>
+      <td>-0.392272</td>
+      <td>0.000075</td>
+      <td>-0.004249</td>
+      <td>0.239381</td>
+    </tr>
+    <tr>
+      <th>895</th>
+      <td>1.0</td>
+      <td>0.278465</td>
+      <td>-0.974898</td>
+      <td>-0.008684</td>
+      <td>0.077543</td>
+      <td>-0.271475</td>
+      <td>-0.002418</td>
+      <td>0.950426</td>
+      <td>0.008466</td>
+      <td>0.000075</td>
+    </tr>
+    <tr>
+      <th>896</th>
+      <td>1.0</td>
+      <td>0.019212</td>
+      <td>-0.014295</td>
+      <td>-0.974898</td>
+      <td>0.000369</td>
+      <td>-0.000275</td>
+      <td>-0.018729</td>
+      <td>0.000204</td>
+      <td>0.013937</td>
+      <td>0.950426</td>
+    </tr>
+    <tr>
+      <th>897</th>
+      <td>1.0</td>
+      <td>-0.872305</td>
+      <td>0.562238</td>
+      <td>-0.014295</td>
+      <td>0.760916</td>
+      <td>-0.490443</td>
+      <td>0.012470</td>
+      <td>0.316112</td>
+      <td>-0.008037</td>
+      <td>0.000204</td>
+    </tr>
+  </tbody>
+</table>
+<p>898 rows × 10 columns</p>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># we create the code representation to make easier to identifacate the index of the regressors we want.</span>
+<span class="c1"># regressor_space(non_degree, xlag, ylag, n_inputs)</span>
+<span class="c1"># 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on</span>
+<span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">regressor_code</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[   0,    0],
+       [1001,    0],
+       [2001,    0],
+       [2002,    0],
+       [1001, 1001],
+       [2001, 1001],
+       [2002, 1001],
+       [2001, 2001],
+       [2002, 2001],
+       [2002, 2002]])
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here, <span class="math notranslate nohighlight">\(0\)</span> is the constant term, <span class="math notranslate nohighlight">\([1001]=y_{k-1}, \quad [100n]=y_{k-n}, \quad [200n] = x1_{k-n}, \quad [300n]=x2_{k-n}\)</span> and so on</p>
+<p>We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span> <span class="c1"># to use in the predict function</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">final_model</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[1001,    0],
+       [2002,    0],
+       [2001, 1001]])
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="defining-the-parameter-estimation-method">
+<h2>Defining the parameter estimation method<a class="headerlink" href="#defining-the-parameter-estimation-method" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># by importing the Estimator class you can use any of the parameters estimator methods</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.</span>
+<span class="c1"># If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. </span>
+
+<span class="c1"># Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate </span>
+<span class="c1"># pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">()</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
+<span class="n">model</span><span class="o">.</span><span class="n">theta</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[0.20014451],
+       [0.89992074],
+       [0.09997088]])
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="simulating-the-model">
+<h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0019184421163563367
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
+<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
+                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Regressors Parameters         ERR
+0         y(k-1)     0.2001  0.00000000
+1        x1(k-2)     0.8999  0.00000000
+2  x1(k-1)y(k-1)     0.1000  0.00000000
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/simulating_a_predefined_model_20_0.png" src="../_images/simulating_a_predefined_model_20_0.png" />
+</div>
+</div>
+</div>
+<div class="section" id="final-code">
+<h2>Final code<a class="headerlink" href="#final-code" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
+
+<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
+<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ylag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span> <span class="c1"># to use in the predict function</span>
+<span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
+<span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">()</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
+<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
+<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
+                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.001879953122213705
+      Regressors Parameters         ERR
+0         y(k-1)     0.2000  0.00000000
+1        x1(k-2)     0.8999  0.00000000
+2  x1(k-1)y(k-1)     0.1001  0.00000000
+</pre></div>
+</div>
+<img alt="../_images/simulating_a_predefined_model_22_1.png" src="../_images/simulating_a_predefined_model_22_1.png" />
+</div>
+</div>
+</div>
+</div>
+
+
+              </div>
+              
+        
+        <div class='prev-next-bottom'>
+            
+    <a class='left-prev' id="prev-link" href="general_estimators.html" title="previous page">Building NARX models using general estimators</a>
+    <a class='right-next' id="next-link" href="system_identification_using_adaptative_filters.html" title="next page">System Identification Using Adaptative Filters</a>
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new file mode 100644
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+   A brief introduction to NARMAX models.
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+     Multiple Inputs usage
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+     Information Criteria - Examples
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+    <a class="reference internal" href="extended_least_squares.html">
+     Important notes and examples of how to use Extended Least Squares
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="defining_lags.html">
+     Setting specific lags
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="parameter_estimation.html">
+     Parameter Estimation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="f_16_benchmark.html">
+     Example: F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="narx_neural_network.html">
+     Building NARX Neural Network using Sysidentpy
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="general_estimators.html">
+     Building NARX models using general estimators
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
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+  </i>
+ </li>
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+   Changes in SysIdentPy
+  </a>
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+   Codes
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+   sysidentpy base
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+   sysidentpy narmax
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+   sysidentpy narx_neural_network
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+   sysidentpy residues
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+   sysidentpy metrics
+  </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#module-sysidentpy.parameter_estimation.estimators">
+   sysidentpy estimators
+  </a>
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+  <a class="reference internal" href="../code.html#module-sysidentpy.utils._check_arrays">
+   sysidentpy utils
+  </a>
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+   sysidentpy generate data
+  </a>
+ </li>
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+   Indices and tables
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#generating-1-input-1-output-sample-data">
+   Generating 1 input 1 output sample data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#building-the-model">
+   Building the model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#simulating-the-model">
+   Simulating the model
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#final-code">
+   Final code
+  </a>
+ </li>
+</ul>
+
+        </nav>
+        
+        </div>
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+        
+              <div>
+                
+  <div class="section" id="system-identification-using-adaptative-filters">
+<h1>System Identification Using Adaptative Filters<a class="headerlink" href="#system-identification-using-adaptative-filters" title="Permalink to this headline">¶</a></h1>
+<p>Example created by Wilson Rocha Lacerda Junior</p>
+<div class="section" id="generating-1-input-1-output-sample-data">
+<h2>Generating 1 input 1 output sample data<a class="headerlink" href="#generating-1-input-1-output-sample-data" title="Permalink to this headline">¶</a></h2>
+<p>The data is generated by simulating the following model:</p>
+<p><span class="math notranslate nohighlight">\(y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}\)</span></p>
+<p>If <em>colored_noise</em> is set to True:</p>
+<p><span class="math notranslate nohighlight">\(e_{k} = 0.8\nu_{k-1} + \nu_{k}\)</span></p>
+<p>where <span class="math notranslate nohighlight">\(x\)</span> is a uniformly distributed random variable and <span class="math notranslate nohighlight">\(\nu\)</span> is a gaussian distributed variable with <span class="math notranslate nohighlight">\(\mu=0\)</span> and <span class="math notranslate nohighlight">\(\sigma=0.1\)</span></p>
+<p>In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
+
+<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>One can create a model object and access the Adaptative Filters available in SysIdentPy individually. If you want to build a regressor matrix and estimate the parameters using only the Adaptative Filter method (not applying FROLS + ERR algorithm), follow the steps bellow.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
+<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ylag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># creating the regressor matrix</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">psi</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>2</th>
+      <th>3</th>
+      <th>4</th>
+      <th>5</th>
+      <th>6</th>
+      <th>7</th>
+      <th>8</th>
+      <th>9</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+      <td>0.000000</td>
+      <td>-0.488925</td>
+      <td>-0.242635</td>
+      <td>0.000000</td>
+      <td>-0.000000</td>
+      <td>-0.000000</td>
+      <td>0.239048</td>
+      <td>0.118630</td>
+      <td>0.058872</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1.0</td>
+      <td>-0.217385</td>
+      <td>0.969823</td>
+      <td>-0.488925</td>
+      <td>0.047256</td>
+      <td>-0.210825</td>
+      <td>0.106285</td>
+      <td>0.940557</td>
+      <td>-0.474171</td>
+      <td>0.239048</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1.0</td>
+      <td>-0.503772</td>
+      <td>-0.628457</td>
+      <td>0.969823</td>
+      <td>0.253787</td>
+      <td>0.316599</td>
+      <td>-0.488570</td>
+      <td>0.394958</td>
+      <td>-0.609492</td>
+      <td>0.940557</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1.0</td>
+      <td>0.804358</td>
+      <td>0.308975</td>
+      <td>-0.628457</td>
+      <td>0.646992</td>
+      <td>0.248526</td>
+      <td>-0.505505</td>
+      <td>0.095465</td>
+      <td>-0.194177</td>
+      <td>0.394958</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.0</td>
+      <td>-0.379308</td>
+      <td>-0.442970</td>
+      <td>0.308975</td>
+      <td>0.143875</td>
+      <td>0.168022</td>
+      <td>-0.117197</td>
+      <td>0.196222</td>
+      <td>-0.136867</td>
+      <td>0.095465</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">regressor_code</span> <span class="c1"># the entire regressor space is our input in this case. But you can define specific subsets to use as an input</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[   0,    0],
+       [1001,    0],
+       [2001,    0],
+       [2002,    0],
+       [1001, 1001],
+       [2001, 1001],
+       [2002, 1001],
+       [2001, 2001],
+       [2002, 2001],
+       [2002, 2002]])
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span> <span class="c1"># defines the model representation</span>
+<span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here we are using the Affine Least Mean Squares method, but you can use any of the methods available on SysIdentPy</p>
+<ul class="simple">
+<li><p>Least Mean Squares (LMS)</p></li>
+<li><p>Affine LMS</p></li>
+<li><p>LMS Sign Error</p></li>
+<li><p>Normalized LMS</p></li>
+<li><p>Normalized LSM Sign Error</p></li>
+<li><p>LSM Sign Regressor</p></li>
+<li><p>Normalized LMS Sign Regressor</p></li>
+<li><p>LMS Sign-Sign</p></li>
+<li><p>Normalized LMS Sign-Sign</p></li>
+<li><p>Normalized LMS Leaky</p></li>
+<li><p>LMS Leaky</p></li>
+<li><p>LMS Mixed Norm</p></li>
+<li><p>LMS Fourth</p></li>
+</ul>
+<p>Also, you can use:</p>
+<ul class="simple">
+<li><p>Least Squares (LS)</p></li>
+<li><p>Total LS</p></li>
+<li><p>Recursive LS</p></li>
+</ul>
+</div>
+<div class="section" id="building-the-model">
+<h2>Building the model<a class="headerlink" href="#building-the-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">affine_least_mean_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="simulating-the-model">
+<h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_terms</span><span class="o">*</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
+                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0024097628318461625
+       Regressors Parameters         ERR
+0               1    -0.0000  0.00000000
+1          y(k-1)     0.1999  0.00000000
+2         x1(k-1)    -0.0000  0.00000000
+3         x1(k-2)     0.8999  0.00000000
+4        y(k-1)^2    -0.0001  0.00000000
+5   x1(k-1)y(k-1)     0.1000  0.00000000
+6   x1(k-2)y(k-1)    -0.0000  0.00000000
+7       x1(k-1)^2     0.0002  0.00000000
+8  x1(k-2)x1(k-1)    -0.0000  0.00000000
+9       x1(k-2)^2     0.0000  0.00000000
+</pre></div>
+</div>
+<img alt="../_images/system_identification_using_adaptative_filters_12_1.png" src="../_images/system_identification_using_adaptative_filters_12_1.png" />
+</div>
+</div>
+</div>
+<div class="section" id="final-code">
+<h2>Final code<a class="headerlink" href="#final-code" title="Permalink to this headline">¶</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
+
+<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
+<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ylag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># creating the regressor matrix</span>
+<span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">regressor_code</span> <span class="c1"># the entire regressor space is our input in this case. But you can define specific subsets to use as an input</span>
+<span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span> <span class="c1"># defines the model representation</span>
+<span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span>
+<span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span><span class="o">.</span><span class="n">affine_least_mean_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">n_terms</span><span class="o">*</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
+                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+
+
+              </div>
+              
+        
+        <div class='prev-next-bottom'>
+            
+    <a class='left-prev' id="prev-link" href="simulating_a_predefined_model.html" title="previous page">Simulate a Predefined Model</a>
+    <a class='right-next' id="next-link" href="identification_of_an_electromechanical_system.html" title="next page">Identification of an electromechanical system</a>
+
+        </div>
+        
+        </div>
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
+        
+            &copy; Copyright 2020, Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins.<br/>
+      </p>
+    </div>
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+</main>
+
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+      </div>
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+  <script src="../_static/js/index.d3f166471bb80abb5163.js"></script>
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+  </body>
+</html>
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diff --git a/docs/genindex.html b/docs/genindex.html
index 74e91f92..05e506a3 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -154,12 +154,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/index.html b/docs/index.html
index 5de3ec5b..4b1f2267 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -155,12 +155,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -645,7 +665,7 @@ <h3>Contents<a class="headerlink" href="#contents" title="Permalink to this head
 <li class="toctree-l1"><a class="reference internal" href="user_guide.html">User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dev_guide.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="notebooks.html">Jupyter notebooks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="changelog/v0.1.4.html">Changes in SysIdentPy</a></li>
+<li class="toctree-l1"><a class="reference internal" href="changelog/v0.1.5.html">Changes in SysIdentPy</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html">Codes</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.base">sysidentpy base</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-narmax">sysidentpy narmax</a></li>
diff --git a/docs/installation.html b/docs/installation.html
index 94419e19..27611f17 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -156,12 +156,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/introduction_to_narmax.html b/docs/introduction_to_narmax.html
index a5946977..9cb97a8d 100644
--- a/docs/introduction_to_narmax.html
+++ b/docs/introduction_to_narmax.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/notebooks.html b/docs/notebooks.html
index 128a8c68..5cacf24b 100644
--- a/docs/notebooks.html
+++ b/docs/notebooks.html
@@ -156,12 +156,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
@@ -369,6 +389,30 @@ <h1>Jupyter notebooks<a class="headerlink" href="#jupyter-notebooks" title="Perm
 <li class="toctree-l1"><a class="reference internal" href="examples/general_estimators.html">Building NARX models using general estimators</a></li>
 </ul>
 </div>
+<div class="toctree-wrapper compound">
+<p class="caption"><span class="caption-text">Simulate a predefined model</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="examples/simulating_a_predefined_model.html">Simulate a Predefined Model</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption"><span class="caption-text">System Identification Using Adaptative Filters</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">System Identification Using Adaptative Filters</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption"><span class="caption-text">Identification of an electromechanical system</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">Identification of an electromechanical system</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption"><span class="caption-text">N-steps-ahead simulation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="examples/n_steps_ahead_prediction.html">Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark</a></li>
+</ul>
+</div>
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diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 9d6c54b7..d7fdcc39 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -157,12 +157,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/search.html b/docs/search.html
index eb7c2180..cf926278 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -160,12 +160,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>
diff --git a/docs/searchindex.js b/docs/searchindex.js
index cd271c2f..f8c65e19 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
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in SysIdentPy","Changes in SysIdentPy","Changes in SysIdentPy","Codes","Contributing","Presenting main functionality","Setting specific lags","Important notes and examples of how to use Extended Least Squares","Example: F-16 Ground Vibration Test benchmark","Building NARX models using general estimators","Identification of an electromechanical system","Information Criteria - Examples","Multiple Inputs usage","Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark","Building NARX Neural Network using Sysidentpy","Parameter Estimation","Simulate a Predefined Model","System Identification Using Adaptative Filters","Welcome to SysIdentPy\u2019s documentation!","Install Guide","A brief introduction to NARMAX models.","Jupyter notebooks","User 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\ No newline at end of file
diff --git a/docs/user_guide.html b/docs/user_guide.html
index d9e74d9d..a84c50c6 100644
--- a/docs/user_guide.html
+++ b/docs/user_guide.html
@@ -158,12 +158,32 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
      Building NARX models using general estimators
     </a>
    </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/simulating_a_predefined_model.html">
+     Simulate a Predefined Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/system_identification_using_adaptative_filters.html">
+     System Identification Using Adaptative Filters
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/identification_of_an_electromechanical_system.html">
+     Identification of an electromechanical system
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="examples/n_steps_ahead_prediction.html">
+     Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark
+    </a>
+   </li>
   </ul>
   <i class="fas fa-chevron-down">
   </i>
  </li>
  <li class="toctree-l1">
-  <a class="reference internal" href="changelog/v0.1.4.html">
+  <a class="reference internal" href="changelog/v0.1.5.html">
    Changes in SysIdentPy
   </a>
  </li>

From 373e1adc730fbd15e13895294079c79850ef257a Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Tue, 9 Mar 2021 19:19:48 -0300
Subject: [PATCH 07/27] added interative notebook examples

---
 docs/.buildinfo                               |  2 +-
 docs/_config.yml                              |  6 ------
 docs/_modules/index.html                      |  4 ++--
 docs/_modules/sysidentpy/base.html            |  4 ++--
 .../sysidentpy/general_estimators/narx.html   |  4 ++--
 .../sysidentpy/metrics/_regression.html       |  4 ++--
 .../parameter_estimation/estimators.html      |  4 ++--
 .../sysidentpy/polynomial_basis/narmax.html   |  4 ++--
 .../residues/residues_correlation.html        |  4 ++--
 .../sysidentpy/utils/_check_arrays.html       |  4 ++--
 .../sysidentpy/utils/generate_data.html       |  4 ++--
 docs/_static/documentation_options.js         |  2 +-
 docs/changelog/v0.1.3.html                    |  4 ++--
 docs/changelog/v0.1.4.html                    |  4 ++--
 docs/changelog/v0.1.5.html                    |  4 ++--
 docs/code.html                                |  4 ++--
 docs/dev_guide.html                           |  4 ++--
 docs/examples/basic_steps.html                | 20 +++++++++++++++++--
 docs/examples/defining_lags.html              | 20 +++++++++++++++++--
 docs/examples/extended_least_squares.html     | 20 +++++++++++++++++--
 docs/examples/f_16_benchmark.html             | 20 +++++++++++++++++--
 docs/examples/general_estimators.html         | 20 +++++++++++++++++--
 ...cation_of_an_electromechanical_system.html | 20 +++++++++++++++++--
 .../information_criteria_examples.html        | 20 +++++++++++++++++--
 docs/examples/multiple_inputs_example.html    | 20 +++++++++++++++++--
 docs/examples/n_steps_ahead_prediction.html   | 20 +++++++++++++++++--
 docs/examples/narx_neural_network.html        | 20 +++++++++++++++++--
 docs/examples/parameter_estimation.html       | 20 +++++++++++++++++--
 .../simulating_a_predefined_model.html        | 20 +++++++++++++++++--
 ...entification_using_adaptative_filters.html | 20 +++++++++++++++++--
 docs/genindex.html                            |  4 ++--
 docs/index.html                               |  4 ++--
 docs/installation.html                        |  4 ++--
 docs/introduction_to_narmax.html              |  4 ++--
 docs/notebooks.html                           |  4 ++--
 docs/py-modindex.html                         |  4 ++--
 docs/search.html                              |  4 ++--
 docs/source/conf.py                           | 13 +++++++++---
 docs/source/examples/basic_steps.ipynb        |  9 +++++++++
 docs/source/examples/defining_lags.ipynb      |  9 +++++++++
 .../examples/extended_least_squares.ipynb     |  9 +++++++++
 docs/source/examples/f_16_benchmark.ipynb     |  9 +++++++++
 docs/source/examples/general_estimators.ipynb |  9 +++++++++
 ...ation_of_an_electromechanical_system.ipynb | 11 +++++++++-
 .../information_criteria_examples.ipynb       |  9 +++++++++
 .../examples/multiple_inputs_example.ipynb    |  9 +++++++++
 .../examples/n_steps_ahead_prediction.ipynb   |  9 +++++++++
 .../source/examples/narx_neural_network.ipynb |  9 +++++++++
 .../simulating_a_predefined_model.ipynb       | 11 +++++++++-
 ...ntification_using_adaptative_filters.ipynb | 11 +++++++++-
 docs/user_guide.html                          |  4 ++--
 51 files changed, 401 insertions(+), 84 deletions(-)

diff --git a/docs/.buildinfo b/docs/.buildinfo
index 8b28a3fe..a689989f 100644
--- a/docs/.buildinfo
+++ b/docs/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 27f8835bea0e2daee3a03c91e8d98724
+config: 6f0e27d0fa412c6c4a2ef5a08c16b660
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_config.yml b/docs/_config.yml
index 91a58738..277f1f2c 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -1,7 +1 @@
 theme: jekyll-theme-cayman
-repository:
-  url                       : https://github.com/wilsonrljr/sysidentpy  # Online location of your book
-  path_to_book              : /docs  # Optional path to your book, relative to the repository root
-  branch                    : master  # Which branch of the repository should be used when creating links (optional)
-launch_buttons:
-  colab_url: "https://colab.research.google.com"
\ No newline at end of file
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index c58b5df8..1d8912b8 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2F_modules/index.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/index.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/base.html b/docs/_modules/sysidentpy/base.html
index 29ab5d34..74651808 100644
--- a/docs/_modules/sysidentpy/base.html
+++ b/docs/_modules/sysidentpy/base.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/base.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/base.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/general_estimators/narx.html b/docs/_modules/sysidentpy/general_estimators/narx.html
index 71d77b47..aeb97c8c 100644
--- a/docs/_modules/sysidentpy/general_estimators/narx.html
+++ b/docs/_modules/sysidentpy/general_estimators/narx.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/general_estimators/narx.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/general_estimators/narx.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/metrics/_regression.html b/docs/_modules/sysidentpy/metrics/_regression.html
index 5ab17b60..1c9c9b79 100644
--- a/docs/_modules/sysidentpy/metrics/_regression.html
+++ b/docs/_modules/sysidentpy/metrics/_regression.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/metrics/_regression.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/metrics/_regression.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/parameter_estimation/estimators.html b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
index 79bcb419..7ccf0ae7 100644
--- a/docs/_modules/sysidentpy/parameter_estimation/estimators.html
+++ b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
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         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/parameter_estimation/estimators.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/polynomial_basis/narmax.html b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
index 8468f9dc..989ab8aa 100644
--- a/docs/_modules/sysidentpy/polynomial_basis/narmax.html
+++ b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/polynomial_basis/narmax.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/residues/residues_correlation.html b/docs/_modules/sysidentpy/residues/residues_correlation.html
index 7ce21c10..8fdf3e08 100644
--- a/docs/_modules/sysidentpy/residues/residues_correlation.html
+++ b/docs/_modules/sysidentpy/residues/residues_correlation.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/residues/residues_correlation.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/_modules/sysidentpy/utils/_check_arrays.html b/docs/_modules/sysidentpy/utils/_check_arrays.html
index 831683d0..e5332f59 100644
--- a/docs/_modules/sysidentpy/utils/_check_arrays.html
+++ b/docs/_modules/sysidentpy/utils/_check_arrays.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/utils/_check_arrays.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
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diff --git a/docs/_modules/sysidentpy/utils/generate_data.html b/docs/_modules/sysidentpy/utils/generate_data.html
index acd1b7ae..1320ec20 100644
--- a/docs/_modules/sysidentpy/utils/generate_data.html
+++ b/docs/_modules/sysidentpy/utils/generate_data.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/utils/generate_data.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2F_modules/sysidentpy/utils/generate_data.html&body=Your%20issue%20content%20here."><button
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diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index 36339b6c..09f1a8bf 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.1.4',
+    VERSION: '0.1.5',
     LANGUAGE: 'None',
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     BUILDER: 'html',
diff --git a/docs/changelog/v0.1.3.html b/docs/changelog/v0.1.3.html
index 6c25920d..6636a022 100644
--- a/docs/changelog/v0.1.3.html
+++ b/docs/changelog/v0.1.3.html
@@ -298,11 +298,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.3.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.3.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/changelog/v0.1.4.html b/docs/changelog/v0.1.4.html
index d058ca5c..7de6a081 100644
--- a/docs/changelog/v0.1.4.html
+++ b/docs/changelog/v0.1.4.html
@@ -298,11 +298,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.4.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.4.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/changelog/v0.1.5.html b/docs/changelog/v0.1.5.html
index 208a7097..f3bb3a94 100644
--- a/docs/changelog/v0.1.5.html
+++ b/docs/changelog/v0.1.5.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.5.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.5.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/code.html b/docs/code.html
index 3d6b64dd..1fcfb8fe 100644
--- a/docs/code.html
+++ b/docs/code.html
@@ -299,11 +299,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fcode.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fcode.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/dev_guide.html b/docs/dev_guide.html
index a052a801..d268e86c 100644
--- a/docs/dev_guide.html
+++ b/docs/dev_guide.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
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-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fdev_guide.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fdev_guide.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/examples/basic_steps.html b/docs/examples/basic_steps.html
index d361ef66..76f26122 100644
--- a/docs/examples/basic_steps.html
+++ b/docs/examples/basic_steps.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/basic_steps.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/basic_steps.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/basic_steps.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/defining_lags.html b/docs/examples/defining_lags.html
index 1d3cbdda..89a1c9d8 100644
--- a/docs/examples/defining_lags.html
+++ b/docs/examples/defining_lags.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/defining_lags.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/defining_lags.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/defining_lags.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/extended_least_squares.html b/docs/examples/extended_least_squares.html
index 2fa02219..ae6f2e8f 100644
--- a/docs/examples/extended_least_squares.html
+++ b/docs/examples/extended_least_squares.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/extended_least_squares.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/extended_least_squares.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/extended_least_squares.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/f_16_benchmark.html b/docs/examples/f_16_benchmark.html
index d402a4d2..a5773fd6 100644
--- a/docs/examples/f_16_benchmark.html
+++ b/docs/examples/f_16_benchmark.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/f_16_benchmark.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/f_16_benchmark.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -320,6 +320,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/f_16_benchmark.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/general_estimators.html b/docs/examples/general_estimators.html
index 6f881f81..05d8f6ca 100644
--- a/docs/examples/general_estimators.html
+++ b/docs/examples/general_estimators.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/general_estimators.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/general_estimators.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -320,6 +320,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/general_estimators.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/identification_of_an_electromechanical_system.html b/docs/examples/identification_of_an_electromechanical_system.html
index 4ba2e784..ef073786 100644
--- a/docs/examples/identification_of_an_electromechanical_system.html
+++ b/docs/examples/identification_of_an_electromechanical_system.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/identification_of_an_electromechanical_system.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/identification_of_an_electromechanical_system.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -320,6 +320,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/identification_of_an_electromechanical_system.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/information_criteria_examples.html b/docs/examples/information_criteria_examples.html
index 929eea2f..fef80b77 100644
--- a/docs/examples/information_criteria_examples.html
+++ b/docs/examples/information_criteria_examples.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/information_criteria_examples.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/information_criteria_examples.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/information_criteria_examples.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/multiple_inputs_example.html b/docs/examples/multiple_inputs_example.html
index 04e9c5cc..32b6b51c 100644
--- a/docs/examples/multiple_inputs_example.html
+++ b/docs/examples/multiple_inputs_example.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/multiple_inputs_example.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/multiple_inputs_example.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/multiple_inputs_example.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/n_steps_ahead_prediction.html b/docs/examples/n_steps_ahead_prediction.html
index 8fcd5f9e..c3cda978 100644
--- a/docs/examples/n_steps_ahead_prediction.html
+++ b/docs/examples/n_steps_ahead_prediction.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/n_steps_ahead_prediction.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/n_steps_ahead_prediction.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -320,6 +320,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/n_steps_ahead_prediction.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/narx_neural_network.html b/docs/examples/narx_neural_network.html
index 288fa371..6cf481a6 100644
--- a/docs/examples/narx_neural_network.html
+++ b/docs/examples/narx_neural_network.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/narx_neural_network.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/narx_neural_network.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/narx_neural_network.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/parameter_estimation.html b/docs/examples/parameter_estimation.html
index c1ceadbc..2e0138a0 100644
--- a/docs/examples/parameter_estimation.html
+++ b/docs/examples/parameter_estimation.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/parameter_estimation.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/parameter_estimation.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/parameter_estimation.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/simulating_a_predefined_model.html b/docs/examples/simulating_a_predefined_model.html
index da07f56a..086e15ca 100644
--- a/docs/examples/simulating_a_predefined_model.html
+++ b/docs/examples/simulating_a_predefined_model.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/simulating_a_predefined_model.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/simulating_a_predefined_model.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/simulating_a_predefined_model.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/examples/system_identification_using_adaptative_filters.html b/docs/examples/system_identification_using_adaptative_filters.html
index 9b93c004..5a7d9d1e 100644
--- a/docs/examples/system_identification_using_adaptative_filters.html
+++ b/docs/examples/system_identification_using_adaptative_filters.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fexamples/system_identification_using_adaptative_filters.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/system_identification_using_adaptative_filters.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
@@ -322,6 +322,22 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
 
             <!-- Launch buttons -->
 
+<div class="dropdown-buttons-trigger">
+    <button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn"
+        aria-label="Launch interactive content"><i class="fas fa-rocket"></i></button>
+    <div class="dropdown-buttons">
+        
+        
+        
+        <a class="colab-button" href="https://colab.research.google.com/github/wilsonrljr/sysidentpy/blob/master/docs/examples/system_identification_using_adaptative_filters.ipynb"><button type="button" class="btn btn-secondary topbarbtn"
+                title="Launch Colab" data-toggle="tooltip" data-placement="left"><img class="colab-button-logo"
+                    src="../_static/images/logo_colab.png"
+                    alt="Interact on Colab">Colab</button></a>
+        
+        
+    </div>
+</div>
+
         </div>
 
         <!-- Table of contents -->
diff --git a/docs/genindex.html b/docs/genindex.html
index 05e506a3..fe570196 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -281,11 +281,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fgenindex.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fgenindex.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/index.html b/docs/index.html
index 4b1f2267..edd18faf 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -299,11 +299,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Findex.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Findex.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/installation.html b/docs/installation.html
index 27611f17..f01d5906 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Finstallation.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Finstallation.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/introduction_to_narmax.html b/docs/introduction_to_narmax.html
index 9cb97a8d..c7058561 100644
--- a/docs/introduction_to_narmax.html
+++ b/docs/introduction_to_narmax.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fintroduction_to_narmax.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fintroduction_to_narmax.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/notebooks.html b/docs/notebooks.html
index 5cacf24b..f6c40140 100644
--- a/docs/notebooks.html
+++ b/docs/notebooks.html
@@ -300,11 +300,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fnotebooks.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fnotebooks.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index d7fdcc39..01188e24 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -284,11 +284,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fpy-modindex.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fpy-modindex.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/search.html b/docs/search.html
index cf926278..902ae5c3 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -287,11 +287,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fsearch.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fsearch.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 4127f6c4..23a2cf83 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -25,7 +25,7 @@
 author = 'Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1.4'
+release = '0.1.5'
 
 autodoc_member_order = "bysource"
 add_function_parentheses = True
@@ -102,8 +102,15 @@
 # html_static_path = ['_static']
 
 html_theme_options = {
-    "repository_url": "https://github.com/wilsonrljr/sysidentpy/tree/master/",
+    "repository_url": "https://github.com/wilsonrljr/sysidentpy/",
     "use_repository_button": True,
     "use_issues_button": True,
-    "use_download_button": True
+    "use_download_button": True,
+    "use_edit_page_button": True,
+    "repository_branch": "master",
+    "path_to_docs": "docs",
+    "launch_buttons": {
+        "colab_url": "https://colab.research.google.com",
+        "notebook_interface": "jupyterlab"
+    },
 }
diff --git a/docs/source/examples/basic_steps.ipynb b/docs/source/examples/basic_steps.ipynb
index 49fd63d3..421ec957 100644
--- a/docs/source/examples/basic_steps.ipynb
+++ b/docs/source/examples/basic_steps.ipynb
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentp"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/defining_lags.ipynb b/docs/source/examples/defining_lags.ipynb
index 744d9f8f..b302d081 100644
--- a/docs/source/examples/defining_lags.ipynb
+++ b/docs/source/examples/defining_lags.ipynb
@@ -16,6 +16,15 @@
     "Different ways to set the maximum lag for input and output"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/docs/source/examples/extended_least_squares.ipynb b/docs/source/examples/extended_least_squares.ipynb
index 49676413..6057ea75 100644
--- a/docs/source/examples/extended_least_squares.ipynb
+++ b/docs/source/examples/extended_least_squares.ipynb
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/f_16_benchmark.ipynb b/docs/source/examples/f_16_benchmark.ipynb
index ad05c646..4e4f0240 100644
--- a/docs/source/examples/f_16_benchmark.ipynb
+++ b/docs/source/examples/f_16_benchmark.ipynb
@@ -34,6 +34,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/general_estimators.ipynb b/docs/source/examples/general_estimators.ipynb
index 1d70e4f0..d626e075 100644
--- a/docs/source/examples/general_estimators.ipynb
+++ b/docs/source/examples/general_estimators.ipynb
@@ -38,6 +38,15 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/docs/source/examples/identification_of_an_electromechanical_system.ipynb b/docs/source/examples/identification_of_an_electromechanical_system.ipynb
index 51619d6e..adcb493a 100644
--- a/docs/source/examples/identification_of_an_electromechanical_system.ipynb
+++ b/docs/source/examples/identification_of_an_electromechanical_system.ipynb
@@ -12,6 +12,15 @@
     "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -355,4 +364,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/docs/source/examples/information_criteria_examples.ipynb b/docs/source/examples/information_criteria_examples.ipynb
index ee5e62c1..bebca16f 100644
--- a/docs/source/examples/information_criteria_examples.ipynb
+++ b/docs/source/examples/information_criteria_examples.ipynb
@@ -23,6 +23,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/multiple_inputs_example.ipynb b/docs/source/examples/multiple_inputs_example.ipynb
index edad040d..3c2cba48 100644
--- a/docs/source/examples/multiple_inputs_example.ipynb
+++ b/docs/source/examples/multiple_inputs_example.ipynb
@@ -27,6 +27,15 @@
     "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.001$\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/n_steps_ahead_prediction.ipynb b/docs/source/examples/n_steps_ahead_prediction.ipynb
index 6e1ee55e..a0bccdd9 100644
--- a/docs/source/examples/n_steps_ahead_prediction.ipynb
+++ b/docs/source/examples/n_steps_ahead_prediction.ipynb
@@ -30,6 +30,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/narx_neural_network.ipynb b/docs/source/examples/narx_neural_network.ipynb
index 820754b3..00010a12 100644
--- a/docs/source/examples/narx_neural_network.ipynb
+++ b/docs/source/examples/narx_neural_network.ipynb
@@ -59,6 +59,15 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/source/examples/simulating_a_predefined_model.ipynb b/docs/source/examples/simulating_a_predefined_model.ipynb
index 43edc03c..4800a33f 100644
--- a/docs/source/examples/simulating_a_predefined_model.ipynb
+++ b/docs/source/examples/simulating_a_predefined_model.ipynb
@@ -9,6 +9,15 @@
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -610,4 +619,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/source/examples/system_identification_using_adaptative_filters.ipynb b/docs/source/examples/system_identification_using_adaptative_filters.ipynb
index 1765a962..a458470e 100644
--- a/docs/source/examples/system_identification_using_adaptative_filters.ipynb
+++ b/docs/source/examples/system_identification_using_adaptative_filters.ipynb
@@ -30,6 +30,15 @@
     "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -419,4 +428,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/docs/user_guide.html b/docs/user_guide.html
index a84c50c6..a3b3ba4b 100644
--- a/docs/user_guide.html
+++ b/docs/user_guide.html
@@ -302,11 +302,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
         aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
     <div class="dropdown-buttons sourcebuttons">
         <a class="repository-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master/"><button type="button" class="btn btn-secondary topbarbtn"
+            href="https://github.com/wilsonrljr/sysidentpy/"><button type="button" class="btn btn-secondary topbarbtn"
                 data-toggle="tooltip" data-placement="left" title="Source repository"><i
                     class="fab fa-github"></i>repository</button></a>
         <a class="issues-button"
-            href="https://github.com/wilsonrljr/sysidentpy/tree/master//issues/new?title=Issue%20on%20page%20%2Fuser_guide.html&body=Your%20issue%20content%20here."><button
+            href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fuser_guide.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
         

From 7e77eca5e2622170d6e82b13593f191698a99fbb Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Tue, 9 Mar 2021 19:21:30 -0300
Subject: [PATCH 08/27] update the examples to use in google colab

---
 examples/basic_steps.ipynb                    |  110 +-
 examples/defining_lags.ipynb                  |   26 +-
 examples/extended_least_squares.ipynb         |   78 +-
 examples/f_16_benchmark.ipynb                 |   65 +-
 examples/general_estimators.ipynb             |   43 +-
 ...ation_of_an_electromechanical_system.ipynb |   11 +-
 examples/information_criteria_examples.ipynb  | 3438 +----------------
 examples/multiple_inputs_example.ipynb        |   55 +-
 examples/n_steps_ahead_prediction.ipynb       |    9 +
 examples/narx_neural_network.ipynb            |   37 +-
 examples/parameter_estimation.ipynb           |   33 +-
 examples/simulating_a_predefined_model.ipynb  |   11 +-
 ...ntification_using_adaptative_filters.ipynb |   11 +-
 13 files changed, 460 insertions(+), 3467 deletions(-)

diff --git a/examples/basic_steps.ipynb b/examples/basic_steps.ipynb
index 03dfe929..421ec957 100644
--- a/examples/basic_steps.ipynb
+++ b/examples/basic_steps.ipynb
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentp"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -35,7 +44,9 @@
    "metadata": {},
    "source": [
     "## Generating 1 input 1 output sample data  \n",
-    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
     "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
     "\n",
     "If *colored_noise* is set to True:\n",
@@ -63,9 +74,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### To obtain a NARMAX model we have to choose some values, *e.g*, the nonlinearity degree (*non_degree*), the maximum lag for the inputs and output (*xlag* and *ylag*). \n",
+    "To obtain a NARMAX model we have to choose some values, *e.g*, the nonlinearity degree (*non_degree*), the maximum lag for the inputs and output (*xlag* and *ylag*). \n",
     "\n",
-    "#### In addition, you can select the information criteria to be used with the Error Reduction Ratio to select the model order and the method to estimate the model paramaters:\n",
+    "In addition, you can select the information criteria to be used with the Error Reduction Ratio to select the model order and the method to estimate the model paramaters:\n",
     "\n",
     "- Information Criteria: aic, bic, lilc, fpe\n",
     "- Parameter Estimation: least_squares, total_least_squares, recursive_least_squares, least_mean_squres and many other (see the docs)\n",
@@ -97,7 +108,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The *fit* method executes the Error Reduction Ratio algorithm using Househoulder reflection to select the model structure. "
+    "## Model Structure Selection\n",
+    "\n",
+    "The *fit* method executes the Error Reduction Ratio algorithm using Househoulder reflection to select the model structure. "
    ]
   },
   {
@@ -109,7 +122,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1c971108280>"
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1fd33cab3d0>"
       ]
      },
      "metadata": {},
@@ -124,7 +137,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The *predict* method is use to generate the predictions. For now we only support *free run simulation* (also known as *infinity steps ahead*). Soon will let the user define a *one-step ahead* or *k-step ahead* prediction."
+    "## Free run simulation\n",
+    "\n",
+    "The *predict* method is use to generate the predictions. For now we only support *free run simulation* (also known as *infinity steps ahead*). Soon will let the user define a *one-step ahead* or *k-step ahead* prediction."
    ]
   },
   {
@@ -140,7 +155,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### In this example we use the *root_relative_squared_error* metric because it is often used in System Idenfication. More metrics and information about it can be found on documentation."
+    "## Evaluate the model\n",
+    "\n",
+    "In this example we use the *root_relative_squared_error* metric because it is often used in System Idenfication. More metrics and information about it can be found on documentation."
    ]
   },
   {
@@ -152,7 +169,7 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "0.0021916740077804796\n"
+      "0.0018401326800931033\n"
      ]
     }
    ],
@@ -165,7 +182,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### *model_object.results* return the selected model regressors, the estimated parameters and the ERR values. As shown below, the algorithm detect the exact model that was used for simulate the data."
+    "*model_object.results* return the selected model regressors, the estimated parameters and the ERR values. As shown below, the algorithm detect the exact model that was used for simulate the data."
    ]
   },
   {
@@ -177,7 +194,7 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9001  0.95764084\n1         y(k-1)     0.2001  0.03929227\n2  x1(k-1)y(k-1)     0.1001  0.00306336\n"
+      "      Regressors Parameters         ERR\n0        x1(k-2)     0.8999  0.95739001\n1         y(k-1)     0.2000  0.03917632\n2  x1(k-1)y(k-1)     0.0999  0.00343057\n3      x1(k-2)^2    -0.0002  0.00000002\n4        x1(k-1)     0.0001  0.00000001\n5  x1(k-1)y(k-2)     0.0002  0.00000001\n"
      ]
     }
    ],
@@ -193,7 +210,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### In addition, you can access the *residuals* and *plot_result* methods to take a look at the prediction and two residual analysis. The *extras* and *lam* values below contain another residues analysis so you can plot it mannualy. This method will be improved soon. "
+    "In addition, you can access the *residuals* and *plot_result* methods to take a look at the prediction and two residual analysis. The *extras* and *lam* values below contain another residues analysis so you can plot it mannualy. This method will be improved soon. "
    ]
   },
   {
@@ -207,8 +224,8 @@
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      },
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     }
@@ -222,11 +239,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### In the example above we let the number of terms to compose the final model to be defined as the minimum value of the information criteria. Once you ran the algorithm and choose the best number of parameters, you can turn *order_selection* to *False* and set the *n_terms* value (3 in this example). Here we have a small dataset, but in bigger data this can be critical because running information criteria algorithm is more computational expensive. Since we already know the best number of regressor, we set *n_terms* and we get the same result.\n",
+    "## Setting the *n_terms* parameter\n",
+    "\n",
+    "In the example above we let the number of terms to compose the final model to be defined as the minimum value of the information criteria. Once you ran the algorithm and choose the best number of parameters, you can turn *order_selection* to *False* and set the *n_terms* value (3 in this example). Here we have a small dataset, but in bigger data this can be critical because running information criteria algorithm is more computational expensive. Since we already know the best number of regressor, we set *n_terms* and we get the same result.\n",
     "\n",
-    "### However, this is not only critical because computational eficiency. In many situation, the minimum value of the information criteria can lead to overfiting. In some cases, the diference between choosing a model with 30 regressors or 10 is minimal, so you can take the model with 10 terms without loosing accuracy.\n",
+    "However, this is not only critical because computational eficiency. In many situation, the minimum value of the information criteria can lead to overfiting. In some cases, the diference between choosing a model with 30 regressors or 10 is minimal, so you can take the model with 10 terms without loosing accuracy.\n",
     "\n",
-    "### In the following we use *info_values* to plot the information criteria values. As you can see, the minimum value relies where $xaxis = 5$ "
+    "In the following we use *info_values* to plot the information criteria values. As you can see, the minimum value relies where $xaxis = 5$ "
    ]
   },
   {
@@ -248,8 +267,8 @@
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iiScs5zl8fHwwe/ZsnDlzxmXhqGs0UT+d9+DeBxG5iNXy8PLq/FsSicRpYejWjIgIhq+PFycLEpHLWC2PqKgofPzxxx3G9u3bh9DQUKeHIvv4+XhjZP9gLpJIRC5j9WqrtLQ0LFq0CGvXrkVERATOnj0LpVKJV155xZX5qIs0KiU2FZ/ClTYj/CXeYschoh7OannI5XK8+eabqKurw/nz59GvXz/07dvXldnIDpooBdYXmXH0zEWMHcTFK4nIuWzO8wgPD0d4eLhTXrypqQmpqalobm6GRCLBq6++itDQUBw5cgQvvfQSvL29odVqMX/+fABAdnY2/vOf/8DHxwfLli3DyJEjodPpsGTJEly5cgV9+vRBZmYmAgICnJK3O1P/dNK8tLqB5UFETifqTSB27dqFIUOGYNu2bYiLi8OmTZsAXL1MOCsrCzt27MDRo0dRUVGBiooKHD58GIWFhVizZg0yMjIAADk5OZg8eTK2b9+O6Oho5Ofni/mWRKMI8sWv+kh50pyIXELU8hgyZIhl0qFer4ePjw/0ej1aW1sRGRkJQRCg1Wpx8OBBlJWVQavVQhAEhIeHw2g0QqfToaysDBMmTAAAxMbG4sCBA2K+JVGNUSlQWt0Ak8ksdhQi6uFsHrb6/PPP8fbbb6O1tdUytnXrVrtfqLCwEFu2bOkwlp6ejs8//xxxcXFobGzEtm3boNfrIZX+vEZTUFAQzpw5Az8/P4SEhHQYb2pqgl6vh0wm6zDmqTRRSuw4fAYnzzdhaJhc7DhE1IPZLI/MzEwsW7YMYWFht/VCCQkJSEhI6DA2f/58PPnkk0hKSsKJEyfwzDPPYMeOHR2WQDEYDJDL5ZBIJDeMy2QySKVSGAwG+Pv7W7b1VBrLzaEaWB5E5FQ2D1v169cP9957LwYNGmT5chS5XG7Za+jVqxcMBgOkUikkEglqampgNptRXFwMjUaDmJgYFBcXw2Qyoa6uDiaTCUqlEjExMSgqKgIA7N+/H2q12mH53E2kMhChMj+e9yAip7O559GrVy+kp6cjOjraslRJYmKiQ1782WefxQsvvIDt27ejvb0dL774IgAgIyMDS5YsgdFohFarxahRowAAGo0GiYmJMJlMSE9PBwDMmzcPaWlpKCgogEKhQFZWlkOyuSNBEKCJUnCyIBE5nc3yiIiIAABcuHDB4S/et29fbNy48Ybxu+++GwUFBTeMP/PMM3jmmWc6jPXu3dtylRZdnSz472PncK7xCsKC/cWOQ0Q9lM3DVvPnz8fw4cPh5+eHoUOHWuZcUPekscz34KErInIem+WRlZWFXbt2QSKR4F//+hdWr17tilx0i6LD5QiQeKO0ioeuiMh5bB62KikpQV5eHgDgT3/6E6ZPn+70UHTrJN5euHtACPc8iMipbO55tLe3w2QyAQDMZrPlpDl1X2NUCnxddwn6lnaxoxBRD2VzzyMuLg4zZszAqFGjUF5ejri4OFfkotugVilhMgNHai5Ce0dvseMQUQ9kszxmzZoFrVaLU6dOIT4+HkOGDHFFLroNMZEh8BKAkiody4OInMJqeRQWFiIhIQFZWVmWQ1Vff/01AGDRokWuSUe3ROYvwdAwOW9LS0ROY7U8ri1Hcv2Mcp7zcA8alQLvlNWi3WiCj7eo618SUQ9k9a/KtZVqv/rqKzz22GOWL09etdadaFRKXG414vhZz10okoicx+qex7Zt27Bu3To0Njbio48+sowPHjzYJcHo9vxysuCIiGCR0xBRT2O1PFJSUpCSkoL169dj7ty5rsxEDhAeEoD+IQEorWrA/4wfKHYcIuphbF5tlZSUhN27d6O9vR1msxnnz5/Hn//8Z1dko9ukjlLg0OkfOT+HiBzOZnksWLAAKpUKJ0+ehJ+fn0feH9xdjVEp8N7ROtQ2NGOAMlDsOETUg3TpMpy//vWvGDhwIN5++200NjY6OxM5iDpKCYCLJBKR43WpPFpaWtDc3AxBEHD58mVnZyIHuTNMBpmfD0q4SCIROZjN8khJScHmzZsxfvx43HfffQ69kyA5l7eXgJgoBcpYHkTkYDbPeTz00EOW/540aRKkUqlTA5FjaaIUyNp7Eo2X2xAcKBE7DhH1EDbLIy8vD3l5eWhtbbWMvf/++04NRY6jUV097/FFTQMeGNpH5DRE1FPYLI+tW7fijTfeQHAwJ5q5o7sHhMDHS0BJlY7lQUQOY7M87rzzTvTr1w/e3t6uyEMOFuDrjbv6B6OUiyQSkQPZLI9x48bhwQcfxIABAyyTzbZu3eqKbOQgmigF/vHfarS0G+Hnww8BRHT7bJZHfn4+XnvtNchkMlfkIScYo1JgU/FpHPv+EtQ/rXlFRHQ7bJZH3759MWLECHh5cVlvd3VtsmBZtY7lQUQOYbM8Wltb8eijj+KOO+6wrI+UlZXl9GDkOKEyP6h6BaKkqgFzYsVOQ0Q9gc3ymDFjBuRyuSuykBOpo5T49JvzXCSRiBzCZnls2rQJO3bscEUWcqIxKgV2flGLUxcMGBzKiZ5EdHtslkdwcDC2bNmCgQMHWs57aLVapwcjx7o2WbCsqoHlQUS3zWZ5KBQKnDhxAidOnLCMsTzcz+DQICgCJSip0mH6mAFixyEiN2ezPDIzM3Hy5El89913GDhwIIYNG+aKXORggiBAHaVEGScLEpED2Lz+Njc3F8uXL8eXX36J5cuXY9OmTa7IRU6gUSlw6oIBF/QtYkchIjdnc89j9+7d2LZtG3x8fNDW1oakpCTMnj3bFdnIwcaors7xKKtuwEN3hYmchojcmc09D7PZDB+fqx0jkUggkXBZb3c1vH8wfH28UFrFOwsS0e2xuecRExODBQsWQK1Wo6ysDKNHj3ZFLnICPx9vjIoI5p0Fiei2Wd3zKCkpAQCkpqZi6tSpaG9vx9SpU5GWluaycOR46iglKuoa0dxqFDsKEbkxq+WxevVqXL58GU8++STGjx+PmTNn4t577+1wUyhyP2NUCrQZzThae1HsKETkxqwetho/fjymTJmCc+fO4eGHHwYAy9IW+/btc1lAcqxrCyOWVTdg3KBeIqchIndltTxSU1ORmpqKtWvX4umnn3ZlJnKikEBf3NFHihKeNCei22DzhPljjz2GjRs3oqXl57kB8+fPd2ooci6NSond5XUwmczw8uIiiURkP5uX6i5cuBB6vR69e/e2fN2OvXv3YvHixZbHR44cQUJCApKSkpCdnW0Zz87ORnx8PJKSklBeXg4A0Ol0mDVrFpKTk7Fw4UI0NzcDAD755BNMmzYNiYmJKCgouK18nkATpUDTlXacPN8kdhQiclM29zyCgoKQmprqkBdbtWoViouLOyxxsmLFCrz++usYMGAA5syZg4qKCgDA4cOHUVhYiLNnz+KZZ57Bzp07kZOTg8mTJ2Pq1Kl44403kJ+fj5SUFGRmZuKdd95BQEAAZsyYgQceeAChoaEOydwTjflpkcTSqgYMDeNy+0RkP5t7HnfccQf27NmDU6dO4fTp0zh9+vQtv1hMTAxWrlxpeazX69Ha2orIyEgIggCtVouDBw+irKwMWq0WgiAgPDwcRqMROp0OZWVlmDBhAgAgNjYWBw4cQGVlJSIjIxEcHAxfX1+o1WqUlpbeckZPMEAZgFCZHycLEtEts7nncfz4cRw/ftzyWBAEbN269aY/U1hYiC1btnQYe/nllxEXF4dDhw5ZxvR6PaTSn5cHDwoKwpkzZ+Dn54eQkJAO401NTdDr9ZZ7qXc2dm1cr9fbelseTRAEjFEpUMpFEonoFtksj9zcXLufNCEhAQkJCTa3k0qlMBgMlscGgwFyuRwSieSGcZlMZtne39/fsm1nz/HLMqHOqaOUeP+rczjXeAVhwf5ixyEiN2O1PBITE63erjQvL88hLy6VSiGRSFBTU4MBAwaguLgY8+fPh7e3N1599VXMnj0b586dg8lkglKpRExMDIqKijB16lTs378farUagwcPRnV1NS5evIjAwECUlpZy4cYuuLZIYmm1DpNHhouchojcjdXyWLNmjUsCZGRkYMmSJTAajdBqtRg1ahQAQKPRIDExESaTCenp6QCAefPmIS0tDQUFBVAoFMjKyoJEIsHSpUsxe/ZsmM1mTJs2DX379nVJdnc2rJ8cARJvlFY1sDyIyG6C2Ww2ix3C2WprazFx4kTs27cPERERYsfpNpI3/heNzW3Ys2CC2FGIqBu62d9Om1dbUc+liVLg+NlL0Le0ix2FiNwMy8ODaVRKmMzAlzW86oqI7MPy8GCjI0PgJVydLEhEZA+WhweT+UswNEyO0mpOFiQi+7A8PNwYlQJf1lxEu9EkdhQiciMsDw+nVilxudWI42e5SCIRdR3Lw8P9crIgEVFXsTw8XL/gAPQPCeBJcyKyC8uDoFEpUFqtgwfMFyUiB2F5EDRRCvxwqQW1Dc1iRyEiN8HyIGiu3RyK5z2IqItYHoQhfWWQ+fughOc9iKiLWB4Eby8BMZEK3lmQiLqM5UEArl6ye/IHPRovt4kdhYjcAMuDAFy9syAAlNVw74OIbGN5EADg7gEh8PESON+DiLqE5UEAgABfb9zVP5jlQURdwvIgizFRChytvYiWdqPYUYiom2N5kIVGpUBLuwnHvr8kdhQi6uZYHmRhOWnOyYJEZAPLgyxCZX5Q9QrkZEEisonlQR1oVEqUVTdwkUQiuimWB3UwRqWAztCKUxcMYkchom6M5UEdWM578NAVEd0Ey4M6GBwaBEWgBCVc54qIboLlQR0IggB1lBKl1dzzICLrWB50gzEqBU5fMOCCvkXsKETUTbE86AYalQIAuFQJEVnF8qAbDO8fDF8fL04WJCKrWB50Az8fb4yKCOZkQSKyiuVBndKolKioa0RzKxdJJKIbsTyoU5ooBdqMZhytvSh2FCLqhlge1Cl11NWT5mW8ZJeIOsHyoE6FBPpiSF8pJwsSUadYHmSVOurqIokmExdJJKKOWB5k1RiVAk1X2nHyfJPYUYiom2F5kFWanxZ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      },
      "metadata": {}
     }
@@ -265,20 +284,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Important Note: \n",
-    "\n",
-    "### Here we are creating random samples with white noise and letting the algorithm choose the number of terms based on the minimum value of information criteria. This is not the best approach in System Identification, but serves as a simple example. The information criteria must be used as an __auxiliary tool__ to select *n_terms*. Plot the information values to help you on that!\n",
+    "```{note}\n",
+    " Here we are creating random samples with white noise and letting the algorithm choose\n",
+    " the number of terms based on the minimum value of information criteria. \n",
+    " This is not the best approach in System Identification, but serves as a simple example. \n",
+    " The information criteria must be used as an __auxiliary tool__ to select *n_terms*. \n",
+    " Plot the information values to help you on that!\n",
     "\n",
-    "### If you run the example above several times you might find some cases where the algorithm choose only the first two regressors, or four (depending on the information criteria method selected). This is because the minimum value of information criteria depends on residual variance (affected by noise) and have some limitations in nonlinear scenarios. However, if you check the ERR values (robust to noise) you will see that the ERR is ordering the regressors in the correct way!\n",
+    " If you run the example above several times you might find some cases where the\n",
+    " algorithm choose only the first two regressors, or four (depending on the information\n",
+    " criteria method selected). This is because the minimum value of information criteria\n",
+    " depends on residual variance (affected by noise) and have some limitations in nonlinear\n",
+    " scenarios. However, if you check the ERR values (robust to noise) you will see that the\n",
+    " ERR is ordering the regressors in the correct way!\n",
     "\n",
-    "### We have some examples on *information_criteria* notebook!"
+    " We have some examples on *information_criteria* notebook!\n",
+    "```"
    ]
   },
+  {
+   "source": [
+    "```{note}\n",
+    "This documentation and the examples below are written with MyST Markdown, a form\n",
+    "of markdown that works with Sphinx. For more information about MyST markdown, and\n",
+    "to use MyST markdown with your Sphinx website,\n",
+    "see [the MyST-parser documentation](https://myst-parser.readthedocs.io/)\n",
+    "```"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The *n_info_values* limits the number of regressors to apply the information criteria. We choose $n_y = n_x = \\ell = 2$, so the candidate regressor is a list of 15 regressors. We can set *n_info_values = 15* and see the information values for all regressors. This option can save some amount of computational resources when dealing with multiples inputs and large datasets."
+    "The *n_info_values* limits the number of regressors to apply the information criteria. We choose $n_y = n_x = \\ell = 2$, so the candidate regressor is a list of 15 regressors. We can set *n_info_values = 15* and see the information values for all regressors. This option can save some amount of computational resources when dealing with multiples inputs and large datasets."
    ]
   },
   {
@@ -300,8 +340,8 @@
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      "data": {
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\n"
      },
      "metadata": {}
     }
@@ -328,7 +368,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now running without executing information criteria methods (setting the *n_terms*) because we already know the optimal number of regressors"
+    "Now running without executing information criteria methods (setting the *n_terms*) because we already know the optimal number of regressors"
    ]
   },
   {
@@ -340,7 +380,7 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "rrse:  0.0021916740077804796\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9001  0.95764084\n1         y(k-1)     0.2001  0.03929227\n2  x1(k-1)y(k-1)     0.1001  0.00306336\n"
+      "rrse:  0.0018136024659265537\n\n       Regressors Parameters         ERR\n0        x1(k-2)     0.9000  0.95739001\n1         y(k-1)     0.2000  0.03917632\n2  x1(k-1)y(k-1)     0.0999  0.00343057\n"
      ]
     }
    ],
@@ -371,7 +411,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can acess some extra information like the list of all candidate regressors"
+    "## Extra information\n",
+    "\n",
+    "You can acess some extra information like the list of all candidate regressors"
    ]
   },
   {
@@ -418,7 +460,7 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[0.95764084 0.03929227 0.00306336 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.90011397]\n [0.20005433]\n [0.10009018]]\n"
+      "[0.95739001 0.03917632 0.00343057 0.         0.         0.\n 0.         0.         0.         0.         0.         0.\n 0.         0.         0.        ] \n\n\n[[0.89995854]\n [0.20004441]\n [0.09991068]]\n"
      ]
     }
    ],
@@ -438,8 +480,12 @@
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/defining_lags.ipynb b/examples/defining_lags.ipynb
index 7a5d3c3b..b302d081 100644
--- a/examples/defining_lags.ipynb
+++ b/examples/defining_lags.ipynb
@@ -4,11 +4,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Different ways to set the maximum lag for input and output\n",
+    "# Setting specific lags\n",
     "\n",
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Different ways to set the maximum lag for input and output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -25,7 +41,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### If you pass int values for *ylag* and *xlag*, the lags are defined as a range from 1-*ylag* and 1-*xlag*. \n",
+    "## Setting lags using a range of values\n",
+    "\n",
+    "If you pass int values for *ylag* and *xlag*, the lags are defined as a range from 1-*ylag* and 1-*xlag*. \n",
     "\n",
     "For example: if *ylag=4* then the candidate regressors are $y_{k-1}, y_{k-2}, y_{k-3}, y_{k-4}$"
    ]
@@ -63,7 +81,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## If you pass the *ylag* and *xlag* as a list, only the lags related to values in the list will be created."
+    "## Setting specific lags using lists\n",
+    "\n",
+    "If you pass the *ylag* and *xlag* as a list, only the lags related to values in the list will be created."
    ]
   },
   {
diff --git a/examples/extended_least_squares.ipynb b/examples/extended_least_squares.ipynb
index 1a2298e7..6057ea75 100644
--- a/examples/extended_least_squares.ipynb
+++ b/examples/extended_least_squares.ipynb
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -35,7 +44,8 @@
    "metadata": {},
    "source": [
     "## Generating 1 input 1 output sample data  \n",
-    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
     "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-2} + e_{k}$\n",
     "\n",
     "If *colored_noise* is set to True:\n",
@@ -63,16 +73,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### First we will train a model without the Extended Least Squares Algorithm for comparison purpose."
+    "## Build the model\n",
+    "\n",
+    "First we will train a model without the Extended Least Squares Algorithm for comparison purpose."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
    "source": [
     "model = PolynomialNarmax(non_degree=2,\n",
     "                         order_selection=True,\n",
@@ -83,7 +89,13 @@
     "                         info_criteria='aic',\n",
     "                         estimator='least_squares',\n",
     "                         )"
-   ]
+   ],
+   "cell_type": "code",
+   "metadata": {
+    "scrolled": false
+   },
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -93,9 +105,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": [
-      "0.5043155844895849\n"
-     ]
+     "text": "0.5626926249339153\n"
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    ],
    "source": [
@@ -109,7 +119,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Clearly we have something wrong with the obtained model. See the *basic_steps* notebook to compare the results obtained using the same data but without colored noise. But let take a look in whats is wrong."
+    "Clearly we have something wrong with the obtained model. See the *basic_steps* notebook to compare the results obtained using the same data but without colored noise. But let take a look in whats is wrong."
    ]
   },
   {
@@ -120,9 +130,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": [
-      "      Regressors Parameters         ERR\n0        x1(k-2)     0.9055  0.74383792\n1         y(k-1)     0.2902  0.08256432\n2  x1(k-1)y(k-1)     0.0732  0.00171561\n"
-     ]
+     "text": "Regressors Parameters         ERR\n0        x1(k-2)     0.9094  0.74535677\n1         y(k-1)     0.2794  0.07821222\n2  x1(k-1)y(k-1)     0.1188  0.00443378\n"
     }
    ],
    "source": [
@@ -137,13 +145,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### As we can observe above, the model structure is exact the same the one that generate the data. You can se that the ERR ordered the terms in the correct way. And this is an importante note regarding the Error Reduction Ratio algorithm used here: __it is very robust to colored noise!!__ \n",
+    "## Biased parameter estimation\n",
+    "\n",
+    "As we can observe above, the model structure is exact the same the one that generate the data. You can se that the ERR ordered the terms in the correct way. And this is an importante note regarding the Error Reduction Ratio algorithm used here: __it is very robust to colored noise!!__ \n",
     "\n",
-    "### That is a greate feature! However, although the structure is correct, the model *parameters* are not ok! Here we have a biased estimation! The real parameter for $y_{k-1}$ is $0.2$, not $0.3$.\n",
+    "That is a greate feature! However, although the structure is correct, the model *parameters* are not ok! Here we have a biased estimation! The real parameter for $y_{k-1}$ is $0.2$, not $0.3$.\n",
     "\n",
-    "### In this case, we are actually modeling using a NARX model, not a NARMAX. The MA part exists to allow a unbiased estimation of the parameters. To achieve a unbiased estimation of the parameters we have the Extend Least Squares algorithm. Remember, if the data have only white noise, NARX is fine. \n",
+    "In this case, we are actually modeling using a NARX model, not a NARMAX. The MA part exists to allow a unbiased estimation of the parameters. To achieve a unbiased estimation of the parameters we have the Extend Least Squares algorithm. Remember, if the data have only white noise, NARX is fine. \n",
     "\n",
-    "###  Before applying the Extended Least Squares Algorithm we will run several NARX models to check how different the estimated parameters are from the real ones."
+    "Before applying the Extended Least Squares Algorithm we will run several NARX models to check how different the estimated parameters are from the real ones."
    ]
   },
   {
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\n"
      },
      "metadata": {}
     }
@@ -184,16 +194,18 @@
     "# plotting a vertical line where the real values must lie\n",
     "ax = plt.axvline(x=0.1, c='k')\n",
     "ax = plt.axvline(x=0.2, c='k')\n",
-    "ax = plt.axvline(x=0.9, c='k')\n"
+    "ax = plt.axvline(x=0.9, c='k')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### As shown in figure above, we have a problem to estimate the parameter for $y_{k-1}$. Now we will use the Extended Least Squares Algorithm.\n",
+    "## Using the Extended Least Squares algorithm\n",
+    "\n",
+    "As shown in figure above, we have a problem to estimate the parameter for $y_{k-1}$. Now we will use the Extended Least Squares Algorithm.\n",
     "\n",
-    "### In SysIdentPy, just set *extended_least_squares* to *True* and the algorithm will be applied."
+    "In SysIdentPy, just set *extended_least_squares* to *True* and the algorithm will be applied."
    ]
   },
   {
@@ -205,8 +217,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -245,9 +257,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Great! Now we have an unbiased estimation of the parameters! \n",
+    "Great! Now we have an unbiased estimation of the parameters! \n",
+    "\n",
+    "## Note\n",
     "\n",
-    "### Note: The Extended Least Squares is an iterative algorithm. In SysIdentpy we fixed 30 iterations because it is knwon from literature that the algorithm converges quickly (about 10 or 20 iterations). Also, we create a specific set of noise regressors for estimation (order 2 and nonlinearity also equal 2). It is suposed to work in most cases, however we will add the possibility to choose the order and nonlinearity degree for noise regressors in future releases."
+    "Note: The Extended Least Squares is an iterative algorithm. In SysIdentpy we fixed 30 iterations because it is knwon from literature that the algorithm converges quickly (about 10 or 20 iterations). Also, we create a specific set of noise regressors for estimation (order 2 and nonlinearity also equal 2). It is suposed to work in most cases, however we will add the possibility to choose the order and nonlinearity degree for noise regressors in future releases."
    ]
   },
   {
@@ -261,10 +275,10 @@
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "display_name": "Python 3.7.6 64-bit ('sys_tests': conda)",
    "metadata": {
     "interpreter": {
-     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+     "hash": "75eea971140cd20bf61a98694439aebfe611e1f965354b67c59fd475d54e3010"
     }
    }
   },
@@ -278,7 +292,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6-final"
+   "version": "3.7.6-final"
   }
  },
  "nbformat": 4,
diff --git a/examples/f_16_benchmark.ipynb b/examples/f_16_benchmark.ipynb
index 421624b7..4e4f0240 100644
--- a/examples/f_16_benchmark.ipynb
+++ b/examples/f_16_benchmark.ipynb
@@ -6,6 +6,8 @@
    "source": [
     "# Example: F-16 Ground Vibration Test benchmark\n",
     "\n",
+    "Example created by Wilson Rocha Lacerda Junior\n",
+    "\n",
     "**Note**: The following examples do not try to replicate the results of the cited manuscripts. Even the model parameters such as ylag and xlag and size of identification and validation data are not the same of the cited papers. Moreover, sampling rate adjustment and other different data preparation are not handled here."
    ]
   },
@@ -13,6 +15,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Reference\n",
+    "\n",
     "**The following text was taken from the link http://www.nonlinearbenchmark.org/#F16**. \n",
     "\n",
     "**Note**: The reader is reffered to the mentioned website for a complete reference concerning the experiment. For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. A more detailed study of this system will be published in the future.  \n",
@@ -30,6 +34,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -51,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_16 = pd.read_csv(r'datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
+    "f_16 = pd.read_csv(r'../../../examples/datasets/f-16.txt', header=None, names=['x1', 'x2', 'y'])"
    ]
   },
   {
@@ -74,6 +87,13 @@
     "f_16.shape"
    ]
   },
+  {
+   "source": [
+    "## Visualizating the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -93,7 +113,7 @@
      "output_type": "display_data",
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233.761697 \r\nL 307.018272 314.973513 \r\nL 307.627303 326.203109 \r\nL 307.931819 330.341565 \r\nL 308.236335 332.766526 \r\nL 308.540851 333.789923 \r\nL 308.845367 333.749329 \r\nL 309.454399 330.877834 \r\nL 309.758915 328.739169 \r\nL 310.063431 325.453187 \r\nL 310.672462 314.507749 \r\nL 311.890526 278.620472 \r\nL 313.10859 235.189646 \r\nL 314.326653 196.868328 \r\nL 315.240201 177.29986 \r\nL 315.849233 171.215025 \r\nL 316.153749 169.911743 \r\nL 316.458264 169.172504 \r\nL 316.76278 169.847647 \r\nL 317.371812 172.458485 \r\nL 318.28536 181.993814 \r\nL 319.198907 190.375417 \r\nL 319.503423 192.460669 \r\nL 319.807939 193.674218 \r\nL 320.112455 193.804546 \r\nL 320.416971 192.009862 \r\nL 321.026003 185.322526 \r\nL 321.939551 170.550565 \r\nL 322.853098 156.139677 \r\nL 323.157614 152.528943 \r\nL 323.46213 150.578292 \r\nL 323.766646 149.894603 \r\nL 324.071162 150.262086 \r\nL 324.375678 152.135822 \r\nL 324.680194 155.607681 \r\nL 325.289225 167.132118 \r\nL 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\n"
      },
      "metadata": {
@@ -136,6 +156,13 @@
     "f_16['y'][0:2000].plot(figsize=(12, 8))"
    ]
   },
+  {
+   "source": [
+    "## Spliting the data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -150,6 +177,13 @@
     "y_id, y_val = f_16['y'][0:16384].values.reshape(-1, 1), f_16['y'][16384::].values.reshape(-1, 1)"
    ]
   },
+  {
+   "source": [
+    "## Setting the lags the inputs"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -172,6 +206,13 @@
     "x2lag"
    ]
   },
+  {
+   "source": [
+    "## Model training and evalutation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -255,7 +296,7 @@
      "output_type": "display_data",
      "data": {
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422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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103.897438 424.311263 \r\nL 103.91365 423.412431 \r\nL 104.027133 418.725821 \r\nL 104.043345 419.642524 \r\nL 104.17304 421.271663 \r\nL 104.221676 418.238914 \r\nL 104.302735 420.822883 \r\nL 104.318947 420.821367 \r\nL 104.416218 423.156657 \r\nL 104.43243 422.096437 \r\nL 104.464854 418.498653 \r\nL 104.562125 419.583529 \r\nL 104.594549 421.42263 \r\nL 104.643185 417.971119 \r\nL 104.77288 414.932632 \r\nL 104.708032 418.53113 \r\nL 104.789092 415.678459 \r\nL 104.821516 419.100431 \r\nL 104.886363 415.682828 \r\nL 104.951211 414.541039 \r\nL 104.967423 413.927152 \r\nL 105.016058 416.059553 \r\nL 105.129541 418.941538 \r\nL 105.178177 418.179504 \r\nL 105.194389 418.10923 \r\nL 105.226813 416.15303 \r\nL 105.259237 418.660117 \r\nL 105.324084 417.541416 \r\nL 105.37272 422.465125 \r\nL 105.421355 416.6598 \r\nL 105.469991 423.279049 \r\nL 105.502415 420.260182 \r\nL 105.518627 418.30897 \r\nL 105.567262 423.155176 \r\nL 105.615898 419.076668 \r\nL 105.63211 418.77679 \r\nL 105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 419.701183 \r\nL 196.094438 418.00952 \r\nL 196.143073 420.649234 \r\nL 196.191709 416.199579 \r\nL 196.28898 417.366366 \r\nL 196.37004 416.308742 \r\nL 196.418675 420.426478 \r\nL 196.467311 416.606475 \r\nL 196.515947 420.559165 \r\nL 196.532159 421.148637 \r\nL 196.580794 418.879609 \r\nL 196.613218 420.68436 \r\nL 196.62943 420.609831 \r\nL 196.661854 418.763258 \r\nL 196.710489 420.967438 \r\nL 196.742913 420.44045 \r\nL 196.759125 420.47238 \r\nL 196.856396 422.149606 \r\nL 196.872608 421.599822 \r\nL 196.905032 420.208393 \r\nL 196.953668 422.230145 \r\nL 196.969879 422.162306 \r\nL 196.986091 422.129023 \r\nL 197.067151 423.465547 \r\nL 197.099575 422.612249 \r\nL 197.164422 423.376404 \r\nL 197.22927 420.278935 \r\nL 197.277905 422.495307 \r\nL 197.310329 420.714202 \r\nL 197.440024 415.336633 \r\nL 197.456236 416.381807 \r\nL 197.472448 417.053758 \r\nL 197.504872 414.877169 \r\nL 197.618355 412.860758 \r\nL 197.634567 413.456525 \r\nL 197.845321 417.763333 \r\nL 197.861533 417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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207.880479 424.276284 \r\nL 207.896691 423.130602 \r\nL 207.945327 425.441628 \r\nL 207.993962 423.160242 \r\nL 208.05881 429.944125 \r\nL 208.091234 424.763835 \r\nL 208.204717 416.212684 \r\nL 208.139869 425.92429 \r\nL 208.237141 418.940256 \r\nL 208.301988 416.710223 \r\nL 208.350624 422.762311 \r\nL 208.39926 412.984957 \r\nL 208.480319 417.608707 \r\nL 208.610014 431.804637 \r\nL 208.65865 424.977678 \r\nL 208.804557 435.098215 \r\nL 208.836981 429.586857 \r\nL 208.982888 404.649571 \r\nL 209.031523 417.541713 \r\nL 209.063947 401.839688 \r\nL 209.17743 363.229753 \r\nL 209.128795 418.006432 \r\nL 209.193642 376.94487 \r\nL 209.226066 418.563381 \r\nL 209.323337 417.31235 \r\nL 209.371973 405.117254 \r\nL 209.404397 417.827475 \r\nL 209.469244 414.816196 \r\nL 209.550304 434.908361 \r\nL 209.696211 425.613001 \r\nL 209.712422 427.085399 \r\nL 209.744846 431.152351 \r\nL 209.793482 424.426384 \r\nL 209.809694 425.004577 \r\nL 209.825906 425.032733 \r\nL 209.939389 412.80147 \r\nL 209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 232.084826 416.190473 \r\nL 232.133462 419.975022 \r\nL 232.149674 419.952847 \r\nL 232.198309 417.562905 \r\nL 232.279369 417.968967 \r\nL 232.311792 418.860766 \r\nL 232.37664 418.458003 \r\nL 232.457699 420.188585 \r\nL 232.506335 418.61909 \r\nL 232.554971 420.101388 \r\nL 232.571183 420.250992 \r\nL 232.619818 419.70995 \r\nL 232.668454 420.20553 \r\nL 232.798149 421.486257 \r\nL 232.733302 419.887746 \r\nL 232.814361 420.974426 \r\nL 232.846785 420.144314 \r\nL 232.89542 421.843862 \r\nL 232.911632 421.947938 \r\nL 232.927844 421.601243 \r\nL 233.008904 421.792597 \r\nL 233.057539 419.999412 \r\nL 233.106175 421.417223 \r\nL 233.154811 419.709311 \r\nL 233.252082 419.086825 \r\nL 233.203446 420.227198 \r\nL 233.268294 419.092501 \r\nL 233.300718 419.981722 \r\nL 233.333141 418.942376 \r\nL 233.365565 418.066132 \r\nL 233.414201 419.749932 \r\nL 233.430413 419.812181 \r\nL 233.446625 419.570255 \r\nL 233.479048 418.806347 \r\nL 233.527684 419.92974 \r\nL 233.57632 419.010058 \r\nL 233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 417.859005 \r\nL 253.873602 420.184962 \r\nL 254.035721 418.095403 \r\nL 254.132992 417.929039 \r\nL 254.181628 419.510053 \r\nL 254.230263 418.071689 \r\nL 254.278899 419.603384 \r\nL 254.295111 419.476309 \r\nL 254.327535 419.968898 \r\nL 254.37617 420.838796 \r\nL 254.424806 420.127549 \r\nL 254.441018 419.995491 \r\nL 254.473442 420.615741 \r\nL 254.522077 420.93466 \r\nL 254.554501 420.394222 \r\nL 254.765256 417.851118 \r\nL 254.781467 418.035927 \r\nL 254.813891 418.507548 \r\nL 254.862527 417.999743 \r\nL 254.911162 417.394926 \r\nL 254.959798 418.049726 \r\nL 255.138129 421.239266 \r\nL 255.170553 419.340974 \r\nL 255.186765 419.051076 \r\nL 255.202976 419.790456 \r\nL 255.332672 422.323792 \r\nL 255.478579 418.17466 \r\nL 255.49479 418.653938 \r\nL 255.527214 420.698175 \r\nL 255.592062 417.685434 \r\nL 255.624485 419.530516 \r\nL 255.673121 416.476161 \r\nL 255.689333 416.318693 \r\nL 255.737969 419.580191 \r\nL 255.83524 419.018934 \r\nL 255.867664 417.453204 \r\nL 255.916299 419.239097 \r\nL 255.948723 418.478675 \r\nL 255.964935 418.062983 \r\nL 255.997359 419.335367 \r\nL 256.029783 420.772411 \r\nL 256.110842 419.980513 \r\nL 256.127054 419.910921 \r\nL 256.159478 418.805964 \r\nL 256.191902 420.383329 \r\nL 256.256749 419.211978 \r\nL 256.305385 421.6177 \r\nL 256.370232 419.52206 \r\nL 256.467504 419.75374 \r\nL 256.516139 422.148109 \r\nL 256.564775 418.976532 \r\nL 256.597199 421.479595 \r\nL 256.613411 421.413651 \r\nL 256.662046 418.230852 \r\nL 256.743106 419.009087 \r\nL 256.759318 418.725649 \r\nL 256.775529 419.479317 \r\nL 256.807953 420.504153 \r\nL 256.840377 418.134207 \r\nL 256.856589 417.642334 \r\nL 256.889013 419.294746 \r\nL 256.905225 419.669281 \r\nL 256.937648 418.091728 \r\nL 256.95386 418.002148 \r\nL 256.986284 419.965207 \r\nL 257.067343 418.592691 \r\nL 257.197039 421.015669 \r\nL 257.21325 420.640207 \r\nL 257.342945 419.392319 \r\nL 257.375369 419.882532 \r\nL 257.407793 418.728267 \r\nL 257.424005 418.707298 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 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277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 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419.127897 \r\nL 285.421933 419.02404 \r\nL 285.438145 419.238088 \r\nL 285.584052 421.399485 \r\nL 285.600264 421.306161 \r\nL 285.713747 420.723431 \r\nL 285.665112 421.551593 \r\nL 285.729959 420.848583 \r\nL 285.762383 421.523148 \r\nL 285.811018 420.718569 \r\nL 285.843442 421.063031 \r\nL 285.859654 421.226424 \r\nL 285.892078 420.604689 \r\nL 286.021773 418.069172 \r\nL 286.037985 418.861932 \r\nL 286.070409 420.281751 \r\nL 286.119044 418.129977 \r\nL 286.151468 419.169154 \r\nL 286.16768 419.212274 \r\nL 286.297375 417.758756 \r\nL 286.248739 419.380523 \r\nL 286.313587 418.196437 \r\nL 286.362223 419.676996 \r\nL 286.42707 418.761542 \r\nL 286.459494 418.35776 \r\nL 286.491918 419.019448 \r\nL 286.524342 419.490665 \r\nL 286.572977 418.492963 \r\nL 286.605401 419.163771 \r\nL 286.621613 419.27445 \r\nL 286.637825 418.943696 \r\nL 286.735096 418.242596 \r\nL 286.702672 419.25391 \r\nL 286.751308 418.331789 \r\nL 286.799944 419.758582 \r\nL 286.881003 419.559625 \r\nL 286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 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300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 302.23366 421.848824 \r\nL 302.282295 419.837668 \r\nL 302.298507 419.86751 \r\nL 302.428202 421.313305 \r\nL 302.379567 419.263019 \r\nL 302.444414 421.064503 \r\nL 302.590321 418.566528 \r\nL 302.606533 418.525703 \r\nL 302.736228 419.951067 \r\nL 302.75244 419.481548 \r\nL 302.801076 417.314433 \r\nL 302.865923 418.241904 \r\nL 302.914559 418.627725 \r\nL 303.01183 418.537991 \r\nL 303.060466 418.162645 \r\nL 303.109102 418.586264 \r\nL 303.125313 418.537529 \r\nL 303.141525 418.541192 \r\nL 303.206373 421.165623 \r\nL 303.287432 419.626272 \r\nL 303.417127 420.912659 \r\nL 303.35228 419.227874 \r\nL 303.433339 420.234489 \r\nL 303.563034 417.383923 \r\nL 303.498187 420.527948 \r\nL 303.579246 417.785278 \r\nL 303.61167 419.122078 \r\nL 303.708941 418.850158 \r\nL 303.790001 416.940751 \r\nL 303.822425 418.158348 \r\nL 303.95212 420.623288 \r\nL 303.968332 420.574389 \r\nL 304.000755 419.157289 \r\nL 304.049391 420.924096 \r\nL 304.081815 419.996026 \r\nL 304.098027 419.833191 \r\nL 304.114239 420.63193 \r\nL 304.146662 422.477989 \r\nL 304.21151 420.603813 \r\nL 304.260146 421.000483 \r\nL 304.292569 420.601905 \r\nL 304.324993 420.263745 \r\nL 304.357417 420.857399 \r\nL 304.373629 421.051835 \r\nL 304.389841 420.669467 \r\nL 304.535748 417.180814 \r\nL 304.55196 417.233379 \r\nL 304.697866 419.693106 \r\nL 304.746502 418.199841 \r\nL 304.795138 420.016161 \r\nL 304.892409 422.099487 \r\nL 304.843773 419.405684 \r\nL 304.924833 420.842236 \r\nL 304.957257 419.950565 \r\nL 305.038316 420.141007 \r\nL 305.103164 421.07975 \r\nL 305.151799 420.304603 \r\nL 305.200435 420.035808 \r\nL 305.249071 418.202238 \r\nL 305.33013 418.766127 \r\nL 305.394978 419.126866 \r\nL 305.411189 419.004515 \r\nL 305.459825 417.790766 \r\nL 305.540885 418.259238 \r\nL 305.605732 419.8005 \r\nL 305.686792 419.632806 \r\nL 305.735427 418.264432 \r\nL 305.784063 419.919016 \r\nL 305.816487 419.280174 \r\nL 305.84891 420.361139 \r\nL 305.881334 421.338921 \r\nL 305.92997 419.011347 \r\nL 305.946182 419.716282 \r\nL 305.962394 420.330532 \r\nL 306.011029 418.341235 \r\nL 306.027241 418.431141 \r\nL 306.075877 420.824438 \r\nL 306.156936 420.465862 \r\nL 306.18936 419.18071 \r\nL 306.221784 417.899323 \r\nL 306.270419 419.539967 \r\nL 306.286631 419.537594 \r\nL 306.44875 417.865568 \r\nL 306.383903 419.62762 \r\nL 306.464962 418.280467 \r\nL 306.594657 419.649187 \r\nL 306.52981 418.217231 \r\nL 306.610869 419.341374 \r\nL 306.643293 418.517664 \r\nL 306.724352 418.951672 \r\nL 306.837836 420.922593 \r\nL 306.886471 419.831932 \r\nL 306.983743 419.400221 \r\nL 306.935107 420.34443 \r\nL 306.999954 419.541588 \r\nL 307.04859 421.403253 \r\nL 307.113438 419.868811 \r\nL 307.129649 420.114854 \r\nL 307.162073 419.627347 \r\nL 307.210709 418.457161 \r\nL 307.291768 418.720636 \r\nL 307.421463 417.313761 \r\nL 307.356616 419.081214 \r\nL 307.437675 417.579635 \r\nL 307.551159 419.940945 \r\nL 307.599794 419.084559 \r\nL 307.616006 419.066325 \r\nL 307.826761 421.719791 \r\nL 307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 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421.454443 \r\nL 554.172038 421.147846 \r\nL 554.950256 417.331036 \r\nL 555.047533 417.425931 \r\nL 555.177236 417.952612 \r\nL 555.663623 421.016189 \r\nL 555.728474 420.918288 \r\nL 555.890603 420.073773 \r\nL 556.247286 418.180469 \r\nL 556.344563 418.184512 \r\nL 556.928227 419.251624 \r\nL 557.252484 420.658555 \r\nL 557.447039 421.01652 \r\nL 557.576742 420.761252 \r\nL 557.998277 419.853911 \r\nL 558.192831 419.423603 \r\nL 558.549515 418.211284 \r\nL 558.614366 418.299006 \r\nL 558.744069 419.011785 \r\nL 559.165604 422.121159 \r\nL 559.19803 422.09821 \r\nL 559.295307 421.736462 \r\nL 559.846545 418.858991 \r\nL 560.041099 418.216155 \r\nL 560.332931 416.960981 \r\nL 560.365357 416.975507 \r\nL 560.462634 417.395606 \r\nL 560.689614 420.175572 \r\nL 560.94902 422.771254 \r\nL 560.981446 422.79149 \r\nL 561.013872 422.72363 \r\nL 561.143575 421.73319 \r\nL 561.727238 415.705604 \r\nL 561.889367 415.5081 \r\nL 561.986644 415.698257 \r\nL 562.148773 416.77426 \r\nL 562.700011 422.925064 \r\nL 562.797288 422.632126 \r\nL 563.08912 420.111424 \r\nL 563.3161 419.098357 \r\nL 563.834912 418.442052 \r\nL 563.867338 418.484039 \r\nL 563.997041 419.105307 \r\nL 564.418576 422.608612 \r\nL 564.483427 422.487829 \r\nL 564.645556 421.102959 \r\nL 565.002239 417.83604 \r\nL 565.067091 417.838705 \r\nL 565.099517 417.894571 \r\nL 565.4562 418.893609 \r\nL 565.521051 418.832347 \r\nL 565.748032 418.535221 \r\nL 565.780457 418.567857 \r\nL 565.91016 419.028707 \r\nL 566.364121 421.468927 \r\nL 566.396547 421.427094 \r\nL 566.52625 420.93934 \r\nL 567.077487 418.427727 \r\nL 567.304468 418.273631 \r\nL 567.466597 418.446063 \r\nL 567.693577 419.174697 \r\nL 568.05026 420.16418 \r\nL 568.179963 420.114224 \r\nL 568.374518 419.659518 \r\nL 568.698775 418.01789 \r\nL 568.990607 416.816907 \r\nL 569.055458 416.884862 \r\nL 569.217587 417.708341 \r\nL 569.671548 420.48049 \r\nL 569.768825 420.420317 \r\nL 569.995805 420.207681 \r\nL 570.125508 420.48224 \r\nL 570.41734 421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 620.061167 418.003242 \r\nL 620.19087 418.645451 \r\nL 620.612405 421.235321 \r\nL 620.709682 421.077662 \r\nL 621.196069 419.4035 \r\nL 621.293346 419.489298 \r\nL 621.520326 419.994866 \r\nL 622.039138 421.679966 \r\nL 622.10399 421.587718 \r\nL 622.233693 420.930498 \r\nL 622.752505 416.711592 \r\nL 622.817356 416.86231 \r\nL 623.044336 418.361535 \r\nL 623.336168 419.722398 \r\nL 623.660426 420.219914 \r\nL 623.919832 420.940888 \r\nL 623.952258 420.927746 \r\nL 624.049535 420.678037 \r\nL 624.244089 419.268491 \r\nL 624.503495 417.88471 \r\nL 624.600773 418.270029 \r\nL 624.860179 421.075906 \r\nL 625.054733 422.323079 \r\nL 625.087159 422.308585 \r\nL 625.184436 421.8396 \r\nL 625.443842 418.822386 \r\nL 625.735674 416.390806 \r\nL 625.832951 416.249893 \r\nL 625.865377 416.265179 \r\nL 625.99508 416.598128 \r\nL 626.319337 418.482026 \r\nL 626.643595 420.096055 \r\nL 626.740872 420.068758 \r\nL 626.903001 419.421786 \r\nL 627.227259 418.006481 \r\nL 627.259684 418.016975 \r\nL 627.356962 418.281019 \r\nL 627.583942 419.987531 \r\nL 627.973051 422.640452 \r\nL 628.13518 422.809665 \r\nL 628.264883 422.571951 \r\nL 628.491863 421.533936 \r\nL 629.205229 417.700021 \r\nL 629.367358 417.639787 \r\nL 629.529487 417.841591 \r\nL 629.788893 418.557277 \r\nL 630.242854 419.869818 \r\nL 630.372557 419.743048 \r\nL 630.794091 418.811946 \r\nL 630.858943 418.884193 \r\nL 631.053497 419.54845 \r\nL 631.410181 420.705905 \r\nL 631.507458 420.604427 \r\nL 631.702012 419.885116 \r\nL 632.02627 418.862784 \r\nL 632.155973 418.890704 \r\nL 632.48023 419.168749 \r\nL 632.512656 419.132265 \r\nL 632.739636 418.575826 \r\nL 632.966617 418.212226 \r\nL 633.09632 418.495965 \r\nL 633.679983 420.945285 \r\nL 633.744835 420.862989 \r\nL 633.939389 420.088669 \r\nL 634.523053 417.229196 \r\nL 634.555479 417.255922 \r\nL 634.685182 417.70161 \r\nL 634.912162 419.742414 \r\nL 635.268845 422.69706 \r\nL 635.333697 422.726906 \r\nL 635.366122 422.662689 \r\nL 635.528251 421.769065 \r\nL 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</g>\r\n    <g id=\"xtick_3\">\r\n     <g id=\"line2d_3\"/>\r\n     <g id=\"text_3\">\r\n      <!-- 5000 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(146.307213 544.641875)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-53\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-48\"/>\r\n       <use x=\"111.230469\" xlink:href=\"#ArialMT-48\"/>\r\n       <use x=\"166.845703\" xlink:href=\"#ArialMT-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_4\"/>\r\n     <g id=\"text_4\">\r\n      <!-- 7500 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(186.83693 544.641875)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 4.734375 62.203125 \r\nL 4.734375 70.65625 \r\nL 51.078125 70.65625 \r\nL 51.078125 63.8125 \r\nQ 44.234375 56.546875 37.515625 44.484375 \r\nQ 30.8125 32.421875 27.15625 19.671875 \r\nQ 24.515625 10.6875 23.78125 0 \r\nL 14.75 0 \r\nQ 14.890625 8.453125 18.0625 20.40625 \r\nQ 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<use x=\"141.796875\" xlink:href=\"#ArialMT-50\"/>\r\n       <use x=\"197.412109\" xlink:href=\"#ArialMT-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_12\"/>\r\n     <g id=\"text_13\">\r\n      <!-- 0.00 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(40.12875 423.037969)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-48\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n       <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n       <use x=\"139.013672\" xlink:href=\"#ArialMT-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_13\"/>\r\n     <g id=\"text_14\">\r\n      <!-- 0.25 -->\r\n      <g style=\"fill:#262626;\" transform=\"translate(40.12875 394.406719)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#ArialMT-48\"/>\r\n       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n       <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n       <use 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426.524687 \r\nL 91.073836 425.8022 \r\nL 91.511557 411.152953 \r\nL 91.527769 411.762218 \r\nL 91.657464 414.450677 \r\nL 91.673676 413.929097 \r\nL 91.706099 411.584217 \r\nL 91.738523 416.587624 \r\nL 91.803371 413.657281 \r\nL 91.852006 420.48783 \r\nL 91.88443 417.237727 \r\nL 91.900642 415.724342 \r\nL 91.933066 420.370916 \r\nL 91.949278 421.614602 \r\nL 91.981701 417.132372 \r\nL 91.997913 416.04414 \r\nL 92.030337 420.668488 \r\nL 92.046549 422.442923 \r\nL 92.095185 418.283297 \r\nL 92.14382 421.93661 \r\nL 92.176244 417.362476 \r\nL 92.22488 423.776247 \r\nL 92.257303 421.234152 \r\nL 92.273515 420.18193 \r\nL 92.322151 424.220352 \r\nL 92.354575 421.784311 \r\nL 92.516694 424.020064 \r\nL 92.549117 423.321335 \r\nL 92.711236 418.9788 \r\nL 92.759872 422.615291 \r\nL 92.808508 418.607581 \r\nL 92.840931 420.940681 \r\nL 92.873355 419.416585 \r\nL 92.905779 417.218426 \r\nL 92.970626 420.487016 \r\nL 93.116533 417.101093 \r\nL 93.165169 418.987263 \r\nL 93.246229 417.917766 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425.315479 \r\nL 98.49888 416.654385 \r\nL 98.644787 421.621551 \r\nL 98.660999 420.653346 \r\nL 98.693422 418.369124 \r\nL 98.774482 419.856938 \r\nL 98.855541 417.295702 \r\nL 98.887965 419.580216 \r\nL 98.904177 420.77373 \r\nL 98.952813 417.096355 \r\nL 99.01766 417.870253 \r\nL 99.066296 415.863663 \r\nL 99.09872 417.820775 \r\nL 99.114931 419.552302 \r\nL 99.163567 413.379261 \r\nL 99.179779 413.047338 \r\nL 99.27705 412.882895 \r\nL 99.325686 420.041495 \r\nL 99.374322 415.736438 \r\nL 99.487805 417.124607 \r\nL 99.633712 420.294174 \r\nL 99.666136 420.768299 \r\nL 99.682348 420.068958 \r\nL 99.714771 418.207888 \r\nL 99.763407 421.385087 \r\nL 99.795831 420.331298 \r\nL 99.828255 419.369265 \r\nL 99.87689 421.359343 \r\nL 99.893102 421.511544 \r\nL 99.909314 420.897574 \r\nL 99.925526 420.29216 \r\nL 99.95795 421.642099 \r\nL 100.039009 419.900003 \r\nL 100.087645 423.580726 \r\nL 100.152492 421.387419 \r\nL 100.184916 423.452786 \r\nL 100.201128 423.851458 \r\nL 100.21734 422.848849 \r\nL 100.314611 422.984634 \r\nL 100.347035 420.293032 \r\nL 100.411882 421.788747 \r\nL 100.460518 419.471942 \r\nL 100.492942 421.180587 \r\nL 100.525366 419.193774 \r\nL 100.557789 417.023734 \r\nL 100.606425 420.73466 \r\nL 100.622637 420.743969 \r\nL 100.719908 421.152935 \r\nL 100.768544 418.345978 \r\nL 100.914451 422.484692 \r\nL 100.930663 421.367716 \r\nL 101.044146 417.647304 \r\nL 101.060358 417.768373 \r\nL 101.141417 415.57841 \r\nL 101.190053 420.893623 \r\nL 101.238689 414.383375 \r\nL 101.33596 414.553467 \r\nL 101.433231 413.873389 \r\nL 101.481867 418.973094 \r\nL 101.530503 415.150964 \r\nL 101.579138 420.017119 \r\nL 101.59535 420.667118 \r\nL 101.627774 418.499104 \r\nL 101.643986 418.005304 \r\nL 101.660198 419.034268 \r\nL 101.692621 421.306483 \r\nL 101.741257 417.820744 \r\nL 101.789893 421.285585 \r\nL 101.838528 419.38293 \r\nL 101.887164 421.353669 \r\nL 101.919588 419.550941 \r\nL 101.968224 422.494944 \r\nL 102.000647 419.359593 \r\nL 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103.897438 424.311263 \r\nL 103.91365 423.412431 \r\nL 104.027133 418.725821 \r\nL 104.043345 419.642524 \r\nL 104.17304 421.271663 \r\nL 104.221676 418.238914 \r\nL 104.302735 420.822883 \r\nL 104.318947 420.821367 \r\nL 104.416218 423.156657 \r\nL 104.43243 422.096437 \r\nL 104.464854 418.498653 \r\nL 104.562125 419.583529 \r\nL 104.594549 421.42263 \r\nL 104.643185 417.971119 \r\nL 104.77288 414.932632 \r\nL 104.708032 418.53113 \r\nL 104.789092 415.678459 \r\nL 104.821516 419.100431 \r\nL 104.886363 415.682828 \r\nL 104.951211 414.541039 \r\nL 104.967423 413.927152 \r\nL 105.016058 416.059553 \r\nL 105.129541 418.941538 \r\nL 105.178177 418.179504 \r\nL 105.194389 418.10923 \r\nL 105.226813 416.15303 \r\nL 105.259237 418.660117 \r\nL 105.324084 417.541416 \r\nL 105.37272 422.465125 \r\nL 105.421355 416.6598 \r\nL 105.469991 423.279049 \r\nL 105.502415 420.260182 \r\nL 105.518627 418.30897 \r\nL 105.567262 423.155176 \r\nL 105.615898 419.076668 \r\nL 105.63211 418.77679 \r\nL 105.648322 419.869207 \r\nL 105.664534 420.576119 \r\nL 105.696957 418.028398 \r\nL 105.713169 416.996019 \r\nL 105.745593 420.276945 \r\nL 105.778017 423.474055 \r\nL 105.826653 418.254978 \r\nL 105.875288 423.264508 \r\nL 105.923924 417.022271 \r\nL 106.004983 419.247743 \r\nL 106.021195 419.143982 \r\nL 106.069831 426.255984 \r\nL 106.118467 418.178375 \r\nL 106.264374 423.729407 \r\nL 106.296797 421.881474 \r\nL 106.442704 416.86051 \r\nL 106.458916 417.649232 \r\nL 106.507552 420.118567 \r\nL 106.572399 418.963126 \r\nL 106.588611 419.218555 \r\nL 106.604823 418.693788 \r\nL 106.653459 415.11548 \r\nL 106.718306 417.280991 \r\nL 106.75073 418.669052 \r\nL 106.83179 418.387668 \r\nL 106.880425 416.15993 \r\nL 106.945273 417.553745 \r\nL 106.993908 419.933771 \r\nL 107.026332 422.2017 \r\nL 107.09118 420.048613 \r\nL 107.139815 418.284691 \r\nL 107.172239 420.557719 \r\nL 107.285722 424.717618 \r\nL 107.237087 419.981563 \r\nL 107.301934 422.967019 \r\nL 107.382994 423.058877 \r\nL 107.431629 415.83455 \r\nL 107.480265 424.19853 \r\nL 107.577536 423.962219 \r\nL 107.626172 413.478964 \r\nL 107.723443 416.656822 \r\nL 107.739655 416.696308 \r\nL 107.788291 421.318685 \r\nL 107.885562 420.88835 \r\nL 107.934198 418.606642 \r\nL 107.95041 419.802142 \r\nL 107.982834 423.879169 \r\nL 108.031469 419.434318 \r\nL 108.063893 421.466667 \r\nL 108.080105 421.941927 \r\nL 108.096317 420.944603 \r\nL 108.12874 418.295011 \r\nL 108.177376 422.011588 \r\nL 108.193588 421.987456 \r\nL 108.307071 417.880205 \r\nL 108.339495 419.227211 \r\nL 108.355707 419.180721 \r\nL 108.404343 414.39801 \r\nL 108.485402 416.476323 \r\nL 108.501614 416.255314 \r\nL 108.517826 416.929477 \r\nL 108.534038 417.407146 \r\nL 108.566461 416.245442 \r\nL 108.647521 416.961731 \r\nL 108.679945 415.393955 \r\nL 108.696157 415.58058 \r\nL 108.744792 421.832506 \r\nL 108.842063 420.703122 \r\nL 108.890699 417.357267 \r\nL 108.923123 421.214096 \r\nL 108.98797 417.841749 \r\nL 109.036606 423.514287 \r\nL 109.182513 416.560504 \r\nL 109.231149 422.205814 \r\nL 109.312208 419.355169 \r\nL 109.360844 416.872139 \r\nL 109.425691 418.831978 \r\nL 109.441903 418.468518 \r\nL 109.474327 419.652014 \r\nL 109.506751 421.637474 \r\nL 109.58781 420.152584 \r\nL 109.652658 420.052082 \r\nL 109.66887 420.146724 \r\nL 109.830989 426.03822 \r\nL 109.8472 424.07983 \r\nL 109.879624 418.245055 \r\nL 109.976896 418.874407 \r\nL 110.025531 424.832292 \r\nL 110.074167 416.785249 \r\nL 110.122803 421.086937 \r\nL 110.155226 416.769159 \r\nL 110.171438 415.644753 \r\nL 110.220074 419.97697 \r\nL 110.236286 420.197585 \r\nL 110.252498 419.322069 \r\nL 110.382193 414.06233 \r\nL 110.333557 419.734912 \r\nL 110.398405 414.265444 \r\nL 110.5281 421.782992 \r\nL 110.544312 421.255846 \r\nL 110.592947 415.725893 \r\nL 110.674007 417.546211 \r\nL 110.722642 421.45872 \r\nL 110.819914 420.108058 \r\nL 110.917185 418.335729 \r\nL 110.933397 418.899948 \r\nL 110.965821 422.093295 \r\nL 111.04688 420.075044 \r\nL 111.063092 420.135623 \r\nL 111.12794 414.856481 \r\nL 111.176575 419.560569 \r\nL 111.225211 417.862603 \r\nL 111.257635 420.236313 \r\nL 111.273846 420.071039 \r\nL 111.403542 415.848538 \r\nL 111.484601 421.424896 \r\nL 111.614296 418.980496 \r\nL 111.64672 420.523375 \r\nL 111.743991 423.605745 \r\nL 111.776415 422.446539 \r\nL 111.90611 415.629663 \r\nL 111.857474 422.606361 \r\nL 111.922322 417.723963 \r\nL 111.954746 424.036023 \r\nL 112.003381 416.764819 \r\nL 112.035805 419.766061 \r\nL 112.052017 420.161252 \r\nL 112.068229 418.189124 \r\nL 112.100653 414.999454 \r\nL 112.149288 419.471414 \r\nL 112.181712 416.521267 \r\nL 112.230348 420.777274 \r\nL 112.278983 417.068652 \r\nL 112.295195 416.549931 \r\nL 112.327619 418.070552 \r\nL 112.457314 423.605046 \r\nL 112.392467 417.876729 \r\nL 112.489738 421.473895 \r\nL 112.603221 415.946156 \r\nL 112.554586 422.215464 \r\nL 112.619433 416.589907 \r\nL 112.749128 423.042092 \r\nL 112.895035 415.307077 \r\nL 112.911247 416.115156 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122.135811 416.015782 \r\nL 122.168234 417.060734 \r\nL 122.265506 417.023861 \r\nL 122.330353 422.071881 \r\nL 122.378989 418.060874 \r\nL 122.427625 422.148821 \r\nL 122.443836 421.543211 \r\nL 122.460048 420.628029 \r\nL 122.508684 424.037491 \r\nL 122.524896 424.387922 \r\nL 122.670803 417.088328 \r\nL 122.622167 424.756431 \r\nL 122.687015 417.33883 \r\nL 122.73565 421.887323 \r\nL 122.784286 416.599423 \r\nL 122.81671 420.291638 \r\nL 122.832922 420.226181 \r\nL 122.881557 413.872739 \r\nL 122.930193 420.823945 \r\nL 122.962617 416.231595 \r\nL 122.978829 415.133836 \r\nL 123.011252 419.136386 \r\nL 123.027464 419.520858 \r\nL 123.043676 417.905036 \r\nL 123.059888 416.727988 \r\nL 123.108524 420.184882 \r\nL 123.140948 418.634373 \r\nL 123.157159 418.025627 \r\nL 123.189583 420.409323 \r\nL 123.222007 422.653718 \r\nL 123.303066 421.684332 \r\nL 123.351702 418.578672 \r\nL 123.384126 423.058996 \r\nL 123.400338 424.535863 \r\nL 123.432762 419.750421 \r\nL 123.448973 418.105659 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416.525255 \r\nL 126.77241 420.638199 \r\nL 126.788622 423.139925 \r\nL 126.837258 419.023368 \r\nL 126.885893 421.371859 \r\nL 126.934529 418.798248 \r\nL 126.966953 421.054099 \r\nL 126.999377 423.009994 \r\nL 127.048012 420.666801 \r\nL 127.064224 420.674962 \r\nL 127.096648 420.876982 \r\nL 127.11286 420.573017 \r\nL 127.161495 419.314414 \r\nL 127.242555 419.55994 \r\nL 127.307402 421.238346 \r\nL 127.323614 420.843822 \r\nL 127.437098 417.340923 \r\nL 127.453309 418.27178 \r\nL 127.566793 420.011193 \r\nL 127.583004 419.582345 \r\nL 127.680276 417.802026 \r\nL 127.696488 418.195305 \r\nL 127.745123 421.101368 \r\nL 127.809971 418.977822 \r\nL 127.826183 418.913806 \r\nL 127.842395 419.204173 \r\nL 128.020725 422.683945 \r\nL 127.89103 418.845631 \r\nL 128.069361 420.637288 \r\nL 128.134209 417.068979 \r\nL 128.199056 419.430531 \r\nL 128.280116 419.786662 \r\nL 128.296328 419.766164 \r\nL 128.442234 415.264141 \r\nL 128.458446 416.383008 \r\nL 128.49087 420.478855 \r\nL 128.588141 419.023306 \r\nL 128.604353 419.036938 \r\nL 128.636777 418.151194 \r\nL 128.669201 419.525294 \r\nL 128.798896 423.57351 \r\nL 128.879955 420.646564 \r\nL 128.928591 423.023248 \r\nL 128.944803 423.290044 \r\nL 128.977227 422.257265 \r\nL 129.009651 422.567286 \r\nL 129.252829 414.979856 \r\nL 129.285253 417.173768 \r\nL 129.301464 417.867306 \r\nL 129.317676 416.422346 \r\nL 129.3501 413.180702 \r\nL 129.398736 417.798789 \r\nL 129.414948 417.546813 \r\nL 129.43116 416.727849 \r\nL 129.463583 418.490545 \r\nL 129.496007 421.068701 \r\nL 129.544643 418.297129 \r\nL 129.577067 420.923088 \r\nL 129.60949 419.936586 \r\nL 129.641914 421.512949 \r\nL 129.755397 424.287039 \r\nL 129.706762 421.26442 \r\nL 129.771609 422.829041 \r\nL 129.885092 420.00102 \r\nL 129.901304 420.027951 \r\nL 129.94994 421.998936 \r\nL 129.982364 419.407374 \r\nL 130.112059 416.513954 \r\nL 130.176906 419.190021 \r\nL 130.20933 416.883411 \r\nL 130.225542 416.192302 \r\nL 130.257966 419.205835 \r\nL 130.274178 419.916114 \r\nL 130.29039 418.230569 \r\nL 130.322813 414.064349 \r\nL 130.371449 422.943723 \r\nL 130.420085 416.648617 \r\nL 130.452508 421.75197 \r\nL 130.46872 424.406678 \r\nL 130.517356 418.494122 \r\nL 130.565992 422.967358 \r\nL 130.598415 420.486813 \r\nL 130.614627 419.008762 \r\nL 130.663263 423.238556 \r\nL 130.679475 423.214094 \r\nL 130.80917 418.909562 \r\nL 130.841594 419.213344 \r\nL 130.857806 419.163764 \r\nL 130.874017 419.246102 \r\nL 130.906441 420.040284 \r\nL 130.922653 419.012674 \r\nL 130.955077 416.56567 \r\nL 131.052348 416.773487 \r\nL 131.06856 416.431234 \r\nL 131.084772 416.958165 \r\nL 131.117196 419.022264 \r\nL 131.214467 418.536016 \r\nL 131.246891 418.038726 \r\nL 131.279315 417.127068 \r\nL 131.311738 418.880426 \r\nL 131.425222 422.647719 \r\nL 131.441434 422.400983 \r\nL 131.490069 419.081507 \r\nL 131.571129 419.734127 \r\nL 131.635976 422.469686 \r\nL 131.6684 420.177617 \r\nL 131.765671 418.207615 \r\nL 131.717036 420.831082 \r\nL 131.798095 419.096135 \r\nL 131.814307 419.6904 \r\nL 131.895366 418.91534 \r\nL 132.041273 416.883923 \r\nL 132.073697 417.711195 \r\nL 132.18718 420.125557 \r\nL 132.203392 419.272069 \r\nL 132.235816 417.941203 \r\nL 132.26824 420.819971 \r\nL 132.333087 420.526051 \r\nL 132.381723 422.291275 \r\nL 132.52763 420.274242 \r\nL 132.576266 421.212625 \r\nL 132.592477 421.355112 \r\nL 132.608689 420.734963 \r\nL 132.738384 416.455703 \r\nL 132.754596 417.09703 \r\nL 132.78702 418.117657 \r\nL 132.835656 416.021487 \r\nL 132.851868 416.593705 \r\nL 132.997775 418.428189 \r\nL 132.900503 416.464981 \r\nL 133.013987 418.172999 \r\nL 133.030198 417.926632 \r\nL 133.062622 418.583999 \r\nL 133.224741 422.266576 \r\nL 133.257165 421.017043 \r\nL 133.3058 422.913327 \r\nL 133.338224 421.513541 \r\nL 133.370648 421.559614 \r\nL 133.38686 421.112062 \r\nL 133.467919 421.491864 \r\nL 133.516555 418.469893 \r\nL 133.548979 420.210594 \r\nL 133.597614 417.2726 \r\nL 133.64625 419.922373 \r\nL 133.694886 418.065986 \r\nL 133.759733 419.656757 \r\nL 133.808369 418.515744 \r\nL 133.824581 417.835458 \r\nL 133.857005 419.080586 \r\nL 133.938064 418.061433 \r\nL 133.9867 423.924658 \r\nL 134.035335 417.285586 \r\nL 134.083971 424.262767 \r\nL 134.132607 417.498575 \r\nL 134.181242 423.442206 \r\nL 134.229878 417.480912 \r\nL 134.262302 422.414035 \r\nL 134.278514 423.062149 \r\nL 134.294726 420.748902 \r\nL 134.327149 415.699435 \r\nL 134.375785 420.900362 \r\nL 134.408209 418.182086 \r\nL 134.424421 417.077839 \r\nL 134.473056 419.374043 \r\nL 134.521692 417.099646 \r\nL 134.58654 417.649468 \r\nL 134.602751 417.496555 \r\nL 134.635175 416.343722 \r\nL 134.667599 418.990736 \r\nL 134.683811 419.369571 \r\nL 134.700023 418.054415 \r\nL 134.76487 418.816686 \r\nL 134.813506 415.857319 \r\nL 134.959413 422.451945 \r\nL 134.975625 422.082689 \r\nL 135.056684 423.570402 \r\nL 135.10532 415.840928 \r\nL 135.153956 425.754352 \r\nL 135.235015 422.028095 \r\nL 135.251227 421.842288 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419.705673 \r\nL 140.2607 421.020217 \r\nL 140.309336 419.383907 \r\nL 140.341759 420.885013 \r\nL 140.390395 416.36194 \r\nL 140.422819 420.482012 \r\nL 140.487666 418.794465 \r\nL 140.552514 423.882592 \r\nL 140.698421 418.258842 \r\nL 140.730845 420.588969 \r\nL 140.763268 422.288537 \r\nL 140.811904 417.853034 \r\nL 140.828116 417.721651 \r\nL 140.86054 419.647507 \r\nL 140.909175 416.836494 \r\nL 140.925387 416.685034 \r\nL 140.941599 417.311085 \r\nL 141.071294 420.003195 \r\nL 141.103718 419.259952 \r\nL 141.152354 417.598304 \r\nL 141.200989 419.45544 \r\nL 141.217201 419.439518 \r\nL 141.233413 419.521966 \r\nL 141.395532 422.576797 \r\nL 141.411744 422.376071 \r\nL 141.460379 419.105256 \r\nL 141.509015 423.116671 \r\nL 141.63871 418.012109 \r\nL 141.703558 418.918427 \r\nL 141.768405 415.968926 \r\nL 141.800829 418.713421 \r\nL 141.849465 414.602008 \r\nL 141.865677 414.268494 \r\nL 141.881889 415.399485 \r\nL 142.027796 420.892852 \r\nL 142.044007 419.368963 \r\nL 142.108855 421.257775 \r\nL 142.157491 418.319807 \r\nL 142.287186 423.899119 \r\nL 142.303398 423.77258 \r\nL 142.384457 419.52314 \r\nL 142.465516 419.563488 \r\nL 142.481728 419.559308 \r\nL 142.49794 419.726086 \r\nL 142.514152 419.523059 \r\nL 142.676271 414.178249 \r\nL 142.692483 414.841279 \r\nL 142.805966 419.632672 \r\nL 142.822178 419.619841 \r\nL 142.870814 416.111054 \r\nL 142.919449 420.411545 \r\nL 143.09778 423.388484 \r\nL 143.146416 421.350452 \r\nL 143.243687 422.690244 \r\nL 143.259899 422.955842 \r\nL 143.292323 422.506657 \r\nL 143.389594 418.640869 \r\nL 143.470653 419.062254 \r\nL 143.503077 419.823936 \r\nL 143.535501 418.378273 \r\nL 143.648984 416.149535 \r\nL 143.600349 418.456231 \r\nL 143.665196 416.204252 \r\nL 143.843527 420.069059 \r\nL 143.875951 419.600991 \r\nL 143.892162 419.343981 \r\nL 143.924586 419.972269 \r\nL 144.038069 421.788064 \r\nL 144.054281 421.472514 \r\nL 144.102917 418.932245 \r\nL 144.151553 422.368183 \r\nL 144.410943 414.503216 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414.83592 \r\nL 146.145615 416.290185 \r\nL 146.178039 419.263877 \r\nL 146.226674 415.718645 \r\nL 146.259098 418.380417 \r\nL 146.307734 416.964339 \r\nL 146.388793 418.194822 \r\nL 146.405005 418.369527 \r\nL 146.437429 417.734483 \r\nL 146.453641 417.634175 \r\nL 146.599548 422.794275 \r\nL 146.615759 421.928753 \r\nL 146.648183 418.810954 \r\nL 146.696819 424.073689 \r\nL 146.713031 424.104249 \r\nL 146.761666 420.005906 \r\nL 146.842726 421.32633 \r\nL 146.87515 419.630553 \r\nL 146.956209 420.412164 \r\nL 147.05348 420.467496 \r\nL 147.069692 419.7998 \r\nL 147.150752 420.245363 \r\nL 147.199387 417.167256 \r\nL 147.231811 419.054747 \r\nL 147.248023 420.347173 \r\nL 147.312871 417.284194 \r\nL 147.474989 421.75503 \r\nL 147.507413 419.917767 \r\nL 147.523625 419.025971 \r\nL 147.572261 421.604891 \r\nL 147.588473 422.264934 \r\nL 147.604685 421.074478 \r\nL 147.637108 418.184183 \r\nL 147.685744 423.537012 \r\nL 147.718168 420.057797 \r\nL 147.831651 418.593226 \r\nL 147.783015 421.731998 \r\nL 147.847863 418.689643 \r\nL 147.880287 419.220973 \r\nL 147.91271 418.1268 \r\nL 148.074829 414.291819 \r\nL 148.091041 414.535061 \r\nL 148.220736 417.59067 \r\nL 148.25316 415.833295 \r\nL 148.285584 417.772816 \r\nL 148.399067 421.230668 \r\nL 148.431491 421.107989 \r\nL 148.577398 422.256918 \r\nL 148.609822 421.968791 \r\nL 148.885424 416.534087 \r\nL 148.674669 422.135328 \r\nL 148.966483 417.223566 \r\nL 149.11239 418.694838 \r\nL 149.161026 419.903339 \r\nL 149.306933 423.393138 \r\nL 149.339356 421.86577 \r\nL 149.387992 423.820951 \r\nL 149.404204 424.13989 \r\nL 149.420416 423.380554 \r\nL 149.550111 418.884982 \r\nL 149.566323 419.5176 \r\nL 149.598747 422.020254 \r\nL 149.663594 418.559811 \r\nL 149.696018 419.709927 \r\nL 149.728442 417.612645 \r\nL 149.793289 418.670601 \r\nL 149.841925 416.445863 \r\nL 149.890561 418.756299 \r\nL 149.955408 417.801494 \r\nL 149.987832 416.384916 \r\nL 150.068891 417.650779 \r\nL 150.214798 421.367 \r\nL 150.263434 418.313081 \r\nL 150.344493 420.246303 \r\nL 150.376917 421.507379 \r\nL 150.393129 422.602849 \r\nL 150.441765 418.668474 \r\nL 150.457977 418.539393 \r\nL 150.57146 421.705842 \r\nL 150.587672 421.152267 \r\nL 150.749791 417.202073 \r\nL 150.863274 420.141652 \r\nL 150.895698 418.690026 \r\nL 150.911909 417.374814 \r\nL 151.009181 417.471504 \r\nL 151.155088 422.225457 \r\nL 151.187512 421.022068 \r\nL 151.203723 419.899155 \r\nL 151.252359 423.092807 \r\nL 151.268571 423.867563 \r\nL 151.317207 420.936227 \r\nL 151.365842 423.620617 \r\nL 151.398266 422.014177 \r\nL 151.511749 418.134697 \r\nL 151.527961 418.534623 \r\nL 151.560385 420.026469 \r\nL 151.592809 417.278564 \r\nL 151.754928 413.68732 \r\nL 151.771139 413.702311 \r\nL 151.900835 418.470888 \r\nL 151.917046 418.392121 \r\nL 151.94947 415.034682 \r\nL 151.998106 420.378331 \r\nL 152.046741 420.34475 \r\nL 152.144013 422.208646 \r\nL 152.241284 423.091099 \r\nL 152.257496 422.363207 \r\nL 152.387191 419.111354 \r\nL 152.338555 422.495742 \r\nL 152.403403 419.467952 \r\nL 152.419615 419.994249 \r\nL 152.452039 418.597188 \r\nL 152.468251 417.888002 \r\nL 152.516886 418.739674 \r\nL 152.565522 418.414202 \r\nL 152.581734 418.374484 \r\nL 152.614158 417.508059 \r\nL 152.662793 419.03899 \r\nL 152.679005 419.113969 \r\nL 152.711429 418.04942 \r\nL 152.743853 419.765123 \r\nL 152.857336 422.128251 \r\nL 152.873548 421.867536 \r\nL 152.905971 420.852363 \r\nL 152.938395 422.66759 \r\nL 152.954607 423.548662 \r\nL 153.019455 421.6383 \r\nL 153.051878 422.927924 \r\nL 153.06809 423.106005 \r\nL 153.084302 422.626808 \r\nL 153.213997 417.654959 \r\nL 153.230209 418.934386 \r\nL 153.262633 422.022494 \r\nL 153.311269 416.559764 \r\nL 153.327481 417.461054 \r\nL 153.359904 419.58634 \r\nL 153.440964 419.139634 \r\nL 153.489599 415.845151 \r\nL 153.554447 418.713247 \r\nL 153.651718 417.435902 \r\nL 153.66793 417.759966 \r\nL 153.716566 419.797764 \r\nL 153.765201 417.19234 \r\nL 153.894897 420.16691 \r\nL 153.92732 418.92945 \r\nL 153.943532 418.701687 \r\nL 153.975956 419.778881 \r\nL 154.024592 420.650236 \r\nL 154.057015 419.485985 \r\nL 154.073227 419.404349 \r\nL 154.121863 421.305809 \r\nL 154.202922 420.322133 \r\nL 154.397465 417.899358 \r\nL 154.413677 417.996937 \r\nL 154.429889 417.835762 \r\nL 154.462313 417.086723 \r\nL 154.494736 417.839299 \r\nL 154.543372 417.388361 \r\nL 154.592008 417.024968 \r\nL 154.883822 423.195044 \r\nL 154.964881 423.283942 \r\nL 155.013517 420.364657 \r\nL 155.062152 424.468075 \r\nL 155.094576 421.084804 \r\nL 155.224271 415.017389 \r\nL 155.272907 418.455804 \r\nL 155.353966 417.24786 \r\nL 155.418814 416.014544 \r\nL 155.46745 416.656612 \r\nL 155.597145 421.350806 \r\nL 155.629568 420.223811 \r\nL 155.64578 419.526632 \r\nL 155.694416 421.719765 \r\nL 155.791687 423.944467 \r\nL 155.743052 421.215316 \r\nL 155.807899 423.866416 \r\nL 155.937594 419.487449 \r\nL 155.953806 420.088671 \r\nL 155.970018 420.394328 \r\nL 155.98623 419.610956 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419.558573 \r\nL 159.731176 420.467296 \r\nL 159.7636 417.595276 \r\nL 159.779811 416.743628 \r\nL 159.812235 420.39599 \r\nL 159.828447 421.32006 \r\nL 159.860871 417.883 \r\nL 159.877083 417.046471 \r\nL 159.909507 420.182649 \r\nL 159.925718 421.360743 \r\nL 159.974354 419.072445 \r\nL 160.02299 420.87906 \r\nL 160.185109 418.443574 \r\nL 160.233744 419.288789 \r\nL 160.266168 419.49715 \r\nL 160.28238 419.399692 \r\nL 160.331016 418.226839 \r\nL 160.395863 418.73531 \r\nL 160.428287 420.180284 \r\nL 160.476923 418.01888 \r\nL 160.525558 420.128769 \r\nL 160.54177 420.112003 \r\nL 160.557982 419.831307 \r\nL 160.590406 420.555329 \r\nL 160.62283 421.207493 \r\nL 160.655253 419.282715 \r\nL 160.671465 419.219649 \r\nL 160.720101 422.698053 \r\nL 160.768737 419.207559 \r\nL 160.784948 419.842638 \r\nL 160.88222 420.928411 \r\nL 160.849796 419.626561 \r\nL 160.898432 420.815571 \r\nL 160.947067 416.806599 \r\nL 161.028127 417.826157 \r\nL 161.06055 418.118249 \r\nL 161.092974 417.762641 \r\nL 161.109186 417.546483 \r\nL 161.14161 418.559561 \r\nL 161.174034 419.83251 \r\nL 161.206457 418.067579 \r\nL 161.255093 419.733418 \r\nL 161.303729 418.91887 \r\nL 161.319941 419.556792 \r\nL 161.401 419.172117 \r\nL 161.449636 421.824472 \r\nL 161.546907 422.376713 \r\nL 161.595543 417.582032 \r\nL 161.644178 421.256572 \r\nL 161.676602 416.267726 \r\nL 161.74145 419.938507 \r\nL 161.790085 413.950581 \r\nL 161.838721 419.796527 \r\nL 161.952204 418.892535 \r\nL 161.984628 416.833175 \r\nL 162.033264 420.581446 \r\nL 162.146747 424.072748 \r\nL 162.098111 417.67058 \r\nL 162.162959 424.018958 \r\nL 162.211594 419.471186 \r\nL 162.276442 422.31621 \r\nL 162.406137 420.406418 \r\nL 162.438561 421.492598 \r\nL 162.487197 419.738999 \r\nL 162.51962 420.616063 \r\nL 162.535832 420.332956 \r\nL 162.616892 421.888945 \r\nL 162.665527 417.306803 \r\nL 162.811434 422.081569 \r\nL 162.827646 420.577572 \r\nL 162.86007 417.216215 \r\nL 162.908706 422.419347 \r\nL 162.924917 422.562635 \r\nL 162.973553 417.316528 \r\nL 163.054613 419.44708 \r\nL 163.184308 417.707612 \r\nL 163.20052 418.441108 \r\nL 163.232943 419.499032 \r\nL 163.281579 417.393211 \r\nL 163.297791 417.228082 \r\nL 163.314003 417.901561 \r\nL 163.346427 419.956496 \r\nL 163.395062 417.716146 \r\nL 163.443698 419.468565 \r\nL 163.540969 421.011275 \r\nL 163.492333 419.002393 \r\nL 163.557181 420.815819 \r\nL 163.605817 417.256424 \r\nL 163.654452 420.851289 \r\nL 163.686876 418.261876 \r\nL 163.784147 417.654278 \r\nL 163.735512 419.712851 \r\nL 163.800359 417.825757 \r\nL 163.848995 420.197907 \r\nL 163.897631 417.339649 \r\nL 163.930054 418.893427 \r\nL 163.994902 418.890945 \r\nL 164.043538 420.660245 \r\nL 164.140809 419.163744 \r\nL 164.157021 420.209067 \r\nL 164.189445 422.749627 \r\nL 164.23808 420.108555 \r\nL 164.286716 422.518236 \r\nL 164.448835 418.294132 \r\nL 164.465047 418.701366 \r\nL 164.481259 419.304825 \r\nL 164.529894 417.268209 \r\nL 164.562318 417.811303 \r\nL 164.610954 416.935511 \r\nL 164.659589 414.934574 \r\nL 164.692013 417.36092 \r\nL 164.805496 419.393979 \r\nL 164.821708 419.105675 \r\nL 164.854132 418.25118 \r\nL 164.886556 419.656896 \r\nL 165.032463 423.269786 \r\nL 165.048675 422.228082 \r\nL 165.17837 419.498644 \r\nL 165.227005 420.731889 \r\nL 165.243217 419.384302 \r\nL 165.3567 415.674106 \r\nL 165.372912 416.320057 \r\nL 165.502607 419.449218 \r\nL 165.551243 417.699916 \r\nL 165.616091 419.282362 \r\nL 165.745786 422.417668 \r\nL 165.761998 421.756085 \r\nL 165.875481 419.535282 \r\nL 165.826845 422.317325 \r\nL 165.891693 420.512611 \r\nL 165.924116 424.065344 \r\nL 165.972752 419.11866 \r\nL 166.021388 423.725891 \r\nL 166.151083 418.264479 \r\nL 166.167295 418.546243 \r\nL 166.199719 422.035259 \r\nL 166.264566 418.135137 \r\nL 166.280778 419.128092 \r\nL 166.394261 421.629245 \r\nL 166.345626 418.034957 \r\nL 166.410473 421.094009 \r\nL 166.55638 415.977082 \r\nL 166.702287 421.925272 \r\nL 166.718499 420.473853 \r\nL 166.799558 421.191011 \r\nL 166.848194 415.613527 \r\nL 166.994101 422.270625 \r\nL 167.026525 418.999826 \r\nL 167.042737 417.872536 \r\nL 167.107584 419.658595 \r\nL 167.123796 419.432055 \r\nL 167.15622 418.975425 \r\nL 167.221067 419.378617 \r\nL 167.253491 419.726833 \r\nL 167.269703 418.933034 \r\nL 167.399398 415.696475 \r\nL 167.350763 419.200951 \r\nL 167.41561 416.006985 \r\nL 167.464246 419.730218 \r\nL 167.545305 418.320172 \r\nL 167.561517 418.19845 \r\nL 167.593941 418.616942 \r\nL 167.691212 421.129575 \r\nL 167.739848 420.261069 \r\nL 167.788483 421.20416 \r\nL 167.820907 420.086661 \r\nL 167.837119 419.713867 \r\nL 167.869543 420.29012 \r\nL 167.918179 422.096743 \r\nL 167.966814 420.030015 \r\nL 167.999238 420.144092 \r\nL 168.047874 417.585523 \r\nL 168.064086 416.647708 \r\nL 168.112721 418.45215 \r\nL 168.161357 417.284436 \r\nL 168.177569 417.232086 \r\nL 168.193781 417.388225 \r\nL 168.242416 418.568819 \r\nL 168.307264 417.793349 \r\nL 168.339688 417.091943 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421.644441 \r\nL 172.279176 423.095429 \r\nL 172.344024 421.659621 \r\nL 172.392659 420.842571 \r\nL 172.489931 417.406182 \r\nL 172.538566 418.233822 \r\nL 172.587202 419.739237 \r\nL 172.603414 419.252319 \r\nL 172.652049 415.254316 \r\nL 172.733109 415.998625 \r\nL 172.797956 421.738981 \r\nL 172.895228 419.711148 \r\nL 172.927652 416.338098 \r\nL 172.992499 421.116648 \r\nL 173.024923 419.650142 \r\nL 173.057347 423.365077 \r\nL 173.073559 424.120649 \r\nL 173.105982 420.562716 \r\nL 173.219466 417.010575 \r\nL 173.235677 418.140903 \r\nL 173.268101 420.332226 \r\nL 173.349161 419.852805 \r\nL 173.43022 416.579845 \r\nL 173.478856 418.733304 \r\nL 173.543703 417.645097 \r\nL 173.592339 421.318369 \r\nL 173.640975 415.130986 \r\nL 173.722034 418.515101 \r\nL 173.738246 418.366241 \r\nL 173.786882 421.845823 \r\nL 173.867941 419.81421 \r\nL 173.900365 419.067039 \r\nL 173.916577 418.633109 \r\nL 173.949 419.656173 \r\nL 174.046272 422.152201 \r\nL 174.078695 420.921992 \r\nL 174.159755 418.301511 \r\nL 174.208391 419.770474 \r\nL 174.273238 421.276629 \r\nL 174.305662 419.956261 \r\nL 174.370509 418.728743 \r\nL 174.419145 416.121943 \r\nL 174.467781 418.616405 \r\nL 174.483993 418.613924 \r\nL 174.500205 418.276705 \r\nL 174.532628 419.206027 \r\nL 174.54884 419.825036 \r\nL 174.581264 418.4675 \r\nL 174.613688 416.087312 \r\nL 174.694747 417.908929 \r\nL 174.743383 420.872151 \r\nL 174.840654 420.617977 \r\nL 174.873078 418.513534 \r\nL 174.921714 421.11338 \r\nL 174.954137 419.557393 \r\nL 174.970349 419.612674 \r\nL 175.018985 423.928241 \r\nL 175.067621 418.908013 \r\nL 175.116256 420.47474 \r\nL 175.132468 419.019611 \r\nL 175.164892 417.429848 \r\nL 175.197316 420.951711 \r\nL 175.213528 421.971749 \r\nL 175.262163 419.378444 \r\nL 175.375646 416.000832 \r\nL 175.391858 416.886181 \r\nL 175.521553 423.562252 \r\nL 175.537765 422.573013 \r\nL 175.570189 418.046716 \r\nL 175.66746 418.658533 \r\nL 175.716096 425.892619 \r\nL 175.797155 423.405735 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421.151668 \r\nL 177.791218 420.088669 \r\nL 177.807429 419.602792 \r\nL 177.856065 421.419847 \r\nL 177.888489 420.016371 \r\nL 177.920913 416.855453 \r\nL 177.969548 421.124477 \r\nL 178.018184 417.254252 \r\nL 178.06682 420.749989 \r\nL 178.099243 417.140366 \r\nL 178.115455 416.072233 \r\nL 178.164091 419.542286 \r\nL 178.212727 416.42808 \r\nL 178.342422 421.470208 \r\nL 178.358634 421.269516 \r\nL 178.391057 418.814414 \r\nL 178.472117 420.152736 \r\nL 178.520752 422.44697 \r\nL 178.5856 420.32816 \r\nL 178.618024 420.538471 \r\nL 178.634236 420.139441 \r\nL 178.666659 418.802107 \r\nL 178.763931 419.311085 \r\nL 178.796355 419.818885 \r\nL 178.828778 418.213491 \r\nL 178.909838 416.500365 \r\nL 178.942262 417.134346 \r\nL 179.055745 419.181804 \r\nL 179.088168 418.917476 \r\nL 179.120592 417.833468 \r\nL 179.153016 418.902835 \r\nL 179.282711 423.57591 \r\nL 179.298923 422.979118 \r\nL 179.428618 419.161944 \r\nL 179.493466 423.610344 \r\nL 179.574525 421.114449 \r\nL 179.623161 416.849961 \r\nL 179.736644 417.487086 \r\nL 179.769068 419.949156 \r\nL 179.833915 416.429539 \r\nL 179.979822 420.934305 \r\nL 180.012246 419.122954 \r\nL 180.028458 418.930061 \r\nL 180.04467 419.722916 \r\nL 180.077094 420.898933 \r\nL 180.125729 418.145027 \r\nL 180.271636 422.255476 \r\nL 180.287848 421.29259 \r\nL 180.320272 418.63294 \r\nL 180.401331 419.497931 \r\nL 180.433755 420.349831 \r\nL 180.482391 418.747615 \r\nL 180.56345 417.153828 \r\nL 180.676933 417.89375 \r\nL 180.725569 419.653808 \r\nL 180.774205 418.312472 \r\nL 180.82284 417.451647 \r\nL 180.9039 417.934117 \r\nL 180.952535 420.573833 \r\nL 181.049807 420.090969 \r\nL 181.082231 418.386339 \r\nL 181.147078 420.752173 \r\nL 181.16329 420.567765 \r\nL 181.195714 421.272619 \r\nL 181.211926 421.532962 \r\nL 181.276773 420.925887 \r\nL 181.325409 418.78933 \r\nL 181.357833 420.964114 \r\nL 181.374045 421.767636 \r\nL 181.42268 420.390522 \r\nL 181.471316 421.656668 \r\nL 181.601011 419.172062 \r\nL 181.617223 419.514306 \r\nL 181.649647 421.614481 \r\nL 181.730706 420.395301 \r\nL 181.779342 417.315648 \r\nL 181.860401 419.369654 \r\nL 181.909037 421.789701 \r\nL 181.925249 422.404013 \r\nL 181.973884 420.734401 \r\nL 182.071156 418.363493 \r\nL 182.103579 419.96151 \r\nL 182.119791 420.858206 \r\nL 182.217063 420.781457 \r\nL 182.233274 420.871418 \r\nL 182.298122 416.750846 \r\nL 182.395393 417.692323 \r\nL 182.427817 419.529128 \r\nL 182.460241 417.508827 \r\nL 182.492665 415.764378 \r\nL 182.573724 416.037722 \r\nL 182.687207 419.185135 \r\nL 182.752055 417.84414 \r\nL 182.768267 417.761048 \r\nL 182.897962 423.169904 \r\nL 182.930386 420.33885 \r\nL 182.995233 421.910425 \r\nL 183.043869 418.112596 \r\nL 183.189776 421.984777 \r\nL 183.205988 421.221716 \r\nL 183.254623 417.706178 \r\nL 183.335683 417.963272 \r\nL 183.351895 417.937455 \r\nL 183.40053 420.928765 \r\nL 183.48159 420.066111 \r\nL 183.530225 418.021499 \r\nL 183.562649 419.929717 \r\nL 183.627497 419.919376 \r\nL 183.676132 421.302814 \r\nL 183.805828 417.671596 \r\nL 183.822039 418.584658 \r\nL 183.854463 421.111323 \r\nL 183.951734 420.487975 \r\nL 184.016582 417.596454 \r\nL 184.065218 419.656173 \r\nL 184.08143 419.889486 \r\nL 184.097641 418.763401 \r\nL 184.162489 420.38231 \r\nL 184.227337 416.874119 \r\nL 184.357032 420.979651 \r\nL 184.373244 420.105129 \r\nL 184.405667 417.288412 \r\nL 184.502939 417.412226 \r\nL 184.632634 420.314349 \r\nL 184.665057 419.664843 \r\nL 184.762329 418.753485 \r\nL 184.778541 419.004978 \r\nL 184.827176 421.398518 \r\nL 184.924448 421.253104 \r\nL 184.94066 421.233462 \r\nL 184.989295 418.707105 \r\nL 185.037931 421.450836 \r\nL 185.086567 420.529609 \r\nL 185.102778 421.379265 \r\nL 185.11899 422.304966 \r\nL 185.167626 419.961027 \r\nL 185.183838 419.660268 \r\nL 185.20005 420.3612 \r\nL 185.216262 421.14937 \r\nL 185.264897 419.09264 \r\nL 185.281109 418.80765 \r\nL 185.297321 419.537359 \r\nL 185.329745 421.432581 \r\nL 185.394592 419.537996 \r\nL 185.427016 419.385831 \r\nL 185.443228 419.647542 \r\nL 185.45944 420.065443 \r\nL 185.491864 419.014413 \r\nL 185.508076 418.295054 \r\nL 185.556711 420.377337 \r\nL 185.572923 420.373288 \r\nL 185.71883 418.46658 \r\nL 185.864737 421.294599 \r\nL 185.880949 420.645115 \r\nL 185.994432 418.555164 \r\nL 186.010644 419.065536 \r\nL 186.075492 418.801831 \r\nL 186.124127 419.694036 \r\nL 186.156551 418.460249 \r\nL 186.270034 416.255272 \r\nL 186.221399 419.565521 \r\nL 186.286246 416.988529 \r\nL 186.31867 420.770989 \r\nL 186.367306 416.194239 \r\nL 186.415941 419.573334 \r\nL 186.432153 419.922904 \r\nL 186.448365 419.22177 \r\nL 186.497001 417.434486 \r\nL 186.561848 418.70929 \r\nL 186.57806 418.076935 \r\nL 186.626696 420.12865 \r\nL 186.642908 420.444271 \r\nL 186.65912 419.887053 \r\nL 186.707755 417.608296 \r\nL 186.756391 420.766988 \r\nL 186.772603 420.988986 \r\nL 186.805027 420.058201 \r\nL 186.83745 419.585524 \r\nL 186.886086 420.324075 \r\nL 186.902298 420.422297 \r\nL 186.950934 420.081626 \r\nL 186.999569 420.230643 \r\nL 187.048205 418.670418 \r\nL 187.064417 419.925818 \r\nL 187.096841 422.145794 \r\nL 187.145476 418.479021 \r\nL 187.1779 420.402474 \r\nL 187.194112 421.141673 \r\nL 187.242747 418.557113 \r\nL 187.258959 418.565147 \r\nL 187.340019 417.951641 \r\nL 187.404866 420.569095 \r\nL 187.453502 417.800596 \r\nL 187.534561 419.047408 \r\nL 187.550773 418.616522 \r\nL 187.583197 419.79913 \r\nL 187.599409 420.331276 \r\nL 187.631833 418.228609 \r\nL 187.648045 417.631222 \r\nL 187.664257 419.024362 \r\nL 187.69668 422.672805 \r\nL 187.745316 418.715973 \r\nL 187.77774 420.079231 \r\nL 187.842587 418.577476 \r\nL 187.891223 421.177849 \r\nL 188.069554 417.980841 \r\nL 188.118189 418.960034 \r\nL 188.150613 418.01143 \r\nL 188.166825 417.681163 \r\nL 188.199249 419.10565 \r\nL 188.215461 419.618465 \r\nL 188.247884 418.028278 \r\nL 188.264096 417.423589 \r\nL 188.312732 419.448547 \r\nL 188.328944 419.445851 \r\nL 188.410003 419.716293 \r\nL 188.442427 418.745615 \r\nL 188.588334 421.644656 \r\nL 188.604546 420.435321 \r\nL 188.685605 422.378461 \r\nL 188.734241 418.178501 \r\nL 188.782877 421.790003 \r\nL 188.863936 420.957334 \r\nL 188.912572 417.192955 \r\nL 189.009843 418.678609 \r\nL 189.090903 417.929091 \r\nL 189.139538 420.144116 \r\nL 189.188174 418.663944 \r\nL 189.23681 419.881853 \r\nL 189.317869 422.373918 \r\nL 189.398928 421.281907 \r\nL 189.431352 419.552549 \r\nL 189.479988 423.128777 \r\nL 189.739378 417.844498 \r\nL 189.788014 419.609698 \r\nL 189.836649 417.445763 \r\nL 189.933921 418.226587 \r\nL 189.950133 417.71624 \r\nL 189.982556 416.651731 \r\nL 190.031192 418.773989 \r\nL 190.063616 419.12532 \r\nL 190.09604 418.693206 \r\nL 190.209523 417.149501 \r\nL 190.225735 417.965298 \r\nL 190.339218 419.613541 \r\nL 190.290582 417.397299 \r\nL 190.35543 419.141804 \r\nL 190.485125 417.138309 \r\nL 190.53376 418.472687 \r\nL 190.582396 419.809312 \r\nL 190.631032 418.211137 \r\nL 190.793151 421.354615 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417.688154 \r\nL 192.689941 418.969106 \r\nL 192.803425 421.323822 \r\nL 192.754789 418.907115 \r\nL 192.819636 420.197504 \r\nL 192.85206 418.49042 \r\nL 192.900696 421.603713 \r\nL 192.93312 419.893994 \r\nL 192.949332 419.861703 \r\nL 193.014179 421.743665 \r\nL 193.079027 420.809765 \r\nL 193.095239 421.149494 \r\nL 193.160086 420.630496 \r\nL 193.176298 420.511391 \r\nL 193.19251 420.641652 \r\nL 193.224934 421.901432 \r\nL 193.273569 419.875 \r\nL 193.322205 421.381661 \r\nL 193.370841 418.275277 \r\nL 193.419476 422.235457 \r\nL 193.4519 419.34324 \r\nL 193.500536 422.62524 \r\nL 193.53296 419.439182 \r\nL 193.597807 421.516323 \r\nL 193.646443 417.688596 \r\nL 193.695078 420.667082 \r\nL 193.727502 416.90809 \r\nL 193.743714 416.329235 \r\nL 193.759926 417.727114 \r\nL 193.776138 419.379421 \r\nL 193.824773 414.717163 \r\nL 193.840985 413.800147 \r\nL 193.889621 416.210449 \r\nL 193.922045 415.162346 \r\nL 194.035528 414.285505 \r\nL 193.986892 415.26023 \r\nL 194.05174 414.698094 \r\nL 194.230071 419.530318 \r\nL 194.246283 419.326574 \r\nL 194.278706 418.918075 \r\nL 194.294918 419.635415 \r\nL 194.39219 423.086526 \r\nL 194.457037 422.753974 \r\nL 194.489461 423.497256 \r\nL 194.538096 422.205792 \r\nL 194.554308 422.577953 \r\nL 194.57052 422.831912 \r\nL 194.586732 422.227989 \r\nL 194.716427 419.975218 \r\nL 194.797487 418.433412 \r\nL 194.82991 419.153575 \r\nL 194.975817 420.961528 \r\nL 195.008241 420.893431 \r\nL 195.040665 421.582013 \r\nL 195.17036 423.771177 \r\nL 195.186572 423.612102 \r\nL 195.316267 420.367444 \r\nL 195.332479 421.281552 \r\nL 195.348691 422.31049 \r\nL 195.381115 420.128521 \r\nL 195.494598 415.927206 \r\nL 195.51081 416.329961 \r\nL 195.624293 417.602774 \r\nL 195.575657 415.511134 \r\nL 195.640505 417.458264 \r\nL 195.68914 415.355604 \r\nL 195.753988 416.890049 \r\nL 195.7702 416.538351 \r\nL 195.802624 418.067962 \r\nL 195.883683 417.999542 \r\nL 195.948531 420.042754 \r\nL 195.997166 418.350226 \r\nL 196.02959 419.701183 \r\nL 196.094438 418.00952 \r\nL 196.143073 420.649234 \r\nL 196.191709 416.199579 \r\nL 196.28898 417.366366 \r\nL 196.37004 416.308742 \r\nL 196.418675 420.426478 \r\nL 196.467311 416.606475 \r\nL 196.515947 420.559165 \r\nL 196.532159 421.148637 \r\nL 196.580794 418.879609 \r\nL 196.613218 420.68436 \r\nL 196.62943 420.609831 \r\nL 196.661854 418.763258 \r\nL 196.710489 420.967438 \r\nL 196.742913 420.44045 \r\nL 196.759125 420.47238 \r\nL 196.856396 422.149606 \r\nL 196.872608 421.599822 \r\nL 196.905032 420.208393 \r\nL 196.953668 422.230145 \r\nL 196.969879 422.162306 \r\nL 196.986091 422.129023 \r\nL 197.067151 423.465547 \r\nL 197.099575 422.612249 \r\nL 197.164422 423.376404 \r\nL 197.22927 420.278935 \r\nL 197.277905 422.495307 \r\nL 197.310329 420.714202 \r\nL 197.440024 415.336633 \r\nL 197.456236 416.381807 \r\nL 197.472448 417.053758 \r\nL 197.504872 414.877169 \r\nL 197.618355 412.860758 \r\nL 197.634567 413.456525 \r\nL 197.845321 417.763333 \r\nL 197.861533 417.52953 \r\nL 197.877745 417.488546 \r\nL 198.023652 422.923767 \r\nL 198.0885 422.093656 \r\nL 198.283042 418.897523 \r\nL 198.299254 419.2313 \r\nL 198.331678 419.890972 \r\nL 198.364102 418.203768 \r\nL 198.380314 417.284749 \r\nL 198.428949 419.714009 \r\nL 198.769399 426.655466 \r\nL 199.20712 413.309266 \r\nL 199.223332 414.093875 \r\nL 199.288179 416.014507 \r\nL 199.336815 414.656416 \r\nL 199.417874 417.257532 \r\nL 199.579993 420.494783 \r\nL 199.661053 420.680523 \r\nL 199.709688 419.489321 \r\nL 199.758324 420.37552 \r\nL 199.774536 419.471232 \r\nL 199.904231 415.185078 \r\nL 199.920443 416.467853 \r\nL 199.952867 419.508576 \r\nL 200.001502 415.875837 \r\nL 200.033926 417.178189 \r\nL 200.163621 419.580488 \r\nL 200.098774 416.131432 \r\nL 200.179833 418.859967 \r\nL 200.212257 417.650523 \r\nL 200.260892 420.16491 \r\nL 200.309528 418.037287 \r\nL 200.358164 420.049823 \r\nL 200.487859 422.473007 \r\nL 200.406799 419.912978 \r\nL 200.504071 421.750889 \r\nL 200.520283 420.934441 \r\nL 200.568918 422.967192 \r\nL 200.601342 422.013594 \r\nL 200.617554 421.707633 \r\nL 200.633766 422.38726 \r\nL 200.682402 424.330993 \r\nL 200.747249 423.390172 \r\nL 200.779673 423.400221 \r\nL 200.941792 420.600722 \r\nL 200.990427 420.893987 \r\nL 201.136334 415.305406 \r\nL 201.152546 415.761002 \r\nL 201.18497 417.297597 \r\nL 201.217394 414.626646 \r\nL 201.233606 413.676867 \r\nL 201.282241 416.092499 \r\nL 201.298453 415.941593 \r\nL 201.314665 415.628855 \r\nL 201.347089 416.998067 \r\nL 201.460572 420.932816 \r\nL 201.492996 419.835853 \r\nL 201.509208 419.454089 \r\nL 201.541632 420.546563 \r\nL 201.638903 423.384926 \r\nL 201.671327 422.186325 \r\nL 201.817234 416.253526 \r\nL 201.849657 416.481805 \r\nL 202.011776 414.237478 \r\nL 202.027988 414.243551 \r\nL 202.222531 423.074221 \r\nL 202.38465 430.309234 \r\nL 202.417073 428.380428 \r\nL 202.530557 422.908393 \r\nL 202.56298 423.489384 \r\nL 202.595404 422.081276 \r\nL 202.806159 411.829192 \r\nL 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209.971813 416.237834 \r\nL 210.004236 422.398995 \r\nL 210.085296 419.10915 \r\nL 210.101508 419.914387 \r\nL 210.133931 417.36211 \r\nL 210.150143 416.711855 \r\nL 210.166355 418.490597 \r\nL 210.312262 429.234975 \r\nL 210.37711 423.258767 \r\nL 210.458169 423.459777 \r\nL 210.506805 426.689525 \r\nL 210.539229 424.326977 \r\nL 210.668924 416.381048 \r\nL 210.701348 417.289346 \r\nL 210.733771 417.917142 \r\nL 210.782407 416.506004 \r\nL 210.863466 414.232403 \r\nL 210.944526 415.61133 \r\nL 210.993161 417.082195 \r\nL 211.058009 416.162271 \r\nL 211.106645 414.303989 \r\nL 211.15528 416.444852 \r\nL 211.268764 420.205207 \r\nL 211.301187 418.410305 \r\nL 211.317399 417.78413 \r\nL 211.366035 419.480347 \r\nL 211.414671 420.819348 \r\nL 211.479518 424.265877 \r\nL 211.544366 422.765729 \r\nL 211.576789 423.340937 \r\nL 211.593001 422.290309 \r\nL 211.625425 419.588064 \r\nL 211.674061 422.707135 \r\nL 211.722696 419.907419 \r\nL 211.738908 419.871158 \r\nL 211.771332 420.844918 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421.229003 \r\nL 217.040195 416.522232 \r\nL 217.121255 415.020173 \r\nL 217.153678 416.476753 \r\nL 217.267162 418.29311 \r\nL 217.202314 415.519712 \r\nL 217.283374 417.404798 \r\nL 217.315797 416.336925 \r\nL 217.348221 418.989067 \r\nL 217.461704 421.983003 \r\nL 217.396857 418.94575 \r\nL 217.51034 421.90011 \r\nL 217.591399 421.682369 \r\nL 217.672459 423.198017 \r\nL 217.818366 421.418635 \r\nL 217.834578 421.841809 \r\nL 217.867001 422.581671 \r\nL 217.915637 421.177662 \r\nL 217.964273 421.240484 \r\nL 218.093968 419.120231 \r\nL 218.158815 419.159339 \r\nL 218.191239 419.910254 \r\nL 218.223663 419.191292 \r\nL 218.337146 416.450886 \r\nL 218.353358 416.592055 \r\nL 218.401994 419.018674 \r\nL 218.499265 418.949814 \r\nL 218.531689 418.384257 \r\nL 218.580324 419.338751 \r\nL 218.612748 419.314797 \r\nL 218.645172 419.578055 \r\nL 218.693808 420.459092 \r\nL 218.758655 419.743572 \r\nL 219.018045 414.456008 \r\nL 219.034257 414.953082 \r\nL 219.050469 415.238677 \r\nL 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418.975331 \r\nL 227.091565 419.802765 \r\nL 227.123989 419.408496 \r\nL 227.156412 419.60928 \r\nL 227.302319 422.179216 \r\nL 227.383379 421.555917 \r\nL 227.464438 421.605361 \r\nL 227.432015 421.379495 \r\nL 227.48065 421.58843 \r\nL 227.513074 421.641553 \r\nL 227.545498 421.139652 \r\nL 227.707617 417.917499 \r\nL 227.756252 419.600355 \r\nL 227.837312 418.700485 \r\nL 227.885947 417.581462 \r\nL 227.934583 418.86571 \r\nL 227.967007 418.468523 \r\nL 228.048066 418.77929 \r\nL 228.08049 418.859215 \r\nL 228.096702 418.666222 \r\nL 228.145338 417.763468 \r\nL 228.226397 418.147001 \r\nL 228.275033 419.039417 \r\nL 228.307456 418.08865 \r\nL 228.372304 416.954824 \r\nL 228.42094 417.649092 \r\nL 228.469575 417.256421 \r\nL 228.485787 417.527576 \r\nL 228.566847 419.030213 \r\nL 228.59927 418.548691 \r\nL 228.631694 417.811788 \r\nL 228.68033 419.572163 \r\nL 228.777601 421.61741 \r\nL 228.810025 420.104055 \r\nL 228.874872 421.284864 \r\nL 228.923508 419.183319 \r\nL 228.93972 419.278318 \r\nL 228.988356 421.400473 \r\nL 229.069415 420.844466 \r\nL 229.150475 419.067665 \r\nL 229.182898 420.222194 \r\nL 229.263958 420.082821 \r\nL 229.312593 421.540447 \r\nL 229.361229 419.59748 \r\nL 229.442289 419.646006 \r\nL 229.490924 421.679432 \r\nL 229.523348 419.552084 \r\nL 229.53956 419.159311 \r\nL 229.555772 420.107399 \r\nL 229.571984 421.104669 \r\nL 229.620619 417.537412 \r\nL 229.669255 420.344904 \r\nL 229.782738 419.007692 \r\nL 229.815162 416.863462 \r\nL 229.863798 419.3718 \r\nL 229.896221 417.663025 \r\nL 229.912433 417.511417 \r\nL 229.928645 418.317532 \r\nL 230.025916 419.801242 \r\nL 230.042128 419.769485 \r\nL 230.090764 418.832259 \r\nL 230.1394 419.599794 \r\nL 230.188035 420.818891 \r\nL 230.269095 420.113999 \r\nL 230.415002 417.169965 \r\nL 230.431214 417.703007 \r\nL 230.463637 419.663461 \r\nL 230.512273 417.37177 \r\nL 230.544697 418.017055 \r\nL 230.560909 418.21665 \r\nL 230.577121 417.560504 \r\nL 230.609544 416.490134 \r\nL 230.641968 419.767125 \r\nL 230.65818 420.510488 \r\nL 230.690604 417.930644 \r\nL 230.706816 417.385242 \r\nL 230.739239 419.629851 \r\nL 230.852723 420.748495 \r\nL 230.804087 418.36805 \r\nL 230.868935 420.730393 \r\nL 230.901358 419.805406 \r\nL 230.933782 422.087447 \r\nL 230.949994 423.368251 \r\nL 231.014842 419.959087 \r\nL 231.063477 421.616853 \r\nL 231.095901 419.915657 \r\nL 231.112113 419.577137 \r\nL 231.128325 420.526519 \r\nL 231.209384 419.299964 \r\nL 231.25802 422.516327 \r\nL 231.306655 417.842061 \r\nL 231.387715 418.953599 \r\nL 231.436351 420.718459 \r\nL 231.484986 418.977945 \r\nL 231.501198 418.683446 \r\nL 231.549834 419.698879 \r\nL 231.598469 418.923955 \r\nL 231.614681 418.986681 \r\nL 231.647105 418.699648 \r\nL 231.663317 418.469905 \r\nL 231.695741 419.326265 \r\nL 231.711953 419.929704 \r\nL 231.760588 418.304078 \r\nL 231.825436 418.57099 \r\nL 231.890283 417.656523 \r\nL 231.922707 419.367046 \r\nL 231.971343 416.405041 \r\nL 231.987555 416.279151 \r\nL 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233.722227 421.28165 \r\nL 233.738438 420.711662 \r\nL 233.851922 418.920401 \r\nL 233.868134 418.999784 \r\nL 233.900557 420.253432 \r\nL 233.932981 418.374801 \r\nL 234.046464 416.676641 \r\nL 233.997829 419.986364 \r\nL 234.062676 417.150959 \r\nL 234.143736 416.749955 \r\nL 234.192371 418.795918 \r\nL 234.241007 416.275376 \r\nL 234.289643 419.066043 \r\nL 234.322066 417.490074 \r\nL 234.338278 417.157906 \r\nL 234.370702 418.76033 \r\nL 234.451761 418.402612 \r\nL 234.500397 420.196172 \r\nL 234.549033 418.923621 \r\nL 234.597668 420.381596 \r\nL 234.630092 419.654114 \r\nL 234.646304 419.570646 \r\nL 234.662516 419.804508 \r\nL 234.775999 421.007161 \r\nL 234.727364 419.479871 \r\nL 234.792211 420.524087 \r\nL 234.824635 419.065949 \r\nL 234.873271 421.16986 \r\nL 234.905694 420.160554 \r\nL 235.002966 419.306032 \r\nL 234.95433 420.837159 \r\nL 235.019178 419.496852 \r\nL 235.067813 421.610734 \r\nL 235.116449 419.122201 \r\nL 235.165085 421.079399 \r\nL 235.262356 420.633384 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421.856496 \r\nL 241.893017 418.749628 \r\nL 241.925441 420.969012 \r\nL 241.941653 420.900051 \r\nL 241.974077 418.544507 \r\nL 242.038924 421.496121 \r\nL 242.071348 419.087358 \r\nL 242.08756 419.169281 \r\nL 242.119984 420.305464 \r\nL 242.16862 418.590442 \r\nL 242.201043 419.401032 \r\nL 242.314527 420.697324 \r\nL 242.330738 420.386076 \r\nL 242.476645 419.268768 \r\nL 242.638764 421.826008 \r\nL 242.6874 418.838482 \r\nL 242.784671 419.475379 \r\nL 242.817095 421.765967 \r\nL 242.865731 418.72414 \r\nL 242.881943 418.188758 \r\nL 242.914366 420.086346 \r\nL 242.930578 420.398111 \r\nL 242.94679 419.613341 \r\nL 242.979214 418.02981 \r\nL 243.044061 420.159411 \r\nL 243.20618 416.655053 \r\nL 243.335875 419.516523 \r\nL 243.352087 419.488502 \r\nL 243.400723 416.907568 \r\nL 243.449359 419.660994 \r\nL 243.46557 419.158007 \r\nL 243.481782 418.54755 \r\nL 243.530418 420.355852 \r\nL 243.54663 420.523765 \r\nL 243.579054 419.91914 \r\nL 243.643901 419.395399 \r\nL 243.676325 420.080894 \r\nL 243.692537 420.193427 \r\nL 243.708749 419.846484 \r\nL 243.724961 419.594888 \r\nL 243.741173 420.08293 \r\nL 243.773596 421.609077 \r\nL 243.822232 419.592032 \r\nL 243.838444 419.748085 \r\nL 243.870868 420.202193 \r\nL 243.88708 419.524216 \r\nL 243.919503 417.304385 \r\nL 243.984351 419.76537 \r\nL 244.000563 419.065379 \r\nL 244.114046 416.969321 \r\nL 244.06541 419.598822 \r\nL 244.130258 417.007422 \r\nL 244.276165 420.089406 \r\nL 244.292377 420.074164 \r\nL 244.3248 419.068922 \r\nL 244.422072 419.189332 \r\nL 244.551767 421.562241 \r\nL 244.600403 420.767775 \r\nL 244.616614 420.730591 \r\nL 244.649038 421.459931 \r\nL 244.681462 420.562115 \r\nL 244.762521 422.039707 \r\nL 244.811157 418.273637 \r\nL 244.859793 420.060689 \r\nL 244.892217 417.69024 \r\nL 244.908428 417.434727 \r\nL 244.92464 418.59638 \r\nL 245.038123 420.423322 \r\nL 244.989488 417.760191 \r\nL 245.054335 420.276999 \r\nL 245.102971 417.39368 \r\nL 245.18403 418.323089 \r\nL 245.200242 418.15394 \r\nL 245.248878 421.35449 \r\nL 245.346149 420.50806 \r\nL 245.362361 420.768562 \r\nL 245.378573 419.996363 \r\nL 245.410997 418.029157 \r\nL 245.459633 420.297014 \r\nL 245.492056 419.106953 \r\nL 245.508268 418.706122 \r\nL 245.52448 419.460565 \r\nL 245.556904 421.063783 \r\nL 245.60554 417.630908 \r\nL 245.751447 420.328483 \r\nL 245.767658 420.283032 \r\nL 245.800082 418.911075 \r\nL 245.881142 419.182926 \r\nL 245.929777 420.091625 \r\nL 246.010837 419.804961 \r\nL 246.059472 419.906986 \r\nL 246.091896 420.331732 \r\nL 246.12432 419.156696 \r\nL 246.140532 418.921452 \r\nL 246.172956 419.760589 \r\nL 246.205379 420.594367 \r\nL 246.286439 420.042229 \r\nL 246.335074 419.210186 \r\nL 246.399922 419.700399 \r\nL 246.529617 420.932636 \r\nL 246.562041 420.38166 \r\nL 246.659312 418.954049 \r\nL 246.691736 419.599361 \r\nL 246.707948 419.971564 \r\nL 246.740372 418.933645 \r\nL 246.870067 417.283411 \r\nL 246.886279 417.623313 \r\nL 246.918702 418.449267 \r\nL 246.98355 417.154489 \r\nL 246.999762 416.851213 \r\nL 247.032186 418.182351 \r\nL 247.145669 419.513647 \r\nL 247.097033 417.645409 \r\nL 247.161881 419.378526 \r\nL 247.178093 419.138414 \r\nL 247.194304 419.438204 \r\nL 247.226728 421.109682 \r\nL 247.291576 419.309997 \r\nL 247.307788 419.634451 \r\nL 247.372635 421.039781 \r\nL 247.453695 420.390017 \r\nL 247.615813 417.585982 \r\nL 247.632025 417.872232 \r\nL 247.745509 418.822834 \r\nL 247.794144 416.549374 \r\nL 247.826568 418.696158 \r\nL 247.891416 417.919467 \r\nL 247.956263 420.828189 \r\nL 247.988687 420.405255 \r\nL 248.004899 420.480408 \r\nL 248.150806 422.792325 \r\nL 248.329136 420.156904 \r\nL 248.345348 420.592817 \r\nL 248.377772 421.187306 \r\nL 248.410196 419.467759 \r\nL 248.475043 419.924537 \r\nL 248.556103 418.74959 \r\nL 248.62095 418.44165 \r\nL 248.637162 418.176797 \r\nL 248.685798 419.196754 \r\nL 248.734434 418.719561 \r\nL 248.766857 419.212008 \r\nL 248.783069 419.325408 \r\nL 248.815493 418.997679 \r\nL 248.847917 418.771879 \r\nL 248.896553 419.237544 \r\nL 248.928976 419.450677 \r\nL 248.993824 419.15095 \r\nL 249.010036 419.094123 \r\nL 249.026248 419.389625 \r\nL 249.058671 420.045703 \r\nL 249.091095 418.810355 \r\nL 249.107307 418.429064 \r\nL 249.155943 419.753175 \r\nL 249.188366 418.979533 \r\nL 249.204578 418.802321 \r\nL 249.22079 419.229182 \r\nL 249.253214 419.976618 \r\nL 249.285638 418.240258 \r\nL 249.30185 417.765832 \r\nL 249.334273 419.431135 \r\nL 249.350485 420.087649 \r\nL 249.382909 418.249372 \r\nL 249.399121 417.338001 \r\nL 249.447757 419.575282 \r\nL 249.496392 417.846006 \r\nL 249.545028 418.636516 \r\nL 249.577452 417.939802 \r\nL 249.609876 417.372977 \r\nL 249.642299 418.446892 \r\nL 249.723359 417.383461 \r\nL 249.771994 419.924644 \r\nL 249.82063 418.17467 \r\nL 249.885478 419.794412 \r\nL 249.90169 419.832364 \r\nL 249.950325 421.760746 \r\nL 250.063808 421.651757 \r\nL 250.096232 421.267791 \r\nL 250.144868 421.964931 \r\nL 250.193503 421.307034 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417.032905 \r\nL 252.317261 419.369218 \r\nL 252.382108 418.601382 \r\nL 252.430744 420.338377 \r\nL 252.479379 418.643111 \r\nL 252.576651 418.980291 \r\nL 252.592863 418.987086 \r\nL 252.641498 421.718865 \r\nL 252.706346 419.243702 \r\nL 252.722558 419.361759 \r\nL 252.771193 418.923635 \r\nL 252.803617 419.153681 \r\nL 252.852253 420.150014 \r\nL 252.900889 419.076957 \r\nL 252.9171 419.036487 \r\nL 252.933312 419.288608 \r\nL 252.965736 419.749527 \r\nL 252.981948 419.246514 \r\nL 253.030584 416.995951 \r\nL 253.079219 419.11394 \r\nL 253.160279 419.494258 \r\nL 253.127855 419.01432 \r\nL 253.176491 419.403494 \r\nL 253.225126 417.583403 \r\nL 253.289974 419.279063 \r\nL 253.419669 420.954057 \r\nL 253.371033 419.105595 \r\nL 253.452093 420.482777 \r\nL 253.484516 419.43557 \r\nL 253.549364 419.986187 \r\nL 253.598 422.149449 \r\nL 253.662847 420.798499 \r\nL 253.760119 420.206028 \r\nL 253.77633 420.372295 \r\nL 253.792542 420.540833 \r\nL 253.824966 419.889706 \r\nL 254.003297 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419.483891 \r\nL 259.450491 418.032023 \r\nL 259.596398 420.197015 \r\nL 259.61261 419.794979 \r\nL 259.726093 418.62365 \r\nL 259.677457 419.875157 \r\nL 259.742305 418.717435 \r\nL 259.807152 420.4772 \r\nL 259.888212 419.970114 \r\nL 259.920635 419.474399 \r\nL 259.969271 420.227208 \r\nL 260.001695 419.929682 \r\nL 260.017907 419.818958 \r\nL 260.034119 420.016637 \r\nL 260.115178 420.734966 \r\nL 260.147602 420.295313 \r\nL 260.228661 419.080748 \r\nL 260.261085 419.558878 \r\nL 260.293509 420.012997 \r\nL 260.342145 419.233433 \r\nL 260.358356 419.3407 \r\nL 260.39078 419.512121 \r\nL 260.423204 419.133055 \r\nL 260.47184 418.477706 \r\nL 260.520475 419.077338 \r\nL 260.569111 419.909567 \r\nL 260.601535 419.148089 \r\nL 260.633958 418.608367 \r\nL 260.682594 419.568605 \r\nL 260.698806 419.368997 \r\nL 260.715018 419.231228 \r\nL 260.73123 419.520802 \r\nL 260.844713 421.088029 \r\nL 260.860925 420.804848 \r\nL 260.925772 418.604363 \r\nL 261.006832 419.278672 \r\nL 261.055468 419.7082 \r\nL 261.087891 419.509392 \r\nL 261.25001 417.254561 \r\nL 261.266222 417.80279 \r\nL 261.314858 419.750367 \r\nL 261.395917 419.387617 \r\nL 261.444553 418.824241 \r\nL 261.460765 419.288067 \r\nL 261.59046 422.015953 \r\nL 261.606672 422.015636 \r\nL 261.655307 420.539879 \r\nL 261.736367 421.028559 \r\nL 261.752579 421.164911 \r\nL 261.801214 420.827517 \r\nL 261.963333 417.149885 \r\nL 261.995757 418.905097 \r\nL 262.011969 419.313396 \r\nL 262.044393 417.388691 \r\nL 262.10924 418.178258 \r\nL 262.157876 416.197771 \r\nL 262.303783 420.055315 \r\nL 262.319995 419.505085 \r\nL 262.336207 418.884636 \r\nL 262.401054 420.103504 \r\nL 262.417266 419.820127 \r\nL 262.433478 419.867414 \r\nL 262.546961 421.243104 \r\nL 262.563173 421.24191 \r\nL 262.692868 419.791589 \r\nL 262.70908 419.818564 \r\nL 262.757716 420.606412 \r\nL 262.790139 419.431365 \r\nL 262.822563 418.533751 \r\nL 262.871199 419.561261 \r\nL 262.919835 418.594479 \r\nL 262.984682 420.053733 \r\nL 263.017106 418.681968 \r\nL 263.033318 418.43045 \r\nL 263.04953 418.995223 \r\nL 263.081953 420.00056 \r\nL 263.114377 417.890088 \r\nL 263.130589 417.345509 \r\nL 263.146801 418.245877 \r\nL 263.22786 418.213502 \r\nL 263.276496 421.316466 \r\nL 263.325132 418.312559 \r\nL 263.438615 419.194683 \r\nL 263.487251 420.746174 \r\nL 263.552098 419.402882 \r\nL 263.584522 419.310969 \r\nL 263.600734 419.159039 \r\nL 263.633158 419.511334 \r\nL 263.714217 419.947679 \r\nL 263.730429 419.746567 \r\nL 263.876336 418.036967 \r\nL 263.924971 419.705751 \r\nL 264.006031 418.60305 \r\nL 264.038455 418.941229 \r\nL 264.070878 418.24916 \r\nL 264.08709 417.997193 \r\nL 264.119514 419.018777 \r\nL 264.249209 420.850618 \r\nL 264.297845 419.198473 \r\nL 264.378904 419.631995 \r\nL 264.42754 421.562426 \r\nL 264.508599 420.263662 \r\nL 264.524811 420.435713 \r\nL 264.541023 420.065545 \r\nL 264.654506 419.253695 \r\nL 264.68693 419.870703 \r\nL 264.735566 418.951199 \r\nL 264.784201 418.397552 \r\nL 264.849049 418.651629 \r\nL 264.978744 420.040075 \r\nL 264.994956 419.693962 \r\nL 265.02738 419.251809 \r\nL 265.076015 420.048092 \r\nL 265.157075 420.968707 \r\nL 265.205711 420.405316 \r\nL 265.367829 419.242131 \r\nL 265.384041 419.288825 \r\nL 265.400253 419.384009 \r\nL 265.416465 419.301538 \r\nL 265.54616 417.084925 \r\nL 265.578584 417.742969 \r\nL 265.594796 417.981025 \r\nL 265.643431 417.514313 \r\nL 265.692067 417.807232 \r\nL 265.724491 418.117993 \r\nL 265.919034 420.832702 \r\nL 265.951457 420.049535 \r\nL 265.967669 419.657244 \r\nL 266.016305 420.345741 \r\nL 266.048729 420.067865 \r\nL 266.064941 420.09342 \r\nL 266.097364 420.730364 \r\nL 266.146 419.808168 \r\nL 266.162212 419.933196 \r\nL 266.178424 420.033051 \r\nL 266.194636 419.643205 \r\nL 266.275695 420.146208 \r\nL 266.324331 418.454568 \r\nL 266.470238 420.091955 \r\nL 266.518873 419.890441 \r\nL 266.535085 420.060166 \r\nL 266.583721 421.423007 \r\nL 266.632357 419.974793 \r\nL 266.648568 419.909178 \r\nL 266.74584 419.427242 \r\nL 266.794475 422.290969 \r\nL 266.940382 417.726981 \r\nL 266.989018 420.790077 \r\nL 267.037654 417.622951 \r\nL 267.070077 420.156793 \r\nL 267.086289 420.411431 \r\nL 267.102501 419.159192 \r\nL 267.134925 416.79989 \r\nL 267.183561 419.59608 \r\nL 267.215984 417.943713 \r\nL 267.232196 417.563482 \r\nL 267.280832 419.009027 \r\nL 267.313256 418.21963 \r\nL 267.361891 418.627796 \r\nL 267.426739 418.35177 \r\nL 267.442951 418.110723 \r\nL 267.475375 418.932545 \r\nL 267.491587 419.141134 \r\nL 267.507798 418.651489 \r\nL 267.572646 419.454121 \r\nL 267.621282 417.633568 \r\nL 267.767189 420.979085 \r\nL 267.783401 420.616269 \r\nL 267.86446 421.963106 \r\nL 267.913096 417.273618 \r\nL 267.961731 422.663272 \r\nL 268.042791 420.804534 \r\nL 268.059003 421.024951 \r\nL 268.075214 420.076838 \r\nL 268.107638 418.953797 \r\nL 268.140062 420.644189 \r\nL 268.188698 419.307964 \r\nL 268.269757 419.874509 \r\nL 268.318393 418.747887 \r\nL 268.350817 418.917084 \r\nL 268.480512 420.457969 \r\nL 268.415664 418.782907 \r\nL 268.496724 419.853301 \r\nL 268.529147 419.079924 \r\nL 268.561571 420.232389 \r\nL 268.610207 419.561511 \r\nL 268.739902 420.982863 \r\nL 268.869597 418.50488 \r\nL 268.902021 419.45376 \r\nL 268.918233 419.704748 \r\nL 268.950656 419.078259 \r\nL 269.080351 417.378184 \r\nL 269.096563 417.908104 \r\nL 269.161411 417.18718 \r\nL 269.226258 419.249865 \r\nL 269.274894 418.089123 \r\nL 269.32353 419.30538 \r\nL 269.339742 419.344382 \r\nL 269.372165 418.650217 \r\nL 269.404589 420.290905 \r\nL 269.420801 420.927467 \r\nL 269.453225 419.00569 \r\nL 269.469437 418.276676 \r\nL 269.50186 420.502698 \r\nL 269.518072 421.439511 \r\nL 269.58292 418.938987 \r\nL 269.599132 419.735175 \r\nL 269.631556 420.857845 \r\nL 269.680191 419.352562 \r\nL 269.712615 420.266302 \r\nL 269.728827 420.256857 \r\nL 269.777463 417.805544 \r\nL 269.858522 418.940946 \r\nL 269.874734 418.898151 \r\nL 269.890946 419.152138 \r\nL 269.955793 420.440625 \r\nL 270.036853 420.045666 \r\nL 270.053065 420.057482 \r\nL 270.085488 419.565101 \r\nL 270.117912 420.441603 \r\nL 270.18276 420.3985 \r\nL 270.231395 421.136953 \r\nL 270.377302 419.6089 \r\nL 270.409726 420.906731 \r\nL 270.458362 419.589628 \r\nL 270.490786 420.004264 \r\nL 270.523209 419.617077 \r\nL 270.539421 419.343294 \r\nL 270.604269 420.029918 \r\nL 270.750176 418.930646 \r\nL 270.7826 419.322501 \r\nL 270.798811 419.461754 \r\nL 270.815023 419.126148 \r\nL 270.831235 418.683225 \r\nL 270.879871 419.922345 \r\nL 270.896083 420.081385 \r\nL 270.912295 419.573325 \r\nL 271.04199 417.085816 \r\nL 271.187897 420.012839 \r\nL 271.204109 419.51191 \r\nL 271.236532 417.825916 \r\nL 271.317592 418.689898 \r\nL 271.333804 418.675256 \r\nL 271.382439 420.601057 \r\nL 271.447287 418.868606 \r\nL 271.56077 420.575454 \r\nL 271.576982 420.558996 \r\nL 271.722889 417.728009 \r\nL 271.803948 417.596538 \r\nL 271.868796 420.041032 \r\nL 271.917432 417.532041 \r\nL 271.998491 418.487572 \r\nL 272.176822 420.859318 \r\nL 272.209246 420.114877 \r\nL 272.225457 419.841012 \r\nL 272.257881 420.786451 \r\nL 272.355153 421.962996 \r\nL 272.387576 421.307848 \r\nL 272.517271 418.621555 \r\nL 272.533483 419.219958 \r\nL 272.630755 419.921403 \r\nL 272.646967 419.777388 \r\nL 272.695602 417.928121 \r\nL 272.76045 418.705434 \r\nL 272.857721 419.075662 \r\nL 272.873933 419.03925 \r\nL 272.906357 418.717704 \r\nL 272.93878 419.123472 \r\nL 272.971204 419.44763 \r\nL 273.003628 418.583183 \r\nL 273.01984 418.440268 \r\nL 273.036052 419.073288 \r\nL 273.068476 421.092307 \r\nL 273.149535 420.309152 \r\nL 273.198171 417.815083 \r\nL 273.246806 420.545998 \r\nL 273.263018 420.643881 \r\nL 273.311654 418.443691 \r\nL 273.36029 420.754679 \r\nL 273.392713 419.396765 \r\nL 273.506197 418.333941 \r\nL 273.554832 420.450153 \r\nL 273.61968 418.797972 \r\nL 273.652103 419.282246 \r\nL 273.765587 419.86661 \r\nL 273.716951 418.411393 \r\nL 273.781799 419.720012 \r\nL 273.79801 419.374299 \r\nL 273.846646 420.623046 \r\nL 273.862858 420.697885 \r\nL 273.87907 420.262246 \r\nL 273.976341 419.600694 \r\nL 273.992553 419.736304 \r\nL 274.13846 420.660757 \r\nL 274.187096 420.287302 \r\nL 274.203308 420.602916 \r\nL 274.21952 420.833692 \r\nL 274.251943 419.798881 \r\nL 274.268155 419.37975 \r\nL 274.300579 420.689255 \r\nL 274.316791 421.190205 \r\nL 274.349215 420.003122 \r\nL 274.47891 417.644142 \r\nL 274.527545 419.051081 \r\nL 274.576181 417.446602 \r\nL 274.608605 418.808572 \r\nL 274.641029 417.675655 \r\nL 274.65724 416.807803 \r\nL 274.722088 418.629959 \r\nL 274.754512 417.531321 \r\nL 274.786936 418.626386 \r\nL 274.819359 420.683704 \r\nL 274.916631 420.61315 \r\nL 274.949054 419.055968 \r\nL 275.013902 421.19141 \r\nL 275.046326 419.545863 \r\nL 275.062538 419.606482 \r\nL 275.094961 421.288022 \r\nL 275.143597 419.211273 \r\nL 275.273292 418.367982 \r\nL 275.289504 418.385677 \r\nL 275.305716 418.360072 \r\nL 275.386775 417.037835 \r\nL 275.484047 417.101573 \r\nL 275.889344 422.02341 \r\nL 275.905556 421.94262 \r\nL 276.051463 420.006965 \r\nL 276.067675 420.282329 \r\nL 276.100098 421.2276 \r\nL 276.132522 419.802588 \r\nL 276.164946 418.762023 \r\nL 276.262217 418.826198 \r\nL 276.294641 419.872526 \r\nL 276.343277 418.189502 \r\nL 276.359489 418.082326 \r\nL 276.408124 420.112025 \r\nL 276.505396 419.718156 \r\nL 276.570243 419.987042 \r\nL 276.602667 419.716715 \r\nL 276.618879 419.633611 \r\nL 276.635091 419.770968 \r\nL 276.780998 420.589234 \r\nL 276.910693 418.939895 \r\nL 276.926905 419.179483 \r\nL 276.959328 420.426246 \r\nL 277.007964 418.588919 \r\nL 277.024176 418.339899 \r\nL 277.040388 418.9129 \r\nL 277.072812 419.746506 \r\nL 277.105235 417.767483 \r\nL 277.170083 418.699019 \r\nL 277.218719 416.692186 \r\nL 277.267354 418.792546 \r\nL 277.364626 418.23126 \r\nL 277.413261 417.515962 \r\nL 277.445685 418.335048 \r\nL 277.591592 420.684416 \r\nL 277.624016 420.016467 \r\nL 277.656439 420.891823 \r\nL 277.769923 422.022119 \r\nL 277.721287 420.623972 \r\nL 277.786135 421.78096 \r\nL 277.883406 419.893667 \r\nL 277.91583 420.553399 \r\nL 277.932042 420.881242 \r\nL 277.964465 419.477085 \r\nL 278.077949 417.489274 \r\nL 278.09416 417.650066 \r\nL 278.223856 418.918682 \r\nL 278.159008 416.947504 \r\nL 278.240067 418.768989 \r\nL 278.288703 417.772659 \r\nL 278.321127 418.574111 \r\nL 278.450822 421.450678 \r\nL 278.467034 421.404595 \r\nL 278.548093 422.592394 \r\nL 278.596729 419.812163 \r\nL 278.612941 419.898384 \r\nL 278.661576 421.534638 \r\nL 278.710212 420.036831 \r\nL 278.937179 416.874789 \r\nL 278.969602 418.345 \r\nL 278.985814 418.61688 \r\nL 279.002026 417.97502 \r\nL 279.03445 417.009543 \r\nL 279.083086 418.491447 \r\nL 279.099297 418.300515 \r\nL 279.115509 418.187117 \r\nL 279.147933 418.613 \r\nL 279.391111 420.895405 \r\nL 279.407323 421.450167 \r\nL 279.455959 419.649204 \r\nL 279.472171 419.610476 \r\nL 279.520806 421.912616 \r\nL 279.569442 419.636394 \r\nL 279.601866 420.257393 \r\nL 279.63429 420.761081 \r\nL 279.666713 419.637103 \r\nL 279.682925 419.314786 \r\nL 279.715349 420.361574 \r\nL 279.731561 420.610647 \r\nL 279.763985 419.488803 \r\nL 279.780197 419.327232 \r\nL 279.796409 419.661171 \r\nL 279.828832 420.485932 \r\nL 279.877468 419.542325 \r\nL 279.909892 419.069506 \r\nL 279.958527 419.688506 \r\nL 279.990951 419.177666 \r\nL 280.007163 419.214789 \r\nL 280.039587 419.844885 \r\nL 280.136858 419.616333 \r\nL 280.169282 419.726388 \r\nL 280.185494 419.677051 \r\nL 280.234129 418.880373 \r\nL 280.266553 420.187117 \r\nL 280.282765 420.494321 \r\nL 280.331401 419.592266 \r\nL 280.347613 419.433214 \r\nL 280.363825 419.729101 \r\nL 280.444884 419.285679 \r\nL 280.49352 421.407474 \r\nL 280.655639 418.857633 \r\nL 280.688062 418.885653 \r\nL 280.720486 418.55476 \r\nL 280.882605 416.317041 \r\nL 280.898817 416.340906 \r\nL 281.028512 417.998667 \r\nL 280.979876 416.338206 \r\nL 281.044724 417.930062 \r\nL 281.077148 417.508228 \r\nL 281.109571 418.930864 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286.962062 419.473909 \r\nL 286.978274 419.576082 \r\nL 287.091758 419.928604 \r\nL 287.043122 419.366023 \r\nL 287.107969 419.860655 \r\nL 287.253876 418.439009 \r\nL 287.270088 418.579073 \r\nL 287.302512 419.230514 \r\nL 287.351148 418.430426 \r\nL 287.383572 418.829977 \r\nL 287.415995 418.728056 \r\nL 287.432207 418.909059 \r\nL 287.610538 420.625928 \r\nL 287.62675 420.453057 \r\nL 287.642962 420.374849 \r\nL 287.675385 420.658933 \r\nL 287.724021 420.506168 \r\nL 287.853716 421.362861 \r\nL 287.805081 420.274743 \r\nL 287.869928 421.359452 \r\nL 288.015835 418.249994 \r\nL 288.032047 418.293247 \r\nL 288.064471 418.831266 \r\nL 288.113106 417.914114 \r\nL 288.14553 418.544529 \r\nL 288.194166 417.525954 \r\nL 288.275225 418.15769 \r\nL 288.40492 419.962631 \r\nL 288.437344 419.537405 \r\nL 288.453556 419.306359 \r\nL 288.48598 419.768199 \r\nL 288.599463 420.922364 \r\nL 288.615675 420.859937 \r\nL 288.74537 419.39241 \r\nL 288.761582 419.533071 \r\nL 288.777794 419.579109 \r\nL 288.794006 419.404378 \r\nL 288.810218 419.303548 \r\nL 288.842641 419.742092 \r\nL 288.858853 419.779618 \r\nL 288.875065 419.569583 \r\nL 288.956125 418.996915 \r\nL 288.988548 419.330152 \r\nL 289.134455 420.350367 \r\nL 289.183091 419.644917 \r\nL 289.215515 420.589413 \r\nL 289.231727 420.793962 \r\nL 289.26415 419.924962 \r\nL 289.280362 419.792946 \r\nL 289.296574 420.149514 \r\nL 289.328998 420.963307 \r\nL 289.377634 419.343249 \r\nL 289.393845 419.327926 \r\nL 289.426269 419.749938 \r\nL 289.458693 418.749909 \r\nL 289.523541 419.327372 \r\nL 289.572176 418.272107 \r\nL 289.620812 419.521596 \r\nL 289.653236 418.210903 \r\nL 289.669448 417.672674 \r\nL 289.718083 419.351996 \r\nL 289.766719 419.093848 \r\nL 289.86399 419.785689 \r\nL 289.977473 419.541116 \r\nL 289.993685 419.54421 \r\nL 290.042321 420.158528 \r\nL 290.090957 419.578945 \r\nL 290.20444 418.265866 \r\nL 290.155804 419.624543 \r\nL 290.236864 418.966167 \r\nL 290.269287 419.453163 \r\nL 290.301711 418.950744 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419.919287 \r\nL 296.446016 420.280443 \r\nL 296.510864 418.980782 \r\nL 296.543287 419.177776 \r\nL 296.575711 418.637907 \r\nL 296.591923 418.455574 \r\nL 296.640559 419.022864 \r\nL 296.656771 419.09588 \r\nL 296.672983 418.803187 \r\nL 296.705406 418.126206 \r\nL 296.754042 419.103712 \r\nL 296.786466 418.621813 \r\nL 296.81889 419.250302 \r\nL 296.916161 420.437615 \r\nL 296.932373 420.371942 \r\nL 296.964797 419.881122 \r\nL 296.99722 421.238615 \r\nL 297.013432 421.417781 \r\nL 297.029644 421.007688 \r\nL 297.110704 421.275067 \r\nL 297.159339 420.178019 \r\nL 297.191763 420.503678 \r\nL 297.207975 420.442603 \r\nL 297.353882 417.99352 \r\nL 297.370094 418.093291 \r\nL 297.402517 417.908595 \r\nL 297.467365 416.867709 \r\nL 297.499789 417.689787 \r\nL 297.629484 419.359167 \r\nL 297.564636 417.171246 \r\nL 297.645696 418.977722 \r\nL 297.661908 418.789335 \r\nL 297.710543 419.397511 \r\nL 297.807815 420.467762 \r\nL 297.888874 419.947111 \r\nL 297.93751 421.485012 \r\nL 298.083417 418.409522 \r\nL 298.11584 420.449348 \r\nL 298.180688 417.702652 \r\nL 298.310383 419.182487 \r\nL 298.342807 418.634251 \r\nL 298.407654 419.36477 \r\nL 298.440078 419.741609 \r\nL 298.472502 419.183976 \r\nL 298.488714 418.84393 \r\nL 298.521138 419.732257 \r\nL 298.553561 420.975216 \r\nL 298.650833 420.739959 \r\nL 298.731892 421.28092 \r\nL 298.780528 419.646308 \r\nL 298.910223 421.436161 \r\nL 298.958859 419.170824 \r\nL 299.039918 420.150133 \r\nL 299.153401 419.083224 \r\nL 299.104766 420.455537 \r\nL 299.169613 419.261895 \r\nL 299.202037 420.220234 \r\nL 299.234461 418.666022 \r\nL 299.299308 419.362447 \r\nL 299.364156 416.984395 \r\nL 299.510063 419.35486 \r\nL 299.526275 418.836727 \r\nL 299.558698 417.412323 \r\nL 299.607334 419.961594 \r\nL 299.639758 418.232332 \r\nL 299.65597 417.870623 \r\nL 299.688393 419.5069 \r\nL 299.801877 421.811926 \r\nL 299.818089 421.172991 \r\nL 299.947784 419.758205 \r\nL 299.980207 420.124077 \r\nL 300.012631 419.370924 \r\nL 300.207174 417.056876 \r\nL 300.061267 419.705799 \r\nL 300.223386 417.287764 \r\nL 300.369293 418.797821 \r\nL 300.580047 421.318308 \r\nL 300.612471 420.856924 \r\nL 300.644895 420.610484 \r\nL 300.709742 420.958688 \r\nL 300.725954 421.051911 \r\nL 300.742166 420.795954 \r\nL 300.952921 417.493261 \r\nL 300.969133 417.47004 \r\nL 301.001556 417.606871 \r\nL 301.03398 417.558087 \r\nL 301.37443 421.243524 \r\nL 301.131251 417.467046 \r\nL 301.406853 420.479282 \r\nL 301.423065 420.2378 \r\nL 301.455489 421.254656 \r\nL 301.471701 421.648473 \r\nL 301.520337 420.562335 \r\nL 301.568972 420.930198 \r\nL 301.601396 419.655697 \r\nL 301.617608 418.886552 \r\nL 301.666244 420.057955 \r\nL 301.714879 418.999692 \r\nL 301.747303 419.413559 \r\nL 301.779727 419.041911 \r\nL 301.89321 418.072773 \r\nL 301.844574 419.274419 \r\nL 301.909422 418.242168 \r\nL 302.039117 420.588161 \r\nL 302.087753 419.085701 \r\nL 302.136388 420.769041 \r\nL 302.185024 419.790703 \r\nL 302.201236 420.36148 \r\nL 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307.956456 419.89713 \r\nL 307.972668 420.009612 \r\nL 308.021303 421.073205 \r\nL 308.069939 419.947859 \r\nL 308.183422 418.395606 \r\nL 308.199634 418.651957 \r\nL 308.329329 420.350639 \r\nL 308.345541 419.840707 \r\nL 308.377965 418.690721 \r\nL 308.4266 419.850539 \r\nL 308.459024 418.860464 \r\nL 308.50766 421.628824 \r\nL 308.621143 420.371485 \r\nL 308.653567 419.611657 \r\nL 308.702202 420.629334 \r\nL 308.750838 419.66164 \r\nL 308.783262 419.930298 \r\nL 308.815686 419.710808 \r\nL 308.896745 417.685688 \r\nL 308.961593 418.807992 \r\nL 309.075076 417.45096 \r\nL 309.156135 417.709417 \r\nL 309.28583 419.58114 \r\nL 309.318254 418.693232 \r\nL 309.334466 418.131296 \r\nL 309.383102 419.811974 \r\nL 309.496585 420.990527 \r\nL 309.431737 419.496841 \r\nL 309.512797 420.27673 \r\nL 309.593856 420.832689 \r\nL 309.642492 419.153776 \r\nL 309.707339 420.999385 \r\nL 309.804611 419.860633 \r\nL 309.837035 418.323119 \r\nL 309.918094 419.448502 \r\nL 310.047789 418.571132 \r\nL 309.982942 419.457406 \r\nL 310.064001 418.630467 \r\nL 310.258544 420.043126 \r\nL 310.274755 419.696728 \r\nL 310.290967 419.280136 \r\nL 310.355815 420.279961 \r\nL 310.388239 420.14871 \r\nL 310.420662 420.448344 \r\nL 310.453086 420.89736 \r\nL 310.501722 420.254022 \r\nL 310.550358 420.02764 \r\nL 310.598993 420.207584 \r\nL 310.631417 420.533785 \r\nL 310.680053 420.002774 \r\nL 310.696265 420.073538 \r\nL 310.712476 420.051064 \r\nL 310.842172 418.350411 \r\nL 310.858383 418.629624 \r\nL 310.874595 418.726164 \r\nL 310.890807 418.474496 \r\nL 311.00429 417.789802 \r\nL 310.955655 418.52431 \r\nL 311.020502 417.824927 \r\nL 311.247469 420.22303 \r\nL 311.296104 419.655583 \r\nL 311.328528 420.463144 \r\nL 311.409588 420.767057 \r\nL 311.425799 420.67855 \r\nL 311.555495 419.878837 \r\nL 311.571706 419.96021 \r\nL 311.587918 420.053991 \r\nL 311.620342 419.616613 \r\nL 311.750037 418.060455 \r\nL 311.766249 418.427035 \r\nL 311.895944 419.637091 \r\nL 311.94458 418.702263 \r\nL 311.977004 419.731245 \r\nL 312.090487 420.69516 \r\nL 312.041851 419.347863 \r\nL 312.106699 420.591775 \r\nL 312.220182 419.711965 \r\nL 312.268818 419.807066 \r\nL 312.317453 420.189231 \r\nL 312.366089 419.792503 \r\nL 312.54442 418.197178 \r\nL 312.593055 418.635405 \r\nL 312.641691 418.152726 \r\nL 312.657903 418.26162 \r\nL 312.674115 418.231805 \r\nL 312.706539 417.545694 \r\nL 312.755174 418.7422 \r\nL 312.80381 419.331093 \r\nL 312.884869 419.244856 \r\nL 313.079412 420.883099 \r\nL 313.111836 420.508246 \r\nL 313.176683 419.597235 \r\nL 313.225319 420.152908 \r\nL 313.257743 420.525084 \r\nL 313.306378 419.83755 \r\nL 313.338802 419.851548 \r\nL 313.355014 419.770425 \r\nL 313.484709 419.226493 \r\nL 313.500921 419.351087 \r\nL 313.565768 420.184302 \r\nL 313.598192 419.443833 \r\nL 313.630616 418.568456 \r\nL 313.711675 418.922751 \r\nL 313.808947 418.830618 \r\nL 313.857582 419.526436 \r\nL 313.906218 418.892415 \r\nL 313.954854 419.321149 \r\nL 314.019701 420.403097 \r\nL 314.100761 419.936793 \r\nL 314.133185 419.51308 \r\nL 314.165608 419.993177 \r\nL 314.246668 419.858724 \r\nL 314.279092 420.430213 \r\nL 314.295303 420.402515 \r\nL 314.424998 418.791575 \r\nL 314.457422 419.326231 \r\nL 314.489846 419.616301 \r\nL 314.538482 419.259427 \r\nL 314.587117 418.228685 \r\nL 314.668177 418.921096 \r\nL 314.797872 420.015878 \r\nL 314.846508 419.428091 \r\nL 314.878931 419.033172 \r\nL 314.927567 419.756616 \r\nL 315.008626 420.242146 \r\nL 315.04105 420.066724 \r\nL 315.105898 418.995258 \r\nL 315.186957 419.188177 \r\nL 315.203169 419.097366 \r\nL 315.235593 419.541242 \r\nL 315.251805 419.590287 \r\nL 315.365288 418.112161 \r\nL 315.397712 418.464406 \r\nL 315.511195 419.795735 \r\nL 315.543619 419.501893 \r\nL 315.576042 419.194994 \r\nL 315.624678 419.562983 \r\nL 315.657102 419.344693 \r\nL 315.705738 421.069218 \r\nL 315.803009 420.261915 \r\nL 315.851645 418.728251 \r\nL 315.932704 419.501863 \r\nL 315.98134 420.065114 \r\nL 316.013763 419.741361 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318.202368 418.819017 \r\nL 318.21858 418.791588 \r\nL 318.251004 418.297416 \r\nL 318.332063 418.686353 \r\nL 318.445546 419.55854 \r\nL 318.461758 419.366629 \r\nL 318.510394 418.675199 \r\nL 318.55903 419.380783 \r\nL 318.656301 420.378915 \r\nL 318.607665 419.141678 \r\nL 318.688725 419.645918 \r\nL 318.721148 419.034388 \r\nL 318.753572 419.845868 \r\nL 318.802208 419.229666 \r\nL 318.867055 420.154379 \r\nL 318.964327 419.98762 \r\nL 318.996751 418.751692 \r\nL 319.07781 419.594844 \r\nL 319.094022 419.61893 \r\nL 319.126446 420.325773 \r\nL 319.191293 419.328783 \r\nL 319.256141 419.795057 \r\nL 319.288564 419.36449 \r\nL 319.304776 419.053378 \r\nL 319.353412 419.881776 \r\nL 319.385836 419.582238 \r\nL 319.515531 419.023364 \r\nL 319.564167 420.232579 \r\nL 319.661438 419.945876 \r\nL 319.710074 419.421524 \r\nL 319.774921 419.875236 \r\nL 319.888404 419.630409 \r\nL 320.001888 418.802424 \r\nL 320.018099 418.982689 \r\nL 320.034311 419.203343 \r\nL 320.082947 418.515108 \r\nL 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417.625228 \r\nL 465.584882 420.681449 \r\nL 465.747011 420.803577 \r\nL 466.006417 420.7599 \r\nL 466.200971 421.148902 \r\nL 466.719783 422.268764 \r\nL 466.914338 422.313537 \r\nL 467.044041 422.096792 \r\nL 467.238595 421.132675 \r\nL 467.951962 416.559323 \r\nL 468.146516 416.667252 \r\nL 468.308645 416.652295 \r\nL 468.535625 416.347001 \r\nL 468.73018 416.230835 \r\nL 468.859883 416.4671 \r\nL 469.054437 417.515794 \r\nL 469.346269 420.603417 \r\nL 469.735378 424.358313 \r\nL 469.80023 424.407818 \r\nL 469.832655 424.343383 \r\nL 469.962358 423.518973 \r\nL 470.708151 416.67195 \r\nL 470.740577 416.697864 \r\nL 470.87028 417.101652 \r\nL 471.09726 418.759524 \r\nL 471.518795 421.772488 \r\nL 471.583646 421.682369 \r\nL 471.745775 420.651061 \r\nL 472.134884 417.777392 \r\nL 472.199735 417.799275 \r\nL 472.361864 418.475444 \r\nL 472.653696 419.704552 \r\nL 472.750973 419.669537 \r\nL 473.172508 418.923634 \r\nL 473.237359 418.98507 \r\nL 473.46434 419.543861 \r\nL 473.756172 420.850125 \r\nL 474.177706 423.192503 \r\nL 474.210132 423.168014 \r\nL 474.307409 422.730638 \r\nL 474.53439 420.084548 \r\nL 474.826221 417.319906 \r\nL 474.891073 417.344761 \r\nL 475.020776 418.067564 \r\nL 475.312608 419.736414 \r\nL 475.409885 419.389375 \r\nL 475.636865 416.904376 \r\nL 475.896271 414.803815 \r\nL 475.961123 414.846365 \r\nL 476.090826 415.599583 \r\nL 476.642064 419.602291 \r\nL 477.063598 420.786485 \r\nL 477.452707 422.074331 \r\nL 477.58241 422.051248 \r\nL 477.744539 421.683992 \r\nL 478.42548 419.90886 \r\nL 478.847015 419.722862 \r\nL 479.463104 418.608844 \r\nL 479.527955 418.662634 \r\nL 479.884639 418.920567 \r\nL 480.046768 418.651017 \r\nL 480.403451 417.935145 \r\nL 480.435877 417.95847 \r\nL 480.56558 418.257019 \r\nL 481.149243 420.616309 \r\nL 481.214095 420.57005 \r\nL 481.376223 420.168884 \r\nL 481.668055 419.587224 \r\nL 481.765332 419.694359 \r\nL 481.959887 420.459232 \r\nL 482.348996 422.132792 \r\nL 482.446273 421.943491 \r\nL 482.608402 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421.454443 \r\nL 554.172038 421.147846 \r\nL 554.950256 417.331036 \r\nL 555.047533 417.425931 \r\nL 555.177236 417.952612 \r\nL 555.663623 421.016189 \r\nL 555.728474 420.918288 \r\nL 555.890603 420.073773 \r\nL 556.247286 418.180469 \r\nL 556.344563 418.184512 \r\nL 556.928227 419.251624 \r\nL 557.252484 420.658555 \r\nL 557.447039 421.01652 \r\nL 557.576742 420.761252 \r\nL 557.998277 419.853911 \r\nL 558.192831 419.423603 \r\nL 558.549515 418.211284 \r\nL 558.614366 418.299006 \r\nL 558.744069 419.011785 \r\nL 559.165604 422.121159 \r\nL 559.19803 422.09821 \r\nL 559.295307 421.736462 \r\nL 559.846545 418.858991 \r\nL 560.041099 418.216155 \r\nL 560.332931 416.960981 \r\nL 560.365357 416.975507 \r\nL 560.462634 417.395606 \r\nL 560.689614 420.175572 \r\nL 560.94902 422.771254 \r\nL 560.981446 422.79149 \r\nL 561.013872 422.72363 \r\nL 561.143575 421.73319 \r\nL 561.727238 415.705604 \r\nL 561.889367 415.5081 \r\nL 561.986644 415.698257 \r\nL 562.148773 416.77426 \r\nL 562.700011 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421.207341 \r\nL 570.514617 420.960904 \r\nL 570.936152 418.669998 \r\nL 571.033429 418.873259 \r\nL 571.519816 421.197058 \r\nL 571.584667 421.114282 \r\nL 571.908925 420.671242 \r\nL 572.135905 420.767754 \r\nL 572.265608 420.302884 \r\nL 572.525014 417.812766 \r\nL 572.751994 416.37081 \r\nL 572.816846 416.438746 \r\nL 572.946549 417.200951 \r\nL 573.432935 420.543824 \r\nL 573.595064 420.641599 \r\nL 573.757193 420.448777 \r\nL 573.951747 419.680246 \r\nL 574.502985 416.115476 \r\nL 574.567836 416.246518 \r\nL 574.729965 417.332907 \r\nL 575.346054 422.248163 \r\nL 575.540609 422.555835 \r\nL 575.637886 422.386116 \r\nL 575.800015 421.474128 \r\nL 576.44853 416.641285 \r\nL 576.480956 416.656184 \r\nL 576.610659 417.056525 \r\nL 576.870065 418.888934 \r\nL 577.2916 421.625755 \r\nL 577.388877 421.555784 \r\nL 577.551006 420.780048 \r\nL 577.940115 418.73845 \r\nL 578.037392 418.818066 \r\nL 578.296798 419.652744 \r\nL 579.139867 422.273388 \r\nL 579.172293 422.229766 \r\nL 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596.390368 419.062022 \r\nL 596.811903 417.911863 \r\nL 596.844328 417.930466 \r\nL 596.974031 418.227298 \r\nL 597.36314 420.051954 \r\nL 597.849527 421.979642 \r\nL 597.946804 421.919191 \r\nL 598.108933 421.210917 \r\nL 598.530467 418.948895 \r\nL 598.627745 419.140422 \r\nL 599.016854 420.542051 \r\nL 599.04928 420.51617 \r\nL 599.178983 420.146932 \r\nL 599.568092 418.8608 \r\nL 599.827498 418.586752 \r\nL 600.054478 417.525864 \r\nL 600.313884 416.678037 \r\nL 600.411161 416.920894 \r\nL 600.702993 418.961131 \r\nL 600.865122 419.519132 \r\nL 600.897547 419.505573 \r\nL 601.02725 419.11859 \r\nL 601.319082 418.037737 \r\nL 601.351508 418.05192 \r\nL 601.448785 418.290901 \r\nL 601.675765 419.83455 \r\nL 602.032449 421.907463 \r\nL 602.194578 422.130976 \r\nL 602.227003 422.10867 \r\nL 602.356706 421.782748 \r\nL 602.583687 420.291308 \r\nL 602.94037 418.095237 \r\nL 603.005221 418.124099 \r\nL 603.134924 418.677932 \r\nL 603.491608 420.271844 \r\nL 603.588885 420.057045 \r\nL 603.977994 418.565607 \r\nL 604.042845 418.660573 \r\nL 604.2374 419.574359 \r\nL 604.594083 421.00072 \r\nL 604.821063 421.096825 \r\nL 605.177747 421.140897 \r\nL 605.30745 420.748059 \r\nL 605.599282 418.78732 \r\nL 605.891113 417.292185 \r\nL 606.053242 417.20865 \r\nL 606.345074 417.591401 \r\nL 606.701757 418.221544 \r\nL 607.317846 419.411431 \r\nL 607.836658 419.500934 \r\nL 608.063639 419.030843 \r\nL 608.355471 418.402492 \r\nL 608.387896 418.42175 \r\nL 608.517599 418.779079 \r\nL 608.777005 420.580062 \r\nL 609.068837 422.089535 \r\nL 609.166114 422.014627 \r\nL 609.360669 421.192631 \r\nL 609.684926 419.994662 \r\nL 609.847055 419.969918 \r\nL 610.625273 420.325072 \r\nL 610.754976 419.860589 \r\nL 611.241363 416.737486 \r\nL 611.33864 416.989491 \r\nL 611.533194 418.762311 \r\nL 611.889878 421.935869 \r\nL 611.954729 421.978823 \r\nL 611.987155 421.926451 \r\nL 612.149284 421.028337 \r\nL 612.830224 415.801802 \r\nL 612.86265 415.824233 \r\nL 612.959927 416.193807 \r\nL 613.186908 418.365004 \r\nL 613.511165 420.897282 \r\nL 613.576017 420.860647 \r\nL 613.70572 420.343548 \r\nL 614.062403 418.832602 \r\nL 614.192106 418.916482 \r\nL 614.354235 419.022387 \r\nL 614.386661 418.99535 \r\nL 614.548789 418.612336 \r\nL 614.743344 418.223185 \r\nL 614.77577 418.258489 \r\nL 614.873047 418.650602 \r\nL 615.100027 420.961801 \r\nL 615.391859 423.140517 \r\nL 615.45671 423.020338 \r\nL 615.618839 421.816747 \r\nL 615.975522 419.195433 \r\nL 616.0728 419.199576 \r\nL 616.52676 419.816242 \r\nL 616.559186 419.786624 \r\nL 616.786166 419.651086 \r\nL 616.980721 419.961888 \r\nL 617.272552 420.38025 \r\nL 617.402256 420.191766 \r\nL 617.59681 419.426617 \r\nL 618.083196 417.416577 \r\nL 618.180474 417.419727 \r\nL 618.342602 417.807752 \r\nL 618.66686 418.65539 \r\nL 618.89384 418.629031 \r\nL 619.023543 418.716344 \r\nL 619.185672 419.158544 \r\nL 619.477504 420.062255 \r\nL 619.509929 420.048703 \r\nL 619.607207 419.784526 \r\nL 619.996316 417.943507 \r\nL 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      },
      "metadata": {}
@@ -266,6 +307,13 @@
     "model.plot_result(y_val, y_hat, a, b, n=600)"
    ]
   },
+  {
+   "source": [
+    "## Information Criteria plot"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -285,7 +333,7 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {}
@@ -299,13 +347,6 @@
     "\n",
     "# You can use the plot below to choose the \"n_terms\" and run the model again with the most adequate value of terms."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/general_estimators.ipynb b/examples/general_estimators.ipynb
index 6352030d..d626e075 100644
--- a/examples/general_estimators.ipynb
+++ b/examples/general_estimators.ipynb
@@ -38,6 +38,15 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -68,7 +77,9 @@
   },
   {
    "source": [
-    "To see the importance of the NARX architecture, lets take a look in the performance of the models without the NARX configuration."
+    "## Importance of the NARX architecture\n",
+    "\n",
+    "To get an idea of the importance of the NARX architecture, lets take a look in the performance of the models without the NARX configuration."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -157,7 +168,7 @@
   },
   {
    "source": [
-    "# Introducing the NARX configuration using SysIdentPy\n",
+    "## Introducing the NARX configuration using SysIdentPy\n",
     "\n",
     "As you can see, you just need to pass the base estimator you want to the NARX class from SysIdentPy do build the NARX model! You can choose the lags of the input and output variables to build the regressor matrix.\n",
     "\n",
@@ -166,6 +177,13 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "source": [
+    "### NARX with Catboost"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -211,6 +229,13 @@
     "catboost_narx.plot_result(y_valid, yhat, ee, ex, n=200)"
    ]
   },
+  {
+   "source": [
+    "### NARX with Gradient Boosting"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 27,
@@ -256,6 +281,13 @@
     "gb_narx.plot_result(y_valid, yhat, ee, ex, n=200)"
    ]
   },
+  {
+   "source": [
+    "### NARX with ARD"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
@@ -298,6 +330,13 @@
     "ARD_narx.plot_result(y_valid, yhat, ee, ex, n=200)"
    ]
   },
+  {
+   "source": [
+    "### NARX with Bayesian Rigde"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
diff --git a/examples/identification_of_an_electromechanical_system.ipynb b/examples/identification_of_an_electromechanical_system.ipynb
index 51619d6e..adcb493a 100644
--- a/examples/identification_of_an_electromechanical_system.ipynb
+++ b/examples/identification_of_an_electromechanical_system.ipynb
@@ -12,6 +12,15 @@
     "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -355,4 +364,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/information_criteria_examples.ipynb b/examples/information_criteria_examples.ipynb
index d61397a4..bebca16f 100644
--- a/examples/information_criteria_examples.ipynb
+++ b/examples/information_criteria_examples.ipynb
@@ -4,6 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Information Criteria - Examples\n",
+    "\n",
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
@@ -11,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using different information criteria methods"
+    "## Comparing different information criteria methods"
    ]
   },
   {
@@ -21,6 +23,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -39,8 +50,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generating 1 input 1 output sample data  \n",
-    "### The data is generated by simulating the following model:\n",
+    "## Generating sample data\n",
+    "  \n",
+    "The data is generated by simulating the following model:\n",
     "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
     "\n",
     "If *colored_noise* is set to True:\n",
@@ -49,7 +61,7 @@
     "\n",
     "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
     "\n",
-    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
+    "In the next example we will generate a data with 3000 samples with white noise and selecting 90% of the data to train the model. "
    ]
   },
   {
@@ -68,21 +80,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The idea is to show the impact of the information criteria to select the number of terms to compose the final model. You will se why it is an auxiliary tool and let the algorithm select the number of terms based on the minimum value is not a good idea when dealing with data highly corrupted by noise (even white noise) "
+    "The idea is to show the impact of the information criteria to select the number of terms to compose the final model. You will se why it is an auxiliary tool and let the algorithm select the number of terms based on the minimum value is not a good idea when dealing with data highly corrupted by noise (even white noise) "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Note: You may find different results when running the examples. This is due the fact we are not setting a fixed random generator for the sample data. However, the main analysis remain. "
+    "Note: You may find different results when running the examples. This is due the fact we are not setting a fixed random generator for the sample data. However, the main analysis remain. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### AIC"
+    "## AIC"
    ]
   },
   {
@@ -93,851 +105,28 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
-     ]
+     "name": "stdout",
+     "text": "0.38202463734760717\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9210  0.84287328\n1         y(k-1)     0.1867  0.03532986\n2  x1(k-1)y(k-1)     0.1215  0.00403228\n3       y(k-1)^2     0.0478  0.00073353\n4      x1(k-1)^2    -0.0342  0.00052802\n"
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Information Criteria')"
-      ]
+      "text/plain": "Text(0, 0.5, 'Information Criteria')"
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -973,17 +162,12 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "array([-2694.09936284, -2920.43235501, -2948.99729043, -2951.5656718 ,\n",
-       "       -2953.60017972, -2952.10447799, -2950.4899516 , -2948.64474158,\n",
-       "       -2946.7871786 , -2944.89120846, -2942.95165921, -2940.98557817,\n",
-       "       -2939.02706707, -2937.03646143, -2935.03708992])"
-      ]
+      "text/plain": "array([-2694.09936284, -2920.43235501, -2948.99729043, -2951.5656718 ,\n       -2953.60017972, -2952.10447799, -2950.4899516 , -2948.64474158,\n       -2946.7871786 , -2944.89120846, -2942.95165921, -2940.98557817,\n       -2939.02706707, -2937.03646143, -2935.03708992])"
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
    "source": [
@@ -994,16 +178,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### As can be seen above, the minimum value make the algorithm choose a model with 5 terms. However, if you check the plot, 3 terms is the best choice. Incresase the number of terms from 3 upwards do not lead to a better model since the difference is very small.\n",
+    "As can be seen above, the minimum value make the algorithm choose a model with 5 terms. However, if you check the plot, 3 terms is the best choice. Incresase the number of terms from 3 upwards do not lead to a better model since the difference is very small.\n",
     "\n",
-    "### In this case, you should run the model again with the parameters *n_terms=3*! The ERR algorithm ordered the terms in a correct way, so you will get the exact model structure again! "
+    "In this case, you should run the model again with the parameters *n_terms=3*! The ERR algorithm ordered the terms in a correct way, so you will get the exact model structure again! "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# BIC "
+    "## BIC "
    ]
   },
   {
@@ -1012,834 +196,28 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "0.38121187399853695\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9235  0.84287328\n",
-      "1         y(k-1)     0.1864  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1188  0.00403228\n"
-     ]
+     "name": "stdout",
+     "text": "0.38121187399853695\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9235  0.84287328\n1         y(k-1)     0.1864  0.03532986\n2  x1(k-1)y(k-1)     0.1188  0.00403228\n"
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Information Criteria')"
-      ]
+      "text/plain": "Text(0, 0.5, 'Information Criteria')"
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -1875,17 +253,12 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "array([-2689.29919277, -2910.83201488, -2934.59678022, -2932.36499152,\n",
-       "       -2929.59932938, -2923.30345758, -2916.88876112, -2910.24338103,\n",
-       "       -2903.58564799, -2896.88950777, -2890.14978846, -2883.38353735,\n",
-       "       -2876.62485618, -2869.83408047, -2863.03453889])"
-      ]
+      "text/plain": "array([-2689.29919277, -2910.83201488, -2934.59678022, -2932.36499152,\n       -2929.59932938, -2923.30345758, -2916.88876112, -2910.24338103,\n       -2903.58564799, -2896.88950777, -2890.14978846, -2883.38353735,\n       -2876.62485618, -2869.83408047, -2863.03453889])"
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
@@ -1903,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# LILC"
+    "## LILC"
    ]
   },
   {
@@ -1912,851 +285,28 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
-     ]
+     "name": "stdout",
+     "text": "0.38202463734760717\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9210  0.84287328\n1         y(k-1)     0.1867  0.03532986\n2  x1(k-1)y(k-1)     0.1215  0.00403228\n3       y(k-1)^2     0.0478  0.00073353\n4      x1(k-1)^2    -0.0342  0.00052802\n"
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Information Criteria')"
-      ]
+      "text/plain": "Text(0, 0.5, 'Information Criteria')"
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -2799,14 +349,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### LILC also includes spurious terms. Like AIC, it fails to automatically select the correct terms but you could select the right number based on the plot above!"
+    "LILC also includes spurious terms. Like AIC, it fails to automatically select the correct terms but you could select the right number based on the plot above!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# FPE"
+    "## FPE"
    ]
   },
   {
@@ -2815,851 +365,28 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "0.38202463734760717\n",
-      "      Regressors Parameters         ERR\n",
-      "0        x1(k-2)     0.9210  0.84287328\n",
-      "1         y(k-1)     0.1867  0.03532986\n",
-      "2  x1(k-1)y(k-1)     0.1215  0.00403228\n",
-      "3       y(k-1)^2     0.0478  0.00073353\n",
-      "4      x1(k-1)^2    -0.0342  0.00052802\n"
-     ]
+     "name": "stdout",
+     "text": "0.38202463734760717\n      Regressors Parameters         ERR\n0        x1(k-2)     0.9210  0.84287328\n1         y(k-1)     0.1867  0.03532986\n2  x1(k-1)y(k-1)     0.1215  0.00403228\n3       y(k-1)^2     0.0478  0.00073353\n4      x1(k-1)^2    -0.0342  0.00052802\n"
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Information Criteria')"
-      ]
+      "text/plain": "Text(0, 0.5, 'Information Criteria')"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -3695,17 +422,12 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "array([-2694.09936201, -2920.4323484 , -2948.99726811, -2951.56561889,\n",
-       "       -2953.60007638, -2952.10429942, -2950.48966802, -2948.64431828,\n",
-       "       -2946.78657589, -2944.89038168, -2942.95055875, -2940.98414946,\n",
-       "       -2939.02525054, -2937.03419259, -2935.03429929])"
-      ]
+      "text/plain": "array([-2694.09936201, -2920.4323484 , -2948.99726811, -2951.56561889,\n       -2953.60007638, -2952.10429942, -2950.48966802, -2948.64431828,\n       -2946.78657589, -2944.89038168, -2942.95055875, -2940.98414946,\n       -2939.02525054, -2937.03419259, -2935.03429929])"
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 9
     }
    ],
    "source": [
@@ -3716,16 +438,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### FPE also failed to automatically select the right number of terms! But, as we pointed out before, Information Criteria is an auxiliary tool! If you look at the plots, all the methods allows you to choose the right numbers of terms!"
+    "FPE also failed to automatically select the right number of terms! But, as we pointed out before, Information Criteria is an auxiliary tool! If you look at the plots, all the methods allows you to choose the right numbers of terms!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Importante Note: Here we are dealing with a known model structure! Concerning real data, we do not know the rigth number of terms so the methods above stands as excelents tools to help you out!\n",
+    "## Importante Note\n",
+    "\n",
+    "Here we are dealing with a known model structure! Concerning real data, we do not know the rigth number of terms so the methods above stands as excelents tools to help you out!\n",
     "\n",
-    "## If you check the metrics above, even with the models with more terms, you will see excelents metrics! But System Identification always search for the best model structure! Model Structure Selection is the core of NARMAX methods! In this respect, the examples are to show basic concepts and how the algorithms work!"
+    "If you check the metrics above, even with the models with more terms, you will see excelents metrics! But System Identification always search for the best model structure! Model Structure Selection is the core of NARMAX methods! In this respect, the examples are to show basic concepts and how the algorithms work!"
    ]
   },
   {
@@ -3752,9 +476,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/multiple_inputs_example.ipynb b/examples/multiple_inputs_example.ipynb
index 896f501c..3c2cba48 100644
--- a/examples/multiple_inputs_example.ipynb
+++ b/examples/multiple_inputs_example.ipynb
@@ -4,6 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Multiple Inputs usage\n",
+    "\n",
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
@@ -11,8 +13,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generating 2 input 1 output sample data  \n",
-    "### The data is generated by simulating the following model:\n",
+    "## Generating 2 input 1 output sample data\n",
+    "\n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
     "$y_k = 0.4y_{k-1}^2 + 0.1y_{k-1}x1_{k-1} + 0.6x2_{k-1} -0.3x1_{k-1}x2_{k-2} + e_{k}$\n",
     "\n",
     "If *colored_noise* is set to True:\n",
@@ -22,6 +27,15 @@
     "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.001$\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -52,14 +66,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### There is specific diferences for multiple input data.\n",
+    "There is specific diferences for multiple input data.\n",
     "\n",
     "- You have to set the number of inputs (*n_inputs=2* in this example)\n",
     "- You have to pass the lags for each input in a nested list (*e.g., [[1, 2], [1, 2]]*)\n",
     "\n",
-    "### The remainder settings remains the same."
+    "The remainder settings remains the same."
    ]
   },
+  {
+   "source": [
+    "## Build the model"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -87,7 +108,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2014449c730>"
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x2191c4db6d0>"
       ]
      },
      "metadata": {},
@@ -98,6 +119,13 @@
     "model.fit(x_train, y_train)"
    ]
   },
+  {
+   "source": [
+    "## Model evaluation"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -107,15 +135,15 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "0.0027888197126054283\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6001  0.91387720\n1  x2(k-2)x1(k-1)    -0.3007  0.04275254\n2        y(k-1)^2     0.3998  0.04309934\n3   x1(k-1)y(k-1)     0.1011  0.00026347\n"
+      "0.002440520762176125\n       Regressors Parameters         ERR\n0         x2(k-1)     0.6000  0.91070733\n1  x2(k-2)x1(k-1)    -0.2997  0.04434814\n2        y(k-1)^2     0.3999  0.04463687\n3   x1(k-1)y(k-1)     0.0996  0.00030042\n"
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      "data": {
       "text/plain": "<Figure size 720x576 with 3 Axes>",
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\n"
      },
      "metadata": {}
     }
@@ -166,6 +194,13 @@
     "plt.xlabel('n_terms')\n",
     "plt.ylabel('Information Criteria')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/n_steps_ahead_prediction.ipynb b/examples/n_steps_ahead_prediction.ipynb
index 6e1ee55e..a0bccdd9 100644
--- a/examples/n_steps_ahead_prediction.ipynb
+++ b/examples/n_steps_ahead_prediction.ipynb
@@ -30,6 +30,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/examples/narx_neural_network.ipynb b/examples/narx_neural_network.ipynb
index e008eaba..00010a12 100644
--- a/examples/narx_neural_network.ipynb
+++ b/examples/narx_neural_network.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "Series-Parallel allows us to use Pytorch directly for training, so we can use all the power of the Pytorch library to build our NARX Neural Network model! \n",
     "\n",
-    "![alt text](examples/figures/narxnet.png \"Title\")\n",
+    "![](figures/narxnet.png)\n",
     "\n",
     "The reader is referred to the following paper for a more in depth discussion about Series-Parallel and Parallel configurations regarding NARX neural network:\n",
     "\n",
@@ -52,13 +52,22 @@
   },
   {
    "source": [
-    "# Building a NARX Neural Network\n",
+    "## Building a NARX Neural Network\n",
     "\n",
     "First, just import the necessary packages"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -83,7 +92,7 @@
   },
   {
    "source": [
-    "# Getting the data\n",
+    "## Getting the data\n",
     "\n",
     "The data is generated by simulating the following model:\n",
     "\n",
@@ -112,7 +121,7 @@
   },
   {
    "source": [
-    "# Choosing the NARX parameters, loss function and optmizer\n",
+    "## Choosing the NARX parameters, loss function and optmizer\n",
     "\n",
     "One can create a NARXNN object and choose the maximum lag of both input and output for building the regressor matrix to serve as input of the network.\n",
     "\n",
@@ -173,7 +182,7 @@
   },
   {
    "source": [
-    "# Building the NARX Neural Network\n",
+    "## Building the NARX Neural Network\n",
     "\n",
     "The configuration of your network follows exactly the same pattern of a network defined in Pytorch. The following representing our NARX neural network."
    ],
@@ -192,13 +201,13 @@
     "        self.lin = nn.Linear(4, 10)\n",
     "        self.lin2 = nn.Linear(10, 10)\n",
     "        self.lin3 = nn.Linear(10, 1)\n",
-    "        self.sigmoid = nn.Tanh()\n",
+    "        self.tanh = nn.Tanh()\n",
     "\n",
     "    def forward(self, xb):\n",
     "        z = self.lin(xb)\n",
-    "        z = self.sigmoid(z)\n",
+    "        z = self.tanh(z)\n",
     "        z = self.lin2(z)\n",
-    "        z = self.sigmoid(z)\n",
+    "        z = self.tanh(z)\n",
     "        z = self.lin3(z)\n",
     "        return z"
    ]
@@ -221,7 +230,7 @@
   },
   {
    "source": [
-    "# Numpy array to Tensor\n",
+    "## Numpy array to Tensor\n",
     "\n",
     "If your inputs are numpy array, you can transform them to tensor using our *data_transform* method. Our function return a Dataloader object that we use to iterate on our training function. You can transform your inputs mannualy using TensorDataset and Dataloader using Pytorch.\n",
     "\n",
@@ -242,7 +251,7 @@
   },
   {
    "source": [
-    "# Fit and Predict\n",
+    "## Fit and Predict\n",
     "\n",
     "Because we have a fit (for training) and predict function for Polynomial NARMAX, we create the same pattern for the NARX net. So, you only have to fit and predict using the following:"
    ],
@@ -491,7 +500,7 @@
   },
   {
    "source": [
-    "# Note\n",
+    "## Note\n",
     "\n",
     "If you built the net configuration before calling the NARXNN, you can just pass the model to the NARXNN as follows:"
    ],
@@ -720,13 +729,13 @@
     "        self.lin = nn.Linear(4, 10)\n",
     "        self.lin2 = nn.Linear(10, 10)\n",
     "        self.lin3 = nn.Linear(10, 1)\n",
-    "        self.sigmoid = nn.Tanh()\n",
+    "        self.tanh = nn.Tanh()\n",
     "\n",
     "    def forward(self, xb):\n",
     "        z = self.lin(xb)\n",
-    "        z = self.sigmoid(z)\n",
+    "        z = self.tanh(z)\n",
     "        z = self.lin2(z)\n",
-    "        z = self.sigmoid(z)\n",
+    "        z = self.tanh(z)\n",
     "        z = self.lin3(z)\n",
     "        return z\n",
     "\n",
diff --git a/examples/parameter_estimation.ipynb b/examples/parameter_estimation.ipynb
index f55bed1a..c32fab6f 100644
--- a/examples/parameter_estimation.ipynb
+++ b/examples/parameter_estimation.ipynb
@@ -35,7 +35,9 @@
    "metadata": {},
    "source": [
     "## Generating 1 input 1 output sample data  \n",
-    "### The data is generated by simulating the following model:\n",
+    "\n",
+    "The data is generated by simulating the following model:\n",
+    "\n",
     "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
     "\n",
     "If *colored_noise* is set to True:\n",
@@ -92,6 +94,13 @@
     "- **Note: The adaptative filters have specifc parameter that need to be tunned. In the following examples we will use the default ones. More examples regarding tunned parameter will be available soon. For now, the user can read the method documentation for more information.** "
    ]
   },
+  {
+   "source": [
+    "## Total Least Squares"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -126,6 +135,13 @@
     "print(results)\n"
    ]
   },
+  {
+   "source": [
+    "## Recursive Least Squares"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -158,6 +174,13 @@
     "print(results)\n"
    ]
   },
+  {
+   "source": [
+    "## Least Mean Squares"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -170,7 +193,6 @@
     }
    ],
    "source": [
-    "# least_mean_squares\n",
     "model = PolynomialNarmax(non_degree=2,\n",
     "                         n_terms=3,\n",
     "                         extended_least_squares=False,\n",
@@ -190,6 +212,13 @@
     "print(results)\n"
    ]
   },
+  {
+   "source": [
+    "## Affine Least Mean Squares"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
diff --git a/examples/simulating_a_predefined_model.ipynb b/examples/simulating_a_predefined_model.ipynb
index 43edc03c..4800a33f 100644
--- a/examples/simulating_a_predefined_model.ipynb
+++ b/examples/simulating_a_predefined_model.ipynb
@@ -9,6 +9,15 @@
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -610,4 +619,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/system_identification_using_adaptative_filters.ipynb b/examples/system_identification_using_adaptative_filters.ipynb
index 1765a962..a458470e 100644
--- a/examples/system_identification_using_adaptative_filters.ipynb
+++ b/examples/system_identification_using_adaptative_filters.ipynb
@@ -30,6 +30,15 @@
     "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -419,4 +428,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file

From 5270cb051ebad6368fa59b60328e61065076871d Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Tue, 9 Mar 2021 19:37:29 -0300
Subject: [PATCH 09/27] fix import code in notebooks to use in colab

---
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+       <use x="166.845703" xlink:href="#ArialMT-48"/>

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+       <use x="55.615234" xlink:href="#ArialMT-50"/>

+       <use x="111.230469" xlink:href="#ArialMT-53"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

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+Q 17.390625 -1.21875 11.203125 4.25 

+Q 5.03125 9.71875 4.15625 18.75 

+z

+" id="ArialMT-53"/>

+       </defs>

+       <use xlink:href="#ArialMT-50"/>

+       <use x="55.615234" xlink:href="#ArialMT-53"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_3">

+     <g id="line2d_3"/>

+     <g id="text_3">

+      <!-- 500 -->

+      <g style="fill:#262626;" transform="translate(128.215008 544.641875)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-53"/>

+       <use x="55.615234" xlink:href="#ArialMT-48"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_4">

+     <g id="line2d_4"/>

+     <g id="text_4">

+      <!-- 750 -->

+      <g style="fill:#262626;" transform="translate(162.072846 544.641875)scale(0.1 -0.1)">

+       <defs>

+        <path d="M 4.734375 62.203125 

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+Q 21.234375 32.375 27.171875 43.484375 

+Q 33.109375 54.59375 39.796875 62.203125 

+z

+" id="ArialMT-55"/>

+       </defs>

+       <use xlink:href="#ArialMT-55"/>

+       <use x="55.615234" xlink:href="#ArialMT-53"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_5">

+     <g id="line2d_5"/>

+     <g id="text_5">

+      <!-- 1000 -->

+      <g style="fill:#262626;" transform="translate(193.150216 544.641875)scale(0.1 -0.1)">

+       <defs>

+        <path d="M 37.25 0 

+L 28.46875 0 

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+Q 18.265625 57.375 23.78125 62.296875 

+Q 29.296875 67.234375 31.59375 71.875 

+L 37.25 71.875 

+z

+" id="ArialMT-49"/>

+       </defs>

+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-48"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_6">

+     <g id="line2d_6"/>

+     <g id="text_6">

+      <!-- 1250 -->

+      <g style="fill:#262626;" transform="translate(227.008054 544.641875)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-50"/>

+       <use x="111.230469" xlink:href="#ArialMT-53"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_7">

+     <g id="line2d_7"/>

+     <g id="text_7">

+      <!-- 1500 -->

+      <g style="fill:#262626;" transform="translate(260.865892 544.641875)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-53"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_8">

+     <g id="line2d_8"/>

+     <g id="text_8">

+      <!-- 1750 -->

+      <g style="fill:#262626;" transform="translate(294.72373 544.641875)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-55"/>

+       <use x="111.230469" xlink:href="#ArialMT-53"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_9">

+     <g id="line2d_9"/>

+     <g id="text_9">

+      <!-- 2000 -->

+      <g style="fill:#262626;" transform="translate(328.581568 544.641875)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-50"/>

+       <use x="55.615234" xlink:href="#ArialMT-48"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="text_10">

+     <!-- Lag -->

+     <g style="fill:#262626;" transform="translate(194.194687 559.21875)scale(0.12 -0.12)">

+      <defs>

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+" id="ArialMT-76"/>

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+" id="ArialMT-97"/>

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+      </defs>

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+      <use x="55.615234" xlink:href="#ArialMT-97"/>

+      <use x="111.230469" xlink:href="#ArialMT-103"/>

+     </g>

+    </g>

+   </g>

+   <g id="matplotlib.axis_2">

+    <g id="ytick_1">

+     <g id="line2d_10"/>

+     <g id="text_11">

+      <!-- −1.00 -->

+      <g style="fill:#262626;" transform="translate(26.502813 537.562969)scale(0.1 -0.1)">

+       <defs>

+        <path d="M 52.828125 31.203125 

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+       </defs>

+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-49"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-48"/>

+       <use x="197.412109" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_2">

+     <g id="line2d_11"/>

+     <g id="text_12">

+      <!-- −0.75 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-55"/>

+       <use x="197.412109" xlink:href="#ArialMT-53"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_3">

+     <g id="line2d_12"/>

+     <g id="text_13">

+      <!-- −0.50 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-53"/>

+       <use x="197.412109" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_4">

+     <g id="line2d_13"/>

+     <g id="text_14">

+      <!-- −0.25 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-50"/>

+       <use x="197.412109" xlink:href="#ArialMT-53"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_5">

+     <g id="line2d_14"/>

+     <g id="text_15">

+      <!-- 0.00 -->

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+       <use xlink:href="#ArialMT-48"/>

+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-48"/>

+       <use x="139.013672" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_6">

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+     <g id="text_16">

+      <!-- 0.25 -->

+      <g style="fill:#262626;" transform="translate(32.343438 394.406719)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-48"/>

+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-50"/>

+       <use x="139.013672" xlink:href="#ArialMT-53"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_7">

+     <g id="line2d_16"/>

+     <g id="text_17">

+      <!-- 0.50 -->

+      <g style="fill:#262626;" transform="translate(32.343438 365.775469)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-48"/>

+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-53"/>

+       <use x="139.013672" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_8">

+     <g id="line2d_17"/>

+     <g id="text_18">

+      <!-- 0.75 -->

+      <g style="fill:#262626;" transform="translate(32.343438 337.144219)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-48"/>

+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-55"/>

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+      </g>

+     </g>

+    </g>

+    <g id="ytick_9">

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+     <g id="text_19">

+      <!-- 1.00 -->

+      <g style="fill:#262626;" transform="translate(32.343438 308.512969)scale(0.1 -0.1)">

+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-48"/>

+       <use x="139.013672" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="text_20">

+     <!-- Cross Correlation: ee, ex -->

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+       <use x="222.460938" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_6">

+     <g id="line2d_6"/>

+     <g id="text_6">

+      <!-- 12500 -->

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+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-50"/>

+       <use x="111.230469" xlink:href="#ArialMT-53"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+       <use x="222.460938" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="xtick_7">

+     <g id="line2d_7"/>

+     <g id="text_7">

+      <!-- 15000 -->

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+       <use xlink:href="#ArialMT-49"/>

+       <use x="55.615234" xlink:href="#ArialMT-53"/>

+       <use x="111.230469" xlink:href="#ArialMT-48"/>

+       <use x="166.845703" xlink:href="#ArialMT-48"/>

+       <use x="222.460938" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="text_8">

+     <!-- Lag -->

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+      </defs>

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+      <use x="55.615234" xlink:href="#ArialMT-97"/>

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+     </g>

+    </g>

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+   <g id="matplotlib.axis_2">

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+     <g id="line2d_8"/>

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+      <!-- −1.00 -->

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+       <defs>

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+       </defs>

+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-49"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-48"/>

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+      </g>

+     </g>

+    </g>

+    <g id="ytick_2">

+     <g id="line2d_9"/>

+     <g id="text_10">

+      <!-- −0.75 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

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+       <use x="141.796875" xlink:href="#ArialMT-55"/>

+       <use x="197.412109" xlink:href="#ArialMT-53"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_3">

+     <g id="line2d_10"/>

+     <g id="text_11">

+      <!-- −0.50 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

+       <use x="141.796875" xlink:href="#ArialMT-53"/>

+       <use x="197.412109" xlink:href="#ArialMT-48"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_4">

+     <g id="line2d_11"/>

+     <g id="text_12">

+      <!-- −0.25 -->

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+       <use xlink:href="#ArialMT-8722"/>

+       <use x="58.398438" xlink:href="#ArialMT-48"/>

+       <use x="114.013672" xlink:href="#ArialMT-46"/>

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+      <!-- 0.00 -->

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+       <use x="83.398438" xlink:href="#ArialMT-48"/>

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+      </g>

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+    </g>

+    <g id="ytick_6">

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+      <!-- 0.25 -->

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+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-50"/>

+       <use x="139.013672" xlink:href="#ArialMT-53"/>

+      </g>

+     </g>

+    </g>

+    <g id="ytick_7">

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+     <g id="text_15">

+      <!-- 0.50 -->

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+       <use x="55.615234" xlink:href="#ArialMT-46"/>

+       <use x="83.398438" xlink:href="#ArialMT-53"/>

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+      </g>

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+    </g>

+    <g id="ytick_8">

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+      <!-- 0.75 -->

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+      </g>

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HcmV?d00001

diff --git a/docs/_sources/examples/basic_steps.ipynb.txt b/docs/_sources/examples/basic_steps.ipynb.txt
index 49fd63d3..980a8cb6 100644
--- a/docs/_sources/examples/basic_steps.ipynb.txt
+++ b/docs/_sources/examples/basic_steps.ipynb.txt
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/defining_lags.ipynb.txt b/docs/_sources/examples/defining_lags.ipynb.txt
index 744d9f8f..b302d081 100644
--- a/docs/_sources/examples/defining_lags.ipynb.txt
+++ b/docs/_sources/examples/defining_lags.ipynb.txt
@@ -16,6 +16,15 @@
     "Different ways to set the maximum lag for input and output"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/docs/_sources/examples/extended_least_squares.ipynb.txt b/docs/_sources/examples/extended_least_squares.ipynb.txt
index 49676413..6057ea75 100644
--- a/docs/_sources/examples/extended_least_squares.ipynb.txt
+++ b/docs/_sources/examples/extended_least_squares.ipynb.txt
@@ -16,6 +16,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/f_16_benchmark.ipynb.txt b/docs/_sources/examples/f_16_benchmark.ipynb.txt
index ad05c646..4e4f0240 100644
--- a/docs/_sources/examples/f_16_benchmark.ipynb.txt
+++ b/docs/_sources/examples/f_16_benchmark.ipynb.txt
@@ -34,6 +34,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/general_estimators.ipynb.txt b/docs/_sources/examples/general_estimators.ipynb.txt
index 1d70e4f0..d626e075 100644
--- a/docs/_sources/examples/general_estimators.ipynb.txt
+++ b/docs/_sources/examples/general_estimators.ipynb.txt
@@ -38,6 +38,15 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt b/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt
index 51619d6e..adcb493a 100644
--- a/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt
+++ b/docs/_sources/examples/identification_of_an_electromechanical_system.ipynb.txt
@@ -12,6 +12,15 @@
     "More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -355,4 +364,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/docs/_sources/examples/information_criteria_examples.ipynb.txt b/docs/_sources/examples/information_criteria_examples.ipynb.txt
index ee5e62c1..bebca16f 100644
--- a/docs/_sources/examples/information_criteria_examples.ipynb.txt
+++ b/docs/_sources/examples/information_criteria_examples.ipynb.txt
@@ -23,6 +23,15 @@
     "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/multiple_inputs_example.ipynb.txt b/docs/_sources/examples/multiple_inputs_example.ipynb.txt
index edad040d..3c2cba48 100644
--- a/docs/_sources/examples/multiple_inputs_example.ipynb.txt
+++ b/docs/_sources/examples/multiple_inputs_example.ipynb.txt
@@ -27,6 +27,15 @@
     "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.001$\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt b/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt
index 6e1ee55e..a0bccdd9 100644
--- a/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt
+++ b/docs/_sources/examples/n_steps_ahead_prediction.ipynb.txt
@@ -30,6 +30,15 @@
     "> J.P. Noël and M. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. 19-23, Brussels, Belgium, April 24-26, 2017."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/narx_neural_network.ipynb.txt b/docs/_sources/examples/narx_neural_network.ipynb.txt
index 820754b3..00010a12 100644
--- a/docs/_sources/examples/narx_neural_network.ipynb.txt
+++ b/docs/_sources/examples/narx_neural_network.ipynb.txt
@@ -59,6 +59,15 @@
    "cell_type": "markdown",
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
index 43edc03c..4800a33f 100644
--- a/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
+++ b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
@@ -9,6 +9,15 @@
     "Example created by Wilson Rocha Lacerda Junior"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -610,4 +619,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt b/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt
index 1765a962..a458470e 100644
--- a/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt
+++ b/docs/_sources/examples/system_identification_using_adaptative_filters.ipynb.txt
@@ -30,6 +30,15 @@
     "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install sysidentpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -419,4 +428,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/docs/changelog/v0.1.3.html b/docs/changelog/v0.1.3.html
index 6636a022..2d731848 100644
--- a/docs/changelog/v0.1.3.html
+++ b/docs/changelog/v0.1.3.html
@@ -305,7 +305,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.3.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/changelog/v0.1.3.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/changelog/v0.1.4.html b/docs/changelog/v0.1.4.html
index 7de6a081..5eb18f19 100644
--- a/docs/changelog/v0.1.4.html
+++ b/docs/changelog/v0.1.4.html
@@ -305,7 +305,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.4.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/changelog/v0.1.4.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/changelog/v0.1.5.html b/docs/changelog/v0.1.5.html
index f3bb3a94..d7a38265 100644
--- a/docs/changelog/v0.1.5.html
+++ b/docs/changelog/v0.1.5.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fchangelog/v0.1.5.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/changelog/v0.1.5.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/code.html b/docs/code.html
index 1fcfb8fe..8e04cab9 100644
--- a/docs/code.html
+++ b/docs/code.html
@@ -306,7 +306,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fcode.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/code.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/dev_guide.html b/docs/dev_guide.html
index d268e86c..d457ed39 100644
--- a/docs/dev_guide.html
+++ b/docs/dev_guide.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fdev_guide.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/dev_guide.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/examples/basic_steps.html b/docs/examples/basic_steps.html
index 76f26122..16d9eaea 100644
--- a/docs/examples/basic_steps.html
+++ b/docs/examples/basic_steps.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/basic_steps.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/basic_steps.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -400,6 +402,13 @@ <h1>Presenting main functionality<a class="headerlink" href="#presenting-main-fu
 <p>Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -526,7 +535,7 @@ <h2>Evaluate the model<a class="headerlink" href="#evaluate-the-model" title="Pe
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/basic_steps_16_0.svg" src="../_images/basic_steps_16_0.svg" /></div>
+<img alt="../_images/basic_steps_17_0.svg" src="../_images/basic_steps_17_0.svg" /></div>
 </div>
 </div>
 <div class="section" id="setting-the-n-terms-parameter">
@@ -547,7 +556,7 @@ <h2>Setting the <em>n_terms</em> parameter<a class="headerlink" href="#setting-t
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/basic_steps_18_1.svg" src="../_images/basic_steps_18_1.svg" /></div>
+<img alt="../_images/basic_steps_19_1.svg" src="../_images/basic_steps_19_1.svg" /></div>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -596,7 +605,7 @@ <h2>Setting the <em>n_terms</em> parameter<a class="headerlink" href="#setting-t
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/basic_steps_22_1.svg" src="../_images/basic_steps_22_1.svg" /></div>
+<img alt="../_images/basic_steps_23_1.svg" src="../_images/basic_steps_23_1.svg" /></div>
 </div>
 <p>Now running without executing information criteria methods (setting the <em>n_terms</em>) because we already know the optimal number of regressors</p>
 <div class="cell docutils container">
diff --git a/docs/examples/defining_lags.html b/docs/examples/defining_lags.html
index 89a1c9d8..16271a0a 100644
--- a/docs/examples/defining_lags.html
+++ b/docs/examples/defining_lags.html
@@ -58,7 +58,7 @@
 <meta property="og:url"         content="http://sysidentpy.org/examples/defining_lags.html" />
 <meta property="og:type"        content="article" />
 <meta property="og:title"       content="Setting specific lags" />
-<meta property="og:description" content="Setting specific lags  Example created by Wilson Rocha Lacerda Junior  Different ways to set the maximum lag for input and output  import numpy as np import pan" />
+<meta property="og:description" content="Setting specific lags  Example created by Wilson Rocha Lacerda Junior  Different ways to set the maximum lag for input and output  pip install sysidentpy  impor" />
 <meta property="og:image"       content="http://sysidentpy.org/_static/sysidentpy-logo.png" />
 
 <meta name="twitter:card" content="summary" />
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/defining_lags.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/defining_lags.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -376,6 +378,13 @@ <h1>Setting specific lags<a class="headerlink" href="#setting-specific-lags" tit
 <p>Different ways to set the maximum lag for input and output</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
diff --git a/docs/examples/extended_least_squares.html b/docs/examples/extended_least_squares.html
index ae6f2e8f..46a8f2a8 100644
--- a/docs/examples/extended_least_squares.html
+++ b/docs/examples/extended_least_squares.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/extended_least_squares.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/extended_least_squares.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -391,6 +393,13 @@ <h1>Important notes and examples of how to use Extended Least Squares<a class="h
 <p>Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -509,7 +518,7 @@ <h2>Biased parameter estimation<a class="headerlink" href="#biased-parameter-est
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/extended_least_squares_11_0.svg" src="../_images/extended_least_squares_11_0.svg" /></div>
+<img alt="../_images/extended_least_squares_12_0.svg" src="../_images/extended_least_squares_12_0.svg" /></div>
 </div>
 </div>
 <div class="section" id="using-the-extended-least-squares-algorithm">
@@ -549,7 +558,7 @@ <h2>Using the Extended Least Squares algorithm<a class="headerlink" href="#using
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/extended_least_squares_13_0.svg" src="../_images/extended_least_squares_13_0.svg" /></div>
+<img alt="../_images/extended_least_squares_14_0.svg" src="../_images/extended_least_squares_14_0.svg" /></div>
 </div>
 <p>Great! Now we have an unbiased estimation of the parameters!</p>
 </div>
diff --git a/docs/examples/f_16_benchmark.html b/docs/examples/f_16_benchmark.html
index a5773fd6..43c552ed 100644
--- a/docs/examples/f_16_benchmark.html
+++ b/docs/examples/f_16_benchmark.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/f_16_benchmark.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/f_16_benchmark.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -416,6 +418,13 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </div></blockquote>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -459,7 +468,7 @@ <h2>Visualizating the data<a class="headerlink" href="#visualizating-the-data" t
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_6_1.svg" src="../_images/f_16_benchmark_6_1.svg" /></div>
+<img alt="../_images/f_16_benchmark_7_1.svg" src="../_images/f_16_benchmark_7_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -471,7 +480,7 @@ <h2>Visualizating the data<a class="headerlink" href="#visualizating-the-data" t
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_7_1.svg" src="../_images/f_16_benchmark_7_1.svg" /></div>
+<img alt="../_images/f_16_benchmark_8_1.svg" src="../_images/f_16_benchmark_8_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="spliting-the-data">
@@ -748,7 +757,7 @@ <h2>Model training and evalutation<a class="headerlink" href="#model-training-an
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/f_16_benchmark_14_0.svg" src="../_images/f_16_benchmark_14_0.svg" /></div>
+<img alt="../_images/f_16_benchmark_15_0.svg" src="../_images/f_16_benchmark_15_0.svg" /></div>
 </div>
 </div>
 <div class="section" id="information-criteria-plot">
@@ -768,7 +777,7 @@ <h2>Information Criteria plot<a class="headerlink" href="#information-criteria-p
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/f_16_benchmark_16_1.svg" src="../_images/f_16_benchmark_16_1.svg" /></div>
+<img alt="../_images/f_16_benchmark_17_1.svg" src="../_images/f_16_benchmark_17_1.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/general_estimators.html b/docs/examples/general_estimators.html
index 05d8f6ca..56b04318 100644
--- a/docs/examples/general_estimators.html
+++ b/docs/examples/general_estimators.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/general_estimators.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/general_estimators.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -396,6 +398,13 @@ <h1>Building NARX models using general estimators<a class="headerlink" href="#bu
 <p>In this example we will create NARX models using different estimator like GrandientBoostingRegressior, Bayesian Regression, Automatic Relevance Determination (ARD) Regression and Catboost</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
 <span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_siso_data</span>
@@ -464,7 +473,7 @@ <h2>Importance of the NARX architecture<a class="headerlink" href="#importance-o
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/general_estimators_8_0.svg" src="../_images/general_estimators_8_0.svg" /></div>
+<img alt="../_images/general_estimators_9_0.svg" src="../_images/general_estimators_9_0.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -475,7 +484,7 @@ <h2>Importance of the NARX architecture<a class="headerlink" href="#importance-o
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/general_estimators_9_0.svg" src="../_images/general_estimators_9_0.svg" /></div>
+<img alt="../_images/general_estimators_10_0.svg" src="../_images/general_estimators_10_0.svg" /></div>
 </div>
 </div>
 <div class="section" id="introducing-the-narx-configuration-using-sysidentpy">
@@ -513,7 +522,7 @@ <h3>NARX with Catboost<a class="headerlink" href="#narx-with-catboost" title="Pe
 0.00015277808907582634
 </pre></div>
 </div>
-<img alt="../_images/general_estimators_12_1.svg" src="../_images/general_estimators_12_1.svg" /></div>
+<img alt="../_images/general_estimators_13_1.svg" src="../_images/general_estimators_13_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="narx-with-gradient-boosting">
@@ -547,7 +556,7 @@ <h3>NARX with Gradient Boosting<a class="headerlink" href="#narx-with-gradient-b
 0.0009623151220631377
 </pre></div>
 </div>
-<img alt="../_images/general_estimators_14_1.svg" src="../_images/general_estimators_14_1.svg" /></div>
+<img alt="../_images/general_estimators_15_1.svg" src="../_images/general_estimators_15_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="narx-with-ard">
@@ -578,7 +587,7 @@ <h3>NARX with ARD<a class="headerlink" href="#narx-with-ard" title="Permalink to
 0.0010634487808187914
 </pre></div>
 </div>
-<img alt="../_images/general_estimators_16_1.svg" src="../_images/general_estimators_16_1.svg" /></div>
+<img alt="../_images/general_estimators_17_1.svg" src="../_images/general_estimators_17_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="narx-with-bayesian-rigde">
@@ -609,7 +618,7 @@ <h3>NARX with Bayesian Rigde<a class="headerlink" href="#narx-with-bayesian-rigd
 0.0010635104552663241
 </pre></div>
 </div>
-<img alt="../_images/general_estimators_18_1.svg" src="../_images/general_estimators_18_1.svg" /></div>
+<img alt="../_images/general_estimators_19_1.svg" src="../_images/general_estimators_19_1.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/identification_of_an_electromechanical_system.html b/docs/examples/identification_of_an_electromechanical_system.html
index ef073786..dca47ed5 100644
--- a/docs/examples/identification_of_an_electromechanical_system.html
+++ b/docs/examples/identification_of_an_electromechanical_system.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/identification_of_an_electromechanical_system.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/identification_of_an_electromechanical_system.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -374,6 +376,13 @@ <h1>Identification of an electromechanical system<a class="headerlink" href="#id
 <p>More details about this data can be found in the following paper (in Portuguese): https://www.researchgate.net/publication/320418710_Identificacao_de_um_motorgerador_CC_por_meio_de_modelos_polinomiais_autorregressivos_e_redes_neurais_artificiais</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -404,7 +413,7 @@ <h1>Identification of an electromechanical system<a class="headerlink" href="#id
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/identification_of_an_electromechanical_system_3_1.png" src="../_images/identification_of_an_electromechanical_system_3_1.png" />
+<img alt="../_images/identification_of_an_electromechanical_system_4_1.png" src="../_images/identification_of_an_electromechanical_system_4_1.png" />
 </div>
 </div>
 <div class="cell docutils container">
@@ -468,7 +477,7 @@ <h2>Building a Polynomial NARX model<a class="headerlink" href="#building-a-poly
 9   x1(k-2)y(k-2)     0.0341  0.00003253
 </pre></div>
 </div>
-<img alt="../_images/identification_of_an_electromechanical_system_7_2.png" src="../_images/identification_of_an_electromechanical_system_7_2.png" />
+<img alt="../_images/identification_of_an_electromechanical_system_8_2.png" src="../_images/identification_of_an_electromechanical_system_8_2.png" />
 </div>
 </div>
 </div>
diff --git a/docs/examples/information_criteria_examples.html b/docs/examples/information_criteria_examples.html
index fef80b77..99e0025b 100644
--- a/docs/examples/information_criteria_examples.html
+++ b/docs/examples/information_criteria_examples.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/information_criteria_examples.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/information_criteria_examples.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -403,6 +405,13 @@ <h2>Comparing different information criteria methods<a class="headerlink" href="
 <p>Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -479,7 +488,7 @@ <h2>AIC<a class="headerlink" href="#aic" title="Permalink to this headline">¶</
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/information_criteria_examples_9_2.svg" src="../_images/information_criteria_examples_9_2.svg" /></div>
+<img alt="../_images/information_criteria_examples_10_2.svg" src="../_images/information_criteria_examples_10_2.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -541,7 +550,7 @@ <h2>BIC<a class="headerlink" href="#bic" title="Permalink to this headline">¶</
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/information_criteria_examples_13_2.svg" src="../_images/information_criteria_examples_13_2.svg" /></div>
+<img alt="../_images/information_criteria_examples_14_2.svg" src="../_images/information_criteria_examples_14_2.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -604,7 +613,7 @@ <h2>LILC<a class="headerlink" href="#lilc" title="Permalink to this headline">¶
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/information_criteria_examples_17_2.svg" src="../_images/information_criteria_examples_17_2.svg" /></div>
+<img alt="../_images/information_criteria_examples_18_2.svg" src="../_images/information_criteria_examples_18_2.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -659,7 +668,7 @@ <h2>FPE<a class="headerlink" href="#fpe" title="Permalink to this headline">¶</
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/information_criteria_examples_21_2.svg" src="../_images/information_criteria_examples_21_2.svg" /></div>
+<img alt="../_images/information_criteria_examples_22_2.svg" src="../_images/information_criteria_examples_22_2.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
diff --git a/docs/examples/multiple_inputs_example.html b/docs/examples/multiple_inputs_example.html
index 32b6b51c..1e37de1d 100644
--- a/docs/examples/multiple_inputs_example.html
+++ b/docs/examples/multiple_inputs_example.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/multiple_inputs_example.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/multiple_inputs_example.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -387,6 +389,13 @@ <h2>Generating 2 input 1 output sample data<a class="headerlink" href="#generati
 <p>where <span class="math notranslate nohighlight">\(x\)</span> is a uniformly distributed random variable and <span class="math notranslate nohighlight">\(\nu\)</span> is a gaussian distributed variable with <span class="math notranslate nohighlight">\(\mu=0\)</span> and <span class="math notranslate nohighlight">\(\sigma=0.001\)</span></p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -473,7 +482,7 @@ <h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permal
 3   x1(k-1)y(k-1)     0.0996  0.00030042
 </pre></div>
 </div>
-<img alt="../_images/multiple_inputs_example_9_1.svg" src="../_images/multiple_inputs_example_9_1.svg" /></div>
+<img alt="../_images/multiple_inputs_example_10_1.svg" src="../_images/multiple_inputs_example_10_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -488,7 +497,7 @@ <h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permal
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;Information Criteria&#39;)
 </pre></div>
 </div>
-<img alt="../_images/multiple_inputs_example_10_1.svg" src="../_images/multiple_inputs_example_10_1.svg" /></div>
+<img alt="../_images/multiple_inputs_example_11_1.svg" src="../_images/multiple_inputs_example_11_1.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/n_steps_ahead_prediction.html b/docs/examples/n_steps_ahead_prediction.html
index c3cda978..a3302b2c 100644
--- a/docs/examples/n_steps_ahead_prediction.html
+++ b/docs/examples/n_steps_ahead_prediction.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/n_steps_ahead_prediction.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/n_steps_ahead_prediction.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -398,6 +400,13 @@ <h1>Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark<a c
 </div></blockquote>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -440,7 +449,7 @@ <h2>Preparing the data<a class="headerlink" href="#preparing-the-data" title="Pe
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/n_steps_ahead_prediction_6_1.svg" src="../_images/n_steps_ahead_prediction_6_1.svg" /></div>
+<img alt="../_images/n_steps_ahead_prediction_7_1.svg" src="../_images/n_steps_ahead_prediction_7_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -452,7 +461,7 @@ <h2>Preparing the data<a class="headerlink" href="#preparing-the-data" title="Pe
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot:&gt;
 </pre></div>
 </div>
-<img alt="../_images/n_steps_ahead_prediction_7_1.svg" src="../_images/n_steps_ahead_prediction_7_1.svg" /></div>
+<img alt="../_images/n_steps_ahead_prediction_8_1.svg" src="../_images/n_steps_ahead_prediction_8_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -739,7 +748,7 @@ <h2>Defining the forecasting horizon<a class="headerlink" href="#defining-the-fo
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/n_steps_ahead_prediction_14_0.svg" src="../_images/n_steps_ahead_prediction_14_0.svg" /></div>
+<img alt="../_images/n_steps_ahead_prediction_15_0.svg" src="../_images/n_steps_ahead_prediction_15_0.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -757,7 +766,7 @@ <h2>Defining the forecasting horizon<a class="headerlink" href="#defining-the-fo
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.22158488532181117
 </pre></div>
 </div>
-<img alt="../_images/n_steps_ahead_prediction_15_1.svg" src="../_images/n_steps_ahead_prediction_15_1.svg" /></div>
+<img alt="../_images/n_steps_ahead_prediction_16_1.svg" src="../_images/n_steps_ahead_prediction_16_1.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -775,7 +784,7 @@ <h2>Defining the forecasting horizon<a class="headerlink" href="#defining-the-fo
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.3090559299602927
 </pre></div>
 </div>
-<img alt="../_images/n_steps_ahead_prediction_16_1.svg" src="../_images/n_steps_ahead_prediction_16_1.svg" /></div>
+<img alt="../_images/n_steps_ahead_prediction_17_1.svg" src="../_images/n_steps_ahead_prediction_17_1.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/narx_neural_network.html b/docs/examples/narx_neural_network.html
index 6cf481a6..26afcb5b 100644
--- a/docs/examples/narx_neural_network.html
+++ b/docs/examples/narx_neural_network.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/narx_neural_network.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/narx_neural_network.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -418,6 +420,13 @@ <h2>Building a NARX Neural Network<a class="headerlink" href="#building-a-narx-n
 <p>First, just import the necessary packages</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -747,7 +756,7 @@ <h2>Fit and Predict<a class="headerlink" href="#fit-and-predict" title="Permalin
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.00045161987584446645
 </pre></div>
 </div>
-<img alt="../_images/narx_neural_network_19_1.svg" src="../_images/narx_neural_network_19_1.svg" /></div>
+<img alt="../_images/narx_neural_network_20_1.svg" src="../_images/narx_neural_network_20_1.svg" /></div>
 </div>
 </div>
 <div class="section" id="note">
@@ -982,7 +991,7 @@ <h2>Note<a class="headerlink" href="#note" title="Permalink to this headline">¶
 01-07 15:37:23 - INFO - Train metrics: 0.000229597191714663 | Validation metrics: 0.00024509568013147346
 </pre></div>
 </div>
-<img alt="../_images/narx_neural_network_21_1.svg" src="../_images/narx_neural_network_21_1.svg" /></div>
+<img alt="../_images/narx_neural_network_22_1.svg" src="../_images/narx_neural_network_22_1.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/parameter_estimation.html b/docs/examples/parameter_estimation.html
index 2e0138a0..702056d3 100644
--- a/docs/examples/parameter_estimation.html
+++ b/docs/examples/parameter_estimation.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/parameter_estimation.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/parameter_estimation.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/examples/simulating_a_predefined_model.html b/docs/examples/simulating_a_predefined_model.html
index 086e15ca..1faa446d 100644
--- a/docs/examples/simulating_a_predefined_model.html
+++ b/docs/examples/simulating_a_predefined_model.html
@@ -58,7 +58,7 @@
 <meta property="og:url"         content="http://sysidentpy.org/examples/simulating_a_predefined_model.html" />
 <meta property="og:type"        content="article" />
 <meta property="og:title"       content="Simulate a Predefined Model" />
-<meta property="og:description" content="Simulate a Predefined Model  Example created by Wilson Rocha Lacerda Junior  import numpy as np import pandas as pd import matplotlib.pyplot as plt from sysiden" />
+<meta property="og:description" content="Simulate a Predefined Model  Example created by Wilson Rocha Lacerda Junior  pip install sysidentpy  import numpy as np import pandas as pd import matplotlib.py" />
 <meta property="og:image"       content="http://sysidentpy.org/_static/sysidentpy-logo.png" />
 
 <meta name="twitter:card" content="summary" />
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/simulating_a_predefined_model.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/simulating_a_predefined_model.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -397,6 +399,13 @@ <h1>Simulate a Predefined Model<a class="headerlink" href="#simulate-a-predefine
 <p>Example created by Wilson Rocha Lacerda Junior</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -771,7 +780,7 @@ <h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/simulating_a_predefined_model_20_0.png" src="../_images/simulating_a_predefined_model_20_0.png" />
+<img alt="../_images/simulating_a_predefined_model_21_0.png" src="../_images/simulating_a_predefined_model_21_0.png" />
 </div>
 </div>
 </div>
@@ -821,7 +830,7 @@ <h2>Final code<a class="headerlink" href="#final-code" title="Permalink to this
 2  x1(k-1)y(k-1)     0.1001  0.00000000
 </pre></div>
 </div>
-<img alt="../_images/simulating_a_predefined_model_22_1.png" src="../_images/simulating_a_predefined_model_22_1.png" />
+<img alt="../_images/simulating_a_predefined_model_23_1.png" src="../_images/simulating_a_predefined_model_23_1.png" />
 </div>
 </div>
 </div>
diff --git a/docs/examples/system_identification_using_adaptative_filters.html b/docs/examples/system_identification_using_adaptative_filters.html
index 5a7d9d1e..7e306992 100644
--- a/docs/examples/system_identification_using_adaptative_filters.html
+++ b/docs/examples/system_identification_using_adaptative_filters.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fexamples/system_identification_using_adaptative_filters.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/examples/system_identification_using_adaptative_filters.ipynb"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
@@ -393,6 +395,13 @@ <h2>Generating 1 input 1 output sample data<a class="headerlink" href="#generati
 <p>In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sysidentpy</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -616,7 +625,7 @@ <h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title
 9       x1(k-2)^2     0.0000  0.00000000
 </pre></div>
 </div>
-<img alt="../_images/system_identification_using_adaptative_filters_12_1.png" src="../_images/system_identification_using_adaptative_filters_12_1.png" />
+<img alt="../_images/system_identification_using_adaptative_filters_13_1.png" src="../_images/system_identification_using_adaptative_filters_13_1.png" />
 </div>
 </div>
 </div>
diff --git a/docs/index.html b/docs/index.html
index edd18faf..8912a006 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -306,7 +306,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Findex.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/index.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/installation.html b/docs/installation.html
index f01d5906..effe594c 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Finstallation.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/installation.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/introduction_to_narmax.html b/docs/introduction_to_narmax.html
index c7058561..3088d06c 100644
--- a/docs/introduction_to_narmax.html
+++ b/docs/introduction_to_narmax.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fintroduction_to_narmax.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/introduction_to_narmax.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/notebooks.html b/docs/notebooks.html
index f6c40140..91bc51f9 100644
--- a/docs/notebooks.html
+++ b/docs/notebooks.html
@@ -307,7 +307,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fnotebooks.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/notebooks.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 
diff --git a/docs/searchindex.js b/docs/searchindex.js
index f8c65e19..d158fe1d 100644
--- a/docs/searchindex.js
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in SysIdentPy","Changes in SysIdentPy","Changes in SysIdentPy","Codes","Contributing","Presenting main functionality","Setting specific lags","Important notes and examples of how to use Extended Least Squares","Example: F-16 Ground Vibration Test benchmark","Building NARX models using general estimators","Identification of an electromechanical system","Information Criteria - Examples","Multiple Inputs usage","Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark","Building NARX Neural Network using Sysidentpy","Parameter Estimation","Simulate a Predefined Model","System Identification Using Adaptative Filters","Welcome to SysIdentPy\u2019s documentation!","Install Guide","A brief introduction to NARMAX models.","Jupyter notebooks","User Guide"],titleterms:{"final":[16,17],"function":[5,14,22],"import":[7,9,22],The:16,There:15,Using:[7,17],adapt:17,affin:15,ahead:13,aic:11,algorithm:7,architectur:9,ard:9,arrai:14,autoregress:10,base:3,bayesian:9,benchmark:[8,13],bias:7,bic:11,boost:9,brief:20,bug:4,build:[7,9,10,12,13,14,17],catboost:[9,18],chang:[0,1,2],changelog:18,choos:14,citat:18,code:[3,16,17,18],commun:18,compar:11,configur:[9,18],content:18,contribut:4,contributor:[0,1,2],criteria:[8,11],data:[3,5,7,8,11,12,13,14,15,16,17,22],defin:[13,16],develop:[4,18],differ:[10,11],document:[4,18],electromechan:10,environ:4,estim:[3,7,9,15,16],evalu:[5,12],evalut:8,exampl:[7,8,11,13,18],extend:7,extra:5,filter:17,fit:14,follow:16,forecast:13,fpe:11,free:5,gener:[3,5,7,9,11,12,15,16,17,22],general_estim:3,get:14,gradient:9,ground:[8,13],guid:[19,22],histori:18,horizon:13,how:[7,18],identif:[10,17],important:11,indic:3,inform:[5,8,11],input:[5,7,8,12,15,16,17,22],inspir:18,instal:19,introduc:9,introduct:20,jupyt:21,lag:[6,8],least:[7,15],lilc:11,list:6,loss:14,main:[5,22],mean:15,method:[11,15,16],metric:3,model:[5,7,8,9,10,12,13,16,17,20],multipl:12,n_term:5,narmax:[3,20],narx:[9,10,14,18],narx_neural_network:3,network:[14,18],neural:[14,18],note:[7,11,14,22],notebook:21,numpi:14,optmiz:14,output:[5,7,12,15,16,17,22],parallel:14,parallen:14,paramet:[5,7,14,15,16],plot:8,polynomi:[10,18,20],predefin:16,predict:[13,14],prepar:13,present:[5,22],project:18,rang:6,recurs:15,refer:8,report:4,represent:20,requir:19,residu:3,rigd:9,run:[5,18],sampl:[5,7,11,12,15,16,17,22],select:5,seri:14,set:[5,6,8],sever:15,simul:[5,16,17],sourc:18,specif:6,splite:8,squar:[7,15],step:13,structur:5,sugest:4,sysidentpi:[0,1,2,3,9,14,18],system:[10,17],tabl:3,tensor:14,test:[8,10,13,18],total:15,train:[8,14],usag:12,use:7,used:15,user:22,using:[6,9,14],util:3,valu:6,vibrat:[8,13],visualiz:8,welcom:18,what:20,without:18}})
\ No newline at end of file
diff --git a/docs/source/examples/basic_steps.ipynb b/docs/source/examples/basic_steps.ipynb
index 421ec957..980a8cb6 100644
--- a/docs/source/examples/basic_steps.ipynb
+++ b/docs/source/examples/basic_steps.ipynb
@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install sysidentp"
+    "pip install sysidentpy"
    ]
   },
   {
diff --git a/docs/user_guide.html b/docs/user_guide.html
index a3b3ba4b..af94355f 100644
--- a/docs/user_guide.html
+++ b/docs/user_guide.html
@@ -309,7 +309,9 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
             href="https://github.com/wilsonrljr/sysidentpy//issues/new?title=Issue%20on%20page%20%2Fuser_guide.html&body=Your%20issue%20content%20here."><button
                 type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
                 title="Open an issue"><i class="fas fa-lightbulb"></i>open issue</button></a>
-        
+        <a class="edit-button" href="https://github.com/wilsonrljr/sysidentpy/edit/master/docs/user_guide.rst"><button
+                type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
+                title="Edit this page"><i class="fas fa-pencil-alt"></i>suggest edit</button></a>
     </div>
 </div>
 

From 92a710c15e5c23d4904f09b68b5f0c620f293bcf Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:42:58 -0300
Subject: [PATCH 10/27] fix code format in basic_steps example

---
 examples/basic_steps.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/basic_steps.ipynb b/examples/basic_steps.ipynb
index 421ec957..980a8cb6 100644
--- a/examples/basic_steps.ipynb
+++ b/examples/basic_steps.ipynb
@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pip install sysidentp"
+    "pip install sysidentpy"
    ]
   },
   {

From 373e3875c587129fca6545c339a681e9d6cd7474 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:44:58 -0300
Subject: [PATCH 11/27] fix code format in base.py

---
 sysidentpy/base.py | 28 ++++++++++------------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/sysidentpy/base.py b/sysidentpy/base.py
index e6f14eb0..a463b7d1 100644
--- a/sysidentpy/base.py
+++ b/sysidentpy/base.py
@@ -88,20 +88,17 @@ def regressor_space(self, non_degree, xlag, ylag, n_inputs):
             )
 
         if not isinstance(ylag, (int, list)) or np.min(np.minimum(ylag, 1)) < 1:
-            raise ValueError(
-                "ylag must be integer or list and > zero. Got %f" % ylag)
+            raise ValueError("ylag must be integer or list and > zero. Got %f" % ylag)
 
         if (
             not isinstance(xlag, (int, list))
             # or np.min(np.minimum(xlag, 1)) < 1):
             or np.min(np.min(list(chain.from_iterable([[xlag]])))) < 1
         ):
-            raise ValueError(
-                "xlag must be integer or list and > zero. Got %f" % xlag)
+            raise ValueError("xlag must be integer or list and > zero. Got %f" % xlag)
 
         if not isinstance(n_inputs, int) or n_inputs < 1:
-            raise ValueError(
-                "n_inputs must be integer and > zero. Got %f" % n_inputs)
+            raise ValueError("n_inputs must be integer and > zero. Got %f" % n_inputs)
 
         if isinstance(ylag, list):
             # create only the lags passed from list
@@ -127,8 +124,7 @@ def regressor_space(self, non_degree, xlag, ylag, n_inputs):
             for i in range(n_inputs):
                 if isinstance(xlag[i], list) and n_inputs > 1:
                     # create 200n, 300n,..., 400n to describe each input
-                    x_vec_tmp.extend(
-                        [lag + 2000 + i * 1000 for lag in xlag[i]])
+                    x_vec_tmp.extend([lag + 2000 + i * 1000 for lag in xlag[i]])
                 elif isinstance(xlag[i], int) and n_inputs > 1:
                     x_vec_tmp.extend(
                         [np.arange(2001 + i * 1000, 2001 + i * 1000 + xlag[i])]
@@ -137,19 +133,17 @@ def regressor_space(self, non_degree, xlag, ylag, n_inputs):
         reg_aux = np.array([0])
         if n_inputs > 1:
             # if x_vec is a nested list, ensure all elements are arrays
-            all_arrays = [np.array([i]) if isinstance(
-                i, int) else i for i in x_vec_tmp]
+            all_arrays = [np.array([i]) if isinstance(i, int) else i for i in x_vec_tmp]
             x_vec = np.concatenate([i for i in all_arrays])
         else:
             x_vec = x_vec_tmp
 
         reg_aux = np.concatenate([reg_aux, y_vec, x_vec])
 
-        regressor_code = list(
-            combinations_with_replacement(reg_aux, non_degree))
+        regressor_code = list(combinations_with_replacement(reg_aux, non_degree))
 
         regressor_code = np.array(regressor_code)
-        regressor_code = regressor_code[:, regressor_code.shape[1]:: -1]
+        regressor_code = regressor_code[:, regressor_code.shape[1] :: -1]
         max_lag = _get_max_lag(ylag, xlag)
         return regressor_code, max_lag
 
@@ -246,7 +240,7 @@ def shift_column(self, col_to_shift, lag):
         """
         n_samples = col_to_shift.shape[0]
         tmp_column = np.zeros((n_samples, 1))
-        aux = col_to_shift[0: n_samples - lag]
+        aux = col_to_shift[0 : n_samples - lag]
         aux = np.reshape(aux, (len(aux), 1))
         tmp_column[lag:, 0] = aux[:, 0]
         return tmp_column
@@ -353,8 +347,7 @@ def _create_lagged_y(self, y, ylag):
             without combinations.
 
         """
-        y_lagged = np.column_stack(
-            [self.shift_column(y[:, 0], lag) for lag in ylag])
+        y_lagged = np.column_stack([self.shift_column(y[:, 0], lag) for lag in ylag])
         return y_lagged
 
     def initial_lagged_matrix(self, X, y, xlag, ylag):
@@ -437,8 +430,7 @@ def build_information_matrix(self, X, y, xlag, ylag, non_degree):
 
         # Create combinations of all columns based on its index
         iterable_list = range(data.shape[1])
-        combinations = list(combinations_with_replacement(
-            iterable_list, non_degree))
+        combinations = list(combinations_with_replacement(iterable_list, non_degree))
 
         psi = np.column_stack(
             [

From 2bded960701d14e5aabfa245c7876f74682e619d Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:46:32 -0300
Subject: [PATCH 12/27] fix code format in general_estimator/narx

---
 sysidentpy/general_estimators/narx.py | 48 +++++++++++++++------------
 1 file changed, 27 insertions(+), 21 deletions(-)

diff --git a/sysidentpy/general_estimators/narx.py b/sysidentpy/general_estimators/narx.py
index a6bc1b48..4ac188af 100644
--- a/sysidentpy/general_estimators/narx.py
+++ b/sysidentpy/general_estimators/narx.py
@@ -13,14 +13,17 @@
 from ..utils._check_arrays import check_X_y
 
 import sys
-logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
-                    datefmt='%m-%d %H:%M:%S',
-                    level=logging.INFO,
-                    stream=sys.stdout)
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    datefmt="%m-%d %H:%M:%S",
+    level=logging.INFO,
+    stream=sys.stdout,
+)
 
 
 class NARX(GenerateRegressors, InformationMatrix, ResiduesAnalysis):
-    """ NARX model build on top of general estimators
+    """NARX model build on top of general estimators
 
     Currently is possible to use any estimator that have a fit/predict
     as an Autoregressive Model. We use our GenerateRegressors and
@@ -75,7 +78,7 @@ def __init__(
         xlag=2,
         n_inputs=1,
         base_estimator=None,
-        fit_params={}
+        fit_params={},
     ):
         self.non_degree = non_degree
         self.ylag = ylag
@@ -115,13 +118,15 @@ def data_preparation(self, X, y):
             The information matrix of the model.
         """
         logging.info("Creating the regressor matrix")
-        reg_matrix = InformationMatrix().build_information_matrix(X, y, self.xlag,
-                                                                  self.ylag, self.non_degree)
-        logging.info("The regressor matrix have " +
-                     str(reg_matrix.shape[1]) + " features")
+        reg_matrix = InformationMatrix().build_information_matrix(
+            X, y, self.xlag, self.ylag, self.non_degree
+        )
+        logging.info(
+            "The regressor matrix have " + str(reg_matrix.shape[1]) + " features"
+        )
         reg_matrix = reg_matrix
 
-        y = y[self.max_lag:]
+        y = y[self.max_lag :]
         return reg_matrix, y
 
     def fit(self, X, y):
@@ -149,10 +154,10 @@ def fit(self, X, y):
 
         check_X_y(X, y)
 
-        logging.info('Training the model')
+        logging.info("Training the model")
         X, y = self.data_preparation(X, y)
         self.base_estimator.fit(X, y, **self.fit_params)
-        logging.info('Done! Model is built!')
+        logging.info("Done! Model is built!")
         return self
 
     def predict(self, X, y_initial):
@@ -184,17 +189,18 @@ def predict(self, X, y_initial):
         yhat = np.zeros((len(X), 1))
 
         # Discard unnecessary initial values
-        yhat[0:self.max_lag] = y_initial[0:self.max_lag]
+        yhat[0 : self.max_lag] = y_initial[0 : self.max_lag]
         analised_elements_number = self.max_lag + 1
 
         for i in range(0, len(X) - self.max_lag):
-            reg_matrix = InformationMatrix().\
-                build_information_matrix(X[i:i+analised_elements_number],
-                                         yhat[i:i+analised_elements_number],
-                                         self.xlag,
-                                         self.ylag,
-                                         self.non_degree)
+            reg_matrix = InformationMatrix().build_information_matrix(
+                X[i : i + analised_elements_number],
+                yhat[i : i + analised_elements_number],
+                self.xlag,
+                self.ylag,
+                self.non_degree,
+            )
 
             a = self.base_estimator.predict(reg_matrix)
-            yhat[i+self.max_lag] = a[0]
+            yhat[i + self.max_lag] = a[0]
         return yhat

From a7a5499b5b9a21c708258ae1b9d24d3280b9a564 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:47:17 -0300
Subject: [PATCH 13/27] fix code format in metrics/_regression

---
 sysidentpy/metrics/_regression.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/sysidentpy/metrics/_regression.py b/sysidentpy/metrics/_regression.py
index 4065637f..3284618c 100644
--- a/sysidentpy/metrics/_regression.py
+++ b/sysidentpy/metrics/_regression.py
@@ -347,8 +347,7 @@ def explained_variance_score(y, y_predicted):
     nonzero_denominator = denominator != 0
     valid_score = nonzero_numerator & nonzero_denominator
     output_scores = np.ones(y.shape[0])
-    output_scores[valid_score] = 1 - \
-        (numerator[valid_score] / denominator[valid_score])
+    output_scores[valid_score] = 1 - (numerator[valid_score] / denominator[valid_score])
     output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0
     return np.average(output_scores)
 
@@ -388,14 +387,12 @@ def r2_score(y, y_predicted):
 
     """
     numerator = ((y - y_predicted) ** 2).sum(axis=0, dtype=np.float64)
-    denominator = ((y - np.average(y, axis=0)) **
-                   2).sum(axis=0, dtype=np.float64)
+    denominator = ((y - np.average(y, axis=0)) ** 2).sum(axis=0, dtype=np.float64)
     nonzero_denominator = denominator != 0
     nonzero_numerator = numerator != 0
     valid_score = nonzero_denominator & nonzero_numerator
     output_scores = np.ones([y.shape[0]])
-    output_scores[valid_score] = 1 - \
-        (numerator[valid_score] / denominator[valid_score])
+    output_scores[valid_score] = 1 - (numerator[valid_score] / denominator[valid_score])
     # arbitrary set to zero to avoid -inf scores, having a constant
     # y_true is not interesting for scoring a regression anyway
     output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0

From c6c5ce25cf7754f8c6d80d4e2f9e9bf6d78bbeee Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:47:55 -0300
Subject: [PATCH 14/27] fix code format in neural_network/narx_nn

---
 sysidentpy/neural_network/narx_nn.py | 71 +++++++++++++++-------------
 1 file changed, 37 insertions(+), 34 deletions(-)

diff --git a/sysidentpy/neural_network/narx_nn.py b/sysidentpy/neural_network/narx_nn.py
index ce1da599..7350e207 100644
--- a/sysidentpy/neural_network/narx_nn.py
+++ b/sysidentpy/neural_network/narx_nn.py
@@ -17,14 +17,17 @@
 import torch.nn.functional as F
 import torch
 import sys
-logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
-                    datefmt='%m-%d %H:%M:%S',
-                    level=logging.INFO,
-                    stream=sys.stdout)
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    datefmt="%m-%d %H:%M:%S",
+    level=logging.INFO,
+    stream=sys.stdout,
+)
 
 
 class NARXNN(GenerateRegressors, InformationMatrix, ResiduesAnalysis):
-    """ NARX Neural Network model build on top of Pytorch
+    """NARX Neural Network model build on top of Pytorch
 
     Currently we support a Series-Parallel (open-loop) Feedforward Network training
     process, which make the training process easier, and we convert the
@@ -119,8 +122,8 @@ def __init__(
         batch_size=100,  # batch size
         learning_rate=0.01,  # learning rate
         epochs=200,  # how many epochs to train for
-        loss_func='mse_loss',
-        optimizer='Adam',
+        loss_func="mse_loss",
+        optimizer="Adam",
         net=None,
         train_percentage=80,
         verbose=False,
@@ -160,9 +163,7 @@ def _validate_params(self):
             )
 
         if not isinstance(self.epochs, int) or self.epochs < 1:
-            raise ValueError(
-                "epochs must be integer and > zero. Got %f" % self.epochs
-            )
+            raise ValueError("epochs must be integer and > zero. Got %f" % self.epochs)
 
         if not isinstance(self.train_percentage, int) or self.train_percentage < 0:
             raise ValueError(
@@ -170,9 +171,7 @@ def _validate_params(self):
             )
 
         if not isinstance(self.verbose, bool):
-            raise TypeError(
-                "verbose must be False or True. Got %f" % self.verbose
-            )
+            raise TypeError("verbose must be False or True. Got %f" % self.verbose)
 
     def define_opt(self):
         opt = getattr(optim, self.optimizer)
@@ -220,12 +219,13 @@ def split_data(self, X, y):
             The information matrix of the model.
         """
         # [:, 1] removes the column of the constant
-        reg_matrix = InformationMatrix().build_information_matrix(X, y, self.xlag,
-                                                                  self.ylag, self.non_degree)[:, 1:]
+        reg_matrix = InformationMatrix().build_information_matrix(
+            X, y, self.xlag, self.ylag, self.non_degree
+        )[:, 1:]
 
         reg_matrix = np.atleast_1d(reg_matrix).astype(np.float32)
 
-        y = np.atleast_1d(y[self.max_lag:]).astype(np.float32)
+        y = np.atleast_1d(y[self.max_lag :]).astype(np.float32)
         return reg_matrix, y
 
     def convert_to_tensor(self, reg_matrix, y):
@@ -327,18 +327,20 @@ def fit(self, train_dl, valid_dl):
                     *[self.loss_batch(X, y) for X, y in train_dl]
                 )
                 self.train_loss.append(
-                    np.sum(np.multiply(train_losses, train_nums)) / np.sum(train_nums))
+                    np.sum(np.multiply(train_losses, train_nums)) / np.sum(train_nums)
+                )
 
                 self.net.eval()
                 with torch.no_grad():
-                    losses, nums = zip(
-                        *[self.loss_batch(X, y) for X, y in valid_dl]
-                    )
-                self.val_loss.append(
-                    np.sum(np.multiply(losses, nums)) / np.sum(nums))
-
-                logging.info("Train metrics: " + str(
-                    self.train_loss[epoch]) + " | Validation metrics: " + str(self.val_loss[epoch]))
+                    losses, nums = zip(*[self.loss_batch(X, y) for X, y in valid_dl])
+                self.val_loss.append(np.sum(np.multiply(losses, nums)) / np.sum(nums))
+
+                logging.info(
+                    "Train metrics: "
+                    + str(self.train_loss[epoch])
+                    + " | Validation metrics: "
+                    + str(self.val_loss[epoch])
+                )
         return self
 
     def one_step_ahead_prediction(self, valid_dl):
@@ -372,20 +374,21 @@ def predict(self, X, y_initial):
         yhat = np.zeros((len(X), 1))
 
         # Discard unnecessary initial values
-        yhat[0:self.max_lag] = y_initial[0:self.max_lag]
+        yhat[0 : self.max_lag] = y_initial[0 : self.max_lag]
         analised_elements_number = self.max_lag + 1
 
-        for i in range(0, len(X)-self.max_lag):
-            reg_matrix = InformationMatrix().\
-                build_information_matrix(X[i:i+analised_elements_number],
-                                         yhat[i:i+analised_elements_number],
-                                         self.xlag,
-                                         self.ylag,
-                                         self.non_degree)[:, 1:]
+        for i in range(0, len(X) - self.max_lag):
+            reg_matrix = InformationMatrix().build_information_matrix(
+                X[i : i + analised_elements_number],
+                yhat[i : i + analised_elements_number],
+                self.xlag,
+                self.ylag,
+                self.non_degree,
+            )[:, 1:]
 
             reg_matrix = np.atleast_1d(reg_matrix).astype(np.float32)
             yhat = yhat.astype(np.float32)
             x_valid, y_valid = map(torch.tensor, (reg_matrix, yhat))
             a = self.net(x_valid)
-            yhat[i+self.max_lag] = a[:, 0].detach().numpy()
+            yhat[i + self.max_lag] = a[:, 0].detach().numpy()
         return yhat

From 4e22302c8d9760d6d1c5e138abf1f894482fca18 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:48:40 -0300
Subject: [PATCH 15/27] fix code format in parameter_estimation/estimators

---
 sysidentpy/parameter_estimation/estimators.py | 27 +++++++------------
 1 file changed, 10 insertions(+), 17 deletions(-)

diff --git a/sysidentpy/parameter_estimation/estimators.py b/sysidentpy/parameter_estimation/estimators.py
index f4c1f04c..42060a7e 100644
--- a/sysidentpy/parameter_estimation/estimators.py
+++ b/sysidentpy/parameter_estimation/estimators.py
@@ -114,7 +114,7 @@ def least_squares(self, psi, y):
         """
         self._check_linear_dependence_rows(psi)
 
-        y = y[self._aux_lag:, 0].reshape(-1, 1)
+        y = y[self._aux_lag :, 0].reshape(-1, 1)
         theta = (np.linalg.pinv(psi.T @ psi)) @ psi.T @ y
         return theta
 
@@ -146,7 +146,7 @@ def total_least_squares(self, psi, y):
             https://en.wikipedia.org/wiki/Total_least_squares
 
         """
-        y = y[self._aux_lag:, 0].reshape(-1, 1)
+        y = y[self._aux_lag :, 0].reshape(-1, 1)
         full = np.hstack((psi, y))
         n = psi.shape[1]
         u, s, v = np.linalg.svd(full, full_matrices=True)
@@ -154,7 +154,7 @@ def total_least_squares(self, psi, y):
         return theta.reshape(-1, 1)
 
     def _initial_values(self, y, psi):
-        y = y[self._aux_lag:, 0].reshape(-1, 1)
+        y = y[self._aux_lag :, 0].reshape(-1, 1)
         n_theta = psi.shape[1]
         n = len(psi)
         theta = np.zeros([n_theta, n])
@@ -197,15 +197,12 @@ def recursive_least_squares(self, psi, y):
         for i in range(2, n):
             psi_tmp = psi[i, :].reshape(-1, 1)
             k_numerator = self._lam ** (-1) * p.dot(psi_tmp)
-            k_denominator = 1 + \
-                self._lam ** (-1) * psi_tmp.T.dot(p).dot(psi_tmp)
+            k_denominator = 1 + self._lam ** (-1) * psi_tmp.T.dot(p).dot(psi_tmp)
             k = np.divide(k_numerator, k_denominator)
             self.xi[i, 0] = y[i, 0] - psi_tmp.T @ theta[:, i - 1]
-            theta[:, i] = list(
-                theta[:, i - 1].reshape(-1, 1) + k.dot(self.xi[i, 0]))
+            theta[:, i] = list(theta[:, i - 1].reshape(-1, 1) + k.dot(self.xi[i, 0]))
 
-            p1 = p.dot(psi[i, :].reshape(-1, 1)
-                       ).dot(psi[i, :].reshape(-1, 1).T).dot(p)
+            p1 = p.dot(psi[i, :].reshape(-1, 1)).dot(psi[i, :].reshape(-1, 1).T).dot(p)
             p2 = (
                 psi[i, :].reshape(-1, 1).T.dot(p).dot(psi[i, :].reshape(-1, 1))
                 + self._lam
@@ -255,8 +252,7 @@ def affine_least_mean_squares(self, psi, y):
                     psi.T @ psi + self._offset_covariance * np.eye(n_theta)
                 )
             )
-            theta[:, i] = list(
-                theta[:, i - 1].reshape(-1, 1) + aux.T.dot(self.xi))
+            theta[:, i] = list(theta[:, i - 1].reshape(-1, 1) + aux.T.dot(self.xi))
 
         return theta[:, -1].reshape(-1, 1)
 
@@ -298,8 +294,7 @@ def least_mean_squares(self, psi, y):
             psi_tmp = psi[i, :].reshape(-1, 1)
             self.xi[i, 0] = y[i, 0] - psi_tmp.T @ theta[:, i - 1]
             theta[:, i] = list(
-                theta[:, i - 1].reshape(-1, 1) + 2 *
-                self._mu * self.xi[i, 0] * psi_tmp
+                theta[:, i - 1].reshape(-1, 1) + 2 * self._mu * self.xi[i, 0] * psi_tmp
             )
 
         return theta[:, -1].reshape(-1, 1)
@@ -691,8 +686,7 @@ def least_mean_squares_normalized_leaky(self, psi, y):
             self.xi[i, 0] = y[i, 0] - psi_tmp.T @ theta[:, i - 1]
             theta[:, i] = list(
                 theta[:, i - 1].reshape(-1, 1) * (1 - self._mu * self._gama)
-                + self._mu * self.xi[i, 0] * psi_tmp /
-                (self._eps + psi_tmp.T @ psi_tmp)
+                + self._mu * self.xi[i, 0] * psi_tmp / (self._eps + psi_tmp.T @ psi_tmp)
             )
 
         return theta[:, -1].reshape(-1, 1)
@@ -795,8 +789,7 @@ def least_mean_squares_fourth(self, psi, y):
             psi_tmp = psi[i, :].reshape(-1, 1)
             self.xi[i, 0] = y[i, 0] - psi_tmp.T @ theta[:, i - 1]
             theta[:, i] = list(
-                theta[:, i - 1].reshape(-1, 1) +
-                self._mu * psi_tmp * self.xi[i, 0] ** 3
+                theta[:, i - 1].reshape(-1, 1) + self._mu * psi_tmp * self.xi[i, 0] ** 3
             )
 
         return theta[:, -1].reshape(-1, 1)

From 2dc3aacea806a8e2a85d813cd3c43379802739f1 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:49:19 -0300
Subject: [PATCH 16/27] fix code format in polynomial_basis/narmax

---
 sysidentpy/polynomial_basis/narmax.py | 56 +++++++++++----------------
 1 file changed, 22 insertions(+), 34 deletions(-)

diff --git a/sysidentpy/polynomial_basis/narmax.py b/sysidentpy/polynomial_basis/narmax.py
index 1d6421e8..5546c282 100644
--- a/sysidentpy/polynomial_basis/narmax.py
+++ b/sysidentpy/polynomial_basis/narmax.py
@@ -22,7 +22,7 @@
 class PolynomialNarmax(
     GenerateRegressors, HouseHolder, InformationMatrix, ResiduesAnalysis, Estimators
 ):
-    """ Polynomial NARXMAX model
+    """Polynomial NARXMAX model
 
     Parameters
     ----------
@@ -244,9 +244,9 @@ def error_reduction_ratio(self, psi, y, process_term_number):
             e Novos Resultados
 
         """
-        squared_y = y[self.max_lag:].T @ y[self.max_lag:]
+        squared_y = y[self.max_lag :].T @ y[self.max_lag :]
         tmp_psi = np.array(psi)
-        y = np.array([y[self.max_lag:, 0]]).T
+        y = np.array([y[self.max_lag :, 0]]).T
         tmp_y = np.copy(y)
         [n, dimension] = tmp_psi.shape
         piv = np.arange(dimension)
@@ -257,8 +257,7 @@ def error_reduction_ratio(self, psi, y, process_term_number):
             for j in np.arange(i, dimension):
                 num = np.array(tmp_psi[i:n, j].T @ tmp_y[i:n])
                 num = np.power(num, 2)
-                den = np.array(
-                    (tmp_psi[i:n, j].T @ tmp_psi[i:n, j]) * squared_y)
+                den = np.array((tmp_psi[i:n, j].T @ tmp_psi[i:n, j]) * squared_y)
                 tmp_err[j] = num / den
 
             if i == process_term_number:
@@ -333,8 +332,7 @@ def fit(self, X, y):
             self.info_values = self.information_criterion(X, y)
 
         if self.n_terms is None and self._order_selection is True:
-            model_length = np.where(
-                self.info_values == np.amin(self.info_values))
+            model_length = np.where(self.info_values == np.amin(self.info_values))
             model_length = int(model_length[0] + 1)
             self.n_terms = model_length
         elif self.n_terms is None and self._order_selection is not True:
@@ -413,16 +411,13 @@ def _unbiased_estimator(
                 ee, y, elag, 1, 2
             )
 
-            psi_extended = psi_extended[:, [
-                2, 3, 7, 8, 11, 12, 13, 14, 15, 16, 17]]
+            psi_extended = psi_extended[:, [2, 3, 7, 8, 11, 12, 13, 14, 15, 16, 17]]
 
             psi_e = np.concatenate([psi, psi_extended], axis=1)
-            unbiased_theta = getattr(
-                parameter_estimation, self.estimator)(psi_e, y)
-            e = y[aux_lag:, 0].reshape(-1, 1) - \
-                psi_e @ unbiased_theta.reshape(-1, 1)
+            unbiased_theta = getattr(parameter_estimation, self.estimator)(psi_e, y)
+            e = y[aux_lag:, 0].reshape(-1, 1) - psi_e @ unbiased_theta.reshape(-1, 1)
 
-        return unbiased_theta[0: len(self.final_model), 0].reshape(-1, 1)
+        return unbiased_theta[0 : len(self.final_model), 0].reshape(-1, 1)
 
     def predict(self, X, y, steps_ahead=None):
         """Return the predicted values given an input.
@@ -452,7 +447,7 @@ def predict(self, X, y, steps_ahead=None):
         elif steps_ahead == 1:
             return self._one_step_ahead_prediction(X, y)
         else:
-            self._check_positive_int(steps_ahead, 'steps_ahead')
+            self._check_positive_int(steps_ahead, "steps_ahead")
             return self._n_step_ahead_prediction(X, y, steps_ahead=steps_ahead)
 
     def _code2exponents(self, code):
@@ -491,9 +486,7 @@ def _code2exponents(self, code):
 
     def _check_positive_int(self, value, name):
         if not isinstance(value, int) or value < 1:
-            raise ValueError(
-                f"{name} must be integer and > zero. Got {value}"
-            )
+            raise ValueError(f"{name} must be integer and > zero. Got {value}")
 
     def _one_step_ahead_prediction(self, X, y):
         """Perform the 1-step-ahead prediction of a model.
@@ -515,10 +508,10 @@ def _one_step_ahead_prediction(self, X, y):
         X_base = InformationMatrix().build_information_matrix(
             X, y, self.xlag, self.ylag, self.non_degree
         )
-        piv_final_model = self.pivv[:len(self.final_model)]
+        piv_final_model = self.pivv[: len(self.final_model)]
         X_base = X_base[:, piv_final_model]
         yhat = np.dot(X_base, self.theta.flatten())
-        yhat = np.concatenate([y[:self.max_lag].flatten(), yhat])
+        yhat = np.concatenate([y[: self.max_lag].flatten(), yhat])
         return yhat.reshape(-1, 1)
 
     def _n_step_ahead_prediction(self, X, y, steps_ahead):
@@ -551,9 +544,9 @@ def _n_step_ahead_prediction(self, X, y, steps_ahead):
             if i + steps_ahead > len(y):
                 steps_ahead = len(y) - i  # predicts the remaining values
 
-            yhat[i:i+steps_ahead] = self._model_prediction(
-                X[k:i+steps_ahead],
-                y[k:i+steps_ahead])[-steps_ahead:].ravel()
+            yhat[i : i + steps_ahead] = self._model_prediction(
+                X[k : i + steps_ahead], y[k : i + steps_ahead]
+            )[-steps_ahead:].ravel()
 
             i += steps_ahead
 
@@ -585,8 +578,7 @@ def _model_prediction(self, X, y_initial):
         y_output.fill(np.nan)
         y_output[: self.max_lag] = y_initial[: self.max_lag, 0]
 
-        model_exponents = [self._code2exponents(
-            model) for model in self.final_model]
+        model_exponents = [self._code2exponents(model) for model in self.final_model]
         raw_regressor = np.zeros(len(model_exponents[0]), dtype=float)
 
         for i in range(self.max_lag, X.shape[0]):
@@ -673,19 +665,17 @@ def information_criterion(self, X, y):
 
         for i in range(0, self.n_info_values):
             n_theta = i + 1
-            regressor_matrix = self.error_reduction_ratio(X_base, y, n_theta)[
-                3]
+            regressor_matrix = self.error_reduction_ratio(X_base, y, n_theta)[3]
 
             tmp_theta = getattr(parameter_estimation, self.estimator)(
                 regressor_matrix, y
             )
 
             tmp_yhat = regressor_matrix @ tmp_theta
-            tmp_residual = y[self.max_lag:] - tmp_yhat
+            tmp_residual = y[self.max_lag :] - tmp_yhat
             e_var = np.var(tmp_residual, ddof=1)
 
-            output_vector[i] = self.compute_info_value(
-                n_theta, n_samples, e_var)
+            output_vector[i] = self.compute_info_value(n_theta, n_samples, e_var)
 
             # output_vector[i] = e_factor + model_factor
 
@@ -755,8 +745,7 @@ def results(self, theta_precision=4, err_precision=8, dtype="dec"):
                         translated_exponent = ""
                     else:
                         delay_string = str(
-                            int(regressor_key -
-                                np.floor(regressor_key / 1000) * 1000)
+                            int(regressor_key - np.floor(regressor_key / 1000) * 1000)
                         )
                         if int(regressor_key / 1000) < 2:
                             translated_key = "y(k-" + delay_string + ")"
@@ -774,8 +763,7 @@ def results(self, theta_precision=4, err_precision=8, dtype="dec"):
                             translated_exponent = "^" + str(
                                 regressor_dic[regressor_key]
                             )
-                    regressor_string.append(
-                        translated_key + translated_exponent)
+                    regressor_string.append(translated_key + translated_exponent)
                 tmp_regressor = "".join(regressor_string)
 
             current_parameter = theta_output_format.format(self.theta[i, 0])

From 5af335ea241c9b61a0cadc4bbf3f3ba42d703c80 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sat, 13 Mar 2021 15:50:16 -0300
Subject: [PATCH 17/27] fix code format in residues/residues_correlation

---
 sysidentpy/residues/residues_correlation.py | 24 ++++++++-------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/sysidentpy/residues/residues_correlation.py b/sysidentpy/residues/residues_correlation.py
index e9e14184..9613b1f4 100644
--- a/sysidentpy/residues/residues_correlation.py
+++ b/sysidentpy/residues/residues_correlation.py
@@ -56,11 +56,9 @@ def residuals(self, X, y, yhat):
         ye = y * e.reshape(-1, 1)
         yeline = (y * e.reshape(-1, 1)) - (y * e.reshape(-1, 1)).mean()
 
-        yeline_x2line = self._input_ccf(
-            yeline, x2line, len(unnormalized_e_acf))
+        yeline_x2line = self._input_ccf(yeline, x2line, len(unnormalized_e_acf))
         lam = np.sqrt(sum((ee - ee.mean()) ** 2) / sum((ye - ye.mean()) ** 2))
-        yeline_eeline = self._input_ccf(
-            yeline, eeline, len(unnormalized_e_acf))
+        yeline_eeline = self._input_ccf(yeline, eeline, len(unnormalized_e_acf))
         return (
             e_acf,
             xe_ccf,
@@ -73,8 +71,7 @@ def _input_ccf(self, X, e, len_confidence):
 
         xe_ccf[:, 0] = self._normalized_correlation(X, e)
 
-        xe_ccf[:, 1] = np.ones(len(xe_ccf[:, 0])) * \
-            (1.96 / np.sqrt(len_confidence))
+        xe_ccf[:, 1] = np.ones(len(xe_ccf[:, 0])) * (1.96 / np.sqrt(len_confidence))
 
         xe_ccf[:, 2] = xe_ccf[:, 1] * (-1)
         return xe_ccf
@@ -118,8 +115,7 @@ def _normalized_correlation(self, signal1, signal2):
         u = (signal2 - np.mean(signal2)).flatten()
         t = int(np.floor(len(signal1) / 2))
         ruy = np.array(np.zeros(t))
-        ruy[0] = np.sum(y * u) / (np.sqrt(np.sum(y ** 2))
-                                  * np.sqrt(np.sum(u ** 2)))
+        ruy[0] = np.sum(y * u) / (np.sqrt(np.sum(y ** 2)) * np.sqrt(np.sum(u ** 2)))
 
         for i in range(1, t):
             y = (signal1 - np.mean(signal1[:i])).flatten()
@@ -152,13 +148,11 @@ def plot_result(self, y, yhat, e_acf, xe_ccf, figsize=(10, 8), n=100):
         plt.style.use("seaborn-white")
         plt.rcParams["axes.facecolor"] = "white"
 
-        fig, axes = plt.subplots(
-            nrows=2, ncols=2, figsize=figsize, facecolor="white")
+        fig, axes = plt.subplots(nrows=2, ncols=2, figsize=figsize, facecolor="white")
         fig.subplots_adjust(hspace=0.7)
         for ax, feature in zip(axes.flatten()[2:], [e_acf, xe_ccf]):
-            ax.plot(feature[:, 0], color='#1f77b4')
-            ax.axhspan(feature[0, 1], feature[0, 2],
-                       color='#ccd9ff', alpha=0.5, lw=0)
+            ax.plot(feature[:, 0], color="#1f77b4")
+            ax.axhspan(feature[0, 1], feature[0, 2], color="#ccd9ff", alpha=0.5, lw=0)
             ax.set_xlabel("Lag", fontsize=12)
             ax.set_ylabel("Cross Correlation: ee, ex", fontsize=12)
             # ax = plt.gca()
@@ -172,7 +166,7 @@ def plot_result(self, y, yhat, e_acf, xe_ccf, figsize=(10, 8), n=100):
 
         ax = plt.subplot(211)
         ax.plot(
-            y[self.max_lag: n],
+            y[self.max_lag : n],
             c="#1f77b4",
             alpha=1,
             marker="o",
@@ -180,7 +174,7 @@ def plot_result(self, y, yhat, e_acf, xe_ccf, figsize=(10, 8), n=100):
             linewidth=1.5,
         )
         ax.plot(
-            yhat[self.max_lag: n],
+            yhat[self.max_lag : n],
             c="#ff7f0e",
             marker="*",
             # linestyle='dashed',

From 68bde4066ac25ae4ffc429e9cab996ab1239a3d2 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:03:54 -0300
Subject: [PATCH 18/27] change the version of the package

---
 sysidentpy/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sysidentpy/__init__.py b/sysidentpy/__init__.py
index 8fdf2fa0..1276d025 100644
--- a/sysidentpy/__init__.py
+++ b/sysidentpy/__init__.py
@@ -1 +1 @@
-__version__ = "0.1.4.2"
+__version__ = "0.1.5"

From b22e7e855c6660b1d96fc73b4a52e41182b00875 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:06:11 -0300
Subject: [PATCH 19/27] update the example
 'simulating_a_predefined_model.ipynb' using the new class for simulation

---
 .../simulating_a_predefined_model.ipynb       | 631 ++++++------------
 examples/basic_steps.ipynb                    |   4 +-
 examples/simulating_a_predefined_model.ipynb  | 631 ++++++------------
 3 files changed, 404 insertions(+), 862 deletions(-)

diff --git a/docs/source/examples/simulating_a_predefined_model.ipynb b/docs/source/examples/simulating_a_predefined_model.ipynb
index 4800a33f..36aa5e2c 100644
--- a/docs/source/examples/simulating_a_predefined_model.ipynb
+++ b/docs/source/examples/simulating_a_predefined_model.ipynb
@@ -20,16 +20,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
     "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.polynomial_basis.simulation import SimulatePolynomialNarmax\n"
    ]
   },
   {
@@ -52,557 +52,328 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)"
+    "x_train, x_test, y_train, y_test = get_siso_data(n=1000,\n",
+    "                                                 colored_noise=False,\n",
+    "                                                 sigma=0.001,\n",
+    "                                                 train_percentage=90)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "## Defining the model\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors follwing a codification pattern:\n",
+    "- $0$ is the constant term,\n",
+    "- $[1001] = y_{k-1}$\n",
+    "- $[100n] = y_{k-n}$\n",
+    "- $[200n] = x1_{k-n}$\n",
+    "- $[300n] = x2_{k-n}$\n",
+    "- $[1011, 1001] = y_{k-11} \\times y_{k-1}$\n",
+    "- $[100n, 100m] = y_{k-n} \\times y_{k-m}$\n",
+    "- $[12001, 1003, 1001] = x11_{k-1} \\times y_{k-3} \\times y_{k-1}$\n",
+    "- and so on\n",
+    "\n",
+    "### Importante Note\n",
+    "\n",
+    "The order of the arrays matter. \n",
     "\n",
-    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+    "If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:\n",
+    "- $[2003, 2001]$ **works**\n",
+    "- $[2001, 2003]$ **do not work**\n",
+    "\n",
+    "We will handle this limitation in upcoming update."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Defining the model"
+    "s = SimulatePolynomialNarmax()\n",
+    "\n",
+    "# the model must be a numpy array\n",
+    "model = np.array(\n",
+    "    [\n",
+    "    [1001,    0], # y(k-1)\n",
+    "    [2001, 1001], # x1(k-1)y(k-1)\n",
+    "    [2002,    0], # x1(k-2)\n",
+    "    ]\n",
+    "                )\n",
+    "# theta must be a numpy array of shape (n, 1) where n is the number of regressors\n",
+    "theta = np.array([[0.2, 0.9, 0.1]]).T"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "# build the object so we can access its functions\n",
-    "model = PolynomialNarmax()\n",
+    "## Simulating the model\n",
     "\n",
-    "# create any psi matrix that contains the combinations we want\n",
-    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
-    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
-    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
-    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
-   ]
+    "After defining the model and theta we just need to use the simulate method.\n",
+    "\n",
+    "The simulate method returns the predicted values and the results where we can look at regressors,\n",
+    "parameters and ERR values."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
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\n"
      },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {}
     }
    ],
    "source": [
-    "pd.DataFrame(psi)"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[   0,    0],\n",
-       "       [1001,    0],\n",
-       "       [2001,    0],\n",
-       "       [2002,    0],\n",
-       "       [1001, 1001],\n",
-       "       [2001, 1001],\n",
-       "       [2002, 1001],\n",
-       "       [2001, 2001],\n",
-       "       [2002, 2001],\n",
-       "       [2002, 2002]])"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2000  0.00000000\n",
+       "1        x1(k-2)     0.9000  0.00000000\n",
+       "2  x1(k-1)y(k-1)     0.1000  0.00000000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1000</td>\n      <td>0.00000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 21,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
    "source": [
-    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
-    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
-    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "regressor_code"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "### Options\n",
     "\n",
-    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
-   ]
+    "You can set the `steps_ahead` to run the prediction/simulation:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[1001,    0],\n",
-       "       [2002,    0],\n",
-       "       [2001, 1001]])"
+       "0.001695176840746673"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
-    "model.final_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.psi = psi[:, [1, 3, 5]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining the parameter estimation method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
-    "from sysidentpy.parameter_estimation import Estimators"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[0.20014451],\n",
-       "       [0.89992074],\n",
-       "       [0.09997088]])"
+       "0.0017301972652496206"
       ]
      },
-     "execution_count": 26,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
-    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
-    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
-    "\n",
-    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
-    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
-    "\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "model.theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulating the model"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=21)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "yhat = model.predict(x_valid, y_valid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0019184421163563367\n"
-     ]
-    }
+    "### Estimating the parameters\n",
+    "\n",
+    "If you have only the model strucuture, you can create an object with `estimate_parameter=True` and\n",
+    "choose the methed for estimation using `estimator`. In this case, you have to pass the training data\n",
+    "for parameters estimation. \n",
+    "\n",
+    "When `estimate_parameter=True`, we also computate the ERR considering only the regressors defined by the user. "
    ],
-   "source": [
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)"
-   ]
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2001  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
-     ]
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
     }
    ],
    "source": [
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "s2 = SimulatePolynomialNarmax(estimate_parameter=True, estimator='recursive_least_squares')\n",
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True)\n",
     "\n",
-    "print(results)"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 9
     }
    ],
    "source": [
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Final code"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n",
+    "\n",
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.001879953122213705\n",
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2000  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
-     ]
-    },
-    {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
-    "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
-    "from sysidentpy.parameter_estimation import Estimators\n",
-    "\n",
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)\n",
-    "\n",
-    "model = PolynomialNarmax()\n",
-    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
-    "model.psi = psi[:, [1, 3, 5]]\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "yhat = model.predict(x_valid, y_valid)\n",
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)\n",
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
-    "\n",
-    "print(results)\n",
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:v0.1.4] *",
-   "language": "python",
-   "name": "conda-env-v0.1.4-py"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -614,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/examples/basic_steps.ipynb b/examples/basic_steps.ipynb
index 980a8cb6..c834f2fe 100644
--- a/examples/basic_steps.ipynb
+++ b/examples/basic_steps.ipynb
@@ -122,7 +122,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1fd33cab3d0>"
+       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x291a2889610>"
       ]
      },
      "metadata": {},
@@ -245,7 +245,7 @@
     "\n",
     "However, this is not only critical because computational eficiency. In many situation, the minimum value of the information criteria can lead to overfiting. In some cases, the diference between choosing a model with 30 regressors or 10 is minimal, so you can take the model with 10 terms without loosing accuracy.\n",
     "\n",
-    "In the following we use *info_values* to plot the information criteria values. As you can see, the minimum value relies where $xaxis = 5$ "
+    "In the following we use *info_values* to plot the information criteria values. As you can see, the minimum value relies where $xaxis = 3$ "
    ]
   },
   {
diff --git a/examples/simulating_a_predefined_model.ipynb b/examples/simulating_a_predefined_model.ipynb
index 4800a33f..36aa5e2c 100644
--- a/examples/simulating_a_predefined_model.ipynb
+++ b/examples/simulating_a_predefined_model.ipynb
@@ -20,16 +20,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
     "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.polynomial_basis.simulation import SimulatePolynomialNarmax\n"
    ]
   },
   {
@@ -52,557 +52,328 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)"
+    "x_train, x_test, y_train, y_test = get_siso_data(n=1000,\n",
+    "                                                 colored_noise=False,\n",
+    "                                                 sigma=0.001,\n",
+    "                                                 train_percentage=90)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "## Defining the model\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors follwing a codification pattern:\n",
+    "- $0$ is the constant term,\n",
+    "- $[1001] = y_{k-1}$\n",
+    "- $[100n] = y_{k-n}$\n",
+    "- $[200n] = x1_{k-n}$\n",
+    "- $[300n] = x2_{k-n}$\n",
+    "- $[1011, 1001] = y_{k-11} \\times y_{k-1}$\n",
+    "- $[100n, 100m] = y_{k-n} \\times y_{k-m}$\n",
+    "- $[12001, 1003, 1001] = x11_{k-1} \\times y_{k-3} \\times y_{k-1}$\n",
+    "- and so on\n",
+    "\n",
+    "### Importante Note\n",
+    "\n",
+    "The order of the arrays matter. \n",
     "\n",
-    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+    "If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:\n",
+    "- $[2003, 2001]$ **works**\n",
+    "- $[2001, 2003]$ **do not work**\n",
+    "\n",
+    "We will handle this limitation in upcoming update."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Defining the model"
+    "s = SimulatePolynomialNarmax()\n",
+    "\n",
+    "# the model must be a numpy array\n",
+    "model = np.array(\n",
+    "    [\n",
+    "    [1001,    0], # y(k-1)\n",
+    "    [2001, 1001], # x1(k-1)y(k-1)\n",
+    "    [2002,    0], # x1(k-2)\n",
+    "    ]\n",
+    "                )\n",
+    "# theta must be a numpy array of shape (n, 1) where n is the number of regressors\n",
+    "theta = np.array([[0.2, 0.9, 0.1]]).T"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "# build the object so we can access its functions\n",
-    "model = PolynomialNarmax()\n",
+    "## Simulating the model\n",
     "\n",
-    "# create any psi matrix that contains the combinations we want\n",
-    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
-    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
-    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
-    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
-   ]
+    "After defining the model and theta we just need to use the simulate method.\n",
+    "\n",
+    "The simulate method returns the predicted values and the results where we can look at regressors,\n",
+    "parameters and ERR values."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
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I4U4k4IUIHZQCPExEEBhPo1jf3YWrP3MxAOBTi/YwcbyPc/kpK+AV+axtXpGv+KpCIpHAfffdh2AwiGAwiFtuuQUXX3wxFEUBpRQcx8E0raj1f/3Xf+Hb3/42vvOd76CtrQ3UuTFVARZBZjAYDEZexHAnxIQGQqyfWW7q3OXyU1bA92AMYwjgHbMDb9DF6JiSRtHQthC9XCe8u1+o4UgZ9YAzQaq0iwVgFd5deOGF4DgOhmHg85//PJYsWYIvf/nLePnll+H1erFo0SIMDg5i+fLl+PGPf4yDDjoIH/rQh/CRj3wEoVAILS0tGBwcnPFY8kFoNeV3jent7cWJJ56IJ554AvPnl5Y0zmAwGAwrB3nlgx/ASm4nDAq8SRejH61InXUnizDOQX7/7Casf+xo/MA4C/cFLswpeF7+wcewbPjv8F27E6LA4miMfZO6TLFQVRWnn346nn322bz7bN68Geeeey5Wr16Ns88+Gxs3bpzFETIYDMa+wfruLgz69sMADeMF80CIxGDieA6z0ngLPKE44f2n45mr1uX8HvnFx6GBJLHtDWbwxth3qTuBnMlk8OUvfxlvv/123n1SqRQ++clPYvXq1XjggQdw+OGH45JLLkEikZjFkTIYDMa+wWL1Lez2HQBz/1OwkuzCca2pWg+JUSbxrc/CpASLVx+fd5/5h70fADDy+t9ma1gMRt1RVwJ569at+MhHPoKdO3P76zk88sgjEEURV111Ffbbbz9cc801CAaD+POf/zxLI2UwGIx9g0R0FAvNPqRbV2Pemg8BAN597sEaj4pRLr6Bl/EuvxANjS1592np2h8DpBVy3/OzODIGo76oK4H80ksv4dhjj8W9995bcL9XX30Vhx12GDjOGj4hBIcddhh6enpmY5gMBoOxz/Dua88AAPxLj8TSFauxi8yDtP2vNR4VoxxMw8Bi5Q0MhlcX3bevoRtLUq/CqILnLYMxF6grgXzeeefhiiuugNfrLbjf0NAQ2trasrY1NzdjYGCgmsNjMBiMfY749n8CABauOhaE47CzZS2Wp3qgJGM1HhmjVHa99QpCSIEseE/xnRcfi2ZE8c6WV6o+LgajHqkrgeyWdDoNSZKytkmSlNNUmsFgMBjlIw++gj7SjsaWDgCA/6APQiYatr7wpxqPjFEqA2/+HwBg3qq1RfftWm3lIQ++9kRVx8Rg1CtzUiDLsjxNDKuqCo/HU6MRMRgMxt7JvOQm9PsPHP955VGnIEG9yLzxSA1HNXfY0NOHM2+8Hy987T0448bf17ZF965/YgxBzN/v4KK7ti8+CMNoBL8rfx5yXf1ujOoR3wP84lQgXplV+hdeeAErVqzAI49k30POOOMMXHXVVUXfn8lksG7duoLH/9KXvjTjcc5Jgdze3o6hoaGsbcPDw2htba3RiBgMBmPvY3SgFx10CFr7oePbPB4vNvvXYOHo06Amy08txIaePlz9wGs4J3kP1pAt+EjyN7j6gddqJiQ7Yq/iXd8qEM7Fo58Q7AodikWJnpzfc739bowq8tR3gZ3PA0/dVLFDLl26FH/84x/Hf96yZQvS6eltrWvJnHQAX716NX784x+DUgpCCCil6OnpwSc/+claD43BYDD2GnrfeAZNAELLsnNW1f1OQutr/4edb76AhauOrs3g5gCnPrQa63lt/OcLhcdxIR5H5iER6B6e1bFEhvdgodmHvvazXL/HXHA0Ot74O3Zs34zFyw7Meq2efjdGmbxyD9BzV/7Xdz4DTO4l99LPrf8IARYem/s93RcAh55f9KNXrlyJHTt2IBaLIRQK4eGHH8YZZ5yB/v5+PPzww7jzzjshSRIWL16Mb3zjG1BVFV/5ylcQi8WwcOHC8eNs2bIF3/zmNwEA4XAY3/72t1396m6YMxHkoaEhKIoCAPjABz6AVCqFG264AVu3bsWNN96IRCKB0047rcajZDAYjL2H1DsvwqAEi6aI4P2OWQ+TEux5+aEajWxusFa5FY/oa8Y1RppKeFA/Fscp/zPrY3l345MAgNDy41y/p/0QKw9596uPT3ttrXIrnjYOqovfjVElOtcAvlaA2FKRcIC/FehaU5HDn3TSSfjrX/8KSik2btyI7u5uRCIR/OAHP8Cdd96Je+65B8FgEPfeey8efPBBLF++HHfffTfOO++88WNce+21uO666/DrX/8a733ve/Gzn/2sImMD5lAE+bjjjsONN96Is88+G4FAALfddhuuu+463HfffVixYgVuv/12BAKBWg+TwWAw9hq8w69iJ78AS4LhrO3t8xZis7Acjb2skUQhxHAn5iVHQIj1swwVCXghhefN+lhS256DRnksPcS9QO7a/1BEEQC381kAX8h6rSHgR7f2NggBdMpBhlaz341RJoeeXzza+4cvAf/6JSB4AEMFDjgTOP3/q8jHn3HGGfj617+OBQsW4IgjjgAAmKaJZcuWjeu5NWvW4OmnnwYArF1rFZeuXr0agt0Cfdu2bbj++usBAJqmYcmSJRUZG1DHAnnLli0Ffz7kkEPw4IPMrJ7BYDCqATVNLEhvxtaGY5HrkTPc+T4ct+s2RId60dA6f9bHNxf44vsW4YA/70K/2Qg/UTBMQ2jnorj8lBWzPpbQ0L+wQ1yK/f1B1+8hHI8d/tXoivaMpzQCgKoZuNb8ETxQoVOCR401GEUQHTX63RhVJDkIHP4fwBH/Abz0CyBROTvdBQsWIJVK4de//jW+/OUvY9euXSCEYNu2bUilUvD5fPjnP/+JJUuWgBCCV155Be9///vx5ptvQtd1AMCSJUtw0003obOzEy+//PK0+rSZMGdSLBgMBoMxewz0bkUTYqCdh+Z8veWwMwEA259lgYp8zNv9F3iIhu8In8EfjaPRQSIYO+UHWN/dNavj0NQMlmS2YKTx0NLfO/9oLMAe7N65fXzbE7/5Lt5r/hN/W/A59KMFGSLitsClSJ1156z/bowqc97dVsS442Dr/+fdXdHDn3baaejv7x+P/DY2NuLzn/88LrroInzkIx/B2NgYzj//fHzsYx/DwMAAzj//fNx9990QRREA8PWvfx1XXnklPvrRj+KWW27BihWVm6ARSidnYO9d9Pb24sQTT8QTTzyB+fNZhIPBYDDc8q8//wKHvXAZ3jrzISw/7H3TXjcNE0M37If+wCoc+pU/zP4A6xzTpNj8zSMRIkl0ffU1bH/pL9jvkfPwbPfNOOZDn5rVsbz9yj+w/4bT8fKaW3D4B0srZn/39Wew6P7T8Hz3d3HUhy7BK/96ASseOh29wdXY/8t/wTvf6saY3IXDrmC2f4y9CxZBZjAYDMY0Mjtfgkp5LDrwyJyvczyHV71HYf/4C/jn145kPrhTeOX5v+JA8y0MH/jvIByPpYefhCHSBGnTA7M+lpFNVg5n1yHvK/m9C1a+Bwl4QXc8g2gsAd8fLkGGeNB18S8BjkOG80HUk5UdcJkwX2ZGJWECmcFgMBjTCI68jnfFJZA9vpyvb+jpw/3xg+AnGRxB3mI+uFNQn/5fxOHDgad+GgBAeAHvdpyCg9MvYnhocFbHIux+EYNoQseCZSW/lxMEbBYPRNvIC0j99yosp+/gHwd+Db4ma1VW472QzNr71zJfZkalYQKZwWAwGFmYhoFFmS0YbViVd59TH1qN24WbAQAcobhQeByb+HNx6kOrZ2uYdcuOd97C4cl/4O3OsyD5QuPbW4/5GGSiY8uTlc3jLEZXfCN6A8W75+ViQ08f/q7sj/24PZjHjWGL2YUrXlswLjx1wQ/ZTFVyuGVx6kOrsYk/FxcKj7s+H1nEmVEIJpAZDAaDkcWura8hSNLgug7Lu89a5VZs0I+BQS1nA+aDO8HOR78PDiYWnXZZ1vZFq47Dbm4eAm/Pnn/0QN92zMMQ1M7yvGtPfWg1Lud/O/7zCq4vS3haArn2EWTnfNQoD6D4+cgizoxiMIHMYDAYjCwGNj8HAGhdmb9LnhjuRAJecKCgFMwH1yYai+HgPRvwRug4NM9fnv0iIeibfxpWZV5Bf9+7szKe3o1PAQCaVq4t6/1rlVvxkH50XuFJRT98tPYC2TkfBRgAip+P5USc6xEWBa8eTCAzGAxGFZmLDzCz92WkqIwFy7vz7nP5KSvQxsXwOl0MHRzuMtbVzOO3ntj4yO1oJHEEjv98zte7jrsAPKHY9mSBFr8VRH3nBShUxJJVR5X1fjHciTh84GFCoeI04WlKAfiggJpmJYddMs75uIO2AwDuN9YWPB+diLNqC3+FinNuBYRFwasLE8gMBoNRJebqAyw89jrelZaBF8S8+6zv7kLqrDvxR24dRGLit76P7dM+uBt6+nDGt+/D4ZtuwhY6Hxu5g3Lu17n8MOzgF6P5nYerPp4zb7wfqwYexGa6EH96Y6Ss4zjC8y7jRJylfgN3GSdmC08pAJEYUBSlgqMvHed87IUlkH/rPbfg+Tg14ixBn3MrIHtLFLxeYQKZwWAwqsRcfICpmQwWa1sRaype1LW+uwsnH2VFmX94+rx9Whxf/cBr+EL6R/ARFcNmCFc/+HreidDQ4tNxgL4ZO7dtqup4zkvehQAUCNDLnpg5wvO2wKXYTBdNawjCyX4AQDIeqeSvUBbru7vQLllC/eYzlhY8Hx3h/xq1GlQ8bnbPuRWQiToAS8qxOoDKUretphkMBmOus1a5FdcId+N0/nkIxESaSnjUWINv6x/Di7UeXB52bnkZy4gGceERrvb3tVh2X4nhnQCOqeLI6pdTH1qN9bw2/vOx/JvYhHOReUgEuoen7b/4+IuAbT/Erv+7Cwv3+1bVx7OKvFtwPMVY392VV2xyHqt1dSYZBdBZ1ngriddIAHDGk5/13V3YgDuxccN/YjXewSPiyXjfGRfNqUmeGO5EIuEFBxMmqwOoOCyCPEvMxTxEBoMxM5xlXB5Wfma9P8A29PTh4QcsC7L/7VFd3afCbQsBAJnRffee5kTynL60xSJ5rQtX4A1uBZreeRhLrvoTjv3O3yr6TJjN/FreY9nYpVOxih+7HHzUalqipQsLZMASySsaLReWCw5vmVPiGLCi4O1cDCaAGHzT018YM4IJ5FlgruYhMhiMmeE8wAz7Vvs389C6fYA596nTzb+BUuB9yhOu7lPN7QtgUgIz2j9LI60/xHAn0pBBCKBRruhEaENPHx7QjsIB3E5skL4KNdJf0WeCMzETYYDS6ubXil4rgqwmay+QqWkiSK0IspaOu3oPb3cBpJnaezmXyvruLgweex14Anih4bbAZ/fpOoBKwwTyLDAX8xAZDMbMWd/dheH3XAmBWBHkZ/j31O0DzLlPLed2gxDgQuEJV/cpSZYxShpAkntmaaT1x+WnrMBCzkpd+P/0DxeN5N382BY8rL0HJgUOIe/gC8IDSGsGbn5sS8XG08bF0Etb8A5tr2pkUbQboWjp2gtkJZ2ERKyiO9OlQHbaZBtzUCADwIGeUQCATDQ88cWj6/LeMldhAnkWKHX5jcFg7D3sp28b//fJ8/W6fYCtVW7F34wJMVzKfWqMb4YnPVDN4dU167u7sHPlxQCAF80DphWyTeWJ9Efwouez4Ajsycjj2OH5KJ5If6Ri40mddScyRMZbdGHR8cwE2W9FkN1GbKtJIjrh1GEq7sYjGXYEWZubAjk9sHX834lYpHYD2QthRXqzgBjuRDohgRBAd7H8xmAw9h703RuhUgFJ4gOX2F3r4eRFDHdiSWoPKAUymO53W4iE1IYGdd+NIAPAEsESZz/53Hq0dC0tuO9HPD/Bxcmf4wP8i/AQDSYl+JPxHtzu/xT+UKHxnHFwO4wNAxievw7PXLKuQkedjuxrAOA+YltNUpMEMs24Fch2kxN1bgpkOvLO+L/TiQjQXvtCyb0FFkGeBS4/ZQU6uTEAwJPmISyRnsHYhwiMvYmd4mIMCx3w1nGU9XOrCRaRAbxBF+X2uy2A6m1Do1Gez+7eghnZBY3yaGpfWHTfiz9wNNKcDxJ0qFQAAUU39zYuPsmdc4gbBnq3QSI6uObCYn2m+AKWQDZcRmyrSTo2cQ4SNeHqPV7TiiATLVmVMVUbOT7RkVFJRGo3kL0QJpBngfXdXdhz1LUAAANCVZe7agFz6GAwckNNE/PVrRgLrkRCbkdIq1+BvOStn0ODiCs8X8vpd1sII9CBRsSgZWrfcrhWiPFeDHIt4ITiC7Pru7twfBfwoHAK1qs34GWyEvO5Ecz/13crNp6RXZsBAIHO5UX2nBleWyBTl4K0mmSSo+P/5jSXAhnWOUv0uXnuNii9UGA19ClmbccoDZZiMUsc2mZZyXT5DDxzVfWWu2Ybp/L9GnoP1vCOQ4dlHL+3TAAYjHLp37UdnYhja8fBUEe2oSXxEiilIITUemhZ9Pdux2Gjj+DV1jPxyOc+XPL7+QZrWXd0z060L9o3V8YCym5EpA64vet1XvJ7/BuAfwNA6Wfw7A8uxjG778KrD6/A6jO/MOPxJPvfBgC0LDxgxscqhCR7oVEeqAOBrNkRVIMS8C4iwtQ04aNpgADcHBTI1DTRYfSjV1qKZdoWaCkmkCsJiyDPEqp94YrG3LsIC8EcOhiM/Ox5y2oH0rD0cCDUhQBJIxYZLfKu2eedh28CB4oFZ1xV1vs9TVazkMjgrkoOa07RpA0g7S0v/5MQgsM//RO8InXjgJe/jsu/8c0Zr8jRka1QqIiWeYvLer9rCEGKeEDU2qcoGOkIAGCINEPQiwv2TCY97nrBz8Fn89BQP4IkjUiDNQnSXXg/M9zDBPIsoSatHGTJnHsXYSHWKrfiOWMiQsEcOhiMCdK7emBSgoUr10BsWgAAGOnfVuRds8voUD9WDzyIV8LvR8eilWUdw29300sN75sCWVHSaKVj0EMLyj6GLMnYdNz3sYu24QbjezP2zPfE38Uefh44ni97TG5JwwveZUpDNTFT1nM2IrZB1IsX3U1uj83PwQjy0LtWGg03zwpIGXVgtbc3wQTyLGHYSx8ynXsXYSHEcCd4WDNwjfLMoYPBmIRn+E3s5ubBGwzD12IVb8UHdtZ4VNlsfui/4ScZtJ16ZdnHaOpYBABQI/tm/cFQ33ZwhEJoKl6gV4iz/7YO+3H98BB9xityYaUXEc/8GY3HLRnOO95wo5YQJYoUlaGIDZDN4gJZSUxEXAVTqebQqkLCTqNpXHY4APfWdgx3MIE8S5j20od3LxPIl5+yAm3E+t1u009nDh0MxiQ6Um9hKGAVSTXaS93KSP0I5Hh0FAf13oNXfMdg4cryHRTCTe3IUBE0tm920xvbba0K+NuWzOg4lWoRbRgG5hn9yIQWz2g8bslwPoh1EIHl1BgSxA9d8LsUyJHxf4tmpoojqw768HYAQNfyw2FSAri0tmO4gwnk2UKJAAB8dO7NUguxvrsL/R7LRigB717n0MFglEtkZAhdGIDaugoA0Ny+0GrJXEdR1tf/8H00IAn/iVfM6Dgcz2GYa4KYrF+XjmqSHrS8aMOdy2Z0HKdFtACr86JU5orcYN92eIgG0rzfjMbjFpX3QTRrH0EW1DhSnB+GGHAVjHJcH2Lwzcn0RyG6A4NohuQNIEk8ICoTyJWECeRZgtgzO5lo0NW5N1MtRJthPRTft9iDZ65ax8QxgwFg1+Z/AgD8C7sBAIIkY4Q0gq+DZiEbevpw1rfuweFvfx8vmcvxBjdzK7CY0AyPsm8KZHNsJwxK0No5swiy0yL6LuNEbDU7MYSGslbkRnZauan+efvPaDxu0QUfZKP2jTYkPQaFD4KKfvhp8fFo9spuhGuEROfeczmY6sWIZBWGpuBzbW3HcAcTyLOEoE0kz6dSe9Esj1K0GdayqltjdgZjXyCx418AgM4DjhzfNia0wJuubcc5x5rxSuVWSMTAGPWXXQg2mZTchpA2XKFRzi34eC+GSRMESZ7RcZwW0bcFLsUfzaPQihjiJ3235KBDYs9bAKpv8eagCwHIdbA66tHjyAhBUDkID9Gga2rB/Z322AmhCZ46GH+ptOq7kfRbhaEK54PABHJFYQJ5lhC0CVGcTuw9ViyxyBCCttE6z5Z3GIxx+IHXMIJwVme1pKcDIW2whqOasGY8ireijCfxPRWxZtR87Wg2RwBKKzHMOYU/1YdRsaMix1rf3YVnrlqHk8++GByhWDr6TMnHoMPboFIBrZ3V7aLnYIo+eF1EbKuN10xAE4MgcgAAkIwXftYatkBW5BZ45lgEOZGIoRVjMMOLAQAK74dUB4WSexNMIM8S8iRPRiWx91ixDNlLeQBc+U4yGPsKzYkt2O3NXuLO+OahxRgCNc0ajcoqBHtYP2r850pZM9LgPPhIBon42EyHOOdo1AeQKtMDOR8rVx+LPWgG99ajJb9Xjr2Lfn6eq65+lcAUA/DXQQTWT5MwpBB4OQgASBdpvUwV61mseVvggVrT67JU9uywnr1iq5VnrvE+iHWQ5lKIudZ1lwnkWcJjJJChVjtIdS/yKozZnq4J6oXIZq8MBgBASaew0NiFVNNBWdtJqBN+kqlpsxAx3ImM3Zq2ktaMYtjuptf/7ozHOJfQNRWt5gi0YGVrLziew/am92L/xItQ06XdW8PKLozNksUbAEDyQyYa1EztorCmYSBIk6CeBnBeq/11UYGcsYI61NcGjlBklPoWmJOJ9lkWbyE7z1wTAvDUQaFkPpzUrnOS98zY43u2YAJ5lvCZCQxzTQAAdS/KQVaHLJuZneKSup+9Mhizxc7NL0MkBqT52WkLou2TO7K7ds1CLj9lBTq4CADgf/SzKmbN6Gm2BFl8qH5s7GaDod07IBIDXOOiih/bs+p0+EgGbz3/J9fvMQ3TsngLVn48+XBSGlI1TB9MJqLgCQU8YYheK4LsuFTkRU0gRWVA9gMA0qm5swqaGdoKAGhbbDX30YUAvC6s7WrFXOy6ywTyLOGnKUSFVgCAnt57BDKJvItRBJH2tNX17JXBmE1Gt1kFeu3Lj8za7m+1m4UM1k5Eru/uQu8RVlOQt+mCilkzhlqtYqH0SO+MxziXGLUnO74ZeiDn4sCjT0OCepF+/Q+u3zPYvwNeos6axRsAcB5LkKaKRGyrSTI6Yo3FG4bkCwEAtFTh1VpejSNJfOAkSyBn5lDwioztQBw+BMNtAABTCsBXB3ng+Vir3IrHje7xn+dC193ZSVDaxzF0DX6iIOnpALTXoe1FKRbeZC+G+Q7Ld7KOZ68MxmxC97yKJPWgY3G2i0C4TpqFrG61bv2fP7Ubq9auq8gxm+1uenp032oWkhzcAQAIz6t8QZzH68NLgSOxZOT/QE0DhCveNnp45yZ0APDNm7l1n1t4jxOxrZ3ATMWstCXBH4bkt1Isij1rOT0JhXjByT4AQCY9dyLIvsRODAidCBICAKBSEH4oMAwTPF9/sU8x3Ak9YY1Lp9yc6Lpbf3/FvZBk1Cpa0QNWlbOZmTsXYTEaM7sR93bBkIKufCcZjH2Bhuhm7JKWguOzBU1z+0IYlMCM1jbvTk1ZS8+OkKgE/mADYtQHEt+3BLIxugMA0NJVnYitvv8H0IIItr/6tKv9k3b74eYFK6synlwIdkqDkqpdikU6ZkWQZX8jPPZ5XWy1VtSTUHgfBDvFQp1DArlJ3Y24dyLPnHiC4AhFslhaSY2wPL6t7+NRY82c6LrLBPIskLAvXAStIpa9RSCbuo42cxCZ4EJACsJHMjB1vdbDYjBqimEYWKRuQyw83YNWECWrWUi8ts1CdDuyJvvDFT3uKN8MKbVvNQvhY70YQRgeX6Aqx19+7NnQKYehlze42t8Y3gaV8mifP3spFm5TGqqJmrAiyHKoGb5gGABgKoXHI+lJqJwfvJ1DrSlzI01Q0zS0mwNQQxN55uNpLrFIjUZVmPXdXXh7yXnWD4TMia67TCDPAum4deFKjfaJkJkbF2ExhvbsgEQMcE2LQeyLM1nDHDQGox7o3fYG/EQBmXdIztfHhFb4lNpGWQ1bIHsDlYsgA0BcbIE/U1uf59nGm9qNEaGtasdvau3AFukgtPf/zdX+cuwd7OE7wAli1cY0FclrCeRa1tfoqQgAwBucEMg0U3g8kpGCJvgheqwIsj5HIsgDvdsgEQN880TeO+dx59xRSxZ5Vfv/ypzoultXAllVVVx77bVYs2YNjj32WPz0pz/Nu+/FF1+MFStWZP33+OOPz+Jo3aPYvqBysAVpKgF13O2mFJ/C0V1WtyZf21IQj3WDTO2DHqgMxmQGt74EAGja7/Ccryc9HQipQ7M5pGk4wqHSAjntaUdIH6noMeudsLoHcU9lPZCnElt0EpYYO7D7nc1F9w2nezEmz6LFG+A6paGaGLZADjQ0QZI9UKlQNBjloSnogh+i14ogG+rcCF6N7toCAPB3TPisi3YUP5OM1GJIrjDt1t4+vT7TQKZSV0V63/3ud9HT04Nf/OIX2LNnD6644gp0dnbigx/84LR9t27diu9973tYs2bN+LaGhsre7CuFlrREoyfYiBTxgtTpRej4FF5D78Ea3vEptGbWuWZ6iQGrertx/nKo9u+oxCOzNl4Gox7Rel+BRnksWJFbIKv+eWiJPw9qmiBcbWIUjv+rv8IC2fC3ozkyBtMwpuVf742YhoE2cwh9wROq+jldR/0bsPX/w67n70fnkv/Kux81TXQYu/F6cE3efaqBHLDEWS3TB2k6AgAINDQDAJLEC6IVFuw+moYh+iF57QjyHEmxSA1YFm+T88ydegK1TnOQAYAoEQBAwJwbRgV1E0FOpVL43e9+h2uuuQarVq3C+9//fnzyk5/EXXfdNW3fRCKBgYEBHHLIIWhtbR3/T5KkGoy8ONr40k8TFOIBr9XnRViqT6Ex/A4MStA+fxlEn3VxKnV8cTIYs4F/9E30CgsgeXy5dwh1wkcyiEWGZ3dgkyCZOJLUU3ERS0KdEImBseHa5ljPFqODfZCJBhJeWHznGbBw2Srs4BbAv+OvBfcb6t8FP8nMqsUbMDHRMoukNFQTkokiTr3g7e6BaeKFoBYW7D6aBpWC8PisFEFTnRuF5nR0O1QqoMV2xQEA2XHuqGEeeDG4jDW2EI2DzoGW9HUjkDdv3gxVVXH44RNRl8MPPxyvvfYa9CmFX1u3boUsy+jsrO6yVqUwbYHsDzUhQ7zgq9BQoxItHNcqt2KDfgxUaj00M1Qo6FMoxHZikLRAkmVIPmf2Gin7d2Aw5jqUUnQpb2MkkL8yW2x0moW8M1vDmganJZAkeQT8DJAarXtyZGDf6KY33Gs5RnhbK++BPJX+9hOwUtmI6Fj+9JyhnW9a4+nYP+8+1UD2BmBSAtRQIPOZGJJkolBSIT7wBbq7qhkFMtFA5QAkO8WCzhGBLMXexR6+PauVuFNwq9exjayo2rUPREWqhpaAbqkbgTw0NISGhgbIsjy+raWlBZqmYXQ0uy3r1q1bEQqF8KUvfQnHHXccPvzhD+Opp56a7SG7xun37m9ohMp7IeqVvQgr1cJRDHciAS9EGAAACXpBn0J/ug+jkvWas8S2N3k8zyWq3eO+2sffG9jQ04cLvvVztCCCF6INef9GgTaroUbc9s+tBYKWgMJ5K35cf4v1uyWG9o1mIYlBa5IT6qi8B/JU3m05HgIxMfS99+a9BpP9Vl1I84LpDirVhHAcUsQDotVOYApaDCnOP/5zhvcV7O6astMBiRyEz29FkGmdpj9OpUHpQ0TOTnv0O84ddfwMlvQJURwbrX+3m7oRyOl0elqKhPOzqqpZ27dt24ZkMol169bhZz/7GY4//nh8+tOfxquvvjpr4y0FokSRpB6IogSV90E00hU9fqVaOFo+hTFsolaEazdtLuhT2KL1I+mzikG89uzVqOPlnb2Vave4r/bx9wacv9HFmV8DAPY3tuX9G4U7rGijMrJrVsc4GUFPIcNVPoLc0GbdOzJj+4ZA1kasSHnr/GVV/ZwNPX34xitepKmI/cjuvNegPrwdGuXRvnB2I8gAkIYXXJGUhmoi63EoQnD8Z433QyokkO222LwnCFGSoVEe0Cr7bK4G1DTRofcjHchO65lw7qjfZ7DHiFsrDQCSkdoWKruhbor0ZFmeJoSdn73e7EjHV77yFXzmM59BKGRFLVeuXIk33ngDv/3tb7F6df319ebUOBLEDz8AnfdXvIJ9rXIrrhHuxun88xCIiTSV8KixBt/WP4YXSzjO+u4ubMCd2LThUhyInfCRDBJn/AJn5yjQU1IJtGIMWxusi9QXbAIAGEV8JxmV59SHVmM9r43/fKHwOC7E48g8JALdpeW5bujpwx2PPoevpm/GN71X4BMfOKqix99bmfo3Opl/GZtwbs6/UUuH3SwkUjsRKRlJZHh/8R1LpLljAUxKYO4j3fS46E5EEEA41FjVz3F7DcqxdzDAtWH+LFq8OaQ5L4QCKQ3VxqPHEZ3kJqILPshq/vNQse3QeNuiTiFSTSPgbhkd7kczSQNN2Wk9ouxFhoogav2mLnjNBAa5FnTQIaTmgECumwhye3s7YrFYlkgeGhqCJEnT3Cl4nh8Xxw5Lly7F4GB9+m9OXvrRBR9kWtlZqpMawcMEpZhRC8f13V1YHLByvhtJAt2hSM79BnZauXei7cPoD9rfUQ1z0PZVnNxx0655KLfH/dRI8UXJX+D1B76Dt8zsCVK5x6811UwTcb4DnVq31EJ/I14QMUyaICRqJyJlIwWNr3wEWRQljJIG8DX83WYTT2o3hvn2qn+Oc34p1BK+GuVznl8N6V6MehZUfTy5UDkv+AqnD5aCz0xCFyd0gS4G4DXzj8dxexAdgQwPOL3+I8hD71pWf5626asWlktW/drIBs0ERiTreZKJM4HsmgMOOACiKKKnp2d828svv4yDDjoIgpAd6P7CF76Ar3/961nbNm3ahCVLql8oUQ6inoDCW0UApuiDhyoVPb6TGrGHNoIQ4B7jhBm1cJT0BBKwovYDm57NuU9ktyWQA/Osi1QQRSSpzARyDRDDnUhBBkcAg5KyJ0hTU3XOEf6B/+J/hYPIu/iXsWxcgM9kAlYrJsT/b6qSJjJ5kmq6mKRGhFZ4lT0V+exykGkKulidzm9jfAvkdH0GKypNKLMHcU9H1T/HOb8k6DAogQADCqSs88taet+NdGBRgSNVjwznK5jSUG38NAFDnhDIphiAr0AwKmOnA0p+6z0qkeeEQI7brcTDXdOf7yniBV+nAtkwDASQQso+P414/ful141A9nq9WL9+Pa6//nps3LgRTzzxBO644w5cdNFFAKxosqJYwnLdunX4/e9/jz/84Q/YsWMHvv/97+Pll18e37fe8OhxqIJdJSv64auwQF7f3YXUWXdiEFaaw93ej82ohaOsJ/GuvBJpKsHofTnnPsrQdgBAy4Ll49tSxAeuiO8ko/JcfsoKLOCsZdbXzSVl97ifGgXVKI/HjW68J/O/GCaNeNSwvFWfNA+Z0QSsFkyI/ydmlKefD2eS2kubsZ12Fv0Okp52NKi1E5E+moJZJYGclFoR0Oo/OjRTqGmi3RhAxl/9phzO+XWXcSIu1y4BIcBh3NtZ59fwYB8CJA3SXJtAkSb4IReI2FYTXdMQJGlAnrTaLAXggwLTMHO/x25a4bg/ZIgHfIXrg6qBPvwOTErQsXj5tNcUzl/TNJdCJKKj4AiFGV4MADBTTCCXxNVXX42DDz4YH//4x3Hdddfh0ksvxWmnnQYAOO644/DII48AANavX48rr7wS3//+93HGGWfgqaeews9//nMsXFhdL8py8ZpJqII9s5X8kIkGXc1U9DPWd3chJFipET/6t6UzauHoMRPISGHslJehcey1nPvQ0R1IUwnNbRMPhzTx1e3sdW9mfXcXdh34aQCARPSye9xnR0EJeJjop82QwvOQOutOfM/3eQBAD3/wjCZgtcAR/3SGaSj5cCapu9GGEYSKfgeqvxMt5jComfvhXVUohY+mqyaQM752NBqjxXec40RGB+EjGSBc/ZQG5/y6LXApHjTfi6fNg9FM4jhp/4mitKF3NwEAvB21mbgagg+yWRuBmYzZ55s3PLFRDoAjFKk8heOG3fXP6QKocR4IRmWDV9WAj+7AEGmC7JleQ5Dh/ZD0+nwGJ6LWd8QHW5GAFyRd//eIuinSA6wo8k033YSbbrpp2mtbtmzJ+vmCCy7ABRdcMFtDmxF+moQpWQ8jIlv/TyZjaJBaK/o5sh2ZTsdmNjPz2cuvMX87VvU/AEVR4PF4svaR4rswwLdj8aROYArvh1ins9e9naVSBADQxCXwzFXryjrG5aesgO/BGEYQxHZzHjbTheiwo6Dru7vwoUM7MXp9CO9timLNLIrjXIWD5Yh/JSGBWAXUkKFWPE1kfXcXtv5RRVJqxjNXFvkOQp3wDqiIjg2hobn6OayTyWRSkIkBIgeL71wGpr8DjSMxZJQU5HzNUvYChnvfRiMAuWXxrHze+u6u8fN+yz9FtDxyDp7ZcCuOveBrAID4bsfibWXeY1QTQ/DDU+H6GrckoqNoAMD7wuPbOI91fqfjUQSC4WnvMRS73br9msZ7IJr1L5CDqV0YljqR666h8X74a9zGPh+pqLXKKfgbESdBCMpYjUdUnLqKIO+NUNOEn6Zg2Es/nP1QqkbHOYlaUenMDHN7AjQJUw5BWngEvETFjk0vTdunIdOHqJzdqEXlfZAMJpBrgW47IoRoouwORU6UKgE/9qB5WhSUEII9wgIEErPX4MLJHT4/edeMcocvP2UFOrmJiEUEgaqkichmGrpQ3B1CarKijsM1aBaSjEWsf8jViSDzYeu+MDpQOxu72SAxYKWZBdur74E8lRVHnow35dVYsfXn4w0XjOGt0CmHtgXVtZzLhykVzvmtJik7KCT6J9xEOI+1aptO5BFidr2M0wVQ571zQiC3aLuR9OVetdDF4mkutfK0zySs+68caEKCb4CkRWblc2cCE8hVJpNOQCQGiH2xch7roaQkKp+r67UjyNoMutllMml4iAbIIcw76DgAwOjbL2TtQ00TbfoeKIHsi1TlA5CZQK4JQtwSyFaHovKX2NZ3dyFEUuhoa8MzV62bFqmNBxahTZ09ezInd/h84e8zyh1e392FvsO+AgB4wuhGE0lg6JBLKp4m4qFpGGJxgexrtdLBEkM7Kvr5bkgnrCVnx96q0siN1t80upd308sMz44Hcj74E65CCyLoeegHAAApugMDXBsEyVPknVVC9MNHMtA1rfi+FcYJCkmTBLJgn9+ZZB7rUTWONJUgiFa/BYP3QqpzgZxOxtGKMZjh3HnmhhiAj+YXyLX0tHcEsifYBEVogFerfJCw0jCBXGWcvBviCQMABFsgqxXudkNNEx5YEWQjWf7SRSJqvZd4QmhdsAIRBMDtzi7Ui44OIUjSoOHsamldDMBb4OIsBuvWVj6e9IQjQnSs/OIvSikCNAVDasj5utG4DM2IIBmdnfyxtcqtGDQnxjKT3OEDwlaO/ryTvoAEvNj/3XsrNk4Ht7m9TfOsqKMyPPtRVsWOqAne6qRYBFutiXNqZC+/fiM7kaQehBormyrnlhXvORWbpVVY9tZPkU6l0JDehVG5+gWD+XDSB1Op2S/UVu1nnifUPL7NsW/LpHILMU5NIkUmeiwYvGc8TbFe2WNbvImtuVctTCkIf4FncKWaipWDbgfufA3NUKUw/Gb990xgArnKOMUDgs96yE9ctJU9OTKZNHhiLa3TdKTs46Ti1o2G94RAOA693gPQGnsja5/BnVY+uDzlIrWW2MoTyKxb28xoUAfG3SeSkfKbd2SUFCSiA57c4km2C4D2bM9dvFlpDvMNoI2zHnA65WZkMafZvpvBjqV4s/U0HBr7O0YGd1dsrKZhwE8UUKm4QG5uXwCdcqDR2W8W4tx7BG/uSdBMaeqwJs7qXt5NT072YZBvB+Fq9BglBDj+SrRjFP966Pto13cjHayNxRsAECfnNzH7kcFx8RVqGt8m2/ZtWp5nLa8lkCYTOfJU9I0HmeqVSK/17A3Om+5gAQCQg/AQDZlM7lQXp1hZc+HXXmnMlKUtAg3N0D1NCDGBzFDsZQUnN0ryWTcRvcKz7HRy4nhEiZR9HCVuC3rb+ibVuhqLjJ2IOXmLAGJ7tgIAwl3Z7UypGICfpsqqzK/lzHauQylFizGEXsGK3CnR8os0ElFrqZLLI54aFx4AAIj0vln2Z7jFMAx8kd6NFJUQpT68aC4v28IOAHT73A42tqHjxEshER1vPfrjio03ZS/lEql4igUvCBgmTTVpqKHbETXJV50Ui4amNmSoCMT37mYhocwexOTqeyAXYuUxZ+ANbgVWbv4BQiSFF4f4mgUVeFsgKzUQyIYdFAqEJyLIsu1OYeRZrRX0JJRJ7dZNwQcPrV+BvKGnD88/+3cAwNf+vD3n9+wU3iYnPa8n4zgVCbCe0bPpaU+VCAxK4AuEQT2NCJI0Mpn6jtgzgVxlVLudpRwIW/+3Z7W6UlmBnElP5J1yavkzs4w9XskWyL7FayAQEztef358H3XYKixqWzBlFusJgicUGaX0POS1yq14WD96/Oe52q2tFkTGRhAkaYwGLdGYiZcfQXZSJ/JFFzuXHAidctAG3ir7M9zyzz/dgQPoNjy2+Ar8i65EmKTKtrADAKRGYFKCYGMrFq48HG9KB2PRO/fCNIyKjNfJ7eXyRN+nEhFa4UvPfrMQJ73LsbeqNITjMMI1QUgOVOX49UKrMQDFX1urww2v7MYt2tloJtb9/wBjS81W3gT7vM+b81tN0pb4cgrugAl3CiPPs1Y0klAnd5MUvZCIDl1Tc+5fS5wV1hPM50Ep8MH0H3J+zxOFibm/A8dPeze1Iu2PGYfPmqc9l4kiTvwgHAfityYysdH6bijEBHKVGc+NClonpMdOtTAzlfUqzEyKSAtq+TN41V6qcgT9glXHAgDi2yYEMh95F2MIwR9qzHovZ3cxSsRKz4EWw53Q7dNxJt3g9kVGdlvV9EbbKgCAnijfxWQ8P9WeIE3F4/Gin2uHFN1e9me4IZNRML/nFuzgF2H9RV+Cv30Jusgwnr7yhLIL64gyhjjxgResdr3p1R9HJx3AG/94sCJjTtvXDu9SICc97QjVoKGG6fi/5rC+qhRRoQVepb4ffjMhHh1FCEnQhtq0dXY49aHVuIO/cfzndfyrNVt5E+3VUbXC6YNumBBf/Pg2ny2WaR6BLBspqPzEag+RLLGcTtWfj7CzwrqS6wUhyLvC6hQm5nPucJyKorC+q/8Tj501T3s+E0OSWOlnYsASyPGx+p5EM4FcZQx7OdMXtE4IT8A6gSstkNX0RNRW1su/QTnLr96AJegb2hZgD2mFNDDRAtyb7MWQMH1pkXMuznik5M+1bLisi/qlGS6l72vEB3YAAEKLuwEAZqr8ArqJFYTGvPuMyAsQTlXXoeClB3+ABbQfqeO+CsILoA0LECIpRMbKF/98ZgxxMpFWcPD7L8QIGmD+82eVGPJ45Iz3uEtdUP3z0GoOzXqzEFOxxum1J8HVICW3IqSVv5JR7wz3Wu1+xeba5fwCEzmlCrVaGtRy5U2y7/9ahQvQ3cCrMSRJdmqT1xeEQQlonmetx0xlOc44Annyamy94HzPZpFGR6K9KuTcx3OxvrsLLZw1aThzCZ21hk+iFkOatwSyHLIKW1MzqJeZDZhArjKm3c4yELYEh8+eZdNMZe3QNDsqlKIyvHr56RvOeCdHh/cEDsC8xKbxn5vU3Uh4p19UTiFioYszH+u7u7Bzxb8DAERizGwpfR9DGdkJAGhedJCV+5kq38XEsQj0BPIL5HRoCebpfaBmZVITppJIxLB88/9ii3QQDnzfRwAAsi1Ehnu3ln1cjxpBip8Qr5LswZbOs7Aq+TwGd709s0EDUG2BLPjcRZBJQxc8REN0lpcZHcEweTm60mi+DjSbI0CZntz1TqzfWkEJ1MADeTJOTqkEAwoVa7ry5qQP5ktpqCaCFkeayy6OJRyHFPGA5Onu6qFpGJM8yzm7diBTAxeOYjjfM0HhYmXZfgZreZw7AMvxKkyt10ls9lJxZCMOhbfujd4GSyCrsfpsauLABHK1USJQKQ+v17p4BVFEmkp5L9py0TKWe8QI1wyfWf6xqR1dmlwNrLV3Yz4GMDjQB13T0GYOQg1OX1qU7ItTKdOHeYnHmjS08qmcHryM3JiRXuiUQ1P7QsRIEHwmUvaxdLvYxRfML5BJy/7wEhVDfdVpcvHqfd9BK8bAnXQ9nNZ3jhCJ7Sk/tcOjR6GI2aJw0UmfBQD0PXIz8ItTgXj5S35O5Ex2WfzmNAsZmeVmIUSNI0Vl8EIVG6mGOuAjGcRd2gHONYtHZXgHAKCpa7+ajsPJKb3LOBFnqd+o6cqb145e6srsR2A9ehyKMH1imoIPnJZ7PH6agilNvIeXrQiyWocR5MtPWYEubhSEAD/VT8v7PTu1Q3qBKH4sMgqJWMENOVU5F59ieI04VNG6NwYa2wAAaoJFkPdpSCaOJPFlWQGliBdEq2wE2bBvSnGpBQE6g2NnokhTCaIkj28KLTsKAND72tMY3L0DEjHANy2e9lanajifrU4xjJglToK0/mbw9YyY2I0RrgmcICLBBSGqkbKP5awg+Bqa8+7jn2e1sh3a8UbefcphQ08fzvvWL/GeHT/GM/RgvCEcOP5asy1EMiPlp3YEjBgyUrbw71qyAv8U12Dl7gdg7ngOD9z6hbLFmVN4K7ssfvO32M1CBneU9XnlwqmJLP/XaiCErcnt6J6dRfctZPFYr8KZRnZBoSJa2mrnOwxM5JTeFrgUm+mimq68eYJ2zm9m9u/fHiMOVZg+MVU4HwR9uuDVNRVeogKTLBl5u0dBPaZYrO/uwsDh/wkA6KH75/2efUFnkpL/GRwdmRDFwczsrV75zSR0yfqOQs1Wo2yanFnX32pTxRACAwAEzUpMn/xYVogHfKUFsh1BTnnaEchshKapEO0OQaXAqQkkiQ+TH5+LVh0D888EqR0vYtQbQCcAX/v07lEeO6fRSJdXJMglrYs1SBOgppFVcMHIj0/ZgzGhDe0A0kID5Jl0KFJiMKdUg0+ldYlVDJjYvRnAh8r/rEk4Iul+cgt4YmLU8OHqByyv5fXdXQi3dlrpI5HigisfIRqH4ckWyMY32nC0mQGsQDXONh4FHnoUxh9k8F8r7eFh2GlOXpcCudFpFjI6u81CeD2Z5f9aDbzNVnQ8PrQTWNldcN9TH1qN9fxE97ULhcdxIR6HtoHHJcYV+H/4G9bwjnC2lsFrubq0oacPDf1vggI486YH8YkPHFXT8azv7qqL1Tafs3JS4foaV59tJjEoTY8gZzgvBH26N38yHkMDkNVuXZCtc0svw4VpNljhte7rV5z7fixbfVzOfXx24S1N55+kJEct55zdpB3NxuwIZEopgjQBU7bujR5fEAoVgRnUy8wGLIJcZazcqOzigQzxgs9x0c4E085p1v1WTlJ8rLylC0GLIzVlvJ5AGLuEBfAPb0RyYBsAoHGKBzIwyVanzCINMW3lI/GEIh6r7wunnghrA0h5raJJVQzBa5QfweEyUSSIFxyff3LSNm8RktQDDFfO6s2p0j6I2wlCgDOEF7KqtAnHY4hvhZQoL4KopFPwEwXwNmVtP0v8ETboR08rflkvlu6PbNqRM4/L3N7m9vnQKD/rzUIELZHl/1oNQnY3PWW0+O/mFCA5jW6ctGWRGLhDuBFnCC/UjTe6M5E7GFvhgcYaGk2CEwSkqAxUOH3QDQGaHBdfk1F5PyRjuuB1XB64SQW1kre+BbI6sgMA0NyVv7W549yBAlH8dMQSyHv8K9GIeFYPhWqRUVLwEA3wTHxHMRIElym/XmY2YAK5ysh6AgqfXTyg8l4IRmUFMtWs45FQJwAgES1PIIt6AsoUgQwAQ6FVWKRsgjHyDgxK0DZ/eu6d385bLXeJzadOjDkxsvdaRFUSwzDQao5A81vfuyY3IjiDDkWcGkcKhRtdcDyH3UIXvPEdZX/OVNYqt+JZYyKlIleVdkTqQDBTXvOJ6KiVvsP5swXya1EvEvCBADAn2Qu+HvWU/iFO8ZvfXQ4yLwgYIY2z3ixEmmJvVQ1a5llFlXqkuHC0CpA84GHCoBwoCH6jn4APZa7H/xmrxoWzQsWae6M7E7kWEi9ot7WvkiZecBVeHS1GJpOGj2RAPdMFsib4IOd41jrd/hxbNACQ7BQLo8IF9JWCRnqRojLCTW159yEcjwT1Amr+Z7BmF8aprYcAAIZ2V9eyEwDiEacBVXh8W4IPQZpBvcxswARylfEacWhi9tKPyvsgVVogq9ZFLTVZOXHpeHkzM0lP5Hx4ks5uNCGGhoEXMMi1QJiUo+wge7xQqVBw9lqIoD6KKLU+OxlhAtkNo4N9kIkGErYidqYcRogmyrYOy7XikYuobxFaM+WnO0zF8sG2otYZKuSs0k77OtGsl1dElxizzicx0JK1vTPsRQuJYRdtxQ7ahruME9FKougMl56jS9QEUlQGV0LxW0Rsm/VmIbKRhCpUVyA/9lYMMerF4m2/KZo7fPkpK7A/1w9CgJv1j+Au40S0cAns9B6InbQdnN31S4Jec290K9p99HiUmzU0yiZNvBVPHyxGIof4ctCFADzm9GdtJukI5Ilns2gX0pt1KpCl5G4M8a1FW5uniBd8AYFsJqx7oX/JYQCA6J4dFRtjPpJ2h1Z+kn1oWgjDo0eq/tkzgQnkKuM1k9MEss77IZu5e6WXjZaGRnl4w9bsMhMvL/ndayamjRcAmldYXe4O0N7EmJT7AUUIQZJ4wRW4OPNCKRrNMewWrcIlpczx72uM9FsOCLKd8wlfE2SiIZGnk1IxJD0x7lVZCDW8DO3mUMUKWi4/ZQWaiHXe/D/1P3NWaZuh+WhBFKkyjPzTEStqItn+m5M/90v4Cp4zD4KXaPiafjG+hK+U5QJAtCTSJRS/bejpQ1/Gi6WZTbNagGbZW1UvxcJJQzDAoZ2MFU1DWN/dhaGWI2FSgnuNE8YLkK474yC0cTH82TgSAPCkeUjNvdHFcCcykEAIoFGeNTSaQoarfPpgMVIxW3zlEMim6IcX05+16ni79Ymos8dnC2R1dsfvloDSj6jUXnQ/hfOB1/OLfJIaQQw+NM+3iq3Tw9X1tAeAtP0dTfbXz4gN8M+gZ8NswARylQnQJKiUveSqCz7ItLICmWgppIkMr92QRE2Ul8PrNVPQc9jlzF+5BioVwBGKlC9/5Xaa+MCVkYOWjI/BS1REg1Z+lRqvb3/EeiExYN3cQm2LAQCcfQOKj5X39/MYCWhCcYEste8PjlD0v7Op6L5uWN/dha0dpwEAXqHLclZpi03Wsv3grm0lH99pv+0LZwvk9d1duPHsg5Higwgjga6wFzeefXBZRU98CQLZEZEdGLJzWe+etVxWL03DEIt/x+XipCE0kqTrNIT94//EVnF/9HznvHGLR8eh4afe/wAAPCXMXtevfExuaPQ/+lmsodEUMrwvZ85vNUnZ9SpSDu92UwrAR9OgU/y4HRs0OTBdINM6FchN+iAUb/GJmML5IBYQyIIygigJo7lzMQDAGKt+kXDG1iNycMIdSfc0IkCZQN5nMXQNPpKBKWcLZFP0wUOVin4Wp6eRgQx/g7WErCfLS7Hw0yQMebpA/uMbI3iLWsL41TEh74M8TXwQClyc+RgbsIp5zBZrVmuUKfD3NbRRu0lIp5UT7qQQJCPlCeRcKx65aJh/AABgbGflrN7aZcvJoOebZ+f0wfbZk4BIf+kCWbcFciA8PX9vfXcXVi5dCC9R8cQXjixbgAl60nXx29SixAuFJ2Ytl9Xyf62eQHaK7jRqpcyki+QOxyJDWKZuxkj79Mr89d1duPsL1sTp1KVizd0a1nd3oe8wy25rU41t1eoRvQrpg8XIxB2B3DTtNSIFIREDGSU7IDUukCdHkO0UC6eep57IKCm0IAIjVNxWUOX9kAo8gz2ZUSSFMGSPDyMIg49X3wtZS1rf0eT+CqanCQ00AcOoTsOpSsAEchVJ2ib5nDe7eICKAfiqIZCJB4GwJZCo3fChFDRNhZ9kgCmC3ol2CdABACuMbXmjXRneDzGH72Qx4iPWsfydB8CkZEbtkvcpYr1QqIigXbjhCVkz9HSsvCJNH03CyGGXNJWOpZbVW2ZP5ZwsSCaGBPXmbWDRZE8ClKEdJR/bTFlLfKHm3AUunO1ukSgz8g4AopGEyruLIDsiMmO3CJ6tArSMkoJEdCDHJLhSOF2/eDt3uFgawrbn/wSeUIRWnZLzdZ8/ZFn8JeujqcBBjdbvdfmHjmINjaag8b7Kpw8W+0w7GOQN5hDIduFdakpNjmnboDm2aADA8bzVxEub3fG7YbjPKqTjGxcW3VcTApDN/ALZp0eQtv3gR4VWeNLVLxI27JxvX2gigkx8TeAJRaxMx63ZgAnkKpKwl37I1NwoyQeZaNDUTMU+izfSUDkZHq/fKpRTIiUfIxm1byKebIHsRLtWclaU9zj+jbzRLlXwQy5jiS09Zl2kwbYFiBMfOKW+7V/qBTHZj2FuonDDG7ImSE5KQSlQ00SApqateOQiGGrEIJrAj5Uezc0Hp8YLNrBonrcEBiUwy/FCTo0hRWXIntzFaYK99JeIli+QJSMNjXcXQXZEpAgreiLNUi5rKm63mK2iQJ7c3W272Y5hNBRMQ9Dfehxx6sXyw0/I+TrhOERIqG7uCapdH+Gdkq7DsHJ+PRVOHyyGnooAyN3cyLFxS0+pyXCcliYLZMDqUVDpJl6VYMxube5rXVR0X13MXZjoEDIjUGXrbxWXO9Cglt891C1m2rp2g40TRdKCvdoZG63+55eLK4F84oknIhKJTNs+MDCAo48+utJj2mtI2zdS0RfO2u48nFLJyuXfCEYaKucFCEGcBMAppTeLSMYjAAB+SsTbiXalqdV4pFDldr6q4WJoEUsgN7TOR5yEINTJw7DeCSh7EJlUuBFotP5tJEovckwlYxCICeJCIAPAoLQAoeSOkj8nH4KWmObBPRlelDDMNUOIl56ny2fGECP5fy8pYD0wUpHyoxmymYLu0h3CEZH3Ge8FALxkLp+VXFbH3orzVE8gT+7udq+xDm0kiuhx1+aMtFLTxMKxZ/GW/4is7p1TSfANkNT6uCfodvevABPI0zBFP7yzLJBNe7U0GJ4ukB2XinQykv2CGkeGipDkbDvHDGRwemVXdytBasiqNWmcV7y1uSn64c/zHZiGgTCNgfrsWiX/PLQaQ2W7HrmFKFGkqAxRmvh7S0FLIKfKTAecDfL6ET3yyCN48sknAQB9fX247rrrIMvZN7Ddu3dDKMHSaF8jk4gAmOiP7sDZ3XuUZBQNjZW5yYqmAp2zTr4EF4Cgli6Q03berzBF0IvhTiQSXsjQoFCx4JKpIQbgpaULZJoYhEZ5hJvbMcwHIc6kG9w+RKM+iF2B94z/HLTPJzNZeopKIjYKP6anBOUjGVyClSN/tTo7EFLy501F1OPIFLGYGxPb4U+XnjMnqREk+PwC2Wvn7mfKmFiMH4OmYbgUyOu7u7ABd+L7j27Cmcpz2MTtj4b1N1d9uV6xhcJk/9dq4BTZ7X53Acw7fouFux8BcPy0/fq2vor5dATvLH5fweOlhAZ4ZtBCvZKYKUuohyp0796boFIAPigwDRMcP0sL1OkoVCrkXB0S7XuZOiUY5bRbnzoly3AyeKP+UiyMsV0wKUFLZ/EIMpWD8COV8zuIjg2hkZiA3zp3SagLvsEMopERNDRV73zmMlEkiR+T19c8DdbnKbH6Fch5z+CjjjoKPM+DtztqcRw3/rPz38qVK/GjH/1o1gY711Dtbj2eYHZ1LWfnRSllWnHlQjQU6Hb+Y5oPQi7DPmVc0PuyBdLkJdOz1G8UrNw2pQD8ZQhkPjmIUdIAnuehCA3w6kwgF0PNZNBKx6AHJ0SV5PEhRWWQMiLw6Zj1Ht7nTiDTpv3QgCTGhivj4ysbSWSKOGgkvZ1o0kpfkvNoEShCflE4XtxaRmqKg4+mSyp+W9/dhWeufj8G+TYcHIzPSi6r4/8qVlkgO3Qu2h9vSgejc+cfckap+l/+IwCg64gzCh5HlRrhN+rjnkDSo4hTb8GI9z6LFABPKNKp6ndnc+DUKOLEn9MfWPRZEWQtlX3ucHkcZ1TiqUuBzMd7MUKswrqiSEHwhCKV4zuIDlvBBTFo1WIItjPQSJWbhQhaDEku+97otwum1Rncc6tN3vBvU1MTbrzxRgBAV1cXLr74Yvh81W1Purfh5EZNLR5wDMkzFbyJSDQDg7ciyBkhCJ9aegRRtaNLUiCctd2Jdt322BbsjqRxW+BSXH7KitwPdDkID9GgqUrWckox5MwwYnwT2gGoUhhtmep7M851hvt3oJNQ8OHsyuY4CYAro0ORYk/oRN90u6RceOetBN4GBra/hsbWmefOeswk4kJnwX304Hy0Rp6AqqqQJMn1sf1GDIMFLJKcIsdyi0NNw7BaWYulN+CISu0IKDObZGzo6cMdjz6Hr6Zvxje9V+ATHzgq5/WppqyJs+h3NwmqBPEVZ2PV61/H9o1PY+mh7816zbvzSbxLurBoaeHUEl1uREO8PgQyn4kgzgVRvSSVuct4UVwyCn9wds4xQY0hRfyYnmABeOzz3HGtGH+PnkQ6h+OMxnkgGPWXYuFL7cao0AY3MV7Onvym4xEEpnwHyTEruOBpsO53/jar6C828A6w6j2oFrIWn9ZRONRcfjrgbOEqP+Jzn/schoaG8Prrr2dZcqiqijfffBOf+cxnqjbAuYxhC2T/lOIBwS4cUFOVu+HLVIEpWDNiTQzBp5QuMHVbIHty+Ek6S6bFcPJXU/EIGpo7XH+2Xx1BXLKieIYcRiA2exGIucpY/zvoBOBpya5sTpbZwjNjC2R5SuFKPpoXWq2h432bAJyc9ZpbwTYZn5mEUSQCyzcuhNBrom/3DnQtXu5qnAAQpDHslvMLf78/BJUKZbm/AEAqFUcAAOTS7dMU7zx0jW0t63OBCZeZb9M7sIbfYjfmsIT61L+5bt9zPLMokFeecAEyr30LI8/9Oksgq+kklqVexb9aP4RiC8emrwUhJKGpmZpHbiU1ihTH5HEueDu3vZKro8UQtXje5kayHewxlOzniagnoeYQyDrvhacOVy8b1AEM+t3d75zCxFQiAky5spSINRH3NVnP5uZ5SwEAmZHKdUXNhWzEkZKyu5j6g41WG/n0HBfI99xzD775zW/CMAwQQsZNtwkhWL16NRPIeaCKdZPwh7IfzJLPOoH1CnUhAwAPMuMC2ZBCCNDSj22kHSsWdxHEXDiz12SsNIEcMkYxLFseyKanEUGkoauZnC2tGRap4R0AgIaOpVnb00KorBQbrcAEKRcdi5ZDpTz0obeztjuC7Rp6T1HBNhkfTcMs4sHsbV0MABjdvc21QNY1DSGahOmdbgPlQDgOMRIo2ykhHY8igPLcIYzQfDSPRaGkEuPNCkrh1IdWYz2vjf98ofA4LsTjyDwkAt3Zy5eOUPBMWSWqJo3NrXjJfzT2G3gMpq6BE0QAwLaX/4oDiAZ55clFjgBwfivIEB0dREvHgqqOtxgeLYq0MHsTjLkEb4szJ5VnNvDosbzpU177PDenCGTZSCEthqftr/MeSLPg6lAK1DTRag6hL5Db5WUqoq0vlMT070CPWW2mg83WalpT+wJolIcZ6a3QaHPjMxOIStnPKcJxiJIg+DouyHeVRf/Tn/4Un/nMZ7Bx40Y0Nzfj73//O/74xz/igAMOwIknnljtMc5ZiBJFgnohimLWdtlvPUR1pXJRUg9VQEVrRkw9YQRpCmaJBtzUdr4INOQXEsVwin+UqVXDBTB1HY00CsNvLfsQv/X58Tqubq0HtFHrptbalX3jyYhh+MqIghh2dNGXw080F4IooZ/vhCeanb/m2AJeKDwOjlBXndQ0NQMfyYB6CguPsB3xSA2+42qMABCLDIMjFMRX+PdKcMGyiluBifOdL8MdwvE2Hd7t/neazFrlVrxoTEwWCrnMmPak3TuLAhkAzIM/giZEseXZP4xvS7zxGDJUwP5H5vY/noxgV7wnxmovXnxGDKrEBHIuRNs1wknlmQ28RiJvcyMnxcCxdXPwmEloObpJGoIXUoV7FMyUseF+eIgGNBRvEgJM1BCpOZ7BZsKaMIft4BUvCBgmTRAT1e3gGaAJGDmumQQXgpCZ4wJ5cHAQ69evhyRJOOigg9DT04Nly5bhq1/9Ku67775qj3HOwqlxJMn0ZRyPfQIbSmUiyJqagUQMwBbI8IbBEYq4bdvmmkwsbzWwWxyBnOvizEd0dAACMcEFrJwkwY4WldsueV+Bi/chBj+8gewbjyE3lLWCYNoTJH8JE6QxzwI0KtnLc2uVW/En/UiYdndXN00wkrEIgOL2Yy3zLZsjfcz9kmDc9tkUA7myFCdI8yHIZTolKHaVfDnuEH47Kh7pL69QRgx3QiZWBFmnXEGXGZqxzgt/YHaK9BwOft+HEaV+KP/6zfi2tsFnsEVehVAoXPT9csjKvkyODVZriK4J0Dg0OVzrYdQlzuqolp49gWw1N8p9PvOCaDX/mCqQaRpGjnoBU/BCppXrT1AJnAI6ubl4kxAAkOz0KS3HJIWkhxBBAII4Ub8xJrbDp1Rv4mkYBoI0BSpPF8gpPgS5jh2rXAnk5uZmjI5axStLly7Fpk2bAABtbW0YHKz9DateEbQYUtz0WarXfjhRtTICOZWyjyNZApm3G5MkS/R05dQ4EjkEfSk4F2emhAhCdMiKhIpha1Yr2U0bUlF2bhXCk+zHMD+9bMOUG9FA4zCNEr0tlSh0ysHrcx8FVRr2Q6fRD12bWOIXGjqwinsHnO38JkEv2gTD6XRFikSQZW8QowiBj7lfEkzZzT/EYEvB/RSxAV6jvAe7ahfcCt7SUyTCtrdpuowOgYDtMoMIAOAx44iCLjMkE0eKylkPyNnA6/XijcYTsSLyf1CSUYz1v4NFxruIdb23+JsB+MLW5DkTrW0E2TQMhGgCtEA++76MPC6QZ6eGhJomgjQJM4f4ckgS37TmH1Y61/Rr1RR88NZZBDk+YK0sBduXuNrfa6fI6enpwlNURhHlwlnbUt4OhLXqPWsTsYi1gje1YRqAjBSGvw5zvh1cCeTTTjsNV155JV5++WWsXbsWv//97/HII4/g+9//PhYtKu7Lt68i6gko/PRZqs9vz3YzlRHIqi2QOVsgC3ZP+lS0NIHMqwmkSPnRY2Ait1EvoQAxMWJZz3ibrPxUp8I2U2a75H2FgDqA+KQmIeP4miASo+QVBKLGkCC+nHZJ+eBb94dEdOzZOdFy+gvhp7GIG8Iu0xKkj5vdRZtgpOPWBFx0YTE3IrTDl3LvhazY55G3oXANuCaF4TfKe7A70Rq5jOK31q7FMCmBPrarrM9e392FlF2kxBOK2wKXInXWnTnzvTktUbBbYTXxHfFR+JDBlid/ix3/tFItWg49zdV7Q3ZRkVZjS6h4bAw8oSAunV72NTz2s82s0OpoMZR0EhLRQT3h/PsQL/hJAtmwHWeolCMQIHrhgVr1xhmloI5YBfctXcWbhACA1y6ydtKpJuNRR5EUwlnbNH8nWszhklMy3ZKwA3VcjmtGk8IImLO32lAqrp6E//mf/4kzzzwTkUgExxxzDM455xx84xvfwGuvvYbrrruu2mOcs3j0ODLC9IuQFwSkqAyolWlpqdjRK062xK1sC2QlXlp1qKjHkS7SqKEYzsVplLDEpoxZYifYYj3QA7YnrRovz3JrX6HFGITim26Lxts53IkSU1QENV7yBCnYZRVWDr/7BgBgy6svYP2eH+Jl8TBcJV0NAPir8L68gs1Bcfx5XQjkhGceGlT3tmiOqPKHc0wmJmHIDQiWkZoCALr9MJrqIe4GSfZgmDRCiJdXKGNoKhaY1jW02JPEM1ety/u35rUk0jNcJSqXVUedhD60QXj9PpBtf8MgGrG/S2spxxLKTNZWIDspHk7RICMbj3P/r2B9TSESUesZV6i5kcL5IOgTz9qk000yl+OM6AVHKDJK6V7+VSPaixSV0WBbURbD5+Rd5/gOAnoEipSdQseF50MiBkYHqlOoN95ReErDNAAwPU1ooPG6mpBMxpVAFgQBn/nMZ8YL8r70pS/h+eefx6OPPorDDjusqgOcy3jNJLQcAhkA0hXs+a6mrePwdgRZtlMUnEYlbpH0BNQcEe9S8OWpGi6EEbPETmObLZCbnIdh/dq/1JpUMoYwEqCh6QLZybVNRkpbNrNaPZeWItC+9GAAQLp/CxKJGMQNn0SK+LDsU7/G//fv7wMAfPigUFGLN8dBQ3bhoKEGutBmDrlOITHs8yhY7AHjbYSPZJBRSr8uDXtJeWo+uFtGhXZ40/1lvXfPjjchEgMq5eHXIwX3FfQEMlxtIsiCIOCl0IlYmXoRq6JP4iVzBf6w0d3vLMkexKkXJF3bSXPCLhx20sAY2fid1dEKpQ8WI2kLZCGH+HJQeR9EY+KaTtsNsUiOegciWc8/JTU743eDlNiNIb7N9cqeLPugUh7ITH8Gh8wINDlbIDu5zaN7yisSLoYSs74jKZCjtsXXBInopddLzRKu+0Q/9dRT2Lx5MzKZzLjNm8MXv/jFig9sb8BPkzByLeMAUIgna9lnJmi2GOVtk3bHd1lPliaQPUYSEa+7Stl8+AMNMCkBcizv5CUxiCT1wG+Li2AwDI3yQJlNG/YFhnq3YxEAoXG65ZUnZH3/6RJXEGQ9jkyJE6Sndpk4gXqxdMvP8Ph3X8B6bic2v/9OrGydD9F+EJnp4ueh01THjf0YF14A7x4Vg0N9aHNj+ZUcgUZ5BIr4OzsuF/HRIcidpf0dnCp5r7+84reEtwMdic1lvXf4nY3oArBVXIH5+o6C+4p6Chm+NhHkDT19uG30CHxIuAcARdBM4FMPvAagsP2fQ4wLQVBqe09w0nU8TCDnRJBkZKg4awLZSc2SCqS8aLwPfnVi5SFtizEuR0Gtk6aoVNCCdaYEMnsQzZVKlw9C7LzrbIGsaxoaaALUl12LEWyz0mQTA+8AeN8MRzudiY7C0wWysxITHx1CaAbuWdXClUC+6aab8Mtf/hIrV65EIJAdYSKEVGVgcx1qmvDTFEw59wMzQ7zg9cos42h2BFm0UywCYeukcyNMJuM1kxgu4kNbDI7nEYcHUN1HkIX0EMa4Rvjtc4njOYyRQFntkvcVYnbhhs92QJiMk8OtlpjDLRtJxDyFO9lNZkNPH65+8HX8HyeijUSwnvwDtxuno82/BisBeH0ha6LjovmGk5LjDRaPIMstiwEAI73bXAlkThlDlATRUiQC40TeE5EhtHQuLnrcydCMdQ36y4wg6/5OtMX+AdMwwPF8Se9Vdr8JABhrPQIH9r8JVUlD8uSOEstmCnG5hIdtBTn1odVYL0wUc67lX8cmnJvTrzkXSb4Bslrbe4LTFrdYPvu+TIp4wc2SQFbtLmxyKP+ERRMC8Exy2nE8mqVcAtl+hmYqGEEup2nSZJr0QWwPFe40OZU08YGf8h1ERgfQQihIIPvcbe60cpvV0fJqIIqh2YG6qQ3TAEBy7BsjgwBK+x1nA1cC+f7778ctt9yC005zV1DBADLpBDzEAPJU5VvLPpURyLpd7Oe0sPYHwtApV3JXMD9NwswT8S6FVI6LsxAeZRgxIXv2GOeCEMu03NoXSA9bN/xwx/TK5mCj3ZEwWVq0zWcmMFLCBGlqgwoA+BT/R2QeegzoHgbhOMSJH1ymeMGmWYIHd6jDuqEnBt1FPEQ1ggQXRGEPi4ll83SJxa0AANVyh/AJrhflsgkvhLRHx/BQH1o63Nk5OYijb6EfreCbFwP9QHSkf5o3toPHTGJMmFkaVbmsVW7FNcLdOJX/J2SiI00lPGqswbf1j+FFF+9PiY0IqLXNQXauqWAjE8j5SBMPuAoFf4qh2atUhSbWhuiHh6bHf1ZtS0Yxh0B20hS1ChUZlts0yUFJJ9GCCN4OuhfUAJCekncNALGR3WgBIAazU80amlqtmqhYdbyQTVuH+Bum34Fle6KZrlPHKtc5yAceeGC1xwJVVXHttddizZo1OPbYY/HTn/40776bN2/Gueeei9WrV+Pss8/Gxo0bqz6+UkhErRspNwsC2bCjV6KdYuEIE+JCmDiYhgE/FNA8fpKlkOZ84HX3N5iAPoq0lD27TPENkLXIjMeyt2KM9cKkBK1di6e9Fmq0boCl5nD7aCqvn2gu1iq3YoN+jJXvhtx+x0kSAO+m+YYSh0p5eDzFl/9b5i8DAGgj7tqpy1oEqSmV27nwhlrsoZSe+85pyRm5Q8gt1jLn6O7SvZDDye0Y9CyGFLIiw7Hh/A86L03DyGFvNRuI4U4k4IUIAwoVC/o150KTGhEo04avUpgpKxoWYgI5Lxnig1Ch9MFiOKlZ/gIRZCoF4JskkDXb/kzK4Tgj2HnJlRLI5TRNmsxQn3U/EJtKmzSrnA/iFIGcGrVqfTxTipUJx2GYb4GUdO8MVAo0PQaDEviD0//e/rD1rNLi9dnzwJVAvvDCC/H9738fqVR1Z4Xf/e530dPTg1/84he4/vrr8eMf/xh/+tOfpu2XSqXwyU9+EqtXr8YDDzyAww8/HJdccgkSifrJG0rGLIHM+8I5X9d5H2QznfO1UjEz1vcyuU1tkgQgZNw/TBJx26vQM3OBrHB+iJr77yJsjkLzZj9wMkIInjLaJe8r8PFejJAwRMkz7TVB8iBJPSWlqJiGgQDSQJ6UoFw4gkeACYWKOf2O03wAklb8e+TUGJLED7hI2Qo0NCMBL0jU3ZKgT48hIxZPffCHLYGsJ0oXyLyWhDIDgRyyPU4TA6UVypi6ji69F8mGZfDaPuKpAt3m/DQNmqNBwmxw+Skr0MbFcJdxIs5Sv1HQrzkXhqcRIVrbewKnjCJGfbPuIz2XyEwpiqsmzippIFxgfUgKwEcy0DUVAKCPF9SGp+0q2hN0J21xpjhNk5yyrUJdLnMRtZsH5UqlK4Qq+CFP+Q4UO0rrb+yY/jliOwKKe2egUuAyjn3o9NSxoB3M0evUsSrveuDxxx8/nl9MKcXAwAD+8pe/oLGxEfyUHLknn3xyxgNJpVL43e9+h5/85CdYtWoVVq1ahU9+8pO466678MEPfjBr30ceeQSiKOKqq64Cx3G45ppr8NRTT+HPf/4zzjnnnBmPpRIoCbt4IE91rS5UUCDbdnHypAYPboWJQyo2ihAK2+W4RRX8kF1GkDNKCg1IwvRnL/tociMC6bfyvIvhTe/BmNCKfHGsGBeEkIm4Pl48HkFDiROky09ZAd+DluC5xzgR5/NPoGOK4FGEEGQXEx3e9ud15S5LCIa5NniS7pYEg2YUQ9LBxfezb9ZGGe4pgp6akTtES1dpUXGHPTvfQifRwLUegECzlT+eieR+0GlqBjLRQOWZp1GVw/ruLmzAnbjtsS3YHUnjtsCluPyUFa7zMamvGT6SQToZh9dfm9+BV6x0ndntQzi30HgvpBJWEGcCUSJWapM8PVAwjm3nlkzE0NDYAmoXkHtyFNQ6jX6cVdmZIoY74UmqIKR4l8tcpIas+4HTTMgtuhCAR8m2bdNjlkAOtUz/7LS3A/Miz5b0GW7hM1EkSAC5lIWTqkRT9elYlVcgX3bZZbM4DCtlQlVVHH744ePbDj/8cPzoRz+CrusQJuX2vfrqqzjssMPA2UU3hBAcdthh6OnpqRuBrNq5Uflsq6jogweV6dhD1ekRZIUPuhImDim70pTL0e2mVHTejwbVXU5RZLAP7QD4UPasVpfDNY8W1TMhbRAjvtx5pgCQ4kIQ3aQ2OPtHR9CA0iZIbgSPJobQmCm+dCdoCaRLsJiLeToQchHxoKaJEI3D8BTPbfYHGqx0kRKLWwFANJIzcocIhZuQoF7QaGlepMM7XkUngNDCVWhotR58Rp7lylQ8ggYApEYCGbDOmVIKlCbDB6woYWxkT80EsqhFkeRq9/ebC2iCH0GX9/+ZwqkxJIgfha48zp70pxMRNDS2wLQdZ3yh6c9mucIC+fJTVmDhg9b1+Jx5IN6hHdOCCIUwxnbBpKTkomFDDMBLs1f8aXIYBiVoaJxud2kE56NpLAo1o0AqNNkoA6u/Qu57Oy+IiMFftwX5eQXyWWedNf7vX/7ylzj11FPR3l696uehoSE0NDRAluXxbS0tLdA0DaOjo2hra8vad8mS7OKk5uZmbN5cnk1SNZiwNsktkE0xAD+tTAQZWgomJZAn5W+qYgjhEpopZGzrm1x5WaWiiwF4ku7ScaLDlkCWGrIFMvE2wkM0ZNKJ8ZsWw4KaJlqNIfT7j8u7T1oIwVNCj/u03eo5X0pQPooJHl1qQIAWdzSR9ERJFnOKrwuLkq+DUlrQSScRjyBIDFedzwjHIUaC4JSI63E4SEYKKal8myIrD7DVdVTcQemzHCzmLVuNYKABaSqBpnIL5KQtkPkc/q9zASFoRZviYwNoX7h/Tcbg0aJIu0jX2ZcxBD88FVodLYagxpEq0tzKOd8Vu0EIMglolIcsT1/xmRDIlUknXd/dhWceWYj9tT7IRMNt/tJWTfh4L4ZJI9pKFK2mFMzKuwYALjWMKAmiKUchMd84H9wuiuHdO9C5ZGVJn1UMWY9BEfKvucRICGKmPgWyqxzkP//5z1i3bh0uuugi3HfffYhGK987O51OQ5Ky87qcn1VVdbXv1P1qiWEXAvjy+WVKPkhEh6ZWIIqspaFAyjIS1+UG+Kj7WbBqW7HkSwkpBVMMwAd3N5iU3Wba35x9wyDj/oj1Wd1aS2JjQ/CRDBDKf5NVpTB8JRQ0KQnn+69sC13qaUCQJot2SvIYCWhCCROh8AI0kCSikcK5a3G7myAXKOZhYZHkghDKcE/xmCnoM3SHiModCGTy5w/nghvZgkE0obGpBYTjECENEFK5nR4ydjMWPkf1/lzAZ9sXpktsgFPRMRhxaEwgF8QU/fBidgSypMeg8IUnfILPOt+dbp2cGkeKeHI23nDSFGmFutxSSrFUexsAMI+PFexymQtfejfGBHcd9LKw8641bUITSZkRxLhwzt29LVYRYKS/9CLhYniNBLQC7kgpPgSpTh2rXAnke++9F3/5y19w/PHH47e//S2OO+44fPrTn8Yf/vCHihXuybI8TeA6P3u9Xlf7ejyVXRqYCeZ48UDuqJKzzJlKzLwlJ6enkCbZv7tpt81128JRS1k3D4+LTmbFoHIQfqqAmsV7u6tRq5NWqDX7piE4nrQltkveFxi2K5ul5vyVzbrsLnLr4KQESS4adZQC8YQhEBOJIp2SPDQFrQR3BbHJcn0Y6t1acD+nm6DkUiCn+GBJufsOHpqGMUOBrPg60WyUJv4aEtsxIC0a/znON0LK5J40OP6vwlwVyPbScCZWu3tCgMahy+Gaff5cwBT98FFlVtoHe/Q4Mnm61TpItkB2nnGW40zupAyvnaZItcromv6+nZiHYWSoiEYzMq3JWjEa1EEkPNOL6opiR81Tk+67HnUMSSH3890pEk4Nl1YD4Qa/mYAmFWgFLjbAq1c+6FoJ3PUuBNDV1YVPfOIT+P3vf48///nPWLFiBa699loce+yx+PKXv4znn39+RgNpb29HLBbLEr5DQ0OQJAkNDQ3T9h0ayr5JDg8Po7W1jqx3lChUysPrzf3QdPrAp5MzPzE4PY0MkbO2EU8YEjGQTrkTSYZ98/DnyMsqGTkIjlCkEsWFhhmzImZNUwSy06kqVcOHYb0SH9wBAAi0Lc67j+lpRIgmXLdj1my7pFyV3TOBs1M2EpHC/rV+moRZggdz0PZ/ju8pHPFwoo2ekDuBXO7N2ksVmDN0hzBDXWhEHKmEu8+npoEubScSDcvGt6WkRvi03ALZ8X91BMNcI9RkCQUjUZt7gmkY1jXlqewqy94GkQIQiYFMpvpRZK9ZODoJAJJ9D9JS1vlvOc7kFsiS7IFGeZAKCeTdb1qFb1v93QiQNBIunokOpmGizRyCFig9Z5+3866TsYnUBb8+BkXKfe62zrfqWbSxyjcLCdBk3oZpAKBKjQgYc1wgA0BfXx9+9rOf4Utf+hJ+9rOf4dBDD8U111yD/fbbD5dddhluvPHGsgdywAEHQBRF9PT0jG97+eWXcdBBB2UV6AHA6tWr0dPTMz4bo5Sip6cHhx56aNmfX2ksaxN/3v7pTlvoTHLmhWi8noY6JYJM7GK7eBFh4mDalb3+0MzbPTpFESkX/dVJchBjCEKSswW+HLImO5kSu8HtC2RGrCYhTTmahDgQbxMEYiIWc2efY9opQd4CfqLlINgpG8kC47C6TqZLcldo6rREYWZ4R8H9xjufhd0tU2piGH6jtFUd0zDhgwJIM8uVn4iKb3O1/1DfO1aqTetEwY8qNyNoRHLur9n2VnKFJ0GzRbCxFQYloGW4jFSCeHQUPKGu8tn3ZYj9bEvFqy96/DRZ1LvdY9fVOPZukpEoWFCrQAK0yoh7ZefLMCmBsngdACAy5N5reHR4N2SigYSLdwudirNKpNgrgwDQQKPQPbnv7z5/CBEEwFW4WYiSTsFLVMATzruPZd8485X0auBKIP/85z/Hhz/8Ybz//e/HY489htNPPx1PPvkkfvnLX+Kcc87BpZdeiquuugq/+93vyh6I1+vF+vXrcf3112Pjxo144okncMcdd+Ciiy4CYEWTFcXK1/3ABz6AVCqFG264AVu3bsWNN96IRCJRV53+eC2OFMkfUXLsZDKpCghkQ4HKZQtkwW8J3ZRLgQQlCp1yWU4YZY/H614gS8oQItx0UR5osv0RE0wgT4VGe6FSHk3t8/Puw9vff8JlDjdNu+9kVwpiwDpepoDPZSoZh0DMkjyYG9u6oFIBNFI44uF4GrvtfGZ6wiXfrNOpGDhCx+2kysVvrwhE97jzQh7c/goAIDB/1fg2w9uMRhrNubzt1EXksreaC/A8jygJgqRr45masFcjeF9lr5G9DSd9MF1CtHRDTx/OvPF+vPC19+CMG3+PDT3FhZppmFZ9Q55mXA4+u0GFqTgCOQW1QEGwUsFOgP7hjdglLIDUak3oY8MlCGQ7lU5uXlRkz+kI9qTA0RdqRkEDkqC+/CtpI3wbPKn+kj+rEE6ArqA7kq8JfpJBusp9NsrBlUD+/e9/jxNOOAGPPfYY7rvvPnz84x+fls5w4IEH4rrrrpvRYK6++mocfPDB+PjHP47rrrsOl1566bjoPe644/DII48AAAKBAG677Tb09PTgrLPOwr/+9S/cfvvtCATqx+1A1OJIFbCtEuwoq1oBgSwaaWhTBLJkC5N0zF20hVPjtpl3SYsKucdjL+E6RUGF8KojSIjTHzgNtqAxkvVZ3VpLhMRuDHMt4PjpxusOop2ikoi6W46mmShUKsCTJyWoXJyIdEGBHHcsBt0LN8LxGORaISUKP0hpahQmJa47n1FvGD6SQUZxf7NO25GymdqnNc6zljmVIlFxh2Sv42Bx6MRGfyskoiOZY3I6bm8VnLsCL86FIObJsa42yYh1LUkVXmXZ23A6umZS7iLITjvmc5L3YA1x2jG/VlQkJxMR8IQWjE4CEwIZ9vkvFymozRAZnD7z4nlqmpivbMFI6ED4mywLxvSYewGaGLQEcrA9/0phPpw0KtVO4YyOWI5WXDD/fTAutSOollYkXIxk1NIfhSaVnM+6nqKjlf3sSpDX5m0yjjAtxPLly7F8+fIZDcbr9eKmm27CTTfdNO21LVu2ZP18yCGH4MEHH5zR51WTYrZVzgmsp2duqC6ayjSLKcdeTnXZoYZXY0gRP8IzHg0g2jlfqov86pA+ir7gIdO2e30BpKlUs2hRvbKhpw+LIzsQIDGccePv8YkPHJWzKtpTYooKl7EmSJWWTr4GuztdsrhA5kv04I5KHQhkCj9wuPQI4sSHBpedzzj7Rh4fG4I8z13kJu24Q8zQPq1l3iLolIPhMg+QG96CUYTQ3DpRxMMHrZWXyFDftNUAakfQcrV8nSsk+TDkGlW8K1HrWpKD7vLZ91Wc5X23q6OnPrQa63lt/OcLhcdxIR5H5iER6M5//0pGRxBE8fuGLPugUR5UtTvomamCBbUq8UCoQAR596530IUI3u04FKFWq4mPFnUvkFU7la61q7QmIQDgsd2onMLE6Eg/WgGIofypZoq/A0vTG0v+rEIotv6QChT/C04wZ2wAmF/6ZKCazDxcyMiJ14hDLVA8INtG97oy89wbiSow+GynD1+DddJpBYTJZKxGDZWJHsrjF2ek4H7UNNFojkH3TZ/VEkIQI0HwdWogXgucSMsysgtBpAtGWhxhqsbdrSDwWhxJUvkVmIB9Hpqp/N+jYgtkwVeacEv5OtGsF446CJkI4sR9ZJq37QWdaKEblKR1DQvemQlkQZQwTJohFImKO4QS29AvLsrygZZtK7TE6PQHMVETSFNpTrdJzkhh+PVITT5btdN1nGuLkRtx3DXCnUBeq9yKDfoxMEtsx5yMWs82oZg1KSFIEi+IagWjvDQNo0C9gMZ5wBszjyDv3vQMAKBx//eg0S5CN+MluNREe5GiMoKNpZ9vnmAYAGDYtUXpMSuC7Ann72VBQ/MRQhKpSXnLM0Wxnz9ygVUrp95IcbnaOZswgVwlvGYSegGB7LVzhAxl5hFk2VRgCNkCOWDfxI0iItVB0pNQSmjUUAivfXHq6cI3yGQiYiXwB3JftJYnbX1Wt9aCUx9ajU38uQiQDAixIi2b+HNx6kOrp+073jY54U4gi1q8Yt//ZPzBRpiUgNq2h7lQ7QisXGKTGjM0H62IIJXKfw1JagRJ3r1AdqKDyaj73HcnTWqmAhkAxsQ2+NPFo0zUNNGpvYt4KDu6NL6UG5k+cbD8X8tvh10PaHIjgmZtOmxO5LNXr2HW3oDsd1ZH3X1PYrgTInRwBKAUrtsxKwk7OunCuz0NL3gtCdMw4S9SUKvxHgjmzAWyuvNl6JTDggOOBC/KiCAAPuVeIEvJ3Rji28pKe/TZEVtqfweZqPW5/sb8lnFio1UMONTrrgbCDZp9b/cWEMi+sC2Q67AgnwnkKhGgSZgFvP/GC2UyM48gy8jAFLKrcp3IXSFhknUMMwG1iJ+kW3x2lbzjjJGPsQGrra4Qyn3RpoQGeLRIRca0N7BWuRV/0o8c/7lQpMUpSjNT7lYQZD2BDF/5CDLH84gTH0gm/0RHtZcBS3VX6KWWmH3rO2vzFvZ49SiUEho7eO3rphT3FC3t2KfNPHUh6Z2HsFY8F290sBdBpEBbsrtehZqdpdwcAllLIj3HBbLhaUYDjc2Kx+5UqN2CPBhmOciFkO3rQHcZ/Ln8lBVYzlnXLiHAH433oN1FO2ZndUx2kROucD7wWhKp8YLa/M86nfdCqoBADoy8hl3CIkh2QX6Ua4SYdn9fCSp7EJXK8EAG4A9Y3wHNWN+BFrMEcti+P+TC22KllMWKWGeWglND5G/I/x0F7AmnXiP7xkIwgVwFDF2Dj2QK2lb5bIFciY49HpoBnRJB5gUBMeoDcdk212smZ9zowMFnR5BpkfSRxLB1U/Q05o4UZMSGkrrB7e2I4U7osArzNMoXjLTwgog4vK573HvMEjvZlUCCBAquBDge3IWiDFPZ0NOHB9+xbl+HkHfyppv4jRjUPN6fufA1WBMLt6lJwESkrNQIeC60QBdazREYul5wvz1bXwEA+OcflLU93GI9UHMt5Qp6EkqF0qhqBfE3W/aF0dmvTSDpMcTgn9MpKrOBN5DtGlGM9d1dUIUARqj1vHxBXIPUWXcW7Tin26ujPhcCOcP7IBmJ8YJarkC9gMF7ZiyQTcPEQuUtjDRMXJ9JsQle1f1522QMQvEVjqLngxMEpKgM2HnXSA5BozyCjfn/Vo3zrPxfxc59rgROgC4Yzp8mErIFslnCPXe2mJFATiQSuPrqq/H1r38dzz33XKXGNOdxcqNIgeIBfvwEnplANgwDXqKCitN9HZPED95lioLlJ1mZCLIky1CoWDQ67lT0Bppz3wh1OQy/WZ/+iLXg8lNWoIOLAACu1y/CXcaJBSMtcRKCkIm4OrbPTBY13C+XFB+EWKA7nbPS4CuhSc2pD63GXfwNAACO0LzpJg00BkN2f9zxyHsJN2snTcpbAfs0LrwQIjEwsqfwQyrZ+zoAoGO/Q7O2S7IHUfjB5YhUSUYSagH/17kAb3dEjNtV+bOJkBlDvAp5+nsbvqAT/HEXQR4b2o0DjLewZf45yFARZ7aPFBXHG3r68MIrrwIAvvybF4o6Xmi8D5KRclVQa/BeyHRmArl3xxY0kjhIZ/f4NkVuRshwd19JJxNoRhRmKL+VZzGSxAfeFsh8egQREgLh8jsftXYuhkmJ6yJhVygRpKkEUc6/ciV5vEhRuS4L8mckkA3DQG9vLz75yU/iL3/5CwyjeGvhfYGE7T1c0PsPQJp4QLSZCWTFzr8k4vQTMMUHIanFI7BOo4ZC3W5KJUl8IFrhG6TTZjrclvsmYNjd4GqxnFqPrO/uwo7l/wEA2Gjuh9sClxaMtKT4IETXE6RURb//yWT4IGQt/0SHOk1qSkixWKvciof0o0ELFPZklJS1kuN1L5ADwbBV8e4yNQWYWClxImczwdNqLXOO7i7SLGR4C6LUj9aO6ddOhDRCyCmQC/u/zgUku6AnGZl9gSyqUaRKyGffV5EkLzTKAxl3Annbsw+CIxQtR5yNd4XF8I9tKri/U6y81ngRAPDB9MNFbeE0wQ/ZTEGxI8h8gWczFbyQkXE19nzs2WwFDJuWv2d8m+5tQ9hlu+kh+/oXmkpvEuKQ5nzg7WewmBlFnC98fxIlGUOkEXzCvVdzMTjVaphWjBgXAu9ytXs2cSWQP/vZz+KRRx4Zb9Th0NDQgF//+teYP38+rrvuOvAFfFn3JdJ2bpRYpLpWIR7wMxTITitpIk8/CRU+CEkvHoFNJWMQiAlSQYGUJj7wRSIIND4AlfLjnsfT8DZBJIarhiP7Ckt91jX4k0tOxjNXrSsYaUkL7toma5oKP1FKatRRCqoYgrfASgDJxJCkHvCCK9dJAFa6SRxWNNTMU9gTs5ukcH73OaOE4xAjAXAuI+/ARKTMVwGB3GB3R0wM7Si4XzC2DbulhTkLeJJCGB51emqN10wW9H+dC3htlw6lBJeRSuHRY0gLc9cib7YgHIcU8YBzGUHm3n4MQ2jEskOOwVhwBeZntgIFRKRTrLyat4rJLhSeyFus7GAIAXjM9Hi9Q6F266bog5fOTCDru16GSnksWHHE+Dbqb0WAKIi76DAY7bd+N1/L4rLHoHB+iLqlL7zqGJJC4UDBhp4+DJoNWD72lOtmLcUQ1GjBfhAOCS4EKcc9q9a4EshLly7F9773PRx99NH4z//8T/z973+HXiRHbl8mY9ukOD3g8+5HvBD0mQlkNW29n5OmL51mxBC8LtrmOv3aSZGIdykonA+iXvgGyaeGMEbCeRteOG2K45ESrHH2cgy7s2BDc/HiDVVsgM/N9x+1b0ye6ghkXQrBb+Y/Fzg1jiQpben/8lNWoI2LYSNdAgrgXuP4aekmiTGrUE0IlGaTlOCCEEsQyCQTR4rKJQn8fLTYnqd6kTzADvVdxAK5/VHTUhP8+vSHjYemYYhzWyAHmqzzXo3PvkD2GzGoJRR87suk4QXRinsJa2oG+yf+ie2Nx4LjeZgdqxBGHMP9+Z0UHFs4g1r2hm5s4UzJDx9NjfsCFxLIEH2QiA5D1/LvU4Tg6OvYKS6BMCm1QAhZubaRweLCMz38LgCgsXNp2WNQeR9Ew9IHAWMMqpy/xsOJygeRRAgp181aiiFpcaT54ql7iugumDPbuBLIX/nKV/DXv/4Vd955J9ra2nDDDTfg2GOPxde+9jW88MILrpYM9iXUhPVw8oQKFx2pvA+CMTND8owdQeZztLnVxSB8BYSJQ9pOCeErUIU/Pi7eP35x5kNWhhDj8/+NnHzDxBgTyOOkRqBQET5/8ZuO4WlEkBZPsXEmSFyJjTrcYsphqyVsnvuEoCWQKrF4bH13F1Jn3YnbhY+BJ8D/Se+dlm6SstucyqHSBHKaD0HS3N+siZasmH1aINSIKPwgsd68+0SGdqMRMZjNuRszaZ5mNJiRadt9NA1aoTqDWhFqtkSGUYMW9AEahyEzgewGhXMX/Hn7pb8iiDTElR8AAAQXHwYA6N/8z7zvEcOdSMADDhQG5VzZwlEpAB8U6LYlY0HHHDvY5KzOlophGFiUeQtj4VVZ22V7fPGR4ikMxuhOmJSgpXNxWWMA7LQSW1+EzSg0T/6VNCcqv5gbLGohWgoeI46MC3csVQzXZUF+STnIhxxyCK688kps2LAB559/Ph566CF8/OMfx/HHH4/vf//7SNVhL+1a4FTXegt0jwGcGV56Rp+lKtZNiJenR+BMuQFBWlwgK7YVi1gk4l3SuAQ/5CICOaCNICnlv2g9trBRSvCk3dvhlVFESciVN6YphxGiyaK1AWnbT7RYSlDZeMKQiQYllft8EPU4MmW4K6zv7sLNl30CJiW4oGvPtHQT1bY2cpbl3WJFM9zfrHkthQzxFN/RJcN8G+Rk/odo/zarOMnXtSrn66avFWHEoWvq+DZdU61i3gL+r3MBvz+EDBWB1OzeE0xdR4gmYXrmbpvu2UTlvBCK3P8BILbxT1CpgOXHnAEAWLByDQAgtfOVvO+5/JQVWGILuVv1s4sWKwMApCB4QqHHrVWlQvUCzmqsUsBfvRC9215HiKRAug7L2j7uUZ6jic9kNvT0YWzn69DA4+xb/lR2FFcXAvCaSSjpJAIkDeRoyOXgROVVaq3mZqjgqllLMXxmArpYfGVS8zQhVCN/80K4FsjxeBwPPvggPvWpT+HYY4/F448/jksuuQSPPfYYbrnlFvzjH//AZz7zmWqOdc7gNOco5P0HADrvg2zOTCBrabuLl2f6g496w/ASFapS+DMyCduHtoICybo4C0+YGoxRqJ780T2/bSCuumx2MVts6OnDmTfejxe+9p6K5Wq5RcqMIc6HXe1L/E3gCEVsrLCYcNqBihVcQZgMZ0+84pHc45CNJDJlWsz5Qk14V1iEwODL015zzptAU/4HQy40sQF+F6kpDpZ9WuXcIWLyPDRk8hehxXe9AQBoXTq9RTsAcAHr941McnpIOnmPBawn5wKE4xAlQfDp2c1XjEdHwBEK4nNf8Lkvo9quEcXoHHwKm72rEbCtQRvCTdhF5kEafiPve9Z3d2FP18kAgIfNY4sWKwMAcVwr4pY4LVQQ7AjkTJ4JfTEGNj8PAGhZflTW9ga7m54WzX9tO6kO3dgCCfqMUh1MKQgfUogMW78zVyDVzIrKeyHAhEkBCTpSkIs2aylGgCagu1h1oZ5GNJAktEmT+nrAVdLcpz71KTz33HNoaWnBaaedhi9/+ctYuXLCoH7RokX4xCc+gWuuuaZqA51LjFflF7GtMoSZC2QjY13Eomd6BI6zq/djkWG0dOSvhnVaQnuClbv5G1IAXpr/BmnoOhppFIY/f3TPEch6HQlk5wZ2Db0Ha/gt9g3M+tsXsyaqBB4t4rpQSLCL0+Jjg2hsyd/9y+lk5ymy4lEugt+KuiWjw0DX4mmve8wk4kJ+A/tiDIdXY8XwX6HrOoRJecDUtmpraCqt85nhCSMYcS+QRSOJTAUFsuqbh+bUK3lfp4ObkKQedMzPnYMs2RHz+Eg/WjoWAgDSiTE0oLC91VwhzjdArGBBz4aePtzx6HP4avpmfNN7BT7xgaOmXcvxsSHr7+dnEWQ3aLwf/iJNnvq2vY6FZh92L7oga/uAb390JN8u+N7FyVcxhEY8+a1/d7Wa5pz3UmoABiXw+PJPyHm74F1Nl5li0fsyFCpiwfLurO0NLdY9zkzkTxk89aHVWM9P5D5fKDyOC/E4Mg+JQHdpqyamFICfpjE40o8OAGIo/7P28lNWwPdgDHcZJ+JtOh83iL/EEdxbRZu1FPx8w0CApkBdFH9z9nUVGR1Ea3v51naVxlUEed68efjFL36Bv//977j88suzxLHDmjVr8OCDD1Z8gHMRokSRoF6IolhwP1P0wYuZCWTdTrFwuvVMhreL3FKxwgLTadRQSYFExSD8NJW3Gjky0g+eUHDB/OIl1GRd0DRZPwLZydW6UHi8oP9utfAbUWRcNr4QA9ZNJ1Wkx73uNOookjNfLpJ9XqXznIdeMwVjBkv//KKjECIp7Nj8r+wX0qNIURkeb2npG9QThp8oUDPuvFAlIwVNqJxApg3zEUIKsUjuv5c/tg19wkJwfO7bt6fBuqaSoxORqnTCsbea+wI5JYQr1mHTmfCek7wHa8iWvBE75xqSgqXls++rGIIPcoEACQD0/nMDAGDBe9Znbc80H4T5tB+peJ5JEKWYH3sF7wYPdd2GWfBaIs2XGUSSeAu+b0Igl5di0TD2BnZK+4Gf0lCGFyVEECzYbtqNfaVr5BAEYiK+xyp49IbzF3Y7NR23BS7FXcbJ+Jt5GOaTYZzQVb51bzwWAU8oiAubTWG83qi+uum5Oruuv/56HHHEEQX3aW5uxqJFiyoyqLkOp8Zdef+ZYgC+GRqSGxnrJpRLIIt+R5gUnnmaivXwLBbxLglPEBIxkMnkvklGh6wHkNiQfwlHlmTEqReokD9iJVIjnFwtrcK5Wm4J0SgMj7vvydPgrse9mba//xI62ZWCbB83k8jtLeynKZgzaFIyb9XxAIChTf/I2s4rY4iR0o/rRDOiLotDZTNdUfs0sdm6j470bZ322oaePrQp29GoD+Q9h51cR2WSV3AmaX3HYgWdampFRmqEv0IV724nvM491E1bYwZgiH54izzb/Dsex7vcfHQtPTBru3eB9bfv3fxSzvf173wL7RiB3nVUztdzIdgTw5A+gjQKF9Q66Yqay1bZk9E1DYvUtxEJ564PiHCNEJX8AR8x3AkTBIQU75ZaDM7+PTJD1n3EcYDJx/ruLjxz1Tq8850PYuFHb4UAHdt/e3nJn+uQtGuHivWDAIC349Zk4oo7/4Zjv/O3WU1bLARrNV0FBC2GtJuiI8kPieiuI1W5cFpVyzmWjBxhouSbiTvHUGIwKRnv314JODvXcdxCbApJu820t6nw0nqMC0HIzHw51W2kqBgTuVrWzFqEXvYNrFQ0NYMQUjC97qJYPrvIUcsjTB2oI5AbqiOQnVawWmL696ipGfhIZkYezB2LD8AoQuB7syvfJXUMiSLm+LlwUlNSLqMZHpqGWcEIsr9tMQAgNpBtdbWhpw/feuA5tJEomhHNew6HW61rSp/UblpNurC3miPociMaaGUE8lrlVvzVmFgKzxex02xve38DiyC7wZQC8NH8q6OJ2BiWKxvR33b8tNc6lluFetF3/jXtNQDY/erfAAAtB73P9Xic+poWcxTpIulQTrqinie4U4idb78KP8mAn394zteTYhN8an6BfPkpK7CAs17/qn6xuwLEPPC2bSc3tgMA0NDqPo1t2crVeKH9PHSPPoodr/y95M8GJlaunZXMfGzo6cO9b1g65pviz6FG+itiMVcJmECuApIeR5ovvmRMbGu2dLL8dsqOQPbkiCB7g44wKSyQuEwMCeLN60dcDpx9caaTuR9kSsQqHAi1FM7bTXIhSGpkxuOpVGqE47/7DrWWsXfStrJvYKUSHbUqsN02vgjaKSpFc7gzUasdqCTPaHz5CIQtUWGmpgvkZCwCACDe8oUb4Tjs9K1CR2xj1naPFkVaKP24kn3dpIpE3h18NA1DrJw7REunlVusDGd7IZ/60Gq8yH8CAMAVsGIKNrRYKxzJCYGvp626CMk/9yPI1NeMEJLQ1Zk1cwCsCa+DQUneiJ1up3kFG0vLZ99nkQKQiQYtz3f09nMPQyIGgoecPu21eQuWYowGQfa8lvO9xo5nEKN+LDlwjevheOw28DLRitYLOKuxhstOgJMZessu0FuRO7qdkZsR0vM/j9d3d2FHl/U3edo4xFUBYj4EO3LrT74LlQrjhZBuOfj8GzCIRph/uhzULD3VQrHtY6UiqZs3P7YFg4Y1Kdmf9OELwgNIawZufmxLyZ9ZaZhArgKynoDqoiqfGxfI5UdDqGrN0r2+6UvJfluYGDmESdY41DhSqGwDAcF2REjn6YJnxCyxF24tfOErQghyCZ60+SjHXD4XTq7WIKy/rUjMsm9gpRK3BbLgMg8y2NACkxKgSNtk3mVKULkE7KgbTUemvTbepMYzM+GWmXcEFtLdGBmcsEfzGTFkpHDJx/I69oIuBDI1TfigABW0T2tqXwCV8jDHdmVtv069CCZF0XOY4zmMkQbwk6zQDFsge0po512vOBNEtykwhbj8lBWYR6xzcAftyBuxo/Y9NBhmEWQ3OMGfVCK3dZe26VHE4MfyI06c/l6Owy55P4Rjm3O+d17kX9juW1VS517vpPNe490JZLPECPKGnj709vwVBiX4/ANv54yA6t5WhGnhdtNdZj8yVMTT3/pY0W6phXBciZrU3RgjDa7ztR0aG5uw5eArsFR7G6//8Uclf75qB+Y8wcIBnSfSH8HT8hcBTEz8d3g+iifSHyn5MysNE8hVwGsmobkwx+adHKEZCGSiJZGhIrgcXbxC9s2cFhHIohZ3lxJSAqK9lJv3d0sMIEG98AcLC6OM1AB/BQzEndQIDtaNaSa5Xeu7uzBPsMbURYaxbgGZ8fjckLI7w8kN7mzLOEFAnPhAlMLfP6/FS27UUQq8ICAOL0iOXHIlURmLuYblxwEAdr765Pi2II1Bl8MlH8tn/32LrbwAVpt2jlAgR6OecuF4HkNcC8TExAN24+N34QbxlxhGAwgAhYoFz+E4H4aYmRi/oVirVJVoh11reNvGLmFPGGfC+u4uvNNyAgBgPhnG7f5Lck54OWUMMfgr0i1xX8BJsUvbXWUnYxoGlkaexdvBI/OuWiXCB2CBtgPmlG52kcE+LDD7kOo4sqTxTPY91ooEr5x0RVN1L5CdFL734l/gQHFO6ne50wQCbfCTDGL2ylnOz4/vxADfPuMVXdn+ndvNIdfWoFM5+kOX4FWyEl0vfwcvfe2Ikmp3dFt3+IoUf3/E8xNs0I+BQi1TA2fif47ntrLGXEmYQK4wG3r64DNjWBB9qejJJNgz1UyZHXsAAHoaaZL7JiPJHqSoDCiFBbioJ6DwlRVITs6X09pz2memhzDGFS820+VGBOkM/j42TmqEAusifNw8bEapEQ3mGLYTy0Kr941nZjw+N2Ri1pK5r4Rl3gQJgs8U/v4lLQ6ligIZAJIkAF6dPg5lPDd2ZsJt8cHHQqU8lO3PAbBsBEM0CVqge1Q+go2WADOTxQVy2o6QkQr7C0fEdvgVKw1p499+i5X/+ALe4vbD63Q/3GWciLPUbxTMT0yKjVm5jjRjC+QSl1nrEaf4tFIdNtthF+ARDb87d37OiB2fiZRV8Lmv4gR/lGR2cGNDTx8u+9YtaEEEf4kvyft85OYdAplo2L0tO83i3VeeAACEV07PXS6ELzCRaqUXabfutQUyzbj3QXZS+FpJrGAnOqfd9FiBdtNhpRdj8sxXJD12OhVPKFJieQX4f3xtD76mfRyNSOBw8nZJtTtOP4hAuHBA5+IPHI0054MEfXzir3A+fOID7oswqwUTyBXEmkW+igak0E4iRU8mJ0dITZUfIeX1NDLInzuaIH7wmcLHn0mjhrzHtGeveh6B7MkMIy4ULwqjnkaEkISp6zMaz/ruLiTO+ClkWMd5jl9TdmqElkkjjAT6W9fCpASJ7S+WPa5SnDW0uCWQQ42Fq5Enk+RDkItYYsmGu5SgmZDkAhDV6eehZnswF8tTK4bHF8A74jI0DPcAsHxrrcYOpRceBkKN0CkHmnYRQbYjZHyORj3lsqGnD+8qPixV38LV11+HFU9dineFJZj/+UcQO+su3Ba4FJvpooL5iRmpGQEjMv4zycSRoSIkuXId/2qF17axU6MzjyADgCe1Z7yD2NA7r+bcR1KjSPFzv8BxtnBs1SavIDpR1k/ovwGlwH761rzPx+ZllmvW0NvZThaZbc9AoSKWrj6upPEQjkeCWu4VZpF6Aaeeh2ruLVjXKrfin8ZE6/d86U/Oak8iT7tpappoN/qhBGfuCOadNBnOyOUVYJ/60Or/n707D4+qvv4H/r6z75ns+w5hJyEsioiIqAgqRUDRurTFLlZb+rVV615trWDr+rNqXaq1rigqiLuIirIvYQ1JICEJ2deZzL7d+/vjzkwSMpPMlmSSnNfz8GhmuXOTSe6ce+75nIPNonvAMAh+BLVFzy/+H+Aq8fIZ6ViQDnwkWowV9r/iI9FiLEjnhqRscSB0vSiCgm3yLXG3nnGG2G8RAAROC2wC/x96JoEaogFqeGWsCTpRbsj74IvCHfC4rH2Dok0l9Si0NUMDM65c94HPxvwenslVRl0bNAmBB4a+zE8X8JfDAVyaasLcEP8AdW0NSAQgTMxHTWsGZK2HQtpOsENHWPdCoZj4wDPIVlEMZAO9/y4TDLLQB3UEtB9CNSTOvlcCHN4e3NqwX6MzfgaKmj6Aw26DobMZWgBCVfAZZEYgQBejgiCA9oKek9tIDeDw/E68yzRDAif+xj6Dci4LpQtew9VxiVgeF9hAGqc8Hlp99/vOOEwwMfJ+TqVHDo37999hjMy4aY29GRXSKZhqPwJLfanPx8icelhDWPA5VnlK7Ow9ro6e/fl4jWg7rsF2n5+PmeOnw8aJ4azvfcIS174fpySTMFXWf6s2X8yMHCpYBhy3LhAKYeEkYByBl1iIY1IwztoAjgNs8F/+pIrnj7PWTt8BcltzHRIZG5i4nIBf2x9lj8FfrhCupAF84H+v6C0sEe6FlHHCxonwmescPOq8HgOlhRibDgZGgRjBwKUiab/5ACsBrAQA3BrSvg4GyiBHkGchmI3jzzsGWggmU7oDZGvoJQQilwV2xn+AbBGqIXH0n0FWcuENavBFodECALizvjdPAJCGNsTBMGCWXajk66gNuvCzRfrWOu//S7tqwtiOp4dzClrUk5FhLvM7EKU/wXbWEJjb0AVFUN0m7BItlGz/AbKCM8ElGdzLx3axBnIf45s9PZjlEZjiKM4+FzLGgdPHdsPkHmsd6mAHo0ANkU034ONs7kvI4gi1T/P8ThQKToNhABHDYYqgBsu2XRTchpSJUDA2mD0DQhxGWJjgg4po5BkgxEYoQI5nW2HUjEcL4iDqqPD5GIWrC/YQFnyOVd7kT4/j/0+sf4WeUwQ0BEMmk6FGlA1FZ/cJi8WgQ66jEvqk/mcy+GMVuH//AzjWWRkpGGfgGeS1WVWIY4zYwU7pt/zJ07XJ39WP1lq+c4M8eXzAr+2PVKbw9uvnlIGtWzmbZ+2OOIS2pkJ7F0zM4F6ZHGwUIEdQz1+mgRbRAN01QmwYAbLQZYWjnwyyXaSGzEdg4sGxLJScCawkstkRuVwFJycAzirv8AQAUsYV0CUbibuXsyfgCYfJfVmrE2rEWM8M8Gj/zB38dhRxaXCmzEAc9GhrqAx6O/OtT+M713Tv1wOdUIlsnehigqvVdUljoGb7v0KhGoT3/2xOcQyUrI8A2V0fr4rAkJqM6RcAADrLf/DWa8ti/I9X7Y9ZqIEkgO4pDk/7tDBrqD08J9kW74IVcUjdVoRq/vvWtfJ1zCKnCdYIjsMeTlKZgh8gFEAJzEBMXR1QwwJOk44WaRY0xiqfj1NzBrgkI3+B41DxfLZ52gs21Z7Ev6XPQAErODABfT52qCYgzXrKm3w4fehbCBkOivHzQ9onT3s3JoByKBukEDgDyyBzLIsZp19BPRJxj+Iv/ZY/aRNS+c5CRt8BsqGRP0GLyyjweX8wGIEAJvdJsWdSXbA8a3fedC1CO6dGFZca8NodsaMLZgEFyMSt5y/TQItogO7ejGwI/RY9JKwFDqH/zJBDooGinwDJZjVDwriACF0i9vD8cTL23q893/o0DrjGeb8eKCiUajzT4MIfQWlz916uUUxDqrMBHMuGtZ2YxHTEjj8HANBwfGfQ2xFr0+DJOzs5wYAfGBJb8IMvOFksNIwJDofD5/02mwUyxgFEeJHZ2VyyGKg4H4terAbYOWHQ46B9Sc7IRxMSIKrfB5uBP6EaaIGIP1ZRDOTOgdcGOC180C+NUAbZc5It9S5YCW0QjdR9YmDs4H9XJU4jbIO8EHModQk0EA3QnSUQbQ38MBZxXCaMmnFIc5zpc1xwOZ1Qc2awAYzMJTyZe1EcazWg7tQx4NUliEMXSrgJAX8+upKnIhZd6Gzhkxmmiu1wcgLkFS0MaZ/s7gBZGEDPdZtABmGAGeQTuz5BgbMcZyb/Gj/csxin11/utz2bQCSGnlFDYPL9eeZqqwLLMUjKikxffc9VI3FMaP27e46gPs7mwAJZwGt3pE4DbAF084pmFCBHUM9fpoEW0QCAwh0ge4Z9hELM2uAS+s8guyQxUHH+A2Sju5m3IMw+tL6YoYDA3jtrKNamIUvQAo4buFUVACjdAY5nklU4XAZ+9K41ZRYUjA3tLXUDPMPPdtw9nGMT05EzZQ4cnBDWmuAX6t25eAKSGD5Luc1VNOAHhtyph1WsDeo1GPfYZIOfqXBGPf/+M/Lgths0WQwUjA02W+8PHYG9CyZGya8AiYA69XRkGI9667VVcaF9MDgCbC/oufojj1D7tGBPsv2Ru/+eLJ3877zEZYYjgtP+hptJGAOpPfwMcldTNQBAkZANJmECVIwFrQ3VvR5j0LWFvOBzrPr+NJ99zSt9DrI3lkDKWdGy4n00XPVBwJ+P6uxiAEBj2R7+65Z9qBLlIUYb2omKwz0O3jNhrj92RgahK8ASi+2PowVxKLrytoAerhfEQmLz/Xkm6qpBiyABYmlkyqGs7v72iiAWdp/NM4JalDIJ45h6/KQwsG3JXQbYxSO7bp8C5AjrOc98oCbfQpEIZk4KJowAWcpZ4eong8zJtFDDApefLhAWd4AsDGBeerAsAiVEzt7B+dqpNiQyXdjPFgQUAKhj3dPgTOEHyIyxBQZODkUmX87RVuO7Ef1ABKZW6Dkl5AolFAoVqkU5ULYdGfiJZ1k+Ix2n4/myABnjGPgDw6WDQxrch4PIEyDrfAfIZvegDmGESgT88QTgXWcF6kKHEeYI1sY6UmcjGe0Qtx6DgxNC466FD5ZLqoW6nxNLD9bdPk0WoQA52JNsf9Tx/IeYp9ZRylrgHOROJUPJItZCPkB3lkBY2/lphdqUHCgzpwAAmqsO9XqM52/H87dE+reppB73f1YFjgMSGT0UsOIG14M4xuYG9fmYMZGvNTbVHILTZkGutQytcaHVHwOA0x0ge+qj++MQyCByWQd83Kn9X2Oy7TBO5v884Ktg/Y2b1pjPoF0SmQXTm0rq0eXka5Af/aw07NHNTPJkyBk7mmt91+mfTcGa4BRTBpmEwcLIwDhCD5AlnA2syH+AwbgDX6Pedw2vp5G7SKENeR/8sQsVEDt7f2+51e/Bxolxv/y+gAIAjTYeLo4ZcNhJIMSWVugEsYjNnAigu94rWCL3djzaNVORZSsPaRxnEqMDAGSLdf1/YHAcYrguOGXBfUiL3YvULHrfAbLnBEk0yBlkobsbiVnf+4NB5DDCEsE6tbhJfH3iOP0u6Bl10NOjvOSxUDEW2G39jzPm3OVRyggO4AgmiPBHm8B/yLqMfK9gBWeGa4D+ryOJXRILVQQGCLl0dWA5BgmpOUjJ50+czWd1sjC7A2RxCB1RxiJ+ncl13otCCsaOT0V3BtYarIe4+AScQQrErcdQe2wHpIwDotzzQtqnTSX1qNPzf8svflsxYLDoEMohZgcOkK3b/oEOqDH9J38IeF9s0nionb4/zxKdjTArswLelj+exfAJ0AEAFlu/Crh/sT8xWfx6mdZTJQE9Xs0Z4ZJpQ369aEAB8jCzMjIIHaHXIMs5K1ix/0unAndgYtL7vhxpdwfIEmXkM4h2oRISV/dCB4uxC5PaPseRmIX48t7lgWXZhULoGRWE1vAvp8rt7TCI45GcOR5OTgBXW/AL6wBAbmuDUdQjk5s+A2pY0Fjlu0VUf2QWPsMXz/a/CNFs6uJrhRXBfUh7xiZb/Jwg2Uz8gVriHuwyWCQqPrD3BOTe251G2CI4pCZ38hyYOSliYIRBEPrlPU97wS4/mXcvuxFmThp1E9bkShXf99XEv+9yzgLXAP1fRxKnLA4aLvwAWWhoQBsTC4lUivjENHRCDaa1vNdjPCPHZRoaMx0Iz0LTsyejBbvQFACaFOORYKpAx4nvAQDZxX1HUw/EEyyO4/jORYvs3wwYLLqEMkgGCJBrj+3AVPNeHM+6AeogrlQ5FUmI5XR9at31nR2Ihx6sNifgbfnjWQyfLuATEteJvg28f7Ef6eNnAADM9ccGfOwHeyohZ+xIr/8qqOl70YYC5GFmY+QQBrha9mwcy0IGGyD2n0EWuwMTs58Aye6ediNVRf7yoUOkhIztziAf++pVqGGBct6vgtqOgdEMOA0uEGpHOyySeEikUjQLEiHWV4e0HZWzExZp94dlfMFcAEBTWfAL9ZR292IyWGDq8n8SoG/na0mDXY2s1PIlKg6j70t6nkEdUvXgXj72bN9m6P09Sl2mAUe/BkMilaJKypfrWMIY7CByZwsHmtYmsJtg6afN4nDSCWIgsrbD5XRAwdgGfSHmkFLEQ8HYYA1nCikAuaURnSJ+nQMjEKBJnAXVWZ0sPH87ihAXfI41noWmPSejhbLQFACs8ZOR5mqE5sy3OM1kICUlI+hteILFqQI+QP6JaNeAwSIrlEHC9X/1SPflenRxCkxdfkdwO6TiWzDq9bpeNzfXngAASJLyg9ueD56TFE+bt3BOUjxiYuPQiESI2/svTdxUUo8nt+wFAKQzrUFN34s2FCAPM5tQAbErtADZZrNAyHDg+skgewJfq8F38OUye/rQakPah/64xCrI2e7vLab0LZwWZGHS7IuD2o5FqIHErgt7f7RsJxxy/kOuXZoBjSW0Vm+xXCcc8u5ANWdSMSycBM4z+/t5lp99crV7Jzy1N/rvzWzq4DPNYnVwH9JK99hkl5+xyZ5Jh4oItFnrj0LDB5yOs/ZDzpngiHBms1rO15Km2KtDzl5I1Pz++su8ewidpqjtL2wUxkJqa4fRwL/HjHT0ZJAF7hNFfVtTWNvROFpglHYv5OxS5yPV3vvv0On+ndXEhtYycKyJ1EJTAJBlFEHAcCiwHUVjzIyQ9ifY+QQA4BIpIOV8Z5A3ldTjlr89hemG7fgIF+L7mv4D6bOJNPz6AH1b70XiXfUnAQDatPBbvHlOUoRgwz5J6alZlgutqf8rr0s2F2KH6BYAgCDY6XtRhgLkYeYQKiAOdLXsWawmvjRDIPEfIMs0fIBs9xMgudyDGpSayNfXsRI1lBwfIFce+REFzgo0jbsu6LpQiygGCmd4GWSLyQA1YwGn4j/kzKosJDl9TzPqj9VsgAoWQNn9YSkWS3BaPA6ajoEvPfXktNsQDz1OuzOeXS3+A2Szjs9kyrTBfUhvq7LAxQGZ5a/5DBZZiw4AoNAMbgZZGcP/fjlNvWvvlJxpwNGvwdhUUo+P2/ksUyyMIWcv5O7abesA3VNETpO3v2q0sUhioXR0etcZCAJYvT9SiFX8iZ+hM/QBQhzLIsHVBoeyO2hg4wsQCwM6Wrp/XzhzB1iOgSqGapADEamFpgBQweR4//87XWJIJ7vBzicAAE4sh8xHBtlTrnGX4wVwHCB3GYM+vsi0fIBsaOv9+WNv5QPP5JxJAW/Ln0iepPRk1hYgw1kHl8Pu9zHzrU9jl6v7e4hE9nq4RFfh3BjkFCqgsfd/Gdcfq7sHKyPxX8PpLzDp3ghfx6cKcbV/fzipGgrGBpfTifbvXkQaJ8Hky34d9HYckhgobKHVC3t0ttRDDkDoPntHbB5i2kzQtzcHNbq5s6UeqQCEmt7P0cdORVHLJricDghF4oC21dFShyQAhvhpQOMhWNr9Z7TtBr4WVhFEgLyppB73birFEoEI6Uy7z1HWnOf9j8Aku/6o3ZlsztL9e8gPqbGAk0YucFuyuRDLRXzPZ88gmv7GvfujCLC9oDiKA2SHLB4acymM7gA5UuOwo4Hc/f5Y9KEdOwHA0NkGDWMDNN2BmzJjCnASaDp1GHFJ/O0CSyc/MjfK6syj2fIZ6SEFxD1tKqnHIz/osFIggoxxYqKzHPd8eNS7/UDduXgCFB/xweI7rkW4TvgNUgYIFjmRAnLYwLFsr4ROMOOy/VG6x017WjB6CHSnoYMa2ggkK5bPSMcmvI4XvyxHg86CF1W34c7FE8J+T4QpkyBpcqLudCkyCop8PkasTYPKbB5w7PZIQBnkYeYSKSBjQ8sg28wDZ5DVWj4TxrprjftupAsmTjYoi4wYd81jW1M1prR/hSOxixATF/xCF5dMG/aCnC735Syp+49U6q7zaq45EdR2DG18puDsP3ZhxkzIGTvOlB8MeFv6Zr7FlCSLb13k6vTfl9npDpBj4gM/yHhq72SM0+/UQsbWBSMnH/RFZmKJDGZOCli7rwSYTQaIGBaIYIAcyuVUX9QDlKZ4SFyWqO0v7JInQMt1wWrQAQBEg9DKcbgotPwJqs1Pd5ZAtDfytcbiuEzvbUl5/Ep9Q91x721Cmx4GZvRk30cKTzcMGcO3KF0h2hHSpfqQMtoSOQQMB7u992fzfOvT2O/qLoEI5fgS4x437dD3DpAVpjNoFUWmxRsQmW44Z4vN5n/2rWe1QuzpzsUTkM60ow2aiGavhwMFyMOMFSsgQ2gBssPKB8hCqf8Mslyugp0TAVadz/uFdgNMzOB8wHsu6Z7+/FkoGSs05wefPQYAVh4HBWxw2kL7OQGA2T1RTBnHH4BiM/hWb1315X6f43s7De7t9A5UkyfxC/XaygNfqGdq5wPimNTx6IAaAmNjPy/cDicngDom8OyCJ1h0cvyfua+DuWAQ3/+zGRklBD0WW5oNfDZZEMBkq0CFcjnVF5Umjv+5DTDOWMqZ4RRGZ4DMqJIgYliYWvhpcRLl6AnyNO4+zy5j6CPoDc3VAABlYrb3tuT0PJg4GdDavRBJ4tDBLBw92feRIpLdMIINFhl3S0RPGaOHWJsGBWMNeNCVL5r4FJ/jphPsDTAogl+EOJQyCorAcgzsDf7LCS8er4EKFmzBhWGX2Aw3CpCHGStWQeFnMcBA7Ba+Q4S4n9nyjECALkYFgZ8AWeQwwDxII2g9Iz0n17+PSkEuJhZfGNJ2BO4JVno/0+AC4dDzwac2kT8ApeRMBMsxcLYGV7rhGbygSex9IMvMn4ouTgGuPvAMsq2TD7a1yZnoFCRAavZfT8lYOqBjNBAIhQFv3xMsCsC3E/J1MBfZu2AZohHEJoEaYnv3lQBPgCyMYA/mSNXeMQIBDIwKggHGGctYS9T2FxZp+HIce+spAIBkkIfBDCWNNoHvj27qHSBvKqnHsnUbsefBcwZcoOkZEhKbluu9jREIUC/OgqKr+7ggc3bBKho9P7uRIpLdMIIlkPInvZ4yRo87F09ACtMRVnZUIBJDx6ghMHd/nlmtFiRzrXDG5ERk/weLQqlGgyAF4g7/MwROlXwHCePCOQuXRTR7PRyiJkDmOA5PPfUU5s6di9mzZ+Oxxx6Dy+V/8MIDDzyACRMm9Pr33//+d+h2OFKkSkgYJ+y24INkpzuDLJL1/wFtZpQQ2X0vchM7jbBGcFBDTxU6/r8amPAxOxebD/eTIe2HyN2JwzRAy63+sIZmsBwDbSJ/cJXJlWhh4iEKstUb29UElmMQm9D7UphAKESNtACxuuN+ntkXZ2iEkxMgLikdBmkSVP3UoottnTAG2dfXEyy+57oQDk6AUi6rz8Fc4jTCKhya7gYWkRpSZ3eAbHEHyKIIBm6RXCBkZFQQ+vm78VBwloguMowkWQxfhuD5HZertMO3MxEmFInQxajA9MjwexZQXW16B7OZ8gEXaLL6ejg4IRKSew9m0CnzkGzrXjCrdOlhl1CAPNQGa6FZIATuq7KeMkaPn0xPhhROfIW5YR1f9MI4SKzd6xuaaiogZDiIEsNv8TbYWuR5SDD7TyyZyreD5RjkzLhoCPdqcETNqoP//ve/+PDDD/HMM8+A4zjccccdiI2Nxa9/7fuy/KlTp3DXXXdh2bJl3ttUquj8oOoPI+H32WIyQCINrp+q0zZwBhkAzEI1xA7f/UKlLiPMIm1QrxuITSX12FRqwHUigOOAVFdDSAssAEDibm1mDmNBjsDUgk5Gg3ixxHtbmyQdKnNwrd4E5hboGDXipNI+9xnip2Niw1uwWU2QDnDSAgBCYzM6GC2ShELY5CnIsvivh5Y5OmEKMovlWajx7JflSDO1I1PQCtOy13BVj5+/1GWERTy4C/Q87CI11Nbuuju7iQ8+pREeUhOJBUIAYBZpIO0nQOZYFgpYAUl0Hnc8ZUBqE58plUdw2l806BLEQGzrzvCfvYBqoAWaImM92plYpJxVf++KL0CS/gt06dqh0cZDzRlRJ9UO2vdBfBushWaB8KzrsVt6B8iNVceQxliRN+18nF55ecjbN4njoHB0B8gddRXIAaBKGR/yNoeKLbYAaXW7YLdaIJH1bXGpbtmLanEu8kZB15eoySC//vrrWLt2LebMmYNzzjkHd9xxB9566y2/j6+qqsLUqVORmJjo/SeXR2c/0v4I3L1JPa2YgsG6A2SJwv8H9KaSenTahci1lvq85ChzRb4PLcB/WG0QPQiA7yZwrei7kHshymPcLbe6+u8o0B+JtQ16Qe9A0KzMRKIjuLZBEkvf7XhIs2dBzLhQe3xvQNuSWlugE7kXUapSEYcu2Ky+x44rnXrYQghkPbV3iunLkMs0Ybq890mGnDXBIRma2lSHWAMl232i5vAOqRmaAD1YVlEMZE7/i0PNJgMEDAdEaX9hTQIfICc4+FIehXp0BcgmoRZSe3eAPN/6NI6x3fXEdk7Yb82qwtKMTnHfrjCytMkAgIZTh+ByOqGBCawsOn9HR7vBWGgWCLE7weE463jcXL4LAJBQcG5Y27dJ46HpMW7a0syXQSVmTwxru0NBnDoFIoZFQ+WRPvdZrRaMt5WiPX7mMOxZ5EVFgNzc3IzGxkbMmjXLe9vMmTPR1NSExsa+l+VbW1uh0+mQm5vb576RRujO/tpMwXdpcNn4HsMyue8PaM8lx2S0QQmrz0uOCs4MlzjyC1DmW5/GZufcfheIBUrlbumUf/ifgCG0vqcKezuMkt5ntE5tLuKh73eC3dnkjnYYxb4XyqVNPg8A0Hlyd0DbUtvbYJLwAbJQyx/42xtrfT+W1cMhC739T+68qwEATXs+7HW7kjPCOQjvvy8uaQxUXPcHzmAOqYkEuyQGStb/36XFFN0DOLRxyXBxDBKgg50TQSqLzsWEobKKtVA6dd6vr5HvxVRBDTiOv2olhqvfmtUYRzNMspQ+tyfmTAMAGGqPocu97oFRDG6fcBJdRO7PZedZGWTnmYMwc1JkTygKa/suRSLiuE6wLn59CNN5GmZO6l0jE83icvkkV/vpw33uqzy8A3LGDmne/KHerUERFQFyayt/EEpK6j6bT0jgA4empr6Tkk6dOgWRSIRnnnkG8+fPx7Jly/Dhhx/2edxIIHIHtzZL8CNTObsng+w7wPG0+coTNPtt86XkTGAlkQ+QxNo0GKCAAFzYCyx+rOdr0ZXGGnz49NqQmsVrnB2wSXu3mJMmjQMANFf3Pzrz7O1Ypb5b1e1ulaGdUyP3xPMBTXDTsu2wy/nfeVk832pK72NYCOtyIYYzgJWH/iEdn5aLk6ICxNd97b2N70Nsjmgf4n7JtFAxFjjcTeZZdw/mwR5SEiqXVAs16//v0mLkA+Ro7S8sFImgc7cnM0XptL9wOKSxULtPYA5/8Bju4F5HIxuLN12LsI8tQCeUfmtWOZZFItvea0iIR2rORNg4MVwt5TC6B/SIlNH5O0oGh8SdQXbae0+51XQcQ7U4HxKJxNfTAqdMgoxxQK/ns8gyYy1aRClBD9EaDunjpsHBCeFo7LveprPsewBAdvGiod6tQTFk74bNZkNNTY3Pf1Yrv0Ct5y+d5//t9r4TW6qq+P6VEydOxMsvv4xVq1bhwQcfxOeffz4E30lkid29Se3m4DPInPuPV6H0ncE6uyeskxP0yuI67FbIGEdE+9B6RGqBheuvSbjmK/5yloABVri+wPLNk+H6a+ADMziWRRyng1PRe0xzTDq/L/pAW71xHGLZTjjlfQNkfijHcZg5CRLQNeACIZvVjFgYwKr4DJYmiV8oZG7tWxNt0LVCyHBgFOHVdHVmXoKJrnI01vFtv6wWMySMC5AOTYDHuLtVGPV8xt4zpEQZrYvHZLFQMxbYbb5HydpM0R0gA0CXQAsAsAxRK7+hsqmkHic7HIjldHjmod+i8Oij2Cubhx8v34Z/q36HPdxkaGCB8fIXfF6W72xtgIRxgonpm7ETikSoF6ZDrj8Jk3vUuFg98uspSeDE7sSVy9qdQWadDmTZT0GnnRL29kUx7nHTrXyrz1hrPfSy6M8eA4BUKke9MA2yzr6dLJSNe3BGkI6YEZAJD8SQLdI7evQorr/+ep/33XnnnQD4YFgsFnv/H4DPuuKf/vSnuPzyy6HVagHwgXJNTQ3eeecdLFmyZBD2fvB4sr9nX8oJiMMMByeEWOJ7cZ9Ymwajke8J6+QEEDEsYhmDN4tr1HcgFgAzCCNoI7XA4irx81hj+g+WCXdCwPClGl+4ZuNV5c3YEuA2unQdiGEcYFS9p98l5/D1XrYAW72ZDJ1QMg5A1XfyXrALhDqaz/AT+WL4bhhx7lZTjs6+AXJXexNiAIhUwQ9Z6Slt7tXA6edQ/eP7SL32Lpi6OiBHd+A62IRKvo7TqGtDbEIKGJsBJk4GZZROKBMo+P3t0rUhIbnv763NzGeXxYro7S9sEscCthpYR1EG2VM69gYqIRACf8Db2Ooqhm7hv3D1OXm4+pw87PusFqK9m1AoaQDQd+FTR+NpxAGQxGX1uQ8AOpR5SDMeR2sXf3VTHpPo83FkdJK5P5fZHhnk+lOHkMnYIUgvDnv7Us+46fYGOJ1TkMo2olVzftjbHSodijwkm3pfeXU6HMi3HEVFwsXI9PO8kWbIMsizZs1CeXm5z39XXnklAKCtrTuQ8JRdJCb2PTAxDOMNjj3y8vLQ3BxafepwknoD5OAzyIzDAivj/1LP2VncNk6N8wTH8adz+ctHZnftrWCQJmxFYoHFUb0cRsjBAGC57l6+x/SBd/zQubOynrN2D7UmFm3QQthZFdB2Opv5s33vuOoezs7WWzlRvzXX+mZ+n6Sx6d59MXEywNC35t7T3k4SE3jW3JeM8UWoF6RCcfoLfrvuRY/CIZqwJnYHyGb36wrsXUM2pCQUQvcJiUnnu/+208JnkCVRHCDbJHxpgE0Ynb2aQ+EpHZslPOm97WLhQVz5+Rzv1ykT+atOnZX7fG7D4C5lUiVl+7zfETseKWwLrO5hPgpNeCenZGSRyvsGyK1l/NqSxAnhLdADAFWcZ9x0I1oaaiBjHBDEjZw1Vba4iUhlW2Axdsctlcf3QcOYIcydN4x7FllRUfCSnJyMtLQ0HDhwwHvbgQMHkJSUhNTUvjVi69evx29+85tet504cQJ5eXmDvq+RJnO3uHLZgs8gC5xmWOE/UOzZE/YYl4dfix+FAyJM2X0nOJdzUPrQRlqaVo4EpgslbD4EDPCe6wIkMnqkaQPPiHnGQ8u0fQPbVnEalKbAWr0Z2v1vp+cENwCQwNlvzbWlnX9NVQJ/rs0wDNqECZCY+9bcW9zt7RTa8AJkMAzqky/CJOsh6DrbvO+/RKkNb7sBkrr7WdsMfIAschgHbUhNJEjc+2v2M87Y4V43II3iANkh40sD7FE67S8UnpNRC8cnB3wtAE7PLoCeU4Jr7LvSHgDsHZ4hITk+75emToKA4SCo4wNsTWyYf3tkRJEp3celHgGyq/4gTJwM2QXTw96+NolPjDi7mtFWy5f4KUZAizcPWfoUCBgO9ScPeW9rP/4tACCz6OJh2qvIi4oAGQCuu+46PPnkk9i1axf27t2LJ598EjfddJP3/o6ODphM/KK0iy66CNu3b8f//vc/1NbW4q233sKmTZtw8803D9fuh0zmHv/KhRQgW2Bn+vbj7alnFvfD+2/C/in3YYL1MA69+xBs7tZyQxUgheLOxRNwO+7A467VAIBP2bm4HXcEVcts1fFZWXV83wy2QZGFBHtgi/6snXzwqoxP63OfJ1v/lusi2DkhSrnsfmuu7Tq+9VZscvfFqC5xIpTWvldBnAY+QFPF9i3tCFbszKsgYVyo+PFD2E06AEP3/ss17mDNyF+5EDuNsEVxgOy5rO6vvSDrDpBlUdxfmFPymU+nKHp/zsHynIxK4fC7AFggFKBWOh6x+lKf2+D0dbBxYsQn+L6qFefuZJHadQgsx0ClpRrksUQikcHBCcE5ugNkre44TkvGQxSBkjBNXAo/CdLQDGMTfyUkPnPwB6BESoK7k0VnTfcJqKR+N5qYRCRkjBuu3Yq4qAmQb775ZlxxxRVYu3Ytfv/73+Pyyy/vFfCuWrUKr776KgBgzpw5eOKJJ/Dee+/h8ssvx1tvvYUnn3yyV5u4keLrk/yHbEbFGwF1PuhJ6LLAxgQ3XGT+yt9jt+JCTKt4Doe3vgEAeH5bWUidIYbC8hnpWLdiGppUk8FyDM6VVGHdimlBlWs49Xxgq03qu3DAGZODJLTDah74BMUzrtrXAgRPtv7fqt+hmktBExL7nbDEGZpg54TQxndno62yZGidfeuVnUb+tpj4vpnrYOUXLUQHYsCUfwaHO0CWqYdmhb7S3c/a6X5dqcsImyg6W6QBgMK9vw5j3/cEAFj3SW00T6gTqvjMpytKp/2FItAFwIbYychynIbL0Xeht9jYiFZBPARC3x+BaXlT4eQEyOCa0MUogxrxTkY+hmFghQSMO0B2OWzIsldBHzstMtsXiqBnNBBa2sB1VMHJCZCYPnICy7S8KXynlyb+BJR1scg1H0Z9TPj12dEkalbHCIVC3H333bj77rt93r9t27ZeXy9duhRLly4dil0bNJtK6nHf5hO4UiBAOtPm7nzAZ3oCCQBFLgscguACZIFQgNPnPoL0rVfhp8yXAIBLbNtwz4dTA37doeaZjFb1cCYukNdgWpD7yBlaYOeE0MT2rWcXJ44DaoGW2jJkTez/BIs1NMPJCRCb4DtQ9eznwX/kYry1Gtn97KfQ1IR2Jg6pPT6gnapUxOs64XI6IBSJvbcz5naYOSkUyvC7JQhEIlTFXYBJ7VtxVH8OAECuGZohCCotH3ByFndrI9YEg6hvNj5aqOL43xeXqdPn/ZyNP7lVRnEG+bRFjjkAMtt34Mp1H+Dmy86Nyr/xYAS6AFiUXgRJ89uoqShB9pRzet2ntDZBJ06Cv7X2EqkMtcJUZLH1MDJqaAfnWyFRzMZIIXBaAAB15QeRzTggyoxcAKgXxkFqbYXDYUSLIBFp4jBbxw0hoUiMalEmFDq+k0X1ySPIgx7VWXOHec8iK2oyyGORZ7GJmGH99inuj5i1wiEMfnX6im0LkSnorqu8SrQj5Cl3Q6lZMxWZ5lJ+CkAQROYWdDCxPntMqtMLAACddQO3ehOaW9HJxEA4QDbJFpOHVFcjnD4yVx5yayv0ot4Lf5iYdIgYFh3uxYDe17V2oouJXJ2rbNoyqBgLlKf5EyTlEPUhlilUsHFiwKoDAMhZM1xROqYZANSaOLg4BjD7HiTD2E2wcBIIo7QLx6aSenxQzndWSWE6B2w9OJIEsgA4oYAPittO9Z1sqXW2wiLrv2SpXc4vmjIJo7fGnAweKyPzBshtFfwCveSJ4S/Q8zCJY6GwdyDGUodO6chri9apzEeSlW8Z2nyUT2CmFY6O/sceFCAPI89iE5ZjAAQ/bU7MWuEUBpdB7vm6Dk4Y0usOFzZtJrQwoKX2RFDPk9ra0CXyHQQmZ08CAFjdoz77I7G2QS8cOJgUJhVAwrjQWOM/6FY72mCW9s5oy+L4g2Rnc+9hIRJ7B4wR/JAumHsFzJwUU218feVQZkCNjBICK9/9QcmZwQ7RmOtQbD7chC7IkXvmQ5/lT4zDCHMUt09bsrkQ74keAICQTsBHuqxxU2HipHDV9574xTqdSGDb4VT1f/XCquUveVtF0fs7SgaPg5FB6OIDZK6+BHpOicy88Hsge9hkiVC7OpHsbIRF5bvdYDRzxk9ECtqg17VDULsTHYhBSu7U4d6tiKIAeRh5Fpsw4MD1aGEW6LQ5CWuDK4QMsud1hWDDnnI3lOIn8u1jGo/9GNTzlI52mCW+F9lo4xKhgwpMAK3elI52mPyMme5Jk8EH3R3Vx/w+JpZth13Re2W8KpE/SJpae4+bljt0MIu1A75uoCQyBQ5LZ0HAcGDBYNljm4Ysq2gUqCCy6+Fw2KFgbMAgTHGMBE+vXQE4JDM6n9lXocMESxQHyJEc9z4SiUQi1IjzoensPfGro/kMRAwLgY8hIT3VCfm/x3RLedDrQ8jIZxPIIHJnkGP1x1EjLYDQT816KFzyBCRx7dAyRnCxORHb7lCRZ/AnCw0VJcg0HEKNqmhETAIMxuj6bkYYz2KTvewEMAzwoWteUNPmpJwVLlHwH9CRmnI31PImzYKJk8JRsyeo58W4OmGX+e5jyjAMmkVpUBhrfd7fEz+ueuDV7Kl5/EIOS5PvDLLF2AUNzOBUvWuZY1NzAAD2jt5t55QuPeySyNUJbyqpx0Yzn0UUgh3SS+8WoRpSZxdM7hGrjDw6s3Oe8qcYxuI3+yp0mmATRG/7tEiOex+p9NrJyLJXgnU6vbd1NPGXhaXx/rN2m0rq8XYVf2yND2AyJhl9nEIZX8ZoMyPTcRqGuMhmRxlVEkQMCwCQJY+cBXoeSfkzAAAdh7YgDa1wZo6u+mOAAuRh5el88JLkRgDAj5Lz++18cDYZbOBCCJB79kcu47Lxouq2oF53uEgkYlRJJkDb6bu3qS8upxOxnB6swn8f0y55JuJtdX7vBwCOdUHL6eHsZzseMfHJ6IAGgo6TPu/vaOaDcVFM70AlNj4Vdk4Etquh1+0atgtOWeTqhJdsLsQToucBDP2ld5tIDanTCFOXO0CWRecCN08ZUn/ZV4nTHNUB8kg9EY4kQVohFIwNjdXdWWSTZ0hIsu8hIQD/N/KR6B4AY7M8hQBOgRwi1obaE/shYVwQZ86M6PZ7DpyKda+FGUlSssbDzEkxoeEjAEDS1IuGeY8ijwLkYbZ8Rjr+3//dCJZjsDqjM6ggVcZZwYpD+4COxJS74dAVV4gseyUcNvPADwbQ2dYAIcOBUftfkOOIyUES2wqH3er3MYbONkgYFxhVYAMDmsWZUBmrfd7X1cJniGVxvX/mAqEAbYJ4iE3dw0LsVgvUjAWcPHIBsif4c4VY+x4OhzgGCtYAi7szhDhKh9R4ypAE4DM8Utj7ZF8lrDmkRbJDZaSeCEdS/LjZAICW8u6Fep4rNAmp/gdLef5GrBzfTWaslacQwCmSQcpa0HmSv2KZOum8iG6/58CppOyJEd32UNh8uAmnuRQkQI8uTo6D1vDbkEYbCpCjgFKtRb0gFbI2/zWrZ3PYbXzAFmKAPFJJcuZAwrhQe3x3QI/Xt/CZYYmP6XcewoQ8CBkOLbUVfh/jGVctjgnsIGBQ5SDZ7ntCn7mD3yd1Yt+J9TpRIhQ9hoXoO/j/FygjN+q2u/YdQ37p3SWNgYozdg+pidIA2ZN9fdt1EYycFJVcWp/sq5Q1R/0AjpF6IhwpmRNmwM6JYK8r6b5RXwczJ0VMXN+2jx6evxEJnGO2PGWsY4VySDgb0FiCDqiRkRPZSXfHu/ghX3ZOiJXPfD2iync8azSk4Ds16TkF7t10YkR9D4GgADlKtKgmIMXsP0A7m8Uz2EIytgLk9GnzAQAd5TsDerypnS9XkMf6X7GuTuWDno4z/rtOdLXx25EF+AHJxo1DPPTQd/YdMuFwT/aL9XGJ1yJLgsbR3YLP4A6QxRr/H+bBGs5L75xUAzVnhs09TU+iGpoezMHyZF9fUP0eW9mZiGMMMFzxcq8AU8Za4BJHd4A81kmlMlSLcqHq6C6xkJgb0SZI6HdBEZWnEFYkhwxWxOtLcUY6IaIL0DaV1OOp3Xw3HzFcI67G3bNGY5yA/yzLFLSPyhIkCpCjhD1xGlLRCn1731HDvtjcAbJgjAXIqek5aEQCRI0HAnq8d8x0gv8V6wnuy1vWZv8nKDYdX/agSghssIUshd9mU6WPemlDE6ycGBof42vtyhQksu3gWP7SvlnH/z5INYGVdgRiWC+9y7UQMBzs7XwdtiyKp9B5sq95869DPGPAOFvvKzxyzgJ2FE2oG606NBORYTvl/ZtSWpugl/T/90TlKYQTy6HgrMh01sAYH5kJeh5LNhdip/A3AEZmjfvZJUhWTjwqS5AoQI4Sqmx+ReiZE4F1aLCZ+SlejGRsZbAYhkG9cgpSDIGVo7i6+MA2Nsl/YLujHjByMmSWvuy3nZNnXHWsj3HVvsRlTwYAdNX17dksMjejXRDnMyPBaNIgZRzeEyWbvgUAoIyNXIAMDN+ld6GCzxhzOr78RDFEU/zCUTBvOaycGKbDm7y3cSwLBazAGPv7G5FSpyMGRrTW8Ytmtc42WOQDXwka6+UpY55YATHjgohhIc3uf8pqsEbqLAKPs0uQJHCOyhIkCpCjRNokfuqTsfpgQI+3WUwAAJF0bGWQAcCWMgOpXAt0Lf13ngAAxtQCIyeHws8wjE0l9bh303HYIEJyP9PGOGML7JwIGm1gtcCpOZPg4IRwtvbNSsutLegS+d6OJJYPwNub+JX2TiNfoqGKGx0LIERKfrGh2Mj/fJXq6A+QZUoNylTnIL9tG1wuFwDAYjZAyHBAFE8CJLyYPH6hXmPZHjjtNiRwnXANMCSEEKbH1dm0SZFtYTZSZxF4jJUSJAqQo0R8UjpaEAdRy9GAHu+w8hlkoXTsfUDHjONXE9ce/WHAx4otregU+A/CPLVU8Yyx30tdInML2hltwHVoYokUTYJkyHSVfe7T+Jii56FI8AwL4QNk1tQOANDGRTaDPFykKj5AVlsbYeeEkMlHRgaWnXg5ktGBioPfAwDMRr5+kJFF56AT0i170iw4OQGstSVoa6qBgOEg0I680b5kaDHu9QWt0CI1Izei2x7pAeZYKUGiADmKNMgLkGD0v1CsJ4eVzyCLZSMjwIik3Gn8pSnL6YHLUWS2dhj8jJkGfF3q8l1LJbW19bsdX9rl2Yi11PS5PY7tgEPhu+2cNoVfuGdp57PjAnM79FBCJJYE9drRSh7D113HO5thYpR8Ad4IMP78VXBwQnQc+BAAP+wFAIQUIEc9hVKNWmEm5O3HoHMPCZEljLzRvmTobCqpx3cn+Ktc5WwmNh9ujOj2R0OAORZKkETDvQOkmyV+Cqad2QOLyQC5sv8PXpc7QBbJx14GWalSo0KUC1VryYCPVTk70K7I93u/WJsGo5G/1AX4H/etcHTAIAmuzMGqyUOqaT9cTieEIv5PzdjVCRVjBdS+txWfnAkXx4DT8wdnka0DXUwMorMZWvAUGv4kIwE61DPJiP4CC546NgnH5IXIat4KjmVhM/EZZAqQR4Y21UTkde1BTQu/OFSTlDO8O0SilqeF2b9xABACas6IX33IX9mNZBC4fEb6qAwqRxPKIEcRaWYRhAyHM2X7B3ys08YHyBLZ2AuQAaBDOw3ZtvJeI2R9iWU74ZD7b5HW81JXExuLBi7e56UurasTNj/jqv0RJBZAyjjQVNs9Ua/DXVssivFdAymWSNHOxEJg5DMWUnsnTKLREh4Dqpjun6FFMLJ+dy35lyOTa0RV6QHYzXwGWSKnAHkkcCVPRwJ0cNXxx9b49MheMiejh6fsboGQD4oLhadHVIcJEjkUIEeRlAn8Qj1d1cABMusOkKWKkRVkRAqTOQsqWFB38pDfx1gtJmhgAqf0P0Wv56WuT9lzkch0wbT0X73O7F1OJ7ScHqwiuF7E6oxJAIC2mu6OG13ugSOyeP+ZA50oATL3sBC5Uw+LeKTkWQemUMV4xzfbhCOrPChv/jVgOQZNe9+Hw+LuYaocPScvo5k6lx8TnNH8Hbo4BTQxkZtMSUYXT9mdhePL2kZahwkSORQgR5HUrPHQQwk0+uidexbWzo9alo3BEgsASJnMDwxpPvGj38d0urtcCDX+A2Sgu5Zq5uIbIGUcyNf3rm3WtQ88rtqX5NypAABLY5n3Nms7Xzqh8TFFz8MkTYLGzrd3U7v0cEi0Qb1uNGMEAhgY/nfWIRpZv7vxKVmokExCcv3XcFj4RbJShWaY94oEInMyn3xI45rQLozc0B0y+ng6TEjhGJEdJkjkUIAcRRiBAGck46DtKhvwsZydzyDLBqhVHq0y86dBDyW4Ov/Z9q5WPhiVaAMLbCefcyk6oIazdEuv2/Wt/BS9QMdMe8QmpEIPJZj2U97bnHp+W3EpfafoedgVKYh3tYFjWWi5LrjkoyvbZfQEyCNwyIY+ZwnGuapgr+cvv8r8tA8k0SVGG4dahi9r6hpgSAgZ20Z6hwkSORQgRxlD7GRkOU7D6bD3/0CHBSzHQCobe32QAUAgFKBaNgmJev9t8cwdfDCqiAtsIYREIkFFzPkYr98Bp93qvd3Uxgfa8tjgMgiMQIAmUSaUhtPdNxqaYOJkUPUzIINVp0HNWNDWVAsJ4wSjDK72OdpZhHxgPBKn0GXNuwYAkNfwKQBAHsWTAElvNeJxAIAUc4XfgUCEjIYOEyQyKECOMqK0QsgYB+pOHu73cYzDDCskEZ0PP9KYE4qQ5ayB2dDp8367e/pdTGLgPU+Fk66EGmac3PuF9zabd1x18AfILlUOkuxnvF+Lzc3oEPSfERZr+ddpKN8HABCMsgDZJuKvenDSkVeekJozEaeE+UgBP8BFMUav4Iw0m0rqsdPM/10lMnq/A4EIAcZGCzMysLEbXUWphPH81Ke2U/v6fRzjtMDKSIdil6JWjWIyhAyH6n9e4DMjxBr4hW6xiYFPzZp8/jKYOSmMhzd7b3N2ubeTHPxwAVdsPpLQAYO+AwCgsLWiS9x/wCt392g11/Jt7CSa0VUzaRe7A+MRGCADwImYCwAALo7BVY9voSBrBFiyuRB/Fr0DABD0MxCIEEI8KECOMpnjp8PKieGs7z+DLHCaYWVkQ7RX0WdTST2eOsEHWBOZWp8ZIYGpBZ3QQCwJ/ERCqVLjhHI2clq/A8fyY4VhaoGZk0IZQr2pJIXvZNFUxXey0DjbYPEzRc8jJokPkCWtxwEAspjRFSA7JfzPUSAfeQHyppJ6/LtlMgBAAI4ykSPEfOvT+Ng5t8dAIOpMQAjpHwXIUUYklqBWnAt1Z2m/jxM6rbCP4Qzyks2F2CP8JQD/GSGJpRW6fsZM++MYvxSJ6EDVYb5DBj+uWgsmhKlv8Vl8MKU/UwqOZRHPdsCp6H+RUEJaDgAgyVQBAFDHBbc4MJptKqnHyTa+vvvr400jLrBcsrkQn4ruBIB+R5OT6CLWpqELCgjBUmcCQkhAKECOQp2aSci0V4JjWb+PEbkssAvkQ7hX0cXTq9LuzgjZOFGfjJDC3g6jOPgOEAXzV8HJCdC2nx8rLB1gXHV/UnInwcUxcLZUoEvXDhnjADT9fyjLFCrooEI66659HiUBsmdC1SSW7+pxrmv/iMu+en7vbBw/GZEykSMDdSYghASLAuRolDIdGpjQWFPh9yEilxVOwdjNIHt6VYrAguUACZwwQtYrI6R2dcAmDX6BW1xCMkql05HauBUAoHK0wyKJD2k/pTIFGgXJEOsq0dnsnqKnHbgmukOQAAHDwcEJoe6n48VI4plQNVPIB8iLhIdGXPbV83snhosykSMIdSYghASLAuQopM3npz41V+z1+xgxa4FDOHYzyD0zQo86fwqGAWYIKr0ZIY5lEcd2DljO4I8x9zJksXWoP3kIWrYT9iDHTPfULsuC1lwDQys/uEQRN/BiP4OU3289ox41nUrOzr5aOfGIy75SJnLkos4EhJBgjI5P3lEma+JsODkBbGdK/D5GwlnhGsMBcs+M0H9cl+MQOw6JAj0WF/ALvwxdnXw5gyq0ADnnvFUAgLod70ALI1hl6MMFLJo8pLnqYWnn2731N0XPwybnh5sYBNqQXzfa9My+2jiRO+s/srKvlIkkhJCxQTTcO0D6kitVOC3MgLz9uN/HSFnbmA6QAT5Y8QQmpbuESP7yWux+fx3O/fmj0LWcgQaASBNa/W5a9nhUCMcju+YDAIAgyDHTPQkSxkPebAfrnvoXlzJwgOxSpQEdgFk0eia13bl4AhQf8dnXd1yLcJ3wG6SMwOxrz987QgghoxNlkKNUm2oCUi0n/d4vhRWsaGwHyD1NnrsEJYrzMPX0q+hoqYehjZ+iJwty+l1PbRkXI4VrBRD8mOmeVOl8q7fUjv3oggKKANrF1bv4uuMUW9WomfpF2VdCCCEjBWWQo5QzaRqSur5GR3Md4nwMqJBxNnDisTlm2p+4n6yD7O2FOP7+gxDlzAUAqEKYfudRnXgRzqt5AQDw+u561CbWhxTMJeVNAwBkc3WoEWRioO6/m0rq8VkNg1UiIA4Gd69dJQCM+GCSsq+EEEJGAsogR6kacT4AoOW5y/pkEFmXC3LGDogpg9xT9oQiHEi4EsUtH8F5eicAQBvEmOmeNpXU4297nNBz/M94gf37kFuSxSdlwODejkE8cDeMJZsL8apoHQDqtUsIIYQMBwqQo9Cmkno8eYxv4VbAnOkzrctqMfIPpAxyH/lXPwI7RJjV+gFYDrjxpe0hBbV8S7LrEMNYAAArRDtCDlIZgQCNYr7u2CIbeLHffOvT2OI8BxzHf029dgkhhJChRQFyFFqyuRB7+5kSZzEZAACMhALks/3YKMR/2CsgZAAGwDXmDSFlfj0tySycBED4QWqjiC8rSO/cP2BNsVibBj1U4MBQr11CCCFkGFCAHIU8wZmT49+es4Mzm8UEAGCkymHbx2i1ZHMh1gr5zhPhlCd4WpJJ4Qg7SN1UUo8DRr6PcgrT0eeKwNmo1y4hhBAyvChAjkKe4EwA/hq7FPZewZndwmeQhRQg9+E5ubByYgChZ34jGaQu2VyIP4reB+D7isDZqNsDIYQQMryiLkDmOA5r1qzB+++/3+/j6uvrsWbNGhQVFWHJkiX4/vvvh2gPB1/P4OwEm4kuKHoFZ3Z3BpkC5L48JxcSOMPK/EYySJ1vfRqfOWfDxTEAAgvaaeoXIYQQMnyiqs0by7L4+9//jh07dmDJkiV+H8dxHG699Vbk5+dj48aN2LZtG9auXYtPPvkEmZkDD2GIdstnpGMTXseLX5bjqOFz/FP8Ejqn/RKr3UGSw71ITySjAPlskRxGEamWZGJtGjqMGjAA1RQTQgghI0DUBMjNzc244447UFdXB42m/06xu3fvxunTp/HWW29BpVJh3Lhx2LlzJzZu3Ijbb799iPZ4cHmCM7NpFrr+8SbyazcCuBYA4LTxGWQxZZD76Hly0aCz4EXVbbhz8YRhzcCOlglyhBBCyFgRNQFyaWkpsrKy8Mwzz2DVqlX9Pvbw4cOYPHkyVCqV97aZM2di//79g72bQ06h1GB34mWY0boF+vZmxMQnw2nlA2SJQj3Mexedom0YRTQG7YQQQgjxL2oC5IULF2LhwoUBPba1tRVJSb37ycbHx6OpqWkwdm3YJS74DaQffIiSL1/GuT+9H6ydD5ClctUAzyTRItqCdkIIIYT4N2QBss1m8xvAxsfH98oGD8RisUAsFve6TSKRwOFwhLWP0Sp/2rko3zwBKafeBcfeC9ZdYiGhAJkQQgghJOKGLEA+evQorr/+ep/3rVu3DitWrAh4W1KpFEajsddtdrsdMpksrH2MZl2Tr8fsIw/ixL6tYO1mAIBcQQEyIYQQQkikDVmAPGvWLJSXl0dkW8nJySgrK+t1W1tbGxITEyOy/Wg05dKfw3h4HYw7XwFisgEAMqpBJoQQQgiJuKjrgxyIwsJClJWVwWw2e287cOAAioqKhm+nBplCFYPjCZdhmm4bBIZG2DgxhKKoKSEnhBBCCBk1RkyA3NHRAZOJr72dM2cO0tLScPfdd+PkyZN46aWXcPjwYVx99dXDvJeDK2HBbyBjHJja8TUsjHS4d4cQQgghZFQaMQHyqlWr8OqrrwIAhEIhnn/+eXR0dGDFihXYvHkz/vWvfyEjI2OY93Jw5U+fixOCcVAyVsg5K65c9wE2ldQP924RQgghhIwqUXmNftu2bQPelp2djTfffHOodikqbCqpx177QjwqOgUJnLjG9Dbu+ZAfFkItxAghhBBCIiMqA2Ti25LNhVgu4lvZMQxwo2grbsRW2DaLgRltw7x3hBBCCCGjw4gpsSDAfOvT2OQ8D3ZOCACwcBJ85JyH863PDPOeEUIIIYSMHhQgjyBibRqMkEMEFlZODCkcMEIOiTZ1uHeNEEIIIWTUoAB5BLlz8QQkCbrwpmsRrrL/FW+6FiFZoMediycM964RQgghhIwaFCCPIMtnpMN81et4UXUbyrhsvKi6DearXqcFeoQQQgghEUSL9EaY5TPSKSAmhBBCCBlElEEmhBBCCCGkBwqQCSGEEEII6YECZEIIIYQQQnqgAJkQQgghhJAeRvUiPZfLBQBoamoa5j0hhBBCCCHRJiUlBSJR33B4VAfIra2tAIDrr79+mPeEEEIIIYREm2+++QYZGRl9bmc4juOGYX+GhNVqxbFjx5CYmAihUDjcu0MIIYQQQqKIvwzyqA6QCSGEEEIICRYt0iOEEEIIIaQHCpAJIYQQQgjpgQLkCLLb7XjggQcwe/ZszJs3Dy+//PJw7xKJgNraWtxyyy2YPXs2LrjgAqxfvx42mw0AUF9fjzVr1qCoqAhLlizB999/P8x7S8J133334cYbb/R+Te/x6OFwOLBu3Tqcc845OOecc/CXv/wFdrsdAL3Po4ler8cdd9yBOXPmYP78+Xj88ce9Xa3ofR757HY7rrjiCuzcudN720Dv6+7du3HllVeisLAQN954I2pqagZ8HQqQI+gf//gHSkpK8Nprr+Hhhx/GCy+8gE8//XS4d4uEwW6345ZbboFEIsG7776Lxx9/HFu3bsVTTz0FjuNw6623QqvVYuPGjbjqqquwdu1anDlzZrh3m4Ro165d2Lhxo/dreo9Hl3/84x/4+uuv8fzzz+OFF17ADz/8gOeee47e51Hm4YcfRnNzM958803885//xKZNm/Daa6/R+zwK2Gw2/PGPf8TJkye9tw30vjY2NuK3v/0tli1bhg8++AAJCQm49dZbwbJs/y/GkYgwmUzctGnTuB07dnhve+6557hrr712GPeKhGvfvn3clClTOKPR6L3t448/5s477zxu586d3LRp0ziDweC972c/+xn35JNPDseukjCZTCZu0aJF3LXXXsvdcMMNHMdx9B6PInq9npsyZQr3448/em/74IMPuJtvvpne51GmuLiY+/rrr71fr1u3jt7nUeDkyZPcsmXLuCuvvJIrKCjwxlsDva9PP/10r1jMbDZzM2bM6BWv+UIZ5AgpKyuD3W7HzJkzvbfNnDkTR48ehdPpHMY9I+HIy8vDSy+9BKVS6b2NYRjY7XYcPnwYkydPhkql8t43c+ZMHDp0aBj2lITrqaeewpw5czBnzhzvbfQejx4HDhyATCbDeeed571txYoVeOWVV+h9HmW0Wi0+/vhjWCwWNDc344cffsCUKVPofR7h9u/fj3nz5mHDhg29bh/ofT18+DBmz57tvU8ul2PKlCkoKSnp9/UoQI6Q1tZWxMTEQCqVem9LSEiAw+FAR0fHMO4ZCUdcXFyvD1SWZfHmm29i5syZaG1tRVJSUq/Hx8fH0+TGEaikpARffPEF/vznP/e6nd7j0aO2thbp6en45JNPcPnll2PhwoV47LHHYLfb6X0eZf7yl79g7969KC4uxgUXXICEhAT8/ve/p/d5hLv22mtx1113QS6X97p9oPfV3/3Nzc39vt6onqQ3lCwWCyQSSa/bPF97FoGQkW/dunU4ceIENm7ciNdeew1isbjX/RKJBA6HY5j2joTCbrfjvvvuw7333ouYmJhe91ksFnqPRwmTyYS6ujq8+eabePjhh2EymfDwww/D6XTS+zzK1NbWYvLkybjttttgNBrxt7/9DY899hi9z6PUQO+rv/hsoNiMAuQIkUqlfX7Ynq/PPtshIw/Hcfj73/+Od955B8888wzGjx8PqVQKo9HY63F2ux0ymWyY9pKE4rnnnkN2djaWLFnS5z56j0cPkUgEo9GIf/7zn8jKygIA3HXXXbjrrrtw1VVX0fs8StTW1uLRRx/Ftm3bkJKSAoD/O16zZg2uvvpqep9HoYGO0/7iM61W2+92KUCOkOTkZHR1dcFut3vPVFpbWyGRSPpkpcjIwrIs7rvvPmzZsgVPPfUULr74YgD8e15WVtbrsW1tbUhMTByO3SQh2rJlC1pbWzFjxgwAfCswl8uFGTNm4De/+Q29x6NEUlISRCKRNzgGgNzcXNhsNiQmJqKioqLX4+l9HpmOHTsGpVLpDY4BYOrUqXC5XPQ+j1IDfRYnJyejtbW1z/3jx4/vd7tUgxwhkyZNglgs7lX0feDAAUyZMsXnjG8ycqxfvx5btmzBs88+i0svvdR7e2FhIcrKymA2m723HThwAEVFRcOwlyRUb7zxBj755BNs2rQJmzZtwtVXX42pU6di06ZN9B6PIkVFRXA6nSgvL/feVllZCaVSiaKiInqfR4mkpCR0dXWhsbHRe1tlZSUAftE1vc+jz0DH6cLCQhw8eNB7n8ViQWlp6YDvOwXIESKXy7F8+XI8/PDDOHLkCL755hu8+uqruOmmm4Z710gYDh06hNdffx1r167F1KlT0dra6v03Z84cpKWl4e6778bJkyfx0ksv4fDhw7j66quHe7dJENLT05Gdne39p9FoIJPJkJ2dTe/xKJKTk4NFixbhnnvuwbFjx7B//348/vjjuOaaazB37lx6n0eJoqIiTJo0Cffccw/Kyspw6NAhPPDAA/jJT36CxYsX0/s8Cg10nF65ciUOHz6MF154AadOncJ9992HtLQ0zJ07t9/tMhzHcUPxDYwFFosFDz30EL766isolUqsWbMGa9asGe7dImF47LHH8Oqrr/q87/jx46ivr8d9992Hw4cPIysrC/fccw/OP//8Id5LEklPPfUUDh48iDfeeAMAUFNTQ+/xKGE0GvH3v/8dX331FUQiEZYvX4477rgDYrGY3udRpLm5GY8++ih2794NsViMyy67DHfccQdkMhm9z6PEhAkT8Nprr3m7TA30vn7//fdYt24dGhsbUVhYiEceeaRXuZUvFCATQgghhBDSA5VYEEIIIYQQ0gMFyIQQQgghhPRAATIhhBBCCCE9UIBMCCGEEEJIDxQgE0IIIYQQ0gMFyIQQQgghhPRAATIhhEQBp9OJ559/HpdccgmmTp2K+fPn44EHHkB7e/uQ78uNN96Ip556ashflxBCogUFyIQQEgWeeOIJfPrpp3jooYfw5Zdf4qmnnkJFRQV+9atfgdrVE0LI0KIAmRBCosCHH36I3//+95g3bx7S09Mxa9YsPP744zh+/DgOHz483LtHCCFjCgXIhBASJXbv3g2Xy+X9OjMzE59++ikmTpwIo9GI++67D3PnzsXUqVOxePFifPnll97HTpgwAZ9++imWLFmCwsJC/OlPf8KZM2dw4403orCwEDfccANaWloAAM8++yzWrl2Le++9F4WFhVi8eDG2bt3qd782bNiARYsWYcaMGbjuuutw5MgR73179uzBihUrMH36dFx44YV48cUXB+EnQwghQ4sCZEIIiQI33XQT3nnnHSxcuBD3338/Pv30U3R1dWHcuHGQyWRYt24dKisr8eqrr+KTTz7B7Nmz8cADD8But3u38f/+3//DunXr8O9//xtffPEFrrvuOtxwww14++23UV9fj1dffdX72G3btsHlcuHDDz/EqlWrsHbtWpSXl/fZr23btuGZZ57BPffcg48++ggXXHABfvazn6GlpQUulwtr167FwoUL8dlnn+HBBx/Ec889hx9++GFIfmaEEDJYRMO9A4QQQoDbbrsNubm5ePvtt/Hhhx/i/fffh1Qqxdq1a/HLX/4SM2fOxE033YQJEyYAANasWYP3338fzc3NyMzMBMAH2UVFRQD4jPL48eOxePFiAMCiRYtQVVXlfT2NRoO//e1vkEgkyM/Px/fff4+NGzfivvvu67Vfr7zyCn7961/j4osvBgD89re/xc6dO/H+++/j+uuvh06nQ3x8PDIyMpCRkYH//ve/3v0hhJCRigJkQgiJEkuXLsXSpUvR1dWFnTt3YsOGDfjnP/+JnJwcLF++HFu3bsX777+PqqoqHD9+HADAsqz3+T0DU6lUirS0NO/XMpmsV7Z58uTJkEgk3q+nTp2KkydP9tmnyspKPPnkk3jmmWe8t9ntdqSkpECr1eLXv/41Hn74YbzwwgtYuHAhli1bhsTExMj8QAghZJhQgEwIIcOsrKwMGzduxP333w+Az+5edtllWLx4MVatWoWdO3fiiy++wMGDB/GTn/wE1113HRITE7F69epe2xGJeh/SBQL/VXRnP9blcoFhmD6Pc7lc+POf/4zzzz+/1+0KhQIA8Kc//QlXXXUVvvnmG3z77be48cYb8cgjj2DlypWB/wAIISTKUA0yIYQMM5fLhTfeeAOHDh3qdTvDMFCr1YiLi8Mnn3yCJ554An/4wx9wySWXQK/XA0DILeAqKip6ZZ+PHTvmLd/oKTc3F01NTcjOzvb+e/XVV7F37160trbioYceQnp6On71q1/h7bffxooVK/D555+HtE+EEBItKEAmhJBhNmXKFCxcuBC/+93v8NFHH+HMmTM4evQonnrqKZw4cQIrV66EXC7HV199hbq6Ovz444/461//CgC9yiaCUV9fj/Xr16Oqqgr//ve/cezYMVx99dV9HveLX/wCb7zxBj766CPU1tbiX//6Fz744APk5eUhJiYGW7duxd///nfU1NTgyJEj2L9/P6ZMmRLWz4MQQoYblVgQQkgUePrpp/HSSy/hxRdfxF/+8hdIJBLMnj0bb731FlJTU/HPf/4Tjz32GN566y1kZGTglltuwbPPPovS0lIUFBQE/XpTp06FwWDAVVddhezsbLz00kvIycnp87ilS5eivb0d//rXv9DS0oK8vDw899xzmDRpEgDghRdewKOPPorly5dDKpVi6dKluO2228L9cRBCyLBiOBrRRAghY8qzzz6LnTt34p133hnuXSGEkKhEJRaEEEIIIYT0QAEyIYQQQgghPVCJBSGEEEIIIT1QBpkQQgghhJAeKEAmhBBCCCGkBwqQCSGEEEII6YECZEIIIYQQQnqgAJkQQgghhJAeKEAmhBBCCCGkBwqQCSGEEEII6YECZEIIIYQQQnqgAJkQQgghhJAeKEAmhBBCCCGkh6gKkA8fPowbb7yxz+3btm3DypUrsXr1arz33nsAAJZl8eCDD2L16tW48cYbUVNTM9S7SwghowYdfwkhpJtouHfA4+WXX8bHH38MuVze63aHw4F169Zh48aNkMvluO6667Bw4UKUlJTAbrdjw4YNOHToENavX48XXnih13OdTieampqQkpICkShqvlVCCIkqdPwlhJDeoiaDnJWVhWeffbbP7ZWVlcjKykJMTAwkEglmzpyJ/fv348CBA5g/fz4AoKioCMeOHevz3KamJixatAhNTU0B74eL5ZB3z6d4/rtToX8zhBAygkTL8ZcQQqJF1ATIixcv9pllMBqNUKvV3q+VSiWMRiOMRiNUKpX3dqFQCKfTGfZ+CBj+vxa7K+xtEULISBAtx19CCIkWURMg+6NSqWAymbxfm0wmqNXqPrezLBuRy3gMw0AiEsDmZMPeFiGEjGRDffwlhJBoEfUBcn5+PmpqaqDT6WC327F//37MmDEDxcXF2L59OwDg0KFDKCgoiNhrSkVC2ByUQSaEjG3DcfwlhJBoELWn/Fu2bIHZbMbq1atx99134+abbwbHcVi5ciWSk5NxySWXYMeOHbj22mvBcRweffTRiL22RCSA3UUZZELI2DScx19CCIkGDMdx3HDvxGCpq6vDokWL8M033yAjIyPg553/2DbMyYnDk6uLBm/nCCFkFAv1+EsIIdEg6ksshoNUJICNMsiEEEIIIWMSBcg+SERC2BwUIBNCCCGEjEUUIPsgpRpkQgghhJAxiwJkHyQiAXWxIIQQQggZoyhA9kFKfZAJIYQQQsYsCpB9kIoEsFOATAghhBAyJlGA7INUJITNSSUWhBBCCCFjEQXIPtAiPUIIIYSQsYsCZB/4RXoUIBNCCCGEjEUUIPtAGWRCCCGEkLGLAmQfKINMCCGEEDJ2UYDsAy3SI4QQQggZuyhA9kEqEoDlACeVWRBCCCGEjDkUIPsgEfE/FhoWQgghhBAy9lCA7IPUHSDTsBBCCCGEkLGHAmQfJCIhAMogE0IIIYSMRRQg+yD1lljQQj1CCCGEkLGGAmQfJFRiQQghhBAyZlGA7IOUFukRQgghhIxZFCD7IBVTDTIhhBBCyFglGu4dAACWZfHQQw+hvLwcEokEjzzyCLKzswEAra2t+OMf/+h97IkTJ/CnP/0J1113HZYvXw61Wg0AyMjIwLp16yKyPxIh1SATQsaGaDv+EkJINIiKAHnr1q2w2+3YsGEDDh06hPXr1+OFF14AACQmJuKNN94AAJSUlOCpp57CNddcA5vNBgDe+yJJKqYaZELI2BBtx19CCIkGUVFiceDAAcyfPx8AUFRUhGPHjvV5DMdx+Nvf/oaHHnoIQqEQZWVlsFgsWLNmDW666SYcOnQoYvvTnUGmAJkQMrpF2/GXEEKiQVRkkI1GI1QqlfdroVAIp9MJkah797Zt24bx48cjLy8PACCTyXDzzTfj6quvRnV1NX71q1/hiy++6PWcUMnEFCATQsaGaDv+EkJINIiKo5lKpYLJZPJ+zbJsnwPtxx9/jJtuusn7dW5uLrKzs8EwDHJzc6HVatHa2orU1NSw90fqHhRCJRaEkNEu2o6/hBASDaKixKK4uBjbt28HABw6dAgFBQV9HnP8+HEUFxd7v964cSPWr18PAGhubobRaERiYmJE9kdCg0IIIWNEtB1/CSEkGkRFBvmSSy7Bjh07cO2114LjODz66KPYsmULzGYzVq9ejY6ODiiVSjAM433OqlWrcM899+C6664DwzB49NFHI3Z5T0qDQgghY0S0HX8JISQaMBzHccO9E4Olrq4OixYtwjfffIOMjIyAn2e2OzH5wS9x95KJuGVB/iDuISGEjE6hHn8JISQaREWJRbTxdLGgDDIhhBBCyNhDAbIPIqEAIgFDNciEEEIIIWMQBch+SEQC2ByUQSaEEEIIGWsoQPZDKhLA7qIAmRBCCCFkrKEA2Q/KIBNCCCGEjE0UIPshFQkpg0wIIYQQMgZRgOyHRCSgRXqEEEIIIWMQBch+SEUCavNGCCGEEDIGUYDsh1QkgI0CZEIIIYSQMYcCZD9okR4hhBBCyNhEAbIfUpEQNlqkRwghhBAy5lCA7AefQaZFeoQQQgghYw0FyH7QoBBCCCGEkLGJAmQ/pCIh1SATQgghhIxBFCD7IaEuFoQQQgghYxIFyH7wfZCpBpkQQgghZKwJK0DevHlzr69dLhcef/zxsHYoWlAfZEJItBrNx15CCIkGYQXIL774Iv7yl7/Abrejrq4O1157LU6ePBmpfRtWnkV6HMcN964QQkgvo/nYSwgh0SCsAHnjxo1wOp1YuXIlrr/+elxzzTV48cUXI7Vvw0oiEoDjAIeLAmRCSHQZzcdeQgiJBmEFyAzDQCKRwGKxgGVZMAwTqf0adlKREACo1RshJOqM5mMvIYREg7AC5GXLlsFoNGLz5s1488038e677+KWW26J1L4NK6mY/9HQsBBCSLQZzcdeQgiJBqJwnnzbbbdh+fLlAAClUol3330XTz75ZNDbYVkWDz30EMrLyyGRSPDII48gOzvbe/9rr72GjRs3Ii4uDgDw8MMPIycnp9/nhEsidAfItFCPEBJlInXsBaLz+EsIIcMtrAB5+fLlOHLkCEpLS7FixQocP34cd911V9Db2bp1K+x2OzZs2IBDhw5h/fr1eOGFF7z3Hz9+HI899himTp3qve2rr77q9znh8mSQ7RQgE0KiTKSOvUB0Hn8JIWS4hVVi8eGHH+Kee+7BK6+8AoPBgFtvvRXvvfde0Ns5cOAA5s+fDwAoKirCsWPHet1//PhxvPTSS7juuuu8C1EGek64JEK+BpkyyISQaBOpYy8QncdfQggZbmEFyG+88QY2bNgAlUqF+Ph4fPjhh3j99deD3o7RaIRKpfJ+LRQK4XQ6vV9ffvnleOihh/D666/jwIED+Pbbbwd8TrikIsogE0KiU6SOvUB0Hn8JIWS4hVViIRAIeh0kU1NTIXRnXoOhUqlgMpm8X7MsC5GI3zWO4/Czn/0MarUaALBgwQKUlpb2+5xI8C7So2l6hJAoE6ljLxCdx19CCBluYWWQtVotTpw44W0x9PHHHyMmJibo7RQXF2P79u0AgEOHDqGgoMB7n9FoxBVXXAGTyQSO47Bnzx5MnTq13+dEgmeRHmWQCSHRJlLHXiA6j7+EEDLcwjrlv/fee/GHP/wBtbW1OP/88yGVSvH8888HvZ1LLrkEO3bswLXXXguO4/Doo49iy5YtMJvNWL16NW6//XbcdNNNkEgkmDt3LhYsWACWZfs8J5KkYqpBJoREp0gde4HoPP4SQshwY7gwZym7XC5UV1fD5XIhNzcXYrE4UvsWtrq6OixatAjffPMNMjIygnpuaUMXlv6/H/DvG4px2dTUQdpDQggJTTQfe4Hwjr+EEDLcwi4aEwqFyM/Pj8S+RJXuGmTKIBNCos9oPfYSQkg0CKsGeTTzdLGgAJkQQgghZGyhANkPCbV5I4QQQggZkyhA9kMqokV6hBBCCCFjUcQbV95///1wuVz49a9/jdzc3Ehvfsh0l1hQH2RCSPQbLcdeQgiJBhHPIE+dOhX3338/2tvbI73pIUV9kAkhI8loOfYSQkg0iHgG+dprrwUAzJo1K9KbHlICAQOxkKESC0LIiDBajr2EEBINwsogm0wmPPzww/jZz34GnU6HBx98sNf40ZFOKhJSBpkQEnVG+7GXEEKGW1gB8iOPPAKNRoP29nZIpVIYjUY8+OCDkdq3YScVCagGmRASdUb7sZcQQoZbWAHyiRMncPvtt0MkEkEul+Pxxx/HiRMnIrVvw04iElAGmRASdUb7sZcQQoZbWAGyQND76S6Xq89tIxmfQaYAmRASXUb7sZcQQoZbWIv0Zs+ejX/+85+wWq344Ycf8NZbb+Gcc86J1L4NO4lIAJuDAmRCSHQZ7cdeQggZbmGlHO644w4oFAqo1Wo89dRTmDBhAu66665I7duwk4qEsLsoQCaERJfRfuwlhJDhFlYGWSwW47bbbsONN94IjUYTqX2KGrRIjxASjUb7sZcQQoZbWBnkqqoqLF26FJdffjmam5uxZMkSVFZWRmrfhh0t0iOERKPRfuwlhJDhFnabt/vuuw/x8fFITk7GDTfcMKpaDdEiPUJINBrtx15CCBluYQXIOp0O8+bN8359/fXXw2g0hr1T0YIyyISQaDTaj72EkIEdqOnEvPXb0NxlHe5dGZXC7gtks9nAMAwAoLW1FSw7egJKqUhIGWRCSFQazcdeQsjAXttxGvU6C3ZXtQ/3roxKYQXIP/3pT3HzzTejvb0dTzzxBFavXo3rrrsuUvs27Pg2b7RIjxASXUb7sZcQ0j+d2Y6vjjcDAA6f0Q/z3oxOYXWxWLVqFbKzs/Hdd9/B6XTib3/7W6/LfiOdVCSgNm+EkKgz2o+9hJD+bT7UALuLRZJaiiN1uuHenVEprAAZ4BvWz549O6xtsCyLhx56COXl5ZBIJHjkkUeQnZ3tvf+TTz7B66+/DqFQiIKCAjz00EMQCARYvnw51Go1ACAjIwPr1q0Laz/OJhUJaVAIISQqReLYC0Tv8ZcQ4t97+89garoGc3Li8fbeGjhdLERCmqYZSWEHyJGwdetW2O12bNiwAYcOHcL69evxwgsvAACsViuefvppbNmyBXK5HH/84x/x7bff4vzzzwcAvPHGG4O2XxKRADbKIBNCRrFoPf4SQnw7Vq/H8YYu/PUnUxAjF+PVHSwqmo2YnEY90SMpKk43Dhw4gPnz5wMAioqKcOzYMe99EokE7777LuRyOQDA6XRCKpWirKwMFosFa9aswU033YRDhw5FfL+k7i4WHMdFfNuEEBINovX4Swjx7f39ZyARCbCsMA2FGVoAwGEqs4i4qAiQjUYjVCqV92uhUAin0wkAEAgESEhIAMBnK8xmM+bNmweZTIabb74Z//nPf/Dwww/jjjvu8D4nUiQi/sdDnSwIIaNVtB5/CSF9WR0ubDrUgMVTUqBVSJAdr0CMXEx1yIMgrAB5w4YNAd02EJVKBZPJ5P2aZVmIRKJeXz/22GPYsWMHnn32WTAMg9zcXCxbtsz7/1qtFq2traF9I35I3QEyLdQjhESTSB17geg9/hJC+vq6tBl6iwPXzMoAADAMg+kZMdTJYhCEFSAfPXo0oNsGUlxcjO3btwMADh06hIKCgl73P/jgg7DZbHj++ee9l/o2btyI9evXAwCam5thNBqRmJgY9Gv3RyoWAgAt1COERJVIHXuB6D3+DsTmdKHDZB/S1yRkIE9vrcCPJ9sGbfvv7T+DdK0c8/ITvLcVZmhR3myAxU5taSOJ4aKgwNazirqiogIcx+HRRx9FaWkpzGYzpk6dipUrV2LWrFnepvg33XQTFixYgHvuuQcNDQ1gGAZ33HEHiouLe223rq4OixYtwjfffIOMjIyg9+u9fWdw1wdHsOPui5CulUfkeyWEkGgSrcffgfx54xF8VdqE7+5YiBiFOOLbJyRYOrMdRX/9GgsKEvH6mjkhb4fjOO/fW0/1OgvOf2wb1l40Hrdf0n0i+3VpM371v/344LdzMTM7LuTXJb2F3cVi79690Ov1vRayXXrppUFtQyAQ4K9//Wuv2/Lz873/X1ZW5vN5TzzxRFCvEyyp2F2DTMNCCCFRJhLHXiB6j7/9adBZ8MHBOjhZDi/9UIk7F08ctn0hxGN/dScAYF91BxwuFuIQ2q79eLINa17fh1/Pz8PvLhoHmftKNgB8cKAOHAesmtn7hLMwIwYAcOiMngLkCAorQL7//vuxffv2Xj0zGYYJ6SAdjSRCqkEmhESf0X7sHcirP54GB2BuXjxe21GNX8zLRYJKOty7Rca4fdUdAACz3YWj9XoUZ8UGvY3vK1rgcLH417en8OnRRjx61TTMzY8Hy3J4/8AZzBsXj8w4Ra/nJGlkSI2RRe1CPY7jcLrNhN1VHTjeoMetC8eNiKvyYQXIu3btwmeffdZrBfRoIpPwZ24mG63OJoREj9F+7O2P3uzAO3trceX0VKxdNB6XPLUdz39biQevnDzcuzam6cx2aBWS4d6NYbW3ugN5iUpUtZqwq7I9pAD5SJ0ehRla3HHpBNz70VFc9/JuXDMrAwsnJOFMhwV3XDrB5/P4hXq6ML+DyOkw2fHFsSbsrmrH7qp2tBhs3vs6zXY8f/3MYdy7wIS1SC81NXVUH6AzY/kznLpOyzDvCSGEdBvtx97+vLmnBia7C79ZkI+8RBVWFqfjzd01aNDRcXq4VDQbMPORrdhW1jzcuzJsLHYXjtXrccnkZExIVmN3VXvQ22BZDsfq9ZieEYPzxyfgy/+7ALcsyMcHB+vx27cOQi0TYfGUFJ/PnZ6hRXW7GTqz74Wrp9tMuPalXUP2d/LApmO496Oj2F3VjnPz4vHoVdOw7U8L8IdF4/HZ0aaoCub9CSuDXFxcjNtvvx0LFy6ETCbz3j5aLvNlxCrAMEB1m3m4d4UQQrxG+7HXH6vDhdd2nMaCgkRMSuWnhq1dNB4fldTj2W0nsW7F9GHew7Hpq+NNcLEcPippwEUTk4d7d4bFoTM6OFwc5uTEweZgsWHfGdidrHeeQiCq2kww2V2Yls7XFMslQty9ZCKWFabhkU9LMW9cQq+a5J6KMrUA+Az0BQV9O8o8/+0p7K7qwH9+PI0Hrhj8qy1H6/W4bEoKXrihuNeCw19dkIc3d9dg/edlePtX5/hcjBgtwgqQS0pKAADvv/++97bRVAcnEwuRFiNHTbtp4AcTQsgQGe3HXn8+OFiHNqMdtyzoXkSYEavAT+dk4c09tfjNBfnISVAO4x6OTd+W8z2wt51ohtXh8hvEjWae+uNZ2XFwuFj8d2c1jtbrglo0d7ReB4DPBvc0OU2Dt391br/PneoOqo/U6foEyC0GKzYfaoBYyGDDvjP4v4vHQy0bvM4vJpsTtR1mXD0zo08ArJKK8LuLxuHhLaXYfrINC3wE86G83g3/2YN7lkzCnNzILVIMK0B+4403APDjRzmOg1g8+lrtZMcrUE0BMiEkioyFY+/ZXCyHl7dXoTAjBufm9f4QvO2icdiw/wye3lqBp6+dMUx7ODbpzHaU1HaiOEuLg7U6bK9oxaV+ygBGs33VHZiQrEaMQoxzcuMBALsq24MKkI/U6SEXC5GfGPxJXoxcjLxEJQ7X9R0Y8r+dNXCwLJ5eXYQ/vHsI7++vw5rzc4N+jUBVNBsAABNS1D7v/+k5WfjPj6fx2OdlmD8uAQJBeFnkbWUtKKnVwcVGtmtxWDXI7e3t+OUvf4mioiJMnz4dN910E5qbR1cNUna8EjXtoZdYmGxO2GlUNRlEP55sQ1lT13DvBhlCY+HYe7Yvjzehut2MWxbk98lKJall+Pl5udh8uAHlTYZh2sPRp91oG/Ax20+2geWAu5dMglYhxufHmkJ6rT1V7Xhnb21Izx1uTheLgzWdmJ3LL8qLVUowMUWN3VUdQW3nWL0ek9M0EIXQHg7gB4acXdtrsbvw5p4aXDIpGT8pSses7Fi8tvN0xIPJnjx/gxNTND7vl4qE+NOlBSht7MKWIw1hv97nxxqRoJJGNHsMhBkg//Wvf0VRURF27tyJnTt3YtasWXjooYcitGvRISdegXaTHV1WR0jPX/7cDjz2he8+ooSEi+M4/N+GEjzyyYnh3hUyhMbCsbcnjuPw7+8rkZug9JudvGVBHlQSEZ74qnyI9250OlKnw6y/b8X3Ff2PEP+urAWxCjFmZsfi0snJ2FraDJsz+NkB//yyHPd+dBSn20beFdsTjQaY7C7MzukO0Obmx2N/TUfAPwsXy+FYfZe3/jgUhRkxaDHY0KS3em/beLAOOrMDv5yfBwC4+fxcnOmw4OvSwTuhLmsyQCERIiPWfyu3nxSmY2KKGk98VRFWEtFsd2JbWQsum5oMYZiZ6LOFVWJRXV2NZ555xvv12rVrcfnll4e9U5HW0gkIZAM/zheNhL/Usf+UGRNTgvvFNdkcONliBCBAw+BNniRjWLvJhjajHQdqOlHbwkIkCOuclwyTtISBH9PTSDn2AuEdfz0O1LbjSJ0ed10yDc0d/j4EJVg9Mw+v7KzA1qM6TE7VhveiY9zHB1vBccAr22swPs53nSjLcdhW1oo5OYlo7mBwbnYq3ttfh48PtGFefuCL9XRmOw7WdoLjgKe+qsTdl46sxZZbj/OZ4uyYOO9nfUFCPKyOamw7pkdhxsCZzao2IywOFzJjYkKOF9LUWgDAt6U6LBifApbj8NL3pzEpJQbpqlg0tAFTkpORopHjhe9OY3rK4JTCHDnThdx4NZr8/q0CAINfnTcRf/pwH178thYrZ+SE9FrbylthdbA4Jys1pJ9bf8fesD5NnU4nbLbuSzAWiyWqVySGIkPLN+Su0wV/VlvTwT+nqs1A0/hGEKvDBb3Fd6ucaHOyhb+UZXG4cKqFLi2PFWPh2AsATpZFg86M/+4+hTiFFEumpPf7+NWzciETCfH58boh2sPR6+AZPtrYWdmCNqPV52PKmvTQWeyYm5sEAJiZlQC1VIRvK4Irs9hd3QKWA6amafH58Xq0+nm9aHW4rgMpGjmS1N0Z0xkZcWAAHDwTWLu3sia+dnhSkIm4nsYnaSAUMDjRpAMA/FjZjDOdJlw3K897fBAJBLi6OAeH6zq8rxlJHMehss2AvATf9cc9nZubiBkZcXh110mY7aHNm9hW3ohYhQRFGfEhPb8/YQXIS5cuxc9//nO8//772LhxI9asWYPFixdHat+iQlpM6AFydbsRAH/p5FQb1YhGO5Pdif/tOYUVL23DL974sdcI32hV2dr9e3W4Prh6NzJyjdZjb1WbAeu+PILfv7cbq17ehoVPfYFVr3yLA7XtuG5WLqSi/rsjKCUizMqOx86qlhHx9xutbA4XjjbocH5+Elwch8+P1/t83K7TLWAAnJPDZ5jFQgHmj0vBD6ea4AhiAu2OyhbEKaR4cGkRXCyLDftPR+LbGBIcx+FwfUefLLFGLsG4JE3gAXKzDgqxEJmxofc3l4qEGJeoRmmjDgDw7v7TSFbLcWFB70zxldMyoRALseFg5H/O7SYb9BYHxiUOHCAzDIPfXjARnWY7NhwIfl+sDhd2VrXgwvEpES+vAMIMkG+77TasWrUKO3bswPbt27FixQr87ne/i9S+RQW5RIQElRR1nQMv1GPPOiBXdxjhecsG40wtFnFpQgAASolJREFU0rosdvzyzR042tA53Lsy5E63GbDq5W349w/lkIqEaOqyoMM88AKV4XaqzYBElQypGjkO11GAPFaM1mPviSYddp1ugdXhwtS0WNxwTj7uWTwNz187Fz+dnRfQNs7NTUJjl8V7BY8E72hDJxwuFssLs1GUEYdPjp3xecKxq6oVk1O1vSboLSxIgcHmxIHawK53O10s9la34ry8RGRolbh4Yho2Ha4Jed3P2TiOgz2EmuhA1enM6DTbUZjet4yiODMeRxs6A3r9E016FCTHhB3oTUrRoqxZj9JGHQ7VdeCamTl9Su9UUjGumJaJrWUNEc/WV7bxVzLzE3wv0Dvb1LRYXDAuGe/srwo6i7yzqgVWpwsLJ6QGvZ+BCKkG2Wg0QqVSQafTYdGiRVi0aJH3Pr1eD61WG6n9iwoZWiXqB8gg76luxb2bD+Ddmy9EooovuKtuNyInXoVOsx1lzdEfIB+u70Rpkw6PfXUU/73x/JBX0o5EG0uqYXW48PL158HicGHte3tQ1WZEvDLM4slBVtnahXGJamjkEuyrbgPHcaPyUjvhjfZj7+VTM3H51MywtjE3l89m7j7dgpz4sTltMFwHzrRDyDAozIiD3mLH3z4/jEN1HZiR2X0Zu9Nsw4kmHW4+r6DXc2dnJ0Ah4cssznWXXvTnaEMnDDYnzsvjH3vDnHx8daIBHx6qxs/PHR/29/Lyjgr8b88p5MSrMTklBpNTtZiUokV+gjoin3GexERhet+x0sWZ8dhw4DSON+p6/ezO5mRZnGztworC7LD3Z3KKFpsO1+KJb45BKRFh2TTff0+rinPw/sFqfFBSjVvmTwz7dT0qW90BcgAZZI/rZ+dh+6lmfFlaj6uKAv8ZfFvRCK1cgqIAarxDEdJvx4033ggAOPfcczF37lzvP8/Xo02GVoE6Xf8Z5B9PNcPicKGkx+WUmg4jsuNUmJQS4zeD3GWxo90UmTM4juPCat1S0cLvY1WbAR8cqonIPo0ENqcLX5c14MKCVExJjUVePP+HXdUWmZrewbrU63CxqG43YlyiBoXpcegw21A/wO8pGdnG2rE3FKkxCuTGq7DrdP/dF4h/B2rbMDElBkqJCAsLUqGUiLDl6Jlej9lT3QoOwNy83gv4JCIhzs9Pwvcnm+AMoMxiR2ULRAIGs91lGuMSNZibm4j3DvBJi3B0mGx4Z38VJqVokayW4cfKFvzj62P4xRs/4pJnv8T2U6G1pOvpUF0HYuRinydjhQHWIVe3G2F3skE3AvDFU8N8okmPZdMzoZT67pGeoVVi/rhkbDpcG/bPuaeqNgMSlFLEyCUDP9htalosJibH4P2D1QF/XlodLuyobMGFBSmDtjg9pK1+9NFHAIDS0lKcOHHC+6+srAy7du2K6A5Gg4xYJdpNtn7T/576z6P1fHmC3elCvc6EnHgVJibH4HS7wecv4f1bDuIP7++NyH6+8EM5fvra9yEHZBUtXciOU+LcnES8sqMiYoF7tNt+qhlGmxOXT8kAAMQppdDKJREJkDcdrsHyF7eFdQA6u3THo6bDCCfLIT9R7c1eUB3y6DbWjr2hOjc3EYfqOkJe+BNpLpbDpsM1eOjTkqhfsG2yO3GiUY+ZWXzGUyYW4tJJadhW0QhDj7KHXVWtiFVIMCG5b1C3sCAVXVYHSgIo+9pR1YwZmfFQSrovaN94zjjoLHZ8clZQHqy39lXC4WLxwJJCPLFyDj699WJs/OVCPHzFDCSqZPjvrlNhbR/gj7nT0+N8XrnTyMQoSNb0Spz54kmgTfTxswxWdrwKMpEQQobB1cX9DwNZPTMXXVYHPj4auf7Tp1q7kBdE9hjga5GvLs5BdYcR+2sDq9nedZovr7ioYHDKK4Awa5BXrlzZ57YbbrghnE1GpQwt3+rNX3bOYHV4Lyt46nfPdJrBcuAD5BQtWA442dI7i9zcZcH+2nZUtRlwpjO8erlWgxXvHTiNM50mNOotIW2jorkLBUkxuH3RFNhdLJ77fnD7Nx+u68DpfoJQq8OFriHoJvHZsTokq+Uozuq+BJaXoA47QNaZ7Xh+exlajVZvdj5QHx6qwW/e3ollL2zFBU9+hq9O9F0kc8q9QG9cogbZ8SpoZGIcqR979eNj0Vg59oZqbl4SHC4WBwL8sA2V1eHC/tq2fk+AS860Y82bP+IfXx/DVyca8H0EspaD6UhdB1wch5lZ3f2vrpyWBbuTxddl/FAHF8thT3Urzs1JhMBHYHhuTiLkYiG2lTf2+1p1OhNqOkze8gqPwvRYTEuLxdv7qwLKQvvSbrLiw0M1WDwpHVlxfHaXYRikaRW4ZGIaVhXnoKxZ7z2OhqLNaEW9zuyz/thjRkY8jjXo+u2HfKJJB6VEhIzY8MekiwQCLJqYihUzspGi8d+HGACKMuIwJVWLp7eV4uf/+wEbDpxGZxhrb5wsf1UzP4AOFmdbNCEVsQoJ3g9w4eC2cnd5RebglFcAIQbIP/vZz1BcXIzy8nIUFxd7/xUVFSE2tm8dzkiXEdt/J4ujDZ3gwF9OOdXaBbPdiZoOvoNFdpzKe9nk7DrkrWXdE2R+rAyvafcbeythdx9ISt0tXoKht9jRbLCgIEmDzFglrinOwRel9Wg1DE4WmeM43LP5AJ75rtTvY/7+xWGseGkbdlW1DMo+APyJxb6aViydkt7rQJ+XoMbpNkNY5RGv7KzwZrA8q4o9LHan33IYm9OF57eXodNswzm5idDIJPjOR9ukylYDxEIBsuKUEDAMpqfHUgZ5lBtrx95QFabHQSEWYtfpwTt2AMD7B6ux9r09WPLcV7h38wF8WVoPo43PsjZ3WfDAloO4bcNudFns+OsVM5CqkePTY9Hdgu5AbTvEQgGmpXX/Pk1I1mB8ogZb3JnG0iYduqwOzM3zXWMsFQtxXp67zIL1H+DudB/b5+X33g7DMLjpnHw0dVmwtbz7c9JgdeCDkmqseeNHXP/a9/2243xzbxWcLg6/mOu7jnnxpHSIhYKw3g9PQqIww//fXnFWPOwuFscbdH4fU9asx4TkGJ8nG6G477JC3H7RlAEfxzAMnlg5B/930WQwDPDMt6VY9u9v8OdN+/HDqeagP//qOs2wu9iAF+j1JBEJsXx6FnZUtgxYKmhzd69YMH7wyiuAEBfpPffcc9DpdLj33nuxbt267o2JREhM9N1QfCRLd2eQ/XWyOFzXAZGAwWp3b8HSJp03QM6KVUIuESFeKe1Th/xVWQMmp2hhc7qwo7IZ180KbJX22VqNVnx8pBZLJqfjm4pGlDbqcPHEtKC2cbKFP4sucF/iuXhiGt7aV4WSunZcOqn/3qOhON1uhM5iR2mjDizH9TkwdJpt+O5kE4QMgzs/2oc/LpqKFUEU7wfq89I6sBywZGpGr9vzElQwO1xo6rIg1d3qrz8sx+G2d3dBLRPj9oumwGx3YdPhGqwoysEPp5pQ2uO9tztdWPXKt1g9Mxc3nTOuz7b2VLfCbHfi78uKcU5OIh75/DB2VDX3+TmdbO1CbrzKe4CYnh6HHytb0GGyIU4pDenncbKlCyzH+bxsSobfWDv2hkosFGBWdgJ2VbUO6sLVI/UdSFbLMS8/CdtPNuG7k00QCRhMT4/D8UY+cbJm7njcMCcfMrEQ1e1GvLbrJJq6LANm94bLgTNtmJKqhUzc3VKPYRhcOS0TT247jvJmPXZVtUDAAHNy/P/OLSxIxTfljThc19ErG93TjsoWZMcpvVdpe5qbl4S8BDXe2FOJJJUcW46dwbcVjbA7WYxLVKNOZ8aDn5TgyZVz+nR+aDdZ8dHhGiyenO43Kxsjl2B+fjK+KK3DrRdMhDiEBXuH6jogEwkxIcn/8bIwPQ4Chq9D7nmV0sPhYnGq1YBrinOCfv1I0MjEuKY4F9cU56Ky1YDPj9fhyxP1+OFUMx5cWojLJmcMvBE3z1XXYBbo9bS8KBv/21uJD0qqsXbhZL+P213dCovDhYUFgzPoxCOk0FulUiEjIwP/+9//kJ6e7v2XlJSE2tqROUu9P0qJCHEKqd8M8uH6DkxIjvEeBI7Wd6K63YgUjRxyd13VxOSYXhnk020GnGzpwqWT0zAvPwmH6zpDbmvz5t5KuDgOa84rwISkmJAyyJ4SgIIk/sxvXKIGKqkIJWcGJyPpqcky2pyo9dGO6euyBrhYDs+uPhdzc5Pw+NZj+MP7e/BBSTWaukIrITnb6TYDPj5yBoUZcX0O0J4m54GWWeytbsPh+k7sqmrF9a9tx/1bDkApFePm88ZjUoq2Vwb5aEMnOs32XlcQetpW3giNTIyZ7lXPxZnx0FscffalstWAcYndZ+qePpyB9t0825lOE27bsAt/+/xwSM8Ph9nuRIdp6NvqcRwX8iXc4TDWjr3hmJuXhGaDBafd/egjjeM4HG/UYWZWPO64eCo23bIIL143F1cX56DDZMP5+cl45xcL8Mt5Bd5gc+mUDHBA1A4y6bI6UNHc5a0/7unSyemQCAX45OgZ7DrdgqlpsdDIfC8AA/huIlKRwO/QEJPdiZIz7ZiX53vinoBhcMOcPJxuN+J37+3GjspmXD4lA6/ecD5ev2k+7rh4CvbVtOHfP/QtBXxzbxVcLg4/n9s3AdHTFdMyoLc4Qr6Ce6S+A1PStP12w1DLxChIivF7XK5qM8DhYiNSfxyu/EQ1fnfhJHz0m4swPlGD/+w82e8VgLNVtnZByDAhd49JVMmwsCAVnxw70+/6gW3ljYiRi32ecERSWLnpd999F8XFxZg0aRImTZqEyZMn4/rrr4/UvkWVjFgF6nzUCducLpxo0qMoIw5qmRi58So+QO4wIieu+5dkYkoMqtuN3jf9q7IGCBi+7ub8/GS4OA67Q7gc2Ga0YvPhWiydkoF0rQKTU7Uob9YH/aFf0dKFZLXMu/JUKGBQmB434OICfyx2J575thTbyht9XqY5VNcBifugcsJHQP/ZsTpMSNZgWlos1i+fhV/NK0BTlwVPfHMcq17ehr3VvVeo/79vS/GnD/b2WkTiz9GGTtz27i5c/9/taDVacYOP/qq53k4WgX24bjpcA61cgrfXLMCMzDjUdJjwy/PGI0YuweRULRr0Zu/lwH01fH/QU62GPsG+zenCj5XuS0fun4/nINCznrLDZEO7ydbrTH1SSgzSYuR4e19V0JfGzHYn7t60H0abE9V+FpQG63BdBzYerB7wcR0mG37+vx/wizd+jOhq6kD87fPDWP7itj4lMNFuLB17Q+Vp9zZYJVoNegt0Frt3pLWAYTAtPQ6/v3Ay3l6zAH+7srjP1ac0rQLFmXH47HhdVA4yKTnTDg7wmfHVyMS4sCAFn5fWo7y5yzs9zx+5RIT541Lw6bEzPv++9te0wclyfeqPe7p4Qhp+du44PLi0EFtuuRh3XjINE1Ni3BntLFxVmIW39lX1Sja0Gfns8WVT0n1mpnuanZ2IRJUspMWAJpsDp1q7+q0/9ijOjMfxRh105r4lIZ7E2cQUbdD7MFhEAgFunjce9TozvvAzJMaXyjYDMmIVAw706c/VxTkw2pz4otT369oc/BX3C8YNbnkFEGaA/NJLL+G1117DggUL8NFHH2Ht2rW4+OKLI7VvUSVDq0R1h7FP3Whpow4OF4vp7j+S6elxONbYidoOI7J7nEVNTI4BBz6Qsjld+PpEPWZmJSBeKcOkFC20cgl2VAZ/IN91ugV2F4vVM/nVqpNSYmBzsqhqD26BWUVzF8afdZloRmY8ajtNfseM+mOyOfDHD/dhw4HTuH/LQax9f0+v7CfHcSg504EF41OgkIhw/KyD56nWLlS0dGGpu6uEUMDgF3PHY8PNF+KdNQuQqJLhf3sqvY9v7rLg/YPV2HW6Fb97b/eAmcjHvjqKmg4Tfjt/Ijb95iLMy++bwVDLxEhSywL6ObYYLNhR2YIrp2UiM1aJx1fMxjtrFmCVe7b8ZPeBz3MisLe6Dclqvr/yjrMyF57yioU9VuamaORIi1H0ykB4fp7je2SQRQIBfn7ueJQ16721fYHgOA5//+IwajqMWFmUDZYDKsOc/MhxHP659Rie3Ha83yE5JpsDf/xgL5oNVrQa+UU1Q+XrsgZ8UVoPi8OJ2zbsikjLp6Eylo69oUpSy5GfoB60dm/HG/n60ynuADlQS6dkol5nxuEoXFB7oLYdUpEAk/20G7tiWqY3yXN2ezdf/m/hZMQppLjzo31o1PcuUdxR2QyVVITpPvoHe4iEAvzm/Am4bHIGpOK+Qdf/XTQFhemxePSLI94ywTf2VsLFcgH1UBYKGCydmoE91a1Br7c52tAJlkOfCXq+XOYekf6XT0v6xBBlTXqoZWKkxURXyc38/GRMTI7Ba7tOBpxwO9VqCKn+uKepqVp3y7fTPk8id1e3wuxw4aJBGg7SU1gBslarRWFhISZNmoT29nb89re/xb59+4LeDsuyePDBB7F69WrceOONqKnp/SG5bds2rFy5EqtXr8Z7770X0HMibV5+EjrNdnx01ge4p0jf80c+LS0WRpsTNifb6zJDUWY8xidq8K/vy3DlC1vRoLdgsbu2VyhgcF5eEnafbgk681vWpIdSIvK+liebUdoYeNcEq8OF2k6jt7zCY4Z7dWggrXo8DFYH/m/jXhyr78RDlxfhjounoqKlCz//3w/eA1hNhwkdZhuKs+IxMTmmT3bhs2N1EAkYXDKxb+1zdpwKK2fk4OCZdu+Y5Q8O1YADh7sumYbaDiNu3bDL78HO7nShpt2IK6Zl4MZz8hGr8F+rmxcfWCeLLUfPgOU4LJueBYCv18uOU3nrHiekxIAB/57oLXaUN+txxbRMZGgVfQJZT3nFrLMuHc3Misehug5vy7eT7u/97FqvyyanIy1Ggf/sPBlQhqrNaMWDn5Tg24om/Hb+RO+0sorm8ALkkjMd3p/dKzsrfD7G5nThz5sOoLLNgPU/mYk5OQl4c28lTCG05nKxHI7UdwTcB7zVaMXjW49hSqoW7958IfITNLhn04GovfR9tkgde4GRcfwN1dy8JByu74DJFpmpbD2VNuogFQmCrrdcWJAChViIz46F18JsMBw8047p6XGQ+MkAFmfGIy1GgQSltNfJuT9xSimeWDkbTheLP32wz3uFj+U47KxqxTk5iWEN6xALBXhkWTHUMjHu2bwfla2GXldUA3H5lAywHL8eJRjfn2yGRCQI6ARpXKLGWxLy4o/lve4ra9ZhYnJM1A14YhgGv5xXgMYuCz4N4LhotjvRoDeHXH/c83WvLs5BTYfJe7UV4Ncl/eu7E3jo0xIkKKXeEsTBFFaALBKJoNfrkZ2djSNHjgAAXK7gL5Fu3boVdrsdGzZswJ/+9CesX7/ee5/D4cC6devw6quv4o033sCGDRvQ2tra73MGw4XjUzA7OwEv7Sjv1R/4cF0HcuNV3tKEaT3OhnuWWCglIvz3pvPxzNXnoDgzHrnxKiwY3525PD8/CQabM+g2XeXNXZiQrPEu3kqPUSBGLvZZtuDPqdYusBxQkNz7gDc+SQOlRIRDQZRZPPRpCcqb9XhkWTEunZSOFUXZeOcXCyATC/Hf3ScBAIfq+O3NcLeYOdna5e0N6nSx+PJEPeblJ/UaX9rTsmmZkIoEeO9gNSx2JzYfqcWC8SlYXpiFp1adg5Yua5+DkEd1hxEujutVu+tPXoIaNe3GfmuwnCyLj4+cwTk5iX4PyJ4TmNImHfbXtIEDv7hlXn4yDtS2w+IOCH2VV3gUZ8bDYHXgZEsXnC4WX5TWI1Uj7xPgi4QC/PzccShr1uPHfq5IcByHDQdO49pXv8cPp5rxy/MK8NPZeUjRyKGWiVEeZFu6s20sqYZGJsYvzh2HnVUt3mxbTx8dqsHBM+24/7JCzM1Lwq/nTYDOYg+4zU9P//6hDLe8s8tvMN4Tx3FY98UR2J0uPLCkEIkqGf61+lwUZcThqW3Hw2pzNFQidewFRsbxN1RzcxPhYrleH7SRcryRD2yCvcwrl4hw0YRUbCtv9P7tR4MOkw1VbQaf9cceAobBw1cU4aHLZwQc0OXEq7HuJ7NQpzPh3o8PwOFiUd6sR4fZ1qd7RSjilTI8+pNitBpt+NVbO+DiOPzMx+JnfzJilZiREYdPjvoep+1Ll9WBL0vrcenENCgkgfU6uHJaFpYXZuHNvZX4toJvf2dzulDZaoiK+mNf5uYmYkqqFv/ddWrAcdmn3Vdb80Jo8Xa27pZv1eiyOvDvH8qw6uVv8c7+KlxYkIoXrjtvSCb9hvUK11xzDX7zm9/gwgsvxIYNG7BixQrk5QXfieHAgQOYP38+AKCoqAjHjh3z3ldZWYmsrCzExMRAIpFg5syZ2L9/f7/PGQwMw+CPi6bA6nDhX+7+wPU6M442dPa6xJKhVUDrDpbPLlRnGAazsxOwfvksvPWLBb0m3MzJSYREKMB3J/1f5rU5Xb1+SZ0uFqdau3p1HGAYBpPPWhQ2kApPB4uzSixEAgGmp8f6XVxw9sGkut2AXadbcfN5BVgwvnt1aZxSipUzcvBdRROq2w0oOdOBBKUUmbFKTE7VwsVy3kWCO6pa0Gm2Y8kU/ytnNXIJLpucgS9P1OPdA6dhsDq8JSZFGXG4dHIavq1o9Fnk7+lXPS6As9y8BDXsLrbfljO7qlrRarRieVFWv9vyLNTbW9MGlVSESSkxmJefBLuLxb5a/sN79+m+5RUenjrkg2fa8cbeSpxs6fK7yrc7i1zh94C/v7Ydz3xbimlpsXjz5xdgzXnjwTAMGIZBQZImrAxyU5cF20814cppmbh+Tj60cgle2XGyz+O+OtGAickxWDyZv1IwOVWL8/OT8M6+qoBqyT2+q2jEW/uqkKiS4fXdp/DDKd8Lblwsh28rGvHrt3did3Urfrdgkrc/qkwsxJ2XTIXF7sIrOwYOsj2+KWvAkue+wgVPfoYLnvwMN7/5Y8DPDUekjr3AyDj+hmpaWiyUElHEyyzsThcqWrowJTW01npLp2bC7HD5XcA2HDzH+f4CZACYkhob9OKo4qx43LN4Og7UtuOxr45iR2ULGADn5oQfIHv26Y6Lp8DqdGHplAykBZg99rhiWibqgih7+eToGVidLqwKsvPE/y2cjCmpWvz988M43WZAZasBTpaLyAS9weDJIjcbLPhkgHZ4oYyY9sfT8m1nVQtWvbwN/9tTifPykvDWLy7AX5YWBXx1IFxhBcirVq3Cq6++Cq1Wiw0bNuDWW2/F008/HfR2jEYjVKruYFIoFMLpdHrvU6u7f+BKpRJGo7Hf5wyW7DgVfjo7D1+W1uOPG/di9X++hc3pwiU9Wqox7n60cYrgRi0qJCKcn5+Mr8vq4fBTZnHPpgO4f8tB79en242wu9g+LbkmpWhxut0Q8BSpihY9NDKxty62p+LMeL4k4qy63srWLlzy7FfYX9udmfnocC3EQgGWTe87+311cS6kIiFe31OJkrp2FGXGg2EY7+Wp0iY9WI7DqztPIi1GgfMGWABydXEO7E4WL++owKSUmF49Oy+fkgGLw+U9S++93wZIhIKAGrIH0sni67IGxCul/S40AfjgT2exY1t5I2ZmxUMkEKAwPQ5KiQg7KltwrKET6748gmS1vE95BcCv7s2KVeLTY3V4bddJXDwxrddJSE8ioQA3zMlHRUuXtxSjz36fqIdCIsL6n8zs87OYkBSDyjZDyN0dPGVIK4qyoZCIcMOcPOypbsWRHj2a6zpNKGvW92lH+Mt5BTDYnPjfnsAmXNV2GPHIF0cwOUWLt35xASYmx+Cvnx3qM3inqcuCG/77Pe77+CA6zTbccXHftoE58WpcVZSNzUdqveU7/rAch1d2VOCBT0qQoVXip7Pz8NPZed6688EWqWMvMHKOv6EQCQWYk5OA3adb+5wsljbq8H8b9+D7fpIS/pxq5TsPTA6y/tijMD0W6VoFPjsePWUWB8+0QyERDVqLxyVTMnDzeePx2fE6vLmvElPTYv1eJQzFldOy8NzqcwPq/3u2C93rYQJZrOdiOXx4qBqFGXF9kkoDkYiE+PuyYkjFQtyz+YB34XW0ZpABYE52Aqanx+L13af6HXZS2WaAXCxEWgBtUQNxVVE24pRSFGfG438/m4+/XVmMnPjwg+9ghNQH+bXXXvN739tvv41f/OIXQW1PpVLBZOr+QGNZFiKRyOd9JpMJarW63+cMpp+fOx5byxpxtKETP52dj6uLc5Co6h1Yrr1wMjotwV+mvXxqBrZVNOLHyuY+WUSTzcFfJmQAo80BlVSMcs/q17P+uCan8pP7ypv1mNFPnQ7rblN0sLYdBUkan5fMPM8vOdOORT2Cma9ONMBsd+KJrcfwv59dAKeLxWfH6rCwIMVnXa9WIcHywixsOHAaHPjyCgBIUMmQpJbheKMOceWNONnahb8sLRrw8kleghqzsxOwr6YNq2fm9tr3qWmx3mDy8qm9g/VTbV3I6dE7uD858Sow4ANkX1ldgD9RmJSiHXB7noV6JrsTs7P5xS1ioQDn5ibi24pGfH2iAfEqKZ5edY7f731GZjw2H6mFVi7BHwf4EPCs4C8509HnIO5wsfj+ZBMuGJfsc+FLQbIGDheL6g5jQKUoPdkcLnx8tBbn5yd7V/CvKMrB2/tO44UfyvH86nPBMIx3IteisxZaFCTF4Mppme5LaSn9ZuhcLIf7txyEWMjgkWXFUEnF+PuyYvzijR9x50f7sG7ZTOQmqNFhsuEP7+9Bp9mGR64sxoLxKX36pnr88rzx+PJEPZ75thTPXH1On78JT2uv13efwo6qFiydkoG7Lpnqt2Yz0iJ97AVG1vE3FHNzk/BtRRMq2/i2iEabAy/+UI4PD9WAA3CiUYfJqdo+x/H+hLpAz4NhGCydkoGXd1SgXmcesoxYfw7UtmNGRtygdgZYM3c8GnRmfF5aP2BSIRT9fd71Ry4R4eKJqfiqtAG3XzS519Xds+2sakGD3oJbL5gU0mslqeV45MpirH1vD176sRxauSRqe2ID3Vnkte/twcdHav2Or65sNSAvQR2xYScJKhm2/HZ4Fx6H9JdQUVHR779gFRcXY/v27QCAQ4cOoaCgwHtffn4+ampqoNPpYLfbsX//fsyYMaPf5wwmmViI/950Pj6+ZRFuvWCiz4NqmlYR0qW3OTmJSFBJ8ZmPSxkHzrTDxXFwsRz2VvNZ2xNNOih8jKec5L5c01+ZxTdlDVj+72/wm7d3oqnL4rekoSBZA4VY2KvMguM4fH+yCfFKKWo6THj/YDW+KmuAye7Eyn6GeVw3O8/bjH1Gj/GQk1O0ONbQiVd2VCA/QY1LJgU25OQ350/A0ikZfWaxMwy/MvlQXUef3tVn9w7uj0wsRFacEtvKG322H3O6WJzpNCE3gJ6P+Ylqb1u7OTndLZTOy0uC0eZEZpwS/75ubr8flOe4n3fHxVMHzLwka+RI1yp8tunbW90Kg83pd5iMJ6AOpczi25ON0FscvTKpMrEQN583HofrOvCDu2vH1rIGFKbHItnHB8PvL5yERJUMf/vscL9t374/2YRTrQb8adFU7wdMaowC634yE10WB37xxo94Y08lbt+4F60GKx5fMRsXTUj1GxwDfPnOL88bj/217bjqpW1Y88aPuH3jXjyw5SDWf3UEa978Eb9+eydKzrTjdwsm4b7Lpg9ZcAxE/tgLjKzjbyjOcZ8s7qxqwbbyRvz01e/x0eEarCrOwWs3ng+7i8VjXx0Nqu3a8UYdElRSJPm46haoJVMywGBoeyJ3mGz49NgZVJ/VG7rFYMGZTtOg95VlGAZ3L56OP186LejyhMF2xdRMWJ0ufDPAeOyNJdVIVMlwwTjf/ZsDMSMzHr+7cBJc7qFM0bZA72wzM+MxIyMO/9tT6V0v1BPHcahs64pI/XE0CemUv+cEJwDo6uqCRhN6a49LLrkEO3bswLXXXguO4/Doo49iy5YtMJvNWL16Ne6++27cfPPN4DgOK1euRHJyss/nDBVVP2eX4RAKGCyZnIG391Wh3WRFvLL74Lu3ug1ysRAioQA7q1pw0YRUfoFekqbPGVusQoq0GHmf9mk9fXqsDmCAhy4vwnl5SX6/J5FAgKLMeOyoasHtLhYioQDV7UbUdppwx8VTsaOyGa/uOokElRTjEtWYmub/xCBRJcPKGdn44VQzsnssYJySqvXWXj+2fFbAZ6CTU7V+L3FeNjkdL/5Qji+O1+OX8/gP704z3zs4mD/iPyycjD99sA+Pbz2G+5cU9rqvTmeCk+WQmzBwgCwWClCQrEGHydarN+clE9PgZFksLEgd8PdqwfgUvLtmgbdudiDFmfH47mRTnwl8X5c1QCMTY3a27+lWmbFKyERClLfosRSBT1ECgC9LG5Cqkff5oL1yeibeP1iN578vQ7JajtPtRvxpke8suEoqxr2XFeIP7+/Biz+W4w8+aq05jsPb+6qQoVX0ye7PyIzHmz+/AOu/OooXfiiDSMDgnytme1sxDuSqomzYnCyq243oNNugs9jRqDfDaHMiXinFHRdPxeLJ6VAGuDgnkiJ97AVG3vE3WIkqGQr+f3t3HhBVuccN/HtmYXHYZHNDVkVQJBZ3RS1MszRzQYNCy65LqS0aF3NJC15NjXvTvL55b1fzWnlzabnm7WpRgr7ihkuKUqIgi4oILjDscN4/kGlGAYGZYRa+n39qzpkzPU8Mv/PjOc/ze1ztsOXIpftT0uywdmJ/1bzPuWF+WP/LBfzvQl6Tax/UpV2/gz5dHLRKbDrbWaOfhzP+m5aLmUN66mzkrSHKiirsOJmJf5+8gtL7CY6Xkw0e9+2Cx327qNaAPGr+sS7IpRJMCGx6zYYh9OniAE9HG+w9l4Pxfbs3+LPNKizGiau3MHuYr9aLxKaGeKKiusaop1fUqx9FnvfVUXx99upDu/4WKitwt6xKJ/OPjYlWET4zMxPz5s1DcXExdu/ejZdeegkbN26Ej49Piz5HIpHg/fff1zim/hlPPPEEnnjiiUdeYw6eCXDD9uOX8b+0PLww4I//B8ezChDS3QkKSxmOXLmJyuoaZBTca3T75SA3Jxy5crPRbVazbyvxWDfHZm0jPTnIA4u+PoEf069hbB83VTIb1qMT+ns448XPkpFdpMSfnwx45A1j3gh/vBrmp/G++iS3z/1FWrrgamuN/p6aN5/Lt5q/QK/eIC9XvDSoB7YezUCQmyPG9f1jykb9JiJezZwXFftkX1TVaI5SyaQSjO/bvJuFIAjNTo6BugWLe8/l4HJBMXreL+FXXlWDwxn5GOXftdGtVaUSAT1cbVWLN5urUFmOE1cLED2wx0M3e5lEgnkj/BDzzUks33sKEgGNTlsBgP4ezpgU5IGdqZnoZGuFqaFeGp95Nu82Lty4g7dHBTQ4IuyosMSa50Lxy+83YG8tb3Sr24bIJHVzuI2ZrmIv0D7i75N+XbE15RJeHd4bk4M9NKYRRIR44uClG/jo5zT083B+5FSLO6WVyLtTimeb+XvblGcC3LBy3xkkXbqBwG4dIZdKYSGVQC6VNPmko7kqqmvw9Zmr+NexDNwtq8ITvl0wrZ8XfrtxF7/8fh1bUy5hS8olWMgksLOSt3hKlTkRBAGTgz2QkJiGfx65pBpYUbf79NW6dTY6+NkLgoDpLai2YWjB3Z0w0NMFmw/9Bg9HG40pMvX3Vh+OIP8hLi4OS5cuxbp169CpUye8+OKLePfdd/HFF1/oqn3tjrujDfp27Yh953MR1d8bgiDg2p1S5N4pRUSIJ+ytLXDg4jX8Ny0XlTW1ja5+Db6/W1NmYclDI6ZVNbW4ca8Uo5s5lWGQlwt6uNji8+OXMaZ3NyRn3EBA1z/m7E0f6INvz2Y3K9mWCAIkUs3A37uLA0b27IzogT46fdT0TEB3rPj+NI5n3cIgLxe1ChYtuwnMHOKLc9du48PE8wjo6qBaKJBZWAwB0BgNb4pPG9986ufjncopVCXIR67cRGlVTaPTK+r1crXHD2m5D40+n8y+hX8dzUB5dQ0qq2sx2r+bqnbyT+nXUSsCYxr5Xg3xdkWouxNSswvR38MZjorGa1ADwLzhfsi/V4YNBy8iKSMfS58KVI2+f3niCuyt5arNZBoiCEKbFJM3BMbelonq743n+3k3mHRKBAFLxwQielsy1hw4h3UT+zUZhy7cL6EZ0NVB63aN6NEZNpYyLP3PqYfOWcmk8HSyQQ8XW/RwtUNPFzv4uNg1ub2zukMZ+UhIPI+bxeUY4OmMucP8VPeLvl07YkqIJwqV5Ui+lI+Dl27gsW6Oeh3FNgWTgjyQnn8XW1Iuwd5arjHftqSiCj+k5WJUry6PjF3m6r1ngvDG7mN457tU/J9nQzDs/iZbqgoWWm4SYmy0ekZw584dDB06VPX6hRdeQEmJfva9b0+eCXBDVlGJqnbn8fv/HODpgkGeLpAKArYdrdtJrrEVxyFqi+sedO1uKWrFukfpzSEIAl4c4IPMwhLsOZ2F3/LvYUSPPyoozBzcE9/MfqLZ9SAfZCmTYtWEUPjreKvNET06wVlhiR0nrwCoW1DnYG3R4uAmlQhY+UwwampE7L/4x5amWYUl6OrQAVYNLHQzBvU78Kl/B35KvwbHDpYIdmv6UapvJzuUVtVolLgTRREfH7yIjIJiWMvrftZ/S7qIs/c3ktl/IQ+9Otk1utJYEAQsGOkPmUTAMwGPfpRtbSHD2on9sGRMIDJu3sOLW5Ox7sdzOJp5E4cv52NykKfR/r/XN8belhEEockRWbeOCswN88ORKzcb3eK2Xtq125AIjcfelrCUS7EhYhDeGdMXi8L74PWR/pgb1guvDOmJZwO7w8ZShsOXb+Kjny9g3ldH8dTGA/hb0sVHfu698iq8998zsLWU4+OpA/HRlIENDqY4KawwMcgD6yMGYuaQR+88Z+4EQUDs6L4I69EJf/35Ag5c/OO7sO98LsqqahpdpNYe2FlbYH3EIPRwscU736WqKsBcuVUMJ4WlTquSGAOtJ9FVVFSo/touKChAbRObKlDzjPbvhi9OXMGq/b/iX9PDcDyrAJ1sreHeUQFBEBDQrSPO5hahg4Ws0SS3i30HdLazxqmcQkx+oPRUfQms5ibIAPBEry7YfOg3bDhYF5zVS4wJggCZ1PhGHixkUkSEeOH/HkrH7zfvIqOguEXTK9Q5Kizh19kep7I1t3tuzgI9Qwrp7oikjHzUiiLSb9zFoYx8TAnxfOTj2/qFer/l31V9T07lFOLSzXtYPLovng10R2llNV78LBmrD/yKuHEhSM+/i9dHNr2y29fVHt+/9iRsLZsXegRBwLi+3dHfwxlbUi7h+/O5+OZsNixkkkanF7UXjL261dypFmk37sDb2bbVAwIP8uts32QdXFEUUaisQEZBMb779Sr+fTITEwLdmyxVuftUFkorq7HimaB2PW2iNWQSCd4fF4yFu48j7oezsLWUY6CXC/aczkJAVwejrVncVuys5FgfMRAL9xzHsv+cwnvjgnH51j2zm38MaDmCHBkZiVdeeQWFhYVISEjAtGnTEBkZqau2tVtWcilWPhOEQmUFPjhwDqnZtzDA01l1Mxx6f+6PbwML9NSFdHfC6Zyih1ZntyZBlkkkiOrvjZpaET7Ots2qI2wMnnvMHR3kUmw/dhlXCou1muYQ6u6ECzfuoKyyWq2ChXEHheD7O/Cdv3a7botOG0vMHPzokSJvZ1tYyaSqbbQB4N8nM+FgbaHaIr2DhQyxo/siu0iJt78+AYmAR07dAOoCbEun0nSys8Y7YwKxe9bjmD7QB2890afdPuYEGHv1oX6qRVNVLWpFERev32n1BiGtIQgCnG2sMMjLBYvCAyCVCvjsaON1wksrq7HzVCaG+bgyOW4lS5kUayf2g4+zLZb8JxVbjlxC7p3SNqtzbuxsLOX465SBCOjqgHe/P4WMm8VmN/8Y0DJBjoiIwBtvvIHx48ejuroacXFxiIqK0lXb2jX/zg6YPbQXDl66geKKagz0dFGdq0+QH/WIL7i7I+6UVSLzgZI+ubeVsLWSt2gjE6BuTq+bQweNhWrGztZKjmcD3ZH423VUVtdq9Usc3N0JNbUifr12W1XB4sHdEo1N/TzkZf85hbw7pXj36SDYNmMOo1wqwfyR/jhx9Ra+PHEFV4tK8P+u3MSkIA+N2skDPV3wdB83FJSUo5+HM5xbUEu2NVxsrDA3zM8oV8G3JcZe/VCfatFQua/sIiWKK6pbXf9YW842Vpj4mAf+dyH3oc1w6n17Nhv3yqtMagGYMVJYyvGXyQPgamuNLSmX4KSwbHJhcXujsJAhYfIABLk5okYUzW7+MaDlFIsZM2Zg27Zt6N+/v67aQ2qi+nvjWNZNnM29rVF+x9PJBovC+2DwI3abU5+HrL5QL+e2Eu6tGAG2kkux80+Pt/g6Q5sW6oVdp7NQUytqNaIS2LUjpBIBp7ILVX+cGHvdx/p5yNfuluLFAT4tKqQ/8TF3pGbfwubDvyHlyk1YSBue1vD6SH9k3y7BtHY8N6+tMfbqz5RgTxy4mIe//pyGAZ4uGovi6mvLt3YHPV2IHuCDb89exdaUS3j36SCNcxXVNdhx8gpC3Z2aLLlJzeOosMRHUwZg0Z4TmBzs0Wjln/aqg4UMH04agL3nsjHCt+GdXU2ZVj/t4uJilJaWPvqN1CpSiYDVE/phU+RgjdHeunI0no/cb76LfQd0srXG6ZwijeM5t5UmM0VCFzrZWeNJv7qyZtrMGba2kKF3ZwecyilEVmFJiypYGNJo/64IcnPErAbKFjVFEAQsHh0IVxsrnM4twpje3Rqc1mBnbYG/Rw3FYD3sjEUNY+zVH6mkbqHWvbIqbErWXBCXdv02OljIDPp776iwxKQgDxy4mIerRZpPB/edz0WhsgIzBnH0WFe62HfAlzNHPLSWh+pYyevW+hiiLry+adUja2trPP744+jVqxc6dPgjWfvkk0+0bhjVsbWSo68WIwEh3R2RklmgqodcUVWD/OJydHdoPwkyACwK74OpIV4Nbq3cEiHuTvj82GXYWcnRxd54K1iomz2sV6uvtbWSI258CNb/csHoawO3J4y9+uXrao9poV748uQVPNXbDUFudZvMXLhxB7072+ukRrE2Xujvg2/OZGNryiWsfCYYQN3Onp8fv4w+XRwQ2sotl4noD1olyFOmTNFVO0hPgrs74YcLecgqLIGXsy1y75ft6u7YvhJkhaVcJ6uPQ7o7YdvRDBzLKtAolG7OendxwOaoIYZuBqlh7NW/V4b0xM+/X8faA+fw2fRhqBWBjJvFeGGA96Mv1jNHhSUmB3vgyxNX8NKgHvB0ssWBi9dw414ZFoX3Mfqti4lMgVYJ8rfffott27bpqi2kB/Vzl1MyC+DlbIuc23WP5NrbCLKu9O3aETKJcH+BnnHPPybzxdirf9YWMsSMCsCir0/gixNXENLdCTWi2KYVLJoS1d8bX5+5ii0pGVjxdBD+dTwDPV3s2s0f7kT6xjnIZq6LfQf07uyAH9JyIYoicm7fH0Hu2PT8ZWqYlVyqWsFu7DWQyXwx9raNwd6uCO/VBduOZqg2EDFUBYsHdexgicnBnkhMv4ZtRzOQXaTEdB3vRkrUnrWLOciuHYGuzoZuheFEDXLDsm/Po6jiHorKlHC2sUTPbs3brpQeNqKXE87m3cYAH9t2/b0iwzGV2AuYfvxdPaU3RiUU4Ltfs+HW0RoBHsZTf3vhGG98cyYLnx75Hd7OCkQN6QIWWiDSDc5BbgfGP9YVcd9fwK7UHGTeUsLLmaPH2ogc6I6qWhH+XTjFggyDsbftuNpaYfFYfyz55hyCujsYujkaHBUWmDHEE5sOXsbckT4GXzxIZE60SpAnTpyIvLw8HD9+HNXV1RgwYAA8PNr39q/GyN5ajjF9OuPb03mQSSUI9+McNW10sbdG7FN+hm4GtWOMvW3r+f7dkVWoNMrYOf+JHvByVmBicDdDN4XIrGj1MObQoUOYPHkyfvrpJyQmJmLKlCn46aefdNU20qGIfm64V16NImUlvFy4QI/IlDH2ti2JRMCSp/0x0Nv4yqd1sJAhol93yDi3gkintBpBXr9+PT7//HP06FFXlPzSpUuIiYnBqFGjdNI40p0hPs7oam+Fa3fL4eXEBJnIlDH2EhHpl1Z/clZVVakCNAD07NkTNTU1WjeKdE8qETA51A0AOIJMZOIYe4mI9EurBNnKygrnzp1TvT537hysra21bhTpx5wRPkiIeAy9OnFxGZEpY+wlItIvraZYxMTEYO7cuarFIZmZmVi/fr1OGka6Z2MpU40iE5HpYuwlItIvrRLkfv36Yd++fTh79ixqa2sRFBSEjh2NY5chIiJzxdhLRKRfrZ5ikZKSgkuXLsHBwQEjRozA1atXkZ6ersu2ERHRAxh7iYj0r1UJcmJiIt5++23cvXtXdcza2hoxMTFISkpq8eeVl5djwYIFiIqKwqxZs1BUVPTQez777DNEREQgIiICGzduBACIooiwsDBER0cjOjoaCQkJrekOEZFJYOwlImojYitMnTpVvHjx4kPHz507J06bNq3Fn7dlyxZxw4YNoiiK4vfffy/GxcVpnM/OzhYnTpwoVldXizU1NeK0adPEixcvillZWeKcOXMa/dycnBzR19dXzMnJaXGbiIiMjanEXlFk/CUi09aqEeSKigr4+T28k1hAQABKS0tb/HmpqakICwsDAAwfPhwpKSka5zt37oxPP/0UUqkUEokE1dXVsLS0RFpaGvLz8xEdHY1Zs2bhypUrrekOEZFJYOwlImobrVqkV1tb2+g5URSbvHbXrl3Ytm2bxjEnJyfY2taVHlMoFCguLtY4L5fL4ejoCFEUsXbtWvTu3RteXl64desWZs+ejbFjx+LkyZOIiYnBnj17WtMlIiKjx9hLRNQ2WpUg9+nTB3v37sX48eM1jn///ffw9PRs8tr6uWzq5s+fD6VSCQBQKpWws7N76LqKigosWbIECoUCK1asAFA3aiKVSgHUrerOz8+HKIoQBKE13SIiMmqMvUREbaNVCfKbb76JyMhIJCcnIyQkBLW1tThz5gxOnDiB7du3t/jzQkJCkJSUhMDAQCQnJyM0NFTjvCiKeO211zBw4EDMnj1bdXzjxo1wcHDArFmzkJ6ejq5duzJAE5HZYuwlImobgvio53KNKCoqwpdffonz589DIpEgKCgI06ZNg729fYs/q6ysDLGxsSgoKIBcLkdCQgJcXFywdetWuLu7o7a2FgsXLkRQUJDqmoULF8Lb2xsxMTEoLS2FVCrFu+++Cx8fH9V7cnNzER4ejsTERLi5cYMMIjJ9phB7AcZfIjJtrU6QTQEDNBGRYTD+EpEpa/VGIURERERE5ogJMhERERGRGibIRERERERqtEqQb926hcTERADAunXrMGPGDKSnp+ukYURE1DDGXiIi/dIqQV68eDFycnKQkpKCQ4cOYcKECYiPj9dV24iIqAGMvURE+qVVgnznzh289NJLSE5Oxrhx4zBp0iSUlZXpqm1ERNQAxl4iIv3SKkGuqqpCVVUVDh06hCFDhqCsrAylpaW6ahsRETWAsZeISL+0SpDDw8MxePBgdOzYEQEBAYiIiMC4ceN01TYiImoAYy8RkX5pvVHIjRs30KlTJwiCgPT0dPj5+emqbVpjoXoiMlfGHHsBxl8iMm1aV7FIS0uDIAhYt24dVq9ezZXURER6xthLRKRfrGJBRGRiGHuJiPSLVSyIiEwMYy8RkX6xigURkYlh7CUi0i9WsSAiMjGMvURE+qWTKhadO3cGAKNbSc1V1ERkrow59gKMv0Rk2mTaXFxbW4u9e/ciOTkZ1dXVGDp0KHr06AGZTKuPJSKiJjD2EhHpl1ZTLBISEnD06FHMmDEDL7/8Mk6fPo21a9fqqm1ERNQAxl4iIv3Sarjh0KFD2LNnD+RyOQBg5MiRePbZZ7FkyRKdNI6IiB7G2EtEpF9ajSCLoqgK0ABgYWGh8ZqIiHSPsZeISL+0SpD9/PywatUqZGdnIycnB6tXr4avr6+u2kZERA1g7CUi0i+tplisWLEC8fHxeP755yGKIoYNG4bly5e3+HPKy8sRExODwsJCKBQKrFmzBo6OjhrviY+Px6lTp6BQKAAAmzZtglwuf+R1RETmhrGXiEi/tEqQN2/ejA8++EDrRuzYsQO+vr5YsGAB9u3bh02bNmHZsmUa70lLS8Onn36qEYS3bt36yOuIiMwNYy8RkX5pNcXi4MGDOmlEamoqwsLCAADDhw9HSkqKxvna2lpcvXoV7777Lp5//nns3r27WdcREZkjxl4iIv3SagTZzc0NM2fOREhIiOrxGwC8/PLLjV6za9cubNu2TeOYk5MTbG1tAQAKhQLFxcUa50tLS/Hiiy/i5ZdfRk1NDaZPn46AgACUlJQ0eR0RkTli7CUi0i+tEmQHBwcAQF5eXrOviYiIQEREhMax+fPnQ6lUAgCUSiXs7Ow0zltbW2P69OmwtrYGAAwaNAjp6emwsbFp8joiInPE2EtEpF9aJcirV69W/XtlZSUsLCxa9TkhISFISkpCYGAgkpOTERoaqnE+KysLb731Fr755hvU1tbi1KlTmDhxIoqKipq8jojIHDH2EhHpV6sS5MrKSixfvhyjRo3Ck08+CQBYsGABHB0dERcX1+LtTiMjIxEbG4vIyEjI5XIkJCQAqFsI4u7ujvDwcIwfPx5Tp06FXC7HhAkT0LNnT7i5uTV4HRGROWLsJSJqG4IoimJLL/rwww+RmZmJ999/H05OTgCA/Px8vPfee+jZsyfeeustnTe0NXJzcxEeHo7ExES4ubkZujlERFoxldgLMP4SkWlrVRWLgwcPIiEhQRWgAaBTp05Yu3YtfvrpJ501joiI/sDYS0TUNlqVIMvlclhZWT103MbGptVz4YiIqGmMvUREbaNVCbJEIkFJSclDx0tKSlBdXa11o4iI6GGMvUREbaNVCfK4ceOwbNkylJaWqo6VlpZi2bJlGD16tM4aR0REf2DsJSJqG61KkGfMmAFbW1sMHToUU6dOxZQpUzB06FDY2dlh3rx5um4jERGBsZeIqK20qopFvby8PKSlpUEikSAwMBCurq66bJvWuIqaiMyRscdegPGXiEybVhuFdOvWDd26ddNVW4iIqBkYe4mI9KtVUyyIiIiIiMwVE2QiIiIiIjVMkImIiIiI1DBBJiIiIiJSwwSZiIiIiEgNE2QiIiIiIjVMkImIiIiI1DBBJiIiIiJSwwSZiIiIiEgNE2QiIiIiIjVMkImIiIiI1DBBJiIiIiJSwwSZiIiIiEgNE2QiIiIiIjUyQzcAAMrLyxETE4PCwkIoFAqsWbMGjo6OqvMXL17EqlWrVK/PnDmDv/3tbwgLC8Pw4cPh6ekJAAgKCsKiRYvauvlERCaJsZeIqGFGkSDv2LEDvr6+WLBgAfbt24dNmzZh2bJlqvP+/v7Yvn07AOCHH36Aq6srhg8fjqtXr6JPnz745JNPDNV0IiKTxdhLRNQwo5hikZqairCwMADA8OHDkZKS0uD7SktL8fHHH2Pp0qUAgLS0NOTn5yM6OhqzZs3ClStX2qzNRESmjrGXiKhhbT6CvGvXLmzbtk3jmJOTE2xtbQEACoUCxcXFDV67e/duPPXUU6pHgC4uLpg9ezbGjh2LkydPIiYmBnv27NFvB4iITBBjLxFR87V5ghwREYGIiAiNY/Pnz4dSqQQAKJVK2NnZNXjt3r17sWHDBtXrgIAASKVSAEC/fv2Qn58PURQhCIKeWk9EZJoYe4mIms8opliEhIQgKSkJAJCcnIzQ0NCH3lNcXIzKykp06dJFdWzjxo2qEZH09HR07dqVAZqIqJkYe4mIGmYUi/QiIyMRGxuLyMhIyOVyJCQkAAC2bt0Kd3d3hIeHIzMzE926ddO4bvbs2YiJiUFSUhKkUilWr15tiOYTEZkkxl4iooYJoiiKhm6EvuTm5iI8PByJiYlwc3MzdHOIiNoNxl8iMmVGMcWCiIiIiMhYMEEmIiIiIlLDBJmIiIiISA0TZCIiIiIiNUyQiYiIiIjUMEEmIiIiIlLDBJmIiIiISA0TZCIiIiIiNUyQiYiIiIjUMEEmIiIiIlLDBJmIiIiISA0TZCIiIiIiNUyQiYiIiIjUMEEmIiIiIlLDBJmIiIiISA0TZCIiIiIiNUyQiYiIiIjUMEEmIiIiIlLDBJmIiIiISA0TZCIiIiIiNUyQiYiIiIjUGFWC/OOPP2LRokUNntu5cycmTZqEqVOn4pdffgEAlJeXY8GCBYiKisKsWbNQVFTUls0lIjILjL1ERJpkhm5Avfj4eBw+fBj+/v4PnSsoKMD27duxZ88eVFRUICoqCkOHDsWOHTvg6+uLBQsWYN++fdi0aROWLVumuq6mpgYAcOPGjTbrBxFRa3Tu3BkyWduHZH3EXoDxl4hMQ2Ox12gS5JCQEIwaNQpfffXVQ+d+/fVXBAcHw8LCAhYWFnB3d0d6ejpSU1Pxpz/9CQAwfPhwbNq0SeO6goICAMALL7yg/w4QEWkhMTERbm5ubf7f1UfsBRh/icg0NBZ72zxB3rVrF7Zt26ZxbNWqVXj66adx7NixBq8pKSmBra2t6rVCoUBJSYnGcYVCgeLiYo3rAgIC8MUXX8DFxQVSqVTHPSEi0p3OnTvr9fPbMvYCjL9EZBoai71tniBHREQgIiKiRdfY2NhAqVSqXiuVStja2mocVyqVsLOz07jOysoK/fr1077RREQmri1jL8D4S0SmzagW6TUmMDAQqampqKioQHFxMS5fvgxfX1+EhIQgKSkJAJCcnIzQ0FADt5SIyHww9hJRe2U0c5AbsnXrVri7uyM8PBzR0dGIioqCKIp46623YGlpicjISMTGxiIyMhJyuRwJCQmGbjIRkclj7CWi9k4QRVE0dCOMRW1tLVauXInffvsNFhYWiI+Ph4eHh6Gb1WpVVVVYsmQJ8vLyUFlZiVdffRU9evTA4sWLIQgCevbsiRUrVkAiMYkHCQ0qLCzEpEmTsGXLFshkMrPo2+bNm/Hzzz+jqqoKkZGRGDBggFn0q6qqCosXL0ZeXh4kEgni4uLM4md29uxZfPjhh9i+fTuuXr3aYH927tyJf//735DJZHj11Vfx+OOPG7rZRsfc4m+95nw/TI2531tqamqwbNkyZGZmQiqVYvXq1RBF0Wz6Z473zXrPPfecan2Em5sb5s6d2/r+iaSyf/9+MTY2VhRFUTx9+rQ4d+5cA7dIO7t37xbj4+NFURTFoqIiccSIEeKcOXPEo0ePiqIoisuXLxcPHDhgyCZqpbKyUnzttdfE0aNHixkZGWbRt6NHj4pz5swRa2pqxJKSEnHDhg1m0S9RFMUff/xRfP3110VRFMXDhw+L8+fPN/m+/f3vfxfHjRsnRkREiKIoNtifmzdviuPGjRMrKirEe/fuqf6dNJlb/BXF5n0/TJG531t+/PFHcfHixaIo1sXkuXPnmk3/zPG+Wa+8vFycMGGCxjFt+me6fyboQWpqKsLCwgAAQUFBOH/+vIFbpJ2nnnoKb7zxhuq1VCpFWloaBgwYAKCuPNORI0cM1TytrVmzBs8//zxcXV0BwCz6dvjwYfj6+mLevHmYO3cuRo4caRb9AgAvLy/U1NSgtrYWJSUlkMlkJt83d3d3fPzxx6rXDfVHvVSara2tqlQaaTK3+As07/thisz93jJq1CjExcUBAK5duwZnZ2ez6Z853jfrpaeno6ysDDNnzsT06dNx5swZrfrHBFlNSUkJbGxsVK+lUimqq6sN2CLtKBQK2NjYoKSkBK+//jrefPNNiKIIQRBU5xsqz2QKvv76azg6OqpuqADMom+3b9/G+fPnsX79erz33nt4++23zaJfANChQwfk5eVh7NixWL58OaKjo02+b2PGjNEoMN9QfxorlUaazC3+As37fpgic7631JPJZIiNjUVcXBzGjBljFv0z1/tmPSsrK7zyyiv45z//qZP7JxNkNQ+WNKqtrTXIzla6dP36dUyfPh0TJkzA+PHjNebeNFaeyRTs2bMHR44cQXR0NC5evIjY2FiN7W5NtW8ODg4YNmwYLCws4O3tDUtLS41faFPtFwB89tlnGDZsGPbv34/vvvsOixcvRlVVleq8KfetXkO/X42VSiNN5hh/H2Qu8Rcw33uLujVr1mD//v1Yvnw5KioqVMdNtX/met+s5+XlhWeffRaCIMDLywsODg4oLCxUnW9p/5ggqwkJCUFycjIA4MyZM/D19TVwi7Rz69YtzJw5EzExMZgyZQoAoHfv3qpNAZKTk022TukXX3yBzz//HNu3b4e/vz/WrFmD4cOHm3zfQkNDcejQIYiiiPz8fJSVlWHw4MEm3y8AsLOzUyWG9vb2qK6uNpvvY72G+tNYqTTSZG7xtyHm8n0353sLAHz77bfYvHkzAMDa2hqCICAgIMDk+2eu9816u3fvxgcffAAAyM/PR0lJCYYOHdrq/rGKhZr6VdS///47RFHEqlWr4OPjY+hmtVp8fDx++OEHeHt7q44tXboU8fHxqKqqgre3N+Lj401+l6vo6GisXLkSEokEy5cvN/m+rV27FseOHVOV1XJzczOLfimVSixZsgQFBQWoqqrC9OnTERAQYPJ9y83NxcKFC7Fz505kZmY22J+dO3fiq6++giiKmDNnDsaMGWPoZhsdc4u/9Zrz/TA15n5vKS0txTvvvINbt26huroas2bNgo+Pj1n87OqZ230TACorK/HOO+/g2rVrEAQBb7/9Njp27Njq/jFBJiIiIiJSwykWRERERERqmCATEREREalhgkxEREREpIYJMhERERGRGibIRERERERqmCATqcnNzUVwcLChm0FE1K4w9pKxYYJMRERERKTGvPbxJNKTzMxMvP/++1AqlSgoKICfnx8++ugjWFpaIikpCR9++CEkEgn8/f1x5MgRfPnll3BzczN0s4mITBpjLxkKR5CJmmHnzp147rnnsHPnThw4cAC5ubk4ePAgbt++jT//+c9Yt24dvvvuOwwcOBD5+fmGbi4RkVlg7CVDYYJM1AwxMTFwdHTEP/7xD6xcuRI3b95EaWkpTp48CR8fH/j5+QEAJk6cCBsbGwO3lojIPDD2kqFwigVRMyxcuBA1NTUYO3YsRo4cievXr0MURUilUjy4W7tEwr87iYh0gbGXDIXfJqJmOHz4MObNm4enn34aAHD27FnU1NQgJCQEWVlZSE9PBwDs378f9+7dgyAIhmwuEZFZYOwlQ+EIMtEDSktLHyo39Oabb2LevHno0KEDbGxs0L9/f2RnZ8PBwQF/+ctfEBsbC4lEgoCAAMhkMlhbWxuo9UREpomxl4yJID74jIKImq2kpASbNm3CggULYG1tjbS0NMyZMweHDh3iSAYRkZ4w9pK+cQSZSAs2NjaQy+WYMmUKZDIZZDIZPvroIwZoIiI9YuwlfeMIMhERERGRGi7SIyIiIiJSwwSZiIiIiEgNE2QiIiIiIjVMkImIiIiI1DBBJiIiIiJS8/8B3/QPvr2MYm8AAAAASUVORK5CYII=\n"
      },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {}
     }
    ],
    "source": [
-    "pd.DataFrame(psi)"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[   0,    0],\n",
-       "       [1001,    0],\n",
-       "       [2001,    0],\n",
-       "       [2002,    0],\n",
-       "       [1001, 1001],\n",
-       "       [2001, 1001],\n",
-       "       [2002, 1001],\n",
-       "       [2001, 2001],\n",
-       "       [2002, 2001],\n",
-       "       [2002, 2002]])"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2000  0.00000000\n",
+       "1        x1(k-2)     0.9000  0.00000000\n",
+       "2  x1(k-1)y(k-1)     0.1000  0.00000000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1000</td>\n      <td>0.00000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 21,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
    "source": [
-    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
-    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
-    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "regressor_code"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "### Options\n",
     "\n",
-    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
-   ]
+    "You can set the `steps_ahead` to run the prediction/simulation:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[1001,    0],\n",
-       "       [2002,    0],\n",
-       "       [2001, 1001]])"
+       "0.001695176840746673"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
-    "model.final_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.psi = psi[:, [1, 3, 5]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining the parameter estimation method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
-    "from sysidentpy.parameter_estimation import Estimators"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[0.20014451],\n",
-       "       [0.89992074],\n",
-       "       [0.09997088]])"
+       "0.0017301972652496206"
       ]
      },
-     "execution_count": 26,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
-    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
-    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
-    "\n",
-    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
-    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
-    "\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "model.theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulating the model"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=21)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "yhat = model.predict(x_valid, y_valid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0019184421163563367\n"
-     ]
-    }
+    "### Estimating the parameters\n",
+    "\n",
+    "If you have only the model strucuture, you can create an object with `estimate_parameter=True` and\n",
+    "choose the methed for estimation using `estimator`. In this case, you have to pass the training data\n",
+    "for parameters estimation. \n",
+    "\n",
+    "When `estimate_parameter=True`, we also computate the ERR considering only the regressors defined by the user. "
    ],
-   "source": [
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)"
-   ]
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2001  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
-     ]
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
     }
    ],
    "source": [
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "s2 = SimulatePolynomialNarmax(estimate_parameter=True, estimator='recursive_least_squares')\n",
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True)\n",
     "\n",
-    "print(results)"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 9
     }
    ],
    "source": [
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Final code"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n",
+    "\n",
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.001879953122213705\n",
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2000  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
-     ]
-    },
-    {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
-    "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
-    "from sysidentpy.parameter_estimation import Estimators\n",
-    "\n",
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)\n",
-    "\n",
-    "model = PolynomialNarmax()\n",
-    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
-    "model.psi = psi[:, [1, 3, 5]]\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "yhat = model.predict(x_valid, y_valid)\n",
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)\n",
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
-    "\n",
-    "print(results)\n",
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:v0.1.4] *",
-   "language": "python",
-   "name": "conda-env-v0.1.4-py"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -614,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,

From 898ae6d723eba4af52e820ab7422f09b66ec5b90 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:06:54 -0300
Subject: [PATCH 20/27] minor changes in code format

---
 sysidentpy/general_estimators/narx.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/sysidentpy/general_estimators/narx.py b/sysidentpy/general_estimators/narx.py
index 4ac188af..d983b12c 100644
--- a/sysidentpy/general_estimators/narx.py
+++ b/sysidentpy/general_estimators/narx.py
@@ -80,11 +80,12 @@ def __init__(
         base_estimator=None,
         fit_params={},
     ):
+
         self.non_degree = non_degree
         self.ylag = ylag
         self.xlag = xlag
         self._n_inputs = n_inputs
-        [self.regressor_code, self.max_lag] = GenerateRegressors().regressor_space(
+        [self.regressor_code, self.max_lag] = self.regressor_space(
             non_degree, xlag, ylag, n_inputs
         )
         self.regressor_code = self.regressor_code[1:]
@@ -118,7 +119,7 @@ def data_preparation(self, X, y):
             The information matrix of the model.
         """
         logging.info("Creating the regressor matrix")
-        reg_matrix = InformationMatrix().build_information_matrix(
+        reg_matrix = self.build_information_matrix(
             X, y, self.xlag, self.ylag, self.non_degree
         )
         logging.info(
@@ -193,7 +194,7 @@ def predict(self, X, y_initial):
         analised_elements_number = self.max_lag + 1
 
         for i in range(0, len(X) - self.max_lag):
-            reg_matrix = InformationMatrix().build_information_matrix(
+            reg_matrix = self.build_information_matrix(
                 X[i : i + analised_elements_number],
                 yhat[i : i + analised_elements_number],
                 self.xlag,

From 39b1aba51611ad4652437cde27b7965c6cbec7f5 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:08:15 -0300
Subject: [PATCH 21/27] minor change in code format in narmax.py

---
 sysidentpy/polynomial_basis/narmax.py | 50 +++++++++++++++++----------
 1 file changed, 31 insertions(+), 19 deletions(-)

diff --git a/sysidentpy/polynomial_basis/narmax.py b/sysidentpy/polynomial_basis/narmax.py
index 5546c282..af75d274 100644
--- a/sysidentpy/polynomial_basis/narmax.py
+++ b/sysidentpy/polynomial_basis/narmax.py
@@ -126,14 +126,14 @@ def __init__(
         n_terms=None,
         n_inputs=1,
         n_info_values=10,
-        estimator="least_squares",
+        estimator="recursive_least_squares",
         extended_least_squares=True,
         aux_lag=1,
         lam=0.98,
         delta=0.01,
         offset_covariance=0.2,
         mu=0.01,
-        eps=np.finfo(np.float).eps,
+        eps=np.finfo(np.float64).eps,
         gama=0.2,
         weight=0.02,
     ):
@@ -159,7 +159,7 @@ def __init__(
         self._lam = lam
         self._delta = delta
         self._gama = gama
-        self._weight = weight  # <0  e <1
+        self._weight = weight  # 0 < weight <1
         self._validate_params()
 
     def _validate_params(self):
@@ -244,9 +244,9 @@ def error_reduction_ratio(self, psi, y, process_term_number):
             e Novos Resultados
 
         """
-        squared_y = y[self.max_lag :].T @ y[self.max_lag :]
+        squared_y = y[self.max_lag:].T @ y[self.max_lag:]
         tmp_psi = np.array(psi)
-        y = np.array([y[self.max_lag :, 0]]).T
+        y = np.array([y[self.max_lag:, 0]]).T
         tmp_y = np.copy(y)
         [n, dimension] = tmp_psi.shape
         piv = np.arange(dimension)
@@ -257,7 +257,8 @@ def error_reduction_ratio(self, psi, y, process_term_number):
             for j in np.arange(i, dimension):
                 num = np.array(tmp_psi[i:n, j].T @ tmp_y[i:n])
                 num = np.power(num, 2)
-                den = np.array((tmp_psi[i:n, j].T @ tmp_psi[i:n, j]) * squared_y)
+                den = np.array(
+                    (tmp_psi[i:n, j].T @ tmp_psi[i:n, j]) * squared_y)
                 tmp_err[j] = num / den
 
             if i == process_term_number:
@@ -332,7 +333,8 @@ def fit(self, X, y):
             self.info_values = self.information_criterion(X, y)
 
         if self.n_terms is None and self._order_selection is True:
-            model_length = np.where(self.info_values == np.amin(self.info_values))
+            model_length = np.where(
+                self.info_values == np.amin(self.info_values))
             model_length = int(model_length[0] + 1)
             self.n_terms = model_length
         elif self.n_terms is None and self._order_selection is not True:
@@ -411,13 +413,16 @@ def _unbiased_estimator(
                 ee, y, elag, 1, 2
             )
 
-            psi_extended = psi_extended[:, [2, 3, 7, 8, 11, 12, 13, 14, 15, 16, 17]]
+            psi_extended = psi_extended[:, [
+                2, 3, 7, 8, 11, 12, 13, 14, 15, 16, 17]]
 
             psi_e = np.concatenate([psi, psi_extended], axis=1)
-            unbiased_theta = getattr(parameter_estimation, self.estimator)(psi_e, y)
-            e = y[aux_lag:, 0].reshape(-1, 1) - psi_e @ unbiased_theta.reshape(-1, 1)
+            unbiased_theta = getattr(
+                parameter_estimation, self.estimator)(psi_e, y)
+            e = y[aux_lag:, 0].reshape(-1, 1) - \
+                psi_e @ unbiased_theta.reshape(-1, 1)
 
-        return unbiased_theta[0 : len(self.final_model), 0].reshape(-1, 1)
+        return unbiased_theta[0: len(self.final_model), 0].reshape(-1, 1)
 
     def predict(self, X, y, steps_ahead=None):
         """Return the predicted values given an input.
@@ -435,6 +440,8 @@ def predict(self, X, y, steps_ahead=None):
             The input data to be used in the prediction process.
         y : ndarray of floats
             The output data to be used in the prediction process.
+        steps_ahead = int (default = None)
+            The forecast horizon.
 
         Returns
         -------
@@ -544,8 +551,8 @@ def _n_step_ahead_prediction(self, X, y, steps_ahead):
             if i + steps_ahead > len(y):
                 steps_ahead = len(y) - i  # predicts the remaining values
 
-            yhat[i : i + steps_ahead] = self._model_prediction(
-                X[k : i + steps_ahead], y[k : i + steps_ahead]
+            yhat[i: i + steps_ahead] = self._model_prediction(
+                X[k: i + steps_ahead], y[k: i + steps_ahead]
             )[-steps_ahead:].ravel()
 
             i += steps_ahead
@@ -578,7 +585,8 @@ def _model_prediction(self, X, y_initial):
         y_output.fill(np.nan)
         y_output[: self.max_lag] = y_initial[: self.max_lag, 0]
 
-        model_exponents = [self._code2exponents(model) for model in self.final_model]
+        model_exponents = [self._code2exponents(
+            model) for model in self.final_model]
         raw_regressor = np.zeros(len(model_exponents[0]), dtype=float)
 
         for i in range(self.max_lag, X.shape[0]):
@@ -665,17 +673,19 @@ def information_criterion(self, X, y):
 
         for i in range(0, self.n_info_values):
             n_theta = i + 1
-            regressor_matrix = self.error_reduction_ratio(X_base, y, n_theta)[3]
+            regressor_matrix = self.error_reduction_ratio(X_base, y, n_theta)[
+                3]
 
             tmp_theta = getattr(parameter_estimation, self.estimator)(
                 regressor_matrix, y
             )
 
             tmp_yhat = regressor_matrix @ tmp_theta
-            tmp_residual = y[self.max_lag :] - tmp_yhat
+            tmp_residual = y[self.max_lag:] - tmp_yhat
             e_var = np.var(tmp_residual, ddof=1)
 
-            output_vector[i] = self.compute_info_value(n_theta, n_samples, e_var)
+            output_vector[i] = self.compute_info_value(
+                n_theta, n_samples, e_var)
 
             # output_vector[i] = e_factor + model_factor
 
@@ -745,7 +755,8 @@ def results(self, theta_precision=4, err_precision=8, dtype="dec"):
                         translated_exponent = ""
                     else:
                         delay_string = str(
-                            int(regressor_key - np.floor(regressor_key / 1000) * 1000)
+                            int(regressor_key -
+                                np.floor(regressor_key / 1000) * 1000)
                         )
                         if int(regressor_key / 1000) < 2:
                             translated_key = "y(k-" + delay_string + ")"
@@ -763,7 +774,8 @@ def results(self, theta_precision=4, err_precision=8, dtype="dec"):
                             translated_exponent = "^" + str(
                                 regressor_dic[regressor_key]
                             )
-                    regressor_string.append(translated_key + translated_exponent)
+                    regressor_string.append(
+                        translated_key + translated_exponent)
                 tmp_regressor = "".join(regressor_string)
 
             current_parameter = theta_output_format.format(self.theta[i, 0])

From 066acf16bfed312a9e63d6ba6476244dcc000710 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:09:45 -0300
Subject: [PATCH 22/27] update np.float to np.float64 in eps value because of
 numpy warning

---
 .../polynomial_basis/tests/test_polynomial_basis.py    | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/sysidentpy/polynomial_basis/tests/test_polynomial_basis.py b/sysidentpy/polynomial_basis/tests/test_polynomial_basis.py
index 1cbba5c7..142ef4ad 100644
--- a/sysidentpy/polynomial_basis/tests/test_polynomial_basis.py
+++ b/sysidentpy/polynomial_basis/tests/test_polynomial_basis.py
@@ -82,14 +82,14 @@ def test_default_values():
         "n_terms": None,
         "n_inputs": 1,
         "n_info_values": 10,
-        "estimator": "least_squares",
+        "estimator": "recursive_least_squares",
         "extended_least_squares": True,
         "aux_lag": 1,
         "lam": 0.98,
         "delta": 0.01,
         "offset_covariance": 0.2,
         "mu": 0.01,
-        "eps": np.finfo(np.float).eps,
+        "eps": np.finfo(np.float64).eps,
         "gama": 0.2,
         "weight": 0.02,
     }
@@ -262,7 +262,8 @@ def test_information_criteria_bic():
         estimator="least_squares",
     )
     model.fit(x, y)
-    info_values = np.array([-1764.885, -2320.101, -2976.391, -4461.908, -72845.768])
+    info_values = np.array(
+        [-1764.885, -2320.101, -2976.391, -4461.908, -72845.768])
     assert_almost_equal(model.info_values[:4], info_values[:4], decimal=3)
 
 
@@ -302,7 +303,8 @@ def test_information_criteria_lilc():
         estimator="least_squares",
     )
     model.fit(x, y)
-    info_values = np.array([-1767.926, -2326.183, -2985.514, -4474.072, -72860.973])
+    info_values = np.array(
+        [-1767.926, -2326.183, -2985.514, -4474.072, -72860.973])
     assert_almost_equal(model.info_values[:4], info_values[:4], decimal=3)
 
 

From 2fa8a78f544ce2e3adfdbc9c2813612c5185f24e Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:10:29 -0300
Subject: [PATCH 23/27] added simple tests for generate_data function

---
 sysidentpy/utils/tests/test_utils.py | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/sysidentpy/utils/tests/test_utils.py b/sysidentpy/utils/tests/test_utils.py
index e343e141..b312c4b8 100644
--- a/sysidentpy/utils/tests/test_utils.py
+++ b/sysidentpy/utils/tests/test_utils.py
@@ -5,7 +5,7 @@
     check_nan,
     check_X_y,
 )
-
+from sysidentpy.utils.generate_data import get_miso_data, get_siso_data
 import numpy as np
 from numpy.testing import assert_raises
 
@@ -44,3 +44,29 @@ def test_check_X_y():
     X = np.ones([10, 2])
     y = np.ones([8, 1])
     assert_raises(ValueError, check_X_y, X, y)
+    
+def test_get_miso_data():
+    x_train, x_valid, y_train, y_valid = get_siso_data(
+        n=1000,
+        colored_noise=False,
+        sigma=0.001,
+        train_percentage=90)
+    assert len(x_train) == 900
+    assert len(x_valid) == 100
+    assert len(y_train) == 900
+    assert len(y_valid) == 100
+    assert x_train.shape[1] == 1
+    assert y_train.shape[1] == 1
+
+def test_get_miso_data():
+    x_train, x_valid, y_train, y_valid = get_miso_data(
+        n=1000,
+        colored_noise=False,
+        sigma=0.001,
+        train_percentage=90)
+    assert len(x_train) == 900
+    assert len(x_valid) == 100
+    assert len(y_train) == 900
+    assert len(y_valid) == 100
+    assert x_train.shape[1] == 2
+    assert y_train.shape[1] == 1

From be5cb4991d5fbc76057fe2c431f0c604ce20759d Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:15:31 -0300
Subject: [PATCH 24/27] Added a new class pre defined model simulation. The
 user only need to pass the model code to simulate the model

---
 sysidentpy/polynomial_basis/__init__.py       |   1 +
 sysidentpy/polynomial_basis/simulation.py     | 244 ++++++++++++++++++
 .../polynomial_basis/tests/test_simulation.py | 123 +++++++++
 3 files changed, 368 insertions(+)
 create mode 100644 sysidentpy/polynomial_basis/simulation.py
 create mode 100644 sysidentpy/polynomial_basis/tests/test_simulation.py

diff --git a/sysidentpy/polynomial_basis/__init__.py b/sysidentpy/polynomial_basis/__init__.py
index 87e79912..04ff7481 100644
--- a/sysidentpy/polynomial_basis/__init__.py
+++ b/sysidentpy/polynomial_basis/__init__.py
@@ -1 +1,2 @@
 from .narmax import PolynomialNarmax
+from .simulation import SimulatePolynomialNarmax
diff --git a/sysidentpy/polynomial_basis/simulation.py b/sysidentpy/polynomial_basis/simulation.py
new file mode 100644
index 00000000..ee2242d0
--- /dev/null
+++ b/sysidentpy/polynomial_basis/simulation.py
@@ -0,0 +1,244 @@
+from sysidentpy.parameter_estimation.estimators import Estimators
+from ..base import GenerateRegressors
+from ..base import InformationMatrix
+from .narmax import PolynomialNarmax
+import numpy as np
+from ..residues.residues_correlation import ResiduesAnalysis
+from ..utils._check_arrays import check_X_y
+
+
+class SimulatePolynomialNarmax(PolynomialNarmax):
+    def __init__(
+        self,
+        n_inputs=1,
+        estimator="recursive_least_squares",
+        extended_least_squares=True,
+        lam=0.98,
+        delta=0.01,
+        offset_covariance=0.2,
+        mu=0.01,
+        eps=np.finfo(np.float).eps,
+        gama=0.2,
+        weight=0.02,
+        estimate_parameter=False
+    ):
+
+        super().__init__(n_inputs,
+                         estimator=estimator,
+                         extended_least_squares=extended_least_squares,
+                         lam=lam,
+                         delta=delta,
+                         offset_covariance=offset_covariance,
+                         mu=mu,
+                         eps=eps,
+                         gama=gama,
+                         weight=weight)
+
+        self.estimate_parameter = estimate_parameter
+
+    def _validate_simulate_params(self):
+        if not isinstance(self.estimate_parameter, bool):
+            raise TypeError(
+                f"estimate_parameter must be False or True. Got {self.estimate_parameter}"
+            )
+
+    def _get_index_from_regressor_code(self, regressor_code, model_code):
+        """Get the index of user regressor in regressor space.
+
+        Took from: https://stackoverflow.com/questions/38674027/find-the-row-indexes-of-several-values-in-a-numpy-array/38674038#38674038
+
+        Parameters
+        ----------
+        regressor_code : ndarray of int
+            Matrix codification of all possible regressors.
+        model_code : ndarray of int
+            Model defined by the user to simulate.
+
+        Returns
+        -------
+        model_index : ndarray of int
+            Index of model code in the regressor space.
+
+        """
+        dims = regressor_code.max(0)+1
+        model_index = np.where(np.in1d(np.ravel_multi_index(regressor_code.T, dims),
+                                       np.ravel_multi_index(model_code.T, dims)))[0]
+        return model_index
+
+    def _list_output_regressor_code(self, model_code):
+        """Create a flattened array of input or output regressors.
+
+        Parameters
+        ----------
+        model_code : ndarray of int
+            Model defined by the user to simulate.
+
+        Returns
+        -------
+        model_code : ndarray of int
+            Flattened list of output regressors.
+
+        """
+        regressor_code = [code for code in model_code.ravel() if (
+            code != 0) and (str(code)[0] == '1')]
+
+        return np.asarray(regressor_code)
+
+    def _list_input_regressor_code(self, model_code):
+        """Create a flattened array of input or output regressors.
+
+        Parameters
+        ----------
+        model_code : ndarray of int
+            Model defined by the user to simulate.
+
+        Returns
+        -------
+        model_code : ndarray of int
+            Flattened list of output regressors.
+
+        """
+        regressor_code = [code for code in model_code.ravel() if (
+            code != 0) and (str(code)[0] != '1')]
+        return np.asarray(regressor_code)
+
+    def _get_lag_from_regressor_code(self, regressors):
+        """Get the maximum lag from array of regressors.
+
+        Parameters
+        ----------
+        regressors : ndarray of int
+            Flattened list of input or output regressors.
+
+        Returns
+        -------
+        max_lag : int
+            Maximum lag of list of regressors.
+
+        """
+        lag_list = [int(i) for i in regressors.astype('str')
+                    for i in [np.sum(int(i[2:]))]]
+        return max(lag_list)
+
+    def simulate(self,
+                 X_train=None,
+                 y_train=None,
+                 X_test=None,
+                 y_test=None,
+                 model_code=None,
+                 steps_ahead=None,
+                 theta=None,
+                 plot=True):
+        """Simulate a model defined by the user.
+
+        Parameters
+        ----------
+        X_train : ndarray of floats
+            The input data to be used in the training process.
+        y_train : ndarray of floats
+            The output data to be used in the training process.
+        X_test : ndarray of floats
+            The input data to be used in the prediction process.
+        y_test : ndarray of floats
+            The output data (initial conditions) to be used in the prediction process.
+        model_code : ndarray of int
+            Flattened list of input or output regressors.
+        steps_ahead = int, default = None
+            The forecast horizon.
+        theta : array-like of shape = number_of_model_elements
+            The parameters of the model.
+        plot : bool, default=True
+            Indicate if the user wants to plot or not.
+        Returns
+        -------
+        yhat : ndarray of floats
+            The predicted values of the model.
+        results : string
+            Where:
+                First column represents each regressor element;
+                Second column represents associated parameter;
+                Third column represents the error reduction ratio associated
+                to each regressor.
+
+        """
+        if y_test is None:
+            raise ValueError("y_test cannot be None")
+
+        if not isinstance(model_code, np.ndarray):
+            raise TypeError(
+                f"model_code must be an np.np.ndarray. Got {model_code}"
+            )
+
+        if not isinstance(steps_ahead, (int, type(None))):
+            raise ValueError(
+                f"steps_ahead must be None or integer > zero. Got {steps_ahead}"
+            )
+        if not isinstance(plot, bool):
+            raise TypeError(
+                f"plot must be True or False. Got {plot}"
+            )
+        if not isinstance(theta, np.ndarray) and not self.estimate_parameter:
+            raise TypeError(
+                f"If estimate_parameter is False, theta must be an np.np.ndarray. Got {theta}"
+            )
+
+        check_X_y(X_test, y_test)
+        if self.estimate_parameter:
+            if not all(isinstance(i, np.ndarray) for i in [X_train, y_train]):
+                raise TypeError(
+                    f"If estimate_parameter is True, X_train and y_train must be an np.ndarray. Got {type(X_train), type(y_train)}"
+                )
+            check_X_y(X_train, y_train)
+            if y_train is None:
+                raise ValueError("y_train cannot be None")
+
+        xlag_code = self._list_input_regressor_code(model_code)
+        ylag_code = self._list_output_regressor_code(model_code)
+        self.xlag = self._get_lag_from_regressor_code(xlag_code)
+        self.ylag = self._get_lag_from_regressor_code(ylag_code)
+        if self._n_inputs != 1:
+            self.xlag = self._n_inputs * [[self.xlag]]
+
+        self.non_degree = model_code.shape[1]
+        [regressor_code, _] = self.regressor_space(
+            self.non_degree, self.xlag, self.ylag, self._n_inputs
+        )
+
+        self.pivv = self._get_index_from_regressor_code(
+            regressor_code, model_code)
+        self.final_model = regressor_code[self.pivv]
+        # self.pivv = model_index
+
+        # to use in the predict function
+        self.n_terms = self.final_model.shape[0]
+        if not self.estimate_parameter:
+            self.theta = theta
+            self.err = self.n_terms*[0]
+            # self.pivv = list(range(1, self.n_terms+1))
+        else:
+            psi = self.build_information_matrix(
+                X_train, y_train, self.xlag, self.ylag, self.non_degree)
+            psi = psi[:, self.pivv]
+
+            parameter_estimation = Estimators(
+                aux_lag=self.max_lag,
+                lam=self._lam,
+                delta=self._delta,
+                offset_covariance=self._offset_covariance,
+                mu=self._mu,
+                eps=self._eps,
+                gama=self._gama,
+                weight=self._weight,
+            )
+            _, self.err, self.pivv, _ = self.error_reduction_ratio(psi,
+                                                                   y_train, self.n_terms)
+            self.theta = getattr(parameter_estimation, self.estimator)(
+                psi,  y_train)
+
+        yhat = self.predict(X_test, y_test, steps_ahead)
+        results = self.results(err_precision=8, dtype='dec')
+
+        if plot:
+            ee, ex, _, _ = self.residuals(X_test, y_test, yhat)
+            self.plot_result(y_test, yhat, ee, ex)
+        return yhat, results
diff --git a/sysidentpy/polynomial_basis/tests/test_simulation.py b/sysidentpy/polynomial_basis/tests/test_simulation.py
new file mode 100644
index 00000000..3c154117
--- /dev/null
+++ b/sysidentpy/polynomial_basis/tests/test_simulation.py
@@ -0,0 +1,123 @@
+from numpy.testing._private.utils import assert_allclose
+from sysidentpy.polynomial_basis import PolynomialNarmax
+from sysidentpy.utils.generate_data import get_miso_data, get_siso_data
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_array_equal
+from numpy.testing import assert_raises
+from sysidentpy.polynomial_basis import SimulatePolynomialNarmax
+
+
+def test_get_index_from_regressor_code():
+    s = SimulatePolynomialNarmax()
+    model = np.array(
+        [
+            [1001,    0],  # y(k-1)
+            [2001, 1001],  # x1(k-1)y(k-1)
+            [2002,    0],  # x1(k-2)
+        ])
+
+    regressor_space = np.array([[   0,    0],
+       [1001,    0],
+       [2001,    0],
+       [2002,    0],
+       [1001, 1001],
+       [2001, 1001],
+       [2002, 1001],
+       [2001, 2001],
+       [2002, 2001],
+       [2002, 2002]])
+    index = s._get_index_from_regressor_code(regressor_code=regressor_space, model_code=model)
+
+    assert (index == np.array([1, 3, 5])).all()
+
+def test_list_output_regressor():
+    s = SimulatePolynomialNarmax()
+    model = np.array(
+        [
+            [1001,    0],  # y(k-1)
+            [2001, 1001],  # x1(k-1)y(k-1)
+            [2002,    0],  # x1(k-2)
+        ])
+
+    y_code = s._list_output_regressor_code(model)
+    assert (y_code == np.array([1001, 1001])).all()
+    
+def test_list_input_regressor():
+    s = SimulatePolynomialNarmax()
+    model = np.array(
+        [
+            [1001,    0],  # y(k-1)
+            [2001, 1001],  # x1(k-1)y(k-1)
+            [2002,    0],  # x1(k-2)
+        ])
+
+    x_code = s._list_input_regressor_code(model)
+    assert (x_code == np.array([2001, 2002])).all()
+
+def test_get_lag_from_regressor_code():
+    s = SimulatePolynomialNarmax()
+    list_regressor1 = np.array([2001, 2002])
+    list_regressor2 = np.array([1004, 1002])
+    max_lag1 = s._get_lag_from_regressor_code(list_regressor1)
+    max_lag2 = s._get_lag_from_regressor_code(list_regressor2)
+
+    assert max_lag1 == 2
+    assert max_lag2 == 4
+
+def test_simulate():
+    x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,
+                                                   colored_noise=False,
+                                                   sigma=0.001,
+                                                   train_percentage=90)
+
+    s = SimulatePolynomialNarmax()
+
+    # the model must be a numpy array
+    model = np.array(
+        [
+        [1001,    0], # y(k-1)
+        [2001, 1001], # x1(k-1)y(k-1)
+        [2002,    0], # x1(k-2)
+        ]
+                    )
+    # theta must be a numpy array of shape (n, 1) where n is the number of regressors
+    theta = np.array([[0.2, 0.9, 0.1]]).T
+
+    yhat, results = s.simulate(
+        X_test=x_valid,
+        y_test=y_valid,
+        model_code=model,
+        theta=theta,
+        plot=False)
+    assert yhat.shape == (100, 1)
+    assert len(results) == 3
+    
+def test_simulate_theta():
+    x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,
+                                                   colored_noise=False,
+                                                   sigma=0.001,
+                                                   train_percentage=90)
+
+    s = SimulatePolynomialNarmax(estimate_parameter=True)
+
+    # the model must be a numpy array
+    model = np.array(
+        [
+        [1001,    0], # y(k-1)
+        [2001, 1001], # x1(k-1)y(k-1)
+        [2002,    0], # x1(k-2)
+        ]
+                    )
+
+    yhat, results = s.simulate(
+        X_train=x_train,
+        y_train=y_train,
+        X_test=x_valid,
+        y_test=y_valid,
+        model_code=model,
+        plot=False)
+    theta = np.array([[0.2, 0.9, 0.1]]).T
+    assert_almost_equal(s.theta, theta, decimal=1)
+
+def test_estimate_parameter():
+    assert_raises(TypeError, SimulatePolynomialNarmax, estimmate_parameter=1)

From afcb2e9ac56d6dcb917c8b1f7b97ea3f9a122779 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:16:16 -0300
Subject: [PATCH 25/27] added catboost folder in gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 11c04f99..addc909d 100755
--- a/.gitignore
+++ b/.gitignore
@@ -35,3 +35,4 @@ pip-wheel-metadata
 # default pytest cache directory
 */.pytest_cache
 .pytest_cache/
+catboost_info/

From 49485654bed8d3062884fb8d15c22db6365113ae Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:25:51 -0300
Subject: [PATCH 26/27] Added changes in Changelog

---
 CHANGELOG                        | 11 ++++++++++-
 docs/source/changelog/v0.1.5.rst | 14 ++++++++++++--
 docs/source/code.rst             |  5 +++++
 3 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG b/CHANGELOG
index 4a0a2beb..2cbb2bab 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -17,10 +17,19 @@ CHANGES
     - Now you can define the numbers of steps ahead in the predict function.
 	- Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
 
+- MAJOR: Simulating predefined models.
+    - Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.
+    - Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.
 
 - Added 4 new notebooks in the example section.
 
-- Added iterative notebooks.
+- Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.
+
+- Fix code format issues.
+
+- Added new tests for SimulatePolynomialNarmax and generate_data.
+
+- Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.
 
 - Fix issues related to html on Jupyter notebooks examples on documentation.
 
diff --git a/docs/source/changelog/v0.1.5.rst b/docs/source/changelog/v0.1.5.rst
index 14bfbfd6..a7a58ce0 100644
--- a/docs/source/changelog/v0.1.5.rst
+++ b/docs/source/changelog/v0.1.5.rst
@@ -14,11 +14,21 @@ CHANGES
 
 - MAJOR: n-steps-ahead prediction.
     - Now you can define the numbers of steps ahead in the predict function.
-	- only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+	- Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+
+- MAJOR: Simulating predefined models.
+    - Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.
+    - Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.
 
 - Added 4 new notebooks in the example section.
 
-- Added iterative notebooks.
+- Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.
+
+- Fix code format issues.
+
+- Added new tests for SimulatePolynomialNarmax and generate_data.
+
+- Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.
 
 - Fix issues related to html on Jupyter notebooks examples on documentation.
 
diff --git a/docs/source/code.rst b/docs/source/code.rst
index 91bb9a94..d7fea24a 100644
--- a/docs/source/code.rst
+++ b/docs/source/code.rst
@@ -15,6 +15,11 @@ sysidentpy narmax
 .. automodule:: sysidentpy.polynomial_basis.narmax
    :members:
 
+sysidentpy simulation
+===============
+.. automodule:: sysidentpy.polynomial_basis.simulation
+   :members:
+
 sysidentpy narx_neural_network
 ===============
 .. automodule:: sysidentpy.narx_neural_network.narx_nn

From d74e7bfc8fa778cbbf33edde5f8d646ba1f30f32 Mon Sep 17 00:00:00 2001
From: Wilson Rocha <wilsonrljr@outlook.com>
Date: Sun, 14 Mar 2021 11:38:39 -0300
Subject: [PATCH 27/27] Updated documentation with the new examples and methods

---
 .../simulating_a_predefined_model_14_0.svg    | 2149 +++++++++++++++++
 .../simulating_a_predefined_model_15_0.svg    | 2149 +++++++++++++++++
 .../simulating_a_predefined_model_16_0.svg    | 2149 +++++++++++++++++
 .../simulating_a_predefined_model_8_0.svg     | 2149 +++++++++++++++++
 docs/_modules/index.html                      |    6 +
 docs/_modules/sysidentpy/base.html            |   33 +-
 .../sysidentpy/general_estimators/narx.html   |   56 +-
 .../sysidentpy/metrics/_regression.html       |   14 +-
 .../parameter_estimation/estimators.html      |   32 +-
 .../sysidentpy/polynomial_basis/narmax.html   |   31 +-
 .../polynomial_basis/simulation.html          |  601 +++++
 .../residues/residues_correlation.html        |   29 +-
 .../sysidentpy/utils/_check_arrays.html       |    5 +
 .../sysidentpy/utils/generate_data.html       |    5 +
 docs/_sources/changelog/v0.1.5.rst.txt        |   14 +-
 docs/_sources/code.rst.txt                    |    5 +
 .../simulating_a_predefined_model.ipynb.txt   |  631 ++---
 docs/changelog/v0.1.3.html                    |    5 +
 docs/changelog/v0.1.4.html                    |    5 +
 docs/changelog/v0.1.5.html                    |   20 +-
 docs/code.html                                |   57 +-
 docs/dev_guide.html                           |    5 +
 docs/examples/basic_steps.html                |    5 +
 docs/examples/defining_lags.html              |    5 +
 docs/examples/extended_least_squares.html     |    5 +
 docs/examples/f_16_benchmark.html             |    5 +
 docs/examples/general_estimators.html         |    5 +
 ...cation_of_an_electromechanical_system.html |    5 +
 .../information_criteria_examples.html        |    5 +
 docs/examples/multiple_inputs_example.html    |    5 +
 docs/examples/n_steps_ahead_prediction.html   |    5 +
 docs/examples/narx_neural_network.html        |    5 +
 docs/examples/parameter_estimation.html       |    5 +
 .../simulating_a_predefined_model.html        |  566 ++---
 ...entification_using_adaptative_filters.html |    5 +
 docs/genindex.html                            |   18 +
 docs/index.html                               |    6 +
 docs/installation.html                        |    5 +
 docs/introduction_to_narmax.html              |    5 +
 docs/notebooks.html                           |    5 +
 docs/objects.inv                              |  Bin 1563 -> 1586 bytes
 docs/py-modindex.html                         |   10 +
 docs/search.html                              |    5 +
 docs/searchindex.js                           |    2 +-
 docs/user_guide.html                          |    5 +
 45 files changed, 9992 insertions(+), 845 deletions(-)
 create mode 100644 docs/_images/simulating_a_predefined_model_14_0.svg
 create mode 100644 docs/_images/simulating_a_predefined_model_15_0.svg
 create mode 100644 docs/_images/simulating_a_predefined_model_16_0.svg
 create mode 100644 docs/_images/simulating_a_predefined_model_8_0.svg
 create mode 100644 docs/_modules/sysidentpy/polynomial_basis/simulation.html

diff --git a/docs/_images/simulating_a_predefined_model_14_0.svg b/docs/_images/simulating_a_predefined_model_14_0.svg
new file mode 100644
index 00000000..451b376a
--- /dev/null
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diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index 1d8912b8..0317c9f4 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -320,6 +325,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="sysidentpy/metrics/_regression.html">sysidentpy.metrics._regression</a></li>
 <li><a href="sysidentpy/parameter_estimation/estimators.html">sysidentpy.parameter_estimation.estimators</a></li>
 <li><a href="sysidentpy/polynomial_basis/narmax.html">sysidentpy.polynomial_basis.narmax</a></li>
+<li><a href="sysidentpy/polynomial_basis/simulation.html">sysidentpy.polynomial_basis.simulation</a></li>
 <li><a href="sysidentpy/residues/residues_correlation.html">sysidentpy.residues.residues_correlation</a></li>
 <li><a href="sysidentpy/utils/_check_arrays.html">sysidentpy.utils._check_arrays</a></li>
 <li><a href="sysidentpy/utils/generate_data.html">sysidentpy.utils.generate_data</a></li>
diff --git a/docs/_modules/sysidentpy/base.html b/docs/_modules/sysidentpy/base.html
index 74651808..af4a2c26 100644
--- a/docs/_modules/sysidentpy/base.html
+++ b/docs/_modules/sysidentpy/base.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -405,20 +410,17 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
             <span class="p">)</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ylag</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">ylag</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;ylag must be integer or list and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">ylag</span><span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;ylag must be integer or list and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">ylag</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="p">(</span>
             <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">xlag</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span>
             <span class="c1"># or np.min(np.minimum(xlag, 1)) &lt; 1):</span>
             <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">([[</span><span class="n">xlag</span><span class="p">]]))))</span> <span class="o">&lt;</span> <span class="mi">1</span>
         <span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;xlag must be integer or list and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">xlag</span><span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;xlag must be integer or list and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">xlag</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n_inputs</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;n_inputs must be integer and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n_inputs</span><span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;n_inputs must be integer and &gt; zero. Got </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n_inputs</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ylag</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
             <span class="c1"># create only the lags passed from list</span>
@@ -444,8 +446,7 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">):</span>
                 <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">xlag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="n">n_inputs</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                     <span class="c1"># create 200n, 300n,..., 400n to describe each input</span>
-                    <span class="n">x_vec_tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-                        <span class="p">[</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">2000</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">1000</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">xlag</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
+                    <span class="n">x_vec_tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">lag</span> <span class="o">+</span> <span class="mi">2000</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">1000</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">xlag</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
                 <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">xlag</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">n_inputs</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                     <span class="n">x_vec_tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
                         <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2001</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2001</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">1000</span> <span class="o">+</span> <span class="n">xlag</span><span class="p">[</span><span class="n">i</span><span class="p">])]</span>
@@ -454,19 +455,17 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
         <span class="n">reg_aux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span>
         <span class="k">if</span> <span class="n">n_inputs</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="c1"># if x_vec is a nested list, ensure all elements are arrays</span>
-            <span class="n">all_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
-                <span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_vec_tmp</span><span class="p">]</span>
+            <span class="n">all_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">else</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_vec_tmp</span><span class="p">]</span>
             <span class="n">x_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">all_arrays</span><span class="p">])</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">x_vec</span> <span class="o">=</span> <span class="n">x_vec_tmp</span>
 
         <span class="n">reg_aux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">reg_aux</span><span class="p">,</span> <span class="n">y_vec</span><span class="p">,</span> <span class="n">x_vec</span><span class="p">])</span>
 
-        <span class="n">regressor_code</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-            <span class="n">combinations_with_replacement</span><span class="p">(</span><span class="n">reg_aux</span><span class="p">,</span> <span class="n">non_degree</span><span class="p">))</span>
+        <span class="n">regressor_code</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">combinations_with_replacement</span><span class="p">(</span><span class="n">reg_aux</span><span class="p">,</span> <span class="n">non_degree</span><span class="p">))</span>
 
         <span class="n">regressor_code</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">regressor_code</span><span class="p">)</span>
-        <span class="n">regressor_code</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[:,</span> <span class="n">regressor_code</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]::</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">regressor_code</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[:,</span> <span class="n">regressor_code</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">::</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">max_lag</span> <span class="o">=</span> <span class="n">_get_max_lag</span><span class="p">(</span><span class="n">ylag</span><span class="p">,</span> <span class="n">xlag</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span></div></div>
 
@@ -563,7 +562,7 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">n_samples</span> <span class="o">=</span> <span class="n">col_to_shift</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">tmp_column</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">aux</span> <span class="o">=</span> <span class="n">col_to_shift</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="n">n_samples</span> <span class="o">-</span> <span class="n">lag</span><span class="p">]</span>
+        <span class="n">aux</span> <span class="o">=</span> <span class="n">col_to_shift</span><span class="p">[</span><span class="mi">0</span> <span class="p">:</span> <span class="n">n_samples</span> <span class="o">-</span> <span class="n">lag</span><span class="p">]</span>
         <span class="n">aux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">aux</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">aux</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
         <span class="n">tmp_column</span><span class="p">[</span><span class="n">lag</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">aux</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">tmp_column</span></div>
@@ -670,8 +669,7 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
 <span class="sd">            without combinations.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">y_lagged</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span>
-            <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">shift_column</span><span class="p">(</span><span class="n">y</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lag</span><span class="p">)</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">ylag</span><span class="p">])</span>
+        <span class="n">y_lagged</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">shift_column</span><span class="p">(</span><span class="n">y</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lag</span><span class="p">)</span> <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">ylag</span><span class="p">])</span>
         <span class="k">return</span> <span class="n">y_lagged</span>
 
 <div class="viewcode-block" id="InformationMatrix.initial_lagged_matrix"><a class="viewcode-back" href="../../code.html#sysidentpy.base.InformationMatrix.initial_lagged_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">initial_lagged_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">xlag</span><span class="p">,</span> <span class="n">ylag</span><span class="p">):</span>
@@ -754,8 +752,7 @@ <h1>Source code for sysidentpy.base</h1><div class="highlight"><pre>
 
         <span class="c1"># Create combinations of all columns based on its index</span>
         <span class="n">iterable_list</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">combinations</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">combinations_with_replacement</span><span class="p">(</span>
-            <span class="n">iterable_list</span><span class="p">,</span> <span class="n">non_degree</span><span class="p">))</span>
+        <span class="n">combinations</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">combinations_with_replacement</span><span class="p">(</span><span class="n">iterable_list</span><span class="p">,</span> <span class="n">non_degree</span><span class="p">))</span>
 
         <span class="n">psi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span>
             <span class="p">[</span>
diff --git a/docs/_modules/sysidentpy/general_estimators/narx.html b/docs/_modules/sysidentpy/general_estimators/narx.html
index aeb97c8c..12478770 100644
--- a/docs/_modules/sysidentpy/general_estimators/narx.html
+++ b/docs/_modules/sysidentpy/general_estimators/narx.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -330,14 +335,17 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
 <span class="kn">from</span> <span class="nn">..utils._check_arrays</span> <span class="kn">import</span> <span class="n">check_X_y</span>
 
 <span class="kn">import</span> <span class="nn">sys</span>
-<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> - </span><span class="si">%(levelname)s</span><span class="s1"> - </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span>
-                    <span class="n">datefmt</span><span class="o">=</span><span class="s1">&#39;%m-</span><span class="si">%d</span><span class="s1"> %H:%M:%S&#39;</span><span class="p">,</span>
-                    <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
-                    <span class="n">stream</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span>
+    <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(asctime)s</span><span class="s2"> - </span><span class="si">%(levelname)s</span><span class="s2"> - </span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span>
+    <span class="n">datefmt</span><span class="o">=</span><span class="s2">&quot;%m-</span><span class="si">%d</span><span class="s2"> %H:%M:%S&quot;</span><span class="p">,</span>
+    <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
+    <span class="n">stream</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">,</span>
+<span class="p">)</span>
 
 
 <div class="viewcode-block" id="NARX"><a class="viewcode-back" href="../../../code.html#sysidentpy.general_estimators.narx.NARX">[docs]</a><span class="k">class</span> <span class="nc">NARX</span><span class="p">(</span><span class="n">GenerateRegressors</span><span class="p">,</span> <span class="n">InformationMatrix</span><span class="p">,</span> <span class="n">ResiduesAnalysis</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; NARX model build on top of general estimators</span>
+    <span class="sd">&quot;&quot;&quot;NARX model build on top of general estimators</span>
 
 <span class="sd">    Currently is possible to use any estimator that have a fit/predict</span>
 <span class="sd">    as an Autoregressive Model. We use our GenerateRegressors and</span>
@@ -392,13 +400,14 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
         <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
         <span class="n">n_inputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="n">base_estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">fit_params</span><span class="o">=</span><span class="p">{}</span>
+        <span class="n">fit_params</span><span class="o">=</span><span class="p">{},</span>
     <span class="p">):</span>
+
         <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span> <span class="o">=</span> <span class="n">non_degree</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span> <span class="o">=</span> <span class="n">ylag</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span> <span class="o">=</span> <span class="n">xlag</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_n_inputs</span> <span class="o">=</span> <span class="n">n_inputs</span>
-        <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">regressor_code</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">GenerateRegressors</span><span class="p">()</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span>
+        <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">regressor_code</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span>
             <span class="n">non_degree</span><span class="p">,</span> <span class="n">xlag</span><span class="p">,</span> <span class="n">ylag</span><span class="p">,</span> <span class="n">n_inputs</span>
         <span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">regressor_code</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">regressor_code</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
@@ -432,13 +441,15 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
 <span class="sd">            The information matrix of the model.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Creating the regressor matrix&quot;</span><span class="p">)</span>
-        <span class="n">reg_matrix</span> <span class="o">=</span> <span class="n">InformationMatrix</span><span class="p">()</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span>
-                                                                  <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span><span class="p">)</span>
-        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;The regressor matrix have &quot;</span> <span class="o">+</span>
-                     <span class="nb">str</span><span class="p">(</span><span class="n">reg_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot; features&quot;</span><span class="p">)</span>
+        <span class="n">reg_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span>
+            <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="s2">&quot;The regressor matrix have &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">reg_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot; features&quot;</span>
+        <span class="p">)</span>
         <span class="n">reg_matrix</span> <span class="o">=</span> <span class="n">reg_matrix</span>
 
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:]</span>
         <span class="k">return</span> <span class="n">reg_matrix</span><span class="p">,</span> <span class="n">y</span></div>
 
 <div class="viewcode-block" id="NARX.fit"><a class="viewcode-back" href="../../../code.html#sysidentpy.general_estimators.narx.NARX.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -466,10 +477,10 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
 
         <span class="n">check_X_y</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 
-        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Training the model&#39;</span><span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Training the model&quot;</span><span class="p">)</span>
         <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_preparation</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">base_estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_params</span><span class="p">)</span>
-        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Done! Model is built!&#39;</span><span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Done! Model is built!&quot;</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="NARX.predict"><a class="viewcode-back" href="../../../code.html#sysidentpy.general_estimators.narx.NARX.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y_initial</span><span class="p">):</span>
@@ -501,19 +512,20 @@ <h1>Source code for sysidentpy.general_estimators.narx</h1><div class="highlight
         <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
 
         <span class="c1"># Discard unnecessary initial values</span>
-        <span class="n">yhat</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_initial</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="n">yhat</span><span class="p">[</span><span class="mi">0</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_initial</span><span class="p">[</span><span class="mi">0</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
         <span class="n">analised_elements_number</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
 
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
-            <span class="n">reg_matrix</span> <span class="o">=</span> <span class="n">InformationMatrix</span><span class="p">()</span><span class="o">.</span>\
-                <span class="n">build_information_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">analised_elements_number</span><span class="p">],</span>
-                                         <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">analised_elements_number</span><span class="p">],</span>
-                                         <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span>
-                                         <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span>
-                                         <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span><span class="p">)</span>
+            <span class="n">reg_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">analised_elements_number</span><span class="p">],</span>
+                <span class="n">yhat</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">analised_elements_number</span><span class="p">],</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span><span class="p">,</span>
+            <span class="p">)</span>
 
             <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reg_matrix</span><span class="p">)</span>
-            <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">yhat</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">yhat</span></div></div>
 </pre></div>
 
diff --git a/docs/_modules/sysidentpy/metrics/_regression.html b/docs/_modules/sysidentpy/metrics/_regression.html
index 1c9c9b79..4d1cadb4 100644
--- a/docs/_modules/sysidentpy/metrics/_regression.html
+++ b/docs/_modules/sysidentpy/metrics/_regression.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -664,8 +669,7 @@ <h1>Source code for sysidentpy.metrics._regression</h1><div class="highlight"><p
     <span class="n">nonzero_denominator</span> <span class="o">=</span> <span class="n">denominator</span> <span class="o">!=</span> <span class="mi">0</span>
     <span class="n">valid_score</span> <span class="o">=</span> <span class="n">nonzero_numerator</span> <span class="o">&amp;</span> <span class="n">nonzero_denominator</span>
     <span class="n">output_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">output_scores</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> \
-        <span class="p">(</span><span class="n">numerator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">])</span>
+    <span class="n">output_scores</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">numerator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">])</span>
     <span class="n">output_scores</span><span class="p">[</span><span class="n">nonzero_numerator</span> <span class="o">&amp;</span> <span class="o">~</span><span class="n">nonzero_denominator</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">output_scores</span><span class="p">)</span></div>
 
@@ -705,14 +709,12 @@ <h1>Source code for sysidentpy.metrics._regression</h1><div class="highlight"><p
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">numerator</span> <span class="o">=</span> <span class="p">((</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_predicted</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-    <span class="n">denominator</span> <span class="o">=</span> <span class="p">((</span><span class="n">y</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="o">**</span>
-                   <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+    <span class="n">denominator</span> <span class="o">=</span> <span class="p">((</span><span class="n">y</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
     <span class="n">nonzero_denominator</span> <span class="o">=</span> <span class="n">denominator</span> <span class="o">!=</span> <span class="mi">0</span>
     <span class="n">nonzero_numerator</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">!=</span> <span class="mi">0</span>
     <span class="n">valid_score</span> <span class="o">=</span> <span class="n">nonzero_denominator</span> <span class="o">&amp;</span> <span class="n">nonzero_numerator</span>
     <span class="n">output_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-    <span class="n">output_scores</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> \
-        <span class="p">(</span><span class="n">numerator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">])</span>
+    <span class="n">output_scores</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">numerator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">]</span> <span class="o">/</span> <span class="n">denominator</span><span class="p">[</span><span class="n">valid_score</span><span class="p">])</span>
     <span class="c1"># arbitrary set to zero to avoid -inf scores, having a constant</span>
     <span class="c1"># y_true is not interesting for scoring a regression anyway</span>
     <span class="n">output_scores</span><span class="p">[</span><span class="n">nonzero_numerator</span> <span class="o">&amp;</span> <span class="o">~</span><span class="n">nonzero_denominator</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
diff --git a/docs/_modules/sysidentpy/parameter_estimation/estimators.html b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
index 7ccf0ae7..fac0cfb4 100644
--- a/docs/_modules/sysidentpy/parameter_estimation/estimators.html
+++ b/docs/_modules/sysidentpy/parameter_estimation/estimators.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -431,7 +436,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_linear_dependence_rows</span><span class="p">(</span><span class="n">psi</span><span class="p">)</span>
 
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">theta</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">psi</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">psi</span><span class="p">))</span> <span class="o">@</span> <span class="n">psi</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y</span>
         <span class="k">return</span> <span class="n">theta</span></div>
 
@@ -463,7 +468,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
 <span class="sd">            https://en.wikipedia.org/wiki/Total_least_squares</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">full</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">psi</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
         <span class="n">n</span> <span class="o">=</span> <span class="n">psi</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">full</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -471,7 +476,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
         <span class="k">return</span> <span class="n">theta</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
 
     <span class="k">def</span> <span class="nf">_initial_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">psi</span><span class="p">):</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_lag</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">n_theta</span> <span class="o">=</span> <span class="n">psi</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">psi</span><span class="p">)</span>
         <span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">n_theta</span><span class="p">,</span> <span class="n">n</span><span class="p">])</span>
@@ -514,15 +519,12 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
             <span class="n">psi_tmp</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
             <span class="n">k_numerator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lam</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">p</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi_tmp</span><span class="p">)</span>
-            <span class="n">k_denominator</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> \
-                <span class="bp">self</span><span class="o">.</span><span class="n">_lam</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi_tmp</span><span class="p">)</span>
+            <span class="n">k_denominator</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lam</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi_tmp</span><span class="p">)</span>
             <span class="n">k</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">k_numerator</span><span class="p">,</span> <span class="n">k_denominator</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
+            <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
 
-            <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                       <span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+            <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
             <span class="n">p2</span> <span class="o">=</span> <span class="p">(</span>
                 <span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
                 <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lam</span>
@@ -572,8 +574,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
                     <span class="n">psi</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">psi</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset_covariance</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_theta</span><span class="p">)</span>
                 <span class="p">)</span>
             <span class="p">)</span>
-            <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">aux</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">))</span>
+            <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">aux</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">))</span>
 
         <span class="k">return</span> <span class="n">theta</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
 
@@ -615,8 +616,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
             <span class="n">psi_tmp</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
             <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">psi_tmp</span>
+                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">psi_tmp</span>
             <span class="p">)</span>
 
         <span class="k">return</span> <span class="n">theta</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
@@ -1008,8 +1008,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
             <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
             <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
                 <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gama</span><span class="p">)</span>
-                <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">psi_tmp</span> <span class="o">/</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_eps</span> <span class="o">+</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">psi_tmp</span><span class="p">)</span>
+                <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">psi_tmp</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_eps</span> <span class="o">+</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">psi_tmp</span><span class="p">)</span>
             <span class="p">)</span>
 
         <span class="k">return</span> <span class="n">theta</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
@@ -1112,8 +1111,7 @@ <h1>Source code for sysidentpy.parameter_estimation.estimators</h1><div class="h
             <span class="n">psi_tmp</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">psi_tmp</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
             <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="n">psi_tmp</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">3</span>
+                <span class="n">theta</span><span class="p">[:,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mu</span> <span class="o">*</span> <span class="n">psi_tmp</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">xi</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">3</span>
             <span class="p">)</span>
 
         <span class="k">return</span> <span class="n">theta</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
diff --git a/docs/_modules/sysidentpy/polynomial_basis/narmax.html b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
index 989ab8aa..052c1e30 100644
--- a/docs/_modules/sysidentpy/polynomial_basis/narmax.html
+++ b/docs/_modules/sysidentpy/polynomial_basis/narmax.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -339,7 +344,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 <div class="viewcode-block" id="PolynomialNarmax"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.narmax.PolynomialNarmax">[docs]</a><span class="k">class</span> <span class="nc">PolynomialNarmax</span><span class="p">(</span>
     <span class="n">GenerateRegressors</span><span class="p">,</span> <span class="n">HouseHolder</span><span class="p">,</span> <span class="n">InformationMatrix</span><span class="p">,</span> <span class="n">ResiduesAnalysis</span><span class="p">,</span> <span class="n">Estimators</span>
 <span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Polynomial NARXMAX model</span>
+    <span class="sd">&quot;&quot;&quot;Polynomial NARXMAX model</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -443,14 +448,14 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
         <span class="n">n_terms</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">n_inputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="n">n_info_values</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;least_squares&quot;</span><span class="p">,</span>
+        <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;recursive_least_squares&quot;</span><span class="p">,</span>
         <span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
         <span class="n">aux_lag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
         <span class="n">lam</span><span class="o">=</span><span class="mf">0.98</span><span class="p">,</span>
         <span class="n">delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
         <span class="n">offset_covariance</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
         <span class="n">mu</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
-        <span class="n">eps</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span>
+        <span class="n">eps</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span>
         <span class="n">gama</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
         <span class="n">weight</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
     <span class="p">):</span>
@@ -476,7 +481,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
         <span class="bp">self</span><span class="o">.</span><span class="n">_lam</span> <span class="o">=</span> <span class="n">lam</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_delta</span> <span class="o">=</span> <span class="n">delta</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_gama</span> <span class="o">=</span> <span class="n">gama</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_weight</span> <span class="o">=</span> <span class="n">weight</span>  <span class="c1"># &lt;0  e &lt;1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weight</span> <span class="o">=</span> <span class="n">weight</span>  <span class="c1"># 0 &lt; weight &lt;1</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_validate_params</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">_validate_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -757,6 +762,8 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 <span class="sd">            The input data to be used in the prediction process.</span>
 <span class="sd">        y : ndarray of floats</span>
 <span class="sd">            The output data to be used in the prediction process.</span>
+<span class="sd">        steps_ahead = int (default = None)</span>
+<span class="sd">            The forecast horizon.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -769,7 +776,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
         <span class="k">elif</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_one_step_ahead_prediction</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_check_positive_int</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">,</span> <span class="s1">&#39;steps_ahead&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_positive_int</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">,</span> <span class="s2">&quot;steps_ahead&quot;</span><span class="p">)</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_step_ahead_prediction</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="o">=</span><span class="n">steps_ahead</span><span class="p">)</span></div>
 
     <span class="k">def</span> <span class="nf">_code2exponents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">code</span><span class="p">):</span>
@@ -808,9 +815,7 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
 
     <span class="k">def</span> <span class="nf">_check_positive_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">value</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> must be integer and &gt; zero. Got </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> must be integer and &gt; zero. Got </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_one_step_ahead_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Perform the 1-step-ahead prediction of a model.</span>
@@ -832,10 +837,10 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
         <span class="n">X_base</span> <span class="o">=</span> <span class="n">InformationMatrix</span><span class="p">()</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span>
             <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span>
         <span class="p">)</span>
-        <span class="n">piv_final_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">)]</span>
+        <span class="n">piv_final_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="p">)]</span>
         <span class="n">X_base</span> <span class="o">=</span> <span class="n">X_base</span><span class="p">[:,</span> <span class="n">piv_final_model</span><span class="p">]</span>
         <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X_base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-        <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">yhat</span><span class="p">])</span>
+        <span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">y</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">yhat</span><span class="p">])</span>
         <span class="k">return</span> <span class="n">yhat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_n_step_ahead_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">):</span>
@@ -868,9 +873,9 @@ <h1>Source code for sysidentpy.polynomial_basis.narmax</h1><div class="highlight
             <span class="k">if</span> <span class="n">i</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
                 <span class="n">steps_ahead</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">i</span>  <span class="c1"># predicts the remaining values</span>
 
-            <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span>
-                <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">],</span>
-                <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">steps_ahead</span><span class="p">])[</span><span class="o">-</span><span class="n">steps_ahead</span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+            <span class="n">yhat</span><span class="p">[</span><span class="n">i</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_prediction</span><span class="p">(</span>
+                <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">steps_ahead</span><span class="p">]</span>
+            <span class="p">)[</span><span class="o">-</span><span class="n">steps_ahead</span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
 
             <span class="n">i</span> <span class="o">+=</span> <span class="n">steps_ahead</span>
 
diff --git a/docs/_modules/sysidentpy/polynomial_basis/simulation.html b/docs/_modules/sysidentpy/polynomial_basis/simulation.html
new file mode 100644
index 00000000..fe38cbbf
--- /dev/null
+++ b/docs/_modules/sysidentpy/polynomial_basis/simulation.html
@@ -0,0 +1,601 @@
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+                
+  <h1>Source code for sysidentpy.polynomial_basis.simulation</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation.estimators</span> <span class="kn">import</span> <span class="n">Estimators</span>
+<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">GenerateRegressors</span>
+<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">InformationMatrix</span>
+<span class="kn">from</span> <span class="nn">.narmax</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">..residues.residues_correlation</span> <span class="kn">import</span> <span class="n">ResiduesAnalysis</span>
+<span class="kn">from</span> <span class="nn">..utils._check_arrays</span> <span class="kn">import</span> <span class="n">check_X_y</span>
+
+
+<div class="viewcode-block" id="SimulatePolynomialNarmax"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax">[docs]</a><span class="k">class</span> <span class="nc">SimulatePolynomialNarmax</span><span class="p">(</span><span class="n">PolynomialNarmax</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n_inputs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">estimator</span><span class="o">=</span><span class="s2">&quot;recursive_least_squares&quot;</span><span class="p">,</span>
+        <span class="n">extended_least_squares</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">lam</span><span class="o">=</span><span class="mf">0.98</span><span class="p">,</span>
+        <span class="n">delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
+        <span class="n">offset_covariance</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+        <span class="n">mu</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
+        <span class="n">eps</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span>
+        <span class="n">gama</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+        <span class="n">weight</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
+        <span class="n">estimate_parameter</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">,</span>
+                         <span class="n">estimator</span><span class="o">=</span><span class="n">estimator</span><span class="p">,</span>
+                         <span class="n">extended_least_squares</span><span class="o">=</span><span class="n">extended_least_squares</span><span class="p">,</span>
+                         <span class="n">lam</span><span class="o">=</span><span class="n">lam</span><span class="p">,</span>
+                         <span class="n">delta</span><span class="o">=</span><span class="n">delta</span><span class="p">,</span>
+                         <span class="n">offset_covariance</span><span class="o">=</span><span class="n">offset_covariance</span><span class="p">,</span>
+                         <span class="n">mu</span><span class="o">=</span><span class="n">mu</span><span class="p">,</span>
+                         <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span>
+                         <span class="n">gama</span><span class="o">=</span><span class="n">gama</span><span class="p">,</span>
+                         <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span> <span class="o">=</span> <span class="n">estimate_parameter</span>
+
+    <span class="k">def</span> <span class="nf">_validate_simulate_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;estimate_parameter must be False or True. Got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_index_from_regressor_code</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">regressor_code</span><span class="p">,</span> <span class="n">model_code</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Get the index of user regressor in regressor space.</span>
+
+<span class="sd">        Took from: https://stackoverflow.com/questions/38674027/find-the-row-indexes-of-several-values-in-a-numpy-array/38674038#38674038</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        regressor_code : ndarray of int</span>
+<span class="sd">            Matrix codification of all possible regressors.</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Model defined by the user to simulate.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        model_index : ndarray of int</span>
+<span class="sd">            Index of model code in the regressor space.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dims</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
+        <span class="n">model_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel_multi_index</span><span class="p">(</span><span class="n">regressor_code</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">dims</span><span class="p">),</span>
+                                       <span class="n">np</span><span class="o">.</span><span class="n">ravel_multi_index</span><span class="p">(</span><span class="n">model_code</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">dims</span><span class="p">)))[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">model_index</span>
+
+    <span class="k">def</span> <span class="nf">_list_output_regressor_code</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_code</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Create a flattened array of input or output regressors.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Model defined by the user to simulate.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Flattened list of output regressors.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">regressor_code</span> <span class="o">=</span> <span class="p">[</span><span class="n">code</span> <span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <span class="n">model_code</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> <span class="k">if</span> <span class="p">(</span>
+            <span class="n">code</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">code</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;1&#39;</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regressor_code</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_list_input_regressor_code</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_code</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Create a flattened array of input or output regressors.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Model defined by the user to simulate.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Flattened list of output regressors.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">regressor_code</span> <span class="o">=</span> <span class="p">[</span><span class="n">code</span> <span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <span class="n">model_code</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> <span class="k">if</span> <span class="p">(</span>
+            <span class="n">code</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">code</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;1&#39;</span><span class="p">)]</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regressor_code</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_lag_from_regressor_code</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">regressors</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Get the maximum lag from array of regressors.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        regressors : ndarray of int</span>
+<span class="sd">            Flattened list of input or output regressors.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        max_lag : int</span>
+<span class="sd">            Maximum lag of list of regressors.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">lag_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">regressors</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;str&#39;</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">:]))]]</span>
+        <span class="k">return</span> <span class="nb">max</span><span class="p">(</span><span class="n">lag_list</span><span class="p">)</span>
+
+<div class="viewcode-block" id="SimulatePolynomialNarmax.simulate"><a class="viewcode-back" href="../../../code.html#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">X_train</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">y_train</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">X_test</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">y_test</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">model_code</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">steps_ahead</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">theta</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Simulate a model defined by the user.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        X_train : ndarray of floats</span>
+<span class="sd">            The input data to be used in the training process.</span>
+<span class="sd">        y_train : ndarray of floats</span>
+<span class="sd">            The output data to be used in the training process.</span>
+<span class="sd">        X_test : ndarray of floats</span>
+<span class="sd">            The input data to be used in the prediction process.</span>
+<span class="sd">        y_test : ndarray of floats</span>
+<span class="sd">            The output data (initial conditions) to be used in the prediction process.</span>
+<span class="sd">        model_code : ndarray of int</span>
+<span class="sd">            Flattened list of input or output regressors.</span>
+<span class="sd">        steps_ahead = int, default = None</span>
+<span class="sd">            The forecast horizon.</span>
+<span class="sd">        theta : array-like of shape = number_of_model_elements</span>
+<span class="sd">            The parameters of the model.</span>
+<span class="sd">        plot : bool, default=True</span>
+<span class="sd">            Indicate if the user wants to plot or not.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        yhat : ndarray of floats</span>
+<span class="sd">            The predicted values of the model.</span>
+<span class="sd">        results : string</span>
+<span class="sd">            Where:</span>
+<span class="sd">                First column represents each regressor element;</span>
+<span class="sd">                Second column represents associated parameter;</span>
+<span class="sd">                Third column represents the error reduction ratio associated</span>
+<span class="sd">                to each regressor.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">y_test</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;y_test cannot be None&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model_code</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;model_code must be an np.np.ndarray. Got </span><span class="si">{</span><span class="n">model_code</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">steps_ahead</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;steps_ahead must be None or integer &gt; zero. Got </span><span class="si">{</span><span class="n">steps_ahead</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">plot</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;plot must be True or False. Got </span><span class="si">{</span><span class="n">plot</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;If estimate_parameter is False, theta must be an np.np.ndarray. Got </span><span class="si">{</span><span class="n">theta</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">check_X_y</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;If estimate_parameter is True, X_train and y_train must be an np.ndarray. Got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">X_train</span><span class="p">),</span> <span class="nb">type</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+            <span class="n">check_X_y</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">y_train</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;y_train cannot be None&quot;</span><span class="p">)</span>
+
+        <span class="n">xlag_code</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_input_regressor_code</span><span class="p">(</span><span class="n">model_code</span><span class="p">)</span>
+        <span class="n">ylag_code</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_output_regressor_code</span><span class="p">(</span><span class="n">model_code</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lag_from_regressor_code</span><span class="p">(</span><span class="n">xlag_code</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lag_from_regressor_code</span><span class="p">(</span><span class="n">ylag_code</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_inputs</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_inputs</span> <span class="o">*</span> <span class="p">[[</span><span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">]]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span> <span class="o">=</span> <span class="n">model_code</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">_</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_inputs</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_index_from_regressor_code</span><span class="p">(</span>
+            <span class="n">regressor_code</span><span class="p">,</span> <span class="n">model_code</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">]</span>
+        <span class="c1"># self.pivv = model_index</span>
+
+        <span class="c1"># to use in the predict function</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_model</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimate_parameter</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">theta</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_terms</span><span class="o">*</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="c1"># self.pivv = list(range(1, self.n_terms+1))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">psi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span>
+                <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">xlag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ylag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_degree</span><span class="p">)</span>
+            <span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">]</span>
+
+            <span class="n">parameter_estimation</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">(</span>
+                <span class="n">aux_lag</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span>
+                <span class="n">lam</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_lam</span><span class="p">,</span>
+                <span class="n">delta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_delta</span><span class="p">,</span>
+                <span class="n">offset_covariance</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_offset_covariance</span><span class="p">,</span>
+                <span class="n">mu</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_mu</span><span class="p">,</span>
+                <span class="n">eps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_eps</span><span class="p">,</span>
+                <span class="n">gama</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_gama</span><span class="p">,</span>
+                <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_weight</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">_</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">err</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pivv</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">error_reduction_ratio</span><span class="p">(</span><span class="n">psi</span><span class="p">,</span>
+                                                                   <span class="n">y_train</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_terms</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">parameter_estimation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">)(</span>
+                <span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">)</span>
+
+        <span class="n">yhat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">)</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+            <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">results</span></div></div>
+</pre></div>
+
+              </div>
+              
+        
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+
+        </div>
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+        </div>
+    </div>
+    <footer class="footer mt-5 mt-md-0">
+    <div class="container">
+      <p>
+        
+          By Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins<br/>
+        
+            &copy; Copyright 2020, Wilson Rocha, Luan Pascoal, Samuel Oliveira, Samir Martins.<br/>
+      </p>
+    </div>
+  </footer>
+</main>
+
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+  </body>
+</html>
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diff --git a/docs/_modules/sysidentpy/residues/residues_correlation.html b/docs/_modules/sysidentpy/residues/residues_correlation.html
index 8fdf3e08..3fb7648e 100644
--- a/docs/_modules/sysidentpy/residues/residues_correlation.html
+++ b/docs/_modules/sysidentpy/residues/residues_correlation.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -373,11 +378,9 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
         <span class="n">ye</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">e</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">yeline</span> <span class="o">=</span> <span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">e</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">-</span> <span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">e</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 
-        <span class="n">yeline_x2line</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_ccf</span><span class="p">(</span>
-            <span class="n">yeline</span><span class="p">,</span> <span class="n">x2line</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">unnormalized_e_acf</span><span class="p">))</span>
+        <span class="n">yeline_x2line</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_ccf</span><span class="p">(</span><span class="n">yeline</span><span class="p">,</span> <span class="n">x2line</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">unnormalized_e_acf</span><span class="p">))</span>
         <span class="n">lam</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">((</span><span class="n">ee</span> <span class="o">-</span> <span class="n">ee</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">((</span><span class="n">ye</span> <span class="o">-</span> <span class="n">ye</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="n">yeline_eeline</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_ccf</span><span class="p">(</span>
-            <span class="n">yeline</span><span class="p">,</span> <span class="n">eeline</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">unnormalized_e_acf</span><span class="p">))</span>
+        <span class="n">yeline_eeline</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_ccf</span><span class="p">(</span><span class="n">yeline</span><span class="p">,</span> <span class="n">eeline</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">unnormalized_e_acf</span><span class="p">))</span>
         <span class="k">return</span> <span class="p">(</span>
             <span class="n">e_acf</span><span class="p">,</span>
             <span class="n">xe_ccf</span><span class="p">,</span>
@@ -390,8 +393,7 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
 
         <span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_normalized_correlation</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
 
-        <span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]))</span> <span class="o">*</span> \
-            <span class="p">(</span><span class="mf">1.96</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">len_confidence</span><span class="p">))</span>
+        <span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]))</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.96</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">len_confidence</span><span class="p">))</span>
 
         <span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">xe_ccf</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">xe_ccf</span>
@@ -435,8 +437,7 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
         <span class="n">u</span> <span class="o">=</span> <span class="p">(</span><span class="n">signal2</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">signal2</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
         <span class="n">t</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">signal1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
         <span class="n">ruy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">t</span><span class="p">))</span>
-        <span class="n">ruy</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">u</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
-                                  <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">u</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)))</span>
+        <span class="n">ruy</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">u</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">u</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)))</span>
 
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
             <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">signal1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">signal1</span><span class="p">[:</span><span class="n">i</span><span class="p">]))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
@@ -469,13 +470,11 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
         <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">&quot;seaborn-white&quot;</span><span class="p">)</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;axes.facecolor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;white&quot;</span>
 
-        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
-            <span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">)</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">)</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">2</span><span class="p">:],</span> <span class="p">[</span><span class="n">e_acf</span><span class="p">,</span> <span class="n">xe_ccf</span><span class="p">]):</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">feature</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#1f77b4&#39;</span><span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">axhspan</span><span class="p">(</span><span class="n">feature</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">feature</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-                       <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#ccd9ff&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">feature</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#1f77b4&quot;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">axhspan</span><span class="p">(</span><span class="n">feature</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">feature</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#ccd9ff&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Lag&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Cross Correlation: ee, ex&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
             <span class="c1"># ax = plt.gca()</span>
@@ -489,7 +488,7 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
 
         <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-            <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">n</span><span class="p">],</span>
+            <span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="n">n</span><span class="p">],</span>
             <span class="n">c</span><span class="o">=</span><span class="s2">&quot;#1f77b4&quot;</span><span class="p">,</span>
             <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
             <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
@@ -497,7 +496,7 @@ <h1>Source code for sysidentpy.residues.residues_correlation</h1><div class="hig
             <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
         <span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-            <span class="n">yhat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">n</span><span class="p">],</span>
+            <span class="n">yhat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="n">n</span><span class="p">],</span>
             <span class="n">c</span><span class="o">=</span><span class="s2">&quot;#ff7f0e&quot;</span><span class="p">,</span>
             <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">,</span>
             <span class="c1"># linestyle=&#39;dashed&#39;,</span>
diff --git a/docs/_modules/sysidentpy/utils/_check_arrays.html b/docs/_modules/sysidentpy/utils/_check_arrays.html
index e5332f59..5ca2a142 100644
--- a/docs/_modules/sysidentpy/utils/_check_arrays.html
+++ b/docs/_modules/sysidentpy/utils/_check_arrays.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/_modules/sysidentpy/utils/generate_data.html b/docs/_modules/sysidentpy/utils/generate_data.html
index 1320ec20..703f34d4 100644
--- a/docs/_modules/sysidentpy/utils/generate_data.html
+++ b/docs/_modules/sysidentpy/utils/generate_data.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/_sources/changelog/v0.1.5.rst.txt b/docs/_sources/changelog/v0.1.5.rst.txt
index 14bfbfd6..a7a58ce0 100644
--- a/docs/_sources/changelog/v0.1.5.rst.txt
+++ b/docs/_sources/changelog/v0.1.5.rst.txt
@@ -14,11 +14,21 @@ CHANGES
 
 - MAJOR: n-steps-ahead prediction.
     - Now you can define the numbers of steps ahead in the predict function.
-	- only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+	- Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.
+
+- MAJOR: Simulating predefined models.
+    - Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.
+    - Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.
 
 - Added 4 new notebooks in the example section.
 
-- Added iterative notebooks.
+- Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.
+
+- Fix code format issues.
+
+- Added new tests for SimulatePolynomialNarmax and generate_data.
+
+- Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.
 
 - Fix issues related to html on Jupyter notebooks examples on documentation.
 
diff --git a/docs/_sources/code.rst.txt b/docs/_sources/code.rst.txt
index 91bb9a94..d7fea24a 100644
--- a/docs/_sources/code.rst.txt
+++ b/docs/_sources/code.rst.txt
@@ -15,6 +15,11 @@ sysidentpy narmax
 .. automodule:: sysidentpy.polynomial_basis.narmax
    :members:
 
+sysidentpy simulation
+===============
+.. automodule:: sysidentpy.polynomial_basis.simulation
+   :members:
+
 sysidentpy narx_neural_network
 ===============
 .. automodule:: sysidentpy.narx_neural_network.narx_nn
diff --git a/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
index 4800a33f..36aa5e2c 100644
--- a/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
+++ b/docs/_sources/examples/simulating_a_predefined_model.ipynb.txt
@@ -20,16 +20,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
     "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data"
+    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
+    "from sysidentpy.polynomial_basis.simulation import SimulatePolynomialNarmax\n"
    ]
   },
   {
@@ -52,557 +52,328 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)"
+    "x_train, x_test, y_train, y_test = get_siso_data(n=1000,\n",
+    "                                                 colored_noise=False,\n",
+    "                                                 sigma=0.001,\n",
+    "                                                 train_percentage=90)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:\n",
+    "## Defining the model\n",
+    "\n",
+    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors follwing a codification pattern:\n",
+    "- $0$ is the constant term,\n",
+    "- $[1001] = y_{k-1}$\n",
+    "- $[100n] = y_{k-n}$\n",
+    "- $[200n] = x1_{k-n}$\n",
+    "- $[300n] = x2_{k-n}$\n",
+    "- $[1011, 1001] = y_{k-11} \\times y_{k-1}$\n",
+    "- $[100n, 100m] = y_{k-n} \\times y_{k-m}$\n",
+    "- $[12001, 1003, 1001] = x11_{k-1} \\times y_{k-3} \\times y_{k-1}$\n",
+    "- and so on\n",
+    "\n",
+    "### Importante Note\n",
+    "\n",
+    "The order of the arrays matter. \n",
     "\n",
-    "We already know that the generated data is a result of the model  $𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘$ . Thus, we can create a model with those regressors and estimate its parameters as follows:"
+    "If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:\n",
+    "- $[2003, 2001]$ **works**\n",
+    "- $[2001, 2003]$ **do not work**\n",
+    "\n",
+    "We will handle this limitation in upcoming update."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Defining the model"
+    "s = SimulatePolynomialNarmax()\n",
+    "\n",
+    "# the model must be a numpy array\n",
+    "model = np.array(\n",
+    "    [\n",
+    "    [1001,    0], # y(k-1)\n",
+    "    [2001, 1001], # x1(k-1)y(k-1)\n",
+    "    [2002,    0], # x1(k-2)\n",
+    "    ]\n",
+    "                )\n",
+    "# theta must be a numpy array of shape (n, 1) where n is the number of regressors\n",
+    "theta = np.array([[0.2, 0.9, 0.1]]).T"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "# build the object so we can access its functions\n",
-    "model = PolynomialNarmax()\n",
+    "## Simulating the model\n",
     "\n",
-    "# create any psi matrix that contains the combinations we want\n",
-    "# in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))\n",
-    "# so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher\n",
-    "# build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)\n",
-    "psi = model.build_information_matrix(x_train, y_train, 2, 1, 2)"
-   ]
+    "After defining the model and theta we just need to use the simulate method.\n",
+    "\n",
+    "The simulate method returns the predicted values and the results where we can look at regressors,\n",
+    "parameters and ERR values."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
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-       "      <td>0.108434</td>\n",
-       "      <td>-0.610171</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.000000</td>\n",
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-       "      <td>-0.066163</td>\n",
-       "      <td>0.372308</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.548087</td>\n",
-       "      <td>-0.370476</td>\n",
-       "      <td>0.108434</td>\n",
-       "      <td>0.300400</td>\n",
-       "      <td>0.203053</td>\n",
-       "      <td>-0.059431</td>\n",
-       "      <td>0.137252</td>\n",
-       "      <td>-0.040172</td>\n",
-       "      <td>0.011758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.009140</td>\n",
-       "      <td>0.730998</td>\n",
-       "      <td>-0.370476</td>\n",
-       "      <td>0.000084</td>\n",
-       "      <td>0.006681</td>\n",
-       "      <td>-0.003386</td>\n",
-       "      <td>0.534358</td>\n",
-       "      <td>-0.270817</td>\n",
-       "      <td>0.137252</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.332688</td>\n",
-       "      <td>0.042290</td>\n",
-       "      <td>0.730998</td>\n",
-       "      <td>0.110682</td>\n",
-       "      <td>-0.014070</td>\n",
-       "      <td>-0.243195</td>\n",
-       "      <td>0.001788</td>\n",
-       "      <td>0.030914</td>\n",
-       "      <td>0.534358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.589887</td>\n",
-       "      <td>0.369362</td>\n",
-       "      <td>0.042290</td>\n",
-       "      <td>0.347967</td>\n",
-       "      <td>0.217882</td>\n",
-       "      <td>0.024947</td>\n",
-       "      <td>0.136428</td>\n",
-       "      <td>0.015620</td>\n",
-       "      <td>0.001788</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>893</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.741870</td>\n",
-       "      <td>0.489266</td>\n",
-       "      <td>-0.685155</td>\n",
-       "      <td>0.550371</td>\n",
-       "      <td>-0.362971</td>\n",
-       "      <td>0.508296</td>\n",
-       "      <td>0.239381</td>\n",
-       "      <td>-0.335223</td>\n",
-       "      <td>0.469437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>894</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.801757</td>\n",
-       "      <td>-0.008684</td>\n",
-       "      <td>0.489266</td>\n",
-       "      <td>0.642815</td>\n",
-       "      <td>0.006963</td>\n",
-       "      <td>-0.392272</td>\n",
-       "      <td>0.000075</td>\n",
-       "      <td>-0.004249</td>\n",
-       "      <td>0.239381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>895</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.278465</td>\n",
-       "      <td>-0.974898</td>\n",
-       "      <td>-0.008684</td>\n",
-       "      <td>0.077543</td>\n",
-       "      <td>-0.271475</td>\n",
-       "      <td>-0.002418</td>\n",
-       "      <td>0.950426</td>\n",
-       "      <td>0.008466</td>\n",
-       "      <td>0.000075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>896</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.019212</td>\n",
-       "      <td>-0.014295</td>\n",
-       "      <td>-0.974898</td>\n",
-       "      <td>0.000369</td>\n",
-       "      <td>-0.000275</td>\n",
-       "      <td>-0.018729</td>\n",
-       "      <td>0.000204</td>\n",
-       "      <td>0.013937</td>\n",
-       "      <td>0.950426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>897</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-0.872305</td>\n",
-       "      <td>0.562238</td>\n",
-       "      <td>-0.014295</td>\n",
-       "      <td>0.760916</td>\n",
-       "      <td>-0.490443</td>\n",
-       "      <td>0.012470</td>\n",
-       "      <td>0.316112</td>\n",
-       "      <td>-0.008037</td>\n",
-       "      <td>0.000204</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>898 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0         1         2         3         4         5         6  \\\n",
-       "0    1.0  0.000000  0.108434 -0.610171  0.000000  0.000000 -0.000000   \n",
-       "1    1.0 -0.548087 -0.370476  0.108434  0.300400  0.203053 -0.059431   \n",
-       "2    1.0  0.009140  0.730998 -0.370476  0.000084  0.006681 -0.003386   \n",
-       "3    1.0 -0.332688  0.042290  0.730998  0.110682 -0.014070 -0.243195   \n",
-       "4    1.0  0.589887  0.369362  0.042290  0.347967  0.217882  0.024947   \n",
-       "..   ...       ...       ...       ...       ...       ...       ...   \n",
-       "893  1.0 -0.741870  0.489266 -0.685155  0.550371 -0.362971  0.508296   \n",
-       "894  1.0 -0.801757 -0.008684  0.489266  0.642815  0.006963 -0.392272   \n",
-       "895  1.0  0.278465 -0.974898 -0.008684  0.077543 -0.271475 -0.002418   \n",
-       "896  1.0  0.019212 -0.014295 -0.974898  0.000369 -0.000275 -0.018729   \n",
-       "897  1.0 -0.872305  0.562238 -0.014295  0.760916 -0.490443  0.012470   \n",
-       "\n",
-       "            7         8         9  \n",
-       "0    0.011758 -0.066163  0.372308  \n",
-       "1    0.137252 -0.040172  0.011758  \n",
-       "2    0.534358 -0.270817  0.137252  \n",
-       "3    0.001788  0.030914  0.534358  \n",
-       "4    0.136428  0.015620  0.001788  \n",
-       "..        ...       ...       ...  \n",
-       "893  0.239381 -0.335223  0.469437  \n",
-       "894  0.000075 -0.004249  0.239381  \n",
-       "895  0.950426  0.008466  0.000075  \n",
-       "896  0.000204  0.013937  0.950426  \n",
-       "897  0.316112 -0.008037  0.000204  \n",
-       "\n",
-       "[898 rows x 10 columns]"
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+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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\n"
      },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {}
     }
    ],
    "source": [
-    "pd.DataFrame(psi)"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[   0,    0],\n",
-       "       [1001,    0],\n",
-       "       [2001,    0],\n",
-       "       [2002,    0],\n",
-       "       [1001, 1001],\n",
-       "       [2001, 1001],\n",
-       "       [2002, 1001],\n",
-       "       [2001, 2001],\n",
-       "       [2002, 2001],\n",
-       "       [2002, 2002]])"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2000  0.00000000\n",
+       "1        x1(k-2)     0.9000  0.00000000\n",
+       "2  x1(k-1)y(k-1)     0.1000  0.00000000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.00000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1000</td>\n      <td>0.00000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 21,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
    "source": [
-    "# we create the code representation to make easier to identifacate the index of the regressors we want.\n",
-    "# regressor_space(non_degree, xlag, ylag, n_inputs)\n",
-    "# 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "regressor_code"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "Here, $0$ is the constant term, $[1001]=y_{k-1}, \\quad [100n]=y_{k-n}, \\quad [200n] = x1_{k-n}, \\quad [300n]=x2_{k-n}$ and so on\n",
+    "### Options\n",
     "\n",
-    "We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function"
-   ]
+    "You can set the `steps_ahead` to run the prediction/simulation:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[1001,    0],\n",
-       "       [2002,    0],\n",
-       "       [2001, 1001]])"
+       "0.001695176840746673"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
-    "model.final_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.psi = psi[:, [1, 3, 5]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining the parameter estimation method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# by importing the Estimator class you can use any of the parameters estimator methods\n",
-    "from sysidentpy.parameter_estimation import Estimators"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=1)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "array([[0.20014451],\n",
-       "       [0.89992074],\n",
-       "       [0.09997088]])"
+       "0.0017301972652496206"
       ]
      },
-     "execution_count": 26,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     }
    ],
    "source": [
-    "# # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.\n",
-    "# If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. \n",
-    "\n",
-    "# Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate \n",
-    "# pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])\n",
-    "\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "model.theta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulating the model"
+    "yhat, results = s.simulate(\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    theta=theta,\n",
+    "    plot=False,\n",
+    "    steps_ahead=21)\n",
+    "rrse = root_relative_squared_error(y_test, yhat)\n",
+    "rrse"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "yhat = model.predict(x_valid, y_valid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0019184421163563367\n"
-     ]
-    }
+    "### Estimating the parameters\n",
+    "\n",
+    "If you have only the model strucuture, you can create an object with `estimate_parameter=True` and\n",
+    "choose the methed for estimation using `estimator`. In this case, you have to pass the training data\n",
+    "for parameters estimation. \n",
+    "\n",
+    "When `estimate_parameter=True`, we also computate the ERR considering only the regressors defined by the user. "
    ],
-   "source": [
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)"
-   ]
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2001  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1000  0.00000000\n"
-     ]
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
     }
    ],
    "source": [
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "s2 = SimulatePolynomialNarmax(estimate_parameter=True, estimator='recursive_least_squares')\n",
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True)\n",
     "\n",
-    "print(results)"
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+       "      Regressors Parameters         ERR\n",
+       "0         y(k-1)     0.2003  0.95597023\n",
+       "1        x1(k-2)     0.9000  0.04072471\n",
+       "2  x1(k-1)y(k-1)     0.1002  0.00330137"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Regressors</th>\n      <th>Parameters</th>\n      <th>ERR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>y(k-1)</td>\n      <td>0.2003</td>\n      <td>0.95597023</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x1(k-2)</td>\n      <td>0.9000</td>\n      <td>0.04072471</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>x1(k-1)y(k-1)</td>\n      <td>0.1002</td>\n      <td>0.00330137</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "output_type": "display_data"
+     "execution_count": 9
     }
    ],
    "source": [
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Final code"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n",
+    "\n",
+    "results = pd.DataFrame(results, columns=['Regressors', 'Parameters', 'ERR'])\n",
+    "results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.001879953122213705\n",
-      "      Regressors Parameters         ERR\n",
-      "0         y(k-1)     0.2000  0.00000000\n",
-      "1        x1(k-2)     0.8999  0.00000000\n",
-      "2  x1(k-1)y(k-1)     0.1001  0.00000000\n"
-     ]
-    },
-    {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 3 Axes>"
-      ]
+      "text/plain": "<Figure size 720x576 with 3 Axes>",
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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
-    "from sysidentpy.metrics import root_relative_squared_error\n",
-    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n",
-    "from sysidentpy.parameter_estimation import Estimators\n",
-    "\n",
-    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
-    "                                                   colored_noise=False,\n",
-    "                                                   sigma=0.001,\n",
-    "                                                   train_percentage=90)\n",
-    "\n",
-    "model = PolynomialNarmax()\n",
-    "psi = model.build_information_matrix(x_train, y_train, xlag=2, ylag=1, non_degree=2)\n",
-    "[regressor_code, max_lag] = model.regressor_space(2, 2, 1, 1)\n",
-    "model.final_model = regressor_code[[1, 3, 5]] # to use in the predict function\n",
-    "model.psi = psi[:, [1, 3, 5]]\n",
-    "model.theta = Estimators().least_squares(model.psi,  y_train[1:, 0].reshape(-1, 1)) \n",
-    "yhat = model.predict(x_valid, y_valid)\n",
-    "rrse = root_relative_squared_error(y_valid, yhat)\n",
-    "print(rrse)\n",
-    "model.err = [0, 0, 0] # just to use the `results` method\n",
-    "model.n_terms = 3 # the number of terms we selected (necessary in the 'results' methods)\n",
-    "results = pd.DataFrame(model.results(err_precision=8,\n",
-    "                                     dtype='dec'),\n",
-    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
-    "\n",
-    "print(results)\n",
-    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
-    "model.plot_result(y_valid, yhat, ee, ex)"
+    "yhat, results = s2.simulate(\n",
+    "    X_train=x_train,\n",
+    "    y_train=y_train,\n",
+    "    X_test=x_test,\n",
+    "    y_test=y_test,\n",
+    "    model_code=model,\n",
+    "    # theta will be estimated using the defined estimator\n",
+    "    plot=True,\n",
+    "    steps_ahead=8)\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:v0.1.4] *",
-   "language": "python",
-   "name": "conda-env-v0.1.4-py"
+   "name": "python3",
+   "display_name": "Python 3.8.6 64-bit ('v0.1.4': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "af0c49d7270b55aedb9d136513e348c9f6bf581fb1aab1dd354844b585f9bbf2"
+    }
+   }
   },
   "language_info": {
    "codemirror_mode": {
@@ -614,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
diff --git a/docs/changelog/v0.1.3.html b/docs/changelog/v0.1.3.html
index 2d731848..be93f757 100644
--- a/docs/changelog/v0.1.3.html
+++ b/docs/changelog/v0.1.3.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/changelog/v0.1.4.html b/docs/changelog/v0.1.4.html
index 5eb18f19..d9656390 100644
--- a/docs/changelog/v0.1.4.html
+++ b/docs/changelog/v0.1.4.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/changelog/v0.1.5.html b/docs/changelog/v0.1.5.html
index d7a38265..6fe55181 100644
--- a/docs/changelog/v0.1.5.html
+++ b/docs/changelog/v0.1.5.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -417,17 +422,28 @@ <h3>CHANGES<a class="headerlink" href="#changes" title="Permalink to this headli
 <dt>MAJOR: n-steps-ahead prediction.</dt><dd><ul>
 <li><dl class="simple">
 <dt>Now you can define the numbers of steps ahead in the predict function.</dt><dd><ul>
-<li><p>only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.</p></li>
+<li><p>Only for Polynomial models for now. Next update will bring this functionality to Neural NARX and General Estimators.</p></li>
+</ul>
+</dd>
+</dl>
+</li>
 </ul>
 </dd>
 </dl>
 </li>
+<li><dl class="simple">
+<dt>MAJOR: Simulating predefined models.</dt><dd><ul>
+<li><p>Added the new class SimulatePolynomialNarmax to handle the simulation of known model structures.</p></li>
+<li><p>Now you can simulate predefined models by just passing the model structure codification. Check the notebook examples.</p></li>
 </ul>
 </dd>
 </dl>
 </li>
 <li><p>Added 4 new notebooks in the example section.</p></li>
-<li><p>Added iterative notebooks.</p></li>
+<li><p>Added iterative notebooks. Now you can run the notebooks in Jupyter notebook section of the documentation in Colab.</p></li>
+<li><p>Fix code format issues.</p></li>
+<li><p>Added new tests for SimulatePolynomialNarmax and generate_data.</p></li>
+<li><p>Started changes related to numpy 1.19.4 update. There are still some Deprecation warnings that will be fixed in next update.</p></li>
 <li><p>Fix issues related to html on Jupyter notebooks examples on documentation.</p></li>
 <li><p>Updated Readme with examples of how to use.</p></li>
 </ul>
diff --git a/docs/code.html b/docs/code.html
index 8e04cab9..3aec1a3f 100644
--- a/docs/code.html
+++ b/docs/code.html
@@ -203,6 +203,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -346,6 +351,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toc-h1 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -555,7 +565,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 <span class="target" id="module-sysidentpy.polynomial_basis.narmax"></span><p>Build Polynomial NARMAX Models</p>
 <dl class="py class">
 <dt id="sysidentpy.polynomial_basis.narmax.PolynomialNarmax">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.polynomial_basis.narmax.</span></code><code class="sig-name descname"><span class="pre">PolynomialNarmax</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">non_degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_criteria</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'aic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_terms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_info_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'least_squares'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_least_squares</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.98</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.220446049250313e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gama</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.polynomial_basis.narmax.</span></code><code class="sig-name descname"><span class="pre">PolynomialNarmax</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">non_degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_criteria</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'aic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_terms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_info_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'recursive_least_squares'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_least_squares</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_lag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.98</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.220446049250313e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gama</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/narmax.html#PolynomialNarmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.narmax.PolynomialNarmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Polynomial NARXMAX model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -710,6 +720,7 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>ndarray of floats</em>) – The input data to be used in the prediction process.</p></li>
 <li><p><strong>y</strong> (<em>ndarray of floats</em>) – The output data to be used in the prediction process.</p></li>
+<li><p><strong>= int</strong><strong> (</strong><strong>default = None</strong><strong>)</strong> (<em>steps_ahead</em>) – The forecast horizon.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -812,6 +823,50 @@ <h1>sysidentpy narmax<a class="headerlink" href="#sysidentpy-narmax" title="Perm
 
 </dd></dl>
 
+</div>
+<div class="section" id="sysidentpy-simulation">
+<h1>sysidentpy simulation<a class="headerlink" href="#sysidentpy-simulation" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-sysidentpy.polynomial_basis.simulation"></span><dl class="py class">
+<dt id="sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">sysidentpy.polynomial_basis.simulation.</span></code><code class="sig-name descname"><span class="pre">SimulatePolynomialNarmax</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'recursive_least_squares'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_least_squares</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.98</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.220446049250313e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gama</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimate_parameter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/simulation.html#SimulatePolynomialNarmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="py method">
+<dt id="sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax.simulate">
+<code class="sig-name descname"><span class="pre">simulate</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sysidentpy/polynomial_basis/simulation.html#SimulatePolynomialNarmax.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax.simulate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Simulate a model defined by the user.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X_train</strong> (<em>ndarray of floats</em>) – The input data to be used in the training process.</p></li>
+<li><p><strong>y_train</strong> (<em>ndarray of floats</em>) – The output data to be used in the training process.</p></li>
+<li><p><strong>X_test</strong> (<em>ndarray of floats</em>) – The input data to be used in the prediction process.</p></li>
+<li><p><strong>y_test</strong> (<em>ndarray of floats</em>) – The output data (initial conditions) to be used in the prediction process.</p></li>
+<li><p><strong>model_code</strong> (<em>ndarray of int</em>) – Flattened list of input or output regressors.</p></li>
+<li><p><strong>= int</strong> (<em>steps_ahead</em>) – The forecast horizon.</p></li>
+<li><p><strong>= None</strong> (<em>default</em>) – The forecast horizon.</p></li>
+<li><p><strong>theta</strong> (<em>array-like of shape = number_of_model_elements</em>) – The parameters of the model.</p></li>
+<li><p><strong>plot</strong> (<em>bool</em><em>, </em><em>default=True</em>) – Indicate if the user wants to plot or not.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul>
+<li><p><strong>yhat</strong> (<em>ndarray of floats</em>) – The predicted values of the model.</p></li>
+<li><p><strong>results</strong> (<em>string</em>) –</p>
+<dl class="simple">
+<dt>Where:</dt><dd><p>First column represents each regressor element;
+Second column represents associated parameter;
+Third column represents the error reduction ratio associated
+to each regressor.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="sysidentpy-narx-neural-network">
 <h1>sysidentpy narx_neural_network<a class="headerlink" href="#sysidentpy-narx-neural-network" title="Permalink to this headline">¶</a></h1>
diff --git a/docs/dev_guide.html b/docs/dev_guide.html
index d457ed39..4a6fa7ea 100644
--- a/docs/dev_guide.html
+++ b/docs/dev_guide.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/basic_steps.html b/docs/examples/basic_steps.html
index 16d9eaea..f8bf46ed 100644
--- a/docs/examples/basic_steps.html
+++ b/docs/examples/basic_steps.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/defining_lags.html b/docs/examples/defining_lags.html
index 16271a0a..b7a2b24b 100644
--- a/docs/examples/defining_lags.html
+++ b/docs/examples/defining_lags.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/extended_least_squares.html b/docs/examples/extended_least_squares.html
index 46a8f2a8..0a8ba066 100644
--- a/docs/examples/extended_least_squares.html
+++ b/docs/examples/extended_least_squares.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/f_16_benchmark.html b/docs/examples/f_16_benchmark.html
index 43c552ed..3c577709 100644
--- a/docs/examples/f_16_benchmark.html
+++ b/docs/examples/f_16_benchmark.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/general_estimators.html b/docs/examples/general_estimators.html
index 56b04318..7c0803ab 100644
--- a/docs/examples/general_estimators.html
+++ b/docs/examples/general_estimators.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/identification_of_an_electromechanical_system.html b/docs/examples/identification_of_an_electromechanical_system.html
index dca47ed5..f8101f09 100644
--- a/docs/examples/identification_of_an_electromechanical_system.html
+++ b/docs/examples/identification_of_an_electromechanical_system.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/information_criteria_examples.html b/docs/examples/information_criteria_examples.html
index 99e0025b..0661296f 100644
--- a/docs/examples/information_criteria_examples.html
+++ b/docs/examples/information_criteria_examples.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/multiple_inputs_example.html b/docs/examples/multiple_inputs_example.html
index 1e37de1d..e6344528 100644
--- a/docs/examples/multiple_inputs_example.html
+++ b/docs/examples/multiple_inputs_example.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/n_steps_ahead_prediction.html b/docs/examples/n_steps_ahead_prediction.html
index a3302b2c..5cf2593a 100644
--- a/docs/examples/n_steps_ahead_prediction.html
+++ b/docs/examples/n_steps_ahead_prediction.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/narx_neural_network.html b/docs/examples/narx_neural_network.html
index 26afcb5b..8058f0e7 100644
--- a/docs/examples/narx_neural_network.html
+++ b/docs/examples/narx_neural_network.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/parameter_estimation.html b/docs/examples/parameter_estimation.html
index 702056d3..2ebd6994 100644
--- a/docs/examples/parameter_estimation.html
+++ b/docs/examples/parameter_estimation.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/examples/simulating_a_predefined_model.html b/docs/examples/simulating_a_predefined_model.html
index 1faa446d..4b60bf88 100644
--- a/docs/examples/simulating_a_predefined_model.html
+++ b/docs/examples/simulating_a_predefined_model.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -366,21 +371,30 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
   <a class="reference internal nav-link" href="#defining-the-model">
    Defining the model
   </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#defining-the-parameter-estimation-method">
-   Defining the parameter estimation method
-  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#importante-note">
+     Importante Note
+    </a>
+   </li>
+  </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#simulating-the-model">
    Simulating the model
   </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#final-code">
-   Final code
-  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#options">
+     Options
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#estimating-the-parameters">
+     Estimating the parameters
+    </a>
+   </li>
+  </ul>
  </li>
 </ul>
 
@@ -409,9 +423,9 @@ <h1>Simulate a Predefined Model<a class="headerlink" href="#simulate-a-predefine
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
 <span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
 <span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
+<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis.simulation</span> <span class="kn">import</span> <span class="n">SimulatePolynomialNarmax</span>
 </pre></div>
 </div>
 </div>
@@ -427,37 +441,82 @@ <h3>The data is generated by simulating the following model:<a class="headerlink
 <p>In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
-                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                                                 <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                 <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+                                                 <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </div>
-<p>One can create a model object and access the build_information_matrix method, the regressor_space method and the Estimator to build a model manually by choosing what regressors you want and estimate the parameters for them. For example:</p>
-<p>We already know that the generated data is a result of the model  <span class="math notranslate nohighlight">\(𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘\)</span> . Thus, we can create a model with those regressors and estimate its parameters as follows:</p>
 </div>
 </div>
 <div class="section" id="defining-the-model">
 <h2>Defining the model<a class="headerlink" href="#defining-the-model" title="Permalink to this headline">¶</a></h2>
+<p>We already know that the generated data is a result of the model  <span class="math notranslate nohighlight">\(𝑦_𝑘=0.2𝑦_{𝑘−1}+0.1𝑦_{𝑘−1}𝑥_{𝑘−1}+0.9𝑥_{𝑘−2}+𝑒_𝑘\)</span> . Thus, we can create a model with those regressors follwing a codification pattern:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(0\)</span> is the constant term,</p></li>
+<li><p><span class="math notranslate nohighlight">\([1001] = y_{k-1}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([100n] = y_{k-n}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([200n] = x1_{k-n}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([300n] = x2_{k-n}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([1011, 1001] = y_{k-11} \times y_{k-1}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([100n, 100m] = y_{k-n} \times y_{k-m}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\([12001, 1003, 1001] = x11_{k-1} \times y_{k-3} \times y_{k-1}\)</span></p></li>
+<li><p>and so on</p></li>
+</ul>
+<div class="section" id="importante-note">
+<h3>Importante Note<a class="headerlink" href="#importante-note" title="Permalink to this headline">¶</a></h3>
+<p>The order of the arrays matter.</p>
+<p>If you use [2001, 1001], it will work, but [1001, 2001] will not (the regressor will be ignored). Always put the highest value first:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\([2003, 2001]\)</span> <strong>works</strong></p></li>
+<li><p><span class="math notranslate nohighlight">\([2001, 2003]\)</span> <strong>do not work</strong></p></li>
+</ul>
+<p>We will handle this limitation in upcoming update.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">SimulatePolynomialNarmax</span><span class="p">()</span>
+
+<span class="c1"># the model must be a numpy array</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+    <span class="p">[</span>
+    <span class="p">[</span><span class="mi">1001</span><span class="p">,</span>    <span class="mi">0</span><span class="p">],</span> <span class="c1"># y(k-1)</span>
+    <span class="p">[</span><span class="mi">2001</span><span class="p">,</span> <span class="mi">1001</span><span class="p">],</span> <span class="c1"># x1(k-1)y(k-1)</span>
+    <span class="p">[</span><span class="mi">2002</span><span class="p">,</span>    <span class="mi">0</span><span class="p">],</span> <span class="c1"># x1(k-2)</span>
+    <span class="p">]</span>
+                <span class="p">)</span>
+<span class="c1"># theta must be a numpy array of shape (n, 1) where n is the number of regressors</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="simulating-the-model">
+<h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title="Permalink to this headline">¶</a></h2>
+<p>After defining the model and theta we just need to use the simulate method.</p>
+<p>The simulate method returns the predicted values and the results where we can look at regressors,
+parameters and ERR values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># build the object so we can access its functions</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
-
-<span class="c1"># create any psi matrix that contains the combinations we want</span>
-<span class="c1"># in this case we want, y(k-1), x(k-2) and y(k-1)x(k-1))</span>
-<span class="c1"># so the maximum lag of X must be 2 or higher; the maximum lag of y must be 1 or higher and the non_degree must be 2 or higher</span>
-<span class="c1"># build_information_matrix(X, y, self.xlag, self.ylag, self.non_degree)</span>
-<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">theta</span><span class="o">=</span><span class="n">theta</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../_images/simulating_a_predefined_model_8_0.svg" src="../_images/simulating_a_predefined_model_8_0.svg" /></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">psi</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+<span class="n">results</span>
 </pre></div>
 </div>
 </div>
@@ -480,357 +539,228 @@ <h2>Defining the model<a class="headerlink" href="#defining-the-model" title="Pe
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>0</th>
-      <th>1</th>
-      <th>2</th>
-      <th>3</th>
-      <th>4</th>
-      <th>5</th>
-      <th>6</th>
-      <th>7</th>
-      <th>8</th>
-      <th>9</th>
+      <th>Regressors</th>
+      <th>Parameters</th>
+      <th>ERR</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.0</td>
-      <td>0.000000</td>
-      <td>0.108434</td>
-      <td>-0.610171</td>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>-0.000000</td>
-      <td>0.011758</td>
-      <td>-0.066163</td>
-      <td>0.372308</td>
+      <td>y(k-1)</td>
+      <td>0.2000</td>
+      <td>0.00000000</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.0</td>
-      <td>-0.548087</td>
-      <td>-0.370476</td>
-      <td>0.108434</td>
-      <td>0.300400</td>
-      <td>0.203053</td>
-      <td>-0.059431</td>
-      <td>0.137252</td>
-      <td>-0.040172</td>
-      <td>0.011758</td>
+      <td>x1(k-2)</td>
+      <td>0.9000</td>
+      <td>0.00000000</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.0</td>
-      <td>0.009140</td>
-      <td>0.730998</td>
-      <td>-0.370476</td>
-      <td>0.000084</td>
-      <td>0.006681</td>
-      <td>-0.003386</td>
-      <td>0.534358</td>
-      <td>-0.270817</td>
-      <td>0.137252</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1.0</td>
-      <td>-0.332688</td>
-      <td>0.042290</td>
-      <td>0.730998</td>
-      <td>0.110682</td>
-      <td>-0.014070</td>
-      <td>-0.243195</td>
-      <td>0.001788</td>
-      <td>0.030914</td>
-      <td>0.534358</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1.0</td>
-      <td>0.589887</td>
-      <td>0.369362</td>
-      <td>0.042290</td>
-      <td>0.347967</td>
-      <td>0.217882</td>
-      <td>0.024947</td>
-      <td>0.136428</td>
-      <td>0.015620</td>
-      <td>0.001788</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>893</th>
-      <td>1.0</td>
-      <td>-0.741870</td>
-      <td>0.489266</td>
-      <td>-0.685155</td>
-      <td>0.550371</td>
-      <td>-0.362971</td>
-      <td>0.508296</td>
-      <td>0.239381</td>
-      <td>-0.335223</td>
-      <td>0.469437</td>
-    </tr>
-    <tr>
-      <th>894</th>
-      <td>1.0</td>
-      <td>-0.801757</td>
-      <td>-0.008684</td>
-      <td>0.489266</td>
-      <td>0.642815</td>
-      <td>0.006963</td>
-      <td>-0.392272</td>
-      <td>0.000075</td>
-      <td>-0.004249</td>
-      <td>0.239381</td>
-    </tr>
-    <tr>
-      <th>895</th>
-      <td>1.0</td>
-      <td>0.278465</td>
-      <td>-0.974898</td>
-      <td>-0.008684</td>
-      <td>0.077543</td>
-      <td>-0.271475</td>
-      <td>-0.002418</td>
-      <td>0.950426</td>
-      <td>0.008466</td>
-      <td>0.000075</td>
-    </tr>
-    <tr>
-      <th>896</th>
-      <td>1.0</td>
-      <td>0.019212</td>
-      <td>-0.014295</td>
-      <td>-0.974898</td>
-      <td>0.000369</td>
-      <td>-0.000275</td>
-      <td>-0.018729</td>
-      <td>0.000204</td>
-      <td>0.013937</td>
-      <td>0.950426</td>
-    </tr>
-    <tr>
-      <th>897</th>
-      <td>1.0</td>
-      <td>-0.872305</td>
-      <td>0.562238</td>
-      <td>-0.014295</td>
-      <td>0.760916</td>
-      <td>-0.490443</td>
-      <td>0.012470</td>
-      <td>0.316112</td>
-      <td>-0.008037</td>
-      <td>0.000204</td>
+      <td>x1(k-1)y(k-1)</td>
+      <td>0.1000</td>
+      <td>0.00000000</td>
     </tr>
   </tbody>
 </table>
-<p>898 rows × 10 columns</p>
 </div></div></div>
 </div>
+<div class="section" id="options">
+<h3>Options<a class="headerlink" href="#options" title="Permalink to this headline">¶</a></h3>
+<p>You can set the <code class="docutils literal notranslate"><span class="pre">steps_ahead</span></code> to run the prediction/simulation:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># we create the code representation to make easier to identifacate the index of the regressors we want.</span>
-<span class="c1"># regressor_space(non_degree, xlag, ylag, n_inputs)</span>
-<span class="c1"># 0 is constant, 100n is y(k-n), 200n is x1(k-n), 300n is x2(k-n) and so on</span>
-<span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">regressor_code</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">theta</span><span class="o">=</span><span class="n">theta</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">rrse</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[   0,    0],
-       [1001,    0],
-       [2001,    0],
-       [2002,    0],
-       [1001, 1001],
-       [2001, 1001],
-       [2002, 1001],
-       [2001, 2001],
-       [2002, 2001],
-       [2002, 2002]])
-</pre></div>
-</div>
-</div>
-</div>
-<p>Here, <span class="math notranslate nohighlight">\(0\)</span> is the constant term, <span class="math notranslate nohighlight">\([1001]=y_{k-1}, \quad [100n]=y_{k-n}, \quad [200n] = x1_{k-n}, \quad [300n]=x2_{k-n}\)</span> and so on</p>
-<p>We want the regressors corresponding to the index 1, 3, and 5. So we select the subset of the psi matrix:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span> <span class="c1"># to use in the predict function</span>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.001695176840746673
 </pre></div>
 </div>
 </div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">final_model</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">theta</span><span class="o">=</span><span class="n">theta</span><span class="p">,</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">21</span><span class="p">)</span>
+<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
+<span class="n">rrse</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[1001,    0],
-       [2002,    0],
-       [2001, 1001]])
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0017301972652496206
 </pre></div>
 </div>
 </div>
 </div>
 </div>
-<div class="section" id="defining-the-parameter-estimation-method">
-<h2>Defining the parameter estimation method<a class="headerlink" href="#defining-the-parameter-estimation-method" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="estimating-the-parameters">
+<h3>Estimating the parameters<a class="headerlink" href="#estimating-the-parameters" title="Permalink to this headline">¶</a></h3>
+<p>If you have only the model strucuture, you can create an object with <code class="docutils literal notranslate"><span class="pre">estimate_parameter=True</span></code> and
+choose the methed for estimation using <code class="docutils literal notranslate"><span class="pre">estimator</span></code>. In this case, you have to pass the training data
+for parameters estimation.</p>
+<p>When <code class="docutils literal notranslate"><span class="pre">estimate_parameter=True</span></code>, we also computate the ERR considering only the regressors defined by the user.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># by importing the Estimator class you can use any of the parameters estimator methods</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">SimulatePolynomialNarmax</span><span class="p">(</span><span class="n">estimate_parameter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="s1">&#39;recursive_least_squares&#39;</span><span class="p">)</span>
+<span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s2</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_train</span><span class="o">=</span><span class="n">x_train</span><span class="p">,</span>
+    <span class="n">y_train</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="c1"># theta will be estimated using the defined estimator</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+<span class="n">results</span>
 </pre></div>
 </div>
 </div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># # we use y_train[1:, 0].reshape(-1, 1) because the maximum lag of the selected regressor is 1.</span>
-<span class="c1"># If maximum lag is 2, we would have used  y_train[2:, 0].reshape(-1, 1) and so on. </span>
+<div class="cell_output docutils container">
+<img alt="../_images/simulating_a_predefined_model_14_0.svg" src="../_images/simulating_a_predefined_model_14_0.svg" /><div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
 
-<span class="c1"># Note: here we are estimating the parameters of the model, but we can just set the values of theta if we want to simulate </span>
-<span class="c1"># pre-defined model. Example: model.theta = np.array([[0.2], [0.9], [0.1]])</span>
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
 
-<span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">()</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
-<span class="n">model</span><span class="o">.</span><span class="n">theta</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[0.20014451],
-       [0.89992074],
-       [0.09997088]])
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="section" id="simulating-the-model">
-<h2>Simulating the model<a class="headerlink" href="#simulating-the-model" title="Permalink to this headline">¶</a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Regressors</th>
+      <th>Parameters</th>
+      <th>ERR</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>y(k-1)</td>
+      <td>0.2003</td>
+      <td>0.95597023</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>x1(k-2)</td>
+      <td>0.9000</td>
+      <td>0.04072471</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>x1(k-1)y(k-1)</td>
+      <td>0.1002</td>
+      <td>0.00330137</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s2</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_train</span><span class="o">=</span><span class="n">x_train</span><span class="p">,</span>
+    <span class="n">y_train</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="c1"># theta will be estimated using the defined estimator</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+
+<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+<span class="n">results</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0019184421163563367
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
-<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
-                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
+<img alt="../_images/simulating_a_predefined_model_15_0.svg" src="../_images/simulating_a_predefined_model_15_0.svg" /><div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
 
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Regressors Parameters         ERR
-0         y(k-1)     0.2001  0.00000000
-1        x1(k-2)     0.8999  0.00000000
-2  x1(k-1)y(k-1)     0.1000  0.00000000
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
-<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<img alt="../_images/simulating_a_predefined_model_21_0.png" src="../_images/simulating_a_predefined_model_21_0.png" />
-</div>
-</div>
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Regressors</th>
+      <th>Parameters</th>
+      <th>ERR</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>y(k-1)</td>
+      <td>0.2003</td>
+      <td>0.95597023</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>x1(k-2)</td>
+      <td>0.9000</td>
+      <td>0.04072471</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>x1(k-1)y(k-1)</td>
+      <td>0.1002</td>
+      <td>0.00330137</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
 </div>
-<div class="section" id="final-code">
-<h2>Final code<a class="headerlink" href="#final-code" title="Permalink to this headline">¶</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.polynomial_basis</span> <span class="kn">import</span> <span class="n">PolynomialNarmax</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.metrics</span> <span class="kn">import</span> <span class="n">root_relative_squared_error</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.utils.generate_data</span> <span class="kn">import</span> <span class="n">get_miso_data</span><span class="p">,</span> <span class="n">get_siso_data</span>
-<span class="kn">from</span> <span class="nn">sysidentpy.parameter_estimation</span> <span class="kn">import</span> <span class="n">Estimators</span>
-
-<span class="n">x_train</span><span class="p">,</span> <span class="n">x_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">get_siso_data</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                                                   <span class="n">colored_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                   <span class="n">sigma</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
-                                                   <span class="n">train_percentage</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
-
-<span class="n">model</span> <span class="o">=</span> <span class="n">PolynomialNarmax</span><span class="p">()</span>
-<span class="n">psi</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">build_information_matrix</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">xlag</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ylag</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">non_degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="p">[</span><span class="n">regressor_code</span><span class="p">,</span> <span class="n">max_lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">regressor_space</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">model</span><span class="o">.</span><span class="n">final_model</span> <span class="o">=</span> <span class="n">regressor_code</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span> <span class="c1"># to use in the predict function</span>
-<span class="n">model</span><span class="o">.</span><span class="n">psi</span> <span class="o">=</span> <span class="n">psi</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
-<span class="n">model</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">Estimators</span><span class="p">()</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">psi</span><span class="p">,</span>  <span class="n">y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> 
-<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
-<span class="n">rrse</span> <span class="o">=</span> <span class="n">root_relative_squared_error</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">rrse</span><span class="p">)</span>
-<span class="n">model</span><span class="o">.</span><span class="n">err</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="c1"># just to use the `results` method</span>
-<span class="n">model</span><span class="o">.</span><span class="n">n_terms</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># the number of terms we selected (necessary in the &#39;results&#39; methods)</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">results</span><span class="p">(</span><span class="n">err_precision</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-                                     <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;dec&#39;</span><span class="p">),</span>
-                       <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Regressors&#39;</span><span class="p">,</span> <span class="s1">&#39;Parameters&#39;</span><span class="p">,</span> <span class="s1">&#39;ERR&#39;</span><span class="p">])</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-<span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">,</span> <span class="n">extras</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">residuals</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">)</span>
-<span class="n">model</span><span class="o">.</span><span class="n">plot_result</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">yhat</span><span class="p">,</span> <span class="n">ee</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">yhat</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">s2</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span>
+    <span class="n">X_train</span><span class="o">=</span><span class="n">x_train</span><span class="p">,</span>
+    <span class="n">y_train</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+    <span class="n">X_test</span><span class="o">=</span><span class="n">x_test</span><span class="p">,</span>
+    <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+    <span class="n">model_code</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="c1"># theta will be estimated using the defined estimator</span>
+    <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">steps_ahead</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.001879953122213705
-      Regressors Parameters         ERR
-0         y(k-1)     0.2000  0.00000000
-1        x1(k-2)     0.8999  0.00000000
-2  x1(k-1)y(k-1)     0.1001  0.00000000
-</pre></div>
-</div>
-<img alt="../_images/simulating_a_predefined_model_23_1.png" src="../_images/simulating_a_predefined_model_23_1.png" />
+<img alt="../_images/simulating_a_predefined_model_16_0.svg" src="../_images/simulating_a_predefined_model_16_0.svg" /></div>
 </div>
 </div>
 </div>
diff --git a/docs/examples/system_identification_using_adaptative_filters.html b/docs/examples/system_identification_using_adaptative_filters.html
index 7e306992..d1e360f2 100644
--- a/docs/examples/system_identification_using_adaptative_filters.html
+++ b/docs/examples/system_identification_using_adaptative_filters.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/genindex.html b/docs/genindex.html
index fe570196..eaa1b227 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -202,6 +202,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -519,6 +524,8 @@ <h2 id="M">M</h2>
         <li><a href="code.html#module-sysidentpy.parameter_estimation.estimators">sysidentpy.parameter_estimation.estimators</a>
 </li>
         <li><a href="code.html#module-sysidentpy.polynomial_basis.narmax">sysidentpy.polynomial_basis.narmax</a>
+</li>
+        <li><a href="code.html#module-sysidentpy.polynomial_basis.simulation">sysidentpy.polynomial_basis.simulation</a>
 </li>
         <li><a href="code.html#module-sysidentpy.residues.residues_correlation">sysidentpy.residues.residues_correlation</a>
 </li>
@@ -590,6 +597,10 @@ <h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="code.html#sysidentpy.base.InformationMatrix.shift_column">shift_column() (sysidentpy.base.InformationMatrix method)</a>
+</li>
+      <li><a href="code.html#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax.simulate">simulate() (sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax method)</a>
+</li>
+      <li><a href="code.html#sysidentpy.polynomial_basis.simulation.SimulatePolynomialNarmax">SimulatePolynomialNarmax (class in sysidentpy.polynomial_basis.simulation)</a>
 </li>
       <li><a href="code.html#sysidentpy.metrics._regression.symmetric_mean_absolute_percentage_error">symmetric_mean_absolute_percentage_error() (in module sysidentpy.metrics._regression)</a>
 </li>
@@ -628,6 +639,13 @@ <h2 id="S">S</h2>
 
       <ul>
         <li><a href="code.html#module-sysidentpy.polynomial_basis.narmax">module</a>
+</li>
+      </ul></li>
+      <li>
+    sysidentpy.polynomial_basis.simulation
+
+      <ul>
+        <li><a href="code.html#module-sysidentpy.polynomial_basis.simulation">module</a>
 </li>
       </ul></li>
       <li>
diff --git a/docs/index.html b/docs/index.html
index 8912a006..c1090c01 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -203,6 +203,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
@@ -671,6 +676,7 @@ <h3>Contents<a class="headerlink" href="#contents" title="Permalink to this head
 <li class="toctree-l1"><a class="reference internal" href="code.html">Codes</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.base">sysidentpy base</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-narmax">sysidentpy narmax</a></li>
+<li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-simulation">sysidentpy simulation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-narx-neural-network">sysidentpy narx_neural_network</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#sysidentpy-general-estimators">sysidentpy general_estimators</a></li>
 <li class="toctree-l1"><a class="reference internal" href="code.html#module-sysidentpy.residues.residues_correlation">sysidentpy residues</a></li>
diff --git a/docs/installation.html b/docs/installation.html
index effe594c..be46cee3 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/introduction_to_narmax.html b/docs/introduction_to_narmax.html
index 3088d06c..e8893dd6 100644
--- a/docs/introduction_to_narmax.html
+++ b/docs/introduction_to_narmax.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
diff --git a/docs/notebooks.html b/docs/notebooks.html
index 91bc51f9..4993ebf2 100644
--- a/docs/notebooks.html
+++ b/docs/notebooks.html
@@ -204,6 +204,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network
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@@ -205,6 +205,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
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+ <li class="toctree-l1">
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+   sysidentpy simulation
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+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="code.html#module-sysidentpy.polynomial_basis.simulation"><code class="xref">sysidentpy.polynomial_basis.simulation</code></a></td><td>
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+   sysidentpy simulation
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in SysIdentPy","Changes in SysIdentPy","Changes in SysIdentPy","Codes","Contributing","Presenting main functionality","Setting specific lags","Important notes and examples of how to use Extended Least Squares","Example: F-16 Ground Vibration Test benchmark","Building NARX models using general estimators","Identification of an electromechanical system","Information Criteria - Examples","Multiple Inputs usage","Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark","Building NARX Neural Network using Sysidentpy","Parameter Estimation","Simulate a Predefined Model","System Identification Using Adaptative Filters","Welcome to SysIdentPy\u2019s documentation!","Install Guide","A brief introduction to NARMAX models.","Jupyter notebooks","User 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diff --git a/docs/user_guide.html b/docs/user_guide.html
index af94355f..112277dd 100644
--- a/docs/user_guide.html
+++ b/docs/user_guide.html
@@ -206,6 +206,11 @@ <h1 class="site-logo" id="site-title">NARMAX models</h1>
    sysidentpy narmax
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="code.html#sysidentpy-simulation">
+   sysidentpy simulation
+  </a>
+ </li>
  <li class="toctree-l1">
   <a class="reference internal" href="code.html#sysidentpy-narx-neural-network">
    sysidentpy narx_neural_network