update documentation for plotting method and required packages

SVInsight/svi.py

@@ -780,10 +780,10 @@ def plot_svi(self, plot_option: int, geopackages: list):
 
         A single figure of a single SVI estimate. The geopackage parameter must be in the format [year, boundary, config, variable] where:
 
-        -Year: SVI estimate year (int)
-        -Boundary: Boundary of interest ('bg' or 'tract', str)
-        -Config: Which config file was used to create the SVI estimate (str)
-        -Variable: Which SVI variable to plot (i.e., the attributes of the SVI geopackages created, str). 
+        - Year: SVI estimate year (int)
+        - Boundary: Boundary of interest ('bg' or 'tract', str)
+        - Config: Which config file was used to create the SVI estimate (str)
+        - Variable: Which SVI variable to plot (i.e., the attributes of the SVI geopackages created, str). 
 
         **Plot Option 2: Simple Comparative Plot**
 
@@ -794,9 +794,7 @@ def plot_svi(self, plot_option: int, geopackages: list):
         A more detailed plotting option, that will produce a comparison plot and calculate a linear regression. Because the difference map and linear regression require the same set of input geoids (i.e., the same locations in the geopackage), it is currently required that the variables come from the same geopackage but compare different rank methodologies. The geopackages input should be formated as follows: [year, boundary, config]. If a variable option is passed it is ignored. The additional plots show the following information:
 
         - Difference plot: Shows the The FA_SVI_Rank minus the RM_SVI_Rank to highlight areas where the factor analysis method is under (negative) and over (positive) predicting SVI rank when compared to the rank method. 
-        -Linear Regression: Shows linear correlation betweeen factor analysis and rank method SVI estimates and automatically computes an r-squared value with p-value, 95% confidence interval, and 95% prediction interval.
-        
-        
+        - Linear Regression: Shows linear correlation betweeen factor analysis and rank method SVI estimates and automatically computes an r-squared value with p-value, 95% confidence interval, and 95% prediction interval.
         
         """
         # ADD CHECKS TO VARIABLE INPUTS

doc/_build/html/.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: 84e8fb852c4885a901327cb14ccc00a0
+config: 6b2bd8b92764ff171da02403603cbf26
 tags: 645f666f9bcd5a90fca523b33c5a78b7

doc/_build/html/apiref/apiref.html

@@ -398,11 +398,38 @@
 <dl class="py method">
 <dt class="sig sig-object py" id="SVInsight.svi.SVInsight.plot_svi">
 <span class="sig-name descname"><span class="pre">plot_svi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_option</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geopackages</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#SVInsight.svi.SVInsight.plot_svi" title="Link to this definition">#</a></dt>
-<dd><p>Method to plot SVI and SVI comparisons
-Option 1: Single plot
-Option 2: Two side by side plots
-Option 3: Comparison plots
-geopackage: [year, boundary, config, variable]</p>
+<dd><p>Simple plotting method to quickly map an SVI variable or compare two SVIs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>plot_option</strong> (<em>int</em>) – Which plot method to use: Either 1 (single SVI map), 2 (two side by side maps), or 3 (full comparison figure).</p></li>
+<li><p><strong>geopackages</strong> (<em>list</em>) – The required information for plotting, must be format: [year, boundary, config, variable]. Nested list if plot_option 2.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object</p>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>ValueError</strong> – If the boundary type is invalid or the year is not between 2010 and 2022,</p>
+</dd>
+</dl>
+<p>This method quickly creates an example SVI plot either by itself or in a comparative format. The plot options and their required information can be found below.</p>
+<p><strong>Plot Option 1: Single Plot</strong></p>
+<p>A single figure of a single SVI estimate. The geopackage parameter must be in the format [year, boundary, config, variable] where:</p>
+<ul class="simple">
+<li><p>Year: SVI estimate year (int)</p></li>
+<li><p>Boundary: Boundary of interest (‘bg’ or ‘tract’, str)</p></li>
+<li><p>Config: Which config file was used to create the SVI estimate (str)</p></li>
+<li><p>Variable: Which SVI variable to plot (i.e., the attributes of the SVI geopackages created, str).</p></li>
+</ul>
+<p><strong>Plot Option 2: Simple Comparative Plot</strong></p>
+<p>A simple two by one figure to visually compare two differnet SVI estimates. These estimates can be from the same or different geopackages. The geopackages parameter should be a nested list of the same variables as described in plot option 1: [[year, boundary, config, variable],[year, boundary, config, variable]].</p>
+<p><strong>Plot Option 1: Complete Comparative Plot</strong></p>
+<p>A more detailed plotting option, that will produce a comparison plot and calculate a linear regression. Because the difference map and linear regression require the same set of input geoids (i.e., the same locations in the geopackage), it is currently required that the variables come from the same geopackage but compare different rank methodologies. The geopackages input should be formated as follows: [year, boundary, config]. If a variable option is passed it is ignored. The additional plots show the following information:</p>
+<ul class="simple">
+<li><p>Difference plot: Shows the The FA_SVI_Rank minus the RM_SVI_Rank to highlight areas where the factor analysis method is under (negative) and over (positive) predicting SVI rank when compared to the rank method.</p></li>
+<li><p>Linear Regression: Shows linear correlation betweeen factor analysis and rank method SVI estimates and automatically computes an r-squared value with p-value, 95% confidence interval, and 95% prediction interval.</p></li>
+</ul>
 </dd></dl>
 
 <dl class="py method">

doc/conf.py

@@ -50,7 +50,13 @@
 #html_theme = 'alabaster'
 #html_theme = 'pydata_sphinx_theme'
 html_theme = 'furo'
-html_theme_options ={"color_scheme": "light"}
+#html_theme_options ={"color_scheme": "light"}
+
+html_context = {
+   # ...
+   "default_mode": "light"
+}
+
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".

pyproject.toml

@@ -13,7 +13,8 @@ dependencies = [
     "pandas>=2.2.2",
     "PyYAML>=6.0.1",
     "scikit_learn>=1.3.0",
-    "openpyxl>=3.0.10"
+    "openpyxl>=3.0.10",
+    "matplotlib"
 ]
 requires-python = ">=3.8"
 authors = [

requirements.txt

@@ -7,4 +7,5 @@ pytest>=8.1.1
 PyYAML>=6.0.1
 scikit_learn>=1.3.0
 setuptools==68.2.2
-openpyxl>=3.0.10
+openpyxl>=3.0.10
+matplotlib