use work toolbox and remove redundant functions

BayesianCalibration_spm/makeData.py

@@ -41,7 +41,6 @@
 # from forwardPass import from_param_list_to_dict, from_param_list_to_str
 from forwardPass import from_param_list_to_str
 from plotsUtil_batt import *
-from prettyPlot.plotsUtil import pretty_labels
 
 # Read command line arguments
 args_spm = argument.initArg()

pinn_spm_param/integration_spm/main.py

@@ -4,7 +4,6 @@
 import argument
 import numpy as np
 from plotsUtil_batt import *
-from prettyPlot.plotsUtil import pretty_labels, pretty_legend
 from thermo import grad_ds_a_cs_a, grad_ds_c_cs_c
 
 # Read command line arguments
@@ -135,25 +134,28 @@
 
 
 # Plot cs
-plotData(
-    [config["r_a"], config["r_c"]],
-    [sol["cs_a"] / params["csanmax"], sol["cs_c"] / params["cscamax"]],
-    params["tmax"],
-    [r"", r""],
-    [r"$c_{s,an}/c_{s,an,max}$", r"$c_{s,ca}/c_{s,ca,max}$"],
-    ["r [m]", "r [m]"],
+pretty_multi_contour(
+    listDatax=[config["r_a"], config["r_c"]],
+    listData=[
+        sol["cs_a"] / params["csanmax"],
+        sol["cs_c"] / params["cscamax"],
+    ],
+    ybound=[0, params["tmax"]],
+    listCBLabel=[r"", r""],
+    listTitle=[r"$c_{s,an}/c_{s,an,max}$", r"$c_{s,ca}/c_{s,ca,max}$"],
+    listXAxisName=["r [m]", "r [m]"],
     vminList=[0, 0],
     vmaxList=[1, 1],
 )
 
 # Plot cs
-plotData(
-    [config["r_a"], config["r_c"]],
-    [sol["cs_a"], sol["cs_c"]],
-    params["tmax"],
-    [r"", r""],
-    [r"$c_{s,an}$", r"$c_{s,ca}$"],
-    ["r [m]", "r [m]"],
+pretty_multi_contour(
+    listDatax=[config["r_a"], config["r_c"]],
+    listData=[sol["cs_a"], sol["cs_c"]],
+    ybound=[0, params["tmax"]],
+    listCBLabel=[r"", r""],
+    listTitle=[r"$c_{s,an}$", r"$c_{s,ca}$"],
+    listXAxisName=["r [m]", "r [m]"],
 )
 
 if args.verbose:

pinn_spm_param/integration_spm/spm_int.py

@@ -1,11 +1,8 @@
 import sys
-
-import numpy as np
-
-sys.path.append("../util")
 import time
 
-from myProgressBar import printProgressBar
+import numpy as np
+from prettyPlot.progressBar import print_progress_bar
 
 
 def get_r_domain(n_r, params):
@@ -188,7 +185,7 @@ def integration(
     dR_c = config["dR_c"]
 
     if verbose:
-        printProgressBar(
+        print_progress_bar(
             0,
             n_t,
             prefix=f"Step= {0} / {n_t}",
@@ -429,7 +426,7 @@ def integration(
             )
 
         if verbose:
-            printProgressBar(
+            print_progress_bar(
                 i_t,
                 n_t - 1,
                 prefix=f"Step= {i_t} / {n_t}",

pinn_spm_param/main.py

@@ -18,8 +18,6 @@
     initialize_params_from_inpt,
 )
 from myNN import *
-from myparser import parseInputFile
-from myProgressBar import printProgressBar
 
 
 def do_training_only(input_params, nn):

pinn_spm_param/postProcess/computeManyMetrics.py

@@ -9,7 +9,7 @@
 
 import argument
 from computeError import *
-from prettyPlot.plotsUtil import plt, pretty_labels, pretty_legend
+from plotsUtil_batt import *
 from scipy import stats
 
 print("\n\nINFO: COMPUTING MANY METRICS\n\n")

pinn_spm_param/postProcess/plotCorrelationPINNvsData.py

@@ -8,7 +8,7 @@
 import argument
 import tensorflow as tf
 from myNN import *
-from prettyPlot.plotsUtil import plt, pretty_labels, pretty_legend
+from plotsUtil_batt import *
 from tensorflow import keras
 from tensorflow.keras import layers, regularizers
 

pinn_spm_param/postProcess/plotData.py

@@ -82,13 +82,13 @@ def plot_pde_data(args):
         plt.savefig(os.path.join(figureFolder, "PhiData.png"))
 
     # Plot cs
-    plotData(
-        [r_a, r_c],
-        [cs_a, cs_c],
-        params["tmax"],
-        [r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
-        [r"$c_{s,an}$", r"$c_{s,ca}$"],
-        ["r [m]", "r [m]"],
+    pretty_multi_contour(
+        listDatax=[r_a, r_c],
+        listData=[cs_a, cs_c],
+        ybound=[0, params["tmax"]],
+        listCBLabel=[r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
+        listTitle=[r"$c_{s,an}$", r"$c_{s,ca}$"],
+        listXAxisName=["r [m]", "r [m]"],
     )
     if not args.verbose:
         plt.savefig(os.path.join(figureFolder, "csData.png"))

pinn_spm_param/postProcess/plotManyLosses.py

@@ -5,7 +5,7 @@
 
 sys.path.append("../util")
 import argument
-from prettyPlot.plotsUtil import plt, pretty_labels, pretty_legend
+from plotsUtil_batt import *
 
 print("\n\nINFO: PLOTTING MANY LOSSES\n\n")
 

pinn_spm_param/postProcess/plotPINNResult.py

@@ -154,13 +154,13 @@ def plot_pinn_result(args):
         field_dat=field_dat,
         verbose=args.verbose,
     )
-    plotData(
-        [r_test_a[:, :, 0, 0], r_test_c[:, :, 0, 0]],
-        [cs_a[:, :, 0, 0], cs_c[:, :, 0, 0]],
-        tmax,
-        [r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
-        [r"$c_{s,an}$", r"$c_{s,ca}$"],
-        ["r [m]", "r [m]"],
+    pretty_multi_contour(
+        listDatax=[r_test_a[:, :, 0, 0], r_test_c[:, :, 0, 0]],
+        listData=[cs_a[:, :, 0, 0], cs_c[:, :, 0, 0]],
+        ybound=[0, tmax],
+        listCBLabel=[r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
+        listTitle=[r"$c_{s,an}$", r"$c_{s,ca}$"],
+        listXAxisName=["r [m]", "r [m]"],
     )
     if not args.verbose:
         plt.savefig(

pinn_spm_param/postProcess/plotPINNResult_movie.py

@@ -125,13 +125,13 @@ def makePlot(movieDir, solDict, params_list, indMov, stepID, data_dict=None):
         field_dat=field_dat,
         verbose=False,
     )
-    plotData(
-        [r_test_a[:, :, 0, 0], r_test_c[:, :, 0, 0]],
-        [cs_a[:, :, 0, 0], cs_c[:, :, 0, 0]],
-        tmax,
-        [r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
-        [r"$c_{s,an}$", r"$c_{s,ca}$"],
-        ["r [m]", "r [m]"],
+    pretty_multi_contour(
+        listDatax=[r_test_a[:, :, 0, 0], r_test_c[:, :, 0, 0]],
+        listData=[cs_a[:, :, 0, 0], cs_c[:, :, 0, 0]],
+        ybound=[0, tmax],
+        listCBLabel=[r"$[kmol/m^3]$", r"$[kmol/m^3]$"],
+        listTitle=[r"$c_{s,an}$", r"$c_{s,ca}$"],
+        listXAxisName=["r [m]", "r [m]"],
     )
     plt.savefig(
         os.path.join(figureFolder, f"cs2D{string_params}_{indMov}.png")

pinn_spm_param/postProcess/plotResidualVariation.py

@@ -6,7 +6,7 @@
 
 sys.path.append("../util")
 import argument
-from prettyPlot.plotsUtil import plt, pretty_labels, pretty_legend
+from plotsUtil_batt import *
 
 print("\n\nINFO: PLOTTING LOSSES\n\n")
 

pinn_spm_param/util/generateOCP_poly_mon.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pandas as pd
 import tf_lineInterp as tfli
-from myProgressBar import printProgressBar
+from prettyPlot.progressBar import print_progress_bar
 from scipy import odr
 from scipy.optimize import minimize
 
@@ -47,7 +47,7 @@ def polyOpt(order, xd, yd):
 
 def iterativeOpt(order_list, xd, yd):
     error = []
-    printProgressBar(
+    print_progress_bar(
         0,
         len(order_list) - 1,
         prefix=f"Poly Order = {0} / {len(order_list)-1}",
@@ -59,7 +59,7 @@ def iterativeOpt(order_list, xd, yd):
         pars = res.x
         poly = np.poly1d(pars)
         error.append(np.sum(abs(poly(xd) - yd)))
-        printProgressBar(
+        print_progress_bar(
             order,
             len(order_list) - 1,
             prefix=f"Poly Order = {order} / {len(order_list)-1}",

pinn_spm_param/util/init_pinn.py

@@ -13,8 +13,7 @@
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 import tensorflow as tf
 from myNN import *
-from myparser import parseInputFile
-from myProgressBar import printProgressBar
+from prettyPlot.parser import parse_input_file
 
 
 def absolute_path_check(path):
@@ -289,7 +288,7 @@ def initialize_params_from_inpt(inpt):
 
 
 def initialize_params(args):
-    inpt = parseInputFile(args.input_file)
+    inpt = parse_input_file(args.input_file)
     return initialize_params_from_inpt(inpt)
 
 

pinn_spm_param/util/myNN.py

@@ -18,7 +18,7 @@
 from custom_activations import swish_activation
 from dataTools import checkDataShape, completeDataset
 from eager_lbfgs import Struct, lbfgs
-from myProgressBar import printProgressBar
+from prettyPlot.progressBar import print_progress_bar
 from tensorflow.keras import backend as K
 from tensorflow.keras import (
     initializers,
@@ -2313,7 +2313,7 @@ def train(
         print("Using collocation points: " + self.collocationMode)
 
         # Train
-        printProgressBar(
+        print_progress_bar(
             0,
             self.nEpochs,
             prefix="Loss=%s  Epoch= %d / %d " % ("?", 0, self.nEpochs),
@@ -2776,7 +2776,7 @@ def train(
                     ) / (step + 1)
 
                     if not optimized and step % self.freq == 0:
-                        printProgressBar(
+                        print_progress_bar(
                             step,
                             self.n_batch,
                             prefix="Loss=%.2f i=%.2f b=%.2f d=%.2f r=%.2f, t/step=%.2g ms,  Epoch= %d / %d "
@@ -2828,7 +2828,7 @@ def train(
                             self.reg_col_weights,
                         )
 
-                printProgressBar(
+                print_progress_bar(
                     epoch + 1,
                     self.nEpochs,
                     prefix="Loss=%.2f i=%.2f b=%.2f d=%.2f r=%.2f, t/step=%.2g ms,  Epoch= %d / %d "