update git ignore and remove unnecessary scripts.

.gitignore

@@ -3,8 +3,12 @@
 *.hdf5
 !BayesValidRox/tests/PA-A/data/ValidationSets/ExpDesign*
 *.pdf
+*.png
+*.svg
+*.zip
 *.vtu
 *.vtp
+*.pvd
 BayesValidRox/.spyderproject/
 BayesValidRox/__pycache__/*
 

BayesValidRox/tests/PA-A/post_plot.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Feb 18 08:28:13 2020
-
-@author: farid
-"""
-import numpy as np
-import os, sys
-import seaborn as sns
-import h5py
-from scipy.stats import norm
-import pandas as pd
-
-try:
-    import cPickle as pickle
-except ModuleNotFoundError:
-    import pickle
-from matplotlib.patches import Patch
-import matplotlib.lines as mlines
-from matplotlib import ticker
-import matplotlib.pylab as plt
-# Add BayesValidRox path
-#sys.path.insert(0,'./../../../BayesValidRox/')
-plt.style.use('seaborn-deep') # talk, paper, poster
-fsize = 50
-params = {'legend.fontsize': fsize,
-          'axes.labelsize': fsize,
-          'axes.titlesize': fsize,
-          'xtick.labelsize' :fsize,
-          'ytick.labelsize': fsize,
-          'grid.color': 'k',
-          'grid.linestyle': ':',
-          'grid.linewidth': 0.5,
-#          'mathtext.fontset' : 'stix',
-#          'mathtext.rm'      : 'serif',
-          'font.family'      : 'serif',
-          'font.serif'       : "Times New Roman", # or "Times"
-          'figure.figsize'   : (32, 24),
-          # 'savefig.dpi':1500,
-          'text.usetex': True
-         }
-plt.rcParams.update(params)
-
-def upper_rugplot(data, height=.05, ax=None, **kwargs):
-    from matplotlib.collections import LineCollection
-    ax = ax or plt.gca()
-    kwargs.setdefault("linewidth", 1)
-    segs = np.stack((np.c_[data, data],
-                     np.c_[np.ones_like(data), np.ones_like(data)-height]),
-                    axis=-1)
-    lc = LineCollection(segs, transform=ax.get_xaxis_transform(), **kwargs)
-    ax.add_collection(lc)
-
-def postPredictiveplot(modelName, errorPrec, averaging=True, case='Calib', bins='auto'):
-    result_folder = './Results_22_06_2021/outputs_{}/'.format(modelName.split('-v')[0])
-    # result_folder = './'
-    directory = result_folder+'Outputs_Bayes_'+modelName+'_'+case
-    OutputDir = ('postPred_'+modelName+'_'+case)
-    if not os.path.exists(OutputDir): os.makedirs(OutputDir)
-
-    ave = '_PNM_averaging' if 'ffpm-stokespnm' in modelName else ''
-    data = pd.read_csv('data/stokesData'+case+ave+'.csv')
-
-    # Load Post pred file
-    f = h5py.File(directory+'/'+"postPredictive.hdf5", 'r+')
-
-    # Load PCEModel
-    with open(result_folder+'PCEModel_'+modelName+'.pkl', 'rb') as input:
-        PCEModel = pickle.load(input)
-
-    # Generate Prior Predictive
-    #priorPred, std = PCEModel.eval_metamodel(nsamples=10000)
-
-
-    for OutputName in ['velocity [m/s]', 'p']:
-        # x_coords = np.array(f["x_values/"+OutputName])
-
-        # Find pointIDs
-        csv_file = 'pressure_points.csv' if OutputName == 'p' else 'velocity_points.csv'
-        if case == 'Calib':
-            pointIDs = pd.read_csv('models/'+csv_file).query("__vtkIsSelected__ \
-                                                                   == 'Calibration'")['vtkOriginalPointIds'].to_numpy()
-        else:
-            pointIDs = pd.read_csv('models/'+csv_file).query("__vtkIsSelected__ \
-                                                                   == 'Validation'")['vtkOriginalPointIds'].to_numpy()
-
-        for idx, x in enumerate(pointIDs):
-            fig, ax = plt.subplots(1, 1)
-
-            # Prior predictive
-            #outputs = priorPred[OutputName][:,idx]
-            #sns.histplot(outputs[outputs>0], ax=ax,# bins=50,
-                         #color='blue', alpha=0.4,stat="count")
-
-            # Posterior predictive
-            postPred = np.array(f["EDY/"+OutputName])[:,idx]
-            sns.histplot(postPred[postPred>0], ax=ax, bins=bins,
-                          color='orange',stat="count") #normalizes counts so that the sum of the bar heights is 1
-
-            # Reference data from the pore-scale simulation
-            ax.axvline(x=data[OutputName][idx], linewidth=15, color='green')
-            #sns.histplot(np.random.normal(data[OutputName][idx], errorPrec*data[OutputName][idx], len(postPred[postPred>0])), ax=ax,bins=bins,
-                          #color='green', alpha=0.5, stat="count")
-
-            # Print conidence interval of ExpDesign.Y (Trained area)
-            modelRuns = PCEModel.ExpDesign.Y[OutputName][:,idx]
-            upper_rugplot(modelRuns, ax=ax, alpha=0.75, color='grey')
-
-            # fmt = ticker.StrMethodFormatter("{x}")
-            # ax.xaxis.set_major_formatter(fmt)
-            # ax.yaxis.set_major_formatter(fmt)
-
-            legend_elements = [
-                Patch(facecolor='orange', edgecolor='orange',label='Posterior Pred.'),
-                Patch(facecolor='green', edgecolor='green',alpha=0.5, label='Ref. Data'),
-                mlines.Line2D([], [], marker='|', color='grey', alpha=0.75,
-                              linestyle='None', markersize=75, markeredgewidth=1.5, label='Orig. Responses')]
-
-            ax.legend(handles=legend_elements, fontsize=75)
-
-            font = {'family': 'serif',
-                    'weight': 'normal',
-                    'size': 100,
-                    }
-            x_label = 'Pressure [Pa]' if OutputName == 'p' else 'velocity [m/s]'
-            ax.set_xlabel(x_label, fontdict=font)
-            ax.set_ylabel('Count', fontdict=font)
-            # ax.set_xscale('log')
-            ax.tick_params(axis='both', which='major', labelsize=font['size'])
-            plt.ticklabel_format(axis="x", style="sci", scilimits=(0,0))
-            plt.ticklabel_format(axis="y", style="sci", scilimits=(0,0))
-            ax.yaxis.get_offset_text().set_fontsize(font['size'])
-            ax.xaxis.get_offset_text().set_fontsize(font['size'])
-            plt.grid(True)
-
-            title = 'Point ID: '+str(x)
-            plt.title(title,fontdict=font)
-            fig.subplots_adjust(top=0.95)
-            plotname = OutputName if OutputName == 'p' else 'velocity'
-            # fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.svg', bbox_inches='tight')
-            fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.pdf', bbox_inches='tight')
-            plt.close()
-
-modelName = 'ffpm-stokesdarcyER' #stokespnm stokesdarcyER stokesdarcyBJ
-postPredictiveplot(modelName, errorPrec=0.05, case='Calib', bins=75)
-#postPredictiveplot(modelName+'-valid', errorPrec=0.05, case='Valid',bins=75)