diff --git a/.gitignore b/.gitignore
index 30bf95043f8b45bce0c013fa12bf7cc258f3841c..5515eb3dd7ff62fa779a91d6af9e61e5f44cab4a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,8 +3,12 @@
*.hdf5
!BayesValidRox/tests/PA-A/data/ValidationSets/ExpDesign*
*.pdf
+*.png
+*.svg
+*.zip
*.vtu
*.vtp
+*.pvd
BayesValidRox/.spyderproject/
BayesValidRox/__pycache__/*
diff --git a/BayesValidRox/tests/PA-A/post_plot.py b/BayesValidRox/tests/PA-A/post_plot.py
deleted file mode 100755
index 7ca0e7779976e8882c8001ef266016ce03d79d3c..0000000000000000000000000000000000000000
--- a/BayesValidRox/tests/PA-A/post_plot.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/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)