diff --git a/BayesValidRox/tests/PA-A/util/postDist_visualization.py b/BayesValidRox/tests/PA-A/util/postDist_visualization.py
index 752757c11770ba913839844e07a754ec56f84c00..e5843e284c0dbcc94dee6ec995add1bfeb0cb33f 100755
--- a/BayesValidRox/tests/PA-A/util/postDist_visualization.py
+++ b/BayesValidRox/tests/PA-A/util/postDist_visualization.py
@@ -5,27 +5,25 @@ Created on Thu Aug 13 09:53:11 2020
@author: farid
"""
-import sys, os, joblib
+import sys
+import os
+import joblib
import numpy as np
import pandas as pd
import h5py
import corner
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import _pickle as pickle
# Local
-sys.path.insert(0,'./../../../../BayesValidRox/')
+sys.path.insert(0, './../../../../BayesValidRox/')
-inletLoc = 'top' # top or left
-inclusion = 'squared' # squared or circular
+inletLoc = 'top' # top or left
+inclusion = 'squared' # squared or circular
-Name = 'stokesdarcyER' #stokesdarcyER stokesdarcyBJ stokespnm stokespnmNA
-modelName = 'ffpm-{}_{}_inclusion_{}Inflow'.format(Name,inclusion,inletLoc)
+name = 'stokespnmNA' # stokesdarcyER stokesdarcyBJ stokespnm stokespnmNA
+modelName = f'ffpm-{name}_{inclusion}_inclusion_{inletLoc}Inflow'
# Load the pickle objects
-data_dir = '../Results_15_12_2021_topInflow/outputs_{}/'.format(modelName)
+data_dir = '../Results_19_01_2022_topInflow/outputs_{}/'.format(modelName)
# data_dir = '../outputs_{}/'.format(modelName)
# with open(data_dir+'PCEModel_ffpm-{}.pkl'.format(modelName), 'rb') as input:
@@ -44,10 +42,9 @@ lw = 3
alpha = 0.45
plt.rc('figure', figsize = (24, 16))
plt.rc('font', family='serif', serif='Arial')
-plt.rc('axes', grid = True)
# plt.rc('text', usetex=True)
plt.rc('axes', linewidth=lw)
-plt.rc('axes', grid=True)
+plt.rc('axes', grid=False)
plt.rc('grid', linestyle="-")
plt.rc('font', size=BIGGER_SIZE) # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title
@@ -58,9 +55,9 @@ plt.rc('legend', fontsize=BIGGER_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
-#=====================================================
-#========== DEFINITION OF THE METAMODEL ============
-#=====================================================
+# =====================================================
+# ========== DEFINITION OF THE METAMODEL ============
+# =====================================================
PCEModel = Bayes.PCEModel
# Update slicing index
# Extract the experimental design
@@ -72,12 +69,16 @@ EDY = PCEModel.ExpDesign.Y
Outputs = ['velocity [m/s]', 'p']
# labels = [PCEModel.Inputs.Marginals[i].Name for i in range(PCEModel.NofPa)]
labels = list(Bayes.Posterior_df.keys())
-#=====================================================
-#================= Visualization ===================
-#=====================================================
+if '$K$' in labels:
+ index = labels.index('$K$')
+ labels[index] = r"$\mathsf{K}$"
+# =====================================================
+# ================= Visualization ===================
+# =====================================================
# Make directory
outDir = './posteriorPlot'
-if not os.path.exists(outDir): os.makedirs(outDir)
+if not os.path.exists(outDir):
+ os.makedirs(outDir)
# ----- Posterior plot ---------
# import emcee
@@ -126,11 +127,16 @@ goodpostSamples = postSamples#[goodIndices]
figPosterior = corner.corner(goodpostSamples, labels=labels,
# range=PCEModel.ExpDesign.BoundTuples,
# color='grey',
- use_math_text = True,
+ use_math_text=True,
quantiles=[0.15, 0.5, 0.85],
show_titles=True,
labelpad=0.2,
title_fmt='.2e',
+ plot_datapoints=False,
+ plot_density=False,
+ fill_contours=True,
+ smooth=0.5,
+ smooth1d=0.5,
title_kwargs={"fontsize": SMALL_SIZE})
# Loop over axes and set x limits
diff --git a/BayesValidRox/tests/PA-A/util/post_plot_PAPER_v2.py b/BayesValidRox/tests/PA-A/util/post_plot_PAPER_v2.py
index 83c172bf98993843467501d716dc973b52b9f764..c4fe87c30bca48c0072916383cf2a5865abd45cc 100644
--- a/BayesValidRox/tests/PA-A/util/post_plot_PAPER_v2.py
+++ b/BayesValidRox/tests/PA-A/util/post_plot_PAPER_v2.py
@@ -9,7 +9,6 @@ import numpy as np
import os
import sys
import joblib
-import seaborn as sns
import h5py
import pandas as pd
import matplotlib.pylab as plt
@@ -18,7 +17,8 @@ import matplotlib.pylab as plt
sys.path.insert(0, "./../../../../BayesValidRox/")
plt.rcParams.update({'lines.markeredgewidth': 1})
-def postPredictiveplot(modelNames, result_folder= ".", case="Calib",
+
+def postPredictiveplot(modelNames, result_folder=".", case="Calib",
inclusion="squared", inletLoc="top", bins="auto"):
OutputDir = f"../{result_folder}/postPredPlots_{case}"
@@ -35,15 +35,19 @@ def postPredictiveplot(modelNames, result_folder= ".", case="Calib",
modelName = f'ffpm-{name}_{inclusion}_inclusion_{inletLoc}Inflow'
if case == 'calib':
- directory = f"../{result_folder}/outputs_{modelName}/Outputs_Bayes_{modelName}_{case}"
+ directory = f"../{result_folder}/outputs_{modelName}/Outputs_"\
+ f"Bayes_{modelName}_{case}"
else:
- directory = f"../{result_folder}/outputs_{modelName}/Outputs_Bayes_{modelName}-valid_{case}"
+ directory = f"../{result_folder}/outputs_{modelName}/Outputs_"\
+ f"Bayes_{modelName}-valid_{case}"
# Load Post pred file
f = h5py.File(f"{directory}/postPredictive.hdf5", "r+")
# Load PCEModel
- with open(f"../{result_folder}/outputs_{modelName}/PCEModel_{modelName}.pkl", "rb") as input:
+ file_name = f"../{result_folder}/outputs_{modelName}/PCEModel_"\
+ f"{modelName}.pkl"
+ with open(file_name, "rb") as input:
PCEModel = joblib.load(input)
if case == "Calib":
@@ -111,12 +115,12 @@ def postPredictiveplot(modelNames, result_folder= ".", case="Calib",
modelNames = ['stokesdarcyBJ', 'stokesdarcyER', 'stokespnm']
-inletLoc = 'top' # top or left
-inclusion = 'squared' # squared or circular
-path = "Results_09_01_2022_topInflow"
+inletLoc = 'top' # top or left
+inclusion = 'squared' # squared or circular
+path = "Results_19_01_2022_topInflow"
case = 'Valid'
-allMeanPred, allStdPred = postPredictiveplot(modelNames, inclusion=inclusion,
+allMeanPred, allStdPred = postPredictiveplot(modelNames, inclusion=inclusion,
inletLoc=inletLoc,
result_folder=path,
case='Valid')
diff --git a/BayesValidRox/tests/PA-A/util/sobol_indices.py b/BayesValidRox/tests/PA-A/util/sobol_indices.py
index 120f6067922fc731a3cd066d8c2d57e1869ffc87..20387ed483c6bcb7308ac73c55a79ee085a9c269 100644
--- a/BayesValidRox/tests/PA-A/util/sobol_indices.py
+++ b/BayesValidRox/tests/PA-A/util/sobol_indices.py
@@ -10,10 +10,10 @@ import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
SIZE = 60
-plt.rc('figure', figsize = (24, 16))
+plt.rc('figure', figsize=(24,16))
plt.rc('font', family='serif', serif='Arial')
plt.rc('font', size=SIZE)
-plt.rc('axes', grid = True)
+plt.rc('axes', grid=True)
plt.rc('text', usetex=True)
plt.rc('axes', linewidth=3)
plt.rc('axes', grid=True)
@@ -25,36 +25,45 @@ plt.rc('ytick', labelsize=SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SIZE) # legend fontsize
plt.rc('figure', titlesize=SIZE) # fontsize of the figure title
-result_dir = '../Results_20_09_2021/'
-modelName = 'stokesdarcyER' #stokesdarcyBJ stokesdarcyER stokespnm
+result_dir = '../Results_09_01_2022_topInflow/'
+
+inletLoc = "top" # top or left
+inclusion = "squared" # squared or circular
+Name = "stokesdarcyBJ" # stokesdarcyER stokesdarcyBJ stokespnm stokespnmNA
+modelName = "ffpm-{}_{}_inclusion_{}Inflow".format(Name, inclusion, inletLoc)
fig = plt.figure()
xlabel = 'Point ID'
-x_values = {'velocity [m_s]':[1,2,3,4,5,6,7,8,9,10], 'p': [1,2,3]}
-outNames = ['velocity [m_s]','p']
+x_values = {'velocity [m_s]': np.arange(1, 11), 'p': np.arange(1, 4)}
+outNames = ['velocity [m_s]', 'p']
for outIdx, Output in enumerate(outNames):
-
- delimiter = ';' if 'pnm' in modelName else ','
-
- total_sobols = pd.read_csv(result_dir+'outputs_ffpm-{}/Outputs_PostProcessing_calib/totalsobol_{}.csv'.format(modelName,Output)
- ,delimiter=delimiter)
+
+ total_sobols = pd.read_csv(f'{result_dir}outputs_{modelName}/'\
+ f'Outputs_PostProcessing_calib/totalsobol_'\
+ f'{Output}.csv',
+ delimiter=',')
total_sobol = total_sobols.to_numpy()
- parNames = list(total_sobols.keys())
-
- ax = fig.add_axes([0,0,1,1])
+ parNames = list(total_sobols.keys())
+ if '$K$' in parNames:
+ index = parNames.index('$K$')
+ parNames[index] = r"$\mathsf{K}$"
+
+ ax = fig.add_axes([0, 0, 1, 1])
dict1 = {xlabel: x_values[Output]}
- dict2 = {param: sobolIndices for param, sobolIndices in zip(parNames,total_sobol.T)}
-
+ dict2 = {param: sobolIndices for param, sobolIndices in zip(parNames,
+ total_sobol.T)}
+
df = pd.DataFrame({**dict1, **dict2})
- df.plot(x=xlabel, y=parNames, kind="bar", ax=ax , rot=0, colormap='Dark2')
+ df.plot(x=xlabel, y=parNames, kind="bar", ax=ax, rot=0, colormap='Dark2',
+ edgecolor="black", lw=1)
ax.set_ylabel('Total Sobol indices, $S^T$')
# save the current figure
- fig.savefig('Sobol_indices_{}_{}.pdf'.format(Output,modelName), bbox_inches='tight')
-
+ fig.savefig(f'Sobol_indices_{Output}_{modelName}.pdf', bbox_inches='tight')
+
# Destroy the current plot
plt.clf()
diff --git a/configure.sh b/configure.sh
index 3300074ea449907c2d3ab89d9087c6cdb75a6186..f482ea2289144316876f0082ebe6519a7175370a 100755
--- a/configure.sh
+++ b/configure.sh
@@ -14,5 +14,8 @@ python3 -m pip install tables==3.6.1 --upgrade --user
python3 -m pip install corner==2.2.1 --user
python3 -m pip install h5py==3.2.1 --user
# python3 -m pip install vtk --user
-sudo apt-get install python3-vtk7
python3 -m pip install meshio==5.0.0 --user
+
+# Here you need super user rights
+sudo apt-get install python3-vtk7
+sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super