From 50e9a7300f5b66bbdeaf77af47c6cb9c8fd15884 Mon Sep 17 00:00:00 2001
From: farid <farid.mohammadi@iws.uni-stuttgart.de>
Date: Mon, 17 May 2021 17:18:58 +0200
Subject: [PATCH] [PA-A] modified post plot.
---
.../tests/PA-A/ffpm_validation_stokesdarcy.py | 4 +-
.../tests/PA-A/ffpm_validation_stokespnm.py | 2 +-
BayesValidRox/tests/PA-A/post_plot.py | 54 +++++++++++--------
3 files changed, 35 insertions(+), 25 deletions(-)
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
index 1ff7de7a4..4cba2ebe9 100644
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
@@ -130,7 +130,7 @@ def run(params, errorPerc=0.1, couplingcond='BJ', PCEEDMethod='normal'):
MetaModelOpts = Metamodel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
- MetaModelOpts.DimRedMethod = 'PCA'
+ # MetaModelOpts.DimRedMethod = 'PCA'
# MetaModelOpts.varPCAThreshold = 99.99
# Select your metamodel method
@@ -296,7 +296,7 @@ def run(params, errorPerc=0.1, couplingcond='BJ', PCEEDMethod='normal'):
# Select the inference method
BayesOptsCalib.SamplingMethod = 'MCMC'
# BayesOptsCalib.MultiProcessMCMC = False
- BayesOptsCalib.MCMCnwalkers = 300 #100
+ BayesOptsCalib.MCMCnwalkers = 500 #100
# BayesOptsCalib.MCMCnSteps = 1500 #5000
BayesOptsCalib.MAP ='mean'
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
index b06dd8ec3..db8222f18 100755
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
@@ -291,7 +291,7 @@ def run(params, errorPerc=0.1, PCEEDMethod='normal'):
BayesOptsCalib.SamplingMethod = 'MCMC'
# import emcee
# BayesOptsCalib.MCMCmoves = emcee.moves.StretchMove()
- BayesOptsCalib.MCMCnwalkers = 300 #300
+ BayesOptsCalib.MCMCnwalkers = 500 #300
# BayesOptsCalib.MCMCnSteps = 1500 #5000
BayesOptsCalib.MAP ='mean'
diff --git a/BayesValidRox/tests/PA-A/post_plot.py b/BayesValidRox/tests/PA-A/post_plot.py
index de4d77a9f..9f26ebec2 100755
--- a/BayesValidRox/tests/PA-A/post_plot.py
+++ b/BayesValidRox/tests/PA-A/post_plot.py
@@ -6,7 +6,7 @@ Created on Tue Feb 18 08:28:13 2020
@author: farid
"""
import numpy as np
-import os
+import os, sys
import seaborn as sns
import h5py
from scipy.stats import norm
@@ -20,7 +20,8 @@ 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,
@@ -34,7 +35,7 @@ params = {'legend.fontsize': fsize,
# 'mathtext.fontset' : 'stix',
# 'mathtext.rm' : 'serif',
'font.family' : 'serif',
- 'font.serif' : "Times New Roman", # or "Times"
+ 'font.serif' : "Times New Roman", # or "Times"
'figure.figsize' : (32, 24),
# 'savefig.dpi':1500,
'text.usetex': True
@@ -50,22 +51,30 @@ def upper_rugplot(data, height=.05, ax=None, **kwargs):
axis=-1)
lc = LineCollection(segs, transform=ax.get_xaxis_transform(), **kwargs)
ax.add_collection(lc)
-
+
def postPredictiveplot(modelName, errorPrec, case='Calib'):
# result_folder = './Results_10_03_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)
-
+
data = pd.read_csv('data/stokesData'+case+'.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':
@@ -77,29 +86,30 @@ def postPredictiveplot(modelName, errorPrec, case='Calib'):
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=50,
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,
color='green', alpha=0.5, stat="count")
-
+
# Print conidence interval of ExpDesign.Y (Trained area)
- with open(result_folder+'PCEModel_'+modelName+'.pkl', 'rb') as input:
- PCEModel = pickle.load(input)
-
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'),
@@ -107,7 +117,7 @@ def postPredictiveplot(modelName, errorPrec, case='Calib'):
linestyle='None', markersize=75, markeredgewidth=1.5, label='Orig. Responses')]
ax.legend(handles=legend_elements, fontsize=75)
-
+
font = {'family': 'serif',
'weight': 'normal',
'size': 100,
@@ -131,6 +141,6 @@ def postPredictiveplot(modelName, errorPrec, case='Calib'):
fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.pdf', bbox_inches='tight')
plt.close()
-# modelName = 'ffpm-stokespnm' #stokespnm stokesdarcyER stokesdarcyBJ
-# postPredictiveplot(modelName, case='Calib')
-# postPredictiveplot(modelName+'-valid', case='Valid')
+# modelName = 'ffpm-stokesdarcyER' #stokespnm stokesdarcyER stokesdarcyBJ
+# postPredictiveplot(modelName, errorPrec=0.1, case='Calib')
+# postPredictiveplot(modelName+'-valid', errorPrec=0.1, case='Valid')
--
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