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SimuResult_Synthe.py
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SimuResult_Synthe.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#Comparaison pour des donnees de terrain
"""
Created on Wed May 13 22:49:07 2020
@author: el
"""
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
import glob
import io
import numpy as np
from param_acquisition import ParamGPRMAX, ParamMVG, Geometry
from Forward import Forward
from F_extractTWT import F_extractTWT
from F_extract_volumes import F_extract_volumes
import math
from outils import read_parameters, rada_plot
pd.set_option('max_columns', 7)
#%% pathounet
data_path='/home/el/OUT/Codes/Porchet-GPR/OUTdtrou35_rtrou4_tr5.0/'
X_path='/home/el/Codes/Porchet-GPR/'
hehe=os.getcwd()
if(hehe==X_path):
print('HEHEHE on est bon')
else:
os.chdir(X_path)
#%%folder
#
foldernama='./OUTRosettadtrou30_rtrou4.0_tr5.0/'
fname=next(os.walk(foldernama))[1]
#%%Data
#CDC
hahat=glob.glob('/home/el/Data/Compil_data-Kriterres/061218-Cul-du-chien/Fit-avec-baseOUTdtrou30_rtrou4_tr5.0/twt*.csv')
ouca='Bilb'
fontouney=20
listounet=[]
for filit in hahat:
fin=filit.find('.csv',-4)
guili=filit.find('/',-30)
debut=filit.find(ouca,guili)+len(ouca)
Nama=filit[debut:fin]
#Nama=filit[-11:-4] #Poligny
#Nama=filit[-12:-4]#Bilbo
#Nama=filit[-12:-4]#Auffargis
#temp=np.genfromtxt(filit, delimiter=',')
temp=np.genfromtxt(filit, delimiter=',',skip_header=1)
TWT_XP=temp[:,1]
Time_TWT_XP=temp[:,0]
temp=np.genfromtxt(filit[0:guili]+'/'+'volumes_'+ouca+Nama+'.csv',delimiter=',',skip_header=1)
VOL_XP_temp=temp[:,1]
Time_VOL_XP=temp[:,0]
VOL_XP=0.001*np.interp(Time_TWT_XP,Time_VOL_XP,VOL_XP_temp)
listounet.append([(TWT_XP),(VOL_XP),[255, 165, 0]])
df_params_XP=pd.DataFrame(listounet,columns=['TWT_XP','VOL_XP','Color'])
#%%
#ouca='Poligny'
#ouca='Bilb'
#ouca='Auffar'
ouca='Tcherno'
fontouney=20
lst=[]
for ii in fname:
p=read_parameters(foldernama+ii)
paramMVG = ParamMVG(tr=p[2], ts=p[0], ti=p[1], Ks=p[5], n=p[3], alpha=p[4])
try:
temp=F_extractTWT(foldernama+ii)
vol=np.genfromtxt(foldernama+ii+'/Volumes_EL.csv',delimiter=',',skip_header=1)
#vol=np.nan
bibi = 0
#rmse=np.sqrt(rmseTwt**2+rmsevol**2)
except:
bibi=1
#rmseTwt=np.nan
#rmsevol=np.nan
rmse=np.nan
lst.append([paramMVG.tr,paramMVG.ts,paramMVG.ti,paramMVG.n,paramMVG.alpha,paramMVG.Ks,bibi,(temp),(vol)])
df_params=pd.DataFrame(lst,columns=['tr','ts','ti','n','alpha','Ks','Converged','TWT','VOL'])
df_params['alpha']=df_params['alpha']*100
#cmap = mpl.cm.jet((df_params_sorted_cut['RMSE'].values - df_params_sorted_cut['RMSE'].min())/\
#(df_params_sorted_cut['RMSE'].max() - df_params_sorted_cut['RMSE'].min()))
plt.close('all')
Time_TWT_XP=[0,0.17, 0.33, 0.50, 0.67, 0.83, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00]
from matplotlib.cm import get_cmap
cmap = mpl.cm.autumn
(f2, ax)= plt.subplots(2,1,figsize=(10,10))
k=0
ax[0].grid()
ax[1].grid()
for (index, row) in df_params.iterrows():
#ax[0].plot(Time_TWT_XP, row['TWT'], c=cmap(k/float(len(df_params))))
#ax[1].plot(Time_TWT_XP, 0.001*row['VOL'], c=cmap(k/float(len(df_params))))
ax[0].plot(Time_TWT_XP, row['TWT'], c='r')
ax[1].plot(Time_TWT_XP, 0.001*row['VOL'], c='r')
#k=k+1
for (index, row) in df_params_XP.iterrows():
ax[0].scatter(Time_TWT_XP, row['TWT_XP'],c='k',marker='*',s=100)
ax[1].scatter(Time_TWT_XP, row['VOL_XP'],c='k',marker='*',s=100)
ax[0].tick_params(axis='both', which='major', labelsize=fontouney)
ax[1].tick_params(axis='both', which='major', labelsize=fontouney)
ax[0].set_ylabel('TWT (ns)',fontsize=fontouney)
ax[1].set_ylabel('Vol (l)',fontsize=fontouney)
ax[1].set_xlabel('Experimental time (min)',fontsize=fontouney)
ax[0].set_xlabel('Experimental time (min)',fontsize=fontouney)
f2.savefig('./plots/Synthe_Rosetta.png',format='png')
#%%
################Sensibility plot
df_params_sorted=df_params.sort_values(by=['RMSE'],inplace=False,ascending=False)
df_params_sorted.reset_index(drop=True, inplace=True)
pc=1#percent
df_params_sorted_cut=df_params_sorted[0:np.int(np.round(pc*len(df_params_sorted)))]
df_params_sorted_cut.reset_index(drop=True, inplace=True)
# df_params_sorted_cut=df_params_sorted[df_params_sorted['RMSE']<0.1]
# df_params_sorted_cut.reset_index(drop=True, inplace=True)
df_params=pd.DataFrame()
df_params=df_params_sorted_cut.copy()
plt.close('all')
legendounet=['ti','ts','n','alpha','Ks']
#df_params=df_params[(df_params['tr']==0.03) & (df_params['Ks']<0.49) & (df_params['Ks']>0.07) & (df_params['n']<10.1) ]
(f1, ax)= plt.subplots(5,5,figsize=(25,15))
#cmap = mpl.cm.jet(vmin=0, vmax=1)
#norma = mpl.colors.Normalize(vmin=0, vmax=1)
maxi=df_params_sorted_cut['RMSE'][len(df_params)-1]
mini=df_params_sorted_cut['RMSE'][0]
#maxi=np.log10(1)
#mini=np.log10(0.05)
maxi=2
mini=0
#df_params['RMSE'] = df_params['RMSE'].apply(np.log10)
norm=plt.Normalize(mini,maxi)
for ii in range(5):
for jj in range(5):
if(ii==jj):
ax[ii,jj].hist(df_params[legendounet[ii]], weights=np.zeros_like(df_params_sorted_cut[legendounet[ii]]) + 1. / df_params_sorted_cut[legendounet[ii]].size)
ax[ii,jj].set_xlabel(legendounet[ii],fontsize=fontouney)
ax[ii,jj].set_ylabel('Rel Freq.',fontsize=fontouney)
ax[ii,jj].tick_params(axis='both', which='major', labelsize=fontouney)
ax[ii,jj].grid()
else:
sc=ax[ii,jj].scatter(df_params[legendounet[ii]],df_params[legendounet[jj]],c=df_params_sorted_cut.RMSE.apply(np.log10),cmap = 'jet',norm=norm)
#plt.colorbar(sc,ax=ax[ii,jj])
ax[ii,jj].grid()
ax[ii,jj].set_xlabel(legendounet[ii],fontsize=fontouney)
ax[ii,jj].set_ylabel(legendounet[jj],fontsize=fontouney)
ax[ii,jj].tick_params(axis='both', which='major', labelsize=fontouney)
left = 0.045 # the left side of the subplots of the figure
right = 0.988 # the right side of the subplots of the figure
bottom = 0.049 # the bottom of the subplots of the figure
top = 0.987 # the top of the subplots of the figure
wspace = 0.224 # the amount of width reserved for blank space between subplots
hspace = 0.290 # the amount of height reserved for white space between subplots
plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
f1.savefig('./plots/'+ouca+'/RMSEVOLANDTWT_'+str(np.round(100*pc))+'pc_'+ouca+Nama+'.png',format='png')
####################
################### histogram
# df_params_sorted=df_params.sort_values(by=['RMSE'],inplace=False)
# df_params_sorted.reset_index(drop=True, inplace=True)
# pc=0.1#10percent
# df_params_sorted_cut=df_params_sorted[0:np.int(np.round(pc*len(df_params_sorted)))]
# df_params_sorted_cut.reset_index(drop=True, inplace=True)
# #df_params_sorted_cut=df_params_sorted[df_params_sorted['RMSE']<0.1]
# #df_params_sorted_cut.reset_index(drop=True, inplace=True)
# plt.close('all')
# legendounet=['ts','n','alpha','Ks']
# (f2, ax)= plt.subplots(2,3,figsize=(25,15))
# kk=0
# for ii in range(2):
# for jj in range(2):
# ax[ii,jj].hist(df_params_sorted_cut[legendounet[kk]], weights=np.zeros_like(df_params_sorted_cut[legendounet[kk]]) + 1. / df_params_sorted_cut[legendounet[kk]].size)
# ax[ii,jj].set_xlabel(legendounet[kk]+' Optim= '+str(df_params_sorted_cut[legendounet[kk]][0]),fontsize=fontouney)
# ax[ii,jj].set_ylabel('Rel Freq.',fontsize=fontouney)
# #ax[ii,jj].plot(df_params_sorted_cut[legendounet[kk]][0], 0.15, marker = '*', markersize = 10, mfc = 'k')
# ax[ii,jj].tick_params(axis='both', which='major', labelsize=fontouney)
# ax[ii,jj].grid()
# ax[ii,jj].set_ylim([0, 0.3])
# kk=kk+1
# #tcherno
# ii=1
# jj=2
# legendounet=['ti']
# kk=0
# ax[ii,jj].hist(df_params_sorted_cut[legendounet[kk]], weights=np.zeros_like(df_params_sorted_cut[legendounet[kk]]) + 1. / df_params_sorted_cut[legendounet[kk]].size)
# ax[ii,jj].set_xlabel(legendounet[kk]+' Optim= '+str(df_params_sorted_cut[legendounet[kk]][0]),fontsize=fontouney)
# ax[ii,jj].set_ylabel('Rel Freq.',fontsize=fontouney)
# #ax[ii,jj].plot(df_params_sorted_cut[legendounet[kk]][0], 0.15, marker = '*', markersize = 10, mfc = 'k')
# ax[ii,jj].tick_params(axis='both', which='major', labelsize=fontouney)
# ax[ii,jj].grid()
# ax[ii,jj].set_ylim([0, 0.3])
# cmap = mpl.cm.jet((df_params_sorted_cut['RMSE'].values - df_params_sorted_cut['RMSE'].min())/\
# (df_params_sorted_cut['RMSE'].max() - df_params_sorted_cut['RMSE'].min()))
# for color, (index, row) in zip(cmap,df_params_sorted_cut.iterrows()):
# ax[0,2].plot(Time_TWT_XP, row['TWT'], c=color)
# #ax[1,2].plot(Time_TWT_XP, row['VOL'], c=color)
# ax[0,2].grid()
# ax[1,2].grid()
# ax[0,2].plot(Time_TWT_XP, TWT_XP, marker = '+', markersize = 10, mfc = 'k')
# ax[0,2].plot(Time_TWT_XP,df_params_sorted_cut['TWT'][0], marker = '*', markersize = 10, mfc = 'k')
# #ax[1,2].plot(Time_TWT_XP, 0.001*VOL_XP, marker = '+', markersize = 10, mfc = 'k')
# #ax[1,2].plot(Time_TWT_XP,df_params_sorted_cut['VOL'][0], marker = '*', markersize = 10, mfc = 'k')
# ax[0,2].tick_params(axis='both', which='major', labelsize=fontouney)
# ax[1,2].tick_params(axis='both', which='major', labelsize=fontouney)
# ax[0,2].set_ylabel('TWT(ns)',fontsize=fontouney)
# ax[0,2].set_xlabel('Exp. Time (min)',fontsize=fontouney)
# #ax[1,2].set_ylabel('Vol. (l)',fontsize=fontouney)
# #ax[1,2].set_xlabel('Exp. Time (min)',fontsize=fontouney)
# f2.suptitle(str(np.round(100*pc))+'% des modèles '+ ouca+'-'+Nama+'\n Nombre de modeles: '+str(len(df_params_sorted_cut))+', RMSE: ['+str(np.round(df_params_sorted_cut['RMSE'][0],4))+';'+str(np.round(df_params_sorted_cut['RMSE'][len(df_params_sorted_cut)-1],4))+'] (ns)', fontsize=fontouney)
# f2.savefig('./plots/'+ouca+'/Histo_'+'pc_'+str(round(100*pc))+'_'+ouca+'-'+Nama+'.png',format='png')
###################
#############Sensibility plot mais en coupant Ti
# plt.close('all')
# legendounet=['ti','ts','n','alpha','Ks']
# gnifalou=df_params['ti'].unique()
# shortl=['tr','ts','n','alpha','Ks']
# fontouney=20
# for kk in gnifalou:
# (f1, ax)= plt.subplots(5,5,figsize=(25,15))
# norm=plt.Normalize(0,2)
# df_params_cutted=df_params[df_params['ti']==round(float(kk),2)].copy()
# for ii in range(5):
# for jj in range(5):
# if(ii==jj and df_params_cutted.empty==False):
# ax[ii,jj].hist(df_params_cutted[shortl[ii]], weights=np.zeros_like(df_params_cutted[shortl[ii]]) + 1. / df_params_cutted[shortl[ii]].size)
# ax[ii,jj].set_xlabel(shortl[ii])
# ax[ii,jj].set_ylabel('Rel Freq.')
# ax[ii,jj].grid()
# else:
# ax[ii,jj].scatter(df_params_cutted[shortl[ii]],df_params_cutted[shortl[jj]],c=df_params_cutted.RMSE,cmap = 'jet',norm=norm)
# #plt.colorbar(sc,ax=ax[ii,jj])
# ax[ii,jj].grid()
# ax[ii,jj].set_xlabel(shortl[ii])
# ax[ii,jj].set_ylabel(shortl[jj])
# f1.suptitle(' ti = '+str(kk), fontsize=fontouney)
# f1.savefig('./plots/'+ouca+'/RMSEVOLANDTWT_'+ouca+Nama+'_ti='+str(kk)+'.png',format='png')
##### RMSE en fonction de param
# plt.close('all')
# legendounet=['ts','n','alpha','Ks']
# (f2, ax)= plt.subplots(2,2,figsize=(25,15))
# kk=0
# norm=plt.Normalize(0,2)
# for ii in range(2):
# for jj in range(2):
# ax[ii,jj].scatter(df_params[legendounet[kk]],df_params['RMSE'],c=df_params.RMSE,cmap = 'jet',norm=norm)
# ax[ii,jj].set_xlabel(legendounet[kk]+' Optim= '+str(df_params_sorted_cut[legendounet[kk]][0]),fontsize=fontouney)
# ax[ii,jj].set_ylabel('RMSE',fontsize=fontouney)
# #ax[ii,jj].plot(df_params_sorted_cut[legendounet[kk]][0], 0.15, marker = '*', markersize = 10, mfc = 'k')
# ax[ii,jj].tick_params(axis='both', which='major', labelsize=fontouney)
# ax[ii,jj].grid()
# ax[ii,jj].set_ylim([0, 5])
# kk=kk+1
# f2.savefig('./plots/'+ouca+'/RMSE_Param'+ouca+'-'+Nama+'.png',format='png')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Compa total les sommant tous
#%% on lit les fichiers de data
#filiname=next(os.walk('/home/el/Data/Compil_data-Kriterres/061218-Cul-du-chien/Fit-avec-baseOUTdtrou30_rtrou4_tr5.0/*.txt'))[2]
hahat=glob.glob('/home/el/Data/Compil_data-Kriterres/190527-Poligny/Fit-avec-baseOUTdtrou30_rtrou4_tr5.0/TWT*.txt')
hahav=glob.glob('/home/el/Data/Compil_data-Kriterres/190527-Poligny/Fit-avec-baseOUTdtrou30_rtrou4_tr5.0/Vol*.txt')
lst=[]
#%% Reading the folder names
fname=next(os.walk('./OUTdtrou30_rtrou4_tr5.0/'))[1]
#%%
for filit,filiv in zip(hahat,hahav):
#Nama=fili[-16:-4]
temp=np.genfromtxt(filit, delimiter=' ')
TWT_XP=temp[:,1]
Time_TWT_XP=temp[:,0]
temp=np.genfromtxt(filiv,delimiter=',')
VOL_XP_temp=temp[:,1]
Time_VOL_XP=temp[:,0]
VOL_XP=np.interp(Time_TWT_XP,Time_VOL_XP,VOL_XP_temp)
for ii in fname:
p=read_parameters('./OUTdtrou30_rtrou4_tr5.0/'+ii)
paramMVG = ParamMVG(tr=p[2], ts=p[0], ti=p[1], Ks=p[5], n=p[3], alpha=p[4])
try:
#temp = np.genfromtxt('./OUTdtrou30_rtrou4_tr5.0/'+ii+'/TWT_EL.csv', delimiter=',',skip_header=1)
temp=F_extractTWT('./OUTdtrou30_rtrou4_tr5.0/'+ii)
vol=np.genfromtxt('./OUTdtrou30_rtrou4_tr5.0/'+ii+'/Volumes_EL.csv',delimiter=',',skip_header=1)
bibi = 0
rmseTwt=np.sqrt(np.mean((temp-TWT_XP)**2))
rmsevol=np.sqrt(np.mean(((0.001*(vol-VOL_XP))**2)))
rmse=np.sqrt(rmseTwt**2+rmsevol**2)
except:
bibi=1
rmseTwt=np.nan
rmsevol=np.nan
rmse=np.nan
lst.append([paramMVG.tr,paramMVG.ts,paramMVG.ti,paramMVG.n,paramMVG.alpha,paramMVG.Ks,rmseTwt,rmsevol,rmse,bibi])
df_params=pd.DataFrame(lst,columns=['tr','ts','ti','n','alpha','Ks','RMSETWT','RMSEVOL','RMSE','Converged'])
#%%
df_params_sorted=df_params.sort_values(by=['RMSE'],inplace=False)
df_params_sorted.reset_index(drop=True, inplace=True)
pc=0.1#10percent
df_params_sorted_cut=df_params_sorted[0:np.int(np.round(pc*len(df_params_sorted)))]
df_params_sorted_cut.reset_index(drop=True, inplace=True)
#df_params=df_params[(df_params['RMSE']0.03) & (df_params['Ks']<0.49) & (df_params['Ks']>0.07) & (df_params['n']<10.1) ]
plt.close('all')
legendounet=['ts','n','alpha','Ks']
(f2, ax)= plt.subplots(2,2,figsize=(25,15))
kk=0
fontouney=20
for ii in range(2):
for jj in range(2):
ax[ii,jj].hist(df_params_sorted_cut[legendounet[kk]], weights=np.zeros_like(df_params_sorted_cut[legendounet[kk]]) + 1. / df_params_sorted_cut[legendounet[kk]].size)
ax[ii,jj].set_xlabel(legendounet[kk],fontsize=fontouney)
ax[ii,jj].set_ylabel('Rel Freq.',fontsize=fontouney)
ax[ii,jj].grid()
kk=kk+1
f2.suptitle(str(100*pc)+'% du meilleur modele - Total-Poligny', fontsize=fontouney)
f2.savefig('./plots/Histo_'+str(100*pc)+'pc_Total-Poligny.png',format='png')