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my_functions.py
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my_functions.py
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import numpy as np
from matplotlib import pylab, mlab, pyplot
from pylab import *
from IPython.core.pylabtools import figsize, getfigs
plt = pyplot
import os
from os import listdir
from os.path import isfile, join
import sys
from pathlib import Path
import re
# from natsort import natsorted
import timeit
# sys.path.append('/home/adrian/Documents/Programmes/Fortran_Jofre')
# from function_jofre import energy_lost
# sélection des fichiers (Python 3)
import tkinter as tk
from tkinter import filedialog
def load_file_GUI(dir_string):
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename(initialdir = dir_string,
multiple=True)
return file_path
def size_data(file_path,row_skip,col_to_read,delim):
all_size = []
m = 0
for k in range(0, len(file_path)):
temp_size = loadtxt(file_path[k], delimiter=delim,
skiprows=row_skip, usecols=col_to_read,
unpack=True)[0].size
all_size.append(temp_size)
if temp_size > m:
m = temp_size
return all_size,m
# import données /usr/share/myspell/dicts/en_GB.dic
def import_data(file_path,row_skip,col_to_read,delim):
dico = {} # creation dictionnaire
for i in file_path: # pour associer à chaque fichier
dico[i] = [] # un nombre de colonnes dépendant de col_to_read
# dico de la forme
# dico = {'my_file1.csv': array([1,2,3]),
# 'my_file2.csv': array([2,4,6]),
# 'my_file3.csv': array([5,10,15])}
# récup valeur dans dico : dico.get('my_file1.csv')
for k in range(0, len(file_path)):
dico[file_path[k]] = loadtxt(file_path[k], delimiter=delim,encoding ='utf-8',
skiprows=row_skip, usecols=col_to_read,
unpack=True)
return dico
def convert_dico_to_var(dico):
file_path = list(dico.keys()) # all the keys from dico
# création variables courantes
n = len(dico) # nombre de fichiers
m = len(dico.get(file_path[0])[0]) # longueur colones
p = len(dico.get(file_path[0])[1:]) # nombre de col : temps + canaux oscillo
shape = (n, m)
TP = zeros(shape) # time
CH = zeros((n, p, m)) # les canaux de l'oscillo (p-1) CH[file:channel:time]
for k in range(0, n):
TP[k, :] = dico.get(file_path[k])[0, :]
for l in range(1, p+1):
CH[k, l-1, :] = dico.get(file_path[k])[l, :]
return TP,CH
##### FUNCTION_JOFRE.IPY #####
def load_T_and_PM_simu(str_load):
data = loadtxt('{}.dat'.format(str_load),comments='%');
return data[:,0],data[:,1:4], data[:,4:7], data[:,7] # tt, T_CM, T_aux, PM
def load_T_and_PM_simu_noGMOL(str_load):
data = loadtxt('{}.dat'.format(str_load),comments='%');
return data[:,0],data[:,1:4] # tt, T_CM, T_aux, PM
def load_xyz_init_bin_DP(str_load):
aux_info = loadtxt(str_load+'.info',comments='%');
n_ions1 = int(aux_info[0])
n_ions2 = int(aux_info[1])
n_ions = n_ions1 + n_ions2
# print(n_ions1, n_ions2)
imax = 12 * n_ions
# print('extra_time', aux_info[19])
fid = open(str_load+'.bin', 'rb')
junk = fromfile(fid, int32,1) # Read record start tag
aux = fromfile(fid, int32, 3);
junk = fromfile(fid, int32,1) # Read record stop tag
junk = fromfile(fid, int32,1) # Read record start tag
aux = fromfile(fid, float64, imax);
junk = fromfile(fid, int32,1) # Read record stop tag
fid.close
aux = reshape(aux, (12, n_ions),order='F')
r_LC = 1.e3*aux[0:3,0:n_ions1]
v_LC = aux[3:6,0:n_ions1]
#xyz = 1.e3*aux[0:3,:]
a_LC = aux[6:9,0:n_ions1]
v_LC_avg = aux[9:12,0:n_ions1]
return r_LC,v_LC,a_LC,v_LC_avg
def plot_XYZ(file_name,fig_name='2',fig_title='XYZ'):
r_LC,v_LC,a_LC,v_LC_avg = load_xyz_init_bin_DP(file_name)
figure(fig_name); clf()
title(fig_title)
subplot(211,aspect=1.0)
plot(r_LC [0,:]*1e3,r_LC [1,:]*1e3,'8',color='xkcd:purplish blue')
xlabel('x[µm]')
ylabel('y[µm]')
grid()
# subplot(212,aspect=1.0)
subplot(212)
plot(r_LC [2,:]*1e3,r_LC [0,:]*1e3,'8',color='xkcd:purplish blue')
xlabel('z[µm]')
ylabel('x[µm]')
grid()
tight_layout()
# def plot_T_and_PM_Init_Inje_Evol(file_dir,file1, file2, file3,fig_name='3'):
def plot_T_and_PM_Init_Inje_Evol(file_dir2,file_name,flag_plot,fig_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
file0 = file_dir2+'Temp_3D_Harmo_N1024_T500uK_F0.15D-18Kg_s_5'
file1 = 'SimuTypeQ' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
file2 = 'SimuType4_01' + file_name[i_aux:]
file3 = 'SimuType2_01' + file_name[i_aux:]
N_ions, j_save, dt_j_save_next, eta, Temp, save_T = load_Temp_init_bin_Lan(file0, flag_print=0)
tt0 = save_T[:,0]
T_CM0 = save_T[:,1:4]
T_aux0 = save_T[:,4:7]
# tt0, T_CM0, T_aux0, PM0 = load_T_and_PM_simu(file_dir2+file0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'Temp_'+file2+'50eV')
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'Temp_'+file3+'50eV')
aux = mean(PM1[-100:])
PM_variation = ( aux - mean(PM3[-100:]) ) / aux
T_variation = mean(T_aux3[-100:,0]) + mean(T_aux3[-100:,1]) + mean(T_aux3[-100:,2])
# Auxiliary arrays:
t_aux1 = array([tt2[ 0],tt2[ 0]])
t_aux2 = array([tt2[-1],tt2[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
tt = concatenate( ( tt0, tt1, tt2, tt3) )
tt_laser = concatenate( ( tt1, tt2, tt3) )
T_CM = concatenate( (T_CM0,T_CM1,T_CM2,T_CM3) )
T_aux = concatenate( (T_aux0,T_aux1,T_aux2,T_aux3) )
PM = concatenate( (PM1,PM2,PM3) )
SNR = np.abs( aux - mean(PM3[-100:]) )/np.sqrt(aux) # Sig-to-Noi ratio considering Poisson noise
if flag_plot == 1 :
#fig_name = file_name[-9:]
fig = figure(fig_name); clf()
ax1 = subplot(211)
semilogy(tt*1.e3,T_aux[:,0], label='Tx')
semilogy(tt*1.e3,T_aux[:,1], label='Ty')
semilogy(tt*1.e3,T_aux[:,2], label='Tz')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax1.grid()
legend()
# ~ xlabel('time[ms]')
# ~ ylabel('T[K]')
plt.setp(ax1.get_xticklabels(),visible=False)
ax2 = subplot(212,sharex=ax1)
plot(tt_laser*1.e3,PM[:])
plot(t_aux1*1.e3,y2_aux,'r')
plot(t_aux2*1.e3,y2_aux,'r')
ax2.grid()
xlabel('time[ms]')
ylabel('Counts')
ax1.set_xlim(xlim1)
ax1.set_ylim(ylim1)
ax2.set_ylim(ylim2)
plt.tight_layout()
subplots_adjust(hspace=0.015)
temps = [tt,t_aux1, t_aux2]
fluo = [PM, PM_variation, SNR]
temperature = [T_aux, T_variation, T_CM]
return temps, temperature, fluo
def plot_T_and_PM_InitQ_Inje_Evol(file_dir2,file_name,flag_plot,fig_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
# file0 = 'Temp_3D_Harmo'+ file_name[i_aux:]
file1 = 'SimuTypeQ' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
file2 = 'SimuType4_01' + file_name[i_aux:]
file3 = 'SimuType2_01' + file_name[i_aux:]
# tt0, T_CM0, T_aux0, PM0 = load_T_and_PM_simu(file_dir2+file0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'Temp_'+file2+'50eV')
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'Temp_'+file3+'50eV')
aux = mean(PM1[-100:])
PM_variation = ( aux - mean(PM3[-100:]) ) / aux
T_variation = mean(T_aux3[-100:,0]) + mean(T_aux3[-100:,1]) + mean(T_aux3[-100:,2])
# Auxiliary arrays:
t_aux1 = array([tt2[ 0],tt2[ 0]])
t_aux2 = array([tt2[-1],tt2[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
tt = concatenate( ( tt1, tt2, tt3) )
T_CM = concatenate( (T_CM1,T_CM2,T_CM3) )
T_aux = concatenate( (T_aux1,T_aux2,T_aux3) )
PM = concatenate( (PM1,PM2,PM3) )
SNR = np.abs( aux - mean(PM3[-100:]) )/np.sqrt(aux) # Sig-to-Noi ratio considering Poisson noise
if flag_plot == 1 :
#fig_name = file_name[-9:]
fig = figure(fig_name); clf()
ax1 = subplot(211)
semilogy(tt*1.e3,T_aux[:,0], label='Tx')
semilogy(tt*1.e3,T_aux[:,1], label='Ty')
semilogy(tt*1.e3,T_aux[:,2], label='Tz')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax1.grid()
legend()
# ~ xlabel('time[ms]')
# ~ ylabel('T[K]')
plt.setp(ax1.get_xticklabels(),visible=False)
ax2 = subplot(212,sharex=ax1)
plot(tt*1.e3,PM[:])
plot(t_aux1*1.e3,y2_aux,'r')
plot(t_aux2*1.e3,y2_aux,'r')
ax2.grid()
xlabel('time[ms]')
ylabel('Counts')
ax1.set_xlim(xlim1)
ax1.set_ylim(ylim1)
ax2.set_ylim(ylim2)
plt.tight_layout()
subplots_adjust(hspace=0.015)
temps = [tt,t_aux1, t_aux2]
fluo = [PM, PM_variation, SNR]
temperature = [T_aux, T_variation, T_CM]
return temps, temperature, fluo
def plot_T_and_PM_InitQ_Inje(file_dir2,file_name,flag_plot,fig_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
# file0 = 'Temp_3D_Harmo'+ file_name[i_aux:]
file1 = 'SimuTypeQ' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
file2 = 'SimuType4_01' + file_name[i_aux:]
# tt0, T_CM0, T_aux0, PM0 = load_T_and_PM_simu(file_dir2+file0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'Temp_'+file2+'50eV')
aux = mean(PM1[-100:])
# Auxiliary arrays:
t_aux1 = array([tt2[ 0],tt2[ 0]])
t_aux2 = array([tt2[-1],tt2[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
tt = concatenate( ( tt1, tt2) )
T_CM = concatenate( (T_CM1,T_CM2) )
T_aux = concatenate( (T_aux1,T_aux2) )
PM = concatenate( (PM1,PM2) )
if flag_plot == 1 :
#fig_name = file_name[-9:]
fig = figure(fig_name); clf()
ax1 = subplot(211)
semilogy(tt*1.e3,T_aux[:,0], label='Tx')
semilogy(tt*1.e3,T_aux[:,1], label='Ty')
semilogy(tt*1.e3,T_aux[:,2], label='Tz')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax1.grid()
legend()
# ~ xlabel('time[ms]')
# ~ ylabel('T[K]')
plt.setp(ax1.get_xticklabels(),visible=False)
ax2 = subplot(212,sharex=ax1)
plot(tt*1.e3,PM[:])
plot(t_aux1*1.e3,y2_aux,'r')
plot(t_aux2*1.e3,y2_aux,'r')
ax2.grid()
xlabel('time[ms]')
ylabel('Counts')
ax1.set_xlim(xlim1)
ax1.set_ylim(ylim1)
ax2.set_ylim(ylim2)
plt.tight_layout()
subplots_adjust(hspace=0.015)
temps = [tt,t_aux1, t_aux2]
fluo = [PM, 0, 0]
temperature = [T_aux, T_variation, T_CM]
return temps, temperature, fluo
def plot_T_and_PM_Init(file_dir2,file_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
# file0 = 'Temp_3D_Harmo'+ file_name[i_aux:]
file1 = 'SimuType0' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
# tt0, T_CM0, T_aux0, PM0 = load_T_and_PM_simu(file_dir2+file0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
# Auxiliary arrays:
t_aux1 = array([tt1[-1],tt1[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
return tt1, T_CM1, T_aux1, PM1
def plot_T_and_PM_Quen(file_dir2,file_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
# file0 = 'Temp_3D_Harmo'+ file_name[i_aux:]
file1 = 'SimuTypeQ' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
# tt0, T_CM0, T_aux0, PM0 = load_T_and_PM_simu(file_dir2+file0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
# Auxiliary arrays:
t_aux1 = array([tt1[-1],tt1[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
return tt1, T_CM1, T_aux1, PM1
def plot_T_and_PM_Inje(file_dir2,file_name,**kwargs):
i_aux = file_name.find('_N')
file2 = 'SimuType4_01' + file_name[i_aux:]
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'/Temp_'+file2+'50eV')
T_variation = mean(T_aux2[-25:,0]) + mean(T_aux2[-25:,1]) + mean(T_aux2[-25:,2])
# Auxiliary arrays:
t_aux1 = array([tt2[ 0],tt2[ 0]])
t_aux2 = array([tt2[-1],tt2[-1]])
y1_aux = array([1.0e-3 ,1.0e-1 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
return tt2, T_CM2, T_aux2, PM2
def plot_T_and_PM_Evol(file_dir2,file_name,**kwargs):
i_aux = file_name.find('_N')
file3 = 'SimuType2_01' + file_name[i_aux:]
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'Temp_'+file3+'50eV')
T_variation = mean(T_aux3[-100:,0]) + mean(T_aux3[-100:,1]) + mean(T_aux3[-100:,2])
return tt3, T_CM3, T_aux3, PM3
def plot_T_and_PM_Init_RFrelax_AfterCooling_Evol(file_dir2,file_name,flag_plot,fig_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.5*1e-3,2e4))
ylim2 = kwargs.get('ylim2', (-2,50))
i_aux = file_name.find('_N')
file1 = 'SimuType0' + file_name[i_aux:]
file2 = 'SimuType4_01' + file_name[i_aux:]
file3 = 'SimuType2_01' + file_name[i_aux:]
file4 = 'SimuType6_01' + file_name[i_aux:]
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'Temp_'+file2+'50eV')
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'Temp_'+file3+'50eV')
try:
tt4, T_CM4, T_aux4, PM4 = load_T_and_PM_simu(file_dir2+'Temp_'+file4+'50eV')
except:
tt4, T_CM4, T_aux4, PM4 = load_T_and_PM_simu(file_dir2+'Temp_'+file4)
T_variation = mean(T_aux2[-100:,0])+mean(T_aux2[-100:,1])+mean(T_aux2[-100:,2]) - mean(T_aux1[-100:,0])+mean(T_aux1[-100:,1])+mean(T_aux1[-100:,2])
aux = mean(PM1[-100:])
PM_variation = ( aux - mean(PM3[-100:]) ) / aux
SNR = np.abs( aux - mean(PM3[-100:]) )/np.sqrt(aux) # Sig-to-Noi ratio considering Poisson noise
# Auxiliary arrays:
t_aux1 = array([tt4[ 0],tt4[ 0]])
t_aux2 = array([tt4[-1],tt4[-1]])
y1_aux = array([1.0e-4 ,3.0e4 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
tt = concatenate( ( tt1, tt4) )
T_CM = concatenate( (T_CM1, T_CM4) )
T_aux = concatenate( (T_aux1,T_aux4) )
PM = concatenate( (PM1,PM4) )
if flag_plot == 1 :
#fig_name = file_name[-9:]
fig = figure(fig_name); clf()
ax1 = subplot(111)
semilogy(tt*1.e3,T_aux[:,0], label='Tx')
semilogy(tt*1.e3,T_aux[:,1], label='Ty')
semilogy(tt*1.e3,T_aux[:,2], label='Tz')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax1.grid()
# ax1.set_xticks([1e-4,1e-2,1,1e2,1e4])
legend()
xlabel('time[ms]')
ylabel('T[K]')
fig.set_size_inches(11.69, 8.27)
plt.tight_layout()
temps = [tt,t_aux1, t_aux2]
fluo = [PM, PM_variation, SNR]
temperature = [T_aux, T_variation, T_CM]
return temps, temperature, fluo
def plot_T_and_PM_Init_RFrelax_AfterInj_Evol(file_dir2,file_name,flag_plot,fig_name,**kwargs):
# ~ xlim1 = (-0.1,6)
# ~ ylim1 = (0.5*1e-3,5e3)
# ~ ylim2 = (-2,120)
xlim1 = kwargs.get('xlim1', (-0.1,6))
ylim1 = kwargs.get('ylim1', (0.2*1e-3,2e4))
ylim3 = kwargs.get('ylim2', (-2,85))
i_aux = file_name.find('_N')
file1 = 'SimuType0' + file_name[i_aux:] ########## file1 = 'SimuType0' + file_name[i_aux:17+36]
file2 = 'SimuType4_01' + file_name[i_aux:]
file3 = 'SimuType2_01' + file_name[i_aux:]
file4 = 'SimuType6_01' + file_name[i_aux:]
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'Temp_'+file1)
tt2, T_CM2, T_aux2, PM2 = load_T_and_PM_simu(file_dir2+'Temp_'+file2+'50eV')
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'Temp_'+file3+'50eV')
try:
tt4, T_CM4, T_aux4, PM4 = load_T_and_PM_simu(file_dir2+'Temp_'+file4+'50eV')
except:
tt4, T_CM4, T_aux4, PM4 = load_T_and_PM_simu(file_dir2+'Temp_'+file4)
aux = mean(PM1[-100:])
PM_variation = ( aux - mean(PM3[-100:]) ) / aux
T_variation = mean(T_aux3[-100:,0]) + mean(T_aux3[-100:,1]) + mean(T_aux3[-100:,2])
SNR = np.abs( aux - mean(PM3[-100:]) )/np.sqrt(aux) # Sig-to-Noi ratio considering Poisson noise
# Auxiliary arrays:
t_aux1 = array([tt2[ 0],tt2[ 0]])
t_aux2 = array([tt2[-1],tt2[-1]])
y1_aux = array([1.0e-3 ,1.0 ])
# y2_aux = array([0 ,20 ])
y2_aux = array([0 ,50 ])
tt = concatenate( ( tt1, tt2, tt3) )
T_aux = concatenate( (T_aux1,T_aux2,T_aux3) )
T_CM = concatenate( (T_CM1,T_CM2,T_CM3) )
PM = concatenate( (PM1,PM2,PM3) )
tt_relax = concatenate( ( tt1, tt2, tt4) )
T_aux_relax = concatenate( (T_aux1,T_aux2,T_aux4) )
PM_relax = concatenate( (PM1,PM2,PM4) )
if flag_plot == 1 :
#fig_name = file_name[-9:]
fig = figure(fig_name); clf()
ax1 = subplot(311)
semilogy(tt*1.e3,T_aux[:,0], label='Tx')
semilogy(tt*1.e3,T_aux[:,1], label='Ty')
semilogy(tt*1.e3,T_aux[:,2], label='Tz')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax1.set_yticks([1e-4,1e-2,1,1e2,1e4])
ax1.grid()
# annotate('Laser ON', xy=(0.5,350), xycoords='data',
# size=24, ha='left', va='top', color='xkcd:azul',
# bbox=dict(boxstyle='round', fc='white',edgecolor='xkcd:azul'))
legend(title='Laser ON',fontsize=18)
# ~ xlabel('time[ms]')
# ~ ylabel('T[K]')
plt.setp(ax1.get_xticklabels(),visible=False)
ax2 = subplot(312,sharex=ax1,sharey=ax1)
semilogy(tt_relax*1.e3,T_aux_relax[:,0], label='Tx',nonposy='mask')
semilogy(tt_relax*1.e3,T_aux_relax[:,1], label='Ty',nonposy='mask')
semilogy(tt_relax*1.e3,T_aux_relax[:,2], label='Tz',nonposy='mask')
semilogy(t_aux1*1.e3,y1_aux,'r')
semilogy(t_aux2*1.e3,y1_aux,'r')
ax2.set_yticks([1e-4,1e-2,1,1e2,1e4])
ax2.grid()
# annotate('laser off après injection', xy=(0.5,350), xycoords='data',
# size=24, ha='left', va='top', color='xkcd:azul',
# bbox=dict(boxstyle='round', fc='white',edgecolor='xkcd:azul'))
legend(title='Laser OFF après injection',fontsize=18)
plt.setp(ax2.get_xticklabels(),visible=False)
ax3 = subplot(313,sharex=ax1)
plot(tt*1.e3,PM[:])
plot(t_aux1*1.e3,y2_aux,'r')
plot(t_aux2*1.e3,y2_aux,'r')
ax3.grid()
xlabel('time[ms]')
ylabel('Counts')
ax1.set_xlim(xlim1)
ax1.set_ylim(ylim1)
ax3.set_ylim(ylim3)
plt.tight_layout()
fig.set_size_inches(11.69, 8.27)
subplots_adjust(hspace=0.015)
temps = [tt,t_aux1, t_aux2]
fluo = [PM, PM_variation, SNR]
temperature = [T_aux, T_variation, T_CM]
return temps, temperature, fluo
def plot_T_and_PM_InitQ_Evol_AfterCool(data, flag_plot, fig_name, **kwargs):
onlyfiles = sort([f for f in listdir(data) if isfile(join(data, f)) and "SimuType" in f and ".dat" in f])
onlyfiles_Lan = sort([f for f in listdir(data) if isfile(join(data, f)) and "Harmo" in f])
xlim1 = kwargs.get('xlim1', (-0.1, 6))
ylim1 = kwargs.get('ylim1', (0.5 * 1e-3, 2e4))
ylim2 = kwargs.get('ylim2', (-2, 50))
N_ions, j_save, dt_j_save_next, eta, Temp, save_T = \
load_Temp_init_bin_Lan(data + os.path.sep + onlyfiles_Lan[0].strip('.bin'), 0)
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(data + os.path.sep + onlyfiles[1].strip('.dat'))
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(data + os.path.sep + onlyfiles[0].strip('.dat'))
tt0 = save_T[:, 0]
T_CM0 = save_T[:, 1:4]
T_aux0 = save_T[:, 4:]
tt = concatenate((tt0, tt1, tt3))
T_CM = concatenate((T_CM0, T_CM1, T_CM3))
T_aux = concatenate((T_aux0, T_aux1, T_aux3))
if flag_plot == 1:
# fig_name = file_name[-9:]
fig = figure(fig_name);
clf()
ax1 = subplot(211)
semilogy(tt * 1.e3, T_aux[:, 0], label='Tx')
semilogy(tt * 1.e3, T_aux[:, 1], label='Ty')
semilogy(tt * 1.e3, T_aux[:, 2], label='Tz')
ax1.grid()
legend()
# ~ xlabel('time[ms]')
# ~ ylabel('T[K]')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = subplot(212, sharex=ax1)
plot(tt * 1.e3, T_aux[:, 0], label='Tx')
plot(tt * 1.e3, T_aux[:, 1], label='Tx')
plot(tt * 1.e3, T_aux[:, 2], label='Tx')
ax2.grid()
xlabel('time[ms]')
ylabel('T [k]')
ax1.set_xlim(xlim1)
ax1.set_ylim(ylim1)
ax2.set_ylim(ylim2)
plt.tight_layout()
subplots_adjust(hspace=0.015)
return tt, T_aux, T_CM
def find_PM_variation_FinalT(file_dir2,file_name):
i_aux = file_name.find('_N')+1
file1 = 'SimuTypeQ_{}'.format(file_name[i_aux:].strip('50eV.dat')) # for N=1024
file3 = 'SimuType2_01_{}50eV'.format(file_name[i_aux:].strip('50eV.dat'))
tt1, T_CM1, T_aux1, PM1 = load_T_and_PM_simu(file_dir2+'/Temp_'+file1)
tt3, T_CM3, T_aux3, PM3 = load_T_and_PM_simu(file_dir2+'/Temp_'+file3)
# 1ms is 2000 pts at 1pt/500ns
aux = mean(PM1[-2000:])
PM_variation = ( aux - mean(PM3[-2000:]) ) / aux
T_variation = ( mean(T_aux3[-2000:,0]) + mean(T_aux3[-2000:,1]) + mean(T_aux3[-2000:,2]) ) / 3
SNR = np.abs( aux - mean(PM3[-2000:]) )/np.sqrt(aux) # Sig-to-Noi ratio considering Poisson noise
return PM_variation, T_variation, SNR
# Look at the lost of energy:
def energy_lost(file_dir2,file_name):
data = loadtxt('{}/{}.dat'.format(file_dir2,file_name))
r_0 = data[0:3]; v_0 = data[3: 6]; # Position and velocity at the start
r_1 = data[6:9]; v_1 = data[9:12]; # Position and velocity at the end
t_c = data[12] ; # Time of crossing
Ec0 = sum(v_0*v_0) ; Ec1 = sum(v_1*v_1)
delta_Ec = (Ec1 - Ec0)
return r_0, r_1, delta_Ec, delta_Ec / Ec1, t_c
def load_trj(str_load):
fid = open(str_load+'.bin', 'rb')
junk = fromfile(fid, int32,1) # Read record start tag
aux = fromfile(fid, int32,1)
junk = fromfile(fid, int32,1) # Read record stop tag
jmax = aux[0]
junk = fromfile(fid, int32,1) # Read record start tag
aux = fromfile(fid, int32,1)
junk = fromfile(fid, int32,1) # Read record stop tag
N_ions1 = aux[0]
junk = fromfile(fid, int32,1) # Read record start tag
aux = fromfile(fid, int32,1)
junk = fromfile(fid, int32,1) # Read record stop tag
N_ions2 = aux[0]
junk = fromfile(fid, int32 ,1 ) # Read record start tag
t = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
r_x = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
r_y = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
r_z = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
v_x = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
v_y = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
v_z = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
a_x = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
a_y = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
junk = fromfile(fid, int32 ,1 ) # Read record start tag
a_z = fromfile(fid, float64,jmax)
junk = fromfile(fid, int32 ,1 ) # Read record stop tag
fid.close
return t,r_x, r_y, r_z, v_x, v_y, v_z, a_x, a_y, a_z
def load_cloud_trj(str_load):
fid = open(str_load + '.bin', 'rb')
junk0 = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk1 = fromfile(fid, int32, 1) # Read record stop tag
jmax = aux[0]
junk2 = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk3 = fromfile(fid, int32, 1) # Read record stop tag
N_ions = aux[0]
junk4 = fromfile(fid, int32, 1) # Read record start tag
r = fromfile(fid, single, int(junk4[0] / 4)) # jmax*N_ions*3
junk5 = fromfile(fid, int32, 1) # Read record stop tag
fid.close
return jmax, N_ions, r
##### FORTRAN ANALYSIS #####
### Fonctions Adrien ###
def load_gui(filter_nocomplete):
# sélection des fichiers (Python 3)
# SELECTIONNER UN FICHIER DE DONNÉES AU FOND DE L'ARBORESCENCE
# Temp_SimuType0_N01024_Vrf0064_Udc0.5000D+00V_D1.0_S1.0RFG.dat
# root = tk.Tk()
# root.withdraw()
# # Sélectionner un fichier du dossier pour récupérer le répertoire et les noms
# file_path = filedialog.askopenfilename(initialdir = '/home/adrian/Documents/Programmes/Fortran_Jofre',
# multiple=None)
file_path = load_file_GUI('/home/adrian/Documents/Programmes/Fortran_Jofre')[0]
dir_path = Path(file_path).parent.absolute()
work_rep = file_path[:len(str(dir_path))]
filename = file_path[len(str(dir_path))+1:]
print('{}{}'.format('> Répertoire : ',work_rep))
print('{}{}'.format('> Filename : ',filename))
## Recover all subdirectories hosting simulation data
# find all '/' in path
myslashpos = [m.start() for m in re.finditer('/', work_rep)]
# print(myslashpos[6]) # choose the right level
if 'home' in file_path:
if 'Hobitton' or 'Rivendel' in file_path :
slashcond = 6 # the slash number just before runs (after date)
else:
slashcond = 6 # the slash number just before runs (after date)
else:
slashcond = -2
print('> myslashpos |',myslashpos)
print('> slashcond |',slashcond)
# Getting a naturally sorted list of all subdirectories in some directory
# throw all the intermediary subdirectories that are not hosting data
all_subdir = [x[0] for x in os.walk(work_rep[0:myslashpos[slashcond]])]
all_subdir = natsorted(all_subdir)
# keep only the long path (by remove short ones)
all_subdir = [i for i in all_subdir if len(i) >= len(str(dir_path))-1]
if filter_nocomplete == 1:
# Name of all points (conditions)
all_points = [point[myslashpos[slashcond] + 1:myslashpos[slashcond + 1]] for point in all_subdir]
all_points = list(dict.fromkeys(all_points))
# how much repetition for first point : usually the first point is complete
# i'm not going to plot the data while not even the first point is complete ...
num_rep = len([k for k in all_subdir if all_points[0] in k])
# determine which points are not complete
# the way the code is done usually leads to
# only one uncomplete point but in any case
# I handle the possibility for multiple uncomplete.
all_subdir_temp = []
pts_to_delete = []
deleted_points = 0
# sweep all points
for i,j in enumerate(all_points):
temp = [k for k in all_subdir if j in k] # all addresses given one point condition
if len(temp) != num_rep: # compare number of repetition for each point
# if different from the reference (num_rep)
# I will remove this point
pts_to_delete.append(j)
# removing uncomplete points from the list all_subdir
if len(pts_to_delete) != 0:
for i,w in enumerate(all_subdir): # sweep all points
# ~ print(w)
for j,x in enumerate(pts_to_delete): # sweep all delete conditions
# ~ print(x)
if x not in w: # if the current point is not complete, delete from all_subdir
all_subdir_temp.append(w)
else:
deleted_points += 1
# print('===== ^ REMOVED ^ =====')
all_subdir = all_subdir_temp
del all_subdir_temp
print('Points deleted because they were not complete',pts_to_delete,' '+str(deleted_points)+' pt(s)')
print('Total number of data directories',len(all_subdir))
else:
print('No points deleted because they were not complete')
print('Total number of data directories',len(all_subdir))
# all_subdir ce sont tous les répertoires contenant des donnés à analyser
file_cfg = [file_path, dir_path, work_rep, filename]
slash_cfg = [myslashpos, slashcond]
return file_cfg, slash_cfg, all_subdir
def load_Temp_init_bin_Lan(str_load, flag_print):
fid = open(str_load+'.bin', 'rb')
a = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
N_ions = aux[0]
b = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
j_save = aux[0]
c = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, float64, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
dt_j_save_next = aux[0]
d = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, float64, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
eta = aux[0]
e = fromfile(fid, int32, 1 ) # Read record start tag
Temp = fromfile(fid, float64, 1)
junk = fromfile(fid, int32, 1 ) # Read record stop tag
f = fromfile(fid, int32, 1 ) # Read record start tag
save_T = fromfile(fid, float64, 7*j_save)
junk = fromfile(fid, int32, 1 ) # Read record stop tag
fid.close
# print('len save_T', junk)
save_T = reshape(save_T, (j_save, 7), order='F')
if flag_print == 1:
print(a, b, c, d, e, f)
print('N_ions', N_ions)
print('j_save', j_save)
print('dt_j_save_next', dt_j_save_next)
print('eta', eta)
print('Temp', Temp)
print(save_T[0])
return N_ions, j_save, dt_j_save_next, eta, Temp, save_T
def load_xva_init_bin_Lan(str_load, flag_print):
fid = open(str_load+'.bin', 'rb')
a = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
N_ions = aux[0]
b = fromfile(fid, int32, 1) # Read record start tag
aux = fromfile(fid, int32, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
j_save = aux[0]
c = fromfile(fid, int32, 1) # Read record start tag
t_act= fromfile(fid, float64, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
d = fromfile(fid, int32, 1) # Read record start tag
dt = fromfile(fid, float64, 1)
junk = fromfile(fid, int32, 1) # Read record stop tag
e = fromfile(fid, int32, 1 ) # Read record start tag
iRF = fromfile(fid, int32, 3)
junk = fromfile(fid, int32, 1 ) # Read record stop tag
f = fromfile(fid, int32, 1 ) # Read record start tag
save_xva = fromfile(fid, float64, 12*N_ions)
junk = fromfile(fid, int32, 1 ) # Read record stop tag
fid.close
# print('len save_T', junk)
save_xva = reshape(save_xva, (12,N_ions), order='F')
# print(save_xva[0])
return N_ions, j_save, t_act, dt, iRF, save_xva
#############################
### Single case functions ###
def file_name_retrieve(address,simu_type):
# get only .dat files in each simulation directory
onlyfiles = [f for f in listdir(address) if isfile(join(address, f)) and not "xva" in f and ".dat" in f]
tmp_file = []
# build path file
if simu_type == 'Collision':
for k in range(3):
tmp_file.append('{}/{}'.format(address,sort(onlyfiles)[k].strip('.dat')))
elif simu_type == 'RF_relax':
for k in range(4):
try:
tmp_file.append('{}/{}'.format(address,sort(onlyfiles)[k].strip('.dat')))
except(IndexError):
tmp_file.append('No file for this SimuType')
print('A file is missing, skiping the file number',k)
return onlyfiles, tmp_file
# Return Temp, Fluo, for SimuType2 RF with laser
def load_T_PM_cloud_GMol(address,flag_plot):
# retrieve files adresses
onlyfiles, data_files = file_name_retrieve(address,simu_type='Collision')
file0 = data_files[0];file2 = data_files[1];file4 = data_files[2]
# load Ec variation for GMol
_,_,deltaEc,deltaEcRel,t_c = energy_lost(address,'xva'+sort(onlyfiles)[2][4:].strip('.dat'))