Module Owntools.Plot
@author: Alexandre Sac–Morane alexandre.sac-morane@uclouvain.be
This file contains the different functions to plot used in the simulation.
Expand source code
# -*- coding: utf-8 -*-
"""
@author: Alexandre Sac--Morane
alexandre.sac-morane@uclouvain.be
This file contains the different functions to plot used in the simulation.
"""
#-------------------------------------------------------------------------------
#Librairy
#-------------------------------------------------------------------------------
from pathlib import Path
from scipy.ndimage import binary_dilation
import numpy as np
import matplotlib.pyplot as plt
import os
import imageio
from Owntools import index_to_str
#-------------------------------------------------------------------------------
def Plot_Diffusion_Solute(dict_algorithm, dict_material, dict_sample):
'''
Plot the delta of solute concentration in the case of a pure diffusion problem with the same initial configuration as phase field simulation.
Input :
an algorithm dictionnary (a dict)
a sample dictionnary (a dict)
Output :
Nothing but a .png file is generated (a file)
'''
#---------------------------------------------------------------------------
#create .i
#---------------------------------------------------------------------------
file_to_write = open('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i','w')
file_to_read = open('Owntools/Debug_Diff_Solute_base.i','r')
lines = file_to_read.readlines()
file_to_read.close()
j = 0
for line in lines :
j = j + 1
if j == 4:
line = line[:-1] + ' ' + str(len(dict_sample['x_L'])-1)+'\n'
elif j == 5:
line = line[:-1] + ' ' + str(len(dict_sample['y_L'])-1)+'\n'
elif j == 7:
line = line[:-1] + ' ' + str(min(dict_sample['x_L']))+'\n'
elif j == 8:
line = line[:-1] + ' ' + str(max(dict_sample['x_L']))+'\n'
elif j == 9:
line = line[:-1] + ' ' + str(min(dict_sample['y_L']))+'\n'
elif j == 10:
line = line[:-1] + ' ' + str(max(dict_sample['y_L']))+'\n'
elif j == 40:
line = line[:-1] + ' ' + str(dict_material['M'])+'\n'
elif j == 53 or j == 57:
line = line[:-1] + str(dict_algorithm['i_PFDEM']) + '.txt\n'
elif j == 87:
line = line[:-1] + ' ' + str(dict_algorithm['dt_PF']*dict_algorithm['n_t_PF']) +'\n'
elif j == 91:
line = line[:-1] + ' ' + str(dict_algorithm['dt_PF']) +'\n'
file_to_write.write(line)
file_to_write.close()
#---------------------------------------------------------------------------
#run simulation
#---------------------------------------------------------------------------
os.system('mpiexec -n '+str(dict_algorithm['np_proc'])+' ~/projects/moose/modules/combined/combined-opt -i '+'Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i')
#---------------------------------------------------------------------------
#sort files
#---------------------------------------------------------------------------
os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_out.e','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_out.e')
os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i')
j = 0
j_str = index_to_str(j)
filepath = Path('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu')
while filepath.exists():
for i_proc in range(dict_algorithm['np_proc']):
os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu')
os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu')
j = j + 1
j_str = index_to_str(j)
filepath = Path('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu')
j_str = index_to_str(j-1)
#---------------------------------------------------------------------------
#read files
#---------------------------------------------------------------------------
solute_diff_M = np.array(np.zeros((len(dict_sample['y_L']),len(dict_sample['x_L']))))
id_L = None
c_selector_len = len(' <DataArray type="Float64" Name="c')
end_len = len(' </DataArray>')
XYZ_selector_len = len(' <DataArray type="Float64" Name="Points"')
data_jump_len = len(' ')
for i_proc in range(dict_algorithm['np_proc']):
L_Work = [[], #X
[], #Y
[]] #c
f = open('Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu','r')
data = f.read()
f.close
lines = data.splitlines()
#iterations on line
for line in lines:
if line[0:c_selector_len] == ' <DataArray type="Float64" Name="c':
id_L = 2
elif line[0:XYZ_selector_len] == ' <DataArray type="Float64" Name="Points"':
id_L = 0
elif (line[0:end_len] == ' </DataArray>' or line[0:len(' <InformationKey')] == ' <InformationKey') and id_L != None:
id_L = None
elif line[0:data_jump_len] == ' ' and id_L == 2: #Read c
line = line[data_jump_len:]
c_start = 0
for c_i in range(0,len(line)):
if line[c_i]==' ':
c_end = c_i
L_Work[id_L].append(float(line[c_start:c_end]))
c_start = c_i+1
L_Work[id_L].append(float(line[c_start:]))
elif line[0:data_jump_len] == ' ' and id_L == 0: #Read [X, Y, Z]
line = line[data_jump_len:]
XYZ_temp = []
c_start = 0
for c_i in range(0,len(line)):
if line[c_i]==' ':
c_end = c_i
XYZ_temp.append(float(line[c_start:c_end]))
if len(XYZ_temp)==3:
L_Work[0].append(XYZ_temp[0])
L_Work[1].append(XYZ_temp[1])
XYZ_temp = []
c_start = c_i+1
XYZ_temp.append(float(line[c_start:]))
L_Work[0].append(XYZ_temp[0])
L_Work[1].append(XYZ_temp[1])
#Adaptating data and update of solute_M
for i in range(len(L_Work[0])):
#Interpolation method
L_dy = []
for y_i in dict_sample['y_L'] :
L_dy.append(abs(y_i - L_Work[1][i]))
L_dx = []
for x_i in dict_sample['x_L'] :
L_dx.append(abs(x_i - L_Work[0][i]))
solute_diff_M[-1-list(L_dy).index(min(L_dy))][list(L_dx).index(min(L_dx))] = L_Work[2][i]
#---------------------------------------------------------------------------
#Compare with initial value
#---------------------------------------------------------------------------
delta_solute = solute_diff_M - dict_sample['solute_M']
#---------------------------------------------------------------------------
#Plot result
#---------------------------------------------------------------------------
#look for the name of the new plot
template_name = 'Debug/Diff_Solute/Diffusion_solute_'
j = 0
plotpath = Path(template_name+str(j)+'.png')
while plotpath.exists():
j = j + 1
plotpath = Path(template_name+str(j)+'.png')
name = template_name+str(j)+'.png'
#plot
plt.figure(1,figsize=(16,9))
plt.subplot(311)
#diffusion coefficient
im = plt.imshow(dict_sample['kc_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.title('Diffusion coefficient solute and grains')
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.subplot(312)
#diffusion coefficient
im = plt.imshow(delta_solute,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.title('Delta solute (= diffused - initial)')
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
grad_y, grad_x = np.gradient(-delta_solute)
plt.subplot(325)
im = plt.imshow(grad_x,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.title('Grad_x(Delta solute)')
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.subplot(326)
im = plt.imshow(grad_y,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.title('Grad_y(Delta solute)')
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.savefig(name)
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_config(dict_algorithm, dict_sample):
'''
Plot the sample configuration.
Input :
a sample dictionnary (a dict)
Output :
Nothing but a .png file is generated (a file)
'''
#look for the name of the new plot
template_name = 'Debug/Configuration/Configuration_'
j = 0
plotpath = Path(template_name+str(j)+'.png')
while plotpath.exists():
j = j + 1
plotpath = Path(template_name+str(j)+'.png')
name = template_name+str(j)+'.png'
#plot
plt.figure(1,figsize=(16,9))
plt.subplot(211)
#solute
im = plt.imshow(dict_sample['solute_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = dict_algorithm['c_min'], vmax = dict_algorithm['c_max'])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.title('Solute c and grains')
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
#eta1_M
plt.subplot(223)
im = plt.imshow(dict_sample['L_g'][0].etai_M,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = 0, vmax = 1)
plt.colorbar(im)
plt.plot(dict_sample['L_g'][0].l_border_x,dict_sample['L_g'][0].l_border_y,'r')
plt.title(r'$\eta$1')
#eta2_M
plt.subplot(224)
im = plt.imshow(dict_sample['L_g'][1].etai_M,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = 0, vmax = 1)
plt.colorbar(im)
plt.plot(dict_sample['L_g'][1].l_border_x,dict_sample['L_g'][1].l_border_y,'r')
plt.title(r'$\eta$2')
plt.savefig(name)
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_mp4(template_name,name_video):
'''The goal of this function is to create a movie with pictures.
from https://www.blog.pythonlibrary.org/2021/06/23/creating-an-animated-gif-with-python/
Input :
the template of the pictures used (a string)
the name of the video (a string)
Output :
a movie file (a .mp4)
'''
#look for the largest iteration
i_f = 0
plotpath = Path(template_name+str(i_f)+'.png')
while plotpath.exists():
i_f = i_f + 1
plotpath = Path(template_name+str(i_f)+'.png')
fileList = []
for i in range(0,i_f):
fileList.append(template_name+str(i)+'.png')
duration_movie = 10 #sec
writer = imageio.get_writer(name_video, fps=int(i_f/duration_movie))
for im in fileList:
writer.append_data(imageio.imread(im))
writer.close()
#-------------------------------------------------------------------------------
def Plot_Ed(dict_sample):
'''
Plot the energy source configuration at the start of the simultion.
Input :
a sample dictionnary (a dict)
Output :
Nothing but a .png file is generated (a file)
'''
#look for the name of the new plot
template_name = 'Debug/Ed/Ed_'
j = 0
plotpath = Path(template_name+str(j)+'.png')
while plotpath.exists():
j = j + 1
plotpath = Path(template_name+str(j)+'.png')
name = template_name+str(j)+'.png'
#plot
plt.figure(1,figsize=(16,9))
#Ed_M
plt.subplot(211)
im = plt.imshow(dict_sample['Ed_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.title('Ed = Emec - Eche')
#Emec_M
plt.subplot(223)
im = plt.imshow(dict_sample['Emec_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.title('Emec')
#Eche_M
plt.subplot(224)
im = plt.imshow(dict_sample['Eche_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#etai
for i in range(len(dict_sample['L_g'])):
plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y)
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.title('Eche')
plt.savefig(name)
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_kc(dict_sample):
'''
Plot the distribution of the diffusion coefficient for the solute.
Input :
a sample dictionnary (a dict)
Output :
Nothing but a .png file is generated (a file)
'''
#look for the name of the new plot
template_name = 'Debug/Kc/Kc_'
j = 0
plotpath = Path(template_name+str(j)+'.png')
while plotpath.exists():
j = j + 1
plotpath = Path(template_name+str(j)+'.png')
name = template_name+str(j)+'.png'
#plot
plt.figure(1,figsize=(16,9))
#kc_M
im = plt.imshow(dict_sample['kc_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])])
plt.colorbar(im)
#plot g1 and g2 boundaries
plt.plot(dict_sample['L_g'][0].l_border_x,dict_sample['L_g'][0].l_border_y)
plt.plot(dict_sample['L_g'][1].l_border_x,dict_sample['L_g'][1].l_border_y)
plt.axis('equal')
plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L']))
plt.title('Diffusion coefficient of the solute')
plt.savefig(name)
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_init_current_shape(dict_sample):
'''
Plot the comparison between initial and current shape for the grain 1.
Input :
a sample dictionnary (a dict)
Output :
Nothing but a .png file is generated (a file)
'''
#look for the name of the new plot
template_name = 'Debug/Comparison_Init_Current/Init_Current_Shape_'
j = 0
plotpath = Path(template_name+str(j)+'.png')
while plotpath.exists():
j = j + 1
plotpath = Path(template_name+str(j)+'.png')
name = template_name+str(j)+'.png'
#prepare plot
L_border_x_init = []
L_border_y_init = []
for i in range(len(dict_sample['L_g'][0].l_border_x_init)):
L_border_x_init.append(dict_sample['L_g'][0].l_border_x_init[i] - dict_sample['L_g'][0].center_init[0])
L_border_y_init.append(dict_sample['L_g'][0].l_border_y_init[i] - dict_sample['L_g'][0].center_init[1])
L_border_x = []
L_border_y = []
for i in range(len(dict_sample['L_g'][0].l_border_x)):
L_border_x.append(dict_sample['L_g'][0].l_border_x[i] - dict_sample['L_g'][0].center[0])
L_border_y.append(dict_sample['L_g'][0].l_border_y[i] - dict_sample['L_g'][0].center[1])
#plot
plt.figure(1,figsize=(16,9))
plt.plot(L_border_x_init,L_border_y_init,'k',label='Initial')
plt.plot(L_border_x,L_border_y,label='Current')
plt.savefig(name)
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_sum_eta_c(dict_tracker):
'''
Plot the trackers.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
#plot Displacement and sum of c and etai
plt.figure(1,figsize=(16,9))
plt.subplot(211)
plt.plot(dict_tracker['L_t'], dict_tracker['L_int_displacement'])
plt.title('Total displacement done')
plt.subplot(234)
plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_eta'])
plt.title('Sum of etai')
plt.subplot(235)
plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_solute'])
plt.title('Sum of c')
plt.subplot(236)
plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_total'])
plt.title('Sum of etai and c')
plt.savefig('Debug/Trackers.png')
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_sphericity(dict_tracker):
'''
Plot the sphericity of the grain 1.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
plt.figure(1,figsize=(16,9))
plt.plot(dict_tracker['L_t'], dict_tracker['L_area_sphericity_g0'],label='Area sphericity')
plt.plot(dict_tracker['L_t'], dict_tracker['L_diameter_sphericity_g0'],label='Diameter sphericity')
plt.plot(dict_tracker['L_t'], dict_tracker['L_circle_ratio_sphericity_g0'],label='Circle sphericity')
plt.plot(dict_tracker['L_t'], dict_tracker['L_perimeter_sphericity_g0'],label='Perimeter sphericity')
plt.plot(dict_tracker['L_t'], dict_tracker['L_width_to_length_ratio_sphericity_g0'],label='Width to length ratio sphericity')
plt.legend()
plt.title('2D sphericity of grain 1')
plt.savefig('Debug/Sphericity_g_1.png')
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_c_at_p(dict_tracker):
'''
Plot the value of the solute concentration at the point defined.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
plt.figure(1,figsize=(16,9))
plt.plot(dict_tracker['L_t'], dict_tracker['c_at_the_center'])
plt.title('Value of the solute concentration at the center')
plt.savefig('Debug/Solute_Contration_Center.png')
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_sum_Ed(dict_tracker):
'''
Plot the value of the total energy in the sample.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
plt.figure(1,figsize=(16,9))
plt.subplot(331)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_Ed_che_L'])
plt.title('Total chemical energy Ed_che')
plt.subplot(334)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_Ed_mec_L'])
plt.title('Total mechanical energy Ed_mec')
plt.subplot(337)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_L'])
plt.title('Total Energy Ed = Ed_mec - Ed_che')
plt.subplot(132)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_plus_L'], label = 'Ed+')
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_minus_L'], label = 'Ed-')
plt.title('Repartition of the energy in a + and a - terms')
plt.subplot(133)
plt.plot(dict_tracker['L_t'], dict_tracker['L_int_displacement'])
plt.title('Total displacement done')
plt.savefig('Debug/Evolution_sum_Ed.png')
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_Sint_SumMinEtai(dict_tracker):
'''
Plot the evolution of the intersection surface and the sum of min of etai.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
plt.figure(1,figsize=(16,9))
plt.subplot(211)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['S_int_L'])
plt.title('Intersection surface')
plt.subplot(212)
plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_min_etai_L'])
plt.title('Sum of minimum etai')
plt.savefig('Debug/Evolution_Sint_SumMinEtai.png')
plt.close(1)
#-------------------------------------------------------------------------------
def Plot_dt_used(dict_tracker):
'''
Plot the evolution of the time step used in the phase field simulation.
Input :
a tracker dictionnary (a dict)
Output :
Nothing but a .png files are generated (files)
'''
plt.figure(1,figsize=(16,9))
plt.plot(dict_tracker['L_dt'])
plt.title('Evolution of the time step used')
plt.savefig('Debug/Evolution_dt_used.png')
plt.close(1)Functions
def Plot_Diffusion_Solute(dict_algorithm, dict_material, dict_sample)-
Plot the delta of solute concentration in the case of a pure diffusion problem with the same initial configuration as phase field simulation.
Input : an algorithm dictionnary (a dict) a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file)Expand source code
def Plot_Diffusion_Solute(dict_algorithm, dict_material, dict_sample): ''' Plot the delta of solute concentration in the case of a pure diffusion problem with the same initial configuration as phase field simulation. Input : an algorithm dictionnary (a dict) a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file) ''' #--------------------------------------------------------------------------- #create .i #--------------------------------------------------------------------------- file_to_write = open('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i','w') file_to_read = open('Owntools/Debug_Diff_Solute_base.i','r') lines = file_to_read.readlines() file_to_read.close() j = 0 for line in lines : j = j + 1 if j == 4: line = line[:-1] + ' ' + str(len(dict_sample['x_L'])-1)+'\n' elif j == 5: line = line[:-1] + ' ' + str(len(dict_sample['y_L'])-1)+'\n' elif j == 7: line = line[:-1] + ' ' + str(min(dict_sample['x_L']))+'\n' elif j == 8: line = line[:-1] + ' ' + str(max(dict_sample['x_L']))+'\n' elif j == 9: line = line[:-1] + ' ' + str(min(dict_sample['y_L']))+'\n' elif j == 10: line = line[:-1] + ' ' + str(max(dict_sample['y_L']))+'\n' elif j == 40: line = line[:-1] + ' ' + str(dict_material['M'])+'\n' elif j == 53 or j == 57: line = line[:-1] + str(dict_algorithm['i_PFDEM']) + '.txt\n' elif j == 87: line = line[:-1] + ' ' + str(dict_algorithm['dt_PF']*dict_algorithm['n_t_PF']) +'\n' elif j == 91: line = line[:-1] + ' ' + str(dict_algorithm['dt_PF']) +'\n' file_to_write.write(line) file_to_write.close() #--------------------------------------------------------------------------- #run simulation #--------------------------------------------------------------------------- os.system('mpiexec -n '+str(dict_algorithm['np_proc'])+' ~/projects/moose/modules/combined/combined-opt -i '+'Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i') #--------------------------------------------------------------------------- #sort files #--------------------------------------------------------------------------- os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_out.e','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_out.e') os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'.i') j = 0 j_str = index_to_str(j) filepath = Path('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu') while filepath.exists(): for i_proc in range(dict_algorithm['np_proc']): os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu') os.rename('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu','Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu') j = j + 1 j_str = index_to_str(j) filepath = Path('Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'.pvtu') j_str = index_to_str(j-1) #--------------------------------------------------------------------------- #read files #--------------------------------------------------------------------------- solute_diff_M = np.array(np.zeros((len(dict_sample['y_L']),len(dict_sample['x_L'])))) id_L = None c_selector_len = len(' <DataArray type="Float64" Name="c') end_len = len(' </DataArray>') XYZ_selector_len = len(' <DataArray type="Float64" Name="Points"') data_jump_len = len(' ') for i_proc in range(dict_algorithm['np_proc']): L_Work = [[], #X [], #Y []] #c f = open('Debug/Diff_Solute/Ite_'+str(dict_algorithm['i_PFDEM'])+'/Debug_Diff_Solute_'+str(dict_algorithm['i_PFDEM'])+'_other_'+j_str+'_'+str(i_proc)+'.vtu','r') data = f.read() f.close lines = data.splitlines() #iterations on line for line in lines: if line[0:c_selector_len] == ' <DataArray type="Float64" Name="c': id_L = 2 elif line[0:XYZ_selector_len] == ' <DataArray type="Float64" Name="Points"': id_L = 0 elif (line[0:end_len] == ' </DataArray>' or line[0:len(' <InformationKey')] == ' <InformationKey') and id_L != None: id_L = None elif line[0:data_jump_len] == ' ' and id_L == 2: #Read c line = line[data_jump_len:] c_start = 0 for c_i in range(0,len(line)): if line[c_i]==' ': c_end = c_i L_Work[id_L].append(float(line[c_start:c_end])) c_start = c_i+1 L_Work[id_L].append(float(line[c_start:])) elif line[0:data_jump_len] == ' ' and id_L == 0: #Read [X, Y, Z] line = line[data_jump_len:] XYZ_temp = [] c_start = 0 for c_i in range(0,len(line)): if line[c_i]==' ': c_end = c_i XYZ_temp.append(float(line[c_start:c_end])) if len(XYZ_temp)==3: L_Work[0].append(XYZ_temp[0]) L_Work[1].append(XYZ_temp[1]) XYZ_temp = [] c_start = c_i+1 XYZ_temp.append(float(line[c_start:])) L_Work[0].append(XYZ_temp[0]) L_Work[1].append(XYZ_temp[1]) #Adaptating data and update of solute_M for i in range(len(L_Work[0])): #Interpolation method L_dy = [] for y_i in dict_sample['y_L'] : L_dy.append(abs(y_i - L_Work[1][i])) L_dx = [] for x_i in dict_sample['x_L'] : L_dx.append(abs(x_i - L_Work[0][i])) solute_diff_M[-1-list(L_dy).index(min(L_dy))][list(L_dx).index(min(L_dx))] = L_Work[2][i] #--------------------------------------------------------------------------- #Compare with initial value #--------------------------------------------------------------------------- delta_solute = solute_diff_M - dict_sample['solute_M'] #--------------------------------------------------------------------------- #Plot result #--------------------------------------------------------------------------- #look for the name of the new plot template_name = 'Debug/Diff_Solute/Diffusion_solute_' j = 0 plotpath = Path(template_name+str(j)+'.png') while plotpath.exists(): j = j + 1 plotpath = Path(template_name+str(j)+'.png') name = template_name+str(j)+'.png' #plot plt.figure(1,figsize=(16,9)) plt.subplot(311) #diffusion coefficient im = plt.imshow(dict_sample['kc_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.title('Diffusion coefficient solute and grains') plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.subplot(312) #diffusion coefficient im = plt.imshow(delta_solute,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.title('Delta solute (= diffused - initial)') plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) grad_y, grad_x = np.gradient(-delta_solute) plt.subplot(325) im = plt.imshow(grad_x,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.title('Grad_x(Delta solute)') plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.subplot(326) im = plt.imshow(grad_y,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.title('Grad_y(Delta solute)') plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.savefig(name) plt.close(1) def Plot_Ed(dict_sample)-
Plot the energy source configuration at the start of the simultion.
Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file)Expand source code
def Plot_Ed(dict_sample): ''' Plot the energy source configuration at the start of the simultion. Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file) ''' #look for the name of the new plot template_name = 'Debug/Ed/Ed_' j = 0 plotpath = Path(template_name+str(j)+'.png') while plotpath.exists(): j = j + 1 plotpath = Path(template_name+str(j)+'.png') name = template_name+str(j)+'.png' #plot plt.figure(1,figsize=(16,9)) #Ed_M plt.subplot(211) im = plt.imshow(dict_sample['Ed_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.title('Ed = Emec - Eche') #Emec_M plt.subplot(223) im = plt.imshow(dict_sample['Emec_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.title('Emec') #Eche_M plt.subplot(224) im = plt.imshow(dict_sample['Eche_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.title('Eche') plt.savefig(name) plt.close(1) def Plot_Sint_SumMinEtai(dict_tracker)-
Plot the evolution of the intersection surface and the sum of min of etai.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_Sint_SumMinEtai(dict_tracker): ''' Plot the evolution of the intersection surface and the sum of min of etai. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' plt.figure(1,figsize=(16,9)) plt.subplot(211) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['S_int_L']) plt.title('Intersection surface') plt.subplot(212) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_min_etai_L']) plt.title('Sum of minimum etai') plt.savefig('Debug/Evolution_Sint_SumMinEtai.png') plt.close(1) def Plot_c_at_p(dict_tracker)-
Plot the value of the solute concentration at the point defined.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_c_at_p(dict_tracker): ''' Plot the value of the solute concentration at the point defined. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' plt.figure(1,figsize=(16,9)) plt.plot(dict_tracker['L_t'], dict_tracker['c_at_the_center']) plt.title('Value of the solute concentration at the center') plt.savefig('Debug/Solute_Contration_Center.png') plt.close(1) def Plot_config(dict_algorithm, dict_sample)-
Plot the sample configuration.
Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file)Expand source code
def Plot_config(dict_algorithm, dict_sample): ''' Plot the sample configuration. Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file) ''' #look for the name of the new plot template_name = 'Debug/Configuration/Configuration_' j = 0 plotpath = Path(template_name+str(j)+'.png') while plotpath.exists(): j = j + 1 plotpath = Path(template_name+str(j)+'.png') name = template_name+str(j)+'.png' #plot plt.figure(1,figsize=(16,9)) plt.subplot(211) #solute im = plt.imshow(dict_sample['solute_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = dict_algorithm['c_min'], vmax = dict_algorithm['c_max']) plt.colorbar(im) #etai for i in range(len(dict_sample['L_g'])): plt.plot(dict_sample['L_g'][i].l_border_x,dict_sample['L_g'][i].l_border_y) plt.title('Solute c and grains') plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) #eta1_M plt.subplot(223) im = plt.imshow(dict_sample['L_g'][0].etai_M,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = 0, vmax = 1) plt.colorbar(im) plt.plot(dict_sample['L_g'][0].l_border_x,dict_sample['L_g'][0].l_border_y,'r') plt.title(r'$\eta$1') #eta2_M plt.subplot(224) im = plt.imshow(dict_sample['L_g'][1].etai_M,interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])], vmin = 0, vmax = 1) plt.colorbar(im) plt.plot(dict_sample['L_g'][1].l_border_x,dict_sample['L_g'][1].l_border_y,'r') plt.title(r'$\eta$2') plt.savefig(name) plt.close(1) def Plot_dt_used(dict_tracker)-
Plot the evolution of the time step used in the phase field simulation.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_dt_used(dict_tracker): ''' Plot the evolution of the time step used in the phase field simulation. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' plt.figure(1,figsize=(16,9)) plt.plot(dict_tracker['L_dt']) plt.title('Evolution of the time step used') plt.savefig('Debug/Evolution_dt_used.png') plt.close(1) def Plot_init_current_shape(dict_sample)-
Plot the comparison between initial and current shape for the grain 1.
Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file)Expand source code
def Plot_init_current_shape(dict_sample): ''' Plot the comparison between initial and current shape for the grain 1. Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file) ''' #look for the name of the new plot template_name = 'Debug/Comparison_Init_Current/Init_Current_Shape_' j = 0 plotpath = Path(template_name+str(j)+'.png') while plotpath.exists(): j = j + 1 plotpath = Path(template_name+str(j)+'.png') name = template_name+str(j)+'.png' #prepare plot L_border_x_init = [] L_border_y_init = [] for i in range(len(dict_sample['L_g'][0].l_border_x_init)): L_border_x_init.append(dict_sample['L_g'][0].l_border_x_init[i] - dict_sample['L_g'][0].center_init[0]) L_border_y_init.append(dict_sample['L_g'][0].l_border_y_init[i] - dict_sample['L_g'][0].center_init[1]) L_border_x = [] L_border_y = [] for i in range(len(dict_sample['L_g'][0].l_border_x)): L_border_x.append(dict_sample['L_g'][0].l_border_x[i] - dict_sample['L_g'][0].center[0]) L_border_y.append(dict_sample['L_g'][0].l_border_y[i] - dict_sample['L_g'][0].center[1]) #plot plt.figure(1,figsize=(16,9)) plt.plot(L_border_x_init,L_border_y_init,'k',label='Initial') plt.plot(L_border_x,L_border_y,label='Current') plt.savefig(name) plt.close(1) def Plot_kc(dict_sample)-
Plot the distribution of the diffusion coefficient for the solute.
Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file)Expand source code
def Plot_kc(dict_sample): ''' Plot the distribution of the diffusion coefficient for the solute. Input : a sample dictionnary (a dict) Output : Nothing but a .png file is generated (a file) ''' #look for the name of the new plot template_name = 'Debug/Kc/Kc_' j = 0 plotpath = Path(template_name+str(j)+'.png') while plotpath.exists(): j = j + 1 plotpath = Path(template_name+str(j)+'.png') name = template_name+str(j)+'.png' #plot plt.figure(1,figsize=(16,9)) #kc_M im = plt.imshow(dict_sample['kc_M'],interpolation='nearest', extent=[min(dict_sample['x_L']),max(dict_sample['x_L']),min(dict_sample['y_L']),max(dict_sample['y_L'])]) plt.colorbar(im) #plot g1 and g2 boundaries plt.plot(dict_sample['L_g'][0].l_border_x,dict_sample['L_g'][0].l_border_y) plt.plot(dict_sample['L_g'][1].l_border_x,dict_sample['L_g'][1].l_border_y) plt.axis('equal') plt.xlim(min(dict_sample['x_L']),max(dict_sample['x_L'])) plt.title('Diffusion coefficient of the solute') plt.savefig(name) plt.close(1) def Plot_mp4(template_name, name_video)-
The goal of this function is to create a movie with pictures.
from https://www.blog.pythonlibrary.org/2021/06/23/creating-an-animated-gif-with-python/
Input : the template of the pictures used (a string) the name of the video (a string) Output : a movie file (a .mp4)Expand source code
def Plot_mp4(template_name,name_video): '''The goal of this function is to create a movie with pictures. from https://www.blog.pythonlibrary.org/2021/06/23/creating-an-animated-gif-with-python/ Input : the template of the pictures used (a string) the name of the video (a string) Output : a movie file (a .mp4) ''' #look for the largest iteration i_f = 0 plotpath = Path(template_name+str(i_f)+'.png') while plotpath.exists(): i_f = i_f + 1 plotpath = Path(template_name+str(i_f)+'.png') fileList = [] for i in range(0,i_f): fileList.append(template_name+str(i)+'.png') duration_movie = 10 #sec writer = imageio.get_writer(name_video, fps=int(i_f/duration_movie)) for im in fileList: writer.append_data(imageio.imread(im)) writer.close() def Plot_sphericity(dict_tracker)-
Plot the sphericity of the grain 1.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_sphericity(dict_tracker): ''' Plot the sphericity of the grain 1. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' plt.figure(1,figsize=(16,9)) plt.plot(dict_tracker['L_t'], dict_tracker['L_area_sphericity_g0'],label='Area sphericity') plt.plot(dict_tracker['L_t'], dict_tracker['L_diameter_sphericity_g0'],label='Diameter sphericity') plt.plot(dict_tracker['L_t'], dict_tracker['L_circle_ratio_sphericity_g0'],label='Circle sphericity') plt.plot(dict_tracker['L_t'], dict_tracker['L_perimeter_sphericity_g0'],label='Perimeter sphericity') plt.plot(dict_tracker['L_t'], dict_tracker['L_width_to_length_ratio_sphericity_g0'],label='Width to length ratio sphericity') plt.legend() plt.title('2D sphericity of grain 1') plt.savefig('Debug/Sphericity_g_1.png') plt.close(1) def Plot_sum_Ed(dict_tracker)-
Plot the value of the total energy in the sample.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_sum_Ed(dict_tracker): ''' Plot the value of the total energy in the sample. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' plt.figure(1,figsize=(16,9)) plt.subplot(331) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_Ed_che_L']) plt.title('Total chemical energy Ed_che') plt.subplot(334) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_Ed_mec_L']) plt.title('Total mechanical energy Ed_mec') plt.subplot(337) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_L']) plt.title('Total Energy Ed = Ed_mec - Ed_che') plt.subplot(132) plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_plus_L'], label = 'Ed+') plt.plot(dict_tracker['L_t'][:-1], dict_tracker['sum_ed_minus_L'], label = 'Ed-') plt.title('Repartition of the energy in a + and a - terms') plt.subplot(133) plt.plot(dict_tracker['L_t'], dict_tracker['L_int_displacement']) plt.title('Total displacement done') plt.savefig('Debug/Evolution_sum_Ed.png') plt.close(1) def Plot_sum_eta_c(dict_tracker)-
Plot the trackers.
Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files)Expand source code
def Plot_sum_eta_c(dict_tracker): ''' Plot the trackers. Input : a tracker dictionnary (a dict) Output : Nothing but a .png files are generated (files) ''' #plot Displacement and sum of c and etai plt.figure(1,figsize=(16,9)) plt.subplot(211) plt.plot(dict_tracker['L_t'], dict_tracker['L_int_displacement']) plt.title('Total displacement done') plt.subplot(234) plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_eta']) plt.title('Sum of etai') plt.subplot(235) plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_solute']) plt.title('Sum of c') plt.subplot(236) plt.plot(dict_tracker['L_t'], dict_tracker['L_sum_total']) plt.title('Sum of etai and c') plt.savefig('Debug/Trackers.png') plt.close(1)