Module Etai
@author: Alexandre Sac–Morane alexandre.sac-morane@uclouvain.be
The goal of this file is to define a new class. The new class is about the unconserved variables used in phase-field simulation.
Expand source code
# -*- coding: utf-8 -*-
"""
@author: Alexandre Sac--Morane
alexandre.sac-morane@uclouvain.be
The goal of this file is to define a new class.
The new class is about the unconserved variables used in phase-field simulation.
"""
#-------------------------------------------------------------------------------
#Libs
#-------------------------------------------------------------------------------
import numpy as np
import random
#Own
import Report
import Grain
#-------------------------------------------------------------------------------
#Class
#-------------------------------------------------------------------------------
class Etai:
#-------------------------------------------------------------------------------
def __init__(self, ID, L_ig, L_g):
'''
Defining the phase variables etai.
One eta represents several grains.
Input :
an id (an integer)
a list of grain id represented by the variable (a list)
the list of the grains (a list)
Output :
an eta (an eta)
'''
self.id = ID
self.l_ig = L_ig
self.update_etai_M(L_g)
for i_grain in self.l_ig :
L_g[i_grain].etai = self.id
#-------------------------------------------------------------------------------
def update_etai_M(self,L_g):
'''
Sum variables field of all the grain assigned to this eta.
Input :
itself (an eta)
the list of grains (a list)
Output :
Nothing, but the phase field attribute is updated (a nx x ny numpy array)
'''
self.etai_M = np.array(L_g[self.l_ig[0]].etai_M.copy())
for i in range(1,len(self.l_ig)):
self.etai_M = self.etai_M + np.array(L_g[self.l_ig[i]].etai_M.copy())
#-------------------------------------------------------------------------------
#Function
#-------------------------------------------------------------------------------
def etai_distribution(dict_algorithm, dict_sample, simulation_report):
'''
Assign grains to etai.
A minimal distance between grains with same eta is computed from the factor variable
Input :
an algorithm dictionnary (a dict)
a sample dictionnary (a dict)
a simulation report (a report)
Output :
Nothing, but the sample dictionnary is updated with the list of the etai (a list)
'''
#initialisation
for grain in dict_sample['L_g']:
grain.ig_near_L = []
grain.eta_near_L = []
#look for grains near
for i_grain1 in range(1,len(dict_sample['L_g'])):
grain1 = dict_sample['L_g'][i_grain1]
for i_grain2 in range(0,i_grain1):
grain2 = dict_sample['L_g'][i_grain2]
if np.linalg.norm(grain1.center - grain2.center) < dict_algorithm['factor_etai'] * (grain1.r_max+grain2.r_max):
grain1.ig_near_L.append(i_grain2)
grain2.ig_near_L.append(i_grain1)
#first try
n_etai_L = [1]
L_etai = [0]
L_ig_etai = [[0]]
dict_sample['L_g'][0].id_eta = 0
for grain in dict_sample['L_g']:
if 0 in grain.ig_near_L:
grain.eta_near_L.append(0)
for i_grain in range(1,len(dict_sample['L_g'])):
grain = dict_sample['L_g'][i_grain]
etai_defined = False
etai = 0
while etai < len(L_etai) and not etai_defined:
if etai not in grain.eta_near_L:
grain.id_eta = etai
etai_defined = True
n_etai_L[etai] = n_etai_L[etai] + 1
L_ig_etai[etai].append(i_grain)
for grain2 in dict_sample['L_g']:
if i_grain in grain2.ig_near_L:
grain2.eta_near_L.append(etai)
etai = etai + 1
if etai == len(L_etai) and not etai_defined:
grain.id_eta = etai
L_etai.append(etai)
n_etai_L.append(1)
L_ig_etai.append([i_grain])
for grain2 in dict_sample['L_g']:
if i_grain in grain2.ig_near_L:
grain2.eta_near_L.append(etai)
#adaptation (try to have the same number of grain assigned to all eta)
n_etai_mean = np.mean(n_etai_L)
adaptation_done = False
adaptation_i = 0
while not adaptation_done :
adaptation_i = adaptation_i + 1
L_ig_over = L_ig_etai[n_etai_L.index(max(n_etai_L))]
id_g_to_work = L_ig_over[random.randint(0,len(L_ig_over)-1)]
etai_over = n_etai_L.index(max(n_etai_L))
etai_under = n_etai_L.index(min(n_etai_L))
grain = dict_sample['L_g'][id_g_to_work]
if etai_under not in grain.eta_near_L:
grain.id_eta = etai_under
n_etai_L[etai_over] = n_etai_L[etai_over] - 1
n_etai_L[etai_under] = n_etai_L[etai_under] + 1
L_ig_etai[etai_over].remove(id_g_to_work)
L_ig_etai[etai_under].append(id_g_to_work)
for grain2 in dict_sample['L_g']:
if id_g_to_work in grain2.ig_near_L:
grain2.eta_near_L.remove(etai_over)
grain2.eta_near_L.append(etai_under)
#check the quality
adaptation_done = True
for n_etai in n_etai_L :
if n_etai_mean-2 < n_etai and n_etai < n_etai_mean+2:
adaptation_done = False
if adaptation_i > len(dict_sample['L_g']):
adaptation_done = True
#Create etai
L_etai = []
for i in range(len(L_ig_etai)) :
etai = Etai(i, L_ig_etai[i], dict_sample['L_g'])
L_etai.append(etai)
#update the dict
dict_sample['L_etai'] = L_etai
simulation_report.write(f"{len(dict_sample['L_etai'])} phase variables used.\n")
simulation_report.write(f"{round(len(dict_sample['L_g'])/len(dict_sample['L_etai']),1)} grains described for one phase variable.\n")
#-------------------------------------------------------------------------------
def PFtoDEM_Multi(FileToRead,dict_algorithm,dict_material,dict_sample):
'''
Read file from MOOSE simulation to reconstruct the phase field of the grain.
Input :
the name of the file to read (a string)
an algorithm dictionnary (a dictionnary)
a material dictionnary (a dictionnary)
a sample dictionnary (a dictionnary)
Output :
Nothing but the grain gets an updated attribute (a n_y x n_x numpy array)
'''
#--------------------------------------------------------------------------
#Global parameters
#---------------------------------------------------------------------------
L_etai_M = np.array(np.zeros((len(dict_sample['L_etai']),len(dict_sample['y_L']),len(dict_sample['x_L']))))
id_L = None
eta_selector_len = len(' <DataArray type="Float64" Name="eta')
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
for etai in dict_sample['L_etai']:
L_Work.append([]) #etai
#---------------------------------------------------------------------------
#Reading file
#---------------------------------------------------------------------------
f = open(f'{FileToRead}_{i_proc}.vtu','r')
data = f.read()
f.close
lines = data.splitlines()
#iterations on line
for line in lines:
if line[0:eta_selector_len] == ' <DataArray type="Float64" Name="eta':
id_L = 1 + int(line[eta_selector_len])
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 id_L != None :
if line[0:data_jump_len] == ' ' and id_L >= 2: #Read etai
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 etai_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]))
for j in range(2,len(L_Work)):
L_etai_M[j-2][-1-list(L_dy).index(min(L_dy))][list(L_dx).index(min(L_dx))] = L_Work[j][i]
#---------------------------------------------------------------------------
#Transmit data to grains
#---------------------------------------------------------------------------
for grain in dict_sample['L_g']:
grain.ExtractPF_from_Eta(L_etai_M, dict_algorithm, dict_material, dict_sample)
grain.geometric_study(dict_sample)Functions
def PFtoDEM_Multi(FileToRead, dict_algorithm, dict_material, dict_sample)-
Read file from MOOSE simulation to reconstruct the phase field of the grain.
Input : the name of the file to read (a string) an algorithm dictionnary (a dictionnary) a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) Output : Nothing but the grain gets an updated attribute (a n_y x n_x numpy array)Expand source code
def PFtoDEM_Multi(FileToRead,dict_algorithm,dict_material,dict_sample): ''' Read file from MOOSE simulation to reconstruct the phase field of the grain. Input : the name of the file to read (a string) an algorithm dictionnary (a dictionnary) a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) Output : Nothing but the grain gets an updated attribute (a n_y x n_x numpy array) ''' #-------------------------------------------------------------------------- #Global parameters #--------------------------------------------------------------------------- L_etai_M = np.array(np.zeros((len(dict_sample['L_etai']),len(dict_sample['y_L']),len(dict_sample['x_L'])))) id_L = None eta_selector_len = len(' <DataArray type="Float64" Name="eta') 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 for etai in dict_sample['L_etai']: L_Work.append([]) #etai #--------------------------------------------------------------------------- #Reading file #--------------------------------------------------------------------------- f = open(f'{FileToRead}_{i_proc}.vtu','r') data = f.read() f.close lines = data.splitlines() #iterations on line for line in lines: if line[0:eta_selector_len] == ' <DataArray type="Float64" Name="eta': id_L = 1 + int(line[eta_selector_len]) 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 id_L != None : if line[0:data_jump_len] == ' ' and id_L >= 2: #Read etai 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 etai_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])) for j in range(2,len(L_Work)): L_etai_M[j-2][-1-list(L_dy).index(min(L_dy))][list(L_dx).index(min(L_dx))] = L_Work[j][i] #--------------------------------------------------------------------------- #Transmit data to grains #--------------------------------------------------------------------------- for grain in dict_sample['L_g']: grain.ExtractPF_from_Eta(L_etai_M, dict_algorithm, dict_material, dict_sample) grain.geometric_study(dict_sample) def etai_distribution(dict_algorithm, dict_sample, simulation_report)-
Assign grains to etai.
A minimal distance between grains with same eta is computed from the factor variable
Input : an algorithm dictionnary (a dict) a sample dictionnary (a dict) a simulation report (a report) Output : Nothing, but the sample dictionnary is updated with the list of the etai (a list)Expand source code
def etai_distribution(dict_algorithm, dict_sample, simulation_report): ''' Assign grains to etai. A minimal distance between grains with same eta is computed from the factor variable Input : an algorithm dictionnary (a dict) a sample dictionnary (a dict) a simulation report (a report) Output : Nothing, but the sample dictionnary is updated with the list of the etai (a list) ''' #initialisation for grain in dict_sample['L_g']: grain.ig_near_L = [] grain.eta_near_L = [] #look for grains near for i_grain1 in range(1,len(dict_sample['L_g'])): grain1 = dict_sample['L_g'][i_grain1] for i_grain2 in range(0,i_grain1): grain2 = dict_sample['L_g'][i_grain2] if np.linalg.norm(grain1.center - grain2.center) < dict_algorithm['factor_etai'] * (grain1.r_max+grain2.r_max): grain1.ig_near_L.append(i_grain2) grain2.ig_near_L.append(i_grain1) #first try n_etai_L = [1] L_etai = [0] L_ig_etai = [[0]] dict_sample['L_g'][0].id_eta = 0 for grain in dict_sample['L_g']: if 0 in grain.ig_near_L: grain.eta_near_L.append(0) for i_grain in range(1,len(dict_sample['L_g'])): grain = dict_sample['L_g'][i_grain] etai_defined = False etai = 0 while etai < len(L_etai) and not etai_defined: if etai not in grain.eta_near_L: grain.id_eta = etai etai_defined = True n_etai_L[etai] = n_etai_L[etai] + 1 L_ig_etai[etai].append(i_grain) for grain2 in dict_sample['L_g']: if i_grain in grain2.ig_near_L: grain2.eta_near_L.append(etai) etai = etai + 1 if etai == len(L_etai) and not etai_defined: grain.id_eta = etai L_etai.append(etai) n_etai_L.append(1) L_ig_etai.append([i_grain]) for grain2 in dict_sample['L_g']: if i_grain in grain2.ig_near_L: grain2.eta_near_L.append(etai) #adaptation (try to have the same number of grain assigned to all eta) n_etai_mean = np.mean(n_etai_L) adaptation_done = False adaptation_i = 0 while not adaptation_done : adaptation_i = adaptation_i + 1 L_ig_over = L_ig_etai[n_etai_L.index(max(n_etai_L))] id_g_to_work = L_ig_over[random.randint(0,len(L_ig_over)-1)] etai_over = n_etai_L.index(max(n_etai_L)) etai_under = n_etai_L.index(min(n_etai_L)) grain = dict_sample['L_g'][id_g_to_work] if etai_under not in grain.eta_near_L: grain.id_eta = etai_under n_etai_L[etai_over] = n_etai_L[etai_over] - 1 n_etai_L[etai_under] = n_etai_L[etai_under] + 1 L_ig_etai[etai_over].remove(id_g_to_work) L_ig_etai[etai_under].append(id_g_to_work) for grain2 in dict_sample['L_g']: if id_g_to_work in grain2.ig_near_L: grain2.eta_near_L.remove(etai_over) grain2.eta_near_L.append(etai_under) #check the quality adaptation_done = True for n_etai in n_etai_L : if n_etai_mean-2 < n_etai and n_etai < n_etai_mean+2: adaptation_done = False if adaptation_i > len(dict_sample['L_g']): adaptation_done = True #Create etai L_etai = [] for i in range(len(L_ig_etai)) : etai = Etai(i, L_ig_etai[i], dict_sample['L_g']) L_etai.append(etai) #update the dict dict_sample['L_etai'] = L_etai simulation_report.write(f"{len(dict_sample['L_etai'])} phase variables used.\n") simulation_report.write(f"{round(len(dict_sample['L_g'])/len(dict_sample['L_etai']),1)} grains described for one phase variable.\n")
Classes
class Etai (ID, L_ig, L_g)-
Defining the phase variables etai.
One eta represents several grains.
Input : an id (an integer) a list of grain id represented by the variable (a list) the list of the grains (a list) Output : an eta (an eta)Expand source code
class Etai: #------------------------------------------------------------------------------- def __init__(self, ID, L_ig, L_g): ''' Defining the phase variables etai. One eta represents several grains. Input : an id (an integer) a list of grain id represented by the variable (a list) the list of the grains (a list) Output : an eta (an eta) ''' self.id = ID self.l_ig = L_ig self.update_etai_M(L_g) for i_grain in self.l_ig : L_g[i_grain].etai = self.id #------------------------------------------------------------------------------- def update_etai_M(self,L_g): ''' Sum variables field of all the grain assigned to this eta. Input : itself (an eta) the list of grains (a list) Output : Nothing, but the phase field attribute is updated (a nx x ny numpy array) ''' self.etai_M = np.array(L_g[self.l_ig[0]].etai_M.copy()) for i in range(1,len(self.l_ig)): self.etai_M = self.etai_M + np.array(L_g[self.l_ig[i]].etai_M.copy())Methods
def update_etai_M(self, L_g)-
Sum variables field of all the grain assigned to this eta.
Input : itself (an eta) the list of grains (a list) Output : Nothing, but the phase field attribute is updated (a nx x ny numpy array)Expand source code
def update_etai_M(self,L_g): ''' Sum variables field of all the grain assigned to this eta. Input : itself (an eta) the list of grains (a list) Output : Nothing, but the phase field attribute is updated (a nx x ny numpy array) ''' self.etai_M = np.array(L_g[self.l_ig[0]].etai_M.copy()) for i in range(1,len(self.l_ig)): self.etai_M = self.etai_M + np.array(L_g[self.l_ig[i]].etai_M.copy())