Newer
Older
import numpy as np
import matplotlib.pyplot as plt
import sympy as sym
import math
import os
import subprocess
import fileinput
import re
import matlab.engine
import time
Klaus Böhnlein
committed
from ClassifyMin import *
# from scipy.io import loadmat #not Needed anymore?
import codecs
import sys
Klaus Böhnlein
committed
def ReadEffectiveQuantities(QFilePath = os.path.dirname(os.getcwd()) + '/outputs/QMatrix.txt', BFilePath = os.path.dirname(os.getcwd())+ '/outputs/BMatrix.txt'):
# Read Output Matrices (effective quantities)
# From Cell-Problem output Files : ../outputs/Qmatrix.txt , ../outputs/Bmatrix.txt
# -- Read Matrix Qhom
X = []
with codecs.open(path + '/outputs/QMatrix.txt', encoding='utf-8-sig') as f:
for line in f:
s = line.split()
X.append([float(s[i]) for i in range(len(s))])
Q = np.array([[X[0][2], X[1][2], X[2][2]],
[X[3][2], X[4][2], X[5][2]],
[X[6][2], X[7][2], X[8][2]] ])
Klaus Böhnlein
committed
# -- Read Beff (as Vector)
X = []
with codecs.open(path + '/outputs/BMatrix.txt', encoding='utf-8-sig') as f:
for line in f:
s = line.split()
X.append([float(s[i]) for i in range(len(s))])
B = np.array([X[0][2], X[1][2], X[2][2]])
return Q, B
Klaus Böhnlein
committed
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
def RunCellProblem(alpha,beta,theta,gamma,mu1,rho1, InputFilePath = os.path.dirname(os.getcwd()) +"/inputs/computeMuGamma.parset"):
with open(InputFilePath, 'r') as file:
filedata = file.read()
filedata = re.sub('(?m)^gamma=.*','gamma='+str(gamma),filedata)
filedata = re.sub('(?m)^alpha=.*','alpha='+str(alpha),filedata)
filedata = re.sub('(?m)^beta=.*','beta='+str(beta),filedata)
filedata = re.sub('(?m)^theta=.*','theta='+str(theta),filedata)
filedata = re.sub('(?m)^mu1=.*','mu1='+str(mu1),filedata)
filedata = re.sub('(?m)^rho1=.*','rho1='+str(rho1),filedata)
f = open(InputFilePath,'w')
f.write(filedata)
f.close()
# --- Run Cell-Problem
# Optional: Check Time
# t = time.time()
subprocess.run(['./build-cmake/src/Cell-Problem', './inputs/cellsolver.parset'],
capture_output=True, text=True)
# print('elapsed time:', time.time() - t)
# --------------------------------------------------------------------------------------
def Compare_Classification(alpha,beta,theta,gamma,mu1,rho1, InputFilePath = os.path.dirname(os.getcwd()) +"/inputs/computeMuGamma.parset"):
# ---------------------------------------------------------------
# Comparison of the analytical Classification 'ClassifyMin'
# and the symbolic Minimizatio + Classification 'symMinimization'
# ----------------------------------------------------------------
comparison_successful = True
eps = 1e-8
# 1. Substitute Input-Parameters for the Cell-Problem
with open(InputFilePath, 'r') as file:
filedata = file.read()
filedata = re.sub('(?m)^gamma=.*','gamma='+str(gamma),filedata)
filedata = re.sub('(?m)^alpha=.*','alpha='+str(alpha),filedata)
filedata = re.sub('(?m)^beta=.*','beta='+str(beta),filedata)
filedata = re.sub('(?m)^theta=.*','theta='+str(theta),filedata)
filedata = re.sub('(?m)^mu1=.*','mu1='+str(mu1),filedata)
filedata = re.sub('(?m)^rho1=.*','rho1='+str(rho1),filedata)
f = open(InputFilePath,'w')
f.write(filedata)
f.close()
# 2. --- Run Cell-Problem
print('Run Cell-Problem ...')
# Optional: Check Time
# t = time.time()
subprocess.run(['./build-cmake/src/Cell-Problem', './inputs/cellsolver.parset'],
capture_output=True, text=True)
# 3. --- Run Matlab symbolic minimization program: 'symMinimization'
eng = matlab.engine.start_matlab()
# s = eng.genpath(path + '/Matlab-Programs')
s = eng.genpath(path)
eng.addpath(s, nargout=0)
# print('current Matlab folder:', eng.pwd(nargout=1))
eng.cd('Matlab-Programs', nargout=0) #switch to Matlab-Programs folder
# print('current Matlab folder:', eng.pwd(nargout=1))
Inp = False
print('Run symbolic Minimization...')
G, angle, type, kappa = eng.symMinimization(Inp,Inp,Inp,Inp, nargout=4) #Name of program:symMinimization
# G, angle, type, kappa = eng.symMinimization(Inp,Inp,Inp,Inp,path + "/outputs", nargout=4) #Optional: add Path
G = np.asarray(G) #cast Matlab Outout to numpy array
# --- print Output ---
print('Minimizer G:')
print(G)
print('angle:', angle)
print('type:', type )
print('curvature:', kappa)
# 4. --- Read the effective quantities (output values of the Cell-Problem)
# Read Output Matrices (effective quantities)
print('Read effective quantities...')
Q, B = ReadEffectiveQuantities()
print('Q:', Q)
print('B:', B)
q1 = Q[0][0]
q2 = Q[1][1]
q3 = Q[2][2]
q12 = Q[0][1]
b1 = B[0]
b2 = B[1]
b3 = B[2]
# print("q1:", q1)
# print("q2:", q2)
# print("q3:", q3)
# print("q12:", q12)
# print("b1:", b1)
# print("b2:", b2)
# print("b3:", b3)
# --- Check Assumptions:
# Assumption of Classification-Lemma1.6: [b3 == 0] & [Q orthotropic]
# Check if b3 is close to zero
assert (b3 < eps), "ClassifyMin only defined for b3 == 0 !"
# Check if Q is orthotropic i.e. q13 = q31 = q23 = q32 == 0
assert(Q[2][0] < eps and Q[0][2] < eps and Q[1][2] < eps and Q[2][1] < eps), "Q is not orthotropic !"
# 5. --- Get output from the analytical Classification 'ClassifyMin'
G_ana, angle_ana, type_ana, kappa_ana = classifyMin(q1, q2, q3, q12, b1, b2)
# print('Minimizer G_ana:')
# print(G_ana)
# print('angle_ana:', angle_ana)
# print('type_ana:', type_ana )
# print('curvature_ana:', kappa_ana)
# 6. Compare
# print('DifferenceMatrix:', G_ana - G )
# print('MinimizerError (FrobeniusNorm):', np.linalg.norm(G_ana - G , 'fro') )
if np.linalg.norm(G_ana - G , 'fro') > eps :
comparison_successful = False
print('Difference between Minimizers is too large !')
if type != type_ana :
comparison_successful = False
print('Difference in type !')
if abs(angle-angle_ana) > eps :
comparison_successful = False
print('Difference in angle is too large!')
if abs(kappa-kappa_ana) > eps :
comparison_successful = False
print('Difference in curvature is too large!')
if comparison_successful:
print('Comparison successful')
return comparison_successful
# ----------------------------------------------------------------------------------------------------------------------------
# ----- Setup Paths -----
InputFile = "/inputs/cellsolver.parset"
OutputFile = "/outputs/output.txt"
# --------- Run from src folder:
path_parent = os.path.dirname(os.getcwd())
os.chdir(path_parent)
path = os.getcwd()
print(path)
InputFilePath = os.getcwd()+InputFile
OutputFilePath = os.getcwd()+OutputFile
print("InputFilepath: ", InputFilePath)
print("OutputFilepath: ", OutputFilePath)
print("Path: ", path)
#1. Define Inputs Gamma-Array..
#2. for(i=0; i<length(array)) ..compute Q_hom, B_eff from Cell-Problem
#3
# matrix = np.loadtxt(path + 'Qmatrix.txt', usecols=range(3))
# print(matrix)
# Use Shell Commands:
# subprocess.run('ls', shell=True)
Klaus Böhnlein
committed
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
#
# #-------------------- PLOT OPTION -------------------------------------------
#
# Gamma_Values = np.linspace(0.01, 2.5, num=6) # TODO variable Input Parameters...alpha,beta...
# print('(Input) Gamma_Values:', Gamma_Values)
# mu_gamma = []
#
#
#
# # --- Options
# RUN = True
# # RUN = False
# # make_Plot = False
# make_Plot = True # vll besser : Plot_muGamma
#
# if RUN:
# for gamma in Gamma_Values:
# print("Run Cell-Problem for Gamma = ", gamma)
# # print('gamma='+str(gamma))
# with open(InputFilePath, 'r') as file:
# filedata = file.read()
# filedata = re.sub('(?m)^gamma=.*','gamma='+str(gamma),filedata)
# f = open(InputFilePath,'w')
# f.write(filedata)
# f.close()
# # --- Run Cell-Problem
# t = time.time()
# # subprocess.run(['./build-cmake/src/Cell-Problem', './inputs/cellsolver.parset'],
# # capture_output=True, text=True)
# # --- Run Cell-Problem_muGama -> faster
# # subprocess.run(['./build-cmake/src/Cell-Problem_muGamma', './inputs/cellsolver.parset'],
# # capture_output=True, text=True)
# # --- Run Compute_muGamma (2D Problem much much faster)
# subprocess.run(['./build-cmake/src/Compute_MuGamma', './inputs/computeMuGamma.parset'],
# capture_output=True, text=True)
# print('elapsed time:', time.time() - t)
#
# #Extract mu_gamma from Output-File TODO: GENERALIZED THIS FOR QUANTITIES OF INTEREST
# with open(OutputFilePath, 'r') as file:
# output = file.read()
# tmp = re.search(r'(?m)^mu_gamma=.*',output).group() # Not necessary for Intention of Program t output Minimizer etc.....
# s = re.findall(r"[-+]?\d*\.\d+|\d+", tmp)
# mu_gammaValue = float(s[0])
# print("mu_gamma:", mu_gammaValue)
# mu_gamma.append(mu_gammaValue)
# # ------------end of for-loop -----------------
# print("(Output) Values of mu_gamma: ", mu_gamma)
# # ----------------end of if-statement -------------
#
# # mu_gamma=[2.06099, 1.90567, 1.905]
# # mu_gamma=[2.08306, 1.905, 1.90482, 1.90479, 1.90478, 1.90477]
#
# ##Gamma_Values = np.linspace(0.01, 20, num=12) :
# #mu_gamma= [2.08306, 1.91108, 1.90648, 1.90554, 1.90521, 1.90505, 1.90496, 1.90491, 1.90487, 1.90485, 1.90483, 1.90482]
#
# ##Gamma_Values = np.linspace(0.01, 2.5, num=12)
# # mu_gamma=[2.08306, 2.01137, 1.96113, 1.93772, 1.92592, 1.91937, 1.91541, 1.91286, 1.91112, 1.90988, 1.90897, 1.90828]
#
# Gamma_Values = np.linspace(0.01, 2.5, num=6)
# mu_gamma=[2.08306, 1.95497, 1.92287, 1.91375, 1.9101, 1.90828]
#
#
#
# # Make Plot
# if make_Plot:
# plt.figure()
# plt.title(r'$\mu_\gamma(\gamma)$-Plot')
# plt.plot(Gamma_Values, mu_gamma)
# plt.scatter(Gamma_Values, mu_gamma)
# # plt.axis([0, 6, 0, 20])
# plt.axhline(y = 1.90476, color = 'b', linestyle = ':', label='$q_1$')
# plt.axhline(y = 2.08333, color = 'r', linestyle = 'dashed', label='$q_2$')
# plt.xlabel("$\gamma$")
# plt.ylabel("$\mu_\gamma$")
# plt.legend()
# plt.show()
#
# # ------------------------------------------------------------------------
Klaus Böhnlein
committed
print('---- Input parameters: -----')
mu1 = 10.0
rho1 = 1.0
alpha = 2.8
beta = 2.0
theta = 1.0/4.0
gamma = 0.75
Klaus Böhnlein
committed
print('mu1: ', mu1)
print('rho1: ', rho1)
print('alpha: ', alpha)
print('beta: ', beta)
print('theta: ', theta)
print('gamma:', gamma)
print('----------------------------')
Klaus Böhnlein
committed
# print('RunCellProblem...')
# RunCellProblem(alpha,beta,theta,gamma,mu1,rho1,InputFilePath)
# TEST read Matrix file
# Test = loadmat(path + '/outputs/QMatrix.mat')
print('Compare_Classification...')
Compare_Classification(alpha,beta,theta,gamma,mu1,rho1,InputFilePath)
# ------------- RUN Matlab symbolic minimization program
eng = matlab.engine.start_matlab()
# s = eng.genpath(path + '/Matlab-Programs')
s = eng.genpath(path)
eng.addpath(s, nargout=0)
# print('current Matlab folder:', eng.pwd(nargout=1))
eng.cd('Matlab-Programs', nargout=0) #switch to Matlab-Programs folder
# print('current Matlab folder:', eng.pwd(nargout=1))
Inp = False
print('Run symbolic Minimization...')
G, angle, type, kappa = eng.symMinimization(Inp,Inp,Inp,Inp, nargout=4) #Name of program:symMinimization
# G, angle, type, kappa = eng.symMinimization(Inp,Inp,Inp,Inp,path + "/outputs", nargout=4) #Optional: add Path
G = np.asarray(G) #cast Matlab Outout to numpy array
# --- print Output ---
print('Minimizer G:')
print(G)
print('angle:', angle)
print('type:', type )
print('curvature:', kappa)