diff --git a/Plot-Scripts/WoodBilayer_ExperimentComparison.py b/Plot-Scripts/WoodBilayer_ExperimentComparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a2d68d0700f6464ca2d763ce82df78b573f3c75
--- /dev/null
+++ b/Plot-Scripts/WoodBilayer_ExperimentComparison.py
@@ -0,0 +1,199 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import os
+import subprocess
+import fileinput
+import re
+import sys
+import matplotlib as mpl
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.cm as cm
+import matplotlib.ticker as ticker
+from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
+import seaborn as sns
+import matplotlib.colors as mcolors
+# ----------------------------------------------------
+# --- Define Plot style:
+# plt.style.use("seaborn-white")
+# plt.style.use("seaborn-pastel")
+# plt.style.use("seaborn-colorblind")
+plt.style.use("seaborn")
+mpl.rcParams['text.usetex'] = True
+mpl.rcParams["font.family"] = "serif"
+mpl.rcParams["font.size"] = "14"
+mpl.rcParams['xtick.bottom'] = True
+mpl.rcParams['xtick.major.size'] = 3
+mpl.rcParams['xtick.minor.size'] = 1.5
+mpl.rcParams['xtick.major.width'] = 0.75
+mpl.rcParams['ytick.left'] = True
+mpl.rcParams['ytick.major.size'] = 3
+mpl.rcParams['ytick.minor.size'] = 1.5
+mpl.rcParams['ytick.major.width'] = 0.75
+
+#Adjust grid:
+mpl.rcParams.update({"axes.grid" : True}) # Add grid
+mpl.rcParams['axes.labelpad'] = 3
+mpl.rcParams['grid.linewidth'] = 0.25
+mpl.rcParams['grid.alpha'] = 0.9 # 0.75
+mpl.rcParams['grid.linestyle'] = '-'
+mpl.rcParams['grid.color'] = 'gray'#'black'
+mpl.rcParams['text.latex.preamble'] = r'\usepackage{amsfonts}' # Makes Use of \mathbb possible.
+# ----------------------------------------------------------------------------------------
+width = 5.79
+height = width / 1.618 # The golden ratio.
+
+
+
+
+#TODO read values from .txt output files....
+
+dataset_numbers = [0, 1, 2, 3, 4 ,5]
+
+for dataset_number in dataset_numbers:
+
+ # fig = plt.figure() #main
+ fig, ax = plt.subplots(figsize=(width,height))
+
+ dataset = [
+
+ [ # Dataset Ratio r = 0.12
+ [14.70179844, 13.6246 , 12.42994508, 11.69773413,11.14159987, 9.500670278, 9.005046347] , # input (moisture-content in %)
+ [1.29323308, 1.83458647 , 2.40601504 , 2.76691729 , 3.03759398, 3.81954887 , 4.03007519], # curvature kappa from Simulation
+ [1.140351217, 1.691038688, 2.243918105, 2.595732726, 2.945361006,4.001528043, 4.312080261] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.17
+ [14.75453569,13.71227639,12.54975012,11.83455959,11.29089521,9.620608917,9.101671742], # input (moisture-content in %)
+ [1.26315789, 1.77443609, 2.34586466, 2.70676692, 2.97744361, 3.78947368,4.03007519], # curvature kappa from Simulation
+ [1.02915975,1.573720805,2.407706364,2.790518802,3.173814476,4.187433094,4.511739072] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.22
+ [14.72680026, 13.64338887, 12.41305478, 11.66482931, 11.09781471, 9.435795985, 8.959564147], # input (moisture-content in %)
+ [1.20401338 , 1.72575251 , 2.28762542 , 2.64882943 , 2.92976589 , 3.73244147 , 3.93311037 ], # curvature kappa from Simulation
+ [1.058078122, 1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.34
+ [14.98380876,13.97154915,12.77309253,12.00959929,11.42001731,9.561447179,8.964704969], # input (moisture-content in %)
+ [0.87218045, 1.26315789, 1.7443609, 2.07518797, 2.28571429, 3.03759398, 3.27819549], # curvature kappa from Simulation
+ [0.789078472,1.1299263,1.738136936,2.159520896,2.370047499,3.088299431,3.18097558] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.43
+ [15.11316339,14.17997082,13.05739844,12.32309209,11.74608518,9.812372466,9.10519385 ], # input (moisture-content in %)
+ [0.63157895, 0.93233083, 1.29323308, 1.53383459, 1.71428571, 2.34586466,2.55639098], # curvature kappa from Simulation
+ [0.577989364,0.829007544,1.094211707,1.325332511,1.400455154,1.832325697,2.047483977] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.49
+ [15.30614414,14.49463867,13.46629742,12.78388234,12.23057715,10.21852839,9.341730605], # input (moisture-content in %)
+ [0.60150376, 0.87218045, 1.23308271, 1.5037594 , 1.71428571, 2.46616541,2.79699248], # curvature kappa from Simulation
+ [0.357615902,0.376287785,0.851008627,0.904475291,1.039744708,1.346405241,1.566568558] # curvature kappa from Experiment
+ ]
+ ]
+
+
+
+ # input = [14.72680026, 13.64338887, 12.41305478, 11.66482931, 11.09781471, 9.435795985, 8.959564147] #moisture-content (in %)
+ # kappa_sim = [1.20401338 , 1.72575251, 2.28762542 , 2.64882943 , 2.92976589 , 3.73244147 , 3.93311037 ] #kappa from Simulation
+ # kappa_exp = [1.058078122,1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825] #kappa from Experiment
+
+ # compute difference:
+ # relative_error = (np.array(kappa_sim) - np.array(kappa_exp)) / np.array(kappa_exp)
+ # print('relative_error:', relative_error)
+
+ relative_error = (np.array(dataset[dataset_number][1]) - np.array(dataset[dataset_number][2])) / np.array(dataset[dataset_number][2])
+ print('relative_error:', relative_error)
+
+
+ # --------------- Plot Lines + Scatter -----------------------
+ line_1 = ax.plot(np.array(dataset[dataset_number][0]), np.array(dataset[dataset_number][1]), # data
+ # color='forestgreen', # linecolor
+ marker='D', # each marker will be rendered as a circle
+ markersize=5, # marker size
+ # markerfacecolor='darkorange', # marker facecolor
+ markeredgecolor='black', # marker edgecolor
+ markeredgewidth=0.75, # marker edge width
+ # linestyle='dashdot', # line style will be dash line
+ linewidth=1.5, # line width
+ zorder=3,
+ label = r"$\kappa_{sim}$")
+
+ line_2 = ax.plot(np.array(dataset[dataset_number][0]), np.array(dataset[dataset_number][2]), # data
+ # color='orangered', # linecolor
+ marker='o', # each marker will be rendered as a circle
+ markersize=5, # marker size
+ # markerfacecolor='cornflowerblue', # marker facecolor
+ markeredgecolor='black', # marker edgecolor
+ markeredgewidth=0.75, # marker edge width
+ # linestyle='--', # line style will be dash line
+ linewidth=1.5, # line width
+ zorder=3,
+ alpha=0.8, # Change opacity
+ label = r"$\kappa_{exp}$")
+
+ # --- Plot order line
+ # x = np.linspace(0.01,1/2,100)
+ # y = CC_L2[0]*x**2
+ # OrderLine = ax.plot(x,y,linestyle='--', label=r"$\mathcal{O}(h)$")
+
+
+
+ # Fix_value = 7.674124
+ # l3 = plt.axhline(y = Fix_value, color = 'black', linewidth=0.75, linestyle = 'dashed')
+ # --------------- Set Axes -----------------------
+ # ax.set_title(r"ratio $r = 0.22$") # Plot - Title
+
+ # Plot - Title
+ match dataset_number:
+ case 0:
+ ax.set_title(r"ratio $r = 0.12$")
+ case 1:
+ ax.set_title(r"ratio $r = 0.17$")
+ case 2:
+ ax.set_title(r"ratio $r = 0.22$")
+ case 3:
+ ax.set_title(r"ratio $r = 0.34$")
+ case 4:
+ ax.set_title(r"ratio $r = 0.43$")
+ case 5:
+ ax.set_title(r"ratio $r = 0.49$")
+
+ # plt.xscale('log') # Use Logarithmic-Scale
+ # plt.yscale('log')
+ ax.set_xlabel(r"Wood moisture content $\omega (\%)$", labelpad=4)
+ ax.set_ylabel(r"Curvature $\kappa$", labelpad=4)
+ plt.tight_layout()
+
+ # # --- Set Line labels
+ # line_labels = [r"$CC_{L_2}$",r"$CC_{H_1}$", r"$\mathcal{O}(h)$"]
+
+ # --- Set Legend
+ legend = ax.legend()
+ # legend = fig.legend([line_1 , line_2, OrderLine],
+ # labels = line_labels,
+ # bbox_to_anchor=[0.97, 0.50],
+ # # bbox_to_anchor=[0.97, 0.53],
+ # # loc='center',
+ # ncol=1, # Number of columns used for legend
+ # # borderaxespad=0.15, # Small spacing around legend box
+ # frameon=True,
+ # prop={'size': 10})
+
+
+ frame = legend.get_frame()
+ frame.set_edgecolor('black')
+
+
+ # --- Adjust left/right spacing:
+ # plt.subplots_adjust(right=0.81)
+ # plt.subplots_adjust(left=0.11)
+
+ # ---------- Output Figure as pdf:
+ fig.set_size_inches(width, height)
+ fig.savefig('WoodBilayer_expComparison_' + str(dataset_number) + '.pdf')
+ # plt.show()
+
+
diff --git a/experiment/wood-bilayer/PolarPlotLocalEnergy.py b/experiment/wood-bilayer/PolarPlotLocalEnergy.py
index 6e623b15cf0658cd74b5fcb3bbe78ed0100a8d2a..daa490a07d3db90a976ef6131c482192efffdd7f 100644
--- a/experiment/wood-bilayer/PolarPlotLocalEnergy.py
+++ b/experiment/wood-bilayer/PolarPlotLocalEnergy.py
@@ -48,48 +48,63 @@ def ReadEffectiveQuantities(QFilePath, BFilePath):
B = np.array([X[0][2], X[1][2], X[2][2]])
return Q, B
+# -------------------------------------------------------------------
+
+
# Number of experiments / folders
number=7
-kappa=np.zeros(number)
-for n in range(0,number):
- # Read from Date
- print(str(n))
- DataPath = './experiment/wood-bilayer/results/'+str(n)
- DataPath = './experiment/wood-bilayer/results/'+str(n)
- QFilePath = DataPath + '/QMatrix.txt'
- BFilePath = DataPath + '/BMatrix.txt'
- Q, B = ReadEffectiveQuantities(QFilePath,BFilePath)
- # Q=0.5*(np.transpose(Q)+Q) # symmetrize
- B=np.transpose([B])
- #
-
- N=300
- length=12
- r, theta = np.meshgrid(np.linspace(0,length,N),np.radians(np.linspace(0, 360, N)))
- E=np.zeros(np.shape(r))
- for i in range(0,N):
- for j in range(0,N):
- if theta[i,j]<np.pi:
- E[i,j]=energy(r[i,j],theta[i,j],Q,B)
- else:
- E[i,j]=energy(-r[i,j],theta[i,j],Q,B)
-
- # Compute Minimizer
- [imin,jmin]=np.unravel_index(E.argmin(),(N,N))
- kappamin=r[imin,jmin]
- alphamin=theta[imin,jmin]
- kappa[n]=kappamin
- fig, ax = plt.subplots(figsize=(6,6),subplot_kw=dict(projection='polar'))
- levs=np.geomspace(E.min(),E.max(),400)
- pcm=ax.contourf(theta, r, E, levs, norm=colors.PowerNorm(gamma=0.2), cmap='brg')
- ax.set_xticks([0,np.pi/2])
- ax.set_yticks([kappamin])
- colorbarticks=np.linspace(E.min(),E.max(),6)
- plt.colorbar(pcm, extend='max', ticks=colorbarticks, pad=0.1)
- plt.show()
-
-f = open("./experiment/wood-bilayer/results/kappa_simulation.txt", "w")
-f.write(str(kappa))
-f.close()
+show_plot = False
+
+dataset_numbers = [0, 1, 2, 3, 4, 5]
+
+for dataset_number in dataset_numbers:
+
+ kappa=np.zeros(number)
+ for n in range(0,number):
+ # Read from Date
+ print(str(n))
+ DataPath = './experiment/wood-bilayer/results_'+ str(dataset_number) + '/' +str(n)
+ QFilePath = DataPath + '/QMatrix.txt'
+ BFilePath = DataPath + '/BMatrix.txt'
+ Q, B = ReadEffectiveQuantities(QFilePath,BFilePath)
+ # Q=0.5*(np.transpose(Q)+Q) # symmetrize
+ B=np.transpose([B])
+ #
+
+ N=400
+ length=12
+ r, theta = np.meshgrid(np.linspace(0,length,N),np.radians(np.linspace(0, 360, N)))
+ E=np.zeros(np.shape(r))
+ for i in range(0,N):
+ for j in range(0,N):
+ if theta[i,j]<np.pi:
+ E[i,j]=energy(r[i,j],theta[i,j],Q,B)
+ else:
+ E[i,j]=energy(-r[i,j],theta[i,j],Q,B)
+
+ # Compute Minimizer
+ [imin,jmin]=np.unravel_index(E.argmin(),(N,N))
+ kappamin=r[imin,jmin]
+ alphamin=theta[imin,jmin]
+ kappa[n]=kappamin
+ fig, ax = plt.subplots(figsize=(6,6),subplot_kw=dict(projection='polar'))
+ levs=np.geomspace(E.min(),E.max(),400)
+ pcm=ax.contourf(theta, r, E, levs, norm=colors.PowerNorm(gamma=0.2), cmap='brg')
+ ax.set_xticks([0,np.pi/2])
+ ax.set_yticks([kappamin])
+ colorbarticks=np.linspace(E.min(),E.max(),6)
+ plt.colorbar(pcm, extend='max', ticks=colorbarticks, pad=0.1)
+ if (show_plot):
+ plt.show()
+ # Save Figure as .pdf
+ width = 5.79
+ height = width / 1.618 # The golden ratio.
+ fig.set_size_inches(width, height)
+ fig.savefig('Plot_dataset_' +str(dataset_number) + '_exp' +str(n) + '.pdf')
+
+ # f = open("./experiment/wood-bilayer/results/kappa_simulation.txt", "w")
+ f = open("./experiment/wood-bilayer/results_" + str(dataset_number) + "/kappa_simulation.txt", "w")
+ f.write(str(kappa))
+ f.close()
diff --git a/experiment/wood-bilayer/wood_european_beech.py b/experiment/wood-bilayer/wood_european_beech.py
index 135ca8b24ef61d2fa0255e4c567bf283fd22d875..89da18ae223f5c8e5a6d38843ca0237fd4a1c0e0 100644
--- a/experiment/wood-bilayer/wood_european_beech.py
+++ b/experiment/wood-bilayer/wood_european_beech.py
@@ -42,13 +42,13 @@ parameterSet.Phases=2
# Parameters of the model
# -- (thickness upper layer) / (thickness)
-param_r = 0.22
+param_r = 0.49
# -- thickness [meter]
-param_h = 0.0053
+param_h = 0.008
# -- moisture content in the flat state [%]
-param_omega_flat = 17.17547062
+param_omega_flat = 17.01520754
# -- moisture content in the target state [%]
-param_omega_target = 8.959564147
+param_omega_target = 9.341730605
# -- Drehwinkel
param_theta = 0
diff --git a/experiment/wood-bilayer/wood_test.py b/experiment/wood-bilayer/wood_test.py
index ad7610cb6e6adc8dc51c9bfc6a8b5c216a01ce47..1e96127ac37496864160a9e4e1131ca8758c884c 100644
--- a/experiment/wood-bilayer/wood_test.py
+++ b/experiment/wood-bilayer/wood_test.py
@@ -101,124 +101,239 @@ def eval_energy(kappa,alpha,Q,B) :
########################
#### SET PARAMETERS ####
########################
-# ----- Setup Paths -----
-# write_LOG = True # writes Cell-Problem output-LOG in "Cell-Problem_output.log"
-# path='/home/klaus/Desktop/Dune_release/dune-microstructure/experiment/wood-bilayer/results/'
-# pythonPath = '/home/klaus/Desktop/Dune_release/dune-microstructure/experiment/wood-bilayer'
-path = os.getcwd() + '/experiment/wood-bilayer/results/'
-pythonPath = os.getcwd() + '/experiment/wood-bilayer'
-pythonModule = "wood_european_beech"
-executable = os.getcwd() + '/build-cmake/src/Cell-Problem'
-# ---------------------------------
-# Setup Experiment
-# ---------------------------------
-gamma = 1.0
-
-# ----- Define Parameters for Material Function --------------------
-# [r, h, omega_flat, omega_target, theta, experimental_kappa]
-# r = (thickness upper layer)/(thickness)
-# h = thickness [meter]
-# omega_flat = moisture content in the flat state before drying [%]
-# omega_target = moisture content in the target state [%]
-# theta = rotation angle (not implemented and used)
-# experimental_kappa = curvature measure in experiment
-
-#First Experiment:
-# Ratio r = 0.22
-# materialFunctionParameter=[
-# [0.22, 0.0053, 17.17547062, 14.72680026, 0, 1.058078122],
-# [0.22, 0.0053, 17.17547062, 13.64338887, 0, 1.544624544],
-# [0.22, 0.0053, 17.17547062, 12.41305478, 0, 2.317033799],
-# [0.22, 0.0053, 17.17547062, 11.66482931, 0, 2.686043143],
-# [0.22, 0.0053, 17.17547062, 11.09781471, 0, 2.967694189],
-# [0.22, 0.0053, 17.17547062, 9.435795985, 0, 3.913528418],
-# [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825]
-# ]
-
-# Ratio r = 0.49
-# materialFunctionParameter=[
-# [0.49, 0.008, 17.01520754, 15.30614414, 0, 0.357615902],
-# [0.49, 0.008, 17.01520754, 14.49463867, 0, 0.376287785],
-# [0.49, 0.008, 17.01520754, 13.46629742, 0, 0.851008627],
-# [0.49, 0.008, 17.01520754, 12.78388234, 0, 0.904475291],
-# [0.49, 0.008, 17.01520754, 12.23057715, 0, 1.039744708],
-# [0.49, 0.008, 17.01520754, 10.21852839, 0, 1.346405241],
-# [0.49, 0.008, 17.01520754, 9.341730605, 0, 1.566568558]
-# ]
-
-#Second Experiment: Rotate "active" bilayer phase
-# materialFunctionParameter=[
-# [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/6.0), 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/3.0), 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/2.0), 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, 2.0*(np.pi/3.0), 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, 5.0*(np.pi/6.0), 4.262750825],
-# [0.22, 0.0053, 17.17547062, 8.959564147, np.pi, 4.262750825]
-# ]
-
-materialFunctionParameter=[
- [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/12.0), 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/6.0), 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/4.0), 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/3.0), 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, 5.0*(np.pi/12.0), 4.262750825],
- [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/2.0), 4.262750825]
-]
-
-# ------ Loops through Parameters for Material Function -----------
-for i in range(0,np.shape(materialFunctionParameter)[0]):
+
+dataset_numbers = [0, 1, 2, 3, 4, 5]
+
+for dataset_number in dataset_numbers:
print("------------------")
- print("New Loop")
+ print(str(dataset_number) + "th data set")
print("------------------")
- # Check output directory
- outputPath = path + str(i)
- isExist = os.path.exists(outputPath)
- if not isExist:
- # Create a new directory because it does not exist
- os.makedirs(outputPath)
- print("The new directory " + outputPath + " is created!")
-
- # thread = threading.Thread(target=run_CellProblem(executable, pythonModule, pythonPath, LOGFILE))
- # thread.start()
-
- #TODO: apperently its not possible to pass a variable via subprocess and "calculate" another input value inside the python file.
- # Therefore we use this instead.
- SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_r",materialFunctionParameter[i][0])
- SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_h",materialFunctionParameter[i][1])
- SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_omega_flat",materialFunctionParameter[i][2])
- SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_omega_target",materialFunctionParameter[i][3])
- SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_theta",materialFunctionParameter[i][4])
-
- LOGFILE = outputPath + "/" + pythonModule + "_output" + "_" + str(i) + ".log"
-
- processList = []
- p = subprocess.Popen(executable + " " + pythonPath + " " + pythonModule
- + " -outputPath " + outputPath
- + " -gamma " + str(gamma)
- + " | tee " + LOGFILE, shell=True)
-
- # p = subprocess.Popen(executable + " " + pythonPath + " " + pythonModule
- # + " -outputPath " + outputPath
- # + " -gamma " + str(gamma)
- # + " -param_r " + str(materialFunctionParameter[i][0])
- # + " -param_h " + str(materialFunctionParameter[i][1])
- # + " -param_omega_flat " + str(materialFunctionParameter[i][2])
- # + " -param_omega_target " + str(materialFunctionParameter[i][3])
- # + " -phase2_angle " + str(materialFunctionParameter[i][4])
- # + " | tee " + LOGFILE, shell=True)
-
- p.wait() # wait
- processList.append(p)
- exit_codes = [p.wait() for p in processList]
- # ---------------------------------------------------
- # wait here for the result to be available before continuing
- # thread.join()
- f = open(outputPath+"/parameter.txt", "w")
- f.write("r = "+str(materialFunctionParameter[i][0])+"\n")
- f.write("h = "+str(materialFunctionParameter[i][1])+"\n")
- f.write("omega_flat = "+str(materialFunctionParameter[i][2])+"\n")
- f.write("omega_target = "+str(materialFunctionParameter[i][3])+"\n")
- f.close()
- #
+
+ # ----- Setup Paths -----
+ # write_LOG = True # writes Cell-Problem output-LOG in "Cell-Problem_output.log"
+ # path='/home/klaus/Desktop/Dune_release/dune-microstructure/experiment/wood-bilayer/results/'
+ # pythonPath = '/home/klaus/Desktop/Dune_release/dune-microstructure/experiment/wood-bilayer'
+ path = os.getcwd() + '/experiment/wood-bilayer/results_' + str(dataset_number) + '/'
+ pythonPath = os.getcwd() + '/experiment/wood-bilayer'
+ pythonModule = "wood_european_beech"
+ executable = os.getcwd() + '/build-cmake/src/Cell-Problem'
+ # ---------------------------------
+ # Setup Experiment
+ # ---------------------------------
+ gamma = 1.0
+
+ # ----- Define Parameters for Material Function --------------------
+ # [r, h, omega_flat, omega_target, theta, experimental_kappa]
+ # r = (thickness upper layer)/(thickness)
+ # h = thickness [meter]
+ # omega_flat = moisture content in the flat state before drying [%]
+ # omega_target = moisture content in the target state [%]
+ # theta = rotation angle (not implemented and used)
+ # experimental_kappa = curvature measure in experiment
+
+ #First Experiment:
+
+
+ # Dataset Ratio r = 0.12
+ # materialFunctionParameter=[
+ # [0.12, 0.0047, 17.32986047, 14.70179844, 0, 1.140351217],
+ # [0.12, 0.0047, 17.32986047, 13.6246, 0, 1.691038688],
+ # [0.12, 0.0047, 17.32986047, 12.42994508, 0, 2.243918105],
+ # [0.12, 0.0047, 17.32986047, 11.69773413, 0, 2.595732726],
+ # [0.12, 0.0047, 17.32986047, 11.14159987, 0, 2.945361006],
+ # [0.12, 0.0047, 17.32986047, 9.500670278, 0, 4.001528043],
+ # [0.12, 0.0047, 17.32986047, 9.005046347, 0, 4.312080261],
+ # ]
+
+ # # Dataset Ratio r = 0.17
+ # materialFunctionParameter=[
+ # [0.17, 0.0049, 17.28772791 , 14.75453569, 0, 1.02915975],
+ # [0.17, 0.0049, 17.28772791 , 13.71227639, 0, 1.573720805],
+ # [0.17, 0.0049, 17.28772791 , 12.54975012, 0, 2.407706364],
+ # [0.17, 0.0049, 17.28772791 , 11.83455959, 0, 2.790518802],
+ # [0.17, 0.0049, 17.28772791 , 11.29089521, 0, 3.173814476],
+ # [0.17, 0.0049, 17.28772791 , 9.620608917, 0, 4.187433094],
+ # [0.17, 0.0049, 17.28772791 , 9.101671742, 0, 4.511739072],
+ # ]
+
+ # # Dataset Ratio r = 0.22
+ # materialFunctionParameter=[
+ # [0.22, 0.0053, 17.17547062, 14.72680026, 0, 1.058078122],
+ # [0.22, 0.0053, 17.17547062, 13.64338887, 0, 1.544624544],
+ # [0.22, 0.0053, 17.17547062, 12.41305478, 0, 2.317033799],
+ # [0.22, 0.0053, 17.17547062, 11.66482931, 0, 2.686043143],
+ # [0.22, 0.0053, 17.17547062, 11.09781471, 0, 2.967694189],
+ # [0.22, 0.0053, 17.17547062, 9.435795985, 0, 3.913528418],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825]
+ # ]
+
+ # # Dataset Ratio r = 0.34
+ # materialFunctionParameter=[
+ # [0.34, 0.0063, 17.14061081 , 14.98380876, 0, 0.789078472],
+ # [0.34, 0.0063, 17.14061081 , 13.97154915 0, 1.1299263],
+ # [0.34, 0.0063, 17.14061081 , 12.77309253, 0, 1.738136936],
+ # [0.34, 0.0063, 17.14061081 , 12.00959929, 0, 2.159520896],
+ # [0.34, 0.0063, 17.14061081 , 11.42001731, 0, 2.370047499],
+ # [0.34, 0.0063, 17.14061081 , 9.561447179, 0, 3.088299431],
+ # [0.34, 0.0063, 17.14061081 , 8.964704969, 0, 3.18097558],
+ # ]
+
+ # # Dataset Ratio r = 0.43
+ # materialFunctionParameter=[
+ # [0.43, 0.0073, 17.07559686 , 15.11316339, 0, 0.577989364],
+ # [0.43, 0.0073, 17.07559686 , 14.17997082, 0, 0.829007544],
+ # [0.43, 0.0073, 17.07559686 , 13.05739844, 0, 1.094211707],
+ # [0.43, 0.0073, 17.07559686 , 12.32309209, 0, 1.325332511],
+ # [0.43, 0.0073, 17.07559686 , 11.74608518, 0, 1.400455154],
+ # [0.43, 0.0073, 17.07559686 , 9.812372466, 0, 1.832325697],
+ # [0.43, 0.0073, 17.07559686 , 9.10519385 , 0, 2.047483977],
+ # ]
+
+ # # Dataset Ratio r = 0.49
+ # materialFunctionParameter=[
+ # [0.49, 0.008, 17.01520754, 15.30614414, 0, 0.357615902],
+ # [0.49, 0.008, 17.01520754, 14.49463867, 0, 0.376287785],
+ # [0.49, 0.008, 17.01520754, 13.46629742, 0, 0.851008627],
+ # [0.49, 0.008, 17.01520754, 12.78388234, 0, 0.904475291],
+ # [0.49, 0.008, 17.01520754, 12.23057715, 0, 1.039744708],
+ # [0.49, 0.008, 17.01520754, 10.21852839, 0, 1.346405241],
+ # [0.49, 0.008, 17.01520754, 9.341730605, 0, 1.566568558]
+ # ]
+
+
+
+
+
+ materialFunctionParameter=[
+ [ # Dataset Ratio r = 0.12
+ [0.12, 0.0047, 17.32986047, 14.70179844, 0, 1.140351217],
+ [0.12, 0.0047, 17.32986047, 13.6246, 0, 1.691038688],
+ [0.12, 0.0047, 17.32986047, 12.42994508, 0, 2.243918105],
+ [0.12, 0.0047, 17.32986047, 11.69773413, 0, 2.595732726],
+ [0.12, 0.0047, 17.32986047, 11.14159987, 0, 2.945361006],
+ [0.12, 0.0047, 17.32986047, 9.500670278, 0, 4.001528043],
+ [0.12, 0.0047, 17.32986047, 9.005046347, 0, 4.312080261]
+ ],
+ [ # Dataset Ratio r = 0.17
+ [0.17, 0.0049, 17.28772791 , 14.75453569, 0, 1.02915975],
+ [0.17, 0.0049, 17.28772791 , 13.71227639, 0, 1.573720805],
+ [0.17, 0.0049, 17.28772791 , 12.54975012, 0, 2.407706364],
+ [0.17, 0.0049, 17.28772791 , 11.83455959, 0, 2.790518802],
+ [0.17, 0.0049, 17.28772791 , 11.29089521, 0, 3.173814476],
+ [0.17, 0.0049, 17.28772791 , 9.620608917, 0, 4.187433094],
+ [0.17, 0.0049, 17.28772791 , 9.101671742, 0, 4.511739072]
+ ],
+ [ # Dataset Ratio r = 0.22
+ [0.22, 0.0053, 17.17547062, 14.72680026, 0, 1.058078122],
+ [0.22, 0.0053, 17.17547062, 13.64338887, 0, 1.544624544],
+ [0.22, 0.0053, 17.17547062, 12.41305478, 0, 2.317033799],
+ [0.22, 0.0053, 17.17547062, 11.66482931, 0, 2.686043143],
+ [0.22, 0.0053, 17.17547062, 11.09781471, 0, 2.967694189],
+ [0.22, 0.0053, 17.17547062, 9.435795985, 0, 3.913528418],
+ [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825]
+ ],
+ [ # Dataset Ratio r = 0.34
+ [0.34, 0.0063, 17.14061081 , 14.98380876, 0, 0.789078472],
+ [0.34, 0.0063, 17.14061081 , 13.97154915, 0, 1.1299263],
+ [0.34, 0.0063, 17.14061081 , 12.77309253, 0, 1.738136936],
+ [0.34, 0.0063, 17.14061081 , 12.00959929, 0, 2.159520896],
+ [0.34, 0.0063, 17.14061081 , 11.42001731, 0, 2.370047499],
+ [0.34, 0.0063, 17.14061081 , 9.561447179, 0, 3.088299431],
+ [0.34, 0.0063, 17.14061081 , 8.964704969, 0, 3.18097558]
+ ],
+ [ # Dataset Ratio r = 0.43
+ [0.43, 0.0073, 17.07559686 , 15.11316339, 0, 0.577989364],
+ [0.43, 0.0073, 17.07559686 , 14.17997082, 0, 0.829007544],
+ [0.43, 0.0073, 17.07559686 , 13.05739844, 0, 1.094211707],
+ [0.43, 0.0073, 17.07559686 , 12.32309209, 0, 1.325332511],
+ [0.43, 0.0073, 17.07559686 , 11.74608518, 0, 1.400455154],
+ [0.43, 0.0073, 17.07559686 , 9.812372466, 0, 1.832325697],
+ [0.43, 0.0073, 17.07559686 , 9.10519385 , 0, 2.047483977]
+ ],
+ [ # Dataset Ratio r = 0.49
+ [0.49, 0.008, 17.01520754, 15.30614414, 0, 0.357615902],
+ [0.49, 0.008, 17.01520754, 14.49463867, 0, 0.376287785],
+ [0.49, 0.008, 17.01520754, 13.46629742, 0, 0.851008627],
+ [0.49, 0.008, 17.01520754, 12.78388234, 0, 0.904475291],
+ [0.49, 0.008, 17.01520754, 12.23057715, 0, 1.039744708],
+ [0.49, 0.008, 17.01520754, 10.21852839, 0, 1.346405241],
+ [0.49, 0.008, 17.01520754, 9.341730605, 0, 1.566568558]
+ ]
+ ]
+
+ # --- Second Experiment: Rotate "active" bilayer phase
+ # materialFunctionParameter=[
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/6.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/3.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/2.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 2.0*(np.pi/3.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 5.0*(np.pi/6.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, np.pi, 4.262750825]
+ # ]
+
+ # materialFunctionParameter=[
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 0, 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/12.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/6.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/4.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/3.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, 5.0*(np.pi/12.0), 4.262750825],
+ # [0.22, 0.0053, 17.17547062, 8.959564147, (np.pi/2.0), 4.262750825]
+ # ]
+
+ # ------ Loops through Parameters for Material Function -----------
+ for i in range(0,np.shape(materialFunctionParameter)[1]):
+ print("------------------")
+ print("New Loop")
+ print("------------------")
+ # Check output directory
+ outputPath = path + str(i)
+ isExist = os.path.exists(outputPath)
+ if not isExist:
+ # Create a new directory because it does not exist
+ os.makedirs(outputPath)
+ print("The new directory " + outputPath + " is created!")
+
+ # thread = threading.Thread(target=run_CellProblem(executable, pythonModule, pythonPath, LOGFILE))
+ # thread.start()
+
+ #TODO: apperently its not possible to pass a variable via subprocess and "calculate" another input value inside the python file.
+ # Therefore we use this instead.
+ SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_r",materialFunctionParameter[dataset_number][i][0])
+ SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_h",materialFunctionParameter[dataset_number][i][1])
+ SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_omega_flat",materialFunctionParameter[dataset_number][i][2])
+ SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_omega_target",materialFunctionParameter[dataset_number][i][3])
+ SetParameterMaterialFunction(pythonPath + "/" + pythonModule, "param_theta",materialFunctionParameter[dataset_number][i][4])
+
+ LOGFILE = outputPath + "/" + pythonModule + "_output" + "_" + str(i) + ".log"
+
+ processList = []
+ p = subprocess.Popen(executable + " " + pythonPath + " " + pythonModule
+ + " -outputPath " + outputPath
+ + " -gamma " + str(gamma)
+ + " | tee " + LOGFILE, shell=True)
+
+ # p = subprocess.Popen(executable + " " + pythonPath + " " + pythonModule
+ # + " -outputPath " + outputPath
+ # + " -gamma " + str(gamma)
+ # + " -param_r " + str(materialFunctionParameter[i][0])
+ # + " -param_h " + str(materialFunctionParameter[i][1])
+ # + " -param_omega_flat " + str(materialFunctionParameter[i][2])
+ # + " -param_omega_target " + str(materialFunctionParameter[i][3])
+ # + " -phase2_angle " + str(materialFunctionParameter[i][4])
+ # + " | tee " + LOGFILE, shell=True)
+
+ p.wait() # wait
+ processList.append(p)
+ exit_codes = [p.wait() for p in processList]
+ # ---------------------------------------------------
+ # wait here for the result to be available before continuing
+ # thread.join()
+ f = open(outputPath+"/parameter.txt", "w")
+ f.write("r = "+str(materialFunctionParameter[dataset_number][i][0])+"\n")
+ f.write("h = "+str(materialFunctionParameter[dataset_number][i][1])+"\n")
+ f.write("omega_flat = "+str(materialFunctionParameter[dataset_number][i][2])+"\n")
+ f.write("omega_target = "+str(materialFunctionParameter[dataset_number][i][3])+"\n")
+ f.close()
+ #