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R12600/notebooks/plot_elastic_properties.py
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plot_elastic_properties.py
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import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from pylab import *
import numpy as np
import math
import os
import csv
from import_atoms import import_atoms
from density_tot import density_tot
from matplotlib import rc
rc('font',**{'family':'serif','serif':['Computer Modern Roman']})
rc('font',**{'family':'serif'})
rc('text', usetex=True)
plt.rcParams.update({'figure.max_open_warning': 0})
P = [5e6]
d0 = 0.003
dmean = 0.003
Size = [2*d0, 3*d0, 6*d0, 9*d0, 12*d0, 15*d0, 18*d0, 21*d0, 24*d0, 27*d0]
Size_title = ["2", "3", "6", "9", "12", "15", "18", "21", "24", "27"]
Seed = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19]
path_image = "Image_elastic_properties"
Table = []
E_glass_bead = 50*10**9
try:
os.mkdir(path_image)
except OSError:
print ("directory already exists")
else:
print ("Successfully created the directory %s" % path_image)
for i in range(len(P)):
pressure = P[i]
Bulk_modulus_mean = []
Shear_modulus1_mean = []
Shear_modulus2_mean = []
Shear_modulus_average_mean = []
Poisson_mean = []
Anisotropy_coefficient_mean = []
Young_modulus_mean = []
Size_box_mean = []
Density_mean = []
Bulk_modulus_all = []
Shear_modulus1_all = []
Shear_modulus2_all = []
Shear_modulus_average_all = []
Poisson_all = []
Anisotropy_coefficient_all = []
Young_modulus_all = []
Size_box_all = []
Density_all = []
Bulk_modulus_error_bar = []
Shear_modulus1_error_bar = []
Shear_modulus2_error_bar = []
Shear_modulus_average_error_bar = []
Poisson_error_bar = []
Anisotropy_coefficient_error_bar = []
Young_modulus_error_bar = []
Density_error_bar = []
Length_box_error_bar = []
n = -1
for j in range(len(Size)):
Lx = Size[j]
s_title = Size_title[j]
C11_average = 0
C12_average = 0
C13_average = 0
C14_average = 0
C15_average = 0
C16_average = 0
C26_average = 0
C21_average = 0
C22_average = 0
C23_average = 0
C24_average = 0
C25_average = 0
C26_average = 0
C31_average = 0
C32_average = 0
C33_average = 0
C34_average = 0
C35_average = 0
C36_average = 0
C41_average = 0
C42_average = 0
C43_average = 0
C44_average = 0
C45_average = 0
C46_average = 0
C51_average = 0
C52_average = 0
C53_average = 0
C54_average = 0
C55_average = 0
C56_average = 0
C61_average = 0
C62_average = 0
C63_average = 0
C64_average = 0
C65_average = 0
C66_average = 0
bulk_modulus_average = 0
shear_modulus1_average = 0
shear_modulus2_average = 0
poisson_ratio_average = 0
anisotropy_coefficient_average = 0
young_modulus_average = 0
density_average = 0
L_average = 0
Bulk_modulus_error_bar.append([])
Shear_modulus1_error_bar.append([])
Shear_modulus2_error_bar.append([])
Shear_modulus_average_error_bar.append([])
Poisson_error_bar.append([])
Anisotropy_coefficient_error_bar.append([])
Young_modulus_error_bar.append([])
Density_error_bar.append([])
Length_box_error_bar.append([])
Bulk_modulus_mean.append([])
Shear_modulus1_mean.append([])
Shear_modulus2_mean.append([])
Shear_modulus_average_mean.append([])
Poisson_mean.append([])
Anisotropy_coefficient_mean.append([])
Young_modulus_mean.append([])
Size_box_mean.append([])
Density_mean.append([])
Bulk_modulus_specific_size = []
Shear_modulus1_specific_size = []
Shear_modulus2_specific_size = []
Shear_modulus_average_specific_size = []
Poisson_specific_size = []
Anisotropy_coefficient_specific_size = []
Young_modulus_specific_size = []
Density_specific_size = []
L_box = []
n = n + 1
for k in range(len(Seed)):
seedChoice = Seed[k]
path = "../data/elastic_properties/box_" + str(Lx) + "/seed_" + str(seedChoice) + "/pressure_" + str(pressure*10**-6) + "/elastic_properties"
# Elastic properties
open1 = "../data/elastic_properties/box_" + str(Lx) + "/seed_" + str(seedChoice) + "/pressure_" + str(pressure*10**-6) + "/elastic_properties/stiffness_matrix_sym.txt"
f1 = open(open1, "r")
A1 = []
A1 = [ line.split() for line in f1]
del A1[0:1]
open2 = "../data/elastic_properties/box_" + str(Lx) + "/seed_" + str(seedChoice) + "/pressure_" + str(pressure*10**-6) + "/elastic_properties/properties.txt"
f2 = open(open2, "r")
A2 = []
A2 = [ line.split() for line in f2]
del A2[0:1]
bulk_modulus_average = bulk_modulus_average + float(A2[0][0])
shear_modulus1_average = shear_modulus1_average + float(A2[0][1])
shear_modulus2_average = shear_modulus2_average + float(A2[0][2])
poisson_ratio_average = poisson_ratio_average + float(A2[0][3])
anisotropy_coefficient_average = anisotropy_coefficient_average + float(A2[0][4])
young_modulus_average = young_modulus_average + float(A2[0][5])
Bulk_modulus_specific_size.append(float(A2[0][0]))
Shear_modulus1_specific_size.append(float(A2[0][1]))
Shear_modulus2_specific_size.append(float(A2[0][2]))
Shear_modulus_average_specific_size.append((float(A2[0][1])+float(A2[0][2]))/2)
Poisson_specific_size.append(float(A2[0][3]))
Anisotropy_coefficient_specific_size.append(float(A2[0][4]))
Young_modulus_specific_size.append(float(A2[0][5]))
# Box_size
input_file = "../data/elastic_properties/box_" + str(Lx) + "/seed_" + str(seedChoice) + "/pressure_" + str(pressure*10**-6) + "/confined_" + str(pressure*10**-6) + "_MPa.data"
atoms, boundaries = import_atoms(input_file)
L = boundaries[1] - boundaries[0]
L_box.append(L)
L_average = L_average + L
# Total density
d_tot = density_tot(input_file)
Density_specific_size.append(d_tot)
density_average = density_average + d_tot
## Stiffness matrix sym
C11_average = C11_average + float(A1[0][0])
C12_average = C12_average + float(A1[0][1])
C13_average = C13_average + float(A1[0][2])
C14_average = C14_average + float(A1[0][3])
C15_average = C15_average + float(A1[0][4])
C16_average = C16_average + float(A1[0][5])
C21_average = C21_average + float(A1[0][6])
C22_average = C22_average + float(A1[0][7])
C23_average = C23_average + float(A1[0][8])
C24_average = C24_average + float(A1[0][9])
C25_average = C25_average + float(A1[0][10])
C26_average = C26_average + float(A1[0][11])
C31_average = C31_average + float(A1[0][12])
C32_average = C32_average + float(A1[0][13])
C33_average = C33_average + float(A1[0][14])
C34_average = C34_average + float(A1[0][15])
C35_average = C35_average + float(A1[0][16])
C36_average = C36_average + float(A1[0][17])
C41_average = C41_average + float(A1[0][18])
C42_average = C42_average + float(A1[0][19])
C43_average = C43_average + float(A1[0][20])
C44_average = C44_average + float(A1[0][21])
C45_average = C45_average + float(A1[0][22])
C46_average = C46_average + float(A1[0][23])
C51_average = C51_average + float(A1[0][24])
C52_average = C52_average + float(A1[0][25])
C53_average = C53_average + float(A1[0][26])
C54_average = C54_average + float(A1[0][27])
C55_average = C55_average + float(A1[0][28])
C56_average = C56_average + float(A1[0][29])
C61_average = C61_average + float(A1[0][30])
C62_average = C62_average + float(A1[0][31])
C63_average = C63_average + float(A1[0][32])
C64_average = C64_average + float(A1[0][33])
C65_average = C65_average + float(A1[0][34])
C66_average = C66_average + float(A1[0][35])
C = []
C.append( [float(A1[0][0]), float(A1[0][1]), float(A1[0][2]), float(A1[0][3]), float(A1[0][4]), float(A1[0][5])] )
C.append( [float(A1[0][6]), float(A1[0][7]), float(A1[0][8]), float(A1[0][9]), float(A1[0][10]), float(A1[0][11])] )
C.append( [float(A1[0][12]), float(A1[0][13]), float(A1[0][14]), float(A1[0][15]), float(A1[0][16]), float(A1[0][17])] )
C.append( [float(A1[0][18]), float(A1[0][19]), float(A1[0][20]), float(A1[0][21]), float(A1[0][22]), float(A1[0][23])] )
C.append( [float(A1[0][24]), float(A1[0][25]), float(A1[0][26]), float(A1[0][27]), float(A1[0][28]), float(A1[0][29])] )
C.append( [float(A1[0][30]), float(A1[0][31]), float(A1[0][32]), float(A1[0][33]), float(A1[0][34]), float(A1[0][35])] )
# Change units of the stiffness marix Pa --> GPa
for i in range(6):
for j in range(6):
C[i][j] = C[i][j]*10**-9
Bulk_modulus_all.append(Bulk_modulus_specific_size)
Shear_modulus1_all.append(Shear_modulus1_specific_size)
Shear_modulus2_all.append(Shear_modulus2_specific_size)
Shear_modulus_average_all.append(Shear_modulus_average_specific_size)
Poisson_all.append(Poisson_specific_size)
Anisotropy_coefficient_all.append(Anisotropy_coefficient_specific_size)
Young_modulus_all.append(Young_modulus_specific_size)
Density_all.append(Density_specific_size)
Size_box_all.append(L_box)
bulk_modulus_average = bulk_modulus_average/len(Seed)
shear_modulus1_average = shear_modulus1_average/len(Seed)
shear_modulus2_average = shear_modulus2_average/len(Seed)
poisson_ratio_average = poisson_ratio_average/len(Seed)
anisotropy_coefficient_average = anisotropy_coefficient_average/len(Seed)
young_modulus_average = young_modulus_average/len(Seed)
density_average = density_average/len(Seed)
L_average = L_average/len(Seed)
Bulk_modulus_mean[n].append(bulk_modulus_average)
Shear_modulus1_mean[n].append(shear_modulus1_average)
Shear_modulus2_mean[n].append(shear_modulus2_average)
Shear_modulus_average_mean[n].append((shear_modulus1_average+shear_modulus2_average)/2)
Poisson_mean[n].append(poisson_ratio_average)
Anisotropy_coefficient_mean[n].append(anisotropy_coefficient_average)
Young_modulus_mean[n].append(young_modulus_average)
Size_box_mean[n].append(L_average)
Density_mean[n].append(density_average)
bulk_error = 0
shear1_error = 0
shear2_error = 0
poisson_error = 0
anisotropy_error = 0
young_error = 0
density_error = 0
length_box_error = 0
for l in range(len(Seed)):
bulk_error = bulk_error + (Bulk_modulus_specific_size[l] - bulk_modulus_average)**2
shear1_error = shear1_error + (Shear_modulus1_specific_size[l] - shear_modulus1_average )**2
shear2_error = shear2_error + (Shear_modulus2_specific_size[l] - shear_modulus2_average)**2
poisson_error = poisson_error + (Poisson_specific_size[l] - poisson_ratio_average)**2
anisotropy_error = anisotropy_error + (Anisotropy_coefficient_specific_size[l] - anisotropy_coefficient_average)**2
young_error = young_error + (Young_modulus_specific_size[l] - young_modulus_average)**2
density_error = density_error + (Density_specific_size[l] - density_average)**2
length_box_error = length_box_error + (L_box[l]-L_average)**2
bulk_error = math.sqrt(bulk_error/len(Bulk_modulus_specific_size))
shear1_error = math.sqrt(shear1_error/len(Shear_modulus1_specific_size))
shear2_error = math.sqrt(shear2_error/len(Shear_modulus2_specific_size))
poisson_error = math.sqrt(poisson_error/len(Poisson_specific_size))
young_error = math.sqrt(young_error/len(Young_modulus_specific_size))
density_error = math.sqrt(density_error/len(Density_specific_size))
length_box_error = math.sqrt(length_box_error/len(L_box))
Bulk_modulus_error_bar[n].append(bulk_error)
Shear_modulus1_error_bar[n].append(shear1_error)
Shear_modulus2_error_bar[n].append(shear2_error)
Shear_modulus_average_error_bar[n].append((shear1_error+shear2_error)/2)
Poisson_error_bar[n].append(poisson_error)
Anisotropy_coefficient_error_bar[n].append(anisotropy_error)
Young_modulus_error_bar[n].append(young_error)
Density_error_bar[n].append(density_error)
Length_box_error_bar[n].append(length_box_error)
C11_average = C11_average/len(Seed)
C12_average = C12_average/len(Seed)
C13_average = C13_average/len(Seed)
C14_average = C14_average/len(Seed)
C15_average = C15_average/len(Seed)
C16_average = C16_average/len(Seed)
C21_average = C21_average/len(Seed)
C22_average = C22_average/len(Seed)
C23_average = C23_average/len(Seed)
C24_average = C24_average/len(Seed)
C25_average = C25_average/len(Seed)
C26_average = C26_average/len(Seed)
C31_average = C31_average/len(Seed)
C32_average = C32_average/len(Seed)
C33_average = C33_average/len(Seed)
C34_average = C34_average/len(Seed)
C35_average = C35_average/len(Seed)
C36_average = C36_average/len(Seed)
C41_average = C41_average/len(Seed)
C42_average = C42_average/len(Seed)
C43_average = C43_average/len(Seed)
C44_average = C44_average/len(Seed)
C45_average = C45_average/len(Seed)
C46_average = C46_average/len(Seed)
C51_average = C51_average/len(Seed)
C52_average = C52_average/len(Seed)
C53_average = C53_average/len(Seed)
C54_average = C54_average/len(Seed)
C55_average = C55_average/len(Seed)
C56_average = C56_average/len(Seed)
C61_average = C61_average/len(Seed)
C62_average = C62_average/len(Seed)
C63_average = C63_average/len(Seed)
C64_average = C64_average/len(Seed)
C65_average = C65_average/len(Seed)
C66_average = C66_average/len(Seed)
C = []
C.append( [C11_average, C12_average, C13_average, C14_average, C15_average, C16_average] )
C.append( [C21_average, C22_average, C23_average, C24_average, C25_average, C26_average] )
C.append( [C31_average, C32_average, C33_average, C34_average, C35_average, C36_average] )
C.append( [C41_average, C42_average, C43_average, C44_average, C45_average, C46_average] )
C.append( [C51_average, C52_average, C53_average, C54_average, C55_average, C56_average] )
C.append( [C61_average, C62_average, C63_average, C64_average, C65_average, C66_average] )
# Change units of the stiffness marix Pa --> GPa
for i in range(6):
for j in range(6):
C[i][j] = C[i][j]*10**-9
Table.append([C11_average, C12_average, C13_average, C14_average, C15_average, C16_average, C21_average, C22_average, C23_average, C24_average, C25_average, C26_average, C31_average, C32_average, C33_average, C34_average, C35_average, C36_average, C41_average, C42_average, C43_average, C44_average, C45_average, C46_average, C51_average, C52_average, C53_average, C54_average, C55_average, C56_average, C61_average, C62_average, C63_average, C64_average, C65_average, C66_average])
if Lx != 2*d0:
## Create plot
fig2 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax2 = plt.gca()
cmap = cm.get_cmap('Blues')
#cmap = cm.get_cmap('bwr')
caxes = plt.imshow(C, cmap=cmap)
Cisotropic= [[r'$\lambda$+2$\mu$', '$\lambda$', '$\lambda$', '$\sim$0', '$\sim$0', '$\sim$0'],['$\lambda$', '$\lambda$+2$\mu$', '$\lambda$', '$\sim$0', '$\sim$0', '$\sim$0'],['$\lambda$', '$\lambda$', '$\lambda$+2$\mu$', '$\sim$0', '$\sim$0', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\mu$', '$\sim$0', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\mu$', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\mu$'] ]
for i in range(6):
for j in range(6):
c = Cisotropic[i][j]
c2 = round(C[i][j],2)
if (i==0 and j==0) or (i==1 and j==1) or (i==2 and j==2):
ax2.text(i, j, str(c) + "\n" + str(c2), va='center', ha='center', fontsize = 6, color = 'w')
if (i==0 and (j==1 or j==2)) or (i==1 and (j==0 or j==2)) or (i==2 and (j==0 or j==1)) or (i==3 and j==3) or (i==4 and j==4) or (i==5 and j==5):
ax2.text(i, j, str(c) + "\n" + str(c2), va='center', ha='center', fontsize = 6, color = 'k')
if (i!=j) and ((i==3 or i==4 or i==5) or (j==3 or j==4 or j==5)):
ax2.text(i, j, str(c) , va='center', ha='center', fontsize = 6, color = 'k')
plt.xticks([0, 1, 2, 3, 4, 5] , ['C$_{i1}$', 'C$_{i2}$', 'C$_{i3}$', 'C$_{i4}$', 'C$_{i5}$', 'C$_{i6}$'])
plt.yticks([0, 1, 2, 3, 4, 5] , ['C$_{1j}$', 'C$_{2j}$', 'C$_{3j}$', 'C$_{4j}$', 'C$_{5j}$', 'C$_{6j}$'])
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
# colorbar
cbar2 = fig2.colorbar(caxes)
plt.clim(vmax=1)
cbar2.ax.tick_params(labelsize=8)
cbar2.set_label(r'GPa', rotation=270, fontsize = 9, labelpad=15)
# title
plt.title(r'Elastic tensor ' + str(s_title) + r'$d_{0}$', fontsize = 9, pad = 10)
# save plot
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".png")
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".pgf")
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".pdf")
if Lx == 2*d0:
fig2 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax2 = plt.gca()
# plot matrix
#cmap = ListedColormap([ 'lightskyblue', 'steelblue', 'k', 'r'])
cmap = cm.get_cmap('Blues')
#cmap = cm.get_cmap('bwr')
caxes = plt.imshow(C, cmap=cmap)
Cisotropic= [[r'$\lambda$+2$\mu$', '$\lambda$', '$\lambda$', '$\sim$0', '$\sim$0', '$\sim$0'],['$\lambda$', '$\lambda$+2$\mu$', '$\lambda$', '$\sim$0', '$\sim$0', '$\sim$0'],['$\lambda$', '$\lambda$', '$\lambda$+2$\mu$', '$\sim$0', '$\sim$0', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\mu$', '$\sim$0', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\mu$', '$\sim$0'], ['$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\sim$0', '$\mu$'] ]
for i in range(6):
for j in range(6):
#c = Cisotropic[i][j]
c = round(C[i][j],2)
if (i==0 and (j==0 or j==1 or j==2)) or (i==1 and (j==0 or j==1 or j==2)) or (i==2 and (j==0 or j==1 or j==2)) or (i==j):
ax2.text(i, j, str(c), va='center', ha='center', fontsize = 6, color = 'w')
else:
ax2.text(i, j, str(c), va='center', ha='center', fontsize = 6, color = 'k')
# abscissa
line1 = ['C$_{i1}$', 'C$_{i2}$', 'C$_{i3}$', 'C$_{i4}$', 'C$_{i5}$', 'C$_{i6}$']
line2 = ['C$_{1j}$', 'C$_{2j}$', 'C$_{3j}$', 'C$_{4j}$', 'C$_{5j}$', 'C$_{6j}$']
ax2.set_xticklabels(['']+line1)
ax2.set_yticklabels(['']+line2)
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
# colorbar
cbar2 = fig2.colorbar(caxes)
plt.clim(vmax=1)
cbar2.ax.tick_params(labelsize=8)
cbar2.set_label(r'GPa', rotation=270, fontsize = 9, labelpad=15)
# title
plt.title(r'Elastic tensor $2d_{0}$', fontsize = 9, pad = 10)
# save plot
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".png")
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".pgf")
fig2.savefig( path_image + "/Elastic_tensor_" + str(Lx) + ".pdf")
#pgf_delete_family(path_image2 + "/Stiffness_matrix_" + str(Lx) + ".pgf", path_image2 + "/Stiffness_matrix_" + str(Lx) + "_latex.pgf")
#Young Modulus error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Young_modulus_error_bar[l][0]*10**-9]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
ax1.errorbar(Size_box_mean[l][0]/dmean, Young_modulus_mean[l][0]*10**-9, xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3)
plt.axhline(y=Young_modulus_mean[4][0]*10**-9, color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(0.732, 1.22)
ax1.set_yticks([0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1.00, 1.05, 1.10, 1.15, 1.20])
ax1.set_yticks([round(Young_modulus_mean[4][0]*10**-9,3)], minor = True)
ax1.yaxis.set_ticklabels([round(Young_modulus_mean[4][0]*10**-9,3)], minor = True, fontsize = 8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 10)
ax1.set_ylabel(r"Young's modulus $E$ (GPa)", fontsize = 9, labelpad = 10)
fig1.savefig( path_image + "/Young_modulus_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Young_modulus_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Young_modulus_error_bar.pdf", bbox_inches="tight")
plt.show()
#Bulk Modulus error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Bulk_modulus_error_bar[l][0]*10**-9]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
ax1.errorbar(Size_box_mean[l][0]/dmean, Bulk_modulus_mean[l][0]*10**-9, xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3)
plt.axhline(y=Bulk_modulus_mean[4][0]*10**-9, color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(0.495, 0.825)
ax1.set_yticks([0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80])
ax1.set_yticks([round(Bulk_modulus_mean[4][0]*10**-9,3)], minor = True)
ax1.yaxis.set_ticklabels([round(Bulk_modulus_mean[4][0]*10**-9,3)], minor = True, fontsize=8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.yticks(fontsize = 8)
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 10)
ax1.set_ylabel(r'Bulk modulus $K$ (GPa)', fontsize = 9, labelpad = 10)
fig1.savefig( path_image + "/Bulk_modulus_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Bulk_modulus_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Bulk_modulus_error_bar.pdf", bbox_inches="tight")
plt.show()
#Shear Modulus 1 and 2 error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Shear_modulus1_error_bar[l][0]*10**-9]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
yerr2 = [2 * Shear_modulus2_error_bar[l][0]*10**-9]
ax1.errorbar(Size_box_mean[l][0]/dmean, Shear_modulus1_mean[l][0]*10**-9, xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3, ls='-.')
eb2=plt.errorbar(Size_box_mean[l][0]/dmean, Shear_modulus2_mean[l][0]*10**-9, xerr=xerr, fmt='o', color='blue', ecolor='skyblue', elinewidth=1.5, errorevery=2, ls='-.', capsize=3, markersize = 3)
eb2[-1][0].set_linestyle('--')
eb3=plt.errorbar(Size_box_mean[l][0]/dmean, Shear_modulus2_mean[l][0]*10**-9, yerr=yerr2, fmt='o', color='blue', ecolor='skyblue', elinewidth=1.5, errorevery=2, ls='-.', capsize=3, markersize = 3)
eb3[-1][0].set_linestyle('--')
plt.axhline(y=Shear_modulus_average_mean[4][0]*10**-9, color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(0.293, 0.487)
#ax13.set_yticks([0.24, 0.26, 0.28 ,0.30, 0.32, 0.34, 0.36, 0.38])
ax1.set_yticks([round(Shear_modulus_average_mean[4][0]*10**-9,3)], minor = True)
ax1.yaxis.set_ticklabels([round(Shear_modulus_average_mean[4][0]*10**-9,3)], minor = True, fontsize=8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 10)
ax1.set_ylabel(r'Shear modulus (GPa)', fontsize = 9, labelpad = 10)
fig1.savefig( path_image + "/Shear_modulus_1_and_2_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Shear_modulus_1_and_2_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Shear_modulus_1_and_2_error_bar.pdf", bbox_inches="tight")
plt.show()
#Poisson ratio error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Poisson_error_bar[l][0]]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
ax1.errorbar(Size_box_mean[l][0]/dmean, Poisson_mean[l][0], xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3)
plt.axhline(y=Poisson_mean[4][0], color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(0, 0.5)
ax1.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5])
ax1.set_yticks([round(Poisson_mean[4][0],3)], minor = True)
ax1.yaxis.set_ticklabels([round(Poisson_mean[4][0],3)], minor = True, fontsize = 8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 10)
ax1.set_ylabel(r"Poisson's ratio $\nu$", fontsize = 9, labelpad = 10)
fig1.savefig( path_image + "/Poisson_ratio_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Poisson_ratio_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Poisson_ratio_error_bar.pdf", bbox_inches="tight")
plt.show()
#Anisotropy coefficient error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Anisotropy_coefficient_error_bar[l][0]]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
ax1.errorbar(Size_box_mean[l][0]/dmean, Anisotropy_coefficient_mean[l][0], xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3)
plt.axhline(y=1, color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(0, 2)
ax1.set_yticks([0, 0.25, 0.50, 0.75, 1.25, 1.50, 1.75, 2.00])
ax1.set_yticks([1.00], minor = True)
ax1.yaxis.set_ticklabels(['1.00'], minor = True, fontsize = 8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 9)
ax1.set_ylabel(r'Zener ratio', fontsize = 9, labelpad = 9)
fig1.savefig( path_image + "/Anisotropy_coefficient_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Anisotropy_coefficient_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Anisotropy_coefficient_error_bar.pdf", bbox_inches="tight")
plt.show()
#Density error bar
fig1 = plt.figure(figsize=(3.5, 2.5))
plt.subplot(1,1,1)
ax1 = plt.gca()
for l in range(1, len(Size)):
yerr = [2 * Density_error_bar[l][0]]
xerr = [2 * Length_box_error_bar[l][0]/dmean]
ax1.errorbar(Size_box_mean[l][0]/dmean, Density_mean[l][0], xerr=xerr, yerr=yerr, fmt='o', color='black', ecolor='gray', elinewidth=1.5, capsize=3, markersize = 3)
plt.axhline(y=Density_mean[4][0], color='r', linestyle='--', linewidth = 1)
ax1.set_ylim(1235, 2058)
ax1.set_yticks([round(Density_mean[4][0])], minor = True)
ax1.yaxis.set_ticklabels([round(Density_mean[4][0])], minor = True, fontsize=8)
ax1.yaxis.set_tick_params(which = 'major', color = 'black', labelcolor = 'black')
ax1.yaxis.set_tick_params(which = 'minor', color = 'red', labelcolor = 'red')
plt.xticks(np.arange(0, ceil(Size_box_mean[len(Size)-1][0]/dmean)+1, 2.0))
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
ax1.set_xlabel(r'$L_{x}/d_{0}$', fontsize = 9, labelpad = 10)
ax1.set_ylabel(r'density (Kg/m$^3$)', fontsize = 9, labelpad = 10)
fig1.savefig( path_image + "/Density_error_bar.png", bbox_inches="tight")
fig1.savefig( path_image + "/Density_error_bar.pgf", bbox_inches="tight")
fig1.savefig( path_image + "/Density_error_bar.pdf", bbox_inches="tight")
plt.show()
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