def ResilienceCurve(self, output_folder):
#delete the first element
valid_P_compression = self.pressure_compression
valid_e_compression = self.porosity_compression[1:]
valid_P_resilience = self.pressure_resilience
valid_e_resilience = self.porosity_resilience
valid_P_recompression = self.pressure_recompression
valid_e_recompression = self.porosity_recompression
if valid_P_compression==[]\
or valid_e_compression==[]\
or valid_P_resilience==[]\
or valid_e_resilience==[]\
or valid_P_recompression==[]\
or valid_e_recompression==[]:
return None
#Logarithm of P
valid_logP_compression = [
np.log10(item) for item in valid_P_compression
]
valid_logP_resilience = [np.log10(item) for item in valid_P_resilience]
valid_logP_recompression = [
np.log10(item) for item in valid_P_recompression
]
#combine all variabls
valid_logP = valid_logP_compression
valid_e = list(valid_e_compression) + list(valid_e_resilience) + list(
valid_e_recompression)
'''canvas'''
fig, ax = plt.subplots(figsize=(8, 8))
#calculation of consolidation pressure
final_Pc = C_P_C.CalculatePcAndCc(valid_logP_compression,
valid_e_compression,
show=1)
#set ticks
plt.tick_params(labelsize=12)
labels = ax.get_xticklabels() + ax.get_yticklabels()
plt.title('ID: ' + str(self.hole_id), FontProperties=title_font)
plt.xlabel('lgP', FontProperties=annotation_font)
plt.ylabel('e', FontProperties=annotation_font)
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
#tick step
x_major_step = (max(valid_logP) - min(valid_logP)) / 10
x_minor_step = (max(valid_logP) - min(valid_logP)) / 20
y_major_step = (max(valid_e) - min(valid_e)) / 10
y_minor_step = (max(valid_e) - min(valid_e)) / 20
#set locator
ax.xaxis.set_major_locator(MultipleLocator(x_major_step))
ax.xaxis.set_minor_locator(MultipleLocator(x_minor_step))
ax.yaxis.set_major_locator(MultipleLocator(y_major_step))
ax.yaxis.set_minor_locator(MultipleLocator(y_minor_step))
#init data for visualization
self.valid_logP_compression = valid_logP_compression
self.valid_e_compression = valid_e_compression
self.valid_logP_resilience = valid_logP_resilience
self.valid_e_resilience = valid_e_resilience
self.valid_logP_recompression = valid_logP_recompression
self.valid_e_recompression = valid_e_recompression
'''visualization of curve'''
self.PerfectDataVisualization(x_major_step, y_major_step)
#visualization of curve
# C_P_C.DataVisualization(valid_logP_compression,
# valid_e_compression,
# x_major_step,
# y_major_step)
#
# C_P_C.DataVisualization(valid_logP_resilience,
# valid_e_resilience,
# x_major_step,
# y_major_step)
#
# C_P_C.DataVisualization(valid_logP_recompression,
# valid_e_recompression,
# x_major_step,
# y_major_step)
#
#add depth
plt.text(0.95 * np.average(valid_logP),
max(valid_e),
'Start Depth: ' + str(self.start_depth) + 'm End Depth: ' +
str(self.end_depth) + 'm',
FontProperties=annotation_font)
#show the grid
plt.grid()
plt.show()
fig_path = output_folder + str(self.hole_id) + '.png'
#save the fig
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
plt.close()
return final_Pc
def ConsolidationCurve(self,output_folder):
#delete the first element
valid_P_compression=self.pressure_compression[1:]
valid_e_compression=self.porosity_compression[1:]
if valid_P_compression==[]\
or valid_e_compression==[]:
return None
#Logarithm of P
valid_logP_compression=[np.log10(item) for item in valid_P_compression]
#combine all variabls
valid_logP=valid_logP_compression
valid_e=list(valid_e_compression)
'''canvas'''
fig,ax=plt.subplots(figsize=(8,8))
#calculation of consolidation pressure
final_Pc=C_P_C.CalculatePcAndCc(valid_logP_compression,valid_e_compression,show=1)
#set ticks
plt.tick_params(labelsize=12)
labels = ax.get_xticklabels() + ax.get_yticklabels()
plt.title('ID: '+str(self.hole_id),FontProperties=title_font)
plt.xlabel('P(kPa)',FontProperties=annotation_font)
plt.ylabel('e',FontProperties=annotation_font)
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
#tick step
x_major_step=(max(valid_logP)-min(valid_logP))/10
x_minor_step=x_major_step/2
y_major_step=(max(valid_e)-min(valid_e))/10
y_minor_step=y_major_step/2
#set locator
# ax.xaxis.set_major_locator(MultipleLocator(x_major_step))
# ax.xaxis.set_minor_locator(MultipleLocator(x_minor_step))
ax.yaxis.set_major_locator(MultipleLocator(y_major_step))
ax.yaxis.set_minor_locator(MultipleLocator(y_minor_step))
'''represent A with B'''
plt.xticks(valid_logP_compression,[int(item) for item in valid_P_compression])
#visualization of curve
O_V.DataVisualization(valid_logP_compression,
valid_e_compression,
x_major_step,
y_major_step,
True)
#add depth
plt.text(0.95*np.average(valid_logP),max(valid_e),
'Start Depth: '+str(self.start_depth)+'m End Depth: '+str(self.end_depth)+'m',
FontProperties=annotation_font)
#show the grid
plt.grid()
plt.show()
if final_Pc<100:
output_folder+='0-100\\'
elif final_Pc<200:
output_folder+='100-200\\'
elif final_Pc<400:
output_folder+='200-400\\'
elif final_Pc<800:
output_folder+='400-800\\'
elif final_Pc<1600:
output_folder+='800-1600\\'
else:
output_folder+='1600-3200\\'
fig_path=output_folder+str(self.hole_id)+'.png'
#save the fig
plt.savefig(fig_path,dpi=300,bbox_inches='tight')
plt.close()
return final_Pc
def CalculatePc(P,
e,
hole_id,
start_depth,
end_depth,
output_folder,
show=False):
#delete the first element
valid_P = P[1:]
valid_e = e[1:]
if valid_P == [] or valid_e == []:
return None
valid_logP = [np.log10(item) for item in valid_P]
# valid_logP=[np.log2(item) for item in valid_P]
if C_P_C.CalculatePcAndCc(valid_logP, valid_e) > max(P):
return None
fig, ax = plt.subplots(figsize=(8, 8))
final_Pc = C_P_C.CalculatePcAndCc(valid_logP, valid_e, show=show)
#set ticks
plt.tick_params(labelsize=12)
labels = ax.get_xticklabels() + ax.get_yticklabels()
#title font
annotation_font = FontProperties(fname=r"C:\Windows\Fonts\GILI____.ttf",
size=16)
#annotation font
title_font = FontProperties(fname="C:\Windows\Fonts\GIL_____.ttf", size=20)
plt.title('ID:' + str(hole_id), FontProperties=title_font)
plt.xlabel('lgP', FontProperties=annotation_font)
plt.ylabel('e', FontProperties=annotation_font)
#label fonts
for this_label in labels:
this_label.set_fontname('Times New Roman')
#tick step
x_major_step = (max(valid_logP) - min(valid_logP)) / 10
x_minor_step = (max(valid_logP) - min(valid_logP)) / 20
y_major_step = (max(valid_e) - min(valid_e)) / 10
y_minor_step = (max(valid_e) - min(valid_e)) / 20
#set locator
ax.xaxis.set_major_locator(MultipleLocator(x_major_step))
ax.xaxis.set_minor_locator(MultipleLocator(x_minor_step))
ax.yaxis.set_major_locator(MultipleLocator(y_major_step))
ax.yaxis.set_minor_locator(MultipleLocator(y_minor_step))
#add depth
plt.text(np.average(valid_logP),
max(valid_e),
'Start Depth:' + str(start_depth) + 'm End Depth:' +
str(end_depth) + 'm',
FontProperties=annotation_font)
#show the grid
plt.grid()
plt.show()
if final_Pc < 100:
output_folder += '0-100\\'
elif final_Pc < 200:
output_folder += '100-200\\'
elif final_Pc < 400:
output_folder += '200-400\\'
elif final_Pc < 800:
output_folder += '400-800\\'
elif final_Pc < 1600:
output_folder += '800-1600\\'
else:
output_folder += '1600-3200\\'
fig_path = output_folder + str(hole_id) + '.png'
#save the fig
plt.savefig(fig_path, dpi=300, bbox_inches='tight')
plt.close()
return final_Pc