def initialize(self):
print(self.Run_name)
# booleans to know which figure to generat
do_catalog = True
#If it's the first time you run it, you have to do the catalog! Then you can save time and not run it again
plot_mfd = True
plot_mfd_detailled = True
plot_Mmax = True
plot_as_rep = True
plot_rup_freq = True
plot_sr_use = True
plot_moment_rate = True
visual_FtF = True # !!! can take a very long time if there are a lot of FtF
OQ_job = OQ_job_Creator(
self.Run_name
) # ask the info about the run and create the job.ini file
Mmin = OQ_job.Mmin
#plot mfd parameters
xmin = float(Mmin)
xmax = self.xmax
ymin = self.ymin
ymax = self.ymax
'''########################
# Extraction of the data
########################'''
time_i = time.time()
(mega_MFD, df_mega_MFD, scenarios_names_list, ScL_complet_list,
ScL_list, Model_list, BG_hyp_list, dimension_used_list,
faults_name_list, sample_list, b_value_list, MFD_type_list, m_Mmax,
mega_bining_in_mag, a_s_model, b_sample, sm_sample, Mt_sample,
sources_Lengths, sources_Areas) = Extract_data.extract(self.Run_name)
print('\nTime to extract the data : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''########################
# plot the Mmax distriution
########################'''
if not os.path.exists(str(self.Run_name) + '/analysis/figures/Mmax'):
os.makedirs(str(self.Run_name) + '/analysis/figures/Mmax')
if plot_Mmax == True:
plt.hist(m_Mmax,
int(round(max(m_Mmax) - min(m_Mmax), 1) * 10. + 1.))
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Mmax.png',
dpi=180)
plt.close()
plt.hist(sources_Lengths, 20)
plt.xlabel('Length of the sources')
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Lengths_all_models.png',
dpi=180)
plt.close()
plt.hist(sources_Areas, 20)
plt.xlabel('Area of the sources')
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Areas_all_models.png',
dpi=180)
plt.close()
if xmax < max(m_Mmax):
xmax = round(max(m_Mmax) + 0.2, 1)
#bining_in_mag = np.linspace(xmin,xmax,int((xmax-xmin)*10)+2)
bining_in_mag = [round(i, 1) for i in np.arange(xmin, xmax + 0.1, 0.1)]
if plot_Mmax == True:
#plot the distribution of Mmax of each rupture for each fault for the mean branch
i_model = 0
for Model in Model_list:
for scenario in scenarios_names_list:
log_Mmax_file = (str(self.Run_name) + '/' + str(Model) +
'/' + 'bg_' + str(BG_hyp_list[0]) + '/' +
ScL_complet_list[0] + '/sc_' +
str(scenario) + '/' + b_value_list[0] +
'/' + 'MFD_' + str(MFD_type_list[0]) +
'/Log/Mmax_sample_1.txt')
log_file = np.genfromtxt(log_Mmax_file,
dtype=[('U10000'), ('f8'),
('U100'), ('f8'), ('f8'),
('f8')],
delimiter='\t')
sources_names = list(
map(lambda i: log_file[i][0], range(len(log_file))))
sources_Mmax = list(
map(lambda i: log_file[i][5], range(len(log_file))))
sources_names = [str(i) for i in sources_names]
for fault in faults_name_list[0]:
Mmax_ruptures_fault = []
for source, Mmax_i in zip(sources_names, sources_Mmax):
if fault in source:
Mmax_ruptures_fault.append(Mmax_i)
n = []
for mag in bining_in_mag:
n.append(sum(i > mag for i in Mmax_ruptures_fault))
plt.plot(bining_in_mag, n)
plt.title(fault + ' ' + Model + ' ' + scenario)
plt.xlabel('Mw')
plt.ylabel('Number of rupture with Mmax >= Mw')
if not os.path.exists(
str(self.Run_name) +
'/analysis/figures/Mmax/' + Model):
os.makedirs(
str(self.Run_name) +
'/analysis/figures/Mmax/' + Model)
if not os.path.exists(
str(self.Run_name) +
'/analysis/figures/Mmax/' + Model + '/' +
scenario):
os.makedirs(
str(self.Run_name) +
'/analysis/figures/Mmax/' + Model + '/' +
scenario)
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/' + Model + '/' +
scenario + '/' + fault + '.png',
dpi=80)
plt.close()
i_model += 1
'''########################
# plot Mmax vs NMS
########################'''
plt.scatter(m_Mmax, a_s_model)
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Mmax_vs_NMS.png',
dpi=180)
plt.title('Mmax vs NMS')
plt.close()
'''#############################
###############################
# Extraction of
# the catalog
###############################
##############################'''
time_i = time.time()
if not os.path.exists(str(self.Run_name) + '/analysis/figures/mfd'):
os.makedirs(str(self.Run_name) + '/analysis/figures/mfd')
if not os.path.exists(
str(self.Run_name) + '/analysis/figures/analyze_branches'):
os.makedirs(
str(self.Run_name) + '/analysis/figures/analyze_branches')
nb_inter = 25
try:
(seismological_moment_rate, catalog_cum_rate, yr_cat_for_map,
M_cat_for_map, lon_cat_for_map,
lat_cat_for_map) = plt_catalog.plt_catalog(
do_catalog, Model_list, self.File_bg, self.catalog_file,
self.Run_name, xmin, xmax, ymin, ymax, self.llcrnrlon,
self.llcrnrlat, self.urcrnrlon, self.urcrnrlat,
self.completness_file, nb_inter, bining_in_mag,
self.end_year_of_catalog, self.sub_area_file)
except:
seismological_moment_rate, catalog_cum_rate = plt_catalog.plt_catalog(
do_catalog, Model_list, self.File_bg, self.catalog_file,
self.Run_name, xmin, xmax, ymin, ymax, self.llcrnrlon,
self.llcrnrlat, self.urcrnrlon, self.urcrnrlat,
self.completness_file, nb_inter, bining_in_mag,
self.end_year_of_catalog, self.sub_area_file)
print('\nTime to plot the catalog : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''################################################
###################################################
### work on and plot the MFD of the sources in the model ###
###################################################
################################################'''
time_i = time.time()
(total_list_ScL, total_list_dimension_used, geologic_moment_rate,
geologic_moment_rate_no_as, total_list_scenario_name,
total_list_MFD_type, mega_mfd_cummulative, total_list_model,
total_list_sample, total_list_BG_hyp) = Plot_mfd.plt_mfd(
self.Run_name, mega_MFD, scenarios_names_list, ScL_complet_list,
ScL_list, Model_list, BG_hyp_list, dimension_used_list,
faults_name_list, sample_list, b_value_list, MFD_type_list,
m_Mmax, mega_bining_in_mag, a_s_model, b_sample, sm_sample,
Mt_sample, plot_mfd, plot_as_rep, plot_Mmax, xmin, xmax, ymin,
ymax, catalog_cum_rate, plot_mfd_detailled, bining_in_mag)
print('\nTime to plot the MFDs : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
#
# print('size variables after mfd')
# for var, obj in locals().items():
# print(var, sys.getsizeof(obj))
'''####################################
# use of the slip rate per fault
#######################################'''
if plot_sr_use == True:
time_i = time.time()
slip_rate_rep.sr_rate(self.Run_name, scenarios_names_list,
mega_MFD, Model_list, MFD_type_list)
print('\nTime to see how the slip rate in distributed : ' +
str(time.time() - time_i) + ' s.\n')
'''####################################
#######################################
# FAULTS RUPTURES
# plot return period of a fault of a mutli faults rupture to occure for
# each set of rupture scenario used
#
#######################################
#######################################'''
'''####################################
# rupture rate fault by fault
#######################################'''
time_i = time.time()
if plot_rup_freq == True:
Participation_rates.plt_EQ_rates(
self.Run_name, mega_MFD, df_mega_MFD, scenarios_names_list,
ScL_complet_list, ScL_list, Model_list, BG_hyp_list,
dimension_used_list, faults_name_list, sample_list,
b_value_list, MFD_type_list, m_Mmax, mega_bining_in_mag,
a_s_model, b_sample, sm_sample, Mt_sample, plot_mfd,
plot_as_rep, plot_Mmax, xmin, xmax, ymin, ymax,
self.file_faults_data, self.File_bg, self.File_geom,
self.sub_area_file)
# #extract the faults data
del mega_MFD, df_mega_MFD
print('\nTime to plot the rupture rates of each faults : ' +
str(time.time() - time_i) + ' s.\n')
'''####################################
# impact of the sampling on the fit
#######################################'''
time_i = time.time()
Sampling_analysis.sampling_analysis(
self.Run_name, Model_list, m_Mmax, b_sample, a_s_model,
mega_mfd_cummulative, catalog_cum_rate, xmin, xmax, ymin, ymax,
total_list_model, bining_in_mag, total_list_MFD_type,
total_list_scenario_name, self.file_faults_data, total_list_sample,
total_list_BG_hyp)
print('\nTime to do the sampling analysis : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''####################################
# visualisation of the FtF ruptures
#######################################'''
time_i = time.time()
plot_FtF_GIF.map_faults(self.Run_name, Model_list,
scenarios_names_list, ScL_complet_list,
BG_hyp_list, sample_list, b_value_list,
MFD_type_list, self.llcrnrlon, self.llcrnrlat,
self.urcrnrlon, self.urcrnrlat, self.File_bg,
self.FileName_Prop, plot_sr_use, visual_FtF,
self.sub_area_file)
print('\nTime to plot the different FtF ruptures : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''#############################
###############################
# comparison of the moment rate
# geologic, geodetic, sismologic
###############################
##############################'''
moment_rate.moment_rate(self.Run_name, plot_moment_rate,
geologic_moment_rate_no_as,
geologic_moment_rate,
seismological_moment_rate,
scenarios_names_list, total_list_scenario_name,
MFD_type_list, total_list_MFD_type)
def initialize(self):
print(self.Run_name)
# booleans to know which figure to generat
plot_mfd = False
plot_mfd_detailled = False
plot_Mmax = False
plot_as_rep = False
plot_rup_freq = False
plot_sr_use = False
plot_moment_rate = False
visual_FtF = False # !!! can take a very long time if there are a lot of FtF
OQ_job = OQ_job_Creator(
self.Run_name
) # ask the info about the run and create the job.ini file
Mmin = OQ_job.Mmin
#plot mfd parameters
xmin = float(Mmin)
xmax = self.xmax
ymin = self.ymin
ymax = self.ymax
'''########################
# Extraction of the data
########################'''
time_i = time.time()
(mega_MFD, df_mega_MFD, scenarios_names_list, ScL_complet_list,
ScL_list, Model_list, BG_hyp_list, dimension_used_list,
faults_name_list, sample_list, b_value_list, MFD_type_list, m_Mmax,
mega_bining_in_mag, a_s_model, b_sample, sm_sample, Mt_sample,
sources_Lengths, sources_Areas) = Extract_data.extract(self.Run_name)
print('\nTime to extract the data : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''########################
# plot the Mmax distriution
########################'''
if not os.path.exists(str(self.Run_name) + '/analysis/figures/Mmax'):
os.makedirs(str(self.Run_name) + '/analysis/figures/Mmax')
if plot_Mmax == True:
plt.hist(m_Mmax,
int(round(max(m_Mmax) - min(m_Mmax), 1) * 10. + 1.))
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Mmax.png',
dpi=180)
plt.close()
plt.hist(sources_Lengths, 20)
plt.xlabel('Length of the sources')
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Lengths_all_models.png',
dpi=180)
plt.close()
plt.hist(sources_Areas, 20)
plt.xlabel('Area of the sources')
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Hist_Areas_all_models.png',
dpi=180)
plt.close()
if xmax < max(m_Mmax):
xmax = max(m_Mmax) + 0.2
bining_in_mag = np.linspace(xmin, xmax, int((xmax - xmin) * 10) + 1)
'''########################
# plot Mmax vs NMS
########################'''
plt.scatter(m_Mmax, a_s_model)
plt.savefig(str(self.Run_name) +
'/analysis/figures/Mmax/Mmax_vs_NMS.png',
dpi=180)
plt.title('Mmax vs NMS')
plt.close()
'''#############################
###############################
# Extraction of
# the catalog of SHARE
###############################
##############################'''
time_i = time.time()
if not os.path.exists(str(self.Run_name) + '/analysis/figures/mfd'):
os.makedirs(str(self.Run_name) + '/analysis/figures/mfd')
if not os.path.exists(
str(self.Run_name) + '/analysis/figures/analyze_branches'):
os.makedirs(
str(self.Run_name) + '/analysis/figures/analyze_branches')
nb_inter = 20
(seismological_moment_rate,
catalog_cum_rate) = plt_catalog.plt_catalog(
Model_list, self.File_bg, self.catalog_file, self.Run_name, xmin,
xmax, ymin, ymax, self.llcrnrlon, self.llcrnrlat, self.urcrnrlon,
self.urcrnrlat, self.completness_file, nb_inter, bining_in_mag,
self.end_year_of_catalog, self.sub_area_file)
print('\nTime to plot the catalog : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''################################################
###################################################
### work on and plot the MFD of the sources in the model ###
###################################################
################################################'''
time_i = time.time()
(total_list_ScL, total_list_dimension_used, geologic_moment_rate,
geologic_moment_rate_no_as, total_list_scenario_name,
total_list_MFD_type,
mega_mfd_cummulative, total_list_model) = Plot_mfd.plt_mfd(
self.Run_name, mega_MFD, scenarios_names_list, ScL_complet_list,
ScL_list, Model_list, BG_hyp_list, dimension_used_list,
faults_name_list, sample_list, b_value_list, MFD_type_list,
m_Mmax, mega_bining_in_mag, a_s_model, b_sample, sm_sample,
Mt_sample, plot_mfd, plot_as_rep, plot_Mmax, xmin, xmax, ymin,
ymax, catalog_cum_rate, plot_mfd_detailled, bining_in_mag)
print('\nTime to plot the MFDs : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''####################################
# impact of the sampling on the fit
#######################################'''
time_i = time.time()
Sampling_analysis.sampling_analysis(self.Run_name, Model_list, m_Mmax,
b_sample, a_s_model,
mega_mfd_cummulative,
catalog_cum_rate, xmin, xmax, ymin,
ymax, total_list_model,
bining_in_mag)
print('\nTime to do the sampling analysis : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''####################################
#######################################
# FAULTS RUPTURES
# plot return period of a fault of a mutli faults rupture to occure for
# each set of rupture scenario used
#
#######################################
#######################################'''
'''####################################
# use of the slip rate per fault
#######################################'''
if plot_sr_use == True:
time_i = time.time()
slip_rate_rep.sr_rate(self.Run_name, scenarios_names_list,
mega_MFD, Model_list, MFD_type_list)
print('\nTime to see how the slip rate in distributed : ' +
str(time.time() - time_i) + ' s.\n')
'''####################################
# rupture rate fault by fault
#######################################'''
if plot_rup_freq == True:
time_i = time.time()
EQ_rates_for_each_fault.plt_EQ_rates(
self.Run_name, mega_MFD, df_mega_MFD, scenarios_names_list,
ScL_complet_list, ScL_list, Model_list, BG_hyp_list,
dimension_used_list, faults_name_list, sample_list,
b_value_list, MFD_type_list, m_Mmax, mega_bining_in_mag,
a_s_model, b_sample, sm_sample, Mt_sample, plot_mfd,
plot_as_rep, plot_Mmax, xmin, xmax, ymin, ymax,
self.file_faults_data, self.File_bg, self.File_geom,
self.sub_area_file)
# #extract the faults data
print('\nTime to plot the rupture rates of each faults : ' +
str(time.time() - time_i) + ' s.\n')
'''####################################
# visualisation of the FtF ruptures
#######################################'''
time_i = time.time()
plot_FtF_GIF.map_faults(self.Run_name, Model_list,
scenarios_names_list, ScL_complet_list,
BG_hyp_list, sample_list, b_value_list,
MFD_type_list, self.llcrnrlon, self.llcrnrlat,
self.urcrnrlon, self.urcrnrlat, self.File_bg,
self.FileName_Prop, plot_sr_use, visual_FtF)
print('\nTime to plot the different FtF ruptures : ' +
str(round(time.time() - time_i, 2)) + ' s.\n')
'''#############################
###############################
# comparison of the moment rate
# geologic, geodetic, sismologic
###############################
##############################'''
moment_rate.moment_rate(self.Run_name, plot_moment_rate,
geologic_moment_rate_no_as,
geologic_moment_rate,
seismological_moment_rate,
scenarios_names_list, total_list_scenario_name,
MFD_type_list, total_list_MFD_type)