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import sys
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# import os
import scipy.io
import logging
from geopy.distance import geodesic
from geopy.point import Point
def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_hour, time_win_type,
End_time, ev_limit, Inpar, time_inj, time_shut_in, time_big_ev, Model_index,
Mc, Mu, G, ssd, C, b_value_type, cl, mag_name):
"""
Main function for application: MAXIMUM_MAGNITUDE_DETERMINISTIC_MODELS
Arguments:
Input catalog: path to input file of type 'catalog'
Input injection rate: path to input file of type 'injection_rate'
**kwargs: Model paramters.
Returns:
'PLOT_Mmax_param': Plot of all results (also check 'application.log' for more info)
'DATA_Mmax_param': Results with 'csv' format
'application.log': Logging file
"""
f_indx = Model_index
b_method = b_value_type
Plot_flag = 1
# Setting up the logfile--------------------------------
logging.basicConfig(filename="application.log",
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
# Importing utilities ----------------------
logger.info("Import utilities")
from util.Find_idx4Time import Find_idx4Time
from util.CandidateEventsTS import CandidateEventsTS
from util.M_max_models import M_max_models
def latlon_to_enu(lat, lon, alt):
lat0 = np.mean(lat)
lon0 = np.mean(lon)
alt0 = 0
# Reference point (origin)
origin = Point(lat0, lon0, alt0)
east = np.zeros_like(lon)
north = np.zeros_like(lat)
up = np.zeros_like(alt)
for i in range(len(lon)):
# Target point
target = Point(lat[i], lon[i], alt[i])
# Calculate East-North-Up
east[i] = geodesic((lat0, lon0), (lat0, lon[i])).meters
if lon[i] < lon0:
east[i] = -east[i]
north[i] = geodesic((lat0, lon0), (lat[i], lon0)).meters
if lat[i] < lat0:
north[i] = -north[i]
up = alt - alt0
return east, north, up
# Importing data
logger.info("Import data")
mat = scipy.io.loadmat(Input_catalog)
Cat_structure = mat['Catalog']
Cat_id, Cat_t, Cat_m = [], [], []
Cat_x, Cat_y, Cat_z = [], [], []
Cat_lat, Cat_lon, Cat_elv, Cat_depth = [], [], [], []
for i in range(1,Cat_structure.shape[1]):
if Cat_structure.T[i][0][0][0] == 'X':
Cat_x = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_x)):
raise ValueError("Catalog-X contains infinite value")
if Cat_structure.T[i][0][3][0] == 'km':
Cat_x *= 1000
if Cat_structure.T[i][0][0][0] == 'Lat':
Cat_lat = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_lat)):
raise ValueError("Catalog-Lat contains infinite value")
if Cat_structure.T[i][0][0][0] == 'Y':
Cat_y = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_y)):
raise ValueError("Catalog-Y contains infinite value")
if Cat_structure.T[i][0][3][0] == 'km':
Cat_y *= 1000
if Cat_structure.T[i][0][0][0] == 'Long':
Cat_lon = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_lon)):
raise ValueError("Catalog-Long contains infinite value")
if Cat_structure.T[i][0][0][0] == 'Z':
Cat_z = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_z)):
raise ValueError("Catalog-Z contains infinite value")
if Cat_structure.T[i][0][3][0] == 'km':
Cat_z *= 1000
if Cat_structure.T[i][0][0][0] == 'Depth':
Cat_depth = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_depth)):
raise ValueError("Catalog-Depth contains infinite value")
if Cat_structure.T[i][0][3][0] == 'km':
Cat_depth *= 1000
if Cat_structure.T[i][0][0][0] == 'Elevation':
Cat_elv = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_elv)):
raise ValueError("Catalog-Elevation contains infinite value")
if Cat_structure.T[i][0][3][0] == 'km':
Cat_elv *= 1000
if Cat_structure.T[i][0][0][0] == 'Time':
Cat_t = Cat_structure.T[i][0][2]
if not all(np.isfinite(Cat_t)):
raise ValueError("Catalog-Time contains infinite value")
if Cat_structure.T[i][0][0][0] == mag_name:
Cat_m = Cat_structure.T[i][0][2]
# if not all(np.isfinite(Cat_m)):
if np.argwhere(all(np.isfinite(Cat_m))):
raise ValueError("Catalog-Magnitude contains infinite value")
if any(Cat_x):
Cat_id = np.linspace(0,Cat_x.shape[0],Cat_x.shape[0]).reshape((Cat_x.shape[0],1))
arg = (Cat_id, Cat_x, Cat_y, Cat_z, Cat_t, Cat_m)
Cat = np.concatenate(arg, axis=1)
elif any(Cat_lat):
if any(Cat_elv):
Cat_x, Cat_y, Cat_z = latlon_to_enu(Cat_lat, Cat_lon, Cat_elv)
elif any(Cat_depth):
Cat_x, Cat_y, Cat_z = latlon_to_enu(Cat_lat, Cat_lon, Cat_depth)
else:
raise ValueError("Catalog Depth or Elevation is not available")
Cat_id = np.linspace(0,Cat_x.shape[0],Cat_x.shape[0]).reshape((Cat_x.shape[0],1))
arg = (Cat_id, Cat_x, Cat_y, Cat_z, Cat_t, Cat_m)
Cat = np.concatenate(arg, axis=1)
else:
raise ValueError("Catalog data are not available")
mat = scipy.io.loadmat(Input_injection_rate)
Inj_structure = mat['d']
if 'Date' in Inj_structure.dtype.names:
inj_date = Inj_structure['Date'][0,0]
inj_rate = Inj_structure['Injection_rate'][0,0]
Hyd = np.concatenate((inj_date,inj_rate), axis=1)
else:
raise ValueError("Injection data are not available")
if Cat[0,4]>np.max(Hyd[:,0]) or Hyd[0,0]>np.max(Cat[:,4]):
raise ValueError('Catalog and injection data do not have time coverage!')
if Hyd[0,0] < Cat[0,4]:
same_time_idx = Find_idx4Time(Hyd[:,0], Cat[0,4])
Hyd = Hyd[same_time_idx:,:]
Hyd[:,0] = (Hyd[:,0] - Cat[0,4])*24*3600
Cat[:,4] = (Cat[:,4] - Cat[0,4])*24*3600
logger.info('Start of the computations is based on the time of catalog data.')
# Model dictionary
Feat_dic = {
'indx' :[0 ,1 ,2 ,3 ,4 ,5 ],
'Full_names':['All_M_max' ,'McGarr' ,'Hallo' ,'Li' ,'van-der-Elst','Shapiro' ],
'Short_name':['max_all' , 'max_mcg', 'max_hlo', 'max_li', 'max_vde' , 'max_shp'],
'f_num' :[5 ,4 ,4 ,1 ,6 ,5 ],
'Param' :[{'Mc': 0.8, 'b_method': ['b'], 'cl': [0.37], 'Inpar': ['dv','d_num'], 'Mu':0.6, 'G': 35*10**(9), 'ssd': 3*10**6, 'C': 0.95, 'ev_limit': 20, 'num_bootstraps': 100}]
}
Feat_dic['Param'][0]['Inpar'] = Inpar
Feat_dic['Param'][0]['ev_limit'] = ev_limit
num_bootstraps = 100 # Number of bootstraping for standard error computation
Feat_dic['Param'][0]['num_bootstraps'] = num_bootstraps
if f_indx in [0,1,2,4]:
if Mc < np.min(Cat[:,-1]) or Mc > np.max(Cat[:,-1]):
raise ValueError("Completeness magnitude (Mc) is out of magnitude range")
Feat_dic['Param'][0]['Mc'] = Mc
if f_indx in [0,1,2]:
if Mu < 0.2 or Mu > 0.8:
raise ValueError("Friction coefficient (Mu) must be between [0.2, 0.8]")
Feat_dic['Param'][0]['Mu'] = Mu
if f_indx in [0,1,2,3]:
if G < 1*10**(9) or G > 100*10**(9):
raise ValueError("Shear modulus of reservoir rock (G) must be between [1, 100] GPa")
Feat_dic['Param'][0]['G'] = G
if f_indx in [0,5]:
if ssd < 0.1*10**(6) or ssd > 100*10**(6):
raise ValueError("Static stress drop (ssd) must be between [0.1, 100] MPa")
Feat_dic['Param'][0]['ssd'] = ssd
if f_indx in [0,5]:
if C < 0.5 or C > 5:
raise ValueError("Geometrical constant (C) of Shaprio's model must be between [0.5, 5]")
Feat_dic['Param'][0]['C'] = C
if f_indx in [0,1,2,4]:
Feat_dic['Param'][0]['b_method'] = b_method
if f_indx in [0,4]:
for cl_i in cl:
if cl_i < 0 or cl_i > 1:
raise ValueError("Confidence level (cl) of van der Elst model must be between [0, 1]")
Feat_dic['Param'][0]['cl'] = cl
# Setting up based on the config and model dic --------------
ModelClass = M_max_models()
Model_name = Feat_dic['Full_names'][f_indx]
ModelClass.f_name = Feat_dic['Short_name'][f_indx]
f_num = Feat_dic['f_num'][f_indx]
if Feat_dic['Param'][0]['Mc']:
ModelClass.Mc = Mc
else:
Mc = np.min(Cat[:,-1])
ModelClass.Mc = Mc
time_win = time_win_in_hours*3600 # in sec
ModelClass.time_win = time_win
if f_indx == 0:
# Only first b_methods is used
Feat_dic['Param'][0]['b_method'] = b_method[0]
ModelClass.b_method = Feat_dic['Param'][0]['b_method']
logger.info(f"All models are based on b_method: { b_method[0]}")
Feat_dic['Param'][0]['cl'] = [cl[0]]
ModelClass.cl = Feat_dic['Param'][0]['cl']
logger.info(f"All models are based on cl: { cl[0]}")
ModelClass.Mu = Feat_dic['Param'][0]['Mu']
ModelClass.G = Feat_dic['Param'][0]['G']
ModelClass.ssd = Feat_dic['Param'][0]['ssd']
ModelClass.C = Feat_dic['Param'][0]['C']
ModelClass.num_bootstraps = Feat_dic['Param'][0]['num_bootstraps']
if f_indx == 1 or f_indx == 2:
ModelClass.b_method = Feat_dic['Param'][0]['b_method']
ModelClass.Mu = Feat_dic['Param'][0]['Mu']
ModelClass.G = Feat_dic['Param'][0]['G']
ModelClass.num_bootstraps = Feat_dic['Param'][0]['num_bootstraps']
Feat_dic['Param'][0]['cl'] = [None]
if f_indx == 3:
Feat_dic['Param'][0]['b_method'] = [None]
ModelClass.G = Feat_dic['Param'][0]['G']
Feat_dic['Param'][0]['cl'] = [None]
if f_indx == 4:
ModelClass.b_method = Feat_dic['Param'][0]['b_method']
ModelClass.num_bootstraps = Feat_dic['Param'][0]['num_bootstraps']
ModelClass.G = Feat_dic['Param'][0]['G']
ModelClass.cl = Feat_dic['Param'][0]['cl']
if f_indx == 5:
ModelClass.ssd = Feat_dic['Param'][0]['ssd']
ModelClass.C = Feat_dic['Param'][0]['C']
Feat_dic['Param'][0]['b_method'] = [None]
Feat_dic['Param'][0]['cl'] = [None]
# Output dictionary --------------------------------
Output_dict = {
'Type' :['idx', 'Time[day]'],
'label' :[None, None],
'b_method' :[None, None],
'cl' :[None, None],
}
c_out = 2
if any(Feat_dic['Param'][0]['b_method']) and any(Feat_dic['Param'][0]['cl']):
for i in range(len(Feat_dic['Param'][0]['b_method'])):
for j in range(len(Feat_dic['Param'][0]['cl'])):
if f_indx == 0: # f_index == 0
for i in Feat_dic['Full_names'][1:]:
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(i)
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 1
elif j == 0: # f_index == 4
Output_dict['Type'].append('b_value')
Output_dict['label'].append('b_value')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('b_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Seismogenic Index')
Output_dict['label'].append('SI')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('si_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(Model_name)
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(Feat_dic['Param'][0]['cl'][j])
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('M_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
c_out += 6
else: # f_index == 4
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(Model_name)
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(Feat_dic['Param'][0]['cl'][j])
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('M_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
c_out += 2
elif any(Feat_dic['Param'][0]['b_method']): # f_index == 1, 2
for i in range(len(Feat_dic['Param'][0]['b_method'])):
Output_dict['Type'].append('b_value')
Output_dict['label'].append('b_value')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('b_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(Model_name)
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
Output_dict['Type'].append('Standard Error')
Output_dict['label'].append('M_std_err')
Output_dict['b_method'].append(Feat_dic['Param'][0]['b_method'][i])
Output_dict['cl'].append(None)
c_out += 4
elif f_indx == 5: # f_index == 5
for i in ['L(max)','L(int)','L(min)', 'L(avg)']:
Output_dict['Type'].append('Length[m]')
Output_dict['label'].append(i)
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(Model_name)
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 5
else: # f_index == 3
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(Model_name)
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 1
# Add True maximum magnitude to the outputs
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append('True Max-Mag')
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 1
# if any(Feat_dic['Param'][0]['Inpar']): # Input parameters
if Inpar:
for i in Feat_dic['Param'][0]['Inpar']:
if i == 'd_num':
Output_dict['Type'].append('Number of events')
Output_dict['label'].append('Ev_Num')
elif i == 'dv':
Output_dict['Type'].append('Volume[m3]')
Output_dict['label'].append('Vol')
elif i == 'Mo':
Output_dict['Type'].append('Seismic moment')
Output_dict['label'].append('Mo')
elif i == 'SER':
Output_dict['Type'].append('Seismic efficiency ratio')
Output_dict['label'].append('SER')
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 1
# Functions ------------------------------
# Computing in extending time or time window
def Feature_in_Time(In_arr):
SER_l = 0
SER_c = 0
i_row = 0
for i in np.arange(1,Times):
# Defining time window and data
if time_win_type == 0:
ModelClass.time_win = i*time_step
candidate_events = CandidateEventsTS(Cat, i*time_step, None, i*time_step)
else:
candidate_events = CandidateEventsTS(Cat, i*time_step, None, time_win)
data = candidate_events.filter_data()
# USER: Check if cumulative dv is correctly computed !!!
end_time_indx = Find_idx4Time(Hyd[:,0], i*time_step)
# ModelClass.dv = np.sum(Hyd[:end_time_indx,1])
ModelClass.dv = np.trapz(Hyd[:end_time_indx,1], Hyd[:end_time_indx,0])/60
if len(data) > Feat_dic['Param'][0]['ev_limit'] and ModelClass.dv > 0:
ModelClass.Mo = np.sum(10**(1.5*data[:end_time_indx,5]+9.1))
if f_indx != 5: # Parameters have not been assinged for f_index == 5
SER_c = ModelClass.Mo/(2.0*ModelClass.G*ModelClass.dv)
SER_l = np.max((SER_c, SER_l))
ModelClass.SER = SER_l
ModelClass.data = data
In_arr[i_row,0] = i # index
In_arr[i_row,1] = i*time_step # Currecnt time
c = 2
if any(Feat_dic['Param'][0]['b_method']) and any(Feat_dic['Param'][0]['cl']):
for ii in range(len(Feat_dic['Param'][0]['b_method'])):
ModelClass.b_method = Feat_dic['Param'][0]['b_method'][ii]
for jj in range(len(Feat_dic['Param'][0]['cl'])): # f_index == 4
ModelClass.cl = Feat_dic['Param'][0]['cl'][jj]
Out = ModelClass.ComputeModel()
if jj == 0:
In_arr[i_row,c:c+f_num] = Out
c += f_num
else:
In_arr[i_row,c:c+2] = Out[-2:]
c += 2
elif any(Feat_dic['Param'][0]['b_method']): # f_index == 1, 2
for ii in range(len(Feat_dic['Param'][0]['b_method'])):
ModelClass.b_method = Feat_dic['Param'][0]['b_method'][ii]
In_arr[i_row,c:c+f_num] = ModelClass.ComputeModel()
c += f_num
else: # f_index = 0, 3, 5
In_arr[i_row,c:c+f_num] = ModelClass.ComputeModel()
c += f_num
# Compute true maximum magnitude in the data
In_arr[i_row,c] = np.max(data[:end_time_indx,5])
c += 1
if Inpar:
for ii in range(len(Feat_dic['Param'][0]['Inpar'])): # Add input parameters
if Feat_dic['Param'][0]['Inpar'][ii] == 'd_num':
In_arr[i_row,c+ii] = data.shape[0]
elif Feat_dic['Param'][0]['Inpar'][ii] == 'dv':
In_arr[i_row,c+ii] = ModelClass.dv
elif Feat_dic['Param'][0]['Inpar'][ii] == 'Mo':
In_arr[i_row,c+ii] = ModelClass.Mo
elif Feat_dic['Param'][0]['Inpar'][ii] == 'SER':
if ModelClass.SER:
In_arr[i_row,c+ii] = ModelClass.SER
i_row += 1
return In_arr[:i_row,:]
# Plotting models and parameters
def Plot_feature(Model_Param_array,Output_dict):
myVars = locals()
# Function for findin min-max of all similar parameters
def Extermom4All(Model_Param_array, itr_loc):
Mat1D = np.reshape(Model_Param_array[:,itr_loc], -1)
NotNone = np.isfinite(Mat1D)
if np.min(Mat1D[NotNone])>0:
return [np.min(Mat1D[NotNone])*0.95, np.max(Mat1D[NotNone])*1.05]
elif np.min(Mat1D[NotNone])<0 and np.max(Mat1D[NotNone])>0:
return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*1.05]
elif np.max(Mat1D[NotNone])<0:
return [np.min(Mat1D[NotNone])*1.05, np.max(Mat1D[NotNone])*0.95]
# Function for setting relevant lagends in the plot
def Legend_label(loc):
l = Output_dict_c['label'][loc]
if Output_dict_c['b_method'][loc]:
if Output_dict_c['cl'][loc]:
l+='('+Output_dict_c['b_method'][loc]+', cl='+str(Output_dict_c['cl'][loc])+')'
else:
l+='('+Output_dict_c['b_method'][loc]+')'
return l
c_NotNone = [] # Removing all parameters with None or constant value
for i in range(Model_Param_array.shape[1]):
NotNone = np.isfinite(Model_Param_array[:,i])
Eq_value = np.mean(Model_Param_array[:,i])
if any(NotNone) and Eq_value != Model_Param_array[0,i]:
c_NotNone.append(i)
else:
logger.info(f"No-PLOT: All values of {Output_dict['Type'][i]} are {Model_Param_array[0,i]}!")
if len(c_NotNone) > 1:
Model_Param_array = Model_Param_array[:,c_NotNone]
# New output dictionary based on valid parameters for plotting
Output_dict_c = {'Type':[], 'label':[], 'b_method':[], 'cl':[]}
for i in range(len(c_NotNone)):
Output_dict_c['Type'].append(Output_dict['Type'][c_NotNone[i]])
Output_dict_c['label'].append(Output_dict['label'][c_NotNone[i]])
Output_dict_c['b_method'].append(Output_dict['b_method'][c_NotNone[i]])
Output_dict_c['cl'].append(Output_dict['cl'][c_NotNone[i]])
coloring=['blue','g','r','c','m','y',
'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',
'purple', 'darksalmon', '#c5b0d5', '#c49c94',
'#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
'#17becf', '#9edae5']
# All parameters to be plotted
All_vars = Output_dict_c['Type'][2:]
Uniqe_var = list(dict.fromkeys([s for s in All_vars if 'Standard Error' not in s])) #list(set(All_vars))
# defining handels and labels to make final legend
All_handels = ['p0']
for i in range(1,len(All_vars)):
All_handels.append('p'+str(i))
handels = []
labels = []
# itr_loc: location of paramteres with similar type
itr_loc = np.where(np.array(All_vars) == Uniqe_var[0])[0]+2
fig, myVars[Output_dict_c['Type'][0]] = plt.subplots(1,1,figsize=(8+int(len(All_vars)/3),6))
fig.subplots_adjust(right=1-len(Uniqe_var)*0.09)
if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]], ls='--')
else:
myVars[All_handels[itr_loc[0]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
handels.append(All_handels[itr_loc[0]-2])
labels.append(Legend_label(itr_loc[0]))
myVars[Output_dict_c['Type'][0]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
myVars[Output_dict_c['Type'][0]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
myVars[Output_dict_c['Type'][0]].set_xlabel('Day (From start of the recording)')
if End_time:
myVars[Output_dict_c['Type'][0]].set_xlim(0,End_time)
# Plotting statndard error (if exists)
if Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
Model_Param_array[:,itr_loc[0]] - Model_Param_array[:,itr_loc[0]+1],
Model_Param_array[:,itr_loc[0]] + Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
# Plotting similar parameters on one axis
for j in range(1,len(itr_loc)):
if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]], ls='--')
else:
myVars[All_handels[itr_loc[j]-2]], = myVars[Output_dict_c['Type'][0]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
handels.append(All_handels[itr_loc[j]-2])
labels.append(Legend_label(itr_loc[j]))
# Plotting statndard error (if exists)
if itr_loc[j]+1 <= len(itr_loc) and Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
myVars[Output_dict_c['Type'][0]].fill_between(Model_Param_array[:,1]/24/3600,
Model_Param_array[:,itr_loc[j]] - Model_Param_array[:,itr_loc[j]+1],
Model_Param_array[:,itr_loc[j]] + Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
first_itr = 0
# Check if there is any more parameter to be plotted in second axes
# The procedure is similar to last plots.
if len(Uniqe_var) > 1:
for i in range(1,len(Uniqe_var)):
itr_loc = np.where(np.array(All_vars) == Uniqe_var[i])[0]+2
myVars[Uniqe_var[i]] = myVars[Output_dict_c['Type'][0]].twinx()
# if it is third or more axis, make a distance between them
if first_itr == 0:
first_itr += 1
set_right = 1
else:
set_right = 1 + first_itr*0.2
first_itr += 1
myVars[Uniqe_var[i]].spines.right.set_position(("axes", set_right))
if Output_dict_c['label'][itr_loc[0]] == 'True Max-Mag': # plot with dash-line
myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]], ls='--')
else:
myVars[All_handels[itr_loc[0]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[0]], c= coloring[itr_loc[0]])
handels.append(All_handels[itr_loc[0]-2])
labels.append(Legend_label(itr_loc[0]))
myVars[Uniqe_var[i]].set_ylabel(Output_dict_c['Type'][itr_loc[0]])
myVars[Uniqe_var[i]].yaxis.label.set_color(coloring[itr_loc[0]])
myVars[Uniqe_var[i]].spines["right"].set_edgecolor(coloring[itr_loc[0]])
myVars[Uniqe_var[i]].tick_params(axis='y', colors= coloring[itr_loc[0]])
myVars[Uniqe_var[i]].set_ylim(Extermom4All(Model_Param_array, itr_loc)[0], Extermom4All(Model_Param_array, itr_loc)[1])
if itr_loc[0]+1 < len(Output_dict_c['Type']) and Output_dict_c['Type'][itr_loc[0]+1] == 'Standard Error':
myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
Model_Param_array[:,itr_loc[0]] - Model_Param_array[:,itr_loc[0]+1],
Model_Param_array[:,itr_loc[0]] + Model_Param_array[:,itr_loc[0]+1], color= coloring[itr_loc[0]], alpha=0.1)
for j in range(1,len(itr_loc)):
if Output_dict_c['label'][itr_loc[j]] == 'True Max-Mag': # plot with dash-line
myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]], ls = '--')
else:
myVars[All_handels[itr_loc[j]-2]], = myVars[Uniqe_var[i]].plot(Model_Param_array[:,1]/24/3600, Model_Param_array[:,itr_loc[j]], c= coloring[itr_loc[j]])
handels.append(All_handels[itr_loc[j]-2])
labels.append(Legend_label(itr_loc[j]))
if Output_dict_c['Type'][itr_loc[j]+1] == 'Standard Error':
myVars[Uniqe_var[i]].fill_between(Model_Param_array[:,1]/24/3600,
Model_Param_array[:,itr_loc[j]] - Model_Param_array[:,itr_loc[j]+1],
Model_Param_array[:,itr_loc[j]] + Model_Param_array[:,itr_loc[j]+1], color= coloring[itr_loc[j]], alpha=0.1)
# If there are timing, plot them as vertical lines
if time_inj:
myVars['l1'], = plt.plot([time_inj,time_inj], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='--', c='k')
handels.append('l1')
labels.append('Start-inj')
if time_shut_in:
myVars['l2'], = plt.plot([time_shut_in,time_shut_in], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='-.', c='k')
handels.append('l2')
labels.append('Shut-in')
if time_big_ev:
myVars['l3'], = plt.plot([time_big_ev,time_big_ev], [Extermom4All(Model_Param_array, itr_loc)[0],Extermom4All(Model_Param_array, itr_loc)[1]], ls='dotted', c='k')
handels.append('l3')
labels.append('Large-Ev')
box = myVars[Output_dict_c['Type'][0]].get_position()
if len(handels) < 6:
myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
bbox_to_anchor=(0.5+0.06*first_itr, -0.15), fancybox=True, shadow=True, ncol=len(handels))
elif len(handels) < 13:
myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
box.width, box.height * (1 - 0.04*int(len(handels)/2))])
plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
bbox_to_anchor=(0.5+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
else:
myVars[Output_dict_c['Type'][0]].set_position([box.x0, box.y0 + box.height * 0.04*int(len(handels)/2),
box.width, box.height * (1 - 0.04*int(len(handels)/2))])
plt.legend([myVars[ii] for ii in handels], labels, loc='upper center',
bbox_to_anchor=(0.6+0.1*first_itr, -0.04*int(len(handels)/2)), fancybox=True, shadow=True, ncol=int(len(handels)/2)+1, handleheight=2)
plt.title(Model_name)
# plt.savefig(cwd+'/Results/'+Model_name+'.png', dpi=300)
plt.savefig('PLOT_Mmax_param.png', dpi=300)
# plt.show()
# Run functions based on the configurations -------------------
# Computing model
if time_step_in_hour > time_win_in_hours:
raise ValueError('Time steps should be <= time window.')
if (time_win_in_hours+3*time_step_in_hour)*3600 > np.max(Cat[:,4]):
raise ValueError('Time window and time steps are like that no more than three computations is possible. Use smaller value for either time window or time step.')
time_step = time_step_in_hour*3600
if End_time:
if End_time*24*3600 > np.max(Cat[:,4])+24*3600:
raise ValueError(f'End_time is longer than the maximum time of catalog, which is {np.ceil(np.max(Cat[:,4])/24/3600)} day!')
Times = int(np.floor((End_time*24*3600 - Cat[0,4]) / time_step))
else:
Times = int(np.floor((np.max(Cat[:,4]) - Cat[0,4]) / time_step))
Model_Param_array = np.zeros((Times,c_out))
logger.info("Computing input parameter(s) and the model(s)")
Model_Param_array = Feature_in_Time(Model_Param_array)
# Plotting
if Plot_flag > 0:
if Model_Param_array.any():
logger.info("Plotting results")
Plot_feature(Model_Param_array, Output_dict)
else:
logger.info("Model_Param_array is empty.")
raise ValueError("No model is generated since no event in all time windows is available due to imporoper values of either time window, minimum limit of events or completeness magnitude!")
# Saving
logger.info("Saving results")
# Save models and parameters in
def OneLineHeader(loc):
l = Output_dict['Type'][loc]
if Output_dict['b_method'][loc]:
if Output_dict['label'][loc] != 'b_value' and Output_dict['label'][loc] != 'b_std_err' and Output_dict['label'][loc] != 'M_std_err':
l += '_'+Output_dict['label'][loc]
else:
l = Output_dict['label'][loc]
if Output_dict['cl'][loc]:
l += '(b_method: '+Output_dict['b_method'][loc]+', q='+str(Output_dict['cl'][loc])+')'
else:
l += '(b_method: '+Output_dict['b_method'][loc]+')'
elif Output_dict['label'][loc]:
l += '('+ Output_dict['label'][loc] +')'
return l
db_df = pd.DataFrame(data = Model_Param_array,
columns = [OneLineHeader(i) for i in range(len(Output_dict['Type']))])
db_df = db_df.round(4)
# db_df.to_excel(cwd+'/Results/'+Full_feat_name+'.xlsx')
db_df.to_csv('DATA_Mmax_param.csv', sep=';', index=False)
# np.savez_compressed(cwd+'/Results/'+Full_feat_name+'.npz', Model_Param_array = Model_Param_array) # change the name!
if __name__=='__main__':
# Input_catalog = 'Data/Cooper_Basin_Catalog_HAB_1_2003_Reprocessed.mat'
# Input_injection_rate = 'Data/Cooper_Basin_HAB_1_2003_Injection_Rate.mat'
# Model_index = 4
# b_value_type = 'TGR'
main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_hour, time_win_type,
End_time, ev_limit, Inpar, time_inj, time_shut_in, time_big_ev, Model_index,
Mc, Mu, G, ssd, C, b_value_type, cl, mag_name)

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maxmagnitude_wrapper.py Normal file
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# -*- coding: utf-8 -*-
# -----------------
# Copyright © 2024 ACK Cyfronet AGH, Poland.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This work was partially funded by DT-GEO Project.
# -----------------
import sys
import argparse
from Mmax import main as Mmax
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("Input_catalog", help="Input catalog: path to input file of type 'catalog'")
parser.add_argument("Input_injection_rate", help="Input injection rate: path to input file of type 'injection_rate'")
parser.add_argument("--time_win_in_hours", help="Time window length (in hours- backward from the current time).", type=int, default=6)
parser.add_argument("--time_step_in_hour", help="Time interval for computation (in hours).", type=int, default=3)
parser.add_argument("--time_win_type", help="Time window type for computation.", type=int, default=0)
parser.add_argument("--End_time", help="End time of the computations (in day).", type=int, default=None)
parser.add_argument("--ev_limit", help="Minimum events number required for model computation.", type=int, default=20)
# Parameters with action='append' are array inputs. We can pass array by using a parameters multiple time. Eg.:
# --Inpar=A --Inpar=B --Inpar=C pass ['A', 'B', 'C'] array as script input.
parser.add_argument("--Inpar", help="Input parameters of the model to be saved and plotted.", type=str, action='append', default=None)
parser.add_argument("--time_inj", help="Injection time for plotting (in day).", type=int, default=None)
parser.add_argument("--time_shut_in", help="Shut-in time for plotting (in day).", type=int, default=None)
parser.add_argument("--time_big_ev", help="Big event time for plotting (in day).", type=int, default=None)
parser.add_argument("--Model_index", help="Model index: parameter of type 'INTEGER'", type=int)
parser.add_argument("--Mc", help="Completeness magnitude.", type=float, default=0.8)
parser.add_argument("--Mu", help="Friction coefficient.", type=float, default=0.6, required=False)
parser.add_argument("--G", help="Shear modulus of reservoir (in Pa).", type=float, default=35000000000)
parser.add_argument("--ssd", help="Static stress drop (in Pa).", type=float, default=3000000)
parser.add_argument("--C", help="Geometrical constant.", type=float, default=0.95)
parser.add_argument("--b_value_type", help="b-value type: parameter of type 'TEXT'", action='append')
parser.add_argument("--cl", help="Confidence level in van der Elst model.", type=float, action='append')
parser.add_argument("--mag_name", help="Magnitude column name", type=str)
args = parser.parse_args()
Mmax(**vars(args))
return
if __name__ == '__main__':
main(sys.argv)

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import numpy as np
class CandidateEventsTS:
def __init__(self, data, current_time, Mc, time_win, space_win=None):
assert time_win > 0, f"Time windows is {time_win}, which should be a positive number"
self.data = data
self.current_time = current_time
self.Mc = Mc
self.time_win = time_win
self.space_win = space_win
def filter_by_time(self):
indx = np.where((self.data[:, 4] > (self.current_time - self.time_win)) & (self.data[:, 4] <= self.current_time))[0]
if len(indx) > 0:
self.data = self.data[indx, :]
else:
self.data = []
def filter_by_magnitude(self):
if self.Mc:
indx = np.where(self.data[:, 5] > self.Mc)[0]
if len(indx) > 0:
self.data = self.data[indx, :]
else:
self.data = []
def filter_by_space(self):
dist = np.sqrt(np.sum((self.data[:, 1:4] - self.data[-1, 1:4]) ** 2, axis=1))
indx = np.where(dist < self.space_win)[0]
if len(indx) > 0:
self.data = self.data[indx, :]
else:
self.data = []
def filter_data(self):
self.filter_by_time()
if len(self.data) > 0:
self.filter_by_magnitude()
if len(self.data) > 0 and self.space_win:
self.filter_by_space()
return self.data

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import numpy as np
def Find_idx4Time(In_mat, t):
# In_mat: time array
# t = target time
In_mat = np.array(In_mat)
t = np.array(t)
if len(np.shape(t)) == 0:
return np.where(abs(In_mat - t) <= min(abs(In_mat - t)))[0][0]
else:
In_mat = In_mat.reshape((len(In_mat), 1))
t = t.reshape((1,len(t)))
target_time = np.matmul(np.ones((len(In_mat),1)), t)
diff_mat = target_time - In_mat
return np.where(abs(diff_mat) <= np.min(abs(diff_mat), axis = 0))

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# -----------------
# Copyright © 2024 ACK Cyfronet AGH, Poland.
# -----------------
import numpy as np
class M_max_models:
def __init__(self, data = None, f_name = None, time_win = None, space_win = None,
Mc = None, b_method = None, num_bootstraps = None,
G = None, Mu = None,
dv = None, Mo = None, SER = None,
cl = None,
ssd = None, C = None,
):
self.data = data # Candidate data table: 2darray n x m, for n events and m clonums: x, y, z, t, mag
self.f_name = f_name # Feature's name to be calculated, check: def ComputeFeaure(self)
self.time_win = time_win # Time window whihc a feature is computed in
self.space_win = space_win # Space window ...
self.Mc = Mc # Magnitude of completeness for computing b-positive
self.b_method = b_method # list of b_methods
self.num_bootstraps = num_bootstraps # Num of bootstraps for standard error estimation of b-value
self.G = G # Shear modulus
self.Mu = Mu # Friction coefficient
self.dv = dv # Injected fluid
self.SER = SER
self.Mo = Mo # Cumulative moment magnitude
self.cl = cl # Confidence level
self.ssd = ssd # Static stress drop (Shapiro et al. 2013)
self.C = C # Geometrical constant (Shapiro et al. 2013)
def b_value(self, b_flag):
if b_flag == '1':
return 1, None
# maximum-likelihood estimate (MLE) of b (Deemer & Votaw 1955; Aki 1965; Kagan 2002):
elif b_flag == 'b':
X = self.data[np.where(self.data[:,-1]>self.Mc)[0],:]
if X.shape[0] > 0:
b = 1/((np.mean(X[:,-1] - self.Mc))*np.log(10))
std_error = b/np.sqrt(X.shape[0])
else:
raise ValueError("All events in the current time window have a magnitude less than 'completeness magnitude'. Use another value either for 'time window', 'minimum number of events' or 'completeness magnitude'.")
return b, std_error
# B-positive (van der Elst 2021)
elif b_flag == 'bp':
# Function to perform bootstrap estimation
def bootstrap_estimate(data, num_bootstraps):
estimates = []
for _ in range(num_bootstraps):
# Generate bootstrap sample
bootstrap_sample = np.random.choice(data, size=len(data), replace=True)
# Perform maximum likelihood estimation on bootstrap sample
diff_mat = np.diff(bootstrap_sample)
diff_mat = diff_mat[np.where(diff_mat>0)[0]]
estimate = 1/((np.mean(diff_mat - np.min(diff_mat)))*np.log(10))
estimates.append(estimate)
return np.array(estimates)
diff_mat = np.diff(self.data[:,-1])
diff_mat = diff_mat[np.where(diff_mat>0)[0]]
bp = 1/((np.mean(diff_mat - np.min(diff_mat)))*np.log(10))
bootstrap_estimates = bootstrap_estimate(diff_mat, self.num_bootstraps)
std_error = np.std(bootstrap_estimates, axis=0)
return bp, std_error
# Tapered Gutenberg_Richter (TGR) distribution (Kagan 2002)
elif b_flag == 'TGR':
from scipy.optimize import minimize
# The logarithm of the likelihood function for the TGR distribution (Kagan 2002)
def log_likelihood(params, data):
beta, Mcm = params
n = len(data)
Mt = np.min(data)
l = n*beta*np.log(Mt)+1/Mcm*(n*Mt-np.sum(data))-beta*np.sum(np.log(data))+np.sum(np.log([(beta/data[i]+1/Mcm) for i in range(len(data))]))
return -l
X = self.data[np.where(self.data[:,-1]>self.Mc)[0],:]
M = 10**(1.5*X[:,-1]+9.1)
initial_guess = [0.5, np.max(M)]
bounds = [(0.0, None), (np.max(M), None)]
# Minimize the negative likelihood function for beta and maximum moment
result = minimize(log_likelihood, initial_guess, args=(M,), bounds=bounds, method='L-BFGS-B',
options={'gtol': 1e-12, 'disp': False})
beta_opt, Mcm_opt = result.x
eta = 1/Mcm_opt
S = M/np.min(M)
dldb2 = -np.sum([1/(beta_opt-eta*S[i])**2 for i in range(len(S))])
dldbde = -np.sum([S[i]/(beta_opt-eta*S[i])**2 for i in range(len(S))])
dlde2 = -np.sum([S[i]**2/(beta_opt-eta*S[i])**2 for i in range(len(S))])
std_error_beta = 1/np.sqrt(dldb2*dlde2-dldbde**2)*np.sqrt(-dlde2)
return beta_opt*1.5, std_error_beta*1.5
def McGarr(self):
b_value, b_stderr = self.b_value(self.b_method)
B = 2/3*b_value
if B < 1:
sigma_m = ((1-B)/B)*(2*self.Mu)*(5*self.G)/3*self.dv
Mmax = (np.log10(sigma_m)-9.1)/1.5
if b_stderr:
Mmax_stderr = b_stderr/np.abs(np.log(10)*(1.5*b_value-b_value**2))
else:
Mmax_stderr = None
else:
Mmax = None
Mmax_stderr = None
return b_value, b_stderr, Mmax, Mmax_stderr
def Hallo(self):
b_value, b_stderr = self.b_value(self.b_method)
B = 2/3*b_value
if b_value < 1.5:
sigma_m = self.SER*((1-B)/B)*(2*self.Mu)*(5*self.G)/3*self.dv
Mmax = (np.log10(sigma_m)-9.1)/1.5
if b_stderr:
Mmax_stderr = self.SER*b_stderr/np.abs(np.log(10)*(1.5*b_value-b_value**2))
else:
Mmax_stderr = None
else:
Mmax = None
Mmax_stderr = None
return b_value, b_stderr, Mmax, Mmax_stderr
def Li(self):
sigma_m = self.SER*2*self.G*self.dv - self.Mo
Mmax = (np.log10(sigma_m)-9.1)/1.5
if Mmax < 0:
return None
else:
return Mmax
def van_der_Elst(self):
b_value, b_stderr = self.b_value(self.b_method)
# Seismogenic_Index
X = self.data
si = np.log10(X.shape[0]) - np.log10(self.dv) + b_value*self.Mc
if b_stderr:
si_stderr = self.Mc*b_stderr
else:
si_stderr = None
Mmax = (si + np.log10(self.dv))/b_value - np.log10(X.shape[0]*(1-self.cl**(1/X.shape[0])))/b_value
if b_stderr:
Mmax_stderr = (np.log10(X.shape[0]) + np.log10(X.shape[0]*(1-self.cl**(1/X.shape[0]))))*b_stderr
else:
Mmax_stderr = None
return b_value, b_stderr, si, si_stderr, Mmax, Mmax_stderr
def L_Shapiro(self):
from scipy.stats import chi2
X = self.data[np.isfinite(self.data[:,1]),1:4]
# Parameters
STD = 2.0 # 2 standard deviations
conf = 2 * chi2.cdf(STD, 2) - 1 # covers around 95% of population
scalee = chi2.ppf(conf, 2) # inverse chi-squared with dof=#dimensions
# Center the data
Mu = np.mean(X, axis=0)
X0 = X - Mu
# Covariance matrix
Cov = np.cov(X0, rowvar=False) * scalee
# Eigen decomposition
D, V = np.linalg.eigh(Cov)
order = np.argsort(D)[::-1]
D = D[order]
V = V[:, order]
# Compute radii
VV = V * np.sqrt(D)
R1 = np.sqrt(VV[0, 0]**2 + VV[1, 0]**2 + VV[2, 0]**2)
R2 = np.sqrt(VV[0, 1]**2 + VV[1, 1]**2 + VV[2, 1]**2)
R3 = np.sqrt(VV[0, 2]**2 + VV[1, 2]**2 + VV[2, 2]**2)
L = (1/3*(1/R1**3+1/R2**3+1/R3**3))**(-1/3)
return R1, R2, R3, L
def Shapiro(self):
R1, R2, R3, L = self.L_Shapiro()
return R1, R2, R3, L, np.log10(R3**2)+(np.log10(self.ssd)-np.log10(self.C)-9.1)/1.5
def All_models(self):
return self.McGarr()[2], self.Hallo()[2], self.Li(), self.van_der_Elst()[-2], self.Shapiro()[-1]
def ComputeModel(self):
if self.f_name == 'max_mcg':
return self.McGarr()
if self.f_name == 'max_hlo':
return self.Hallo()
if self.f_name == 'max_li':
return self.Li()
if self.f_name == 'max_vde':
return self.van_der_Elst()
if self.f_name == 'max_shp':
return self.Shapiro()
if self.f_name == 'max_all':
return self.All_models()