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Add MaxMagnitudeDPModels application code

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Mieszko Makuch 2024-12-08 23:14:50 +09:00
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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
from util.base_logger import getDefaultLogger
def main(Input_catalog, Input_injection_rate, time_win_in_hours, time_step_in_hour, time_win_type,
End_time, ev_limit, Model_index, Mc, Mu, G, ssd, C, b_value_type, cl, mag_name, time_inj=None, time_shut_in=None,
time_big_ev=None, Inpar = ["SER", "dv", "d_num"]):
"""
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 = 0
# Setting up the logfile--------------------------------
logger = getDefaultLogger(__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 ,4 ],
'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
for i in ['Shapiro (Lmax)','Shapiro (Lint)','Shapiro (Lmin)', 'Shapiro (Lavg)']:
Output_dict['Type'].append('Maximum magnitude')
Output_dict['label'].append(i)
Output_dict['b_method'].append(None)
Output_dict['cl'].append(None)
c_out += 4
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',
'brown', 'darkolivegreen', 'teal', 'steelblue', 'slateblue',]
# 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= 'k', ls='--', lw = 2)
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 itr_loc[0]+1 < len(Output_dict_c['Type']) and 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= 'k', ls='--', lw = 2)
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[0]+1 < len(Output_dict_c['Type']) 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= 'k', ls='--', lw = 2)
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= 'k', ls = '--', lw = 2)
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 itr_loc[j]+1 < len(Output_dict_c['Type']) and 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+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!')
elif time_step > 0:
Times = int(np.floor((End_time*24*3600 - Cat[0,4]) / time_step))
else:
Times = 2
time_step = time_win
elif time_step > 0:
Times = int(np.floor((np.max(Cat[:,4]) - Cat[0,4]) / time_step))
else:
Times = 2
time_step = time_win
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 or not enough values to plot. Check 'csv' file.")
# 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)

49
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)
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)

43
util/CandidateEventsTS.py Normal file
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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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util/M_max_models.py Normal file
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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'. Also check 'time window type'.")
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()
Sh_lmax = np.log10((2*R1)**2)+(np.log10(self.ssd)-np.log10(self.C)-9.1)/1.5
Sh_lint = np.log10((2*R2)**2)+(np.log10(self.ssd)-np.log10(self.C)-9.1)/1.5
Sh_lmin = np.log10((2*R3)**2)+(np.log10(self.ssd)-np.log10(self.C)-9.1)/1.5
Sh_lavg = np.log10((2*L)**2)+(np.log10(self.ssd)-np.log10(self.C)-9.1)/1.5
return Sh_lmax, Sh_lint, Sh_lmin, Sh_lavg
# 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()

62
util/base_logger.py Normal file
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#
# -----------------
# Copyright © 2024 ACK Cyfronet AGH, Poland.
# -----------------
#
import os
import logging
def getDefaultLogger(name):
"""
Retrieves or creates a logger with the specified name and sets it up with a file handler.
The logger is configured to write log messages to the file path specified by the
'APP_LOG_FILE' environment variable. If the environment variable is not set,
the logger will write to the file 'base-logger-log.log' in the current
working directory. The logger uses the 'INFO' level as the default logging level
and writes log entries in the following format:
'YYYY-MM-DD HH:MM:SS,ms LEVEL logger_name message'
If the logger does not already have handlers, a file handler is created, and the
logging output is appended to the file. The log format includes the timestamp with
milliseconds, log level, logger name, and the log message.
Parameters:
-----------
name : str
The name of the logger. This can be the name of the module or any identifier
that you want to associate with the logger.
Returns:
--------
logger : logging.Logger
A logger instance with the specified name. The logger is configured with a
file handler that writes to the file specified by the 'APP_LOG_FILE'
environment variable, or to 'base-logger-log.log' if the environment
variable is not set.
Example:
--------
logger = getDefaultLogger(__name__)
logger.info("This is an info message.")
try:
# some code causing exception
except Exception:
logger.exception('An error occurred')
Notes:
------
- The 'APP_LOG_FILE' environment variable should specify the full path to the log file.
- If 'APP_LOG_FILE' is not set, logs will be written to 'base-logger-log.log'.
"""
logger = logging.getLogger(name)
if not logger.hasHandlers():
file_handler = logging.FileHandler(os.environ.get('APP_LOG_FILE', 'base-logger-log.log'), mode='a')
formatter = logging.Formatter('%(asctime)s,%(msecs)d %(levelname)s %(name)s %(message)s')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
return logger