From 292f63c2198f6fe32f9aba9c8d94e388fde2b409 Mon Sep 17 00:00:00 2001
From: EPISODES Platform Official Apps <>
Date: Tue, 25 Mar 2025 12:31:44 +0100
Subject: [PATCH] Initial commit
---
.gitignore | 7 +
README.md | 3 +
src/Mmax.py | 552 ++++++++++++++++++++++++++++++++++++++++++
src/Mmax_plot.py | 206 ++++++++++++++++
src/maxmagnitude_wrapper.py | 49 ++++
src/util/CandidateEventsTS.py | 43 ++++
src/util/Find_idx4Time.py | 14 ++
src/util/M_max_models.py | 217 +++++++++++++++++
src/util/base_logger.py | 62 +++++
9 files changed, 1153 insertions(+)
create mode 100644 .gitignore
create mode 100644 README.md
create mode 100644 src/Mmax.py
create mode 100644 src/Mmax_plot.py
create mode 100644 src/maxmagnitude_wrapper.py
create mode 100644 src/util/CandidateEventsTS.py
create mode 100644 src/util/Find_idx4Time.py
create mode 100644 src/util/M_max_models.py
create mode 100644 src/util/base_logger.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..dfd7964
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+# Intellij
+.idea/
+*.iml
+*.iws
+
+# Mac
+.DS_Store
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..547646e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,3 @@
+MaxMagnitudeDPModels app official repository
+
+Link to remote: https://epos-apps.grid.cyfronet.pl/official-apps/MaxMagnitudeDPModels
\ No newline at end of file
diff --git a/src/Mmax.py b/src/Mmax.py
new file mode 100644
index 0000000..3b747b6
--- /dev/null
+++ b/src/Mmax.py
@@ -0,0 +1,552 @@
+import sys
+import numpy as np
+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_name = scipy.io.whosmat(Input_catalog)[0][0]
+ Cat_structure = mat[Cat_structure_name]
+ 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]:
+ if not b_method:
+ raise ValueError("Please chose an option for b-value")
+
+ 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,:]
+
+ # 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")
+
+ import Mmax_plot # Import locally to ensure Mmax_plot is required only when Plot_flag > 0
+ Mmax_plot.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)
diff --git a/src/Mmax_plot.py b/src/Mmax_plot.py
new file mode 100644
index 0000000..1d30e2c
--- /dev/null
+++ b/src/Mmax_plot.py
@@ -0,0 +1,206 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def Plot_feature(Model_Param_array,
+ Output_dict,
+ End_time=None,
+ time_inj=None,
+ time_shut_in=None,
+ time_big_ev=None,
+ Model_name="",
+ logger=None):
+ """
+ Plotting function extracted from Mmax.py for plotting models and parameters.
+
+ Parameters
+ ----------
+ Model_Param_array : np.ndarray
+ Computed matrix of model parameters as rows in time.
+ Output_dict : dict
+ Dictionary describing each column in Model_Param_array.
+ End_time : float, optional
+ The last time to show in the X-axis (days), if desired.
+ time_inj : float, optional
+ Time of injection start (days), if you want a vertical line.
+ time_shut_in : float, optional
+ Time of shut-in (days), if you want a vertical line.
+ time_big_ev : float, optional
+ Time of large event (days), if you want a vertical line.
+ Model_name : str, optional
+ Model name used for the plot title.
+ logger : logging.Logger, optional
+ Logger for printing info messages. If None, no logging happens.
+ """
+ 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()
diff --git a/src/maxmagnitude_wrapper.py b/src/maxmagnitude_wrapper.py
new file mode 100644
index 0000000..de02925
--- /dev/null
+++ b/src/maxmagnitude_wrapper.py
@@ -0,0 +1,49 @@
+# -*- 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)
\ No newline at end of file
diff --git a/src/util/CandidateEventsTS.py b/src/util/CandidateEventsTS.py
new file mode 100644
index 0000000..cf8dd5e
--- /dev/null
+++ b/src/util/CandidateEventsTS.py
@@ -0,0 +1,43 @@
+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
diff --git a/src/util/Find_idx4Time.py b/src/util/Find_idx4Time.py
new file mode 100644
index 0000000..f6cf838
--- /dev/null
+++ b/src/util/Find_idx4Time.py
@@ -0,0 +1,14 @@
+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))
\ No newline at end of file
diff --git a/src/util/M_max_models.py b/src/util/M_max_models.py
new file mode 100644
index 0000000..c2fccc5
--- /dev/null
+++ b/src/util/M_max_models.py
@@ -0,0 +1,217 @@
+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()
\ No newline at end of file
diff --git a/src/util/base_logger.py b/src/util/base_logger.py
new file mode 100644
index 0000000..188b72f
--- /dev/null
+++ b/src/util/base_logger.py
@@ -0,0 +1,62 @@
+#
+# -----------------
+# 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
\ No newline at end of file