From 3ef8d2c35044c64a7964076d4f3194526193fb7f Mon Sep 17 00:00:00 2001 From: Mieszko Makuch Date: Fri, 28 Mar 2025 12:52:11 +0100 Subject: [PATCH] Clear src directory --- 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 ----- 7 files changed, 1143 deletions(-) delete mode 100644 src/Mmax.py delete mode 100644 src/Mmax_plot.py delete mode 100644 src/maxmagnitude_wrapper.py delete mode 100644 src/util/CandidateEventsTS.py delete mode 100644 src/util/Find_idx4Time.py delete mode 100644 src/util/M_max_models.py delete mode 100644 src/util/base_logger.py diff --git a/src/Mmax.py b/src/Mmax.py deleted file mode 100644 index 3b747b6..0000000 --- a/src/Mmax.py +++ /dev/null @@ -1,552 +0,0 @@ -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 deleted file mode 100644 index 1d30e2c..0000000 --- a/src/Mmax_plot.py +++ /dev/null @@ -1,206 +0,0 @@ -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 deleted file mode 100644 index de02925..0000000 --- a/src/maxmagnitude_wrapper.py +++ /dev/null @@ -1,49 +0,0 @@ -# -*- 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 deleted file mode 100644 index cf8dd5e..0000000 --- a/src/util/CandidateEventsTS.py +++ /dev/null @@ -1,43 +0,0 @@ -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 deleted file mode 100644 index f6cf838..0000000 --- a/src/util/Find_idx4Time.py +++ /dev/null @@ -1,14 +0,0 @@ -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 deleted file mode 100644 index c2fccc5..0000000 --- a/src/util/M_max_models.py +++ /dev/null @@ -1,217 +0,0 @@ -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 deleted file mode 100644 index 188b72f..0000000 --- a/src/util/base_logger.py +++ /dev/null @@ -1,62 +0,0 @@ -# -# ----------------- -# 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