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