1238 lines
57 KiB
Python
1238 lines
57 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Function to run automated picking algorithms using AIC,
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HOS and AR prediction. Uses objects CharFuns and Picker and
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function conglomerate utils.
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import traceback
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from obspy.taup import TauPyModel
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from pylot.core.pick.charfuns import CharacteristicFunction
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from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
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from pylot.core.pick.picker import AICPicker, PragPicker
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from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
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getSNR, fmpicker, checkPonsets, wadaticheck
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from pylot.core.util.utils import getPatternLine, gen_Pool, \
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real_Bool, identifyPhaseID
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def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None, ncores=0, metadata=None, origin=None):
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"""
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:param data: ObsPy stream object containing waveform data of all stations in the event
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:type data: ~obspy.core.stream.Stream
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:param param: PylotParameter object containing parameters used for picking
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:type param: pylot.core.io.inputs.PylotParameter
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:param iplot: logical variable for plotting: 0=none, 1=partial, 2=all
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:type iplot: int, Boolean or String
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:param fig_dict: dictionary containing Matplotlib figures used for plotting picking results during tuning
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:type fig_dict: dict
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:param fig_dict_wadatijack: dictionary containing Matplotlib figures used for plotting jackknife-, wadati- and
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mediantest results
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:type fig_dict_wadatijack: dict
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:param ncores: number of cores used for parallel processing. Default (0) uses all available cores
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:type ncores: int
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:param metadata: tuple containing metadata type string and Parser object read from inventory file
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:type metadata: tuple (str, ~obspy.io.xseed.parser.Parser)
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:param origin: list containing origin objects representing origins for all events
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:type origin: list(~obspy.core.event.origin)
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:return: dictionary containing picked stations and pick information
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:rtype: dictionary
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"""
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stations = []
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all_onsets = {}
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input_tuples = []
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try:
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iplot = int(iplot)
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except ValueError:
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if iplot is True or iplot == 'True':
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iplot = 2
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else:
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iplot = 0
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# get some parameters for quality control from
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# parameter input file (usually pylot.in).
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wdttolerance = param.get('wdttolerance')
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mdttolerance = param.get('mdttolerance')
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jackfactor = param.get('jackfactor')
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apverbose = param.get('apverbose')
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for n in range(len(data)):
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station = data[n].stats.station
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if station not in stations:
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stations.append(station)
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else:
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continue
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for station in stations:
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topick = data.select(station=station)
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input_tuples.append((topick, param, apverbose, iplot, fig_dict, metadata, origin))
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if iplot > 0:
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print('iPlot Flag active: NO MULTIPROCESSING possible.')
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ncores = 1
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# rename ncores for string representation in case ncores == 0 (use all cores)
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ncores_str = ncores if ncores != 0 else 'all available'
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print('Autopickstation: Distribute autopicking for {} '
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'stations on {} cores.'.format(len(input_tuples), ncores_str))
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if ncores == 1:
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results = serial_picking(input_tuples)
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else:
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results = parallel_picking(input_tuples, ncores)
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for result, station in results:
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if type(result) == dict:
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all_onsets[station] = result
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else:
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if result is None:
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result = 'Picker exited unexpectedly.'
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print('Could not pick a station: {}\nReason: {}'.format(station, result))
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# no Wadati/JK for single station (also valid for tuning mode)
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if len(stations) == 1:
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return all_onsets
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# quality control
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# median check and jackknife on P-onset times
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jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, jackfactor, iplot, fig_dict_wadatijack)
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# check S-P times (Wadati)
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wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, iplot, fig_dict_wadatijack)
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return wadationsets
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def serial_picking(input_tuples):
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result = []
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for input_tuple in input_tuples:
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result.append(call_autopickstation(input_tuple))
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return result
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def parallel_picking(input_tuples, ncores):
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pool = gen_Pool(ncores)
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result = pool.imap_unordered(call_autopickstation, input_tuples)
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pool.close()
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return result
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def call_autopickstation(input_tuple):
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"""
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helper function used for multiprocessing
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:param input_tuple: contains all parameters used for autopicking
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:type input_tuple: tuple
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:return: dictionary containing P pick, S pick and station name
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:rtype: dict
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"""
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wfstream, pickparam, verbose, iplot, fig_dict, metadata, origin = input_tuple
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if fig_dict:
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print('Running in interactive mode')
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# multiprocessing not possible with interactive plotting
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try:
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return autopickstation(wfstream, pickparam, verbose, fig_dict=fig_dict, iplot=iplot, metadata=metadata,
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origin=origin)
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except Exception as e:
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tbe = traceback.format_exc()
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return tbe, wfstream[0].stats.station
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def autopickstation(wfstream, pickparam, verbose=False,
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iplot=0, fig_dict=None, metadata=None, origin=None):
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"""
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picks a single station
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:param wfstream: stream object containing waveform of all traces
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:type wfstream: ~obspy.core.stream.Stream
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:param pickparam: container of picking parameters from input file, usually pylot.in
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:type pickparam: pylot.core.io.inputs.PylotParameter
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:param verbose: used to control output to log during picking. True = more information printed
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:type verbose: bool
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:param iplot: logical variable for plotting: 0=none, 1=partial, 2=all
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:type iplot: int, (Boolean or String)
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:param fig_dict: dictionary containing Matplotlib figures used for plotting picking results during tuning
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:type fig_dict: dict
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:param metadata: tuple containing metadata type string and Parser object read from inventory file
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:type metadata: tuple (str, ~obspy.io.xseed.parser.Parser)
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:param origin: list containing origin objects representing origins for all events
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:type origin: list(~obspy.core.event.origin)
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:return: dictionary containing P pick, S pick and station name
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:rtype: dict
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"""
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# declaring pickparam variables (only for convenience)
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# read your pylot.in for details!
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plt_flag = 0
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# special parameters for P picking
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algoP = pickparam.get('algoP')
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pstart = pickparam.get('pstart')
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pstop = pickparam.get('pstop')
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thosmw = pickparam.get('tlta')
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tsnrz = pickparam.get('tsnrz')
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hosorder = pickparam.get('hosorder')
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bpz1 = pickparam.get('bpz1')
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bpz2 = pickparam.get('bpz2')
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pickwinP = pickparam.get('pickwinP')
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aictsmoothP = pickparam.get('aictsmooth')
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tsmoothP = pickparam.get('tsmoothP')
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ausP = pickparam.get('ausP')
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nfacP = pickparam.get('nfacP')
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tpred1z = pickparam.get('tpred1z')
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tdet1z = pickparam.get('tdet1z')
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tpred2z = pickparam.get('tpred2z')
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tdet2z = pickparam.get('tdet2z')
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Parorder = pickparam.get('Parorder')
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addnoise = pickparam.get('addnoise')
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Precalcwin = pickparam.get('Precalcwin')
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minAICPslope = pickparam.get('minAICPslope')
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minAICPSNR = pickparam.get('minAICPSNR')
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timeerrorsP = pickparam.get('timeerrorsP')
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# special parameters for S picking
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algoS = pickparam.get('algoS')
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sstart = pickparam.get('sstart')
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sstop = pickparam.get('sstop')
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use_taup = real_Bool(pickparam.get('use_taup'))
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taup_model = pickparam.get('taup_model')
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bph1 = pickparam.get('bph1')
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bph2 = pickparam.get('bph2')
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tsnrh = pickparam.get('tsnrh')
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pickwinS = pickparam.get('pickwinS')
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tpred1h = pickparam.get('tpred1h')
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tdet1h = pickparam.get('tdet1h')
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tpred2h = pickparam.get('tpred2h')
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tdet2h = pickparam.get('tdet2h')
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Sarorder = pickparam.get('Sarorder')
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aictsmoothS = pickparam.get('aictsmoothS')
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tsmoothS = pickparam.get('tsmoothS')
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ausS = pickparam.get('ausS')
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minAICSslope = pickparam.get('minAICSslope')
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minAICSSNR = pickparam.get('minAICSSNR')
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Srecalcwin = pickparam.get('Srecalcwin')
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nfacS = pickparam.get('nfacS')
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timeerrorsS = pickparam.get('timeerrorsS')
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# parameters for first-motion determination
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minFMSNR = pickparam.get('minFMSNR')
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fmpickwin = pickparam.get('fmpickwin')
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minfmweight = pickparam.get('minfmweight')
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# parameters for checking signal length
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minsiglength = pickparam.get('minsiglength')
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minpercent = pickparam.get('minpercent')
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nfacsl = pickparam.get('noisefactor')
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# parameter to check for spuriously picked S onset
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zfac = pickparam.get('zfac')
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# path to inventory-, dataless- or resp-files
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# initialize output
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Pweight = 4 # weight for P onset
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Sweight = 4 # weight for S onset
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FM = 'N' # first motion (polarity)
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SNRP = None # signal-to-noise ratio of P onset
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SNRPdB = None # signal-to-noise ratio of P onset [dB]
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SNRS = None # signal-to-noise ratio of S onset
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SNRSdB = None # signal-to-noise ratio of S onset [dB]
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mpickP = None # most likely P onset
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lpickP = None # latest possible P onset
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epickP = None # earliest possible P onset
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mpickS = None # most likely S onset
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lpickS = None # latest possible S onset
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epickS = None # earliest possible S onset
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Perror = None # symmetrized picking error P onset
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Serror = None # symmetrized picking error S onset
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aicSflag = 0
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aicPflag = 0
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Pflag = 0
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Sflag = 0
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Pmarker = []
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Ao = None # Wood-Anderson peak-to-peak amplitude
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picker = 'auto' # type of picks
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# split components
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zdat = wfstream.select(component="Z")
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if len(zdat) == 0: # check for other components
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print('HIT: 3')
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zdat = wfstream.select(component="3")
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edat = wfstream.select(component="E")
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if len(edat) == 0: # check for other components
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edat = wfstream.select(component="2")
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print('HIT: 2')
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ndat = wfstream.select(component="N")
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if len(ndat) == 0: # check for other components
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ndat = wfstream.select(component="1")
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print('HIT: 1')
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picks = {}
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station = wfstream[0].stats.station
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if not zdat:
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print('No z-component found for station {}. STOP'.format(station))
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return picks, station
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if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
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msg = '##################################################\nautopickstation:' \
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' Working on P onset of station {station}\nFiltering vertical ' \
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'trace ...\n{data}'.format(station=wfstream[0].stats.station,
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data=str(zdat))
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if verbose: print(msg)
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z_copy = zdat.copy()
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tr_filt = zdat[0].copy()
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# remove constant offset from data to avoid unwanted filter response
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tr_filt.detrend(type='demean')
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# filter and taper data
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tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1],
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zerophase=False)
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tr_filt.taper(max_percentage=0.05, type='hann')
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z_copy[0].data = tr_filt.data
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##############################################################
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# check length of waveform and compare with cut times
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# for global seismology: use tau-p method for estimating travel times (needs source and station coords.)
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# if not given: sets Lc to infinity to use full stream
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if use_taup is True:
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Lc = np.inf
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print('autopickstation: use_taup flag active.')
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if not metadata:
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print('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
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else:
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station_id = wfstream[0].get_id()
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station_coords = metadata.get_coordinates(station_id, time=wfstream[0].stats.starttime)
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if station_coords and origin:
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source_origin = origin[0]
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model = TauPyModel(taup_model)
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arrivals = model.get_travel_times_geo(
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source_origin.depth,
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source_origin.latitude,
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source_origin.longitude,
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station_coords['latitude'],
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station_coords['longitude']
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)
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phases = {'P': [],
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'S': []}
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for arr in arrivals:
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phases[identifyPhaseID(arr.phase.name)].append(arr)
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# get first P and S onsets from arrivals list
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arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
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arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
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print('autopick: estimated first arrivals for P: {} s, S:{} s after event'
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' origin time using TauPy'.format(estFirstP, estFirstS))
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# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
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pstart += (source_origin.time + estFirstP) - zdat[0].stats.starttime
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pstop += (source_origin.time + estFirstP) - zdat[0].stats.starttime
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print('autopick: CF calculation times respectively:'
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' pstart: {} s, pstop: {} s'.format(pstart, pstop))
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elif not origin:
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print('No source origins given!')
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# make sure pstart and pstop are inside zdat[0]
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pstart = max(pstart, 0)
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pstop = min(pstop, len(zdat[0]) * zdat[0].stats.delta)
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if not use_taup is True or origin:
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Lc = pstop - pstart
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Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
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if not Lwf > 0:
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print('autopickstation: empty trace! Return!')
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return picks, station
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Ldiff = Lwf - abs(Lc)
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if Ldiff <= 0 or pstop <= pstart or pstop - pstart <= thosmw:
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msg = 'autopickstation: Cutting times are too large for actual ' \
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'waveform!\nUsing entire waveform instead!'
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if verbose: print(msg)
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pstart = 0
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pstop = len(zdat[0].data) * zdat[0].stats.delta
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cuttimes = [pstart, pstop]
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cf1 = None
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if algoP == 'HOS':
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# calculate HOS-CF using subclass HOScf of class
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# CharacteristicFunction
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cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) # instance of HOScf
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elif algoP == 'ARZ':
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# calculate ARZ-CF using subclass ARZcf of class
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# CharcteristicFunction
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cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z,
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addnoise) # instance of ARZcf
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##############################################################
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# calculate AIC-HOS-CF using subclass AICcf of class
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# CharacteristicFunction
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# class needs stream object => build it
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assert isinstance(cf1, CharacteristicFunction), 'cf2 is not set ' \
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'correctly: maybe the algorithm name ({algoP}) is ' \
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'corrupted'.format(
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algoP=algoP)
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tr_aic = tr_filt.copy()
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tr_aic.data = cf1.getCF()
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z_copy[0].data = tr_aic.data
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aiccf = AICcf(z_copy, cuttimes) # instance of AICcf
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##############################################################
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# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
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# of class AutoPicking
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key = 'aicFig'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig, linecolor=linecolor)
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# add pstart and pstop to aic plot
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if fig:
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for ax in fig.axes:
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ax.vlines(pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
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ax.vlines(pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
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ax.legend(loc=1)
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##############################################################
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if aicpick.getpick() is not None:
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# check signal length to detect spuriously picked noise peaks
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# use all available components to avoid skipping correct picks
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# on vertical traces with weak P coda
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z_copy[0].data = tr_filt.data
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zne = z_copy
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if len(ndat) == 0 or len(edat) == 0:
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msg = 'One or more horizontal component(s) missing!\nSignal ' \
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'length only checked on vertical component!\n' \
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'Decreasing minsiglengh from {0} to ' \
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'{1}'.format(minsiglength, minsiglength / 2)
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if verbose: print(msg)
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key = 'slength'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2,
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nfacsl, minpercent, iplot,
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fig, linecolor)
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else:
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# filter and taper horizontal traces
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trH1_filt = edat.copy()
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trH2_filt = ndat.copy()
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# remove constant offset from data to avoid unwanted filter response
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trH1_filt.detrend(type='demean')
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trH2_filt.detrend(type='demean')
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trH1_filt.filter('bandpass', freqmin=bph1[0],
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freqmax=bph1[1],
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zerophase=False)
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trH2_filt.filter('bandpass', freqmin=bph1[0],
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freqmax=bph1[1],
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zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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zne += trH1_filt
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zne += trH2_filt
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if fig_dict:
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fig = fig_dict['slength']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
|
|
minsiglength,
|
|
nfacsl, minpercent, iplot,
|
|
fig, linecolor)
|
|
|
|
if Pflag == 1:
|
|
# check for spuriously picked S onset
|
|
# both horizontal traces needed
|
|
if len(ndat) == 0 or len(edat) == 0:
|
|
msg = 'One or more horizontal components missing!\n' \
|
|
'Skipping control function checkZ4S.'
|
|
if verbose: print(msg)
|
|
else:
|
|
if iplot > 1:
|
|
if fig_dict:
|
|
fig = fig_dict['checkZ4s']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
|
|
tsnrz[2], iplot, fig, linecolor)
|
|
if Pflag == 0:
|
|
Pmarker = 'SinsteadP'
|
|
Pweight = 9
|
|
else:
|
|
Pmarker = 'shortsignallength'
|
|
Pweight = 9
|
|
##############################################################
|
|
# go on with processing if AIC onset passes quality control
|
|
slope = aicpick.getSlope()
|
|
if not slope:
|
|
slope = 0
|
|
if slope >= minAICPslope and aicpick.getSNR() >= minAICPSNR and Pflag == 1:
|
|
aicPflag = 1
|
|
msg = 'AIC P-pick passes quality control: Slope: {0} counts/s, ' \
|
|
'SNR: {1}\nGo on with refined picking ...\n' \
|
|
'autopickstation: re-filtering vertical trace ' \
|
|
'...'.format(aicpick.getSlope(), aicpick.getSNR())
|
|
if verbose: print(msg)
|
|
# re-filter waveform with larger bandpass
|
|
z_copy = zdat.copy()
|
|
tr_filt = zdat[0].copy()
|
|
tr_filt.detrend(type='demean')
|
|
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
|
|
zerophase=False)
|
|
tr_filt.taper(max_percentage=0.05, type='hann')
|
|
z_copy[0].data = tr_filt.data
|
|
#############################################################
|
|
# re-calculate CF from re-filtered trace in vicinity of initial
|
|
# onset
|
|
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])),
|
|
round(min([len(zdat[0].data) * zdat[0].stats.delta,
|
|
aicpick.getpick() + Precalcwin]))]
|
|
cf2 = None
|
|
if algoP == 'HOS':
|
|
# calculate HOS-CF using subclass HOScf of class
|
|
# CharacteristicFunction
|
|
cf2 = HOScf(z_copy, cuttimes2, thosmw,
|
|
hosorder) # instance of HOScf
|
|
elif algoP == 'ARZ':
|
|
# calculate ARZ-CF using subclass ARZcf of class
|
|
# CharcteristicFunction
|
|
cf2 = ARZcf(z_copy, cuttimes2, tpred2z, Parorder, tdet2z,
|
|
addnoise) # instance of ARZcf
|
|
##############################################################
|
|
# get refined onset time from CF2 using class Picker
|
|
assert isinstance(cf2, CharacteristicFunction), 'cf2 is not set ' \
|
|
'correctly: maybe the algorithm name ({algoP}) is ' \
|
|
'corrupted'.format(
|
|
algoP=algoP)
|
|
if fig_dict:
|
|
fig = fig_dict['refPpick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
|
|
aicpick.getpick(), fig, linecolor)
|
|
mpickP = refPpick.getpick()
|
|
#############################################################
|
|
if mpickP is not None:
|
|
# quality assessment
|
|
# get earliest/latest possible pick and symmetrized uncertainty
|
|
if iplot:
|
|
if fig_dict:
|
|
fig = fig_dict['el_Ppick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
|
|
mpickP, iplot, fig=fig,
|
|
linecolor=linecolor)
|
|
else:
|
|
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
|
|
mpickP, iplot)
|
|
|
|
# get SNR
|
|
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP)
|
|
|
|
# weight P-onset using symmetric error
|
|
if Perror is None:
|
|
Pweight = 4
|
|
else:
|
|
if Perror <= timeerrorsP[0]:
|
|
Pweight = 0
|
|
elif timeerrorsP[0] < Perror <= timeerrorsP[1]:
|
|
Pweight = 1
|
|
elif timeerrorsP[1] < Perror <= timeerrorsP[2]:
|
|
Pweight = 2
|
|
elif timeerrorsP[2] < Perror <= timeerrorsP[3]:
|
|
Pweight = 3
|
|
elif Perror > timeerrorsP[3]:
|
|
Pweight = 4
|
|
|
|
##############################################################
|
|
# get first motion of P onset
|
|
# certain quality required
|
|
if Pweight <= minfmweight and SNRP >= minFMSNR:
|
|
if iplot:
|
|
if fig_dict:
|
|
fig = fig_dict['fm_picker']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig, linecolor)
|
|
else:
|
|
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
|
|
else:
|
|
FM = 'N'
|
|
|
|
msg = "autopickstation: P-weight: {0}, " \
|
|
"SNR: {1}, SNR[dB]: {2}, Polarity: {3}".format(Pweight,
|
|
SNRP,
|
|
SNRPdB,
|
|
FM)
|
|
print(msg)
|
|
msg = 'autopickstation: Refined P-Pick: {} s | P-Error: {} s'.format(zdat[0].stats.starttime \
|
|
+ mpickP, Perror)
|
|
print(msg)
|
|
Sflag = 1
|
|
|
|
else:
|
|
msg = 'Bad initial (AIC) P-pick, skipping this onset!\n' \
|
|
'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
|
|
'(min. AIC-SNR={2}, ' \
|
|
'min. AIC-Slope={3}counts/s)'.format(aicpick.getSNR(),
|
|
aicpick.getSlope(),
|
|
minAICPSNR,
|
|
minAICPslope)
|
|
if verbose: print(msg)
|
|
Sflag = 0
|
|
|
|
else:
|
|
print('autopickstation: No vertical component data available!, '
|
|
'Skipping station!')
|
|
|
|
if ((len(edat) > 0 and len(ndat) == 0) or (len(ndat) > 0 and len(edat) == 0)) and Pweight < 4:
|
|
msg = 'Go on picking S onset ...\n' \
|
|
'##################################################\n' \
|
|
'Only one horizontal component available!\n' \
|
|
'ARH prediction requires at least 2 components!\n' \
|
|
'Copying existing horizontal component ...'
|
|
if verbose: print(msg)
|
|
|
|
# check which component is missing
|
|
if len(edat) == 0:
|
|
edat = ndat
|
|
else:
|
|
ndat = edat
|
|
|
|
pickSonset = (edat is not None and ndat is not None and len(edat) > 0 and len(
|
|
ndat) > 0 and Pweight < 4)
|
|
|
|
if pickSonset:
|
|
# determine time window for calculating CF after P onset
|
|
cuttimesh = [
|
|
round(max([mpickP + sstart, 0])), # MP MP relative time axis
|
|
round(min([
|
|
mpickP + sstop,
|
|
edat[0].stats.endtime - edat[0].stats.starttime,
|
|
ndat[0].stats.endtime - ndat[0].stats.starttime
|
|
]))
|
|
]
|
|
|
|
if not cuttimesh[1] >= cuttimesh[0]:
|
|
print('Cut window for horizontal phases too small! Will not pick S onsets.')
|
|
pickSonset = False
|
|
|
|
if pickSonset:
|
|
msg = 'Go on picking S onset ...\n' \
|
|
'##################################################\n' \
|
|
'Working on S onset of station {0}\nFiltering horizontal ' \
|
|
'traces ...'.format(edat[0].stats.station)
|
|
if verbose: print(msg)
|
|
|
|
if algoS == 'ARH':
|
|
# re-create stream object including both horizontal components
|
|
hdat = edat.copy()
|
|
hdat += ndat
|
|
h_copy = hdat.copy()
|
|
# filter and taper data
|
|
trH1_filt = hdat[0].copy()
|
|
trH2_filt = hdat[1].copy()
|
|
trH1_filt.detrend(type='demean')
|
|
trH2_filt.detrend(type='demean')
|
|
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
|
zerophase=False)
|
|
trH1_filt.taper(max_percentage=0.05, type='hann')
|
|
trH2_filt.taper(max_percentage=0.05, type='hann')
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
elif algoS == 'AR3':
|
|
# re-create stream object including all components
|
|
hdat = zdat.copy()
|
|
hdat += edat
|
|
hdat += ndat
|
|
h_copy = hdat.copy()
|
|
# filter and taper data
|
|
trH1_filt = hdat[0].copy()
|
|
trH2_filt = hdat[1].copy()
|
|
trH3_filt = hdat[2].copy()
|
|
trH1_filt.detrend(type='demean')
|
|
trH2_filt.detrend(type='demean')
|
|
trH3_filt.detrend(type='demean')
|
|
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
|
zerophase=False)
|
|
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
|
zerophase=False)
|
|
trH1_filt.taper(max_percentage=0.05, type='hann')
|
|
trH2_filt.taper(max_percentage=0.05, type='hann')
|
|
trH3_filt.taper(max_percentage=0.05, type='hann')
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
h_copy[2].data = trH3_filt.data
|
|
##############################################################
|
|
if algoS == 'ARH':
|
|
# calculate ARH-CF using subclass ARHcf of class
|
|
# CharcteristicFunction
|
|
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
|
|
addnoise) # instance of ARHcf
|
|
elif algoS == 'AR3':
|
|
# calculate ARH-CF using subclass AR3cf of class
|
|
# CharcteristicFunction
|
|
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
|
|
addnoise) # instance of ARHcf
|
|
##############################################################
|
|
# calculate AIC-ARH-CF using subclass AICcf of class
|
|
# CharacteristicFunction
|
|
# class needs stream object => build it
|
|
tr_arhaic = trH1_filt.copy()
|
|
tr_arhaic.data = arhcf1.getCF()
|
|
h_copy[0].data = tr_arhaic.data
|
|
# calculate ARH-AIC-CF
|
|
haiccf = AICcf(h_copy, cuttimesh) # instance of AICcf
|
|
##############################################################
|
|
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
|
|
# of class AutoPicking
|
|
if fig_dict:
|
|
fig = fig_dict['aicARHfig']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
|
|
aictsmoothS, fig=fig, linecolor=linecolor)
|
|
###############################################################
|
|
# go on with processing if AIC onset passes quality control
|
|
slope = aicarhpick.getSlope()
|
|
if not slope:
|
|
slope = 0
|
|
if (slope >= minAICSslope and
|
|
aicarhpick.getSNR() >= minAICSSNR and aicarhpick.getpick() is not None):
|
|
aicSflag = 1
|
|
msg = 'AIC S-pick passes quality control: Slope: {0} counts/s, ' \
|
|
'SNR: {1}\nGo on with refined picking ...\n' \
|
|
'autopickstation: re-filtering horizontal traces ' \
|
|
'...'.format(aicarhpick.getSlope(), aicarhpick.getSNR())
|
|
if verbose: print(msg)
|
|
# re-calculate CF from re-filtered trace in vicinity of initial
|
|
# onset
|
|
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin),
|
|
round(aicarhpick.getpick() + Srecalcwin)]
|
|
# re-filter waveform with larger bandpass
|
|
h_copy = hdat.copy()
|
|
# filter and taper data
|
|
if algoS == 'ARH':
|
|
trH1_filt = hdat[0].copy()
|
|
trH2_filt = hdat[1].copy()
|
|
trH1_filt.detrend(type='demean')
|
|
trH2_filt.detrend(type='demean')
|
|
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
|
zerophase=False)
|
|
trH1_filt.taper(max_percentage=0.05, type='hann')
|
|
trH2_filt.taper(max_percentage=0.05, type='hann')
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
#############################################################
|
|
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
|
|
addnoise) # instance of ARHcf
|
|
elif algoS == 'AR3':
|
|
trH1_filt = hdat[0].copy()
|
|
trH2_filt = hdat[1].copy()
|
|
trH3_filt = hdat[2].copy()
|
|
trH1_filt.detrend(type='demean')
|
|
trH2_filt.detrend(type='demean')
|
|
trH3_filt.detrend(type='demean')
|
|
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
|
zerophase=False)
|
|
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
|
zerophase=False)
|
|
trH1_filt.taper(max_percentage=0.05, type='hann')
|
|
trH2_filt.taper(max_percentage=0.05, type='hann')
|
|
trH3_filt.taper(max_percentage=0.05, type='hann')
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
h_copy[2].data = trH3_filt.data
|
|
#############################################################
|
|
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
|
|
addnoise) # instance of ARHcf
|
|
|
|
# get refined onset time from CF2 using class Picker
|
|
if fig_dict:
|
|
fig = fig_dict['refSpick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
|
|
tsmoothS, aicarhpick.getpick(), fig, linecolor)
|
|
mpickS = refSpick.getpick()
|
|
#############################################################
|
|
if mpickS is not None:
|
|
# quality assessment
|
|
# get earliest/latest possible pick and symmetrized uncertainty
|
|
h_copy[0].data = trH1_filt.data
|
|
if iplot:
|
|
if fig_dict:
|
|
fig = fig_dict['el_S1pick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
|
|
tsnrh,
|
|
mpickS, iplot,
|
|
fig=fig,
|
|
linecolor=linecolor)
|
|
else:
|
|
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
|
|
tsnrh,
|
|
mpickS, iplot)
|
|
|
|
h_copy[0].data = trH2_filt.data
|
|
if iplot:
|
|
if fig_dict:
|
|
fig = fig_dict['el_S2pick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = ''
|
|
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
|
|
tsnrh,
|
|
mpickS, iplot,
|
|
fig=fig,
|
|
linecolor=linecolor)
|
|
else:
|
|
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
|
|
tsnrh,
|
|
mpickS, iplot)
|
|
if epickS1 is not None and epickS2 is not None:
|
|
if algoS == 'ARH':
|
|
# get earliest pick of both earliest possible picks
|
|
epick = [epickS1, epickS2]
|
|
lpick = [lpickS1, lpickS2]
|
|
pickerr = [Serror1, Serror2]
|
|
if epickS1 is None and epickS2 is not None:
|
|
ipick = 1
|
|
elif epickS1 is not None and epickS2 is None:
|
|
ipick = 0
|
|
elif epickS1 is not None and epickS2 is not None:
|
|
ipick = np.argmin([epickS1, epickS2])
|
|
elif algoS == 'AR3':
|
|
[epickS3, lpickS3, Serror3] = earllatepicker(h_copy,
|
|
nfacS,
|
|
tsnrh,
|
|
mpickS,
|
|
iplot)
|
|
# get earliest pick of all three picks
|
|
epick = [epickS1, epickS2, epickS3]
|
|
lpick = [lpickS1, lpickS2, lpickS3]
|
|
pickerr = [Serror1, Serror2, Serror3]
|
|
if epickS1 is None and epickS2 is not None \
|
|
and epickS3 is not None:
|
|
ipick = np.argmin([epickS2, epickS3])
|
|
elif epickS1 is not None and epickS2 is None \
|
|
and epickS3 is not None:
|
|
ipick = np.argmin([epickS2, epickS3])
|
|
elif epickS1 is not None and epickS2 is not None \
|
|
and epickS3 is None:
|
|
ipick = np.argmin([epickS1, epickS2])
|
|
elif epickS1 is not None and epickS2 is not None \
|
|
and epickS3 is not None:
|
|
ipick = np.argmin([epickS1, epickS2, epickS3])
|
|
|
|
epickS = epick[ipick]
|
|
lpickS = lpick[ipick]
|
|
Serror = pickerr[ipick]
|
|
|
|
msg = 'autopickstation: Refined S-Pick: {} s | S-Error: {} s'.format(hdat[0].stats.starttime \
|
|
+ mpickS, Serror)
|
|
print(msg)
|
|
|
|
# get SNR
|
|
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, mpickS)
|
|
|
|
# weight S-onset using symmetric error
|
|
if Serror <= timeerrorsS[0]:
|
|
Sweight = 0
|
|
elif timeerrorsS[0] < Serror <= timeerrorsS[1]:
|
|
Sweight = 1
|
|
elif timeerrorsS[1] < Serror <= timeerrorsS[2]:
|
|
Sweight = 2
|
|
elif timeerrorsS[2] < Serror <= timeerrorsS[3]:
|
|
Sweight = 3
|
|
elif Serror > timeerrorsS[3]:
|
|
Sweight = 4
|
|
|
|
print('autopickstation: S-weight: {0}, SNR: {1}, '
|
|
'SNR[dB]: {2}\n'
|
|
'##################################################'
|
|
''.format(Sweight, SNRS, SNRSdB))
|
|
################################################################
|
|
# get Wood-Anderson peak-to-peak amplitude
|
|
# initialize Data object
|
|
# re-create stream object including both horizontal components
|
|
hdat = edat.copy()
|
|
hdat += ndat
|
|
else:
|
|
msg = 'Bad initial (AIC) S-pick, skipping this onset!\n' \
|
|
'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
|
|
'(min. AIC-SNR={2}, ' \
|
|
'min. AIC-Slope={3}counts/s)\n' \
|
|
'##################################################' \
|
|
''.format(aicarhpick.getSNR(),
|
|
aicarhpick.getSlope(),
|
|
minAICSSNR,
|
|
minAICSslope)
|
|
if verbose: print(msg)
|
|
|
|
############################################################
|
|
# get Wood-Anderson peak-to-peak amplitude
|
|
# initialize Data object
|
|
# re-create stream object including both horizontal components
|
|
hdat = edat.copy()
|
|
hdat += ndat
|
|
|
|
else:
|
|
print('autopickstation: No horizontal component data available or '
|
|
'bad P onset, skipping S picking!')
|
|
|
|
##############################################################
|
|
try:
|
|
iplot = int(iplot)
|
|
except ValueError:
|
|
if iplot is True or iplot == 'True':
|
|
iplot = 2
|
|
else:
|
|
iplot = 0
|
|
|
|
if iplot > 0:
|
|
# plot vertical trace
|
|
if fig_dict is None or fig_dict == 'None':
|
|
fig = plt.figure()
|
|
plt_flag = 1
|
|
linecolor = 'k'
|
|
else:
|
|
fig = fig_dict['mainFig']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
fig._tight = True
|
|
ax1 = fig.add_subplot(311)
|
|
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
|
|
tr_filt.stats.delta)
|
|
# check equal length of arrays, sometimes they are different!?
|
|
wfldiff = len(tr_filt.data) - len(tdata)
|
|
if wfldiff < 0:
|
|
tdata = tdata[0:len(tdata) - abs(wfldiff)]
|
|
ax1.plot(tdata, tr_filt.data / max(tr_filt.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if Pweight < 4:
|
|
ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
|
|
'b', label='CF1')
|
|
if aicPflag == 1:
|
|
ax1.plot(cf2.getTimeArray(),
|
|
cf2.getCF() / max(cf2.getCF()), 'm', label='CF2')
|
|
ax1.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
|
|
'r', label='Initial P Onset')
|
|
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
|
[1, 1], 'r')
|
|
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
|
[-1, -1], 'r')
|
|
ax1.plot([refPpick.getpick(), refPpick.getpick()],
|
|
[-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
|
|
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
|
[1.3, 1.3], 'r', linewidth=2)
|
|
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
|
[-1.3, -1.3], 'r', linewidth=2)
|
|
ax1.plot([lpickP, lpickP], [-1.1, 1.1], 'r--', label='lpp')
|
|
ax1.plot([epickP, epickP], [-1.1, 1.1], 'r--', label='epp')
|
|
ax1.set_title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
|
|
'Polarity: %s' % (tr_filt.stats.station,
|
|
tr_filt.stats.channel,
|
|
Pweight,
|
|
SNRP,
|
|
SNRPdB,
|
|
FM))
|
|
else:
|
|
ax1.set_title('%s, P Weight=%d, SNR=None, '
|
|
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
|
else:
|
|
ax1.set_title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
|
|
tr_filt.stats.channel,
|
|
Pweight))
|
|
ax1.legend(loc=1)
|
|
ax1.set_yticks([])
|
|
ax1.set_ylim([-1.5, 1.5])
|
|
ax1.set_ylabel('Normalized Counts')
|
|
# fig.suptitle(tr_filt.stats.starttime)
|
|
# only continue if one horizontal stream exists
|
|
if (ndat or edat) and Sflag == 1:
|
|
# mirror components in case one does not exist
|
|
if not edat:
|
|
edat = ndat
|
|
if not ndat:
|
|
ndat = edat
|
|
if len(edat[0]) > 1 and len(ndat[0]) > 1:
|
|
# plot horizontal traces
|
|
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
|
|
th1data = np.arange(0,
|
|
trH1_filt.stats.npts /
|
|
trH1_filt.stats.sampling_rate,
|
|
trH1_filt.stats.delta)
|
|
# check equal length of arrays, sometimes they are different!?
|
|
wfldiff = len(trH1_filt.data) - len(th1data)
|
|
if wfldiff < 0:
|
|
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
|
ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if Pweight < 4:
|
|
ax2.plot(arhcf1.getTimeArray(),
|
|
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
|
if aicSflag == 1 and Sweight < 4:
|
|
ax2.plot(arhcf2.getTimeArray(),
|
|
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
|
ax2.plot(
|
|
[aicarhpick.getpick(), aicarhpick.getpick()],
|
|
[-1, 1], 'g', label='Initial S Onset')
|
|
ax2.plot(
|
|
[aicarhpick.getpick() - 0.5,
|
|
aicarhpick.getpick() + 0.5],
|
|
[1, 1], 'g')
|
|
ax2.plot(
|
|
[aicarhpick.getpick() - 0.5,
|
|
aicarhpick.getpick() + 0.5],
|
|
[-1, -1], 'g')
|
|
ax2.plot([refSpick.getpick(), refSpick.getpick()],
|
|
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
|
ax2.plot(
|
|
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
|
[1.3, 1.3], 'g', linewidth=2)
|
|
ax2.plot(
|
|
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
|
[-1.3, -1.3], 'g', linewidth=2)
|
|
ax2.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
|
ax2.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
|
ax2.set_title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
|
|
trH1_filt.stats.channel,
|
|
Sweight, SNRS, SNRSdB))
|
|
else:
|
|
ax2.set_title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
|
|
trH1_filt.stats.channel, Sweight))
|
|
ax2.legend(loc=1)
|
|
ax2.set_yticks([])
|
|
ax2.set_ylim([-1.5, 1.5])
|
|
ax2.set_ylabel('Normalized Counts')
|
|
# fig.suptitle(trH1_filt.stats.starttime)
|
|
|
|
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
|
|
th2data = np.arange(0,
|
|
trH2_filt.stats.npts /
|
|
trH2_filt.stats.sampling_rate,
|
|
trH2_filt.stats.delta)
|
|
# check equal length of arrays, sometimes they are different!?
|
|
wfldiff = len(trH2_filt.data) - len(th2data)
|
|
if wfldiff < 0:
|
|
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
|
ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if Pweight < 4:
|
|
p22, = ax3.plot(arhcf1.getTimeArray(),
|
|
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
|
if aicSflag == 1:
|
|
ax3.plot(arhcf2.getTimeArray(),
|
|
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
|
ax3.plot(
|
|
[aicarhpick.getpick(), aicarhpick.getpick()],
|
|
[-1, 1], 'g', label='Initial S Onset')
|
|
ax3.plot(
|
|
[aicarhpick.getpick() - 0.5,
|
|
aicarhpick.getpick() + 0.5],
|
|
[1, 1], 'g')
|
|
ax3.plot(
|
|
[aicarhpick.getpick() - 0.5,
|
|
aicarhpick.getpick() + 0.5],
|
|
[-1, -1], 'g')
|
|
ax3.plot([refSpick.getpick(), refSpick.getpick()],
|
|
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
|
ax3.plot(
|
|
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
|
[1.3, 1.3], 'g', linewidth=2)
|
|
ax3.plot(
|
|
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
|
[-1.3, -1.3], 'g', linewidth=2)
|
|
ax3.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
|
ax3.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
|
ax3.legend(loc=1)
|
|
ax3.set_yticks([])
|
|
ax3.set_ylim([-1.5, 1.5])
|
|
ax3.set_xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
|
ax3.set_ylabel('Normalized Counts')
|
|
ax3.set_title(trH2_filt.stats.channel)
|
|
if plt_flag == 1:
|
|
fig.show()
|
|
try:
|
|
input()
|
|
except SyntaxError:
|
|
pass
|
|
plt.close(fig)
|
|
##########################################################################
|
|
# calculate "real" onset times
|
|
if lpickP is not None and lpickP == mpickP:
|
|
lpickP += zdat[0].stats.delta
|
|
if epickP is not None and epickP == mpickP:
|
|
epickP -= zdat[0].stats.delta
|
|
if mpickP is not None and epickP is not None and lpickP is not None:
|
|
lpickP = zdat[0].stats.starttime + lpickP
|
|
epickP = zdat[0].stats.starttime + epickP
|
|
mpickP = zdat[0].stats.starttime + mpickP
|
|
else:
|
|
# dummy values (start of seismic trace) in order to derive
|
|
# theoretical onset times for iteratve picking
|
|
lpickP = zdat[0].stats.starttime + timeerrorsP[3]
|
|
epickP = zdat[0].stats.starttime - timeerrorsP[3]
|
|
mpickP = zdat[0].stats.starttime
|
|
|
|
# create dictionary
|
|
# for P phase
|
|
ccode = zdat[0].stats.channel
|
|
ncode = zdat[0].stats.network
|
|
ppick = dict(channel=ccode, network=ncode, lpp=lpickP, epp=epickP, mpp=mpickP, spe=Perror, snr=SNRP,
|
|
snrdb=SNRPdB, weight=Pweight, fm=FM, w0=None, fc=None, Mo=None,
|
|
Mw=None, picker=picker, marked=Pmarker)
|
|
|
|
if edat:
|
|
hdat = edat[0]
|
|
elif ndat:
|
|
hdat = ndat[0]
|
|
else:
|
|
# no horizontal components given
|
|
picks = dict(P=ppick)
|
|
return picks, station
|
|
|
|
if lpickS is not None and lpickS == mpickS:
|
|
lpickS += hdat.stats.delta
|
|
if epickS is not None and epickS == mpickS:
|
|
epickS -= hdat.stats.delta
|
|
if mpickS is not None and epickS is not None and lpickS is not None:
|
|
lpickS = hdat.stats.starttime + lpickS
|
|
epickS = hdat.stats.starttime + epickS
|
|
mpickS = hdat.stats.starttime + mpickS
|
|
else:
|
|
# dummy values (start of seismic trace) in order to derive
|
|
# theoretical onset times for iteratve picking
|
|
lpickS = hdat.stats.starttime + timeerrorsS[3]
|
|
epickS = hdat.stats.starttime - timeerrorsS[3]
|
|
mpickS = hdat.stats.starttime
|
|
|
|
# add S phase
|
|
ccode = hdat.stats.channel
|
|
ncode = hdat.stats.network
|
|
spick = dict(channel=ccode, network=ncode, lpp=lpickS, epp=epickS, mpp=mpickS, spe=Serror, snr=SNRS,
|
|
snrdb=SNRSdB, weight=Sweight, fm=None, picker=picker, Ao=Ao)
|
|
# merge picks into returning dictionary
|
|
picks = dict(P=ppick, S=spick)
|
|
return picks, station
|
|
|
|
|
|
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter, fig_dict=None):
|
|
"""
|
|
Repicking of bad onsets. Uses theoretical onset times from NLLoc-location file.
|
|
:param wf: waveform, obspy stream object
|
|
:type wf: ~obspy.core.stream.Stream
|
|
:param NLLocfile: path/name of NLLoc-location file
|
|
:type NLLocfile: str
|
|
:param picks: dictionary of available onset times
|
|
:type picks: dict
|
|
:param badpicks: picks to be repicked
|
|
:type badpicks:
|
|
:param pickparameter: picking parameters from autoPyLoT-input file
|
|
:type pickparameter: pylot.core.io.inputs.PylotParameter
|
|
:param fig_dict: dictionary containing Matplotlib figures used for plotting results
|
|
:type fig_dict: dict
|
|
:return: dictionary containing iterative picks
|
|
:rtype: dict
|
|
"""
|
|
|
|
msg = '##################################################\n' \
|
|
'autoPyLoT: Found {0} bad onsets at station(s) {1}, ' \
|
|
'starting re-picking them ...'.format(len(badpicks), badpicks)
|
|
print(msg)
|
|
|
|
newpicks = {}
|
|
for i in range(0, len(badpicks)):
|
|
if len(badpicks[i][0]) > 4:
|
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
|
elif len(badpicks[i][0]) == 4:
|
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
|
elif len(badpicks[i][0]) < 4:
|
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
|
nllocline = getPatternLine(NLLocfile, Ppattern)
|
|
res = nllocline.split(None)[16]
|
|
# get theoretical P-onset time from residuum
|
|
badpicks[i][1] = picks[badpicks[i][0]]['P']['mpp'] - float(res)
|
|
|
|
# get corresponding waveform stream
|
|
msg = '##################################################\n' \
|
|
'iteratepicker: Re-picking station {0}'.format(badpicks[i][0])
|
|
print(msg)
|
|
wf2pick = wf.select(station=badpicks[i][0])
|
|
|
|
# modify some picking parameters
|
|
pstart_old = pickparameter.get('pstart')
|
|
pstop_old = pickparameter.get('pstop')
|
|
sstop_old = pickparameter.get('sstop')
|
|
pickwinP_old = pickparameter.get('pickwinP')
|
|
Precalcwin_old = pickparameter.get('Precalcwin')
|
|
noisefactor_old = pickparameter.get('noisefactor')
|
|
zfac_old = pickparameter.get('zfac')
|
|
twindows = pickparameter.get('tsnrz')
|
|
tsafety = twindows[1]
|
|
pstart = max([0, badpicks[i][1] - wf2pick[0].stats.starttime - pickparameter.get('tlta')])
|
|
if abs(float(res)) <= tsafety / 2 or pstart == 0:
|
|
print("iteratepicker: Small residuum, leave parameters unchanged for this phase!")
|
|
else:
|
|
pickparameter.setParam(pstart=pstart)
|
|
pickparameter.setParam(pstop=pickparameter.get('pstart') + (pickparameter.get('Precalcwin')))
|
|
pickparameter.setParam(sstop=pickparameter.get('sstop') / 2)
|
|
pickparameter.setParam(pickwinP=pickparameter.get('pickwinP') / 2)
|
|
pickparameter.setParam(Precalcwin=pickparameter.get('Precalcwin') / 2)
|
|
pickparameter.setParam(noisefactor=1.0)
|
|
pickparameter.setParam(zfac=1.0)
|
|
|
|
print(
|
|
"iteratepicker: The following picking parameters have been modified for iterative picking:")
|
|
print(
|
|
"pstart: %fs => %fs" % (pstart_old, pickparameter.get('pstart')))
|
|
print(
|
|
"pstop: %fs => %fs" % (pstop_old, pickparameter.get('pstop')))
|
|
print(
|
|
"sstop: %fs => %fs" % (sstop_old, pickparameter.get('sstop')))
|
|
print("pickwinP: %fs => %fs" % (
|
|
pickwinP_old, pickparameter.get('pickwinP')))
|
|
print("Precalcwin: %fs => %fs" % (
|
|
Precalcwin_old, pickparameter.get('Precalcwin')))
|
|
print("noisefactor: %f => %f" % (
|
|
noisefactor_old, pickparameter.get('noisefactor')))
|
|
print("zfac: %f => %f" % (zfac_old, pickparameter.get('zfac')))
|
|
|
|
# repick station
|
|
newpicks, _ = autopickstation(wf2pick, pickparameter, fig_dict=fig_dict)
|
|
|
|
# replace old dictionary with new one
|
|
picks[badpicks[i][0]] = newpicks
|
|
|
|
# reset temporary change of picking parameters
|
|
print("iteratepicker: Resetting picking parameters ...")
|
|
pickparameter.setParam(pstart=pstart_old)
|
|
pickparameter.setParam(pstop=pstop_old)
|
|
pickparameter.setParam(sstop=sstop_old)
|
|
pickparameter.setParam(pickwinP=pickwinP_old)
|
|
pickparameter.setParam(Precalcwin=Precalcwin_old)
|
|
pickparameter.setParam(noisefactor=noisefactor_old)
|
|
pickparameter.setParam(zfac=zfac_old)
|
|
|
|
return picks
|