2047 lines
103 KiB
Python
2047 lines
103 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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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, get_pickparams, get_quality_class
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from pylot.core.util.utils import getPatternLine, gen_Pool,\
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real_Bool, identifyPhaseID, real_None, correct_iplot
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from obspy.taup import TauPyModel
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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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if iplot is None or iplot == 'None' or iplot == 0:
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input_tuples.append((topick, param, apverbose, metadata, origin))
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if iplot > 0:
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all_onsets[station] = autopickstation(topick, param, verbose=apverbose,
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iplot=iplot, fig_dict=fig_dict,
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metadata=metadata, origin=origin)
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if iplot > 0:
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print('iPlot Flag active: NO MULTIPROCESSING possible.')
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return all_onsets
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# rename str for ncores 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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pool = gen_Pool(ncores)
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result = pool.map(call_autopickstation, input_tuples)
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pool.close()
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for pick in result:
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if pick:
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station = pick['station']
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pick.pop('station')
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all_onsets[station] = pick
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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, 1, fig_dict_wadatijack)
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# check S-P times (Wadati)
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wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, 1, fig_dict_wadatijack)
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return wadationsets
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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, metadata, origin = input_tuple
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# multiprocessing not possible with interactive plotting
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return autopickstation(wfstream, pickparam, verbose, iplot=0, metadata=metadata, origin=origin)
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def get_source_coords(parser, station_id):
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"""
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retrieves station coordinates from metadata
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:param parser: Parser object containing metadata read from inventory file
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:type parser: ~obspy.io.xseed.parser.Parser
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:param station_id: station id of which the coordinates should be retrieved, for example 'BW.RJOB..EHZ'. Only
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network and station name is required, channel id (last part) is ignored.
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:type station_id: str
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:return: dictionary containing 'latitude', 'longitude', 'elevation' and 'local_depth' of station
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:rtype: dict
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"""
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station_coords = None
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try:
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station_coords = parser.get_coordinates(station_id)
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except Exception as e:
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print('Could not get source coordinates for station {}: {}'.format(station_id, e))
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return station_coords
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class PickingParameters(object):
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"""
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Stores parameters used for picking a single station.
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@DynamicAttrs (mark class so that PyCharm doesnt warn when accessing dynamically added attributes)
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"""
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def __init__(self, *args, **kwargs):
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"""
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Add dictionaries given as positional arguments and the keyword argument dictionary to the instance
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as attributes. Positional arguments with types differing from dict are ignored.
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"""
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# add entries from dictionaries given as positional arguments
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for arg in args:
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if type(arg) == dict:
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self.add_params_from_dict(arg)
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# add values given as keyword arguments
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self.add_params_from_dict(kwargs)
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def add_params_from_dict(self, d):
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"""
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Add all key-value pairs from dictionary d to the class namespace as attributes.
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:param d:
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:type d: dict
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:rtype: None
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"""
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for key, value in d.items():
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setattr(self, key, value)
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class PickingResults(dict):
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def __init__(self):
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# initialize output
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self.Pweight = 4 # weight for P onset
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self.Sweight = 4 # weight for S onset
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self.FM = 'N' # first motion (polarity)
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self.SNRP = None # signal-to-noise ratio of P onset
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self.SNRPdB = None # signal-to-noise ratio of P onset [dB]
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self.SNRS = None # signal-to-noise ratio of S onset
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self.SNRSdB = None # signal-to-noise ratio of S onset [dB]
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self.mpickP = None # most likely P onset
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self.lpickP = None # latest possible P onset
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self.epickP = None # earliest possible P onset
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self.mpickS = None # most likely S onset
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self.lpickS = None # latest possible S onset
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self.epickS = None # earliest possible S onset
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self.Perror = None # symmetrized picking error P onset
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self.Serror = None # symmetrized picking error S onset
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self.Pmarker = []
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self.Ao = None # Wood-Anderson peak-to-peak amplitude
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self.picker = 'auto' # type of picks
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# these get added during the construction of the p pick results dictionary
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self.w0 = None
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self.fc = None
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self.Mw = None
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# flags for plotting
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self.aicPflag = 0
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self.aicSflag = 0
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self.Pflag = 0
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self.Sflag = 0
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def __setattr__(self, key, value):
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self[key] = value
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def __getattr__(self, key):
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return self[key]
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class MissingTraceException(ValueError):
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"""
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Used to indicate missing traces in a obspy.core.stream.Stream object
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"""
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pass
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class PickingFailedException(Exception):
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"""
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Raised when picking fails due to missing values etc.
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"""
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pass
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class AutopickStation(object):
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def __init__(self, wfstream, pickparam, verbose, iplot, fig_dict, metadata, origin):
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# save given parameters
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self.wfstream = wfstream
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self.pickparam = pickparam
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self.verbose = verbose
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self.iplot = correct_iplot(iplot)
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self.fig_dict = real_None(fig_dict)
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self.metadata = metadata
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self.origin = origin
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# extract additional information
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pickparams = self.extract_pickparams(pickparam)
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self.p_params, self.s_params, self.first_motion_params, self.signal_length_params = pickparams
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self.results = PickingResults()
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self.channelorder = {'Z': 3, 'N': 1, 'E': 2} # TODO get this from the pylot preferences
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self.station_name = wfstream[0].stats.station
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self.network_name = wfstream[0].stats.network
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self.station_id = '{}.{}'.format(self.network_name, self.station_name)
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# save streams and traces
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# TODO: error handling of missing traces
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self.zstream, self.nstream, self.estream = self.get_components_from_waveformstream()
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self.ztrace = self.zstream[0]
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try:
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self.ntrace = self.nstream[0]
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except IndexError:
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self.nstream = self.estream
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self.ntrace = self.nstream[0]
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try:
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self.etrace = self.estream[0]
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except IndexError:
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self.estream = self.nstream
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self.etrace = self.estream[0]
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# default values used in old autopickstation function #TODO way for user to set those
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self.detrend_type = 'demean'
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self.filter_type = 'bandpass'
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self.zerophase = False
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self.taper_max_percentage = 0.05
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self.taper_type = 'hann'
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# initialize picking results
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self.p_results = PickingResults()
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self.s_results = PickingResults()
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def vprint(self, s):
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"""Only print statement if verbose picking is set to true."""
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if self.verbose:
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print(s)
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def extract_pickparams(self, pickparam):
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"""
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Get parameter names out of pickparam dictionary into PickingParameters objects and return them.
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:return: PickingParameters objects containing 1. p pick parameters, 2. s pick parameters, 3. first motion determinatiion
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parameters, 4. signal length parameters
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:rtype: (PickingParameters, PickingParameters, PickingParameters, PickingParameters)
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"""
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# Define names of all parameters in different groups
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p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP'.split(
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' ')
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s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac'.split(
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' ')
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first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
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signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
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# Get list of values from pickparam by name
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p_parameter_values = map(pickparam.get, p_parameter_names)
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s_parameter_values = map(pickparam.get, s_parameter_names)
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fm_parameter_values = map(pickparam.get, first_motion_names)
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sl_parameter_values = map(pickparam.get, signal_length_names)
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# construct dicts from names and values
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p_params = dict(zip(p_parameter_names, p_parameter_values))
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s_params = dict(zip(s_parameter_names, s_parameter_values))
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first_motion_params = dict(zip(first_motion_names, fm_parameter_values))
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signal_length_params = dict(zip(signal_length_names, sl_parameter_values))
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p_params['use_taup'] = real_Bool(p_params['use_taup'])
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return PickingParameters(p_params), PickingParameters(s_params), PickingParameters(first_motion_params), PickingParameters(signal_length_params)
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def get_components_from_waveformstream(self):
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"""
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Splits waveformstream into multiple components zdat, ndat, edat. For traditional orientation (ZNE) these contain
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the vertical, north-south or east-west component. Otherwise they contain components numbered 123 with
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orientation diverging from the traditional orientation.
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:param waveformstream: Stream containing all three components for one station either by ZNE or 123 channel code
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(mixture of both options is handled as well)
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:type waveformstream: obspy.core.stream.Stream
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:return: Tuple containing (z waveform, n waveform, e waveform) selected by the given channels
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:rtype: (obspy.core.stream.Stream, obspy.core.stream.Stream, obspy.core.stream.Stream)
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"""
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waveform_data = {}
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for key in self.channelorder:
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waveform_data[key] = self.wfstream.select(component=key) # try ZNE first
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if len(waveform_data[key]) == 0:
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waveform_data[key] = self.wfstream.select(component=str(self.channelorder[key])) # use 123 as second option
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return waveform_data['Z'], waveform_data['N'], waveform_data['E']
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def prepare_wfstream(self, wfstream, freqmin=None, freqmax=None):
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"""
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Prepare a waveformstream for picking by applying detrending, filtering and tapering. Creates a copy of the
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waveform the leave the original unchanged.
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:param wfstream: waveform stream
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:type wfstream: obspy.core.stream.Stream
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:param freqmin: Lower frequency of bandpass or highpass
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:type freqmin: float
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:param freqmax: Upper frequency of bandpass or lowpass
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:type freqmax: float
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:return: Tuple containing the changed waveform stream and the changed first trace of the stream
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:rtype: (obspy.core.trace.Trace, obspy.core.stream.Stream)
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"""
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wfstream_copy = wfstream.copy()
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trace_copy = wfstream[0].copy()
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trace_copy.detrend(type=self.detrend_type)
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trace_copy.filter(self.filter_type, freqmin=freqmin, freqmax=freqmax, zerophase=self.zerophase)
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trace_copy.taper(max_percentage=self.taper_max_percentage, type=self.taper_type)
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wfstream_copy[0].data = trace_copy.data
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return trace_copy, wfstream_copy
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def modify_starttimes_taupy(self):
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"""
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Calculate theoretical arrival times for a source at self.origin and a station at self.metadata. Modify
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self.pstart and self.pstop so they are based on a time window around these theoretical arrival times.
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:rtype: None
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"""
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def create_arrivals(metadata, origin, station_id, taup_model):
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"""
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Create List of arrival times for all phases for a given origin and station
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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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:param station_id: Station id with format NETWORKNAME.STATIONNAME
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:type station_id: str
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:param taup_model: Model name to use. See obspy.taup.tau.TauPyModel for options
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:type taup_model: str
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:return: List of Arrival objects
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:rtype: obspy.taup.tau.Arrivals
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:raises:
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AttributeError when no metadata or source origins is given
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"""
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parser = metadata[1]
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station_coords = get_source_coords(parser, station_id)
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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(source_depth_in_km=source_origin.depth,
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source_latitude_in_deg=source_origin.latitude,
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source_longitude_in_deg=source_origin.longitude,
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receiver_latitude_in_deg=station_coords['latitude'],
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receiver_longitude_in_deg=station_coords['longitude'])
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return arrivals
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def first_PS_onsets(arrivals):
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"""
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Get first P and S onsets from arrivals list
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:param arrivals: List of Arrival objects
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:type arrivals: obspy.taup.tau.Arrivals
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:return:
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:rtype:
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"""
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phases = {'P': [], 'S': []}
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# sort phases in P and S phases
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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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return estFirstP, estFirstS
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if self.p_params.use_taup is False and self.p_params.pstart < 0:
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# correct user mistake where a relative cuttime is selected (pstart < 0) but use of taupy is disabled/ has
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# not the required parameters
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self.p_params.pstart = 0
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return
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print('autopickstation: use_taup flag active.')
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# catch missing metadata or origin information. Onset calculation is stopped, given cuttimes are then used.
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if not self.metadata[1]:
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raise AttributeError('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
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if not self.origin:
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raise AttributeError('No source origins given!')
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arrivals = create_arrivals(self.metadata, self.origin, self.station_id, self.p_params.taup_model)
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estFirstP, estFirstS = first_PS_onsets(arrivals)
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# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
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self.p_params.pstart += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
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self.p_params.pstop += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
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print('autopick: CF calculation times respectively:'
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' pstart: {} s, pstop: {} s'.format(self.p_params.pstart, self.p_params.pstop))
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# make sure pstart and pstop are inside the starttime/endtime of vertical trace
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self.p_params.pstart = max(self.p_params.pstart, 0)
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self.p_params.pstop = min(self.p_params.pstop, len(self.ztrace) * self.ztrace.stats.delta)
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def autopickstation(self):
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try:
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self.pick_p_phase()
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#TODO handle exceptions correctly (goal is to be compatible with old code first)
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# requires an overlook of what should be returned in case picking fails at various stages
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except MissingTraceException as mte:
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print(mte)
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except PickingFailedException as pfe:
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print(pfe)
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if self.estream is not None and self.nstream is not None and len(self.estream) > 0 and len(self.nstream) > 0 and self.p_results.Pweight is not None and self.p_results.Pweight < 4:
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try:
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self.pick_s_phase()
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except MissingTraceException as mte:
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print(mte)
|
|
except PickingFailedException as pfe:
|
|
print(pfe)
|
|
|
|
self.finish_picking()
|
|
self.plot_pick_results()
|
|
return {'P': self.p_results, 'S':self.s_results, 'station':self.ztrace.stats.station} #TODO method to format picking results as a dict correctly
|
|
|
|
def finish_picking(self):
|
|
|
|
# calculate "real" onset times, save them in PickingResults
|
|
if self.p_results.lpickP is not None and self.p_results.lpickP == self.p_results.mpickP:
|
|
self.p_results.lpickP += self.ztrace.stats.delta
|
|
if self.p_results.epickP is not None and self.p_results.epickP == self.p_results.mpickP:
|
|
self.p_results.epickP -= self.ztrace.stats.delta
|
|
if self.p_results.mpickP is not None and self.p_results.epickP is not None and self.p_results.lpickP is not None:
|
|
lpickP = self.ztrace.stats.starttime + self.p_results.lpickP
|
|
epickP = self.ztrace.stats.starttime + self.p_results.epickP
|
|
mpickP = self.ztrace.stats.starttime + self.p_results.mpickP
|
|
else:
|
|
# dummy values (start of seismic trace) in order to derive
|
|
# theoretical onset times for iterative picking
|
|
lpickP = self.ztrace.stats.starttime + self.p_params.timeerrorsP[3]
|
|
epickP = self.ztrace.stats.starttime - self.p_params.timeerrorsP[3]
|
|
mpickP = self.ztrace.stats.starttime
|
|
|
|
# add picking results to PickingResults instance
|
|
self.p_results.channel = self.ztrace.stats.channel
|
|
self.p_results.network = self.ztrace.stats.network
|
|
self.p_results.lpp = lpickP
|
|
self.p_results.epp = epickP
|
|
self.p_results.mpp = mpickP
|
|
self.p_results.snrdb = self.p_results.SNRPdB
|
|
self.p_results.snr = self.p_results.SNRP
|
|
self.p_results.marked = self.p_results.Pmarker # TODO add equal implementation as in old function
|
|
self.p_results.weight = self.p_results.Pweight
|
|
self.p_results.fm = self.p_results.FM
|
|
self.p_results.spe = self.p_results.Perror
|
|
self.p_results.Mo = None # TODO what is this parameter for?
|
|
|
|
#
|
|
# S results
|
|
#
|
|
if self.etrace:
|
|
hdat = self.etrace
|
|
elif self.ntrace:
|
|
hdat = self.ntrace
|
|
|
|
# TODO picks are calculated with stats from E trace if it exists, otherwise stats from N trace is used.
|
|
# What if the stats are different?
|
|
if self.s_results.lpickS is not None and self.s_results.lpickS == self.s_results.mpickS:
|
|
self.s_results.lpickS += hdat.stats.delta
|
|
if self.s_results.epickS is not None and self.s_results.epickS == self.s_results.mpickS:
|
|
self.s_results.epickS -= hdat.stats.delta
|
|
if self.s_results.mpickS is not None and self.s_results.epickS is not None and self.s_results.lpickS is not None:
|
|
lpickS = hdat.stats.starttime + self.s_results.lpickS
|
|
epickS = hdat.stats.starttime + self.s_results.epickS
|
|
mpickS = hdat.stats.starttime + self.s_results.mpickS
|
|
else:
|
|
# dummy values (start of seismic trace) in order to derive
|
|
# theoretical onset times for iteratve picking
|
|
lpickS = hdat.stats.starttime + self.s_params.timeerrorsS[3]
|
|
epickS = hdat.stats.starttime - self.s_params.timeerrorsS[3]
|
|
mpickS = hdat.stats.starttime
|
|
|
|
self.s_results.channel = self.etrace.stats.channel
|
|
self.s_results.network = self.etrace.stats.network
|
|
self.s_results.lpp = lpickS
|
|
self.s_results.epp = epickS
|
|
self.s_results.mpp = mpickS
|
|
self.s_results.spe = self.s_results.Serror
|
|
self.s_results.snr = self.s_results.SNRS
|
|
self.s_results.snrdb = self.s_results.SNRSdB
|
|
self.s_results.weight = self.s_results.Sweight
|
|
self.s_results.fm = None
|
|
self.s_results.picker='auto'
|
|
self.s_results.Ao = None
|
|
|
|
def plot_pick_results(self):
|
|
if self.iplot > 0:
|
|
# plot vertical trace
|
|
if self.fig_dict is None:
|
|
fig = plt.figure()
|
|
plt_flag = 1
|
|
linecolor = 'k'
|
|
else:
|
|
fig = self.fig_dict['mainFig']
|
|
linecolor = self.fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
plt_flag = 0
|
|
fig._tight = True
|
|
ax1 = fig.add_subplot(311)
|
|
tdata = np.linspace(start=0, stop=self.ztrace.stats.npts*self.ztrace.stats.delta, num=self.ztrace.stats.npts)
|
|
# plot tapered trace filtered with bpz2 filter settings
|
|
ax1.plot(tdata, self.tr_filt_z_bpz2.data/max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if self.p_results.Pweight < 4:
|
|
# plot CF of initial onset (HOScf or ARZcf)
|
|
ax1.plot(self.cf1.getTimeArray(), self.cf1.getCF()/max(self.cf1.getCF()), 'b', label='CF1')
|
|
if self.p_results.aicPflag == 1:
|
|
aicpick = self.p_results.aicpick
|
|
refPpick = self.p_results.refPpick
|
|
# plot CF of precise pick (HOScf or ARZcf)
|
|
ax1.plot(self.cf2.getTimeArray(), self.cf2.getCF() / max(self.cf2.getCF()), 'm', label='CF2')
|
|
# plot inital P pick
|
|
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')
|
|
# plot precise P pick
|
|
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)
|
|
# plot latest possible P pick
|
|
ax1.plot([self.p_results.lpickP, self.p_results.lpickP], [-1.1, 1.1], 'r--', label='lpp')
|
|
# plot earliest possible P pick
|
|
ax1.plot([self.p_results.epickP, self.p_results.epickP], [-1.1, 1.1], 'r--', label='epp')
|
|
# add title to plot
|
|
title = '{station}, {channel}, P weight={pweight:d}, SNR={snr:7.2}, SNR[dB]={snrdb:7.2}, Polarity: {polarity}'
|
|
ax1.set_title(title.format(station=self.ztrace.stats.station,
|
|
channel=self.ztrace.stats.channel,
|
|
pweight=self.p_results.Pweight,
|
|
snr=self.p_results.SNRP,
|
|
snrdb=self.p_results.SNRPdB,
|
|
polarity=self.p_results.FM))
|
|
else:
|
|
title = '{channel}, P weight={pweight}, SNR=None, SNR[dB]=None'
|
|
ax1.set_title(title.format(channel=self.ztrace.stats.channel, pweight=self.p_results.Pweight))
|
|
|
|
ax1.legend(loc=1)
|
|
ax1.set_yticks([])
|
|
ax1.set_ylim([-1.5, 1.5])
|
|
ax1.set_ylabel('Normalized Counts')
|
|
|
|
if self.s_results.Sflag == 1:
|
|
# plot E trace
|
|
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
|
|
th1data = np.linspace(0, self.etrace.stats.npts*self.etrace.stats.delta, self.etrace.stats.npts)
|
|
# plot filtered and tapered waveform
|
|
ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if self.p_results.Pweight < 4:
|
|
# plot initial CF (ARHcf or AR3Ccf)
|
|
ax2.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
|
|
if self.s_results.aicSflag == 1 and self.s_results.Sweight <= 4:
|
|
aicarhpick = self.aicarhpick
|
|
refSpick = self.refSpick
|
|
# plot second cf, used for determing precise onset (ARHcf or AR3Ccf)
|
|
ax2.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm', label='CF2')
|
|
# plot preliminary onset time, calculated from CF1
|
|
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')
|
|
# plot precise onset time, calculated from CF2
|
|
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([self.s_results.lpickS, self.s_results.lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
|
ax2.plot([self.s_results.epickS, self.s_results.epickS], [-1.1, 1.1], 'g--', label='epp')
|
|
title = '{channel}, S weight={sweight}, SNR={snr:7.2}, SNR[dB]={snrdb:7.2}'
|
|
ax2.set_title(title.format(channel=self.etrace.stats.channel,
|
|
sweight=self.s_results.Sweight,
|
|
snr=self.s_results.SNRS,
|
|
snrdb=self.s_results.SNRSdB))
|
|
else:
|
|
title = '{channel}, S weight={sweight}, SNR=None, SNR[dB]=None'
|
|
ax2.set_title(title.format(channel=self.etrace.stats.channel, sweight=self.s_results.Sweight))
|
|
ax2.legend(loc=1)
|
|
ax2.set_yticks([])
|
|
ax2.set_ylim([-1.5, 1.5])
|
|
ax2.set_ylabel('Normalized Counts')
|
|
|
|
# plot N trace
|
|
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
|
|
th2data= np.linspace(0, self.ntrace.stats.npts*self.ntrace.stats.delta, self.ntrace.stats.npts)
|
|
# plot trace
|
|
ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7, label='Data')
|
|
if self.p_results.Pweight < 4:
|
|
p22, = ax3.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
|
|
if self.s_results.aicSflag == 1:
|
|
aicarhpick = self.aicarhpick
|
|
refSpick = self.refSpick
|
|
ax3.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.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([self.s_results.lpickS, self.s_results.lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
|
ax3.plot([self.s_results.epickS, self.s_results.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' % self.ntrace.stats.starttime)
|
|
ax3.set_ylabel('Normalized Counts')
|
|
ax3.set_title(self.ntrace.stats.channel)
|
|
if plt_flag == 1:
|
|
fig.show()
|
|
try:
|
|
input()
|
|
except SyntaxError:
|
|
pass
|
|
plt.close(fig)
|
|
|
|
def pick_p_qc1(self, aicpick, z_copy, tr_filt):
|
|
"""
|
|
Quality control of first pick using minseglength and checkZ4S.
|
|
:param aicpick: Instance of AICPicker to run quality control on
|
|
:type aicpick: AICPicker
|
|
:param z_copy: Stream if vertical trace, data replaced with values from from initial CF (HOScf or ARHcf)
|
|
:type z_copy: obspy.core.stream.Stream
|
|
:param tr_filt: Filtered and tapered trace of vertical component
|
|
:type tr_filt: obspy.core.trace.trace
|
|
:return: Flag if P onset passed quality control, 1 if passed, 0 if failed.
|
|
:rtype: int
|
|
"""
|
|
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'slength')
|
|
if aicpick.getpick() is None:
|
|
msg = "Bad initial (AIC) P-pick, skipping this onset!\nAIC-SNR={0}, AIC-Slope={1}counts/s\n " \
|
|
"(min. AIC-SNR={2}, min. AIC-Slope={3}counts/s)"
|
|
msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.p_params.minAICPSNR, self.p_params.minAICPslope)
|
|
self.vprint(msg)
|
|
return 0
|
|
# Quality check initial pick with minimum signal length
|
|
z_copy[0].data = tr_filt.data # save filtered, tapered trace in z_copy stream object
|
|
zne = z_copy
|
|
if len(self.nstream) == 0 or len(self.estream) == 0:
|
|
msg = 'One or more horizontal component(s) missing!\n' \
|
|
'Signal length only checked on vertical component!\n' \
|
|
'Decreasing minsiglengh from {0} to {1}'\
|
|
.format(self.signal_length_params.minsiglength, self.signal_length_params.minsiglength / 2)
|
|
self.vprint(msg)
|
|
minsiglength = self.signal_length_params.minsiglength / 2
|
|
else:
|
|
# filter, taper other traces as well since signal length is compared on all traces
|
|
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
zne += trH1_filt
|
|
zne += trH2_filt
|
|
minsiglength = self.signal_length_params.minsiglength
|
|
Pflag = checksignallength(zne, aicpick.getpick(), self.p_params.tsnrz, minsiglength,
|
|
self.signal_length_params.noisefactor, self.signal_length_params.minpercent,
|
|
self.iplot, fig, linecolor)
|
|
if Pflag == 0:
|
|
self.p_results.Pmarker = 'shortsignallength'
|
|
self.p_results.Pweight = 9
|
|
return 0
|
|
|
|
if self.nstream == self.estream:
|
|
# todo: old implementation skipped this test if one component was misisng, why not use one component?
|
|
msg = 'One or more horizontal components missing!\n Skipping control function checkZ4S.'
|
|
self.vprint(msg)
|
|
return 1
|
|
|
|
if self.iplot > 1: fig, linecolor = get_fig_from_figdict(self.fig_dict, 'checkZ4s')
|
|
Pflag = checkZ4S(zne, aicpick.getpick(), self.s_params.zfac, self.p_params.tsnrz[2], self.iplot, fig, linecolor)
|
|
if Pflag == 0:
|
|
self.p_results.Pmarker = 'SinsteadP'
|
|
self.p_results.Pweight = 9
|
|
return 0
|
|
return 1
|
|
|
|
def pick_p_phase(self):
|
|
"""
|
|
Pick p phase, return results
|
|
:return: P pick results
|
|
:rtype: PickingResults
|
|
:raises:
|
|
MissingTraceException: If vertical trace is missing.
|
|
"""
|
|
if not self.zstream or self.zstream is None:
|
|
raise MissingTraceException('No z-component found for station {}'.format(self.station_name))
|
|
|
|
msg = '##################################################\nautopickstation:' \
|
|
' Working on P onset of station {station}\nFiltering vertical ' \
|
|
'trace ...\n{data}'.format(station=self.station_name, data=str(self.zstream))
|
|
self.vprint(msg)
|
|
|
|
tr_filt, z_copy = self.prepare_wfstream(self.zstream, self.p_params.bpz1[0], self.p_params.bpz1[1])
|
|
# save filtered trace in instance for later plotting
|
|
self.tr_filt_z_bpz2 = tr_filt
|
|
try:
|
|
# modify pstart, pstop to be around theoretical onset if taupy should be used, else does nothing
|
|
self.modify_starttimes_taupy()
|
|
except AttributeError as ae:
|
|
print(ae)
|
|
except MissingTraceException as mte:
|
|
print(mte)
|
|
cuttimes = [self.p_params.pstart, self.p_params.pstop]
|
|
|
|
# calculate first CF
|
|
if self.p_params.algoP == 'HOS':
|
|
cf1 = HOScf(z_copy, cuttimes, self.p_params.tlta, self.p_params.hosorder)
|
|
elif self.p_params.algoP == 'ARZ':
|
|
cf1 = ARZcf(z_copy, cuttimes, self.p_params.tpred1z, self.p_params.Parorder, self.p_params.tdet1z,
|
|
self.p_params.addnoise)
|
|
else:
|
|
cf1 = None
|
|
# save cf1 for plotting
|
|
self.cf1 = cf1
|
|
assert isinstance(cf1, CharacteristicFunction), 'cf1 is not set ' \
|
|
'correctly: maybe the algorithm name ({algoP}) is ' \
|
|
'corrupted'.format(algoP=self.p_params.algoP)
|
|
|
|
# calculate AIC cf from first cf (either HOS or ARZ)
|
|
z_copy[0].data = cf1.getCF()
|
|
aiccf = AICcf(z_copy, cuttimes)
|
|
# get preliminary onset time from AIC-CF
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'aicFig')
|
|
aicpick = AICPicker(aiccf, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot,
|
|
Tsmooth=self.p_params.aictsmooth, fig=fig, linecolor=linecolor)
|
|
# add pstart and pstop to aic plot
|
|
if fig:
|
|
for ax in fig.axes:
|
|
ax.vlines(self.p_params.pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
|
|
ax.vlines(self.p_params.pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
|
|
ax.legend(loc=1)
|
|
|
|
Pflag = self.pick_p_qc1(aicpick, z_copy, tr_filt)
|
|
# go on with processing if AIC onset passes quality control
|
|
slope = aicpick.getSlope()
|
|
if not slope: slope = 0
|
|
if slope >= self.p_params.minAICPslope and aicpick.getSNR() >= self.p_params.minAICPSNR and Pflag == 1:
|
|
self.p_results.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())
|
|
self.vprint(msg)
|
|
# refilter waveform with larger bandpass
|
|
tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.p_params.bpz2[0], freqmax=self.p_params.bpz2[1])
|
|
# save filtered trace in instance for later plotting
|
|
self.tr_filt_z_bpz2 = tr_filt
|
|
cuttimes2 = [round(max([aicpick.getpick() - self.p_params.Precalcwin, 0])),
|
|
round(min([len(self.ztrace.data) * self.ztrace.stats.delta,
|
|
aicpick.getpick() + self.p_params.Precalcwin]))]
|
|
if self.p_params.algoP == 'HOS':
|
|
cf2 = HOScf(z_copy, cuttimes2, self.p_params.tlta, self.p_params.hosorder)
|
|
elif self.p_params.algoP == 'ARZ':
|
|
cf2 = ARZcf(z_copy, cuttimes2, self.p_params.tpred1z, self.p_params.Parorder, self.p_params.tdet1z, self.p_params.addnoise)
|
|
else:
|
|
cf2 = None
|
|
# save cf2 for plotting
|
|
self.cf2 = cf2
|
|
# get refined onset time from CF2
|
|
assert isinstance(cf2, CharacteristicFunction), 'cf2 is not set ' \
|
|
'correctly: maybe the algorithm name ({algoP}) is ' \
|
|
'corrupted'.format(algoP=self.p_params.algoP)
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'refPpick')
|
|
refPpick = PragPicker(cf2, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot, self.p_params.ausP,
|
|
self.p_params.tsmoothP, aicpick.getpick(), fig, linecolor)
|
|
# save PragPicker result for plotting
|
|
self.p_results.refPpick = refPpick
|
|
self.p_results.mpickP = refPpick.getpick()
|
|
if self.p_results.mpickP is not None:
|
|
# quality assessment, get earliest/latest pick and symmetrized uncertainty
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'el_Ppick')
|
|
self.p_results.epickP, self.p_results.lpickP, self.p_results.Perror = earllatepicker(z_copy, self.p_params.nfacP, self.p_params.tsnrz, self.p_results.mpickP,
|
|
self.iplot, fig=fig, linecolor=linecolor)
|
|
self.p_results.SNRP, self.p_results.SNRPdB, self.p_results.Pnoiselevel = getSNR(z_copy, self.p_params.tsnrz, self.p_results.mpickP)
|
|
|
|
# weight P-onset using symmetric error
|
|
self.p_results.Pweight = get_quality_class(self.p_results.Perror, self.p_params.timeerrorsP)
|
|
if self.p_results.Pweight <= self.first_motion_params.minfmweight and self.p_results.SNRP >= self.first_motion_params.minFMSNR:
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'fm_picker')
|
|
self.p_results.FM = fmpicker(self.zstream, z_copy, self.first_motion_params.fmpickwin, self.p_results.mpickP, self.iplot,
|
|
fig, linecolor)
|
|
else:
|
|
self.p_results.FM = 'N'
|
|
msg = "autopickstation: P-weight: {0}, " \
|
|
"SNR: {1}, SNR[dB]: {2}, Polarity: {3}".format(self.p_results.Pweight, self.p_results.SNRP, self.p_results.SNRPdB, self.p_results.FM)
|
|
print(msg)
|
|
msg = 'autopickstation: Refined P-Pick: {} s | P-Error: {} s'.format(self.p_results.mpickP, self.p_results.Perror)
|
|
print(msg)
|
|
self.s_results.Sflag = 1
|
|
|
|
self.p_results.aicpick = aicpick
|
|
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(),
|
|
self.p_params.minAICPSNR, self.p_params.minAICPslope)
|
|
self.vprint(msg)
|
|
self.s_results.Sflag = 0
|
|
else:
|
|
#todo why did picking fail should be saved in the pick dictionary
|
|
raise PickingFailedException('AIC P onset did not pass quality control')
|
|
|
|
def pick_s_phase(self):
|
|
|
|
def check_existence_ne_traces():
|
|
"""
|
|
Checks if both N and E stream contain traces and copy over the trace to the missing stream if only
|
|
one trace is missing
|
|
:rtype: None
|
|
:raises:
|
|
MissingTraceException when both traces are missing
|
|
"""
|
|
# check existence of vertical traces #Maybe do this in __init__?
|
|
nstream_exists, estream_exists = bool(len(self.nstream)), bool(len(self.estream))
|
|
if not all((nstream_exists, estream_exists)):
|
|
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 ...'
|
|
self.vprint(msg)
|
|
if estream_exists == 0:
|
|
self.estream = self.nstream
|
|
elif nstream_exists == 0:
|
|
self.nstream = self.estream
|
|
else:
|
|
raise MissingTraceException("N and E stream missing, can't pick S phase.")
|
|
|
|
|
|
#check_existence_ne_traces()
|
|
# determine time window for calculating CF after P onset
|
|
start = round(max(self.p_results.mpickP + self.s_params.sstart, 0))
|
|
stop = round(min([
|
|
self.p_results.mpickP + self.s_params.sstop,
|
|
self.etrace.stats.endtime - self.etrace.stats.starttime,
|
|
self.ntrace.stats.endtime - self.ntrace.stats.starttime
|
|
]))
|
|
cuttimesh = (start, stop)
|
|
|
|
if cuttimesh[1] <= cuttimesh[0]:
|
|
pickSonset = False
|
|
raise PickingFailedException('Cut window for horizontal phases too small! Will not pick S onsets.')
|
|
|
|
# prepare traces for picking by filtering, taper
|
|
if self.s_params.algoS == 'ARH':
|
|
hdat = self.nstream.copy() + self.estream.copy()
|
|
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
h_copy = hdat.copy()
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
if self.s_params.algoS == 'AR3':
|
|
hdat = self.zstream.copy() + self.estream.copy() + self.nstream.copy()
|
|
trH1_filt, _ = self.prepare_wfstream(self.zstream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
trH2_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
trH3_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.s_params.bph1[0], freqmax=self.s_params.bph1[1])
|
|
h_copy = hdat.copy()
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
h_copy[2].data = trH3_filt.data
|
|
|
|
# calculate inital CF based on autoregression
|
|
if self.s_params.algoS == 'ARH':
|
|
arhcf1 = ARHcf(h_copy, cuttimesh, self.s_params.tpred1h, self.s_params.Sarorder, self.s_params.tdet1h, self.p_params.addnoise)
|
|
elif self.s_params.algoS == 'AR3':
|
|
arhcf1 = AR3Ccf(h_copy, cuttimesh, self.s_params.tpred1h, self.s_params.Sarorder, self.s_params.tdet1h, self.p_params.addnoise)
|
|
# save cf for later plotting
|
|
self.arhcf1 = arhcf1
|
|
|
|
tr_arhaic = trH1_filt.copy()
|
|
tr_arhaic.data = arhcf1.getCF()
|
|
h_copy[0].data = tr_arhaic.data
|
|
|
|
# calculate AIC cf
|
|
haiccf = AICcf(h_copy, cuttimesh)
|
|
|
|
# get preliminary onset time from AIC cf
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'aicARHfig')
|
|
aicarhpick = AICPicker(haiccf, self.s_params.tsnrh, self.s_params.pickwinS, self.iplot, Tsmooth=self.s_params.aictsmoothS, fig=fig, linecolor=linecolor)
|
|
# save pick for later plotting
|
|
self.aicarhpick = aicarhpick
|
|
|
|
# go on with processing if AIC onset passes quality control
|
|
slope = aicarhpick.getSlope()
|
|
|
|
if not slope:
|
|
slope = 0
|
|
|
|
if slope >= self.s_params.minAICSslope and aicarhpick.getSNR() >= self.s_params.minAICSSNR and aicarhpick.getpick() is not None:
|
|
self.s_results.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())
|
|
self.vprint(msg)
|
|
|
|
# recalculate cf from refiltered trace in vicinity of initial onset
|
|
start = round(aicarhpick.getpick() - self.s_params.Srecalcwin)
|
|
stop = round(aicarhpick.getpick() + self.s_params.Srecalcwin)
|
|
cuttimesh2 = (start, stop)
|
|
|
|
# refilter waveform with larger bandpass
|
|
if self.s_params.algoS == 'ARH':
|
|
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.s_params.bph2[0], freqmax=self.s_params.bph2[1])
|
|
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.s_params.bph2[0], freqmax=self.s_params.bph2[1])
|
|
h_copy = hdat.copy()
|
|
h_copy[0].data = trH1_filt.data
|
|
h_copy[1].data = trH2_filt.data
|
|
elif self.s_params.algoS == 'AR3':
|
|
trH3_filt, _ = self.prepare_wfstream(self.zstream, freqmin=self.s_params.bph2[0], freqmax=self.s_params.bph2[1])
|
|
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.s_params.bph2[0], freqmax=self.s_params.bph2[1])
|
|
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.s_params.bph2[0], freqmax=self.s_params.bph2[1])
|
|
h_copy = hdat.copy()
|
|
h_copy[0].data = trH3_filt.data
|
|
h_copy[1].data = trH1_filt.data
|
|
h_copy[2].data = trH2_filt.data
|
|
|
|
# save filtered traces for plotting
|
|
self.estream_bph2 = trH1_filt
|
|
self.nstream_bph2 = trH2_filt
|
|
|
|
# calculate second cf
|
|
if self.s_params.algoS == 'ARH':
|
|
arhcf2 = ARHcf(h_copy, cuttimesh2, self.s_params.tpred2h, self.s_params.Sarorder, self.s_params.tdet2h, self.p_params.addnoise)
|
|
elif self.s_params.algoS == 'AR3':
|
|
arhcf2 = AR3Ccf(h_copy, cuttimesh2, self.s_params.tpred2h, self.s_params.Sarorder, self.s_params.tdet2h, self.p_params.addnoise)
|
|
# save cf for later plotting
|
|
self.arhcf2 = arhcf2
|
|
|
|
# get refined onset time from CF2
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'refSpick')
|
|
refSpick = PragPicker(arhcf2, self.s_params.tsnrh, self.s_params.pickwinS, self.iplot, self.s_params.ausS, self.s_params.tsmoothS, aicarhpick.getpick(), fig, linecolor)
|
|
# save refSpick for later plotitng
|
|
self.refSpick = refSpick
|
|
self.s_results.mpickS = refSpick.getpick()
|
|
|
|
if self.s_results.mpickS is not None:
|
|
# quality assessment
|
|
# get earliest/latest possible pick and symmetrized uncertainty
|
|
h_copy[0].data = trH1_filt.data
|
|
if self.iplot:
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'el_Slpick')
|
|
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, self.s_params.nfacS, self.s_params.tsnrh, self.s_results.mpickS, self.iplot, fig=fig, linecolor=linecolor)
|
|
|
|
h_copy[0].data = trH2_filt.data
|
|
if self.iplot:
|
|
fig, linecolor = get_fig_from_figdict(self.fig_dict, 'el_S2pick')
|
|
else:
|
|
# why is it set to empty here? DA
|
|
linecolor = ''
|
|
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, self.s_params.nfacS, self.s_params.tsnrh, self.s_results.mpickS, self.iplot, fig=fig, linecolor=linecolor)
|
|
|
|
if epickS1 is not None and epickS2 is not None:
|
|
if self.s_params.algoS == 'ARH':
|
|
# get earliest pick of both earliest possible picks
|
|
epick = [epickS1, epickS2]
|
|
lpick = [lpickS1, lpickS2]
|
|
pickerr = [Serror1, Serror2]
|
|
ipick = np.argmin(epick)
|
|
if self.s_params.algoS == 'AR3':
|
|
epickS3, lpickS3, Serror3 = earllatepicker(h_copy, self.s_params.nfacS, self.s_params.tsnrh, self.s_results.mpickS, self.iplot)
|
|
# get earliest of all three picks
|
|
epick = [epickS1, epickS2, epickS3]
|
|
lpick = [lpickS1, lpickS2, lpickS3]
|
|
pickerr = [Serror1, Serror2, Serror3]
|
|
|
|
if epickS3 is not None:
|
|
ipick = np.argmin(epick)
|
|
else:
|
|
ipick = np.argmin([epickS1, epickS2])
|
|
self.s_results.epickS = epick[ipick]
|
|
self.s_results.lpickS = lpick[ipick]
|
|
self.s_results.Serror = pickerr[ipick]
|
|
|
|
msg = 'autopickstation: Refined S-Pick: {} s | S-Error: {} s'.format(self.s_results.mpickS, self.s_results.Serror)
|
|
print(msg)
|
|
|
|
# get SNR
|
|
self.s_results.SNRS, self.s_results.SNRSdB, self.s_results.Snoiselevel = getSNR(h_copy, self.s_params.tsnrh, self.s_results.mpickS)
|
|
|
|
self.s_results.Sweight = get_quality_class(self.s_results.Serror, self.s_params.timeerrorsS)
|
|
|
|
print('autopickstation: S-weight: {0}, SNR: {1}, '
|
|
'SNR[dB]: {2}\n'
|
|
'##################################################'
|
|
''.format(self.s_results.Sweight, self.s_results.SNRS, self.s_results.SNRSdB))
|
|
|
|
else:
|
|
print('autopickstation: No horizontal component data available or '
|
|
'bad P onset, skipping S picking!')
|
|
|
|
def get_fig_from_figdict(figdict, figkey):
|
|
"""
|
|
Helper method to extract a figure by name from dictionary
|
|
:param figdict: Dictionary of matplotlib figures
|
|
:type figdict: dict
|
|
:param figkey: which figure to extract from figdict
|
|
:type figkey: str
|
|
:return: Figure and linecolor as a tuple
|
|
:rtype:
|
|
"""
|
|
if figdict is None:
|
|
return None, None
|
|
fig = figdict.get(figkey, None)
|
|
linecolor = figdict.get('plot_style', 'k')
|
|
linecolor = linecolor['linecolor']['rgba_mpl']
|
|
return fig, linecolor
|
|
|
|
|
|
def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None, metadata=None, origin=None):
|
|
station = AutopickStation(wfstream, pickparam, verbose, iplot, fig_dict, metadata, origin)
|
|
return station.autopickstation()
|
|
|
|
|
|
def nautopickstation(wfstream, pickparam, verbose=False,
|
|
iplot=0, fig_dict=None, metadata=None, origin=None):
|
|
"""
|
|
picks a single station
|
|
:param wfstream: stream object containing waveform of all traces
|
|
:type wfstream: ~obspy.core.stream.Stream
|
|
:param pickparam: container of picking parameters from input file, usually pylot.in
|
|
:type pickparam: pylot.core.io.inputs.PylotParameter
|
|
:param verbose: used to control output to log during picking. True = more information printed
|
|
:type verbose: bool
|
|
:param iplot: logical variable for plotting: 0=none, 1=partial, 2=all
|
|
:type iplot: int, (Boolean or String)
|
|
:param fig_dict: dictionary containing Matplotlib figures used for plotting picking results during tuning
|
|
:type fig_dict: dict
|
|
:param metadata: tuple containing metadata type string and Parser object read from inventory file
|
|
:type metadata: tuple (str, ~obspy.io.xseed.parser.Parser)
|
|
:param origin: list containing origin objects representing origins for all events
|
|
:type origin: list(~obspy.core.event.origin)
|
|
:return: dictionary containing P pick, S pick and station name
|
|
:rtype: dict
|
|
"""
|
|
|
|
# declaring pickparam variables (only for convenience)
|
|
# read your pylot.in for details!
|
|
plt_flag = 0
|
|
|
|
# get picking parameter dictionaries
|
|
p_params, s_params, first_motion_params, signal_length_params = get_pickparams(pickparam)
|
|
|
|
# initialize output
|
|
Pweight = 4 # weight for P onset
|
|
Sweight = 4 # weight for S onset
|
|
FM = 'N' # first motion (polarity)
|
|
SNRP = None # signal-to-noise ratio of P onset
|
|
SNRPdB = None # signal-to-noise ratio of P onset [dB]
|
|
SNRS = None # signal-to-noise ratio of S onset
|
|
SNRSdB = None # signal-to-noise ratio of S onset [dB]
|
|
mpickP = None # most likely P onset
|
|
lpickP = None # latest possible P onset
|
|
epickP = None # earliest possible P onset
|
|
mpickS = None # most likely S onset
|
|
lpickS = None # latest possible S onset
|
|
epickS = None # earliest possible S onset
|
|
Perror = None # symmetrized picking error P onset
|
|
Serror = None # symmetrized picking error S onset
|
|
|
|
aicSflag = 0
|
|
aicPflag = 0
|
|
Pflag = 0
|
|
Sflag = 0
|
|
Pmarker = []
|
|
Ao = None # Wood-Anderson peak-to-peak amplitude
|
|
picker = 'auto' # type of picks
|
|
|
|
def get_components_from_waveformstream(waveformstream):
|
|
"""
|
|
Splits waveformstream into multiple components zdat, ndat, edat. For traditional orientation (ZNE) these contain
|
|
the vertical, north-south or east-west component. Otherwise they contain components numbered 123 with
|
|
orientation diverging from the traditional orientation.
|
|
:param waveformstream: Stream containing all three components for one station either by ZNE or 123 channel code
|
|
(mixture of both options is handled as well)
|
|
:type waveformstream: obspy.core.stream.Stream
|
|
:return: Tuple containing (z waveform, n waveform, e waveform) selected by the given channels
|
|
:rtype: (obspy.core.stream.Stream, obspy.core.stream.Stream, obspy.core.stream.Stream)
|
|
"""
|
|
|
|
#TODO: get this order from the pylot preferences
|
|
channelorder_default = {'Z': 3, 'N': 1, 'E': 2}
|
|
waveform_data = {}
|
|
for key in channelorder_default:
|
|
waveform_data[key] = waveformstream.select(component=key) # try ZNE first
|
|
if len(waveform_data[key]) == 0:
|
|
waveform_data[key] = waveformstream.select(component=str(channelorder_default[key])) # use 123 as second option
|
|
return waveform_data['Z'], waveform_data['N'], waveform_data['E']
|
|
|
|
|
|
def prepare_wfstream_component(wfstream, detrend_type='demean', filter_type='bandpass', freqmin=None, freqmax=None, zerophase=False, taper_max_percentage=0.05, taper_type='hann'):
|
|
"""
|
|
Prepare a waveformstream for picking by applying detrending, filtering and tapering. Creates a copy of the
|
|
waveform the leave the original unchanged.
|
|
:param wfstream:
|
|
:type wfstream:
|
|
:param detrend_type:
|
|
:type detrend_type:
|
|
:param filter_type:
|
|
:type filter_type:
|
|
:param freqmin:
|
|
:type freqmin:
|
|
:param freqmax:
|
|
:type freqmax:
|
|
:param zerophase:
|
|
:type zerophase:
|
|
:param taper_max_percentage:
|
|
:type taper_max_percentage:
|
|
:param taper_type:
|
|
:type taper_type:
|
|
:return: Tuple containing the changed waveform stream and the first trace of the stream
|
|
:rtype: (obspy.core.stream.Stream, obspy.core.trace.Trace)
|
|
"""
|
|
wfstream_copy = wfstream.copy()
|
|
trace_copy = wfstream[0].copy()
|
|
trace_copy.detrend(type=detrend_type)
|
|
trace_copy.filter(filter_type, freqmin=freqmin, freqmax=freqmax, zerophase=zerophase)
|
|
trace_copy.taper(max_percentage=taper_max_percentage, type=taper_type)
|
|
wfstream_copy[0].data = trace_copy.data
|
|
return wfstream_copy, trace_copy
|
|
|
|
# split components
|
|
zdat, ndat, edat = get_components_from_waveformstream(wfstream)
|
|
|
|
if not zdat:
|
|
print('No z-component found for station {}. STOP'.format(wfstream[0].stats.station))
|
|
return
|
|
|
|
if p_params['algoP'] == 'HOS' or p_params['algoP'] == 'ARZ' and zdat is not None:
|
|
msg = '##################################################\nautopickstation:' \
|
|
' Working on P onset of station {station}\nFiltering vertical ' \
|
|
'trace ...\n{data}'.format(station=wfstream[0].stats.station, data=str(zdat))
|
|
if verbose: print(msg)
|
|
z_copy, tr_filt = prepare_wfstream_component(zdat, freqmin=p_params['bpz1'][0], freqmax=p_params['bpz1'][1])
|
|
##############################################################
|
|
# check length of waveform and compare with cut times
|
|
|
|
# for global seismology: use tau-p method for estimating travel times (needs source and station coords.)
|
|
# if not given: sets Lc to infinity to use full stream
|
|
if p_params['use_taup'] is True:
|
|
Lc = np.inf
|
|
print('autopickstation: use_taup flag active.')
|
|
if not metadata[1]:
|
|
print('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
|
|
else:
|
|
station_id = wfstream[0].get_id()
|
|
parser = metadata[1]
|
|
station_coords = get_source_coords(parser, station_id)
|
|
if station_coords and origin:
|
|
source_origin = origin[0]
|
|
model = TauPyModel(p_params['taup_model'])
|
|
arrivals = model.get_travel_times_geo(
|
|
source_origin.depth,
|
|
source_origin.latitude,
|
|
source_origin.longitude,
|
|
station_coords['latitude'],
|
|
station_coords['longitude']
|
|
)
|
|
phases = {'P': [],
|
|
'S': []}
|
|
for arr in arrivals:
|
|
phases[identifyPhaseID(arr.phase.name)].append(arr)
|
|
|
|
# get first P and S onsets from arrivals list
|
|
arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
|
|
arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
|
|
print('autopick: estimated first arrivals for P: {} s, S:{} s after event'
|
|
' origin time using TauPy'.format(estFirstP, estFirstS))
|
|
|
|
# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
|
|
p_params['pstart'] += (source_origin.time + estFirstP) - zdat[0].stats.starttime
|
|
p_params['pstop']+= (source_origin.time + estFirstP) - zdat[0].stats.starttime
|
|
print('autopick: CF calculation times respectively:'
|
|
' pstart: {} s, pstop: {} s'.format(p_params['pstart'], p_params['pstop']))
|
|
elif not origin:
|
|
print('No source origins given!')
|
|
|
|
# make sure pstart and pstop are inside zdat[0]
|
|
pstart = max(p_params['pstart'], 0)
|
|
pstop = min(p_params['pstop'], len(zdat[0])*zdat[0].stats.delta)
|
|
|
|
if p_params['use_taup'] is False or origin:
|
|
Lc = p_params['pstop'] - p_params['pstart']
|
|
|
|
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
|
|
if not Lwf > 0:
|
|
print('autopickstation: empty trace! Return!')
|
|
return
|
|
|
|
Ldiff = Lwf - abs(Lc)
|
|
if Ldiff < 0 or pstop <= pstart:
|
|
msg = 'autopickstation: Cutting times are too large for actual ' \
|
|
'waveform!\nUsing entire waveform instead!'
|
|
if verbose: print(msg)
|
|
pstart = 0
|
|
pstop = len(zdat[0].data) * zdat[0].stats.delta
|
|
cuttimes = [pstart, pstop]
|
|
cf1 = None
|
|
if p_params['algoP'] == 'HOS':
|
|
# calculate HOS-CF using subclass HOScf of class
|
|
# CharacteristicFunction
|
|
cf1 = HOScf(z_copy, cuttimes, p_params['tlta'], p_params['hosorder']) # instance of HOScf
|
|
elif p_params['algoP'] == 'ARZ':
|
|
# calculate ARZ-CF using subclass ARZcf of class
|
|
# CharcteristicFunction
|
|
cf1 = ARZcf(z_copy, cuttimes, p_params['tpred1z'], p_params['Parorder'], p_params['tdet1z'],
|
|
p_params['addnoise']) # instance of ARZcf
|
|
##############################################################
|
|
# calculate AIC-HOS-CF using subclass AICcf of class
|
|
# CharacteristicFunction
|
|
# class needs stream object => build it
|
|
assert isinstance(cf1, CharacteristicFunction), 'cf2 is not set ' \
|
|
'correctly: maybe the algorithm name ({algoP}) is ' \
|
|
'corrupted'.format(algoP=p_params['algoP'])
|
|
tr_aic = tr_filt.copy()
|
|
tr_aic.data = cf1.getCF()
|
|
z_copy[0].data = tr_aic.data
|
|
aiccf = AICcf(z_copy, cuttimes) # instance of AICcf
|
|
##############################################################
|
|
# get preliminary onset time from AIC-HOS-CF using subclass AICPicker
|
|
# of class AutoPicking
|
|
key = 'aicFig'
|
|
if fig_dict:
|
|
fig = fig_dict[key]
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
aicpick = AICPicker(aiccf, p_params['tsnrz'], p_params['pickwinP'], iplot, Tsmooth=p_params['aictsmooth'],
|
|
fig=fig, linecolor=linecolor)
|
|
# add pstart and pstop to aic plot
|
|
if fig:
|
|
for ax in fig.axes:
|
|
ax.vlines(pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
|
|
ax.vlines(pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
|
|
ax.legend(loc=1)
|
|
##############################################################
|
|
if aicpick.getpick() is not None:
|
|
# check signal length to detect spuriously picked noise peaks
|
|
# use all available components to avoid skipping correct picks
|
|
# on vertical traces with weak P coda
|
|
z_copy[0].data = tr_filt.data
|
|
zne = z_copy
|
|
if len(ndat) == 0 or len(edat) == 0:
|
|
msg = 'One or more horizontal component(s) missing!\n' \
|
|
'Signal length only checked on vertical component!\n' \
|
|
'Decreasing minsiglengh from {0} to {1}' \
|
|
.format(signal_length_params['minsiglength'], signal_length_params['minsiglength'] / 2)
|
|
if verbose: print(msg)
|
|
key = 'slength'
|
|
if fig_dict:
|
|
fig = fig_dict[key]
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
Pflag = checksignallength(zne, aicpick.getpick(), p_params['tsnrz'],
|
|
signal_length_params['minsiglength'] / 2,
|
|
signal_length_params['noisefactor'], signal_length_params['minpercent'], iplot,
|
|
fig, linecolor)
|
|
else:
|
|
trH1_filt, _ = prepare_wfstream_component(edat, freqmin=s_params['bph1'][0], freqmax=s_params['bph1'][1])
|
|
trH2_filt, _ = prepare_wfstream_component(ndat, freqmin=s_params['bph1'][0], freqmax=s_params['bph1'][1])
|
|
zne += trH1_filt
|
|
zne += trH2_filt
|
|
if fig_dict:
|
|
fig = fig_dict['slength']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
Pflag = checksignallength(zne, aicpick.getpick(), p_params['tsnrz'],
|
|
signal_length_params['minsiglength'],
|
|
signal_length_params['noisefactor'], signal_length_params['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(), s_params['zfac'],
|
|
p_params['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 >= p_params['minAICPslope'] and aicpick.getSNR() >= p_params['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, tr_filt = prepare_wfstream_component(zdat, freqmin=p_params['bpz2'][0], freqmax=p_params['bpz2'][1])
|
|
#############################################################
|
|
# re-calculate CF from re-filtered trace in vicinity of initial
|
|
# onset
|
|
cuttimes2 = [round(max([aicpick.getpick() - p_params['Precalcwin'], 0])),
|
|
round(min([len(zdat[0].data) * zdat[0].stats.delta,
|
|
aicpick.getpick() + p_params['Precalcwin']]))]
|
|
cf2 = None
|
|
if p_params['algoP'] == 'HOS':
|
|
# calculate HOS-CF using subclass HOScf of class
|
|
# CharacteristicFunction
|
|
cf2 = HOScf(z_copy, cuttimes2, p_params['tlta'],
|
|
p_params['hosorder']) # instance of HOScf
|
|
elif p_params['algoP'] == 'ARZ':
|
|
# calculate ARZ-CF using subclass ARZcf of class
|
|
# CharcteristicFunction
|
|
cf2 = ARZcf(z_copy, cuttimes2, p_params['tpred1z'], p_params['Parorder'], p_params['tdet1z'],
|
|
p_params['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=p_params['algoP'])
|
|
if fig_dict:
|
|
fig = fig_dict['refPpick']
|
|
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
|
else:
|
|
fig = None
|
|
linecolor = 'k'
|
|
refPpick = PragPicker(cf2, p_params['tsnrz'], p_params['pickwinP'], iplot, p_params['ausP'],
|
|
p_params['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, p_params['nfacP'], p_params['tsnrz'],
|
|
mpickP, iplot, fig=fig,
|
|
linecolor=linecolor)
|
|
|
|
# get SNR
|
|
SNRP, SNRPdB, Pnoiselevel = getSNR(z_copy, p_params['tsnrz'], mpickP)
|
|
|
|
# weight P-onset using symmetric error
|
|
Pweight = get_quality_class(Perror, p_params['timeerrorsP'])
|
|
|
|
##############################################################
|
|
# get first motion of P onset
|
|
# certain quality required
|
|
if Pweight <= first_motion_params['minfmweight'] and SNRP >= first_motion_params['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, first_motion_params['fmpickwin'], mpickP, iplot, fig, linecolor)
|
|
else:
|
|
FM = fmpicker(zdat, z_copy, first_motion_params['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(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(),
|
|
p_params['minAICPSNR'],
|
|
p_params['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 + s_params['sstart'], 0])), # MP MP relative time axis
|
|
round(min([
|
|
mpickP + s_params['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 s_params['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=s_params['bph1'][0], freqmax=s_params['bph1'][1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=s_params['bph1'][0], freqmax=s_params['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 s_params['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=s_params['bph1'][0], freqmax=s_params['bph1'][1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=s_params['bph1'][0], freqmax=s_params['bph1'][1],
|
|
zerophase=False)
|
|
trH3_filt.filter('bandpass', freqmin=s_params['bph1'][0], freqmax=s_params['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 s_params['algoS'] == 'ARH':
|
|
# calculate ARH-CF using subclass ARHcf of class
|
|
# CharcteristicFunction
|
|
arhcf1 = ARHcf(h_copy, cuttimesh, s_params['tpred1h'], s_params['Sarorder'], s_params['tdet1h'],
|
|
p_params['addnoise']) # instance of ARHcf
|
|
elif s_params['algoS'] == 'AR3':
|
|
# calculate ARH-CF using subclass AR3cf of class
|
|
# CharcteristicFunction
|
|
arhcf1 = AR3Ccf(h_copy, cuttimesh, s_params['tpred1h'], s_params['Sarorder'], s_params['tdet1h'],
|
|
p_params['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, s_params['tsnrh'], s_params['pickwinS'], iplot, None,
|
|
s_params['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 >= s_params['minAICSslope'] and
|
|
aicarhpick.getSNR() >= s_params['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() - s_params['Srecalcwin']),
|
|
round(aicarhpick.getpick() + s_params['Srecalcwin'])]
|
|
# re-filter waveform with larger bandpass
|
|
h_copy = hdat.copy()
|
|
# filter and taper data
|
|
if s_params['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=s_params['bph2'][0], freqmax=s_params['bph2'][1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=s_params['bph2'][0], freqmax=s_params['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, s_params['tpred2h'], s_params['Sarorder'], s_params['tdet2h'],
|
|
p_params['addnoise']) # instance of ARHcf
|
|
elif s_params['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=s_params['bph2'][0], freqmax=s_params['bph2'][1],
|
|
zerophase=False)
|
|
trH2_filt.filter('bandpass', freqmin=s_params['bph2'][0], freqmax=s_params['bph2'][1],
|
|
zerophase=False)
|
|
trH3_filt.filter('bandpass', freqmin=s_params['bph2'][0], freqmax=s_params['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, s_params['tpred2h'], s_params['Sarorder'], s_params['tdet2h'],
|
|
p_params['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, s_params['tsnrh'], s_params['pickwinS'], iplot, s_params['ausS'],
|
|
s_params['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, s_params['nfacS'], s_params['tsnrh'], mpickS,
|
|
iplot, fig=fig, linecolor=linecolor)
|
|
else:
|
|
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, s_params['nfacS'], s_params['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, s_params['nfacS'], s_params['tsnrh'], mpickS,
|
|
iplot, fig=fig, linecolor=linecolor)
|
|
else:
|
|
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, s_params['nfacS'], s_params['tsnrh'], mpickS, iplot)
|
|
if epickS1 is not None and epickS2 is not None:
|
|
if s_params['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 s_params['algoS'] == 'AR3':
|
|
[epickS3, lpickS3, Serror3] = earllatepicker(h_copy, s_params['nfacS'], s_params['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(mpickS, Serror)
|
|
print(msg)
|
|
|
|
# get SNR
|
|
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, s_params['tsnrh'], mpickS)
|
|
|
|
# weight S-onset using symmetric error
|
|
if Serror <= s_params['timeerrorsS'][0]:
|
|
Sweight = 0
|
|
elif s_params['timeerrorsS'][0] < Serror <= s_params['timeerrorsS'][1]:
|
|
Sweight = 1
|
|
elif Perror > s_params['timeerrorsS'][1] and Serror <= s_params['timeerrorsS'][2]:
|
|
Sweight = 2
|
|
elif s_params['timeerrorsS'][2] < Serror <= s_params['timeerrorsS'][3]:
|
|
Sweight = 3
|
|
elif Serror > s_params['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(), s_params['minAICSSNR'], s_params['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)
|
|
try:
|
|
len(edat[0])
|
|
except:
|
|
edat = ndat
|
|
try:
|
|
len(ndat[0])
|
|
except:
|
|
ndat = edat
|
|
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 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 + p_params['timeerrorsP'][3]
|
|
epickP = zdat[0].stats.starttime - p_params['timeerrorsP'][3]
|
|
mpickP = zdat[0].stats.starttime
|
|
|
|
if edat:
|
|
hdat = edat[0]
|
|
elif ndat:
|
|
hdat = ndat[0]
|
|
else:
|
|
return
|
|
|
|
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 + s_params['timeerrorsS'][3]
|
|
epickS = hdat.stats.starttime - s_params['timeerrorsS'][3]
|
|
mpickS = hdat.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)
|
|
# 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, station=zdat[0].stats.station)
|
|
return picks
|
|
|
|
|
|
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
|