[remove] old autopickstation code
This commit is contained in:
Darius Arnold
parent
b7d3568498
commit
dcd0bc40d7
@ -1184,983 +1184,6 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None,
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return None, station_name
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def nautopickstation(wfstream, pickparam, verbose=False,
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iplot=0, fig_dict=None, metadata=None, origin=None):
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"""
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picks a single station
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:param wfstream: stream object containing waveform of all traces
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:type wfstream: ~obspy.core.stream.Stream
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:param pickparam: container of picking parameters from input file, usually pylot.in
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:type pickparam: pylot.core.io.inputs.PylotParameter
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:param verbose: used to control output to log during picking. True = more information printed
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:type verbose: bool
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:param iplot: logical variable for plotting: 0=none, 1=partial, 2=all
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:type iplot: int, (Boolean or String)
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:param fig_dict: dictionary containing Matplotlib figures used for plotting picking results during tuning
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:type fig_dict: dict
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:param metadata: tuple containing metadata type string and Parser object read from inventory file
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:type metadata: tuple (str, ~obspy.io.xseed.parser.Parser)
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:param origin: list containing origin objects representing origins for all events
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:type origin: list(~obspy.core.event.origin)
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:return: dictionary containing P pick, S pick and station name
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:rtype: dict
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"""
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# declaring pickparam variables (only for convenience)
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# read your pylot.in for details!
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plt_flag = 0
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# special parameters for P picking
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algoP = pickparam.get('algoP')
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pstart = pickparam.get('pstart')
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pstop = pickparam.get('pstop')
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thosmw = pickparam.get('tlta')
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tsnrz = pickparam.get('tsnrz')
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hosorder = pickparam.get('hosorder')
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bpz1 = pickparam.get('bpz1')
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bpz2 = pickparam.get('bpz2')
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pickwinP = pickparam.get('pickwinP')
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aictsmoothP = pickparam.get('aictsmooth')
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tsmoothP = pickparam.get('tsmoothP')
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ausP = pickparam.get('ausP')
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nfacP = pickparam.get('nfacP')
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tpred1z = pickparam.get('tpred1z')
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tdet1z = pickparam.get('tdet1z')
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tpred2z = pickparam.get('tpred2z')
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tdet2z = pickparam.get('tdet2z')
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Parorder = pickparam.get('Parorder')
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addnoise = pickparam.get('addnoise')
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Precalcwin = pickparam.get('Precalcwin')
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minAICPslope = pickparam.get('minAICPslope')
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minAICPSNR = pickparam.get('minAICPSNR')
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timeerrorsP = pickparam.get('timeerrorsP')
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# special parameters for S picking
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algoS = pickparam.get('algoS')
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sstart = pickparam.get('sstart')
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sstop = pickparam.get('sstop')
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use_taup = real_Bool(pickparam.get('use_taup'))
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taup_model = pickparam.get('taup_model')
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bph1 = pickparam.get('bph1')
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bph2 = pickparam.get('bph2')
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tsnrh = pickparam.get('tsnrh')
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pickwinS = pickparam.get('pickwinS')
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tpred1h = pickparam.get('tpred1h')
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tdet1h = pickparam.get('tdet1h')
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tpred2h = pickparam.get('tpred2h')
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tdet2h = pickparam.get('tdet2h')
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Sarorder = pickparam.get('Sarorder')
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aictsmoothS = pickparam.get('aictsmoothS')
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tsmoothS = pickparam.get('tsmoothS')
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ausS = pickparam.get('ausS')
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minAICSslope = pickparam.get('minAICSslope')
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minAICSSNR = pickparam.get('minAICSSNR')
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Srecalcwin = pickparam.get('Srecalcwin')
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nfacS = pickparam.get('nfacS')
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timeerrorsS = pickparam.get('timeerrorsS')
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# parameters for first-motion determination
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minFMSNR = pickparam.get('minFMSNR')
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fmpickwin = pickparam.get('fmpickwin')
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minfmweight = pickparam.get('minfmweight')
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# parameters for checking signal length
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minsiglength = pickparam.get('minsiglength')
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minpercent = pickparam.get('minpercent')
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nfacsl = pickparam.get('noisefactor')
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# parameter to check for spuriously picked S onset
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zfac = pickparam.get('zfac')
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# path to inventory-, dataless- or resp-files
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# initialize output
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Pweight = 4 # weight for P onset
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Sweight = 4 # weight for S onset
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FM = 'N' # first motion (polarity)
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SNRP = None # signal-to-noise ratio of P onset
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SNRPdB = None # signal-to-noise ratio of P onset [dB]
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SNRS = None # signal-to-noise ratio of S onset
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SNRSdB = None # signal-to-noise ratio of S onset [dB]
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mpickP = None # most likely P onset
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lpickP = None # latest possible P onset
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epickP = None # earliest possible P onset
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mpickS = None # most likely S onset
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lpickS = None # latest possible S onset
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epickS = None # earliest possible S onset
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Perror = None # symmetrized picking error P onset
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Serror = None # symmetrized picking error S onset
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aicSflag = 0
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aicPflag = 0
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Pflag = 0
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Sflag = 0
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Pmarker = []
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Ao = None # Wood-Anderson peak-to-peak amplitude
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picker = 'auto' # type of picks
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def get_components_from_waveformstream(waveformstream):
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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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#TODO: get this order from the pylot preferences
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channelorder_default = {'Z': 3, 'N': 1, 'E': 2}
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waveform_data = {}
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for key in channelorder_default:
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waveform_data[key] = waveformstream.select(component=key) # try ZNE first
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if len(waveform_data[key]) == 0:
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waveform_data[key] = waveformstream.select(component=str(channelorder_default[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_component(wfstream, detrend_type='demean', filter_type='bandpass', freqmin=None, freqmax=None, zerophase=False, taper_max_percentage=0.05, taper_type='hann'):
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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:
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:type wfstream:
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:param detrend_type:
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:type detrend_type:
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:param filter_type:
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:type filter_type:
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:param freqmin:
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:type freqmin:
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:param freqmax:
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:type freqmax:
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:param zerophase:
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:type zerophase:
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:param taper_max_percentage:
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:type taper_max_percentage:
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:param taper_type:
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:type taper_type:
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:return: Tuple containing the changed waveform stream and the first trace of the stream
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:rtype: (obspy.core.stream.Stream, obspy.core.trace.Trace)
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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=detrend_type)
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trace_copy.filter(filter_type, freqmin=freqmin, freqmax=freqmax, zerophase=zerophase)
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trace_copy.taper(max_percentage=taper_max_percentage, type=taper_type)
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wfstream_copy[0].data = trace_copy.data
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return wfstream_copy, trace_copy
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# split components
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zdat, ndat, edat = get_components_from_waveformstream(wfstream)
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picks = {}
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station = wfstream[0].stats.station
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if not zdat:
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print('No z-component found for station {}. STOP'.format(station))
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return picks, station
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if p_params['algoP'] == 'HOS' or p_params['algoP'] == 'ARZ' and zdat is not None:
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msg = '##################################################\nautopickstation:' \
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' Working on P onset of station {station}\nFiltering vertical ' \
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'trace ...\n{data}'.format(station=wfstream[0].stats.station, data=str(zdat))
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if verbose: print(msg)
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z_copy, tr_filt = prepare_wfstream_component(zdat, freqmin=p_params['bpz1'][0], freqmax=p_params['bpz1'][1])
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##############################################################
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# check length of waveform and compare with cut times
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# for global seismology: use tau-p method for estimating travel times (needs source and station coords.)
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# if not given: sets Lc to infinity to use full stream
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if p_params['use_taup'] is True:
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Lc = np.inf
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print('autopickstation: use_taup flag active.')
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if not metadata:
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print('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
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else:
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station_id = wfstream[0].get_id()
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station_coords = metadata.get_coordinates(station_id, time=wfstream[0].stats.starttime)
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if station_coords and origin:
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source_origin = origin[0]
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model = TauPyModel(p_params['taup_model'])
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arrivals = model.get_travel_times_geo(
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source_origin.depth,
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source_origin.latitude,
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source_origin.longitude,
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station_coords['latitude'],
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station_coords['longitude']
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)
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phases = {'P': [],
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'S': []}
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for arr in arrivals:
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phases[identifyPhaseID(arr.phase.name)].append(arr)
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# get first P and S onsets from arrivals list
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arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
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arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
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print('autopick: estimated first arrivals for P: {} s, S:{} s after event'
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' origin time using TauPy'.format(estFirstP, estFirstS))
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# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
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p_params['pstart'] += (source_origin.time + estFirstP) - zdat[0].stats.starttime
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p_params['pstop']+= (source_origin.time + estFirstP) - zdat[0].stats.starttime
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print('autopick: CF calculation times respectively:'
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' pstart: {} s, pstop: {} s'.format(p_params['pstart'], p_params['pstop']))
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elif not origin:
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print('No source origins given!')
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# make sure pstart and pstop are inside zdat[0]
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pstart = max(p_params['pstart'], 0)
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pstop = min(p_params['pstop'], len(zdat[0])*zdat[0].stats.delta)
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if p_params['use_taup'] is False or origin:
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Lc = p_params['pstop'] - p_params['pstart']
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Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
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if not Lwf > 0:
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print('autopickstation: empty trace! Return!')
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return picks, station
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Ldiff = Lwf - abs(Lc)
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if Ldiff <= 0 or pstop <= pstart or pstop - pstart <= thosmw:
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msg = 'autopickstation: Cutting times are too large for actual ' \
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'waveform!\nUsing entire waveform instead!'
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if verbose: print(msg)
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pstart = 0
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pstop = len(zdat[0].data) * zdat[0].stats.delta
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cuttimes = [pstart, pstop]
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cf1 = None
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if p_params['algoP'] == 'HOS':
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# calculate HOS-CF using subclass HOScf of class
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# CharacteristicFunction
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cf1 = HOScf(z_copy, cuttimes, p_params['tlta'], p_params['hosorder']) # instance of HOScf
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elif p_params['algoP'] == 'ARZ':
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# calculate ARZ-CF using subclass ARZcf of class
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# CharcteristicFunction
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cf1 = ARZcf(z_copy, cuttimes, p_params['tpred1z'], p_params['Parorder'], p_params['tdet1z'],
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p_params['addnoise']) # instance of ARZcf
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##############################################################
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# calculate AIC-HOS-CF using subclass AICcf of class
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# CharacteristicFunction
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# class needs stream object => build it
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assert isinstance(cf1, CharacteristicFunction), 'cf2 is not set ' \
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'correctly: maybe the algorithm name ({algoP}) is ' \
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'corrupted'.format(algoP=p_params['algoP'])
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tr_aic = tr_filt.copy()
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tr_aic.data = cf1.getCF()
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z_copy[0].data = tr_aic.data
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aiccf = AICcf(z_copy, cuttimes) # instance of AICcf
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##############################################################
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# get preliminary onset time from AIC-HOS-CF using subclass AICPicker
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# of class AutoPicking
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key = 'aicFig'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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aicpick = AICPicker(aiccf, p_params['tsnrz'], p_params['pickwinP'], iplot, Tsmooth=p_params['aictsmooth'],
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fig=fig, linecolor=linecolor)
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# add pstart and pstop to aic plot
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if fig:
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for ax in fig.axes:
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ax.vlines(pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
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ax.vlines(pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
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ax.legend(loc=1)
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##############################################################
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if aicpick.getpick() is not None:
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# check signal length to detect spuriously picked noise peaks
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# use all available components to avoid skipping correct picks
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# on vertical traces with weak P coda
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z_copy[0].data = tr_filt.data
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zne = z_copy
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if len(ndat) == 0 or len(edat) == 0:
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msg = 'One or more horizontal component(s) missing!\n' \
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'Signal length only checked on vertical component!\n' \
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'Decreasing minsiglengh from {0} to {1}' \
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.format(signal_length_params['minsiglength'], signal_length_params['minsiglength'] / 2)
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if verbose: print(msg)
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key = 'slength'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), p_params['tsnrz'],
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signal_length_params['minsiglength'] / 2,
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signal_length_params['noisefactor'], signal_length_params['minpercent'], iplot,
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fig, linecolor)
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else:
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trH1_filt, _ = prepare_wfstream_component(edat, freqmin=s_params['bph1'][0], freqmax=s_params['bph1'][1])
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trH2_filt, _ = prepare_wfstream_component(ndat, freqmin=s_params['bph1'][0], freqmax=s_params['bph1'][1])
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zne += trH1_filt
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zne += trH2_filt
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if fig_dict:
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fig = fig_dict['slength']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), p_params['tsnrz'],
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signal_length_params['minsiglength'],
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signal_length_params['noisefactor'], signal_length_params['minpercent'], iplot,
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fig, linecolor)
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if Pflag == 1:
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# check for spuriously picked S onset
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# both horizontal traces needed
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if len(ndat) == 0 or len(edat) == 0:
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msg = 'One or more horizontal components missing!\n' \
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'Skipping control function checkZ4S.'
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if verbose: print(msg)
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else:
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if iplot > 1:
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if fig_dict:
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fig = fig_dict['checkZ4s']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checkZ4S(zne, aicpick.getpick(), s_params['zfac'],
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p_params['tsnrz'][2], iplot, fig, linecolor)
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if Pflag == 0:
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Pmarker = 'SinsteadP'
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Pweight = 9
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else:
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Pmarker = 'shortsignallength'
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Pweight = 9
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##############################################################
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# go on with processing if AIC onset passes quality control
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slope = aicpick.getSlope()
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if not slope:
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slope = 0
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if slope >= p_params['minAICPslope'] and aicpick.getSNR() >= p_params['minAICPSNR'] and Pflag == 1:
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aicPflag = 1
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msg = 'AIC P-pick passes quality control: Slope: {0} counts/s, ' \
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'SNR: {1}\nGo on with refined picking ...\n' \
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'autopickstation: re-filtering vertical trace ' \
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'...'.format(aicpick.getSlope(), aicpick.getSNR())
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if verbose: print(msg)
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# re-filter waveform with larger bandpass
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z_copy, tr_filt = prepare_wfstream_component(zdat, freqmin=p_params['bpz2'][0], freqmax=p_params['bpz2'][1])
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#############################################################
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# re-calculate CF from re-filtered trace in vicinity of initial
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# onset
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cuttimes2 = [round(max([aicpick.getpick() - p_params['Precalcwin'], 0])),
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round(min([len(zdat[0].data) * zdat[0].stats.delta,
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aicpick.getpick() + p_params['Precalcwin']]))]
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cf2 = None
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if p_params['algoP'] == 'HOS':
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# calculate HOS-CF using subclass HOScf of class
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# CharacteristicFunction
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cf2 = HOScf(z_copy, cuttimes2, p_params['tlta'],
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p_params['hosorder']) # instance of HOScf
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elif p_params['algoP'] == 'ARZ':
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# calculate ARZ-CF using subclass ARZcf of class
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# CharcteristicFunction
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cf2 = ARZcf(z_copy, cuttimes2, tpred2z, Parorder, tdet2z,
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addnoise) # instance of ARZcf
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##############################################################
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# get refined onset time from CF2 using class Picker
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assert isinstance(cf2, CharacteristicFunction), 'cf2 is not set ' \
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'correctly: maybe the algorithm name ({algoP}) is ' \
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'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(zdat[0].stats.starttime \
|
||||
+ mpickP, Perror)
|
||||
print(msg)
|
||||
Sflag = 1
|
||||
|
||||
else:
|
||||
msg = 'Bad initial (AIC) P-pick, skipping this onset!\n' \
|
||||
'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
|
||||
'(min. AIC-SNR={2}, ' \
|
||||
'min. AIC-Slope={3}counts/s)'.format(aicpick.getSNR(),
|
||||
aicpick.getSlope(),
|
||||
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(hdat[0].stats.starttime \
|
||||
+ 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)
|
||||
# only continue if one horizontal stream exists
|
||||
if (ndat or edat) and Sflag == 1:
|
||||
# mirror components in case one does not exist
|
||||
if not edat:
|
||||
edat = ndat
|
||||
if not ndat:
|
||||
ndat = edat
|
||||
if len(edat[0]) > 1 and len(ndat[0]) > 1:
|
||||
# plot horizontal traces
|
||||
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
|
||||
th1data = np.arange(0,
|
||||
trH1_filt.stats.npts /
|
||||
trH1_filt.stats.sampling_rate,
|
||||
trH1_filt.stats.delta)
|
||||
# check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH1_filt.data) - len(th1data)
|
||||
if wfldiff < 0:
|
||||
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
||||
ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), color=linecolor, linewidth=0.7, label='Data')
|
||||
if Pweight < 4:
|
||||
ax2.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
||||
if aicSflag == 1 and Sweight < 4:
|
||||
ax2.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g', label='Initial S Onset')
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
ax2.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
||||
ax2.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
ax2.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
ax2.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
||||
ax2.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
||||
ax2.set_title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
|
||||
trH1_filt.stats.channel,
|
||||
Sweight, SNRS, SNRSdB))
|
||||
else:
|
||||
ax2.set_title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
|
||||
trH1_filt.stats.channel, Sweight))
|
||||
ax2.legend(loc=1)
|
||||
ax2.set_yticks([])
|
||||
ax2.set_ylim([-1.5, 1.5])
|
||||
ax2.set_ylabel('Normalized Counts')
|
||||
# fig.suptitle(trH1_filt.stats.starttime)
|
||||
|
||||
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
|
||||
th2data = np.arange(0,
|
||||
trH2_filt.stats.npts /
|
||||
trH2_filt.stats.sampling_rate,
|
||||
trH2_filt.stats.delta)
|
||||
# check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH2_filt.data) - len(th2data)
|
||||
if wfldiff < 0:
|
||||
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
||||
ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), color=linecolor, linewidth=0.7, label='Data')
|
||||
if Pweight < 4:
|
||||
p22, = ax3.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
||||
if aicSflag == 1:
|
||||
ax3.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g', label='Initial S Onset')
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
ax3.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
||||
ax3.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
ax3.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
ax3.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
||||
ax3.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
||||
ax3.legend(loc=1)
|
||||
ax3.set_yticks([])
|
||||
ax3.set_ylim([-1.5, 1.5])
|
||||
ax3.set_xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
ax3.set_ylabel('Normalized Counts')
|
||||
ax3.set_title(trH2_filt.stats.channel)
|
||||
if plt_flag == 1:
|
||||
fig.show()
|
||||
try:
|
||||
input()
|
||||
except SyntaxError:
|
||||
pass
|
||||
plt.close(fig)
|
||||
##########################################################################
|
||||
# calculate "real" onset times
|
||||
if lpickP is not None and lpickP == mpickP:
|
||||
lpickP += zdat[0].stats.delta
|
||||
if epickP is not None and epickP == mpickP:
|
||||
epickP -= zdat[0].stats.delta
|
||||
if mpickP is not None and epickP is not None and lpickP is not None:
|
||||
lpickP = zdat[0].stats.starttime + lpickP
|
||||
epickP = zdat[0].stats.starttime + epickP
|
||||
mpickP = zdat[0].stats.starttime + mpickP
|
||||
else:
|
||||
# dummy values (start of seismic trace) in order to derive
|
||||
# theoretical onset times for iteratve picking
|
||||
lpickP = zdat[0].stats.starttime + p_params['timeerrorsP'][3]
|
||||
epickP = zdat[0].stats.starttime - p_params['timeerrorsP'][3]
|
||||
mpickP = zdat[0].stats.starttime
|
||||
|
||||
# create dictionary
|
||||
# for P phase
|
||||
ccode = zdat[0].stats.channel
|
||||
ncode = zdat[0].stats.network
|
||||
ppick = dict(channel=ccode, network=ncode, lpp=lpickP, epp=epickP, mpp=mpickP, spe=Perror, snr=SNRP,
|
||||
snrdb=SNRPdB, weight=Pweight, fm=FM, w0=None, fc=None, Mo=None,
|
||||
Mw=None, picker=picker, marked=Pmarker)
|
||||
|
||||
if edat:
|
||||
hdat = edat[0]
|
||||
elif ndat:
|
||||
hdat = ndat[0]
|
||||
else:
|
||||
# no horizontal components given
|
||||
picks = dict(P=ppick)
|
||||
return picks, station
|
||||
|
||||
if lpickS is not None and lpickS == mpickS:
|
||||
lpickS += hdat.stats.delta
|
||||
if epickS is not None and epickS == mpickS:
|
||||
epickS -= hdat.stats.delta
|
||||
if mpickS is not None and epickS is not None and lpickS is not None:
|
||||
lpickS = hdat.stats.starttime + lpickS
|
||||
epickS = hdat.stats.starttime + epickS
|
||||
mpickS = hdat.stats.starttime + mpickS
|
||||
else:
|
||||
# dummy values (start of seismic trace) in order to derive
|
||||
# theoretical onset times for iteratve picking
|
||||
lpickS = hdat.stats.starttime + s_params['timeerrorsS'][3]
|
||||
epickS = hdat.stats.starttime - s_params['timeerrorsS'][3]
|
||||
mpickS = hdat.stats.starttime
|
||||
|
||||
# add S phase
|
||||
ccode = hdat.stats.channel
|
||||
ncode = hdat.stats.network
|
||||
spick = dict(channel=ccode, network=ncode, lpp=lpickS, epp=epickS, mpp=mpickS, spe=Serror, snr=SNRS,
|
||||
snrdb=SNRSdB, weight=Sweight, fm=None, picker=picker, Ao=Ao)
|
||||
# merge picks into returning dictionary
|
||||
picks = dict(P=ppick, S=spick)
|
||||
return picks, station
|
||||
|
||||
|
||||
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter, fig_dict=None):
|
||||
"""
|
||||
Repicking of bad onsets. Uses theoretical onset times from NLLoc-location file.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user