core.py 15.9 KB
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import math
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import logging
import os.path as op
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import shutil
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from collections import defaultdict

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import numpy as num

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from pyrocko import pile, trace, util, io, model
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from pyrocko.parstack import parstack, argmax as pargmax
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from pyrocko.guts import Object, Timestamp, String, Float
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from lassie import common, plot, grid as gridmod, geo
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from lassie.config import write_config
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logger = logging.getLogger('lassie.core')


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class Detection(Object):
    id = String.T()
    time = Timestamp.T()
    location = geo.Point.T()
    ifm = Float.T()

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    def get_event(self):
        lat, lon = geo.point_coords(self.location, system='latlon')

        event = model.Event(
            lat=lat,
            lon=lon,
            depth=self.location.z,
            time=self.time,
            name='%s (%g)' % (self.id, self.ifm))

        return event

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def check_data_consistency(p, config):
    receivers = config.get_receivers()
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    nsl_ids = [nslc_id[:3] for nslc_id in p.nslc_ids.keys()]
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    r_ids = [r.codes for r in receivers]

    r_not_in_p = []
    t_not_in_r = []
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    to_be_blacklisted = []

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    for r in receivers:
        if r.codes[:3] not in nsl_ids:
            r_not_in_p.append(r.codes)
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        if '.'.join(r.codes[:3]) in config.blacklist:
            to_be_blacklisted.append('.'.join(r.codes))
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    for nsl_id in nsl_ids:
        if nsl_id not in r_ids:
            t_not_in_r.append(nsl_id)

    if len(r_not_in_p) != 0.:
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        logger.warn('Following receivers have no traces in data set:')
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        for nsl_id in r_not_in_p:
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            logger.warn('  %s' % '.'.join(nsl_id))
        logger.warn('-' * 40)
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    if len(t_not_in_r) != 0.:
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        logger.warn('Following traces have no associated receivers:')
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        for nsl_id in t_not_in_r:
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            logger.warn('  %s' % '.'.join(nsl_id))
        logger.warn('-' * 40)

    if len(to_be_blacklisted):
        logger.info('Blacklisted receivers:')
        for code in to_be_blacklisted:
            logger.info('  %s' % code)
        logger.info('-' * 40)

    if len(config.blacklist) and\
            len(to_be_blacklisted) != len(config.blacklist):
        logger.warn('Blacklist NSL codes that did not match any receiver:')
        for code in config.blacklist:
            if code not in to_be_blacklisted:
                logger.warn('  %s' % code)
        logger.info('-' * 40)
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def zero_fill(trs, tmin, tmax):
    trs = trace.degapper(trs)

    d = defaultdict(list)
    for tr in trs:
        d[tr.nslc_id].append(tr)

    trs_out = []
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    for nslc, trs_group in d.items():
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        if not all(tr.deltat == trs_group[0].deltat for tr in trs_group):
            logger.warn('inconsistent sample rate, cannot merge traces')
            continue

        if not all(tr.ydata.dtype == trs_group[0].ydata.dtype
                   for tr in trs_group):

            logger.warn('inconsistent data type, cannot merge traces')
            continue

        tr_combi = trs_group[0].copy()
        tr_combi.extend(tmin, tmax, fillmethod='zeros')

        for tr in trs_group[1:]:
            tr_combi.add(tr)

        trs_out.append(tr_combi)

    return trs_out

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def search(
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        config,
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        override_tmin=None,
        override_tmax=None,
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        show_detections=False,
        show_movie=False,
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        show_window_traces=False,
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        force=False,
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        stop_after_first=False,
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        nparallel=None,
        bark=False):
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    fp = config.expand_path
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    run_path = fp(config.run_path)

    if op.exists(run_path):
        if force:
            shutil.rmtree(run_path)
        else:
            raise common.LassieError(
                'run directory already exists: %s' %
                run_path)

    util.ensuredir(run_path)

    write_config(config, op.join(run_path, 'config.yaml'))
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    ifm_path_template = config.get_ifm_path_template()
    detections_path = config.get_detections_path()
    events_path = config.get_events_path()
    figures_path_template = config.get_figures_path_template()
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    config.setup_image_function_contributions()
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    ifcs = config.image_function_contributions

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    grid = config.get_grid()
    receivers = config.get_receivers()

    norm_map = gridmod.geometrical_normalization(grid, receivers)

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    data_paths = fp(config.data_paths)
    for data_path in fp(data_paths):
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        if not op.exists(data_path):
            raise common.LassieError(
                'waveform data path does not exist: %s' % data_path)

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    p = pile.make_pile(data_paths, fileformat='detect')
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    if p.is_empty():
        raise common.LassieError('no usable waveforms found')

    for ifc in ifcs:
        ifc.prescan(p)
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    shift_tables = []
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    tshift_minmaxs = []
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    for ifc in ifcs:
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        shift_tables.append(ifc.get_table(grid, receivers))
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        tshift_minmaxs.append(num.nanmin(shift_tables[-1]))
        tshift_minmaxs.append(num.nanmax(shift_tables[-1]))
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    fsmooth_min = min(ifc.get_fsmooth() for ifc in ifcs)

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    tshift_min = min(tshift_minmaxs)
    tshift_max = max(tshift_minmaxs)
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    if config.detector_tpeaksearch is not None:
        tpeaksearch = config.detector_tpeaksearch
    else:
        tpeaksearch = (tshift_max - tshift_min) + 1.0 / fsmooth_min
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    tpad = max(ifc.get_tpad() for ifc in ifcs) + \
        (tshift_max - tshift_min) + tpeaksearch
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    tinc = (tshift_max - tshift_min) * 10. + 3.0 * tpad
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    tavail = p.tmax - p.tmin
    tinc = min(tinc, tavail - 2.0 * tpad)

    if tinc <= 0:
        raise common.LassieError(
            'available waveforms too short \n'
            'required: %g s\n'
            'available: %g s\n' % (2.*tpad, tavail))
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    blacklist = set(tuple(s.split('.')) for s in config.blacklist)
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    whitelist = set(tuple(s.split('.')) for s in config.whitelist)

    distances = grid.distances(receivers)
    distances_to_grid = num.min(distances, axis=0)
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    distance_min = num.min(distances)
    distance_max = num.max(distances)

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    station_index = dict(
        (rec.codes, i) for (i, rec) in enumerate(receivers)
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        if rec.codes not in blacklist and (
            not whitelist or rec.codes in whitelist) and (
            config.distance_max is None or
                distances_to_grid[i] <= config.distance_max))
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    check_data_consistency(p, config)
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    deltat_cf = max(p.deltats.keys())
    assert deltat_cf > 0.0

    while True:
        if not all(ifc.deltat_cf_is_available(deltat_cf * 2) for ifc in ifcs):
            break

        deltat_cf *= 2

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    logger.info('CF sampling interval (rate): %g s (%g Hz)' % (
        deltat_cf, 1.0/deltat_cf))
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    ngridpoints = grid.size()

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    logger.info('number of grid points: %i' % ngridpoints)
    logger.info('minimum source-receiver distance: %g m' % distance_min)
    logger.info('maximum source-receiver distance: %g m' % distance_max)
    logger.info('minimum travel-time: %g s' % tshift_min)
    logger.info('maximum travel-time: %g s' % tshift_max)

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    idetection = 0

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    tmin = override_tmin or config.tmin or p.tmin + tpad
    tmax = override_tmax or config.tmax or p.tmax - tpad
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    events = config.get_events()
    twindows = []
    if events is not None:
        for ev in events:
            if tmin <= ev.time <= tmax:
                twindows.append((
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                    ev.time + tshift_min - (tshift_max - tshift_min) *
                    config.event_time_window_factor,
                    ev.time + tshift_min + (tshift_max - tshift_min) *
                    config.event_time_window_factor))
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    else:
        twindows.append((tmin, tmax))
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    for iwindow_group, (tmin_win, tmax_win) in enumerate(twindows):

        nwin = int(math.ceil((tmax_win - tmin_win) / tinc))

        logger.info('start processing time window group %i/%i: %s - %s' % (
            iwindow_group + 1, len(twindows),
            util.time_to_str(tmin_win),
            util.time_to_str(tmax_win)))

        logger.info('number of time windows: %i' % nwin)
        logger.info('time window length: %g s' % (tinc + 2.0*tpad))
        logger.info('time window payload: %g s' % tinc)
        logger.info('time window padding: 2 x %g s' % tpad)
        logger.info('time window overlap: %g%%' % (
            100.0*2.0*tpad / (tinc+2.0*tpad)))

        iwin = -1
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        for trs in p.chopper(tmin=tmin_win, tmax=tmax_win, tinc=tinc, tpad=tpad,
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                want_incomplete=config.fill_incomplete_with_zeros,
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                trace_selector=lambda tr: tr.nslc_id[:3] in station_index):
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            iwin += 1
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            trs_ok = []
            for tr in trs:
                if tr.ydata.size == 0:
                    logger.warn(
                        'skipping empty trace: %s.%s.%s.%s' % tr.nslc_id)
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                    continue
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                if not num.all(num.isfinite(tr.ydata)):
                    logger.warn(
                        'skipping trace because of invalid values: '
                        '%s.%s.%s.%s' % tr.nslc_id)
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                    continue
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                trs_ok.append(tr)
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            trs = trs_ok
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            if not trs:
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                continue

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            logger.info('processing time window %i/%i: %s - %s' % (
                iwin + 1, nwin,
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                util.time_to_str(trs[0].wmin),
                util.time_to_str(trs[0].wmax)))
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            wmin = trs[0].wmin
            wmax = trs[0].wmax
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            if config.fill_incomplete_with_zeros:
                trs = zero_fill(trs, wmin - tpad, wmax + tpad)
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            frames = None
            pdata = []
            trs_debug = []
            shift_maxs = []
            for iifc, ifc in enumerate(ifcs):
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                dataset = ifc.preprocess(
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                    trs, wmin-tpeaksearch, wmax+tpeaksearch,
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                    tshift_max - tshift_min, deltat_cf)
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                if not dataset:
                    continue
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                nstations_selected = len(dataset)
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                nsls_selected, trs_selected = zip(*dataset)
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                for tr in trs_selected:
                    tr.meta = {'tabu': True}

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                trs_debug.extend(trs + list(trs_selected))
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                t0 = (wmin / deltat_cf) * deltat_cf
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                istations_selected = num.array(
                    [station_index[nsl] for nsl in nsls_selected],
                    dtype=num.int)
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                arrays = [tr.ydata.astype(num.float) for tr in trs_selected]
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                offsets = num.array(
                    [int(round((tr.tmin-t0) / deltat_cf))
                     for tr in trs_selected],
                    dtype=num.int32)
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                w = ifc.get_weights(nsls_selected)
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                weights = num.ones((ngridpoints, nstations_selected))
                weights *= w[num.newaxis, :]
                weights *= ifc.weight
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                shift_table = shift_tables[iifc]
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                ok = num.isfinite(shift_table[:, istations_selected])
                bad = num.logical_not(ok)

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                shifts = -num.round(
                    shift_table[:, istations_selected] /
                    deltat_cf).astype(num.int32)
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                weights[bad] = 0.0
                shifts[bad] = num.max(shifts[ok])

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                pdata.append((list(trs_selected), shift_table, ifc))
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                iwmin = int(round((wmin-tpeaksearch-t0) / deltat_cf))
                iwmax = int(round((wmax+tpeaksearch-t0) / deltat_cf))
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                lengthout = iwmax - iwmin + 1
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                shift_maxs.append(num.max(-shifts) * deltat_cf)
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                frames, ioff = parstack(
                    arrays, offsets, shifts, weights, 0,
                    offsetout=iwmin,
                    lengthout=lengthout,
                    result=frames,
                    nparallel=nparallel,
                    impl='openmp')
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            shift_max = max(shift_maxs)
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            if config.sharpness_normalization:
                frame_maxs = frames.max(axis=0)
                frame_means = num.abs(frames).mean(axis=0)
                frames *= (frame_maxs / frame_means)[num.newaxis, :]
                frames *= norm_map[:, num.newaxis]
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            if config.ifc_count_normalization:
                frames *= 1.0 / len(ifcs)

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            frame_maxs = frames.max(axis=0)
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            tmin_frames = t0 + ioff * deltat_cf
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            tr_stackmax = trace.Trace(
                '', 'SMAX', '', '',
                tmin=tmin_frames,
                deltat=deltat_cf,
                ydata=frame_maxs)
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            tr_stackmax.meta = {'tabu': True}

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            trs_debug.append(tr_stackmax)
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            if show_window_traces:
                trace.snuffle(trs_debug)
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            ydata_window = tr_stackmax.chop(
                wmin, wmax, inplace=False).get_ydata()
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            logger.info('CF stats: min %g, max %g, median %g' % (
                num.min(ydata_window),
                num.max(ydata_window),
                num.median(ydata_window)))
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            tpeaks, apeaks = list(zip(*[(tpeak, apeak) for (tpeak, apeak) in zip(
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                *tr_stackmax.peaks(config.detector_threshold, tpeaksearch)) if
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                wmin <= tpeak and tpeak < wmax])) or ([], [])
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            tr_stackmax_indx = tr_stackmax.copy(data=False)
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            imaxs = pargmax(frames)
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            tr_stackmax_indx.set_ydata(imaxs.astype(num.int32))
            tr_stackmax_indx.set_location('i')

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            for (tpeak, apeak) in zip(tpeaks, apeaks):
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                iframe = int(round(((tpeak-t0) - ioff*deltat_cf) / deltat_cf))
                frame = frames[:, iframe]
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                imax = imaxs[iframe]
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                latpeak, lonpeak, xpeak, ypeak, zpeak = \
                    grid.index_to_location(imax)
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                idetection += 1
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                detection = Detection(
                    id='%06i' % idetection,
                    time=tpeak,
                    location=geo.Point(
                        lat=float(latpeak),
                        lon=float(lonpeak),
                        x=float(xpeak),
                        y=float(ypeak),
                        z=float(zpeak)),
                    ifm=float(apeak))

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                if bark:
                    common.bark()

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                logger.info('detection: %s' % str(detection))

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                f = open(detections_path, 'a')
                f.write('%06i %s %g %g %g %g %g %g\n' % (
                    idetection,
                    util.time_to_str(
                        tpeak,
                        format='%Y-%m-%d %H:%M:%S.6FRAC'),
                    apeak,
                    latpeak, lonpeak, xpeak, ypeak, zpeak))
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                f.close()
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                ev = detection.get_event()
                f = open(events_path, 'a')
                model.dump_events([ev], stream=f)
                f.close()

                if show_detections or config.save_figures:
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                    fmin = min(ifc.fmin for ifc in ifcs)
                    fmax = min(ifc.fmax for ifc in ifcs)
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                    fn = figures_path_template % {
                        'id': idetection,
                        'format': 'png'}

                    util.ensuredirs(fn)

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                    try:
                        plot.plot_detection(
                            grid, receivers, frames, tmin_frames,
                            deltat_cf, imax, iframe, fsmooth_min, xpeak, ypeak,
                            zpeak,
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                            tr_stackmax, tpeaks, apeaks,
                            config.detector_threshold,
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                            wmin, wmax,
                            pdata, trs, fmin, fmax, idetection,
                            grid_station_shift_max=shift_max,
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                            movie=show_movie,
                            show=show_detections,
                            save_filename=fn)
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                    except AttributeError as e:
                        logger.warn(e)
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                del frame
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                if stop_after_first:
                    return
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            tr_stackmax.chop(wmin, wmax)
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            tr_stackmax_indx.chop(wmin, wmax)
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            io.save([tr_stackmax, tr_stackmax_indx], ifm_path_template)
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            del frames
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        logger.info('end processing time window group: %s - %s' % (
            util.time_to_str(tmin_win),
            util.time_to_str(tmax_win)))

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__all__ = [
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    'search',
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]