core.py 8.2 KB
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import sys
import os
import logging
import os.path as op
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import shutil
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import numpy as num

from pyrocko import pile, trace, util, io
from pyrocko.parstack import parstack

from lassie import common, plot, grid as gridmod

logger = logging.getLogger('lassie.core')


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

    r_not_in_p = []
    t_not_in_r = []
    for r in receivers:
        if r.codes[:3] not in nsl_ids:
            r_not_in_p.append(r.codes)

    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.:
        logger.warn('following receivers have no traces in data set:')
        for nsl_id in r_not_in_p:
            logger.warn(nsl_id)

    if len(t_not_in_r) != 0.:
        logger.warn('following traces have no associated receivers:')
        for nsl_id in t_not_in_r:
            logger.warn(nsl_id)



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def scan(
        config,
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        override_tmin=None,
        override_tmax=None,
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        show_detections=False,
        show_movie=False,
        force=False,
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        stop_after_first=False,
        nparallel=None):
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    if config.detections_path:
        if op.exists(config.detections_path):
            if force:
                os.unlink(config.detections_path)
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                shutil.rmtree('stackmax')
                os.mkdir('stackmax')
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            else:
                raise common.LassieError(
                    'detections file already exists: %s' % config.detections_path)

        util.ensuredirs(config.detections_path)

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

    norm_map = gridmod.geometrical_normalization(grid, receivers)

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

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

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    ifcs = config.image_function_contributions
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    for ifc in ifcs:
        ifc.prescan(p)
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    shift_tables = []
    tshift_maxs = []
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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_maxs.append(shift_tables[-1].max())

    tshift_max = max(tshift_maxs) * 1.0

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    tpad = max(ifc.get_tpad() for ifc in ifcs) + tshift_max
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    tinc = tshift_max * 10 + 3*tpad

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    fsmooth_min = min(ifc.get_fsmooth() for ifc in ifcs)
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    blacklist = set(tuple(s.split('.')) for s in config.blacklist)

    station_index = dict(
        (rec.codes, i) for (i, rec) in enumerate(receivers)
        if rec.codes not in blacklist)

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    for data_path in config.data_paths:
        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(config.data_paths, fileformat='detect')
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    if p.is_empty():
        raise common.LassieError('no usable waveforms found')
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    check_data_consistency(p, receivers)

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    ngridpoints = grid.size()

    idetection = 0

    station_weights = {}

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    tmin = override_tmin or config.tmin
    tmax = override_tmax or config.tmax
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    for trs in p.chopper(
            tmin=tmin, tmax=tmax, tinc=tinc, tpad=tpad,
            want_incomplete=False,
            trace_selector=lambda tr: tr.nslc_id[:3] in station_index):

        if not trs:
            continue

        logger.info('processing time window %s - %s' % (
            util.time_to_str(trs[0].wmin),
            util.time_to_str(trs[0].wmax)))

        wmin = trs[0].wmin
        wmax = trs[0].wmax

        frames = None
        pdata = []
        trs_debug = []
        shift_maxs = []
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        for iifc, ifc in enumerate(ifcs):
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            dataset = ifc.preprocess(trs, wmin, wmax, tshift_max)
            if not dataset:
                continue

            nstations_selected = len(dataset)

            nsls_selected, trs_selected = zip(*dataset)

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            #trs_debug.extend(trs + list(trs_selected))
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            deltat_cf = trs_selected[0].deltat

            t0 = (wmin / deltat_cf) * deltat_cf

            istations_selected = num.array(
                [station_index[nsl] for nsl in nsls_selected], dtype=num.int)

            arrays = [tr.ydata.astype(num.float) for tr in trs_selected]

            offsets = num.array(
                [int(round((tr.tmin-t0) / deltat_cf)) for tr in trs_selected],
                dtype=num.int32)

            w = num.array(
                [station_weights.get(nsl, 1.0) for nsl in nsls_selected],
                dtype=num.float)


            weights = num.ones((ngridpoints, nstations_selected))
            weights *= w[num.newaxis, :]
            weights *= ifc.weight

            shift_table = shift_tables[iifc]

            shifts = -num.round(
                shift_table[:, istations_selected] /
                deltat_cf).astype(num.int32)

            pdata.append((list(trs_selected), shift_table, ifc))

            iwmin = int(round((wmin-t0) / deltat_cf))
            iwmax = int(round((wmax-t0) / deltat_cf))

            lengthout = iwmax - iwmin + 1

            shift_maxs.append(num.max(-shifts) * deltat_cf)

            frames, ioff = parstack(
                arrays, offsets, shifts, weights, 0,
                offsetout=iwmin,
                lengthout=lengthout,
                result=frames,
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                nparallel=nparallel,
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                impl='openmp')

        shift_max = max(shift_maxs)

        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]

        frame_maxs = frames.max(axis=0)

        tmin_frames = t0 + ioff * deltat_cf

        tr_stackmax = trace.Trace(
            '', 'SMAX', '', '',
            tmin=tmin_frames,
            deltat=deltat_cf,
            ydata=frame_maxs)

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        #trs_debug.append(tr_stackmax)
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        # trace.snuffle(trs_debug)

        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' % (
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            num.min(ydata_window),
            num.max(ydata_window),
            num.median(ydata_window)))

        tpeaks, apeaks = tr_stackmax.peaks(
            config.detector_threshold, shift_max + 1.0/fsmooth_min)

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        tr_stackmax_indx = tr_stackmax.copy(data=False)
        imaxs = num.argmax(frames, axis=0)
        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):
            if not (wmin <= tpeak and tpeak < wmax):
                continue

            logger.info('detection: %s %g' % (
                util.time_to_str(tpeak),
                apeak))

            iframe = int(round(((tpeak-t0) - ioff*deltat_cf) / deltat_cf))
            frame = frames[:, iframe]
            imax = num.argmax(frame)

            latpeak, lonpeak, xpeak, ypeak, zpeak = grid.index_to_location(imax)

            idetection += 1

            if config.detections_path:
                f = open(config.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))

                f.close()

            if show_detections:
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                fmin = min(ifc.fmin for ifc in ifcs)
                fmax = min(ifc.fmax for ifc in ifcs)
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                plot.plot_detection(
                    grid, receivers, frames, tmin_frames,
                    deltat_cf, imax, iframe, fsmooth_min, xpeak, ypeak, zpeak,
                    tr_stackmax, tpeaks, apeaks, config.detector_threshold,
                    pdata, trs, fmin, fmax, idetection,
                    grid_station_shift_max=shift_max,
                    movie=show_movie)

            if stop_after_first:
                return
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        tr_stackmax.chop(wmin, wmax)
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        io.save([tr_stackmax, tr_stackmax_indx], 'stackmax/trace_%(tmin_ms)s.mseed')
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__all__ = [
    'scan',
]