Source code for

import logging
import os
import pycbc
import numpy
import lal
import json
from six import u as unicode
from glue.ligolw import ligolw
from glue.ligolw import lsctables
from glue.ligolw import utils as ligolw_utils
from glue.ligolw.utils import process as ligolw_process
from glue.ligolw import param as ligolw_param
from pycbc import version as pycbc_version
from pycbc import pnutils
from pycbc.tmpltbank import return_empty_sngl
from pycbc.results import ifo_color
from pycbc.results import source_color
from pycbc.mchirp_area import calc_probabilities

#FIXME Legacy build PSD xml helpers, delete me when we move away entirely from
# xml formats
def _build_series(series, dim_names, comment, delta_name, delta_unit):
    from glue.ligolw import array as ligolw_array
    Attributes = ligolw.sax.xmlreader.AttributesImpl
    elem = ligolw.LIGO_LW(
            Attributes({u"Name": unicode(series.__class__.__name__)}))
    if comment is not None:
        elem.appendChild(ligolw.Comment()).pcdata = comment
    elem.appendChild(ligolw.Time.from_gps(series.epoch, u"epoch"))
    elem.appendChild(ligolw_param.Param.from_pyvalue(u"f0", series.f0,
    delta = getattr(series, delta_name)
    if numpy.iscomplexobj(
        data = numpy.row_stack((numpy.arange(len( * delta,
        data = numpy.row_stack((numpy.arange(len( * delta,
    a =, data, dim_names=dim_names)
    a.Unit = str(series.sampleUnits)
    dim0 = a.getElementsByTagName(ligolw.Dim.tagName)[0]
    dim0.Unit = delta_unit
    dim0.Start = series.f0
    dim0.Scale = delta
    return elem

[docs]def snr_series_to_xml(snr_series, document, sngl_inspiral_id): """Save an SNR time series into an XML document, in a format compatible with BAYESTAR. """ snr_lal = snr_series.lal() = 'snr' snr_lal.sampleUnits = '' snr_xml = _build_series(snr_lal, (u'Time', u'Time,Real,Imaginary'), None, 'deltaT', 's') snr_node = document.childNodes[-1].appendChild(snr_xml) eid_param ='event_id', u'ilwd:char', sngl_inspiral_id) snr_node.appendChild(eid_param)
[docs]def make_psd_xmldoc(psddict, xmldoc=None): """Add a set of PSDs to a LIGOLW XML document. If the document is not given, a new one is created first. """ xmldoc = ligolw.Document() if xmldoc is None else xmldoc.childNodes[0] # the PSDs must be children of a LIGO_LW with name "psd" root_name = u"psd" Attributes = ligolw.sax.xmlreader.AttributesImpl lw = xmldoc.appendChild( ligolw.LIGO_LW(Attributes({u"Name": root_name}))) for instrument, psd in psddict.items(): xmlseries = _build_series(psd, (u"Frequency,Real", u"Frequency"), None, 'deltaF', 's^-1') fs = lw.appendChild(xmlseries) fs.appendChild(ligolw_param.Param.from_pyvalue(u"instrument", instrument)) return xmldoc
[docs]class SingleCoincForGraceDB(object): """Create xml files and submit them to gracedb from PyCBC Live""" def __init__(self, ifos, coinc_results, **kwargs): """Initialize a ligolw xml representation of a zerolag trigger for upload from pycbc live to gracedb. Parameters ---------- ifos: list of strs A list of the ifos participating in this trigger. coinc_results: dict of values A dictionary of values. The format is defined in pycbc/events/ and matches the on disk representation in the hdf file for this time. psds: dict of FrequencySeries Dictionary providing PSD estimates for all involved detectors. low_frequency_cutoff: float Minimum valid frequency for the PSD estimates. high_frequency_cutoff: float, optional Maximum frequency considered for the PSD estimates. Default None. followup_data: dict of dicts, optional Dictionary providing SNR time series for each detector, to be used in sky localization with BAYESTAR. The format should be `followup_data['H1']['snr_series']`. More detectors can be present than given in `ifos`. If so, the extra detectors will only be used for sky localization. channel_names: dict of strings, optional Strain channel names for each detector. Will be recorded in the sngl_inspiral table. mc_area_args: dict of dicts, optional Dictionary providing arguments to be used in source probability estimation with pycbc/ """ self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]] self.coinc_results = coinc_results self.ifos = ifos # remember if this should be marked as HWINJ self.is_hardware_injection = ('HWINJ' in coinc_results and coinc_results['HWINJ']) # Check if we need to apply a time offset (this may be permerger) self.time_offset = 0 rtoff = 'foreground/{}/time_offset'.format(ifos[0]) if rtoff in coinc_results: self.time_offset = coinc_results[rtoff] if 'followup_data' in kwargs: fud = kwargs['followup_data'] assert len({fud[ifo]['snr_series'].delta_t for ifo in fud}) == 1, \ "delta_t for all ifos do not match" self.snr_series = {ifo: fud[ifo]['snr_series'] for ifo in fud} usable_ifos = fud.keys() followup_ifos = list(set(usable_ifos) - set(ifos)) for ifo in self.snr_series: self.snr_series[ifo].start_time += self.time_offset else: self.snr_series = None usable_ifos = ifos followup_ifos = [] # Set up the bare structure of the xml document outdoc = ligolw.Document() outdoc.appendChild(ligolw.LIGO_LW()) proc_id = ligolw_process.register_to_xmldoc( outdoc, 'pycbc', {}, ifos=usable_ifos, comment='', version=pycbc_version.version, cvs_repository='pycbc/'+pycbc_version.git_branch, # Set up coinc_definer table coinc_def_table = lsctables.New(lsctables.CoincDefTable) coinc_def_id = lsctables.CoincDefID(0) coinc_def_row = lsctables.CoincDef() = "inspiral" coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs" coinc_def_row.coinc_def_id = coinc_def_id coinc_def_row.search_coinc_type = 0 coinc_def_table.append(coinc_def_row) outdoc.childNodes[0].appendChild(coinc_def_table) # Set up coinc inspiral and coinc event tables coinc_id = lsctables.CoincID(0) coinc_event_table = lsctables.New(lsctables.CoincTable) coinc_event_row = lsctables.Coinc() coinc_event_row.coinc_def_id = coinc_def_id coinc_event_row.nevents = len(usable_ifos) coinc_event_row.instruments = ','.join(usable_ifos) coinc_event_row.time_slide_id = lsctables.TimeSlideID(0) coinc_event_row.process_id = proc_id coinc_event_row.coinc_event_id = coinc_id coinc_event_row.likelihood = 0. coinc_event_table.append(coinc_event_row) outdoc.childNodes[0].appendChild(coinc_event_table) # Set up sngls sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable) coinc_event_map_table = lsctables.New(lsctables.CoincMapTable) sngl_populated = None network_snrsq = 0 for sngl_id, ifo in enumerate(usable_ifos): sngl = return_empty_sngl(nones=True) sngl.event_id = lsctables.SnglInspiralID(sngl_id) sngl.process_id = proc_id sngl.ifo = ifo names = [n.split('/')[-1] for n in coinc_results if 'foreground/%s' % ifo in n] for name in names: val = coinc_results['foreground/%s/%s' % (ifo, name)] if name == 'end_time': val += self.time_offset sngl.set_end(lal.LIGOTimeGPS(val)) else: try: setattr(sngl, name, val) except AttributeError: pass if sngl.mass1 and sngl.mass2: sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta( sngl.mass1, sngl.mass2) sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta( sngl.mass1, sngl.mass2) sngl_populated = sngl if sngl.snr: sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr network_snrsq += sngl.snr ** 2.0 if 'channel_names' in kwargs and ifo in kwargs['channel_names']: = kwargs['channel_names'][ifo] sngl_inspiral_table.append(sngl) # Set up coinc_map entry coinc_map_row = lsctables.CoincMap() coinc_map_row.table_name = 'sngl_inspiral' coinc_map_row.coinc_event_id = coinc_id coinc_map_row.event_id = sngl.event_id coinc_event_map_table.append(coinc_map_row) if self.snr_series is not None: snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id) # set merger time to the average of the ifo peaks self.merger_time = numpy.mean( [coinc_results['foreground/{}/end_time'.format(ifo)] for ifo in ifos]) + self.time_offset # for subthreshold detectors, respect BAYESTAR's assumptions and checks bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x ' 'spin1y spin1z spin2x spin2y spin2z').split() for sngl in sngl_inspiral_table: if sngl.ifo in followup_ifos: for bcf in bayestar_check_fields: setattr(sngl, bcf, getattr(sngl_populated, bcf)) sngl.set_end(lal.LIGOTimeGPS(self.merger_time)) outdoc.childNodes[0].appendChild(coinc_event_map_table) outdoc.childNodes[0].appendChild(sngl_inspiral_table) # Set up the coinc inspiral table coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable) coinc_inspiral_row = lsctables.CoincInspiral() # This seems to be used as FAP, which should not be in gracedb coinc_inspiral_row.false_alarm_rate = 0 coinc_inspiral_row.minimum_duration = 0. coinc_inspiral_row.set_ifos(usable_ifos) coinc_inspiral_row.coinc_event_id = coinc_id coinc_inspiral_row.mchirp = sngl_populated.mchirp coinc_inspiral_row.mass = sngl_populated.mtotal coinc_inspiral_row.end_time = sngl_populated.end_time coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns coinc_inspiral_row.snr = network_snrsq ** 0.5 far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar']) coinc_inspiral_row.combined_far = far coinc_inspiral_table.append(coinc_inspiral_row) outdoc.childNodes[0].appendChild(coinc_inspiral_table) # append the PSDs self.psds = kwargs['psds'] psds_lal = {} for ifo in self.psds: psd = self.psds[ifo] kmin = int(kwargs['low_frequency_cutoff'] / psd.delta_f) fseries = lal.CreateREAL8FrequencySeries( "psd", psd.epoch, kwargs['low_frequency_cutoff'], psd.delta_f, lal.StrainUnit**2 / lal.HertzUnit, len(psd) - kmin) = psd.numpy()[kmin:] / pycbc.DYN_RANGE_FAC ** 2.0 psds_lal[ifo] = fseries make_psd_xmldoc(psds_lal, outdoc) # source probabilities estimation if 'mc_area_args' in kwargs: eff_distances = [sngl.eff_distance for sngl in sngl_inspiral_table] probabilities = calc_probabilities(coinc_inspiral_row.mchirp, coinc_inspiral_row.snr, min(eff_distances), kwargs['mc_area_args']) self.probabilities = probabilities else: self.probabilities = None self.outdoc = outdoc self.time = sngl_populated.get_end()
[docs] def save(self, filename): """Write this trigger to gracedb compatible xml format Parameters ---------- filename: str Name of file to write to disk. """ gz = filename.endswith('.gz') ligolw_utils.write_filename(self.outdoc, filename, gz=gz) # save source probabilities in a json file if self.probabilities is not None: prob_fname = filename.replace('.xml.gz', '_probs.json') with open(prob_fname, 'w') as prob_outfile: json.dump(self.probabilities, prob_outfile)'Source probabilities file saved as %s', prob_fname)
[docs] def upload(self, fname, gracedb_server=None, testing=True, extra_strings=None, search='AllSky'): """Upload this trigger to gracedb Parameters ---------- fname: str The name to give the xml file associated with this trigger gracedb_server: string, optional URL to the GraceDB web API service for uploading the event. If omitted, the default will be used. testing: bool Switch to determine if the upload should be sent to gracedb as a test trigger (True) or a production trigger (False). search: str String going into the "search" field of the GraceDB event. """ from import GraceDb import matplotlib matplotlib.use('Agg') import pylab as pl # first of all, make sure the event is saved on disk # as GraceDB operations can fail later if self.snr_series is not None: if fname.endswith('.xml.gz'): snr_series_fname = fname.replace('.xml.gz', '.hdf') else: snr_series_fname = fname.replace('.xml', '.hdf') snr_series_plot_fname = snr_series_fname.replace('.hdf', '_snr.png') psd_series_plot_fname = snr_series_fname.replace('.hdf', '_psd.png') pl.figure() ref_time = int(self.merger_time) for ifo in sorted(self.snr_series): curr_snrs = self.snr_series[ifo], group='%s/snr' % ifo) pl.plot(curr_snrs.sample_times - ref_time, abs(curr_snrs), c=ifo_color(ifo), label=ifo) if ifo in self.ifos: base = 'foreground/{}/'.format(ifo) snr = self.coinc_results[base + 'snr'] mt = (self.coinc_results[base + 'end_time'] + self.time_offset) pl.plot([mt - ref_time], [snr], c=ifo_color(ifo), marker='x') pl.legend() pl.xlabel('GPS time from {:d} (s)'.format(ref_time)) pl.ylabel('SNR') pl.savefig(snr_series_plot_fname) pl.close() pl.figure() for ifo in sorted(self.snr_series): # Undo dynamic range factor curr_psd = self.psds[ifo].astype(numpy.float64) curr_psd /= pycbc.DYN_RANGE_FAC ** 2.0, group='%s/psd' % ifo) # Can't plot log(0) so start from point 1 pl.loglog(curr_psd.sample_frequencies[1:], curr_psd[1:]**0.5, c=ifo_color(ifo), label=ifo) pl.legend() pl.xlim([10, 1300]) pl.ylim([3E-24, 1E-20]) pl.xlabel('Frequency (Hz)') pl.ylabel('ASD') pl.savefig(psd_series_plot_fname) pl.close() if self.probabilities is not None: prob_fname = fname.replace('.xml.gz', '_probs.json') prob_plot_fname = prob_fname.replace('.json', '.png') prob_plot = {k: v for (k, v) in self.probabilities.items() if v != 0.0} labels, sizes = zip(*prob_plot.items()) colors = [source_color(label) for label in labels] fig, ax = pl.subplots() ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', textprops={'fontsize': 15}) ax.axis('equal') fig.savefig(prob_plot_fname) pl.close() gid = None try: # try connecting to GraceDB gracedb = GraceDb(gracedb_server) \ if gracedb_server is not None else GraceDb() # create GraceDB event group = 'Test' if testing else 'CBC' r = gracedb.createEvent(group, "pycbc", fname, search).json() gid = r["graceid"]"Uploaded event %s", gid) if self.is_hardware_injection: gracedb.writeLabel(gid, 'INJ')"Tagging event %s as an injection", gid) # upload PSDs. Note that the PSDs are already stored in the # original event file and we just upload a copy of that same file # here. This keeps things as they were in O2 and can be removed # after updating the follow-up infrastructure psd_fname = 'psd.xml.gz' if fname.endswith('.gz') else 'psd.xml' gracedb.writeLog(gid, "PyCBC PSD estimate from the time of event", psd_fname, open(fname, "rb").read(), "psd")"Uploaded PSDs for event %s", gid) # add info for tracking code version version_str = 'Using PyCBC version {}{} at {}' version_str = version_str.format( pycbc_version.version, ' (release)' if pycbc_version.release else '', os.path.dirname(pycbc.__file__)) gracedb.writeLog(gid, version_str) extra_strings = [] if extra_strings is None else extra_strings for text in extra_strings: gracedb.writeLog(gid, text, tag_name=['analyst_comments']) # upload SNR series in HDF format and plots if self.snr_series is not None: gracedb.writeLog(gid, 'SNR timeseries HDF file upload', filename=snr_series_fname) gracedb.writeLog(gid, 'SNR timeseries plot upload', filename=snr_series_plot_fname, tag_name=['background'], displayName=['SNR timeseries']) gracedb.writeLog(gid, 'PSD plot upload', filename=psd_series_plot_fname, tag_name=['psd'], displayName=['PSDs']) # upload source probabilities in json format and plot if self.probabilities is not None: gracedb.writeLog(gid, 'source probabilities JSON file upload', filename=prob_fname, tag_name=['em_follow'])'Uploaded source probabilities for event %s', gid) gracedb.writeLog(gid, 'source probabilities plot upload', filename=prob_plot_fname, tag_name=['em_follow'])'Uploaded source probabilities pie chart for ' 'event %s', gid) except Exception as exc: logging.error('Something failed during the upload/annotation of ' 'event %s on GraceDB. The event may not have been ' 'uploaded!', fname) logging.error(str(exc)) return gid
__all__ = ['SingleCoincForGraceDB', 'make_psd_xmldoc', 'snr_series_to_xml']