def get_tmpltbank_data(self):
"""Read inspiral horizon data from the TmpltBank cache
"""
tmpltsegs = find_cache_segments(self.tmpltbankcache)
ifo = self.channel.split(':')[0]
rangechannel = '%s:horizon_distance' % ifo
sizechannel = '%s:tmpltbank_size' % ifo
globalv.DATA[rangechannel] = TimeSeriesList()
globalv.DATA[sizechannel] = TimeSeriesList()
for seg in tmpltsegs:
segcache = self.tmpltbankcache.sieve(segment=seg)
rangedata = []
sizedata = []
for ce in segcache:
xmldoc = llwutils.load_filename(ce.path)
svtable = SummValueTable.get_table(xmldoc)
svtable.sort(key=lambda row: float(row.comment.split('_')[0]))
rangedata.append(svtable[0].value * (1.4)**(5 / 6.))
sizedata.append(len(SnglInspiralTable.get_table(xmldoc)))
if rangedata:
dt = float(abs(segcache[0].segment))
epoch = segcache[0].segment[0] + dt / 2.
globalv.DATA[rangechannel].append(
TimeSeries(rangedata,
sample_rate=1 / dt,
epoch=epoch,
name=rangechannel))
try:
globalv.DATA[rangechannel].coalesce()
except ValueError:
pass
globalv.DATA[sizechannel].append(
TimeSeries(sizedata,
sample_rate=1 / dt,
epoch=epoch,
name=sizechannel))
try:
globalv.DATA[sizechannel].coalesce()
except ValueError:
pass
def get_tmpltbank_data(self):
"""Read inspiral horizon data from the TmpltBank cache
"""
tmpltsegs = find_cache_segments(self.tmpltbankcache)
ifo = self.channel.split(':')[0]
rangechannel = '%s:horizon_distance' % ifo
sizechannel = '%s:tmpltbank_size' % ifo
globalv.DATA[rangechannel] = TimeSeriesList()
globalv.DATA[sizechannel] = TimeSeriesList()
for seg in tmpltsegs:
segcache = self.tmpltbankcache.sieve(segment=seg)
rangedata = []
sizedata = []
for ce in segcache:
xmldoc = llwutils.load_filename(ce.path)
svtable = SummValueTable.get_table(xmldoc)
svtable.sort(key=lambda row: float(row.comment.split('_')[0]))
rangedata.append(svtable[0].value * (1.4)**(5/6.))
sizedata.append(len(SnglInspiralTable.get_table(xmldoc)))
if rangedata:
dt = float(abs(segcache[0].segment))
epoch = segcache[0].segment[0] + dt/2.
globalv.DATA[rangechannel].append(
TimeSeries(rangedata, sample_rate=1/dt, epoch=epoch,
name=rangechannel))
try:
globalv.DATA[rangechannel].coalesce()
except ValueError:
pass
globalv.DATA[sizechannel].append(
TimeSeries(sizedata, sample_rate=1/dt, epoch=epoch,
name=sizechannel))
try:
globalv.DATA[sizechannel].coalesce()
except ValueError:
pass
def _make_events(self, doc, psd_file, coinc_def):
# Look up necessary tables.
coinc_table = CoincTable.get_table(doc)
coinc_map_table = CoincMapTable.get_table(doc)
sngl_inspiral_table = SnglInspiralTable.get_table(doc)
try:
time_slide_table = TimeSlideTable.get_table(doc)
except ValueError:
offsets_by_time_slide_id = None
else:
offsets_by_time_slide_id = time_slide_table.as_dict()
# Indices to speed up lookups by ID.
key = operator.attrgetter('coinc_event_id')
event_ids_by_coinc_event_id = {
coinc_event_id:
tuple(coinc_map.event_id for coinc_map in coinc_maps)
for coinc_event_id, coinc_maps
in itertools.groupby(sorted(coinc_map_table, key=key), key=key)}
sngl_inspirals_by_event_id = {
row.event_id: row for row in sngl_inspiral_table}
# Filter rows by coinc_def if requested.
if coinc_def is not None:
coinc_def_table = CoincDefTable.get_table(doc)
coinc_def_ids = {
row.coinc_def_id for row in coinc_def_table
if (row.search, row.search_coinc_type) ==
(coinc_def.search, coinc_def.search_coinc_type)}
coinc_table = [
row for row in coinc_table
if row.coinc_def_id in coinc_def_ids]
snr_dict = dict(self._snr_series_by_sngl_inspiral(doc))
process_table = ProcessTable.get_table(doc)
program_for_process_id = {
row.process_id: row.program for row in process_table}
try:
process_params_table = ProcessParamsTable.get_table(doc)
except ValueError:
psd_filenames_by_process_id = {}
else:
psd_filenames_by_process_id = {
process_param.process_id: process_param.value
for process_param in process_params_table
if process_param.param == '--reference-psd'}
ts0 = TimeSlideID(0)
for time_slide_id in {coinc.time_slide_id for coinc in coinc_table}:
if offsets_by_time_slide_id is None and time_slide_id == ts0:
log.warning(
'Time slide record is missing for %s, '
'guessing that this is zero-lag', time_slide_id)
for program in {program_for_process_id[coinc.process_id]
for coinc in coinc_table}:
invert_phases = self._phase_convention(program)
if invert_phases:
log.warning(
'Using anti-FINDCHIRP phase convention; inverting phases. '
'This is currently the default and it is appropriate for '
'gstlal and MBTA but not pycbc as of observing run 1 '
'("O1"). The default setting is likely to change in the '
'future.')
for coinc in coinc_table:
coinc_event_id = coinc.coinc_event_id
coinc_event_num = int(coinc_event_id)
sngls = [sngl_inspirals_by_event_id[event_id] for event_id
in event_ids_by_coinc_event_id[coinc_event_id]]
if offsets_by_time_slide_id is None and coinc.time_slide_id == ts0:
offsets = defaultdict(float)
else:
offsets = offsets_by_time_slide_id[coinc.time_slide_id]
template_args = [
{key: getattr(sngl, key) for key in self._template_keys}
for sngl in sngls]
if any(d != template_args[0] for d in template_args[1:]):
raise ValueError(
'Template arguments are not identical for all detectors!')
template_args = template_args[0]
invert_phases = self._phase_convention(
program_for_process_id[coinc.process_id])
singles = tuple(LigoLWSingleEvent(
self, sngl.ifo, sngl.snr, sngl.coa_phase,
float(sngl.end + offsets[sngl.ifo]), float(sngl.end),
psd_file or psd_filenames_by_process_id.get(sngl.process_id),
snr_dict.get(sngl.event_id), invert_phases)
for sngl in sngls)
event = LigoLWEvent(coinc_event_num, singles, template_args)
yield coinc_event_num, event