def append_process(xmldoc, cluster_algorithm, comment):
return ligolw_process.register_to_xmldoc(
xmldoc,
program = process_program_name,
paramdict = {
"cluster_algorithm": cluster_algorithm
},
version = __version__,
cvs_repository = u"lscsoft",
cvs_entry_time = __date__,
comment = comment
)
def gen_likelihood_control(coinc_params_distributions, seglists, name = u"ligolw_burca_tailor", comment = u""):
xmldoc = ligolw.Document()
node = xmldoc.appendChild(ligolw.LIGO_LW())
process = ligolw_process.register_to_xmldoc(xmldoc, program = process_program_name, paramdict = {}, version = __version__, cvs_repository = "lscsoft", cvs_entry_time = __date__, comment = comment)
coinc_params_distributions.process_id = process.process_id
ligolw_search_summary.append_search_summary(xmldoc, process, ifos = seglists.keys(), inseg = seglists.extent_all(), outseg = seglists.extent_all())
node.appendChild(coinc_params_distributions.to_xml(name))
ligolw_process.set_process_end_time(process)
return xmldoc
def save_xml(self, filename):
"""
Save the MDC set as an XML SimBurstTable.
Parameters
----------
filename : str
The location to save the xml file. The output is gzipped, so ending it with
a ".gz" would stick with convention.
"""
xmldoc = ligolw.Document()
lw = xmldoc.appendChild(ligolw.LIGO_LW())
sim = lsctables.New(self.table_type)
lw.appendChild(sim)
# This needs to be given the proper metadata once the package has the maturity to
# write something sensible.
for waveform in self.waveforms:
procrow = process.register_to_xmldoc(xmldoc, "minke_burst_mdc+{}".format(minke.__version__), {}) # waveform.params)
try:
waveform_row = waveform._row(sim)
waveform_row.process_id = procrow.process_id
except:
row = sim.RowType()
for a in self.table_type.validcolumns.keys():
if a in waveform.params.keys():
setattr(row, a, waveform.params[a])
else:
if not hasattr(waveform, a):
setattr(row, a, 0)
else:
setattr(row, a, getattr(waveform, a))
row.waveform = waveform.waveform
if self.table_type == lsctables.SimBurstTable:
# Fill in the time
row.set_time_geocent(GPS(float(waveform.time)))
# Get the sky locations
row.ra, row.dec, row.psi = waveform.ra, waveform.dec, waveform.psi
row.simulation_id = waveform.simulation_id
row.waveform_number = random.randint(0,int(2**32)-1)
### !! This needs to be updated.
row.process_id = "process:process_id:0" #procrow.process_id
waveform_row = row
sim.append(waveform_row)
#del waveform_row
# Write out the xml and gzip it.
utils.write_filename(xmldoc, filename, gz=True)
def append_process(xmldoc, match_algorithm, comment):
"""
Convenience wrapper for adding process metadata to the document.
"""
return ligolw_process.register_to_xmldoc(
xmldoc,
program = process_program_name,
paramdict = {
"match_algorithm": match_algorithm
},
version = __version__,
cvs_repository = u"lscsoft",
cvs_entry_time = __date__,
comment = comment
)
def new_doc(comment = None, **kwargs): doc = ligolw.Document() doc.appendChild(ligolw.LIGO_LW()) process = ligolw_process.register_to_xmldoc( doc, program = u"ligolw_tisi", paramdict = kwargs, version = __version__, cvs_repository = u"lscsoft", cvs_entry_time = __date__, comment = comment ) timeslidetable = lsctables.New(lsctables.TimeSlideTable) doc.childNodes[0].appendChild(timeslidetable) return doc, process
def save_xml(self, filename):
"""
Save the MDC set as an XML SimBurstTable.
Parameters
----------
filename : str
The location to save the xml file. The output is gzipped, so ending it with
a ".gz" would stick with convention.
"""
xmldoc = ligolw.Document()
lw = xmldoc.appendChild(ligolw.LIGO_LW())
sim = lsctables.New(lsctables.SimBurstTable)
lw.appendChild(sim)
# This needs to be given the proper metadata once the package has the maturity to
# write something sensible.
for waveform in self.waveforms:
procrow = process.register_to_xmldoc(xmldoc, "minke_burst_mdc", {}) # waveform.params)
waveform_row = waveform._row(sim)
waveform_row.process_id = procrow.process_id
sim.append(waveform_row)
#del waveform_row
# Write out the xml and gzip it.
utils.write_filename(xmldoc, filename, gz=True)
def setup_analysislogging(workflow, segs_list, insps, args, output_dir,
program_name="workflow", tags=[]):
"""
This module sets up the analysis logging xml file that contains the
following information:
* Command line arguments that the code was run with
* Segment list of times marked as SCIENCE
* Segment list of times marked as SCIENCE and "OK" ie. not CAT_1 vetoed
* Segment list of times marked as SCIENCE_OK and present on the cluster
* The times that will be analysed by the matched-filter jobs
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance.
segs_list : pycbc.workflow.core.FileList
A list of Files containing the information needed to generate the
segments above. For segments generated at run time the associated
segmentlist is a property of this object.
insps : pycbc.workflow.core.FileList
The output files from the matched-filtering module. Used to identify
what times have been analysed in this workflow.
output_dir : path
Directory to output any files to.
program_name : string (optional, default = "workflow")
The program name to stick in the process/process_params tables.
tags : list (optional, default = [])
If given restrict to considering inspiral and segment files that
are tagged with all tags in this list.
"""
logging.info("Entering analysis logging module.")
make_analysis_dir(output_dir)
# Construct the summary XML file
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# Add process and process_params tables
proc_id = process.register_to_xmldoc(outdoc, program_name,
vars(args) ).process_id
# Now add the various segment lists to this file
summ_segs = segmentlist([workflow.analysis_time])
# If tags is given filter by tags
if tags:
for tag in tags:
segs_list = segs_list.find_output_with_tag(tag)
insps = insps.find_output_with_tag(tag)
for ifo in workflow.ifos:
# Lets get the segment lists we need
seg_ifo_files = segs_list.find_output_with_ifo(ifo)
# SCIENCE
sci_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE')
if len(sci_seg_file) == 1:
sci_seg_file = sci_seg_file[0]
print sci_seg_file.segment_dict.keys()
sci_segs = sci_seg_file.segment_dict['%s:%s' %(ifo, 'RESULT')]
sci_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id,
ifo, "CBC_WORKFLOW_SCIENCE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_def_id,
sci_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sci_def_id,
summ_segs, comment='')
elif sci_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_seg_file), ifo, 'SCIENCE')
#raise ValueError(err_msg)
# SCIENCE_OK
sci_ok_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE_OK')
if len(sci_ok_seg_file) == 1:
sci_ok_seg_file = sci_ok_seg_file[0]
sci_ok_segs = \
sci_ok_seg_file.segment_dict['%s:%s' %(ifo,'SCIENCE_OK')]
sci_ok_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_SCIENCE_OK", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_ok_def_id,
sci_ok_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_ok_def_id, summ_segs, comment='')
elif sci_ok_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_ok_seg_file), ifo, 'SCIENCE_OK')
#raise ValueError(err_msg)
# SCIENCE_AVAILABLE
sci_available_seg_file = seg_ifo_files.find_output_with_tag(\
'SCIENCE_AVAILABLE')
if len(sci_available_seg_file) == 1:
sci_available_seg_file = sci_available_seg_file[0]
sci_available_segs = sci_available_seg_file.segment_dict
sci_available_segs = \
sci_available_segs['%s:%s' %(ifo, 'SCIENCE_AVAILABLE')]
sci_available_def_id = segmentdb_utils.add_to_segment_definer(\
outdoc, proc_id, ifo, "CBC_WORKFLOW_SCIENCE_AVAILABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id,
sci_available_def_id, sci_available_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_available_def_id, summ_segs, comment='')
elif sci_available_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_available_seg_file), ifo, 'SCIENCE_AVAILABLE')
#raise ValueError(err_msg)
# ANALYSABLE - This one needs to come from inspiral outs
ifo_insps = insps.find_output_with_ifo(ifo)
analysable_segs = ifo_insps.get_times_covered_by_files()
analysable_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_ANALYSABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, analysable_def_id,
analysable_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
analysable_def_id, summ_segs, comment='')
summ_file = File(workflow.ifos, "WORKFLOW_SUMMARY",
workflow.analysis_time, extension=".xml",
directory=output_dir)
summ_file.PFN(summ_file.storage_path, site='local')
utils.write_filename(outdoc, summ_file.storage_path)
return FileList([summ_file])
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
lsctables.SnglInspiralTable.RowType = SnglInspiralTable
tbl = lsctables.New(lsctables.SnglInspiralTable)
xmldoc.childNodes[-1].appendChild(tbl)
# initialize random seed
np.random.mtrand.seed(opts.seed)
#
# prepare process table with information about the current program
#
opts_dict = dict((k, v) for k, v in opts.__dict__.iteritems() if v is not False and v is not None)
process = ligolw_process.register_to_xmldoc(xmldoc, "lalapps_cbc_sbank",
opts_dict, version="no version",
cvs_repository="sbank", cvs_entry_time=strftime('%Y/%m/%d %H:%M:%S'))
#
# populate params dictionary to be passed to the generators
#
if opts.trial_waveforms:
trialdoc = utils.load_filename(opts.trial_waveforms, contenthandler=ContentHandler, gz=opts.trial_waveforms.endswith('.gz'))
trial_sngls = lsctables.SnglInspiralTable.get_table(trialdoc)
proposal = [tmplt_class.from_sngl(t, bank=bank) for t in trial_sngls]
else:
params = {'mass1': (opts.mass1_min, opts.mass1_max),
'mass2': (opts.mass2_min, opts.mass2_max),
del CMAP["mass2"]
CMAP[("mass1", "mass2")] = ("mass1", "mass2")
ar = numpy.random.random((len(CMAP), 10))
samp_dict = dict(zip(CMAP, ar))
ar = samp_dict[("mass1", "mass2")]
samp_dict[("mass1", "mass2")] = numpy.array([ar, ar])
del CMAP[("mass1", "mass2")]
CMAP["mass1"] = "mass1"
CMAP["mass2"] = "mass2"
samp_dict["samp_n"] = numpy.array(range(0, 10))
CMAP["sample_n"] = "sample_n"
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], {})
append_samples_to_xmldoc(xmldoc, samp_dict)
def gaussian(x, mu=0, std=1):
return 1 / numpy.sqrt(numpy.pi * 2) / std * numpy.exp(
-(x - mu)**2 / 2 / std**2)
m1m, m2m = 1.4, 1.5
m1, m2 = numpy.random.random(2000).reshape(2, 1000) * 1.0 + 1.0
loglikes = [
gaussian(m1i, m1m) * gaussian(m2i, m2m) for m1i, m2i in zip(m1, m2)
]
#loglikelihood = - 7.5**2/2
#append_likelihood_result_to_xmldoc(xmldoc, loglikelihood, **{"mass1": 1.4, "mass2": 1.4, "ifos": "H1,L1,V1"})
for m1i, m2i, loglikelihood in zip(m1, m2, loglikes):
def to_coinc_xml_object(self, file_name):
# FIXME: This function will only work with two ifos!!
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
ifos = [ifo for ifo in self.sngl_files.keys()]
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
'pycbc', {},
ifos=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
search_summ_table = lsctables.New(lsctables.SearchSummaryTable)
coinc_h5file = self.coinc_file.h5file
start_time = coinc_h5file['segments']['coinc']['start'][:].min()
end_time = coinc_h5file['segments']['coinc']['end'][:].max()
num_trigs = len(self.sort_arr)
search_summary = return_search_summary(start_time, end_time, num_trigs,
ifos)
search_summ_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summ_table)
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
time_slide_table = lsctables.New(lsctables.TimeSlideTable)
# Set up time_slide table
time_slide_id = lsctables.TimeSlideID(0)
for ifo in ifos:
time_slide_row = lsctables.TimeSlide()
time_slide_row.instrument = ifo
time_slide_row.time_slide_id = time_slide_id
time_slide_row.offset = 0
time_slide_row.process_id = proc_id
time_slide_table.append(time_slide_row)
# Set up coinc_definer table
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincidences"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
bank_col_names = ['mass1', 'mass2', 'spin1z', 'spin2z']
bank_col_vals = {}
for name in bank_col_names:
bank_col_vals[name] = self.get_bankfile_array(name)
coinc_event_names = ['ifar', 'time1', 'fap', 'stat']
coinc_event_vals = {}
for name in coinc_event_names:
coinc_event_vals[name] = self.get_coincfile_array(name)
sngl_col_names = [
'snr', 'chisq', 'chisq_dof', 'bank_chisq', 'bank_chisq_dof',
'cont_chisq', 'cont_chisq_dof', 'end_time', 'template_duration',
'coa_phase', 'sigmasq'
]
sngl_col_vals = {}
for name in sngl_col_names:
sngl_col_vals[name] = self.get_snglfile_array_dict(name)
for idx in xrange(len(self.sort_arr)):
# Set up IDs and mapping values
coinc_id = lsctables.CoincID(idx)
# Set up sngls
# FIXME: As two-ifo is hardcoded loop over all ifos
sngl_combined_mchirp = 0
sngl_combined_mtot = 0
for ifo in ifos:
sngl_id = self.trig_id[ifo][idx]
event_id = lsctables.SnglInspiralID(sngl_id)
sngl = return_empty_sngl()
sngl.event_id = event_id
sngl.ifo = ifo
for name in sngl_col_names:
val = sngl_col_vals[name][ifo][idx]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
setattr(sngl, name, val)
for name in bank_col_names:
val = bank_col_vals[name][idx]
setattr(sngl, name, val)
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.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_combined_mchirp += sngl.mchirp
sngl_combined_mtot += sngl.mtotal
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 = event_id
coinc_event_map_table.append(coinc_map_row)
sngl_combined_mchirp = sngl_combined_mchirp / len(ifos)
sngl_combined_mtot = sngl_combined_mtot / len(ifos)
# Set up coinc inspiral and coinc event tables
coinc_event_row = lsctables.Coinc()
coinc_inspiral_row = lsctables.CoincInspiral()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_inspiral_row.set_ifos(ifos)
coinc_event_row.time_slide_id = time_slide_id
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_combined_mchirp
coinc_inspiral_row.mass = sngl_combined_mtot
coinc_inspiral_row.set_end(\
LIGOTimeGPS(coinc_event_vals['time1'][idx]))
coinc_inspiral_row.snr = coinc_event_vals['stat'][idx]
coinc_inspiral_row.false_alarm_rate = coinc_event_vals['fap'][idx]
coinc_inspiral_row.combined_far = 1. / coinc_event_vals['ifar'][idx]
# Transform to Hz
coinc_inspiral_row.combined_far = \
coinc_inspiral_row.combined_far / YRJUL_SI
coinc_event_row.likelihood = 0.
coinc_inspiral_row.minimum_duration = 0.
coinc_event_table.append(coinc_event_row)
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
outdoc.childNodes[0].appendChild(coinc_event_table)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(time_slide_table)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
ligolw_utils.write_filename(outdoc, file_name)
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/coinc.py 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/mchirp_area.py
"""
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,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "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']:
sngl.channel = 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.instruments = tuple(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)
fseries.data.data = 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()
return options, filenames
options, filenames = parse_command_line()
opts_dict = dict((k, v) for k, v in options.__dict__.iteritems() if v is not False and v is not None)
for fname in filenames:
xmldoc=utils.load_filename(fname, gz=fname.endswith(".gz"), verbose = options.verbose, contenthandler=ContentHandler)
sngl_inspiral_table=lsctables.SnglInspiralTable.get_table(xmldoc)
process_params_table = lsctables.ProcessParamsTable.get_table(xmldoc)
# Prepare process table with information about the current program
process = ligolw_process.register_to_xmldoc(xmldoc,
"lalapps_cbc_sbank_splitter", opts_dict,
version="no version", cvs_repository="sbank",
cvs_entry_time=strftime('%Y/%m/%d %H:%M:%S'))
# sort template rows
sngl_inspiral_table.sort(key=attrgetter(options.sort_by))
sngl_inspiral_table_split = lsctables.table.new_from_template(sngl_inspiral_table)
sngl_inspiral_table.parentNode.replaceChild(sngl_inspiral_table_split, sngl_inspiral_table)
if len(sngl_inspiral_table) < options.nbanks:
raise ValueError, "Not enough templates to create the requested number of subbanks."
# override/read instrument column
if options.instrument:
for row in sngl_inspiral_table:
row.ifo = options.instrument
else:
def register_to_xmldoc(xmldoc, parser, opts, **kwargs):
from glue.ligolw.utils import process
return process.register_to_xmldoc(
xmldoc, parser.prog,
{key: (value.name if hasattr(value, 'read') else value)
for key, value in opts.__dict__.items()})
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 pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
"""
self.ifos = ifos
if 'followup_ifos' in kwargs and kwargs['followup_ifos'] is not None:
self.followup_ifos = kwargs['followup_ifos']
else:
self.followup_ifos = []
self.template_id = coinc_results['foreground/%s/template_id' %
self.ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results)
# 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=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "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(ifos)
coinc_event_row.instruments = ','.join(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_event_id_map = {}
sngl_populated = None
for sngl_id, ifo in enumerate(ifos + self.followup_ifos):
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl_event_id_map[ifo] = sngl.event_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':
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
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)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/%s/end_time' % ifo] for ifo in ifos])
for sngl in sngl_inspiral_table:
if sngl.ifo in self.followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.set_end(lal.LIGOTimeGPS(subthreshold_sngl_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(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 = coinc_results['foreground/stat']
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)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
# compute SNR time series
self.upload_snr_series = kwargs['upload_snr_series']
if self.upload_snr_series:
htilde = kwargs['bank'][self.template_id]
self.snr_series = {}
self.snr_series_psd = {}
for ifo in self.ifos + self.followup_ifos:
if ifo in ifos:
trig_time = coinc_results['foreground/%s/end_time' % ifo]
else:
trig_time = subthreshold_sngl_time
self.snr_series[ifo], self.snr_series_psd[ifo] = \
compute_followup_snr_series(
kwargs['data_readers'][ifo], htilde,
trig_time)
snr_series_to_xml(self.snr_series[ifo], outdoc,
sngl_event_id_map[ifo])
#
# Main
#
# =============================================================================
#
options, filenames = parse_command_line()
if options.verbose:
print >> sys.stderr, "time-frequency tiles have %g degrees of freedom" % (
2 * options.delta_t * options.delta_f)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process = ligolw_process.register_to_xmldoc(xmldoc,
u"lalapps_binj_pic",
options.options_dict,
version=git_version.version)
time_slide_table = xmldoc.childNodes[-1].appendChild(
lsctables.TimeSlideTable.get_table(
ligolw_utils.load_filename(options.time_slide_xml,
verbose=options.verbose,
contenthandler=LIGOLWContentHandler)))
time_slide_id = time_slide_table[0].time_slide_id
if options.verbose:
print >> sys.stderr, "associating injections with time slide ID \"%s\": %s" % (
time_slide_id, time_slide_table.as_dict()[time_slide_id])
for row in time_slide_table:
row.process_id = process.process_id
sim_burst_tbl = xmldoc.childNodes[-1].appendChild(
lsctables.New(lsctables.SimBurstTable, [
"process_id", "simulation_id", "time_slide_id", "waveform",
def excess_power(
ts_data, # Time series from magnetic field data
band=None, # Channel bandwidth
channel_name='channel-name', # Channel name
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
impulse=False, # Impulse response
make_plot=True, # Condition to produce plots
max_duration=None, # Maximum duration of the tile
nchans=256, # Total number of channels
psd_estimation='median-mean', # Average method
psd_segment_length=60, # Length of each segment in seconds
psd_segment_stride=30, # Separation between 2 consecutive segments in seconds
station='station-name', # Station name
tile_fap=1e-7, # Tile false alarm probability threshold in Gaussian noise.
verbose=True, # Print details
window_fraction=0, # Withening window fraction
wtype='tukey'): # Whitening type, can tukey or hann
'''
Perform excess-power search analysis on magnetic field data.
This method will produce a bunch of time-frequency plots for every
tile duration and bandwidth analysed as well as a XML file identifying
all the triggers found in the selected data within the user-defined
time range.
Parameters
----------
ts_data : TimeSeries
Time Series from magnetic field data
psd_segment_length : float
Length of each segment in seconds
psd_segment_stride : float
Separation between 2 consecutive segments in seconds
psd_estimation : string
Average method
window_fraction : float
Withening window fraction
tile_fap : float
Tile false alarm probability threshold in Gaussian noise.
nchans : int
Total number of channels
band : float
Channel bandwidth
fmin : float
Lowest frequency of the filter bank.
fmax : float
Highest frequency of the filter bank
Examples
--------
The program can be ran as an executable by using the ``excesspower`` command
line as follows::
excesspower --station "mainz01" \\
--start-time "2017-04-15-17-1" \\
--end-time "2017-04-15-18" \\
--rep "/Users/vincent/ASTRO/data/GNOME/GNOMEDrive/gnome/serverdata/" \\
--resample 512 \\
--verbose
'''
# Determine sampling rate based on extracted time series
sample_rate = ts_data.sample_rate
# Check if tile maximum frequency is not defined
if fmax is None or fmax > sample_rate / 2.:
# Set the tile maximum frequency equal to the Nyquist frequency
# (i.e. half the sampling rate)
fmax = sample_rate / 2.0
# Check whether or not tile bandwidth and channel are defined
if band is None and nchans is None:
# Exit program with error message
exit("Either bandwidth or number of channels must be specified...")
else:
# Check if tile maximum frequency larger than its minimum frequency
assert fmax >= fmin
# Define spectral band of data
data_band = fmax - fmin
# Check whether tile bandwidth or channel is defined
if band is not None:
# Define number of possible filter bands
nchans = int(data_band / band)
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans -= 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
if verbose: print '|- Estimating PSD from segments of',
if verbose:
print '%.2f s, with %.2f s stride...' % (psd_segment_length,
psd_segment_stride)
# Convert time series as array of float
data = ts_data.astype(numpy.float64)
# Define segment length for PSD estimation in sample unit
seg_len = int(psd_segment_length * sample_rate)
# Define separation between consecutive segments in sample unit
seg_stride = int(psd_segment_stride * sample_rate)
# Minimum frequency of detectable signal in a segment
delta_f = 1. / psd_segment_length
# Calculate PSD length counting the zero frequency element
fd_len = fmax / delta_f + 1
# Calculate the overall PSD from individual PSD segments
if impulse:
# Produce flat data
flat_data = numpy.ones(int(fd_len)) * 2. / fd_len
# Create PSD frequency series
fd_psd = types.FrequencySeries(flat_data, 1. / psd_segment_length,
ts_data.start_time)
else:
# Create overall PSD using Welch's method
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
if make_plot:
# Plot the power spectral density
plot_spectrum(fd_psd)
# We need this for the SWIG functions
lal_psd = fd_psd.lal()
# Create whitening window
if verbose: print "|- Whitening window and spectral correlation..."
if wtype == 'hann': window = lal.CreateHannREAL8Window(seg_len)
elif wtype == 'tukey':
window = lal.CreateTukeyREAL8Window(seg_len, window_fraction)
else:
raise ValueError("Can't handle window type %s" % wtype)
# Create FFT plan
fft_plan = lal.CreateForwardREAL8FFTPlan(len(window.data.data), 1)
# Perform two point spectral correlation
spec_corr = lal.REAL8WindowTwoPointSpectralCorrelation(window, fft_plan)
# Determine length of individual filters
filter_length = int(2 * band / fd_psd.delta_f) + 1
# Initialise filter bank
if verbose:
print "|- Create bank of %i filters of %i Hz bandwidth..." % (
nchans, filter_length)
# Initialise array to store filter's frequency series and metadata
lal_filters = []
# Initialise array to store filter's time series
fdb = []
# Loop over the channels
for i in range(nchans):
# Define central position of the filter
freq = fmin + band / 2 + i * band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(freq, band, lal_psd,
spec_corr)
# Testing spectral correlation on filter
#print lalburst.ExcessPowerFilterInnerProduct(lal_filter, lal_filter, spec_corr, None)
# Append entire filter structure
lal_filters.append(lal_filter)
# Append filter's spectrum
fdb.append(FrequencySeries.from_lal(lal_filter))
#print fdb[0].frequencies
#print fdb[0]
if make_plot:
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
if verbose:
print "|- Convert all the frequency domain to the time domain..."
tdb = []
# Loop for each filter's spectrum
for fdt in fdb:
zero_padded = numpy.zeros(int((fdt.f0 / fdt.df).value) + len(fdt))
st = int((fdt.f0 / fdt.df).value)
zero_padded[st:st + len(fdt)] = numpy.real_if_close(fdt.value)
n_freq = int(sample_rate / 2 / fdt.df.value) * 2
tdt = numpy.fft.irfft(zero_padded, n_freq) * math.sqrt(sample_rate)
tdt = numpy.roll(tdt, len(tdt) / 2)
tdt = TimeSeries(tdt,
name="",
epoch=fdt.epoch,
sample_rate=sample_rate)
tdb.append(tdt)
# Plot time series filter
plot_filters(tdb, fmin, band)
# Computer whitened inner products of input filters with themselves
#white_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None) for f in lal_filters])
# Computer unwhitened inner products of input filters with themselves
#unwhite_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, lal_psd) for f in lal_filters])
# Computer whitened filter inner products between input adjacent filters
#white_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, None) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Computer unwhitened filter inner products between input adjacent filters
#unwhite_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, lal_psd) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Check filter's bandwidth is equal to user defined channel bandwidth
min_band = (len(lal_filters[0].data.data) - 1) * lal_filters[0].deltaF / 2
assert min_band == band
# Create an event list where all the triggers will be stored
event_list = lsctables.New(lsctables.SnglBurstTable, [
'start_time', 'start_time_ns', 'peak_time', 'peak_time_ns', 'duration',
'bandwidth', 'central_freq', 'chisq_dof', 'confidence', 'snr',
'amplitude', 'channel', 'ifo', 'process_id', 'event_id', 'search',
'stop_time', 'stop_time_ns'
])
# Create repositories to save TF and time series plots
os.system('mkdir -p segments/time-frequency')
os.system('mkdir -p segments/time-series')
# Define time edges
t_idx_min, t_idx_max = 0, seg_len
# Loop over each segment
while t_idx_max <= len(ts_data):
# Define first and last timestamps of the block
start_time = ts_data.start_time + t_idx_min / float(
ts_data.sample_rate)
end_time = ts_data.start_time + t_idx_max / float(ts_data.sample_rate)
if verbose:
print "\n|- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# Debug for impulse response
if impulse:
for i in range(t_idx_min, t_idx_max):
ts_data[i] = 1000. if i == (t_idx_max + t_idx_min) / 2 else 0.
# Model a withen time series for the block
tmp_ts_data = types.TimeSeries(ts_data[t_idx_min:t_idx_max] *
window.data.data,
delta_t=1. / ts_data.sample_rate,
epoch=start_time)
# Save time series in relevant repository
os.system('mkdir -p segments/%i-%i' % (start_time, end_time))
if make_plot:
# Plot time series
plot_ts(tmp_ts_data,
fname='segments/time-series/%i-%i.png' %
(start_time, end_time))
# Convert times series to frequency series
fs_data = tmp_ts_data.to_frequencyseries()
if verbose:
print "|- Frequency series data has variance: %s" % fs_data.data.std(
)**2
# Whitening (FIXME: Whiten the filters, not the data)
fs_data.data /= numpy.sqrt(fd_psd) / numpy.sqrt(2 * fd_psd.delta_f)
if verbose:
print "|- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
if verbose: print "|- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),
# fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=lal_filters[0].f0,
# high_frequency_cutoff=lal_filters[0].f0+2*band)
if verbose: print "|- Filtering all %d channels...\n" % nchans,
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(lal_filters[i].f0 / fd_psd.delta_f)
# Index of last frequency bin
f2 = int((lal_filters[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = lal_filters[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank,
delta_f=fd_psd.delta_f,
copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(
template,
fs_data,
h_norm=None,
psd=None,
low_frequency_cutoff=lal_filters[i].f0,
high_frequency_cutoff=lal_filters[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
if make_plot:
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
dt=tmp_ts_data.delta_t,
df=band,
ymax=ts_data.sample_rate / 2.,
t0=start_time,
t1=end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
plot_tiles_ts(numpy.abs(tf_map),
2,
1,
sample_rate=ts_data.sample_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/ts.png' %
(start_time, end_time))
#plot_tiles_tf(numpy.abs(tf_map),2,1,ymax=ts_data.sample_rate/2,
# sample_rate=ts_data.sample_rate,t0=start_time,t1=end_time,
# fname='segments/%i-%i/tf.png'%(start_time,end_time))
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
# Calculate total number of summed channels
nc_sum = 2**nc_sum
if verbose:
print "\n\t|- Contructing tiles containing %d narrow band channels" % nc_sum
# Compute full bandwidth of virtual channel
df = band * nc_sum
# Compute minimal signal's duration in virtual channel
dt = 1.0 / (2 * df)
# Compute under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
if verbose:
print "\t|- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
if verbose: print "\t|- Calculating tiles..."
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Undersample narrow band channel's time series
# Apply clipping condition because [0:-0] does not give the full array
tf_map_temp = tf_map[:,clip_samples:-clip_samples:us_rate] \
if clip_samples > 0 else tf_map[:,::us_rate]
# Initialise final tile time-frequency map
tiles = numpy.zeros(((nchans + 1) / nc_sum, tf_map_temp.shape[1]))
# Loop over tile index
for i in xrange(len(tiles)):
# Sum all inner narrow band channels
ts_tile = numpy.absolute(tf_map_temp[nc_sum * i:nc_sum *
(i + 1)].sum(axis=0))
# Define index of last narrow band channel for given tile
n = (i + 1) * nc_sum - 1
n = n - 1 if n == len(lal_filters) else n
# Computer withened inner products of each input filter with itself
mu_sq = nc_sum * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n], lal_filters[n], spec_corr, None)
#kmax = nc_sum-1 if n==len(lal_filters) else nc_sum-2
# Loop over the inner narrow band channels
for k in xrange(0, nc_sum - 1):
# Computer whitened filter inner products between input adjacent filters
mu_sq += 2 * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n - k], lal_filters[n - 1 - k], spec_corr,
None)
# Normalise tile's time series
tiles[i] = ts_tile.real**2 / mu_sq
if verbose: print "\t|- TF-plane is %dx%s samples" % tiles.shape
if verbose:
print "\t|- Tile energy mean %f, var %f" % (numpy.mean(tiles),
numpy.var(tiles))
# Define maximum number of degrees of freedom and check it larger or equal to 2
max_dof = 32 if max_duration == None else int(max_duration / dt)
assert max_dof >= 2
# Loop through multiple degrees of freedom
for j in [2**l for l in xrange(0, int(math.log(max_dof, 2)))]:
# Duration is fixed by the NDOF and bandwidth
duration = j * dt
if verbose: print "\n\t\t|- Summing DOF = %d ..." % (2 * j)
if verbose:
print "\t\t|- Explore signal duration of %f s..." % duration
# Construct filter
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
# Calculate length of filtered time series
tlen = tiles.shape[1] - sum_filter.shape[0] + 1
# Initialise filtered time series array
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
# Loop over tiles
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
if verbose:
print "\t\t|- Summed tile energy mean: %f" % (
numpy.mean(dof_tiles))
if verbose:
print "\t\t|- Variance tile energy: %f" % (
numpy.var(dof_tiles))
if make_plot:
plot_spectrogram(
dof_tiles.T,
dt,
df,
ymax=ts_data.sample_rate / 2,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_ts(
dof_tiles,
2 * j,
df,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_ts.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_tf(
dof_tiles,
2 * j,
df,
ymax=ts_data.sample_rate / 2,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_tf.png' %
(start_time, end_time, nc_sum, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
if verbose:
print "\t\t|- Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
if verbose:
print "\t\t|- Processing %.2fx%.2f time-frequency map." % (
spant, spanf)
# Since we clip the data, the start time needs to be adjusted accordingly
window_offset_epoch = fs_data.epoch + psd_segment_length * window_fraction / 2
window_offset_epoch = LIGOTimeGPS(float(window_offset_epoch))
for i, j in zip(*numpy.where(dof_tiles > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(window_offset_epoch + float(j * dt))
event.set_stop(window_offset_epoch + float(j * dt) +
duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = lal_filters[
0].f0 + band / 2 + i * df + 0.5 * df
event.duration = duration
event.bandwidth = df
event.chisq_dof = 2 * duration * df
event.snr = math.sqrt(dof_tiles[i, j] / event.chisq_dof -
1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(
event.snr * 0.62, event.chisq_dof * 0.62)
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
for event in event_list[::-1]:
if event.amplitude != None:
continue
etime_min_idx = float(event.get_start()) - float(
fs_data.epoch)
etime_min_idx = int(etime_min_idx / tmp_ts_data.delta_t)
etime_max_idx = float(event.get_start()) - float(
fs_data.epoch) + event.duration
etime_max_idx = int(etime_max_idx / tmp_ts_data.delta_t)
# (band / 2) to account for sin^2 wings from finest filters
flow_idx = int((event.central_freq - event.bandwidth / 2 -
(df / 2) - fmin) / df)
fhigh_idx = int((event.central_freq + event.bandwidth / 2 +
(df / 2) - fmin) / df)
# TODO: Check that the undersampling rate is always commensurate
# with the indexing: that is to say that
# mod(etime_min_idx, us_rate) == 0 always
z_j_b = tf_map[flow_idx:fhigh_idx,
etime_min_idx:etime_max_idx:us_rate]
# FIXME: Deal with negative hrss^2 -- e.g. remove the event
try:
event.amplitude = measure_hrss(
z_j_b, unwhite_filter_ip[flow_idx:fhigh_idx],
unwhite_ss_ip[flow_idx:fhigh_idx - 1],
white_ss_ip[flow_idx:fhigh_idx - 1],
fd_psd.delta_f, tmp_ts_data.delta_t,
len(lal_filters[0].data.data), event.chisq_dof)
except ValueError:
event.amplitude = 0
if verbose:
print "\t\t|- Total number of events: %d" % len(event_list)
t_idx_min += int(seg_len * (1 - window_fraction))
t_idx_max += int(seg_len * (1 - window_fraction))
setname = "MagneticFields"
__program__ = 'pyburst_excesspower_gnome'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time, end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc,
__program__,
straindict,
ifos=[ifo],
version=git_version.id,
cvs_repository=git_version.branch,
cvs_entry_time=git_version.date)
dt_stride = psd_segment_length
sample_rate = ts_data.sample_rate
# Amount to overlap successive blocks so as not to lose data
window_overlap_samples = window_fraction * sample_rate
outseg = inseg.contract(window_fraction * dt_stride / 2)
# With a given dt_stride, we cannot process the remainder of this data
remainder = math.fmod(abs(outseg), dt_stride * (1 - window_fraction))
# ...so make an accounting of it
outseg = segment(outseg[0], outseg[1] - remainder)
ss = append_search_summary(xmldoc,
proc_row,
ifos=(station, ),
inseg=inseg,
outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
ifostr = ifo if isinstance(ifo, str) else "".join(ifo)
st_rnd, end_rnd = int(math.floor(inseg[0])), int(math.ceil(inseg[1]))
dur = end_rnd - st_rnd
fname = "%s-excesspower-%d-%d.xml.gz" % (ifostr, st_rnd, dur)
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
plot_triggers(fname)
def to_coinc_xml_object(self, file_name):
# FIXME: This function will only work with two ifos!!
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
ifos = [ifo for ifo in self.sngl_files.keys()]
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
"pycbc",
{},
ifos=ifos,
comment="",
version=pycbc_version.git_hash,
cvs_repository="pycbc/" + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date,
).process_id
search_summ_table = lsctables.New(lsctables.SearchSummaryTable)
coinc_h5file = self.coinc_file.h5file
start_time = coinc_h5file["segments"]["coinc"]["start"][:].min()
end_time = coinc_h5file["segments"]["coinc"]["end"][:].max()
num_trigs = len(self.sort_arr)
search_summary = return_search_summary(start_time, end_time, num_trigs, ifos)
search_summ_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summ_table)
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
time_slide_table = lsctables.New(lsctables.TimeSlideTable)
# Set up time_slide table
time_slide_id = lsctables.TimeSlideID(0)
for ifo in ifos:
time_slide_row = lsctables.TimeSlide()
time_slide_row.instrument = ifo
time_slide_row.time_slide_id = time_slide_id
time_slide_row.offset = 0
time_slide_row.process_id = proc_id
time_slide_table.append(time_slide_row)
# Set up coinc_definer table
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral-sngl_inspiral coincidences"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
bank_col_names = ["mass1", "mass2", "spin1z", "spin2z"]
bank_col_vals = {}
for name in bank_col_names:
bank_col_vals[name] = self.get_bankfile_array(name)
coinc_event_names = ["ifar", "time1", "fap", "stat"]
coinc_event_vals = {}
for name in coinc_event_names:
coinc_event_vals[name] = self.get_coincfile_array(name)
sngl_col_names = [
"snr",
"chisq",
"chisq_dof",
"bank_chisq",
"bank_chisq_dof",
"cont_chisq",
"cont_chisq_dof",
"end_time",
"template_duration",
"coa_phase",
"sigmasq",
]
sngl_col_vals = {}
for name in sngl_col_names:
sngl_col_vals[name] = self.get_snglfile_array_dict(name)
for idx, coinc_idx in enumerate(self.sort_arr):
# Set up IDs and mapping values
curr_tmplt_id = self.template_id[idx]
coinc_id = lsctables.CoincID(idx)
# Set up sngls
# FIXME: As two-ifo is hardcoded loop over all ifos
sngl_combined_mchirp = 0
sngl_combined_mtot = 0
for ifo in ifos:
sngl_id = self.trig_id[ifo][idx]
event_id = lsctables.SnglInspiralID(sngl_id)
sngl = return_empty_sngl()
sngl.event_id = event_id
sngl.ifo = ifo
curr_sngl_file = self.sngl_files[ifo].h5file[ifo]
for name in sngl_col_names:
val = sngl_col_vals[name][ifo][idx]
setattr(sngl, name, val)
for name in bank_col_names:
val = bank_col_vals[name][idx]
setattr(sngl, name, val)
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(sngl.mass1, sngl.mass2)
sngl.mchirp, junk = pnutils.mass1_mass2_to_mchirp_eta(sngl.mass1, sngl.mass2)
sngl.eff_distance = (sngl.sigmasq) ** 0.5 / sngl.snr
sngl_combined_mchirp += sngl.mchirp
sngl_combined_mtot += sngl.mtotal
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 = event_id
coinc_event_map_table.append(coinc_map_row)
sngl_combined_mchirp = sngl_combined_mchirp / len(ifos)
sngl_combined_mtot = sngl_combined_mtot / len(ifos)
# Set up coinc inspiral and coinc event tables
coinc_event_row = lsctables.Coinc()
coinc_inspiral_row = lsctables.CoincInspiral()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ",".join(ifos)
coinc_inspiral_row.set_ifos(ifos)
coinc_event_row.time_slide_id = time_slide_id
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_combined_mchirp
coinc_inspiral_row.mass = sngl_combined_mtot
coinc_inspiral_row.set_end(LIGOTimeGPS(coinc_event_vals["time1"][idx]))
coinc_inspiral_row.snr = coinc_event_vals["stat"][idx]
coinc_inspiral_row.false_alarm_rate = coinc_event_vals["fap"][idx]
coinc_inspiral_row.combined_far = 1.0 / coinc_event_vals["ifar"][idx]
# Transform to Hz
coinc_inspiral_row.combined_far = coinc_inspiral_row.combined_far / YRJUL_SI
coinc_event_row.likelihood = 0.0
coinc_inspiral_row.minimum_duration = 0.0
coinc_event_table.append(coinc_event_row)
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
outdoc.childNodes[0].appendChild(coinc_event_table)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(time_slide_table)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
ligolw_utils.write_filename(outdoc, file_name)
if not opts.output_file:
print >>sys.stderr, "Must specify an output file"
sys.exit(1)
if not opts.veto_file:
print >>sys.stderr, "Must specify a veto file"
sys.exit(1)
trigs, livetime = get_loud_trigs(fList, opts.veto_file, opts.new_snr_cut)
output_doc=ligolw.Document()
output_doc.appendChild(ligolw.LIGO_LW())
proc_id = process.register_to_xmldoc(output_doc,
"get_loud_trigs", opts.__dict__, comment="", ifos=[""],
version=git_version.id, cvs_repository=git_version.branch,
cvs_entry_time=git_version.date).process_id
vartable = lsctables.New(lsctables.SearchSummVarsTable)
var = lsctables.SearchSummVars()
var.process_id = proc_id
var.value = livetime
var.name = "livetime"
var.string = "Single detector trigger collection time in seconds."
var.search_summvar_id = vartable.next_id
vartable.append(var)
output_doc.childNodes[0].appendChild(vartable)
output_doc.childNodes[0].appendChild(trigs)
utils.write_filename(output_doc, opts.output_file, gz=opts.output_file.endswith("gz"))
# FIXME: sample rate could be a command line option; template duration and data
# duration should be determined from chirp time
sample_rate = 4096 # sample rate in Hz
template_duration = 128 # template duration in seconds
template_length = sample_rate * template_duration # template length in samples
data_duration = 512 # data duration in seconds
data_length = sample_rate * data_duration # data length in samples
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = ligolw_process.register_to_xmldoc(out_xmldoc, parser.get_prog_name(),
opts.__dict__, ifos=opts.detector, comment="Little hope!")
# Add search summary to output file.
all_time = segments.segment([glue.lal.LIGOTimeGPS(0), glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc, process,
inseg=all_time, outseg=all_time)
# Read template bank file.
progress.update(-1, 'reading ' + opts.template_bank)
xmldoc = ligolw_utils.load_filename(
opts.template_bank, contenthandler=ligolw_bayestar.LSCTablesContentHandler)
# Determine the low frequency cutoff from the template bank file.
template_bank_f_low = ligolw_bayestar.get_temlate_bank_f_low(xmldoc)
#========================
# iterate through classifiers -> generate segments
#========================
### set up xml document
from glue.ligolw import ligolw
from glue.ligolw import utils as ligolw_utils
from glue.ligolw import lsctables
from glue.ligolw.utils import process
xmldoc = ligolw.Document()
xml_element = ligolw.LIGO_LW()
xmldoc.appendChild( xml_element )
proc_id = process.register_to_xmldoc( xmldoc, __prog__, opts.__dict__, version=__version__ ).process_id
segdef = lsctables.New( lsctables.SegmentDefTable, columns=["process_id", "segment_def_id", "ifos", "name", "version", "comment"] )
segsum = lsctables.New( lsctables.SegmentSumTable, columns=["process_id", "segment_sum_id", "start_time", "start_time_ns", "end_time", "end_time_ns", "comment", "segment_def_id"] )
segtab = lsctables.New( lsctables.SegmentTable, columns=["process_id", "segment_def_id", "segment_id", "start_time", "start_time_ns", "end_time", "end_time_ns"] )
xml_element.appendChild( segdef )
xml_element.appendChild( segsum )
xml_element.appendChild( segtab )
### iterate through classifiers
for classifier in opts.classifier:
logger.info('Begin: generating segments for %s'%classifier)
faps = fapsD[classifier]
logger.info(' found %d files'%(len(faps)))
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 pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py 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.
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.
"""
self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results
and coinc_results['HWINJ'])
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))
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.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "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':
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']:
sngl.channel = 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)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/{}/end_time'.format(ifo)]
for ifo in ifos])
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(subthreshold_sngl_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)
fseries.data.data = psd.numpy()[kmin:] / pycbc.DYN_RANGE_FAC ** 2.0
psds_lal[ifo] = fseries
make_psd_xmldoc(psds_lal, outdoc)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
workflow, scienceSegs, dfDir, segsList)
# This is needed to know what times will be analysed by daily ahope
# Template bank stuff
banks = _workflow.setup_tmpltbank_workflow(workflow, scienceSegs, datafinds,
dfDir)
# Do matched-filtering
insps = _workflow.setup_matchedfltr_workflow(workflow, scienceSegs, datafinds,
banks, dfDir)
# Now construct the summary XML file
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# FIXME: PROGRAM NAME and dictionary of opts should be variables defined up above
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'dayhopetest',
vars(args)).process_id
for ifo in workflow.ifos:
# Lets get the segment lists we need
segIfoFiles = segsList.find_output_with_ifo(ifo)
# SCIENCE
sciSegFile = segIfoFiles.find_output_with_tag('SCIENCE')
assert (len(sciSegFile) == 1)
sciSegFile = sciSegFile[0]
sciSegs = sciSegFile.segmentList
# SCIENCE_OK
sciokSegFile = segIfoFiles.find_output_with_tag('SCIENCE_OK')
assert (len(sciokSegFile) == 1)
sciokSegFile = sciokSegFile[0]
sciokSegs = sciokSegFile.segmentList
# SCIENCE_AVAILABLE
sciavailableSegFile = segIfoFiles.find_output_with_tag('SCIENCE_AVAILABLE')
# BAYESTAR imports.
from lalinference.bayestar import ligolw as ligolw_bayestar
from lalinference.bayestar import timing
# Other imports.
import numpy as np
import scipy.linalg
# Open output file.
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = ligolw_process.register_to_xmldoc(xmldoc, parser.get_prog_name(),
opts.__dict__)
# Create a SnglInspiral table and initialize its row ID counter.
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
xmldoc.childNodes[0].appendChild(sngl_inspiral_table)
sngl_inspiral_table.set_next_id(lsctables.SnglInspiralID(0))
f_low = 10.
f_high = 2048.
df = 0.1
initial_mchirp = lalinspiral.sbank.tau0tau3.m1m2_to_mchirp(opts.initial_mass1, opts.initial_mass2)
initial_eta = opts.initial_mass1 * opts.initial_mass2 / (opts.initial_mass1 + opts.initial_mass2)**2
initial_chi = 0.
initial_chirp_times = lalsimulation.SimInspiralTaylorF2RedSpinChirpTimesFromMchirpEtaChi(initial_mchirp, initial_eta, initial_chi, f_low)
initial_theta0_theta3 = initial_chirp_times[:2]
if options.verbose: print >>sys.stderr, "warning: %s already processed," % (filename or "stdin"), if not options.force: if options.verbose: print >>sys.stderr, "skipping" continue if options.verbose: print >>sys.stderr, "continuing by --force" # # Add an entry to the process table. # process = ligolw_process.register_to_xmldoc(xmldoc, process_program_name, paramdict, comment = options.comment, version = __version__, cvs_repository = u"lscsoft", cvs_entry_time = __date__ ) # # Run coincidence algorithm. # burca.burca( xmldoc = xmldoc, process_id = process.process_id, EventListType = EventListType, CoincTables = CoincTables, coinc_definer_row = CoincDef, thresholds = options.threshold, ntuple_comparefunc = ntuple_comparefunc,
def register_to_xmldoc(xmldoc, parser, opts, **kwargs):
from glue.ligolw.utils import process
params = {key: value.name if hasattr(value, 'read') else value
for key, value in opts.__dict__.items()}
return process.register_to_xmldoc(xmldoc, parser.prog, params)
f_low, = f_lows
except ValueError:
raise ValueError(
"sim_inspiral:f_lower columns are not unique, got values: "
+ ' '.join(f_lows))
else:
f_low = opts.f_low
# Open output file.
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file. Masquerade as lalapps_tmpltbank and
# encode low frequency cutoff in command line arguments.
process = ligolw_process.register_to_xmldoc(out_xmldoc, "tmpltbank",
dict(opts.__dict__, low_frequency_cutoff=f_low), ifos="H1",
comment="Exact-match template bank")
# Record low-frequency cutoff in the SearchSummVars table.
search_summvars_table = lsctables.New(lsctables.SearchSummVarsTable)
out_xmldoc.childNodes[0].appendChild(search_summvars_table)
search_summvars = lsctables.SearchSummVars()
search_summvars.search_summvar_id = search_summvars_table.get_next_id()
search_summvars.process_id = process.process_id
search_summvars.name = "low-frequency cutoff"
search_summvars.string = None
search_summvars.value = f_low
search_summvars_table.append(search_summvars)
# Create a SnglInspiral table and initialize its row ID counter.
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
# In the interest of not blocking later DAGs for completion in an
# uberdag, this is now dependent on the SQL step
pos_plot_node.add_parent(sql_node)
dag_name = opts.output_name
dag.set_dag_file(dag_name)
dag.write_concrete_dag()
if opts.write_script:
dag.write_script()
print("Created a DAG named %s\n" % dag_name)
print("This will run %i instances of %s in parallel\n" %
(len(tmplt_bnk), ile_sub_name))
# FIXME: Adjust name on command line
if use_bayespe_postproc:
ppdag_name = "posterior_pp"
ppdag.set_dag_file(ppdag_name)
ppdag.add_maxjobs_category("ANALYSIS", MAXJOBS["ANALYSIS"])
ppdag.add_maxjobs_category("POST", MAXJOBS["POST"])
ppdag.write_concrete_dag()
if opts.write_script:
ppdag.write_script()
print("Created a postprocessing DAG named %s\n" % ppdag_name)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__)
utils.write_filename(xmldoc, opts.output_name + ".xml.gz", gz=True)
input_table = lsctables.SimInspiralTable.get_table(indoc)
inputtabletype = lsctables.SimInspiralTable
#
#print tabletype
length = len(input_table)
else:
print ("Waning: No input table given to append to, will construct one from scratch")
inputtabletype = lsctables.SimInspiralTable
#raise IOError("Please give a table to add the information about NR waveforms to.")
# Re-write the input table files
# create a blank xml document and add the process id
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(outdoc,
PROGRAM_NAME, options.__dict__, ifos=["G1"],
version=git_version.id, cvs_repository=git_version.branch,
cvs_entry_time=git_version.date).process_id
out_table = lsctables.New(inputtabletype,columns=['mass1','mass2','mchirp','eta','spin1x','spin1y','spin1z','spin2x','spin2y','spin2z','inclination','polarization','latitude','longitude','bandpass','alpha','alpha1','alpha2','process_id','waveform','numrel_data','numrel_mode_min','numrel_mode_max','t_end_time','f_lower'])
outdoc.childNodes[0].appendChild(out_table)
if options.input_catalog is not None:
# Fill in the INPUT table
for point in input_table:
if not os.path.exists( point.numrel_data ):
print "waveform file %s for %s does NOT exist! " % \
(point.numrel_data, point.waveform)
if options.force_file_exists:
print "(SKIPPING)\n"
continue
fmm = interp1d( qmt, qmm )
mismatches.append( [qmt, qmm, fmm] )
dicfmm = {}
for idx in range(len(mass_ratios)):
dicfmm.update({mass_ratios[idx]:mismatches[idx][2]})
################### Process the bank amd write it to disk ############
subfile_name = options.output_bank
if options.verbose:
print "Writing substitute-bank file %s" % subfile_name
out_subbank_doc = ligolw.Document()
out_subbank_doc.appendChild(ligolw.LIGO_LW())
out_proc_id = ligolw_process.register_to_xmldoc(out_subbank_doc,
PROGRAM_NAME, options.__dict__, comment=options.comment,
version=git_version.id, cvs_repository=git_version.branch,
cvs_entry_time=git_version.date).process_id
# Use exactly the same columns as in the input bank table
tmp0 = in_bank_table[0]
cols_list = tmp0.__slots__
cols = []
for tmpc in cols_list:
if hasattr(tmp0, tmpc):
cols.append( tmpc )
if options.table == 'sim':
out_subbank_table = lsctables.New(lsctables.SimInspiralTable,columns=cols)
else:
out_subbank_table = lsctables.New(lsctables.SnglInspiralTable,columns=cols)
(options, args) = parser.parse_args()
##############################################################################
# Sanity check of input arguments
if not options.ihope_cache:
raise ValueError, "An ihope-cache file is required."
if not options.config_file:
raise ValueError, "A config-file is required."
if not options.log_path:
raise ValueError, "A log-path is required."
##############################################################################
# Create log file
logdoc = ligolw.Document()
logdoc.appendChild(ligolw.LIGO_LW())
proc_id = process.register_to_xmldoc(logdoc, __prog__, options.__dict__, version = git_version.id)
##############################################################################
# parse the ini file and initialize
cp = pipeline.DeepCopyableConfigParser()
cp.read(options.config_file)
tmp_space = cp.get('pipeline', 'node-tmp-dir')
experiment_start = options.gps_start_time
experiment_duration = str(int(options.gps_end_time) - int(options.gps_start_time))
try:
do_cats = cp.get('pipeline', 'pipedown-cats')
do_cats = do_cats.split(',')
except:
def setup_analysislogging(workflow, segs_list, insps, args, output_dir,
program_name="workflow", tags=[]):
"""
This module sets up the analysis logging xml file that contains the
following information:
* Command line arguments that the code was run with
* Segment list of times marked as SCIENCE
* Segment list of times marked as SCIENCE and "OK" ie. not CAT_1 vetoed
* Segment list of times marked as SCIENCE_OK and present on the cluster
* The times that will be analysed by the matched-filter jobs
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance.
segs_list : pycbc.workflow.core.FileList
A list of Files containing the information needed to generate the
segments above. For segments generated at run time the associated
segmentlist is a property of this object.
insps : pycbc.workflow.core.FileList
The output files from the matched-filtering module. Used to identify
what times have been analysed in this workflow.
output_dir : path
Directory to output any files to.
program_name : string (optional, default = "workflow")
The program name to stick in the process/process_params tables.
tags : list (optional, default = [])
If given restrict to considering inspiral and segment files that
are tagged with all tags in this list.
"""
logging.info("Entering analysis logging module.")
make_analysis_dir(output_dir)
# Construct the summary XML file
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# Add process and process_params tables
proc_id = process.register_to_xmldoc(outdoc, program_name,
vars(args) ).process_id
# Now add the various segment lists to this file
summ_segs = segmentlist([workflow.analysis_time])
# If tags is given filter by tags
if tags:
for tag in tags:
segs_list = segs_list.find_output_with_tag(tag)
insps = insps.find_output_with_tag(tag)
for ifo in workflow.ifos:
# Lets get the segment lists we need
seg_ifo_files = segs_list.find_output_with_ifo(ifo)
# SCIENCE
sci_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE')
if len(sci_seg_file) == 1:
sci_seg_file = sci_seg_file[0]
sci_segs = sci_seg_file.segmentList
sci_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id,
ifo, "CBC_WORKFLOW_SCIENCE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_def_id,
sci_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sci_def_id,
summ_segs, comment='')
elif sci_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_seg_file), ifo, 'SCIENCE')
#raise ValueError(err_msg)
# SCIENCE_OK
sci_ok_seg_file = seg_ifo_files.find_output_with_tag('SCIENCE_OK')
if len(sci_ok_seg_file) == 1:
sci_ok_seg_file = sci_ok_seg_file[0]
sci_ok_segs = sci_ok_seg_file.segmentList
sci_ok_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_SCIENCE_OK", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_ok_def_id,
sci_ok_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_ok_def_id, summ_segs, comment='')
elif sci_ok_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_ok_seg_file), ifo, 'SCIENCE_OK')
#raise ValueError(err_msg)
# SCIENCE_AVAILABLE
sci_available_seg_file = seg_ifo_files.find_output_with_tag(\
'SCIENCE_AVAILABLE')
if len(sci_available_seg_file) == 1:
sci_available_seg_file = sci_available_seg_file[0]
sci_available_segs = sci_available_seg_file.segmentList
sci_available_def_id = segmentdb_utils.add_to_segment_definer(\
outdoc, proc_id, ifo, "CBC_WORKFLOW_SCIENCE_AVAILABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id,
sci_available_def_id, sci_available_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
sci_available_def_id, summ_segs, comment='')
elif sci_available_seg_file:
# FIXME: While the segment module is still fractured (#127) this
# may not work. Please update when #127 is resolved
pass
#err_msg = "Got %d files matching %s and %s. Expected 1 or 0." \
# %(len(sci_available_seg_file), ifo, 'SCIENCE_AVAILABLE')
#raise ValueError(err_msg)
# ANALYSABLE - This one needs to come from inspiral outs
ifo_insps = insps.find_output_with_ifo(ifo)
analysable_segs = ifo_insps.get_times_covered_by_files()
analysable_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_WORKFLOW_ANALYSABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, analysable_def_id,
analysable_segs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id,
analysable_def_id, summ_segs, comment='')
summ_file = File(workflow.ifos, "WORKFLOW_SUMMARY",
workflow.analysis_time, extension=".xml",
directory=output_dir)
summ_file.PFN(summ_file.storage_path, site='local')
utils.write_filename(outdoc, summ_file.storage_path)
return FileList([summ_file])
def to_coinc_xml_object(self, file_name):
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
ifos = list(self.sngl_files.keys())
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'pycbc',
{}, ifos=ifos, comment='', version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
search_summ_table = lsctables.New(lsctables.SearchSummaryTable)
coinc_h5file = self.coinc_file.h5file
try:
start_time = coinc_h5file['segments']['coinc']['start'][:].min()
end_time = coinc_h5file['segments']['coinc']['end'][:].max()
except KeyError:
start_times = []
end_times = []
for ifo_comb in coinc_h5file['segments']:
if ifo_comb == 'foreground_veto':
continue
seg_group = coinc_h5file['segments'][ifo_comb]
start_times.append(seg_group['start'][:].min())
end_times.append(seg_group['end'][:].max())
start_time = min(start_times)
end_time = max(end_times)
num_trigs = len(self.sort_arr)
search_summary = return_search_summary(start_time, end_time,
num_trigs, ifos)
search_summ_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summ_table)
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
time_slide_table = lsctables.New(lsctables.TimeSlideTable)
# Set up time_slide table
time_slide_id = lsctables.TimeSlideID(0)
for ifo in ifos:
time_slide_row = lsctables.TimeSlide()
time_slide_row.instrument = ifo
time_slide_row.time_slide_id = time_slide_id
time_slide_row.offset = 0
time_slide_row.process_id = proc_id
time_slide_table.append(time_slide_row)
# Set up coinc_definer table
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = \
"sngl_inspiral<-->sngl_inspiral coincidences"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
bank_col_names = ['mass1', 'mass2', 'spin1z', 'spin2z']
bank_col_vals = {}
for name in bank_col_names:
bank_col_vals[name] = self.get_bankfile_array(name)
coinc_event_names = ['ifar', 'time', 'fap', 'stat']
coinc_event_vals = {}
for name in coinc_event_names:
if name == 'time':
coinc_event_vals[name] = self.get_end_time()
else:
coinc_event_vals[name] = self.get_coincfile_array(name)
sngl_col_names = ['snr', 'chisq', 'chisq_dof', 'bank_chisq',
'bank_chisq_dof', 'cont_chisq', 'cont_chisq_dof',
'end_time', 'template_duration', 'coa_phase',
'sigmasq']
sngl_col_vals = {}
for name in sngl_col_names:
sngl_col_vals[name] = self.get_snglfile_array_dict(name)
sngl_event_count = 0
for idx in range(len(self.sort_arr)):
# Set up IDs and mapping values
coinc_id = lsctables.CoincID(idx)
# Set up sngls
sngl_mchirps = []
sngl_mtots = []
net_snrsq = 0
triggered_ifos = []
for ifo in ifos:
# If this ifo is not participating in this coincidence then
# ignore it and move on.
if not sngl_col_vals['snr'][ifo][1][idx]:
continue
triggered_ifos += [ifo]
event_id = lsctables.SnglInspiralID(sngl_event_count)
sngl_event_count += 1
sngl = return_empty_sngl()
sngl.event_id = event_id
sngl.ifo = ifo
net_snrsq += sngl_col_vals['snr'][ifo][0][idx]**2
for name in sngl_col_names:
val = sngl_col_vals[name][ifo][0][idx]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
setattr(sngl, name, val)
for name in bank_col_names:
val = bank_col_vals[name][idx]
setattr(sngl, name, val)
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.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
# If exact match is not used, then take mean
# masses from the single triggers
sngl_mchirps += [sngl.mchirp]
sngl_mtots += [sngl.mtotal]
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 = event_id
coinc_event_map_table.append(coinc_map_row)
sngl_combined_mchirp = np.mean(sngl_mchirps)
sngl_combined_mtot = np.mean(sngl_mtots)
# Set up coinc inspiral and coinc event tables
coinc_event_row = lsctables.Coinc()
coinc_inspiral_row = lsctables.CoincInspiral()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(triggered_ifos)
coinc_event_row.instruments = ','.join(triggered_ifos)
coinc_inspiral_row.set_ifos(triggered_ifos)
coinc_event_row.time_slide_id = time_slide_id
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_combined_mchirp
coinc_inspiral_row.mass = sngl_combined_mtot
coinc_inspiral_row.set_end(
LIGOTimeGPS(coinc_event_vals['time'][idx])
)
coinc_inspiral_row.snr = net_snrsq**0.5
coinc_inspiral_row.false_alarm_rate = coinc_event_vals['fap'][idx]
coinc_inspiral_row.combined_far = 1./coinc_event_vals['ifar'][idx]
# Transform to Hz
coinc_inspiral_row.combined_far = \
coinc_inspiral_row.combined_far / YRJUL_SI
coinc_event_row.likelihood = coinc_event_vals['stat'][idx]
coinc_inspiral_row.minimum_duration = 0.
coinc_event_table.append(coinc_event_row)
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
outdoc.childNodes[0].appendChild(coinc_event_table)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(time_slide_table)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
ligolw_utils.write_filename(outdoc, file_name)
del CMAP["mass2"]
CMAP[("mass1", "mass2")] = ("mass1", "mass2")
ar = numpy.random.random((len(CMAP), 10))
samp_dict = dict(zip(CMAP, ar))
ar = samp_dict[("mass1", "mass2")]
samp_dict[("mass1", "mass2")] = numpy.array([ar, ar])
del CMAP[("mass1", "mass2")]
CMAP["mass1"] = "mass1"
CMAP["mass2"] = "mass2"
samp_dict["samp_n"] = numpy.array(range(0,10))
CMAP["sample_n"] = "sample_n"
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], {})
append_samples_to_xmldoc(xmldoc, samp_dict)
def gaussian(x, mu=0, std=1):
return 1/numpy.sqrt(numpy.pi*2)/std * numpy.exp(-(x-mu)**2/2/std**2)
m1m, m2m = 1.4, 1.5
m1, m2 = numpy.random.random(2000).reshape(2,1000)*1.0+1.0
loglikes = [gaussian(m1i, m1m)*gaussian(m2i, m2m) for m1i, m2i in zip(m1, m2)]
#loglikelihood = - 7.5**2/2
#append_likelihood_result_to_xmldoc(xmldoc, loglikelihood, **{"mass1": 1.4, "mass2": 1.4, "ifos": "H1,L1,V1"})
for m1i, m2i, loglikelihood in zip(m1, m2, loglikes):
append_likelihood_result_to_xmldoc(xmldoc, loglikelihood, **{"mass1": m1i, "mass2": m2i})
from glue.ligolw import utils
def register_to_xmldoc(xmldoc, parser, opts, **kwargs):
from glue.ligolw.utils import process
params = {key: _sanitize_arg_value_for_xmldoc(value)
for key, value in opts.__dict__.items()}
return process.register_to_xmldoc(xmldoc, parser.prog, params, **kwargs)
# Main # # ============================================================================= # options, filenames = parse_command_line() if options.verbose: print >>sys.stderr, "time-frequency tiles have %g degrees of freedom" % (2 * options.delta_t * options.delta_f) xmldoc = ligolw.Document() xmldoc.appendChild(ligolw.LIGO_LW()) process = ligolw_process.register_to_xmldoc(xmldoc, u"lalapps_binj_pic", options.options_dict, version = git_version.version) time_slide_table = xmldoc.childNodes[-1].appendChild(lsctables.TimeSlideTable.get_table(ligolw_utils.load_filename(options.time_slide_xml, verbose = options.verbose, contenthandler = LIGOLWContentHandler))) time_slide_id = time_slide_table[0].time_slide_id if options.verbose: print >>sys.stderr, "associating injections with time slide ID \"%s\": %s" % (time_slide_id, time_slide_table.as_dict()[time_slide_id]) for row in time_slide_table: row.process_id = process.process_id sim_burst_tbl = xmldoc.childNodes[-1].appendChild(lsctables.New(lsctables.SimBurstTable, ["process_id", "simulation_id", "time_slide_id", "waveform", "waveform_number", "ra", "dec", "psi", "time_geocent_gps", "time_geocent_gps_ns", "duration", "frequency", "bandwidth", "egw_over_rsquared", "pol_ellipse_angle", "pol_ellipse_e"])) for filename in filenames: if options.verbose: print >>sys.stderr, "loading %s ..." % filename img = Image.open(filename) width, height = img.size
file=sys.stderr)
if not options.force:
if options.verbose:
print("skipping", file=sys.stderr)
continue
if options.verbose:
print("continuing by --force", file=sys.stderr)
#
# Add an entry to the process table.
#
process = ligolw_process.register_to_xmldoc(xmldoc,
process_program_name,
process_params,
comment=options.comment,
version=__version__,
cvs_repository=u"lscsoft",
cvs_entry_time=__date__)
#
# LAL writes all triggers with event_id = 0. Detect this and barf
# on it.
#
tbl = lsctables.SnglInspiralTable.get_table(xmldoc)
assert len(set(tbl.getColumnByName("event_id"))) == len(
tbl), "degenerate sngl_inspiral event_id detected"
#
# Extract veto segments if present.
def output_sngl_inspiral_table(outputFile, tempBank, metricParams,
ethincaParams, programName="", optDict = None,
outdoc=None, **kwargs):
"""
Function that converts the information produced by the various pyCBC bank
generation codes into a valid LIGOLW xml file containing a sngl_inspiral
table and outputs to file.
Parameters
-----------
outputFile : string
Name of the file that the bank will be written to
tempBank : iterable
Each entry in the tempBank iterable should be a sequence of
[mass1,mass2,spin1z,spin2z] in that order.
metricParams : metricParameters instance
Structure holding all the options for construction of the metric
and the eigenvalues, eigenvectors and covariance matrix
needed to manipulate the space.
ethincaParams: {ethincaParameters instance, None}
Structure holding options relevant to the ethinca metric computation
including the upper frequency cutoff to be used for filtering.
NOTE: The computation is currently only valid for non-spinning systems
and uses the TaylorF2 approximant.
programName (key-word-argument) : string
Name of the executable that has been run
optDict (key-word argument) : dictionary
Dictionary of the command line arguments passed to the program
outdoc (key-word argument) : ligolw xml document
If given add template bank to this representation of a xml document and
write to disk. If not given create a new document.
kwargs : key-word arguments
All other key word arguments will be passed directly to
ligolw_process.register_to_xmldoc
"""
if optDict is None:
optDict = {}
if outdoc is None:
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# get IFO to put in search summary table
ifos = []
if 'channel_name' in optDict.keys():
if optDict['channel_name'] is not None:
ifos = [optDict['channel_name'][0:2]]
proc_id = ligolw_process.register_to_xmldoc(outdoc, programName, optDict,
ifos=ifos, **kwargs).process_id
sngl_inspiral_table = convert_to_sngl_inspiral_table(tempBank, proc_id)
# Calculate Gamma components if needed
if ethincaParams is not None:
if ethincaParams.doEthinca:
for sngl in sngl_inspiral_table:
# Set tau_0 and tau_3 values needed for the calculation of
# ethinca metric distances
(sngl.tau0,sngl.tau3) = pnutils.mass1_mass2_to_tau0_tau3(
sngl.mass1, sngl.mass2, metricParams.f0)
fMax_theor, GammaVals = calculate_ethinca_metric_comps(
metricParams, ethincaParams,
sngl.mass1, sngl.mass2, spin1z=sngl.spin1z,
spin2z=sngl.spin2z, full_ethinca=ethincaParams.full_ethinca)
# assign the upper frequency cutoff and Gamma0-5 values
sngl.f_final = fMax_theor
for i in xrange(len(GammaVals)):
setattr(sngl, "Gamma"+str(i), GammaVals[i])
# If Gamma metric components are not wanted, assign f_final from an
# upper frequency cutoff specified in ethincaParams
elif ethincaParams.cutoff is not None:
for sngl in sngl_inspiral_table:
sngl.f_final = pnutils.frequency_cutoff_from_name(
ethincaParams.cutoff,
sngl.mass1, sngl.mass2, sngl.spin1z, sngl.spin2z)
# set per-template low-frequency cutoff
if 'f_low_column' in optDict and 'f_low' in optDict and \
optDict['f_low_column'] is not None:
for sngl in sngl_inspiral_table:
setattr(sngl, optDict['f_low_column'], optDict['f_low'])
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# get times to put in search summary table
start_time = 0
end_time = 0
if 'gps_start_time' in optDict.keys() and 'gps_end_time' in optDict.keys():
start_time = optDict['gps_start_time']
end_time = optDict['gps_end_time']
# make search summary table
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
search_summary = return_search_summary(start_time, end_time,
len(sngl_inspiral_table), ifos, **kwargs)
search_summary_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summary_table)
# write the xml doc to disk
proctable = table.get_table(outdoc, lsctables.ProcessTable.tableName)
ligolw_utils.write_filename(outdoc, outputFile,
gz=outputFile.endswith('.gz'))
else:
raise IOError("Could not split mchirp into bins suitably")
if options.verbose:
logging.info("Mchirp bin edges chosen are: {}".format(mc_bins_edges))
sys.stdout.flush()
for i in range(len(mc_bins_edges) - 1):
# create a blank xml document and add points that fall within the i'th bin
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
new_inspiral_table = lsctables.New(tabletype,
columns=template_bank_table.columnnames)
outdoc.childNodes[0].appendChild(new_inspiral_table)
out_proc_id = ligolw_process.register_to_xmldoc(
outdoc, PROGRAM_NAME, options.__dict__,
comment=options.comment).process_id
for p in template_bank_table:
if p.mchirp >= mc_bins_edges[i] and p.mchirp < mc_bins_edges[i + 1]:
p.process_id = out_proc_id
new_inspiral_table.append(p)
if options.verbose:
logging.info("\t {} templates in sub-bank {}.".format(\
len(new_inspiral_table), i))
# write the xml doc to disk
proctable = table.get_table(outdoc, lsctables.ProcessTable.tableName)
proctable[0].end_time = gpstime.GpsSecondsFromPyUTC(time.time())
outname = options.named + '%06d.xml' % i
# Clear the statistics book-keeping object. # distributions = stringutils.StringCoincParamsDistributions() segs = segments.segmentlistdict() # # Start output document # xmldoc = ligolw.Document() xmldoc.appendChild(ligolw.LIGO_LW()) process = ligolw_process.register_to_xmldoc(xmldoc, program = u"lalapps_string_meas_likelihood", paramdict = paramdict, version = __version__, cvs_repository = "lscsoft", cvs_entry_time = __date__, comment = u"") distributions.process_id = process.process_id # # Iterate over files # for n, filename in enumerate(filenames): # # Open the database file. # if options.verbose: print >>sys.stderr, "%d/%d: %s" % (n + 1, len(filenames), filename)
dfDir, segsList)
# This is needed to know what times will be analysed by daily ahope
# Template bank stuff
banks = _workflow.setup_tmpltbank_workflow(workflow, scienceSegs, datafinds,
dfDir)
# Do matched-filtering
insps = _workflow.setup_matchedfltr_workflow(workflow, scienceSegs, datafinds,
banks, dfDir)
# Now construct the summary XML file
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# FIXME: PROGRAM NAME and dictionary of opts should be variables defined up above
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'dayhopetest',
vars(args) ).process_id
for ifo in workflow.ifos:
# Lets get the segment lists we need
segIfoFiles = segsList.find_output_with_ifo(ifo)
# SCIENCE
sciSegFile = segIfoFiles.find_output_with_tag('SCIENCE')
assert(len(sciSegFile) == 1)
sciSegFile = sciSegFile[0]
sciSegs = sciSegFile.segmentList
# SCIENCE_OK
sciokSegFile = segIfoFiles.find_output_with_tag('SCIENCE_OK')
assert(len(sciokSegFile) == 1)
sciokSegFile = sciokSegFile[0]
sciokSegs = sciokSegFile.segmentList
# SCIENCE_AVAILABLE
sciavailableSegFile = segIfoFiles.find_output_with_tag('SCIENCE_AVAILABLE')
lnL[i] = factored_likelihood.factored_log_likelihood_time_marginalized(tvals, P, rholms_intp, rholms, cross_terms, det_epochs, opts.l_max,interpolate=opts.interpolate_time)
i+=1
return numpy.exp(lnL)
res, var, neff, dict_return = sampler.integrate(likelihood_function, *unpinned_params, **pinned_params)
print " lnLmarg is ", numpy.log(res), " with expected relative error ", numpy.sqrt(var)/res
print " note neff is ", neff
# FIXME: This is turning into a mess
if opts.output_file:
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__)
result_dict = {"mass1": opts.mass1, "mass2": opts.mass2, "event_duration": numpy.sqrt(var)/res, "ttotal": sampler.ntotal}
if opts.spin1z is not None or sngl_inspiral_table:
result_dict["spin1z"] = opts.spin1z or 0.0
if opts.spin2z is not None or sngl_inspiral_table:
result_dict["spin2z"] = opts.spin2z or 0.0
if opts.eff_lambda is not None:
result_dict["psi0"] = opts.eff_lambda
if opts.deff_lambda is not None:
result_dict["psi3"] = opts.deff_lambda
xmlutils.append_likelihood_result_to_xmldoc(xmldoc, numpy.log(res), neff=neff, **result_dict)
utils.write_filename(xmldoc, opts.output_file, gz=opts.output_file.endswith(".gz"))
if opts.save_samples:
samples = sampler._rvs
samples["distance"] = samples["distance"]
# iterate through classifiers -> generate segments
#========================
### set up xml document
from glue.ligolw import ligolw
from glue.ligolw import utils as ligolw_utils
from glue.ligolw import lsctables
from glue.ligolw.utils import process
xmldoc = ligolw.Document()
xml_element = ligolw.LIGO_LW()
xmldoc.appendChild(xml_element)
proc_id = process.register_to_xmldoc(xmldoc,
__prog__,
opts.__dict__,
version=__version__).process_id
segdef = lsctables.New(lsctables.SegmentDefTable,
columns=[
"process_id", "segment_def_id", "ifos", "name",
"version", "comment"
])
segsum = lsctables.New(lsctables.SegmentSumTable,
columns=[
"process_id", "segment_sum_id", "start_time",
"start_time_ns", "end_time", "end_time_ns",
"comment", "segment_def_id"
])
segtab = lsctables.New(lsctables.SegmentTable,
columns=[
import lalsimulation
# Other imports.
import numpy as np
from scipy import linalg
# BAYESTAR imports.
from lalinference import bayestar.ligolw
# Read input file.
xmldoc = ligolw_utils.load_filename(,
infilename, contenthandler=bayestar.ligolw.LSCTablesContentHandler)
# Write process metadata to output file.
process = ligolw_process.register_to_xmldoc(xmldoc, parser.get_prog_name(),
opts.__dict__)
# Determine the low frequency cutoff from the template bank file.
f_low = bayestar.ligolw.get_template_bank_f_low(xmldoc)
# Get the SnglInspiral table.
sngl_inspiral_table = ligolw_table.get_table(xmldoc,
lsctables.SnglInspiralTable.tableName)
# Determine central values of intrinsic parameters.
mchirp0 = lalinspiral.sbank.tau0tau3.m1m2_to_mchirp(opts.mass1, opts.mass2)
eta0 = opts.mass1 * opts.mass2 / np.square(opts.mass1 + opts.mass2)
chi0 = 0.
# Transform to chirp times.
thetas_0 = lalsimulation.SimInspiralTaylorF2RedSpinChirpTimesFromMchirpEtaChi(
#
# FIXME: don't hard-code instruments
distributions = stringutils.StringCoincParamsDistributions(["H1", "L1", "V1"])
segs = segments.segmentlistdict()
#
# Start output document
#
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
process = ligolw_process.register_to_xmldoc(
xmldoc,
program=u"lalapps_string_meas_likelihood",
paramdict=paramdict,
version=__version__,
cvs_repository="lscsoft",
cvs_entry_time=__date__,
comment=u"")
#
# Iterate over files
#
for n, filename in enumerate(filenames):
#
# Open the database file.
#
if options.verbose:
print >> sys.stderr, "%d/%d: %s" % (n + 1, len(filenames), filename)
from lalinference.bayestar import filter
from lalinference.bayestar import timing
# Other imports.
import numpy as np
progress = pylal.progress.ProgressBar()
# Open output file.
progress.update(-1, 'setting up output document')
out_xmldoc = ligolw.Document()
out_xmldoc.appendChild(ligolw.LIGO_LW())
# Write process metadata to output file.
process = ligolw_process.register_to_xmldoc(out_xmldoc, parser.get_prog_name(),
opts.__dict__, ifos=opts.detector, comment="Simulated coincidences")
# Add search summary to output file.
all_time = segments.segment(
[glue.lal.LIGOTimeGPS(0), glue.lal.LIGOTimeGPS(2e9)])
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
out_xmldoc.childNodes[0].appendChild(search_summary_table)
summary = ligolw_search_summary.append_search_summary(out_xmldoc, process,
inseg=all_time, outseg=all_time)
# Read PSDs.
progress.update(-1, 'reading ' + opts.reference_psd)
xmldoc = ligolw_utils.load_filename(
opts.reference_psd, contenthandler=lal.series.PSDContentHandler)
psds = lal.series.read_psd_xmldoc(xmldoc)
psds = dict(
def __init__(self, ifos, coinc_results):
"""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 pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is define
in pycbc/events/coinc.py
and matches the on disk representation in the hdf file for this time.
"""
# remember if this should be marked as HWINJ
self.is_hardware_injection = False
if 'HWINJ' in coinc_results:
self.is_hardware_injection = True
# 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=ifos, comment='', version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "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(ifos)
coinc_event_row.instruments = ','.join(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_id = 0
for ifo in ifos:
names = [n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n]
sngl_id += 1
sngl = return_empty_sngl()
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.ifo = ifo
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
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.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
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)
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(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl.mchirp
coinc_inspiral_row.mass = sngl.mtotal
coinc_inspiral_row.end_time = sngl.end_time
coinc_inspiral_row.end_time_ns = sngl.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (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)
self.outdoc = outdoc
def to_coinc_xml_object(self, file_name):
# FIXME: This function will only work with two ifos!!
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
ifos = [ifo for ifo in self.sngl_files.keys()]
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'pycbc',
{}, ifos=ifos, comment='', version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
search_summ_table = lsctables.New(lsctables.SearchSummaryTable)
coinc_h5file = self.coinc_file.h5file
start_time = coinc_h5file['segments']['coinc']['start'][:].min()
end_time = coinc_h5file['segments']['coinc']['end'][:].max()
num_trigs = len(self.sort_arr)
search_summary = return_search_summary(start_time, end_time,
num_trigs, ifos)
search_summ_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summ_table)
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
time_slide_table = lsctables.New(lsctables.TimeSlideTable)
# Set up time_slide table
time_slide_id = lsctables.TimeSlideID(0)
for ifo in ifos:
time_slide_row = lsctables.TimeSlide()
time_slide_row.instrument = ifo
time_slide_row.time_slide_id = time_slide_id
time_slide_row.offset = 0
time_slide_row.process_id = proc_id
time_slide_table.append(time_slide_row)
# Set up coinc_definer table
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincidences"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
bank_col_names = ['mass1', 'mass2', 'spin1z', 'spin2z']
bank_col_vals = {}
for name in bank_col_names:
bank_col_vals[name] = self.get_bankfile_array(name)
coinc_event_names = ['ifar', 'time1', 'fap', 'stat']
coinc_event_vals = {}
for name in coinc_event_names:
coinc_event_vals[name] = self.get_coincfile_array(name)
sngl_col_names = ['snr', 'chisq', 'chisq_dof', 'bank_chisq',
'bank_chisq_dof', 'cont_chisq', 'cont_chisq_dof',
'end_time', 'template_duration', 'coa_phase',
'sigmasq']
sngl_col_vals = {}
for name in sngl_col_names:
sngl_col_vals[name] = self.get_snglfile_array_dict(name)
for idx in xrange(len(self.sort_arr)):
# Set up IDs and mapping values
coinc_id = lsctables.CoincID(idx)
# Set up sngls
# FIXME: As two-ifo is hardcoded loop over all ifos
sngl_combined_mchirp = 0
sngl_combined_mtot = 0
for ifo in ifos:
sngl_id = self.trig_id[ifo][idx]
event_id = lsctables.SnglInspiralID(sngl_id)
sngl = return_empty_sngl()
sngl.event_id = event_id
sngl.ifo = ifo
for name in sngl_col_names:
val = sngl_col_vals[name][ifo][idx]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
setattr(sngl, name, val)
for name in bank_col_names:
val = bank_col_vals[name][idx]
setattr(sngl, name, val)
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.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_combined_mchirp += sngl.mchirp
sngl_combined_mtot += sngl.mtotal
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 = event_id
coinc_event_map_table.append(coinc_map_row)
sngl_combined_mchirp = sngl_combined_mchirp / len(ifos)
sngl_combined_mtot = sngl_combined_mtot / len(ifos)
# Set up coinc inspiral and coinc event tables
coinc_event_row = lsctables.Coinc()
coinc_inspiral_row = lsctables.CoincInspiral()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_inspiral_row.set_ifos(ifos)
coinc_event_row.time_slide_id = time_slide_id
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_combined_mchirp
coinc_inspiral_row.mass = sngl_combined_mtot
coinc_inspiral_row.set_end(\
LIGOTimeGPS(coinc_event_vals['time1'][idx]))
coinc_inspiral_row.snr = coinc_event_vals['stat'][idx]
coinc_inspiral_row.false_alarm_rate = coinc_event_vals['fap'][idx]
coinc_inspiral_row.combined_far = 1./coinc_event_vals['ifar'][idx]
# Transform to Hz
coinc_inspiral_row.combined_far = \
coinc_inspiral_row.combined_far / YRJUL_SI
coinc_event_row.likelihood = 0.
coinc_inspiral_row.minimum_duration = 0.
coinc_event_table.append(coinc_event_row)
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
outdoc.childNodes[0].appendChild(coinc_event_table)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(time_slide_table)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
ligolw_utils.write_filename(outdoc, file_name)
# Dump data: p(<x)
with open(fnameBase + "-pp-data.dat", 'w') as f:
for key in ['ra', 'dec', 'tref', 'phi', 'incl', 'psi', 'dist', 'lnL']:
f.write(key + " " + str(ppdata[key][0]) + ' ' +
str(ppdata[key][1]) + '\n')
# Save the outputs in CP's format, for comparison. NOT YET ACTIVE CODE -- xmlutils has a bug on master (lacking terms in dictionary)
if True: # opts.points_file_base:
print("==== Exporting to xml: <base>.xml.gz =====")
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
opts.NR_template_param = ""
# process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__) # the process-params generation has not been reliable
process.register_to_xmldoc(
xmldoc, sys.argv[0],
{}) # the process-params generation has not been reliable
samples = {}
samples["distance"] = ret[:, -3 - 1] #retNiceIndexed['dist']
samples["t_ref"] = ret[:, -3 - 5] #retNiceIndexed['tref']
samples["polarization"] = ret[:, -3 - 1] #retNiceIndexed['psi']
samples["coa_phase"] = ret[:, -3 - 4] #retNiceIndexed['phi']
samples["latitude"] = retNiceIndexed['dec']
samples["longitude"] = retNiceIndexed['ra']
samples["inclination"] = retNiceIndexed['incl']
samples["loglikelihood"] = ret[:, -1] #alpha1
samples["joint_prior"] = ret[:, -3] #alpha2
samples["joint_s_prior"] = ret[:, -2] #alpha3
# samples = sampler._rvs
# samples["distance"] = samples["distance"]
# samples["polarization"] = samples["psi"]
def output_sngl_inspiral_table(outputFile, tempBank, metricParams,
ethincaParams, programName="", optDict = None,
outdoc=None, **kwargs):
"""
Function that converts the information produced by the various pyCBC bank
generation codes into a valid LIGOLW xml file containing a sngl_inspiral
table and outputs to file.
Parameters
-----------
outputFile : string
Name of the file that the bank will be written to
tempBank : iterable
Each entry in the tempBank iterable should be a sequence of
[mass1,mass2,spin1z,spin2z] in that order.
metricParams : metricParameters instance
Structure holding all the options for construction of the metric
and the eigenvalues, eigenvectors and covariance matrix
needed to manipulate the space.
ethincaParams: {ethincaParameters instance, None}
Structure holding options relevant to the ethinca metric computation
including the upper frequency cutoff to be used for filtering.
NOTE: The computation is currently only valid for non-spinning systems
and uses the TaylorF2 approximant.
programName (key-word-argument) : string
Name of the executable that has been run
optDict (key-word argument) : dictionary
Dictionary of the command line arguments passed to the program
outdoc (key-word argument) : ligolw xml document
If given add template bank to this representation of a xml document and
write to disk. If not given create a new document.
kwargs : key-word arguments
All other key word arguments will be passed directly to
ligolw_process.register_to_xmldoc
"""
if optDict is None:
optDict = {}
if outdoc is None:
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# get IFO to put in search summary table
ifos = []
if 'channel_name' in optDict.keys():
if optDict['channel_name'] is not None:
ifos = [optDict['channel_name'][0:2]]
proc_id = ligolw_process.register_to_xmldoc(outdoc, programName, optDict,
ifos=ifos, **kwargs).process_id
sngl_inspiral_table = convert_to_sngl_inspiral_table(tempBank, proc_id)
# Calculate Gamma components if needed
if ethincaParams is not None:
if ethincaParams.doEthinca:
for sngl in sngl_inspiral_table:
# Set tau_0 and tau_3 values needed for the calculation of
# ethinca metric distances
(sngl.tau0,sngl.tau3) = pnutils.mass1_mass2_to_tau0_tau3(
sngl.mass1, sngl.mass2, metricParams.f0)
fMax_theor, GammaVals = calculate_ethinca_metric_comps(
metricParams, ethincaParams,
sngl.mass1, sngl.mass2, spin1z=sngl.spin1z,
spin2z=sngl.spin2z, full_ethinca=ethincaParams.full_ethinca)
# assign the upper frequency cutoff and Gamma0-5 values
sngl.f_final = fMax_theor
for i in range(len(GammaVals)):
setattr(sngl, "Gamma"+str(i), GammaVals[i])
# If Gamma metric components are not wanted, assign f_final from an
# upper frequency cutoff specified in ethincaParams
elif ethincaParams.cutoff is not None:
for sngl in sngl_inspiral_table:
sngl.f_final = pnutils.frequency_cutoff_from_name(
ethincaParams.cutoff,
sngl.mass1, sngl.mass2, sngl.spin1z, sngl.spin2z)
# set per-template low-frequency cutoff
if 'f_low_column' in optDict and 'f_low' in optDict and \
optDict['f_low_column'] is not None:
for sngl in sngl_inspiral_table:
setattr(sngl, optDict['f_low_column'], optDict['f_low'])
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# get times to put in search summary table
start_time = 0
end_time = 0
if 'gps_start_time' in optDict.keys() and 'gps_end_time' in optDict.keys():
start_time = optDict['gps_start_time']
end_time = optDict['gps_end_time']
# make search summary table
search_summary_table = lsctables.New(lsctables.SearchSummaryTable)
search_summary = return_search_summary(start_time, end_time,
len(sngl_inspiral_table), ifos, **kwargs)
search_summary_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summary_table)
# write the xml doc to disk
ligolw_utils.write_filename(outdoc, outputFile,
gz=outputFile.endswith('.gz'))
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 pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is define
in pycbc/events/coinc.py
and matches the on disk representation in the hdf file for this time.
"""
self.ifos = ifos
self.template_id = coinc_results['foreground/%s/template_id' %
self.ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = False
if 'HWINJ' in coinc_results:
self.is_hardware_injection = True
# 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=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "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(ifos)
coinc_event_row.instruments = ','.join(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_id = 0
sngl_event_id_map = {}
for ifo in ifos:
names = [
n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n
]
sngl_id += 1
sngl = return_empty_sngl()
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl_event_id_map[ifo] = sngl.event_id
sngl.ifo = ifo
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
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.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
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)
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(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl.mchirp
coinc_inspiral_row.mass = sngl.mtotal
coinc_inspiral_row.end_time = sngl.end_time
coinc_inspiral_row.end_time_ns = sngl.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
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)
self.outdoc = outdoc
self.time = sngl.get_end()
# compute SNR time series
self.upload_snr_series = kwargs['upload_snr_series']
if self.upload_snr_series:
data_readers = kwargs['data_readers']
bank = kwargs['bank']
htilde = bank[self.template_id]
self.snr_series = {}
self.snr_series_psd = {}
for ifo in self.ifos:
stilde = data_readers[ifo].overwhitened_data(htilde.delta_f)
norm = 4.0 * htilde.delta_f / (htilde.sigmasq(stilde.psd)**0.5)
qtilde = zeros((len(htilde) - 1) * 2, dtype=htilde.dtype)
correlate(htilde, stilde, qtilde)
snr = qtilde * 0
ifft(qtilde, snr)
valid_end = int(len(qtilde) - data_readers[ifo].trim_padding)
valid_start = int(valid_end - data_readers[ifo].blocksize *
data_readers[ifo].sample_rate)
seg = slice(valid_start, valid_end)
snr = snr[seg]
snr *= norm
delta_t = 1.0 / data_readers[ifo].sample_rate
start = data_readers[ifo].start_time
snr = TimeSeries(snr, delta_t=delta_t, epoch=start)
self.snr_series[ifo] = snr
self.snr_series_psd[ifo] = stilde.psd
# store the on-source slice of the series into the XML doc
snr_onsource_time = coinc_results['foreground/%s/end_time' %
ifo] - snr.start_time
snr_onsource_dur = lal.REARTH_SI / lal.C_SI
onsource_idx = round(snr_onsource_time * snr.sample_rate)
onsource_start = onsource_idx - int(
snr.sample_rate * snr_onsource_dur / 2)
onsource_end = onsource_idx + int(
snr.sample_rate * snr_onsource_dur / 2)
onsource_slice = slice(onsource_start, onsource_end + 1)
snr_lal = snr[onsource_slice].lal()
snr_lal.name = 'snr'
snr_lal.sampleUnits = ''
snr_xml = _build_series(snr_lal,
(u'Time', u'Time,Real,Imaginary'),
None, 'deltaT', 's')
snr_node = outdoc.childNodes[-1].appendChild(snr_xml)
eid_param = ligolw_param.new_param(
u'event_id', u'ilwd:char', unicode(sngl_event_id_map[ifo]))
snr_node.appendChild(eid_param)
