def save(self, filename):
"""Write this trigger to gracedb compatible xml format
Parameters
----------
filename: str
Name of file to write to disk.
"""
ligolw_utils.write_filename(self.outdoc, filename)
def save(self, filename):
"""Write this trigger to gracedb compatible xml format
Parameters
----------
filename: str
Name of file to write to disk.
"""
ligolw_utils.write_filename(self.outdoc, filename)
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
ligolw_utils.write_filename(outdoc, outputFile,
gz=outputFile.endswith('.gz'))
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 make_exttrig_file(cp, ifos, sci_seg, out_dir):
'''
Make an ExtTrig xml file containing information on the external trigger
Parameters
----------
cp : pycbc.workflow.configuration.WorkflowConfigParser object
The parsed configuration options of a pycbc.workflow.core.Workflow.
ifos : str
String containing the analysis interferometer IDs.
sci_seg : glue.segments.segment
The science segment for the analysis run.
out_dir : str
The output directory, destination for xml file.
Returns
-------
xml_file : pycbc.workflow.File object
The xml file with external trigger information.
'''
# Initialise objects
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
tbl = lsctables.New(lsctables.ExtTriggersTable)
cols = tbl.validcolumns
xmldoc.childNodes[-1].appendChild(tbl)
row = tbl.appendRow()
# Add known attributes for this GRB
setattr(row, "event_ra", float(cp.get("workflow", "ra")))
setattr(row, "event_dec", float(cp.get("workflow", "dec")))
setattr(row, "start_time", int(cp.get("workflow", "trigger-time")))
setattr(row, "event_number_grb", str(cp.get("workflow", "trigger-name")))
# Fill in all empty rows
for entry in cols.keys():
if not hasattr(row, entry):
if cols[entry] in ['real_4', 'real_8']:
setattr(row, entry, 0.)
elif cols[entry] == 'int_4s':
setattr(row, entry, 0)
elif cols[entry] == 'lstring':
setattr(row, entry, '')
elif entry == 'process_id':
row.process_id = ilwd.ilwdchar("external_trigger:process_id:0")
elif entry == 'event_id':
row.event_id = ilwd.ilwdchar("external_trigger:event_id:0")
else:
print("Column %s not recognized" % (entry), file=sys.stderr)
raise ValueError
# Save file
xml_file_name = "triggerGRB%s.xml" % str(cp.get("workflow",
"trigger-name"))
xml_file_path = os.path.join(out_dir, xml_file_name)
utils.write_filename(xmldoc, xml_file_path)
xml_file_url = urlparse.urljoin("file:",
urllib.pathname2url(xml_file_path))
xml_file = File(ifos, xml_file_name, sci_seg, file_url=xml_file_url)
xml_file.PFN(xml_file_url, site="local")
return xml_file
# And add these to the output file
# Start with the segment summary
summSegs = segments.segmentlist([workflow.analysis_time])
sci_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id, ifo,
"CBC_DAYHOPE_SCIENCE", 0)
sciok_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id, ifo,
"CBC_DAYHOPE_SCIENCE_OK", 0)
sciavailable_def_id = segmentdb_utils.add_to_segment_definer(outdoc,
proc_id, ifo, "CBC_DAYHOPE_SCIENCE_AVAILABLE", 0)
analysable_def_id = segmentdb_utils.add_to_segment_definer(outdoc, proc_id,
ifo, "CBC_DAYHOPE_ANALYSABLE", 0)
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_def_id, sciSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, sciok_def_id, sciokSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, sciavailable_def_id,
sciavailableSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, analysable_def_id,
analysableSegs)
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sci_def_id,
summSegs, comment='')
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sciok_def_id,
summSegs, comment='')
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, sciavailable_def_id,
summSegs, comment='')
segmentdb_utils.add_to_segment_summary(outdoc, proc_id, analysable_def_id,
summSegs, comment='')
ligolw_utils.write_filename(outdoc, "SUMMARY.xml")
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
ligolw_utils.write_filename(outdoc,
outputFile,
gz=outputFile.endswith('.gz'))
def save(self, path, group=None, ifo='P1'):
"""
Save frequency series to a Numpy .npy, hdf, or text file. The first column
contains the sample frequencies, the second contains the values.
In the case of a complex frequency series saved as text, the imaginary
part is written as a third column. When using hdf format, the data is stored
as a single vector, along with relevant attributes.
Parameters
----------
path: string
Destination file path. Must end with either .hdf, .npy or .txt.
group: string
Additional name for internal storage use. Ex. hdf storage uses
this as the key value.
Raises
------
ValueError
If path does not end in .npy or .txt.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
_numpy.save(path, output)
elif ext == '.txt':
if self.kind == 'real':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
elif self.kind == 'complex':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy().real,
self.numpy().imag)).T
_numpy.savetxt(path, output)
elif ext == '.xml' or path.endswith('.xml.gz'):
from pycbc.io.live import make_psd_xmldoc
from pycbc.ligolw import utils
if self.kind != 'real':
raise ValueError('XML only supports real frequency series')
output = self.lal()
# When writing in this format we must *not* have the 0 values at
# frequencies less than flow. To resolve this we set the first
# non-zero value < flow.
data_lal = output.data.data
first_idx = _numpy.argmax(data_lal>0)
if not first_idx == 0:
data_lal[:first_idx] = data_lal[first_idx]
psddict = {ifo: output}
utils.write_filename(make_psd_xmldoc(psddict), path,
gz=path.endswith(".gz"))
elif ext =='.hdf':
key = 'data' if group is None else group
f = h5py.File(path)
ds = f.create_dataset(key, data=self.numpy(), compression='gzip',
compression_opts=9, shuffle=True)
ds.attrs['epoch'] = float(self.epoch)
ds.attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy, .txt, .xml, .xml.gz '
'or .hdf')
def save(self, path, group=None, ifo='P1'):
"""
Save frequency series to a Numpy .npy, hdf, or text file. The first column
contains the sample frequencies, the second contains the values.
In the case of a complex frequency series saved as text, the imaginary
part is written as a third column. When using hdf format, the data is stored
as a single vector, along with relevant attributes.
Parameters
----------
path: string
Destination file path. Must end with either .hdf, .npy or .txt.
group: string
Additional name for internal storage use. Ex. hdf storage uses
this as the key value.
Raises
------
ValueError
If path does not end in .npy or .txt.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy())).T
_numpy.save(path, output)
elif ext == '.txt':
if self.kind == 'real':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy())).T
elif self.kind == 'complex':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy().real,
self.numpy().imag)).T
_numpy.savetxt(path, output)
elif ext == '.xml' or path.endswith('.xml.gz'):
from pycbc.io.live import make_psd_xmldoc
from pycbc.ligolw import utils
if self.kind != 'real':
raise ValueError('XML only supports real frequency series')
output = self.lal()
# When writing in this format we must *not* have the 0 values at
# frequencies less than flow. To resolve this we set the first
# non-zero value < flow.
data_lal = output.data.data
first_idx = _numpy.argmax(data_lal > 0)
if not first_idx == 0:
data_lal[:first_idx] = data_lal[first_idx]
psddict = {ifo: output}
utils.write_filename(make_psd_xmldoc(psddict),
path,
gz=path.endswith(".gz"))
elif ext == '.hdf':
key = 'data' if group is None else group
f = h5py.File(path)
ds = f.create_dataset(key,
data=self.numpy(),
compression='gzip',
compression_opts=9,
shuffle=True)
ds.attrs['epoch'] = float(self.epoch)
ds.attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy, .txt, .xml, .xml.gz '
'or .hdf')
segmentdb_utils.add_to_segment(outdoc, proc_id, sci_def_id, sciSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, sciok_def_id, sciokSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, sciavailable_def_id,
sciavailableSegs)
segmentdb_utils.add_to_segment(outdoc, proc_id, analysable_def_id,
analysableSegs)
segmentdb_utils.add_to_segment_summary(outdoc,
proc_id,
sci_def_id,
summSegs,
comment='')
segmentdb_utils.add_to_segment_summary(outdoc,
proc_id,
sciok_def_id,
summSegs,
comment='')
segmentdb_utils.add_to_segment_summary(outdoc,
proc_id,
sciavailable_def_id,
summSegs,
comment='')
segmentdb_utils.add_to_segment_summary(outdoc,
proc_id,
analysable_def_id,
summSegs,
comment='')
ligolw_utils.write_filename(outdoc, "SUMMARY.xml")
def make_exttrig_file(cp, ifos, sci_seg, out_dir):
'''
Make an ExtTrig xml file containing information on the external trigger
Parameters
----------
cp : pycbc.workflow.configuration.WorkflowConfigParser object
The parsed configuration options of a pycbc.workflow.core.Workflow.
ifos : str
String containing the analysis interferometer IDs.
sci_seg : glue.segments.segment
The science segment for the analysis run.
out_dir : str
The output directory, destination for xml file.
Returns
-------
xml_file : pycbc.workflow.File object
The xml file with external trigger information.
'''
# Initialise objects
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
tbl = lsctables.New(lsctables.ExtTriggersTable)
cols = tbl.validcolumns
xmldoc.childNodes[-1].appendChild(tbl)
row = tbl.appendRow()
# Add known attributes for this GRB
setattr(row, "event_ra", float(cp.get("workflow", "ra")))
setattr(row, "event_dec", float(cp.get("workflow", "dec")))
setattr(row, "start_time", int(cp.get("workflow", "trigger-time")))
setattr(row, "event_number_grb", str(cp.get("workflow", "trigger-name")))
# Fill in all empty rows
for entry in cols.keys():
if not hasattr(row, entry):
if cols[entry] in ['real_4','real_8']:
setattr(row,entry,0.)
elif cols[entry] == 'int_4s':
setattr(row,entry,0)
elif cols[entry] == 'lstring':
setattr(row,entry,'')
elif entry == 'process_id':
row.process_id = ilwd.ilwdchar("external_trigger:process_id:0")
elif entry == 'event_id':
row.event_id = ilwd.ilwdchar("external_trigger:event_id:0")
else:
print("Column %s not recognized" %(entry), file=sys.stderr)
raise ValueError
# Save file
xml_file_name = "triggerGRB%s.xml" % str(cp.get("workflow",
"trigger-name"))
xml_file_path = os.path.join(out_dir, xml_file_name)
utils.write_filename(xmldoc, xml_file_path)
xml_file_url = urlparse.urljoin("file:", urllib.pathname2url(xml_file_path))
xml_file = File(ifos, xml_file_name, sci_seg, file_url=xml_file_url)
xml_file.PFN(xml_file_url, site="local")
return xml_file
def upload(
self,
fname,
psds,
low_frequency_cutoff,
testing=True,
extra_strings=None,
):
"""Upload this trigger to gracedb
Parameters
----------
fname: str
The name to give the xml file associated with this trigger
pds: dict of pybc.types.FrequencySeries
A ifo keyed dictionary of psds to be uploaded in association
with this trigger.
low_frequency_cutoff: float
The low frequency cutoff of the psds.
testing: bool
Switch to determine if the upload should be sent to gracedb as a
test trigger (True) or a production trigger (False)
"""
from ligo.gracedb.rest import GraceDb
# first of all, make sure the event and PSDs are saved on disk
# as GraceDB operations can fail later
self.save(fname)
psds_lal = {}
for ifo in psds:
psd = psds[ifo]
kmin = int(low_frequency_cutoff / psd.delta_f)
fseries = lal.CreateREAL8FrequencySeries(
"psd", psd.epoch, 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
psd_xmldoc = make_psd_xmldoc(psds_lal)
psd_xml_path = os.path.splitext(fname)[0] + '-psd.xml.gz'
ligolw_utils.write_filename(psd_xmldoc, psd_xml_path, gz=True)
if self.upload_snr_series:
snr_series_fname = os.path.splitext(fname)[0] + '.hdf'
for ifo in self.snr_series:
self.snr_series[ifo].save(snr_series_fname,
group='%s/snr' % ifo)
self.snr_series_psd[ifo].save(snr_series_fname,
group='%s/psd' % ifo)
# try connecting to GraceDB
try:
gracedb = GraceDb(self.gracedb_server) \
if self.gracedb_server is not None else GraceDb()
except Exception as exc:
logging.error('Cannot connect to GraceDB')
logging.error(str(exc))
logging.error('Carrying on, but event %s will NOT be uploaded!',
fname)
return None
# create GraceDB event
group = 'Test' if testing else 'CBC'
try:
r = gracedb.createEvent(group, "pycbc", fname, "AllSky").json()
except Exception as exc:
logging.error('Cannot create GraceDB event')
logging.error(str(exc))
logging.error('Carrying on, but event %s will NOT be uploaded!',
fname)
return None
logging.info("Uploaded event %s", r["graceid"])
if self.is_hardware_injection:
try:
gracedb.writeLabel(r['graceid'], 'INJ')
except Exception as exc:
logging.error("Cannot tag event %s as an injection",
r["graceid"])
logging.error(str(exc))
logging.info("Tagging event %s as an injection", r["graceid"])
# upload PSDs
try:
gracedb.writeLog(r["graceid"],
"PyCBC PSD estimate from the time of event",
"psd.xml.gz",
open(psd_xml_path, "rb").read(), "psd").json()
except Exception as exc:
logging.error("Cannot upload PSDs for event %s", r["graceid"])
logging.error(str(exc))
logging.info("Uploaded PSDs for event %s", r["graceid"])
# add other tags and comments
try:
gracedb.writeLog(
r["graceid"],
"Using PyCBC code hash %s" % pycbc_version.git_hash).json()
extra_strings = [] if extra_strings is None else extra_strings
for text in extra_strings:
gracedb.writeLog(r["graceid"], text).json()
except Exception as exc:
logging.error("Cannot write comments for event %s", r["graceid"])
logging.error(str(exc))
# upload SNR series in HDF format
if self.upload_snr_series:
try:
gracedb.writeFile(r['graceid'], snr_series_fname)
except Exception as exc:
logging.error("Cannot upload HDF SNR series for event %s",
r["graceid"])
logging.error(str(exc))
return r['graceid']
def upload(self, fname, psds, low_frequency_cutoff,
testing=True,
extra_strings=None,
):
"""Upload this trigger to gracedb
Parameters
----------
fname: str
The name to give the xml file associated with this trigger
pds: dict of pybc.types.FrequencySeries
A ifo keyed dictionary of psds to be uploaded in association
with this trigger.
low_frequency_cutoff: float
The low frequency cutoff of the psds.
testing: bool
Switch to determine if the upload should be sent to gracedb as a
test trigger (True) or a production trigger (False)
"""
from ligo.gracedb.rest import GraceDb
# first of all, make sure the event and PSDs are saved on disk
# as GraceDB operations can fail later
self.save(fname)
psds_lal = {}
for ifo in psds:
psd = psds[ifo]
kmin = int(low_frequency_cutoff / psd.delta_f)
fseries = lal.CreateREAL8FrequencySeries(
"psd", psd.epoch, 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
psd_xmldoc = make_psd_xmldoc(psds_lal)
psd_xml_path = os.path.splitext(fname)[0] + '-psd.xml.gz'
ligolw_utils.write_filename(psd_xmldoc, psd_xml_path, gz=True)
if self.upload_snr_series:
snr_series_fname = os.path.splitext(fname)[0] + '.hdf'
for ifo in self.snr_series:
self.snr_series[ifo].save(snr_series_fname,
group='%s/snr' % ifo)
self.snr_series_psd[ifo].save(snr_series_fname,
group='%s/psd' % ifo)
# try connecting to GraceDB
try:
gracedb = GraceDb(self.gracedb_server) \
if self.gracedb_server is not None else GraceDb()
except Exception as exc:
logging.error('Cannot connect to GraceDB')
logging.error(str(exc))
logging.error('Carrying on, but event %s will NOT be uploaded!', fname)
return None
# create GraceDB event
group = 'Test' if testing else 'CBC'
try:
r = gracedb.createEvent(group, "pycbc", fname, "AllSky").json()
except Exception as exc:
logging.error('Cannot create GraceDB event')
logging.error(str(exc))
logging.error('Carrying on, but event %s will NOT be uploaded!', fname)
return None
logging.info("Uploaded event %s", r["graceid"])
if self.is_hardware_injection:
try:
gracedb.writeLabel(r['graceid'], 'INJ')
except Exception as exc:
logging.error("Cannot tag event %s as an injection", r["graceid"])
logging.error(str(exc))
logging.info("Tagging event %s as an injection", r["graceid"])
# upload PSDs
try:
gracedb.writeLog(r["graceid"],
"PyCBC PSD estimate from the time of event",
"psd.xml.gz", open(psd_xml_path, "rb").read(),
"psd").json()
except Exception as exc:
logging.error("Cannot upload PSDs for event %s", r["graceid"])
logging.error(str(exc))
logging.info("Uploaded PSDs for event %s", r["graceid"])
# add other tags and comments
try:
gracedb.writeLog(r["graceid"],
"Using PyCBC code hash %s" % pycbc_version.git_hash).json()
extra_strings = [] if extra_strings is None else extra_strings
for text in extra_strings:
gracedb.writeLog(r["graceid"], text).json()
except Exception as exc:
logging.error("Cannot write comments for event %s", r["graceid"])
logging.error(str(exc))
# upload SNR series in HDF format
if self.upload_snr_series:
try:
gracedb.writeFile(r['graceid'], snr_series_fname)
except Exception as exc:
logging.error("Cannot upload HDF SNR series for event %s",
r["graceid"])
logging.error(str(exc))
return r['graceid']