def coinc_without_inj(coinc, tmpdir):
"""Produce a coinc.xml file with the found coincs stripped out."""
filename = str(tmpdir / 'coinc_without_inj.xml')
xmldoc = ligolw_utils.load_filename(coinc, contenthandler=ContentHandler)
# Prune coinc_def table
coinc_def_table = lsctables.CoincDefTable.get_table(xmldoc)
included = [row for row in coinc_def_table
if row.search_coinc_type == InspiralCoincDef.search_coinc_type
and row.search == InspiralCoincDef.search]
included_coinc_def_ids = {row.coinc_def_id for row in included}
coinc_def_table[:] = included
# Prune coinc table
coinc_table = lsctables.CoincTable.get_table(xmldoc)
included = [row for row in coinc_table
if row.coinc_def_id in included_coinc_def_ids]
included_coinc_ids = {row.coinc_event_id for row in included}
coinc_table[:] = included
# Prune coinc_map table
coinc_map_table = lsctables.CoincMapTable.get_table(xmldoc)
coinc_map_table[:] = [row for row in coinc_map_table
if row.coinc_event_id in included_coinc_ids]
ligolw_utils.write_filename(xmldoc, filename)
return filename
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 pylal import series as lalseries
from glue.ligolw import utils
assert(self.kind == 'real')
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(lalseries.make_psd_xmldoc(psddict), path,
gz=path.endswith(".gz"))
elif ext =='.hdf':
key = 'data' if group is None else group
d = h5py.File(path)
d[key] = self.numpy()
d[key].attrs['epoch'] = float(self.epoch)
d[key].attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy or .txt')
def write_to_xml(cells, intr_prms, fvals=None, fname=None, verbose=False):
"""
Write a set of cells, with dimensions corresponding to intr_prms to an XML file as sim_inspiral rows.
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
procrow = process.append_process(xmldoc, program=sys.argv[0])
procid = procrow.process_id
process.append_process_params(xmldoc, procrow, process.process_params_from_dict(opts.__dict__))
rows = ["simulation_id", "process_id", "numrel_data"] + list(intr_prms)
if fvals is not None:
rows.append("alpha1")
sim_insp_tbl = lsctables.New(lsctables.SimInspiralTable, rows)
for itr, intr_prm in enumerate(cells):
sim_insp = sim_insp_tbl.RowType()
# FIXME: Need better IDs
sim_insp.numrel_data = "INTR_SET_%d" % itr
sim_insp.simulation_id = ilwd.ilwdchar("sim_inspiral:sim_inspiral_id:%d" % itr)
sim_insp.process_id = procid
if fvals:
sim_insp.alpha1 = fvals[itr]
for p, v in zip(intr_prms, intr_prm._center):
setattr(sim_insp, p, v)
sim_insp_tbl.append(sim_insp)
xmldoc.childNodes[0].appendChild(sim_insp_tbl)
if fname is None:
channel_name = ["H=H", "L=L"]
ifos = "".join([o.split("=")[0][0] for o in channel_name])
#start = int(event_time)
start = 0
fname = "%s-MASS_POINTS-%d-1.xml.gz" % (ifos, start)
utils.write_filename(xmldoc, fname, gz=True, verbose=verbose)
def write_xml(trig_times,freqs,snrs,channel,start_time,length,thresh,outdir):
if len(trig_times):
print 'number of triggers is ' + str(len(trig_times))
print 'trigger rate is ' + str(float(len(trig_times))/length)
if (float(len(trig_times))/length > 16):
print 'Trigger rate too high, skipping channel'
return
sngl_burst_table = lsctables.New(lsctables.SnglBurstTable, ["peak_time", "peak_time_ns","peak_frequency","snr"])
for t,f,s in zip(trig_times, freqs, snrs):
row = sngl_burst_table.RowType()
row.set_peak(t)
row.peak_frequency = f
row.snr = s
sngl_burst_table.append(row)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
xmldoc.childNodes[0].appendChild(sngl_burst_table)
# define trigger directory
trig_dir = (outdir + channel[:2] + '/' +
channel[3:] + '_' + str(thresh) + '_DAC/' + str(start_time)[:5] + '/')
if not os.path.exists(trig_dir):
os.makedirs(trig_dir)
# create filename string
utils.write_filename(xmldoc, trig_dir + channel[:2] + "-" + channel[3:6] +
"_" + channel[7:] + "_" + str(thresh) + "_DAC-" + str(start_time) + "-" + str(length) +
".xml.gz", gz=True)
print 'wrote XML for channel: ' + str(channel)
print 'number of triggers: ' + str(np.size(trig_times))
else:
print 'No triggers found for channel: ' + str(channel)
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 write_likelihood_data(filename,
coincparamsdistributions,
seglists,
verbose=False):
utils.write_filename(ligolw_burca_tailor.gen_likelihood_control(
coincparamsdistributions, seglists, name=u"string_cusp_likelihood"),
filename,
verbose=verbose,
gz=(filename or "stdout").endswith(".gz"))
def upload(self, fname, psds, low_frequency_cutoff, testing=True):
"""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
import lal
import lal.series
self.save(fname)
if testing:
group = 'Test'
else:
group = 'CBC'
gracedb = GraceDb()
r = gracedb.createEvent(group, "pycbc", fname, "AllSky").json()
logging.info("Uploaded event %s.", r["graceid"])
if self.is_hardware_injection:
gracedb.writeLabel(r['graceid'], 'INJ')
logging.info("Tagging event %s as an injection", r["graceid"])
# Convert our psds to the xml psd format.
# FIXME: we should not use lal.series!!!
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 = lal.series.make_psd_xmldoc(psds_lal)
ligolw_utils.write_filename(psd_xmldoc, "tmp_psd.xml.gz", gz=True)
gracedb.writeLog(r["graceid"],
"PyCBC PSD estimate from the time of event",
"psd.xml.gz",
open("tmp_psd.xml.gz", "rb").read(), "psd").json()
logging.info("Uploaded file psd.xml.gz to event %s.", r["graceid"])
def upload(self, fname, psds, low_frequency_cutoff, testing=True):
"""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
import lal
import lal.series
self.save(fname)
if testing:
group = 'Test'
else:
group = 'CBC'
gracedb = GraceDb()
r = gracedb.createEvent(group, "pycbc", fname, "AllSky").json()
logging.info("Uploaded event %s.", r["graceid"])
if self.is_hardware_injection:
gracedb.writeLabel(r['graceid'], 'INJ')
logging.info("Tagging event %s as an injection", r["graceid"])
# Convert our psds to the xml psd format.
# FIXME: we should not use lal.series!!!
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 = lal.series.make_psd_xmldoc(psds_lal)
ligolw_utils.write_filename(psd_xmldoc, "tmp_psd.xml.gz", gz=True)
gracedb.writeLog(r["graceid"],
"PyCBC PSD estimate from the time of event",
"psd.xml.gz", open("tmp_psd.xml.gz", "rb").read(),
"psd").json()
logging.info("Uploaded file psd.xml.gz to event %s.", r["graceid"])
def write_tables(target, tables, append=False, overwrite=False, **kwargs):
"""Write an LIGO_LW table to file
Parameters
----------
target : `str`, `file`, :class:`~glue.ligolw.ligolw.Document`
the file or document to write into
tables : `list`, `tuple` of :class:`~glue.ligolw.table.Table`
the tables to write
append : `bool`, optional, default: `False`
if `True`, append to an existing file/table, otherwise `overwrite`
overwrite : `bool`, optional, default: `False`
if `True`, delete an existing instance of the table type, otherwise
append new rows
**kwargs
other keyword arguments to pass to
:func:`~glue.ligolw.utils.load_filename`, or
:func:`~glue.ligolw.utils.load_fileobj` as appropriate
"""
from glue.ligolw.ligolw import (Document, LIGO_LW, LIGOLWContentHandler)
from glue.ligolw import utils as ligolw_utils
# allow writing directly to XML
if isinstance(target, (Document, LIGO_LW)):
xmldoc = target
# open existing document, if possible
elif append and not overwrite:
xmldoc = open_xmldoc(
target, contenthandler=kwargs.pop('contenthandler',
LIGOLWContentHandler))
# fail on existing document and not overwriting
elif (not overwrite and isinstance(target, string_types) and
os.path.isfile(target)):
raise IOError("File exists: {}".format(target))
else: # or create a new document
xmldoc = Document()
# convert table to format
write_tables_to_document(xmldoc, tables, overwrite=overwrite)
# write file
if isinstance(target, string_types):
kwargs.setdefault('gz', target.endswith('.gz'))
ligolw_utils.write_filename(xmldoc, target, **kwargs)
elif isinstance(target, FILE_LIKE):
kwargs.setdefault('gz', target.name.endswith('.gz'))
ligolw_utils.write_fileobj(xmldoc, target, **kwargs)
def write_tables(target, tables, append=False, overwrite=False, **kwargs):
"""Write an LIGO_LW table to file
Parameters
----------
target : `str`, `file`, :class:`~glue.ligolw.ligolw.Document`
the file or document to write into
tables : `list`, `tuple` of :class:`~glue.ligolw.table.Table`
the tables to write
append : `bool`, optional, default: `False`
if `True`, append to an existing file/table, otherwise `overwrite`
overwrite : `bool`, optional, default: `False`
if `True`, delete an existing instance of the table type, otherwise
append new rows
**kwargs
other keyword arguments to pass to
:func:`~glue.ligolw.utils.load_filename`, or
:func:`~glue.ligolw.utils.load_fileobj` as appropriate
"""
from glue.ligolw.ligolw import (Document, LIGO_LW, LIGOLWContentHandler)
from glue.ligolw import utils as ligolw_utils
# allow writing directly to XML
if isinstance(target, (Document, LIGO_LW)):
xmldoc = target
# open existing document, if possible
elif append and not overwrite:
xmldoc = open_xmldoc(target,
contenthandler=kwargs.pop('contenthandler',
LIGOLWContentHandler))
# fail on existing document and not overwriting
elif (not overwrite and isinstance(target, string_types)
and os.path.isfile(target)):
raise IOError("File exists: {}".format(target))
else: # or create a new document
xmldoc = Document()
# convert table to format
write_tables_to_document(xmldoc, tables, overwrite=overwrite)
# write file
if isinstance(target, string_types):
kwargs.setdefault('gz', target.endswith('.gz'))
ligolw_utils.write_filename(xmldoc, target, **kwargs)
elif isinstance(target, FILE_LIKE):
kwargs.setdefault('gz', target.name.endswith('.gz'))
ligolw_utils.write_fileobj(xmldoc, target, **kwargs)
def write_to_xml(cells,
intr_prms,
pin_prms={},
fvals=None,
fname=None,
verbose=False):
"""
Write a set of cells, with dimensions corresponding to intr_prms to an XML file as sim_inspiral rows.
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
procrow = process.append_process(xmldoc, program=sys.argv[0])
procid = procrow.process_id
process.append_process_params(
xmldoc, procrow, process.process_params_from_dict(opts.__dict__))
rows = ["simulation_id", "process_id", "numrel_data"]
# Override eff_lambda to with psi0, its shoehorn column
if "eff_lambda" in intr_prms:
intr_prms[intr_prms.index("eff_lambda")] = "psi0"
if "deff_lambda" in intr_prms:
intr_prms[intr_prms.index("deff_lambda")] = "psi3"
rows += list(intr_prms)
rows += list(pin_prms)
if fvals is not None:
rows.append("alpha1")
sim_insp_tbl = lsctables.New(lsctables.SimInspiralTable, rows)
for itr, intr_prm in enumerate(cells):
sim_insp = sim_insp_tbl.RowType()
# FIXME: Need better IDs
sim_insp.numrel_data = "INTR_SET_%d" % itr
sim_insp.simulation_id = ilwd.ilwdchar(
"sim_inspiral:sim_inspiral_id:%d" % itr)
sim_insp.process_id = procid
if fvals:
sim_insp.alpha1 = fvals[itr]
for p, v in zip(intr_prms, intr_prm._center):
setattr(sim_insp, p, v)
for p, v in pin_prms.iteritems():
setattr(sim_insp, p, v)
sim_insp_tbl.append(sim_insp)
xmldoc.childNodes[0].appendChild(sim_insp_tbl)
if fname is None:
channel_name = ["H=H", "L=L"]
ifos = "".join([o.split("=")[0][0] for o in channel_name])
#start = int(event_time)
start = 0
fname = "%s-MASS_POINTS-%d-1.xml.gz" % (ifos, start)
utils.write_filename(xmldoc, fname, gz=True, verbose=verbose)
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 checkpoint_save(xmldoc, fout, process):
print >>sys.stderr, "\t[Checkpointing ...]"
# save rng state
rng_state = np.random.get_state()
np.savez(fout + "_checkpoint.rng.npz",
state1=rng_state[1],
state2=np.array(rng_state[2]),
state3=np.array(rng_state[3]),
state4=np.array(rng_state[4]))
# write out the document
ligolw_process.set_process_end_time(process)
utils.write_filename(xmldoc, fout + "_checkpoint.gz", gz=True)
def checkpoint_save(xmldoc, fout, process):
print >> sys.stderr, "\t[Checkpointing ...]"
# save rng state
rng_state = np.random.get_state()
np.savez(fout + "_checkpoint.rng.npz",
state1=rng_state[1],
state2=np.array(rng_state[2]),
state3=np.array(rng_state[3]),
state4=np.array(rng_state[4]))
# write out the document
ligolw_process.set_process_end_time(process)
utils.write_filename(xmldoc, fout + "_checkpoint.gz", gz=True)
def write_rows(rows, table_type, filename):
"""
Create an empty LIGO_LW XML document, add a table of table_type,
insert the given rows, then write the document to a file.
"""
# prepare a new XML document
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
tbl = lsctables.New(table_type)
xmldoc.childNodes[-1].appendChild(tbl)
# insert our rows
tbl.extend(rows)
# write out the document
utils.write_filename(xmldoc, filename)
def write_rows(rows, table_type, filename):
"""
Create an empty LIGO_LW XML document, add a table of table_type,
insert the given rows, then write the document to a file.
"""
# prepare a new XML document
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
tbl = lsctables.New(table_type)
xmldoc.childNodes[-1].appendChild(tbl)
# insert our rows
tbl.extend(rows)
# write out the document
utils.write_filename(xmldoc, filename)
def save(self, filename):
"""Write this trigger to gracedb compatible xml format
Parameters
----------
filename: str
Name of file to write to disk.
"""
gz = filename.endswith('.gz')
ligolw_utils.write_filename(self.outdoc, filename, gz=gz)
# save source probabilities in a json file
if self.probabilities is not None:
prob_fname = filename.replace('.xml.gz', '_probs.json')
with open(prob_fname, 'w') as prob_outfile:
json.dump(self.probabilities, prob_outfile)
logging.info('Source probabilities file saved as %s', prob_fname)
def write_tables(f, tables, append=False, overwrite=False, **kwargs):
"""Write an LIGO_LW table to file
Parameters
----------
f : `str`, `file`, :class:`~glue.ligolw.ligolw.Document`
the file or document to write into
tables : `list` of :class:`~glue.ligolw.table.Table`
the tables to write
append : `bool`, optional, default: `False`
if `True`, append to an existing file/table, otherwise `overwrite`
overwrite : `bool`, optional, default: `False`
if `True`, delete an existing instance of the table type, otherwise
append new rows
"""
from glue.ligolw.ligolw import (Document, LIGO_LW, LIGOLWContentHandler)
from glue.ligolw import utils as ligolw_utils
# allow writing directly to XML
if isinstance(f, (Document, LIGO_LW)):
xmldoc = f
# open existing document, if possible
elif append and not overwrite:
xmldoc = open_xmldoc(f,
contenthandler=kwargs.pop('contenthandler',
LIGOLWContentHandler))
# fail on existing document and not overwriting
elif not overwrite and isinstance(f, string_types) and os.path.isfile(f):
raise IOError("File exists: %s" % f)
else: # or create a new document
xmldoc = Document()
# convert table to format
write_tables_to_document(xmldoc, tables, overwrite=overwrite)
# write file
if isinstance(f, string_types):
kwargs.setdefault('gz', f.endswith('.gz'))
ligolw_utils.write_filename(xmldoc, f, **kwargs)
elif not isinstance(f, Document):
kwargs.setdefault('gz', f.name.endswith('.gz'))
ligolw_utils.write_fileobj(xmldoc, f, **kwargs)
def create_xml(ts_data,psd_segment_length,window_fraction,event_list,station,setname="MagneticFields"):
__program__ = 'pyburst_excesspower'
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 = 'H1'#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)
outseg = determine_output_segment(inseg, psd_segment_length, ts_data.sample_rate, window_fraction)
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)
fname = make_filename(station, inseg)
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
def multi_segments_to_file(seg_list, filename, names, ifos):
""" Save segments to an xml file
Parameters
----------
seg_list: glue.segments.segmentlist
List of segment lists to write to disk
filename : str
name of the output file
names :
name of each segment list
ifos :
list of ifos
Returns
-------
File : Return a pycbc.core.File reference to the file
"""
from pycbc.workflow.core import File
# create XML doc and add process table
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
process = ligolw_utils.process.register_to_xmldoc(outdoc, argv[0], {})
for segs, ifo, name in zip(seg_list, ifos, names):
fsegs = [(lal.LIGOTimeGPS(seg[0]), lal.LIGOTimeGPS(seg[1])) \
for seg in segs]
# add segments, segments summary, and segment definer tables using glue library
with ligolw_segments.LigolwSegments(outdoc, process) as xmlsegs:
xmlsegs.insert_from_segmentlistdict({ifo: fsegs}, name)
# write file
ligolw_utils.write_filename(outdoc, filename)
# return a File instance
url = urlparse.urlunparse(
['file', 'localhost', filename, None, None, None])
f = File(ifo, name, segs, file_url=url, tags=[name])
f.PFN(os.path.abspath(filename), site='local')
return f
def multi_segments_to_file(seg_list, filename, names, ifos):
""" Save segments to an xml file
Parameters
----------
seg_list: glue.segments.segmentlist
List of segment lists to write to disk
filename : str
name of the output file
names :
name of each segment list
ifos :
list of ifos
Returns
-------
File : Return a pycbc.core.File reference to the file
"""
from pycbc.workflow.core import File
# create XML doc and add process table
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
process = ligolw_utils.process.register_to_xmldoc(outdoc, argv[0], {})
for segs, ifo, name in zip(seg_list, ifos, names):
fsegs = [(lal.LIGOTimeGPS(seg[0]), lal.LIGOTimeGPS(seg[1])) \
for seg in segs]
# add segments, segments summary, and segment definer tables using glue library
with ligolw_segments.LigolwSegments(outdoc, process) as xmlsegs:
xmlsegs.insert_from_segmentlistdict({ifo : fsegs}, name)
# write file
ligolw_utils.write_filename(outdoc, filename)
# return a File instance
url = urlparse.urlunparse(['file', 'localhost', filename, None, None, None])
f = File(ifo, name, segs, file_url=url, tags=[name])
f.PFN(os.path.abspath(filename), site='local')
return f
def write(filename, samples, write_params=None, static_args=None):
"""Writes the injection samples to the given xml.
Parameters
----------
filename : str
The name of the file to write to.
samples : io.FieldArray
FieldArray of parameters.
write_params : list, optional
Only write the given parameter names. All given names must be keys
in ``samples``. Default is to write all parameters in ``samples``.
static_args : dict, optional
Dictionary mapping static parameter names to values. These are
written to the ``attrs``.
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
simtable = lsctables.New(lsctables.SimInspiralTable)
xmldoc.childNodes[0].appendChild(simtable)
if static_args is None:
static_args = {}
if write_params is None:
write_params = samples.fieldnames
for ii in range(samples.size):
sim = lsctables.SimInspiral()
# initialize all elements to None
for col in sim.__slots__:
setattr(sim, col, None)
for field in write_params:
data = samples[ii][field]
set_sim_data(sim, field, data)
# set any static args
for (field, value) in static_args.items():
set_sim_data(sim, field, value)
simtable.append(sim)
ligolw_utils.write_filename(xmldoc,
filename,
gz=filename.endswith('gz'))
def write(filename, samples, write_params=None, static_args=None):
"""Writes the injection samples to the given xml.
Parameters
----------
filename : str
The name of the file to write to.
samples : io.FieldArray
FieldArray of parameters.
write_params : list, optional
Only write the given parameter names. All given names must be keys
in ``samples``. Default is to write all parameters in ``samples``.
static_args : dict, optional
Dictionary mapping static parameter names to values. These are
written to the ``attrs``.
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
simtable = lsctables.New(lsctables.SimInspiralTable)
xmldoc.childNodes[0].appendChild(simtable)
if static_args is None:
static_args = {}
if write_params is None:
write_params = samples.fieldnames
for ii in range(samples.size):
sim = lsctables.SimInspiral()
# initialize all elements to None
for col in sim.__slots__:
setattr(sim, col, None)
for field in write_params:
data = samples[ii][field]
set_sim_data(sim, field, data)
# set any static args
for (field, value) in static_args.items():
set_sim_data(sim, field, value)
simtable.append(sim)
ligolw_utils.write_filename(xmldoc, filename,
gz=filename.endswith('gz'))
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 write_ligolw(flag, fobj, **kwargs):
"""Write this `DataQualityFlag` to XML in LIGO_LW format
"""
# if given a Document, just add data
if isinstance(fobj, Document):
return write_to_xmldoc(flag, fobj, **kwargs)
# otherwise build a new Document
xmldoc = Document()
xmldoc.appendChild(LIGO_LW())
# TODO: add process information
write_to_xmldoc(flag, xmldoc)
# and write
if isinstance(fobj, string_types):
return write_filename(xmldoc, fobj, gz=fobj.endswith('.gz'))
else:
return write_fileobj(xmldoc, fobj, gz=fobj.name.endswith('.gz'))
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
# FIXME: As two-ifo is hardcoded loop over all ifos
sngl_combined_mchirp = 0
sngl_combined_mtot = 0
net_snrsq = 0
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
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
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['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 lsctables.ProcessTable.validcolumns['jobid']
del lsctables.ProcessTable.validcolumns['is_online']
segment_map_map = {}
segment_sum_map_map = {}
# Create two random files, each with 2 procs, 4 segment definers each with 8 segs and 8 seg summaries
tmp_dir = tempfile.mkdtemp()
for filename in ['file1.xml', 'file2.xml']:
doc, segment_map, segment_sum_map = make_random_document(
2, 4, 8, 8, 800000000, 800000200, 10)
segment_map_map[filename] = segment_map
segment_map_map[filename] = segment_map
segment_sum_map_map[filename] = segment_sum_map
utils.write_filename(doc, tmp_dir + "/" + filename)
# find the intersection between two segment definers
do_test(
'cat %s/file1.xml | ligolw_segment_intersect -i H1:TEST_SEG_1,H1:TEST_SEG_2 --segment | ligolw_print -t segment -c start_time -c end_time'
% tmp_dir, segment_map_map['file1.xml']['TEST_SEG_1']
& segment_map_map['file1.xml']['TEST_SEG_2'],
'intersect between segments')
# union
do_test(
'cat %s/file1.xml | ligolw_segment_union -i H1:TEST_SEG_1,H1:TEST_SEG_2 --segment | ligolw_print -t segment -c start_time -c end_time'
% tmp_dir, segment_map_map['file1.xml']['TEST_SEG_1']
| segment_map_map['file1.xml']['TEST_SEG_2'], 'union between segments')
# difference
# Convert to m1, m2
m1m2_grid = np.array([lsu.m1m2(cart_grid[i][0], cart_grid[i][1])
for i in xrange(len(cart_grid))])
m1m2_grid /= lal.MSUN_SI
if opts.mass_points_xml:
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
procrow = process.append_process(xmldoc, program=sys.argv[0])
procid = procrow.process_id
process.append_process_params(xmldoc, procrow, process.process_params_from_dict(opts.__dict__))
sim_insp_tbl = lsctables.New(lsctables.SimInspiralTable, ["simulation_id", "process_id", "numrel_data", "mass1", "mass2", "psi0", "psi3"])
for itr, (m1, m2) in enumerate(m1m2_grid):
for l1 in np.linspace(common_cl.param_limits["lam_tilde"][0], common_cl.param_limits["lam_tilde"][1], Nlam):
sim_insp = sim_insp_tbl.RowType()
sim_insp.numrel_data = "MASS_SET_%d" % itr
sim_insp.simulation_id = ilwd.ilwdchar("sim_inspiral:sim_inspiral_id:%d" % itr)
sim_insp.process_id = procid
sim_insp.mass1, sim_insp.mass2 = m1, m2
sim_insp.psi0, sim_insp.psi3 = opts.eff_lambda or l1, opts.delta_eff_lambda or 0
sim_insp_tbl.append(sim_insp)
xmldoc.childNodes[0].appendChild(sim_insp_tbl)
if opts.channel_name:
ifos = "".join([o.split("=")[0][0] for o in opts.channel_name])
else:
ifos = "HLV"
start = int(event_time)
fname = "%s-MASS_POINTS-%d-1.xml.gz" % (ifos, start)
utils.write_filename(xmldoc, fname, gz=True)
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 >> sys.stderr, "Column %s not recognized" % (entry)
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
#
# Call clustering library
#
xmldoc, changed = bucluster.bucluster(
xmldoc,
program = options.program,
process = process,
prefunc = prefunc,
postfunc = postfunc,
testfunc = testfunc,
clusterfunc = clusterfunc,
sortfunc = sortfunc,
bailoutfunc = bailoutfunc,
verbose = options.verbose
)
#
# Finish process information
#
ligolw_process.set_process_end_time(process)
#
# Write document
#
if changed:
utils.write_filename(xmldoc, filename, gz = (filename or "stdout").endswith(".gz"), verbose = options.verbose)
xmldoc.unlink()
del lsctables.ProcessTable.validcolumns['jobid']
del lsctables.ProcessTable.validcolumns['is_online']
segment_map_map = {}
segment_sum_map_map = {}
# Create two random files, each with 2 procs, 4 segment definers each with 8 segs and 8 seg summaries
tmp_dir = tempfile.mkdtemp()
for filename in ['file1.xml', 'file2.xml']:
doc, segment_map, segment_sum_map = make_random_document(2, 4, 8, 8, 800000000, 800000200, 10)
segment_map_map[filename] = segment_map
segment_map_map[filename] = segment_map
segment_sum_map_map[filename] = segment_sum_map
utils.write_filename(doc, tmp_dir + "/" + filename)
# find the intersection between two segment definers
do_test('cat %s/file1.xml | ligolw_segment_intersect -i H1:TEST_SEG_1,H1:TEST_SEG_2 --segment | ligolw_print -t segment -c start_time -c end_time' % tmp_dir,
segment_map_map['file1.xml']['TEST_SEG_1'] & segment_map_map['file1.xml']['TEST_SEG_2'],
'intersect between segments')
# union
do_test('cat %s/file1.xml | ligolw_segment_union -i H1:TEST_SEG_1,H1:TEST_SEG_2 --segment | ligolw_print -t segment -c start_time -c end_time' % tmp_dir,
segment_map_map['file1.xml']['TEST_SEG_1'] | segment_map_map['file1.xml']['TEST_SEG_2'],
'union between segments')
# difference
do_test('cat %s/file1.xml | ligolw_segment_diff -i H1:TEST_SEG_1,H1:TEST_SEG_2 --segment | ligolw_print -t segment -c start_time -c end_time' % tmp_dir,
import sys
from glue.lal import CacheEntry
from glue.ligolw import lsctables, utils
for filename in (CacheEntry(line).path for line in file(sys.argv[1])):
xmldoc = utils.load_filename(filename,
gz=(filename or "stdin").endswith(".gz"))
try:
lsctables.table.get_table(xmldoc,
lsctables.SnglInspiralTable.tableName)
except ValueError:
xmldoc.childNodes[-1].appendChild(
lsctables.New(
lsctables.SnglInspiralTable,
columns=("process_id", "ifo", "search", "channel", "end_time",
"end_time_ns", "end_time_gmst", "impulse_time",
"impulse_time_ns", "template_duration",
"event_duration", "amplitude", "eff_distance",
"coa_phase", "mass1", "mass2", "mchirp", "mtotal",
"eta", "kappa", "chi", "tau0", "tau2", "tau3", "tau4",
"tau5", "ttotal", "psi0", "psi3", "alpha", "alpha1",
"alpha2", "alpha3", "alpha4", "alpha5", "alpha6",
"beta", "f_final", "snr", "chisq", "chisq_dof",
"bank_chisq", "bank_chisq_dof", "cont_chisq",
"cont_chisq_dof", "sigmasq", "rsqveto_duration",
"Gamma0", "Gamma1", "Gamma2", "Gamma3", "Gamma4",
"Gamma5", "Gamma6", "Gamma7", "Gamma8", "Gamma9",
"event_id")))
utils.write_filename(filename,
xmldoc,
gz=(filename or "stdout").endswith(".gz"))
coinc.coinc_event_id = coinc_table.get_next_id()
coinc.process_id = process.process_id
coinc.coinc_def_id = coinc_def_id
coinc.time_slide_id = time_slide_id
coinc.set_instruments(opts.detector)
coinc.nevents = len(opts.detector)
coinc.likelihood = None
coinc_table.append(coinc)
# Record all sngl_inspiral records and associate them with coincidences.
for sngl_inspiral in sngl_inspirals:
# Give this sngl_inspiral record an id and add it to the table.
sngl_inspiral.event_id = sngl_inspiral_table.get_next_id()
sngl_inspiral_table.append(sngl_inspiral)
# Add CoincMap entry.
coinc_map = lsctables.CoincMap()
coinc_map.coinc_event_id = coinc.coinc_event_id
coinc_map.table_name = sngl_inspiral_table.tableName
coinc_map.event_id = sngl_inspiral.event_id
coinc_map_table.append(coinc_map)
# Record process end time.
progress.update(-1, 'writing ' + opts.output)
ligolw_process.set_process_end_time(process)
# Write output file.
ligolw_utils.write_filename(out_xmldoc, opts.output,
gz=(os.path.splitext(opts.output)[-1]==".gz"))
def excess_power2(
ts_data, # Time series from magnetic field data
psd_segment_length, # Length of each segment in seconds
psd_segment_stride, # Separation between 2 consecutive segments in seconds
psd_estimation, # Average method
window_fraction, # Withening window fraction
tile_fap, # Tile false alarm probability threshold in Gaussian noise.
station, # Station
nchans=None, # Total number of channels
band=None, # Channel bandwidth
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
max_duration=None, # Maximum duration of the tile
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
"""
# 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) - 1
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans = nchans - 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
print '|- Estimating PSD from segments of time',
print '%.2f s in length, 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)
# Calculate the overall PSD from individual PSD segments
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
# We need this for the SWIG functions...
lal_psd = fd_psd.lal()
# Plot the power spectral density
plot_spectrum(fd_psd)
# Create whitening window
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)
# Initialise filter bank
print "|- Create filter..."
filter_bank, fdb = [], []
# Loop for each channels
for i in range(nchans):
channel_flow = fmin + band / 2 + i * band
channel_width = band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(channel_flow,
channel_width, lal_psd,
spec_corr)
filter_bank.append(lal_filter)
fdb.append(Spectrum.from_lal(lal_filter))
# Calculate the minimum bandwidth
min_band = (len(filter_bank[0].data.data) - 1) * filter_bank[0].deltaF / 2
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
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)
# Compute the renormalization for the base filters up to a given bandwidth.
mu_sq_dict = {}
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2))):
nc_sum = 2**nc_sum - 1
print "|- Calculating renormalization for resolution level containing %d %fHz channels" % (
nc_sum + 1, min_band)
mu_sq = (nc_sum + 1) * numpy.array([
lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None)
for f in filter_bank
])
# Uncomment to get all possible frequency renormalizations
#for n in xrange(nc_sum, nchans): # channel position index
for n in xrange(nc_sum, nchans, nc_sum + 1): # channel position index
for k in xrange(0, nc_sum): # channel sum index
# FIXME: We've precomputed this, so use it instead
mu_sq[n] += 2 * lalburst.ExcessPowerFilterInnerProduct(
filter_bank[n - k], filter_bank[n - 1 - k], spec_corr,
None)
#print mu_sq[nc_sum::nc_sum+1]
mu_sq_dict[nc_sum] = mu_sq
# 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
while t_idx_max <= len(ts_data):
# Define starting and ending time of the segment in seconds
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)
print "\n|-- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# 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
segfolder = 'segments/%i-%i' % (start_time, end_time)
os.system('mkdir -p ' + segfolder)
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()
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)
print "|-- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
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=filter_bank[0].f0,
# high_frequency_cutoff=filter_bank[0].f0+2*band)
print "|-- Filtering all %d channels..." % 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(filter_bank[i].f0 / fd_psd.delta_f)
# Index of ending frequency
f2 = int((filter_bank[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = filter_bank[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=filter_bank[i].f0,
high_frequency_cutoff=filter_bank[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
tmp_ts_data.delta_t,
band,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
# Loop through all summed channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
nc_sum = 2**nc_sum - 1
mu_sq = mu_sq_dict[nc_sum]
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Constructing tile and calculate their energy
print "\n|--- Constructing tile with %d summed channels..." % (
nc_sum + 1)
# Current bandwidth of the time-frequency map tiles
df = band * (nc_sum + 1)
dt = 1.0 / (2 * df)
# How much each "step" is in the time domain -- under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
print "|--- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
print "|--- Calculating tiles..."
# Making independent tiles
# 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]
tiles = tf_map_temp.copy()
# Here's the deal: we're going to keep only the valid output and
# it's *always* going to exist in the lowest available indices
stride = nc_sum + 1
for i in xrange(tiles.shape[0] / stride):
numpy.absolute(tiles[stride * i:stride * (i + 1)].sum(axis=0),
tiles[stride * (i + 1) - 1])
tiles = tiles[nc_sum::nc_sum + 1].real**2 / mu_sq[nc_sum::nc_sum +
1].reshape(
-1, 1)
print "|--- TF-plane is %dx%s samples" % tiles.shape
print "|--- 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 2 * max_duration * df
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
print "\n|----- Explore signal duration of %f s..." % duration
print "|----- Summing DOF = %d ..." % (2 * j)
tlen = tiles.shape[1] - 2 * j + 1 + 1
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
print "|----- Summed tile energy mean: %f, var %f" % (
numpy.mean(dof_tiles), numpy.var(dof_tiles))
plot_spectrogram(
dof_tiles.T,
dt,
df,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/%i-%i/tf_%02ichans_%02idof.png' %
(start_time, end_time, nc_sum + 1, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
print "|------ Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
print "|------ 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 = filter_bank[
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]
event.amplitude = 0
print "|------ 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'
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 = 'H1' #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)
fname = 'excesspower.xml.gz'
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
#
# Fill in some metadata about the flags
#
name = "detchar %s threshold flags" % channel
comment = " ".join(["%s %s %s" % (channel, stringify(op), str(v)) for op, v in opthresholds])
lwsegs.insert_from_segmentlistdict(segmentlistdict({channel[:2]: seglist}), name=name, comment=comment)
#
# After recording segments, one can take the intersection (all must be on) or
# union (any can be on)
#
# Possible enhancement: instead of giving all keys, give a user selection
# corresponding to their demands
if opts.intersection:
intersection = segmentlistdict(all_segs).intersection(all_segs.keys())
# FIXME: ifos
lwsegs.insert_from_segmentlistdict(segmentlistdict({channel[:2]: seglist}), name=opts.name_result or "INTERSECTION", comment="%s intersection" % " ".join(all_segs.keys()))
elif opts.union:
union = segmentlistdict(all_segs).union(all_segs.keys())
# FIXME: ifos
lwsegs.insert_from_segmentlistdict(segmentlistdict({channel[:2]: seglist}), name=opts.name_result or "UNION", comment="%s union" % " ".join(all_segs.keys()))
#
# Finish up
#
lwsegs.finalize(procrow)
# FIXME: Determine the true extent of the cache
seg = cache.to_segmentlistdict()[ifos[0][0]][0]
utils.write_filename(xmldoc, "%s-%s-%d-%d.xml.gz" % ("".join(ifos), opts.output_tag, seg[0], abs(seg)), gz = True, verbose=opts.verbose)
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import numpy
import sys
from glue.ligolw import ligolw
from glue.ligolw import array as ligolw_array
from glue.ligolw import param as ligolw_param
from glue.ligolw import utils as ligolw_utils
class ContentHandler(ligolw.LIGOLWContentHandler):
pass
ligolw_array.use_in(ContentHandler)
ligolw_param.use_in(ContentHandler)
xmldoc = ligolw_utils.load_filename("ligo_lw_test_01.xml", contenthandler = ContentHandler, verbose = True)
ligolw_utils.write_filename(xmldoc, "/dev/null")
t, = xmldoc.getElementsByTagName(ligolw.Time.tagName)
print("%s: %s" % (t.Name, t.pcdata), file=sys.stderr)
for n, a in enumerate(xmldoc.getElementsByTagName(ligolw.Array.tagName)):
print("found %s array '%s'" % ("x".join(map(str, a.array.shape)), a.Name), file=sys.stderr)
fig = figure.Figure()
FigureCanvas(fig)
axes = fig.gca()
axes.loglog()
axes.grid(True)
for i in range(1, a.array.shape[0]):
axes.plot(numpy.fabs(a.array[0]), numpy.fabs(a.array[i]))
axes.set_title(a.Name)
print("saving as 'ligo_lw_test_01_%d.png' ..." % n, file=sys.stderr)
# Blockwise separation of Fisher matrix. Parameters are in the following order:
# theta0, theta3, theta3S, t0, phi0
IA = I[0:3, 0:3] # intrinsic block
IB = I[0:3, 3:5] # cross block
ID = I[3:5, 3:5] # extrinsic block
metric = IA - np.dot(IB, linalg.solve(ID, IB.T, sym_pos=True))
def predicate(sngl):
"""Return True if a template is within a 1-sigma radius of the central
template, False otherwise"""
thetas = lalsimulation.SimInspiralTaylorF2RedSpinChirpTimesFromMchirpEtaChi(
sngl.mchirp, sngl.eta, sngl.chi, f_low)
dtheta = np.asarray(thetas) - thetas_0
distance = np.dot(dtheta, np.dot(metric, dtheta))
return distance <= 1
# Grab the templates that are at most 1 sigma from the central (mass1, mass2).
rows_to_keep = filter(predicate, sngl_inspiral_table)
del sngl_inspiral_table[:]
sngl_inspiral_table.extend(rows_to_keep)
# Record process end time.
ligolw_process.set_process_end_time(process)
# Write output.
ligolw_utils.write_filename(xmldoc, opts.output,
gz=(os.path.splitext(opts.output)[-1] == '.gz'))
#=============================================
if opts.ignore_science_segments:
logger.info("ignoring science segments")
scisegs = [[gpsstart, gpsstart+stride]]
else:
logger.info("generating science segments")
try:
seg_xml_file = idq.segment_query(config, gpsstart, gpsstart+stride, url=config.get("get_science_segments","segdb"))
lsctables.use_in(ligolw.LIGOLWContentHandler)
xmldoc = utils.load_fileobj(seg_xml_file, contenthandler=ligolw.LIGOLWContentHandler)[0]
seg_file = "%s/science_segements-%d-%d.xml.gz"%(this_sumdir, int(gpsstart), int(stride))
logger.info("writting science segments to file : %s"%seg_file)
utils.write_filename(xmldoc, seg_file, gz=seg_file.endswith(".gz"))
(scisegs, coveredseg) = idq.extract_dq_segments(seg_file, config.get('get_science_segments', 'include'))
except Exception as e:
traceback.print_exc()
logger.info("ERROR: segment generation failed. Skipping this summary period.")
gpsstart += stride
continue
#=============================================
# generating summary datfiles filtered by segments
#=============================================
### get all relevant datfiles
datfiles = idq.get_all_files_in_range(realtimedir, gpsstart, gpsstart+stride, suffix=".dat")
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 >> sys.stderr, "Column %s not recognized" %(entry)
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
parser.add_option("-o",
"--output-name",
action="store",
dest="outputname",
help="Name of XML file to output.")
#parser.add_option("-s", "--segment-file", action="store", dest="segfile",
#help="Name of segment file to integrate.")
opts, rootfiles = parser.parse_args()
if rootfiles == None:
sys.exit("Must specify input Coherent WaveBurst ROOT file with -r option")
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
create_tables(xmldoc, rootfiles)
if opts.outputname == None:
print("Assigning name to output xml")
output = "convertROOT.xml.gz"
else:
output = "%s.xml.gz" % opts.outputname
utils.write_filename(xmldoc,
output,
verbose=True,
gz=(output or "stdout").endswith(".gz"))
xmldoc.unlink()
def write_likelihood_data(filename, coincparamsdistributions, seglists, verbose = False):
utils.write_filename(ligolw_burca_tailor.gen_likelihood_control(coincparamsdistributions, seglists, name = u"string_cusp_likelihood"), filename, verbose = verbose, gz = (filename or "stdout").endswith(".gz"))
math.sqrt((coincs.H1.get_effective_snr())**2 +
(coincs.L1.get_effective_snr())**2))
elif opts.statistic == 'effective_snr' and opts.coincs == "H2L1" and hasattr(
coincs, "H2") and hasattr(coincs, "L1"):
distributions.add_injection(double_params_func(coincs, timeslide))
x_back_param.append(
math.sqrt((coincs.H2.get_effective_snr())**2 +
(coincs.L1.get_effective_snr())**2))
X_back_param = asarray(x_back_param)
#############################################################################
# Finish Smoothening of the Data using Gaussian Filter
#############################################################################
xmldoc = ligolw_burca_tailor.gen_likelihood_control(distributions)
utils.write_filename(xmldoc, "distributions.xml")
distributions.finish()
#3###########################################################################
# Construction of Histrogram
#############################################################################
p = arange(0, 50.0, 1.0)
X_Inj_norm = hist(X_inj_param, p)[0] * 1.0 / max(hist(X_back_param, p)[0])
clf()
X_Back_norm = hist(X_back_param, p)[0] * 1.0 / max(hist(X_back_param, p)[0])
clf()
##########################################################################
# Reload X nd Y parameters
psd_dict_raw[ifo] = lalsimutils.get_psd_series_from_xmldoc(
opts.psd_file, ifo)
npts_orig = len(psd_dict_raw[ifo].data.data)
df = psd_dict_raw[ifo].deltaF
f0 = psd_dict_raw[ifo].f0
nyquist = int(len(psd_dict_raw[ifo].data.data) * df + f0)
epoch = psd_dict_raw[ifo].epoch
print(ifo, len(psd_dict_raw[ifo].data.data), 1. / psd_dict_raw[ifo].deltaF,
nyquist)
npts_desired = int(nyquist / df + 0.5)
indx_start = int(f0 / df + 0.5)
# Loop
for ifo in ifos:
print(" Writing for ", ifo)
dat_here = psd_dict_raw[ifo].data.data
psddict = {}
psd_s = lal.CreateREAL8FrequencySeries(name=ifo,
epoch=epoch,
f0=0,
deltaF=df,
sampleUnits="s",
length=npts_desired)
psd_s.data.data[indx_start:indx_start + npts_orig] = dat_here[:npts_orig -
1]
psddict[ifo] = psd_s
xmldoc = make_psd_xmldoc(psddict)
utils.write_filename(xmldoc, ifo + "-psd.xml.gz", gz=True)
# 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")
#!/usr/bin/python
import sys
from glue.lal import CacheEntry
from glue.ligolw import lsctables, utils
for filename in (CacheEntry(line).path for line in file(sys.argv[1])):
xmldoc = utils.load_filename(filename, gz = (filename or "stdin").endswith(".gz"))
try:
lsctables.table.get_table(xmldoc, lsctables.SnglInspiralTable.tableName)
except ValueError:
xmldoc.childNodes[-1].appendChild(lsctables.New(lsctables.SnglInspiralTable, columns = ("process_id", "ifo", "search", "channel", "end_time", "end_time_ns", "end_time_gmst", "impulse_time", "impulse_time_ns", "template_duration", "event_duration", "amplitude", "eff_distance", "coa_phase", "mass1", "mass2", "mchirp", "mtotal", "eta", "kappa", "chi", "tau0", "tau2", "tau3", "tau4", "tau5", "ttotal", "psi0", "psi3", "alpha", "alpha1", "alpha2", "alpha3", "alpha4", "alpha5", "alpha6", "beta", "f_final", "snr", "chisq", "chisq_dof", "bank_chisq", "bank_chisq_dof", "cont_chisq", "cont_chisq_dof", "sigmasq", "rsqveto_duration", "Gamma0", "Gamma1", "Gamma2", "Gamma3", "Gamma4", "Gamma5", "Gamma6", "Gamma7", "Gamma8", "Gamma9", "event_id")))
utils.write_filename(filename, xmldoc, gz = (filename or "stdout").endswith(".gz"))
# FIXME convert to years (use some lal or pylal thing in the future) vA.array /= secs_in_year vA2.array /= secs_in_year * secs_in_year #two powers for this squared quantity #Trim the array to have sane values outside the total mass area of interest try: minvol = scipy.unique(vA.array)[1]/10.0 except: minvol = 0 UL.trim_mass_space(dvA, instruments, minthresh=0.0, minM=UL.mintotal, maxM=UL.maxtotal) UL.trim_mass_space(vA, instruments, minthresh=minvol, minM=UL.mintotal, maxM=UL.maxtotal) UL.trim_mass_space(vA2, instruments, minthresh=0.0, minM=UL.mintotal, maxM=UL.maxtotal) #output an XML file with the result xmldoc = ligolw.Document() xmldoc.appendChild(ligolw.LIGO_LW()) xmldoc.childNodes[-1].appendChild(rate.binned_array_to_xml(vA, "2DsearchvolumeFirstMoment")) xmldoc.childNodes[-1].appendChild(rate.binned_array_to_xml(vA2, "2DsearchvolumeSecondMoment")) xmldoc.childNodes[-1].appendChild(rate.binned_array_to_xml(dvA, "2DsearchvolumeDerivative")) # DONE with vA, so it is okay to mess it up... # Compute range vA.array = (vA.array * secs_in_year / UL.livetime[instruments] / (4.0/3.0 * pi)) **(1.0/3.0) UL.trim_mass_space(vA, instruments, minthresh=0.0, minM=UL.mintotal, maxM=UL.maxtotal) xmldoc.childNodes[-1].appendChild(rate.binned_array_to_xml(vA, "2DsearchvolumeDistance")) # make a live time UL.trim_mass_space(ltA, instruments, minthresh=0.0, minM=UL.mintotal, maxM=UL.maxtotal) xmldoc.childNodes[-1].appendChild(rate.binned_array_to_xml(ltA, "2DsearchvolumeLiveTime")) utils.write_filename(xmldoc, "2Dsearchvolume-%s-%s.xml" % (opts.output_name_tag, "".join(sorted(list(instruments)))))
print('Accepting triggers with FAN <', maxFAN)
print('Accepted', len(loudestTrig), ' SINGLE triggers.')
flag = False
integ = 0
while not flag:
try:
outputFile=utils.load_filename(corsefiles[integ],\
gz=corsefiles[integ].endswith('.gz'))
origtbl = lsctables.SnglInspiralTable.get_table(outputFile)
flag = True
except:
integ = integ + 1
parent = origtbl.parentNode
parent.replaceChild(combinedTrigs, origtbl)
utils.write_filename(outputFile,opts.output_file, \
gz = opts.output_file.endswith('gz'))
if opts.min_rate:
for trig in loudestTrig:
trig.alpha = trig.alpha / (FrgrndTime / 3.15567360E7)
newtbl = lsctables.SnglInspiralTable.get_table(outputFile)
parent = newtbl.parentNode
parent.replaceChild(loudestTrig, newtbl)
utils.write_filename(outputFile,opts.output_file_loudest, \
gz = opts.output_file.endswith('gz'))
#searchSumm = lsctables.New(lsctables.SearchSummaryTable)
#summVal = lsctables.New(lsctables.SummValueTable)
#
#outputFile = open(opts.output_file,"w")
#outputFile.write('''<?xml version='1.0' encoding='utf-8' ?> \n''')
#outputFile.write('''<!DOCTYPE LIGO_LW SYSTEM "http://ldas-sw.ligo.caltech.edu/doc/ligolwAPI/html/ligolw_dtd.txt"><LIGO_LW> \n''')
# If cehck passed, proceed to appending it to the final table
npoint = lsctables.SimInspiral()
# Initialize columns
for nn in out_table.columnnames:
if 'process_id' in nn:
npoint.process_id = proc_id
elif 'waveform' in nn:
npoint.waveform = 'NR'
else:
npoint.__setattr__(nn, 0)
# Copy over columns
for nn in point.__slots__:
if hasattr(point, nn):
npoint.__setattr__(nn, point.__getattribute__(nn))
#
out_table.append(npoint)
#
if options.verbose:
print(len(out_table), " points copied", file=sys.stderr)
if options.verbose:
print("Total %d points in final table" % len(out_table), file=sys.stderr)
# write the xml doc to disk
proctable = lsctables.ProcessTable.get_table(outdoc)
proctable[0].end_time = gpstime.GpsSecondsFromPyUTC(time.time())
outname = options.output_catalog + '.xml'
ligolw_utils.write_filename(outdoc, outname)
print(len(out_table))
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")
if opts.checkpoint and not len(bank) % opts.checkpoint:
checkpoint_save(xmldoc, opts.output_filename, process)
# clear the proposal template if caching is not enabled
if not opts.cache_waveforms:
tmplt.clear()
if opts.verbose:
print "\ntotal number of proposed templates: %d" % nprop
print "total number of match calculations: %d" % bank._nmatch
print "final bank size: %d" % len(bank)
bank.clear() # clear caches
# write out the document
if opts.output_filename.endswith(('.xml', '.xml.gz')):
ligolw_process.set_process_end_time(process)
utils.write_filename(xmldoc,
opts.output_filename,
gz=opts.output_filename.endswith("gz"))
elif opts.output_filename.endswith(('.hdf', '.h5', '.hdf5')):
hdf_fp = h5py.File(opts.output_filename, 'w')
if len(tbl) == 0:
hdf_fp.attrs['empty_file'] = True
else:
params = tbl.dtype.names
hdf_fp.attrs['parameters'] = params
for param in params:
hdf_fp[param] = tbl[param]
fname = "m1_m2_pts_%i.txt" % i
np.savetxt(fname, cart_grid3[i])
elapsed = elapsed_time() - elapsed
print("Time to distribute points, split and write to file:", elapsed)
#dag_utils.write_integrate_likelihood_extrinsic_sub('test')
#dag_utils.write_extrinsic_marginalization_dag(cart_grid2, 'test.sub')
xmldoc = ligolw.Document()
xmldoc.childNodes.append(ligolw.LIGO_LW())
#proc_id = process.register_to_xmldoc(xmldoc, sys.argv[0], opts.__dict__)
proc_id = process.register_to_xmldoc(xmldoc, sys.argv[0], {})
proc_id = proc_id.process_id
xmldoc.childNodes[0].appendChild(write_sngl_params(cart_grid3, proc_id))
utils.write_filename(xmldoc, "m1m2_grid.xml.gz", gz=True)
#
# N.B. Below here, the real code will divy up cart_grid into blocks of intrinsic
# parameters and compute the marginalized likelihood on each point
# in intrinsic parameter space
#
# For testing purposes, simply evaluate the overlap on cart_grid and plot
# to confirm it is placing points in the expected ellipsoid with the
# proper distribution, and the overlap behaves properly.
#
# Evaluate IP on the grid inside the ellipsoid
rhos2 = eff.evaluate_ip_on_grid(hfSIG, PTMPLT, IP, param_names, cart_grid)
# Plot the ambiguity function, effective Fisher and ellipsoid points
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'))
timeslidetable = lsctables.TimeSlideTable.get_table(xmldoc)
#
# How many slides will go into this file?
#
N = int(round(float(len(time_slides)) / len(filenames)))
#
# Put them in.
#
for offsetvect in time_slides[:N]:
timeslidetable.append_offsetvector(offsetvect, process)
del time_slides[:N]
#
# Finish off the document.
#
ligolw_process.set_process_end_time(process)
#
# Write.
#
filename = filenames.pop(0)
ligolw_utils.write_filename(xmldoc, filename, verbose = options.verbose, gz = (filename or "stdout").endswith(".gz"))
assert not time_slides
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)
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:
for row in process_params_table:
if row.param=='--ifos':
options.instrument = row.value
# split into disjoint sub-banks
if min([row.f_final for row in sngl_inspiral_table]) > 0:
# check that this column is actually populated...
weights = [row.tau0*row.f_final for row in sngl_inspiral_table]
else:
weights = [1 for row in sngl_inspiral_table]
weights_cum = numpy.array(weights).cumsum()
first_row = 0
for bank in range(options.nbanks):
last_row = numpy.searchsorted(weights_cum, (bank+1)*weights_cum[-1]/options.nbanks)
sngl_inspiral_table_split[:] = sngl_inspiral_table[first_row:last_row]
first_row = last_row
ligolw_process.set_process_end_time(process)
utils.write_filename(xmldoc, "%s-SBANK_SPLIT_%04d-%s.xml"%(options.instrument,bank+1,options.user_tag), gz = False, verbose = options.verbose)
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"]
if not opts.time_marginalization:
samples["t_ref"] += float(fiducial_epoch)
else:
samples["t_ref"] = float(fiducial_epoch)*numpy.ones(len(sampler._rvs["psi"]))
samples["polarization"] = samples["psi"]
samples["coa_phase"] = samples["phi_orb"]
if ("declination", "right_ascension") in sampler.params:
samples["latitude"], samples["longitude"] = samples[("declination", "right_ascension")]
else:
samples["latitude"] = samples["declination"]
samples["longitude"] = samples["right_ascension"]
samples["loglikelihood"] = numpy.log(samples["integrand"])