def get_found_param(injfile, bankfile, trigfile, param, ifo):
"""
Translates some popular trigger parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
bankfile: hdf5 File object or None
Template bank file
trigfile: hdf5 File object or None
Single-detector trigger file
param: string
Parameter to be calculated for the recovered triggers
ifo: string or None
Standard ifo name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
foundtmp = injfile["found_after_vetoes/template_id"][:]
if trigfile is not None:
# get the name of the ifo in the injection file, eg "detector_1"
# and the integer from that name
ifolabel = [name for name, val in injfile.attrs.items() if \
"detector" in name and val == ifo][0]
foundtrg = injfile["found_after_vetoes/trigger_id" + ifolabel[-1]]
if bankfile is not None and param in bankfile.keys():
return bankfile[param][:][foundtmp]
elif trigfile is not None and param in trigfile[ifo].keys():
return trigfile[ifo][param][:][foundtrg]
else:
b = bankfile
found_param_dict = {
"mtotal": (b['mass1'][:] + b['mass2'][:])[foundtmp],
"mchirp":
pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[0][foundtmp],
"eta":
pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[1][foundtmp],
"effective_spin":
pnutils.phenomb_chi(b['mass1'][:], b['mass2'][:], b['spin1z'][:],
b['spin2z'][:])[foundtmp]
}
return found_param_dict[param]
def get_found_param(injfile, bankfile, trigfile, param, ifo):
"""
Translates some popular trigger parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
bankfile: hdf5 File object or None
Template bank file
trigfile: hdf5 File object or None
Single-detector trigger file
param: string
Parameter to be calculated for the recovered triggers
ifo: string or None
Standard ifo name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
foundtmp = injfile["found_after_vetoes/template_id"][:]
if trigfile is not None:
# get the name of the ifo in the injection file, eg "detector_1"
# and the integer from that name
ifolabel = [name for name, val in injfile.attrs.items() if \
"detector" in name and val == ifo][0]
foundtrg = injfile["found_after_vetoes/trigger_id" + ifolabel[-1]]
if bankfile is not None and param in bankfile.keys():
return bankfile[param][:][foundtmp]
elif trigfile is not None and param in trigfile[ifo].keys():
return trigfile[ifo][param][:][foundtrg]
else:
b = bankfile
found_param_dict = {
"mtotal" : (b['mass1'][:] + b['mass2'][:])[foundtmp],
"mchirp" : pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[0][foundtmp],
"eta" : pnutils.mass1_mass2_to_mchirp_eta(b['mass1'][:],
b['mass2'][:])[1][foundtmp],
"effective_spin" : pnutils.phenomb_chi(b['mass1'][:],
b['mass2'][:],
b['spin1z'][:],
b['spin2z'][:])[foundtmp]
}
return found_param_dict[param]
def __init__(self, filename, f_lower, sample_rate, minimum_buffer,
approximant=None, increment=8, parameters=None,
load_compressed=True, load_compressed_now=False,
**kwds):
self.increment = increment
self.f_lower = f_lower
self.filename = filename
self.sample_rate = sample_rate
self.minimum_buffer = minimum_buffer
super(LiveFilterBank, self).__init__(filename, approximant=approximant,
parameters=parameters, load_compressed=load_compressed,
load_compressed_now=load_compressed_now, **kwds)
if not hasattr(self.table, 'template_duration'):
self.table = self.table.add_fields(numpy.zeros(len(self.table),
dtype=numpy.float32), 'template_duration')
from pycbc.pnutils import mass1_mass2_to_mchirp_eta
self.table = sorted(self.table, key=lambda t: mass1_mass2_to_mchirp_eta(t.mass1, t.mass2)[0])
self.hash_lookup = {}
for i, p in enumerate(self.table):
hash_value = hash((p.mass1, p.mass2, p.spin1z, p.spin2z))
self.hash_lookup[hash_value] = i
def convert_to_sngl_inspiral_table(params, proc_id):
"""
Convert a list of m1,m2,spin1z,spin2z values into a basic sngl_inspiral
table with mass and spin parameters populated and event IDs assigned
Parameters
-----------
params : iterable
Each entry in the params iterable should be a sequence of
[mass1, mass2, spin1z, spin2z] in that order
proc_id : ilwd char
Process ID to add to each row of the sngl_inspiral table
Returns
----------
SnglInspiralTable
Bank of templates in SnglInspiralTable format
"""
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
col_names = ["mass1", "mass2", "spin1z", "spin2z"]
for values in params:
tmplt = return_empty_sngl()
tmplt.process_id = proc_id
for colname, value in zip(col_names, values):
setattr(tmplt, colname, value)
tmplt.mtotal, tmplt.eta = pnutils.mass1_mass2_to_mtotal_eta(tmplt.mass1, tmplt.mass2)
tmplt.mchirp, junk = pnutils.mass1_mass2_to_mchirp_eta(tmplt.mass1, tmplt.mass2)
tmplt.template_duration = 0 # FIXME
tmplt.event_id = sngl_inspiral_table.get_next_id()
sngl_inspiral_table.append(tmplt)
return sngl_inspiral_table
def get_param(par, args, m1, m2, s1z, s2z):
"""
Helper function
Parameters
----------
par : string
Name of parameter to calculate
args : Namespace object returned from ArgumentParser instance
Calling code command line options, used for f_lower value
m1 : float or array of floats
First binary component mass (etc.)
Returns
-------
parvals : float or array of floats
Calculated parameter values
"""
if par == 'mchirp':
parvals, _ = pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
elif par == 'mtotal':
parvals = m1 + m2
elif par == 'template_duration':
# default to SEOBNRv4 duration function
parvals = pnutils.get_imr_duration(m1, m2, s1z, s2z, args.f_lower,
args.approximant or "SEOBNRv4")
if args.min_duration:
parvals += args.min_duration
elif par in pnutils.named_frequency_cutoffs.keys():
parvals = pnutils.frequency_cutoff_from_name(par, m1, m2, s1z, s2z)
else:
# try asking for a LALSimulation frequency function
parvals = pnutils.get_freq(par, m1, m2, s1z, s2z)
return parvals
def get_param(par, args, m1, m2, s1z, s2z):
"""
Helper function
Parameters
----------
par : string
Name of parameter to calculate
args : Namespace object returned from ArgumentParser instance
Calling code command line options, used for f_lower value
m1 : float or array of floats
First binary component mass (etc.)
Returns
-------
parvals : float or array of floats
Calculated parameter values
"""
if par == 'mchirp':
parvals, _ = pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
elif par == 'mtotal':
parvals = m1 + m2
elif par == 'template_duration':
# default to SEOBNRv4 duration function
parvals = pnutils.get_imr_duration(m1, m2, s1z, s2z, args.f_lower,
args.approximant or "SEOBNRv4")
if args.min_duration:
parvals += args.min_duration
elif par in pnutils.named_frequency_cutoffs.keys():
parvals = pnutils.frequency_cutoff_from_name(par, m1, m2, s1z, s2z)
else:
# try asking for a LALSimulation frequency function
parvals = pnutils.get_freq(par, m1, m2, s1z, s2z)
return parvals
def read_samples_from_walker(self, variable_arg, nwalker,
thin_start=None, thin_interval=1):
""" Reads all samples from a specific walker for a parameter.
Parameters
-----------
variable_arg : str
Name of parameter to get independent samples.
nwalker : int
Index of the walker to get samples.
thin_start : int
Index of the sample to begin returning samples.
thin_interval : int
Interval to accept every i-th sample.
Returns
-------
numpy.array
Samples from a specific walker for a parameter.
"""
# default is to skip burn in samples
thin_start = self.attrs["burn_in_iterations"] if thin_start is None else thin_start
# derived parameter case for mchirp will calculate mchrip
# from mass1 and mass2
if variable_arg == "mchirp" and "mchirp" not in self.keys():
mass1 = self.read_samples_from_walker("mass1", nwalker,
thin_start=thin_start,
thin_interval=thin_interval)
mass2 = self.read_samples_from_walker("mass2", nwalker,
thin_start=thin_start,
thin_interval=thin_interval)
return pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)[0]
# derived parameter case for eta will calculate eta
# from mass1 and mass2
elif variable_arg == "eta" and "eta" not in self.keys():
mass1 = self.read_samples_from_walker("mass1", nwalker,
thin_start=thin_start,
thin_interval=thin_interval)
mass2 = self.read_samples_from_walker("mass2", nwalker,
thin_start=thin_start,
thin_interval=thin_interval)
return pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)[1]
return self[variable_arg]["walker%d"%nwalker][thin_start::thin_interval]
def outside_mchirp_window(bank, sim, w):
# template mchirp
if hasattr(bank, "mchirp"):
bmchirp = bank.mchirp
elif hasattr(bank, "mass1") and hasattr(bank, "mass2"):
bmchirp, eta =\
pnutils.mass1_mass2_to_mchirp_eta(bank.mass1, bank.mass2)
elif hasattr(bank, "mtotal") and hasattr(bank, "eta"):
bmchirp = bank.mtotal * (bank.eta**0.6)
# signal / injection / proposal mchirp
if hasattr(sim, "mchirp"):
smchirp = sim.mchirp
elif hasattr(sim, "mass1") and hasattr(sim, "mass2"):
smchirp, eta =\
pnutils.mass1_mass2_to_mchirp_eta(sim.mass1, sim.mass2)
elif hasattr(sim, "mtotal") and hasattr(sim, "eta"):
smchirp = sim.mtotal * (sim.eta**0.6)
return abs(smchirp - bmchirp) > (w * bmchirp)
def get_inj_param(injfile, param, ifo):
"""
Translates some popular injection parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
param: string
Parameter to be calculated for the injected signals
ifo: string
Standard detector name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
det = pycbc.detector.Detector(ifo)
time_delay = numpy.vectorize(#lambda dec, ra, t :
det.time_delay_from_earth_center)#(dec, ra, t))
inj = injfile["injections"]
if param in inj.keys():
return inj["injections/"+param]
inj_param_dict = {
"mtotal" : inj['mass1'][:] + inj['mass2'][:],
"mchirp" : pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:],
inj['mass2'][:])[0],
"eta" : pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:],
inj['mass2'][:])[1],
"effective_spin" : pnutils.phenomb_chi(inj['mass1'][:],
inj['mass2'][:],
inj['spin1z'][:],
inj['spin2z'][:]),
"end_time_"+ifo[0].lower() :
inj['end_time'][:] + time_delay(inj['longitude'][:],
inj['latitude'][:],
inj['end_time'][:]),
}
return inj_param_dict[param]
def get_inj_param(injfile, param, ifo):
"""
Translates some popular injection parameters into functions that calculate
them from an hdf found injection file
Parameters
----------
injfile: hdf5 File object
Injection file of format known to ANitz (DOCUMENTME)
param: string
Parameter to be calculated for the injected signals
ifo: string
Standard detector name, ex. 'L1'
Returns
-------
[return value]: NumPy array of floats
The calculated parameter values
"""
det = pycbc.detector.Detector(ifo)
time_delay = numpy.vectorize( #lambda dec, ra, t :
det.time_delay_from_earth_center) #(dec, ra, t))
inj = injfile["injections"]
if param in inj.keys():
return inj["injections/" + param]
inj_param_dict = {
"mtotal":
inj['mass1'][:] + inj['mass2'][:],
"mchirp":
pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:], inj['mass2'][:])[0],
"eta":
pnutils.mass1_mass2_to_mchirp_eta(inj['mass1'][:], inj['mass2'][:])[1],
"effective_spin":
pnutils.phenomb_chi(inj['mass1'][:], inj['mass2'][:], inj['spin1z'][:],
inj['spin2z'][:]),
"end_time_" + ifo[0].lower():
inj['end_time'][:] + time_delay(
inj['longitude'][:], inj['latitude'][:], inj['end_time'][:]),
}
return inj_param_dict[param]
def __init__(self, filename, f_lower, sample_rate, minimum_buffer,
approximant=None,
**kwds):
self.f_lower = f_lower
self.filename = filename
self.sample_rate = sample_rate
self.minimum_buffer = minimum_buffer
super(LiveFilterBank, self).__init__(filename, approximant=approximant, **kwds)
from pycbc.pnutils import mass1_mass2_to_mchirp_eta
self.table = sorted(self.table, key=lambda t: mass1_mass2_to_mchirp_eta(t.mass1, t.mass2)[0])
def __init__(self, filename, f_lower, sample_rate, minimum_buffer,
approximant=None, increment=8,
**kwds):
self.increment = increment
self.f_lower = f_lower
self.filename = filename
self.sample_rate = sample_rate
self.minimum_buffer = minimum_buffer
super(LiveFilterBank, self).__init__(filename, approximant=approximant, **kwds)
from pycbc.pnutils import mass1_mass2_to_mchirp_eta
self.table = sorted(self.table, key=lambda t: mass1_mass2_to_mchirp_eta(t.mass1, t.mass2)[0])
self.hash_lookup = {}
for i, p in enumerate(self.table):
hash_value = hash((p.mass1, p.mass2, p.spin1z, p.spin2z))
self.hash_lookup[hash_value] = i
def __init__(self,
filename,
f_lower,
sample_rate,
minimum_buffer,
approximant=None,
**kwds):
self.f_lower = f_lower
self.filename = filename
self.sample_rate = sample_rate
self.minimum_buffer = minimum_buffer
super(LiveFilterBank, self).__init__(filename,
approximant=approximant,
**kwds)
from pycbc.pnutils import mass1_mass2_to_mchirp_eta
self.table = sorted(
self.table,
key=lambda t: mass1_mass2_to_mchirp_eta(t.mass1, t.mass2)[0])
def _imrphenombfreq(**p):
import lalinspiral
params = lalinspiral.InspiralTemplate()
m1 = p['mass1']
m2 = p['mass2']
mc, et = pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
params.approximant = lalsimulation.IMRPhenomB
params.fLower = p['f_lower']
params.eta = et
params.distance = p['distance'] * lal.PC_SI * 1e6
params.mass1 = m1
params.mass2 = m2
params.spin1[2] = p['spin1z']
params.spin2[2] = p['spin2z']
params.startPhase = p['coa_phase']*2 - lal.PI
params.startTime = 0
params.tSampling = 8192
N = int(params.tSampling / p['delta_f'])
n = N / 2
# Create temporary memory to hold the results and call the generator
hpt = zeros(N, dtype=float32)
hct = zeros(N, dtype=float32)
hpt=hpt.lal()
hct=hct.lal()
lalinspiral.BBHPhenWaveBFreqDomTemplates(hpt, hct, params)
# Copy the results to a complex frequencyseries format
hctc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc.data += hpt.data[0:n]
hptc.data[1:n] += hpt.data[N:N-n:-1] * 1j
hctc.data += hct.data[0:n]
hctc.data[1:n] += hct.data[N:N-n:-1] * 1j
return hptc.astype(complex128), hctc.astype(complex128)
def __init__(self,
filename,
f_lower,
sample_rate,
minimum_buffer,
approximant=None,
increment=8,
parameters=None,
load_compressed=True,
load_compressed_now=False,
**kwds):
self.increment = increment
self.f_lower = f_lower
self.filename = filename
self.sample_rate = sample_rate
self.minimum_buffer = minimum_buffer
super(LiveFilterBank,
self).__init__(filename,
approximant=approximant,
parameters=parameters,
load_compressed=load_compressed,
load_compressed_now=load_compressed_now,
**kwds)
if not hasattr(self.table, 'template_duration'):
self.table = self.table.add_fields(
numpy.zeros(len(self.table), dtype=numpy.float32),
'template_duration')
from pycbc.pnutils import mass1_mass2_to_mchirp_eta
self.table = sorted(
self.table,
key=lambda t: mass1_mass2_to_mchirp_eta(t.mass1, t.mass2)[0])
self.hash_lookup = {}
for i, p in enumerate(self.table):
hash_value = hash((p.mass1, p.mass2, p.spin1z, p.spin2z))
self.hash_lookup[hash_value] = i
def _imrphenombfreq(**p):
params = lalinspiral.InspiralTemplate()
m1 = p['mass1']
m2 = p['mass2']
mc, et = pnutils.mass1_mass2_to_mchirp_eta(m1, m2)
params.approximant = lalsimulation.IMRPhenomB
params.fLower = p['f_lower']
params.eta = et
params.distance = p['distance'] * lal.PC_SI * 1e6
params.mass1 = m1
params.mass2 = m2
params.spin1[2] = p['spin1z']
params.spin2[2] = p['spin2z']
params.startPhase = p['coa_phase'] * 2 - lal.PI
params.startTime = 0
params.tSampling = 8192
N = int(params.tSampling / p['delta_f'])
n = N / 2
# Create temporary memory to hold the results and call the generator
hpt = zeros(N, dtype=float32)
hct = zeros(N, dtype=float32)
hpt = hpt.lal()
hct = hct.lal()
lalinspiral.BBHPhenWaveBFreqDomTemplates(hpt, hct, params)
# Copy the results to a complex frequencyseries format
hctc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc = FrequencySeries(zeros(n, dtype=complex64), delta_f=p['delta_f'])
hptc.data += hpt.data[0:n]
hptc.data[1:n] += hpt.data[N:N - n:-1] * 1j
hctc.data += hct.data[0:n]
hctc.data[1:n] += hct.data[N:N - n:-1] * 1j
return hptc.astype(complex128), hctc.astype(complex128)
def convert_to_sngl_inspiral_table(params, proc_id):
'''
Convert a list of m1,m2,spin1z,spin2z values into a basic sngl_inspiral
table with mass and spin parameters populated and event IDs assigned
Parameters
-----------
params : iterable
Each entry in the params iterable should be a sequence of
[mass1, mass2, spin1z, spin2z] in that order
proc_id : ilwd char
Process ID to add to each row of the sngl_inspiral table
Returns
----------
SnglInspiralTable
Bank of templates in SnglInspiralTable format
'''
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
col_names = ['mass1', 'mass2', 'spin1z', 'spin2z']
for values in params:
tmplt = return_empty_sngl()
tmplt.process_id = proc_id
for colname, value in zip(col_names, values):
setattr(tmplt, colname, value)
tmplt.mtotal, tmplt.eta = pnutils.mass1_mass2_to_mtotal_eta(
tmplt.mass1, tmplt.mass2)
tmplt.mchirp, junk = pnutils.mass1_mass2_to_mchirp_eta(
tmplt.mass1, tmplt.mass2)
tmplt.template_duration = 0 # FIXME
tmplt.event_id = sngl_inspiral_table.get_next_id()
sngl_inspiral_table.append(tmplt)
return sngl_inspiral_table
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos participating in this trigger.
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
psds: dict of FrequencySeries
Dictionary providing PSD estimates for all involved detectors.
low_frequency_cutoff: float
Minimum valid frequency for the PSD estimates.
high_frequency_cutoff: float, optional
Maximum frequency considered for the PSD estimates. Default None.
followup_data: dict of dicts, optional
Dictionary providing SNR time series for each detector,
to be used in sky localization with BAYESTAR. The format should
be `followup_data['H1']['snr_series']`. More detectors can be
present than given in `ifos`. If so, the extra detectors will only
be used for sky localization.
channel_names: dict of strings, optional
Strain channel names for each detector
Will be recorded in the sngl_inspiral table.
padata: PAstroData instance, organizes info relevant to p astro.
mc_area_args: dict of dicts, optional
Dictionary providing arguments to be used in source probability
estimation with pycbc/mchirp_area.py
"""
self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]]
self.coinc_results = coinc_results
self.ifos = ifos
self.basename = None
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results
and coinc_results['HWINJ'])
# Check if we need to apply a time offset (this may be permerger)
self.time_offset = 0
rtoff = 'foreground/{}/time_offset'.format(ifos[0])
if rtoff in coinc_results:
self.time_offset = coinc_results[rtoff]
if 'followup_data' in kwargs:
fud = kwargs['followup_data']
assert len({fud[ifo]['snr_series'].delta_t for ifo in fud}) == 1, \
"delta_t for all ifos do not match"
self.snr_series = {ifo: fud[ifo]['snr_series'] for ifo in fud}
usable_ifos = fud.keys()
followup_ifos = list(set(usable_ifos) - set(ifos))
for ifo in self.snr_series:
self.snr_series[ifo].start_time += self.time_offset
else:
self.snr_series = None
usable_ifos = ifos
followup_ifos = []
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# FIXME is it safe (in terms of downstream operations) to let
# `program_name` default to the actual script name?
proc_id = create_process_table(outdoc,
program_name='pycbc',
detectors=usable_ifos).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(usable_ifos)
coinc_event_row.instruments = ','.join(usable_ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_populated = None
network_snrsq = 0
for sngl_id, ifo in enumerate(usable_ifos):
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.process_id = proc_id
sngl.ifo = ifo
names = [
n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n
]
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
val += self.time_offset
sngl.end = lal.LIGOTimeGPS(val)
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
if sngl.mass1 and sngl.mass2:
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl_populated = sngl
if sngl.snr:
sngl.eff_distance = sngl.sigmasq**0.5 / sngl.snr
network_snrsq += sngl.snr**2.0
if 'channel_names' in kwargs and ifo in kwargs['channel_names']:
sngl.channel = kwargs['channel_names'][ifo]
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
if self.snr_series is not None:
snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id)
# Set merger time to the average of the ifo peaks
self.merger_time = numpy.mean([
coinc_results['foreground/{}/end_time'.format(ifo)] for ifo in ifos
]) + self.time_offset
# For subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
for sngl in sngl_inspiral_table:
if sngl.ifo in followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.end = lal.LIGOTimeGPS(self.merger_time)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.instruments = tuple(usable_ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_populated.mchirp
coinc_inspiral_row.mass = sngl_populated.mtotal
coinc_inspiral_row.end_time = sngl_populated.end_time
coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns
coinc_inspiral_row.snr = network_snrsq**0.5
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
# Append the PSDs
self.psds = kwargs['psds']
psds_lal = {}
for ifo in self.psds:
psd = self.psds[ifo]
kmin = int(kwargs['low_frequency_cutoff'] / psd.delta_f)
fseries = lal.CreateREAL8FrequencySeries(
"psd", psd.epoch, kwargs['low_frequency_cutoff'], psd.delta_f,
lal.StrainUnit**2 / lal.HertzUnit,
len(psd) - kmin)
fseries.data.data = psd.numpy()[kmin:] / pycbc.DYN_RANGE_FAC**2.0
psds_lal[ifo] = fseries
make_psd_xmldoc(psds_lal, outdoc)
# P astro calculation
if 'padata' in kwargs:
padata = kwargs['padata']
trigger_data = {
'mass1': sngl_populated.mass1,
'mass2': sngl_populated.mass2,
'spin1z': sngl_populated.spin1z,
'spin2z': sngl_populated.spin2z,
'network_snr': network_snrsq**0.5,
'far': far
}
horizons = {ifo: self.psds[ifo].dist for ifo in self.psds}
self.p_astro, self.p_terr = \
padata.do_pastro_calc(trigger_data, horizons)
else:
self.p_astro, self.p_terr = None, None
# Source probabilities estimation
if 'mc_area_args' in kwargs:
eff_distances = [sngl.eff_distance for sngl in sngl_inspiral_table]
self.probabilities = calc_probabilities(coinc_inspiral_row.mchirp,
coinc_inspiral_row.snr,
min(eff_distances),
kwargs['mc_area_args'])
else:
self.probabilities = None
# Combine p astro and source probs
if self.p_astro is not None and self.probabilities is not None:
self.astro_probs = {
cl: pr * self.p_astro
for cl, pr in self.probabilities.items()
}
self.astro_probs['p_terr'] = self.p_terr
else:
self.astro_probs = None
self.outdoc = outdoc
self.time = sngl_populated.end
def get_td_waveform_resp(params):
"""
Generate time domain data of gw detector response
This function will produce data of a gw detector response based on a
numerical relativity waveform.
Parameters
----------
params: object
The fields of this object correspond to the kwargs of the
`pycbc.waveform.get_td_waveform()` method and the positional
arguments of `pycbc.detector.Detector.antenna_pattern()`. For the later
the fields should be supplied as `params.ra`, `.dec`, `.polarization`
and `.geocentric_end_time`
Returns
-------
h_plus: pycbc.Types.TimeSeries
h_cross: pycbc.Types.TimeSeries
pats: dictionary
Dictionary containing 'H1' and 'L1' keys. Each key maps to an object
of containing the field `.f_plus` and `.f_cross` corresponding to
the plus and cross antenna patterns for the two ifos 'H1' and 'L1'.
"""
# # construct waveform string that can be parsed by lalsimulation
waveform_string = params.approximant
if not pn_orders[params.order] == -1:
waveform_string += params.order
name, phase_order = legacy_approximant_name(waveform_string)
# Populate additional fields of params object
params.mchirp, params.eta = pnutils.mass1_mass2_to_mchirp_eta(
params.mass1, params.mass2)
params.waveform = waveform_string
params.approximant = name
params.phase_order = phase_order
# generate waveform
h_plus, h_cross = get_td_waveform(params)
# Generate antenna patterns for all ifos
pats = {}
for ifo in params.instruments:
# get Detector instance for IFO
det = Detector(ifo)
# get antenna pattern
f_plus, f_cross = det.antenna_pattern(
params.ra,
params.dec,
params.polarization,
params.geocentric_end_time)
# Populate antenna patterns with new pattern
pat = type('AntennaPattern', (object,), {})
pat.f_plus = f_plus
pat.f_cross = f_cross
pats[ifo] = pat
return h_plus, h_cross, pats
def test_get_random_mass(self):
# Want to do this for a variety of mass combinations
for i in update_mass_parameters(self):
curr_min_mass = self.min_total_mass
curr_max_mass = self.max_total_mass
try:
pycbc.tmpltbank.verify_mass_range_options(self, parser=parser)
except ValueError:
# Some of the inputs are unphysical and will fail.
# These cases are known to fail, the inputs are unphysical
# 35 has inconsistent total mass and eta restrictions
# 38 Component mass, [upper] chirp mass and [lower] eta limits
# rule out the entire space.
# 41 Same as 38
# 44 Same as 38
# 62 From component mass and total mass limits only total masses
# between 7 and 7.5 are possible. This range all has eta
# lower than the limit of 0.17.
# 65 Same as 38
# 68 Same as 38
# 71 Same as 38
# 80 Same as 62
if i in [35,38,41,44,62,65,68,71,80]:
continue
raise
# Check that if the mass limits have changed, it was right to do so
# This is not exhaustive, but gets most things
if not self.min_total_mass == curr_min_mass:
min_comp_mass = self.min_mass1 + self.min_mass2
min_eta = self.min_mass1 * self.min_mass2 /\
(min_comp_mass * min_comp_mass)
min_chirp_mass = min_comp_mass * min_eta**(3./5.)
if self.min_total_mass == min_comp_mass:
# Okay, the total mass is changed by the components
pass
elif min_eta < self.min_eta or min_eta > self.max_eta:
# Okay, not possible from eta
pass
elif min_chirp_mass < self.min_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Minimum total mass changed unexpectedly."
print self.min_total_mass, curr_min_mass
print self.min_mass1, self.min_mass2, min_comp_mass
print min_eta, self.min_eta, self.max_eta
print min_chirp_mass, self.min_chirp_mass
self.fail(err_msg)
if not self.max_total_mass == curr_max_mass:
max_comp_mass = self.max_mass1 + self.max_mass2
max_eta = self.max_mass1 * self.max_mass2 /\
(max_comp_mass * max_comp_mass)
max_chirp_mass = max_comp_mass * max_eta**(3./5.)
if self.max_total_mass == max_comp_mass:
# Okay, the total mass is changed by the components
pass
elif max_eta < self.min_eta or max_eta > self.max_eta:
# Okay, not possible from eta
pass
elif max_chirp_mass > self.max_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Maximum total mass changed unexpectedly."
self.fail(err_msg)
massRangeParams = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
ns_bh_boundary_mass=self.ns_bh_boundary_mass)
# And again with the nsbh flag
massRangeParams2 = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
nsbhFlag=True)
mass,eta,beta,sigma,gamma,spin1z,spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams)
errMsg = "pycbc.tmpltbank.get_random_mass returns invalid ranges."
if (mass < self.min_total_mass).any():
idx = mass.argmin()
diff = (mass*mass * (1-4*eta))**0.5
mass1 = (mass + diff)/2.
mass2 = (mass - diff)/2.
self.assertTrue(not (mass < self.min_total_mass).any(),msg=errMsg)
self.assertTrue(not (mass > self.max_total_mass).any(),msg=errMsg)
# Get individual masses
diff = (mass*mass * (1-4*eta))**0.5
mass1 = (mass + diff)/2.
mass2 = (mass - diff)/2.
self.assertTrue(not (mass1 > self.max_mass1 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass1 < self.min_mass1 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass2 > self.max_mass2 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass2 < self.min_mass2 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass1 < mass2).any(),msg=errMsg)
# Chirp mass and eta
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(mass1,mass2)
if self.max_chirp_mass:
self.assertTrue(not (mchirp > self.max_chirp_mass*1.0001).any(),
msg=errMsg)
if self.min_chirp_mass:
self.assertTrue(not (mchirp < self.min_chirp_mass*0.9999).any(),
msg=errMsg)
if self.min_eta:
self.assertTrue(not (eta < self.min_eta*0.9999).any(),
msg=errMsg)
self.assertTrue(not (eta > self.max_eta*1.0001).any(),
msg=errMsg)
nsSpin1 = spin1z[mass1 < self.ns_bh_boundary_mass]
nsSpin2 = spin2z[mass2 < self.ns_bh_boundary_mass]
bhSpin1 = spin1z[mass1 > self.ns_bh_boundary_mass]
bhSpin2 = spin2z[mass2 > self.ns_bh_boundary_mass]
self.assertTrue(not (abs(nsSpin1) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(nsSpin2) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin1) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin2) > 0.9).any(), msg=errMsg)
# Check that *some* spins are bigger than 0.5
if len(bhSpin1):
self.assertTrue((abs(bhSpin1) > 0.5).any(), msg=errMsg)
if len(bhSpin2):
self.assertTrue((abs(bhSpin2) > 0.5).any(), msg=errMsg)
# This can be used to test the boundaries are all applied visually
# FIXME: This would need to be updated for all of the mass limits
#pylab.plot(mass1, mass2, 'b.')
#pylab.plot([3.0,5.0],[3.0,1.0],'r-')
#pylab.plot([1.7216566400945545,1.9921146662296347,3.4433132801891091,4.01175560949798],[1.1477710933963694,1.0066274466204264,2.2955421867927388,1.002938902374495],'mx')
#pylab.ylim([0.8,3.0])
#pylab.xlim([1.5,5.0])
#pylab.savefig("masses_%d.png" %(idx,))
# Check nsbh flag
mass,eta,beta,sigma,gamma,spin1z,spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams2)
self.assertTrue(not (abs(spin1z) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(spin2z) > 0.5).any(), msg=errMsg)
self.assertTrue((abs(spin1z) > 0.5).any(), msg=errMsg)
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
for table in tables:
xmldoc.childNodes[0].appendChild(table)
return xmldoc
input_bank = 'H1L1-UBERBANK_MAXM100_NS0p05_ER8HMPSD-1126033217-223200.xml.gz'
output_bank = 'H1L1-EDGEBIN_FROM_UBERBANK_ER8HMPSD-1126033217-223200.xml.gz'
xmldoc = utils.load_filename(input_bank, contenthandler=LIGOLWContentHandler,
gz=True)
all_templates = table.get_table(xmldoc, 'sngl_inspiral')
# get the other tables that are in the document
other_table_names = [y.Name for y in xmldoc.childNodes[0].getElements(
lambda x: x.tagName == ligolw.Table.tagName) if y.Name != u'sngl_inspiral']
other_tables = [table.get_table(xmldoc, x) for x in other_table_names]
keep_templates = lsctables.New(lsctables.SnglInspiralTable)
# only add templates that fall in the edge bin
for tmplt in all_templates:
mchirp = pnutils.mass1_mass2_to_mchirp_eta(tmplt.mass1, tmplt.mass2)[0]
peakfreq = pnutils.get_freq('fSEOBNRv2Peak', tmplt.mass1, tmplt.mass2,
tmplt.spin1z, tmplt.spin2z)
if mchirp >= 1.74 and peakfreq < 220.:
keep_templates.append(tmplt)
outdoc = create_xmldoc(other_tables + [keep_templates])
utils.write_filename(outdoc, output_bank, gz=True)
def writeHybrid_h5(path_name_part, metadata, approx, sim_name, h1, h1_ts, h2,
h2_ts, delta_t, ip2, fp2):
solar_mass_mpc = MRSUN_SI / ((1 * 10**6) * PC_SI)
grav_m1 = metadata[0][0]
grav_m2 = metadata[0][1]
baryon_m1 = metadata[1][0]
baryon_m2 = metadata[1][1]
m1 = metadata[2][0]
m2 = metadata[2][1]
s1x = metadata[3][0]
s1y = metadata[3][1]
s1z = metadata[3][2]
s2x = metadata[3][3]
s2y = metadata[3][4]
s2z = metadata[3][5]
LNhatx = metadata[4][0]
LNhaty = metadata[4][1]
LNhatz = metadata[4][2]
n_hatx = 1.0 #metadata[5][0]
n_haty = 0.0 #metadata[5][1]
n_hatz = 0.0 #metadata[5][2]
tidal_1 = metadata[6][0]
tidal_2 = metadata[6][1]
type_sim = metadata[7]
f_low = metadata[8]
EOS = metadata[9]
f_low_num = metadata[10]
M = 300 ## length of the windowing function
total_mass = grav_m1 + grav_m2 ### used masses are the masses used in the characterization procedure
### hybridize numerical (h2) and analytical waveform (h1) at a particular match window length set by fp2
hybridPN_Num = hy.hybridize(h1,
h1_ts,
h2,
h2_ts,
match_i=0,
match_f=fp2,
delta_t=delta_t,
M=M,
info=1)
shift_time = (hybridPN_Num[6], fp2)
hh_freq = (hybridPN_Num[7], fp2)
match = hybridPN_Num[0]
### path_name_part contains simulation details that must be passed into this function.
path_name = path_name_part + 'fp2_' + str(fp2) + '.h5'
f_low_M = f_low * (TWOPI * total_mass * MTSUN_SI)
with h5py.File(path_name, 'w') as fd:
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(grav_m1, grav_m2)
fd.attrs['type'] = 'Hybrid:%s' % type_sim
fd.attrs['hgroup'] = 'Read_CSUF'
fd.attrs['Format'] = 1
fd.attrs['Lmax'] = 2
fd.attrs['approx'] = approx
fd.attrs['sim_name'] = sim_name
fd.attrs['f_lower_at_1MSUN'] = f_low
fd.attrs['eta'] = eta
fd.attrs['spin1x'] = s1x
fd.attrs['spin1y'] = s1y
fd.attrs['spin1z'] = s1z
fd.attrs['spin2x'] = s2x
fd.attrs['spin2y'] = s2y
fd.attrs['spin2z'] = s2z
fd.attrs['LNhatx'] = LNhatx
fd.attrs['LNhaty'] = LNhaty
fd.attrs['LNhatz'] = LNhatz
fd.attrs['nhatx'] = 1.0 #n_hatx
fd.attrs['nhaty'] = 0.0 #n_haty
fd.attrs['nhatz'] = 0.0 #n_hatz
fd.attrs['mass1'] = m1
fd.attrs['mass2'] = m2
fd.attrs['grav_mass1'] = grav_m1
fd.attrs['grav_mass2'] = grav_m2
fd.attrs['baryon_mass1'] = baryon_m1
fd.attrs['baryon_mass2'] = baryon_m2
fd.attrs['lambda1'] = tidal_1
fd.attrs['lambda2'] = tidal_2
fd.attrs['fp1'] = len(h1)
fd.attrs['ip2'] = ip2
fd.attrs['hybrid_match'] = match
fd.attrs['shift_time'] = shift_time
fd.attrs['hybridize_freq'] = hh_freq
fd.attrs['EOS'] = EOS
fd.attrs['f_low_num'] = f_low_num
gramp = fd.create_group('amp_l2_m2')
grphase = fd.create_group('phase_l2_m2')
times = hybridPN_Num[5][0]
hplus = hybridPN_Num[5][1]
hcross = hybridPN_Num[5][2]
massMpc = total_mass * solar_mass_mpc
hplusMpc = pycbc.types.TimeSeries(hplus / massMpc, delta_t=delta_t)
hcrossMpc = pycbc.types.TimeSeries(hcross / massMpc, delta_t=delta_t)
times_M = times / (MTSUN_SI * total_mass)
HlmAmp = wfutils.amplitude_from_polarizations(hplusMpc, hcrossMpc).data
HlmPhase = wfutils.phase_from_polarizations(hplusMpc, hcrossMpc).data
sAmph = romspline.ReducedOrderSpline(times_M,
HlmAmp,
rel=True,
verbose=False)
sPhaseh = romspline.ReducedOrderSpline(times_M,
HlmPhase,
rel=True,
verbose=False)
sAmph.write(gramp)
sPhaseh.write(grphase)
fd.close()
## this function returns the hybridization frequency (hh_freq) and the time shift (shift_time) associated with each hybrid.
return (shift_time, hh_freq)
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
psds: dict of FrequencySeries
Dictionary providing PSD estimates for all involved detectors.
low_frequency_cutoff: float
Minimum valid frequency for the PSD estimates.
followup_data: dict of dicts, optional
Dictionary providing SNR time series for each detector,
to be used in sky localization with BAYESTAR. The format should
be `followup_data['H1']['snr_series']`. More detectors can be
present than given in `ifos`. If so, the extra detectors will only
be used for sky localization.
channel_names: dict of strings, optional
Strain channel names for each detector.
Will be recorded in the sngl_inspiral table.
"""
self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]]
self.coinc_results = coinc_results
self.ifos = ifos
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results
and coinc_results['HWINJ'])
if 'followup_data' in kwargs:
fud = kwargs['followup_data']
assert len({fud[ifo]['snr_series'].delta_t for ifo in fud}) == 1, \
"delta_t for all ifos do not match"
self.snr_series = {ifo: fud[ifo]['snr_series'] for ifo in fud}
usable_ifos = fud.keys()
followup_ifos = list(set(usable_ifos) - set(ifos))
else:
self.snr_series = None
usable_ifos = ifos
followup_ifos = []
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc, 'pycbc', {}, ifos=usable_ifos, comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(usable_ifos)
coinc_event_row.instruments = ','.join(usable_ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_populated = None
network_snrsq = 0
for sngl_id, ifo in enumerate(usable_ifos):
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.process_id = proc_id
sngl.ifo = ifo
names = [n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n]
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
if sngl.mass1 and sngl.mass2:
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl_populated = sngl
if sngl.snr:
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
network_snrsq += sngl.snr ** 2.0
if 'channel_names' in kwargs and ifo in kwargs['channel_names']:
sngl.channel = kwargs['channel_names'][ifo]
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
if self.snr_series is not None:
snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/{}/end_time'.format(ifo)]
for ifo in ifos])
for sngl in sngl_inspiral_table:
if sngl.ifo in followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.set_end(lal.LIGOTimeGPS(subthreshold_sngl_time))
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(usable_ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_populated.mchirp
coinc_inspiral_row.mass = sngl_populated.mtotal
coinc_inspiral_row.end_time = sngl_populated.end_time
coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns
coinc_inspiral_row.snr = network_snrsq ** 0.5
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
# append the PSDs
self.psds = kwargs['psds']
psds_lal = {}
for ifo in self.psds:
psd = self.psds[ifo]
kmin = int(kwargs['low_frequency_cutoff'] / psd.delta_f)
fseries = lal.CreateREAL8FrequencySeries(
"psd", psd.epoch, kwargs['low_frequency_cutoff'], psd.delta_f,
lal.StrainUnit**2 / lal.HertzUnit, len(psd) - kmin)
fseries.data.data = psd.numpy()[kmin:] / pycbc.DYN_RANGE_FAC ** 2.0
psds_lal[ifo] = fseries
make_psd_xmldoc(psds_lal, outdoc)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
out_left2 = time[l:r] < (time[l:r][idx_mxsnr] - 0.1)
out_left = time[l:r][numpy.invert(
(out_left1 - out_left2))].shape[0]
out_right1 = time[l:r] <= (time[l:r][idx_mxsnr] +
int(args.window))
out_right2 = time[l:r] > (time[l:r][idx_mxsnr] + 0.1)
out_right = time[l:r][numpy.invert(
(out_right1 - out_right2))].shape[0]
count_out.append(out_left + out_right)
#determining the chirp mass
m1_snr = mass1_full[l:r][idx_mxsnr]
m2_snr = mass2_full[l:r][idx_mxsnr]
m1_new_snr = mass1_full[l:r][idx_mxnewsnr]
m2_new_snr = mass2_full[l:r][idx_mxnewsnr]
chirp_snr = pnutils.mass1_mass2_to_mchirp_eta(
m1_snr, m2_snr)
chirp_m.append(chirp_snr)
chirp_new_snr = pnutils.mass1_mass2_to_mchirp_eta(
m1_new_snr, m2_new_snr)
#Calculating delta chirp and ratio of chirp masses
delta_chirp.append(
log(chirp_snr[0]) - log(chirp_new_snr[0]))
ratio_chirp.append(chirp_snr[0] / chirp_new_snr[0])
for i, (l, r) in enumerate(zip(left_inj, right_inj)):
logging.info('%s: %2.1f complete', ifo,
float(i) / len(right_inj) * 100)
closest_inj_id = numpy.argmin(abs(time_injc - window_times[i]))
closest_injt = time_injc[closest_inj_id]
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
"""
followup_ifos = kwargs.get('followup_ifos') or []
self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results)
# remember if we want to use a non-standard gracedb server
self.gracedb_server = kwargs.get('gracedb_server')
# compute SNR time series if needed, and figure out which of
# the followup detectors are usable
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/%s/end_time' % ifo]
for ifo in ifos])
self.upload_snr_series = kwargs.get('upload_snr_series')
usable_ifos = []
if self.upload_snr_series:
self.snr_series = {}
self.snr_series_psd = {}
htilde = kwargs['bank'][self.template_id]
for ifo in ifos + followup_ifos:
if ifo in ifos:
trig_time = coinc_results['foreground/%s/end_time' % ifo]
else:
trig_time = subthreshold_sngl_time
# NOTE we only check the state/DQ of followup IFOs here.
# IFOs producing the coincidence are assumed to also
# produce valid SNR series.
snr_series, snr_series_psd = compute_followup_snr_series(
kwargs['data_readers'][ifo], htilde, trig_time,
check_state=(ifo in followup_ifos))
if snr_series is not None:
self.snr_series[ifo] = snr_series
self.snr_series_psd[ifo] = snr_series_psd
usable_ifos.append(ifo)
else:
usable_ifos = ifos
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc, 'pycbc', {}, ifos=usable_ifos, comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(usable_ifos)
coinc_event_row.instruments = ','.join(usable_ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_populated = None
for sngl_id, ifo in enumerate(usable_ifos):
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.process_id = proc_id
sngl.ifo = ifo
names = [n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n]
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
if sngl.mass1 and sngl.mass2:
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl_populated = sngl
if sngl.snr:
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
if self.upload_snr_series:
snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
for sngl in sngl_inspiral_table:
if sngl.ifo in followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.set_end(lal.LIGOTimeGPS(subthreshold_sngl_time))
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(usable_ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_populated.mchirp
coinc_inspiral_row.mass = sngl_populated.mtotal
coinc_inspiral_row.end_time = sngl_populated.end_time
coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
def test_get_random_mass(self):
# Want to do this for a variety of mass combinations
for i in update_mass_parameters(self):
curr_min_mass = self.min_total_mass
curr_max_mass = self.max_total_mass
try:
pycbc.tmpltbank.verify_mass_range_options(self, parser=parser)
except ValueError:
# Some of the inputs are unphysical and will fail.
# These cases are known to fail, the inputs are unphysical
# 35 has inconsistent total mass and eta restrictions
# 38 Component mass, [upper] chirp mass and [lower] eta limits
# rule out the entire space.
# 41 Same as 38
# 44 Same as 38
# 62 From component mass and total mass limits only total masses
# between 7 and 7.5 are possible. This range all has eta
# lower than the limit of 0.17.
# 65 Same as 38
# 68 Same as 38
# 71 Same as 38
# 80 Same as 62
if i in [35,38,41,44,62,65,68,71,80]:
continue
raise
# Check that if the mass limits have changed, it was right to do so
# This is not exhaustive, but gets most things
if not self.min_total_mass == curr_min_mass:
min_comp_mass = self.min_mass1 + self.min_mass2
min_eta = self.min_mass1 * self.min_mass2 /\
(min_comp_mass * min_comp_mass)
min_chirp_mass = min_comp_mass * min_eta**(3./5.)
if self.min_total_mass == min_comp_mass:
# Okay, the total mass is changed by the components
pass
elif min_eta < self.min_eta or min_eta > self.max_eta:
# Okay, not possible from eta
pass
elif min_chirp_mass < self.min_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Minimum total mass changed unexpectedly."
print(self.min_total_mass, curr_min_mass)
print(self.min_mass1, self.min_mass2, min_comp_mass)
print(min_eta, self.min_eta, self.max_eta)
print(min_chirp_mass, self.min_chirp_mass)
self.fail(err_msg)
if not self.max_total_mass == curr_max_mass:
max_comp_mass = self.max_mass1 + self.max_mass2
max_eta = self.max_mass1 * self.max_mass2 /\
(max_comp_mass * max_comp_mass)
max_chirp_mass = max_comp_mass * max_eta**(3./5.)
if self.max_total_mass == max_comp_mass:
# Okay, the total mass is changed by the components
pass
elif max_eta < self.min_eta or max_eta > self.max_eta:
# Okay, not possible from eta
pass
elif max_chirp_mass > self.max_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Maximum total mass changed unexpectedly."
self.fail(err_msg)
massRangeParams = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
ns_bh_boundary_mass=self.ns_bh_boundary_mass)
# And again with the nsbh flag
massRangeParams2 = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
nsbhFlag=True)
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams)
mass = mass1 + mass2
errMsg = "pycbc.tmpltbank.get_random_mass returns invalid ranges."
self.assertTrue(not (mass < self.min_total_mass).any(),msg=errMsg)
self.assertTrue(not (mass > self.max_total_mass).any(),msg=errMsg)
self.assertTrue(not (mass1 > self.max_mass1 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass1 < self.min_mass1 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass2 > self.max_mass2 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass2 < self.min_mass2 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass1 < mass2).any(),msg=errMsg)
# Chirp mass and eta
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(mass1,mass2)
if self.max_chirp_mass:
self.assertTrue(not (mchirp > self.max_chirp_mass*1.0001).any(),
msg=errMsg)
if self.min_chirp_mass:
self.assertTrue(not (mchirp < self.min_chirp_mass*0.9999).any(),
msg=errMsg)
if self.min_eta:
self.assertTrue(not (eta < self.min_eta*0.9999).any(),
msg=errMsg)
self.assertTrue(not (eta > self.max_eta*1.0001).any(),
msg=errMsg)
nsSpin1 = spin1z[mass1 < self.ns_bh_boundary_mass]
nsSpin2 = spin2z[mass2 < self.ns_bh_boundary_mass]
bhSpin1 = spin1z[mass1 > self.ns_bh_boundary_mass]
bhSpin2 = spin2z[mass2 > self.ns_bh_boundary_mass]
self.assertTrue(not (abs(nsSpin1) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(nsSpin2) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin1) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin2) > 0.9).any(), msg=errMsg)
# Check that *some* spins are bigger than 0.5
if len(bhSpin1):
self.assertTrue((abs(bhSpin1) > 0.5).any(), msg=errMsg)
if len(bhSpin2):
self.assertTrue((abs(bhSpin2) > 0.5).any(), msg=errMsg)
# Check nsbh flag
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams2)
self.assertTrue(not (abs(spin1z) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(spin2z) > 0.5).any(), msg=errMsg)
self.assertTrue((abs(spin1z) > 0.5).any(), msg=errMsg)
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is define
in pycbc/events/coinc.py
and matches the on disk representation in the hdf file for this time.
"""
self.ifos = ifos
self.template_id = coinc_results['foreground/%s/template_id' %
self.ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = False
if 'HWINJ' in coinc_results:
self.is_hardware_injection = True
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
'pycbc', {},
ifos=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_id = 0
sngl_event_id_map = {}
for ifo in ifos:
names = [
n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n
]
sngl_id += 1
sngl = return_empty_sngl()
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl_event_id_map[ifo] = sngl.event_id
sngl.ifo = ifo
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl.mchirp
coinc_inspiral_row.mass = sngl.mtotal
coinc_inspiral_row.end_time = sngl.end_time
coinc_inspiral_row.end_time_ns = sngl.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
self.outdoc = outdoc
self.time = sngl.get_end()
# compute SNR time series
self.upload_snr_series = kwargs['upload_snr_series']
if self.upload_snr_series:
data_readers = kwargs['data_readers']
bank = kwargs['bank']
htilde = bank[self.template_id]
self.snr_series = {}
self.snr_series_psd = {}
for ifo in self.ifos:
stilde = data_readers[ifo].overwhitened_data(htilde.delta_f)
norm = 4.0 * htilde.delta_f / (htilde.sigmasq(stilde.psd)**0.5)
qtilde = zeros((len(htilde) - 1) * 2, dtype=htilde.dtype)
correlate(htilde, stilde, qtilde)
snr = qtilde * 0
ifft(qtilde, snr)
valid_end = int(len(qtilde) - data_readers[ifo].trim_padding)
valid_start = int(valid_end - data_readers[ifo].blocksize *
data_readers[ifo].sample_rate)
seg = slice(valid_start, valid_end)
snr = snr[seg]
snr *= norm
delta_t = 1.0 / data_readers[ifo].sample_rate
start = data_readers[ifo].start_time
snr = TimeSeries(snr, delta_t=delta_t, epoch=start)
self.snr_series[ifo] = snr
self.snr_series_psd[ifo] = stilde.psd
# store the on-source slice of the series into the XML doc
snr_onsource_time = coinc_results['foreground/%s/end_time' %
ifo] - snr.start_time
snr_onsource_dur = lal.REARTH_SI / lal.C_SI
onsource_idx = round(snr_onsource_time * snr.sample_rate)
onsource_start = onsource_idx - int(
snr.sample_rate * snr_onsource_dur / 2)
onsource_end = onsource_idx + int(
snr.sample_rate * snr_onsource_dur / 2)
onsource_slice = slice(onsource_start, onsource_end + 1)
snr_lal = snr[onsource_slice].lal()
snr_lal.name = 'snr'
snr_lal.sampleUnits = ''
snr_xml = _build_series(snr_lal,
(u'Time', u'Time,Real,Imaginary'),
None, 'deltaT', 's')
snr_node = outdoc.childNodes[-1].appendChild(snr_xml)
eid_param = ligolw_param.new_param(
u'event_id', u'ilwd:char', unicode(sngl_event_id_map[ifo]))
snr_node.appendChild(eid_param)
def __init__(self, ifos, coinc_results):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is define
in pycbc/events/coinc.py
and matches the on disk representation in the hdf file for this time.
"""
# remember if this should be marked as HWINJ
self.is_hardware_injection = False
if 'HWINJ' in coinc_results:
self.is_hardware_injection = True
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
'pycbc', {},
ifos=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_id = 0
for ifo in ifos:
names = [
n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n
]
sngl_id += 1
sngl = return_empty_sngl()
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.ifo = ifo
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl.mchirp
coinc_inspiral_row.mass = sngl.mtotal
coinc_inspiral_row.end_time = sngl.end_time
coinc_inspiral_row.end_time_ns = sngl.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
self.outdoc = outdoc
best_chirp_mass = np.zeros(len(results_data))
sigma_best_match = np.zeros(len(results_data))
sigma_best_mass = np.zeros(len(results_data))
sigma_best_chirp_mass = np.zeros(len(results_data))
best_a1dotL = np.zeros(len(results_data))
best_a2dotL = np.zeros(len(results_data))
best_mass_ratio = np.zeros(len(results_data))
for d,data in enumerate(results_data):
results_file = os.path.join(data[0], data[0]+'_'+sys.argv[2]+'.pickle')
injected_mass[d] = float(data[1])+float(data[2])
injected_chirp_mass[d], injected_eta = pnutils.mass1_mass2_to_mchirp_eta(
float(data[1]),float(data[2]))
injected_a1z[d] = float(data[3])
injected_a2z[d] = float(data[4])
matches, masses, inclinations, config, simulations, _, _, _ = pickle.load(
open(results_file, 'rb'))
# Label figures according to the pickle file
if opts.user_tag is None:
user_tag=results_file.replace('.pickle','')
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Manipulation and derived FOMs
#
def mchirp(self):
mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
self.mass1, self.mass2)
return mchirp
def to_coinc_xml_object(self, file_name):
# FIXME: This function will only work with two ifos!!
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
ifos = [ifo for ifo in self.sngl_files.keys()]
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
"pycbc",
{},
ifos=ifos,
comment="",
version=pycbc_version.git_hash,
cvs_repository="pycbc/" + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date,
).process_id
search_summ_table = lsctables.New(lsctables.SearchSummaryTable)
coinc_h5file = self.coinc_file.h5file
start_time = coinc_h5file["segments"]["coinc"]["start"][:].min()
end_time = coinc_h5file["segments"]["coinc"]["end"][:].max()
num_trigs = len(self.sort_arr)
search_summary = return_search_summary(start_time, end_time, num_trigs, ifos)
search_summ_table.append(search_summary)
outdoc.childNodes[0].appendChild(search_summ_table)
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
time_slide_table = lsctables.New(lsctables.TimeSlideTable)
# Set up time_slide table
time_slide_id = lsctables.TimeSlideID(0)
for ifo in ifos:
time_slide_row = lsctables.TimeSlide()
time_slide_row.instrument = ifo
time_slide_row.time_slide_id = time_slide_id
time_slide_row.offset = 0
time_slide_row.process_id = proc_id
time_slide_table.append(time_slide_row)
# Set up coinc_definer table
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral-sngl_inspiral coincidences"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
bank_col_names = ["mass1", "mass2", "spin1z", "spin2z"]
bank_col_vals = {}
for name in bank_col_names:
bank_col_vals[name] = self.get_bankfile_array(name)
coinc_event_names = ["ifar", "time1", "fap", "stat"]
coinc_event_vals = {}
for name in coinc_event_names:
coinc_event_vals[name] = self.get_coincfile_array(name)
sngl_col_names = [
"snr",
"chisq",
"chisq_dof",
"bank_chisq",
"bank_chisq_dof",
"cont_chisq",
"cont_chisq_dof",
"end_time",
"template_duration",
"coa_phase",
"sigmasq",
]
sngl_col_vals = {}
for name in sngl_col_names:
sngl_col_vals[name] = self.get_snglfile_array_dict(name)
for idx, coinc_idx in enumerate(self.sort_arr):
# Set up IDs and mapping values
curr_tmplt_id = self.template_id[idx]
coinc_id = lsctables.CoincID(idx)
# Set up sngls
# FIXME: As two-ifo is hardcoded loop over all ifos
sngl_combined_mchirp = 0
sngl_combined_mtot = 0
for ifo in ifos:
sngl_id = self.trig_id[ifo][idx]
event_id = lsctables.SnglInspiralID(sngl_id)
sngl = return_empty_sngl()
sngl.event_id = event_id
sngl.ifo = ifo
curr_sngl_file = self.sngl_files[ifo].h5file[ifo]
for name in sngl_col_names:
val = sngl_col_vals[name][ifo][idx]
setattr(sngl, name, val)
for name in bank_col_names:
val = bank_col_vals[name][idx]
setattr(sngl, name, val)
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(sngl.mass1, sngl.mass2)
sngl.mchirp, junk = pnutils.mass1_mass2_to_mchirp_eta(sngl.mass1, sngl.mass2)
sngl.eff_distance = (sngl.sigmasq) ** 0.5 / sngl.snr
sngl_combined_mchirp += sngl.mchirp
sngl_combined_mtot += sngl.mtotal
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = "sngl_inspiral"
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = event_id
coinc_event_map_table.append(coinc_map_row)
sngl_combined_mchirp = sngl_combined_mchirp / len(ifos)
sngl_combined_mtot = sngl_combined_mtot / len(ifos)
# Set up coinc inspiral and coinc event tables
coinc_event_row = lsctables.Coinc()
coinc_inspiral_row = lsctables.CoincInspiral()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ",".join(ifos)
coinc_inspiral_row.set_ifos(ifos)
coinc_event_row.time_slide_id = time_slide_id
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_combined_mchirp
coinc_inspiral_row.mass = sngl_combined_mtot
coinc_inspiral_row.set_end(LIGOTimeGPS(coinc_event_vals["time1"][idx]))
coinc_inspiral_row.snr = coinc_event_vals["stat"][idx]
coinc_inspiral_row.false_alarm_rate = coinc_event_vals["fap"][idx]
coinc_inspiral_row.combined_far = 1.0 / coinc_event_vals["ifar"][idx]
# Transform to Hz
coinc_inspiral_row.combined_far = coinc_inspiral_row.combined_far / YRJUL_SI
coinc_event_row.likelihood = 0.0
coinc_inspiral_row.minimum_duration = 0.0
coinc_event_table.append(coinc_event_row)
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
outdoc.childNodes[0].appendChild(coinc_event_table)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(time_slide_table)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
ligolw_utils.write_filename(outdoc, file_name)
def mchirp(self):
mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(self.mass1, self.mass2)
return mchirp
def test_get_random_mass(self):
# Want to do this for a variety of mass combinations
for i in update_mass_parameters(self):
curr_min_mass = self.min_total_mass
curr_max_mass = self.max_total_mass
try:
pycbc.tmpltbank.verify_mass_range_options(self, parser=parser)
except ValueError:
# Some of the inputs are unphysical and will fail.
# These cases are known to fail, the inputs are unphysical
# 35 has inconsistent total mass and eta restrictions
# 38 Component mass, [upper] chirp mass and [lower] eta limits
# rule out the entire space.
# 41 Same as 38
# 44 Same as 38
# 62 From component mass and total mass limits only total masses
# between 7 and 7.5 are possible. This range all has eta
# lower than the limit of 0.17.
# 65 Same as 38
# 68 Same as 38
# 71 Same as 38
# 80 Same as 62
if i in [35, 38, 41, 44, 62, 65, 68, 71, 80]:
continue
raise
# Check that if the mass limits have changed, it was right to do so
# This is not exhaustive, but gets most things
if (curr_min_mass is None) or \
not math.isclose(self.min_total_mass, curr_min_mass,
rel_tol=1e-06):
min_comp_mass = self.min_mass1 + self.min_mass2
min_eta = self.min_mass1 * self.min_mass2 /\
(min_comp_mass * min_comp_mass)
min_chirp_mass = min_comp_mass * min_eta**(3. / 5.)
if (min_comp_mass is not None) and \
math.isclose(self.min_total_mass, min_comp_mass,
rel_tol=1e-06):
# Okay, the total mass is changed by the components
pass
elif (self.min_eta and min_eta < self.min_eta) or \
(self.max_eta and min_eta > self.max_eta):
# Okay, not possible from eta
pass
elif self.min_chirp_mass and \
min_chirp_mass < self.min_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Minimum total mass changed unexpectedly."
self.fail(err_msg)
if (curr_max_mass is None) or \
not math.isclose(self.max_total_mass, curr_max_mass,
rel_tol=1e-06):
max_comp_mass = self.max_mass1 + self.max_mass2
max_eta = self.max_mass1 * self.max_mass2 /\
(max_comp_mass * max_comp_mass)
max_chirp_mass = max_comp_mass * max_eta**(3. / 5.)
if (max_comp_mass is not None) and \
math.isclose(self.max_total_mass, max_comp_mass,
rel_tol=1e-06):
# Okay, the total mass is changed by the components
pass
elif (self.min_eta and max_eta < self.min_eta) or\
(self.max_eta and max_eta > self.max_eta):
# Okay, not possible from eta
pass
elif self.max_chirp_mass and \
max_chirp_mass > self.max_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Maximum total mass changed unexpectedly."
self.fail(err_msg)
massRangeParams = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
ns_bh_boundary_mass=self.ns_bh_boundary_mass)
# And again with the nsbh flag
massRangeParams2 = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
nsbhFlag=True)
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams)
mass = mass1 + mass2
errMsg = "pycbc.tmpltbank.get_random_mass returns invalid ranges."
self.assertTrue(not (mass < self.min_total_mass).any(), msg=errMsg)
self.assertTrue(not (mass > self.max_total_mass).any(), msg=errMsg)
self.assertTrue(not (mass1 > self.max_mass1 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass1 < self.min_mass1 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass2 > self.max_mass2 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass2 < self.min_mass2 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass1 < mass2).any(), msg=errMsg)
# Chirp mass and eta
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)
if self.max_chirp_mass:
self.assertTrue(
not (mchirp > self.max_chirp_mass * 1.0001).any(),
msg=errMsg)
if self.min_chirp_mass:
self.assertTrue(
not (mchirp < self.min_chirp_mass * 0.9999).any(),
msg=errMsg)
if self.min_eta:
self.assertTrue(not (eta < self.min_eta * 0.9999).any(),
msg=errMsg)
self.assertTrue(not (eta > self.max_eta * 1.0001).any(),
msg=errMsg)
nsSpin1 = spin1z[mass1 < self.ns_bh_boundary_mass]
nsSpin2 = spin2z[mass2 < self.ns_bh_boundary_mass]
bhSpin1 = spin1z[mass1 > self.ns_bh_boundary_mass]
bhSpin2 = spin2z[mass2 > self.ns_bh_boundary_mass]
self.assertTrue(not (abs(nsSpin1) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(nsSpin2) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin1) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin2) > 0.9).any(), msg=errMsg)
# Check that *some* spins are bigger than 0.5
if len(bhSpin1):
self.assertTrue((abs(bhSpin1) > 0.5).any(), msg=errMsg)
if len(bhSpin2):
self.assertTrue((abs(bhSpin2) > 0.5).any(), msg=errMsg)
# Check nsbh flag
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams2)
self.assertTrue(not (abs(spin1z) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(spin2z) > 0.5).any(), msg=errMsg)
self.assertTrue((abs(spin1z) > 0.5).any(), msg=errMsg)
def eta(self):
_, eta = pnutils.mass1_mass2_to_mchirp_eta(self.mass1, self.mass2)
return eta
times, strainVal1, strainVal2 = np.loadtxt(datafile,
usecols=(0, 1, 2),
unpack=True)
# run conversion functions for correct PyCBC conventions
grav_mass1, grav_mass2 = convert_mass(massFormat, grav_mass1,
grav_mass2, total_grav_mass)
strainVal1, strainVal2 = convert_strain(strainFormat, strainVal1,
strainVal2, total_grav_mass)
times = convert_time(timeFormat, times, total_grav_mass)
with h5py.File(h5file, 'w') as fd:
# Set metadata
# still need to add values for love number, radius, lambda, etc.
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(
grav_mass1, grav_mass2)
fd.attrs.create('NR_group', 'CSUF_GWPAC_NS')
fd.attrs.create('sim_name', 'Deitrich')
fd.attrs.create('EOS', 'ALF2')
hashtag = hashlib.md5()
fd.attrs.create('type', 'BNS')
hashtag.update(fd.attrs['type'])
fd.attrs.create('hashtag', hashtag.digest())
fd.attrs.create('Format', 1)
fd.attrs.create('f_lower_at_1MSUN', f_lower)
fd.attrs.create('eta', eta)
fd.attrs.create('spin1x', spin1x)
fd.attrs.create('spin1y', spin1y)
fd.attrs.create('spin1z', spin1z)
fd.attrs.create('spin2x', spin2x)
fd.attrs.create('spin2y', spin2y)
def __init__(self, ifos, coinc_results):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is define
in pycbc/events/coinc.py
and matches the on disk representation in the hdf file for this time.
"""
# remember if this should be marked as HWINJ
self.is_hardware_injection = False
if 'HWINJ' in coinc_results:
self.is_hardware_injection = True
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc, 'pycbc',
{}, ifos=ifos, comment='', version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_id = 0
for ifo in ifos:
names = [n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n]
sngl_id += 1
sngl = return_empty_sngl()
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.ifo = ifo
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl.mchirp
coinc_inspiral_row.mass = sngl.mtotal
coinc_inspiral_row.end_time = sngl.end_time
coinc_inspiral_row.end_time_ns = sngl.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
self.outdoc = outdoc
def eta(self):
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(self.mass1, self.mass2)
return eta
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)
elif ((spin1z != 0) or (spin2z != 0)):
simtype = 'aligned-spin'
else:
spinval = False
# calculate the value for mean_anomaly (assuming this hasn't been calculated)
# 0 is default for low eccentricity values (<= .001)
mean_anom = 0
if (id_eccentricity > .001):
# default value for when it hasn't been properly calculated
mean_anom = -1
# create all relevant attributes for converted .h5 file
# default Format 1 attributes
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(
id_mass_starA, id_mass_starB)
hashtag = hashlib.md5()
fd.attrs.create('Format', 1)
fd.attrs.create('type', 'BNS')
fd.attrs.create('name', 'CoRe:' + database_key)
fd.attrs.create('alternative-names', simulation_name)
fd.attrs.create('NR-group', 'CoRe')
fd.attrs.create('NR-code', id_code + ', ' + evolution_code)
fd.attrs.create('modification-date', '')
fd.attrs.create('point-of-contact-email',
'*****@*****.**')
fd.attrs.create('simulation-type', simtype)
fd.attrs.create('INSPIRE-bibtex-keys', reference_bibkeys)
fd.attrs.create('license', 'public')
fd.attrs.create('Lmax', lmax)
fd.attrs.create('files-in-error-series', '')
def get_params(file):
"""
Read waveform meta data from hdf5 file
"""
f = h5py.File(file, "r")
# Metadata parameters:
params = {}
params["eta"] = float(f.attrs["eta"])
params["spin1x"] = float(f.attrs["spin1x"])
params["spin1y"] = float(f.attrs["spin1y"])
params["spin1z"] = float(f.attrs["spin1z"])
params["spin2x"] = float(f.attrs["spin2x"])
params["spin2y"] = float(f.attrs["spin2y"])
params["spin2z"] = float(f.attrs["spin2z"])
params["Mmin30Hz"] = float(f.attrs["f_lower_at_1MSUN"]) / 30.0
mass1, mass2 = pnutils.mtotal_eta_to_mass1_mass2(params["Mmin30Hz"], params["eta"])
params["Mchirpmin30Hz"], _ = pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)
params["q"] = mass1 / mass2
# --- Derived spin configuration
params["a1"] = np.linalg.norm([params["spin1x"], params["spin1y"], params["spin1z"]])
params["a2"] = np.linalg.norm([params["spin2x"], params["spin2y"], params["spin2z"]])
params["a1dotL"], vec = nrbu.a_with_L(params["spin1x"], params["spin1y"], params["spin1z"])
params["a1crossL"] = np.linalg.norm(vec)
params["a2dotL"], vec = nrbu.a_with_L(params["spin2x"], params["spin2y"], params["spin2z"])
params["a2crossL"] = np.linalg.norm(vec)
params["theta_a12"] = nrbu.spin_angle(
params["spin1x"], params["spin1y"], params["spin1z"], params["spin2x"], params["spin2y"], params["spin2z"]
)
params["SeffdotL"], vec = nrbu.effspin_with_L(
params["q"],
params["spin1x"],
params["spin1y"],
params["spin1z"],
params["spin2x"],
params["spin2y"],
params["spin2z"],
)
params["SeffcrossL"] = np.linalg.norm(vec)
params["SdotL"], params["theta_SdotL"] = nrbu.totspin_dot_L(
params["q"],
params["spin1x"],
params["spin1y"],
params["spin1z"],
params["spin2x"],
params["spin2y"],
params["spin2z"],
)
params["wavefile"] = os.path.abspath(file)
return params
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)
Hlm = pwave.construct_Hlm(*quadrupole_data[1:])
wavelen = len(Hlm['l=2, m=2'])
times = np.arange(0, wavelen * delta_t, delta_t)
times_M = times / (lal.MTSUN_SI * total_mass)
# GEOMETERIZE HLM (might be already!) see line 241+ of pmns_waveform.py
l = 2
with h5py.File(eos + '_' + mass + '.h5', 'w') as fd:
#
# Set metadata
#
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)
fd.attrs.create('NR_group', 'Bauswein')
fd.attrs.create(
'name',
'Bauswein:BNS:%s' % (waveform_data.eos + '_' + waveform_data.mass +
'_' + waveform_data.viscosity))
hashtag = hashlib.md5()
hashtag.update(fd.attrs['name'])
fd.attrs.create('hashtag', hashtag.digest())
fd.attrs.create('f_lower_at_1MSUN', f_lower)
fd.attrs.create('eta', eta)
fd.attrs.create('LNhatx', 0.0)
fd.attrs.create('LNhaty', 0.0)
fd.attrs.create('LNhatz', 0.0)
fd.attrs.create('lnhatx', 0.0)
fd.attrs.create('lnhaty', 0.0)
def test_get_random_mass(self):
# Want to do this for a variety of mass combinations
for i in update_mass_parameters(self):
curr_min_mass = self.min_total_mass
curr_max_mass = self.max_total_mass
try:
pycbc.tmpltbank.verify_mass_range_options(self, parser=parser)
except ValueError:
# Some of the inputs are unphysical and will fail.
# These cases are known to fail, the inputs are unphysical
# 35 has inconsistent total mass and eta restrictions
# 38 Component mass, [upper] chirp mass and [lower] eta limits
# rule out the entire space.
# 41 Same as 38
# 44 Same as 38
# 62 From component mass and total mass limits only total masses
# between 7 and 7.5 are possible. This range all has eta
# lower than the limit of 0.17.
# 65 Same as 38
# 68 Same as 38
# 71 Same as 38
# 80 Same as 62
if i in [35, 38, 41, 44, 62, 65, 68, 71, 80]:
continue
raise
# Check that if the mass limits have changed, it was right to do so
# This is not exhaustive, but gets most things
if not self.min_total_mass == curr_min_mass:
min_comp_mass = self.min_mass1 + self.min_mass2
min_eta = self.min_mass1 * self.min_mass2 /\
(min_comp_mass * min_comp_mass)
min_chirp_mass = min_comp_mass * min_eta**(3. / 5.)
if self.min_total_mass == min_comp_mass:
# Okay, the total mass is changed by the components
pass
elif min_eta < self.min_eta or min_eta > self.max_eta:
# Okay, not possible from eta
pass
elif min_chirp_mass < self.min_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Minimum total mass changed unexpectedly."
print(self.min_total_mass, curr_min_mass)
print(self.min_mass1, self.min_mass2, min_comp_mass)
print(min_eta, self.min_eta, self.max_eta)
print(min_chirp_mass, self.min_chirp_mass)
self.fail(err_msg)
if not self.max_total_mass == curr_max_mass:
max_comp_mass = self.max_mass1 + self.max_mass2
max_eta = self.max_mass1 * self.max_mass2 /\
(max_comp_mass * max_comp_mass)
max_chirp_mass = max_comp_mass * max_eta**(3. / 5.)
if self.max_total_mass == max_comp_mass:
# Okay, the total mass is changed by the components
pass
elif max_eta < self.min_eta or max_eta > self.max_eta:
# Okay, not possible from eta
pass
elif max_chirp_mass > self.max_chirp_mass:
# Okay, not possible from chirp mass
pass
else:
err_msg = "Maximum total mass changed unexpectedly."
self.fail(err_msg)
massRangeParams = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
ns_bh_boundary_mass=self.ns_bh_boundary_mass)
# And again with the nsbh flag
massRangeParams2 = pycbc.tmpltbank.massRangeParameters(\
self.min_mass1,\
self.max_mass1, self.min_mass2, self.max_mass2,\
maxNSSpinMag=self.max_ns_spin_mag,\
maxBHSpinMag=self.max_bh_spin_mag,\
maxTotMass=self.max_total_mass,\
minTotMass=self.min_total_mass,\
max_chirp_mass=self.max_chirp_mass,\
min_chirp_mass=self.min_chirp_mass,\
maxEta=self.max_eta,\
minEta=self.min_eta,\
nsbhFlag=True)
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams)
mass = mass1 + mass2
errMsg = "pycbc.tmpltbank.get_random_mass returns invalid ranges."
self.assertTrue(not (mass < self.min_total_mass).any(), msg=errMsg)
self.assertTrue(not (mass > self.max_total_mass).any(), msg=errMsg)
self.assertTrue(not (mass1 > self.max_mass1 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass1 < self.min_mass1 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass2 > self.max_mass2 * 1.001).any(),
msg=errMsg)
self.assertTrue(not (mass2 < self.min_mass2 * 0.999).any(),
msg=errMsg)
self.assertTrue(not (mass1 < mass2).any(), msg=errMsg)
# Chirp mass and eta
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(mass1, mass2)
if self.max_chirp_mass:
self.assertTrue(
not (mchirp > self.max_chirp_mass * 1.0001).any(),
msg=errMsg)
if self.min_chirp_mass:
self.assertTrue(
not (mchirp < self.min_chirp_mass * 0.9999).any(),
msg=errMsg)
if self.min_eta:
self.assertTrue(not (eta < self.min_eta * 0.9999).any(),
msg=errMsg)
self.assertTrue(not (eta > self.max_eta * 1.0001).any(),
msg=errMsg)
nsSpin1 = spin1z[mass1 < self.ns_bh_boundary_mass]
nsSpin2 = spin2z[mass2 < self.ns_bh_boundary_mass]
bhSpin1 = spin1z[mass1 > self.ns_bh_boundary_mass]
bhSpin2 = spin2z[mass2 > self.ns_bh_boundary_mass]
self.assertTrue(not (abs(nsSpin1) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(nsSpin2) > 0.5).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin1) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(bhSpin2) > 0.9).any(), msg=errMsg)
# Check that *some* spins are bigger than 0.5
if len(bhSpin1):
self.assertTrue((abs(bhSpin1) > 0.5).any(), msg=errMsg)
if len(bhSpin2):
self.assertTrue((abs(bhSpin2) > 0.5).any(), msg=errMsg)
# This can be used to test the boundaries are all applied visually
# FIXME: This would need to be updated for all of the mass limits
#pylab.plot(mass1, mass2, 'b.')
#pylab.plot([3.0,5.0],[3.0,1.0],'r-')
#pylab.plot([1.7216566400945545,1.9921146662296347,3.4433132801891091,4.01175560949798],[1.1477710933963694,1.0066274466204264,2.2955421867927388,1.002938902374495],'mx')
#pylab.ylim([0.8,3.0])
#pylab.xlim([1.5,5.0])
#pylab.savefig("masses_%d.png" %(idx,))
# Check nsbh flag
mass1, mass2, spin1z, spin2z = \
pycbc.tmpltbank.get_random_mass(100000, massRangeParams2)
self.assertTrue(not (abs(spin1z) > 0.9).any(), msg=errMsg)
self.assertTrue(not (abs(spin2z) > 0.5).any(), msg=errMsg)
self.assertTrue((abs(spin1z) > 0.5).any(), msg=errMsg)
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
"""
self.ifos = ifos
followup_ifos = kwargs.get('followup_ifos') or []
self.template_id = coinc_results['foreground/%s/template_id' %
self.ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results)
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc,
'pycbc', {},
ifos=ifos,
comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/' + pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(ifos)
coinc_event_row.instruments = ','.join(ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# compute SNR time series
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/%s/end_time' % ifo] for ifo in ifos])
self.upload_snr_series = kwargs.get('upload_snr_series')
if self.upload_snr_series:
self.snr_series = {}
self.snr_series_psd = {}
htilde = kwargs['bank'][self.template_id]
for ifo in ifos + followup_ifos:
if ifo in ifos:
trig_time = coinc_results['foreground/%s/end_time' % ifo]
else:
trig_time = subthreshold_sngl_time
# NOTE we only check the state/DQ of followup IFOs here.
# IFOs producing the coincidence are assumed to also
# produce valid SNR series.
snr_series, snr_series_psd = compute_followup_snr_series(
kwargs['data_readers'][ifo],
htilde,
trig_time,
check_state=(ifo in followup_ifos))
if snr_series is not None:
self.snr_series[ifo] = snr_series
self.snr_series_psd[ifo] = snr_series_psd
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_populated = None
for sngl_id, ifo in enumerate(ifos + followup_ifos):
if self.upload_snr_series and ifo not in self.snr_series:
# SNR series could not be computed, so skip this
continue
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.process_id = proc_id
sngl.ifo = ifo
names = [
n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n
]
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
if sngl.mass1 and sngl.mass2:
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl_populated = sngl
if sngl.snr:
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
if self.upload_snr_series and ifo in self.snr_series:
snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
for sngl in sngl_inspiral_table:
if sngl.ifo in followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.set_end(lal.LIGOTimeGPS(subthreshold_sngl_time))
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_populated.mchirp
coinc_inspiral_row.mass = sngl_populated.mtotal
coinc_inspiral_row.end_time = sngl_populated.end_time
coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns
coinc_inspiral_row.snr = coinc_results['foreground/stat']
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
def __init__(self, ifos, coinc_results, **kwargs):
"""Initialize a ligolw xml representation of a zerolag trigger
for upload from pycbc live to gracedb.
Parameters
----------
ifos: list of strs
A list of the ifos pariticipating in this trigger
coinc_results: dict of values
A dictionary of values. The format is defined in
pycbc/events/coinc.py and matches the on disk representation
in the hdf file for this time.
psds: dict of FrequencySeries
Dictionary providing PSD estimates for all involved detectors.
low_frequency_cutoff: float
Minimum valid frequency for the PSD estimates.
followup_data: dict of dicts, optional
Dictionary providing SNR time series for each detector,
to be used in sky localization with BAYESTAR. The format should
be `followup_data['H1']['snr_series']`. More detectors can be
present than given in `ifos`. If so, the extra detectors will only
be used for sky localization.
channel_names: dict of strings, optional
Strain channel names for each detector.
Will be recorded in the sngl_inspiral table.
"""
self.template_id = coinc_results['foreground/%s/template_id' % ifos[0]]
# remember if this should be marked as HWINJ
self.is_hardware_injection = ('HWINJ' in coinc_results
and coinc_results['HWINJ'])
if 'followup_data' in kwargs:
fud = kwargs['followup_data']
assert len({fud[ifo]['snr_series'].delta_t for ifo in fud}) == 1, \
"delta_t for all ifos do not match"
self.snr_series = {ifo: fud[ifo]['snr_series'] for ifo in fud}
usable_ifos = fud.keys()
followup_ifos = list(set(usable_ifos) - set(ifos))
else:
self.snr_series = None
usable_ifos = ifos
followup_ifos = []
# Set up the bare structure of the xml document
outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc(
outdoc, 'pycbc', {}, ifos=usable_ifos, comment='',
version=pycbc_version.git_hash,
cvs_repository='pycbc/'+pycbc_version.git_branch,
cvs_entry_time=pycbc_version.date).process_id
# Set up coinc_definer table
coinc_def_table = lsctables.New(lsctables.CoincDefTable)
coinc_def_id = lsctables.CoincDefID(0)
coinc_def_row = lsctables.CoincDef()
coinc_def_row.search = "inspiral"
coinc_def_row.description = "sngl_inspiral<-->sngl_inspiral coincs"
coinc_def_row.coinc_def_id = coinc_def_id
coinc_def_row.search_coinc_type = 0
coinc_def_table.append(coinc_def_row)
outdoc.childNodes[0].appendChild(coinc_def_table)
# Set up coinc inspiral and coinc event tables
coinc_id = lsctables.CoincID(0)
coinc_event_table = lsctables.New(lsctables.CoincTable)
coinc_event_row = lsctables.Coinc()
coinc_event_row.coinc_def_id = coinc_def_id
coinc_event_row.nevents = len(usable_ifos)
coinc_event_row.instruments = ','.join(usable_ifos)
coinc_event_row.time_slide_id = lsctables.TimeSlideID(0)
coinc_event_row.process_id = proc_id
coinc_event_row.coinc_event_id = coinc_id
coinc_event_row.likelihood = 0.
coinc_event_table.append(coinc_event_row)
outdoc.childNodes[0].appendChild(coinc_event_table)
# Set up sngls
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
sngl_populated = None
network_snrsq = 0
for sngl_id, ifo in enumerate(usable_ifos):
sngl = return_empty_sngl(nones=True)
sngl.event_id = lsctables.SnglInspiralID(sngl_id)
sngl.process_id = proc_id
sngl.ifo = ifo
names = [n.split('/')[-1] for n in coinc_results
if 'foreground/%s' % ifo in n]
for name in names:
val = coinc_results['foreground/%s/%s' % (ifo, name)]
if name == 'end_time':
sngl.set_end(lal.LIGOTimeGPS(val))
else:
try:
setattr(sngl, name, val)
except AttributeError:
pass
if sngl.mass1 and sngl.mass2:
sngl.mtotal, sngl.eta = pnutils.mass1_mass2_to_mtotal_eta(
sngl.mass1, sngl.mass2)
sngl.mchirp, _ = pnutils.mass1_mass2_to_mchirp_eta(
sngl.mass1, sngl.mass2)
sngl_populated = sngl
if sngl.snr:
sngl.eff_distance = (sngl.sigmasq)**0.5 / sngl.snr
network_snrsq += sngl.snr ** 2.0
if 'channel_names' in kwargs and ifo in kwargs['channel_names']:
sngl.channel = kwargs['channel_names'][ifo]
sngl_inspiral_table.append(sngl)
# Set up coinc_map entry
coinc_map_row = lsctables.CoincMap()
coinc_map_row.table_name = 'sngl_inspiral'
coinc_map_row.coinc_event_id = coinc_id
coinc_map_row.event_id = sngl.event_id
coinc_event_map_table.append(coinc_map_row)
if self.snr_series is not None:
snr_series_to_xml(self.snr_series[ifo], outdoc, sngl.event_id)
# for subthreshold detectors, respect BAYESTAR's assumptions and checks
bayestar_check_fields = ('mass1 mass2 mtotal mchirp eta spin1x '
'spin1y spin1z spin2x spin2y spin2z').split()
subthreshold_sngl_time = numpy.mean(
[coinc_results['foreground/{}/end_time'.format(ifo)]
for ifo in ifos])
for sngl in sngl_inspiral_table:
if sngl.ifo in followup_ifos:
for bcf in bayestar_check_fields:
setattr(sngl, bcf, getattr(sngl_populated, bcf))
sngl.set_end(lal.LIGOTimeGPS(subthreshold_sngl_time))
outdoc.childNodes[0].appendChild(coinc_event_map_table)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
# Set up the coinc inspiral table
coinc_inspiral_table = lsctables.New(lsctables.CoincInspiralTable)
coinc_inspiral_row = lsctables.CoincInspiral()
# This seems to be used as FAP, which should not be in gracedb
coinc_inspiral_row.false_alarm_rate = 0
coinc_inspiral_row.minimum_duration = 0.
coinc_inspiral_row.set_ifos(usable_ifos)
coinc_inspiral_row.coinc_event_id = coinc_id
coinc_inspiral_row.mchirp = sngl_populated.mchirp
coinc_inspiral_row.mass = sngl_populated.mtotal
coinc_inspiral_row.end_time = sngl_populated.end_time
coinc_inspiral_row.end_time_ns = sngl_populated.end_time_ns
coinc_inspiral_row.snr = network_snrsq ** 0.5
far = 1.0 / (lal.YRJUL_SI * coinc_results['foreground/ifar'])
coinc_inspiral_row.combined_far = far
coinc_inspiral_table.append(coinc_inspiral_row)
outdoc.childNodes[0].appendChild(coinc_inspiral_table)
# append the PSDs
self.psds = kwargs['psds']
psds_lal = {}
for ifo in self.psds:
psd = self.psds[ifo]
kmin = int(kwargs['low_frequency_cutoff'] / psd.delta_f)
fseries = lal.CreateREAL8FrequencySeries(
"psd", psd.epoch, kwargs['low_frequency_cutoff'], psd.delta_f,
lal.StrainUnit**2 / lal.HertzUnit, len(psd) - kmin)
fseries.data.data = psd.numpy()[kmin:] / pycbc.DYN_RANGE_FAC ** 2.0
psds_lal[ifo] = fseries
make_psd_xmldoc(psds_lal, outdoc)
self.outdoc = outdoc
self.time = sngl_populated.get_end()
elif ((spin1z != 0) or (spin2z != 0)):
simtype = 'aligned-spin'
else:
spinval = False
# calculate the value for mean_anomaly (assuming this hasn't been calculated)
# 0 is default for low eccentricity values (<= .001)
mean_anom = 0
if (eccentricity > .001):
# default value for when it hasn't been properly calculated
mean_anom = -1
# create all relevant attributes for converted .h5 file
# default Format 1 attributes
mchirp, eta = pnutils.mass1_mass2_to_mchirp_eta(
initial_mass1, initial_mass2)
hashtag = hashlib.md5()
fd.attrs.create('Format', 1)
fd.attrs.create('type', 'BBH')
fd.attrs.create('name', simulation_name)
fd.attrs.create('alternative-names', alternative_names)
fd.attrs.create('NR-group', 'SXS')
fd.attrs.create('NR-code', 'SpEC')
fd.attrs.create('modification-date', '')
fd.attrs.create('point-of-contact-email', '')
fd.attrs.create('simulation-type', '')
fd.attrs.create('INSPIRE-bibtex-keys', '')
fd.attrs.create('license', 'public')
fd.attrs.create('Lmax', lmax)
fd.attrs.create('files-in-error-series', '')
fd.attrs.create('comparable-simulation', '')