def create_empty_sim_inspiral_row():
""" Create an empty sim_inspiral or sngl_inspiral row where the columns
have default values of None for a float.
Retuns
----------
row: SimInspiral
An empty sim_inspiral row.
"""
# create sim_inspiral row
row = lsctables.SimInspiral()
cols = lsctables.SimInspiralTable.validcolumns
# populate columns with default values
for entry in cols.keys():
if cols[entry] in ['real_4', 'real_8']:
setattr(row, entry, None)
elif cols[entry] == 'int_4s':
setattr(row, entry, None)
elif cols[entry] == 'lstring':
setattr(row, entry, "")
elif entry == 'process_id':
row.process_id = "process:process_id:0"
elif entry == 'simulation_id':
row.simulation_id = "sim_inspiral:simulation_id:0"
return row
def create_empty_row(obj):
"""Create an empty sngl_inspiral row where the
columns have default values of 0.0 for a float, 0 for an int, '' for
a string. The ilwd columns have a default where the index is 0.
"""
# check if sim_inspiral or sngl_inspiral
row = lsctables.SimInspiral()
cols = lsctables.SimInspiralTable.validcolumns
# populate columns with default values
for entry in cols.keys():
if cols[entry] in ['real_4', 'real_8']:
setattr(row, entry, 0.)
elif cols[entry] == 'int_4s':
setattr(row, entry, 0)
elif cols[entry] == 'lstring':
setattr(row, entry, '')
elif entry == 'process_id':
row.process_id = ilwd.ilwdchar("sim_inspiral:process_id:0")
elif entry == 'simulation_id':
row.simulation_id = ilwd.ilwdchar("sim_inspiral:simulation_id:0")
else:
raise ValueError("Column %s not recognized." % (entry))
return row
def get_new_sample_point():
"""This function returns an instance of lsctables.SimInspiral, with elements
corresponding to various physical parameters uniformly sampled within their
respective ranges. """
p = lsctables.SimInspiral()
# Masses
p.mchirp = sample_mchirp()
p.eta = sample_eta_uniform()
while not accept_point(p.mchirp, p.eta):
p.mchirp = sample_mchirp()
p.eta = sample_eta_uniform()
p.mass1, p.mass2 = mchirp_eta_to_mass1_mass2(p.mchirp, p.eta)
# Spins
p.spin1x = sample_sxyz()
p.spin1y = sample_sxyz()
p.spin1z = sample_sxyz()
smag = np.sqrt(p.spin1x**2. + p.spin1y**2. + p.spin1z**2.)
if smag > smag_max or smag < smag_min:
newsmag = sample_smag()
p.spin1x *= (newsmag / smag)
p.spin1y *= (newsmag / smag)
p.spin1z *= (newsmag / smag)
p.spin2x = sample_sxyz()
p.spin2y = sample_sxyz()
p.spin2z = sample_sxyz()
smag = np.sqrt(p.spin2x**2. + p.spin2y**2. + p.spin2z**2.)
if smag > smag_max or smag < smag_min:
newsmag = sample_smag()
p.spin2x *= (newsmag / smag)
p.spin2y *= (newsmag / smag)
p.spin2z *= (newsmag / smag)
# Orbital parameters
p.alpha = sample_ecc()
p.alpha1 = sample_mean_per_ano()
p.alpha2 = sample_long_asc_nodes()
p.coa_phase = sample_coa_phase()
# Orientation and location
p.inclination = sample_inc()
p.distance = sample_dist()
# Polarization
p.polarization = sample_pol()
# Sky angles
p.latitude, p.longitude = sample_lat_lon()
# Unique HASH
p.simulation_id = get_sim_hash()
# Process ID
p.process_id = out_proc_id
return p
def write(filename, samples, write_params=None, static_args=None):
"""Writes the injection samples to the given xml.
Parameters
----------
filename : str
The name of the file to write to.
samples : io.FieldArray
FieldArray of parameters.
write_params : list, optional
Only write the given parameter names. All given names must be keys
in ``samples``. Default is to write all parameters in ``samples``.
static_args : dict, optional
Dictionary mapping static parameter names to values. These are
written to the ``attrs``.
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
simtable = lsctables.New(lsctables.SimInspiralTable)
xmldoc.childNodes[0].appendChild(simtable)
if static_args is None:
static_args = {}
if write_params is None:
write_params = samples.fieldnames
for ii in range(samples.size):
sim = lsctables.SimInspiral()
# initialize all elements to None
for col in sim.__slots__:
setattr(sim, col, None)
for field in write_params:
data = samples[ii][field]
set_sim_data(sim, field, data)
# set any static args
for (field, value) in static_args.items():
set_sim_data(sim, field, value)
simtable.append(sim)
ligolw_utils.write_filename(xmldoc,
filename,
gz=filename.endswith('gz'))
print("Reading from %s" % input_catalog, file=sys.stderr)
#
for point in input_table:
# Apply the mass-ratio threshold
qth = options.upper_q_threshold
if point.eta < (qth / (1. + qth)**2):
if options.verbose:
print(" -- Not including %s" % point.waveform,
file=sys.stdout)
sys.stdout.flush()
continue
# Check if the waveform location is specified.
# if not os.path.exists(point.numrel_data): continue
# if not (os.path.getsize(point.numrel_data) > 0): continue
# If cehck passed, proceed to appending it to the final table
npoint = lsctables.SimInspiral()
# Initialize columns
for nn in out_table.columnnames:
if 'process_id' in nn:
npoint.process_id = proc_id
elif 'waveform' in nn:
npoint.waveform = 'NR'
else:
npoint.__setattr__(nn, 0)
# Copy over columns
for nn in point.__slots__:
if hasattr(point, nn):
npoint.__setattr__(nn, point.__getattribute__(nn))
#
out_table.append(npoint)
#
print param_names
if options.type == "sngl":
sngl_inspiral_table = lsctables.New(lsctables.SnglInspiralTable,
columns=col_names)
elif options.type == "sim":
sngl_inspiral_table = lsctables.New(lsctables.SimInspiralTable,
columns=col_names)
outdoc.childNodes[0].appendChild(sngl_inspiral_table)
for values in params:
if options.type == "sngl":
tmplt = lsctables.SnglInspiral()
elif options.type == "sim":
tmplt = lsctables.SimInspiral()
tmplt.process_id = proc_id
index = 0
for value in values:
if value is 'skip':
continue
setattr(tmplt, param_names[index], value)
index += 1
sngl_inspiral_table.append(tmplt)
# write the xml doc to disk
proctable = table.get_table(outdoc, lsctables.ProcessTable.tableName)
outname = 'table.xml'
ligolw_utils.write_filename(outdoc, outname)
def calculate_faithfulness(m1, m2,
s1x=0, s1y=0, s1z=0,
s2x=0, s2y=0, s2z=0,
tc=0, phic=0,
ra=0, dec=0, polarization=0,
signal_approx='IMRPhenomD',
signal_file=None,
tmplt_approx='IMRPhenomC',
tmplt_file=None,
aligned_spin_tmplt_only=True,
non_spin_tmplt_only=False,
f_lower=15.0,
sample_rate=4096,
signal_duration=256,
psd_string='aLIGOZeroDetHighPower',
verbose=True,
debug=False):
"""
Calculates the match for a signal of given physical
parameters, as modelled by a given signal approximant, against
templates of another approximant.
This function allows turning off x,y components of
spin for templates.
IN PROGRESS: Adding facility to use "FromDataFile" waveforms
"""
# {{{
# 0) OPTION CHECKING
if aligned_spin_tmplt_only:
print(
"WARNING: Spin components parallel to L allowed, others set to 0 in templates.")
# 1) GENERATE FILTERING META-PARAMETERS
filter_N = signal_duration * sample_rate
filter_n = filter_N / 2 + 1
delta_t = 1./sample_rate
delta_f = 1./signal_duration
# LIGO Noise PSD
psd = from_string(psd_string, filter_n, delta_f, f_lower)
# 2) GENERATE THE TARGET SIGNAL
# Get the signal waveform first
if signal_approx in pywf.fd_approximants():
generator = pywfg.FDomainDetFrameGenerator(pywfg.FDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_f=delta_f, f_lower=f_lower,
approximant=signal_approx)
elif signal_approx in pywf.td_approximants():
generator = pywfg.TDomainDetFrameGenerator(pywfg.TDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_t=delta_t, f_lower=f_lower,
approximant=signal_approx)
elif 'FromDataFile' in signal_approx:
if os.path.getsize(signal_file) == 0:
raise RuntimeError(
" ERROR:...OOPS. Waveform file %s empty!!" % signal_file)
try:
_ = np.loadtxt(signal_file)
except:
raise RuntimeError(
" WARNING: FAILURE READING DATA FROM %s.." % signal_file)
waveform_params = lsctables.SimInspiral()
waveform_params.latitude = 0
waveform_params.longitude = 0
waveform_params.polarization = 0
waveform_params.spin1x = 0
waveform_params.spin1y = 0
waveform_params.spin1z = 0
waveform_params.spin2x = 0
waveform_params.spin2y = 0
waveform_params.spin2z = 0
# try:
if True:
if verbose:
print(".. generating signal waveform ")
signal_htilde, _params = get_waveform(signal_approx,
-1, -1, -1,
waveform_params,
f_lower,
sample_rate,
filter_N,
datafile=signal_file)
print(".. generated signal waveform ")
m1, m2, w_value, _ = _params
waveform_params.mass1 = m1
waveform_params.mass2 = m2
signal_h = make_frequency_series(signal_htilde)
signal_h = extend_waveform_FrequencySeries(signal_h, filter_n)
# except: raise IOError("Approximant %s not found.." % signal_approx)
else:
raise IOError("Signal Approximant %s not found.." % signal_approx)
if verbose:
print("..Generating signal with masses = %3f, %.3f, spin1 = (%.3f, %.3f, %.3f), and spin2 = (%.3f, %.3f, %.3f)" %
(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z))
sys.stdout.flush()
if signal_approx in pywf.fd_approximants():
signal = generator.generate_from_args(m1, m2,
s1x, s1y, s1z,
s2x, s2y, s2z,
phic, tc, ra, dec, polarization)
# NOTE: SEOBNRv4 has extra high frequency content, it seems..
if 'SEOBNRv4_ROM' in signal_approx or 'SEOBNRv2_ROM' in signal_approx:
signal_h = extend_waveform_FrequencySeries(
signal['H1'], filter_n, force_fit=True)
else:
signal_h = extend_waveform_FrequencySeries(signal['H1'], filter_n)
elif signal_approx in pywf.td_approximants():
signal = generator.generate_from_args(m1, m2,
s1x, s1y, s1z,
s2x, s2y, s2z,
phic, tc, ra, dec, polarization)
signal_h = make_frequency_series(signal['H1'])
signal_h = extend_waveform_FrequencySeries(signal_h, filter_n)
elif 'FromDataFile' in signal_approx:
pass
else:
raise IOError("Signal Approximant %s not found.." % signal_approx)
# 3) GENERATE THE TARGET TEMPLATE
# Get the signal waveform first
if tmplt_approx in pywf.fd_approximants():
generator = pywfg.FDomainDetFrameGenerator(pywfg.FDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_f=delta_f, f_lower=f_lower,
approximant=tmplt_approx)
elif tmplt_approx in pywf.td_approximants():
generator = pywfg.TDomainDetFrameGenerator(pywfg.TDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_t=delta_t, f_lower=f_lower,
approximant=tmplt_approx)
elif 'FromDataFile' in tmplt_approx:
if os.path.getsize(tmplt_file) == 0:
raise RuntimeError(
" ERROR:...OOPS. Waveform file %s empty!!" % tmplt_file)
try:
_ = np.loadtxt(tmplt_file)
except:
raise RuntimeError(
" WARNING: FAILURE READING DATA FROM %s.." % tmplt_file)
waveform_params = lsctables.SimInspiral()
waveform_params.latitude = 0
waveform_params.longitude = 0
waveform_params.polarization = 0
waveform_params.spin1x = 0
waveform_params.spin1y = 0
waveform_params.spin1z = 0
waveform_params.spin2x = 0
waveform_params.spin2y = 0
waveform_params.spin2z = 0
# try:
if True:
if verbose:
print(".. generating signal waveform ")
tmplt_htilde, _params = get_waveform(tmplt_approx,
-1, -1, -1,
waveform_params,
f_lower,
1./delta_t,
filter_N,
datafile=tmplt_file)
print(".. generated signal waveform ")
m1, m2, w_value, _ = _params
waveform_params.mass1 = m1
waveform_params.mass2 = m2
tmplt_h = make_frequency_series(tmplt_htilde)
tmplt_h = extend_waveform_FrequencySeries(tmplt_h, filter_n)
# except: raise IOError("Approximant %s not found.." % tmplt_approx)
else:
raise IOError("Template Approximant %s not found.." % tmplt_approx)
#
if aligned_spin_tmplt_only:
_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z = m1, m2, 0, 0, s1z, 0, 0, s2z
elif non_spin_tmplt_only:
_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z = m1, m2, 0, 0, 0, 0, 0, 0
else:
_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z = m1, m2, s1x, s1y, s1z, s2x, s2y, s2z
#
# template = generator.generate_from_args(_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z,\
# phic, tc, ra, dec, polarization)
#
if verbose:
print(
"..Generating template with masses = %3f, %.3f, spin1 = (%.3f, %.3f, %.3f), and spin2 = (%.3f, %.3f, %.3f)" %
(_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z))
sys.stdout.flush()
if tmplt_approx in pywf.fd_approximants():
try:
template = generator.generate_from_args(_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z,
phic, tc, ra, dec, polarization)
except RuntimeError as rerr:
print("""FAILED TO GENERATE %s waveform for
masses = %.3f, %.3f
spins = (%.3f, %.3f, %.3f), (%.3f, %.3f, %.3f)
phic, tc, ra, dec, pol = (%.3f, %.3f, %.3f, %.3f, %.3f)""" %
(tmplt_approx, _m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z,
phic, tc, ra, dec, polarization))
raise RuntimeError(rerr)
# NOTE: SEOBNRv4 has extra high frequency content, it seems..
if 'SEOBNRv4_ROM' in tmplt_approx or 'SEOBNRv2_ROM' in tmplt_approx:
template_h = extend_waveform_FrequencySeries(
template['H1'], filter_n, force_fit=True)
else:
template_h = extend_waveform_FrequencySeries(
template['H1'], filter_n)
elif tmplt_approx in pywf.td_approximants():
try:
template = generator.generate_from_args(_m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z,
phic, tc, ra, dec, polarization)
except RuntimeError as rerr:
print("""FAILED TO GENERATE %s waveform for
masses = %.3f, %.3f
spins = (%.3f, %.3f, %.3f), (%.3f, %.3f, %.3f)
phic, tc, ra, dec, pol = (%.3f, %.3f, %.3f, %.3f, %.3f)""" %
(tmplt_approx, _m1, _m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z,
phic, tc, ra, dec, polarization))
raise RuntimeError(rerr)
template_h = make_frequency_series(template['H1'])
template_h = extend_waveform_FrequencySeries(template_h, filter_n)
elif 'FromDataFile' in tmplt_approx:
pass
else:
raise IOError("Template Approximant %s not found.." % tmplt_approx)
# 4) COMPUTE MATCH
m, idx = match(signal_h, template_h, psd=psd, low_frequency_cutoff=f_lower)
if debug:
print(
"MATCH IS %.6f for parameters" % m, m1, m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z)
sys.stderr.flush()
#
# 5) RETURN OPTIMIZED MATCH
return m, idx
def frominjectionfile(file, type, ifo=None, start=None, end=None):
"""
Read generic injection file object file containing injections of the given
type string. Returns an 'Sim' lsctable of the corresponding type.
Arguments:
file : file object
type : [ "inspiral" | "burst" | "ringdown" ]
Keyword arguments:
ifo : [ "G1" | "H1" | "H2" | "L1" | "V1" ]
"""
# read type
type = type.lower()
# read injection xml
xml = re.compile('(xml$|xml.gz$)')
if re.search(xml,file.name):
xmldoc,digest = utils.load_fileobj(file)
injtable = table.get_table(xmldoc,'sim_%s:table' % (type))
# read injection txt
else:
cchar = re.compile('[#%<!()_\[\]{}:;\'\"]+')
#== construct new Sim{Burst,Inspiral,Ringdown}Table
injtable = lsctables.New(lsctables.__dict__['Sim%sTable' % (type.title())])
if type=='inspiral':
columns = ['geocent_end_time.geocent_end_time_ns',\
'h_end_time.h_end_time_ns',\
'l_end_time.l_end_time_ns',\
'v_end_time.v_end_time_ns',\
'distance']
for line in file.readlines():
if re.match(cchar,line):
continue
# set up siminspiral object
inj = lsctables.SimInspiral()
# split data
sep = re.compile('[\s,=]+')
data = sep.split(line)
# set attributes
inj.geocent_end_time = int(data[0].split('.')[0])
inj.geocent_end_time_ns = int(data[0].split('.')[1])
inj.h_end_time = int(data[1].split('.')[0])
inj.h_end_time_ns = int(data[1].split('.')[1])
inj.l_end_time = int(data[2].split('.')[0])
inj.l_end_time_ns = int(data[2].split('.')[1])
inj.v_end_time = int(data[3].split('.')[0])
inj.v_end_time_ns = int(data[3].split('.')[1])
inj.distance = float(data[4])
injtable.append(inj)
if type=='burst':
if file.readlines()[0].startswith('filestart'):
# if given parsed burst file
file.seek(0)
snrcol = { 'G1':23, 'H1':19, 'L1':21, 'V1':25 }
for line in file.readlines():
inj = lsctables.SimBurst()
# split data
sep = re.compile('[\s,=]+')
data = sep.split(line)
# set attributes
# gps time
if 'burstgps' in data:
idx = data.index('burstgps')+1
geocent = LIGOTimeGPS(data[idx])
inj.time_geocent_gps = geocent.seconds
inj.time_geocent_gps_ns = geocent.nanoseconds
else:
continue
#inj.waveform = data[4]
#inj.waveform_number = int(data[5])
# frequency
if 'freq' in data:
idx = data.index('freq')+1
inj.frequency = float(data[idx])
else:
continue
# SNR a.k.a. amplitude
if ifo and 'snr%s' % ifo in data:
idx = data.index('snr%s' % ifo)+1
inj.amplitude = float(data[idx])
elif 'rmsSNR' in data:
idx = data.index('rmsSNR')+1
inj.amplitude = float(data[idx])
else:
continue
if 'phi' in data:
idx = data.index('phi' )+1
inj.ra = float(data[idx])*24/(2*math.pi)
if 'theta' in data:
idx = data.index('theta' )+1
inj.ra = 90-(float(data[idx])*180/math.pi)
if ifo and 'hrss%s' % ifo in data:
idx = data.index('hrss%s' % ifo)+1
inj.hrss = float(data[idx])
elif 'hrss' in data:
idx = data.index('hrss')+1
inj.hrss = float(data[idx])
# extra columns to be added when I know how
#inj.q = 0
#inj.q = float(data[11])
#h_delay = LIGOTimeGPS(data[41])
#inj.h_peak_time = inj.time_geocent_gps+h_delay.seconds
#inj.h_peak_time_ns = inj.time_geocent_gps_ns+h_delay.nanoseconds
#l_delay = LIGOTimeGPS(data[43])
#inj.l_peak_time = inj.time_geocent_gps+l_delay.seconds
#inj.l_peak_time_ns = inj.time_geocent_gps_ns+l_delay.nanoseconds
#v_delay = LIGOTimeGPS(data[43])
#inj.v_peak_time = inj.time_geocent_gps+v_delay.seconds
#inj.v_peak_time_ns = inj.time_geocent_gps_ns+v_delay.nanoseconds
injtable.append(inj)
else:
# if given parsed burst file
file.seek(0)
for line in file.readlines():
inj = lsctables.SimBurst()
# split data
sep = re.compile('[\s,]+')
data = sep.split(line)
# set attributes
geocent = LIGOTimeGPS(data[0])
inj.time_geocent_gps = geocent.seconds
inj.time_geocent_gps_ns = geocent.nanoseconds
injtable.append(inj)
injections = table.new_from_template(injtable)
if not start: start = 0
if not end: end = 9999999999
span = segments.segmentlist([ segments.segment(start, end) ])
get_time = dqTriggerUtils.def_get_time(injections.tableName)
injections.extend(inj for inj in injtable if get_time(inj) in span)
return injections
def calculate_fitting_factor(m1, m2,
s1x=0, s1y=0, s1z=0,
s2x=0, s2y=0, s2z=0,
tc=0, phic=0,
ra=0, dec=0, polarization=0,
signal_approx='IMRPhenomD',
signal_file=None,
tmplt_approx='IMRPhenomC',
vary_masses_only=True,
vary_masses_and_aligned_spin_only=False,
chirp_mass_window=0.1,
effective_spin_window=0.5,
num_retries=4,
f_lower=15.0,
sample_rate=4096,
signal_duration=256,
psd_string='aLIGOZeroDetHighPower',
pso_swarm_size=500,
pso_omega=0.5,
pso_phip=0.5,
pso_phig=0.25,
pso_minfunc=1e-8,
verbose=True,
debug=False):
"""
Calculates the fitting factor for a signal of given physical
parameters, as modelled by a given signal approximant, against
templates of another approximant.
This function uses a particle swarm optimization to maximize
the overlaps between signal and templates. Algorithm parameters
are tunable, depending on how many dimensions we are optimizing
over.
IN PROGRESS: Adding facility to use "FromDataFile" waveforms
"""
# {{{
# 0) OPTION CHECKING
if vary_masses_only:
print("WARNING: Only component masses are allowed to be varied in templates. Setting the rest to signal values.")
if vary_masses_and_aligned_spin_only:
print("WARNING: Only component masses and spin components parallel to L allowed to be varied in templates. Setting the rest to signal values.")
if vary_masses_only and vary_masses_and_aligned_spin_only:
raise IOError(
"Inconsistent options: vary_masses_only and vary_masses_and_aligned_spin_only")
if (not vary_masses_only) and (not vary_masses_and_aligned_spin_only):
print("WARNING: All mass and spin components varied in templates. Sky parameters still fixed to signal values.")
# 1) GENERATE FILTERING META-PARAMETERS
signal_duration = int(signal_duration)
sample_rate = int(sample_rate)
filter_N = signal_duration * sample_rate
filter_n = filter_N / 2 + 1
delta_t = 1./sample_rate
delta_f = 1./signal_duration
if verbose:
print("signal_duration = %d, sample_rate = %d, filter_N = %d, filter_n = %d" % (
signal_duration, sample_rate, filter_N, filter_n))
print("deltaT = %f, deltaF = %f" % (delta_t, delta_f))
# LIGO Noise PSD
psd = from_string(psd_string, filter_n, delta_f, f_lower)
# 2) GENERATE THE TARGET SIGNAL
# PREPARATORY: Get the signal generator
if signal_approx in pywf.fd_approximants():
generator = pywfg.FDomainDetFrameGenerator(pywfg.FDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_f=delta_f, f_lower=f_lower,
approximant=signal_approx)
elif signal_approx in pywf.td_approximants():
generator = pywfg.TDomainDetFrameGenerator(pywfg.TDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z',
'coa_phase',
'tc', 'ra', 'dec', 'polarization'],
detectors=['H1'],
delta_t=delta_t, f_lower=f_lower,
approximant=signal_approx)
elif 'FromDataFile' in signal_approx:
if os.path.getsize(signal_file) == 0:
raise RuntimeError(
" ERROR:...OOPS. Waveform file %s empty!!" % signal_file)
try:
_ = np.loadtxt(signal_file)
except:
raise RuntimeError(
" WARNING: FAILURE READING DATA FROM %s.." % signal_file)
waveform_params = lsctables.SimInspiral()
waveform_params.latitude = 0
waveform_params.longitude = 0
waveform_params.polarization = 0
waveform_params.spin1x = 0
waveform_params.spin1y = 0
waveform_params.spin1z = 0
waveform_params.spin2x = 0
waveform_params.spin2y = 0
waveform_params.spin2z = 0
# try:
if True:
if verbose:
print(".. generating signal waveform ")
signal_htilde, _params = get_waveform(signal_approx,
-1, -1, -1,
waveform_params,
f_lower,
sample_rate,
filter_N,
datafile=signal_file)
print(".. generated signal waveform ")
m1, m2, w_value, _ = _params
waveform_params.mass1 = m1
waveform_params.mass2 = m2
signal_h = make_frequency_series(signal_htilde)
signal_h = extend_waveform_FrequencySeries(signal_h, filter_n)
# except: raise IOError("Approximant %s not found.." % signal_approx)
else:
raise IOError("Approximant %s not found.." % signal_approx)
if verbose:
print(
"\nGenerating signal with masses = %3f, %.3f, spin1 = (%.3f, %.3f, %.3f), and spin2 = (%.3f, %.3f, %.3f)" %
(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z))
sys.stdout.flush()
# Actually GENERATE THE SIGNAL
if signal_approx in pywf.fd_approximants():
signal = generator.generate_from_args(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z,
phic, tc, ra, dec, polarization)
signal_h = extend_waveform_FrequencySeries(signal['H1'], filter_n)
elif signal_approx in pywf.td_approximants():
signal = generator.generate_from_args(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z,
phic, tc, ra, dec, polarization)
signal_h = make_frequency_series(signal['H1'])
signal_h = extend_waveform_FrequencySeries(signal_h, filter_n)
elif 'FromDataFile' in signal_approx:
pass
else:
raise IOError("Approximant %s not found.." % signal_approx)
###
# NOW : Set up PSO calculation of the optimal overlap parameter set, i.e. \theta(FF)
###
# 3) INITIALIZE THE WAVEFORM GENERATOR FOR TEMPLATES
# We allow all intrinsic parameters to vary, and fix them to the signal
# values, in case only masses or only mass+aligned-spin components are
# requested to be varied. This fixing is done inside the objective function.
if tmplt_approx in pywf.fd_approximants():
generator_tmplt = pywfg.FDomainDetFrameGenerator(pywfg.FDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z'
],
detectors=['H1'],
coa_phase=phic,
tc=tc, ra=ra, dec=dec, polarization=polarization,
delta_f=delta_f, f_lower=f_lower,
approximant=tmplt_approx)
elif tmplt_approx in pywf.td_approximants():
raise IOError(
"Time-domain templates not supported yet (TDomainDetFrameGenerator doesn't exist)")
generator_tmplt = pywfg.TDomainDetFrameGenerator(pywfg.TDomainCBCGenerator, 0,
variable_args=['mass1', 'mass2',
'spin1x', 'spin1y', 'spin1z',
'spin2x', 'spin2y', 'spin2z'
],
detectors=['H1'],
coa_phase=phic,
tc=tc, ra=ra, dec=dec, polarization=polarization,
delta_t=delta_t, f_lower=f_lower,
approximant=tmplt_approx)
elif 'FromDataFile' in tmplt_approx:
raise RuntimeError(
"Using **templates** from data files is not implemented yet")
else:
raise IOError("Approximant %s not found.." % tmplt_approx)
# 4) DEFINE AN OBJECTIVE FUNCTION FOR PSO TO MINIMIZE
def objective_function_fitting_factor(x, *args):
"""
This function is to be minimized if the fitting factor is to be found
"""
objective_function_fitting_factor.counter += 1
# 1) OBTAIN THE TEMPLATE PARAMETERS FROM X. ASSUME THAT ONLY
# THOSE ARE PASSED THAT ARE NEEDED BY THE GENERATOR
if len(x) == 2:
m1, m2 = x
if vary_masses_only:
_s1x = _s1y = _s1z = _s2x = _s2y = _s2z = 0
else:
_s1x, _s1y, _s1z = s1x, s1y, s1z
_s2x, _s2y, _s2z = s2x, s2y, s2z
elif len(x) == 4:
m1, m2, _s1z, _s2z = x
if vary_masses_and_aligned_spin_only:
_s1x = _s1y = _s2x = _s2y = 0
else:
_s1x, _s1y = s1x, s1y
_s2x, _s2y = s2x, s2y
elif len(x) == 8:
m1, m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z = x
else:
raise IOError(
"No of vars %d not supported (should be 2 or 4 or 8)" % len(x))
# 2) CHECK FOR CONSISTENCY
if (_s1x**2 + _s1y**2 + _s1z**2) > s_max or (_s2x**2 + _s2y**2 + _s2z**2) > s_max:
return 1e99
# 2) ASSUME THAT
signal_h, tmplt_generator = args
tmplt = tmplt_generator.generate_from_args(
m1, m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z)
tmplt_h = make_frequency_series(tmplt['H1'])
if debug:
print("IN FF Objective-> for parameters:", m1,
m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z)
if debug:
print("IN FF Objective-> Length(tmplt) = %d, making it %d" %
(len(tmplt['H1']), filter_n))
# NOTE: SEOBNRv4 has extra high frequency content, it seems..
if 'SEOBNRv4_ROM' in tmplt_approx or 'SEOBNRv2_ROM' in tmplt_approx:
tmplt_h = extend_waveform_FrequencySeries(
tmplt_h, filter_n, force_fit=True)
else:
tmplt_h = extend_waveform_FrequencySeries(tmplt_h, filter_n)
# 3) COMPUTE MATCH
m, _ = match(signal_h, tmplt_h, psd=psd, low_frequency_cutoff=f_lower)
if debug:
print("MATCH IS %.6f for parameters:" %
m, m1, m2, _s1x, _s1y, _s1z, _s2x, _s2y, _s2z)
retval = np.log10(1. - m)
# We do not want PSO to go berserk, so we stop when FF = 0.999999
if retval <= -6.0:
retval = -6.0
return retval
objective_function_fitting_factor.counter = 0
# 5) DEFINE A CONSTRAINT FUNCTION FOR PSO TO RESPECT
def constraint_function_fitting_factor(x, *args):
"""
This function implements constraints on the optimization of fitting
factors:
1) spin magnitudes on both holes should be <= 1
"""
if len(x) == 2:
m1, m2 = x
s1x = s1y = s1z = s2x = s2y = s2z = 0
elif len(x) == 4:
m1, m2, s1z, s2z = x
s1x = s1y = s2x = s2y = 0
elif len(x) == 8:
m1, m2, s1x, s1y, s1z, s2x, s2y, s2z = x
# 1) Constraint on spin magnitudes
s1_mag = (s1x**2 + s1y**2 + s1z**2)**0.5
s2_mag = (s2x**2 + s2y**2 + s2z**2)**0.5
##
if (s1_mag > s_max) or (s2_mag > s_max):
return -1
# 2) Constraint on effective spin
s_eff = (s1z * m1 + s2z * m2) / (m1 + m2)
##
if (s_eff > s_eff_max) or (s_eff < s_eff_min):
return -1
# FINALLY) DEFAULT
return 1
# 6) FINALLY, CALL THE PSO TO COMPUTE THE FITTING FACTOR
# 6a) FIRST CONSTRUCT THE FIXED ARGUMENTS FOR THE PSO's OBJECTIVE FUNCTION
pso_args = (signal_h, generator_tmplt)
# 6b) NOW SET THE RANGE OF PARAMETERS TO BE PROBED
mt = m1 + m2 * 1.0
et = m1 * m2 / mt / mt
mc = mt * et**0.6
mc_min = mc * (1.0 - chirp_mass_window)
mc_max = mc * (1.0 + chirp_mass_window)
et_max = 0.25
et_min = 10. / 121. # Lets say we trust waveform models up to q = 10
m1_max, _ = pnutils.mchirp_eta_to_mass1_mass2(mc_max, et_min)
m1_min, _ = pnutils.mchirp_eta_to_mass1_mass2(mc_min, et_max)
_, m2_max = pnutils.mchirp_eta_to_mass1_mass2(mc_max, et_max)
_, m2_min = pnutils.mchirp_eta_to_mass1_mass2(mc_min, et_min)
s_min = -0.99
s_max = +0.99
s_eff = (s1z * m1 + s2z * m2) / (m1 + m2)
s_eff_min = s_eff - effective_spin_window
s_eff_max = s_eff + effective_spin_window
if verbose:
print(m1, m2, mt, et, mc, mc_min, mc_max, et_min,
et_max, m1_min, m1_max, m2_min, m2_max)
if vary_masses_only:
low_lim = [m1_min, m2_min]
high_lim = [m1_max, m2_max]
elif vary_masses_and_aligned_spin_only:
low_lim = [m1_min, m2_min, s_min, s_min]
high_lim = [m1_max, m2_max, s_max, s_max]
else:
low_lim = [m1_min, m2_min, s_min, s_min, s_min, s_min, s_min, s_min]
high_lim = [m1_max, m2_max, s_max, s_max, s_max, s_max, s_max, s_max]
#
if verbose:
print("\nSearching within limits:\n", low_lim, " and \n", high_lim)
print("\nCalculating overlap now..")
sys.stdout.flush()
olap, idx = calculate_faithfulness(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z,
tc=tc, phic=phic,
ra=ra, dec=dec,
polarization=polarization,
signal_approx=signal_approx,
signal_file=signal_file,
tmplt_approx=tmplt_approx,
tmplt_file=None,
aligned_spin_tmplt_only=vary_masses_and_aligned_spin_only,
non_spin_tmplt_only=vary_masses_only,
f_lower=f_lower, sample_rate=sample_rate,
signal_duration=signal_duration,
verbose=verbose, debug=debug)
#
if verbose:
print("Overlap with aligned_spin_tmplt_only = ", vary_masses_and_aligned_spin_only,
" and non_spin_tmplt_only = ", vary_masses_only, ": ", olap, np.log10(
1. - olap))
sys.stdout.flush()
#
idx = 1
ff = 0.0
while ff < olap:
if idx and idx % 2 == 0:
pso_minfunc *= 0.1
pso_phig *= 1.1
if idx > num_retries:
print(
"WARNING: Failed to improve on overlap in %d iterations. Set ff = olap now" % num_retries)
ff = olap
break
if verbose:
print("\nTry %d to compute fitting factor" % idx)
sys.stdout.flush()
params, ff = pso(objective_function_fitting_factor,
low_lim, high_lim,
f_ieqcons=constraint_function_fitting_factor,
args=pso_args,
swarmsize=pso_swarm_size,
omega=pso_omega,
phip=pso_phip,
phig=pso_phig,
minfunc=pso_minfunc,
maxiter=500,
debug=verbose)
# Restore fitting factor from 1-ff
ff = 1.0 - 10**ff
if verbose:
print("\nLoop will continue till %.12f < %.12f" % (ff, olap))
sys.stdout.flush()
idx += 1
if verbose:
print("optimization took %d objective func evals" %
objective_function_fitting_factor.counter)
sys.stdout.flush()
#
# 7) RETURN OPTIMIZED PARAMETERS
return [params, olap, ff]
# Prepare xml document
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
proc_id = ligolw_process.register_to_xmldoc\
(xmldoc, "nr_catalog", args.__dict__, comment="",
version=lalapps.git_version.version,
cvs_repository='lalsuite/' + lalapps.git_version.branch,
cvs_entry_time=lalapps.git_version.date).process_id
sim_table = lsctables.New(lsctables.SimInspiralTable)
inj_list = args.inputs
for count, inj in enumerate(inj_list):
curr_sim = lsctables.SimInspiral()
# Add the empty columns
fill_missing_columns(curr_sim)
# Set id columns
curr_sim.process_id = proc_id
curr_sim.simulation_id = ilwd.ilwdchar("sim_inspiral:simulation_id:%d"\
%(count))
curr_sim.numrel_data = inj
f = h5py.File(inj, 'r')
curr_sim.eta = f.attrs['eta']
if curr_sim.eta > 0.25 and curr_sim.eta < 0.2501:
curr_sim.eta = 0.25
# Populate spins columns with spins in LAL frame! Need to be
# transformed from NR frame
curr_sim.f_lower = f.attrs['f_lower_at_1MSUN']
f.close()
def new_row(tabletype):
if tabletype == lsctables.SnglInspiralTable:
return lsctables.SnglInspiral()
if tabletype == lsctables.SimInspiralTable:
return lsctables.SimInspiral()
raise IOError("Input table type neither Sim or Sngl Inspiral")
eta = q / (1. + q)**2
mchirp = mtotal * eta**0.6
m1, m2 = mchirp_eta_to_m1_m2(mchirp, eta)
if (mtotal <= mtotal_max) and (mtotal >= mtotal_min) and (
eta >= eta_min) and (eta <= eta_max) and (m1 > mass1_min) and (
m2 > mass2_min) and (m1 < mass1_max) and (m2 < mass2_max):
return True
else:
print("sample mtotal = %f, q = %f REJECTED!" % (mtotal, q))
return False
for i in np.arange(npoints):
if i % 1000 == 0 and options.verbose:
print("Point %d" % i, file=sys.stderr)
smplpt = lsctables.SimInspiral()
smplpt.process_id = proc_id
# Using the field alpha as the index associated with the point. This value
# remains the same, even if these points get split up between jobs
smplpt.bandpass = i
smplpt.alpha = i
smplpt.alpha1 = sample_range(ecc_min, ecc_max)
smplpt.alpha2 = sample_range(anom_min, anom_max)
# Get the masses
if options.sample_m1_m2:
if options.mass1 is not None:
smplpt.mass1 = options.mass1
def get_new_sample_point():
"""This function returns an instance of lsctables.SimInspiral, with elements corresponding to various physical parameters uniformly sampled within their respective ranges. """
p = lsctables.SimInspiral()
p.alpha = -1
p.alpha1 = -1
p.alpha2 = -1
p.eta, chi = sample_eta_chi()
p.spin1z = chi
p.spin2z = chi
# Get the allowed range of mchirp for this eta
#q = (0.5/p.eta) * (1. + sqrt(1. - 4.*p.eta)) - 1.
mtot_max = mtotal_max #mass_max * (1. + (1./q) )
mtot_min = 1.5581 * chi**2 + 11.438 * chi + 63.875
mtot = sample_bound(mtot_min, mtot_max)
p.mchirp = mtot * p.eta**0.6
p.mass1, p.mass2 = mchirp_eta_to_mass1_mass2(p.mchirp, p.eta)
#mchirp_max = (mtot_max) * p.eta**0.6
#mchirp_min = -63.5 * p.eta**2 + 65.9 * p.eta + 19.7 - 0.50
#p.mchirp = sample_bound(mchirp_min,mchirp_max)
#tm1,tm2 = qm.mchirp_eta_to_m1_m2(p.mchirp,p.eta)
# Just in case the values fall outside the bank's range
#while (tm1 < mass_min) or (tm1 > mass_max) or (tm2 < mass_min) or (tm2 > mass_max):
# p.eta = sample_eta()
# p.mchirp = sample_bound(mchirp_min,mchirp_max)
# tm1,tm2 = qm.mchirp_eta_to_m1_m2(p.mchirp,p.eta)
#p.eta = eta
#p.mass1 = m1
#p.mass2 = m2
#tm1 = sample_mass()
#tm2 = sample_mass()
#p.mass1 = max( tm1, tm2 )
#p.mass2 = min( tm1, tm2 )
#p.mchirp, p.eta = qm.m1_m2_to_mchirp_eta( p.mass1, p.mass2 )
#while not accept_point( p.mchirp, p.eta ):
# tm1 = sample_mass()
# tm2 = sample_mass()
# p.mass1 = max( tm1, tm2 )
# p.mass2 = min( tm1, tm2 )
# p.mchirp, p.eta = qm.m1_m2_to_mchirp_eta( p.mass1, p.mass2 )
p.spin1x = 0. #sample_sxyz()
p.spin1y = 0. #sample_sxyz()
#p.spin1z = sample_sxyz()
#smag = np.sqrt( p.spin1x**2. + p.spin1y**2. + p.spin1z**2. )
#if smag:
# newsmag = sample_smag()
# p.spin1x *= (newsmag/smag)
# p.spin1y *= (newsmag/smag)
# p.spin1z *= (newsmag/smag)
p.spin2x = 0. #sample_sxyz()
p.spin2y = 0. #sample_sxyz()
#p.spin2z = sample_sxyz()
#smag = np.sqrt( p.spin2x**2. + p.spin2y**2. + p.spin2z**2. )
#if smag:
# newsmag = sample_smag()
# p.spin2x *= (newsmag/smag)
# p.spin2y *= (newsmag/smag)
# p.spin2z *= (newsmag/smag)
p.inclination = sample_inc()
p.polarization = sample_pol()
p.latitude = 0.
p.longitude = 0.
return p
