def setUp(self, *args):
self.numtests = 1000
self.precision = 1e-8
self.f_lower = 10.
# create some component masses to work with
self.m1 = numpy.random.uniform(1., 100., size=self.numtests)
self.m2 = numpy.random.uniform(1., 100., size=self.numtests)
# create some spins to work with
spin_angledist = distributions.UniformSolidAngle()
rvals = spin_angledist.rvs(size=self.numtests)
self.spin1_polar = rvals['theta']
self.spin1_az = rvals['phi']
self.spin1_amp = numpy.random.uniform(0., 1., size=self.numtests)
rvals = spin_angledist.rvs(size=self.numtests)
self.spin2_polar = rvals['theta']
self.spin2_az = rvals['phi']
self.spin2_amp = numpy.random.uniform(0., 1., size=self.numtests)
# calculate derived parameters from each
self.mp = conversions.primary_mass(self.m1, self.m2)
self.ms = conversions.secondary_mass(self.m1, self.m2)
self.mtotal = conversions.mtotal_from_mass1_mass2(self.m1, self.m2)
self.q = conversions.q_from_mass1_mass2(self.m1, self.m2)
self.invq = conversions.invq_from_mass1_mass2(self.m1, self.m2)
self.mchirp = conversions.mchirp_from_mass1_mass2(self.m1, self.m2)
self.eta = conversions.eta_from_mass1_mass2(self.m1, self.m2)
self.tau0 = conversions.tau0_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.tau3 = conversions.tau3_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.spin1x, self.spin1y, self.spin1z = \
coordinates.spherical_to_cartesian(self.spin1_amp, self.spin1_az,
self.spin1_polar)
self.spin2x, self.spin2y, self.spin2z = \
coordinates.spherical_to_cartesian(self.spin2_amp, self.spin2_az,
self.spin2_polar)
self.effective_spin = conversions.chi_eff(self.m1, self.m2,
self.spin1z, self.spin2z)
self.chi_p = conversions.chi_p(self.m1, self.m2, self.spin1x,
self.spin1y, self.spin2x, self.spin2y)
self.primary_spinx = conversions.primary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.primary_spiny = conversions.primary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.primary_spinz = conversions.primary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
self.secondary_spinx = conversions.secondary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.secondary_spiny = conversions.secondary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.secondary_spinz = conversions.secondary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
def setUp(self, *args):
self.numtests = 1000
self.precision = 1e-8
self.f_lower = 10.
# create some component masses to work with
self.m1 = numpy.random.uniform(1., 100., size=self.numtests)
self.m2 = numpy.random.uniform(1., 100., size=self.numtests)
# create some spins to work with
spin_angledist = distributions.UniformSolidAngle()
rvals = spin_angledist.rvs(size=self.numtests)
self.spin1_polar = rvals['theta']
self.spin1_az = rvals['phi']
self.spin1_amp = numpy.random.uniform(0., 1., size=self.numtests)
rvals = spin_angledist.rvs(size=self.numtests)
self.spin2_polar = rvals['theta']
self.spin2_az = rvals['phi']
self.spin2_amp = numpy.random.uniform(0., 1., size=self.numtests)
# calculate derived parameters from each
self.mp = conversions.primary_mass(self.m1, self.m2)
self.ms = conversions.secondary_mass(self.m1, self.m2)
self.mtotal = conversions.mtotal_from_mass1_mass2(self.m1, self.m2)
self.q = conversions.q_from_mass1_mass2(self.m1, self.m2)
self.invq = conversions.invq_from_mass1_mass2(self.m1, self.m2)
self.mchirp = conversions.mchirp_from_mass1_mass2(self.m1, self.m2)
self.eta = conversions.eta_from_mass1_mass2(self.m1, self.m2)
self.tau0 = conversions.tau0_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.tau3 = conversions.tau3_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.spin1x, self.spin1y, self.spin1z = \
coordinates.spherical_to_cartesian(self.spin1_amp, self.spin1_az,
self.spin1_polar)
self.spin2x, self.spin2y, self.spin2z = \
coordinates.spherical_to_cartesian(self.spin2_amp, self.spin2_az,
self.spin2_polar)
self.effective_spin = conversions.chi_eff(self.m1, self.m2,
self.spin1z, self.spin2z)
self.primary_spinx = conversions.primary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.primary_spiny = conversions.primary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.primary_spinz = conversions.primary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
self.secondary_spinx = conversions.secondary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.secondary_spiny = conversions.secondary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.secondary_spinz = conversions.secondary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
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" : conversions.mchirp_from_mass1_mass2(b['mass1'][:],
b['mass2'][:])[foundtmp],
"eta" : conversions.eta_from_mass1_mass2(b['mass1'][:],
b['mass2'][:])[foundtmp],
"effective_spin" : conversions.chi_eff(b['mass1'][:],
b['mass2'][:],
b['spin1z'][:],
b['spin2z'][:])[foundtmp]
}
return found_param_dict[param]
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 = conversions.mchirp_from_mass1_mass2(m1, m2)
elif par == 'mtotal':
parvals = m1 + m2
elif par == 'eta':
parvals = conversions.eta_from_mass1_mass2(m1, m2)
elif par in ['chi_eff', 'effective_spin']:
parvals = conversions.chi_eff(m1, m2, s1z, s2z)
elif par == 'template_duration':
# default to SEOBNRv4 duration function
if not hasattr(args, 'approximant') or args.approximant is None:
args.approximant = "SEOBNRv4"
parvals = pnutils.get_imr_duration(m1, m2, s1z, s2z, args.f_lower,
args.approximant)
if args.min_duration:
parvals += args.min_duration
elif par == 'tau0':
parvals = conversions.tau0_from_mass1_mass2(m1, m2, args.f_lower)
elif par == 'tau3':
parvals = conversions.tau3_from_mass1_mass2(m1, m2, args.f_lower)
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 =='eta':
parvals = conversions.eta_from_mass1_mass2(m1, m2)
elif par in ['chi_eff', 'effective_spin']:
parvals = conversions.chi_eff(m1, m2, s1z, s2z)
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 == 'tau0':
parvals = conversions.tau0_from_mass1_mass2(m1, m2, args.f_lower)
elif par == 'tau3':
parvals = conversions.tau3_from_mass1_mass2(m1, m2, args.f_lower)
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_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" : conversions.mchirp_from_mass1_mass2(inj['mass1'][:],
inj['mass2'][:]),
"eta" : conversions.eta_from_mass1_mass2(inj['mass1'][:],
inj['mass2'][:]),
"effective_spin" : conversions.chi_eff(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 transform_masses(mass1, mass2, mchirp_mod, eta_mod):
"""Modifies masses given a difference in mchirp and eta.
Parameters
----------
mass1 : float
Mass of the larger object (already modified).
mass2 : float
Mass of the smaller object (already modified).
mchirp_mod : tuple of (float, str) or None
Tuple giving the modification value for ``mchirp``, and a string
indicating whether the given modification is a fractional difference
(``'fdiff'``), an absolute difference (``'absdiff'``), or a replacement
(``'replace'``). If ``None``, the chirp mass will not be modified.
eta_mod : tuple of (float, str)
Same as ``mchirp_mod``, but for ``eta``.
Returns
-------
mass1 : float
Modified mass1.
mass2 : float
Modified mass2.
"""
mchirp = conversions.mchirp_from_mass1_mass2(mass1, mass2)
eta = conversions.eta_from_mass1_mass2(mass1, mass2)
if mchirp_mod is not None:
diff, modtype = mchirp_mod
mchirp = apply_mod(mchirp, diff, modtype)
if eta_mod is not None:
diff, modtype = eta_mod
eta = apply_mod(eta, diff, modtype)
# make sure values are physical
if (eta < 0 or eta > 0.25) or mchirp < 0:
raise NoWaveformError("unphysical masses")
m1 = conversions.mass1_from_mchirp_eta(mchirp, eta)
m2 = conversions.mass2_from_mchirp_eta(mchirp, eta)
return m1, m2
def eta(self):
return conversions.eta_from_mass1_mass2(self.mass1, self.mass2)
def eta(self):
return conversions.eta_from_mass1_mass2(self.mass1, self.mass2)
def get_random_mass(numPoints, massRangeParams):
"""
This function will generate a large set of points within the chosen mass
and spin space, and with the desired minimum remnant disk mass (this applies
to NS-BH systems only). It will also return the corresponding PN spin
coefficients for ease of use later (though these may be removed at some
future point).
Parameters
----------
numPoints : int
Number of systems to simulate
massRangeParams : massRangeParameters instance
Instance holding all the details of mass ranges and spin ranges.
Returns
--------
mass1 : float
Mass of heavier body.
mass2 : float
Mass of lighter body.
spin1z : float
Spin of body 1.
spin2z : float
Spin of body 2.
"""
# WARNING: We expect mass1 > mass2 ALWAYS
# Check if EM contraints are required, i.e. if the systems must produce
# a minimum remnant disk mass. If this is not the case, proceed treating
# the systems as point particle binaries
if massRangeParams.remnant_mass_threshold is None:
mass1, mass2, spin1z, spin2z = \
get_random_mass_point_particles(numPoints, massRangeParams)
# otherwise, load EOS dependent data, generate the EM constraint
# (i.e. compute the minimum symmetric mass ratio needed to
# generate a given remnant disk mass as a function of the NS
# mass and the BH spin along z) and then proceed by accepting
# only systems that can yield (at least) the desired remnant
# disk mass and that pass the mass and spin range cuts.
else:
ns_sequence, max_ns_g_mass = load_ns_sequence(massRangeParams.ns_eos)
boundary_mass = massRangeParams.ns_bh_boundary_mass
if max_ns_g_mass < boundary_mass:
warn_msg = "WARNING: "
warn_msg += "Option of ns-bh-boundary-mass is %s " % (
boundary_mass)
warn_msg += "which is higher than the maximum NS gravitational "
warn_msg += "mass admitted by the EOS that was prescribed "
warn_msg += "(%s). " % (max_ns_g_mass)
warn_msg += "The code will proceed using the latter value "
warn_msg += "as the boundary mass."
logging.warn(warn_msg)
boundary_mass = max_ns_g_mass
# Empty arrays to store points that pass all cuts
mass1_out = []
mass2_out = []
spin1z_out = []
spin2z_out = []
# As the EM cut can remove several randomly generated
# binaries, track the number of accepted points that pass
# all cuts and stop only once enough of them are generated
numPointsFound = 0
while numPointsFound < numPoints:
# Generate the random points within the required mass
# and spin cuts
mass1, mass2, spin1z, spin2z = \
get_random_mass_point_particles(numPoints-numPointsFound,
massRangeParams)
# Now proceed with cutting out EM dim systems
# Use a logical mask to track points that do not correspond to
# BBHs. The remaining points will be BNSs and NSBHs.
# Further down, EM-dim NSBHs will also be removed.
mask_not_bbh = numpy.zeros(len(mass1), dtype=bool)
# Keep a point if:
# 1) the secondary object is a not a BH (mass2 < boundary mass)
# [Store masses and spins of non BBH]
mask_not_bbh[mass2 < boundary_mass] = True
mass1_not_bbh = mass1[mask_not_bbh]
mass2_not_bbh = mass2[mask_not_bbh]
spin1z_not_bbh = spin1z[mask_not_bbh]
spin2z_not_bbh = spin2z[mask_not_bbh]
# 2) and if the primary mass is a NS (i.e., it is a BNS), or...
mask_nsbh = numpy.zeros(len(mass1_not_bbh), dtype=bool)
# [mask_nsbh identifies NSBH systems]
mask_nsbh[mass1_not_bbh > boundary_mass] = True
# [mask_bns identifies BNS systems]
mask_bns = ~mask_nsbh
# [Store masses and spins of BNSs]
mass1_bns = mass1_not_bbh[mask_bns]
mass2_bns = mass2_not_bbh[mask_bns]
spin1z_bns = spin1z_not_bbh[mask_bns]
spin2z_bns = spin2z_not_bbh[mask_bns]
# 3) ...it is an NS-BH with remnant mass greater than the threshold
# required to have a counterpart
# [Store masses and spins of all NSBHs]
mass1_nsbh = mass1_not_bbh[mask_nsbh]
mass2_nsbh = mass2_not_bbh[mask_nsbh]
spin1z_nsbh = spin1z_not_bbh[mask_nsbh]
spin2z_nsbh = spin2z_not_bbh[mask_nsbh]
# [Store etas of all NSBHs]
eta_nsbh = conversions.eta_from_mass1_mass2(mass1_nsbh, mass2_nsbh)
# [mask_bright_nsbh will identify NSBH systems with high enough
# threshold mass]
mask_bright_nsbh = numpy.zeros(len(mass1_nsbh), dtype=bool)
if eta_nsbh.size != 0:
remnant = remnant_masses(eta_nsbh, mass2_nsbh, ns_sequence,
spin1z_nsbh, 0.)
mask_bright_nsbh[
remnant > massRangeParams.remnant_mass_threshold] = True
# Keep only points that correspond to binaries that can produce an
# EM counterpart (i.e., BNSs and EM-bright NSBHs) and add their
# properties to the pile of accpeted points to output
mass1_out = numpy.concatenate(
(mass1_out, mass1_bns, mass1_nsbh[mask_bright_nsbh]))
mass2_out = numpy.concatenate(
(mass2_out, mass2_bns, mass2_nsbh[mask_bright_nsbh]))
spin1z_out = numpy.concatenate(
(spin1z_out, spin1z_bns, spin1z_nsbh[mask_bright_nsbh]))
spin2z_out = numpy.concatenate(
(spin2z_out, spin2z_bns, spin2z_nsbh[mask_bright_nsbh]))
# Number of points that survived all cuts
numPointsFound = len(mass1_out)
# Ready to go
mass1 = mass1_out
mass2 = mass2_out
spin1z = spin1z_out
spin2z = spin2z_out
return mass1, mass2, spin1z, spin2z
def mass1_mass2_to_mchirp_eta(mass1, mass2):
m_chirp = conversions.mchirp_from_mass1_mass2(mass1, mass2)
eta = conversions.eta_from_mass1_mass2(mass1, mass2)
return m_chirp,eta
def mass1_mass2_to_mtotal_eta(mass1, mass2):
m_total = conversions.mtotal_from_mass1_mass2(mass1, mass2)
eta = conversions.eta_from_mass1_mass2(mass1, mass2)
return m_total,eta
def mass1_mass2_to_mchirp_eta(mass1, mass2):
m_chirp = conversions.mchirp_from_mass1_mass2(mass1, mass2)
eta = conversions.eta_from_mass1_mass2(mass1, mass2)
return m_chirp,eta
def mass1_mass2_to_mtotal_eta(mass1, mass2):
m_total = conversions.mtotal_from_mass1_mass2(mass1, mass2)
eta = conversions.eta_from_mass1_mass2(mass1, mass2)
return m_total,eta