def inverse_transform(self, maps):
""" This function transforms from component masses and caretsian spins
to chi_p.
Parameters
----------
maps : a mapping object
Examples
--------
Convert a dict of numpy.array:
>>> import numpy
>>> from pycbc import transforms
>>> from pycbc.waveform import parameters
>>> cl = transforms.DistanceToRedshift()
>>> cl.transform({parameters.distance : numpy.array([1000])})
{'distance': array([1000]), 'redshift': 0.19650987609144363}
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
"""
out = {}
out["chi_p"] = conversions.chi_p(
maps[parameters.mass1], maps[parameters.mass2],
maps[parameters.spin1x], maps[parameters.spin1y],
maps[parameters.spin2x], maps[parameters.spin2y])
return self.format_output(maps, out)
def inverse_transform(self, maps):
""" This function transforms from component masses and caretsian spins
to chi_p.
Parameters
----------
maps : a mapping object
Examples
--------
Convert a dict of numpy.array:
>>> import numpy
>>> from pycbc import transforms
>>> from pycbc.waveform import parameters
>>> cl = transforms.DistanceToRedshift()
>>> cl.transform({parameters.distance : numpy.array([1000])})
{'distance': array([1000]), 'redshift': 0.19650987609144363}
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
"""
out = {}
out["chi_p"] = conversions.chi_p(maps[parameters.mass1],
maps[parameters.mass2],
maps[parameters.spin1x],
maps[parameters.spin1y],
maps[parameters.spin2x],
maps[parameters.spin2y])
return self.format_output(maps, out)
def test_chip_compare_lalsuite(self):
"""Compares effective precession parameter bewteen
the pycbc implementation and the lalsuite implementation.
"""
import lal
import lalsimulation as lalsim
msg = '{} does not recover same {}; max difference: {}; inputs: {}'
f_ref = self.f_lower
chip_lal = []
for i in range(len(self.m1)):
_, _, tmp, _, _, _, _ = lalsim.SimIMRPhenomPCalculateModelParametersFromSourceFrame(
self.m1[i] * lal.MSUN_SI, self.m2[i] * lal.MSUN_SI, f_ref, 0.,
0., self.spin1x[i], self.spin1y[i], self.spin1z[i],
self.spin2x[i], self.spin2y[i], self.spin2z[i],
lalsim.IMRPhenomPv2_V)
chip_lal.append(tmp)
chip_pycbc = conversions.chi_p(self.m1, self.m2, self.spin1x,
self.spin1y, self.spin2x, self.spin2y)
passed, maxdiff, maxidx = almost_equal(chip_lal, chip_pycbc,
self.precision)
failinputs = (self.m1[maxidx], self.m2[maxidx], self.spin1x[maxidx],
self.spin1y[maxidx], self.spin2x[maxidx],
self.spin2y[maxidx])
self.assertTrue(
passed,
msg.format("conversions.chi_p", "chi_p", maxdiff, failinputs))
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)