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 transform(self, maps):
""" This function transforms from spherical to cartesian spins.
Parameters
----------
maps : a mapping object
Examples
--------
Convert a dict of numpy.array:
>>> import numpy
>>> from pycbc import transforms
>>> t = transforms.SphericalSpin1ToCartesianSpin1()
>>> t.transform({'spin1_a': numpy.array([0.1]), 'spin1_azimuthal': numpy.array([0.1]), 'spin1_polar': numpy.array([0.1])})
{'spin1_a': array([ 0.1]), 'spin1_azimuthal': array([ 0.1]), 'spin1_polar': array([ 0.1]),
'spin2x': array([ 0.00993347]), 'spin2y': array([ 0.00099667]), 'spin2z': array([ 0.09950042])}
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
"""
a, az, po = self._inputs
data = coordinates.spherical_to_cartesian(maps[a], maps[az], maps[po])
out = {param: val for param, val in zip(self._outputs, data)}
return self.format_output(maps, out)
def transform(self, maps):
""" This function transforms from spherical to cartesian spins.
Parameters
----------
maps : a mapping object
Examples
--------
Convert a dict of numpy.array:
>>> import numpy
>>> from pycbc import transforms
>>> t = transforms.SphericalSpin1ToCartesianSpin1()
>>> t.transform({'spin1_a': numpy.array([0.1]), 'spin1_azimuthal': numpy.array([0.1]), 'spin1_polar': numpy.array([0.1])})
{'spin1_a': array([ 0.1]), 'spin1_azimuthal': array([ 0.1]), 'spin1_polar': array([ 0.1]),
'spin2x': array([ 0.00993347]), 'spin2y': array([ 0.00099667]), 'spin2z': array([ 0.09950042])}
Returns
-------
out : dict
A dict with key as parameter name and value as numpy.array or float
of transformed values.
"""
a, az, po = self._inputs
data = coordinates.spherical_to_cartesian(maps[a], maps[az], maps[po])
out = {param : val for param, val in zip(self._outputs, data)}
return self.format_output(maps, out)
def generator_spin_spherical_to_spin_cartesian(generator):
"""Converts spherical spin magnitude and angles in `current_params`,
to cartesian component spins.
"""
x, y, z = coordinates.spherical_to_cartesian(
generator.current_params["spin1_a"],
generator.current_params["spin1_azimuthal"],
generator.current_params["spin1_polar"])
generator.current_params["spin1x"] = x
generator.current_params["spin1y"] = y
generator.current_params["spin1z"] = z
x, y, z = coordinates.spherical_to_cartesian(
generator.current_params["spin2_a"],
generator.current_params["spin2_azimuthal"],
generator.current_params["spin2_polar"])
generator.current_params["spin2x"] = x
generator.current_params["spin2y"] = y
generator.current_params["spin2z"] = z
def generator_spin_spherical_to_spin_cartesian(generator):
"""Converts spherical spin magnitude and angles in `current_params`,
to cartesian component spins.
"""
x, y, z = coordinates.spherical_to_cartesian(
generator.current_params["spin1_a"],
generator.current_params["spin1_azimuthal"],
generator.current_params["spin1_polar"])
generator.current_params["spin1x"] = x
generator.current_params["spin1y"] = y
generator.current_params["spin1z"] = z
x, y, z = coordinates.spherical_to_cartesian(
generator.current_params["spin2_a"],
generator.current_params["spin2_azimuthal"],
generator.current_params["spin2_polar"])
generator.current_params["spin2x"] = x
generator.current_params["spin2y"] = y
generator.current_params["spin2z"] = z
azimuthal_bounds=(phi_low,phi_high))
# Now we can take a random variable sample from that distribution.
# In this case we want 50000 samples.
solid_angle_samples = uniform_solid_angle_distribution.rvs(size=10000)
# Make a spin 1 magnitude since solid angle is only 2 dimensions and we need a
# 3rd dimension for a 3D plot that we make later on.
spin_mag = numpy.ndarray(shape=(10000), dtype=float)
for i in range(0,10000):
spin_mag[i] = 1.
# Use pycbc.coordinates as co. Use spherical_to_cartesian function to
# convert from spherical polar coordinates to cartesian coordinates.
spinx, spiny, spinz = co.spherical_to_cartesian(spin_mag,
solid_angle_samples['phi'],
solid_angle_samples['theta'])
# Plot the spherical distribution of spins to make sure that we
# distributed across the surface of a sphere.
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(spinx, spiny, spinz, s=1)
ax.set_xlabel('Spin X Axis')
ax.set_ylabel('Spin Y Axis')
ax.set_zlabel('Spin Z Axis')
plt.show()
polar_bounds=(theta_low, theta_high), azimuthal_bounds=(phi_low, phi_high))
# Now we can take a random variable sample from that distribution.
# In this case we want 50000 samples.
solid_angle_samples = uniform_solid_angle_distribution.rvs(size=10000)
# Make a spin 1 magnitude since solid angle is only 2 dimensions and we need a
# 3rd dimension for a 3D plot that we make later on.
spin_mag = numpy.ndarray(shape=(10000), dtype=float)
for i in range(0, 10000):
spin_mag[i] = 1.
# Use pycbc.coordinates as co. Use spherical_to_cartesian function to
# convert from spherical polar coordinates to cartesian coordinates.
spinx, spiny, spinz = co.spherical_to_cartesian(spin_mag,
solid_angle_samples['phi'],
solid_angle_samples['theta'])
# Plot the spherical distribution of spins to make sure that we
# distributed across the surface of a sphere.
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(spinx, spiny, spinz, s=1)
ax.set_xlabel('Spin X Axis')
ax.set_ylabel('Spin Y Axis')
ax.set_zlabel('Spin Z Axis')
plt.show()