def lambda_tilde_here(delta):
# compute m1,m2
eta = 0.25 * (1.0 - delta * delta)
m1, m2 = lalsimutils.m1m2(mc_source, eta)
lam1 = lam_fit(m1)
lam2 = lam_fit(m2)
lambda_tilde, dLt = lalsimutils.tidal_lambda_tilde(m1, m2, lam1, lam2)
return lambda_tilde
def extract_combination_from_LI(samples_LI, p):
"""
extract_combination_from_LI
- reads in known columns from posterior samples
- for selected known combinations not always available, it will compute them from standard quantities
Unike version in ConstructIntrinsicPosterior, this code does not rely on ChooseWaveformParams to perform coordinate changes...
"""
if p in samples_LI.dtype.names: # e.g., we have precomputed it
return samples_LI[p]
if p in remap_ILE_2_LI.keys():
if remap_ILE_2_LI[p] in samples_LI.dtype.names:
return samples_LI[remap_ILE_2_LI[p]]
# Return cartesian components of spin1, spin2. NOTE: I may already populate these quantities in 'Add important quantities'
if (p == 'chi_eff' or p == 'xi') and 'a1z' in samples_LI.dtype.names:
m1 = samples_LI['m1']
m2 = samples_LI['m2']
a1z = samples_LI['a1z']
a2z = samples_LI['a2z']
return (m1 * a1z + m2 * a2z) / (m1 + m2)
if p == 'chiz_plus':
print(" Transforming ")
if 'a1z' in samples_LI.dtype.names:
return (samples_LI['a1z'] + samples_LI['a2z']) / 2.
if 'theta1' in samples_LI.dtype.names:
return (samples_LI['a1'] * np.cos(samples_LI['theta1']) +
samples_LI['a2'] * np.cos(samples_LI['theta2'])) / 2.
# return (samples_LI['a1']+ samples_LI['a2'])/2.
if p == 'chiz_minus':
print(" Transforming ")
if 'a1z' in samples_LI.dtype.names:
return (samples_LI['a1z'] - samples_LI['a2z']) / 2.
if 'theta1' in samples_LI.dtype.names:
return (samples_LI['a1'] * np.cos(samples_LI['theta1']) -
samples_LI['a2'] * np.cos(samples_LI['theta2'])) / 2.
# return (samples_LI['a1']- samples_LI['a2'])/2.
if 'theta1' in samples_LI.dtype.names:
if p == 's1x':
return samples_LI["a1"] * np.sin(samples_LI['theta1']) * np.cos(
samples_LI['phi1'])
if p == 's1y':
return samples_LI["a1"] * np.sin(samples_LI['theta1']) * np.sin(
samples_LI['phi1'])
if p == 's2x':
return samples_LI["a2"] * np.sin(samples_LI['theta2']) * np.cos(
samples_LI['phi2'])
if p == 's2y':
return samples_LI["a2"] * np.sin(samples_LI['theta2']) * np.sin(
samples_LI['phi2'])
if p == 'chi1_perp':
return samples_LI["a1"] * np.sin(samples_LI['theta1'])
if p == 'chi2_perp':
return samples_LI["a2"] * np.sin(samples_LI['theta2'])
elif 'tilt1' in samples_LI.dtype.names:
if p == 'chi1_perp':
return samples_LI["a1"] * np.sin(samples_LI['tilt1'])
if p == 'chi2_perp':
return samples_LI["a2"] * np.sin(samples_LI['tilt2'])
else: # aligned
if p == 'chi1_perp':
return np.zeros(len(samples_LI["m1"]))
if p == 'chi2_perp':
return np.zeros(len(samples_LI["m1"]))
if 'lambdat' in samples_LI.dtype.names: # LI does sampling in these tidal coordinates
lambda1, lambda2 = lalsimutils.tidal_lambda_from_tilde(
samples_LI["m1"], samples_LI["m2"], samples_LI["lambdat"],
samples_LI["dlambdat"])
if p == "lambda1":
return lambda1
if p == "lambda2":
return lambda2
if p == 'delta' or p == 'delta_mc':
return (samples_LI['m1'] - samples_LI['m2']) / (
(samples_LI['m1'] + samples_LI['m2']))
# Return cartesian components of Lhat
if p == 'product(sin_beta,sin_phiJL)':
return np.sin(samples_LI[remap_ILE_2_LI['beta']]) * np.sin(
samples_LI['phi_jl'])
if p == 'product(sin_beta,cos_phiJL)':
return np.sin(samples_LI[remap_ILE_2_LI['beta']]) * np.cos(
samples_LI['phi_jl'])
if p == 'mc':
m1v = samples["m1"]
m2v = samples["m2"]
return lalsimutils.mchirp(m1v, m2v)
if p == 'eta':
m1v = samples["m1"]
m2v = samples["m2"]
return lalsimutils.symRatio(m1v, m2v)
# Backup : access lambdat if not present
if (p == 'lambdat'
or p == 'dlambdat') and 'lambda1' in samples.dtype.names:
Lt, dLt = lalsimutils.tidal_lambda_tilde(samples['m1'], samples['m2'],
samples['lambda1'],
samples['lambda2'])
if p == 'lambdat':
return Lt
if p == 'dlambdat':
return dLt
if p == "q" and 'm1' in samples.dtype.names:
return samples["m2"] / samples["m1"]
print(" No access for parameter ", p, " in ", samples.dtype.names)
return np.zeros(len(samples_LI['m1'])) # to avoid causing a hard failure
samples['chi_eff'] = (samples["m1"] * samples["a1z"] +
samples["m2"] * samples["a2z"]) / (
samples["m1"] + samples["m2"])
elif 'a1x' in samples.dtype.names:
chi1_perp = np.sqrt(samples['a1x']**2 + samples['a1y']**2)
chi2_perp = np.sqrt(samples['a2x']**2 + samples['a2y']**2)
samples = add_field(samples, [('chi1_perp', float)])
samples['chi1_perp'] = chi1_perp
samples = add_field(samples, [('chi2_perp', float)])
samples['chi2_perp'] = chi2_perp
if 'lambda1' in samples.dtype.names and not ('lambdat'
in samples.dtype.names):
Lt, dLt = lalsimutils.tidal_lambda_tilde(samples['m1'],
samples['m2'],
samples['lambda1'],
samples['lambda2'])
samples = add_field(samples, [('lambdat', float)])
samples['lambdat'] = Lt
samples = add_field(samples, [('dlambdat', float)])
samples['dlambdat'] = dLt
if 'chi1_perp' in samples.dtype.names:
# impose Kerr limit, if neede
npts = len(samples["m1"])
indx_ok = np.arange(npts)
chi1_squared = samples['chi1_perp']**2 + samples["a1z"]**2
chi2_squared = samples["chi2_perp"]**2 + samples["a2z"]**2
indx_ok = np.logical_and(chi1_squared < opts.chi_max,
chi2_squared < opts.chi_max)
npts_out = np.sum(indx_ok)