mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 15:08:02 +00:00
Absmag (#149)
* Add spacing * Add various cleanups of the code * Add basic updates * Add
This commit is contained in:
Richard Stiskalek
GitHub
parent
4fa0e04f6e
commit
5336c0296c
1 changed files with 75 additions and 60 deletions
|
|
@ -45,13 +45,13 @@ H0 = 100 # km / s / Mpc
|
|||
# JAX Flow model #
|
||||
###############################################################################
|
||||
|
||||
def dist2redshift(dist, Omega_m):
|
||||
def dist2redshift(dist, Omega_m, h=1.):
|
||||
"""
|
||||
Convert comoving distance to cosmological redshift if the Universe is
|
||||
flat and z << 1.
|
||||
"""
|
||||
eta = 3 * Omega_m / 2
|
||||
return 1 / eta * (1 - (1 - 2 * H0 * dist / SPEED_OF_LIGHT * eta)**0.5)
|
||||
return 1 / eta * (1 - (1 - 2 * 100 * h * dist / SPEED_OF_LIGHT * eta)**0.5)
|
||||
|
||||
|
||||
def redshift2dist(z, Omega_m):
|
||||
|
|
@ -366,11 +366,6 @@ def sample_calibration(Vext_min, Vext_max, Vmono_min, Vmono_max, beta_min,
|
|||
}
|
||||
|
||||
|
||||
###############################################################################
|
||||
# PV calibration model #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def sample_gaussian_hyperprior(param, name, xmin, xmax):
|
||||
"""Sample MNR Gaussian hyperprior mean and standard deviation."""
|
||||
mean = sample(f"{param}_mean_{name}", Uniform(xmin, xmax))
|
||||
|
|
@ -378,9 +373,15 @@ def sample_gaussian_hyperprior(param, name, xmin, xmax):
|
|||
return mean, std
|
||||
|
||||
|
||||
###############################################################################
|
||||
# PV calibration model without absolute calibration #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class PV_LogLikelihood(BaseFlowValidationModel):
|
||||
"""
|
||||
Peculiar velocity validation model log-likelihood.
|
||||
Peculiar velocity validation model log-likelihood with numerical
|
||||
integration of the true distances.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -408,11 +409,15 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
Catalogue kind, either "TFR", "SN", or "simple".
|
||||
name : str
|
||||
Name of the catalogue.
|
||||
with_num_dist_marginalisation : bool, optional
|
||||
Whether to use numerical distance marginalisation, in which case
|
||||
the tracers cannot be coupled by a covariance matrix. By default
|
||||
`True`.
|
||||
"""
|
||||
|
||||
def __init__(self, los_density, los_velocity, RA, dec, z_obs, e_zobs,
|
||||
calibration_params, abs_calibration_params, mag_selection,
|
||||
r_xrange, Omega_m, kind, name):
|
||||
r_xrange, Omega_m, kind, name, with_num_dist_marginalisation):
|
||||
if e_zobs is not None:
|
||||
e2_cz_obs = jnp.asarray((SPEED_OF_LIGHT * e_zobs)**2)
|
||||
else:
|
||||
|
|
@ -433,8 +438,12 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
self.kind = kind
|
||||
self.name = name
|
||||
self.Omega_m = Omega_m
|
||||
self.with_num_dist_marginalisation = with_num_dist_marginalisation
|
||||
self.norm = - self.ndata * jnp.log(self.num_sims)
|
||||
|
||||
# TODO: Somewhere here prepare the interpolators in case of no
|
||||
# numerical marginalisation.
|
||||
|
||||
if mag_selection is not None:
|
||||
self.mag_selection_kind = mag_selection["kind"]
|
||||
|
||||
|
|
@ -485,6 +494,9 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
Vmono = field_calibration_params["Vmono"]
|
||||
Vext_rad = project_Vext(Vext[0], Vext[1], Vext[2], self.RA, self.dec)
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# 1. Sample true observables and obtain the distance estimate
|
||||
# ------------------------------------------------------------
|
||||
e_mu = distmod_params["e_mu"]
|
||||
if self.kind == "SN":
|
||||
mag_cal = distmod_params["mag_cal"]
|
||||
|
|
@ -532,10 +544,6 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
|
||||
mu = distmod_SN(
|
||||
mag_true, x1_true, c_true, mag_cal, alpha_cal, beta_cal)
|
||||
|
||||
if field_calibration_params["sample_h"]:
|
||||
raise NotImplementedError("H0 for SN not implemented.")
|
||||
|
||||
elif self.kind == "TFR":
|
||||
a = distmod_params["a"]
|
||||
b = distmod_params["b"]
|
||||
|
|
@ -605,11 +613,6 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
e2_mu = jnp.ones_like(mag_true) * e_mu**2
|
||||
|
||||
mu = distmod_TFR(mag_true, eta_true, a, b, c)
|
||||
|
||||
if field_calibration_params["sample_h"]:
|
||||
raise NotImplementedError("H0 for TFR not implemented.")
|
||||
# mu -= 5 * jnp.log10(field_calibration_params["h"])
|
||||
|
||||
elif self.kind == "simple":
|
||||
dmu = distmod_params["dmu"]
|
||||
|
||||
|
|
@ -628,61 +631,73 @@ class PV_LogLikelihood(BaseFlowValidationModel):
|
|||
e2_mu = jnp.ones_like(mag_true) * e_mu**2
|
||||
|
||||
mu = mu_true + dmu
|
||||
|
||||
if field_calibration_params["sample_h"]:
|
||||
raise NotImplementedError("H0 for simple not implemented.")
|
||||
else:
|
||||
raise ValueError(f"Unknown kind: `{self.kind}`.")
|
||||
|
||||
# h = field_calibration_params["h"]
|
||||
# ----------------------------------------------------------------
|
||||
# 2. Log-likelihood of the true distance and observed redshifts.
|
||||
# The marginalisation of the true distance can be done numerically.
|
||||
# ----------------------------------------------------------------
|
||||
if self.with_num_dist_marginalisation:
|
||||
|
||||
if field_calibration_params["sample_h"]:
|
||||
raise NotImplementedError("Sampling of h not implemented.")
|
||||
# Rescale the grid to account for the sampled H0. For distance
|
||||
# modulus going from Mpc / h to Mpc implies larger numerical
|
||||
# values, so there has to be a minus sign since h < 1.
|
||||
# mu_xrange = self.mu_xrange - 5 * jnp.log(h)
|
||||
|
||||
# The redshift should also be boosted since now the object are
|
||||
# further away?
|
||||
|
||||
# Actually, the redshift ought to remain the same?
|
||||
else:
|
||||
mu_xrange = self.mu_xrange
|
||||
|
||||
# Calculate p(r) (Malmquist bias). Shape is (ndata, nxrange)
|
||||
log_ptilde = log_ptilde_wo_bias(
|
||||
self.mu_xrange[None, :], mu[:, None], e2_mu[:, None],
|
||||
mu_xrange[None, :], mu[:, None], e2_mu[:, None],
|
||||
self.log_r2_xrange[None, :])
|
||||
|
||||
# Inhomogeneous Malmquist bias. Shape is (n_sims, ndata, nxrange)
|
||||
# Inhomogeneous Malmquist bias. Shape: (nsims, ndata, nxrange)
|
||||
alpha = distmod_params["alpha"]
|
||||
log_ptilde = log_ptilde[None, ...] + alpha * self.log_los_density
|
||||
|
||||
ptilde = jnp.exp(log_ptilde)
|
||||
|
||||
# Normalization of p(r). Shape is (n_sims, ndata)
|
||||
# Normalization of p(r). Shape: (nsims, ndata)
|
||||
pnorm = simpson(ptilde, x=self.r_xrange, axis=-1)
|
||||
|
||||
# Calculate z_obs at each distance. Shape is (n_sims, ndata, nxrange)
|
||||
# Calculate z_obs at each distance. Shape: (nsims, ndata, nxrange)
|
||||
vrad = field_calibration_params["beta"] * self.los_velocity
|
||||
vrad += (Vext_rad[None, :, None] + Vmono)
|
||||
zobs = (1 + self.z_xrange[None, None, :]) * (1 + vrad / SPEED_OF_LIGHT)
|
||||
zobs = 1 + self.z_xrange[None, None, :]
|
||||
zobs *= 1 + vrad / SPEED_OF_LIGHT
|
||||
zobs -= 1.
|
||||
|
||||
# Shape remains (n_sims, ndata, nxrange)
|
||||
# Shape remains (nsims, ndata, nxrange)
|
||||
ptilde *= likelihood_zobs(
|
||||
self.z_obs[None, :, None], zobs, e2_cz[None, :, None])
|
||||
|
||||
if self.with_absolute_calibration:
|
||||
raise NotImplementedError("Absolute calibration not implemented.")
|
||||
# Absolute calibration likelihood, the shape is now
|
||||
# (ndata_with_calibration, ncalib, nxrange)
|
||||
# ll_calibration = normal_logpdf(
|
||||
# self.mu_xrange[None, None, :],
|
||||
# self.calibration_distmod[..., None],
|
||||
# self.calibration_edistmod[..., None])
|
||||
raise NotImplementedError(
|
||||
"Absolute calibration not implemented for this model. "
|
||||
"Use `PV_LogLikelihood_NoDistMarg` instead.")
|
||||
|
||||
# # Average the likelihood over the calibration points. The shape
|
||||
# is
|
||||
# # now (ndata, nxrange)
|
||||
# ll_calibration = logsumexp(
|
||||
# jnp.nan_to_num(ll_calibration, nan=-jnp.inf), axis=1)
|
||||
# # This is the normalisation because we want the *average*.
|
||||
# ll_calibration -= self.log_length_calibration[:, None]
|
||||
|
||||
# ptilde = ptilde.at[:, self.data_with_calibration, :].
|
||||
# multiply(jnp.exp(ll_calibration))
|
||||
|
||||
# Integrate over the radial distance. Shape is (n_sims, ndata)
|
||||
# Integrate over the radial distance. Shape: (nsims, ndata)
|
||||
ll = jnp.log(simpson(ptilde, x=self.r_xrange, axis=-1))
|
||||
ll -= jnp.log(pnorm)
|
||||
|
||||
return ll0 + jnp.sum(logsumexp(ll, axis=0)) + self.norm
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"No distance marginalisation not implemented yet.")
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Combining several catalogues #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def PV_validation_model(models, distmod_hyperparams_per_model,
|
||||
|
|
|
|||
Loading…
Reference in a new issue