Data API

Data API#

CHIMERA.data.compute_localization_areas(theta, phi, percentile=0.9, unit='deg2')#

Compute the localization area of each event in the dataset.

Parameters:

  • theta – Polar angle samples for each event

  • phi – Azimuthal angle samples for each event

  • percentile – Confidence level for localization area (0-100)

  • unit – Output unit for area (‘deg2’ or ‘rad2’)

Returns:

Array of localization areas for each event

CHIMERA.data.compute_localization_volumes(theta, phi, dL, cosmo_params_min, cosmo_param_max, percentile=90)#

Compute the localization volume of each event in the dataset.

Parameters:

  • theta – Polar angle samples for each event

  • phi – Azimuthal angle samples for each event

  • dL – Luminosity distance samples for each event

  • cosmo_params_min – Cosmological parameters for minimum distance bound

  • cosmo_param_max – Cosmological parameters for maximum distance bound

  • percentile – Confidence level for localization volume (0-100)

Returns:

Array of localization volumes for each event in Gpc^3

CHIMERA.data.compute_sky_conf_event(healpix_pe, sky_conf, nside)#

Return all the Healpix pixel indices where the probability of an event is above a given threshold.

Parameters:

  • healpix_pe (ndarray) – Healpix index of PE for a particular events and nside

  • event (int) – number of the event

  • nside (int) – nside parameter for Healpix

Returns:

Healpix indices of the skymap where the probability of an event is above a given threshold.

CHIMERA.data.load_galaxy_catalog(file_path, parameters=['ra_gal', 'dec_gal', 'z_cgal'], units='rad', backend='numpy')#

Load galaxy catalog data with optional unit conversion.

Parameters:

  • file_path – Path to HDF5 file containing galaxy data

  • parameters – List of parameters to load (default: [‘ra_gal’, ‘dec_gal’, ‘z_cgal’])

  • units – Output units for angular coordinates (‘rad’ or ‘deg’)

  • backend – Array backend (‘numpy’ or ‘jax’)

Returns:

Dictionary with ‘ra’ (right ascension), ‘dec’ (declination), ‘z’ (redshift).

CHIMERA.data.load_gw_pe_samples(file_ev_pe, parameters=['dL', 'm1det', 'm2det', 'phi', 'theta'], group='posteriors', nevents=None, nsamples=None, return_struct=True)#

Load GW parameter estimation samples with flexible selection.

Parameters:

  • file_ev_pe – Path to HDF5 file with PE samples

  • parameters – List of parameters to load

  • group – HDF5 group containing the data

  • nevents – Number/indices of events to select

  • nsamples – Number of samples per event to select

  • return_struct – Return theta_pe_det struct if True, else dict

Returns:

theta_pe_det struct or dictionary of arrays

CHIMERA.data.load_injection_data(file_inj, snr_cut=None, ninj=None, group=None, key_mapping=None, return_struct=True)#

Load injection data with SNR cut and optional downsampling.

Parameters:

  • file_inj – Path to injection file

  • snr_cut – Minimum SNR threshold

  • ninj – Number/indices of injections to select

  • group – HDF5 group containing data

  • key_mapping – Dictionary for custom key names

  • return_struct – Return theta_inj_det if True, else tuple (data, prior)

Returns:

theta_inj_det or tuple of (data dictionary, prior array)

CHIMERA.data.load_pixelated_gw_catalog(fname)#

Load pixelated GW catalog into a theta_pe_det struct

CHIMERA.data.pixelize_gw_catalog(theta_gw, nside_list, mean_npixels_event, sky_conf, nest=False, prefix=None, ret_datastruct=True)#

Pre-compute columns of corresponding Healpix indices for all the provided nside_list pixelization parameters.

Parameters:

  • theta_gw – struct with the detector frame PE of the GW catalog

  • nside_list – list of nside parameters for Healpix

  • mean_npixels_event – approximate number of desired pixels per event

  • sky_conf – percentage of the GW are to pixelize

  • nest – if the healpy map is nested or not

  • prefix – if provided is the prefix of the h5 file to save with the pixelated gw catalog

Returns:

Instance of theta_pe_det with all pixelization fields

class CHIMERA.data.theta_generic#

Bases: Module

update(**kwargs)#
class CHIMERA.data.theta_inj_det(m1det: jax.Array | None = None, m2det: jax.Array | None = None, dL: jax.Array | None = None, p_draw: jax.Array | None = None)#

Bases: theta_generic

dL: Array | None = None#
m1det: Array | None = None#
m2det: Array | None = None#
p_draw: Array | None = None#
class CHIMERA.data.theta_pe_det(m1det: jax.Array | None = None, m2det: jax.Array | None = None, dL: jax.Array | None = None, phi: jax.Array | None = None, theta: jax.Array | None = None, ra: jax.Array | None = None, dec: jax.Array | None = None, pe_prior: jax.Array | None = None, pixels_pe_all_nsides: Dict[str, jax.Array] | None = None, opt_nsides: jax.Array | None = None, pixels_opt_nsides: jax.Array | None = None, ra_pix: jax.Array | None = None, dec_pix: jax.Array | None = None, gw_loc2d_pdf: jax.Array | None = None, pixels_pe_opt_nside: jax.Array | None = None)#

Bases: theta_generic

dL: Array | None = None#
dec: Array | None = None#
dec_pix: Array | None = None#
gw_loc2d_pdf: Array | None = None#
m1det: Array | None = None#
m2det: Array | None = None#
opt_nsides: Array | None = None#
pe_prior: Array | None = None#
phi: Array | None = None#
pixels_opt_nsides: Array | None = None#
pixels_pe_all_nsides: Dict[str, Array] | None = None#
pixels_pe_opt_nside: Array | None = None#
ra: Array | None = None#
ra_pix: Array | None = None#
theta: Array | None = None#
class CHIMERA.data.theta_src(m1src: jax.Array | None = None, m2src: jax.Array | None = None, z: jax.Array | None = None, original_distances: jax.Array | None = None)#

Bases: theta_generic

m1src: Array | None = None#
m2src: Array | None = None#
original_distances: Array | None = None#
z: Array | None = None#
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