sp_validation.cosmo_val.catalog_characterization

Catalog characterization diagnostic for cosmology validation.

This mixin holds the survey-statistics and catalog-diagnostic machinery: effective survey statistics (area, effective number density, shape noise), the per-version area / n_eff / ellipticity-dispersion calculations, the catalog diagnostic plots (footprints, ellipticity, weight, and separation histograms), and the additive-bias (c1/c2) estimation. It depends on healpy, the cs_util plotting helpers, and astropy.io.fits.

class CatalogCharacterizationMixin[source]

Bases: object

compute_survey_stats(ver, weights_key_override=None, mask_path=None, nside=None, overwrite_config=False)[source]

Compute effective survey statistics for a catalog version.

Parameters:

  • ver (str) – Version string registered in the catalog config.

  • weights_key_override (str, optional) – Override the weight column key (defaults to the configured w_col).

  • mask_path (str, optional) – Explicit mask path to use when measuring survey area.

  • nside (int, optional) – If provided, compute survey area from the catalog using this NSIDE when no mask path is available.

  • overwrite_config (bool, optional) – If True, persist the derived statistics back to the catalog configuration.

Returns:

Dictionary containing: - area_deg2: Survey area in square degrees. - n_eff: Effective number density per arcmin^2. - sigma_e: Per-component shape noise. - sum_w: Sum of weights. - sum_w2: Sum of squared weights. - catalog_size: Number of galaxies processed.

Return type:

dict

_area_from_catalog(catalog_path, nside)[source]
_area_from_mask(mask_map_path)[source]
property area
property n_eff_gal
property ellipticity_dispersion
_get_binned_catalog_mask(ver)[source]
calculate_area()[source]
calculate_area_from_binned_catalog(ver)[source]
calculate_n_eff_gal()[source]
calculate_ellipticity_dispersion()[source]
plot_footprints()[source]
plot_ellipticity(nbins=200)[source]
plot_weights(nbins=200)[source]
plot_separation(nbins=200)[source]
calculate_additive_bias()[source]
property c1
property c2