Selection functions

A selection turns a catalog into a boolean mask. select() applies it to one catalog and returns the Sample; select_ensemble() applies the same selection across a list of catalogs, yielding one sample each.

Selections operate on the catalog columns: sfr_corr, sfr_raw, mstar_corr, mstar_raw, mpeak, and the position / velocity components x,y,z / vx,vy,vz.

NumberDensity

Select the highest-SFR objects down to a mean density nbar [(h/Mpc)³].

from covariance_mocks.selection import NumberDensity, build_ensemble_nsfr

ens = build_ensemble_nsfr(paths)
sel = NumberDensity(nbar=1e-3, ensemble=ens)        # threshold fixed from the ensemble

• With ensemble= set, the threshold is solved once from the ensemble n(>sfr_corr) table and applied to every realization.

• NumberDensity(nbar=1e-3, per_realization=True) solves the threshold from each catalog instead, matching nbar exactly in every catalog.

• sfr_col defaults to "sfr_corr"; pass sfr_col="sfr_raw" to use the raw SFR.

The threshold and achieved density are recorded in sample.metadata["selection"]["threshold"] and sample.metadata["achieved_nbar"].

Threshold

A lo <= column [< hi] cut on any column. hi=None means no upper edge.

from covariance_mocks.selection import Threshold

Threshold("sfr_corr", lo=10.0)               # sfr_corr >= 10 Msun/yr
Threshold("sfr_corr", lo=10.0, hi=100.0)     # an SFR bin
Threshold("mstar_corr", lo=10**10.5)         # a stellar-mass cut

Joint

The logical AND of several selections.

from covariance_mocks.selection import Joint, Threshold

sel = Joint([Threshold("mstar_corr", lo=10**10.0),
             Threshold("sfr_corr",   lo=10.0)])

Callable

Wrap any function fn(columns) -> bool mask. columns serves any catalog column; the mask must have length len(catalog).

from covariance_mocks.selection import Callable

sel = Callable(lambda c: (c["sfr_corr"] > 10.0) & (c["mstar_corr"] > 1e10))