xlens.catalog.redshift

Attributes

`NUM_Z_GRIDS`
`Z_MIN`
`Z_MAX`
`Z_GRIDS`
`PROBS`
`INV1PZ`
`GAMMA_RISK`

Classes

`zEstimator`	Helper class that provides a standard way to create an ABC using
`flexzboostEstimator`	Wraps a FlexZBoost-like predictor object with a uniform get_z API.
`bpzEstimator`	Wraps BPZ template/prior configuration with a uniform get_z API.

Functions

`risk`(→ float)
`get_point_estimate`(p)
`get_point_estimates_from_pdfs`(pdfs)	Compute point estimates from PDF samples on a redshift grid.
`get_color`(→ numpy.ndarray)
`load_bpz_templates`(data_path, bands[, filter_name, ...])	Load BPZ template fluxes on Z_GRIDS for provided filter set.

Module Contents

NUM_Z_GRIDS = 501[source]

Z_MIN = 0.0[source]

Z_MAX = 5.0[source]

Z_GRIDS[source]

PROBS[source]

INV1PZ[source]

GAMMA_RISK = 0.15[source]

risk(zx: float, p_norm: numpy.ndarray) → float[source]

get_point_estimate(p)[source]

get_point_estimates_from_pdfs(pdfs: numpy.ndarray)[source]

Compute point estimates from PDF samples on a redshift grid.

Returns dict of arrays (shape (N,)):

zmode : z_grid[argmax p(z)]
z025, z160, z500, z840, z975 : CDF percentiles at [0.025, 0.16, 0.50, 0.84, 0.975]
zbest : argmin_zx ∫ p_norm(z) * loss(zx,z) dz (bounded to [z_grid[0], z_grid[-1]])

get_color(src: numpy.ndarray, *, bands: str = 'grizy', ref_band: str = 'i', mag_zero: float = 30.0, comp: int = 1, dg: float = 0.0, flux_name: str = 'gauss2', include_mag_err: bool = False, extinction: numpy.ndarray | None = None) → numpy.ndarray[source]

Returns:

If include_mag_err=False: shape (N, 1 + (len(bands)-1)): [ref_mag, (b0-b1), (b1-b2), …]
If include_mag_err=True: shape (N, 1 + 2*(len(bands)-1)): [ref_mag, (b0-b1), err01, (b1-b2), err12, …]

Return type:

np.ndarray

class zEstimator[source]

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

abstract get_z(src: numpy.ndarray, *, mag_zero: float = 30.0, flux_name: str = 'gauss2', bands: str = 'grizy', ref_band: str = 'i', comp: int = 1, dg: float = 0.0, flux_name2: str | None = None, flux_name3: str | None = None, extinction: numpy.ndarray | None = None, **kwargs) → dict[source]: Method to get redshift point estimates

get_zsel(src: numpy.ndarray, *, mag_zero: float = 30.0, flux_name: str = 'gauss2', bands: str = 'grizy', ref_band: str = 'i', comp: int = 1, dg: float = 0.0, z_point_name: str = 'zmode', flux_name2: str | None = None, flux_name3: str | None = None, extinction: numpy.ndarray | None = None, **kwargs)[source]

class flexzboostEstimator(pz_obj)[source]

Bases: zEstimator

Wraps a FlexZBoost-like predictor object with a uniform get_z API.

pz_obj[source]

get_z(src: numpy.ndarray, *, mag_zero: float = 30.0, flux_name: str = 'gauss2', bands: str = 'grizy', ref_band: str = 'i', comp: int = 1, dg: float = 0.0, flux_name2: str | None = None, flux_name3: str | None = None, include_mag_err: bool = False, return_pdfs: bool = False, extinction: numpy.ndarray | None = None, **kwargs) → dict[source]: Method to get redshift point estimates

load_bpz_templates(data_path: str, bands: str, filter_name: str = 'DC2LSST', spectra_name: str = 'cosmossedswdust136.list')[source]: Load BPZ template fluxes on Z_GRIDS for provided filter set.

class bpzEstimator(flux_templates: numpy.ndarray, prior_dict: dict, zp_errors)[source]

Bases: zEstimator

Wraps BPZ template/prior configuration with a uniform get_z API.

flux_templates[source]

prior_dict[source]

zp_errors[source]

_measure_one_source(flux: numpy.ndarray, flux_err: numpy.ndarray, mag_0: float)[source]

get_z(src: numpy.ndarray, *, mag_zero: float = 30.0, flux_name: str = 'gauss2', bands: str = 'grizy', ref_band: str = 'i', comp: int = 1, dg: float = 0.0, return_pdfs: bool = False, extinction: numpy.ndarray | None = None, **kwargs) → dict[source]: Method to get redshift point estimates