rail.creation.degraders.spectroscopic_selections module

Degrader that applies selection functions to catalog.

class rail.creation.degraders.spectroscopic_selections.SpecSelection

Bases: Selector

The super class of spectroscopic selections.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

__init__(args, **kwargs)

Constructor: Do RailStage specific initialization

downsampling_N_tot()

Randomly sample down the objects to a given number of data objects.

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection'
invalid_cut(data)

Removes entries in the data that have invalid magnitude values (NaN or nondetect_val).

name = 'SpecSelection'
selection(data)

Selection functions.

This should be overwritten by the subclasses corresponding to different spec selections.

validate_colnames(data)

Validate the column names of data table to make sure they have necessary information for each selection.

Parameters:

colnames (list of str) – A list of column names

class rail.creation.degraders.spectroscopic_selections.SpecSelection_BOSS

Bases: SpecSelection

The class of spectroscopic selections with BOSS.

BOSS selection function is based on http://www.sdss3.org/dr9/algorithms/boss_galaxy_ts.php

The selection has changed slightly compared to Dawson+13.

BOSS covers an area of 9100 deg^2 with 893,319 galaxies.

For BOSS selection, the data should at least include gri bands.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_BOSS'
name = 'SpecSelection_BOSS'
selection(data)

The BOSS selection function.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_DEEP2

Bases: SpecSelection

The class of spectroscopic selections with DEEP2.

DEEP2 has a sky coverage of 2.8 deg^2 with ~53000 spectra.

For DEEP2, one needs R band magnitude, B-R/R-I colors–which are not available for the time being, so we use LSST gri bands now. When the conversion degrader is ready, this subclass will be updated accordingly.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_DEEP2'
name = 'SpecSelection_DEEP2'
photometryCut(data)

Applies DEEP2 photometric cut based on Newman+13.

This modified selection gives the best match to the data n(z) with its cut at z~0.75 and the B-R/R-I distribution (Newman+13, Fig. 12).

Notes

We cannot apply the surface brightness cut and do not apply the Gaussian weighted sampling near the original colour cuts.

selection(data)

DEEP2 selection function.

speczSuccess(data)

Spec-z success rate as function of r_AB for Q>=3 read of Figure 13 in Newman+13 for DEEP2 fields 2-4. Values are binned in steps of 0.2 mag with the first and last bin centered on 19 and 24.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_DEEP2_LSST

Bases: SpecSelection

The class of spectroscopic selections with DEEP2.

Approximate Rubin->CFHT12K transforms based off of CWWSB SED colors

B = g + 0.35 * (g-r) R = r - 0.3 * (r-i) I = i - 0.5 * (r-i)

transform the cuts accordingly

Also, original has B-R < 0.5 modify to B-R < 0.33 to exclude a few more low-z galaxies leave speczSuccess unchanged from original implementation

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_DEEP2_LSST'
name = 'SpecSelection_DEEP2_LSST'
photometryCut(data)

Applies DEEP2 photometric cut based on Newman+13.

This modified selection gives the best match to the data n(z) with its cut at z~0.75 and the B-R/R-I distribution (Newman+13, Fig. 12).

Notes

We cannot apply the surface brightness cut and do not apply the Gaussian weighted sampling near the original colour cuts.

selection(data)

DEEP2 selection function.

speczSuccess(data)

Spec-z success rate as function of r_AB for Q>=3 read of Figure 13 in Newman+13 for DEEP2 fields 2-4. Values are binned in steps of 0.2 mag with the first and last bin centered on 19 and 24.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_BGS

Bases: SpecSelection

The class of spectroscopic selections with DESI BGS .

Implements a minimal DESI Bright Galaxy Survey (BGS) selection using:

  • r < 19.5

Required bands in data (via config.colnames): r

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_DESI_BGS'
name = 'SpecSelection_DESI_BGS'
selection(data)

The DESI BGS selection function (simplified cut).

class rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_ELG_LOP

Bases: SpecSelection

The class of spectroscopic selections with DESI ELG LOP.

Implements the simplified DESI ELG_LOP photometric selection using:

  • (g > 20) AND (gfib < 24.1)

  • 0.15 < (r − z)

  • (g − r) < 0.5 × (r − z) + 0.1

  • (g − r) < −1.2 × (r − z) + 1.3

All of the above are combined with AND.

Required bands in data (via config.colnames): g, r, z, gfib

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_ELG_LOP'
name = 'SpecSelection_DESI_ELG_LOP'
selection(data)

The DESI ELG_LOP selection function.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_LRG

Bases: SpecSelection

The class of spectroscopic selections with DESI LRG (simplified).

This implements a simplified DESI LRG photometric selection using:

  • zfiber < 21.60 (here approximated with z)

  • z − W1 > 0.8 × (r − z) − 0.6

  • (g − W1 > 2.9) OR (r − W1 > 1.8)

  • [ ((r − W1 > 1.8 × (W1 − 17.14)) AND (r − W1 > W1 − 16.33)) OR (r − W1 > 3.3) ]

All of the above are combined with AND.

Required bands in data (via config.colnames): g, r, z, W1

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'W1': 'W1', 'redshift': 'redshift'}) – a dictionary that includes necessary columns (magnitudes, colors and redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_DESI_LRG'
name = 'SpecSelection_DESI_LRG'
selection(data)

The DESI LRG selection function (simplified).

class rail.creation.degraders.spectroscopic_selections.SpecSelection_GAMA

Bases: SpecSelection

The class of spectroscopic selections with GAMA.

The GAMA survey covers an area of 286 deg^2, with ~238000 objects.

The necessary column is r band.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_GAMA'
name = 'SpecSelection_GAMA'
selection(data)

GAMA selection function.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_HSC

Bases: SpecSelection

The class of spectroscopic selections with HSC.

For HSC, the data should at least include giz bands and redshift.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_HSC'
name = 'SpecSelection_HSC'
photometryCut(data)

HSC galaxies were binned in color magnitude space with i-band mag from -2 to 6 and g-z color from 13 to 26.

selection(data)

Selection functions.

This should be overwritten by the subclasses corresponding to different spec selections.

speczSuccess(data)

HSC galaxies were binned in color magnitude space with i-band mag from -2 to 6 and g-z color from 13 to 26 (200 bins in each direction). The ratio of galaxies with spectroscopic redshifts (training galaxies) to galaxies with only photometry in HSC wide field (application galaxies) was computed for each pixel. We divide the data into the same pixels and randomly select galaxies into the training sample based on the HSC ratios.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_VVDSf02

Bases: SpecSelection

The class of spectroscopic selections with VVDSf02.

It covers an area of 0.5 deg^2 with ~10000 sources.

Necessary columns are i band magnitude and redshift.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_VVDSf02'
name = 'SpecSelection_VVDSf02'
photometryCut(data)

Photometric cut of VVDS 2h-field based on LeFèvre+05.

Notes

The oversight of 1.0 magnitudes on the bright end misses 0.2% of galaxies.

selection(data)

Selection functions.

This should be overwritten by the subclasses corresponding to different spec selections.

speczSuccess(data)

Success rate of VVDS 2h-field.

Notes

We use a redshift-based and I-band based success rate independently here since we do not know their correlation, which makes the success rate worse than in reality.

Spec-z success rate as function of i_AB read of Figure 16 in LeFevre+05 for the VVDS 2h field. Values are binned in steps of 0.5 mag with the first starting at 17 and the last bin ending at 24.

class rail.creation.degraders.spectroscopic_selections.SpecSelection_zCOSMOS

Bases: SpecSelection

The class of spectroscopic selections with zCOSMOS.

It covers an area of 1.7 deg^2 with ~20000 galaxies.

For zCOSMOS, the data should at least include i band and redshift.

Parameters:

  • output_mode ([str] default=default) – What to do with the outputs. The options are ‘default’, where outputs will be written to files and some returned, and ‘return’, where outputs will only be returned and not written.

  • drop_rows ([bool] default=True) – Drop selected rows from output table

  • seed ([type not specified] default=None) – Set to an int to force reproducible results.

  • N_tot ([int] default=10000) – Number of selected sources

  • nondetect_val ([float] default=99.0) – value to be removed for non detects

  • downsample ([bool] default=True) – If true, downsample the selected sources into a total number of N_tot

  • success_rate_dir ([str] default=/home/docs/checkouts/readthedocs.org/user_builds/rail-hub/conda/stable/lib/python3.14/site-packages/rail/examples_data/creation_data/data/success_rate_data) – The path to the directory containing success rate files.

  • percentile_cut ([int] default=100) – cut redshifts above this percentile

  • colnames ([dict] default={'u': 'mag_u_lsst', 'g': 'mag_g_lsst', 'r': 'mag_r_lsst', 'i': 'mag_i_lsst', 'z': 'mag_z_lsst', 'y': 'mag_y_lsst', 'redshift': 'redshift'}) –

    a dictionary that includes necessary columns (magnitudes, colors and

    redshift) for selection. For magnitudes, the keys are ugrizy; for colors, the keys are, for example, gr standing for g-r; for redshift, the key is ‘redshift’

  • random_seed ([int] default=42) – random seed for reproducibility

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'spec_selection_zCOSMOS'
name = 'SpecSelection_zCOSMOS'
photometryCut(data)

Photometry cut for zCOSMOS based on Lilly+09.

Updates the internal state.

NOTE: This only includes zCOSMOS bright.

selection(data)

Selection functions.

This should be overwritten by the subclasses corresponding to different spec selections.

speczSuccess(data)

Spec-z success rate as function of redshift (x) and I_AB (y) read of Figure 3 in Lilly+09 for zCOSMOS bright sample.