rail.interactive.creation.degraders.spectroscopic_selections module

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection(**kwargs)

The super class of spectroscopic selections.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_BOSS(**kwargs)

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.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_BOSS.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_DEEP2(**kwargs)

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.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_DEEP2.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_DEEP2_LSST(**kwargs)

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

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_DEEP2_LSST.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_DESI_BGS(**kwargs)

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

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_BGS.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_DESI_LRG(**kwargs)

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

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_LRG.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_ELG_LOP(**kwargs)

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

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_DESI_ELG_LOP.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_GAMA(**kwargs)

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.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_GAMA.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_HSC(**kwargs)

The class of spectroscopic selections with HSC.

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

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_HSC.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_VVDSf02(**kwargs)

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.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_VVDSf02.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.spectroscopic_selections.spec_selection_zCOSMOS(**kwargs)

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.

—

The main interface method for Selector.

Adds noise to the input catalog

This will attach the input to this Selector

Then it will call the select() which add a flag column to the catalog. flag=1 means selected, 0 means dropped.

If dropRows = True, the dropped rows will not be presented in the output catalog, otherwise, all rows will be presented.

Finally, this will return a PqHandle providing access to that output data.

—

This function was generated from the function rail.creation.degraders.spectroscopic_selections.SpecSelection_zCOSMOS.__call__

Parameters:

• sample (TableLike, required) – The sample to be selected

• drop_rows (bool, optional) – Drop selected rows from output table Default: True

• seed (int, optional) – random seed for reproducibility Default: 42

• n_tot (int, optional) – Number of selected sources Default: 10000

• nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

• downsample (bool, optional) – If true, downsample the selected sources into a total number of n_tot Default: True

• success_rate_dir (str, optional) – The path to the directory containing success rate files. Default: rail/examples_data/creation_data/data/success_rate_data

• percentile_cut (int, optional) – cut redshifts above this percentile Default: 100

• colnames (dict, optional) – 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’ Default: {‘u’: ‘mag_u_lsst’, ‘g’: ‘mag_g_lsst’, ‘r’: ‘mag_r_lsst’, ‘i’:…}

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame