rail.interactive.tools.photometry_tools module

rail.interactive.tools.photometry_tools.dereddener(**kwargs)

Utility stage that does dereddening

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Return a converted table

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This function was generated from the function rail.tools.photometry_tools.Dereddener.__call__

Parameters:

data (table-like, required) – The data to be converted
dustmap_dir (str, required) – Directory with dustmaps
ra_name (str, optional) – Name of the RA column Default: ra
dec_name (str, optional) – Name of the DEC column Default: dec
mag_name (str, optional) – Template for the magnitude columns Default: mag_{band}_lsst
band_a_env (dict, optional) – Reddening parameters Default: {‘mag_u_lsst’: 4.81, ‘mag_g_lsst’: 3.64, ‘mag_r_lsst’: 2.7,…}
dustmap_name (str, optional) – Name of the dustmap in question Default: sfd
copy_cols (list, optional) – Additional columns to copy Default: []
copy_all_cols (bool, optional) – Copy all the columns Default: False

Returns:

The converted version of the table

Return type:

pandas.core.frame.DataFrame

rail.interactive.tools.photometry_tools.dust_map_base(**kwargs)

Utility stage that does dereddening

Note: set copy_all_cols=True to copy all columns in data, copy_cols will be ignored

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Return a converted table

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This function was generated from the function rail.tools.photometry_tools.DustMapBase.__call__

Parameters:

data (table-like, required) – The data to be converted
dustmap_dir (str, required) – Directory with dustmaps
ra_name (str, optional) – Name of the RA column Default: ra
dec_name (str, optional) – Name of the DEC column Default: dec
mag_name (str, optional) – Template for the magnitude columns Default: mag_{band}_lsst
band_a_env (dict, optional) – Reddening parameters Default: {‘mag_u_lsst’: 4.81, ‘mag_g_lsst’: 3.64, ‘mag_r_lsst’: 2.7,…}
dustmap_name (str, optional) – Name of the dustmap in question Default: sfd
copy_cols (list, optional) – Additional columns to copy Default: []
copy_all_cols (bool, optional) – Copy all the columns Default: False

Returns:

The converted version of the table

Return type:

pandas.core.frame.DataFrame

rail.interactive.tools.photometry_tools.hyperbolic_magnitudes(**kwargs)

Convert a set of classical magnitudes to hyperbolic magnitudes (Lupton et al. 1999). Requires input from the initial stage (HyperbolicSmoothing) to supply optimal values for the smoothing parameters (b).

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Main method to call. Outputs hyperbolic magnitudes compuated from a set of smoothing parameters and input catalogue with classical magitudes and their respective errors.

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This function was generated from the function rail.tools.photometry_tools.HyperbolicMagnitudes.compute

Parameters:

data (PqHandle, required) – Input table with photometry (magnitudes or flux columns and their respective uncertainties) as defined by the configuration.
parameters (PqHandle, required) – Table witdh smoothing parameters per photometric band, determined by HyperbolicSmoothing.
value_columns (list, optional) – list of columns that prove photometric measurements (fluxes or magnitudes) Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]
error_columns (list, optional) – list of columns with errors corresponding to value_columns (assuming same ordering) Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]
zeropoints (list, optional) – optional list of magnitude zeropoints for value_columns (assuming same ordering, defaults to 0.0) Default: []
is_flux (bool, optional) – whether the provided quantities are fluxes or magnitudes Default: False

Returns:

Output table containting hyperbolic magnitudes and their uncertainties. If the columns in the input table contain a prefix mag_, this output tabel will replace the prefix with hyp_mag_, otherwise the column names will be identical to the input table.

Return type:

pandas.core.frame.DataFrame

rail.interactive.tools.photometry_tools.hyperbolic_smoothing(**kwargs)

Initial stage to compute hyperbolic magnitudes (Lupton et al. 1999). Estimates the smoothing parameter b that is used by the second stage (HyperbolicMagnitudes) to convert classical to hyperbolic magnitudes.

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Main method to call. Computes the set of smoothing parameters (b) for an input catalogue with classical photometry and their respective errors. These parameters are required by the follow-up stage HyperbolicMagnitudes and are parsed as tabular data.

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This function was generated from the function rail.tools.photometry_tools.HyperbolicSmoothing.compute

Parameters:

data (PqHandle, required) – Input table with magnitude and magnitude error columns as defined in the configuration.
value_columns (list, optional) – list of columns that prove photometric measurements (fluxes or magnitudes) Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]
error_columns (list, optional) – list of columns with errors corresponding to value_columns (assuming same ordering) Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]
zeropoints (list, optional) – optional list of magnitude zeropoints for value_columns (assuming same ordering, defaults to 0.0) Default: []
is_flux (bool, optional) – whether the provided quantities are fluxes or magnitudes Default: False

Returns:

Table with smoothing parameters per photometric band and additional meta data.

Return type:

pandas.core.frame.DataFrame

rail.interactive.tools.photometry_tools.lsst_flux_to_mag_converter(**kwargs)

Utility stage that converts from fluxes to magnitudes

Note, this is hardwired to take parquet files as input and provide hdf5 files as output

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Return a converted table

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This function was generated from the function rail.tools.photometry_tools.LSSTFluxToMagConverter.__call__

Parameters:

data (table-like, required) – The data to be converted
bands (list, optional) – Names of the bands Default: [‘u’, ‘g’, ‘r’, ‘i’, ‘z’, ‘y’]
flux_name (str, optional) – Template for band names Default: {band}_gaap1p0Flux
flux_err_name (str, optional) – Template for band error column names Default: {band}_gaap1p0FluxErr
mag_name (str, optional) – Template for magnitude column names Default: mag_{band}_lsst
mag_err_name (str, optional) – Template for magnitude error column names Default: mag_err_{band}_lsst
copy_col_dict (dict, optional) – Map of other columns to copy Default: {}
mag_offset (float, optional) – Magntidue offset value Default: 31.4

Returns:

The converted version of the table

Return type:

pandas.core.frame.DataFrame

rail.interactive.tools.photometry_tools.reddener(**kwargs)

Utility stage that does reddening

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Return a converted table

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This function was generated from the function rail.tools.photometry_tools.Reddener.__call__

Parameters:

data (table-like, required) – The data to be converted
dustmap_dir (str, required) – Directory with dustmaps
ra_name (str, optional) – Name of the RA column Default: ra
dec_name (str, optional) – Name of the DEC column Default: dec
mag_name (str, optional) – Template for the magnitude columns Default: mag_{band}_lsst
band_a_env (dict, optional) – Reddening parameters Default: {‘mag_u_lsst’: 4.81, ‘mag_g_lsst’: 3.64, ‘mag_r_lsst’: 2.7,…}
dustmap_name (str, optional) – Name of the dustmap in question Default: sfd
copy_cols (list, optional) – Additional columns to copy Default: []
copy_all_cols (bool, optional) – Copy all the columns Default: False

Returns:

The converted version of the table

Return type:

pandas.core.frame.DataFrame