rail.creation.degraders.gaussian_skewt_scatter_selector module

Add a bias to redshift using a Gaussian core + skewed Student-t tail error model.

class rail.creation.degraders.gaussian_skewt_scatter_selector.GaussianSkewtScatterSelector

Bases: Selector

Add a mock photometric redshift column to a dataframe with a Gaussian + skew Student-t error model

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.

  • col_name ([str] default=photoz_mock) – Name of the mock photometric redshift column to make

  • col_name_mag_i ([str] default=mag_i) – Name of the i-band magnitude column

  • col_name_z ([str] default=z) – Name of the (true) redshift column

  • selector_model_dict ([dict] default={'mag_i_bin_edges': array([15.5, 22. , 23. , 24. , 29. ]), 'z_bin_edges': array([0. , 0.3, 0.7, 1. , 1.5, 2. , 2.5, 3. , 4. ]), 'bias_median_lookup_table': array([[ 0. , 0.002, -0.002, 0.001, 0.001, 0.001, 0.001, 0.001],) –

    [ 0. , -0. , -0.002, -0.004, 0.01 , 0.01 , 0.01 , 0.01 ],

    [ 0.003, -0. , -0.001, -0.006, -0.002, 0.024, 0.007, 0. ], [ 0.008, -0.005, 0.007, -0.015, -0.019, 0.017, 0.011, 0. ]]), ‘bias_std_lookup_table’: array([[0.01 , 0.02 , 0.026, 0.038, 0.038, 0.038, 0.038, 0.038], [0.011, 0.019, 0.025, 0.036, 0.062, 0.062, 0.062, 0.062], [0.011, 0.022, 0.027, 0.044, 0.063, 0.115, 0.093, 0.074], [0.013, 0.023, 0.025, 0.051, 0.069, 0.12 , 0.103, 0.069]]), ‘f_tail_by_mag_i’: array([0.088 , 0.1377, 0.4312, 0.4312]), ‘tail_loc_by_mag_i’: array([-0.0055, 0.1568, 0.2 , 0.2 ]), ‘tail_scale_by_mag_i’: array([0.2041, 0.3522, 0.237 , 0.237 ]), ‘tail_a_by_mag_i’: array([ 3.7662, 10.1149, 2. , 2. ]), ‘tail_b_by_mag_i’: array([ 4. , 11.2095, 4. , 4. ])}

    Dictionary of model parameters for Gaussian core and skew-t tail distribution components

  • input (PqHandle (INPUT))

  • output (PqHandle (OUTPUT))

GaussianSkewtScatterSelector(sample, seed=None, **kwargs)
Parameters:

  • sample (Any)

  • seed (int | None)

  • kwargs (Any)

__init__(args, **kwargs)

Constructor Does standard Selector initialization

Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

None

entrypoint_function: str | None = '__call__'
interactive_function: str | None = 'GaussianSkewtScatterSelector'
name = 'GaussianSkewtScatterSelector'