rail.interactive.creation.degraders.specz_som module

rail.interactive.creation.degraders.specz_som.som_spec_selector(**kwargs)

Class that creates a specz sample by training a SOM on data with spec-z, classifying all galaxies from a larger sample via the SOM, then selecting the same number of galaxies in each SOM cell as there are in the specz sample. If fewer galaxies are available in the large sample for a cell, it just takes as many as possible, so you can still mismatch the distribution numbers, i.e. if you have a lot of bright galaxies with speczs from a really wide survey like SDSS and the second dataset does not have the same areal coverage, then there may not be enough bright objects in the second dataset to select, so you will end up with fewer.

For the columns used to construct the SOM, there are two sets of columns, noncolor_cols is a config option where you supply a list of columns that will be used directly in the SOM, e.g. redshift, i-magnitude, etc… color_cols, on the other hand, is a config parameter where the user supplies an ordered list of columns that will be differenced before being used as SOM inputs, e.g. if you supply [‘u’, ‘g’,’r’] then a function in the code will compute u-g and g-r and use those in SOM construction. The code combines the noncolor_cols and color_cols features and all are used in construction of the SOM.

As this degrader inherits from Selector, it simply computes a mask, the Selector parent class code will perform the masking, and will return the final dataset that mimics the input reference sample.

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Entrypoint function for SomSpecSelector

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This function was generated from the function rail.creation.degraders.specz_som.SOMSpecSelector.__call__

Parameters:

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

  • spec_data (TableLike, required) – A reference/spectroscopic data set

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

  • seed (unknown type, optional) – Set to an int to force reproducible results. Default: None

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

  • noncolor_cols (list, optional) – data columns used for SOM, can be a single band ifyou will also be using colordata in ‘color_cols’, or can be as many as you want Default: [‘i’, ‘redshift’]

  • noncolor_nondet (list, optional) – list of nondetect replacement values for the non-color cols Default: [28.62, -1.0]

  • color_cols (list, optional) – columns that will be differenced to make colors. This will be done in order, so put in increasing WL order Default: [‘u’, ‘g’, ‘r’, ‘i’, ‘z’, ‘y’]

  • color_nondet (list, optional) – list of nondetect replacement vals for color columns Default: [27.79, 29.04, 29.06, 28.62, 27.98, 27.05]

  • som_size (list, optional) – tuple containing the size (x, y) of the SOM Default: [32, 32]

  • n_epochs (int, optional) – number of training epochs. Default: 10

Returns:

A handle giving access to a table with selected sample

Return type:

pandas.core.frame.DataFrame