rail_cmnn

RAIL interface to Melissa Graham's CMNN algorithm. A slight modification of the original code found here:
dirac-institute/CMNN_Photoz_estimator

See https://ui.adsabs.harvard.edu/abs/2018AJ....155....1G/abstract for more details on the code Any use of rail_cmnn in a paper or report should cite Graham et al. (2018).

The current version of the code consists of a training stage, Inform_CMNNPDF, that computes colors for a set of training data (replacing non-detections with the 1-sigma limiting magnitude), and an estimation stage CMNNPDF that calculates the Mahalanobis distance to each training galaxy for each test galaxy and returns a single Guassian PDF for each galaxy where the mean can be estimated in one of three ways (see selection mode below), and the width is determined by the standard deviation of training galaxy redshifts within the threshold Mahalanobis distance. Future implementation improvements may change the output format to include multiple Gaussians.

Inform_CMNNPDF takes in a training data set and returns a model file that simply consists of the computed colors and color errors (magnitude errors added in quadrature) for that dataset, the model to be used in the CMNNPDF stage. A modification of the original CMNN algorithm, "nondetections" are now replaced by the 1-sigma limiting magnitudes and the non-detect magnitude errors replaced with a value of 1.0. The config parameters that can be set by the user for Inform_CMNNPDF are:

• bands: list of the band names that should be present in the input data.
  
• err_bands: list of the magnitude error column names that should be present in the input data.
  
• redshift_col: a string giving the name for the redshift column present in the input data.
  
• mag_limits: a dictionary with keys that match those in bands and a float with the 1 sigma limiting magnitude for each band.
  
• nondetect_val: float or np.nan, the value indicating a non-detection, which will be replaced by the values in mag_limits.
  

The parameters that can be set via the config_params in CMNNPDF are described in brief below:

• bands, err_bands, redshift_col, mag_limits are all the same as described above for Inform_CMNNPDF.
  
• ppf_value: float, usually 0.68 or 0.95, which sets the value of the PPF used in the Mahalanobis distance calculation.
  
• selection_mode: int, selects how the central value of the Gaussian PDF is calculated in the algorithm, if set to 0 randomly chooses from set within the Mahalanobis distance, if set to 1 chooses the nearest neighbor point, if set to 2 adds a distance weight to the random choice.
  
• min_n: int, the minimum number of training galaxies to use.
  
• min_thresh: float, the minimum threshold cutoff. Values smaller than this threshold value will be ignored.
  
• min_dist: float, the minimum Mahalanobis distance. Values smaller than this will be ignored.
  
• bad_redshift_val: float, in the unlikely case that there are not enough training galaxies, this central redshift will be assigned to galaxies.
  
• bad_redshift_err: float, in the unlikely case that there are not enough training galaxies, this Gaussian width will be assigned to galaxies.