macauff Documentation

This page details the inputs and outputs for various Python and Fortran functions/subroutines used internally within macauff. While CrossMatch is the main input for most users, it is possible to overwrite each of the four main steps within the matching process (AUF component creation, island group creation, photometric likelihood derivation, and final match assignment) and hence it may be important to understand the required I/O to a given step for compatibility purposes.

For the details of the inputs that CrossMatch expects and parses through its read_metadata function, see Input Parameters for specifics.

Python

Functions

calculate_proper_motions(d, l, b, temp, n)

Calculates the statistical distribution of proper motions of a sightline, from a set of theoretical sources within the particular sky area.

compute_photometric_likelihoods(cm)

Derives the photometric likelihoods and priors for use in the catalogue cross-match process.

create_galaxy_counts(cmau_array, mag_bins, ...)

Create a simulated distribution of galaxy magnitudes for a particular bandpass by consideration of double Schechter functions (for blue and red galaxies) in a specified range of redshifts, following [Rf776fcfe5044-1].

create_single_perturb_auf(r, dr, j0s, ...[, ...])

Creates the associated parameters for describing a single perturbation AUF component, for a single sky position.

csv_to_npy(input_filename, astro_cols, ...)

Convert a .csv file representation of a photometric catalogue into the appropriate .npy binary files used in the cross-matching process.

generate_speclite_filters(group_name, ...)

Convenience function to create a new set of speclite filters, if bandpasses other than those provided in the module already are required.

generate_trilegal_histogram_cube(auf_points, ...)

Convenience function to generate the hyper-cube and accompanying ID array that wraps the TRILEGAL-simulation-generated sky density maps, generated for a series of sky pointings and filters relevant to a particular cross-match.

make_island_groupings(cm)

Function to handle the creation of "islands" of astrometrically coeval sources, and identify which overlap to some probability based on their combined AUFs.

make_perturb_aufs(cm, which_cat)

cm : Class

npy_to_csv(input_csv_file_paths, cm, ...[, ...])

Function to convert output .npy files, as created during the cross-match process, and create a .csv file of matches and non-matches, combining columns from the original .csv catalogues.

rect_slice_csv(input_folder, output_folder, ...)

Convenience function to take a small rectangular slice of a larger .csv catalogue, based on its given orthogonal sky coordinates in the large catalogue.

rect_slice_npy(input_folder, output_folder, ...)

Convenience function to take a small rectangular slice of a larger catalogue, represented by three or four binary .npy files, based on its given orthogonal sky coordinates in the large catalogue.

set_list(aindices, bindices, aoverlap, ...)

Creates the inter-catalogue groupings between catalogues "a" and "b", based on previously determined individual source "overlaps" in astrometry.

source_pairing(cm[, force_no_phot_like])

Function to iterate over all grouped islands of sources, calculating the probabilities of all permutations of matches and deriving the most likely counterparts for sources in the two catalogues.

Classes

AstrometricCorrections(psf_fwhms, numtrials, ...)

Class to calculate any potential corrections to quoted astrometric precisions in photometric catalogues, based on reliable cross-matching to a well-understood second dataset.

CrossMatch(crossmatch_params_file_path, ...)

A class to cross-match two photometric catalogues with one another, producing a composite catalogue of merged sources.

FitPSFPerturbations(psf_fwhm, d_di, d_li, ...)

Class that derives a parameterisation of the effect of a hidden, blended contaminant within a brighter source in a photometric image, based on fitting the composite object with a single Gaussian PSF in the limit that sky background dominates and noise is constant across the image.

Fortran

Perturbation AUF

Group Sources

Photometric Likelihood

Counterpart Pairing

Miscellaneous Functions

Shared Library