API reference

galsampler.crossmatch.crossmatch(x, y, skip_bounds_checking=False)[source]

Finds where the elements of x appear in the array y, including repeats.

The elements in x may be repeated, but the elements in y must be unique. The arrays x and y may be only partially overlapping.

Applications of this function involve cross-matching two catalogs which share an ID.

Parameters:

  • x (integer array) – Array of integers with possibly repeated entries.

  • y (integer array) – Array of unique integers.

  • skip_bounds_checking (bool, optional) – The first step in the crossmatch function is to test that the input arrays satisfy the assumptions of the algorithm (namely that x and y store integers, and that all values in y are unique). If skip_bounds_checking is set to True, this testing is bypassed and the function evaluates faster. Default is False.

Returns:

  • idx_x (integer array) – Integer array used to apply a mask to x such that x[idx_x] = y[idx_y]

  • y_idx (integer array) – Integer array used to apply a mask to y such that x[idx_x] = y[idx_y]

galsampler.crossmatch.compute_richness(unique_halo_ids, halo_id_of_galaxies)[source]

For every ID in unique_halo_ids, calculate the number of times the ID appears in halo_id_of_galaxies.

Parameters:

  • unique_halo_ids (ndarray) – Numpy array of shape (num_halos, ) storing unique integers

  • halo_id_of_galaxies (ndarray) – Numpy integer array of shape (num_galaxies, ) storing the host ID of each galaxy

Returns:

richness – Numpy integer array of shape (num_halos, ) storing richness of each host halo

Return type:

ndarray

Examples

>>> num_hosts = 100
>>> num_sats = int(1e5)
>>> unique_halo_ids = np.arange(5, num_hosts + 5)
>>> halo_id_of_galaxies = np.random.randint(0, 5000, num_sats)
>>> richness = compute_richness(unique_halo_ids, halo_id_of_galaxies)
galsampler.galmatch.galsample(source_galaxies_host_halo_id, source_halo_ids, target_halo_ids, source_halo_props, target_halo_props)[source]

Calculate the indexing array that transfers source galaxies to target halos

Parameters:

  • source_galaxies_host_halo_id (ndarray of shape (n_source_gals, )) – Integer array storing the value of the halo ID of the source halo of each source galaxy Each entry in source_galaxies_host_halo_id must appear in source_halo_ids This quantity can be computed via the following function in halotools: halotools.utils.value_added_halo_table_functions.compute_uber_hostid

  • source_halo_ids (ndarray of shape (n_source_halos, )) –

  • target_halo_ids (ndarray of shape (n_target_halos, )) –

  • source_halo_props (sequence of n_props ndarrays) – Sequence of n_props of ndarrays, each with shape (n_source_halos, ) These properties determine the correspondence between source and target halos

  • target_halo_props (sequence of n_props ndarrays) – Sequence of n_props of ndarrays, each with shape (n_target_halos, ) These properties determine the correspondence between source and target halos

Returns:

  • target_gals_selection_indx (ndarray of shape (n_target_gals, )) – Integer array storing values in the range [0, n_source_gals-1]

  • target_galaxy_target_halo_ids (ndarray of shape (n_target_gals, )) – Integer array storing values appearing in target_halo_ids

  • target_galaxy_source_halo_ids (ndarray of shape (n_target_gals, )) – Integer array storing values appearing in source_halo_ids