Source code for halotools.mock_observables.occupation_stats

""" Module storing functions to calculate common halo occupation statistics
such as the first occupation moment.
import numpy as np
from scipy.stats import binned_statistic
from ..utils import crossmatch

__all__ = ('hod_from_mock', 'get_haloprop_of_galaxies')
__author__ = ('Andrew Hearin', )

[docs]def hod_from_mock(haloprop_galaxies, haloprop_halos, haloprop_bins=None): r""" Calculate the HOD of a mock galaxy sample. It returns the expected number of galaxies per halo, in bins of whatever halo property ``haloprop_galaxies`` and ``haloprop_halos`` are given in. Parameters ---------- haloprop_galaxies : ndarray Array of shape (num_galaxies, ) used to bin the galaxies according to the property of their host halo, e.g., host halo mass. If this quantity is not readily available in the mock galaxy catalog, the `get_haloprop_of_galaxies` function can be used to calculate it. haloprop_halos : ndarray Array of shape (num_halos, ) used to bin the halos in the same manner as the galaxies so that the counts in each bin can be properly normalized. Note that this property (e.g. halo mass) must be the same as used for ``haloprop_halos``. haloprop_bins : ndarray, optional Array defining the bin edges. If None, this defaults to 10 linearly spaced bins and so you will probably obtain better results if you pass in logarithmic quantities for the ``haloprop_galaxies`` and ``haloprop_halos`` arrays. Returns ------- mean_occupation : ndarray Array of shape (num_bins-1, ) storing the mean occupation of the input galaxy sample as a function of the input halo property. bin_edges : ndarray Array of shape (num_bins, ) storing the bin edges used in the calculation. Examples -------- In the following calculation, we'll populate a mock catalog and then manually compute the central galaxy HOD (number of central galaxies above the mass threshold as a function of halo mass) from the ``galaxy_table``. >>> from halotools.empirical_models import PrebuiltHodModelFactory >>> from halotools.sim_manager import FakeSim >>> from halotools.mock_observables import hod_from_mock >>> model = PrebuiltHodModelFactory('leauthaud11', threshold=10.75) >>> halocat = FakeSim() >>> model.populate_mock(halocat) Now compute :math:`\langle N_{\rm cen} \rangle(M_{\rm vir})`: >>> cenmask = model.mock.galaxy_table['gal_type'] == 'centrals' >>> central_host_mass = model.mock.galaxy_table['halo_mvir'][cenmask] >>> halo_mass = model.mock.halo_table['halo_mvir'] >>> haloprop_bins = np.logspace(11, 15, 15) >>> mean_ncen, bin_edges = hod_from_mock(central_host_mass, halo_mass, haloprop_bins) """ _result = binned_statistic(haloprop_galaxies, haloprop_galaxies, bins=haloprop_bins, statistic='count') galaxy_counts, bin_edges, bin_number = _result _result = binned_statistic(haloprop_halos, haloprop_halos, bins=haloprop_bins, statistic='count') halo_counts, bin_edges, bin_number = _result bins_with_galaxies_but_no_halos = (galaxy_counts > 0) & (halo_counts == 0) if np.any(bins_with_galaxies_but_no_halos): bad_bin_index = np.where(bins_with_galaxies_but_no_halos == True)[0][0] bad_bin_edge_low, bad_bin_edge_high = bin_edges[bad_bin_index], bin_edges[bad_bin_index+1] msg = ("The bin with edges ({0:.3f}, {1:.3f}) has galaxies but no halos.\n" "This must mean that the input ``haloprop_galaxies`` and ``haloprop_halos`` arrays \n" "have not been consistently computed.\n".format(bad_bin_edge_low, bad_bin_edge_high)) raise ValueError(msg) mean_occupation = np.zeros(len(haloprop_bins)-1) halo_mask = halo_counts > 0 mean_occupation[halo_mask] = galaxy_counts[halo_mask]/halo_counts[halo_mask].astype('f4') return mean_occupation, bin_edges
[docs]def get_haloprop_of_galaxies(halo_id_galaxies, halo_id_halos, haloprop_halos): """ Determine the halo property in ``haloprop_halos`` for each galaxy. This crossmatches the galaxy catalog with the halo catalog using their ``halo_id``. Return the halo property for galaxies with a match, else nan. Parameters ---------- halo_id_galaxies : ndarray Integer array of shape (num_galaxies, ) storing the ``halo_id`` that each galaxy belongs to. halo_id_halos : ndarray Integer array of shape (num_halos, ) storing the ``halo_id`` of every host halo in the entire halo catalog used to populate the mock. Repeated entries are not permissible, but halos with zero or multiple galaxies are accepted. haloprop_halos : ndarray Array of shape (num_halos, ) storing the halo property of interest, e.g., ``halo_vpeak`` or ``halo_spin``. Returns ------- haloprop_galaxies : ndarray Array of shape (num_galaxies, ) storing the property of the halo that each galaxy belongs to. Galaxies with no matching halo will receive value of `~numpy.nan` Examples -------- When you populate a mock catalog, the host halo mass of every galaxy is automatically included in the ``galaxy_table``. However, you may wish to know other halo properties for each mock galaxy, such as the spin of the halo the galaxy lives in. The code below demonstrates how to use the `get_haloprop_of_galaxies` function to do this. >>> from halotools.empirical_models import PrebuiltHodModelFactory >>> from halotools.sim_manager import FakeSim >>> from halotools.mock_observables import get_haloprop_of_galaxies >>> model = PrebuiltHodModelFactory('leauthaud11') >>> halocat = FakeSim() >>> model.populate_mock(halocat) >>> halo_id_halos = halocat.halo_table['halo_id'] >>> halo_id_galaxies = model.mock.galaxy_table['halo_id'] >>> haloprop_halos = halocat.halo_table['halo_spin'] >>> halo_spin_galaxies = get_haloprop_of_galaxies(halo_id_galaxies, halo_id_halos, haloprop_halos) >>> model.mock.galaxy_table['halo_spin'] = halo_spin_galaxies Note that we needed to use the original halo catalog to retrieve the ``halo_spin`` of the halos; in order to save memory, the version of the ``halo_table`` that is bound to ``model.mock`` has a restricted subset of columns. """ halo_id_galaxies = np.atleast_1d(halo_id_galaxies) halo_id_halos = np.atleast_1d(halo_id_halos) haloprop_halos = np.atleast_1d(haloprop_halos) result = np.zeros_like(halo_id_galaxies) + np.nan idxA, idxB = crossmatch(halo_id_galaxies, halo_id_halos) result[idxA] = haloprop_halos[idxB] return result