# hod_from_mock¶

halotools.mock_observables.hod_from_mock(haloprop_galaxies, haloprop_halos, haloprop_bins=None)[source] [edit on github]

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. 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 $$\langle N_{\rm cen} \rangle(M_{\rm vir})$$:

>>> cenmask = model.mock.galaxy_table['gal_type'] == 'centrals'