npairs_per_object_3d

halotools.mock_observables.pair_counters.npairs_per_object_3d(sample1, sample2, rbins, period=None, verbose=False, num_threads=1, approx_cell1_size=None, approx_cell2_size=None)[source] [edit on github]

Function counts the number of points in sample2 separated by a distance r from each point in sample1, where r is defined by the input rbins.

Parameters:

sample1 : array_like

Numpy array of shape (Npts1, 3) containing 3-D positions of points. See the Formatting your xyz coordinates for Mock Observables calculations documentation page, or the Examples section below, for instructions on how to transform your coordinate position arrays into the format accepted by the sample1 and sample2 arguments. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.

sample2 : array_like

Numpy array of shape (Npts2, 3) containing 3-D positions of points. Should be identical to sample1 for cases of auto-sample pair counts.

rbins : array_like

Boundaries defining the bins in which pairs are counted. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.

period : array_like, optional

Length-3 array defining the periodic boundary conditions. If only one number is specified, the enclosing volume is assumed to be a periodic cube (by far the most common case). If period is set to None, the default option, PBCs are set to infinity.

verbose : Boolean, optional

If True, print out information and progress.

num_threads : int, optional

Number of threads to use in calculation, where parallelization is performed using the python multiprocessing module. Default is 1 for a purely serial calculation, in which case a multiprocessing Pool object will never be instantiated. A string ‘max’ may be used to indicate that the pair counters should use all available cores on the machine.

approx_cell1_size : array_like, optional

Length-3 array serving as a guess for the optimal manner by how points will be apportioned into subvolumes of the simulation box. The optimum choice unavoidably depends on the specs of your machine. Default choice is to use Lbox/10 in each dimension, which will return reasonable result performance for most use-cases. Performance can vary sensitively with this parameter, so it is highly recommended that you experiment with this parameter when carrying out performance-critical calculations.

approx_cell2_size : array_like, optional

Analogous to approx_cell1_size, but for sample2. See comments for approx_cell1_size for details.

Returns:

num_pairs : array_like

Numpy array of shape (Npts1, len(rbins)) storing the numbers of points in sample2 inside spheres surrounding each point in sample1.

Examples

For illustration purposes, we’ll create some fake data and call the pair counter:

>>> Npts1, Npts2, Lbox = 1000, 1000, 250.
>>> period = [Lbox, Lbox, Lbox]
>>> rbins = np.logspace(-1, 1.5, 15)
>>> x1 = np.random.uniform(0, Lbox, Npts1)
>>> y1 = np.random.uniform(0, Lbox, Npts1)
>>> z1 = np.random.uniform(0, Lbox, Npts1)
>>> x2 = np.random.uniform(0, Lbox, Npts2)
>>> y2 = np.random.uniform(0, Lbox, Npts2)
>>> z2 = np.random.uniform(0, Lbox, Npts2)

We transform our x, y, z points into the array shape used by the pair-counter by taking the transpose of the result of numpy.vstack. This boilerplate transformation is used throughout the mock_observables sub-package:

>>> sample1 = np.vstack([x1, y1, z1]).T
>>> sample2 = np.vstack([x2, y2, z2]).T
>>> result = npairs_per_object_3d(sample1, sample2, rbins, period=period)