halotools.mock_observables.npairs_3d(sample1, sample2, rbins, period=None, num_threads=1, approx_cell1_size=None, approx_cell2_size=None)[source] [edit on github]

Function counts the number of pairs of points separated by a three-dimensional distance smaller than the input rbins.

Note that if sample1 == sample2 that the npairs_3d function double-counts pairs. If your science application requires sample1==sample2 inputs and also pairs to not be double-counted, simply divide the final counts by 2.

A common variation of pair-counting calculations is to count pairs with separations between two different distances r1 and r2. You can retrieve this information from the npairs_3d by taking numpy.diff of the returned array.

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.

period : array_like, optional

Length-3 sequence defining the periodic boundary conditions in each dimension. If you instead provide a single scalar, Lbox, period is assumed to be the same in all Cartesian directions.

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.

num_pairs : numpy.array

Numpy array of length len(rbins) storing the numbers of pairs in the input bins.


For demonstration purposes we create randomly distributed sets of points within a periodic unit cube.

>>> 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_3d(sample1, sample2, rbins, period = period)