npairs_per_object_3d¶

halotools.mock_observables.pair_counters.
npairs_per_object_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 points in
sample2
separated by a distancer
from each point insample1
, wherer
is defined by the inputrbins
.Parameters:  sample1 : array_like
Numpy array of shape (Npts1, 3) containing 3D 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
andsample2
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 3D positions of points. Should be identical to sample1 for cases of autosample 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
Length3 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.
 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
Length3 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 usecases. Performance can vary sensitively with this parameter, so it is highly recommended that you experiment with this parameter when carrying out performancecritical calculations.
 approx_cell2_size : array_like, optional
Analogous to
approx_cell1_size
, but for sample2. See comments forapprox_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 insample1
.
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 paircounter by taking the transpose of the result of
numpy.vstack
. This boilerplate transformation is used throughout themock_observables
subpackage:>>> sample1 = np.vstack([x1, y1, z1]).T >>> sample2 = np.vstack([x2, y2, z2]).T
>>> result = npairs_per_object_3d(sample1, sample2, rbins, period=period)