Source code for halotools.mock_observables.pair_counters.npairs_per_object_3d

""" Module containing the `~halotools.mock_observables.npairs_per_object_3d` function
used to count pairs as a function of separation.
from __future__ import (absolute_import, division, print_function, unicode_literals)
import numpy as np
import multiprocessing
from functools import partial

from .rectangular_mesh import RectangularDoubleMesh
from .mesh_helpers import _set_approximate_cell_sizes, _cell1_parallelization_indices
from .cpairs import npairs_per_object_3d_engine
from .npairs_3d import _npairs_3d_process_args

__author__ = ('Andrew Hearin', 'Duncan Campbell')

__all__ = ('npairs_per_object_3d', )

[docs] def npairs_per_object_3d(sample1, sample2, rbins, period=None, num_threads=1, approx_cell1_size=None, approx_cell2_size=None): """ 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 :ref:`mock_obs_pos_formatting` 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. 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 `~halotools.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) """ # Process the inputs with the helper function result = _npairs_3d_process_args(sample1, sample2, rbins, period, num_threads, approx_cell1_size, approx_cell2_size) x1in, y1in, z1in, x2in, y2in, z2in = result[0:6] rbins, period, num_threads, PBCs, approx_cell1_size, approx_cell2_size = result[6:] xperiod, yperiod, zperiod = period rmax = np.max(rbins) search_xlength, search_ylength, search_zlength = rmax, rmax, rmax # Compute the estimates for the cell sizes approx_cell1_size, approx_cell2_size = ( _set_approximate_cell_sizes(approx_cell1_size, approx_cell2_size, period) ) approx_x1cell_size, approx_y1cell_size, approx_z1cell_size = approx_cell1_size approx_x2cell_size, approx_y2cell_size, approx_z2cell_size = approx_cell2_size # Build the rectangular mesh double_mesh = RectangularDoubleMesh(x1in, y1in, z1in, x2in, y2in, z2in, approx_x1cell_size, approx_y1cell_size, approx_z1cell_size, approx_x2cell_size, approx_y2cell_size, approx_z2cell_size, search_xlength, search_ylength, search_zlength, xperiod, yperiod, zperiod, PBCs) # Create a function object that has a single argument, for parallelization purposes engine = partial(npairs_per_object_3d_engine, double_mesh, x1in, y1in, z1in, x2in, y2in, z2in, rbins) # Calculate the cell1 indices that will be looped over by the engine num_threads, cell1_tuples = _cell1_parallelization_indices( double_mesh.mesh1.ncells, num_threads) if num_threads > 1: pool = multiprocessing.Pool(num_threads) result =, cell1_tuples) counts = np.sum(np.array(result), axis=0) pool.close() else: result = engine(cell1_tuples[0]) counts = np.vstack(result) return np.array(counts)