pairwise_distance_3d¶

halotools.mock_observables.pair_counters.
pairwise_distance_3d
(data1, data2, r_max, period=None, verbose=False, num_threads=1, approx_cell1_size=None, approx_cell2_size=None)[source] [edit on github]¶ Function returns pairs of points separated by a threedimensional distance smaller than or eqaul to the input
r_max
.Note that if data1 == data2 that the
pairwise_distance_3d
function doublecounts pairs.Parameters: data1 : array_like
N1 by 3 numpy array of 3dimensional positions. Values of each dimension should be between zero and the corresponding dimension of the input period.
data2 : array_like
N2 by 3 numpy array of 3dimensional positions. Values of each dimension should be between zero and the corresponding dimension of the input period.
r_max : array_like
radius of spheres to search for pairs around galaxies in
sample1
. If a single float is given,r_max
is assumed to be the same for each galaxy insample1
. You may optionally pass in an array of length Npts1, in which case each point insample1
will have its own individual pairsearch radius.Length units 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.
verbose : Boolean, optional
If True, print out information and progress.
num_threads : int, optional
Number of CPU cores to use in the pair counting. If
num_threads
is set to the string ‘max’, use all available cores. Default is 1 thread for a serial calculation that does not open a multiprocessing pool.approx_cell1_size : array_like, optional
Length3 array serving as a guess for the optimal manner by which the
RectangularDoubleMesh
will apportion thedata
points into subvolumes of the simulation box. The optimum choice unavoidably depends on the specs of your machine. Default choice is to use 1/10 of the box size 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
See comments for
approx_cell1_size
.Returns: distance :
coo_matrix
sparse matrix in COO format containing distances between the ith entry in
data1
and jth indata2
.Examples
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] >>> r_max = 1.0
>>> 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:>>> data1 = np.vstack([x1, y1, z1]).T >>> data2 = np.vstack([x2, y2, z2]).T
>>> dist_matrix = pairwise_distance_3d(data1, data2, r_max, period = period)