marked_npairs_xy_z¶

halotools.mock_observables.
marked_npairs_xy_z
(sample1, sample2, rp_bins, pi_bins, period=None, weights1=None, weights2=None, weight_func_id=0, verbose=False, num_threads=1, approx_cell1_size=None, approx_cell2_size=None)[source] [edit on github]¶ Calculate the number of weighted pairs with separations greater than or equal to \(r_{\perp}\) and \(r_{\parallel}\), \(W(>r_{\perp},>r_{\parallel})\).
\(r_{\perp}\) and \(r_{\parallel}\) are defined wrt the zdirection.
The weight given to each pair is determined by the weights for a pair, \(w_1\), \(w_2\), and a userspecified “weighting function”, indicated by the
wfunc
parameter, \(f(w_1,w_2)\).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, optional
Numpy array of shape (Npts2, 3) containing 3D positions of points. Should be identical to sample1 for cases of autosample pair counts.
rp_bins : array_like
array of boundaries defining the radial bins perpendicular to the LOS in which pairs are counted. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.
pi_bins : array_like
array of boundaries defining the p radial bins parallel to the LOS 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 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.
weights1 : array_like, optional
Either a 1D array of length N1, or a 2D array of length N1 x N_weights, containing the weights used for the weighted pair counts. If this parameter is None, the weights are set to np.ones((N1,N_weights)).
weights2 : array_like, optional
Either a 1D array of length N1, or a 2D array of length N1 x N_weights, containing the weights used for the weighted pair counts. If this parameter is None, the weights are set to np.ones((N1,N_weights)).
wfunc : int, optional
weighting function integer ID. Each weighting function requires a specific number of weights per point, N_weights. See the Notes for a description of available weighting functions.
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
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: wN_pairs : numpy.ndarray
2D array of shape (Nrp_bins,Npi_bins) containing the weighted number counts of pairs
Notes
See the docstring of the
marked_tpcf
function for a description of the available marking functions that can be passed in via thewfunc
optional argument.