wp¶

halotools.mock_observables.
wp
(sample1, rp_bins, pi_max, sample2=None, randoms=None, period=None, do_auto=True, do_cross=True, estimator=u'Natural', num_threads=1, approx_cell1_size=None, approx_cell2_size=None, approx_cellran_size=None, seed=None)[source] [edit on github]¶ Calculate the projected two point correlation function, \(w_{p}(r_p)\), where \(r_p\) is the separation perpendicular to the lineofsight (LOS).
The first two dimensions define the plane for perpendicular distances. The third dimension is used for parallel distances, i.e. x,y positions are on the plane of the sky, and z is the redshift coordinate. This is the ‘distant observer’ approximation.
Note in particular that the
wp
function does not accept angular coordinates for the inputsample1
orsample2
. If you are trying to calculate projected galaxy clustering from a set of observational data, thewp
function is not what you want. To perform such a calculation, refer to the appropriate function of the Corrfunc code written by Manodeep Sinha, available at https://github.com/manodeep/Corrfunc, which can be used to calculate projected clustering from a set of observational data.Example calls to the
wp
function appear in the documentation below. See the Formatting your xyz coordinates for Mock Observables calculations documentation page for instructions on how to transform your coordinate position arrays into the format accepted by thesample1
andsample2
arguments.See also Galaxy Catalog Analysis Example: Projected galaxy clustering for a stepbystep tutorial on how to use this function on a mock galaxy catalog.
Parameters: sample1 : array_like
Npts1 x 3 numpy array 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.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_max : float
maximum LOS distance defining the projection integral lengthscale in the zdimension. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.
sample2 : array_like, optional
Npts2 x 3 array containing 3D positions of points. Passing
sample2
as an input permits the calculation of the crosscorrelation function. Default is None, in which case only the autocorrelation function will be calculated.randoms : array_like, optional
Nran x 3 array containing 3D positions of randomly distributed points. If no randoms are provided (the default option), calculation of the tpcf can proceed using analytical randoms (only valid for periodic boundary conditions).
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. If set to None (the default option), PBCs are set to infinity, in which case
randoms
must be provided. Length units are comoving and assumed to be in Mpc/h, here and throughout Halotools.do_auto : boolean, optional
Boolean determines whether the autocorrelation function will be calculated and returned. Default is True.
do_cross : boolean, optional
Boolean determines whether the crosscorrelation function will be calculated and returned. Only relevant when
sample2
is also provided. Default is True for the case wheresample2
is provided, otherwise False.estimator : string, optional
Statistical estimator for the tpcf. Options are ‘Natural’, ‘DavisPeebles’, ‘Hewett’ , ‘Hamilton’, ‘LandySzalay’ Default is
Natural
.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.approx_cellran_size : array_like, optional
Analogous to
approx_cell1_size
, but for randoms. See comments forapprox_cell1_size
for details.seed : int, optional
Random number seed used to randomly downsample data, if applicable. Default is None, in which case downsampling will be stochastic.
Returns: correlation_function(s) : numpy.array
len(rp_bins)1 length array containing the correlation function \(w_p(r_p)\) computed in each of the bins defined by input
rp_bins
.If
sample2
is not None (and not exactly the same assample1
), three arrays of length len(rp_bins)1 are returned:\[w_{p11}(r_p), \ w_{p12}(r_p), \ w_{p22}(r_p),\]the autocorrelation of
sample1
, the crosscorrelation betweensample1
andsample2
, and the autocorrelation ofsample2
. Ifdo_auto
ordo_cross
is set to False, the appropriate result(s) is not returned.Notes
The projected correlation function is calculated by integrating the redshift space two point correlation function using
rp_pi_tpcf
:\[w_p(r_p) = \int_0^{\pi_{\rm max}}2.0\xi(r_p,\pi)\mathrm{d}\pi\]where \(\pi_{\rm max}\) is
pi_max
and \(\xi(r_p,\pi)\) is the redshift space correlation function.For a higherperformance implementation of the wp function, see the Corrfunc code written by Manodeep Sinha, available at https://github.com/manodeep/Corrfunc.
Examples
For demonstration purposes we create a randomly distributed set of points within a periodic cube with Lbox = 250 Mpc/h.
>>> Npts = 1000 >>> Lbox = 250.
>>> x = np.random.uniform(0, Lbox, Npts) >>> y = np.random.uniform(0, Lbox, Npts) >>> z = np.random.uniform(0, Lbox, Npts)
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:>>> coords = np.vstack((x,y,z)).T
Alternatively, you may use the
return_xyz_formatted_array
convenience function for this same purpose, which provides additional wrapper behavior aroundnumpy.vstack
such as placing points into redshiftspace.>>> rp_bins = np.logspace(1,1,10) >>> pi_max = 10 >>> xi = wp(coords, rp_bins, pi_max, period=Lbox)