Converting 3D pair counts into a correlation function

3D pair counts can be converted into a correlation function by using the helper function Corrfunc.utils.convert_3d_counts_to_cf. First, we have to compute the relevant pair counts using the python wrapper Corrfunc.theory.DD

>>> import numpy as np
>>> from os.path import dirname, abspath, join as pjoin
>>> from Corrfunc.theory.DD import DD
>>> from Corrfunc.io import read_catalog
>>> from Corrfunc.utils import convert_3d_counts_to_cf

>>> # Read the supplied galaxies on a periodic box
>>> X, Y, Z = read_catalog()
>>> N = len(X)
>>> boxsize = 420.0
>>> nthreads = 2

# Generate randoms on the box
>>> rand_N = 3*N
>>> rand_X = np.random.uniform(0, boxsize, rand_N)
>>> rand_Y = np.random.uniform(0, boxsize, rand_N)
>>> rand_Z = np.random.uniform(0, boxsize, rand_N)

# Setup the bins
>>> nbins = 10
>>> bins = np.linspace(0.1, 10.0, nbins + 1) # note that +1 to nbins

# Auto pair counts in DD
>>> autocorr=1
>>> DD_counts = DD(autocorr, nthreads, bins, X, Y, Z,
...               periodic=False, verbose=True)

# Cross pair counts in DR
>>> autocorr=0
>>> DR_counts = DD(autocorr, nthreads, bins, X, Y, Z,
...               X2=rand_X, Y2=rand_Y, Z2=rand_Z,
...               periodic=False, verbose=True)

# Auto pairs counts in RR
>>> autocorr=1
>>> RR_counts = DD(autocorr, nthreads, bins, rand_X, rand_Y, rand_Z,
...                periodic=False, verbose=True)

# All the pair counts are done, get the correlation function
>>> cf = convert_3d_counts_to_cf(N, N, rand_N, rand_N,
...                             DD_counts, DR_counts,
...                             DR_counts, RR_counts)

See the complete reference here Corrfunc.