.. _converting_rp_pi_counts: Converting :math:`(r_p, \pi)` pairs into a projected correlation function ========================================================================== Pair counts in :math:`(r_p, \pi)` can be converted into a projected correlation function by using the helper function :py:mod:`Corrfunc.utils.convert_rp_pi_counts_to_wp`. .. code-block:: python >>> import numpy as np >>> from Corrfunc.theory import DDrppi >>> from Corrfunc.io import read_catalog >>> from Corrfunc.utils import convert_rp_pi_counts_to_wp # Read the supplied galaxies on a periodic box >>> X, Y, Z = read_catalog() >>> N = len(X) >>> boxsize = 420.0 # 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) >>> nthreads = 2 >>> pimax = 40.0 # Setup the bins >>> nrpbins = 10 >>> bins = np.linspace(0.1, 10.0, nrpbins + 1) # Auto pair counts in DD >>> autocorr=1 >>> DD_counts = DDrppi(autocorr, nthreads, pimax, bins, X, Y, Z, ... periodic=False, verbose=True) # Cross pair counts in DR >>> autocorr=0 >>> DR_counts = DDrppi(autocorr, nthreads, pimax, 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 = DDrppi(autocorr, nthreads, pimax, bins, rand_X, rand_Y, rand_Z, ... periodic=False, verbose=True) # All the pair counts are done, get the correlation function >>> wp = convert_rp_pi_counts_to_wp(N, N, rand_N, rand_N, ... DD_counts, DR_counts, ... DR_counts, RR_counts, nrpbins, pimax) See the complete reference here :py:mod:`Corrfunc`.