Corrfunc forked:

I rewrite the utils.py, so that the convert_rp_pi_counts_to_wp and convert_3d_counts_to_cf works more properly. The formal call is still valid, and I added that if you provide weight in the pair-counting, it will account for the weighting.

# load some catalogue...

# Code 1
wei_norm = galaxy['w'] / (galaxy['w'].mean())
wei_norm_r = random['w'] / (random['w'].mean())

dd = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
                   RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=wei_norm, is_comoving_dist=True, weight_type='pair_product')
dr = DDrppi_mocks(
    autocorr=False, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin, 
    RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=wei_norm, 
    RA2=random['ra'], DEC2=random['dec'], CZ2=random['distance'], weights2=wei_norm_r, 
    is_comoving_dist=True, weight_type='pair_product')
rr = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
                   RA1=random['ra'], DEC1=random['dec'], CZ1=random['distance'], weights1=wei_norm_r, is_comoving_dist=True, weight_type='pair_product')

Nd = len(galaxy)
Nr = len(random)

wp_1 = convert_rp_pi_counts_to_wp(Nd, Nd, Nr, Nr, dd, dr, dr, rr, pimax=pimax, nrpbins=Nbins)

# Code 2
dd = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
                   RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=galaxy['w'], is_comoving_dist=True, weight_type='pair_product')
dr = DDrppi_mocks(
    autocorr=False, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin, 
    RA1=galaxy['ra'], DEC1=galaxy['dec'], CZ1=galaxy['distance'], weights1=galaxy['w'], 
    RA2=random['ra'], DEC2=random['dec'], CZ2=random['distance'], weights2=random['w'], 
    is_comoving_dist=True, weight_type='pair_product')
rr = DDrppi_mocks(autocorr=True, cosmology=1, nthreads=50, pimax=pimax, binfile=rp_bin,
                   RA1=random['ra'], DEC1=random['dec'], CZ1=random['distance'], weights1=random['w'], is_comoving_dist=True, weight_type='pair_product')

Nd = galaxy['w'].sum()
Nr = random['w'].sum()

wp_2 = convert_rp_pi_counts_to_wp(Nd, Nd, Nr, Nr, dd, dr, dr, rr, pimax=pimax, nrpbins=Nbins)
assert np.isclose(wp_1, wp_2).all()

This code will work.

Note that, for simplicity, I didn't add new parameters to the function. Instead you can

normalize the weight first, e.g. weight_normal = weight / weight.mean(), and pass the parameter in the old way.
pass the sum of weight of dataset1 to ND1, sum of weight of dataset2 to ND2, etc. This is reasonable, because if you assume no weighting(that is weight of every point is 1), then the sum of weights equals to number of points.

Both way will work.

manodeep / Corrfunc

some modify to utils convert function #335

Corrfunc forked: