def reference_survey_paircount(pos1,
w1,
redges,
Nmu,
pos2=None,
w2=None,
los=2):
"""Reference pair counting via kdcount"""
tree1 = correlate.points(pos1, boxsize=None, weights=w1)
if pos2 is None:
tree2 = tree1
else:
tree2 = correlate.points(pos2, boxsize=None, weights=w2)
bins = correlate.RmuBinning(redges,
Nmu,
observer=(0, 0, 0),
mu_min=0.,
absmu=True)
pc = correlate.paircount(tree1,
tree2,
bins,
np=0,
compute_mean_coords=True)
return numpy.nan_to_num(pc.pair_counts), numpy.nan_to_num(
pc.mean_centers[0]), pc.sum1
def reference_2pcf_smu(sedges,muedges,position1,weight1,position2=None,weight2=None,los='midpoint'):
"""Reference pair counting via kdcount"""
tree1 = correlate.points(position1,boxsize=None,weights=weight1)
if position2 is None: tree2 = tree1
else: tree2 = correlate.points(position2,boxsize=None,weights=weight2)
if los=='midpoint':
bins = correlate.RmuBinning(np.asarray(sedges),(len(muedges)-1),observer=(0,0,0),mu_min=muedges[0],mu_max=muedges[-1],absmu=False)
else:
bins = correlate.FlatSkyBinning(np.asarray(sedges),(len(muedges)-1),los='xyz'.index(los),mu_min=muedges[0],mu_max=muedges[-1],absmu=False)
pc = correlate.paircount(tree2,tree1,bins,np=0,usefast=False,compute_mean_coords=True)
return pc.sum1