def _do_z_match(self, rz, z, weights=None):
"""
all the other match methods call this one
"""
import weighting
wr = weighting.hist_match_remove(rz, z, self['binsize'],
extra_weights1=weights)
if weights is None:
weights_out=None
else:
weights_out=weights[wr]
return wr,weights_out
def compare_hist_match(binsize, binnum=9, l=None, r=None):
"""
Compare hist_match with weights and with remove method.
"""
import weighting
import lensing
if l is None or r is None:
l,r = load_test_data()
binner=lensing.binning.LambdaBinner(12)
print("selecting ",binner.bin_label(binnum))
w=binner.select_bin(l, binnum)
print(" kept %d/%d" % (w.size,l.size))
print("using remove method")
keep = weighting.hist_match_remove(r['z'], l['z'][w], binsize)
perc = keep.size/(1.*r.size)
print(" used number: %d/%d = %0.2f" % (keep.size,r.size, perc))
print(" combining")
comb_rm = average_lensums(r[keep])
for i in xrange(comb_rm['r'].size):
print("%0.3f %15.12f %15.12f" % \
(comb_rm['r'][0,i], comb_rm['dsig'][0,i], comb_rm['wsum_mean'][0,i]))
print("using weights method")
weights = weighting.hist_match(r['z'], l['z'][w], binsize)
effnum = weights.sum()
effperc = effnum/r.size
print(" effective number: %d/%d = %0.2f" % (effnum,r.size, effperc))
print(" combining")
comb = average_lensums(r, weights=weights)
for i in xrange(comb_rm['r'].size):
print("%0.3f %15.12f %15.12f %15.12f %15.12f" % \
(comb['r'][0,i],
comb['dsig'][0,i], comb_rm['dsig'][0,i],
comb['wsum_mean'][0,i], comb_rm['wsum_mean'][0,i]))
