def _1d_correlation(pos, min_cut, max_cut, end_s=0.02, ns=15, n_randoms=1, n_cores=1):
"""
Does a 1d correlation in either redshift or comoving distance space.
"""
if pos.max() < 10.0:
A, g, t = sf.fit_selection_simple_z(pos)
else:
A, g, t = sf.fit_selection_simple(pos)
DD = _correlation_1d_DD_wrapper(pos1=pos, end_s=end_s, ns=ns, n_cores=n_cores)
print "Done DD"
RR = np.zeros((ns))
DR = np.zeros((ns))
print "made zeros"
for i in range(n_randoms):
rand_r = sf.create_radial_selection(A, g, t, min_cut, max_cut, len(pos))
if i == 0:
plt.hist(rand_r, normed=True)
plt.hist(pos, normed=True)
plt.show()
go_ahead = str(raw_input("do you like what you see?? [Y/n]"))
if go_ahead == 'n':
raise ValueError("Okay we'll stop then")
print len(rand_r)
RR += _correlation_1d_DD_wrapper(pos1=rand_r, end_s=end_s, ns=ns, n_cores=n_cores)
print "Done RR"
DR += _correlation_1d_DR_wrapper(pos1=pos, pos2=rand_r, end_s=end_s, ns=ns, n_cores=n_cores)
print "done DR"
RR /= n_randoms
DR /= n_randoms
#Use the Landy-Szalay Estimator
Corr = 1 + DD / RR - 2 * DR * (len(pos) - 1.0) / len(pos) / RR
#Calculate the centre of bins
s_bins = np.linspace(end_s / (2 * ns), end_s * (1 - 0.5 / ns), ns)
print "got to the end..."
print Corr
return s_bins.copy(), Corr.copy()
