afmin = 3e-3
indfm = (af1 >= afmin) & (af1 <= 1 - afmin) & (af2 >= afmin) & (af2 <= 1 - afmin)
nsites = len(np.unique(indfm.nonzero()[1]))
# Estimate the template number
mea = 0.5 * (af1[indfm] + af2[indfm])
var = ((af1[indfm] - af2[indfm]) / 2)**2
# In binomial sampling, the variance on k is var(k) = nx (1 - x), so
# for the frequency var(k/n) = x (1 - x) / n
n_all = mea * (1 - mea) / var
# NOTE: pseudocounts that come from the F4 dilution estimate, so we
# only listen to the data if there is enough data points to listen to
len_pseudo = 1
n_pseudo = sample.get_n_templates_dilutions()
n_allp = np.concatenate([n_all, ([n_pseudo] * len_pseudo)])
if VERBOSE >= 2:
print 'Number of doubly polymorphic sites:', nsites, 'n_pseudo:', n_pseudo
# NOTE: the estimate of n has a bad distribution because some points are
# exactly on the diagonal, so we average the inverse (which is well
# behaved) and also take the medians as alternatives
n = 1.0 / (1.0 / n_allp).mean()
ninv = n_allp.mean()
nmed = np.median(n_allp)
if VERBOSE >= 2:
print fr1, fr2, n, ninv, nmed
key = (samplename, fr1, fr2)
