def starr(counts, r=1, **args):
"""The Starr estimator for the unseen mass
Shorthand notation to be called as AlphaDiversityCalc
"""
sample = expand_counts(counts)
shuffle(sample)
# misuse r param for starr lookahead
return starr_est(sample, m=r)[-1]
def lladser_pe(counts, r=10, **args):
"""Single point estimate of the conditional uncovered probability
This function is just a wrapper around the full point estimator,
intended to be called fo a single best estimate on a complete sample.
"""
sample = expand_counts(counts)
shuffle(sample)
try:
pe = list(lladser_point_estimates(sample, r))[-1][0]
except IndexError:
pe = 'NaN'
return pe
def lladser_ci(counts, r, alpha=0.95, f=10, ci_type='ULCL', **args):
"""Single CI of the conditional uncovered probability
This function is just a wrapper around the full point estimator,
intended to be called for a single best estimate on a complete sample.
"""
sample = expand_counts(counts)
shuffle(sample)
try:
pe = list(lladser_ci_series(sample, r))[-1]
except IndexError:
pe = ('NaN','NaN')
return pe
def test_expand_counts(self):
"""expand_counts should return correct expanded array"""
c = array([2,0,1,2])
self.assertEqual(expand_counts(c), array([0,0,2,3,3]))
