def test_uniform_binomial(N, m, p):
"""Check that the false positive probability is right
In particular, run m trials with N uniformly-distributed photons
and check that the number of false positives is consistent with
a binomial distribution. The more trials, the tighter the bounds
but the longer the runtime.
"""
with NumpyRNGContext(1234):
fpps = np.array(
[funcs.kuiper(np.random.random(N))[1] for i in range(m)])
assert (fpps >= 0).all()
assert (fpps <= 1).all()
low = scipy.stats.binom(n=m, p=p).ppf(0.01)
high = scipy.stats.binom(n=m, p=p).ppf(0.99)
assert (low < sum(fpps < p) < high)
def test_uniform_binomial(N, m, p):
"""Check that the false positive probability is right
In particular, run m trials with N uniformly-distributed photons
and check that the number of false positives is consistent with
a binomial distribution. The more trials, the tighter the bounds
but the longer the runtime.
"""
with NumpyRNGContext(1234):
fpps = np.array([funcs.kuiper(np.random.random(N))[1]
for i in range(m)])
assert (fpps >= 0).all()
assert (fpps <= 1).all()
low = scipy.stats.binom(n=m, p=p).ppf(0.01)
high = scipy.stats.binom(n=m, p=p).ppf(0.99)
assert (low < sum(fpps < p) < high)
def test_uniform(N):
with NumpyRNGContext(12345):
assert funcs.kuiper(np.random.random(N))[1] > 0.01
def test_uniform(N):
with NumpyRNGContext(12345):
assert funcs.kuiper(np.random.random(N))[1] > 0.01
