def test_binned_binom_proportion():
# Check that it works.
nbins = 20
x = np.linspace(0., 10., 100) # Guarantee an `x` in every bin.
success = np.ones(len(x), dtype=bool)
bin_ctr, bin_hw, p, perr = funcs.binned_binom_proportion(x,
success,
bins=nbins)
# Check shape of outputs
assert bin_ctr.shape == (nbins, )
assert bin_hw.shape == (nbins, )
assert p.shape == (nbins, )
assert perr.shape == (2, nbins)
# Check that p is 1 in all bins, since success = True for all `x`.
assert (p == 1.).all()
# Check that p is 0 in all bins if success = False for all `x`.
success[:] = False
bin_ctr, bin_hw, p, perr = funcs.binned_binom_proportion(x,
success,
bins=nbins)
assert (p == 0.).all()
def test_binned_binom_proportion():
# Check that it works.
nbins = 20
x = np.linspace(0., 10., 100) # Guarantee an `x` in every bin.
success = np.ones(len(x), dtype=bool)
bin_ctr, bin_hw, p, perr = funcs.binned_binom_proportion(x, success,
bins=nbins)
# Check shape of outputs
assert bin_ctr.shape == (nbins,)
assert bin_hw.shape == (nbins,)
assert p.shape == (nbins,)
assert perr.shape == (2, nbins)
# Check that p is 1 in all bins, since success = True for all `x`.
assert (p == 1.).all()
# Check that p is 0 in all bins if success = False for all `x`.
success[:] = False
bin_ctr, bin_hw, p, perr = funcs.binned_binom_proportion(x, success,
bins=nbins)
assert (p == 0.).all()
def test_binned_binom_proportion_exception():
with pytest.raises(ValueError):
with pytest.warns(AstropyDeprecationWarning):
funcs.binned_binom_proportion([0], [1, 2], conf=0.75)
def test_binned_binom_proportion_exception():
with pytest.raises(ValueError):
funcs.binned_binom_proportion([0], [1, 2], confidence_level=0.75)