def test_helper_poisson_exact():
pytest.skip('distribution stretch goal not yet implemented')
centerq = [1, 5, 30, 400] * u.one
ds.poisson(centerq, n_samples=1000)
with pytest.raises(u.UnitsError) as exc:
centerq = [1, 5, 30, 400] * u.kpc
ds.poisson(centerq, n_samples=1000)
assert exc.value.args[0] == ("Poisson distribution can only be computed "
"for dimensionless quantities")
def test_helper_poisson_exact():
pytest.skip('distribution stretch goal not yet implemented')
centerq = [1, 5, 30, 400] * u.one
ds.poisson(centerq, n_samples=1000)
with pytest.raises(u.UnitsError) as exc:
centerq = [1, 5, 30, 400] * u.kpc
ds.poisson(centerq, n_samples=1000)
assert exc.value.args[0] == ("Poisson distribution can only be computed "
"for dimensionless quantities")
def test_helper_poisson_samples():
centerqcounts = [1, 5, 30, 400] * u.count
with NumpyRNGContext(12345):
p_dist = ds.poisson(centerqcounts, n_samples=100)
assert p_dist.shape == (4,)
assert p_dist.distribution.shape == (4, 100)
assert p_dist.unit == u.count
p_min = np.min(p_dist)
assert isinstance(p_min, Distribution)
assert p_min.shape == ()
assert np.all(p_min >= 0)
assert np.all(np.abs(p_dist.pdf_mean - centerqcounts) < centerqcounts)
def test_helper_poisson_samples():
centerqcounts = [1, 5, 30, 400] * u.count
with NumpyRNGContext(12345):
p_dist = ds.poisson(centerqcounts, n_samples=100)
assert p_dist.shape == (4,)
assert p_dist.distribution.shape == (4, 100)
assert p_dist.unit == u.count
p_min = np.min(p_dist)
assert isinstance(p_min, Distribution)
assert p_min.shape == ()
assert np.all(p_min >= 0)
assert np.all(np.abs(p_dist.pdf_mean - centerqcounts) < centerqcounts)