def test_helper_normal_samples():
centerq = [1, 5, 30, 400] * u.kpc
with NumpyRNGContext(12345):
n_dist = ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.kpc, n_samples=100)
assert n_dist.distribution.shape == (4, 100)
assert n_dist.shape == (4, )
assert n_dist.unit == u.kpc
assert np.all(n_dist.pdf_std > 100*u.pc)
n_dist2 = ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.pc, n_samples=20000)
assert n_dist2.distribution.shape == (4, 20000)
assert n_dist2.shape == (4, )
assert n_dist2.unit == u.kpc
assert np.all(n_dist2.pdf_std < 100*u.pc)
def test_helper_normal_samples():
centerq = [1, 5, 30, 400] * u.kpc
with NumpyRNGContext(12345):
n_dist = ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.kpc, n_samples=100)
assert n_dist.distribution.shape == (4, 100)
assert n_dist.shape == (4, )
assert n_dist.unit == u.kpc
assert np.all(n_dist.pdf_std > 100*u.pc)
n_dist2 = ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.pc, n_samples=20000)
assert n_dist2.distribution.shape == (4, 20000)
assert n_dist2.shape == (4, )
assert n_dist2.unit == u.kpc
assert np.all(n_dist2.pdf_std < 100*u.pc)
def test_index_assignment_array():
arr = np.random.randn(2, 1000)
distr = Distribution(arr)
d1a, d2a = distr
assert isinstance(d1a, Distribution)
assert isinstance(d2a, Distribution)
ndistr = ds.normal(center=[1, 2], std=[3, 4], n_samples=1000)
n1, n2 = ndistr
assert isinstance(n1, ds.Distribution)
assert isinstance(n2, ds.Distribution)
def test_index_assignment_quantity():
arr = np.random.randn(2, 1000)
distr = Distribution(arr*u.kpc)
d1q, d2q = distr
assert isinstance(d1q, Distribution)
assert isinstance(d2q, Distribution)
ndistr = ds.normal(center=[1, 2]*u.kpc, std=[3, 4]*u.kpc, n_samples=1000)
n1, n2 = ndistr
assert isinstance(n1, ds.Distribution)
assert isinstance(n2, ds.Distribution)
def test_index_assignment_array():
arr = np.random.randn(2, 1000)
distr = Distribution(arr)
d1a, d2a = distr
assert isinstance(d1a, Distribution)
assert isinstance(d2a, Distribution)
ndistr = ds.normal(center=[1, 2], std=[3, 4], n_samples=1000)
n1, n2 = ndistr
assert isinstance(n1, ds.Distribution)
assert isinstance(n2, ds.Distribution)
def test_index_assignment_quantity():
arr = np.random.randn(2, 1000)
distr = Distribution(arr*u.kpc)
d1q, d2q = distr
assert isinstance(d1q, Distribution)
assert isinstance(d2q, Distribution)
ndistr = ds.normal(center=[1, 2]*u.kpc, std=[3, 4]*u.kpc, n_samples=1000)
n1, n2 = ndistr
assert isinstance(n1, ds.Distribution)
assert isinstance(n2, ds.Distribution)
def test_distr_noq_to_value():
distr = ds.normal(10, n_samples=100, std=1)
with pytest.raises(AttributeError):
distr.to_value(u.m)
def test_distr_to_value():
distr = ds.normal(10*u.cm, n_samples=100, std=1*u.cm)
tovdistr = distr.to_value(u.m)
assert np.allclose(distr.pdf_mean.to_value(u.m), tovdistr.pdf_mean)
def test_distr_noq_to():
# this is an array distribution not a quantity
distr = ds.normal(10, n_samples=100, std=1)
with pytest.raises(AttributeError):
distr.to(u.m)
def test_distr_to():
distr = ds.normal(10*u.cm, n_samples=100, std=1*u.cm)
todistr = distr.to(u.m)
assert_quantity_allclose(distr.pdf_mean.to(u.m), todistr.pdf_mean)
def test_helper_normal_exact():
pytest.skip('distribution stretch goal not yet implemented')
centerq = [1, 5, 30, 400] * u.kpc
ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.kpc)
ds.normal(centerq, var=[0.04, 2.25, 16, 1]*u.kpc**2)
ds.normal(centerq, ivar=[25, 0.44444444, 0.625, 1]*u.kpc**-2)
def test_helper_normal_exact():
pytest.skip('distribution stretch goal not yet implemented')
centerq = [1, 5, 30, 400] * u.kpc
ds.normal(centerq, std=[0.2, 1.5, 4, 1]*u.kpc)
ds.normal(centerq, var=[0.04, 2.25, 16, 1]*u.kpc**2)
ds.normal(centerq, ivar=[25, 0.44444444, 0.625, 1]*u.kpc**-2)
