def test_pdf(rng, allclose):
s = 0.25
f = lambda x: (
np.exp(-0.5 * (x + 0.5) ** 2 / s ** 2) + np.exp(-0.5 * (x - 0.5) ** 2 / s ** 2)
)
xref = np.linspace(-2, 2, 101)
pref = f(xref)
pref /= pref.sum()
dist = PDF(xref, pref)
n = 100000
samples = dist.sample(n, rng=rng)
h, xedges = np.histogram(samples, bins=101)
x = 0.5 * (xedges[:-1] + xedges[1:])
dx = np.diff(xedges)
y = h / float(h.sum()) / dx
z = f(x)
z = z / z.sum() / dx
assert allclose(y, z, atol=0.05)
with pytest.raises(ValidationError, match="PDF must sum to one"):
dist = PDF([0, 1, 2], [0.1, 1.1, 0.1])
with pytest.raises(ValidationError, match="`x` and `p` must be the same length"):
dist = PDF([0, 1], [0, 1, 0])
def test_pdf(rng):
s = 0.25
f = lambda x: (np.exp(-0.5 *
(x + 0.5)**2 / s**2) + np.exp(-0.5 *
(x - 0.5)**2 / s**2))
xref = np.linspace(-2, 2, 101)
pref = f(xref)
pref /= pref.sum()
dist = PDF(xref, pref)
n = 100000
samples = dist.sample(n, rng=rng)
h, xedges = np.histogram(samples, bins=101)
x = 0.5 * (xedges[:-1] + xedges[1:])
dx = np.diff(xedges)
y = h / float(h.sum()) / dx
z = f(x)
z = z / z.sum() / dx
assert np.allclose(y, z, atol=0.05)