def _test_betaincinv22(plt, allclose):
scipy_special = pytest.importorskip("scipy.special")
# call once to load table, so that doesn't effect timing
_betaincinv22.lookup(5, [0.1])
dims = np.concatenate([
np.arange(1, 50),
np.round(np.logspace(np.log10(51), 3.1)).astype(np.int64)
])
x = np.linspace(0, 1, 1000)
results = []
for dim in dims:
ref_timer = time.time()
yref = scipy_special.betaincinv(dim / 2, 0.5, x)
ref_timer = time.time() - ref_timer
timer = time.time()
y = _betaincinv22.lookup(dim, x)
timer = time.time() - timer
results.append((yref, y, ref_timer, timer))
n_show = 5
resultsT = list(zip(*results))
errors = np.abs(np.array(resultsT[0]) - np.array(resultsT[1])).max(axis=1)
show_inds = np.argsort(errors)[-n_show:]
subplots = plt.subplots(nrows=2, sharex=True)
if isinstance(subplots, tuple):
_, ax = subplots
for i in show_inds:
yref, y, ref_timer, timer = results[i]
dim = dims[i]
ax[0].plot(x, y, label=f"dims={dim}")
ax[1].plot(x, y - yref)
speedups = np.array(resultsT[2]) / np.array(resultsT[3])
ax[0].set_title(f"average speedup = {speedups.mean():0.1f} times")
ax[0].set_ylabel("value")
ax[1].set_xlabel("input")
ax[1].set_ylabel("error")
ax[0].legend()
for i, (yref, y, ref_timer, timer) in enumerate(results):
# allow error to increase for higher dimensions (to 5e-3 when dims=1000)
atol = 1e-3 + (np.log10(dims[i]) / 3) * 4e-3
assert allclose(y, yref, atol=atol), f"dims={dims[i]}"
def test_betaincinv22_errors():
x = np.linspace(0.1, 0.9)
_betaincinv22.lookup(3, x)
with pytest.raises(ValidationError, match="must be an integer >= 1"):
_betaincinv22.lookup(0, x)
with pytest.raises(ValidationError, match="must be an integer >= 1"):
_betaincinv22.lookup(2.2, x)