def test_boostraphistogram_fast_and_slow_filling_give_identical_results(
withweight: bool, withseed: bool
) -> None:
size = 10000
hist_slow = BootstrapHistogram(
bh.axis.Regular(100, 0.0, 1.0), numsamples=100, rng=1234
)
hist_fast = BootstrapHistogram(
bh.axis.Regular(100, 0.0, 1.0), numsamples=100, rng=1234
)
data = np.random.uniform(size=size)
weight = np.random.uniform(size=size) if withweight else None
seed = np.arange(size) if withseed else None
hist_slow._fill_slow(data, weight=weight, seed=seed)
hist_fast._fill_fast(data, weight=weight, seed=seed)
assert hist_slow == hist_fast
def _run(
withweight: bool,
withseed: bool,
numsamples: int = 10000,
arraysize: int = 100,
numfills: int = 5,
fast: bool = True,
) -> float:
hist = BootstrapHistogram(bh.axis.Regular(100, 0.0, 1.0),
numsamples=numsamples,
rng=1234)
data = np.random.uniform(size=arraysize)
weight = np.random.uniform(size=arraysize) if withweight else None
seed = np.arange(arraysize) if withseed else None
if fast:
return timeit(lambda: hist._fill_fast(data, weight=weight, seed=seed),
number=numfills)
else:
return timeit(lambda: hist._fill_slow(data, weight=weight, seed=seed),
number=numfills)
def test_boostraphistogram_fast_is_faster_than_slow(
withweight: bool, withseed: bool
) -> None:
numsamples = 10000
arraysize = 100
hist_slow = BootstrapHistogram(
bh.axis.Regular(100, 0.0, 1.0), numsamples=numsamples, rng=1234
)
hist_fast = BootstrapHistogram(
bh.axis.Regular(100, 0.0, 1.0), numsamples=numsamples, rng=1234
)
data = np.random.uniform(size=arraysize)
weight = np.random.uniform(size=arraysize) if withweight else None
seed = np.arange(arraysize) if withseed else None
slowtime = timeit(
lambda: hist_slow._fill_slow(data, weight=weight, seed=seed), number=5
)
fasttime = timeit(
lambda: hist_fast._fill_fast(data, weight=weight, seed=seed), number=5
)
assert slowtime > fasttime