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from __future__ import annotations
import numpy as np
import pytest
import boost_histogram as bh
@pytest.mark.benchmark(group="IntCategory")
@pytest.mark.parametrize("dtype", ["i", tuple])
def test_IntCategory(benchmark, growth, dtype):
np.random.seed(42)
values = np.random.choice(np.arange(5), size=[100000])
h = bh.Histogram(
bh.axis.IntCategory([] if growth else np.arange(4), growth=growth),
storage=bh.storage.Double(),
)
def run(h, data):
h.fill(data)
benchmark(run, h, tuple(values) if dtype is tuple else values.astype(dtype))
@pytest.mark.benchmark(group="StrCategory")
@pytest.mark.parametrize("dtype", ["S", "U", "O", tuple])
def test_StrCategory(benchmark, growth, dtype):
np.random.seed(42)
values = np.random.choice(["A", "B", "C", "D", "E"], size=[100000])
h = bh.Histogram(
bh.axis.StrCategory([] if growth else ["A", "B", "C", "D"], growth=growth),
storage=bh.storage.Double(),
)
def run(h, data):
h.fill(data)
benchmark(run, h, tuple(values) if dtype is tuple else values.astype(dtype))
@pytest.mark.benchmark(group="Pick")
def test_pick_only(benchmark):
h = bh.Histogram(
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.Regular(32, 0, 320),
)
h[...] = 1.0
def run(h):
return h[bh.loc("13"), bh.loc("13"), bh.loc("13"), :].view()
benchmark(run, h)
@pytest.mark.benchmark(group="Pick")
def test_pick_and_slice(benchmark):
h = bh.Histogram(
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.StrCategory([str(i) for i in range(32)]),
bh.axis.Regular(32, 0, 320),
)
h[...] = 1.0
def run(h):
return h[3:29, bh.loc("13"), bh.loc("13"), :].view()
benchmark(run, h)
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