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# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE
from __future__ import annotations
import numpy as np
import pytest # noqa: F401
import awkward as ak
def test():
array = ak.Array([[1, 2], [], [-np.inf]])
# monoidal reducers (have identities)
assert ak.count(array, axis=1)[1] == 0
assert ak.count_nonzero(array, axis=1)[1] == 0
assert ak.sum(array, axis=1)[1] == 0
assert ak.prod(array, axis=1)[1] == 1
assert ak.all(array, axis=1)[1]
assert not ak.any(array, axis=1)[1]
# semigroup reducers (no identity; arguable in the case of min/max on floats)
assert ak.max(array, axis=1)[1] is None
assert ak.min(array, axis=1)[1] is None
assert ak.argmax(array, axis=1)[1] is None
assert ak.argmin(array, axis=1)[1] is None
# defined in terms of reducers, with ak.count in the denominator
assert np.isnan(ak.moment(array, 1, axis=1)[1])
assert np.isnan(ak.mean(array, axis=1)[1])
assert np.isnan(ak.var(array, axis=1)[1])
assert np.isnan(ak.std(array, axis=1)[1])
assert np.isnan(ak.corr(array, array, axis=1)[1])
assert np.isnan(ak.covar(array, array, axis=1)[1])
assert np.isnan(ak.linear_fit(array, array, axis=1)["intercept", 1])
assert np.isnan(ak.linear_fit(array, array, axis=1)["slope", 1])
assert np.isnan(ak.linear_fit(array, array, axis=1)["intercept_error", 1])
assert np.isnan(ak.linear_fit(array, array, axis=1)["slope_error", 1])
# defined in terms of reducers, but a map-like operation
assert ak.softmax(array, axis=1)[1].to_list() == []
assert np.isnan(ak.softmax(array, axis=1)[2, 0])
# defined in terms of reducers, computed from ak.min and ak.max
assert ak.ptp(array, axis=1)[1] is None
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