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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
import awkward as ak
array = ak.Array([[0, 2, 3.0], [4, 5, 6, 7, 8], [], [9, 8, None], [10, 1], []])
def test_sum():
assert ak.sum(array, axis=None) == pytest.approx(63.0)
assert ak.almost_equal(
ak.sum(array, axis=None, keepdims=True), ak.to_regular([[63.0]])
)
assert ak.almost_equal(
ak.sum(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[63.0]]).mask[[[True]]]),
)
assert ak.sum(array[2], axis=None, mask_identity=True) is None
def test_prod():
assert ak.prod(array[1:], axis=None) == pytest.approx(4838400.0)
assert ak.prod(array, axis=None) == 0
assert ak.almost_equal(
ak.prod(array, axis=None, keepdims=True), ak.to_regular([[0.0]])
)
assert ak.almost_equal(
ak.prod(array[1:], axis=None, keepdims=True), ak.to_regular([[4838400.0]])
)
assert ak.almost_equal(
ak.prod(array[1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[4838400.0]]).mask[[[True]]]),
)
assert ak.prod(array[2], axis=None, mask_identity=True) is None
def test_min():
assert ak.min(array, axis=None) == pytest.approx(0.0)
assert ak.almost_equal(
ak.min(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[0.0]]),
)
assert ak.almost_equal(
ak.min(array, axis=None, keepdims=True, initial=-100, mask_identity=False),
ak.to_regular([[-100.0]]),
)
assert ak.almost_equal(
ak.min(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0.0]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.min(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[np.inf]]).mask[[[False]]]),
)
assert ak.min(array[2], axis=None, mask_identity=True) is None
def test_max():
assert ak.max(array, axis=None) == pytest.approx(10.0)
assert ak.almost_equal(
ak.max(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[10.0]]),
)
assert ak.almost_equal(
ak.max(array, axis=None, keepdims=True, initial=100, mask_identity=False),
ak.to_regular([[100.0]]),
)
assert ak.almost_equal(
ak.max(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[10.0]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.max(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[np.inf]]).mask[[[False]]]),
)
assert ak.max(array[2], axis=None, mask_identity=True) is None
def test_count():
assert ak.count(array, axis=None) == 12
assert ak.almost_equal(
ak.count(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[12]]),
)
assert ak.almost_equal(
ak.count(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[12]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.count(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0]]).mask[[[False]]]),
)
assert ak.count(array[2], axis=None, mask_identity=True) is None
assert ak.count(array[2], axis=None, mask_identity=False) == 0
def test_count_nonzero():
assert ak.count_nonzero(array, axis=None) == 11
assert ak.almost_equal(
ak.count_nonzero(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[11]]),
)
assert ak.almost_equal(
ak.count_nonzero(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[11]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.count_nonzero(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0]]).mask[[[False]]]),
)
assert ak.count_nonzero(array[2], axis=None, mask_identity=True) is None
assert ak.count_nonzero(array[2], axis=None, mask_identity=False) == 0
def test_std():
assert ak.std(array, axis=None) == pytest.approx(3.139134700306227)
assert ak.almost_equal(
ak.std(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[3.139134700306227]]),
)
assert ak.almost_equal(
ak.std(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[3.139134700306227]]).mask[[[True]]]),
)
assert np.isnan(ak.std(array[2], axis=None, mask_identity=False))
def test_std_no_mask_axis_none():
assert ak.almost_equal(
ak.std(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0.0]]).mask[[[False]]]),
)
assert ak.std(array[2], axis=None, mask_identity=True) is None
def test_var():
assert ak.var(array, axis=None) == pytest.approx(9.854166666666666)
assert ak.almost_equal(
ak.var(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[9.854166666666666]]),
)
assert ak.almost_equal(
ak.var(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[9.854166666666666]]).mask[[[True]]]),
)
assert np.isnan(ak.var(array[2], axis=None, mask_identity=False))
def test_var_no_mask_axis_none():
assert ak.almost_equal(
ak.var(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0.0]]).mask[[[False]]]),
)
assert ak.var(array[2], axis=None, mask_identity=True) is None
def test_mean():
assert ak.mean(array, axis=None) == pytest.approx(5.25)
assert ak.almost_equal(
ak.mean(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[5.25]]),
)
assert ak.almost_equal(
ak.mean(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[5.25]]).mask[[[True]]]),
)
assert np.isnan(ak.mean(array[2], axis=None, mask_identity=False))
def test_mean_no_mask_axis_none():
assert ak.almost_equal(
ak.mean(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0.0]]).mask[[[False]]]),
)
assert ak.mean(array[2], axis=None, mask_identity=True) is None
def test_ptp():
assert ak.ptp(array, axis=None) == pytest.approx(10.0)
assert ak.almost_equal(
ak.ptp(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[10.0]]),
)
assert ak.almost_equal(
ak.ptp(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[10.0]]).mask[[[True]]]),
)
assert ak.ptp(array[2], axis=None, mask_identity=False) == pytest.approx(0.0)
def test_ptp_no_mask_axis_none():
assert ak.almost_equal(
ak.ptp(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0.0]]).mask[[[False]]]),
)
assert ak.ptp(array[2], axis=None, mask_identity=True) is None
def test_argmax():
assert ak.argmax(array, axis=None) == 11
assert ak.almost_equal(
ak.argmax(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[11]]),
)
assert ak.almost_equal(
ak.argmax(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[11]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.argmax(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0]]).mask[[[False]]]),
)
assert ak.argmax(array[2], axis=None, mask_identity=True) is None
assert ak.argmax(array[2], axis=None, mask_identity=False) == -1
def test_argmin():
assert ak.argmin(array, axis=None) == 0
assert ak.almost_equal(
ak.argmin(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[0]]),
)
assert ak.almost_equal(
ak.argmin(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[0]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.argmin(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[999]]).mask[[[False]]]),
)
assert ak.argmin(array[2], axis=None, mask_identity=True) is None
assert ak.argmin(array[2], axis=None, mask_identity=False) == -1
def test_any():
assert ak.any(array, axis=None)
assert ak.almost_equal(
ak.any(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[True]]),
)
assert ak.almost_equal(
ak.any(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[True]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.any(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[True]]).mask[[[False]]]),
)
assert ak.any(array[2], axis=None, mask_identity=True) is None
assert not ak.any(array[2], axis=None, mask_identity=False)
def test_all():
assert not ak.all(array, axis=None)
assert ak.almost_equal(
ak.all(array, axis=None, keepdims=True, mask_identity=False),
ak.to_regular([[False]]),
)
assert ak.almost_equal(
ak.all(array, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[False]]).mask[[[True]]]),
)
assert ak.almost_equal(
ak.all(array[-1:], axis=None, keepdims=True, mask_identity=True),
ak.to_regular(ak.Array([[False]]).mask[[[False]]]),
)
assert ak.all(array[2], axis=None, mask_identity=True) is None
assert ak.all(array[2], axis=None, mask_identity=False)
def test_prod_numpy():
array_reg = ak.from_numpy(np.arange(7 * 5 * 3, dtype=np.int64).reshape((7, 5, 3)))
result_reg = ak.from_numpy(np.array([[[5460]]], dtype=np.int64))
assert ak.sum(array_reg, axis=None) == 5460
assert ak.almost_equal(ak.sum(array_reg, axis=None, keepdims=True), result_reg)
assert ak.almost_equal(
ak.sum(array_reg, axis=None, keepdims=True, mask_identity=True),
ak.to_regular(result_reg.mask[[[[True]]]], axis=None),
)
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