1381 lines
54 KiB
Python
1381 lines
54 KiB
Python
"""Tests for the array padding functions.
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"""
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import pytest
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import numpy as np
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from numpy.testing import assert_array_equal, assert_allclose, assert_equal
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from numpy.lib.arraypad import _as_pairs
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_numeric_dtypes = (
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np.sctypes["uint"]
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+ np.sctypes["int"]
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+ np.sctypes["float"]
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+ np.sctypes["complex"]
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)
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_all_modes = {
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'constant': {'constant_values': 0},
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'edge': {},
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'linear_ramp': {'end_values': 0},
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'maximum': {'stat_length': None},
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'mean': {'stat_length': None},
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'median': {'stat_length': None},
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'minimum': {'stat_length': None},
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'reflect': {'reflect_type': 'even'},
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'symmetric': {'reflect_type': 'even'},
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'wrap': {},
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'empty': {}
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}
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class TestAsPairs:
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def test_single_value(self):
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"""Test casting for a single value."""
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expected = np.array([[3, 3]] * 10)
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for x in (3, [3], [[3]]):
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result = _as_pairs(x, 10)
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assert_equal(result, expected)
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# Test with dtype=object
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obj = object()
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assert_equal(
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_as_pairs(obj, 10),
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np.array([[obj, obj]] * 10)
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)
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def test_two_values(self):
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"""Test proper casting for two different values."""
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# Broadcasting in the first dimension with numbers
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expected = np.array([[3, 4]] * 10)
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for x in ([3, 4], [[3, 4]]):
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result = _as_pairs(x, 10)
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assert_equal(result, expected)
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# and with dtype=object
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obj = object()
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assert_equal(
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_as_pairs(["a", obj], 10),
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np.array([["a", obj]] * 10)
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)
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# Broadcasting in the second / last dimension with numbers
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assert_equal(
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_as_pairs([[3], [4]], 2),
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np.array([[3, 3], [4, 4]])
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)
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# and with dtype=object
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assert_equal(
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_as_pairs([["a"], [obj]], 2),
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np.array([["a", "a"], [obj, obj]])
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)
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def test_with_none(self):
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expected = ((None, None), (None, None), (None, None))
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assert_equal(
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_as_pairs(None, 3, as_index=False),
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expected
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)
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assert_equal(
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_as_pairs(None, 3, as_index=True),
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expected
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)
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def test_pass_through(self):
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"""Test if `x` already matching desired output are passed through."""
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expected = np.arange(12).reshape((6, 2))
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assert_equal(
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_as_pairs(expected, 6),
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expected
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)
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def test_as_index(self):
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"""Test results if `as_index=True`."""
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assert_equal(
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_as_pairs([2.6, 3.3], 10, as_index=True),
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np.array([[3, 3]] * 10, dtype=np.intp)
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)
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assert_equal(
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_as_pairs([2.6, 4.49], 10, as_index=True),
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np.array([[3, 4]] * 10, dtype=np.intp)
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)
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for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]],
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[[1, 2]] * 9 + [[1, -2]]):
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with pytest.raises(ValueError, match="negative values"):
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_as_pairs(x, 10, as_index=True)
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def test_exceptions(self):
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"""Ensure faulty usage is discovered."""
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with pytest.raises(ValueError, match="more dimensions than allowed"):
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_as_pairs([[[3]]], 10)
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with pytest.raises(ValueError, match="could not be broadcast"):
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_as_pairs([[1, 2], [3, 4]], 3)
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with pytest.raises(ValueError, match="could not be broadcast"):
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_as_pairs(np.ones((2, 3)), 3)
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class TestConditionalShortcuts:
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@pytest.mark.parametrize("mode", _all_modes.keys())
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def test_zero_padding_shortcuts(self, mode):
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test = np.arange(120).reshape(4, 5, 6)
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pad_amt = [(0, 0) for _ in test.shape]
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assert_array_equal(test, np.pad(test, pad_amt, mode=mode))
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@pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
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def test_shallow_statistic_range(self, mode):
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test = np.arange(120).reshape(4, 5, 6)
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pad_amt = [(1, 1) for _ in test.shape]
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assert_array_equal(np.pad(test, pad_amt, mode='edge'),
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np.pad(test, pad_amt, mode=mode, stat_length=1))
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@pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
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def test_clip_statistic_range(self, mode):
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test = np.arange(30).reshape(5, 6)
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pad_amt = [(3, 3) for _ in test.shape]
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assert_array_equal(np.pad(test, pad_amt, mode=mode),
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np.pad(test, pad_amt, mode=mode, stat_length=30))
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class TestStatistic:
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def test_check_mean_stat_length(self):
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a = np.arange(100).astype('f')
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a = np.pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), ))
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b = np.array(
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[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
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0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
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0.5, 0.5, 0.5, 0.5, 0.5,
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0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
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10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
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20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
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30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
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40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
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50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
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60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
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70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
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80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
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90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
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98., 98., 98., 98., 98., 98., 98., 98., 98., 98.,
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98., 98., 98., 98., 98., 98., 98., 98., 98., 98.
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])
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assert_array_equal(a, b)
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def test_check_maximum_1(self):
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a = np.arange(100)
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a = np.pad(a, (25, 20), 'maximum')
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b = np.array(
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[99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
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99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
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99, 99, 99, 99, 99,
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
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10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
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20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
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30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
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40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
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50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
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60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
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70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
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80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
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90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
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99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
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99, 99, 99, 99, 99, 99, 99, 99, 99, 99]
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)
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assert_array_equal(a, b)
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def test_check_maximum_2(self):
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a = np.arange(100) + 1
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a = np.pad(a, (25, 20), 'maximum')
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b = np.array(
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[100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
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100, 100, 100, 100, 100,
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
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21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
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31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
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41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
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51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
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61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
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71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
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81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
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91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
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)
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assert_array_equal(a, b)
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def test_check_maximum_stat_length(self):
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a = np.arange(100) + 1
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a = np.pad(a, (25, 20), 'maximum', stat_length=10)
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b = np.array(
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[10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
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10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
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10, 10, 10, 10, 10,
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
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21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
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31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
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41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
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51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
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61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
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71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
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81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
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91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
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100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
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)
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assert_array_equal(a, b)
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def test_check_minimum_1(self):
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a = np.arange(100)
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a = np.pad(a, (25, 20), 'minimum')
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b = np.array(
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0,
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
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10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
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20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
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30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
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40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
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50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
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60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
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70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
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80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
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90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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)
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assert_array_equal(a, b)
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def test_check_minimum_2(self):
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a = np.arange(100) + 2
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a = np.pad(a, (25, 20), 'minimum')
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b = np.array(
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[2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2,
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2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
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12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
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22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
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32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
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42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
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52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
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62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
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72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
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82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
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92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
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)
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assert_array_equal(a, b)
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def test_check_minimum_stat_length(self):
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a = np.arange(100) + 1
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a = np.pad(a, (25, 20), 'minimum', stat_length=10)
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b = np.array(
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[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1,
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
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21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
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31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
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41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
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51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
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61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
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71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
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81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
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91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
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91, 91, 91, 91, 91, 91, 91, 91, 91, 91,
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91, 91, 91, 91, 91, 91, 91, 91, 91, 91]
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)
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assert_array_equal(a, b)
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def test_check_median(self):
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a = np.arange(100).astype('f')
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a = np.pad(a, (25, 20), 'median')
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b = np.array(
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[49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
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49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
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49.5, 49.5, 49.5, 49.5, 49.5,
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0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
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10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
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20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
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30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
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40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
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50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
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60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
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70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
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80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
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90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
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49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
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49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
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)
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assert_array_equal(a, b)
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def test_check_median_01(self):
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a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
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a = np.pad(a, 1, 'median')
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b = np.array(
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[[4, 4, 5, 4, 4],
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[3, 3, 1, 4, 3],
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[5, 4, 5, 9, 5],
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[8, 9, 8, 2, 8],
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[4, 4, 5, 4, 4]]
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)
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assert_array_equal(a, b)
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def test_check_median_02(self):
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a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
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a = np.pad(a.T, 1, 'median').T
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b = np.array(
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[[5, 4, 5, 4, 5],
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[3, 3, 1, 4, 3],
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[5, 4, 5, 9, 5],
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[8, 9, 8, 2, 8],
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[5, 4, 5, 4, 5]]
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)
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assert_array_equal(a, b)
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def test_check_median_stat_length(self):
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a = np.arange(100).astype('f')
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a[1] = 2.
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a[97] = 96.
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a = np.pad(a, (25, 20), 'median', stat_length=(3, 5))
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b = np.array(
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[ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
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2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
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2., 2., 2., 2., 2.,
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0., 2., 2., 3., 4., 5., 6., 7., 8., 9.,
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10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
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20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
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30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
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40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
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50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
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60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
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70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
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80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
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90., 91., 92., 93., 94., 95., 96., 96., 98., 99.,
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96., 96., 96., 96., 96., 96., 96., 96., 96., 96.,
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96., 96., 96., 96., 96., 96., 96., 96., 96., 96.]
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)
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assert_array_equal(a, b)
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def test_check_mean_shape_one(self):
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a = [[4, 5, 6]]
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a = np.pad(a, (5, 7), 'mean', stat_length=2)
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b = np.array(
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[[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
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|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
|
|
[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_mean_2(self):
|
|
a = np.arange(100).astype('f')
|
|
a = np.pad(a, (25, 20), 'mean')
|
|
b = np.array(
|
|
[49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
|
|
49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
|
|
49.5, 49.5, 49.5, 49.5, 49.5,
|
|
|
|
0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
|
|
10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
|
|
20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
|
|
30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
|
|
40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
|
|
50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
|
|
60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
|
|
70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
|
|
80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
|
|
90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
|
|
|
|
49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
|
|
49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
@pytest.mark.parametrize("mode", [
|
|
"mean",
|
|
"median",
|
|
"minimum",
|
|
"maximum"
|
|
])
|
|
def test_same_prepend_append(self, mode):
|
|
""" Test that appended and prepended values are equal """
|
|
# This test is constructed to trigger floating point rounding errors in
|
|
# a way that caused gh-11216 for mode=='mean'
|
|
a = np.array([-1, 2, -1]) + np.array([0, 1e-12, 0], dtype=np.float64)
|
|
a = np.pad(a, (1, 1), mode)
|
|
assert_equal(a[0], a[-1])
|
|
|
|
@pytest.mark.parametrize("mode", ["mean", "median", "minimum", "maximum"])
|
|
@pytest.mark.parametrize(
|
|
"stat_length", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))]
|
|
)
|
|
def test_check_negative_stat_length(self, mode, stat_length):
|
|
arr = np.arange(30).reshape((6, 5))
|
|
match = "index can't contain negative values"
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(arr, 2, mode, stat_length=stat_length)
|
|
|
|
def test_simple_stat_length(self):
|
|
a = np.arange(30)
|
|
a = np.reshape(a, (6, 5))
|
|
a = np.pad(a, ((2, 3), (3, 2)), mode='mean', stat_length=(3,))
|
|
b = np.array(
|
|
[[6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
|
|
[6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
|
|
|
|
[1, 1, 1, 0, 1, 2, 3, 4, 3, 3],
|
|
[6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
|
|
[11, 11, 11, 10, 11, 12, 13, 14, 13, 13],
|
|
[16, 16, 16, 15, 16, 17, 18, 19, 18, 18],
|
|
[21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
|
|
[26, 26, 26, 25, 26, 27, 28, 29, 28, 28],
|
|
|
|
[21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
|
|
[21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
|
|
[21, 21, 21, 20, 21, 22, 23, 24, 23, 23]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
@pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning")
|
|
@pytest.mark.filterwarnings(
|
|
"ignore:invalid value encountered in( scalar)? divide:RuntimeWarning"
|
|
)
|
|
@pytest.mark.parametrize("mode", ["mean", "median"])
|
|
def test_zero_stat_length_valid(self, mode):
|
|
arr = np.pad([1., 2.], (1, 2), mode, stat_length=0)
|
|
expected = np.array([np.nan, 1., 2., np.nan, np.nan])
|
|
assert_equal(arr, expected)
|
|
|
|
@pytest.mark.parametrize("mode", ["minimum", "maximum"])
|
|
def test_zero_stat_length_invalid(self, mode):
|
|
match = "stat_length of 0 yields no value for padding"
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1., 2.], 0, mode, stat_length=0)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1., 2.], 0, mode, stat_length=(1, 0))
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1., 2.], 1, mode, stat_length=0)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1., 2.], 1, mode, stat_length=(1, 0))
|
|
|
|
|
|
class TestConstant:
|
|
def test_check_constant(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'constant', constant_values=(10, 20))
|
|
b = np.array(
|
|
[10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
|
10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
|
10, 10, 10, 10, 10,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
|
|
20, 20, 20, 20, 20, 20, 20, 20, 20, 20]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_constant_zeros(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'constant')
|
|
b = np.array(
|
|
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_constant_float(self):
|
|
# If input array is int, but constant_values are float, the dtype of
|
|
# the array to be padded is kept
|
|
arr = np.arange(30).reshape(5, 6)
|
|
test = np.pad(arr, (1, 2), mode='constant',
|
|
constant_values=1.1)
|
|
expected = np.array(
|
|
[[ 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
|
|
|
[ 1, 0, 1, 2, 3, 4, 5, 1, 1],
|
|
[ 1, 6, 7, 8, 9, 10, 11, 1, 1],
|
|
[ 1, 12, 13, 14, 15, 16, 17, 1, 1],
|
|
[ 1, 18, 19, 20, 21, 22, 23, 1, 1],
|
|
[ 1, 24, 25, 26, 27, 28, 29, 1, 1],
|
|
|
|
[ 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
|
[ 1, 1, 1, 1, 1, 1, 1, 1, 1]]
|
|
)
|
|
assert_allclose(test, expected)
|
|
|
|
def test_check_constant_float2(self):
|
|
# If input array is float, and constant_values are float, the dtype of
|
|
# the array to be padded is kept - here retaining the float constants
|
|
arr = np.arange(30).reshape(5, 6)
|
|
arr_float = arr.astype(np.float64)
|
|
test = np.pad(arr_float, ((1, 2), (1, 2)), mode='constant',
|
|
constant_values=1.1)
|
|
expected = np.array(
|
|
[[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1],
|
|
|
|
[ 1.1, 0. , 1. , 2. , 3. , 4. , 5. , 1.1, 1.1],
|
|
[ 1.1, 6. , 7. , 8. , 9. , 10. , 11. , 1.1, 1.1],
|
|
[ 1.1, 12. , 13. , 14. , 15. , 16. , 17. , 1.1, 1.1],
|
|
[ 1.1, 18. , 19. , 20. , 21. , 22. , 23. , 1.1, 1.1],
|
|
[ 1.1, 24. , 25. , 26. , 27. , 28. , 29. , 1.1, 1.1],
|
|
|
|
[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1],
|
|
[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]]
|
|
)
|
|
assert_allclose(test, expected)
|
|
|
|
def test_check_constant_float3(self):
|
|
a = np.arange(100, dtype=float)
|
|
a = np.pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2))
|
|
b = np.array(
|
|
[-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
|
|
-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
|
|
-1.1, -1.1, -1.1, -1.1, -1.1,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
-1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2,
|
|
-1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2]
|
|
)
|
|
assert_allclose(a, b)
|
|
|
|
def test_check_constant_odd_pad_amount(self):
|
|
arr = np.arange(30).reshape(5, 6)
|
|
test = np.pad(arr, ((1,), (2,)), mode='constant',
|
|
constant_values=3)
|
|
expected = np.array(
|
|
[[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
|
|
|
|
[ 3, 3, 0, 1, 2, 3, 4, 5, 3, 3],
|
|
[ 3, 3, 6, 7, 8, 9, 10, 11, 3, 3],
|
|
[ 3, 3, 12, 13, 14, 15, 16, 17, 3, 3],
|
|
[ 3, 3, 18, 19, 20, 21, 22, 23, 3, 3],
|
|
[ 3, 3, 24, 25, 26, 27, 28, 29, 3, 3],
|
|
|
|
[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]]
|
|
)
|
|
assert_allclose(test, expected)
|
|
|
|
def test_check_constant_pad_2d(self):
|
|
arr = np.arange(4).reshape(2, 2)
|
|
test = np.lib.pad(arr, ((1, 2), (1, 3)), mode='constant',
|
|
constant_values=((1, 2), (3, 4)))
|
|
expected = np.array(
|
|
[[3, 1, 1, 4, 4, 4],
|
|
[3, 0, 1, 4, 4, 4],
|
|
[3, 2, 3, 4, 4, 4],
|
|
[3, 2, 2, 4, 4, 4],
|
|
[3, 2, 2, 4, 4, 4]]
|
|
)
|
|
assert_allclose(test, expected)
|
|
|
|
def test_check_large_integers(self):
|
|
uint64_max = 2 ** 64 - 1
|
|
arr = np.full(5, uint64_max, dtype=np.uint64)
|
|
test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
|
|
expected = np.full(7, uint64_max, dtype=np.uint64)
|
|
assert_array_equal(test, expected)
|
|
|
|
int64_max = 2 ** 63 - 1
|
|
arr = np.full(5, int64_max, dtype=np.int64)
|
|
test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
|
|
expected = np.full(7, int64_max, dtype=np.int64)
|
|
assert_array_equal(test, expected)
|
|
|
|
def test_check_object_array(self):
|
|
arr = np.empty(1, dtype=object)
|
|
obj_a = object()
|
|
arr[0] = obj_a
|
|
obj_b = object()
|
|
obj_c = object()
|
|
arr = np.pad(arr, pad_width=1, mode='constant',
|
|
constant_values=(obj_b, obj_c))
|
|
|
|
expected = np.empty((3,), dtype=object)
|
|
expected[0] = obj_b
|
|
expected[1] = obj_a
|
|
expected[2] = obj_c
|
|
|
|
assert_array_equal(arr, expected)
|
|
|
|
def test_pad_empty_dimension(self):
|
|
arr = np.zeros((3, 0, 2))
|
|
result = np.pad(arr, [(0,), (2,), (1,)], mode="constant")
|
|
assert result.shape == (3, 4, 4)
|
|
|
|
|
|
class TestLinearRamp:
|
|
def test_check_simple(self):
|
|
a = np.arange(100).astype('f')
|
|
a = np.pad(a, (25, 20), 'linear_ramp', end_values=(4, 5))
|
|
b = np.array(
|
|
[4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56,
|
|
2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96,
|
|
0.80, 0.64, 0.48, 0.32, 0.16,
|
|
|
|
0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00,
|
|
10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0,
|
|
20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0,
|
|
30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0,
|
|
40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0,
|
|
50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0,
|
|
60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0,
|
|
70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0,
|
|
80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0,
|
|
90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0,
|
|
|
|
94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0,
|
|
47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.]
|
|
)
|
|
assert_allclose(a, b, rtol=1e-5, atol=1e-5)
|
|
|
|
def test_check_2d(self):
|
|
arr = np.arange(20).reshape(4, 5).astype(np.float64)
|
|
test = np.pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0))
|
|
expected = np.array(
|
|
[[0., 0., 0., 0., 0., 0., 0., 0., 0.],
|
|
[0., 0., 0., 0.5, 1., 1.5, 2., 1., 0.],
|
|
[0., 0., 0., 1., 2., 3., 4., 2., 0.],
|
|
[0., 2.5, 5., 6., 7., 8., 9., 4.5, 0.],
|
|
[0., 5., 10., 11., 12., 13., 14., 7., 0.],
|
|
[0., 7.5, 15., 16., 17., 18., 19., 9.5, 0.],
|
|
[0., 3.75, 7.5, 8., 8.5, 9., 9.5, 4.75, 0.],
|
|
[0., 0., 0., 0., 0., 0., 0., 0., 0.]])
|
|
assert_allclose(test, expected)
|
|
|
|
@pytest.mark.xfail(exceptions=(AssertionError,))
|
|
def test_object_array(self):
|
|
from fractions import Fraction
|
|
arr = np.array([Fraction(1, 2), Fraction(-1, 2)])
|
|
actual = np.pad(arr, (2, 3), mode='linear_ramp', end_values=0)
|
|
|
|
# deliberately chosen to have a non-power-of-2 denominator such that
|
|
# rounding to floats causes a failure.
|
|
expected = np.array([
|
|
Fraction( 0, 12),
|
|
Fraction( 3, 12),
|
|
Fraction( 6, 12),
|
|
Fraction(-6, 12),
|
|
Fraction(-4, 12),
|
|
Fraction(-2, 12),
|
|
Fraction(-0, 12),
|
|
])
|
|
assert_equal(actual, expected)
|
|
|
|
def test_end_values(self):
|
|
"""Ensure that end values are exact."""
|
|
a = np.pad(np.ones(10).reshape(2, 5), (223, 123), mode="linear_ramp")
|
|
assert_equal(a[:, 0], 0.)
|
|
assert_equal(a[:, -1], 0.)
|
|
assert_equal(a[0, :], 0.)
|
|
assert_equal(a[-1, :], 0.)
|
|
|
|
@pytest.mark.parametrize("dtype", _numeric_dtypes)
|
|
def test_negative_difference(self, dtype):
|
|
"""
|
|
Check correct behavior of unsigned dtypes if there is a negative
|
|
difference between the edge to pad and `end_values`. Check both cases
|
|
to be independent of implementation. Test behavior for all other dtypes
|
|
in case dtype casting interferes with complex dtypes. See gh-14191.
|
|
"""
|
|
x = np.array([3], dtype=dtype)
|
|
result = np.pad(x, 3, mode="linear_ramp", end_values=0)
|
|
expected = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype)
|
|
assert_equal(result, expected)
|
|
|
|
x = np.array([0], dtype=dtype)
|
|
result = np.pad(x, 3, mode="linear_ramp", end_values=3)
|
|
expected = np.array([3, 2, 1, 0, 1, 2, 3], dtype=dtype)
|
|
assert_equal(result, expected)
|
|
|
|
|
|
class TestReflect:
|
|
def test_check_simple(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'reflect')
|
|
b = np.array(
|
|
[25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
|
|
15, 14, 13, 12, 11, 10, 9, 8, 7, 6,
|
|
5, 4, 3, 2, 1,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
98, 97, 96, 95, 94, 93, 92, 91, 90, 89,
|
|
88, 87, 86, 85, 84, 83, 82, 81, 80, 79]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_odd_method(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'reflect', reflect_type='odd')
|
|
b = np.array(
|
|
[-25, -24, -23, -22, -21, -20, -19, -18, -17, -16,
|
|
-15, -14, -13, -12, -11, -10, -9, -8, -7, -6,
|
|
-5, -4, -3, -2, -1,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
|
|
110, 111, 112, 113, 114, 115, 116, 117, 118, 119]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_large_pad(self):
|
|
a = [[4, 5, 6], [6, 7, 8]]
|
|
a = np.pad(a, (5, 7), 'reflect')
|
|
b = np.array(
|
|
[[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_shape(self):
|
|
a = [[4, 5, 6]]
|
|
a = np.pad(a, (5, 7), 'reflect')
|
|
b = np.array(
|
|
[[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
|
|
[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_01(self):
|
|
a = np.pad([1, 2, 3], 2, 'reflect')
|
|
b = np.array([3, 2, 1, 2, 3, 2, 1])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_02(self):
|
|
a = np.pad([1, 2, 3], 3, 'reflect')
|
|
b = np.array([2, 3, 2, 1, 2, 3, 2, 1, 2])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_03(self):
|
|
a = np.pad([1, 2, 3], 4, 'reflect')
|
|
b = np.array([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
|
|
assert_array_equal(a, b)
|
|
|
|
|
|
class TestEmptyArray:
|
|
"""Check how padding behaves on arrays with an empty dimension."""
|
|
|
|
@pytest.mark.parametrize(
|
|
# Keep parametrization ordered, otherwise pytest-xdist might believe
|
|
# that different tests were collected during parallelization
|
|
"mode", sorted(_all_modes.keys() - {"constant", "empty"})
|
|
)
|
|
def test_pad_empty_dimension(self, mode):
|
|
match = ("can't extend empty axis 0 using modes other than 'constant' "
|
|
"or 'empty'")
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([], 4, mode=mode)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(np.ndarray(0), 4, mode=mode)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(np.zeros((0, 3)), ((1,), (0,)), mode=mode)
|
|
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_pad_non_empty_dimension(self, mode):
|
|
result = np.pad(np.ones((2, 0, 2)), ((3,), (0,), (1,)), mode=mode)
|
|
assert result.shape == (8, 0, 4)
|
|
|
|
|
|
class TestSymmetric:
|
|
def test_check_simple(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'symmetric')
|
|
b = np.array(
|
|
[24, 23, 22, 21, 20, 19, 18, 17, 16, 15,
|
|
14, 13, 12, 11, 10, 9, 8, 7, 6, 5,
|
|
4, 3, 2, 1, 0,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
99, 98, 97, 96, 95, 94, 93, 92, 91, 90,
|
|
89, 88, 87, 86, 85, 84, 83, 82, 81, 80]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_odd_method(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'symmetric', reflect_type='odd')
|
|
b = np.array(
|
|
[-24, -23, -22, -21, -20, -19, -18, -17, -16, -15,
|
|
-14, -13, -12, -11, -10, -9, -8, -7, -6, -5,
|
|
-4, -3, -2, -1, 0,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
|
|
109, 110, 111, 112, 113, 114, 115, 116, 117, 118]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_large_pad(self):
|
|
a = [[4, 5, 6], [6, 7, 8]]
|
|
a = np.pad(a, (5, 7), 'symmetric')
|
|
b = np.array(
|
|
[[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
[7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
|
|
)
|
|
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_large_pad_odd(self):
|
|
a = [[4, 5, 6], [6, 7, 8]]
|
|
a = np.pad(a, (5, 7), 'symmetric', reflect_type='odd')
|
|
b = np.array(
|
|
[[-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6],
|
|
[-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6],
|
|
[-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8],
|
|
[-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8],
|
|
[ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10],
|
|
|
|
[ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10],
|
|
[ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12],
|
|
|
|
[ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12],
|
|
[ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14],
|
|
[ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14],
|
|
[ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
|
|
[ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
|
|
[ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18],
|
|
[ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_shape(self):
|
|
a = [[4, 5, 6]]
|
|
a = np.pad(a, (5, 7), 'symmetric')
|
|
b = np.array(
|
|
[[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
|
|
[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_01(self):
|
|
a = np.pad([1, 2, 3], 2, 'symmetric')
|
|
b = np.array([2, 1, 1, 2, 3, 3, 2])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_02(self):
|
|
a = np.pad([1, 2, 3], 3, 'symmetric')
|
|
b = np.array([3, 2, 1, 1, 2, 3, 3, 2, 1])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_03(self):
|
|
a = np.pad([1, 2, 3], 6, 'symmetric')
|
|
b = np.array([1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3])
|
|
assert_array_equal(a, b)
|
|
|
|
|
|
class TestWrap:
|
|
def test_check_simple(self):
|
|
a = np.arange(100)
|
|
a = np.pad(a, (25, 20), 'wrap')
|
|
b = np.array(
|
|
[75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
|
|
85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
|
|
95, 96, 97, 98, 99,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
|
|
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
|
|
30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
|
|
40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
|
|
50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
|
|
60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
|
|
70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
|
|
80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
|
|
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
|
|
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_large_pad(self):
|
|
a = np.arange(12)
|
|
a = np.reshape(a, (3, 4))
|
|
a = np.pad(a, (10, 12), 'wrap')
|
|
b = np.array(
|
|
[[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11],
|
|
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
|
|
3, 0, 1, 2, 3, 0, 1, 2, 3],
|
|
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
|
|
7, 4, 5, 6, 7, 4, 5, 6, 7],
|
|
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
|
|
11, 8, 9, 10, 11, 8, 9, 10, 11]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_01(self):
|
|
a = np.pad([1, 2, 3], 3, 'wrap')
|
|
b = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_02(self):
|
|
a = np.pad([1, 2, 3], 4, 'wrap')
|
|
b = np.array([3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1])
|
|
assert_array_equal(a, b)
|
|
|
|
def test_pad_with_zero(self):
|
|
a = np.ones((3, 5))
|
|
b = np.pad(a, (0, 5), mode="wrap")
|
|
assert_array_equal(a, b[:-5, :-5])
|
|
|
|
def test_repeated_wrapping(self):
|
|
"""
|
|
Check wrapping on each side individually if the wrapped area is longer
|
|
than the original array.
|
|
"""
|
|
a = np.arange(5)
|
|
b = np.pad(a, (12, 0), mode="wrap")
|
|
assert_array_equal(np.r_[a, a, a, a][3:], b)
|
|
|
|
a = np.arange(5)
|
|
b = np.pad(a, (0, 12), mode="wrap")
|
|
assert_array_equal(np.r_[a, a, a, a][:-3], b)
|
|
|
|
def test_repeated_wrapping_multiple_origin(self):
|
|
"""
|
|
Assert that 'wrap' pads only with multiples of the original area if
|
|
the pad width is larger than the original array.
|
|
"""
|
|
a = np.arange(4).reshape(2, 2)
|
|
a = np.pad(a, [(1, 3), (3, 1)], mode='wrap')
|
|
b = np.array(
|
|
[[3, 2, 3, 2, 3, 2],
|
|
[1, 0, 1, 0, 1, 0],
|
|
[3, 2, 3, 2, 3, 2],
|
|
[1, 0, 1, 0, 1, 0],
|
|
[3, 2, 3, 2, 3, 2],
|
|
[1, 0, 1, 0, 1, 0]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
|
|
class TestEdge:
|
|
def test_check_simple(self):
|
|
a = np.arange(12)
|
|
a = np.reshape(a, (4, 3))
|
|
a = np.pad(a, ((2, 3), (3, 2)), 'edge')
|
|
b = np.array(
|
|
[[0, 0, 0, 0, 1, 2, 2, 2],
|
|
[0, 0, 0, 0, 1, 2, 2, 2],
|
|
|
|
[0, 0, 0, 0, 1, 2, 2, 2],
|
|
[3, 3, 3, 3, 4, 5, 5, 5],
|
|
[6, 6, 6, 6, 7, 8, 8, 8],
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
[9, 9, 9, 9, 10, 11, 11, 11]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
def test_check_width_shape_1_2(self):
|
|
# Check a pad_width of the form ((1, 2),).
|
|
# Regression test for issue gh-7808.
|
|
a = np.array([1, 2, 3])
|
|
padded = np.pad(a, ((1, 2),), 'edge')
|
|
expected = np.array([1, 1, 2, 3, 3, 3])
|
|
assert_array_equal(padded, expected)
|
|
|
|
a = np.array([[1, 2, 3], [4, 5, 6]])
|
|
padded = np.pad(a, ((1, 2),), 'edge')
|
|
expected = np.pad(a, ((1, 2), (1, 2)), 'edge')
|
|
assert_array_equal(padded, expected)
|
|
|
|
a = np.arange(24).reshape(2, 3, 4)
|
|
padded = np.pad(a, ((1, 2),), 'edge')
|
|
expected = np.pad(a, ((1, 2), (1, 2), (1, 2)), 'edge')
|
|
assert_array_equal(padded, expected)
|
|
|
|
|
|
class TestEmpty:
|
|
def test_simple(self):
|
|
arr = np.arange(24).reshape(4, 6)
|
|
result = np.pad(arr, [(2, 3), (3, 1)], mode="empty")
|
|
assert result.shape == (9, 10)
|
|
assert_equal(arr, result[2:-3, 3:-1])
|
|
|
|
def test_pad_empty_dimension(self):
|
|
arr = np.zeros((3, 0, 2))
|
|
result = np.pad(arr, [(0,), (2,), (1,)], mode="empty")
|
|
assert result.shape == (3, 4, 4)
|
|
|
|
|
|
def test_legacy_vector_functionality():
|
|
def _padwithtens(vector, pad_width, iaxis, kwargs):
|
|
vector[:pad_width[0]] = 10
|
|
vector[-pad_width[1]:] = 10
|
|
|
|
a = np.arange(6).reshape(2, 3)
|
|
a = np.pad(a, 2, _padwithtens)
|
|
b = np.array(
|
|
[[10, 10, 10, 10, 10, 10, 10],
|
|
[10, 10, 10, 10, 10, 10, 10],
|
|
|
|
[10, 10, 0, 1, 2, 10, 10],
|
|
[10, 10, 3, 4, 5, 10, 10],
|
|
|
|
[10, 10, 10, 10, 10, 10, 10],
|
|
[10, 10, 10, 10, 10, 10, 10]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
|
|
def test_unicode_mode():
|
|
a = np.pad([1], 2, mode='constant')
|
|
b = np.array([0, 0, 1, 0, 0])
|
|
assert_array_equal(a, b)
|
|
|
|
|
|
@pytest.mark.parametrize("mode", ["edge", "symmetric", "reflect", "wrap"])
|
|
def test_object_input(mode):
|
|
# Regression test for issue gh-11395.
|
|
a = np.full((4, 3), fill_value=None)
|
|
pad_amt = ((2, 3), (3, 2))
|
|
b = np.full((9, 8), fill_value=None)
|
|
assert_array_equal(np.pad(a, pad_amt, mode=mode), b)
|
|
|
|
|
|
class TestPadWidth:
|
|
@pytest.mark.parametrize("pad_width", [
|
|
(4, 5, 6, 7),
|
|
((1,), (2,), (3,)),
|
|
((1, 2), (3, 4), (5, 6)),
|
|
((3, 4, 5), (0, 1, 2)),
|
|
])
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_misshaped_pad_width(self, pad_width, mode):
|
|
arr = np.arange(30).reshape((6, 5))
|
|
match = "operands could not be broadcast together"
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(arr, pad_width, mode)
|
|
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_misshaped_pad_width_2(self, mode):
|
|
arr = np.arange(30).reshape((6, 5))
|
|
match = ("input operand has more dimensions than allowed by the axis "
|
|
"remapping")
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(arr, (((3,), (4,), (5,)), ((0,), (1,), (2,))), mode)
|
|
|
|
@pytest.mark.parametrize(
|
|
"pad_width", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))])
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_negative_pad_width(self, pad_width, mode):
|
|
arr = np.arange(30).reshape((6, 5))
|
|
match = "index can't contain negative values"
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad(arr, pad_width, mode)
|
|
|
|
@pytest.mark.parametrize("pad_width, dtype", [
|
|
("3", None),
|
|
("word", None),
|
|
(None, None),
|
|
(object(), None),
|
|
(3.4, None),
|
|
(((2, 3, 4), (3, 2)), object),
|
|
(complex(1, -1), None),
|
|
(((-2.1, 3), (3, 2)), None),
|
|
])
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_bad_type(self, pad_width, dtype, mode):
|
|
arr = np.arange(30).reshape((6, 5))
|
|
match = "`pad_width` must be of integral type."
|
|
if dtype is not None:
|
|
# avoid DeprecationWarning when not specifying dtype
|
|
with pytest.raises(TypeError, match=match):
|
|
np.pad(arr, np.array(pad_width, dtype=dtype), mode)
|
|
else:
|
|
with pytest.raises(TypeError, match=match):
|
|
np.pad(arr, pad_width, mode)
|
|
with pytest.raises(TypeError, match=match):
|
|
np.pad(arr, np.array(pad_width), mode)
|
|
|
|
def test_pad_width_as_ndarray(self):
|
|
a = np.arange(12)
|
|
a = np.reshape(a, (4, 3))
|
|
a = np.pad(a, np.array(((2, 3), (3, 2))), 'edge')
|
|
b = np.array(
|
|
[[0, 0, 0, 0, 1, 2, 2, 2],
|
|
[0, 0, 0, 0, 1, 2, 2, 2],
|
|
|
|
[0, 0, 0, 0, 1, 2, 2, 2],
|
|
[3, 3, 3, 3, 4, 5, 5, 5],
|
|
[6, 6, 6, 6, 7, 8, 8, 8],
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
[9, 9, 9, 9, 10, 11, 11, 11],
|
|
[9, 9, 9, 9, 10, 11, 11, 11]]
|
|
)
|
|
assert_array_equal(a, b)
|
|
|
|
@pytest.mark.parametrize("pad_width", [0, (0, 0), ((0, 0), (0, 0))])
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_zero_pad_width(self, pad_width, mode):
|
|
arr = np.arange(30).reshape(6, 5)
|
|
assert_array_equal(arr, np.pad(arr, pad_width, mode=mode))
|
|
|
|
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_kwargs(mode):
|
|
"""Test behavior of pad's kwargs for the given mode."""
|
|
allowed = _all_modes[mode]
|
|
not_allowed = {}
|
|
for kwargs in _all_modes.values():
|
|
if kwargs != allowed:
|
|
not_allowed.update(kwargs)
|
|
# Test if allowed keyword arguments pass
|
|
np.pad([1, 2, 3], 1, mode, **allowed)
|
|
# Test if prohibited keyword arguments of other modes raise an error
|
|
for key, value in not_allowed.items():
|
|
match = "unsupported keyword arguments for mode '{}'".format(mode)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1, 2, 3], 1, mode, **{key: value})
|
|
|
|
|
|
def test_constant_zero_default():
|
|
arr = np.array([1, 1])
|
|
assert_array_equal(np.pad(arr, 2), [0, 0, 1, 1, 0, 0])
|
|
|
|
|
|
@pytest.mark.parametrize("mode", [1, "const", object(), None, True, False])
|
|
def test_unsupported_mode(mode):
|
|
match= "mode '{}' is not supported".format(mode)
|
|
with pytest.raises(ValueError, match=match):
|
|
np.pad([1, 2, 3], 4, mode=mode)
|
|
|
|
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_non_contiguous_array(mode):
|
|
arr = np.arange(24).reshape(4, 6)[::2, ::2]
|
|
result = np.pad(arr, (2, 3), mode)
|
|
assert result.shape == (7, 8)
|
|
assert_equal(result[2:-3, 2:-3], arr)
|
|
|
|
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_memory_layout_persistence(mode):
|
|
"""Test if C and F order is preserved for all pad modes."""
|
|
x = np.ones((5, 10), order='C')
|
|
assert np.pad(x, 5, mode).flags["C_CONTIGUOUS"]
|
|
x = np.ones((5, 10), order='F')
|
|
assert np.pad(x, 5, mode).flags["F_CONTIGUOUS"]
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", _numeric_dtypes)
|
|
@pytest.mark.parametrize("mode", _all_modes.keys())
|
|
def test_dtype_persistence(dtype, mode):
|
|
arr = np.zeros((3, 2, 1), dtype=dtype)
|
|
result = np.pad(arr, 1, mode=mode)
|
|
assert result.dtype == dtype
|