name : test_arraypad.py """Tests for the array padding functions. """ import pytest import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal from numpy.lib.arraypad import _as_pairs _numeric_dtypes = ( np.sctypes["uint"] + np.sctypes["int"] + np.sctypes["float"] + np.sctypes["complex"] ) _all_modes = { 'constant': {'constant_values': 0}, 'edge': {}, 'linear_ramp': {'end_values': 0}, 'maximum': {'stat_length': None}, 'mean': {'stat_length': None}, 'median': {'stat_length': None}, 'minimum': {'stat_length': None}, 'reflect': {'reflect_type': 'even'}, 'symmetric': {'reflect_type': 'even'}, 'wrap': {}, 'empty': {} } class TestAsPairs: def test_single_value(self): """Test casting for a single value.""" expected = np.array([[3, 3]] * 10) for x in (3, [3], [[3]]): result = _as_pairs(x, 10) assert_equal(result, expected) # Test with dtype=object obj = object() assert_equal( _as_pairs(obj, 10), np.array([[obj, obj]] * 10) ) def test_two_values(self): """Test proper casting for two different values.""" # Broadcasting in the first dimension with numbers expected = np.array([[3, 4]] * 10) for x in ([3, 4], [[3, 4]]): result = _as_pairs(x, 10) assert_equal(result, expected) # and with dtype=object obj = object() assert_equal( _as_pairs(["a", obj], 10), np.array([["a", obj]] * 10) ) # Broadcasting in the second / last dimension with numbers assert_equal( _as_pairs([[3], [4]], 2), np.array([[3, 3], [4, 4]]) ) # and with dtype=object assert_equal( _as_pairs([["a"], [obj]], 2), np.array([["a", "a"], [obj, obj]]) ) def test_with_none(self): expected = ((None, None), (None, None), (None, None)) assert_equal( _as_pairs(None, 3, as_index=False), expected ) assert_equal( _as_pairs(None, 3, as_index=True), expected ) def test_pass_through(self): """Test if `x` already matching desired output are passed through.""" expected = np.arange(12).reshape((6, 2)) assert_equal( _as_pairs(expected, 6), expected ) def test_as_index(self): """Test results if `as_index=True`.""" assert_equal( _as_pairs([2.6, 3.3], 10, as_index=True), np.array([[3, 3]] * 10, dtype=np.intp) ) assert_equal( _as_pairs([2.6, 4.49], 10, as_index=True), np.array([[3, 4]] * 10, dtype=np.intp) ) for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]], [[1, 2]] * 9 + [[1, -2]]): with pytest.raises(ValueError, match="negative values"): _as_pairs(x, 10, as_index=True) def test_exceptions(self): """Ensure faulty usage is discovered.""" with pytest.raises(ValueError, match="more dimensions than allowed"): _as_pairs([[[3]]], 10) with pytest.raises(ValueError, match="could not be broadcast"): _as_pairs([[1, 2], [3, 4]], 3) with pytest.raises(ValueError, match="could not be broadcast"): _as_pairs(np.ones((2, 3)), 3) class TestConditionalShortcuts: @pytest.mark.parametrize("mode", _all_modes.keys()) def test_zero_padding_shortcuts(self, mode): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(0, 0) for _ in test.shape] assert_array_equal(test, np.pad(test, pad_amt, mode=mode)) @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',]) def test_shallow_statistic_range(self, mode): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(1, 1) for _ in test.shape] assert_array_equal(np.pad(test, pad_amt, mode='edge'), np.pad(test, pad_amt, mode=mode, stat_length=1)) @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',]) def test_clip_statistic_range(self, mode): test = np.arange(30).reshape(5, 6) pad_amt = [(3, 3) for _ in test.shape] assert_array_equal(np.pad(test, pad_amt, mode=mode), np.pad(test, pad_amt, mode=mode, stat_length=30)) class TestStatistic: def test_check_mean_stat_length(self): a = np.arange(100).astype('f') a = np.pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), )) b = np.array( [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98. ]) assert_array_equal(a, b) def test_check_maximum_1(self): a = np.arange(100) a = np.pad(a, (25, 20), 'maximum') b = np.array( [99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 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, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99] ) assert_array_equal(a, b) def test_check_maximum_2(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'maximum') b = np.array( [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 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, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_maximum_stat_length(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'maximum', stat_length=10) 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, 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, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_minimum_1(self): a = np.arange(100) a = np.pad(a, (25, 20), 'minimum') 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_minimum_2(self): a = np.arange(100) + 2 a = np.pad(a, (25, 20), 'minimum') b = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] ) assert_array_equal(a, b) def test_check_minimum_stat_length(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'minimum', stat_length=10) 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, 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, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91] ) assert_array_equal(a, b) def test_check_median(self): a = np.arange(100).astype('f') a = np.pad(a, (25, 20), 'median') 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) def test_check_median_01(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = np.pad(a, 1, 'median') b = np.array( [[4, 4, 5, 4, 4], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [4, 4, 5, 4, 4]] ) assert_array_equal(a, b) def test_check_median_02(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = np.pad(a.T, 1, 'median').T b = np.array( [[5, 4, 5, 4, 5], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [5, 4, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_median_stat_length(self): a = np.arange(100).astype('f') a[1] = 2. a[97] = 96. a = np.pad(a, (25, 20), 'median', stat_length=(3, 5)) b = np.array( [ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 2., 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., 96., 98., 99., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96.] ) assert_array_equal(a, b) def test_check_mean_shape_one(self): a = [[4, 5, 6]] a = np.pad(a, (5, 7), 'mean', stat_length=2) b = np.array( [[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], [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