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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/numpy/lib/tests/test_arraypad.py

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Created starter files for the project.
2020-10-03 01:26:03 +00:00
"""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 (true_divide|double_scalars):"
"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)
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=u'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
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