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517 lines
18 KiB
517 lines
18 KiB
import pytest
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import numpy as np
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from numpy.testing import (
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assert_, assert_equal, assert_array_equal, assert_almost_equal,
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assert_array_almost_equal, assert_raises, assert_raises_regex,
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)
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from numpy.lib.index_tricks import (
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mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from,
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index_exp, ndindex, r_, s_, ix_
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)
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class TestRavelUnravelIndex:
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def test_basic(self):
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assert_equal(np.unravel_index(2, (2, 2)), (1, 0))
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# test that new shape argument works properly
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assert_equal(np.unravel_index(indices=2,
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shape=(2, 2)),
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(1, 0))
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# test that an invalid second keyword argument
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# is properly handled, including the old name `dims`.
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with assert_raises(TypeError):
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np.unravel_index(indices=2, hape=(2, 2))
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with assert_raises(TypeError):
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np.unravel_index(2, hape=(2, 2))
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with assert_raises(TypeError):
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np.unravel_index(254, ims=(17, 94))
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with assert_raises(TypeError):
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np.unravel_index(254, dims=(17, 94))
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assert_equal(np.ravel_multi_index((1, 0), (2, 2)), 2)
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assert_equal(np.unravel_index(254, (17, 94)), (2, 66))
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assert_equal(np.ravel_multi_index((2, 66), (17, 94)), 254)
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assert_raises(ValueError, np.unravel_index, -1, (2, 2))
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assert_raises(TypeError, np.unravel_index, 0.5, (2, 2))
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assert_raises(ValueError, np.unravel_index, 4, (2, 2))
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assert_raises(ValueError, np.ravel_multi_index, (-3, 1), (2, 2))
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assert_raises(ValueError, np.ravel_multi_index, (2, 1), (2, 2))
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assert_raises(ValueError, np.ravel_multi_index, (0, -3), (2, 2))
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assert_raises(ValueError, np.ravel_multi_index, (0, 2), (2, 2))
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assert_raises(TypeError, np.ravel_multi_index, (0.1, 0.), (2, 2))
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assert_equal(np.unravel_index((2*3 + 1)*6 + 4, (4, 3, 6)), [2, 1, 4])
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assert_equal(
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np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (2*3 + 1)*6 + 4)
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arr = np.array([[3, 6, 6], [4, 5, 1]])
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assert_equal(np.ravel_multi_index(arr, (7, 6)), [22, 41, 37])
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assert_equal(
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np.ravel_multi_index(arr, (7, 6), order='F'), [31, 41, 13])
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assert_equal(
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np.ravel_multi_index(arr, (4, 6), mode='clip'), [22, 23, 19])
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assert_equal(np.ravel_multi_index(arr, (4, 4), mode=('clip', 'wrap')),
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[12, 13, 13])
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assert_equal(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), 1621)
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assert_equal(np.unravel_index(np.array([22, 41, 37]), (7, 6)),
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[[3, 6, 6], [4, 5, 1]])
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assert_equal(
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np.unravel_index(np.array([31, 41, 13]), (7, 6), order='F'),
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[[3, 6, 6], [4, 5, 1]])
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assert_equal(np.unravel_index(1621, (6, 7, 8, 9)), [3, 1, 4, 1])
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def test_empty_indices(self):
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msg1 = 'indices must be integral: the provided empty sequence was'
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msg2 = 'only int indices permitted'
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assert_raises_regex(TypeError, msg1, np.unravel_index, [], (10, 3, 5))
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assert_raises_regex(TypeError, msg1, np.unravel_index, (), (10, 3, 5))
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assert_raises_regex(TypeError, msg2, np.unravel_index, np.array([]),
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(10, 3, 5))
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assert_equal(np.unravel_index(np.array([],dtype=int), (10, 3, 5)),
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[[], [], []])
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assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], []),
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(10, 3))
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assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], ['abc']),
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(10, 3))
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assert_raises_regex(TypeError, msg2, np.ravel_multi_index,
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(np.array([]), np.array([])), (5, 3))
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assert_equal(np.ravel_multi_index(
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(np.array([], dtype=int), np.array([], dtype=int)), (5, 3)), [])
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assert_equal(np.ravel_multi_index(np.array([[], []], dtype=int),
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(5, 3)), [])
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def test_big_indices(self):
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# ravel_multi_index for big indices (issue #7546)
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if np.intp == np.int64:
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arr = ([1, 29], [3, 5], [3, 117], [19, 2],
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[2379, 1284], [2, 2], [0, 1])
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assert_equal(
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np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
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[5627771580, 117259570957])
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# test unravel_index for big indices (issue #9538)
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assert_raises(ValueError, np.unravel_index, 1, (2**32-1, 2**31+1))
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# test overflow checking for too big array (issue #7546)
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dummy_arr = ([0],[0])
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half_max = np.iinfo(np.intp).max // 2
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assert_equal(
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np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
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assert_raises(ValueError,
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np.ravel_multi_index, dummy_arr, (half_max+1, 2))
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assert_equal(
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np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
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assert_raises(ValueError,
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np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F')
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def test_dtypes(self):
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# Test with different data types
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for dtype in [np.int16, np.uint16, np.int32,
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np.uint32, np.int64, np.uint64]:
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coords = np.array(
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[[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0]], dtype=dtype)
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shape = (5, 8)
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uncoords = 8*coords[0]+coords[1]
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assert_equal(np.ravel_multi_index(coords, shape), uncoords)
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assert_equal(coords, np.unravel_index(uncoords, shape))
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uncoords = coords[0]+5*coords[1]
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assert_equal(
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np.ravel_multi_index(coords, shape, order='F'), uncoords)
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assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
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coords = np.array(
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[[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0], [1, 3, 1, 0, 9, 5]],
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dtype=dtype)
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shape = (5, 8, 10)
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uncoords = 10*(8*coords[0]+coords[1])+coords[2]
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assert_equal(np.ravel_multi_index(coords, shape), uncoords)
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assert_equal(coords, np.unravel_index(uncoords, shape))
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uncoords = coords[0]+5*(coords[1]+8*coords[2])
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assert_equal(
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np.ravel_multi_index(coords, shape, order='F'), uncoords)
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assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
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def test_clipmodes(self):
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# Test clipmodes
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assert_equal(
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np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode='wrap'),
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np.ravel_multi_index([1, 1, 6, 2], (4, 3, 7, 12)))
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assert_equal(np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12),
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mode=(
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'wrap', 'raise', 'clip', 'raise')),
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np.ravel_multi_index([1, 1, 0, 2], (4, 3, 7, 12)))
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assert_raises(
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ValueError, np.ravel_multi_index, [5, 1, -1, 2], (4, 3, 7, 12))
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def test_writeability(self):
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# See gh-7269
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x, y = np.unravel_index([1, 2, 3], (4, 5))
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assert_(x.flags.writeable)
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assert_(y.flags.writeable)
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def test_0d(self):
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# gh-580
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x = np.unravel_index(0, ())
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assert_equal(x, ())
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assert_raises_regex(ValueError, "0d array", np.unravel_index, [0], ())
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assert_raises_regex(
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ValueError, "out of bounds", np.unravel_index, [1], ())
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@pytest.mark.parametrize("mode", ["clip", "wrap", "raise"])
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def test_empty_array_ravel(self, mode):
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res = np.ravel_multi_index(
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np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode)
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assert(res.shape == (0,))
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with assert_raises(ValueError):
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np.ravel_multi_index(
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np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode)
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def test_empty_array_unravel(self):
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res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0))
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# res is a tuple of three empty arrays
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assert(len(res) == 3)
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assert(all(a.shape == (0,) for a in res))
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with assert_raises(ValueError):
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np.unravel_index([1], (2, 1, 0))
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class TestGrid:
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def test_basic(self):
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a = mgrid[-1:1:10j]
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b = mgrid[-1:1:0.1]
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assert_(a.shape == (10,))
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assert_(b.shape == (20,))
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assert_(a[0] == -1)
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assert_almost_equal(a[-1], 1)
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assert_(b[0] == -1)
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assert_almost_equal(b[1]-b[0], 0.1, 11)
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assert_almost_equal(b[-1], b[0]+19*0.1, 11)
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assert_almost_equal(a[1]-a[0], 2.0/9.0, 11)
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def test_linspace_equivalence(self):
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y, st = np.linspace(2, 10, retstep=True)
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assert_almost_equal(st, 8/49.0)
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assert_array_almost_equal(y, mgrid[2:10:50j], 13)
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def test_nd(self):
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c = mgrid[-1:1:10j, -2:2:10j]
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d = mgrid[-1:1:0.1, -2:2:0.2]
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assert_(c.shape == (2, 10, 10))
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assert_(d.shape == (2, 20, 20))
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assert_array_equal(c[0][0, :], -np.ones(10, 'd'))
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assert_array_equal(c[1][:, 0], -2*np.ones(10, 'd'))
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assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11)
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assert_array_almost_equal(c[1][:, -1], 2*np.ones(10, 'd'), 11)
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assert_array_almost_equal(d[0, 1, :] - d[0, 0, :],
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0.1*np.ones(20, 'd'), 11)
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assert_array_almost_equal(d[1, :, 1] - d[1, :, 0],
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0.2*np.ones(20, 'd'), 11)
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def test_sparse(self):
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grid_full = mgrid[-1:1:10j, -2:2:10j]
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grid_sparse = ogrid[-1:1:10j, -2:2:10j]
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# sparse grids can be made dense by broadcasting
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grid_broadcast = np.broadcast_arrays(*grid_sparse)
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for f, b in zip(grid_full, grid_broadcast):
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assert_equal(f, b)
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@pytest.mark.parametrize("start, stop, step, expected", [
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(None, 10, 10j, (200, 10)),
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(-10, 20, None, (1800, 30)),
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])
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def test_mgrid_size_none_handling(self, start, stop, step, expected):
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# regression test None value handling for
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# start and step values used by mgrid;
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# internally, this aims to cover previously
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# unexplored code paths in nd_grid()
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grid = mgrid[start:stop:step, start:stop:step]
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# need a smaller grid to explore one of the
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# untested code paths
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grid_small = mgrid[start:stop:step]
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assert_equal(grid.size, expected[0])
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assert_equal(grid_small.size, expected[1])
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def test_accepts_npfloating(self):
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# regression test for #16466
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grid64 = mgrid[0.1:0.33:0.1, ]
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grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1), ]
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assert_(grid32.dtype == np.float64)
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assert_array_almost_equal(grid64, grid32)
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# different code path for single slice
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grid64 = mgrid[0.1:0.33:0.1]
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grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1)]
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assert_(grid32.dtype == np.float64)
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assert_array_almost_equal(grid64, grid32)
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def test_accepts_npcomplexfloating(self):
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# Related to #16466
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assert_array_almost_equal(
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mgrid[0.1:0.3:3j, ], mgrid[0.1:0.3:np.complex64(3j), ]
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)
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# different code path for single slice
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assert_array_almost_equal(
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mgrid[0.1:0.3:3j], mgrid[0.1:0.3:np.complex64(3j)]
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)
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class TestConcatenator:
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def test_1d(self):
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assert_array_equal(r_[1, 2, 3, 4, 5, 6], np.array([1, 2, 3, 4, 5, 6]))
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b = np.ones(5)
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c = r_[b, 0, 0, b]
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assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
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def test_mixed_type(self):
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g = r_[10.1, 1:10]
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assert_(g.dtype == 'f8')
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def test_more_mixed_type(self):
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g = r_[-10.1, np.array([1]), np.array([2, 3, 4]), 10.0]
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assert_(g.dtype == 'f8')
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def test_complex_step(self):
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# Regression test for #12262
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g = r_[0:36:100j]
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assert_(g.shape == (100,))
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# Related to #16466
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g = r_[0:36:np.complex64(100j)]
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assert_(g.shape == (100,))
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def test_2d(self):
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b = np.random.rand(5, 5)
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c = np.random.rand(5, 5)
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d = r_['1', b, c] # append columns
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assert_(d.shape == (5, 10))
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assert_array_equal(d[:, :5], b)
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assert_array_equal(d[:, 5:], c)
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d = r_[b, c]
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assert_(d.shape == (10, 5))
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assert_array_equal(d[:5, :], b)
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assert_array_equal(d[5:, :], c)
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def test_0d(self):
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assert_equal(r_[0, np.array(1), 2], [0, 1, 2])
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assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3])
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assert_equal(r_[np.array(0), [1, 2, 3]], [0, 1, 2, 3])
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class TestNdenumerate:
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def test_basic(self):
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a = np.array([[1, 2], [3, 4]])
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assert_equal(list(ndenumerate(a)),
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[((0, 0), 1), ((0, 1), 2), ((1, 0), 3), ((1, 1), 4)])
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class TestIndexExpression:
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def test_regression_1(self):
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# ticket #1196
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a = np.arange(2)
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assert_equal(a[:-1], a[s_[:-1]])
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assert_equal(a[:-1], a[index_exp[:-1]])
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def test_simple_1(self):
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a = np.random.rand(4, 5, 6)
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assert_equal(a[:, :3, [1, 2]], a[index_exp[:, :3, [1, 2]]])
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assert_equal(a[:, :3, [1, 2]], a[s_[:, :3, [1, 2]]])
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class TestIx_:
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def test_regression_1(self):
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# Test empty untyped inputs create outputs of indexing type, gh-5804
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a, = np.ix_(range(0))
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assert_equal(a.dtype, np.intp)
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a, = np.ix_([])
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assert_equal(a.dtype, np.intp)
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# but if the type is specified, don't change it
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a, = np.ix_(np.array([], dtype=np.float32))
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assert_equal(a.dtype, np.float32)
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def test_shape_and_dtype(self):
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sizes = (4, 5, 3, 2)
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# Test both lists and arrays
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for func in (range, np.arange):
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arrays = np.ix_(*[func(sz) for sz in sizes])
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for k, (a, sz) in enumerate(zip(arrays, sizes)):
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assert_equal(a.shape[k], sz)
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assert_(all(sh == 1 for j, sh in enumerate(a.shape) if j != k))
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assert_(np.issubdtype(a.dtype, np.integer))
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def test_bool(self):
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bool_a = [True, False, True, True]
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int_a, = np.nonzero(bool_a)
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assert_equal(np.ix_(bool_a)[0], int_a)
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def test_1d_only(self):
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idx2d = [[1, 2, 3], [4, 5, 6]]
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assert_raises(ValueError, np.ix_, idx2d)
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def test_repeated_input(self):
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length_of_vector = 5
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x = np.arange(length_of_vector)
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out = ix_(x, x)
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assert_equal(out[0].shape, (length_of_vector, 1))
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assert_equal(out[1].shape, (1, length_of_vector))
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# check that input shape is not modified
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assert_equal(x.shape, (length_of_vector,))
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def test_c_():
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a = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])]
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assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]])
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class TestFillDiagonal:
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def test_basic(self):
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a = np.zeros((3, 3), int)
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fill_diagonal(a, 5)
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assert_array_equal(
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a, np.array([[5, 0, 0],
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[0, 5, 0],
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[0, 0, 5]])
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)
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def test_tall_matrix(self):
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a = np.zeros((10, 3), int)
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fill_diagonal(a, 5)
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assert_array_equal(
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a, np.array([[5, 0, 0],
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[0, 5, 0],
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[0, 0, 5],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]])
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)
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def test_tall_matrix_wrap(self):
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a = np.zeros((10, 3), int)
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fill_diagonal(a, 5, True)
|
|
assert_array_equal(
|
|
a, np.array([[5, 0, 0],
|
|
[0, 5, 0],
|
|
[0, 0, 5],
|
|
[0, 0, 0],
|
|
[5, 0, 0],
|
|
[0, 5, 0],
|
|
[0, 0, 5],
|
|
[0, 0, 0],
|
|
[5, 0, 0],
|
|
[0, 5, 0]])
|
|
)
|
|
|
|
def test_wide_matrix(self):
|
|
a = np.zeros((3, 10), int)
|
|
fill_diagonal(a, 5)
|
|
assert_array_equal(
|
|
a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0]])
|
|
)
|
|
|
|
def test_operate_4d_array(self):
|
|
a = np.zeros((3, 3, 3, 3), int)
|
|
fill_diagonal(a, 4)
|
|
i = np.array([0, 1, 2])
|
|
assert_equal(np.where(a != 0), (i, i, i, i))
|
|
|
|
def test_low_dim_handling(self):
|
|
# raise error with low dimensionality
|
|
a = np.zeros(3, int)
|
|
with assert_raises_regex(ValueError, "at least 2-d"):
|
|
fill_diagonal(a, 5)
|
|
|
|
def test_hetero_shape_handling(self):
|
|
# raise error with high dimensionality and
|
|
# shape mismatch
|
|
a = np.zeros((3,3,7,3), int)
|
|
with assert_raises_regex(ValueError, "equal length"):
|
|
fill_diagonal(a, 2)
|
|
|
|
|
|
def test_diag_indices():
|
|
di = diag_indices(4)
|
|
a = np.array([[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16]])
|
|
a[di] = 100
|
|
assert_array_equal(
|
|
a, np.array([[100, 2, 3, 4],
|
|
[5, 100, 7, 8],
|
|
[9, 10, 100, 12],
|
|
[13, 14, 15, 100]])
|
|
)
|
|
|
|
# Now, we create indices to manipulate a 3-d array:
|
|
d3 = diag_indices(2, 3)
|
|
|
|
# And use it to set the diagonal of a zeros array to 1:
|
|
a = np.zeros((2, 2, 2), int)
|
|
a[d3] = 1
|
|
assert_array_equal(
|
|
a, np.array([[[1, 0],
|
|
[0, 0]],
|
|
[[0, 0],
|
|
[0, 1]]])
|
|
)
|
|
|
|
|
|
class TestDiagIndicesFrom:
|
|
|
|
def test_diag_indices_from(self):
|
|
x = np.random.random((4, 4))
|
|
r, c = diag_indices_from(x)
|
|
assert_array_equal(r, np.arange(4))
|
|
assert_array_equal(c, np.arange(4))
|
|
|
|
def test_error_small_input(self):
|
|
x = np.ones(7)
|
|
with assert_raises_regex(ValueError, "at least 2-d"):
|
|
diag_indices_from(x)
|
|
|
|
def test_error_shape_mismatch(self):
|
|
x = np.zeros((3, 3, 2, 3), int)
|
|
with assert_raises_regex(ValueError, "equal length"):
|
|
diag_indices_from(x)
|
|
|
|
|
|
def test_ndindex():
|
|
x = list(ndindex(1, 2, 3))
|
|
expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))]
|
|
assert_array_equal(x, expected)
|
|
|
|
x = list(ndindex((1, 2, 3)))
|
|
assert_array_equal(x, expected)
|
|
|
|
# Test use of scalars and tuples
|
|
x = list(ndindex((3,)))
|
|
assert_array_equal(x, list(ndindex(3)))
|
|
|
|
# Make sure size argument is optional
|
|
x = list(ndindex())
|
|
assert_equal(x, [()])
|
|
|
|
x = list(ndindex(()))
|
|
assert_equal(x, [()])
|
|
|
|
# Make sure 0-sized ndindex works correctly
|
|
x = list(ndindex(*[0]))
|
|
assert_equal(x, [])
|
|
|