1328 lines
54 KiB
Python
1328 lines
54 KiB
Python
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import sys
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import numpy
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from numpy.testing import (assert_, assert_equal, assert_array_equal,
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assert_array_almost_equal, assert_allclose,
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suppress_warnings)
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import pytest
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from pytest import raises as assert_raises
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import scipy.ndimage as ndimage
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from . import types
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eps = 1e-12
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ndimage_to_numpy_mode = {
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'mirror': 'reflect',
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'reflect': 'symmetric',
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'grid-mirror': 'symmetric',
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'grid-wrap': 'wrap',
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'nearest': 'edge',
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'grid-constant': 'constant',
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}
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class TestNdimageInterpolation:
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@pytest.mark.parametrize(
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'mode, expected_value',
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[('nearest', [1.5, 2.5, 3.5, 4, 4, 4, 4]),
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('wrap', [1.5, 2.5, 3.5, 1.5, 2.5, 3.5, 1.5]),
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('grid-wrap', [1.5, 2.5, 3.5, 2.5, 1.5, 2.5, 3.5]),
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('mirror', [1.5, 2.5, 3.5, 3.5, 2.5, 1.5, 1.5]),
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('reflect', [1.5, 2.5, 3.5, 4, 3.5, 2.5, 1.5]),
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('constant', [1.5, 2.5, 3.5, -1, -1, -1, -1]),
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('grid-constant', [1.5, 2.5, 3.5, 1.5, -1, -1, -1])]
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)
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def test_boundaries(self, mode, expected_value):
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def shift(x):
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return (x[0] + 0.5,)
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data = numpy.array([1, 2, 3, 4.])
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assert_array_equal(
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expected_value,
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ndimage.geometric_transform(data, shift, cval=-1, mode=mode,
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output_shape=(7,), order=1))
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@pytest.mark.parametrize(
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'mode, expected_value',
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[('nearest', [1, 1, 2, 3]),
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('wrap', [3, 1, 2, 3]),
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('grid-wrap', [4, 1, 2, 3]),
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('mirror', [2, 1, 2, 3]),
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('reflect', [1, 1, 2, 3]),
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('constant', [-1, 1, 2, 3]),
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('grid-constant', [-1, 1, 2, 3])]
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)
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def test_boundaries2(self, mode, expected_value):
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def shift(x):
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return (x[0] - 0.9,)
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data = numpy.array([1, 2, 3, 4])
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assert_array_equal(
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expected_value,
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ndimage.geometric_transform(data, shift, cval=-1, mode=mode,
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output_shape=(4,)))
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@pytest.mark.parametrize('mode', ['mirror', 'reflect', 'grid-mirror',
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'grid-wrap', 'grid-constant',
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'nearest'])
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@pytest.mark.parametrize('order', range(6))
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def test_boundary_spline_accuracy(self, mode, order):
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"""Tests based on examples from gh-2640"""
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data = numpy.arange(-6, 7, dtype=float)
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x = numpy.linspace(-8, 15, num=1000)
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y = ndimage.map_coordinates(data, [x], order=order, mode=mode)
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# compute expected value using explicit padding via numpy.pad
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npad = 32
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pad_mode = ndimage_to_numpy_mode.get(mode)
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padded = numpy.pad(data, npad, mode=pad_mode)
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expected = ndimage.map_coordinates(padded, [npad + x], order=order,
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mode=mode)
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atol = 1e-5 if mode == 'grid-constant' else 1e-12
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assert_allclose(y, expected, rtol=1e-7, atol=atol)
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@pytest.mark.parametrize('order', range(2, 6))
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@pytest.mark.parametrize('dtype', types)
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def test_spline01(self, dtype, order):
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data = numpy.ones([], dtype)
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out = ndimage.spline_filter(data, order=order)
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assert_array_almost_equal(out, 1)
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@pytest.mark.parametrize('order', range(2, 6))
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@pytest.mark.parametrize('dtype', types)
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def test_spline02(self, dtype, order):
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data = numpy.array([1], dtype)
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out = ndimage.spline_filter(data, order=order)
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assert_array_almost_equal(out, [1])
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@pytest.mark.parametrize('order', range(2, 6))
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@pytest.mark.parametrize('dtype', types)
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def test_spline03(self, dtype, order):
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data = numpy.ones([], dtype)
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out = ndimage.spline_filter(data, order, output=dtype)
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assert_array_almost_equal(out, 1)
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@pytest.mark.parametrize('order', range(2, 6))
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@pytest.mark.parametrize('dtype', types)
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def test_spline04(self, dtype, order):
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data = numpy.ones([4], dtype)
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out = ndimage.spline_filter(data, order)
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assert_array_almost_equal(out, [1, 1, 1, 1])
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@pytest.mark.parametrize('order', range(2, 6))
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@pytest.mark.parametrize('dtype', types)
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def test_spline05(self, dtype, order):
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data = numpy.ones([4, 4], dtype)
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out = ndimage.spline_filter(data, order=order)
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assert_array_almost_equal(out, [[1, 1, 1, 1],
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[1, 1, 1, 1],
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[1, 1, 1, 1],
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[1, 1, 1, 1]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform01(self, order):
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data = numpy.array([1])
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def mapping(x):
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return x
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [1])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform02(self, order):
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data = numpy.ones([4])
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def mapping(x):
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return x
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [1, 1, 1, 1])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform03(self, order):
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data = numpy.ones([4])
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def mapping(x):
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return (x[0] - 1,)
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [0, 1, 1, 1])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform04(self, order):
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data = numpy.array([4, 1, 3, 2])
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def mapping(x):
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return (x[0] - 1,)
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [0, 4, 1, 3])
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@pytest.mark.parametrize('order', range(0, 6))
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@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
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def test_geometric_transform05(self, order, dtype):
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data = numpy.array([[1, 1, 1, 1],
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[1, 1, 1, 1],
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[1, 1, 1, 1]], dtype=dtype)
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expected = numpy.array([[0, 1, 1, 1],
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[0, 1, 1, 1],
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[0, 1, 1, 1]], dtype=dtype)
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if data.dtype.kind == 'c':
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data -= 1j * data
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expected -= 1j * expected
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def mapping(x):
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return (x[0], x[1] - 1)
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, expected)
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform06(self, order):
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data = numpy.array([[4, 1, 3, 2],
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[7, 6, 8, 5],
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[3, 5, 3, 6]])
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def mapping(x):
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return (x[0], x[1] - 1)
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [[0, 4, 1, 3],
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[0, 7, 6, 8],
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[0, 3, 5, 3]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform07(self, order):
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data = numpy.array([[4, 1, 3, 2],
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[7, 6, 8, 5],
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[3, 5, 3, 6]])
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def mapping(x):
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return (x[0] - 1, x[1])
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [[0, 0, 0, 0],
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[4, 1, 3, 2],
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[7, 6, 8, 5]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform08(self, order):
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data = numpy.array([[4, 1, 3, 2],
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[7, 6, 8, 5],
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[3, 5, 3, 6]])
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def mapping(x):
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return (x[0] - 1, x[1] - 1)
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out = ndimage.geometric_transform(data, mapping, data.shape,
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order=order)
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assert_array_almost_equal(out, [[0, 0, 0, 0],
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[0, 4, 1, 3],
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[0, 7, 6, 8]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform10(self, order):
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data = numpy.array([[4, 1, 3, 2],
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[7, 6, 8, 5],
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[3, 5, 3, 6]])
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def mapping(x):
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return (x[0] - 1, x[1] - 1)
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if (order > 1):
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filtered = ndimage.spline_filter(data, order=order)
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else:
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filtered = data
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out = ndimage.geometric_transform(filtered, mapping, data.shape,
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order=order, prefilter=False)
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assert_array_almost_equal(out, [[0, 0, 0, 0],
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[0, 4, 1, 3],
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[0, 7, 6, 8]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform13(self, order):
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data = numpy.ones([2], numpy.float64)
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def mapping(x):
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return (x[0] // 2,)
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out = ndimage.geometric_transform(data, mapping, [4], order=order)
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assert_array_almost_equal(out, [1, 1, 1, 1])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform14(self, order):
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data = [1, 5, 2, 6, 3, 7, 4, 4]
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def mapping(x):
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return (2 * x[0],)
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out = ndimage.geometric_transform(data, mapping, [4], order=order)
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assert_array_almost_equal(out, [1, 2, 3, 4])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform15(self, order):
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data = [1, 2, 3, 4]
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def mapping(x):
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return (x[0] / 2,)
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out = ndimage.geometric_transform(data, mapping, [8], order=order)
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assert_array_almost_equal(out[::2], [1, 2, 3, 4])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform16(self, order):
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data = [[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9.0, 10, 11, 12]]
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def mapping(x):
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return (x[0], x[1] * 2)
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out = ndimage.geometric_transform(data, mapping, (3, 2),
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order=order)
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assert_array_almost_equal(out, [[1, 3], [5, 7], [9, 11]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform17(self, order):
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data = [[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 10, 11, 12]]
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def mapping(x):
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return (x[0] * 2, x[1])
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out = ndimage.geometric_transform(data, mapping, (1, 4),
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order=order)
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assert_array_almost_equal(out, [[1, 2, 3, 4]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform18(self, order):
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data = [[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 10, 11, 12]]
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def mapping(x):
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return (x[0] * 2, x[1] * 2)
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out = ndimage.geometric_transform(data, mapping, (1, 2),
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order=order)
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assert_array_almost_equal(out, [[1, 3]])
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform19(self, order):
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data = [[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 10, 11, 12]]
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def mapping(x):
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return (x[0], x[1] / 2)
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out = ndimage.geometric_transform(data, mapping, (3, 8),
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order=order)
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assert_array_almost_equal(out[..., ::2], data)
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@pytest.mark.parametrize('order', range(0, 6))
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def test_geometric_transform20(self, order):
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data = [[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 10, 11, 12]]
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def mapping(x):
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return (x[0] / 2, x[1])
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|
|
out = ndimage.geometric_transform(data, mapping, (6, 4),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ...], data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_geometric_transform21(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] / 2, x[1] / 2)
|
||
|
|
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (6, 8),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_geometric_transform22(self, order):
|
||
|
|
data = numpy.array([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]], numpy.float64)
|
||
|
|
|
||
|
|
def mapping1(x):
|
||
|
|
return (x[0] / 2, x[1] / 2)
|
||
|
|
|
||
|
|
def mapping2(x):
|
||
|
|
return (x[0] * 2, x[1] * 2)
|
||
|
|
|
||
|
|
out = ndimage.geometric_transform(data, mapping1,
|
||
|
|
(6, 8), order=order)
|
||
|
|
out = ndimage.geometric_transform(out, mapping2,
|
||
|
|
(3, 4), order=order)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_geometric_transform23(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (1, x[0] * 2)
|
||
|
|
|
||
|
|
out = ndimage.geometric_transform(data, mapping, (2,), order=order)
|
||
|
|
out = out.astype(numpy.int32)
|
||
|
|
assert_array_almost_equal(out, [5, 7])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_geometric_transform24(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
|
||
|
|
def mapping(x, a, b):
|
||
|
|
return (a, x[0] * b)
|
||
|
|
|
||
|
|
out = ndimage.geometric_transform(
|
||
|
|
data, mapping, (2,), order=order, extra_arguments=(1,),
|
||
|
|
extra_keywords={'b': 2})
|
||
|
|
assert_array_almost_equal(out, [5, 7])
|
||
|
|
|
||
|
|
def test_geometric_transform_grid_constant_order1(self):
|
||
|
|
# verify interpolation outside the original bounds
|
||
|
|
x = numpy.array([[1, 2, 3],
|
||
|
|
[4, 5, 6]], dtype=float)
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 0.5), (x[1] - 0.5)
|
||
|
|
|
||
|
|
expected_result = numpy.array([[0.25, 0.75, 1.25],
|
||
|
|
[1.25, 3.00, 4.00]])
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.geometric_transform(x, mapping, mode='grid-constant',
|
||
|
|
order=1),
|
||
|
|
expected_result,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('mode', ['grid-constant', 'grid-wrap', 'nearest',
|
||
|
|
'mirror', 'reflect'])
|
||
|
|
@pytest.mark.parametrize('order', range(6))
|
||
|
|
def test_geometric_transform_vs_padded(self, order, mode):
|
||
|
|
x = numpy.arange(144, dtype=float).reshape(12, 12)
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return (x[0] - 0.4), (x[1] + 2.3)
|
||
|
|
|
||
|
|
# Manually pad and then extract center after the transform to get the
|
||
|
|
# expected result.
|
||
|
|
npad = 24
|
||
|
|
pad_mode = ndimage_to_numpy_mode.get(mode)
|
||
|
|
xp = numpy.pad(x, npad, mode=pad_mode)
|
||
|
|
center_slice = tuple([slice(npad, -npad)] * x.ndim)
|
||
|
|
expected_result = ndimage.geometric_transform(
|
||
|
|
xp, mapping, mode=mode, order=order)[center_slice]
|
||
|
|
|
||
|
|
assert_allclose(
|
||
|
|
ndimage.geometric_transform(x, mapping, mode=mode,
|
||
|
|
order=order),
|
||
|
|
expected_result,
|
||
|
|
rtol=1e-7,
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_geometric_transform_endianness_with_output_parameter(self):
|
||
|
|
# geometric transform given output ndarray or dtype with
|
||
|
|
# non-native endianness. see issue #4127
|
||
|
|
data = numpy.array([1])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
for out in [data.dtype, data.dtype.newbyteorder(),
|
||
|
|
numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder())]:
|
||
|
|
returned = ndimage.geometric_transform(data, mapping, data.shape,
|
||
|
|
output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [1])
|
||
|
|
|
||
|
|
def test_geometric_transform_with_string_output(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
|
||
|
|
def mapping(x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
out = ndimage.geometric_transform(data, mapping, output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_map_coordinates01(self, order, dtype):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
expected = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data = data - 1j * data
|
||
|
|
expected = expected - 1j * expected
|
||
|
|
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
idx -= 1
|
||
|
|
|
||
|
|
out = ndimage.map_coordinates(data, idx, order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_map_coordinates02(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
idx = numpy.indices(data.shape, numpy.float64)
|
||
|
|
idx -= 0.5
|
||
|
|
|
||
|
|
out1 = ndimage.shift(data, 0.5, order=order)
|
||
|
|
out2 = ndimage.map_coordinates(data, idx, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
def test_map_coordinates03(self):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]], order='F')
|
||
|
|
idx = numpy.indices(data.shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data, idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data, (1, 1)))
|
||
|
|
idx = numpy.indices(data[::2].shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data[::2], idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data[::2], (1, 1)))
|
||
|
|
idx = numpy.indices(data[:, ::2].shape) - 1
|
||
|
|
out = ndimage.map_coordinates(data[:, ::2], idx)
|
||
|
|
assert_array_almost_equal(out, [[0, 0], [0, 4], [0, 7]])
|
||
|
|
assert_array_almost_equal(out, ndimage.shift(data[:, ::2], (1, 1)))
|
||
|
|
|
||
|
|
def test_map_coordinates_endianness_with_output_parameter(self):
|
||
|
|
# output parameter given as array or dtype with either endianness
|
||
|
|
# see issue #4127
|
||
|
|
data = numpy.array([[1, 2], [7, 6]])
|
||
|
|
expected = numpy.array([[0, 0], [0, 1]])
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
idx -= 1
|
||
|
|
for out in [
|
||
|
|
data.dtype,
|
||
|
|
data.dtype.newbyteorder(),
|
||
|
|
numpy.empty_like(expected),
|
||
|
|
numpy.empty_like(expected).astype(expected.dtype.newbyteorder())
|
||
|
|
]:
|
||
|
|
returned = ndimage.map_coordinates(data, idx, output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, expected)
|
||
|
|
|
||
|
|
def test_map_coordinates_with_string_output(self):
|
||
|
|
data = numpy.array([[1]])
|
||
|
|
idx = numpy.indices(data.shape)
|
||
|
|
out = ndimage.map_coordinates(data, idx, output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [[1]])
|
||
|
|
|
||
|
|
@pytest.mark.skipif('win32' in sys.platform or numpy.intp(0).itemsize < 8,
|
||
|
|
reason='do not run on 32 bit or windows '
|
||
|
|
'(no sparse memory)')
|
||
|
|
def test_map_coordinates_large_data(self):
|
||
|
|
# check crash on large data
|
||
|
|
try:
|
||
|
|
n = 30000
|
||
|
|
a = numpy.empty(n**2, dtype=numpy.float32).reshape(n, n)
|
||
|
|
# fill the part we might read
|
||
|
|
a[n - 3:, n - 3:] = 0
|
||
|
|
ndimage.map_coordinates(a, [[n - 1.5], [n - 1.5]], order=1)
|
||
|
|
except MemoryError as e:
|
||
|
|
raise pytest.skip('Not enough memory available') from e
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform01(self, order):
|
||
|
|
data = numpy.array([1])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], order=order)
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform02(self, order):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform03(self, order):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform04(self, order):
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 4, 1, 3])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_affine_transform05(self, order, dtype):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]], dtype=dtype)
|
||
|
|
expected = numpy.array([[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]], dtype=dtype)
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data -= 1j * data
|
||
|
|
expected -= 1j * expected
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[0, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform06(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[0, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8],
|
||
|
|
[0, 3, 5, 3]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform07(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[-1, 0], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform08(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 1]],
|
||
|
|
[-1, -1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform09(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
out = ndimage.affine_transform(filtered, [[1, 0], [0, 1]],
|
||
|
|
[-1, -1], order=order,
|
||
|
|
prefilter=False)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform10(self, order):
|
||
|
|
data = numpy.ones([2], numpy.float64)
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], output_shape=(4,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 0])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform11(self, order):
|
||
|
|
data = [1, 5, 2, 6, 3, 7, 4, 4]
|
||
|
|
out = ndimage.affine_transform(data, [[2]], 0, (4,), order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 2, 3, 4])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform12(self, order):
|
||
|
|
data = [1, 2, 3, 4]
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], 0, (8,), order=order)
|
||
|
|
assert_array_almost_equal(out[::2], [1, 2, 3, 4])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform13(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9.0, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0], [0, 2]], 0, (3, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3], [5, 7], [9, 11]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform14(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[2, 0], [0, 1]], 0, (1, 4),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 2, 3, 4]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform15(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[2, 0], [0, 2]], 0, (1, 2),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [[1, 3]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform16(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[1, 0.0], [0, 0.5]], 0,
|
||
|
|
(3, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[..., ::2], data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform17(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 1]], 0,
|
||
|
|
(6, 4), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ...], data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform18(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform19(self, order):
|
||
|
|
data = numpy.array([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]], numpy.float64)
|
||
|
|
out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
out = ndimage.affine_transform(out, [[2.0, 0], [0, 2.0]], 0,
|
||
|
|
(3, 4), order=order)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform20(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[0], [2]], 0, (2,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 3])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform21(self, order):
|
||
|
|
data = [[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]]
|
||
|
|
out = ndimage.affine_transform(data, [[2], [0]], 0, (2,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 9])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform22(self, order):
|
||
|
|
# shift and offset interaction; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
out = ndimage.affine_transform(data, [[2]], [-1], (3,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 2])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform23(self, order):
|
||
|
|
# shift and offset interaction; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
out = ndimage.affine_transform(data, [[0.5]], [-1], (8,),
|
||
|
|
order=order)
|
||
|
|
assert_array_almost_equal(out[::2], [0, 4, 1, 3])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform24(self, order):
|
||
|
|
# consistency between diagonal and non-diagonal case; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
'The behavior of affine_transform with a 1-D array .* '
|
||
|
|
'has changed')
|
||
|
|
out1 = ndimage.affine_transform(data, [2], -1, order=order)
|
||
|
|
out2 = ndimage.affine_transform(data, [[2]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform25(self, order):
|
||
|
|
# consistency between diagonal and non-diagonal case; see issue #1547
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
'The behavior of affine_transform with a 1-D array .* '
|
||
|
|
'has changed')
|
||
|
|
out1 = ndimage.affine_transform(data, [0.5], -1, order=order)
|
||
|
|
out2 = ndimage.affine_transform(data, [[0.5]], -1, order=order)
|
||
|
|
assert_array_almost_equal(out1, out2)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform26(self, order):
|
||
|
|
# test homogeneous coordinates
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
tform_original = numpy.eye(2)
|
||
|
|
offset_original = -numpy.ones((2, 1))
|
||
|
|
tform_h1 = numpy.hstack((tform_original, offset_original))
|
||
|
|
tform_h2 = numpy.vstack((tform_h1, [[0, 0, 1]]))
|
||
|
|
out1 = ndimage.affine_transform(filtered, tform_original,
|
||
|
|
offset_original.ravel(),
|
||
|
|
order=order, prefilter=False)
|
||
|
|
out2 = ndimage.affine_transform(filtered, tform_h1, order=order,
|
||
|
|
prefilter=False)
|
||
|
|
out3 = ndimage.affine_transform(filtered, tform_h2, order=order,
|
||
|
|
prefilter=False)
|
||
|
|
for out in [out1, out2, out3]:
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
def test_affine_transform27(self):
|
||
|
|
# test valid homogeneous transformation matrix
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
tform_h1 = numpy.hstack((numpy.eye(2), -numpy.ones((2, 1))))
|
||
|
|
tform_h2 = numpy.vstack((tform_h1, [[5, 2, 1]]))
|
||
|
|
assert_raises(ValueError, ndimage.affine_transform, data, tform_h2)
|
||
|
|
|
||
|
|
def test_affine_transform_1d_endianness_with_output_parameter(self):
|
||
|
|
# 1d affine transform given output ndarray or dtype with
|
||
|
|
# either endianness. see issue #7388
|
||
|
|
data = numpy.ones((2, 2))
|
||
|
|
for out in [numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder()),
|
||
|
|
data.dtype, data.dtype.newbyteorder()]:
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
'The behavior of affine_transform with a 1-D array '
|
||
|
|
'.* has changed')
|
||
|
|
returned = ndimage.affine_transform(data, [1, 1], output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [[1, 1], [1, 1]])
|
||
|
|
|
||
|
|
def test_affine_transform_multi_d_endianness_with_output_parameter(self):
|
||
|
|
# affine transform given output ndarray or dtype with either endianness
|
||
|
|
# see issue #4127
|
||
|
|
data = numpy.array([1])
|
||
|
|
for out in [data.dtype, data.dtype.newbyteorder(),
|
||
|
|
numpy.empty_like(data),
|
||
|
|
numpy.empty_like(data).astype(data.dtype.newbyteorder())]:
|
||
|
|
returned = ndimage.affine_transform(data, [[1]], output=out)
|
||
|
|
result = out if returned is None else returned
|
||
|
|
assert_array_almost_equal(result, [1])
|
||
|
|
|
||
|
|
def test_affine_transform_output_shape(self):
|
||
|
|
# don't require output_shape when out of a different size is given
|
||
|
|
data = numpy.arange(8, dtype=numpy.float64)
|
||
|
|
out = numpy.ones((16,))
|
||
|
|
|
||
|
|
ndimage.affine_transform(data, [[1]], output=out)
|
||
|
|
assert_array_almost_equal(out[:8], data)
|
||
|
|
|
||
|
|
# mismatched output shape raises an error
|
||
|
|
with pytest.raises(RuntimeError):
|
||
|
|
ndimage.affine_transform(
|
||
|
|
data, [[1]], output=out, output_shape=(12,))
|
||
|
|
|
||
|
|
def test_affine_transform_with_string_output(self):
|
||
|
|
data = numpy.array([1])
|
||
|
|
out = ndimage.affine_transform(data, [[1]], output='f')
|
||
|
|
assert_(out.dtype is numpy.dtype('f'))
|
||
|
|
assert_array_almost_equal(out, [1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('shift',
|
||
|
|
[(1, 0), (0, 1), (-1, 1), (3, -5), (2, 7)])
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform_shift_via_grid_wrap(self, shift, order):
|
||
|
|
# For mode 'grid-wrap', integer shifts should match numpy.roll
|
||
|
|
x = numpy.array([[0, 1],
|
||
|
|
[2, 3]])
|
||
|
|
affine = numpy.zeros((2, 3))
|
||
|
|
affine[:2, :2] = numpy.eye(2)
|
||
|
|
affine[:, 2] = shift
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.affine_transform(x, affine, mode='grid-wrap', order=order),
|
||
|
|
numpy.roll(x, shift, axis=(0, 1)),
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_affine_transform_shift_reflect(self, order):
|
||
|
|
# shift by x.shape results in reflection
|
||
|
|
x = numpy.array([[0, 1, 2],
|
||
|
|
[3, 4, 5]])
|
||
|
|
affine = numpy.zeros((2, 3))
|
||
|
|
affine[:2, :2] = numpy.eye(2)
|
||
|
|
affine[:, 2] = x.shape
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.affine_transform(x, affine, mode='reflect', order=order),
|
||
|
|
x[::-1, ::-1],
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift01(self, order):
|
||
|
|
data = numpy.array([1])
|
||
|
|
out = ndimage.shift(data, [1], order=order)
|
||
|
|
assert_array_almost_equal(out, [0])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift02(self, order):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
out = ndimage.shift(data, [1], order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 1, 1, 1])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift03(self, order):
|
||
|
|
data = numpy.ones([4])
|
||
|
|
out = ndimage.shift(data, -1, order=order)
|
||
|
|
assert_array_almost_equal(out, [1, 1, 1, 0])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift04(self, order):
|
||
|
|
data = numpy.array([4, 1, 3, 2])
|
||
|
|
out = ndimage.shift(data, 1, order=order)
|
||
|
|
assert_array_almost_equal(out, [0, 4, 1, 3])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_shift05(self, order, dtype):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]], dtype=dtype)
|
||
|
|
expected = numpy.array([[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]], dtype=dtype)
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data -= 1j * data
|
||
|
|
expected -= 1j * expected
|
||
|
|
out = ndimage.shift(data, [0, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('mode', ['constant', 'grid-constant'])
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_shift_with_nonzero_cval(self, order, mode, dtype):
|
||
|
|
data = numpy.array([[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1],
|
||
|
|
[1, 1, 1, 1]], dtype=dtype)
|
||
|
|
|
||
|
|
expected = numpy.array([[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1],
|
||
|
|
[0, 1, 1, 1]], dtype=dtype)
|
||
|
|
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data -= 1j * data
|
||
|
|
expected -= 1j * expected
|
||
|
|
cval = 5.0
|
||
|
|
expected[:, 0] = cval # specific to shift of [0, 1] used below
|
||
|
|
out = ndimage.shift(data, [0, 1], order=order, mode=mode, cval=cval)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift06(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.shift(data, [0, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8],
|
||
|
|
[0, 3, 5, 3]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift07(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.shift(data, [1, 0], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift08(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
out = ndimage.shift(data, [1, 1], order=order)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift09(self, order):
|
||
|
|
data = numpy.array([[4, 1, 3, 2],
|
||
|
|
[7, 6, 8, 5],
|
||
|
|
[3, 5, 3, 6]])
|
||
|
|
if (order > 1):
|
||
|
|
filtered = ndimage.spline_filter(data, order=order)
|
||
|
|
else:
|
||
|
|
filtered = data
|
||
|
|
out = ndimage.shift(filtered, [1, 1], order=order, prefilter=False)
|
||
|
|
assert_array_almost_equal(out, [[0, 0, 0, 0],
|
||
|
|
[0, 4, 1, 3],
|
||
|
|
[0, 7, 6, 8]])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('shift',
|
||
|
|
[(1, 0), (0, 1), (-1, 1), (3, -5), (2, 7)])
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift_grid_wrap(self, shift, order):
|
||
|
|
# For mode 'grid-wrap', integer shifts should match numpy.roll
|
||
|
|
x = numpy.array([[0, 1],
|
||
|
|
[2, 3]])
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, shift, mode='grid-wrap', order=order),
|
||
|
|
numpy.roll(x, shift, axis=(0, 1)),
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('shift',
|
||
|
|
[(1, 0), (0, 1), (-1, 1), (3, -5), (2, 7)])
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift_grid_constant1(self, shift, order):
|
||
|
|
# For integer shifts, 'constant' and 'grid-constant' should be equal
|
||
|
|
x = numpy.arange(20).reshape((5, 4))
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, shift, mode='grid-constant', order=order),
|
||
|
|
ndimage.shift(x, shift, mode='constant', order=order),
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_shift_grid_constant_order1(self):
|
||
|
|
x = numpy.array([[1, 2, 3],
|
||
|
|
[4, 5, 6]], dtype=float)
|
||
|
|
expected_result = numpy.array([[0.25, 0.75, 1.25],
|
||
|
|
[1.25, 3.00, 4.00]])
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, (0.5, 0.5), mode='grid-constant', order=1),
|
||
|
|
expected_result,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_shift_reflect(self, order):
|
||
|
|
# shift by x.shape results in reflection
|
||
|
|
x = numpy.array([[0, 1, 2],
|
||
|
|
[3, 4, 5]])
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, x.shape, mode='reflect', order=order),
|
||
|
|
x[::-1, ::-1],
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('prefilter', [False, True])
|
||
|
|
def test_shift_nearest_boundary(self, order, prefilter):
|
||
|
|
# verify that shifting at least order // 2 beyond the end of the array
|
||
|
|
# gives a value equal to the edge value.
|
||
|
|
x = numpy.arange(16)
|
||
|
|
kwargs = dict(mode='nearest', order=order, prefilter=prefilter)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, order // 2 + 1, **kwargs)[0], x[0],
|
||
|
|
)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.shift(x, -order // 2 - 1, **kwargs)[-1], x[-1],
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('mode', ['grid-constant', 'grid-wrap', 'nearest',
|
||
|
|
'mirror', 'reflect'])
|
||
|
|
@pytest.mark.parametrize('order', range(6))
|
||
|
|
def test_shift_vs_padded(self, order, mode):
|
||
|
|
x = numpy.arange(144, dtype=float).reshape(12, 12)
|
||
|
|
shift = (0.4, -2.3)
|
||
|
|
|
||
|
|
# manually pad and then extract center to get expected result
|
||
|
|
npad = 32
|
||
|
|
pad_mode = ndimage_to_numpy_mode.get(mode)
|
||
|
|
xp = numpy.pad(x, npad, mode=pad_mode)
|
||
|
|
center_slice = tuple([slice(npad, -npad)] * x.ndim)
|
||
|
|
expected_result = ndimage.shift(
|
||
|
|
xp, shift, mode=mode, order=order)[center_slice]
|
||
|
|
|
||
|
|
assert_allclose(
|
||
|
|
ndimage.shift(x, shift, mode=mode, order=order),
|
||
|
|
expected_result,
|
||
|
|
rtol=1e-7,
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_zoom1(self, order):
|
||
|
|
for z in [2, [2, 2]]:
|
||
|
|
arr = numpy.array(list(range(25))).reshape((5, 5)).astype(float)
|
||
|
|
arr = ndimage.zoom(arr, z, order=order)
|
||
|
|
assert_equal(arr.shape, (10, 10))
|
||
|
|
assert_(numpy.all(arr[-1, :] != 0))
|
||
|
|
assert_(numpy.all(arr[-1, :] >= (20 - eps)))
|
||
|
|
assert_(numpy.all(arr[0, :] <= (5 + eps)))
|
||
|
|
assert_(numpy.all(arr >= (0 - eps)))
|
||
|
|
assert_(numpy.all(arr <= (24 + eps)))
|
||
|
|
|
||
|
|
def test_zoom2(self):
|
||
|
|
arr = numpy.arange(12).reshape((3, 4))
|
||
|
|
out = ndimage.zoom(ndimage.zoom(arr, 2), 0.5)
|
||
|
|
assert_array_equal(out, arr)
|
||
|
|
|
||
|
|
def test_zoom3(self):
|
||
|
|
arr = numpy.array([[1, 2]])
|
||
|
|
out1 = ndimage.zoom(arr, (2, 1))
|
||
|
|
out2 = ndimage.zoom(arr, (1, 2))
|
||
|
|
|
||
|
|
assert_array_almost_equal(out1, numpy.array([[1, 2], [1, 2]]))
|
||
|
|
assert_array_almost_equal(out2, numpy.array([[1, 1, 2, 2]]))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_zoom_affine01(self, order, dtype):
|
||
|
|
data = numpy.asarray([[1, 2, 3, 4],
|
||
|
|
[5, 6, 7, 8],
|
||
|
|
[9, 10, 11, 12]], dtype=dtype)
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data -= 1j * data
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(UserWarning,
|
||
|
|
'The behavior of affine_transform with a 1-D array .* '
|
||
|
|
'has changed')
|
||
|
|
out = ndimage.affine_transform(data, [0.5, 0.5], 0,
|
||
|
|
(6, 8), order=order)
|
||
|
|
assert_array_almost_equal(out[::2, ::2], data)
|
||
|
|
|
||
|
|
def test_zoom_infinity(self):
|
||
|
|
# Ticket #1419 regression test
|
||
|
|
dim = 8
|
||
|
|
ndimage.zoom(numpy.zeros((dim, dim)), 1. / dim, mode='nearest')
|
||
|
|
|
||
|
|
def test_zoom_zoomfactor_one(self):
|
||
|
|
# Ticket #1122 regression test
|
||
|
|
arr = numpy.zeros((1, 5, 5))
|
||
|
|
zoom = (1.0, 2.0, 2.0)
|
||
|
|
|
||
|
|
out = ndimage.zoom(arr, zoom, cval=7)
|
||
|
|
ref = numpy.zeros((1, 10, 10))
|
||
|
|
assert_array_almost_equal(out, ref)
|
||
|
|
|
||
|
|
def test_zoom_output_shape_roundoff(self):
|
||
|
|
arr = numpy.zeros((3, 11, 25))
|
||
|
|
zoom = (4.0 / 3, 15.0 / 11, 29.0 / 25)
|
||
|
|
out = ndimage.zoom(arr, zoom)
|
||
|
|
assert_array_equal(out.shape, (4, 15, 29))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('zoom', [(1, 1), (3, 5), (8, 2), (8, 8)])
|
||
|
|
@pytest.mark.parametrize('mode', ['nearest', 'constant', 'wrap', 'reflect',
|
||
|
|
'mirror', 'grid-wrap', 'grid-mirror',
|
||
|
|
'grid-constant'])
|
||
|
|
def test_zoom_by_int_order0(self, zoom, mode):
|
||
|
|
# order 0 zoom should be the same as replication via numpy.kron
|
||
|
|
# Note: This is not True for general x shapes when grid_mode is False,
|
||
|
|
# but works here for all modes because the size ratio happens to
|
||
|
|
# always be an integer when x.shape = (2, 2).
|
||
|
|
x = numpy.array([[0, 1],
|
||
|
|
[2, 3]], dtype=float)
|
||
|
|
# x = numpy.arange(16, dtype=float).reshape(4, 4)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.zoom(x, zoom, order=0, mode=mode),
|
||
|
|
numpy.kron(x, numpy.ones(zoom))
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('shape', [(2, 3), (4, 4)])
|
||
|
|
@pytest.mark.parametrize('zoom', [(1, 1), (3, 5), (8, 2), (8, 8)])
|
||
|
|
@pytest.mark.parametrize('mode', ['nearest', 'reflect', 'mirror',
|
||
|
|
'grid-wrap', 'grid-constant'])
|
||
|
|
def test_zoom_grid_by_int_order0(self, shape, zoom, mode):
|
||
|
|
# When grid_mode is True, order 0 zoom should be the same as
|
||
|
|
# replication via numpy.kron. The only exceptions to this are the
|
||
|
|
# non-grid modes 'constant' and 'wrap'.
|
||
|
|
x = numpy.arange(numpy.prod(shape), dtype=float).reshape(shape)
|
||
|
|
assert_array_almost_equal(
|
||
|
|
ndimage.zoom(x, zoom, order=0, mode=mode, grid_mode=True),
|
||
|
|
numpy.kron(x, numpy.ones(zoom))
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('mode', ['constant', 'wrap'])
|
||
|
|
def test_zoom_grid_mode_warnings(self, mode):
|
||
|
|
# Warn on use of non-grid modes when grid_mode is True
|
||
|
|
x = numpy.arange(9, dtype=float).reshape((3, 3))
|
||
|
|
with pytest.warns(UserWarning,
|
||
|
|
match="It is recommended to use mode"):
|
||
|
|
ndimage.zoom(x, 2, mode=mode, grid_mode=True),
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate01(self, order):
|
||
|
|
data = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
out = ndimage.rotate(data, 0, order=order)
|
||
|
|
assert_array_almost_equal(out, data)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate02(self, order):
|
||
|
|
data = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
out = ndimage.rotate(data, 90, order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
@pytest.mark.parametrize('dtype', [numpy.float64, numpy.complex128])
|
||
|
|
def test_rotate03(self, order, dtype):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]], dtype=dtype)
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=dtype)
|
||
|
|
if data.dtype.kind == 'c':
|
||
|
|
data -= 1j * data
|
||
|
|
expected -= 1j * expected
|
||
|
|
out = ndimage.rotate(data, 90, order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate04(self, order):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0]], dtype=numpy.float64)
|
||
|
|
out = ndimage.rotate(data, 90, reshape=False, order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate05(self, order):
|
||
|
|
data = numpy.empty((4, 3, 3))
|
||
|
|
for i in range(3):
|
||
|
|
data[:, :, i] = numpy.array([[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
out = ndimage.rotate(data, 90, order=order)
|
||
|
|
for i in range(3):
|
||
|
|
assert_array_almost_equal(out[:, :, i], expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate06(self, order):
|
||
|
|
data = numpy.empty((3, 4, 3))
|
||
|
|
for i in range(3):
|
||
|
|
data[:, :, i] = numpy.array([[0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0],
|
||
|
|
[0, 0, 0, 0]], dtype=numpy.float64)
|
||
|
|
expected = numpy.array([[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0]], dtype=numpy.float64)
|
||
|
|
out = ndimage.rotate(data, 90, order=order)
|
||
|
|
for i in range(3):
|
||
|
|
assert_array_almost_equal(out[:, :, i], expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate07(self, order):
|
||
|
|
data = numpy.array([[[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
data = data.transpose()
|
||
|
|
expected = numpy.array([[[0, 0, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 0, 0],
|
||
|
|
[0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
expected = expected.transpose([2, 1, 0])
|
||
|
|
out = ndimage.rotate(data, 90, axes=(0, 1), order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('order', range(0, 6))
|
||
|
|
def test_rotate08(self, order):
|
||
|
|
data = numpy.array([[[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
data = data.transpose()
|
||
|
|
expected = numpy.array([[[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]]] * 2, dtype=numpy.float64)
|
||
|
|
expected = expected.transpose()
|
||
|
|
out = ndimage.rotate(data, 90, axes=(0, 1), reshape=False, order=order)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate09(self):
|
||
|
|
data = numpy.array([[0, 0, 0, 0, 0],
|
||
|
|
[0, 1, 1, 0, 0],
|
||
|
|
[0, 0, 0, 0, 0]] * 2, dtype=numpy.float64)
|
||
|
|
with assert_raises(ValueError):
|
||
|
|
ndimage.rotate(data, 90, axes=(0, data.ndim))
|
||
|
|
|
||
|
|
def test_rotate10(self):
|
||
|
|
data = numpy.arange(45, dtype=numpy.float64).reshape((3, 5, 3))
|
||
|
|
|
||
|
|
# The output of ndimage.rotate before refactoring
|
||
|
|
expected = numpy.array([[[0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0],
|
||
|
|
[6.54914793, 7.54914793, 8.54914793],
|
||
|
|
[10.84520162, 11.84520162, 12.84520162],
|
||
|
|
[0.0, 0.0, 0.0]],
|
||
|
|
[[6.19286575, 7.19286575, 8.19286575],
|
||
|
|
[13.4730712, 14.4730712, 15.4730712],
|
||
|
|
[21.0, 22.0, 23.0],
|
||
|
|
[28.5269288, 29.5269288, 30.5269288],
|
||
|
|
[35.80713425, 36.80713425, 37.80713425]],
|
||
|
|
[[0.0, 0.0, 0.0],
|
||
|
|
[31.15479838, 32.15479838, 33.15479838],
|
||
|
|
[35.45085207, 36.45085207, 37.45085207],
|
||
|
|
[0.0, 0.0, 0.0],
|
||
|
|
[0.0, 0.0, 0.0]]])
|
||
|
|
|
||
|
|
out = ndimage.rotate(data, angle=12, reshape=False)
|
||
|
|
assert_array_almost_equal(out, expected)
|
||
|
|
|
||
|
|
def test_rotate_exact_180(self):
|
||
|
|
a = numpy.tile(numpy.arange(5), (5, 1))
|
||
|
|
b = ndimage.rotate(ndimage.rotate(a, 180), -180)
|
||
|
|
assert_equal(a, b)
|
||
|
|
|
||
|
|
|
||
|
|
def test_zoom_output_shape():
|
||
|
|
"""Ticket #643"""
|
||
|
|
x = numpy.arange(12).reshape((3, 4))
|
||
|
|
ndimage.zoom(x, 2, output=numpy.zeros((6, 8)))
|