# Copyright (C) 2003-2005 Peter J. Verveer # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # 3. The name of the author may not be used to endorse or promote # products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS # OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE # GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import math import sys import numpy from numpy import fft from numpy.testing import (assert_, assert_equal, assert_array_equal, assert_array_almost_equal, assert_almost_equal, suppress_warnings) import pytest from pytest import raises as assert_raises import scipy.ndimage as ndimage eps = 1e-12 def sumsq(a, b): return math.sqrt(((a - b)**2).sum()) class TestNdimage: def setup_method(self): # list of numarray data types self.integer_types = [ numpy.int8, numpy.uint8, numpy.int16, numpy.uint16, numpy.int32, numpy.uint32, numpy.int64, numpy.uint64] self.float_types = [numpy.float32, numpy.float64] self.types = self.integer_types + self.float_types # list of boundary modes: self.modes = ['nearest', 'wrap', 'reflect', 'mirror', 'constant'] def test_correlate01(self): array = numpy.array([1, 2]) weights = numpy.array([2]) expected = [2, 4] output = ndimage.correlate(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, expected) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, expected) def test_correlate01_overlap(self): array = numpy.arange(256).reshape(16, 16) weights = numpy.array([2]) expected = 2 * array ndimage.correlate1d(array, weights, output=array) assert_array_almost_equal(array, expected) def test_correlate02(self): array = numpy.array([1, 2, 3]) kernel = numpy.array([1]) output = ndimage.correlate(array, kernel) assert_array_almost_equal(array, output) output = ndimage.convolve(array, kernel) assert_array_almost_equal(array, output) output = ndimage.correlate1d(array, kernel) assert_array_almost_equal(array, output) output = ndimage.convolve1d(array, kernel) assert_array_almost_equal(array, output) def test_correlate03(self): array = numpy.array([1]) weights = numpy.array([1, 1]) expected = [2] output = ndimage.correlate(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, expected) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, expected) def test_correlate04(self): array = numpy.array([1, 2]) tcor = [2, 3] tcov = [3, 4] weights = numpy.array([1, 1]) output = ndimage.correlate(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, tcov) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, tcov) def test_correlate05(self): array = numpy.array([1, 2, 3]) tcor = [2, 3, 5] tcov = [3, 5, 6] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel) assert_array_almost_equal(tcor, output) output = ndimage.convolve(array, kernel) assert_array_almost_equal(tcov, output) output = ndimage.correlate1d(array, kernel) assert_array_almost_equal(tcor, output) output = ndimage.convolve1d(array, kernel) assert_array_almost_equal(tcov, output) def test_correlate06(self): array = numpy.array([1, 2, 3]) tcor = [9, 14, 17] tcov = [7, 10, 15] weights = numpy.array([1, 2, 3]) output = ndimage.correlate(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, tcov) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, tcov) def test_correlate07(self): array = numpy.array([1, 2, 3]) expected = [5, 8, 11] weights = numpy.array([1, 2, 1]) output = ndimage.correlate(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, expected) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, expected) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, expected) def test_correlate08(self): array = numpy.array([1, 2, 3]) tcor = [1, 2, 5] tcov = [3, 6, 7] weights = numpy.array([1, 2, -1]) output = ndimage.correlate(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve(array, weights) assert_array_almost_equal(output, tcov) output = ndimage.correlate1d(array, weights) assert_array_almost_equal(output, tcor) output = ndimage.convolve1d(array, weights) assert_array_almost_equal(output, tcov) def test_correlate09(self): array = [] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel) assert_array_almost_equal(array, output) output = ndimage.convolve(array, kernel) assert_array_almost_equal(array, output) output = ndimage.correlate1d(array, kernel) assert_array_almost_equal(array, output) output = ndimage.convolve1d(array, kernel) assert_array_almost_equal(array, output) def test_correlate10(self): array = [[]] kernel = numpy.array([[1, 1]]) output = ndimage.correlate(array, kernel) assert_array_almost_equal(array, output) output = ndimage.convolve(array, kernel) assert_array_almost_equal(array, output) def test_correlate11(self): array = numpy.array([[1, 2, 3], [4, 5, 6]]) kernel = numpy.array([[1, 1], [1, 1]]) output = ndimage.correlate(array, kernel) assert_array_almost_equal([[4, 6, 10], [10, 12, 16]], output) output = ndimage.convolve(array, kernel) assert_array_almost_equal([[12, 16, 18], [18, 22, 24]], output) def test_correlate12(self): array = numpy.array([[1, 2, 3], [4, 5, 6]]) kernel = numpy.array([[1, 0], [0, 1]]) output = ndimage.correlate(array, kernel) assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output) output = ndimage.convolve(array, kernel) assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output) def test_correlate13(self): kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) for type2 in self.types: output = ndimage.correlate(array, kernel, output=type2) assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output) assert_equal(output.dtype.type, type2) output = ndimage.convolve(array, kernel, output=type2) assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output) assert_equal(output.dtype.type, type2) def test_correlate14(self): kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) for type2 in self.types: output = numpy.zeros(array.shape, type2) ndimage.correlate(array, kernel, output=output) assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output) assert_equal(output.dtype.type, type2) ndimage.convolve(array, kernel, output=output) assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output) assert_equal(output.dtype.type, type2) def test_correlate15(self): kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output=numpy.float32) assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output) assert_equal(output.dtype.type, numpy.float32) output = ndimage.convolve(array, kernel, output=numpy.float32) assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output) assert_equal(output.dtype.type, numpy.float32) def test_correlate16(self): kernel = numpy.array([[0.5, 0], [0, 0.5]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output=numpy.float32) assert_array_almost_equal([[1, 1.5, 2.5], [2.5, 3, 4]], output) assert_equal(output.dtype.type, numpy.float32) output = ndimage.convolve(array, kernel, output=numpy.float32) assert_array_almost_equal([[3, 4, 4.5], [4.5, 5.5, 6]], output) assert_equal(output.dtype.type, numpy.float32) def test_correlate17(self): array = numpy.array([1, 2, 3]) tcor = [3, 5, 6] tcov = [2, 3, 5] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel, origin=-1) assert_array_almost_equal(tcor, output) output = ndimage.convolve(array, kernel, origin=-1) assert_array_almost_equal(tcov, output) output = ndimage.correlate1d(array, kernel, origin=-1) assert_array_almost_equal(tcor, output) output = ndimage.convolve1d(array, kernel, origin=-1) assert_array_almost_equal(tcov, output) def test_correlate18(self): kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output=numpy.float32, mode='nearest', origin=-1) assert_array_almost_equal([[6, 8, 9], [9, 11, 12]], output) assert_equal(output.dtype.type, numpy.float32) output = ndimage.convolve(array, kernel, output=numpy.float32, mode='nearest', origin=-1) assert_array_almost_equal([[2, 3, 5], [5, 6, 8]], output) assert_equal(output.dtype.type, numpy.float32) def test_correlate_mode_sequence(self): kernel = numpy.ones((2, 2)) array = numpy.ones((3, 3), float) with assert_raises(RuntimeError): ndimage.correlate(array, kernel, mode=['nearest', 'reflect']) with assert_raises(RuntimeError): ndimage.convolve(array, kernel, mode=['nearest', 'reflect']) def test_correlate19(self): kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output=numpy.float32, mode='nearest', origin=[-1, 0]) assert_array_almost_equal([[5, 6, 8], [8, 9, 11]], output) assert_equal(output.dtype.type, numpy.float32) output = ndimage.convolve(array, kernel, output=numpy.float32, mode='nearest', origin=[-1, 0]) assert_array_almost_equal([[3, 5, 6], [6, 8, 9]], output) assert_equal(output.dtype.type, numpy.float32) def test_correlate20(self): weights = numpy.array([1, 2, 1]) expected = [[5, 10, 15], [7, 14, 21]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis=0, output=output) assert_array_almost_equal(output, expected) ndimage.convolve1d(array, weights, axis=0, output=output) assert_array_almost_equal(output, expected) def test_correlate21(self): array = numpy.array([[1, 2, 3], [2, 4, 6]]) expected = [[5, 10, 15], [7, 14, 21]] weights = numpy.array([1, 2, 1]) output = ndimage.correlate1d(array, weights, axis=0) assert_array_almost_equal(output, expected) output = ndimage.convolve1d(array, weights, axis=0) assert_array_almost_equal(output, expected) def test_correlate22(self): weights = numpy.array([1, 2, 1]) expected = [[6, 12, 18], [6, 12, 18]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis=0, mode='wrap', output=output) assert_array_almost_equal(output, expected) ndimage.convolve1d(array, weights, axis=0, mode='wrap', output=output) assert_array_almost_equal(output, expected) def test_correlate23(self): weights = numpy.array([1, 2, 1]) expected = [[5, 10, 15], [7, 14, 21]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis=0, mode='nearest', output=output) assert_array_almost_equal(output, expected) ndimage.convolve1d(array, weights, axis=0, mode='nearest', output=output) assert_array_almost_equal(output, expected) def test_correlate24(self): weights = numpy.array([1, 2, 1]) tcor = [[7, 14, 21], [8, 16, 24]] tcov = [[4, 8, 12], [5, 10, 15]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis=0, mode='nearest', output=output, origin=-1) assert_array_almost_equal(output, tcor) ndimage.convolve1d(array, weights, axis=0, mode='nearest', output=output, origin=-1) assert_array_almost_equal(output, tcov) def test_correlate25(self): weights = numpy.array([1, 2, 1]) tcor = [[4, 8, 12], [5, 10, 15]] tcov = [[7, 14, 21], [8, 16, 24]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis=0, mode='nearest', output=output, origin=1) assert_array_almost_equal(output, tcor) ndimage.convolve1d(array, weights, axis=0, mode='nearest', output=output, origin=1) assert_array_almost_equal(output, tcov) def test_correlate26(self): # test fix for gh-11661 (mirror extension of a length 1 signal) y = ndimage.convolve1d(numpy.ones(1), numpy.ones(5), mode='mirror') assert_array_equal(y, numpy.array(5.)) y = ndimage.correlate1d(numpy.ones(1), numpy.ones(5), mode='mirror') assert_array_equal(y, numpy.array(5.)) def test_gauss01(self): input = numpy.array([[1, 2, 3], [2, 4, 6]], numpy.float32) output = ndimage.gaussian_filter(input, 0) assert_array_almost_equal(output, input) def test_gauss02(self): input = numpy.array([[1, 2, 3], [2, 4, 6]], numpy.float32) output = ndimage.gaussian_filter(input, 1.0) assert_equal(input.dtype, output.dtype) assert_equal(input.shape, output.shape) def test_gauss03(self): # single precision data" input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) output = ndimage.gaussian_filter(input, [1.0, 1.0]) assert_equal(input.dtype, output.dtype) assert_equal(input.shape, output.shape) # input.sum() is 49995000.0. With single precision floats, we can't # expect more than 8 digits of accuracy, so use decimal=0 in this test. assert_almost_equal(output.sum(dtype='d'), input.sum(dtype='d'), decimal=0) assert_(sumsq(input, output) > 1.0) def test_gauss04(self): input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output = ndimage.gaussian_filter(input, [1.0, 1.0], output=otype) assert_equal(output.dtype.type, numpy.float64) assert_equal(input.shape, output.shape) assert_(sumsq(input, output) > 1.0) def test_gauss05(self): input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output = ndimage.gaussian_filter(input, [1.0, 1.0], order=1, output=otype) assert_equal(output.dtype.type, numpy.float64) assert_equal(input.shape, output.shape) assert_(sumsq(input, output) > 1.0) def test_gauss06(self): input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output1 = ndimage.gaussian_filter(input, [1.0, 1.0], output=otype) output2 = ndimage.gaussian_filter(input, 1.0, output=otype) assert_array_almost_equal(output1, output2) def test_gauss_memory_overlap(self): input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) output1 = ndimage.gaussian_filter(input, 1.0) ndimage.gaussian_filter(input, 1.0, output=input) assert_array_almost_equal(output1, input) def test_prewitt01(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1) output = ndimage.prewitt(array, 0) assert_array_almost_equal(t, output) def test_prewitt02(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1) output = numpy.zeros(array.shape, type_) ndimage.prewitt(array, 0, output) assert_array_almost_equal(t, output) def test_prewitt03(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 0) output = ndimage.prewitt(array, 1) assert_array_almost_equal(t, output) def test_prewitt04(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.prewitt(array, -1) output = ndimage.prewitt(array, 1) assert_array_almost_equal(t, output) def test_sobel01(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1) output = ndimage.sobel(array, 0) assert_array_almost_equal(t, output) def test_sobel02(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1) output = numpy.zeros(array.shape, type_) ndimage.sobel(array, 0, output) assert_array_almost_equal(t, output) def test_sobel03(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 0) output = numpy.zeros(array.shape, type_) output = ndimage.sobel(array, 1) assert_array_almost_equal(t, output) def test_sobel04(self): for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) t = ndimage.sobel(array, -1) output = ndimage.sobel(array, 1) assert_array_almost_equal(t, output) def test_laplace01(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0) tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1) output = ndimage.laplace(array) assert_array_almost_equal(tmp1 + tmp2, output) def test_laplace02(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0) tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1) output = numpy.zeros(array.shape, type_) ndimage.laplace(array, output=output) assert_array_almost_equal(tmp1 + tmp2, output) def test_gaussian_laplace01(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2]) output = ndimage.gaussian_laplace(array, 1.0) assert_array_almost_equal(tmp1 + tmp2, output) def test_gaussian_laplace02(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2]) output = numpy.zeros(array.shape, type_) ndimage.gaussian_laplace(array, 1.0, output) assert_array_almost_equal(tmp1 + tmp2, output) def test_generic_laplace01(self): def derivative2(input, axis, output, mode, cval, a, b): sigma = [a, b / 2.0] input = numpy.asarray(input) order = [0] * input.ndim order[axis] = 2 return ndimage.gaussian_filter(input, sigma, order, output, mode, cval) for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = numpy.zeros(array.shape, type_) tmp = ndimage.generic_laplace(array, derivative2, extra_arguments=(1.0,), extra_keywords={'b': 2.0}) ndimage.gaussian_laplace(array, 1.0, output) assert_array_almost_equal(tmp, output) def test_gaussian_gradient_magnitude01(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1]) output = ndimage.gaussian_gradient_magnitude(array, 1.0) expected = tmp1 * tmp1 + tmp2 * tmp2 expected = numpy.sqrt(expected).astype(type_) assert_array_almost_equal(expected, output) def test_gaussian_gradient_magnitude02(self): for type_ in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1]) output = numpy.zeros(array.shape, type_) ndimage.gaussian_gradient_magnitude(array, 1.0, output) expected = tmp1 * tmp1 + tmp2 * tmp2 expected = numpy.sqrt(expected).astype(type_) assert_array_almost_equal(expected, output) def test_generic_gradient_magnitude01(self): array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], numpy.float64) def derivative(input, axis, output, mode, cval, a, b): sigma = [a, b / 2.0] input = numpy.asarray(input) order = [0] * input.ndim order[axis] = 1 return ndimage.gaussian_filter(input, sigma, order, output, mode, cval) tmp1 = ndimage.gaussian_gradient_magnitude(array, 1.0) tmp2 = ndimage.generic_gradient_magnitude( array, derivative, extra_arguments=(1.0,), extra_keywords={'b': 2.0}) assert_array_almost_equal(tmp1, tmp2) def test_uniform01(self): array = numpy.array([2, 4, 6]) size = 2 output = ndimage.uniform_filter1d(array, size, origin=-1) assert_array_almost_equal([3, 5, 6], output) def test_uniform02(self): array = numpy.array([1, 2, 3]) filter_shape = [0] output = ndimage.uniform_filter(array, filter_shape) assert_array_almost_equal(array, output) def test_uniform03(self): array = numpy.array([1, 2, 3]) filter_shape = [1] output = ndimage.uniform_filter(array, filter_shape) assert_array_almost_equal(array, output) def test_uniform04(self): array = numpy.array([2, 4, 6]) filter_shape = [2] output = ndimage.uniform_filter(array, filter_shape) assert_array_almost_equal([2, 3, 5], output) def test_uniform05(self): array = [] filter_shape = [1] output = ndimage.uniform_filter(array, filter_shape) assert_array_almost_equal([], output) def test_uniform06(self): filter_shape = [2, 2] for type1 in self.types: array = numpy.array([[4, 8, 12], [16, 20, 24]], type1) for type2 in self.types: output = ndimage.uniform_filter( array, filter_shape, output=type2) assert_array_almost_equal([[4, 6, 10], [10, 12, 16]], output) assert_equal(output.dtype.type, type2) def test_minimum_filter01(self): array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([2]) output = ndimage.minimum_filter(array, filter_shape) assert_array_almost_equal([1, 1, 2, 3, 4], output) def test_minimum_filter02(self): array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([3]) output = ndimage.minimum_filter(array, filter_shape) assert_array_almost_equal([1, 1, 2, 3, 4], output) def test_minimum_filter03(self): array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([2]) output = ndimage.minimum_filter(array, filter_shape) assert_array_almost_equal([3, 2, 2, 1, 1], output) def test_minimum_filter04(self): array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([3]) output = ndimage.minimum_filter(array, filter_shape) assert_array_almost_equal([2, 2, 1, 1, 1], output) def test_minimum_filter05(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) filter_shape = numpy.array([2, 3]) output = ndimage.minimum_filter(array, filter_shape) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 2, 1, 1, 1], [5, 3, 3, 1, 1]], output) def test_minimum_filter05_overlap(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) filter_shape = numpy.array([2, 3]) ndimage.minimum_filter(array, filter_shape, output=array) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 2, 1, 1, 1], [5, 3, 3, 1, 1]], array) def test_minimum_filter06(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 1, 1], [1, 1, 1]] output = ndimage.minimum_filter(array, footprint=footprint) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 2, 1, 1, 1], [5, 3, 3, 1, 1]], output) # separable footprint should allow mode sequence output2 = ndimage.minimum_filter(array, footprint=footprint, mode=['reflect', 'reflect']) assert_array_almost_equal(output2, output) def test_minimum_filter07(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint=footprint) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) with assert_raises(RuntimeError): ndimage.minimum_filter(array, footprint=footprint, mode=['reflect', 'constant']) def test_minimum_filter08(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint=footprint, origin=-1) assert_array_almost_equal([[3, 1, 3, 1, 1], [5, 3, 3, 1, 1], [3, 3, 1, 1, 1]], output) def test_minimum_filter09(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint=footprint, origin=[-1, 0]) assert_array_almost_equal([[2, 3, 1, 3, 1], [5, 5, 3, 3, 1], [5, 3, 3, 1, 1]], output) def test_maximum_filter01(self): array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([2]) output = ndimage.maximum_filter(array, filter_shape) assert_array_almost_equal([1, 2, 3, 4, 5], output) def test_maximum_filter02(self): array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([3]) output = ndimage.maximum_filter(array, filter_shape) assert_array_almost_equal([2, 3, 4, 5, 5], output) def test_maximum_filter03(self): array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([2]) output = ndimage.maximum_filter(array, filter_shape) assert_array_almost_equal([3, 3, 5, 5, 4], output) def test_maximum_filter04(self): array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([3]) output = ndimage.maximum_filter(array, filter_shape) assert_array_almost_equal([3, 5, 5, 5, 4], output) def test_maximum_filter05(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) filter_shape = numpy.array([2, 3]) output = ndimage.maximum_filter(array, filter_shape) assert_array_almost_equal([[3, 5, 5, 5, 4], [7, 9, 9, 9, 5], [8, 9, 9, 9, 7]], output) def test_maximum_filter06(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 1, 1], [1, 1, 1]] output = ndimage.maximum_filter(array, footprint=footprint) assert_array_almost_equal([[3, 5, 5, 5, 4], [7, 9, 9, 9, 5], [8, 9, 9, 9, 7]], output) # separable footprint should allow mode sequence output2 = ndimage.maximum_filter(array, footprint=footprint, mode=['reflect', 'reflect']) assert_array_almost_equal(output2, output) def test_maximum_filter07(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint=footprint) assert_array_almost_equal([[3, 5, 5, 5, 4], [7, 7, 9, 9, 5], [7, 9, 8, 9, 7]], output) # non-separable footprint should not allow mode sequence with assert_raises(RuntimeError): ndimage.maximum_filter(array, footprint=footprint, mode=['reflect', 'reflect']) def test_maximum_filter08(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint=footprint, origin=-1) assert_array_almost_equal([[7, 9, 9, 5, 5], [9, 8, 9, 7, 5], [8, 8, 7, 7, 7]], output) def test_maximum_filter09(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint=footprint, origin=[-1, 0]) assert_array_almost_equal([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) def test_rank01(self): array = numpy.array([1, 2, 3, 4, 5]) output = ndimage.rank_filter(array, 1, size=2) assert_array_almost_equal(array, output) output = ndimage.percentile_filter(array, 100, size=2) assert_array_almost_equal(array, output) output = ndimage.median_filter(array, 2) assert_array_almost_equal(array, output) def test_rank02(self): array = numpy.array([1, 2, 3, 4, 5]) output = ndimage.rank_filter(array, 1, size=[3]) assert_array_almost_equal(array, output) output = ndimage.percentile_filter(array, 50, size=3) assert_array_almost_equal(array, output) output = ndimage.median_filter(array, (3,)) assert_array_almost_equal(array, output) def test_rank03(self): array = numpy.array([3, 2, 5, 1, 4]) output = ndimage.rank_filter(array, 1, size=[2]) assert_array_almost_equal([3, 3, 5, 5, 4], output) output = ndimage.percentile_filter(array, 100, size=2) assert_array_almost_equal([3, 3, 5, 5, 4], output) def test_rank04(self): array = numpy.array([3, 2, 5, 1, 4]) expected = [3, 3, 2, 4, 4] output = ndimage.rank_filter(array, 1, size=3) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 50, size=3) assert_array_almost_equal(expected, output) output = ndimage.median_filter(array, size=3) assert_array_almost_equal(expected, output) def test_rank05(self): array = numpy.array([3, 2, 5, 1, 4]) expected = [3, 3, 2, 4, 4] output = ndimage.rank_filter(array, -2, size=3) assert_array_almost_equal(expected, output) def test_rank06(self): array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) expected = [[2, 2, 1, 1, 1], [3, 3, 2, 1, 1], [5, 5, 3, 3, 1]] output = ndimage.rank_filter(array, 1, size=[2, 3]) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 17, size=(2, 3)) assert_array_almost_equal(expected, output) def test_rank06_overlap(self): array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) array_copy = array.copy() expected = [[2, 2, 1, 1, 1], [3, 3, 2, 1, 1], [5, 5, 3, 3, 1]] ndimage.rank_filter(array, 1, size=[2, 3], output=array) assert_array_almost_equal(expected, array) ndimage.percentile_filter(array_copy, 17, size=(2, 3), output=array_copy) assert_array_almost_equal(expected, array_copy) def test_rank07(self): array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) expected = [[3, 5, 5, 5, 4], [5, 5, 7, 5, 4], [6, 8, 8, 7, 5]] output = ndimage.rank_filter(array, -2, size=[2, 3]) assert_array_almost_equal(expected, output) def test_rank08(self): array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) expected = [[3, 3, 2, 4, 4], [5, 5, 5, 4, 4], [5, 6, 7, 5, 5]] output = ndimage.percentile_filter(array, 50.0, size=(2, 3)) assert_array_almost_equal(expected, output) output = ndimage.rank_filter(array, 3, size=(2, 3)) assert_array_almost_equal(expected, output) output = ndimage.median_filter(array, size=(2, 3)) assert_array_almost_equal(expected, output) # non-separable: does not allow mode sequence with assert_raises(RuntimeError): ndimage.percentile_filter(array, 50.0, size=(2, 3), mode=['reflect', 'constant']) with assert_raises(RuntimeError): ndimage.rank_filter(array, 3, size=(2, 3), mode=['reflect']*2) with assert_raises(RuntimeError): ndimage.median_filter(array, size=(2, 3), mode=['reflect']*2) def test_rank09(self): expected = [[3, 3, 2, 4, 4], [3, 5, 2, 5, 1], [5, 5, 8, 3, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = ndimage.rank_filter(array, 1, footprint=footprint) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 35, footprint=footprint) assert_array_almost_equal(expected, output) def test_rank10(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) expected = [[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]] footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.rank_filter(array, 0, footprint=footprint) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 0.0, footprint=footprint) assert_array_almost_equal(expected, output) def test_rank11(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) expected = [[3, 5, 5, 5, 4], [7, 7, 9, 9, 5], [7, 9, 8, 9, 7]] footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.rank_filter(array, -1, footprint=footprint) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 100.0, footprint=footprint) assert_array_almost_equal(expected, output) def test_rank12(self): expected = [[3, 3, 2, 4, 4], [3, 5, 2, 5, 1], [5, 5, 8, 3, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = ndimage.rank_filter(array, 1, footprint=footprint) assert_array_almost_equal(expected, output) output = ndimage.percentile_filter(array, 50.0, footprint=footprint) assert_array_almost_equal(expected, output) output = ndimage.median_filter(array, footprint=footprint) assert_array_almost_equal(expected, output) def test_rank13(self): expected = [[5, 2, 5, 1, 1], [5, 8, 3, 5, 5], [6, 6, 5, 5, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = ndimage.rank_filter(array, 1, footprint=footprint, origin=-1) assert_array_almost_equal(expected, output) def test_rank14(self): expected = [[3, 5, 2, 5, 1], [5, 5, 8, 3, 5], [5, 6, 6, 5, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = ndimage.rank_filter(array, 1, footprint=footprint, origin=[-1, 0]) assert_array_almost_equal(expected, output) def test_rank15(self): "rank filter 15" expected = [[2, 3, 1, 4, 1], [5, 3, 7, 1, 1], [5, 5, 3, 3, 3]] footprint = [[1, 0, 1], [0, 1, 0]] for type_ in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type_) output = ndimage.rank_filter(array, 0, footprint=footprint, origin=[-1, 0]) assert_array_almost_equal(expected, output) def test_generic_filter1d01(self): weights = numpy.array([1.1, 2.2, 3.3]) def _filter_func(input, output, fltr, total): fltr = fltr / total for ii in range(input.shape[0] - 2): output[ii] = input[ii] * fltr[0] output[ii] += input[ii + 1] * fltr[1] output[ii] += input[ii + 2] * fltr[2] for type_ in self.types: a = numpy.arange(12, dtype=type_) a.shape = (3, 4) r1 = ndimage.correlate1d(a, weights / weights.sum(), 0, origin=-1) r2 = ndimage.generic_filter1d( a, _filter_func, 3, axis=0, origin=-1, extra_arguments=(weights,), extra_keywords={'total': weights.sum()}) assert_array_almost_equal(r1, r2) def test_generic_filter01(self): filter_ = numpy.array([[1.0, 2.0], [3.0, 4.0]]) footprint = numpy.array([[1, 0], [0, 1]]) cf = numpy.array([1., 4.]) def _filter_func(buffer, weights, total=1.0): weights = cf / total return (buffer * weights).sum() for type_ in self.types: a = numpy.arange(12, dtype=type_) a.shape = (3, 4) r1 = ndimage.correlate(a, filter_ * footprint) if type_ in self.float_types: r1 /= 5 else: r1 //= 5 r2 = ndimage.generic_filter( a, _filter_func, footprint=footprint, extra_arguments=(cf,), extra_keywords={'total': cf.sum()}) assert_array_almost_equal(r1, r2) # generic_filter doesn't allow mode sequence with assert_raises(RuntimeError): r2 = ndimage.generic_filter( a, _filter_func, mode=['reflect', 'reflect'], footprint=footprint, extra_arguments=(cf,), extra_keywords={'total': cf.sum()}) def test_extend01(self): array = numpy.array([1, 2, 3]) weights = numpy.array([1, 0]) expected_values = [[1, 1, 2], [3, 1, 2], [1, 1, 2], [2, 1, 2], [0, 1, 2]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate1d(array, weights, 0, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend02(self): array = numpy.array([1, 2, 3]) weights = numpy.array([1, 0, 0, 0, 0, 0, 0, 0]) expected_values = [[1, 1, 1], [3, 1, 2], [3, 3, 2], [1, 2, 3], [0, 0, 0]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate1d(array, weights, 0, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend03(self): array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 1]) expected_values = [[2, 3, 3], [2, 3, 1], [2, 3, 3], [2, 3, 2], [2, 3, 0]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate1d(array, weights, 0, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend04(self): array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) expected_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [1, 2, 3], [0, 0, 0]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate1d(array, weights, 0, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend05(self): array = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) weights = numpy.array([[1, 0], [0, 0]]) expected_values = [[[1, 1, 2], [1, 1, 2], [4, 4, 5]], [[9, 7, 8], [3, 1, 2], [6, 4, 5]], [[1, 1, 2], [1, 1, 2], [4, 4, 5]], [[5, 4, 5], [2, 1, 2], [5, 4, 5]], [[0, 0, 0], [0, 1, 2], [0, 4, 5]]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend06(self): array = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) weights = numpy.array([[0, 0, 0], [0, 0, 0], [0, 0, 1]]) expected_values = [[[5, 6, 6], [8, 9, 9], [8, 9, 9]], [[5, 6, 4], [8, 9, 7], [2, 3, 1]], [[5, 6, 6], [8, 9, 9], [8, 9, 9]], [[5, 6, 5], [8, 9, 8], [5, 6, 5]], [[5, 6, 0], [8, 9, 0], [0, 0, 0]]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend07(self): array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) expected_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [1, 2, 3], [0, 0, 0]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend08(self): array = numpy.array([[1], [2], [3]]) weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]]) expected_values = [[[3], [3], [3]], [[2], [3], [1]], [[2], [1], [1]], [[1], [2], [3]], [[0], [0], [0]]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend09(self): array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) expected_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [1, 2, 3], [0, 0, 0]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_extend10(self): array = numpy.array([[1], [2], [3]]) weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]]) expected_values = [[[3], [3], [3]], [[2], [3], [1]], [[2], [1], [1]], [[1], [2], [3]], [[0], [0], [0]]] for mode, expected_value in zip(self.modes, expected_values): output = ndimage.correlate(array, weights, mode=mode, cval=0) assert_array_equal(output, expected_value) def test_boundaries(self): def shift(x): return (x[0] + 0.5,) data = numpy.array([1, 2, 3, 4.]) expected = {'constant': [1.5, 2.5, 3.5, -1, -1, -1, -1], 'wrap': [1.5, 2.5, 3.5, 1.5, 2.5, 3.5, 1.5], 'mirror': [1.5, 2.5, 3.5, 3.5, 2.5, 1.5, 1.5], 'nearest': [1.5, 2.5, 3.5, 4, 4, 4, 4]} for mode in expected: assert_array_equal( expected[mode], ndimage.geometric_transform(data, shift, cval=-1, mode=mode, output_shape=(7,), order=1)) def test_boundaries2(self): def shift(x): return (x[0] - 0.9,) data = numpy.array([1, 2, 3, 4]) expected = {'constant': [-1, 1, 2, 3], 'wrap': [3, 1, 2, 3], 'mirror': [2, 1, 2, 3], 'nearest': [1, 1, 2, 3]} for mode in expected: assert_array_equal( expected[mode], ndimage.geometric_transform(data, shift, cval=-1, mode=mode, output_shape=(4,))) def test_fourier_gaussian_real01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.float32, numpy.float64], [6, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a), 1, decimal=dec) def test_fourier_gaussian_complex01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.complex64, numpy.complex128], [6, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec) def test_fourier_uniform_real01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.float32, numpy.float64], [6, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec) def test_fourier_uniform_complex01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.complex64, numpy.complex128], [6, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec) def test_fourier_shift_real01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.float32, numpy.float64], [4, 11]): expected = numpy.arange(shape[0] * shape[1], dtype=type_) expected.shape = shape a = fft.rfft(expected, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1], decimal=dec) assert_array_almost_equal(a.imag, numpy.zeros(shape), decimal=dec) def test_fourier_shift_complex01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.complex64, numpy.complex128], [4, 11]): expected = numpy.arange(shape[0] * shape[1], dtype=type_) expected.shape = shape a = fft.fft(expected, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1], decimal=dec) assert_array_almost_equal(a.imag, numpy.zeros(shape), decimal=dec) def test_fourier_ellipsoid_real01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.float32, numpy.float64], [5, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec) def test_fourier_ellipsoid_complex01(self): for shape in [(32, 16), (31, 15)]: for type_, dec in zip([numpy.complex64, numpy.complex128], [5, 14]): a = numpy.zeros(shape, type_) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec) def test_fourier_ellipsoid_1d_complex(self): # expected result of 1d ellipsoid is the same as for fourier_uniform for shape in [(32, ), (31, )]: for type_, dec in zip([numpy.complex64, numpy.complex128], [5, 14]): x = numpy.ones(shape, dtype=type_) a = ndimage.fourier_ellipsoid(x, 5, -1, 0) b = ndimage.fourier_uniform(x, 5, -1, 0) assert_array_almost_equal(a, b, decimal=dec) def test_spline01(self): for type_ in self.types: data = numpy.ones([], type_) for order in range(2, 6): out = ndimage.spline_filter(data, order=order) assert_array_almost_equal(out, 1) def test_spline02(self): for type_ in self.types: data = numpy.array([1], type_) for order in range(2, 6): out = ndimage.spline_filter(data, order=order) assert_array_almost_equal(out, [1]) def test_spline03(self): for type_ in self.types: data = numpy.ones([], type_) for order in range(2, 6): out = ndimage.spline_filter(data, order, output=type_) assert_array_almost_equal(out, 1) def test_spline04(self): for type_ in self.types: data = numpy.ones([4], type_) for order in range(2, 6): out = ndimage.spline_filter(data, order) assert_array_almost_equal(out, [1, 1, 1, 1]) def test_spline05(self): for type_ in self.types: data = numpy.ones([4, 4], type_) for order in range(2, 6): out = ndimage.spline_filter(data, order=order) assert_array_almost_equal(out, [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) def test_geometric_transform01(self): data = numpy.array([1]) def mapping(x): return x for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [1]) def test_geometric_transform02(self): data = numpy.ones([4]) def mapping(x): return x for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [1, 1, 1, 1]) def test_geometric_transform03(self): data = numpy.ones([4]) def mapping(x): return (x[0] - 1,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [0, 1, 1, 1]) def test_geometric_transform04(self): data = numpy.array([4, 1, 3, 2]) def mapping(x): return (x[0] - 1,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [0, 4, 1, 3]) def test_geometric_transform05(self): data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) def mapping(x): return (x[0], x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) def test_geometric_transform06(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0], x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]) def test_geometric_transform07(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]) def test_geometric_transform08(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) assert_array_almost_equal(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) def test_geometric_transform10(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1] - 1) for order in range(0, 6): if (order > 1): filtered = ndimage.spline_filter(data, order=order) else: filtered = data out = ndimage.geometric_transform(filtered, mapping, data.shape, order=order, prefilter=False) assert_array_almost_equal(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) def test_geometric_transform13(self): data = numpy.ones([2], numpy.float64) def mapping(x): return (x[0] // 2,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [4], order=order) assert_array_almost_equal(out, [1, 1, 1, 1]) def test_geometric_transform14(self): data = [1, 5, 2, 6, 3, 7, 4, 4] def mapping(x): return (2 * x[0],) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [4], order=order) assert_array_almost_equal(out, [1, 2, 3, 4]) def test_geometric_transform15(self): data = [1, 2, 3, 4] def mapping(x): return (x[0] / 2,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [8], order=order) assert_array_almost_equal(out[::2], [1, 2, 3, 4]) def test_geometric_transform16(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9.0, 10, 11, 12]] def mapping(x): return (x[0], x[1] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (3, 2), order=order) assert_array_almost_equal(out, [[1, 3], [5, 7], [9, 11]]) def test_geometric_transform17(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] * 2, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (1, 4), order=order) assert_array_almost_equal(out, [[1, 2, 3, 4]]) def test_geometric_transform18(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] * 2, x[1] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (1, 2), order=order) assert_array_almost_equal(out, [[1, 3]]) def test_geometric_transform19(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0], x[1] / 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (3, 8), order=order) assert_array_almost_equal(out[..., ::2], data) def test_geometric_transform20(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] / 2, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (6, 4), order=order) assert_array_almost_equal(out[::2, ...], data) def test_geometric_transform21(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] / 2, x[1] / 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (6, 8), order=order) assert_array_almost_equal(out[::2, ::2], data) def test_geometric_transform22(self): 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) for order in range(0, 6): 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) def test_geometric_transform23(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (1, x[0] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (2,), order=order) out = out.astype(numpy.int32) assert_array_almost_equal(out, [5, 7]) def test_geometric_transform24(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x, a, b): return (a, x[0] * b) for order in range(0, 6): 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_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]) def test_map_coordinates01(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) idx = numpy.indices(data.shape) idx -= 1 for order in range(0, 6): out = ndimage.map_coordinates(data, idx, order=order) assert_array_almost_equal(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) def test_map_coordinates02(self): 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 for order in range(0, 6): 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: raise pytest.skip("Not enough memory available") def test_affine_transform01(self): data = numpy.array([1]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], order=order) assert_array_almost_equal(out, [1]) def test_affine_transform02(self): data = numpy.ones([4]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], order=order) assert_array_almost_equal(out, [1, 1, 1, 1]) def test_affine_transform03(self): data = numpy.ones([4]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], -1, order=order) assert_array_almost_equal(out, [0, 1, 1, 1]) def test_affine_transform04(self): data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], -1, order=order) assert_array_almost_equal(out, [0, 4, 1, 3]) def test_affine_transform05(self): data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 1]], [0, -1], order=order) assert_array_almost_equal(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) def test_affine_transform06(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_affine_transform07(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_affine_transform08(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_affine_transform09(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_affine_transform10(self): data = numpy.ones([2], numpy.float64) for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5]], output_shape=(4,), order=order) assert_array_almost_equal(out, [1, 1, 1, 0]) def test_affine_transform11(self): data = [1, 5, 2, 6, 3, 7, 4, 4] for order in range(0, 6): out = ndimage.affine_transform(data, [[2]], 0, (4,), order=order) assert_array_almost_equal(out, [1, 2, 3, 4]) def test_affine_transform12(self): data = [1, 2, 3, 4] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5]], 0, (8,), order=order) assert_array_almost_equal(out[::2], [1, 2, 3, 4]) def test_affine_transform13(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9.0, 10, 11, 12]] for order in range(0, 6): 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]]) def test_affine_transform14(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2, 0], [0, 1]], 0, (1, 4), order=order) assert_array_almost_equal(out, [[1, 2, 3, 4]]) def test_affine_transform15(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2, 0], [0, 2]], 0, (1, 2), order=order) assert_array_almost_equal(out, [[1, 3]]) def test_affine_transform16(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0.0], [0, 0.5]], 0, (3, 8), order=order) assert_array_almost_equal(out[..., ::2], data) def test_affine_transform17(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5, 0], [0, 1]], 0, (6, 4), order=order) assert_array_almost_equal(out[::2, ...], data) def test_affine_transform18(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0, (6, 8), order=order) assert_array_almost_equal(out[::2, ::2], data) def test_affine_transform19(self): data = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], numpy.float64) for order in range(0, 6): 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) def test_affine_transform20(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0], [2]], 0, (2,), order=order) assert_array_almost_equal(out, [1, 3]) def test_affine_transform21(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2], [0]], 0, (2,), order=order) assert_array_almost_equal(out, [1, 9]) def test_affine_transform22(self): # shift and offset interaction; see issue #1547 data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.affine_transform(data, [[2]], [-1], (3,), order=order) assert_array_almost_equal(out, [0, 1, 2]) def test_affine_transform23(self): # shift and offset interaction; see issue #1547 data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5]], [-1], (8,), order=order) assert_array_almost_equal(out[::2], [0, 4, 1, 3]) def test_affine_transform24(self): # consistency between diagonal and non-diagonal case; see issue #1547 data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): 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) def test_affine_transform25(self): # consistency between diagonal and non-diagonal case; see issue #1547 data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): 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) def test_affine_transform26(self): # test homogeneous coordinates data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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_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]) def test_shift01(self): data = numpy.array([1]) for order in range(0, 6): out = ndimage.shift(data, [1], order=order) assert_array_almost_equal(out, [0]) def test_shift02(self): data = numpy.ones([4]) for order in range(0, 6): out = ndimage.shift(data, [1], order=order) assert_array_almost_equal(out, [0, 1, 1, 1]) def test_shift03(self): data = numpy.ones([4]) for order in range(0, 6): out = ndimage.shift(data, -1, order=order) assert_array_almost_equal(out, [1, 1, 1, 0]) def test_shift04(self): data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.shift(data, 1, order=order) assert_array_almost_equal(out, [0, 4, 1, 3]) def test_shift05(self): data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) for order in range(0, 6): out = ndimage.shift(data, [0, 1], order=order) assert_array_almost_equal(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) def test_shift06(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_shift07(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_shift08(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_shift09(self): data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 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]]) def test_zoom1(self): for order in range(0, 6): 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]])) def test_zoom_affine01(self): data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): 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)) def test_rotate01(self): data = numpy.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 0, 0]], dtype=numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 0) assert_array_almost_equal(out, data) def test_rotate02(self): 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) for order in range(0, 6): out = ndimage.rotate(data, 90) assert_array_almost_equal(out, expected) def test_rotate03(self): 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, 1, 0], [0, 0, 0]], dtype=numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90) assert_array_almost_equal(out, expected) def test_rotate04(self): 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) for order in range(0, 6): out = ndimage.rotate(data, 90, reshape=False) assert_array_almost_equal(out, expected) def test_rotate05(self): 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) for order in range(0, 6): out = ndimage.rotate(data, 90) for i in range(3): assert_array_almost_equal(out[:, :, i], expected) def test_rotate06(self): 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) for order in range(0, 6): out = ndimage.rotate(data, 90) for i in range(3): assert_array_almost_equal(out[:, :, i], expected) def test_rotate07(self): 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]) for order in range(0, 6): out = ndimage.rotate(data, 90, axes=(0, 1)) assert_array_almost_equal(out, expected) def test_rotate08(self): 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() for order in range(0, 6): out = ndimage.rotate(data, 90, axes=(0, 1), reshape=False) 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_watershed_ift01(self): data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]) expected = [[-1, -1, -1, -1, -1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift02(self): data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers) expected = [[-1, -1, -1, -1, -1, -1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift03(self): data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers) expected = [[-1, -1, -1, -1, -1, -1, -1], [-1, -1, 2, -1, 3, -1, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, -1, 2, -1, 3, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift04(self): data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]) expected = [[-1, -1, -1, -1, -1, -1, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift05(self): data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]) expected = [[-1, -1, -1, -1, -1, -1, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift06(self): data = numpy.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]) expected = [[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_watershed_ift07(self): shape = (7, 6) data = numpy.zeros(shape, dtype=numpy.uint8) data = data.transpose() data[...] = numpy.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = numpy.zeros(shape, dtype=numpy.int16) out = out.transpose() ndimage.watershed_ift(data, markers, structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]], output=out) expected = [[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]] assert_array_almost_equal(out, expected) def test_distance_transform_bf01(self): # brute force (bf) distance transform for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_bf(data, 'euclidean', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_bf02(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_bf(data, 'cityblock', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 2, 3, 2, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] assert_array_almost_equal(out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 3, 1, 3, 3, 3, 3], [4, 4, 4, 4, 7, 4, 4, 4, 4], [5, 5, 6, 7, 7, 7, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(expected, ft) def test_distance_transform_bf03(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_bf(data, 'chessboard', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] assert_array_almost_equal(out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 4, 2, 2, 2, 4, 3, 3], [4, 4, 5, 6, 6, 6, 5, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 1, 2, 6, 7, 7, 7, 8], [0, 1, 2, 2, 6, 6, 7, 7, 8], [0, 1, 2, 4, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_bf04(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) tdt, tft = ndimage.distance_transform_bf(data, return_indices=1) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype=numpy.float64) ndimage.distance_transform_bf(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_bf( data, return_distances=False, return_indices=1) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_bf( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_bf( data, return_indices=1) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.float64) ft = ndimage.distance_transform_bf( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) dt = ndimage.distance_transform_bf( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.float64) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_bf( data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) def test_distance_transform_bf05(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_bf( data, 'euclidean', return_indices=True, sampling=[2, 2]) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 4, 16, 32, 16, 4, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_bf06(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_bf( data, 'euclidean', return_indices=True, sampling=[2, 1]) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 4, 9, 4, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 2, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 6, 5, 5, 5, 5], [6, 6, 6, 6, 7, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 6, 6, 6, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 1, 1, 7, 7, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_cdt01(self): # chamfer type distance (cdt) transform for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_cdt( data, 'cityblock', return_indices=True) bf = ndimage.distance_transform_bf(data, 'cityblock') assert_array_almost_equal(bf, out) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 1, 1, 1, 2, 3, 3], [4, 4, 4, 4, 1, 4, 4, 4, 4], [5, 5, 5, 5, 7, 7, 6, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_cdt02(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_cdt(data, 'chessboard', return_indices=True) bf = ndimage.distance_transform_bf(data, 'chessboard') assert_array_almost_equal(bf, out) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 2, 1, 2, 2, 3, 3], [4, 4, 3, 2, 2, 2, 3, 4, 4], [5, 5, 4, 6, 7, 6, 4, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 3, 4, 6, 7, 8], [0, 1, 1, 2, 2, 6, 6, 7, 8], [0, 1, 1, 1, 2, 6, 7, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] assert_array_almost_equal(ft, expected) def test_distance_transform_cdt03(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) tdt, tft = ndimage.distance_transform_cdt(data, return_indices=True) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype=numpy.int32) ndimage.distance_transform_cdt(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_cdt( data, return_distances=False, return_indices=True) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_cdt( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_cdt( data, return_indices=True) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.int32) ft = ndimage.distance_transform_cdt( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) dt = ndimage.distance_transform_cdt( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.int32) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_cdt(data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) def test_distance_transform_edt01(self): # euclidean distance transform (edt) for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) out, ft = ndimage.distance_transform_edt(data, return_indices=True) bf = ndimage.distance_transform_bf(data, 'euclidean') assert_array_almost_equal(bf, out) dt = ft - numpy.indices(ft.shape[1:], dtype=ft.dtype) dt = dt.astype(numpy.float64) numpy.multiply(dt, dt, dt) dt = numpy.add.reduce(dt, axis=0) numpy.sqrt(dt, dt) assert_array_almost_equal(bf, dt) def test_distance_transform_edt02(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) tdt, tft = ndimage.distance_transform_edt(data, return_indices=True) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype=numpy.float64) ndimage.distance_transform_edt(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_edt( data, return_distances=0, return_indices=True) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_edt( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_edt( data, return_indices=True) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.float64) ft = ndimage.distance_transform_edt( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype=numpy.int32) dt = ndimage.distance_transform_edt( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype=numpy.float64) ft = numpy.indices(data.shape, dtype=numpy.int32) ndimage.distance_transform_edt( data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) def test_distance_transform_edt03(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 2]) out = ndimage.distance_transform_edt(data, sampling=[2, 2]) assert_array_almost_equal(ref, out) def test_distance_transform_edt4(self): for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type_) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 1]) out = ndimage.distance_transform_edt(data, sampling=[2, 1]) assert_array_almost_equal(ref, out) def test_distance_transform_edt5(self): # Ticket #954 regression test out = ndimage.distance_transform_edt(False) assert_array_almost_equal(out, [0.]) def test_generate_structure01(self): struct = ndimage.generate_binary_structure(0, 1) assert_array_almost_equal(struct, 1) def test_generate_structure02(self): struct = ndimage.generate_binary_structure(1, 1) assert_array_almost_equal(struct, [1, 1, 1]) def test_generate_structure03(self): struct = ndimage.generate_binary_structure(2, 1) assert_array_almost_equal(struct, [[0, 1, 0], [1, 1, 1], [0, 1, 0]]) def test_generate_structure04(self): struct = ndimage.generate_binary_structure(2, 2) assert_array_almost_equal(struct, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_iterate_structure01(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] out = ndimage.iterate_structure(struct, 2) assert_array_almost_equal(out, [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]) def test_iterate_structure02(self): struct = [[0, 1], [1, 1], [0, 1]] out = ndimage.iterate_structure(struct, 2) assert_array_almost_equal(out, [[0, 0, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [0, 0, 1]]) def test_iterate_structure03(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] out = ndimage.iterate_structure(struct, 2, 1) expected = [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]] assert_array_almost_equal(out[0], expected) assert_equal(out[1], [2, 2]) def test_binary_erosion01(self): for type_ in self.types: data = numpy.ones([], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, 1) def test_binary_erosion02(self): for type_ in self.types: data = numpy.ones([], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, 1) def test_binary_erosion03(self): for type_ in self.types: data = numpy.ones([1], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [0]) def test_binary_erosion04(self): for type_ in self.types: data = numpy.ones([1], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [1]) def test_binary_erosion05(self): for type_ in self.types: data = numpy.ones([3], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [0, 1, 0]) def test_binary_erosion06(self): for type_ in self.types: data = numpy.ones([3], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [1, 1, 1]) def test_binary_erosion07(self): for type_ in self.types: data = numpy.ones([5], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [0, 1, 1, 1, 0]) def test_binary_erosion08(self): for type_ in self.types: data = numpy.ones([5], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [1, 1, 1, 1, 1]) def test_binary_erosion09(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [0, 0, 0, 0, 0]) def test_binary_erosion10(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [1, 0, 0, 0, 1]) def test_binary_erosion11(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, [1, 0, 1, 0, 1]) def test_binary_erosion12(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value=1, origin=-1) assert_array_almost_equal(out, [0, 1, 0, 1, 1]) def test_binary_erosion13(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value=1, origin=1) assert_array_almost_equal(out, [1, 1, 0, 1, 0]) def test_binary_erosion14(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 struct = [1, 1] out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, [1, 1, 0, 0, 1]) def test_binary_erosion15(self): for type_ in self.types: data = numpy.ones([5], type_) data[2] = 0 struct = [1, 1] out = ndimage.binary_erosion(data, struct, border_value=1, origin=-1) assert_array_almost_equal(out, [1, 0, 0, 1, 1]) def test_binary_erosion16(self): for type_ in self.types: data = numpy.ones([1, 1], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [[1]]) def test_binary_erosion17(self): for type_ in self.types: data = numpy.ones([1, 1], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [[0]]) def test_binary_erosion18(self): for type_ in self.types: data = numpy.ones([1, 3], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [[0, 0, 0]]) def test_binary_erosion19(self): for type_ in self.types: data = numpy.ones([1, 3], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [[1, 1, 1]]) def test_binary_erosion20(self): for type_ in self.types: data = numpy.ones([3, 3], type_) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, [[0, 0, 0], [0, 1, 0], [0, 0, 0]]) def test_binary_erosion21(self): for type_ in self.types: data = numpy.ones([3, 3], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_binary_erosion22(self): expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, expected) def test_binary_erosion23(self): struct = ndimage.generate_binary_structure(2, 2) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) def test_binary_erosion24(self): struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) def test_binary_erosion25(self): struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) def test_binary_erosion26(self): struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_erosion(data, struct, border_value=1, origin=(-1, -1)) assert_array_almost_equal(out, expected) def test_binary_erosion27(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=2) assert_array_almost_equal(out, expected) def test_binary_erosion28(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=2, output=out) assert_array_almost_equal(out, expected) def test_binary_erosion29(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=3) assert_array_almost_equal(out, expected) def test_binary_erosion30(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=3, output=out) assert_array_almost_equal(out, expected) # test with output memory overlap ndimage.binary_erosion(data, struct, border_value=1, iterations=3, output=data) assert_array_almost_equal(data, expected) def test_binary_erosion31(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=1, output=out, origin=(-1, -1)) assert_array_almost_equal(out, expected) def test_binary_erosion32(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=2) assert_array_almost_equal(out, expected) def test_binary_erosion33(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = [[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]] data = numpy.array([[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value=1, mask=mask, iterations=-1) assert_array_almost_equal(out, expected) def test_binary_erosion34(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value=1, mask=mask) assert_array_almost_equal(out, expected) def test_binary_erosion35(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) tmp = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] expected = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) expected = numpy.logical_or(expected, tmp) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=1, output=out, origin=(-1, -1), mask=mask) assert_array_almost_equal(out, expected) def test_binary_erosion36(self): struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] tmp = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) expected = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) expected = numpy.logical_or(expected, tmp) out = ndimage.binary_erosion(data, struct, mask=mask, border_value=1, origin=(-1, -1)) assert_array_almost_equal(out, expected) def test_binary_erosion37(self): a = numpy.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=bool) b = numpy.zeros_like(a) out = ndimage.binary_erosion(a, structure=a, output=b, iterations=0, border_value=True, brute_force=True) assert_(out is b) assert_array_equal( ndimage.binary_erosion(a, structure=a, iterations=0, border_value=True), b) def test_binary_erosion38(self): data = numpy.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=bool) iterations = 2.0 with assert_raises(TypeError): _ = ndimage.binary_erosion(data, iterations=iterations) def test_binary_erosion39(self): iterations = numpy.int32(3) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=iterations, output=out) assert_array_almost_equal(out, expected) def test_binary_erosion40(self): iterations = numpy.int64(3) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value=1, iterations=iterations, output=out) assert_array_almost_equal(out, expected) def test_binary_dilation01(self): for type_ in self.types: data = numpy.ones([], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, 1) def test_binary_dilation02(self): for type_ in self.types: data = numpy.zeros([], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, 0) def test_binary_dilation03(self): for type_ in self.types: data = numpy.ones([1], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [1]) def test_binary_dilation04(self): for type_ in self.types: data = numpy.zeros([1], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [0]) def test_binary_dilation05(self): for type_ in self.types: data = numpy.ones([3], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [1, 1, 1]) def test_binary_dilation06(self): for type_ in self.types: data = numpy.zeros([3], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [0, 0, 0]) def test_binary_dilation07(self): for type_ in self.types: data = numpy.zeros([3], type_) data[1] = 1 out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [1, 1, 1]) def test_binary_dilation08(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 data[3] = 1 out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [1, 1, 1, 1, 1]) def test_binary_dilation09(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [1, 1, 1, 0, 0]) def test_binary_dilation10(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 out = ndimage.binary_dilation(data, origin=-1) assert_array_almost_equal(out, [0, 1, 1, 1, 0]) def test_binary_dilation11(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 out = ndimage.binary_dilation(data, origin=1) assert_array_almost_equal(out, [1, 1, 0, 0, 0]) def test_binary_dilation12(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, [1, 0, 1, 0, 0]) def test_binary_dilation13(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, border_value=1) assert_array_almost_equal(out, [1, 0, 1, 0, 1]) def test_binary_dilation14(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, origin=-1) assert_array_almost_equal(out, [0, 1, 0, 1, 0]) def test_binary_dilation15(self): for type_ in self.types: data = numpy.zeros([5], type_) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, origin=-1, border_value=1) assert_array_almost_equal(out, [1, 1, 0, 1, 0]) def test_binary_dilation16(self): for type_ in self.types: data = numpy.ones([1, 1], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [[1]]) def test_binary_dilation17(self): for type_ in self.types: data = numpy.zeros([1, 1], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [[0]]) def test_binary_dilation18(self): for type_ in self.types: data = numpy.ones([1, 3], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [[1, 1, 1]]) def test_binary_dilation19(self): for type_ in self.types: data = numpy.ones([3, 3], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_binary_dilation20(self): for type_ in self.types: data = numpy.zeros([3, 3], type_) data[1, 1] = 1 out = ndimage.binary_dilation(data) assert_array_almost_equal(out, [[0, 1, 0], [1, 1, 1], [0, 1, 0]]) def test_binary_dilation21(self): struct = ndimage.generate_binary_structure(2, 2) for type_ in self.types: data = numpy.zeros([3, 3], type_) data[1, 1] = 1 out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_binary_dilation22(self): expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, expected) def test_binary_dilation23(self): expected = [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 1, 0, 1], [1, 0, 0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation24(self): expected = [[1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, origin=(1, 1)) assert_array_almost_equal(out, expected) def test_binary_dilation25(self): expected = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, origin=(1, 1), border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation26(self): struct = ndimage.generate_binary_structure(2, 2) expected = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, expected) def test_binary_dilation27(self): struct = [[0, 1], [1, 1]] expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, expected) def test_binary_dilation28(self): expected = [[1, 1, 1, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1, 1, 1, 1]] for type_ in self.types: data = numpy.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation29(self): struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations=2) assert_array_almost_equal(out, expected) def test_binary_dilation30(self): struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations=2, output=out) assert_array_almost_equal(out, expected) def test_binary_dilation31(self): struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations=3) assert_array_almost_equal(out, expected) def test_binary_dilation32(self): struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations=3, output=out) assert_array_almost_equal(out, expected) def test_binary_dilation33(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations=-1, mask=mask, border_value=0) assert_array_almost_equal(out, expected) def test_binary_dilation34(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.zeros(mask.shape, bool) out = ndimage.binary_dilation(data, struct, iterations=-1, mask=mask, border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation35(self): tmp = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) expected = numpy.logical_or(expected, tmp) for type_ in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_dilation(data, mask=mask, origin=(1, 1), border_value=1) assert_array_almost_equal(out, expected) def test_binary_propagation01(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_propagation(data, struct, mask=mask, border_value=0) assert_array_almost_equal(out, expected) def test_binary_propagation02(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.zeros(mask.shape, bool) out = ndimage.binary_propagation(data, struct, mask=mask, border_value=1) assert_array_almost_equal(out, expected) def test_binary_opening01(self): expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_opening(data) assert_array_almost_equal(out, expected) def test_binary_opening02(self): struct = ndimage.generate_binary_structure(2, 2) expected = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_opening(data, struct) assert_array_almost_equal(out, expected) def test_binary_closing01(self): expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_closing(data) assert_array_almost_equal(out, expected) def test_binary_closing02(self): struct = ndimage.generate_binary_structure(2, 2) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_closing(data, struct) assert_array_almost_equal(out, expected) def test_binary_fill_holes01(self): expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) def test_binary_fill_holes02(self): expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) def test_binary_fill_holes03(self): expected = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) def test_grey_erosion01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.grey_erosion(array, footprint=footprint) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) def test_grey_erosion01_overlap(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] ndimage.grey_erosion(array, footprint=footprint, output=array) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], array) def test_grey_erosion02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] output = ndimage.grey_erosion(array, footprint=footprint, structure=structure) assert_array_almost_equal([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) def test_grey_erosion03(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[1, 1, 1], [1, 1, 1]] output = ndimage.grey_erosion(array, footprint=footprint, structure=structure) assert_array_almost_equal([[1, 1, 0, 0, 0], [1, 2, 0, 2, 0], [4, 4, 2, 2, 0]], output) def test_grey_dilation01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] output = ndimage.grey_dilation(array, footprint=footprint) assert_array_almost_equal([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) def test_grey_dilation02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] structure = [[0, 0, 0], [0, 0, 0]] output = ndimage.grey_dilation(array, footprint=footprint, structure=structure) assert_array_almost_equal([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) def test_grey_dilation03(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] structure = [[1, 1, 1], [1, 1, 1]] output = ndimage.grey_dilation(array, footprint=footprint, structure=structure) assert_array_almost_equal([[8, 8, 10, 10, 6], [8, 10, 9, 10, 8], [9, 9, 9, 8, 8]], output) def test_grey_opening01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] tmp = ndimage.grey_erosion(array, footprint=footprint) expected = ndimage.grey_dilation(tmp, footprint=footprint) output = ndimage.grey_opening(array, footprint=footprint) assert_array_almost_equal(expected, output) def test_grey_opening02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = ndimage.grey_dilation(tmp, footprint=footprint, structure=structure) output = ndimage.grey_opening(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_grey_closing01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] tmp = ndimage.grey_dilation(array, footprint=footprint) expected = ndimage.grey_erosion(tmp, footprint=footprint) output = ndimage.grey_closing(array, footprint=footprint) assert_array_almost_equal(expected, output) def test_grey_closing02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_dilation(array, footprint=footprint, structure=structure) expected = ndimage.grey_erosion(tmp, footprint=footprint, structure=structure) output = ndimage.grey_closing(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_morphological_gradient01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 - tmp2 output = numpy.zeros(array.shape, array.dtype) ndimage.morphological_gradient(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_morphological_gradient02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 - tmp2 output = ndimage.morphological_gradient(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_morphological_laplace01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 + tmp2 - 2 * array output = numpy.zeros(array.shape, array.dtype) ndimage.morphological_laplace(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_morphological_laplace02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 + tmp2 - 2 * array output = ndimage.morphological_laplace(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_white_tophat01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_opening(array, footprint=footprint, structure=structure) expected = array - tmp output = numpy.zeros(array.shape, array.dtype) ndimage.white_tophat(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_white_tophat02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_opening(array, footprint=footprint, structure=structure) expected = array - tmp output = ndimage.white_tophat(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_white_tophat03(self): array = numpy.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1]], dtype=numpy.bool_) structure = numpy.ones((3, 3), dtype=numpy.bool_) expected = numpy.array([[0, 1, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 1, 0], [1, 0, 0, 1, 1, 1, 0], [0, 1, 1, 0, 0, 0, 1], [0, 1, 1, 0, 1, 0, 1], [0, 1, 1, 0, 0, 0, 1], [0, 0, 0, 1, 1, 1, 1]], dtype=numpy.bool_) output = ndimage.white_tophat(array, structure=structure) assert_array_equal(expected, output) def test_white_tophat04(self): array = numpy.eye(5, dtype=numpy.bool_) structure = numpy.ones((3, 3), dtype=numpy.bool_) # Check that type mismatch is properly handled output = numpy.empty_like(array, dtype=numpy.float64) ndimage.white_tophat(array, structure=structure, output=output) def test_black_tophat01(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_closing(array, footprint=footprint, structure=structure) expected = tmp - array output = numpy.zeros(array.shape, array.dtype) ndimage.black_tophat(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_black_tophat02(self): array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_closing(array, footprint=footprint, structure=structure) expected = tmp - array output = ndimage.black_tophat(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) def test_black_tophat03(self): array = numpy.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1]], dtype=numpy.bool_) structure = numpy.ones((3, 3), dtype=numpy.bool_) expected = numpy.array([[0, 1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 0]], dtype=numpy.bool_) output = ndimage.black_tophat(array, structure=structure) assert_array_equal(expected, output) def test_black_tophat04(self): array = numpy.eye(5, dtype=numpy.bool_) structure = numpy.ones((3, 3), dtype=numpy.bool_) # Check that type mismatch is properly handled output = numpy.empty_like(array, dtype=numpy.float64) ndimage.black_tophat(array, structure=structure, output=output) def test_hit_or_miss01(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 1, 0, 0, 0], [1, 1, 1, 0, 0], [0, 1, 0, 1, 1], [0, 0, 1, 1, 1], [0, 1, 1, 1, 0], [0, 1, 1, 1, 1], [0, 1, 1, 1, 1], [0, 0, 0, 0, 0]], type_) out = numpy.zeros(data.shape, bool) ndimage.binary_hit_or_miss(data, struct, output=out) assert_array_almost_equal(expected, out) def test_hit_or_miss02(self): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_hit_or_miss(data, struct) assert_array_almost_equal(expected, out) def test_hit_or_miss03(self): struct1 = [[0, 0, 0], [1, 1, 1], [0, 0, 0]] struct2 = [[1, 1, 1], [0, 0, 0], [1, 1, 1]] expected = [[0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type_ in self.types: data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type_) out = ndimage.binary_hit_or_miss(data, struct1, struct2) assert_array_almost_equal(expected, out) class TestDilateFix: def setup_method(self): # dilation related setup self.array = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=numpy.uint8) self.sq3x3 = numpy.ones((3, 3)) dilated3x3 = ndimage.binary_dilation(self.array, structure=self.sq3x3) self.dilated3x3 = dilated3x3.view(numpy.uint8) def test_dilation_square_structure(self): result = ndimage.grey_dilation(self.array, structure=self.sq3x3) # +1 accounts for difference between grey and binary dilation assert_array_almost_equal(result, self.dilated3x3 + 1) def test_dilation_scalar_size(self): result = ndimage.grey_dilation(self.array, size=3) assert_array_almost_equal(result, self.dilated3x3) class TestBinaryOpeningClosing: def setup_method(self): a = numpy.zeros((5,5), dtype=bool) a[1:4, 1:4] = True a[4,4] = True self.array = a self.sq3x3 = numpy.ones((3,3)) self.opened_old = ndimage.binary_opening(self.array, self.sq3x3, 1, None, 0) self.closed_old = ndimage.binary_closing(self.array, self.sq3x3, 1, None, 0) def test_opening_new_arguments(self): opened_new = ndimage.binary_opening(self.array, self.sq3x3, 1, None, 0, None, 0, False) assert_array_equal(opened_new, self.opened_old) def test_closing_new_arguments(self): closed_new = ndimage.binary_closing(self.array, self.sq3x3, 1, None, 0, None, 0, False) assert_array_equal(closed_new, self.closed_old)