import os.path import numpy as np from numpy.testing import (assert_, assert_array_almost_equal, assert_equal, assert_almost_equal, assert_array_equal, suppress_warnings) from pytest import raises as assert_raises import scipy.ndimage as ndimage from . import types class Test_measurements_stats: """ndimage._measurements._stats() is a utility used by other functions.""" def test_a(self): x = [0, 1, 2, 6] labels = [0, 0, 1, 1] index = [0, 1] for shp in [(4,), (2, 2)]: x = np.array(x).reshape(shp) labels = np.array(labels).reshape(shp) counts, sums = ndimage._measurements._stats( x, labels=labels, index=index) assert_array_equal(counts, [2, 2]) assert_array_equal(sums, [1.0, 8.0]) def test_b(self): # Same data as test_a, but different labels. The label 9 exceeds the # length of 'labels', so this test will follow a different code path. x = [0, 1, 2, 6] labels = [0, 0, 9, 9] index = [0, 9] for shp in [(4,), (2, 2)]: x = np.array(x).reshape(shp) labels = np.array(labels).reshape(shp) counts, sums = ndimage._measurements._stats( x, labels=labels, index=index) assert_array_equal(counts, [2, 2]) assert_array_equal(sums, [1.0, 8.0]) def test_a_centered(self): x = [0, 1, 2, 6] labels = [0, 0, 1, 1] index = [0, 1] for shp in [(4,), (2, 2)]: x = np.array(x).reshape(shp) labels = np.array(labels).reshape(shp) counts, sums, centers = ndimage._measurements._stats( x, labels=labels, index=index, centered=True) assert_array_equal(counts, [2, 2]) assert_array_equal(sums, [1.0, 8.0]) assert_array_equal(centers, [0.5, 8.0]) def test_b_centered(self): x = [0, 1, 2, 6] labels = [0, 0, 9, 9] index = [0, 9] for shp in [(4,), (2, 2)]: x = np.array(x).reshape(shp) labels = np.array(labels).reshape(shp) counts, sums, centers = ndimage._measurements._stats( x, labels=labels, index=index, centered=True) assert_array_equal(counts, [2, 2]) assert_array_equal(sums, [1.0, 8.0]) assert_array_equal(centers, [0.5, 8.0]) def test_nonint_labels(self): x = [0, 1, 2, 6] labels = [0.0, 0.0, 9.0, 9.0] index = [0.0, 9.0] for shp in [(4,), (2, 2)]: x = np.array(x).reshape(shp) labels = np.array(labels).reshape(shp) counts, sums, centers = ndimage._measurements._stats( x, labels=labels, index=index, centered=True) assert_array_equal(counts, [2, 2]) assert_array_equal(sums, [1.0, 8.0]) assert_array_equal(centers, [0.5, 8.0]) class Test_measurements_select: """ndimage._measurements._select() is a utility used by other functions.""" def test_basic(self): x = [0, 1, 6, 2] cases = [ ([0, 0, 1, 1], [0, 1]), # "Small" integer labels ([0, 0, 9, 9], [0, 9]), # A label larger than len(labels) ([0.0, 0.0, 7.0, 7.0], [0.0, 7.0]), # Non-integer labels ] for labels, index in cases: result = ndimage._measurements._select( x, labels=labels, index=index) assert_(len(result) == 0) result = ndimage._measurements._select( x, labels=labels, index=index, find_max=True) assert_(len(result) == 1) assert_array_equal(result[0], [1, 6]) result = ndimage._measurements._select( x, labels=labels, index=index, find_min=True) assert_(len(result) == 1) assert_array_equal(result[0], [0, 2]) result = ndimage._measurements._select( x, labels=labels, index=index, find_min=True, find_min_positions=True) assert_(len(result) == 2) assert_array_equal(result[0], [0, 2]) assert_array_equal(result[1], [0, 3]) assert_equal(result[1].dtype.kind, 'i') result = ndimage._measurements._select( x, labels=labels, index=index, find_max=True, find_max_positions=True) assert_(len(result) == 2) assert_array_equal(result[0], [1, 6]) assert_array_equal(result[1], [1, 2]) assert_equal(result[1].dtype.kind, 'i') def test_label01(): data = np.ones([]) out, n = ndimage.label(data) assert_array_almost_equal(out, 1) assert_equal(n, 1) def test_label02(): data = np.zeros([]) out, n = ndimage.label(data) assert_array_almost_equal(out, 0) assert_equal(n, 0) def test_label03(): data = np.ones([1]) out, n = ndimage.label(data) assert_array_almost_equal(out, [1]) assert_equal(n, 1) def test_label04(): data = np.zeros([1]) out, n = ndimage.label(data) assert_array_almost_equal(out, [0]) assert_equal(n, 0) def test_label05(): data = np.ones([5]) out, n = ndimage.label(data) assert_array_almost_equal(out, [1, 1, 1, 1, 1]) assert_equal(n, 1) def test_label06(): data = np.array([1, 0, 1, 1, 0, 1]) out, n = ndimage.label(data) assert_array_almost_equal(out, [1, 0, 2, 2, 0, 3]) assert_equal(n, 3) def test_label07(): data = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) out, n = ndimage.label(data) assert_array_almost_equal(out, [[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]]) assert_equal(n, 0) def test_label08(): data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]]) out, n = ndimage.label(data) assert_array_almost_equal(out, [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) assert_equal(n, 4) def test_label09(): data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]]) struct = ndimage.generate_binary_structure(2, 2) out, n = ndimage.label(data, struct) assert_array_almost_equal(out, [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [2, 2, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0]]) assert_equal(n, 3) def test_label10(): data = np.array([[0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) struct = ndimage.generate_binary_structure(2, 2) out, n = ndimage.label(data, struct) assert_array_almost_equal(out, [[0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) assert_equal(n, 1) def test_label11(): for type in types: data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]], type) out, n = ndimage.label(data) expected = [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]] assert_array_almost_equal(out, expected) assert_equal(n, 4) def test_label11_inplace(): for type in types: data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]], type) n = ndimage.label(data, output=data) expected = [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]] assert_array_almost_equal(data, expected) assert_equal(n, 4) def test_label12(): for type in types: data = np.array([[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 1, 0]], type) out, n = ndimage.label(data) expected = [[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 1, 0]] assert_array_almost_equal(out, expected) assert_equal(n, 1) def test_label13(): for type in types: data = np.array([[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], type) out, n = ndimage.label(data) expected = [[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] assert_array_almost_equal(out, expected) assert_equal(n, 1) def test_label_output_typed(): data = np.ones([5]) for t in types: output = np.zeros([5], dtype=t) n = ndimage.label(data, output=output) assert_array_almost_equal(output, 1) assert_equal(n, 1) def test_label_output_dtype(): data = np.ones([5]) for t in types: output, n = ndimage.label(data, output=t) assert_array_almost_equal(output, 1) assert output.dtype == t def test_label_output_wrong_size(): data = np.ones([5]) for t in types: output = np.zeros([10], t) assert_raises((RuntimeError, ValueError), ndimage.label, data, output=output) def test_label_structuring_elements(): data = np.loadtxt(os.path.join(os.path.dirname( __file__), "data", "label_inputs.txt")) strels = np.loadtxt(os.path.join( os.path.dirname(__file__), "data", "label_strels.txt")) results = np.loadtxt(os.path.join( os.path.dirname(__file__), "data", "label_results.txt")) data = data.reshape((-1, 7, 7)) strels = strels.reshape((-1, 3, 3)) results = results.reshape((-1, 7, 7)) r = 0 for i in range(data.shape[0]): d = data[i, :, :] for j in range(strels.shape[0]): s = strels[j, :, :] assert_equal(ndimage.label(d, s)[0], results[r, :, :]) r += 1 def test_ticket_742(): def SE(img, thresh=.7, size=4): mask = img > thresh rank = len(mask.shape) la, co = ndimage.label(mask, ndimage.generate_binary_structure(rank, rank)) _ = ndimage.find_objects(la) if np.dtype(np.intp) != np.dtype('i'): shape = (3, 1240, 1240) a = np.random.rand(np.prod(shape)).reshape(shape) # shouldn't crash SE(a) def test_gh_issue_3025(): """Github issue #3025 - improper merging of labels""" d = np.zeros((60, 320)) d[:, :257] = 1 d[:, 260:] = 1 d[36, 257] = 1 d[35, 258] = 1 d[35, 259] = 1 assert ndimage.label(d, np.ones((3, 3)))[1] == 1 def test_label_default_dtype(): test_array = np.random.rand(10, 10) label, no_features = ndimage.label(test_array > 0.5) assert_(label.dtype in (np.int32, np.int64)) # Shouldn't raise an exception ndimage.find_objects(label) def test_find_objects01(): data = np.ones([], dtype=int) out = ndimage.find_objects(data) assert_(out == [()]) def test_find_objects02(): data = np.zeros([], dtype=int) out = ndimage.find_objects(data) assert_(out == []) def test_find_objects03(): data = np.ones([1], dtype=int) out = ndimage.find_objects(data) assert_equal(out, [(slice(0, 1, None),)]) def test_find_objects04(): data = np.zeros([1], dtype=int) out = ndimage.find_objects(data) assert_equal(out, []) def test_find_objects05(): data = np.ones([5], dtype=int) out = ndimage.find_objects(data) assert_equal(out, [(slice(0, 5, None),)]) def test_find_objects06(): data = np.array([1, 0, 2, 2, 0, 3]) out = ndimage.find_objects(data) assert_equal(out, [(slice(0, 1, None),), (slice(2, 4, None),), (slice(5, 6, None),)]) def test_find_objects07(): data = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) out = ndimage.find_objects(data) assert_equal(out, []) def test_find_objects08(): data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) out = ndimage.find_objects(data) assert_equal(out, [(slice(0, 1, None), slice(0, 1, None)), (slice(1, 3, None), slice(2, 5, None)), (slice(3, 5, None), slice(0, 2, None)), (slice(5, 6, None), slice(3, 5, None))]) def test_find_objects09(): data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) out = ndimage.find_objects(data) assert_equal(out, [(slice(0, 1, None), slice(0, 1, None)), (slice(1, 3, None), slice(2, 5, None)), None, (slice(5, 6, None), slice(3, 5, None))]) def test_value_indices01(): "Test dictionary keys and entries" data = np.array([[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) vi = ndimage.value_indices(data, ignore_value=0) true_keys = [1, 2, 4] assert_equal(list(vi.keys()), true_keys) truevi = {} for k in true_keys: truevi[k] = np.where(data == k) vi = ndimage.value_indices(data, ignore_value=0) assert_equal(vi, truevi) def test_value_indices02(): "Test input checking" data = np.zeros((5, 4), dtype=np.float32) msg = "Parameter 'arr' must be an integer array" with assert_raises(ValueError, match=msg): ndimage.value_indices(data) def test_value_indices03(): "Test different input array shapes, from 1-D to 4-D" for shape in [(36,), (18, 2), (3, 3, 4), (3, 3, 2, 2)]: a = np.array((12*[1]+12*[2]+12*[3]), dtype=np.int32).reshape(shape) trueKeys = np.unique(a) vi = ndimage.value_indices(a) assert_equal(list(vi.keys()), list(trueKeys)) for k in trueKeys: trueNdx = np.where(a == k) assert_equal(vi[k], trueNdx) def test_sum01(): for type in types: input = np.array([], type) output = ndimage.sum(input) assert_equal(output, 0.0) def test_sum02(): for type in types: input = np.zeros([0, 4], type) output = ndimage.sum(input) assert_equal(output, 0.0) def test_sum03(): for type in types: input = np.ones([], type) output = ndimage.sum(input) assert_almost_equal(output, 1.0) def test_sum04(): for type in types: input = np.array([1, 2], type) output = ndimage.sum(input) assert_almost_equal(output, 3.0) def test_sum05(): for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.sum(input) assert_almost_equal(output, 10.0) def test_sum06(): labels = np.array([], bool) for type in types: input = np.array([], type) output = ndimage.sum(input, labels=labels) assert_equal(output, 0.0) def test_sum07(): labels = np.ones([0, 4], bool) for type in types: input = np.zeros([0, 4], type) output = ndimage.sum(input, labels=labels) assert_equal(output, 0.0) def test_sum08(): labels = np.array([1, 0], bool) for type in types: input = np.array([1, 2], type) output = ndimage.sum(input, labels=labels) assert_equal(output, 1.0) def test_sum09(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels=labels) assert_almost_equal(output, 4.0) def test_sum10(): labels = np.array([1, 0], bool) input = np.array([[1, 2], [3, 4]], bool) output = ndimage.sum(input, labels=labels) assert_almost_equal(output, 2.0) def test_sum11(): labels = np.array([1, 2], np.int8) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels=labels, index=2) assert_almost_equal(output, 6.0) def test_sum12(): labels = np.array([[1, 2], [2, 4]], np.int8) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels=labels, index=[4, 8, 2]) assert_array_almost_equal(output, [4.0, 0.0, 5.0]) def test_sum_labels(): labels = np.array([[1, 2], [2, 4]], np.int8) for type in types: input = np.array([[1, 2], [3, 4]], type) output_sum = ndimage.sum(input, labels=labels, index=[4, 8, 2]) output_labels = ndimage.sum_labels( input, labels=labels, index=[4, 8, 2]) assert (output_sum == output_labels).all() assert_array_almost_equal(output_labels, [4.0, 0.0, 5.0]) def test_mean01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels=labels) assert_almost_equal(output, 2.0) def test_mean02(): labels = np.array([1, 0], bool) input = np.array([[1, 2], [3, 4]], bool) output = ndimage.mean(input, labels=labels) assert_almost_equal(output, 1.0) def test_mean03(): labels = np.array([1, 2]) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels=labels, index=2) assert_almost_equal(output, 3.0) def test_mean04(): labels = np.array([[1, 2], [2, 4]], np.int8) with np.errstate(all='ignore'): for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels=labels, index=[4, 8, 2]) assert_array_almost_equal(output[[0, 2]], [4.0, 2.5]) assert_(np.isnan(output[1])) def test_minimum01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels=labels) assert_almost_equal(output, 1.0) def test_minimum02(): labels = np.array([1, 0], bool) input = np.array([[2, 2], [2, 4]], bool) output = ndimage.minimum(input, labels=labels) assert_almost_equal(output, 1.0) def test_minimum03(): labels = np.array([1, 2]) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels=labels, index=2) assert_almost_equal(output, 2.0) def test_minimum04(): labels = np.array([[1, 2], [2, 3]]) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels=labels, index=[2, 3, 8]) assert_array_almost_equal(output, [2.0, 4.0, 0.0]) def test_maximum01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels=labels) assert_almost_equal(output, 3.0) def test_maximum02(): labels = np.array([1, 0], bool) input = np.array([[2, 2], [2, 4]], bool) output = ndimage.maximum(input, labels=labels) assert_almost_equal(output, 1.0) def test_maximum03(): labels = np.array([1, 2]) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels=labels, index=2) assert_almost_equal(output, 4.0) def test_maximum04(): labels = np.array([[1, 2], [2, 3]]) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels=labels, index=[2, 3, 8]) assert_array_almost_equal(output, [3.0, 4.0, 0.0]) def test_maximum05(): # Regression test for ticket #501 (Trac) x = np.array([-3, -2, -1]) assert_equal(ndimage.maximum(x), -1) def test_median01(): a = np.array([[1, 2, 0, 1], [5, 3, 0, 4], [0, 0, 0, 7], [9, 3, 0, 0]]) labels = np.array([[1, 1, 0, 2], [1, 1, 0, 2], [0, 0, 0, 2], [3, 3, 0, 0]]) output = ndimage.median(a, labels=labels, index=[1, 2, 3]) assert_array_almost_equal(output, [2.5, 4.0, 6.0]) def test_median02(): a = np.array([[1, 2, 0, 1], [5, 3, 0, 4], [0, 0, 0, 7], [9, 3, 0, 0]]) output = ndimage.median(a) assert_almost_equal(output, 1.0) def test_median03(): a = np.array([[1, 2, 0, 1], [5, 3, 0, 4], [0, 0, 0, 7], [9, 3, 0, 0]]) labels = np.array([[1, 1, 0, 2], [1, 1, 0, 2], [0, 0, 0, 2], [3, 3, 0, 0]]) output = ndimage.median(a, labels=labels) assert_almost_equal(output, 3.0) def test_median_gh12836_bool(): # test boolean addition fix on example from gh-12836 a = np.asarray([1, 1], dtype=bool) output = ndimage.median(a, labels=np.ones((2,)), index=[1]) assert_array_almost_equal(output, [1.0]) def test_median_no_int_overflow(): # test integer overflow fix on example from gh-12836 a = np.asarray([65, 70], dtype=np.int8) output = ndimage.median(a, labels=np.ones((2,)), index=[1]) assert_array_almost_equal(output, [67.5]) def test_variance01(): with np.errstate(all='ignore'): for type in types: input = np.array([], type) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "Mean of empty slice") output = ndimage.variance(input) assert_(np.isnan(output)) def test_variance02(): for type in types: input = np.array([1], type) output = ndimage.variance(input) assert_almost_equal(output, 0.0) def test_variance03(): for type in types: input = np.array([1, 3], type) output = ndimage.variance(input) assert_almost_equal(output, 1.0) def test_variance04(): input = np.array([1, 0], bool) output = ndimage.variance(input) assert_almost_equal(output, 0.25) def test_variance05(): labels = [2, 2, 3] for type in types: input = np.array([1, 3, 8], type) output = ndimage.variance(input, labels, 2) assert_almost_equal(output, 1.0) def test_variance06(): labels = [2, 2, 3, 3, 4] with np.errstate(all='ignore'): for type in types: input = np.array([1, 3, 8, 10, 8], type) output = ndimage.variance(input, labels, [2, 3, 4]) assert_array_almost_equal(output, [1.0, 1.0, 0.0]) def test_standard_deviation01(): with np.errstate(all='ignore'): for type in types: input = np.array([], type) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "Mean of empty slice") output = ndimage.standard_deviation(input) assert_(np.isnan(output)) def test_standard_deviation02(): for type in types: input = np.array([1], type) output = ndimage.standard_deviation(input) assert_almost_equal(output, 0.0) def test_standard_deviation03(): for type in types: input = np.array([1, 3], type) output = ndimage.standard_deviation(input) assert_almost_equal(output, np.sqrt(1.0)) def test_standard_deviation04(): input = np.array([1, 0], bool) output = ndimage.standard_deviation(input) assert_almost_equal(output, 0.5) def test_standard_deviation05(): labels = [2, 2, 3] for type in types: input = np.array([1, 3, 8], type) output = ndimage.standard_deviation(input, labels, 2) assert_almost_equal(output, 1.0) def test_standard_deviation06(): labels = [2, 2, 3, 3, 4] with np.errstate(all='ignore'): for type in types: input = np.array([1, 3, 8, 10, 8], type) output = ndimage.standard_deviation(input, labels, [2, 3, 4]) assert_array_almost_equal(output, [1.0, 1.0, 0.0]) def test_standard_deviation07(): labels = [1] with np.errstate(all='ignore'): for type in types: input = np.array([-0.00619519], type) output = ndimage.standard_deviation(input, labels, [1]) assert_array_almost_equal(output, [0]) def test_minimum_position01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.minimum_position(input, labels=labels) assert_equal(output, (0, 0)) def test_minimum_position02(): for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input) assert_equal(output, (1, 2)) def test_minimum_position03(): input = np.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) assert_equal(output, (1, 2)) def test_minimum_position04(): input = np.array([[5, 4, 2, 5], [3, 7, 1, 2], [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) assert_equal(output, (0, 0)) def test_minimum_position05(): labels = [1, 2, 0, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 2, 3]], type) output = ndimage.minimum_position(input, labels) assert_equal(output, (2, 0)) def test_minimum_position06(): labels = [1, 2, 3, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input, labels, 2) assert_equal(output, (0, 1)) def test_minimum_position07(): labels = [1, 2, 3, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input, labels, [2, 3]) assert_equal(output[0], (0, 1)) assert_equal(output[1], (1, 2)) def test_maximum_position01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output = ndimage.maximum_position(input, labels=labels) assert_equal(output, (1, 0)) def test_maximum_position02(): for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input) assert_equal(output, (1, 2)) def test_maximum_position03(): input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], bool) output = ndimage.maximum_position(input) assert_equal(output, (0, 0)) def test_maximum_position04(): labels = [1, 2, 0, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels) assert_equal(output, (1, 1)) def test_maximum_position05(): labels = [1, 2, 0, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels, 1) assert_equal(output, (0, 0)) def test_maximum_position06(): labels = [1, 2, 0, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels, [1, 2]) assert_equal(output[0], (0, 0)) assert_equal(output[1], (1, 1)) def test_maximum_position07(): # Test float labels labels = np.array([1.0, 2.5, 0.0, 4.5]) for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels, [1.0, 4.5]) assert_equal(output[0], (0, 0)) assert_equal(output[1], (0, 3)) def test_extrema01(): labels = np.array([1, 0], bool) for type in types: input = np.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels=labels) output2 = ndimage.minimum(input, labels=labels) output3 = ndimage.maximum(input, labels=labels) output4 = ndimage.minimum_position(input, labels=labels) output5 = ndimage.maximum_position(input, labels=labels) assert_equal(output1, (output2, output3, output4, output5)) def test_extrema02(): labels = np.array([1, 2]) for type in types: input = np.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels=labels, index=2) output2 = ndimage.minimum(input, labels=labels, index=2) output3 = ndimage.maximum(input, labels=labels, index=2) output4 = ndimage.minimum_position(input, labels=labels, index=2) output5 = ndimage.maximum_position(input, labels=labels, index=2) assert_equal(output1, (output2, output3, output4, output5)) def test_extrema03(): labels = np.array([[1, 2], [2, 3]]) for type in types: input = np.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels=labels, index=[2, 3, 8]) output2 = ndimage.minimum(input, labels=labels, index=[2, 3, 8]) output3 = ndimage.maximum(input, labels=labels, index=[2, 3, 8]) output4 = ndimage.minimum_position(input, labels=labels, index=[2, 3, 8]) output5 = ndimage.maximum_position(input, labels=labels, index=[2, 3, 8]) assert_array_almost_equal(output1[0], output2) assert_array_almost_equal(output1[1], output3) assert_array_almost_equal(output1[2], output4) assert_array_almost_equal(output1[3], output5) def test_extrema04(): labels = [1, 2, 0, 4] for type in types: input = np.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output1 = ndimage.extrema(input, labels, [1, 2]) output2 = ndimage.minimum(input, labels, [1, 2]) output3 = ndimage.maximum(input, labels, [1, 2]) output4 = ndimage.minimum_position(input, labels, [1, 2]) output5 = ndimage.maximum_position(input, labels, [1, 2]) assert_array_almost_equal(output1[0], output2) assert_array_almost_equal(output1[1], output3) assert_array_almost_equal(output1[2], output4) assert_array_almost_equal(output1[3], output5) def test_center_of_mass01(): expected = [0.0, 0.0] for type in types: input = np.array([[1, 0], [0, 0]], type) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass02(): expected = [1, 0] for type in types: input = np.array([[0, 0], [1, 0]], type) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass03(): expected = [0, 1] for type in types: input = np.array([[0, 1], [0, 0]], type) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass04(): expected = [1, 1] for type in types: input = np.array([[0, 0], [0, 1]], type) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass05(): expected = [0.5, 0.5] for type in types: input = np.array([[1, 1], [1, 1]], type) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass06(): expected = [0.5, 0.5] input = np.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input) assert_array_almost_equal(output, expected) def test_center_of_mass07(): labels = [1, 0] expected = [0.5, 0.0] input = np.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels) assert_array_almost_equal(output, expected) def test_center_of_mass08(): labels = [1, 2] expected = [0.5, 1.0] input = np.array([[5, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels, 2) assert_array_almost_equal(output, expected) def test_center_of_mass09(): labels = [1, 2] expected = [(0.5, 0.0), (0.5, 1.0)] input = np.array([[1, 2], [1, 1]], bool) output = ndimage.center_of_mass(input, labels, [1, 2]) assert_array_almost_equal(output, expected) def test_histogram01(): expected = np.ones(10) input = np.arange(10) output = ndimage.histogram(input, 0, 10, 10) assert_array_almost_equal(output, expected) def test_histogram02(): labels = [1, 1, 1, 1, 2, 2, 2, 2] expected = [0, 2, 0, 1, 1] input = np.array([1, 1, 3, 4, 3, 3, 3, 3]) output = ndimage.histogram(input, 0, 4, 5, labels, 1) assert_array_almost_equal(output, expected) def test_histogram03(): labels = [1, 0, 1, 1, 2, 2, 2, 2] expected1 = [0, 1, 0, 1, 1] expected2 = [0, 0, 0, 3, 0] input = np.array([1, 1, 3, 4, 3, 5, 3, 3]) output = ndimage.histogram(input, 0, 4, 5, labels, (1, 2)) assert_array_almost_equal(output[0], expected1) assert_array_almost_equal(output[1], expected2) def test_stat_funcs_2d(): a = np.array([[5, 6, 0, 0, 0], [8, 9, 0, 0, 0], [0, 0, 0, 3, 5]]) lbl = np.array([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 2, 2]]) mean = ndimage.mean(a, labels=lbl, index=[1, 2]) assert_array_equal(mean, [7.0, 4.0]) var = ndimage.variance(a, labels=lbl, index=[1, 2]) assert_array_equal(var, [2.5, 1.0]) std = ndimage.standard_deviation(a, labels=lbl, index=[1, 2]) assert_array_almost_equal(std, np.sqrt([2.5, 1.0])) med = ndimage.median(a, labels=lbl, index=[1, 2]) assert_array_equal(med, [7.0, 4.0]) min = ndimage.minimum(a, labels=lbl, index=[1, 2]) assert_array_equal(min, [5, 3]) max = ndimage.maximum(a, labels=lbl, index=[1, 2]) assert_array_equal(max, [9, 5]) class TestWatershedIft: def test_watershed_ift01(self): data = np.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]], np.uint8) markers = np.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]], np.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 = np.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]], np.uint8) markers = np.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]], np.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 = np.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]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 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]], np.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 = np.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]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 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]], np.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 = np.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]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 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]], np.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 = np.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]], np.uint8) markers = np.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]], np.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 = np.zeros(shape, dtype=np.uint8) data = data.transpose() data[...] = np.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]], np.uint8) markers = np.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]], np.int8) out = np.zeros(shape, dtype=np.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_watershed_ift08(self): # Test cost larger than uint8. See gh-10069. shape = (2, 2) data = np.array([[256, 0], [0, 0]], np.uint16) markers = np.array([[1, 0], [0, 0]], np.int8) out = ndimage.watershed_ift(data, markers) expected = [[1, 1], [1, 1]] assert_array_almost_equal(out, expected)