1610 lines
58 KiB
Python
1610 lines
58 KiB
Python
import os
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import os.path
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import numpy as np
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from numpy.testing import suppress_warnings
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from scipy._lib._array_api import (
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is_jax,
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is_torch,
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array_namespace,
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xp_assert_equal,
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xp_assert_close,
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assert_array_almost_equal,
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assert_almost_equal,
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)
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import pytest
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from pytest import raises as assert_raises
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import scipy.ndimage as ndimage
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from . import types
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from scipy.conftest import array_api_compatible
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skip_xp_backends = pytest.mark.skip_xp_backends
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pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends"),
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skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'],)]
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IS_WINDOWS_AND_NP1 = os.name == 'nt' and np.__version__ < '2'
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@skip_xp_backends(np_only=True, reason='test internal numpy-only helpers')
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class Test_measurements_stats:
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"""ndimage._measurements._stats() is a utility used by other functions.
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Since internal ndimage/_measurements.py code is NumPy-only,
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so is this this test class.
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"""
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def test_a(self, xp):
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x = [0, 1, 2, 6]
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labels = [0, 0, 1, 1]
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index = [0, 1]
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for shp in [(4,), (2, 2)]:
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x = np.array(x).reshape(shp)
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labels = np.array(labels).reshape(shp)
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counts, sums = ndimage._measurements._stats(
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x, labels=labels, index=index)
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dtype_arg = {'dtype': np.int64} if IS_WINDOWS_AND_NP1 else {}
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xp_assert_equal(counts, np.asarray([2, 2], **dtype_arg))
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xp_assert_equal(sums, np.asarray([1.0, 8.0]))
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def test_b(self, xp):
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# Same data as test_a, but different labels. The label 9 exceeds the
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# length of 'labels', so this test will follow a different code path.
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x = [0, 1, 2, 6]
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labels = [0, 0, 9, 9]
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index = [0, 9]
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for shp in [(4,), (2, 2)]:
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x = np.array(x).reshape(shp)
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labels = np.array(labels).reshape(shp)
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counts, sums = ndimage._measurements._stats(
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x, labels=labels, index=index)
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dtype_arg = {'dtype': np.int64} if IS_WINDOWS_AND_NP1 else {}
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xp_assert_equal(counts, np.asarray([2, 2], **dtype_arg))
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xp_assert_equal(sums, np.asarray([1.0, 8.0]))
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def test_a_centered(self, xp):
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x = [0, 1, 2, 6]
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labels = [0, 0, 1, 1]
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index = [0, 1]
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for shp in [(4,), (2, 2)]:
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x = np.array(x).reshape(shp)
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labels = np.array(labels).reshape(shp)
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counts, sums, centers = ndimage._measurements._stats(
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x, labels=labels, index=index, centered=True)
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dtype_arg = {'dtype': np.int64} if IS_WINDOWS_AND_NP1 else {}
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xp_assert_equal(counts, np.asarray([2, 2], **dtype_arg))
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xp_assert_equal(sums, np.asarray([1.0, 8.0]))
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xp_assert_equal(centers, np.asarray([0.5, 8.0]))
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def test_b_centered(self, xp):
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x = [0, 1, 2, 6]
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labels = [0, 0, 9, 9]
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index = [0, 9]
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for shp in [(4,), (2, 2)]:
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x = np.array(x).reshape(shp)
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labels = np.array(labels).reshape(shp)
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counts, sums, centers = ndimage._measurements._stats(
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x, labels=labels, index=index, centered=True)
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dtype_arg = {'dtype': np.int64} if IS_WINDOWS_AND_NP1 else {}
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xp_assert_equal(counts, np.asarray([2, 2], **dtype_arg))
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xp_assert_equal(sums, np.asarray([1.0, 8.0]))
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xp_assert_equal(centers, np.asarray([0.5, 8.0]))
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def test_nonint_labels(self, xp):
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x = [0, 1, 2, 6]
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labels = [0.0, 0.0, 9.0, 9.0]
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index = [0.0, 9.0]
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for shp in [(4,), (2, 2)]:
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x = np.array(x).reshape(shp)
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labels = np.array(labels).reshape(shp)
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counts, sums, centers = ndimage._measurements._stats(
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x, labels=labels, index=index, centered=True)
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dtype_arg = {'dtype': np.int64} if IS_WINDOWS_AND_NP1 else {}
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xp_assert_equal(counts, np.asarray([2, 2], **dtype_arg))
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xp_assert_equal(sums, np.asarray([1.0, 8.0]))
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xp_assert_equal(centers, np.asarray([0.5, 8.0]))
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class Test_measurements_select:
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"""ndimage._measurements._select() is a utility used by other functions."""
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def test_basic(self, xp):
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x = [0, 1, 6, 2]
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cases = [
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([0, 0, 1, 1], [0, 1]), # "Small" integer labels
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([0, 0, 9, 9], [0, 9]), # A label larger than len(labels)
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([0.0, 0.0, 7.0, 7.0], [0.0, 7.0]), # Non-integer labels
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]
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for labels, index in cases:
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result = ndimage._measurements._select(
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x, labels=labels, index=index)
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assert len(result) == 0
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result = ndimage._measurements._select(
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x, labels=labels, index=index, find_max=True)
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assert len(result) == 1
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xp_assert_equal(result[0], [1, 6])
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result = ndimage._measurements._select(
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x, labels=labels, index=index, find_min=True)
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assert len(result) == 1
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xp_assert_equal(result[0], [0, 2])
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result = ndimage._measurements._select(
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x, labels=labels, index=index, find_min=True,
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find_min_positions=True)
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assert len(result) == 2
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xp_assert_equal(result[0], [0, 2])
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xp_assert_equal(result[1], [0, 3])
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assert result[1].dtype.kind == 'i'
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result = ndimage._measurements._select(
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x, labels=labels, index=index, find_max=True,
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find_max_positions=True)
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assert len(result) == 2
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xp_assert_equal(result[0], [1, 6])
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xp_assert_equal(result[1], [1, 2])
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assert result[1].dtype.kind == 'i'
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def test_label01(xp):
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data = xp.ones([])
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out, n = ndimage.label(data)
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assert out == 1
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assert n == 1
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def test_label02(xp):
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data = xp.zeros([])
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out, n = ndimage.label(data)
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assert out == 0
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assert n == 0
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@pytest.mark.thread_unsafe # due to Cython fused types, see cython#6506
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def test_label03(xp):
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data = xp.ones([1])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray([1]))
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assert n == 1
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def test_label04(xp):
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data = xp.zeros([1])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray([0]))
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assert n == 0
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def test_label05(xp):
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data = xp.ones([5])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray([1, 1, 1, 1, 1]))
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assert n == 1
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def test_label06(xp):
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data = xp.asarray([1, 0, 1, 1, 0, 1])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray([1, 0, 2, 2, 0, 3]))
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assert n == 3
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def test_label07(xp):
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data = xp.asarray([[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0]])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray(
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[[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0]]))
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assert n == 0
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def test_label08(xp):
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data = xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0]])
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out, n = ndimage.label(data)
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assert_array_almost_equal(out, xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 2, 2, 0, 0],
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[0, 0, 2, 2, 2, 0],
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[3, 3, 0, 0, 0, 0],
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[3, 3, 0, 0, 0, 0],
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[0, 0, 0, 4, 4, 0]]))
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assert n == 4
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def test_label09(xp):
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data = xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0]])
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struct = ndimage.generate_binary_structure(2, 2)
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struct = xp.asarray(struct)
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out, n = ndimage.label(data, struct)
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assert_array_almost_equal(out, xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 2, 2, 0, 0],
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[0, 0, 2, 2, 2, 0],
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[2, 2, 0, 0, 0, 0],
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[2, 2, 0, 0, 0, 0],
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[0, 0, 0, 3, 3, 0]]))
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assert n == 3
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def test_label10(xp):
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data = xp.asarray([[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0]])
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struct = ndimage.generate_binary_structure(2, 2)
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struct = xp.asarray(struct)
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out, n = ndimage.label(data, struct)
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assert_array_almost_equal(out, xp.asarray([[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0]]))
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assert n == 1
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def test_label11(xp):
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for type in types:
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dtype = getattr(xp, type)
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data = xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0]], dtype=dtype)
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out, n = ndimage.label(data)
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expected = [[1, 0, 0, 0, 0, 0],
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[0, 0, 2, 2, 0, 0],
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[0, 0, 2, 2, 2, 0],
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[3, 3, 0, 0, 0, 0],
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[3, 3, 0, 0, 0, 0],
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[0, 0, 0, 4, 4, 0]]
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expected = xp.asarray(expected)
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assert_array_almost_equal(out, expected)
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assert n == 4
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@skip_xp_backends(np_only=True, reason='inplace output is numpy-specific')
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def test_label11_inplace(xp):
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for type in types:
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dtype = getattr(xp, type)
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data = xp.asarray([[1, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 0],
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[1, 1, 0, 0, 0, 0],
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[1, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 0]], dtype=dtype)
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n = ndimage.label(data, output=data)
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expected = [[1, 0, 0, 0, 0, 0],
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[0, 0, 2, 2, 0, 0],
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[0, 0, 2, 2, 2, 0],
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[3, 3, 0, 0, 0, 0],
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[3, 3, 0, 0, 0, 0],
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[0, 0, 0, 4, 4, 0]]
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expected = xp.asarray(expected)
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assert_array_almost_equal(data, expected)
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assert n == 4
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def test_label12(xp):
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for type in types:
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dtype = getattr(xp, type)
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data = xp.asarray([[0, 0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 1, 0, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 0, 1, 1, 0]], dtype=dtype)
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out, n = ndimage.label(data)
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expected = [[0, 0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0, 1],
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[0, 0, 1, 0, 1, 1],
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[0, 0, 1, 1, 1, 1],
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[0, 0, 0, 1, 1, 0]]
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expected = xp.asarray(expected)
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assert_array_almost_equal(out, expected)
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assert n == 1
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def test_label13(xp):
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for type in types:
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dtype = getattr(xp, type)
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data = xp.asarray([[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1],
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[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],
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[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
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dtype=dtype)
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out, n = ndimage.label(data)
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expected = [[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1],
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[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],
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[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
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expected = xp.asarray(expected)
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assert_array_almost_equal(out, expected)
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assert n == 1
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@skip_xp_backends(np_only=True, reason='output=dtype is numpy-specific')
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def test_label_output_typed(xp):
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data = xp.ones([5])
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for t in types:
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dtype = getattr(xp, t)
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output = xp.zeros([5], dtype=dtype)
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n = ndimage.label(data, output=output)
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assert_array_almost_equal(output,
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xp.ones(output.shape, dtype=output.dtype))
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assert n == 1
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@skip_xp_backends(np_only=True, reason='output=dtype is numpy-specific')
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def test_label_output_dtype(xp):
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data = xp.ones([5])
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for t in types:
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dtype = getattr(xp, t)
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output, n = ndimage.label(data, output=dtype)
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assert_array_almost_equal(output,
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xp.ones(output.shape, dtype=output.dtype))
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assert output.dtype == t
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def test_label_output_wrong_size(xp):
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if is_jax(xp):
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pytest.xfail("JAX does not raise")
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data = xp.ones([5])
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for t in types:
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dtype = getattr(xp, t)
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output = xp.zeros([10], dtype=dtype)
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# TypeError is from non-numpy arrays as output
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assert_raises((ValueError, TypeError),
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ndimage.label, data, output=output)
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def test_label_structuring_elements(xp):
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data = np.loadtxt(os.path.join(os.path.dirname(
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__file__), "data", "label_inputs.txt"))
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strels = np.loadtxt(os.path.join(
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os.path.dirname(__file__), "data", "label_strels.txt"))
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results = np.loadtxt(os.path.join(
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os.path.dirname(__file__), "data", "label_results.txt"))
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data = data.reshape((-1, 7, 7))
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strels = strels.reshape((-1, 3, 3))
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results = results.reshape((-1, 7, 7))
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data = xp.asarray(data)
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strels = xp.asarray(strels)
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results = xp.asarray(results)
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r = 0
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for i in range(data.shape[0]):
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d = data[i, :, :]
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for j in range(strels.shape[0]):
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s = strels[j, :, :]
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xp_assert_equal(ndimage.label(d, s)[0], results[r, :, :], check_dtype=False)
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r += 1
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@skip_xp_backends("cupy",
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reason="`cupyx.scipy.ndimage` does not have `find_objects`"
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)
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def test_ticket_742(xp):
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def SE(img, thresh=.7, size=4):
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mask = img > thresh
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rank = len(mask.shape)
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struct = ndimage.generate_binary_structure(rank, rank)
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struct = xp.asarray(struct)
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la, co = ndimage.label(mask,
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struct)
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_ = ndimage.find_objects(la)
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if np.dtype(np.intp) != np.dtype('i'):
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shape = (3, 1240, 1240)
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a = np.random.rand(np.prod(shape)).reshape(shape)
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a = xp.asarray(a)
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# shouldn't crash
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SE(a)
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def test_gh_issue_3025(xp):
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"""Github issue #3025 - improper merging of labels"""
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d = np.zeros((60, 320))
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d[:, :257] = 1
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d[:, 260:] = 1
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d[36, 257] = 1
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d[35, 258] = 1
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d[35, 259] = 1
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d = xp.asarray(d)
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assert ndimage.label(d, xp.ones((3, 3)))[1] == 1
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@skip_xp_backends("cupy", reason="cupyx.scipy.ndimage does not have find_object")
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class TestFindObjects:
|
|
def test_label_default_dtype(self, xp):
|
|
test_array = np.random.rand(10, 10)
|
|
test_array = xp.asarray(test_array)
|
|
label, no_features = ndimage.label(test_array > 0.5)
|
|
assert label.dtype in (xp.int32, xp.int64)
|
|
# Shouldn't raise an exception
|
|
ndimage.find_objects(label)
|
|
|
|
|
|
def test_find_objects01(self, xp):
|
|
data = xp.ones([], dtype=xp.int64)
|
|
out = ndimage.find_objects(data)
|
|
assert out == [()]
|
|
|
|
|
|
def test_find_objects02(self, xp):
|
|
data = xp.zeros([], dtype=xp.int64)
|
|
out = ndimage.find_objects(data)
|
|
assert out == []
|
|
|
|
|
|
def test_find_objects03(self, xp):
|
|
data = xp.ones([1], dtype=xp.int64)
|
|
out = ndimage.find_objects(data)
|
|
assert out == [(slice(0, 1, None),)]
|
|
|
|
|
|
def test_find_objects04(self, xp):
|
|
data = xp.zeros([1], dtype=xp.int64)
|
|
out = ndimage.find_objects(data)
|
|
assert out == []
|
|
|
|
|
|
def test_find_objects05(self, xp):
|
|
data = xp.ones([5], dtype=xp.int64)
|
|
out = ndimage.find_objects(data)
|
|
assert out == [(slice(0, 5, None),)]
|
|
|
|
|
|
def test_find_objects06(self, xp):
|
|
data = xp.asarray([1, 0, 2, 2, 0, 3])
|
|
out = ndimage.find_objects(data)
|
|
assert out == [(slice(0, 1, None),),
|
|
(slice(2, 4, None),),
|
|
(slice(5, 6, None),)]
|
|
|
|
|
|
def test_find_objects07(self, xp):
|
|
data = xp.asarray([[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 out == []
|
|
|
|
|
|
def test_find_objects08(self, xp):
|
|
data = xp.asarray([[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 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(self, xp):
|
|
data = xp.asarray([[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 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(xp):
|
|
"Test dictionary keys and entries"
|
|
data = xp.asarray([[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 list(vi.keys()) == true_keys
|
|
|
|
nnz_kwd = {'as_tuple': True} if is_torch(xp) else {}
|
|
|
|
truevi = {}
|
|
for k in true_keys:
|
|
truevi[k] = xp.nonzero(data == k, **nnz_kwd)
|
|
|
|
vi = ndimage.value_indices(data, ignore_value=0)
|
|
assert vi.keys() == truevi.keys()
|
|
for key in vi.keys():
|
|
assert len(vi[key]) == len(truevi[key])
|
|
for v, true_v in zip(vi[key], truevi[key]):
|
|
xp_assert_equal(v, true_v)
|
|
|
|
|
|
def test_value_indices02(xp):
|
|
"Test input checking"
|
|
data = xp.zeros((5, 4), dtype=xp.float32)
|
|
msg = "Parameter 'arr' must be an integer array"
|
|
with assert_raises(ValueError, match=msg):
|
|
ndimage.value_indices(data)
|
|
|
|
|
|
def test_value_indices03(xp):
|
|
"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 = xp.asarray((12*[1]+12*[2]+12*[3]), dtype=xp.int32)
|
|
a = xp.reshape(a, shape)
|
|
|
|
nnz_kwd = {'as_tuple': True} if is_torch(xp) else {}
|
|
|
|
unique_values = array_namespace(a).unique_values
|
|
trueKeys = unique_values(a)
|
|
vi = ndimage.value_indices(a)
|
|
assert list(vi.keys()) == list(trueKeys)
|
|
for k in [int(x) for x in trueKeys]:
|
|
trueNdx = xp.nonzero(a == k, **nnz_kwd)
|
|
assert len(vi[k]) == len(trueNdx)
|
|
for vik, true_vik in zip(vi[k], trueNdx):
|
|
xp_assert_equal(vik, true_vik)
|
|
|
|
|
|
def test_sum01(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([], dtype=dtype)
|
|
output = ndimage.sum(input)
|
|
assert output == 0
|
|
|
|
|
|
def test_sum02(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.zeros([0, 4], dtype=dtype)
|
|
output = ndimage.sum(input)
|
|
assert output == 0
|
|
|
|
|
|
def test_sum03(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.ones([], dtype=dtype)
|
|
output = ndimage.sum(input)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_sum04(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 2], dtype=dtype)
|
|
output = ndimage.sum(input)
|
|
assert_almost_equal(output, xp.asarray(3.0), check_0d=False)
|
|
|
|
|
|
def test_sum05(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.sum(input)
|
|
assert_almost_equal(output, xp.asarray(10.0), check_0d=False)
|
|
|
|
|
|
def test_sum06(xp):
|
|
labels = np.asarray([], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels)
|
|
assert output == 0
|
|
|
|
|
|
def test_sum07(xp):
|
|
labels = np.ones([0, 4], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.zeros([0, 4], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels)
|
|
assert output == 0
|
|
|
|
|
|
def test_sum08(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 2], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels)
|
|
assert output == 1
|
|
|
|
|
|
def test_sum09(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(4.0), check_0d=False)
|
|
|
|
|
|
def test_sum10(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
input = np.asarray([[1, 2], [3, 4]], dtype=bool)
|
|
|
|
labels = xp.asarray(labels)
|
|
input = xp.asarray(input)
|
|
output = ndimage.sum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(2.0), check_0d=False)
|
|
|
|
|
|
def test_sum11(xp):
|
|
labels = xp.asarray([1, 2], dtype=xp.int8)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels,
|
|
index=2)
|
|
assert_almost_equal(output, xp.asarray(6.0), check_0d=False)
|
|
|
|
|
|
def test_sum12(xp):
|
|
labels = xp.asarray([[1, 2], [2, 4]], dtype=xp.int8)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.sum(input, labels=labels, index=xp.asarray([4, 8, 2]))
|
|
assert_array_almost_equal(output, xp.asarray([4.0, 0.0, 5.0]))
|
|
|
|
|
|
def test_sum_labels(xp):
|
|
labels = xp.asarray([[1, 2], [2, 4]], dtype=xp.int8)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output_sum = ndimage.sum(input, labels=labels, index=xp.asarray([4, 8, 2]))
|
|
output_labels = ndimage.sum_labels(
|
|
input, labels=labels, index=xp.asarray([4, 8, 2]))
|
|
|
|
assert xp.all(output_sum == output_labels)
|
|
assert_array_almost_equal(output_labels, xp.asarray([4.0, 0.0, 5.0]))
|
|
|
|
|
|
def test_mean01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.mean(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(2.0), check_0d=False)
|
|
|
|
|
|
def test_mean02(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
input = np.asarray([[1, 2], [3, 4]], dtype=bool)
|
|
|
|
labels = xp.asarray(labels)
|
|
input = xp.asarray(input)
|
|
output = ndimage.mean(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_mean03(xp):
|
|
labels = xp.asarray([1, 2])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.mean(input, labels=labels,
|
|
index=2)
|
|
assert_almost_equal(output, xp.asarray(3.0), check_0d=False)
|
|
|
|
|
|
def test_mean04(xp):
|
|
labels = xp.asarray([[1, 2], [2, 4]], dtype=xp.int8)
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.mean(input, labels=labels,
|
|
index=xp.asarray([4, 8, 2]))
|
|
# XXX: output[[0, 2]] does not work in array-api-strict; annoying
|
|
# assert_array_almost_equal(output[[0, 2]], xp.asarray([4.0, 2.5]))
|
|
assert output[0] == 4.0
|
|
assert output[2] == 2.5
|
|
assert xp.isnan(output[1])
|
|
|
|
|
|
def test_minimum01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.minimum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_minimum02(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
input = np.asarray([[2, 2], [2, 4]], dtype=bool)
|
|
|
|
labels = xp.asarray(labels)
|
|
input = xp.asarray(input)
|
|
output = ndimage.minimum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_minimum03(xp):
|
|
labels = xp.asarray([1, 2])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.minimum(input, labels=labels,
|
|
index=2)
|
|
assert_almost_equal(output, xp.asarray(2.0), check_0d=False)
|
|
|
|
|
|
def test_minimum04(xp):
|
|
labels = xp.asarray([[1, 2], [2, 3]])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.minimum(input, labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
assert_array_almost_equal(output, xp.asarray([2.0, 4.0, 0.0]))
|
|
|
|
|
|
def test_maximum01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.maximum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(3.0), check_0d=False)
|
|
|
|
|
|
def test_maximum02(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
input = np.asarray([[2, 2], [2, 4]], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
input = xp.asarray(input)
|
|
output = ndimage.maximum(input, labels=labels)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_maximum03(xp):
|
|
labels = xp.asarray([1, 2])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.maximum(input, labels=labels,
|
|
index=2)
|
|
assert_almost_equal(output, xp.asarray(4.0), check_0d=False)
|
|
|
|
|
|
def test_maximum04(xp):
|
|
labels = xp.asarray([[1, 2], [2, 3]])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.maximum(input, labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
assert_array_almost_equal(output, xp.asarray([3.0, 4.0, 0.0]))
|
|
|
|
|
|
def test_maximum05(xp):
|
|
# Regression test for ticket #501 (Trac)
|
|
x = xp.asarray([-3, -2, -1])
|
|
assert ndimage.maximum(x) == -1
|
|
|
|
|
|
def test_median01(xp):
|
|
a = xp.asarray([[1, 2, 0, 1],
|
|
[5, 3, 0, 4],
|
|
[0, 0, 0, 7],
|
|
[9, 3, 0, 0]])
|
|
labels = xp.asarray([[1, 1, 0, 2],
|
|
[1, 1, 0, 2],
|
|
[0, 0, 0, 2],
|
|
[3, 3, 0, 0]])
|
|
output = ndimage.median(a, labels=labels, index=xp.asarray([1, 2, 3]))
|
|
assert_array_almost_equal(output, xp.asarray([2.5, 4.0, 6.0]))
|
|
|
|
|
|
def test_median02(xp):
|
|
a = xp.asarray([[1, 2, 0, 1],
|
|
[5, 3, 0, 4],
|
|
[0, 0, 0, 7],
|
|
[9, 3, 0, 0]])
|
|
output = ndimage.median(a)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_median03(xp):
|
|
a = xp.asarray([[1, 2, 0, 1],
|
|
[5, 3, 0, 4],
|
|
[0, 0, 0, 7],
|
|
[9, 3, 0, 0]])
|
|
labels = xp.asarray([[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, xp.asarray(3.0), check_0d=False)
|
|
|
|
|
|
def test_median_gh12836_bool(xp):
|
|
# test boolean addition fix on example from gh-12836
|
|
a = np.asarray([1, 1], dtype=bool)
|
|
a = xp.asarray(a)
|
|
output = ndimage.median(a, labels=xp.ones((2,)), index=xp.asarray([1]))
|
|
assert_array_almost_equal(output, xp.asarray([1.0]))
|
|
|
|
|
|
def test_median_no_int_overflow(xp):
|
|
# test integer overflow fix on example from gh-12836
|
|
a = xp.asarray([65, 70], dtype=xp.int8)
|
|
output = ndimage.median(a, labels=xp.ones((2,)), index=xp.asarray([1]))
|
|
assert_array_almost_equal(output, xp.asarray([67.5]))
|
|
|
|
|
|
def test_variance01(xp):
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([], dtype=dtype)
|
|
with suppress_warnings() as sup:
|
|
sup.filter(RuntimeWarning, "Mean of empty slice")
|
|
output = ndimage.variance(input)
|
|
assert xp.isnan(output)
|
|
|
|
|
|
def test_variance02(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1], dtype=dtype)
|
|
output = ndimage.variance(input)
|
|
assert_almost_equal(output, xp.asarray(0.0), check_0d=False)
|
|
|
|
|
|
def test_variance03(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 3], dtype=dtype)
|
|
output = ndimage.variance(input)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_variance04(xp):
|
|
input = np.asarray([1, 0], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.variance(input)
|
|
assert_almost_equal(output, xp.asarray(0.25), check_0d=False)
|
|
|
|
|
|
def test_variance05(xp):
|
|
labels = xp.asarray([2, 2, 3])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
|
|
input = xp.asarray([1, 3, 8], dtype=dtype)
|
|
output = ndimage.variance(input, labels, 2)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_variance06(xp):
|
|
labels = xp.asarray([2, 2, 3, 3, 4])
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 3, 8, 10, 8], dtype=dtype)
|
|
output = ndimage.variance(input, labels, xp.asarray([2, 3, 4]))
|
|
assert_array_almost_equal(output, xp.asarray([1.0, 1.0, 0.0]))
|
|
|
|
|
|
def test_standard_deviation01(xp):
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([], dtype=dtype)
|
|
with suppress_warnings() as sup:
|
|
sup.filter(RuntimeWarning, "Mean of empty slice")
|
|
output = ndimage.standard_deviation(input)
|
|
assert xp.isnan(output)
|
|
|
|
|
|
def test_standard_deviation02(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1], dtype=dtype)
|
|
output = ndimage.standard_deviation(input)
|
|
assert_almost_equal(output, xp.asarray(0.0), check_0d=False)
|
|
|
|
|
|
def test_standard_deviation03(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 3], dtype=dtype)
|
|
output = ndimage.standard_deviation(input)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_standard_deviation04(xp):
|
|
input = np.asarray([1, 0], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.standard_deviation(input)
|
|
assert_almost_equal(output, xp.asarray(0.5), check_0d=False)
|
|
|
|
|
|
def test_standard_deviation05(xp):
|
|
labels = xp.asarray([2, 2, 3])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 3, 8], dtype=dtype)
|
|
output = ndimage.standard_deviation(input, labels, 2)
|
|
assert_almost_equal(output, xp.asarray(1.0), check_0d=False)
|
|
|
|
|
|
def test_standard_deviation06(xp):
|
|
labels = xp.asarray([2, 2, 3, 3, 4])
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([1, 3, 8, 10, 8], dtype=dtype)
|
|
output = ndimage.standard_deviation(
|
|
input, labels, xp.asarray([2, 3, 4])
|
|
)
|
|
assert_array_almost_equal(output, xp.asarray([1.0, 1.0, 0.0]))
|
|
|
|
|
|
def test_standard_deviation07(xp):
|
|
labels = xp.asarray([1])
|
|
with np.errstate(all='ignore'):
|
|
for type in types:
|
|
if is_torch(xp) and type == 'uint8':
|
|
pytest.xfail("value cannot be converted to type uint8 "
|
|
"without overflow")
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([-0.00619519], dtype=dtype)
|
|
output = ndimage.standard_deviation(input, labels, xp.asarray([1]))
|
|
assert_array_almost_equal(output, xp.asarray([0]))
|
|
|
|
|
|
def test_minimum_position01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.minimum_position(input, labels=labels)
|
|
assert output == (0, 0)
|
|
|
|
|
|
def test_minimum_position02(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 0, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.minimum_position(input)
|
|
assert output == (1, 2)
|
|
|
|
|
|
def test_minimum_position03(xp):
|
|
input = np.asarray([[5, 4, 2, 5],
|
|
[3, 7, 0, 2],
|
|
[1, 5, 1, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.minimum_position(input)
|
|
assert output == (1, 2)
|
|
|
|
|
|
def test_minimum_position04(xp):
|
|
input = np.asarray([[5, 4, 2, 5],
|
|
[3, 7, 1, 2],
|
|
[1, 5, 1, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.minimum_position(input)
|
|
assert output == (0, 0)
|
|
|
|
|
|
def test_minimum_position05(xp):
|
|
labels = xp.asarray([1, 2, 0, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 0, 2],
|
|
[1, 5, 2, 3]], dtype=dtype)
|
|
output = ndimage.minimum_position(input, labels)
|
|
assert output == (2, 0)
|
|
|
|
|
|
def test_minimum_position06(xp):
|
|
labels = xp.asarray([1, 2, 3, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 0, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.minimum_position(input, labels, 2)
|
|
assert output == (0, 1)
|
|
|
|
|
|
def test_minimum_position07(xp):
|
|
labels = xp.asarray([1, 2, 3, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 0, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.minimum_position(input, labels,
|
|
xp.asarray([2, 3]))
|
|
assert output[0] == (0, 1)
|
|
assert output[1] == (1, 2)
|
|
|
|
|
|
def test_maximum_position01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output = ndimage.maximum_position(input,
|
|
labels=labels)
|
|
assert output == (1, 0)
|
|
|
|
|
|
def test_maximum_position02(xp):
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.maximum_position(input)
|
|
assert output == (1, 2)
|
|
|
|
|
|
def test_maximum_position03(xp):
|
|
input = np.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.maximum_position(input)
|
|
assert output == (0, 0)
|
|
|
|
|
|
def test_maximum_position04(xp):
|
|
labels = xp.asarray([1, 2, 0, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.maximum_position(input, labels)
|
|
assert output == (1, 1)
|
|
|
|
|
|
def test_maximum_position05(xp):
|
|
labels = xp.asarray([1, 2, 0, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.maximum_position(input, labels, 1)
|
|
assert output == (0, 0)
|
|
|
|
|
|
def test_maximum_position06(xp):
|
|
labels = xp.asarray([1, 2, 0, 4])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.maximum_position(input, labels,
|
|
xp.asarray([1, 2]))
|
|
assert output[0] == (0, 0)
|
|
assert output[1] == (1, 1)
|
|
|
|
|
|
def test_maximum_position07(xp):
|
|
# Test float labels
|
|
if is_torch(xp):
|
|
pytest.xfail("output[1] is wrong on pytorch")
|
|
|
|
labels = xp.asarray([1.0, 2.5, 0.0, 4.5])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output = ndimage.maximum_position(input, labels,
|
|
xp.asarray([1.0, 4.5]))
|
|
assert output[0] == (0, 0)
|
|
assert output[1] == (0, 3)
|
|
|
|
|
|
def test_extrema01(xp):
|
|
labels = np.asarray([1, 0], dtype=bool)
|
|
labels = xp.asarray(labels)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
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 output1 == (output2, output3, output4, output5)
|
|
|
|
|
|
def test_extrema02(xp):
|
|
labels = xp.asarray([1, 2])
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
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 output1 == (output2, output3, output4, output5)
|
|
|
|
|
|
def test_extrema03(xp):
|
|
labels = xp.asarray([[1, 2], [2, 3]])
|
|
for type in types:
|
|
if is_torch(xp) and type in ("uint16", "uint32", "uint64"):
|
|
pytest.xfail("https://github.com/pytorch/pytorch/issues/58734")
|
|
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 2], [3, 4]], dtype=dtype)
|
|
output1 = ndimage.extrema(input,
|
|
labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
output2 = ndimage.minimum(input,
|
|
labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
output3 = ndimage.maximum(input, labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
output4 = ndimage.minimum_position(input,
|
|
labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
output5 = ndimage.maximum_position(input,
|
|
labels=labels,
|
|
index=xp.asarray([2, 3, 8]))
|
|
assert_array_almost_equal(output1[0], output2)
|
|
assert_array_almost_equal(output1[1], output3)
|
|
assert output1[2] == output4
|
|
assert output1[3] == output5
|
|
|
|
|
|
def test_extrema04(xp):
|
|
labels = xp.asarray([1, 2, 0, 4])
|
|
for type in types:
|
|
if is_torch(xp) and type in ("uint16", "uint32", "uint64"):
|
|
pytest.xfail("https://github.com/pytorch/pytorch/issues/58734")
|
|
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[5, 4, 2, 5],
|
|
[3, 7, 8, 2],
|
|
[1, 5, 1, 1]], dtype=dtype)
|
|
output1 = ndimage.extrema(input, labels, xp.asarray([1, 2]))
|
|
output2 = ndimage.minimum(input, labels, xp.asarray([1, 2]))
|
|
output3 = ndimage.maximum(input, labels, xp.asarray([1, 2]))
|
|
output4 = ndimage.minimum_position(input, labels,
|
|
xp.asarray([1, 2]))
|
|
output5 = ndimage.maximum_position(input, labels,
|
|
xp.asarray([1, 2]))
|
|
assert_array_almost_equal(output1[0], output2)
|
|
assert_array_almost_equal(output1[1], output3)
|
|
assert output1[2] == output4
|
|
assert output1[3] == output5
|
|
|
|
|
|
def test_center_of_mass01(xp):
|
|
expected = (0.0, 0.0)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 0], [0, 0]], dtype=dtype)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass02(xp):
|
|
expected = (1, 0)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[0, 0], [1, 0]], dtype=dtype)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass03(xp):
|
|
expected = (0, 1)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[0, 1], [0, 0]], dtype=dtype)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass04(xp):
|
|
expected = (1, 1)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[0, 0], [0, 1]], dtype=dtype)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass05(xp):
|
|
expected = (0.5, 0.5)
|
|
for type in types:
|
|
dtype = getattr(xp, type)
|
|
input = xp.asarray([[1, 1], [1, 1]], dtype=dtype)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass06(xp):
|
|
expected = (0.5, 0.5)
|
|
input = np.asarray([[1, 2], [3, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.center_of_mass(input)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass07(xp):
|
|
labels = xp.asarray([1, 0])
|
|
expected = (0.5, 0.0)
|
|
input = np.asarray([[1, 2], [3, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.center_of_mass(input, labels)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass08(xp):
|
|
labels = xp.asarray([1, 2])
|
|
expected = (0.5, 1.0)
|
|
input = np.asarray([[5, 2], [3, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.center_of_mass(input, labels, 2)
|
|
assert output == expected
|
|
|
|
|
|
def test_center_of_mass09(xp):
|
|
labels = xp.asarray((1, 2))
|
|
expected = xp.asarray([(0.5, 0.0), (0.5, 1.0)], dtype=xp.float64)
|
|
input = np.asarray([[1, 2], [1, 1]], dtype=bool)
|
|
input = xp.asarray(input)
|
|
output = ndimage.center_of_mass(input, labels, xp.asarray([1, 2]))
|
|
xp_assert_equal(xp.asarray(output), xp.asarray(expected))
|
|
|
|
|
|
def test_histogram01(xp):
|
|
expected = xp.ones(10)
|
|
input = xp.arange(10)
|
|
output = ndimage.histogram(input, 0, 10, 10)
|
|
assert_array_almost_equal(output, expected)
|
|
|
|
|
|
def test_histogram02(xp):
|
|
labels = xp.asarray([1, 1, 1, 1, 2, 2, 2, 2])
|
|
expected = xp.asarray([0, 2, 0, 1, 1])
|
|
input = xp.asarray([1, 1, 3, 4, 3, 3, 3, 3])
|
|
output = ndimage.histogram(input, 0, 4, 5, labels, 1)
|
|
assert_array_almost_equal(output, expected)
|
|
|
|
|
|
@skip_xp_backends(np_only=True, reason='object arrays')
|
|
def test_histogram03(xp):
|
|
labels = xp.asarray([1, 0, 1, 1, 2, 2, 2, 2])
|
|
expected1 = xp.asarray([0, 1, 0, 1, 1])
|
|
expected2 = xp.asarray([0, 0, 0, 3, 0])
|
|
input = xp.asarray([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(xp):
|
|
a = xp.asarray([[5, 6, 0, 0, 0], [8, 9, 0, 0, 0], [0, 0, 0, 3, 5]])
|
|
lbl = xp.asarray([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 2, 2]])
|
|
|
|
mean = ndimage.mean(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
xp_assert_equal(mean, xp.asarray([7.0, 4.0], dtype=xp.float64))
|
|
|
|
var = ndimage.variance(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
xp_assert_equal(var, xp.asarray([2.5, 1.0], dtype=xp.float64))
|
|
|
|
std = ndimage.standard_deviation(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
assert_array_almost_equal(std, xp.sqrt(xp.asarray([2.5, 1.0], dtype=xp.float64)))
|
|
|
|
med = ndimage.median(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
xp_assert_equal(med, xp.asarray([7.0, 4.0], dtype=xp.float64))
|
|
|
|
min = ndimage.minimum(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
xp_assert_equal(min, xp.asarray([5, 3]), check_dtype=False)
|
|
|
|
max = ndimage.maximum(a, labels=lbl, index=xp.asarray([1, 2]))
|
|
xp_assert_equal(max, xp.asarray([9, 5]), check_dtype=False)
|
|
|
|
|
|
@skip_xp_backends("cupy", reason="no watershed_ift on CuPy")
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|
class TestWatershedIft:
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|
|
|
def test_watershed_ift01(self, xp):
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|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[-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]], dtype=xp.int8)
|
|
structure=xp.asarray([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
out = ndimage.watershed_ift(data, markers, structure=structure)
|
|
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, xp.asarray(expected))
|
|
|
|
def test_watershed_ift02(self, xp):
|
|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[-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]], dtype=xp.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, xp.asarray(expected))
|
|
|
|
def test_watershed_ift03(self, xp):
|
|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[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]], dtype=xp.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, xp.asarray(expected))
|
|
|
|
def test_watershed_ift04(self, xp):
|
|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[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]],
|
|
dtype=xp.int8)
|
|
|
|
structure=xp.asarray([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
out = ndimage.watershed_ift(data, markers, structure=structure)
|
|
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, xp.asarray(expected))
|
|
|
|
def test_watershed_ift05(self, xp):
|
|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[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]],
|
|
dtype=xp.int8)
|
|
structure = xp.asarray([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
out = ndimage.watershed_ift(data, markers, structure=structure)
|
|
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, xp.asarray(expected))
|
|
|
|
def test_watershed_ift06(self, xp):
|
|
data = xp.asarray([[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]], dtype=xp.uint8)
|
|
markers = xp.asarray([[-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]], dtype=xp.int8)
|
|
structure=xp.asarray([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
out = ndimage.watershed_ift(data, markers, structure=structure)
|
|
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, xp.asarray(expected))
|
|
|
|
@skip_xp_backends(np_only=True, reason="inplace ops are numpy-specific")
|
|
def test_watershed_ift07(self, xp):
|
|
shape = (7, 6)
|
|
data = np.zeros(shape, dtype=np.uint8)
|
|
data = data.transpose()
|
|
data[...] = np.asarray([[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]], dtype=np.uint8)
|
|
data = xp.asarray(data)
|
|
markers = xp.asarray([[-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]], dtype=xp.int8)
|
|
out = xp.zeros(shape, dtype=xp.int16)
|
|
out = out.T
|
|
structure=xp.asarray([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
ndimage.watershed_ift(data, markers, structure=structure,
|
|
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, xp.asarray(expected))
|
|
|
|
@skip_xp_backends("cupy", reason="no watershed_ift on CuPy")
|
|
def test_watershed_ift08(self, xp):
|
|
# Test cost larger than uint8. See gh-10069.
|
|
data = xp.asarray([[256, 0],
|
|
[0, 0]], dtype=xp.uint16)
|
|
markers = xp.asarray([[1, 0],
|
|
[0, 0]], dtype=xp.int8)
|
|
out = ndimage.watershed_ift(data, markers)
|
|
expected = [[1, 1],
|
|
[1, 1]]
|
|
assert_array_almost_equal(out, xp.asarray(expected))
|
|
|
|
@skip_xp_backends("cupy", reason="no watershed_ift on CuPy" )
|
|
def test_watershed_ift09(self, xp):
|
|
# Test large cost. See gh-19575
|
|
data = xp.asarray([[xp.iinfo(xp.uint16).max, 0],
|
|
[0, 0]], dtype=xp.uint16)
|
|
markers = xp.asarray([[1, 0],
|
|
[0, 0]], dtype=xp.int8)
|
|
out = ndimage.watershed_ift(data, markers)
|
|
expected = [[1, 1],
|
|
[1, 1]]
|
|
xp_assert_close(out, xp.asarray(expected), check_dtype=False)
|
|
|
|
|
|
@skip_xp_backends(np_only=True)
|
|
@pytest.mark.parametrize("dt", [np.intc, np.uintc])
|
|
def test_gh_19423(dt, xp):
|
|
rng = np.random.default_rng(123)
|
|
max_val = 8
|
|
image = rng.integers(low=0, high=max_val, size=(10, 12)).astype(dtype=dt)
|
|
val_idx = ndimage.value_indices(image)
|
|
assert len(val_idx.keys()) == max_val
|