Python scipy.ndimage.minimum_filter() Examples

def test_valid_origins():
    """Regression test for #1311."""
    func = lambda x: np.mean(x)
    data = np.array([1,2,3,4,5], dtype=np.float64)
    assert_raises(ValueError, sndi.generic_filter, data, func, size=3,
                  origin=2)
    func2 = lambda x, y: np.mean(x + y)
    assert_raises(ValueError, sndi.generic_filter1d, data, func,
                  filter_size=3, origin=2)
    assert_raises(ValueError, sndi.percentile_filter, data, 0.2, size=3,
                  origin=2)

    for filter in [sndi.uniform_filter, sndi.minimum_filter,
                   sndi.maximum_filter, sndi.maximum_filter1d,
                   sndi.median_filter, sndi.minimum_filter1d]:
        # This should work, since for size == 3, the valid range for origin is
        # -1 to 1.
        list(filter(data, 3, origin=-1))
        list(filter(data, 3, origin=1))
        # Just check this raises an error instead of silently accepting or
        # segfaulting.
        assert_raises(ValueError, filter, data, 3, origin=2) 

def test_valid_origins():
    """Regression test for #1311."""
    func = lambda x: np.mean(x)
    data = np.array([1,2,3,4,5], dtype=np.float64)
    assert_raises(ValueError, sndi.generic_filter, data, func, size=3,
                  origin=2)
    func2 = lambda x, y: np.mean(x + y)
    assert_raises(ValueError, sndi.generic_filter1d, data, func,
                  filter_size=3, origin=2)
    assert_raises(ValueError, sndi.percentile_filter, data, 0.2, size=3,
                  origin=2)

    for filter in [sndi.uniform_filter, sndi.minimum_filter,
                   sndi.maximum_filter, sndi.maximum_filter1d,
                   sndi.median_filter, sndi.minimum_filter1d]:
        # This should work, since for size == 3, the valid range for origin is
        # -1 to 1.
        list(filter(data, 3, origin=-1))
        list(filter(data, 3, origin=1))
        # Just check this raises an error instead of silently accepting or
        # segfaulting.
        assert_raises(ValueError, filter, data, 3, origin=2) 

def test_minimum_filter02(self):
        array = numpy.array([1, 2, 3, 4, 5])
        filter_shape = numpy.array([3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([1, 1, 2, 3, 4], output) 

def test_minmax_filter(self):
        d = np.random.randn(500, 500)
        os = np.empty([4] + list(d.shape))
        ot = np.empty_like(os)
        self.check_func_serial(4, sndi.maximum_filter, (d, 3), os)
        self.check_func_thread(4, sndi.maximum_filter, (d, 3), ot)
        assert_array_equal(os, ot)
        self.check_func_serial(4, sndi.minimum_filter, (d, 3), os)
        self.check_func_thread(4, sndi.minimum_filter, (d, 3), ot)
        assert_array_equal(os, ot) 

def test_multiple_modes():
    # Test that the filters with multiple mode cababilities for different
    # dimensions give the same result as applying a single mode.
    arr = np.array([[1., 0., 0.],
                    [1., 1., 0.],
                    [0., 0., 0.]])

    mode1 = 'reflect'
    mode2 = ['reflect', 'reflect']

    assert_equal(sndi.gaussian_filter(arr, 1, mode=mode1),
                 sndi.gaussian_filter(arr, 1, mode=mode2))
    assert_equal(sndi.prewitt(arr, mode=mode1),
                 sndi.prewitt(arr, mode=mode2))
    assert_equal(sndi.sobel(arr, mode=mode1),
                 sndi.sobel(arr, mode=mode2))
    assert_equal(sndi.laplace(arr, mode=mode1),
                 sndi.laplace(arr, mode=mode2))
    assert_equal(sndi.gaussian_laplace(arr, 1, mode=mode1),
                 sndi.gaussian_laplace(arr, 1, mode=mode2))
    assert_equal(sndi.maximum_filter(arr, size=5, mode=mode1),
                 sndi.maximum_filter(arr, size=5, mode=mode2))
    assert_equal(sndi.minimum_filter(arr, size=5, mode=mode1),
                 sndi.minimum_filter(arr, size=5, mode=mode2))
    assert_equal(sndi.gaussian_gradient_magnitude(arr, 1, mode=mode1),
                 sndi.gaussian_gradient_magnitude(arr, 1, mode=mode2))
    assert_equal(sndi.uniform_filter(arr, 5, mode=mode1),
                 sndi.uniform_filter(arr, 5, mode=mode2)) 

def test_minimum_filter09(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                             [7, 6, 9, 3, 5],
                             [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array, footprint=footprint,
                                        origin=[-1, 0])
        assert_array_almost_equal([[2, 3, 1, 3, 1],
                                   [5, 5, 3, 3, 1],
                                   [5, 3, 3, 1, 1]], output) 

def test_minimum_filter08(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                             [7, 6, 9, 3, 5],
                             [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array, footprint=footprint, origin=-1)
        assert_array_almost_equal([[3, 1, 3, 1, 1],
                                   [5, 3, 3, 1, 1],
                                   [3, 3, 1, 1, 1]], output) 

def test_minimum_filter07(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                             [7, 6, 9, 3, 5],
                             [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array, footprint=footprint)
        assert_array_almost_equal([[2, 2, 1, 1, 1],
                                   [2, 3, 1, 3, 1],
                                   [5, 5, 3, 3, 1]], output) 

def test_minimum_filter05(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                             [7, 6, 9, 3, 5],
                             [5, 8, 3, 7, 1]])
        filter_shape = numpy.array([2, 3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([[2, 2, 1, 1, 1],
                                   [2, 2, 1, 1, 1],
                                   [5, 3, 3, 1, 1]], output) 

def test_minimum_filter04(self):
        array = numpy.array([3, 2, 5, 1, 4])
        filter_shape = numpy.array([3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([2, 2, 1, 1, 1], output) 

def test_minimum_filter03(self):
        array = numpy.array([3, 2, 5, 1, 4])
        filter_shape = numpy.array([2])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([3, 2, 2, 1, 1], output) 

def test_minimum_filter01(self):
        array = numpy.array([1, 2, 3, 4, 5])
        filter_shape = numpy.array([2])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([1, 1, 2, 3, 4], output) 

def test_minimum_filter01(self):
        array = numpy.array([1, 2, 3, 4, 5])
        filter_shape = numpy.array([2])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([1, 1, 2, 3, 4], output) 

def test_minimum_filter09(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                                [7, 6, 9, 3, 5],
                                [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array,
                                  footprint=footprint, origin=[-1, 0])
        assert_array_almost_equal([[2, 3, 1, 3, 1],
                              [5, 5, 3, 3, 1],
                              [5, 3, 3, 1, 1]], output) 

def test_minimum_filter08(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                                [7, 6, 9, 3, 5],
                                [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array,
                                       footprint=footprint, origin=-1)
        assert_array_almost_equal([[3, 1, 3, 1, 1],
                              [5, 3, 3, 1, 1],
                              [3, 3, 1, 1, 1]], output) 

def test_minimum_filter07(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                                [7, 6, 9, 3, 5],
                                [5, 8, 3, 7, 1]])
        footprint = [[1, 0, 1], [1, 1, 0]]
        output = ndimage.minimum_filter(array,
                                                 footprint=footprint)
        assert_array_almost_equal([[2, 2, 1, 1, 1],
                              [2, 3, 1, 3, 1],
                              [5, 5, 3, 3, 1]], output) 

def test_minimum_filter05(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                                [7, 6, 9, 3, 5],
                                [5, 8, 3, 7, 1]])
        filter_shape = numpy.array([2, 3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([[2, 2, 1, 1, 1],
                              [2, 2, 1, 1, 1],
                              [5, 3, 3, 1, 1]], output) 

def test_minimum_filter04(self):
        array = numpy.array([3, 2, 5, 1, 4])
        filter_shape = numpy.array([3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([2, 2, 1, 1, 1], output) 

def test_minimum_filter03(self):
        array = numpy.array([3, 2, 5, 1, 4])
        filter_shape = numpy.array([2])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([3, 2, 2, 1, 1], output) 

def test_minimum_filter02(self):
        array = numpy.array([1, 2, 3, 4, 5])
        filter_shape = numpy.array([3])
        output = ndimage.minimum_filter(array, filter_shape)
        assert_array_almost_equal([1, 1, 2, 3, 4], output)