Python numpy.AxisError方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_prepend(self):
        x = np.arange(5) + 1
        assert_array_equal(diff(x, prepend=0), np.ones(5))
        assert_array_equal(diff(x, prepend=[0]), np.ones(5))
        assert_array_equal(np.cumsum(np.diff(x, prepend=0)), x)
        assert_array_equal(diff(x, prepend=[-1, 0]), np.ones(6))

        x = np.arange(4).reshape(2, 2)
        result = np.diff(x, axis=1, prepend=0)
        expected = [[0, 1], [2, 1]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=1, prepend=[[0], [0]])
        assert_array_equal(result, expected)

        result = np.diff(x, axis=0, prepend=0)
        expected = [[0, 1], [2, 2]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=0, prepend=[[0, 0]])
        assert_array_equal(result, expected)

        assert_raises(ValueError, np.diff, x, prepend=np.zeros((3,3)))

        assert_raises(np.AxisError, diff, x, prepend=0, axis=3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_invalid(self):
        """ Test it errors when indices has too few dimensions """
        a = np.ones((10, 10))
        ai = np.ones((10, 2), dtype=np.intp)

        # sanity check
        take_along_axis(a, ai, axis=1)

        # not enough indices
        assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
        # bool arrays not allowed
        assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
        # float arrays not allowed
        assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
        # invalid axis
        assert_raises(np.AxisError, take_along_axis, a, ai, axis=10) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)
        assert_raises(np.AxisError, ott.count, axis=1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_axis_argument_errors(self):
        msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
        for ndmin in range(5):
            for mask in [False, True]:
                x = array(1, ndmin=ndmin, mask=mask)

                # Valid axis values should not raise exception
                args = itertools.product(range(-ndmin, ndmin), [False, True])
                for axis, over in args:
                    try:
                        np.ma.median(x, axis=axis, overwrite_input=over)
                    except Exception:
                        raise AssertionError(msg % (mask, ndmin, axis, over))

                # Invalid axis values should raise exception
                args = itertools.product([-(ndmin + 1), ndmin], [False, True])
                for axis, over in args:
                    try:
                        np.ma.median(x, axis=axis, overwrite_input=over)
                    except np.AxisError:
                        pass
                    else:
                        raise AssertionError(msg % (mask, ndmin, axis, over)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_bad_args(self):
        # Check that bad arguments raise the appropriate exceptions.

        A = self.array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)

        # Using `axis=<integer>` or passing in a 1-D array implies vector
        # norms are being computed, so also using `ord='fro'`
        # or `ord='nuc'` raises a ValueError.
        assert_raises(ValueError, norm, A, 'fro', 0)
        assert_raises(ValueError, norm, A, 'nuc', 0)
        assert_raises(ValueError, norm, [3, 4], 'fro', None)
        assert_raises(ValueError, norm, [3, 4], 'nuc', None)

        # Similarly, norm should raise an exception when ord is any finite
        # number other than 1, 2, -1 or -2 when computing matrix norms.
        for order in [0, 3]:
            assert_raises(ValueError, norm, A, order, None)
            assert_raises(ValueError, norm, A, order, (0, 1))
            assert_raises(ValueError, norm, B, order, (1, 2))

        # Invalid axis
        assert_raises(np.AxisError, norm, B, None, 3)
        assert_raises(np.AxisError, norm, B, None, (2, 3))
        assert_raises(ValueError, norm, B, None, (0, 1, 2)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_errors(self):
        x = np.random.randn(1, 2, 3)
        assert_raises_regex(np.AxisError, 'source.*out of bounds',
                            np.moveaxis, x, 3, 0)
        assert_raises_regex(np.AxisError, 'source.*out of bounds',
                            np.moveaxis, x, -4, 0)
        assert_raises_regex(np.AxisError, 'destination.*out of bounds',
                            np.moveaxis, x, 0, 5)
        assert_raises_regex(ValueError, 'repeated axis in `source`',
                            np.moveaxis, x, [0, 0], [0, 1])
        assert_raises_regex(ValueError, 'repeated axis in `destination`',
                            np.moveaxis, x, [0, 1], [1, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, 0, [0, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, [0, 1], [0]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_broadcasting_shapes(self):
        u = np.ones((2, 1, 3))
        v = np.ones((5, 3))
        assert_equal(np.cross(u, v).shape, (2, 5, 3))
        u = np.ones((10, 3, 5))
        v = np.ones((2, 5))
        assert_equal(np.cross(u, v, axisa=1, axisb=0).shape, (10, 5, 3))
        assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=2)
        assert_raises(np.AxisError, np.cross, u, v, axisa=3, axisb=0)
        u = np.ones((10, 3, 5, 7))
        v = np.ones((5, 7, 2))
        assert_equal(np.cross(u, v, axisa=1, axisc=2).shape, (10, 5, 3, 7))
        assert_raises(np.AxisError, np.cross, u, v, axisa=-5, axisb=2)
        assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=-4)
        # gh-5885
        u = np.ones((3, 4, 2))
        for axisc in range(-2, 2):
            assert_equal(np.cross(u, u, axisc=axisc).shape, (3, 4)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_specific_axes(self):
        # Testing that gradient can work on a given axis only
        v = [[1, 1], [3, 4]]
        x = np.array(v)
        dx = [np.array([[2., 3.], [2., 3.]]),
              np.array([[0., 0.], [1., 1.]])]
        assert_array_equal(gradient(x, axis=0), dx[0])
        assert_array_equal(gradient(x, axis=1), dx[1])
        assert_array_equal(gradient(x, axis=-1), dx[1])
        assert_array_equal(gradient(x, axis=(1, 0)), [dx[1], dx[0]])

        # test axis=None which means all axes
        assert_almost_equal(gradient(x, axis=None), [dx[0], dx[1]])
        # and is the same as no axis keyword given
        assert_almost_equal(gradient(x, axis=None), gradient(x))

        # test vararg order
        assert_array_equal(gradient(x, 2, 3, axis=(1, 0)),
                           [dx[1]/2.0, dx[0]/3.0])
        # test maximal number of varargs
        assert_raises(TypeError, gradient, x, 1, 2, axis=1)

        assert_raises(np.AxisError, gradient, x, axis=3)
        assert_raises(np.AxisError, gradient, x, axis=-3)
        # assert_raises(TypeError, gradient, x, axis=[1,]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_bad_args(self):
        # Check that bad arguments raise the appropriate exceptions.

        A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)

        # Using `axis=<integer>` or passing in a 1-D array implies vector
        # norms are being computed, so also using `ord='fro'`
        # or `ord='nuc'` raises a ValueError.
        assert_raises(ValueError, norm, A, 'fro', 0)
        assert_raises(ValueError, norm, A, 'nuc', 0)
        assert_raises(ValueError, norm, [3, 4], 'fro', None)
        assert_raises(ValueError, norm, [3, 4], 'nuc', None)

        # Similarly, norm should raise an exception when ord is any finite
        # number other than 1, 2, -1 or -2 when computing matrix norms.
        for order in [0, 3]:
            assert_raises(ValueError, norm, A, order, None)
            assert_raises(ValueError, norm, A, order, (0, 1))
            assert_raises(ValueError, norm, B, order, (1, 2))

        # Invalid axis
        assert_raises(np.AxisError, norm, B, None, 3)
        assert_raises(np.AxisError, norm, B, None, (2, 3))
        assert_raises(ValueError, norm, B, None, (0, 1, 2)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_axes(self):
        assert_raises(np.AxisError, np.flip, np.ones(4), axis=1)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=2)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=-3)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=(0, 3)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_multidim(self):
        a = [[1, 1, 1]]
        r = [[2, 2, 2],
             [1, 1, 1]]
        assert_equal(insert(a, 0, [1]), [1, 1, 1, 1])
        assert_equal(insert(a, 0, [2, 2, 2], axis=0), r)
        assert_equal(insert(a, 0, 2, axis=0), r)
        assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]])

        a = np.array([[1, 1], [2, 2], [3, 3]])
        b = np.arange(1, 4).repeat(3).reshape(3, 3)
        c = np.concatenate(
            (a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T,
             a[:, 1:2]), axis=1)
        assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b)
        assert_equal(insert(a, [1], [1, 2, 3], axis=1), c)
        # scalars behave differently, in this case exactly opposite:
        assert_equal(insert(a, 1, [1, 2, 3], axis=1), b)
        assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c)

        a = np.arange(4).reshape(2, 2)
        assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a)
        assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a)

        # negative axis value
        a = np.arange(24).reshape((2, 3, 4))
        assert_equal(insert(a, 1, a[:,:, 3], axis=-1),
                     insert(a, 1, a[:,:, 3], axis=2))
        assert_equal(insert(a, 1, a[:, 2,:], axis=-2),
                     insert(a, 1, a[:, 2,:], axis=1))

        # invalid axis value
        assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=3)
        assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=-4)

        # negative axis value
        a = np.arange(24).reshape((2, 3, 4))
        assert_equal(insert(a, 1, a[:, :, 3], axis=-1),
                     insert(a, 1, a[:, :, 3], axis=2))
        assert_equal(insert(a, 1, a[:, 2, :], axis=-2),
                     insert(a, 1, a[:, 2, :], axis=1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_axis(self):
        x = np.zeros((10, 20, 30))
        x[:, 1::2, :] = 1
        exp = np.ones((10, 19, 30))
        exp[:, 1::2, :] = -1
        assert_array_equal(diff(x), np.zeros((10, 20, 29)))
        assert_array_equal(diff(x, axis=-1), np.zeros((10, 20, 29)))
        assert_array_equal(diff(x, axis=0), np.zeros((9, 20, 30)))
        assert_array_equal(diff(x, axis=1), exp)
        assert_array_equal(diff(x, axis=-2), exp)
        assert_raises(np.AxisError, diff, x, axis=3)
        assert_raises(np.AxisError, diff, x, axis=-4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_append(self):
        x = np.arange(5)
        result = diff(x, append=0)
        expected = [1, 1, 1, 1, -4]
        assert_array_equal(result, expected)
        result = diff(x, append=[0])
        assert_array_equal(result, expected)
        result = diff(x, append=[0, 2])
        expected = expected + [2]
        assert_array_equal(result, expected)

        x = np.arange(4).reshape(2, 2)
        result = np.diff(x, axis=1, append=0)
        expected = [[1, -1], [1, -3]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=1, append=[[0], [0]])
        assert_array_equal(result, expected)

        result = np.diff(x, axis=0, append=0)
        expected = [[2, 2], [-2, -3]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=0, append=[[0, 0]])
        assert_array_equal(result, expected)

        assert_raises(ValueError, np.diff, x, append=np.zeros((3,3)))

        assert_raises(np.AxisError, diff, x, append=0, axis=3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import AxisError [as 別名]
def test_extended_axis_invalid(self):
        d = np.ones((3, 5, 7, 11))
        assert_raises(np.AxisError, np.percentile, d, axis=-5, q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=(0, -5), q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=4, q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=(0, 4), q=25)
        # each of these refers to the same axis twice
        assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)
        assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25)
        assert_raises(ValueError, np.percentile, d, axis=(3, -1), q=25)