dpnp.divide
- dpnp.divide(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)
Calculates the ratio for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.
For full documentation refer to
numpy.divide
.- Parameters:
x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type. Default:
None
.order ({"C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, if parameter out is
None
. Default:"K"
.
- Returns:
out -- An array containing the result of element-wise division. The data type of the returned array is determined by the Type Promotion Rules.
- Return type:
dpnp.ndarray
Limitations
Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise
NotImplementedError
exception will be raised.Notes
Equivalent to x1 / x2 in terms of array-broadcasting.
The
true_divide(x1, x2)
function is an alias fordivide(x1, x2)
.Examples
>>> import dpnp as np >>> np.divide(dp.array([1, -2, 6, -9]), np.array([-2, -2, -2, -2])) array([-0.5, 1. , -3. , 4.5])
>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.divide(x1, x2) array([[nan, 1. , 1. ], [inf, 4. , 2.5], [inf, 7. , 4. ]])
The
/
operator can be used as a shorthand fordivide
ondpnp.ndarray
.>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = 2 * np.ones(3) >>> x1/x2 array([[0. , 0.5, 1. ], [1.5, 2. , 2.5], [3. , 3.5, 4. ]])