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 for divide(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 for divide on dpnp.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. ]])