xarray.ufuncs.divide

xarray.ufuncs.divide = <xarray.ufuncs._binary_ufunc object>

xarray specific variant of numpy.divide(). Handles xarray objects by dispatching to the appropriate function for the underlying array type.

Documentation from numpy:

Divide arguments element-wise.

Parameters

• x1 (array_like) – Dividend array.

• x2 (array_like) – Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

• out (ndarray, None, or tuple of ndarray and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

• where (array_like, optional) – This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

• **kwargs – For other keyword-only arguments, see the ufunc docs.

Returns

y (ndarray or scalar) – The quotient x1/x2, element-wise. This is a scalar if both x1 and x2 are scalars.

See also

seterr

Set whether to raise or warn on overflow, underflow and division by zero.

Notes

Equivalent to x1 / x2 in terms of array-broadcasting.

The true_divide(x1, x2) function is an alias for divide(x1, x2).

Examples

>>> np.divide(2.0, 4.0)
0.5
>>> 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 np.divide on ndarrays.

>>> 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. ]])