hypot= <xarray.ufuncs._UFuncDispatcher object>¶
xarray specific variant of numpy.hypot. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.
Documentation from numpy:
Given the “legs” of a right triangle, return its hypotenuse.
sqrt(x1**2 + x2**2), element-wise. If x1 or x2 is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument. (See Examples)
x1, x2 (array_like) – Leg of the triangle(s). If
x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
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.
ndarray) – The hypotenuse of the triangle(s). This is a scalar if both x1 and x2 are scalars.
>>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3))) array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]])
Example showing broadcast of scalar_like argument:
>>> np.hypot(3*np.ones((3, 3)), ) array([[ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.]])