xskillscore.me

xskillscore.me(a, b, dim=None, weights=None, skipna=False, keep_attrs=False)

Mean Error.

\[\mathrm{ME} = \frac{1}{n}\sum_{i=1}^{n}(a_{i} - b_{i})\]
Parameters:

  • a (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • b (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • dim (str, list) – The dimension(s) to apply the me along. Note that this dimension will be reduced as a result. Defaults to None reducing all dimensions.

  • weights (xarray.Dataset or xarray.DataArray or None) – Weights matching dimensions of dim to apply during the function.

  • skipna (bool) – If True, skip NaNs when computing function.

  • keep_attrs (bool) – If True, the attributes (attrs) will be copied from the first input to the new one. If False (default), the new object will be returned without attributes.

Returns:

Mean Error.

Return type:

xarray.Dataset or xarray.DataArray

Examples

>>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
>>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
>>> xs.me(a, b, dim="time")
<xarray.DataArray (x: 3, y: 3)> Size: 72B
array([[ 0.01748202, -0.14165293,  0.22455357],
       [ 0.13893709, -0.23513353, -0.18174132],
       [-0.29317762,  0.16887445, -0.17297527]])
Dimensions without coordinates: x, y