xskillscore.mse

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

Mean Squared Error.

\[\mathrm{MSE} = \frac{1}{n}\sum_{i=1}^{n}(a_{i} - b_{i})^{2}\]
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 mse 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 Squared Error.

Return type:

xarray.Dataset or xarray.DataArray

See also

sklearn.metrics.mean_squared_error

References

https://en.wikipedia.org/wiki/Mean_squared_error

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.mse(a, b, dim='time')
<xarray.DataArray (x: 3, y: 3)>
array([[0.09221469, 0.26497971, 0.32959323],
       [0.08784395, 0.13821504, 0.16454288],
       [0.31009429, 0.14584225, 0.04544392]])
Dimensions without coordinates: x, y