xskillscore.rmse¶
- xskillscore.rmse(a, b, dim=None, weights=None, skipna=False, keep_attrs=False)¶
Root Mean Squared Error.
\[\mathrm{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(a_{i} - b_{i})^{2}}\]- 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 rmse 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
Root Mean Squared Error.
- Return type
See also
References
https://en.wikipedia.org/wiki/Root-mean-square_deviation
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.rmse(a, b, dim='time') <xarray.DataArray (x: 3, y: 3)> array([[0.30366872, 0.5147618 , 0.57410211], [0.2963848 , 0.37177283, 0.40563885], [0.55686111, 0.38189299, 0.21317579]]) Dimensions without coordinates: x, y