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
See also
References
https://en.wikipedia.org/wiki/Mean_squared_error
Examples
>>> import numpy as np >>> import xarray as xr >>> from xskillscore import mse >>> 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']) >>> mse(a, b, dim='time')