xskillscore.linslope¶
- xskillscore.linslope(a, b, dim=None, weights=None, skipna=False, keep_attrs=False)¶
Slope of linear fit.
\[s_{ab} = \frac{ \sum_{i=i}^{n} (a_{i} - \bar{a}) (b_{i} - \bar{b}) } { \sum_{i=1}^{n} (a_{i} - \bar{a})^{2} }\]- 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 slope of linear fit 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
Slope of linear fit.
- Return type
See also
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.linslope(a, b, dim='time') <xarray.DataArray (x: 3, y: 3)> array([[-0.30948771, -0.21562529, -0.63141304], [ 0.31446077, 2.23858011, 0.44743617], [-0.22243944, 0.47034784, 1.08512859]]) Dimensions without coordinates: x, y