xskillscore.spearman_r

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

Spearman’s correlation coefficient.

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 correlation 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:

Spearman’s correlation coefficient.

Return type:

xarray.DataArray or xarray.Dataset

See also

scipy.stats.spearman_r

References

https://github.com/scipy/scipy/blob/v1.3.1/scipy/stats/stats.py#L3613-L3764 https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient

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.spearman_r(a, b, dim="time")
<xarray.DataArray (x: 3, y: 3)> Size: 72B
array([[-0.6, -0.5, -0.7],
       [ 0.4,  0.3,  0.3],
       [-0.3, -0.1,  0.9]])
Dimensions without coordinates: x, y