iris.analysis.stats#

Statistical operations between cubes.

iris.analysis.stats.pearsonr(cube_a, cube_b, corr_coords=None, weights=None, mdtol=1.0, common_mask=False)[source]#

Calculate the Pearson’s r correlation coefficient over specified dimensions.

Parameters:

cube_a (iris.cube.Cube) – Cubes between which the correlation will be calculated. The cubes should either be the same shape and have the same dimension coordinates or one cube should be broadcastable to the other. Broadcasting rules are the same as those for cube arithmetic (see Cube Maths).
cube_b (iris.cube.Cube) – Cubes between which the correlation will be calculated. The cubes should either be the same shape and have the same dimension coordinates or one cube should be broadcastable to the other. Broadcasting rules are the same as those for cube arithmetic (see Cube Maths).
corr_coords (str or list of str, optional) – The cube coordinate name(s) over which to calculate correlations. If no names are provided then correlation will be calculated over all common cube dimensions.
weights (numpy.ndarray, optional) – Weights array of same shape as (the smaller of) cube_a and cube_b. Note that latitude/longitude area weights can be calculated using iris.analysis.cartography.area_weights().
mdtol (float, default=1.0) – Tolerance of missing data. The missing data fraction is calculated based on the number of grid cells masked in both cube_a and cube_b. If this fraction exceed mdtol, the returned value in the corresponding cell is masked. mdtol =0 means no missing data is tolerated while mdtol =1 means the resulting element will be masked if and only if all contributing elements are masked in cube_a or cube_b.
common_mask (bool, default=False) – If True, applies a common mask to cube_a and cube_b so only cells which are unmasked in both cubes contribute to the calculation. If False, the variance for each cube is calculated from all available cells.

Returns:

A cube of the correlation between the two input cubes along the specified dimensions, at each point in the remaining dimensions of the cubes.

For example providing two time/altitude/latitude/longitude cubes and corr_coords of ‘latitude’ and ‘longitude’ will result in a time/altitude cube describing the latitude/longitude (i.e. pattern) correlation at each time/altitude point.

Return type:

Cube

Notes

If either of the input cubes has lazy data, the result will have lazy data.

Reference:: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient