Defined in awkward.operations.ak_corr on line 12.
- ak.corr(x, y, weight=None, axis=None, *, keepdims=False, mask_identity=False)#
x – One coordinate to use in the correlation (anything
y – The other coordinate to use in the correlation (anything
weight – Data that can be broadcasted to
yto give each point a weight. Weighting points equally is the same as no weights; weighting some points higher increases the significance of those points. Weights can be zero or negative.
axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis:
0is the outermost,
1is the first level of nested lists, etc., and negative
axiscounts from the innermost:
-1is the innermost,
-2is the next level up, etc.
keepdims (bool) – If False, this function decreases the number of dimensions by 1; if True, the output values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.
mask_identity (bool) – If True, the application of this function on empty lists results in None (an option type); otherwise, the calculation is followed through with the reducers’ identities, usually resulting in floating-point
Computes the correlation of
y (many types supported, including
all Awkward Arrays and Records, must be broadcastable to each other).
The grouping is performed the same way as for reducers, though this
operation is not a reducer and has no identity.
This function has no NumPy equivalent.
Passing all arguments to the reducers, the correlation is calculated as
ak.sum((x - ak.mean(x))*(y - ak.mean(y))*weight) / np.sqrt(ak.sum((x - ak.mean(x))**2)) / np.sqrt(ak.sum((y - ak.mean(y))**2))
ak.sum for a complete description of handling nested lists and
missing values (None) in reducers, and
ak.mean for an example with another