ak.corr#

Defined in awkward.operations.ak_corr on line 24.

ak.corr(x, y, weight=None, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#

Parameters:

x – One coordinate to use in the correlation (anything ak.to_layout recognizes).
y – The other coordinate to use in the correlation (anything ak.to_layout recognizes).
weight – Data that can be broadcasted to x and y to 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: 0 is the outermost, 1 is the first level of nested lists, etc., and negative axis counts from the innermost: -1 is the innermost, -2 is 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 nan.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

Computes the correlation of x and 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))

See ak.sum for a complete description of handling nested lists and missing values (None) in reducers, and ak.mean for an example with another non-reducer.