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)#

Computes the correlation of x and y over one or all levels of nesting.

Many types are supported, including all Awkward Arrays and Records, which 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.

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 or str) – 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; if a str, it is interpreted as the name of the axis which maps to an int if named axes are present. Named axes are attached to an array using ak.with_named_axis and removed with ak.without_named_axis; also see the Named axes user guide.

  • 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.

Returns:

The correlation of x and y.