ak.covar#

Defined in awkward.operations.ak_covar on line 23.

ak.covar(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 covariance calculation (anything ak.to_layout recognizes).

  • y – The other coordinate to use in the covariance calculation (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.

Computes the covariance 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 covariance is calculated as:

ak.sum((x - ak.mean(x))*(y - ak.mean(y))*weight) / ak.sum(weight)

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.