ak.linear_fit ============= Defined in `awkward.operations.ak_linear_fit `__ on `line 17 `__. .. py:function:: ak.linear_fit(x, y, weight=None, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None) Computes the linear fit of y against x 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 linear fit is calculated as:: sumw = ak.sum(weight) sumwx = ak.sum(weight * x) sumwy = ak.sum(weight * y) sumwxx = ak.sum(weight * x**2) sumwxy = ak.sum(weight * x * y) delta = (sumw*sumwxx) - (sumwx*sumwx) intercept = ((sumwxx*sumwy) - (sumwx*sumwxy)) / delta slope = ((sumw*sumwxy) - (sumwx*sumwy)) / delta intercept_error = np.sqrt(sumwxx / delta) slope_error = np.sqrt(sumw / delta) The results, ``intercept``, ``slope``, ``intercept_error``, and ``slope_error``, are given as an :py:obj:`ak.Record` with four fields. The values of these fields might be arrays or even nested arrays; they match the structure of ``x`` and ``y``. See :py:obj:`ak.sum` for a complete description of handling nested lists and missing values (None) in reducers, and :py:obj:`ak.mean` for an example with another non-reducer. :param x: One coordinate to use in the linear fit (anything :py:obj:`ak.to_layout` recognizes). :param y: The other coordinate to use in the linear fit (anything :py:obj:`ak.to_layout` recognizes). :param 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. :param axis: 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 :py:obj:`ak.with_named_axis` and removed with :py:obj:`ak.without_named_axis`; also see the `Named axes user guide <../../user-guide/how-to-array-properties-named-axis.html>`__. :type axis: None or int or str :param keepdims: 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. :type keepdims: bool :param mask_identity: 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``. :type mask_identity: bool :param highlevel: If True, return an :py:obj:`ak.Array`; otherwise, return a low-level :py:obj:`ak.contents.Content` subclass. :type highlevel: bool :param behavior: Custom :py:obj:`ak.behavior` for the output array, if high-level. :type behavior: None or dict :param attrs: Custom attributes for the output array, if high-level. :type attrs: None or dict :returns: The linear fit of ``y`` with respect to ``x``.