linear_fit(x, y, weight=None, axis=None, keepdims=False, mask_identity=True)¶
x – one coordinate to use in the linear fit.
y – the other coordinate to use in the linear fit.
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 descreases 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 linear fit of
y with respect to
x (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 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)
are given as an
ak.Record with four fields. The values of these fields
might be arrays or even nested arrays; they match the structure of