ADR Suggestion Fitting weights

[damskii9992]

General

When fitting data to a model it is possible to add weights to different data-points to prioritize the fit to certain parts of the data which might be more important or more accurate than other parts. This is especially useful when working with data with uncertainties, as it is possible to weigh more accurate data-points higher than more inaccurate points.

Current Implementation

Currently, if the user does not provide a list of weights to the fitter, a list of default weights will be used, according to:

if weights is None:
    weights = np.sqrt(np.abs(y))

i.e. the weights are set to the square-root of the data values y. This is an issue for 2 reasons:

1. When the data y-values are 0, the weights become 0 and since we divide by weights we get Inf/ NaN errors.
2. If the data y-values varies greatly, such as in a diffraction spectrum, the peaks would be valued infinitely higher than any other points in the spectrum. I.e. this is in general an issue when the data y-values are not neutron counts.

Proposed implementation

Based on the discussion of this issue in #133, we propose to completely remove default weights from the core EasyScience library and require users to provide a list of weights explicitly whenever making a fit. We additionally propose to add a check on this input to ensure that the user doesn't accidentally add weights with 0-values or NaNs/Infs to increase robustness.

Sub-projects can then implement their own defaults depending on the technique of the relevant project.