Size Distribution calculator clean up

[jellybean2004]

Description

Reformat, clean up and type hints for the Size distribution calculator.

How Has This Been Tested?

Manually tested functionality, same as before.

Review Checklist:

Documentation

• There is nothing that needs documenting
• Documentation changes are in this PR
• There is an issue open for the documentation (link?)

Installers

• There is a chance this will affect the installers, if so
  • Windows installer (GH artifact) has been tested (installed and worked)
  • MacOSX installer (GH artifact) has been tested (installed and worked)
  • Wheels installer (GH artifact) has been tested (installed and worked)

Licensing

• The introduced changes comply with SasView license (BSD 3-Clause)

[Copilot]

Pull request overview

This PR refactors the Size Distribution calculator implementation to improve readability and maintainability by reformatting code, adding type hints, and clarifying docstrings/logging, while aiming to preserve existing behavior.

Changes:

• Added module/class docstrings and extensive type annotations across helper functions and the sizeDistribution class.
• Refactored/cleaned up several methods (e.g., background fit, MaxEnt preparation/execution) and improved logging output.
• Reorganized initialization and internal state fields for binning, weighting, and result storage.

Comments suppressed due to low confidence (2)

src/sas/sascalc/size_distribution/SizeDistribution.py:570

• In prep_maxEnt, if an exception occurs while trimming (try block), the code logs the exception but still assigns trim_data_pars[pkey] = data_vals. Since data_vals may be undefined on error, this can raise UnboundLocalError and hide the original problem. Consider re-raising, continue, or setting a safe fallback when trimming fails.

            if check_data:
                item = self._data.__dict__[pkey]
                try:
                    if pkey == "y":
                        item = item - sub_intensities.y
                    elif pkey == "dy":
                        item = item + sub_intensities.dy

                    data_vals = item[self.ndx_qmin : self.ndx_qmax]

                except Exception as e:
                    logger.exception("Error trimming data in prep_maxEnt: %s", e)
                trim_data_pars[pkey] = data_vals

src/sas/sascalc/size_distribution/SizeDistribution.py:539

• In the prep_maxEnt docstring, the function name is misspelled as "add_gausisan_noise". This should match the actual helper name add_gaussian_noise to avoid confusion.

        """
        1. Subtract intensities from the raw data.
        2. Trim the data to the correct q-range for maxEnt; Create new trimmed Data1D object to return after MaxEnt.
        3. Generate Model Data based of the trimmed data.
        4. Create a list of intensities for maxEnt, if full_fit == True , call add_gausisan_noise nreps times;
            pass just subtracted intensities.
        5. Calculate initial bin weights, sigma, and return.

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[DrPaulSharp]

A few things to have a look at here.

[DrPaulSharp]

Looking good, that's one more perspective down!