465 add scaling step

[Elena-kal]

Description

fixes #465
This issue introduces a new step in which the intensity values can be scaled.

Changes

Added the new step to transformations in data_preprocessing and added new tests.

Testing

Create a standard workflow. Add the scaling step (I would assume one would usually want the scaling step after the normalization and log transformation). And check out how the data scales. The min value of the protein df given should map to the minimum value set in the parameters, same with the maximum values.

PR checklist

Development

• If necessary, I have updated the documentation (README, docstrings, etc.)
• If necessary, I have created / updated tests.

Mergeability

• main-branch has been merged into local branch to resolve conflicts
• The tests and linter have passed AFTER local merge
• The backend code has been formatted with black
• The frontend code has been formatted with pnpm format and checked with pnpm lint

Code review

• I have self-reviewed my code.
• At least one other developer reviewed and approved the changes

[github-actions]

Coverage report

Click to see where and how coverage changed

File	Statements	Missing	Coverage	Coverage (new stmts)	Lines missing
backend/protzilla/data_preprocessing
transformation.py					132
backend/protzilla/methods
data_preprocessing.py					518, 530, 565-570
Project Total

This report was generated by python-coverage-comment-action

[jorisfu]

Tested working, but I'd personally add an error when the input max is smaller than the input min. It should work anyway (with the data flipped), but I think most of the time this is not intentional, so I suppose we need at least a warning or an error.

Also we do already have a lerp helper function that could make the code cleaner by using .apply() on the df with that function. Not insisting on this though

[NeleRiediger]

Seems to work fine and code looks good as well