for-a-few-dpps-more / structure-factor Goto Github PK

I have the impression that current error bars on 1D plots of S(k) are computed as the std of the values of S falling in each bin. But we are rather interested in the standard deviation of the empirical mean, which is the same value but divided by the sqrt of the number of points that fall in each bin. A quick sanity check is that the error bar in a bin should get smaller as the number of points in that bin increases.

It would be great to use an automatic code formating tool to standardize and clean code before pushing
Most IDEs and text editors already include this feature, let's use it

• indentation
• trailing whitespaces
• spacing between function/class declarations
• spacing
• orthograph/grammar
• ...

It would be great to be able to pass a random_state or seed or rng argument for reproducibility when calling function involving random variables.
See also the check_random_state function in sklearn
https://github.com/scikit-learn/scikit-learn/blob/a45c0c99a38cffca6724cb8fd38b12edd4fb6b35/sklearn/utils/validation.py#L926

When calling pp.plot(), there is a lot of whitespace around the observation window. To make sure that the user understands that this white space is there because it is outside of the observation window, and not because by chance no points fell there, I would plot the boundary of the observation window.

It is important to write more explicit docstrings, and decide which docstring format to use and stick to it.
you can have a look at this compact post https://stackoverflow.com/a/24385103

Note: you can partially automate docstring generation in your text editor, in VS-Code for example there's https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring

Let's organize files according to Python standards.

• add a .gitignore file see https://github.com/github/gitignore/blob/master/Python.gitignore
• add a LICENCE file (basic MIT should be fine)
• structure the project using src/ layout https://packaging.python.org/tutorials/packaging-projects/#uploading-your-project-to-pypi
• add pyproject.toml file (replaces setup.py and requirements.txt)

More generally, I suggest we use poetry as a tool to manage dependencies and in a near future to release the package on PyPI. I'll show you how to work with it ;)