Benchmarking visualization

[YitongPan1]

Summary

Major changes:

supports #252

• feature 1: Integrate CRC dataset into package structure under pyEQL/database;
• feature 2: Add benchmarking calculations for pyEQL properties (activity coefficient, conductivity);
• feature 3: Enable visualization of experimental CRC data against pyEQL simulation results;
• feature 4: Add usage example for running benchmark function;

Example usage:

benchmark(cond, ["Mg[+2]", "Cl[-1]"], "conductivity")

Todos

If this is work in progress, what else needs to be done?

• limitation 1: Current dataset only supports a limited set of electrolytes including NaCl, KCl, CaCl2, MgCl2, Na2SO4, K2SO4, and MgSO4;
• Improvement 1: Add error handling for unsupported ion combinations and missing data cases.

Checklist

• Google format doc strings added.
• Code linted with ruff. (For guidance in fixing rule violates, see rule list)
• Type annotations included. Check with mypy.
• Tests added for new features/fixes.
• I have run the tests locally and they passed.

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

pip install -U pre-commit
pre-commit install

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.54%. Comparing base (dd1be5f) to head (074d8c0).
⚠️ Report is 5 commits behind head on main.

Additional details and impacted files

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  Coverage   87.54%   87.54%           
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  Files          14       14           
  Lines        1951     1951           
  Branches      340      340           
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  Hits         1708     1708           
  Misses        192      192           
  Partials       51       51           

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[rkingsbury]

Thank you @YitongPan1 , it will be great to have a built-in way to generate these types of plots. Please modify a few things:

1. create a new folder benchmarks under /src/pyEQL and move all your data files into it
2. rename Benchmarking_plot benchmark(.py) and keep it in the src/pyEQL directory. (another python convention - module names are always lowercase)
3. Include bibliographic information for the specific CRC tables you used in the data files. You can probably do this as the first line of your .CSVs, which you can then tell pandas to skip when loading the file
4. Add a docstring for the user-facing function benchmark
5. prefix any private functions (not intended for direct use by end user) with a _. So filter_df -> _filter_df. (Yet another python convention)
6. Add a page under docs that shows plots for each of the salts you have and includes the reference information. Make a separate subfolder called plots under docs to save the images. (This item can be a separate PR if you prefer)

[rkingsbury]

Also remember to lint by running pre-commit - this would likely have auto-corrected or flagged some of the issues I raised like missing docstrings.