Galpynostatic

- Create the `base` module with the `MapSpline` class.
- Change the project description.

- Change the name of the `bmx_fc` metric to `umbem`, which is the name of the metric in the cited PhD thesis.
- Replace the `test_metric` to use `pytest.mark.parametrize` and have a test for each value insted of the dataframe all togheter.

- Allow the modification of C-rate with minutes parameter in `bmx_fc` of `metric` module.
- Citation of theoretical framework in `CITATION.bib` file.

- An implementation of a new module with two metrics for benchmarking an extreme fast-charging of battery electrode materials.
- Fixed test errors in `make_prediction` module due to uncertaintes calculations.

- Fixed the citation link, the BibTeX file and the doi.
- The test of the plots with Python3.9+ instead of Python3.8
- Fixed the uncertainties calculations.

This is the first Python object-oriented version of galpynostatic.

Features

- A galvanostatic regressor to fit maximum State-of-Charge (SOC) values versus C-rates experimental data with the physics-based heuristic model implemented here.
- Visualization in different formats through a plotter.
- Make predictions of the optimal particle size for the fifteen-minute charging electrode material.
- A preprocessing tool to obtain discharge capacities from galvanostatic profiles, useful to define the maximum SOC values.
- Surface datasets of the continuous computational physics previous model for different single-particle geometries.

Software quality assurance

- Runs on Ubuntu with Python 3.8+.
- Documentation available in readthedocs with installation guide, tutorials and API reference.
- Multiple unit tests.
- 100% coverage.
- PEP8 code style assured with flake8 and extensions.
- CI/CD on GitHub Actions.
- MIT LICENSE, encouraging its use in both academic and commercial settings.
- PyPI package distribution.