Py50

Updates

Minor update for minor features. The following has been added:
- Option to convert x-axis to picomolar (pM) scale.
- Update how modules are imported for readability.

Bug fixes 🪲
- Fixed calculate_pic50 label for pM

Updates

Bug fixes

Dependencies were changed to try to force the correct installation to the py50-streamlit application. This did not work for py50-streamlit. I have discovered the issue and issued a fix. In the meantime, the py50 v1.0.6 was only modified for PyPI installation. All underlying code have remained the same.

Updates

Minor update to fix bugs. During the [py50-streamlit application](https://github.com/tlint101/py50-streamlit) updates,
several errors were encountered for during my
sample data. This has been corrected in py50. The py50-streamlit application will be updated shortly.

Bug fixes 🪲

- Corrected pair generation if the groups in the subgroup column contain hyphens.

Updates

Minor quality of life update.

All plots now include a **hide_ns** parameter. This will automatically hide pairs that are labeled as
n.s. (not significant) from the plot.

Bug fixes 🪲

- Corrected a few typos for the documentation

Bug fixes 🪲

- Included dependencies with installatoin. This includes:
- seaborn = <0.12.0
- scikit-posthocs = >=0.7.0
- pingouin = >=0.5.4
- statannotations >=0.6.0

Release Notes
A great milestone has been reached!🎉

The release of py50 v1.0 includes the main ideas and concepts I first envisioned for the
project. Future work will include refactoring the code for maintainability and bug fixes. Updates to the Streamlit web
application will be undertaken next. As always, if there are suggestions for improvement, feel free to share!

Updates
**Calculate Statistics**
Need to calculate significance to data? You're in luck. py50 now includes statistical calculations! Special thanks must
be given to the maintainers and contributors of the [Pingouin](https://github.com/raphaelvallat/pingouin?tab=GPL-3.0-1-ov-file#readme) package. Py50 now includes a wrapper for some of
Pingouin's features. Pingouin was chosen for its friendliness with Pandas dataframes.

**Plot Statistic Results with Annotations!**
The calculations can now be used to annotate plots! To achieve this, py50 has wrapped functions from [Statannotations](https://github.com/trevismd/statannotations/tree/master).
This will allow quick plotting of the results on a Seaborn plot. Special thanks to the maintainers and contributors to
Statannotations!

Minor Changes
- Updated parameters for consistency.
- Added parameters in class initializers in Calculator() and PlotCurve()
- Update tutorials to demo code. Check them out [here](https://github.com/tlint101/py50/tree/main/tutorials)!