✨ starbars ✨

This Python tool helps visualizing statistical significance on existing Matplotlib plots by adding significance bars and p-value labels between chosen pairs of columns.

Features

• Converts p-values to asterisk notations for easy interpretation.
• Draws statistical significance bars on Matplotlib plots.
• Customizable bar margins, tip lengths, font sizes, and top margins.

Installation

You can install the package via pip:

pip install starbars

Example

import starbars
import matplotlib.pyplot as plt

# Example data
categories = ['A', 'B', 'C']
values = [10, 20, 15]
annotations = [('A', 'B', 0.01), ('B', 'C', 0.05)]
plt.bar(categories, values)

# Annotate significance
starbars.draw_annotation(annotations)

plt.show()

This example creates a simple bar plot and uses the draw_annotation function to add statistical significance annotations between the specified pairs. For more detailed examples, please check the example.

Parameters

• annotations: List of tuples (x1, x2, p) containing the x-axis labels and the p-value of the pair.
• ns_show: Whether to show bars for non-statistical p-values. (Default: True)
• ax: The axis of subplots to draw annotations on. If ax is not provided, it implies that you are working with a single plot rather than a set of subplots. In such cases, the annotations apply to the only existing plot in the figure. (Default: None)
• bar_gap: Gap in between the bars of data. Default is 3% of the y-axis.
• tip_length: Length of the tip of the statistical bar. Default is 3% of the y-axis.
• top_margin: Margin of the last annotation from the top of the graph. Default is 5% of the y-axis.
• text_distance: Distance between the bar and the text. Default is 2% of the y-axis.
• fontsize: Font size of the annotations. Default is 10.

Contributing

We welcome contributions! If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add some amazing feature').
4. Push to the branch (git push origin feature-branch)
5. Open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for more details.