https://github.com/tmcclintock/betterviolinplots

Better violin plots for common scenarios.
https://github.com/tmcclintock/betterviolinplots

Last synced: 20 days ago
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Better violin plots for common scenarios.

• Host: GitHub
• URL: https://github.com/tmcclintock/betterviolinplots
• Owner: tmcclintock
• License: mit
• Created: 2020-06-27T18:30:51.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-14T00:53:06.000Z (over 4 years ago)
• Last Synced: 2024-10-27T11:08:30.565Z (2 months ago)
• Language: Jupyter Notebook
• Size: 1.14 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# BetterViolinPlots [![Build Status](https://travis-ci.com/tmcclintock/BetterViolinPlots.svg?branch=master)](https://travis-ci.com/tmcclintock/BetterViolinPlots.svg?branch=master)

Better violin plots for common scenarios.

This package allows for violin plots of any and all of:

- analytic distributions

- KDE estimates from samples

- box plots

- single or double sided violin plots

Packages like `matplotlib` or `seaborn` are limited in that they

do not have all of these options.

## Installation

After cloning, make sure the requirements are installed with

```bash

pip install -r requirements.txt

```

then do the actual install of this package by performing

```bash

python setup.py install

```

and run the tests

```bash

pytest

```

Please report any issues

[here](https://github.com/tmcclintock/BetterViolinPlots/issues).

To contribute to the development, there are a few more requirements

found in `environment.yml`. To install, the environment, follow the directions

on [creating and environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file), with

```bash

conda env create -f environment.yml

```

Followed by the installation instructions above.

## Usage

Import the package or a single routine and use it with the appropriate kinds of data (either a `scipy`-like distribution or samples of points).

```python

from bvp import analytic_violin

from scipy.stats import norm

# Five normal distributions

distributions = [norm() for _ in range(5)]

fig, ax = analytic_violin(distributions)

```