MPSPlots 1.8.7

A matplotlib wrapper for defered plots and plotting styles.

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Overview

MPSPlots is a personal plotting library developed as a streamlined wrapper around two popular visualization tools: Matplotlib for 2D plotting and PyVista for 3D visualization. This library was created with the goal of balancing ease-of-use with flexibility, allowing users to produce consistent plots for scientific publications. Initially developed to standardize the author’s scientific plots, MPSPlots continues to evolve, providing a customizable yet simple interface for a wide range of plotting needs.

Key Features: - Intuitive and straightforward API, abstracting common plotting tasks. - High-quality outputs tailored for scientific journals. - Seamless integration with Matplotlib and PyVista. - Easily customizable plots without sacrificing flexibility.

The motivation behind this library was to make complex plotting routines more accessible while maintaining the ability to fine-tune results as needed, making it ideal for researchers and scientists who require consistent, publication-ready plots.

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Installation

To install the library from PyPI, simply use pip, or conda:

pip install MPSPlots
conda install --channels martinpdes mpsplots

For a development version, clone the GitHub repository and install the dependencies manually:

git clone https://github.com/MartinPdeS/MPSPlots.git
cd MPSPlots
pip install -r requirements/requirements.txt

This setup ensures that you have access to the latest updates and features under active development.

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Usage

MPSPlots can be integrated into your scientific workflow with minimal effort. Here’s a simple example showing how you can create a 2D Matplotlib plot:

from MPSPlots.render2D import Scene

plot = Scene()
plot.add_line(x_data, y_data, label="Sample Line")
plot.show()

For more complex 3D visualizations using PyVista:

from MPSPlots.render3D import Scene

plot = Scene()
plot.add_surface(mesh)
plot.show()

Whether it’s a 2D line chart or a 3D surface plot, MPSPlots makes it simple to generate publication-quality visualizations quickly.

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Testing and Development

If you want to contribute to the project or test it locally, follow these steps to set up your development environment:

1. Clone the repository:

   git clone https://github.com/MartinPdeS/MPSPlots.git
   cd MPSPlots

2. Install dependencies:

   pip install -r requirements/requirements.txt

3. Run the tests with coverage:

   coverage run --source=MPSPlots --module pytest --verbose tests
   coverage report --show-missing

These commands will ensure that you have all the necessary dependencies and will run the tests, providing you with a detailed report on code coverage and any potential issues.

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Documentation

Detailed documentation for MPSPlots is available here, where you’ll find a comprehensive guide to the library’s usage, examples, and API references. Whether you’re a beginner or an advanced user, the documentation provides clear instructions and examples to help you get the most out of the library.

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Contributing

MPSPlots is an open-source project under continuous development, and contributions are welcome! Whether it’s bug fixes, new features, or improvements to documentation, any help is appreciated. If you’re interested in collaborating, please feel free to reach out to the author.

If you’d like to contribute:

1. Fork the repository and create your feature branch:

   git checkout -b feature-branch

2. Commit your changes and push your branch:

   git commit -m "Add new feature"
   git push origin feature-branch

3. Create a Pull Request on GitHub.

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Contact Information

As of 2023, MPSPlots is actively maintained and open to collaboration. If you’re interested in contributing or have any questions, don’t hesitate to reach out. The author, Martin Poinsinet de Sivry-Houle, can be contacted via:

• Email: [e-mail]

The project continues to evolve, and your contributions are encouraged!

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