ez-animate 0.1.1

A simple package to streamline Matplotlib animations.

ez-animate

A high-level, declarative Python package for creating common Matplotlib animations with minimal boilerplate code.

Project Goals

ez-animate aims to make it easy for data scientists, analysts, educators, and researchers to create standard Matplotlib animations quickly and with minimal code. It abstracts away the complexity of FuncAnimation, state management, and repetitive setup, letting you focus on your data and story.

Why?

• Complex Setup: No need to write custom init and update functions.
• State Management: Simplifies handling data and artist states between frames.
• Repetitive Code: Reduces boilerplate for standard animations.

Who is it for?

• Primary: Data scientists & analysts (exploratory analysis, presentations, notebooks).
• Secondary: Students, educators, and researchers (learning, teaching, publications).

Features

• Simple API: Create animations with a few lines of code.
• Tested & Linted: High code quality with pytest and ruff.
• Documentation: See documentation for usage examples and API references.

Installation

pip install ez-animate

Quickstart

from ez_animate import RegressionAnimation

# Create and run the animation
animator = RegressionAnimation(
    model=Lasso,    # Scikit-learn or sega_learn model class
    X=X,
    y=y,
    test_size=0.25,
    dynamic_parameter="alpha",
    static_parameters={"max_iter": 1, "fit_intercept": True},
    keep_previous=True,
    metric_fn=Metrics.mean_squared_error,
)

# Set up the plot
animator.setup_plot(
    title="Regression Animation",
    xlabel="Feature Coefficient",
    ylabel="Target Value",
)

# Create the animation
animator.animate(frames=np.arange(0.01, 1.0, 0.01))

# Show and save the animation
animator.show()
animator.save("regression_animation.gif")

Full Documentation

See the documentation site for complete usage instructions, API references, and examples.

Development/Contributing

See DEVELOPMENT.md for full development and contributing guidelines.

Project Structure

ez-animate/
├─ .github/
│  ├─ ISSUE_TEMPLATE
│  └─ workflows
├─ examples/
│  ├─ plots
│  ├─ sega_learn
│  └─ sklearn
├─ src/
│  └─ ez_animate
└─ tests

License

This project is licensed under the terms of the MIT License.

Acknowledgments

• Built with inspiration from the Matplotlib community.
• Thanks to all contributors!

Example GIFs

Stochastic Gradient Descent (SGD) Regression

Here's an example of a Stochastic Gradient Descent (SGD) regression animation created using ez-animate. This animation shows how the fit and the metrics evolve over time as the model learns from the data.

K-Means Clustering

Here's an example of a K-Means clustering animation created using ez-animate. This animation shows how the cluster centroids and the data points evolve over time as the algorithm iteratively refines the clusters.