mplviz

Viz - A Fluent API Wrapper for Matplotlib

Viz is a user-friendly, fluent API wrapper for matplotlib designed to streamline the process of creating, customizing, and managing plots in Python. By supporting method chaining and reusable functions, Viz makes complex plotting workflows easier to handle, while still offering complete control over matplotlib functionalities.

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📦 Installation

To install the Viz package, use pip from PyPI (once it's published) or from the source.

Install via PyPI:

pip install mplviz

Install from Source:

git clone https://github.com/BBEK-Anand/mplviz.git
cd mplviz
pip install .

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🚀 Quick Start

Here’s how you can use Viz to create a simple plot:

import matplotlib.pyplot as plt
from mplviz import Viz

# Create a figure and axis
fig, ax = plt.subplots()

# Initialize Viz with the axis
viz = Viz(ax, fig)

# Plot a line with customization
viz.plot([1, 2, 3], [4, 5, 6], label='Line') \
   .set_title("My Plot") \
   .xlabel("X Axis") \
   .ylabel("Y Axis") \
   .legend() \
   .grid(True) \
   .show()

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🌟 Key Features

• Method Chaining: Easily create readable and customizable plots with fluent syntax.
• Support for Multiple Plot Types: Create line, scatter, bar charts, and more.
• Grid and Axis Customization: Customize axis labels, grid visibility, and other settings.
• Flexible Subplot Layout: Seamlessly create grid-based layouts with subplots.
• Save and Export: Save plots to images or PDFs.
• Reusability & Extensibility: Define reusable styles and settings for plots.

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📊 Supported Plot Types

• Line Plot: viz.plot()
• Scatter Plot: viz.scatter()
• Bar Chart: viz.bar()
• Contour Plot: viz.contour()
• Heatmaps/Images: viz.imshow()
• Annotations: viz.annotate()
• Axis Customization: viz.grid(), viz.set_xlim(), viz.set_ylim()
• Subplots Layout: viz.add_subplot()
• Multiple Y-Axes: viz.twinx()

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🛠️ Method Overview

Basic Plotting

• viz.plot(x, y, **kwargs) - Create a line plot.
• viz.scatter(x, y, **kwargs) - Create a scatter plot.
• viz.bar(x, y, **kwargs) - Create a bar chart.

Customization

• viz.set_title(title, **kwargs) - Set the plot title.
• viz.xlabel(text, **kwargs) - Set the x-axis label.
• viz.ylabel(text, **kwargs) - Set the y-axis label.
• viz.grid(flag=True, **kwargs) - Toggle grid lines on or off.
• viz.legend(**kwargs) - Show the legend.

Layout and Saving

• viz.show(clear=False) - Display the plot in the notebook.
• viz.tight_layout(**kwargs) - Adjust subplot layout to prevent overlap.
• viz.save(path, **kwargs) - Save the plot to a file.

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🔄 Combine Multiple Plots

Combine multiple Viz objects into a single figure with subplots for side-by-side comparisons.

fig, axs = plt.subplots(2, 2, figsize=(10, 10))
viz1 = Viz(axs[0, 0])
viz2 = Viz(axs[0, 1])

viz1.plot([1, 2, 3], [4, 5, 6]).set_title("Plot 1")
viz2.scatter([1, 2, 3], [2, 3, 4]).set_title("Plot 2")

combined_viz = Viz.combine_viz([viz1, viz2])
combined_viz.show()

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⚙️ Customization and Style

You can apply predefined matplotlib styles using plt.style for quick and consistent styling:

viz.style('seaborn-darkgrid')

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🤝 Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes (git commit -am 'Add feature').
4. Push your branch (git push origin feature-name).
5. Open a Pull Request.

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📄 License

Distributed under the MIT License. See the LICENSE file for more information.

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🙋‍♂️ Questions?

If you have any questions or issues, feel free to open an issue on the GitHub repository. You can also contribute by improving the documentation or adding new features!

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📍 Future Enhancements

• Adding interactive features with libraries like mplcursors or plotly.
• Improving subplot layout and positioning for more complex figure structures.
• Expanding export options (e.g., support for SVG, LaTeX-friendly outputs).
• Automatic color-mapping for scatter plots or heatmaps.