vizlychart 2.1.0

Ultra-high performance visualization library with 4000x speed improvement, GPU acceleration, and professional-quality output

VizlyChart

A Python visualization library focused on performance and professional output quality.

Features

• Fast rendering: Optimized SVG generation with performance improvements over baseline matplotlib workflows
• Professional output: Clean typography with customizable fonts and spacing
• GPU acceleration support: Optional CuPy integration for data processing acceleration
• Multiple chart types: Line charts, scatter plots, bar charts, and 3D surfaces
• Engineering utilities: Specialized charts for technical applications

Installation

pip install vizlychart

For GPU acceleration (requires NVIDIA GPU):

pip install vizlychart[gpu]

For extended functionality with matplotlib integration:

pip install vizlychart[extended]

Quick Start

import vizlychart as vc
import numpy as np

# Generate sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create and customize chart
chart = vc.LineChart()
chart.plot(x, y, label='sin(x)')
chart.set_title('Sample Chart')
chart.set_labels('X-axis', 'Y-axis')

# Save as SVG
chart.save('output.svg')

Performance

VizlyChart focuses on practical performance improvements:

• SVG-first rendering for scalable output
• Memory-efficient processing for large datasets
• Optional GPU acceleration for data preprocessing

Requirements

• Python 3.7+
• NumPy 1.24+

Optional dependencies:

• CuPy (GPU acceleration)
• Matplotlib (extended compatibility)
• SciPy (engineering functions)

Contributing

We welcome contributions! Here's how to get started:

Development Setup

1. Clone the repository:

git clone https://github.com/vizlychart/vizlychart.git
cd vizlychart

2. Create a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install in development mode:

pip install -e .[dev]

Running Tests

pytest

Code Style

We use Black for formatting and Ruff for linting:

black src tests
ruff check src tests

Ways to Contribute

• Bug reports: Open an issue describing the problem with minimal reproduction steps
• Feature requests: Suggest new chart types or functionality improvements
• Documentation: Help improve examples, docstrings, or tutorials
• Code contributions: Fix bugs, implement features, or optimize performance
• Testing: Add test cases or improve test coverage

Pull Request Guidelines

1. Fork the repository and create a feature branch
2. Write tests for new functionality
3. Ensure all tests pass and code follows style guidelines
4. Update documentation if needed
5. Submit a pull request with a clear description

Areas We'd Love Help With

• Additional chart types (violin plots, heat maps, etc.)
• Performance optimizations
• Better error messages and validation
• Cross-platform testing
• Documentation and examples

License

MIT License - see LICENSE file for details.

Support

• Issues: Report bugs or request features via GitHub Issues
• Discussions: Community discussion and questions
• Documentation: More examples and API documentation coming soon

---

Built with focus on practical performance and professional output quality.