Metadata-Version: 2.4
Name: cnsplots
Version: 0.1.0
Summary: A comprehensive plotting library for bioinformatics and data visualization
License: BSD-3-Clause
Project-URL: Homepage, https://github.com/faridrashidi/cnsplots
Project-URL: Documentation, https://cnsplots.farid.one
Project-URL: Repository, https://github.com/faridrashidi/cnsplots
Project-URL: Issues, https://github.com/faridrashidi/cnsplots/issues
Keywords: plotting,visualization,bioinformatics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: <4.0,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: adjustText
Requires-Dist: biopython
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Dynamic: license-file

# cnsplots

  cnsplots logo

[![Tests](https://img.shields.io/github/actions/workflow/status/faridrashidi/cnsplots/ci-tests.yml?branch=main&logo=github&logoColor=white&style=flat-square&label=tests&labelColor=000000&cacheSeconds=0)](https://github.com/faridrashidi/cnsplots/actions/workflows/ci-tests.yml)
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**Publication-Ready Scientific Plots for Cell, Nature, and Science Journals**

Create visually stunning, journal-quality figures with minimal code. Built on matplotlib, fully compatible with seaborn, and optimized for Adobe Illustrator.

[Documentation](https://cnsplots.farid.one/) · [Examples Gallery](https://cnsplots.farid.one/examples/index.html) · [Report Bug](https://github.com/faridrashidi/cnsplots/issues) · [Request Feature](https://github.com/faridrashidi/cnsplots/issues)

---

## Overview

[![Overview](https://raw.githubusercontent.com/faridrashidi/cnsplots/main/docs/_static/images/overview.png)](https://cnsplots.farid.one/examples/showcase.html#figure-1)

**cnsplots** is a Python visualization library designed specifically for creating publication-ready scientific figures. It takes care of the tedious styling details so you can focus on your science.

### Why cnsplots?

- 🎨 **Publication-Ready**: Pre-configured styles matching Cell, Nature, and Science journal requirements
- 🎯 **Simple API**: Create complex multi-panel figures with just a few lines of code
- 📐 **Precise Control**: Specify dimensions in pixels, perfect for journal submission guidelines
- 🖋️ **Adobe Illustrator Compatible**: SVG exports with editable fonts (no text-to-path conversion)
- 📊 **Statistical Integration**: Built-in statistical tests and annotations
- 🔧 **Highly Customizable**: Full control over colors, fonts, and styling
- 🌈 **Rich Color Palettes**: Curated color schemes optimized for scientific visualization
- 🧩 **Multi-Panel Support**: Easy creation of complex figure layouts

## Features

### 📊 25+ Plot Types

**Basic Plots**

- Box plots, violin plots, bar plots, strip plots
- Scatter plots, line plots, regression plots
- Histograms, KDE plots, ridge plots

**Scientific Plots**

- Survival plots (Kaplan-Meier)
- Cumulative incidence plots
- ROC curves and forest plots
- Volcano plots and GSEA plots
- Confusion matrices

**Specialized Plots**

- Heatmaps with hierarchical clustering
- Dot plots for enrichment
- Venn diagrams and UpSet plots
- Sankey diagrams
- Pie and donut charts
- QQ plots and slope plots

### 🎨 Beautiful Color Palettes

Multiple curated palettes including:

- **Qualitative**: Cell, Nature, Science, Ecotyper1-6, Set1-3, Tableau, Bold
- **Sequential**: Parula, gnuplot, custom gradients
- **Diverging**: BlueRed, BuRd_custom, OrBu_custom

### 📐 Multi-Panel Figures

Create complex layouts with automatic panel labeling (A, B, C...):

```python
import cnsplots as cns

mp = cns.multipanel(max_width=540)

# Panel A
mp.panel("A", height=150, width=150)
cns.boxplot(data=df1, x="group", y="value")

# Panel B
mp.panel("B", height=150, width=150)
cns.scatterplot(data=df2, x="x", y="y")

# Continues...
```

## Installation

### From PyPI

```bash
pip install cnsplots
```

### For Development

First install [uv](https://docs.astral.sh/uv/), then:

```bash
git clone https://github.com/faridrashidi/cnsplots
cd cnsplots
make install
```

This installs the package with its development and documentation extras. Core
scientific integrations such as `scanpy`, `lifelines`, and `gseapy` remain
runtime dependencies of `cnsplots` itself.

## Quick Start

### Basic Usage

```python
import cnsplots as cns
import seaborn as sns

# Load example data
df = sns.load_dataset("tips")

# Create a figure (height, width in pixels)
cns.figure(height=150, width=100)

# Create a publication-ready boxplot
cns.boxplot(data=df, x="day", y="total_bill")

# Save as vector graphic
cns.savefig("figure.svg")
```

### Statistical Comparisons

```python
# Add statistical significance annotations
cns.figure(150, 150)
cns.boxplot(
    data=df,
    x="day",
    y="total_bill",
    pairs=[("Thur", "Fri"), ("Sat", "Sun")],  # Compare these pairs
)
# Prints: P-values were determined by two-sided Mann-Whitney U test.
```

### Custom Colors

```python
# Use custom color palette
cns.figure(150, 200, color_cycle="Ecotyper1")
cns.violinplot(data=df, x="day", y="total_bill", hue="sex")
```

## Examples Gallery

Explore our comprehensive [examples gallery](https://cnsplots.farid.one/examples/index.html) featuring:

- 📦 Basic statistical plots
- 🧬 Genomics and bioinformatics visualizations
- 📈 Time-series and survival analysis
- 🎯 Machine learning results (ROC, confusion matrices)
- 🔬 Multi-omics data visualization
- 🎨 Custom color schemes and styling

## Documentation

Full documentation is available at [cnsplots.farid.one](https://cnsplots.farid.one/)

- [Installation Guide](https://cnsplots.farid.one/installation.html)
- [API Reference](https://cnsplots.farid.one/api.html)
- [Examples Gallery](https://cnsplots.farid.one/examples/index.html)

## Key Concepts

### Figure Dimensions

Specify sizes in **pixels** for precise control:

```python
cns.figure(height=150, width=100)  # Starts from a 150px × 100px canvas
```

With `cns.settings.figure_autofit=True` (the default), the final rendered
canvas can expand or trim on draw to keep long titles, outside legends, and
annotations tightly in bounds.

### Color Palettes

Access curated color palettes:

```python
# Qualitative palettes (for categorical data)
cns.figure(color_cycle="Ecotyper1")  # Default, optimized for journals
cns.figure(color_cycle="Cell")  # Custom Cell-inspired journal palette
cns.figure(color_cycle="Nature")  # Nature-inspired journal palette
cns.figure(color_cycle="Science")  # Science-inspired journal palette
cns.figure(color_cycle="Set1")  # ColorBrewer Set1

# Sequential palettes (for continuous data)
cns.figure(color_map="parula")  # MATLAB-style
cns.figure(color_map="gnuplot")  # Default sequential

# Get individual colors
red = cns.RED
blue = cns.BLUE
```

### Statistical Tests

Many plot functions include built-in statistical testing:

```python
# Boxplot with Mann-Whitney U test
cns.boxplot(data=df, x="group", y="value", pairs="all")

# Barplot with Welch's t-test
cns.barplot(data=df, x="group", y="value", pairs=[("A", "B")])

# Stackplot with Fisher's exact test
cns.stackplot(data=df, x="group", y="category", pairs=[("A", "B")])
```

### Export for Publication

```python
# SVG for vector graphics (recommended)
cns.savefig("figure.svg")

# High-resolution PNG
cns.savefig("figure.png")

# PDF with editable text
cns.savefig("figure.pdf")
```

For Illustrator-optimized SVG post-processing, install MuPDF's `mutool`.
Without it, `cns.savefig("figure.svg")` falls back to a standard matplotlib SVG
and emits a warning instead of failing.

## Requirements

- Python ≥ 3.9
- Core: matplotlib, numpy, pandas, seaborn
- Included integrations: lifelines, gseapy, scanpy, and other plotting backends
- Optional external tool: MuPDF's `mutool` for enhanced SVG post-processing

See [pyproject.toml](pyproject.toml) for complete dependency list.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

## Citation

If you use cnsplots in your research, please cite:

```bibtex
@software{cnsplots,
  author = {Rashidi, Farid},
  title = {cnsplots: Publication-Ready Scientific Plots},
  year = {2026},
  url = {https://github.com/faridrashidi/cnsplots}
}
```

## License

This project is licensed under the BSD 3-Clause License - see the [LICENSE.md](LICENSE.md) file for details.

## Acknowledgments

Built with:

- [matplotlib](https://matplotlib.org/) - Core plotting library
- [seaborn](https://seaborn.pydata.org/) - Statistical visualizations
- [lifelines](https://lifelines.readthedocs.io/) - Survival analysis
- [PyComplexHeatmap](https://github.com/DingWB/PyComplexHeatmap) - Complex heatmaps
- [UpSetPlot](https://upsetplot.readthedocs.io/) - Set intersections

Inspired by the visualization standards of Cell, Nature, and Science journals.

## Support

- 📖 [Documentation](https://cnsplots.farid.one/)
- 🐛 [Issue Tracker](https://github.com/faridrashidi/cnsplots/issues)
- 💬 [Discussions](https://github.com/faridrashidi/cnsplots/discussions)

## Related Projects

- [matplotlib](https://matplotlib.org/) - The foundation of Python plotting
- [seaborn](https://seaborn.pydata.org/) - Statistical data visualization
- [plotnine](https://plotnine.readthedocs.io/) - Grammar of graphics for Python
- [altair](https://altair-viz.github.io/) - Declarative visualization

---

Made with ❤️ for the scientific community

[⭐ Star us on GitHub](https://github.com/faridrashidi/cnsplots) · [📖 Documentation](https://cnsplots.farid.one/)

