slanplot 0.1.0

A Python visualization library replicating slandarer's MATLAB plots

slanplot

slanplot is a high-level, elegant Python visualization library that reproduces the incredibly aesthetic MATLAB scientific visualization suite authored by slandarer. By bridging the gap between MATLAB and Python, slanplot makes stunning and publication-ready statistical and relational diagrams accessible to the Python data science ecosystem.

✨ Features

• Rich Aesthetics: Ships with 53 highly-curated scientific colormaps directly adapted from MATLAB's premium slanColor palette. Automatically registers them into Matplotlib's global colormaps registry.
• Relational Diagrams:
  • ChordDiagram (和弦图)
  • SSankey (带智能排板的多级桑基图)
  • STree, PiTree, CircularTree (层级树、环形树)
• Distribution Diagrams:
  • JoyPlot (峰峦图 / Ridgeline Plot)
  • MarginalPlot (带地毯式误差带和误差棒的联合边缘分布)
  • HatchedBar, HatchedPie (支持密集交叉纹理的经典带阴影条形图与饼图)
  • VennDiagram (平滑贝塞尔三相维恩图)
  • CalendarHeatmap (GitHub 风格日历热力图)
  • SHeatmap (支持六边形、扇形、自定义填充的特种相关性热力矩阵)
• Scientific Ready: Includes built-in support for rendering *** significance stars, overlaying nested distribution patches, and fill_between dynamic standard deviation bounds.

📦 Installation

Install slanplot easily via pip:

pip install slanplot

🚀 Quick Start

import numpy as np
import matplotlib.pyplot as plt
from slanplot import SHeatmap

# 1. Generate correlation data
data = np.random.randn(10, 10)
pvals = np.random.rand(10, 10)

# 2. Draw a gorgeous special heatmap
fig, ax = plt.subplots(figsize=(8, 8))
hm = SHeatmap(data, format_type='pie', ax=ax, pval=pvals, cmap='slan_batlow')
hm.draw()

plt.show()

📖 Documentation & Examples

Please check the interactive Jupyter Notebooks in the examples/notebooks/ directory for an exhaustive visual walkthrough of every chart type:

1. 01_Relational_Plots.ipynb: Focuses on network and relation distributions (Sankey, Chord).
2. 02_Distribution_Plots.ipynb: Focuses on complex statistical comparisons (Joyplot, Marginal, Venn).
3. 03_Scientific_Visualization.ipynb: Step-by-step reproduction of top-tier biological scientific plots.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📜 License

This project is licensed under the MIT License.