pubfig

<div align="center"> <img src="LOGO.png" alt="pubfig logo" width="100%"/> <p> <img src="https://img.shields.io/badge/Python-3.10%2B-3776AB?style=flat-square&logo=python&logoColor=white" alt="Python 3.10+"/> <a href="https://pypi.org/project/pubfig/"><img src="https://img.shields.io/pypi/v/pubfig?style=flat-square&logo=pypi&logoColor=white" alt="PyPI version"/></a> <a href="https://pypi.org/project/pubfig/"><img src="https://img.shields.io/badge/pip%20install-pubfig-3775A9?style=flat-square&logo=pypi&logoColor=white" alt="pip install pubfig"/></a> <img src="https://img.shields.io/badge/Matplotlib-3.8%2B-11557C?style=flat-square" alt="Matplotlib 3.8+"/> <img src="https://img.shields.io/badge/License-MIT-green?style=flat-square" alt="License"/> <a href="https://github.com/Galaxy-Dawn/pubfig"><img src="https://img.shields.io/github/stars/Galaxy-Dawn/pubfig?style=flat-square" alt="GitHub Stars"/></a> </p>

<strong>Language</strong>: <a href="https://github.com/Galaxy-Dawn/pubfig/blob/main/README.md">English</a> | <a href="https://github.com/Galaxy-Dawn/pubfig/blob/main/README.zh-CN.md">中文</a>

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Publication-style figures for papers, from single plots to panel-first Figma assembly.

pubfig is a Matplotlib-native plotting library for researchers who want figures that already look close to the final paper figure. It combines common scientific plot families, journal-aware export, and a panel-first Figma workflow for assembling large multi-panel figures without rebuilding artwork by hand.

Project links: PyPI · GitHub · Examples

Highlights

• Paper-Ready Defaults, Not a Blank Canvas — Titles, legends, fonts, line widths, and spacing start from a more publication-like baseline.
• Common Scientific Plot Families in One API — Statistical plots, distributions, trends, dimensionality reduction, evaluation curves, heatmaps, and flow plots live in one consistent surface.
• Journal-Aware Export Without Boilerplate — save_figure(...) and batch_export(...) handle explicit output suffixes, column widths, DPI, and trimming directly.
• Panel-First Workflow for Composite Figures — Export clean subplot assets, then assemble and refresh full figures in Figma instead of re-drawing panels manually.
• Matplotlib-Native and Script-Friendly — Plot functions return standard Matplotlib Figure objects, so they drop into existing analysis code easily.

Recent News

• 2026-03-31: pubfig 0.2.2 adds ECDF, QQ, Bland–Altman, Calibration, and UpSet, and brings them into the homepage showcase and full gallery.
• 2026-03-30: pubfig 0.2.1 refreshed the PyPI release with stronger polar/composition coverage and fuller gallery examples.
• 2026-03-29: pubfig 0.2.0 landed on PyPI with pip install pubfig, suffix-based export, and the documented panel-first Figma workflow.

<details> <summary><strong>View older changelog</strong></summary>

• 2026-03-25: Panel-first Figma loop polish — panel export now defaults to title-free assets for cleaner Figma assembly, pubfig-sync now keeps shared title / legend placeholders off by default, and bridge/watch flows now surface bundle provenance plus the exact manual-fallback bundle path.
• 2026-03-20: Local bridge automation for Figma sync — added a bridge-backed pubfig figma bridge|sync|watch workflow, upgraded pubfig-sync with bridge connection mode, and enabled CLI-triggered vector import/refresh after one-time plugin connection.
• 2026-03-20: Figma plugin v2 workflow polish — added auto / hero_top relayout presets, upgraded shared title / legend placeholders, and improved refresh behavior so manual Figma positioning is preserved more reliably unless relayout is requested.
• 2026-03-20: CLI + Figma plugin workflow — added pubfig figma package|validate|inspect, introduced a single-file Figma bundle JSON format for exported panels, and scaffolded the figma-plugin/pubfig-sync plugin for node-level import and refresh.
• 2026-03-20: Figma-first panel export workflow — added export_panel(...) and export_panels(...) for stable subplot asset export, introduced a minimal panel-index.json sync index, and documented the Codex + Figma MCP refinement path for multi-panel figures.
• 2026-03-20: README alignment with pubtab style and homepage refresh — reorganized the README into a pubtab-style homepage with centered badges, language switch, highlights, dated recent news, showcase examples, and an embedded gallery hero.
• 2026-03-20: Default full install and metadata simplification — changed pip install pubfig to install the full plotting stack by default, removed user-facing extras from the main install path, and aligned package metadata, GitHub About, and README wording.
• 2026-03-19: Raincloud plot support and gallery refresh — added raincloud(...), tuned its default styling, integrated it into the gallery, and regenerated the exported figure set.
• 2026-03-19: PCA biplot and radar default updates — expanded pca_biplot(...) with loading panel modes and group ellipses, refreshed radar defaults, unified font handling, and re-exported the gallery.

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Examples

Showcase

Single-plot examples

<p align="center"> <a href="examples/bar_scatter.png"><img src="examples/bar_scatter.png" width="32%" alt="Bar scatter example"></a> <a href="examples/raincloud.png"><img src="examples/raincloud.png" width="32%" alt="Raincloud example"></a> <a href="examples/line.png"><img src="examples/line.png" width="32%" alt="Line example"></a> </p> <p align="center"> <a href="examples/radar.png"><img src="examples/radar.png" width="32%" alt="Radar example"></a> <a href="examples/scatter.png"><img src="examples/scatter.png" width="32%" alt="Scatter example"></a> <a href="examples/heatmap.png"><img src="examples/heatmap.png" width="32%" alt="Heatmap example"></a> </p>

New plot families

<p align="center"> <a href="examples/ecdf.png"><img src="examples/ecdf.png" width="32%" alt="ECDF example"></a> <a href="examples/qq.png"><img src="examples/qq.png" width="32%" alt="QQ plot example"></a> <a href="examples/bland_altman.png"><img src="examples/bland_altman.png" width="32%" alt="Bland-Altman example"></a> </p> <p align="center"> <a href="examples/calibration.png"><img src="examples/calibration.png" width="48%" alt="Calibration example"></a> <a href="examples/upset.png"><img src="examples/upset.png" width="48%" alt="UpSet example"></a> </p> <p align="center"> <a href="examples/dumbbell.png"><img src="examples/dumbbell.png" width="48%" alt="Dumbbell example"></a> <a href="examples/forest_plot.png"><img src="examples/forest_plot.png" width="48%" alt="Forest plot example"></a> </p> <p align="center"> <a href="examples/hexbin.png"><img src="examples/hexbin.png" width="48%" alt="Hexbin example"></a> <a href="examples/volcano.png"><img src="examples/volcano.png" width="48%" alt="Volcano example"></a> </p> <p align="center"> <a href="examples/grouped_scatter.png"><img src="examples/grouped_scatter.png" width="48%" alt="Grouped scatter example"></a> <a href="examples/radial_hierarchy.png"><img src="examples/radial_hierarchy.png" width="48%" alt="Radial hierarchy example"></a> </p> <p align="center"> <a href="examples/circular_stacked_bar.png"><img src="examples/circular_stacked_bar.png" width="48%" alt="Circular stacked bar example"></a> <a href="examples/circular_grouped_bar.png"><img src="examples/circular_grouped_bar.png" width="48%" alt="Circular grouped bar example"></a> </p>

Composite figure examples assembled in Figma

<p align="center"> <a href="examples/composite-showcase-benchmark.png"><img src="examples/composite-showcase-benchmark.png" width="96%" alt="Benchmark composite figure assembled in Figma"></a> </p> <p align="center"> <a href="examples/composite-showcase-intervention.png"><img src="examples/composite-showcase-intervention.png" width="96%" alt="Intervention composite figure assembled in Figma"></a> </p> <p align="center"> <a href="examples/composite-showcase-stratification.png"><img src="examples/composite-showcase-stratification.png" width="96%" alt="Stratification composite figure assembled in Figma"></a> </p> <details> <summary><strong>Full Gallery</strong></summary>

The full gallery below also includes the newest diagnostics, evaluation, and set/composition families such as ECDF, QQ, Bland–Altman, Calibration, and UpSet.

<p align="center"> <img src="examples/gallery-hero.png" width="100%" alt="Full gallery contact sheet"> </p> </details>

Quick Start

pip install pubfig

Python Quick Start

Start with the fewest possible parameters first:

import numpy as np
import pubfig as pf

rng = np.random.default_rng(0)
means = np.array([
    [0.78, 0.96],
    [0.88, 1.08],
    [0.84, 1.00],
], dtype=float)

data = rng.normal(loc=means[..., None], scale=0.08, size=(3, 2, 18))
data = np.clip(data, 0.0, None)

fig = pf.bar_scatter(data)
pf.save_figure(fig, "figure1.pdf")

This is enough to get your first figure out. You do not need to understand layout, export, or publication-specific parameters before the first run.

Most common next parameters

Once the minimal example works, these are usually the first parameters worth adding:

fig = pf.bar_scatter(
    data,
    category_names=["Condition A", "Condition B", "Condition C"],
    series_names=["Ctrl", "Treatment"],
    title="Bar + Scatter",
)

pf.save_figure(fig, "figure1.pdf", spec="nature", width="single")

• category_names: names on the x-axis
• series_names: names in the legend
• title: figure title
• spec / width: journal-style export presets

Only add parameters like aspect_ratio or trim when you already know why you need them.

Where to look up detailed parameters

If you want to unde