FigRecipe (scitex-plt) - Reproducible Scientific Figures

FigRecipe is a framework for creating reproducible, style-editable scientific figures via YAML recipes. It wraps matplotlib with automatic recording, enabling figures to be reproduced, modified, and shared. Part of SciTeX.

FigRecipe: Reproducible Scientific Figures

FigRecipe separates data, style, and specification for fully reproducible scientific figures.

Getting Started

User Guide

API Reference

Key Features

  • Automatic Recording: All matplotlib calls automatically recorded to YAML

  • Reproducibility: Recreate any figure from its recipe file

  • Style Editing: Change styles without regenerating data

  • Data Preservation: Plot data saved to CSV files

  • CSV Column Input: Reference CSV columns in declarative specs

  • Statistical Annotations: Add significance brackets with p-values

  • Multi-Panel Composition: Combine figures with fr.compose()

  • Schematic Diagrams: Box-and-arrow schematics with mm-based coordinates and 8 validation rules (R1–R8)

  • MCP Integration: AI agents can create figures via MCP server

  • 47 Plot Types: All standard matplotlib plot types supported

Quick Example

Python API:

import figrecipe as fr
import numpy as np

# Create figure (auto-recording enabled)
fig, ax = fr.subplots()

# Standard matplotlib API
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label="sin(x)")
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.legend()

# Save creates image + recipe + data CSVs
fr.save(fig, "my_plot.png")

CLI:

# Reproduce a figure from recipe
figrecipe reproduce my_plot.yaml -o reproduced.png

# Launch GUI editor
figrecipe gui my_plot.yaml

# Validate reproduction fidelity
figrecipe validate my_plot.yaml

MCP Server:

# Start MCP server for AI agent integration
figrecipe mcp start

Indices and tables