figrecipe 0.22.0

Reproducible matplotlib wrapper with mm-precision layouts

FigRecipe (scitex-plt)

Reproducible scientific figures as first-class objects

Full Documentation · pip install figrecipe

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Reproducible, editable, publication-ready scientific figures. Part of SciTeX.

SciTeX users: pip install scitex[plt] includes FigRecipe. scitex.plt delegates to figrecipe — they share the same API.

Overview

FigRecipe treats recipe, data, and style as first-class attributes of every figure. This enables data governance and style editing without losing scientific rigor.

Styling

FigRecipe provides millimeter-precise control over every visual element. The SCITEX style preset is applied by default, producing publication-ready figures with standard matplotlib plotting.

Millimeter-based Layout

fig, ax = fr.subplots(
    axes_width_mm=60,
    axes_height_mm=40,
    margin_left_mm=15,
)

Style Presets

fr.load_style("SCITEX")       # Publication defaults
fr.load_style("SCITEX_DARK")  # Dark theme
fr.load_style("MATPLOTLIB")   # Pure Matplotlib

GUI Editor

For precise adjustments, GUI editor is available.

Migration from Matplotlib

Matplotlib-compatibility

FigRecipe is a drop-in replacement for matplotlib — just change your import:

# Before
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(x, y)
plt.savefig("fig.png")

# After
import figrecipe as fr
fig, ax = fr.subplots()
ax.plot(x, y, id="my_trace")
fr.save(fig, "fig.png")  # → fig.png + fig.yaml + fig_data/

Systematic Migration

scitex-linter detects and auto-fixes matplotlib patterns into mm-based FigRecipe equivalents (check, format, python). It also works as a pre-commit hook, ensuring AI agents follow FigRecipe conventions.

Schematic Diagrams

Create publication-quality box-and-arrow schematics with mm-based coordinates:

s = fr.Schematic(title="EEG Analysis Pipeline", width_mm=350, height_mm=100)
s.add_box("raw", "Raw EEG", subtitle="64 ch", emphasis="muted")
s.add_box("filter", "Bandpass Filter", subtitle="0.5-45 Hz", emphasis="primary")
s.add_box("ica", "ICA", subtitle="Artifact removal", emphasis="primary")
s.add_arrow("raw", "filter")
s.add_arrow("filter", "ica")
s.auto_layout(layout="lr", gap_mm=15)

fig, ax = fr.subplots()
ax.schematic(s, id="pipeline")
fr.save(fig, "pipeline.png")

Three Interfaces

🐍 Python API

Create and save — standard matplotlib API with auto-recording:

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
ax.plot(np.sin(np.linspace(0, 10, 100)), id="sine")
fr.save(fig, "figure.png")  # Saves + validates pixel-identical reproduction

Output:

figure.png                # Publication-ready image
figure.yaml               # Reproducible recipe (validated on save)
figure_data/
  sine.csv                # Plot data (one CSV per trace)

Save / Load Formats — from recipe or bundle:

fr.save(fig, "fig.png")     # fig.png + fig.yaml
fr.save(fig, "fig.zip")     # self-contained zip bundle
fr.load("fig.png")          # reload from any format

Format	Save	Load
PNG / PDF / SVG	✓	✓
YAML	✓	✓
Directory / ZIP	✓	✓

Reproduce and edit — from recipe or bundle:

fig, ax = fr.reproduce("figure.yaml")
fr.gui(fig)  # Launch visual editor (at http://127.0.0.1:5050 by default)

Compose — multi-panel figures:

fr.compose(
    sources=["panel_a.yaml", "panel_b.yaml"],
    output_path="composed.png",
    layout="horizontal",
)

Statistics — significance brackets:

ax.add_stat_annotation(x1=0, x2=1, p_value=0.01, style="stars")

Full API reference

🖥️ CLI Commands

figrecipe --help-recursive            # Show all commands
figrecipe reproduce fig.yaml          # Recreate figure from recipe
figrecipe gui figure.png              # Launch visual editor
figrecipe validate fig.yaml           # Verify pixel-identical reproduction
figrecipe extract fig.yaml            # Extract plotted data as CSV
figrecipe compose a.yaml b.yaml       # Compose multi-panel figure
figrecipe crop figure.png             # Auto-crop whitespace
figrecipe info fig.yaml               # Show recipe metadata

Full CLI reference

🔧 MCP Server — for AI Agents

AI agents can create, compose, and reproduce publication-ready figures autonomously.

Tool	Description
plot	Create figure from declarative YAML spec
reproduce	Recreate figure from recipe
compose	Combine panels into multi-panel layout
crop	Auto-crop whitespace
info	Inspect recipe metadata
validate	Verify reproduction fidelity

# Install to Claude Code
figrecipe mcp install

Full MCP specification

47 matplotlib plot types supported

Category	Plot Types
Line & Curve	plot, step, fill, fill_between, fill_betweenx, errorbar, stackplot, stairs
Scatter & Points	scatter
Bar & Categorical	bar, barh
Distribution	hist, hist2d, boxplot, violinplot, ecdf
2D Image & Matrix	imshow, matshow, pcolor, pcolormesh, hexbin, spy
Contour & Surface	contour, contourf, tricontour, tricontourf, tripcolor, triplot
Spectral & Signal	specgram, psd, csd, cohere, angle_spectrum, magnitude_spectrum, phase_spectrum, acorr, xcorr
Vector & Flow	quiver, barbs, streamplot
Special	pie, stem, eventplot, loglog, semilogx, semilogy, graph

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AGPL-3.0 · [e-mail]