figrecipe 0.25.1
Reproducible matplotlib wrapper with mm-precision layouts
Requires Python: >=3.9
Keywords: figrecipe, matplotlib, millimeter, plotting, publication, recipe, reproducibility, scientific, scitex, visualization, yaml

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!-- Timestamp: 2026-02-16 10:20:09
!-- Author: ywatanabe
!-- File: /home/ywatanabe/proj/figrecipe/README.md
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# FigRecipe (<code>scitex-plt</code>)

<p align="center">
  <a href="https://scitex.ai">
    <img src="docs/scitex-logo-blue-cropped.png" alt="SciTeX" width="400">
  </a>
</p>

<p align="center"><b>Reproducible scientific figures as first-class objects</b></p>

<p align="center">
  <a href="https://badge.fury.io/py/figrecipe"><img src="https://badge.fury.io/py/figrecipe.svg" alt="PyPI version"></a>
  <a href="https://figrecipe.readthedocs.io/"><img src="https://readthedocs.org/projects/figrecipe/badge/?version=latest" alt="Documentation"></a>
  <a href="https://github.com/ywatanabe1989/figrecipe/actions/workflows/test.yml"><img src="https://github.com/ywatanabe1989/figrecipe/actions/workflows/test.yml/badge.svg" alt="Tests"></a>
  <a href="https://www.gnu.org/licenses/agpl-3.0"><img src="https://img.shields.io/badge/License-AGPL--3.0-blue.svg" alt="License: AGPL-3.0"></a>
</p>

<p align="center">
  <a href="https://figrecipe.readthedocs.io/">Full Documentation</a> · <code>pip install figrecipe</code>
</p>

---

# FigRecipe — **Reproducible, editable, publication-ready scientific figures.** Part of [**SciTeX**](https://scitex.ai).

>Four Freedoms for Research
>
>0. The freedom to **run** your research anywhere — your machine, your terms.
>1. The freedom to **study** how every step works — from raw data to final manuscript.
>2. The freedom to **redistribute** your workflows, not just your papers.
>3. The freedom to **modify** any module and share improvements with the community.
>
>AGPL-3.0 — because research infrastructure deserves the same freedoms as the software it runs on.

> **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.

<p align="center">
  <img src="examples/10b_figrecipe_concept_diagram_fixed_out/figrecipe_concept.png" alt="FigRecipe: Reproducible Scientific Figures" width="100%"/>
</p>

<p align="center"><sub>Created with <a href="#diagrams">Diagrams</a></sub></p>

## 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.

<p align="center">
  <img src="docs/scitex_style_anatomy_out/scitex_style_anatomy.jpg" alt="SCITEX Style Anatomy" width="100%"/>
</p>

<details>
<summary><b>Millimeter-based Layout</b></summary>

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

</details>

<details>
<summary><b>Style Presets</b></summary>

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

</details>

## GUI Editor

For precise adjustments, GUI editor is available.

<p align="center">
  <img src="docs/figrecipe-gui-demo.png" alt="FigRecipe GUI Editor" width="100%"/>
</p>

## Migration from Matplotlib

#### Matplotlib-compatibility

<details>

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

```python
# 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`](https://github.com/ywatanabe1989/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.

</details>

## Diagrams

Create publication-quality box-and-arrow diagrams with mm-based coordinates. See [Overview](#overview) for an example output.

<details>
<summary><strong>Usage</strong></summary>

<br>

```python
from figrecipe._diagram import Diagram

d = Diagram(title="EEG Pipeline", gap_mm=10)

# Boxes
d.add_box(
    "raw", "Raw EEG", subtitle="64 ch", emphasis="muted", shape="cylinder"
)
d.add_box("filter", "Bandpass", subtitle="0.5-45 Hz", emphasis="primary")
d.add_box("ica", "ICA", subtitle="Artifact removal", emphasis="primary")

# Arrows
d.add_arrow("raw", "filter")
d.add_arrow("filter", "ica")

d.save(
    "pipeline.png"
)  # → pipeline.png + pipeline.yaml + pipeline_hitmap.png + pipeline_debug.png
```

</details>

<details>
<summary><strong>Containers & Flex Layout</strong></summary>

<br>

Use `gap_mm` on the Diagram for automatic flex layout (no manual x/y needed):

```python
d = Diagram(title="System Overview", gap_mm=10)

d.add_box("a", "Module A")
d.add_box("b", "Module B")
d.add_container("grp", title="Core", children=["a", "b"], direction="row")
d.add_box("out", "Output", shape="document")
d.add_arrow("grp", "out")
d.save("overview.png")
```

</details>

<details>
<summary><strong>Auto-Fix & Save Options</strong></summary>

<br>

`auto_fix=True` automatically resolves layout violations (overlaps, container enclosure, canvas bounds, arrow collisions):

```python
fig, ax = d.render(auto_fix=True)

# d.save() renders, auto-crops, and optionally watermarks:
d.save("out.png", watermark=True)  # "Plotted by FigRecipe" stamp
```

**Output files from `d.save()`**:
| File | Content |
|------|---------|
| `out.png` | Auto-cropped diagram |
| `out.yaml` | Recipe for reproduction |
| `out_hitmap.png` | Click-target regions for GUI editing |
| `out_debug.png` | Debug overlay showing positions and anchors |

</details>

<details>
<summary><strong>Shapes & Anchors</strong></summary>

<br>

**Shapes**: `rounded` (default), `box`, `stadium`, `cylinder`, `document`, `file`, `codeblock`.
Use `node_class` for semantic defaults: `"code"` → codeblock, `"input"` → cylinder, `"claim"` → document.

**Anchors**: `top`, `bottom`, `left`, `right`, `top-left`, `top-right`, `bottom-left`, `bottom-right`, `center`, or `auto` (default). Aliases like `n`/`s`/`e`/`w`, `tl`/`tr`/`bl`/`br` are normalized automatically.

</details>

<details>
<summary><strong>Validation Rules</strong></summary>

<br>

All rules are enforced on render. Failed figures are saved with a `_FAILED` suffix for inspection.

| Rule | Check | Severity |
|------|-------|----------|
| W | Width exceeds 185 mm (double-column max) | Warning |
| R1 | Container must enclose all children | Error |
| R2 | No two boxes may overlap | Error |
| R3 | Container title must clear children (5 mm zone) | Warning |
| R4 | Box text must fit within padded inner area | Warning |
| R5 | Text-to-text margin >= 2 mm | Error |
| R6 | Text-to-edge margin >= 2 mm | Error |
| R7 | Arrow visible-length ratio >= 90% | Error |
| R8 | Curved-arrow label on same side as arc | Error |
| R9 | All elements within canvas bounds | Error |

</details>

## Three Interfaces

<details>
<summary><strong>🐍 Python API</strong></summary>

<br>

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

```python
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:

```python
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:

```python
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:

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

<p align="center">
  <img src="examples/03_composition_out/composed_grid.png" alt="Composed multi-panel figure" width="100%"/>
</p>

**Statistics** — significance brackets:

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

> **[Full API reference](https://figrecipe.readthedocs.io/en/latest/)**

</details>

<details>
<summary><strong>🖥️ CLI Commands</strong></summary>

<br>

```bash
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](https://figrecipe.readthedocs.io/en/latest/cli_reference.html)**

</details>

<details>
<summary><strong>🔧 MCP Server — for AI Agents</strong></summary>

<br>

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 |

```bash
# Install to Claude Code
figrecipe mcp install
```

> **[Full MCP specification](https://figrecipe.readthedocs.io/en/latest/mcp_spec.html)**

</details>

## 47 matplotlib plot types supported

<details>

| 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 |

</details>

---

<p align="center">
  <a href="https://scitex.ai" target="_blank"><img src="docs/scitex-icon-navy-inverted.png" alt="SciTeX" width="40"/></a>
</p>

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