# nature-figure skill

Submission-grade scientific figures for Nature-tier journals and high-impact academic venues,
with both Python and R plotting tracks.

The skill starts from a figure contract: core conclusion, evidence hierarchy, archetype,
backend choice, journal/export constraints, statistics, and source-data traceability.
Plotting templates are used only after the scientific logic is clear.

Python remains the best-supported low-level layout path through `matplotlib`, `seaborn`,
`subplot_mosaic`, and `statsmodels`. R is supported through `ggplot2`, `patchwork`,
`ComplexHeatmap`, `ggrepel`, `svglite`, `cairo_pdf`, and `ragg`. If private template
collections are used, their paths, filenames, and provenance must not appear in
user-facing output.

Derived from production scripts in [figures4papers](https://github.com/ChenLiu-1996/figures4papers)
(published in *Nature Machine Intelligence* and top ML/bioinformatics venues).
The original figures4papers demo scripts and preview images are also bundled under
`assets/figures4papers/` for pattern-level adaptation.

---

## Example output gallery

The images below are simulated data mockups generated with this skill's rules:
editable SVG-first export, restrained semantic palettes, lowercase panel labels, and
asymmetric multi-panel information architecture. They are PNG previews for README display;
production use should still export SVG/PDF from the plotting script.

| Figure | Preview | What the skill demonstrates |
|--------|---------|-----------------------------|
| Material design and physical validation | <a href="assets/gallery/fig1-material-mechanism-rich.png"><img src="assets/gallery/fig1-material-mechanism-rich.png" width="260" alt="Material design and physical validation"></a> | Schematic-led composite, SEM-like image panel, rheology, release kinetics, retention map, correlation and endpoint quantification |
| Spatial retention and uptake | <a href="assets/gallery/fig2-spatial-imaging-rich.png"><img src="assets/gallery/fig2-spatial-imaging-rich.png" width="260" alt="Spatial retention and uptake"></a> | Dark microscopy plate, channel rows, zoom crops, depth profiles, uptake histograms, 3D penetration heatmap and image-derived correlation |
| In vivo efficacy and tolerability | <a href="assets/gallery/fig3-in-vivo-efficacy-rich.png"><img src="assets/gallery/fig3-in-vivo-efficacy-rich.png" width="260" alt="In vivo efficacy and tolerability"></a> | Experimental timeline, longitudinal tumour curves, individual growth traces, waterfall response, forest plot, histology, immune composition and toxicity panels |
| Single-cell systems figure | <a href="assets/gallery/fig4-single-cell-systems-rich.png"><img src="assets/gallery/fig4-single-cell-systems-rich.png" width="260" alt="Single-cell systems figure"></a> | UMAP-style embedding, composition, marker heatmap, pseudotime, volcano plot, enrichment, ligand-receptor bubble matrix and spatial niche adjacency |
| Perturbation validation | <a href="assets/gallery/fig5-validation-perturbation-rich.png"><img src="assets/gallery/fig5-validation-perturbation-rich.png" width="260" alt="Perturbation validation"></a> | Mechanistic perturbation timeline, relapse endpoint, polar summary, dose response, synergy matrix, biodistribution, cytokines, flow-like scatter and safety score |

**Gallery file policy**  
Keep only lightweight PNG previews in `assets/gallery/`. Do not commit large generated
SVG/PDF outputs unless they are needed for a tutorial, because real users should regenerate
editable outputs from source data and scripts.

---

## Chart-type atlas

The gallery below classifies the skill by chart family. Each preview is a dense 4 x 4
atlas of small panels, designed to show the range of visual grammars that can be combined
inside a larger *Nature*-style result figure.

| Type | Preview | Common use |
|------|---------|------------|
| Bar charts | <a href="assets/chart-atlas/atlas-01-bar-charts.png"><img src="assets/chart-atlas/atlas-01-bar-charts.png" width="240" alt="Bar chart atlas"></a> | Group comparisons, signed deltas, grouped-within-grouped designs, stacked composition |
| Line and longitudinal trends | <a href="assets/chart-atlas/atlas-02-line-trends.png"><img src="assets/chart-atlas/atlas-02-line-trends.png" width="240" alt="Line chart atlas"></a> | Time courses, uncertainty ribbons, intervention marks, individual traces |
| Heatmaps | <a href="assets/chart-atlas/atlas-03-heatmaps.png"><img src="assets/chart-atlas/atlas-03-heatmaps.png" width="240" alt="Heatmap atlas"></a> | Z-score matrices, sequential abundance maps, annotated tables, clustered blocks |
| Scatter and bubble plots | <a href="assets/chart-atlas/atlas-04-scatter-bubble.png"><img src="assets/chart-atlas/atlas-04-scatter-bubble.png" width="240" alt="Scatter and bubble atlas"></a> | Correlation, clusters, volcano-style tests, quadrant summaries, third-variable bubbles |
| Radar and polar charts | <a href="assets/chart-atlas/atlas-05-radar-polar.png"><img src="assets/chart-atlas/atlas-05-radar-polar.png" width="240" alt="Radar and polar atlas"></a> | Multi-axis benchmarking, circular summaries, polar histograms, directional density |
| Distribution plots | <a href="assets/chart-atlas/atlas-06-distributions.png"><img src="assets/chart-atlas/atlas-06-distributions.png" width="240" alt="Distribution plot atlas"></a> | Histograms, violins, boxes, ridgelines and sample-level spread |
| Forest and interval plots | <a href="assets/chart-atlas/atlas-07-forest-interval.png"><img src="assets/chart-atlas/atlas-07-forest-interval.png" width="240" alt="Forest and interval atlas"></a> | Effect sizes, confidence intervals, point ranges, paired slope comparisons |
| Area and stacked trends | <a href="assets/chart-atlas/atlas-08-area-stacked.png"><img src="assets/chart-atlas/atlas-08-area-stacked.png" width="240" alt="Area and stacked trend atlas"></a> | Filled trajectories, stacked shares, cumulative curves, stream-like compositions |
| Image plates | <a href="assets/chart-atlas/atlas-09-image-plates.png"><img src="assets/chart-atlas/atlas-09-image-plates.png" width="240" alt="Image plate atlas"></a> | Microscopy channels, overlays, crops, scale bars and dark-panel layouts |
| Network and matrix charts | <a href="assets/chart-atlas/atlas-10-network-matrix.png"><img src="assets/chart-atlas/atlas-10-network-matrix.png" width="240" alt="Network and matrix atlas"></a> | Bubble matrices, adjacency maps, node-link diagrams and bipartite interaction panels |

---

## File structure

```
nature-figure/
├── SKILL.md                     ← skill trigger & overview (loaded by Claude automatically)
├── README.md                    ← this file
├── assets/
│   ├── gallery/                 ← result-figure preview PNGs
│   ├── chart-atlas/             ← chart-type taxonomy preview PNGs
│   └── figures4papers/          ← original demo scripts and preview assets
└── references/
    ├── figure-contract.md       ← core conclusion, evidence hierarchy, panel map
    ├── backend-selection.md     ← Python vs R decision rules
    ├── r-workflow.md            ← R scaffold, patchwork, ComplexHeatmap, export
    ├── r-template-index.md      ← local R template atlas
    ├── qa-contract.md           ← submission/revision QA checklist
    ├── api.md                   ← PALETTE constants, helper function signatures
    ├── design-theory.md         ← typography, color theory, layout, export policy
    ├── common-patterns.md       ← reusable code patterns (bars, legends, heatmaps)
    ├── tutorials.md             ← end-to-end walkthroughs
    ├── chart-types.md           ← radar, 3D sphere, scatter, fill_between, log-scale
    └── demos.md                 ← figures4papers demo map and routing guide
```

---

## Backend and contract rules

Ask the user to choose **Python or R** unless the backend is already specified.
If they ask for a recommendation, use `references/backend-selection.md`.

After a backend is selected, use it exclusively for plotting, previews, exports,
and visual QA. If the selected runtime or packages are missing, stop and report the
blocker; do not render a fallback preview with the other language. This applies in
both directions: no Python substitute for R, and no R substitute for Python.

Before plotting, write or infer the core conclusion, figure archetype, panel map,
evidence hierarchy, target output, statistics/source-data needs, and export bundle.
The figure must serve the scientific logic first. Aesthetic polish and template
matching are secondary.

User-facing output must not disclose private local paths, private filenames, internal
reference documents, template identifiers, or private-material provenance unless the
user explicitly asks for that audit trail.

---

## Python mandatory rules

### 1. Three required rcParams — editable SVG text

```python
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']
plt.rcParams['svg.fonttype'] = 'none'
```

**Why `svg.fonttype = 'none'`**  
Matplotlib's default (`'path'`) converts every glyph to a bezier curve. The result is
visually identical but every `<text>` element becomes a `<path d="M...">` — unselectable,
unsearchable, and impossible to realign in Illustrator or Inkscape.  
With `'none'`, text stays as SVG `<text>` nodes. Font substitution happens at render time.

**Why three fonts in the stack**  
`Arial` is standard on macOS/Windows. `DejaVu Sans` ships with matplotlib and is the
Linux fallback. `Liberation Sans` is metric-compatible with Arial on RHEL/Ubuntu.
The cascade guarantees identical letter-spacing on all platforms.

### 2. Primary output format is SVG

```python
fig.savefig('figure.svg', bbox_inches='tight')        # primary — editable text
fig.savefig('figure.png', dpi=300, bbox_inches='tight')  # optional raster preview
```

Never use PNG alone when the figure will go into a paper or slide deck that requires
post-hoc text adjustment.

### 3. Always close the figure

```python
plt.close(fig)
```

---

## Quick-start template

```python
import matplotlib
matplotlib.use('Agg')                    # headless / server rendering
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np

# ── MANDATORY ─────────────────────────────────────────────────────────────────
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']
plt.rcParams['svg.fonttype'] = 'none'

# ── Style ──────────────────────────────────────────────────────────────────────
plt.rcParams.update({
    'font.size': 12,
    'axes.spines.right': False,
    'axes.spines.top': False,
    'axes.linewidth': 2.0,
    'legend.frameon': False,
    'xtick.major.width': 1.5,
    'ytick.major.width': 1.5,
})

# ── Figure ──────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(8, 5))
ax.spines['bottom'].set_linewidth(2)
ax.spines['left'].set_linewidth(2)

# ... your plot code ...

fig.tight_layout(pad=2)
fig.savefig('output.svg', bbox_inches='tight')
fig.savefig('output.png', dpi=300, bbox_inches='tight')
plt.close(fig)
```

---

## Color palette

```python
PALETTE = {
    # Primary / hero method
    'blue_main':      '#0F4D92',
    'blue_secondary': '#3775BA',

    # Positive / improvement shades
    'green_1': '#DDF3DE',
    'green_2': '#AADCA9',
    'green_3': '#8BCF8B',

    # Baseline / contrast
    'red_1':      '#F6CFCB',
    'red_2':      '#E9A6A1',
    'red_strong': '#B64342',

    # Neutral support
    'neutral_light': '#CFCECE',
    'neutral_mid':   '#767676',
    'neutral_dark':  '#4D4D4D',
    'neutral_black': '#272727',

    # Accent (use sparingly)
    'gold':    '#FFD700',
    'teal':    '#42949E',
    'violet':  '#9A4D8E',
    'magenta': '#EA84DD',
}
```

**Semantic mapping convention**  
`blue_main` = your method / hero series. `green_3` = positive variants. `red_strong` = baselines.
`neutral_light` = reference / background. Apply this consistently across every panel in the figure.

**Unified palette policy (recommended for recent Nature Machine Intelligence-style layouts)**  
Do not maximize hue separation by default. In dense multi-panel figures, prefer **one coherent baseline family**
and **one coherent hero family**, then reserve green/red for delta markers or genuinely signed semantics.

```python
PALETTE_NMI_PASTEL = {
    # Baseline / comparison family (cool blue-grey)
    'baseline_dark': '#484878',
    'baseline_mid':  '#7884B4',
    'baseline_soft': '#B4C0E4',

    # Hero / proposed family (lilac → rose)
    'ours_tiny':  '#E4E4F0',
    'ours_base':  '#E4CCD8',
    'ours_large': '#F0C0CC',

    # Background blocks for overview / concept panels
    'bg_lilac': '#E0E0F0',
    'bg_aqua':  '#E0F0F0',
    'bg_peach': '#F0E0D0',

    # Neutral support
    'neutral_light': '#D8D8D8',
    'neutral_mid':   '#A8A8A8',
    'neutral_dark':  '#606060',

    # Accent only for directional annotations
    'delta_up':   '#2E9E44',
    'delta_down': '#E53935',
}

DEFAULT_COLORS_NMI_PASTEL = [
    PALETTE_NMI_PASTEL['baseline_dark'],
    PALETTE_NMI_PASTEL['baseline_mid'],
    PALETTE_NMI_PASTEL['baseline_soft'],
    PALETTE_NMI_PASTEL['ours_tiny'],
    PALETTE_NMI_PASTEL['ours_base'],
    PALETTE_NMI_PASTEL['ours_large'],
]
```

Use `DEFAULT_COLORS_NMI_PASTEL` when:
- comparing related model families such as `Tiny / Base / Large`
- building 1-page result atlases where multiple panels must feel visually unified
- matching low-saturation editorial styling rather than maximum category separation

**Practical rule**  
The same method family keeps the same hue family in every panel. Do not recolor a model from blue-grey in panel `a`
to green in panel `d` just because that panel needs more contrast.

---

## Supported chart types

| Chart | File | Key pattern |
|-------|------|-------------|
| Grouped bar | `tutorials.md` | `ax.bar()` with `x + offset`, legend-only last panel |
| Stacked bar | `common-patterns.md` | iterate `col_order`, accumulate `bottom` |
| Horizontal ablation bar | `tutorials.md` | `ax.barh()`, alpha-gradient for completeness encoding |
| Trend / line | `tutorials.md` + `api.md` | `make_trend()`, `fill_between` for uncertainty shadow |
| Heatmap (sequential) | `api.md` | `make_heatmap()`, `YlOrRd`, cell annotation with luminance check |
| Heatmap (diverging / z-score) | `design-theory.md §11` | `RdBu_r`, `vmin=-2.5, vmax=2.5` |
| Bubble scatter | `design-theory.md §11` | x/y = two compartments, `s=` = third variable |
| Radar / polar | `chart-types.md` | `projection='polar'`, custom spokes, per-spoke normalization |
| 3D sphere / illustration | `chart-types.md` | Lambertian shading via ray-cast on numpy grid |
| Fill-between (stacked area) | `chart-types.md` | hatch for print-safe grayscale |
| Log-scale bar | `chart-types.md` | `set_yscale('log')`, expand top for annotations |
| Multi-panel GridSpec | `chart-types.md` | `GridSpec(rows, cols)`, `gs[0, :]` for full-width spans |

---

## Multi-panel information architecture

Each panel in a multi-panel figure must answer a **unique** scientific question.
Covering any one panel should leave a gap that cannot be recovered from the others.

### Three-level progressive complexity (recommended)

| Level | Question | Encoding |
|-------|----------|----------|
| Overview | "What is the landscape?" | Stacked bar, composition |
| Deviation | "What is distinctive per group?" | Z-score heatmap, diverging cmap |
| Relationship | "How do variables co-vary?" | Bubble scatter, correlation |

### Common redundancy traps

| Trap | Example | Fix |
|------|---------|-----|
| Absolute + absolute | Stacked bar (%) + heatmap of same % | Replace heatmap with z-score deviation |
| Subset of parent | Tumor-only ranked bar is just one column of the stacked bar | Swap for scatter: tumor % vs immune % |
| Two rankings | Two ranked bars on related metrics | Replace one with bubble scatter |
| Different chart, same data | Pie + stacked bar | Merge or replace with a relationship plot |

### Z-score deviation heatmap

```python
z = (heat - heat.mean(axis=0)) / heat.std(axis=0)
im = ax.imshow(z.values, cmap='RdBu_r', aspect='auto', vmin=-2.5, vmax=2.5)
cbar.set_label('Z-score vs pan-cohort mean')
```

`RdBu_r`: red = enriched above average, blue = depleted. Orthogonal to absolute % shown in panel a.

### Bubble scatter with quadrant labels

```python
ax.scatter(x, y, s=size_var * scale, c=colors, edgecolors='white', linewidth=0.8, alpha=0.9)
ax.axvline(np.median(x), lw=1.2, ls='--', color='#767676', alpha=0.6)
ax.axhline(np.median(y), lw=1.2, ls='--', color='#767676', alpha=0.6)
```

Label quadrants at corners with small grey italic text (`fontsize=7.5, color='#888888', style='italic'`).

---

## Layout rules

### Figure sizes

| Type | `figsize` |
|------|-----------|
| Multi-metric bar (3–4 metrics + legend panel) | `(28–45, 6–12)` |
| Grand multi-panel (3 panels, 2-row GridSpec) | `(22, 17)` |
| Compact single bar | `(9–16, 5–8)` |
| Trend / line multi-panel | `(14, 4)` or `(9, 8)` |
| Heatmap single | `(8–20, 5–9)` |
| Radar polar | `(12, 10)` |

**Rule**: width ≈ 3–4× height for comparison bar panels.

### Panel labels

```python
ax.text(-0.05, 1.06, 'a', transform=ax.transAxes,
        fontsize=22, fontweight='bold', va='top', ha='right')
```

Use lowercase bold (`a`, `b`, `c`) at top-left of each subplot axes, placed via `transAxes`.

### Legend

- For multi-axis figures: give the legend its own axis (`ax.set_axis_off()`).
- Always `frameon=False`.
- When the legend is large, place it `bbox_to_anchor=(0.5, -0.24), loc='upper center'` below the panel.

---

## Font size hierarchy

| Context | `font.size` |
|---------|-------------|
| Base (compact subfigures) | 12–16 |
| Large bar panels (figsize > 28 in) | 24 |
| Axis labels (large panels) | 32–54 via per-label override |
| In-bar / in-cell annotations | 6.5–12 |
| Panel letter labels | 20–22 |
| Legend | 8–14 |

---

## Axes & spines rules

```python
plt.rcParams['axes.spines.right'] = False   # always off
plt.rcParams['axes.spines.top'] = False     # always off
plt.rcParams['legend.frameon'] = False

ax.spines['bottom'].set_linewidth(2)        # thicker for emphasis
ax.spines['left'].set_linewidth(2)
```

No gridlines by default. Use sparse `set_yticks` to guide the eye.  
Y-limits tightened to data range — never use `0–100` when all values sit in `80–95`.

---

## In-cell / in-bar text contrast

```python
def luminance_text_color(hex_color):
    c = hex_color.lstrip('#')
    r, g, b = int(c[0:2],16)/255, int(c[2:4],16)/255, int(c[4:6],16)/255
    return 'white' if 0.299*r + 0.587*g + 0.114*b < 0.5 else '#333333'
```

---

## Reproduction checklist

- [ ] Core conclusion and panel map are clear before styling
- [ ] Backend is explicitly Python or R
- [ ] **Lines 1–3**: `font.family`, `font.sans-serif` (three fonts), `svg.fonttype = 'none'`
- [ ] Primary output is **SVG** (`bbox_inches='tight'`)
- [ ] Right and top spines off; `legend.frameon = False`
- [ ] Font size matches final use: 5–7 pt for dense journal output, larger only for slide-sized panels
- [ ] Colors come from one coherent palette system: either semantic `PALETTE` or unified `PALETTE_NMI_PASTEL`
- [ ] Related model sizes / variants share a hue family; do not assign unrelated saturated colors to siblings
- [ ] Green / red reserved for gains, drops, thresholds, or truly signed semantics
- [ ] Y-limits tightened to data range
- [ ] Multi-panel figures: each panel answers a **different** question (anti-redundancy checklist passed)
- [ ] Panel labels (`a`, `b`, `c`) are bold lowercase and sized for final output
- [ ] Statistics, `n`, source data, and image-integrity notes are documented when manuscript-facing
- [ ] `tight_layout(pad=2)` before save
- [ ] `plt.close(fig)` after save
