nature-figure

name	nature-figure
description	Generate publication-ready matplotlib figures matching Nature journal standards. Use when user says 'Nature figure', 'Nature style plot', or needs high-impact journal figures with Nature typography, color systems, and SVG/PDF export.
argument-hint	["figure-plan-or-data-path"]
allowed-tools	Bash(*), Read, Write, Edit, Grep, Glob, Agent, mcp__codex__codex, mcp__codex__codex-reply

Nature Figure: Publication-Quality Figures for Nature/High-Impact Journals

Generate Nature-style figures from: $ARGUMENTS

Constants

• FIG_DIR = figures/

• PRIMARY_FORMAT = pdf (LaTeX embedding, vector)

• DPI = 300

• CUSTOM_REQUIREMENTS — User-specified requirements, highest priority.

Mandatory rcParams (apply at top of EVERY script)

import matplotlib.pyplot as plt



plt.rcParams['font.family'] = 'sans-serif'

plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']

plt.rcParams['svg.fonttype'] = 'none'          # editable text in SVG/PDF

plt.rcParams['font.size'] = 16                 # 24 for large bar panels

plt.rcParams['axes.spines.right'] = False

plt.rcParams['axes.spines.top'] = False

plt.rcParams['axes.linewidth'] = 2.5           # 3 for big bars, 2 for compact

plt.rcParams['legend.frameon'] = False

Integration with plot_utils.py

Try setup_style(palette='nature') first. If unavailable, use inline rcParams above as fallback:

import os, sys, shutil

os.makedirs('_utils', exist_ok=True)

for src in ['plot_utils.py']:

    for search in ['skills/shared-scripts', '../skills/shared-scripts']:

        p = os.path.join(search, src)

        if os.path.isfile(p):

            shutil.copy2(p, f'_utils/{src}')

            break

sys.path.insert(0, '.')

try:

    from _utils.plot_utils import setup_style, save_fig, PALETTE

    setup_style(palette='nature')

except (ImportError, TypeError):

    # Fallback: apply Nature rcParams directly

    plt.rcParams['font.family'] = 'sans-serif'

    plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']

    plt.rcParams['svg.fonttype'] = 'none'

    plt.rcParams['font.size'] = 16

    plt.rcParams['axes.spines.right'] = False

    plt.rcParams['axes.spines.top'] = False

    plt.rcParams['axes.linewidth'] = 2.5

    plt.rcParams['legend.frameon'] = False

Nature Color Palette

PALETTE_NATURE = {

    "blue_main":      "#0F4D92",   # deep blue — hero method

    "blue_secondary": "#3775BA",   # medium blue

    "green_1": "#DDF3DE",          # light positive

    "green_2": "#AADCA9",          # mid positive

    "green_3": "#8BCF8B",          # strong positive

    "red_1":   "#F6CFCB",          # light baseline

    "red_2":   "#E9A6A1",          # mid baseline

    "red_strong": "#B64342",       # strong baseline/negative

    "neutral_light": "#CFCECE",

    "neutral_mid":   "#767676",

    "neutral_dark":  "#4D4D4D",

    "neutral_black": "#272727",

    "gold":   "#FFD700",

    "teal":   "#42949E",

    "violet": "#9A4D8E",

}



# For unified-family figures (NMI-style dense pages)

PALETTE_NMI_PASTEL = {

    "baseline_dark": "#484878",

    "baseline_mid":  "#7884B4",

    "baseline_soft": "#B4C0E4",

    "ours_tiny":  "#E4E4F0",

    "ours_base":  ,

    : ,

    :   ,

    : ,

}

Semantic rules:

• Blue = proposed/hero method

• Green = positive variants/improvements

• Red/pink = baselines/contrast

• Neutral grays = reference/background

• Use NMI pastel when comparing method families on dense pages

Default Operating Stance

1. Classify the figure into one of 5 Nature page archetypes (see below)

2. Hero panel concept: one dominant panel + subordinate evidence panels

3. Direct labels over legends when categories are spatially fixed

4. White background for plots; black only for microscopy/imaging plates

5. One restrained palette per figure: neutral + signal + accent families

6. Panel labels: small bold lowercase (a, b, c) near top-left edge

5 Nature Page Archetypes

| Archetype | Layout | When to use |

|-----------|--------|-------------|

| Schematic-led composite | Wide story panel + smaller quant panels below | Method explanation + validation |

| Dark image plate | Black tiles with fluorescent channels | Microscopy, imaging, volume rendering |

| Clinical triptych | Top longitudinal, middle forest, bottom summary | Clinical/longitudinal studies |

| Dense categorical | Grid of equal panels, unified palette | Multi-metric comparisons |

| Asymmetric hero | One dominant panel spanning grid cells + small supports | Single key result + context |

Layout Rules

• Hero panel gets visual hierarchy; support panels validate, not compete

• Panel labels: ax.set_title('a', loc='left', pad=3, fontsize=14, fontweight='bold')

• Tight gutters; increase spacing when dark/light modalities touch

• Prefer shared legend strip above a row over per-panel legends

• Dynamic y-axis: tighten to data range, never fixed 0–100 for narrow bands

• figsize guidance: journal-width composite (7.0–7.4, 5.5–7.8); bar panels (28–45, 6–12)

Export Policy

import os

os.makedirs('./figures/', exist_ok=True)

fig.tight_layout(pad=0.5)



# PDF output for LaTeX compilation

fig.savefig('./figures/name.pdf', dpi=300, bbox_inches='tight')



plt.close(fig)

• Output PDF only (LaTeX \includegraphics requires PDF)

• Always dpi=300, bbox_inches='tight'

• Always plt.close(fig) after save

• PNG optional at 300 dpi for preview

• Always bbox_inches='tight'

• Always plt.close(fig) after save

Workflow

Step 1: Read data + classify figure type

Read PAPER_PLAN.md and data files. For each figure, classify into archetype and choose palette.

Step 2: Read references + Generate scripts

⛔ 必须在写任何绑图脚本之前，先读取以下参考文件：

# 必读：配色方案和 helper 函数

cat _references/api.md



# 必读：根据图表类型选择对应教程

cat _references/tutorials.md



# 按需读取（多面板/复杂布局时）

cat _references/common-patterns.md



# 按需读取（需要了解 Nature 真实页面风格时）

cat _references/nature-2026-observations.md



# 按需读取（雷达图/3D/特殊图表时）

cat _references/chart-types.md

One script per figure. Each starts with Nature rcParams setup (setup_style(palette='nature') or inline rcParams). Follow the patterns from _references/tutorials.md as starting point.

Step 3: Execute and validate

Run each script. Verify PDF output exists in figures/. Check:

• No plt.title() (captions in LaTeX only)

• Font ≥ 9pt final size

• Grayscale-distinguishable

• Panel labels present for multi-panel figures

• Colors from Nature palette, not matplotlib defaults

Step 4: Generate latex_includes.tex

Include all figures with [H] float specifier and English captions.

Related Files

| File | Open when |

|------|-----------|

| references/api.md | Palette constants, helper function signatures, validation rules |

| references/design-theory.md | Typography, color theory, layout rationale |

| references/chart-types.md | Radar, 3D sphere, fill_between, scatter patterns |

| references/common-patterns.md | Ultra-wide panels, legend-only axes, print-safe bars |

| references/nature-2026-observations.md | Real Nature page archetypes from 2026 issues |

| references/tutorials.md | End-to-end walkthroughs: bars, trends, heatmaps |

| _utils/plot_utils.py | Shared plotting infrastructure |

Key Rules

• ⛔ Never use svg.fonttype = 'path' — breaks text editability

• ⛔ No plt.title() — captions belong in LaTeX

• ⛔ No matplotlib default colors — always use Nature palette

• ⛔ No grid lines by default — sparse y-ticks guide the eye

• Active voice in axis labels; concise legend entries

• For ablation: single color with varying alpha (0.2–1.0)

• Error bars: elinewidth=2, capthick=2, capsize=10

• Heatmap text contrast: white on dark cells, black on light cells