paper-figure

# Paper Figure: Publication-Quality Plots from 求解数据 Generate all figures and tables for a paper based on: **$ARGUMENTS** ## Scope: What This Skill Can and Cannot Do | Category | Can auto-generate? | Examples | |----------|-------------------|----------| | **Data-driven plots** | ✅ Yes | Line plots (求解过程曲线), bar charts (method comparison), scatter plots, heatmaps, box/violin plots | | **Comparison tables** | ✅ Yes | LaTeX tables comparing prior bounds, method features, 灵敏度分析 results | | **Multi-panel figures** | ✅ Yes | Subfigure grids combining multiple plots (e.g., 3×3 dataset × method) | | **Architecture/pipeline diagrams** | ❌ No — manual | Model architecture, data flow diagrams, system overviews. At best can generate a rough TikZ skeleton, but **expect to draw these yourself** using tools like draw.io, Figma, or TikZ | | **Generated image grids** | ❌ No — manual | Grids of generated samples (e.g., GAN/diffusion outputs). These come from running your model, not from this skill | | **Photographs / screenshots** | ❌ No — manual | Real-world images, UI screenshots, qualitative examples | **In practice:** For a typical ML paper, this skill handles ~60% of figures (all data plots + tables). The remaining ~40% (hero figure, architecture diagram, qualitative results) need to be created manually and placed in `figures/` before running `/paper-write`. The skill will detect these as "existing figures" and preserve them. ## Constants - **STYLE = `publication`** — Visual style preset. Options: `publication` (default, clean for print), `poster` (larger fonts), `slide` (bold colors) - **DPI = 300** — Output resolution - **FORMAT = `pdf`** — Output format. Options: `pdf` (vector, best for LaTeX), `png` (raster fallback) - **COLOR_PALETTE = `tab10`** — Default matplotlib color cycle. Options: `tab10`, `Set2`, `colorblind` (deuteranopia-safe) - **FONT_SIZE = 10** — Base font size (matches typical conference body text) - **FIG_DIR = `figures/`** — Output directory for generated figures - **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for figure quality review. ## Inputs 1. **PAPER_PLAN.md** — figure plan table (from `/paper-plan`) 2. **Experiment data** — JSON files, CSV files, or screen logs in `figures/` or project root 3. **Existing figures** — any manually created figures to preserve If no PAPER_PLAN.md exists, scan for data files and ask the user which figures to generate. ## Workflow ### Step 1: Read Figure Plan Parse the Figure Plan table from PAPER_PLAN.md: ```markdown | ID | Type | Description | Data Source | Priority | |----|------|-------------|-------------|----------| | Fig 1 | Architecture | ... | manual | HIGH | | Fig 2 | Line plot | ... | figures/exp.json | HIGH | ``` Identify: - Which figures can be auto-generated from data - Which need manual creation (architecture diagrams, etc.) - Which are comparison tables (generate as LaTeX) ### Step 2: Set Up Plotting Environment Create a shared style configuration script: ```python # paper_plot_style.py — shared across all figure scripts import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams.update({ 'font.size': FONT_SIZE, 'font.family': 'serif', 'font.serif': ['Times New Roman', 'Times', 'DejaVu Serif'], 'axes.labelsize': FONT_SIZE, 'axes.titlesize': FONT_SIZE + 1, 'xtick.labelsize': FONT_SIZE - 1, 'ytick.labelsize': FONT_SIZE - 1, 'legend.fontsize': FONT_SIZE - 1, 'figure.dpi': DPI, 'savefig.dpi': DPI, 'savefig.bbox': 'tight', 'savefig.pad_inches': 0.05, 'axes.grid': False, 'axes.spines.top': False, 'axes.spines.right': False, 'text.usetex': False, # set True if LaTeX is available 'mathtext.fontset': 'stix', }) # Color palette COLORS = plt.cm.tab10.colors # or Set2, or colorblind-safe def save_fig(fig, name, fmt=FORMAT): """Save figure to FIG_DIR with consistent naming.""" fig.savefig(f'{FIG_DIR}/{name}.{fmt}') print(f'Saved: {FIG_DIR}/{name}.{fmt}') ``` ### Step 3: Auto-Select Figure Type Use this decision tree for data-driven figures (inspired by Imbad0202/academic-research-skills): | Data Pattern | Recommended Type | Size | |-------------|-----------------|------| | X=time/steps, Y=metric | Line plot | 0.48\textwidth | | Methods × 1 metric | Bar chart | 0.48\textwidth | | Methods × multiple metrics | Grouped bar / radar | 0.95\textwidth | | Two continuous variables | Scatter plot | 0.48\textwidth | | Matrix / grid values | Heatmap | 0.48\textwidth | | Distribution comparison | Box/violin plot | 0.48\textwidth | | Multi-dataset results | Multi-panel (subfigure) | 0.95\textwidth | | Prior work comparison | LaTeX table | — | ### Step 4: Generate Each Figure For each figure in the plan, create a standalone Python script: **Line plots** (求解过程曲线, scaling): ```python # gen_fig2_training_curves.py from paper_plot_style import * import json with open('figures/exp_results.json') as f: data = json.load(f) fig, ax = plt.subplots(1, 1, figsize=(5, 3.5)) ax.plot(data['steps'], data['fac_loss'], label='Factorized', color=COLORS[0]) ax.plot(data['steps'], data['crf_loss'], label='CRF-LR', color=COLORS[1]) ax.set_xlabel('Training Steps') ax.set_ylabel('Cross-Entropy Loss') ax.legend(frameon=False) save_fig(fig, 'fig2_training_curves') ``` **Bar charts** (comparison, 灵敏度分析): ```python fig, ax = plt.subplots(1, 1, figsize=(5, 3)) methods = ['Baseline', 'Method A', 'Method B', 'Ours'] values = [82.3, 85.1, 86.7, 89.2] bars = ax.bar(methods, values, color=[COLORS[i] for i in range(len(methods))]) ax.set_ylabel('Accuracy (%)') # Add value labels on bars for bar, val in zip(bars, values): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3, f'{val:.1f}', ha='center', va='bottom', fontsize=FONT_SIZE-1) save_fig(fig, 'fig3_comparison') ``` **Comparison tables** (LaTeX, for theory papers): ```latex \begin{table}[t] \centering \caption{Comparison of estimation error bounds. $n$: sample size, $D$: ambient dim, $d$: latent dim, $K$: subspaces, $n_k$: modes.} \label{tab:bounds} \begin{tabular}{lccc} \toprule Method & Rate & Depends on $D$? & Multi-modal? \\ \midrule \citet{MinimaxOkoAS23} & $n^{-s'/D}$ & Yes (curse) & No \\ \citet{ScoreMatchingdistributionrecovery} & $n^{-2/d}$ & No & No \\ \textbf{Ours} & $\sqrt{\sum n_k d_k / n}$ & No & Yes \\ \bottomrule \end{tabular} \end{table} ``` **Architecture/pipeline diagrams** (MANUAL — outside this skill's scope): - These require manual creation using draw.io, Figma, Keynote, or TikZ - This skill can generate a rough TikZ skeleton as a starting point, but **do not expect publication-quality results** - If the figure already exists in `figures/`, preserve it and generate only the LaTeX `\includegraphics` snippet - Flag as `[MANUAL]` in the figure plan and `latex_includes.tex` ### Step 5: Run All Scripts ```bash # Run all figure generation scripts for script in gen_fig*.py; do python "$script" done ``` Verify all output files exist and are non-empty. ### Step 6: Generate LaTeX Include Snippets For each figure, output the LaTeX code to include it: ```latex % === Fig 2: Training Curves === \begin{figure}[t] \centering \includegraphics[width=0.48\textwidth]{figures/fig2_training_curves.pdf} \caption{Training curves comparing factorized and CRF-LR denoising.} \label{fig:training_curves} \end{figure} ``` Save all snippets to `figures/latex_includes.tex` for easy copy-paste into the paper. ### Step 7: Figure Quality Review with REVIEWER_MODEL Send figure descriptions and captions to GPT-5.4 for review: ``` mcp__codex__codex: model: gpt-5.4 config: {"model_reasoning_effort": "xhigh"} prompt: | Review these figure/table plans for a [VENUE] submission. For each figure: 1. Is the caption informative and self-contained? 2. Does the figure type match the data being shown? 3. Is the comparison fair and clear? 4. Any missing baselines or 灵敏度分析s? 5. Would a different visualization be more effective? [list all figures with captions and descriptions] ``` ### Step 8: Quality Checklist Before finishing, verify each figure (from pedrohcgs/claude-code-my-workflow): - [ ] Font size readable at printed paper size (not too small) - [ ] Colors distinguishable in grayscale (print-friendly) - [ ] **No title inside figures** — titles go only in LaTeX `\caption{}` (from pedrohcgs) - [ ] Legend does not overlap data - [ ] Axis labels have units where applicable - [ ] Axis labels are publication-quality (not variable names like `emp_rate`) - [ ] Figure width fits single column (0.48\textwidth) or full width (0.95\textwidth) - [ ] PDF output is vector (not rasterized text) - [ ] No matplotlib default title (remove `plt.title` for publications) - [ ] Serif font matches paper body text (Times / Computer Modern) - [ ] Colorblind-accessible (if using colorblind palette) ## Output ``` figures/ ├── paper_plot_style.py # shared style config ├── gen_fig1_architecture.py # per-figure scripts ├── gen_fig2_training_curves.py ├── gen_fig3_comparison.py ├── fig1_architecture.pdf # generated figures ├── fig2_training_curves.pdf ├── fig3_comparison.pdf ├── latex_includes.tex # LaTeX snippets for all figures └── TABLE_*.tex # standalone table LaTeX files ``` ## Key Rules - **Every figure must be reproducible** — save the generation script alongside the output - **Do NOT hardcode data** — always read from JSON/CSV files - **Use vector format (PDF)** for all plots — PNG only as fallback - **No decorative elements** — no background colors, no 3D effects, no chart junk - **Consistent style across all figures** — same fonts, colors, line widths - **Colorblind-safe** — verify with https://davidmathlogic.com/colorblind/ if needed - **One script per figure** — easy to re-run individual figures when data changes - **No titles inside figures** — captions are in LaTeX only - **Comparison tables count as figures** — generate them as standalone .tex files ## Figure Type Reference | Type | When to Use | Typical Size | |------|------------|--------------| | Line plot | Training curves, scaling trends | 0.48\textwidth | | Bar chart | Method comparison, 灵敏度分析 | 0.48\textwidth | | Grouped bar | Multi-metric comparison | 0.95\textwidth | | Scatter plot | Correlation analysis | 0.48\textwidth | | Heatmap | Attention, 混淆矩阵 | 0.48\textwidth | | Box/violin | Distribution comparison | 0.48\textwidth | | Architecture | System overview | 0.95\textwidth | | Multi-panel | Combined results (subfigures) | 0.95\textwidth | | Comparison table | Prior bounds vs. ours (theory) | full width | ## Acknowledgements Design pattern (type × style matrix) inspired by [baoyu-skills](https://github.com/jimliu/baoyu-skills). Publication style defaults and figure rules from [pedrohcgs/claude-code-my-workflow](https://github.com/pedrohcgs/claude-code-my-workflow). Visualization decision tree from [Imbad0202/academic-research-skills](https://github.com/Imbad0202/academic-research-skills).