aggregate-plots

name

aggregate-plots

description

Create publication-quality figures from experiment results for a scientific paper. Use this skill when the user wants to generate paper figures, create publication plots, make professional charts from experiment data, aggregate multiple plots into final figures, or prepare visualizations for a paper submission. Also use when the user says "make the plots," "prepare figures," or mentions needing camera-ready figures.

Aggregate Plots

You are creating the final publication-quality figures for a scientific paper. Your job is to read the experiment results (.npy files, JSON summaries, intermediate plots) and produce a comprehensive, professional set of figures saved to figures/.

Prerequisites

• Completed experiment results in experiment_results/ (.npy files, diagnostic plots)
• Summary JSONs (baseline_summary.json, research_summary.json, ablation_summary.json)
• idea.md for understanding the research context

Step-by-Step Process

1. Survey Available Data

Read all summary JSONs and list all .npy files to understand what data is available:

ls experiment_results/*.npy
cat baseline_summary.json
cat research_summary.json
cat ablation_summary.json

2. Plan the Figures

A good ML paper typically needs these types of figures (pick what's relevant):

Figure Type	When to Use	Example
Learning curves	Almost always	Loss/accuracy vs. epoch for each method
Bar chart comparison	Comparing methods on metrics	Accuracy across methods with error bars
Ablation table/chart	Ablation studies	Performance impact of removing components
Hyperparameter sensitivity	Parameter sweeps	Metric vs. learning rate/hidden size
Sample outputs	Generative models, vision	Generated images, attention maps
Distribution plots	Statistical analysis	Histogram of scores, box plots
Scaling plots	Scaling experiments	Performance vs. dataset size/model size
t-SNE/UMAP	Representation learning	Learned embeddings visualization

Aim for 4-8 figures total. Each figure should communicate one clear message.

3. Write the Plotting Script

Create auto_plot_aggregator.py in the experiment root. Follow these rules:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import json
import os

matplotlib.rcParams.update({
    'font.size': 14,
    'axes.titlesize': 16,
    'axes.labelsize': 14,
    'xtick.labelsize': 12,
    'ytick.labelsize': 12,
    'legend.fontsize': 12,
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox_inches': 'tight',
})

os.makedirs("figures", exist_ok=True)

# ── Figure 1: Learning Curves ──────────────────────────────
try:
    fig, ax = plt.subplots(1, 1, figsize=(8, 5))

    proposed_loss = np.load("experiment_results/proposed_train_losses.npy")
    baseline_loss = np.load("experiment_results/baseline_train_losses.npy")

    ax.plot(proposed_loss, label="Proposed Method", linewidth=2)
    ax.plot(baseline_loss, label="Baseline", linewidth=2, linestyle="--")
    ax.set_xlabel("Epoch")
    ax.set_ylabel("Training Loss")
    ax.set_title("Training Convergence Comparison")
    ax.legend()
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)

    plt.savefig("figures/learning_curves.png")
    plt.close()
    print("Created: figures/learning_curves.png")
except Exception as e:
    print(f"Failed to create learning curves: {e}")

# ── Figure 2: Method Comparison ────────────────────────────
try:
    # ... next figure ...
    pass
except Exception as e:
    print(f"Failed to create comparison: {e}")

4. Professional Styling Rules

Follow these to make figures publication-ready:

• Remove top and right spines — cleaner look
• DPI: 300 — required for most venues
• Font size: 14+ — readable when printed
• No underscores in labels — use spaces ("Test Accuracy" not "test_accuracy")
• Informative legends — "Proposed Method (Ours)" not "method_1"
• Error bars / shading — show std when you have multiple seeds
• Consistent colors — use the same color for the same method across all figures
• Tight layout — use plt.tight_layout() or bbox_inches='tight'
• Meaningful titles — describe the finding, not just the plot type
• Group related plots — use subplots (up to 3 per row) for related comparisons

5. Subplot Aggregation

When multiple plots are closely related, combine them:

fig, axes = plt.subplots(1, 3, figsize=(18, 5))

# Left: Learning curves
axes[0].plot(...)
axes[0].set_title("(a) Training Loss")

# Middle: Validation accuracy
axes[1].plot(...)
axes[1].set_title("(b) Validation Accuracy")

# Right: Test performance
axes[2].bar(...)
axes[2].set_title("(c) Final Test Results")

plt.tight_layout()
plt.savefig("figures/main_results.png")

6. Execute and Iterate

Run the plotting script:

python auto_plot_aggregator.py

Check the output — look at each generated figure and ask:

• Is the message clear?
• Are labels readable?
• Are colors distinguishable?
• Is anything misleading?
• Can any plots be combined into subplots?

Fix issues and re-run. Repeat up to 5 times until the figures are publication-ready.

7. Common Patterns

Error bars (multiple seeds):

mean = np.mean(results, axis=0)
std = np.std(results, axis=0)
ax.plot(epochs, mean, label="Method")
ax.fill_between(epochs, mean - std, mean + std, alpha=0.2)

Bar chart with error bars:

methods = ["Baseline", "Variant A", "Proposed"]
means = [0.82, 0.85, 0.91]
stds = [0.03, 0.02, 0.01]
ax.bar(methods, means, yerr=stds, capsize=5, color=["#999", "#66b", "#c44"])

Ablation heatmap:

import seaborn as sns
sns.heatmap(ablation_matrix, annot=True, fmt=".2f", xticklabels=components, yticklabels=metrics)

Output

experiments/{idea_name}/
├── figures/
│   ├── learning_curves.png
│   ├── method_comparison.png
│   ├── ablation_results.png
│   └── ... (4-8 figures total)
└── auto_plot_aggregator.py

Every .png file in figures/ will be available for inclusion in the paper during the write-paper stage.