Academic Plotting

1. FRAMING (5 lines): "Create a [STYLE_NAME]-style technical diagram for a
   [VENUE] paper. The diagram should feel [ADJECTIVES]..."

2. VISUAL STYLE (20-30 lines): Copy the full style block from above (A/B/C/D).
   This is the most important section — it determines the entire visual character.

3. COLOR PALETTE (10 lines): Exact hex codes for every color used.

4. LAYOUT (50-150 lines): Every component, box, section — exact text, spatial
   arrangement, and grouping. Be exhaustively specific.

5. CONNECTIONS (30-80 lines): Every arrow individually — source, target, style,
   label, routing direction.

6. CONSTRAINTS (10 lines): What NOT to include. Adapt per style — e.g., sketch
   style allows slight irregularity but still no clip art.

#!/usr/bin/env python3
"""Generate [FIGURE_NAME] diagram using Gemini image generation."""
import os, sys, time
from google import genai

API_KEY = os.environ.get("GEMINI_API_KEY")
if not API_KEY:
    print("ERROR: Set GEMINI_API_KEY environment variable.")
    print("  Get a key at: https://aistudio.google.com/apikey")
    sys.exit(1)

MODEL = "gemini-3-pro-image-preview"
OUTPUT_DIR = os.path.dirname(os.path.abspath(__file__))
client = genai.Client(api_key=API_KEY)

PROMPT = """
[PASTE YOUR 6-SECTION PROMPT HERE]
"""

def generate_image(prompt_text, attempt_num):
    print(f"\n{'='*60}\nAttempt {attempt_num}\n{'='*60}")
    try:
        response = client.models.generate_content(
            model=MODEL,
            contents=prompt_text,
            config=genai.types.GenerateContentConfig(
                response_modalities=["IMAGE", "TEXT"],
            ),
        )
        output_path = os.path.join(OUTPUT_DIR, f"fig_NAME_attempt{attempt_num}.png")
        for part in response.candidates[0].content.parts:
            if part.inline_data:
                with open(output_path, "wb") as f:
                    f.write(part.inline_data.data)
                print(f"Saved: {output_path} ({os.path.getsize(output_path):,} bytes)")
                return output_path
            elif part.text:
                print(f"Text: {part.text[:300]}")
        print("WARNING: No image in response")
        return None
    except Exception as e:
        print(f"ERROR: {e}")
        return None

def main():
    results = []
    for i in range(1, 4):
        if i > 1:
            time.sleep(2)
        path = generate_image(PROMPT, i)
        if path:
            results.append(path)
    if not results:
        print("All attempts failed!")
        sys.exit(1)
    print(f"\nGenerated {len(results)} attempts. Review and pick the best.")

if __name__ == "__main__":
    main()

import matplotlib.pyplot as plt
import numpy as np

# --- Publication defaults (polished, not generic) ---
plt.rcParams.update({
    "font.family": "serif", "font.serif": ["Times New Roman", "DejaVu Serif"],
    "font.size": 10, "axes.titlesize": 11, "axes.titleweight": "bold",
    "axes.labelsize": 10, "legend.fontsize": 8.5, "legend.frameon": False,
    "figure.dpi": 300, "savefig.dpi": 300, "savefig.bbox": "tight",
    "axes.spines.top": False, "axes.spines.right": False,
    "axes.grid": True, "grid.alpha": 0.15, "grid.linestyle": "-",
    "lines.linewidth": 1.8, "lines.markersize": 5,
})

# --- "Ocean Dusk" palette (professional, distinctive, colorblind-safe) ---
COLORS = ["#264653", "#2A9D8F", "#E9C46A", "#F4A261", "#E76F51",
          "#0072B2", "#56B4E9", "#8C8C8C"]
OUR_COLOR = "#E76F51"       # coral — warm, stands out
BASELINE_COLOR = "#B0BEC5"  # cool gray — recedes
FIG_SINGLE, FIG_FULL = (3.25, 2.5), (6.75, 2.8)

fig, ax = plt.subplots(figsize=FIG_SINGLE)
markers = ["o", "s", "^", "D", "v"]
for i, (method, (mean, std)) in enumerate(results.items()):
    color = OUR_COLOR if method == "Ours" else COLORS[i]
    ax.plot(steps, mean, label=method, color=color,
            marker=markers[i % 5], markevery=max(1, len(steps)//8),
            markersize=4, zorder=3)
    ax.fill_between(steps, mean - std, mean + std, color=color, alpha=0.12)
ax.set_xlabel("Training Steps")
ax.set_ylabel("Accuracy (%)")
ax.legend(loc="lower right")
fig.savefig("figures/fig_training.pdf")
fig.savefig("figures/fig_training.png", dpi=300)

fig, ax = plt.subplots(figsize=FIG_FULL)
x = np.arange(len(categories))
n = len(methods)
width = 0.7 / n
for i, (method, scores) in enumerate(methods.items()):
    color = OUR_COLOR if method == "Ours" else COLORS[i]
    offset = (i - n / 2 + 0.5) * width
    bars = ax.bar(x + offset, scores, width * 0.9, label=method, color=color,
                  edgecolor="white", linewidth=0.5)
    for bar, s in zip(bars, scores):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
                f"{s:.1f}", ha="center", va="bottom", fontsize=7, color="#444")
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.set_ylabel("Score")
ax.legend(ncol=min(n, 4))
fig.savefig("figures/fig_ablation.pdf")

import seaborn as sns
fig, ax = plt.subplots(figsize=(4, 3.5))
sns.heatmap(matrix, annot=True, fmt=".2f", cmap="YlOrRd", ax=ax,
            cbar_kws={"shrink": 0.75, "aspect": 20},
            linewidths=1.5, linecolor="white",
            annot_kws={"size": 8, "weight": "medium"})
ax.set_xlabel("Predicted")
ax.set_ylabel("Actual")
fig.savefig("figures/fig_confusion.pdf")

fig, ax = plt.subplots(figsize=FIG_SINGLE)
y_pos = np.arange(len(models))
colors = [BASELINE_COLOR] * len(models)
colors[our_idx] = OUR_COLOR
bars = ax.barh(y_pos, scores, color=colors, height=0.55,
               edgecolor="white", linewidth=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(models)
ax.set_xlabel("Accuracy (%)")
ax.invert_yaxis()
for bar, s in zip(bars, scores):
    ax.text(bar.get_width() + 0.3, bar.get_y() + bar.get_height()/2,
            f"{s:.1f}", va="center", fontsize=8, color="#444")
fig.savefig("figures/fig_leaderboard.pdf")

figures/
├── gen_fig_<name>.py      # Generation script (always save for reproducibility)
├── fig_<name>.pdf         # Final vector output (for LaTeX)
├── fig_<name>.png         # Raster output (300 DPI, for AI-generated or fallback)
└── fig_<name>_attempt*.png # Gemini attempts (keep for comparison)