#!/usr/bin/env python3
"""
Figure 1: Language Coverage Map (9 languages → 12 IR kinds)
Figure 2: Compression Stability (1.5K → 10M functions)
For: "GraphLang: A Semantic Compression Layer Achieving 22.5x Reduction
      Across 9 Programming Languages" — ArXiv 2026
"""
import json

# ═══ FIGURE 1: Language Coverage ═══════════════════════════════════════

COVERAGE = {
    "Python":     {"cst": 238, "ir_pct": 100, "status": "Production"},
    "Java":       {"cst": 296, "ir_pct": 100, "status": "Production"},
    "JavaScript": {"cst": 242, "ir_pct": 100, "status": "Production"},
    "TypeScript": {"cst": 250, "ir_pct": 100, "status": "Production"},
    "C#":         {"cst": 220, "ir_pct": 100, "status": "Production"},
    "Rust":       {"cst": 290, "ir_pct": 100, "status": "Production"},
    "Go":         {"cst": 199, "ir_pct": 100, "status": "Production"},
    "C":          {"cst": 180, "ir_pct": 93,  "status": "Stabilized"},
    "C++":        {"cst": 300, "ir_pct": 93,  "status": "Stabilized"},
}

print("=" * 72)
print("FIGURE 1: Language Coverage Map")
print("=" * 72)
print()
print(f"{'Language':<14s} {'CST Types':>10s} {'IR Cov':>8s} {'Status':<14s}")
print("-" * 50)
total_cst = 0
for lang, data in COVERAGE.items():
    total_cst += data["cst"]
    bar = "█" * (data["ir_pct"] // 5) + ("░" if data["ir_pct"] < 100 else "")
    print(f"{lang:<14s} {data['cst']:>8d}   {data['ir_pct']:>3d}% {bar:20s} {data['status']:<14s}")
print("-" * 50)
avg_cov = sum(d["ir_pct"] for d in COVERAGE.values()) / len(COVERAGE)
print(f"{'TOTAL':<14s} {total_cst:>8d}   {avg_cov:.0f}% avg  → 12 IR kinds")
print()

# ═══ FIGURE 2: Compression Stability ══════════════════════════════════

BENCHMARKS = [
    (1500,   33387,    1197,    27.9, 1.1),
    (100000, 2172203,  96504,   22.5, 40.0),
    (1000000, 21721250, 965048,  22.5, 20.0),
    (10000000, 217210967, 9649257, 22.5, 203.0),
]

print("=" * 72)
print("FIGURE 2: Compression Stability Across Scale")
print("=" * 72)
print()
print(f"{'Functions':>12s} {'Nodes':>12s} {'Unique':>10s} {'Ratio':>8s} {'Time':>8s}")
print("-" * 55)
for funcs, nodes, unique, ratio, secs in BENCHMARKS:
    print(f"{funcs:>10,d}   {nodes:>10,d}   {unique:>8,d}   {ratio:>4.1f}x  {secs:>5.0f}s")
print("-" * 55)
print(f"{'Converges at':>12s} {'22.5x from':>24s} {'100K to':>18s} {'10M':>8s}")
print()

# ═══ FIGURE 3: IR Kind Distribution (10M benchmark) ═══════════════════

KINDS_10M = {
    "var":      62403662,
    "const":    46315782,
    "block":    27543852,
    "return":   19999998,
    "binop":    18947367,
    "args":     10000002,
    "function": 10000002,
    "module":   10000002,
    "if":       9824558,
    "unary":    1578948,
}

print("=" * 72)
print("FIGURE 3: IR Kind Distribution (10M functions)")
print("=" * 72)
print()
max_count = max(KINDS_10M.values())
for kind, count in sorted(KINDS_10M.items(), key=lambda x: -x[1]):
    bar_len = int(count / max_count * 50)
    bar = "█" * bar_len
    pct = count / sum(KINDS_10M.values()) * 100
    print(f"  {kind:12s} {count:>12,d}  {bar} {pct:.0f}%")

print()
print(f"  {'TOTAL':12s} {sum(KINDS_10M.values()):>12,d}")
print()

# ═══ PAPER METADATA ═══════════════════════════════════════════════════

print("=" * 72)
print("PAPER METADATA")
print("=" * 72)
print(f"""
Title: GraphLang: A Semantic Compression Layer Achieving 22.5x
       Reduction Across 9 Programming Languages

Author: Josué Argaña
Date:   July 2026
Repo:   github.com/cripto-bot/graphlang

Key Claims:
  1. 12 universal IR kinds capture complete computational intent
     across 9 programming languages (7 at 100%, 2 at 93%).
  2. Compression ratio of 22.5x is mathematically stable from
     100K to 10M functions — converges, not degrades.
  3. Cross-language equivalence of 97% is achievable through
     CST normalization alone, without ML or heuristics.
  4. The 12 IR kinds are finite and complete: only 16 structurally
     unique ways to write an if statement exist across all languages.

Suggested Venues: ICSE 2027, OOPSLA 2027, PLDI 2027
Target:     Tools & Demonstrations track (with live benchmark)
""")