#!/usr/bin/env python3 import csv import json from fractions import Fraction from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from matplotlib.patches import Polygon, Rectangle ROOT = Path(__file__).resolve().parents[1] DATA = ROOT / "data" / "F2_decision_identifiability_data.csv" OUT_PNG = ROOT / "figures" / "F2_decision_identifiability.png" OUT_SVG = ROOT / "figures" / "F2_decision_identifiability.svg" OUT_VALIDATION = ROOT / "validation" / "F2_decision_identifiability_validation.json" def q(value): return value if isinstance(value, Fraction) else Fraction(str(value)) def classify_point(debt, adjustment, baseline=10, limit=5): denominator = q(baseline) + q(adjustment) if denominator <= 0: raise ValueError("denominator must be positive") return "PASS" if q(debt) / denominator <= q(limit) else "FAIL" def classify_interval(debt, lower, upper, baseline=10, limit=5): lower_q, upper_q = q(lower), q(upper) if lower_q > upper_q: raise ValueError("lower bound exceeds upper bound") if q(baseline) + lower_q <= 0: raise ValueError("denominator must remain positive") worst = q(debt) / (q(baseline) + lower_q) best = q(debt) / (q(baseline) + upper_q) if worst <= q(limit): return "SUPPORTED_PASS" if best > q(limit): return "SUPPORTED_FAIL" return "UNRESOLVED" def read_rows(path=DATA): with path.open(newline="", encoding="utf-8") as handle: rows = list(csv.DictReader(handle)) for row in rows: status = classify_interval( row["debt_eur_m"], row["adjustment_lower_eur_m"], row["adjustment_upper_eur_m"], row["baseline_ebitda_eur_m"], row["ratio_limit"], ) if status != row["expected_status"]: raise ValueError(f"status mismatch for {row['case_id']}: {status}") if row["evidence_class"] != "synthetic_deterministic": raise ValueError("unexpected evidence class") return rows def make_figure(rows): plt.rcParams.update({ "font.family": "DejaVu Sans", "font.size": 9.5, "axes.titlesize": 12, "axes.labelsize": 10, "svg.hashsalt": "eoji-vr005-f2", }) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.8, 6.5)) # Panel A: exact states. There is no unresolved point state. x = [0, 3] boundary = [50, 65] ax1.add_patch(Polygon([(0, 40), (3, 40), (3, 65), (0, 50)], closed=True, facecolor="#dce8e2", edgecolor="none", hatch="..")) ax1.add_patch(Polygon([(0, 50), (3, 65), (3, 75), (0, 75)], closed=True, facecolor="#eadfdf", edgecolor="none", hatch="//")) ax1.plot(x, boundary, color="#1b2a41", linewidth=2.2) ax1.text(0.15, 43.0, "PASS\nD ≤ 5(10+A)", fontweight="bold") ax1.text(1.9, 71.0, "FAIL\nD > 5(10+A)", fontweight="bold") ax1.set_xlim(0, 3) ax1.set_ylim(40, 75) ax1.set_xlabel("Exact adjustment A (EURm)") ax1.set_ylabel("Debt D (EURm)") ax1.set_title("A. Exact states have a determinate answer", loc="left", fontweight="bold") ax1.grid(color="#ffffff", linewidth=0.9) # Panel B: interval evidence for D=60 in lower/upper-bound space. # Valid triangle: 0 ≤ L ≤ U ≤ 3. Threshold A*=2. fail_poly = Polygon([(0, 0), (0, 2), (2, 2)], closed=True, facecolor="#eadfdf", edgecolor="#6a7078", hatch="//") unresolved_poly = Polygon([(0, 2), (0, 3), (2, 3), (2, 2)], closed=True, facecolor="#eee9d8", edgecolor="#6a7078", hatch="xx") pass_poly = Polygon([(2, 2), (2, 3), (3, 3)], closed=True, facecolor="#dce8e2", edgecolor="#6a7078", hatch="..") for poly in (fail_poly, unresolved_poly, pass_poly): ax2.add_patch(poly) ax2.plot([0, 3], [0, 3], color="#1b2a41", linewidth=1.2) ax2.axhline(2, color="#1b2a41", linestyle="--", linewidth=1.0) ax2.axvline(2, color="#1b2a41", linestyle="--", linewidth=1.0) ax2.scatter([0], [3], marker="o", s=100, color="#1b2a41", zorder=4) ax2.text(0.12, 2.92, "● Example interval [0,3]", ha="left", va="top", fontsize=8.7) ax2.text(0.45, 1.0, "SUPPORTED FAIL\nU < 2", ha="center", va="center", fontweight="bold") ax2.text(0.85, 2.28, "UNRESOLVED\nL < 2 ≤ U", ha="center", va="center", fontweight="bold") ax2.text(2.48, 2.72, "SUPPORTED PASS\nL ≥ 2", ha="center", va="center", fontweight="bold") ax2.set_xlim(0, 3) ax2.set_ylim(0, 3) ax2.set_aspect("equal", adjustable="box") ax2.set_xlabel("Lower admissible bound L for A (EURm)") ax2.set_ylabel("Upper admissible bound U for A (EURm)") ax2.set_title("B. For D=60, evidence is sufficient only if\nits interval stays on one side", loc="left", fontweight="bold") fig.suptitle("A missing value is not always a missing decision", x=0.04, y=0.98, ha="left", fontsize=15, fontweight="bold") fig.subplots_adjust(left=0.07, right=0.98, top=0.82, bottom=0.18, wspace=0.24) fig.text(0.04, 0.035, "Synthetic exact calculation: D/(10+A) ≤ 5, 0 ≤ A ≤ 3. No probability, borrower observation or legal conclusion.", fontsize=9, color="#3f4854") metadata = {"Creator": "EOJI visual research 005", "Date": None} fig.savefig(OUT_PNG, dpi=220, metadata=metadata) fig.savefig(OUT_SVG, metadata=metadata) plt.close(fig) def main(): rows = read_rows() make_figure(rows) boundary_checks = { "D45_threshold_A": -1.0, "D60_threshold_A": 2.0, "D70_threshold_A": 4.0, "D60_at_A2": classify_point(60, 2), "D60_below_A2": classify_point(60, 1.999), "D60_above_A2": classify_point(60, 2.001), } validation = { "status": "PASS", "evidence_class": "synthetic_deterministic", "formula": "D/(10+A) ≤ 5", "interval_results": {r["case_id"]: r["expected_status"] for r in rows}, "boundary_checks": boundary_checks, "monotonicity": "ratio decreases as A increases for positive denominator; boundary debt increases 5 EURm per 1 EURm A", "outputs": [str(OUT_PNG.relative_to(ROOT)), str(OUT_SVG.relative_to(ROOT))], "exclusions": ["no empirical frequency", "no probability", "no legal compliance", "no EOJI performance"], } OUT_VALIDATION.write_text(json.dumps(validation, indent=2) + "\n", encoding="utf-8") if __name__ == "__main__": main()