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task1/export_paper_tables.py

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+from __future__ import annotations
+
+import math
+from pathlib import Path
+
+import pandas as pd
+
+
+ROOT = Path(__file__).resolve().parent
+OUT_DIR = ROOT / "paper_tables"
+
+
+def _to_int_series(s: pd.Series) -> pd.Series:
+    return s.map(lambda x: int(round(float(x))) if pd.notna(x) else x)
+
+
+def _to_float_series(s: pd.Series, ndigits: int) -> pd.Series:
+    return s.map(lambda x: round(float(x), ndigits) if pd.notna(x) else x)
+
+
+def _format_table1_metrics_summary(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out["E1_total_service"] = _to_int_series(out["E1_total_service"])
+    out["E2_quality_weighted"] = _to_int_series(out["E2_quality_weighted"])
+    out["F1_gini"] = _to_float_series(out["F1_gini"], 4)
+    out["F2_min_satisfaction"] = _to_float_series(out["F2_min_satisfaction"], 2)
+    out["F3_cv_satisfaction"] = _to_float_series(out["F3_cv_satisfaction"], 4)
+    out["E1_total_service_pct"] = _to_float_series(out["E1_total_service_pct"], 2)
+    out["E2_quality_weighted_pct"] = _to_float_series(out["E2_quality_weighted_pct"], 2)
+    return out
+
+
+def _format_table2_corrected_sites(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out["site_id"] = _to_int_series(out["site_id"])
+    out["mu"] = _to_float_series(out["mu"], 1)
+    out["sigma"] = _to_float_series(out["sigma"], 1)
+    out["p_trunc"] = _to_float_series(out["p_trunc"], 3)
+    out["mu_tilde"] = _to_float_series(out["mu_tilde"], 1)
+    out["mu_delta"] = _to_float_series(out["mu_delta"], 1)
+    out["mu_delta_pct"] = _to_float_series(out["mu_delta_pct"], 1)
+    return out
+
+
+def _format_table3_backtest_summary(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out["value"] = out["value"].map(lambda x: round(float(x), 6) if pd.notna(x) else x)
+    out["p_value"] = out["p_value"].map(lambda x: round(float(x), 6) if pd.notna(x) else x)
+    return out
+
+
+def _format_appendix_site_details(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out["site_id"] = _to_int_series(out["site_id"])
+    out["mu"] = _to_float_series(out["mu"], 1)
+    out["mu_tilde"] = _to_float_series(out["mu_tilde"], 1)
+    out["k"] = _to_int_series(out["k"])
+    out["annual_service"] = _to_int_series(out["annual_service"])
+    out["r"] = _to_float_series(out["r"], 4)
+    out["quality_factor"] = _to_float_series(out["quality_factor"], 4)
+    out["service_quality_weighted"] = _to_int_series(out["service_quality_weighted"])
+    return out
+
+
+def export_tables() -> list[Path]:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    written: list[Path] = []
+
+    # Table 1: overall performance comparison
+    metrics_xlsx = ROOT / "04_metrics.xlsx"
+    metrics = pd.read_excel(metrics_xlsx, sheet_name="metrics_summary")
+    metrics_paper = _format_table1_metrics_summary(metrics)
+    p1 = OUT_DIR / "table1_metrics_summary.csv"
+    metrics_paper.to_csv(p1, index=False, encoding="utf-8-sig")
+    written.append(p1)
+
+    # Table 2: truncation correction (corrected sites)
+    backtest_xlsx = ROOT / "07_backtest.xlsx"
+    corrected = pd.read_excel(backtest_xlsx, sheet_name="corrected_sites")
+    corrected = corrected.copy()
+    corrected["mu_delta"] = corrected["mu_tilde"] - corrected["mu"]
+    corrected["mu_delta_pct"] = corrected["mu_delta"] / corrected["mu"] * 100
+    corrected_paper = _format_table2_corrected_sites(corrected)
+    p2 = OUT_DIR / "table2_corrected_sites.csv"
+    corrected_paper.to_csv(p2, index=False, encoding="utf-8-sig")
+    written.append(p2)
+
+    # Table 3: backtest & fit statistics
+    summary = pd.read_excel(backtest_xlsx, sheet_name="summary_metrics")
+    summary_paper = _format_table3_backtest_summary(summary)
+    p3 = OUT_DIR / "table3_backtest_summary.csv"
+    summary_paper.to_csv(p3, index=False, encoding="utf-8-sig")
+    written.append(p3)
+
+    # Table 4: constraint validation results
+    validate_xlsx = ROOT / "06_validate.xlsx"
+    validation = pd.read_excel(validate_xlsx, sheet_name="validation_results")
+    p4 = OUT_DIR / "table4_validation_results.csv"
+    validation.to_csv(p4, index=False, encoding="utf-8-sig")
+    written.append(p4)
+
+    # Appendix: per-site allocation details (optional for paper appendix)
+    site_details = pd.read_excel(metrics_xlsx, sheet_name="site_details")
+    site_details_paper = _format_appendix_site_details(site_details)
+    p5 = OUT_DIR / "appendix_site_details.csv"
+    site_details_paper.to_csv(p5, index=False, encoding="utf-8-sig")
+    written.append(p5)
+
+    return written
+
+
+def main() -> None:
+    written = export_tables()
+    rel = [p.relative_to(ROOT) for p in written]
+    print("Wrote:")
+    for p in rel:
+        print(f" - {p}")
+
+
+if __name__ == "__main__":
+    main()