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zi!{G;)qg+1FY^hJIfNkO4hIqbZ{VM#7J17-jnP7K_kkM8bD#W=h5xH!uYNM`xGVsGnY#@PF2Mi${XZj2R3;7kqwffZf9Iu1|2@Z@4h9|~b2yop qf3MyKVdOY;*Efs>0D%6&6e1Tnx&KT>z{LP8LEh)OkAu(g8}fft6B2d+ diff --git a/task1/export_paper_tables.py b/task1/export_paper_tables.py new file mode 100644 index 0000000..a5a3450 --- /dev/null +++ b/task1/export_paper_tables.py @@ -0,0 +1,121 @@ +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() diff --git a/task1/figures/fig1_site_map.png b/task1/figures/fig1_site_map.png index 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