#!/usr/bin/env python3 """ Auto-generated by submit_fisher_condor.py. Merges per-job Fisher CSVs and renders a compact LaTeX table. Usage: python merge_results.py python merge_results.py --N-near 100 # change nearby-event target python merge_results.py --csv-glob "/path/**/fisher_table.csv" """ import argparse, importlib.util, math, os, pathlib, sys import numpy as np import pandas as pd OUTDIR = pathlib.Path(r"/home/chrism/cosmem/fisher_run21") FISHER_SCRIPT = pathlib.Path(r"/home/chrism/cosmem/fisher_population_table.py") PYTHONPATH = r"" CSV_FILES = [ r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var04/scenario_0004/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var05/scenario_0005/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var06/scenario_0006/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var07/scenario_0007/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/aLIGO_HLV_var08/scenario_0008/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var04/scenario_0004/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var05/scenario_0005/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var06/scenario_0006/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var07/scenario_0007/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_var08/scenario_0008/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var04/scenario_0004/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var05/scenario_0005/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var06/scenario_0006/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var07/scenario_0007/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/CE_var08/scenario_0008/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var04/scenario_0004/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var05/scenario_0005/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var06/scenario_0006/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var07/scenario_0007/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/ET_CE_var08/scenario_0008/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_light_seeds_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_light_seeds_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_light_seeds_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_light_seeds_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_light_seeds_var04/scenario_0004/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_heavy_seeds_baseline/scenario_0000/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_heavy_seeds_var01/scenario_0001/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_heavy_seeds_var02/scenario_0002/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_heavy_seeds_var03/scenario_0003/fisher_table.csv", r"/home/chrism/cosmem/fisher_run21/jobs/LISA_heavy_seeds_var04/scenario_0004/fisher_table.csv", ] def parse_args(): p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) p.add_argument("--outdir", default=None) p.add_argument("--fisher-script", default=None) p.add_argument("--N-near", type=int, default=100, help="Events at dL_near for sigma_near column (default: 100)") p.add_argument("--csv-glob", default=None, help="Glob to find CSVs instead of hard-coded list") return p.parse_args() def load_csvs(files): dfs, missing = [], [] for p in files: p = pathlib.Path(p) if p.exists(): dfs.append(pd.read_csv(p)) else: missing.append(str(p)) if missing: print(f"WARNING: {len(missing)} CSV(s) not found:", file=sys.stderr) for m in missing: print(f" {m}", file=sys.stderr) if not dfs: sys.exit("No job outputs found.") combined = pd.concat(dfs, ignore_index=True) print(f"Loaded {len(dfs)} CSVs ({len(combined)} rows)") for det in combined["detector"].unique(): print(f" {det}: {len(combined[combined['detector']==det])} rows") return combined def load_fisher_mod(fisher_script, pythonpath=None): for _p in [pythonpath or ""] + os.environ.get("PYTHONPATH","").split(os.pathsep): if _p and _p not in sys.path: sys.path.insert(0, _p) spec = importlib.util.spec_from_file_location("fisher_pop", fisher_script) mod = importlib.util.module_from_spec(spec) spec.loader.exec_module(mod) return mod def print_summary(combined, N_near): print() print(f"Summary — baseline rows (sigma km/s/Mpc, N_near={N_near})") print("-" * 70) has_ext = "sigma_ext_lo" in combined.columns for det in combined["detector"].unique(): row = combined[combined["detector"]==det].iloc[0] sigma = float(row.get("sigma_H0", float("nan"))) N_det = float(row.get("N_det", float("nan"))) try: s_near = sigma * math.sqrt(N_det / N_near) if ( np.isfinite(sigma) and N_det > 0) else float("nan") except Exception: s_near = float("nan") line = (f" {det:<12} N_det={int(N_det) if np.isfinite(N_det) else '?':>5}" f" sigma(N={N_near})={s_near:.2f}") if has_ext: lo = float(row.get("sigma_ext_lo", float("nan"))) hi = float(row.get("sigma_ext_hi", float("nan"))) line += f" sigma(N_lo)={lo:.2f} sigma(N_hi)={hi:.3f}" print(line) def main(): args = parse_args() outdir = pathlib.Path(args.outdir) if args.outdir else OUTDIR fscript = pathlib.Path(args.fisher_script) if args.fisher_script else FISHER_SCRIPT outdir.mkdir(parents=True, exist_ok=True) if args.csv_glob: import glob files = sorted(glob.glob(args.csv_glob, recursive=True)) if not files: sys.exit(f"No files matched: {args.csv_glob}") else: files = CSV_FILES combined = load_csvs(files) out_csv = outdir / "fisher_combined.csv" combined.to_csv(out_csv, index=False) print(f"Saved: {out_csv}") if fscript.exists(): try: mod = load_fisher_mod(fscript, PYTHONPATH) tex = mod.format_combined_latex_table(combined, N_near=args.N_near) tex_path = outdir / "fisher_combined.tex" tex_path.write_text(tex) print(f"Saved: {tex_path}") except Exception as exc: import traceback print(f"LaTeX render failed: {exc}", file=sys.stderr) traceback.print_exc(file=sys.stderr) else: print(f"WARNING: Fisher script not found at {fscript}", file=sys.stderr) print_summary(combined, args.N_near) print("\nMerge complete.") if __name__ == "__main__": main()