#!/usr/bin/env python3 import csv import hashlib import math from eval_utils import ( desktop_root, print_result, read_numeric_csv, run_submission, ) JOB_HASHES = { "job_baseline.py": "49a962bcda502b901c1a8d9fc5e018bd05e9a3218438e20f0cec9f3655c8a736", "job_yaw.py": "b0f1c852d0ce7b514a649582c23b6a9c2bddd3af8e13970f768aa1c57c1842b5", } EXPECTED = [2190.397160, 436.442701, 2304.300414, 660.711994, 5.200119, 51.385736] def check() -> bool: root = desktop_root() for name, expected_hash in JOB_HASHES.items(): actual_hash = hashlib.sha256((root / name).read_bytes()).hexdigest() if actual_hash != expected_hash: return False run_submission( root, "run_pipeline.py", ["baseline.csv", "yaw.csv"], timeout=300, ) run_submission(root, "compare.py", ["comparison_report.txt"]) with (root / "comparison_report.txt").open("r", encoding="utf-8", newline="") as handle: rows = list(csv.reader(handle)) if len(rows) != 4 or [row[0] for row in rows] != [ "baseline", "yaw", "Gain_Total_Percent", "Gain_Downstream_Percent", ]: return False values = [ float(rows[0][1]), float(rows[0][2]), float(rows[1][1]), float(rows[1][2]), float(rows[2][1]), float(rows[3][1]), ] baseline_values = read_numeric_csv(root / "baseline.csv", 1, 2)[0] yaw_values = read_numeric_csv(root / "yaw.csv", 1, 2)[0] if not all( math.isclose(actual, source, rel_tol=1e-6, abs_tol=1e-6) for actual, source in zip(values[:4], baseline_values + yaw_values) ): return False if not all(math.isclose(a, e, rel_tol=1e-4, abs_tol=1e-3) for a, e in zip(values, EXPECTED)): return False total_gain = (values[2] - values[0]) / values[0] * 100.0 downstream_gain = (values[3] - values[1]) / values[1] * 100.0 return abs(values[4] - total_gain) <= 1e-3 and abs(values[5] - downstream_gain) <= 1e-3 if __name__ == "__main__": try: result = check() except Exception: result = False raise SystemExit(print_result(result))