# -*- coding: utf-8 -*- """Verify outputs from 01_full_to_n64_windows.py.""" from __future__ import annotations import argparse import json from pathlib import Path import numpy as np import pandas as pd DEFAULT_CHUNK_SIZE = 25_000 TOL = 2.0e-6 BASE_REQUIRED = { "source_row_id", "original_x", "image_width", "image_height", "aspect_ratio", "k1", "full_estimated_midline_x", "full_feature_x_min", "full_feature_x_max", "full_feature_x_span", } WINDOW_REQUIRED = { "source_row_id", "window_id", "window_y0_norm", "window_y1_norm", "estimated_midline_x", "feature_x_min", "feature_x_max", "feature_x_span", "feature_y_min_norm", "feature_y_max_norm", "feature_y_span_norm", "feature_y_valid", "n_observed_samples", "hgm_sample_count", "reobserve_protocol", } def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--manifest", type=Path, required=True) parser.add_argument("--full-scan", action="store_true") parser.add_argument("--chunk-size", type=int, default=DEFAULT_CHUNK_SIZE) parser.add_argument("--report", type=Path, default=None) return parser.parse_args() def finite_max(values: np.ndarray) -> float: arr = np.asarray(values, dtype=np.float64) arr = arr[np.isfinite(arr)] return float(np.max(arr)) if arr.size else 0.0 def scan_window(path: Path, chunk_size: int) -> dict[str, float | int]: rows = 0 hgm_err = 0.0 x_span_err = 0.0 y_span_err = 0.0 support_leaks = 0 for chunk in pd.read_csv(path, chunksize=chunk_size, low_memory=False): rows += len(chunk) valid = pd.to_numeric(chunk["feature_y_valid"], errors="coerce").fillna(0).to_numpy(dtype=np.int64) == 1 x_min = pd.to_numeric(chunk["feature_x_min"], errors="coerce").to_numpy(dtype=np.float64) x_max = pd.to_numeric(chunk["feature_x_max"], errors="coerce").to_numpy(dtype=np.float64) span = pd.to_numeric(chunk["feature_x_span"], errors="coerce").to_numpy(dtype=np.float64) hgm = pd.to_numeric(chunk["estimated_midline_x"], errors="coerce").to_numpy(dtype=np.float64) y_min = pd.to_numeric(chunk["feature_y_min_norm"], errors="coerce").to_numpy(dtype=np.float64) y_max = pd.to_numeric(chunk["feature_y_max_norm"], errors="coerce").to_numpy(dtype=np.float64) y_span = pd.to_numeric(chunk["feature_y_span_norm"], errors="coerce").to_numpy(dtype=np.float64) low = pd.to_numeric(chunk["window_y0_norm"], errors="coerce").to_numpy(dtype=np.float64) high = pd.to_numeric(chunk["window_y1_norm"], errors="coerce").to_numpy(dtype=np.float64) hgm_err = max(hgm_err, finite_max(np.abs(hgm[valid] - 0.5 * (x_min[valid] + x_max[valid])))) x_span_err = max(x_span_err, finite_max(np.abs(span[valid] - (x_max[valid] - x_min[valid])))) y_span_err = max(y_span_err, finite_max(np.abs(y_span[valid] - (y_max[valid] - y_min[valid])))) support_leaks += int(((y_min[valid] < low[valid] - TOL) | (y_max[valid] > high[valid] + TOL)).sum()) return { "rows": rows, "max_hgm_formula_abs_err": hgm_err, "max_x_span_formula_abs_err": x_span_err, "max_y_span_formula_abs_err": y_span_err, "support_leak_count": support_leaks, } def main() -> None: args = parse_args() manifest = json.loads(args.manifest.read_text(encoding="utf-8-sig")) failures: list[str] = [] if manifest.get("status") != "complete": failures.append("manifest status is not complete") if manifest.get("random_x_resampling") is not False: failures.append("random_x_resampling must be false") if manifest.get("camera_jitter") is not False: failures.append("camera_jitter must be false") if not manifest.get("validation", {}).get("overall_valid", False): failures.append("producer internal validation failed") base_path = Path(manifest["outputs"]["base_feature_reference"]) if not base_path.exists(): failures.append(f"missing base reference: {base_path}") base_columns: set[str] = set() else: base_columns = set(pd.read_csv(base_path, nrows=0).columns) missing = BASE_REQUIRED - base_columns if missing: failures.append(f"base reference missing columns: {sorted(missing)}") window_reports: dict[str, object] = {} expected_rows = int(manifest["rows_written"]) for window_id, value in manifest["outputs"]["window_observations"].items(): path = Path(value) if not path.exists(): failures.append(f"missing window output: {path}") continue columns = set(pd.read_csv(path, nrows=0).columns) missing = WINDOW_REQUIRED - columns if missing: failures.append(f"{window_id} missing columns: {sorted(missing)}") if args.full_scan: report = scan_window(path, args.chunk_size) window_reports[window_id] = report if int(report["rows"]) != expected_rows: failures.append(f"{window_id} row count {report['rows']} != {expected_rows}") if float(report["max_hgm_formula_abs_err"]) > TOL: failures.append(f"{window_id} HGM formula error exceeds tolerance") if float(report["max_x_span_formula_abs_err"]) > TOL: failures.append(f"{window_id} x-span formula error exceeds tolerance") if float(report["max_y_span_formula_abs_err"]) > TOL: failures.append(f"{window_id} y-span formula error exceeds tolerance") if int(report["support_leak_count"]) != 0: failures.append(f"{window_id} support leakage detected") result = { "manifest": str(args.manifest), "full_scan": bool(args.full_scan), "expected_rows_per_output": expected_rows, "window_count": len(manifest["outputs"]["window_observations"]), "window_reports": window_reports, "failures": failures, "overall_valid": len(failures) == 0, } report_path = args.report or args.manifest.with_name("shortest_path_verification.json") report_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(result, ensure_ascii=False, indent=2)) if failures: raise SystemExit(1) if __name__ == "__main__": main()