| """Evaluate the ensemble and render a 2D composite-reflectivity summary.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
| import sys |
| ROOT=Path(__file__).resolve().parents[1];sys.path.insert(0,str(ROOT)) |
|
|
| from model.echocast_3d import load_config, evaluate_ensemble |
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|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--config", default="conf/config.yaml") |
| parser.add_argument("--predictions", default="result/output/predictions.npz") |
| parser.add_argument("--metrics", default="result/evaluation/metrics.json") |
| parser.add_argument("--figure", default="result/evaluation/comparison.png") |
| args = parser.parse_args() |
| config = load_config(ROOT / args.config) |
| with np.load(ROOT / args.predictions) as data: |
| ensemble = data["ensemble"] |
| truth = data["truth"] |
| valid = data["validity"].astype(bool) |
| result = evaluate_ensemble(ensemble, truth, valid, config["evaluation"]["thresholds_dbz"]) |
| metrics_path = ROOT / args.metrics |
| metrics_path.parent.mkdir(parents=True, exist_ok=True) |
| metrics_path.write_text(json.dumps(result, indent=2), encoding="utf-8") |
| prediction = ensemble.mean(axis=0) |
| fig, axes = plt.subplots(2, 5, figsize=(15, 6), constrained_layout=True) |
| for lead in range(5): |
| for row, field in enumerate((truth, prediction)): |
| composite = np.where(valid[lead], field[lead], np.nan).max(axis=0) |
| image = axes[row, lead].imshow(composite.T, origin="lower", vmin=0, vmax=60, cmap="turbo", aspect="auto") |
| axes[row, lead].set_title(f"{'Truth' if row == 0 else 'Ensemble mean'} +{(lead + 1) * 6} min") |
| axes[row, lead].set_xlabel("azimuth index") |
| if lead == 0: |
| axes[row, lead].set_ylabel("range bin") |
| fig.colorbar(image, ax=axes, label="composite reflectivity (dBZ)", shrink=0.8) |
| figure_path=ROOT/args.figure;figure_path.parent.mkdir(parents=True,exist_ok=True);fig.savefig(figure_path,dpi=120) |
| plt.close(fig) |
| print(json.dumps(result, indent=2)) |
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|
|
| if __name__ == "__main__": |
| main() |
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|
