File size: 2,372 Bytes
e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from __future__ import annotations
import argparse
import numpy as np
from common import load_config, project_path, relative_errors, write_json
def plot_result(prediction: np.ndarray, target: np.ndarray, cfg: dict, figure_path) -> bool:
try:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
except Exception as exc:
print(f"matplotlib unavailable, skip figure: {exc}")
return False
figure_path.parent.mkdir(parents=True, exist_ok=True)
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
x_min, x_max = cfg["data"]["x_range"]
t_min, t_max = cfg["data"]["t_range"]
items = [
(prediction, "Predicted u(x,t)"),
(target, "Exact u(x,t)"),
(np.abs(prediction - target), "Absolute Error"),
]
for ax, (data, title) in zip(axes, items):
image = ax.imshow(data, extent=[x_min, x_max, t_max, t_min], aspect="auto")
ax.set_xlabel("x")
ax.set_ylabel("t")
ax.set_title(title)
fig.colorbar(image, ax=ax)
plt.tight_layout()
plt.savefig(figure_path)
plt.close(fig)
return True
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate and plot PINNsformer inference output.")
parser.add_argument("--config", default=None, help="Path to config.yaml.")
parser.add_argument("--prediction", default=None, help="Path to prediction .npz.")
args = parser.parse_args()
cfg = load_config(args.config)
prediction_path = project_path(args.prediction or cfg["paths"]["prediction"])
if not prediction_path.exists():
raise FileNotFoundError(f"Prediction file not found: {prediction_path}. Run scripts/inference.py first.")
data = np.load(prediction_path)
prediction = data["prediction"]
target = data["target"]
metrics = relative_errors(prediction, target)
metrics_path = project_path(cfg["paths"]["metrics"])
write_json(metrics_path, metrics)
figure_path = project_path(cfg["paths"]["figure"])
plotted = plot_result(prediction, target, cfg, figure_path)
print(f"metrics saved to {metrics_path}")
if plotted:
print(f"figure saved to {figure_path}")
print(f"relative L1 error: {metrics['relative_l1']:.6f}")
print(f"relative L2 error: {metrics['relative_l2']:.6f}")
if __name__ == "__main__":
main()
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