File size: 4,979 Bytes
950fc23 | 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 73 74 75 76 77 78 79 80 81 82 83 84 | """Compute daily/annual detection metrics and a 7x7 occlusion trend map."""
import sys
from pathlib import Path
import numpy as np
import torch
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.precipdd import correlation, ensemble_predict, linear_trend, load_config, load_ensemble, write_json
def main():
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
config = load_config(ROOT / "conf/config.yaml")
values = np.load(ROOT / config["paths"]["predictions"])
prediction, target, year = values["prediction"], values["target"], values["year"].astype(float)
if prediction.shape != target.shape or values["precipitation"].shape[1:] != (1, 55, 160):
raise ValueError("inference output violates scalar target or [N,1,55,160] input contract")
years = np.unique(year).astype(int)
annual_prediction = np.array([prediction[year == current].mean() for current in years])
annual_target = np.array([target[year == current].mean() for current in years])
threshold = config["evaluation"]["emergence_threshold_c"]
em_fraction = np.array([(prediction[year == current] > threshold).mean() for current in years])
metrics = {
"daily": {"correlation": correlation(target, prediction), "rmse_c": float(np.sqrt(np.mean((prediction - target) ** 2)))},
"annual": {"correlation": correlation(annual_target, annual_prediction), "rmse_c": float(np.sqrt(np.mean((annual_prediction - annual_target) ** 2)))},
"emergence": {"threshold_c": threshold, "fraction_all_days": float((prediction > threshold).mean()),
"fraction_trend_per_decade": linear_trend(em_fraction, years.astype(float))},
"trend_c_per_decade": {"daily_prediction": linear_trend(prediction, year), "annual_prediction": linear_trend(annual_prediction, years.astype(float)),
"annual_target": linear_trend(annual_target, years.astype(float))},
"samples": {"daily": len(prediction), "years": len(years)}
}
device = torch.device("cuda" if torch.cuda.is_available() and config["runtime"]["device"] != "cpu" else "cpu")
models, _ = load_ensemble(ROOT / config["paths"]["checkpoint"], device)
count = min(config["evaluation"]["occlusion_max_days"], len(prediction))
indices = np.linspace(0, len(prediction) - 1, count, dtype=int)
selected = torch.from_numpy(values["precipitation"][indices]).float().to(device)
baseline = ensemble_predict(models, selected).cpu().numpy()
selected_year = year[indices]
patch, stride = config["evaluation"]["occlusion_patch"], config["evaluation"]["occlusion_stride"]
half = patch // 2
sensitivity = np.zeros((55, 160), dtype=np.float32)
for lat_start in range(0, 55, stride):
for lon_start in range(0, 160, stride):
masked = selected.clone()
lat_stop, lon_stop = min(lat_start + patch, 55), min(lon_start + patch, 160)
masked[:, :, lat_start:lat_stop, lon_start:lon_stop] = 0.0
delta = baseline - ensemble_predict(models, masked).cpu().numpy()
score = linear_trend(delta, selected_year) if np.ptp(selected_year) else float(delta.mean())
sensitivity[lat_start:min(lat_start + stride, 55), lon_start:min(lon_start + stride, 160)] = score
metrics["occlusion"] = {"patch": [patch, patch], "map_shape": [55, 160], "stride": stride, "sampled_days": count,
"quantity": "AGMT occlusion-sensitivity trend in degC per decade"}
write_json(ROOT / config["paths"]["evaluation_metrics"], metrics)
fig, axes = plt.subplots(3, 1, figsize=(11, 11), constrained_layout=True)
axes[0].plot(years, annual_target, color="#202020", label="target AGMT")
axes[0].plot(years, annual_prediction, color="#c84c32", label="DD estimate")
axes[0].axhline(threshold, color="#777777", linestyle="--", label="0.42 C EM threshold")
axes[0].set(ylabel="AGMT anomaly (C)", title="Annual mean of daily estimates")
axes[0].legend(ncol=3)
axes[1].plot(years, em_fraction, color="#196f82")
axes[1].set(xlabel="Year", ylabel="Fraction", ylim=(-0.03, 1.03), title="Emergence days (estimated AGMT > 0.42 C)")
limit = float(np.max(np.abs(sensitivity))) or 1e-6
image = axes[2].imshow(sensitivity, origin="lower", aspect="auto", extent=(0, 400, values["latitude"][0], values["latitude"][-1]),
cmap="RdBu_r", vmin=-limit, vmax=limit)
axes[2].set(xlabel="Longitude (degrees E, extended)", ylabel="Latitude", title="7x7 occlusion-sensitivity trend")
fig.colorbar(image, ax=axes[2], label="C decade-1")
figure = ROOT / config["paths"]["comparison_figure"]
figure.parent.mkdir(parents=True, exist_ok=True)
fig.savefig(figure, dpi=160)
plt.close(fig)
print(f"metrics={config['paths']['evaluation_metrics']} figure={config['paths']['comparison_figure']}")
if __name__ == "__main__":
main()
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