File size: 4,682 Bytes
fa2b79f | 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 85 86 87 | """Generate correlated 113-feature WoFS ensemble storm-track examples."""
import argparse
from pathlib import Path
import numpy as np
import yaml
ROOT = Path(__file__).resolve().parents[1]
def correlated_block(rng, latent, count, noise, offset):
projection_rng = np.random.default_rng(1000 + int(offset * 100))
weights = projection_rng.normal(0, 0.35, (latent.shape[1], count))
for index in range(min(latent.shape[1], count)):
weights[index, index::latent.shape[1]] += 0.8
values = latent @ weights + rng.normal(0, noise, (len(latent), count))
values += 0.08 * np.sin(np.arange(count)[None, :] * 0.21 + offset)
return values.astype(np.float32)
def make_split(path, samples, config, seed):
rng = np.random.default_rng(seed)
lead_start = rng.choice(np.arange(0, 125, 5), samples)
lead_group = (lead_start > 60).astype(np.int64)
organization = rng.normal(size=samples)
rotation = 0.65 * organization + rng.normal(0, 0.75, samples)
instability = rng.normal(size=samples)
shear = 0.35 * instability + rng.normal(0, 0.9, samples)
cold_pool = 0.45 * organization + rng.normal(0, 0.85, samples)
spread = np.maximum(0.15, rng.lognormal(-0.2 + 0.2 * lead_group, 0.35, samples))
latent = np.column_stack((organization, rotation, instability, shear, cold_pool, spread))
amplitude = correlated_block(rng, latent, 30, 0.32, 0.0)
spatial = correlated_block(rng, latent, 76, 0.48 + 0.08 * lead_group[:, None], 0.7)
area = np.exp(0.45 * organization + rng.normal(5.8, 0.35, samples))
eccentricity = 1 / (1 + np.exp(-(0.5 * shear + rng.normal(0, 0.6, samples))))
major = np.sqrt(area) * (1.2 + eccentricity)
minor = area / np.maximum(major, 1)
orientation = np.arctan2(spatial[:, 4], spatial[:, 3]) / np.pi
extent = np.clip(0.72 - 0.15 * spread + rng.normal(0, 0.08, samples), 0.15, 1)
initialization_time = rng.uniform(0, 1, samples)
object_properties = np.column_stack((np.log1p(area), eccentricity, orientation, major / 100,
minor / 100, extent, initialization_time)).astype(np.float32)
features = np.concatenate((amplitude, spatial, object_properties), axis=1).astype(np.float32)
risk = np.column_stack((1.25 * rotation + 0.70 * shear + 0.35 * organization,
1.15 * instability + 0.55 * organization + 0.45 * amplitude[:, 5],
1.00 * cold_pool + 0.65 * shear + 0.35 * spatial[:, 12]))
risk -= lead_group[:, None] * np.array((0.35, 0.22, 0.18))
# Elevated synthetic rates make the tiny engineering dataset trainable; paper rates were about 1.2/2.5/4%.
intercept = np.array((-2.35, -1.75, -1.45))
probabilities = 1 / (1 + np.exp(-(risk + intercept)))
targets = (rng.random((samples, 3)) < probabilities).astype(np.float32)
np.savez_compressed(path, features=features, targets=targets, lead_group=lead_group,
lead_start_minutes=lead_start.astype(np.int16),
lead_end_minutes=(lead_start + 30).astype(np.int16),
format_version=np.asarray(config["data"]["format_version"]),
feature_group_sizes=np.asarray((30, 76, 7), dtype=np.int16),
hazards=np.asarray(config["data"]["hazards"]),
lead_group_names=np.asarray(config["data"]["lead_groups"]),
ensemble_members=np.asarray(18), grid_spacing_km=np.asarray(3),
forecast_window_minutes=np.asarray(30), forecast_interval_minutes=np.asarray(5),
data_source=np.asarray("structured_synthetic_elevated_event_rates"),
paper_event_rates=np.asarray((0.012, 0.025, 0.040), dtype=np.float32))
return targets.mean(0)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--force", action="store_true")
args = parser.parse_args()
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
output = ROOT / config["data"]["root"]
output.mkdir(parents=True, exist_ok=True)
for offset, (name, count) in enumerate((("train.npz", config["data"]["train_samples"]),
("test.npz", config["data"]["test_samples"]))):
path = output / name
if args.force or not path.exists():
rates = make_split(path, int(count), config, int(config["seed"]) + offset)
else:
rates = np.load(path)["targets"].mean(0)
print(f"generated={path.relative_to(ROOT)} shape=({count},113) event_rates={rates.round(3).tolist()}")
if __name__ == "__main__":
main()
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