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807a08b | 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | """Run ClimODE global forecasts and save machine-readable outputs."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.utils.data import DataLoader
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from model.climode import load_checkpoint
from scripts.data_loader import ClimODESeriesDataset, load_constants
from scripts.metrics import evaluate, save_metrics
from scripts.velocity import fit_velocity_cache, load_velocity_cache
def _load_config(path: Path) -> dict:
with path.open("r", encoding="utf-8") as handle:
return yaml.safe_load(handle)
def _parse_years(value: str | None, fallback: list[int]) -> list[int]:
if value is None:
return list(fallback)
years = [int(item.strip()) for item in value.split(",") if item.strip()]
if not years:
raise ValueError("year override must contain at least one integer")
return years
def _device(value: str | None) -> torch.device:
if value:
return torch.device(value)
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
def _resolve(path: str | Path) -> Path:
value = Path(path)
return value if value.is_absolute() else PROJECT_ROOT / value
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, default=PROJECT_ROOT / "conf/config.yaml")
parser.add_argument("--checkpoint", type=Path, default=None)
parser.add_argument("--device", type=str, default=None)
parser.add_argument("--test-years", type=str, default=None)
parser.add_argument("--sequence-length", type=int, default=None)
parser.add_argument("--max-samples", type=int, default=None)
parser.add_argument("--velocity-epochs", type=int, default=None)
parser.add_argument("--velocity-cache", type=Path, default=None)
parser.add_argument("--data-dir", type=Path, default=None, help="Override data.data_dir")
parser.add_argument("--stats-dir", type=Path, default=None, help="Override data.stats_dir")
parser.add_argument("--static-file", type=Path, default=None, help="Override data.static_file")
parser.add_argument("--output-dir", type=Path, default=None)
args = parser.parse_args()
args.config = _resolve(args.config)
args.checkpoint = _resolve(args.checkpoint) if args.checkpoint is not None else None
args.velocity_cache = (
_resolve(args.velocity_cache) if args.velocity_cache is not None else None
)
args.data_dir = _resolve(args.data_dir) if args.data_dir is not None else None
args.stats_dir = _resolve(args.stats_dir) if args.stats_dir is not None else None
args.static_file = _resolve(args.static_file) if args.static_file is not None else None
args.output_dir = _resolve(args.output_dir) if args.output_dir is not None else None
config = _load_config(args.config)
data_cfg, model_cfg, vel_cfg = config["data"], config["model"], config["velocity"]
root = _resolve(args.data_dir or data_cfg["data_dir"])
stats_dir = _resolve(args.stats_dir or data_cfg.get("stats_dir", root / "static"))
test_years = _parse_years(args.test_years, data_cfg["test_years"])
sequence_length = args.sequence_length or data_cfg.get("sequence_length", 8)
dataset = ClimODESeriesDataset(
root,
test_years,
stats_dir=stats_dir,
model_size=(data_cfg["model_height"], data_cfg["model_width"]),
sequence_length=sequence_length,
normalize=data_cfg.get("normalize", True),
)
loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
static_file = _resolve(args.static_file or data_cfg["static_file"])
constants, lat, lon = load_constants(
static_file, (data_cfg["model_height"], data_cfg["model_width"])
)
device = _device(args.device)
constants = constants.to(device)
lat_device, lon_device = lat.unsqueeze(0).to(device), lon.unsqueeze(0).to(device)
velocity_root = _resolve(vel_cfg["cache_dir"])
velocity_path = args.velocity_cache or (velocity_root / "test.pt")
if velocity_path.is_file():
velocity = load_velocity_cache(velocity_path, len(dataset))
else:
velocity = fit_velocity_cache(
dataset,
constants,
lat,
lon,
velocity_path,
epochs=args.velocity_epochs if args.velocity_epochs is not None else vel_cfg["epochs"],
learning_rate=vel_cfg["learning_rate"],
smoothing_alpha=vel_cfg["smoothing_alpha"],
kernel_sigma=vel_cfg["kernel_sigma"],
)
checkpoint_path = args.checkpoint
if checkpoint_path is None:
checkpoint_path = _resolve(model_cfg["default_checkpoint"])
if not checkpoint_path.is_file():
pretrained = _resolve(model_cfg["pretrained_checkpoint"])
if pretrained.is_file():
checkpoint_path = pretrained
if checkpoint_path is None or not checkpoint_path.is_file():
raise FileNotFoundError(
"No checkpoint found; pass --checkpoint or provide model.default_checkpoint"
)
model = load_checkpoint(checkpoint_path, map_location="cpu").to(device).eval()
predictions, uncertainties, targets = [], [], []
with torch.no_grad():
for sample_index, batch in enumerate(loader):
if args.max_samples is not None and sample_index >= args.max_samples:
break
observations = batch["observations"].squeeze(0).to(device)
time_steps = batch["time_steps"].squeeze(0).to(device)
initial = observations[0].unsqueeze(1)
model.update_param([velocity[sample_index].to(device), constants, lat_device, lon_device])
mean, std, _ = model(
time_steps,
initial,
atol=model_cfg["atol"],
rtol=model_cfg["rtol"],
)
# Index 0 is the analysis state used to initialize the ODE. Official
# evaluation starts at index 1, corresponding to a six-hour lead.
if mean.shape[0] > 1:
predictions.append(mean[1:].detach().cpu().numpy())
uncertainties.append(std[1:].detach().cpu().numpy())
targets.append(observations[1:].detach().cpu().numpy())
if not predictions:
raise RuntimeError("No test samples were processed")
valid_lengths = np.asarray([item.shape[0] for item in predictions], dtype=np.int64)
max_lead = int(valid_lengths.max())
def _pad(items: list[np.ndarray]) -> np.ndarray:
shape = (len(items), max_lead) + tuple(items[0].shape[1:])
padded = np.full(shape, np.nan, dtype=np.float32)
for index, item in enumerate(items):
padded[index, : item.shape[0]] = item
return padded
pred_array = _pad(predictions)
std_array = _pad(uncertainties)
target_array = _pad(targets)
scale = (dataset.maximum - dataset.minimum).numpy().reshape(1, 1, 1, 5, 1, 1)
offset = dataset.minimum.numpy().reshape(1, 1, 1, 5, 1, 1)
pred_physical = pred_array * scale + offset
target_physical = target_array * scale + offset
std_physical = std_array * scale
output_dir = args.output_dir or _resolve(data_cfg["output_dir"])
output_dir.mkdir(parents=True, exist_ok=True)
np.save(output_dir / "predictions.npy", pred_array)
np.save(output_dir / "std.npy", std_array)
np.save(output_dir / "targets.npy", target_array)
np.save(output_dir / "valid_lengths.npy", valid_lengths)
metrics = evaluate(
pred_physical,
target_physical,
lat.numpy(),
std_physical,
crps_predictions=pred_array,
crps_targets=target_array,
crps_std=std_array,
valid_lengths=valid_lengths,
)
metrics["checkpoint"] = str(checkpoint_path)
metrics["outputs_normalized"] = True
metrics_path = _resolve(config["output"]["metrics_file"])
if args.output_dir is not None:
metrics_path = output_dir.parent / "metrics.json"
save_metrics(metrics, metrics_path)
manifest = {
"checkpoint": str(checkpoint_path),
"samples": int(pred_array.shape[0]),
"shape": list(pred_array.shape),
"valid_lengths": valid_lengths.tolist(),
"variables": ["z", "t", "t2m", "u10", "v10"],
"output_dir": str(output_dir),
"metrics": str(metrics_path),
}
(output_dir / "inference_manifest.json").write_text(
json.dumps(manifest, indent=2), encoding="utf-8"
)
print(json.dumps(manifest))
if __name__ == "__main__":
main()
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