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import argparse |
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from dataclasses import dataclass |
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from typing import Tuple |
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import numpy as np |
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import torch |
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import xarray as xr |
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from torch.utils.data import DataLoader, Dataset |
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@dataclass(frozen=True) |
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class WindowSpec: |
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in_days: int = 7 |
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out_days: int = 7 |
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stride_days: int = 7 |
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class PacificSSTForecastDataset(Dataset): |
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"""Forecasting dataset over pacific_sst.zarr. |
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Returns: |
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x: (in_days, H, W) float32 |
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y: (out_days, H, W) float32 |
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""" |
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def __init__(self, zarr_path: str, *, split: str, window: WindowSpec = WindowSpec()) -> None: |
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self.ds = xr.open_zarr(zarr_path, consolidated=True) |
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self.var = self.ds["analysed_sst"] |
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self.window = window |
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t = self.ds["time"].values |
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if t.dtype.kind != "M": |
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raise ValueError("Expected datetime64 time coordinate") |
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train_end = np.datetime64("2018-12-30T09:00:00") |
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val_end = np.datetime64("2019-06-30T09:00:00") |
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if split == "train": |
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t0 = 0 |
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t1 = int(np.searchsorted(t, train_end, side="right")) - 1 |
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elif split == "val": |
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t0 = int(np.searchsorted(t, train_end, side="right")) |
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t1 = int(np.searchsorted(t, val_end, side="right")) - 1 |
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elif split == "test": |
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t0 = int(np.searchsorted(t, val_end, side="right")) |
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t1 = len(t) - 1 |
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else: |
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raise ValueError("split must be one of: train, val, test") |
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n = window.in_days + window.out_days |
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self.start_indices = list(range(t0, t1 - n + 2, window.stride_days)) |
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def __len__(self) -> int: |
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return len(self.start_indices) |
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: |
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i = self.start_indices[idx] |
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x = self.var.isel(time=slice(i, i + self.window.in_days)).values.astype(np.float32) |
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y = self.var.isel( |
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time=slice(i + self.window.in_days, i + self.window.in_days + self.window.out_days) |
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).values.astype(np.float32) |
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return torch.from_numpy(x), torch.from_numpy(y) |
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def main() -> None: |
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p = argparse.ArgumentParser() |
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p.add_argument("--zarr", default="pacific_sst.zarr") |
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p.add_argument("--split", default="train", choices=["train", "val", "test"]) |
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p.add_argument("--batch-size", type=int, default=1) |
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p.add_argument("--num-workers", type=int, default=0) |
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args = p.parse_args() |
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ds = PacificSSTForecastDataset(args.zarr, split=args.split) |
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dl = DataLoader(ds, batch_size=args.batch_size, shuffle=(args.split == "train"), num_workers=args.num_workers) |
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x, y = next(iter(dl)) |
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print("x:", tuple(x.shape), x.dtype, "y:", tuple(y.shape), y.dtype) |
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if __name__ == "__main__": |
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main() |
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