credit.preblock.norm

ERA5Normalizer: normalizes per-variable ERA5 tensors using mean/std NC files.

Operates on the raw batch structure from MultiSourceDataset:

batch["era5"]["input"]["era5/field_type/3d/varname"]  = (B, n_levels, T, H, W)
batch["era5"]["input"]["era5/field_type/2d/varname"]  = (B, 1, T, H, W)

Variables absent from the mean/std file are passed through unchanged.

Registered in the preblock registry as "era5_normalizer" so it can be included via the config’s preblocks: section:

preblocks:
  norm:
    type: era5_normalizer
    args:
      mean_path: /path/to/mean.nc
      std_path:  /path/to/std.nc

Attributes

logger

Classes

ERA5Normalizer

Normalizes per-variable ERA5 tensors using pre-computed mean/std files.

Module Contents

credit.preblock.norm.logger

class credit.preblock.norm.ERA5Normalizer(mean_path: str, std_path: str, levels: list[int] | None = None)

Bases: credit.preblock.base.BasePreblock

Normalizes per-variable ERA5 tensors using pre-computed mean/std files.

Normalization: (x - mean) / std applied per variable. Variables not found in the statistics file are passed through unchanged.

Parameters:

• mean_path – Path to NetCDF file containing per-variable means.

• std_path – Path to NetCDF file containing per-variable standard deviations.

• levels – Optional list of 1-indexed model levels to select from the full 137-level stats (e.g. [60, 90, 120, 137] for a 4-level smoke test). When omitted, all levels in the stats file are used.

_mean: dict[str, torch.Tensor]

_std: dict[str, torch.Tensor]

_normalize_tensor(key: str, tensor: torch.Tensor) → torch.Tensor

Normalize tensor using the variable name extracted from key.

forward(batch: dict) → dict

Normalize all input/target tensors, returning a new batch dict.