credit.transforms.transforms_les

Content

• NormalizeLES

• ToTensorLES

Attributes

logger

Classes

NormalizeLES
ToTensorLES

Module Contents

credit.transforms.transforms_les.logger

class credit.transforms.transforms_les.NormalizeLES(conf)

mean_ds

std_ds

mean_tensors

std_tensors

levels

varname_upper_air

num_upper_air

flag_surface

flag_dyn_forcing

flag_diagnostic

flag_forcing

flag_static

__call__(sample, inverse: bool = False)

transform_array(x: torch.Tensor) → torch.Tensor

This function applies to y_pred, so there won’t be dynamic forcing, forcing, and static variables.

transform(sample: Dict[str, numpy.ndarray]) → Dict[str, numpy.ndarray]

This function transforms training batches, it handles forcing & static as follows:

• forcing & static don’t need to be transformed; users should transform them and save them to the file

• other variables (upper-air, surface, dynamic forcing, diagnostics) need to be transformed

inverse_transform(x: torch.Tensor) → torch.Tensor

This function applies to y_pred, so there won’t be dynamic forcing, forcing, and static vars

inverse_transform_input(x: torch.Tensor) → torch.Tensor

Inverse transform for input x. Forcing and static variables are not transformed (they were not transformed in the transform function).

class credit.transforms.transforms_les.ToTensorLES(conf)

conf

output_dtype = Ellipsis

hist_len

for_len

flag_surface

flag_dyn_forcing

flag_diagnostic

flag_forcing

flag_static

varname_upper_air

num_forcing_static = 0

__call__(sample)