credit.transforms.transforms_wrf#
- Content
NormalizeWRF
ToTensorWRF
Attributes#
Classes#
Module Contents#
- credit.transforms.transforms_wrf.logger#
- class credit.transforms.transforms_wrf.NormalizeWRF(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#
- mean_ds_outside#
- std_ds_outside#
- mean_tensors_outside#
- std_tensors_outside#
- levels_outside#
- varname_upper_air_outside#
- num_upper_air_outside#
- flag_surface_outside#
- __call__(sample, inverse: bool = False)#
- transform_array(x: torch.Tensor) torch.Tensor#
This function applies to y_pred, so there won’t be boundary input, forcing, and static variables.
- transform(sample: Dict[str, numpy.ndarray]) Dict[str, numpy.ndarray]#
- This function transforms training batches
forcing & static don’t need to be transformed; users should transform them and save them to the file
other variables 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
- class credit.transforms.transforms_wrf.ToTensorWRF(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#
- hist_len_outside#
- for_len_outside#
- flag_surface_outside#
- varname_upper_air_outside#
- __call__(sample)#