credit.transforms.transforms_quantile

Contents

credit.transforms.transforms_quantile#

Content

  • BridgescalerScaleState

  • NormalizeState_Quantile_Bridgescalar

  • ToTensor_BridgeScaler

Attributes#

read_scaler

logger

Classes#

BridgescalerScaleState

Convert to rescaled tensor using Bridgescaler.

NormalizeState_Quantile_Bridgescalar

Class to use the bridgescaler Quantile functionality.

ToTensor_BridgeScaler

Convert to reshaped tensor.

Module Contents#

credit.transforms.transforms_quantile.read_scaler = None#
credit.transforms.transforms_quantile.logger#
class credit.transforms.transforms_quantile.BridgescalerScaleState(conf)#

Bases: object

Convert to rescaled tensor using Bridgescaler.

scaler_file#
variables#
surface_variables#
n_levels#
var_levels = []#
n_surface_variables#
n_3dvar_levels#
scaler_df#
scaler_3d#
scaler_surf#
inverse_transform(x: torch.Tensor) → torch.Tensor#

Inverse transform.

Inverse transform.

Parameters:

x – batch.

Returns:

inverse transformed batch.

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

Transform.

Transform.

Parameters:

x – batch.

Returns:

transformed batch.

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

Transform.

Transform.

Parameters:

sample – batch.

Returns:

transformed batch.

class credit.transforms.transforms_quantile.NormalizeState_Quantile_Bridgescalar(conf)#

Class to use the bridgescaler Quantile functionality.

Some hoops have to be jumped thorugh, and the efficiency could be improved if we were to retrain the bridgescaler.

scaler_file#
variables#
surface_variables#
levels#
scaler_df#
scaler_3ds#
scaler_surfs#
scaler_3d#
scaler_surf#
__call__(sample: credit.data.Sample, inverse: bool = False) → credit.data.Sample#

Normalize via quantile transform with bridgescaler.

Normalize via provided scaler file/s.

Parameters:

sample (iterator) – batch.

Returns:

transformed torch tensor.

Return type:

torch.tensor

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

Inverse transform.

Inverse transform via provided scaler file/s.

Parameters:

x – batch.

Returns:

inverse transformed torch tensor.

transform(sample)#

Transform.

Transform via provided scaler file/s.

Parameters:

sample (iterator) – batch.

Returns:

transformed torch tensor.

Return type:

torch.Tensor

class credit.transforms.transforms_quantile.ToTensor_BridgeScaler(conf)#

Convert to reshaped tensor.

conf#
hist_len#
for_len#
variables#
surface_variables#
allvars#
static_variables#
latN#
lonN#
levels#
one_shot#
__call__(sample: credit.data.Sample) → credit.data.Sample#

Convert to reshaped tensor.

Reshape and convert to torch tensor.

Parameters:

sample (interator) – batch.

Returns:

reshaped torch tensor.

Return type:

torch.tensor

Contents