PreprocManager

class torch_ecg._preprocessors.PreprocManager(*pps: Tuple[PreProcessor, ...] | None, random: bool = False)

Bases: ReprMixin

Manager of preprocessors.

This class is used to manage a sequence of preprocessors. It can be used to add preprocessors to the manager, and apply the preprocessors to a signal.

Parameters:

• pps (Tuple[PreProcessor], optional) – The sequence of preprocessors to be added to the manager.

• random (bool, default False) – Whether to apply the augmenters in random order.

Examples

import torch
from torch_ecg.cfg import CFG
from torch_ecg._preprocessors import PreprocManager
config = CFG(
    random=False,
    resample={"fs": 500},
    bandpass={"filter_type": "fir"},
    normalize={"method": "min-max"},
)
ppm = PreprocManager.from_config(config)
sig = torch.randn(12, 80000).numpy()
sig, fs = ppm(sig, 200)

add_(pp: PreProcessor, pos: int = -1) → None

Add a (custom) preprocessor to the manager.

This method is preferred against directly manipulating the internal list of preprocessors via PreprocManager.preprocessors.append(pp).

Parameters:

• pp (PreProcessor) – The PreProcessor to be added.

• pos (int, default -1) – The position to insert the preprocessor, should be >= -1, with -1 the indicator of the end.

extra_repr_keys() → List[str]

Extra keys for __repr__() and __str__().

classmethod from_config(config: dict) → PreprocManager

Create a new instance of PreprocManager from a configuration.

Parameters:

config (dict) – The configuration of the preprocessors, better to be an OrderedDict.

Returns:

ppm – A new instance of PreprocManager.

Return type:

PreprocManager

rearrange(new_ordering: List[str]) → None

Rearrange the preprocessors.

Parameters:

new_ordering (List[str]) – The new ordering of the preprocessors.