PreprocManager¶

class torch_ecg._preprocessors.PreprocManager(*pps: Optional[Tuple[torch_ecg._preprocessors.base.PreProcessor, ...]], random: bool = False)[source]¶

Bases: torch_ecg.utils.misc.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: torch_ecg._preprocessors.base.PreProcessor, pos: int = - 1) → None[source]¶

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][source]¶: Extra keys for __repr__() and __str__().

classmethod from_config(config: dict) → torch_ecg._preprocessors.preproc_manager.PreprocManager[source]¶

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[source]¶

Rearrange the preprocessors.

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