"""BandPass filter preprocessor."""
from numbers import Real
from typing import Any, List, Literal, Optional, Tuple
import numpy as np
from .base import PreProcessor, preprocess_multi_lead_signal
__all__ = [
"BandPass",
]
[docs]
class BandPass(PreProcessor):
"""Bandpass filtering preprocessor.
Parameters
----------
lowcut : numbers.Real, optional
Low cutoff frequency
highcut : numbers.Real, optional
High cutoff frequency.
filter_type : {"butter", "fir"}, , default "butter"
Type of the bandpass filter.
filter_order : int, optional
Order of the bandpass filter.
**kwargs : dict, optional
Other arguments for :class:`PreProcessor`.
Examples
--------
.. code-block:: python
from torch_ecg.cfg import DEFAULTS
sig = DEFAULTS.RNG.randn(1000)
pp = BandPass(lowcut=0.5, highcut=45, filter_type="butter", filter_order=4)
sig, _ = pp(sig, 500)
"""
__name__ = "BandPass"
def __init__(
self,
lowcut: Optional[Real] = 0.5,
highcut: Optional[Real] = 45,
filter_type: Literal["butter", "fir"] = "butter",
filter_order: Optional[int] = None,
**kwargs: Any,
) -> None:
self.lowcut = lowcut
self.highcut = highcut
assert any([self.lowcut is not None, self.highcut is not None]), "At least one of lowcut and highcut should be set"
if not self.lowcut:
self.lowcut = 0
if not self.highcut:
self.highcut = float("inf")
self.filter_type = filter_type
self.filter_order = filter_order
[docs]
def apply(self, sig: np.ndarray, fs: int) -> Tuple[np.ndarray, int]:
"""Apply the preprocessor to `sig`.
Parameters
----------
sig : numpy.ndarray
The ECG signal, can be
- 1d array, which is a single-lead ECG;
- 2d array, which is a multi-lead ECG of "lead_first" format;
- 3d array, which is a tensor of several ECGs, of shape ``(batch, lead, siglen)``.
fs : int
Sampling frequency of the ECG signal.
Returns
-------
filtered_sig : :class:`numpy.ndarray`
Bandpass filtered ECG signal.
fs : :class:`int`
Sampling frequency of the filtered ECG signal.
"""
self._check_sig(sig)
filtered_sig = preprocess_multi_lead_signal(
raw_sig=sig,
fs=fs,
band_fs=[self.lowcut, self.highcut],
filter_type=self.filter_type,
filter_order=self.filter_order,
)
return filtered_sig, fs
[docs]
def extra_repr_keys(self) -> List[str]:
return [
"lowcut",
"highcut",
"filter_type",
"filter_order",
] + super().extra_repr_keys()
