Transformer
Filter
Transformer that filters Series data.
FIR, IIR, band pass filters.
Provides a simple wrapper around mne.filter.filter_data.
Quickstart
python
from sktime.transformations.filter import Filter
estimator = Filter(sfreq, l_freq=None, h_freq=None, filter_kwargs=None)Tags
Capabilities
- Multivariate
- Unequal-length series
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- Yes
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(4)
- sfreq: int or float
- sampling frequency of the recorded data in Hz
- l_freq: float or None
- For FIR filters, the lower pass-band edge; for IIR filters, the lower cutoff frequency. If None the data are only low-passed.
- h_freq: float or None
- For FIR filters, the upper pass-band edge; for IIR filters, the upper cutoff frequency. If None the data are only high-passed.
- filter_kwargs: dict or None
Additional parameters passed on to
mne.filter.filter_data. Seemne.filter.filter_datadocumentation for a detailed description of all options.
Examples
>>> from sktime.transformations.filter import Filter
>>> from sktime.datasets import load_arrow_head
>>> X, y = load_arrow_head (return_X_y = True, return_type = "pd-multiindex")
>>> transformer = Filter (sfreq = 128, l_freq = 0.5, h_freq = 40)
>>> X_filtered = transformer. fit_transform (X)