elephant.signal_processing.butter¶

elephant.signal_processing.
butter
(signal, highpass_frequency=None, lowpass_frequency=None, order=4, filter_function='filtfilt', sampling_frequency=1.0, axis= 1)[source]¶ Butterworth filtering function for neo.AnalogSignal.
Filter type is determined according to how values of highpass_frequency and lowpass_frequency are given (see “Parameters” section for details).
Parameters:  signalneo.AnalogSignal or pq.Quantity or np.ndarray
Time series data to be filtered. If pq.Quantity or np.ndarray, the sampling frequency should be given through the keyword argument fs.
 highpass_frequencypq.Quantity of float, optional
Highpass cutoff frequency. If float, the given value is taken as frequency in Hz. Default: None
 lowpass_frequencypq.Quantity or float, optional
Lowpass cutoff frequency. If float, the given value is taken as frequency in Hz. Filter type is determined depending on the values of lowpass_frequency and highpass_frequency:
 highpass_frequency only (lowpass_frequency is None):
highpass filter
 lowpass_frequency only (highpass_frequency is None):
lowpass filter
 highpass_frequency < lowpass_frequency: bandpass filter
 highpass_frequency > lowpass_frequency: bandstop filter
Default: None
 orderint, optional
Order of the Butterworth filter. Default: 4
 filter_function{‘filtfilt’, ‘lfilter’, ‘sosfiltfilt’}, optional
Filtering function to be used. Available filters:
 ‘filtfilt’: scipy.signal.filtfilt;
 ‘lfilter’: scipy.signal.lfilter;
 ‘sosfiltfilt’: scipy.signal.sosfiltfilt.
In most applications ‘filtfilt’ should be used, because it doesn’t bring about phase shift due to filtering. For numerically stable filtering, in particular higher order filters, use ‘sosfiltfilt’ (see https://github.com/NeuralEnsemble/elephant/issues/220). Default: ‘filtfilt’
 sampling_frequencypq.Quantity or float, optional
The sampling frequency of the input time series. When given as float, its value is taken as frequency in Hz. When signal is given as neo.AnalogSignal, its attribute is used to specify the sampling frequency and this parameter is ignored. Default: 1.0
 axisint, optional
Axis along which filter is applied. Default: last axis (1)
Returns:  filtered_signalneo.AnalogSignal or pq.Quantity or np.ndarray
Filtered input data. The shape and type is identical to those of the input signal.
Raises:  ValueError
If filter_function is not one of ‘lfilter’, ‘filtfilt’, or ‘sosfiltfilt’.
If both highpass_frequency and lowpass_frequency are None.
Examples
>>> import neo >>> import numpy as np >>> import quantities as pq >>> from elephant.signal_processing import butter >>> noise = neo.AnalogSignal(np.random.normal(size=5000), ... sampling_rate=1000 * pq.Hz, units='mV') >>> filtered_noise = butter(noise, highpass_frequency=250.0 * pq.Hz) >>> filtered_noise AnalogSignal with 1 channels of length 5000; units mV; datatype float64 sampling rate: 1000.0 Hz time: 0.0 s to 5.0 s
Let’s check that the normal noise power spectrum at zero frequency is close to zero.
>>> from elephant.spectral import welch_psd >>> freq, psd = welch_psd(filtered_noise, fs=1000.0) >>> psd.shape (1, 556) >>> freq[0], psd[0, 0] (array(0.) * Hz, array(7.21464674e08) * mV**2/Hz)