neurodsp.filt.filter_signal_iir¶
- neurodsp.filt.filter_signal_iir(sig, fs, pass_type, f_range, butterworth_order, print_transitions=False, plot_properties=False, return_filter=False)[source]¶
Apply an IIR filter to a signal.
- Parameters
- sigarray
Time series to be filtered.
- fsfloat
Sampling rate, in Hz.
- pass_type{‘bandpass’, ‘bandstop’, ‘lowpass’, ‘highpass’}
Which kind of filter to apply:
‘bandpass’: apply a bandpass filter
‘bandstop’: apply a bandstop (notch) filter
‘lowpass’: apply a lowpass filter
‘highpass’ : apply a highpass filter
- f_rangetuple of (float, float) or float
Cutoff frequency(ies) used for filter, specified as f_lo & f_hi. For ‘bandpass’ & ‘bandstop’, must be a tuple. For ‘lowpass’ or ‘highpass’, can be a float that specifies pass frequency, or can be a tuple and is assumed to be (None, f_hi) for ‘lowpass’, and (f_lo, None) for ‘highpass’.
- butterworth_orderint
Order of the butterworth filter, if using an IIR filter. See input ‘N’ in scipy.signal.butter.
- print_transitionsbool, optional, default: False
If True, print out the transition and pass bandwidths.
- plot_propertiesbool, optional, default: False
If True, plot the properties of the filter, including frequency response and/or kernel.
- return_filterbool, optional, default: False
If True, return the second order series coefficients of the IIR filter.
- Returns
- sig_filt1d array
Filtered time series.
- sos2d array
Second order series coefficients of the IIR filter. Has shape of (n_sections, 6). Only returned if return_filter is True.
Examples
Apply a bandstop IIR filter to a simulated signal:
>>> from neurodsp.sim import sim_combined >>> sig = sim_combined(n_seconds=10, fs=500, ... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}}) >>> filt_sig = filter_signal_iir(sig, fs=500, pass_type='bandstop', ... f_range=(55, 65), butterworth_order=7)