neurodsp.filt.filter_signal_fir

neurodsp.filt.filter_signal_fir(sig, fs, pass_type, f_range, n_cycles=3, n_seconds=None, remove_edges=True, print_transitions=False, plot_properties=False, return_filter=False)

Apply an FIR 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’.

n_cyclesfloat, optional, default: 3

Length of filter, in number of cycles, defined at the ‘f_lo’ frequency. This parameter is overwritten by n_seconds, if provided.

n_secondsfloat, optional

Length of filter, in seconds. This parameter overwrites n_cycles.

remove_edgesbool, optional

If True, replace samples within half the kernel length to be np.nan.

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 filter coefficients of the FIR filter.

Returns

sig_filtarray

Filtered time series.

filter_coefs1d array

Filter coefficients of the FIR filter. Only returned if return_filter is True.

Examples

Apply a band pass FIR 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_fir(sig, fs=500, pass_type='bandpass', f_range=(1, 25))

Apply a high pass FIR filter to a signal, with a specified number of cycles:

>>> sig = sim_combined(n_seconds=10, fs=500,
...                    components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> filt_sig = filter_signal_fir(sig, fs=500, pass_type='highpass',
...                              f_range=(2, None), n_cycles=5)