neurodsp.filt.filter_signal¶

neurodsp.filt.filter_signal(sig, fs, pass_type, f_range, filter_type=None, print_transitions=False, plot_properties=False, return_filter=False, **filter_kwargs)[source]¶

Apply a bandpass, bandstop, highpass, or lowpass filter to a neural 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’.

filter_type{‘fir’, ‘iir’}, optional

Whether to use an FIR or IIR filter. IIR option is a butterworth filter. If None, type is inferred from input parameters, and/or defaults to FIR.

print_transitionsbool, optional, default: True

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.

**filter_kwargs

Additional parameters for the filtering function, specific to filtering type.

For FIR filters, can include:

n_cycles : float, optional

Filter length, in number of cycles, defined at ‘f_lo’ frequency.
Either n_cycles or n_seconds can be set for the filter length, but not both.
If not provided, and n_seconds is also not defined, defaults to 3.

n_seconds : float, optional

Filter length, in seconds.

Either n_cycles or n_seconds can be set for the filter length, but not both.

remove_edges : bool, optional, default: True

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

For IIR filters, can include:

butterworth_order : int, optional

Order of the butterworth filter. See input ‘N’ in scipy.signal.butter.

Returns:

sig_filt1d array: Filtered time series.
kernel1d array or tuple of (1d array, 1d array): Filter coefficients. Only returned if return_filter is True.

Examples

Apply an FIR band pass filter to a signal, for the range of 1 to 25 Hz:

>>> 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(sig, fs=500, pass_type='bandpass',
...                          filter_type='fir', f_range=(1, 25))

Examples using `neurodsp.filt.filter_signal`¶

Time-frequency analysis

neurodsp.filt.filter_signal¶

Examples using neurodsp.filt.filter_signal¶

Examples using `neurodsp.filt.filter_signal`¶