"""Filter time series."""
from neurodsp.filt.fir import filter_signal_fir
from neurodsp.filt.iir import filter_signal_iir
from neurodsp.utils.checks import check_param_options
from neurodsp.utils.decorators import multidim
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[docs]@multidim(pass_2d_input=True)
def filter_signal(sig, fs, pass_type, f_range, filter_type=None,
print_transitions=False, plot_properties=False, return_filter=False,
**filter_kwargs):
"""Apply a bandpass, bandstop, highpass, or lowpass filter to a neural signal.
Parameters
----------
sig : array
Time series to be filtered.
fs : float
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_range : tuple 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_transitions : bool, optional, default: True
If True, print out the transition and pass bandwidths.
plot_properties : bool, optional, default: False
If True, plot the properties of the filter, including frequency response and/or kernel.
return_filter : bool, 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_filt : 1d array
Filtered time series.
kernel : 1d 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))
"""
if filter_type is not None:
check_param_options(filter_type, 'filter_type', ['fir', 'iir'])
else:
# Infer IIR if relevant parameter set, otherwise, assume FIR
filter_type = 'iir' if 'butterworth_order' in filter_kwargs else 'fir'
_filter_input_checks(filter_type, filter_kwargs)
if filter_type.lower() == 'fir':
return filter_signal_fir(sig, fs, pass_type, f_range, **filter_kwargs,
print_transitions=print_transitions,
plot_properties=plot_properties,
return_filter=return_filter)
elif filter_type.lower() == 'iir':
return filter_signal_iir(sig, fs, pass_type, f_range, **filter_kwargs,
print_transitions=print_transitions,
plot_properties=plot_properties,
return_filter=return_filter)
FILTER_INPUTS = {
'fir' : ['n_cycles', 'n_seconds', 'remove_edges'],
'iir' : ['butterworth_order'],
}
def _filter_input_checks(filter_type, filter_kwargs):
"""Check inputs to `filter_signal` match filter type."""
for param in filter_kwargs.keys():
assert param in FILTER_INPUTS[filter_type], \
'Parameter {} not expected for {} filter'.format(param, filter_type)