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# Authors: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
from scipy import signal, interpolate
from .. import pick_types
def eliminate_stim_artifact(raw, events, event_id, tmin=-0.005,
tmax=0.01, mode='linear'):
"""Eliminates stimulations artifacts from raw data
The raw object will be modified in place (no copy)
Parameters
----------
raw : Raw object
raw data object.
events : array, shape (n_events, 3)
The list of events.
event_id : int
The id of the events generating the stimulation artifacts.
tmin : float
Start time of the interpolation window in seconds.
tmax : float
End time of the interpolation window in seconds.
mode : 'linear' | 'window'
way to fill the artifacted time interval.
'linear' does linear interpolation
'window' applies a (1 - hanning) window.
Returns
-------
raw: Raw object
raw data object.
"""
if not raw.preload:
raise RuntimeError('Modifying data of Raw is only supported '
'when preloading is used. Use preload=True '
'(or string) in the constructor.')
events_sel = (events[:, 2] == event_id)
event_start = events[events_sel, 0]
s_start = int(np.ceil(raw.info['sfreq'] * tmin))
s_end = int(np.ceil(raw.info['sfreq'] * tmax))
picks = pick_types(raw.info, meg=True, eeg=True, eog=True, ecg=True,
emg=True, ref_meg=True, misc=True, chpi=True,
exclude='bads', stim=False, resp=False)
if mode == 'window':
window = 1 - np.r_[signal.hann(4)[:2],
np.ones(np.abs(s_end - s_start) - 4),
signal.hann(4)[-2:]].T
for k in range(len(event_start)):
first_samp = int(event_start[k]) - raw.first_samp + s_start
last_samp = int(event_start[k]) - raw.first_samp + s_end
data, _ = raw[picks, first_samp:last_samp]
if mode == 'linear':
x = np.array([first_samp, last_samp])
f = interpolate.interp1d(x, data[:, (0, -1)])
xnew = np.arange(first_samp, last_samp)
interp_data = f(xnew)
raw[picks, first_samp:last_samp] = interp_data
elif mode == 'window':
raw[picks, first_samp:last_samp] = data * window[np.newaxis, :]
return raw
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