def resample_rec(d, factor, max_marker_delay=0):
'''Resample a recording to length d.ninstances * factor'''
new_len = int(d.ninstances * factor)
labels = resample_markers(d.labels.flatten(), new_len,
max_delay=max_marker_delay)
# calculate data and ids
data, ids = signal.resample(d.data, new_len, t=d.ids.ravel(), axis=1)
data = data.astype(d.data.dtype) # keep old dtype
# construct new DataSet
return DataSet(data=data, labels=labels, ids=ids, default=d)
def resample_rec(d, factor, max_marker_delay=0):
'''Resample a recording to length d.ninstances * factor'''
new_len = int(d.ninstances * factor)
ys = resample_markers(d.ys.flatten(), new_len,
max_delay=max_marker_delay).reshape(-1, 1)
# calculate xs and ids
xs, ids = signal.resample(d.xs, new_len, t=d.ids)
xs = xs.astype(d.xs.dtype) # keep old dtype
# construct new DataSet
extra = d.extra.copy()
return DataSet(xs=xs, ys=ys, ids=ids.reshape(-1, 1), extra=extra, default=d)
def decimate_rec(d, factor, max_marker_delay=0):
'''Decimate a recording using an anti-aliasing filter.'''
assert isinstance(factor, int), 'Decimation factor should be an int'
# anti-aliasing filter
(b, a) = signal.iirfilter(8, .8 / factor, btype='lowpass', rp=0.05,
ftype='cheby1')
data = d.data.copy()
data = signal.filtfilt(b, a, data, axis=1)
data = np.ascontiguousarray(data[:, ::factor]).astype(d.data.dtype)
labels = resample_markers(d.labels.flatten(), data.shape[1],
max_delay=max_marker_delay)
ids = np.ascontiguousarray(d.ids[:, ::factor]).astype(d.ids.dtype)
# construct new DataSet
return DataSet(data=data, labels=labels, ids=ids, default=d)
def decimate_rec(d, factor, max_marker_delay=0):
'''Decimate a recording using an anti-aliasing filter.'''
assert isinstance(factor, int), 'Decimation factor should be an int'
# anti-aliasing filter
(b, a) = signal.iirfilter(8, .8 / factor, btype='lowpass', rp=0.05,
ftype='cheby1')
xs = d.xs.copy()
for i in range(d.nfeatures):
xs[:,i] = signal.filtfilt(b, a, xs[:, i])
xs = np.ascontiguousarray(xs[::factor,:]).astype(d.xs.dtype)
ys = resample_markers(d.ys.flatten(), xs.shape[0],
max_delay=max_marker_delay).reshape(-1, 1)
ids = np.ascontiguousarray(d.ids[::factor,:]).astype(d.ids.dtype)
# construct new DataSet
return DataSet(xs=xs, ys=ys, ids=ids.reshape(-1, 1), default=d)
