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# -*- coding: utf-8 -*-
# Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
from ..utils import logger
# adapted from SciPy
def check_cola(win, nperseg, noverlap, tol=1e-10):
"""Check whether the Constant OverLap Add (COLA) constraint is met."""
nperseg = int(nperseg)
if nperseg < 1:
raise ValueError('nperseg must be a positive integer')
if noverlap >= nperseg:
raise ValueError('noverlap must be less than nperseg.')
noverlap = int(noverlap)
step = nperseg - noverlap
binsums = np.sum([win[ii * step:(ii + 1) * step]
for ii in range(nperseg // step)], axis=0)
if nperseg % step != 0:
binsums[:nperseg % step] += win[-(nperseg % step):]
deviation = binsums - np.median(binsums)
return np.max(np.abs(deviation)) < tol
def _get_lims_cola(n_samp, n_times, sfreq, picks):
from scipy.signal import get_window
if n_samp > n_times:
raise ValueError('Effective duration (%s) must be at most the '
'duration of the raw instance (%s)'
% (n_samp / sfreq, n_times / sfreq))
# Eventually this could be configurable
window = 'hann'
step = n_samp // 2
win = get_window(window, n_samp)
n_overlap = n_samp - step
if not check_cola(win, n_samp, n_overlap, tol=1e-2):
raise RuntimeError('COLA not met')
starts = np.arange(0, n_times - n_samp + 1, step)
stops = starts + n_samp
delta = n_times - stops[-1]
stops[-1] = n_times
pl = 's' if len(starts) != 1 else ''
logger.info(' Processing %s data chunk%s of (at least) %0.1f sec with '
'%s windowing'
% (len(starts), pl, n_samp / sfreq, window))
if delta > 0:
logger.info(' The final %0.3f sec will be lumped into the final '
'window' % (delta / sfreq,))
windows = list(np.tile(win[np.newaxis], (len(starts), 1)))
# First and last windows are special, fix them
windows[0][:n_overlap] = 1.
windows[-1] = np.concatenate([windows[-1], np.ones(delta)])
windows[-1][n_overlap:] = 1.
return starts, stops, windows
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