def preprocessing(dat, MRK_DEF, JUMPING_MEANS_IVALS): dat = proc.sort_channels(dat) fs_n = dat.fs / 2 b, a = proc.signal.butter(5, [30 / fs_n], btype='low') dat = proc.lfilter(dat, b, a) b, a = proc.signal.butter(5, [.4 / fs_n], btype='high') dat = proc.lfilter(dat, b, a) dat = proc.subsample(dat, 60) epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL) fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS) fv = proc.create_feature_vectors(fv) return fv, epo
def test_sort_channels_copy(self): """sort_channels must not modify argument.""" cpy = self.dat.copy() sort_channels(self.dat) self.assertEqual(self.dat, cpy)
def test_sort_channels_swapaxis(self): """sort_channels must workt with nonstandard chanaxis.""" sorted_ = sort_channels(swapaxes(self.dat, 1, -1), 1) sorted_ = swapaxes(sorted_, 1, -1) sorted2 = sort_channels(self.dat) self.assertEqual(sorted_, sorted2)
def test_sort_channels_with_unknown_channel(self): """Unknown channels move to the back.""" self.dat.axes[-1][7] = 'XX' dat = sort_channels(self.dat) self.assertEqual(dat.axes[-1][-1], 'XX')
def test_sort_channels(self): """sort_channels must sort correctly.""" dat = sort_channels(self.dat) np.testing.assert_array_equal(dat.axes[-1], self.sorted_channels)