def test_logarithm(self): """logarithm basics must work.""" dat = logarithm(self.dat) # works elementwise (does not alter the shape) self.assertEqual(self.dat.data.shape, dat.data.shape) # actual log was computed np.testing.assert_array_almost_equal(np.e**dat.data, self.dat.data)
def preprocess(data, filt=None): dat = data.copy() fs_n = 250 # sample rate is 250 for us b, a = proc.signal.butter(5, [13 / fs_n], btype='low') dat = proc.filtfilt(dat, b, a) b, a = proc.signal.butter(5, [9 / fs_n], btype='high') dat = proc.filtfilt(dat, b, a) dat = proc.subsample(dat, 50) if filt is None: filt, pattern, _ = proc.calculate_csp(dat) plot_csp_pattern(pattern) dat = proc.apply_csp(dat, filt) dat = proc.variance(dat) dat = proc.logarithm(dat) return dat, filt
def preprocess(data, filt=None): dat = data.copy() fs_n = dat.fs / 2 b, a = proc.signal.butter(5, [13 / fs_n], btype='low') dat = proc.filtfilt(dat, b, a) b, a = proc.signal.butter(5, [9 / fs_n], btype='high') dat = proc.filtfilt(dat, b, a) dat = proc.subsample(dat, 50) if filt is None: filt, pattern, _ = proc.calculate_csp(dat) plot_csp_pattern(pattern) dat = proc.apply_csp(dat, filt) dat = proc.variance(dat) dat = proc.logarithm(dat) return dat, filt
def preprocess(dat,filt=None): '''fs_n = dat.fs / 2 b, a = proc.signal.butter(5, [13 / fs_n], btype='low') dat = proc.lfilter(dat, b, a) b, a = proc.signal.butter(5, [8 / fs_n], btype='high') dat = proc.lfilter(dat, b, a) print dat''' dat = proc.subsample(dat, 64) #epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL) #fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS) #fv = proc.create_feature_vectors(dat) if filt is None: filt, pattern, _ = proc.calculate_csp(dat) #plot_csp_pattern(pattern) dat = proc.apply_csp(dat, filt) dat = proc.variance(dat) dat = proc.logarithm(dat) return dat, filt
def test_logarithm_copy(self): """Rectify channels must not change the original parameter.""" cpy = self.dat.copy() logarithm(self.dat) self.assertEqual(cpy, self.dat)