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 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)
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)
