def preprocess(data, filt=None):
# copying data
dat = data.copy()
fs_n = dat.fs / 2.0
# butter filtering low
b, a = proc.signal.butter(5, [13 / fs_n], btype='low')
#dat = proc.filtfilt(dat, b, a)
# butter filtering high
b, a = proc.signal.butter(4, [9 / fs_n], btype='high')
dat = proc.filtfilt(dat, b, a)
# subsampling
#dat = proc.subsample(dat, 50)
if filt is None:
# calculate_csp
filt, pattern, _ = proc.calculate_csp(dat)
# plot_csp_pattern
#plot_csp_pattern(pattern)
# apply_csp
dat = proc.apply_csp(dat, filt)
# variance and logarithm
dat = proc.variance(dat)
#dat = proc.logarithm(dat)
return dat, filt
def test_apply_csp(self):
"""apply_csp."""
dat = apply_csp(self.epo, self.filter)
# reduce the channels down to 2, the rest of the shape should
# stay the same
self.assertEqual(self.epo.data.shape[0], dat.data.shape[0])
self.assertEqual(self.epo.data.shape[1], dat.data.shape[1])
self.assertEqual(2, dat.data.shape[2])
# new name for csp axis
self.assertEqual(dat.names[-1], 'CSP Channel')
# check if the dot product was calculated correctly
d = np.array([np.dot(self.epo.data[i], self.filter[:, [0, -1]]) for i in range(self.epo.data.shape[0])])
np.testing.assert_array_equal(d, dat.data)
def test_apply_csp(self):
"""apply_csp."""
dat = apply_csp(self.epo, self.filter)
# reduce the channels down to 2, the rest of the shape should
# stay the same
self.assertEqual(self.epo.data.shape[0], dat.data.shape[0])
self.assertEqual(self.epo.data.shape[1], dat.data.shape[1])
self.assertEqual(2, dat.data.shape[2])
# new name for csp axis
self.assertEqual(dat.names[-1], 'CSP Channel')
# check if the dot product was calculated correctly
d = np.array([
np.dot(self.epo.data[i], self.filter[:, [0, -1]])
for i in range(self.epo.data.shape[0])
])
np.testing.assert_array_equal(d, 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
cD_Energy.append(abs(np.sum(np.square(cD_values[x]))))
features.append(abs(np.sum(np.square(cD_values[x]))))
return features
final_epoch1 = append_epo(epoch_subject1_class1, epoch_subject1_class2)
final_epoch_ch1 = append_epo(
epoch_subject1_ch1_class1,
epoch_subject1_ch1_class2) #appended both the epoch data sets
w1, a1, d1 = calculate_csp(final_epoch1)
w2, a2, d2 = calculate_csp(
final_epoch_ch1
) #calculate csp but why we need to append the data and calculate the csp paramters waht if we calculate it individually
fil_epoch_subject1_class1 = apply_csp(
epoch_subject1_class1, w1,
[0, 1, 2, 3, 4, -5, -4, -3, -2, -1
]) # brackets number are the column number to use
fil_epoch_subject1_class2 = apply_csp(epoch_subject1_class2, w1,
[0, 1, 2, 3, 4, -5, -4, -3, -2, -1])
fil_final_epoch1 = append_epo(
fil_epoch_subject1_class1,
fil_epoch_subject1_class2) # final filtered epo class*time*channel
fil_epoch_subject1_ch1_class1 = apply_csp(
epoch_subject1_ch1_class1, w2,
[0, 1, 2, 3, 4, -5, -4, -3, -2, -1
]) # brackets number are the column number to use
fil_epoch_subject1_ch1_class2 = apply_csp(epoch_subject1_ch1_class2, w2,
[0, 1, 2, 3, 4, -5, -4, -3, -2, -1])
fil_final_epoch_ch1 = append_epo(
fil_epoch_subject1_class1,
fil_epoch_subject1_class2) # final filtered epo class*time*channel
def test_apply_csp_copy(self):
"""apply_csp must not modify argument."""
cpy = self.epo.copy()
apply_csp(self.epo, self.filter)
self.assertEqual(self.epo, cpy)
def test_apply_csp_copy(self):
"""apply_csp must not modify argument."""
cpy = self.epo.copy()
apply_csp(self.epo, self.filter)
self.assertEqual(self.epo, cpy)