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_variance(self):
"""Variance."""
dat = variance(self.dat)
# test the resulting dat has one axis less (the middle one)
self.assertEqual(dat.data.shape, self.dat.data.shape[::2])
# each epoch should have a variance of zero, test if the var of
# all epochs is 0
self.assertEqual(dat.data.var(), 0)
def test_variance(self):
"""Variance."""
dat = variance(self.dat)
# test the resulting dat has one axis less (the middle one)
self.assertEqual(dat.data.shape, self.dat.data.shape[::2])
# each epoch should have a variance of zero, test if the var of
# all epochs is 0
self.assertEqual(dat.data.var(), 0)
self.assertEqual(len(dat.axes), len(self.dat.axes) - 1)
def test_variance_with_cnt(self):
"""variance must work with cnt argument."""
data = self.dat.data[1]
axes = self.dat.axes[1:]
names = self.dat.names[1:]
units = self.dat.units[1:]
dat = self.dat.copy(data=data, axes=axes, names=names, units=units)
dat = variance(dat)
self.assertEqual(dat.data.var(), 0)
def test_variance_with_cnt(self):
"""variance must work with cnt argument."""
data = self.dat.data[1]
axes = self.dat.axes[1:]
names = self.dat.names[1:]
units = self.dat.units[1:]
dat = self.dat.copy(data=data, axes=axes, names=names, units=units)
dat = variance(dat)
self.assertEqual(dat.data.var(), 0)
self.assertEqual(len(dat.axes), len(self.dat.axes) - 2)
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_variance_copy(self):
"""variance must not modify argument."""
cpy = self.dat.copy()
variance(self.dat)
self.assertEqual(self.dat, cpy)
def test_variance_swapaxes(self):
"""variance must work with nonstandard timeaxis."""
dat = variance(swapaxes(self.dat, 1, 2), timeaxis=2)
# we don't swap back here as variance removes the timeaxis
dat2 = variance(self.dat)
self.assertEqual(dat, dat2)
def test_variance_copy(self):
"""variance must not modify argument."""
cpy = self.dat.copy()
variance(self.dat)
self.assertEqual(self.dat, cpy)
def test_variance_swapaxes(self):
"""variance must work with nonstandard timeaxis."""
dat = variance(swapaxes(self.dat, 1, 2), timeaxis=2)
# we don't swap back here as variance removes the timeaxis
dat2 = variance(self.dat)
self.assertEqual(dat, dat2)
