def test_filtfilt_copy(self):
"""filtfilt must not modify argument."""
cpy = self.dat.copy()
fn = self.dat.fs / 2
b, a = butter(4, [6 / fn, 8 / fn], btype='band')
filtfilt(self.dat, b, a)
self.assertEqual(cpy, self.dat)
def test_filtfilt_swapaxes(self):
"""filtfilt must work with nonstandard timeaxis."""
fn = self.dat.fs / 2
b, a = butter(4, [6 / fn, 8 / fn], btype='band')
dat = filtfilt(swapaxes(self.dat, 0, 1), b, a, timeaxis=1)
dat = swapaxes(dat, 0, 1)
dat2 = filtfilt(self.dat, b, a)
self.assertEqual(dat, dat2)
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 offline_experiment(filename_, cfy_, true_labels_):
print("\n")
cnt = io.load_bcicomp3_ds2(filename_)
fs_n = cnt.fs / 2
b, a = proc.signal.butter(5, [HIGH_CUT / fs_n], btype='low')
cnt = proc.filtfilt(cnt, b, a)
b, a = proc.signal.butter(5, [LOWER_CUT / fs_n], btype='high')
cnt = proc.filtfilt(cnt, b, a)
cnt = proc.subsample(cnt, SUBSAMPLING)
epo = proc.segment_dat(cnt, MARKER_DEF_TEST, SEG_IVAL)
fv = proc.jumping_means(epo, JUMPING_MEANS_INTERVALS)
fv = proc.create_feature_vectors(fv)
lda_out = proc.lda_apply(fv, cfy_)
markers = [fv.class_names[cls_idx] for cls_idx in fv.axes[0]]
result = zip(markers, lda_out)
endresult = []
markers_processed = 0
letter_prob = {i: 0 for i in 'abcdefghijklmnopqrstuvwxyz123456789_'}
for s, score in result:
if markers_processed == 180:
endresult.append(
sorted(letter_prob.items(), key=lambda x: x[1])[-1][0])
letter_prob = {
i: 0
for i in 'abcdefghijklmnopqrstuvwxyz123456789_'
}
markers_processed = 0
for letter in s:
letter_prob[letter] += score
markers_processed += 1
print('Letras Encontradas-: %s' % "".join(endresult))
print('Letras Corretas----: %s' % true_labels_)
acc = np.count_nonzero(
np.array(endresult) == np.array(
list(true_labels_.lower()[:len(endresult)]))) / len(endresult)
print("Acertividade Final : %d" % (acc * 100))
def highpass_filt_filt_cnt(cnt, low_cut_off_hz, filt_order=3):
if (low_cut_off_hz is None) or (low_cut_off_hz == 0):
log.info("Not doing any highpass, since low 0 or None")
return cnt.copy()
b, a = scipy.signal.butter(filt_order,
low_cut_off_hz / (cnt.fs / 2.0),
btype='highpass')
assert filter_is_stable(a)
cnt_highpassed = filtfilt(cnt, b, a)
return cnt_highpassed
def preprocessing_simple(dat, MRK_DEF, *args, **kwargs):
"""Simple preprocessing that reaches 97% accuracy.
"""
fs_n = dat.fs / 2
b, a = proc.signal.butter(5, [10 / fs_n], btype='low')
dat = proc.filtfilt(dat, b, a)
dat = proc.subsample(dat, 20)
epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL)
fv = proc.create_feature_vectors(epo)
return fv, epo
def lowpass_filt_filt_cnt(cnt, high_cut_off_hz, filt_order=3):
if (high_cut_off_hz is None) or (high_cut_off_hz == cnt.fs):
log.info(
"Not doing any lowpass, since ince high cut hz is None or current fs"
)
b, a = scipy.signal.butter(filt_order,
high_cut_off_hz / (cnt.fs / 2.0),
btype='lowpass')
assert filter_is_stable(a)
cnt_lowpassed = filtfilt(cnt, b, a)
return cnt_lowpassed
def preprocessing_simple(dat, MRK_DEF, *args, **kwargs):
"""Simple preprocessing that reaches 97% accuracy.
"""
fs_n = dat.fs / 2
b, a = proc.signal.butter(5, [10 / fs_n], btype='low')
dat = proc.filtfilt(dat, b, a)
dat = proc.subsample(dat, 20)
epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL)
fv = proc.create_feature_vectors(epo)
return fv, epo
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 test_bandpass(self):
"""Band pass filtering."""
# bandpass around the middle frequency
fn = self.dat.fs / 2
b, a = butter(4, [6 / fn, 8 / fn], btype='band')
ans = filtfilt(self.dat, b, a)
# check if the desired band is not damped
dat = spectrum(ans)
mask = dat.axes[0] == 7
self.assertTrue((dat.data[mask] > 6.5).all())
# check if the outer freqs are damped close to zero
mask = (dat.axes[0] <= 6) & (dat.axes[0] > 8)
self.assertTrue((dat.data[mask] < .5).all())
def bandpass_filt_filt_cnt(cnt, low_cut_hz, high_cut_hz, filt_order=3):
"""Bandpass cnt signal using butterworth filter.
Uses lowpass in case low cut hz is exactly zero."""
if (low_cut_hz == 0 or low_cut_hz == None) and (high_cut_hz == None
or high_cut_hz == cnt.fs):
log.info("Not doing any bandpass, since low 0 or None and "
"high None or current fs")
return cnt.copy()
if low_cut_hz == 0 or low_cut_hz == None:
log.info("Using lowpass filter since low cut hz is 0 or None")
return lowpass_filt_filt_cnt(cnt, high_cut_hz, filt_order=filt_order)
if high_cut_hz == None or high_cut_hz == cnt.fs:
log.info(
"Using highpass filter since high cut hz is None or current fs")
return highpass_filt_filt_cnt(cnt, low_cut_hz, filt_order=filt_order)
nyq_freq = 0.5 * cnt.fs
low = low_cut_hz / nyq_freq
high = high_cut_hz / nyq_freq
b, a = scipy.signal.butter(filt_order, [low, high], btype='bandpass')
assert filter_is_stable(a), "Filter should be stable..."
cnt_bandpassed = filtfilt(cnt, b, a)
return cnt_bandpassed
def highpass_filt_filt_cnt(cnt, low_cut_off_hz, filt_order=3):
b,a = scipy.signal.butter(filt_order, low_cut_off_hz/(cnt.fs/2.0),btype='highpass')
assert filter_is_stable(a)
cnt_highpassed = filtfilt(cnt,b,a)
return cnt_highpassed
