def test_append_until_full(self):
"""Appending fractions of block_length, must accumulate in the buffer until block_length is reached."""
b = BlockBuffer(5)
for i in range(4):
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.empty_dat, ret)
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.dat_5, ret)
def test_append_until_full(self):
"""Appending fractions of block_length, must accumulate in the buffer until block_length is reached."""
b = BlockBuffer(5)
for i in range(4):
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.empty_dat, ret)
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.dat_5, ret)
def test_append_with_markers(self):
"""Check if markers are handled correctly."""
markers = [[i, 'x'] for i in range(5)]
b = BlockBuffer(5)
for i in range(4):
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.empty_dat, ret)
b.append(self.dat_1)
ret = b.get()
self.assertEqual(ret.markers, markers)
def test_append_with_markers(self):
"""Check if markers are handled correctly."""
markers = [[i, 'x'] for i in range(5)]
b = BlockBuffer(5)
for i in range(4):
b.append(self.dat_1)
ret = b.get()
self.assertEqual(self.empty_dat, ret)
b.append(self.dat_1)
ret = b.get()
self.assertEqual(ret.markers, markers)
def test_append_empty(self):
"""Appending several emtpy dats must not modify the Block Buffer."""
b = BlockBuffer(5)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
self.assertEqual(self.empty_dat, b.get())
def test_append_empty(self):
"""Appending several emtpy dats must not modify the Block Buffer."""
b = BlockBuffer(5)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
b.append(self.empty_dat)
self.assertEqual(self.empty_dat, b.get())
def online_experiment(amp, clf):
amp_fs = amp.get_sampling_frequency()
amp_channels = amp.get_channels()
buf = BlockBuffer(4)
rb = RingBuffer(5000)
fn = amp.get_sampling_frequency() / 2
b_low, a_low = proc.signal.butter(16, [30 / fn], btype='low')
b_high, a_high = proc.signal.butter(5, [.4 / fn], btype='high')
zi_low = proc.lfilter_zi(b_low, a_low, len(amp_channels))
zi_high = proc.lfilter_zi(b_high, a_high, len(amp_channels))
amp.start()
markers_processed = 0
current_letter_idx = 0
current_letter = TRUE_LABELS[current_letter_idx].lower()
letter_prob = {i: 0 for i in 'abcdefghijklmnopqrstuvwxyz123456789_'}
endresult = []
while True:
# turn on for 'real time'
#time.sleep(0.01)
# get fresh data from the amp
data, markers = amp.get_data()
# we should rather wait for a specific end-of-experiment marker
if len(data) == 0:
break
# convert to cnt
cnt = io.convert_mushu_data(data, markers, amp_fs, amp_channels)
# enter the block buffer
buf.append(cnt)
cnt = buf.get()
if not cnt:
continue
# band-pass and subsample
cnt, zi_low = proc.lfilter(cnt, b_low, a_low, zi=zi_low)
cnt, zi_high = proc.lfilter(cnt, b_high, a_high, zi=zi_high)
cnt = proc.subsample(cnt, 60)
newsamples = cnt.data.shape[0]
# enter the ringbuffer
rb.append(cnt)
cnt = rb.get()
# segment
epo = proc.segment_dat(cnt,
MARKER_DEF_TEST,
SEG_IVAL,
newsamples=newsamples)
if not epo:
continue
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
fv = proc.create_feature_vectors(fv)
logger.debug(markers_processed)
lda_out = proc.lda_apply(fv, clf)
markers = [fv.class_names[cls_idx] for cls_idx in fv.axes[0]]
result = zip(markers, lda_out)
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
current_letter_idx += 1
current_letter = TRUE_LABELS[current_letter_idx].lower()
for letter in s:
letter_prob[letter] += score
markers_processed += 1
logger.debug("".join([
i[0] for i in sorted(
letter_prob.items(), key=lambda x: x[1], reverse=True)
]).replace(current_letter, " %s " % current_letter))
logger.debug(TRUE_LABELS)
logger.debug("".join(endresult))
# calculate the current accuracy
if len(endresult) > 0:
acc = np.count_nonzero(
np.array(endresult) == np.array(
list(TRUE_LABELS.lower()[:len(endresult)]))) / len(
endresult)
print "Current accuracy:", acc * 100
if len(endresult) == len(TRUE_LABELS):
break
#logger.debug("Result: %s" % result)
acc = np.count_nonzero(
np.array(endresult) == np.array(
list(TRUE_LABELS.lower()[:len(endresult)]))) / len(endresult)
print "Accuracy:", acc * 100
amp.stop()
def online_erp(fs, n_channels, subsample):
logger.debug('Running Online ERP with {fs}Hz, and {channels}channels'.format(fs=fs, channels=n_channels))
target_fs = 100
# blocklen in ms
blocklen = 1000 * 1 / target_fs
# blocksize given the original fs and blocklen
blocksize = fs * (blocklen / 1000)
MRK_DEF = {'target': 'm'}
SEG_IVAL = [0, 700]
JUMPING_MEANS_IVALS = [150, 220], [200, 260], [310, 360], [550, 660]
RING_BUFFER_CAP = 1000
cfy = [0, 0]
fs_n = fs / 2
b_l, a_l = proc.signal.butter(5, [30 / fs_n], btype='low')
b_h, a_h = proc.signal.butter(5, [.4 / fs_n], btype='high')
zi_l = proc.lfilter_zi(b_l, a_l, n_channels)
zi_h = proc.lfilter_zi(b_h, a_h, n_channels)
ax_channels = np.array([str(i) for i in range(n_channels)])
names = ['time', 'channel']
units = ['ms', '#']
blockbuf = BlockBuffer(blocksize)
ringbuf = RingBuffer(RING_BUFFER_CAP)
times = []
# time since the last data was acquired
t_last = time.time()
# time since the last marker
t_last_marker = time.time()
# time since the experiment started
t_start = time.time()
full_iterations = 0
while full_iterations < 500:
t0 = time.time()
dt = time.time() - t_last
samples = int(dt * fs)
if samples == 0:
continue
t_last = time.time()
# get data
data = np.random.random((samples, n_channels))
ax_times = np.linspace(0, 1000 * (samples / fs), samples, endpoint=False)
if t_last_marker + .01 < time.time():
t_last_marker = time.time()
markers = [[ax_times[-1], 'm']]
else:
markers = []
cnt = Data(data, axes=[ax_times, ax_channels], names=names, units=units)
cnt.fs = fs
cnt.markers = markers
# blockbuffer
blockbuf.append(cnt)
cnt = blockbuf.get()
if not cnt:
continue
# filter
cnt, zi_l = proc.lfilter(cnt, b_l, a_l, zi=zi_l)
cnt, zi_h = proc.lfilter(cnt, b_h, a_h, zi=zi_h)
# subsample
if subsample:
cnt = proc.subsample(cnt, target_fs)
newsamples = cnt.data.shape[0]
# ringbuffer
ringbuf.append(cnt)
cnt = ringbuf.get()
# epoch
epo = proc.segment_dat(cnt, MRK_DEF, SEG_IVAL, newsamples=newsamples)
if not epo:
continue
# feature vectors
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
rv = proc.create_feature_vectors(fv)
# classification
proc.lda_apply(fv, cfy)
# don't measure in the first second, where the ringbuffer is not
# full yet.
if time.time() - t_start < (RING_BUFFER_CAP / 1000):
continue
dt = time.time() - t0
times.append(dt)
full_iterations += 1
return np.array(times)
def online_erp(fs, n_channels, subsample):
logger.debug('Running Online ERP with {fs}Hz, and {channels}channels'.format(fs=fs, channels=n_channels))
target_fs = 100
# blocklen in ms
blocklen = 1000 * 1 / target_fs
# blocksize given the original fs and blocklen
blocksize = fs * (blocklen / 1000)
MRK_DEF = {'target': 'm'}
SEG_IVAL = [0, 700]
JUMPING_MEANS_IVALS = [150, 220], [200, 260], [310, 360], [550, 660]
RING_BUFFER_CAP = 1000
cfy = [0, 0]
fs_n = fs / 2
b_l, a_l = proc.signal.butter(5, [30 / fs_n], btype='low')
b_h, a_h = proc.signal.butter(5, [.4 / fs_n], btype='high')
zi_l = proc.lfilter_zi(b_l, a_l, n_channels)
zi_h = proc.lfilter_zi(b_h, a_h, n_channels)
ax_channels = np.array([str(i) for i in range(n_channels)])
names = ['time', 'channel']
units = ['ms', '#']
blockbuf = BlockBuffer(blocksize)
ringbuf = RingBuffer(RING_BUFFER_CAP)
times = []
# time since the last data was acquired
t_last = time.time()
# time since the last marker
t_last_marker = time.time()
# time since the experiment started
t_start = time.time()
full_iterations = 0
while full_iterations < 500:
t0 = time.time()
dt = time.time() - t_last
samples = int(dt * fs)
if samples == 0:
continue
t_last = time.time()
# get data
data = np.random.random((samples, n_channels))
ax_times = np.linspace(0, 1000 * (samples / fs), samples, endpoint=False)
if t_last_marker + .01 < time.time():
t_last_marker = time.time()
markers = [[ax_times[-1], 'm']]
else:
markers = []
cnt = Data(data, axes=[ax_times, ax_channels], names=names, units=units)
cnt.fs = fs
cnt.markers = markers
# blockbuffer
blockbuf.append(cnt)
cnt = blockbuf.get()
if not cnt:
continue
# filter
cnt, zi_l = proc.lfilter(cnt, b_l, a_l, zi=zi_l)
cnt, zi_h = proc.lfilter(cnt, b_h, a_h, zi=zi_h)
# subsample
if subsample:
cnt = proc.subsample(cnt, target_fs)
newsamples = cnt.data.shape[0]
# ringbuffer
ringbuf.append(cnt)
cnt = ringbuf.get()
# epoch
epo = proc.segment_dat(cnt, MRK_DEF, SEG_IVAL, newsamples=newsamples)
if not epo:
continue
# feature vectors
fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
rv = proc.create_feature_vectors(fv)
# classification
proc.lda_apply(fv, cfy)
# don't measure in the first second, where the ringbuffer is not
# full yet.
if time.time() - t_start < (RING_BUFFER_CAP / 1000):
continue
dt = time.time() - t0
times.append(dt)
full_iterations += 1
return np.array(times)