def process(self):
# A stimulation set is a chunk which starts at current time and end time is the time step between two calls
# init here and filled within triger()
self.stimSet = OVStimulationSet(self.getCurrentTime(), self.getCurrentTime()+1./self.getClock())
if self.init == False :
local_time = local_clock()
initSecond=int(local_time)
initMillis=int((local_time-initSecond)*1000)
self.stimSet.append(OVStimulation(self.initLabel, self.getCurrentTime(), 0.))
self.stimSet.append(OVStimulation(initSecond, self.getCurrentTime(), 0.))
self.stimSet.append(OVStimulation(initMillis, self.getCurrentTime(), 0.))
self.init=True
# read all available stream
samples=[]
sample,timestamp = self.inlet.pull_sample(0)
while sample != None:
samples += sample
sample,timestamp = self.inlet.pull_sample(0)
# every value will be converted to openvibe code and a stim will be create
for label in samples:
label = str(label)
if self.debug:
print "Got label: ", label
self.stimSet.append(OVStimulation(float(label), self.getCurrentTime(), 0.))
# even if it's empty we have to send stim list to keep the rest in sync
self.output[0].append(self.stimSet)
def update(self):
now = local_clock()
if (now > self.next_transition):
# Transition phase
self.in_phase = self.phases[self.in_phase]['next']
self.next_transition = now + self.phases[self.in_phase]['duration']
# Send markers
out_string = "undefined"
if self.in_phase == 'precue':
# transition from evaluate to precue
# print("Previous class_id: {}, target_id: {}".format(self.class_id, self.target_id))
self.trial_ix += 1
self.target_id = random.choice(self.target_list) if self.targets_rand else self.target_list[
(self.target_list.index(self.target_id) + 1) % len(self.target_list)]
self.class_id = random.choice(self.class_list) if self.classes_rand else self.class_list[
(self.class_list.index(self.class_id) + 1) % len(self.class_list)]
# print("New class_id: {}, target_id: {}".format(self.class_id, self.target_id))
out_string = "NewTrial {}, Class {}, Target {}".format(self.trial_ix, self.class_id, self.target_id)
elif self.in_phase == 'cue':
# transition from precue to cue
out_string = "TargetCue, Class {}, Target {}".format(self.class_id, self.target_id)
elif self.in_phase == 'go':
# transition from cue to go
out_string = "GoCue, Class {}, Target {}".format(self.class_id, self.target_id)
elif self.in_phase == 'evaluate':
# transition from go to evaluate
hit_string = "Hit" if random.randint(0, 1) == 1 else "Miss"
out_string = "{}, Class {}, Target {}".format(hit_string, self.class_id, self.target_id)
print("Marker outlet pushing string: {}".format(out_string))
self.outlet.push_sample([out_string,])
return True
return False
def __init__(self, stream, buf_size, col="w", chnames=False, invert=False):
""" Initializes the grapher.
Args:
stream: <pylsl.StreamInfo instance> pointer to a stream
buf_size: <integer> visualization buffer length in samples
col: <char> color of the line plot (b,r,g,c,m,y,k and w)
"""
self.stream = stream
self.inlet = pylsl.StreamInlet(stream)
self.buf_size = buf_size
self.channel_count = self.inlet.channel_count
self.gbuffer = np.zeros(self.buf_size * self.channel_count)
self.gtimes = np.zeros(self.buf_size) + pylsl.local_clock()
self.col = col
if chnames:
if self.channel_count == len(chnames):
self.chnames = chnames
else:
print("Channel names vs channel count mismatch, skipping")
else:
self.chnames = False
if invert:
pg.setConfigOption("background", "w")
pg.setConfigOption("foreground", "k")
self.fill_buffer()
self.start_graph()
def update(self, task={'phase': 'precue', 'class': 1}):
# Convert phase and class_id into beta_amp
if task['phase'] in ['cue', 'go']:
beta_amp = 0 if task['class'] == 3 else self.AmpBeta
else:
beta_amp = self.AmpBeta / 5.0
this_tvec = self.tvec + self.last_time # Sample times
# Put the signal together
this_sig = self.AmpNoise * np.asarray(self.pinkNoiseGen.generate(), dtype=np.float32) # Start with some pink noise
this_sig += beta_amp * np.sin(this_tvec * 2 * np.pi * self.FreqBeta) # Add our beta signal
this_sig = np.atleast_2d(this_sig).T * np.ones((1, len(self.channels)), dtype=np.float32) # Tile across channels
time_to_sleep = max(0, this_tvec[-1] - local_clock())
time.sleep(time_to_sleep)
print("Beta outlet pushing signal with shape {},{} and Beta amp {}".format(this_sig.shape[0], this_sig.shape[1],
beta_amp))
self.eeg_outlet.push_chunk(this_sig, timestamp=this_tvec[-1])
self.last_time = local_clock()
def __init__(self, Fs=2**14, FreqBeta=20.0, AmpBeta=100.0, AmpNoise=20.0, NCyclesPerChunk=4,
channels=["RAW1", "SPK1", "RAW2", "SPK2", "RAW3", "SPK3"]):
"""
:param Fs: Sampling rate
:param FreqBeta: Central frequency of beta band
:param AmpBeta: Amplitude of beta (uV)
:param AmpNoise: Amplitude of pink noise (uV)
:param NCyclesPerChunk: Minimum number of cycles of beta in a chunk.
:param channels: List of channel names
"""
# Saved arguments
self.FreqBeta = FreqBeta
self.AmpBeta = AmpBeta # Amplitude of Beta (uV)
self.AmpNoise = AmpNoise # Amplitude of pink noise
self.channels = channels
# Derived variables
chunk_dur = NCyclesPerChunk / self.FreqBeta # Duration, in sec, of one chunk
chunk_len = int(Fs * chunk_dur) # Number of samples in a chunk
self.tvec = 1.0 * (np.arange(chunk_len) + 1) / Fs # time vector for chunk (sec)
# Pink noise generator
self.pinkNoiseGen = PinkNoiseGenerator(nSampsPerBlock=chunk_len)
# Create a stream of fake 'raw' data
raw_info = StreamInfo(name='BetaGen', type='EEG',
channel_count=len(self.channels), nominal_srate=Fs,
channel_format='float32', source_id='betagen1234')
raw_xml = raw_info.desc()
chans = raw_xml.append_child("channels")
for channame in self.channels:
chn = chans.append_child("channel")
chn.append_child_value("label", channame)
chn.append_child_value("unit", "microvolts")
chn.append_child_value("type", "generated")
self.eeg_outlet = StreamOutlet(raw_info)
print("Created outlet with name BetaGen and type EEG")
self.last_time = local_clock()
tracker = EyeLink("100.1.1.1")
print "Established a primary connection with the eye tracker."
# uncomment if you want to get pupil size in diameter, otherwise it is the area
# tracker.setPupilSizeDiameter(YES)
# tracker.setVelocityThreshold(22)
beginRealTimeMode(100)
getEYELINK().openDataFile(edfFileName)
getEYELINK().startRecording(1, 1, 1, 1)
print "Now reading samples..."
print "Press \'Esc\' to quit"
while quit != 1:
sample = getEYELINK().getNewestSample()
quit = getEYELINK().escapePressed();
if sample is not None:
now = pylsl.local_clock()
ppd = sample.getPPD()
#values = [0,0, 0,0, 0, 0, sample.getTargetX(),sample.getTargetY(),sample.getTargetDistance(), ppd[0],ppd[1]]
values = [0,0, 0,0, 0, 0, ppd[0],ppd[1], sample.getTime(), now]
if (sample.isLeftSample()) or (sample.isBinocular()):
values[0:2] = sample.getLeftEye().getGaze()
values[4] = sample.getLeftEye().getPupilSize()
if (sample.isRightSample()) or (sample.isBinocular()):
values[2:4] = sample.getRightEye().getGaze()
values[5] = sample.getRightEye().getPupilSize()
outlet.push_sample(pylsl.vectord(values), now, True)
time.sleep(1.0/SR)
# first create a new stream info (here we set the name to BioSemi,
# the content-type to EEG, 8 channels, 100 Hz, and float-valued data) The
# last value would be the serial number of the device or some other more or
# less locally unique identifier for the stream as far as available (you
# could also omit it but interrupted connections wouldn't auto-recover)
fs = 1000
info = StreamInfo('python', 'EEG', 2)
# next make an outlet
outlet = StreamOutlet(info)
from pylsl import StreamInlet, resolve_stream
print('resolving stream')
streams = resolve_stream('name', 'matlab')
# create a new inlet to read from the stream
inlet = StreamInlet(streams[0])
print('resolved')
t = 0
mean_time = 0
while True:
#time.sleep(0.002)
t += 1
clock = local_clock()
outlet.push_sample([0, 1])
sample, timestamp = inlet.pull_sample(timeout=1)
dt = local_clock() - clock
mean_time += dt
print(mean_time / t, dt)
#time.sleep(0.001)
def get_prob(self, timestamp=False):
"""
Read the latest window
Input
-----
timestamp: If True, returns LSL timestamp of the leading edge of the window used for decoding.
Returns
-------
The likelihood P(X|C), where X=window, C=model
"""
if self.fake:
# fake deocder: biased likelihood for the first class
probs = [random.uniform(0.0, 1.0)]
# others class likelihoods are just set to equal
p_others = (1 - probs[0]) / (len(self.labels) - 1)
for x in range(1, len(self.labels)):
probs.append(p_others)
time.sleep(0.0625) # simulated delay
t_prob = pylsl.local_clock()
else:
self.sr.acquire(blocking=True)
w, ts = self.sr.get_window() # w = times x channels
t_prob = ts[-1]
w = w.T # -> channels x times
# re-reference channels
# TODO: add re-referencing function to preprocess()
# apply filters. Important: maintain the original channel order at this point.
w = pu.preprocess(w, sfreq=self.sfreq, spatial=self.spatial, spatial_ch=self.spatial_ch,
spectral=self.spectral, spectral_ch=self.spectral_ch, notch=self.notch,
notch_ch=self.notch_ch, multiplier=self.multiplier, decim=self.decim)
# select the same channels used for training
w = w[self.picks]
# debug: show max - min
# c=1; print( '### %d: %.1f - %.1f = %.1f'% ( self.picks[c], max(w[c]), min(w[c]), max(w[c])-min(w[c]) ) )
# psd = channels x freqs
psd = self.psde.transform(w.reshape((1, w.shape[0], w.shape[1])))
# make a feautre vector and classify
feats = np.concatenate(psd[0]).reshape(1, -1)
# compute likelihoods
probs = self.cls.predict_proba(feats)[0]
# update psd buffer ( < 1 msec overhead )
'''
if self.psd_buffer is None:
self.psd_buffer = psd
else:
self.psd_buffer = np.concatenate((self.psd_buffer, psd), axis=0)
# TODO: CHECK THIS BLOCK
self.ts_buffer.append(ts[0])
if ts[0] - self.ts_buffer[0] > self.buffer_sec:
# search speed comparison for ordered arrays:
# http://stackoverflow.com/questions/16243955/numpy-first-occurence-of-value-greater-than-existing-value
#t_index = np.searchsorted(self.ts_buffer, ts[0] - 1.0)
t_index = np.searchsorted(self.ts_buffer, ts[0] - self.buffer_sec)
self.ts_buffer = self.ts_buffer[t_index:]
self.psd_buffer = self.psd_buffer[t_index:, :, :] # numpy delete is slower
# assert ts[0] - self.ts_buffer[0] <= self.buffer_sec
'''
if timestamp:
return probs, t_prob
else:
return probs
# the content-type to EEG, 8 channels, 100 Hz, and float-valued data) The
# last value would be the serial number of the device or some other more or
# less locally unique identifier for the stream as far as available (you
# could also omit it but interrupted connections wouldn't auto-recover)
fs = 1000
info = StreamInfo('BioSemi', 'EEG', 2)
# next make an outlet
outlet = StreamOutlet(info)
print("now sending data...")
n_samples = fs * 100
recorder = np.zeros((n_samples, 2))
t = 0
t_start = local_clock()
while True:
if t > n_samples - 2:
break
clock = local_clock()
if clock - t_start > (t+1)/fs:
# make a new random 8-channel sample; this is converted into a
# pylsl.vectorf (the data type that is expected by push_sample)
second = int(clock)
mysample = [clock, int(second%5 == 0) if second - t_start > 5 else -1]
t += 1
outlet.push_sample(mysample, clock)
recorder[t] = mysample
print(local_clock() - t_start)
np.save('sent_data.npy', recorder)
def drawAndRecordConfigSeq(dot, lslStream, sequence, pygameWindow):
""" Draws the configuration sequence on the screen and
sends dot positions to LSL.
Keywords arguments:
dot ---------- MovingDot object, which is used to store
information about the current position of
the dot.
lslStream ---- LSL StreamOutlet object, which is used to
stream positions of the dot to LSL.
sequence ----- Array of tuples, which stores the tour
of the dot.
pygameWindow - Pygame window object to visualize the
Experiment.
"""
# Create a Clock object.
clock = pygame.time.Clock()
# Display dot at start position until
# subject press a button.
pygameWindow.fill(GRAY)
pygame.draw.circle(pygameWindow, WHITE, (dot.x, dot.y), dot.size, THICKNESS)
pygame.display.update()
eventOcc = False
while True:
if eventOcc:
break
for event in pygame.event.get():
if event.type == KEYDOWN and event.key == K_RETURN:
eventOcc = True
# Start the calibration sequence.
for part in sequence:
# Check whether you have fixation or smooth persuit.
if (part[1]==0 and part[2]==0): # fixation
dot.x = part[0][0]
dot.y = part[0][1]
dot.dx = 0
dot.dy = 0
pygameWindow.fill(GRAY)
pygame.draw.circle(pygameWindow, WHITE, (dot.x, dot.y), dot.size, THICKNESS)
pygame.display.update()
startTime = currentTime()
while (currentTime() < startTime+FIXATIONTIME):
pygameWindow.fill(GRAY)
pygame.draw.circle(pygameWindow, WHITE, (dot.x, dot.y), dot.size, THICKNESS)
clock.tick(SAMPLERATE)
pygame.display.update()
lslStream.push_sample([dot.x, dot.y], pylsl.local_clock())
else: # smooth persuit
pygameWindow.fill(GRAY)
pygame.draw.circle(pygameWindow, WHITE, (dot.x, dot.y), dot.size, THICKNESS)
clock.tick(SAMPLERATE)
pygame.display.update()
lslStream.push_sample([dot.x, dot.y], pylsl.local_clock())
dot.dx = part[1]
dot.dy = part[2]
dot.calcDirection()
while True:
dot.move()
pygameWindow.fill(GRAY)
pygame.draw.circle(pygameWindow, WHITE, (dot.x, dot.y), dot.size, THICKNESS)
clock.tick(SAMPLERATE)
pygame.display.update()
lslStream.push_sample([dot.x, dot.y], pylsl.local_clock())
if dot.reachedGoal(part[0]):
break
# Indicate that the calibration sequence is over.
afterTime = currentTime()
while (currentTime() < afterTime+500):
lslStream.push_sample([-100, -100], pylsl.local_clock())
# graphics
def loadImage(filename):
return visual.ImageStim(win=mywin, image=filename)
mywin = visual.Window([1920, 1080], monitor="testMonitor", units="deg",
fullscr=True)
targets = map(loadImage, glob('stimulus_presentation/stim/cats_dogs/target-*.jpg'))
nontargets = map(loadImage, glob('stimulus_presentation/stim/cats_dogs/nontarget-*.jpg'))
for ii, trial in trials.iterrows():
# inter trial interval
core.wait(iti + np.random.rand() * jitter)
# onset
pos = trials['position'].iloc[ii]
image = choice(targets if pos == 1 else nontargets)
image.draw()
timestamp = local_clock()
outlet.push_sample([markernames[pos]], timestamp)
mywin.flip()
# offset
core.wait(soa)
mywin.flip()
if len(event.getKeys()) > 0 or (time() - start) > record_duration:
break
event.clearEvents()
# Cleanup
mywin.close()
def add_trial(self, label):
self.trial_time_stamps.append(pylsl.local_clock())
self.Y.append(label)
import time
import random
from pylsl import local_clock
for i in range(20):
t1 = local_clock()
t2 = time.time()
print(t2 - t1)
time.sleep(random.random() * 10)
def stream_player(fif_file, server_name='StreamPlayer', chunk_size=8, auto_restart=True, wait_start=True, repeat=float('inf'), high_resolution=False, trigger_file=None):
"""
Input
=====
server_name: LSL server name.
fif_file: fif file to replay.
chunk_size: number of samples to send at once (usually 16-32 is good enough).
auto_restart: play from beginning again after reaching the end.
wait_start: wait for user to start in the beginning.
repeat: number of loops to play.
high_resolution: use perf_counter() instead of sleep() for higher time resolution
but uses much more cpu due to polling.
trigger_file: used to convert event numbers into event strings for readability.
Note: Run pycnbi.set_log_level('DEBUG') to print out the relative time stamps since started.
"""
raw, events = pu.load_raw(fif_file)
sfreq = raw.info['sfreq'] # sampling frequency
n_channels = len(raw.ch_names) # number of channels
if trigger_file is not None:
tdef = trigger_def(trigger_file)
try:
event_ch = raw.ch_names.index('TRIGGER')
except ValueError:
event_ch = None
if raw is not None:
logger.info_green('Successfully loaded %s' % fif_file)
logger.info('Server name: %s' % server_name)
logger.info('Sampling frequency %.3f Hz' % sfreq)
logger.info('Number of channels : %d' % n_channels)
logger.info('Chunk size : %d' % chunk_size)
for i, ch in enumerate(raw.ch_names):
logger.info('%d %s' % (i, ch))
logger.info('Trigger channel : %s' % event_ch)
else:
raise RuntimeError('Error while loading %s' % fif_file)
# set server information
sinfo = pylsl.StreamInfo(server_name, channel_count=n_channels, channel_format='float32',\
nominal_srate=sfreq, type='EEG', source_id=server_name)
desc = sinfo.desc()
channel_desc = desc.append_child("channels")
for ch in raw.ch_names:
channel_desc.append_child('channel').append_child_value('label', str(ch))\
.append_child_value('type','EMG').append_child_value('unit','microvolts')
desc.append_child('amplifier').append_child('settings').append_child_value('is_slave', 'false')
desc.append_child('acquisition').append_child_value('manufacturer', 'PyCNBI').append_child_value('serial_number', 'N/A')
outlet = pylsl.StreamOutlet(sinfo, chunk_size=chunk_size)
# if wait_start:
# input('Press Enter to start streaming.')
logger.info('Streaming started')
idx_chunk = 0
t_chunk = chunk_size / sfreq
finished = False
if high_resolution:
t_start = time.perf_counter()
else:
t_start = time.time()
# start streaming
played = 1
while played < repeat:
idx_current = idx_chunk * chunk_size
chunk = raw._data[:, idx_current:idx_current + chunk_size]
data = chunk.transpose().tolist()
if idx_current >= raw._data.shape[1] - chunk_size:
finished = True
if high_resolution:
# if a resolution over 2 KHz is needed
t_sleep_until = t_start + idx_chunk * t_chunk
while time.perf_counter() < t_sleep_until:
pass
else:
# time.sleep() can have 500 us resolution using the tweak tool provided.
t_wait = t_start + idx_chunk * t_chunk - time.time()
if t_wait > 0.001:
time.sleep(t_wait)
outlet.push_chunk(data)
logger.debug('[%8.3fs] sent %d samples (LSL %8.3f)' % (time.perf_counter(), len(data), pylsl.local_clock()))
if event_ch is not None:
event_values = set(chunk[event_ch]) - set([0])
if len(event_values) > 0:
if trigger_file is None:
logger.info('Events: %s' % event_values)
else:
for event in event_values:
if event in tdef.by_value:
logger.info('Events: %s (%s)' % (event, tdef.by_value[event]))
else:
logger.info('Events: %s (Undefined event)' % event)
idx_chunk += 1
if finished:
if auto_restart is False:
input('Reached the end of data. Press Enter to restart or Ctrl+C to stop.')
else:
logger.info('Reached the end of data. Restarting.')
idx_chunk = 0
finished = False
if high_resolution:
t_start = time.perf_counter()
else:
t_start = time.time()
played += 1
def log_decoding(decoder, logfile, amp_name=None, amp_serial=None, pklfile=True, matfile=False, autostop=False, prob_smooth=False):
"""
Decode online and write results with event timestamps
input
-----
decoder: Decoder or DecoderDaemon class object.
logfile: File name to contain the result in Python pickle format.
amp_name: LSL server name (if known).
amp_serial: LSL server serial number (if known).
pklfile: Export results to Python pickle format.
matfile: Export results to Matlab .mat file if True.
autostop: Automatically finish when no more data is received.
prob_smooth: Use smoothed probability values according to decoder's smoothing parameter.
"""
import cv2
import scipy
# run event acquisition process in the background
state = mp.Value('i', 1)
event_queue = mp.Queue()
proc = mp.Process(target=log_decoding_helper, args=[state, event_queue, amp_name, amp_serial, autostop])
proc.start()
logger.info_green('Spawned event acquisition process.')
# init variables and choose decoding function
labels = decoder.get_label_names()
probs = []
prob_times = []
if prob_smooth:
decode_fn = decoder.get_prob_smooth_unread
else:
decode_fn = decoder.get_prob_unread
# simple controller UI
cv2.namedWindow("Decoding", cv2.WINDOW_AUTOSIZE)
cv2.moveWindow("Decoding", 1400, 50)
img = np.zeros([100, 400, 3], np.uint8)
cv2.putText(img, 'Press any key to start', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
cv2.imshow("Decoding", img)
cv2.waitKeyEx()
img *= 0
cv2.putText(img, 'Press ESC to stop', (40, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
cv2.imshow("Decoding", img)
key = 0
started = False
tm_watchdog = qc.Timer(autoreset=True)
tm_cls = qc.Timer()
while key != 27:
prob, prob_time = decode_fn(True)
t_lsl = pylsl.local_clock()
key = cv2.waitKeyEx(1)
if prob is None:
# watch dog
if tm_cls.sec() > 5:
if autostop and started:
logger.info('No more streaming data. Finishing.')
break
tm_cls.reset()
tm_watchdog.sleep_atleast(0.001)
continue
probs.append(prob)
prob_times.append(prob_time)
txt = '[%.3f] ' % prob_time
txt += ', '.join(['%s: %.2f' % (l, p) for l, p in zip(labels, prob)])
txt += ' (%d ms, LSL Diff = %.3f)' % (tm_cls.msec(), (t_lsl-prob_time))
logger.info(txt)
if not started:
started = True
tm_cls.reset()
# finish up processes
cv2.destroyAllWindows()
logger.info('Cleaning up event acquisition process.')
state.value = 0
decoder.stop()
event_times, event_values = event_queue.get()
proc.join()
# save values
if len(prob_times) == 0:
logger.error('No decoding result. Please debug.')
import pdb
pdb.set_trace()
t_start = prob_times[0]
probs = np.vstack(probs)
event_times = np.array(event_times) - t_start
prob_times = np.array(prob_times) - t_start
event_values = np.array(event_values)
data = dict(probs=probs, prob_times=prob_times, event_times=event_times, event_values=event_values, labels=labels)
if pklfile:
qc.save_obj(logfile, data)
logger.info('Saved to %s' % logfile)
if matfile:
pp = qc.parse_path(logfile)
matout = '%s/%s.mat' % (pp.dir, pp.name)
scipy.io.savemat(matout, data)
logger.info('Saved to %s' % matout)
"EEG",
len(headset.channel_mask),
headset.sampling_rate,
pylsl.cf_int16,
str(headset.serial_number),
)
info_desc = info.desc()
info_desc.append_child_value("manufacturer", "Emotiv")
channels = info_desc.append_child("channels")
for ch in headset.channel_mask:
channels.append_child("channel").append_child_value("label", ch).append_child_value(
"unit", "microvolts"
).append_child_value("type", "EEG")
# Outgoing buffer size = 360 seconds, transmission chunk size = 32 samples
outlet = pylsl.stream_outlet(info, 1, 32)
while True:
try:
s = headset.get_sample()
except epoc.EPOCTurnedOffError, e:
print "Headset is turned off, waiting..."
time.sleep(0.02)
else:
if s:
outlet.push_sample(pylsl.vectori(s), pylsl.local_clock())
headset.disconnect()
import sys; sys.path.append('..') # help python find pylsl relative to this example program
from pylsl import StreamInfo, StreamOutlet, local_clock
import random
import time
# first create a new stream info (here we set the name to BioSemi, the content-type to EEG, 8 channels, 100 Hz, and float-valued data)
# The last value would be the serial number of the device or some other more or less locally unique identifier for the stream as far as available (you could also omit it but interrupted connections wouldn't auto-recover).
info = StreamInfo('BioSemi','EEG',8,100,'float32','myuid2424');
# append some meta-data
info.desc().append_child_value("manufacturer","BioSemi")
channels = info.desc().append_child("channels")
for c in ["C3","C4","Cz","FPz","POz","CPz","O1","O2"]:
channels.append_child("channel").append_child_value("name",c).append_child_value("unit","microvolts").append_child_value("type","EEG")
# next make an outlet; we set the transmission chunk size to 32 samples and the outgoing buffer size to 360 seconds (max.)
outlet = StreamOutlet(info,32,360)
print("now sending data...")
while True:
# make a new random 8-channel sample; this is converted into a pylsl.vectorf (the data type that is expected by push_sample)
mysample = [random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random()]
# get a time stamp in seconds (we pretend that our samples are actually 125ms old, e.g., as if coming from some external hardware)
stamp = local_clock()-0.125
# now send it and wait for a bit
outlet.push_sample(mysample,stamp)
time.sleep(0.01)
print("LSL INCOMING TRIGGERS TEST\n")
name = "stream_trigger2"
si = lsl.StreamInfo(name=name,
type='Markers',
source_id='testing_dep',
channel_format=lsl.cf_string)
so = lsl.stream_outlet(si)
print("It will take 5 seconds to start sending triggers...")
time.sleep(
5
) # Make sure that eego got the news of the new stram and subcribes to it. Not needed from the python side
triggers = int(sys.argv[1]) #number of triggers
sleeping_time = float(sys.argv[2]) #time in seconds
cont = 1
while (cont <= triggers or triggers is -1):
string = "%i -> %s" % (cont, datetime.datetime.now().time())
print(string)
so.push_sample([string], lsl.local_clock())
cont += 1
time.sleep(sleeping_time)