imagestimulus=imagestimulus,
textstimulus=textstimulus,
markernumerate=markernumerate)
self.end_time = time.time()
self.elapsed_time = self.end_time - self.start_time
self.logger.info("!!ON_PLAY ELAPSED TIME!! : {}".format(
self.elapsed_time))
if __name__ == "__main__":
#WARNING TO SIMULATE: USE Generated data or Replay data ( - Generated(outlet_actichamp.py,sine waves), Replay(PREFERABLY file_stream_player.py or class replayamp - does not simulate all)
replayamp = False
#replay sample data
filetoreplay = my.get_filetoreplay() #filepath
#save folder dir
test_folder_path = my.get_test_folder() #folderpath
#1.Start Class
prime = primeclass(replayamp=replayamp, filetoreplay=filetoreplay)
try:
#2.RE-INIT Vars
#SAVING - Reinitialize parameters
#Start dialogue
info = {
'SAVE': ['no', 'yes'],
'savefolder': test_folder_path,
'subject': 1,
'session': 1,
self.end_time = time.time()
self.elapsed_time = self.end_time - self.start_time
self.logger.info("!!ON_PLAY ELAPSED TIME!! : {}".format(
self.elapsed_time))
if __name__ == "__main__":
#WARNING TO SIMULATE: USE ONLY Replay data - Replay(PREFERABLY file_stream_player.py instead of using replayamp of nftclass)
#replayamp
replayamp = False
#replay sample data (using same for online and offline if you don't acquire data)
folderdir = 'e2_sample'
filename = "EG_S001_REST_ss01_b01_08102019_14h13m_rest_1"
amp_ref = ['Cz'] #[] - no_ref (give manual information about the ref)
filetoreplay = my.get_filetoreplay(folderdir=folderdir,
filename=filename,
filetype=".meta") #filepath
#save folder dir
test_folder_path = os.path.join(my.get_test_folder(),
'nftclass') #folderpath
#1.Start Class
nft = nftclass(replayamp=replayamp, filetoreplay=filetoreplay)
nft.logger.debug("#WARNING replayamp only useful for online routines")
try:
#2.RE-INIT Vars
#SAVING
#Start dialogue
info = {
'SAVE': ['no', 'yes'],
)
else:
logger.info('Reached the end of data. Restarting.')
idx_chunk = 0
finished = False
# t_start = timeit.default_timer() #chooses the best default timer depending on os and python version
if high_resolution:
t_start = time.perf_counter()
else:
t_start = timeit.default_timer()
# sample code
if __name__ == '__main__':
server_name = 'BrainVision RDA' #'Actichamp-0'
server_data_unit = 'uV'
chunk_size = 50 # chunk samples ; for a fs=1000Hz , 10 Samples are sent at a 10ms interval
#filetoreplay
folderdir = 'e2_sample' #folder inside "sampledata"
filename = 'EG_S001_REST_ss01_b01_08102019_14h13m_rest_1' #
filetype = ".meta" #".vhdr"#".meta"
filetoreplay = my.get_filetoreplay(
folderdir=folderdir, filename=filename,
filetype=filetype) #filepath "sampledata"
#stream
stream_player(server_name,
filetoreplay,
chunk_size,
server_data_unit=server_data_unit,
auto_restart=True)
def test_1():
save = False
plot = True
on_filter = True # DC Offset
rereference = False
ref_channels = ['Fp1'] # or average
#load data from sample folder
folderdir = 'e2_sample'
filename = "EG_S001_REST_ss01_b01_08102019_14h13m_rest_1"
filetype = ".meta"
filepath = my.get_filetoreplay(folderdir=folderdir, filename=filename, filetype=filetype)
# wyrm test data: mushu2wyrm
raw_wyrm = my.load_mushu_data(filepath) #format: raw_wyrm.data samples x channels
if on_filter:
order=4
fs_n= raw_wyrm.fs / 2
l_freq, h_freq=1, 40#bandpass
b, a = wyrm.processing.signal.butter(order, [l_freq / fs_n, h_freq / fs_n], btype= 'band' )
raw_m_2 = wyrm.processing.lfilter(raw_wyrm, b, a)
if plot:
spectrum = wyrm.processing.spectrum(raw_wyrm)
psds=spectrum.data.transpose() #chs, psds
freqs =spectrum.axes[0]
fmax=freqs<40
psds=psds[:,fmax]
freqs=freqs[fmax]
eeg.plot_psd(psds, freqs, log_scale=False)
# mne test data: wyrm2mne or mushu2mne
st=time.time()
raw, event_id = my.raw_wyrm_to_mne(raw_wyrm) #raw, event_id = my.mushu_to_mne(filepath)
print("Event id 1: ", event_id)
et=time.time()-st
stim_ch_1 = raw.pick_types(stim=True)
print("Time raw: ", et)
raw_m=raw
print(raw_m.ch_names)
if on_filter:
l_freq, h_freq = 1, None # highpass
raw_m.filter(l_freq, h_freq)
l_freq, h_freq = None, 40 # lowpass
raw_m.filter(l_freq, h_freq)
if rereference:
raw_m.set_eeg_reference(ref_channels=ref_channels, projection=False)
if plot:
picks = mne.pick_types(raw_m.info, eeg=True)
raw_m.plot_psd(fmax=40, picks=picks)
raw_m.pick_types(eeg=True).plot(n_channels=1)
if save:
filedir, filname, ext = my.parse_path_list(filepath)
nfilename = filename + ".fif"
out_filepath = os.path.join(filedir, nfilename)
print('saving eeg fif'), nfilename
raw_m.save(out_filepath)
#simulate an online conversion
st=time.time()
raw, event_id = my.online_raw_wyrm2mne(raw_wyrm)
print("Event id 2: ", event_id)
et=time.time()-st
stim_ch_2=raw.pick_types(stim=True)
print("Time online: ", et)
print("Same : ", (stim_ch_1.get_data()==stim_ch_2.get_data()).all() )