def create(self, *args, **kwargs):
self.markers = []
self.timeStamps = []
if 'data' in kwargs.keys():
if kwargs['data']:
self.markers = kwargs['markers']
self.timeStamps = kwargs['timeStamps']
self.trialIndices = kwargs['trialIndices']
self.rawMarkers = kwargs['rawMarkers']
self.sessionStartTime = kwargs['sessionStartTime']
self.samplingRate = kwargs['sampleRate']
self.numSets = 1
return self
nevFile = glob.glob("*.nev")
if len(nevFile) == 0:
print("No .nev files in directory. Returning empty object...")
# create empty object
DPT.DPObject.create(self, dirs=[], *args, **kwargs)
else:
# create object
DPT.DPObject.create(self, *args, **kwargs)
reader = BlackrockIO(nevFile[0])
print('Opening .nev file, creating new RPLParallel object...')
ev_rawtimes, _, ev_markers = reader.get_event_timestamps()
ev_times = reader.rescale_event_timestamp(ev_rawtimes,
dtype="float64")
if ev_markers[0] == 128:
self.rawMarkers = ev_markers
self.markers = ev_markers[::2]
self.timeStamps = ev_times[::2]
return self
self.samplingRate = 30000
self.rawMarkers = ev_markers
self.sessionStartTime = ev_times[0]
self.numSets = 1
self.markers, self.timeStamps, self.trialIndices = arrangeMarkers(
ev_markers, ev_times)
return self
def create(self, *args, **kwargs):
if not self.args['SkipParallel']:
print('Calling RPLParallel...')
rp = RPLParallel(saveLevel=1)
ns5File = glob.glob('*.ns5')
if len(ns5File) > 1:
print('Too many .ns5 files, do not know which one to use.')
# create empty object
DPT.DPObject.create(self, dirs=[], *args, **kwargs)
return
if len(ns5File) == 0:
print('.ns5 file missing')
# create empty object
DPT.DPObject.create(self, dirs=[], *args, **kwargs)
return
# create object
DPT.DPObject.create(self, *args, **kwargs)
reader = BlackrockIO(ns5File[0])
bl = reader.read_block(lazy=True)
print('.ns5 file loaded.')
segment = bl.segments[0]
if len(
glob.glob('*.ns2')
) == 0: # Check if .ns2 file is present, if its not present adjust the index for raw signals accordingly
index = 1
else:
index = 2
chx = bl.channel_indexes[index] # For the raw data.
analogInfo = {}
analogInfo['SampleRate'] = float(
segment.analogsignals[index].sampling_rate)
annotations = chx.annotations
names = list(map(lambda x: str(x), chx.channel_names))
def process_channel(data,
annotations,
chxIndex,
analogInfo,
channelNumber,
returnData=False):
analogInfo['Units'] = 'uV'
analogInfo['HighFreqCorner'] = float(
annotations['nev_hi_freq_corner'][chxIndex])
analogInfo['HighFreqOrder'] = annotations['nev_hi_freq_order'][
chxIndex]
analogInfo['HighFilterType'] = annotations['nev_hi_freq_type'][
chxIndex]
analogInfo['LowFreqCorner'] = float(
annotations['nev_lo_freq_corner'][chxIndex])
analogInfo['LowFreqOrder'] = annotations['nev_lo_freq_order'][
chxIndex]
analogInfo['LowFilterType'] = annotations['nev_lo_freq_type'][
chxIndex]
analogInfo['MaxVal'] = np.amax(data)
analogInfo['MinVal'] = np.amin(data)
analogInfo['NumberSamples'] = len(data)
analogInfo['ProbeInfo'] = names[chxIndex]
if returnData:
return analogInfo
arrayNumber = annotations['connector_ID'][chxIndex] + 1
arrayDir = "array{:02d}".format(int(arrayNumber))
channelDir = "channel{:03d}".format(int(channelNumber))
directory = os.getcwd(
) # Go to the channel directory and save file there.
if arrayDir not in os.listdir('.'):
os.mkdir(arrayDir)
os.chdir(arrayDir)
if channelDir not in os.listdir('.'):
os.mkdir(channelDir)
os.chdir(channelDir)
print('Calling RPLRaw for channel {:03d}'.format(channelNumber))
rplraw.RPLRaw(analogData=data, analogInfo=analogInfo, saveLevel=1)
if self.args['SkipHPC']:
if not self.args['SkipLFP']:
print('Calling RPLLFP for channel {:03d}'.format(
channelNumber))
rpllfp.RPLLFP(saveLevel=1)
if not self.args['SkipHighPass']:
print('Calling RPLHighPass for channel {:03d}'.format(
channelNumber))
rplhighpass.RPLHighPass(saveLevel=1)
if DPT.levels.get_level_name('session',
os.getcwd()) != 'sessioneye':
if not self.args['SkipSort']:
print(
'Calling Mountain Sort for channel {:03d}'.format(
channelNumber))
# mountain_batch()
# export_mountain_cells()
else:
if 'HPCScriptsDir' not in kwargs.keys():
kwargs['HPCScriptsDir'] = ''
print(
'Adding RPLLFP slurm script for channel {:03d} to job queue'
.format(channelNumber))
os.system('sbatch ' + kwargs['HPCScriptsDir'] +
'rpllfp-slurm.sh')
if not self.args['SkipSort']:
print(
'Adding RPLHighPass and Mountain Sort slurm script for channel {:03d} to job queue'
.format(channelNumber))
os.system('sbatch ' + kwargs['HPCScriptsDir'] +
'rplhighpass-sort-slurm.sh')
else:
print(
'Adding RPLHighPass slurm script for channel {:03d} to job queue'
.format(channelNumber))
os.system('sbatch ' + kwargs['HPCScriptsDir'] +
'rplhighpass-slurm.sh')
os.chdir(directory)
print('Channel {:03d} processed'.format(channelNumber))
return
if 'returnData' in kwargs.keys():
i = self.args['channel'][0]
chxIndex = names.index(
list(filter(lambda x: str(i) in x, names))[0])
data = np.array(segment.analogsignals[index].load(
time_slice=None, channel_indexes=[chx.index[chxIndex]]))
analogInfo = process_channel(data,
annotations,
chxIndex,
analogInfo,
i,
returnData=True)
print('Returning data and analogInfo to RPLRaw')
self.data = data
self.analogInfo = analogInfo
return
channelNumbers = []
channelIndexes = []
for i in self.args['channel']:
chxIndex = list(
filter(lambda x: int(i) == int(x[6:len(x) - 1]), names))
if len(chxIndex) > 0:
chxIndex = names.index(chxIndex[0])
channelNumbers.append(i)
channelIndexes.append(chxIndex)
if kwargs.get('byChannel', 0):
for ind, idx in enumerate(channelIndexes):
data = np.array(segment.analogsignals[index].load(
time_slice=None, channel_indexes=[idx]))
print('Processing channel {:03d}'.format(channelNumbers[ind]))
process_channel(data, annotations, idx, analogInfo,
channelNumbers[ind])
del data # to create RAM space to load in the next channel data.
return
else:
if len(channelIndexes) > 0:
numOfIterations = int(np.ceil(len(channelIndexes) / 32))
for k in range(numOfIterations):
tempIndexes = channelIndexes[k * 32:(k + 1) * 32]
tempNumbers = channelNumbers[k * 32:(k + 1) * 32]
data = np.array(segment.analogsignals[index].load(
time_slice=None, channel_indexes=tempIndexes))
for ind, idx in enumerate(tempIndexes):
print('Processing channel {:03d}'.format(
tempNumbers[ind]))
process_channel(np.array(data[:, ind]), annotations,
idx, analogInfo, tempNumbers[ind])
del data # to create RAM space to load in the next set of channel data.
return
def load_data(settings):
reader = BlackrockIO(filename=os.path.join(settings.path2data,
settings.file_stem),
nsx_to_load=3)
return reader
import numpy as np
import matplotlib.pyplot as plt
from pyedfread import edf
from neo.io import BlackrockIO
# 2c
pos_data = np.loadtxt("session01/RawData_T1-400/session_1_5112018105323.txt",
skiprows=14)
# 3b
samples, events, messages = edf.pread('181105.edf',
trial_marker=b'Start Trial')
# 4c
reader = BlackrockIO('session01/181105_Block1.ns5')
bl = reader.read_block(lazy=True)
# 4e
broadband_data = np.array(bl.segments[0].analogsignals[1].load(
time_slice=None, channel_indexes=[2]))
# 4f
sr = float(bl.segments[0].analogsignals[1].sampling_rate)
# 4g
reader = BlackrockIO('session01/181105_Block1.nev')
ev_rawtimes, _, ev_markers = reader.get_event_timestamps()
# 2d
uind = np.arange(300)
def source2raw(subs=cfg.subs):
from neo.io import BlackrockIO
TimeStart = time.time()
# ITERATE SUBJECT LIST =============================================================================
for iSub, sub in enumerate(subs):
print(
f"\n--- {sub} ----------------------------------------------------\n"
)
# add paths to parameters
SP = cf.sub_params(sub)
cf.display_progress(f"Loading source data, {sub}", iSub, len(subs),
TimeStart)
ieeg = []
ttl = []
# 2 files per session
for i, source_file in enumerate(SP['source_files']):
reader = BlackrockIO(filename=source_file)
blks = reader.read(lazy=False)
ttl_ = np.squeeze(1. * (np.diff(1. * (np.array(
blks[0].segments[-1].analogsignals[0]).T[0, :] > 4000.)) >
0)).astype(np.int8)
ieeg_ = np.array(blks[0].segments[-1].analogsignals[1],
dtype=np.int16).T
# start five seconds before first fixation
t0 = np.argmax(ttl_ > 0.5) - int(5 * cfg.source_srate)
ttl += [ttl_[t0:]]
ieeg += [ieeg_[:, t0:]]
# loop over sessions
ieeg_full = []
ttl_full = []
for i in range(0, len(ttl), 2):
m = min(len(ttl[i]), len(ttl[i + 1]))
ieeg_session = np.vstack((ieeg[i][:, :m], ieeg[i + 1][:, :m]))
ieeg_full += [ieeg_session]
ttl_full += [ttl[i][:m]]
# concatenate sessions
ieeg = np.concatenate(ieeg_full, axis=-1)
ttl = np.concatenate(ttl_full)
print(
f"\n{ieeg.shape[0]} channels, {int(len(ttl)/cfg.source_srate)} seconds, {np.sum(ttl)} events, {round(ieeg.nbytes/1e9,2)} GB of data loaded"
)
cf.print_d("saving...")
# save one ieeg, one ttl file per subject
fname = os.path.join(SP['RawPath'], f"{sub}_ttl.csv")
ttl.tofile(fname, sep=',')
if len(SP['ChNames']) != ieeg.shape[0]:
dp = fix_channels(dp, len(ieeg))
cf.display_progress(f"Making MNE Raw, {sub} ", iSub, len(subs),
TimeStart)
# Create MNE info
info = mne.create_info(SP['ChNames'],
sfreq=cfg.source_srate,
ch_types='eeg')
# Finally, create the Raw object
raw = mne.io.RawArray(ieeg, info)
montage = mne.channels.make_dig_montage(
dict(zip(SP['ChNames'], SP['coords'])))
raw.set_montage(montage)
#print(raw.get_montage().get_positions()['ch_pos']['001-AH1'])
# downsample for memory
if cfg.srate >= raw.info['sfreq']:
print(
f"Error: Original sampling freq smaller than or equal to target sampling freq ({raw.info['sfreq']} Hz <={cfg.srate} Hz)"
)
else:
# resample raw
cf.display_progress(
f"Resampling from {cfg.source_srate} to {cfg.srate}, {sub}",
iSub, len(subs), TimeStart)
raw.resample(cfg.srate)
cf.display_progress(
f"Applying notch filtering at {cfg.landline_noise} Hz, and 4 harmonics, {sub}",
iSub, len(subs), TimeStart)
raw.notch_filter(cfg.landline_noise * np.arange(1, 5),
filter_length='auto',
phase='zero',
picks='all')
cf.print_d(f"Saving mne raw files for {sub}")
fname = os.path.join(SP['RawPath'], f"{sub}_raw.fif")
raw.save(fname, picks='all', overwrite=True)
print(' ')