def launch(self, matfile):
mat = loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = SensorsMEG(storage_path=self.storage_path)
ts = TimeSeriesEEG(storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = mat['dat'].T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:ns] * 1.0 / fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(fs)
ts.close_file()
return ts
def launch(self, matfile, fdtfile):
self.mat = loadmat(matfile)
self.fs = self.mat['EEG']['srate'][0, 0][0, 0]
self.nsamp = self.mat['EEG']['pnts'][0, 0][0, 0]
self.data = numpy.fromfile(fdtfile, dtype=numpy.float32)
self.data = self.data.reshape((self.nsamp, -1)).T
self.nchan = self.data.shape[0]
self.labels = [
c[0] for c in self.mat['EEG']['chanlocs'][0, 0]['labels'][0]
]
ch = SensorsEEG(storage_path=self.storage_path,
labels=self.labels,
number_of_sensors=len(self.labels))
self._capture_operation_results([ch])
ts = TimeSeriesEEG(sensors=ch, storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = self.data.T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:dat.shape[0]] * 1.0 / self.fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(self.fs)
ts.close_file()
return ts
def launch(self, matfile):
mat = loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = SensorsMEG(storage_path=self.storage_path)
ts = TimeSeriesEEG(storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = mat['dat'].T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:ns] * 1.0 / fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(fs)
ts.close_file()
return ts
def launch(self, matfile, fdtfile):
self.mat = loadmat(matfile)
self.fs = self.mat["EEG"]["srate"][0, 0][0, 0]
self.nsamp = self.mat["EEG"]["pnts"][0, 0][0, 0]
self.data = numpy.fromfile(fdtfile, dtype=numpy.float32)
self.data = self.data.reshape((self.nsamp, -1)).T
self.nchan = self.data.shape[0]
self.labels = [c[0] for c in self.mat["EEG"]["chanlocs"][0, 0]["labels"][0]]
ch = SensorsEEG(storage_path=self.storage_path, labels=self.labels, number_of_sensors=len(self.labels))
self._capture_operation_results([ch])
ts = TimeSeriesEEG(sensors=ch, storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = self.data.T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = "Time 1 Channel 1".split()
ts.write_time_slice(numpy.r_[: dat.shape[0]] * 1.0 / self.fs)
ts.start_time = 0.0
ts.sample_period_unit = "s"
ts.sample_period = 1.0 / float(self.fs)
ts.close_file()
return ts
def launch(self, vhdr, dat):
self.filename = vhdr
self.wd, _ = os.path.split(vhdr)
# read file
with open(vhdr, 'r') as fd:
self.srclines = fd.readlines()
# config parser expects each section to have header
# but vhdr has some decorative information at the beginning
while not self.srclines[0].startswith('['):
self.srclines.pop(0)
self.sio = StringIO()
self.sio.write('\n'.join(self.srclines))
self.sio.seek(0)
self.cp = ConfigParser.ConfigParser()
self.cp.readfp(self.sio)
for opt in self.cp.options('Common Infos'):
setattr(self, opt, self.cp.get('Common Infos', opt))
self.binaryformat = self.cp.get('Binary Infos', 'BinaryFormat')
self.labels = [
self.cp.get('Channel Infos', o).split(',')[0]
for o in self.cp.options('Channel Infos')
]
self.fs = self.srate = 1e6 / float(self.samplinginterval)
self.nchan = int(self.numberofchannels)
# important if not in same directory
self.datafile = os.path.join(self.wd, self.datafile)
self.read_data()
# create TVB datatypes
ch = SensorsEEG(storage_path=self.storage_path,
labels=self.labels,
number_of_sensors=len(self.labels))
uid = vhdr + '-sensors'
self._capture_operation_results([ch], uid=uid)
ts = TimeSeriesEEG(sensors=ch, storage_path=self.storage_path)
dat = self.data.T[:, numpy.newaxis, :, numpy.newaxis]
ts.write_data_slice(dat)
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:dat.shape[0]] * 1.0 / self.fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(self.fs)
ts.close_file()
return ts
def launch(self, vhdr, dat):
self.filename = vhdr
self.wd, _ = os.path.split(vhdr)
# read file
with open(vhdr, "r") as fd:
self.srclines = fd.readlines()
# config parser expects each section to have header
# but vhdr has some decorative information at the beginning
while not self.srclines[0].startswith("["):
self.srclines.pop(0)
self.sio = StringIO()
self.sio.write("\n".join(self.srclines))
self.sio.seek(0)
self.cp = ConfigParser.ConfigParser()
self.cp.readfp(self.sio)
for opt in self.cp.options("Common Infos"):
setattr(self, opt, self.cp.get("Common Infos", opt))
self.binaryformat = self.cp.get("Binary Infos", "BinaryFormat")
self.labels = [self.cp.get("Channel Infos", o).split(",")[0] for o in self.cp.options("Channel Infos")]
self.fs = self.srate = 1e6 / float(self.samplinginterval)
self.nchan = int(self.numberofchannels)
# important if not in same directory
self.datafile = os.path.join(self.wd, self.datafile)
self.read_data()
# create TVB datatypes
ch = SensorsEEG(storage_path=self.storage_path, labels=self.labels, number_of_sensors=len(self.labels))
uid = vhdr + "-sensors"
self._capture_operation_results([ch], uid=uid)
ts = TimeSeriesEEG(sensors=ch, storage_path=self.storage_path)
dat = self.data.T[:, numpy.newaxis, :, numpy.newaxis]
ts.write_data_slice(dat)
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = "Time 1 Channel 1".split()
ts.write_time_slice(numpy.r_[: dat.shape[0]] * 1.0 / self.fs)
ts.start_time = 0.0
ts.sample_period_unit = "s"
ts.sample_period = 1.0 / float(self.fs)
ts.close_file()
return ts
