def launch(self, matfile):
mat = scipy.io.loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = Sensors(storage_path=self.storage_path)
ts = TimeSeries(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()
# setup sensors information
# ch.labels = numpy.array(
# [str(l[0]) for l in hdr['label'][0, 0][:, 0]])
# ch.number_of_sensors = ch.labels.size
return ts #, ch
def launch(self, matfile):
mat = scipy.io.loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = Sensors(storage_path=self.storage_path)
ts = TimeSeries(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()
# setup sensors information
# ch.labels = numpy.array(
# [str(l[0]) for l in hdr['label'][0, 0][:, 0]])
# ch.number_of_sensors = ch.labels.size
return ts #, ch
def launch(self, array_data):
array_data = numpy.loadtxt(array_data)
ts = TimeSeries()
ts.storage_path = self.storage_path
#ts.configure()
ts.write_data_slice(array_data)
ts.close_file()
return ts
def launch(self, array_data):
array_data = numpy.loadtxt(array_data)
ts = TimeSeries()
ts.storage_path = self.storage_path
#ts.configure()
ts.write_data_slice(array_data)
ts.close_file()
return ts
def _create_timeseries(self):
"""Launch adapter to persist a TimeSeries entity"""
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
time_series = TimeSeries()
time_series.sample_period = 10.0
time_series.start_time = 0.0
time_series.storage_path = storage_path
time_series.write_data_slice(numpy.array([1.0, 2.0, 3.0]))
time_series.close_file()
time_series.sample_period_unit = 'ms'
self._store_entity(time_series, "TimeSeries", "tvb.datatypes.time_series")
count_ts = self.count_all_entities(TimeSeries)
self.assertEqual(1, count_ts, "Should be only one TimeSeries")
def _create_timeseries(self):
"""Launch adapter to persist a TimeSeries entity"""
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
time_series = TimeSeries()
time_series.sample_period = 10.0
time_series.start_time = 0.0
time_series.storage_path = storage_path
time_series.write_data_slice(numpy.array([1.0, 2.0, 3.0]))
time_series.close_file()
time_series.sample_period_unit = 'ms'
self._store_entity(time_series, "TimeSeries", "tvb.datatypes.time_series")
count_ts = self.count_all_entities(TimeSeries)
self.assertEqual(1, count_ts, "Should be only one TimeSeries")
