def _parse_timeseries(self, data_arrays):
# Create TVB time series to be filled
time_series = TimeSeriesSurface()
time_series.storage_path = self.storage_path
time_series.set_operation_id(self.operation_id)
time_series.start_time = 0.0
time_series.sample_period = 1.0
# First process first data_array and extract important data from it's metadata
meta_dict = self._get_meta_dict(data_arrays[0])
gid = meta_dict.get(self.UNIQUE_ID_ATTR)
sample_period = meta_dict.get(self.TIME_STEP_ATTR)
time_series.subject = meta_dict.get(self.SUBJECT_ATTR)
time_series.title = meta_dict.get(self.NAME_ATTR)
if gid:
time_series.gid = gid.replace("{", "").replace("}", "")
if sample_period:
time_series.sample_period = float(sample_period)
# todo : make sure that write_time_slice is not required here
# Now read time series data
for data_array in data_arrays:
time_series.write_data_slice([data_array.data])
# Close file after writing data
time_series.close_file()
return time_series
def _parse_timeseries(self, data_arrays):
# Create TVB time series to be filled
time_series = TimeSeriesSurface()
time_series.storage_path = self.storage_path
time_series.set_operation_id(self.operation_id)
time_series.start_time = 0.0
time_series.sample_period = 1.0
# First process first data_array and extract important data from it's metadata
meta_dict = self._get_meta_dict(data_arrays[0])
gid = meta_dict.get(self.UNIQUE_ID_ATTR)
sample_period = meta_dict.get(self.TIME_STEP_ATTR)
time_series.subject = meta_dict.get(self.SUBJECT_ATTR)
time_series.title = meta_dict.get(self.NAME_ATTR)
if gid:
time_series.gid = gid.replace("{", "").replace("}", "")
if sample_period:
time_series.sample_period = float(sample_period)
# todo : make sure that write_time_slice is not required here
# Now read time series data
for data_array in data_arrays:
time_series.write_data_slice([data_array.data])
# Close file after writing data
time_series.close_file()
return time_series
def _parse_timeseries(self, data_arrays):
# Create TVB time series to be filled
time_series = TimeSeriesSurface()
time_series.start_time = 0.0
time_series.sample_period = 1.0
# First process first data_array and extract important data from it's metadata
meta_dict = self._get_meta_dict(data_arrays[0])
sample_period = meta_dict.get(self.TIME_STEP_ATTR)
time_series.subject = meta_dict.get(self.SUBJECT_ATTR)
time_series.title = meta_dict.get(self.NAME_ATTR)
if sample_period:
time_series.sample_period = float(sample_period)
time_series.sample_rate = 1 / time_series.sample_period
return time_series, data_arrays
