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