def save_to_hdf5(self, file_name=None, system=None): """ Stores NLfp() object to HDF5 file Parameters ---------- file_name : str Full file directory for the lfp data system : str Recoring system or data format Returns ------- None Also see -------- nc_hdf.Nhdf().save_lfp() """ hdf = Nhdf() if file_name and system: if os.path.exists(file_name): self.set_filename(file_name) self.set_system(system) self.load() else: logging.error('Specified file cannot be found!') hdf.save_lfp(lfp=self) hdf.close()
def load_lfp_NWB(self, file_name): """ Decodes LFP data from NWB (HDF5) file format Parameters ---------- file_name : str Full file directory for the lfp data Returns ------- None """ file_name, path = file_name.split('+') if os.path.exists(file_name): hdf = Nhdf() hdf.set_filename(file_name) _record_info = {} if path in hdf.f: g = hdf.f[path] elif '/processing/Neural Continuous/LFP/' + path in hdf.f: path = '/processing/Neural Continuous/LFP/' + path g = hdf.f[path] else: logging.error('Specified path does not exist!') for key, value in g.attrs.items(): _record_info[key] = value self.set_record_info(_record_info) self._set_samples(hdf.get_dataset(group=g, name='data')) self._set_timestamp(hdf.get_dataset(group=g, name='timestamps')) self._set_total_samples( hdf.get_dataset(group=g, name='num_samples')) hdf.close() else: logging.error(file_name + ' does not exist!')