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!')