def read_spiketrain(fh, block_id, array_id):
nix_block = fh.handle.blocks[block_id]
nix_da = nix_block.data_arrays[array_id]
params = {
'times': nix_da[:], # TODO think about lazy data loading
'dtype': nix_da.dtype,
't_start': Reader.Help.read_quantity(nix_da.metadata, 't_start'),
't_stop': Reader.Help.read_quantity(nix_da.metadata, 't_stop')
}
name = Reader.Help.get_obj_neo_name(nix_da)
if name:
params['name'] = name
if 'left_sweep' in nix_da.metadata:
params['left_sweep'] = Reader.Help.read_quantity(nix_da.metadata, 'left_sweep')
if len(nix_da.dimensions) > 0:
s_dim = nix_da.dimensions[0]
params['sampling_rate'] = s_dim.sampling_interval * getattr(pq, s_dim.unit)
if nix_da.unit:
params['units'] = nix_da.unit
st = SpikeTrain(**params)
for key, value in Reader.Help.read_attributes(nix_da.metadata, 'spiketrain').items():
setattr(st, key, value)
st.annotations = Reader.Help.read_annotations(nix_da.metadata, 'spiketrain')
return st
def _align_to_zero(self, spiketrains=None):
if spiketrains is None:
spiketrains = self.spiketrains
t_lims = [(st.t_start, st.t_stop) for st in spiketrains]
tmin = min(t_lims, key=lambda f: f[0])[0]
tmax = max(t_lims, key=lambda f: f[1])[1]
unit = spiketrains[0].units
for count, spiketrain in enumerate(spiketrains):
annotations = spiketrain.annotations
spiketrains[count] = SpikeTrain(
np.array(spiketrain.tolist()) * unit - tmin,
t_start=0 * unit,
t_stop=tmax - tmin)
spiketrains[count].annotations = annotations
return spiketrains
