def __init__(self,
action_id_0,
action_id_1,
actions,
channel_group=None,
max_dissimilarity=10,
dissimilarity_function=None,
verbose=False):
data_path_0 = get_data_path(actions[action_id_0])
data_path_1 = get_data_path(actions[action_id_1])
self._actions = actions
self.action_id_0 = action_id_0
self.action_id_1 = action_id_1
self._channel_group = channel_group
self.action_ids = [action_id_0, action_id_1]
self.max_dissimilarity = max_dissimilarity
self.dissimilarity_function = dissimilarity_function
self._verbose = verbose
if channel_group is None:
channel_groups = get_channel_groups(data_path_0)
self.matches = {}
self.templates = {}
self.unit_ids = {}
for chan in channel_groups:
self.matches[chan] = dict()
self.templates[chan] = list()
self.unit_ids[chan] = list()
else:
self.matches = {channel_group: dict()}
self.templates = {channel_group: list()}
self.unit_ids = {channel_group: list()}
for channel_group in self.matches.keys():
unit_annotations_0 = load_unit_annotations(
data_path_0, channel_group=channel_group)
unit_annotations_1 = load_unit_annotations(
data_path_1, channel_group=channel_group)
unit_ids_0 = []
unit_ids_1 = []
for st in unit_annotations_0:
unit_ids_0.append(get_unit_id(st))
for st in unit_annotations_1:
unit_ids_1.append(get_unit_id(st))
self.unit_ids[channel_group] = [
np.array(unit_ids_0),
np.array(unit_ids_1)
]
self.templates[channel_group] = [
compute_templates(self.waveforms_0(channel_group)),
compute_templates(self.waveforms_1(channel_group))
]
if len(unit_annotations_0) > 0 and len(unit_annotations_1) > 0:
self._do_dissimilarity(channel_group)
self._do_matching(channel_group)
def __init__(self,
actions,
action_list=None,
channel_group=None,
max_dissimilarity=None,
max_timedelta=None,
verbose=False,
progress_bar=None,
data_path=None):
self.data_path = Path.cwd() if data_path is None else Path(data_path)
self.data_path.mkdir(parents=True, exist_ok=True)
self.action_list = [a for a in actions
] if action_list is None else action_list
self._actions = actions
self._channel_group = channel_group
self.max_dissimilarity = max_dissimilarity or np.inf
self.max_timedelta = max_timedelta or datetime.MAXYEAR
self._verbose = verbose
self._pbar = tqdm if progress_bar is None else progress_bar
if self._channel_group is None:
dp = get_data_path(self._actions[self.action_list[0]])
self._channel_groups = get_channel_groups(dp)
else:
self._channel_groups = [self._channel_group]
def waveforms_1(self, channel_group):
action_1 = self._actions[self.action_id_1]
data_path_1 = get_data_path(action_1)
spike_trains_1 = load_spiketrains(data_path_1,
channel_group=channel_group,
load_waveforms=True)
return [np.array(sptr.waveforms) for sptr in spike_trains_1]
def load_waveforms(self, action_id, unit_id, channel_group):
action = self._actions[action_id]
data_path = get_data_path(action)
spike_trains = load_spiketrains(
data_path, channel_group=channel_group, load_waveforms=True)
wfs = [np.array(sptr.waveforms) for sptr in spike_trains if get_unit_id(sptr)==unit_id]
if len(wfs) != 1:
raise ValueError(
f'Unable to load waveforms from unit {unit_id}' +
f' in {action_id} channel group {channel_group}')
return wfs[0]
