def readkwikinfo(kwik, grupete=3):
model = KwikModel(kwik) # load kwik model from file
spiketimes = model.spike_times # extract the absolute spike times
clusters = model.cluster_groups # extract the cluster names
sample_rate = model.sample_rate # extract sampling freq
spikedata = {} # initialise dictionary
for cluster in clusters.keys():
clustergroup = clusters[cluster]
if clustergroup==grupete: # only look at specified type of cluster, 0 = noise, 1 = MUA, 2 = GOOD, 3 = unsorted
spiketimematrix = AttrDict({'spike_times': np.zeros(len(spiketimes[where(model.spike_clusters==cluster)]))})
spiketimematrix.spike_times = spiketimes[where(model.spike_clusters==cluster)]
spikedata[cluster] = spiketimematrix # data structure is a dictionary with attribute accessible spiketimes
# attribute accessible means that spikedata.spike_times works, normal dictionaries would be spikedata[spike_times]
model.close()
return spikedata, sample_rate
def readkwikinfo(kwik, grupete=3):
model = KwikModel(kwik) # load kwik model from file
spiketimes = model.spike_times # extract the absolute spike times
clusters = model.cluster_groups # extract the cluster names
sample_rate = model.sample_rate # extract sampling freq
spikedata = {} # initialise dictionary
for cluster in clusters.keys():
clustergroup = clusters[cluster]
if clustergroup == grupete: # only look at specified type of cluster, 0 = noise, 1 = MUA, 2 = GOOD, 3 = unsorted
spiketimematrix = AttrDict({
'spike_times':
np.zeros(
len(spiketimes[where(model.spike_clusters == cluster)]))
})
spiketimematrix.spike_times = spiketimes[where(
model.spike_clusters == cluster)]
spikedata[
cluster] = spiketimematrix # data structure is a dictionary with attribute accessible spiketimes
# attribute accessible means that spikedata.spike_times works, normal dictionaries would be spikedata[spike_times]
model.close()
return spikedata, sample_rate
