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 spike_clusters_into_kwik(scname, kwikname, clustering_name):
try:
print("Loading {0}".format(scname))
sc = np.loadtxt(scname, dtype=int) - 1
print("Loaded {0} spikes".format(len(sc)))
except(IOError, OSError) as e:
print("Error opening file: {1}".format(e))
return
model = KwikModel(kwikname)
print("Adding {0} into {1}, clustering {2}".format(sys.argv[1], sys.argv[2], sys.argv[3]))
try:
model.add_clustering(clustering_name, sc)
except ValueError:
print("Overwriting old clustering!")
time.sleep(5)
# Hack to switch then overwrite clustering...
model.add_clustering('123_interim_temp', sc)
model.clustering = '123_interim_temp'
model.delete_clustering(clustering_name)
model.copy_clustering('123_interim_temp', clustering_name)
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 add_clustering_to_kwik(kwik_path_dir, filename, clustering_name, clu_array):
basename = os.path.join(kwik_path_dir, filename)
model = KwikModel(basename)
model.add_clustering(clustering_name, clu_array)
def spike_clusters_into_kwik(scname, kwikname, clustering_name):
try:
print("Loading {0}".format(scname))
sc = np.loadtxt(scname, dtype=int) - 1
print("Loaded {0} spikes".format(len(sc)))
except (IOError, OSError) as e:
print("Error opening file: {1}".format(e))
return
model = KwikModel(kwikname)
print("Adding {0} into {1}, clustering {2}".format(sys.argv[1],
sys.argv[2],
sys.argv[3]))
try:
model.add_clustering(clustering_name, sc)
except ValueError:
print("Overwriting old clustering!")
time.sleep(5)
# Hack to switch then overwrite clustering...
model.add_clustering('123_interim_temp', sc)
model.clustering = '123_interim_temp'
model.delete_clustering(clustering_name)
model.copy_clustering('123_interim_temp', clustering_name)
