def cluster(self,
clustering=None,
algorithm='klustakwik',
spike_ids=None,
channel_group=None,
**kwargs):
"""Run an automatic clustering algorithm on all or some of the spikes.
Parameters
----------
clustering : str
The name of the clustering in which to save the results.
algorithm : str
The algorithm name. Only `klustakwik` currently.
spike_ids : array-like
Array of spikes to cluster.
Returns
-------
spike_clusters : array
The spike_clusters assignements returned by the algorithm.
"""
if clustering is None:
clustering = 'main'
if channel_group is not None:
self.change_channel_group(channel_group)
kk2_dir = op.join(self.settings.exp_settings_dir, 'klustakwik2')
_ensure_dir_exists(kk2_dir)
# Take KK2's default parameters.
from klustakwik2.default_parameters import default_parameters
params = default_parameters.copy()
# Update the PRM ones, by filtering them.
params.update({
k: v
for k, v in self.model.metadata.items() if k in default_parameters
})
# Update the ones passed to the function.
params.update(kwargs)
# Original spike_clusters array.
if self.model.spike_clusters is None:
n_spikes = (len(spike_ids)
if spike_ids is not None else self.model.n_spikes)
spike_clusters_orig = np.zeros(n_spikes, dtype=np.int32)
else:
spike_clusters_orig = self.model.spike_clusters.copy()
# HACK: there needs to be one clustering.
if 'empty' not in self.model.clusterings:
self.model.add_clustering('empty', spike_clusters_orig)
# Instantiate the KlustaKwik instance.
kk = KlustaKwik(**params)
# Save the current clustering in the Kwik file.
@kk.connect
def on_iter(sc):
# Update the original spike clusters.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Save to a text file.
path = op.join(kk2_dir, 'spike_clusters.txt')
# Backup.
if op.exists(path):
shutil.copy(path, path + '~')
np.savetxt(path, spike_clusters, fmt='%d')
info("Running {}...".format(algorithm))
# Run KK.
sc = kk.cluster(model=self.model, spike_ids=spike_ids)
info("The automatic clustering process has finished.")
# Save the results in the Kwik file.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Add a new clustering and switch to it.
if clustering in self.model.clusterings:
self.change_clustering('empty')
self.model.delete_clustering(clustering)
self.model.add_clustering(clustering, spike_clusters)
# Copy the main clustering to original (only if this is the very
# first run of the clustering algorithm).
if clustering == 'main':
self.model.copy_clustering('main', 'original')
self.change_clustering(clustering)
# Set the new clustering metadata.
params = kk.params
params['version'] = kk.version
metadata = {
'{}_{}'.format(algorithm, name): value
for name, value in params.items()
}
self.model.clustering_metadata.update(metadata)
self.save()
info("The clustering has been saved in the "
"`{}` clustering in the `.kwik` file.".format(clustering))
self.model.delete_clustering('empty')
return sc
def cluster(self,
clustering=None,
algorithm='klustakwik',
spike_ids=None,
**kwargs):
"""Run an automatic clustering algorithm on all or some of the spikes.
Parameters
----------
clustering : str
The name of the clustering in which to save the results.
algorithm : str
The algorithm name. Only `klustakwik` currently.
spike_ids : array-like
Array of spikes to cluster.
Returns
-------
spike_clusters : array
The spike_clusters assignements returned by the algorithm.
"""
if clustering is None:
clustering = 'main'
# Make sure the clustering name does not exist already.
if clustering in self.model.clusterings:
raise ValueError("The clustering `{}` ".format(clustering) +
"already exists.")
# Take KK2's default parameters.
from klustakwik2.default_parameters import default_parameters
params = default_parameters.copy()
# Update the PRM ones, by filtering them.
params.update({k: v for k, v in self.model.metadata.items()
if k in default_parameters})
# Update the ones passed to the function.
params.update(kwargs)
# Original spike_clusters array.
if self.model.spike_clusters is None:
n_spikes = (len(spike_ids) if spike_ids is not None
else self.model.n_spikes)
spike_clusters_orig = np.zeros(n_spikes, dtype=np.int32)
else:
spike_clusters_orig = self.model.spike_clusters.copy()
# HACK: there needs to be one clustering.
if not self.model.clusterings:
self.model.add_clustering('empty', spike_clusters_orig)
# Instantiate the KlustaKwik instance.
kk = KlustaKwik(**kwargs)
# Save the current clustering in the Kwik file.
@kk.connect
def on_iter(sc):
# Update the original spike clusters.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Replace the kk2_current clustering.
if 'kk2_current' in self.model.clusterings:
self.model.delete_clustering('kk2_current')
self.model.add_clustering('kk2_current', spike_clusters)
info("Updated `kk2_current` clustering in the `.kwik` file.")
info("Running {}...".format(algorithm))
# Run KK.
sc = kk.cluster(model=self.model, spike_ids=spike_ids)
info("The automatic clustering process has finished.")
# Save the results in the Kwik file.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Add a new clustering and switch to it.
self.model.add_clustering(clustering, spike_clusters)
# Copy the main clustering to original (only if this is the very
# first run of the clustering algorithm).
if clustering == 'main':
self.model.copy_clustering('main', 'original')
self.change_clustering(clustering)
# Set the new clustering metadata.
params = kk.params
params['version'] = kk.version
metadata = {'{}_{}'.format(algorithm, name): value
for name, value in params.items()}
self.model.clustering_metadata.update(metadata)
self.save()
info("The clustering has been saved in the "
"`{}` clustering in the `.kwik` file.".format(clustering))
return sc
def cluster(self,
clustering=None,
algorithm='klustakwik',
spike_ids=None,
channel_group=None,
**kwargs):
"""Run an automatic clustering algorithm on all or some of the spikes.
Parameters
----------
clustering : str
The name of the clustering in which to save the results.
algorithm : str
The algorithm name. Only `klustakwik` currently.
spike_ids : array-like
Array of spikes to cluster.
Returns
-------
spike_clusters : array
The spike_clusters assignements returned by the algorithm.
"""
if clustering is None:
clustering = 'main'
if channel_group is not None:
self.change_channel_group(channel_group)
kk2_dir = op.join(self.settings.exp_settings_dir, 'klustakwik2')
_ensure_dir_exists(kk2_dir)
# Take KK2's default parameters.
from klustakwik2.default_parameters import default_parameters
params = default_parameters.copy()
# Update the PRM ones, by filtering them.
params.update({k: v for k, v in self.model.metadata.items()
if k in default_parameters})
# Update the ones passed to the function.
params.update(kwargs)
# Original spike_clusters array.
if self.model.spike_clusters is None:
n_spikes = (len(spike_ids) if spike_ids is not None
else self.model.n_spikes)
spike_clusters_orig = np.zeros(n_spikes, dtype=np.int32)
else:
spike_clusters_orig = self.model.spike_clusters.copy()
# HACK: there needs to be one clustering.
if 'empty' not in self.model.clusterings:
self.model.add_clustering('empty', spike_clusters_orig)
# Instantiate the KlustaKwik instance.
kk = KlustaKwik(**params)
# Save the current clustering in the Kwik file.
@kk.connect
def on_iter(sc):
# Update the original spike clusters.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Save to a text file.
path = op.join(kk2_dir, 'spike_clusters.txt')
# Backup.
if op.exists(path):
shutil.copy(path, path + '~')
np.savetxt(path, spike_clusters, fmt='%d')
info("Running {}...".format(algorithm))
# Run KK.
sc = kk.cluster(model=self.model, spike_ids=spike_ids)
info("The automatic clustering process has finished.")
# Save the results in the Kwik file.
spike_clusters = spike_clusters_orig.copy()
spike_clusters[spike_ids] = sc
# Add a new clustering and switch to it.
if clustering in self.model.clusterings:
self.change_clustering('empty')
self.model.delete_clustering(clustering)
self.model.add_clustering(clustering, spike_clusters)
# Copy the main clustering to original (only if this is the very
# first run of the clustering algorithm).
if clustering == 'main':
self.model.copy_clustering('main', 'original')
self.change_clustering(clustering)
# Set the new clustering metadata.
params = kk.params
params['version'] = kk.version
metadata = {'{}_{}'.format(algorithm, name): value
for name, value in params.items()}
self.model.clustering_metadata.update(metadata)
self.save()
info("The clustering has been saved in the "
"`{}` clustering in the `.kwik` file.".format(clustering))
self.model.delete_clustering('empty')
return sc
