def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spikes = spikes_per_cluster(cluster)
# Regular subselection.
if batch_size is None or len(spikes) <= max(batch_size, n):
spikes = regular_subset(spikes, n_spikes_max=n)
else:
# Batch selections of spikes.
spikes = get_excerpts(spikes, n // batch_size, batch_size)
selection[cluster] = spikes
return _flatten_per_cluster(selection)
def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up
def merge(self, cluster_ids, to=None):
"""Merge several clusters to a new cluster.
Parameters
----------
cluster_ids : array-like
List of clusters to merge.
to : integer or None
The id of the new cluster. By default, this is `new_cluster_id()`.
Returns
-------
up : UpdateInfo instance
"""
if not _is_array_like(cluster_ids):
raise ValueError("The first argument should be a list or "
"an array.")
cluster_ids = sorted(cluster_ids)
if not set(cluster_ids) <= set(self.cluster_ids):
raise ValueError("Some clusters do not exist.")
# Find the new cluster number.
if to is None:
to = self.new_cluster_id()
if to < self.new_cluster_id():
raise ValueError("The new cluster numbers should be higher than "
"{0}.".format(self.new_cluster_id()))
# NOTE: we could have called self.assign() here, but we don't.
# We circumvent self.assign() for performance reasons.
# assign() is a relatively costly operation, whereas merging is a much
# cheaper operation.
# Find all spikes in the specified clusters.
spike_ids = _spikes_in_clusters(self.spike_clusters, cluster_ids)
up = self._do_merge(spike_ids, cluster_ids, to)
undo_state = self.emit('request_undo_state', up)
# Add to stack.
self._undo_stack.add((spike_ids, [to], undo_state))
self.emit('cluster', up)
return up
def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None):
"""Return a selection of spikes belonging to the specified clusters."""
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spikes = spikes_per_cluster(cluster)
selection[cluster] = regular_subset(spikes, n_spikes_max=n)
return _flatten_per_cluster(selection)
def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None):
"""Return a selection of spikes belonging to the specified clusters."""
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spikes = spikes_per_cluster(cluster)
selection[cluster] = regular_subset(spikes, n_spikes_max=n)
return _flatten_per_cluster(selection)
def select_spikes(cluster_ids=None,
max_n_spikes_per_cluster=None,
spikes_per_cluster=None,
batch_size=None,
subset=None,
):
"""Return a selection of spikes belonging to the specified clusters."""
subset = subset or 'regular'
assert _is_array_like(cluster_ids)
if not len(cluster_ids):
return np.array([], dtype=np.int64)
if max_n_spikes_per_cluster in (None, 0):
selection = {c: spikes_per_cluster(c) for c in cluster_ids}
else:
assert max_n_spikes_per_cluster > 0
selection = {}
n_clusters = len(cluster_ids)
for cluster in cluster_ids:
# Decrease the number of spikes per cluster when there
# are more clusters.
n = int(max_n_spikes_per_cluster * exp(-.1 * (n_clusters - 1)))
n = max(1, n)
spike_ids = spikes_per_cluster(cluster)
if subset == 'regular':
# Regular subselection.
if batch_size is None or len(spike_ids) <= max(batch_size, n):
spike_ids = regular_subset(spike_ids, n_spikes_max=n)
else:
# Batch selections of spikes.
spike_ids = get_excerpts(spike_ids,
n // batch_size,
batch_size)
elif subset == 'random' and len(spike_ids) > n:
# Random subselection.
spike_ids = np.random.choice(spike_ids, n, replace=False)
spike_ids = np.unique(spike_ids)
selection[cluster] = spike_ids
return _flatten_per_cluster(selection)
