def _init_data(self):
self.cache_dir = self.config_dir
self.n_samples_waveforms = 31
self.n_samples_t = 20000
self.n_channels = 11
self.n_clusters = 4
self.n_spikes_per_cluster = 50
n_spikes_total = self.n_clusters * self.n_spikes_per_cluster
n_features_per_channel = 4
self.n_channels = self.n_channels
self.n_spikes = n_spikes_total
self.sample_rate = 20000.
self.duration = self.n_samples_t / float(self.sample_rate)
self.spike_times = np.arange(0, self.duration, 100. / self.sample_rate)
self.spike_clusters = np.repeat(np.arange(self.n_clusters),
self.n_spikes_per_cluster)
assert len(self.spike_times) == len(self.spike_clusters)
self.cluster_ids = np.unique(self.spike_clusters)
self.channel_positions = staggered_positions(self.n_channels)
sc = self.spike_clusters
self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c])
self.spike_count = lambda c: len(self.spikes_per_cluster(c))
self.n_features_per_channel = n_features_per_channel
self.cluster_groups = {c: None for c in range(self.n_clusters)}
self.all_traces = artificial_traces(self.n_samples_t, self.n_channels)
self.all_masks = artificial_masks(n_spikes_total, self.n_channels)
self.all_waveforms = artificial_waveforms(n_spikes_total,
self.n_samples_waveforms,
self.n_channels)
self.all_features = artificial_features(n_spikes_total,
self.n_channels,
self.n_features_per_channel)
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids,
spike_ids,
assume_unique=True)
return extended_spike_ids
def test_manual_clustering_split_2(gui, quality, similarity):
spike_clusters = np.array([0, 0, 1])
mc = ManualClustering(spike_clusters,
lambda c: _spikes_in_clusters(spike_clusters, [c]),
similarity=similarity,
)
mc.attach(gui)
mc.add_column(quality, name='quality', default=True)
mc.set_default_sort('quality', 'desc')
mc.split([0])
assert mc.selected == [3, 2]
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 test_manual_clustering_split_2(gui, quality, similarity):
spike_clusters = np.array([0, 0, 1])
mc = ManualClustering(
spike_clusters,
lambda c: _spikes_in_clusters(spike_clusters, [c]),
similarity=similarity,
)
mc.attach(gui)
mc.add_column(quality, name='quality', default=True)
mc.set_default_sort('quality', 'desc')
mc.split([0])
assert mc.selected == [3, 2]
def _init_data(self):
self.cache_dir = self.config_dir
self.n_samples_waveforms = 31
self.n_samples_t = 20000
self.n_channels = 11
self.n_clusters = 4
self.n_spikes_per_cluster = 200
n_spikes_total = self.n_clusters * self.n_spikes_per_cluster
n_features_per_channel = 4
self.n_channels = self.n_channels
self.n_spikes = n_spikes_total
self.sample_rate = 20000.
self.duration = self.n_samples_t / float(self.sample_rate)
self.spike_times = np.arange(
0, self.duration,
5000. / (self.sample_rate * self.n_spikes_per_cluster))
self.spike_clusters = np.repeat(np.arange(self.n_clusters),
self.n_spikes_per_cluster)
assert len(self.spike_times) == len(self.spike_clusters)
self.cluster_ids = np.unique(self.spike_clusters)
self.channel_positions = staggered_positions(self.n_channels)
self.channel_order = np.arange(self.n_channels)
sc = self.spike_clusters
self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c])
self.spike_count = lambda c: len(self.spikes_per_cluster(c))
self.n_features_per_channel = n_features_per_channel
self.cluster_groups = {c: None for c in range(self.n_clusters)}
self.all_traces = artificial_traces(self.n_samples_t, self.n_channels)
self.all_masks = artificial_masks(n_spikes_total, self.n_channels)
self.all_waveforms = artificial_waveforms(n_spikes_total,
self.n_samples_waveforms,
self.n_channels)
self.all_features = artificial_features(n_spikes_total,
self.n_channels,
self.n_features_per_channel)
def spikes_in_template(self, template_id):
return _spikes_in_clusters(self.spike_templates, [template_id])
def _assert_spikes(clusters):
ae(info.spike_ids, _spikes_in_clusters(spike_clusters, clusters))
def spikes_in_clusters(self, clusters):
"""Return the array of spike ids belonging to a list of clusters."""
return _spikes_in_clusters(self.spike_clusters, clusters)
def spikes_in_clusters(self, clusters):
"""Return the array of spike ids belonging to a list of clusters."""
return _spikes_in_clusters(self.spike_clusters, clusters)
