def _update_cluster_ids(self, to_remove=None, to_add=None):
# Update the list of non-empty cluster ids.
self._cluster_ids = _unique(self._spike_clusters)
# Clusters to remove.
if to_remove is not None:
for clu in to_remove:
self._spikes_per_cluster.pop(clu, None)
# Clusters to add.
if to_add:
for clu, spk in to_add.items():
self._spikes_per_cluster[clu] = spk
# If spikes_per_cluster is invalid, recompute the entire
# spikes_per_cluster array.
coherent = np.all(np.in1d(self._cluster_ids,
sorted(self._spikes_per_cluster),
))
if not coherent:
logger.debug("Recompute spikes_per_cluster manually: "
"this is long.")
sc = self._spike_clusters
self._spikes_per_cluster = _spikes_per_cluster(sc)
def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up
def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids, old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up
def _update_cluster_ids(self, to_remove=None, to_add=None):
# Update the list of non-empty cluster ids.
self._cluster_ids = _unique(self._spike_clusters)
# Clusters to remove.
if to_remove is not None:
for clu in to_remove:
self._spikes_per_cluster.pop(clu, None)
# Clusters to add.
if to_add:
for clu, spk in to_add.items():
self._spikes_per_cluster[clu] = spk
# If spikes_per_cluster is invalid, recompute the entire
# spikes_per_cluster array.
coherent = np.all(
np.in1d(
self._cluster_ids,
sorted(self._spikes_per_cluster),
))
if not coherent:
logger.debug("Recompute spikes_per_cluster manually: "
"this is long.")
sc = self._spike_clusters
self._spikes_per_cluster = _spikes_per_cluster(sc)
def test_feature_view(qtbot, tempdir, n_channels):
nc = n_channels
ns = 500
features = artificial_features(ns, nc, 4)
spike_clusters = artificial_spike_clusters(ns, 4)
spike_times = np.linspace(0., 1., ns)
spc = _spikes_per_cluster(spike_clusters)
def get_spike_ids(cluster_id):
return (spc[cluster_id] if cluster_id is not None else np.arange(ns))
def get_features(cluster_id=None,
channel_ids=None,
spike_ids=None,
load_all=None):
if load_all:
spike_ids = spc[cluster_id]
else:
spike_ids = get_spike_ids(cluster_id)
return Bunch(
data=features[spike_ids],
spike_ids=spike_ids,
masks=np.random.rand(ns, nc),
channel_ids=(channel_ids
if channel_ids is not None else np.arange(nc)[::-1]),
)
def get_time(cluster_id=None, load_all=None):
return Bunch(
data=spike_times[get_spike_ids(cluster_id)],
lim=(0., 1.),
)
v = FeatureView(
features=get_features,
attributes={'time': get_time},
)
v.set_state(GUIState(scaling=None))
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
gui.emit('select', [0, 2])
qtbot.wait(10)
v.increase()
v.decrease()
v.on_channel_click(channel_id=3, button=1, key=2)
v.clear_channels()
v.toggle_automatic_channel_selection()
# Split without selection.
spike_ids = v.on_request_split()
assert len(spike_ids) == 0
# Draw a lasso.
def _click(x, y):
qtbot.mouseClick(v.native,
Qt.LeftButton,
pos=QPoint(x, y),
modifier=Qt.ControlModifier)
_click(10, 10)
_click(10, 100)
_click(100, 100)
_click(100, 10)
# Split lassoed points.
spike_ids = v.on_request_split()
assert len(spike_ids) > 0
# qtbot.stop()
gui.close()
def test_feature_view(qtbot, tempdir, n_channels):
nc = n_channels
ns = 500
features = artificial_features(ns, nc, 4)
spike_clusters = artificial_spike_clusters(ns, 4)
spike_times = np.linspace(0., 1., ns)
spc = _spikes_per_cluster(spike_clusters)
def get_spike_ids(cluster_id):
return (spc[cluster_id] if cluster_id is not None else np.arange(ns))
def get_features(cluster_id=None, channel_ids=None, spike_ids=None,
load_all=None):
if load_all:
spike_ids = spc[cluster_id]
else:
spike_ids = get_spike_ids(cluster_id)
return Bunch(data=features[spike_ids],
spike_ids=spike_ids,
masks=np.random.rand(ns, nc),
channel_ids=(channel_ids
if channel_ids is not None
else np.arange(nc)[::-1]),
)
def get_time(cluster_id=None, load_all=None):
return Bunch(data=spike_times[get_spike_ids(cluster_id)],
lim=(0., 1.),
)
v = FeatureView(features=get_features,
attributes={'time': get_time},
)
v.set_state(GUIState(scaling=None))
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
gui.emit('select', [0, 2])
qtbot.wait(10)
v.increase()
v.decrease()
v.on_channel_click(channel_id=3, button=1, key=2)
v.clear_channels()
v.toggle_automatic_channel_selection()
# Split without selection.
spike_ids = v.on_request_split()
assert len(spike_ids) == 0
# Draw a lasso.
def _click(x, y):
qtbot.mouseClick(v.native, Qt.LeftButton, pos=QPoint(x, y),
modifier=Qt.ControlModifier)
_click(10, 10)
_click(10, 100)
_click(100, 100)
_click(100, 10)
# Split lassoed points.
spike_ids = v.on_request_split()
assert len(spike_ids) > 0
# qtbot.stop()
gui.close()
