def on_request_split_FTT(self):
"""Return the spikes enclosed by the lasso."""
if (self.lasso.count < 3
or not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
#assert len(self.channel_ids)
id_str = [str(id) for id in self.cluster_ids]
dlg = QtGui.QInputDialog(None)
[cl,
ok] = QtGui.QInputDialog.getItem(dlg,
"Which cluster id should be split:",
"Which cluster id should be split:",
id_str, 0, False)
if not ok:
return np.array([], dtype=np.int64)
cluster_index = id_str.index(cl)
bunchs = self._get_data(self.cluster_ids)
pos = np.vstack((bunchs[cluster_index]['x'],
bunchs[cluster_index]['y'])).transpose()
# Normalize the points.
ra = Range(self.data_bounds)
pos = ra.apply(pos)
# Find lassoed spikes.
ind = self.lasso.in_polygon(pos)
ids = bunchs[cluster_index]['spike_ids'][ind]
self.lasso.clear()
return np.unique(ids)
def on_request_split(self, sender=None):
"""Return the spikes enclosed by the lasso."""
if (self.canvas.lasso.count < 3
or not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
assert len(self.channel_ids)
# Get the dimensions of the lassoed subplot.
i, j = self.canvas.layout.active_box
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
# Get all points from all clusters.
pos = []
spike_ids = []
for cluster_id in self.cluster_ids:
# Load all spikes.
bunch = self.features(cluster_id,
channel_ids=self.channel_ids,
load_all=True)
px = self._get_axis_data(bunch,
dim_x,
cluster_id=cluster_id,
load_all=True)
py = self._get_axis_data(bunch,
dim_y,
cluster_id=cluster_id,
load_all=True)
points = np.c_[px.data, py.data]
# Normalize the points.
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
r = Range((xmin, ymin, xmax, ymax))
points = r.apply(points)
pos.append(points)
spike_ids.append(bunch.spike_ids)
pos = np.vstack(pos)
spike_ids = np.concatenate(spike_ids)
# Find lassoed spikes.
ind = self.canvas.lasso.in_polygon(pos)
self.canvas.lasso.clear()
return np.unique(spike_ids[ind])
def on_request_split(self):
"""Return the spikes enclosed by the lasso."""
if (self.lasso.count < 3 or
not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
assert len(self.channel_ids)
# Get the dimensions of the lassoed subplot.
i, j = self.lasso.box
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
# Get all points from all clusters.
pos = []
spike_ids = []
for cluster_id in self.cluster_ids:
# Load all spikes.
bunch = self.features(cluster_id,
channel_ids=self.channel_ids,
load_all=True)
px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id,
load_all=True)
py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id,
load_all=True)
points = np.c_[px.data, py.data]
# Normalize the points.
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
r = Range((xmin, ymin, xmax, ymax))
points = r.apply(points)
pos.append(points)
spike_ids.append(bunch.spike_ids)
pos = np.vstack(pos)
spike_ids = np.concatenate(spike_ids)
# Find lassoed spikes.
ind = self.lasso.in_polygon(pos)
self.lasso.clear()
return np.unique(spike_ids[ind])