def _plot_traces(self, traces, color=None):
traces = traces.T
n_samples = traces.shape[1]
n_ch = self.n_channels
assert traces.shape == (n_ch, n_samples)
if self.origin == 'bottom':
traces = traces[::-1, ...]
vmin, vmax = self.vrange
image = _continuous_colormap(colormaps.diverging, traces, vmin=vmin, vmax=vmax)
image = add_alpha(image, alpha=1.)
self.trace_visual.set_data(image=image)
def _plot_cluster(self, bunch):
"""Make the scatter plot."""
ms = self._marker_size
if not len(bunch.histogram):
return
# Histogram in the background.
self.hist_visual.add_batch_data(
hist=bunch.histogram,
ylim=self._ylim,
color=add_alpha(bunch.color, self.histogram_alpha))
# Scatter plot.
self.visual.add_batch_data(
pos=bunch.pos, color=bunch.color, size=ms, data_bounds=self.data_bounds)
def get_clusters_data(self, load_all=None):
ccg = self.correlograms(self.cluster_ids, self.bin_size,
self.window_size)
fr = self.firing_rate(self.cluster_ids,
self.bin_size) if self.firing_rate else None
assert ccg.ndim == 3
n_bins = ccg.shape[2]
bunchs = []
m = ccg.max()
for i, j in self._iter_subplots(len(self.cluster_ids)):
b = Bunch()
b.correlogram = ccg[i, j, :]
if not self.uniform_normalization:
# Normalization row per row.
m = ccg[i, j, :].max()
b.firing_rate = fr[i, j] if fr is not None else None
b.data_bounds = (0, 0, n_bins, m)
b.pair_index = i, j
b.color = selected_cluster_color(i, 1)
if i != j:
b.color = add_alpha(_override_hsv(b.color[:3], s=.1, v=1))
bunchs.append(b)
return bunchs
