def plot(self):
correlograms, bins = compute_correlograms(self.sorting, **self.compute_kwargs)
bin_width = bins[1] - bins[0]
unit_ids = self.sorting.unit_ids
for i, unit_id in enumerate(unit_ids):
ccg = correlograms[i, i]
ax = self.axes.flatten()[i]
color = 'g'
ax.bar(x=bins[:-1], height=ccg, width=bin_width, color=color, align='edge')
ax.set_title(str(unit_id))
def test_compute_correlograms():
repo = 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'
remote_path = 'mearec/mearec_test_10s.h5'
local_path = download_dataset(repo=repo,
remote_path=remote_path,
local_folder=None)
# ~ recording = se.MEArecRecordingExtractor(local_path)
sorting = se.MEArecSortingExtractor(local_path)
unit_ids = sorting.unit_ids
sorting2 = sorting.select_units(unit_ids[:3])
correlograms, bins = compute_correlograms(sorting2)
def compute_correlograms(self):
correlograms_1, bins = compute_correlograms(self.sorting1,
**self.compute_kwargs)
correlograms_2, bins = compute_correlograms(self.sorting2,
**self.compute_kwargs)
self.good_sorting = self.get_well_detected_units(
self.well_detected_score)
self.good_gt = self.hungarian_match_21[self.good_sorting].values
self.good_idx_gt = self.sorting1.ids_to_indices(self.good_gt)
self.good_idx_sorting = self.sorting2.ids_to_indices(self.good_sorting)
#order = np.argsort(self.good_idx_gt)
#self.good_idx_gt = self.good_idx_gt[order]
if len(self.good_idx_gt) > 0:
correlograms_1 = correlograms_1[self.good_idx_gt, :, :]
self.correlograms['true'] = correlograms_1[:, self.good_idx_gt, :]
if len(self.good_idx_sorting) > 0:
correlograms_2 = correlograms_2[self.good_idx_sorting, :, :]
self.correlograms[
'estimated'] = correlograms_2[:, self.good_idx_sorting, :]
if len(self.good_idx_gt) > 0:
self.nb_cells = self.correlograms['true'].shape[0]
self.nb_timesteps = self.correlograms['true'].shape[2]
else:
self.nb_cells = 0
self.nb_timesteps = 11
self.correlograms['true'] = np.zeros((0, 0, self.nb_timesteps))
self.correlograms['estimated'] = np.zeros(
(0, 0, self.nb_timesteps))
self._center = self.nb_timesteps // 2
def plot(self):
correlograms, bins = compute_correlograms(self.sorting, **self.compute_kwargs)
bin_width = bins[1] - bins[0]
unit_ids = self.sorting.unit_ids
for i, unit_id1 in enumerate(unit_ids):
for j, unit_id2 in enumerate(unit_ids):
ccg = correlograms[i, j]
ax = self.axes[i, j]
if i == j:
color = 'g'
else:
color = 'k'
ax.bar(x=bins[:-1], height=ccg, width=bin_width, color=color, align='edge')
for i, unit_id in enumerate(unit_ids):
self.axes[0, i].set_title(str(unit_id))
self.axes[-1, i].set_xlabel('CCG (ms)')