def test_recompute_correlation():
l, c = load()
clusters_unique = l.get_clusters_unique()
# Select three clusters
clusters_selected = [2, 4, 6]
spikes = l.get_spikes(clusters=clusters_selected)
# cluster_spikes = l.get_clusters(clusters=clusters_selected)
# Select half of the spikes in these clusters.
spikes_sample = spikes[::2]
# Get the correlation matrix parameters.
features = get_array(l.get_features('all'))
masks = get_array(l.get_masks('all', full=True))
clusters0 = get_array(l.get_clusters('all'))
clusters_all = l.get_clusters_unique()
similarity_matrix = CacheMatrix()
correlations0 = compute_correlations(features, clusters0, masks)
similarity_matrix.update(clusters_unique, correlations0)
matrix0 = normalize(similarity_matrix.to_array().copy())
# Merge these clusters.
action, output = c.merge_clusters(clusters_selected)
cluster_new = output['cluster_merged']
# Compute the new matrix
similarity_matrix.invalidate([2, 4, 6, cluster_new])
clusters1 = get_array(l.get_clusters('all'))
correlations1 = compute_correlations(features, clusters1, masks,#)
[cluster_new])
similarity_matrix.update([cluster_new], correlations1)
matrix1 = normalize(similarity_matrix.to_array().copy())
# Undo.
assert c.can_undo()
action, output = c.undo()
# Compute the new matrix
similarity_matrix.invalidate([2, 4, 6, cluster_new])
clusters2 = get_array(l.get_clusters('all'))
correlations2 = compute_correlations(features, clusters2, masks,)
correlations2b = compute_correlations(features, clusters2, masks,#)
clusters_selected)
for (clu0, clu1) in correlations2b.keys():
assert np.allclose(correlations2[clu0, clu1], correlations2b[clu0, clu1]), (clu0, clu1, correlations2[clu0, clu1], correlations2b[clu0, clu1])
similarity_matrix.update(clusters_selected, correlations2b)
matrix2 = normalize(similarity_matrix.to_array().copy())
assert np.array_equal(clusters0, clusters2)
# assert np.allclose(matrix0, matrix2)
l.close()
def _similarity_matrix_computed(self, clusters_selected, matrix, clusters,
cluster_groups, target_next=None):
self.mainwindow.set_busy(computing_matrix=False)
# spikes_slice = _get_similarity_matrix_slice(
# self.loader.nspikes,
# len(self.loader.get_clusters_unique()))
# clusters_now = self.loader.get_clusters(
# spikes=self.loader.background_spikes)
# if not np.array_equal(clusters, clusters_now):
# return False
if len(matrix) == 0:
return []
self.statscache.similarity_matrix.update(clusters_selected, matrix)
self.statscache.similarity_matrix_normalized = normalize(
self.statscache.similarity_matrix.to_array(copy=True))
# Update the cluster view with cluster quality.
quality = np.diag(self.statscache.similarity_matrix_normalized).copy()
self.statscache.cluster_quality = pd.Series(
quality,
index=self.statscache.similarity_matrix.indices,
)
self.get_view('ClusterView').set_quality(
self.statscache.cluster_quality)
return [('_wizard_update', (target_next,)),
('_update_similarity_matrix_view',),
]
def _similarity_matrix_computed(self,
clusters_selected,
matrix,
clusters,
cluster_groups,
target_next=None):
self.mainwindow.set_busy(computing_matrix=False)
# spikes_slice = _get_similarity_matrix_slice(
# self.loader.nspikes,
# len(self.loader.get_clusters_unique()))
clusters_now = self.loader.get_clusters(
spikes=self.loader.background_spikes)
if not np.array_equal(clusters, clusters_now):
return False
self.statscache.similarity_matrix.update(clusters_selected, matrix)
self.statscache.similarity_matrix_normalized = normalize(
self.statscache.similarity_matrix.to_array(copy=True))
# Update the cluster view with cluster quality.
quality = np.diag(self.statscache.similarity_matrix_normalized)
self.statscache.cluster_quality = pd.Series(
quality,
index=self.statscache.similarity_matrix.indices,
)
self.get_view('ClusterView').set_quality(
self.statscache.cluster_quality)
return [
('_wizard_update', (target_next, )),
('_update_similarity_matrix_view', ),
]
def test_recompute_correlation():
l, c = load()
clusters_unique = l.get_clusters_unique()
# Select three clusters
clusters_selected = [2, 4, 6]
spikes = l.get_spikes(clusters=clusters_selected)
# cluster_spikes = l.get_clusters(clusters=clusters_selected)
# Select half of the spikes in these clusters.
spikes_sample = spikes[::2]
# Get the correlation matrix parameters.
features = get_array(l.get_features('all'))
masks = get_array(l.get_masks('all', full=True))
clusters0 = get_array(l.get_clusters('all'))
clusters_all = l.get_clusters_unique()
similarity_matrix = CacheMatrix()
correlations0 = compute_correlations(features, clusters0, masks)
similarity_matrix.update(clusters_unique, correlations0)
matrix0 = normalize(similarity_matrix.to_array().copy())
# Merge these clusters.
action, output = c.merge_clusters(clusters_selected)
cluster_new = output['cluster_merged']
# Compute the new matrix
similarity_matrix.invalidate([2, 4, 6, cluster_new])
clusters1 = get_array(l.get_clusters('all'))
correlations1 = compute_correlations(
features,
clusters1,
masks, #)
[cluster_new])
similarity_matrix.update([cluster_new], correlations1)
matrix1 = normalize(similarity_matrix.to_array().copy())
# Undo.
assert c.can_undo()
action, output = c.undo()
# Compute the new matrix
similarity_matrix.invalidate([2, 4, 6, cluster_new])
clusters2 = get_array(l.get_clusters('all'))
correlations2 = compute_correlations(
features,
clusters2,
masks,
)
correlations2b = compute_correlations(
features,
clusters2,
masks, #)
clusters_selected)
for (clu0, clu1) in correlations2b.keys():
assert np.allclose(correlations2[clu0, clu1],
correlations2b[clu0,
clu1]), (clu0, clu1,
correlations2[clu0, clu1],
correlations2b[clu0, clu1])
similarity_matrix.update(clusters_selected, correlations2b)
matrix2 = normalize(similarity_matrix.to_array().copy())
assert np.array_equal(clusters0, clusters2)
assert np.allclose(matrix0, matrix2)
l.close()