def test_compute_correlations():
n = 1000
nspikes = 3 * n
clusters = np.repeat([0, 1, 2], n)
features = np.zeros((nspikes, 2))
masks = np.ones((nspikes, 2))
# clusters 0 and 1 are close, 2 is far away from 0 and 1
features[:n, :] = np.random.randn(n, 2)
features[n:2*n, :] = np.random.randn(n, 2)
features[2*n:, :] = np.array([[10, 10]]) + np.random.randn(n, 2)
# compute the correlation matrix
sm = SimilarityMatrix(features, masks)
correlations = sm.compute_matrix(clusters)
matrix = matrix_of_pairs(correlations)
# check that correlation between 0 and 1 is much higher than the
# correlation between 2 and 0/1
assert matrix[0,1] > 100 * matrix[0, 2]
assert matrix[0,1] > 100 * matrix[1, 2]
def test_compute_correlations():
n = 1000
nspikes = 3 * n
clusters = np.repeat([0, 1, 2], n)
features = np.zeros((nspikes, 2))
masks = np.ones((nspikes, 2))
# clusters 0 and 1 are close, 2 is far away from 0 and 1
features[:n, :] = np.random.randn(n, 2)
features[n : 2 * n, :] = np.random.randn(n, 2)
features[2 * n :, :] = np.array([[10, 10]]) + np.random.randn(n, 2)
# compute the correlation matrix
sm = SimilarityMatrix(features, masks)
correlations = sm.compute_matrix(clusters)
matrix = matrix_of_pairs(correlations)
# check that correlation between 0 and 1 is much higher than the
# correlation between 2 and 0/1
assert matrix[0, 1] > 100 * matrix[0, 2]
assert matrix[0, 1] > 100 * matrix[1, 2]
def test_recompute_correlations():
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()
sm = SimilarityMatrix(features, masks)
correlations0 = sm.compute_matrix(clusters0)
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'))
# sm = SimilarityMatrix(features, clusters1, masks)
correlations1 = sm.compute_matrix(clusters1, [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'))
# sm = SimilarityMatrix(features, clusters2, masks,)
correlations2 = sm.compute_matrix(clusters2)
correlations2b = sm.compute_matrix(clusters2, 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 test_recompute_correlations():
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()
sm = SimilarityMatrix(features, masks)
correlations0 = sm.compute_matrix(clusters0)
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"))
# sm = SimilarityMatrix(features, clusters1, masks)
correlations1 = sm.compute_matrix(clusters1, [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"))
# sm = SimilarityMatrix(features, clusters2, masks,)
correlations2 = sm.compute_matrix(clusters2)
correlations2b = sm.compute_matrix(clusters2, 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()