def test_wizard_merge():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster = w.current_candidate()
# Simulate a merge: target and cluster ==> cluster_new.
cluster_new = clusters_unique.max() + 1
clusters[clusters == target] = cluster_new
clusters[clusters == cluster] = cluster_new
log.debug("Merged {0:d} and {1:d} to {2:d}".format(target, cluster,
cluster_new))
similarity_matrix = create_similarity_matrix(nclusters - 1)
indices = [
x for x in xrange(cluster_offset, cluster_offset + nclusters + 1)
if x != cluster and x != target
]
cluster_groups = pd.Series(np.array(np.ones(nclusters - 1) * 3,
dtype=np.int32),
index=np.array(indices))
# Update the wizard.
quality = np.diag(similarity_matrix)
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates(cluster_new)
assert w.current_target() == cluster_new
c = w.current_candidate()
assert c is not None
assert w.previous_candidate() == w.current_candidate()
assert w.next_candidate() == c
for _ in xrange(nclusters):
c = w.next_candidate()
assert c not in (target, cluster)
def test_wizard():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
best_cluster = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
# Check the first target cluster.
assert w.current_target() == best_cluster
# Test impossible previous.
assert w.previous_candidate() == w.current_candidate()
# Check next/previous.
c0 = w.next_candidate()
c1 = w.next_candidate()
assert w.previous_candidate() == c0
assert w.next_candidate() == c1
# Check skip target.
t0 = w.current_target()
w.skip_target()
w.update_candidates()
t1 = w.current_target()
assert t0 != t1
def test_wizard():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
best_cluster = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
# Check the first target cluster.
assert w.current_target() == best_cluster
# Test impossible previous.
assert w.previous_candidate() == w.current_candidate()
# Check next/previous.
c0 = w.next_candidate()
c1 = w.next_candidate()
assert w.previous_candidate() == c0
assert w.next_candidate() == c1
# Check skip target.
t0 = w.current_target()
w.skip_target()
w.update_candidates()
t1 = w.current_target()
assert t0 != t1
def test_wizard_merge():
# Create mock data.
clusters = create_clusters(nspikes, nclusters)
cluster_groups = create_cluster_groups(nclusters)
similarity_matrix = create_similarity_matrix(nclusters)
quality = np.diag(similarity_matrix)
# Get the best clusters.
clusters_unique = np.unique(clusters)
target = clusters_unique[np.argmax(quality)]
# Initialize the wizard.
w = Wizard()
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates()
cluster = w.current_candidate()
# Simulate a merge: target and cluster ==> cluster_new.
cluster_new = clusters_unique.max() + 1
clusters[clusters == target] = cluster_new
clusters[clusters == cluster] = cluster_new
log.debug("Merged {0:d} and {1:d} to {2:d}".format(
target, cluster, cluster_new))
similarity_matrix = create_similarity_matrix(nclusters - 1)
indices = [x for x in xrange(cluster_offset, cluster_offset + nclusters + 1)
if x != cluster and x != target]
cluster_groups = pd.Series(np.array(np.ones(nclusters - 1) * 3, dtype=np.int32),
index=np.array(indices))
# Update the wizard.
quality = np.diag(similarity_matrix)
w.set_data(similarity_matrix=similarity_matrix,
cluster_groups=cluster_groups)
w.update_candidates(cluster_new)
assert w.current_target() == cluster_new
c = w.current_candidate()
assert c is not None
assert w.previous_candidate() == w.current_candidate()
assert w.next_candidate() == c
for _ in xrange(nclusters):
c = w.next_candidate()
assert c not in (target, cluster)