def test_kwik_save():
"""WARNING: this test should occur at the end of the module since it
changes the mock data sets."""
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KwikLoader(filename=xmlfile)
clusters = l.get_clusters()
cluster_colors = l.get_cluster_colors()
cluster_groups = l.get_cluster_groups()
group_colors = l.get_group_colors()
group_names = l.get_group_names()
# Set clusters.
indices = get_indices(clusters)
l.set_cluster(indices[::2], 2)
l.set_cluster(indices[1::2], 3)
# Set cluster info.
cluster_indices = l.get_clusters_unique()
l.set_cluster_colors(cluster_indices[::2], 10)
l.set_cluster_colors(cluster_indices[1::2], 20)
l.set_cluster_groups(cluster_indices[::2], 1)
l.set_cluster_groups(cluster_indices[1::2], 0)
# Save.
l.remove_empty_clusters()
l.save()
clusters = l.get_clusters()
cluster_colors = l.get_cluster_colors()
cluster_groups = l.get_cluster_groups()
group_colors = l.get_group_colors()
group_names = l.get_group_names()
assert np.all(clusters[::2] == 2)
assert np.all(clusters[1::2] == 3)
assert np.all(cluster_colors[::2] == 10)
assert np.all(cluster_colors[1::2] == 20)
print cluster_groups
assert np.all(cluster_groups[::2] == 1)
assert np.all(cluster_groups[1::2] == 0)
l.close()
def test_klusters_loader_control():
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KlustersLoader(filename=xmlfile)
# Take all spikes in cluster 3.
spikes = get_indices(l.get_clusters(clusters=3))
# Put them in cluster 4.
l.set_cluster(spikes, 4)
spikes_new = get_indices(l.get_clusters(clusters=4))
# Ensure all spikes in old cluster 3 are now in cluster 4.
assert np.all(np.in1d(spikes, spikes_new))
# Change cluster groups.
clusters = [2, 3, 4]
group = 0
l.set_cluster_groups(clusters, group)
groups = l.get_cluster_groups(clusters)
assert np.all(groups == group)
# Change cluster colors.
clusters = [2, 3, 4]
color = 12
l.set_cluster_colors(clusters, color)
colors = l.get_cluster_colors(clusters)
assert np.all(colors == color)
# Change group name.
group = 0
name = l.get_group_names(group)
name_new = 'Noise new'
assert name == 'Noise'
l.set_group_names(group, name_new)
assert l.get_group_names(group) == name_new
# Change group color.
groups = [1, 2]
colors = l.get_group_colors(groups)
color_new = 10
l.set_group_colors(groups, color_new)
assert np.all(l.get_group_colors(groups) == color_new)
# Add cluster and group.
spikes = get_indices(l.get_clusters(clusters=3))[:10]
# Create new group 100.
l.add_group(100, 'New group', 10)
# Create new cluster 10000 and put it in group 100.
l.add_cluster(10000, 100, 10)
# Put some spikes in the new cluster.
l.set_cluster(spikes, 10000)
clusters = l.get_clusters(spikes=spikes)
assert np.all(clusters == 10000)
groups = l.get_cluster_groups(10000)
assert groups == 100
l.set_cluster(spikes, 2)
# Remove the new cluster and group.
l.remove_cluster(10000)
l.remove_group(100)
assert np.all(~np.in1d(10000, l.get_clusters()))
assert np.all(~np.in1d(100, l.get_cluster_groups()))
l.close()
def test_renumber_clusters():
# Create clusters.
clusters = np.random.randint(size=20, low=10, high=100)
clusters_unique = np.unique(clusters)
n = len(clusters_unique)
# Create cluster info.
cluster_info = np.zeros((n, 3), dtype=np.int32)
cluster_info[:, 0] = clusters_unique
cluster_info[:, 1] = np.mod(np.arange(n, dtype=np.int32), 35) + 1
# Set groups.
k = n // 3
cluster_info[:k, 2] = 1
cluster_info[k:2 * n // 3, 2] = 0
cluster_info[2 * k:, 2] = 2
cluster_info[n // 2, 2] = 1
cluster_info = pd.DataFrame({
'color': cluster_info[:, 1],
'group': cluster_info[:, 2]},
dtype=np.int32, index=cluster_info[:, 0])
# Renumber
clusters_renumbered, cluster_info_renumbered = renumber_clusters(clusters,
cluster_info)
# Test.
c0 = clusters_unique[k] # group 0
c1 = clusters_unique[0] # group 1
c2 = clusters_unique[2 * k] # group 2
cm = clusters_unique[n // 2] # group 1
c0next = clusters_unique[k + 1]
c1next = clusters_unique[0 + 1]
c2next = clusters_unique[2 * k + 1]
# New order:
# c0 ... cm-1, cm+1, ..., c2-1, c1, ..., c0-1, cm, c2, ...
assert np.array_equal(clusters == c0, clusters_renumbered == 0 + 2)
assert np.array_equal(clusters == c0next,
clusters_renumbered == 1 + 2)
assert np.array_equal(clusters == c1, clusters_renumbered == k - 1 + 2)
assert np.array_equal(clusters == c1next,
clusters_renumbered == k + 2)
assert np.array_equal(clusters == c2, clusters_renumbered == 2 * k + 2)
assert np.array_equal(clusters == c2next,
clusters_renumbered == 2 * k + 1 + 2)
assert np.array_equal(get_indices(cluster_info_renumbered),
np.arange(n) + 2)
# Increasing groups with the new numbering.
assert np.all(np.diff(get_array(cluster_info_renumbered)[:,1]) >= 0)
assert np.all(select(cluster_info_renumbered, 0 + 2) ==
select(cluster_info, c0))
assert np.all(select(cluster_info_renumbered, 1 + 2) ==
select(cluster_info, c0next))
assert np.all(select(cluster_info_renumbered, k - 1 + 2) ==
select(cluster_info, c1))
assert np.all(select(cluster_info_renumbered, k + 2) ==
select(cluster_info, c1next))
assert np.all(select(cluster_info_renumbered, 2 * k + 2) ==
select(cluster_info, c2))
assert np.all(select(cluster_info_renumbered, 2 * k + 1 + 2) ==
select(cluster_info, c2next))
def test_klusters_loader_control():
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KlustersLoader(filename=xmlfile)
# Take all spikes in cluster 3.
spikes = get_indices(l.get_clusters(clusters=3))
# Put them in cluster 4.
l.set_cluster(spikes, 4)
spikes_new = get_indices(l.get_clusters(clusters=4))
# Ensure all spikes in old cluster 3 are now in cluster 4.
assert np.all(np.in1d(spikes, spikes_new))
# Change cluster groups.
clusters = [2, 3, 4]
group = 0
l.set_cluster_groups(clusters, group)
groups = l.get_cluster_groups(clusters)
assert np.all(groups == group)
# Change cluster colors.
clusters = [2, 3, 4]
color = 12
l.set_cluster_colors(clusters, color)
colors = l.get_cluster_colors(clusters)
assert np.all(colors == color)
# Change group name.
group = 0
name = l.get_group_names(group)
name_new = 'Noise new'
assert name == 'Noise'
l.set_group_names(group, name_new)
assert l.get_group_names(group) == name_new
# Change group color.
groups = [1, 2]
colors = l.get_group_colors(groups)
color_new = 10
l.set_group_colors(groups, color_new)
assert np.all(l.get_group_colors(groups) == color_new)
# Add cluster and group.
spikes = get_indices(l.get_clusters(clusters=3))[:10]
# Create new group 100.
l.add_group(100, 'New group', 10)
# Create new cluster 10000 and put it in group 100.
l.add_cluster(10000, 100, 10)
# Put some spikes in the new cluster.
l.set_cluster(spikes, 10000)
clusters = l.get_clusters(spikes=spikes)
assert np.all(clusters == 10000)
groups = l.get_cluster_groups(10000)
assert groups == 100
l.set_cluster(spikes, 2)
# Remove the new cluster and group.
l.remove_cluster(10000)
l.remove_group(100)
assert np.all(~np.in1d(10000, l.get_clusters()))
assert np.all(~np.in1d(100, l.get_cluster_groups()))
l.close()
def te_SKIP_st_renumber_clusters():
# Create clusters.
clusters = np.random.randint(size=20, low=10, high=100)
clusters_unique = np.unique(clusters)
n = len(clusters_unique)
# Create cluster info.
cluster_info = np.zeros((n, 3), dtype=np.int32)
cluster_info[:, 0] = clusters_unique
cluster_info[:, 1] = np.mod(np.arange(n, dtype=np.int32), 35) + 1
# Set groups.
k = n // 3
cluster_info[:k, 2] = 1
cluster_info[k:2 * n // 3, 2] = 0
cluster_info[2 * k:, 2] = 2
cluster_info[n // 2, 2] = 1
cluster_info = pd.DataFrame(
{
'color': cluster_info[:, 1],
'group': cluster_info[:, 2]
},
dtype=np.int32,
index=cluster_info[:, 0])
# Renumber
clusters_renumbered, cluster_info_renumbered = renumber_clusters(
clusters, cluster_info)
# Test.
c0 = clusters_unique[k] # group 0
c1 = clusters_unique[0] # group 1
c2 = clusters_unique[2 * k] # group 2
cm = clusters_unique[n // 2] # group 1
c0next = clusters_unique[k + 1]
c1next = clusters_unique[0 + 1]
c2next = clusters_unique[2 * k + 1]
# New order:
# c0 ... cm-1, cm+1, ..., c2-1, c1, ..., c0-1, cm, c2, ...
assert np.array_equal(clusters == c0, clusters_renumbered == 0 + 2)
assert np.array_equal(clusters == c0next, clusters_renumbered == 1 + 2)
assert np.array_equal(clusters == c1, clusters_renumbered == k - 1 + 2)
assert np.array_equal(clusters == c1next, clusters_renumbered == k + 2)
assert np.array_equal(clusters == c2, clusters_renumbered == 2 * k + 2)
assert np.array_equal(clusters == c2next,
clusters_renumbered == 2 * k + 1 + 2)
assert np.array_equal(get_indices(cluster_info_renumbered),
np.arange(n) + 2)
# Increasing groups with the new numbering.
assert np.all(np.diff(get_array(cluster_info_renumbered)[:, 1]) >= 0)
assert np.all(
select(cluster_info_renumbered, 0 + 2) == select(cluster_info, c0))
assert np.all(
select(cluster_info_renumbered, 1 + 2) == select(cluster_info, c0next))
assert np.all(
select(cluster_info_renumbered, k - 1 + 2) == select(cluster_info, c1))
assert np.all(
select(cluster_info_renumbered, k + 2) == select(cluster_info, c1next))
assert np.all(
select(cluster_info_renumbered, 2 * k + 2) == select(cluster_info, c2))
assert np.all(
select(cluster_info_renumbered, 2 * k + 1 +
2) == select(cluster_info, c2next))
