def get_correlogramsview_data(loader, statscache):
clusters_selected0 = loader.get_clusters_selected()
# Subset of selected clusters if there are too many clusters.
max_nclusters = USERPREF['correlograms_max_nclusters']
if len(clusters_selected0) < max_nclusters:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:max_nclusters]
correlograms = statscache.correlograms.submatrix(
clusters_selected)
# Compute the baselines.
sizes = get_array(select(loader.get_cluster_sizes(), clusters_selected))
colors = select(loader.get_cluster_colors(), clusters_selected)
corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = corrbin * ncorrbins
baselines = get_baselines(sizes, duration, corrbin)
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
)
return data
def get_correlogramsview_data(loader, statscache):
clusters_selected0 = loader.get_clusters_selected()
# Subset of selected clusters if there are too many clusters.
max_nclusters = USERPREF['correlograms_max_nclusters']
if len(clusters_selected0) < max_nclusters:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:max_nclusters]
correlograms = statscache.correlograms.submatrix(clusters_selected)
# Compute the baselines.
sizes = get_array(select(loader.get_cluster_sizes(), clusters_selected))
colors = select(loader.get_cluster_colors(), clusters_selected)
corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = corrbin * ncorrbins
baselines = get_baselines(sizes, duration, corrbin)
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
)
return data
def test_klusters_loader_2():
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KlustersLoader(filename=xmlfile)
# Get full data sets.
features = l.get_features()
masks = l.get_masks()
waveforms = l.get_waveforms()
clusters = l.get_clusters()
spiketimes = l.get_spiketimes()
nclusters = len(Counter(clusters))
probe = l.get_probe()
cluster_colors = l.get_cluster_colors()
cluster_groups = l.get_cluster_groups()
group_colors = l.get_group_colors()
group_names = l.get_group_names()
cluster_sizes = l.get_cluster_sizes()
# Check selection.
# ----------------
index = nspikes / 2
waveform = select(waveforms, index)
cluster = clusters[index]
spikes_in_cluster = np.nonzero(clusters == cluster)[0]
nspikes_in_cluster = len(spikes_in_cluster)
l.select(clusters=[cluster])
# Check the size of the selected data.
# ------------------------------------
assert check_shape(l.get_features(), (nspikes_in_cluster,
nchannels * fetdim + 1))
assert check_shape(l.get_masks(full=True), (nspikes_in_cluster,
nchannels * fetdim + 1))
assert check_shape(l.get_waveforms(),
(nspikes_in_cluster, nsamples, nchannels))
assert check_shape(l.get_clusters(), (nspikes_in_cluster,))
assert check_shape(l.get_spiketimes(), (nspikes_in_cluster,))
# Check waveform sub selection.
# -----------------------------
waveforms_selected = l.get_waveforms()
assert np.array_equal(get_array(select(waveforms_selected, index)),
get_array(waveform))
l.close()
def test_klusters_loader_2():
# Open the mock data.
dir = TEST_FOLDER
xmlfile = os.path.join(dir, 'test.xml')
l = KlustersLoader(filename=xmlfile)
# Get full data sets.
features = l.get_features()
masks = l.get_masks()
waveforms = l.get_waveforms()
clusters = l.get_clusters()
spiketimes = l.get_spiketimes()
nclusters = len(Counter(clusters))
probe = l.get_probe()
cluster_colors = l.get_cluster_colors()
cluster_groups = l.get_cluster_groups()
group_colors = l.get_group_colors()
group_names = l.get_group_names()
cluster_sizes = l.get_cluster_sizes()
# Check selection.
# ----------------
index = nspikes / 2
waveform = select(waveforms, index)
cluster = clusters[index]
spikes_in_cluster = np.nonzero(clusters == cluster)[0]
nspikes_in_cluster = len(spikes_in_cluster)
l.select(clusters=[cluster])
# Check the size of the selected data.
# ------------------------------------
assert check_shape(l.get_features(),
(nspikes_in_cluster, nchannels * fetdim + 1))
assert check_shape(l.get_masks(full=True),
(nspikes_in_cluster, nchannels * fetdim + 1))
assert check_shape(l.get_waveforms(),
(nspikes_in_cluster, nsamples, nchannels))
assert check_shape(l.get_clusters(), (nspikes_in_cluster, ))
assert check_shape(l.get_spiketimes(), (nspikes_in_cluster, ))
# Check waveform sub selection.
# -----------------------------
waveforms_selected = l.get_waveforms()
assert np.array_equal(get_array(select(waveforms_selected, index)),
get_array(waveform))
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 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))
