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_klusters_loader_1():
# 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()
# features_some = l.get_some_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 the shape of the data sets.
# ---------------------------------
assert check_shape(features, (nspikes, nchannels * fetdim + 1))
# assert features_some.shape[1] == nchannels * fetdim + 1
assert check_shape(masks, (nspikes, nchannels))
assert check_shape(waveforms, (nspikes, nsamples, nchannels))
assert check_shape(clusters, (nspikes,))
assert check_shape(spiketimes, (nspikes,))
assert check_shape(probe, (nchannels, 2))
assert check_shape(cluster_colors, (nclusters,))
assert check_shape(cluster_groups, (nclusters,))
assert check_shape(group_colors, (4,))
assert check_shape(group_names, (4,))
assert check_shape(cluster_sizes, (nclusters,))
# Check the data type of the data sets.
# -------------------------------------
assert check_dtype(features, np.float32)
assert check_dtype(masks, np.float32)
# HACK: Panel has no dtype(s) attribute
# assert check_dtype(waveforms, np.float32)
assert check_dtype(clusters, np.int32)
assert check_dtype(spiketimes, np.float32)
assert check_dtype(probe, np.float32)
assert check_dtype(cluster_colors, np.int32)
assert check_dtype(cluster_groups, np.int32)
assert check_dtype(group_colors, np.int32)
assert check_dtype(group_names, object)
assert check_dtype(cluster_sizes, np.int32)
l.close()
def test_klusters_loader_1():
# 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()
# features_some = l.get_some_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 the shape of the data sets.
# ---------------------------------
assert check_shape(features, (nspikes, nchannels * fetdim + 1))
# assert features_some.shape[1] == nchannels * fetdim + 1
assert check_shape(masks, (nspikes, nchannels))
assert check_shape(waveforms, (nspikes, nsamples, nchannels))
assert check_shape(clusters, (nspikes, ))
assert check_shape(spiketimes, (nspikes, ))
assert check_shape(probe, (nchannels, 2))
assert check_shape(cluster_colors, (nclusters, ))
assert check_shape(cluster_groups, (nclusters, ))
assert check_shape(group_colors, (4, ))
assert check_shape(group_names, (4, ))
assert check_shape(cluster_sizes, (nclusters, ))
# Check the data type of the data sets.
# -------------------------------------
assert check_dtype(features, np.float32)
assert check_dtype(masks, np.float32)
# HACK: Panel has no dtype(s) attribute
# assert check_dtype(waveforms, np.float32)
assert check_dtype(clusters, np.int32)
assert check_dtype(spiketimes, np.float32)
assert check_dtype(probe, np.float32)
assert check_dtype(cluster_colors, np.int32)
assert check_dtype(cluster_groups, np.int32)
assert check_dtype(group_colors, np.int32)
assert check_dtype(group_names, object)
assert check_dtype(cluster_sizes, np.int32)
l.close()
def test_klusters_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 = KlustersLoader(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 = read_clusters(l.filename_aclu)
cluster_info = read_cluster_info(l.filename_acluinfo)
assert np.all(clusters[::2] == 2)
assert np.all(clusters[1::2] == 3)
assert np.array_equal(cluster_info.index, cluster_indices)
assert np.all(cluster_info.values[::2, 0] == 10)
assert np.all(cluster_info.values[1::2, 0] == 20)
assert np.all(cluster_info.values[::2, 1] == 1)
assert np.all(cluster_info.values[1::2, 1] == 0)
l.close()
def test_klusters_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 = KlustersLoader(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 = read_clusters(l.filename_aclu)
cluster_info = read_cluster_info(l.filename_acluinfo)
assert np.all(clusters[::2] == 2)
assert np.all(clusters[1::2] == 3)
assert np.array_equal(cluster_info.index, cluster_indices)
assert np.all(cluster_info.values[::2, 0] == 10)
assert np.all(cluster_info.values[1::2, 0] == 20)
assert np.all(cluster_info.values[::2, 1] == 1)
assert np.all(cluster_info.values[1::2, 1] == 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_hdf5_loader1():
# Open the mock data.
dir = TEST_FOLDER
filename = os.path.join(dir, 'test.xml')
global nspikes
nspikes_total = nspikes
# Convert in HDF5.
klusters_to_hdf5(filename)
# Open the file.
filename_h5 = os.path.join(dir, 'test.kwik')
l = HDF5Loader(filename=filename_h5)
lk = KlustersLoader(filename=filename)
# Open probe.
probe = l.get_probe()
assert np.array_equal(probe[1]['channels'], np.arange(nchannels))
# Select cluster.
cluster = 3
l.select(clusters=[cluster])
lk.select(clusters=[cluster])
# Get clusters.
clusters = l.get_clusters('all')
clusters_k = lk.get_clusters('all')
nspikes = np.sum(clusters == cluster)
# Check the clusters are correct.
assert np.array_equal(get_array(clusters), get_array(clusters_k))
# Get the spike times.
spiketimes = l.get_spiketimes()
spiketimes_k = lk.get_spiketimes()
assert np.all(spiketimes <= 60)
# Check the spiketimes are correct.
assert np.allclose(get_array(spiketimes), get_array(spiketimes_k))
# Get features.
features = l.get_features()
spikes = l.get_spikes()
# Assert the indices in the features Pandas object correspond to the
# spikes in the selected cluster.
assert np.array_equal(features.index, spikes)
# Assert the array has the right number of spikes.
assert features.shape[0] == nspikes
assert l.fetdim == fetdim
assert l.nextrafet == 1
# Get all features.
features = l.get_features('all')
features_k = lk.get_features('all')
assert type(features) == pd.DataFrame
assert features.shape[0] == nspikes_total
# Check the features are correct.
f = get_array(features)[:,:-1]
f_k = get_array(features_k)[:,:-1]
normalize_inplace(f)
normalize_inplace(f_k)
assert np.allclose(f, f_k, atol=1e-5)
# Get masks.
masks = l.get_masks('all')
masks_k = lk.get_masks('all')
assert masks.shape[0] == nspikes_total
# Check the masks.
assert np.allclose(masks.values, masks_k.values, atol=1e-2)
# Get waveforms.
waveforms = l.get_waveforms().values
waveforms_k = lk.get_waveforms().values
assert np.array_equal(waveforms.shape, (nspikes, nsamples, nchannels))
# Check waveforms
normalize_inplace(waveforms)
normalize_inplace(waveforms_k)
assert np.allclose(waveforms, waveforms_k, atol=1e-4)
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()