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_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()
