def import_callback(self, checked=None):
folder = SETTINGS['main_window.last_data_dir']
path = QtGui.QFileDialog.getOpenFileName(self,
"Open a .kwik file", folder, ".kwik file")[0]
# If a file has been selected, open it.
if path and self.loader is not None:
clu = read_clusters(path)
# TODO
self.open_done()
def import_callback(self, checked=None):
folder = SETTINGS['main_window.last_data_dir']
path = QtGui.QFileDialog.getOpenFileName(self,
"Open a .clu file", folder, "CLU file (*.clu.* *.clu_original.*)")[0]
# If a file has been selected, open it.
if path and self.loader is not None:
clu = read_clusters(path)
# TODO
self.open_done()
def test_clusters():
dir = TEST_FOLDER
clufile = os.path.join(dir, 'test.aclu.1')
clufile2 = os.path.join(dir, 'test.aclu.1.saved')
clusters = read_clusters(clufile)
assert clusters.dtype == np.int32
assert clusters.shape == (1000,)
# Save.
save_clusters(clufile2, clusters)
# Open again.
clusters2 = read_clusters(clufile2)
assert np.array_equal(clusters, clusters2)
# Check the headers.
clusters_with_header = load_text(clufile, np.int32, skiprows=0)
clusters2_with_header = load_text(clufile2, np.int32, skiprows=0)
assert np.array_equal(clusters_with_header, clusters2_with_header)
def test_hdf5():
dir = TEST_FOLDER
filename = os.path.join(dir, 'test.xml')
# Convert in HDF5.
with HDF5Writer(filename) as writer:
writer.convert()
# Open the HDF5 file.
filename = os.path.join(dir, 'test.kwik')
with tables.openFile(filename) as file:
# Shank 1.
# --------
spikes = file.root.shanks.shank1.spikes
clusters_hdf5 = spikes.col('cluster_manual')
features_hdf5 = spikes.col('features')
# Check that the arrays correspond to the original values.
clusters = read_clusters(os.path.join(dir, 'test.clu.1'))
features = read_features(os.path.join(dir, 'test.fet.1'),
nchannels, fetdim, freq, do_process=False)
np.testing.assert_equal(clusters_hdf5, clusters)
np.testing.assert_equal(features_hdf5, features)
# Shank 2.
# --------
spikes = file.root.shanks.shank2.spikes
clusters_hdf5 = spikes.col('cluster_manual')
features_hdf5 = spikes.col('features')
# Check that the arrays correspond to the original values.
clusters = read_clusters(os.path.join(dir, 'test.clu.2'))
features = read_features(os.path.join(dir, 'test.fet.2'),
nchannels, fetdim, freq, do_process=False)
np.testing.assert_equal(clusters_hdf5, clusters)
np.testing.assert_equal(features_hdf5, features)
def test_clusters():
dir = TEST_FOLDER
clufile = os.path.join(dir, 'test.aclu.1')
clufile2 = os.path.join(dir, 'test.aclu.1.saved')
clusters = read_clusters(clufile)
assert clusters.dtype == np.int32
assert clusters.shape == (1000, )
# Save.
save_clusters(clufile2, clusters)
# Open again.
clusters2 = read_clusters(clufile2)
assert np.array_equal(clusters, clusters2)
# Check the headers.
clusters_with_header = load_text(clufile, np.int32, skiprows=0)
clusters2_with_header = load_text(clufile2, np.int32, skiprows=0)
assert np.array_equal(clusters_with_header, clusters2_with_header)
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_cluster_info():
dir = TEST_FOLDER
clufile = os.path.join(dir, 'test.aclu.1')
cluinfofile = os.path.join(dir, 'test.acluinfo.1')
clusters = read_clusters(clufile)
indices = np.unique(clusters)
colors = np.random.randint(low=0, high=10, size=len(indices))
groups = np.random.randint(low=0, high=2, size=len(indices))
cluster_info = pd.DataFrame({'color': pd.Series(colors, index=indices),
'group': pd.Series(groups, index=indices)})
save_cluster_info(cluinfofile, cluster_info)
cluster_info2 = read_cluster_info(cluinfofile)
assert np.array_equal(cluster_info.values, cluster_info2.values)
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_cluster_info():
dir = TEST_FOLDER
clufile = os.path.join(dir, 'test.aclu.1')
cluinfofile = os.path.join(dir, 'test.acluinfo.1')
clusters = read_clusters(clufile)
indices = np.unique(clusters)
colors = np.random.randint(low=0, high=10, size=len(indices))
groups = np.random.randint(low=0, high=2, size=len(indices))
cluster_info = pd.DataFrame({
'color': pd.Series(colors, index=indices),
'group': pd.Series(groups, index=indices)
})
save_cluster_info(cluinfofile, cluster_info)
cluster_info2 = read_cluster_info(cluinfofile)
assert np.array_equal(cluster_info.values, cluster_info2.values)
def test_conversion_1():
# Convert klusters data to kwik.
klusters_to_kwik(filename='test', dir=TEST_FOLDER)
fet = read_features(os.path.join(TEST_FOLDER, 'test.fet.1'),
nchannels, fetdim, freq, do_process=False)
clu = read_clusters(os.path.join(TEST_FOLDER, 'test.clu.1'))
with Experiment('test', dir=TEST_FOLDER, mode='r') as exp:
# Check cluster / cluster group metadata.
assert np.allclose(sorted(exp.channel_groups[1].clusters.main.keys()),
range(2, 22))
assert np.allclose(exp.channel_groups[1].clusters.main.color[:],
range(1, 21))
assert np.all(exp.channel_groups[1].clusters.main.group[:] == 3)
# Check original == main.
assert np.allclose(
sorted(exp.channel_groups[1].clusters.main.keys()),
sorted(exp.channel_groups[1].clusters.original.keys()),
)
assert np.allclose(
exp.channel_groups[1].clusters.main.color[:],
exp.channel_groups[1].clusters.original.color[:],
)
assert np.allclose(
exp.channel_groups[1].clusters.main.group[:],
exp.channel_groups[1].clusters.original.group[:],
)
# Test spike clusters.
assert np.allclose(
exp.channel_groups[1].spikes.clusters.main[:],
clu)
assert np.allclose(
exp.channel_groups[1].spikes.clusters.main[:],
exp.channel_groups[1].spikes.clusters.original[:])
# Ensure features masks is contiguous.
assert isinstance(exp.channel_groups[1].spikes.features_masks, tb.Array)
assert not isinstance(exp.channel_groups[1].spikes.features_masks, tb.EArray)
# Check features and waveforms.
nspikes = len(exp.channel_groups[1].spikes.clusters.main[:])
assert exp.channel_groups[1].spikes.features_masks.shape[0] == nspikes
# No uspk file ==> no waveforms_raw
assert exp.channel_groups[1].spikes.waveforms_raw.shape[0] == 0
assert exp.channel_groups[1].spikes.waveforms_filtered.shape[0] == nspikes
assert exp.channel_groups[1].spikes.time_samples[:].sum() > 0
assert exp.channel_groups[1].spikes.features_masks[:].sum() > 0
assert exp.channel_groups[1].spikes.waveforms_filtered[:].sum() > 0
fet_kwik = exp.channel_groups[1].spikes.features[:]
# Check equality between original and kwik (normalized) features array.
fet = fet.ravel()
fet_kwik = fet_kwik.ravel()
ind = fet !=0
d = (fet[ind] / fet_kwik[ind])
assert d.max() - d.min() <= .1
def test_conversion_1():
# Convert klusters data to kwik.
klusters_to_kwik(filename='test', dir=TEST_FOLDER)
fet = read_features(os.path.join(TEST_FOLDER, 'test.fet.1'),
nchannels,
fetdim,
freq,
do_process=False)
clu = read_clusters(os.path.join(TEST_FOLDER, 'test.clu.1'))
with Experiment('test', dir=TEST_FOLDER, mode='r') as exp:
# Check cluster / cluster group metadata.
assert np.allclose(sorted(exp.channel_groups[1].clusters.main.keys()),
range(2, 22))
assert np.allclose(exp.channel_groups[1].clusters.main.color[:],
range(1, 21))
assert np.all(exp.channel_groups[1].clusters.main.group[:] == 3)
# Check original == main.
assert np.allclose(
sorted(exp.channel_groups[1].clusters.main.keys()),
sorted(exp.channel_groups[1].clusters.original.keys()),
)
assert np.allclose(
exp.channel_groups[1].clusters.main.color[:],
exp.channel_groups[1].clusters.original.color[:],
)
assert np.allclose(
exp.channel_groups[1].clusters.main.group[:],
exp.channel_groups[1].clusters.original.group[:],
)
# Test spike clusters.
assert np.allclose(exp.channel_groups[1].spikes.clusters.main[:], clu)
assert np.allclose(exp.channel_groups[1].spikes.clusters.main[:],
exp.channel_groups[1].spikes.clusters.original[:])
# Ensure features masks is contiguous.
assert isinstance(exp.channel_groups[1].spikes.features_masks,
tb.Array)
assert not isinstance(exp.channel_groups[1].spikes.features_masks,
tb.EArray)
# Check features and waveforms.
nspikes = len(exp.channel_groups[1].spikes.clusters.main[:])
assert exp.channel_groups[1].spikes.features_masks.shape[0] == nspikes
# No uspk file ==> no waveforms_raw
assert exp.channel_groups[1].spikes.waveforms_raw.shape[0] == 0
assert exp.channel_groups[1].spikes.waveforms_filtered.shape[
0] == nspikes
assert exp.channel_groups[1].spikes.time_samples[:].sum() > 0
assert exp.channel_groups[1].spikes.features_masks[:].sum() > 0
assert exp.channel_groups[1].spikes.waveforms_filtered[:].sum() > 0
fet_kwik = exp.channel_groups[1].spikes.features[:]
# Check equality between original and kwik (normalized) features array.
fet = fet.ravel()
fet_kwik = fet_kwik.ravel()
ind = fet != 0
d = (fet[ind] / fet_kwik[ind])
assert d.max() - d.min() <= .1