def test_run_1():
"""Read from NumPy array file."""
# Run the algorithm.
with Experiment('myexperiment', dir=DIRPATH, mode='a') as exp:
run(raw_data, experiment=exp, prm=prm, probe=Probe(prb),)
# Open the data files.
with Experiment('myexperiment', dir=DIRPATH) as exp:
nspikes = len(exp.channel_groups[0].spikes)
assert exp.channel_groups[0].spikes.clusters.main.shape[0] == nspikes
assert exp.channel_groups[0].spikes.features_masks.shape[0] == nspikes
assert exp.channel_groups[0].spikes.waveforms_filtered.shape[0] == nspikes
assert isinstance(exp.channel_groups[0]._node.pca_waveforms,
tb.Array)
# Assert the log file exists.
logfile = exp.gen_filename('log')
assert os.path.exists(logfile)
assert exp.recordings[0].raw.shape == (nsamples, nchannels)
assert exp.recordings[0].high.shape == (nsamples, nchannels)
assert exp.recordings[0].low.shape[0] in range(nsamples // 16 - 2,
nsamples // 16 + 3)
assert exp.recordings[0].low.shape[1] == nchannels
def test_run_nospikes():
"""Read from NumPy array file."""
# Run the algorithm.
with Experiment('myexperiment', dir=DIRPATH, mode='a') as exp:
run(np.zeros((nsamples, nchannels)),
experiment=exp, prm=prm, probe=Probe(prb))
# Open the data files.
with Experiment('myexperiment', dir=DIRPATH) as exp:
assert len(exp.channel_groups[0].spikes) == 0
def test_run_2():
"""Read from .dat file."""
path = os.path.join(DIRPATH, 'mydatfile.dat')
(raw_data * 1e4).astype(np.int16).tofile(path)
# Run the algorithm.
with Experiment('myexperiment', dir=DIRPATH, mode='a') as exp:
run(path, experiment=exp, prm=prm, probe=Probe(prb))
# Open the data files.
with Experiment('myexperiment', dir=DIRPATH) as exp:
assert len(exp.channel_groups[0].spikes)
def test_run_canonical_pcs():
prm_canonical = prm.copy()
canonical_pcs = np.ones((prm['nfeatures_per_channel'],
prm['waveforms_nsamples'],
prm['nchannels']))
prm_canonical['canonical_pcs'] = canonical_pcs
with Experiment('myexperiment', dir=DIRPATH, mode='a') as exp:
run(raw_data, experiment=exp, prm=prm_canonical, probe=Probe(prb),)
def test_diagnostics():
dir = tempfile.mkdtemp()
path = op.join(dir, 'diagnostics.py')
with open(path, 'w') as f:
f.write(
'def diagnostics(prm=None, **kwargs):\n'
' print(prm)\n'
'\n')
prm['diagnostics_path'] = path
with Experiment('myexperiment', dir=DIRPATH, mode='a') as exp:
run(np.zeros((nsamples, nchannels)),
experiment=exp, prm=prm, probe=Probe(prb))
shutil.rmtree(dir)
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