def test_probe_merge_1(tempdir):
out_dir = tempdir / 'merged'
# Create two identical datasets.
probe_names = ('probe_left', 'probe_right')
for name in probe_names:
(tempdir / name).mkdir(exist_ok=True, parents=True)
_make_dataset(tempdir / name, param='dense', has_spike_attributes=False)
subdirs = [tempdir / name for name in probe_names]
# Merge them.
m = Merger(subdirs, out_dir)
single = load_model(tempdir / probe_names[0] / 'params.py')
# Test the merged dataset.
merged = m.merge()
for name in ('n_spikes', 'n_channels', 'n_templates'):
assert getattr(merged, name) == getattr(single, name) * 2
assert merged.sample_rate == single.sample_rate
def test_probe_merge_2(tempdir):
out_dir = tempdir / 'merged'
# Create two identical datasets.
probe_names = ('probe_left', 'probe_right')
for name in probe_names:
(tempdir / name).mkdir(exist_ok=True, parents=True)
_make_dataset(tempdir / name, param='dense', has_spike_attributes=False)
subdirs = [tempdir / name for name in probe_names]
# Add small shift in the spike times of the second probe.
single = load_model(tempdir / probe_names[0] / 'params.py')
st_path = tempdir / 'probe_right/spike_times.npy'
np.save(st_path, single.spike_samples + 1)
# make amplitudes unique and growing so they can serve as key and sorting indices
single.amplitudes = np.linspace(5, 15, single.n_spikes)
# single.spike_clusters[single.spike_clusters == 0] = 12
for m, subdir in enumerate(subdirs):
np.save(subdir / 'amplitudes.npy', single.amplitudes + 20 * m)
np.save(subdir / 'spike_clusters.npy', single.spike_clusters)
# Merge them.
m = Merger(subdirs, out_dir)
merged = m.merge()
# Test the merged dataset.
for name in ('n_spikes', 'n_channels', 'n_templates'):
assert getattr(merged, name) == getattr(single, name) * 2
assert merged.sample_rate == single.sample_rate
# Check the spikes.
single = load_model(tempdir / probe_names[0] / 'params.py')
def test_merged_single(merged, merged_original_amps=None):
if merged_original_amps is None:
merged_original_amps = merged.amplitudes
_, im1, i1 = np.intersect1d(merged_original_amps, single.amplitudes, return_indices=True)
_, im2, i2 = np.intersect1d(merged_original_amps, single.amplitudes + 20,
return_indices=True)
# intersection spans the full vector
assert i1.size + i2.size == merged.amplitudes.size
# test spikes
assert np.allclose(merged.spike_times[im1], single.spike_times[i1])
assert np.allclose(merged.spike_times[im2], single.spike_times[i2] + 4e-5)
# test clusters
assert np.allclose(merged.spike_clusters[im2], single.spike_clusters[i2] + 64)
assert np.allclose(merged.spike_clusters[im1], single.spike_clusters[i1])
# test templates
assert np.all(merged.spike_templates[im1] - single.spike_templates[i1] == 0)
assert np.all(merged.spike_templates[im2] - single.spike_templates[i2] == 64)
# test probes
assert np.all(merged.channel_probes == np.r_[single.channel_probes,
single.channel_probes + 1])
assert np.all(merged.templates_channels[merged.templates_probes == 0] < single.n_channels)
assert np.all(merged.templates_channels[merged.templates_probes == 1] >= single.n_channels)
spike_probes = merged.templates_probes[merged.spike_templates]
assert np.all(merged_original_amps[spike_probes == 0] <= 15)
assert np.all(merged_original_amps[spike_probes == 1] >= 20)
np.all(merged.sparse_templates.data[:64, :, 0:32] == single.sparse_templates.data)
# Convert into ALF and load.
alf = EphysAlfCreator(merged).convert(tempdir / 'alf')
test_merged_single(merged)
test_merged_single(alf, merged_original_amps=merged.amplitudes)
# specific test channel ids only for ALF merge dataset: the raw indices are still individual
# file indices, the merged channel mapping is in `channels._phy_ids.npy`
chid = np.load(tempdir.joinpath('alf', 'channels.rawInd.npy'))
assert np.all(chid == np.r_[single.channel_mapping, single.channel_mapping])
out_files = list(tempdir.joinpath('alf').glob('*.*'))
cl_shape = [np.load(f).shape[0] for f in out_files if f.name.startswith('clusters.') and
f.name.endswith('.npy')]
sp_shape = [np.load(f).shape[0] for f in out_files if f.name.startswith('spikes.')]
ch_shape = [np.load(f).shape[0] for f in out_files if f.name.startswith('channels.')]
assert len(set(cl_shape)) == 1
assert len(set(sp_shape)) == 1
assert len(set(ch_shape)) == 1
def test_template_describe(qtbot, tempdir):
model = load_model(
_make_dataset(tempdir, param='dense', has_spike_attributes=False))
with captured_output() as (stdout, stderr):
template_describe(model.dir_path / 'params.py')
assert '314' in stdout.getvalue()
def _create_dataset(cls, tempdir):
return _make_dataset(tempdir,
param='dense',
has_spike_attributes=False)
def _create_dataset(cls, tempdir):
return _make_dataset(tempdir, param='misc')