def test_extend_spikes():
n_spikes = 1000
n_clusters = 10
spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
spike_ids = np.unique(np.random.randint(size=5, low=0, high=n_spikes))
# These spikes belong to the following clusters.
clusters = np.unique(spike_clusters[spike_ids])
# These are the spikes belonging to those clusters, but not in the
# originally-specified spikes.
extended = _extend_spikes(spike_ids, spike_clusters)
assert np.all(np.in1d(spike_clusters[extended], clusters))
# The function only returns spikes that weren't in the passed spikes.
assert len(np.intersect1d(extended, spike_ids)) == 0
# Check that all spikes from our clusters have been selected.
rest = np.setdiff1d(np.arange(n_spikes), extended)
rest = np.setdiff1d(rest, spike_ids)
assert not np.any(np.in1d(spike_clusters[rest], clusters))
def test_clustering_long():
n_spikes = 1000
n_clusters = 10
spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
spike_clusters_base = spike_clusters.copy()
# Instantiate a Clustering instance.
clustering = Clustering(spike_clusters)
ae(clustering.spike_clusters, spike_clusters)
# Test clustering.spikes_in_clusters() function.:
assert np.all(spike_clusters[clustering.spikes_in_clusters([5])] == 5)
# Test cluster ids.
ae(clustering.cluster_ids, np.arange(n_clusters))
assert clustering.new_cluster_id() == n_clusters
assert clustering.n_clusters == n_clusters
# Updating a cluster, method 1.
spike_clusters_new = spike_clusters.copy()
spike_clusters_new[:10] = 100
clustering.spike_clusters[:] = spike_clusters_new[:]
# Need to update explicitely.
clustering._new_cluster_id = 101
clustering._update_cluster_ids()
ae(clustering.cluster_ids, np.r_[np.arange(n_clusters), 100])
# Updating a cluster, method 2.
clustering.spike_clusters[:] = spike_clusters_base[:]
clustering.spike_clusters[:10] = 100
# HACK: need to update manually here.
clustering._new_cluster_id = 101
ae(clustering.cluster_ids, np.r_[np.arange(n_clusters), 100])
# Assign.
new_cluster = 101
clustering.assign(np.arange(0, 10), new_cluster)
assert new_cluster in clustering.cluster_ids
assert np.all(clustering.spike_clusters[:10] == new_cluster)
# Merge.
my_spikes_0 = np.nonzero(np.in1d(clustering.spike_clusters, [2, 3]))[0]
info = clustering.merge([2, 3])
my_spikes = info.spike_ids
ae(my_spikes, my_spikes_0)
assert (new_cluster + 1) in clustering.cluster_ids
assert np.all(clustering.spike_clusters[my_spikes] == (new_cluster + 1))
# Merge to a given cluster.
clustering.spike_clusters[:] = spike_clusters_base[:]
clustering._new_cluster_id = 11
my_spikes_0 = np.nonzero(np.in1d(clustering.spike_clusters, [4, 6]))[0]
info = clustering.merge([4, 6], 11)
my_spikes = info.spike_ids
ae(my_spikes, my_spikes_0)
assert 11 in clustering.cluster_ids
assert np.all(clustering.spike_clusters[my_spikes] == 11)
# Split.
my_spikes = [1, 3, 5]
clustering.split(my_spikes)
assert np.all(clustering.spike_clusters[my_spikes] == 12)
# Assign.
clusters = [0, 1, 2]
clustering.assign(my_spikes, clusters)
clu = clustering.spike_clusters[my_spikes]
ae(clu - clu[0], clusters)
def test_clustering_assign():
n_spikes = 1000
n_clusters = 10
spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
clustering = Clustering(spike_clusters)
checkpoints = {}
def _checkpoint(index=None):
if index is None:
index = len(checkpoints)
checkpoints[index] = clustering.spike_clusters.copy()
def _assert_is_checkpoint(index):
ae(clustering.spike_clusters, checkpoints[index])
@clustering.connect
def on_request_undo_state(up):
return 'hello'
# Checkpoint 0.
_checkpoint()
_assert_is_checkpoint(0)
my_spikes_1 = np.unique(np.random.randint(low=0, high=n_spikes, size=5))
my_spikes_2 = np.unique(np.random.randint(low=0, high=n_spikes, size=10))
my_spikes_3 = np.unique(np.random.randint(low=0, high=n_spikes, size=1000))
my_spikes_4 = np.arange(n_spikes - 5)
# Edge cases.
clustering.assign([])
with raises(ValueError):
clustering.merge([], 1)
# Checkpoint 1.
info = clustering.split(my_spikes_1)
_checkpoint()
assert info.description == 'assign'
assert 10 in info.added
assert info.history is None
_assert_is_checkpoint(1)
# Checkpoint 2.
info = clustering.split(my_spikes_2)
assert info.description == 'assign'
assert info.history is None
_checkpoint()
_assert_is_checkpoint(2)
# Checkpoint 3.
info = clustering.assign(my_spikes_3)
assert info.description == 'assign'
assert info.history is None
assert info.undo_state is None
_checkpoint()
_assert_is_checkpoint(3)
# Undo checkpoint 3.
info = clustering.undo()
assert info.description == 'assign'
assert info.history == 'undo'
assert info.undo_state == ['hello']
_checkpoint()
_assert_is_checkpoint(2)
# Checkpoint 4.
info = clustering.assign(my_spikes_4)
assert info.description == 'assign'
assert info.history is None
_checkpoint(4)
assert len(info.deleted) >= 2
_assert_is_checkpoint(4)
def test_clustering_merge():
n_spikes = 1000
n_clusters = 10
spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
clustering = Clustering(spike_clusters)
spk0 = clustering.spikes_per_cluster[0]
spk1 = clustering.spikes_per_cluster[1]
checkpoints = {}
def _checkpoint():
index = len(checkpoints)
checkpoints[index] = clustering.spike_clusters.copy()
def _assert_is_checkpoint(index):
ae(clustering.spike_clusters, checkpoints[index])
def _assert_spikes(clusters):
ae(info.spike_ids, _spikes_in_clusters(spike_clusters, clusters))
@clustering.connect
def on_request_undo_state(up):
return 'hello'
# Checkpoint 0.
_checkpoint()
_assert_is_checkpoint(0)
# Checkpoint 1.
info = clustering.merge([0, 1], 11)
_checkpoint()
_assert_spikes([11])
ae(clustering.spikes_per_cluster[11], np.sort(np.r_[spk0, spk1]))
assert 0 not in clustering.spikes_per_cluster
assert info.added == [11]
assert info.deleted == [0, 1]
_assert_is_checkpoint(1)
# Checkpoint 2.
info = clustering.merge([2, 3], 12)
_checkpoint()
_assert_spikes([12])
assert info.added == [12]
assert info.deleted == [2, 3]
assert info.history is None
assert info.undo_state is None # undo_state is only returned when undoing.
_assert_is_checkpoint(2)
# Undo once.
info = clustering.undo()
assert info.added == [2, 3]
assert info.deleted == [12]
assert info.history == 'undo'
assert info.undo_state == ['hello']
_assert_is_checkpoint(1)
ae(clustering.spikes_per_cluster[11], np.sort(np.r_[spk0, spk1]))
# Redo.
info = clustering.redo()
_assert_spikes([12])
assert info.added == [12]
assert info.deleted == [2, 3]
assert info.history == 'redo'
assert info.undo_state is None
_assert_is_checkpoint(2)
# No redo.
info = clustering.redo()
_assert_is_checkpoint(2)
# Merge again.
info = clustering.merge([4, 5, 6], 13)
_checkpoint()
_assert_spikes([13])
assert info.added == [13]
assert info.deleted == [4, 5, 6]
assert info.history is None
_assert_is_checkpoint(3)
# One more merge.
info = clustering.merge([8, 7]) # merged to 14
_checkpoint()
_assert_spikes([14])
assert info.added == [14]
assert info.deleted == [7, 8]
assert info.history is None
_assert_is_checkpoint(4)
# Now we undo.
info = clustering.undo()
assert info.added == [7, 8]
assert info.deleted == [14]
assert info.history == 'undo'
_assert_is_checkpoint(3)
# We merge again.
# NOTE: 14 has been wasted, move to 15: necessary to avoid explicit cache
# invalidation when caching clusterid-based functions.
assert clustering.new_cluster_id() == 15
assert any(clustering.spike_clusters == 13)
assert all(clustering.spike_clusters != 14)
info = clustering.merge([8, 7], 15)
_assert_spikes([15])
assert info.added == [15]
assert info.deleted == [7, 8]
assert info.history is None
# Same as checkpoint with 4, but replace 14 with 15.
res = checkpoints[4]
res[res == 14] = 15
ae(clustering.spike_clusters, res)
# Undo all.
for i in range(3, -1, -1):
info = clustering.undo()
_assert_is_checkpoint(i)
_assert_is_checkpoint(0)
# Redo all.
for i in range(5):
_assert_is_checkpoint(i)
info = clustering.redo()
def test_trace_view(tempdir, qtbot):
nc = 5
ns = 9
sr = 1000.
ch = list(range(nc))
duration = 1.
st = np.linspace(0.1, .9, ns)
sc = artificial_spike_clusters(ns, nc)
traces = 10 * artificial_traces(int(round(duration * sr)), nc)
cs = ColorSelector()
m = Bunch(spike_times=st, spike_clusters=sc, sample_rate=sr)
s = Bunch(cluster_meta={}, selected=[0])
sw = _iter_spike_waveforms(
interval=[0., 1.],
traces_interval=traces,
model=m,
supervisor=s,
n_samples_waveforms=ns,
get_best_channels=lambda cluster_id: ch,
color_selector=cs,
)
assert len(list(sw))
def get_traces(interval):
out = Bunch(
data=select_traces(traces, interval, sample_rate=sr),
color=(.75, ) * 4,
)
a, b = st.searchsorted(interval)
out.waveforms = []
k = 20
for i in range(a, b):
t = st[i]
c = sc[i]
s = int(round(t * sr))
d = Bunch(
data=traces[s - k:s + k, :],
start_time=t - k / sr,
color=cs.get(c),
channel_ids=np.arange(5),
spike_id=i,
spike_cluster=c,
)
out.waveforms.append(d)
return out
v = TraceView(
traces=get_traces,
n_channels=nc,
sample_rate=sr,
duration=duration,
channel_vertical_order=np.arange(nc)[::-1],
)
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
# qtbot.waitForWindowShown(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
# ac(v.stacked.box_size, (1., .08181), atol=1e-3)
v.set_interval((.375, .625))
assert v.time == .5
v.go_to(.25)
assert v.time == .25
v.go_to(-.5)
assert v.time == .125
v.go_left()
assert v.time == .125
v.go_right()
assert v.time == .175
# Change interval size.
v.interval = (.25, .75)
ac(v.interval, (.25, .75))
v.widen()
ac(v.interval, (.125, .875))
v.narrow()
ac(v.interval, (.25, .75))
# Widen the max interval.
v.set_interval((0, duration))
v.widen()
v.toggle_show_labels()
# v.toggle_show_labels()
v.go_right()
assert v.do_show_labels
# Change channel scaling.
bs = v.stacked.box_size
v.increase()
v.decrease()
ac(v.stacked.box_size, bs, atol=1e-3)
v.origin = 'upper'
assert v.origin == 'upper'
# Simulate spike selection.
_clicked = []
@v.gui.connect_
def on_spike_click(channel_id=None, spike_id=None, cluster_id=None):
_clicked.append((channel_id, spike_id, cluster_id))
v.events.key_press(key=keys.Key('Control'))
v.events.mouse_press(pos=(400., 200.),
button=1,
modifiers=(keys.CONTROL, ))
v.events.key_release(key=keys.Key('Control'))
assert _clicked == [(1, 4, 1)]
# qtbot.stop()
gui.close()
def test_feature_view(qtbot, tempdir, n_channels):
nc = n_channels
ns = 500
features = artificial_features(ns, nc, 4)
spike_clusters = artificial_spike_clusters(ns, 4)
spike_times = np.linspace(0., 1., ns)
spc = _spikes_per_cluster(spike_clusters)
def get_spike_ids(cluster_id):
return (spc[cluster_id] if cluster_id is not None else np.arange(ns))
def get_features(cluster_id=None,
channel_ids=None,
spike_ids=None,
load_all=None):
if load_all:
spike_ids = spc[cluster_id]
else:
spike_ids = get_spike_ids(cluster_id)
return Bunch(
data=features[spike_ids],
spike_ids=spike_ids,
masks=np.random.rand(ns, nc),
channel_ids=(channel_ids
if channel_ids is not None else np.arange(nc)[::-1]),
)
def get_time(cluster_id=None, load_all=None):
return Bunch(
data=spike_times[get_spike_ids(cluster_id)],
lim=(0., 1.),
)
v = FeatureView(
features=get_features,
attributes={'time': get_time},
)
v.set_state(GUIState(scaling=None))
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
gui.emit('select', [0, 2])
qtbot.wait(10)
v.increase()
v.decrease()
v.on_channel_click(channel_id=3, button=1, key=2)
v.clear_channels()
v.toggle_automatic_channel_selection()
# Split without selection.
spike_ids = v.on_request_split()
assert len(spike_ids) == 0
# Draw a lasso.
def _click(x, y):
qtbot.mouseClick(v.native,
Qt.LeftButton,
pos=QPoint(x, y),
modifier=Qt.ControlModifier)
_click(10, 10)
_click(10, 100)
_click(100, 100)
_click(100, 10)
# Split lassoed points.
spike_ids = v.on_request_split()
assert len(spike_ids) > 0
# qtbot.stop()
gui.close()
def test_feature_view(qtbot, tempdir, n_channels):
nc = n_channels
ns = 500
features = artificial_features(ns, nc, 4)
spike_clusters = artificial_spike_clusters(ns, 4)
spike_times = np.linspace(0., 1., ns)
spc = _spikes_per_cluster(spike_clusters)
def get_spike_ids(cluster_id):
return (spc[cluster_id] if cluster_id is not None else np.arange(ns))
def get_features(cluster_id=None, channel_ids=None, spike_ids=None,
load_all=None):
if load_all:
spike_ids = spc[cluster_id]
else:
spike_ids = get_spike_ids(cluster_id)
return Bunch(data=features[spike_ids],
spike_ids=spike_ids,
masks=np.random.rand(ns, nc),
channel_ids=(channel_ids
if channel_ids is not None
else np.arange(nc)[::-1]),
)
def get_time(cluster_id=None, load_all=None):
return Bunch(data=spike_times[get_spike_ids(cluster_id)],
lim=(0., 1.),
)
v = FeatureView(features=get_features,
attributes={'time': get_time},
)
v.set_state(GUIState(scaling=None))
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
gui.emit('select', [0, 2])
qtbot.wait(10)
v.increase()
v.decrease()
v.on_channel_click(channel_id=3, button=1, key=2)
v.clear_channels()
v.toggle_automatic_channel_selection()
# Split without selection.
spike_ids = v.on_request_split()
assert len(spike_ids) == 0
# Draw a lasso.
def _click(x, y):
qtbot.mouseClick(v.native, Qt.LeftButton, pos=QPoint(x, y),
modifier=Qt.ControlModifier)
_click(10, 10)
_click(10, 100)
_click(100, 100)
_click(100, 10)
# Split lassoed points.
spike_ids = v.on_request_split()
assert len(spike_ids) > 0
# qtbot.stop()
gui.close()
def test_trace_view(tempdir, qtbot):
nc = 5
ns = 9
sr = 1000.
ch = list(range(nc))
duration = 1.
st = np.linspace(0.1, .9, ns)
sc = artificial_spike_clusters(ns, nc)
traces = 10 * artificial_traces(int(round(duration * sr)), nc)
cs = ColorSelector()
m = Bunch(spike_times=st, spike_clusters=sc, sample_rate=sr)
s = Bunch(cluster_meta={}, selected=[0])
sw = _iter_spike_waveforms(interval=[0., 1.],
traces_interval=traces,
model=m,
supervisor=s,
n_samples_waveforms=ns,
get_best_channels=lambda cluster_id: ch,
color_selector=cs,
)
assert len(list(sw))
def get_traces(interval):
out = Bunch(data=select_traces(traces, interval, sample_rate=sr),
color=(.75,) * 4,
)
a, b = st.searchsorted(interval)
out.waveforms = []
k = 20
for i in range(a, b):
t = st[i]
c = sc[i]
s = int(round(t * sr))
d = Bunch(data=traces[s - k:s + k, :],
start_time=t - k / sr,
color=cs.get(c),
channel_ids=np.arange(5),
spike_id=i,
spike_cluster=c,
)
out.waveforms.append(d)
return out
v = TraceView(traces=get_traces,
n_channels=nc,
sample_rate=sr,
duration=duration,
channel_vertical_order=np.arange(nc)[::-1],
)
gui = GUI(config_dir=tempdir)
gui.show()
v.attach(gui)
qtbot.addWidget(gui)
# qtbot.waitForWindowShown(gui)
v.on_select([])
v.on_select([0])
v.on_select([0, 2, 3])
v.on_select([0, 2])
# ac(v.stacked.box_size, (1., .08181), atol=1e-3)
v.set_interval((.375, .625))
assert v.time == .5
v.go_to(.25)
assert v.time == .25
v.go_to(-.5)
assert v.time == .125
v.go_left()
assert v.time == .125
v.go_right()
assert v.time == .175
# Change interval size.
v.interval = (.25, .75)
ac(v.interval, (.25, .75))
v.widen()
ac(v.interval, (.125, .875))
v.narrow()
ac(v.interval, (.25, .75))
# Widen the max interval.
v.set_interval((0, duration))
v.widen()
v.toggle_show_labels()
# v.toggle_show_labels()
v.go_right()
assert v.do_show_labels
# Change channel scaling.
bs = v.stacked.box_size
v.increase()
v.decrease()
ac(v.stacked.box_size, bs, atol=1e-3)
v.origin = 'upper'
assert v.origin == 'upper'
# Simulate spike selection.
_clicked = []
@v.gui.connect_
def on_spike_click(channel_id=None, spike_id=None, cluster_id=None):
_clicked.append((channel_id, spike_id, cluster_id))
v.events.key_press(key=keys.Key('Control'))
v.events.mouse_press(pos=(400., 200.), button=1, modifiers=(keys.CONTROL,))
v.events.key_release(key=keys.Key('Control'))
assert _clicked == [(1, 4, 1)]
# qtbot.stop()
gui.close()
