def _init_data(self):
self.cache_dir = self.config_dir
self.n_samples_waveforms = 31
self.n_samples_t = 20000
self.n_channels = 11
self.n_clusters = 4
self.n_spikes_per_cluster = 50
n_spikes_total = self.n_clusters * self.n_spikes_per_cluster
n_features_per_channel = 4
self.n_channels = self.n_channels
self.n_spikes = n_spikes_total
self.sample_rate = 20000.
self.duration = self.n_samples_t / float(self.sample_rate)
self.spike_times = np.arange(0, self.duration, 100. / self.sample_rate)
self.spike_clusters = np.repeat(np.arange(self.n_clusters),
self.n_spikes_per_cluster)
assert len(self.spike_times) == len(self.spike_clusters)
self.cluster_ids = np.unique(self.spike_clusters)
self.channel_positions = staggered_positions(self.n_channels)
sc = self.spike_clusters
self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c])
self.spike_count = lambda c: len(self.spikes_per_cluster(c))
self.n_features_per_channel = n_features_per_channel
self.cluster_groups = {c: None for c in range(self.n_clusters)}
self.all_traces = artificial_traces(self.n_samples_t, self.n_channels)
self.all_masks = artificial_masks(n_spikes_total, self.n_channels)
self.all_waveforms = artificial_waveforms(n_spikes_total,
self.n_samples_waveforms,
self.n_channels)
self.all_features = artificial_features(n_spikes_total,
self.n_channels,
self.n_features_per_channel)
def test_probe_view(qtbot, tempdir):
n = 50
positions = staggered_positions(n)
best_channels = lambda cluster_id: range(1, 9, 2)
v = ProbeView(positions=positions,
best_channels=best_channels,
)
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])
# qtbot.stop()
gui.close()
def test_probe_view(qtbot, tempdir):
n = 50
positions = staggered_positions(n)
best_channels = lambda cluster_id: range(1, 9, 2)
v = ProbeView(positions=positions,
best_channels=best_channels,
)
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])
# qtbot.stop()
gui.close()
def _init_data(self):
self.cache_dir = self.config_dir
self.n_samples_waveforms = 31
self.n_samples_t = 20000
self.n_channels = 11
self.n_clusters = 4
self.n_spikes_per_cluster = 200
n_spikes_total = self.n_clusters * self.n_spikes_per_cluster
n_features_per_channel = 4
self.n_channels = self.n_channels
self.n_spikes = n_spikes_total
self.sample_rate = 20000.
self.duration = self.n_samples_t / float(self.sample_rate)
self.spike_times = np.arange(
0, self.duration,
5000. / (self.sample_rate * self.n_spikes_per_cluster))
self.spike_clusters = np.repeat(np.arange(self.n_clusters),
self.n_spikes_per_cluster)
assert len(self.spike_times) == len(self.spike_clusters)
self.cluster_ids = np.unique(self.spike_clusters)
self.channel_positions = staggered_positions(self.n_channels)
self.channel_order = np.arange(self.n_channels)
sc = self.spike_clusters
self.spikes_per_cluster = lambda c: _spikes_in_clusters(sc, [c])
self.spike_count = lambda c: len(self.spikes_per_cluster(c))
self.n_features_per_channel = n_features_per_channel
self.cluster_groups = {c: None for c in range(self.n_clusters)}
self.all_traces = artificial_traces(self.n_samples_t, self.n_channels)
self.all_masks = artificial_masks(n_spikes_total, self.n_channels)
self.all_waveforms = artificial_waveforms(n_spikes_total,
self.n_samples_waveforms,
self.n_channels)
self.all_features = artificial_features(n_spikes_total,
self.n_channels,
self.n_features_per_channel)
def test_get_boxes():
positions = [[-1, 0], [1, 0]]
boxes = _get_boxes(positions)
ac(boxes, [[-1, -.25, 0, .25],
[+0, -.25, 1, .25]], atol=1e-4)
positions = [[-1, 0], [1, 0]]
boxes = _get_boxes(positions, keep_aspect_ratio=False)
ac(boxes, [[-1, -1, 0, 1],
[0, -1, 1, 1]], atol=1e-4)
positions = linear_positions(4)
boxes = _get_boxes(positions)
ac(boxes, [[-0.5, -1.0, +0.5, -0.5],
[-0.5, -0.5, +0.5, +0.0],
[-0.5, +0.0, +0.5, +0.5],
[-0.5, +0.5, +0.5, +1.0],
], atol=1e-4)
positions = staggered_positions(8)
boxes = _get_boxes(positions)
ac(boxes[:, 1], np.arange(.75, -1.1, -.25), atol=1e-6)
ac(boxes[:, 3], np.arange(1, -.76, -.25), atol=1e-7)
def get_waveforms(cluster_id):
return Bunch(
data=artificial_waveforms(10, 20, nc),
channel_ids=np.arange(nc),
channel_positions=staggered_positions(nc),
)
def site_positions(n_channels):
yield staggered_positions(n_channels)
def site_positions(n_channels):
yield staggered_positions(n_channels)
def get_waveforms(cluster_id):
return Bunch(
data=artificial_waveforms(10, 20, nc), channel_ids=np.arange(nc), channel_positions=staggered_positions(nc)
)