def test_feature_extractor():
base.features.Provide("SignalSource", DummySignalSource())
base.features.Provide("SpikeMarkerSource", DummySpikeDetector())
base.features.Provide("SpikeSource", components.SpikeExtractor())
feat_comp = components.FeatureExtractor(normalize=False)
feat_comp.add_feature("P2P")
features = feat_comp.features
ok_((features['data'] == spike_amp).all())
def test_feature_extractor_hide_features_not_found():
base.features.Provide("SignalSource", DummySignalSource())
base.features.Provide("SpikeMarkerSource", DummySpikeDetector())
base.features.Provide("SpikeSource", components.SpikeExtractor())
feat_comp = components.FeatureExtractor(normalize=False)
feat_comp.add_feature("P2P")
feat_comp.update()
assert_raises(ValueError, feat_comp.hide_features, "NonExistingFeature")
def test_propagate_truncate_to_features():
spike_src = DummySpikeSource()
sp_waves = spike_src._sp_waves
is_valid = np.ones(sp_waves['data'].shape[1]).astype(bool)
spike_src._sp_waves['is_valid'] = is_valid
base.features.Provide("SpikeSource", spike_src)
feat_comp = components.FeatureExtractor(normalize=False)
feat_comp.add_feature("P2P")
features = feat_comp.features
ok_((features['is_valid'] == is_valid).all())
def test_pipeline_update():
base.features.Provide("SignalSource", DummySignalSource())
base.features.Provide("SpikeMarkerSource",
components.SpikeDetector(thresh=spike_amp / 2.))
base.features.Provide("SpikeSource", components.SpikeExtractor())
base.features.Provide("FeatureSource",
components.FeatureExtractor(normalize=False))
base.features.Provide("ClusterAnalyzer",
components.ClusterAnalyzer("k_means", 2))
base.features['FeatureSource'].add_feature("P2P")
cl1 = base.features["ClusterAnalyzer"].labels
base.features["SignalSource"].update()
cl2 = base.features["ClusterAnalyzer"].labels
ok_(~(len(cl1) == len(cl2)))
def test_feature_extractor_hide_features():
base.features.Provide("SignalSource", DummySignalSource())
base.features.Provide("SpikeMarkerSource", DummySpikeDetector())
base.features.Provide("SpikeSource", components.SpikeExtractor())
feat_comp = components.FeatureExtractor(normalize=False)
feat_comp.add_feature("P2P")
feat_comp.add_feature("SpIdx")
feat_comp.hide_features("Sp*")
feat_comp.update()
features = feat_comp.features
test1 = features['names'] == ["P2P:Ch0:P2P"] # it's P2P
test2 = features['data'].shape[1] == 1
test3 = (features['data'] == spike_amp).all() # with corresponding data
ok_(test1 and test2 and test3)
def test_null_labels_returned_for_truncated_spikes():
signal_src = DummySignalSource()
signal_src._spikes = signal_src._spikes[:, :-period / 1000. * FS * 2.5]
base.features.Provide("SignalSource", signal_src)
base.features.Provide("SpikeMarkerSource", DummySpikeDetector())
base.features.Provide("SpikeSource", components.SpikeExtractor())
base.features.Provide("FeatureSource",
components.FeatureExtractor(normalize=False))
base.features.Provide("ClusterAnalyzer",
components.ClusterAnalyzer("k_means", 1))
base.features['FeatureSource'].add_feature("P2P")
cl = base.features["ClusterAnalyzer"].labels
true_labels = np.ones(n_spikes - 2)
true_labels[-1] = 0
ok_((cl == true_labels).all())
io = ABFSource(file_name, electrodes=[1, 2])
io_filter = components.FilterStack()
base.features.Provide("RawSource", io)
base.features.Provide("EventsOutput", io)
base.features.Provide("SignalSource", io_filter)
base.features.Provide(
"SpikeMarkerSource",
MultiChannelSpikeDetector(contact=contact,
thresh=thresh,
type=type,
sp_win=[-0.6, 0.8],
resample=10,
align=True))
base.features.Provide("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
base.features.Provide("FeatureSource", components.FeatureExtractor())
base.features.Provide("LabelSource",
components.ClusterAnalyzer("gmm", n_clusters))
src = base.features['SignalSource']
features = base.features['FeatureSource']
clusters = base.features['LabelSource']
events = base.features['SpikeMarkerSource']
labels = base.features['LabelSource']
browser = components.SpikeBrowserWithLabels()
plot1 = components.PlotFeaturesTimeline()
plot2 = components.PlotSpikes()
legend = components.Legend()
export = components.ExportCells()
io = components.PyTablesSource(hdf5file, dataset)
io_filter = components.FilterStack()
base.register("RawSource", io)
base.register("EventsOutput", io)
base.register("SignalSource", io_filter)
base.register(
"SpikeMarkerSource",
components.SpikeDetector(contact=contact,
thresh=thresh,
type=detection_type,
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
base.register("FeatureSource", components.FeatureExtractor())
base.register("LabelSource", components.ClusterAnalyzer("gmm", 4))
browser = components.SpikeBrowser()
feature_plot = components.PlotFeaturesTimeline()
wave_plot = components.PlotSpikes()
legend = components.Legend()
export = components.ExportCells()
#############################################################
# Add filters here:
base.features["SignalSource"].add_filter("LinearIIR", *filter_freq)
# Add the features here:
base.features["FeatureSource"].add_feature("P2P")
base.features["FeatureSource"].add_feature("PCA", ncomps=2)