def test_spike_extractor():
base.features.Provide("SignalSource", DummySignalSource())
base.features.Provide("SpikeMarkerSource", DummySpikeDetector())
sp_waves = components.SpikeExtractor().spikes
mean_wave = sp_waves['data'][:, :, 0].mean(1)
time = sp_waves['time']
true_spike = spike_amp * ((time >= 0) & (time < spike_dur))
ok_(np.sum(np.abs(mean_wave - true_spike)) < 0.01 * spike_amp)
def test_truncated_spikes_from_end():
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())
sp_waves = components.SpikeExtractor().spikes
correct_mask = np.ones(n_spikes - 2).astype(np.bool)
correct_mask[-1] = False
ok_((sp_waves['is_valid'] == correct_mask).all())
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_truncated_spikes_from_begin():
signal_src = DummySignalSource()
detector = DummySpikeDetector()
spt = detector._spt_data['data']
detector._spt_data['data'] = np.insert(spt, 0, 0)
base.features.Provide("SignalSource", signal_src)
base.features.Provide("SpikeMarkerSource", detector)
sp_waves = components.SpikeExtractor().spikes
correct_mask = np.ones(n_spikes - 1).astype(np.bool)
correct_mask[0] = False
ok_((sp_waves['is_valid'] == correct_mask).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())
import os
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()
from spike_sort.io import neo_filters
####################################
# Adjust these fields for your needs
sp_win = [-0.6, 0.8]
url = 'https://portal.g-node.org/neo/axon/File_axon_1.abf'
path = 'file_axon.abf'
import urllib
urllib.urlretrieve(url, path)
io = neo_filters.NeoSource(path)
base.register("SignalSource", io)
base.register(
"SpikeMarkerSource",
components.SpikeDetector(contact=0,
thresh='auto',
type='max',
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
browser = components.SpikeBrowser()
browser.show()