def test1(): bl = generate_block_for_sorting(nb_unit = 6, duration = 10.*pq.s, noise_ratio = 0.7, nb_segment = 2, ) rcg = bl.recordingchannelgroups[0] spikesorter = SpikeSorter(rcg) spikesorter.ButterworthFilter( f_low = 200.) spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'MAD', threshold_mode = 'peak', peak_span = 0.4*pq.ms) spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'biggest_amplitude') spikesorter.PcaFeature(n_components = 4) spikesorter.SklearnGaussianMixtureEm(n_cluster = 5) spikesorter.check_display_attributes() from OpenElectrophy.gui.spikesorting import FilteredBandSignal, AverageWaveforms app = QApplication([ ]) w1 = AverageWaveforms(spikesorter = spikesorter) w1.refresh() w1.show() w2 = FilteredBandSignal(spikesorter = spikesorter) w2.refresh() w2.show() app.exec_()
class BasicTest(unittest.TestCase): def setUp(self): bl = generate_block_for_sorting( nb_unit=3, duration=1. * pq.s, noise_ratio=0.2, nb_segment=2, ) rcg = bl.recordingchannelgroups[0] self.sps = SpikeSorter(rcg, initial_state='full_band_signal') def tearDown(self): pass def test_getattr_aliases(self): self.assertIs(self.sps.segs, self.sps.segments) self.assertRaises(AttributeError, getattr, self.sps, 'i_love_my_mother') def test_getattr_runstep(self): self.sps.ButterworthFilter(f_low=200.) self.assertIsInstance(self.sps.history[-1]['methodInstance'], ButterworthFilter) def test_one_standart_pipeline(self): self.sps.ButterworthFilter(f_low=200.) self.assertIsNotNone(self.sps.filtered_sigs) self.sps.MedianThresholdDetection( sign='-', median_thresh=6, ) self.assertIsNotNone(self.sps.spike_index_array) self.sps.AlignWaveformOnDetection(left_sweep=1 * pq.ms, right_sweep=2 * pq.ms) self.assertIsNotNone(self.sps.seg_spike_slices) self.assertIsNotNone(self.sps.spike_waveforms) self.assertIsNotNone(self.sps.left_sweep) self.assertIsNotNone(self.sps.right_sweep) self.sps.PcaFeature(n_components=3) self.assertIsNotNone(self.sps.waveform_features) self.sps.SklearnGaussianMixtureEm(n_cluster=12, n_iter=500) self.assertIsNotNone(self.sps.spike_clusters) self.assertIsNotNone(self.sps.cluster_names) def test_apply_history_to_other(self): sps2 = SpikeSorter(self.sps.rcg, initial_state='full_band_signal') self.sps.apply_history_to_other(sps2)
spikesorter = SpikeSorter(rcg) # display unit before sorting for u, unit in enumerate(rcg.units): print u, 'unit name', unit.name for s, seg in enumerate(rcg.block.segments): sptr = seg.spiketrains[u] print ' in Segment', s, 'has SpikeTrain with ', sptr.size # Apply a chain spikesorter.ButterworthFilter( f_low = 200.) # equivalent to # spikesorter.run_step(ButterworthFilter, f_low = 200.) spikesorter.MedianThresholdDetection(sign= '-',median_thresh = 6) spikesorter.AlignWaveformOnDetection(left_sweep = 1*pq.ms ,right_sweep = 2*pq.ms) spikesorter.PcaFeature(n_components = 6) spikesorter.SklearnGaussianMixtureEm(n_cluster = 6, n_iter = 200 ) print # display unit after sorting rcg = spikesorter.populate_recordingchannelgroup() for u, unit in enumerate(rcg.units): print u, 'unit name', unit.name for s, seg in enumerate(rcg.block.segments): sptr = seg.spiketrains[u] print ' in Segment', s, 'has SpikeTrain with ', sptr.size