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
