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 = 'crossing',
#~ consistent_across_channels = False,
#~ consistent_across_segments = True,
#~ )
#~ print spikesorter
spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'MAD', threshold_mode = 'peak', peak_span = 0.53*pq.ms)
print spikesorter.detection_thresholds
print spikesorter
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'STD', threshold_mode = 'crossing', )
#~ print spikesorter
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'STD', threshold_mode = 'peak', peak_span = 0.3*pq.ms )
#~ print spikesorter
spikesorter.populate_recordingchannelgroup(with_waveforms = False)
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import FilteredBandSignal
app = QApplication([ ])
w2 = FilteredBandSignal(spikesorter = spikesorter)
w2.refresh()
w2.show()
app.exec_()
def test1():
#save to neo
bl = generate_block_for_sorting(nb_unit = 6,
duration = 10.*pq.s,
noise_ratio = 0.2,
nb_segment = 2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
spikesorter.ButterworthFilter( f_low = 200.)
spikesorter.MedianThresholdDetection(sign= '-', median_thresh = 6.,)
spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-')
spikesorter.PcaFeature(n_components = 4)
spikesorter.CombineFeature(use_peak = True, use_peak_to_valley = True, n_pca = 3, n_ica = 3, n_haar = 3, sign = '-')
spikesorter.SklearnKMeans(n_cluster = 3)
for u, unit in enumerate(rcg.units):
for s, seg in enumerate(rcg.block.segments):
sptr = seg.spiketrains[u]
print 'u', u, 's', s, seg.spiketrains[u] is unit.spiketrains[s], sptr.size
rcg = spikesorter.populate_recordingchannelgroup()
print
for u, unit in enumerate(rcg.units):
for s, seg in enumerate(rcg.block.segments):
sptr = seg.spiketrains[u]
print 'u', u, 's', s, seg.spiketrains[u] is unit.spiketrains[s], sptr.size
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 = 'crossing',
#~ consistent_across_channels = False,
#~ consistent_across_segments = True,
#~ )
#~ print spikesorter
spikesorter.RelativeThresholdDetection(sign='-',
relative_thresh=3.5,
noise_estimation='MAD',
threshold_mode='peak',
peak_span=0.53 * pq.ms)
print spikesorter.detection_thresholds
print spikesorter
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'STD', threshold_mode = 'crossing', )
#~ print spikesorter
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 3.5,noise_estimation = 'STD', threshold_mode = 'peak', peak_span = 0.3*pq.ms )
#~ print spikesorter
spikesorter.populate_recordingchannelgroup(with_waveforms=False)
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import FilteredBandSignal
app = QApplication([])
w2 = FilteredBandSignal(spikesorter=spikesorter)
w2.refresh()
w2.show()
app.exec_()
def test1():
#save to neo
bl = generate_block_for_sorting(
nb_unit=6,
duration=10. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
spikesorter.ButterworthFilter(f_low=200.)
spikesorter.MedianThresholdDetection(
sign='-',
median_thresh=6.,
)
spikesorter.AlignWaveformOnPeak(left_sweep=1 * pq.ms,
right_sweep=2 * pq.ms,
sign='-')
spikesorter.PcaFeature(n_components=4)
spikesorter.CombineFeature(use_peak=True,
use_peak_to_valley=True,
n_pca=3,
n_ica=3,
n_haar=3,
sign='-')
spikesorter.SklearnKMeans(n_cluster=3)
for u, unit in enumerate(rcg.units):
for s, seg in enumerate(rcg.block.segments):
sptr = seg.spiketrains[u]
print 'u', u, 's', s, seg.spiketrains[u] is unit.spiketrains[
s], sptr.size
rcg = spikesorter.populate_recordingchannelgroup()
print
for u, unit in enumerate(rcg.units):
for s, seg in enumerate(rcg.block.segments):
sptr = seg.spiketrains[u]
print 'u', u, 's', s, seg.spiketrains[u] is unit.spiketrains[
s], sptr.size
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
