def test1():
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.DerivativeFilter()
spikesorter.SlidingMedianFilter(window_size = 50.*pq.ms,
sliding_step = 25.*pq.ms, interpolation = 'spline')
spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'peak', peak_span = 0.5*pq.ms)
print spikesorter
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import FilteredBandSignal
app = QApplication([ ])
w1 = FilteredBandSignal(spikesorter = spikesorter)
w1.refresh()
w1.show()
app.exec_()
def test1():
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.DerivativeFilter()
spikesorter.SlidingMedianFilter(window_size=50. * pq.ms,
sliding_step=25. * pq.ms,
interpolation='spline')
spikesorter.RelativeThresholdDetection(sign='-',
relative_thresh=4.,
noise_estimation='MAD',
threshold_mode='peak',
peak_span=0.5 * pq.ms)
print spikesorter
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import FilteredBandSignal
app = QApplication([])
w1 = FilteredBandSignal(spikesorter=spikesorter)
w1.refresh()
w1.show()
app.exec_()
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_()
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():
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.RelativeThresholdDetection(sign='-',
relative_thresh=4.,
noise_estimation='MAD',
threshold_mode='crossing')
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'peak')
spikesorter.AlignWaveformOnDetection(left_sweep=1 * pq.ms,
right_sweep=2 * pq.ms,
sign='-')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'biggest_amplitude')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'closer')
#~ spikesorter.AlignWaveformOnCentralWaveform(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, )
#~ print spikesorter.spike_waveforms.shape
#~ s0 = spikesorter.spike_waveforms.shape[0]
#~ wf2 = spikesorter.spike_waveforms.reshape(s0, -1)
#~ from matplotlib import pyplot
#~ pyplot.plot(wf2[:-10, :].transpose())
#~ pyplot.show()
print spikesorter
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import AverageWaveforms, AllWaveforms
app = QApplication([])
w1 = AverageWaveforms(spikesorter=spikesorter)
w1.refresh()
w1.show()
w2 = AllWaveforms(spikesorter=spikesorter)
w2.refresh()
w2.show()
app.exec_()
def test1():
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.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'crossing')
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'peak')
spikesorter.AlignWaveformOnDetection(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'biggest_amplitude')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'closer')
#~ spikesorter.AlignWaveformOnCentralWaveform(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, )
#~ print spikesorter.spike_waveforms.shape
#~ s0 = spikesorter.spike_waveforms.shape[0]
#~ wf2 = spikesorter.spike_waveforms.reshape(s0, -1)
#~ from matplotlib import pyplot
#~ pyplot.plot(wf2[:-10, :].transpose())
#~ pyplot.show()
print spikesorter
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import AverageWaveforms, AllWaveforms
app = QApplication([ ])
w1 = AverageWaveforms(spikesorter = spikesorter)
w1.refresh()
w1.show()
w2 = AllWaveforms(spikesorter = spikesorter)
w2.refresh()
w2.show()
app.exec_()
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_()
# read or create datasets
bl = generate_block_for_sorting(nb_unit=6, duration=5.0 * pq.s, noise_ratio=0.2)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
# Apply a chain
spikesorter.ButterworthFilter(f_low=200.0)
# 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)
from PyQt4.QtGui import QApplication
app = QApplication([])
spikesorter.check_display_attributes()
w1 = AverageWaveforms(spikesorter=spikesorter)
w2 = FeaturesNDViewer(spikesorter=spikesorter)
w3 = FilteredBandSignal(spikesorter=spikesorter)
all = [w1, w2, w3]
for w in all:
w.refresh()
w.show()
app.exec_()
noise_ratio=0.2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
# 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)
from PyQt4.QtGui import QApplication
app = QApplication([])
spikesorter.check_display_attributes()
w1 = AverageWaveforms(spikesorter=spikesorter)
w2 = FeaturesNDViewer(spikesorter=spikesorter)
w3 = FilteredBandSignal(spikesorter=spikesorter)
all = [w1, w2, w3]
for w in all:
w.refresh()
w.show()
app.exec_()
