def test1():
bl1 = generate_block_for_sorting(
nb_unit=2,
duration=3. * pq.s,
nb_segment=2,
)
oebl1 = OEBase.from_neo(bl1, dbinfo.mapped_classes, cascade=True)
oebl1.save()
bl2 = generate_block_for_sorting(
nb_unit=3,
duration=2.5 * pq.s,
nb_segment=1,
)
oebl2 = OEBase.from_neo(bl2, dbinfo.mapped_classes, cascade=True)
oebl2.save()
oebl3 = merge_blocks([oebl1, oebl2], )
print 'name', oebl3.name
#~ print oebl3.recordingchannelgroups
for rcg in oebl3.recordingchannelgroups:
print '#' * 3
print rcg
for rc in rcg.recordingchannels:
print rc
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=0.2,
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 = '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 test3():
# add a spike
#save to neo
bl = generate_block_for_sorting(nb_unit = 6,
duration = 1.*pq.s,
noise_ratio = 0.2,
nb_segment = 2,
)
rcg = bl.recordingchannelgroups[0]
sps = spikesorter = SpikeSorter(rcg)
print sps.spike_index_array[0].shape
print sps.spike_waveforms.shape
print sps.seg_spike_slices
print sps.waveform_features
print sps.spike_clusters
sps.add_one_spike(0, .05)
print sps.spike_index_array[0].shape
print sps.spike_waveforms.shape
print sps.seg_spike_slices
print sps.waveform_features
print sps.spike_clusters
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 test4():
# add a spike
#save to neo
bl = generate_block_for_sorting(
nb_unit=6,
duration=1. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
sps = spikesorter = SpikeSorter(rcg)
print sps.spike_index_array[0].shape
print sps.spike_waveforms.shape
print sps.seg_spike_slices
print sps.waveform_features
print sps.spike_clusters
sps.delete_one_cluster(0)
print
print sps.spike_index_array[0].shape
print sps.spike_waveforms.shape
print sps.seg_spike_slices
print sps.waveform_features
print sps.spike_clusters
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 test2():
#save to db
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(url = url, use_global_session = True, myglobals = globals(),)
session = dbinfo.Session()
bl = generate_block_for_sorting(nb_unit = 6,
duration = 10.*pq.s,
noise_ratio = 0.2,
nb_segment = 2,
)
rcg = bl.recordingchannelgroups[0]
oebl = OEBase.from_neo(bl, dbinfo.mapped_classes, cascade =True)
#~ print oebl is bl.OEinstance
oebl.save()
id_bl = oebl.id
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)
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)
spikesorter.save_in_database(session, dbinfo)
dbinfo = open_db(url = url, use_global_session = True, myglobals = globals(),)
session = dbinfo.Session()
oebl = Block.load(id_bl)
rcg = oebl.recordingchannelgroups[0]
for s, seg in enumerate(rcg.block.segments):
print 's', s, len(seg.spiketrains)
for u, unit in enumerate(rcg.units):
print 'u',u, len(unit.spiketrains)
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.to_neo().size
def test1():
bl1 = generate_block_for_sorting(nb_unit = 2,
duration = 3.*pq.s,
nb_segment = 2,
)
oebl1 = OEBase.from_neo(bl1, dbinfo.mapped_classes, cascade =True)
oebl1.save()
bl2 = generate_block_for_sorting(nb_unit = 3,
duration = 2.5*pq.s,
nb_segment = 1,
)
oebl2 = OEBase.from_neo(bl2, dbinfo.mapped_classes, cascade =True)
oebl2.save()
oebl3 = merge_blocks([oebl1, oebl2], )
print 'name', oebl3.name
#~ print oebl3.recordingchannelgroups
for rcg in oebl3.recordingchannelgroups:
print '#'*3
print rcg
for rc in rcg.recordingchannels:
print rc
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 test_runtest(self):
bl = generate_block_for_sorting(
nb_segment=5,
nb_recordingchannel=4,
sampling_rate=10.e3 * pq.Hz,
duration=0.5 * pq.s,
nb_unit=6,
spikerate_range=[.5 * pq.Hz, 12 * pq.Hz],
t_start=0. * pq.s,
noise_ratio=0.2,
use_memmap_path=None,
)
print(bl)
self.assertIsInstance(bl, neo.Block)
self.assertEqual(len(bl.segments), 5)
self.assertEqual(len(bl.recordingchannelgroups), 1)
self.assertEqual(len(bl.recordingchannelgroups[0].recordingchannels),
4)
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 test_runtest(self):
bl = generate_block_for_sorting(
nb_segment = 5,
nb_recordingchannel = 4,
sampling_rate = 10.e3 * pq.Hz,
duration = 0.5*pq.s,
nb_unit = 6,
spikerate_range = [.5*pq.Hz, 12*pq.Hz],
t_start = 0.*pq.s,
noise_ratio = 0.2,
use_memmap_path = None,
)
print bl
self.assertIsInstance(bl, neo.Block)
self.assertEqual(len(bl.segments), 5)
self.assertEqual(len(bl.recordingchannelgroups), 1)
self.assertEqual(len(bl.recordingchannelgroups[0].recordingchannels), 4)
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=1.5 * pq.ms,
right_sweep=2.5 * pq.ms,
sign='-')
spikesorter.PcaFeature(n_components=4)
spikesorter.SklearnKMeans(n_cluster=3)
#~ 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,
shift_estimation_method='taylor order1',
#~ shift_estimation_method = 'taylor order2',
#~ shift_estimation_method ='optimize',
#~ shift_method = 'spline',
shift_method='sinc',
max_iter=3)
step = spikesorter.history[-1]
instance = step['methodInstance']
fig = pyplot.figure()
instance.plot_iterative_centers(fig, spikesorter)
pyplot.show()
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_()
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')
* add_one_spike
* regroup_small_cluster
"""
import sys
sys.path.append('..')
if __name__== '__main__':
import quantities as pq
from OpenElectrophy.spikesorting import (generate_block_for_sorting, SpikeSorter)
# read or create datasets
bl = generate_block_for_sorting(nb_unit = 6,
duration = 5.*pq.s,
noise_ratio = 0.2,
)
rcg = bl.recordingchannelgroups[0]
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.)
If you have closed your widget, you can show them again of course::
w1.show()
w2.show()
"""
# DO NOT FORGET this IPython comand
%gui qt
from OpenElectrophy.spikesorting import SpikeSorter, generate_block_for_sorting
import quantities as pq
# generate dataset
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, initial_state='full_band_signal')
# Apply a chain
spikesorter.ButterworthFilter( f_low = 200.)
spikesorter.MedianThresholdDetection(sign= '-', median_thresh = 6.,)
spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-')
# display widget interactivively
from OpenElectrophy.gui.spikesorting import AverageWaveforms
w1 = AverageWaveforms(spikesorter = spikesorter)
def test2():
#save to db
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(
url=url,
use_global_session=True,
myglobals=globals(),
)
session = dbinfo.Session()
bl = generate_block_for_sorting(
nb_unit=6,
duration=10. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
oebl = OEBase.from_neo(bl, dbinfo.mapped_classes, cascade=True)
#~ print oebl is bl.OEinstance
oebl.save()
id_bl = oebl.id
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)
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)
spikesorter.save_in_database(session, dbinfo)
dbinfo = open_db(
url=url,
use_global_session=True,
myglobals=globals(),
)
session = dbinfo.Session()
oebl = Block.load(id_bl)
rcg = oebl.recordingchannelgroups[0]
for s, seg in enumerate(rcg.block.segments):
print 's', s, len(seg.spiketrains)
for u, unit in enumerate(rcg.units):
print 'u', u, len(unit.spiketrains)
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.to_neo().size
def test1():
# open DB and create new column
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(url = url, use_global_session = True, myglobals = globals(),)
session = dbinfo.Session()
for attrname, attrtype in dbinfo.classes_by_name['SpikeTrain'].usable_attributes.items():
print attrname, attrtype
#Create acolumn on table SpikeTrain (maybe this could be store at Unit level)
create_column_if_not_exists(dbinfo.metadata.tables['SpikeTrain'],'detection_thresholds', np.ndarray)
#test is creation is OK
for attrname, attrtype in dbinfo.classes_by_name['SpikeTrain'].usable_attributes.items():
print attrname, attrtype
# re open DB for sorting
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(url = url, use_global_session = True, myglobals = globals(),)
session = dbinfo.Session()
print dbinfo.classes_by_name['SpikeTrain']
for attrname, attrtype in dbinfo.classes_by_name['SpikeTrain'].usable_attributes.items():
print attrname, attrtype
neobl = generate_block_for_sorting(nb_unit = 6,
duration = 10.*pq.s,
noise_ratio = 0.2,
nb_segment = 2,
)
neorcg = neobl.recordingchannelgroups[0]
bl = OEBase.from_neo(neobl, dbinfo.mapped_classes, cascade =True)
bl.save()
rcg = OEBase.from_neo(neorcg, dbinfo.mapped_classes, cascade =True)
rcg_id = rcg.id
# spike sorting chain
spikesorter = SpikeSorter(neorcg)
spikesorter.ButterworthFilter( f_low = 200.)
spikesorter.RelativeThresholdDetection(sign= '-', relative_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)
spikesorter.save_in_database(session, dbinfo)
# Note that threshold is per channel/segment
for unit in rcg.units:
for sptr in unit.spiketrains:
print 'writte threshold on spiketrain.id',sptr.id
print spikesorter.detection_thresholds
sptr.detection_thresholds = spikesorter.detection_thresholds
session.commit()
# re open DB for sorting for reading
dbinfo = open_db(url = url, use_global_session = True, myglobals = globals(),)
session = dbinfo.Session()
rcg = RecordingChannelGroup.load(rcg_id)
for unit in rcg.units:
for sptr in unit.spiketrains:
print 'read threshold on spiketrain.id',sptr.id
print sptr.detection_thresholds
def test1():
# open DB and create new column
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(
url=url,
use_global_session=True,
myglobals=globals(),
)
session = dbinfo.Session()
for attrname, attrtype in dbinfo.classes_by_name[
'SpikeTrain'].usable_attributes.items():
print attrname, attrtype
#Create acolumn on table SpikeTrain (maybe this could be store at Unit level)
create_column_if_not_exists(dbinfo.metadata.tables['SpikeTrain'],
'detection_thresholds', np.ndarray)
#test is creation is OK
for attrname, attrtype in dbinfo.classes_by_name[
'SpikeTrain'].usable_attributes.items():
print attrname, attrtype
# re open DB for sorting
url = 'sqlite:///test_spikesorter.sqlite'
dbinfo = open_db(
url=url,
use_global_session=True,
myglobals=globals(),
)
session = dbinfo.Session()
print dbinfo.classes_by_name['SpikeTrain']
for attrname, attrtype in dbinfo.classes_by_name[
'SpikeTrain'].usable_attributes.items():
print attrname, attrtype
neobl = generate_block_for_sorting(
nb_unit=6,
duration=10. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
neorcg = neobl.recordingchannelgroups[0]
bl = OEBase.from_neo(neobl, dbinfo.mapped_classes, cascade=True)
bl.save()
rcg = OEBase.from_neo(neorcg, dbinfo.mapped_classes, cascade=True)
rcg_id = rcg.id
# spike sorting chain
spikesorter = SpikeSorter(neorcg)
spikesorter.ButterworthFilter(f_low=200.)
spikesorter.RelativeThresholdDetection(
sign='-',
relative_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)
spikesorter.save_in_database(session, dbinfo)
# Note that threshold is per channel/segment
for unit in rcg.units:
for sptr in unit.spiketrains:
print 'writte threshold on spiketrain.id', sptr.id
print spikesorter.detection_thresholds
sptr.detection_thresholds = spikesorter.detection_thresholds
session.commit()
# re open DB for sorting for reading
dbinfo = open_db(
url=url,
use_global_session=True,
myglobals=globals(),
)
session = dbinfo.Session()
rcg = RecordingChannelGroup.load(rcg_id)
for unit in rcg.units:
for sptr in unit.spiketrains:
print 'read threshold on spiketrain.id', sptr.id
print sptr.detection_thresholds
