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)
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.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():
#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=0.2,
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=0.7,
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_()
%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)
w1.show()
# test another step methods
spikesorter.AlignWaveformOnDetection(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms)
w1.refresh()
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 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
