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
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
