def build_detection_limits():
# silence warnings about fp issues
np.seterr(all='ignore')
# read all pair records from DB
classifier = strength_analysis.get_pair_classifier(seed=0,
use_vc_features=False)
conns = strength_analysis.query_all_pairs(classifier)
# filter
mask = np.isfinite(conns['ic_deconv_amp_mean'])
filtered = conns[mask]
# remove recordings with gain errors
mask = filtered['ic_deconv_amp_mean'] < 0.02
# remove recordings with high crosstalk
mask &= abs(filtered['ic_crosstalk_mean']) < 60e-6
# remove recordings with low sample count
mask &= filtered['ic_n_samples'] > 50
typs = filtered['pre_cre_type']
mask &= typs == filtered['post_cre_type']
typ_mask = ((typs == 'sim1') | (typs == 'tlx3') | (typs == 'unknown') |
(typs == 'rorb') | (typs == 'ntsr1'))
mask &= typ_mask
filtered = filtered[mask]
c_mask = filtered['synapse'] == True
u_mask = ~c_mask
signal = filtered['confidence']
background = filtered['ic_base_deconv_amp_mean']
session = db.session()
# do selected connections first
count = 0
for i, rec in enumerate(filtered):
print("================== %d/%d ===================== " %
(i, len(filtered)))
pair = session.query(
db.Pair).filter(db.Pair.id == rec['pair_id']).all()[0]
if pair.detection_limit is not None:
print(" skip!")
continue
try:
measure_limit(pair, session, classifier)
except Exception:
sys.excepthook(*sys.exc_info())
count += 1
if count > 100:
print("Bailing out before memory fills up.")
sys.exit(0)
def build_detection_limits():
# silence warnings about fp issues
np.seterr(all='ignore')
# read all pair records from DB
classifier = strength_analysis.get_pair_classifier(seed=0, use_vc_features=False)
conns = strength_analysis.query_all_pairs(classifier)
# filter
mask = np.isfinite(conns['ic_deconv_amp_mean'])
filtered = conns[mask]
# remove recordings with gain errors
mask = filtered['ic_deconv_amp_mean'] < 0.02
# remove recordings with high crosstalk
mask &= abs(filtered['ic_crosstalk_mean']) < 60e-6
# remove recordings with low sample count
mask &= filtered['ic_n_samples'] > 50
typs = filtered['pre_cre_type']
mask &= typs == filtered['post_cre_type']
typ_mask = ((typs == 'sim1') | (typs == 'tlx3') | (typs == 'unknown') | (typs == 'rorb') | (typs == 'ntsr1'))
mask &= typ_mask
filtered = filtered[mask]
c_mask = filtered['synapse'] == True
u_mask = ~c_mask
signal = filtered['confidence']
background = filtered['ic_base_deconv_amp_mean']
session = db.Session()
# do selected connections first
count = 0
for i,rec in enumerate(filtered):
print("================== %d/%d ===================== " % (i, len(filtered)))
pair = session.query(db.Pair).filter(db.Pair.id==rec['pair_id']).all()[0]
if pair.detection_limit is not None:
print(" skip!")
continue
try:
measure_limit(pair, session, classifier)
except Exception:
sys.excepthook(*sys.exc_info())
count += 1
if count > 100:
print("Bailing out before memory fills up.")
sys.exit(0)
pg.mkQApp()
pg.dbg()
pg.setConfigOption('background', 'w')
pg.setConfigOption('foreground', 'k')
win = pg.GraphicsLayoutWidget()
win.show()
win.resize(1600, 600)
scatter_plot = win.addPlot(0, 0, rowspan=len(show_conns))
scatter_plot.setLogMode(True, True)
scatter_plot.setAspectLocked()
scatter_plot.setFixedWidth(500)
# read all pair records from DB
conns = strength_analysis.query_all_pairs()
# filter
mask = np.isfinite(conns['abs_deconv_base_amp_med'])
filtered = conns[mask]
# remove recordings with gain errors
mask = filtered['abs_deconv_base_amp_med'] < 0.02
# remove recordings likely to have high crosstalk
# cutoff = strength_analysis.datetime_to_timestamp(datetime(2017,4,1))
# mask &= (filtered['electrode_distance'] > 1) | (filtered['acq_timestamp'] > cutoff)
# mask &= filtered['electrode_distance'] > 1
# remove recordings with low sample count
mask &= filtered['n_samples'] > 50
win = pg.GraphicsLayoutWidget()
win.show()
win.resize(900, 900)
# set up scatter plot
scatter_plot = win.addPlot(0, 0, rowspan=len(show_conns))
scatter_plot.setLogMode(True, True)
scatter_plot.setAspectLocked()
scatter_plot.setFixedWidth(350)
scatter_plot.showGrid(True, True, alpha=0.5)
# read all pair records from DB
classifier = strength_analysis.get_pair_classifier(seed=0,
use_vc_features=False)
conns = strength_analysis.query_all_pairs(classifier)
# filter
mask = np.isfinite(conns['ic_deconv_amp_mean'])
filtered = conns[mask]
# remove recordings with gain errors
mask = filtered['ic_deconv_amp_mean'] < 0.02
# remove recordings with high crosstalk
mask &= abs(filtered['ic_crosstalk_mean']) < 60e-6
# remove recordings with low sample count
mask &= filtered['ic_n_samples'] > 50
typs = filtered['pre_cre_type']
pg.setConfigOption('foreground', 'k')
win = pg.GraphicsLayoutWidget()
win.show()
win.resize(900, 900)
# set up scatter plot
scatter_plot = win.addPlot(0, 0, rowspan=len(show_conns))
scatter_plot.setLogMode(True, True)
scatter_plot.setAspectLocked()
scatter_plot.setFixedWidth(350)
scatter_plot.showGrid(True, True, alpha=0.5)
# read all pair records from DB
classifier = strength_analysis.get_pair_classifier(seed=0, use_vc_features=False)
conns = strength_analysis.query_all_pairs(classifier)
# filter
mask = np.isfinite(conns['ic_deconv_amp_mean'])
filtered = conns[mask]
# remove recordings with gain errors
mask = filtered['ic_deconv_amp_mean'] < 0.02
# remove recordings with high crosstalk
mask &= abs(filtered['ic_crosstalk_mean']) < 60e-6
# remove recordings with low sample count
mask &= filtered['ic_n_samples'] > 50
typs = filtered['pre_cre_type']