train_response,
rec_t,
holding,
thresh=sweep_threshold,
rec_dict=recovery_grand,
offset_dict=pulse_offset_rec,
uid=(expt.uid, pre, post))
for f, freq in enumerate(freqs):
if freq not in induction_grand.keys():
print("%d Hz not represented in data set for %s" % (freq, c_type))
continue
ind_offsets = pulse_offset_ind[freq]
qc_plot.clear()
ind_pass_qc = train_qc(induction_grand[freq],
ind_offsets,
amp=qc_params[1][c],
sign=qc_params[0],
plot=qc_plot)
n_synapses = len(ind_pass_qc[0])
if n_synapses > 0:
induction_grand_trace = TraceList(ind_pass_qc[0]).mean()
ind_rec_grand_trace = TraceList(ind_pass_qc[1]).mean()
ind_amp = train_amp(ind_pass_qc, ind_offsets, '+')
ind_amp_grand = np.nanmean(ind_amp, 0)
if f == 0:
ind_plot[f, c].setTitle(connection_types[c])
type = pg.LabelItem('%s -> %s' % connection_types[c])
type.setParentItem(summary_plot[c, 0])
type.setPos(50, 0)
if c == 0:
rec_dict=grand_recovery,
offset_dict=offset_rec)
if len(grand_pulse_response) > 0:
grand_pulse_trace = TSeriesList(grand_pulse_response).mean()
p2 = trace_plot(grand_pulse_trace,
color=avg_color,
plot=p2,
x_range=[0, 27e-3],
name=('n = %d' % len(grand_pulse_response)))
if len(grand_induction) > 0:
for f, freq in enumerate(freqs):
if freq in grand_induction:
offset = offset_ind[freq]
ind_pass_qc = train_qc(grand_induction[freq],
offset,
amp=amp_thresh,
sign=sign)
n = len(ind_pass_qc[0])
if n > 0:
ind_amp = train_amp(ind_pass_qc, offset, sign)
grand_ind_amp = np.nanmean(ind_amp, 0)
ind_amp_sem = stats.sem(ind_amp)
if freq not in ind_index.keys():
ind_index[freq] = {}
if key not in ind_index[freq].keys():
ind_index[freq][key] = []
for n in range(ind_amp.shape[0]):
ind_index[freq][key].append(ind_amp[n, 7] /
ind_amp[n, 0])
if freq == 50:
grand_ind_trace = TSeriesList(
train_response, artifact = get_response(expt, pre, post, type='train')
if threshold[c] is not None and artifact > threshold[c]:
continue
induction_grand, pulse_offset_ind = induction_summary(train_response, freqs, holding, thresh=sweep_threshold,
ind_dict=induction_grand, offset_dict=pulse_offset_ind,
uid=(expt.uid, pre, post))
recovery_grand, pulse_offset_rec = recovery_summary(train_response, rec_t, holding, thresh=sweep_threshold,
rec_dict=recovery_grand, offset_dict=pulse_offset_rec,
uid=(expt.uid, pre, post))
for f, freq in enumerate(freqs):
if freq not in induction_grand.keys():
print ("%d Hz not represented in data set for %s" % (freq, c_type))
continue
ind_offsets = pulse_offset_ind[freq]
qc_plot.clear()
ind_pass_qc = train_qc(induction_grand[freq], ind_offsets, amp=qc_params[1][c], sign=qc_params[0], plot=qc_plot)
n_synapses = len(ind_pass_qc[0])
if n_synapses > 0:
induction_grand_trace = TraceList(ind_pass_qc[0]).mean()
ind_rec_grand_trace = TraceList(ind_pass_qc[1]).mean()
ind_amp = train_amp(ind_pass_qc, ind_offsets, '+')
ind_amp_grand = np.nanmean(ind_amp, 0)
if f == 0:
ind_plot[f, c].setTitle(connection_types[c])
type = pg.LabelItem('%s -> %s' % connection_types[c])
type.setParentItem(summary_plot[c, 0])
type.setPos(50, 0)
if c == 0:
label = pg.LabelItem('%d Hz Induction' % freq)
label.setParentItem(ind_plot[f, c].vb)