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])
train_cache_change.append(cache_change)
if (50, 0.25) in [
(k[0], np.round(k[1], 2))
for k in train_response['responses'].keys()
]:
grand_induction, offset_ind = induction_summary(
train_response,
freqs,
holding,
thresh=sweep_threshold,
ind_dict=grand_induction,
offset_dict=offset_ind)
grand_recovery, offset_rec = recovery_summary(
train_response,
t_rec,
holding,
thresh=sweep_threshold,
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]
summary_plot[c, 1].addLegend()
for expt in expt_list:
if expt.connections is None:
continue
for pre, post in expt.connections:
if [expt.uid, pre, post] in no_include:
continue
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]:
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)