def train_response_plot(expt_list, name=None, summary_plots=[None, None], color=None):
ind_base_subtract = []
rec_base_subtract = []
train_plots = pg.plot()
train_plots.setLabels(left=('Vm', 'V'))
tau =15e-3
lp = 1000
for expt in expt_list:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]:
print ('Processing experiment: %s' % (expt.nwb_file))
ind = []
rec = []
analyzer = DynamicsAnalyzer(expt, pre, post)
train_responses = analyzer.train_responses
artifact = analyzer.cross_talk()
if artifact > 0.03e-3:
continue
for i, stim_params in enumerate(train_responses.keys()):
rec_t = int(np.round(stim_params[1] * 1e3, -1))
if stim_params[0] == 50 and rec_t == 250:
pulse_offsets = analyzer.pulse_offsets
if len(train_responses[stim_params][0]) != 0:
ind_group = train_responses[stim_params][0]
rec_group = train_responses[stim_params][1]
for j in range(len(ind_group)):
ind.append(ind_group.responses[j])
rec.append(rec_group.responses[j])
if len(ind) > 5:
ind_avg = TraceList(ind).mean()
rec_avg = TraceList(rec).mean()
rec_avg.t0 = 0.3
base = float_mode(ind_avg.data[:int(10e-3 / ind_avg.dt)])
ind_base_subtract.append(ind_avg.copy(data=ind_avg.data - base))
rec_base_subtract.append(rec_avg.copy(data=rec_avg.data - base))
train_plots.plot(ind_avg.time_values, ind_avg.data - base)
train_plots.plot(rec_avg.time_values, rec_avg.data - base)
app.processEvents()
if len(ind_base_subtract) != 0:
print (name + ' n = %d' % len(ind_base_subtract))
ind_grand_mean = TraceList(ind_base_subtract).mean()
rec_grand_mean = TraceList(rec_base_subtract).mean()
ind_grand_mean_dec = bessel_filter(exp_deconvolve(ind_grand_mean, tau), lp)
train_plots.addLegend()
train_plots.plot(ind_grand_mean.time_values, ind_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
train_plots.plot(rec_grand_mean.time_values, rec_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
#train_plots.plot(ind_grand_mean_dec.time_values, ind_grand_mean_dec.data, pen={'color': 'g', 'dash': [1,5,3,2]})
train_amps = train_amp([ind_base_subtract, rec_base_subtract], pulse_offsets, '+')
if ind_grand_mean is not None:
train_plots = summary_plot_train(ind_grand_mean, plot=summary_plots[0], color=color,
name=(legend + ' 50 Hz induction'))
train_plots = summary_plot_train(rec_grand_mean, plot=summary_plots[0], color=color)
train_plots2 = summary_plot_train(ind_grand_mean_dec, plot=summary_plots[1], color=color,
name=(legend + ' 50 Hz induction'))
return train_plots, train_plots2, train_amps
else:
print ("No Traces")
return None
def train_response_plot(expt_list, name=None, summary_plots=[None, None], color=None):
grand_train = [[], []]
train_plots = pg.plot()
train_plots.setLabels(left=('Vm', 'V'))
tau =15e-3
lp = 1000
for expt in expt_list:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[post].cre_type == cre_type[1]:
print ('Processing experiment: %s' % (expt.nwb_file))
train_responses, artifact = get_response(expt, pre, post, analysis_type='train')
if artifact > 0.03e-3:
continue
train_filter = response_filter(train_responses['responses'], freq_range=[50, 50], train=0, delta_t=250)
pulse_offsets = response_filter(train_responses['pulse_offsets'], freq_range=[50, 50], train=0, delta_t=250)
if len(train_filter[0]) > 5:
ind_avg = TraceList(train_filter[0]).mean()
rec_avg = TraceList(train_filter[1]).mean()
rec_avg.t0 = 0.3
grand_train[0].append(ind_avg)
grand_train[1].append(rec_avg)
train_plots.plot(ind_avg.time_values, ind_avg.data)
train_plots.plot(rec_avg.time_values, rec_avg.data)
app.processEvents()
if len(grand_train[0]) != 0:
print (name + ' n = %d' % len(grand_train[0]))
ind_grand_mean = TraceList(grand_train[0]).mean()
rec_grand_mean = TraceList(grand_train[1]).mean()
ind_grand_mean_dec = bessel_filter(exp_deconvolve(ind_grand_mean, tau), lp)
train_plots.addLegend()
train_plots.plot(ind_grand_mean.time_values, ind_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
train_plots.plot(rec_grand_mean.time_values, rec_grand_mean.data, pen={'color': 'g', 'width': 3}, name=name)
train_amps = train_amp([grand_train[0], grand_train[1]], pulse_offsets, '+')
if ind_grand_mean is not None:
train_plots = summary_plot_train(ind_grand_mean, plot=summary_plots[0], color=color,
name=(legend + ' 50 Hz induction'))
train_plots = summary_plot_train(rec_grand_mean, plot=summary_plots[0], color=color)
train_plots2 = summary_plot_train(ind_grand_mean_dec, plot=summary_plots[1], color=color,
name=(legend + ' 50 Hz induction'))
return train_plots, train_plots2, train_amps
else:
print ("No Traces")
return None
def train_response_plot(expt_list,
name=None,
summary_plots=[None, None],
color=None):
grand_train = [[], []]
train_plots = pg.plot()
train_plots.setLabels(left=('Vm', 'V'))
tau = 15e-3
lp = 1000
for expt in expt_list:
for pre, post in expt.connections:
if expt.cells[pre].cre_type == cre_type[0] and expt.cells[
post].cre_type == cre_type[1]:
print('Processing experiment: %s' % (expt.nwb_file))
train_responses, artifact = get_response(expt,
pre,
post,
analysis_type='train')
if artifact > 0.03e-3:
continue
train_filter = response_filter(train_responses['responses'],
freq_range=[50, 50],
train=0,
delta_t=250)
pulse_offsets = response_filter(
train_responses['pulse_offsets'],
freq_range=[50, 50],
train=0,
delta_t=250)
if len(train_filter[0]) > 5:
ind_avg = TraceList(train_filter[0]).mean()
rec_avg = TraceList(train_filter[1]).mean()
rec_avg.t0 = 0.3
grand_train[0].append(ind_avg)
grand_train[1].append(rec_avg)
train_plots.plot(ind_avg.time_values, ind_avg.data)
train_plots.plot(rec_avg.time_values, rec_avg.data)
app.processEvents()
if len(grand_train[0]) != 0:
print(name + ' n = %d' % len(grand_train[0]))
ind_grand_mean = TraceList(grand_train[0]).mean()
rec_grand_mean = TraceList(grand_train[1]).mean()
ind_grand_mean_dec = bessel_filter(exp_deconvolve(ind_grand_mean, tau),
lp)
train_plots.addLegend()
train_plots.plot(ind_grand_mean.time_values,
ind_grand_mean.data,
pen={
'color': 'g',
'width': 3
},
name=name)
train_plots.plot(rec_grand_mean.time_values,
rec_grand_mean.data,
pen={
'color': 'g',
'width': 3
},
name=name)
train_amps = train_amp([grand_train[0], grand_train[1]], pulse_offsets,
'+')
if ind_grand_mean is not None:
train_plots = summary_plot_train(ind_grand_mean,
plot=summary_plots[0],
color=color,
name=(legend +
' 50 Hz induction'))
train_plots = summary_plot_train(rec_grand_mean,
plot=summary_plots[0],
color=color)
train_plots2 = summary_plot_train(ind_grand_mean_dec,
plot=summary_plots[1],
color=color,
name=(legend +
' 50 Hz induction'))
return train_plots, train_plots2, train_amps
else:
print("No Traces")
return None
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(
ind_pass_qc[0]).mean()
grand_rec_trace = TSeriesList(
ind_pass_qc[1]).mean()
for ind in ind_pass_qc[0]:
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)
label.setPos(50, 0)
summary_plot[c, 0].setTitle('Induction')
ind_plot[f, c].addLegend()
[
ind_plot[f, c].plot(ind.time_values, ind.data, pen=trace_color)
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)
label.setPos(50, 0)
summary_plot[c, 0].setTitle('Induction')
ind_plot[f, c].addLegend()
[ind_plot[f, c].plot(ind.time_values, ind.data, pen=trace_color) for ind in ind_pass_qc[0]]
[ind_plot[f, c].plot(rec.time_values, rec.data, pen=trace_color) for rec in ind_pass_qc[1]]