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
trace_color = (0, 0, 0, 30)
trace_plot(avg_trace, trace_color, plot=synapse_plot[c, 0], x_range=[0, 27e-3])
app.processEvents()
# decay_response = response_filter(pulse_response, freq_range=[0, 20], holding_range=holding)
# qc_list = pulse_qc(response_subset, baseline=2, pulse=None, plot=qc_plot)
# if len(qc_list) >= sweep_threshold:
# avg_trace, avg_amp, amp_sign, peak_t = get_amplitude(qc_list)
# if amp_sign is '-':
# continue
# psp_fits = fit_psp(avg_trace, sign=amp_sign, yoffset=0, amp=avg_amp, method='leastsq', stacked = False, fit_kws={})
# grand_response[type[0]]['decay'].append(psp_fits.best_values['decay_tau'])
if len(grand_response[type[0]]['trace']) == 0:
continue
if len(grand_response[type[0]]['trace']) > 1:
grand_trace = TraceList(grand_response[type[0]]['trace']).mean()
grand_trace.t0 = 0
else:
grand_trace = grand_response[type[0]]['trace'][0]
n_synapses = len(grand_response[type[0]]['trace'])
trace_plot(grand_trace, color={'color': color, 'width': 2}, plot=synapse_plot[c, 0], x_range=[0, 27e-3],
name=('%s, n = %d' % (connection_types[c], n_synapses)))
synapse_plot[c, 0].hideAxis('bottom')
# all_amps = np.hstack(np.asarray(grand_response[cre_type[0]]['fail_rate']))
# y, x = np.histogram(all_amps, bins=np.linspace(0, 2e-3, 40))
# synapse_plot[c, 1].plot(x, y, stepMode=True, fillLevel=0, brush='k')
# synapse_plot[c, 1].setLabels(bottom=('Vm', 'V'))
# synapse_plot[c, 1].setXRange(0, 2e-3)
print ('%s kinetics n = %d' % (type[0], len(grand_response[type[0]]['latency'])))
feature_list = (grand_response[type[0]]['amp'], grand_response[type[0]]['CV'], grand_response[type[0]]['latency'],
grand_response[type[0]]['rise'])
labels = (['Vm', 'V'], ['CV', ''], ['t', 's'], ['t', 's'])
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
pre_spike.dt)])
sweep_list['response'].append(
sweep_trace.copy(data=sweep_trace.data - post_base))
sweep_list['spike'].append(
pre_spike.copy(data=pre_spike.data - pre_base))
if len(sweep_list['response']) > 5:
n = len(sweep_list['response'])
if plot_sweeps is True:
for sweep in range(n):
current_sweep = sweep_list['response'][sweep]
current_sweep.t0 = 0
grid[row[1], 0].plot(current_sweep.time_values,
current_sweep.data,
pen=sweep_color)
avg_first_pulse = TraceList(sweep_list['response']).mean()
avg_first_pulse.t0 = 0
avg_spike = TraceList(sweep_list['spike']).mean()
avg_spike.t0 = 0
grid[row[1], 0].setLabels(left=('Vm', 'V'))
grid[row[1], 0].setLabels(bottom=('t', 's'))
grid[row[1], 0].setXRange(-2e-3, 27e-3)
grid[row[1], 0].plot(avg_first_pulse.time_values,
avg_first_pulse.data,
pen={
'color': (255, 0, 255),
'width': 2
})
grid[row[0], 0].setLabels(left=('Vm', 'V'))
sweep_list['spike'][0].t0 = 0
grid[row[0], 0].plot(avg_spike.time_values, avg_spike.data, pen='k')
grid[row[0], 0].setXLink(grid[row[1], 0])