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 bsub_mean(self):
"""Return a baseline-subtracted, average evoked response trace between two cells.
All traces are downsampled to the minimum sample rate in the set.
"""
if len(self) == 0:
return None
if self._bsub_mean is None:
responses = self.responses
baselines = self.baselines
# downsample all traces to the same rate
# yarg: how does this change SNR?
avg = TraceList([r.copy(t0=0) for r in responses]).mean()
avg_baseline = TraceList([b.copy(t0=0) for b in baselines]).mean().data
# subtract baseline
baseline = np.median(avg_baseline)
bsub = avg.data - baseline
result = avg.copy(data=bsub)
assert len(result.time_values) == len(result)
# Attach some extra metadata to the result:
result.meta['baseline'] = avg_baseline
result.meta['baseline_med'] = baseline
if len(avg_baseline) == 0:
result.meta['baseline_std'] = None
else:
result.meta['baseline_std'] = scipy.signal.detrend(avg_baseline).std()
self._bsub_mean = result
return self._bsub_mean
def bsub_mean(self):
"""Return a baseline-subtracted, average evoked response trace between two cells.
All traces are downsampled to the minimum sample rate in the set.
"""
if len(self) == 0:
return None
if self._bsub_mean is None:
responses = self.responses
baselines = self.baselines
# downsample all traces to the same rate
# yarg: how does this change SNR?
avg = TraceList([r.copy(t0=0) for r in responses]).mean()
avg_baseline = TraceList([b.copy(t0=0) for b in baselines]).mean().data
# subtract baseline
baseline = np.median(avg_baseline)
bsub = avg.data - baseline
result = avg.copy(data=bsub)
assert len(result.time_values) == len(result)
# Attach some extra metadata to the result:
result.meta['baseline'] = avg_baseline
result.meta['baseline_med'] = baseline
if len(avg_baseline) == 0:
result.meta['baseline_std'] = None
else:
result.meta['baseline_std'] = scipy.signal.detrend(avg_baseline).std()
self._bsub_mean = result
return self._bsub_mean
def get_amplitude(response_list):
avg_trace = TraceList(response_list).mean()
data = avg_trace.data
dt = avg_trace.dt
base = float_mode(data[:int(10e-3 / dt)])
bsub_mean = avg_trace.copy(data=data - base)
neg = bsub_mean.data[int(13e-3 / dt):].min()
pos = bsub_mean.data[int(13e-3 / dt):].max()
avg_amp = neg if abs(neg) > abs(pos) else pos
amp_sign = '-' if avg_amp < 0 else '+'
peak_ind = list(bsub_mean.data).index(avg_amp)
peak_t = bsub_mean.time_values[peak_ind]
return bsub_mean, avg_amp, amp_sign, peak_t
def first_pulse_features(pair, pulse_responses, pulse_response_amps):
avg_psp = TraceList(pulse_responses).mean()
dt = avg_psp.dt
avg_psp_baseline = float_mode(avg_psp.data[:int(10e-3 / dt)])
avg_psp_bsub = avg_psp.copy(data=avg_psp.data - avg_psp_baseline)
lower_bound = -float('inf')
upper_bound = float('inf')
xoffset = pair.connection_strength.ic_fit_xoffset
if xoffset is None:
xoffset = 14 * 10e-3
synapse_type = pair.connection_strength.synapse_type
if synapse_type == 'ex':
amp_sign = '+'
elif synapse_type == 'in':
amp_sign = '-'
else:
raise Exception(
'Synapse type is not defined, reconsider fitting this pair %s %d->%d'
% (pair.expt_id, pair.pre_cell_id, pair.post_cell_id))
weight = np.ones(len(
avg_psp.data)) * 10. # set everything to ten initially
weight[int(10e-3 / dt):int(12e-3 / dt)] = 0. # area around stim artifact
weight[int(12e-3 / dt):int(19e-3 / dt)] = 30. # area around steep PSP rise
psp_fits = fit_psp(avg_psp,
xoffset=(xoffset, lower_bound, upper_bound),
yoffset=(avg_psp_baseline, lower_bound, upper_bound),
sign=amp_sign,
weight=weight)
amp_cv = np.std(pulse_response_amps) / np.mean(pulse_response_amps)
features = {
'ic_fit_amp': psp_fits.best_values['amp'],
'ic_fit_latency': psp_fits.best_values['xoffset'] - 10e-3,
'ic_fit_rise_time': psp_fits.best_values['rise_time'],
'ic_fit_decay_tau': psp_fits.best_values['decay_tau'],
'ic_amp_cv': amp_cv,
'avg_psp': avg_psp_bsub.data
}
#'ic_fit_NRMSE': psp_fits.nrmse()} TODO: nrmse not returned from psp_fits?
return features
def first_pulse_features(pair, pulse_responses, pulse_response_amps):
avg_psp = TraceList(pulse_responses).mean()
dt = avg_psp.dt
avg_psp_baseline = float_mode(avg_psp.data[:int(10e-3/dt)])
avg_psp_bsub = avg_psp.copy(data=avg_psp.data - avg_psp_baseline)
lower_bound = -float('inf')
upper_bound = float('inf')
xoffset = pair.connection_strength.ic_fit_xoffset
if xoffset is None:
xoffset = 14*10e-3
synapse_type = pair.connection_strength.synapse_type
if synapse_type == 'ex':
amp_sign = '+'
elif synapse_type == 'in':
amp_sign = '-'
else:
raise Exception('Synapse type is not defined, reconsider fitting this pair %s %d->%d' %
(pair.expt_id, pair.pre_cell_id, pair.post_cell_id))
weight = np.ones(len(avg_psp.data)) * 10. # set everything to ten initially
weight[int(10e-3 / dt):int(12e-3 / dt)] = 0. # area around stim artifact
weight[int(12e-3 / dt):int(19e-3 / dt)] = 30. # area around steep PSP rise
psp_fits = fit_psp(avg_psp,
xoffset=(xoffset, lower_bound, upper_bound),
yoffset=(avg_psp_baseline, lower_bound, upper_bound),
sign=amp_sign,
weight=weight)
amp_cv = np.std(pulse_response_amps)/np.mean(pulse_response_amps)
features = {'ic_fit_amp': psp_fits.best_values['amp'],
'ic_fit_latency': psp_fits.best_values['xoffset'] - 10e-3,
'ic_fit_rise_time': psp_fits.best_values['rise_time'],
'ic_fit_decay_tau': psp_fits.best_values['decay_tau'],
'ic_amp_cv': amp_cv,
'avg_psp': avg_psp_bsub.data}
#'ic_fit_NRMSE': psp_fits.nrmse()} TODO: nrmse not returned from psp_fits?
return features
name=("n = %d" % n_synapses))
rec_plot[t, c].plot(recovery_grand_trace.time_values,
recovery_grand_trace.data,
pen={
'color': color,
'width': 2
})
rec_plot[t, c].setLabels(left=('Vm', 'V'))
rec_plot[t, c].setLabels(bottom=('t', 's'))
if deconv is True:
rec_deconv = deconv_train(rec_pass_qc[:2])
rec_deconv_grand = TraceList(rec_deconv[0]).mean()
rec_ind_deconv_grand = TraceList(rec_deconv[1]).mean()
#log_rec_plt.plot(rec_deconv_grand.time_values, rec_deconv_grand.data,
# pen={'color': color, 'width': 2})
rec_deconv_ind_grand2 = rec_ind_deconv_grand.copy(t0=delta +
0.2)
#log_rec_plt.plot(rec_deconv_ind_grand2.time_values, rec_deconv_ind_grand2.data,
# pen={'color': color, 'width': 2})
[
deconv_rec_plot[t, c].plot(ind.time_values,
ind.data,
pen=trace_color)
for ind in rec_deconv[0]
]
[
deconv_rec_plot[t, c].plot(rec.time_values,
rec.data,
pen=trace_color)
for rec in rec_deconv[1]
]
deconv_rec_plot[t, c].plot(rec_ind_deconv_grand.time_values,
summary_plot[c, 1].setTitle('Recovery')
rec_plot[t, c].addLegend()
[rec_plot[t, c].plot(ind.time_values, ind.data, pen=trace_color) for ind in rec_pass_qc[0]]
[rec_plot[t, c].plot(rec.time_values, rec.data, pen=trace_color) for rec in rec_pass_qc[1]]
rec_plot[t, c].plot(rec_ind_grand_trace.time_values, rec_ind_grand_trace.data, pen={'color': color, 'width': 2},
name=("n = %d" % n_synapses))
rec_plot[t, c].plot(recovery_grand_trace.time_values, recovery_grand_trace.data, pen={'color': color, 'width': 2})
rec_plot[t, c].setLabels(left=('Vm', 'V'))
rec_plot[t, c].setLabels(bottom=('t', 's'))
if deconv is True:
rec_deconv = deconv_train(rec_pass_qc[:2])
rec_deconv_grand = TraceList(rec_deconv[0]).mean()
rec_ind_deconv_grand = TraceList(rec_deconv[1]).mean()
#log_rec_plt.plot(rec_deconv_grand.time_values, rec_deconv_grand.data,
# pen={'color': color, 'width': 2})
rec_deconv_ind_grand2 = rec_ind_deconv_grand.copy(t0=delta + 0.2)
#log_rec_plt.plot(rec_deconv_ind_grand2.time_values, rec_deconv_ind_grand2.data,
# pen={'color': color, 'width': 2})
[deconv_rec_plot[t, c].plot(ind.time_values, ind.data, pen=trace_color) for ind in rec_deconv[0]]
[deconv_rec_plot[t, c].plot(rec.time_values, rec.data, pen=trace_color) for rec in rec_deconv[1]]
deconv_rec_plot[t, c].plot(rec_ind_deconv_grand.time_values, rec_ind_deconv_grand.data,
pen={'color': color, 'width': 2}, name=("n = %d" % n_synapses))
deconv_rec_plot[t, c].plot(rec_deconv_grand.time_values, rec_deconv_grand.data,
pen={'color': color, 'width': 2})
summary_plot[c, 1].setLabels(left=('Norm Amp', ''))
summary_plot[c, 1].setLabels(bottom=('Pulse Number', ''))
f_color = pg.hsvColor(hue=hue, sat=float(t+0.5) / len(rec_t), val=1)
summary_plot[c, 1].plot(rec_amp_grand/rec_amp_grand[0], name=(' %d ms' % delta), pen=f_color, symbol=symbols[t],
symbolBrush=f_color, symbolPen=None)
if connection_types[c] not in rec_amp_summary.keys():
rec_amp_summary[connection_types[c]] = []