def load_pair(self, pair, default_session, record=None):
self.record = record
self.initial_fit_parameters = OrderedDict([
('vc', {'-55': {}, '-70': {}}),
('ic', {'-55': {}, '-70': {}}),
])
self.output_fit_parameters = OrderedDict([
('vc', {'-55': {}, '-70': {}}),
('ic', {'-55': {}, '-70': {}}),
])
self.fit_params = {'initial': self.initial_fit_parameters, 'fit': self.output_fit_parameters}
with pg.BusyCursor():
self.reset_display()
self.pair = pair
print ('loading responses for %s...' % pair)
q = response_query(default_session, pair)
self.pulse_responses = [q.PulseResponse for q in q.all()]
print('got %d pulse responses' % len(self.pulse_responses))
if pair.has_synapse is True:
synapse_type = pair.synapse.synapse_type
else:
synapse_type = None
pair_params = {'Synapse call': synapse_type, 'Gap junction call': pair.has_electrical}
self.ctrl_panel.update_user_params(**pair_params)
self.ctrl_panel.update_fit_params(self.fit_params['fit'], fit_pass=True)
def plot_metric_pairs(pair_list, metric, db, ax, align='pulse', norm_amp=None, perc=False, labels=None, max_ind_freq=50):
pairs = [get_pair(eid, pre, post, db) for eid, pre, post in pair_list]
_, metric_name, units, scale, _, cmap, cmap_log, clim, _ = get_metric_data(metric, db)
cmap = matplotlib.cm.get_cmap(cmap)
if cmap_log:
norm = matplotlib.colors.LogNorm(vmin=clim[0], vmax=clim[1], clip=False)
else:
norm = matplotlib.colors.Normalize(vmin=clim[0], vmax=clim[1], clip=False)
colors = [map_color_by_metric(pair, metric, cmap, norm, scale) for pair in pairs]
for i, pair in enumerate(pairs):
s = db.session()
q= response_query(s, pair, max_ind_freq=max_ind_freq)
prs = [q.PulseResponse for q in q.all()]
sort_prs = sort_responses(prs)
prs = sort_prs[('ic', -55)]['qc_pass']
if pair.synapse.synapse_type=='ex':
prs = prs + sort_prs[('ic', -70)]['qc_pass']
if perc:
prs_amp = [abs(pr.pulse_response_fit.fit_amp) for pr in prs if pr.pulse_response_fit is not None]
amp_85, amp_95 = np.percentile(prs_amp, [85, 95])
mask = (prs_amp >= amp_85) & (prs_amp <= amp_95)
prs = np.asarray(prs)[mask]
prl = PulseResponseList(prs)
post_ts = prl.post_tseries(align='spike', bsub=True, bsub_win=0.1e-3)
trace = post_ts.mean()*scale
if norm_amp=='exc':
trace = post_ts.mean()/pair.synapse.psp_amplitude
if norm_amp=='inh':
trace = post_ts.mean()/pair.synapse.psp_amplitude*-1
latency = pair.synapse.latency
if align=='pulse':
trace.t0 = trace.t0 - latency
label = labels[i] if labels is not None else None
ax.plot(trace.time_values*scale, trace.data, color=colors[i], linewidth=2, label=label)
ax.set_xlim(-2, 10)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
if labels is not None:
ax.legend(loc='upper left', bbox_to_anchor=(1, 1))