dist_plot=None,
color=color[0])
avg_est_age2, summary_plots = first_pulse_plot(
expts,
name=legend,
summary_plot=summary_plots,
color=color[1],
scatter=1,
features=True)
if args.trains is True:
summary_train, summary_dec, summary_amps = train_response_plot(
expts,
name=(legend + ' 50 Hz induction'),
summary_plots=[summary_train, summary_dec],
color=color[1])
write_cache(summary_amps, 'age_train_amps.pkl')
ks = stats.ks_2samp(avg_est_age1['amp'], avg_est_age2['amp'])
print('p = %f (KS test, Amplitude)' % ks.pvalue)
ks = stats.ks_2samp(avg_est_age1['latency'],
avg_est_age2['latency'])
print('p = %f (KS test, Latency)' % ks.pvalue)
ks = stats.ks_2samp(avg_est_age1['rise'], avg_est_age2['rise'])
print('p = %f (KS test, Rise time)' % ks.pvalue)
elif args.cre_type is None and (args.calcium is not None
or args.age is not None):
print(
'Error: in order to compare across conditions a single cre-type connection must be specified'
)
else:
dist_plots = None
rec_index[delta] = {}
if key not in rec_index[delta].keys():
rec_index[delta][key] = []
for n in range(rec_amp.shape[0]):
rec_index[delta][key].append(rec_amp[n, 8] /
rec_amp[n, 0])
t_color = pg.hsvColor(hue,
sat=float(t + 0.5) /
len(t_rec),
val=1)
p5.plot(grand_rec_amp / grand_rec_amp[0],
name=(' %d ms, n = %d' % (delta, n)),
pen=t_color,
symbol='t',
symbolBrush=t_color,
symbolPen=None)
# rec_err = pg.ErrorBarItem(x=np.arange(12),y=np.array(grand_rec_amp / grand_rec_amp[0]),
# height=np.array(rec_amp_sem), beam=0.3)
# p5.addItem(rec_err)
row += 1
# if sum(pulse_cache_change) > 0:
# write_cache(pulse_response_cache, pulse_cache_file)
# if sum(train_cache_change) > 0:
# write_cache(train_response_cache, train_cache_file)
feature_cache = {}
feature_cache['Amplitudes'] = pulse_amp
feature_cache['Induction'] = ind_index
feature_cache['Recovery'] = rec_index
write_cache(feature_cache, 'feature_cache_file.pkl')
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]] = []
rec_uid[connection_types[c]] = []
rec_amp_summary[connection_types[c]].append([delta, rec_amp])
rec_uid[connection_types[c]].append([delta, rec_pass_qc[2]])
# if sum(cache_change) > 0:
# write_cache(response_cache, cache_file)
print ('Exporting train pulse amplitudes and experiment IDs for further analysis')
write_cache([ind_amp_summary, rec_amp_summary], "train_amps_human.pkl")
write_cache([ind_uid, rec_uid], "expt_ids_human.pkl")
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]] = []
rec_uid[connection_types[c]] = []
rec_amp_summary[connection_types[c]].append([delta, rec_amp])
rec_uid[connection_types[c]].append([delta, rec_pass_qc[2]])
# if sum(cache_change) > 0:
# write_cache(response_cache, cache_file)
print(
'Exporting train pulse amplitudes and experiment IDs for further analysis')
write_cache([ind_amp_summary, rec_amp_summary], "train_amps3.pkl")
write_cache([ind_uid, rec_uid], "expt_ids3.pkl")
ages = args.age.split(',')
expts, legend, dist_plots = get_expts(all_expts, cre_type, calcium='high', age=ages[0], start=args.start,
stop=args.stop, dist_plot=None, color=color[0])
avg_est_age1, summary_plots = first_pulse_plot(expts, name=legend, summary_plot=None, color=color[0], scatter=0,
features=True)
if args.trains is True:
summary_train, summary_dec = train_response_plot(expts, name=(legend + ' 50 Hz induction'), summary_plots=[None,None],
color=color[0])
expts, legend, dist_plots = get_expts(all_expts, cre_type, calcium='high', age=ages[1], start=args.start,
stop=args.stop, dist_plot=None, color=color[0])
avg_est_age2, summary_plots = first_pulse_plot(expts, name=legend, summary_plot=summary_plots, color=color[1],
scatter=1, features=True)
if args.trains is True:
summary_train, summary_dec, summary_amps = train_response_plot(expts, name=(legend + ' 50 Hz induction'),
summary_plots=[summary_train, summary_dec], color=color[1])
write_cache(summary_amps, 'age_train_amps.pkl')
ks = stats.ks_2samp(avg_est_age1['amp'], avg_est_age2['amp'])
print('p = %f (KS test, Amplitude)' % ks.pvalue)
ks = stats.ks_2samp(avg_est_age1['latency'], avg_est_age2['latency'])
print('p = %f (KS test, Latency)' % ks.pvalue)
ks = stats.ks_2samp(avg_est_age1['rise'], avg_est_age2['rise'])
print('p = %f (KS test, Rise time)' % ks.pvalue)
elif args.cre_type is None and (args.calcium is not None or args.age is not None):
print('Error: in order to compare across conditions a single cre-type connection must be specified')
else:
dist_plots = None
summary_plot = None
train_plots = None
train_plots2 = None
for i, type in enumerate(cre_types):