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])