def get_spont(nFiles): spikeFreq = np.zeros(nFiles) for fid in range(nFiles): fname = format(fid, '05d') v = np.genfromtxt('sim_gocnet_GoC_{}.v.dat'.format(fname)) spikes = np.genfromtxt('sim_gocnet_GoC_{}.v.spikes'.format(fname)) if not np.isnan(v[-1, 1]): spikeFreq[fid] = analysis.mean_spike_frequency(spikes[:, 1] * 1e3) else: spikeFreq[fid] = np.nan temp = spikeFreq temp[np.isnan(temp)] = 0 useParams = np.where(abs(temp - 7) <= 2) print(useParams, spikeFreq[useParams]) return spikeFreq
def generate_current_vs_frequency_curve(nml2_file, cell_id, start_amp_nA=-0.1, end_amp_nA=0.1, step_nA=0.01, custom_amps_nA=[], analysis_duration=1000, analysis_delay=0, pre_zero_pulse=0, post_zero_pulse=0, dt=0.05, temperature="32degC", spike_threshold_mV=0., plot_voltage_traces=False, plot_if=True, plot_iv=False, xlim_if=None, ylim_if=None, xlim_iv=None, ylim_iv=None, label_xaxis=True, label_yaxis=True, show_volts_label=True, grid=True, font_size=12, if_iv_color='k', linewidth=1, bottom_left_spines_only=False, show_plot_already=True, save_voltage_traces_to=None, save_if_figure_to=None, save_iv_figure_to=None, save_if_data_to=None, save_iv_data_to=None, simulator="jNeuroML", num_processors=1, include_included=True, title_above_plot=False, return_axes=False, verbose=False): print_comment( "Running generate_current_vs_frequency_curve() on %s (%s)" % (nml2_file, os.path.abspath(nml2_file)), verbose) from pyelectro.analysis import max_min from pyelectro.analysis import mean_spike_frequency import numpy as np traces_ax = None if_ax = None iv_ax = None sim_id = 'iv_%s' % cell_id total_duration = pre_zero_pulse + analysis_duration + analysis_delay + post_zero_pulse pulse_duration = analysis_duration + analysis_delay end_stim = pre_zero_pulse + analysis_duration + analysis_delay ls = LEMSSimulation(sim_id, total_duration, dt) ls.include_neuroml2_file(nml2_file, include_included=include_included) stims = [] if len(custom_amps_nA) > 0: stims = [float(a) for a in custom_amps_nA] stim_info = ['%snA' % float(a) for a in custom_amps_nA] else: amp = start_amp_nA while amp <= end_amp_nA: stims.append(amp) amp += step_nA stim_info = '(%snA->%snA; %s steps of %snA; %sms)' % ( start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration) print_comment_v("Generating an IF curve for cell %s in %s using %s %s" % (cell_id, nml2_file, simulator, stim_info)) number_cells = len(stims) pop = nml.Population(id="population_of_%s" % cell_id, component=cell_id, size=number_cells) # create network and add populations net_id = "network_of_%s" % cell_id net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature) ls.assign_simulation_target(net_id) net_doc = nml.NeuroMLDocument(id=net.id) net_doc.networks.append(net) net_doc.includes.append(nml.IncludeType(nml2_file)) net.populations.append(pop) for i in range(number_cells): stim_amp = "%snA" % stims[i] input_id = ("input_%s" % stim_amp).replace('.', '_').replace('-', 'min') pg = nml.PulseGenerator(id=input_id, delay="%sms" % pre_zero_pulse, duration="%sms" % pulse_duration, amplitude=stim_amp) net_doc.pulse_generators.append(pg) # Add these to cells input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id) input = nml.Input(id='0', target="../%s[%i]" % (pop.id, i), destination="synapses") input_list.input.append(input) net.input_lists.append(input_list) net_file_name = '%s.net.nml' % sim_id pynml.write_neuroml2_file(net_doc, net_file_name) ls.include_neuroml2_file(net_file_name) disp0 = 'Voltage_display' ls.create_display(disp0, "Voltages", "-90", "50") of0 = 'Volts_file' ls.create_output_file(of0, "%s.v.dat" % sim_id) for i in range(number_cells): ref = "v_cell%i" % i quantity = "%s[%i]/v" % (pop.id, i) ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color()) ls.add_column_to_output_file(of0, ref, quantity) lems_file_name = ls.save_to_file() print_comment( "Written LEMS file %s (%s)" % (lems_file_name, os.path.abspath(lems_file_name)), verbose) if simulator == "jNeuroML": results = pynml.run_lems_with_jneuroml(lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, verbose=verbose) elif simulator == "jNeuroML_NEURON": results = pynml.run_lems_with_jneuroml_neuron(lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, verbose=verbose) elif simulator == "jNeuroML_NetPyNE": results = pynml.run_lems_with_jneuroml_netpyne( lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, num_processors=num_processors, verbose=verbose) else: raise Exception( "Sorry, cannot yet run current vs frequency analysis using simulator %s" % simulator) print_comment( "Completed run in simulator %s (results: %s)" % (simulator, results.keys()), verbose) #print(results.keys()) times_results = [] volts_results = [] volts_labels = [] if_results = {} iv_results = {} for i in range(number_cells): t = np.array(results['t']) * 1000 v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000 if plot_voltage_traces: times_results.append(t) volts_results.append(v) volts_labels.append("%s nA" % stims[i]) mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV) spike_times = mm['maxima_times'] freq = 0 if len(spike_times) > 2: count = 0 for s in spike_times: if s >= pre_zero_pulse + analysis_delay and s < ( pre_zero_pulse + analysis_duration + analysis_delay): count += 1 freq = 1000 * count / float(analysis_duration) mean_freq = mean_spike_frequency(spike_times) #print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq)) if_results[stims[i]] = freq if freq == 0: if post_zero_pulse == 0: iv_results[stims[i]] = v[-1] else: v_end = None for j in range(len(t)): if v_end == None and t[j] >= end_stim: v_end = v[j] iv_results[stims[i]] = v_end if plot_voltage_traces: traces_ax = pynml.generate_plot( times_results, volts_results, "Membrane potential traces for: %s" % nml2_file, xaxis='Time (ms)' if label_xaxis else ' ', yaxis='Membrane potential (mV)' if label_yaxis else '', xlim=[total_duration * -0.05, total_duration * 1.05], show_xticklabels=label_xaxis, font_size=font_size, bottom_left_spines_only=bottom_left_spines_only, grid=False, labels=volts_labels if show_volts_label else [], show_plot_already=False, save_figure_to=save_voltage_traces_to, title_above_plot=title_above_plot, verbose=verbose) if plot_if: stims = sorted(if_results.keys()) stims_pA = [ii * 1000 for ii in stims] freqs = [if_results[s] for s in stims] if_ax = pynml.generate_plot( [stims_pA], [freqs], "Firing frequency versus injected current for: %s" % nml2_file, colors=[if_iv_color], linestyles=['-'], markers=['o'], linewidths=[linewidth], xaxis='Input current (pA)' if label_xaxis else ' ', yaxis='Firing frequency (Hz)' if label_yaxis else '', xlim=xlim_if, ylim=ylim_if, show_xticklabels=label_xaxis, show_yticklabels=label_yaxis, font_size=font_size, bottom_left_spines_only=bottom_left_spines_only, grid=grid, show_plot_already=False, save_figure_to=save_if_figure_to, title_above_plot=title_above_plot, verbose=verbose) if save_if_data_to: with open(save_if_data_to, 'w') as if_file: for i in range(len(stims_pA)): if_file.write("%s\t%s\n" % (stims_pA[i], freqs[i])) if plot_iv: stims = sorted(iv_results.keys()) stims_pA = [ii * 1000 for ii in sorted(iv_results.keys())] vs = [iv_results[s] for s in stims] xs = [] ys = [] xs.append([]) ys.append([]) for si in range(len(stims)): stim = stims[si] if len(custom_amps_nA) == 0 and si > 1 and ( stims[si] - stims[si - 1]) > step_nA * 1.01: xs.append([]) ys.append([]) xs[-1].append(stim * 1000) ys[-1].append(iv_results[stim]) iv_ax = pynml.generate_plot( xs, ys, "V at %sms versus I below threshold for: %s" % (end_stim, nml2_file), colors=[if_iv_color for s in xs], linestyles=['-' for s in xs], markers=['o' for s in xs], xaxis='Input current (pA)' if label_xaxis else '', yaxis='Membrane potential (mV)' if label_yaxis else '', xlim=xlim_iv, ylim=ylim_iv, show_xticklabels=label_xaxis, show_yticklabels=label_yaxis, font_size=font_size, linewidths=[linewidth for s in xs], bottom_left_spines_only=bottom_left_spines_only, grid=grid, show_plot_already=False, save_figure_to=save_iv_figure_to, title_above_plot=title_above_plot, verbose=verbose) if save_iv_data_to: with open(save_iv_data_to, 'w') as iv_file: for i in range(len(stims_pA)): iv_file.write("%s\t%s\n" % (stims_pA[i], vs[i])) if show_plot_already: from matplotlib import pyplot as plt plt.show() if return_axes: return traces_ax, if_ax, iv_ax return if_results
def generate_current_vs_frequency_curve(nml2_file, cell_id, start_amp_nA, end_amp_nA, step_nA, analysis_duration, analysis_delay, dt=0.05, temperature="32degC", spike_threshold_mV=0., plot_voltage_traces=False, plot_if=True, plot_iv=False, xlim_if=None, ylim_if=None, xlim_iv=None, ylim_iv=None, show_plot_already=True, save_if_figure_to=None, save_iv_figure_to=None, simulator="jNeuroML", include_included=True): from pyelectro.analysis import max_min from pyelectro.analysis import mean_spike_frequency import numpy as np print_comment_v( "Generating FI curve for cell %s in %s using %s (%snA->%snA; %snA steps)" % (cell_id, nml2_file, simulator, start_amp_nA, end_amp_nA, step_nA)) sim_id = 'iv_%s' % cell_id duration = analysis_duration + analysis_delay ls = LEMSSimulation(sim_id, duration, dt) ls.include_neuroml2_file(nml2_file, include_included=include_included) stims = [] amp = start_amp_nA while amp <= end_amp_nA: stims.append(amp) amp += step_nA number_cells = len(stims) pop = nml.Population(id="population_of_%s" % cell_id, component=cell_id, size=number_cells) # create network and add populations net_id = "network_of_%s" % cell_id net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature) ls.assign_simulation_target(net_id) net_doc = nml.NeuroMLDocument(id=net.id) net_doc.networks.append(net) net_doc.includes.append(nml.IncludeType(nml2_file)) net.populations.append(pop) for i in range(number_cells): stim_amp = "%snA" % stims[i] input_id = ("input_%s" % stim_amp).replace('.', '_').replace('-', 'min') pg = nml.PulseGenerator(id=input_id, delay="0ms", duration="%sms" % duration, amplitude=stim_amp) net_doc.pulse_generators.append(pg) # Add these to cells input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id) input = nml.Input(id='0', target="../%s[%i]" % (pop.id, i), destination="synapses") input_list.input.append(input) net.input_lists.append(input_list) net_file_name = '%s.net.nml' % sim_id pynml.write_neuroml2_file(net_doc, net_file_name) ls.include_neuroml2_file(net_file_name) disp0 = 'Voltage_display' ls.create_display(disp0, "Voltages", "-90", "50") of0 = 'Volts_file' ls.create_output_file(of0, "%s.v.dat" % sim_id) for i in range(number_cells): ref = "v_cell%i" % i quantity = "%s[%i]/v" % (pop.id, i) ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color()) ls.add_column_to_output_file(of0, ref, quantity) lems_file_name = ls.save_to_file() if simulator == "jNeuroML": results = pynml.run_lems_with_jneuroml(lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces, show_plot_already=False) elif simulator == "jNeuroML_NEURON": results = pynml.run_lems_with_jneuroml_neuron(lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces, show_plot_already=False) #print(results.keys()) if_results = {} iv_results = {} for i in range(number_cells): t = np.array(results['t']) * 1000 v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000 mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV) spike_times = mm['maxima_times'] freq = 0 if len(spike_times) > 2: count = 0 for s in spike_times: if s >= analysis_delay and s < (analysis_duration + analysis_delay): count += 1 freq = 1000 * count / float(analysis_duration) mean_freq = mean_spike_frequency(spike_times) # print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq)) if_results[stims[i]] = freq if freq == 0: iv_results[stims[i]] = v[-1] if plot_if: stims = sorted(if_results.keys()) stims_pA = [ii * 1000 for ii in stims] freqs = [if_results[s] for s in stims] pynml.generate_plot([stims_pA], [freqs], "Frequency versus injected current for: %s" % nml2_file, colors=['k'], linestyles=['-'], markers=['o'], xaxis='Input current (pA)', yaxis='Firing frequency (Hz)', xlim=xlim_if, ylim=ylim_if, grid=True, show_plot_already=False, save_figure_to=save_if_figure_to) if plot_iv: stims = sorted(iv_results.keys()) stims_pA = [ii * 1000 for ii in sorted(iv_results.keys())] vs = [iv_results[s] for s in stims] pynml.generate_plot( [stims_pA], [vs], "Final membrane potential versus injected current for: %s" % nml2_file, colors=['k'], linestyles=['-'], markers=['o'], xaxis='Input current (pA)', yaxis='Membrane potential (mV)', xlim=xlim_iv, ylim=ylim_iv, grid=True, show_plot_already=False, save_figure_to=save_iv_figure_to) if show_plot_already: from matplotlib import pyplot as plt plt.show() return if_results
def generate_current_vs_frequency_curve( nml2_file, cell_id, start_amp_nA, end_amp_nA, step_nA, analysis_duration, analysis_delay, dt=0.05, temperature="32degC", spike_threshold_mV=0.0, plot_voltage_traces=False, plot_if=True, simulator="jNeuroML", ): from pyelectro.analysis import max_min from pyelectro.analysis import mean_spike_frequency import numpy as np sim_id = "iv_%s" % cell_id duration = analysis_duration + analysis_delay ls = LEMSSimulation(sim_id, duration, dt) ls.include_neuroml2_file(nml2_file) stims = [] amp = start_amp_nA while amp <= end_amp_nA: stims.append(amp) amp += step_nA number_cells = len(stims) pop = nml.Population(id="population_of_%s" % cell_id, component=cell_id, size=number_cells) # create network and add populations net_id = "network_of_%s" % cell_id net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature) ls.assign_simulation_target(net_id) net_doc = nml.NeuroMLDocument(id=net.id) net_doc.networks.append(net) net.populations.append(pop) for i in range(number_cells): stim_amp = "%snA" % stims[i] input_id = ("input_%s" % stim_amp).replace(".", "_") pg = nml.PulseGenerator(id=input_id, delay="0ms", duration="%sms" % duration, amplitude=stim_amp) net_doc.pulse_generators.append(pg) # Add these to cells input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id) input = nml.Input(id="0", target="../%s[%i]" % (pop.id, i), destination="synapses") input_list.input.append(input) net.input_lists.append(input_list) net_file_name = "%s.net.nml" % sim_id pynml.write_neuroml2_file(net_doc, net_file_name) ls.include_neuroml2_file(net_file_name) disp0 = "Voltage_display" ls.create_display(disp0, "Voltages", "-90", "50") of0 = "Volts_file" ls.create_output_file(of0, "%s.v.dat" % sim_id) for i in range(number_cells): ref = "v_cell%i" % i quantity = "%s[%i]/v" % (pop.id, i) ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color()) ls.add_column_to_output_file(of0, ref, quantity) lems_file_name = ls.save_to_file() if simulator == "jNeuroML": results = pynml.run_lems_with_jneuroml( lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces ) elif simulator == "jNeuroML_NEURON": results = pynml.run_lems_with_jneuroml_neuron( lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces ) # print(results.keys()) if_results = {} for i in range(number_cells): t = np.array(results["t"]) * 1000 v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000 mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV) spike_times = mm["maxima_times"] freq = 0 if len(spike_times) > 2: count = 0 for s in spike_times: if s >= analysis_delay and s < (analysis_duration + analysis_delay): count += 1 freq = 1000 * count / float(analysis_duration) mean_freq = mean_spike_frequency(spike_times) # print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq)) if_results[stims[i]] = freq if plot_if: from matplotlib import pyplot as plt plt.xlabel("Input current (nA)") plt.ylabel("Firing frequency (Hz)") plt.grid("on") stims = sorted(if_results.keys()) freqs = [] for s in stims: freqs.append(if_results[s]) plt.plot(stims, freqs, "o") plt.show() return if_results
def generate_current_vs_frequency_curve(nml2_file, cell_id, start_amp_nA, end_amp_nA, step_nA, analysis_duration, analysis_delay, dt = 0.05, temperature = "32degC", spike_threshold_mV=0., plot_voltage_traces=False, plot_if=True, plot_iv=False, xlim_if = None, ylim_if = None, xlim_iv = None, ylim_iv = None, show_plot_already=True, save_if_figure_to=None, save_iv_figure_to=None, simulator="jNeuroML", include_included=True): from pyelectro.analysis import max_min from pyelectro.analysis import mean_spike_frequency import numpy as np print_comment_v("Generating FI curve for cell %s in %s using %s (%snA->%snA; %snA steps)"% (cell_id, nml2_file, simulator, start_amp_nA, end_amp_nA, step_nA)) sim_id = 'iv_%s'%cell_id duration = analysis_duration+analysis_delay ls = LEMSSimulation(sim_id, duration, dt) ls.include_neuroml2_file(nml2_file, include_included=include_included) stims = [] amp = start_amp_nA while amp<=end_amp_nA : stims.append(amp) amp+=step_nA number_cells = len(stims) pop = nml.Population(id="population_of_%s"%cell_id, component=cell_id, size=number_cells) # create network and add populations net_id = "network_of_%s"%cell_id net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature) ls.assign_simulation_target(net_id) net_doc = nml.NeuroMLDocument(id=net.id) net_doc.networks.append(net) net_doc.includes.append(nml.IncludeType(nml2_file)) net.populations.append(pop) for i in range(number_cells): stim_amp = "%snA"%stims[i] input_id = ("input_%s"%stim_amp).replace('.','_').replace('-','min') pg = nml.PulseGenerator(id=input_id, delay="0ms", duration="%sms"%duration, amplitude=stim_amp) net_doc.pulse_generators.append(pg) # Add these to cells input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id) input = nml.Input(id='0', target="../%s[%i]"%(pop.id, i), destination="synapses") input_list.input.append(input) net.input_lists.append(input_list) net_file_name = '%s.net.nml'%sim_id pynml.write_neuroml2_file(net_doc, net_file_name) ls.include_neuroml2_file(net_file_name) disp0 = 'Voltage_display' ls.create_display(disp0,"Voltages", "-90", "50") of0 = 'Volts_file' ls.create_output_file(of0, "%s.v.dat"%sim_id) for i in range(number_cells): ref = "v_cell%i"%i quantity = "%s[%i]/v"%(pop.id, i) ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color()) ls.add_column_to_output_file(of0, ref, quantity) lems_file_name = ls.save_to_file() if simulator == "jNeuroML": results = pynml.run_lems_with_jneuroml(lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces, show_plot_already=False) elif simulator == "jNeuroML_NEURON": results = pynml.run_lems_with_jneuroml_neuron(lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces, show_plot_already=False) #print(results.keys()) if_results = {} iv_results = {} for i in range(number_cells): t = np.array(results['t'])*1000 v = np.array(results["%s[%i]/v"%(pop.id, i)])*1000 mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV) spike_times = mm['maxima_times'] freq = 0 if len(spike_times) > 2: count = 0 for s in spike_times: if s >= analysis_delay and s < (analysis_duration+analysis_delay): count+=1 freq = 1000 * count/float(analysis_duration) mean_freq = mean_spike_frequency(spike_times) # print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq)) if_results[stims[i]] = freq if freq == 0: iv_results[stims[i]] = v[-1] if plot_if: stims = sorted(if_results.keys()) stims_pA = [ii*1000 for ii in stims] freqs = [if_results[s] for s in stims] pynml.generate_plot([stims_pA], [freqs], "Frequency versus injected current for: %s"%nml2_file, colors = ['k'], linestyles=['-'], markers=['o'], xaxis = 'Input current (pA)', yaxis = 'Firing frequency (Hz)', xlim = xlim_if, ylim = ylim_if, grid = True, show_plot_already=False, save_figure_to = save_if_figure_to) if plot_iv: stims = sorted(iv_results.keys()) stims_pA = [ii*1000 for ii in sorted(iv_results.keys())] vs = [iv_results[s] for s in stims] pynml.generate_plot([stims_pA], [vs], "Final membrane potential versus injected current for: %s"%nml2_file, colors = ['k'], linestyles=['-'], markers=['o'], xaxis = 'Input current (pA)', yaxis = 'Membrane potential (mV)', xlim = xlim_iv, ylim = ylim_iv, grid = True, show_plot_already=False, save_figure_to = save_iv_figure_to) if show_plot_already: from matplotlib import pyplot as plt plt.show() return if_results
def generate_current_vs_frequency_curve(nml2_file, cell_id, start_amp_nA = -0.1, end_amp_nA = 0.1, step_nA = 0.01, custom_amps_nA = [], analysis_duration = 1000, analysis_delay = 0, pre_zero_pulse = 0, post_zero_pulse = 0, dt = 0.05, temperature = "32degC", spike_threshold_mV = 0., plot_voltage_traces = False, plot_if = True, plot_iv = False, xlim_if = None, ylim_if = None, xlim_iv = None, ylim_iv = None, label_xaxis = True, label_yaxis = True, show_volts_label = True, grid = True, font_size = 12, if_iv_color = 'k', linewidth = 1, bottom_left_spines_only = False, show_plot_already = True, save_voltage_traces_to = None, save_if_figure_to = None, save_iv_figure_to = None, save_if_data_to = None, save_iv_data_to = None, simulator = "jNeuroML", num_processors = 1, include_included = True, title_above_plot = False, return_axes = False, verbose = False): print_comment("Running generate_current_vs_frequency_curve() on %s (%s)"%(nml2_file,os.path.abspath(nml2_file)), verbose) from pyelectro.analysis import max_min from pyelectro.analysis import mean_spike_frequency import numpy as np traces_ax = None if_ax = None iv_ax = None sim_id = 'iv_%s'%cell_id total_duration = pre_zero_pulse+analysis_duration+analysis_delay+post_zero_pulse pulse_duration = analysis_duration+analysis_delay end_stim = pre_zero_pulse+analysis_duration+analysis_delay ls = LEMSSimulation(sim_id, total_duration, dt) ls.include_neuroml2_file(nml2_file, include_included=include_included) stims = [] if len(custom_amps_nA)>0: stims = [float(a) for a in custom_amps_nA] stim_info = ['%snA'%float(a) for a in custom_amps_nA] else: amp = start_amp_nA while amp<=end_amp_nA : stims.append(amp) amp+=step_nA stim_info = '(%snA->%snA; %s steps of %snA; %sms)'%(start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration) print_comment_v("Generating an IF curve for cell %s in %s using %s %s"% (cell_id, nml2_file, simulator, stim_info)) number_cells = len(stims) pop = nml.Population(id="population_of_%s"%cell_id, component=cell_id, size=number_cells) # create network and add populations net_id = "network_of_%s"%cell_id net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature) ls.assign_simulation_target(net_id) net_doc = nml.NeuroMLDocument(id=net.id) net_doc.networks.append(net) net_doc.includes.append(nml.IncludeType(nml2_file)) net.populations.append(pop) for i in range(number_cells): stim_amp = "%snA"%stims[i] input_id = ("input_%s"%stim_amp).replace('.','_').replace('-','min') pg = nml.PulseGenerator(id=input_id, delay="%sms"%pre_zero_pulse, duration="%sms"%pulse_duration, amplitude=stim_amp) net_doc.pulse_generators.append(pg) # Add these to cells input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id) input = nml.Input(id='0', target="../%s[%i]"%(pop.id, i), destination="synapses") input_list.input.append(input) net.input_lists.append(input_list) net_file_name = '%s.net.nml'%sim_id pynml.write_neuroml2_file(net_doc, net_file_name) ls.include_neuroml2_file(net_file_name) disp0 = 'Voltage_display' ls.create_display(disp0,"Voltages", "-90", "50") of0 = 'Volts_file' ls.create_output_file(of0, "%s.v.dat"%sim_id) for i in range(number_cells): ref = "v_cell%i"%i quantity = "%s[%i]/v"%(pop.id, i) ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color()) ls.add_column_to_output_file(of0, ref, quantity) lems_file_name = ls.save_to_file() print_comment("Written LEMS file %s (%s)"%(lems_file_name,os.path.abspath(lems_file_name)), verbose) if simulator == "jNeuroML": results = pynml.run_lems_with_jneuroml(lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, verbose=verbose) elif simulator == "jNeuroML_NEURON": results = pynml.run_lems_with_jneuroml_neuron(lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, verbose=verbose) elif simulator == "jNeuroML_NetPyNE": results = pynml.run_lems_with_jneuroml_netpyne(lems_file_name, nogui=True, load_saved_data=True, plot=False, show_plot_already=False, num_processors = num_processors, verbose=verbose) else: raise Exception("Sorry, cannot yet run current vs frequency analysis using simulator %s"%simulator) print_comment("Completed run in simulator %s (results: %s)"%(simulator,results.keys()), verbose) #print(results.keys()) times_results = [] volts_results = [] volts_labels = [] if_results = {} iv_results = {} for i in range(number_cells): t = np.array(results['t'])*1000 v = np.array(results["%s[%i]/v"%(pop.id, i)])*1000 if plot_voltage_traces: times_results.append(t) volts_results.append(v) volts_labels.append("%s nA"%stims[i]) mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV) spike_times = mm['maxima_times'] freq = 0 if len(spike_times) > 2: count = 0 for s in spike_times: if s >= pre_zero_pulse + analysis_delay and s < (pre_zero_pulse + analysis_duration+analysis_delay): count+=1 freq = 1000 * count/float(analysis_duration) mean_freq = mean_spike_frequency(spike_times) #print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq)) if_results[stims[i]] = freq if freq == 0: if post_zero_pulse==0: iv_results[stims[i]] = v[-1] else: v_end = None for j in range(len(t)): if v_end==None and t[j]>=end_stim: v_end = v[j] iv_results[stims[i]] = v_end if plot_voltage_traces: traces_ax = pynml.generate_plot(times_results, volts_results, "Membrane potential traces for: %s"%nml2_file, xaxis = 'Time (ms)' if label_xaxis else ' ', yaxis = 'Membrane potential (mV)' if label_yaxis else '', xlim = [total_duration*-0.05,total_duration*1.05], show_xticklabels = label_xaxis, font_size = font_size, bottom_left_spines_only = bottom_left_spines_only, grid = False, labels = volts_labels if show_volts_label else [], show_plot_already=False, save_figure_to = save_voltage_traces_to, title_above_plot = title_above_plot, verbose=verbose) if plot_if: stims = sorted(if_results.keys()) stims_pA = [ii*1000 for ii in stims] freqs = [if_results[s] for s in stims] if_ax = pynml.generate_plot([stims_pA], [freqs], "Firing frequency versus injected current for: %s"%nml2_file, colors = [if_iv_color], linestyles=['-'], markers=['o'], linewidths = [linewidth], xaxis = 'Input current (pA)' if label_xaxis else ' ', yaxis = 'Firing frequency (Hz)' if label_yaxis else '', xlim = xlim_if, ylim = ylim_if, show_xticklabels = label_xaxis, show_yticklabels = label_yaxis, font_size = font_size, bottom_left_spines_only = bottom_left_spines_only, grid = grid, show_plot_already=False, save_figure_to = save_if_figure_to, title_above_plot = title_above_plot, verbose=verbose) if save_if_data_to: with open(save_if_data_to,'w') as if_file: for i in range(len(stims_pA)): if_file.write("%s\t%s\n"%(stims_pA[i],freqs[i])) if plot_iv: stims = sorted(iv_results.keys()) stims_pA = [ii*1000 for ii in sorted(iv_results.keys())] vs = [iv_results[s] for s in stims] xs = [] ys = [] xs.append([]) ys.append([]) for si in range(len(stims)): stim = stims[si] if len(custom_amps_nA)==0 and si>1 and (stims[si]-stims[si-1])>step_nA*1.01: xs.append([]) ys.append([]) xs[-1].append(stim*1000) ys[-1].append(iv_results[stim]) iv_ax = pynml.generate_plot(xs, ys, "V at %sms versus I below threshold for: %s"%(end_stim,nml2_file), colors = [if_iv_color for s in xs], linestyles=['-' for s in xs], markers=['o' for s in xs], xaxis = 'Input current (pA)' if label_xaxis else '', yaxis = 'Membrane potential (mV)' if label_yaxis else '', xlim = xlim_iv, ylim = ylim_iv, show_xticklabels = label_xaxis, show_yticklabels = label_yaxis, font_size = font_size, linewidths = [linewidth for s in xs], bottom_left_spines_only = bottom_left_spines_only, grid = grid, show_plot_already=False, save_figure_to = save_iv_figure_to, title_above_plot = title_above_plot, verbose=verbose) if save_iv_data_to: with open(save_iv_data_to,'w') as iv_file: for i in range(len(stims_pA)): iv_file.write("%s\t%s\n"%(stims_pA[i],vs[i])) if show_plot_already: from matplotlib import pyplot as plt plt.show() if return_axes: return traces_ax, if_ax, iv_ax return if_results