sim_refs = [] for gj_conn_type in ['2010', '2012']: sim_config_name = 'input_resistance_' + gj_conn_type + 'gap' sim_config = project.simConfigInfo.getSimConfig(sim_config_name) project.neuronSettings.setNoConsole() for trial in range(n_trials): # generate pm.doGenerate(sim_config_name, 1234) while pm.isGenerating(): time.sleep(0.02) print('network generated') for amplitude in stim_amplitude_range: sim_ref = utils.ir_sim_ref(timestamp, gj_conn_type, amplitude, trial) sim_refs.append(sim_ref) sim_path = '../simulations/' + sim_ref project.simulationParameters.setReference(sim_ref) # set stim rate amplitude_in_nA = amplitude / 1000. for cell_type in ['Vervaeke', 'reduced']: stim = project.elecInputInfo.getStim('cclamp_network_' + cell_type) stim.setAmp(NumberGenerator(amplitude_in_nA)) project.elecInputInfo.updateStim(stim) # generate and compile neuron files print "Generating NEURON scripts..." project.neuronFileManager.setSuggestedRemoteRunTime(10) simulator_seed = random.getrandbits(32) project.neuronFileManager.generateTheNeuronFiles(
dash_styles = [(4, 1.5), (None, None)] n_trials = 1 averages = {} sigmas = {} lines = [] for gj_conn_type, dashes in zip(gj_conn_types, dash_styles): averages[gj_conn_type] = {} sigmas[gj_conn_type] = {} for cell_type, color in zip(cell_types, colors): averages[gj_conn_type][cell_type] = [] sigmas[gj_conn_type][cell_type] = [] for stim_level in stim_range: responses = [] for trial in range(n_trials): sim_ref = utils.ir_sim_ref(timestamp, gj_conn_type, stim_level, trial) filename = '../simulations/{0}/Golgi_network_{1}_TTX_0.dat'.format( sim_ref, cell_type) try: responses.append(np.loadtxt(filename)[-1]) except IOError: print('Data file not found: {0}'.format(sim_ref)) responses = np.array(responses) averages[gj_conn_type][cell_type].append(responses.mean()) sigmas[gj_conn_type][cell_type].append(responses.std()) baseline = averages[gj_conn_type][cell_type][4] averages[gj_conn_type][ cell_type] = averages[gj_conn_type][cell_type] - baseline label = '{}, {} net'.format(cell_type, gj_conn_type) if n_trials > 1: ax.errorbar(stim_range,
for gj_conn_type in ['2010', '2012']: sim_config_name = 'input_resistance_' + gj_conn_type + 'gap' sim_config = project.simConfigInfo.getSimConfig(sim_config_name) project.neuronSettings.setNoConsole() for trial in range(n_trials): # generate pm.doGenerate(sim_config_name, 1234) while pm.isGenerating(): time.sleep(0.02) print('network generated') for amplitude in stim_amplitude_range: sim_ref = utils.ir_sim_ref(timestamp, gj_conn_type, amplitude, trial) sim_refs.append(sim_ref) sim_path = '../simulations/' + sim_ref project.simulationParameters.setReference(sim_ref) # set stim rate amplitude_in_nA = amplitude/1000. for cell_type in ['Vervaeke', 'reduced']: stim = project.elecInputInfo.getStim('cclamp_network_' + cell_type) stim.setAmp(NumberGenerator(amplitude_in_nA)) project.elecInputInfo.updateStim(stim) # generate and compile neuron files print "Generating NEURON scripts..." project.neuronFileManager.setSuggestedRemoteRunTime(10) simulator_seed = random.getrandbits(32) project.neuronFileManager.generateTheNeuronFiles(sim_config, None, NeuronFileManager.RUN_HOC,simulator_seed)