) # object of class SimConfig to store simulation configuration simConfig.duration = 1 * 1e3 # Duration of the simulation, in ms simConfig.dt = 0.025 # Internal integration timestep to use simConfig.verbose = 0 # Show detailed messages simConfig.recordTraces = { 'V_soma': { 'sec': 'soma', 'loc': 0.5, 'var': 'v' } } # Dict with traces to record simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'model_output' # Set file output name simConfig.savePickle = False # Save params, network and sim output to pickle file simConfig.analysis['plotRaster'] = { 'orderInverse': True, 'saveFig': 'tut_import_raster.png' } # Plot a raster simConfig.analysis['plotTraces'] = { 'include': [0] } # Plot recorded traces for this list of cells # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut_import')
for x in [30, 90]] simConfig.analysis['plotTraces'] = { 'include': [('E', 0)], 'oneFigPer': 'cell', 'overlay': True, 'figSize': (5, 3), 'saveFig': True } # Plot recorded traces for this list of cells simConfig.analysis['plotLFP'] = { 'includeAxon': False, 'plots': ['timeSeries', 'locations'], 'figSize': (5, 9), 'saveFig': True } simConfig.analysis['getCSD'] = { 'timeRange': [10, 45], 'spacing_um': 150, 'vaknin': True } simConfig.analysis['plotCSD'] = {'LFP_overlay': True} #sim.analysis.getCSD(...args...) #simConfig.analysis['plotCSD'] = {} # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) #sim.analysis.plotCSD() # check model output sim.checkOutput('cell_lfp')
sim.runSim() # run parallel Neuron simulation sim.gatherData() # gather spiking data and cell info from each node sim.saveData( ) # save params, cell info and sim output to file (pickle,mat,txt,etc) sim.analysis.plotData() # plot spike raster # modify cells geometry sim.net.modifyCells({ 'conds': { 'pop': 'hop' }, 'secs': { 'soma': { 'geom': { 'L': 160 } } } }) sim.simulate() from netpyne import __gui__ if __gui__: sim.analysis.plotRaster(syncLines=True) sim.analysis.plotTraces(include=[1]) # check model output sim.checkOutput('tut7')
import M1 # import parameters file from netpyne import sim # import netpyne init module sim.createSimulateAnalyze( netParams=M1.netParams, simConfig=M1.simConfig) # create and simulate network # check model output sim.checkOutput('M1')
def test_tutorial_3(): import tut3 sim.checkOutput('tut3')
simConfig.saveCellSecs = 0 #False simConfig.saveCellConns = 0 simConfig.recordLFP = [[150, y, 150] for y in [600, 800, 1000]] # only L5 (Zagha) [[150, y, 150] for y in range(200,1300,100)] simConfig.analysis['plotRaster'] = True # Plot a raster simConfig.analysis['plotTraces'] = {'include': [1]} # Plot recorded traces for this list of cells #simConfig.analysis['plot2Dnet'] = True # plot 2D visualization of cell positions and connections simConfig.analysis['plotLFP'] = True def modifyGnabar(t): params = {'conds': {'cellType': 'PYR'}, 'secs': {'soma': {'mechs': {'hh': {'gnabar': 0.0}}}}} sim.net.modifyCells(params) print(sim.net.cells[0].secs['soma']['mechs']['hh']) # Create network and run simulation sim.create(netParams=netParams, simConfig=simConfig) sim.runSimWithIntervalFunc(500, modifyGnabar) sim.gatherData() # gather spiking data and cell info from each node sim.saveData() # save params, cell info and sim output to file (pickle,mat,txt,etc)# sim.analysis.plotData() # plot spike raster etc # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut2')
Starting script to run NetPyNE-based M1 model. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi init.py """ import matplotlib matplotlib.use('Agg') # to avoid graphics error in servers from netpyne import sim from cfg import cfg from netParams import netParams print("Starting sim ...") (pops, cells, conns, stims, rxd, simData) = sim.create(netParams, cfg, output=True) # saveInterval defines how often the data is saved sim.runSimWithIntervalFunc(cfg.saveInterval, sim.intervalSave) # we run fileGather() instead of gather sim.fileGather() sim.analyze() sim.checkOutput('M1detailed')
def test_run(self, pkg_setup): import M1_run sim.checkOutput('M1')
def test_init(self, pkg_setup): import init sim.checkOutput('saving')
def test_tutorial_1(): import tut1 sim.checkOutput('tut1')
def test_init(self, pkg_setup): import init sim.checkOutput('PTcell')
def test_tutorial_7(): import tut7 sim.checkOutput('tut7')
def test_tutorial_6(): import tut6 sim.checkOutput('tut6')
def test_tutorial_5(): import tut5 sim.checkOutput('tut5')
simConfig.recordTraces = { 'V_soma': { 'sec': 'soma', 'loc': 0.5, 'var': 'v' } } # Dict with traces to record simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'model_output' # Set file output name simConfig.savePickle = False # Save params, network and sim output to pickle file simConfig.saveMat = False # Save params, network and sim output to pickle file simConfig.analysis['plotRaster'] = { 'orderBy': 'y', 'orderInverse': True } # Plot a raster simConfig.analysis['plotTraces'] = { 'include': [('E2', 0), ('E4', 0), ('E5', 5)] } # Plot recorded traces for this list of cells simConfig.analysis[ 'plot2Dnet'] = True # plot 2D visualization of cell positions and connections simConfig.analysis['plotConn'] = True # plot connectivity matrix # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut5')
def test_run(self, pkg_setup): import HybridTut_run sim.checkOutput('HybridTut')
simConfig = specs.SimConfig( ) # object of class SimConfig to store simulation configuration simConfig.duration = 1 * 1e3 # Duration of the simulation, in ms simConfig.dt = 0.025 # Internal integration timestep to use simConfig.verbose = False # Show detailed messages simConfig.recordTraces = { 'V_soma': { 'sec': 'soma', 'loc': 0.5, 'var': 'v' } } # Dict with traces to record simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'tut6' # Set file output name simConfig.savePickle = False # Save params, network and sim output to pickle file simConfig.analysis['plotRaster'] = {'saveFig': True} # Plot a raster simConfig.analysis['plotTraces'] = { 'include': [('S', 0), ('M', 0)], 'saveFig': True } # Plot recorded traces for this list of cells # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut6')
import HHTut # import parameters file from netpyne import sim # import netpyne sim module sim.createSimulateAnalyze( netParams=HHTut.netParams, simConfig=HHTut.simConfig) # create and simulate network # check model output sim.checkOutput('HHTut')
'weight': 0.01, # synaptic weight 'delay': 5, # transmission delay (ms) 'sec': 'dend', # section to connect to 'loc': 1.0, # location of synapse 'synMech': 'exc'} # target synaptic mechanism # Simulation options simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration simConfig.duration = 1*1e3 # Duration of the simulation, in ms simConfig.dt = 0.025 # Internal integration timestep to use simConfig.verbose = False # Show detailed messages simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'tut3' # Set file output name simConfig.savePickle = False # Save params, network and sim output to pickle file simConfig.analysis['plotRaster'] = {'saveFig': True} # Plot a raster simConfig.analysis['plotTraces'] = {'include': [1], 'saveFig': True} # Plot recorded traces for this list of cells simConfig.analysis['plot2Dnet'] = {'saveFig': True} # plot 2D cell positions and connections # Create network and run simulation sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut3')
'probability': 0.1, # probability of connection 'weight': 0.005, # synaptic weight 'delay': 5, # transmission delay (ms) 'sec': 'dend', # section to connect to 'loc': 1.0, # location in section to connect to 'synMech': 'exc'} # target synapse # Simulation options simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration simConfig.duration = 1*1e3 # Duration of the simulation, in ms simConfig.dt = 0.025 # Internal integration timestep to use simConfig.verbose = False # Show detailed messages simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'tut4' # Set file output name simConfig.savePickle = False # Save params, network and sim output to pickle file simConfig.analysis['plotRaster'] = {'saveFig': True} # Plot a raster simConfig.analysis['plotTraces'] = {'include': [1], 'saveFig': True} # Plot recorded traces for this list of cells simConfig.analysis['plot2Dnet'] = {'saveFig': True} # plot 2D cell positions and connections # Create network and run simulation sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty # check model output sim.checkOutput('tut4')
import HybridTut # import parameters file from netpyne import sim # import netpyne init module sim.createSimulateAnalyze( netParams=HybridTut.netParams, simConfig=HybridTut.simConfig) # create and simulate network # check model output sim.checkOutput('HybridTut')
""" init.py Starting script to run NetPyNE-based PT model. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi init.py Contributors: [email protected] """ #import matplotlib; matplotlib.use('Agg') # to avoid graphics error in servers from netpyne import sim from cfg import cfg from netParams import netParams sim.createSimulateAnalyze(netParams, cfg) #SimulateAnalyze(netParams, cfg) # check model output sim.checkOutput('PTcell')
sim.createSimulateAnalyze(netParams, cfg) # Saving different network components to file sim.cfg.saveJson = True # save network params (rules) sim.saveData(include=['netParams'], filename='out_netParams') # save network instance sim.saveData(include=['net'], filename='out_netInstance') # save network params and instance together sim.saveData(include=['netParams', 'net'], filename='out_netParams_netInstance') # save sim config sim.saveData(include=['simConfig'], filename='out_simConfig') # save sim output data sim.saveData(include=['simData'], filename='out_simData') # save network instance with compact conn format (list instead of dict) sim.cfg.compactConnFormat = [ 'preGid', 'sec', 'loc', 'synMech', 'weight', 'delay' ] sim.gatherData() sim.saveData(include=['net'], filename='out_netInstanceCompact') # check model output sim.checkOutput('saving')
def test_tutorial_2(): import tut2 sim.checkOutput('tut2')