) # 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')
