def run(): # Folder that contains x86_64 folder NETPYNE_WORKDIR_PATH = "../../../" neuron.load_mechanisms(NETPYNE_WORKDIR_PATH) # read cfg and netParams from command line arguments if available; otherwise use default simConfig, netParams = sim.readCmdLineArgs(simConfigDefault="cfg.py", netParamsDefault="netParams.py") # Create network and run simulation sim.createSimulate(netParams=netParams, simConfig=simConfig) sim.saveData()
import time from netpyne import sim # import netpyne init module from neuron import h simConfig, netParams = sim.readCmdLineArgs('cfg.py', 'netParams.py') sim.create(simConfig=simConfig, netParams=netParams) seed = int(time.time() * 1e7) & 0xffffffff rndm = sim.h.Random() rndm.Random123( sim.rank, 0, 0 ) #initialize with seed as second argument to achieve different results for each run for TCsoma in [ x.secs.soma for x in sim.net.cells if x.tags['cellType'] == 'TC' ]: TCsoma.hObj.ghbar_iar = rndm.normal(17.5, 0.0008) * 1e-6 TCsoma.pointps.kleak_0.hObj.gmax = rndm.normal(40, 0.003) * 1e-4 sim.simulate() sim.analyze()
""" init.py Starting script to run NetPyNE-based model. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi init.py Contributors: [email protected] """ from netpyne import sim cfg, netParams = sim.readCmdLineArgs() # read cfg and netParams from command line arguments sim.createSimulateAnalyze(simConfig = cfg, netParams = netParams)
from netpyne import sim # import netpyne init module from neuron import h simConfig, netParams = sim.readCmdLineArgs( simConfigDefault='cfg.py', netParamsDefault='netParams_SGGA_markov.py') ############################################################################### # create, simulate, and analyse network ############################################################################### sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig) ## Plot comparison to original import json import matplotlib.pyplot as plt with open('./data/original/NaV_0.json', 'rb') as f: data = json.load(f) plt.figure(figsize=(10, 6)) plt.plot(data['simData']['t'], data['simData']['V_soma']['cell_0'], label='V_soma_B_Na', linestyle='dotted') plt.plot(sim.simData['t'], sim.simData['V_soma']['cell_0'], label='V_soma_Na1.3a', linewidth=2) plt.xlabel('Time(ms)') plt.ylabel('voltage (mV)') plt.legend() plt.savefig(simConfig.saveFolder + '/' + simConfig.simLabel)
""" init.py 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 cfg, netParams = sim.readCmdLineArgs() sim.initialize( simConfig=cfg, netParams=netParams) # create network object and set cfg and net params sim.net.createPops() # instantiate network populations sim.net.createCells() # instantiate network cells based on defined populations sim.net.connectCells() # create connections between cells based on params sim.net.addStims() # add network stimulation sim.setupRecording() # setup variables to record (spikes, V traces, etc) # 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 #sim.analysis.plotData() # plot spike raster etc # connDict = {}
import matplotlib matplotlib.use('Agg') from netpyne import sim # read cfg and netParams from command line arguments if available; otherwise use default simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='MSN_cfg.py', netParamsDefault='MSN_params.py') # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)
""" init.py Starting script to run NetPyNE-based model. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi init.py """ from netpyne import sim cfg, netParams = sim.readCmdLineArgs(simConfigDefault='cfg.py', netParamsDefault='netParams.py') # sim.createSimulateAnalyze(netParams, cfg) # sim.initialize( simConfig=cfg, netParams=netParams) # create network object and set cfg and net params sim.net.createPops() # instantiate network populations sim.net.createCells() # instantiate network cells based on defined populations sim.net.connectCells() # create connections between cells based on params sim.net.addStims() # add network stimulation sim.setupRecording( ) # setup variables to record for each cell (spikes, V traces, etc) sim.runSim() # run parallel Neuron simulation sim.gatherData() # gather spiking data and cell info from each node # distributed saving (to avoid errors with large output data) #sim.saveDataInNodes()
try: import batch_utils except: sys.path.append(curpath) import batch_utils try: from __main__ import cfg # import SimConfig object with params from parent module except: print("Couldn't import cfg from __main__") print("Attempting to import cfg from cfg.") try: from cfg import cfg # if no simConfig in parent module, import directly from cfg module except: print("Couldn't import cfg from cfg") cfg, null = sim.readCmdLineArgs() ############################################################################### # # NETWORK PARAMETERS # ############################################################################### netParams = specs.NetParams( ) # object of class NetParams to store the network parameters netParams.defaultThreshold = -20.0 ############################################################################### # Cell parameters ###############################################################################
import sys sys.path.append('/Applications/NEURON-7.6/nrn/lib/python') from netpyne import sim # read cfg and netParams from command line arguments if available; otherwise use default simConfig, netParams = sim.readCmdLineArgs( simConfigDefault='tut8_cfg.py', netParamsDefault='tut8_netParams.py') # Create network and run simulation sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)
A modularized framework to develop and run large-scale network simulations. Built solely in Python with MPI support. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi main.py Contributors: [email protected] """ from netpyne import sim from neuron import h,gui cfg, netParams = sim.readCmdLineArgs(simConfigDefault= 'cfg.py', netParamsDefault= 'M1_detailed.py') #M1_cell originally, test change rn sim.initialize( simConfig=cfg, netParams=netParams) # create network object and set cfg and net params sim.net.createPops() # instantiate network populations sim.net.createCells() # instantiate network cells based on defined populations sim.net.connectCells() # create connections between cells based on params sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc) 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
A modularized framework to develop and run large-scale network simulations. Built solely in Python with MPI support. Usage: python init.py # Run simulation, optionally plot a raster MPI usage: mpiexec -n 4 nrniv -python -mpi main.py Contributors: [email protected] """ from netpyne import sim cfg, _ = sim.readCmdLineArgs() from M1_cell import netParams sim.initialize( simConfig=cfg, netParams=netParams) # create network object and set cfg and net params sim.net.createPops() # instantiate network populations sim.net.createCells() # instantiate network cells based on defined populations sim.net.connectCells() # create connections between cells based on params sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc) 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
from netpyne import sim cfg, netParams = sim.readCmdLineArgs( simConfigDefault='saving_int_cfg.py', netParamsDefault='saving_netParams.py') sim.initialize(simConfig=cfg, netParams=netParams) sim.net.createPops() sim.net.createCells() sim.net.connectCells() sim.net.addStims() sim.setupRecording() #sim.runSim() ##### new ##### sim.runSimIntervalSaving(1000) ##### end new ##### sim.gatherData() sim.saveData() sim.analysis.plotData()
# init.py - Starting script to run NetPyNE-based model. # Usage: python init.py # Run simulation, optionally plot a raster # MPI usage: mpiexec -n 4 nrniv -python -mpi init.py from netpyne import sim cfg, netParams = sim.readCmdLineArgs( 'cfg.py', 'netParams.py') # read cfg and netParams from command line arguments sim.createSimulateAnalyze(simConfig=cfg, netParams=netParams)
from netpyne import sim from Forage import * from utility import * import random import statistics import numpy import mat4py import os ''' netParams is a dict containing a set of network parameters using a standardized structure simConfig is a dict containing a set of simulation configurations using a standardized structure ''' # read cfg and netParams from command line arguments if available; otherwise use default simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='config.py', netParamsDefault='network.py') # Create network and run simulation sim.create(simConfig=simConfig, netParams=netParams) # Initialisation sim.updateInterval = cfg.epochPeriod sim.excOutWeights = [] # Store weight changes here sim.inhOutWeights = [] # Store inhibitory neuron weights here #sim.excOutWeightsStats = {} # Store stats about weights here sim.performances = [] # Store post-epoch performance here sim.outputFrequencies = [] # Store output cell firing frequencies here # Choose indices of weights to store as this data is too large. Choose evenly spaced indices # including first and last of size ~sqrt(numEpochs) weight_indices = numpy.round(
""" init.py 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 simConfig, netParams = sim.readCmdLineArgs() #sim.createSimulateAnalyze() sim.initialize( simConfig=simConfig, netParams=netParams) # create network object and set cfg and net params sim.net.createPops() # instantiate network populations sim.net.createCells() # instantiate network cells based on defined populations sim.net.connectCells() # create connections between cells based on params sim.net.addStims() # add network stimulation sim.setupRecording( ) # setup variables to record for each cell (spikes, V traces, etc) 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)#
'''this is the code for the DHN model involved with mechanical pain network created by K. Sekiguchi (25thMay20) ''' from netpyne import sim from neuron import h simConfig, netParams = sim.readCmdLineArgs( simConfigDefault='cfg_mechanical_GA.py', netParamsDefault='netParams_mechanical_GA.py') # Create network and run simulation # sim.createSimulate(netParams = netParams, simConfig = simConfig) sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)