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)
