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')
simConfig.saveDataInclude = ['simData', 'simConfig', 'netParams'] #, 'net']
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
simConfig.printPopAvgRates = 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(200, sim.saveSimDataInNode)
#sim.gatherData() # gather spiking data and cell info from each node
sim.fileGather()
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')
simConfig.saveDataInclude = ['simData', 'simConfig', 'netParams']#, 'net']
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')
def saveWeights(t):
# if a list for weights is not initialized make one
if not hasattr(sim, 'allWeights'):
sim.allWeights=[]
# save the weights
for cell in sim.net.cells:
for conn in cell.conns:
sim.allWeights.append(float(conn['hObj'].weight[0]))
# if the sim time matches the saveInterval then save data
# NOTE: intervalRun must divide evenly into saveInterval (saveInterval % intervalRun == 0)
if (round(t, 4) % cfg.saveInterval == 0):
sim.intervalSave(t)
print("Starting sim ...")
(pops, cells, conns, stims, rxd, simData) = sim.create(netParams, cfg, output=True)
# we run with an interval function defined above
# if you just want to save the data and not the wieghts you can use the sim.intervalSave function instead of saveWeights
sim.runSimWithIntervalFunc(cfg.intervalRun, saveWeights)
# we run fileGather() instead of gather
sim.fileGather()
sim.analyze()
sim.checkOutput('M1detailed')
from pylab import figure, loadtxt, xlabel, ylabel, xlim, ylim, show, pcolor, array, colorbar
figure()
weightdata = loadtxt(sim.weightsfilename)
weightdataT = list(map(list, list(zip(*weightdata))))
vmax = max([max(row) for row in weightdata])
vmin = min([min(row) for row in weightdata])
pcolor(array(weightdataT), cmap='hot_r', vmin=vmin, vmax=vmax)
xlim((0, len(weightdata)))
ylim((0, len(weightdata[0])))
xlabel('Time (weight updates)')
ylabel('Synaptic connection id')
colorbar()
show()
###############################################################################
# Run Network with virtual arm
###############################################################################
sim.runSimWithIntervalFunc(sim.updateInterval,
runArm) # 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
sim.arm.close(sim)
if sim.plotWeights:
saveWeights(sim)
plotWeights()
# )
## First we have to make a dictionary of the arguments we want to feed into plotShape:"
plotArgs = {
'includePre' : [0],
'includePost': [0],
'cvar' : 'voltage',
'clim' : [-70, -20],
'saveFig' : 'movie',
'showFig' : False,
}
## Then we can replace `sim.runSim()` with:
sim.runSimWithIntervalFunc(1.0, sim.analysis.plotShape, timeRange=[10, 20], funcArgs=plotArgs)
## This will execute `sim.analysis.plotShape` every 1.0 ms from 10 to 20 ms in the simulation and feed it the plotArgs dictionary we created above.
## Once we're done simulating, we need to wrap up the final steps manually:
sim.gatherData()
sim.saveData()
sim.analysis.plotData()
## Once everything is complete, we'll need to install a couple Python packages to make a movie from our frames.
# python3 -m pip install natsort imageio
