def getGeppettoModel(self, netParams, simConfig):
sim.create(netParams, simConfig, True)
sim.gatherData()
modelInterpreter = NetPyNEModelInterpreter()
geppettoModel = modelInterpreter.getGeppettoModel(sim)
sim.analyze()
def M1Cell():
owd = os.getcwd()
# os.chdir('models/DuraBernal')
sys.path.append('models/DuraBernal')
sys.path.append('models/DuraBernal/cells')
from netParams_unified import netParams
from cfg_unified import cfg
from netpyne import sim
sim.create(netParams, cfg)
os.chdir(owd)
return sim
def instantiateNetPyNEModel(self):
with redirect_stdout(sys.__stdout__):
saveData = sim.allSimData if hasattr(
sim, 'allSimData') and 'spkt' in sim.allSimData.keys() and len(
sim.allSimData['spkt']) > 0 else False
sim.create(self.netParams, self.simConfig)
sim.net.defineCellShapes(
) # creates 3d pt for cells with stylized geometries
sim.gatherData(gatherLFP=False)
if saveData:
sim.allSimData = saveData # preserve data from previous simulation
return sim
def instantiateModel(self):
"""
Instantiates model
Sets netParams and cellParams given cfg values
Takes snapshot of cfg
Returns
-------
SimConfig
generated SimConfig
"""
netParams_snapshot = set_netParams(self.cfg)
netParams_snapshot.cellParams = set_cellParams(self.cfg)
sim.create(simConfig=self.cfg, netParams=netParams_snapshot)
sim.gatherData(gatherLFP=False)
self.last_cfg_snapshot = self.cfg.__dict__.copy()
return sim
def loadModel(self, args):
""" Imports a model stored as file in json format.
:param args:
:return:
"""
def remove(dictionary):
# remove reserved keys such as __dict__, __Method__, etc
# they appear when we do sim.loadAll(json_file)
if isinstance(dictionary, dict):
for key, value in list(dictionary.items()):
if key.startswith('__'):
dictionary.pop(key)
else:
remove(value)
if not any([
args[option] for option in
['loadNetParams', 'loadSimCfg', 'loadSimData', 'loadNet']
]):
return utils.getJSONError(
"Error while loading data",
'You have to select at least one option')
try:
owd = os.getcwd()
compileModMechFiles(args['compileMod'], args['modFolder'])
except:
return utils.getJSONError("Error while importing/compiling mods",
sys.exc_info())
finally:
os.chdir(owd)
try:
with redirect_stdout(sys.__stdout__):
sim.initialize()
wake_up_geppetto = False
if all([
args[option] for option in
['loadNetParams', 'loadSimCfg', 'loadSimData', 'loadNet']
]):
wake_up_geppetto = True
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.initialize()
sim.loadAll(args['jsonModelFolder'])
self.netParams = sim.net.params
self.simConfig = sim.cfg
remove(self.netParams.todict())
remove(self.simConfig.todict())
else:
if args['loadNet']:
wake_up_geppetto = True
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.initialize()
sim.loadNet(args['jsonModelFolder'])
if args['loadSimData']: # TODO (https://github.com/Neurosim-lab/netpyne/issues/360)
wake_up_geppetto = True
if not self.doIhaveInstOrSimData()['haveInstance']:
sim.create(specs.NetParams(), specs.SimConfig())
sim.net.defineCellShapes()
sim.gatherData(gatherLFP=False)
sim.loadSimData(args['jsonModelFolder'])
if args['loadSimCfg']:
sim.loadSimCfg(args['jsonModelFolder'])
self.simConfig = sim.cfg
remove(self.simConfig.todict())
if args['loadNetParams']:
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.loadNetParams(args['jsonModelFolder'])
self.netParams = sim.net.params
remove(self.netParams.todict())
if wake_up_geppetto:
if len(sim.net.cells) > 0:
section = list(sim.net.cells[0].secs.keys())[0]
if 'pt3d' not in list(
sim.net.cells[0].secs[section].geom.keys()):
sim.net.defineCellShapes()
sim.gatherData()
sim.loadSimData(args['jsonModelFolder'])
sim.gatherData()
self.geppetto_model = self.model_interpreter.getGeppettoModel(
sim)
return json.loads(
GeppettoModelSerializer.serialize(self.geppetto_model))
else:
return utils.getJSONReply()
except:
return utils.getJSONError("Error while loading the NetPyNE model",
sys.exc_info())
simConfig.createPyStruct = True
simConfig.verbose = False
simConfig.recordCells = ['all']
simConfig.recordTraces = {'v_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'v'}}
simConfig.recordStim = True
simConfig.recordStep = simConfig.dt
simConfig.filename = 'Netpyne-cell_output'
simConfig.saveTxt = True
#simConfig.analysis['plotRaster'] = True
#simConfig.analysis['plotTraces'] = {'include': [0,1,2], 'oneFigPer': 'trace'}
#simConfig.analysis['plot2Dnet'] = True # Plot 2D net cells and connections
data = sim.create(netParams=netParams, simConfig=simConfig, output=True)
soma = data[1][0].secs.soma.hSec
dend = data[1][0].secs.dend.hSec
mutype = 'S'
if mutype == "S" or mutype == "s":
mus(soma, dend)
elif mutype == "FR" or mutype == "fr":
mufr(soma, dend)
elif mutype == "FF" or mutype == "ff":
muff(soma, dend)
#for i in range(len(data[1])):
# data[1][i].secs.soma.hSec.L = 75 + 5*i
# data[1][i].secs.soma.hSec.diam = 75 + 5*i
#data[1][0].secs.soma.hSec.__getattribute__('ena')
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')
'ca': {
'sec': 'soma',
'loc': 0.5,
'var': 'cai'
},
'buf': {
'sec': 'soma',
'loc': 0.5,
'var': 'bufi'
},
'cabuf': {
'sec': 'soma',
'loc': 0.5,
'var': 'cabufi'
}
}
cfg.analysis['plotTraces'] = {
'include': ['cell'],
'ylim': [0, 0.0001],
'overlay': True
}
#------------------------------------------------------------------------------
# RUN MODEL
#------------------------------------------------------------------------------
sim.create(netParams, cfg)
h.finitialize()
sim.simulate()
sim.analyze()
from netpyne import sim import params # Create network and save sim.create(netParams=params.netParams, simConfig=params.simConfig) sim.gatherData() sim.saveData()
simConfig.verbose = False
simConfig.recordCells = ['all']
simConfig.recordTraces = {'v_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'v'}}
simConfig.recordStim = True
simConfig.recordStep = simConfig.dt
simConfig.filename = 'Netpyne-cell_output'
simConfig.saveTxt = True
#simConfig.analysis['plotRaster'] = True
#simConfig.analysis['plotTraces'] = {'include': [0,1,2], 'oneFigPer': 'trace'}
#simConfig.analysis['plot2Dnet'] = True # Plot 2D net cells and connections
(pops, cells, conns, stims, simData) = sim.create(netParams=netParams,
simConfig=simConfig,
output=True)
mutype = []
for n in range(len(cells)):
soma = cells[n].secs.soma.hSec
dend = cells[n].secs.dend.hSec
if n == 0:
ner.mus(soma, dend)
mutype.append('S')
elif n == 1:
ner.mufr(soma, dend)
mutype.append('FR')
elif n == 2:
ner.muff(soma, dend)
mutype.append('FF')
from netParams_unified import netParams
from cfg_unified import cfg
from netpyne import sim as s
s.create(netParams, cfg)
sec_num = 'apic_20' # indicates stimulated section
loc = 0.5 # indicates stimulated compartment (0-1)
seg = s.net.cells[0].secs[sec_num]['hObj'](loc)
soma_seg = s.net.cells[0].secs['soma']['hObj'](0.5)
## record voltages
from neuron import h, gui
soma_v = h.Vector().record(soma_seg._ref_v)
dend_v = h.Vector().record(seg._ref_v)
time = h.Vector().record(h._ref_t)
## parameters of the chirp stimulus
from chirpUtils import applyChirp, getChirp
amp = 0.025 # amplitude of chirp stim
f0, f1, t0, Fs, delay = 0.5, 20, 20, 1000, 5 # initial and final frequencies, duration, sampling freq, delay
I, t = getChirp(f0, f1, t0, amp, Fs, delay) # defines current clamp
## run simulation
print('Running chirp on ' + str(seg))
out = applyChirp(I, t, seg, soma_seg, t0, delay, Fs, f1)
## plotting
from matplotlib import pyplot as plt
### traces
trace_fig = plt.figure(figsize=(8,10))
plt.subplot(3,1,1)
simConfig.duration = 620000 # 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 = 0.25 # Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.filename = 'model_output' # Set file output name
simConfig.saveMat = True # Save params, network a sim output to mat file
sim.create()
sim.simulate()
simConfig.analysis['plotRaster'] = True # Plot a raster
simConfig.analysis['plotTraces'] = {
'include': all,
'showFig': False
} # Plot recorded traces for this list of cells
simConfig.analysis['plot2Dnet'] = {
'saveData': True
} # plot 2D visualization of cell positions and connections
simConfig.analysis['plotConn'] = {'feature': 'numConns'}
sim.analyze()
#simConfig.timestampFilename = True
# Create network and run simulation
#sim.createSimulateAnalyze(netParams, simConfig)
# Create network and run simulation
sim.createSimulate(netParams=netParams, simConfig=simConfig)
sim.saveData()
import pylab
pylab.show(
) # this line is only necessary in certain systems where figures appear empty
'''
# Methods to modify your network
modifyCells(params) # Modify the cells in your network
modifySynMechs(params) # Modify the Synapse Mechanisms
modifyConns(params) # Modify the connections of your network
modifyStims(params) # Modify the stimulations of you network
'''
'''
#### Simulation setup to run multiple runs at once
simConfig = specs.SimConfig() # Object of class SimConfig to store simulation configuration
simConfig.duration = simTime # Duration of the simulation, in ms
simConfig.dt = 0.05 # Internal integration timestep to use
simConfig.hParams = {'celsius': 31,'clamp_resist': 0.001,'steps_per_ms': 20}
simConfig.verbose = 0 # Show detailed messages
simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record
simConfig.recordStep = 0.01 # Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.saveMat = True; # Save .mat file after simulation
simConfig.timestampFilename = True # Add time stamp to filename
# Create simulation
sim.create(netParams, simConfig)
# Create network and run simulation
from netpyne import sim
from neuron import h
import wlutils as wl
import numpy as np
from netpyne import sim
from simConfig import cfg
from netParams import npar
sim.create(npar, cfg)
ax = sim.net.cells[0].secs.axon.hObj
def lp():
for perc in np.linspace(0.0, 0.8, 9): # now is percent block
h.gnablock_ina2005 = perc # h.perc_ina2005
sim.runSim()
sim.gatherData()
sim.saveData(include=['simData'],
filename='data/19dec06blkA_%02d' % (100 * perc))
lp()
def loadModel(self, args):
""" Imports a model stored as file in json format.
:param args:
:return:
"""
if not any([
args[option] for option in
['loadNetParams', 'loadSimCfg', 'loadSimData', 'loadNet']
]):
return utils.getJSONError(
"Error while loading data",
'You have to select at least one option')
try:
owd = os.getcwd()
compileModMechFiles(args['compileMod'], args['modFolder'])
except Exception:
message = "Error while importing/compiling mods"
logging.exception(message)
return utils.getJSONError(message, sys.exc_info())
finally:
os.chdir(owd)
try:
with redirect_stdout(sys.__stdout__):
sim.initialize()
wake_up_geppetto = False
if all([
args[option] for option in
['loadNetParams', 'loadSimCfg', 'loadSimData', 'loadNet']
]):
wake_up_geppetto = True
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.initialize()
sim.loadAll(args['jsonModelFolder'])
self.netParams = sim.net.params
self.simConfig = sim.cfg
netpyne_ui_utils.remove(self.netParams.todict())
netpyne_ui_utils.remove(self.simConfig.todict())
else:
if args['loadNet']:
wake_up_geppetto = True
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.initialize()
sim.loadNet(args['jsonModelFolder'])
# TODO (https://github.com/Neurosim-lab/netpyne/issues/360)
if args['loadSimData']:
wake_up_geppetto = True
if not self.doIhaveInstOrSimData()['haveInstance']:
sim.create(specs.NetParams(), specs.SimConfig())
sim.net.defineCellShapes()
sim.gatherData(gatherLFP=False)
sim.loadSimData(args['jsonModelFolder'])
if args['loadSimCfg']:
sim.loadSimCfg(args['jsonModelFolder'])
self.simConfig = sim.cfg
netpyne_ui_utils.remove(self.simConfig.todict())
if args['loadNetParams']:
if self.doIhaveInstOrSimData()['haveInstance']:
sim.clearAll()
sim.loadNetParams(args['jsonModelFolder'])
self.netParams = sim.net.params
netpyne_ui_utils.remove(self.netParams.todict())
if wake_up_geppetto:
self._create3D_shapes(args['jsonModelFolder'])
# TODO: Fix me - gatherData will remove allSimData!
sim.gatherData()
self.geppetto_model = self.model_interpreter.getGeppettoModel(
sim)
return json.loads(
GeppettoModelSerializer.serialize(self.geppetto_model))
else:
return utils.getJSONReply()
except Exception:
message = "Error while loading the NetPyNE model"
logging.exception(message)
return utils.getJSONError(message, sys.exc_info())
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')
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()
netparms = M1.netParams
print(
"********************\n*\n* Note: setting noise to 1, since noise can only be 0 or 1 in NeuroML export currently!\n*\n********************"
)
netparms.stimSourceParams['background_E']['noise'] = 1
netparms.stimSourceParams['background_I']['noise'] = 1
if disable_output:
M1.simConfig.analysis = {}
M1.simConfig.recordTraces = {}
M1.simConfig.recordCellsSpikes = []
if do_simulate:
tic = time.time()
sim.create(netparms, M1.simConfig, output=False)
sim.runSim()
print(sim.simData, len(sim.simData['spkid']))
print(sim.timingData)
# print( len(sim.simData) )
# print( len( sim.simData['V']['cell_10'] ) )
# print( M1.simConfig.analysis )
# print( sim.simData['spkid'] )
# print( len( sim.simData['spkid'] ) )
# print( [ x for x in sim.simData['V']['cell_10'] ] )
else:
sim.createExportNeuroML2(
netParams=netparms,
simConfig=M1.simConfig,
