How to use the netpyne.sim.createSimulateAnalyze function in netpyne

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github Neurosim-lab / netpyne / examples / sandbox / sandbox.py View on Github external
simConfig.recordStep = 0.1 			# Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.filename = 'model3'  # Set file output name
simConfig.saveJson = True 	
simConfig.seeds['conn'] = 4321
 

simConfig.hParams = {'celsius': 34, 'v_init': -80}

simConfig.printPopAvgRates = [0, simConfig.duration]

simConfig.analysis['plotRaster'] = True 			# Plot a raster
simConfig.analysis['plotTraces'] = {'include': [0]} 			# Plot recorded traces for this list of cells
#simConfig.analysis['plot2Dnet'] = True           # plot 2D visualization of cell positions and connections

# Create network and run simulation
sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)
github Neurosim-lab / netpyne / examples / M1detailed / init.py View on Github external
Usage:
    python init.py # Run simulation, optionally plot a raster

MPI usage:
    mpiexec -n 4 nrniv -python -mpi init.py

Contributors: salvadordura@gmail.com
"""

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)
github Neurosim-lab / netpyne / examples / PTcell / init.py View on Github external
Usage:
    python init.py # Run simulation, optionally plot a raster

MPI usage:
    mpiexec -n 4 nrniv -python -mpi init.py

Contributors: salvadordura@gmail.com
"""

#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')
github Neurosim-lab / netpyne / doc / source / code / tut3.py View on Github external
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 = 'model_output'  # Set file output name
simConfig.savePickle = False 		# Save params, network and sim output to pickle file

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


# 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
github Neurosim-lab / netpyne / examples / paper / fig5 / fig5.py View on Github external
simConfig.dt = 0.025                # Internal integration timestep to use
simConfig.verbose = False            # Show detailed messages 
simConfig.recordCells = [('E2',0), ('E4', 0), ('E5', 5)]
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)    

from matplotlib import pyplot as plt

pops = ['E2', 'I2', 'E4', 'I4', 'E5', 'I5']

# fig 5A
sim.analysis.plotConn(includePre=pops, includePost=pops, fontSize=20)
h=plt.axes()
h.xaxis.set_label_coords(0.5, 1.10)
plt.title ('Connection strength matrix', y=1.12)
plt.savefig('paper_fig5A.png')

# fig 5B
sim.analysis.plotConn(includePre=pops, includePost=pops, fontSize=20, feature='convergence', graphType='bar')
plt.title ('Connection convergence stacked bar graph', y=1.08)
plt.tight_layout()
github Neurosim-lab / netpyne / examples / HHTut / HHTut_run.py View on Github external
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
import pylab
pylab.show()
github Neurosim-lab / netpyne / examples / LFPrecording / net_lfp.py View on Github external
# Simulation configuration
simConfig = specs.SimConfig()        # object of class SimConfig to store simulation configuration
simConfig.duration = 3.0*1e3           # Duration of the simulation, in ms
simConfig.dt = 0.1                # Internal integration timestep to use
simConfig.verbose = False            # Show detailed messages 
simConfig.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)
simConfig.filename = 'net_lfp'   # Set file output name

simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/5.0), int(netParams.sizeY), int(netParams.sizeY/5.0))]

simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig':True, 'figSize': (9,3)}      # Plot a raster
simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'NFFT': 256, 'noverlap': 48, 'nperseg': 64, 'saveFig': True} 


# Create network and run simulation
sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)    
github Neurosim-lab / netpyne / doc / source / code / tut4.py View on Github external
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 = 'model_output'  # Set file output name
simConfig.savePickle = False 		# Save params, network and sim output to pickle file

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


# 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')
github Neurosim-lab / netpyne / doc / source / code / tut_import.py View on Github external
# 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 = 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')
github Neurosim-lab / netpyne / examples / saving / init.py View on Github external
"""
init.py 

Example of saving different network components to file

Contributors: salvadordura@gmail.com
"""

from netpyne import sim

from netParams import netParams
from cfg import cfg

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')