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Auto-generated NEURON file for LEMS_2007One.xml LEMS file
'''
Neuron simulator export for:
Components:
RS (Type: izhikevich2007Cell: v0=-0.06 (SI voltage) k=7.0E-7 (SI conductance_per_voltage) vr=-0.06 (SI voltage) vt=-0.04 (SI voltage) vpeak=0.035 (SI voltage) a=30.0 (SI per_time) b=-2.0E-9 (SI conductance) c=-0.05 (SI voltage) d=1.0E-10 (SI current) C=1.0E-10 (SI capacitance))
RS_Iext (Type: pulseGenerator: delay=0.5 (SI time) duration=1.0 (SI time) amplitude=1.0E-10 (SI current))
net1 (Type: network)
sim1 (Type: Simulation: length=1.6 (SI time) step=2.5E-6 (SI time))
This NEURON file has been generated by org.neuroml.export (see https://github.com/NeuroML/org.neuroml.export)
org.neuroml.export v1.5.2
org.neuroml.model v1.5.2
jLEMS v0.9.8.9
'''
import neuron
import time
import hashlib
h = neuron.h
h.load_file("nrngui.hoc")
h("objref p")
h("p = new PythonObject()")
class NeuronSimulation():
def __init__(self, tstop, dt, seed=123456789):
print("\n Starting simulation in NEURON generated from NeuroML2 model...\n")
self.seed = seed
self.randoms = []
self.next_global_id = 0 # Used in Random123 classes for elements using random(), etc.
self.next_spiking_input_id = 0 # Used in Random123 classes for elements using random(), etc.
'''
Adding simulation Component(id=sim1 type=Simulation) of network/component: net1 (Type: network)
'''
# ###################### Population: RS_pop
print("Population RS_pop contains 1 instance(s) of component: RS of type: izhikevich2007Cell")
h(" {n_RS_pop = 1} ")
'''
Population RS_pop contains instances of Component(id=RS type=izhikevich2007Cell)
whose dynamics will be implemented as a mechanism (RS) in a mod file
'''
h(" create RS_pop[1]")
h(" objectvar m_RS_RS_pop[1] ")
for i in range(int(h.n_RS_pop)):
h.RS_pop[i].L = 10.0
h.RS_pop[i](0.5).diam = 10.0
h.RS_pop[i](0.5).cm = 31.830988618379067
h.RS_pop[i].push()
h(" RS_pop[%i] { m_RS_RS_pop[%i] = new RS(0.5) } "%(i,i))
h.m_RS_RS_pop[i].v0 = -60.0
h.m_RS_RS_pop[i].k = 7.0E-4
h.m_RS_RS_pop[i].vr = -60.0
h.m_RS_RS_pop[i].vt = -40.0
h.m_RS_RS_pop[i].vpeak = 35.0
h.m_RS_RS_pop[i].a = 0.030000001
h.m_RS_RS_pop[i].b = -0.002
h.m_RS_RS_pop[i].c = -50.0
h.m_RS_RS_pop[i].d = 0.1
h.m_RS_RS_pop[i].C = 1.0E-4
h.pop_section()
self.next_global_id+=1
# Adding single input: Component(id=null type=explicitInput)
h("objref explicitInput_RS_IextRS_pop0")
h("RS_pop[0] { explicitInput_RS_IextRS_pop0 = new RS_Iext(0.5) } ")
trec = h.Vector()
trec.record(h._ref_t)
h.tstop = tstop
h.dt = dt
h.steps_per_ms = 1/h.dt
# ###################### Display: self.display_d1
self.display_d1 = h.Graph(0)
self.display_d1.size(0,h.tstop,-80.0,50.0)
self.display_d1.view(0, -80.0, h.tstop, 130.0, 80, 330, 330, 250)
h.graphList[0].append(self.display_d1)
# Line, plotting: RS_pop[0]/v
self.display_d1.addexpr("RS_pop[0].v(0.5)", "RS_pop[0].v(0.5)", 1, 1, 0.8, 0.9, 2)
# ###################### Display: self.display_d2
self.display_d2 = h.Graph(0)
self.display_d2.size(0,h.tstop,-80.0,50.0)
self.display_d2.view(0, -80.0, h.tstop, 130.0, 80, 330, 330, 250)
h.graphList[0].append(self.display_d2)
# Line, plotting: RS_pop[0]/u
self.display_d2.addexpr("m_RS_RS_pop[0].u", "m_RS_RS_pop[0].u", 1, 1, 0.8, 0.9, 2)
# ###################### File to save: RS_One.dat (of0)
# Column: RS_pop[0]/v
h(' objectvar v_v_of0 ')
h(' { v_v_of0 = new Vector() } ')
h(' { v_v_of0.record(&RS_pop[0].v(0.5)) } ')
h.v_v_of0.resize((h.tstop * h.steps_per_ms) + 1)
# Column: RS_pop[0]/u
h(' objectvar v_u_of0 ')
h(' { v_u_of0 = new Vector() } ')
h(' { v_u_of0.record(&m_RS_RS_pop[0].u) } ')
h.v_u_of0.resize((h.tstop * h.steps_per_ms) + 1)
# ###################### File to save: time.dat (time)
# Column: time
h(' objectvar v_time ')
h(' { v_time = new Vector() } ')
h(' { v_time.record(&t) } ')
h.v_time.resize((h.tstop * h.steps_per_ms) + 1)
self.initialized = False
self.sim_end = -1 # will be overwritten
h.nrncontrolmenu()
def run(self):
self.initialized = True
sim_start = time.time()
print("Running a simulation of %sms (dt = %sms; seed=%s)" % (h.tstop, h.dt, self.seed))
h.run()
self.sim_end = time.time()
sim_time = self.sim_end - sim_start
print("Finished NEURON simulation in %f seconds (%f mins)..."%(sim_time, sim_time/60.0))
self.save_results()
def advance(self):
if not self.initialized:
h.finitialize()
self.initialized = True
h.fadvance()
###############################################################################
# Hash function to use in generation of random value
# This is copied from NetPyNE: https://github.com/Neurosim-lab/netpyne/blob/master/netpyne/simFuncs.py
###############################################################################
def _id32 (self,obj):
return int(hashlib.md5(obj).hexdigest()[0:8],16) # convert 8 first chars of md5 hash in base 16 to int
###############################################################################
# Initialize the stim randomizer
# This is copied from NetPyNE: https://github.com/Neurosim-lab/netpyne/blob/master/netpyne/simFuncs.py
###############################################################################
def _init_stim_randomizer(self,rand, stimType, gid, seed):
print("INIT STIM %s; %s; %s; %s; %s"%(rand, stimType, self._id32(stimType), gid, seed))
rand.Random123(self._id32(stimType), gid, seed)
def save_results(self):
print("Saving results at t=%s..."%h.t)
if self.sim_end < 0: self.sim_end = time.time()
self.display_d1.exec_menu("View = plot")
self.display_d2.exec_menu("View = plot")
# ###################### File to save: time.dat (time)
py_v_time = [ t/1000 for t in h.v_time.to_python() ] # Convert to Python list for speed...
f_time_f2 = open('time.dat', 'w')
num_points = len(py_v_time) # Simulation may have been stopped before tstop...
for i in range(num_points):
f_time_f2.write('%f'% py_v_time[i]) # Save in SI units...+ '\n')
f_time_f2.close()
print("Saved data to: time.dat")
# ###################### File to save: RS_One.dat (of0)
py_v_v_of0 = [ float(x / 1000.0) for x in h.v_v_of0.to_python() ] # Convert to Python list for speed, variable has dim: voltage
py_v_u_of0 = [ float(x / 1.0E9) for x in h.v_u_of0.to_python() ] # Convert to Python list for speed, variable has dim: current
f_of0_f2 = open('RS_One.dat', 'w')
num_points = len(py_v_time) # Simulation may have been stopped before tstop...
for i in range(num_points):
f_of0_f2.write('%e\t'% py_v_time[i] + '%e\t'%(py_v_v_of0[i]) + '%e\t'%(py_v_u_of0[i]) + '\n')
f_of0_f2.close()
print("Saved data to: RS_One.dat")
save_end = time.time()
save_time = save_end - self.sim_end
print("Finished saving results in %f seconds"%(save_time))
print("Done")
if __name__ == '__main__':
ns = NeuronSimulation(tstop=1600, dt=0.0025, seed=123456789)
ns.run()
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