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multipop_c_12.py
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# to build c libraries do
# make -f Makefile12 all
#change to multipop_c_11.py: based on Makefile12, which also has direct simulation of master equation, also correction of threshold update as in 2nd version of PLOS submission
version=12
import numpy as np
import numpy.ctypeslib as npct
from ctypes import c_int,c_double
import multiprocessing as multproc
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
print dir_path
array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS')
array_1d_int = npct.ndpointer(dtype=np.intc, ndim=1, flags='CONTIGUOUS')
array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS')
# load library for simulation of population equations
#lib = npct.load_library("../glm_popdyn_%d"%(version,), ".")
lib = npct.load_library("glm_popdyn_%d"%(version,), dir_path)
lib.get_trajectory_with_2D_arrays.restype = None
lib.get_trajectory_with_2D_arrays.argtypes = [c_int, array_2d_double, array_2d_double, \
c_int, array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, array_1d_double, \
array_1d_int, array_2d_double,\
array_2d_double, array_2d_double, array_1d_int,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, c_double,c_double,c_int,c_int] #dt,dtbin,mode
lib.get_psd_with_2D_arrays.restype = None
lib.get_psd_with_2D_arrays.argtypes = [c_int, array_2d_double, c_int, \
c_int, array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, \
array_1d_double, array_1d_double,\
array_1d_double, array_1d_double,\
array_1d_int, array_2d_double,\
array_2d_double, array_1d_int,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, c_double,c_double,c_int] #dt,dtbin,mode
# load library for full spiking network simulation
lib2 = npct.load_library("glm_netw_sim_%d"%(version,), dir_path)
lib2.get_trajectory_srm_with_2D_arrays.restype = None
lib2.get_trajectory_srm_with_2D_arrays.argtypes = [c_int, array_2d_double, \
c_int, array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, array_1d_double, \
array_1d_int, array_2d_double, \
array_2d_double, array_2d_double, array_1d_int, \
array_1d_double, array_1d_double, array_1d_double, \
array_1d_double, c_double,c_double,c_int,c_int,c_int]
lib2.get_trajectory_voltage_srm_with_2D_arrays.restype = None
lib2.get_trajectory_voltage_srm_with_2D_arrays.argtypes = [c_int, array_2d_double, \
array_2d_double, \
array_1d_int, c_double, c_int, \
array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, array_1d_double, array_1d_int, array_2d_double,\
array_2d_double, array_2d_double, array_1d_int,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, c_double,c_double, c_int,c_int,c_int]
lib2.get_psd_srm_with_2D_arrays.restype = None
lib2.get_psd_srm_with_2D_arrays.argtypes = [c_int, array_2d_double, c_int, \
c_int, array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double,array_1d_double, \
array_1d_int, array_2d_double, \
array_2d_double, array_1d_int,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, c_double,c_double,c_int]
lib2.get_isih_with_2D_arrays.restype = None
lib2.get_isih_with_2D_arrays.argtypes = [c_int, array_2d_double, c_int, \
c_int, array_1d_double, array_1d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_2d_double, array_2d_double, \
array_1d_double,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double,array_1d_double, \
array_1d_int, array_2d_double, \
array_2d_double, array_1d_int,\
array_1d_double, array_1d_double, array_1d_double,\
array_1d_double, c_double,c_double,c_int]
# ################################################################################
# # Master equation (occupation number)
# ##############################
# lib3 = npct.load_library("../glm_mastereq_%d"%(version,), ".")
# lib3.get_trajectory_with_2D_arrays.restype = None
# lib3.get_trajectory_with_2D_arrays.argtypes = [c_int, array_2d_double, array_2d_double, \
# c_int, array_1d_double, array_1d_double, \
# array_2d_double, array_2d_double, \
# array_2d_double, array_2d_double, \
# array_2d_double, array_2d_double, \
# array_1d_double,\
# array_1d_double, array_1d_double, array_1d_double,\
# array_1d_double, array_1d_double, \
# array_1d_int, array_2d_double,\
# array_2d_double, array_2d_double, array_1d_int,\
# array_1d_double, array_1d_double, array_1d_double,\
# array_1d_double, c_double,c_double,c_int,c_int] #dt,dtbin,mode
# lib3.get_psd_with_2D_arrays.restype = None
# lib3.get_psd_with_2D_arrays.argtypes = [c_int, array_2d_double, c_int, \
# c_int, array_1d_double, array_1d_double, \
# array_2d_double, array_2d_double, \
# array_2d_double, array_2d_double, \
# array_2d_double, array_2d_double, \
# array_1d_double,\
# array_1d_double, \
# array_1d_double, array_1d_double,\
# array_1d_double, array_1d_double,\
# array_1d_int, array_2d_double,\
# array_2d_double, array_1d_int,\
# array_1d_double, array_1d_double, array_1d_double,\
# array_1d_double, c_double,c_double,c_int] #dt,dtbin,mode
################################################################################
#wrapper functions for multiprocessing of psd's
def get_psd_pop(params):
lib.get_psd_with_2D_arrays(*params)
return params[1]
# def get_psd_master(params):
# lib3.get_psd_with_2D_arrays(*params)
# return params[1]
def get_psd_neuron(params):
lib2.get_psd_srm_with_2D_arrays(*params)
return params[1]
class Multipop(object):
def __init__(self, dtbin,dt, \
tref=[0.002,0.002],taum=[0.01,0.01], \
taus1=[[0,0.],[0.,0.]], taus2=[[0,0.],[0.,0.]], \
taur1=[[0,0.],[0.,0.]], taur2=[[0,0.],[0.,0.]], \
a1=[[0,0.],[0.,0.]], a2=[[0,0.],[0.,0.]], \
mu=[0.,0.], c=[10.,5.], DeltaV=[4., 4.], \
delay=[0.002,0.002], vth=[0.,0.], vreset=[0.,0.], \
N=[400,100], J=[[20,-22.],[20.,-22.]], \
p_conn=[[1.,1.],[1.,1.]], \
Jref=[0.,0.], J_theta=[[1.],[]], tau_theta=[[3.],[]], sigma=[0.,0.], mode=10):
self.dt=dt
self.dtbin=dtbin
self.tref=np.array(tref)
self.taum=np.array(taum)
self.sigma=np.array(sigma)
self.taus1=np.ascontiguousarray(taus1)
self.taus2=np.ascontiguousarray(taus2)
self.taur1=np.ascontiguousarray(taur1)
self.taur2=np.ascontiguousarray(taur2)
self.a1=np.ascontiguousarray(a1)
self.a2=np.ascontiguousarray(a2)
self.mu=np.array(mu)
self.c=np.array(c)
self.D=np.array(DeltaV)
self.delay=np.array(delay)
self.vth=np.array(vth)
self.vreset=np.array(vreset)
self.N=np.array(N,dtype=np.intc)
self.J=np.ascontiguousarray(J)
self.p_conn=np.ascontiguousarray(p_conn)
self.Jref=np.array(Jref)
self.mode=mode #0:GLM, 10:GLIF, 20:GLM master, 30:GLIF master
#bereinige Nullen in J_theta
self.J_theta=[]
self.tau_theta=[]
for i in range(len(J_theta)):
L=J_theta[i]
TAU=tau_theta[i]
if L==[]:
self.J_theta.append([])
self.tau_theta.append([])
else:
Jlist=[]
taulist=[]
for j in range(len(L)):
J=L[j]
if J!=0.:
Jlist.append(J)
taulist.append(TAU[j])
self.J_theta.append(Jlist)
self.tau_theta.append(taulist)
N_theta=[len(i) for i in self.J_theta]
self.N_theta=np.array(N_theta,dtype=np.intc)
self.J_theta_1d=np.hstack(self.J_theta)
self.tau_theta_1d=np.hstack(self.tau_theta)
self.Npop=len(tref)
assert len(taum)==self.Npop
assert len(mu)==self.Npop
assert len(c)==self.Npop
assert len(DeltaV)==self.Npop
assert len(delay)==self.Npop
assert len(vth)==self.Npop
assert len(N)==self.Npop
assert len(N_theta)==self.Npop
assert len(Jref)==self.Npop
def get_trajectory_pop(self, Tsim,step=[],tstep=[], seed=365):
self.Nbin=int(Tsim/self.dtbin)
self.A=np.zeros((self.Npop,self.Nbin),dtype=float)
self.a=np.zeros((self.Npop,self.Nbin),dtype=float)
t=np.arange(self.Nbin) * self.dtbin
self.signal=np.zeros((self.Npop,self.Nbin),dtype=float)
if step is not None:
step = np.array(step)
tstep = np.array(tstep)
for k in range(self.Npop):
#sort step times to ensure temporal order
s=np.argsort(tstep[k])
step_time=np.array(tstep[k])[s]
amp=np.array(step[k])[s]
for i in range(len(amp)):
for j in range(self.Nbin):
if (t[j]>=step_time[i]):
self.signal[k,j] = amp[i]
if (self.mode<20):
lib.get_trajectory_with_2D_arrays(self.Nbin, self.A, self.a, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.signal, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, self.dtbin,self.mode,seed)
# else:
# lib3.get_trajectory_with_2D_arrays(self.Nbin, self.A, self.a, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.signal, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, self.dtbin,self.mode,seed)
def get_trajectory_voltage_neuron(self, Tsim, Nrecord, Vspike=90.,step=0,tstep=0., seed=365, seed_quenched=1):
self.Nbin=int(Tsim/self.dtbin)
self.A=np.zeros((self.Npop,self.Nbin),dtype=float)
self.a=np.zeros((self.Npop,self.Nbin),dtype=float)
t=np.arange(self.Nbin) * self.dtbin
self.signal=np.zeros((self.Npop,self.Nbin),dtype=float)
if step is not None:
step = np.array(step)
tstep = np.array(tstep)
for k in range(self.Npop):
#sort step times to ensure temporal order
s=np.argsort(tstep[k])
step_time=np.array(tstep[k])[s]
amp=np.array(step[k])[s]
for i in range(len(amp)):
for j in range(self.Nbin):
if (t[j]>=step_time[i]):
self.signal[k,j] = amp[i]
Nrecord=np.array(Nrecord,dtype=np.intc)
assert len(Nrecord)==self.Npop
Nrecord_tot=Nrecord.sum()
voltage_matrix=np.zeros((2*Nrecord_tot,self.Nbin),dtype=float) #factor 2 arises because voltage and threshold are saved in voltage_matrix in C program
print voltage_matrix.shape
lib2.get_trajectory_voltage_srm_with_2D_arrays(self.Nbin, self.A, voltage_matrix, Nrecord, Vspike, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.signal, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, self.dtbin,self.mode, seed, seed_quenched)
self.voltage=[]
self.threshold=[]
indx=0
for i in range(self.Npop):
self.voltage.append(voltage_matrix[indx:indx+Nrecord[i],:])
indx2=indx+Nrecord_tot
self.threshold.append(voltage_matrix[indx2:indx2+Nrecord[i],:]+self.vth[i])
indx+=Nrecord[i]
def get_psd_pop(self, df=0.1, dt_sample=0.001, Ntrials=10,nproc=1):
Nbin=int(1./(df*dt_sample)+0.5)
Nf=int(Nbin/2)
self.f = df * (np.arange(Nf) + 1)
if (nproc > 1):
Ntrials_part=int(Ntrials/nproc)
SA_list=[np.zeros((self.Npop, Nf),dtype=float) for i in range(nproc)]
pool=multproc.Pool(processes=nproc)
params=[(Nf, SA_list[i], Ntrials_part, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_sample,self.mode) for i in range(nproc)]
if (self.mode<20):
SA_list=pool.map(get_psd_pop, params)
else:
SA_list=pool.map(get_psd_master, params)
self.SA= np.array(SA_list).mean(axis=0)
else:
self.SA= np.zeros((self.Npop, Nf),dtype=float)
if (self.mode<20):
lib.get_psd_with_2D_arrays(Nf, self.SA, Ntrials, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_sample,self.mode)
# else:
# lib3.get_psd_with_2D_arrays(Nf, self.SA, Ntrials, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_sample,self.mode)
def get_trajectory_neuron(self, Tsim,step=0,tstep=0., seed=365, seed_quenched=1):
self.Nbin=int(Tsim/self.dtbin)
self.A=np.zeros((self.Npop,self.Nbin),dtype=float)
t=np.arange(self.Nbin) * self.dtbin
self.signal=np.zeros((self.Npop,self.Nbin),dtype=float)
if step is not None:
step = np.array(step)
tstep = np.array(tstep)
for k in range(self.Npop):
#sort step times to ensure temporal order
s=np.argsort(tstep[k])
step_time=np.array(tstep[k])[s]
amp=np.array(step[k])[s]
for i in range(len(amp)):
for j in range(self.Nbin):
if (t[j]>=step_time[i]):
self.signal[k,j] = amp[i]
lib2.get_trajectory_srm_with_2D_arrays(self.Nbin, self.A, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.signal, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, self.dtbin,self.mode, seed, seed_quenched)
def get_psd_neuron(self, df=0.1, dt_sample=0.001, Ntrials=10, nproc=1):
Nbin=int(1./(df*dt_sample)+0.5)
Nf=int(Nbin/2)
self.f = df * (np.arange(Nf) + 1)
if (nproc > 1):
Ntrials_part=int(Ntrials/nproc)
SA_list=[np.zeros((self.Npop, Nf),dtype=float) for i in range(nproc)]
pool=multproc.Pool(processes=nproc)
params=[(Nf, SA_list[i], Ntrials_part, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_sample,self.mode) for i in range(nproc)]
SA_list=pool.map(get_psd_neuron, params)
self.SA= np.array(SA_list).mean(axis=0)
else:
self.SA= np.zeros((self.Npop, Nf),dtype=float)
lib2.get_psd_srm_with_2D_arrays(Nf, self.SA, Ntrials, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_sample,self.mode)
def get_isih_neuron(self, Nisi,dt_isi, Nspikes):
self.isih=np.zeros((self.Npop, Nisi),dtype=float)
lib2.get_isih_with_2D_arrays(Nisi, self.isih, Nspikes, self.Npop, self.tref, self.taum, self.taus1, self.taus2, self.taur1, self.taur2, self.a1, self.a2, self.mu, self.c, self.D, self.delay, self.vth, self.vreset, self.N, self.J, self.p_conn, self.N_theta, self.Jref, self.J_theta_1d, self.tau_theta_1d, self.sigma, self.dt, dt_isi,self.mode)