import numpy as np
import numpy.fft as npft
# TODO: license
# TODO: normalize function call names
# TODO: document functions
[docs]def create_basis_IPSP(neye,ncos,kpeaks,ks,DTsim,t0,I_stim,nkt,flag_exp,npcut):
kbasprs = {}
kbasprs['neye'] = neye #No of 'identity' basis vectors near time of spike
kbasprs['ncos'] = ncos #No of raised-cosines to use
kbasprs['kpeaks'] = kpeaks #Position of first and last bump
kbasprs['b'] = 0.1 #Offset for non-linear scaling
kbasprs['ks'] = ks
gg0 = makeFitStruct_GLM(DTsim,kbasprs,nkt,flag_exp)
#Create spike-stim with which to convolve post spike filter
spike_stim = np.zeros(np.shape(I_stim))
for kk in range(len(t0)):
spind = int(t0[kk])
#print int(t0[kk]), spind-190000
spike_stim[spind]=1.0
##Convolve temporal basis functions with spike-stim
c = np.zeros((len(spike_stim),ncos))
for jj in range(ncos):
basisfilt = gg0['ktbas'][:,jj]
bconv = np.convolve(spike_stim,np.flipud(basisfilt),'full')
c[:,jj] = bconv[range(len(spike_stim))]
basis_IPSP = c;
return basis_IPSP, gg0
[docs]def makeFitStruct_GLM(dtsim,kbasprs,nkt,flag_exp):
gg = {}
gg['k'] = []
gg['dc'] = 0
gg['kt'] = np.zeros((nkt,1))
gg['ktbas'] = []
gg['kbasprs'] = kbasprs
gg['dt'] = dtsim
nkt = nkt
if flag_exp==0:
ktbas = makeBasis_StimKernel(kbasprs,nkt)
else:
ktbas = makeBasis_StimKernel_exp(kbasprs,nkt)
gg['ktbas'] = ktbas
gg['k'] = gg['ktbas']*gg['kt']
return gg
[docs]def makeBasis_StimKernel(kbasprs,nkt):
neye = kbasprs['neye']
ncos = kbasprs['ncos']
kpeaks = kbasprs['kpeaks']
kdt = 1
b = kbasprs['b']
yrnge = nlin(kpeaks + b*np.ones(np.shape(kpeaks)))
#db = np.diff(yrnge)/(ncos-1)
db = (yrnge[-1]-yrnge[0])/(ncos-1)
ctrs = yrnge
mxt = invnl(yrnge[ncos-1]+2*db)-b
print(mxt)
kt0 = np.arange(0,mxt,kdt)
nt = len(kt0)
e1 = np.tile(nlin(kt0+b*np.ones(np.shape(kt0))),(ncos,1))
e2 = np.transpose(e1)
e3 = np.tile(ctrs,(nt,1))
kbasis0 = []
for kk in range(ncos):
kbasis0.append(ff(e2[:,kk],e3[:,kk],db))
#Concatenate identity vectors
nkt0 = np.size(kt0,0)
a1 = np.concatenate((np.eye(neye), np.zeros((nkt0,neye))),axis=0)
a2 = np.concatenate((np.zeros((neye,ncos)),np.array(kbasis0).T),axis=0)
kbasis = np.concatenate((a1,a2),axis=1)
kbasis = np.flipud(kbasis)
nkt0 = np.size(kbasis,0)
if nkt0 < nkt:
kbasis = np.concatenate((np.zeros((nkt-nkt0,ncos+neye)),kbasis),axis=0)
elif nkt0 > nkt:
kbasis = kbasis[-1-nkt:-1,:]
kbasis = normalizecols(kbasis)
return kbasis
[docs]def makeBasis_StimKernel_exp(kbasprs,nkt):
ks = kbasprs['ks']
b = kbasprs['b']
x0 = np.arange(0,nkt)
kbasis = np.zeros((nkt,len(ks)))
for ii in range(len(ks)):
kbasis[:,ii] = invnl(-ks[ii]*x0) #(1.0/ks[ii])*
kbasis = np.flipud(kbasis)
return kbasis
[docs]def nlin(x):
eps = 1e-20
return np.log(x+eps)
[docs]def invnl(x):
eps = 1e-20
return np.exp(x)-eps
[docs]def ff(x,c,dc):
rowsize = np.size(x,0)
m = []
for i in range(rowsize):
xi = x[i]
ci = c[i]
val=(np.cos(np.max([-pi,np.min([pi,(xi-ci)*pi/dc/2])]))+1)/2
m.append(val)
return np.array(m)
[docs]def normalizecols(A):
B = A/np.tile(np.sqrt(sum(A**2,0)),(np.size(A,0),1))
return B
[docs]def sameconv(A,B):
am = np.size(A)
bm = np.size(B)
nn = am+bm-1
q = npft.fft(A,nn)*npft.fft(np.flipud(B),nn)
p = q
G = npft.ifft(p)
G = G[range(am)]
return G