def sim_dcm_dataset(noise_sd=5,verbose=False,hrflags=[]):
sys.path.insert(0,'../utils')
from mkdesign import create_design_singlecondition
# first let's build the model without the bilinear influence (aka PPI)
# after http://spm.martinpyka.de/?p=81
nregions=5
z=numpy.zeros(nregions)
# intrinsic connectivity
A=numpy.zeros((z.shape[0],z.shape[0]))
A=numpy.diag(numpy.ones(z.shape[0])*-1)
# add some structure
#A=A + numpy.diag(numpy.ones(z.shape[0]-1),k=-1)
A[2,1]=1
A[3,1]=1
B=numpy.zeros(A.shape)
B[2,0]=1
B[4,0]=1
C=numpy.zeros((z.shape[0],1))
C[0]=1
u=0
if verbose:
print 'A'
print A
print 'B'
print B
print 'C'
print C
# we are assuming a 1 second TR for the resulting data
# but the neural data are at a 1/16 millisecond time resolution
tslength=300
stepsize=.01
timepoints=numpy.arange(0,tslength,stepsize)
# create a blocked design
d,design=create_design_singlecondition(blockiness=1.0,deslength=tslength,blocklength=20,offset=20)
u=scipy.interpolate.griddata(numpy.arange(1,d.shape[0]),d,timepoints,fill_value=0)
data,data_conv=mk_dcm_dataset(A,B,C,u,timepoints,noise_sd,stepsize=stepsize,hrflags=hrflags)
params={'A':A,'B':B,'C':C,'u':u,'noise_sd':noise_sd,'stepsize':stepsize}
return data,data_conv,params
C=numpy.zeros((z.shape[0],1))
C[0]=1
u=0
print A
print B
print C
# we are assuming a 1 second TR for the resulting data
# but the neural data are at a 1/16 millisecond time resolution
stepsize=.01
tslength=300
timepoints=numpy.arange(0,tslength,stepsize)
# create a blocked design
d,design=create_design_singlecondition(blockiness=1.0,deslength=tslength,blocklength=20,offset=20)
u=scipy.interpolate.griddata(numpy.arange(1,d.shape[0]),d,timepoints,fill_value=0)
def dcm_model(t,z,A,B,C,u):
ut=numpy.abs(timepoints - t).argmin()
return (A.dot(z)+ u[ut]*B.dot(z) + C.dot(u[ut]).T)[0]
def mk_dcm_dataset(timepoints,z,noise_sd):
data=numpy.zeros((len(timepoints),len(z)))
for i in range(1,len(timepoints)):
data[i,:]=data[i-1,:] + dcm_model(timepoints[i],data[i-1,:],A,B,C,u) + numpy.random.randn(len(z))*noise_sd
hrf=spm_hrf(stepsize,oversampling=1)
data_conv=numpy.zeros(data.shape)
for i in range(len(z)):
data_conv[:,i]=numpy.convolve(data[:,i],hrf)[:data.shape[0]]