def G(intensName, D, tau) :
def real_fftnd(a) :
a = asarray(a)
for i in range(len(a.shape)) :
a = real_fft(a, axis=i)
return a
def inverse_real_fftnd(a) :
a = asarray(a)
for i in range(len(a.shape)) :
a = inverse_real_fft(a, axis=i)
return a
def unwrap(data) :
(Nx,Ny,Nz) = data.shape
r = zeros((Nx,Ny,Nz), data.dtype())
(r[:Nx/2,:,:],r[Nx/2:,:,:]) = (data[Nx/2:,:,:],data[:Nx/2,:,:])
(r[:,:Ny/2,:],r[:,Ny/2:,:]) = (r[:,Ny/2:,:],r[:,:Ny/2,:].copy())
(r[:,:,:Nz/2],r[:,:,Nz/2:]) = (r[:,:,Nz/2:],r[:,:,:Nz/2].copy())
return r
Psi = lambda r2, D, tau : \
asarray(1./(4.*pi*D*tau)**(1.5),'f') * \
exp(asarray(-1./(4.*D*tau),'f')*r2)
(O, Ohat, r2) = fetchData(intensName)
return sum(sum(sum(O*unwrap(absolute(inverse_fftnd(
fftnd(Psi(r2, D, tau))*Ohat ))) )))
def prepIntens(intens, Pad) :
from math import ceil, log
from numpy import repeat, transpose, reshape
from numpy.oldnumeric.fft import fftnd
if Pad :
dyadicize = lambda N: 2**int(ceil(log(N)/log(2)))
name = "__padded_" + intens.name
else :
dyadicize = lambda N: N+1
name = intens.name
if name in intensPrep : return (name, intensPrep[name])
smds.msg("Preparing intens <%s>" % name, smds.MSG_DBUG)
(Nx, Ny, Nz) = map(lambda x : dyadicize(2*x+1),
(intens.numBins_x, intens.numBins_x, intens.numBins_z))
r2 = (transpose(repeat([repeat([range(-Nx/2, Nx/2)], Ny, 0)], Nz, 0),
(2, 1, 0))*intens.binWidth_x) ** 2 # X
r2 += (transpose(repeat([repeat([range(-Ny/2, Ny/2)], Nx, 0)], Nz, 0),
(1, 2, 0))*intens.binWidth_x) ** 2 # Y
r2 += (repeat([repeat([range(-Nz/2, Nz/2)], Ny, 0)], Nx, 0) * \
intens.binWidth_z) ** 2 # Z
r2 = r2.astype('f')
O = pad(intens.getData().astype('f'), (Nx, Ny, Nz))
del intens
Ohat = fftnd(O)
intensPrep[name] = (O, Ohat, r2)
return (name, intensPrep[name])
