def handler(points, mr, gofscale, gof, sigma):
from pdf2py import readwrite
from meg import density
from mri import transform
from scipy import ndimage
from nifti import NiftiImage
from numpy import float32, int16, array
report = {}
fids = eval(mr.description)
lpa = fids[0]
rpa = fids[1]
nas = fids[2]
# self.points = array([[0,0,0],[10,0,0],[0,20,0]])#DEBUG-----------------
xyz = transform.meg2mri(lpa, rpa, nas, dipole=points)
# readwrite.writedata(xyz, os.path.dirname(mripath)+'/'+'xyz')
print "lpa, rpa, nas", lpa, rpa, nas
print mr.pixdim
# do some scaling of the dips using the GOF as a weight.
VoxDim = mr.voxdim[::-1]
xyzscaled = (xyz / VoxDim).T
print xyzscaled
d = density.calc(xyz)
gofscale = float32(gofscale)
print "gofscale", gofscale
s = gof - gofscale
sf = (1 / (1 - gofscale)) * s
ds = d * sf
# apply a 1D gaussian filter
z = density.val2img(mr.data, ds, xyzscaled)
# sigma = float32(self.sigmaval.GetValue())
print "sigma", sigma
# sigma = 3
print "filtering 1st dimension"
f = ndimage.gaussian_filter1d(z, sigma * 1 / VoxDim[0], axis=0)
print "filtering 2nd dimension"
f = ndimage.gaussian_filter1d(f, sigma * 1 / VoxDim[1], axis=1)
print "filtering 3rd dimension"
f = ndimage.gaussian_filter1d(f, sigma * 1 / VoxDim[2], axis=2)
scaledf = int16((z.max() / f.max()) * f * 1000)
print "writing nifti output image"
overlay = NiftiImage(int16(scaledf))
overlay.setDescription(mr.description)
overlay.setFilename(mr.filename + "dd")
overlay.setQForm(mr.getQForm())
return overlay
