def decimate(self, dec):
'''nim is the mri data in python format
dec is the decimation factor'''
#nim = self.nifti
header = self.nifti.get_header()
self.filename = self.nifti.get_filename()
xend = self.nifti.get_shape()[0]
yend = self.nifti.get_shape()[1]
zend = self.nifti.get_shape()[2]
if type(dec) != float and type(dec) != int:
print 'nonuniform decimation'
xstartval=ceil(dec[0]/2);
print xstartval,
ystartval=ceil(dec[1]/2);
zstartval=ceil(dec[2]/2);
decimg=array(self.data[xstartval::dec[0],:,:][:,ystartval::dec[1],:][:,:,zstartval::dec[2]])
nonz = where(decimg > 0)
x = ((nonz[0])*dec[0])+(dec[0])
y = ((nonz[1])*dec[1])+(dec[1])
z = ((nonz[2])*dec[2])+(dec[2])
else:
print 'uniform decimation'
startval=ceil(dec/2);
decimg=array(self.data[startval::dec,:,:][:,startval::dec,:][:,:,startval::dec])
nonz = where(decimg > 0)
x = ((nonz[0]+1)*dec)#-(dec)
y = ((nonz[1]+1)*dec)#-(dec)
z = ((nonz[2]+1)*dec)#-(dec)
#--20090702--danc--not sure about reordering
#mrixyz = array([x,y,z])
#--20090702--danc--adding voxel scaling. this had to be a huge bug, so why i haven't noticed it so far???
# The effect would be non existant on isotropic volumes, so thats probably why.
voxdim = header['pixdim'][1:4]#.voxdim[::-1] #flipped voxel dims
print voxdim
#print 'WARNING: assuming voxdim reverse of img.voxdim. Could be wrong'
mrixyz = (array([x,y,z]).T*voxdim).T
self.mrixyz = mrixyz
self.img = decimg
self.factor = dec
self.ind = array(nonz)
try:
[t,r] = transform.meg2mri(self.lpa,self.rpa,self.nas)
self.megxyz = transform.mri2meg(t,r,self.mrixyz)
except AttributeError:
print 'Transform Error, skipping'
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
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 mri import img_nibabel as img
from numpy import float32, int16, array
from pdf2py import readwrite
import nibabel,os
report = {}
filename = mr.nifti.get_filename()
#try: xfm = readwrite.readdata(os.path.splitext(filename)[0]+'.pym')
#except: print 'Error reading coregistration info'
lpa = mr.lpa
rpa = mr.rpa
nas = mr.nas
#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 'xyz in mri space', xyz
print 'pixdim', mr.pixdim
#do some scaling of the dips using the GOF as a weight.
#VoxDim = mr.voxdim[::-1]
VoxDim = mr.pixdim
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 = nibabel.Nifti1Image(scaledf,mr.nifti.get_affine(),mr.nifti.get_header())
#overlay = NiftiImage(int16(scaledf))
#overlay.setDescription(mr.description)
filename = os.path.splitext(mr.nifti.get_filename())[0]
overlay.to_filename(filename+'dd.nii.gz')
print 'Density Image Saved', filename+'dd.nii.gz'
#overlay.setFilename(mr.filename+'dd')
#overlay.setQForm(mr.getQForm())
return overlay