def gq_tn_calc_save():
for simfile in simdata:
dataname = simfile
print dataname
sim_data=np.loadtxt(simdir+dataname)
marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt'
b_vals_dirs=np.loadtxt(marta_table_fname)
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
gq = dgqs.GeneralizedQSampling(sim_data,bvals,gradients)
gqfile = simdir+'gq/'+dataname+'.pkl'
pkl.save_pickle(gqfile,gq)
'''
gq.IN gq.__doc__ gq.glob_norm_param
gq.QA gq.__init__ gq.odf
gq.__class__ gq.__module__ gq.q2odf_params
'''
tn = ddti.Tensor(sim_data,bvals,gradients)
tnfile = simdir+'tn/'+dataname+'.pkl'
pkl.save_pickle(tnfile,tn)
'''
tn.ADC tn.__init__ tn._getevals
tn.B tn.__module__ tn._getevecs
tn.D tn.__new__ tn._getndim
tn.FA tn.__reduce__ tn._getshape
tn.IN tn.__reduce_ex__ tn._setevals
tn.MD tn.__repr__ tn._setevecs
tn.__class__ tn.__setattr__ tn.adc
tn.__delattr__ tn.__sizeof__ tn.evals
tn.__dict__ tn.__str__ tn.evecs
tn.__doc__ tn.__subclasshook__ tn.fa
tn.__format__ tn.__weakref__ tn.md
tn.__getattribute__ tn._evals tn.ndim
tn.__getitem__ tn._evecs tn.shape
tn.__hash__ tn._getD
'''
''' file has one row for every voxel, every voxel is repeating 1000
def test_gqi_small():
#read bvals,gradients and data
bvals=np.load(opj(os.path.dirname(__file__), \
'data','small_64D.bvals.npy'))
gradients=np.load(opj(os.path.dirname(__file__), \
'data','small_64D.gradients.npy'))
img =ni.load(os.path.join(os.path.dirname(__file__),\
'data','small_64D.nii'))
data=img.get_data()
print(bvals.shape)
print(gradients.shape)
print(data.shape)
t1=time.clock()
gqs = gq.GeneralizedQSampling(data,bvals,gradients)
t2=time.clock()
print('GQS in %d' %(t2-t1))
eds=np.load(opj(os.path.dirname(__file__),\
'..','matrices',\
'evenly_distributed_sphere_362.npz'))
odf_vertices=eds['vertices']
odf_faces=eds['faces']
#Yeh et.al, IEEE TMI, 2010
#calculate the odf using GQI
scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free
#water diffusion coefficient
#l_values sqrt(6 D tau) D free water
#diffusion coefficiet and tau included in the b-value
tmp=np.tile(scaling,(3,1))
b_vector=gradients.T*tmp
Lambda = 1.2 # smoothing parameter - diffusion sampling length
q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi)
#implements equation no. 9 from Yeh et.al.
S=data.copy()
x,y,z,g=S.shape
S=S.reshape(x*y*z,g)
QA = np.zeros((x*y*z,5))
IN = np.zeros((x*y*z,5))
fwd = 0
#Calculate Quantitative Anisotropy and find the peaks and the indices
#for every voxel
for (i,s) in enumerate(S):
odf = Q2odf(s,q2odf_params)
peaks,inds=rp.peak_finding(odf,odf_faces)
fwd=max(np.max(odf),fwd)
peaks = peaks - np.min(odf)
l=min(len(peaks),5)
QA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
QA/=fwd
QA=QA.reshape(x,y,z,5)
IN=IN.reshape(x,y,z,5)
print('Old %d secs' %(time.clock() - t2))
assert_equal((gqs.QA-QA).max(),0.,'Frank QA different than dipy QA')
assert_equal((gqs.QA.shape),QA.shape, 'Frank QA shape is different')
assert_equal(len(tp.FACT_Delta(QA,IN,seeds_no=100).tracks),100,
'FACT_Delta is not generating the right number of '
'tracks for this dataset')
def test_gqiodf():
#read bvals,gradients and data
bvals=np.load(opj(os.path.dirname(__file__), \
'data','small_64D.bvals.npy'))
gradients=np.load(opj(os.path.dirname(__file__), \
'data','small_64D.gradients.npy'))
img =ni.load(os.path.join(os.path.dirname(__file__),\
'data','small_64D.nii'))
data=img.get_data()
#print(bvals.shape)
#print(gradients.shape)
#print(data.shape)
# t1=time.clock()
gq.GeneralizedQSampling(data,bvals,gradients)
ten = dt.Tensor(data,bvals,gradients,thresh=50)
ten.fa()
x,y,z,a,b=ten.evecs.shape
evecs=ten.evecs
xyz=x*y*z
evecs = evecs.reshape(xyz,3,3)
#vs = np.sign(evecs[:,2,:])
#print vs.shape
#print np.hstack((vs,vs,vs)).reshape(1000,3,3).shape
#evecs = np.hstack((vs,vs,vs)).reshape(1000,3,3)
#print evecs.shape
evals=ten.evals
evals = evals.reshape(xyz,3)
#print evals.shape
#print('GQS in %d' %(t2-t1))
eds=np.load(opj(os.path.dirname(__file__),\
'..','matrices',\
'evenly_distributed_sphere_362.npz'))
odf_vertices=eds['vertices']
odf_faces=eds['faces']
#Yeh et.al, IEEE TMI, 2010
#calculate the odf using GQI
scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free
#water diffusion coefficient
#l_values sqrt(6 D tau) D free water
#diffusion coefficiet and tau included in the b-value
tmp=np.tile(scaling,(3,1))
b_vector=gradients.T*tmp
Lambda = 1.2 # smoothing parameter - diffusion sampling length
q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi)
#implements equation no. 9 from Yeh et.al.
S=data.copy()
x,y,z,g=S.shape
S=S.reshape(x*y*z,g)
QA = np.zeros((x*y*z,5))
IN = np.zeros((x*y*z,5))
fwd = 0
#Calculate Quantitative Anisotropy and find the peaks and the indices
#for every voxel
summary = {}
summary['vertices'] = odf_vertices
v = odf_vertices.shape[0]
summary['faces'] = odf_faces
f = odf_faces.shape[0]
'''
If e = number_of_edges
the Euler formula says f-e+v = 2 for a mesh on a sphere
Here, assuming we have a healthy triangulation
every face is a triangle, all 3 of whose edges should belong to
exactly two faces = so 2*e = 3*f
to avoid division we test whether 2*f - 3*f + 2*v == 4
or equivalently 2*v - f == 4
'''
assert_equal(2*v-f, 4,'Direct Euler test fails')
assert_true(meshes.euler_characteristic_check(odf_vertices, odf_faces,chi=2),'euler_characteristic_check fails')
coarse = meshes.coarseness(odf_faces)
print 'coarseness: ', coarse
for (i,s) in enumerate(S):
#print 'Volume %d' % i
istr = str(i)
summary[istr] = {}
odf = Q2odf(s,q2odf_params)
peaks,inds=rp.peak_finding(odf,odf_faces)
fwd=max(np.max(odf),fwd)
peaks = peaks - np.min(odf)
l=min(len(peaks),5)
QA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
summary[istr]['odf'] = odf
summary[istr]['peaks'] = peaks
summary[istr]['inds'] = inds
summary[istr]['evecs'] = evecs[i,:,:]
summary[istr]['evals'] = evals[i,:]
QA /= fwd
# QA=QA.reshape(x,y,z,5)
# IN=IN.reshape(x,y,z,5)
#print('Old %d secs' %(time.clock() - t2))
# assert_equal((gqs.QA-QA).max(),0.,'Frank QA different than our QA')
# assert_equal((gqs.QA.shape),QA.shape, 'Frank QA shape is different')
# assert_equal((gqs.QA-QA).max(), 0.)
#import dipy.core.track_propagation as tp
#tp.FACT_Delta(QA,IN)
#return tp.FACT_Delta(QA,IN,seeds_no=10000).tracks
peaks_1 = [i for i in range(1000) if len(summary[str(i)]['inds'])==1]
peaks_2 = [i for i in range(1000) if len(summary[str(i)]['inds'])==2]
peaks_3 = [i for i in range(1000) if len(summary[str(i)]['inds'])==3]
# correct numbers of voxels with respectively 1,2,3 ODF/QA peaks
assert_array_equal((len(peaks_1),len(peaks_2),len(peaks_3)), (790,196,14),
'error in numbers of QA/ODF peaks')
# correct indices of odf directions for voxels 0,10,44
# with respectively 1,2,3 ODF/QA peaks
assert_array_equal(summary['0']['inds'],[116],
'wrong peak indices for voxel 0')
assert_array_equal(summary['10']['inds'],[105, 78],
'wrong peak indices for voxel 10')
assert_array_equal(summary['44']['inds'],[95, 84, 108],
'wrong peak indices for voxel 44')
assert_equal(np.argmax(summary['0']['odf']), 116)
assert_equal(np.argmax(summary['10']['odf']), 105)
assert_equal(np.argmax(summary['44']['odf']), 95)
# pole_1 = summary['vertices'][116]
#print 'pole_1', pole_1
# pole_2 = summary['vertices'][105]
#print 'pole_2', pole_2
# pole_3 = summary['vertices'][95]
#print 'pole_3', pole_3
vertices = summary['vertices']
width = 0.02#0.3 #0.05
'''
print 'pole_1 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_1)) < width])
print 'pole_2 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_2)) < width])
print 'pole_3 equator contains:', len([i for i,v in enumerate(vertices) if np.abs(np.dot(v,pole_3)) < width])
'''
#print 'pole_1 equator contains:', len(meshes.equatorial_vertices(vertices,pole_1,width))
#print 'pole_2 equator contains:', len(meshes.equatorial_vertices(vertices,pole_2,width))
#print 'pole_3 equator contains:', len(meshes'equatorial_vertices(vertices,pole_3,width))
#print triple_odf_maxima(vertices,summary['0']['odf'],width)
#print triple_odf_maxima(vertices,summary['10']['odf'],width)
#print triple_odf_maxima(vertices,summary['44']['odf'],width)
#print summary['0']['evals']
'''
pole=np.array([0,0,1])
from dipy.viz import fos
r=fos.ren()
fos.add(r,fos.point(pole,fos.green))
for i,ev in enumerate(vertices):
if np.abs(np.dot(ev,pole))<width:
fos.add(r,fos.point(ev,fos.red))
fos.show(r)
'''
triple = triple_odf_maxima(vertices, summary['10']['odf'], width)
indmax1, odfmax1 = triple[0]
indmax2, odfmax2 = triple[1]
indmax3, odfmax3 = triple[2]
'''
from dipy.viz import fos
r=fos.ren()
for v in vertices:
fos.add(r,fos.point(v,fos.cyan))
fos.add(r,fos.sphere(upper_hemi_map(vertices[indmax1]),radius=0.1,color=fos.red))
#fos.add(r,fos.line(np.array([0,0,0]),vertices[indmax1]))
fos.add(r,fos.sphere(upper_hemi_map(vertices[indmax2]),radius=0.05,color=fos.green))
fos.add(r,fos.sphere(upper_hemi_map(vertices[indmax3]),radius=0.025,color=fos.blue))
fos.add(r,fos.sphere(upper_hemi_map(summary['0']['evecs'][:,0]),radius=0.1,color=fos.red,opacity=0.7))
fos.add(r,fos.sphere(upper_hemi_map(summary['0']['evecs'][:,1]),radius=0.05,color=fos.green,opacity=0.7))
fos.add(r,fos.sphere(upper_hemi_map(summary['0']['evecs'][:,2]),radius=0.025,color=fos.blue,opacity=0.7))
fos.add(r,fos.sphere([0,0,0],radius=0.01,color=fos.white))
fos.show(r)
'''
mat = np.vstack([vertices[indmax1],vertices[indmax2],vertices[indmax3]])
print np.dot(mat,np.transpose(mat))
# this is to assess how othogonal the triple is/are
print np.dot(summary['0']['evecs'],np.transpose(mat))
"""
**Read the b-vectors**, the unit gradient directions.
"""
gradients = np.loadtxt(fbvecs).T
"""
Crossings and Generalized Q-Sampling
------------------------------------
You probably have heard about the problem of crossings in diffusion MRI.
The single tensor model cannot detect a simple crossing of two fibres.
However with *Generalized Q-Sampling (GQS)* this is possible even up to a quadruple crossing
or higher depending on the resolution of your datasets. Resolution will
typically depend on signal-to-noise ratio and voxel-size.
"""
gqs = gqi.GeneralizedQSampling(data, bvals, gradients)
"""
A useful metric derived from GQS is *Quantitative Anisotropy* (QA).
"""
QA = gqs.qa()
print('QA.shape (%d,%d,%d,%d)' % QA.shape)
"""
QA is a 4-d array with up to 5 peak QA values for each voxel::
QA.shape (6,10,10,5)
The QA array is
significantly different in shape from the FA array,
however it too can be directly input to the EuDX class:
def run_small_data():
smalldir = '/home/eg309/Devel/dipy/dipy/data/'
bvals=np.load(smalldir+'small_64D.bvals.npy')
gradients=np.load(smalldir+'small_64D.gradients.npy')
img=nibabel.load(smalldir+'small_64D.nii')
small_data=img.get_data()
print 'real_data', small_data.shape
gqsmall = dgqs.GeneralizedQSampling(small_data,bvals,gradients)
tnsmall = ddti.Tensor(small_data,bvals,gradients)
x,y,z,a,b=tnsmall.evecs.shape
evecs=tnsmall.evecs
xyz=x*y*z
evecs = evecs.reshape(xyz,3,3)
evals=tnsmall.evals
evals = evals.reshape(xyz,3)
"""
eds=np.load(opj(os.path.dirname(__file__),\
'..','matrices',\
'evenly_distributed_sphere_362.npz'))
"""
from dipy.data import get_sphere
odf_vertices,odf_faces=get_sphere('symmetric362')
scaling=np.sqrt(bvals*0.01506) # 0.01506 = 6*D where D is the free
#water diffusion coefficient
#l_values sqrt(6 D tau) D free water
#diffusion coefficiet and tau included in the b-value
tmp=np.tile(scaling,(3,1))
b_vector=gradients.T*tmp
Lambda = 1.2 # smoothing parameter - diffusion sampling length
q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi)
#implements equation no. 9 from Yeh et.al.
S=small_data.copy()
x,y,z,g=S.shape
S=S.reshape(x*y*z,g)
# QA = np.zeros((x*y*z,5))
IN = np.zeros((x*y*z,5))
FA = tnsmall.fa().reshape(x*y*z)
fwd = 0
#Calculate Quantitative Anisotropy and find the peaks and the indices
#for every voxel
summary = {}
summary['vertices'] = odf_vertices
# v = odf_vertices.shape[0]
summary['faces'] = odf_faces
# f = odf_faces.shape[0]
for (i,s) in enumerate(S):
istr = str(i)
summary[istr] = {}
t0, t1, t2, npa = gqsmall.npa(s, width = 5)
summary[istr]['triple']=(t0,t1,t2)
summary[istr]['npa']=npa
odf = Q2odf(s,q2odf_params)
peaks,inds=rp.peak_finding(odf,odf_faces)
fwd=max(np.max(odf),fwd)
n_peaks=min(len(peaks),5)
peak_heights = [odf[i] for i in inds[:n_peaks]]
IN[i][:n_peaks] = inds[:n_peaks]
summary[istr]['odf'] = odf
summary[istr]['peaks'] = peaks
summary[istr]['inds'] = inds
summary[istr]['evecs'] = evecs[i,:,:]
summary[istr]['evals'] = evals[i,:]
summary[istr]['n_peaks'] = n_peaks
summary[istr]['peak_heights'] = peak_heights
summary[istr]['fa'] = FA[i]
"""
QA/=fwd
QA=QA.reshape(x,y,z,5)
IN=IN.reshape(x,y,z,5)
"""
peaks_1 = [i for i in range(1000) if summary[str(i)]['n_peaks']==1]
peaks_2 = [i for i in range(1000) if summary[str(i)]['n_peaks']==2]
peaks_3 = [i for i in range(1000) if summary[str(i)]['n_peaks']==3]
print '#voxels with 1, 2, 3 peaks', len(peaks_1),len(peaks_2),len(peaks_3)
return FA, summary
