def test_eudx():
# read bvals,gradients and data
fimg, fbvals, fbvecs = get_data("small_64D")
bvals = np.load(fbvals)
gradients = np.load(fbvecs)
img = ni.load(fimg)
data = img.get_data()
print(data.shape)
gqs = GeneralizedQSampling(data, bvals, gradients)
ten = Tensor(data, bvals, gradients, thresh=50)
seed_list = np.dot(np.diag(np.arange(10)), np.ones((10, 3)))
iT = iter(EuDX(gqs.qa(), gqs.ind(), seeds=seed_list))
T = []
for t in iT:
T.append(t)
iT2 = iter(EuDX(ten.fa(), ten.ind(), seeds=seed_list))
T2 = []
for t in iT2:
T2.append(t)
print("length T ", sum([length(t) for t in T]))
print("length T2", sum([length(t) for t in T2]))
print(gqs.QA[1, 4, 8, 0])
print(gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]), np.array(gqs.QA.strides), 4, 8)])
assert_almost_equal(
gqs.QA[1, 4, 8, 0], gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]), np.array(gqs.QA.strides), 4, 8)]
)
assert_almost_equal(sum([length(t) for t in T]), 70.999996185302734, places=3)
assert_almost_equal(sum([length(t) for t in T2]), 56.999997615814209, places=3)
def test_eudx():
#read bvals,gradients and data
fimg,fbvals, fbvecs = get_data('small_64D')
bvals=np.load(fbvals)
gradients=np.load(fbvecs)
img =ni.load(fimg)
data=img.get_data()
print(data.shape)
gqs = GeneralizedQSampling(data,bvals,gradients)
ten = Tensor(data,bvals,gradients,thresh=50)
seed_list=np.dot(np.diag(np.arange(10)),np.ones((10,3)))
iT=iter(EuDX(gqs.qa(),gqs.ind(),seed_list=seed_list))
T=[]
for t in iT:
T.append(t)
iT2=iter(EuDX(ten.fa(),ten.ind(),seed_list=seed_list))
T2=[]
for t in iT2:
T2.append(t)
print('length T ',sum([length(t) for t in T]))
print('length T2',sum([length(t) for t in T2]))
print(gqs.QA[1,4,8,0])
print(gqs.QA.ravel()[ndarray_offset(np.array([1,4,8,0]),np.array(gqs.QA.strides),4,8)])
assert_equal(gqs.QA[1,4,8,0], gqs.QA.ravel()[ndarray_offset(np.array([1,4,8,0]),np.array(gqs.QA.strides),4,8)])
#assert_equal, sum([length(t) for t in T ]) , 77.999996662139893
#assert_equal, sum([length(t) for t in T2]) , 63.499998092651367
assert_equal(sum([length(t) for t in T ]) , 75.214988201856613)
assert_equal(sum([length(t) for t in T2]) , 60.202986091375351)
def generate_random_tracks(rand_no):
img=nib.load(fbet)
data=img.get_data()
affine=img.get_affine()
bvals=np.loadtxt(fbvals)
bvecs=np.loadtxt(fbvecs).T
t=time()
gqs=GeneralizedQSampling(data,bvals,bvecs)
print 'gqs time',time()-t,'s'
for (i,sds) in enumerate(seeds):
print i,sds
t=time()
eu=EuDX(gqs.qa(),gqs.ind(),seeds=sds,a_low=0.0239)
T=[downsample(e,12) for e in eu]
np.save('/tmp/random_T.npy',np.array(T,dtype=np.object))
###################
print time()-t
del T
print outs
def generate_skeletons():
img=nib.load(fbet)
data=img.get_data()
affine=img.get_affine()
bvals=np.loadtxt(fbvals)
bvecs=np.loadtxt(fbvecs).T
t=time()
gqs=GeneralizedQSampling(data,bvals,bvecs)
print 'gqs time',time()-t,'s'
for (i,sds) in enumerate(seeds):
print i,sds
t=time()
eu=EuDX(gqs.qa(),gqs.ind(),seeds=sds,a_low=0.0239)
T=[downsample(e,12) for e in eu]
#np.save(dout+outs[i]+'.npy',np.array(T,dtype=np.object))
C=local_skeleton_clustering(T,4.)
save_pickle(dout+outs[i]+'.skl',C)
print time()-t
del T
print outs
def test_eudx():
#read bvals,gradients and data
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
gradients = np.load(fbvecs)
img = ni.load(fimg)
data = img.get_data()
print(data.shape)
gqs = GeneralizedQSampling(data, bvals, gradients)
ten = Tensor(data, bvals, gradients, thresh=50)
seed_list = np.dot(np.diag(np.arange(10)), np.ones((10, 3)))
iT = iter(EuDX(gqs.qa(), gqs.ind(), seeds=seed_list))
T = []
for t in iT:
T.append(t)
iT2 = iter(EuDX(ten.fa(), ten.ind(), seeds=seed_list))
T2 = []
for t in iT2:
T2.append(t)
print('length T ', sum([length(t) for t in T]))
print('length T2', sum([length(t) for t in T2]))
print(gqs.QA[1, 4, 8, 0])
print(gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]),
np.array(gqs.QA.strides), 4, 8)])
assert_almost_equal(
gqs.QA[1, 4, 8, 0],
gqs.QA.ravel()[ndarray_offset(np.array([1, 4, 8, 0]),
np.array(gqs.QA.strides), 4, 8)])
assert_almost_equal(sum([length(t) for t in T]),
70.999996185302734,
places=3)
assert_almost_equal(sum([length(t) for t in T2]),
56.999997615814209,
places=3)
def simulations_marta():
#gq_tn_calc_save()
#a_few_phantoms()
#sd=['fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_60_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7']
sd=['fibres_2_SNR_100_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_1_SNR_100_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_20_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#for simfile in simdata:
np.set_printoptions(2)
dotpow=6
width=6
sincpow=2
sampling_length=1.2
print dotpow,width,sincpow
print sampling_length
verts,faces=get_sphere('symmetric362')
for simfile in sd:
data=np.loadtxt(simdir+simfile)
sf=simfile.split('_')
b_vals_dirs=np.loadtxt(simdir+'Dir_and_bvals_DSI_marta.txt')
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
data2=data[::1000,:]
table={'fibres':sf[1],'snr':sf[3],'angle':sf[5],'l1':sf[7],'l2':sf[9],\
'l3':sf[11],'iso':sf[13],'diso':sf[15],\
'data':data2,'bvals':bvals,'gradients':gradients}
print table['data'].shape
pdi=ProjectiveDiffusivity(table['data'],table['bvals'],table['gradients'],dotpow,width,sincpow)
gqs=GeneralizedQSampling(table['data'],table['bvals'],table['gradients'],sampling_length)
ten=Tensor(table['data'],table['bvals'],table['gradients'])
r=fvtk.ren()
for i in range(10):#range(len(sdi.xa())):
print 'No:',i
print 'simulation fibres ',table['fibres'], ' snr ',table['snr'],' angle ', table['angle']
pdiind=pdi.ind()[i]
gqsind=gqs.ind()[i]
print 'indices',pdiind,gqsind,\
np.rad2deg(np.arccos(np.dot(verts[pdiind[0]],verts[pdiind[1]]))),\
np.rad2deg(np.arccos(np.dot(verts[gqsind[0]],verts[gqsind[1]])))
#ten.ind()[i],
print 'peaks', pdi.xa()[i]*10**3,gqs.qa()[i]
pd=pdi.spherical_diffusivity(table['data'][i])#*10**3
#print 'pd stat',pd.min(),pd.max(),pd.mean(),pd.std()
#colors=fvtk.colors(sdf,'jet')
sdfcol=np.interp(pd,[pd.mean()-4*pd.std(),pd.mean()+4*pd.std()],[0,1])
colors=fvtk.colors(sdfcol,'jet',False)
fvtk.add(r,fvtk.point(5*pdi.odf_vertices+np.array([12*i,0,0]),colors,point_radius=.6,theta=10,phi=10))
odf=gqs.odf(table['data'][i])
colors=fvtk.colors(odf,'jet')
fvtk.add(r,fvtk.point(5*gqs.odf_vertices+np.array([12*i,-12,0]),colors,point_radius=.6,theta=10,phi=10))
fvtk.show(r)
fvtk.show(r) ten=Tensor(data,bvals,bvecs,mask) FA=ten.fa() FA[np.isnan(FA)]=0 eu=EuDX(FA,ten.ind(),seeds=10**4,a_low=fa_thr,length_thr=length_thr) T =[e for e in eu] #show(T,FA,ten.ind(),eu.odf_vertices,scale=1) #r=fvtk.ren() #fvtk.add(r,fvtk.point(eu.odf_vertices,cm.orient2rgb(eu.odf_vertices),point_radius=.5,theta=30,phi=30)) #fvtk.show(r) gqs=GeneralizedQSampling(data,bvals,bvecs,Lambda=1.,mask=mask,squared=False) eu2=EuDX(gqs.qa(),gqs.ind(),seeds=10**4,a_low=0,length_thr=length_thr) T2=[e for e in eu2] show(T2,gqs.qa(),gqs.ind(),eu2.odf_vertices,scale=1) ds=DiffusionSpectrum(data,bvals,bvecs,mask=mask) eu3=EuDX(ds.gfa(),ds.ind()[...,0],seeds=10**4,a_low=0,length_thr=length_thr) T3=[e for e in eu3] #show(T3,ds.gfa(),ds.ind()[...,0],eu3.odf_vertices,scale=1) eu4=EuDX(ds.nfa(),ds.ind(),seeds=10**4,a_low=0,length_thr=length_thr) T4=[e for e in eu4] #show(T4,ds.nfa(),ds.ind(),eu4.odf_vertices,scale=1) eu5=EuDX(ds.qa(),ds.ind(),seeds=10**4,a_low=0,length_thr=length_thr) T5=[e for e in eu5] #show(T5,ds.qa(),ds.ind(),eu5.odf_vertices,scale=1)
def generate_gqi_tracks_and_warp_in_MNI_space(fname,dname):
fbvals=fname+'.bval'
fbvecs=fname+'.bvec'
fdata=fname+'.nii.gz'
if os.path.isfile(fdata):
pass
else:
fdata=fname+'.nii'
if os.path.isfile(fdata)==False:
print('Data do not exist')
return
dti_dname=os.path.join(dname,'DTI')
if os.path.isdir(dti_dname):
pass
else:
os.mkdir(dti_dname)
print dti_dname
gqi_dname=os.path.join(dname,'GQI')
if os.path.isdir(gqi_dname):
pass
else:
os.mkdir(gqi_dname)
print gqi_dname
fdatabet=fname+'_bet.nii.gz'
if os.path.isfile(fdatabet):
pass
else:
print('fdatabet does not exist')
img=nib.load(fdatabet)
data=img.get_data()
affine=img.get_affine()
bvals=np.loadtxt(fbvals)
bvecs=np.loadtxt(fbvecs).T
gqs=GeneralizedQSampling(data,bvals,bvecs)
eu=EuDX(gqs.qa(),gqs.ind(),seeds=10**6,a_low=0.0239)
fdpy=pjoin(gqi_dname,'lsc_QA.dpy')
dpw=Dpy(fdpy,'w',compression=1)
for track in eu:
dpw.write_track(track.astype(np.float32))
dpw.close()
local=dti_dname
fmat=pjoin(local,'flirt.mat')
fnon=pjoin(local,'fnirt.nii.gz')
finv=pjoin(local,'invw.nii.gz')
fdis=pjoin(local,'dis.nii.gz')
fdisa=pjoin(local,'disa.nii.gz')
ffa=pjoin(local,'FA_bet.nii.gz')
fdpyw=pjoin(gqi_dname,'lsc_QA_ref.dpy')
#print fdatabet
#print fmat
#print fnon
print fdpy
print fdpyw
read_warp_save_tracks(fdpy,ffa,fmat,finv,fdis,fdisa,fref,fdpyw)
ds=DiffusionSpectrum(data,bvals,gradients,\
odf_sphere='symmetric642',\
mask=famask,\
half_sphere_grads=True,\
auto=True,\
save_odfs=False)
"""
# ds.PK[FA<.2]=np.zeros(5)
# euler=EuDX(a=FA,ind=tensors.ind(),seeds=seeds,a_low=.2)
# euler=EuDX(a=ds.PK,ind=ds.IN,seeds=seeds,odf_vertices=ds.odf_vertices,a_low=.2)
# euler=EuDX(a=ei.PK,ind=ei.IN,seeds=seeds,odf_vertices=ei.odf_vertices,\
# a_low=.2,step_sz=0.25,ang_thr=80.)
# euler=EuDX(a=gqs.qa(),ind=gqs.ind(),seeds=seeds,odf_vertices=gqs.odf_vertices,a_low=.0239)
euler = EuDX(
a=gqs.qa(),
ind=gqs.ind(),
seeds=3 * 10 ** 6,
odf_vertices=gqs.odf_vertices,
a_low=0.0239,
step_sz=0.25,
ang_thr=80.0,
)
# euler=EuDX(a=ds.pk(),ind=ds.ind(),seeds=5*10**6, odf_vertices=gqs.odf_vertices,\
# a_low=.1,step_sz=0.25,ang_thr=80.)
lT = get_luigi_SLFI()
T = [track for track in euler]
print len(T)
# T=[track for track in euler if track_range(track,100/2.5,200/2.5)]
