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_further():
""" Cause we love testin.. ;-)
"""
fimg, fbvals, fbvecs = get_data("small_101D")
img = ni.load(fimg)
affine = img.get_affine()
bvals = np.loadtxt(fbvals)
gradients = np.loadtxt(fbvecs).T
data = img.get_data()
ten = Tensor(data, bvals, gradients, thresh=50)
x, y, z = data.shape[:3]
seeds = np.zeros((10 ** 4, 3))
for i in range(10 ** 4):
rx = (x - 1) * np.random.rand()
ry = (y - 1) * np.random.rand()
rz = (z - 1) * np.random.rand()
seeds[i] = np.ascontiguousarray(np.array([rx, ry, rz]), dtype=np.float64)
# print seeds
# """
eu = EuDX(a=ten.fa(), ind=ten.ind(), seeds=seeds, a_low=0.2)
T = [e for e in eu]
# check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel() < 0), 0)
"""
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 test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.get_affine()
bvals = np.loadtxt(fbvals)
gradients = np.loadtxt(fbvecs).T
data = img.get_data()
ten = Tensor(data, bvals, gradients, thresh=50)
x, y, z = data.shape[:3]
seeds = np.zeros((10**4, 3))
for i in range(10**4):
rx = (x - 1) * np.random.rand()
ry = (y - 1) * np.random.rand()
rz = (z - 1) * np.random.rand()
seeds[i] = np.ascontiguousarray(np.array([rx, ry, rz]),
dtype=np.float64)
#print seeds
#"""
eu = EuDX(a=ten.fa(), ind=ten.ind(), seeds=seeds, a_low=.2)
T = [e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel() < 0), 0)
"""
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)
nii_filename = base_filename + '_bet.nii.gz'
bvec_filename = base_filename + '.bvec'
bval_filename = base_filename + '.bval'
img = nib.load(nii_filename)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(bval_filename)
gradients = np.loadtxt(bvec_filename).T # this is the unitary direction of the gradient
base_dir2 = base_dir+ 'DTI/'
tensors = Tensor(data, bvals, gradients, thresh=50)
create_save_tracks(tensors.fa(), tensors.ind(), 10**4, .2, base_dir2+'tracks_dti_10K_new.dpy')
# create_save_tracks(tensors.fa(), tensors.ind(), 10**6, .2, base_dir2+'tracks_dti_1M.dpy')
# create_save_tracks(tensors.fa(), tensors.ind(), 3*10**6, .2, base_dir2+'tracks_dti_3M.dpy')
#
# gqs=GeneralizedQSampling(data,bvals,gradients)
# create_save_tracks(gqs.qa(), gqs.ind(), 10**4, .0239, base_dir2+'tracks_gqi_10K.dpy')
# create_save_tracks(gqs.qa(), gqs.ind(), 10**6, .0239, base_dir2+'tracks_gqi_1M.dpy')
# create_save_tracks(gqs.qa(), gqs.ind(), 3*10**6, .0239, base_dir2+'tracks_gqi_3M.dpy')
#
flirt_filename =base_dir2+'flirt.mat'
fa_filename= base_dir2 + 'fa.nii.gz'
tracks_filename = base_dir2+'tracks_dti_10K_new.dpy'
linear_filename = base_dir2+'tracks_dti_10K_linear_new.dpy'
warp_tracks_linearly(flirt_filename,fa_filename, tracks_filename,linear_filename)
nii_filename = base_dir + "DTI/data_resample.nii.gz"
bvec_filename = base_filename + ".bvec"
bval_filename = base_filename + ".bval"
img = nib.load(nii_filename)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(bval_filename)
gradients = np.loadtxt(bvec_filename).T # this is the unitary direction of the gradient
base_dir2 = base_dir + "DTI/"
tensors = Tensor(data, bvals, gradients, thresh=50)
create_save_tracks(tensors.fa(), tensors.ind(), 10 ** 4, 0.2, base_dir2 + "tracks_dti_10K.dpy")
create_save_tracks(tensors.fa(), tensors.ind(), 10 ** 6, 0.2, base_dir2 + "tracks_dti_1M.dpy")
create_save_tracks(tensors.fa(), tensors.ind(), 3 * 10 ** 6, 0.2, base_dir2 + "tracks_dti_3M.dpy")
# ===================== gqi ========================================
# gqs=GeneralizedQSampling(data,bvals,gradients)
# create_save_tracks(gqs.qa(), gqs.ind(), 10**4, .0239, base_dir2+'tracks_gqi_10K.dpy')
# create_save_tracks(gqs.qa(), gqs.ind(), 10**6, .0239, base_dir2+'tracks_gqi_1M.dpy')
# create_save_tracks(gqs.qa(), gqs.ind(), 3*10**6, .0239, base_dir2+'tracks_gqi_3M.dpy')
# ===================== gqi ========================================
print "Done warped"
flirt_filename = base_dir2 + "flirt.mat"
fa_filename = base_dir2 + "fa_resample.nii.gz"
def humans():
no_seeds=10**6
visualize = False
save_odfs = False
dirname = "data/"
for root, dirs, files in os.walk(dirname):
if root.endswith('101_32'):
base_dir = root+'/'
filename = 'raw'
base_filename = base_dir + filename
nii_filename = base_filename + 'bet.nii.gz'
bvec_filename = base_filename + '.bvec'
bval_filename = base_filename + '.bval'
flirt_mat = base_dir + 'DTI/flirt.mat'
fa_filename = base_dir + 'DTI/fa.nii.gz'
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
dpy_filename = base_dir + 'DTI/res_tracks_dti.dpy'
print bvec_filename
img = nib.load(nii_filename)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(bval_filename)
gradients = np.loadtxt(bvec_filename).T # this is the unitary direction of the gradient
tensors = Tensor(data, bvals, gradients, thresh=50)
FA = tensors.fa()
FA = tensors.fa()
famask=FA>=.2
ds=DiffusionSpectrum(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=True,save_odfs=save_odfs)
gq=GeneralizedQSampling(data,bvals,gradients,1.2,odf_sphere='symmetric642',mask=famask,squared=False,save_odfs=save_odfs)
ei=EquatorialInversion(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=False,save_odfs=save_odfs,fast=True)
ei.radius=np.arange(0,5,0.4)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
ds.PK[FA<.2]=np.zeros(5)
ei.PK[FA<.2]=np.zeros(5)
gq.PK[FA<.2]=np.zeros(5)
print 'create seeds'
x,y,z,g=ei.PK.shape
seeds=np.zeros((no_seeds,3))
sid=0
while sid<no_seeds:
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seed=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
seeds[sid]=seed
sid+=1
euler = EuDX(a=FA, ind=tensors.ind(), seeds=seeds, a_low=.2)
dt_tracks = [track for track in euler]
euler2 = EuDX(a=ds.PK, ind=ds.IN, seeds=seeds, odf_vertices=ds.odf_vertices, a_low=.2)
ds_tracks = [track for track in euler2]
euler3 = EuDX(a=gq.PK, ind=gq.IN, seeds=seeds, odf_vertices=gq.odf_vertices, a_low=.2)
gq_tracks = [track for track in euler3]
euler4 = EuDX(a=ei.PK, ind=ei.IN, seeds=seeds, odf_vertices=ei.odf_vertices, a_low=.2)
ei_tracks = [track for track in euler4]
if visualize:
renderer = fvtk.ren()
fvtk.add(renderer, fvtk.line(tensor_tracks, fvtk.red, opacity=1.0))
fvtk.show(renderer)
print 'Load images to be used for registration'
img_fa =nib.load(fa_filename)
img_ref =nib.load(fsl_ref)
mat=flirt2aff(np.loadtxt(flirt_mat),img_fa,img_ref)
del img_fa
del img_ref
print 'transform the tracks'
dt_linear = transform_tracks(dt_tracks,mat)
ds_linear = transform_tracks(ds_tracks,mat)
gq_linear = transform_tracks(gq_tracks,mat)
ei_linear = transform_tracks(ei_tracks,mat)
print 'save tensor tracks'
dpy_filename = base_dir + 'DTI/dt_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(dt_linear)
dpr_linear.close()
print 'save ei tracks'
dpy_filename = base_dir + 'DTI/ei_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ei_linear)
dpr_linear.close()
print 'save ds tracks'
dpy_filename = base_dir + 'DTI/ds_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ds_linear)
dpr_linear.close()
print 'save gq tracks'
dpy_filename = base_dir + 'DTI/gq_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(gq_linear)
dpr_linear.close()
print 'save lengths'
pkl_filename = base_dir + 'DTI/dt_lengths.pkl'
save_pickle(pkl_filename,lengths(dt_linear))
pkl_filename = base_dir + 'DTI/ei_lengths.pkl'
save_pickle(pkl_filename,lengths(ei_linear))
pkl_filename = base_dir + 'DTI/gq_lengths.pkl'
save_pickle(pkl_filename,lengths(gq_linear))
pkl_filename = base_dir + 'DTI/ds_lengths.pkl'
save_pickle(pkl_filename,lengths(ds_linear))
all,allo=fvtk.crossing(A,IND,VERTS,scale,True)
colors=np.zeros((len(all),3))
for (i,a) in enumerate(all):
if allo[i][0]==0 and allo[i][1]==0 and allo[i][2]==1:
pass
else:
colors[i]=cm.boys2rgb(allo[i])
fvtk.add(r,fvtk.line(all,colors))
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]
for i in range(w):
if i not in ind[1:]:
data2[:,:,:,cnt]=data[:,:,:,i]
bvals2[cnt]=bvals[i]
bvecs2[cnt,:]=bvecs[i,:]
cnt+=1
print 'after',bvals2.shape,bvecs2.shape
#always check the threshold but 50 should be okay
ten=Tensor(data2,bvals2,bvecs2,thresh=50)
#ten.ind is indices of the eigen directions projected on a sphere
#stopping criteria is FA of .2
eu=EuDX(a=ten.fa(),ind=ten.ind(),seeds=5000,a_low=0.2)
#generate tracks
ten_tracks=[track for track in eu]
print 'No tracks ',len(ten_tracks)
#remove short tracks smaller than 40mm i.e. 20 in native units
ten_tracks=[t for t in ten_tracks if length(t)>20]
print 'No reduced tracks ',len(ten_tracks)
raw_input('Press enter...')
#load the rois
imsk1=nib.load(dname+'/'+froi1)
roi1=imsk1.get_data()
ei.fit()
print 'EITL', time()-t
t=time()
#DSI
ds=DiffusionSpectrum(data,bvals,gradients,\
odf_sphere='symmetric642',\
mask=famask,\
half_sphere_grads=True,\
auto=True,\
save_odfs=False)
print 'DSI', time()-t
#ds.PK[FA<.2]=np.zeros(5)
t=time()
euler=EuDX(a=FA,ind=tensors.ind(),seeds=seeds,a_low=.2)
T=[track for track in euler]
print 'Eudx ten',time()-t,len(T)
dpr_linear = Dpy(ftracks[0], 'w')
dpr_linear.write_tracks(T)
dpr_linear.close()
del T
for i,qgrid in enumerate([ds,gqs,ei]):
t=time()
euler=EuDX(a=qgrid.PK,ind=qgrid.IN,seeds=seeds,odf_vertices=qgrid.odf_vertices,a_low=.2)
T=[track for track in euler]
print 'Eudx ',time()-t, len(T)
dpr_linear = Dpy(ftracks[i+1], 'w')