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():
# 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_phantom():
def f(t):
x=np.sin(t)
y=np.cos(t)
z=np.linspace(-1,1,len(x))
return x,y,z
fimg,fbvals,fbvecs=get_data('small_64D')
bvals=np.load(fbvals)
bvecs=np.load(fbvecs)
bvecs[np.isnan(bvecs)]=0
N=50 #timepoints
vol=orbital_phantom(bvals=bvals,
bvecs=bvecs,
func=f,
t=np.linspace(0,2*np.pi,N),
datashape=(10,10,10,len(bvals)),
origin=(5,5,5),
scale=(3,3,3),
angles=np.linspace(0,2*np.pi,16),
radii=np.linspace(0.2,2,6))
ten=Tensor(vol,bvals,bvecs)
FA=ten.fa()
FA[np.isnan(FA)]=0
assert_equal(np.round(FA.max()*1000),707)
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 show_phantom_crossing(fname,type='dsi',weight=None):
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
#fname='/home/eg309/Desktop/t5'
#fname='/home/eg309/Data/orbital_phantoms/20.0'
#fname='/tmp/t5'
fvol = np.memmap(fname, dtype='f8', mode='r', shape=(64,64,64,len(bvals)))
data=fvol[32-10:32+10,32-10:32+10,31:34,:]
#data=fvol[32,32,32,:][None,None,None,:]
ten=Tensor(data,bvals,bvecs,thresh=50)
FA=ten.fa()
def H(x):
res=(2*x*np.cos(x) + (x**2-2)*np.sin(x))/x**3
res[np.isnan(res)]=1/3.
return res
if type=='dsi':
mod=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
if type=='gqi':
mod=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
if type=='gqi2':
mod=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
if type=='eitl':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('laplacian')
ei.update()
ei.fit()
mod=ei
if type=='eitl2':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('laplap')
ei.update()
ei.fit()
mod=ei
if type=='eits':
ei=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=weight
ei.set_operator('signal')
ei.update()
ei.fit()
mod=ei
show_blobs(mod.ODF[:,:,0,:][:,:,None,:],mod.odf_vertices,mod.odf_faces,fa_slice=FA[:,:,0],size=1.5,scale=1.)
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)
base_filename = base_dir + 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"
img = nib.load(nii_bet_ecc_filename)
old_data = img.get_data()
old_affine = img.get_affine()
from dipy.align.aniso2iso import resample
zooms=img.get_header().get_zooms()[:3]
print 'old zooms:', zooms
new_zooms=(2.,2.,2.)
print 'new zoom', new_zooms
data,affine=resample(old_data,old_affine,zooms,new_zooms)
print("Computing FA...")
tensors = Tensor(data, bvals, gradients, thresh=b0_threshold)
FA = tensors.fa()
print("FA:", FA.shape)
# print("Computing EuDX reconstruction.")
# euler = EuDX(a=FA, ind=tensors.ind(),
# odf_vertices=get_sphere('symmetric362').vertices,
# seeds=eudx_seeds, a_low=eudx_fa_stop)
# tensor_tracks_old = [track for track in euler]
# tensor_tracks = [track for track in tensor_tracks_old if track.shape[0]>1]
print("Saving results.")
fa_filename = basedir_reconstruction + 'fa_resample.nii.gz'
print "Saving FA:", fa_filename
fa_img = nib.Nifti1Image(data=FA, affine=affine)
#D=data[20:90,20:90,18:22] #D=data[40:44,40:44,18:22] #del data D=data from time import time t0=time() ds=DiffusionSpectrum(D,bvals,bvecs,mask=mask) t1=time() print t1-t0,' secs' GFA=ds.gfa() t2=time() ten=Tensor(D,bvals,bvecs,mask=mask) t3=time() print t3-t2,' secs' FA=ten.fa() from dipy.tracking.propagation import EuDX IN=ds.ind() eu=EuDX(ten.fa(),IN[:,:,:,0],seeds=10000,a_low=0.2) tracks=[e for e in eu] #FAX=np.zeros(IN.shape) #for i in range(FAX.shape[-1]): # FAX[:,:,:,i]=GFA
#D=data[20:90,20:90,18:22]
#D=data[40:44,40:44,18:22]
#del data
D = data
from time import time
t0 = time()
ds = DiffusionSpectrum(D, bvals, bvecs, mask=mask)
t1 = time()
print t1 - t0, ' secs'
GFA = ds.gfa()
t2 = time()
ten = Tensor(D, bvals, bvecs, mask=mask)
t3 = time()
print t3 - t2, ' secs'
FA = ten.fa()
from dipy.tracking.propagation import EuDX
IN = ds.ind()
eu = EuDX(ten.fa(), IN[:, :, :, 0], seeds=10000, a_low=0.2)
tracks = [e for e in eu]
#FAX=np.zeros(IN.shape)
#for i in range(FAX.shape[-1]):
# FAX[:,:,:,i]=GFA
bvecs2=np.zeros((w-len(ind)+1,3))
#copy data
cnt=0
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...')
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))
print IND.shape
print VERTS.shape
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)
def create_FA_displacements_warp_FAs(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)
local=dti_dname
print('-------------------------')
print('----Working with DTI-----')
print('-------------------------')
print local
print('1.Remove the sculp using bet')
fdatabet=fname+'_bet.nii.gz'
bet(fdata,fdatabet)
print('2.Load data and save S0')
data,affine=load_img(fdatabet)
fs0=pjoin(local,'S0_bet.nii.gz')
#print fs0
save_img(data[...,0],affine,fs0)
print('3.Create Tensors and save FAs')
bvals=np.loadtxt(fbvals)
gradients=np.loadtxt(fbvecs).T
ten=Tensor(data,bvals,gradients,thresh=50)
ffa=pjoin(local,'FA_bet.nii.gz')
print ffa
save_img(ten.fa(),affine,ffa)
fmd=pjoin(local,'MD_bet.nii.gz')
#print fmd
save_img(ten.md(),affine,fmd)
print('4.Create the displacements using fnirt')
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')
create_displacements(ffa,fmat,fnon,finv,fdis,fdisa)
print('5.Warp FA')
ffaw=pjoin(local,'FAW_bet.nii.gz')
apply_warp(ffa,fmat,fnon,ffaw)
##warp_displacements(ffa,fmat,fdis,fref,ffaw2,order=1)
print('6.Warp S0')
fs0w=pjoin(local,'S0W_bet.nii.gz')
apply_warp(fs0,fmat,fnon,fs0w)
##warp_displacements(fs0,fmat,fdis,fref,fs0w2,order=1)
print('7.Warp MD')
fmdw=pjoin(local,'MDW_bet.nii.gz')
apply_warp(fmd,fmat,fnon,fmdw)
ei=EquatorialInversion(dat,bvals,bvecs,odf_sphere='symmetric642',
half_sphere_grads=True,auto=False,save_odfs=True,fast=True)
ei.radius=np.arange(0,5,0.1)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
ds=DiffusionSpectrum(dat,bvals,bvecs,
odf_sphere='symmetric642',
mask=None,
half_sphere_grads=True,
auto=True,
save_odfs=True)
ten=Tensor(dat,bvals,bvecs)
FA=ten.fa()
#gq=GeneralizedQSampling(dat,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
#gq2=GeneralizedQSampling(dat,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
#show_blobs(ds.ODF,ds.odf_vertices,ds.odf_faces,FA.squeeze(),size=1.5,scale=1.)
show_blobs(ei.ODF,ei.odf_vertices,ei.odf_faces,FA.squeeze(),size=1.5,scale=1.)
"""
#dti reconstruction
ten=Tensor(data,bvals,bvecs2,mask=mask)
#generate tensor tracks with random seeds
eu=EuDX(ten.fa(),ten.ind(),seeds=10000,a_low=0.2)
tenT=[e for e in eu]
#dsi reconstruction
ds=DiffusionSpectrum(data,bvals,bvecs2,mask=mask)
base_filename = base_dir + filename
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'
ftracks=[ftracks_dti,ftracks_dsi,ftracks_gqi,ftracks_eit]
#load data
fraw=dname+'data.nii.gz'
fbval=dname+'bvals'
fbvec=dname+'bvecs'
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(fbval)
gradients = np.loadtxt(fbvec).T
print 'Data shape',data.shape
t=time()
#calculate FA
tensors = Tensor(data, bvals, gradients, thresh=50)
FA = tensors.fa()
print 'ten',time()-t
famask=FA>=.2
maskimg=nib.Nifti1Image(famask.astype(np.uint8),affine)
nib.save(maskimg,dname+'mask.nii.gz')
#create seeds for first mask
seeds=get_seeds(famask,3,1)
print 'no of seeds', len(seeds)
t=time()
#GQI
gqs=GeneralizedQSampling(data,bvals,gradients,1.2,
odf_sphere='symmetric642',
