def test_peak_finding():
vertices, faces=get_sphere('symmetric724')
odf=np.zeros(len(vertices))
odf = np.abs(vertices.sum(-1))
odf[1] = 10.
odf[505] = 505.
odf[143] = 143.
peaks, inds=peak_finding(odf.astype('f8'), faces.astype('uint16'))
print peaks, inds
edges = unique_edges(faces)
peaks, inds = local_maxima(odf, edges)
print peaks, inds
vertices_half, edges_half, faces_half = reduce_antipodal(vertices, faces)
n = len(vertices_half)
peaks, inds = local_maxima(odf[:n], edges_half)
print peaks, inds
mevals=np.array(([0.0015,0.0003,0.0003],
[0.0015,0.0003,0.0003]))
e0=np.array([1,0,0.])
e1=np.array([0.,1,0])
mevecs=[all_tensor_evecs(e0),all_tensor_evecs(e1)]
odf = multi_tensor_odf(vertices, [0.5,0.5], mevals, mevecs)
peaks, inds=peak_finding(odf, faces)
print peaks, inds
peaks2, inds2 = local_maxima(odf[:n], edges_half)
print peaks2, inds2
assert_equal(len(peaks), 2)
assert_equal(len(peaks2), 2)
def standard_dsi_algorithm(S,bvals,bvecs):
#volume size
sz=16
#shifting
origin=8
#hanning width
filter_width=32.
#number of signal sampling points
n=515
#odf radius
#radius=np.arange(2.1,30,.1)
radius=np.arange(2.1,6,.2)
#radius=np.arange(.1,6,.1)
bv=bvals
bmin=np.sort(bv)[1]
bv=np.sqrt(bv/bmin)
qtable=np.vstack((bv,bv,bv)).T*bvecs
qtable=np.floor(qtable+.5)
#calculate radius for the hanning filter
r = np.sqrt(qtable[:,0]**2+qtable[:,1]**2+qtable[:,2]**2)
#setting hanning filter width and hanning
hanning=.5*np.cos(2*np.pi*r/filter_width)
#center and index in q space volume
q=qtable+origin
q=q.astype('i8')
#apply the hanning filter
values=S*hanning
#create the signal volume
Sq=np.zeros((sz,sz,sz))
for i in range(n):
Sq[q[i][0],q[i][1],q[i][2]]+=values[i]
#apply fourier transform
Pr=fftshift(np.abs(np.real(fftn(fftshift(Sq),(sz,sz,sz)))))
#vertices, edges, faces = create_unit_sphere(5)
#vertices, faces = sphere_vf_from('symmetric362')
vertices, faces = sphere_vf_from('symmetric724')
odf = np.zeros(len(vertices))
for m in range(len(vertices)):
xi=origin+radius*vertices[m,0]
yi=origin+radius*vertices[m,1]
zi=origin+radius*vertices[m,2]
PrI=map_coordinates(Pr,np.vstack((xi,yi,zi)),order=1)
for i in range(len(radius)):
odf[m]=odf[m]+PrI[i]*radius[i]**2
peaks,inds=peak_finding(odf.astype('f8'),faces.astype('uint16'))
return Pr,odf,peaks
def standard_dsi_algorithm(S, bvals, bvecs):
#volume size
sz = 16
#shifting
origin = 8
#hanning width
filter_width = 32.
#number of signal sampling points
n = 515
#odf radius
#radius=np.arange(2.1,30,.1)
radius = np.arange(2.1, 6, .2)
#radius=np.arange(.1,6,.1)
bv = bvals
bmin = np.sort(bv)[1]
bv = np.sqrt(bv / bmin)
qtable = np.vstack((bv, bv, bv)).T * bvecs
qtable = np.floor(qtable + .5)
#calculate radius for the hanning filter
r = np.sqrt(qtable[:, 0]**2 + qtable[:, 1]**2 + qtable[:, 2]**2)
#setting hanning filter width and hanning
hanning = .5 * np.cos(2 * np.pi * r / filter_width)
#center and index in q space volume
q = qtable + origin
q = q.astype('i8')
#apply the hanning filter
values = S * hanning
#create the signal volume
Sq = np.zeros((sz, sz, sz))
for i in range(n):
Sq[q[i][0], q[i][1], q[i][2]] += values[i]
#apply fourier transform
Pr = fftshift(np.abs(np.real(fftn(fftshift(Sq), (sz, sz, sz)))))
#vertices, edges, faces = create_unit_sphere(5)
#vertices, faces = sphere_vf_from('symmetric362')
vertices, faces = sphere_vf_from('symmetric724')
odf = np.zeros(len(vertices))
for m in range(len(vertices)):
xi = origin + radius * vertices[m, 0]
yi = origin + radius * vertices[m, 1]
zi = origin + radius * vertices[m, 2]
PrI = map_coordinates(Pr, np.vstack((xi, yi, zi)), order=1)
for i in range(len(radius)):
odf[m] = odf[m] + PrI[i] * radius[i]**2
peaks, inds = peak_finding(odf.astype('f8'), faces.astype('uint16'))
return Pr, odf, peaks
def revised_peak_no(odf,odf_faces,peak_thr):
peaks,inds=peak_finding(odf,odf_faces)
ibigp=np.where(peaks>peak_thr*peaks[0])[0]
l=len(ibigp)
if l>3:
l=3
if l==0:
return np.sum(peaks[l]/np.float(peaks[0])>0)
if l>0:
return np.sum(peaks[:l]/np.float(peaks[0])>0)
def test_performance():
# test this implementation against Frank Yeh implementation
vertices, faces = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
adj = vertinds_to_neighbors(vert_inds, faces)
np.random.seed(42)
vert_vals = np.random.uniform(size=(n_vertices, ))
maxinds = argmax_from_adj(vert_vals, vert_inds, adj)
maxes, pfmaxinds = dcr.peak_finding(vert_vals, faces)
assert_array_equal(maxinds, pfmaxinds[::-1])
def test_performance():
# test this implementation against Frank Yeh implementation
vertices, faces = SPHERE_DATA
n_vertices = vertices.shape[0]
vert_inds = sym_hemisphere(vertices)
adj = vertinds_to_neighbors(vert_inds, faces)
np.random.seed(42)
vert_vals = np.random.uniform(size=(n_vertices,))
maxinds = argmax_from_adj(vert_vals, vert_inds, adj)
maxes, pfmaxinds = dcr.peak_finding(vert_vals, faces)
assert_array_equal(maxinds, pfmaxinds[::-1])
def simple_peaks(ODF,faces,thr):
x,g=ODF.shape
PK=np.zeros((x,5))
IN=np.zeros((x,5))
for (i,odf) in enumerate(ODF):
peaks,inds=peak_finding(odf,faces)
ibigp=np.where(peaks>thr*peaks[0])[0]
l=len(ibigp)
if l>3:
l=3
PK[i,:l]=peaks[:l]
IN[i,:l]=inds[:l]
return PK,IN
def test_dandelion():
fimg,fbvals,fbvecs=get_data('small_64D')
bvals=np.load(fbvals)
gradients=np.load(fbvecs)
data=nib.load(fimg).get_data()
print(bvals.shape, gradients.shape, data.shape)
sd=SphericalDandelion(data,bvals,gradients)
sdf=sd.spherical_diffusivity(data[5,5,5])
print(sdf.shape)
gq=GeneralizedQSampling(data,bvals,gradients)
sodf=gq.odf(data[5,5,5])
eds=np.load(get_sphere('symmetric362'))
vertices=eds['vertices']
faces=eds['faces']
print(faces.shape)
peaks,inds=peak_finding(np.squeeze(sdf),faces)
print(peaks, inds)
peaks2,inds2=peak_finding(np.squeeze(sodf),faces)
print(peaks2, inds2)
'''
def extended_peak_filtering(odfs,odf_faces,thr=0.3):
new_peaks=[]
for (i,odf) in enumerate(odfs):
peaks,inds=peak_finding(odf,odf_faces)
ismallp=np.where(peaks/peaks[0]<thr)
if len(ismallp[0])>0:
l=ismallp[0][0]
else:
l=len(peaks)
print '#',i,peaknos[i]
if l==0:
print peaks[0]/peaks[0]
else:
print peaks[:l]/peaks[0]
def simple_peaks(ODF,faces,thr,low):
x,y,z,g=ODF.shape
S=ODF.reshape(x*y*z,g)
f,g=S.shape
PK=np.zeros((f,5))
IN=np.zeros((f,5))
for (i,odf) in enumerate(S):
if odf.max()>low:
peaks,inds=peak_finding(odf,faces)
ibigp=np.where(peaks>thr*peaks[0])[0]
l=len(ibigp)
if l>3:
l=3
PK[i,:l]=peaks[:l]/np.float(peaks[0])
IN[i,:l]=inds[:l]
PK=PK.reshape(x,y,z,5)
IN=IN.reshape(x,y,z,5)
return PK,IN
def super_reduced_peaks(odfs,odf_vertices,odf_faces,angle):
final=[]
for (i,odf) in enumerate(odfs):
pks,ins=peak_finding(odf,odf_faces)
peaks=pks[:3]
inds=ins[:3]
print '#', peaks
del_peaks=[]
for (j,ind) in enumerate(inds):
pts=radial_points_on_sphere(ind,odf_vertices,angle)
for p in pts:
if peaks[j]<odf[p]:
del_peaks.append(j)
peaks=np.delete(peaks,del_peaks)
print '@',peaks
print ' ', len(peaks)
final.append(len(peaks))
print(final)
def extended_peak_filtering(odfs, odf_faces):
new_peaks = []
for (i, odf) in enumerate(odfs):
peaks, inds = peak_finding(odf, odf_faces)
dpeaks = np.abs(np.diff(peaks[:3]))
print "#", i, peaknos[i]
print peaks[:3]
print dpeaks
print odf.min()
print peaks[:3] / peaks[0]
print peaks[1:3] / peaks[1]
print
"""
ismallp=np.where(dpeaks<2)
if len(ismallp[0])>0:
l=ismallp[0][0]
else:
l=len(peaks)
"""
"""
def fit(self):
#memory allocations for 4D volumes
if len(self.datashape) == 4:
x, y, z, g = self.datashape
S = self.data.reshape(x * y * z, g)
GFA = np.zeros((x * y * z))
IN = np.zeros((x * y * z, 5))
NFA = np.zeros((x * y * z, 5))
QA = np.zeros((x * y * z, 5))
PK = np.zeros((x * y * z, 5))
if self.save_odfs:
ODF = np.zeros((x * y * z, self.odfn))
if self.mask != None:
if self.mask.shape[:3] == self.datashape[:3]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(self.datashape[:3])
msk = self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(self.datashape) == 2:
x, g = self.datashape
S = self.data
GFA = np.zeros(x)
IN = np.zeros((x, 5))
NFA = np.zeros((x, 5))
QA = np.zeros((x, 5))
PK = np.zeros((x, 5))
if self.save_odfs:
ODF = np.zeros((x, self.odfn))
if self.mask != None:
if mask.shape[0] == self.datashape[0]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(self.datashape[:1])
msk = self.mask.ravel().copy()
#find the global normalization parameter
#useful for quantitative anisotropy
glob_norm_param = 0.
#loop over all voxels
for (i, s) in enumerate(S):
if msk[i] > 0:
#calculate the diffusion propagator or spectrum
Pr = self.pdf(s)
#calculate the orientation distribution function
odf = self.odf(Pr)
if self.save_odfs:
ODF[i] = odf
#normalization for QA
glob_norm_param = max(np.max(odf), glob_norm_param)
#calculate the generalized fractional anisotropy
GFA[i] = self.std_over_rms(odf)
#find peaks
peaks, inds = peak_finding(odf, self.odf_faces)
#remove small peaks
l = self.reduce_peaks(peaks, odf.min())
#print '#',l,peaks[:l]
if l == 0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l] - np.min(odf)
PK[i][l] = peaks[l]
if l > 0 and l < 5:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l] - np.min(odf)
PK[i][:l] = peaks[:l]
"""
if len(peaks)>0:
ismallp=np.where(peaks/peaks.min()<self.peak_thr)
l=ismallp[0][0]
if l<5 and l>0:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l]-np.min(odf)
if l==0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l]-np.min(odf)
"""
if len(self.datashape) == 4:
self.GFA = GFA.reshape(x, y, z)
self.NFA = NFA.reshape(x, y, z, 5)
self.QA = QA.reshape(x, y, z, 5) / glob_norm_param
self.IN = IN.reshape(x, y, z, 5)
self.PK = PK.reshape(x, y, z, 5)
if self.save_odfs:
self.ODF = ODF.reshape(x, y, z, ODF.shape[-1])
self.QA_norm = glob_norm_param
if len(self.datashape) == 2:
self.GFA = GFA
self.NFA = NFA
self.QA = QA
self.IN = IN
self.PK = PK
if self.save_odfs:
self.ODF = ODF
self.QA_norm = None
def test_gqiodf():
#read bvals,gradients and data
fimg,fbvals,fbvecs=get_data('small_64D')
bvals=np.load(fbvals)
gradients=np.load(fbvecs)
data=nib.load(fimg).get_data()
#print(bvals.shape)
#print(gradients.shape)
#print(data.shape)
t1=time.clock()
gqs = gq.GeneralizedQSampling(data,bvals,gradients)
ten = dt.Tensor(data,bvals,gradients,thresh=50)
fa=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
t2=time.clock()
#print('GQS in %d' %(t2-t1))
eds=np.load(get_sphere('symmetric362'))
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,'Euler test fails')
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)
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)
def test():
# img=nib.load('/home/eg309/Data/project01_dsi/connectome_0001/tp1/RAWDATA/OUT/mr000001.nii.gz')
btable = np.loadtxt(get_data("dsi515btable"))
# volume size
sz = 16
# shifting
origin = 8
# hanning width
filter_width = 32.0
# number of signal sampling points
n = 515
# odf radius
radius = np.arange(2.1, 6, 0.2)
# create q-table
bv = btable[:, 0]
bmin = np.sort(bv)[1]
bv = np.sqrt(bv / bmin)
qtable = np.vstack((bv, bv, bv)).T * btable[:, 1:]
qtable = np.floor(qtable + 0.5)
# copy bvals and bvecs
bvals = btable[:, 0]
bvecs = btable[:, 1:]
# S=img.get_data()[38,50,20]#[96/2,96/2,20]
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (60, 0), (90, 90)], fractions=[0, 0, 0], snr=None
)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
dn = DiffusionNabla(S2, bvals, bvecs, auto=False)
pR = dn.equators
odf = dn.odf(S)
# Xs=dn.precompute_interp_coords()
peaks, inds = peak_finding(odf.astype("f8"), dn.odf_faces.astype("uint16"))
print peaks
print peaks / peaks.min()
# print dn.PK
dn.fit()
print dn.PK
# """
ren = fvtk.ren()
colors = fvtk.colors(odf, "jet")
fvtk.add(ren, fvtk.point(dn.odf_vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
stop
# ds=DiffusionSpectrum(S2,bvals,bvecs)
# tpr=ds.pdf(S)
# todf=ds.odf(tpr)
"""
#show projected signal
Bvecs=np.concatenate([bvecs[1:],-bvecs[1:]])
X0=np.dot(np.diag(np.concatenate([S[1:],S[1:]])),Bvecs)
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(X0,fvtk.yellow,1,2,16,16))
fvtk.show(ren)
"""
# qtable=5*matrix[:,1:]
# calculate radius for the hanning filter
r = np.sqrt(qtable[:, 0] ** 2 + qtable[:, 1] ** 2 + qtable[:, 2] ** 2)
# setting hanning filter width and hanning
hanning = 0.5 * np.cos(2 * np.pi * r / filter_width)
# center and index in q space volume
q = qtable + origin
q = q.astype("i8")
# apply the hanning filter
values = S * hanning
"""
#plot q-table
ren=fvtk.ren()
colors=fvtk.colors(values,'jet')
fvtk.add(ren,fvtk.point(q,colors,1,0.1,6,6))
fvtk.show(ren)
"""
# create the signal volume
Sq = np.zeros((sz, sz, sz))
for i in range(n):
Sq[q[i][0], q[i][1], q[i][2]] += values[i]
# apply fourier transform
Pr = fftshift(np.abs(np.real(fftn(fftshift(Sq), (sz, sz, sz)))))
# """
ren = fvtk.ren()
vol = fvtk.volume(Pr)
fvtk.add(ren, vol)
fvtk.show(ren)
# """
"""
from enthought.mayavi import mlab
mlab.pipeline.volume(mlab.pipeline.scalar_field(Sq))
mlab.show()
"""
# vertices, edges, faces = create_unit_sphere(5)
vertices, faces = sphere_vf_from("symmetric362")
odf = np.zeros(len(vertices))
for m in range(len(vertices)):
xi = origin + radius * vertices[m, 0]
yi = origin + radius * vertices[m, 1]
zi = origin + radius * vertices[m, 2]
PrI = map_coordinates(Pr, np.vstack((xi, yi, zi)), order=1)
for i in range(len(radius)):
odf[m] = odf[m] + PrI[i] * radius[i] ** 2
"""
ren=fvtk.ren()
colors=fvtk.colors(odf,'jet')
fvtk.add(ren,fvtk.point(vertices,colors,point_radius=.05,theta=8,phi=8))
fvtk.show(ren)
"""
"""
#Pr[Pr<500]=0
ren=fvtk.ren()
#ren.SetBackground(1,1,1)
fvtk.add(ren,fvtk.volume(Pr))
fvtk.show(ren)
"""
peaks, inds = peak_finding(odf.astype("f8"), faces.astype("uint16"))
Eq = np.zeros((sz, sz, sz))
for i in range(n):
Eq[q[i][0], q[i][1], q[i][2]] += S[i] / S[0]
LEq = laplace(Eq)
# Pr[Pr<500]=0
ren = fvtk.ren()
# ren.SetBackground(1,1,1)
fvtk.add(ren, fvtk.volume(Eq))
fvtk.show(ren)
phis = np.linspace(0, 2 * np.pi, 100)
planars = []
for phi in phis:
planars.append(sphere2cart(1, np.pi / 2, phi))
planars = np.array(planars)
planarsR = []
for v in vertices:
R = vec2vec_rotmat(np.array([0, 0, 1]), v)
planarsR.append(np.dot(R, planars.T).T)
"""
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(planarsR[0],fvtk.green,1,0.1,8,8))
fvtk.add(ren,fvtk.point(2*planarsR[1],fvtk.red,1,0.1,8,8))
fvtk.show(ren)
"""
azimsums = []
for disk in planarsR:
diskshift = 4 * disk + origin
# Sq0=map_coordinates(Sq,diskshift.T,order=1)
# azimsums.append(np.sum(Sq0))
# Eq0=map_coordinates(Eq,diskshift.T,order=1)
# azimsums.append(np.sum(Eq0))
LEq0 = map_coordinates(LEq, diskshift.T, order=1)
azimsums.append(np.sum(LEq0))
azimsums = np.array(azimsums)
# """
ren = fvtk.ren()
colors = fvtk.colors(azimsums, "jet")
fvtk.add(ren, fvtk.point(vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
# for p in planarsR[0]:
"""
def overlaps_faces(arrays):
n = len(arrays)
m = set([])
for i in range(n - 1):
for j in range(i + 1, n):
m = set.union(m, set.intersection(set(faces[arrays[i]].ravel()), set(faces[arrays[j]].ravel())))
return m
import pickle
f = open("dn.dump", "r")
print "picked dn reloading ...\n"
[faces, vertices, odf] = pickle.load(f)
print "picked dn reloaded ...\n"
print "old peak hunting ...\n"
peaks, peakinds = peak_finding(odf, faces)
print "new peak hunting ...\n"
newpeaks = dominant(faces, odf)
# oldpeaks = [305, 317, 171, 170, 172, 169, 40, 45, 2]
newpeaks = [2, 40, 45, 169, 170, 171, 172, 305, 317, 323, 361, 366, 490, 491, 492, 493, 626, 638]
# newpeaks = [2, 40, 45, 169, 170, 171, 172, 305][::-1]
print "starting green paint job ...\n"
# print 'old peaks', peakinds
# print 'half the (old) peaks:', oldpeaks, '\n'
# print 'half the peaks:', newpeaks, '\n'
print "all the new peaks:", newpeaks, "\n"
white = 0.0
red = 1.0
def fit(self):
#memory allocations for 4D volumes
if len(self.datashape)==4:
x,y,z,g=self.datashape
S=self.data.reshape(x*y*z,g)
GFA=np.zeros((x*y*z))
IN=np.zeros((x*y*z,5))
NFA=np.zeros((x*y*z,5))
QA=np.zeros((x*y*z,5))
PK=np.zeros((x*y*z,5))
if self.save_odfs:
ODF=np.zeros((x*y*z,self.odfn))
#BODF=np.zeros((x*y*z,self.odfn))
if self.mask != None:
if self.mask.shape[:3]==self.datashape[:3]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(self.datashape[:3])
msk=self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(self.datashape)==2:
x,g= self.datashape
S=self.data
GFA=np.zeros(x)
IN=np.zeros((x,5))
NFA=np.zeros((x,5))
QA=np.zeros((x,5))
PK=np.zeros((x,5))
if self.save_odfs:
ODF=np.zeros((x,self.odfn))
#BODF=np.zeros((x,self.odfn))
if self.mask != None:
if self.mask.shape[0]==self.datashape[0]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(self.datashape[:1])
msk=self.mask.ravel().copy()
#find the global normalization parameter
#useful for quantitative anisotropy
glob_norm_param = 0.
#loop over all voxels
for (i,s) in enumerate(S):
if msk[i]>0:
#calculate the orientation distribution function
#odf=self.odf(s)
odf=self.odf(s)
odf=self.angular_weighting(odf)
if self.save_odfs:
ODF[i]=odf
#normalization for QA
glob_norm_param=max(np.max(odf),glob_norm_param)
#calculate the generalized fractional anisotropy
GFA[i]=self.std_over_rms(odf)
odf_max=odf.max()
#if not in isotropic case
#if odf.min()<self.iso_thr*odf_max:
if np.std(odf)/np.mean(odf) > self.iso_thr:
#find peaks
peaks,inds=peak_finding(odf,self.odf_faces)
ismallp=np.where(peaks/peaks[0]<self.peak_thr)
if len(ismallp[0])>0:
l=ismallp[0][0]
#do not allow more that three peaks
if l>3:
l=3
else:
l=len(peaks)
if l==0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l]-np.min(odf)
PK[i][l] = peaks[l]
if l>0 and l<=3:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l]-np.min(odf)
PK[i][:l] = peaks[:l]
if len(self.datashape) == 4:
self.GFA=GFA.reshape(x,y,z)
self.NFA=NFA.reshape(x,y,z,5)
self.QA=QA.reshape(x,y,z,5)/glob_norm_param
self.PK=PK.reshape(x,y,z,5)
self.IN=IN.reshape(x,y,z,5)
if self.save_odfs:
self.ODF=ODF.reshape(x,y,z,ODF.shape[-1])
self.QA_norm=glob_norm_param
if len(self.datashape) == 2:
self.GFA=GFA
self.NFA=NFA
self.QA=QA
self.PK=PK
self.IN=IN
if self.save_odfs:
self.ODF=ODF
#self.BODF=BODF
self.QA_norm=None
def __init__(self, data, bvals, gradients,
Lambda=1.2, odf_sphere='symmetric362', mask=None):
""" Generates a model-free description for every voxel that can
be used from simple to very complicated configurations like
quintuple crossings if your datasets support them.
You can use this class for every kind of DWI image but it will
perform much better when you have a balanced sampling scheme.
Implements equation [9] from Generalized Q-Sampling as
described in Fang-Cheng Yeh, Van J. Wedeen, Wen-Yih Isaac Tseng.
Generalized Q-Sampling Imaging. IEEE TMI, 2010.
Parameters
-----------
data: array, shape(X,Y,Z,D)
bvals: array, shape (N,)
gradients: array, shape (N,3) also known as bvecs
Lambda: float, optional
smoothing parameter - diffusion sampling length
odf_sphere : None or str or tuple, optional
input that will result in vertex, face arrays for a sphere.
mask : None or ndarray, optional
Key Properties
---------------
QA : array, shape(X,Y,Z,5), quantitative anisotropy
IN : array, shape(X,Y,Z,5), indices of QA, qa unit directions
fwd : float, normalization parameter
Notes
-------
In order to reconstruct the spin distribution function a nice symmetric
evenly distributed sphere is provided using 362 points. This is usually
sufficient for most of the datasets.
See also
--------
dipy.tracking.propagation.EuDX, dipy.reconst.dti.Tensor,
dipy.data.__init__.get_sphere
"""
odf_vertices, odf_faces = sphere_vf_from(odf_sphere)
self.odf_vertices=odf_vertices
# 0.01506 = 6*D where D is the free water diffusion coefficient
# l_values sqrt(6 D tau) D free water diffusion coefficient and
# tau included in the b-value
scaling = np.sqrt(bvals*0.01506)
tmp=np.tile(scaling, (3,1))
#the b vectors might have nan values where they correspond to b
#value equals with 0
gradients[np.isnan(gradients)]= 0.
gradsT = gradients.T
b_vector=gradsT*tmp # element-wise also known as the Hadamard product
#q2odf_params=np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi)
q2odf_params=np.real(np.sinc(np.dot(b_vector.T, odf_vertices.T) * Lambda/np.pi))
#q2odf_params[np.isnan(q2odf_params)]= 1.
#define total mask
#tot_mask = (mask > 0) & (data[...,0] > thresh)
S=data
datashape=S.shape #initial shape
msk=None #tmp mask
if len(datashape)==4:
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))
if mask != None:
if mask.shape[:3]==datashape[:3]:
msk=mask.ravel().copy()
#print 'msk.shape',msk.shape
if len(datashape)==2:
x,g= S.shape
QA = np.zeros((x,5))
IN = np.zeros((x,5))
glob_norm_param = 0
self.q2odf_params=q2odf_params
#Calculate Quantitative Anisotropy and
#find the peaks and the indices
#for every voxel
if mask !=None:
for (i,s) in enumerate(S):
if msk[i]>0:
#Q to ODF
odf=np.dot(s,q2odf_params)
peaks,inds=rp.peak_finding(odf,odf_faces)
glob_norm_param=max(np.max(odf),glob_norm_param)
#remove the isotropic part
peaks = peaks - np.min(odf)
l=min(len(peaks),5)
QA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
if mask==None:
for (i,s) in enumerate(S):
#Q to ODF
odf=np.dot(s,q2odf_params)
peaks,inds=rp.peak_finding(odf,odf_faces)
glob_norm_param=max(np.max(odf),glob_norm_param)
#remove the isotropic part
peaks = peaks - np.min(odf)
l=min(len(peaks),5)
QA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
#normalize
QA/=glob_norm_param
if len(datashape) == 4:
self.QA=QA.reshape(x,y,z,5)
self.IN=IN.reshape(x,y,z,5)
if len(datashape) == 2:
self.QA=QA
self.IN=IN
self.glob_norm_param = glob_norm_param
def fit(self):
""" process all voxels
"""
S = self.data
datashape = S.shape #initial shape
#memory allocations for 4D volumes
if len(datashape) == 4:
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))
if self.save_odfs:
ODF = np.zeros((x * y * z, self.odfn))
if self.mask != None:
if self.mask.shape[:3] == datashape[:3]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(datashape[:3])
msk = self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(datashape) == 2:
x, g = S.shape
QA = np.zeros((x, 5))
IN = np.zeros((x, 5))
if self.save_odfs:
ODF = np.zeros((x, self.odfn))
if self.mask != None:
if self.mask.shape[0] == datashape[0]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(datashape[:1])
msk = self.mask.ravel().copy()
glob_norm_param = 0
#Calculate Quantitative Anisotropy and
#find the peaks and the indices
#for every voxel
for (i, s) in enumerate(S):
if msk[i] > 0:
#Q to ODF
odf = np.dot(s, self.q2odf_params)
min_odf = np.min(odf)
if self.save_odfs:
ODF[i] = odf #-min_odf
peaks, inds = rp.peak_finding(odf, self.odf_faces)
glob_norm_param = max(np.max(odf), glob_norm_param)
#print peaks,min_odf
#remove the isotropic part
l = self.reduce_peaks(peaks, min_odf)
if l == 0:
QA[i][0] = peaks[0] - min_odf
IN[i][0] = inds[0]
if l > 0 and l < 5:
QA[i][:l] = peaks[:l] - min_odf
IN[i][:l] = inds[:l]
#normalize QA
QA /= glob_norm_param
if len(datashape) == 4:
self.QA = QA.reshape(x, y, z, 5)
self.IN = IN.reshape(x, y, z, 5)
if self.save_odfs:
self.ODF = ODF.reshape(x, y, z, ODF.shape[-1])
self.QA_norm = glob_norm_param
if len(datashape) == 2:
self.QA = QA
self.IN = IN
if self.save_odfs:
self.ODF = ODF
self.QA_norm = None
self.glob_norm_param = glob_norm_param
def fit(self):
""" process all voxels
"""
S=self.data
datashape=S.shape #initial shape
#memory allocations for 4D volumes
if len(datashape)==4:
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))
if self.save_odfs:
ODF=np.zeros((x*y*z,self.odfn))
if self.mask != None:
if self.mask.shape[:3]==datashape[:3]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(datashape[:3])
msk=self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(datashape)==2:
x,g= S.shape
QA = np.zeros((x,5))
IN = np.zeros((x,5))
if self.save_odfs:
ODF=np.zeros((x,self.odfn))
if self.mask != None:
if self.mask.shape[0]==datashape[0]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(datashape[:1])
msk=self.mask.ravel().copy()
glob_norm_param = 0
#Calculate Quantitative Anisotropy and
#find the peaks and the indices
#for every voxel
for (i,s) in enumerate(S):
if msk[i]>0:
#Q to ODF
odf=np.dot(s,self.q2odf_params)
min_odf=np.min(odf)
if self.save_odfs:
ODF[i]=odf#-min_odf
peaks,inds=rp.peak_finding(odf,self.odf_faces)
glob_norm_param=max(np.max(odf),glob_norm_param)
#print peaks,min_odf
#remove the isotropic part
l=self.reduce_peaks(peaks,min_odf)
if l==0:
QA[i][0] = peaks[0]-min_odf
IN[i][0] = inds[0]
if l>0 and l<5:
QA[i][:l] = peaks[:l]-min_odf
IN[i][:l] = inds[:l]
#normalize QA
QA/=glob_norm_param
if len(datashape) == 4:
self.QA=QA.reshape(x,y,z,5)
self.IN=IN.reshape(x,y,z,5)
if self.save_odfs:
self.ODF=ODF.reshape(x,y,z,ODF.shape[-1])
self.QA_norm= glob_norm_param
if len(datashape) == 2:
self.QA=QA
self.IN=IN
if self.save_odfs:
self.ODF=ODF
self.QA_norm=None
self.glob_norm_param = glob_norm_param
def test_gqi_small():
#read bvals,gradients and data
fimg,fbvals,fbvecs=get_data('small_64D')
bvals=np.load(fbvals)
gradients=np.load(fbvecs)
data=nib.load(fimg).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(get_sphere('symmetric362'))
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')
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))
def fit(self):
#memory allocations for 4D volumes
if len(self.datashape) == 4:
x, y, z, g = self.datashape
S = self.data.reshape(x * y * z, g)
GFA = np.zeros((x * y * z))
IN = np.zeros((x * y * z, 5))
NFA = np.zeros((x * y * z, 5))
QA = np.zeros((x * y * z, 5))
PK = np.zeros((x * y * z, 5))
if self.save_odfs:
ODF = np.zeros((x * y * z, self.odfn))
#BODF=np.zeros((x*y*z,self.odfn))
if self.mask != None:
if self.mask.shape[:3] == self.datashape[:3]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(self.datashape[:3])
msk = self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(self.datashape) == 2:
x, g = self.datashape
S = self.data
GFA = np.zeros(x)
IN = np.zeros((x, 5))
NFA = np.zeros((x, 5))
QA = np.zeros((x, 5))
PK = np.zeros((x, 5))
if self.save_odfs:
ODF = np.zeros((x, self.odfn))
#BODF=np.zeros((x,self.odfn))
if self.mask != None:
if self.mask.shape[0] == self.datashape[0]:
msk = self.mask.ravel().copy()
if self.mask == None:
self.mask = np.ones(self.datashape[:1])
msk = self.mask.ravel().copy()
#find the global normalization parameter
#useful for quantitative anisotropy
glob_norm_param = 0.
#loop over all voxels
for (i, s) in enumerate(S):
if msk[i] > 0:
#calculate the orientation distribution function
#odf=self.odf(s)
odf = self.odf(s)
odf = self.angular_weighting(odf)
if self.save_odfs:
ODF[i] = odf
#normalization for QA
glob_norm_param = max(np.max(odf), glob_norm_param)
#calculate the generalized fractional anisotropy
GFA[i] = self.std_over_rms(odf)
odf_max = odf.max()
#if not in isotropic case
#if odf.min()<self.iso_thr*odf_max:
if np.std(odf) / np.mean(odf) > self.iso_thr:
#find peaks
peaks, inds = peak_finding(odf, self.odf_faces)
ismallp = np.where(peaks / peaks[0] < self.peak_thr)
if len(ismallp[0]) > 0:
l = ismallp[0][0]
#do not allow more that three peaks
if l > 3:
l = 3
else:
l = len(peaks)
if l == 0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l] - np.min(odf)
PK[i][l] = peaks[l]
if l > 0 and l <= 3:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l] - np.min(odf)
PK[i][:l] = peaks[:l]
if len(self.datashape) == 4:
self.GFA = GFA.reshape(x, y, z)
self.NFA = NFA.reshape(x, y, z, 5)
self.QA = QA.reshape(x, y, z, 5) / glob_norm_param
self.PK = PK.reshape(x, y, z, 5)
self.IN = IN.reshape(x, y, z, 5)
if self.save_odfs:
self.ODF = ODF.reshape(x, y, z, ODF.shape[-1])
self.QA_norm = glob_norm_param
if len(self.datashape) == 2:
self.GFA = GFA
self.NFA = NFA
self.QA = QA
self.PK = PK
self.IN = IN
if self.save_odfs:
self.ODF = ODF
#self.BODF=BODF
self.QA_norm = None
for fib in fibs:
dix=get_sim_voxels(fib)
data=dix['data']
bvals=dix['bvals']
gradients=dix['gradients']
no=10
print(bvals.shape, gradients.shape, data.shape)
print(dix['fibres'])
np.set_printoptions(2)
for no in range(len(data)):
sd=SphericalDandelion(data,bvals,gradients)
sdf=sd.spherical_diffusivity(data[no])
gq=GeneralizedQSampling(data,bvals,gradients)
sodf=gq.odf(data[no])
#print(faces.shape)
peaks,inds=peak_finding(np.squeeze(sdf),faces)
#print(peaks, inds)
peaks2,inds2=peak_finding(np.squeeze(sodf),faces)
#print(peaks2, inds2)
print 'sdi',inds,'sodf',inds2, vertices[inds[0]]-vertices[inds2[0]]
#print data[no]
def __init__(self, data, bvals, gradients, smoothing=1.,
odf_sphere='symmetric362', mask=None):
'''
Parameters
-----------
data : array, shape(X,Y,Z,D)
bvals : array, shape (N,)
gradients : array, shape (N,3) also known as bvecs
smoothing : float, smoothing parameter
odf_sphere : str or tuple, optional
If str, then load sphere of given name using ``get_sphere``.
If tuple, gives (vertices, faces) for sphere.
See also
----------
dipy.reconst.dti.Tensor, dipy.reconst.gqi.GeneralizedQSampling
'''
odf_vertices, odf_faces = sphere_vf_from(odf_sphere)
self.odf_vertices=odf_vertices
self.bvals=bvals
gradients[np.isnan(gradients)] = 0.
self.gradients=gradients
self.weighting=np.abs(np.dot(gradients,self.odf_vertices.T))
#self.weighting=self.weighting/np.sum(self.weighting,axis=0)
S=data
datashape=S.shape #initial shape
msk=None #tmp mask
if len(datashape)==4:
x,y,z,g=S.shape
S=S.reshape(x*y*z,g)
XA = np.zeros((x*y*z,5))
IN = np.zeros((x*y*z,5))
if mask != None:
if mask.shape[:3]==datashape[:3]:
msk=mask.ravel().copy()
if len(datashape)==2:
x,g= S.shape
XA = np.zeros((x,5))
IN = np.zeros((x,5))
if mask !=None:
for (i,s) in enumerate(S):
if msk[i]>0:
odf=self.spherical_diffusivity(s)
peaks,inds=peak_finding(odf,odf_faces)
l=min(len(peaks),5)
XA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
if mask==None:
for (i,s) in enumerate(S):
odf=self.spherical_diffusivity(s)
peaks,inds=peak_finding(odf,odf_faces)
l=min(len(peaks),5)
XA[i][:l] = peaks[:l]
IN[i][:l] = inds[:l]
if len(datashape) == 4:
self.XA=XA.reshape(x,y,z,5)
self.IN=IN.reshape(x,y,z,5)
if len(datashape) == 2:
self.XA=XA
self.IN=IN
def fit(self):
#memory allocations for 4D volumes
if len(self.datashape)==4:
x,y,z,g=self.datashape
S=self.data.reshape(x*y*z,g)
GFA=np.zeros((x*y*z))
IN=np.zeros((x*y*z,5))
NFA=np.zeros((x*y*z,5))
QA=np.zeros((x*y*z,5))
PK=np.zeros((x*y*z,5))
if self.save_odfs:
ODF=np.zeros((x*y*z,self.odfn))
if self.mask != None:
if self.mask.shape[:3]==self.datashape[:3]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(self.datashape[:3])
msk=self.mask.ravel().copy()
#memory allocations for a series of voxels
if len(self.datashape)==2:
x,g= self.datashape
S=self.data
GFA=np.zeros(x)
IN=np.zeros((x,5))
NFA=np.zeros((x,5))
QA=np.zeros((x,5))
PK=np.zeros((x,5))
if self.save_odfs:
ODF=np.zeros((x,self.odfn))
if self.mask != None:
if mask.shape[0]==self.datashape[0]:
msk=self.mask.ravel().copy()
if self.mask == None:
self.mask=np.ones(self.datashape[:1])
msk=self.mask.ravel().copy()
#find the global normalization parameter
#useful for quantitative anisotropy
glob_norm_param = 0.
#loop over all voxels
for (i,s) in enumerate(S):
if msk[i]>0:
#calculate the diffusion propagator or spectrum
Pr=self.pdf(s)
#calculate the orientation distribution function
odf=self.odf(Pr)
if self.save_odfs:
ODF[i]=odf
#normalization for QA
glob_norm_param=max(np.max(odf),glob_norm_param)
#calculate the generalized fractional anisotropy
GFA[i]=self.std_over_rms(odf)
#find peaks
peaks,inds=peak_finding(odf,self.odf_faces)
#remove small peaks
l=self.reduce_peaks(peaks,odf.min())
#print '#',l,peaks[:l]
if l==0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l]-np.min(odf)
PK[i][l] = peaks[l]
if l>0 and l<5:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l]-np.min(odf)
PK[i][:l] = peaks[:l]
"""
if len(peaks)>0:
ismallp=np.where(peaks/peaks.min()<self.peak_thr)
l=ismallp[0][0]
if l<5 and l>0:
IN[i][:l] = inds[:l]
NFA[i][:l] = GFA[i]
QA[i][:l] = peaks[:l]-np.min(odf)
if l==0:
IN[i][l] = inds[l]
NFA[i][l] = GFA[i]
QA[i][l] = peaks[l]-np.min(odf)
"""
if len(self.datashape) == 4:
self.GFA=GFA.reshape(x,y,z)
self.NFA=NFA.reshape(x,y,z,5)
self.QA=QA.reshape(x,y,z,5)/glob_norm_param
self.IN=IN.reshape(x,y,z,5)
self.PK=PK.reshape(x,y,z,5)
if self.save_odfs:
self.ODF=ODF.reshape(x,y,z,ODF.shape[-1])
self.QA_norm=glob_norm_param
if len(self.datashape) == 2:
self.GFA=GFA
self.NFA=NFA
self.QA=QA
self.IN=IN
self.PK=PK
if self.save_odfs:
self.ODF=ODF
self.QA_norm=None
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(30, 0),(60,0),(90,90)],
fractions=[0,0,0], snr=snr)
data[13]=S.copy()
return data
if __name__ == '__main__':
#def test_dni():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data=sim_data(bvals,bvecs)
dn=DiffusionNabla(data,bvals,bvecs,save_odfs=True)
pks=dn.pk()
#assert_array_equal(np.sum(pks>0,axis=1),
# np.array([0, 1, 2, 3, 3, 3, 3, 3, 3, 2, 2, 1, 1, 0]))
odfs=dn.odfs()
peaks,inds=peak_finding(odfs[10],dn.odf_faces)
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 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
