def test_dandelion():
fimg,fbvals,fbvecs=get_data('small_101D')
bvals=np.loadtxt(fbvals)
gradients=np.loadtxt(fbvecs).T
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])
XA=sd.xa()
np.set_printoptions(2)
print XA.min(),XA.max(),XA.mean()
print sdf*10**4
"""
print(sdf.shape)
gq=GeneralizedQSampling(data,bvals,gradients)
sodf=gq.odf(data[5,5,5])
vertices, faces = get_sphere('symmetric362')
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 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)
'''
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]
