def test_phantom():
N = 50
vol = orbital_phantom(gtab,
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),
S0=100)
m = TensorModel(gtab)
t = m.fit(vol)
FA = t.fa
# print vol
FA[np.isnan(FA)] = 0
# 686 -> expected FA given diffusivities of [1500, 400, 400]
l1, l2, l3 = 1500e-6, 400e-6, 400e-6
expected_fa = (np.sqrt(0.5) *
np.sqrt((l1 - l2)**2 + (l2-l3)**2 + (l3-l1)**2) /
np.sqrt(l1**2 + l2**2 + l3**2))
assert_array_almost_equal(FA.max(), expected_fa, decimal=2)
def test_phantom():
N = 50
vol = orbital_phantom(gtab,
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),
S0=100)
m = TensorModel(gtab)
t = m.fit(vol)
FA = t.fa
# print vol
FA[np.isnan(FA)] = 0
# 686 -> expected FA given diffusivities of [1500, 400, 400]
l1, l2, l3 = 1500e-6, 400e-6, 400e-6
expected_fa = (np.sqrt(0.5) * np.sqrt((l1 - l2)**2 + (l2 - l3)**2 +
(l3 - l1)**2) /
np.sqrt(l1**2 + l2**2 + l3**2))
assert_array_almost_equal(FA.max(), expected_fa, decimal=2)
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_add_noise():
np.random.seed(1980)
N = 50
S0 = 100
options = dict(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),
S0=S0)
vol = orbital_phantom(gtab, **options)
for snr in [10, 20, 30, 50]:
vol_noise = orbital_phantom(gtab, snr=snr, **options)
sigma = S0 / snr
assert_(np.abs(np.var(vol_noise - vol) - sigma ** 2) < 1)
def test_add_noise():
np.random.seed(1980)
N = 50
S0 = 100
options = dict(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),
S0=S0)
vol = orbital_phantom(gtab, **options)
for snr in [10, 20, 30, 50]:
vol_noise = orbital_phantom(gtab, snr=snr, **options)
sigma = S0 / snr
assert_(np.abs(np.var(vol_noise - vol) - sigma**2) < 1)
def build_phantom(fname):
N = 200
S0 = 100
N_angles = 32
N_radii = 20
vol_shape = (100, 100, 100)
origin = (50, 50, 50)
scale = (30, 30, 30)
t = np.linspace(0, 2 * np.pi, N)
angles = np.linspace(0, 2 * np.pi, N_angles)
radii = np.linspace(0.2, 2, N_radii)
vol1 = orbital_phantom(gtab,
func=f1,
t=t,
datashape=vol_shape + (len(bvals), ),
origin=origin,
scale=scale,
angles=angles,
radii=radii,
S0=S0)
vol2 = orbital_phantom(gtab,
func=f2,
t=t,
datashape=vol_shape + (len(bvals), ),
origin=origin,
scale=scale,
angles=angles,
radii=radii,
S0=S0)
vol = vol1 + vol2
np.save(fname, vol)
def create_software_phantom_no_pv_effects():
#if __name__=='__main__':
SNR=5.
final_name='/home/eg309/Data/orbital_phantoms/'+str(SNR)+'_fat'
print 'Loading data'
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
def f2(t):
x=np.linspace(-1,1,len(t))
y=np.linspace(-1,1,len(t))
z=np.zeros(x.shape)
return x,y,z
vol2=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0003,0.0003]),func=f2,datashape=(64,64,64,len(bvals)))
fvol2 = np.memmap('/tmp/t2', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvol2[:]=vol2[:]
del vol2
print 'Created first direction'
norm=fvol2[...,0]/100.
norm[norm==0]=1
fvol2[:]=fvol2[:]/norm[...,None]
print 'Removed partial volume effects'
def f3(t):
x=np.linspace(-1,1,len(t))
y=-np.linspace(-1,1,len(t))
z=np.zeros(x.shape)
return x,y,z
#second direction
vol3=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0003,0.0003]),func=f3,datashape=(64,64,64,len(bvals)))
fvol3 = np.memmap('/tmp/t3', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvol3[:]=vol3[:]
del vol3
print 'Created second direction'
norm=fvol3[...,0]/100.
norm[norm==0]=1
fvol3[:]=fvol3[:]/norm[...,None]
print 'Removed partial volume effects'
fvolfinal = np.memmap(final_name, dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvolfinal[:]=fvol2[:]+fvol3[:]#+fvol4
print 'Adding two directions together'
norm=fvolfinal[...,0]/100.
norm[norm==0]=1
fvolfinal[:]=fvolfinal[:]/norm[...,None]
print 'Removed partial volume effects'
print 'Adding noise'
def gaussian_noise(vol,snr):
voln=np.random.randn(*vol.shape[:3])
pvol=np.sum(vol[:,:,:,0]**2) #power of initial volume
pnoise=np.sum(np.random.randn(*voln.shape[:3])**2) #power of noise volume
K=pvol/pnoise
#print pvol,pnoise,K
return np.sqrt(K/np.float(snr))*np.random.randn(*vol.shape)
print 'Noise 1'
voln=np.random.randn(*fvolfinal[:].shape[:3])
pvol=np.sum(fvolfinal[:,:,:,0]**2) #power of initial volume
pnoise=np.sum(np.random.randn(*voln.shape)**2) #power of noise volume
K=pvol/pnoise
noise1 = np.memmap('/tmp/n1', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
noise1[:] = np.random.randn(*fvolfinal[:].shape)[:]
noise1[:] = np.sqrt(K/np.float(SNR))*noise1[:]#*np.random.randn(*fvolfinal[:].shape)
print 'Noise 2'
voln=np.random.randn(*fvolfinal[:].shape[:3])
pvol=np.sum(fvolfinal[:,:,:,0]**2) #power of initial volume
pnoise=np.sum(np.random.randn(*voln.shape)**2) #power of noise volume
K=pvol/pnoise
noise2 = np.memmap('/tmp/n2', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
noise2[:] = np.random.randn(*fvolfinal[:].shape)[:]
noise2[:] = np.sqrt(K/np.float(SNR))*noise2[:]
print 'Adding both noise components'
fvolfinal[:]=np.sqrt((fvolfinal[:]+noise1[:])**2+noise2[:]**2)
print 'Noise added'
print 'Obtaining only a part from the data'
data=fvolfinal[32-10:32+10,32-10:32+10,31:34,:]
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
print 'Showing data'
show_blobs(ds.ODF[:,:,0,:][:,:,None,:],ds.odf_vertices,ds.odf_faces,scale=5.)
def create_phantom():
SNR=100.
no_seeds=20**3
final_name='/home/eg309/Data/orbital_phantoms/'+str(SNR)+'_beauty2'
print 'Loading data'
#btable=np.loadtxt(get_data('dsi515btable'))
#bvals=btable[:,0]
#bvecs=btable[:,1:]
bvals=np.loadtxt('data/subj_01/101_32/raw.bval')
bvecs=np.loadtxt('data/subj_01/101_32/raw.bvec').T
print 'generate first simulation'
f2=f2_ellipse
vol2=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f2,
t=np.linspace(0.,3*np.pi/2.-np.pi/4.,1000),datashape=(64,64,64,len(bvals)))
fvol2 = np.memmap('/tmp/t2', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvol2[:]=vol2[:]
del vol2
print 'Created first component'
norm=fvol2[...,0]/100.
norm[norm==0]=1
print 'Removing partial volume effects'
fvol2[:]=fvol2[:]/norm[...,None]
print 'creating first mask A'
vol2=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f2,
t=np.linspace(0.,np.pi/8.,1000),datashape=(64,64,64,len(bvals)))
maskA=vol2[...,0]>0
np.save('/tmp/maskA.npy',maskA)
del vol2
print 'creating second mask B'
vol2=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f2,
t=np.linspace(3*np.pi/2.-3*np.pi/8.,3*np.pi/2.-np.pi/4.,1000),datashape=(64,64,64,len(bvals)))
maskB=vol2[...,0]>0
np.save('/tmp/maskB.npy',maskB)
del vol2
print 'generate second simulation'
vol3=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f3,datashape=(64,64,64,len(bvals)))
fvol3 = np.memmap('/tmp/t3', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvol3[:]=vol3[:]
del vol3
print 'Created second direction'
norm=fvol3[...,0]/100.
norm[norm==0]=1
print 'Removing partial volume effects'
fvol3[:]=fvol3[:]/norm[...,None]
print 'creating mask C'
vol3=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f3C,datashape=(64,64,64,len(bvals)))
maskC=vol3[...,0]>0
np.save('/tmp/maskC.npy',maskC)
del vol3
print 'creating mask D'
vol3=orbital_phantom(bvals=bvals,bvecs=bvecs,evals=np.array([0.0017,0.0001,0.0001]),func=f3D,datashape=(64,64,64,len(bvals)))
maskD=vol3[...,0]>0
np.save('/tmp/maskD.npy',maskD)
del vol3
print 'Creating final volume'
fvolfinal = np.memmap(final_name, dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
fvolfinal[:]=fvol2[:]+fvol3[:]
print 'Adding two directions together'
norm=fvolfinal[...,0]/100.
norm[norm==0]=1
print 'Removing partial volume effects'
fvolfinal[:]=fvolfinal[:]/norm[...,None]
print 'Adding noise'
print 'Noise 1'
voln=np.random.randn(*fvolfinal[:].shape[:3])
pvol=np.sum(fvolfinal[:,:,:,0]**2) #power of initial volume
pnoise=np.sum(np.random.randn(*voln.shape)**2) #power of noise volume
K=pvol/pnoise
noise1 = np.memmap('/tmp/n1', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
noise1[:] = np.random.randn(*fvolfinal[:].shape)[:]
noise1[:] = np.sqrt(K/np.float(SNR))*noise1[:]#*np.random.randn(*fvolfinal[:].shape)
print 'Noise 2'
voln=np.random.randn(*fvolfinal[:].shape[:3])
pvol=np.sum(fvolfinal[:,:,:,0]**2) #power of initial volume
pnoise=np.sum(np.random.randn(*voln.shape)**2) #power of noise volume
K=pvol/pnoise
noise2 = np.memmap('/tmp/n2', dtype='f8', mode='w+', shape=(64,64,64,len(bvals)))
noise2[:] = np.random.randn(*fvolfinal[:].shape)[:]
noise2[:] = np.sqrt(K/np.float(SNR))*noise2[:]
print 'Adding both noise components'
fvolfinal[:]=np.sqrt((fvolfinal[:]+noise1[:])**2+noise2[:]**2)
print 'Noise added'
print 'Okay phantom created! Done!'
def f2(t):
x = -np.sin(t)
y = np.linspace(-1, 1, len(x))
z = y
return x, y, z
def f3(t):
x = np.linspace(-1, 1, len(t))
y = np.sin(t)
z = x
return x, y, z
t = np.linspace(0, 2 * np.pi, 100)
# kissing fibre #1
vol = orbital_phantom(func=f1, t=t)
# kissing fibre #2
vol += orbital_phantom(func=f2, t=t)
# kissing fibre #3
vol += orbital_phantom(func=f3, t=t)
#vol = add_rician_noise(vol)
r=fvtk.ren()
fvtk.add(r, fvtk.volume(vol[...,0]))
fvtk.show(r)
