def test_dsi():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0),(90,0),(90,90)], fractions=[50,50,0], snr=None)
pdf0,odf0,peaks0=standard_dsi_algorithm(S,bvals,bvecs)
S2=S.copy()
S2=S2.reshape(1,len(S))
ds=DiffusionSpectrum(S2,bvals,bvecs)
assert_almost_equal(np.sum(ds.pdf(S)-pdf0),0)
assert_almost_equal(np.sum(ds.odf(ds.pdf(S))-odf0),0)
#compare gfa
psi=odf0/odf0.max()
numer=len(psi)*np.sum((psi-np.mean(psi))**2)
denom=(len(psi)-1)*np.sum(psi**2)
GFA=np.sqrt(numer/denom)
assert_almost_equal(ds.gfa()[0],GFA)
#compare indices
#print ds.ind()
#print peak_finding(odf0,odf_faces)
#print peaks0
data=np.zeros((3,3,3,515))
data[:,:,:]=S
ds=DiffusionSpectrum(data,bvals,bvecs)
ds2=DiffusionSpectrum(data,bvals,bvecs,auto=False)
r = np.sqrt(ds2.qtable[:,0]**2+ds2.qtable[:,1]**2+ds2.qtable[:,2]**2)
ds2.filter=.5*np.cos(2*np.pi*r/32)
ds2.fit()
assert_almost_equal(np.sum(ds2.qa()-ds.qa()),0)
#1 fiber
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0),(90,0),(90,90)], fractions=[100,0,0], snr=None)
ds=DiffusionSpectrum(S.reshape(1,len(S)),bvals,bvecs)
QA=ds.qa()
assert_equal(np.sum(QA>0),1)
#2 fibers
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0),(90,0),(90,90)], fractions=[50,50,0], snr=None)
ds=DiffusionSpectrum(S.reshape(1,len(S)),bvals,bvecs)
QA=ds.qa()
assert_equal(np.sum(QA>0),2)
#3 fibers
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0),(90,0),(90,90)], fractions=[33,33,33], snr=None)
ds=DiffusionSpectrum(S.reshape(1,len(S)),bvals,bvecs)
QA=ds.qa()
assert_equal(np.sum(QA>0),3)
#isotropic
S,stics=SticksAndBall(bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0),(90,0),(90,90)], fractions=[0,0,0], snr=None)
ds=DiffusionSpectrum(S.reshape(1,len(S)),bvals,bvecs)
QA=ds.qa()
assert_equal(np.sum(QA>0),0)
print data.shape mask=data[:,:,:,0]>50 #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]
print data.shape
mask = data[:, :, :, 0] > 50
#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]
def test_dsi():
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
pdf0, odf0, peaks0 = standard_dsi_algorithm(S, bvals, bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
ds = DiffusionSpectrum(S2, bvals, bvecs)
assert_almost_equal(np.sum(ds.pdf(S) - pdf0), 0)
assert_almost_equal(np.sum(ds.odf(ds.pdf(S)) - odf0), 0)
#compare gfa
psi = odf0 / odf0.max()
numer = len(psi) * np.sum((psi - np.mean(psi))**2)
denom = (len(psi) - 1) * np.sum(psi**2)
GFA = np.sqrt(numer / denom)
assert_almost_equal(ds.gfa()[0], GFA)
#compare indices
#print ds.ind()
#print peak_finding(odf0,odf_faces)
#print peaks0
data = np.zeros((3, 3, 3, 515))
data[:, :, :] = S
ds = DiffusionSpectrum(data, bvals, bvecs)
ds2 = DiffusionSpectrum(data, bvals, bvecs, auto=False)
r = np.sqrt(ds2.qtable[:, 0]**2 + ds2.qtable[:, 1]**2 +
ds2.qtable[:, 2]**2)
ds2.filter = .5 * np.cos(2 * np.pi * r / 32)
ds2.fit()
assert_almost_equal(np.sum(ds2.qa() - ds.qa()), 0)
#1 fiber
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 1)
#2 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 2)
#3 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 3)
#isotropic
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
ds = DiffusionSpectrum(S.reshape(1, len(S)), bvals, bvecs)
QA = ds.qa()
assert_equal(np.sum(QA > 0), 0)
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] #show(T3,ds.gfa(),ds.ind()[...,0],eu3.odf_vertices,scale=1) eu4=EuDX(ds.nfa(),ds.ind(),seeds=10**4,a_low=0,length_thr=length_thr) T4=[e for e in eu4] #show(T4,ds.nfa(),ds.ind(),eu4.odf_vertices,scale=1) eu5=EuDX(ds.qa(),ds.ind(),seeds=10**4,a_low=0,length_thr=length_thr) T5=[e for e in eu5] #show(T5,ds.qa(),ds.ind(),eu5.odf_vertices,scale=1) """ #data[mask == False]=0 #ind=np.zeros(data.shape)