bvals=btable[:,0] bvecs=btable[:,1:] img=nib.load(fname) data=img.get_data() 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()
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)
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)
bvals = btable[:, 0]
bvecs = btable[:, 1:]
img = nib.load(fname)
data = img.get_data()
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()
def compare_dni_dsi_gqi_gqi2_eit():
#stop
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
type=3
axesn=200
SNR=20
data,gold,angs,anglesn,axesn,angles=generate_gold_data(bvals,bvecs,fibno=type,axesn=axesn,SNR=SNR)
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,save_odfs=True)
gq2=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,save_odfs=True)
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True)
ds.filter_width=32.
ds.update()
ds.fit()
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=None#0.01
ei.set_operator('laplacian')
ei.update()
ei.fit()
ei2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei2.radius=np.arange(0,5,0.1)
ei2.gaussian_weight=None
ei2.set_operator('laplap')
ei2.update()
ei2.fit()
ei3=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',auto=False,save_odfs=True,fast=True)
ei3.radius=np.arange(0,5,0.1)
ei3.gaussian_weight=None
ei3.set_operator('signal')
ei3.update()
ei3.fit()
"""
blobs=np.zeros((2,4,642))
no=200
blobs[0,0,:]=gq.ODF[no]
blobs[0,1,:]=gq2.ODF[no]
blobs[0,2,:]=ds.ODF[no]
blobs[0,3,:]=ei.ODF[no]
no=399
blobs[1,0,:]=gq.ODF[no]
blobs[1,1,:]=gq2.ODF[no]
blobs[1,2,:]=ds.ODF[no]
blobs[1,3,:]=ei.ODF[no]
show_blobs(blobs[None,:,:,:],ei.odf_vertices,ei.odf_faces,1.2)
"""
#stop
vts=gq.odf_vertices
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
thresh=0.5
PK,IN=simple_peaks(ds.ODF,ds.odf_faces,thresh)
res,me,st =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(gq.ODF,ds.odf_faces,thresh)
res2,me2,st2 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(gq2.ODF,ds.odf_faces,thresh)
res3,me3,st3 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei.ODF,ds.odf_faces,thresh)
res4,me4,st4 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei2.ODF,ds.odf_faces,thresh)
res5,me5,st5 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
PK,IN=simple_peaks(ei3.ODF,ei3.odf_faces,thresh)
res6,me6,st6 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
#res7,me7,st7 =do_compare(ei2.PK,ei2.IN,0,anglesn,axesn,type)
if type==1:
plt.figure(1)
plt.plot(res,'r',label='DSI')
plt.plot(res2,'g',label='GQI')
plt.plot(res3,'b',label='GQI2')
plt.plot(res4,'k',label='EITL')
plt.plot(res5,'k--',label='EITL2')
plt.plot(res6,'k-.',label='EITS')
#plt.xlabel('angle')
#plt.ylabel('resolution')
#plt.title('Angular accuracy')
plt.legend()
plt.show()
else:
if type==3:
x,y,z=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.zeros(len(angles)))
x2,y2,z2=sphere2cart(np.ones(len(angles)),np.deg2rad(angles),np.deg2rad(120*np.ones(len(angles))))
angles2=[]
for i in range(len(x)):
angles2.append(np.rad2deg(np.arccos(np.dot([x[i],y[i],z[i]],[x2[i],y2[i],z2[i]]))))
angles=angles2
plt.figure(1)
plt.plot(angles,me,'r',linewidth=3.,label='DSI')
plt.plot(angles,me2,'g',linewidth=3.,label='GQI')
plt.plot(angles,me3,'b',linewidth=3.,label='GQI2')
plt.plot(angles,me4,'k',linewidth=3.,label='EITL')
plt.plot(angles,me5,'k--',linewidth=3.,label='EITL2')
plt.plot(angles,me6,'k-.',linewidth=3.,label='EITS')
#plt.plot(angles,me7,'r--',linewidth=3.,label='EITL2')
plt.xlabel('angle')
plt.ylabel('similarity')
title='Angular similarity of ' + str(type) + '-fibres crossing with SNR ' + str(SNR)
plt.title(title)
plt.legend(loc='center right')
plt.savefig('/tmp/test.png',dpi=300)
plt.show()
def show_simulated_2fiber_crossings():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,47,5),snr=None)
#data=create_data_2fibers(bvals,bvecs,d=0.0015,S0=100,angles=np.arange(0,92,5),snr=None)
print data.shape
#stop
#"""
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
dn.peak_thr=.4
dn.iso_thr=.05
dn.radius=np.arange(0,5,0.1)
dn.radiusn=len(dn.radius)
dn.create_qspace(bvals,bvecs,16,8)
dn.radon_params(64)
dn.precompute_interp_coords()
dn.precompute_fast_coords()
dn.precompute_equator_indices(5.)
dn.precompute_angular(None)#0.01)
dn.fit()
dn2=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=False)
dn2.peak_thr=.4
dn2.iso_thr=.05
dn2.radius=np.arange(0,5,0.1)
dn2.radiusn=len(dn.radius)
dn2.create_qspace(bvals,bvecs,16,8)
dn2.radon_params(64)
dn2.precompute_interp_coords()
dn2.precompute_fast_coords()
dn2.precompute_equator_indices(5.)
dn2.precompute_angular(None)#0.01)
dn2.fit()
#"""
eis=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eis.radius=np.arange(0,6,0.2)
eis.radius=np.arange(0,5,0.1)
eis.gaussian_weight=None
eis.set_operator('signal')#'laplap')
eis.update()
eis.fit()
eil=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil.radius=np.arange(0,6,0.2)
eil.radius=np.arange(0,5,0.1)
eil.gaussian_weight=None
eil.set_operator('laplacian')#'laplap')
eil.update()
eil.fit()
eil2=EquatorialInversion(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=True)
#eil2.radius=np.arange(0,6,0.2)
eil2.radius=np.arange(0,5,0.1)
eil2.gaussian_weight=None
eil2.set_operator('laplap')
eil2.update()
eil2.fit()
ds=DiffusionSpectrum(data,bvals,bvecs,odf_sphere='symmetric642',auto=True,save_odfs=True)
gq=GeneralizedQSampling(data,bvals,bvecs,1.2,odf_sphere='symmetric642',squared=False,auto=False,save_odfs=True)
gq.fit()
gq2=GeneralizedQSampling(data,bvals,bvecs,3.,odf_sphere='symmetric642',squared=True,auto=False,save_odfs=True)
gq2.fit()
#blobs=np.zeros((19,1,8,dn.odfn))
blobs=np.zeros((10,1,6,dn.odfn))
"""
blobs[:,0,0]=dn.odfs()
blobs[:,0,1]=dn2.odfs()
blobs[:,0,2]=eis.odfs()
blobs[:,0,3]=eil.odfs()
blobs[:,0,4]=eil2.odfs()
blobs[:,0,5]=ds.odfs()
blobs[:,0,6]=gq.odfs()
blobs[:,0,7]=gq2.odfs()
"""
blobs[:,0,0]=dn2.odfs()
blobs[:,0,1]=eil.odfs()
eo=eil2.odfs()
#eo[eo<0.05*eo.max()]=0
blobs[:,0,2]=eo
blobs[:,0,3]=ds.odfs()
blobs[:,0,4]=gq.odfs()
blobs[:,0,5]=gq2.odfs()
show_blobs(blobs,dn.odf_vertices,dn.odf_faces,scale=0.5)
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) 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
