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)
def compare_sdni_eitl():
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)
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
ei.set_operator('laplacian')
#{'reflect','constant','nearest','mirror', 'wrap'}
ei.set_mode(order=1,zoom=1,mode='constant')
ei.update()
ei.fit()
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric642',
auto=False,save_odfs=True,fast=False)
dn.radius=np.arange(0,5,0.1)
dn.gaussian_weight=None
dn.update()
dn.fit()
vts=ei.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
print "test0"
thresh=0.5
PK,IN=simple_peaks(ei.ODF,ei.odf_faces,thresh)
res,me,st =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
#PKG,ING=simple_peaks(eig.ODF,ei.odf_faces,thresh)
#resg,meg,stg =do_compare(gold,vts,PKG,ING,0,anglesn,axesn,type)
PK,IN=simple_peaks(dn.ODF,dn.odf_faces,thresh)
res2,me2,st2 =do_compare(gold,vts,PK,IN,0,anglesn,axesn,type)
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
print "test1"
plt.figure(1)
plt.plot(angles,me,'k:',linewidth=3.,label='EITL')
plt.plot(angles,me2,'k--',linewidth=3.,label='sDNI')
#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_multiple_crossings():
'''
S=np.array([[-1,0,0],[0,1,0],[0,0,1]])
#T=np.array([[1,0,0],[0,0,]])
T=np.array([[1,0,0],[-0.76672114, 0.26105963, -0.58650369]])
print 'yo ',compare_orientation_sets(S,T)
'''
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
"""
path='/home/eg309/Data/PROC_MR10032/subj_03/101_32/1312211075232351192010121313490254679236085ep2dadvdiffDSI10125x25x25STs004a001'
#path='/home/eg309/Data/101_32/1312211075232351192010121313490254679236085ep2dadvdiffDSI10125x25x25STs004a001'
bvals=np.loadtxt(path+'.bval')
bvecs=np.loadtxt(path+'.bvec').T
bvals=np.append(bvals.copy(),bvals[1:].copy())
bvecs=np.append(bvecs.copy(),-bvecs[1:].copy(),axis=0)
"""
#bvals=np.load('/tmp/bvals.npy')
#bvecs=np.load('/tmp/bvecs.npy')
#data=create_data(bvals,bvecs,d=0.00075,S0=100,snr=None)
#dvals=[0.0003, 0.0006, 0.0012, 0.0024, 0.0048]
dvals=[0.0003, 0.0005, 0.0010, 0.0015, 0.0020, 0.0025, 0.0030]
data = multi_d_isotropic_data(bvals,bvecs,dvals=dvals,S0=100,snr=None)
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric362',
auto=False,save_odfs=True,fast=False,rotation=None)
#rotation=np.array([[0,0,1],[np.sqrt(0.5),np.sqrt(0.5),0.],[np.sqrt(0.5),-np.sqrt(0.5),0.]])
dn.peak_thr=.4
dn.iso_thr=.05
dn.radius=np.arange(0,6,0.2)
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(0.01)
dn.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.peak_thr=.6
gq.iso_thr=.7
gq.fit()
gq2=GeneralizedQSampling(data,bvals,bvecs,2.,odf_sphere='symmetric642',squared=True,auto=False,save_odfs=True)
gq2.peak_thr=.6
gq2.iso_thr=.7
gq2.fit()
'''
dn_odfs=dn.odfs()
#blobs=np.zeros((1,19,4,dn.odfn))
blobs=np.zeros((1,len(dvals),1,dn.odfn))
#blobs=np.zeros((1,19,1,dn.odfn))
#blobs[0,:,0]=dn.odfs()
for i,d in enumerate(dvals):
blobs[0,i,0]=dn.ODF[i]/dn.ODF[i].mean()
'''
blobs[0,:,1]=ds.odfs()
blobs[0,:,2]=gq.odfs()
gq2odfs=gq2.odfs()
gq2odfs[gq2odfs<0]=0
print 'gq2',gq2odfs.min(),gq2odfs.max()
blobs[0,:,3]=gq2odfs
'''
'''
peaknos=np.array([0,1,2,3,3,3,3,3,3,2,2,1,1,2,3,0,3,3,3])
print peaknos
print np.sum(dn.pk()>0,axis=1),np.sum(np.sum(dn.pk()>0,axis=1)-peaknos),len(peaknos)
'''
'''
print np.sum(ds.qa()>0,axis=1),np.sum(np.sum(ds.qa()>0,axis=1)-peaknos),len(peaknos)
print np.sum(gq.qa()>0,axis=1),np.sum(np.sum(gq.qa()>0,axis=1)-peaknos),len(peaknos)
print np.sum(gq2.qa()>0,axis=1),np.sum(np.sum(gq2.qa()>0,axis=1)-peaknos),len(peaknos)
'''
show_blobs(blobs,dn.odf_vertices,dn.odf_faces,colormap='jet')
for i, d in enumerate(dvals):
m = dn.ODF[i].min()
M = dn.ODF[i].max()
vM = np.argmax(dn.ODF[i])
print i, d, 50*(M-m)/(M+m), dn.odf_vertices[vM]
#odf=dn.fast_odf(data[0])
#r=fvtk.ren()
#fvtk.add(r,fvtk.volume(dn.Eq))
#fvtk.show(r)
"""
blobs2=np.zeros((1,1,3,dn.odfn))
blobs2[0,:,0]=dn.log_fast_odf(data[15])
blobs2[0,:,1]=dn.fast_odf(data[15])
blobs2[0,:,2]=dn.log_slow_odf(data[15])
show_blobs(blobs2,dn.odf_vertices,dn.odf_faces)
e=data[15]/data[15,0]
ne=np.sqrt(-np.log(e))
norm=np.sum(dn.qtable**2,axis=1)
"""
"""
bvecs=np.loadtxt(path+'.bvec').T
bvals=np.append(bvals.copy(),bvals[1:].copy())
bvecs=np.append(bvecs.copy(),-bvecs[1:].copy(),axis=0)
"""
#bvals=np.load('/tmp/bvals.npy')
#bvecs=np.load('/tmp/bvecs.npy')
#data=create_data(bvals,bvecs,d=0.00075,S0=100,snr=None)
#dvals=[0.0003, 0.0006, 0.0012, 0.0024, 0.0048]
dvals=[0.0003, 0.0005, 0.0010, 0.0015, 0.0020, 0.0025, 0.0030]
data = multi_d_isotropic_data(bvals,bvecs,dvals=dvals,S0=100,snr=None)
dn=DiffusionNabla(data,bvals,bvecs,odf_sphere='symmetric362',
auto=False,save_odfs=True,fast=False,rotation=None)
#rotation=np.array([[0,0,1],[np.sqrt(0.5),np.sqrt(0.5),0.],[np.sqrt(0.5),-np.sqrt(0.5),0.]])
dn.peak_thr=.4
dn.iso_thr=.05
dn.radius=np.arange(0,6,0.2)
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(0.01)
dn.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)