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 test():
# img=nib.load('/home/eg309/Data/project01_dsi/connectome_0001/tp1/RAWDATA/OUT/mr000001.nii.gz')
btable = np.loadtxt(get_data("dsi515btable"))
# volume size
sz = 16
# shifting
origin = 8
# hanning width
filter_width = 32.0
# number of signal sampling points
n = 515
# odf radius
radius = np.arange(2.1, 6, 0.2)
# create q-table
bv = btable[:, 0]
bmin = np.sort(bv)[1]
bv = np.sqrt(bv / bmin)
qtable = np.vstack((bv, bv, bv)).T * btable[:, 1:]
qtable = np.floor(qtable + 0.5)
# copy bvals and bvecs
bvals = btable[:, 0]
bvecs = btable[:, 1:]
# S=img.get_data()[38,50,20]#[96/2,96/2,20]
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (60, 0), (90, 90)], fractions=[0, 0, 0], snr=None
)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
dn = DiffusionNabla(S2, bvals, bvecs, auto=False)
pR = dn.equators
odf = dn.odf(S)
# Xs=dn.precompute_interp_coords()
peaks, inds = peak_finding(odf.astype("f8"), dn.odf_faces.astype("uint16"))
print peaks
print peaks / peaks.min()
# print dn.PK
dn.fit()
print dn.PK
# """
ren = fvtk.ren()
colors = fvtk.colors(odf, "jet")
fvtk.add(ren, fvtk.point(dn.odf_vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
stop
# ds=DiffusionSpectrum(S2,bvals,bvecs)
# tpr=ds.pdf(S)
# todf=ds.odf(tpr)
"""
#show projected signal
Bvecs=np.concatenate([bvecs[1:],-bvecs[1:]])
X0=np.dot(np.diag(np.concatenate([S[1:],S[1:]])),Bvecs)
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(X0,fvtk.yellow,1,2,16,16))
fvtk.show(ren)
"""
# qtable=5*matrix[:,1:]
# calculate radius for the hanning filter
r = np.sqrt(qtable[:, 0] ** 2 + qtable[:, 1] ** 2 + qtable[:, 2] ** 2)
# setting hanning filter width and hanning
hanning = 0.5 * np.cos(2 * np.pi * r / filter_width)
# center and index in q space volume
q = qtable + origin
q = q.astype("i8")
# apply the hanning filter
values = S * hanning
"""
#plot q-table
ren=fvtk.ren()
colors=fvtk.colors(values,'jet')
fvtk.add(ren,fvtk.point(q,colors,1,0.1,6,6))
fvtk.show(ren)
"""
# create the signal volume
Sq = np.zeros((sz, sz, sz))
for i in range(n):
Sq[q[i][0], q[i][1], q[i][2]] += values[i]
# apply fourier transform
Pr = fftshift(np.abs(np.real(fftn(fftshift(Sq), (sz, sz, sz)))))
# """
ren = fvtk.ren()
vol = fvtk.volume(Pr)
fvtk.add(ren, vol)
fvtk.show(ren)
# """
"""
from enthought.mayavi import mlab
mlab.pipeline.volume(mlab.pipeline.scalar_field(Sq))
mlab.show()
"""
# vertices, edges, faces = create_unit_sphere(5)
vertices, faces = sphere_vf_from("symmetric362")
odf = np.zeros(len(vertices))
for m in range(len(vertices)):
xi = origin + radius * vertices[m, 0]
yi = origin + radius * vertices[m, 1]
zi = origin + radius * vertices[m, 2]
PrI = map_coordinates(Pr, np.vstack((xi, yi, zi)), order=1)
for i in range(len(radius)):
odf[m] = odf[m] + PrI[i] * radius[i] ** 2
"""
ren=fvtk.ren()
colors=fvtk.colors(odf,'jet')
fvtk.add(ren,fvtk.point(vertices,colors,point_radius=.05,theta=8,phi=8))
fvtk.show(ren)
"""
"""
#Pr[Pr<500]=0
ren=fvtk.ren()
#ren.SetBackground(1,1,1)
fvtk.add(ren,fvtk.volume(Pr))
fvtk.show(ren)
"""
peaks, inds = peak_finding(odf.astype("f8"), faces.astype("uint16"))
Eq = np.zeros((sz, sz, sz))
for i in range(n):
Eq[q[i][0], q[i][1], q[i][2]] += S[i] / S[0]
LEq = laplace(Eq)
# Pr[Pr<500]=0
ren = fvtk.ren()
# ren.SetBackground(1,1,1)
fvtk.add(ren, fvtk.volume(Eq))
fvtk.show(ren)
phis = np.linspace(0, 2 * np.pi, 100)
planars = []
for phi in phis:
planars.append(sphere2cart(1, np.pi / 2, phi))
planars = np.array(planars)
planarsR = []
for v in vertices:
R = vec2vec_rotmat(np.array([0, 0, 1]), v)
planarsR.append(np.dot(R, planars.T).T)
"""
ren=fvtk.ren()
fvtk.add(ren,fvtk.point(planarsR[0],fvtk.green,1,0.1,8,8))
fvtk.add(ren,fvtk.point(2*planarsR[1],fvtk.red,1,0.1,8,8))
fvtk.show(ren)
"""
azimsums = []
for disk in planarsR:
diskshift = 4 * disk + origin
# Sq0=map_coordinates(Sq,diskshift.T,order=1)
# azimsums.append(np.sum(Sq0))
# Eq0=map_coordinates(Eq,diskshift.T,order=1)
# azimsums.append(np.sum(Eq0))
LEq0 = map_coordinates(LEq, diskshift.T, order=1)
azimsums.append(np.sum(LEq0))
azimsums = np.array(azimsums)
# """
ren = fvtk.ren()
colors = fvtk.colors(azimsums, "jet")
fvtk.add(ren, fvtk.point(vertices, colors, point_radius=0.05, theta=8, phi=8))
fvtk.show(ren)
# """
# for p in planarsR[0]:
"""
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)
"""
"""
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()
S,stics=SticksAndBall(bvals, bvecs, d, S0,
angles=[(30, 0),(60,0),(90,90)],
fractions=[0,0,0], snr=snr)
data[13]=S.copy()
return data
if __name__ == '__main__':
#def test_dni():
btable=np.loadtxt(get_data('dsi515btable'))
bvals=btable[:,0]
bvecs=btable[:,1:]
data=sim_data(bvals,bvecs)
dn=DiffusionNabla(data,bvals,bvecs,save_odfs=True)
pks=dn.pk()
#assert_array_equal(np.sum(pks>0,axis=1),
# np.array([0, 1, 2, 3, 3, 3, 3, 3, 3, 2, 2, 1, 1, 0]))
odfs=dn.odfs()
peaks,inds=peak_finding(odfs[10],dn.odf_faces)
"""
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()
'''
np.save('/tmp/test.npy',fvol)
#stop
"""
fvol=np.load('/tmp/test.npy')
#data=np.load('/tmp/test.npy')
dat=fvol[22:42,22:42,32:34]
del fvol
t1=time()
#"""
dn=DiffusionNabla(dat,bvals,bvecs,odf_sphere='symmetric362',
auto=True,save_odfs=True,fast=True)
"""
dn.radius=np.arange(0,6,.2)
dn.radiusn=len(dn.radius)
dn.create_qspace(bvals,bvecs,16,8)
dn.peak_thr=.4
dn.iso_thr=.7
dn.radon_params(64)
dn.precompute_interp_coords()
dn.fit()
"""
#from dipy.reconst.dsi import DiffusionSpectrum
#mask=dat[...,0]>0
#dn=DiffusionSpectrum(dat,bvals,bvecs,save_odfs=True)