def test_dandelion():
fimg,fbvals,fbvecs=get_data('small_64D')
bvals=np.load(fbvals)
gradients=np.load(fbvecs)
data=nib.load(fimg).get_data()
print(bvals.shape, gradients.shape, data.shape)
sd=SphericalDandelion(data,bvals,gradients)
sdf=sd.spherical_diffusivity(data[5,5,5])
print(sdf.shape)
gq=GeneralizedQSampling(data,bvals,gradients)
sodf=gq.odf(data[5,5,5])
eds=np.load(get_sphere('symmetric362'))
vertices=eds['vertices']
faces=eds['faces']
print(faces.shape)
peaks,inds=peak_finding(np.squeeze(sdf),faces)
print(peaks, inds)
peaks2,inds2=peak_finding(np.squeeze(sodf),faces)
print(peaks2, inds2)
'''
for fib in fibs:
dix=get_sim_voxels(fib)
data=dix['data']
bvals=dix['bvals']
gradients=dix['gradients']
no=10
print(bvals.shape, gradients.shape, data.shape)
print(dix['fibres'])
np.set_printoptions(2)
for no in range(len(data)):
sd=SphericalDandelion(data,bvals,gradients)
sdf=sd.spherical_diffusivity(data[no])
gq=GeneralizedQSampling(data,bvals,gradients)
sodf=gq.odf(data[no])
#print(faces.shape)
peaks,inds=peak_finding(np.squeeze(sdf),faces)
#print(peaks, inds)
peaks2,inds2=peak_finding(np.squeeze(sodf),faces)
#print(peaks2, inds2)
print 'sdi',inds,'sodf',inds2, vertices[inds[0]]-vertices[inds2[0]]
#print data[no]
def simulations_marta():
#gq_tn_calc_save()
#a_few_phantoms()
#sd=['fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_60_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_100_angle_60_l1_1.4_l2_0.35_l3_0.35_iso_1_diso_0.7']
sd=['fibres_2_SNR_100_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_1_SNR_100_angle_00_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#sd=['fibres_2_SNR_20_angle_90_l1_1.4_l2_0.35_l3_0.35_iso_0_diso_00']
#for simfile in simdata:
np.set_printoptions(2)
dotpow=6
width=6
sincpow=2
sampling_length=1.2
print dotpow,width,sincpow
print sampling_length
verts,faces=get_sphere('symmetric362')
for simfile in sd:
data=np.loadtxt(simdir+simfile)
sf=simfile.split('_')
b_vals_dirs=np.loadtxt(simdir+'Dir_and_bvals_DSI_marta.txt')
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
data2=data[::1000,:]
table={'fibres':sf[1],'snr':sf[3],'angle':sf[5],'l1':sf[7],'l2':sf[9],\
'l3':sf[11],'iso':sf[13],'diso':sf[15],\
'data':data2,'bvals':bvals,'gradients':gradients}
print table['data'].shape
pdi=ProjectiveDiffusivity(table['data'],table['bvals'],table['gradients'],dotpow,width,sincpow)
gqs=GeneralizedQSampling(table['data'],table['bvals'],table['gradients'],sampling_length)
ten=Tensor(table['data'],table['bvals'],table['gradients'])
r=fvtk.ren()
for i in range(10):#range(len(sdi.xa())):
print 'No:',i
print 'simulation fibres ',table['fibres'], ' snr ',table['snr'],' angle ', table['angle']
pdiind=pdi.ind()[i]
gqsind=gqs.ind()[i]
print 'indices',pdiind,gqsind,\
np.rad2deg(np.arccos(np.dot(verts[pdiind[0]],verts[pdiind[1]]))),\
np.rad2deg(np.arccos(np.dot(verts[gqsind[0]],verts[gqsind[1]])))
#ten.ind()[i],
print 'peaks', pdi.xa()[i]*10**3,gqs.qa()[i]
pd=pdi.spherical_diffusivity(table['data'][i])#*10**3
#print 'pd stat',pd.min(),pd.max(),pd.mean(),pd.std()
#colors=fvtk.colors(sdf,'jet')
sdfcol=np.interp(pd,[pd.mean()-4*pd.std(),pd.mean()+4*pd.std()],[0,1])
colors=fvtk.colors(sdfcol,'jet',False)
fvtk.add(r,fvtk.point(5*pdi.odf_vertices+np.array([12*i,0,0]),colors,point_radius=.6,theta=10,phi=10))
odf=gqs.odf(table['data'][i])
colors=fvtk.colors(odf,'jet')
fvtk.add(r,fvtk.point(5*gqs.odf_vertices+np.array([12*i,-12,0]),colors,point_radius=.6,theta=10,phi=10))
fvtk.show(r)
