def run_gq_sims(sample_data=[35]):
for simfile in [simdata[sample] for sample in sample_data]:
dataname = simfile
print dataname
sim_data=np.loadtxt(simdir+dataname)
marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt'
b_vals_dirs=np.loadtxt(marta_table_fname)
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
for j in range(10):
s = sim_data[10000+j,:]
gqs = dp.GeneralizedQSampling(s.reshape((1,102)),bvals,gradients,Lambda=7)
t0, t1, t2, npa = gqs.npa(s, width = 5)
print t0, t1, t2, npa
"""
def gq_tn_calc_save():
for simfile in simdata:
dataname = simfile
print dataname
sim_data=np.loadtxt(simdir+dataname)
marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt'
b_vals_dirs=np.loadtxt(marta_table_fname)
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
gq = dp.GeneralizedQSampling(sim_data,bvals,gradients)
gqfile = simdir+'gq/'+dataname+'.pkl'
pkl.save_pickle(gqfile,gq)
"""
gq.IN gq.__doc__ gq.glob_norm_param
gq.QA gq.__init__ gq.odf
gq.__class__ gq.__module__ gq.q2odf_params
"""
tn = dp.Tensor(sim_data,bvals,gradients)
tnfile = simdir+'tn/'+dataname+'.pkl'
pkl.save_pickle(tnfile,tn)
"""
tn.ADC tn.__init__ tn._getevals
tn.B tn.__module__ tn._getevecs
tn.D tn.__new__ tn._getndim
tn.FA tn.__reduce__ tn._getshape
tn.IN tn.__reduce_ex__ tn._setevals
tn.MD tn.__repr__ tn._setevecs
tn.__class__ tn.__setattr__ tn.adc
tn.__delattr__ tn.__sizeof__ tn.evals
tn.__dict__ tn.__str__ tn.evecs
tn.__doc__ tn.__subclasshook__ tn.fa
tn.__format__ tn.__weakref__ tn.md
tn.__getattribute__ tn._evals tn.ndim
tn.__getitem__ tn._evecs tn.shape
tn.__hash__ tn._getD
"""
""" file has one row for every voxel, every voxel is repeating 1000
def run_gq_sims(sample_data=[35,23,46,39,40,10,37,27,21,20]):
# results = []
out = open('/home/ian/Data/SimVoxels/Out/'+'npa+fa','w')
for j in range(len(sample_data)):
sample = sample_data[j]
simfile = simdata[sample]
dataname = simfile
print dataname
sim_data=np.loadtxt(simdir+dataname)
marta_table_fname='/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt'
b_vals_dirs=np.loadtxt(marta_table_fname)
bvals=b_vals_dirs[:,0]*1000
gradients=b_vals_dirs[:,1:]
for j in np.vstack((np.arange(100)*1000,np.arange(100)*1000+1)).T.ravel():
# 0,1,1000,1001,2000,2001,...
s = sim_data[j,:]
gqs = dp.GeneralizedQSampling(s.reshape((1,102)),bvals,gradients,Lambda=3.5)
tn = dp.Tensor(s.reshape((1,102)),bvals,gradients,fit_method='LS')
t0, t1, t2, npa = gqs.npa(s, width = 5)
print >> out, dataname, j, npa, tn.fa()[0]
"""
for (i,o) in enumerate(gqs.odf(s)):
print i,o
for (i,o) in enumerate(gqs.odf_vertices):
print i,o
"""
out.close()
def run_gq_sims(sample_data=[35, 23, 46, 39, 40, 10, 37, 27, 21, 20]):
# results = []
out = open('/home/ian/Data/SimVoxels/Out/' + 'npa+fa', 'w')
for j in range(len(sample_data)):
sample = sample_data[j]
simfile = simdata[sample]
dataname = simfile
print dataname
sim_data = np.loadtxt(simdir + dataname)
marta_table_fname = '/home/ian/Data/SimData/Dir_and_bvals_DSI_marta.txt'
b_vals_dirs = np.loadtxt(marta_table_fname)
bvals = b_vals_dirs[:, 0] * 1000
gradients = b_vals_dirs[:, 1:]
for j in np.vstack(
(np.arange(100) * 1000, np.arange(100) * 1000 + 1)).T.ravel():
# 0,1,1000,1001,2000,2001,...
s = sim_data[j, :]
gqs = dp.GeneralizedQSampling(s.reshape((1, 102)),
bvals,
gradients,
Lambda=3.5)
tn = dp.Tensor(s.reshape((1, 102)),
bvals,
gradients,
fit_method='LS')
t0, t1, t2, npa = gqs.npa(s, width=5)
print >> out, dataname, j, npa, tn.fa()[0]
"""
for (i,o) in enumerate(gqs.odf(s)):
print i,o
for (i,o) in enumerate(gqs.odf_vertices):
print i,o
"""
#o = gqs.odf(s)
#v = gqs.odf_vertices
#pole = v[t0[0]]
#eqv = dgqs.equatorial_zone_vertices(v, pole, 5)
#print 'Number of equatorial vertices: ', len(eqv)
#print np.max(o[eqv]),np.min(o[eqv])
#cos_e_pole = [np.dot(pole.T, v[i]) for i in eqv]
#print np.min(cos1), np.max(cos1)
#print 'equatorial max in equatorial vertices:', t1[0] in eqv
#x = np.cross(v[t0[0]],v[t1[0]])
#x = x/np.sqrt(np.sum(x**2))
#print x
#ptchv = dgqs.patch_vertices(v, x, 5)
#print len(ptchv)
#eqp = eqv[np.argmin([np.abs(np.dot(v[t1[0]].T,v[p])) for p in eqv])]
#print (eqp, o[eqp])
#print t2[0] in ptchv, t2[0] in eqv
#print np.dot(pole.T, v[t1[0]]), np.dot(pole.T, v[t2[0]])
#print ptchv[np.argmin([o[v] for v in ptchv])]
#gq_indices = np.array(gq.IN[:,0],dtype='int').reshape((100,1000))
#gq_first_directions_in=odf_vertices[np.array(gq.IN[:,0],dtype='int')]
#print gq_first_directions_in.shape
#gq_results = analyze_maxima(gq_indices, gq_first_directions_in.reshape((100,1000,3)),range(100))
#for gqi see example dicoms_2_tracks gq.IN[:,0]
#np.set_printoptions(precision=6, suppress=True, linewidth=200, threshold=5000)
#out = open('/home/ian/Data/SimVoxels/Out/'+'+++_'+dataname,'w')
#results = np.hstack((np.vstack(dt_results), np.vstack(gq_results)))
#results = np.vstack(dt_results)
#print >> out, results[:,:]
out.close()
#fname='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/results_SNR030_isotropic'
marta_table_fname = '/home/eg01/Data_Backup/Data/Marta/DSI/SimData/Dir_and_bvals_DSI_marta.txt'
sim_data = np.loadtxt(fname)
#bvalsf='/home/eg01/Data_Backup/Data/Marta/DSI/SimData/bvals101D_float.txt'
dname = '/home/eg01/Data_Backup/Data/Frank_Eleftherios/frank/20100511_m030y_cbu100624/08_ep2d_advdiff_101dir_DSI'
#real_data,affine,bvals,gradients=dp.load_dcm_dir(dname)
b_vals_dirs = np.loadtxt(marta_table_fname)
bvals = b_vals_dirs[:, 0] * 1000
gradients = b_vals_dirs[:, 1:]
sim_data = sim_data
gq = dp.GeneralizedQSampling(sim_data, bvals, gradients)
tn = dp.Tensor(sim_data, bvals, gradients)
evals = tn.evals[0]
evecs = tn.evecs[0]
setup()
batch = pyglet.graphics.Batch()
eds = np.load(
'/home/eg01/Devel/dipy/dipy/core/matrices/evenly_distributed_sphere_362.npz'
)
vertices = eds['vertices']
faces = eds['faces']
