def create_dataset_from_tractography(size1, size2, same=True):
if same: assert(size2 >= size1)
filename = 'data/tracks_dti_10K_linear.dpy'
print "Loading", filename
dpr = Dpy(filename, 'r')
tractography = dpr.read_tracks()
dpr.close()
print len(tractography), "streamlines"
print "Removing streamlines that are too short"
tractography = filter(lambda x: len(x) > 20, tractography) # remove too short streamlines
print len(tractography), "streamlines"
tractography = np.array(tractography, dtype=np.object)
print "Creating two simulated tractographies of sizes", size1, "and", size2
if same:
ids = fft(tractography, k=max([size1, size2]), distance=bundles_distances_mam)
tractography1 = tractography[ids[:size1]]
else:
# ids1 = np.random.permutation(len(tractography))[:size1]
# ids1 = sff(tractography, k=size1, distance=bundles_distances_mam)
ids1 = fft(tractography, k=size1, distance=bundles_distances_mam)
tractography1 = tractography[ids1[:size1]]
if same:
tractography2 = tractography[ids[:size2]]
else:
# ids2 = np.random.permutation(len(tractography))[:size2]
# ids2 = sff(tractography, k=size2, distance=bundles_distances_mam)
ids2 = fft(tractography, k=size2, distance=bundles_distances_mam)
tractography2 = tractography[ids2]
print "Done."
print "Computing the distance matrices for each tractography."
dm1 = bundles_distances_mam(tractography1, tractography1)
dm2 = bundles_distances_mam(tractography2, tractography2)
print("Computing similarity matrices.")
sigma2 = np.mean([np.median(dm1), np.median(dm2)]) ** 2.0
print("sigma2 = %f" % sigma2)
A = np.exp(-dm1 * dm1 / sigma2)
B = np.exp(-dm2 * dm2 / sigma2)
# Note: the optimization works even using distance instead of similarity:
# A = dm1
# B = dm2
return A, B
filename = 'data/tracks_dti_10K_linear.dpy'
print "Loading", filename
dpr = Dpy(filename, 'r')
tractography = dpr.read_tracks()
dpr.close()
print len(tractography), "streamlines"
print "Removing streamlines that are too short"
tractography = filter(lambda x: len(x) > 20, tractography) # remove too short streamlines
print len(tractography), "streamlines"
tractography = np.array(tractography, dtype=np.object)
print "Creating two simulated tractographies of sizes", size1, "and", size2
# np.random.seed(0) # this is the random seed to create tractography1 and tractography2
ids1 = np.random.permutation(len(tractography))[:size1]
# ids1 = sff(tractography, k=size1, distance=bundles_distances_mam)
ids1 = fft(tractography, k=size1, distance=bundles_distances_mam)
ids = fft(tractography, k=max([size1, size2]), distance=bundles_distances_mam)
tractography1 = tractography[ids[:size1]]
print "Done."
# ids2 = np.random.permutation(len(tractography))[:size2]
# ids2 = sff(tractography, k=size2, distance=bundles_distances_mam)
# ids2 = fft(tractography, k=size2, distance=bundles_distances_mam)
# tractography2 = tractography[ids2]
# solution = permutation_mapping(size1, size2)
# tractography2 = tractography1[solution]
# inverse_solution = np.argsort(solution)
tractography2 = tractography[ids[:size2]]
print "Done."
print "Computing the distance matrices for each tractography."
dm1 = bundles_distances_mam(tractography1, tractography1)