def QB_reps(limits=[0, np.Inf], reps=1):
ids = ["02", "03", "04", "05", "06", "08", "09", "10", "11", "12"]
sizes = []
for id in range(len(test_tractography_sizes)):
filename = "subj_" + ids[id] + "_QB_reps.pkl"
f = open(filename, "w")
ur_tracks = get_tracks(id, limits)
res = {}
# res['filtered'] = len(ur_tracks)
res["qb_threshold"] = qb_threshold
res["limits"] = limits
# res['ur_tracks'] = ur_tracks
print "Dataset", id, res["filtered"], "filtered tracks"
res["shuffle"] = {}
res["clusters"] = {}
res["nclusters"] = {}
res["centroids"] = {}
res["cluster_sizes"] = {}
for i in range(reps):
print "Subject", ids[id], "shuffle", i
shuffle = np.random.permutation(np.arange(len(ur_tracks)))
res["shuffle"][i] = shuffle
tracks = [ur_tracks[i] for i in shuffle]
qb = QuickBundles(tracks, qb_threshold, downsampling)
res["clusters"][i] = {}
for k in qb.clusters().keys():
# this would be improved if
# we 'enumerated' QB's keys and used the enumerator as
# as the key in the result
res["clusters"][i][k] = qb.clusters()[k]["indices"]
res["centroids"][i] = qb.centroids
res["nclusters"][i] = qb.total_clusters
res["cluster_sizes"][i] = qb.clusters_sizes()
print "QB for has", qb.total_clusters, "clusters"
sizes.append(qb.total_clusters)
pickle.dump(res, f)
f.close()
print "Results written to", filename
def QB_reps_singly(limits=[0, np.Inf], reps=1):
ids = ["02", "03", "04", "05", "06", "08", "09", "10", "11", "12"]
replabs = [str(i) for i in range(reps)]
for id in range(len(test_tractography_sizes)):
ur_tracks = get_tracks(id, limits)
for i in range(reps):
res = {}
# res['filtered'] = len(ur_tracks)
res["qb_threshold"] = qb_threshold
res["limits"] = limits
res["shuffle"] = {}
res["clusters"] = {}
res["nclusters"] = {}
res["centroids"] = {}
res["cluster_sizes"] = {}
print "Subject", ids[id], "shuffle", i
shuffle = np.random.permutation(np.arange(len(ur_tracks)))
res["shuffle"] = shuffle
tracks = [ur_tracks[j] for j in shuffle]
print "... starting QB"
qb = QuickBundles(tracks, qb_threshold, downsampling)
print "... finished QB"
res["clusters"] = {}
for k in qb.clusters().keys():
# this would be improved if
# we 'enumerated' QB's keys and used the enumerator as
# as the key in the result
res["clusters"][k] = qb.clusters()[k]["indices"]
res["centroids"] = qb.centroids
res["nclusters"] = qb.total_clusters
res["cluster_sizes"] = qb.clusters_sizes()
print "QB for has", qb.total_clusters, "clusters"
filename = "subj_" + ids[id] + "_QB_rep_" + replabs[i] + ".pkl"
f = open(filename, "w")
pickle.dump(res, f)
f.close()
print "Results written to", filename
return sizes
eu = EuDX(a=fa, ind=ind, seeds=100000, odf_vertices=sphere.vertices,
a_low=0.2) # FA uses a_low = 0.2
streamlines = [line for line in eu]
print('Number of streamlines %i' % len(streamlines))
'''
for line in streamlines:
print(line.shape)
'''
# Do steamline clustering using QuickBundles (QB) using Eular's Method
# dist_thr (distance threshold) which affects number of clusters and their size
# pts (number of points in each streamline) which will be used for downsampling before clustering
# Default values : dist_thr = 4 & pts = 12
qb = QuickBundles(streamlines, dist_thr=20, pts=20)
clusters = qb.clusters()
print('Number of clusters %i' % qb.total_clusters)
print('Cluster size', qb.clusters_sizes())
# Display streamlines
ren = window.Renderer()
ren.add(actor.streamtube(streamlines, window.colors.white))
window.show(ren)
window.record(ren, out_path=filename + '_stream_lines_eu.png', size=(600, 600))
# Display centroids
window.clear(ren)
colormap = actor.create_colormap(np.arange(qb.total_clusters))
ren.add(actor.streamtube(streamlines, window.colors.white, opacity=0.1))
ren.add(actor.streamtube(qb.centroids, colormap, linewidth=0.5))
window.show(ren)
window.record(ren, out_path=filename + '_centroids_eu.png', size=(600, 600))
'response_dhollander/101107/Structure/T1w_acpc_dc_restore_brain1.25.nii.gz'
img = nib.load(img_path)
fa = Fasciculus(fib)
streamlines = fa.get_data()
length_t = fa.get_lengths()
ind = length_t > 10
streamlines = streamlines[ind]
fa.set_data(streamlines)
fibcluster = FibClustering(fa)
print len(streamlines)
# 1
qb = QuickBundles(streamlines, 2)
clusters = qb.clusters()
print qb.clusters_sizes()
indexs = []
for i in range(len(clusters)):
if clusters[i]['N'] >= 400:
indexs += clusters[i]['indices']
# 2
streamlines = streamlines[indexs]
qb = QuickBundles(streamlines, 2)
clusters = qb.clusters()
centroids = qb.centroids
centroids_lengths = np.array(list(length(centroids)))
print centroids_lengths
indexs_c = []
