if __name__ == '__main__':
np.random.seed(0)
nb_points = 16
subset_size = 20000
filename_template = 'data_emanuele/%s_%s_tracks.trk'
subjects = ['100206', '100307']
label_y = {'plausible': 1, 'unplausible': 0}
X = []
y = []
groups = []
for subject in subjects:
for label in label_y.keys():
filename = filename_template % (subject, label)
streamlines, header, lengths, idxs = load_streamlines(filename,
idxs=subset_size,
verbose=True)
print("Resampling streamlines to %s points" % nb_points)
streamlines = set_number_of_points(streamlines, nb_points)
print("Embedding streamlines into vectors")
# tmp = embed_flattened(streamlines)
# tmp = embed_flattened_plus_flipped(streamlines)
# tmp = embed_ordered(streamlines)
# tmp = embed_flattened_plus_length(streamlines)
# tmp = embed_flattened_plus_flipped_plus_length(streamlines)
tmp = embed_flattened_plus_flipped_plus_length_plus_curvature(streamlines)
X.append(tmp)
y.append([label_y[label]] * len(tmp))
groups.append([subject] * len(tmp))
if __name__ == '__main__':
from nilab.load_trk import load_streamlines
from dipy.tracking.streamline import set_number_of_points
from dipy.tracking.distances import bundles_distances_mdf
from time import time
trk_fn = 'sub-100206_var-FNAL_tract.trk'
nb_points = 32
nb_prototypes = 100
streamline_distance = bundles_distances_mdf
streamlines, header, lengths, idxs = load_streamlines(trk_fn,
idxs=None,
apply_affine=True,
container='list',
verbose=True)
print("Resampling to %s points" % nb_points)
streamlines = set_number_of_points(streamlines, nb_points)
# streamlines = np.array(set_number_of_points(streamlines, nb_points),
# dtype=np.object)
print("embed_flattened():")
t0 = time()
X_flattened = embed_flattened(streamlines)
print("%s sec." % (time() - t0))
print("embed_flattened_plus_flipped():")
t0 = time()
X_flattened_plus_flipped = embed_flattened_plus_flipped(streamlines)
N_streamlines1 = 50000 #00
N_streamlines2 = 50000 #00
N_points = 32
k = 1000
lap_flag = False
MWFBM_flag = False
MATLAB_flag = True
# %% Load Trk
print("laod stramlines...")
filename1 = "data1/1M_len20-250mm_coff0001_step1_seedimage_30deg_SD_STREAM.trk"
filename2 = "data2/1M_len20-250mm_coff0001_step1_seedimage_30deg_SD_STREAM.trk"
track1, header1, lengths1, indices1 = load_streamlines(filename1,
container="array",
verbose=True,
idxs=N_streamlines1,
apply_affine=True)
track2, header2, lengths2, indices2 = load_streamlines(filename2,
container="array",
verbose=True,
idxs=N_streamlines2,
apply_affine=True)
# %% Resampling
print("setting the same number of points for both the tracts...")
track1 = np.array(set_number_of_points(track1, N_points))
track2 = np.array(set_number_of_points(track2, N_points))
# %% Nearest Neightbours