skoudoro on master
NF: add unbiased groupwise bund… TEST: add tests for groupwise s… DOC: add groupwise slr example and 12 more (compare)
""" Computes the mam distance between two streamlines. """
# For simplicity, features will be the vector between endpoints of a streamline. super(mam, self).__init__(feature=VectorOfEndpointsFeature()) def are_compatible(self, shape1, shape2): """ Checks if two features are vectors of same dimension. Basically this method exists so we don't have to do this check inside the `dist` method (speedup). """ return shape1 == shape2 and shape1 == 1 def dist(self, v1, v2): track1 = np.ascontiguousarray(v1, dtype=np.float32) t1_len = track1.shape track2 = np.ascontiguousarray(v2, dtype=np.float32) t2_len = track2.shape # preallocate buffer array for track distance calculations #distances_buffer = np.zeros((t1_len ,), dtype=np.float32) min_t2t1 = np.zeros((t2_len ,), dtype=np.float32) min_t1t2 = np.zeros((t1_len ,), dtype=np.float32) for t2_pi in range(0,t2_len): min_t2t1[t2_pi] = np.inf for t1_pi in range(0,t1_len): min_t1t2[t1_pi] = np.inf # pointer to current point in track 1 t1_pt = track1 t2_pt = track2 # calculate min squared distance between each point in the two # lines. Squared distance to delay doing the sqrt until after this # speed-critical loop for t1_pi in range(0,t1_len): # pointer to current point in track 2 for t2_pi in range(0,t2_len): d0 = t1_pt[t1_pi] - t2_pt[t2_pi] d1 = t1_pt[t1_pi] - t2_pt[t2_pi] delta2 = d0*d0 + d1*d1 #+ d2*d2 if delta2 < min_t1t2[t1_pi]: min_t1t2[t1_pi]=delta2 for t2_pi in range(0,t2_len): # pointer to current point in track 2 for t1_pi in range(0,t1_len): d0 = t1_pt[t1_pi] - t2_pt[t2_pi] d1 = t1_pt[t1_pi] - t2_pt[t2_pi] delta2 = d0*d0 + d1*d1 #+ d2*d2 if delta2 < min_t2t1[t2_pi]: min_t2t1[t2_pi]=delta2 # sqrt to get Euclidean distance from squared distance for t1_pi in range(0,t1_len): min_t1t2[t1_pi]=math.sqrt(min_t1t2[t1_pi]) for t2_pi in range(0,t2_len): min_t2t1[t2_pi]=math.sqrt(min_t2t1[t2_pi]) mean_t2t1 = 0 mean_t1t2 = 0 for t1_pi in range(0, t1_len): mean_t1t2+=min_t1t2[t1_pi] mean_t1t2=mean_t1t2 / t1_len for t2_pi in range(0, t2_len): mean_t2t1+=min_t2t1[t2_pi] mean_t2t1=mean_t2t1 / t2_len return np.min((mean_t2t1,mean_t1t2))
metric = mam()
qb2 = QuickBundles(threshold=0.15, metric=metric)
clus = qb2.cluster(streamlines)
load_tractogramusing the b0 image as a reference and plotting them with spherical ROIs using
stream_actor, the two are in very different spaces. It looks like the origin is shifted and the spatial scaling is different. Is there some transformation happening under the hood in
load_tractogramsomewhere? If so, I'm guessing applying the same transformation to my coordinates will solve the issue.
VectorOfEndpointsFeature()and then I will remove
shape1 == 1on your function
are_compatible. You do not need vector in your case for MAM. Let me know if it works, otherwise create an issue on DIPY, it will be easier to help you
to_xxxxmethods after loading, but without success. They shift the tracks, but not into the space my ROI coordinates are in.
I would be grateful for your thoughts on what could be wrong when feeding labels in utils.connectivity.matrix: M, grouping = utils.connectivitymatrix(streamlines, labels.astype(int), affine_labels,
File "/Users/alex/py3/lib/python3.7/site-packages/dipy/tracking/utils.py", line 157, in connectivity_matrix
raise ValueError("label_volume must be a 3d integer array with labelvolume must be a 3d integer array withnon-negative label values
labels.shape= (200, 400, 200) when labels.max() = 332 and labels.min() = 0. Thanks much!