def isolated(XYZ, k=18):
"""
Outputs an index I of isolated points from their integer coordinates,
XYZ (3, n), and under k-connectivity, k = 6, 18 or 24.
"""
A, B, D = FG.graph_3d_grid(XYZ.transpose(),k)
# Number of vertices
V = max(A) + 1
# Labels of connected components
label = FG.graph_cc(A,B,D,V)
# Isolated points
ncc = label.max() + 1
p = XYZ.shape[1]
size = np.zeros(ncc, float)
ones = np.ones((p, 1), float)
add_lines(ones, size.reshape(ncc, 1), label)
return np.where(size[label] == 1)[0]
def test_onesample_graph(self):
data, vardata, XYZ = make_data()
A, B, D = fg.graph_3d_grid(XYZ.transpose())
V = max(A) + 1
E = len(A)
edges = np.concatenate((A.reshape(E, 1), B.reshape(E, 1)), axis=1)
weights = D
G = fg.WeightedGraph(V, edges, weights)
# rfx calibration
P = PT.permutation_test_onesample_graph(data, G, ndraws=ndraws)
c = [(P.random_Tvalues[P.ndraws*(0.95)],None)]
r = np.ones(data.shape[1],int)
r[data.shape[1]/2:] *= 10
#p_values, cluster_results, region_results = P.calibrate(nperms=100, clusters=c, regions=[r])
# mfx calibration
P = PT.permutation_test_onesample_graph(data, G, vardata=vardata, stat_id="student_mfx", ndraws=ndraws)
p_values, cluster_results, region_results = P.calibrate(nperms=nperms, clusters=c, regions=[r])
def extract_clusters_from_thresh(T,XYZ,th,k=18):
"""
Extract clusters from statistical map
above specified threshold
In: T (p) statistical map
XYZ (3,p) voxels coordinates
th <float> threshold
k <int> the number of neighbours considered. (6,18 or 26)
Out: labels (p) cluster labels
"""
labels = -np.ones(len(T),int)
I = np.where(T >= th)[0]
if len(I)>0:
SupraThreshXYZ = XYZ[:, I]
# Compute graph associated to suprathresh_coords
A, B, D = graph_3d_grid(SupraThreshXYZ.transpose(),k)
# Number of vertices
V = max(A) + 1
# Labels of connected components
CC_label = graph_cc(A,B,D,V)
labels[I] = CC_label
return labels