def label_object_principal_axes(dataset, label_value):
import numpy as np
from tomviz import utils
labels = dataset.active_scalars
num_voxels = np.sum(labels == label_value)
xx, yy, zz = utils.get_coordinate_arrays(dataset)
data = np.zeros((num_voxels, 3))
selection = labels == label_value
assert np.any(selection), \
"No voxels with label %d in label map" % label_value
data[:, 0] = xx[selection]
data[:, 1] = yy[selection]
data[:, 2] = zz[selection]
# Compute PCA on coordinates
from scipy import linalg as la
m, n = data.shape
center = data.mean(axis=0)
data -= center
R = np.cov(data, rowvar=False)
evals, evecs = la.eigh(R)
idx = np.argsort(evals)[::-1]
evecs = evecs[:, idx]
evals = evals[idx]
return (evecs, center)
def label_object_principal_axes(dataset, label_value):
import numpy as np
from tomviz import utils
labels = utils.get_array(dataset)
num_voxels = np.sum(labels == label_value)
xx, yy, zz = utils.get_coordinate_arrays(dataset)
data = np.zeros((num_voxels, 3))
selection = labels == label_value
assert np.any(selection), \
"No voxels with label %d in label map" % label_value
data[:, 0] = xx[selection]
data[:, 1] = yy[selection]
data[:, 2] = zz[selection]
# Compute PCA on coordinates
from scipy import linalg as la
m, n = data.shape
center = data.mean(axis=0)
data -= center
R = np.cov(data, rowvar=False)
evals, evecs = la.eigh(R)
idx = np.argsort(evals)[::-1]
evecs = evecs[:, idx]
evals = evals[idx]
return (evecs, center)
