def test_anisotropy():
x = np.arange(10)
# perfectly anisotropic
output = cluster_morphology.measure_3d(x, x, x)
np.testing.assert_almost_equal(output['anisotropy'][0], 1.)
# isotropic
output = cluster_morphology.measure_3d(x_cube, y_cube, z_cube)
np.testing.assert_almost_equal(output['anisotropy'][0], 0.)
def execute(self, namespace):
from PYME.Analysis.points import cluster_morphology as cmorph
import numpy as np
inp = namespace[self.inputName]
# make sure labeling scheme is consistent with what pyme conventions
if np.min(inp[self.labelKey]) < 0:
raise UserWarning(
'This module expects 0-label for unclustered points, and no negative labels'
)
labels = inp[self.labelKey].astype(np.int)
I = np.argsort(labels)
I = I[labels[I] > 0]
x_vals, y_vals, z_vals = inp['x'][I], inp['y'][I], inp['z'][I]
labels = labels[I]
maxLabel = labels[-1]
#find the unique labels, and their separation in the sorted list of points
unique_labels, counts = np.unique(labels, return_counts=True)
#allocate memory to store results in
measurements = np.zeros(maxLabel, cmorph.measurement_dtype)
# loop over labels, recalling that input is now sorted, and we know how many points are in each label.
# Note that missing labels result in zeroed entries (i.e. the initial values are not changed).
# Missing values can be filtered out later, if desired, by filtering on the 'counts' column, but having a dense
# array where index == label number makes any postprocessing in which we might want to find the data
# corresponding to a particular label MUCH easier and faster.
indi = 0
for label_num, ct in zip(unique_labels, counts):
indf = indi + ct
# create x,y,z arrays for this cluster, and calculate center of mass
x, y, z = x_vals[indi:indf], y_vals[indi:indf], z_vals[indi:indf]
cluster_index = label_num - 1 # we ignore the unclustered points, and start labeling at 1
cmorph.measure_3d(x, y, z, output=measurements[cluster_index])
indi = indf
meas = tabular.RecArraySource(measurements)
try:
meas.mdh = namespace[self.inputName].mdh
except AttributeError:
pass
namespace[self.outputName] = meas
def test_principle_axes():
x = np.arange(10)
nill = np.zeros_like(x)
# perfectly anisotropic
output = cluster_morphology.measure_3d(x, nill, nill)
np.testing.assert_array_almost_equal(np.array([output['sigma0'][0], output['sigma1'][0], output['sigma2'][0]]),
np.array([np.std(x, ddof=1), 0, 0]))
np.testing.assert_array_almost_equal(np.array([output['sigma_x'][0], output['sigma_y'][0], output['sigma_z'][0]]),
np.array([np.std(x, ddof=1), 0, 0]))
np.testing.assert_almost_equal(np.array([1, 0, 0]), output['axis0'][0])