def test_IdentifyOverlappingROIs():
mdh = NestedClassMDHandler()
mdh['voxelsize.x'] = 0.115
roi_size = 256
roi_size_um = roi_size * mdh['voxelsize.x']
max_distance = np.sqrt(2) * roi_size_um
points = tabular.RandomSource(100 * roi_size_um, 100 * roi_size_um,
int(1e3))
points = tabular.MappingFilter(points, **{
'x_um': 'x',
'y_um': 'y'
}) # pretend we defined points in um
points.mdh = mdh
recipe = base.ModuleCollection()
recipe.add_module(
measurement.IdentifyOverlappingROIs(roi_size_pixels=roi_size,
output='mapped'))
recipe.add_module(
tablefilters.FilterTable(inputName='mapped',
filters={'rejected': [-0.5, 0.5]},
outputName='output'))
recipe.namespace['input'] = points
filtered = recipe.execute()
positions = np.stack([filtered['x'], filtered['y']], axis=1)
kdt = KDTree(positions)
distances, indices = kdt.query(positions, k=2, p=2)
assert (distances[:, 1] > max_distance).all()
def test_TravelingSalesperson():
r = 10
theta = np.linspace(0, 2 * np.pi, 5)
dt = theta[1] - theta[0]
x, y = r * np.cos(theta), r * np.sin(theta)
x = np.concatenate([x, r * np.cos(theta + 0.5 * dt)])
y = np.concatenate([y, r * np.sin(theta + 0.5 * dt)])
points = tabular.DictSource({
'x_um':
np.concatenate([x, 1.1 * r * np.cos(theta)]),
'y_um':
np.concatenate([y, 1.1 * r * np.sin(theta)])
})
recipe = base.ModuleCollection()
recipe.add_module(
measurement.TravelingSalesperson(output='output', epsilon=0.001))
recipe.namespace['input'] = points
ordered = recipe.execute()
# should be not too much more than the rough circumference.
assert ordered.mdh['TravelingSalesperson.Distance'] < 1.25 * (2 * np.pi *
r)
def test_ChunkedTravelingSalesman():
n = 500
x = np.random.rand(n) * 4e3
y = np.random.rand(n) * 4e3
points = tabular.DictSource({'x_um': x, 'y_um': y})
recipe = base.ModuleCollection()
recipe.add_module(
measurement.ChunkedTravelingSalesperson(output='output',
epsilon=0.001,
points_per_chunk=50))
recipe.namespace['input'] = points
ordered = recipe.execute()
assert ordered.mdh['TravelingSalesperson.Distance'] < ordered.mdh[
'TravelingSalesperson.OriginalDistance']
epsilon=0.001,
points_per_chunk=50))
recipe.namespace['input'] = points
ordered = recipe.execute()
assert ordered.mdh['TravelingSalesperson.Distance'] < ordered.mdh[
'TravelingSalesperson.OriginalDistance']
if __name__ == '__main__':
import time
n = 10000
x = np.random.rand(n) * 4e3
y = np.random.rand(n) * 4e3
points = tabular.DictSource({'x_um': x, 'y_um': y})
recipe = base.ModuleCollection()
recipe.add_module(
measurement.ChunkedTravelingSalesperson(output='output',
epsilon=0.001,
points_per_chunk=500))
recipe.namespace['input'] = points
t = time.time()
ordered = recipe.execute()
print('n_points: %d, runtime: %f' % (n, time.time() - t))
print('og distance: %f, distance: %f' %
(ordered.mdh['TravelingSalesperson.OriginalDistance'],
ordered.mdh['TravelingSalesperson.Distance']))
