def test_calc_efpl_outside_roi():
import wknml
nml = wknml.NML(
parameters=wknml.NMLParameters(
name='',
scale=(1, 1, 1),
),
trees=[
wknml.Tree(
id=0,
color=(255, 255, 0, 1),
name='',
nodes=[
wknml.Node(id=4, position=(2, 1, 1), radius=1),
wknml.Node(id=5, position=(3, 1, 1), radius=1),
wknml.Node(id=6, position=(20, 1, 1), radius=1)
],
edges=[
wknml.Edge(source=4, target=5),
wknml.Edge(source=5, target=6),
],
),
],
branchpoints=[],
comments=[],
groups=[],
)
Vlbls = np.ones((5, 5, 5), dtype=np.uint32)
dataset_start = np.zeros((1, 3), dtype=int)
calc_eftpl(nml, Vlbls, dataset_start)
def util_get_nml():
import wknml
trees = [
wknml.Tree(
id=0,
color=(255, 255, 0, 1),
name="Synapse 1",
nodes=[
wknml.Node(id=0, position=(1, 1, 1), radius=1),
wknml.Node(id=1, position=(1, 1, 2), radius=1),
wknml.Node(id=2, position=(1, 1, 3), radius=1),
wknml.Node(id=3, position=(1, 2, 2), radius=1)
],
edges=[
wknml.Edge(source=0, target=1),
wknml.Edge(source=1, target=2),
wknml.Edge(source=1, target=3)
],
groupId=1,
),
wknml.Tree(
id=1,
color=(255, 0, 255, 1),
name="Synapse 2",
nodes=[
wknml.Node(id=0, position=(2, 1, 1), radius=1),
wknml.Node(id=1, position=(3, 1, 1), radius=1),
wknml.Node(id=2, position=(4, 1, 1), radius=1)
],
edges=[
wknml.Edge(source=0, target=1),
wknml.Edge(source=1, target=2)
],
groupId=1,
),
]
nml = wknml.NML(
parameters=wknml.NMLParameters(
name="Test",
scale=(1, 1, 1),
),
trees=trees,
branchpoints=[],
comments=[],
groups=[],
)
return nml
def test__calc_efpl__use_correct_edge_lengths():
""" Github issue: https://github.com/elhuhdron/emdrp/issues/4
Check that the norm between edge node positions is calculated along the
correct axis.
"""
import wknml
trees = [
wknml.Tree(
id=0,
color=(255, 255, 0, 1),
name="neuron1",
nodes=[
wknml.Node(id=0, position=(1, 1, 1), radius=1),
wknml.Node(id=1, position=(1, 1, 3), radius=1),
wknml.Node(id=2, position=(1, 2, 1), radius=1)
],
edges=[
wknml.Edge(source=0, target=1),
wknml.Edge(source=0, target=2)
],
groupId=1,
)
]
nml = wknml.NML(
parameters=wknml.NMLParameters(
name="Test",
scale=(1, 1, 1),
),
trees=trees,
branchpoints=[],
comments=[],
groups=[],
)
Vlbls = np.ones((5, 5, 5), dtype=np.uint32)
dataset_start = np.zeros((1, 3), dtype=int)
Vlbls[:, 1:, :] = 2
efpl = calc_eftpl(nml, Vlbls, dataset_start)
assert (efpl == 2 / 3)
def _edges_to_nml_edges(edges):
""" Converts skeleton edges (numpy array) to wknml edges (list of named tuples)."""
nml_edges = []
for idx in range(edges.shape[0]):
nml_edge = wknml.Edge(
source=int(edges[idx, 0]),
target=int(edges[idx, 1]),
)
nml_edges.append(nml_edge)
return nml_edges