def test_skeleton_read_write(simple_skeleton, overwrite_flag, file_exist):
sk = simple_skeleton
with tempfile.NamedTemporaryFile(suffix='.h5',
delete=(not file_exist)) as tf:
fname = tf.name
skeleton_io.write_skeleton_h5(sk, fname, overwrite=overwrite_flag)
if file_exist and not overwrite_flag:
with pytest.raises(OSError):
skback = skeleton_io.read_skeleton_h5(fname)
else:
skback = skeleton_io.read_skeleton_h5(fname)
assert np.all(skback.vertices == sk.vertices)
assert np.all(skback.edges == sk.edges)
assert skback.root == sk.root
def test_skeleton_read_write_with_props(simple_skeleton_with_properties,
tmp_path):
sk = simple_skeleton_with_properties
fname = os.path.join(tmp_path, 'test.swc')
skeleton_io.write_skeleton_h5(sk, fname, overwrite=True)
skback = skeleton_io.read_skeleton_h5(fname)
assert np.all(
skback.vertex_properties['test'] == sk.vertex_properties['test'])
assert np.all(skback.mesh_to_skel_map == sk.mesh_to_skel_map)
def load_skeleton(seg_id): """ Load skeleton. """ path_skel = "../../data/smoothed_skeletons_v185/" skel_lab = np.load(path_skel+str(seg_id)+"_skeleton_label.npy") seg_skel = skeleton_io.read_skeleton_h5(path_skel+str(seg_id)+"_skeleton.h5") return seg_skel, skel_lab
def build_full_cell_skeleton():
filepath = 'test/test_files/sk_648518346349499581.h5'
skel = skeleton_io.read_skeleton_h5(filepath)
yield skel
def build_full_cell_skeleton():
filepath = 'test/test_files/sk_648518346349499581.h5'
sk = skeleton_io.read_skeleton_h5(filepath, remove_zero_length_edges=False)
sk._rooted._mesh_index = np.array(sk.vertex_properties['mesh_index'])
yield sk
def sk_dist(neuronID,
pts,
data_root,
dataset_name='pinky100',
filt=True,
max_dist=2000):
'''
Calculates distance of any point/points in the space to the closest point on the skeleton neuron of interest
INPUTS:
neuronID = String, id associated with the neuron of interest
pts = np.array 3xN, N: number of points
data_root = Location of the dataset
datset_name (optional) = string,(defaul pinky100)
filt (optional) = bool, filter the mesh based on segment size(default True)
max_dist (optional) = float, maximum expected distance from the neuron in nm (defaul 500nm)
RETURNS:
dists = Nx1 np array, distances of each point to soma in nm
'''
# set values
voxel_size = [4, 4, 40]
# Folders for the mesh and skeleton
mesh_folder = os.path.join(data_root, 'meshes')
skeleton_folder = os.path.join(data_root, 'skeletons')
# Mesh meta data
mm = trimesh_io.MeshMeta(
cv_path=
'graphene://https://swdb.dynamicannotationframework.com/segmentation/1.0/pinky100_sv16',
disk_cache_path=mesh_folder,
cache_size=2)
# load the mesh for the neuron
mesh = mm.mesh(seg_id=neuronID)
#load the skeleton for the neuron
sk = skeleton_io.read_skeleton_h5(skeleton_folder + '/' + str(neuronID) +
'.h5')
if filt:
# filter out the segmented portions of the mesh
mask = mesh_filters.filter_largest_component(mesh)
neuron_mesh = mesh.apply_mask(mask)
else:
neuron_mesh = mesh
#load the skeleton for the neuron
sk = skeleton_io.read_skeleton_h5(skeleton_folder + '/' + str(neuron) +
'.h5')
#convert vertecies to nm
pt_nm = np.vstack(pts) * np.array(voxel_size)
# use kdtree to find the shortest distance from the point to the mesh and the index associated with that
dist, ind = neuron_mesh.kdtree.query(pt_nm, distance_upper_bound=max_dist)
if filt:
#find the index on original mask
ind = neuron_mesh.map_indices_to_unmasked(ind_masked)
neuron_mesh = mesh
#find skeleton vertex of the point on the mesh
syn_sk_ind = sk.mesh_to_skel_map[ind]
syn_sk_mesh_ind = np.array(sk.vertex_properties['mesh_index'])[syn_sk_ind]
dd = scipy.sparse.csgraph.dijkstra(neuron_mesh.csgraph,
directed=False,
indices=ind)
dists = [dd[ind, mesh_ind] for ind, mesh_ind in enumerate(syn_sk_mesh_ind)]
dists = np.array(dists)
return dists
def soma_dist(neuronID,
pts,
data_root,
dataset_name='pinky100',
filt=True,
max_dist=500):
'''
Calculates distance of any point/points in the space to the center of the soma of neuron of interest
INPUTS:
neuronID = String, id associated with the neuron of interest
pts = np.array 3xN, N: number of points
data_root = Location of the dataset
datset_name (optional) = string,(defaul pinky100)
filt (optional) = bool, filter the mesh based on segment size(default True)
max_dist (optional) = float, maximum expected distance from the neuron in nm (defaul 500nm)
RETURNS:
pt_soma_dist = Nx1 np array, distances of each point to soma in nm
'''
# set values
voxel_size = [4, 4, 40]
# Folders for the mesh and skeleton
mesh_folder = os.path.join(data_root, 'meshes')
skeleton_folder = os.path.join(data_root, 'skeletons')
# Mesh meta data
mm = trimesh_io.MeshMeta(
cv_path=
'graphene://https://swdb.dynamicannotationframework.com/segmentation/1.0/pinky100_sv16',
disk_cache_path=mesh_folder,
cache_size=2)
# load the mesh for the neuron
mesh = mm.mesh(seg_id=neuronID)
#load the skeleton for the neuron
sk = skeleton_io.read_skeleton_h5(skeleton_folder + '/' + str(neuronID) +
'.h5')
if filt:
# filter out the segmented portions of the mesh
mask = mesh_filters.filter_largest_component(mesh)
neuron_mesh = mesh.apply_mask(mask)
else:
neuron_mesh = mesh
# convert vertecies to nm
pt_nm = np.vstack(pts) * np.array(voxel_size)
# use kdtree to find the shortest distance from the point to the mesh and the index associated with that
dist, ind = neuron_mesh.kdtree.query(pt_nm, distance_upper_bound=500)
# use kdtree to find the shortest distance from the point to the mesh and the index associated with that
dist, ind = neuron_mesh.kdtree.query(pt_nm, distance_upper_bound=max_dist)
#find the vertices of the synapse point on the mesh
pt_pos_mesh = neuron_mesh.vertices[ind]
#find skeleton vertex of the point on the mesh
if filt:
ind_orig = neuron_mesh.map_indices_to_unmasked(ind)
pt_sk_vert = sk.mesh_to_skel_map[ind_orig]
else:
pt_sk_vert = sk.mesh_to_skel_map[ind]
pt_soma_dist = sk.distance_to_root[pt_sk_vert] + dist
+sk_dist(neuronID,
pts,
data_root,
dataset_name='pinky100',
filt=True,
max_dist=2000)
return pt_soma_dist
def read_smoothed_neuron_skel(segid):
filename = f"{SMOOTHED_SKELDIR}/{segid}_skeleton.h5"
return skeleton_io.read_skeleton_h5(filename)
