def make(self,key):
"""
Purpose: To decimate a mesh by a perscribed
decimation ratio and algorithm
Pseudocode:
1) Get the current mesh
2) Decimationt he current mesh with the corresponding decimate method
3) Get n_verts,n_faces and save as h5py file
"""
segment_id = key["segment_id"]
decimation_method = key["decimation_method"]
mesh_fragment_method_set = key["mesh_fragment_method_set"]
ver =key["ver"]
if verbose:
print(f"\n\n--Working on {segment_id}: (decimation_method = {decimation_method})")
param_tables = MeshFragmentMethodSet.restrict_one_part_with_hash(mesh_fragment_method_set)
soma_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Soma'").fetch1("mesh_fragment_method")).fetch1()
glia_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Glia'").fetch1("mesh_fragment_method")).fetch1()
nuclei_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Nuclei'").fetch1("mesh_fragment_method")).fetch1()
all_kwargs = gu.merge_dicts([glia_kwargs,nuclei_kwargs,soma_kwargs])
mesh_fragment_method_soma = (param_tables & dict(table_type='Soma')).fetch1("mesh_fragment_method")
mesh_fragment_method_nuclei = (param_tables & dict(table_type='Nuclei')).fetch1("mesh_fragment_method")
mesh_fragment_method_glia = (param_tables & dict(table_type='Glia')).fetch1("mesh_fragment_method")
st = time.time()
#1) Get the current mesh
new_mesh = (h01mor.Decimation.Object() & key).fetch1("mesh")
if verbose:
print(f"Fetching Decimation time: {time.time()-st}")
current_mesh_verts,current_mesh_faces = new_mesh.vertices,new_mesh.faces
segment_id = key["segment_id"]
(total_soma_list,
run_time,
total_soma_list_sdf,
glia_pieces,
nuclei_pieces) = sm.extract_soma_center(
segment_id,
current_mesh_verts,
current_mesh_faces,
return_glia_nuclei_pieces=True,
verbose = True,
**all_kwargs
)
# -------- 1/9 Addition: Going to save off the glia and nuclei pieces ----------- #
"""
Psuedocode:
For both glia and nuclie pieces
1) If the length of array is greater than 0 --> combine the mesh and map the indices to original mesh
2) If not then just put None
"""
orig_mesh = new_mesh
if len(glia_pieces)>0:
glia_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(glia_pieces))
n_glia_faces = len(glia_faces)
else:
glia_faces = None
n_glia_faces = 0
if len(nuclei_pieces)>0:
nuclei_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(nuclei_pieces))
n_nuclei_faces = len(nuclei_faces)
else:
nuclei_faces = None
n_nuclei_faces = 0
# --------- saving the nuclei and glia saves
glia_path,nuclei_path = hdju.save_glia_nuclei_files(
glia_faces=glia_faces,
nuclei_faces=nuclei_faces,
segment_id=segment_id,
filename_append = filename_append)
print(f" glia_path = {glia_path} \n nuclei_path = {nuclei_path}")
glia_key = dict(key,
mesh_fragment_method = mesh_fragment_method_glia,
n_faces = n_glia_faces,
faces=glia_path)
nuclei_key = dict(key,
mesh_fragment_method = mesh_fragment_method_nuclei,
n_faces = n_nuclei_faces,
faces=nuclei_path)
# ---------------- End of 1/9 Addition --------------------------------- #
self.insert1(key,
skip_duplicates=True,
ignore_extra_fields=True,
allow_direct_insert=True)
print(f"Run time was {run_time} \n total_soma_list = {total_soma_list}"
f"\n with sdf values = {total_soma_list_sdf}")
#check if soma list is empty and did not find soma
if len(total_soma_list) <= 0:
print("There were no somas found for this mesh so just writing empty data")
returned_file_path = tu.write_h5_file(
vertices=np.array([]),
faces=np.array([]),
segment_id=segment_id,
filename = f'{segment_id}_soma_0.h5',
filepath=str(hdju.external_path_mesh)
)
insert_dict = dict(key,
soma_index=0,
centroid_x=None,
centroid_y=None,
centroid_z=None,
centroid_x_nm=None,
centroid_y_nm=None,
centroid_z_nm=None,
#distance_from_prediction=None,
#prediction_matching_index = None,
n_vertices=0,
n_faces=0,
mesh=returned_file_path,
multiplicity=0,
sdf = None,
volume = None,
surface_area = None,
max_side_ratio = None,
bbox_volume_ratio = None,
max_hole_length=None,
run_time=np.round(run_time,4),
mesh_fragment_method = mesh_fragment_method_soma,
)
#raise Exception("to prevent writing because none were found")
self.Soma.insert1(insert_dict,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.SomaObject.insert1(insert_dict,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
return
#if there is one or more soma found, get the volume and side length checks
max_side_ratio = [np.max(sm.side_length_ratios(m)) for m in total_soma_list]
bbox_volume_ratio = [sm.soma_volume_ratio(m) for m in total_soma_list]
dicts_to_insert = []
for i,(current_soma,soma_sdf,sz_ratio,vol_ratio) in enumerate(zip(total_soma_list,total_soma_list_sdf,max_side_ratio,bbox_volume_ratio)):
print("Trying to write off file")
""" Currently don't need to export the meshes
current_soma.export(f"{key['segment_id']}/{key['segment_id']}_soma_{i}.off")
"""
auto_prediction_center_nm = np.mean(current_soma.vertices,axis=0)
auto_prediction_center = auto_prediction_center_nm / hu.voxel_to_nm_scaling
auto_prediction_center = auto_prediction_center.astype("int")
print(f"Predicted Coordinates are {auto_prediction_center}")
max_hole_length = tu.largest_hole_length(current_soma)
if max_hole_length is not None:
max_hole_length = np.round(max_hole_length,3)
returned_file_path = tu.write_h5_file(
vertices=current_soma.vertices,
faces=current_soma.faces,
segment_id=segment_id,
filename = f'{segment_id}_soma_{i+1}.h5',
filepath=str(hdju.external_path_mesh)
)
divisor=1000000
insert_dict = dict(key,
soma_index=i+1,
centroid_x=auto_prediction_center[0],
centroid_y=auto_prediction_center[1],
centroid_z=auto_prediction_center[2],
centroid_x_nm=auto_prediction_center_nm[0],
centroid_y_nm=auto_prediction_center_nm[1],
centroid_z_nm=auto_prediction_center_nm[2],
n_vertices = len(current_soma.vertices),
n_faces = len(current_soma.faces),
mesh=returned_file_path,
multiplicity=len(total_soma_list),
sdf = np.round(soma_sdf,3),
volume = np.round(current_soma.convex_hull.volume/1000000000,3),
surface_area = np.round(current_soma.area/divisor,3),
max_side_ratio = np.round(sz_ratio,3),
bbox_volume_ratio = np.round(vol_ratio,3),
max_hole_length = max_hole_length,
run_time=np.round(run_time,4),
mesh_fragment_method = mesh_fragment_method_soma,
)
dicts_to_insert.append(insert_dict)
self.Glia.insert1(glia_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.GliaObject.insert1(glia_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.Nuclei.insert1(nuclei_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.NucleiObject.insert1(nuclei_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.Soma.insert(dicts_to_insert,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.SomaObject.insert(dicts_to_insert,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
def combine_branches(
branch_upstream,
branch_downstream,
add_skeleton=True,
add_labels=False,
verbose=True,
common_endpoint=None,
return_jitter_segment=False,
):
"""
Purpose: To combine two branch objects together
WHERE IT IS ASSUMED THEY SHARE ONE COMMON ENDPOINT
Ex:
import branch_utils as bu
branch_upstream = copy.deepcopy(neuron_obj[0][upstream_branch])
branch_downstream= copy.deepcopy(neuron_obj[0][downstream_branch])
branch_upstream.labels = ["hellow"]
branch_downstream.labels = ["my","new","labels"]
b_out = bu.combine_branches(
branch_upstream,
branch_downstream,
verbose = True,
add_skeleton = False,
add_labels = False
)
"""
debug_time = False
st = time.time()
b_up = branch_upstream
b_d = branch_downstream
b_obj = copy.deepcopy(b_up)
if debug_time:
print(f"Copying branch: {time.time() - st}")
st = time.time()
# --working on the non-optional_features
face_offset = len(b_up.mesh.faces)
b_obj.mesh = tu.combine_meshes([b_up.mesh, b_d.mesh])
if verbose:
print(f"b_up.mesh.faces = {b_up.mesh.faces.shape}")
print(f"b_d.mesh.faces = {b_d.mesh.faces.shape}")
print(f"b_obj.mesh = {b_obj.mesh.faces.shape}")
if debug_time:
print(f"Combining meshes: {time.time() - st}")
st = time.time()
b_obj.mesh_face_idx = np.concatenate(
[b_up.mesh_face_idx, b_d.mesh_face_idx])
# if verbose:
# print(f"b_up.mesh_face_idx = {b_up.mesh_face_idx}")
# print(f"b_d.mesh_face_idx = {b_d.mesh_face_idx}")
# print(f"b_obj.mesh_face_idx= {b_obj.mesh_face_idx}")
# --------- setting the spine info -----------
if b_d.spines is not None:
if b_d.spines_obj is not None:
d_spines_obj = [
spu.adjust_obj_with_face_offset(k, face_offset=face_offset)
for k in b_d.spines_obj
]
else:
d_spines_obj = []
for sp_attr in ["spines", "spines_obj", "spines_volume"]:
if getattr(b_obj, sp_attr) is None:
setattr(b_obj, sp_attr, [])
if sp_attr == "spines_obj":
curr_value = d_spines_obj
else:
curr_value = getattr(b_d, sp_attr)
setattr(b_obj, sp_attr, getattr(b_obj, sp_attr) + curr_value)
if debug_time:
print(f"Spine adjustment: {time.time() - st}")
st = time.time()
if verbose:
print(f"Total number of spines = {b_obj.n_spines}")
if debug_time:
print(f"computing number of spines : {time.time() - st}")
st = time.time()
# --------- setting the bouton and spines info -----------
for k in [
'boutons',
'boutons_cdfs',
'boutons_volume',
"head_neck_shaft_idx",
]:
if debug_time:
print(f"combining {k}: {time.time() - st}")
st = time.time()
setattr(b_obj, k, combine_attr_lists(getattr(b_up, k), getattr(b_d,
k)))
if debug_time:
print(f"combining lists: {time.time() - st}")
st = time.time()
#------ adjusting the synapses ------------------
synapses_to_add = [
syu.adjust_obj_with_face_offset(k, face_offset) for k in b_d.synapses
]
if debug_time:
print(f"synapses offset: {time.time() - st}")
st = time.time()
# ---------------- PARTS TO RECALCULATE
b_obj._mesh_volume = None
b_obj.mesh_volume
if verbose:
print(f"b_obj.mesh_volume = {b_obj.mesh_volume}")
# ---------------- Do skeleton combination ------------------------------
try:
match_idx_1, match_idx_2 = sk.matching_endpoint_singular(
b_obj.endpoints, b_d.endpoints, return_indices=True, verbose=False)
jitter_segment = None
except Exception as e:
if common_endpoint is None:
raise Exception(f"{e}")
match_idx_1 = nu.matching_row_index(b_obj.endpoints, common_endpoint)
match_idx_2 = nu.matching_row_index(b_d.endpoints, common_endpoint)
jitter_segment = bu.add_jitter_to_endpoint(b_d,
b_d.endpoints[1 -
match_idx_2],
verbose=verbose)
print(f"b_d.endpoints = {b_d.endpoints}")
if add_skeleton:
if verbose:
print(f"Adding skeleton of downstream branch")
sk_len_weights = [b_up.skeletal_length, b_d.skeletal_length]
if match_idx_2 == 1:
skeleton_add = sk.flip_skeleton(b_d.skeleton)
width_array_add = {
k: np.flip(v)
for k, v in b_d.width_array.items()
}
else:
skeleton_add = b_d.skeleton
width_array_add = b_d.width_array
b_obj.skeleton = sk.stack_skeletons([b_up.skeleton, skeleton_add])
b_obj.endpoints[match_idx_1] = b_d.endpoints[1 - match_idx_2]
if verbose:
print(f"Original lengths = {sk_len_weights}")
print(f"New skeleton length = {b_obj.skeletal_length}")
print(
f"New endpoints calculated = {sk.find_skeleton_endpoint_coordinates(b_obj.skeleton)}"
)
print(f"Adjusted endpoints = {b_obj.endpoints}")
b_obj.width_array = {
k: np.concatenate([v, width_array_add[k]])
for k, v in b_obj.width_array.items()
}
b_obj.width = nu.weighted_average([b_up.width, b_d.width],
sk_len_weights)
b_obj.width_new = {
k: nu.weighted_average([v, b_d.width_new[k]], sk_len_weights)
for k, v in b_obj.width_new.items()
}
else:
if verbose:
print(f"Not adding skeleton")
# adjust the closest skeleton coordinates of all the synapses to add
for sy in synapses_to_add:
sy.closest_sk_coordinate = sk.closest_skeleton_coordinate(
b_obj.skeleton, sy.closest_face_coordinate)
if debug_time:
print(f"Skeleton adjustment: {time.time() - st}")
st = time.time()
# ------------ Parts to recalculates ----------
b_obj._skeleton_graph = None
b_obj._endpoints_nodes = None
b_obj.skeleton_graph
b_obj.endpoints_nodes
if debug_time:
print(f"Ccalculating skeleton graph and endpoints: {time.time() - st}")
st = time.time()
# ----- adjusting the synapses --------
if verbose:
print(
f"--- Adjusting the synapse features based on new skeleton additions ------"
)
b_obj.synapses = combine_attr_lists(b_obj.synapses, synapses_to_add)
for syn in b_obj.synapses:
syu.calculate_endpoints_dist(b_obj, syn)
syu.calculate_upstream_downstream_dist_from_down_idx(
syn, down_idx=match_idx_1)
if debug_time:
print(f"synapse distances: {time.time() - st}")
st = time.time()
# --------- adding labels -------
if add_labels:
b_obj.labels += b_d.labels
if return_jitter_segment:
return b_obj, jitter_segment
else:
return b_obj
def make(self,key):
"""
Pseudocode:
1) Compute all of the
2) Save the mesh as an h5 py file
3) Store the saved path as the decomposition part of the dictionary and erase the vertices and faces
4) Insert
"""
#get the mesh data
print(f"\n\n\n---- Working on Neuron {key['segment_id']} ----")
print(key)
new_mesh = (minnie.Decimation() & key).fetch1("mesh")
current_mesh_verts,current_mesh_faces = new_mesh.vertices,new_mesh.faces
segment_id = key["segment_id"]
(total_soma_list,
run_time,
total_soma_list_sdf,
glia_pieces,
nuclei_pieces) = sm.extract_soma_center(
segment_id,
current_mesh_verts,
current_mesh_faces,
return_glia_nuclei_pieces=True,
)
# -------- 1/9 Addition: Going to save off the glia and nuclei pieces ----------- #
"""
Psuedocode:
For both glia and nuclie pieces
1) If the length of array is greater than 0 --> combine the mesh and map the indices to original mesh
2) If not then just put None
"""
orig_mesh = trimesh.Trimesh(vertices=current_mesh_verts,
faces=current_mesh_faces)
if len(glia_pieces)>0:
glia_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(glia_pieces))
n_glia_faces = len(glia_faces)
else:
glia_faces = None
n_glia_faces = 0
if len(nuclei_pieces)>0:
nuclei_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(nuclei_pieces))
n_nuclei_faces = len(nuclei_faces)
else:
nuclei_faces = None
n_nuclei_faces = 0
# --------- saving the nuclei and glia saves
glia_path,nuclei_path = du.save_glia_nuclei_files(glia_faces=glia_faces,
nuclei_faces=nuclei_faces,
segment_id=segment_id)
print(f" glia_path = {glia_path} \n nuclei_path = {nuclei_path}")
glia_nuclei_key = dict(key,
ver=current_version,
n_glia_faces=n_glia_faces,
#glia_faces = glia_faces,
glia_faces = glia_path,
n_nuclei_faces = n_nuclei_faces,
#nuclei_faces = nuclei_faces
nuclei_faces = nuclei_path,
)
NeuronGliaNuclei.insert1(glia_nuclei_key,replace=True)
print(f"Finished saving off glia and nuclei information : {glia_nuclei_key}")
# ---------------- End of 1/9 Addition --------------------------------- #
print(f"Run time was {run_time} \n total_soma_list = {total_soma_list}"
f"\n with sdf values = {total_soma_list_sdf}")
#check if soma list is empty and did not find soma
if len(total_soma_list) <= 0:
print("There were no somas found for this mesh so just writing empty data")
returned_file_path = tu.write_h5_file(
vertices=np.array([]),
faces=np.array([]),
segment_id=segment_id,
filename = f'{segment_id}_0.h5',
filepath=str(du.get_somas_path())
)
insert_dict = dict(key,
soma_index=0,
ver=current_version,
centroid_x=None,
centroid_y=None,
centroid_z=None,
#distance_from_prediction=None,
#prediction_matching_index = None,
n_vertices=0,
n_faces=0,
mesh=returned_file_path,
multiplicity=0,
sdf = None,
volume = None,
max_side_ratio = None,
bbox_volume_ratio = None,
max_hole_length=None,
run_time=run_time
)
#raise Exception("to prevent writing because none were found")
self.insert1(insert_dict,skip_duplicates=True)
return
#if there is one or more soma found, get the volume and side length checks
max_side_ratio = [np.max(sm.side_length_ratios(m)) for m in total_soma_list]
bbox_volume_ratio = [sm.soma_volume_ratio(m) for m in total_soma_list]
dicts_to_insert = []
for i,(current_soma,soma_sdf,sz_ratio,vol_ratio) in enumerate(zip(total_soma_list,total_soma_list_sdf,max_side_ratio,bbox_volume_ratio)):
print("Trying to write off file")
""" Currently don't need to export the meshes
current_soma.export(f"{key['segment_id']}/{key['segment_id']}_soma_{i}.off")
"""
auto_prediction_center = np.mean(current_soma.vertices,axis=0) / np.array([4,4,40])
auto_prediction_center = auto_prediction_center.astype("int")
print(f"Predicted Coordinates are {auto_prediction_center}")
max_hole_length = tu.largest_hole_length(current_soma)
returned_file_path = tu.write_h5_file(
vertices=current_soma.vertices,
faces=current_soma.faces,
segment_id=segment_id,
filename = f'{segment_id}_{i}.h5',
filepath=str(du.get_somas_path())
)
insert_dict = dict(key,
soma_index=i+1,
ver=current_version,
centroid_x=auto_prediction_center[0],
centroid_y=auto_prediction_center[1],
centroid_z=auto_prediction_center[2],
n_vertices = len(current_soma.vertices),
n_faces = len(current_soma.faces),
mesh=returned_file_path,
multiplicity=len(total_soma_list),
sdf = np.round(soma_sdf,3),
volume = current_soma.convex_hull.volume/1000000000,
max_side_ratio = np.round(sz_ratio,3),
bbox_volume_ratio = np.round(vol_ratio,3),
max_hole_length = np.round(max_hole_length,3),
run_time=np.round(run_time,4)
)
dicts_to_insert.append(insert_dict)
self.insert(dicts_to_insert,skip_duplicates=True)
