def make(self,key):
"""
Pseudocode:
1) Pull Down all the Neuron Objects associated with a segment_id
For each neuron:
2) Run the full axon preprocessing
3) Save off the neuron
4) Save dict entry to list
5) Write the new entry to the table
"""
# 1) Pull Down All of the Neurons
segment_id = key["segment_id"]
if verbose:
print(f"------- Working on Neuron {segment_id} -----")
whole_pass_time = time.time()
#1) Pull Down all the Neuron Objects associated with a segment_id
neuron_objs,neuron_split_idxs = du.decomposition_with_spine_recalculation(segment_id,
ignore_DecompositionAxon=True,
ignore_DecompositionCellType = True)
if verbose:
print(f"Number of Neurons found ={len(neuron_objs)}")
#For each neuron:
dict_to_write = []
# -------- getting the nuclei info to match
try:
segment_map_dict = (minnie.AutoProofreadValidationSegmentMap4() & dict(old_segment_id=segment_id)).fetch1()
except:
segment_map_dict = (minnie.AutoProofreadValidationSegmentMapInh() & dict(old_segment_id=segment_id)).fetch1()
nucleus_id = segment_map_dict["nucleus_id"]
nuc_center_coords = du.nuclei_id_to_nucleus_centers(nucleus_id)
nucleus_ids = [nucleus_id]
nucleus_centers = [nuc_center_coords]
print(f"nucleus_ids = {nucleus_ids}")
print(f"nucleus_centers = {nucleus_centers}")
for split_index,neuron_obj in zip(neuron_split_idxs,neuron_objs):
if verbose:
print(f"--> Working on Split Index {split_index} -----")
st = time.time()
# ------------- Does all of the processing -------------------
#1) ------ Getting the paired nuclei ------
winning_nucleus_id, nucleus_info = nru.pair_neuron_obj_to_nuclei(neuron_obj,
"S0",
nucleus_ids,
nucleus_centers,
nuclei_distance_threshold = 15000,
return_matching_info = True,
verbose=True)
# else:
# winning_nucleus_id = 12345
# nucleus_info = dict()
# nucleus_info["nucleus_id"] = winning_nucleus_id
# nucleus_info["nuclei_distance"] = 0
# nucleus_info["n_nuclei_in_radius"] = 1
# nucleus_info["n_nuclei_in_bbox"] = 1
if verbose:
print(f"nucleus_info = {nucleus_info}")
print(f"winning_nucleus_id = {winning_nucleus_id}")
#2) ------- Finding the Allen Cell Types -------
allen_cell_type_info = ctu.allen_nuclei_classification_info_from_nucleus_id(winning_nucleus_id)
if verbose:
print(f"allen_cell_type_info = {allen_cell_type_info}")
#4) -------- Running the cell classification and stats--------------
if verbose:
print(f"\n\n ------ Part C: Inhibitory Excitatory Classification ---- \n\n")
filter_time = time.time()
#---- adding the synapses and spines data -----#
neuron_obj_exc_syn = syu.add_synapses_to_neuron_obj(neuron_obj,
validation = True,
verbose = True,
original_mesh = None,
plot_valid_error_synapses = False,
calculate_synapse_soma_distance = False,
add_valid_synapses = True,
add_error_synapses=False,)
neuron_obj_exc_syn_sp = spu.add_head_neck_shaft_spine_objs(neuron_obj_exc_syn,
verbose = True
)
limb_branch_dict = ctu.postsyn_branches_near_soma_for_syn_post_density(
neuron_obj = neuron_obj_exc_syn_sp,
verbose = False,)
(syn_density_post,
syn_density_head,
syn_density_neck,
syn_density_shaft,
skeletal_length_processed_syn) = ctu.synapse_density_stats(neuron_obj = neuron_obj_exc_syn_sp,
limb_branch_dict = limb_branch_dict,
verbose = True)
(spine_density,
skeletal_length_processed_spine) = ctu.spine_density_near_soma(neuron_obj = neuron_obj_exc_syn_sp,
verbose = True,
multiplier = 1000)
if verbose:
print(f"Total time for density calculations = {time.time() - filter_time}")
baylor_cell_type_info = dict(
syn_density_post = syn_density_post,
syn_density_head = syn_density_head,
syn_density_neck = syn_density_neck,
syn_density_shaft = syn_density_shaft,
skeletal_length_processed_syn=skeletal_length_processed_syn,
spine_density=spine_density,
skeletal_length_processed_spine = skeletal_length_processed_spine
)
n_dict = dict(key,
split_index = split_index,
run_time = np.round(time.time() - st,2),
)
dicts_for_update = [baylor_cell_type_info,
allen_cell_type_info,
nucleus_info,]
for d in dicts_for_update:
n_dict.update(d)
self.insert1(n_dict,skip_duplicates=True,allow_direct_insert=True)
print(f"\n\n ***------ Total time for {key['segment_id']} = {time.time() - whole_pass_time} ------ ***")
def make(self, key):
"""
Pseudocode:
1) Pull Down all the Neuron Objects associated with a segment_id
For each neuron:
2) Run the full axon preprocessing
3) Save off the neuron
4) Save dict entry to list
5) Write the new entry to the table
"""
# 1) Pull Down All of the Neurons
segment_id = key["segment_id"]
if len(key_source_inh & dict(segment_id=segment_id)) > 0:
manual_e_i = "inhibitory"
elif len(key_source_exc & dict(segment_id=segment_id)) > 0:
manual_e_i = "excitatory"
else:
raise Exception("Not in exc or inh table")
if verbose:
print(f"------- Working on Neuron {segment_id} -----")
whole_pass_time = time.time()
#1) Pull Down all the Neuron Objects associated with a segment_id
neuron_objs, neuron_split_idxs = du.decomposition_with_spine_recalculation(
segment_id,
ignore_DecompositionAxon=True,
ignore_DecompositionCellType=True)
if verbose:
print(f"Number of Neurons found ={len(neuron_objs)}")
#For each neuron:
dict_to_write = []
''' ------ Old way of getting the nucleus info for the manual proofread data -------
# -------- getting the nuclei info to match
try:
segment_map_dict = (minnie.AutoProofreadValidationSegmentMap4() & dict(old_segment_id=segment_id)).fetch1()
except:
segment_map_dict = (minnie.AutoProofreadValidationSegmentMapInh() & dict(old_segment_id=segment_id)).fetch1()
nucleus_id = segment_map_dict["nucleus_id"]
nuc_center_coords = du.nuclei_id_to_nucleus_centers(nucleus_id)
nucleus_ids = [nucleus_id]
nucleus_centers = [nuc_center_coords]
print(f"nucleus_ids = {nucleus_ids}")
print(f"nucleus_centers = {nucleus_centers}")'''
nucleus_ids, nucleus_centers = du.segment_to_nuclei(
segment_id,
#nuclei_version=ver
)
if verbose:
print(f"Number of Corresponding Nuclei = {len(nucleus_ids)}")
print(f"nucleus_ids = {nucleus_ids}")
print(f"nucleus_centers = {nucleus_centers}")
for split_index, neuron_obj in zip(neuron_split_idxs, neuron_objs):
if verbose:
print(f"--> Working on Split Index {split_index} -----")
st = time.time()
# ------------- Does all of the processing -------------------
#1) ------ Getting the paired nuclei ------
winning_nucleus_id, nucleus_info = nru.pair_neuron_obj_to_nuclei(
neuron_obj,
"S0",
nucleus_ids,
nucleus_centers,
nuclei_distance_threshold=15000,
return_matching_info=True,
verbose=True)
# else:
# winning_nucleus_id = 12345
# nucleus_info = dict()
# nucleus_info["nucleus_id"] = winning_nucleus_id
# nucleus_info["nuclei_distance"] = 0
# nucleus_info["n_nuclei_in_radius"] = 1
# nucleus_info["n_nuclei_in_bbox"] = 1
if verbose:
print(f"nucleus_info = {nucleus_info}")
print(f"winning_nucleus_id = {winning_nucleus_id}")
#2) ------- Finding the Allen Cell Types -------
allen_cell_type_info = ctu.allen_nuclei_classification_info_from_nucleus_id(
winning_nucleus_id)
if verbose:
print(f"allen_cell_type_info = {allen_cell_type_info}")
# 3) ---- Doing Baylor Cell Type Classification ---------
# 3a) --- Adding the synapses and spine labels
if verbose:
print(
f"\n\n ------ Part C: Inhibitory Excitatory Classification ---- \n\n"
)
st = time.time()
if verbose:
print(f"Adding the synapses and the head_neck_shaft")
neuron_obj = syu.add_synapses_to_neuron_obj(
neuron_obj,
validation=validation,
verbose=verbose,
original_mesh=None,
plot_valid_error_synapses=False,
calculate_synapse_soma_distance=False,
add_valid_synapses=True,
add_error_synapses=False,
)
neuron_obj = spu.add_head_neck_shaft_spine_objs(neuron_obj,
verbose=verbose)
if verbose:
print(
f"Done adding synapses and head_neck_shaft: {time.time() - st}"
)
# 3b) --- Running the stats for Baylor Classification
filter_time = time.time()
'''
limb_branch_dict = ctu.postsyn_branches_near_soma_for_syn_post_density(
neuron_obj = neuron_obj,
verbose = False,)
(syn_density_post,
syn_density_head,
syn_density_neck,
syn_density_shaft,
skeletal_length_processed_syn) = ctu.synapse_density_stats(neuron_obj = neuron_obj,
limb_branch_dict = limb_branch_dict,
verbose = True)
(spine_density,
skeletal_length_processed_spine) = ctu.spine_density_near_soma(neuron_obj = neuron_obj,
verbose = True,
multiplier = 1000)
if verbose:
print(f"Total time for density calculations = {time.time() - filter_time}")
# 4) ------ Predicting the E/I Group Based on the data collected --------
baylor_cell_type_info = dict(
syn_density_post = syn_density_post,
syn_density_head = syn_density_head,
syn_density_neck = syn_density_neck,
syn_density_shaft = syn_density_shaft,
skeletal_length_processed_syn=skeletal_length_processed_syn,
spine_density=spine_density,
skeletal_length_processed_spine = skeletal_length_processed_spine
)
baylor_e_i = ctu.e_i_classification_single(data=[syn_density_shaft,spine_density],
features=["syn_density_shaft","spine_density"],
verbose = True,
return_label_name = True
)
'''
baylor_e_i, baylor_cell_type_info = ctu.e_i_classification_from_neuron_obj(
neuron_obj,
verbose=True,
return_cell_type_info=True,
return_dendrite_branch_stats=True)
baylor_cell_type_info["baylor_e_i"] = baylor_e_i
#5) ----- Deciding on cell type to use for axon
if inh_exc_class_to_use_for_axon == "Allen" and allen_cell_type_info[
"allen_e_i"] is not None:
e_i_class = allen_cell_type_info["allen_e_i"]
cell_type_used = "allen"
else:
e_i_class = baylor_e_i
cell_type_used = "baylor"
if verbose:
print(
f"e_i_class = {e_i_class} with cell_type_used = {cell_type_used}"
)
#3) ------ Axon Classification (and getting the axon features)------------------
o_neuron, filtering_info, axon_angles_dict = au.complete_axon_processing(
neuron_obj,
cell_type=e_i_class,
add_synapses_and_head_neck_shaft_spines=False,
validation=validation,
plot_initial_axon=False,
plot_axon_on_dendrite=False,
return_filtering_info=True,
return_axon_angle_info=True,
verbose=verbose)
filtering_info = {
k: np.round(v, 2)
for k, v in filtering_info.items()
if "area" in k or "length" in k
}
axon_features = au.axon_features_from_neuron_obj(o_neuron)
# ------- Saving off the neuron object ----------------
save_time = time.time()
ret_file_path = o_neuron.save_compressed_neuron(
output_folder=str(du.get_decomposition_path()),
#output_folder = "./",
file_name=
f"{o_neuron.segment_id}_{split_index}_split_axon_v{au.axon_version}_pipe_v6_e_i_val_3",
return_file_path=True,
export_mesh=False,
suppress_output=True)
ret_file_path_str = str(ret_file_path.absolute()) + ".pbz2"
if verbose:
print(f"ret_file_path_str = {ret_file_path_str}")
print(f"Save time = {time.time() - save_time}")
# ----------------
# ---- 8/10 Addition ----------
if save_axon_skeleton:
axon_skeleton_file = du.save_proofread_skeleton(
o_neuron.axon_skeleton,
segment_id=o_neuron.segment_id,
split_index=split_index,
file_name_ending=f"decomp_cell_type_axon_skeleton_e_i_val_3"
)
else:
axon_skeleton_file = None
#---- 8/29 Addition: Will compute the soma center of the mesh in nm ---
soma_x_nm, soma_y_nm, soma_z_nm = neuron_obj["S0"].mesh_center
if verbose:
print(
f"soma_x_nm, soma_y_nm, soma_z_nm = {soma_x_nm, soma_y_nm, soma_z_nm}"
)
n_dict = dict(
key,
split_index=split_index,
axon_version=au.axon_version,
decomposition=ret_file_path_str,
run_time=np.round(time.time() - st, 2),
manual_e_i=manual_e_i,
cell_type=e_i_class,
cell_type_used=cell_type_used,
axon_skeleton=str(axon_skeleton_file),
soma_x_nm=soma_x_nm,
soma_y_nm=soma_y_nm,
soma_z_nm=soma_z_nm,
n_syn_pre=neuron_obj.n_synapses_pre,
n_syn_post=neuron_obj.n_synapses_post,
)
soma_stats_dict = ctu.soma_stats_for_cell_type(neuron_obj)
dicts_for_update = [
baylor_cell_type_info, allen_cell_type_info, nucleus_info,
filtering_info, axon_features, axon_angles_dict,
soma_stats_dict
]
for d in dicts_for_update:
n_dict.update(d)
self.insert1(n_dict,
skip_duplicates=True,
allow_direct_insert=True)
print(
f"\n\n ***------ Total time for {key['segment_id']} = {time.time() - whole_pass_time} ------ ***"
)
def make(self, key):
"""
Purpose: To extract the axon/dendrite of a split neuron
1) Pull down the neuron
2) Get the neuron ids and nucleus centers corresponding to
that segent id
Iterate through all the neuron objects
a0) Recompute the width
a) Get the winning nucleus_id
b) Get the cell type info from the central database
c) Add synapses to neuron obj
d) Add spine categories to neuorn object
e) classifiy E/I cell type according to Baylor rules
f) Pick the cell type to use
g) Perfrom complete aon processing
h) Get aon Features
i) Save neurong object
j) Save Axon/Dendrite before proofreading
k) Write to dj table
"""
global_time = time.time()
segment_id = key["segment_id"]
decomposition_cell_type_hash = key["decomposition_cell_type_method"]
decomposition_split_method = hdju.decomposition_split_method_hash_from_segment_id(
segment_id, verbose=True)
if verbose:
print(
f"\n\n--Working on {segment_id}: (decomposition_cell_type_hash = "
f"{decomposition_cell_type_hash}, decomposition_split_method = {decomposition_split_method})"
)
#0) Visualizing the neuron
if plotting:
print(f"Visualizing the intial neuron")
hdju.plot_mesh_with_somas(
segment_id=segment_id,
#split_index=0,
with_skeleton=True,
align_from_soma_center=True)
# ---1) Pulling down the neuron---
st = time.time()
n_objs, sp_indexes = hdju.neuron_objs_from_decomposition_stage(
segment_id, verbose=True, return_one=False)
if verbose:
print(f"---1) Pulling down the neuron---: {time.time() - st}")
st = time.time()
# ---2) Get the nucleus ids and nucleus centers for that segment id---
nucleus_ids, nucleus_centers = hdju.nuclei_from_segment_id(
segment_id, return_centers=True, return_nm=True)
if verbose:
print(f"Number of Corresponding Nuclei = {len(nucleus_ids)}")
print(f"nucleus_ids = {nucleus_ids}")
print(f"nucleus_centers = {nucleus_centers}")
curr_idx = 0
neuron_obj_pre_filt = n_objs[curr_idx]
split_index = sp_indexes[curr_idx]
if plot_initial_neuron:
neuron_obj_rot = hu.align_neuron_obj(neuron_obj_pre_filt)
nviz.visualize_neuron(neuron_obj_rot, limb_branch_dict="all")
if verbose:
print(f"--> Working on Split Index {split_index} -----")
if verbose:
print(
f"---2) Get the nucleus ids and nucleus centers--- {time.time() - st}"
)
st = time.time()
# -- a0) Prep work: Recompute the Widths --
if filter_low_branch_cluster_dendrite:
neuron_obj, filtering_info_low_branch = pru.apply_proofreading_filters_to_neuron(
input_neuron=neuron_obj_pre_filt,
filter_list=[pru.low_branch_length_clusters_dendrite_filter],
plot_limb_branch_filter_with_disconnect_effect=False,
plot_limb_branch_filter_away=
plot_limb_branch_filter_away_low_branch,
plot_final_neuron=False,
return_error_info=True,
verbose=False,
verbose_outline=verbose)
else:
neuron_obj = neuron_obj_pre_filt
filtering_info_low_branch = {}
neuron_obj = wu.neuron_width_calculation_standard(neuron_obj,
verbose=True)
if verbose:
print(f"a0) Prep work: Recompute the Widths: {time.time() - st}")
st = time.time()
# --- a) Get the winning nucleus_id and nucleus info
winning_nucleus_id, nucleus_info = nru.pair_neuron_obj_to_nuclei(
neuron_obj,
"S0",
nucleus_ids,
nucleus_centers,
nuclei_distance_threshold=15000,
return_matching_info=True,
verbose=True)
if verbose:
print(f"nucleus_info = {nucleus_info}")
print(f"winning_nucleus_id = {winning_nucleus_id}")
if winning_nucleus_id is None:
if verbose:
print(
f"No winning nuclues found so assigning the only nucleus id"
)
winning_nucleus_id = nucleus_ids[0]
if verbose:
print(
f"--- a) Get the winning nucleus_id and nucleus info: {time.time() - st}"
)
st = time.time()
# ---b) Get the cell type info from database
database_cell_type_info = hdju.nuclei_classification_info_from_nucleus_id(
winning_nucleus_id)
database_e_i_class = database_cell_type_info[
f"{data_type}_e_i_cell_type"]
if verbose:
print(f"database_cell_type_info = {database_cell_type_info}")
print(f"database_e_i_class = {database_e_i_class}")
if verbose:
print(
f"---b) Get the cell type info from database: {time.time() - st}"
)
st = time.time()
# ---c/d) Add synapses and spine categories
import synapse_utils as syu
neuron_obj = syu.add_synapses_to_neuron_obj(
neuron_obj,
validation=False,
verbose=verbose,
original_mesh=None,
plot_valid_error_synapses=False,
calculate_synapse_soma_distance=False,
add_valid_synapses=True,
add_error_synapses=False,
)
neuron_obj = spu.add_head_neck_shaft_spine_objs(neuron_obj,
verbose=verbose)
if plot_synapses:
syu.plot_synapses(neuron_obj)
if plot_spines:
spu.plot_spines_head_neck(neuron_obj)
if verbose:
print(
f"---c/d) Add synapses and spine categories: {time.time() - st}"
)
st = time.time()
#---e) classifiy E/I cell type according to Baylor rules
baylor_e_i, baylor_cell_type_info = ctu.e_i_classification_from_neuron_obj(
neuron_obj,
plot_on_model_map=False,
plot_spines_and_sk_filter_for_syn=plot_spines_and_sk_filter_for_syn,
plot_spines_and_sk_filter_for_spine=
plot_spines_and_sk_filter_for_spine,
verbose=True,
return_cell_type_info=True)
baylor_cell_type_info["baylor_e_i"] = baylor_e_i
if verbose:
print(f"baylor_cell_type_info = \n{baylor_cell_type_info}")
if verbose:
print(
f"---e) classifiy E/I cell type according to Baylor rules: {time.time() - st}"
)
st = time.time()
#--- f) Pick the cell type to use
if (inh_exc_class_to_use_for_axon == "h01"
and database_e_i_class in ["excitatory", "inhibitory"]):
e_i_class = database_e_i_class
if verbose:
print(f"Using h01 e/i cell type")
cell_type_used = "h01"
else:
if verbose:
print(f"Using baylor e/i cell type")
e_i_class = baylor_e_i
cell_type_used = "baylor"
if verbose:
print(
f"e_i_class = {e_i_class} with cell_type_used = {cell_type_used}"
)
if verbose:
print(f"---f) Pick the cell type to use: {time.time() - st}")
st = time.time()
#---# g) Perfrom complete aon processing
if plot_aligned_neuron:
print(f"plot_aligned_neuron")
neuron_obj_rot = hu.align_neuron_obj(neuron_obj)
nviz.visualize_neuron(neuron_obj_rot, limb_branch_dict="all")
o_neuron_unalign, filtering_info, axon_angles_dict = au.complete_axon_processing(
neuron_obj,
cell_type=e_i_class,
add_synapses_and_head_neck_shaft_spines=False,
validation=False,
plot_initial_axon=plot_initial_axon,
plot_axon_on_dendrite=plot_axon_on_dendrite,
return_filtering_info=True,
return_axon_angle_info=True,
plot_high_fidelity_axon=plot_high_fidelity_axon,
plot_boutons_web=plot_boutons_web,
add_synapses_after_high_fidelity_axon=True,
verbose=verbose)
#o_neuron_unalign = hu.unalign_neuron_obj(o_neuron)
# if verbose:
# print(f"Readding Synapses to the high fidelity axon after all processing donw")
# o_neuron_unalign = syu.add_synapses_to_neuron_obj(o_neuron_unalign,
# validation = False,
# verbose = verbose,
# original_mesh = None,
# plot_valid_error_synapses = False,
# calculate_synapse_soma_distance = False,
# add_valid_synapses = True,
# add_error_synapses=False,
# limb_branch_dict_to_add_synapses=o_neuron_unalign.axon_limb_branch_dict)
if verbose:
print(
f"After add_synapses_after_high_fidelity_axon: # of neuron_obj.synapses_somas = {len(o_neuron_unalign.synapses_somas)}"
)
if plot_unaligned_synapses:
syu.plot_synapses(o_neuron_unalign, total_synapses=True)
if plot_unaligned_axon:
nviz.plot_axon(o_neuron_unalign)
if verbose:
print(f"---g) Perfrom complete aon processing: {time.time() - st}")
st = time.time()
# --- h) Get the axon and dendrite stats ----
dendrite_stats = nst.skeleton_stats_dendrite(o_neuron_unalign,
include_centroids=False)
axon_stats = nst.skeleton_stats_axon(o_neuron_unalign,
include_centroids=False)
stats_dict = o_neuron_unalign.neuron_stats(
stats_to_ignore=[
"n_not_processed_soma_containing_meshes", "n_error_limbs",
"n_same_soma_multi_touching_limbs",
"n_multi_soma_touching_limbs", "n_somas", "spine_density"
],
include_skeletal_stats=False,
include_centroids=True,
voxel_adjustment_vector=voxel_adjustment_vector,
)
if verbose:
print(
f"--- h) Get the axon and dendrite stats: {time.time() - st}")
st = time.time()
#---- i) Calculating the synapse info ------
syn_dict = syu.n_synapses_analysis_axon_dendrite(o_neuron_unalign,
verbose=True)
# --- j) saving neuron and skeleton ----
#4) Save the neuron object in a certain location
file_name = f"{o_neuron_unalign.segment_id}_{split_index}_{decomposition_cell_type_hash}"
file_name_decomp = f"{file_name}_{dataset}_cell_type_decomp"
output_folder = str(target_dir_decomp)
ret_file_path = o_neuron_unalign.save_compressed_neuron(
output_folder=output_folder,
file_name=file_name_decomp,
return_file_path=True,
export_mesh=False,
suppress_output=True,
)
ret_file_path_str = str(ret_file_path.absolute()) + ".pbz2"
axon_skeleton = o_neuron_unalign.axon_skeleton
file_name_decomp_sk_axon = f"{file_name_decomp}_axon_sk"
ret_sk_filepath_ax = su.compressed_pickle(
axon_skeleton,
filename=file_name_decomp_sk_axon,
folder=str(target_dir_sk),
return_filepath=True)
dendrite_skeleton = o_neuron_unalign.dendrite_skeleton
file_name_decomp_sk_dendr = f"{file_name_decomp}_dendr_sk"
ret_sk_filepath_dendr = su.compressed_pickle(
dendrite_skeleton,
filename=file_name_decomp_sk_dendr,
folder=str(target_dir_sk),
return_filepath=True)
if verbose:
print(f"neuron ret_file_path_str = {ret_file_path_str}")
print(f"ret_sk_filepath_ax = {ret_sk_filepath_ax}")
print(f"ret_sk_filepath_dendr = {ret_sk_filepath_dendr}")
if verbose:
print(f"--- i) saving neuron and skeleton ----")
st = time.time()
nucleus_info
h01_e_i_cell_type = database_e_i_class
database_cell_type_info
baylor_e_i, baylor_cell_type_info
e_i_class
cell_type_used
filtering_info, axon_angles_dict
o_neuron_unalign
dendrite_stats
axon_stats
stats_dict
ret_file_path_str
ret_sk_filepath_ax
ret_sk_filepath_dendr
# 7) make the insertions
run_time = run_time = np.round(time.time() - global_time, 4)
# -- decomp table --
n_dict = dict(
key.copy(),
decomposition_split_method=decomposition_split_method,
multiplicity=1,
split_index=split_index,
decomposition=str(ret_file_path_str),
axon_skeleton=str(ret_sk_filepath_ax),
dendrite_skeleton=str(ret_sk_filepath_dendr),
#--- cell types
h01_e_i_cell_type=database_e_i_class,
cell_type=e_i_class,
cell_type_used=cell_type_used,
#----- synapses ---
n_syn_pre=neuron_obj.n_synapses_pre,
n_syn_post=neuron_obj.n_synapses_post,
run_time=run_time,
# statistics for the split
)
dicts_for_update = [
nucleus_info, database_cell_type_info, filtering_info,
axon_angles_dict, dendrite_stats, axon_stats, stats_dict,
baylor_cell_type_info, filtering_info_low_branch, syn_dict
]
for d in dicts_for_update:
n_dict.update(d)
print(f"n_dict = {n_dict}")
for curr_obj in [self, SkeletonAxonDendrite]:
curr_obj.insert1(n_dict,
allow_direct_insert=True,
ignore_extra_fields=True,
skip_duplicates=True)
curr_obj.Object.insert1(n_dict,
allow_direct_insert=True,
ignore_extra_fields=True,
skip_duplicates=True)