def neuron_proof_with_restriction(verbose=False,
query_type="dendrite",
add_messy_and_multi_soma_filter=True,
**kwargs):
"""
Ex 1: only dendrite restriction:
import ease_utils as ez
restr_table = ez.neuron_proof_with_restriction(query_type = ["dendrite"],verbose = True)
restr_table
Ex 2: soma and dednrite restriction
restr_table = ez.neuron_proof_with_restriction(verbose = True)
restr_table
"""
curr_query = ez.ease_query(query_type=query_type,
verbose=verbose,
**kwargs)
if add_messy_and_multi_soma_filter:
curr_table_before = mgf.neurons_minus_false_and_multi_soma_mergers(
add_allen_e_i_filter=False)
else:
curr_table_before = du.proofreading_neurons_table()
curr_table = curr_table_before & curr_query
if verbose:
print(f"number of neurons in restriction: {len(curr_table)}")
return curr_table
def node_names_from_manual_cell_type(cell_type,verbose = False):
"""
Purpose: Return all the node names for the cells that were manually proofread
"""
seg_ids,sp_idxs = (du.proofreading_neurons_table() & (du.manauL_cell_types_table() & f"cell_type='{cell_type}'").proj()).fetch("segment_id","split_index")
node_names = [pv.node_name(s_i,sp_i) for s_i,sp_i in zip(seg_ids,sp_idxs)]
if verbose:
print(f"Found {len(node_names)} {cell_type} cells")
return node_names
def restrict_proof_neuron_table(segment_id,
split_index = None):
"""
Purpose: to restrict the proofreading
table by the node name
"""
segement_id,split_index = pv.segment_id_and_split_index(segment_id,split_index)
return du.proofreading_neurons_table() & dict(segment_id=segment_id,split_index = split_index)
def add_node_attributes_to_proofread_graph(G,
attributes=None,
add_visual_area=True,
debug=False):
"""
Pseudocode:
1) Download all of the attributes want to store in the nodes
2) Create a dictionar mapping the nuclei to a dict of attribute values
3) set the attributes of the original graph
"""
if attributes is None:
attributes_to_download = [
"spine_category",
"cell_type_predicted",
"n_axons",
"axon_length",
"n_apicals",
"n_spines",
"n_boutons",
"n_nuclei_in_radius",
"skeletal_length",
]
if "nucleus_id" not in attributes_to_download:
attributes_to_download.append("nucleus_id")
if add_visual_area:
for s_t in ["soma_x", "soma_y", "soma_z"]:
if s_t not in attributes_to_download:
attributes_to_download.append(s_t)
neuron_data = du.proofreading_neurons_table().fetch(
*attributes_to_download, as_dict=True)
if add_visual_area:
soma_points = np.array([[k["soma_x"], k["soma_y"], k["soma_z"]]
for k in neuron_data])
visual_area_labels = mru.EM_coordinates_to_visual_areas(soma_points)
layer_labels = mru.EM_coordinates_to_layer(soma_points)
if debug:
print(f"soma_points.shape = {soma_points.shape}")
print(f"visual_area_labels.shape = {visual_area_labels.shape}")
attr_dict = dict()
for j, k in enumerate(neuron_data):
curr_dict = {k1: v for k1, v in k.items() if k != "nucleus_id"}
if add_visual_area:
curr_dict["visual_area"] = visual_area_labels[j]
curr_dict["layer"] = layer_labels[j]
attr_dict[k["nucleus_id"]] = curr_dict
nx.set_node_attributes(G, attr_dict)
return G
"""
False somas or messy neuron filters
"""
import datajoint_utils as du
default_min_n_syn_soma = 3
proof_version = 6
#proof_version = 7
neuron_proof_table = du.proofreading_neurons_table(version = proof_version)
allen_e_i_filter = "(allen_e_i is NULL) or (allen_e_i = baylor_e_i)"
small_n_syn_low_dendrite_median_exc_inh = (neuron_proof_table &
"((n_syn_soma <= 17) AND (cell_type = 'excitatory') AND (dendrite_branch_length_mean < 35))"
f" OR "
"((n_syn_soma <= 17) AND (cell_type = 'inhibitory') AND (dendrite_branch_length_mean < 28))" ).proj()
def small_n_syn_soma_errors(min_n_syn_soma = default_min_n_syn_soma):
#aprint(f"min_n_syn_soma = {min_n_syn_soma}")
small_n_syn_soma_errors_table= (neuron_proof_table & f"n_syn_soma <= {min_n_syn_soma}").proj()
return small_n_syn_soma_errors_table
def false_messy_soma_filter(min_n_syn_soma = default_min_n_syn_soma):
"""
Code to verify this restriction:
def restricted_proof_table_by_node_names(node_names):
return du.proofreading_neurons_table() & restriction_list_by_node_names(node_names)