# Copyright CNRS/Inria/UNS # Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020) # # eric.debreuve@cnrs.fr # # This software is governed by the CeCILL license under French law and # abiding by the rules of distribution of free software. You can use, # modify and/ or redistribute the software under the terms of the CeCILL # license as circulated by CEA, CNRS and INRIA at the following URL # "http://www.cecill.info". # # As a counterpart to the access to the source code and rights to copy, # modify and redistribute granted by the license, users are provided only # with a limited warranty and the software's author, the holder of the # economic rights, and the successive licensors have only limited # liability. # # In this respect, the user's attention is drawn to the risks associated # with loading, using, modifying and/or developing or reproducing the # software by the user in light of its specific status of free software, # that may mean that it is complicated to manipulate, and that also # therefore means that it is reserved for developers and experienced # professionals having in-depth computer knowledge. Users are therefore # encouraged to load and test the software's suitability as regards their # requirements in conditions enabling the security of their systems and/or # data to be ensured and, more generally, to use and operate it in the # same conditions as regards security. # # The fact that you are presently reading this means that you have had # knowledge of the CeCILL license and that you accept its terms. import brick.processing.dijkstra_1_to_n as dk_ from brick.component.extension import extension_t from brick.component.glial_cmp import glial_cmp_t from brick.component.soma import soma_t from brick.general.type import array_t, site_h, site_path_h import itertools as it_ from typing import Callable, Sequence, Tuple import numpy as np_ def CandidateConnections( somas: Sequence[soma_t], influence_map: array_t, dist_to_closest: array_t, extensions: Sequence[extension_t], max_straight_sq_dist: float = np_.inf, ) -> list: # candidate_conn_nfo = [] # conn=connection extensions = filter(lambda ext: ext.is_unconnected, extensions) for soma, extension in it_.product(somas, extensions): new_candidates = extension.EndPointsForSoma(soma.uid, influence_map) candidate_conn_nfo.extend( (ep_idx, soma, extension, end_point) for ep_idx, end_point in enumerate(new_candidates) if dist_to_closest[end_point] <= max_straight_sq_dist ) candidate_conn_nfo.sort(key=lambda elm: dist_to_closest[elm[3]]) return candidate_conn_nfo def ShortestPathFromToN( point: site_h, costs: array_t, candidate_points_fct: Callable, max_straight_sq_dist: float = np_.inf, ) -> Tuple[site_path_h, float]: # candidate_points, candidate_indexing = candidate_points_fct( point, max_straight_sq_dist ) if candidate_points is None: return (), np_.inf costs[point] = 0.0 costs[candidate_indexing] = 0.0 path, length = dk_.DijkstraShortestPath(costs, point, candidate_points) costs[point] = np_.inf costs[candidate_indexing] = np_.inf return path, length def ValidateConnection( glial_cmp: glial_cmp_t, extension: glial_cmp_t, end_point: tuple, ep_idx: int, dijkstra_path: site_path_h, costs: array_t ) -> None: # connection_path = tuple(zip(*dijkstra_path[1:-1])) if connection_path.__len__() == 0: connection_path = None glial_cmp.connection_path[extension.uid] = connection_path glial_cmp.extensions.append(extension) extension.BackReferenceSoma(glial_cmp) # Add the site of the connexion between the extension and the soma, for each soma and for ech extension if type(glial_cmp) is soma_t: # restrain the verification to the soma <-> ext step extension.ext_root.append([end_point, ep_idx]) glial_cmp.ext_root.append([end_point, ep_idx]) # TODO: Ideally, these paths should be dilated + put this outside # but in ext-ext connections, there must not be dilation around the current ext # (current ext plays the role of a soma in soma-ext step) if connection_path is not None: costs[connection_path] = np_.inf