connection.py

# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
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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_
import matplotlib.pyplot as pl_
import skimage


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
    # pl_.imshow(costs[10,:,:])
    # pl_.show(block=True)
    path, length = dk_.DijkstraShortestPath(costs, point, candidate_points)
    # print(skimage.graph.route_through_array(image, point, candidate_points))
    # We could use here skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True)
    # -> (path: list, cost: float)
    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
        glial_cmp.ext_roots.append(end_point)
        # TODO: delete one of this info (ep_idx or end_point) according to the code following and its needs


    # 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