"""

Dijkstra Search library

author: Atsushi Sakai (@Atsushi_twi)

"""

import matplotlib.pyplot as plt
import math
import numpy as np


class DijkstraSearch:
    class Node:
        """
        Node class for dijkstra search
        """

        def __init__(self, x, y, cost=None, parent=None, edge_ids=None):
            self.x = x
            self.y = y
            self.cost = cost
            self.parent = parent
            self.edge_ids = edge_ids

        def __str__(self):
            return str(self.x) + "," + str(self.y) + "," + str(
                self.cost) + "," + str(self.parent)

    def __init__(self, show_animation):
        self.show_animation = show_animation

    def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list):
        """
        Search shortest path

        s_x: start x positions [m]
        s_y: start y positions [m]
        gx: goal x position [m]
        gx: goal x position [m]
        node_x: node x position
        node_y: node y position
        edge_ids_list: edge_list each item includes a list of edge ids
        """

        start_node = self.Node(sx, sy, 0.0, -1)
        goal_node = self.Node(gx, gy, 0.0, -1)
        current_node = None

        open_set, close_set = dict(), dict()
        open_set[self.find_id(node_x, node_y, start_node)] = start_node

        while True:
            if self.has_node_in_set(close_set, goal_node):
                print("goal is found!")
                goal_node.parent = current_node.parent
                goal_node.cost = current_node.cost
                break
            elif not open_set:
                print("Cannot find path")
                break

            current_id = min(open_set, key=lambda o: open_set[o].cost)
            current_node = open_set[current_id]

            # show graph
            if self.show_animation and len(
                    close_set.keys()) % 2 == 0:  # pragma: no cover
                plt.plot(current_node.x, current_node.y, "xg")
                # for stopping simulation with the esc key.
                plt.gcf().canvas.mpl_connect(
                    'key_release_event',
                    lambda event: [exit(0) if event.key == 'escape' else None])
                plt.pause(0.1)

            # Remove the item from the open set
            del open_set[current_id]
            # Add it to the closed set
            close_set[current_id] = current_node

            # expand search grid based on motion model
            for i in range(len(edge_ids_list[current_id])):
                n_id = edge_ids_list[current_id][i]
                dx = node_x[n_id] - current_node.x
                dy = node_y[n_id] - current_node.y
                d = math.hypot(dx, dy)
                node = self.Node(node_x[n_id], node_y[n_id],
                                 current_node.cost + d, current_id)

                if n_id in close_set:
                    continue
                # Otherwise if it is already in the open set
                if n_id in open_set:
                    if open_set[n_id].cost > node.cost:
                        open_set[n_id] = node
                else:
                    open_set[n_id] = node

        # generate final course
        rx, ry = self.generate_final_path(close_set, goal_node)

        return rx, ry

    @staticmethod
    def generate_final_path(close_set, goal_node):
        rx, ry = [goal_node.x], [goal_node.y]
        parent = goal_node.parent
        while parent != -1:
            n = close_set[parent]
            rx.append(n.x)
            ry.append(n.y)
            parent = n.parent
        rx, ry = rx[::-1], ry[::-1]  # reverse it
        return rx, ry

    def has_node_in_set(self, target_set, node):
        for key in target_set:
            if self.is_same_node(target_set[key], node):
                return True
        return False

    def find_id(self, node_x_list, node_y_list, target_node):
        for i, _ in enumerate(node_x_list):
            if self.is_same_node_with_xy(node_x_list[i], node_y_list[i],
                                         target_node):
                return i
        return None

    @staticmethod
    def is_same_node_with_xy(node_x, node_y, node_b):
        dist = np.hypot(node_x - node_b.x,
                        node_y - node_b.y)
        return dist <= 0.1

    @staticmethod
    def is_same_node(node_a, node_b):
        dist = np.hypot(node_a.x - node_b.x,
                        node_a.y - node_b.y)
        return dist <= 0.1