subproblem_og.py

import numpy as np
import random 

class Subproblem:
    """
    Subproblem class responsible for finding and creating subproblems
    initialization inputs:
        - map: map of the environment (obstacle map)
        - conflict: the conflict the subproblem must cover
        - all_conflicts: All of the conflicts, so that we can maximize the number of conflicts resolved
        - robots: all of the robots
    """
    def __init__(self, map, conflict, robots, temp_starts, temp_goals,all_conflicts, type):

        self.map = map
        self.conflict = conflict
        self.conflicts = all_conflicts
        self.all_robots = robots
        self.type = type
        self.all_robots_involved_in_subproblem = []

        # The temporary starts/goals of the subpproblem. 
        # These are still in the original environment's coordinates
        self.temp_starts = temp_starts
        self.temp_goals = temp_goals
        print(f"temp starts = {self.temp_starts}")
        print(f"temp goals = {self.temp_goals}")

        # Location of the subproblem in the original environment's coordinates
        self.top_left = None
        self.bottom_right = None

        # get the indices of the robots that are actually in conflict
        self.robots_in_conflict = [r.label for r in conflict]

        # get the smallest/biggest x/y value of the robots in conflict
        # this gives us the area that our subproblem needs to cover
        self.row_range, self.col_range, self.leftmost_robot, self.rightmost_robot, self.topmost_robot, self.bottommost_robot = self.get_range_of_subproblem()
        
        self.subproblem_layout = None
        self.score = None
        self.waiting_score = None
        self.conflicts_covered = None
        
    def get_range_of_subproblem(self):
        """
        Find the smallest and largest x/y values. 
        This will tell us what range our subproblem needs to cover
        """
        smallest_row = None
        smallest_col = None
        largest_row = None
        largest_col = None
        leftmost_robot = None
        rightmost_robot = None
        topmost_robot = None
        bottommost_robot = None
        for robot_idx in self.robots_in_conflict:
            robot_position =  self.all_robots[robot_idx].current_position
            if smallest_row is None or smallest_col is None:
                smallest_row = robot_position[0]
                smallest_col = robot_position[1]
                topmost_robot = robot_idx
                leftmost_robot = robot_idx

            else:
                if robot_position[0] < smallest_row:
                    smallest_row = robot_position[0]
                    topmost_robot = robot_idx
                if robot_position[1] < smallest_col:
                    smallest_col = robot_position[1]
                    leftmost_robot = robot_idx
            
            if largest_row is None or largest_col is None:
                largest_row = robot_position[0]
                largest_col = robot_position[1]
                rightmost_robot = robot_idx
                bottommost_robot = robot_idx

            else:
                if robot_position[0] > largest_row:
                    largest_row = robot_position[0]
                    bottommost_robot = robot_idx
                if robot_position[1] > largest_col:
                    largest_col = robot_position[1]
                    rightmost_robot = robot_idx
        row_range = abs(largest_row - smallest_row)
        col_range = abs(largest_col - smallest_col)

        return row_range, col_range, leftmost_robot, rightmost_robot, topmost_robot, bottommost_robot

    def find_subproblem(self,S,find_best,prioritize_goal_assignment):
        """
        Find the subproblem that maximizes the number of robots covered and minimizes 
        the space occupied by the subproblem

        inputs:
            S - list of subproblems that have already been placed

        outputs:
            best_local - the best subproblem, relative to the matching 
                         template, not to the actual environment
        """
        # Find the best in the original environment
        best_global = self.find_global_subproblem(S,find_best)

        print(f"best global = {best_global}")

        if best_global is None: 
            return None
        self.score = best_global[1]
        self.conflicts_covered = best_global[2]
        self.waiting_score = best_global[3]

        # translate the found subproblem to the matching template
        best_local = self.match_template(best_global, prioritize_goal_assignment)
        self.subproblem_layout = best_local

        return best_local

    def get_robots_involved_in_subproblem(self, top_left, bottom_right):
        """
        Get all of the robots from all_robots that should be included in the subproblem

        inputs:
            top_left (node (r,c))- The top left node of the subproblem
            bottom_right (node (r,c))- The top left node of the subproblem
        outputs:
            all_robots (list of robots) - the robots involved in the subproblem
        """
        all_robots = []
        
        max_x = bottom_right[0]
        min_x = top_left[0]
        min_y = top_left[1]
        max_y = bottom_right[1]
        
        # any robot whose current and next node are in the subproblem are considered 
        # involved in the subproblem
        for r in self.all_robots:
            pos_curr = r.current_position 
            pos_next = r.goal
            x = pos_curr[0]
            y = pos_curr[1]
            x_next = pos_next[0]
            y_next = pos_next[1]

            if x <= max_x and x >= min_x and y <= max_y and y >= min_y and \
                x_next <= max_x and x_next >= min_x and y_next <= max_y and y_next >= min_y:
                all_robots.append(r)

        return all_robots
    
    def subproblem_contains_conflict(self, min_x, max_x, min_y, max_y, conflict):
        """
        Check if a given conflict is contained i n the subproblem with the input maxes and mins
        """
        robots_in_conflict = [r.label for r in conflict]
        for r in robots_in_conflict:
            pos = self.all_robots[r].current_position
            x = pos[0]
            y = pos[1]

            if x > max_x or x < min_x or y > max_y or y < min_y:
                return False
            
        return True

    def score_subproblem(self, top_left, bottom_right):
        """
        Score a subproblem. It''s score is the number of conflicts that it covers
        """
        max_x = bottom_right[0]
        min_x = top_left[0]
        min_y = top_left[1]
        max_y = bottom_right[1]

        
        # part of the score will be the number of conflicts
        conflicts = []
        for c in self.conflicts:
            if self.subproblem_contains_conflict(min_x, max_x, min_y, max_y, c):
                conflicts.append(c)

        num_conflicts = len(conflicts)

        # the other part of the score will be minimizing the number of waiting robots
        # get the number robots whose next desired cell is in this subproblem
        # num_waiting = 0
        # for r in self.all_robots:
        #     next = r.get_next_node()
        #     current = r.current_position

        #     if (next[0] <= max_x and next[0] >= min_x and next[1] <= max_y and next[1] >= min_y) \
        #         and not ( current[0] <= max_x and current[0] >= min_x and current[1] <= max_y and current[1] >= min_y):

        #         num_waiting += 1




        return conflicts, num_conflicts, 0

    def assign_temp_starts(self):
        temp_starts =[]
        for r in self.all_robots_involved_in_subproblem:
            x, y = r.current_position
            # cell_x = min(max(int((x - self.min_x) // self.cell_size), 0), self.grid_size - 1)
            # cell_y = min(max(int((y - self.min_y) // self.cell_size), 0), self.grid_size - 1)
            temp_starts.append([x, y])

        return temp_starts

    def assign_temp_goals(self, top_left, bottom_right):
        max_x = bottom_right[0]
        min_x = top_left[0]
        min_y = top_left[1]
        max_y = bottom_right[1]
        temp_goals = []

        # print(f"top left = {top_left}")
        # print(f"bottom right = {bottom_right}")



        # print(f"Assigning temp goals. Robots involved in subproblem = {self.robots_involved_in_subproblem}")
        for r in self.all_robots_involved_in_subproblem:
            # print(f"assigning temp goal for robot {r.get_label()}")
            # print(f"max x = {max_x}")
            # print(f"max y = {max_y}")
            # print(f"min x = {min_x}")
            # print(f"min y = {min_y}")
            assigned = False
            desired_goal = r.goal
            if (desired_goal[0], desired_goal[1]) not in temp_goals:
                temp_goals.append((desired_goal[0], desired_goal[1]))
                assigned = True
           
            else:
                # print("cant assign desired goal")
                x_rand = random.randint(min_x,max_x)
                y_rand = random.randint(min_y,max_y)
                # print(f"x rand = {x_rand}")
                # print(f"y rand = {y_rand}")
                # print(f"obs map = {self.map[x_rand][y_rand]}")
                while(((x_rand, y_rand) in temp_goals) or (self.map[x_rand][y_rand])):
                    x_rand = random.randint(min_x,max_x)
                    y_rand = random.randint(min_y,max_y)
                #     print(f"x rand = {x_rand}")
                #     print(f"y rand = {y_rand}")
                #     print(f"obs map = {self.map[x_rand][y_rand]}")
                # print(f"assigning {(x_rand, y_rand)}")
                
                temp_goals.append((x_rand, y_rand))
                # print(f"temp goals = {temp_goals}")
                assigned = True
                # temp_goals.append(path[0])
                
            if not assigned:       
                # assign a random unassigned node
                x_rand = random.randint(min_x,max_x)
                y_rand = random.randint(min_y,max_y)
                while((x_rand, y_rand) in temp_goals or (self.map[x_rand][y_rand])):
                    x_rand = random.randint(min_x,max_x)
                    y_rand = random.randint(min_y,max_y)
                temp_goals.append((x_rand, y_rand))
                assigned = True
                # temp_goals.append(path[0])
                

        # print(f"temp goals assigned = {temp_goals}")
        return temp_goals

    def assign_temp_goals_prioritized(self, top_left, bottom_right):
        max_x = bottom_right[0]
        min_x = top_left[0]
        min_y = top_left[1]
        max_y = bottom_right[1]
        temp_goals = [None]*len(self.all_robots_involved_in_subproblem)

        # print(f"top left = {top_left}")
        # print(f"bottom right = {bottom_right}")

        cost_to_go = []
        for r in self.all_robots_involved_in_subproblem:
            path = r.plan_astar(self.map)
            cost_to_go.append(len(path))

        

        # robots_and_costtogo.sort(key=lambda x: x[1])
        # robots_and_costtogo.reverse()
        
        cost_to_go = np.asarray(cost_to_go)
        cost_to_go_sorted = np.sort(cost_to_go)
        cost_to_go_sorted = np.flip(cost_to_go_sorted)
        # for r in self.all_robots_involved_in_subproblem: print(r.get_label())
        # print(f"CTG = {cost_to_go_sorted}")
        if abs(cost_to_go_sorted[0] - cost_to_go_sorted[1]) <= 1 and self.type ==52:
            # print("using non prioritised goal assignments")
            return self.assign_temp_goals(top_left, bottom_right)
        
        cost_to_go_sorted_idxs = np.argsort(cost_to_go)
        cost_to_go_sorted_idxs = np.flip(cost_to_go_sorted_idxs)

    


        # print(f"robots involved {[r.get_label() for r in self.all_robots_involved_in_subproblem]}")
        # print(cost_to_go_sorted_idxs)

        # print(f"Assigning temp goals. Robots involved in subproblem = {self.robots_involved_in_subproblem}")
        for i in cost_to_go_sorted_idxs:
            r = self.all_robots_involved_in_subproblem[i]
            # print(f"assigning temp goal for robot {r.get_label()}")
            # print(f"max x = {max_x}")
            # print(f"max y = {max_y}")
            # print(f"min x = {min_x}")
            # print(f"min y = {min_y}")
            assigned = False
            path = r.plan_astar(self.map)
            # print(f"path = {path}")
            for j in range(len(path)):
                node = path[j]
                x = node[0]
                y = node[1]
                # when we encounter the first node outside of our subproblem,
                # we want the node that comes directly before this
                if x > max_x or x < min_x or y > max_y or y < min_y:
                    # print(f"triggered node = {node}")
                    # print(f"want to assign node {path[j-1]}")
                    # print(f"temp goals = {temp_goals}")
                    if [path[j-1][0], path[j-1][1]] not in temp_goals:
                        # print(f"Assigned it")
                        temp_goals[i] = [path[j-1][0], path[j-1][1]]
                        assigned = True
                        break
                    else:
                        # print("cant assign desired goal")
                        x_rand = random.randint(min_x,max_x)
                        y_rand = random.randint(min_y,max_y)
                        # print(f"x rand = {x_rand}")
                        # print(f"y rand = {y_rand}")
                        # print(f"obs map = {self.map[x_rand][y_rand]}")
                        while(([x_rand, y_rand] in temp_goals) or (self.map[x_rand][y_rand])):
                            x_rand = random.randint(min_x,max_x)
                            y_rand = random.randint(min_y,max_y)
                        #     print(f"x rand = {x_rand}")
                        #     print(f"y rand = {y_rand}")
                        #     print(f"obs map = {self.map[x_rand][y_rand]}")
                        # print(f"assigning {(x_rand, y_rand)}")
                        
                        temp_goals[i] = [x_rand, y_rand]
                        # print(f"temp goals = {temp_goals}")
                        assigned = True
                        # temp_goals.append(path[0])
                        
                        
                        break
            if not assigned:       
                # assign a random unassigned node
                x_rand = random.randint(min_x,max_x)
                y_rand = random.randint(min_y,max_y)
                while([x_rand, y_rand] in temp_goals or (self.map[x_rand][y_rand])):
                    x_rand = random.randint(min_x,max_x)
                    y_rand = random.randint(min_y,max_y)
                temp_goals[i] = [x_rand, y_rand]
                assigned = True
                # temp_goals.append(path[0])
                

        # print(f"temp goals assigned = {temp_goals}")
        return temp_goals

    def get_starts(self):
        return self.transformed_starts
    
    def get_goals(self):
        return self.transformed_goals

    def get_world_coordinates(self,r,c):
        """
        Given the local coordinates of a subproblem template, 
        return the world coordinates
        """
        subproblem = self.subproblem_layout
        # print(f"subproblem = {subproblem}\n {subproblem.shape}")
        lst = subproblem[r,c]
        return lst[0]

    def update_local_starts_and_goals(self):
        """
        get the starts/goals in terms of the subproblem matched to the template
        """
        self.transformed_starts = []
        self.transformed_goals = []
        for i in range(len(self.temp_starts)):
            for r in range(len(self.subproblem_layout)):
                for c in range(len(self.subproblem_layout[r])):
                    # find robot i
                    these_starts = self.subproblem_layout[r][c][1]
                    these_goals = self.subproblem_layout[r][c][2]

                    if i in these_starts:
                        self.transformed_starts.append([r,c])
                    if i in these_goals:
                        self.transformed_goals.append([r,c])

    def rotate(self):
        """
        Rotate the subproblem 180 degrees and then update starts and goals
        """
        subproblem = self.subproblem_layout
        subproblem = np.rot90(subproblem)
        # subproblem = np.rot90(subproblem)
        self.subproblem_layout = subproblem

        self.update_local_starts_and_goals()

    def flip(self):
        """
        Flip the subproblem over the horizontal axis and then update starts and goals
        """
        subproblem = self.subproblem_layout
        subproblem = np.flip(subproblem,1)
        self.subproblem_layout = subproblem
        self.update_local_starts_and_goals()
        
    def find_global_subproblem(self, S, find_best):
        """
        Find the best subproblem that contains the conflict completely. 
        "best" in this case means covering the most conflicts
        inputs: 
            S- list of subproblems that cannot be overlapped with
        outputs: 
            (top_left,bottom_right) - location of best subproblem of this type
        """
                
        # We start by finding all possible valid subproblems (of this type) 
        # and then choosing the best one at the end
        # initialize the list of all found subproblems to be empty.
        possible_subproblems = []

        if self.type == 23: 
            row_val = 2
            col_val = 3
        elif self.type == 33: 
            row_val = 3
            col_val = 3
        else:
            row_val = 2
            col_val = 5 
        
        if self.row_range <= row_val and self.col_range <= col_val:
            # look for a nxm
            row_shift = row_val-1
            col_shift = col_val-1
            left_robot = self.all_robots[self.leftmost_robot].current_position
            top_robot = self.all_robots[self.topmost_robot].current_position

            for r in range(row_shift+1):
                for c in range(col_shift+1):

                    top_left = (top_robot[0]-r, left_robot[1]-c)
                    bottom_right = (top_left[0]+row_shift, top_left[1]+col_shift)

                    if self.is_valid(top_left, bottom_right,S):
                        
                        conflicts, score, num_waiting = self.score_subproblem(top_left, bottom_right)
                        if not find_best: 
                            return ((top_left, bottom_right), score, conflicts,num_waiting)
                        possible_subproblems.append(((top_left, bottom_right), score, conflicts, num_waiting))

                    right_robot = self.all_robots[self.rightmost_robot].current_position
                    bottom_robot = self.all_robots[self.bottommost_robot].current_position
                    bottom_right = (bottom_robot[0]+r, right_robot[1]+c)
                    top_left = (bottom_right[0]-row_shift, bottom_right[1]-col_shift)

                    if self.is_valid(top_left, bottom_right,S):
                        
                        conflicts, score, num_waiting = self.score_subproblem(top_left, bottom_right)
                        if not find_best: 
                            return ((top_left, bottom_right), score, conflicts,num_waiting)
                        possible_subproblems.append(((top_left, bottom_right), score, conflicts, num_waiting))

        if row_val != col_val:
            if self.row_range <= col_val and self.col_range <= row_val:
                # look for a mxn
                row_shift = col_val-1
                col_shift = row_val-1
                left_robot = self.all_robots[self.leftmost_robot].current_position
                top_robot = self.all_robots[self.topmost_robot].current_position
                for r in range(row_shift+1):
                    for c in range(col_shift+1):
                        top_left = (left_robot[0]-r, top_robot[1]-c)
                        bottom_right = (top_left[0]+row_shift, top_left[1]+col_shift)

                        if self.is_valid(top_left, bottom_right,S):
                            
                            conflicts, score, num_waiting = self.score_subproblem(top_left, bottom_right)
                            if not find_best: 
                                return ((top_left, bottom_right), score, conflicts, num_waiting)
                            possible_subproblems.append(((top_left, bottom_right), score, conflicts, num_waiting))

                        right_robot = self.all_robots[self.rightmost_robot].current_position
                        bottom_robot = self.all_robots[self.bottommost_robot].current_position
                        bottom_right = (right_robot[0]+r, bottom_robot[1]+c)
                        top_left = (bottom_right[0]-row_shift, bottom_right[1]-col_shift)

                        if self.is_valid(top_left, bottom_right,S):
                            
                            conflicts, score, num_waiting = self.score_subproblem(top_left, bottom_right)
                            if not find_best: 
                                return ((top_left, bottom_right), score, conflicts, num_waiting)
                            possible_subproblems.append(((top_left, bottom_right), score, conflicts, num_waiting))

        # if there are no valid subproblems, return None. 
        # This conflict will be handled by waiting instead of a subproblem
        if possible_subproblems == []: 
            return None 
        
        # otherwise, return the best of what we have    
        best = max(possible_subproblems, key=lambda x:x[1])
        # print(f"best subproblem = {best[0]}")
        # best_subproblem = best[0]

        most_conflicts_covered = best[1]
        # print(f"best score = {best_score}")
        cands_with_best_score = []
        for cand in possible_subproblems:
            if cand[1] == most_conflicts_covered:
                cands_with_best_score.append(cand)

        best = min(cands_with_best_score, key=lambda x:x[3])
        return best

    def match_template(self, s_loc_global, prioritize_goal_assignment):
        """
        Match a subproblem, s, from the original environment, to its
        corresponding template

        inputs: 
            s_loc_global (location, score, conflicts_covered)- the location of global subproblem 
        outputs: 
            a templated subproblem
        """

        # 
        self.top_left = s_loc_global[0][0]
        self.bottom_right = s_loc_global[0][1]
        top_left = self.top_left
        bottom_right = self.bottom_right

        self.all_robots_involved_in_subproblem = self.get_robots_involved_in_subproblem(top_left, bottom_right)

        self.temp_starts = self.assign_temp_starts()
        if prioritize_goal_assignment: self.temp_goals = self.assign_temp_goals(top_left, bottom_right)
        else: self.temp_goals = self.assign_temp_goals(top_left, bottom_right)

        print(f"temp starts afer reassignment= {self.temp_starts}")
        print(f"temp goals after reassignment= {self.temp_goals}")
        
        row_range = abs(bottom_right[0] - top_left[0])
        col_range = abs(top_left[1] - bottom_right[1])

        subproblem = np.zeros((col_range+1,row_range+1),dtype=object)

        start_idxs = []
        goal_idxs = []
        
        y_count = 0
        for i in range(len(subproblem)):
            x_count = 0
            for j in range(len(subproblem[0])):
                
                x = bottom_right[0]-x_count
                y = bottom_right[1]-y_count

                starts = []
                goals = []

                for k in range(len(self.temp_starts)):
                    start = self.temp_starts[k]
                    goal = self.temp_goals[k]

                    start_x = start[0] 
                    start_y = start[1] 
                    goal_x = goal[0] 
                    goal_y = goal[1] 
                    if start_x == x and start_y == y: 
                        starts.append(k)
                        start_idxs.append([i,j])
                    if goal_x == x and goal_y == y: 
                        goals.append(k)
                        goal_idxs.append([i,j])
            
                subproblem[i][j] = [[x, y], starts, goals]
                x_count +=1
            y_count +=1


        if self.type == 23 and row_range == 1: 
            # we need to rotate (transpose + flip)
            subproblem = np.transpose(subproblem)
            subproblem = np.flip(subproblem,1)
        if self.type == 25 and row_range != 1:
            # we need to rotate (transpose + flip)
            subproblem = np.transpose(subproblem)
            subproblem = np.flip(subproblem,1)

        # get the starts/goals in terms of the subproblem matched to the template
        self.transformed_starts = []
        self.transformed_goals = []
        for i in range(len(self.temp_starts)):
            for r in range(len(subproblem)):
                for c in range(len(subproblem[r])):
                    # find robot i
                    these_starts = subproblem[r][c][1]
                    these_goals = subproblem[r][c][2]

                    if i in these_starts:
                        self.transformed_starts.append([r,c])
                    if i in these_goals:
                        self.transformed_goals.append([r,c])
        return subproblem

    def is_valid(self, top_left, bottom_right, S):
        """
        Determine if a given subproblem is valid. A subproblem is valid if:
        1. It covers at least the conflict in question
        2. It matches its template's obstacle layout
        3. It does not overlap with any subproblem in S
        """
        max_x = bottom_right[0]
        min_x = top_left[0]
        min_y = top_left[1]
        max_y = bottom_right[1]

        # check that we are in bounds of our environment
        if min_x < 0 or min_y < 0 or max_x > len(self.map)-1 or max_y > len(self.map[0])-1: 
            # print("out of bounds")
            return False


        # if (max_x - min_x > 5 and max_y - min_y > 2) or (max_x - min_x > 2 and max_y - min_y > 5):
        #     return False
        

        count = 0
        for x in range(min_x, max_x+1):
            for y in range(min_y, max_y+1):
                if self.map[x][y]: 
                    count += 1
                    if self.type != 25: return False
                if self.type == 25 and count > 1: 
                    # print("More than one obstacle detected")
                    return False

        if self.type == 25:
            # Check that the obstacle is in the correct place using exclusive or
            if max_x - min_x == 1:
                if (self.map[top_left[0]][bottom_right[1]-2]) == (self.map[top_left[0]+1][bottom_right[1]-2]):
                    # invalid
                    # print("obstacle in the wrong place")
                    return False
            else:
                if (self.map[top_left[0]+2][bottom_right[1]]) == (self.map[top_left[0]+2][bottom_right[1]-1]):
                    # invalid
                    # print("obstacle in the wrong place")
                    return False

        # check that all robots in conflict are in the subproblem
        for robot_idx in self.robots_in_conflict:
            pos = self.all_robots[robot_idx].current_position
            x = pos[0]
            y = pos[1]

            if x > max_x or x < min_x or y > max_y or y < min_y:
                # print(f"robot {robot_idx} is left out at position {pos}")
                return False
            
        # check that there is no overlap with any other subproblem in S
        x = set(range(min_x,max_x+1))
        y = set(range(min_y,max_y+1))
        for s in S:
            s_top_left = s.top_left
            s_bottom_right = s.bottom_right
            s_max_x = s_bottom_right[0]
            s_min_x = s_top_left[0]
            s_min_y = s_top_left[1]
            s_max_y = s_bottom_right[1]

            s_x = set(range(s_min_x,s_max_x+1))
            s_y = set(range(s_min_y,s_max_y+1))


            x_overlap = s_x.intersection(x)  
            y_overlap = s_y.intersection(y)  
            if len(x_overlap) > 0 and len(y_overlap) > 0:
                return False
            
        return True

def find_subproblem(c, conflicts, S, robots, starts, goals, obstacle_map, find_best, prioritised):
    print(f"finding best subproblem")
    two_by_three = Subproblem(obstacle_map, c, robots, starts, goals, conflicts,23)
    three_by_three = Subproblem(obstacle_map, c, robots, starts, goals, conflicts,33)
    two_by_five = Subproblem(obstacle_map, c, robots, starts, goals, conflicts,25)

    best_2x3 = two_by_three.find_subproblem(S,find_best, prioritised)
    # best_2x3 = two_by_three.subproblem
    best_3x3 = three_by_three.find_subproblem(S,find_best, prioritised)
    # best_3x3 = three_by_three.subproblem
    best_2x5 = two_by_five.find_subproblem(S,find_best, prioritised)
    # best_2x5 = two_by_five.subproblem


    candidates = []
    if best_2x3 is not None:
        candidates.append([two_by_three, two_by_three.score, two_by_three.waiting_score, 2])
    if best_3x3 is not None:
        candidates.append([three_by_three, three_by_three.score, three_by_three.waiting_score, 3])
    if best_2x5 is not None:
        candidates.append([two_by_five, two_by_five.score, two_by_five.waiting_score, 5])

    if len(candidates) == 0: return [],None 

    if not find_best:
        # print(f"best = {best}")
        best = candidates[0]
        best_subproblem = best[0]
        
        conflicts = best[0].conflicts_covered

        return conflicts, best_subproblem

    best = max(candidates, key=lambda x:x[1])
    # print(f"best subproblem = {best[0]}")
    # best_subproblem = best[0]

    best_score = best[1]
    # print(f"best score = {best_score}")
    cands_with_best_score = []
    for cand in candidates:
        if cand[1] == best_score:
            cands_with_best_score.append(cand)

    best_waiting = min(cands_with_best_score, key=lambda x:x[2])
    best_waiting_score = best_waiting[2]
    # print(f"best score = {best_score}")
    cands_with_best_score_and_best_waiting = []
    for cand in candidates:
        if cand[1] == best_score and cand[2] == best_waiting:
            cands_with_best_score_and_best_waiting.append(cand)

    if cands_with_best_score_and_best_waiting == []:
        final_candidates = cands_with_best_score
    else: final_candidates = cands_with_best_score_and_best_waiting 

    best = min(final_candidates, key=lambda x:x[3])
    

    # print(f"best = {best}")
    best_subproblem = best[0]
    
    conflicts = best[0].conflicts_covered
    # print(f"conflicts = {conflicts}")
    # type = best[3]  

    return conflicts, best_subproblem


def order_query(starts, goals):
    """
    Order the starts and goals in the way that the library expects to see them
    Ordering is determined the start node.
    We use row major ordering:
        ___ ___ ___
    |_6_|_7_|_7_|
    |_3_|_4_|_5_|
    |_0_|_1_|_2_|

    Inputs:
        starts - the starts of each robot to be reordered
        goals - the goals of each robot to be reordered
    Outputs - 
        ordered_starts - The starts in the correct order
        ordered_goals - The goals in the correct order
        new_to_old - The mapping of indices, so that we can recover the original order.
    """
    fake_starts = []
    for start in starts:
        fake_starts.append([3*(start[0]), start[1]])
        
    # get the sums of the starts
    sum_starts = []
    for start in fake_starts:
        sum_starts.append(sum(start))

    # use argsort to sort them
    sum_starts = np.asarray(sum_starts)

    sum_starts_sorted_idxs = np.argsort(sum_starts)
    # sum_starts_sorted_idxs = np.flip(sum_starts_sorted_idxs)

    ordered_starts = [starts[i] for i in sum_starts_sorted_idxs]
    ordered_goals = [goals[i] for i in sum_starts_sorted_idxs]

    new_to_old= [0]*len(sum_starts_sorted_idxs)
    for i in range(len(sum_starts_sorted_idxs)):
        new_to_old[sum_starts_sorted_idxs[i]] = i

    return ordered_starts, ordered_goals, new_to_old
    
def restore_original_order(ordered, new_to_old_idx):
    """
    Order a solution according to its original start and goal ordering. 
    Inputs:
        ordered - list of paths, given in order from the database
        new_to_old_idx - a mapping from the ordered index to the old order index.
    Outputs:
        og_order - the same solution, but in the order of the orignal order given by new_to_old_idx
    """
    og_order = [ordered[new_to_old_idx[i]] for i in range(len(ordered))]
    return og_order

def query_library(obstacle_map, s, lib_2x3, lib_3x3, lib_2x5):
    """
    Query the library to get a solution for the subproblem s

    inputs:
        s - (instance of subproblem class) The subproblem the we need a solution for
    outputs:
        sol - (list of paths) A valid solution for s. This is a list of paths 
    """

    start_nodes = s.get_starts()
    goal_nodes = s.get_goals()
    if start_nodes == goal_nodes:
        # print(f"start and goal are equal")
        sol = []
        for goal in goal_nodes: sol.append([goal])
        return sol
        
    
    sol = None
    count = 0
    while sol is None:
        for i in range(8):
            if s.type == 23:
                start_nodes = s.get_starts()
                goal_nodes = s.get_goals()


                # print(f"type = {s.type}")
                # print(f"tl = {s.top_left}")
                # print(f"br = {s.bottom_right}")
                # for r in s.all_robots_involved_in_subproblem: print(r.get_label())
                # print(f"temp starts = {s.temp_starts}")
                # print(f"temp goals = {s.temp_goals}")
                # print(f"goal nodes before = {goal_nodes}")
                # print(f"start nodes before = {start_nodes}")
                # print(f"goal nodes before = {goal_nodes}")

                # reorder the starts and goals for the query
                start_nodes, goal_nodes, mapping_idxs = order_query(start_nodes, goal_nodes)
                sol = lib_2x3.get_matching_solution(start_nodes, goal_nodes)

                # reorder the solution to match the robots in our og order
                if sol is not None: sol = restore_original_order(sol, mapping_idxs)

                # print(f"subproblem = {s.subproblem_layout}")
                # print(f"start nodes after= {start_nodes}")
                # print(f"goal nodes after = {goal_nodes}\n\n\n")

                
                if sol is not None: break

            elif s.type == 33:
                start_nodes = s.get_starts()
                goal_nodes = s.get_goals()

                # print(f"type = {s.type}")
                # print(f"tl = {s.top_left}")
                # print(f"br = {s.bottom_right}")
                # print(f"temp starts = {s.temp_starts}")
                # print(f"temp goals = {s.temp_goals}")
                # print(f"goal nodes before = {goal_nodes}")
                # print(f"start nodes before = {start_nodes}")
                # print(f"goal nodes before = {goal_nodes}")

                # reorder the starts and goals for the query
                start_nodes, goal_nodes, mapping_idxs = order_query(start_nodes, goal_nodes)
                sol = lib_3x3.get_matching_solution(start_nodes, goal_nodes)

                # reorder the solution to match the robots in our og order
                if sol is not None: sol = restore_original_order(sol, mapping_idxs)

                # print(f"subproblem = {s.subproblem_layout}")
                # print(f"start nodes after= {start_nodes}")
                # print(f"goal nodes after = {goal_nodes}")

                if sol is not None: break

            elif s.type == 25:
                start_nodes = s.get_starts()
                goal_nodes = s.get_goals()


                # print(f"subproblem = {len(s.subproblem_layout)}")

                

                if len(s.subproblem_layout) == 5:
                    obs_locat = s.subproblem_layout[2][1][0]

                    if obstacle_map[obs_locat[0]][obs_locat[1]]:

                        # print(f"type = {s.type}")
                        # print(f"tl = {s.top_left}")
                        # print(f"br = {s.bottom_right}")
                        # print(f"temp starts = {s.temp_starts}")
                        # print(f"temp goals = {s.temp_goals}")
                        # print(f"start nodes before = {start_nodes}")
                        # print(f"goal nodes before = {goal_nodes}")


                        # reorder the starts and goals for the query
                        start_nodes, goal_nodes, mapping_idxs = order_query(start_nodes, goal_nodes)
                        sol = lib_2x5.get_matching_solution(start_nodes, goal_nodes)

                        # reorder the solution to match the robots in our og order
                        if sol is not None: 
                            sol = restore_original_order(sol, mapping_idxs)

                        # print(f"subproblem = {s.subproblem_layout}")
                        # print(f"start nodes after= {start_nodes}")
                        # print(f"goal nodes after = {goal_nodes}\n\n\n")
                        if sol is not None: 
                            break
                #     else:
                #         print("obs in wrong spot")
                # else:
                #     print("subproblem wrong shape")
                
            s.rotate()
            count += 1
        if sol is None: 
            s.flip()
        if count >= 10: break

    if sol == None: print("returning None")
    return sol