3D RRT hole obstacle.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d96665f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "from random import random, randrange, uniform, choice\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import deque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fb62bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The vertices of the path are:\n",
      "[(0.0, 0.0, 0.0), (0.44099440064182815, 0.8341443705885389, -0.33125082281860807), (1.146800423664291, 0.692449711196814, 0.3628388553679197), (2.59009256067749, -0.5660847497595677, 1.1628283944933755), (2.7742769382149293, -0.5522077445913185, 0.7504500698745895), (5.0, 0.0, 0.0)]\n",
      "\n"
     ]
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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    {
     "name": "stdout",
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     "text": [
      "A path was found, with length: 6.94\n",
      "The direct path is of length: 5.0\n",
      "The RRT generation took: 52.82s\n",
      "The Dijkstra path finding took: 0.024491071701049805s\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Created on Mon Nov 28 21:26:12 2022\n",
    "\n",
    "@author: Gebra\n",
    "\"\"\"\n",
    "\n",
    "# line class with some metrics to ease graph creation\n",
    "class Line():\n",
    "    # initialize some metrics\n",
    "    def __init__(self, p0, p1):\n",
    "        self.p0 = np.array(p0)\n",
    "        self.p1 = np.array(p1)\n",
    "        self.direction = np.array(p1) - np.array(p0)\n",
    "        self.distance = np.linalg.norm(self.direction)\n",
    "        self.direction = self.direction/self.distance # normalize\n",
    "\n",
    "    def path(self, t):\n",
    "        return self.p0 + t * self.dirn\n",
    "\n",
    "# graph class\n",
    "class Graph:\n",
    "    # initialize some metrix\n",
    "    def __init__(self, startposition, endposition):\n",
    "        self.startposition = startposition\n",
    "        self.endposition = endposition\n",
    "        self.found_path = False\n",
    "        # size of searchbox\n",
    "        self.searchboxsize_x = 10. #(startposition[0] - endposition[0])\n",
    "        self.searchboxsize_y = 10. #(startposition[1] - endposition[1])\n",
    "        self.searchboxsize_z = 10. #(startposition[2] - endposition[2])\n",
    "        # location of seachbox startpoint: two times size of box between start and end position\n",
    "        self.searchbegin_x = -2.5  \n",
    "        self.searchbegin_y = -5.\n",
    "        self.searchbegin_z = -5.\n",
    "        \n",
    "        self.vertices = [startposition]\n",
    "        self.edges = [] # contains the indices of the vertices\n",
    "        self.indices = {startposition:0} # dictonary with indices for the vertices\n",
    "        self.distances = {0:0.} # dictonary with distances for each index\n",
    "        self.connections = {0:[]} # dictonary with connecting node(s)\n",
    "        \n",
    "    # creating a random position within search frame\n",
    "    def randpos(self, obstacles = None, rand_radius = None, bias = None, obstacle_bias = False):\n",
    "        if obstacle_bias == False:\n",
    "            # create random values between 0 and 1\n",
    "            x = random()\n",
    "            y = random()\n",
    "            z = random()\n",
    "            # convert to value within searchbox\n",
    "            posx = self.searchbegin_x + x*self.searchboxsize_x\n",
    "            posy = self.searchbegin_y + y*self.searchboxsize_y\n",
    "            posz = self.searchbegin_z + z*self.searchboxsize_z\n",
    "            \n",
    "        else:\n",
    "            select = random()\n",
    "            if select > bias:\n",
    "                # create random values between 0 and 1\n",
    "                x = random()\n",
    "                y = random()\n",
    "                z = random()\n",
    "                # convert to value within searchbox\n",
    "                posx = self.searchbegin_x + x*self.searchboxsize_x\n",
    "                posy = self.searchbegin_y + y*self.searchboxsize_y\n",
    "                posz = self.searchbegin_z + z*self.searchboxsize_z\n",
    "            else:\n",
    "                rand_obs = randrange(0,len(obstacles))\n",
    "                direction = np.array((uniform(-1,1), uniform(-1,1), uniform(-1,1)))\n",
    "                length = np.linalg.norm(direction)\n",
    "                rand_pos = (direction/length)*(obstacles[rand_obs][3] + uniform(0.,rand_radius))\n",
    "                posx = obstacles[rand_obs][0]+ rand_pos[0]\n",
    "                posy = obstacles[rand_obs][1]+ rand_pos[1]\n",
    "                posz = obstacles[rand_obs][2]+ rand_pos[2]\n",
    "                \n",
    "        return posx, posy, posz\n",
    "    def add_vertex(self, vertex):\n",
    "        try: # check if vertex already exists\n",
    "            idx = self.indices[vertex]\n",
    "        except:# otherwise add to list and dictionaries\n",
    "            idx = len(self.vertices)\n",
    "            self.vertices.append(vertex)\n",
    "            self.indices[vertex] = idx\n",
    "            self.connections[idx] = []\n",
    "        return idx\n",
    "\n",
    "    def add_edge(self, idx1, idx2, cost):\n",
    "        self.edges.append((idx1, idx2)) # add edge based on indices\n",
    "        self.connections[idx1].append((idx2, cost)) #add the connecting nodes and costs\n",
    "        self.connections[idx2].append((idx1, cost))\n",
    "\n",
    "# RRT algorithm\n",
    "def RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal):\n",
    "    RRT_Graph = Graph(startposition, endposition)\n",
    "    optimal = distance(startposition,endposition)\n",
    "    if goal is not None: iterations = 10000\n",
    "    for i in range(iterations):\n",
    "        randpos = RRT_Graph.randpos(obstacles, rand_radius, bias, obstacle_bias)\n",
    "        \n",
    "        if RRT_Graph.found_path and goal is not None:             \n",
    "            found_path = dijkstra(RRT_Graph)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            \n",
    "            if goal is not None and (length/optimal) <= goal:\n",
    "                break\n",
    "        \n",
    "        if in_obstacle(obstacles, randpos):\n",
    "            continue\n",
    "            \n",
    "        near_vex, near_idx = nearest_node(RRT_Graph, randpos, obstacles)\n",
    "        if near_vex is None:\n",
    "            continue\n",
    "        \n",
    "        if stepsize is not None:\n",
    "            randpos = reshape(randpos, near_vex, stepsize)\n",
    "        \n",
    "        new_idx = RRT_Graph.add_vertex(randpos)\n",
    "        dist = distance(near_vex,randpos)\n",
    "        RRT_Graph.add_edge(new_idx,near_idx,dist)\n",
    "        \n",
    "        end_distance = distance(randpos,RRT_Graph.endposition)\n",
    "        if end_distance < threshold:\n",
    "            end_index = RRT_Graph.add_vertex(RRT_Graph.endposition)\n",
    "            RRT_Graph.add_edge(new_idx,end_index,end_distance)\n",
    "            RRT_Graph.found_path = True\n",
    "    return RRT_Graph\n",
    "\n",
    "# RRT* algorithm\n",
    "def RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal):\n",
    "    RRT_Graph = Graph(startposition, endposition)\n",
    "    gamma = 10*stepsize*pow((1+1/3),(1/3))\n",
    "    optimal = distance(startposition,endposition)\n",
    "    if goal is not None: iterations = 5000\n",
    "    for i in range(iterations):\n",
    "        randpos = RRT_Graph.randpos(obstacles, rand_radius, bias, obstacle_bias)\n",
    "        \n",
    "        if RRT_Graph.found_path and goal is not None:             \n",
    "            found_path = dijkstra(RRT_Graph)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            \n",
    "            if goal is not None and (length/optimal) <= goal:\n",
    "                break\n",
    "            \n",
    "        if in_obstacle(obstacles, randpos):\n",
    "            continue\n",
    "            \n",
    "        near_vex, near_idx = nearest_node(RRT_Graph, randpos, obstacles)\n",
    "        if near_vex is None:\n",
    "            continue\n",
    "        \n",
    "        if stepsize is not None:\n",
    "            randpos = reshape(randpos, near_vex, stepsize)\n",
    "        \n",
    "        new_idx = RRT_Graph.add_vertex(randpos)\n",
    "        dist = distance(near_vex,randpos)\n",
    "        RRT_Graph.add_edge(new_idx,near_idx,dist)\n",
    "        RRT_Graph.distances[new_idx] = RRT_Graph.distances[near_idx] + dist #update the distances\n",
    "        n = len(RRT_Graph.vertices)\n",
    "        \n",
    "        # update nearby vertices distance (if shorter)\n",
    "        for vertex in RRT_Graph.vertices:\n",
    "            if vertex == randpos:\n",
    "                continue\n",
    "\n",
    "            dist = distance(vertex, randpos)\n",
    "            if dist > gamma*pow((np.log(n)/n),1/3):\n",
    "                continue\n",
    "                \n",
    "            line = Line(vertex, randpos)\n",
    "            if trough_obstacle(obstacles, line):\n",
    "                continue\n",
    "\n",
    "            idx = RRT_Graph.indices[vertex]\n",
    "            if (RRT_Graph.distances[new_idx] + dist) < (RRT_Graph.distances[idx]):\n",
    "                RRT_Graph.add_edge(idx, new_idx, dist)\n",
    "                RRT_Graph.distances[idx] = RRT_Graph.distances[new_idx] + dist\n",
    "\n",
    "        \n",
    "        end_distance = distance(randpos,RRT_Graph.endposition)\n",
    "        if end_distance < threshold:\n",
    "            end_index = RRT_Graph.add_vertex(RRT_Graph.endposition)\n",
    "            RRT_Graph.add_edge(new_idx,end_index,end_distance)\n",
    "            try:\n",
    "                RRT_Graph.distances[end_index] = min(RRT_Graph.distances[end_index], RRT_Graph.distances[new_idx]+end_distance)\n",
    "            except:\n",
    "                RRT_Graph.distances[end_index] = RRT_Graph.distances[new_idx]+end_distance\n",
    "\n",
    "            RRT_Graph.found_path = True\n",
    "    return RRT_Graph\n",
    "\n",
    "# Informed RRT* algorithm\n",
    "def iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal):\n",
    "    RRT_Graph = Graph(startposition, endposition)\n",
    "    gamma = 10*stepsize*pow((1+1/3),(1/3))\n",
    "    optimal = distance(startposition,endposition)\n",
    "    if goal is not None: iterations = 5000\n",
    "    for i in range(iterations):\n",
    "        randpos = RRT_Graph.randpos(obstacles, rand_radius, bias, obstacle_bias)\n",
    "        \n",
    "        if RRT_Graph.found_path:             \n",
    "            found_path = dijkstra(RRT_Graph)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            \n",
    "            if goal is not None and (length/optimal) <= goal:\n",
    "                break\n",
    "            \n",
    "            if not in_ellipsoid(startposition, endposition, randpos, length):\n",
    "                continue\n",
    "            \n",
    "            \n",
    "            \n",
    "        if in_obstacle(obstacles, randpos):\n",
    "            continue\n",
    "            \n",
    "        near_vex, near_idx = nearest_node(RRT_Graph, randpos, obstacles)\n",
    "        if near_vex is None:\n",
    "            continue\n",
    "        \n",
    "        if stepsize is not None:\n",
    "            randpos = reshape(randpos, near_vex, stepsize)\n",
    "        \n",
    "        new_idx = RRT_Graph.add_vertex(randpos)\n",
    "        dist = distance(near_vex,randpos)\n",
    "        RRT_Graph.add_edge(new_idx,near_idx,dist)\n",
    "        RRT_Graph.distances[new_idx] = RRT_Graph.distances[near_idx] + dist #update the distances\n",
    "        \n",
    "        n = len(RRT_Graph.vertices)\n",
    "        # update nearby vertices distance (if shorter)\n",
    "        for vertex in RRT_Graph.vertices:\n",
    "            if vertex == randpos:\n",
    "                continue\n",
    "\n",
    "            dist = distance(vertex, randpos)\n",
    "            if dist > gamma*pow((np.log(n)/n),1/3):\n",
    "                continue\n",
    "                \n",
    "            line = Line(vertex, randpos)\n",
    "            if trough_obstacle(obstacles, line):\n",
    "                continue\n",
    "\n",
    "            idx = RRT_Graph.indices[vertex]\n",
    "            if (RRT_Graph.distances[new_idx] + dist) < (RRT_Graph.distances[idx]):\n",
    "                RRT_Graph.add_edge(idx, new_idx, dist)\n",
    "                RRT_Graph.distances[idx] = RRT_Graph.distances[new_idx] + dist\n",
    "\n",
    "        \n",
    "        end_distance = distance(randpos,RRT_Graph.endposition)\n",
    "        if end_distance < threshold:\n",
    "            end_index = RRT_Graph.add_vertex(RRT_Graph.endposition)\n",
    "            RRT_Graph.add_edge(new_idx,end_index,end_distance)\n",
    "            try:\n",
    "                RRT_Graph.distances[end_index] = min(RRT_Graph.distances[end_index], RRT_Graph.distances[new_idx]+end_distance)\n",
    "            except:\n",
    "                RRT_Graph.distances[end_index] = RRT_Graph.distances[new_idx]+end_distance\n",
    "\n",
    "            RRT_Graph.found_path = True\n",
    "        \n",
    "        \n",
    "            \n",
    "    \n",
    "    return RRT_Graph\n",
    "\n",
    "# Plotting function\n",
    "def plot_graph(Graph, obstacles, startposition, endposition, RRT_time, found_path = None, dijkstra_time = None):\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = plt.axes(projection ='3d')\n",
    "    vertices = np.asarray(Graph.vertices)\n",
    "   \n",
    "    size_x = max(vertices[:,0:1]) - min(vertices[:,0:1])\n",
    "    size_y = max(vertices[:,1:2]) - min(vertices[:,1:2])\n",
    "    size_z = max(vertices[:,2:]) - min(vertices[:,2:])\n",
    "    \n",
    "    vex_x = [x for x, y, z in Graph.vertices]\n",
    "    vex_y = [y for x, y, z in Graph.vertices]\n",
    "    vex_z = [z for x, y, z in Graph.vertices]\n",
    "    \n",
    "    for obstacle in range(len(obstacles)):\n",
    "        u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]\n",
    "        x = obstacles[obstacle][0]+obstacles[obstacle][3]*np.cos(u)*np.sin(v)\n",
    "        y = obstacles[obstacle][1]+obstacles[obstacle][3]*np.sin(u)*np.sin(v)\n",
    "        z = obstacles[obstacle][2]+obstacles[obstacle][3]*np.cos(v)\n",
    "        ax.plot_surface(x, y, z, color=\"r\")\n",
    "    \n",
    "    ax.scatter(vex_x,vex_y,vex_z, s=10, color=\"b\")\n",
    "    ax.scatter(Graph.startposition[0],Graph.startposition[1],Graph.startposition[2],s=50, color=\"y\")\n",
    "    ax.scatter(Graph.endposition[0],Graph.endposition[1],Graph.endposition[2],s=50, color=\"y\")\n",
    "    \n",
    "    for edge in Graph.edges:\n",
    "        x = np.array([Graph.vertices[edge[0]][0],Graph.vertices[edge[1]][0]])\n",
    "        y = np.array([Graph.vertices[edge[0]][1],Graph.vertices[edge[1]][1]])\n",
    "        z = np.array([Graph.vertices[edge[0]][2],Graph.vertices[edge[1]][2]])\n",
    "        ax.plot3D(x, y, z, color=\"b\",linewidth=0.3)\n",
    "    \n",
    "    if found_path is not None:\n",
    "        length = 0.\n",
    "        for path in range(len(found_path)-1):\n",
    "            x = np.array([found_path[path][0],found_path[path+1][0]])\n",
    "            y = np.array([found_path[path][1],found_path[path+1][1]])\n",
    "            z = np.array([found_path[path][2],found_path[path+1][2]])\n",
    "            length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            ax.plot3D(x, y, z, color=\"r\",linewidth=3, alpha=0.5)\n",
    "        print('A path was found, with length:', round(length,2))\n",
    "        print('The direct path is of length:', round(distance(startposition,endposition),2))\n",
    "        print('The RRT generation took: '+str(round(RRT_time,2))+ 's')\n",
    "        print('The Dijkstra path finding took: '+str(dijkstra_time)+'s')\n",
    "   \n",
    "    if found_path is None:\n",
    "        print('No path was found.')\n",
    "        print('The RRT generation took: '+str(round(RRT_time,2))+ 's')\n",
    "        \n",
    "    ax.set_box_aspect(aspect = (size_x/size_z,size_y/size_z,1))\n",
    "    #ax.set_aspect('equal', adjustable='box')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# calculate intersection between line and obstacle\n",
    "# still needs some work, only works for spheres\n",
    "def intersection(obstacle, line):\n",
    "    r = obstacle[3]\n",
    "    C = obstacle[:3]\n",
    "    P = line.p0\n",
    "    U = line.direction\n",
    "    Q=P-C\n",
    "    a = np.dot(U,U)\n",
    "    b= 2*np.dot(U,Q)\n",
    "    c = np.dot(Q,Q)-r*r\n",
    "    d = np.dot(b,b)-4*np.dot(a,c)\n",
    "    if (line.p0[0] > (obstacle[0]+r) and line.p1[0] > (obstacle[0]+r)) or (line.p0[0] < (obstacle[0]-r) and line.p1[0] < (obstacle[0]-r)) or (line.p0[1] > (obstacle[1]+r) and line.p1[1] > (obstacle[1]+r)) or (line.p0[1] < (obstacle[1]-r) and line.p1[1] < (obstacle[1]-r)) or (line.p0[2] > (obstacle[2]+r) and line.p1[2] > (obstacle[2]+r)) or (line.p0[2] < (obstacle[2]-r) and line.p1[2] < (obstacle[2]-r)):\n",
    "        return False\n",
    "    if d < 0:\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "# determine distance between 2 points\n",
    "def distance(x, y):\n",
    "    return np.linalg.norm(np.array(x) - np.array(y))\n",
    "\n",
    "# determine whether a vertex (point) is in one of the obstacles\n",
    "def in_obstacle(obstacles, vertex):\n",
    "    for obstacle in obstacles:\n",
    "        if distance(obstacle[:3],vertex) < obstacle[3]:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "# determine whether a edge (line) goes through one of the obstacles\n",
    "def trough_obstacle(obstacles,line):\n",
    "    for obstacle in obstacles:\n",
    "        if intersection(obstacle, line):\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "#find the nearest node\n",
    "def nearest_node(Graph, vertex, obstacles):\n",
    "    near_vex = None\n",
    "    near_idx = None\n",
    "    min_distance = 10.e10\n",
    "    for idx, graph_vertex in enumerate(Graph.vertices):\n",
    "        line = Line(graph_vertex, vertex)\n",
    "        if trough_obstacle(obstacles,line):\n",
    "            continue\n",
    "        \n",
    "        dist = distance(graph_vertex, vertex)\n",
    "        if dist < min_distance:\n",
    "            min_distance = dist\n",
    "            near_idx = idx\n",
    "            near_vex = graph_vertex\n",
    "    return near_vex, near_idx\n",
    "\n",
    "def reshape(randpos, near_vex, stepsize):\n",
    "    direction = np.array(randpos) - np.array(near_vex)\n",
    "    length = np.linalg.norm(direction)\n",
    "    direction = (direction / length) * min (stepsize, length) # find whether stepsize or length is smallest\n",
    "\n",
    "    shortend_vertex = (near_vex[0]+direction[0], near_vex[1]+direction[1], near_vex[2]+direction[2]) # create a shorter line\n",
    "    return shortend_vertex\n",
    "\n",
    "def average(lst):\n",
    "    try:\n",
    "        average = sum(lst) / len(lst)\n",
    "    except:\n",
    "        average = None\n",
    "    return average\n",
    "\n",
    "#define rotated ellipsoid for informed RRT*\n",
    "def in_ellipsoid(startposition, endposition, pos, path_length):\n",
    "    startposition = np.array(startposition)\n",
    "    endposition = np.array(endposition)\n",
    "    c_min = np.linalg.norm(endposition - startposition)\n",
    "    c_best = path_length\n",
    "    a_squared = 0.25 * (np.square(c_best)+np.square(c_min))\n",
    "    c_squared = np.square(0.5*c_best)\n",
    "    r = (endposition-startposition)/np.linalg.norm(endposition-startposition)\n",
    "    p = 0.5 * endposition + startposition\n",
    "    z_squared = np.square((np.dot((pos-p),r)))\n",
    "    xy_squared = np.dot((pos-p),(pos-p))-z_squared\n",
    "                          \n",
    "    if (xy_squared/a_squared + z_squared/c_squared) < 1:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "    \n",
    "    \n",
    "def dijkstra(Graph):\n",
    "    \n",
    "    # Dijkstra algorithm for finding shortest path from start position to end, from MIT License Copyright (c) 2019 Fanjin Zeng\n",
    "    \n",
    "    srcIdx = Graph.indices[Graph.startposition]\n",
    "    dstIdx = Graph.indices[Graph.endposition]\n",
    "\n",
    "    # build dijkstra\n",
    "    nodes = list(Graph.connections.keys())\n",
    "    dist = {node: float('inf') for node in nodes}\n",
    "    prev = {node: None for node in nodes}\n",
    "    dist[srcIdx] = 0\n",
    "\n",
    "    while nodes:\n",
    "        curNode = min(nodes, key=lambda node: dist[node])\n",
    "        nodes.remove(curNode)\n",
    "        if dist[curNode] == float('inf'):\n",
    "            break\n",
    "\n",
    "        for neighbor, cost in Graph.connections[curNode]:\n",
    "            newCost = dist[curNode] + cost\n",
    "            if newCost < dist[neighbor]:\n",
    "                dist[neighbor] = newCost\n",
    "                prev[neighbor] = curNode\n",
    "\n",
    "    # retrieve path\n",
    "    path = deque()\n",
    "    curNode = dstIdx\n",
    "    while prev[curNode] is not None:\n",
    "        path.appendleft(Graph.vertices[curNode])\n",
    "        curNode = prev[curNode]\n",
    "    path.appendleft(Graph.vertices[curNode])\n",
    "    return list(path)\n",
    "\n",
    "def rand_obst():\n",
    "    \n",
    "    x = uniform(1, 4)\n",
    "    y = uniform(-1, 1)\n",
    "    z = uniform(-1, 1)\n",
    "    r = uniform(0.25, 0.75)\n",
    "    \n",
    "    return (x,y,z,r)\n",
    "if __name__ == '__main__':\n",
    "       \n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (5.,0.,0.)\n",
    "    #obstacles = []\n",
    "    #obstacles = [(5.,2.5,2.5,3)]\n",
    "    #obstacles = [(2.5,-1,-1,1),(2.5,-1,1,1),(2.5,1,-1,1),(2.5,1,1,1)]\n",
    "    obstacles = []\n",
    "    for i in range(10):\n",
    "        obstacles.append(rand_obst())\n",
    "    #obstacles = [(1.,1.,1.,.5),(3.,4.,5.,1.),(4,2,3,1),(6,3,1,2),(6,1,1,2),(8,1,4,1)]\n",
    "    #obstacles = [(5,-1,-1,1.5),(5,1,-1,1.5),(5,3,-1,1.5),(5,5,-1,1.5),(5,-1,1,1.5),(5,1,1,1.5),(5,3,1,1.5),(5,5,1,1.5),(5,-1,3,1.5),(5,3.5,3,1.5),(5,0,3,1.5),(5,5,3,1.5),(5,-1,5,1.5),(5,1,5,1.5),(5,3,5,1.5),(5,5,5,1.5)] #hole\n",
    "    #obstacles = [(5,2,3,0.5),(5,2,2,0.5),(5,3,2,0.5),(5,3,3,0.5),(5,1,2.5,1),(5,4,2.5,1),(5,2.5,1,1),(5,2.5,4,1),(5,1,1,1),(5,1,4,1),(5,4,1,1),(5,4,4,1)]\n",
    "    iterations = 500\n",
    "    threshold = 1. #for marking the end position as found\n",
    "    stepsize = 1. # stepsize of newly generated vertices\n",
    "    \n",
    "    #some parameters for the obstacle bias attempt\n",
    "    obstacle_bias = False\n",
    "    \n",
    "    bias = 0.2\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    #path length/optimal length\n",
    "    goal = 1.4\n",
    "    \n",
    "    start_RRT = time.time() #record start time\n",
    "    #G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "    #G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "    G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "    \n",
    "    end_RRT = time.time()\n",
    "    RRT_time = end_RRT - start_RRT\n",
    "    if G.found_path:\n",
    "        start_dijkstra = time.time()\n",
    "        path = dijkstra(G)\n",
    "        end_dijkstra = time.time()\n",
    "        dijkstra_time = end_dijkstra - start_dijkstra\n",
    "        print('The vertices of the path are:')\n",
    "        print(path)\n",
    "        print()\n",
    "        plot_graph(G, obstacles, startposition, endposition, RRT_time, path, dijkstra_time)\n",
    "    else:\n",
    "        plot_graph(G, obstacles, startposition, endposition, RRT_time)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f377651a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1.608590841293335\n"
     ]
    }
   ],
   "source": [
    "from src.prm import PRM\n",
    "\n",
    "def evaluate_hole(variation, startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size):\n",
    "    \n",
    "    iterations = 200\n",
    "    radii = np.linspace(1.,1.1,5)\n",
    "    \n",
    "    average_RRT_times = []\n",
    "    average_BRRT_times = []\n",
    "    std_RRT_times = []\n",
    "    std_BRRT_times = []\n",
    "    for radius in radii:\n",
    "        obstacles = [(2.5,-1,-1,radius),(2.5,-1,1,radius),(2.5,1,-1,radius),(2.5,1,1,radius)]\n",
    "        RRT_times = []\n",
    "        BRRT_times = []\n",
    "        print(radius)\n",
    "        for sample in range(sample_size):\n",
    "            obstacle_bias = False\n",
    "            print(sample, obstacle_bias)            \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'prm':\n",
    "                start_RRT = time.time() #record start time\n",
    "                prm_planner = PRM(iterations, obstacles= obstacles, start= startposition, destination = endposition, goal=goal, fix_room_size=True)\n",
    "                prm_planner.runPRM()\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            RRT_time = end_RRT - start_RRT\n",
    "            RRT_times.append(RRT_time)\n",
    "            print(RRT_time)\n",
    "            obstacle_bias = True\n",
    "            print(sample, obstacle_bias)  \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'prm':\n",
    "                start_RRT = time.time() #record start time\n",
    "                prm_planner = PRM(iterations, obstacles= obstacles, start= startposition, destination = endposition, goal=goal, fix_room_size=True)\n",
    "                prm_planner.runPRM()\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            BRRT_time = end_RRT - start_RRT\n",
    "            BRRT_times.append(BRRT_time)\n",
    "            print(BRRT_time)\n",
    "        average_RRT_time = average(RRT_times)\n",
    "        std_RRT_time = np.std(RRT_times)\n",
    "        average_RRT_times.append(average_RRT_time)\n",
    "        std_RRT_times.append(std_RRT_time) \n",
    "        \n",
    "        average_BRRT_time = average(BRRT_times)\n",
    "        std_BRRT_time = np.std(BRRT_times)\n",
    "        average_BRRT_times.append(average_BRRT_time)\n",
    "        std_BRRT_times.append(std_BRRT_time) \n",
    "     \n",
    "    return average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (5.,0.,0.)\n",
    "    \n",
    "    threshold = 1. \n",
    "    stepsize = 1. \n",
    "    goal = 1.5\n",
    "    bias = 0.2\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    sample_size = 5\n",
    "    \n",
    "    #average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times = evaluate_single('RRT', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)\n",
    "    # average_RRTs_times, average_BRRTs_times, std_RRTs_times, std_BRRTs_times = evaluate_hole('RRT_s', startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size)\n",
    "    # average_iRRTs_times, average_iBRRTs_times, std_iRRTs_times, std_iBRRTs_times = evaluate_hole('iRRT_s', startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size)\n",
    "    average_prm_times, average_Bprm_times, std_prm_times, std_Bprm_times = evaluate_hole('prm', startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "211cfd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_to_disk = False\n",
    "load_from_disk = True\n",
    "if save_to_disk:\n",
    "        np.save(\"saved_results_hole/{average_RRTs_times}.npy\", average_RRTs_times)\n",
    "        np.save(\"saved_results_hole/{average_BRRTs_times}.npy\", average_BRRTs_times)\n",
    "        np.save(\"saved_results_hole/{average_iRRTs_times}.npy\", average_iRRTs_times)\n",
    "        np.save(\"saved_results_hole/{average_iBRRTs_times}.npy\", average_iBRRTs_times)\n",
    "        np.save(\"saved_results_hole/{average_prm_times}.npy\", average_prm_times)\n",
    "elif not save_to_disk and load_from_disk:\n",
    "        average_RRTs_times = np.load('saved_results_hole/{average_RRTs_times}.npy')\n",
    "        average_BRRTs_times = np.load('saved_results_hole/{average_BRRTs_times}.npy')\n",
    "        average_iRRTs_times = np.load('saved_results_hole/{average_iRRTs_times}.npy')\n",
    "        average_iBRRTs_times = np.load('saved_results_hole/{average_iBRRTs_times}.npy')\n",
    "        average_prm_times = np.load('saved_results_hole/{average_prm_times}.npy')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "622f5894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20.71067811865476, 19.220422833239272, 17.766515237711957, 16.347641560149153, 14.962550589195004]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "radii = np.linspace(1.,1.1,5)\n",
    "axis = []\n",
    "for radius in radii:\n",
    "    axis.append((2*np.sqrt(2)-2*radius)/(2+2*radius)*100)\n",
    "print(axis)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Generation Time in s')\n",
    "plt.xlabel('Hole diameter as percentage of obstacle width and height')\n",
    "#plt.errorbar(radii, average_RRT_times, yerr = std_RRT_times, label = \"RRT\", color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_BRRT_times, yerr = std_BRRT_times, label = \"Biased RRT\", linestyle='--', color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_RRTs_times, yerr = std_RRTs_times, label = \"RRT*\", color = 'g', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_BRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_BRRTs_times] , label = \"Biased RRT*\", linestyle='--', color = 'g', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_iRRTs_times, yerr = std_iRRTs_times, label = \"informed RRT*\", color = 'b', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_iBRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_iBRRTs_times], label = \"Biased informed RRT*\", linestyle='--', color = 'b', ecolor='black', capsize=10)\n",
    "plt.plot(axis, average_RRTs_times, label = \"RRT*\", color = 'g')\n",
    "plt.plot(axis, average_BRRTs_times, label = \"Biased RRT*\", linestyle='--', color = 'g')\n",
    "plt.plot(axis, average_iRRTs_times, label = \"informed RRT*\", color = 'b')\n",
    "plt.plot(axis, average_iBRRTs_times, label = \"Biased informed RRT*\", linestyle='--', color = 'b')\n",
    "plt.plot(axis, average_prm_times, label = \"PRM\", color = 'r')\n",
    "plt.title('Computation time compared to hole size')\n",
    "plt.legend()\n",
    "# print(average_RRTs_times)\n",
    "# print(std_RRTs_times)\n",
    "plt.savefig('Computation_time_compared_to_hole_size.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a644ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 5, 7, 9, 11]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [14:50<00:00,  8.91s/it] \n",
      "100%|██████████| 100/100 [16:17<00:00,  9.78s/it]\n",
      "100%|██████████| 100/100 [21:32<00:00, 12.92s/it]\n",
      "100%|██████████| 100/100 [21:53<00:00, 13.13s/it]\n",
      "100%|██████████| 100/100 [19:28<00:00, 11.69s/it]\n",
      "100%|██████████| 5/5 [1:34:02<00:00, 1128.57s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm, trange\n",
    "\n",
    "def evaluate_multiple(variation, startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size):\n",
    "    \n",
    "    iterations = 200\n",
    "    \n",
    "    num_obstacles_list = [3,5,7,9,11]\n",
    "    print(num_obstacles_list)\n",
    "    average_RRT_times = []\n",
    "    average_BRRT_times = []\n",
    "    std_RRT_times = []\n",
    "    std_BRRT_times = []\n",
    "    for num_obst in tqdm(num_obstacles_list, position=0, leave=True):\n",
    "        \n",
    "        RRT_times = []\n",
    "        BRRT_times = []\n",
    "        \n",
    "        for sample in trange((sample_size), position=0, leave=True):\n",
    "            obstacles = []\n",
    "            for i in range(num_obst):\n",
    "                obstacles.append(rand_obst())\n",
    "            \n",
    "            obstacle_bias = False\n",
    "                   \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            RRT_time = end_RRT - start_RRT\n",
    "            RRT_times.append(RRT_time)\n",
    "            \n",
    "            obstacle_bias = True\n",
    "             \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            BRRT_time = end_RRT - start_RRT\n",
    "            BRRT_times.append(BRRT_time)\n",
    "            \n",
    "        average_RRT_time = average(RRT_times)\n",
    "        std_RRT_time = np.std(RRT_times)\n",
    "        average_RRT_times.append(average_RRT_time)\n",
    "        std_RRT_times.append(std_RRT_time) \n",
    "        \n",
    "        average_BRRT_time = average(BRRT_times)\n",
    "        std_BRRT_time = np.std(BRRT_times)\n",
    "        average_BRRT_times.append(average_BRRT_time)\n",
    "        std_BRRT_times.append(std_BRRT_time) \n",
    "     \n",
    "    return average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (5.,0.,0.)\n",
    "    \n",
    "    threshold = 1. \n",
    "    stepsize = 1. \n",
    "    goal = 5.\n",
    "    bias = 0.2\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    sample_size = 100\n",
    "    \n",
    "    #average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times = evaluate_single('RRT', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)\n",
    "    average_RRTs_times, average_BRRTs_times, std_RRTs_times, std_BRRTs_times = evaluate_multiple('RRT_s', startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size)\n",
    "    #average_iRRTs_times, average_iBRRTs_times, std_iRRTs_times, std_iBRRTs_times = evaluate_hole('iRRT_s', startposition, endposition, threshold, rand_radius, bias, goal, stepsize, sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74f2e666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.944017724990845, 6.19951984167099, 9.31525425195694, 9.918867259025573, 7.7349007487297055]\n",
      "[8.849795378480607, 13.956287298449494, 16.365236259804643, 24.895112833155707, 13.87394611590351]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28c329b4009c466e8525e7c5f9ec9171",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "num_obstacles_list = [3,5,7,9,11]\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Generation Time in s')\n",
    "plt.xlabel('Number of obstacles')\n",
    "#plt.errorbar(radii, average_RRT_times, yerr = std_RRT_times, label = \"RRT\", color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_BRRT_times, yerr = std_BRRT_times, label = \"Biased RRT\", linestyle='--', color = 'o', ecolor='black', capsize=10)\n",
    "plt.errorbar(num_obstacles_list, average_RRTs_times, yerr = ([0,0,0,0,0],[i * (1.95/np.sqrt(sample_size)) for i in std_RRTs_times]), label = \"RRT*\", color = 'g', ecolor='black', capsize=10)\n",
    "plt.errorbar(num_obstacles_list, average_BRRTs_times, yerr = ([0,0,0,0,0],[i * (1.95/np.sqrt(sample_size)) for i in std_BRRTs_times]) , label = \"Biased RRT*\", linestyle='--', color = 'g', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_iRRTs_times, yerr = std_iRRTs_times, label = \"informed RRT*\", color = 'b', ecolor='black', capsize=10)\n",
    "#plt.errorbar(radii, average_iBRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_iBRRTs_times], label = \"Biased informed RRT*\", linestyle='--', color = 'b', ecolor='black', capsize=10)\n",
    "#plt.plot(num_obstacles_list, average_RRTs_times, label = \"RRT*\", color = 'g')\n",
    "#plt.plot(num_obstacles_list, average_BRRTs_times, label = \"Biased RRT*\", linestyle='--', color = 'g')\n",
    "#plt.plot(axis, average_iRRTs_times, label = \"informed RRT*\", color = 'b')\n",
    "#plt.plot(axis, average_iBRRTs_times, label = \"Biased informed RRT*\", linestyle='--', color = 'b')\n",
    "plt.title('Computation time compared to number of obstacles')\n",
    "plt.legend()\n",
    "print(average_RRTs_times)\n",
    "print(std_RRTs_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "123f23db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_single(variation, startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size):\n",
    "    obstacles = [(5.,2.5,2.5,3)]\n",
    "    goals = np.linspace(1.34,1.39,6)\n",
    "    iterations = 200\n",
    "    \n",
    "    \n",
    "    average_RRT_times = []\n",
    "    average_BRRT_times = []\n",
    "    std_RRT_times = []\n",
    "    std_BRRT_times = []\n",
    "    for goal in goals:\n",
    "        RRT_times = []\n",
    "        BRRT_times = []\n",
    "        for sample in range(sample_size):\n",
    "            obstacle_bias = False\n",
    "                        \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            RRT_time = end_RRT - start_RRT\n",
    "            RRT_times.append(RRT_time)\n",
    "            \n",
    "            obstacle_bias = True\n",
    "            \n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            BRRT_time = end_RRT - start_RRT\n",
    "            BRRT_times.append(BRRT_time)\n",
    "\n",
    "        average_RRT_time = average(RRT_times)\n",
    "        std_RRT_time = np.std(RRT_times)\n",
    "        average_RRT_times.append(average_RRT_time)\n",
    "        std_RRT_times.append(std_RRT_time) \n",
    "        \n",
    "        average_BRRT_time = average(BRRT_times)\n",
    "        std_BRRT_time = np.std(BRRT_times)\n",
    "        average_BRRT_times.append(average_BRRT_time)\n",
    "        std_BRRT_times.append(std_BRRT_time) \n",
    "     \n",
    "    return average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (10.,5.,5.)\n",
    "    \n",
    "    threshold = 2. \n",
    "    stepsize = 1. \n",
    "\n",
    "    bias = 0.2\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    sample_size = 3\n",
    "    \n",
    "    #average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times = evaluate_single('RRT', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)\n",
    "    average_RRTs_times, average_BRRTs_times, std_RRTs_times, std_BRRTs_times = evaluate_single('RRT_s', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)\n",
    "    average_iRRTs_times, average_iBRRTs_times, std_iRRTs_times, std_iBRRTs_times = evaluate_single('iRRT_s', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4ba81782",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.120678106943767, 2.8324126402537027, 5.540261427561442, 2.836888074874878, 1.540477991104126, 2.575699806213379]\n",
      "[3.406527246966583, 2.6566968698585014, 5.0901724331493154, 2.376923103618451, 1.615809316316339, 1.7793620753156472]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf10183a9e774dfdb02f2fceb227b7de",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "goals = np.linspace(1.34,1.39,6)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Generation Time in s')\n",
    "plt.xlabel('Path length/direct path length')\n",
    "#plt.errorbar(goals, average_RRT_times, yerr = std_RRT_times, label = \"RRT\", color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(goals, average_BRRT_times, yerr = std_BRRT_times, label = \"Biased RRT\", linestyle='--', color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(goals, average_RRTs_times, yerr = std_RRTs_times, label = \"RRT*\", color = 'g', ecolor='black', capsize=10)\n",
    "plt.errorbar(goals, average_BRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_BRRTs_times] , label = \"Biased RRT*\", linestyle='--', color = 'g', ecolor='black', capsize=10)\n",
    "#plt.errorbar(goals, average_iRRTs_times, yerr = std_iRRTs_times, label = \"informed RRT*\", color = 'b', ecolor='black', capsize=10)\n",
    "plt.errorbar(goals, average_iBRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_iBRRTs_times], label = \"Biased informed RRT*\", linestyle='--', color = 'b', ecolor='black', capsize=10)\n",
    "plt.plot(goals, average_RRTs_times, label = \"RRT*\", color = 'g')\n",
    "#plt.plot(goals, average_BRRTs_times, label = \"Biased RRT*\", linestyle='--', color = 'g')\n",
    "plt.plot(goals, average_iRRTs_times, label = \"informed RRT*\", color = 'b')\n",
    "#plt.plot(goals, average_iBRRTs_times, label = \"Biased informed RRT*\", linestyle='--', color = 'b')\n",
    "plt.title('Computation time compared to path length')\n",
    "plt.legend()\n",
    "print(average_BRRTs_times)\n",
    "print(std_BRRTs_times)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3f5bd8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_bias(variation, startposition, endposition, threshold, rand_radius, goal, stepsize, sample_size):\n",
    "    obstacles = [(5.,2.5,2.5,3)]\n",
    "    bias_list = np.linspace(0,0.8,9)\n",
    "    iterations = 200\n",
    "    obstacle_bias = True\n",
    "    average_BRRT_times = []\n",
    "   \n",
    "    std_BRRT_times = []\n",
    "    for bias in bias_list:\n",
    "        \n",
    "        BRRT_times = []\n",
    "        for sample in range(sample_size):\n",
    "\n",
    "            if variation == 'RRT':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'RRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            elif variation == 'iRRT_s':\n",
    "                start_RRT = time.time()\n",
    "                G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize, goal)\n",
    "                end_RRT = time.time()\n",
    "            \n",
    "            BRRT_time = end_RRT - start_RRT\n",
    "            BRRT_times.append(BRRT_time)\n",
    "        \n",
    "        average_BRRT_time = average(BRRT_times)\n",
    "        std_BRRT_time = np.std(BRRT_times)\n",
    "        average_BRRT_times.append(average_BRRT_time)\n",
    "        std_BRRT_times.append(std_BRRT_time) \n",
    "     \n",
    "    return average_BRRT_times, std_BRRT_times\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (10.,5.,5.)\n",
    "    \n",
    "    threshold = 2. \n",
    "    stepsize = 1. \n",
    "\n",
    "    goal = 1.36\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    sample_size = 20\n",
    "    \n",
    "    #average_RRT_times, average_BRRT_times, std_RRT_times, std_BRRT_times = evaluate_single('RRT', startposition, endposition, threshold, rand_radius, bias, stepsize, sample_size)\n",
    "    average_BRRTs_times, std_BRRTs_times = evaluate_bias('RRT_s', startposition, endposition, threshold, rand_radius, goal, stepsize, sample_size)\n",
    "    average_iBRRTs_times, std_iBRRTs_times = evaluate_bias('iRRT_s', startposition, endposition, threshold, rand_radius, goal, stepsize, sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c0718066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.659072875976562, 4.531684494018554, 2.9920475363731383, 11.022029900550843, 4.689996552467346, 3.2892539739608764, 27.342872273921966, 29.530092203617095, 145.61395404338836]\n",
      "[19.431146942585773, 7.059116105975554, 3.473166774730473, 33.270934910304256, 8.757011423194806, 5.624928151783951, 83.4055115519692, 69.61861403941711, 427.30161637880167]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f8e4581d01f40feb4565e0f51cc68da",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "bias_list = np.linspace(0,0.8,9)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Generation Time in s')\n",
    "plt.xlabel('Bias fraction beta')\n",
    "\n",
    "#plt.errorbar(bias_list, average_BRRT_times, yerr = std_BRRT_times, label = \"Biased RRT\", linestyle='--', color = 'o', ecolor='black', capsize=10)\n",
    "#plt.errorbar(bias_list, average_BRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_BRRTs_times] , label = \"Biased RRT*\", linestyle='--', color = 'g')\n",
    "#plt.errorbar(bias_list, average_iRRTs_times, yerr = std_iRRTs_times, label = \"informed RRT*\", color = 'b', ecolor='black', capsize=10)\n",
    "#plt.errorbar(bias_list, average_iBRRTs_times, yerr = [i * (1.95/np.sqrt(sample_size)) for i in std_iBRRTs_times], label = \"Biased informed RRT*\", linestyle='--', color = 'b')\n",
    "\n",
    "plt.plot(bias_list, average_BRRTs_times, label = \"Biased RRT*\", linestyle='--', color = 'g')\n",
    "\n",
    "plt.plot(bias_list, average_iBRRTs_times, label = \"Biased informed RRT*\", linestyle='--', color = 'b')\n",
    "plt.title('Computation time compared to bias beta')\n",
    "plt.legend()\n",
    "print(average_iBRRTs_times)\n",
    "print(std_iBRRTs_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbfeedad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "690cd878",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Doing many iterations and compare, will take some time\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    startposition = (0.,0.,0.)\n",
    "    endposition = (10.,5.,5.)\n",
    "    obstacles = [(1.,1.,1.,.5),(3.,4.,5.,1.),(4,2,3,1),(6,3,1,2),(6,1,1,2),(8,1,4,1)]\n",
    "    iterations = 200\n",
    "    threshold = 2. #for marking the end position as found\n",
    "    stepsize = 1. # stepsize of newly generated vertices\n",
    "    \n",
    "    #some parameters for the obstacle bias attempt\n",
    "    obstacle_bias = False\n",
    "    bias = 0.2\n",
    "    rand_radius = 0.5\n",
    "    \n",
    "    sample_size = 100\n",
    "    \n",
    "    RRT_times = []\n",
    "    RRT_lengths = []\n",
    "    RRT_succesrate = 0\n",
    "    for sample in range(sample_size):\n",
    "        \n",
    "        start_RRT = time.time() #record start time\n",
    "        G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize)\n",
    "        end_RRT = time.time()\n",
    "        RRT_time = end_RRT - start_RRT\n",
    "        RRT_times.append(RRT_time)\n",
    "        if G.found_path:\n",
    "            found_path = dijkstra(G)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            RRT_lengths.append(length)\n",
    "            RRT_succesrate = RRT_succesrate + 1\n",
    "        else:\n",
    "            RRT_succesrate = RRT_succesrate\n",
    "    \n",
    "    average_RRT_time = average(RRT_times)\n",
    "    average_RRT_length = average(RRT_lengths)\n",
    "    RRT_succes_percentage = (RRT_succesrate/sample_size)*100\n",
    "    \n",
    "    obstacle_bias = True\n",
    "    BRRT_times = []\n",
    "    BRRT_lengths = []\n",
    "    BRRT_succesrate = 0\n",
    "    for sample in range(sample_size):\n",
    "        \n",
    "        start_RRT = time.time() #record start time\n",
    "        G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize)\n",
    "        end_RRT = time.time()\n",
    "        BRRT_time = end_RRT - start_RRT\n",
    "        BRRT_times.append(BRRT_time)\n",
    "        if G.found_path:\n",
    "            found_path = dijkstra(G)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            BRRT_lengths.append(length)\n",
    "            BRRT_succesrate = BRRT_succesrate + 1\n",
    "        else:\n",
    "            BRRT_succesrate = BRRT_succesrate\n",
    "    \n",
    "    average_BRRT_time = average(BRRT_times)\n",
    "    average_BRRT_length = average(BRRT_lengths)\n",
    "    BRRT_succes_percentage = (BRRT_succesrate/sample_size)*100\n",
    "    \n",
    "    print('The direct path is of length:', round(distance(startposition,endposition),2))\n",
    "    print('The average time for RRT was: '+str(round(average_RRT_time,2))+ 's')\n",
    "    print('The average time for Biased RRT was: '+str(round(average_BRRT_time,2))+ 's')\n",
    "    print('The average length for RRT was: '+str(round(average_RRT_length,2)))\n",
    "    print('The average length for Biased RRT was: '+str(round(average_BRRT_length,2)))\n",
    "    print('The RRT generation had a succesrate of: '+str(round(RRT_succes_percentage,2))+ '%')\n",
    "    print('The Biased RRT generation had a succesrate of: '+str(round(BRRT_succes_percentage,2))+ '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af132ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate at various bias settings, will take some time\n",
    "\n",
    "\n",
    "startposition = (0.,0.,0.)\n",
    "endposition = (10.,5.,5.)\n",
    "obstacles = [(5.,2.5,2.5,3)]\n",
    "iterations = 300\n",
    "threshold = 2. #for marking the end position as found\n",
    "stepsize = 1. # stepsize of newly generated vertices\n",
    "\n",
    "#some parameters for the obstacle bias attempt\n",
    "obstacle_bias = True\n",
    "bias_list = np.linspace(0,0.8,9)\n",
    "print(bias_list)\n",
    "rand_radius = 0.5\n",
    "\n",
    "sample_size = 5\n",
    "\n",
    "average_BRRT_times = []\n",
    "average_BRRT_lengths = []\n",
    "BRRT_succes_percentages = []\n",
    "std_BRRT_times = []\n",
    "std_BRRT_lengths = []\n",
    "\n",
    "average_BRRTs_times = []\n",
    "average_BRRTs_lengths = []\n",
    "BRRTs_succes_percentages = []\n",
    "std_BRRTs_times = []\n",
    "std_BRRTs_lengths = []\n",
    "\n",
    "average_iBRRTs_times = []\n",
    "average_iBRRTs_lengths = []\n",
    "iBRRTs_succes_percentages = []\n",
    "std_iBRRTs_times = []\n",
    "std_iBRRTs_lengths = []\n",
    "\n",
    "for bias in bias_list:\n",
    "    BRRT_times = []\n",
    "    BRRT_lengths = []\n",
    "    BRRT_succesrate = 0\n",
    "    for sample in range(sample_size):\n",
    "\n",
    "        start_RRT = time.time() #record start time\n",
    "        G = RRT(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize)\n",
    "        end_RRT = time.time()\n",
    "        BRRT_time = end_RRT - start_RRT\n",
    "        BRRT_times.append(BRRT_time)\n",
    "        if G.found_path:\n",
    "            found_path = dijkstra(G)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            BRRT_lengths.append(length)\n",
    "            BRRT_succesrate = BRRT_succesrate + 1\n",
    "        else:\n",
    "            BRRT_succesrate = BRRT_succesrate\n",
    "\n",
    "    average_BRRT_time = average(BRRT_times)\n",
    "    average_BRRT_length = average(BRRT_lengths)\n",
    "    BRRT_succes_percentage = (BRRT_succesrate/sample_size)*100\n",
    "    std_BRRT_time = np.std(BRRT_times)\n",
    "    if len(BRRT_lengths) == 0:\n",
    "        std_BRRT_length = 0\n",
    "    else:\n",
    "        std_BRRT_length = np.std(BRRT_lengths)\n",
    "\n",
    "    average_BRRT_times.append(average_BRRT_time)\n",
    "    average_BRRT_lengths.append(average_BRRT_length)\n",
    "    BRRT_succes_percentages.append(BRRT_succes_percentage)\n",
    "    std_BRRT_times.append(std_BRRT_time) \n",
    "    std_BRRT_lengths.append(std_BRRT_length) \n",
    "\n",
    "for bias in bias_list:\n",
    "    BRRT_times = []\n",
    "    BRRT_lengths = []\n",
    "    BRRT_succesrate = 0\n",
    "    for sample in range(sample_size):\n",
    "\n",
    "        start_RRT = time.time() #record start time\n",
    "        G = RRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize)\n",
    "        end_RRT = time.time()\n",
    "        BRRT_time = end_RRT - start_RRT\n",
    "        BRRT_times.append(BRRT_time)\n",
    "        if G.found_path:\n",
    "            found_path = dijkstra(G)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            BRRT_lengths.append(length)\n",
    "            BRRT_succesrate = BRRT_succesrate + 1\n",
    "        else:\n",
    "            BRRT_succesrate = BRRT_succesrate\n",
    "\n",
    "    average_BRRT_time = average(BRRT_times)\n",
    "    average_BRRT_length = average(BRRT_lengths)\n",
    "    BRRT_succes_percentage = (BRRT_succesrate/sample_size)*100\n",
    "    std_BRRT_time = np.std(BRRT_times)\n",
    "    if len(BRRT_lengths) == 0:\n",
    "        std_BRRT_length = 0\n",
    "    else:\n",
    "        std_BRRT_length = np.std(BRRT_lengths)\n",
    "\n",
    "    average_BRRTs_times.append(average_BRRT_time)\n",
    "    average_BRRTs_lengths.append(average_BRRT_length)\n",
    "    BRRTs_succes_percentages.append(BRRT_succes_percentage)\n",
    "    std_BRRTs_times.append(std_BRRT_time) \n",
    "    std_BRRTs_lengths.append(std_BRRT_length) \n",
    "\n",
    "for bias in bias_list:\n",
    "    BRRT_times = []\n",
    "    BRRT_lengths = []\n",
    "    BRRT_succesrate = 0\n",
    "    for sample in range(sample_size):\n",
    "\n",
    "        start_RRT = time.time() #record start time\n",
    "        G = iRRT_s(startposition, endposition, obstacles, iterations, threshold, rand_radius, bias, obstacle_bias, stepsize)\n",
    "        end_RRT = time.time()\n",
    "        BRRT_time = end_RRT - start_RRT\n",
    "        BRRT_times.append(BRRT_time)\n",
    "        if G.found_path:\n",
    "            found_path = dijkstra(G)\n",
    "            length = 0.\n",
    "            for path in range(len(found_path)-1):\n",
    "                length = length + distance((found_path[path][0],found_path[path][1],found_path[path][2]),(found_path[path+1][0],found_path[path+1][1],found_path[path+1][2]))\n",
    "            BRRT_lengths.append(length)\n",
    "            BRRT_succesrate = BRRT_succesrate + 1\n",
    "        else:\n",
    "            BRRT_succesrate = BRRT_succesrate\n",
    "\n",
    "    average_BRRT_time = average(BRRT_times)\n",
    "    average_BRRT_length = average(BRRT_lengths)\n",
    "    BRRT_succes_percentage = (BRRT_succesrate/sample_size)*100\n",
    "    std_BRRT_time = np.std(BRRT_times)\n",
    "    if len(BRRT_lengths) == 0:\n",
    "        std_BRRT_length = 0\n",
    "    else:\n",
    "        std_BRRT_length = np.std(BRRT_lengths)\n",
    "\n",
    "    average_iBRRTs_times.append(average_BRRT_time)\n",
    "    average_iBRRTs_lengths.append(average_BRRT_length)\n",
    "    iBRRTs_succes_percentages.append(BRRT_succes_percentage)\n",
    "    std_iBRRTs_times.append(std_BRRT_time) \n",
    "    std_iBRRTs_lengths.append(std_BRRT_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a40c674",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'NoneType' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [4], line 17\u001b[0m\n\u001b[0;32m     15\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39merrorbar(bias_list, average_BRRT_lengths, yerr \u001b[38;5;241m=\u001b[39m std_BRRT_lengths, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRRT\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     16\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39merrorbar(bias_list, average_BRRTs_lengths, yerr \u001b[38;5;241m=\u001b[39m std_BRRTs_lengths, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRRT*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 17\u001b[0m \u001b[43max\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrorbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbias_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage_iBRRTs_lengths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myerr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mstd_iBRRTs_lengths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mInformed RRT*\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m ax[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(bias_list, BRRT_succes_percentages, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRRT\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     21\u001b[0m ax[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(bias_list, BRRTs_succes_percentages, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRRT*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\Programs\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:1361\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1358\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1359\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1360\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1361\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1363\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1364\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1365\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mD:\\Programs\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:3495\u001b[0m, in \u001b[0;36mAxes.errorbar\u001b[1;34m(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)\u001b[0m\n\u001b[0;32m   3491\u001b[0m             caplines\u001b[38;5;241m.\u001b[39mappend(mlines\u001b[38;5;241m.\u001b[39mLine2D(\n\u001b[0;32m   3492\u001b[0m                 xo, yo, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39meb_cap_style))\n\u001b[0;32m   3494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yerr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 3495\u001b[0m     lower, upper \u001b[38;5;241m=\u001b[39m \u001b[43mextract_err\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myerr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlolims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muplims\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3496\u001b[0m     barcols\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvlines(\n\u001b[0;32m   3497\u001b[0m         \u001b[38;5;241m*\u001b[39mapply_mask([x, lower, upper], everymask), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39meb_lines_style))\n\u001b[0;32m   3498\u001b[0m     \u001b[38;5;66;03m# select points without upper/lower limits in y and\u001b[39;00m\n\u001b[0;32m   3499\u001b[0m     \u001b[38;5;66;03m# draw normal errorbars for these points\u001b[39;00m\n",
      "File \u001b[1;32mD:\\Programs\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:3455\u001b[0m, in \u001b[0;36mAxes.errorbar.<locals>.extract_err\u001b[1;34m(name, err, data, lolims, uplims)\u001b[0m\n\u001b[0;32m   3451\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(e):\n\u001b[0;32m   3452\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   3453\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe lengths of the data (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) and the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3454\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 3455\u001b[0m low \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mif\u001b[39;00m lo \u001b[38;5;28;01melse\u001b[39;00m v \u001b[38;5;241m-\u001b[39m e \u001b[38;5;28;01mfor\u001b[39;00m v, e, lo \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(data, a, lolims)]\n\u001b[0;32m   3456\u001b[0m high \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mif\u001b[39;00m up \u001b[38;5;28;01melse\u001b[39;00m v \u001b[38;5;241m+\u001b[39m e \u001b[38;5;28;01mfor\u001b[39;00m v, e, up \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(data, b, uplims)]\n\u001b[0;32m   3457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m low, high\n",
      "File \u001b[1;32mD:\\Programs\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:3455\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   3451\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(e):\n\u001b[0;32m   3452\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   3453\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe lengths of the data (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) and the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3454\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 3455\u001b[0m low \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mif\u001b[39;00m lo \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43me\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v, e, lo \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(data, a, lolims)]\n\u001b[0;32m   3456\u001b[0m high \u001b[38;5;241m=\u001b[39m [v \u001b[38;5;28;01mif\u001b[39;00m up \u001b[38;5;28;01melse\u001b[39;00m v \u001b[38;5;241m+\u001b[39m e \u001b[38;5;28;01mfor\u001b[39;00m v, e, up \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(data, b, uplims)]\n\u001b[0;32m   3457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m low, high\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'NoneType' and 'int'"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc3412724ecb4940a7136eaa7cc4ae36",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(3,sharex=True)\n",
    "\n",
    "#plt.plot(bias_list, average_BRRT_times, label = \"Generation time\")\n",
    "\n",
    "ax[0].set_ylabel('Generation Time in s')\n",
    "ax[0].errorbar(bias_list, average_BRRT_times, yerr = std_BRRT_times, label = \"RRT\")\n",
    "ax[0].errorbar(bias_list, average_BRRTs_times, yerr = std_BRRTs_times, label = \"RRT*\")\n",
    "ax[0].errorbar(bias_list, average_iBRRTs_times, yerr = std_iBRRTs_times, label = \"informed RRT*\")\n",
    "\n",
    "\n",
    "#plt.plot(bias_list, average_BRRT_lengths, label = \"RRT\")\n",
    "#plt.plot(bias_list, average_BRRTs_lengths, label = \"RRT*\")\n",
    "\n",
    "ax[1].set_ylabel('Path length')\n",
    "ax[1].errorbar(bias_list, average_BRRT_lengths, yerr = std_BRRT_lengths, label = \"RRT\")\n",
    "ax[1].errorbar(bias_list, average_BRRTs_lengths, yerr = std_BRRTs_lengths, label = \"RRT*\")\n",
    "ax[1].errorbar(bias_list, average_iBRRTs_lengths, yerr = std_iBRRTs_lengths, label = \"Informed RRT*\")\n",
    "\n",
    "\n",
    "ax[2].plot(bias_list, BRRT_succes_percentages, label = \"RRT\")\n",
    "ax[2].plot(bias_list, BRRTs_succes_percentages, label = \"RRT*\")\n",
    "ax[2].plot(bias_list, iBRRTs_succes_percentages, label = \"Informed RRT*\")\n",
    "ax[2].set_xlabel('Obstacle Bias')\n",
    "ax[2].set_ylabel('Succes Rate')\n",
    "\n",
    "handles, labels = ax[2].get_legend_handles_labels()\n",
    "fig.set_figheight(10)\n",
    "fig.legend(handles, labels, loc=\"lower left\", bbox_to_anchor=(0.12,0.1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6834ee3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2ced5e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pdm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 05:59:23) \n[Clang 11.1.0 ]"
  },
  "vscode": {
   "interpreter": {
    "hash": "3fc3b5e72a1d5b0e92c451cfa0a46410bb5636c6e750e98447d3a634ba4029bd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}