car_obs_path_visualization_sst.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "obs_idx: \n",
      "8\n",
      "p_idx:\n",
      "986\n",
      "generated.\n",
      "(5, 2)\n"
     ]
    }
   ],
   "source": [
    "## from ctypes import *\n",
    "#ctypes.cdll.LoadLibrary('')\n",
    "#lib1 = CDLL(\"deps/sparse_rrt/deps/trajopt/build/lib/libsco.so\")\n",
    "#lib2 = CDLL(\"deps/sparse_rrt/deps/trajopt/build/lib/libutils.so\")\n",
    "\n",
    "import sys\n",
    "sys.path.append('deps/sparse_rrt')\n",
    "sys.path.append('.')\n",
    "\n",
    "from sparse_rrt.planners import SST\n",
    "#from env.cartpole_obs import CartPoleObs\n",
    "#from env.cartpole import CartPole\n",
    "#from sparse_rrt.systems.cartpole import Cartpole\n",
    "from sparse_rrt.systems import standard_cpp_systems\n",
    "from sparse_rrt import _sst_module\n",
    "import numpy as np\n",
    "import time\n",
    "from tools.pcd_generation import rectangle_pcd\n",
    "from plan_utility.line_line_cc import *\n",
    "import pickle\n",
    "import os\n",
    "obs_list = []\n",
    "LENGTH = 20.\n",
    "width = 6.\n",
    "near = width * 1.2\n",
    "# convert from obs to point cloud\n",
    "# load generated point cloud\n",
    "obs_list_total = []\n",
    "obc_list_total = []\n",
    "for i in range(10):\n",
    "    file = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/obs_%d.pkl' % (i), 'rb')\n",
    "    obs_list_total.append(pickle.load(file))\n",
    "    file = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/obc_%d.pkl' % (i), 'rb')\n",
    "    obc_list_total.append(pickle.load(file))\n",
    "\n",
    "#[(0, 932), (1, 935), (2, 923), (8, 141), (5,931), (6, 969), (7, 927), (9, 911),]\n",
    "# (5,931), (6, 286)\n",
    "obs_idx = 4\n",
    "\n",
    "p_idx = 975\n",
    "found = False\n",
    "# make sure start and goal are far enough\n",
    "ignore_list = [(0,922), (4,939), (0, 949), (9, 911), (9,937), (1,982), (5,946),(6,944), (7,921), (1,918),(1,928), (1,937)]\n",
    "ignore_list += [(1,976), (1,984), (2,913), (2,942), (2,962), (2,968), (2,986), (2,996), (3,965), (3,975), (4,924)]\n",
    "ignore_list += [(4,993), (5,904), (5,971), (6,914), (6,937), (7,923), (8,901), (8,986)]\n",
    "found = False\n",
    "print(os.path.isfile('/media/arclabdl1/HD1/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/car_obs/default/paths/path_%d_%d.npy' % (obs_idx, p_idx)))\n",
    "for obs_idx in range(1,9):\n",
    "    for p_idx in range(900,1000):\n",
    "        if (obs_idx, p_idx) in ignore_list:\n",
    "            continue\n",
    "        if not os.path.isfile('/media/arclabdl1/HD1/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/car_obs/default/paths/path_%d_%d.npy' % (obs_idx, p_idx)):\n",
    "            continue\n",
    "        cost = np.load('/media/arclabdl1/HD1/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/car_obs/default/paths/path_%d_%d.npy' % (obs_idx, p_idx),\n",
    "               allow_pickle=True)[2].sum()\n",
    "        data_cost = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/cost_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "        data_cost = pickle.load(data_cost).sum()\n",
    "        start_goal = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/start_goal_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "        start_goal = pickle.load(start_goal)\n",
    "        s = start_goal[0]\n",
    "        g = start_goal[1]\n",
    "        \n",
    "        time = np.load('/media/arclabdl1/HD1/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/car_obs/default/time_10_200.npy',\n",
    "               allow_pickle=True)[obs_idx, p_idx-800]\n",
    "        sst_time = np.load('/home/arclabdl1/YLmiao/kinodynamics/KinoMPNet/plan_results/car_obs_sst/plan_time.npy',\n",
    "               allow_pickle=True)[obs_idx, p_idx-800]\n",
    "        if time <= 20 and 2*time <= sst_time and cost < 1.5*data_cost and np.linalg.norm(s-g) >= 50.:\n",
    "            found = True\n",
    "            break\n",
    "    if found:\n",
    "        break\n",
    "obs_idx = 8\n",
    "\n",
    "p_idx = 986\n",
    "print('obs_idx: ')\n",
    "print(obs_idx)\n",
    "print('p_idx:')\n",
    "print(p_idx)       \n",
    "\n",
    "# Create custom system\n",
    "#obs_list = [[-10., -3.],\n",
    "#            [0., 3.],\n",
    "#            [10, -3.]]\n",
    "obs_list = obs_list_total[obs_idx]\n",
    "obc_list = obc_list_total[obs_idx]\n",
    "print('generated.')\n",
    "print(obs_list.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfor p_idx in range(900, 1000):\\n    sgs = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/acrobot_obs_backup/%d/start_goal_%d.pkl' % (obs_idx, p_idx), 'rb')\\n    sgs = pickle.load(sgs)\\n    dis = np.abs(sgs[1]-np.array([np.pi, 0, 0., 0.]))\\n    if dis[0] >= np.pi:\\n        dis[0] = 2*np.pi - dis[0]\\n    if dis[1] >= np.pi:\\n        dis[1] = 2*np.pi - dis[1]\\n        \\n    if np.linalg.norm(dis) <= 1e-1:\\n        break\\nprint(p_idx)\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# search for goal\n",
    "\"\"\"\n",
    "for p_idx in range(900, 1000):\n",
    "    sgs = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/acrobot_obs_backup/%d/start_goal_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "    sgs = pickle.load(sgs)\n",
    "    dis = np.abs(sgs[1]-np.array([np.pi, 0, 0., 0.]))\n",
    "    if dis[0] >= np.pi:\n",
    "        dis[0] = 2*np.pi - dis[0]\n",
    "    if dis[1] >= np.pi:\n",
    "        dis[1] = 2*np.pi - dis[1]\n",
    "        \n",
    "    if np.linalg.norm(dis) <= 1e-1:\n",
    "        break\n",
    "print(p_idx)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load path\n",
    "path = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/path_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "path = pickle.load(path)\n",
    "controls = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/control_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "controls = pickle.load(controls)\n",
    "costs = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/cost_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "costs = pickle.load(costs)\n",
    "sgs = open('/media/arclabdl1/HD1/YLmiao/data/kinodynamic/car_obs/%d/start_goal_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "sgs = pickle.load(sgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def enforce_bounds(state):\n",
    "    '''\n",
    "    check if state satisfies the bound\n",
    "    apply threshold to velocity and angle\n",
    "    return a new state toward which the bound has been enforced\n",
    "    '''\n",
    "    WIDTH = 2.0\n",
    "    LENGTH = 1.0\n",
    "\n",
    "    STATE_X = 0\n",
    "    STATE_Y = 1\n",
    "\n",
    "    STATE_THETA = 2 \n",
    "    MIN_X = -25\n",
    "    MAX_X = 25\n",
    "    MIN_Y = -35\n",
    "    MAX_Y = 35\n",
    "    \n",
    "    \n",
    "    new_state = np.array(state)\n",
    "    \"\"\"\n",
    "    if state[STATE_V] < MIN_V/30.:\n",
    "        new_state[STATE_V] = MIN_V/30.\n",
    "    elif state[STATE_V] > MAX_V/30.:\n",
    "        new_state[STATE_V] = MAX_V/30.\n",
    "    \"\"\"\n",
    "    if state[2] < -np.pi:\n",
    "        new_state[2] += 2*np.pi\n",
    "    elif state[2] > np.pi:\n",
    "        new_state[2] -= 2*np.pi\n",
    "    return new_state\n",
    "\n",
    "\n",
    "\n",
    "def overlap(b1corner,b1axis,b1orign,b1dx,b1dy,b2corner,b2axis,b2orign,b2dx,b2dy):\n",
    "    # this only checks overlap of b1 w.r.t. b2\n",
    "    # a complete check should do in both directions\n",
    "    b2ds = [b2dx, b2dy]\n",
    "    for a in range(0,2):\n",
    "        t=b1corner[0][0]*b2axis[a][0]+b1corner[0][1]*b2axis[a][1] # project corner to the axis by inner product\n",
    "\n",
    "        tMin = t\n",
    "        tMax = t\n",
    "        for c in range(1,4):\n",
    "            t = b1corner[c][0]*b2axis[a][0]+b1corner[c][1]*b2axis[a][1] # project corner to the axis by inner product\n",
    "            # find range by [tMin, tMax]\n",
    "            if t < tMin:\n",
    "                tMin = t\n",
    "            elif t > tMax:\n",
    "                tMax = t\n",
    "        # since b2 the other corners (corner 1, 2, 3) are larger than b2orign (project of corner 0 to axis)\n",
    "        # specifically, the range is [b2orign[i], b2orign[i]+size(i)] (of the projected point by dot product)\n",
    "        # we only need to compare tMax with b2orign[i], and tMin with size(i)+b2orign[i]\n",
    "        if ((tMin > (b2ds[a] + b2orign[a])) or (tMax < b2orign[a])):\n",
    "            return False\n",
    "\n",
    "    return True\n",
    "\n",
    "def IsInCollision(x, obc, obc_width=4.):\n",
    "    car_width = 2.0\n",
    "    car_len = 1.0\n",
    "    width = 8.0\n",
    "    WIDTH = car_width\n",
    "    LENGTH = car_len\n",
    "    STATE_X = 0\n",
    "    STATE_Y = 1\n",
    "    STATE_THETA = 2\n",
    "    MIN_X = -25\n",
    "    MAX_X = 25\n",
    "    MIN_Y = -35\n",
    "    MAX_Y = 35\n",
    "    if x[0] < MIN_X or x[0] > MAX_X or x[1] < MIN_Y or x[1] > MAX_Y:\n",
    "        return True\n",
    "        \n",
    "    robot_corner=np.zeros((4,2),dtype=np.float32)\n",
    "    robot_axis=np.zeros((2,2),dtype=np.float32)\n",
    "    robot_orign=np.zeros(2,dtype=np.float32)\n",
    "    length=np.zeros(2,dtype=np.float32)\n",
    "    X1=np.zeros(2,dtype=np.float32)\n",
    "    Y1=np.zeros(2,dtype=np.float32)\n",
    "\n",
    "    X1[0]=np.cos(x[STATE_THETA])*(WIDTH/2.0)\n",
    "    X1[1]=-np.sin(x[STATE_THETA])*(WIDTH/2.0)\n",
    "    Y1[0]=np.sin(x[STATE_THETA])*(LENGTH/2.0)\n",
    "    Y1[1]=np.cos(x[STATE_THETA])*(LENGTH/2.0)\n",
    "\n",
    "    for j in range(0,2):\n",
    "        # order: (left-bottom, right-bottom, right-upper, left-upper)\n",
    "        # assume angle (state_theta) is clockwise\n",
    "        robot_corner[0][j]=x[j]-X1[j]-Y1[j]\n",
    "        robot_corner[1][j]=x[j]+X1[j]-Y1[j]\n",
    "        robot_corner[2][j]=x[j]+X1[j]+Y1[j]\n",
    "        robot_corner[3][j]=x[j]-X1[j]+Y1[j]\n",
    "\n",
    "        # axis: horizontal and vertical\n",
    "        robot_axis[0][j] = robot_corner[1][j] - robot_corner[0][j]\n",
    "        robot_axis[1][j] = robot_corner[3][j] - robot_corner[0][j]\n",
    "\n",
    "    #print('robot corners:')\n",
    "    #print(robot_corner)\n",
    "    length[0]=np.sqrt(robot_axis[0][0]*robot_axis[0][0]+robot_axis[0][1]*robot_axis[0][1])\n",
    "    length[1]=np.sqrt(robot_axis[1][0]*robot_axis[1][0]+robot_axis[1][1]*robot_axis[1][1])\n",
    "    # normalize the axis\n",
    "    for i in range(0,2):\n",
    "        for j in range(0,2):\n",
    "            robot_axis[i][j]=robot_axis[i][j]/float(length[i])\n",
    "\n",
    "    # obtain the projection of the left-bottom corner to the axis, to obtain the minimal projection length\n",
    "    robot_orign[0]=robot_corner[0][0]*robot_axis[0][0]+ robot_corner[0][1]*robot_axis[0][1]\n",
    "    robot_orign[1]=robot_corner[0][0]*robot_axis[1][0]+ robot_corner[0][1]*robot_axis[1][1]\n",
    "    #print('robot orign:')\n",
    "    #print(robot_orign)\n",
    "    for i in range(len(obc)):\n",
    "        cf=True\n",
    "\n",
    "        obs_corner=np.zeros((4,2),dtype=np.float32)\n",
    "        obs_axis=np.zeros((2,2),dtype=np.float32)\n",
    "        obs_orign=np.zeros(2,dtype=np.float32)\n",
    "        length2=np.zeros(2,dtype=np.float32)\n",
    "\n",
    "        for j in range(0,2):\n",
    "            # order: (left-bottom, right-bottom, right-upper, left-upper)\n",
    "            obs_corner[0][j] = obc[i][j]\n",
    "            obs_corner[1][j] = obc[i][2+j]\n",
    "            obs_corner[2][j] = obc[i][2*2+j]\n",
    "            obs_corner[3][j] = obc[i][3*2+j]\n",
    "            \n",
    "            # horizontal axis and vertical\n",
    "            obs_axis[0][j] = obs_corner[1][j] - obs_corner[0][j]\n",
    "            obs_axis[1][j] = obs_corner[3][j] - obs_corner[0][j]\n",
    "            #obs_axis[0][j] = obs_corner[3][j] - obs_corner[0][j]\n",
    "            #obs_axis[1][j] = obs_corner[1][j] - obs_corner[0][j]\n",
    "\n",
    "        length2[0]=np.sqrt(obs_axis[0][0]*obs_axis[0][0]+obs_axis[0][1]*obs_axis[0][1])\n",
    "        length2[1]=np.sqrt(obs_axis[1][0]*obs_axis[1][0]+obs_axis[1][1]*obs_axis[1][1])\n",
    "\n",
    "        # normalize the axis\n",
    "        for i1 in range(0,2):\n",
    "            for j1 in range(0,2):\n",
    "                obs_axis[i1][j1]=obs_axis[i1][j1]/float(length2[i1])\n",
    "\n",
    "        # obtain the inner product of the left-bottom corner with the axis to obtain the minimal of projection value\n",
    "        obs_orign[0]=obs_corner[0][0]*obs_axis[0][0]+ obs_corner[0][1]*obs_axis[0][1]  # dot product at 0-th corner\n",
    "        obs_orign[1]=obs_corner[0][0]*obs_axis[1][0]+ obs_corner[0][1]*obs_axis[1][1]\n",
    "        # do checking in both direction (b1 -> b2, b2 -> b1). If at least one shows not-overlapping, then it is not overlapping\n",
    "        cf=overlap(robot_corner,robot_axis,robot_orign,car_width,car_len,obs_corner,obs_axis,obs_orign,width,width)\n",
    "        cf=cf and overlap(obs_corner,obs_axis,obs_orign,width,width,robot_corner,robot_axis,robot_orign,car_width,car_len)\n",
    "        if cf==True:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wrap_angle(x, system):\n",
    "    circular = system.is_circular_topology()\n",
    "    res = np.array(x)\n",
    "    for i in range(len(x)):\n",
    "        if circular[i]:\n",
    "            # use our previously saved version\n",
    "            res[i] = x[i] - np.floor(x[i] / (2*np.pi))*(2*np.pi)\n",
    "            if res[i] > np.pi:\n",
    "                res[i] = res[i] - 2*np.pi\n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from visual.visualizer import Visualizer\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "padding = 3\n",
    "class CarVisualizer(Visualizer):\n",
    "    def __init__(self, system, params):\n",
    "        super(CarVisualizer, self).__init__(system, params)\n",
    "        self.dt = 2\n",
    "        self.fig = plt.gcf()\n",
    "        self.fig.set_figheight(10)\n",
    "        self.fig.set_figwidth(10)\n",
    "        self.ax1 = plt.subplot(111)\n",
    "    def _init(self):\n",
    "        ax = self.ax1\n",
    "        ax.clear()\n",
    "        ax.set_xlim(-25-padding, 25+padding)\n",
    "        ax.set_ylim(-35, 35)\n",
    "        # add patches\n",
    "        state = self.states[0]\n",
    "        self.car = patches.Rectangle((state[0]-self.params['car_w']/2,state[1]-self.params['car_l']/2),\\\n",
    "                                       self.params['car_w'],self.params['car_l'],\\\n",
    "                                      linewidth=.5,edgecolor='blue',facecolor='blue')\n",
    "        self.car_direction = patches.Arrow(state[0],state[1],\\\n",
    "                                           self.params['car_w']/2,0,\\\n",
    "                                           linewidth=1.0, edgecolor='yellow')\n",
    "        self.recs = []\n",
    "        self.recs.append(self.car)\n",
    "        self.recs.append(self.car_direction)\n",
    "        for i in range(len(self.obs)):\n",
    "            x, y = self.obs[i]\n",
    "            obs = patches.Rectangle((x-self.params['obs_w']/2,y-params['obs_h']/2),\\\n",
    "                                       self.params['obs_w'],self.params['obs_h'],\\\n",
    "                                      linewidth=.5,edgecolor='black',facecolor='black')\n",
    "            self.recs.append(obs)\n",
    "            ax.add_patch(obs)\n",
    "        # transform pole according to state\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], state[1], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.car.set_transform(t)\n",
    "        ax.add_patch(self.car)\n",
    "        self.car_direction.set_transform(t)\n",
    "        self.car_direction_patch = ax.add_patch(self.car_direction)\n",
    "\n",
    "        # add goal patch\n",
    "        state = self.states[-1]\n",
    "        self.car_goal = patches.Rectangle((state[0]-self.params['car_w']/2,state[1]-self.params['car_l']/2),\\\n",
    "                                       self.params['car_w'],self.params['car_l'],\\\n",
    "                                      linewidth=.5,edgecolor='red',facecolor='red')\n",
    "        self.car_direction_goal = patches.Arrow(state[0],state[1],\\\n",
    "                                           self.params['car_w']/2,0,\\\n",
    "                                           linewidth=1.0, edgecolor='yellow')\n",
    "        self.recs.append(self.car_goal)\n",
    "        self.recs.append(self.car_direction_goal)\n",
    "        \n",
    "        # transform pole according to state\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], state[1], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.car_goal.set_transform(t)\n",
    "        ax.add_patch(self.car_goal)\n",
    "        self.car_direction_goal.set_transform(t)\n",
    "        self.car_direction_goal_patch = ax.add_patch(self.car_direction_goal)\n",
    "\n",
    "\n",
    "        return self.recs\n",
    "    def _animate(self, i):\n",
    "        ax = self.ax1\n",
    "        ax.set_xlim(-25-padding, 25+padding)\n",
    "        ax.set_ylim(-35, 35)\n",
    "        state = self.states[i]\n",
    "        self.recs[0].set_xy((state[0]-self.params['car_w']/2,state[1]-self.params['car_l']/2))\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], state[1], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.recs[0].set_transform(t)\n",
    "\n",
    "        # modify car_direction\n",
    "        self.car_direction_patch.remove()\n",
    "        self.car_direction = patches.Arrow(state[0],state[1],\\\n",
    "                                           self.params['car_w']/2,0,\\\n",
    "                                      linewidth=1.0, edgecolor='yellow')\n",
    "        self.recs[1] = self.car_direction\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], state[1], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.recs[1].set_transform(t)\n",
    "        self.car_direction_patch = ax.add_patch(self.car_direction)\n",
    "        \n",
    "        return self.recs\n",
    "\n",
    "\n",
    "\n",
    "    def animate(self, states, actions, costs, obstacles, sg, wrap_system):\n",
    "        '''\n",
    "        given a list of states, actions and obstacles, animate the robot\n",
    "        '''\n",
    "\n",
    "        new_obs_i = []\n",
    "        obs_width = 8.0\n",
    "        for k in range(len(obstacles)):\n",
    "            obs_pt = []\n",
    "            obs_pt.append(obstacles[k][0]-obs_width/2)\n",
    "            obs_pt.append(obstacles[k][1]-obs_width/2)\n",
    "            obs_pt.append(obstacles[k][0]+obs_width/2)\n",
    "            obs_pt.append(obstacles[k][1]-obs_width/2)\n",
    "            obs_pt.append(obstacles[k][0]+obs_width/2)\n",
    "            obs_pt.append(obstacles[k][1]+obs_width/2)\n",
    "            obs_pt.append(obstacles[k][0]-obs_width/2)\n",
    "            obs_pt.append(obstacles[k][1]+obs_width/2)\n",
    "            new_obs_i.append(obs_pt)\n",
    "        obs_i = new_obs_i\n",
    "        self.cc_obs = obs_i\n",
    "\n",
    "        # transform the waypoint states and actions into trajectory\n",
    "        traj = []\n",
    "        s = states[0]\n",
    "        for i in range(len(states)-1):\n",
    "            print('state: %d, remaining: %d' % (i, len(states)-i))\n",
    "            \n",
    "            \n",
    "            action = actions[i]\n",
    "            # number of steps for propagtion\n",
    "            #num_steps = int(np.round(costs[i]/self.params['integration_step']))\n",
    "            num_steps = 100000\n",
    "            for j in range(num_steps):\n",
    "                traj.append(np.array(s))\n",
    "                #print(\"porpagating...\")\n",
    "                #print(s)\n",
    "                #print('st:')\n",
    "                #print(sT)\n",
    "                s = self.system(s, action, self.params['integration_step'])\n",
    "                assert not IsInCollision(s, obs_i)\n",
    "                if np.linalg.norm(s - states[i+1]) <= 1e-10:\n",
    "                    break\n",
    "\n",
    "            \n",
    "        traj = np.array(traj)\n",
    "        print(\"animating...\")\n",
    "        # animate\n",
    "        self.states = traj\n",
    "        self.obs = obstacles\n",
    "        print(len(self.states))\n",
    "        self.total = len(self.states)\n",
    "        self._init()\n",
    "        \n",
    "        to_plot_list_x = []\n",
    "        to_plot_list_y = []\n",
    "        traj_recs = []\n",
    "        plot_step_sz = 500\n",
    "        for i in list(range(1,len(traj))) + [0]:\n",
    "            if i % plot_step_sz == 0:\n",
    "                # plot the scene change\n",
    "                if i == 0:\n",
    "                    car_color = \"green\"\n",
    "                    direction_color = \"yellow\"\n",
    "                else:\n",
    "                    car_color = \"blue\"\n",
    "                    direction_color = \"yellow\"\n",
    "                ax = self.ax1\n",
    "                ax.set_xlim(-25-padding, 25+padding)\n",
    "                ax.set_ylim(-35, 35)\n",
    "                state = traj[i]\n",
    "                car = patches.Rectangle((state[0]-self.params['car_w']/2,state[1]-self.params['car_l']/2),\\\n",
    "                                                       self.params['car_w'],self.params['car_l'],\\\n",
    "                                                      linewidth=.5,edgecolor=car_color,facecolor=car_color)\n",
    "                car_direction = patches.Arrow(state[0],state[1],\\\n",
    "                                                       self.params['car_w']/2,0,\\\n",
    "                                                       linewidth=1.0, edgecolor=direction_color)\n",
    "                t = mpl.transforms.Affine2D().rotate_deg_around(state[0], state[1], \\\n",
    "                                                                -state[2]/np.pi * 180) + ax.transData\n",
    "                car.set_transform(t)\n",
    "                ax.add_patch(car)\n",
    "                car_direction.set_transform(t)\n",
    "                car_direction_patch = ax.add_patch(car_direction)\n",
    "\n",
    "                traj_recs.append(car)\n",
    "                traj_recs.append(car_direction)\n",
    "                \n",
    "        ax.add_patch(self.car_goal)\n",
    "        ax.add_patch(self.car_direction_goal)\n",
    "        plt.axis('off')\n",
    "        plt.savefig(\"car_sst_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "\n",
    "\n",
    "        \n",
    "        \"\"\"\n",
    "        # for thesis\n",
    "        fig = plt.figure()\n",
    "        ax = plt.subplot(121)\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-40, 40)\n",
    "        fig.set_figheight(5)\n",
    "        fig.set_figwidth(10)\n",
    "        \n",
    "        for i in range(len(self.obs)):\n",
    "            x, y = self.obs[i]\n",
    "            obs = patches.Rectangle((x-self.params['obs_w']/2,y-params['obs_h']/2),\\\n",
    "                                       self.params['obs_w'],self.params['obs_h'],\\\n",
    "                                      linewidth=.5,edgecolor='black',facecolor='black')\n",
    "            self.recs.append(obs)\n",
    "            ax.add_patch(obs)\n",
    "        ax.plot(to_plot_list_x[0], to_plot_list_y[0], alpha=1, c='green')\n",
    "        ax.plot(to_plot_list_x[-1], to_plot_list_y[-1], alpha=1, c='red')\n",
    "        \n",
    "        ax = plt.subplot(122)\n",
    "        ax.set_xlim(-np.pi, np.pi)\n",
    "        ax.set_ylim(-np.pi, np.pi)\n",
    "        dtheta = 0.1\n",
    "        feasible_points = []\n",
    "        infeasible_points = []\n",
    "        imin = 0\n",
    "        imax = int(2*np.pi/dtheta)\n",
    "\n",
    "        for i in range(imin, imax):\n",
    "            for j in range(imin, imax):\n",
    "                x = np.array([dtheta*i-np.pi, dtheta*j-np.pi, 0., 0.])\n",
    "                if IsInCollision(x, self.cc_obs):\n",
    "                    infeasible_points.append(x)\n",
    "                else:\n",
    "                    feasible_points.append(x)\n",
    "        feasible_points = np.array(feasible_points)\n",
    "        infeasible_points = np.array(infeasible_points)\n",
    "\n",
    "        print('feasible points')\n",
    "        print(feasible_points)\n",
    "        print('infeasible points')\n",
    "        print(infeasible_points)\n",
    "        #scat_feas =ax.scatter(feasible_points[:,0], feasible_points[:,1], c='white')\n",
    "        scat_infeas = ax.scatter(infeasible_points[:,0], infeasible_points[:,1], c='black')\n",
    "        scat_state = ax.scatter(sg[0][0], sg[0][1], c='green', s=50.0)\n",
    "        scat_state = ax.scatter(sg[1][0], sg[1][1], c='red', s=50.0, marker='*')\n",
    "        \"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "states:\n",
      "[[-1.58104181e+01  3.00272620e+01  3.09608061e+00]\n",
      " [-1.64222644e+01  2.99877424e+01  3.05786929e+00]\n",
      " [-1.66695386e+01  2.99669810e+01  3.05778642e+00]\n",
      " [-1.69013691e+01  2.99352180e+01  2.95208741e+00]\n",
      " [-1.71249635e+01  2.98843861e+01  2.88360185e+00]\n",
      " [-1.73162711e+01  2.98281215e+01  2.82662693e+00]\n",
      " [-1.75850372e+01  2.97267288e+01  2.73454738e+00]\n",
      " [-1.78947966e+01  2.95847288e+01  2.68872314e+00]\n",
      " [-1.82839870e+01  2.94160747e+01  2.77712188e+00]\n",
      " [-1.86998847e+01  2.92624945e+01  2.79874372e+00]\n",
      " [-1.90388341e+01  2.91130628e+01  2.65304083e+00]\n",
      " [-1.92353783e+01  2.89994522e+01  2.58118532e+00]\n",
      " [-1.95613016e+01  2.87736790e+01  2.48975763e+00]\n",
      " [-1.98192204e+01  2.85797594e+01  2.50420156e+00]\n",
      " [-2.00194601e+01  2.84241623e+01  2.45722668e+00]\n",
      " [-2.02642769e+01  2.82418934e+01  2.54646025e+00]\n",
      " [-2.08090641e+01  2.78720303e+01  2.54384549e+00]\n",
      " [-2.09767931e+01  2.77505254e+01  2.48463661e+00]\n",
      " [-2.15249964e+01  2.72434050e+01  2.30466267e+00]\n",
      " [-2.18570307e+01  2.68404207e+01  2.21461830e+00]\n",
      " [-2.22111644e+01  2.62861106e+01  2.06311993e+00]\n",
      " [-2.23349606e+01  2.60808117e+01  2.16429954e+00]\n",
      " [-2.24601956e+01  2.58939272e+01  2.15801249e+00]\n",
      " [-2.24948093e+01  2.58422935e+01  2.16474183e+00]\n",
      " [-2.27644355e+01  2.54682289e+01  2.22631425e+00]\n",
      " [-2.30200631e+01  2.50756281e+01  2.06876380e+00]\n",
      " [-2.32494143e+01  2.46882434e+01  2.14249904e+00]\n",
      " [-2.33893245e+01  2.44499216e+01  2.06005179e+00]\n",
      " [-2.35236650e+01  2.41828361e+01  2.01328441e+00]\n",
      " [-2.37134017e+01  2.37909686e+01  2.03025820e+00]\n",
      " [-2.38351326e+01  2.35344571e+01  1.99708697e+00]\n",
      " [-2.39611844e+01  2.32513168e+01  1.98207308e+00]\n",
      " [-2.40802865e+01  2.29628411e+01  1.94241782e+00]\n",
      " [-2.42746429e+01  2.24457881e+01  1.91812301e+00]\n",
      " [-2.44097027e+01  2.20963575e+01  1.96149389e+00]\n",
      " [-2.44676901e+01  2.19317920e+01  1.85700852e+00]\n",
      " [-2.45832638e+01  2.14918318e+01  1.79792745e+00]\n",
      " [-2.46515995e+01  2.11873967e+01  1.78519919e+00]\n",
      " [-2.47521590e+01  2.07887024e+01  1.85087717e+00]\n",
      " [-2.48993988e+01  2.00308921e+01  1.67362171e+00]\n",
      " [-2.49383894e+01  1.97293877e+01  1.72549796e+00]\n",
      " [-2.49745758e+01  1.94748493e+01  1.69821097e+00]\n",
      " [-2.49940986e+01  1.91155338e+01  1.55116330e+00]\n",
      " [-2.49911341e+01  1.89121837e+01  1.56145565e+00]\n",
      " [-2.49955449e+01  1.85539496e+01  1.60502174e+00]\n",
      " [-2.49958222e+01  1.81254353e+01  1.53747038e+00]\n",
      " [-2.49779213e+01  1.78651743e+01  1.46624997e+00]\n",
      " [-2.49542982e+01  1.75448155e+01  1.52870311e+00]\n",
      " [-2.49471269e+01  1.73462529e+01  1.54087044e+00]\n",
      " [-2.49394077e+01  1.72665144e+01  1.40673721e+00]\n",
      " [-2.48973279e+01  1.69970509e+01  1.42526369e+00]\n",
      " [-2.48642146e+01  1.67913449e+01  1.39688538e+00]\n",
      " [-2.48165161e+01  1.65568073e+01  1.34297338e+00]\n",
      " [-2.47598504e+01  1.62707280e+01  1.40829680e+00]\n",
      " [-2.47280015e+01  1.60338272e+01  1.46686740e+00]\n",
      " [-2.46934067e+01  1.56578683e+01  1.49146085e+00]\n",
      " [-2.46348150e+01  1.51922200e+01  1.39927869e+00]\n",
      " [-2.46002080e+01  1.48428125e+01  1.54578976e+00]\n",
      " [-2.45937147e+01  1.45411864e+01  1.55280949e+00]\n",
      " [-2.45931122e+01  1.43373405e+01  1.58324305e+00]\n",
      " [-2.46497739e+01  1.37005866e+01  1.73665586e+00]\n",
      " [-2.46859677e+01  1.33607316e+01  1.61652575e+00]\n",
      " [-2.46899129e+01  1.31603891e+01  1.56348137e+00]\n",
      " [-2.46504050e+01  1.27070922e+01  1.40335245e+00]\n",
      " [-2.45760328e+01  1.22322151e+01  1.42769914e+00]\n",
      " [-2.45366715e+01  1.18675190e+01  1.49922760e+00]\n",
      " [-2.45381461e+01  1.15188616e+01  1.65172574e+00]\n",
      " [-2.45474644e+01  1.11421441e+01  1.53860289e+00]\n",
      " [-2.45716869e+01  1.04013610e+01  1.66903246e+00]\n",
      " [-2.46228343e+01  9.93549127e+00  1.69144920e+00]\n",
      " [-2.46433675e+01  9.62230418e+00  1.58050836e+00]\n",
      " [-2.46408831e+01  9.38189481e+00  1.53996106e+00]\n",
      " [-2.46211397e+01  9.09750514e+00  1.46215153e+00]\n",
      " [-2.45975957e+01  8.74723737e+00  1.54612146e+00]\n",
      " [-2.45693571e+01  8.41205235e+00  1.42651211e+00]\n",
      " [-2.45315615e+01  8.02412267e+00  1.52131193e+00]\n",
      " [-2.45136312e+01  7.87854377e+00  1.37418921e+00]\n",
      " [-2.44596263e+01  7.48858089e+00  1.49295595e+00]\n",
      " [-2.44055801e+01  7.07613661e+00  1.38724313e+00]\n",
      " [-2.42707960e+01  6.49345245e+00  1.29925077e+00]\n",
      " [-2.41497449e+01  6.02819755e+00  1.33345332e+00]\n",
      " [-2.40939657e+01  5.84497389e+00  1.21643357e+00]\n",
      " [-2.39276191e+01  5.47404703e+00  1.08128810e+00]\n",
      " [-2.36810561e+01  5.02823731e+00  1.04969887e+00]\n",
      " [-2.35122115e+01  4.73086686e+00  1.05914665e+00]\n",
      " [-2.34056452e+01  4.55865365e+00  9.73543956e-01]\n",
      " [-2.32483211e+01  4.32677287e+00  9.75816465e-01]\n",
      " [-2.30926618e+01  4.09784583e+00  9.71458378e-01]\n",
      " [-2.28268612e+01  3.67270005e+00  1.05313896e+00]\n",
      " [-2.26730925e+01  3.42253988e+00  9.85327686e-01]\n",
      " [-2.25451537e+01  3.24378131e+00  9.13343448e-01]\n",
      " [-2.22792344e+01  2.85984083e+00  1.01728487e+00]\n",
      " [-2.20749870e+01  2.47709624e+00  1.14477895e+00]\n",
      " [-2.19856973e+01  2.28964854e+00  1.10706244e+00]\n",
      " [-2.18153493e+01  1.94148279e+00  1.12457822e+00]\n",
      " [-2.16805923e+01  1.67195972e+00  1.08929415e+00]\n",
      " [-2.15788568e+01  1.48435691e+00  1.05803294e+00]\n",
      " [-2.12465058e+01  9.43649040e-01  9.80866668e-01]\n",
      " [-2.09764778e+01  5.64788288e-01  9.21799457e-01]\n",
      " [-2.08117038e+01  3.73655480e-01  7.95872927e-01]\n",
      " [-2.03736637e+01  1.38573154e-03  6.11971601e-01]\n",
      " [-1.98566248e+01 -4.26498067e-01  7.71536135e-01]\n",
      " [-1.93844994e+01 -8.69218595e-01  7.34777111e-01]\n",
      " [-1.92363308e+01 -9.96663727e-01  6.85222940e-01]\n",
      " [-1.87076756e+01 -1.39478506e+00  6.05243851e-01]\n",
      " [-1.85154874e+01 -1.51245582e+00  4.92631018e-01]\n",
      " [-1.82975364e+01 -1.63681333e+00  5.45038250e-01]\n",
      " [-1.81496624e+01 -1.73978863e+00  6.72471971e-01]\n",
      " [-1.79897515e+01 -1.86004456e+00  6.16123218e-01]\n",
      " [-1.77193624e+01 -2.03554120e+00  5.34477495e-01]\n",
      " [-1.75185307e+01 -2.14925742e+00  4.95742286e-01]\n",
      " [-1.73050237e+01 -2.25664269e+00  4.35444187e-01]\n",
      " [-1.68504787e+01 -2.44080020e+00  3.33775114e-01]\n",
      " [-1.66576606e+01 -2.50626494e+00  3.20701226e-01]\n",
      " [-1.64729654e+01 -2.56126742e+00  2.57496035e-01]\n",
      " [-1.61771958e+01 -2.63055398e+00  2.02364357e-01]\n",
      " [-1.59583766e+01 -2.66823651e+00  1.38064331e-01]\n",
      " [-1.57615585e+01 -2.69077641e+00  8.97410579e-02]\n",
      " [-1.55308887e+01 -2.70337501e+00  1.88087873e-02]\n",
      " [-1.50668527e+01 -2.70471258e+00 -1.32763636e-02]\n",
      " [-1.46967197e+01 -2.72238177e+00  1.09602665e-01]\n",
      " [-1.44973592e+01 -2.74758025e+00  1.42132597e-01]\n",
      " [-1.43391546e+01 -2.75657559e+00 -2.94853465e-02]\n",
      " [-1.41187448e+01 -2.76101682e+00  7.06739110e-02]\n",
      " [-1.38276061e+01 -2.78655263e+00  1.04527359e-01]\n",
      " [-1.35745525e+01 -2.80737530e+00  5.93262338e-02]\n",
      " [-1.31117948e+01 -2.82887612e+00  3.33851368e-02]\n",
      " [-1.28883033e+01 -2.83561977e+00  2.68576962e-02]\n",
      " [-1.24192773e+01 -2.84653579e+00  1.96217122e-02]\n",
      " [-1.22026673e+01 -2.84778054e+00 -8.51424209e-03]\n",
      " [-1.15567160e+01 -2.89679835e+00  1.60840074e-01]\n",
      " [-1.12490516e+01 -2.93629830e+00  9.39924153e-02]\n",
      " [-1.08901875e+01 -2.95316022e+00 -8.52549148e-04]\n",
      " [-1.02080768e+01 -2.89659496e+00 -1.65522279e-01]\n",
      " [-9.65147887e+00 -2.80991884e+00 -1.43303350e-01]\n",
      " [-9.38906473e+00 -2.77674047e+00 -1.07716571e-01]\n",
      " [-9.15214115e+00 -2.75693784e+00 -5.83846954e-02]\n",
      " [-8.97301991e+00 -2.73814400e+00 -1.51497923e-01]\n",
      " [-8.41482421e+00 -2.61918283e+00 -2.69208553e-01]\n",
      " [-7.94185252e+00 -2.52227780e+00 -1.34003053e-01]\n",
      " [-7.67210462e+00 -2.49623968e+00 -5.78366665e-02]\n",
      " [-7.24119490e+00 -2.49590853e+00  5.69556547e-02]\n",
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      "animating...\n",
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     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {}\n",
    "car_width = 2.0\n",
    "car_len = 1.0\n",
    "width = 8.\n",
    "\n",
    "params['car_w'] = car_width\n",
    "params['car_l'] = car_len\n",
    "params['obs_w'] = width\n",
    "params['obs_h'] = width\n",
    "params['integration_step'] = 0.002\n",
    "\n",
    "system = _sst_module.Car()\n",
    "cpp_propagator = _sst_module.SystemPropagator()\n",
    "dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)\n",
    "\n",
    "vis = CarVisualizer(dynamics, params)\n",
    "states = path\n",
    "actions = controls\n",
    "sgs[0] = wrap_angle(sgs[0], system)\n",
    "sgs[1] = wrap_angle(sgs[1], system)\n",
    "print('states:')\n",
    "print(states)\n",
    "anim = vis.animate(np.array(states), np.array(actions), np.array(costs), obs_list, np.array(sgs), system)\n",
    "#HTML(anim.to_html5_video())\n",
    "#anim.save('acrobot_env%d_path%d.mp4' % (obs_idx, p_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "986\n"
     ]
    }
   ],
   "source": [
    "print(obs_idx)\n",
    "print(p_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 2800, 2)\n",
      "(14000, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize point cloud\n",
    "import matplotlib.pyplot as plt\n",
    "print(obc_list.shape)\n",
    "obc_all = np.concatenate(obc_list_total[obs_idx], axis=0)\n",
    "print(obc_all.shape)\n",
    "plt.scatter(obc_all[:,0], obc_all[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14000, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14000, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    obc_all = np.concatenate(obc_list_total[i], axis=0)\n",
    "    print(obc_all.shape)\n",
    "    plt.scatter(obc_all[:,0], obc_all[:,1])\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:ylmiao_kino_c++]",
   "language": "python",
   "name": "conda-env-ylmiao_kino_c++-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}