acrobot_obs_path_visualization_mpc2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  False\n",
      "distance:  [0.83337859 0.12085471 0.         0.        ]\n",
      "0.8333785911754266\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.12979371 1.40053261 0.         0.        ]\n",
      "1.4005326089545216\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.28699115 0.7183223  0.         0.        ]\n",
      "0.7183223020852352\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.57238505 0.74222797 0.         0.        ]\n",
      "0.7422279692428084\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.26436432 1.2084817  0.         0.        ]\n",
      "1.208481698666348\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "found:  False\n",
      "distance:  [1.00678423 0.48260759 0.         0.        ]\n",
      "1.0067842347365152\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.17608277 0.18430442 0.         0.        ]\n",
      "0.18430442110874523\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.73029707 1.17824286 0.         0.        ]\n",
      "1.1782428625359351\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [1.38410367 1.14164773 0.         0.        ]\n",
      "1.3841036669349531\n",
      "threshold:  0.17453292519943295\n",
      "larger than threshold\n",
      "distance:  [0.04665222 0.06879026 0.         0.        ]\n",
      "0.06879026071459116\n",
      "threshold:  0.17453292519943295\n",
      "found time: 5.548271\n",
      "setting found\n",
      "found:  True\n",
      "obs_idx: \n",
      "1\n",
      "p_idx:\n",
      "972\n",
      "generated.\n",
      "(4, 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('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/deps/sparse_rrt-1')\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 import standard_cpp_systems\n",
    "from sparse_rrt import _sst_module\n",
    "import numpy as np\n",
    "import time\n",
    "from plan_utility.line_line_cc import *\n",
    "import pickle\n",
    "from sparse_rrt import _deep_smp_module, _sst_module\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/YLmiao_from_unicorn/YLmiao/data/kinodynamic/acrobot_obs_backup/obs_%d.pkl' % (i), 'rb')\n",
    "    obs_list_total.append(pickle.load(file))\n",
    "    file = open('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/data/kinodynamic/acrobot_obs_backup/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), (7, 927)]\n",
    "# (5,931), (6, 286)\n",
    "\n",
    "obs_idx = 7\n",
    "\n",
    "p_idx = 927\n",
    "\n",
    "found = False\n",
    "print('found: ', found)\n",
    "#print(os.path.isfile('/media/arclabdl1/HD1/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/acrobot_obs/default_small_model_batch/paths/path_%d_%d.npy' % (obs_idx, p_idx)))\n",
    "for obs_idx in range(10):\n",
    "    for p_idx in range(900,1000):\n",
    "        if (obs_idx, p_idx) in [(1,903), (1,934)]:#(1,903), (1, 934)]:\n",
    "            continue\n",
    "        if not os.path.isfile('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/acrobot_obs/default_small_model_batch/paths/path_%d_%d.npy' % (obs_idx, p_idx)):\n",
    "            continue\n",
    "        cost = np.load('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/acrobot_obs/default_small_model_batch/paths/path_%d_%d.npy' % (obs_idx, p_idx),\n",
    "               allow_pickle=True)[2].sum()\n",
    "        data_cost = open('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/data/kinodynamic/acrobot_obs_backup/%d/cost_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "        data_cost = pickle.load(data_cost).sum()\n",
    "        if cost >= 1.*data_cost:\n",
    "            continue\n",
    "        # check if goal is up\n",
    "        sgs = open('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/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",
    "        print('distance: ', dis)\n",
    "        print(np.linalg.norm(dis, ord=np.inf))\n",
    "        threshold = 10*np.pi/180\n",
    "        print('threshold: ', threshold)\n",
    "        if np.linalg.norm(dis, ord=np.inf) > threshold:\n",
    "            print('larger than threshold')\n",
    "            continue\n",
    "        # check time\n",
    "        time = np.load('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/acrobot_obs/default_small_model_batch/time_10_100.npy',\n",
    "               allow_pickle=True)[obs_idx, p_idx-900]\n",
    "        print('found time: %f' % (time))\n",
    "        print('setting found')\n",
    "        found = True\n",
    "        break\n",
    "    print('found: ', found)\n",
    "    if found:\n",
    "        break\n",
    "\n",
    "\n",
    "obs_idx = 1\n",
    "p_idx = 972\n",
    "        \n",
    "print('obs_idx: ')\n",
    "print(obs_idx)\n",
    "print('p_idx:')\n",
    "print(p_idx)\n",
    "        \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": true
   },
   "outputs": [],
   "source": [
    "\n",
    "params = {\n",
    "        'n_problem': 1,\n",
    "        'n_sample': 1024,\n",
    "        'n_elite': 128,\n",
    "        'n_t': 1,\n",
    "        'max_it': 500,\n",
    "        #'converge_r': 5e-1,\n",
    "        'converge_r': 1e-10,\n",
    "    \n",
    "        'dt': 2e-2,\n",
    "        'mu_u': [0],\n",
    "        'sigma_u': [4],\n",
    "         'mu_t': 4e-1,\n",
    "         'sigma_t': 5e-1,\n",
    "         't_max': 2.,\n",
    "#         'mu_t': 2e-1,\n",
    "#         'sigma_t': 2e-1,\n",
    "#         't_max': .5,\n",
    "\n",
    "\n",
    "        'verbose': True,# False,#\n",
    "        'step_size': 0,\n",
    "\n",
    "        \"goal_radius\": 2.0,\n",
    "\n",
    "        \"sst_delta_near\": 1,\n",
    "        \"sst_delta_drain\": 1e-1,\n",
    "        \"goal_bias\": 0.04,\n",
    "\n",
    "        \"width\": 6,\n",
    "        \"hybrid\": False,#True,# \n",
    "        \"hybrid_p\": 0.01,\n",
    "        \n",
    "        \"min_time_steps\": 5,\n",
    "        \"max_time_steps\": 100,\n",
    "    \n",
    "        \"cost_samples\": 1,\n",
    "        \"mpnet_weight_path\":\"/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/mpnet/exported/output/acrobot_obs/mpnet_10k.pt\",\n",
    "        #\"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v3_multigoal.pt\",\n",
    "        #\"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v2_deep.pt\",\n",
    "#         \"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_10k.pt\",\n",
    "#         \"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_10k_branch.pt\",\n",
    "\n",
    "\n",
    "        # \"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_10k_nonorm.pt\",\n",
    "        # \"mpnet_weight_path\":\"mpnet/exported/output/cartpole_obs/mpnet_subsample0.5_10k.pt\",\n",
    "\n",
    "        \"cost_predictor_weight_path\": \"/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/mpnet/exported/output/cartpole_obs/cost_10k.pt\",\n",
    "        \"cost_to_go_predictor_weight_path\": \"/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/mpnet/exported/output/cartpole_obs/cost_to_go_10k.pt\",\n",
    "\n",
    "        \"refine\": False,\n",
    "        \"using_one_step_cost\": False,\n",
    "        \"refine_lr\": 0,\n",
    "        \"refine_threshold\": 0,\n",
    "        \"device_id\": \"cuda:3\",\n",
    "\n",
    "        \"cost_reselection\": False,\n",
    "        \"number_of_iterations\": 500,\n",
    "        \"weights_array\": [1, 1, 1, 1],\n",
    "\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# (7, 927)\\npath = np.array(\\n[[ 0.,          0.,          0.,          0.        ],\\n [-0.30235064,  0.66357245, -1.12404058,  2.63216073],\\n [-0.42513114,  0.98700913, -0.29263858,  1.22161321],\\n [ 0.81097696, -2.21064598, -1.87366238, -0.44346807],\\n [ 0.43625224, -2.17189627, -2.81821699,  1.07151433],\\n [ 1.30897045,  2.50860498,  0.25455659, -1.13373327],\\n [ 1.30296453,  2.41413713, -0.5262052, -0.66861023],\\n [-1.58742542, -1.43178197, -2.36883756,  4.15048263],\\n [-1.88335465, -0.77853767, -0.38750337,  2.06091217],\\n [ 1.86185939, -1.89328253,  1.66468725,  3.40569328],\\n [ 1.88586336, -0.82441263, -1.47474734,  5.44456514],\\n [ 3.02422565,  2.29042714, -1.37086011,  0.0215158 ],\\n [ 2.59949331,  2.31621808, -1.51831319,  0.3535678 ],\\n [ 2.42948197,  2.37665629, -2.00484496,  0.99423899],\\n [-1.57991341, -1.36811145, -2.2772159,   2.70299448],\\n [ 2.80394195,  0.71981286,  1.12148533, -1.98584426],\\n [ 3.00295891,  0.34951812,  0.73202747, -1.4486524 ],\\n [ 3.13364725,  0.10208493, -0.41396637,  0.97824909]])\\n\\n# (1, 916)\\npath = np.array(\\n [[ 0.,          0.,          0.,          0.        ],\\n [-0.14637182,  0.36741907,  0.16802393, -0.09198922],\\n [ 0.33910537, -0.53233128,  1.01860863, -2.2462016 ],\\n [ 0.45433237, -0.84326897,  0.00979799, -0.67701909],\\n [ 0.15750956, -0.76430055, -1.76589893,  2.27511201],\\n [ 0.05599173,  2.06468595,  2.89323376, -1.13509309],\\n [ 0.70446343,  1.75184533,  1.34005915, -0.63608721],\\n [-0.6347062,  -3.02094395, -3.14349578,  2.88615537],\\n [-1.29986757, -2.26094529, -0.70598324,  1.6203048 ],\\n [ 1.52734201,  1.99942129,  2.90282734, -3.52070003],\\n [ 1.89659985,  1.48138215,  1.52561572, -2.66617007],\\n [ 2.02352651,  1.23006466,  0.90312322, -2.28778868],\\n [ 2.09466351,  0.73102972, -0.39847056, -1.59188   ],\\n [ 2.07385742,  0.67449809, -0.88810876, -0.87647985]])\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load path\n",
    "path = np.load('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/mpc-mpnet-cuda-yinglong/results/cpp_full/acrobot_obs/default_small_model_batch/paths/path_%d_%d.npy' % (obs_idx, p_idx),\n",
    "               allow_pickle=True)[0]\n",
    "\n",
    "sgs = open('/media/arclabdl1/HD1/YLmiao/YLmiao_from_unicorn/YLmiao/data/kinodynamic/acrobot_obs_backup/%d/start_goal_%d.pkl' % (obs_idx, p_idx), 'rb')\n",
    "sgs = pickle.load(sgs)\n",
    "\n",
    "\"\"\"\n",
    "# (7, 927)\n",
    "path = np.array(\n",
    "[[ 0.,          0.,          0.,          0.        ],\n",
    " [-0.30235064,  0.66357245, -1.12404058,  2.63216073],\n",
    " [-0.42513114,  0.98700913, -0.29263858,  1.22161321],\n",
    " [ 0.81097696, -2.21064598, -1.87366238, -0.44346807],\n",
    " [ 0.43625224, -2.17189627, -2.81821699,  1.07151433],\n",
    " [ 1.30897045,  2.50860498,  0.25455659, -1.13373327],\n",
    " [ 1.30296453,  2.41413713, -0.5262052, -0.66861023],\n",
    " [-1.58742542, -1.43178197, -2.36883756,  4.15048263],\n",
    " [-1.88335465, -0.77853767, -0.38750337,  2.06091217],\n",
    " [ 1.86185939, -1.89328253,  1.66468725,  3.40569328],\n",
    " [ 1.88586336, -0.82441263, -1.47474734,  5.44456514],\n",
    " [ 3.02422565,  2.29042714, -1.37086011,  0.0215158 ],\n",
    " [ 2.59949331,  2.31621808, -1.51831319,  0.3535678 ],\n",
    " [ 2.42948197,  2.37665629, -2.00484496,  0.99423899],\n",
    " [-1.57991341, -1.36811145, -2.2772159,   2.70299448],\n",
    " [ 2.80394195,  0.71981286,  1.12148533, -1.98584426],\n",
    " [ 3.00295891,  0.34951812,  0.73202747, -1.4486524 ],\n",
    " [ 3.13364725,  0.10208493, -0.41396637,  0.97824909]])\n",
    "\n",
    "# (1, 916)\n",
    "path = np.array(\n",
    " [[ 0.,          0.,          0.,          0.        ],\n",
    " [-0.14637182,  0.36741907,  0.16802393, -0.09198922],\n",
    " [ 0.33910537, -0.53233128,  1.01860863, -2.2462016 ],\n",
    " [ 0.45433237, -0.84326897,  0.00979799, -0.67701909],\n",
    " [ 0.15750956, -0.76430055, -1.76589893,  2.27511201],\n",
    " [ 0.05599173,  2.06468595,  2.89323376, -1.13509309],\n",
    " [ 0.70446343,  1.75184533,  1.34005915, -0.63608721],\n",
    " [-0.6347062,  -3.02094395, -3.14349578,  2.88615537],\n",
    " [-1.29986757, -2.26094529, -0.70598324,  1.6203048 ],\n",
    " [ 1.52734201,  1.99942129,  2.90282734, -3.52070003],\n",
    " [ 1.89659985,  1.48138215,  1.52561572, -2.66617007],\n",
    " [ 2.02352651,  1.23006466,  0.90312322, -2.28778868],\n",
    " [ 2.09466351,  0.73102972, -0.39847056, -1.59188   ],\n",
    " [ 2.07385742,  0.67449809, -0.88810876, -0.87647985]])\n",
    "\"\"\"\n",
    "# (8, 914)\n",
    "# path = np.array(\n",
    "# [[ 0.,          0.,          0.,          0.        ],\n",
    "#  [ 0.1708568,  -0.39391056,  0.19467657, -0.62469995],\n",
    "#  [ 0.28792115, -0.91607445,  0.44898465, -1.63658985],\n",
    "#  [-0.95989468,  1.96289103,  0.9317956,   1.43428469],\n",
    "#  [-0.30129242,  2.94121546, -1.83117369,  0.90737314],\n",
    "#  [-0.70691949, -3.12132232, -1.05535802,  0.6535526 ],\n",
    "#  [-0.82442304, -3.03540106,  0.01787491,  0.13220202],\n",
    "#  [ 0.52775011,  2.27083079,  0.37915111, -0.44081038],\n",
    "#  [-1.07891294, -2.37855552, -0.30601515,  1.50195129],\n",
    "#  [ 1.64625095,  2.19734582,  1.28417728, -1.54819197],\n",
    "#  [ 1.75929464,  2.02474293,  0.19036604, -0.80510894],\n",
    "#  [-1.9036383,  -1.9494513,  -1.02537086,  0.82188096],\n",
    "#  [ 2.3872588,   1.49882542,  1.73340551, -2.18197651],])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "planner = _deep_smp_module.DSSTMPCWrapper(\n",
    "    system_type='acrobot_obs',\n",
    "    solver_type=\"cem\",\n",
    "    start_state=np.array(path[0]),\n",
    "#             goal_state=np.array(ref_path[-1]),\n",
    "    goal_state=np.array(sgs[-1]),\n",
    "    goal_radius=params['goal_radius'],\n",
    "    random_seed=0,\n",
    "    sst_delta_near=params['sst_delta_near'],\n",
    "    sst_delta_drain=params['sst_delta_drain'],\n",
    "    obs_list=obs_list,\n",
    "    width=params['width'],\n",
    "    verbose=params['verbose'],\n",
    "    mpnet_weight_path=params['mpnet_weight_path'], \n",
    "    cost_predictor_weight_path=params['cost_predictor_weight_path'],\n",
    "    cost_to_go_predictor_weight_path=params['cost_to_go_predictor_weight_path'],\n",
    "    num_sample=params['cost_samples'],\n",
    "    np=params['n_problem'], ns=params['n_sample'], nt=params['n_t'], ne=params['n_elite'], max_it=params['max_it'],\n",
    "    converge_r=params['converge_r'], mu_u=params['mu_u'], std_u=params['sigma_u'], mu_t=params['mu_t'], \n",
    "    std_t=params['sigma_t'], t_max=params['t_max'], step_size=params['step_size'], integration_step=params['dt'], \n",
    "    device_id=params['device_id'], refine_lr=params['refine_lr'],\n",
    "    weights_array=params['weights_array'],\n",
    "    obs_voxel_array=obc_list.reshape(-1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from plan_utility.line_line_cc import line_line_cc\n",
    "\n",
    "def IsInCollision(x, obc, obc_width=6.):\n",
    "    STATE_THETA_1, STATE_THETA_2, STATE_V_1, STATE_V_2 = 0, 1, 2, 3\n",
    "    MIN_V_1, MAX_V_1 = -6., 6.\n",
    "    MIN_V_2, MAX_V_2 = -6., 6.\n",
    "    MIN_TORQUE, MAX_TORQUE = -4., 4.\n",
    "\n",
    "    MIN_ANGLE, MAX_ANGLE = -np.pi, np.pi\n",
    "\n",
    "    LENGTH = 20.\n",
    "    m = 1.0\n",
    "    lc = 0.5\n",
    "    lc2 = 0.25\n",
    "    l2 = 1.\n",
    "    I1 = 0.2\n",
    "    I2 = 1.0\n",
    "    l = 1.0\n",
    "    g = 9.81\n",
    "    pole_x0 = 0.\n",
    "    pole_y0 = 0.\n",
    "    pole_x1 = LENGTH * np.cos(x[STATE_THETA_1] - np.pi / 2)\n",
    "    pole_y1 = LENGTH * np.sin(x[STATE_THETA_1] - np.pi / 2)\n",
    "    pole_x2 = pole_x1 + LENGTH * np.cos(x[STATE_THETA_1] + x[STATE_THETA_2] - np.pi / 2)\n",
    "    pole_y2 = pole_y1 + LENGTH * np.sin(x[STATE_THETA_1] + x[STATE_THETA_2] - np.pi / 2)\n",
    "    for i in range(len(obc)):\n",
    "        for j in range(0, 8, 2):\n",
    "            x1 = obc[i][j]\n",
    "            y1 = obc[i][j+1]\n",
    "            x2 = obc[i][(j+2) % 8]\n",
    "            y2 = obc[i][(j+3) % 8]\n",
    "            if line_line_cc(pole_x0, pole_y0, pole_x1, pole_y1, x1, y1, x2, y2):\n",
    "                return True\n",
    "            if line_line_cc(pole_x1, pole_y1, pole_x2, pole_y2, x1, y1, x2, y2):\n",
    "                return True\n",
    "    return False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "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",
    "class AcrobotVisualizer(Visualizer):\n",
    "    def __init__(self, system, params, color_dict={'obs_color': 'black'}):\n",
    "        super(AcrobotVisualizer, self).__init__(system, params)\n",
    "        self.dt = 2\n",
    "        self.fig = plt.gcf()\n",
    "        self.fig.set_figheight(5)\n",
    "        self.fig.set_figwidth(10)\n",
    "        self.ax1 = plt.subplot(121)\n",
    "        self.ax2 = plt.subplot(122)\n",
    "        self.color_dict = color_dict\n",
    "\n",
    "    def _state_to_xy(self, state):\n",
    "        angle0 = state[0]\n",
    "        angle1 = state[1]\n",
    "        x0 = 0.\n",
    "        y0 = 0.\n",
    "        x1 = LENGTH * np.cos(angle0 - np.pi/2)\n",
    "        y1 = LENGTH * np.sin(angle0 - np.pi/2)\n",
    "        x2 = x1 + LENGTH * np.cos(angle0 + angle1 - np.pi/2)\n",
    "        y2 = y1 + LENGTH * np.sin(angle0 + angle1 - np.pi/2)\n",
    "        return x0, y0, x1, y1, x2, y2\n",
    "\n",
    "    def _init(self):\n",
    "        ##### handle the animation\n",
    "        # clear the current ax\n",
    "        print(\"in init\")\n",
    "        ax = self.ax1\n",
    "        ax.clear()\n",
    "        # add patches\n",
    "        state = self.states[0]\n",
    "        print('state:')\n",
    "        print(state)\n",
    "        x0, y0, x1, y1, x2, y2 = self._state_to_xy(state)\n",
    "        print('x1: %f, y1: %f, x2: %f, y2: %f' % (x1, y1, x2, y2))\n",
    "        self.l1 = ax.plot([x0,x1,x2], [y0,y1,y2])[0]\n",
    "        self.recs = []\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=self.color_dict['obs_color'],facecolor=self.color_dict['obs_color'])\n",
    "            self.recs.append(obs)\n",
    "            ax.add_patch(obs)\n",
    "        self.recs.append(self.l1)\n",
    "\n",
    "        #### handle search space\n",
    "        ax = self.ax2\n",
    "        ax.clear()\n",
    "        ax.set_xlim(-np.pi, np.pi)\n",
    "        ax.set_ylim(-np.pi, np.pi)\n",
    "\n",
    "\n",
    "        dtheta = 0.1\n",
    "        feasible_points = []\n",
    "        infeasible_points = []\n",
    "        imin = 0\n",
    "        imax = int(2*np.pi/dtheta)\n",
    "\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=self.color_dict['obs_color'])\n",
    "\n",
    "        #self.recs.append(scat_feas)\n",
    "        self.recs.append(scat_infeas)\n",
    "\n",
    "        #scat_state = ax.scatter(state[0], state[1], c='green')\n",
    "        #self.recs.append(scat_state)\n",
    "        print(\"after init\")\n",
    "\n",
    "        return self.recs\n",
    "    def _animate(self, i):\n",
    "        print('animating, frame %d/%d' % (i, self.total))\n",
    "\n",
    "        ax = self.ax1\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-40, 40)\n",
    "        state = self.states[i]\n",
    "        x0, y0, x1, y1, x2, y2 = self._state_to_xy(state)\n",
    "        self.l1.set_xdata([x0,x1,x2])\n",
    "        self.l1.set_ydata([y0,y1,y2])\n",
    "\n",
    "        # handle search space\n",
    "        ax = self.ax2\n",
    "        ax.set_xlim(-np.pi, np.pi)\n",
    "        ax.set_ylim(-np.pi, np.pi)\n",
    "        self.recs[-1].set_offsets([state[0], state[1]])\n",
    "        # print location of point\n",
    "        return self.recs\n",
    "\n",
    "\n",
    "\n",
    "    def animate(self, states, actions, costs, obstacles, sg):\n",
    "        '''\n",
    "        given a list of states, actions and obstacles, animate the robot\n",
    "        '''\n",
    "\n",
    "        new_obs_i = []\n",
    "        obs_width = 6.0\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",
    "            s = states[i]\n",
    "            print('state: %d, remaining: %d' % (i, len(states)-i))\n",
    "            #action = actions[i]\n",
    "            \n",
    "            # connect from this state to next\n",
    "            solution_u, solution_t = planner.steer_solution(states[i], states[i+1])\n",
    "            print('solution_u:')\n",
    "            print(solution_u)\n",
    "            print('solution_t:')\n",
    "            print(solution_t)\n",
    "#             # go one step until we reach state[i+1]\n",
    "#             while True:\n",
    "#                 solution_u, solution_t = planner.steer_solution(s, states[i+1])\n",
    "#                 print('solution_u:')\n",
    "#                 print(solution_u)\n",
    "#                 print('solution_t:')\n",
    "#                 print(solution_t)\n",
    "#                 print('distance: %f' % (np.linalg.norm(wrap_angle(s - states[i+1], self.params['wrap_angle_system']))))\n",
    "#                 num_step_max = 20\n",
    "#                 num_step_total = 0\n",
    "#                 for j in range(len(solution_u)):\n",
    "#                     action = solution_u[j]\n",
    "#                     num_steps = int(np.round(solution_t[j]/self.params['integration_step']))\n",
    "                    \n",
    "#                     for k in range(num_steps):\n",
    "#                         traj.append(np.array(s))\n",
    "#                         s = self.system(s, action, self.params['integration_step'])\n",
    "#                         num_step_total += 1\n",
    "#                         if num_step_total == num_step_max:\n",
    "#                             break\n",
    "#                         if np.linalg.norm(wrap_angle(s - states[i+1],self.params['wrap_angle_system'])) <= 1e-7:\n",
    "#                             break\n",
    "#                     if num_step_total == num_step_max:\n",
    "#                         break\n",
    "#                     if np.linalg.norm(wrap_angle(s - states[i+1],self.params['wrap_angle_system'])) <= 1e-7:\n",
    "#                         break\n",
    "#                 if np.linalg.norm(wrap_angle(s - states[i+1],self.params['wrap_angle_system'])) <= 1e-7:\n",
    "#                     break\n",
    "                \n",
    "                \n",
    "\n",
    "                \n",
    "            for j in range(len(solution_u)):\n",
    "                action = solution_u[j]\n",
    "                num_steps = int(np.round(solution_t[j]/self.params['integration_step']))\n",
    "\n",
    "                for k 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",
    "            print('after steering state: ', s)\n",
    "            print('next state: ', states[i+1])\n",
    "        self.states = traj\n",
    "        self.obs = obstacles\n",
    "\n",
    "        return np.array(traj)\n",
    "\n",
    "    # plot the trajectory\n",
    "    def plot(self, traj, obstacles, sg, color_dict, fig):\n",
    "        self.fig = fig\n",
    "        self.fig.set_figheight(5)\n",
    "        self.fig.set_figwidth(10)\n",
    "        self.ax1 = fig.add_subplot(121)\n",
    "        self.ax2 = fig.add_subplot(122)\n",
    "\n",
    "        traj = np.array(traj)\n",
    "        self.obs = obstacles\n",
    "\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",
    "        for i in range(len(traj)):\n",
    "            if i % 10 == 0:\n",
    "                x0, y0, x1, y1, x2, y2 = self._state_to_xy(traj[i])\n",
    "                ax = self.ax1\n",
    "                ax.set_xlim(-40, 40)\n",
    "                ax.set_ylim(-40, 40)\n",
    "                #if i == 0:\n",
    "                #    ax.plot([x0,x1,x2],[y0,y1,y2],alpha=1, c='green')\n",
    "                #else:\n",
    "                #    ax.plot([x0,x1,x2],[y0,y1,y2],alpha=float(i)/len(traj), c='blue')\n",
    "                to_plot_list_x.append([x0,x1,x2])\n",
    "                to_plot_list_y.append([y0,y1,y2])\n",
    "            ax = self.ax2\n",
    "            scat_state = ax.scatter(traj[i][0], traj[i][1], c=color_dict['intermediate_color'], s=25.0)\n",
    "        #colors = ['green', 'red']\n",
    "        colors = [color_dict['start_color'], color_dict['goal_color']]\n",
    "        ax = self.ax1\n",
    "        cm = LinearSegmentedColormap.from_list(\"Custom\", colors, N=len(to_plot_list_x))\n",
    "        for i in range(len(to_plot_list_x)):\n",
    "            #ax.plot(to_plot_list_x[i], to_plot_list_y[i], alpha=float(i)/len(to_plot_list_x), c=cm(float(i)/len(to_plot_list_x)))\n",
    "            if i == 0:\n",
    "                ax.plot(to_plot_list_x[i], to_plot_list_y[i], alpha=1, c=color_dict['start_color'])\n",
    "            else:\n",
    "                ax.plot(to_plot_list_x[i], to_plot_list_y[i], alpha=float(i)/len(to_plot_list_x), c=color_dict['intermediate_color'])\n",
    "\n",
    "            \n",
    "        # start and goal\n",
    "        x0, y0, x1, y1, x2, y2 = self._state_to_xy(sg[1])\n",
    "        ax = self.ax1\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-40, 40)\n",
    "        ax.plot([x0,x1,x2],[y0,y1,y2],alpha=1, c=color_dict['goal_color'])\n",
    "        ax.axis('off')\n",
    "\n",
    "        ax = self.ax2\n",
    "        scat_state = ax.scatter(sg[0][0], sg[0][1], c=color_dict['start_color'], s=50.0)\n",
    "        scat_state = ax.scatter(sg[1][0], sg[1][1], c=color_dict['goal_color'], s=50.0, marker='*')\n",
    "        ax.axis('off')\n",
    "        return self.fig\n",
    "        #plt.savefig(\"acrobot_mpnettree_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "states:\n",
      "[[ 0.          0.          0.          0.        ]\n",
      " [ 0.26494066 -0.44239045  1.31444481 -2.71870503]\n",
      " [ 0.46437818 -0.88517501  0.75149249 -1.97289054]\n",
      " [-0.95270271  1.53762599 -0.64811259  2.47243378]\n",
      " [-0.72560115  2.04871561  1.87278817  0.10513623]\n",
      " [-0.08540697  1.89757874  2.42397801 -1.05843124]\n",
      " [-0.3229462   2.69115857 -2.62707216  2.32058159]\n",
      " [-1.11453096 -2.82958832 -0.35992638  0.88925587]\n",
      " [-0.99930524 -2.74153352  1.63207357 -0.61712159]\n",
      " [ 1.1124813   2.22549635  0.19004929 -0.35917918]\n",
      " [-0.75517213 -2.52689946 -3.25381218  2.99359231]\n",
      " [-1.22846856 -2.04802722 -1.27853844  1.61316584]\n",
      " [-1.32004686 -1.91240278 -0.37387504  0.95594433]\n",
      " [-1.32655847 -1.85671481  0.40321651  0.26581402]\n",
      " [ 2.10253174  1.78591848  1.10614117 -1.58223094]\n",
      " [ 2.20747508  1.60423167  0.25316857 -0.82706646]\n",
      " [ 2.21253845  1.58769034  0.08380811 -0.60147075]\n",
      " [ 2.16532079  1.54414206 -0.78397215  0.13973998]\n",
      " [-1.70363463 -1.41006879 -2.93356788  4.01046703]\n",
      " [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      " [-1.87635461 -1.0539289   2.08106892 -2.24554798]\n",
      " [ 1.88671424  2.02592942  3.13229375 -3.05467472]\n",
      " [ 2.41904694  1.43188883  1.52872173 -1.84446655]\n",
      " [ 2.50439048  1.3280372   1.22840678 -1.52723572]\n",
      " [ 2.73746926  0.9989843   0.61361667 -1.06100284]\n",
      " [ 2.88241113  0.55015397  0.1046093  -0.87832573]]\n",
      "state: 0, remaining: 26\n",
      "solution_u:\n",
      "[[-3.85134331]]\n",
      "solution_t:\n",
      "[0.34]\n",
      "after steering state:  [ 0.20465282 -0.44241343  1.07092288 -2.39526334]\n",
      "next state:  [ 0.26494066 -0.44239045  1.31444481 -2.71870503]\n",
      "state: 1, remaining: 25\n",
      "solution_u:\n",
      "[[-4.]]\n",
      "solution_t:\n",
      "[0.16]\n",
      "after steering state:  [ 0.46821927 -0.88730944  1.12498051 -2.71582429]\n",
      "next state:  [ 0.46437818 -0.88517501  0.75149249 -1.97289054]\n",
      "state: 2, remaining: 24\n",
      "solution_u:\n",
      "[[2.32066767]]\n",
      "solution_t:\n",
      "[1.02]\n",
      "after steering state:  [-0.95236414  1.53781451 -0.06480706  1.62136812]\n",
      "next state:  [-0.95270271  1.53762599 -0.64811259  2.47243378]\n",
      "state: 3, remaining: 23\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.38]\n",
      "after steering state:  [-0.72560115  2.04871561  1.87278817  0.10513623]\n",
      "next state:  [-0.72560115  2.04871561  1.87278817  0.10513623]\n",
      "state: 4, remaining: 22\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.28]\n",
      "after steering state:  [-0.08543565  1.89765429  2.42378808 -1.05785405]\n",
      "next state:  [-0.08540697  1.89757874  2.42397801 -1.05843124]\n",
      "state: 5, remaining: 21\n",
      "solution_u:\n",
      "[[3.84543352]]\n",
      "solution_t:\n",
      "[0.96]\n",
      "after steering state:  [-0.33599006  2.69064612 -2.75067407  2.4948515 ]\n",
      "next state:  [-0.3229462   2.69115857 -2.62707216  2.32058159]\n",
      "state: 6, remaining: 20\n",
      "solution_u:\n",
      "[[3.67179409]]\n",
      "solution_t:\n",
      "[0.46]\n",
      "after steering state:  [-1.11373783 -2.83048067 -0.16199167  0.68237507]\n",
      "next state:  [-1.11453096 -2.82958832 -0.35992638  0.88925587]\n",
      "state: 7, remaining: 19\n",
      "solution_u:\n",
      "[[3.65894985]]\n",
      "solution_t:\n",
      "[0.24]\n",
      "after steering state:  [-1.00235106 -2.73468616  1.41527725 -0.13361704]\n",
      "next state:  [-0.99930524 -2.74153352  1.63207357 -0.61712159]\n",
      "state: 8, remaining: 18\n",
      "solution_u:\n",
      "[[-0.06504812]]\n",
      "solution_t:\n",
      "[0.92]\n",
      "after steering state:  [ 1.11170201  2.22533753 -0.53645587  0.46029342]\n",
      "next state:  [ 1.1124813   2.22549635  0.19004929 -0.35917918]\n",
      "state: 9, remaining: 17\n",
      "solution_u:\n",
      "[[2.22482189]]\n",
      "solution_t:\n",
      "[0.7]\n",
      "after steering state:  [-0.74278822 -2.5268977  -3.35213855  3.28030199]\n",
      "next state:  [-0.75517213 -2.52689946 -3.25381218  2.99359231]\n",
      "state: 10, remaining: 16\n",
      "solution_u:\n",
      "[[-1.46340105]]\n",
      "solution_t:\n",
      "[0.2]\n",
      "after steering state:  [-1.22471751 -2.05653273 -1.18506225  1.40154248]\n",
      "next state:  [-1.22846856 -2.04802722 -1.27853844  1.61316584]\n",
      "state: 11, remaining: 15\n",
      "solution_u:\n",
      "[[1.62063539]]\n",
      "solution_t:\n",
      "[0.1]\n",
      "after steering state:  [-1.32016898 -1.91213392 -0.37696642  0.9627403 ]\n",
      "next state:  [-1.32004686 -1.91240278 -0.37387504  0.95594433]\n",
      "state: 12, remaining: 14\n",
      "solution_u:\n",
      "[[0.59240324]]\n",
      "solution_t:\n",
      "[0.08]\n",
      "after steering state:  [-1.32708266 -1.85557344  0.38623802  0.30253262]\n",
      "next state:  [-1.32655847 -1.85671481  0.40321651  0.26581402]\n",
      "state: 13, remaining: 13\n",
      "solution_u:\n",
      "[[-3.94223062]]\n",
      "solution_t:\n",
      "[1.06]\n",
      "after steering state:  [ 2.10975693  1.7848655   1.15160145 -1.65414047]\n",
      "next state:  [ 2.10253174  1.78591848  1.10614117 -1.58223094]\n",
      "state: 14, remaining: 12\n",
      "solution_u:\n",
      "[[-4.]]\n",
      "solution_t:\n",
      "[0.14]\n",
      "after steering state:  [ 2.20747508  1.60423167  0.29800652 -0.924074  ]\n",
      "next state:  [ 2.20747508  1.60423167  0.25316857 -0.82706646]\n",
      "state: 15, remaining: 11\n",
      "solution_u:\n",
      "[[-1.47991009]]\n",
      "solution_t:\n",
      "[0.02]\n",
      "after steering state:  [ 2.21253845  1.58769034  0.11160409 -0.66157035]\n",
      "next state:  [ 2.21253845  1.58769034  0.08380811 -0.60147075]\n",
      "state: 16, remaining: 10\n",
      "solution_u:\n",
      "[[-3.76255969]]\n",
      "solution_t:\n",
      "[0.16]\n",
      "after steering state:  [ 2.16485106  1.5451402  -0.79083685  0.15402375]\n",
      "next state:  [ 2.16532079  1.54414206 -0.78397215  0.13973998]\n",
      "state: 17, remaining: 9\n",
      "solution_u:\n",
      "[[1.76445057]]\n",
      "solution_t:\n",
      "[0.94]\n",
      "after steering state:  [-1.72692922 -1.4119313  -3.21578873  4.52034355]\n",
      "next state:  [-1.70363463 -1.41006879 -2.93356788  4.01046703]\n",
      "state: 18, remaining: 8\n",
      "solution_u:\n",
      "[[-4.]]\n",
      "solution_t:\n",
      "[0.2]\n",
      "after steering state:  [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      "next state:  [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      "state: 19, remaining: 7\n",
      "solution_u:\n",
      "[[-0.80960881]]\n",
      "solution_t:\n",
      "[0.24]\n",
      "after steering state:  [-1.87370729 -1.05922868  2.05743156 -2.19213896]\n",
      "next state:  [-1.87635461 -1.0539289   2.08106892 -2.24554798]\n",
      "state: 20, remaining: 6\n",
      "solution_u:\n",
      "[[-3.39019124]]\n",
      "solution_t:\n",
      "[0.8]\n",
      "after steering state:  [ 1.91733315  2.02739314  3.43278351 -3.55226736]\n",
      "next state:  [ 1.88671424  2.02592942  3.13229375 -3.05467472]\n",
      "state: 21, remaining: 5\n",
      "solution_u:\n",
      "[[-3.63796697]]\n",
      "solution_t:\n",
      "[0.22]\n",
      "after steering state:  [ 2.41898374  1.43248644  1.72674224 -2.27162718]\n",
      "next state:  [ 2.41904694  1.43188883  1.52872173 -1.84446655]\n",
      "state: 22, remaining: 4\n",
      "solution_u:\n",
      "[[-1.79705229]]\n",
      "solution_t:\n",
      "[0.06]\n",
      "after steering state:  [ 2.50402894  1.32881556  1.20058323 -1.4671703 ]\n",
      "next state:  [ 2.50439048  1.3280372   1.22840678 -1.52723572]\n",
      "state: 23, remaining: 3\n",
      "solution_u:\n",
      "[[-3.93137754]]\n",
      "solution_t:\n",
      "[0.26]\n",
      "after steering state:  [ 2.73957807  0.99447246  0.63264928 -1.10115114]\n",
      "next state:  [ 2.73746926  0.9989843   0.61361667 -1.06100284]\n",
      "state: 24, remaining: 2\n",
      "solution_u:\n",
      "[[-2.86015239]]\n",
      "solution_t:\n",
      "[0.68]\n",
      "after steering state:  [ 2.87096465  0.54832071 -0.05067094 -0.59448594]\n",
      "next state:  [ 2.88241113  0.55015397  0.1046093  -0.87832573]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAEzCAYAAAAGisbbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAQ7ElEQVR4nO3df6jl913n8de7GaPYrVXMCJJJbMSpdawL7V5CRdAu1mUSIfmjKgkUrYQO/ogsKAuRLlXiXyoqCNl1B7ZUBRujfywDTgmoKYFiaqa0xiYlMsZqJspmrLX/lDYN+94/7lFvrzO959573vec3DweMHB+fLnn88mZvHnO937vudXdAQBgxmvWvQAAgONMbAEADBJbAACDxBYAwCCxBQAwSGwBAAzaM7aq6v1V9WJVffI6z1dV/WZVXa6qp6rqratfJsDBmGHAui1zZusDSc5+hefvSHJ68edckv95+GUBrMwHYoYBa7RnbHX340n+6SsccneS3+ltTyT5+qr65lUtEOAwzDBg3VZxzdbNSZ7fcf/K4jGAVwIzDBh14ihfrKrOZfs0fV772tf+pze96U1H+fLAmn3sYx/7x+4+ue51HIT5Ba9uh5lfq4itF5LcsuP+qcVj/053n09yPkm2trb60qVLK3h54JWiqv523Wu4hqVmmPkFr26HmV+r+DbihSQ/uviJnrcl+Vx3/8MKvi7AUTDDgFF7ntmqqg8meXuSm6rqSpJfSPJVSdLdv5XkYpI7k1xO8vkkPz61WID9MsOAddsztrr73j2e7yQ/vbIVAayQGQasm0+QBwAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABi0VW1V1tqqerarLVfXANZ6/taoeq6qPV9VTVXXn6pcKsH/mF7Bue8ZWVd2Q5KEkdyQ5k+Teqjqz67D/nuSR7n5LknuS/I9VLxRgv8wvYBMsc2br9iSXu/u57n4pycNJ7t51TCf5usXt1yf5+9UtEeDAzC9g7ZaJrZuTPL/j/pXFYzv9YpJ3VdWVJBeT/My1vlBVnauqS1V16erVqwdYLsC+mF/A2q3qAvl7k3ygu08luTPJ71bVv/va3X2+u7e6e+vkyZMremmAQzG/gFHLxNYLSW7Zcf/U4rGd7kvySJJ0958l+ZokN61igQCHYH4Ba7dMbD2Z5HRV3VZVN2b7AtILu475uyTfnyRV9R3ZHlbOswPrZn4Ba7dnbHX3y0nuT/Jokk9l+6d2nq6qB6vqrsVhP5fkPVX1F0k+mOTd3d1TiwZYhvkFbIITyxzU3RezfeHozsfet+P2M0m+Z7VLAzg88wtYN58gDwAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBoqdiqqrNV9WxVXa6qB65zzI9U1TNV9XRV/d5qlwlwMOYXsG4n9jqgqm5I8lCSH0hyJcmTVXWhu5/ZcczpJD+f5Hu6+7NV9U1TCwZYlvkFbIJlzmzdnuRydz/X3S8leTjJ3buOeU+Sh7r7s0nS3S+udpkAB2J+AWu3TGzdnOT5HfevLB7b6Y1J3lhVH6mqJ6rq7KoWCHAI5hewdnt+G3EfX+d0krcnOZXk8ar6ru7+550HVdW5JOeS5NZbb13RSwMcivkFjFrmzNYLSW7Zcf/U4rGdriS50N1f6u6/SfJX2R5eX6a7z3f3VndvnTx58qBrBliW+QWs3TKx9WSS01V1W1XdmOSeJBd2HfN/sv2vwlTVTdk+Lf/cCtcJcBDmF7B2e8ZWd7+c5P4kjyb5VJJHuvvpqnqwqu5aHPZoks9U1TNJHkvy37r7M1OLBliG+QVsgurutbzw1tZWX7p0aS2vDaxHVX2su7fWvY7DMr/g1ecw88snyAMADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMWiq2qupsVT1bVZer6oGvcNw7q6qramt1SwQ4OPMLWLc9Y6uqbkjyUJI7kpxJcm9VnbnGca9L8l+TfHTViwQ4CPML2ATLnNm6Pcnl7n6uu19K8nCSu69x3C8l+eUkX1jh+gAOw/wC1m6Z2Lo5yfM77l9ZPPavquqtSW7p7j9a4doADsv8Atbu0BfIV9Vrkvx6kp9b4thzVXWpqi5dvXr1sC8NcCjmF3AUlomtF5LcsuP+qcVj/+J1Sd6c5MNV9ekkb0ty4VoXmXb3+e7e6u6tkydPHnzVAMsxv4C1Wya2nkxyuqpuq6obk9yT5MK/PNndn+vum7r7Dd39hiRPJLmruy+NrBhgeeYXsHZ7xlZ3v5zk/iSPJvlUkke6++mqerCq7ppeIMBBmV/AJjixzEHdfTHJxV2Pve86x7798MsCWA3zC1g3nyAPADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMWiq2qupsVT1bVZer6oFrPP+zVfVMVT1VVX9SVd+y+qUC7J/5BazbnrFVVTckeSjJHUnOJLm3qs7sOuzjSba6+z8m+cMkv7LqhQLsl/kFbIJlzmzdnuRydz/X3S8leTjJ3TsP6O7Huvvzi7tPJDm12mUCHIj5BazdMrF1c5Lnd9y/snjseu5L8qFrPVFV56rqUlVdunr16vKrBDgY8wtYu5VeIF9V70qyleRXr/V8d5/v7q3u3jp58uQqXxrgUMwvYMqJJY55IcktO+6fWjz2ZarqHUnem+T7uvuLq1kewKGYX8DaLXNm68kkp6vqtqq6Mck9SS7sPKCq3pLkfyW5q7tfXP0yAQ7E/ALWbs/Y6u6Xk9yf5NEkn0rySHc/XVUPVtVdi8N+Ncl/SPIHVfWJqrpwnS8HcGTML2ATLPNtxHT3xSQXdz32vh2337HidQGshPkFrJtPkAcAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYtFRsVdXZqnq2qi5X1QPXeP6rq+r3F89/tKresOqFAhyE+QWs256xVVU3JHkoyR1JziS5t6rO7DrsviSf7e5vS/IbSX551QsF2C/zC9gEy5zZuj3J5e5+rrtfSvJwkrt3HXN3kt9e3P7DJN9fVbW6ZQIciPkFrN0ysXVzkud33L+yeOyax3T3y0k+l+QbV7FAgEMwv4C1O3GUL1ZV55KcW9z9YlV98ihff9BNSf5x3YtYkeOyl+Oyj+R47eXb172AgzK/XhHsZfMcl30kh5hfy8TWC0lu2XH/1OKxax1zpapOJHl9ks/s/kLdfT7J+SSpqkvdvXWQRW8ae9k8x2UfyfHbyxG/pPm1B3vZTMdlL8dlH8nh5tcy30Z8Msnpqrqtqm5Mck+SC7uOuZDkxxa3fyjJn3Z3H3RRACtifgFrt+eZre5+uaruT/JokhuSvL+7n66qB5Nc6u4LSf53kt+tqstJ/inbAw1grcwvYBMsdc1Wd19McnHXY+/bcfsLSX54n699fp/HbzJ72TzHZR+JvRyK+bUne9lMx2Uvx2UfySH2Us6WAwDM8et6AAAGjcfWcflVGUvs42er6pmqeqqq/qSqvmUd61zGXnvZcdw7q6qramN/kmSZvVTVjyzem6er6veOeo3LWuLv2K1V9VhVfXzx9+zOdaxzL1X1/qp68XofjVDbfnOxz6eq6q1HvcZlHZf5lZhhR7m+ZZlfm2dsfnX32J9sX5D610m+NcmNSf4iyZldx/xUkt9a3L4nye9PrmlwH/85ydcubv/kJu5j2b0sjntdkseTPJFka93rPsT7cjrJx5N8w+L+N6173YfYy/kkP7m4fSbJp9e97uvs5XuTvDXJJ6/z/J1JPpSkkrwtyUfXveZDvCcbP7/2sRczbMP2YX6tZS8j82v6zNZx+VUZe+6jux/r7s8v7j6R7c/z2UTLvCdJ8kvZ/h1xXzjKxe3TMnt5T5KHuvuzSdLdLx7xGpe1zF46ydctbr8+yd8f4fqW1t2PZ/un+q7n7iS/09ueSPL1VfXNR7O6fTku8ysxwzaR+bWBpubXdGwdl1+Vscw+drov2+W7ifbcy+K06C3d/UdHubADWOZ9eWOSN1bVR6rqiao6e2Sr259l9vKLSd5VVVey/dN1P3M0S1u5/f7/tC7HZX4lZtgmMr9emQ40v4701/W8GlTVu5JsJfm+da/lIKrqNUl+Pcm717yUVTmR7VPxb8/2v9Qfr6rv6u5/XuuqDubeJB/o7l+rqu/O9mdDvbm7/9+6F8bxYYZtFPPrmJg+s7WfX5WR+gq/KmPNltlHquodSd6b5K7u/uIRrW2/9trL65K8OcmHq+rT2f6e9IUNvcB0mfflSpIL3f2l7v6bJH+V7eG1aZbZy31JHkmS7v6zJF+T7d879kqz1P9PG+C4zK/EDNvEGWZ+vZrm1/CFZieSPJfktvzbRXPfueuYn86XX2D6yFFeDLfCfbwl2xcInl73eg+7l13HfzgbeHHpPt6Xs0l+e3H7pmyf/v3Gda/9gHv5UJJ3L25/R7aveah1r/06+3lDrn+B6Q/myy8w/fN1r/cQ78nGz6997MUM27B9mF9r28/K59dRLPrObNf4Xyd57+KxB7P9L6dku27/IMnlJH+e5FvX/R/6gPv44yT/N8knFn8urHvNB93LrmM3clDt432pbH9L4Zkkf5nknnWv+RB7OZPkI4tB9okk/2Xda77OPj6Y5B+SfCnb/zK/L8lPJPmJHe/JQ4t9/uUr/O/XK2J+LbkXM2zD9mF+rWUfI/PLJ8gDAAzyCfIAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAz6/wcocW/Qu+xHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {}\n",
    "params['obs_w'] = width\n",
    "params['obs_h'] = width\n",
    "params['integration_step'] = 0.02\n",
    "system = _sst_module.TwoLinkAcrobot()\n",
    "params['wrap_angle_system'] = system\n",
    "\n",
    "cpp_propagator = _sst_module.SystemPropagator()\n",
    "dynamics = lambda x, u, t: cpp_propagator.propagate(system, x, u, t)\n",
    "\n",
    "vis = AcrobotVisualizer(dynamics, params)\n",
    "states = path\n",
    "sgs[0] = wrap_angle(sgs[0], system)\n",
    "sgs[1] = wrap_angle(sgs[1], system)\n",
    "print('states:')\n",
    "print(states)\n",
    "traj = vis.animate(np.array(states), None, None, obs_list, np.array(sgs))\n",
    "#HTML(anim.to_html5_video())\n",
    "#anim.save('acrobot_env%d_path%d.mp4' % (obs_idx, p_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animating...\n",
      "531\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n",
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'springgreen', 'intermediate_color': 'dodgerblue', 'goal_color': 'red', 'obs_color':'slategray'}\n",
    "\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig, system)\n",
    "\n",
    "fig.savefig(\"acrobot_sst_obs_%d_p_%d.pdf\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try Color Schemes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "states:\n",
      "[[ 0.          0.          0.          0.        ]\n",
      " [ 0.26494066 -0.44239045  1.31444481 -2.71870503]\n",
      " [ 0.46437818 -0.88517501  0.75149249 -1.97289054]\n",
      " [-0.95270271  1.53762599 -0.64811259  2.47243378]\n",
      " [-0.72560115  2.04871561  1.87278817  0.10513623]\n",
      " [-0.08540697  1.89757874  2.42397801 -1.05843124]\n",
      " [-0.3229462   2.69115857 -2.62707216  2.32058159]\n",
      " [-1.11453096 -2.82958832 -0.35992638  0.88925587]\n",
      " [-0.99930524 -2.74153352  1.63207357 -0.61712159]\n",
      " [ 1.1124813   2.22549635  0.19004929 -0.35917918]\n",
      " [-0.75517213 -2.52689946 -3.25381218  2.99359231]\n",
      " [-1.22846856 -2.04802722 -1.27853844  1.61316584]\n",
      " [-1.32004686 -1.91240278 -0.37387504  0.95594433]\n",
      " [-1.32655847 -1.85671481  0.40321651  0.26581402]\n",
      " [ 2.10253174  1.78591848  1.10614117 -1.58223094]\n",
      " [ 2.20747508  1.60423167  0.25316857 -0.82706646]\n",
      " [ 2.21253845  1.58769034  0.08380811 -0.60147075]\n",
      " [ 2.16532079  1.54414206 -0.78397215  0.13973998]\n",
      " [-1.70363463 -1.41006879 -2.93356788  4.01046703]\n",
      " [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      " [-1.87635461 -1.0539289   2.08106892 -2.24554798]\n",
      " [ 1.88671424  2.02592942  3.13229375 -3.05467472]\n",
      " [ 2.41904694  1.43188883  1.52872173 -1.84446655]\n",
      " [ 2.50439048  1.3280372   1.22840678 -1.52723572]\n",
      " [ 2.73746926  0.9989843   0.61361667 -1.06100284]\n",
      " [ 2.88241113  0.55015397  0.1046093  -0.87832573]]\n",
      "state: 0, remaining: 26\n",
      "solution_u:\n",
      "[[-3.85228808]]\n",
      "solution_t:\n",
      "[0.34]\n",
      "after steering state:  [ 0.20470219 -0.44252029  1.0711702  -2.39582096]\n",
      "next state:  [ 0.26494066 -0.44239045  1.31444481 -2.71870503]\n",
      "state: 1, remaining: 25\n",
      "solution_u:\n",
      "[[-0.92173153]]\n",
      "solution_t:\n",
      "[0.2]\n",
      "after steering state:  [ 0.46049911 -0.88414779  0.50061005 -1.49270856]\n",
      "next state:  [ 0.46437818 -0.88517501  0.75149249 -1.97289054]\n",
      "state: 2, remaining: 24\n",
      "solution_u:\n",
      "[[2.32440386]]\n",
      "solution_t:\n",
      "[1.02]\n",
      "after steering state:  [-0.95287534  1.53946071 -0.06434177  1.62257475]\n",
      "next state:  [-0.95270271  1.53762599 -0.64811259  2.47243378]\n",
      "state: 3, remaining: 23\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.4]\n",
      "after steering state:  [-0.68814539  2.05081834  1.97065585 -0.01181697]\n",
      "next state:  [-0.72560115  2.04871561  1.87278817  0.10513623]\n",
      "state: 4, remaining: 22\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.28]\n",
      "after steering state:  [-0.08543565  1.89765429  2.42378808 -1.05785405]\n",
      "next state:  [-0.08540697  1.89757874  2.42397801 -1.05843124]\n",
      "state: 5, remaining: 21\n",
      "solution_u:\n",
      "[[3.84755103]]\n",
      "solution_t:\n",
      "[0.96]\n",
      "after steering state:  [-0.33624232  2.69161748 -2.75061264  2.49678007]\n",
      "next state:  [-0.3229462   2.69115857 -2.62707216  2.32058159]\n",
      "state: 6, remaining: 20\n",
      "solution_u:\n",
      "[[3.82678684]]\n",
      "solution_t:\n",
      "[0.44]\n",
      "after steering state:  [-1.11354392 -2.83057693 -0.33885929  0.84749506]\n",
      "next state:  [-1.11453096 -2.82958832 -0.35992638  0.88925587]\n",
      "state: 7, remaining: 19\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.24]\n",
      "after steering state:  [-1.00677094 -2.72479163  1.37498573 -0.04406997]\n",
      "next state:  [-0.99930524 -2.74153352  1.63207357 -0.61712159]\n",
      "state: 8, remaining: 18\n",
      "solution_u:\n",
      "[[-0.06512113]]\n",
      "solution_t:\n",
      "[0.92]\n",
      "after steering state:  [ 1.11170783  2.22530222 -0.53644685  0.46021501]\n",
      "next state:  [ 1.1124813   2.22549635  0.19004929 -0.35917918]\n",
      "state: 9, remaining: 17\n",
      "solution_u:\n",
      "[[2.22521613]]\n",
      "solution_t:\n",
      "[0.7]\n",
      "after steering state:  [-0.74281726 -2.52678898 -3.35214095  3.28066111]\n",
      "next state:  [-0.75517213 -2.52689946 -3.25381218  2.99359231]\n",
      "state: 10, remaining: 16\n",
      "solution_u:\n",
      "[[-1.3399946]]\n",
      "solution_t:\n",
      "[0.2]\n",
      "after steering state:  [-1.22585433 -2.05387168 -1.19781244  1.43184344]\n",
      "next state:  [-1.22846856 -2.04802722 -1.27853844  1.61316584]\n",
      "state: 11, remaining: 15\n",
      "solution_u:\n",
      "[[1.59946131]]\n",
      "solution_t:\n",
      "[0.1]\n",
      "after steering state:  [-1.32011933 -1.91224323 -0.37570957  0.95997731]\n",
      "next state:  [-1.32004686 -1.91240278 -0.37387504  0.95594433]\n",
      "state: 12, remaining: 14\n",
      "solution_u:\n",
      "[[-0.6545502]]\n",
      "solution_t:\n",
      "[0.08]\n",
      "after steering state:  [-1.3252531  -1.85955721  0.44779063  0.16936345]\n",
      "next state:  [-1.32655847 -1.85671481  0.40321651  0.26581402]\n",
      "state: 13, remaining: 13\n",
      "solution_u:\n",
      "[[-3.93979317]]\n",
      "solution_t:\n",
      "[1.06]\n",
      "after steering state:  [ 2.10944653  1.78681241  1.15073012 -1.6496214 ]\n",
      "next state:  [ 2.10253174  1.78591848  1.10614117 -1.58223094]\n",
      "state: 14, remaining: 12\n",
      "solution_u:\n",
      "[[-4.]]\n",
      "solution_t:\n",
      "[0.14]\n",
      "after steering state:  [ 2.20747508  1.60423167  0.29800652 -0.924074  ]\n",
      "next state:  [ 2.20747508  1.60423167  0.25316857 -0.82706646]\n",
      "state: 15, remaining: 11\n",
      "solution_u:\n",
      "[[4.]]\n",
      "solution_t:\n",
      "[0.02]\n",
      "after steering state:  [ 2.21253845  1.58769034  0.03701832 -0.50030335]\n",
      "next state:  [ 2.21253845  1.58769034  0.08380811 -0.60147075]\n",
      "state: 16, remaining: 10\n",
      "solution_u:\n",
      "[[-3.73683665]]\n",
      "solution_t:\n",
      "[0.16]\n",
      "after steering state:  [ 2.1646491   1.54556936 -0.7937878   0.16016404]\n",
      "next state:  [ 2.16532079  1.54414206 -0.78397215  0.13973998]\n",
      "state: 17, remaining: 9\n",
      "solution_u:\n",
      "[[1.76759874]]\n",
      "solution_t:\n",
      "[0.94]\n",
      "after steering state:  [-1.72709996 -1.40975617 -3.2164659   4.52675924]\n",
      "next state:  [-1.70363463 -1.41006879 -2.93356788  4.01046703]\n",
      "state: 18, remaining: 8\n",
      "solution_u:\n",
      "[[-4.]]\n",
      "solution_t:\n",
      "[0.2]\n",
      "after steering state:  [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      "next state:  [-2.05705068 -0.89236667 -0.33581711  0.65020231]\n",
      "state: 19, remaining: 7\n",
      "solution_u:\n",
      "[[-0.52594271]]\n",
      "solution_t:\n",
      "[0.24]\n",
      "after steering state:  [-1.88047229 -1.04510999  1.99732787 -2.07067011]\n",
      "next state:  [-1.87635461 -1.0539289   2.08106892 -2.24554798]\n",
      "state: 20, remaining: 6\n",
      "solution_u:\n",
      "[[-3.39353577]]\n",
      "solution_t:\n",
      "[0.8]\n",
      "after steering state:  [ 1.91736585  2.02568827  3.43255391 -3.55762047]\n",
      "next state:  [ 1.88671424  2.02592942  3.13229375 -3.05467472]\n",
      "state: 21, remaining: 5\n",
      "solution_u:\n",
      "[[-3.47205765]]\n",
      "solution_t:\n",
      "[0.22]\n",
      "after steering state:  [ 2.41668322  1.43768289  1.70184352 -2.21566921]\n",
      "next state:  [ 2.41904694  1.43188883  1.52872173 -1.84446655]\n",
      "state: 22, remaining: 4\n",
      "solution_u:\n",
      "[[-1.62337777]]\n",
      "solution_t:\n",
      "[0.06]\n",
      "after steering state:  [ 2.50387599  1.32914541  1.19284568 -1.45042371]\n",
      "next state:  [ 2.50439048  1.3280372   1.22840678 -1.52723572]\n",
      "state: 23, remaining: 3\n",
      "solution_u:\n",
      "[[-3.91249548]]\n",
      "solution_t:\n",
      "[0.26]\n",
      "after steering state:  [ 2.7390879   0.99552116  0.62822321 -1.091815  ]\n",
      "next state:  [ 2.73746926  0.9989843   0.61361667 -1.06100284]\n",
      "state: 24, remaining: 2\n",
      "solution_u:\n",
      "[[-2.90536515]]\n",
      "solution_t:\n",
      "[0.58]\n",
      "after steering state:  [ 2.88224931  0.58727286  0.03398653 -0.62745253]\n",
      "next state:  [ 2.88241113  0.55015397  0.1046093  -0.87832573]\n",
      "animating...\n",
      "528\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-02b7e84fb6d6>:205: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  self.ax1 = fig.add_subplot(121)\n",
      "<ipython-input-8-02b7e84fb6d6>:206: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  self.ax2 = fig.add_subplot(122)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'springgreen', 'intermediate_color': 'dodgerblue', 'goal_color': 'red', 'obs_color':'slategrey'}\n",
    "\n",
    "vis = AcrobotVisualizer(dynamics, params, color_dict=color_dict)\n",
    "states = path\n",
    "sgs[0] = wrap_angle(sgs[0], system)\n",
    "sgs[1] = wrap_angle(sgs[1], system)\n",
    "print('states:')\n",
    "print(states)\n",
    "traj = vis.animate(np.array(states), None, None, obs_list, np.array(sgs))\n",
    "#HTML(anim.to_html5_video())\n",
    "#anim.save('acrobot_env%d_path%d.mp4' % (obs_idx, p_idx))\n",
    "\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig)\n",
    "\n",
    "#fig.savefig(\"acrobot_mpnettree_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animating...\n",
      "528\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n",
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'springgreen', 'intermediate_color': 'dodgerblue', 'goal_color': 'red', 'obs_color':'slategray'}\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig)\n",
    "\n",
    "vis.color_dict = color_dict\n",
    "fig.savefig(\"acrobot_mpnettree_obs_%d_p_%d.pdf\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animating...\n",
      "528\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n",
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'green', 'intermediate_color': 'cornflowerblue', 'goal_color': 'red', 'obs_color':'dimgrey'}\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig)\n",
    "\n",
    "vis.color_dict = color_dict\n",
    "#fig.savefig(\"acrobot_mpnettree_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animating...\n",
      "528\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n",
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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bKEqcBqahC/uLwCR7Ma/Wdp2Dxjhk3+y3o7TX7tXcnlIwPnujNNawrKc6Udrr38TshxUzH6M0VhKvAFvU6hi0tbUdjqbOrwG8C5wxfvz4W8u1/KaL8KBulqDoQ5S3gRWNnzev2kyEbuUBgcd/gInh/6OmWL8wPjsZn8HZb7SprfFoNszxLrXlcPRtjE8/47Ov8ZkAvA/8BXlMvorSJocDRwBno5uhO4H3jc8txucC43NOFboBv0hPLwzUZvxEqfQmvbUSEktJ6a1NgHNrcQys2LkOMCiTYIDr7ONloRkFzxR0dxEneMD5eKKEgicMc56Ljt1g4AV0Uk6E+DJ0G0G7Hdga+HrFt9bhcFQdK3T2QyLnPiRqRhfw1sH2dYfTXQRNqaAAyjdo9Ezq3M9ShvTWUcCrCYtvQcegauX6ES4hfpbYJeVaQdMJnsCjHYmbJMHjfDwZwh+F/tA1WPRv9rFt0I/TYKAN/WjF8SAKYX/P+D1CsA6Ho4GxwuAF1CRvND07AxfLYDQGIVsA7VuOC3ABper9UESqrhvzVbh6qwXYHqpu70hqC1O2djFN5+EBMD5nopDfjoGXUcfG5xHgX4HHhTXbuHoildoU5XQPIp2+B8D4bAT8F91VvArMBXZCXT2/G3j8I3sxxmct4NfAE4HHVTHPrwC0Bx6fV2hPHA5HmbFi5y3k06s07SiCdAtl8P9YEfAISmMNiHnJUuDAwOOB0ja3JzH+lIeAvemFXyWyH9ugyE624FyETNB3kHW87Of3LsnRuKnA+tXy87S1tc1AaaxsOpAI6/Uxa7oIj2UyOjmyleMUXIQnSnZKi8BjMvA0Mi+vDzyOfojGABfHjZ8IPKYD9wO7GJ9No8/ZqM943Cwzh6NhqLLYAYmSskV+siI9cRf0VuCyckV5EvwpJ9FLv0oB6a1BKMXYI9pj01snEh/pAh3vavp5Hkp4vB9lOmbNLHigZ1rrHdyIiSg9BI/lh8gstxi1Lz8K3UlsC1xofEbELOtO4CPUm6drPIWdzP4v4Fs20uNwOOoY+/v4PPnFTicwG0WJ70DRmVuAi1CaO8k4Wwhh6usuShQ+kVL11xJeYiifiTnOn5JNSX6VPOktkDhIMjNPRNVpcameFHAG1fPz7F3Ce4o6Zs0qeKahC3Scj2cAsHrVt6g+6ebhCbFensmovHEpcBrwHfv0t5GJOfs97SiSMwrdjUS5HX25Di7nxjscjoqwN3SP1GaRBmaiQZmjAo+vBR6HBh5H2n9no672+wMT6C6CFhW5LWHkpyThE/GzzIx5upXyzdsq1IfSG79KUvVWSA8zcyRC9GTCMvshP1A1RgWVuu8Fv68pBY/9kN8g2bjsRkyIrrL0mOdOtX/nATuic+kaFEI9Fk1X75ZPDjxeRw0M9zZ+JnUYeHxiH9/N+ImdRx0OR42xF8prSDYnz0FCZrXA4/4k/4eNSjwQeByVJYIOBs4BLkQCKCndkk3JwieS2lmYsNyx9N7A+26ZX9eDrPTWLfTcn1gzs33f70ie7dUCjK9ClKfUfS/4fbGdcpuEycAhxqc18Lom185AH/o6ZDoLNzNJKS0Cj8eMz/vI8/Q0Citug8rPNwN+gkxvf816683AlkCb8Tk9cuzvRF2ZDwWuKPN+OJqUtra2TirQwKyJ2YvkcQhzgJWsgCgae+F9yP4Lm8HuBVwGrEo6PajrxanEYrCo8Hnf+JwCTCzAeDsR/Y7FmZgHAX8wPpNK3TeUGvpTzLKjLAWGRs7Zog264TE0PpOAlelpZg579YQ9hy6w75kIPIEKUOKEzUrIz3N+BU3MhRyjbNrt+wqiKSM8lsnowI4JH7An83SccTkkNqUV4RJ0Di1G3VTPRimtOehLerHx2Tj6hsBjAXCtfX7/yOOfIXG0S9KcLoejBCrSwKyJOZj460YaOK4XgqAHYRQIWH/t2Y9duc5nE5eM+exhBi/5lFTnEshdYVxUxCfLxLw05iWj6X2per6S6FZgBcpg0C3AzDyASLou8vokP0wL+n2vdI+iYsvGi3p9swseiE9rOcEjcqW0AP6I8sU7olz8scCqyM+zBHXtvCw7TRV4PI/upg4yPqtFnrob/dgcWq4dcDgsZW1g1sQkXTM+Rj23yk7g0bHhrL8cvP5nD/ff4LOJ7Dz9PDb/8HorfJbmu+AVLHwiJuakFMlGlJ7augQyxRolUtQ5XICZuVt3abv/5yOTeRKVnLlVyjEaiDMtF8T7wOfEV2qNMj7LVH+T6o7ElBZ0GZGvt8+PBD5EX67HoKvfzvbAb43PoKy3/xEZFNtCr0/gMQeFcb9hfGccd5SdsjUwa0asUNg24enrK9yvpeuzS5Fm9JevWuFzHSh1ns/rEwqfe4C3jc+5CZ2IO4BTiI/ytKLUVikX+3Kde6UsJ1936exIT5KJO2QtKtOYseKm5ab18AQeaePzOj0FzxT7d13UQbSZCX/Acn3BLwd+hELdJ6F8+w9Qe/Jt0ZTebwMzjM+5dsYWgcdc4/Mn+9490JcS4F5gT9S5+Vdl3RtHs9PZG3+Eg2+hCG4chZqLS+VdsprSWeHTuedbJ6390bCvzfrv6CPp6Dd4BLk9IK3ogn0eMvV+FOPzmYgyAF+NeX+Y2ip2wGaP7S+Ros/hwKPD+OyOzOA/p+fxiUZ6Lgg8lhifE1FULOlYbojOh6TeOaVQ6jEq2LTczBEe0Em9brQvDLAMsCa6UDc3asPdTg7BE3gEKKLTHxmWJyET2YooxTUHfWm+jxpgRXkShU+PDHvw2G7L9wNjjc/a5dwdR9NTtgZmTcrmxF8A24GXKrzuxKZ0KdKpVea/NGr3d04bttbsv/2ewiI+kOnlcw8Rb0oBUY5NKb5nzBnEp5WKpaRzOJKu+ycFRHrI9OdJ6pU0ADi0zFGeUo9RwaLLCR4dg/Ujj3Wii7crTRdLyO+av9L+PZJMKus3gccU1KNnMaoMuNj4mSGiNtoTlrieGCljvx+d+O4C5KgWzuOTn6SLWwcab1BJ8gqMFOkhX5l170Ho9/xgChc+rSia827o8YmUqsdFcVLANcVc7G3k5XggQEbbSqT/cp7DBcwR64r02P/fHfVIS+JwytiUsBfHqGDx2bQpLUvUuPxf+99LkbdkTOw7gLa2tmspnyCaMn78+BPKtKxKsITcKS1QddW7KNx9LHApcIHx2Qu4FZU6HoIqEK4zPnsGHu+BevAYn1uA49CX7R+Bx3zj8xfgCOOzbuB19UdyOCqJ8/jkJukC1A9dHMuZ3sim4MZ99sL+gPGZiEzG16AS7XyMRtGeycZnKxTlmEX8rKmV0IX2/gK3K7yg3wpd7RIqQc7jZNNbF6DeQnEl+GGkZyz6TG8DDop5HUj4fYPu5e29osRj5Dw8hWAvtp/S3cfTjnK7a0RMXNmshz7oZiBnSgu6vkTXom6p45CPZxzwG9Tb4ScoHL4qCiHfZHz2tWMlQGmwHYDjjM/LNq31ALAvSoOdV+Z9cjji6KxU356YwZGN6Bl6Ef0exF0kN6OygqdQf0fXZ7iXPca2J83zyHeSL1odRns+Bh5GxRVn0LPRYhjleajEC325PD3Z5PX4FOPpQb/pbxDvZ4JMN+qxxmf3MhvXCz1GzsNTBJPpLniWoAiPGzEhCklpgRpGLbb//TNU6bAqcIYVMMfb5z5BXp8rI50+O1EqbAiK9BB4LER3W5sbnw3LsysOR06i/oiyeXoSBkc2omdoEvBBwnOVvnkuVEz1+AxtemoL4EAyXZxzVSGBqk6PAH5Bcq+XUZRepl4uT082BXl8CvT0nAG8iUrVp+dYZ7fy9vLsBlD4MXIeniKYDKxpp3aDLvAL0bFx/XgKS2kReMxE4d1ONEPnS+BGNCx0k8DjRfRDMxwNET0ICaPw/e8hgbOD8bsM4w+j0RVHlmtnHI4CKaenJ25wZMN5huxF8s8JT1eyJB16OVgy7EkTeFwYeJyDoh6vEF9+HqWF5OtkC0rVF02VPD3ZdDvnCvD0tKKI/J3AF6gAJYls03OvKeIYFXxuOMGT8fF8xf5dgiIVnTjBAwWktCJcixpBtQNnoVTUHOBy+yW4GnX9HArMBU43fqbbMipJfx/N4RoceCxCX7ZNjM8mZdgXh6MYKt07pRE9Q0kzHSo9Z6msPVqyoj4flbpR5B6impPx48ffOn78+DXHjx+fS1SVm27Ho4BID0j4bEr+68AA4ieyl0yBx8h5eIogalx+Dn3oaZTDdYKnwAiP5QnUqXoImq77KxSWvBY4OvC4wfi0Af9AonIwGkr3TuDxqu3/MB64GIWTr0fVHweg0vWfh318HI4qUK6+PUlehEbsC5RkJK10hKJUz0uiLytibg49PhtR/DVxWP6XFESlPD3Z9DgeBXh6QgrZ1yFUzs+VdIych6dQAo95KKoQ+njCEOdHaIhos1OohycsM78WeXfmoyjPnUjgnG98Vgo8PkWNCVezjw8D7jY+y9tlvIVSWXsYnw1sN+c7keFws3LumMORh3L17UnsIVOm5VeTpGtGpW+eS/W85PVlZUV7ppIs6uKYUcI2xVEpT082sccjK9KzqBfLbwEOrNBk9bhjtAA3PLRoosblMKw3E1jJjZgoKsIDakK4GEV69kANvE5FE4cvBgg8nkQT0XdA3TxXB/4Sadl+K/ApGjvR3y7zExTlSRyT7HBUkWI8OL3yn9QZSZGcwyt0kQPK5nlJPKbRQaVI+MyksMGUV5SwHT2oM0/POPKbunOxIeU3MMcdowA4vphIqEtpicnA7sZnOTLzoz6xf9dB5ZjNSgsF3vFYMTIORcw2Bj4Dzg489jE+vwN+ZnxuCTz+DxnlxgI7oxL0bwN/ND5HBx6LjM81aDrvQYHHbcbnduDHSEA9V95ddDhKouDeMBVefjV5EUUAsmfjrUL5Rw10o0x9bPL2qQHuMz4PorT81cT34QH5Ecs2MLVKfXqyifP0hGm+t9AYjmLp6uVT7lL16DEqBRfhEVEfTyh4Ztm/ze7jGYoqrvJiU1rjgddQNdYLwC7GZyzqzTMNTU8fZMPIJyClPhKJmIOAn9plvYxGT+xvfAzwfyjNeISL8jjqhEK9AwV7DMr0vkoyCQ0JzmYw1U05V/SY2ojPfSj1vj/6/fnY/nsC2A/YrYIDU6v12ceux/4+r0/ucvRcDAC2o/Sy/YrgBI94E0UxooJnMaowanbBMwy6GgTmJfD4GPBQbnVjJBzPtBVXp6Ayx1Pta9+1j22B/DzvA2can33t4v6ExFYbEka3oTuO7Xq9Vw5H72incO9AKf6MpcDQtra2zra2thn14uexF/hbEp6uZsbgDIofWFr0MQ2FT+CxS+Ax2v7bOfC4v8LT4avh6cl5PMogegajG9xKV/AVjBM8QOCxAH2oG5H5EvUH3sEJnoIjPBE6UHn6Z6in0XoAgccTyLNzivF1XAOP+1Fvjx8A59rX32B8Ngo8vkCdTtdBdwr/AN5DfgF37jpqScHVgjHeg0/J3/+lFY1iqUcTc9KFvlppmJBiKzbr+Zh2o8RzpljyHo+I6HmF3J/v9ITtM1TAz1Mq7qKRITQuh1/m/sh4O6bJL66lCJ5w7MavUY54vvFZ1j52JvIA/Dry+jNRlO0c9CUfADxofEYCTwH/QYPqVkT529WBHYveE4ejfAykCFNxVj+RLyk+GlJPJuak38Ojq3hhuwR9Br2hno5pD8pwzhRL7PGIVLHtj6J7r6E+avPRDMoDkSiKS4+FoyfK1pCwNzTzhTybychLshJSqqHgGUB9mgcrTyrViva/WMGzM+qQPBvdPcwCfmF8htuU1ytE+inYMRLHA8sARyEfz2g0lDSFSt07gO8DT6NS0MPq4QvkaGqqbUaul9+hpAjPqpSx6VweKt0Ust6o1nYmNWrsCDweCDyODDw2CTxGBh7DA4+vBh73WlF0CvEl7XXj53GCJ0O2cXkAdE3pbtZ+PEPt31IEz1zgO8gL9QskJH9mfNYDtke9dboIPN5Aeeudkdi8DnX3nBB4fAbcbP9/Z3SXsbL9b4ejVlTbjFwvJuZwiGg24RDRalCuY9FZbz6pBGpqYi6QiSgiH3duDEbDVotpcVJ2nODJ8DaK7ISCpxVFEjpoXh9P0YLH+KyBjMkp4DDg5cDjP8DvUMXD1falccQ5VNsAACAASURBVMbHP6MS9TPtf/8DVWmdDTyKpvYeg8ol3wEONb5rreCoGaWWYFf7feUm1xDRal3QymXqrcjA2ApQrc++5PVkzeaK8/OsDLxVS9HjBI/FdvSdQiTCYx+bQfMKnrCVeMFVWmSiLueiO751jc/6gcd/0UT0LVBZ68fZb7Rl7aeg8vPr0NDQaSgHfAAqeR8AfA+YgKYV71rcLjmajEo2cSuloWAt3ldW7IXtpoSnK9qAMKRCjfrq2dNTrc++V+uJdGxOihStBTxfKzuCEzzdmYyGiEYHZjZzpVYpKa2dkWdnBEpnfQbcZnzWRgKqFTV1PDVO6dtRHyegaNDpyBMwD83VGoWqvLa1y3kTOMT4hY2+cDQfFR7M2KweHlCPrTi/RtiAsOJUaPhmPR3jKDX18BSDFT2nkFxVthE18vM4wdOdyegiP5CM4JmCRkwMr9lW1Y6iBI9tCLgzasz1LZSCOhCFjG9DpuPPgAtRjx4vISXVH52bM22vnkPs4w8C/0JRtxOAe4DlURjV4UiiUv6Hantx6slvktSAcBC1mXnX1z09jeDhiTKRjC82m1Zq5OdpKsFjfNY1PvcZny0TXhJ+QEPoHuGB5ozyFBvhWQ/laZ9CxuRJgcc7aPL5cJSWeijweBy4AfgacFI0vGlbAFyEPAJXAwQe/0LRnuXQD+31KIK0GfAqcLDxe12i6ui7VMr/UOpyKzYIs1pE7uIXZj2VonLDI3PR1z091RouWpbvij0/tiK5aWFN/DxNJXhQ5dA62EZ4MUxDX+BlyEwIj6vUmoLGHpTj35Te71bFKFbwhP6dDiQYJwEEHq8D96LzbSvjs0Lg8XdUebUVcEKk19E4VI11gS1Xxy7jeuAPaDDdpcjcvCvwLBI/NS95dNQtlfI/lLTcSg/CrCITif/9Wp8qfx/7uqenisNFy/ZdiTQtTBpEuhZVFj3NVuHyKfAFMCbuycCj0/i8gaIT4bH5DAmlLpE0fvz4Eyq8nfVCKYLnPWBzdMz+DV2prl1Q/50RwK3G55DA4xHrvxkHLDE+t6GBof9BAimb09HnsDsSojPRRPZX0F3lpKhIcjgslfI/lLzcagzCrDSBR4fxuRfYhO43z4PQSIGJFR6/0I0KDd+sG09PlYaLlnV/A48lxudEZD+I0xuhiXmLapwrTRXhsVVA00gQPJbJwLLYLp72PW/TnL14Cq7SshGab6Ahe3sAjwZel2ntq8ioNh44DpWtT7CNCB8E7gN2Am5ExuSz7HHvhv1CHIbMym0o7TUa+BylzPbNfo/DQf15eMq1nHroy5PUk6dq5uUEav3ZVJp6P6ej5PLzQBVNzE0leCxT0QU3ickoh7t85LFmHTFRTIRnU+SxmYHms0yKPPcdVNFxl/XjnIiq4W4yPkOQ+n8GnfQvowqQWOx8rf1RJdjPkIDdGn1G+xm/a5sdjpBK+B/KOdyzKoMwK0S9mZdDyvGZFzMgttpU4pyuyP4W4Oepmom52S7goAvkcsZnRMLzoRJdIfLY2yjiszp0mZ+PNH5XBKSvEoqHQr5YoX9nWZRffgzA+AwGDgYesCXnWNPyj1Dq64/IL/VVVMb+ORI0iQQeM1ADwnY0iqITRXiGAvsVsmOO5qFCgxjLPYiyIQdh1qF5GSjbZ17sZ1I1KnROV2x/C5i8vjJV6M/TjIJnqv2bFOX5AH15oxGesFIrTGutgiqMqpafrhFDgUWk04Xs507IwLg98EzgMcc+vg8ygd8cfXHgMRE4Fc1YuRtVcP0aeBg4wPjsk2tldvL62ahl+dYotTUX2Nf4LFPA9jqaiCoMYuyNwbXRB2HWjXk5Shk+86IGxFabCpzTFd3fAkzMm0Du3/3e0oyCZ5r9m2RcTqPGeCMjD09HUYTQuDwMiZ24xlt9iYImpdtQ5A7IpLwpPdNZAeqf043A4140RuJ/0Ll4FSpXfxoYZ/y8/XX+YF8/CtgAfWYjkHhyOJKoNxNzQw/CtFGee9FvZJTQvFwPQ35r/dlUmoY4h6zoOZH4YEELcEMlU1vNKHg+QkIll4/nI2C48RkEXWMnZpCJ8AwD5scZa/sYBQkeYEt0TMKw9iQA47MWEkI3B16PH8OQhSgU24lakoOEzPPAUcZnF+OzbNwb7fH/BUqfrYbuHlLAXjlSlg5HvRk++4LBtl7NyyF9odljLhqp8eJE5MGMYyQVTG01neCxF958lVofogvn+pHH3ibTfLBQIdDoDKOw/dwZ5X/XQNGwMLx9hH381rg3WX/PuUjcXIAiM5cg8XMVMjD/BHjY+N0+iy4Cj0Wo6/KryGO1GvqRPaiA7XY0J32lEWG51l8O6tW8HFLqsanXRoTZlOuzr/j+2ojg90n2DFUstdV0gseST/C8iz70jSKPvQ2MtiMmhlHcQM1GZSiF7efOSHCMRd2V01ahHwE8HniJk5VPQuLkjMDjCuBK4HAkgjoAH1VvrQb8MGnlgccnwHdRD6AxKMW1r/G7+bAcjpC+2Iiw5PWXg3o1L0cox7Gpm0aEMVTis6/k/j4E/DfhuRbg6kqcM80qeKai+VhJVVbzUJolW/CA0lrDUAPDvk7eSJaN0myLROQgMv6dXZCY+XPC+1YGPODBwONZ+/BvkCfnOOB0m+/9DYr07Gf8rkqwHgQerwHno4vFaJSyPCTp9Y6mpt48POUahFlrv0mSeXkMtU9rNYTHpRfU3Tmdi0ip+pyEl6xEBaI8zdZpOSQ0Lq+FIhPZtKM7lY2Mz9ZI/ERnag2neSI8H+V5zbaorLw/OiZP2cePQl2qJyW870wUPj0vfMBGhs5HwumHxmdh4PF743MK8AhwvvH5LPB63hkYn9EoTLoAVdoNAg41PvfYCFBO2trariV55EixTGmibtyNyLsoZF+J5ZZrOaVsX6ftwPsucIaNHFWNHJ2Xh6K0Vi1TbuX6zOu5EWE9n9M9sF2YjwX+giKBUVLIwDzR3viWhWaO8ECycXkJEjwGlVt/HfU5mIuqgcKLe1+nEK/STmSiYY8HHouNzwrAnsAd1vDdDePzVeBQ4A+BRxB9zhqRz0BVH6cbn+8FHtNQ6fpywKnGZ8Os5a2GujWvjLotP4h8QGujKrFCWA91ii7Hv3IJJ0dlqNQgxnJd0Bt5uOiL9Nz2ekhr9QWfVC7q/ZzOtfyPE54ru4G5WQXPe0jUJPl4lqCTJ4Wae7XaC/E7ZIzMzWBaLkTw7IKaNa5KJpozDonCm7NfbOdqXYwE5GVxC7TG8tNQT55zjM8RSPB8iHpFnGZ8GchtJdj96MtxSODxNPBb1K15AHCMTZ85HEBFBzGWxUfR4MNFJ5G5oYxS07RWX/BJ5aLez+kkCjAwb0oZU1tNKXjsQZ5B7ghPKHhWIpP6m0KmUqsZIjw5q7RsufiWZPKwj1pBcxTwgp2Sns0+KGJ2iR0TEYudw/Uj4O/oh3tN9GWeY//91PjsgsKhg4ADAo8X7XvnolTZNFS5dVphu+toFsrkmcmmbH6HRvX05OjJMwQ1a60ZjXpMC6Xez+kc5IrypCijgbkpBY8lV6XWEqSQZyIDbHiw30EX12by8OTaz+3RObQCEjgfo3ERGxIf3RmIhMhk4JZ8K480qXoa+F/kJ1oLuAtFb+62fw8IvO7D6QKPd1CYdxHwHeNrLIjDEUO998GpdX+fYolLay0F+tdBtVZIox3TYqn3c7qLAqI8ZTMwN7PgmQqsai/C2YQmqSnIFxJGeN5G4mckfV3wpFL9UVoqV0prZzT/aj0y6azvIP/TPTGvPxHdMZxtT/K8BB6LUcn5CyhyNAxNTB+LfkRfJKZizvi0ogje+0gUnVXI+hxNSSnDO7Op5KDJUravloMvJ6Gu69HfyAFolMwjdSJ6GnlgayHU+zmdTa4y9bJFeZpZ8ExD+79mzHOh4JmK5kANt/8/HR38vi94CpuUvjM6jilgkp18fhBwX+DxefSFxmdF7A9e4PGPYjYk8FgAHI0iQxsj4TQX2AO1EPiF8Vkpsq7lUCPDPVFvn8eAPY3PdsWs19FU9LZreqW7rhe7/Jp1gbc3M7sjj170ojsM2Ibal6iHNOTA1iKo93O6i0iZetJ1tSxRnmYWPKGxLi6tFX5Jp6EPfWXoGjHxKbBMXPVRHyOn4LECZhOU+vsIeAX4NvpR65HOQncKg4BzStmYwGM+6r48CN0tPm49O5cif8Dlxmc147MRMi2vidJgr6II0FLgwFLW7ejzlGN4ZyUHL5ayfTUdfGkvYEvp2fpkKJqTV+soT6MPbM1HvZ/TPbAWhgcSni5LlKeZBU9ApnQ5myVZrxkdee5TMhGfvky+CM+O9q8B/mqr2I5CIvHp6AutCDkCuD7wYis48mJ8vglcg8ZQfAB83/hsEXi8j2ZvrYmaFl6M7hLOQl6i05Ah7lfAusbvmofmcITUe1O6Rh18+RIJJerUPrVV7595b2nU/buD3F6evXqz8KYVPDZCE44iyCYUPGlkXB4VeW4eMNSOmOjLhF2okwTPLsirMwils8YgX83N0aGqtmrrInTcflvKhhifPYE/AW+hKNLxKLJ0l/H5CvAG+mHdGFjWvrYN2BXlhs9GHZ+/QMLL4YhS7wbPRh18GXp5FmU9Ppjap7bq/TPvLY00TDTKRHJXbB3am4U3reCxTCV3hKc/ivKsaC/cAJ+ji22uWVx9gTDCk5RT3RmdmIuBJ1B0pxO4Let1u6OJ6b+y5eJFYXwOAK5FKbODAo85wKPAX9EP550o1bUMaj7YjsTNCODiwGNC4LHE+oDuBbYwPhsUux2OPk29N6VryEaEES/P3fS8aw+7L9eKev/Me0vDDBONUkDF1j69iQw2u+CZBqxpK3qiRAXPdBTFCJvXLUAX9nUpAONzoO0X02gkprRsZ+N17GueRKLnMOCxwMuMojA+A4ALUbXbTcVugPE5DJmO/w0cGhqhbQTpdhR9Ww3dKU4CVkSfzSI01iI7fTYRRZqOLHZbHH2Xem9K18iNCO0F7A56/o7UtPtyvX/mZaDRholGeYieUcGQYfTCvNzsgmcqUrDZPVqigiecu7WJjfK0oNTIlgV+WTdE0YZGI5eHZyf7dyQSGv+DfE7ZZuXvor455xQ7D8XOWLkMCaojAy+zHVagroJSjW8gEXMSKo//PXCC3Z7Tjc+g8H2BxyLUw2dT47NJMdvj6NvUe1O6et++PNRt9+UGPqb5qDc/WcFYkfxcjpf8uNRlN7vgCcVMdnoqrMAKU1qdyB8yCAmkZ1FK5xbj8/WkhduL7VBkdG40cgme0L+TRqmlo4BZ9r8BMD7LI8Pw3wOPvxWzYuPzfXQn8QhwdOCxMPLccmgq+q6oC/NKdt0pJCzfDzxeQpGhMWj21oDI4v8KzAaOiKQpHY4o9e6ZaaimefXcfTlCQx3TAqg3P1mx/C7Hc18pdaHNLnim27/Zgmep/dsfpWvmIMETioDrgdNRefQVxudy48f281nB/u0zgseKhJ3t45NR6HEP4LasKM5PUTVbwUZl45Oyk9HPRQNAj4+W/9tqr9+gqNGfkZ9qOfT5HIAE6XXGZ/nA43lUvbUhcLLx6Q9dZvU70Zemlh4CR/1Sqv+hWn6HUrevln6Teu++3IjHNBeNOkw0up6krEDJ5fZNLXiskfVDehqXoxGepcgPsgGZcvT5gceTaEjm5WjA2e3G53Tjd0tfLW//flaBza80SVVaY9Cg0BEoVH0o6rUxIXyB8VkfOAYJyj0Tull3wwqpnyOhdBfQFgooK4T2RWMpFqBhoQciwfmc3ZZ/IRG0GjDB+CwTeDwF/BF9Pj+M/LA+BnyCi/I44imH/6GSfodSt6+WfpN6777ciMc0kUYdJhpio4JJFcLLhjewxdLUgscSN1MrVJYDkOCZjSIe69nH54MaJQUet6Lowj3oInyP8TncfiBhhGd25Ta/YoQRnuy7hF2gSyRMQums5wKPtyKvuQCdrKehnO/xuYSFfe58lJu9GTjZDg/F+AwGfoI6Lb+CRkXsj0rUXwLeRIJnRyR+/oQm2v/Z+AwLPJ5AhuktUO+eFiuk7kDG860KPySOJqHee5g0XF+eBui+3HDHNB8NPEw0JKmqN0WJDWyd4JGZbi3jdzsWUdNyGOFJQZfRtVupduAxN/D4DapUehU4GVURbQ/MK9awWycMBRaSTmffGexMpgoqhURgl1nZ+PwP8E3gt4HHP9Fx2AKJlB7Y4/5rdDdyHfCzwFOu31aD/Rr9IP4fMipviqJJlyAh+REyLR+EvDyzUA+erwI3GJ/BgcdjqFx+WzLi6+8oFVbPreEdtaHee7Q0pN8kR/flevDyNOQxLYJ6P6fjmJDjuZJ+t53gUYRnILqYhkQFTwe6oC5GkQNI6E0TeEwPPE5GkYqlwLHAHsbvigw1EkPp6d9pAb6BhM4j6KQL+9tgo1oXoFTW9fZtjwH/QP0Tts1aXivgozLxK4Bzw6aFxmcsEjvDUCh8LBJaZwUeD9nXfYY+o9eQJ2cw+jF9DonObYBrjc+AwGMiisLtCPwSmeJGAoNcWsuRRb0PXizFn1Evgy/jui/Xg5fHDRPNT7UH0l5AT6N7yJBSFugET6ZcMurjyfbwALwDbI1Cetl3KN0IPJ5FYuAZlG652ficZfyuFFcj0EPwABuhXjetKEJyIHCvnXMFmpS+HipDb4eunjk3oxTUceFoByuOxqPIzK8Cj0sDj7Tx6WdL0k9F0ZrZwJZ2fb8IvC6jOfa5pUhgpdHnAzAq8Lgf+YG+AYw3PsPsa1ZBn80GSGz9MNoZ2uGw1O3gxRh/xqdkfqeSqJfBl/Xs5XHDRCv7/qKwmZF/JTz9ZinLdIInpjTdplQ66S54JqBS7LHoi+kZv6sZYRwDgBnoonsL8qC8bHyOLcTEWwfECZ5d0DnTjiIvQ7HpLGvW/hk6Qf8afZP141yJBMqP7HG7HjWQOj/wuNwuYyTy8uwNvI3E4gjgd4HHdYHH4qztmY0+p8UoyrMlOu4r2vXegaI5+6MqkeOQCL0OmZaXD71CDkeEuh+8mOXP+JI8N2Ex1LIRYT16edww0fzUYiDtZQmPX17Kwor9kvQ5Ao/Pjc+nxBuXo4JnCnAjKonuh6IZ3zE+TyKfyvNZkYIwmvN+4PGo9aPsDfwA2N/4XAU8WsfRhWH0FDw7oR+pp1Bk5m10twYyFo8Ezozbp8BjvvHxUcn5Y6iC7ReBx40AdibWT+x630XDQF8Hrgq8xCq30AzegRT/OqiJ5CjjszoSOjsho/MaSIydhe5UjgX2NT6LbTRoSv5DUjDlXJaj+tS7ablc66mJ4Tbw6DB+zknqk6wwqiaN9pkXS6Pu30Mour89upltR7/jJZXHN73gsUyjZ2n6EjJVWqBjNQx4I/C40fiMBg5GaZ2dganG53bgYVvuHpakhz14+qGZU9cDp6CRC+OMz2WBx2vl3BnjsyzwRWj+LZFuER7rt9kJHYf/IJ/SuTYNNQaF2CcEHpNzLPNzJCzXRr1wbrL+mX1QtdciFEVbCYnIB/LsQzjXrBOFlf8D7IZK049GX45H0Yytw9BxvwANE/0T+nwPNj7twfjxJxR0VBzNwLsoRVGO5VSDUre3lobb0MszLPJYOEl9ZeOze5VFT6N95sXSkPtnxfFuKPL3NeBlKF0Qu5SWmAqMyTKvLkEX92zBE5akzww8fo8usOeii+uZKN11GvKIpMlEIUYAcwOPF9HF+AI0/uAG43OhFVC9xvgMtdu0ZS8XNZTuefYtUB+iDhTJ6SAzKPR8JFYSw53GZxkkYjYAfoWOzeFkSs4XopDp58gDdF8+wWYjSXOQmByDIjzrInP5PcB3A49rAo+ZKAT6BxSZC41316EI1RG2uszhgPI1V6tmk7Zqvq8c1NskdTdMtLrLKZjAo8MWqlxk/5YshJ3gEVPRBX7FyGPZEZ7+9BQBBB7tgccDwBGo2d5TqBnfJcgwu7WtbhqBLs4EHp2Bx0MoLfRHFCG62/j8wPiluc+hq4pqB3Sn9Eapy7Fke3h2RufLO6iz8l8Dj4+Nzw72/38XeMxK2K7lkADZFEWCLkBK/UxgPyQWB6C5WT8LPN4pZAPtyIg1UKQtFCyvoinuTwYe88LXWnF0CWpOeCKq4upvt2MpcIxNgzkc5WquVq2mdA3XNC/PJPWql6m7YaJVX05NcCktETUuf2L/u53uHp5hSEgklaSnUWO8V2w11hXoS3sVCgOOQb1kou9ZAPzB+NyPhl+GvpKrgYdKSElthlJp/ww8vijyvdlkC55votTRGyiCNMFWVFyE9u+auIUYn1Goyd9awDGBx9+Nz3boLg7UtfljJJiSHPnZyxwO7Im+fGujH6e/I3P4GihduAUyjXdh02+XoM/iHCSUpiEh+iyavu5wNJrfoaE8PCE2XXEHuumJpra6ytSrmdayoudWgLa2tlLtAM7DU8c4wSOipenP2P9eSqYPD0TGSuRbWODxqfF5A0U1FiB/ikHjDYYBtwde5mJsUy5n2y//KchYe4jx+V3g8UIhO2B81kDpojcDryx51i7BY4egjkWCZzkkUB5FKamNUOoou4IKW411N7Ay6rXzjPE5Bvg2Cl1PQVG1ORRQZmjF035ocOhA5Nn5CyqF/xJF0R5G4elvoeMfdmveHImsTex+vGffdz/w6zo2jzuqT6P5HRrRwxMSpra2ISN6wjL1sTXw8oQ08jGNo9HO6YrgUlpiNhlDbUhchAcKEDw2LTUE+CTwmITKtd9AF+gDgL8Yn6uNzzeiHZ6tefl7SPAsA1xtfH6TL9ViIx5fR434Xsq3fQUSrdL6OhIYC1AU6TYkWM5EkZEHYrZpDWQWHoVmjk1GXp9D7bLnorupg1FZ+clWmPTA+IwxPqcD1yIh8xTwo8DjAjJVYkuA5WzU7DlgK+Ozh/E5FfX7+QGKMj2OZnJtbrfvJOCQoo+Ooy/TaH6OUre35n6TOi5Tb0RfVC4a7ZyuCE7w0JWOmkr3Sq1sD0/BgoeeFVoj0MnmI7/LVXZdlwMPGJ/vWFMvgUc68HgUCYGr0aynO4zPyVbYdMOmlXZAeed/5kqD2SGc+Rt7pVIDUPQvFDw7oXTeNGQQnoAiUSugzsfdoiPGZ20kJpa1+zEfTU3fAZ1zU1D/nTsDjw/t8VgZODEUgHZbNzc+F6KuyFuiaMzxgcflgUdgVxeawjuBlWyH5gHobuYsVN7+OHZWV+AxIfB42/bfaUNdoH9tfL6d97g4moJG83P0Ynvrwo9RpyMnGs4XlYtGO6crhUtpZZiGGuuFZFdpFSN4wh48Yf+YkfbvnMBjNnC98bkRGYEPReKhzfhMROmut22K6E/G5wEUnTgM2Mv4XIu6G4cn7BZ2+U8EXuJ02XBA5xbAcOPzZB5/UDg4NNzXvdGXZCRK+bUD3wfuCDxezlrP+mjaeQsqMV0b+C6qSPsE+ZiujW5r4PG68ZmAKqgOMT4zUSRsTSRobkQm6bg7lBQSS5sjkbMtihi9Z7f/1KTmgoFHu/E5Hg0Xvdz25Plr3GsdzUWj+TlK3N568mPElanXxMtjaUhfVC4a7ZyuBE7wZJgKHGh8RgYec5DgGYIiB2kkAtIUFhZcnp4l6RCZ/movwo8Bj9lZW4cCewEHGJ8XUNroCdt07yLjcycSRqej3jGXAx+iMuzXA48PkjYmInbGoD5C+U72UPB8aaNKm6LjsDzqrHwuukO4KGs9GyOD8hLk79kDmYuHoSaFf7T7FOeXeRbYF1VwfYIM4Fegaqtuw1dtynBzNEpiU3sMFqPxFTegyqvz7D6PRdVfsQQeC43PcShqNd74HBdowrrDEdJofo5Ct7ee/Bj15uVptM+8WPr6/sXiBE+G0Lg8Bnlt2oFlbWXPUqynpcDKqeUhUxJNRvDMi3ktgccU4ALbiXg/5Cn5LTDT+NyFIjpTjE8bGn75Y+D3SBjcCt2jLFFixM5/C9j+LsGDOly2omjVEtS1eF80/+qjyHo2RyLtC+BHKAL0ddRf53Hg99HXR943EjUe3BMJzHdRZOaKwOOtyOtCkRMaj8Ntegx5esJy/s/RcZ6KTMnfJkbwWPP4pihkvhmZGWk/BSd4HN04g0yjykKp9qDFKIVsb9fgS/SdO8NGAGqCrdjaHVVP/pzMtke9PNX0j5TymdfVMc1Do53TZcEJngxhafraSPAsJXMydKAL6uyY98WxAoo+HInSMSOQWFqS6022b8xNxudmJGwOQ+LhROPzCHBb4PGk8fk3Kr3e3T6/ivG51kamgK6ePCCRUIzYge6CZ0+UNhqIBM05KLJ0ZWRdX0cRklnApcikvQ5KK01Aqa9uaSU7amN/lNbrBzyNKq7eR52Qf2h8fotGRWyDxEk/MiLn38BUK0i3s8drLjIup43PLDSaYiObZkshYRP+W5eMh2068vK8jMSZw5FNsVV8ta76y7f+cPAlZAZfUgeiJ5eXp9qG2VKHiUKdHNM8NNo53Wuc4MkwE6WrwkqtdjLHZykxTQfjsBGVFdCFeZR9uKvpYCHYKNITwBPWAHwo8tHsY3z+i/Ldr6JOwfuiBoa7G58bgDsDTSofgwTFR8ArRYgdyISUQ8GTRoJhJkpVfT/wWGj3d0fkgXkfeXd+avf3eeCywOPVcKH22GyI/Dlbo2P8GHBfGP2xkZen0F3eg6i66xNUBv8sMC0mJRYK0XZgeeOzEvL/bIiiQf+ArrL5efb4PYwEzstRoehwxFDK4MVw0GItLnalbG84+LLWF+d68fKUc5horY9pHI12TpcFJ3gsNiownUylVlilBfrCDaEww/JgYBASAKPsRX4EEf9Okds1DbjE+FyBxM1xqIPzTLuuP6FeNx5KdR1gfH6P0lgrILNvMWIHbITnldW37IeEw1LUJ+dwNHX8XgDjsyuaDTYDeW6ORymsm4GrA4/P7etaUJTmADT24Qs0ZmJi4DHP+AyxHZsPRGmwd1DEbXUUfTkrKZVoS9kNElAj7Pa2opYCA+xx6o+8QU8BM1zPHUeRTnoxMwAAIABJREFUNJqBtdG2N0q9eHn6eqO+Rj5HSsYJnu5MRRdcULRgOeNzMbAxitasYHyWB2bnuGiGIc0AXYSHouqmRFNxIQSaNv4wirasiPwp30d9ex5DZt3bkbH5GiTY/BLEDnabuW3b4zdGaZ8OlMYaCxwbeHQan31Q2fx05JvZEx2//wUeswJyAKp82w9YBTUs/APwN5Se2sL4XZ6cfiiK9DqatTUFpamOQRGuW61wWhulpDZHYe4N7HuXQSm19+1xeMc+/wzKO3cEHtNLOBYOR6kGz84a+Tka1pBaR16evt6or2HPkd7gBE93pqHRDsOAR9DU7jHIPzIClVcfBCwyPu8jj8r79l/432EPnqlI8Iyy7801RTwvxqc/EgCLUVn3Itvcbxwy5n4LiYV/owjTBsD3bLfjq5PmXCUwFOBls82u9v8Xo35Afwk8njc+B6KZVAtQlGk0EjEXBR7vG5/hxu8a/bAsEh+/RlGgzZDvKBQ5nwJ/Rc0CB6Ho1XtWML2GhNYptpJqZbp3vH4ZNRV8yS53ll3fQ/Z9GyOx9iIq6b8rqUTd4cjBQ6hBZbGEPa+q7ecodXvroqlcnXh5zkCWgZJnG1rq4pjG0NDnSKk4wdOdrkqtwOMV4DQA43MWGk45AXlxVrf/1kEiJNrMbzA6rgPR2IX9UXSj3fikepFOCUO8fws8TRi2IyR+Y3yuQiXtP0ARnqWoPPwL+/g3jc+fgVvC9+ZhKMCnw0dtZf9/JvriX2B8jgB+g1J0KyKBdylKrY20fW12s/v/AgpRD0KRs+PpKXKmWXEzEEWJtgO+Znw2gq4O00ORuJqEhNVLyLDcleYyml8WiqHlAo93jc8cu433o8aD2yE/j8NRDOVotlZNP0dvmub9sJwb0gtq6uUZP378rW1tbaDPbA3UliN/09ae1NMxjdIXzpGicYKnO9FKrVcij7egFNG/oyZcAOPTiiJBqwOroRLrtdBQzPWAE1AEpA2ZjsOIUHaEaE6SGLJ9egwy2H6S/XzgscD4TEPpos1Q9GR3lP4KIycnAvsbnyuBRwtpPPjlwOHLoS/6KJRm2g39ALTbx2cDi1AE5WSU8kqjNNJ05Kc5Cf1QtAOvoflW4XObAUfbkvavIJE02D7/PIoivYQiRD9B0ZsnA4+PY7Z5NooALSATZfsEic0HkHn72zjB4yieRvNz9AV/Rs29PH28UV9fOEcypFIp5DG9lXQ6MajgBE933oeuCqcoob+kh2nZpkg+sP+wFUJPBh43GJ/z0TH+Fqo4motE0XpoXEN0tMeCiBiKCqH5yID8IUpZ9cD4bIGiTbPQRf5sFJU5BEWYlrGPD0Xl7OOMBpMm+XuGASzsPwTo2r+FKC2VRuLvQSRsrgTuRMLkDSSANkJpwFkokvMGMlyHfp2vkelNtACJy+vsfq8K3GxHTkT38XKU1z/Z+FwU01V6NhJMH6MBp9j1b4B+KB9A5f3rBh5vJ+y3wxFHo/k5Gt6fUUdenpCGP6ZZlLo/n7W1tc1Awqeeeg1tgTIwb0LywG0neCLYL9kMus/UAh2nFvJUaUVK0sNGgDNRafTnwAPRyec2MrQyusCHKbJVURXTTnZ9KWR4XmyXuXOMd2htJHbetH+3Bx63guFy43MN+nE4DPWeaUXeopusCfrKmIaAQxe1DkqnW1pSyAPzEkoJpe0670aC5SgkMNZA0ZfVUTn+6/Z9qyFj9VooytSKTsa/2v15CZgS3qlZsXggqqrqRuAxy0anTkdjOC7LusMLS9M76B7hAaW1HgOORsLrf7OX73DkoFS/Q9xyqkGp/pO68mfUiZcnpK95XkrdnxHUU6+hVGoldJ08Gl2fjiaV+oB0embcy53g6ck0FIWI0p8CBA/6Ig4iMzT0EzLzuTqNz2XA/YHHkzYy9J7992x0IRExtDeKUHyKhM8GqFFfGBkaSmao5xQkfgYDo41PAMyznp2/GJ/7UArpUORHWh5VQIXzuW4MZ1WlYeiXA4el7Dpmo3LypcjjNMO+P4x4vW2X9TXgv8irE96NzUKi5k67zSsEHufmOH5hY8Yeggcg8HjL+NyESvMPQ4o+JBQ8aWCg7cwcGrVHWTP134A9jM8fA6+0NgGOpqRcAxOr4n/ohf+kHv0ZRXl5UuenhqNCjnVQKvyO9LnpL8qwHX3N81Lq/sSJz9r05kmlNkN2ik6UWUihwqLQRtEDJ3h6MhU18RsUMfi2ooOZz/AbRhbCoaFhGqkVCZudkEhInO0ESpPZSrHPUYXVm+FztlprNBJSX0MRjRZURbWWXdeFSDzMNz4fkBFWHyBfzLXI43Mo+mG4CDjBluDf89wyK6/W3joQJB5G22XNRmJqM2QOnotE2QD7umWQX+ZG9CP1MvBh6Euy3ZC/aXz65+g43W7/xgoee2z+aXxWQcLlo8Dr6ow8j+4TgJezImcBivCA0lp7oxlftyetw+HIotE8PH1hmGhIwV6e1Pmp7YGHYeBgWNwKgzthyRUt5w/Zo/PcBb317vUtz0t5t6s2+5hOv0QqtQ9wC7o2ga4diQK3JemJJmYaEjdrRR7rDywpoMIqDPVFIzwD7fvnoi/udvk2wPisiITFe1GxA2DFwopIfN0YeJyGPDs3IqV9D8p5X4FK6+chT83RwFnI2HwTSu28g6aXv4eiQzcCL725yqY7LBgwFHscUkjQLIdSVMOQiFuERkH8HNgVDevsj3rwTAw8Psg6XqFYHJRj13NGeCLchXw/h9tqLuy65pBR9tG01ij7mvfQHcHeNormcBRCuXwYtRwmWs7XVQ0rZnYHLiNzQwTdvTxhZOdhYLjEDsDCFlg6GFJ/H3DR5sv0clNKPTZ1d0wt5dyu2u1jOv0QcBW6TnXYv1clvdwJnp6EpelRH09/KKgiIJySHo4q+ATdjSyxKayngNWN31Vu3QNbnr09GuvwbMzzmyMvzpu2dB7UbwYUwZmHJpLfEXhcFnicEngchCJCh6HRD1egUQ3z7PZ9iaqYOoFN0rCsFTzY/Z6NPDBnovTWg8CfA49TA4+bA4/XgN+hE/9S2/04m1DwxD0XUpDgsRVm19htPsn2GsJu5zDkeYoal5ePzBZ7AH1OeYWnw2E5g+4X21Ko9TDRfNtft4MhrejJ5eUBGKfIThypfq0tYy7p5WaUcg7U7TFF27WgyPcstf+i1MM+HoG0zH327xFJL3SCpycBuvBHK7UG0PODjmMFVF4eiqO56OIdvvdp+zf2YmtNz9uhKMg/7Uys6POh2HkrFDvGZxCq+pqGohsL40rObWToYyTGFiMlvCwqDzcoPTUYSD239vY8seEeIPG2EKXh1kCRpRQSGiZr+QuBX9jHvZjdCwVP4vwWu92dFDDB16YbL0fH9mTjMxQJnpH2b1Tw9Iv8/3NkStQdjkLp7TiSWo8zybf+Wm9fPkIvT5QuLw+wTiay0500X9KSGlkOH1afGbZpU57Ho+tdGmUl8l3j4vantvuYSvUD3gK2I50+CLVGmZL0chfWzyLwWGJ83qWn4Ck0whOms8L5XJ1kTorpSHRshxoDZrMR8sE8F3jdJ7NniZ2XI09tiC7or6Lw7gIbzVjdvn49+3ddJFoGIdER9haKGhrTQPrKXc9MgZSNfd3y9t93UCpsMDDIzh6bbP+9G3g8a3zuAL5rfB6ykZ+QhfZvrggPdpvypbQACDw+tXPDfoqMao/a7V1MZnBrtFLrU/uZPIg8S+sEHu8Usi5HU1OOQZL1Pky03gdD5vTyQOsd0G9pnOhJMZSB/bZcuZcNC/vcsM0sn9cMMpaMJOJ+l2u7j+l0B+oPF/7/s93+PwsX4YlnKt1TWnkjPFlT0qOEk8ZDn8lTwLaRFEv4/pVQ75oZ2X1iksSONTBvhTw1+6FQ3lGoad9EFAH5EaqcGoTK5F9FZePzUG+fTrtvLfZvR5p2rL5rt9u/GN1dTUaCbQEyLh+EysSvAe4wPuehnjtfAr/K8smE08pzeXigCMEDYI/VjUj47WQf7gSG2NTaHLszoyJvexRFnPYtdD2OpqbhTMslrrdeDbZ5vTwrD39tLixeGPtmWhgy4LD+qClsqfQ103I2vdnORtlHF+FJYBrqeRNeePuhsvJcoyGGIrWbLXha7XtbbMrmaeSD2RA7X8temMcid/lz0TdHxE4ApI3PQWQiN5uiLs9foIv6QOTjeRmJl4H2bwcSHANQymcRKr1fFqWr+qMfkUXACp0dszpIDUq19lshhUTRiig9NxT14ZmJ+jHMRJGbTiSC1kF3YO1IfDxsfG6x+xl2sS5E8BR1XgYeT1kfz75kOjqDKrU+MD6fkqnUIvD4MlKifoMrUXfkodEaD8att5Dtr9Ww04LI1Zenf7/11wf2hNZHUwwcnOZLUgwFWlhx2MO0pIangKuNz4MlRnkabYBssfTmHK9XY3YPnOCJZyqKeKyB/CDRKEjSlyWsCupKadkoTisSFcvZ556xT28HTLaRobFIjPwdiZp1kKjZBQmTZey/kIWowmo68LhdZn8U3Vlit3UhmQv/ykgYtdp/q5KJpAxCImEeitosXbzkxZZ+/Ua3tPZbARThec8uYw6ZRoidSNw9haIn69llvojSSAcCW9r1LbbLWR4YYX1HkwOvhzjEbkteD08M99htPAK6OkgvhwTgJ0iMRXEl6o5CafRBkoU2mavVsNNiSOzLs8aI9DMffbH56GEDjn93aee7y/ZvWYchA8bRkup66QqU3qG51HOgEY4p9K65Zr02V+yBS2nFE0YjxqAvVif5Iw9h/jN6EV/Wvm8xEhwEHp8iU9V2NipxNBoB8Q3gBpSOug/4vX18IIoK/R74MbpA74Uu2PPs82ORuOiHxM8E1NF4PVTF0B+JlVGoeeFClA8fZp/7xD4/BHg/zcfp9vbnl9ptXxb9oLyAqsHetO9vRYbnFZAx+A+oXLzDrvM99MOUQiXrt9r//zoyN08wPn8yPqcZnz2Mz+pW/BWV0gqxkbdrUdRpb7svUePyYNvbKHy9K1F3FESMwbNUH0i5GhhWY71hQ7l6I/TyRJvAhl6eR1Ye/uKXwwf+4OiRgy9NDxv43ajYAf1mbV7KSst0DtTrMYXenZu1Oq+Lxv3QxzMNndRrowt3OD8qjFbEsTwSRnMij4WCpxPY3vhsgkTIqsA3UZRnCIpqTEddi/9FJpr0r8DjPzbltS4aO3E0irKsi9JMTyKX+kfoy7Qi6qDciSI8S5HPZ2O7Lf9CwuhbyGsTGnZmogGeL6fTC1df2vlqB5l017Loh2YeEluXo5Tcqigq1IFE1yzkp1mMhM1QFKW6FImh51Gp/WvIoL0xEmrftMfrc7s9nxifyWgieiHVcQAEHouNz53A94BtUVdoyHRcXpHuP5QPIDHmpqg7ctLggyT7jP+kiBlbM+jeSy3kGONzcSlprQY/B/LhPDzNSuCxyPh8iCI8LyDx0E7u47U8umCva/wuj83XyfTI2dQ+Px9dgNdAEYZnUE+buQDGZzMkbD5H/WN+ZF8bdnp+BxmDp6N+OPPQxf0YFFl5EbgfiZld7WPDkJh62v7/bihVNxUJjlfQj8PnwOud6S/2Tqfnp9GPxkZI8G0AXIwEwo9QKmtrJAo/QKJrMBpW+gESVveiVNNWdj0b2ONwGxI+99ljs4o9ThvZbdsUia924/MGmUqwN2z5ey5mAP9BqcCDjM9dREZM2OMW8jwSem6KuqMYGm2QZKNtb05yeHmGorESk1Bz1fNi3r46mQh5b+hTxxTn4ak8bW1t1yJhUA6mjB8//oQyLQskBqIpra4vmPXmrEr3ku/dyAzUBEVTvrD/XkWC45eoymkIuoAvRd2SPzc+y6PQ4DYoqjIPCY13gb8hQfGe/bLva5/7HyQUWtBF/WXkR9kSCZuV0QX9MRS1+jaKZryHLvxj7Ho+Ryf7v4EF6fTczs70AlC+fGMUwRlm338cusvZ1e7LI0jQbENmYvoa6IdnOnABmZlXD9t1botMzXOQ8HkGeDTweMT4vIDEystkRNBhSPB1Gp+pZATQ5MDrFlEDCbnZdl1HAscC19v9XDH6wsCj0/g8gCtRdxRHow2S7BPDRLOI8/KkkHdwZdRcNc4P2ApcZnwm9qJEHRrvHMhHU3h4ah3hWQ95V/6/vfeOj+O8zv2/u+gARZEiKRaVEdV7pyVLVuIaRW5x4jgucnKdxE6uGd+My/2lyJZlO3bi+CZ2xkmY4po4sn6K5cRRpCi6LpKt3gspSiQliiNSLKLECoAkQOzeP57z7g4G27BYALvA+3w++ADYnfLO7Ow7zzznOec0IzYihWYpCuksBP44iDgGGWCT9WS2oC/fvajdwgYU630bKsJ3K7AkDtkOEEScirKflgNvtP/PRKGqF1Da9DrgWdfQ09brDSLegsjEftvGnbbfk9HE9lvIuzII3IVITAaRhvNtuzspenO+hmLTWUReenK53eTzB/JIAbka+YT22xhPR/V4/sv+3o36c70ZuMj2/SgiGOfbMf4Qpc2/jAjOP9l7r0applcBW4OI+zA1LQ65G6lEWCPQ0ymGwd5s28OUuDUUSZAjQGvt9TfYZ/ESKcJj+L8Uu6h/ucT7Hh5ptFQjyRnWTNTBeXkuZ3TmZw96+AI9fJ1XYt3lTFzlaalroAZM1MPTjMc0BtNNeJoZz6Gngz9HT0Z5REieQqGa9YjYPIvIwieB/4zDQjVlgoh5FFWaM4OIE1EY6Epb5zTURmKfbecu1BYin9hGBk1Sl6P+Wicj784qpIJkEQF4JwovjSBS84htcxEiQS48d4CiYnMX6mT+aURQngR+Lp/fm7fCyLtsbK6K8VwUsroO+WTuBP4PMlJ/DZEQVw9nA8ogW4iI1T6kuPx9HHIITVYPmJF4BSI/77Tl86bkPBiH7DfS96j9uG7yJ1FUgC6lWGxqr31O3YgQZlGT1DuAo9LNSxMp6r9gXdT34uFRGS3niZlBzUSBUV6eb6OHskzi7V4UFl+BVOj5qdWzKCFkIoSn5a6BKvAenlmOn1A0CrcjovCncTg2XhlEnGB/ptOsl6Iv14XoqeOPUPfxbWjy+SgK+7wErI9DHktssxMpJpejpp2HEJF5ESkxzwBXILJxNMoCG0AkxKW+Hw182H7/DD3VHUQqjMuseq+Nca8d5+E8A8laQ1sRyepCCs3xKJx1M5KVTwS+gyaQbyC2fx5Sg+YileWfkfLyVWQa/CkiMwfjkH5ERu4IIhYicvIWpCK9L4hYg1ShR40oYUbmdfbzfSOFx1JUgN5p4+iBQsXq01Bo8RiKZmaHm22fV+FT1D2qo9X9G7WOv1nGWxJGem5ED1pj0tSRkvVflO6t9IYJVl5u9WsgDe/hmc2wJ/0vBREfQzfOoyl/vlwNnr1BxBkoVHcquunOReGjXYgw3IXCXgeA/4nUkM87shNELEEkZwVSKbahDKeHEcFZiJSnTyJC1Y3I0Hbkzflv5CVaDKxE6syP0FPPIRvHsUghGkSEZ7etfwkwlMsP5DVX0IHIwYl2jHsQUXojIlVPIxVpBSI9745DbrLWHK+zc3YxMgr+GJGJq22f5wYRDyEiM2zn/OUg4ido5z9EYa9Lgd9BBubHbL9rkhOVKWKb3fEHES+ip7olic/tTSgsdk4Q8TBS6tYAa+OQF2zbbw0ibhpPZpjHrMQ1wLcYX72oZmiy6FDL+JtpvJVQpeUEf0PxoS6Jo5hYWKvVr4E0ajkeNy8m74PNfExj4OvwVMcciqnMowhPEJExT8/rkarxMeC3UXhmDyIr9wGfQgpKBoWs+pExdwsiH08GERdaRtYfYUUJgQj4UhxyDyIsZ9t+PojITg6pQ3ehcNVLiGC5Bp5taEJwdW0eQCrIQ8gfczFSsdYjFeVi4HA+P2Cbptu2uQcpIwehQCbehpj9HBQyWwz8cxAxLw55EBmVY/QFWow8Ms8gifkiRJxeA3wwiLjAGgBCsWP6zjjke8D/Rmnt99jxfxT4qyDiN4KIU0zdSWOX7fcepCR9EKk3m9GXdhiR0WuBG4KIr9l5W2CfnYdHNbR6I8lWbyYKVG85gSayTSVW7UTm5Vq8TOXQ6tdAGvVcE81+TKPgFZ4KsJvpHHSjBGgPIo5A4RGn4sxBysFhRDzWAxvjkMNBxBWIICxC5KE/DtkcRJxv23gQpZN/hWLl4v8EHjBS5MYxF/gEIiTrUegqg8jIj+3/d6Ev/NnIhLsPhbeOsn3/AD3RDKACh+0Us5/utmUvBZbl84N5Mh0ZKPSiegb5h4YQeYptLM4j9AxSq74OfCOI+A1r6fBNRCxOsTEstHN5EiJ8q5Bq9Xrg4iDiHoodkTuhoN6sB9YHEd9FhO3V6OntdcArQcT9wP1xyBZbd5ftbwA4IojojEPWWo2eX0DK1B/buM5K/GDHdRd1whS63S785jEj0eqNJGdCM9ECqqSpvwvNnTcyVr2YSIp6q18DadRyPM3XPHSc8ISnMrqQStKHnP2/lXivH7sRoxvvzjgck543D4WuLsX6TlmdndchlWQpUm66UDG/Z1KG5Q709PILiFRtREQGdFN+Gt3YO207x6EQ2Ysoc2seCqf9u41/KRDFIXuCiGOR/2cLxYai+7Wcq5VIr+3vfvS01IOI3QZEVE5AhOm4OORuU6j+FvjHIOK34pCDwE1BxKtsXDk7b9sQQfrvOORG80C9BqkwIzbmMV8uCzU9ATxh7SkuQOTnKuAtQcQWG+t2W8Wdy6PstfvsPLwG2BqH3GbH/b2EDyid5l4z7DhOQqRufb3b8Wh6tLphteWbiZZAtTT1LYxuCA1FlaeeFPVWvwbS8KZlj0K641moON4aijVxtsUhebtRvg2pHAVYJtFcFM7ZiDKq3obIz7C99n1EZA7FIU8n1s2gG/mb0c2/DakW+yn6dNooErIMChMdj4jOw6gC8iIU0tqHVJR7gQetjtDb0ORwPyIwPfb7/Lbsydl8fjCPro89yLg8hMzLa9BEsY5in6/jUSf3HwYRfwD8JfA3QcSH45DDcciD5qv5ACIfzyB/zT8EEe+IQzYEETFSXK5CGRbvDCL+3VpAjIGRqfuA+0wBW2Hn9ldtfAuQwtNJkfDsTHxO7woitjvvVMIHNG7Y53UKIpzb7Tx6zFy0eiPJGdFMNIVqaepfAf6Q0irPt838fNs4iI83LY9etyXgPTyVsRe4CWUZ3Qf8IA65Iw7ZmlBi5qAv0cupdeci0nEkesL4FZS5FAN/AXwmDvkvlD11qYslBxGnIx/PexHJWIv8M/uRLPt9RHSyiOx0Au9D6eBrUZisE5GQnyJC836kXPyTjXsOIgb7Uar3dqTmbAb2dnVc1p7JzgURqW1IlYlt/OuRErIWEal5iLQBEIf8AKW5vx74S3dccciLKGzmJpTrESH7UhDxRqAzDlmPzM9P23n7tSDinUGkPmTlEIfsi0N+HId8AfgDO08diDD+MvC7QcQlUEg5vw898f1uEHFcpW1Xg5HHM9HE+QIyQbdUXNtj3Ki30Jp7OHGNJN/XuCGNC7WOv1nGWxUJL89NjPWV9KKHzC3p9dBceTWq/n77ODw99V4DzVqkbyLjatZjGgNPeCogDsnHIfejG3CO0oqYy9AqpKRbuOVqdBM8HmUbbUUqz1fjkMcSTxL3opv7a4KID6Nql3Oh4Gd5Ffqifg55do6i2OaiB4XZrkCK0bPoy34yIjq3oHTxhcA34pD9pkZciEjK86je0E70VJQFHmpvP72tLXusm+y22r4ehEJdoTZbdi0Kcb0mdd6uB76E/DufM3P3sYi4PYVUsj0oPOXCXe8PIo63Y9uKPEc/RUrQ+4OItwVRoRloWcQhO+OQm1FG2L8jtStAIbRP2VjfgcoODAKhKUTjhk2O59oYn41DNniyMyvQiGaJ09lIciY1Ey3A5tQbkbKbhPP3fAIrMJaC82pehjw9tWAihQebEbOieagnPLWhVDqeQ6FLehBxbBDxbtRC4t223h3oBv8viLgcnVr/CaQEXYvCInejAoDnIWKxGhmZu9DTyAE0+fQhcnQequTcj27gZ6J49k0ozHMF8NNEjZ8e1CIia/vZgtSQDpyBOZ+jq31Fu21zt+33TkR2VlCszfMUCrWdboSmgDjkH5Ap+T3A5ymSvm2IZF2PQnMjyNN0GKkxr7HjzsYhDyMj9P3Ie/OBIOIXzDheDbsQSb0N9Rb7op2XAFVf/lVEUhcA/8v8UjXDlr8QEdCn45B4POt7tDQa5VnwzUQbj0rd1D+CHiT7KZ1d1EPtmVsz7RzOCg+PJzy1oRLhcUX//gdKoV6BVIqH0M19L/KN3JlYniCizSqFfgQ9dXSjG/OpKNPKEaURdGM+ZD9zEdn53+jm/U0k1x5GF97TqG7PQkS6XmK0g/5o5JOJEfHYhr7ormv79uGR1SPtbWe0oYnBPS1tQKrMJSgMtgCRr3XIgH2VhXeS+LKN5bcQybjfjqfbQlx/i4jg6ch/sxplr70Khc2IQw5ZWv7XEWE5E/jtIOK1QTSqvUcau2xcrkL0NhSO+x4q3PgUxZj1yajQYU0wBe8i9FS4Og7ZWsM6C4KIs8uk0Xu0FhrlWZjOZqJTud6UoYY09a8iu8D1qfcdjkHzYzXUey5yK1euzK1cuXJTk4UIJ/LZNv114eAJT21w4acC4QkiFgYRvwT8LrphdqMQyq1Itdljr62OQx6NQ4aQWnJ0ELECtWd4K7oR34Iynt6CwktfRU8ilyHCtAmpHq6I4TX299+hzzCHCM5zyOOTR9kJc4FvxqFISxDRhSaDuSj8tQURoh77GQb6h4YfGM5kujPIqO0IzzAiCcvQRHHAjvsJe+8CiqndDnNQWOpnqA7Pr1Mkd854/Elb5pfRhHQHIm9vDCJebxWniUMG45A7EMF7BqkrHwwiXu2WSWGXnbNh+9xeZ6/fhQjkt1Gtoq8jI3a6/HxJBBF9KHW9C3g8Dgud2Mst3xZEBRLbRenUTo/WwjUUyydMBNPZTLT27WxTAAAgAElEQVSe8beEV8NIT6U09dtQAkU5T88FNeym3nPYrL4o7+HxKMARns4g4lzz2nwKNT7tRx/4n6F08GXopr4UkYW7U9t5F1ITDqMmmk8ib0w3Ukv+ARGKK+y1e9DNuBeRmmttOxEKhQ2gUFg/Uoj2oAyvS4EfxyGrE/ufg4jHPkQadhoR60WTQx7oz+cHyOV35ZDa4iaPDjuWLjvux5Ey8wQiP/NRufZuAFNfLrV1fxupKp9C5upCFoV5Xj6MSMgvU0yVfw5lgb0/aSw2g/J/27mLESn8oBVuTErRrqVEFnmpFqDijC4Ta1EcciAOuScO+cs45C+oAvP6XIQmrEdLdGpPL3+ELb/U9vuYnW+PFoZlK30IXX95qLs9wbR4HyYw/pbxalBMU0/Cpanfbv9/jLGenizKEK0Y1mrQNdBMvqhZ4eHxaek1wNLPR1BLhQwiFbehbJ9PIPPuPBTOcrVqDgAvxiG5IGIRupm/Cnk+voJUm19CmUo/tHUXIkm1D4WnNqHPaAQpQB9HPpiv2//9qO7LQYqm5QB9qV9E4RtglMH2bESytlOsV9ODvuh5YDCbnZ85PLJxBN7gmvD1U2w2+lFEeL6LiFUPmljyiOxdEqjr+aXoaebeOKQ/iPgo8I8oHX4XasDqzu+OIOJP0Jd/iZ2DYZSx9XPArwQRTwJ3J9pQvALcbIX+rkAKzkW277W2jwwiTR2o7s7eICoQjkWMQ4oNIhbYtoYQcTlQYdkMIlkn2PJPxCF7at2XR/OjzmacafhmopOHamnqVyE1fh1jO6qfSg0FCVv9GkjBe3g8RuEZ9OX4BvC5OCw8JXQiAnQFulkfRMRjANgfRLwLyZ9noC/galvug4jMfCsO+Q56InkD+kJ2IyVll23vQpRuvR75XgLbvgs5PYdaNvQiYtVj203elI9ATDxr29kBbDffTZeNZSAOyWcz8zMjI5tc5efXI8JzBCIIm1EBrx5Ess6jGHIbRKbjX7CxPBiHKpRoysZKW+c3gojXps7vvyKPz+vteAIUfvuxnZtzkdqTNkdvtxYUNyGSeSXyUx2DSM0SRIA6bPkDdjxp83hZWFr8eXZ8D1chO9227HIUnnzYk50Zj1b3xNQ6jmYZb1VUSVPvQ/5G0INX+v3xmJcdZss10Oh1pxSe8NSIOORrccjfxyGr4xDH5l1K+nHITLsfmYYfR0rK6YgIPQ18AXlgzkbKyJ1I6dlgCtB6pNbsR2pPHpGsX0Qp1fcDX0Phq0EUQhqhqNYcsO1eCNxeopDhAtRA05GWfXHIICJXGURY+gEymfnZXP7lPEqZPw2pOx2234eQSnWR/T0XEb5OdIM/1cbxSBwWwkruHA4Cf418Q6usNo57L4eIoesSfxe6Pt+HSN33kFfpnWZY7khtO45D/oXiU9nv2NiziJAlU9p3IjJUFRZOO9uO0Xmxyi27GPl75qDMrad9I9JZgWsobYCthGZquljL+JtpvDWhQpp6MrT1OKVT1V3biVoxG66BwzBmPjsM9DWpEXsMPOGZGBagG+np6GJ5HN2UP2mv7UYG5H9DtV9eg75ct5pClEFy4Hxk3B1BRQKPRmrQB9CTyG0oq+BMRDp6EEHZCIXGor0oRBYj83QSfUg9mod5d5BZGtsWiND0BxHZbHZeJpfbnUckaxiRKBA5uh8zFSPFqx8pRH32M2LL9ZU5Z3vRJLQZ+HoQFeXkOGQTarFxLjJEf9OO8U1Ihr7JzvF5wNWBGreOQhyyARGxDagY5AUoJf7YxBPbS8CCak9wQcSJiMDtRAblkuQliGgPIs5En3k/UnVeqrRtjxmHVm8kOSOaiZaAC22lSU0PCne54qpp1NNcdDZeA+3IitGMRuwx8IRnYliObsygG/MVKAW7Dd0kb0OEJkQhlv8fhbTazQB7PLpgtiJD7RDyomxFBQPfhGLEtyD/yAFbvguFpH5iyshiFDLrBL5donHlHFR1eB8KF+1jtH8nb+v2A/MgQy6/K48miYcQAemx7TyGVKhz7f+HbTzOWL3BtvPz1l4jDRcO+g0UMvuWZTE5fAMpMu9G6es3oSKBp6Asr/WI+AD8ahDx80m1J4g4BaW032nb2mbjWwG8x4oX7kTXvlPoRsEKJZ6OPt+tMErVSy87H6k6C1Ehxycs+8xj9mAijSSbAeNpJtpSqBLa6kZtcD5BaXVjPCrPbLgGOqju+20mI/YYTLdpuZENFqe0WaOlQr8dnUNHVA4hcvJT9CVbgcjIk8AtVun4F9HNex1Sa7Yj5WUxCn2dhwx1i4C/Qf6TcxH5yKOLsh/4YRwyZJ6RM1Am0N+YwpEcZzdSKc5B6dfbgUHnrUEXKIgA9GMkIJffnUdq1V3IbH0GOsfbbTsXozT0B4HftPU2I9/RUmTYPR8RoiQO2r52IQJzI/DPQcS7LSw1EkR8DYUA/xj4Q+vFtQWpZL+OfD3Xoye084HlQcQP7bM4CdgSh6wzf9KLqHCj63K/mESmFoxWYmyds5DKtikOeY4SsOWWI3I1iMJd/aWW9ZjxaPUidDOxmWgBNqfciIqNdqfeXobmuYk2F50t18BUb6uhmFbCs2rVqt+Zzv1PEO9HEt4hdOO8D91Y86i68FIUPvp2HLIOCuTjMFJ7Xka+kGWots5epNoci1SJz6Mv4bmIUB1C5CSPyM6A3XQdmVmP0tLTOAI9pWRtPHspqjsg5cYVw+vX2EbI5/c5wrMTeY9WINVkHVJ9LkcZVFvsWDpQrHyLHU8/cFkQ8UyKCDj1ozsO2RxE/AYiPd8x0rMNmb5/hhScmy11fGsQ8U1kvH4T+lLdZsu+CSlrB2yMT9k+nIcoh6kvdqzn2+ujfDymSJ2L1Kr15RqXWi2eM1DYbivwXDkFyGNWoNUbSc7EZqJp3Ia+q2lS041KhXwCzUOlmotWzdhi9lwDtW6rKeFDWnUgiHgb8oZ0IaLxRfRlOQWljr8KEYt/TpCdo9BN+hVEDvYjheBIRHSWoXRvkIKxGZllh5GCkEGE546EGXgpUpnagJtdynZinB22/Tcib89mrJqyve+aj7o4dT+wIJfb427eOXSN/MiO9ULzsTyCyMVr7Tz81Na9BJGqXUg5WYQMzEkUCA9AHPIsyqo6EpGeBXbMP0Mk5U+DSCpUHHIwFeL6APIM3U6x9tHZ9hso1MmZhz6POXFYCHU9SuKLaYrdhbbsUxXIzrG2XCcKdW3wZGfWo9UbSc64ZqJpmELzMRiTYekMzB+hfCHCWrw8rV7MsZHjaJZjGgNPeOrDWUjVeBzVihlGCsO70c3+P+z3TjO0Hos8HvuR+tCFQlediIgEKHupH4WwTrF9jNhrB239+6wlgytqdwkyMm9ASkcacxCJmm/v70CExxEmZ1h2St8AsNDCWSA1yTUJ3QpcbNWan0eEZjlSkH5g/59n491q52QbcH4QFQgIpAgPQByyBhUnXIoKCnbavj+JFK+PJQ8qDnkQtaTIomysX0ZFEf8aTWDvCiKuQIStHxnLd9lv4pD9sbreb7Rz2YNCdL3Ig5NUwLBluoKIc1HIbDfwUDoLzWPWotUbSc7IZqIlcCvKPC1nYL6BOttNzIBijo0cR7Mc0xh4wlMf/g8iOrtQSOmjSPr8D+DvofDEfwiRmR5gh4VrDiKScCQyOl8C/IUt+0XkebkU3bj32z5OAJ6MQ/mULPRyGvIJbUaEZ9TThYW7+pBZeS/6Ig7aOByhSRKeIUQ0OvP5Pe79EdTEM49UnqNR+CyHyMwcip3Tn0DkxIXy9trY+4ArEj2kxhAeAGsU+mFE9j6FVLDHkVfnA0FUCEO55beiVPWsjek4pFxdj4zhF6KU9hFEdF4B5qWf1IKIOYjstKOCgq+QgpUNuBh9ZuvjkDVpNc1jVmO2+jeaZfw1oQYD83sorfJ0U8xULYtVq1Z9d9WqVSesWrUqS+331mY5h7PCw+MJTx2wL47L8DkDmY2/HIfcb+GNeeiG3Yc8OzGwL4hYhs75MCIYb0etInai1PWN6Mbci1QZF9baCIVu56Cb+1XoZn4r+vKma030IQXmHKTIvGD7TqoXvYi8OCO0DMu5XUmFJ2Nk5Q5Eyt6MVKXnbZ0rEOm5B5GGN5hnxzXU3IhUkTPsf0d4xjT+jEN+Bvw+IpHvs2P4ElKP/iyVjdVrx3YHUntOQCGuBXHIT1BqvvPkrECZaVlEWtw25qGaP3lUN2hvcjzWB+s0O94DKN28VAqrx+xGqzeSbPXx14xEbZ50Jivo4bQU4QE9dE1GIcJmOYeN9N14D88MxH5EHv4pDvmuy3oyL8iJSKHZgj78HLohz0dp4XuAq4HfQ2bnu1E20XLkCcoh8vEq28c9TpUxj8urbNlboJBtlSY8RyBSlKUYzhqBUc0uXVuIPoqEJ58rKjzOw4ORmDUoG20OCmPtR2n5S5HKNICMyl0Ua/QMIELxanvdEZ6SKZBWn+hLdg7/0pa/FpGg/2nnwKWZZ1Bo6W6KIa5fDyIujkNesNdW27l3zUMX2DYWIv/RECIyo86flQ24GFVqjqnSTsJjVqNez0IbkMmRDdYteOu3T/uL3WuCiM+mi2pOAWZaI8xqcAbmNNpRaKsUau2i7tBqvijv4fEojzjkmTgkikOeca8FEUeiC3c+8FKsTtrd6ObdjQjQLqQYvA6RhjvsvTz6wr2AfD5vRoTiJy4l0m70J6P2C+uRWdcV+CtMWKZ+9KAWDxuQQpJDzUKTseUepFzMQeRkIbA3EX7OYRlcFkZ7BYW9epH6tBYZk89GE8he+/8CIxDuaWkDMmVfTAWFJ4Fbgf+yc/QllHn1n8BHrD7OChtHgahYiMsVKnxjEPEr6Pq+3c6nu4nMN0+R6xH2cLJujtXgOYFix+TH45BNiTCgh0cadXsWcmT54Ul/zrMLruo42D7vLODTwF7zlU0JZmAjzIqoYGAGyqo4LqmhVrSaL8p7eDxqg4U+lqEaLwcQUXjFQibLbbHnkZrwZyjGuRrFks+x93OIDD2MDNEnAHe5VgYWVgrQU8YQygAboVjdOCnRzkFPKnORcvQsUlQK4Sx7imxH4bVkSCvpYXFepDZEMg4hledCO87HEZF4NUXPTgZ4lY13PSI4w0jVugCFlIYYWw8jiSFUHfUfUPXozwJ/YmNcZcf3aDoElcriOhmFuLpsbHeimkEjiHDutm0UvDh2k7kAnecdiAyN2kcS9rkfawZyj9mLuj0L6xe8mcNtvZDJ6EfoAfbYA86UoE7/SRpN690oAWdgHk87iPGEtVrNF+U9PB7VYV+AABGPnXHIFuThGUIy6CBSHXpQ24SLUKPMXYg4LEPqyMuobcNrETk6hFoVOLg+TccA/2mKBrbfwUTIqxPd5N+MbupbkcKTRzdxB/cE6b7Aw4hIJAmPUzUuROrPIzbGI1AW2VpEfC60ccSI4CwBAuudtRldZ8/ZebnclqlEeBwJuQGRnvehbKzv2jk5pZS52CGVxfU2e/kIdI7n2Xl4Iql2mepzsZ2XtabglXzStcy749Bnebxt02P2om7PwotzX1XurU4gnobwFrR+I8yqSBiYv8jY/lDlMJ7Ky612Dr2Hx6NmZBHB2WMEaBm6ae9CJOBoVDX5BJRq/QAiCK9DHpwYeXlegzKK/sm292ooFLo7GRX6W4cyphx6Ge3fOQJ9MS+wZbejyXNXqvGlIzzuGnAFt9IKz+mIZK1FStE22+brEZHbisJYZ6EQVhcia64x6Ho7Fxlb9hQ79loITyfKiLsedVpfjurz/E66a3oaiRCXq5R8DiInW1CdnRxI6QoizkYeob1I1dlZYpNu2QARnePQZ/dkuZo9HrMG9TSOBCCXqchnFgPrpsnT08qNMGuCkZ7PUfsNup3a+2u12jmst3loqWWatpmoJzwThH1pdqAb/VLk1zkK2GwZPacAf4uIyMdRn6hBW3YRCmHdi9SFY4H7rRjfo8DlCQXpTYhIfCdV6K5AeGzZXuTdcYrOM7bvdG2ZXkQsHPFxBCRJeE62MT1vLRZcxeSHkYpzNEpHn4c8MS/a3+uAs4KIPvPHxOhaixGJOZ7KHh5HeDpMuboRhaPehsJoAJ9PpLqXhO373ykaxTfGIesSathR6LwfhaolP1miDxlBRKf5ei5C5G838vakq0h7zF7U5fHqHRpT8imN5UwP6Wn1Rpg1IeHnqZWcLKd2lafVzmEjGsg2dTNRT3gagDhkPyId51IMb2wLIi4CIvRl+l9I8elFBGQ+unHehjwlp6HKvets/XtRKveZFLOFfhCHo8JSYCEt+3sOIhRXIUVmEIVxYCzhSRqW8/b/oMtEassem0GEZyvWp8wUoiGk7OyzcT1tr11OMfNhK7rwL7b/NyDvTIet6xSrcigQniDiZKSmfMrO1YcoNmp9R4VtOKP1Bei83xGHPG+vZ63J6Dm2r0csFJlevytQx/QLEUF9BWVrrbdwnYcH1Nc4EoATd99Ry2JTTXpavRHmeHEr5dPR08iixs7V0GrnsFHNQ9NoKjO7JzyNww5EMJYj0nE+8OeIaHwE3Vh7kbpxLPKxrIFCiOR5pOo43Is+n6tQaOtpZLxNow8YMLVjDgolzUNf4OcQudpX4gadJDwDFIvzkc0cnWlvO73NjukpGKWk9Nu6dyD16iUKPbgKKZZ9tv+Lg4isqSab0Bdmp+33ZNcyogQc4TkOEaMtccjTqEbP3SgrDOBTlqY/Bmb4vMiOf3WiQvUce32ZjfHREinp3UHESYjoLLYxP2ZtJHxqukcadZs0lwysZv7gOsjn9VMey4GHxlkLpl60muF2QkioPLV6ed5Qw+fQaudwMvfbNNeFJzwNgvWYegApGV3Ib/Mcukl3oJDRsUjZ6UOqyVFIqdgB3J1KfX4GKSdvQ+ToX9Kp0aZgdCHC0os+z7dQbEz6lO1je2q9Llv2gI3lACJJrwQRCzvaz2nL5ffmUZNQGJ2q2Y9I0s/QBHEqUpMWohDXYXSBP4Cyvk6y9Z61c+NS7+eiitKlzmWeogrkSJdTmD5MkRjOA65Lr29EyhmQH49DXrJ08+MQiWlH3ptRTT+DiB5Tfi5A4cYdiBA9l0xd9/BIoW6TZoY8r97y18wfXF/L4mcDn54C0jNrChEmcCvFpsPVsIRiMkQ5tNo5nEyjcdOYmD3haSBMydiOhUmAv0Nhk7lIUXCNMV9CYZmLSdXagUIK+nHIIHwi8P04LISmknAKySBSMpbYNp/ATNT2fro6cE9qvQ6KxusV+fwgw4cfG0mMKanw7KeYzv4ACrk9RzF1fQsiPE8iInWJnZshpGL1oFDeMHBGEHF0+qCslcMSG98TSaJnStVvI5IF8JYg4o2JdV3BwCwKVe0O1KX+PHQuX0bFCncn1ukNIk5FRGeBna9H45CNpTw9Hh4pTKjQWoY8J+7+CdUkHvQd+yPg9kkmPRMqpEgTejeqwea6FWiOqgW/X+X9VjuHk1kssGkKEXrC03jcj/wmEVI0ulEI5VSKTHfYXp+DyE76proIqUGuGGG5LCBHeIYRaXmT7XMXCoEtAA64KtAl1juIlJQuFIZbDgwPHX70cELdHWH0deJMukegru559AUdRD6kPcAJVt/mUeB0q0cEUnkOJ8Y4h9F9tggi5iPi0Y/M0mM6kdvxfACRRoDPBRFHmAn5Qtv+I3HIfiNUF9u+nolD1poaRxAxxwoZno+UtxdtvU2pjDYPj0qYcKG1xQNrmHdg4yGqh1U6gcuo3ThbDxpROK6pvBu1wOas06iN9JxZ5f1WO4eTWSywaQoResLTYNiTwk/QDfZIpHJ0ISLQg7wuHfb6FlLp2Vb8bilK+96EzMGXldmdq7KcQU8Gb0c9t3KosOEixqo72DgO2b6d5+Z4G9P9qTYzrmO6gyM8c8zouxal229HIa0MsMTCZg/a/xfbuRlGJKUDkbRXkPJ1qh37EYgcHkCZXmUNclaH59cRSVmMihOeZ+s+DAwFEWcg4/cASjff4fZj752LPqPNiOjEvimoRx2YsEchQ57LNn/FmVarkZ4eak+PrgeN8lw0jXejVoyD9FRLWmi1c+g9PB71wRQEF9o5E92Ab0OelKOQmvATpLAsdutZh/OAYqG/b9h2KhGeDCI4F9l2N6CwUz9jm4U6JA3LbTaug8ADJdKsRyk8NiEcsnVBKk8PChX12esZ4DirZ7MRuCgxOT+HJos2W8f12ZqLJOXDyDt0ECpnpcQh24H3IzL5HnS+H7HxXIwI3ybk4zkYRMwNIs5CYcYjkOL2cByy2ak+Hh51oCEehQz53JvX/d613cOv1BDdGndvp/GgUZ6LpvFujAcJ0rO7wmLXV9lMq53DydxP03i7POGZPLyM1Ift6Cbv0r/3IvViACksSQ/LMvtZgTwwD6CChJeWeZrrQxL3AFJ3tiOy8SQiUq74YQEWPupGhOcIpM50AQ/GYcHzk4QLWSXhjMvYvrYhkjGESEcbxZYaDyAV5QwokMFnbQyHkbqyADVTzVDsbTVEFcJjaEPhwz2o/tDxSOnJocyqGDjSiguejWTiTVgqerlqyh4e40CjPAptGfKZs3b8a3s2X1Vo7ALePUkqT73NRNNoGu/GeGGk5xhK9xXbjQoWVkKrncPJ3E/TeLs84ZkkJG7sO5EB9zx76wHghyick0cF+npM4ViEqi/3A981s+69yPR8Vond9CFS0Icynu5HF9YTiPBsL9H00oWxBlHX9T5EOp4tcyg5IJsq8tcPzAkiMuaxuR1NwK63Vy9FwvM0Csu9KrGNdbbskYioLERPVOsTCpPzJZWEZV2dhozID6OeW/9B0ez9CNAeRJyLVLZuJFM/Eods9UTHo4FoqEdh8cAa+oZeqqbyZIBfZRIMzA1qJgpN5N2oB1aCog8VPt2FHii/ACyuFvpuwXM4VfuZVm/XeIsIeYwPA0jR6UCE5Ql0w80DLwQRzyOF5xQU6nIhr28mjMb32e/LkJqSxAIU+vlF9IV6yf4/bPssF86CYhXlFxHpKNebqtAxPYF+xNqdUnQn8D/seOciMnW81eAZCSIeRkRukY1xEE0gTt3aZ+fpbIoGbXcMY2ChvzMRqYttDCei8J8r3Hg2mqwOIYVtZykDtIdHA9BQj0KGPEv6H2N/1zLGfvVGoRuVtfh0EPG5RpJ4u2F/F2DlypX1fm+axrtRLyyh5D31rNti53AqP6tpuy68wjO5GEBKRT8iBU+mFBcXN+1BBOR1yARcKEBo5txnKO3jOcr28UtICToStV5YgghQqZ5QrvjhYkRynNJULl6dY+x1UjAu2xgPIGLm6g31IqXFPXk+bPtwnRIP2v6WIdLzODoXyxM9skqGtOxp9jyKZMeduz1IwTkOqUVtSLV6NA7Z4cmOxySi4f6HIw9upi0/XEsp/04mP1W91RphNiOa/RxO5Wc1bdeFJzyTCJM9hymSjwVWE8bBhbtAN+39KOR1XKqM/L3AhZbBBRSyufrQjX8BqtqcRyrQEqRolHriO8Xef4FinZ49FZ4OnYcnHdICeYAcbkcqTSciQidhhMW8QeuB8+y4hpHak0PEZxMKQ+VRmnqWEiEt6wR/ITJnu6yweUgt6qAYRluP/DsvlQjpeXg0GnU3Dy2HoweeGuoYGVxb43Y7Ub2ryTIx1+NHaeomktOAeq6RqTyHtXzGtTQPrYZpvS484Zl8OHPyS4j4LLUbOohw9AFvROTh75BK0YdaLyww38u96IZ+UWK7R9h2z0FkIYsyorJI9RgTzgoijkGhnhcRMXLG41JFDR2cMlIgPEbkDibWx/btQm5dSG06JnGs99lxnYsMzF0o/PQiImz77DiOQuG/YeQdarOxu3pGc9E5XWTby9m5GAHWxSGPxyEve6LjMcVo6PWWIZ9/7fPX/RnwRWq7UfYCHw0i3joFnp6XqX7ja+omktOE8V4jU3YOa/yMG3GNT+t14QnP5GMACplR2xBxcd6VdnvvZBTWWW0hrGdtvSXIm7IGEYDLAEwl6UJKxynAj5DS8YSt4zqlF2DViy+07T5k4+i0t8v5d6B4kVfK1HKVlO9GKg/2XjKs9SxSYt6MLvRtiPBtsOPYh9QeZ6Z263UEEX0ozdwVMFxGkfjsB56OQ56wc+eON2MVlBdZ/ywPj8lC3c1DK6CrLX/4Cygb6C6o2toki2p33cAkGZlXrVp1wqpVq7Loe9fSTSSnAY24Rib1HNbwGdfTPLQapvS68IRnkmGmtxGgz7wurwBzrcjeDooO/tXAuVYQ7xhktt2DiNIy5OO53DbrQkln27oxIkRrEeHZlawUbJWOV6AnxbXoYnZFC6Ey4XEKT3oCLWRqJV57gqLp2Bm12+w85G2cZ9mxvWDH5kzHSxAh2mavuwahCxDZWYoIz/G2743A2jjkSWBPgtycEEScidSgs1Da/VEVjs/DY6KYLBPm8RZqvhK4iepP2MkGwpMV3oLWa4zZDPCFCJtgXz5La2owABxh5GAXRe/NJlRc8AUUguqxn16KakbG/t4G/FIQcT664Q+j0NAjKNvqaUQy5pJogmfqxiWIZKxDxOEAo+v/VFN4ytXiceEzF/vdi9SmC22dC1BYKosIzTBScuahJ9buOCQfRPL3oCyrA8jbtNDO02WIALrKzI9QDA/OCyKW2fly4xtB53uH/R70jT89JhkvINWy0citXLky9xZ44elFv/y9jUe9aQDGqJUjjH0Y6UM1em6bpPIL9R5vzrKVXgCusTDKbEGjrpGpOoeTdU2X29eUwCs8UwMX1uo1pcO1e1gK/CgOWReH7I1Dtschz8cha5His4FijZw70Od1Naq5816k9MSIHDyLiAGYf8d8L5ci8nG/rZ8z1clNnP1VGmRWUnhg9AS8D2WYuYyv0+33YuQ12ooqM59jY3IG7s12jo63sb9i270A+HkkBbsCjp0oVHYCCoWNJN57Mg551M7n5jhklyc7HlOAySraVijYdvrOH6zsHt4dMza0VSp0NWk1egyt1hizGdCoQoRTdQ6nslkbj+MAAB4KSURBVGjklO3LE56pwUFEHHqhYPrdgW74JcMtcchIHDJgBtzNqKjednSTfwk1Cj1gi2fRl+DVKAS0LIgIbJk+1DJigGJLCSgSlUrqDhRl9Iqp6Tbmw0jlcWn1xyCycyHy2jyKstA6URZXt62XR4TtaKRQXYqUneVIEXsYhbD22Tl4FpGbx4zcbDFy4zube0wHJr1oW4Z87+s2XjuH8qGttMG0m8kLbbVaY8xpRwMLESYxmedwKotGTtm+fEhrCmBhm0Gg16oT562Tdx9KVR80f0+lbbgCfuehTK5TkKrRhsJIMQpxvYg+18sQeVgDnBBEHEIEYqf5h460TVcjPDlKhLTikMNBxEFGp6aDCM8dyEDZgYp2PYQKLh4GNgURLyGVpieIWE6xB9epaJLuRerWt4CfAQO+e7lHE2NKPAhZcsejqr/vYLSyehCpxstTq7jMLaCh4a1W86M0BRpUiDCNSfOPTdJ2p3VfnvBMHVz83VUnBik1PShVPa5hQnocpbCvRETkCaS87EaTXb+9dqq99jOKXdqPQAToEFJXzkRq0TrzwRxAE+coydzIGpSWzkdlahlcr7BdKGR3BvAdYKHV0elDSs0C9FS6GE3MfUjFmQ/cA6zx7R88WgRT5XfIvXnd791833EfO7SnZ/nBfKatEz2MdJTZv8vcugR4IIi4skHfqUYd72wuTNjs53AqPTxT5u3yIa2pwyCanHrdC1b9dxsiE4vLrAdAENGFCE8OpXavRqTiGaSGnIzIwlm2rSfjkE1xyJ44ZBsKBW0EHkMhIUdu+lFNmwBVKD63LXtSNps9JmNZT0cwtvCgQz/Q5zK1zJycQ56iHYhQn4zI2AlIVToE/BTVeXC9tw6g7LH/C9xgKeae7Hi0CqbKg9CWIZ959eavdF/04j9m5hzaei/KvGxj9FyeDHlNRuZWqzXGbEY06tgn6xxO5WczZd4uT3imCOZTcc3okq8fRDf/OUFUCDOVQi/KYnrR/nc9th5DWU1DiGCcjxSUran1XZXmfqQ25VBz0UdRwcBnUFhsJwyTzfRlkAfnJKQYnRFEnBhELAsi5gcRvbaNBcCZQcQ5KBX8TBR224F8O502ttVIfdqCLvCXkdo1iLw9m1Em1v4K58DDoxkxpU0yM+RZPLC6a3H/k2cztrZLDj3YpH0+LnNrwibmFmyM2Yxo1LFP1jmcrs9mUr1dPqQ1tRhAPp6upME2Dtltfp6jg4gDZfwqWTSx/AjJ1M8hcvEkUmgOoxDZHkQmTjavzC4jWz3A4Thk2PaVwfw79r5TfHZ3XfeCiy+vsfWOQ+qRq4XTgybabqTc5JCvaI/9vwmlxp9qyx6PVKUzbP0cUnkOAk/FIYcsfX7EfD7jxsqVK//R9tcIrF+1atXvNGhbHjMf0+JFmX9g45EUQ+UOQ8gn8rHU6y5za2kjQlst1hizGdHsPqjp/Gwmbd+e8EwtnAzsungnsR1JekuDiBdKtEbIoKepPwe+jEzJT8Qh+4KIk2yZrUgtaUfFCpegWjVbqTFDK4hoz2TmkKHXSYx9KHuqDYXQtiBylazPsxepMxejsNXzto+NqGryG5EKNYLitFuRp2itjb8DEbqJyOSnohR2D4+pxlT6HQpYNLD2BfQg8VoSlcnR3PCA/e5JrJLM3GpkyKLe4/censZsZzIwLdd0Yt+TAk94phCWaXUAyXa7Uu8dDiK2ozDSQsZ2Os+gGjqugduPE+912fKPGVEaBuIgYi5SZU607a635R3h2WPL9FE0Dne1Z09qSyw3gMJNOeD5tAITRByNCE8bRS/ObjSx7rHXT0dEaJNtZxkiSy4818UE1B0Pj2nGNcDXSPjzpgCHs+T6Ltryd69/bNkHyWULfXbb0HfvauQPvJrR/rteFNq6AIXDG5G9Vc/xDwG3rFy5chN6op9txQgbcc0UGnHS+PN3DcqS7Uy85ubnyeQNk3lM3sMzDRgEOlPd0AGwWjl7gPkWdkoiS/nS8g/EIfebCTq5vX3oCbAfhaSWBxEnQ6F9xTHIqHwsIjuDwJbDuY0jwyNrRsw8/CxSn5zvJ41+pCQtQorT43FIjAzW3we+juT1NWiSOwqFvZYCuYS642voeLQk6myuOVG0Awv3dx+byWXa0gkFvahJ743oe5tEHoW2PkOD+m7VefwZWydgFhYjbNA1M9mNONP3m6loyDypx+QVnqnHADL69iFyk8ZOJEMvCSI2JZ6+XEhrDCp1BjdVaTdSUxai8NRSin21+lH7hcKXreu6Mb7hHKWztEAm40VI/dmYeH0DsCEO+aFlb3VDocJzp+0zb9lnXt3xaGmkPC2b0Hdt0jH34Gba8kOMZLqTLw+hB5knUWjL1bY6hL57buFk9taEQlx1HP+YBz6KhtVZofJMwjXTyPNXqtlpqc9sstHQa8IrPFMMu7EfgjEKjnvftZ5w/accXMp3PehHWVg/Ae5EPpydNo79NZKNcoTnBVQzZ13yRSuu6IiYe4Jst599cciAV3c8ZiimzPB59MBTzDuwibbcQcjnQD65DuB9wPW22NXAdUhxLaUGnd/gYU3k+GerkbnZTMzN9Dk0bCye8EwPBoGuclKyZWm9hGrczLeXyyo81RCH5KxFxb44ZAdwM6p5swSFuXoqb4FuKF3pOA45GIdsqbK+I2u9KFPMERyv7njMREyZGTdDnku2/DUXbP0my/Y9nM/mhtoo1uVxCk4uDvk8CnGlEwMOAacFEW9tYN+tiRz/bDUyN+q4cytXrsytXLly0wRDQc30OTTqmDzhmSa4uHpJlQcgDtmLlJmFFvaZiMKT3vaQ+Ww2o6fBE4OIJRZ6GgWrjuwMyfWiDQrHMGDbdeqObxnhMdPQqMJ8NUF1edYwZ3hHJpcZ41JIKji3oRBXP5pLRlCI6300yM9jmMjxz9ZihM3WXLTUeA4xPfN1wwoTesIzDTAFZ5gKhMewA01KS5mAwlNhHM7UvAv5ik62yspJuAKDB+vZv5GoLJpYDyXUnC705Dlc5/A9PJoSk9QosiqcnyeFIUzBsf+vBN6LPBHDjFWDJlyNeYLHPyuLETZbc9ES44mB3wJ+s8FjHC8mVJjQm5anDwPAkUFENp1d5WCG4+2o8N+YVPZGwEzR24KIvYhYHZ/NHpPJ5XbkE2bjiTx5tCFiN+y2k1B3JqIaeXg0LSapUWRFOD/Pnp4TGMlYNnEm6/w870DqzpVxyC2Wlt6Z2oRTgyasskzg+JvJOzKlaLbmosnxpDCl13UJ1H1MXuGZPgwi1aZiHYY4ZBBlcx1BdUWobth+NgI7spneTHvb8ixKV3djrRddiPQMJoidV3c8ZhOmxA8x2s/zENn8YSiv4DzG1Ph5YHzH30zekelEo87DZJ7P6fqs6vb0eMIzTbAeWq55ZjXsQpPRoiCaPFXOMqtePjyyKZfPH8gjJr0US0eslP5eCtZUdC4wbMeLz8zymIWYMk9P0c/zEmP8PPl8H1Pv54Ha/SCDtqxHazRonVKvWgJ1e3o84ZleuN5a5VK+HTIoayuHWk9UW36CGGYk91IeeYgOo47ni0uZmqugB11j+xKveXXHY1ahjB/ib5lEL0QpP4/93xFEXIuUnquYZD8P1OwHiYEPzaJKyxXRwGtm0jxR0+VVS2Fcnh7v4ZleDCAFpIfKTDlLsTnoUfYzpg9WI5HJ9LgeWRtR+vocxmFaNnI0Bz3FDdlr3rvjMStRxg/xEZgcL0Taz9OWHyKTG8mQ7fo4ukkMYp4eKOvnaVgLimp+EI+xaNA1M6meqOnwqpVAzcfoFZ7pxUGk2lQLazlFZy9SSxbUUDtnAsiS0fw3aJPcDmDjOENafej62p9YrxOv7nh4pNFwL0TSz3Pqy7ewfNePyWXb8+ghJK3ilPLzNLwFhUdDUes1M5U+m+ny9NS8X094phFGBAapHtZyn1MeqTzDKLQ1KZ9fpsilkkrMeNSdDkRuhjCZ07xHbXjvjodHGpPihXB+nlN2/TdZcrlcpqNUleVPAF9F380h9ADmvvfdTEKIy6MhqOWaGWJqPVHT4ekZ1zF6wjP9GKBYmK8c3ESVt0ynbbbO4kYPJojIZDLdmTxDpCTs8ao7rnaP24b37nh4lEANfo0JNyOde3BzdkyNnnw+C7wWWI7C6p22vweZmhYUHnWixuajU9Hss9KYGn4dl8C4jtF7eKYfB9CH1ocIQik4YpoDZXgFEa+gKswDVkCwUeiCLJal5VBz0cMgopvRlZlzCXXHe3c8PEqgil9jExNsLDm2Rk9ZQbkN+LkSrw9SNDxP2NPjMXHU0Hy0iyluxjrZ13EJjOsYvcIzzTDF5gCVfTwZW7ZAOuKQXWgSOtraPzQKveJVYyq2ViU8FmLrRSG3EaTo5PHqjofHRDBh42m6Ro9eLEt6MoxmRP3oO/1xvKenWVHuGmmmQo6TNRZvWm4xDADt1jOrFDKU7qO1HRGRhqSqmxLTmVR3xrndXjRWF6bLee+Oh8eE0RAzaLJGzzgwjGq5dFDa8OzRHCh3jTRTIcfJGkvN2/UhreaAM3r1UpoYZCmhsMQhh4OIHcAyJBXunOA4egDydUSejNh0UQzRgVSebqZG3VnfpNvy8JgorgG+RpWq7LVi7sHN6Cta07NMB/BL6HucRMPaUHg0BKWukWYr5NjQ6ziBmq/BTD4/pb4mjzIIIpYB2ThkS4n3FgAdccj2MuseDcwDtliLiHr2nwEWAUPbd52yCeDQZzfMs9fbgcOV0tKDiCMRMdtjy3eguFgvcNAapnp4eNQBqyb7p0i+z0H94aQ8GX504p8y1D63UlirGvqBLyMTqvf0NAFS18gLwDXNVsixxBhvQcURJ3Jdx6tWrTqhlgU94WkSGGFYAGxOqyFBxCJEhnaUWdeV2W4DNtUz8VhdnyOBXdt3nfISFAhP1rZblvCYUbkP6I9DDlloLmPrZeKQ/vGOx8PDozSswNuEQtjb+s7l0WN+tx7Ck0ch62H0UDOqiKEnPR71YgLXdX7VqlU12XO8h6d5MGC/S8l9FbOkjIhsQ5/nkjr334tITUklpgLZcQ1Qh+OwEI7LUiQ8Xtnx8GgsJuyFWDKwmvaR/fWQkzwiOz14T49HY1Hvde0LD7Ya4pDDiByUytbKUtq0nFz/EPLw9AUR88azbysU2EHptPFqjNsZlQdtW47stKO6QZ7weHg0FhMu8JYhP3jsvociqswrJZAF5lPe0+PhUS/qva5r9vB4wtNcGAC6S6R71lQHJw7ZY9tYVCHjqxR6bPvjcivbOLuRR8cVlGqj2M3WZ2Z5eDQYE2gsOZJY/kObjnrDHwCrq+yuVkJUqNMTRLzVp6x7jBcTuK5rbpDqs7SaCwPo6amP0R3Gy6Wll8J25OdZGkTE1fpfWUiqB5GWcvsot40+ey/JyrNILRr26o6Hx+SgzsaS2bTXIYhYAaxD1ZZLroPmnkoPxyMU6/QUPD1B5D09HuNDnde1r8PTijCCMMxYH0/JtPQy2xhBpKcTOLqGVXpIhKRKoGRIyxSkDtRgNDm2dry64+Exnai5JoslSJwGPF9lm5W+zzngCLynx2NyMeFaQ57wNB8GgZ5UY9CaWzsAWGr6LuDIIGJOlcWd4bhSnZxR+04YlQ/HYbEdho25m+ITn4eHx9SjlBeibE2WBOkpWfYC3SfuAzZSuhdSB2OjBd7T49FojOu6LgVPeJoPA1AgFMlKx+M1F76CenMttqKAY2Bm5XbG3+OqB107A6nX2+0nrfp4eHhMEcp4IT5UqSaLkZ7fpXxzx8vt903UlnnpPT0eDUU913Ua3sPTZLDGoCNYXRuKpHRcBCIOyQcR25HUXC6O3kt1s/Iodckmrh7gUMKonNxeDq/ueHhMK8p4IarhVhQeOLHEex32+jL0oOMySstlcXbjPT0eDUad13UBXuFpTriwVrKJ33gVHuKQoTjklVJqSyL8dKAGNSb5viNJo6RFI0KdeHXHw6MlYWTkY1R+AOpG5OfLwPVQDGmn0I739Hg0GTzhaU4MoM/GhY5gnApPDahmVh7TONS6sjtSkyZgri6HNyt7eLQubgXupTyRASk7h4EPAPdQW4jLe3o8ph0+pNWcOIAUnWR6+rgVniroQWblcjH7JPIJo/JI0qgMo9SdIcqHzzw8PJoccchIEHEl8BbgW8BRJRbLAL9pv7+KCNIfojmgHAaBJ4OItwIX4PtveUwDvMLThLCQ0CAiGA1XeEypaWd8VS27UUHBtFEZ1CU9i3w9Ppzl4dHCiENG4pCbEakpF95aDnwGhbUuA+5CnsNyD2b9wOeAG2y9G4DbvZnZYyrhCU/zYhAKlYyhsSEtZy6uJFtD0T/kihMOlWhs2oZ1U8erOx4eMwkuvFUuZJWh6M/5KvBe4A5Kz1WLgfPwvh6PaYQnPM2LQTRxuDo6DQlpjdOs7OD6e5VTd0CZWY0Ou3l4eEwTLNx0JfBFKvt0+oBz45BbgL+i9DxRKpvL+3o8phSe8DQpzBR8gCLhaZTC46o411J7xzUB7UQEaRShSak74BUeD48ZBSM9n0Mhq3KkZwToDCKuRfeUB1AIq9qc5Wv1eEwpPOFpbgygFNCOCn2uxgsXmqrFrAx6eisX/uqk2NQt7/07Hh4zDzUoPe2o2u1nkKcH4Goqp61DsVbPZ/CeHo8pgCc8zY1Biv6ZCcP6X7VRu1nZmZEH0mTGJqYONAFm8eqOh8eMRUrpKUVi2hjtzclRTFsvZ2b2tXo8phSe8DQxbJIZokGEBzMrp9PKSyMDUnfKdT136s4w4+vm7uHh0YJIKD03UTlcNQe4ILF8JTNzEt7T4zGp8ISn+XEAaCvXD6tWmCLTRY3qTkYcK4OeztLbyjJa3QGv8Hh4zHgYibmR0sbkJDqs5o5r7PjVGtYp1Orxvh6PyYAvPNj8OIiejPqAvRPYjlOJajArt0GmK2P7LqXcdNmYhjClx/t3PDxmDW5DxuRLKN9P60OoTUWhl1ZinWR9sSR2Al9B/bo68T24PBoMr/A0P0ZQu4a+agtWQS8qDFh14shkXJmeseQoqe4YyfH+HQ+PWYRUqOp6Sj9ELWGsP6darZ7jUIPSbryvx2MS4AlP8yOLpODueuXdICpMIFXDWTI2t2fy+QNuQkpPTAV1x8iP9+94eMwyWDXmW5AxuVrvLdADV7VaPaUiDt7X49EweMLT/Ej6aHorLVgBrgdWxcaexX5Zh/Olsk8T6s5wQt0Br/B4eMxKjMPInKd0rZ4clYsaDgFrvK/HoxHwHp7mRxY9PXWisNb+8axsZufOGtfrBbL5/CghKDmJuarKjji14f07Hh6zGtZw9EbgHRQLpaaRBf4/Et4cVKvnXPQQ9fEy63YA37TfBT+Q9/V41AOv8DQ/Moh0DAA9prKMBzWZle2pqRs4KMEmk34/7d0B79/x8PAQnJG5XIXlDGO9Obk45POovk9S8UmiDZiPr9fj0QB4wtP8yKJJYACRlpo/MwtR9QAHa6jU3AeFLu2lMErd8f4dDw8PhxqNzEkUvDm+Xo/HVMGHtJoYRlhAYaODwPZxbqIms3IQ0YnUm4E4JN91XfG9OCRfQd0Br/B4eHhQIC63BBG3AUupnLY+hNXcAS4AHkMqEbZeudAYiBCtSa/rQ1we1eAJT3PDTRT1qii9wOEylZKBAqnqs+USmRaj5qi0dwe8f8fDw6MEzNNzJQo7fRR4HWOV6QMolHUSo2v1XMXoej2H0INbmjR9Jr2u9/V4VIMnPM0NN0mMm1SYWbmD6mblHttPqeXKqTtubD6c5eHhMQYJtQdKKzZHAucl/nfenCvt5yoUtjoNeB+jCU8GOIvi/Svp67mlgYfhMcPgPTzNjUJIq451e229srF0Myr3oIKEqe7phfml034PJdbL2AL+acrDw6MSypmZS917eoHzXY0fMzTfSOmQfPph3ft6PKrCKzzNjbpCWuMwKztSVM7jk0GEZyi1HVcHwys8Hh4zGEFEB3At8HZEUnahOeNF4F+BWyuFkWoMbzkMoh5c11L09CTbWLgQV1eJdUt6gnyIyyMJT3iaG/WGtHoQWSlrVjajcicwWIEUddjvtAcoi/w7nvB4eMxQBBE9wCsUS1uk8V5gdRCxIg4ZLredGsJbDt2oHk/S01MuxJXGHnwfLo8q8ISnuVGvabkXVUMuOQkVKyozEoflQl5Z0MRRSiVqq2NMHh4eLQJTdiqRHdAkcR6wI4j4L6orPtWajrZTJEMFX461o7gFkaa3MrbAYQ44CvXvIr0+3tfjYfAenubGuBUem6jaqVwHoxuRllL9bAydbr+j1B3v3/HwmBX4NJXJThLzUdXkfwfWBxFvL9X+IVVv5ydUf2gq5ctJeoJyaJ7LU/QaVlvfYxbDKzzNjXoUnopmZSMs3ciXU1aGzmQ6QCpRKXVnvGPy8PBoLVxdxzpZ1O38e8CWIOJjpBSfcYS3QGGpUrV6aglxARwGzgwiPg08ivf0zHp4hae5MS6Fx1LIu4ED5erj2Ot7qajuQD5/ACjZbNT7dzw8Zj7qbVQMUlsc8Smn+FRrRQHwHPD7wA2o7s4NwO0ANWRxuXG8F/isW9c3Hp3d8ISnuZGBAkmpBVXNyra9XHXCMgKlVRzv3/HwmPkoFxIfAXZTW0jbEZ8bSZGNGsJbbs67nMp9tNIhrkMltkWZdT1mGTzhaW6Mt1dVDwpDHa66ZNXdjoX373h4zBrsLfN6Fvht4FeQAlPL/NQNXAF8Ok16zJD8V4x9SMugTurdqddH+XJSxOk6pCqVQx/wbq/yzF54D09zI0vt4axO9HmWm6gaAe/f8fCYHbiZ0ZWQHTLAN4DFwK3AW4BfA96AMqVKmYex168B3l/C21Mue6tcD67VJertVMriSo79ncA5QcS/4X09sw5e4WlujEfh6bVlD1ZbsFaUCKV5/46Hx+zAnzC2/pbDfGAdkI1Dbo5D3g8cC7wLqT7l1munGOJ6JIj4tBEUqD17qwP4FilfT0K1SYa4SqEHEbnPAN+3cXSUWdZjhsETnuZGTQpPLWbl8aF0SAvv3/HwmBWwDM73Uv77vhxY58iChaduRllTjviUC613UyQdo4zIlA5vJdGGCFdJX08qxPVp4A5Kz6Guivx5wEM+zDU74AlPcyNDbSEtVy+jUu2dcWL0br1/x8Nj1uE/gNUV3l9OiiykiM+fUl7tAc0ncxjt70mbkCut7zDG12NZXH8CfJkqGanAGbb/a4OIt3ryM3PhCU9zo9aO5L2ors4EzcoOJRUe79/x8JhFMLVkBfB8hcXOQj6eUut+DriL6g9incAfAY8AnwS+iuoAXQd8kfLhKYdCvZ4SpCVNoMrt/xqUvv49fPr6jIUnPM2NqgpPENGFyEjFVPQGwPt3PDxmGSy0dRrlSU878A+lfDCJ8NJ7UIjrIJVJhwtzXY/q7/wZIk111esJItpSIa7PVDkOV5T1tZQgcR6tD094mhu1mJZ7gVwcNs6sLIyZW7x/x8NjFqIG0rOEhJ8ntW7a2/MZ4AnKh6pGhbnstQnV60mFuE5L7D8HJavNt9lYPWYYPOFpbmxDXYArYYTqMepxomRIa4jSk4OHh8cMR4L0bC+zyBg/T2r9JOm4CIWqKvlzXJirmqG5pno9qeO4CKWnXwfcXWb/xwQR37Gfkr3BPFoPmXy+AUk9HjMKXdedsQdGOPTZ9fOmeyweHh7NgyDi7Sidu1QNtxHgC8DnqtW2MQJxO1Jiein/8H3A9nejLX8b5butJ9GPfEA5RtfrGTUuO55/g1GEJm8/bkwjwJ3Alb5mT2vDKzweo2DZWB4eHh6lcCvwVJn32pDpuGptmxLemnJhrh5EXG4AHkIm6C9TuV7PQeBByvh6ShzPnRS7rh9iNNlxx3U5vi1Fy8MTHg8PDw+PmlBD5lYbNda2GUeYy/l6zkNhqI8DCymdqOHUmQUUu7GX68OVJF6/hjxD36O0ctRFiRCZR2vBEx6PMvChTg8Pj7FI+HmeoHxdrjMYhyKSSmOvlJHlyM9JKDsrvWwGqUJnoLBXEuV8PY54ue7rpRJADgGP13o8Hs0JT3g80vAhLQ8Pj4pImH+/QGnS0844FZFUmOt6KrfJ6UXennLZW+2MTbIYpDppuQ24l9HHNALcY+95tDC8adnDw8PDoy5Y2OoRxjYa7Qfea9lV9W63kqm5sH3rx3UDoxuG9iMF6CRbfxDV86lqPLZ9v4Viavr3GN3s1KNF4QmPh4eHh4eHx4yHD2l5eHh4eHh4zHh4wuPh4eHh4eEx4+EJj4eHh4eHh8eMhyc8Hh4eHh4eHjMenvB4eHh4eHh4zHj8Pzksc8obq4PVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'green', \n",
    "              'intermediate_color': 'dodgerblue', \n",
    "              'goal_color': 'red', \n",
    "              'obs_color':'black'}\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig)\n",
    "\n",
    "vis.color_dict = color_dict\n",
    "#fig.savefig(\"acrobot_mpnettree_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animating...\n",
      "528\n",
      "in init\n",
      "state:\n",
      "[0. 0. 0. 0.]\n",
      "x1: 0.000000, y1: -20.000000, x2: 0.000000, y2: -40.000000\n",
      "feasible points\n",
      "[[-3.14159265 -3.14159265  0.          0.        ]\n",
      " [-3.14159265 -3.04159265  0.          0.        ]\n",
      " [-3.14159265 -2.94159265  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  2.75840735  0.          0.        ]\n",
      " [ 2.95840735  2.85840735  0.          0.        ]\n",
      " [ 2.95840735  2.95840735  0.          0.        ]]\n",
      "infeasible points\n",
      "[[-3.14159265  1.45840735  0.          0.        ]\n",
      " [-3.14159265  1.55840735  0.          0.        ]\n",
      " [-3.14159265  1.65840735  0.          0.        ]\n",
      " ...\n",
      " [ 2.95840735  1.75840735  0.          0.        ]\n",
      " [ 2.95840735  1.85840735  0.          0.        ]\n",
      " [ 2.95840735  1.95840735  0.          0.        ]]\n",
      "after init\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {'start_color': 'green', \n",
    "              'intermediate_color': 'cornflowerblue', \n",
    "              'goal_color': 'red', \n",
    "              'obs_color':'slategrey'}\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig = vis.plot(traj, obs_list, np.array(sgs), color_dict, fig)\n",
    "\n",
    "vis.color_dict = color_dict\n",
    "#fig.savefig(\"acrobot_mpnettree_obs_%d_p_%d.png\" % (obs_idx, p_idx), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:ylmiao_mpc]",
   "language": "python",
   "name": "conda-env-ylmiao_mpc-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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}