2017-11-06_JC_low-dim-model.ipynb

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   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy import clip\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch.optim import SGD\n",
    "from IPython.display import clear_output\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "w = widgets.IntText()\n",
    "%matplotlib inline\n",
    "%run utils.py"
   ]
  },
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   "outputs": [
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     "text": [
      "The Cython extension is already loaded. To reload it, use:\n",
      "  %reload_ext Cython\n"
     ]
    }
   ],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
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   "source": [
    "M, N = (40,140) "
   ]
  },
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   "source": [
    "%%cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "cdef int M, N\n",
    "M, N = (40,140)\n",
    "\n",
    "def min_cost(tuple source, tuple target, np.ndarray[float, ndim=2] w):\n",
    "    cdef int sx, sy, tx, ty, i, j\n",
    "    sx, sy = source\n",
    "    tx, ty = target\n",
    "\n",
    "    cdef int hor_dir, ver_dir\n",
    "    hor_dir = np.sign(tx - sx)\n",
    "    ver_dir = np.sign(ty - sy)\n",
    "\n",
    "    cdef int X, Y\n",
    "    cdef np.ndarray[double, ndim=2] cost\n",
    "    cdef np.ndarray[list, ndim=2] paths\n",
    "\n",
    "    X = np.abs(tx - sx) + 1\n",
    "    Y = np.abs(ty - sy) + 1\n",
    "    cost = np.zeros((X, Y))\n",
    "    paths = np.zeros((X, Y), dtype=object)\n",
    "\n",
    "    cost[0, 0] = w[sx, sy]\n",
    "    paths[0, 0] = [source]\n",
    "\n",
    "    for i in range(1, X):\n",
    "        cost[i, 0] = cost[i - 1, 0] + w[i * hor_dir + sx, sy]\n",
    "        paths[i, 0] = paths[i - 1, 0] + [(i * hor_dir + sx, sy)]\n",
    "\n",
    "    for j in range(1, Y):\n",
    "        cost[0, j] = cost[0, j - 1] + w[sx, sy + j * ver_dir]\n",
    "        paths[0, j] = paths[0, j - 1] + [(sx, sy + j * ver_dir)]\n",
    "\n",
    "    for i in range(1, X):\n",
    "        for j in range(1, Y):\n",
    "            if cost[i, j - 1] <= cost[i - 1, j]:\n",
    "                cost[i, j] = cost[i, j - 1] + \\\n",
    "                    w[sx + i * hor_dir, sy + j * ver_dir]\n",
    "                paths[i, j] = paths[i, j - 1] + \\\n",
    "                    [(sx + i * hor_dir, sy + j * ver_dir)]\n",
    "            else:\n",
    "                cost[i - 1, j] = cost[i - 1, j] + \\\n",
    "                    w[sx + i * hor_dir, sy + j * ver_dir]\n",
    "                paths[i, j] = paths[i - 1, j] + \\\n",
    "                    [(sx + i * hor_dir, sy + j * ver_dir)]\n",
    "\n",
    "    cdef np.ndarray st\n",
    "    st = np.zeros((M,N))\n",
    "\n",
    "    for i, j in paths[X-1,Y-1]:\n",
    "        st[i,j] = 1\n",
    "    return st\n",
    "\n"
   ]
  },
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   "source": [
    "def random_path(source, target):\n",
    "    sx, sy = source\n",
    "    tx, ty = target\n",
    "    hor_dir = np.sign(tx - sx)\n",
    "    ver_dir = np.sign(ty - sy)\n",
    "    X = np.abs(tx - sx) \n",
    "    Y = np.abs(ty - sy) \n",
    "    path = np.zeros(X+Y)\n",
    "    path[:X] = 1\n",
    "    path = np.random.permutation(path)\n",
    "    st = np.zeros((M,N))\n",
    "    st[sx, sy] = 1\n",
    "    lastx, lasty = sx, sy\n",
    "    \n",
    "    for i in path:\n",
    "        if i == 0:\n",
    "            lasty += ver_dir\n",
    "            st[lastx, lasty] = 1\n",
    "        else:\n",
    "            lastx += hor_dir\n",
    "            st[lastx, lasty] = 1\n",
    "    return st"
   ]
  },
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      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:123: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['start_x'] = (starts[:, 0] + 84.125113) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:124: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['start_y'] = (starts[:, 1] + 7.853407) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:127: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['end_x'] = (ends[:, 0] + 84.125113) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:128: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['end_y'] = (ends[:, 1] + 7.853407) / 0.005\n"
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      "\n"
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   "source": [
    "# %run utils.py\n",
    "# chunks = pd.read_csv('data/yellow_tripdata_2009-01_train.csv', chunksize=100000)\n",
    "\n",
    "# h = True\n",
    "# for df in tqdm(chunks, total=93):\n",
    "#     df = rotate_manhattan(filter_manhattan(unnormalize(df)))\n",
    "#     df.to_csv('data/yellow_tripdata_2009-01_train_JC.csv', mode='a',\n",
    "#               index=False,\n",
    "#               header=h)\n",
    "#     h = False"
   ]
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   "outputs": [],
   "source": [
    "# chunks = pd.read_csv('data/yellow_tripdata_2009-01_train_JC.csv', chunksize=1000)"
   ]
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   "cell_type": "code",
   "execution_count": 22,
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   "source": [
    "# grid = np.load('grid.npy')\n",
    "# gr = np.zeros((20,70))\n",
    "# for i, j in grid:\n",
    "#     gr[int(i), int(j)] = 1.0\n",
    "\n",
    "weights = Variable((15 * torch.ones((M,N))).float(), requires_grad=True)\n",
    "optimizer = SGD([weights], lr = 0.0001)"
   ]
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   "execution_count": 24,
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   "source": [
    "@interact(x=w)\n",
    "def plot(x):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.imshow(torch.nn.functional.softplus(weights).data.numpy().T, \n",
    "               clim=(0,50), cmap='bwr', origin='lower')\n",
    "    plt.savefig('{}.png'.format(x))\n",
    "    plt.show()\n",
    "    "
   ]
  },
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   "execution_count": 25,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-b22e8d7333e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 pred = (Variable(Tensor(path)) \n\u001b[0m\u001b[1;32m     21\u001b[0m                         * torch.nn.functional.softplus(weights)).sum()\n\u001b[1;32m     22\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'duration'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
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   "source": [
    "optimizer = SGD([weights], lr = 0.001)\n",
    "num_epochs = 5\n",
    "with tqdm(total=num_epochs * 8030 * 1000) as pbar:\n",
    "    for epoch in range(num_epochs):\n",
    "        chunks = pd.read_csv('data/yellow_tripdata_2009-01_train_JC.csv', \n",
    "                             chunksize=10000)\n",
    "        total_loss, n = 0, 0\n",
    "        for df in chunks:\n",
    "            for _, row in df.iterrows():\n",
    "                pbar.update(1)\n",
    "                source = (int(clip(round(M/10 * row['start_x']), 0, M-1)), \n",
    "                          int(clip(round(M/10 * row['start_y']), 0, N-1)))\n",
    "                target = (int(clip(round(M/10 * row['end_x']), 0, M-1)), \n",
    "                          int(clip(round(M/10 * row['end_y']), 0, N-1)))\n",
    "                if np.random.rand() < 0.5:\n",
    "                    path = min_cost(source, target, \n",
    "                                    torch.nn.functional.softplus(weights).data.numpy())\n",
    "                else:\n",
    "                    path = random_path(source, target)\n",
    "                pred = (Variable(Tensor(path)) \n",
    "                        * torch.nn.functional.softplus(weights)).sum()\n",
    "                loss = (pred - row['duration']) ** 2\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                total_loss += np.sqrt(loss.data[0])\n",
    "                n += 1\n",
    "                if pbar.n % 2000 == 0:\n",
    "                    print(total_loss/n/60)\n",
    "                    w.value += 1\n",
    "                    clear_output(wait=True)"
   ]
  },
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ko9lajTYG57Ww7TZlCsrvtcM9mLvuwvGLjtwKsh88BA+gWK5yyoGm8jlSLdhi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\nCBFBi0OICP8fuYzYEhnTT6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b02e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(torch.nn.functional.softplus(torch.load('weights_4x_min_cost.pt')).data.numpy().T, \n",
    "           clim=(0,50), cmap='bwr', origin='lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T19:18:29.344218Z",
     "start_time": "2017-11-15T19:18:29.335323Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.save(weights, 'weights_4x_min_cost.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T18:01:41.526127Z",
     "start_time": "2017-11-15T18:01:41.517744Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.nn.functional.softplus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T17:21:56.217574Z",
     "start_time": "2017-11-15T17:21:56.210062Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "torch.save(weights, 'weights_min_cost_route.fyre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T17:58:50.960620Z",
     "start_time": "2017-11-15T17:58:50.908111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0\n",
       " 0\n",
       " 0\n",
       " 0\n",
       " 1\n",
       " 2\n",
       "[torch.FloatTensor of size 6]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.nn.functional.relu(torch.arange(-3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:57:00.355654Z",
     "start_time": "2017-11-15T03:57:00.348737Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cs = pd.read_csv('data/yellow_tripdata_2009-01_train_JC.csv', chunksize=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:57:06.550083Z",
     "start_time": "2017-11-15T03:57:05.289144Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = next(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T04:02:37.326829Z",
     "start_time": "2017-11-15T04:02:37.322538Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = weights.data.numpy().copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T04:04:04.717522Z",
     "start_time": "2017-11-15T04:04:04.709616Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = weights.data.numpy().copy()\n",
    "w[(w > 700)] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T04:04:06.458252Z",
     "start_time": "2017-11-15T04:04:05.992102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   2.,    0.,    3.,   12.,    9.,   37.,   75.,  139.,   20.,    1.]),\n",
       " array([-290.10040283, -240.25682678, -190.41325073, -140.56967468,\n",
       "         -90.72609863,  -40.88252258,    8.96105347,   58.80462952,\n",
       "         108.64820557,  158.49178162,  208.33535767]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sFc6dMQ86V8Z8rt0qZH1VHe+WTwDru+Xmfg5JJoFXA3dzFox7nLdA+ALwS/N0XVdVt1XV\ndcB1Sa4F3gVcf0YLXAGLjbnb5jrgFHDzmaxtpSxlzDr3VFUlafIrf0leDHwSeG9VPTP4MdK4xj22\noK+q1y9x05uZ/T7+9Sx8a4Un+clUx2D7WWWxMSd5B/BmYFs3NQGNj3kBq3rMy7DorUIa81SSDVV1\nvJuGO9m1N/NzSPI8ZkP+5qr6VNc89nGflVM3SbYMrG4HHumWDwA7kpyXZDOwBbin+2fRM0ku6b6F\n8UfAqjpbTHIZcA3wlqr63kBXs2M+jXNlzOfarUIOADu75Z385L2b9/0eQ329dL+TNwKHq+rDA13j\nH/e4P6le4NPrTwIPAg8A/wJsHOi7jtlPpx9l4BsXwFS3zzeAv6O7GGy1PJj9IOYJ4P7u8dFzYMy/\nz+y85LPAU8DnWx/zPD+Dy5n9dsY3mJ3OGntNIxrXLcBx4Afde3wV8AvAIeAI8AXg/MXe79X0AH6X\n2S+OPDDw5/jys2HcXhkrSY07K6duJEmjY9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4\n/wdbfY+3Z4VIOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1230b0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist((w[~np.isnan(w)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T17:56:50.226047Z",
     "start_time": "2017-11-15T17:56:50.216588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\n",
       "Columns 0 to 7 \n",
       "      nan       nan       nan       nan       nan       nan       nan       nan\n",
       "      nan       nan       nan       nan       nan       nan    1.2149  119.3661\n",
       "  28.4302   27.2659   25.6951  -46.5341  -76.7424  290.5429   88.2641   86.9757\n",
       " 158.9543   57.4560   84.4407   77.9714   60.1472   48.4417  103.5183   92.4683\n",
       "  92.5302  103.2559   71.5246   70.1352   65.4769   92.2900   65.6773  101.5462\n",
       " -52.9651   61.4082   64.1475   74.4736   93.0974   93.3437   97.0056   79.4190\n",
       "  87.6263   49.4165   75.8425   12.3904   84.2782  111.3638  110.7038   89.3369\n",
       "  71.6831  -31.6727       nan   87.6937   72.8622   99.2550  112.5938   75.7369\n",
       "      nan       nan       nan  -59.6603   46.6569   85.6310   52.6893  121.0769\n",
       "      nan       nan       nan       nan  -30.4932   21.3744   82.2779   52.6452\n",
       "\n",
       "Columns 8 to 15 \n",
       "  66.2011   79.7420  123.9766   88.2895   65.5448  134.9269   48.3167   84.0836\n",
       " 133.7874   80.9889   -6.5578  129.8626   72.5021   58.5735   67.9946   55.9030\n",
       "  68.7275  102.1875   72.4769   65.1549   83.7256   76.6139   70.2139   87.2871\n",
       "  61.3568   67.5932  125.2247  100.2852  116.2367   86.4720  138.7307   83.8466\n",
       "  81.0449   90.9605   64.6456  118.3248   73.2256  108.0304   72.8653   84.9810\n",
       " 148.6138   87.3286   99.2214   86.0648  120.4840   90.8166  105.1514  109.4788\n",
       "  62.3610   76.2195   83.0611   84.4270   55.4226   83.4574   70.2826   69.8542\n",
       "  92.0019   62.5878   61.2258   67.0254   65.9244   48.6365   53.2632   78.6076\n",
       " 126.2436  106.9763   68.9933   -6.9690  -47.6346  116.7236   82.0272  -10.3885\n",
       "  80.3508   72.0726   37.5485   28.5604       nan   14.3554  194.3205  229.5631\n",
       "\n",
       "Columns 16 to 23 \n",
       "  86.9752   79.7163   75.7279   29.6621  109.4779   23.4227   26.3501   25.6720\n",
       "  70.6947   93.8652   73.9635   61.7822   62.7389   66.8755  108.8599   69.2450\n",
       "  75.3690   75.7544   84.6907   65.3686   64.5996   63.3908   77.2708   86.3136\n",
       "  81.4060   71.9328   87.6088   71.5086   76.7032   52.8096   53.8237   53.5959\n",
       "  80.9191   72.4942   76.4573   87.5530   70.8531   70.7850   55.7707   66.2935\n",
       "  83.8919   90.5259   68.0362   92.8561   86.9495   90.0074   73.7306   40.2011\n",
       "  85.6300   63.9154   96.9211   77.2109   73.3917   82.7606   52.4783   51.0358\n",
       "  69.3958  103.9745   56.1934   80.0066   66.2800   51.6259   80.6528   24.5243\n",
       "  78.2972  152.8549   67.4728   56.9932   84.3550   95.2122   93.1318   35.3153\n",
       " 174.7923  168.5734  170.5553  127.7559  150.9278   94.4327  116.4682   97.1518\n",
       "\n",
       "Columns 24 to 31 \n",
       "  31.8485   20.9520   47.8682   10.1494    1.9927       nan       nan    0.0000\n",
       "  47.0318   41.3283   44.6785  -30.9194  -54.9372       nan       nan    1.3173\n",
       "  74.9474  226.1721   32.3135  -26.9062  -91.8156       nan       nan       nan\n",
       "  83.1324   20.2280  101.5981   -9.6593  106.1487       nan       nan       nan\n",
       "  36.8098  138.8740    9.1504    4.8957   -1.9681       nan       nan       nan\n",
       "  80.0507   40.7358   60.3395    8.3734  -37.7540       nan       nan       nan\n",
       "  32.2540   64.5999  121.2816  -28.7534  -29.1482       nan       nan       nan\n",
       "  61.2965   45.2339  119.1448   23.5831  -24.7997       nan  -54.4299  -57.8184\n",
       " -30.7213  -52.5046  -55.5307  -13.3646    2.3564       nan  -50.5391  -43.8609\n",
       "      nan       nan       nan   16.6927  -20.6044       nan   10.6516    1.7513\n",
       "\n",
       "Columns 32 to 34 \n",
       "   0.0000       nan       nan\n",
       "      nan       nan       nan\n",
       "      nan       nan       nan\n",
       " 140.6451       nan       nan\n",
       "      nan  112.0817       nan\n",
       " -55.4940  -10.7493       nan\n",
       "      nan       nan       nan\n",
       "      nan       nan   77.7220\n",
       " -45.5696  -82.8708       nan\n",
       " -14.1827       nan  389.4066\n",
       "[torch.FloatTensor of size 10x35]"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tensor(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T18:17:43.799967Z",
     "start_time": "2017-11-15T18:17:43.249162Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'AxesSubplot' object has no attribute 'colorbar'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-367-3465533e16ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'start_x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'start_y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'equal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'colorbar'"
     ]
    },
    {
     "data": {
      "image/png": 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vPY6ui1fNdg8CgJ6mzMMYYyWlRKgvjCs2IVLlyB0eIl18tMxDiIiIsq+xtgIl\nHrUltuHClpUE0GsTclTDFgCcuTyAv//1Ufxsfye8Hg2eweXBEo+ARwwdNWSwu7TRrmKiz5PytY9O\nXsRTOw4hYNNzzGB0ojfuzb5chY8zXERElBcNNX5sXX8b/u61I/jwRPrwocIohh8rrwYYJVcxHWio\nvgZfGGw/ASClgaqdyiml2PxIAxpq/Lhx9pSU783YGPCz/Sfxt6sXY92y6pRrsC9X8WHgIiKivGmo\n8aO0JHUWaKQ+U1me0MJhtJIbzA9E9ZT2E03tPbhvyRwcPHkRS+ZOxS9/15XQb+u+JdeaAam3P7Eg\n3yqmSzy9qxULK8tHdYwPFRYGLiIiyquVi2cnnGWYrLK8FN29qW0kDPctmYNJpd4RBy5jZXC4mbHr\npk8yf29tNWG0lui+3I+Fs8px+PTQvXsHhkJWe1IX+2QxXWJ7S5DBahxgDRcREeXVumXVWFRZnvbr\nHo9AukkwAWDBrHLUJTU1TebVBO5cMD3hvdVL5uDuG2cN+7lfftSFQEcIgY4Qnt55CNHBsAXEg1o4\noieELeN9g92ZjNMn+xJef3ymF+t/egCr/8d72NLcOezzOCHQEcLmvceHrSmjseMMFxER5V1vfyTt\n1yb6vLh5rj+hFkogXrzu0QS6Ll7FqYtX037+2qkT8N8frsf2lmDC+wtmlad8bupELy5eHZqhisn4\nEuKpi1eR3IJLABA2xWPWjvZ2s2fnkzYAWI8G+igYP+/Rrq7LCWw/kTsMXAUu/b8Jk+xWGHOH4rX+\nVGGMytypQid9AMDw/wCN+6LitYjIlSYMU8f1Z7dfh86eKwmBa/WSOSgr9eLVQBBbP4zvJvRq8UL3\n5JAz0Rf/q+7l/YmzR/4yH7qSAldyY1IB4I22bpR6U3+o3bFgOo6d6U3pmm90sB+t11pPm4HL6ean\nbD+RO1xSJCKivFuxKP2/rBZWluPH7ybuCvzFwS6cvNCHaCweFqIxHTLNwTklmkBTe0/KQdJvHz2L\nNfVV8Fj+JrzQlzjTJgF8FLyED0+EEtpBeD0Cc6eV2bar6Lp4dUzLcysXzwYwNPv0gzeO4pEXmxxZ\n8mP7idzhDBcREeVd+cSStF97/p1PUpbzdMAstNdEfGYqZqmvsjp18Sr8Zb6U1hFvHj6D2umTUoKY\nBuCmqmvwu+ClhPGzp07E9Ek+lHo1HOgIpa232vphJ7a1BPHSo41pvyeraWUlWDpvGs5c7seDt1Sb\ns1u5mH0KWU0DAAAgAElEQVRi+4ncYeAiIqK8a6ytSDno2fDW71MPtLa66dprcFttRdreWJf7o9i0\nuw1L5yXWgUnED8ZO5h1sxpr8KKdDV3H64lVIOfzORmtAUvG1pXOx4d5FKe/7y3zxJU4pHZ19YvuJ\n3OCSIhERuYJd2AKQMgOVbPG11ww7QwbEAxCAhEXHEo/APYPnNCbcT0p8FLyU8r6OeJjK1GBVsyzP\n+ZI66fs8At//6k2omjoB08pK8O07a23DVqAjhE2726BLCU2Ln8vIUFTYOMNFRER599c7Do3qcx4N\nWFNfFf+9QMrSo0HTRMLs1qLKcnzvqzfhqE3vrnQBz9gZCWE/pkQD/mb1TQj1hc3luRtnT8FBS3hr\nrK3AumXVGXchWpcTBeSYC/Ep/xi4iIgo7z459+mIPyMAPHRLNRpq/PGC8jTn+wgB1Ewrw/FzQ01I\nfz8YtDbvPaZ8v88vmI5ltRVorK3AnrZuvN7WjakTS8xAFdGBzp4r5oxVoCOUELYAoPkPPebXhqub\nMorZI1GdxexFgkuKRESUd9dkWBK0U+LVzNmtbS3BtDNTUqYeXi0Rn0UKXVGfObIeQbTh3kV4+/9c\njotXE3c1WmvCkvt+AcBAVGJLcycefP6DYXcfGsXsf/nlheyNVSQYuIiIKO/+j7sX2r4/ZUL6hZj7\nG6rMIHJ+mKN/AOD0pf6U9/xlPghh30rCzltHzuAffn0UD/94KCRFk1Ke9fW5NM9kdKzXZbwTfbri\n+oYaPx5fPp9hq0hwSdHNYpmHLJmmeK25CmMuKF7rosKY9E2jh0xQvN/wR5HF/XvFa9n/TCeiPFuY\n5mif5EBjJRBfmmuo8WN6eemw148kXUcTwHd/2ZZy9I7XIyB1CY8nvpwnLeONSbJwVDfPPzyd1PTU\n+jpd3ZW1zkwgHvw27z3OtgxFjjNcRESUd3/32hHb9/si6QPX1g87zSW5tfVV8AwzWeXzJP51N6HE\nkxK2bpnnx8vrb8NDt1bj+umTEsrBZk5ODHRmEEspGpPmuYQqy5Vzp5Vh0+42R5ubkjswcBERUd4d\nPzvyonldAgORodmmv73vprSh67rpkxJef9nm0OrfnryIo9292NYSxJGk3YvdvQPwavEZKZ9HYO1g\n7diUCYm1ZzEd5rJjct2Yncv9EXM3Yn9Ex1++fDDjZ9LhIdTuxiVFIiLKq0BHCJeGObx6OBLAKwdO\nYk19ldlq4UmbFhOTk2rBui5eTdnUGI1JvLy/M2Xmy1A35xrUXXsN1tYP1Y71DkRTxknElx3PZqgr\nA4D5MyYnHF7dcaEPf/pPzfjpN5dl/Kwh0BHCtpYgXg0EEY3xEGq34gwXERHlVVN7T9qmpypiujQL\nz9PVTXUlFc1/eCIEzeZvwEtXI/Bq9tNkHwUv4dVA4s7DdGON57JTMjgNV+IR+M7KRebroWdTLagd\nOm9xa3NnyjFA5C4MXERElFf+Mh8UVt9saUmHLtv1q/JqgLBJdHb1+Cd6+nDXwplp7xeO6nj+nU/M\n1xE99SLGsqPXZn2zvNSDv/njxfj8gun4mz9ejIYaf8oz3zpPdTfUUINU47sT4CHUbsUlRSIiyqtQ\nXzhdz9JhffnGWfjs3KkJu/v22JyNGNWBroupbSHSybTjcc/hM+buyKjNbvLSEg2fmVWe0vQUiPcb\n++4vWhGJSTS392BhZTl++s1l+NN/asaHJy7g1nnTRrScaG2Q6tEEHlg6F2ssS57kHgxcRESUV421\nFSgt0dA/zI7EZF6PQO30SdjS3IF/eu8PuHPBdPSFY9hz2P6ga9UrCwBr66vwsw870866ScQbrTbU\n+G1DYn9Etw1bAHC2dwDhwb4Q4Zg0rzOSkGVlNEgdrms9uQMDFxER5VVDjR8bV9XZFruns3j2FDy3\nr918vfNg14jv6/UIfHHhTPzu5EWcvxLGtVMn4ocPLkFDjR9eTZjByE7bqUvY0tw54ntWTPIl9OpK\nVwGW6egfq4YaP4NWAWDgKnT1iuP2K4xRLRuYqjBGpeZTpYEqANj3Q0ykeirIccVxRJQzgY4QNu1u\nG9Fn0s0gpaMJQBMCj95xHS4PRCGAYZfehgtbAPC74CW0dbWO6BkAYGpZYuC6MhBNaXpqFMKHo9xx\nWEwYuIiIKK+a2ntGtJw4UtPLffiPn7suq0tuEoA+ikr/5P5eOw92mYXuW78VD1ZGIbx1xyEDV+Hj\nLkUiIsorf5nP0evXz83+mYQeAfhKNPiGa2+vyOjbZex+NArhPYI7DosJZ7iIiCiv0vXOypbh2jzY\nydSpfUKJhv/9iwtw7EzvqGrH0nnryNDuRxbCFx8GLiIiyqvG2goIgTE1Px1OW9fI6r0yNQ0tLdHQ\n1N6Dtq7LI36WEg1It3oqJczlQxbCFx8uKRIRUV411Pjx2OdrHbu+So6znkOYaQnvUl8U7x47jwsK\nh1MniyY9jEC8W72G+BIllw+LF2e4iIgo7zbcuyihzUM2GQdNp2O3K9ApZT4PrgwMdUstK/Xgp3+2\njMuH4wBnuIiIKO8y1U2NRaYQY7crcKzS/eV696JZCa//ZFnNmO9FhYEzXERElFfGDFO+WI/HKfFq\n6L0ayfiZeRVlKNEEjp27Yvv1dE0u/nA+cXxLZwgvvvcH6FKy51aR4wwXERHllTHDNFajbdBg7Ar8\nyy8vxMZVdXjh3cxLmz/42hL4J428ncWhU4kF/B+eCCGqS+gy3hoiG7Nr5E6c4XIzm0NRU6h0kAeA\nPoUxlYrXuklxXCavqg0L2R+NlsCv0tkeACYojiOinGmsrYAmBPQxblOcNaUU3ZYu7iNh7ArcvPd4\n2jMUrZrae9B6amS7HwEMe20BsGi+iHGGi4iI8m75Z9R7ZXk1+7+8LvdHlT5v3ZGYrLG2Qmmm7O9/\nfRR9We6Or2mjb6I63PdE7sAZLiIiyhujfmtgBOGlyl+GmeWl+PBEYrjoC9svC1gPggaAh1/4AJGY\nRIlHYOv62xJqphpq/KiY7MP5T51txmpH16XtMT6ZDrLm2YuFgYGLiIjyxqjfGsli4qcDUXRdvKo8\n3hpGPr9ghnkwdTgmsa0lmBBOAh0hXE0T3JykpTnGRyVM8ezFwsAlRSIiyhtjh+BI/jI6/2nYDE0q\nrGHk7OX+hK9ZF/GMcHMlD4FLl8A9dZVKYSoZz14sDAxcRESUN8YOwS/dOCvz4FGyhpEHb6mGz6tB\nDL6/xtIUNVu7JUdr58EubGnuTHhPJUxZd1lyOdG9uKRIRER51VDjR3/EmVklDcCa+iqIwV8bavxY\nWFmO7S3BlGVMI9z0Z7kYfiSe3HEI65ZVJ9RtqRxkzbMX3Y+Bi4iI8m7l4tl499j5rF9XB/CzDztT\nZrO2tQQRjurY3hI0Z4WMmaKHX/hgREuW2Xbf/3gPB4PxlhMTSjRsXFWX8PVMRfTkTgxcRERU1JKL\nyTMVmUfyGLYAmGELAPojOjbuajU70X/jtnnsTF+gGLjcbORNjNNTOa7rDsVr1SqMUZiRj6k0YwXg\nn6wwSPXM22WK44jIUcmzNC/v78z8oTGw1j811lbAqwlEYhIeTZjvG0Xz+Y1bqXQZ70Q/ENHx/L52\n8/nC3JFYUBi4iIgop+xaHZR6ndvD9dmqa7DxK3WJwUQIAHLw17im9p4R9QPLFZ9XQziip/w7VhOC\nOxILyJj/Hy6EmCuE2CuEOCyEaBNC/MXg+9OEEHuEEMcGf2UEJyIifOOfm9EfGVrS29YSTDnUOZuS\nw1ZTew+isXjvr1hsqNWCv8znutktANi4qg63L5ie0MJCE8Cm1Ys5u1VAsvFPiiiAv5JS3gigEcDj\nQogbAWwA8JaUcgGAtwZfExHROHbHM2+hd2BoR2JMAi/v78Q5Bzu7J4eSdK0WQn1hjOF0Hce0dV3C\nEytuQGmJBk0AXk3ge/fdhHXLqlPG8ogf9xrzkqKU8jSA04O/7xVCHAFwLYDVAO4aHPYTAG8D+M5Y\n70dERIXr1MX+lPdiOVzF29LciddaT+Mbt81D+cQS+Mt85gyXUduVzx2Kds71Dpg7KHnET+HKag2X\nEGIegJsBNAOYNRjGAKAbgG1XOyHEegDrAaC6OjWtExG5GX+Gjcy1UycgaAldpR6BgRwFnC3NnXhy\nxyEAwLvHzuPbd9bi//nNsYSA8sDSuXip2dkC/pGaXl4KIHOvLR7x425Zq1IUQkwGsA3AE1LKy9av\nSSklYL80LqV8QUq5VEq5dMaMGdl6HCKinODPsJF5b8OXUDV1AgSAqqkTcP/SuY7f01hee631dML7\nr7d1pwQUa68ut1ir+Ew84sfdsjLDJYQoQTxsvSSl3D749hkhxGwp5WkhxGwAZ7NxLyIiKmzvbfiS\n+fvko2yc8MiLTXjp0caU5qr31FXiXz44gUhUNwOKG2eEVJ9JZdmR8mfMgUsIIQD8E4AjUsofWr70\nCwBfB/DM4K+7xnovIiIqLqE+54rlDcbs1ePL5wOIz3StXDwb65ZV4+66SjS19yTUcrnNlubOhAL5\n4TrN84gf98rGDNftAP4EwCEhxMHB955EPGj9XAjxTQAdAL6WhXsREVERaaytgNcjEHWwjsu6vLZu\nWXVCeGmo8eNody827mpFTJcoLXGuH9hoPb2rFQsry9FQ42dhfAHLxi7F9wCk20j7pTTvkwqVf2yV\nK14rdWPQ6MYASl3kVZ7Ls1Lxfirf4yHFa61QHEdEOdFQ48fyhTOx5/AZx+4xXCgJdISwcVcrono8\n8IVd2Pg0pktsbwkqHUtE7uW+KE9ERONGoCOE3/zeubClYfgaqKb2HuhyaHZNc2MjLsRbQwAsjC9k\nPNqHiIjyZltL0NE+XMal09U9GQEmHNWhCYFH77gOz+1TPZw1d4xIyML4wsXARUREeXN+cObGScPV\nPRkBZltLEALAJw4eMTQWMwd7cQEsjC9UDFxERJQXW5o78dbvne8YlKnu6Wh3L17efxIxXbryaB8A\nKC/14qkdhyAR78uVKXANt5OR8oOBi4iIcs4oVo/pzneZ95f5zGVDIQT8Zb6E53ja8hw5eJxRsS5z\nvnrgJLauv23YjQDcyeg+LJonIqKc294SNHcGOi3UF8bGVXXQhEBMl9i0u83sPt/U3pOT0JdNkZgc\ntmeY3Ywe5R8DFxER5VSgI4RXDpzM2f0aaysQ6gtDlxISiSGksbYCJR6XriMOSv6LusQjht2dyJ2M\n7sQlRSIiyqmm9h5EcnRg9ZKqa8zlNJ9XSzjGB4gXoD+wdG5OjhgajcryUvzJ5+bBX+ZDW9elhBqu\ndHVa3MnoTgxcbqbSiHSB4rXKFMZ8RvFalzMPwTGFMarP/qrCmFrFa5UojiMix+RyxqX78tAP0rX1\nVbZF52vrq/Dz/SdztsSpSgD4zytuQKgvjIWV5SnH+wxXp8WdjO7DwEVERDnVUONHmc+DK+GY4/eq\nnlaWEk7W1lelPI8b+299tuoabNrdZhuq2HG+8LCGi4iIcirQEUJfDsIWAMyZOjEhnIQjOp5982Oz\naN5weSCak+cZidauS2mL34er0wp0hLB57/GU75HyizNcRESUU7ncNbfv43P4k9vmxdtCRHToAP7t\n+HnsP3EBG1fVIdQXRmNtRdoDgfMpqgNeLb60aIQqa92WXZ0WW0K4FwMXERHlVGNtBUpLNAxEdDhd\nNdUfjR/u89KjjXj2zY/xb8fPx2e6ojo27mqFLiV8Xg0bV9WZvbrcRJfAQ7dWm8ugyWHq8eXzE8Zz\nqdG9uKRIREQ5Y8zQbFxVhzsWTHe8s3tfOIa1P3of//rBCTyx4gZzGc7oyWUEk1BfGFu/1YgZk30Z\nr5lLugSunToRDTX+xKXRNP212BLCvTjDRUREOWEsdw1EdHi0+EHRze09COegRcTOg12onDLBXIbz\nl/mwaXdbQpuIhho/+iO5qS1T5bOEJn+Zz+yEr8v46+TWEGwJ4V4MXERElBNN7T3mMmJUl3jxvT9g\nwczJONLdm5P7v97WjQ33LjJDyMLKcjOYAMCTOw6hd8Bdgeu7X6kznzfUF4YAIBFfnmrtumS7i5Et\nIdyJS4pERJQTjbUV8FjWEHUpEeoL5+z+S+ZOTXjdUOM3a6AeebEJW13Y/NR6DJFR++YRgK9EgwB4\nhE8B4QyXm0UUxixUvFaXwpgTiteaqzBmscKY3yreb4PCmA8UrzVFcRwRZV1DjR+bVi9OKFa/b8m1\nOel/JQAsmFVu+zWjNspdbU/jrIXvycuFALCtJZjSPZ/ciYGLiIhyxuiW/lrraaxcPBvrllXj9bZu\nnOjpc+R+E0pSj/NJZhSaR6I6cnTikDKPNnRu4pbmTvPPzVgyZL1W4WDgIiKinAl0hMy6o/0nLgAA\nTl9SOcdsdFQDiXHsj9vOVHxg6Vw01PixpbkTT+44BAB499h5APHwynqtwsHARUREOZPc9f2vdx6C\nk0cYZgokyY1C3cTrEVgz2H/r5f2JQfDl/Z0JZyuS+zFwERFRzliX7yTgaNgajtFOoevi1YTCc7fQ\nBPDy+tvMsDhrygQAl8yvx19TIXFXnCcioqJmFH4/dGs1ZA7Clt15gsas1g/eOIpXDpyE1zPUKNQt\nvJpImJl77AvXo8QT3+FZ4hF47AvX5+vRaJQ4w0VERDnXeupSTnYF2h1tY13WjOkSD946F9dOnQh/\nmc+sk8q3aZMSO9431Pjxs/W3ZaxHS26ESu7BwEVERDljzC71R3KzfGfdmWiEEX+Zz1zWLPFqWFtf\nhYYaPzbvPZ6TZ1IxuTT1r2cjQBn9tpIDVaAjhId/3GR+X1u/xYOr3YSBi4iIcsaYXcoVI3AkF8dv\nXFWHUF84YSbIX+aecxSvmzE55b3k78HoLG/Y3hI0/2zDUR3bW4IMXC7ingVrIiIqetbDlX1eDfNn\nTHL0fkYNl3UZ0Tis+vHl8xMCyc7fBh19FlUeAXzbpkYr+XtI7iyfvETrspZi4x5nuNxMpTXNScVr\nfV5hzEXFa5UpjFHpkv9Zxfu9qjDGvoF0qvmK44jIEUbR/PaWICSAxXOucbRuatvgLE9jbQW8mkAk\nJhOaiRq2NHfiwxOpBfa5tm5ZtbnEmcy6w9Oukeva+iq8euAkIjGJEo/A2sGWEuQODFxERJRz2waX\nv5zuffXKgZNDwUPEj36Wg/cH4gEw0BHC0zvdUSz//a/eBMC++D35aJ/kUNZQ48dWS2E9AGzee5wF\n9C7BwEVERDm1vSWIgUi8D9eAw8XzkZjE9pYg5kydiGgsfs9ITGJLcydePXASW9ffhm0tQVcd6WNX\nqwXADFLGgdt2jEavgY4QHn7hA3O2a6ulpxflBwMXERHlTKAjhJf3d5r1RbnIOR+f6YVE/FxC3ZKs\nwjGJbS1BiBw8gypjZstaq7W9JZgwI5hcLG98zvhe1tRXxccPfq/G98nAlV8MXERElDPbWoLIdUP3\nls6LCHSEbLvat526hAdvqYbPq+V092Q6xiyWtVZLAinF8tbwZMxmGQHrlUAQd90wI+G6bgqV4xUD\nFxER5cz53oGc3zM2zPlBHwUvobXrEG6YWY4j3b05fCp7f//ro/j2nbUJtVpAfBnWrlg+0BHCs29+\nbIYtIB7KZpSXJoS2NSygzzsGLiIiypk3D5/J9yOkiOlwRdgyPLevHd2X+/HsQzeb79kVy1trvaw0\nTaBuzjVYU1/FrvMuwj5cRESUM/lftCsMuw52JZwD2VDjT+gbZs5sDS41agKYP2MSPALQdYlNu9sA\nIKXXGOUPAxcREeWE3UHSZE8CKY1NDcbM1nvHzpthy+fVcGttBeTgZ+0ao1J+cUnRzVSah2Zzdn6q\n4rhrFcbcrzDmHsX7qcz0qzY+va5WcSARZZMREvJtYomGq0mtKOLdudwn3VFDxi5Gifisye3zp+OJ\nFTcASF/rRfnHwEVERI7L9RmK6SSHLcA9YWuwL2s8SAkg1Be2HZe8i/GJFTeYvbfW1FeZrSG4lOgu\nDFxEROQ4IySEIzrruNK4pcaP3526lHGGyq7jvLWA3quJhLHkDgxcRETkOOsZii81d+b7cVzpvpur\n8J2Vi5R2Fhod5Q3WZqnhmMRLzZ3Y1hK0bZJK+cGieSIiyomGGj/mTJ2Yt/u7/S+8nb8NpuxGVGUc\nzm0VjrBw3k3c/v8/IiIqIvks5Hb7Uubxs5+avw90hLB573HlnZ0NNX48sHRuwntC5PfPmxJxSZGI\niHKmocaPyiml6L6c+47zbufzati89zj8ZT5s2t027NmJdtbUV+GVAyfNrvMSwNHuXi4pugRnuIiI\nKKc2P9LAs/1snOkdwA/eOIqNu1oxEInXYw1EdGxrCSp9PnmWS5fAxl2t7H/mEgxcRESUUw01frz6\n55/DZ6uuyfejuIuMhyRdShjlWBLAzw+cxFM7DtkGp+SlxysD0YSvR3XJOi6X4JJioWtXHHdYYcxP\nblS71hGFix1TuI7i7fBOFq+FBtWBROSghho/HrylGh8FD+X7UVyjxCMQ0yVKvBruXDADew6fgQQQ\njUlsSdp1GOgIYXtLEK8cOImoLs2lR7twla6BKuUWAxcREeVFusae45EmgAeWzsWcqRPNQvd9x85h\nIKLbHtfzyItN5tdg+Vr1tLKU+jj+ObsDAxcREeUFZ16G+LxaSnf4lx5txLaWIF4NBBGLDTVDtR7t\nA8SPJjK+1lhbgQeeex/64Be9HsGdii7BwEVERHkR6gu79hzDXPF5BP5ixQ22jU6N5qZr66tSmqEa\nR/t4PBrub6jCWktY+959N+HpnYcQkyzUdhMGLiIiyovG2gpoGhBze4MsB3127lQ8vnz+sGOSu8rb\nHe1jFeoLmyE2Nlg0z9YQ+cfARUREebGnrXtchy1g9MuqySHMKvlway4pugMDFxER5VSgI4Sm9h7s\nPHgq34+SdzPKSwEM/ZlkOkPRTvJnM82AUX4wcBERUc4EOkJ45MUmhKM6hGD70zX1VQl/Jnad5YcL\nY9bPepPquRi03IWBi4iIcsbYYadLQJMSMyb7cO7T8dm24MbZ5QAS/0ysrR+a2nsyHvNj/Ww4qmNL\ncye2W/p1kXswcBERUc74y3zQhIAuJXRg3IYtADh8uhcP/7gJ3/1K3dCuQ03go5MX8d/e/BiRmIQQ\ngJTxnZzGMT/WIOUv85ktIAzhiM5CeRdi4HKzaQpjht/cMmSGwpj/pNKOHsD3Fcb8SGHMHLXbXWnN\nPGaSR+1auPeVzGPuUrwWEY1IoCOEJ3ews7xVOKqjretSQs8to8M8EA9bBgng1UAwoQWEXVNTTWPv\nLTdiiw4iIsqJB370fr4fwZVeOXASAHDt1ImIxvRh+5LFYnrC8T2NtRXweYZq4TyawKbVizm75UKc\n4SIiopwY5x0g0grHJJ575xPMLC+FV4ufpyg0AV2XkBLweAQ0IRK6zRsaavzYuv42bGsJQgAp3erJ\nPRi4iIgoJ7waEGXqsrXn8BnziJ4vLpoJAeA3vz9jdov/7lfqEOoLD9uRntyNgYuIiHLi+Pf/CNdt\n+F/j+iif4UgA0aiOvb8/i5guE7rFh/rCGTvSk7uxhouIiBwX6Ahh897jWL1EcbfMOCQQL3jXpUwI\npR4WwRcFznAREZGjrM05k1sYUJwAcMeC6Vi5eLbZd8v8s2KD2KLAGS4iInKUtTknpRIASks0rFw8\nG6G+MDauqsPt86fDiFnJOxOpMHGGi4iIHGUcpjwQGb7lwXglAdxTV5nQUX7jqjrsP3GBB1AXEQYu\nN+tVGDNX8VoqZRN/rnit3yiM+azCmP9N7XaTJisMiqldS+nPlIiyyjhM+a9+fhAnevry/Tiu9PbH\n58xAGonqCPWFeQB1keGSIhEROa6hxo/1d16f78dwrUt9EUjE/1I2ZrQaavx4fPn8jGHL2JAQ6Ajl\n5FlpdDjDRUREObGwshwC4LKiDQlAE8Dt86fjiRU3KM9oWTck2B1uTe7BGS4iInKcEQwYtuwJAD5v\nvHC+qb1n2Nkq64yWdUNCJMriejfjDBcRETnOCAYUN9GrIaLr8aN7NIG7Fs6EBPDdX7YhGks/W5U8\no7VxVR18Xo3F9QWAgYuIiBxn7FTsjzB0AcDVqA4BYMWNs7B84Uxs2t2WsIvTmK1KDlzJM1osri8c\nXFIkIiLHGTsVSz1s4mmQAPb+/ixauy4hHB0KW8aZinazVUZw9YjU4noALJ53Mc5wERFRThzt7kU4\nxiouK11Ks34rEtXh0QQeWDoXa+qrbGerjOCaPKPF4nn3Y+AiIiLHBTpCeGrHIRbNJ/F5Naypr8Ka\n+irlZcGGGn/GpUa75UjKLwYuIiJy3F8zbNmyzkSNJSAZS40snncvBi4361cYU694rZMKYw4rXuuP\nyjKP+U8K3aQXKN6vS2HMDMVrhRXHEVHWBDpCONLNYx7s7GnrHvNMlNEeYuOqOoT6wiyedykGLiIi\nctRIe0PVTCtDx4XxcQTQc/vacXdd5agDEmu3Cgd3KRIRkaP8Zb4Rjb/QN76mov/utSMpuwtVj+th\n49PCwRkuIiJyTKAjhCd3HBrRZ3r7ow49jTt9eCKEAx0hs5Fpa9clvBoIDtsA1cDarcLBwEVERI7h\njIsaXQLhqI6Nu1oR02XGBqiGdG0iyH0YuIiIyDGccVEjAGhCJISt4RqgWtm1iSD3YQ0XERE5pqHG\nj/uWzMn3Y7hOxaSShNcSwOeur0BpSbyLvM8jsG5ZNYvgiwhnuIiIyFHPPnQzftt5MWHn4aLK8nHb\nKqKyvBTdvQMp7+87dh7f/+pNbO1QpDjDRUREjvvhg0vgGfwbRxPAJ+ev5PeB8sgubBnePnoWjy+f\nbxu2VHcukjtxhsvNFHZSdz6ldqnqRQqD7le7Fh5U6I9Tq3CdSsX76Vm8FhHljUfToOvx/6DDUZX/\nsMefNw6fwZbmTqxbVp3wPvttFb6szHAJIf5ZCHFWCNFqeW+aEGKPEOLY4K/8fwYR0TjV1N6DaEyH\nRHxHHqX3WuvplPfYb6vwZWtJ8V8A3JP03gYAb0kpFwB4a/A1ERGNM4GOELouXoUm8v0khWHl4tkp\n72Vg6HQAABvxSURBVBn9tjxCbeciuU9WlhSllPuEEPOS3l4N4K7B3/8EwNsAvpON+xERUWGwLoUJ\nIQDLEdYeLd4GgYZ8/6s3pSwnAuy3VQycrOGaJaU05kW7Acxy8F5ERORC1qUwDRJeTUCXEpoQ2LR6\nMQCYnegFgLtvnIWTF/rG7Q7GhZXlab/GfluFLSdF81JKKYSw/WeMEGI9gPUAUF2dmuqJiNyMP8OG\nl3z0zMZVdSltDxZWlmNbSxCvBoLYc/gMxvOc1/aWYEqoCnSEOLNVBJwMXGeEELOllKeFELMBnLUb\nJKV8AcALALB06dLx/N8ZERUg/gwbXvJSGJB63M/R7l68f/w8dy4CeKm5E1cGonj2oZsBcHdiMXEy\ncP0CwNcBPDP46y4H70VERC7VUOPHnrZufOun+xG6EgEQr9/atHoxPvxDD3Ye7MrzE7rLzoNd2Pfx\nOfz467fY7k5k4CpMWQlcQoitiBfITxdCBAH8V8SD1s+FEN8E0AHga9m4FxERFZZnfnUEz+1rT3gv\nqks8tePQuF4+HM6FvgjW/uh9TJ3oTViS5e7EwpWtXYoPp/nSl7JxfUqvWrWxx/+rMOa/K15LJTq/\nqTBGtdH0UoUxNYrXUmgmS0TZ9Xpbt+37DFuZXbwaxYzJPnzji9exhqvA8WgfIiJy1D11PApiLM59\nGk573A8VDh7tQ0REjtpwb/xssZ0HT2HutDI0VPtTlhhJTaAjhG0tQQgAa+qrGMIKCAMXERE5bsO9\ni8zgdfcP3s7vwxQg48Dqh1/4AOFYfDH2lUAQW7/FXYuFgkuKRESUU8fOqRZwji8LZkzChBL7v5ab\n2nvQ1N6DSGyo8i0S1fHsmx+bYYzcjYGLiIhyhuHA3tSJXtxXX4WNq+ps/2L2l/nQWFuBEs/QgZQS\nwLvHzuORF5v451oAGLiIiChnkpueUtzFq1H8/a+P4skdh2DX/jXUF0ZDjR9b19+Gu2+cBWE5CDwc\n0fnnWgAYuIiIKGf8ZezNMhpG/62GGj+WzJ0KS96Cpgn25yoADFxERJQzob5wvh+h4AggoTC+sbYC\nXo8GgaGO/Sycdz8GLiIiypnkOiTKbPrkxFnBo929iMV0SAAeET/8m9yPbSHcbILCmEcVr/WPCmPs\nm0GnekNhzPPpDh+w+Outave7UWHMf1S7FD5SHEdEWRHoCJkHVzfU+NFQ48ddC2diz+Ez+X60gnHu\n06FZwUBHCBt3tcLYrBiJSZ6vWCAYuIiIyBGBjhAeebEJ4agOn1fDS482AgDe+fhcnp+s8AQ6Qmio\n8aOpvQcxfag1hATr4goFAxcRETmiqb0H4agOXcZ7Rm1vCaLzQh8iUbt9eDScB59/H5tW34RTF69C\n04QZugRYF1coGLiIiMgRjbUV8Hk1RKI6PB4Nrxw4iUhM8tDqUYjqwNM7D0EivivRIwApAV+Jxh2K\nBYKBi4iIHNFQ48dLjzaiqb0Hpy5exc8+7GTYGgOzybwu8fCt1ZgzdaJZG0fux8BFRESOMQrlAx0h\nbG8JIjK4xMjgNTJicEYLAHQJ1M25BuuWVef3oWhE2BaCiIgcZ8x2/eWXF+KWeZyRGamlNX6z2akG\n1m0VIgYuIiLKiYYaPx5fPh/fWbkI3sFeXB4NYFeuzEJ9EQjE/6y8HnaWL0RcUiQiopxqqPHj5fW3\nmf25jnb34qkdh7jMOIzjZz81fx/jH1RBYuByszKFMb9RvNZTY3mQJHcrjHlboanpHYr3e19hzE8V\nr/VVxXFE5Cij0LupvQf+Mh9W3DiLzVAVxXSJ7S1BFssXGAYuIiLKOaMp6kAkfkSNJuJLZVFO3yj5\n+Exvvh+BRog1XERElHNGU1RLpwPEYhKLeC6gkoMnL2JLc2e+H4NGgIGLiIhyKtARQtfFq/BqIuEv\nIQngk/NX8O07a/P1aAUjEpN4cschhq4CwsBFREQ5Yywlbv2wExACDy2rxpdvnGXuVIzGdHzQ3pPX\nZywkr7WezvcjkCIGLiIiyhnr+YqxmI5rp07EY1+4HqUlGjQRX1r8KHgp349ZMFYunp3vRyBFDFxE\nRJQzxvmKHgGUeDXzaJqXHm3E7fOnsyfXCNy3ZA67zRcQ7lIkIqKcsZ6vaD0HsKHGjydW3ID9Jy4g\nHNGh5/k53UwAeOzOWmy4d1G+H4VGgIGLiIhyyjhf0e79lx5txLNvfox/O34eOjtEAADuXDAdP/3m\nMgQ6QilBlQoHA5ebTVAYo7qZx6Mw5qTitf5RYcwyhTEPKK5o3zMv85gt7WrXUvke/53apYgo+6wz\nXf0RznMBQNU0lS7Y5Has4SIiIlcxZrrYkyvu+Jlec3fnD944ikdebEKgI5Tvx6IRYuAiIiLXaajx\nY9Vn5+T7MVwh0BHC9pagubszEtXRxNYZBYdLikRE5BrWOqXG2gpMKNHMoDFeSRlvCuvzaohEdXN3\nJxUWBi4iInIFY9ksHNXh82p46dFGvPRoI779rwdw7tNwvh8vpzwaEBssYfN6BNbWV2FtfRWL5gsY\nAxcREbmCtSmqsWz2+PL5uBqJ5fvRcs4IWwLAA0vnJgQsYzmRoauwMHAREVHeJC8h2i2brVg0CzsP\nduX5SfOjxKuhvNSLFT98B+c+7cflvigkgAkl8RlAhq7CwcBFRER5kW4JMXnZ7NmHbkbbqUs4du5K\nnp8492IxHc/tS217MxCJzwAycBUO7lIkIqK8sFtCbKjx4/Hl81OCxDP3fxblpSoNBYtLLM1mASHA\nwvkCwxkuIiLKi3RLiMmMmTA2Qh2y/vO1nN0qMAxcbqbyjzmVbvQA8BuFMR8pXuv7CmMuKoz5peIP\nz5hCF3nVDUyqf15E5Lh05yomM2bCKO7LN87iOYoFiIGLiIgcoXL2X7pzFa2MmbDxNMOlAVhYWY4z\nl/sxa8oEfHL+CmKx+EzgY1+4Pt+PR6PAwEVERFlnVxA/liWwtfVVkAAOn7qE3526VNSNUGumleGH\nDy5J+PPiwdWFj4GLiIiyLl1B/EjZBbftLUG81NzpwFPnz6LKcnx89lNIKXGmtz/l6yozgeRu3KVI\nRERZZywDegTGdBSNXXBbU18FTWT5gfPII4DyCV5IKXlWYhHjDBcREWWdakF8JnY7GRtq/PhMZTkO\nn+7N8lPnR0wCH54IwaMJeCB5VmKRYuAiIiJHZGMZLF1wS961KBA/4LmQXTPBi29+vpZ1WkWKgYuI\niFzNLrjVzpiM45bO85+pLMeR7sKe8Zo/czIeXz4/4T0WyxcP1nAREVHBeewL18PriRdyeT0C9UUQ\nRr6zMrG3lrFh4AdvHMUjLzYh0BHK05NRNnCGq9BNVRx3SWHMGcVrvao4LpNKxXEqTVv/s+K1xt/J\nIERFqaHGj5fX32bO/hwt4NktrwZ87ZbqlPeztdOT3IEzXEREVJCMcxcBYNPutjw/zehpmoaffdiZ\nMouVrZ2e5A6c4SIiooLW1N5T0F3ojQ0AybNY2drpSe7AwEVERAWtsbai4HcpCgBCCPjLfAnvs+Fp\n8eCSIhERFbSGGj9WL5mT78cYs5gusWl3G4vjixQDFxERFbxnH7oZ376zFvMqynDnguk5uefsKaX4\n8o2zlMdXlpeaNVlej0joli8H/8cu88WLS4pERFQUNty7CHfXVeLhHzfl5H7nr4TRfEItHN23ZA6e\nfejmhL5a21qC2NrcaS6FCrA4vpgxcBERUdHY3hJM6ULvlEhM4lJfVGlsXzgGILUma3tLEJGoDo9H\nw/0NVVhbX8WarSLFwEVEREXjbO9Avh/BVvu5T1Pe4y7E8YWBi4iIisbM8tJ8P4Kt2hmTAaQe1cNd\niOMHA5eblSiMUd2Yc05hjGrtp8pzvacwRuWZAGCGwpgJitfiNhGioramvgpbP+yEnoMeEZN9Hsyd\nVoaILnH8bOIMlkcAscFn8GgCj33hejzzqyN44d12SAmUlmh46dFGhq1xhIGLiIiKRkONH9+77yY8\nueOQ4/dq3XQPAGDls/tsn2P+rHIIxEPg0e5ePLev3fz6QETtqB4eXl08GLiIiKiorFtWjf/ZdAKH\nTzt7vmKgI4SGGj9OhvpSvjYQ1fH9r95kvn72zY8Tvi4EMu5GNA6vDkd1+LycESt0XGAhIqKcCnSE\nsHnvcUcbfFb5yxy7tsHolzV7SmpNw4NJh1GvXDw74fX6z9dmDE92h1dT4eIMFxEROc5YGvOX+bBp\nd5vjszbTc1A8b8xQ9fSFE973iNSx65bFA9hrraexcvFs83Wm6/u8GiJRnf25igADFxEROcq6NCYw\nVEyefFhzNtzxzFsIXuzP2vWGs6etG8+/8wkuXIkkvB+TwJM7DuHto2fx2BeuR0ONH4GOEEJ9YTyx\n4gbl75dtI4oLAxcRETnKujRm5dFEVmdtbvneHpz7NJx5YJZYi+DtvHH4DN76/Vl8647r8C8fnBjV\nrB7bRhQPBi4iInKUsTQ2ENETjrF5YOncrIWJQEcop2FLVUyXeP7ddgggoRbL7vsOdITw/Duf4Mzl\nflw3fRL+cP4KfF4NN8wqxxp2oC94DFxEROQoY2lsW0sQrwaCiMXiNUlr6quydo+/fPlg1q6VbVLG\nZ/MEZNparEBHCA+98AEig+utHwUvmV/bfyKEVwJBbP0WdykWMgYuN1NpMDpX8Voqu6NPKl7rM5mH\ntLVmHqPas9W/THGgCl8Wr0VEyoylsbX1VVmvSQp0hNBxIbU1g1t4NIFNqxcj1BdO+303tfeYYcuO\nE/VulFsMXERElDNO1CS5vV3C365enHFXYmNtxf/f3v2GWHrVdwD/npnJWDbEdlwlG02yupJKdils\nO2O6StEUYpqEQrTSEE3RF6axoqVQX3T7ByuCEFqkrxbtdhEtjQaLpIpdoiakDaWMNQOlZhtCt6uj\nCTFJ1ymFRrqZndMXO7OZbGZm7+zMuc+9cz8fGObeO3ef53fnXs5895zznJPLxsu6octVisNP4AJg\nqA1yEHnHda/taQmI6b1Tuf+et5nDtYMJXAAMtem9U3nN5ZP5yf8O1qT5N+7elb/+UO9zIqb3TuXo\nB2YaVkSXrDQPwNC7Y3r7JuBvl8/ccfCS/l0/VuKn/wQuAIbeuw7sydgaK7xvZJNP37TVQ4C9hqiV\nRWI/860nc9exWaFrBzGkCMDQ28zE+bGSTIyPZXHxpXXBttvuXS9dZr6ZTajX2j/R3K2dQQ8XAENv\nZXHVXnqtlmqy67KxLDWs5+gH33r+9mY2oV55HePFlYk7jR4uAIbe6n0Hp3ZN5h+efC4nnvmfPL3w\n0zWf/98/XWxWy837r3xZr9RmNqG2f+LOJXANsl4WPn1Lj8d6XQ/P2XhbsJf00HP/jR4O8wd39Hi+\nm3p4zs/1eCxgx1q9xtf7f/nafOk7P8wfPfC9vtfx4Xe++RV1bSZE2T9xZxK4ABh6c/MLLws0c/ML\n+ZO/63/YunxyfM2wJEQhcAEw1NaalD576nSWWs2I38ALZ87mS9/54Ybb+DCaBC4Ahtpak9Kndk1m\nrKST0PWJrz2epVovekUio8VVigAMtQuv7JvaNZlPfeNEagdha3ys5OxS7emKREaLHi4AhtqFk9JX\nerz6nbfe+sapTF87lWP/9P0sLVXLOvAyAhcAQ+/CSekryzCc7VPqes2uy3L41utz17HZLNWasbGS\nT/z6AcOJnGdIEYAdZaXH6/dvfksuG2+9gc85t/7CVS+bS1ZrzcILg7WZNt3SwwXAjrPS43Vo3+68\n97P/3Px8B17/s3nLnit6XuCU0SNwAbBjTe+dylc/8vb81rHZ/PTFdpv5LLxwxirxbEjgGmTjPTzn\nih6P1cvzPtnjsXpYRv6mb/VwnI/3eL5efg9nezwWMHKm907lb+4+1Kyna3JVb5YFTllP8zlcpZRb\nSilPllJOllIOtz4fAFxoeu9Urt/T6/9Qe7f/qivy5d+21hYX1zRwlVLGkxxJcmuS/UneV0rZ3/Kc\nALCWX9zmUHTd6y7P8d97h7BFT1r3cN2Q5GSt9VSt9UyS+5Pc3vicAPAK7/2lq7ftj96eV78q3/74\njdt0NEZB68D1hiQ/WnX/qeXHziul3FNKeayU8tjzzz/fuByA7aUNGy4TE+v/2fvqR96edx98fU/H\nOXLX9HaVxIjofNJ8rfVokqNJMjMz08FGDACXThs2PGZPnc7i2VdeqXjz/itz9AMzSc7N9drz6p/J\n5x49teYxxkryt7/zdsOIbFrrwPV0kmtW3b96+TEA6KuVPRfPvLiUpZwLT5MTY/nwO9+cufmF88s5\nHL7t+rzrwJ587h//Mw8/8WyW6rnnmRzPVrQOXN9Ncl0p5U05F7TuTPL+xucEYICsDjNdBpbV62RN\n7ZrMwgtnzi/ncNex2ZxZXMrkxFjuu/tcsDp4zc/l4SeeTZKcPXtuI+rN1D8or5vB0DRw1VoXSykf\nS/LNnFtN6fO11hMtzwnA4JibX1gzzHRlrXWyjjxy8vyWPC8uvhSsDu3bnYmxkhfP1oyPlU2tHN/r\n6xbKRkfzOVy11uNJjrc+z440PaDTQW64+FOmP9W+DGDwrd5fcHWYGSQrQ41rbslTSpK6/L13vbzu\nQQujtNX5pHkAdq4Nw8yAWG9LnpVJ9jWbH1Ls5XUPQxhl+whcADQzLPsLrjXUuJWw2MvrHoYwyvYp\ntQ7OsNXMzEx97LHHui4D6KNSylytdabrOraDNmznaT3Hyhyu4baZ9ksPFwCso/Vm1Da7Hh3NN68G\nABh1AhcAQGMCFwBAYwIXAEBjAhcA22pufiFHHjmZufmFrkuBgeEqRQC2jdXTYW16uADYNmutng4I\nXABso5XV08dLrJ4OqxhSBGDbDMtWPtBvAhcA28rq6fBKhhQBABoTuAAYeZayoDVDigCMNEtZ0A96\nuAAYaZayoB8ELgBGmqUs6AdDigCMNEtZ0A8CFwAjz1IWtGZIEQCgMYELAKAxgQsAoDGBCwCgMYEL\nAKAxgQsAoDGBCwCgMYELAKAxgQuAkXLv8Sdy458/knuPP9F1KYwQK80DMDLuPf5EPvfoqSQ5//3w\nbdd3WRIjQg8XACPjwRM/3vA+tCJwATAybjmwZ8P70IohRQBGxsrw4YMnfpxbDuwxnEjfCFwAjJTD\nt10vaNF3hhQBABoTuAAAGhO4AAAaE7gAGDhz8ws58sjJzM0vdF0KbAuT5gEYKHPzC7nr2GzOLC5l\ncmIs9919KNN7p7ouC7ZEDxcAA2X21OmcWVzKUk1eXFzK7KnTXZcEWyZwAdAXvQ4THtq3O5MTYxkv\nyWUTYzm0b3efKoR2DCkC0Nxmhgmn907lvrsPZfbU6Rzat9twIjuCwAVAc2sNE24UpKb3Tgla7CiG\nFAFozjAho04PFwDNGSZk1AlcAPSFYUJGmSFFAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQu\nAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCA\nxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYE\nLgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGBC4AgMYELgCAxgQuAIDGthS4Sim/WUo5UUpZKqXMXPCz\nPyylnCylPFlK+bWtlQkAMLwmtvjvH0/yG0n+cvWDpZT9Se5MciDJ65M8VEr5+Vrr2S2eDwBg6Gyp\nh6vW+kSt9ck1fnR7kvtrrf9Xa/1+kpNJbtjKuQAAhlWrOVxvSPKjVfefWn4MAGDkXHRIsZTyUJI9\na/zoj2utX9tqAaWUe5LckyTXXnvtVg8H0FfaMKAXFw1ctdabLuG4Tye5ZtX9q5cfW+v4R5McTZKZ\nmZl6CecC6Iw2DOhFqyHFrye5s5TyqlLKm5Jcl+RfGp0LAGCgbXVZiPeUUp5K8rYkf19K+WaS1FpP\nJPlKkn9P8mCSj7pCEQAYVVtaFqLW+kCSB9b52aeTfHorxwcA2AmsNA8A0JjABQDQmMAFANCYwAUA\n0JjABQDQmMAFANCYwAUA0JjABQDQmMAFANCYwAUA0JjABQDQmMAFANCYwAUA0JjABQDQmMAFANCY\nwAUA0JjABQDQmMAFANCYwAUA0JjABQDQmMAFANCYwAXAjjQ3v5Ajj5zM3PxC16VAJrouAAC229z8\nQu46Npszi0uZnBjLfXcfyvTeqa7LYoTp4QJgx5k9dTpnFpeyVJMXF5cye+p01yUx4gQuAHacQ/t2\nZ3JiLOMluWxiLIf27e66JEacIUUAdpzpvVO57+5DmT11Oof27TacSOcELgB2pOm9U4IWA8OQIgBA\nYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMC\nFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGOl1tp1DeeVUp5PMt+H\nU702yX/14TwtqL0bw1r7MNS9t9b6uq6L2A7asIsa1roTtXdl0Gvvuf0aqMDVL6WUx2qtM13XcSnU\n3o1hrX1Y62Zjw/q+Dmvdidq7Msy1X8iQIgBAYwIXAEBjoxq4jnZdwBaovRvDWvuw1s3GhvV9Hda6\nE7V3ZZhrf5mRnMMFANBPo9rDBQDQNyMbuEopnyylPF1K+dflr9u6rmkjpZRbSilPllJOllIOd13P\nZpRSflBK+d7y7/mxruvZSCnl86WU50opj6967DWllG+XUv5j+ftUlzWuZ53ah+pzTm+G8X3VhvWH\nNmxwjWzgWvYXtdaDy1/Huy5mPaWU8SRHktyaZH+S95VS9ndb1ab96vLvedAv7/1CklsueOxwkodr\nrdcleXj5/iD6Ql5ZezIkn3M2bWjeV21YX30h2rCBNOqBa1jckORkrfVUrfVMkvuT3N5xTTtSrfXR\nJD+54OHbk3xx+fYXk7y7r0X1aJ3aYRBow/pEGza4Rj1w/W4p5d+WuzEHsot12RuS/GjV/aeWHxsW\nNclDpZS5Uso9XRdzCa6stT6zfPvHSa7ssphLMCyfczZnmN5XbVi3tGEDYEcHrlLKQ6WUx9f4uj3J\nZ5PsS3IwyTNJPtNpsTvbr9RaD+bccMJHSynv6LqgS1XPXdY7TJf2+pwPKe3XQNGGdWfHfNYnui6g\npVrrTb08r5TyV0m+0bicrXg6yTWr7l+9/NhQqLU+vfz9uVLKAzk3vPBot1VtyrOllKtqrc+UUq5K\n8lzXBfWq1vrsyu0h+Jyzyg5qvxJtWNe0YQNgR/dwbWT5Q7fiPUkeX++5A+C7Sa4rpbyplDKZ5M4k\nX++4pp6UUi4vpVyxcjvJzRns3/Vavp7kg8u3P5jkax3WsilD9jmnR0P4vmrDuqUNGwA7uofrIv6s\nlHIw57pWf5Dkw92Ws75a62Ip5WNJvplkPMnna60nOi6rV1cmeaCUkpz7vH2p1vpgtyWtr5Ty5SQ3\nJnltKeWpJH+a5N4kXymlfCjJfJI7uqtwfevUfuOwfM7ZlKFpvxJtWD9pwwaXleYBABob2SFFAIB+\nEbgAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABr7f7IhrrYd0aP+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129f14da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = torch.nn.functional.softplus(weights).data.numpy().copy()\n",
    "w[(w > 400)|(w<-100)] = np.nan\n",
    "f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, figsize=(10,10))\n",
    "ax1.imshow(w.T,origin='lower', clim=(0,150), cmap='hot')\n",
    "ax2.plot(t['start_x'], t['start_y'], '.')\n",
    "ax2.axis('equal')\n",
    "ax1.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T17:56:03.014295Z",
     "start_time": "2017-11-15T17:56:02.524699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.2025609488574673,\n",
       " 12.767860741294614,\n",
       " -15.658745946636046,\n",
       " 42.421876456482572)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VwNEzl/DYtgPY1NSRk2uvXTqHYYsckdNdilLK80KI3QDuAnBKCDFDSnlSCDEDwOlc3ouI\niIpTuDcCNeWf6P9j1yGsXpI+g6jtPPSX+RDujXD2ilwrF7sUpwKIDoSt8QDuBPB9AL8A8DUATw38\numO09yIiouJntivv0/5Y2nvGAnsJQBGw3Lcq341F2ciUcjHDNQPATwbquBQAP5dS7hRCfADg50KI\nbwBoB/DVHNyLiIiKXKDGj1vnTsGeI2f19/4vk/5WxgJ7AJb7VuW7sSgbmRKQm12KvwNwvcn73QC+\nMNrrE7EBLNHY8607rsV7R89ClYmZqz+9aXbaGL3APqpCRWKclb5V+Wosqs1qnTh/mY1MiZ3miYjI\nfRrbuvXfi4HXqSHFuPNwODVc+WgsapzV8noUeBWBuCrZyHQMY+AiIiLXGSoUpdZDDXe2yK4WEZmO\nDorHVdx/YzVmThrPGq4xjIGLiIhcJ1Mo2tTUgfU7WhBXJUpLRl4PlevGotmODlpZX8WgNcYxcBER\nkeuY7eoLtoexfkcLYgM9IyJR83qobDsC7dgxaOfRQVQcGLiIiMhVMu3qa2zrhmrYRKMoIq0eKrV2\n6iuBKqwyzC7ZtWOw0I4OovzLSad5IiKiXDHbRQgMhhpFAF5FYMOKhUlBauPuo9jaHNK/NhJTsbmp\nAw++2Ihge3jIa4+W1iH/s3OmYP3yOgYtSsMZLiIicpWG2goIIQApATE4i5WpritpVksR8HoSM03a\nmXLRmIotzSF9N6MdOxSD7WFs2NmKSEzF3mPnMK+ynKGLkjBwERGRq+xq7UJ8oE4rrkrsau3Sw4vZ\nMl3SjkBV4r4bZ0EAeHlvB+IqIATwajCEWHywoD3XxwDlq7dXNsb6NO25WEPmDgxcLlYhRNYxfTm8\n36VOa+MmzMzhTXNkgsVxl6yMYaNVIkdt338i7fW6u+dnHJ9aP7WqvgoA8EowhLiqQkLoM15aQfva\npXNy+sz+Mh8UIQAkem35y3zYuPtoXsNOav0apERMlexu7xIMXERE5Cqp/9TM9k9Ps6XGjbuPIhZP\nhCxVlVAGLmJH41FtOTGuSngUga/fNBtP/rJVD4CbH85P2NnSHNLPlYzGVACDS6parRpnvJzDwEVE\nRK5yoS/5oOpTF/uxqakDq5dUZ/waLUAYC+w1EokdjfcunpW0YzFXjGc6SinxwcBrIFG4v7U5ZHvA\nCbaH8WowpJ8r6fEIKIDe3d5f5uN5jg5j4CIiItcItofRF4knvacCeGzbAQBICl3GeqVdrV14/t02\nqBIYV6LgrrpKqIbqgGhc4qpJ4xGo8WNTUwdeazmJZQtnZLzecMJI6pLmtInjAFzQP89HkUJjWzdi\n8UTIEwDuWzwLK+urTDvf8zxHZzBwERGRo7Sg4y/zYcPOVqgZxj2/5xN995+xXklRBGLxwVjTH1Xx\n9uEzSV8rkAhGm5o69PD27pGz6Oi+hPLxJfCX+Ua8DJh6pmNr5wV4FSCuAiUeodeUWfn+R1rMnxr6\ntM72xuvYfX4kDY2Bi4iIHJMUnITQdyeaae/uxQMvNOIrgSoIQJ+xUeMpXyOA26+diu37B3cCrVg0\nE4EaP55583DS0OffbUt8ieHeI1kG1MZq3wsAXDN1Av7iltohr2P8/lWZCIYjObIo2/mQdp0fSdYx\ncBERkWOMS12AhNZ+y0grmpcYbGZa4kn024prhfGGr1lc48cz91+Pyonj8HprF+6qq9R3OS5bOAPv\nHjmrj5UycV2RctORLAMmfy/A0TOX8OQvW4fsyZX6NcYi95Ecyj3U17DzvbMYuIiIyDGpS2Ffv2k2\nnt3TljautETRd+BJDPbbumrSePjLfFi/4wBiKuBVgHXLEuFq3d3z09pJzKssR4lHIBqX8CiAR0mE\nNo9HQVxVEVcBr4VlwEzfiyJE0vFD2cKT9v1roUuBPTspyXkMXERE5Bizpa7//e9/QL9hmXB8iYJ/\ne6gBW5tDejNTjyKSdhzOqyy3tFzW2NY9uGwpga8EqvTQ9uQvWqBCjvjMu0CNHxtWLMQT2w9Ae/yh\nwpNWu6U1Yh1NDRe5HwMXERE5KnWpq7TEg/74YGsIr0fRP9eamSKlMbTV5TKzJql63y5V6rNnxlmp\n4exeXL2kGvMqy7GlOQQBYOXATFlqE1S7DtEm92LgcrG5FsYcyOH9rHaQn2phzCQLY45Yux3+0sKY\nH1m81t0WxxGRc0q9iulrrfWBBBCPj7zOyax4PDWIabNSm5o68Pi2A4leXgJ45ZufzXpPY/jLFKzY\npmHsYeAiIiJXSd2pqL3OFIqGK9Ns2Kr6KsiBX7XWE1rYAhKF+U9sP4BffetWy/fKFKxy9b1Q4WDg\nIiIiVznXGzV9PdzWBlaXAlNnobSC+ca27rTdin84a+VE1kENtRXwKlqRvtCDldn3MtLGq1QYGLiI\niKjoDKdGKtMsVM/laNrYaeXjhv8wA4daD1V3xpqu4sfARUREruIRgLGXqWcgp1gJJdosUef5y5Zr\npBpqK+D1JJb3PJ7B5b03D55KG3vL3CnD+l6s1p2xpqv4MXAREZGrjCvx4JLhPMVxJR4A2UOJMZB5\nlcHGqMYaqUzLdqqUAw1UDUkvZUYKAOpmXjms78VqrRZruoofAxcREblKNK6avs4WSoyBLK5KfH7+\nNPRF41i2cIZeI3Xf8x8gFpdQBPDde67D6iXV2Noc0s9ijMWlfqzPX9x8tX7uoibcG9F/b6Xmymrd\nmZuO3mEtmT0YuIiIyFUy7VLMFkqMgcyjCLx18BRUCbz/yVnMqyzHc+98ogcrVQKPbzuAls4LONPT\nn3Qd7e7zKsuhiMFjg3wekTRTZrXmymqPMDccvcNaMvswcBERkauknl9tfD1UKDEGsp0fdeJgVw8A\nIK4C33/tYNruRwlgc1MHlJTW8hNLE381bm0OJd170axJ+r2dqrnKNvs02tkp1pLZh4HLxX5mYUyP\nxWs1Whjz8EfWrlX/mexjmqdbuFCX1V42n2Yd8SPRbOlKr6SeiktErpP6X+lw/qvVAtmP30s+j/Ho\nmU8xZ+oVpvdKWcHEmwdPYd3d83HkVPJP2L3Hwgi2h3PWR2u44Sjb7FMuZqdYS2YfBi4iIio608vH\n4dylwRktxaQAXgD6kmFSqBsY+3HnxaTxEtBnfFKXNwHz43syBaqRhKNss0+5mJ1yUy1ZsWHgIiIi\n19jU1JH2nmJoC2E1CNTX+PUlRQDo/jSCcMqS4h0LpmPRrEloauvGniNn9fcXzpwIALgcjSPVifOX\nsampQz9keu3SOabhCUDSe9oB1dqzm4UjAEN+f9lmn+zuxE+jw8BFRESOMgap11pOpn2uysSYB15o\n1MPEk19KDjCpVtZX4ZVgCJFYYr1QAlBTisOWzpuG1Uuq9bCj6b4UQbA9jCvGeXHhcizps81NHZBI\nzI55FIENKxYi3BtBfzTRaysSHQxPWqCKxFSs39ECVUo9kKWGI3+ZL+uMV7bZJ85OuRsDFxEROWZT\nU0dSGLlx9uS0MeWlHmxtHgxPkZiKJ3a0QBoCjFn42PxwA7Y2h/DKvuOIqxJCCMRVqQcmrcVD3YyJ\neNcww1UxIRF++qIpxV0YXHqUAGKqxPodLXjolqsHz1sE4C/zYV5luR6ojPfVZrPWLp2TFI6sLgdm\nm33i7JR7MXAREZEjNjV14PHtB6DtZYnEVOxrD6eN+5u7F2D3odNJ72mtIqyEk5X1VWhs60bP5Sie\n3ZMoppdIBCMA+CTlfMSWExf0cJeNKiVaT17Ua8EUkQhy2mzTluYQ3j96Fse6ewEgaakvNRyxWL24\nMXAREVHePfqz32L7/s6093sj6XVT4d4IUkveFZGYpbISTrRg83hKE9PWzgsA0g+k7ovG4fMqaTNc\nChJBzbgw6VEE+qJx/T3jAdUA8PLejqRdkHfVVWYMh1wOLG4MXERElFdP/eqgadiaMWk8OsOX09pA\nNNRWYP/x80nvBWr8uH3etGGFk0ztJiaXlSS9P3PSePzPB+qx+oVG9Btmusp8HkTiKiKGgx6jcYm9\nx8JJr3e1dunnOaa2nPjN75Nn6lK/Jwat4sXARUREebV9/wnT9+tmTMTJ85eR2i5vV2sXppWXJr13\n7fRyrF06Z8j7pO5qXJhyDqL22iyIBWr8qJ0yIWmn46zJZZg1uQxvfJx+qLXRs3vaoAjAq6S3ojDb\n+UhjAwOXi/2DhTF/YvFaD1to+PmQSZ8aM83y9axj3hZ3ZR1z+3V7LN2vtcXSMCIqENWTy9B1sT/t\n/dvnTTMNM6+3duHpry7CK8GQXuO0sr5qyHtoxfhxVaK0JFFYry1NphbNHz6d3OC080IfACCasqux\npy+Ktw9lnqEy0s5zFAJJAdLaT1kqRgxcRESUV99ZNh+rfvR+0nsTSz34xzcOmY7X6p42P5zcaPTe\nZ9/H8XO9uGfRVbizrjLpsycGwhYA9A+0avCX+ZJ2GR451YM7fvgOLvQmt34YX+IBAFw9ZQKOnh48\n6UIisWRohSIS9WWQQJ9hWdKsAWsmPES6uDBwERFRXgVq/Njyl5/FX/98v75772J/HOg3X267s64S\nAHCoqwdvtHbhnUOnse9YGFqMeXZPG55/N7H70OdVcOvcqUkHYEsAPZejeKO1K+m6ZnVkADDzynEA\nEn26dhlm3Lou9lk+ZkgRAuuX1+Gp1z5GnyHP9cVU/XigofAQ6eLDwEVERHkXqPHj3sWz8I+/PpQ1\nxKz60fuYeoUPZz6NZByj5av+qIoPPjmb9vmL7/0BsdRTsTM4fKoHG3cfRef5y3q7BwFAzdApQhtj\nJKVEuDeCSyYh0sqROzxEuvgo2YcQERHlXkNtBUo81pbYhgpbRhJAj0nIsRq2AODUxX784NeH8LO9\nHfB6FHgGlgdLPAIeMXjUkMbs0lq7ivE+T9pnHx0/j8e3HUDQpOeYRutEr92bfbkKH2e4iIjIEYEa\nPzavuQnff+0gPjyWOXxYoRXDj5ZXAbSSq7gKBKqvxG0D7ScApDVQNVM5sRQbHwwgUOPHghkT0743\nbWPAz/Yex9+vWIjVS6rTrsG+XMWHgYuIiBwTqPGjtCR9Fmi4/qiyPKmFw0ilNpjvj6lp7Sca27px\nz6KZ2H/8PBbNmoRf/q4zqd/WPYuu0gNST19yQb5RXJV4YkcL5lWWj+gYHyosDFxEROSoZQtnJJ1l\nmKqyvBRdPeltJDT3LJqJCaXeYQcubWVwqJmxq6dM0H9vbDWhtZboutiHedPL8fHJwXv39A+GrLaU\nLvap4qrE1uYQg9UYwBouIiJy1Ool1ZhfWZ7xc49HINMkmAAwd3o56lKamqbyKgK3zp2S9N6KRTNx\n54LpQ37dLz/qRLA9jGB7GE9sP4DYQNgCEkEtElWTwpb2vsbsTMYpV/iSXh8+1YM1P92HFf/8HjY1\ndQz5PHYItoexcffRIWvKaPQ4w0VERI7r6Ytm/Gy8z4vrZ/mTaqEEEsXrHkWg8/xlnDh/OePXXzVp\nHP7pgXpsbQ4lvT93enna100a78X5y4MzVHGZWEI8cf4yUltwCQDCpHjM2NHebPbsbMoGAOPRQB+F\nEuc9mtV12YHtJ/KHgcvF6i2MmWn1Yquz7wR68Qlrl9pqoYv8ykezX+eFZ6zd7+GhG0onhLIPISL3\nGjdEHddf3Hw1OrovJQWuFYtmoqzUi1eDIWz+MLGb0KskCt1TQ854X+Kvupf3Js8e+ct86EwJXKmN\nSQWAN1q7UOpNXxC6Ze4UHDnVk9Y1X+tgP1KvtZzUA5fdzU/ZfiJ/uKRIRESOu2N+5qW9eZXleOHd\n5F2Bv9jfiePnehGLJ8JCLK5CZjg4p0QRaGzrTjtI+u1Dp7Gyvgoew9+E53qTZ9okgI9CF/DhsXBS\nOwivR2DW5DLTdhWd5y+Panlu2cIZAAZnn55+4xAefLHRliU/tp/IH85wERGR48rHl2T87Ll3Pklb\nzlMBvdBeEYmZqbihvsroxPnL8Jf50lpHvPnxKdROmZAWxBQA11Vdid+FLiSNnzFpPKZM8KHUq2Bf\nezhjvdXmDzuwpTmElx5qyPg9GU0uK8Hi2ZNx6mIf7ruhWp/dysfsE9tP5A8DFxEROa6htiLtoGfN\nW79PP9Da6LqrrsRNtRUZe2Nd7Ithw85WLJ6dXAcmkTgYO5V3oBlr6qOcDF/GyfOXIeXQOxuNAcmK\nry6ehXWpZy9gAAAgAElEQVR3z09731/mSyxxSmnr7BPbT+QHlxSJiMgVzMIWgLQZqFQLr7pyyBky\nIBGAACQtOpZ4BO4aOKcx6X5S4qPQhbT3VSTCVLYGq4phec6X0knf5xH43pevQ9WkcZhcVoJv3lpr\nGraC7WFs2NkKVUooSuJcRoaiwsYZLiIictzfbjswoq/zKMDK+sTOGo9A2tKjRlFE0uzW/MpyfPfL\n1+GQSe+uTAFP2xkJYT6mRAH+bsV1CPdG9OW5BTMmYr8hvDXUVmD1kuqsuxCNy4kCctSF+OQ8Bi4i\nInLcJ2c+HfbXCAD331CNQI0/UVCe4XwfIYCayWU4emawCenvB4LWxt1HLN/vc3OnYEltBRpqK7Cr\ntQuvt3Zh0vgSPVBFVaCj+5I+YxVsDyeFLQBo+kO3/tlQdVNaMXs0prKYvUhwSZGIiBx3ZZYlQTMl\nXkWf3drSHMo4MyVl+uHVEolZpPAl6zNHxiOI1t09H2//l6U4fzl5V6OxJiy17xcA9MckNjV14L7n\nPhhy96FWzP7tL85jb6wiwcBFRESO+893zjN9f+K4zAsxXwlU6UHk7BBH/wDAyQt9ae/5y3wQInuP\nQs1bB0/hH399CA+8MBiSYikpz/j6TIZn0jrWqzLRiT5TcX2gxo+1S+cwbBUJLim62HELY65ZavFi\ni7MPify1tUuttHCt31hoavoVa7fDdyw0Nf0Xi9fCWxZ+uH4hW0ksEeXavAxH+6QGGiOBxNJcoMaP\nKeWlQ14/mnIdRQBP/rI17egdr0dAqhIeT2I5TxrGa5NkkZiqn394MqXpqfF1prorY52ZQCL4bdx9\nlG0ZihxnuIiIyHHff+2g6fu90cyBa/OHHfqS3Kr6KniG+PeUz5P81924Ek9a2Lphth8vr7kJ999Y\njWumTEgqB5t2RXKg04NYWtGY1M8ltLJcOWtyGTbsbLW1uSm5AwMXERE57ujp4RfNqxLojw7ONv39\nPddlDF1XT5mQ9PqLJodW//b4eRzq6sGW5hAOpuxe7Orph1dJzEj5PAKrBmrHJo5Lrj2Lq9CXHVPr\nxsxc7IvquxH7oiq+/fL+rF+TCQ+hdjcuKRIRkaOC7WFcGOLw6qFIAK/sO46V9VV6q4XHTFpMXJFS\nC9Z5/nLapsZYXOLlvR1pM1+auplXou6qK7GqfrB2rKc/ljZOIrHseDpLXRkAzJl6RdLh1e3nevFn\nP27CT7+xJOvXaoLtYWxpDuHVYAixOA+hdivOcBERkaMa27ozNj21Iq5KvfA8U91UZ0rR/IfHwlBM\n/ga8cDkKr2I+TfZR6AJeDSYXlWYaqz2XmZKBabgSj8B3ls3XXw8+27mM10ylnbe4uakj7RggchcG\nLiIicpS/zAcLq2+mlJRDl836VXkVQJgkOrN6/GPdvbh93rSM94vEVDz3zif666iafhFt2dFrsr5Z\nXurB3/3xQnxu7hT83R8vRKDGn/bMN86enPH+qbQGqdp3J8BDqN2KS4pEROSocG8kU8/SIX1xwXR8\nZtakpN19u0zORoypQOf59LYQmWTb8bjr41P67shYPP3z0hIFfzS9PK3pKZDoN/bkL1oQjUs0tXVj\nXmU5fvqNJfizHzfhw2PncOPsycNaTjQ2SPUoAvcunoWVhiVPcg8GLiIiclRDbQVKSxT0DbEjMZXX\nI1A7ZQI2NbXjx+/9AbfOnYLeSBy7PjY/6NrqlQWAVfVV+NmHHRln3SQSjVYDNX7TkNgXVU3DFgCc\n7ulHZKAvRCQu9esMJ2QZaQ1Sh+paT+7AwEVERI4K1PixfnmdabF7JgtnTMSze9r019v3dw77vl6P\nwOfnTcPvjp/H2UsRXDVpPH543yIEavzwKkIPRmZaT1zApqaOYd+zYoIvqVdXpgqwbEf/GAVq/Axa\nBYCBy8U82YcA2y1e7NujeJBUH2Uf8vlV2cdc2mLtdt+30pDV6j8OvzD0gbFElH/B9jA27Gwd1tdk\nmkHKRBGAIgQeuuVqXOyPQQBDLr0NFbYA4HehC2jtbBnWMwDApLLkwHWpP5bW9FQrhI/EuOOwmDBw\nERGRoxrbuoe1nDhcU8p9+PPPXp3TJTcJQB1BpX9qf6/t+zv1QvfNDyeClVYIb9xxyMBV+LhLkYiI\nHOUv89l6/fpZuT+T0CMAX4kC31Dt7S3S+nZpux+1QniP4I7DYsIZLiIiclSm3lm5MlSbBzPZOrWP\nK1HwHz8/F0dO9YyodiyTtw4O7n5kIXzxYeAiIiJHNdRWQAiMqvnpUFo7h1fvla1paGmJgsa2brR2\nXhz2s5QoQKbVUymhLx+yEL74cEmRiIgcFajx45HP1dp2fSs5zngOYbYlvAu9Mbx75CzOWTicOlUs\n5WEEEt3qFSSWKLl8WLw4w0VERI5bd/f8pDYPuaQdNJ2J2a5Au5T5PLjUP9gttazUg5/+xRIuH44B\nnOEiIiLHZaubGo1sIcZsV+BoZfrL9c7505Ne/+mSmlHfiwoDZ7iIiMhR2gyTU4zH45R4FfRcjmb9\nmtkVZShRBI6cuWT6eaYmF384mzy+uSOMF9/7A1Qp2XOryHGGi4iIHKXNMI3WSBs0aLsCv/3FeVi/\nvA7Pv5t9afPpry6Cf8Lw21kcOJFcwP/hsTBiqoQqE60hcjG7Ru7EGS4Xs7T/5U6LF7OwScfkDFZz\nSy2MuTv7kAkLrN3u4t9nHzNxp7Vr4ZcWjuL4ksVrEVFONNRWQBEC6ii3KU6fWIouQxf34dB2BW7c\nfTTjGYpGjW3daDkxvN2PAIa8tgBYNF/EOMNFRESOW/pH1ntleRXzv7wu9sUsfb1xR2KqhtoKSzNl\nP/j1IfTmuDu+ooy8iepQ3xO5A2e4iIjIMVr9Vv8wwkuVvwzTykvx4bHkcNEbMZ+nNx4EDQAPPP8B\nonGJEo/A5jU3JdVMBWr8qLjCh7Of2tuM1YyqStNjfLIdZM2zFwsDAxcRETlGq98azmLip/0xdJ6/\nbHm8MYx8bu5U/WDqSFxiS3MoKZwE28O4nCG42UnJcIyPlTDFsxcLA5cUiYjIMdoOweH8ZXT204ge\nmqwwhpHTF/uSPjMu4mnh5pIDgUuVwF11lZbCVCqevVgYGLiIiMgx2g7BLyyYnn3wCBnDyH03VMPn\nVSAG3l9paIqaq92SI7V9fyc2NSVv7LESpoy7LLmc6F5cUiQiIkcFavzoi9ozq6QAWFlfBTHwa6DG\nj3mV5djaHEpbxtTCTV+Oi+GH47FtB7B6SXVS3ZaVg6x59qL7MXAREZHjli2cgXePnM35dVUAP/uw\nI202a0tzCJGYiq3NIX1WSJspeuD5D4a1ZJlr9/zze9gfSrScGFeiYP3yuqTPsxXRkzsxcBERUVFL\nLSbPVmQedTBsAdDDFgD0RVWs39Gid6L/+k2z2Zm+QDFwudj/6LEw6HqLFxuXfcj4r1u8VqeFMVae\n/WNrt5u42MKg31q7Fv7W2R+kRJSQOkvz8l4LTYlHwVj/1FBbAa8iEI1LeBShv68Vzbvtp4QqE53o\n+6MqntvTpj9fhDsSCwoDFxER5ZVZq4NSr317uD5TdSXWf6kuOZgIAUAO/JrQ2NY9rH5g+eLzKohE\n1bTzGRUhuCOxgIz6/+FCiFlCiN1CiI+FEK1CiG8NvD9ZCLFLCHFk4FdGcCIiwtf/pQl90cElvS3N\nobRDnXMpNWw1tnUjFk/0/orHB1st+Mt8rpvdAoD1y+tw89wpSS0sFAFsWLGQs1sFJBf/pIgB+Gsp\n5QIADQDWCiEWAFgH4C0p5VwAbw28JiKiMeyWp95CT//gjsS4BF7e24EzNnZ2Tw0lmVothHsjGMXp\nOrZp7byAR++4FqUlChQBeBWB795zHVYvqU4byyN+3GvUS4pSypMATg78vkcIcRDAVQBWALh9YNhP\nALwN4DujvR8RERWuE+f70t6L53EVb1NTB15rOYmv3zQb5eNL4C/z6TNcWm2XkzsUzZzp6dd3UPKI\nn8KV0xouIcRsJMq4mwBMHwhjANAFwLSrnRBiDYA1AFBdnZ7WiYjcjD/DhueqSeMQMoSuUo9Af54C\nzqamDjy27QAA4N0jZ/HNW2vx//7mSFJAuXfxLLzUZG8B/3BNKS8FkL3XFo/4cbecVSkKIa4AsAXA\no1LKi8bPpJQSMF8al1I+L6VcLKVcPHXq1Fw9DhFRXvBn2PC8t+4LqJo0DgJA1aRx+MriWbbfU1te\ne63lZNL7r7d2pQUUY68ut1hl8Zl4xI+75WSGSwhRgkTYeklKuXXg7VNCiBlSypNCiBkATufiXkRE\nVNjeW/cF/fepR9nY4cEXG/HSQw1pzVXvqqvEv35wDNGYqgcUN84IWX0mK8uO5JxRBy4hhADwYwAH\npZQ/NHz0CwBfA/DUwK87RnsvIiIqLuFe+4rlNdrs1dqlcwAkZrqWLZyB1UuqcWddJRrbupNqudxm\nU1NHUoH8UJ3mecSPe+VihutmAH8K4IAQYv/Ae48hEbR+LoT4BoB2AF/Nwb2IiKiINNRWwOsRiNlY\nx2VcXlu9pDopvARq/DjU1YP1O1oQVyVKS+zrBzZST+xowbzKcgRq/CyML2C52KX4HoBMG2m/kOF9\nsuCH5dnHfLvG4sUmWBhzxuK1llsYU2lhzCSL97NS4vEPFq/1t//JwqB/sngxIhqtQI0fS+dNw66P\nT9l2j6FCSbA9jPU7WhBTE4Ev4sLGp3FVYmtzyNKxRORe7ovyREQ0ZgTbw/jN7+0LWwqGroFqbOuG\nKgdn1xQ3NuJCojUEwML4QsajfYiIyDFbmkO29uHSLp2p7kkLMJGYCkUIPHTL1Xh2T5t9DzRCWiRk\nYXzhYuAiIiLHnB2YubHTUHVPWoDZ0hyCAPCJjUcMjca0gV5cAAvjCxUDFxEROWJTUwfe+r39HYOy\n1T0d6urBy3uPI65KVx7tAwDlpV48vu0AJBJ9ubIFrqF2MpIzGLiIiCjvtGL1uGp/l3l/mU9fNhRC\nwF/mS3qOJwzPkYfHGRHjMuer+45j85qbhtwIwJ2M7sOieSIiyrutzSF9Z6Ddwr0RrF9eB0UIxFWJ\nDTtb9e7zjW3deQl9uRSNyyF7hpnN6JHzGLiIiCivgu1hvLLveN7u11BbgXBvBKqUkEgOIQ21FSjx\nuHQdcUDqX9QlHjHk7kTuZHQnLikSEVFeNbZ1I5qnA6sXVV2pL6f5vErSMT5AogD93sWz8nLE0EhU\nlpfiTz87G/4yH1o7LyTVcGWq0+JORndi4HKxb/+ZhUF9Fi/2GQtjLDRaBQA0Wxhz1MKYJy3eb5mF\nMYstXotNTYkcl88Zl66Lgz8kV9VXmRadr6qvws/3Hs/bEqdVAsB/uuNahHsjmFdZnna8z1B1WtzJ\n6D4MXERElFeBGj/KfB5cisRtv1f15LK0cLKqvirtedzYf+szVVdiw85W01DFjvOFhzVcRESUV8H2\nMHrzELYAYOak8UnhJBJV8cybh/Wiec3F/lhenmc4WjovZCx+H6pOK9gexsbdR9O+R3IWZ7iIiCiv\n8rlrbs/hM/jTm2Yn2kJEVagA/v3oWew9dg7rl9ch3BtBQ21FxgOBnRRTAa+SWFrUQpWxbsusTost\nIdyLgYuIiPKqobYCpSUK+qMq7K6a6oslDvd56aEGPPPmYfz70bOJma6YivU7WqBKCZ9XwfrldXqv\nLjdRJXD/jdX6MmhqmFq7dE7SeC41uheXFImIKG+0GZr1y+twy9wptnd2743EsepH7+PfPjiGR++4\nVl+G03pyacEk3BvB5ocbMPUKX9Zr5pMqgasmjUegxp+8NJqhvxZbQrgXZ7iIiCgvtOWu/qgKj5I4\nKLqprRuRPLSI2L6/E5UTx+nLcP4yHzbsbE1qExGo8aMvmp/aMqt8htDkL/PpnfBVmXid2hqCLSHc\ni4GLiIjyorGtW19GjKkSL773B8yddgUOdvXk5f6vt3Zh3d3z9RAyr7JcDyYA8Ni2A+jpd1fgevJL\ndfrzhnsjEAAkEstTLZ0XTHcxsiWEO3FJkYiI8qKhtgIewxqiKiXCvZG83X/RrElJrwM1fr0G6sEX\nG7HZhc1PjccQabVvHgH4ShQIgEf4FBDOcLnZZAtjbrN4rW0Wxlj9b9VjYcw3LIz5jxbvd9DCmGvc\n1bCQiNIFavzYsGJhUrH6PYuuykv/KwFg7nTz7s5abZQbf4oYC99TlwsBYEtzKK17PrkTAxcREeWN\n1i39tZaTWLZwBlYvqcbrrV041t1ry/3GlaQf55NKKzSPxlTk6cQhyzzK4LmJm5o69D83bcmQ9VqF\ng4GLiIjyJtge1uuO9h47BwA4ecHqGWXDZzWQaMf+uO1MxXsXz0Kgxo9NTR14bNsBAMC7R84CSIRX\n1msVDgYuIiLKm9Su73+7/QDsPMIwWyBJbRTqJl6PwMqB/lsv700Ogi/v7Ug6W5Hcj4GLiIjyxrh8\nJwFbw9ZQtHYKnecvJxWeu4UigJfX3KSHxekTxwG4oH+eeE2FxF1xnoiIippW+H3/jdWQeQhbZucJ\narNaT79xCK/sOw6vZ7BRqFt4FZE0M/fIbdegxJPY4VniEXjktmucejQaIc5wERFR3rWcuJCXXYFm\nR9sYlzXjqsR9N87CVZPGw1/m0+uknDZ5QnLH+0CNHz9bc1PWerTURqjkHgxcRESUN9rsUl80P8t3\nxp2JWhjxl/n0Zc0Sr4JV9VUI1PixcffRvDyTFVeUpv/1rAUord9WaqAKtofxwAuN+ve1+WEeXO0m\nDFxERJQ32uxSvmiBI7U4fv3yOoR7I0kzQf4y95yjePXUK9LeS/0etM7ymq3NIf3PNhJTsbU5xMDl\nIu5ZsCYioqJnPFzZ51UwZ+oEW++n1XAZlxG1w6rXLp2TFEi2/zZk67NY5RHAN01qtFK/h9TO8qlL\ntC5rKTbmcYbLzWZbGPMji9eaamHMAovXssLKz60/t3ithy2MmSeyjwGAH/FHEJGTtKL5rc0hSAAL\nZ15pa93UloFZnobaCngVgWhcJjUT1Wxq6sCHx9IL7PNt9ZJqfYkzlXGHp1kj11X1VXh133FE4xIl\nHoFVAy0lyB0YuIiIKO+2DCx/2d376pV9xweDh0gc/SwH7g8kAmCwPYwntrujWP57X74OgHnxe+rR\nPqmhLFDjx2ZDYT0AbNx9lAX0LsHARUREebW1OYT+aKIPV7/NxfPRuMTW5hBmThqPWDxxz2hcYlNT\nB17ddxyb19yELc0hVx3pY1arBUAPUtqB22a0Rq/B9jAeeP4DfbZrs6GnFzmDgYuIiPIm2B7Gy3s7\n9PqifOScw6d6IJE4l1A1JKtIXGJLcwgWCxLyQpvZMtZqbW0OJc0IphbLa1+nfS8r66sS4we+V+37\nZOByFgMXERHlzZbmEPLd0L254zyC7WHTrvatJy7gvhuq4fMqed09mYk2i2Ws1ZJAWrG8MTxps1la\nwHolGMLt1yYX7ropVI5VDFxERJQ3Z3v6837P+BDnB30UuoCWzgO4dlo5Dnb15PGpzP3g14fwzVtr\nk2q1gMQyrFmxfLA9jGfePKyHLSARyqaWlyaFtpUsoHccAxcREeXNmx+fcvoR0sRVuCJsaZ7d04au\ni3145v7r9ffMiuWNtV5GiiJQN/NKrKyvYtd5F2EfLiIiyhvnF+0Kw479nUnnQAZq/El9w/SZrYGl\nRkUAc6ZOgEcAqiqxYWcrAKT1GiPnMHAREVFemB0kTeYkkNbYVKPNbL135KwetnxeBTfWVkAOfK1Z\nY1RyFpcU3czKDPdui9f6QY7uBwAlFsastTDmyxbvZ6Eh64SN1i51yWqjWCLKKS0kOG18iYLLKa0o\nEt253CfTUUPaLkaJxKzJzXOm4NE7rgWQudaLnMfARUREtsv3GYqZpIYtwD1ha6AvayJICSDcGzEd\nl7qL8dE7rtV7b62sr9JbQ3Ap0V0YuIiIyHZaSIhEVdZxZXBDjR+/O3Eh6wyVWcd5YwG9VxFJY8kd\nGLiIiMh2xjMUX2rqcPpxXOme66vwnWXzLe0s1DrKa4zNUiNxiZeaOrClOWTaJJWcwaJ5IiLKi0CN\nHzMnjXfs/m7/C2/7b0NpuxGt0g7nNopEWTjvJm7//x8RERURJwu53b6UefT0p/rvg+1hbNx91PLO\nzkCNH/cunpX0nhDO/nlTMi4pEhFR3gRq/KicWIqui/nvOO92Pq+CjbuPwl/mw4adrUOenWhmZX0V\nXtl3XO86LwEc6urhkqJLcIaLiIjyauODAZ7tZ+JUTz+efuMQ1u9oQX80UY/VH1WxpTlk6etTZ7lU\nCazf0cL+Zy7BwEVERHkVqPHj1b/8LD5TdaXTj+IuMhGSVCmhlWNJAD/fdxyPbztgGpxSlx4v9ceS\nPo+pknVcLsElRRfreCL7mOpbLF5sp4Uxu6wMArByefYxD1i7lBWXLTQ1vbQud/cjIvsFavy474Zq\nfBQ64PSjuEaJRyCuSpR4Fdw6dyp2fXwKEkAsLrEpZddhsD2Mrc0hvLLvOGKq1JcezcJVpgaqlF8M\nXERE5IhMjT3HIkUA9y6ehZmTxuuF7nuOnEF/VDU9rufBFxv1z2D4rHpyWVp9HP+c3YGBi4iIHMGZ\nl0E+r5LWHf6lhxqwpTmEV4MhxOODzVCNR/sAiaOJtM8aaitw77PvQx340OsR3KnoEgxcRETkiHBv\nxLXnGOaLzyPwrTuuNW10qjU3XVVfldYMVTvax+NR8JVAFVYZwtp377kOT2w/gLhkobabMHAREZEj\nGmoroChA3O0Nsmz0mVmTsHbpnCHHpHaVNzvaxyjcG9FDbHygaJ6tIZzHwEVERI7Y1do1psMWMPJl\n1dQQZpR6uDWXFN2BgYuIiPIq2B5GY1s3tu8/4fSjOG5qeSmAwT+TbGcomkn92mwzYOQMBi4iIsqb\nYHsYD77YiEhMhRBsf7qyvirpz8Sss/xQYcz4td6Uei4GLXdh4CIiorzRdtipElCkxNQrfDjz6dhs\nW7BgRjmA5D8TY+uHxrburMf8GL82ElOxqakDWw39usg9GLiIiChv/GU+KEJAlRIqMGbDFgB8fLIH\nD7zQiCe/VDe461AR+Oj4efzPNw8jGpcQApAysZNTO+bHGKT8ZT69BYQmElVZKO9CDFwuVi1fzD5o\n0kPWLvZlC2N+aKGDPAD82MKYHRbGWOySf+7N7GOu+odaS9e628ISxq/kWN6kTmSfYHsYj21jZ3mj\nSExFa+eFpJ5bWod5IBG2NBLAq8FQUgsIs6amisLeW27EFh1ERJQX9/7ofacfwZVe2XccAHDVpPGI\nxdUh+5LF42rS8T0NtRXweQb/IelRBDasWMjZLRfiDBcREeXFGO8AkVEkLvHsO59gWnkpvEriPEWh\nCKiqhJSAxyOgCJHUbV4TqPFj85qbsKU5BAGkdasn92DgIiKivPAqQIypy9Suj0/pR/R8fv40CAC/\n+f0pvVv8k1+qQ7g3MmRHenI3Bi4iIsqLo9/7D7h63f83po/yGYoEEIup2P3704irMqlbfLg3krUj\nPbkba7iIiMh2wfYwNu4+ihWLZjr9KK4lkCh4V6VMCqUeFsEXBc5wERGRrYzNOVNbGFCCAHDL3ClY\ntnCG3ndL/7Nig9iiwBkuIiKylbE5J6UTAEpLFCxbOAPh3gjWL6/DzXOmQItZqTsTqTBxhouIiGyl\nHabcHx265cFYJQHcVVeZ1FF+/fI67D12jgdQFxEGLjdbZKGp6W0WrzXbwphvV1u71sqO7GO2fjb7\nGI+1njxXXZ99zATRZulal9jUlCjvtMOU//rn+3Gsu9fpx3Gltw+f0QNpNKYi3BvhAdRFhkuKRERk\nu0CNH2tuvcbpx3CtC71RSCT+UtZmtAI1fqxdOidr2NI2JATbw3l5VhoZznAREVFezKsshwC4rGhC\nAlAEcPOcKXj0jmstz2gZNySYHW5N7sEZLiIisp0WDBi2zAkAPm+icL6xrXvI2SrjjJZxQ0I0xuJ6\nN+MMFxER2U4LBpQw3qsgqqqJo3sUgdvnTYME8OQvWxGLZ56tSp3RWr+8Dj6vwuL6AsDARUREttN2\nKvZFGboA4HJMhQBwx4LpWDpvGjbsbE3axanNVqUGrtQZLRbXFw4uKRIRke20nYqlHjbx1EgAu39/\nGi2dFxCJDYYt7UxFs9kqLbh6RHpxPQAWz7sYZ7iIiCgvDnX1IBJnFZeRKqVevxWNqfAoAvcunoWV\n9VWms1VacE2d0WLxvPsxcBERke2C7WE8vu0Ai+ZT+LwKVtZXYWV9leVlwUCNP+tSo9lyJDmLgYuI\niGz3twxbpowzUaMJSNpSI4vn3YuBy83WWBjzV39j7VqL/iH7mOUWOsgDwNYF2cf8wEIX+b+3djvs\nsDjOgu9bOAT2O+xGT5RTwfYwDnb1OP0YrrSrtWvUM1Fae4j1y+sQ7o2weN6lGLiIiMhWw+0NVTO5\nDO3nxsYRQM/uacOddZUjDkis3Soc3KVIRES28pf5hjX+XG/Epidxp++/djBtd6HV43rY+LRwcIaL\niIhsE2wP47FtB4b1NT19MZuexp0+PBbGvvaw3si0pfMCXg2GhmyAqmHtVuFg4CIiIttwxsUaVQKR\nmIr1O1oQV2XWBqiaTG0iyH0YuIiIyDaccbFGAFCESApbQzVANTJrE0HuwxouIiKyTaDGj3sWzXT6\nMVynYkJJ0msJ4LPXVKC0JNFF3ucRWL2kmkXwRYQzXEREZKtn7r8ev+04n7TzcH5l+ZhtFVFZXoqu\nnv609/ccOYvvffk6tnYoUpzhIiIi2/3wvkXwDPyNowjgk7OXnH0gB5mFLc3bh05j7dI5pmHL6s5F\ncifOcLnZX30565AJwkJDUwCX/tjCoMADlq6F6zZnH/OChev8ytrt0Jl9yKVbrV3ql3ss3pOIcs6j\nKFBVFUCiQJzSvfHxKWxq6sDqJdVJ77PfVuHLyQyXEOJfhBCnhRAthvcmCyF2CSGODPzK/2cQEY1R\njW3diMVVSCR25FFmr7WcTHuP/bYKX66WFP8VwF0p760D8JaUci6AtwZeExHRGBNsD6Pz/GUo2U/W\nIoxT9R8AABwdSURBVADLFs5Ie0/rt+UR1nYukvvkZElRSrlHCDE75e0VAG4f+P1PALwN4Du5uB8R\nERUG41KYEAIwHGHtURJtEGjQ9758XdpyIsB+W8XAzhqu6VJKbV60C8B0G+9FREQuZFwKUyDhVQRU\nKaEIgQ0rFgKA3oleALhzwXQcP9c7Zncwzqssz/gZ+20VtrwUzUsppRDC9J8xQog1ANYAQHV1eqon\nInIz/gwbWurRM+uX16W1PZhXWY4tzSG8Ggxh18enMJbnvLY2h9JCVbA9zJmtImBn4DolhJghpTwp\nhJgB4LTZICnl8wCeB4DFixeP5f/OiKgA8WfY0FKXwoD0434OdfXg/aNnuXMRwEtNHbjUH8Mz918P\ngLsTi4mdgesXAL4G4KmBX3fYeC8iInKpQI0fu1q78PBP9yJ8KQogUb+1YcVCfPiHbmzfb6H3yxiy\nfX8n9hw+gxe+doPp7kQGrsKUk8AlhNiMRIH8FCFECMB/QyJo/VwI8Q0A7QC+mot7ERFRYXnqVwfx\n7J62pPdiqsTj2w6M6eXDoZzrjWLVj97HpPHepCVZ7k4sXLnapZipY+YXcnH9Mes/b8s65NJ/sXit\nr1kY81kLDU0B4L9aGLPXwpjj1m6HkuxD8Iq1S33pYYv3JKKceb21y/R9hq3szl+OYeoVPnz981ez\nhqvA8WgfIiKy1V11lU4/QkE782kk43E/VDh4tA8REdlq3d3zAQDb95/ArMllCFT705YYyZpgexhb\nmkMQAFbWVzGEFRAGLiIist26u+frwevOp9929mEKkHZg9QPPf4BIPLEY+0owhM0Pc9dioeCSIhER\n5dWRM5ecfgRXmjt1AsaVmP+13NjWjca2bkTjg5Vv0ZiKZ948rIcxcjcGLiIiyhuGA3OTxntxT30V\n1i+vM/2L2V/mQ0NtBUo8gwdSSgDvHjmLB19s5J9rAWDgIiKivEltekoJ5y/H8INfH8Jj2w7ArP1r\nuDeCQI0fm9fchDsXTIcwHAQeiar8cy0ADFxERJQ3/jKf049QkLT+W4EaPxbNmgRD3oKiCPbnKgAM\nXERElDfh3ojTj1BwBJBUGN9QWwGvR4HAYMd+Fs67HwMXERHlTWodEmU35YrkWcFDXT2Ix1VIAB6R\nOPyb3I9tIdzsHQtjmv/Z2rVu+7+zj7Ha+f2AhTH//XL2MbXjrd3vexbGTPtza9ea+7+tjSOinAi2\nh/WDqwM1fgRq/Lh93jTs+viU049WMM58OjgrGGwPY/2OFmibFaNxyfMVCwQDFxER2SLYHsaDLzYi\nElPh8yp46aEGAMA7h884/GSFJ9geRqDGj8a2bsTVwdYQEqyLKxQMXEREZIvGtm5EYipUmegZtbU5\nhI5zvYjGzPbh0VDue+59bFhxHU6cvwxFEXroEmBdXKFg4CIiIls01FbA51UQjanweBS8su84onHJ\nQ6tHIKYCT2w/AInErkSPAKQEfCUKdygWCAYuIiKyRaDGj5ceakBjWzdOnL+Mn33YwbA1CnqTeVXi\ngRurMXPSeL02jtyPgYuIiGyjFcoH28PY2hxCdGCJkcFreMTAjBYAqBKom3klVi+pdvahaFjYFoKI\niGynzXZ9+4vzcMNszsgM1+Iav97sVAHrtgoRAxcREeVFoMaPtUvn4DvL5sM70IvLowDsypVduDcK\ngcSfldfDzvKFiEuKRESUV4EaP15ec5Pen+tQVw8e33aAy4xDOHr6U/33cf5BFSQGLjdrtvJfVcDa\ntVZaGDPd2qWwwMKYb1loavo3Fu/3moUx939i6VKXns4+ZsI/WroUEY2CVujd2NYNf5kPdyyYzmao\nFsVVia3NIRbLFxgGLiIiyjutKWp/NHFEjSISS2UxTt9YcvhUj9OPQMPEGi4iIso7rSmqodMB4nGJ\n+TwX0JL9x89jU1OH049Bw8DARUREeRVsD6Pz/GV4FZH0l5AE8MnZS/jmrbVOPVrBiMYlHtt2gKGr\ngDBwERFR3mhLiZs/7ACEwP1LqvHFBdP1nYqxuIoP2rodfcZC8lrLSacfgSxi4CIiorwxnq8Yj6u4\natJ4PHLbNSgtUaCIxNLiR6ELTj9mwVi2cIbTj0AWMXAREVHeaOcregRQ4lX0o2leeqgBN8+Zwp5c\nw3DPopnsNl9AuEuRiIjyxni+ovEcwECNH4/ecS32HjuHSFSF6vBzupkA8MittVh393ynH4WGgYGL\niIjySjtf0ez9lx5qwDNvHsa/Hz0LlR0iAAC3zp2Cn35jCYLt4bSgSoWDgavATRLNlsad32hhUL3F\nm95vYcwPLIz5nMX7PezLOuSS2GPpUhMet3hPInKEcaarL8p5LgComlzm9CNQDrCGi4iIXEWb6WJP\nroSjp3r03Z1Pv3EID77YiGB72OnHomFi4CIiItcJ1Pix/DMznX4MVwi2h7G1OaTv7ozGVDSydUbB\n4ZIiERG5hrFOqaG2AuNKFD1ojFVSJprC+rwKojFV391JhYWBi4iIXEFbNovEVPi8Cl56qAEvPdSA\nb/7bPpz5NOL04+WVRwHiAyVsXo/AqvoqrKqvYtF8AWPgIiIiVzA2RdWWzdYunYPL0bjTj5Z3WtgS\nAO5dPCspYGnLiQxdhYWBi4iIHJO6hGi2bHbH/OnYvr/T4Sd1RolXQXmpF3f88B2c+bQPF3tjkADG\nlSRmABm6CgcDFxEROSLTEmLqstkz91+P1hMXcOTMJYefOP/icRXP7mlLe78/mpgBZOAqHNylSERE\njjBbQgzU+LF26Zy0IPHUVz6D8lKPQ0/qnHiGzQJCgIXzBYYzXERE5IhMS4iptJkwNkIdtOZztZzd\nKjAMXAXuvMzdXukOYe3Y2OrNFgadtzCmxNLtgMbsu5Mm/Hdrl/qr/5p9zP/6rrVrEdHoZDpXMZU2\nE0YJX1wwnecoFiAGLiIisoWVs/8ynatopM2EjaUZLgXAvMpynLrYh+kTx+GTs5cQjydmAh+57Rqn\nH49GgIGLiIhyzqwgfjRLYKvqqyABfHziAn534kJRN0KtmVyGH963KOnPiwdXFz4GLiIiyrlMBfHD\nZRbctjaH8FJThw1P7Zz5leU4fPpTSClxqqcv7XMrM4HkbtylSEREOactA3oERnUUjVlwW1lfBcVa\nyWlB8AigfJwXUkqelVjEOMNFREQ5Z7UgPhuznYyBGj/+qLIcH5/syfFTOyMugQ+PheFRBDyQPCux\nSDFwERGRLXKxDJYpuKXuWhRIHPBcyK4c58U3PlfLOq0ixcBFRESuZhbcaqdegaOGzvN/VFmOg12F\nPeM1Z9oVWLt0TtJ7LJYvHqzhIiKigvPIbdfA60kUcnk9AvVFEEa+syy5t5a2YeDpNw7hwRcbEWwP\nO/RklAuc4RojJlhoanppusWLPWVhzOMWxvw/Fu/XnH3I4W3WLvUTC2P+l7VLEZGDAjV+vLzmJn32\n51ABz255FeCrN1SnvZ+rnZ7kDpzhIiKigqSduwgAG3a2Ovw0I6coCn72YUfaLFaudnqSO3CGi4iI\nClpjW3dBd6HXNgCkzmLlaqcnuQMDFxERFbSG2oqC36UoAAgh4C/zJb3PhqfFg0uKRERU0AI1fqxY\nNNPpxxi1uCqxYWcri+OLFAMXEREVvGfuvx7fvLUWsyvKcOvcKXm554yJpfjiAqu7jYDK8lK9Jsvr\nEUnd8uXA/9hlvnhxSZGIiIrCurvn4866SjzwQmNe7nf2UgRNx6yFo3sWzcQz91+f1FdrS3MIm5s6\n9KVQARbHFzMGLiIiKhpbm0NpXejtEo1LXOiNWRrbG4kDSK/J2tocQjSmwuNR8JVAFVbVV7Fmq0gx\ncBERUdE43dPv9COYajvzadp73IU4tjBwERFR0ZhWXur0I5iqnXoFgPSjergLcexg4KJBX7Y47qiF\nMRY6v39+s7Xb/eafso+53mKn+UuykDeOE1E2K+ursPnDDqh5+E/9Cp8HsyaXIapKHD2dPIPlEUB8\n4Bk8isAjt12Dp351EM+/2wYpgdISBS891MCwNYYwcBERUdEI1Pjx3Xuuw2PbDth+r5YNdwEAlj2z\nx/Q55kwvh0AiBB7q6sGze9r0z/uj1o7q4eHVxYOBi4iIisrqJdX4P43H8PFJe89XDLaHEajx43i4\nN+2z/piK7335Ov31M28eTvpcCGTdjagdXh2JqfB5OSNW6NiHi4iI8irYHsbG3UdtbfBZ5S+z7doa\nrV/WjInj0j67L+Uw6mULZyS9XvO52qzhyezwaipcnOEiIiLbaUtj/jIfNuxstX3WZkoeiue1Garu\n3kjS+x6RPnb1kkQAe63lJJYtnKG/znZ9n1dBNKayP1cRYOAiIiJbGZfGBAaLyVMPa86FW556C6Hz\nfTm73lB2tXbhuXc+wblL0aT34xJ4bNsBvH3oNB657RoEavwItocR7o3g0Tuutfz9sm1EcWHgIiIi\nWxmXxow8isjprM0N392FM59Gsg/MEWMRvJk3Pj6Ft35/Gg/fcjX+9YNjI5rVY9uI4sHARUREttKW\nxvqjatIxNvcunpWzMBFsD+c1bFkVVyWee7cNAkiqxTL7voPtYTz3zic4dbEPV0+ZgD+cvQSfV8G1\n08uxkh3oCx4DFxER2UpbGtvSHMKrwRDi8URN0sr6qpzd49sv78/ZtXJNysRsnoDMWIsVbA/j/uc/\nQHRgvfWj0AX9s73HwnglGMLmh7lL8f9v735j67zqO4B/j+0ElKpsJqCm0DYQ1KEmTMpwgIAm6DTo\n2mpSYGio0Ale0JUhqKZtL5b9EZuY0KpNaNKkCJZVCNAKFRPqiljUjnbdqmkyI5am0ayqlgVMW/Xf\ngqdNLVqa+OxF7OCmdnwd+9znXt/PR7J87/WT5/nd+Orkm3POc84wE7hGxG/0ctCVPZ7ssR6O6aHt\n+/sP93i921ZfwfC523o8F9CJxaGx97/5ig2fkzQzO5fZH750aYZBMT5W8ukDb8rc86dWfN/TJ06e\nC1vLaTHfjf4SuADomxZzkgZ9uYQ/OvCmVe9K3L9re7aMlxVDl7sUh5/ABcBQG+Qg8s6rX9XTEhBT\nOydz161vN4drExO4ABhqUzsn88pLtuaHzw3WpPnXbd+WL3/0bT0fP7VzMoc/vK9hRXTJSvMADL0P\nTG3cBPyN8tkP7L2oP9ePlfjpP4ELgKH3nj07MrbMCu8XssbD12zpEGCvIWpxkdjP/t2jufmOaaFr\nEzGkCMDQW8vE+bGSTIyP5fTpH68LttG2b9ty7vFaNqFebv9Ec7c2Bz1cAAy9xcVVe+m1mq/Jti1j\nmW9Yz+GPvOXc47VsQr34PsaLOxM3Gz1cAAy9pfsOTm7bmn949Jkce/J/8sTcj5Y9/r9/dLpZLdft\nvuxFvVJr2YTa/ombl8A1Il7ey0F/3OPJZno45udXP+SpL/d2uTd8efX/sz5XWw0MAMNi6RpfH3rb\nVfnKt3+Q3737u32v42PvesNL6lpLiLJ/4uYkcAEw9GZm514UaGZm5/L7f9P/sHXJ1vFlw5IQhcAF\nwFBbblL69ImTme+g4/v5U2fylW//4ILb+DCaBC4Ahtpyk9Int23NWEknoetT9zyc+VpXvSOR0eIu\nRQCG2vl39k1u25pPf/NYupjaOT5Wcma+9nRHIqNFDxcAQ+38SemLPV79zltved1kpq6azB3/9L3M\nz1fLOvAiAhcAQ+/8SemLyzCc6VPqeuW2LTl4wzW5+Y7pzNeasbGST/3iHsOJnGNIEYBNZbHH6zev\ne2O2jLfewOesG3768hfNJau1Zu75wdpMm27p4QJg01ns8dq/a3ve/7l/bn69Pa/5ibxxx6U9L3DK\n6BG4ANi0pnZO5usff0d+5Y7p/OiFdpv5zD1/yirxXJDANSK2rH5I8r89Tna4a/Uu+kseX/00z/15\nb5d77r7ejgNYztTOyfzVLfub9XRtXdKbZYFTVtJ8Dlcp5fpSyqOllOOllIOtrwcA55vaOZlrdly6\n4efdffml+eqvWmuL1TUNXKWU8SSHktyQZHeSD5ZSdre8JgAs52c2OBRd/epLcuTX3yls0ZPWPVxv\nTXK81nqi1noqyV1JDjS+JgC8xPvffMWG/aO34xUvy7d+69oNOhujoHXgem2Sx5Y8f3zhtXNKKbeW\nUo6WUo4+++yzjcsB2FjasOEyMbHyP3tf//g78t69r+npPIduntqokhgRnU+ar7UeTnI4Sfbt29fB\nRgwAF08bNjymT5zM6TMvvVPxut2X5fCH9yU5O9drxytens8/dGLZc4yV5K9/7R2GEVmz1oHriSRX\nLnl+xcJrANBXi3sunnphPvM5G562TozlY+96Q2Zm584t53Dwxmvynj078vl//M888MjTma9njzM5\nnvVoHbi+k+TqUsrrczZo3ZTkQ42vCcAAWRpmugwsS9fJmty2NXPPnzq3nMPNd0zn1On5bJ0Yy523\nnA1We6/8yTzwyNNJkjNnzm5EvZb6B+V9MxiaBq5a6+lSyieT3JdkPMkXaq3HWl4TgMExMzu3bJjp\nynLrZB168Pi5LXleOP3jYLV/1/ZMjJW8cKZmfKysaeX4Xt+3UDY6ms/hqrUeSXKk9XW4sN+uGzi1\n5KbVz/XcTRt3udy2gecC+mrp/oJLw8wgWRxqXHZLnlKS1IXvvevlfQ9aGKWtzifNA7B5XTDMDIiV\ntuRZnGRfs/YhxV7e9zCEUTaOwAVAM8Oyv+ByQ43rCYu9vO9hCKNsnFI3cqhpnfbt21ePHj3adRlA\nH5VSZmqt+7quYyNowzaf1nOszOEabmtpv/RwAcAKWm9GbbPr0dF882oAgFEncAEANCZwAQA0JnAB\nADQmcAGwoWZm53LoweOZmZ3ruhQYGO5SBGDDWD0dlqeHC4ANs9zq6YDABcAGWlw9fbzE6umwhCFF\nADbMsGzlA/0mcAGwoayeDi9lSBEAoDGBC4CRZykLWjOkCMBIs5QF/aCHC4CRZikL+kHgAmCkWcqC\nfjCkCMBIs5QF/SBwATDyLGVBa4YUAQAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gA\nABoTuAAYKbcfeSTX/umDuf3II12Xwgix0jwAI+P2I4/k8w+dSJJz3w/eeE2XJTEi9HABMDLuPfbU\nBZ9DKwIXACPj+j07LvgcWjGkCMDIWBw+vPfYU7l+zw7DifSNwAXASDl44zWCFn1nSBEAoDGBCwCg\nMYELAKAxgQuAgTMzO5dDDx7PzOxc16XAhjBpHoCBMjM7l5vvmM6p0/PZOjGWO2/Zn6mdk12XBeui\nhwuAgTJ94mROnZ7PfE1eOD2f6RMnuy4J1k3gAqAveh0m3L9re7ZOjGW8JFsmxrJ/1/Y+VQjtGFIE\noLm1DBNO7ZzMnbfsz/SJk9m/a7vhRDYFgQuA5pYbJrxQkJraOSlosakYUgSgOcOEjDo9XAA0Z5iQ\nUSdwAdAXhgkZZYYUAQAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4\nAAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAA\nGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaE7gAABoT\nuAAAGhO4AAAaE7gAABoTuAAAGhO4AAAaW1fgKqX8cinlWCllvpSy77yf/U4p5Xgp5dFSyi+sr0wA\ngOE1sc4//3CSX0ryF0tfLKXsTnJTkj1JXpPk/lLKT9Vaz6zzegAAQ2ddPVy11kdqrY8u86MDSe6q\ntf5frfV7SY4neet6rgUAMKxazeF6bZLHljx/fOE1AICRs+qQYinl/iQ7lvnR79Va71lvAaWUW5Pc\nmiRXXXXVek8H0FfaMKAXqwauWuu7L+K8TyS5csnzKxZeW+78h5McTpJ9+/bVi7gWQGe0YUAvWg0p\nfiPJTaWUl5VSXp/k6iT/0uhaAAADbb3LQryvlPJ4krcn+dtSyn1JUms9luRrSf49yb1JPuEORQBg\nVK1rWYha691J7l7hZ59J8pn1nB8AYDOw0jwAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcA\nQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBjAhcAQGMCFwBAYwIXAEBj\nAhcAQGMCFwBAYwIXAEBjAhcAm9LM7FwOPXg8M7NzXZcCmei6AADYaDOzc7n5jumcOj2frRNjufOW\n/ZnaOdl1WYwwPVwAbDrTJ07m1On5zNfkhdPzmT5xsuuSGHECFwCbzv5d27N1YizjJdkyMZb9u7Z3\nXRIjzpAiAJvO1M7J3HnL/kyfOJn9u7YbTqRzAhcAm9LUzklBi4FhSBEAoDGBCwCgMYELAKAxgQsA\noDGBCwCgMYELAKAxgQsAoDGBCwCgMYELAKAxgQsAoDGBCwCgMYELAKAxgQsAoDGBCwCgMYELAKAx\ngQsAoDGBCwCgMYELAKAxgQsAoDGBCwCgMYELAKCxUmvtuoZzSinPJpntw6VeleS/+nCdFtTejWGt\nfRjq3llrfXXXRWwEbdiqhrXuRO1dGfTae26/Bipw9Usp5WitdV/XdVwMtXdjWGsf1rq5sGH9vQ5r\n3YnauzLMtZ/PkCIAQGMCFwBAY6MauA53XcA6qL0bw1r7sNbNhQ3r73VY607U3pVhrv1FRnIOFwBA\nP41qDxcAQN+MbOAqpfxhKeWJUsq/Lnzd2HVNF1JKub6U8mgp5Xgp5WDX9axFKeX7pZTvLvw9H+26\nngsppXyhlPJMKeXhJa+9spTyrVLKfyx8n+yyxpWsUPtQfc7pzTD+XrVh/aENG1wjG7gW/Fmtde/C\n15Gui1lJKWU8yaEkNyTZneSDpZTd3Va1Zj+38Pc86Lf3fjHJ9ee9djDJA7XWq5M8sPB8EH0xL609\nGZLPOWs2NL9XbVhffTHasIE06oFrWLw1yfFa64la66kkdyU50HFNm1Kt9aEkPzzv5QNJvrTw+EtJ\n3tvXonq0Qu0wCLRhfaING1yjHrhuK6X820I35kB2sS54bZLHljx/fOG1YVGT3F9KmSml3Np1MRfh\nslrrkwuPn0pyWZfFXIRh+ZyzNsP0e9WGdUsbNgA2deAqpdxfSnl4ma8DST6XZFeSvUmeTPLZTovd\n3H621ro3Z4cTPlFKeWfXBV2seva23mG6tdfnfEhpvwaKNqw7m+azPtF1AS3VWt/dy3GllL9M8s3G\n5azHE0muXPL8ioXXhkKt9YmF78+UUu7O2eGFh7qtak2eLqVcXmt9spRyeZJnui6oV7XWpxcfD8Hn\nnCU2UfuVaMO6pg0bAJu6h+tCFj50i96X5OGVjh0A30lydSnl9aWUrUluSvKNjmvqSSnlklLKpYuP\nk1yXwf67Xs43knxk4fFHktzTYS1rMmSfc3o0hL9XbVi3tGEDYFP3cK3iT0ope3O2a/X7ST7WbTkr\nq7WeLqV8Msl9ScaTfKHWeqzjsnp1WZK7SynJ2c/bV2qt93Zb0spKKV9Ncm2SV5VSHk/yB0luT/K1\nUspHk8wm+UB3Fa5shdqvHZbPOWsyNO1Xog3rJ23Y4LLSPABAYyM7pAgA0C8CFwBAYwIXAEBjAhcA\nQGMCFwBAYwIXAEBjAhcAQGMCFwBAY/8PZ9DySWW51/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a3b7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "w = torch.load('weights_min_cost_route.fyre').data.numpy().copy()\n",
    "w[(w > 400)|(w<-100)] = np.nan\n",
    "f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True, figsize=(10,10))\n",
    "ax1.imshow(w.T,origin='lower', clim=(0,150),cmap='hot')\n",
    "ax2.plot(t['start_x'], t['start_y'], '.')\n",
    "ax2.axis('equal')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:17:25.584810Z",
     "start_time": "2017-11-15T16:16:24.445442Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93dcd416b8234047aa910a00b31d14e5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:123: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['start_x'] = (starts[:, 0] + 84.125113) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:124: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['start_y'] = (starts[:, 1] + 7.853407) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:127: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['end_x'] = (ends[:, 0] + 84.125113) / 0.005\n",
      "/Users/jiafengchen/Documents/Junior/CS 281/final-project/utils.py:128: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df['end_y'] = (ends[:, 1] + 7.853407) / 0.005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "chunks = pd.read_csv('data/yellow_tripdata_2009-01_test.csv', chunksize=100000)\n",
    "\n",
    "h = True\n",
    "for df in tqdm(chunks, total=93):\n",
    "    df = rotate_manhattan(filter_manhattan(unnormalize(df)))\n",
    "    df.to_csv('data/yellow_tripdata_2009-01_test_JC.csv', mode='a',\n",
    "              index=False,\n",
    "              header=h)\n",
    "    h = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:33:02.631770Z",
     "start_time": "2017-11-15T16:21:05.271446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "chunks = pd.read_csv('data/yellow_tripdata_2009-01_test_JC.csv', \n",
    "                     chunksize=1000)\n",
    "losses = []\n",
    "for df in tqdm(chunks):\n",
    "    for i, row in df.iterrows():\n",
    "        pbar.update(1)\n",
    "        source = (int(clip(round(row['start_x']), 0, 9)), \n",
    "                  int(clip(round(row['start_y']), 0, 34)))\n",
    "        target = (int(clip(round(row['end_x']), 0, 9)), \n",
    "                  int(clip(round(row['end_y']), 0, 34)))\n",
    "        path = min_cost(source, target, weights.data.numpy())\n",
    "        pred = (Variable(Tensor(path)) * weights).sum()\n",
    "        loss = (pred - row['duration']) ** 2\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "        losses.append(loss.data[0])\n",
    "        if i % 50 == 0:\n",
    "            print(np.sqrt(loss.data[0])/60)\n",
    "            clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:34:34.345085Z",
     "start_time": "2017-11-15T16:34:34.290159Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "losses = np.array(losses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:35:58.898153Z",
     "start_time": "2017-11-15T16:35:58.884574Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minute_loss = np.sqrt(losses)/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:39:15.402968Z",
     "start_time": "2017-11-15T16:39:15.380776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.493657230987296"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(minute_loss, 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:40:37.679046Z",
     "start_time": "2017-11-15T16:40:37.671613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2759178529053168"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute_loss.mean()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:41:16.288107Z",
     "start_time": "2017-11-15T16:41:15.817540Z"
    }
   },
   "source": [
    "plt.hist(minute_loss[minute_loss < 40], bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:37:52.207631Z",
     "start_time": "2017-11-15T16:37:41.490918Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('data/yellow_tripdata_2009-01_test_JC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:40:31.645799Z",
     "start_time": "2017-11-15T16:40:31.620371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1142.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(test['duration'],99.99)/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T16:38:10.943297Z",
     "start_time": "2017-11-15T16:38:10.938576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1439.0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "86340.0/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:18:13.337428Z",
     "start_time": "2017-11-15T03:18:13.216000Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.966198</td>\n",
       "      <td>40.762005</td>\n",
       "      <td>-73.959052</td>\n",
       "      <td>40.768965</td>\n",
       "      <td>5.909830</td>\n",
       "      <td>15.360036</td>\n",
       "      <td>6.311452</td>\n",
       "      <td>17.314256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429018</th>\n",
       "      <td>571.0</td>\n",
       "      <td>-0.456648</td>\n",
       "      <td>-0.493930</td>\n",
       "      <td>-0.173769</td>\n",
       "      <td>-0.075451</td>\n",
       "      <td>-0.039256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.990078</td>\n",
       "      <td>40.749276</td>\n",
       "      <td>-73.978317</td>\n",
       "      <td>40.752132</td>\n",
       "      <td>3.385799</td>\n",
       "      <td>10.572496</td>\n",
       "      <td>5.012011</td>\n",
       "      <td>12.365412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429019</th>\n",
       "      <td>1101.0</td>\n",
       "      <td>0.729522</td>\n",
       "      <td>1.081921</td>\n",
       "      <td>1.606344</td>\n",
       "      <td>-0.637841</td>\n",
       "      <td>0.186027</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.952378</td>\n",
       "      <td>40.789555</td>\n",
       "      <td>-73.992933</td>\n",
       "      <td>40.757949</td>\n",
       "      <td>5.104986</td>\n",
       "      <td>21.471666</td>\n",
       "      <td>1.939674</td>\n",
       "      <td>11.687665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429020</th>\n",
       "      <td>486.0</td>\n",
       "      <td>-0.411026</td>\n",
       "      <td>0.707604</td>\n",
       "      <td>0.814112</td>\n",
       "      <td>-0.027508</td>\n",
       "      <td>0.289355</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.961333</td>\n",
       "      <td>40.771629</td>\n",
       "      <td>-73.977071</td>\n",
       "      <td>40.760617</td>\n",
       "      <td>5.634846</td>\n",
       "      <td>17.499187</td>\n",
       "      <td>4.265827</td>\n",
       "      <td>13.909795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429021</th>\n",
       "      <td>188.0</td>\n",
       "      <td>-0.411026</td>\n",
       "      <td>0.564356</td>\n",
       "      <td>0.485791</td>\n",
       "      <td>0.945361</td>\n",
       "      <td>1.109352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.964760</td>\n",
       "      <td>40.764200</td>\n",
       "      <td>-73.951787</td>\n",
       "      <td>40.781790</td>\n",
       "      <td>5.901475</td>\n",
       "      <td>15.884788</td>\n",
       "      <td>6.074429</td>\n",
       "      <td>20.252667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429022</th>\n",
       "      <td>418.0</td>\n",
       "      <td>-0.411026</td>\n",
       "      <td>-0.318204</td>\n",
       "      <td>-1.491784</td>\n",
       "      <td>-0.060868</td>\n",
       "      <td>-0.733774</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.985874</td>\n",
       "      <td>40.719453</td>\n",
       "      <td>-73.977938</td>\n",
       "      <td>40.734199</td>\n",
       "      <td>7.429295</td>\n",
       "      <td>6.107797</td>\n",
       "      <td>7.087712</td>\n",
       "      <td>9.439509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429023</th>\n",
       "      <td>197.0</td>\n",
       "      <td>-0.684758</td>\n",
       "      <td>0.028107</td>\n",
       "      <td>1.588092</td>\n",
       "      <td>0.264268</td>\n",
       "      <td>1.822343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.977589</td>\n",
       "      <td>40.789142</td>\n",
       "      <td>-73.969488</td>\n",
       "      <td>40.800200</td>\n",
       "      <td>0.978173</td>\n",
       "      <td>18.573387</td>\n",
       "      <td>1.077878</td>\n",
       "      <td>21.313147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429024</th>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.866388</td>\n",
       "      <td>-1.066796</td>\n",
       "      <td>-1.201867</td>\n",
       "      <td>0.084924</td>\n",
       "      <td>0.989372</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-74.003783</td>\n",
       "      <td>40.726013</td>\n",
       "      <td>-73.974149</td>\n",
       "      <td>40.778692</td>\n",
       "      <td>3.728467</td>\n",
       "      <td>5.183401</td>\n",
       "      <td>2.720600</td>\n",
       "      <td>17.229738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429025</th>\n",
       "      <td>958.0</td>\n",
       "      <td>0.136437</td>\n",
       "      <td>-0.293459</td>\n",
       "      <td>0.334778</td>\n",
       "      <td>-0.582241</td>\n",
       "      <td>-0.899841</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.985282</td>\n",
       "      <td>40.760783</td>\n",
       "      <td>-73.991488</td>\n",
       "      <td>40.729911</td>\n",
       "      <td>2.888028</td>\n",
       "      <td>13.015595</td>\n",
       "      <td>5.326108</td>\n",
       "      <td>7.208740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429026</th>\n",
       "      <td>383.0</td>\n",
       "      <td>-0.821624</td>\n",
       "      <td>0.678052</td>\n",
       "      <td>0.680468</td>\n",
       "      <td>0.581863</td>\n",
       "      <td>0.809710</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.962040</td>\n",
       "      <td>40.768605</td>\n",
       "      <td>-73.961234</td>\n",
       "      <td>40.774053</td>\n",
       "      <td>5.857258</td>\n",
       "      <td>16.919265</td>\n",
       "      <td>5.379142</td>\n",
       "      <td>17.911544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429027</th>\n",
       "      <td>537.0</td>\n",
       "      <td>-0.639136</td>\n",
       "      <td>-0.498236</td>\n",
       "      <td>0.140145</td>\n",
       "      <td>-0.678050</td>\n",
       "      <td>-0.241845</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.990181</td>\n",
       "      <td>40.756379</td>\n",
       "      <td>-73.993978</td>\n",
       "      <td>40.746901</td>\n",
       "      <td>2.571443</td>\n",
       "      <td>11.736693</td>\n",
       "      <td>3.006825</td>\n",
       "      <td>9.741591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429028</th>\n",
       "      <td>444.0</td>\n",
       "      <td>-0.319782</td>\n",
       "      <td>0.967975</td>\n",
       "      <td>1.185214</td>\n",
       "      <td>0.207244</td>\n",
       "      <td>0.324288</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.955104</td>\n",
       "      <td>40.780026</td>\n",
       "      <td>-73.970970</td>\n",
       "      <td>40.761519</td>\n",
       "      <td>5.723375</td>\n",
       "      <td>19.588341</td>\n",
       "      <td>5.174475</td>\n",
       "      <td>14.743935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429029</th>\n",
       "      <td>144.0</td>\n",
       "      <td>-0.867245</td>\n",
       "      <td>-0.321924</td>\n",
       "      <td>0.404384</td>\n",
       "      <td>-0.566927</td>\n",
       "      <td>0.487489</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.985963</td>\n",
       "      <td>40.762358</td>\n",
       "      <td>-73.991090</td>\n",
       "      <td>40.765733</td>\n",
       "      <td>2.598510</td>\n",
       "      <td>13.199862</td>\n",
       "      <td>1.370998</td>\n",
       "      <td>13.183024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429970</th>\n",
       "      <td>177.0</td>\n",
       "      <td>-0.776002</td>\n",
       "      <td>0.782259</td>\n",
       "      <td>0.796037</td>\n",
       "      <td>0.853862</td>\n",
       "      <td>0.986235</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.959547</td>\n",
       "      <td>40.771220</td>\n",
       "      <td>-73.954165</td>\n",
       "      <td>40.778611</td>\n",
       "      <td>5.976392</td>\n",
       "      <td>17.631963</td>\n",
       "      <td>6.037640</td>\n",
       "      <td>19.459519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429971</th>\n",
       "      <td>506.0</td>\n",
       "      <td>-0.502270</td>\n",
       "      <td>0.439208</td>\n",
       "      <td>0.541078</td>\n",
       "      <td>-0.339523</td>\n",
       "      <td>0.440318</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.967754</td>\n",
       "      <td>40.765451</td>\n",
       "      <td>-73.985180</td>\n",
       "      <td>40.764515</td>\n",
       "      <td>5.265455</td>\n",
       "      <td>15.755792</td>\n",
       "      <td>2.485998</td>\n",
       "      <td>13.644801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429972</th>\n",
       "      <td>1465.0</td>\n",
       "      <td>0.090815</td>\n",
       "      <td>-0.523357</td>\n",
       "      <td>0.127373</td>\n",
       "      <td>0.396477</td>\n",
       "      <td>0.143581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.990782</td>\n",
       "      <td>40.756090</td>\n",
       "      <td>-73.966052</td>\n",
       "      <td>40.756853</td>\n",
       "      <td>2.504400</td>\n",
       "      <td>11.621393</td>\n",
       "      <td>6.512306</td>\n",
       "      <td>14.523615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429973</th>\n",
       "      <td>896.0</td>\n",
       "      <td>0.182059</td>\n",
       "      <td>-0.321966</td>\n",
       "      <td>0.657884</td>\n",
       "      <td>-0.410746</td>\n",
       "      <td>-0.586567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.985964</td>\n",
       "      <td>40.768094</td>\n",
       "      <td>-73.987031</td>\n",
       "      <td>40.738000</td>\n",
       "      <td>1.954483</td>\n",
       "      <td>14.149229</td>\n",
       "      <td>5.155891</td>\n",
       "      <td>9.048006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429974</th>\n",
       "      <td>765.0</td>\n",
       "      <td>-0.319782</td>\n",
       "      <td>-0.377434</td>\n",
       "      <td>0.034829</td>\n",
       "      <td>-0.142787</td>\n",
       "      <td>-0.687261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.987291</td>\n",
       "      <td>40.753996</td>\n",
       "      <td>-73.980067</td>\n",
       "      <td>40.735400</td>\n",
       "      <td>3.317314</td>\n",
       "      <td>11.666635</td>\n",
       "      <td>6.600488</td>\n",
       "      <td>9.399332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429975</th>\n",
       "      <td>625.0</td>\n",
       "      <td>-0.228539</td>\n",
       "      <td>-1.113319</td>\n",
       "      <td>-0.602280</td>\n",
       "      <td>-1.288075</td>\n",
       "      <td>-1.536847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-74.004896</td>\n",
       "      <td>40.739580</td>\n",
       "      <td>-74.009832</td>\n",
       "      <td>40.713463</td>\n",
       "      <td>2.021347</td>\n",
       "      <td>7.304210</td>\n",
       "      <td>4.135905</td>\n",
       "      <td>2.427007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429976</th>\n",
       "      <td>492.0</td>\n",
       "      <td>-0.274160</td>\n",
       "      <td>0.250774</td>\n",
       "      <td>1.814059</td>\n",
       "      <td>-0.228515</td>\n",
       "      <td>0.680473</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.972262</td>\n",
       "      <td>40.794255</td>\n",
       "      <td>-73.982295</td>\n",
       "      <td>40.770716</td>\n",
       "      <td>1.286020</td>\n",
       "      <td>20.017693</td>\n",
       "      <td>2.267494</td>\n",
       "      <td>14.995091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429977</th>\n",
       "      <td>357.0</td>\n",
       "      <td>-0.684758</td>\n",
       "      <td>-0.376891</td>\n",
       "      <td>-0.015155</td>\n",
       "      <td>-0.132591</td>\n",
       "      <td>0.291407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.987278</td>\n",
       "      <td>40.752865</td>\n",
       "      <td>-73.979802</td>\n",
       "      <td>40.760670</td>\n",
       "      <td>3.446420</td>\n",
       "      <td>11.480880</td>\n",
       "      <td>3.807816</td>\n",
       "      <td>13.612016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429978</th>\n",
       "      <td>518.0</td>\n",
       "      <td>-0.228539</td>\n",
       "      <td>0.444684</td>\n",
       "      <td>0.137538</td>\n",
       "      <td>-0.572621</td>\n",
       "      <td>-0.088364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.967623</td>\n",
       "      <td>40.756320</td>\n",
       "      <td>-73.991238</td>\n",
       "      <td>40.750864</td>\n",
       "      <td>6.312087</td>\n",
       "      <td>14.259044</td>\n",
       "      <td>3.015533</td>\n",
       "      <td>10.705148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429979</th>\n",
       "      <td>249.0</td>\n",
       "      <td>-0.821624</td>\n",
       "      <td>0.607745</td>\n",
       "      <td>0.657398</td>\n",
       "      <td>0.779754</td>\n",
       "      <td>0.483113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.963722</td>\n",
       "      <td>40.768083</td>\n",
       "      <td>-73.956091</td>\n",
       "      <td>40.765620</td>\n",
       "      <td>5.637431</td>\n",
       "      <td>16.644055</td>\n",
       "      <td>7.177059</td>\n",
       "      <td>17.092929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429980</th>\n",
       "      <td>337.0</td>\n",
       "      <td>-0.639136</td>\n",
       "      <td>-1.186720</td>\n",
       "      <td>-0.089755</td>\n",
       "      <td>-1.149632</td>\n",
       "      <td>-0.461203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-74.006652</td>\n",
       "      <td>40.751177</td>\n",
       "      <td>-74.006234</td>\n",
       "      <td>40.741237</td>\n",
       "      <td>0.428922</td>\n",
       "      <td>9.026750</td>\n",
       "      <td>1.613871</td>\n",
       "      <td>7.428304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429981</th>\n",
       "      <td>413.0</td>\n",
       "      <td>-0.593514</td>\n",
       "      <td>-0.620082</td>\n",
       "      <td>-0.016171</td>\n",
       "      <td>-0.169067</td>\n",
       "      <td>0.290826</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.993096</td>\n",
       "      <td>40.752842</td>\n",
       "      <td>-73.980750</td>\n",
       "      <td>40.760655</td>\n",
       "      <td>2.485949</td>\n",
       "      <td>10.824007</td>\n",
       "      <td>3.652577</td>\n",
       "      <td>13.503120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429982</th>\n",
       "      <td>553.0</td>\n",
       "      <td>-0.182917</td>\n",
       "      <td>-0.126009</td>\n",
       "      <td>1.225652</td>\n",
       "      <td>0.288471</td>\n",
       "      <td>1.948366</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.981276</td>\n",
       "      <td>40.780941</td>\n",
       "      <td>-73.968859</td>\n",
       "      <td>40.803454</td>\n",
       "      <td>1.288421</td>\n",
       "      <td>16.802015</td>\n",
       "      <td>0.816738</td>\n",
       "      <td>21.922386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429983</th>\n",
       "      <td>807.0</td>\n",
       "      <td>-0.730380</td>\n",
       "      <td>-0.030956</td>\n",
       "      <td>-0.119807</td>\n",
       "      <td>0.078190</td>\n",
       "      <td>0.277465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.979002</td>\n",
       "      <td>40.750497</td>\n",
       "      <td>-73.974324</td>\n",
       "      <td>40.760310</td>\n",
       "      <td>5.082151</td>\n",
       "      <td>12.017880</td>\n",
       "      <td>4.754998</td>\n",
       "      <td>14.167326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429984</th>\n",
       "      <td>2202.0</td>\n",
       "      <td>2.371911</td>\n",
       "      <td>0.905275</td>\n",
       "      <td>1.398188</td>\n",
       "      <td>-0.638918</td>\n",
       "      <td>-0.549504</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.956604</td>\n",
       "      <td>40.784845</td>\n",
       "      <td>-73.992961</td>\n",
       "      <td>40.738957</td>\n",
       "      <td>4.934151</td>\n",
       "      <td>20.217656</td>\n",
       "      <td>4.066876</td>\n",
       "      <td>8.540780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429985</th>\n",
       "      <td>288.0</td>\n",
       "      <td>-0.639136</td>\n",
       "      <td>0.927513</td>\n",
       "      <td>1.172707</td>\n",
       "      <td>0.266885</td>\n",
       "      <td>1.279525</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.956072</td>\n",
       "      <td>40.779743</td>\n",
       "      <td>-73.969420</td>\n",
       "      <td>40.786184</td>\n",
       "      <td>5.594908</td>\n",
       "      <td>19.432839</td>\n",
       "      <td>2.662420</td>\n",
       "      <td>19.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429986</th>\n",
       "      <td>1120.0</td>\n",
       "      <td>0.273303</td>\n",
       "      <td>0.314185</td>\n",
       "      <td>0.297213</td>\n",
       "      <td>-1.027812</td>\n",
       "      <td>-0.543268</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.970745</td>\n",
       "      <td>40.759933</td>\n",
       "      <td>-74.003068</td>\n",
       "      <td>40.739118</td>\n",
       "      <td>5.389747</td>\n",
       "      <td>14.506661</td>\n",
       "      <td>2.375795</td>\n",
       "      <td>7.432927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429987</th>\n",
       "      <td>644.0</td>\n",
       "      <td>-0.502270</td>\n",
       "      <td>0.079855</td>\n",
       "      <td>0.301765</td>\n",
       "      <td>-0.442220</td>\n",
       "      <td>-0.195022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.976351</td>\n",
       "      <td>40.760036</td>\n",
       "      <td>-73.987849</td>\n",
       "      <td>40.748110</td>\n",
       "      <td>4.450225</td>\n",
       "      <td>13.894442</td>\n",
       "      <td>3.885648</td>\n",
       "      <td>10.629692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429988</th>\n",
       "      <td>303.0</td>\n",
       "      <td>-0.776002</td>\n",
       "      <td>1.181906</td>\n",
       "      <td>1.501912</td>\n",
       "      <td>0.963792</td>\n",
       "      <td>1.564179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.949986</td>\n",
       "      <td>40.787192</td>\n",
       "      <td>-73.951308</td>\n",
       "      <td>40.793534</td>\n",
       "      <td>5.766178</td>\n",
       "      <td>21.349019</td>\n",
       "      <td>4.835463</td>\n",
       "      <td>22.250416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429989</th>\n",
       "      <td>679.0</td>\n",
       "      <td>0.182059</td>\n",
       "      <td>0.582289</td>\n",
       "      <td>0.320415</td>\n",
       "      <td>-0.697058</td>\n",
       "      <td>-0.647371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.964331</td>\n",
       "      <td>40.760458</td>\n",
       "      <td>-73.994472</td>\n",
       "      <td>40.736430</td>\n",
       "      <td>6.392524</td>\n",
       "      <td>15.313531</td>\n",
       "      <td>4.100415</td>\n",
       "      <td>7.952877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429990</th>\n",
       "      <td>613.0</td>\n",
       "      <td>0.045193</td>\n",
       "      <td>0.585173</td>\n",
       "      <td>2.423679</td>\n",
       "      <td>0.873870</td>\n",
       "      <td>1.245367</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.964262</td>\n",
       "      <td>40.808049</td>\n",
       "      <td>-73.953645</td>\n",
       "      <td>40.785302</td>\n",
       "      <td>1.061893</td>\n",
       "      <td>23.199005</td>\n",
       "      <td>5.372656</td>\n",
       "      <td>20.625448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429991</th>\n",
       "      <td>292.0</td>\n",
       "      <td>-0.547892</td>\n",
       "      <td>0.391682</td>\n",
       "      <td>0.218767</td>\n",
       "      <td>-0.012348</td>\n",
       "      <td>-0.361167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.968891</td>\n",
       "      <td>40.758158</td>\n",
       "      <td>-73.976677</td>\n",
       "      <td>40.743820</td>\n",
       "      <td>5.895881</td>\n",
       "      <td>14.420956</td>\n",
       "      <td>6.216496</td>\n",
       "      <td>11.173617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429992</th>\n",
       "      <td>376.0</td>\n",
       "      <td>-0.593514</td>\n",
       "      <td>0.582163</td>\n",
       "      <td>2.397162</td>\n",
       "      <td>0.780677</td>\n",
       "      <td>2.048750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.964334</td>\n",
       "      <td>40.807449</td>\n",
       "      <td>-73.956067</td>\n",
       "      <td>40.806046</td>\n",
       "      <td>1.117324</td>\n",
       "      <td>23.091605</td>\n",
       "      <td>2.643245</td>\n",
       "      <td>23.787331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429993</th>\n",
       "      <td>270.0</td>\n",
       "      <td>-0.821624</td>\n",
       "      <td>-0.163002</td>\n",
       "      <td>-0.338968</td>\n",
       "      <td>0.053641</td>\n",
       "      <td>-0.442574</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.982161</td>\n",
       "      <td>40.745538</td>\n",
       "      <td>-73.974962</td>\n",
       "      <td>40.741718</td>\n",
       "      <td>5.115886</td>\n",
       "      <td>10.842423</td>\n",
       "      <td>6.736327</td>\n",
       "      <td>11.018181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429994</th>\n",
       "      <td>830.0</td>\n",
       "      <td>0.227681</td>\n",
       "      <td>-0.395450</td>\n",
       "      <td>0.309720</td>\n",
       "      <td>0.202050</td>\n",
       "      <td>1.698180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.987722</td>\n",
       "      <td>40.760216</td>\n",
       "      <td>-73.971105</td>\n",
       "      <td>40.796994</td>\n",
       "      <td>2.547781</td>\n",
       "      <td>12.647851</td>\n",
       "      <td>1.170088</td>\n",
       "      <td>20.600951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429995</th>\n",
       "      <td>503.0</td>\n",
       "      <td>-0.137295</td>\n",
       "      <td>0.256292</td>\n",
       "      <td>0.421002</td>\n",
       "      <td>-0.672586</td>\n",
       "      <td>-0.022913</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.972130</td>\n",
       "      <td>40.762734</td>\n",
       "      <td>-73.993836</td>\n",
       "      <td>40.752554</td>\n",
       "      <td>4.846078</td>\n",
       "      <td>14.814845</td>\n",
       "      <td>2.395785</td>\n",
       "      <td>10.693270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429996</th>\n",
       "      <td>839.0</td>\n",
       "      <td>0.273303</td>\n",
       "      <td>-0.605285</td>\n",
       "      <td>-0.705651</td>\n",
       "      <td>-1.429519</td>\n",
       "      <td>-1.924480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.992742</td>\n",
       "      <td>40.737241</td>\n",
       "      <td>-74.013508</td>\n",
       "      <td>40.703454</td>\n",
       "      <td>4.295747</td>\n",
       "      <td>8.281313</td>\n",
       "      <td>4.650920</td>\n",
       "      <td>0.357591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429997</th>\n",
       "      <td>258.0</td>\n",
       "      <td>-0.182917</td>\n",
       "      <td>0.300725</td>\n",
       "      <td>-0.086441</td>\n",
       "      <td>-0.464114</td>\n",
       "      <td>-0.983069</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.971067</td>\n",
       "      <td>40.751252</td>\n",
       "      <td>-73.988418</td>\n",
       "      <td>40.727762</td>\n",
       "      <td>6.310882</td>\n",
       "      <td>13.033553</td>\n",
       "      <td>6.075508</td>\n",
       "      <td>7.197622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429998</th>\n",
       "      <td>355.0</td>\n",
       "      <td>-0.776002</td>\n",
       "      <td>-0.688843</td>\n",
       "      <td>-0.103897</td>\n",
       "      <td>-0.962285</td>\n",
       "      <td>0.145595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.994741</td>\n",
       "      <td>40.750857</td>\n",
       "      <td>-74.001365</td>\n",
       "      <td>40.756905</td>\n",
       "      <td>2.436468</td>\n",
       "      <td>10.310781</td>\n",
       "      <td>0.661115</td>\n",
       "      <td>10.568366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429999</th>\n",
       "      <td>61.0</td>\n",
       "      <td>-1.004111</td>\n",
       "      <td>-0.337641</td>\n",
       "      <td>0.618949</td>\n",
       "      <td>-0.420211</td>\n",
       "      <td>0.525366</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-73.986339</td>\n",
       "      <td>40.767213</td>\n",
       "      <td>-73.987277</td>\n",
       "      <td>40.766711</td>\n",
       "      <td>1.991301</td>\n",
       "      <td>13.961304</td>\n",
       "      <td>1.892383</td>\n",
       "      <td>13.772918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration  Trip_Distance  Start_Lon  Start_Lat   End_Lon   End_Lat  \\\n",
       "429000     220.0      -0.593514   0.432520   2.163505  0.726924  2.266675   \n",
       "429001    1416.0       0.227681   0.140130   0.088084 -1.229397 -0.220854   \n",
       "429002     690.0      -0.182917   0.960200   0.671054 -0.118238  0.331298   \n",
       "429003     976.0       0.455790  -0.159783   0.090780  1.278270  0.938482   \n",
       "429004     754.0       0.866388   1.050404   1.002249 -0.605827 -1.125745   \n",
       "429005     450.0      -0.365404  -1.068510  -0.423866 -0.449724  0.245398   \n",
       "429006     288.0      -0.821624  -0.202377  -1.169119 -0.167528 -1.060991   \n",
       "429007    1513.0       0.638278  -0.312603   0.423963 -0.676319 -1.249947   \n",
       "429008     242.0      -0.730380  -0.839615  -1.263430 -0.901451 -0.760187   \n",
       "429009     208.0      -0.228539   0.375630   0.205023  0.921005  1.092932   \n",
       "429010    1211.0       0.866388  -1.623486  -2.076832 -0.418903 -0.649113   \n",
       "429011     707.0      -0.182917  -0.124755   0.029261  0.538960  0.787170   \n",
       "429012     626.0       0.182059   0.435362   2.127663 -0.214240  0.482687   \n",
       "429013     774.0       0.045193   0.157979   1.791564  0.066108  0.416849   \n",
       "429014    1242.0       0.683900  -1.172299  -1.938016 -0.708755 -0.104552   \n",
       "429015     306.0      -0.456648   0.052727  -0.442737 -0.594092 -0.688152   \n",
       "429016     875.0       0.182059   0.499149   0.762935 -0.639072 -0.121825   \n",
       "429017     155.0      -0.730380   0.504248   0.388784  0.665821  0.612660   \n",
       "429018     571.0      -0.456648  -0.493930  -0.173769 -0.075451 -0.039256   \n",
       "429019    1101.0       0.729522   1.081921   1.606344 -0.637841  0.186027   \n",
       "429020     486.0      -0.411026   0.707604   0.814112 -0.027508  0.289355   \n",
       "429021     188.0      -0.411026   0.564356   0.485791  0.945361  1.109352   \n",
       "429022     418.0      -0.411026  -0.318204  -1.491784 -0.060868 -0.733774   \n",
       "429023     197.0      -0.684758   0.028107   1.588092  0.264268  1.822343   \n",
       "429024    1092.0       0.866388  -1.066796  -1.201867  0.084924  0.989372   \n",
       "429025     958.0       0.136437  -0.293459   0.334778 -0.582241 -0.899841   \n",
       "429026     383.0      -0.821624   0.678052   0.680468  0.581863  0.809710   \n",
       "429027     537.0      -0.639136  -0.498236   0.140145 -0.678050 -0.241845   \n",
       "429028     444.0      -0.319782   0.967975   1.185214  0.207244  0.324288   \n",
       "429029     144.0      -0.867245  -0.321924   0.404384 -0.566927  0.487489   \n",
       "...          ...            ...        ...        ...       ...       ...   \n",
       "429970     177.0      -0.776002   0.782259   0.796037  0.853862  0.986235   \n",
       "429971     506.0      -0.502270   0.439208   0.541078 -0.339523  0.440318   \n",
       "429972    1465.0       0.090815  -0.523357   0.127373  0.396477  0.143581   \n",
       "429973     896.0       0.182059  -0.321966   0.657884 -0.410746 -0.586567   \n",
       "429974     765.0      -0.319782  -0.377434   0.034829 -0.142787 -0.687261   \n",
       "429975     625.0      -0.228539  -1.113319  -0.602280 -1.288075 -1.536847   \n",
       "429976     492.0      -0.274160   0.250774   1.814059 -0.228515  0.680473   \n",
       "429977     357.0      -0.684758  -0.376891  -0.015155 -0.132591  0.291407   \n",
       "429978     518.0      -0.228539   0.444684   0.137538 -0.572621 -0.088364   \n",
       "429979     249.0      -0.821624   0.607745   0.657398  0.779754  0.483113   \n",
       "429980     337.0      -0.639136  -1.186720  -0.089755 -1.149632 -0.461203   \n",
       "429981     413.0      -0.593514  -0.620082  -0.016171 -0.169067  0.290826   \n",
       "429982     553.0      -0.182917  -0.126009   1.225652  0.288471  1.948366   \n",
       "429983     807.0      -0.730380  -0.030956  -0.119807  0.078190  0.277465   \n",
       "429984    2202.0       2.371911   0.905275   1.398188 -0.638918 -0.549504   \n",
       "429985     288.0      -0.639136   0.927513   1.172707  0.266885  1.279525   \n",
       "429986    1120.0       0.273303   0.314185   0.297213 -1.027812 -0.543268   \n",
       "429987     644.0      -0.502270   0.079855   0.301765 -0.442220 -0.195022   \n",
       "429988     303.0      -0.776002   1.181906   1.501912  0.963792  1.564179   \n",
       "429989     679.0       0.182059   0.582289   0.320415 -0.697058 -0.647371   \n",
       "429990     613.0       0.045193   0.585173   2.423679  0.873870  1.245367   \n",
       "429991     292.0      -0.547892   0.391682   0.218767 -0.012348 -0.361167   \n",
       "429992     376.0      -0.593514   0.582163   2.397162  0.780677  2.048750   \n",
       "429993     270.0      -0.821624  -0.163002  -0.338968  0.053641 -0.442574   \n",
       "429994     830.0       0.227681  -0.395450   0.309720  0.202050  1.698180   \n",
       "429995     503.0      -0.137295   0.256292   0.421002 -0.672586 -0.022913   \n",
       "429996     839.0       0.273303  -0.605285  -0.705651 -1.429519 -1.924480   \n",
       "429997     258.0      -0.182917   0.300725  -0.086441 -0.464114 -0.983069   \n",
       "429998     355.0      -0.776002  -0.688843  -0.103897 -0.962285  0.145595   \n",
       "429999      61.0      -1.004111  -0.337641   0.618949 -0.420211  0.525366   \n",
       "\n",
       "        week_0  week_1  week_2  week_3    ...      hour_22  hour_23  \\\n",
       "429000       0       0       0       0    ...            0        1   \n",
       "429001       1       0       0       0    ...            0        0   \n",
       "429002       1       0       0       0    ...            0        0   \n",
       "429003       1       0       0       0    ...            0        0   \n",
       "429004       0       0       1       0    ...            1        0   \n",
       "429005       0       1       0       0    ...            0        0   \n",
       "429006       0       1       0       0    ...            0        0   \n",
       "429007       0       0       0       0    ...            0        0   \n",
       "429008       0       0       1       0    ...            0        0   \n",
       "429009       0       0       0       1    ...            0        0   \n",
       "429010       1       0       0       0    ...            0        0   \n",
       "429011       0       1       0       0    ...            0        0   \n",
       "429012       1       0       0       0    ...            0        0   \n",
       "429013       0       1       0       0    ...            0        0   \n",
       "429014       0       1       0       0    ...            0        0   \n",
       "429015       1       0       0       0    ...            1        0   \n",
       "429016       1       0       0       0    ...            0        0   \n",
       "429017       0       1       0       0    ...            0        0   \n",
       "429018       0       0       1       0    ...            1        0   \n",
       "429019       0       1       0       0    ...            0        0   \n",
       "429020       1       0       0       0    ...            0        0   \n",
       "429021       0       0       1       0    ...            0        0   \n",
       "429022       0       0       0       0    ...            0        0   \n",
       "429023       1       0       0       0    ...            0        0   \n",
       "429024       1       0       0       0    ...            0        0   \n",
       "429025       0       0       0       0    ...            0        0   \n",
       "429026       0       1       0       0    ...            0        0   \n",
       "429027       0       1       0       0    ...            0        0   \n",
       "429028       0       1       0       0    ...            0        0   \n",
       "429029       0       1       0       0    ...            0        0   \n",
       "...        ...     ...     ...     ...    ...          ...      ...   \n",
       "429970       0       0       0       1    ...            0        0   \n",
       "429971       0       0       0       1    ...            0        0   \n",
       "429972       0       0       0       1    ...            0        0   \n",
       "429973       0       0       0       1    ...            0        0   \n",
       "429974       0       0       1       0    ...            0        0   \n",
       "429975       0       0       0       1    ...            0        0   \n",
       "429976       0       0       0       1    ...            0        0   \n",
       "429977       0       0       0       1    ...            0        0   \n",
       "429978       0       0       1       0    ...            0        1   \n",
       "429979       0       0       0       1    ...            0        0   \n",
       "429980       0       0       1       0    ...            0        0   \n",
       "429981       1       0       0       0    ...            0        0   \n",
       "429982       1       0       0       0    ...            0        0   \n",
       "429983       0       0       0       1    ...            0        0   \n",
       "429984       0       0       0       1    ...            0        0   \n",
       "429985       1       0       0       0    ...            0        0   \n",
       "429986       0       1       0       0    ...            0        0   \n",
       "429987       0       0       0       1    ...            0        0   \n",
       "429988       0       0       0       1    ...            0        0   \n",
       "429989       0       0       0       1    ...            0        0   \n",
       "429990       0       0       0       1    ...            0        0   \n",
       "429991       1       0       0       0    ...            0        0   \n",
       "429992       0       1       0       0    ...            0        0   \n",
       "429993       0       0       0       1    ...            0        0   \n",
       "429994       0       0       0       1    ...            0        0   \n",
       "429995       0       0       0       1    ...            1        0   \n",
       "429996       0       0       0       1    ...            0        0   \n",
       "429997       0       1       0       0    ...            0        0   \n",
       "429998       1       0       0       0    ...            0        0   \n",
       "429999       0       0       0       1    ...            1        0   \n",
       "\n",
       "        start_lon_raw  start_lat_raw  end_lon_raw  end_lat_raw   start_x  \\\n",
       "429000     -73.967914      40.802162   -73.957464    40.811673  1.118189   \n",
       "429001     -73.974909      40.755201   -74.008307    40.747443  5.231644   \n",
       "429002     -73.955290      40.768392   -73.979429    40.761700  6.998494   \n",
       "429003     -73.982084      40.755262   -73.943135    40.777378  4.037120   \n",
       "429004     -73.953132      40.775886   -73.992101    40.724078  6.514511   \n",
       "429005     -74.003824      40.743617   -73.988044    40.759482  1.745645   \n",
       "429006     -73.983103      40.726754   -73.980710    40.725750  7.068446   \n",
       "429007     -73.985740      40.762801   -73.993933    40.720871  2.585697   \n",
       "429008     -73.998348      40.724620   -73.999784    40.733517  4.784484   \n",
       "429009     -73.969275      40.757847   -73.952420    40.781366  5.867227   \n",
       "429010     -74.017101      40.706215   -73.987243    40.736385  3.746251   \n",
       "429011     -73.981246      40.753870   -73.962349    40.773471  4.332085   \n",
       "429012     -73.967846      40.801351   -73.981924    40.765609  1.220480   \n",
       "429013     -73.974482      40.793746   -73.974638    40.763909  0.975679   \n",
       "429014     -74.006307      40.709356   -73.994776    40.750446  5.180405   \n",
       "429015     -73.977000      40.743190   -73.991796    40.735377  6.233746   \n",
       "429016     -73.966320      40.770471   -73.992965    40.750000  4.939334   \n",
       "429017     -73.966198      40.762005   -73.959052    40.768965  5.909830   \n",
       "429018     -73.990078      40.749276   -73.978317    40.752132  3.385799   \n",
       "429019     -73.952378      40.789555   -73.992933    40.757949  5.104986   \n",
       "429020     -73.961333      40.771629   -73.977071    40.760617  5.634846   \n",
       "429021     -73.964760      40.764200   -73.951787    40.781790  5.901475   \n",
       "429022     -73.985874      40.719453   -73.977938    40.734199  7.429295   \n",
       "429023     -73.977589      40.789142   -73.969488    40.800200  0.978173   \n",
       "429024     -74.003783      40.726013   -73.974149    40.778692  3.728467   \n",
       "429025     -73.985282      40.760783   -73.991488    40.729911  2.888028   \n",
       "429026     -73.962040      40.768605   -73.961234    40.774053  5.857258   \n",
       "429027     -73.990181      40.756379   -73.993978    40.746901  2.571443   \n",
       "429028     -73.955104      40.780026   -73.970970    40.761519  5.723375   \n",
       "429029     -73.985963      40.762358   -73.991090    40.765733  2.598510   \n",
       "...               ...            ...          ...          ...       ...   \n",
       "429970     -73.959547      40.771220   -73.954165    40.778611  5.976392   \n",
       "429971     -73.967754      40.765451   -73.985180    40.764515  5.265455   \n",
       "429972     -73.990782      40.756090   -73.966052    40.756853  2.504400   \n",
       "429973     -73.985964      40.768094   -73.987031    40.738000  1.954483   \n",
       "429974     -73.987291      40.753996   -73.980067    40.735400  3.317314   \n",
       "429975     -74.004896      40.739580   -74.009832    40.713463  2.021347   \n",
       "429976     -73.972262      40.794255   -73.982295    40.770716  1.286020   \n",
       "429977     -73.987278      40.752865   -73.979802    40.760670  3.446420   \n",
       "429978     -73.967623      40.756320   -73.991238    40.750864  6.312087   \n",
       "429979     -73.963722      40.768083   -73.956091    40.765620  5.637431   \n",
       "429980     -74.006652      40.751177   -74.006234    40.741237  0.428922   \n",
       "429981     -73.993096      40.752842   -73.980750    40.760655  2.485949   \n",
       "429982     -73.981276      40.780941   -73.968859    40.803454  1.288421   \n",
       "429983     -73.979002      40.750497   -73.974324    40.760310  5.082151   \n",
       "429984     -73.956604      40.784845   -73.992961    40.738957  4.934151   \n",
       "429985     -73.956072      40.779743   -73.969420    40.786184  5.594908   \n",
       "429986     -73.970745      40.759933   -74.003068    40.739118  5.389747   \n",
       "429987     -73.976351      40.760036   -73.987849    40.748110  4.450225   \n",
       "429988     -73.949986      40.787192   -73.951308    40.793534  5.766178   \n",
       "429989     -73.964331      40.760458   -73.994472    40.736430  6.392524   \n",
       "429990     -73.964262      40.808049   -73.953645    40.785302  1.061893   \n",
       "429991     -73.968891      40.758158   -73.976677    40.743820  5.895881   \n",
       "429992     -73.964334      40.807449   -73.956067    40.806046  1.117324   \n",
       "429993     -73.982161      40.745538   -73.974962    40.741718  5.115886   \n",
       "429994     -73.987722      40.760216   -73.971105    40.796994  2.547781   \n",
       "429995     -73.972130      40.762734   -73.993836    40.752554  4.846078   \n",
       "429996     -73.992742      40.737241   -74.013508    40.703454  4.295747   \n",
       "429997     -73.971067      40.751252   -73.988418    40.727762  6.310882   \n",
       "429998     -73.994741      40.750857   -74.001365    40.756905  2.436468   \n",
       "429999     -73.986339      40.767213   -73.987277    40.766711  1.991301   \n",
       "\n",
       "          start_y     end_x      end_y  \n",
       "429000  21.814597  1.780374  24.561955  \n",
       "429001  13.255969  0.574109   8.222889  \n",
       "429002  17.641689  3.753942  13.824380  \n",
       "429003  12.460678  8.001837  20.493529  \n",
       "429004  19.124404  5.879388   6.174396  \n",
       "429005   8.092785  2.576871  12.490208  \n",
       "429006   7.627372  7.577257   7.729793  \n",
       "429007  13.298224  5.936121   5.437902  \n",
       "429008   5.562892  3.548102   6.874421  \n",
       "429009  14.326372  6.017243  20.111428  \n",
       "429010   0.411307  5.302081   8.756878  \n",
       "429011  12.324325  5.259905  17.690048  \n",
       "429012  21.687985  2.902163  14.191374  \n",
       "429013  19.684245  4.299036  14.727822  \n",
       "429014   2.142854  2.476809  10.238819  \n",
       "429015  11.033077  4.661571   8.078953  \n",
       "429016  16.747717  2.826646  10.368276  \n",
       "429017  15.360036  6.311452  17.314256  \n",
       "429018  10.572496  5.012011  12.365412  \n",
       "429019  21.471666  1.939674  11.687665  \n",
       "429020  17.499187  4.265827  13.909795  \n",
       "429021  15.884788  6.074429  20.252667  \n",
       "429022   6.107797  7.087712   9.439509  \n",
       "429023  18.573387  1.077878  21.313147  \n",
       "429024   5.183401  2.720600  17.229738  \n",
       "429025  13.015595  5.326108   7.208740  \n",
       "429026  16.919265  5.379142  17.911544  \n",
       "429027  11.736693  3.006825   9.741591  \n",
       "429028  19.588341  5.174475  14.743935  \n",
       "429029  13.199862  1.370998  13.183024  \n",
       "...           ...       ...        ...  \n",
       "429970  17.631963  6.037640  19.459519  \n",
       "429971  15.755792  2.485998  13.644801  \n",
       "429972  11.621393  6.512306  14.523615  \n",
       "429973  14.149229  5.155891   9.048006  \n",
       "429974  11.666635  6.600488   9.399332  \n",
       "429975   7.304210  4.135905   2.427007  \n",
       "429976  20.017693  2.267494  14.995091  \n",
       "429977  11.480880  3.807816  13.612016  \n",
       "429978  14.259044  3.015533  10.705148  \n",
       "429979  16.644055  7.177059  17.092929  \n",
       "429980   9.026750  1.613871   7.428304  \n",
       "429981  10.824007  3.652577  13.503120  \n",
       "429982  16.802015  0.816738  21.922386  \n",
       "429983  12.017880  4.754998  14.167326  \n",
       "429984  20.217656  4.066876   8.540780  \n",
       "429985  19.432839  2.662420  19.000714  \n",
       "429986  14.506661  2.375795   7.432927  \n",
       "429987  13.894442  3.885648  10.629692  \n",
       "429988  21.349019  4.835463  22.250416  \n",
       "429989  15.313531  4.100415   7.952877  \n",
       "429990  23.199005  5.372656  20.625448  \n",
       "429991  14.420956  6.216496  11.173617  \n",
       "429992  23.091605  2.643245  23.787331  \n",
       "429993  10.842423  6.736327  11.018181  \n",
       "429994  12.647851  1.170088  20.600951  \n",
       "429995  14.814845  2.395785  10.693270  \n",
       "429996   8.281313  4.650920   0.357591  \n",
       "429997  13.033553  6.075508   7.197622  \n",
       "429998  10.310781  0.661115  10.568366  \n",
       "429999  13.961304  1.892383  13.772918  \n",
       "\n",
       "[1000 rows x 45 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:13:09.257317Z",
     "start_time": "2017-11-15T03:13:09.252496Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = np.zeros((10,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:13:12.462537Z",
     "start_time": "2017-11-15T03:13:12.449556Z"
    }
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "arrays used as indices must be of integer (or boolean) type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-81-8a96321866df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: arrays used as indices must be of integer (or boolean) type"
     ]
    }
   ],
   "source": [
    "g[grid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T03:12:49.246955Z",
     "start_time": "2017-11-15T03:12:49.209551Z"
    }
   },
   "outputs": [
    {
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     "execution_count": 78,
     "metadata": {},
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    }
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    "ExecuteTime": {
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     "start_time": "2017-11-15T03:13:26.103075Z"
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    "collapsed": true
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    "ExecuteTime": {
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     "start_time": "2017-11-15T03:13:42.625041Z"
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   },
   "outputs": [
    {
     "data": {
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     "execution_count": 85,
     "metadata": {},
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   "source": [
    "500 - gr * 500"
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     "start_time": "2017-11-08T03:50:20.122090Z"
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    {
     "data": {
      "text/plain": [
       "(-0.5736560024074322,\n",
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       " -2.9819052614255535,\n",
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g1cHjrK0+CsCsovPb17Em5kwaktb2lGovXXJR7osKigbEy38Hfwx8KuUgZUNQ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\nvc9rMXrgOXA6yUbCF3Uo5QY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12024cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['start_x']/0.005, df['start_y']/0.005,'.')\n",
    "plt.plot(grid[:,0]/0.005,grid[:,1]/0.005,'.')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-15T02:48:50.106588Z",
     "start_time": "2017-11-15T02:48:50.095958Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight = np.arange(350).reshape(10, 35)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}