docs/notebooks/cross_swath.ipynb

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    "# Cross-swath analysis\n",
    "\n",
    "Cross-swath profile refers to swath analysis along tranverse rather than longitudinal direction. It has been demonstrated useful in many previous studies [(e.g., Foster et al. 2010 or Dortch et al. 2011)](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008JF001135).\n",
    "\n",
    "However, these research have restricted the cross-swath inside of a rectangular box, which without reference to geomorphic irregular boudary and are oftentimes subjective about their lengths and orientations.\n",
    "\n",
    "To address these shorcomings, PyOSP provides more powerful and objective method to perform cross-swath analysis.\n",
    "\n",
    "## Step 1. Generate a swath objective\n",
    "\n",
    "We use the same synthetic landscape that being used in the section [Fixed-width curvilinear swath profile](./fix_width_curv.ipynb). \n",
    "\n"
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      "Processing: [#########################] 71 of 71 lineSteps"
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   "source": [
    "import pyosp\n",
    "\n",
    "baseline = pyosp.datasets.get_path(\"homo_baseline.shp\") # the path to baseline shapefile\n",
    "raster = pyosp.datasets.get_path(\"homo_mount.tif\")  # the path to raster file\n",
    "\n",
    "elev = pyosp.Elev_curv(baseline, raster, width=100,\n",
    "                       min_elev=0.01,\n",
    "                       line_stepsize=3, cross_stepsize=None)"
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    "## Step 2. Define the starting and ending location of cross-swath\n",
    "\n",
    "As we demonstrated in previous section, the starting and ending locations can be either defined by inputing values indicating distances from the starting point of baseline, or using points drawn on the baseline to specify the starting and ending locations. Here, we use the second method to bound the area. \n",
    "\n",
    "<img alt=\"homo_case\" src=\"https://i.imgur.com/9QUKGDT.png\" height=\"250\"/>"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pyosp import point_coords, read_shape\n",
    "\n",
    "pointsPath = pyosp.datasets.get_path(\"homo_start_end.shp\") # the path to the points\n",
    "\n",
    "pointsCoords = point_coords(pointsPath)\n",
    "\n",
    "# The sequence of the points follows drawing order on the map\n",
    "# In this case, we drew the ending point first.\n",
    "starting = pointsCoords[1]\n",
    "ending = pointsCoords[0]\n",
    "\n",
    "# let's verify the locations by polygon (optional)\n",
    "# read the baseline shape\n",
    "line_shape = read_shape(baseline)\n",
    "lx, ly = line_shape.xy\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "swath_polygon = elev.out_polygon(start=starting, end=ending)\n",
    "px, py = swath_polygon.exterior.xy\n",
    "ax.plot(px, py)\n",
    "ax.plot(lx, ly, color='C3', label=\"Baseline\")\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "ax.set_title(\"Cross-swath polygon\")\n",
    "ax.legend()"
   ]
  },
  {
   "source": [
    "## Step 3. Plot the cross-swath profile\n",
    "\n",
    "The cross-swath profile can be easily plotted by method *cross_plot*. The distance along x-axis means the transvers distance from the baseline.\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "elev.cross_plot(start=starting, end=ending)"
   ]
  },
  {
   "source": [
    "## Step 4. Plot scatters with cross-swath profile (optional)\n",
    "\n",
    "Note we can also plot scatters with cross-swath, just serve the path of points shapefile to the *cross_plot* method\n",
    "\n",
    "<img alt=\"homo_case\" src=\"https://i.imgur.com/AM5fnnC.png\" height=\"250\"/>\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "checkingPoints = pyosp.datasets.get_path(\"checking_points.shp\") # the path to the points\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# Note parameter \"color\" means color of swath profile, \"c\" and additional\n",
    "# parameters are passed to the Matplolib scatter function.\n",
    "elev.cross_plot(ax=ax, start=starting, end=ending, points=checkingPoints, color=\"navy\", c=\"red\")"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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rKCklH32US3NzF8uXJ1i3t7V1U1HRSmSkz4i+vmJOZLj33ntttrm5uXHw4MFRGtHYpQKUogxCVVWrU+/XnDpVRUtLN93dBioqRjY49bR/fwnTpoUQHn4pxTwnp04FqCsgIyPjimbCjWcqQCnKIFzM3nOGxsZOtm3Lxhkza1Ka0OubMRhacHMLRat1G+B4+OyzApvMvubmLqqr2wgN9ernTEW5clSAUhQHOXt679y5mmEFJ72+hYqKD2lsPEV3dz1SmtfJCKHB0zMWb+9kAgPn4O1tf8FmXl4DRUWNNskRubn1KkApY4YKUIriIGdP7509WzOk84zGLqqqdlFZ+RFGYxf+/jMICMjE1TUIFxcv2ttLaW3No7r6C6qqPsXffybR0bfh4RHR67k++6yQb3wj0/q4rq6dhoYOAgIGXripKCNNBShFcZAz7xHV1XUMOiFCSkl9/SFKSt5Fr2+0BJ5b8fCItDkuKGg+cDGQfUpFxU7OnHmKkJDFxMbeiUZz6de+tLSZ7Ow6pky5VNU8P7+BOXPGfoBau/Z1pz7fBx98zanPpwyfSjNXFAcYjc6d3jt7tnpQx7e1FXP+/K/Jz38Jnc6PtLQfkJLyrV7BqSet1o3IyJuYMeOXhIZeR03NFxQWvtIrnfzzzwtstlVWtqpeUYP09ttvM23aNDQaDcNZs7ly5UrrIthHH30Uo9E45OcaKz2dhkNdQSmKA6qr2zAaTQMf6KBz5xyf3qus/JiSkndxcfEmPv4+goOvRQjHP1vqdD7Exd2FTudDWdlWdDp/YmJus+6vqWknN7fe2htK9YoavOnTp7NlyxYeeeSRYT3PW2+9ha+vL1JK1q9fz9tvv81dd93V5/HjoafTcKgApSgOcOb0XnV1GzU17QMeJ6WkrGwrFRU7CAiYQ3z8vbi4eA75dSMiVtPd3Uhl5UfodH6Ehy+37jt+vNImIBUXN5GcHIgQYsivNxH98pe/5JVXXiEmJoaQkBDmzJnD97///UFVuOjo6OAb3/gG586dIz09ncLCQv70pz8xd+5cfH19AXPg6e7utvv3/9RTT1FeXk5hYSHBwcH84Q9/4NFHH6W42Nz769lnn2XBggW9zvH29ub73/8+eXl5fOtb36KmpsZaAb2zs5Mf/vCHdHR0kJmZ6XAB2ZGmApSiDMBkklRVOS9AOZIcIaWJ4uI3qa7+nJCQRcTF3WP3qkmrFXh46Ght7R7wOYUQxMV9DYOhmZKSt3B3D8HffwZgzt5ra+vGy8sVMPezqqlpVxl9PRw9epQ33niD48ePYzAYmD17NnPmzBn08/zlL3/B09OTU6dOcerUKWbPnm2zf8WKFRw6dIhVq1b12TtqPPV0Gg4VoBRlADU1bRgMV256T0oThYWvUFu7j/DwG4mOvt3uJ+mgIE9uuWUKISGeHD1awVdfldDeru/3uYXQkJj4MOfO/ZLi4jfw9U1Ho9FhNEpOnqzi2mtjrMcWFTWqANXDl19+ya233oqnp/kqdqht3ffs2cN3vvMdAGbMmMGMGTNs9u/cuZPOzk7uuecePvvsM2644YZezzGeejoNhwpQijIAZ07vlZY2U1/fd7XynsEpMnItkZFregUnIWDu3EiWL09Ap9MCcPXV0cyaFc7hw+UcPVrRb8NDjUZHTMwdXLjwLFVVnxERsQKAEycqbQJUVVUbnZ0G3N3V28RFzpryHOh53N3dWbduHVu3brUboMZTT6fhUP/zFKUfJpN0an28fftK+txnG5zWERW1ptcx7u4u3HLLFLsJDG5uLixcGMu118Zw4UIdR49WkJ/fYPe1/Pym4ueXQUXFdoKDr0Gn86WursOmseHFXlFTpgQP8U87sq50WvjixYt54IEHePLJJzEYDHzwwQdDSopYvHgxmzdv5rrrruPMmTOcOnUKMF/dtLS0EBERgcFgYMeOHSxatGjA5xvrPZ2GQ6WZK0o/6ura0euHnurbU1VVKxcu2K9Wbg5Or1qC0xq7wSk83JtvfnP2gNl1Go0gLS2Ye+7J4OtfzyAgwN3ucTEx6zEauykr+8C67dixSptjVK+oS2bPns2dd95JZmYmt99+u03weO+994iOjmb//v3cdNNNrFixos/n+Zd/+RdaW1uZMWMGv/71r5k/37xura2tjXXr1jFjxgxmzpxJaGioQ9l3Y72n03CoflCK0o+TJyspLrbf6G+w3n03q8/7T6WlW6mo2E5ExE1ERa3rNQU0c2YYq1en4OIy+M+Uer2RvXtL2L+/BKPR9ve9qOh1qqt3M336z/DwiESn0/LEE1fh5nZpcmXmzHBiY3u3i7/Sxlo/qJ6ZccOxdOlSnnnmGebO7dUOaUJS/aAUxQmkdN70Xl1dO1lZ9oNTbe1+y1TbQrvBac6cCNatmzKk4ASg02m57rp4li1L6LUvMnINWq07JSVvA+ZgduaM7Thzc+tVryhlVKh7UIrSh9radrq7nTO9t3dvid3CsC0tORQWbsLHZwpxcXf3Ck6zZoWzalWyU8Ywd24kBw+W2SRQ6HQ+RESsprT0XVpacvDxSeHw4XLmzLlUt0/1irLvqaee6nf/zp07+dGPfmSzLSEhgffee89m2+7du508solDXUEpSh+c1VqjoaGDM2d6lzbq7KwhN/cvuLkFkZz8qE2NPDBP6910U4rTMsdcXDQsXhzXa3to6FJcXHwpK9sGmNPqL0+uyMkZuU6/gzGeruRWrFjBiRMnbL4uD06TzWD//VSAUhQ7nDm9t29faa9EA5PJQF7e/yGliZSUb+PiYrveKCUlkLVrU51eyWHmzDCCgmyrUWi1bkRErKSlJZvm5vMAHDhQanPMxV5Ro8nd3Z26urpxFaSUS6SU1NXV4e5uP2nHnhGf4hNCaIEjQJmUco0QIhB4E4gHCoE7pJT2c2EVZZQ4a3qvtbWbkyerem0vK3uf9vZikpP/BXf3MJt9AQHu3HJL2oiUGdJoBEuXxvHuu7aVBkJDl1BZ+TFlZdvw8ZlCfn4DtbXtBAdfCmY5OXWjunA3Ojqa0tJSamqG1qZEGX3u7u5ER0c7fPyVuAf1XSAL8LU8fhLYJaV8WgjxpOXxj/o6WVFGg7Om9w4fLutVZLap6RyVlR8TErKEgIBZNvtcXDSsXz91RBfHpqcHExHhbbMAWaPRERm5mqKi12huPoef3zQOHizjpptSrMfU13f0ClpXkk6nIyGhd6KHMnGN6BSfECIauAl4scfmm4GL5XJfBm4ZyTEoymBJKZ1SPaK728jRoxU22/T6FgoK/o67ewQxMb3rrK1enUx4uPewX7s/QghWrkzGw8M2CAYHL8DVNZCysm2WBZxVvUonnT5dpdZFKVfMSN+Dehb4IdDzI2SYlLICwPI91N6JQoiNQogjQogj6pJeuZJqa52zOPfEiUo6Oi71VZJSUlj4CgZDG0lJD6PVutkcP2dOBDNnhg/7dR0RHe3Lxo1zbNY3ma+ibqKtrYCmpjMYDCaOHbMNsK2t3X0uNlYUZxuxACWEWANUSymPDuV8KeULUsq5Usq5ISEhTh6dovTNGdN7JpPslWhQW7uXxsaTREffiqdnjM2+pKQAVq50Tjq5o3x93bj33hksWhTLxdtdQUHX4OoaRHn5dqSUvdLSAfLy6vut9acozjKSV1ALgHVCiELgDWCZEOJVoEoIEQFg+T641qKK4mQNDR1kZdVYv5wxvZeVVUNT06U38c7OaoqL38LXN42wsOU2x4aFeXH77eloNFe+95I5aSKe1NQgy2MXIiJW0taWT0vLedrb9bz22mk6Oi5N9ZlMkpMnK1U2nTLiRixASSl/LKWMllLGA3cBn0kpvw5sA+63HHY/MHYLQSmTQn5+A7m59dav4U7v6fVGDhwosz6W0kRBwd8RQkN8/AM2fZ18fFy5667pNqWFRsOcOZdaxwcHX4tO5095+XbA3HH3jTfO2vy9NDZ2kpenkm+VkTUavxVPA28JIR4CioENAxyvKCPGaDQ5ZX3P4cPlnDhRSVNTp819J4CKip20tuaRmPgQbm6B1u1CwB13TMPX1+3yp7viEhP9CQhwp6GhE41GR3j4CkpK3rRWlygtbebdd7PYsGEqWq05wJqvEjuZPj101AOsMjFdkYW6UsrdUso1lp/rpJTLpZQplu/1V2IMimJPTU37sJsRNjZ28umn+VRWtvYKTu3tpZSXbyMgYA6BgfNt9qWnh4yZ8kFCCJurqJCQhbi4+FivogBycurZv9/2vlp5eQuff17otIK6itKTqiShTGrOSIj4+OO8PoNcWdn7aDRuxMffY7PwVghYvDh22K/tTDNnhlkL0mq1boSH30hz8zlaWwusx+zfX0pnp20Q1uuNnDxZSVWV8/pmKQqoAKVMYiaTHPabam5uPdnZ9tOuW1sLaGw8RXj4Dbi42K5tSk8PISRkbLVT9/TUkZ5+qTlhaOgSXFy8KCt735oQ0dlpYP9++00Xc3LUZIjiXCpAKZNWTU3bsKb3jEYTH3+c1+f+srJtuLh49craEwIWLRr5qycXFw0eHrpBZQf2rGKu1boTEXETzc1ZNDWdsW4/eLCctrbuXuc2NHRQUzO69fqUiUXd2VQmreFO7x04UEZdXYfdfS0tuTQ3nyU6+na0WtvimFOmBI9oTbvAQA9iY/2IjPSxJjQYDCZaW7upqGihrKzFJm28p5gYP0JDvayJI6GhS6mu3k1JyTv4+U1FCC16vZEvvyy2u24rJ6d+zF0ZKuOXuoJSJiXz9N7QP+13dOj56qviPvebr558CA1darN9JO89ubpqWbgwlgULYomJ8bMGJzBfTfn7u5OeHsL11ycyb15Un8+zfHmC9X6ZRuNCTMztdHZWUFOz13rMsWPmjMXL1dW1U19vP2grymCpAKVMSsMtZ3TwYBldXfbPb27OpqXlPBERq3qVM0pODiQszPm19tzdXViwIJaAAA+Hjg8P9+7VduOi5ORAbrgh0frY3z8TH58Uysq2YTSag4/RaGLv3r7uRalSSIpzqAClTEoVFUOf3uvqMnD4cLndfVJKysreR6fzJzR0ca/98+f3feUyVF5erixcGIu3t+ugzktKCuhz31VXRTF3rjntXAhBTMwGDIYWyss/tB5z5ky1NchH7v6A5Q8tY83N6WSsvYr2F/8x+D+IolxGBShl0mlt7aa0tHnI5x86VN4r1fqixsaTtLbmERm5Bo3GNmAEBXmSkOA/5Ne1R6fTsmBBDB4eukGfGxbm3W9QW7EiyRrEvLziCQq6mqqqT+nuNleQ6O42kp1dR+TuD5j5p5/gWVOOkBLPmnLcHn8MNm8e2h9KUSxUgFImnVOnht4yorvbyMGDpXb3SWmktPQ93N3DCAlZ0Gv/vHkRTm9CmJYWPKwqDklJgX3u02gEt92WjqenOfhFRa0DTFRUXLqKOn26mvRNv8ely/Z+lLazA+OTPx7yuBQFVIBSJpmSkibq6tqHfP7hw+W9qkVcVFu7n87OCqKibsXcSPoSV1ctM2aE2T1vqHx93YiL8xv4wH5ER/v2G+Dc3V249lpz5XU3t2CCgq6lpmav9SoqP78Bj5oKu+dqyuwHckVxlApQyqSh1xs5d27ovcXMRWDtv+kajV2UlW3DyyuhV5dcMFdpcHa9uoyMsGFfkWk0YsBpx7lzI6xTgZGRq+l5FWUySRr97LfD6QiOcFpnYmVyUgFKmTTOnauhu3t4mXuXd5i9qLr6c/T6RqKjb+sVNISAefMi7Z43VFFRvgQGOpaxN5D4eH+blPTL6XRa68JiN7dggoMXUFOzl64uc+WIvyVtwOBmu9bL4OZO1r1PcP58rerAqwyZClDKhHX+fC379pVw4EApBw+WDqugaXu7nn377KdVGwytVFR8iJ9fBr6+U3rtj4/37zOleyhcXDRMneq8Jp46nZb4eP9+j5k1K5yAAHMQiohYDUjrVdSb2pl8df9/0B4SiRSC9pBITn7rF5QvXUtbWzdFRY1OG6syuahKEsqE1N6uJze33mlN9fbsKepz3ZN5fVAn0dG39doXG+vHihVJThkDgJ+fOzNnhuHu7txf3aSkAAoLGzEa7Zd+0mo1LF4cx9at2bi5BREcvJDa2r1ERKzEzS2IrV5zaX7pM7vnZmfXERzsiY/P6LcVUcYXdQWlTEjODE719R0cO2Y/EaCjo5zq6j2Ehi7B0/PSGqfgYE/uumsa998/0ymlfzQaQXp6CIsWxeLn5z7wCYPk5uYyYMJFRkYoISHmK8GIiFWAoLT0fcC8Jqqvv2+93sj+/aW0tKg28crgqAClTDidnQZKSpzXn+jzzwsxGnu/+UopKS5+E63WncjItdbtAQHuPPLIHFJSgpzy+kIIFiyIJTk50Olp6j0lJQX2W1hWCMHChRfvRQUSHn4j9fUHaWnJo7GxkwsX+q5m3tVlYP/+UlpbexeZVZS+qAClTDi5ufVOuzFfVtZMVpb9zL+mplM0N2cRGbkWne5S48HMzPBBVRAfSHJyIP7+zr9qupy7uwuxsf1fRU2bFmK9nxYRsQqdzp/i4jeQ0sSePUX9ntvVZWDfvhK7ldAVxR4VoJQJpavL4LSb8kajiQ8/zMXezJXJpKe4+G3c3cMJDV1i3a7RCGbOdN56J29vV1JTnXMl5ojkZEeuoszrorRaN2Jibqe9vYja2n1UVrb22Rvroq4uA8eOVTht+lWZ2FSAUiaUvLwGp1097dpVQEWF/YaG1dVf0NVVTUzMBjSaSwkLycmBTk0GmDnTuVdjA/Hw0BEd7dvvMdOnh1oz+gID5+PtnURp6XsYDO3s2VM0YPAxTweqgrLKwFSAUiYMo9HktKun3Nx6Dh0qs7vPZOqmouIjfHym4O+fYbNv1qxwp7w+mNPTnbXWaTAGutel0Vy6FyWEIDb2LgyGVsrLtzt0FQXmvlENDaoth9I/FaCUCaO6engdci9qbe1m69Zsu1N7ADU1ezEYmomMXGOz3dvbleTkvmvbDcbF3k2jwcvLlbCw/jMPMzJCrffFvLziCA5eQHX1Z3R2VvHll8UDXkVJKTl+vLLPtHZFARWglAnEGWV1pJRs3ZrdZ8UIk0lPRcVHeHsn4+OTarMvMzPMKdNxUVG+XHttDC4uo/frOVCg1Wo11qsogOjoWxBCR0nJ21RWtnLsWOWAQaqtrZsTJypVpQmlTypAKROC0WgaVofci06cqCI/v6HP/bW1+9DrG4mMvMlmGkwIc/becE2ZEszs2RH9lh66EgICPAacXszMDCMqypy9qNP5Ehm5msbGUzQ1nWXHjhyeffYgO3bk9JvyX17ewt69xSqzT7FLBShlQqiqahv2dFFbWze7duX3ud9kMlBR8RFeXvH4+k612Rcf7+9wN9u+TJsWekUz9gYy0FWUEIKbbkqxXjWGhS3HzS2E4uK3kNJIa2s3R49WsGnTKbq67FeAB2hq6mTPnqJhNZFUJiYVoJQJwRnTe598kt9nKw2AurqDdHfXERm5plcSwcViqkMVHOxJYmLfHW5HQ1iY94AZiWFh3tYuwRqNjpiY9XR2VlBd/YX1GKNRkpPT9yJeAIPBxJEj5Rw6VEZTU2e/xyqThwpQyrhnMJiorh7e9F5+fgOnT1f3ud9o7KKiYjuenjH4+dlm7qWkBBIX5z/k19ZqNcyc6bzsP2fqry38RUuWxOHraw5k/v6Z+PikUVb2AQbDpRT9rKxah16vqqqVPXuKOHy4TE37KSpAKeNfVVXrsKb39HojO3bk9HtMScnbdHXVERNzR697T8uWJQz5tQHS04OtXWvHmpgYP+bMiez3SsrVVcuqVcnAxbTzOzAaOygtfc96TF5ePXq9461OKitb+/3AoEwOKkAp495wp/cOHiyjoaHvaaXGxpPU1OwhPPyGXu00MjLCCA0dejHYoCBPEhLG1tTe5SIjfVi6NJ45cyLx8LAfSFNTg5gyxXz/zNMzmrCwZdTU7KW11XxPT683kZvbd/KJPTU1bWq6b5JTAUoZ14Y7vdfdbeTgQfsLcgH0+mYKCl7BwyOaqKibbfZptRqWLo0b8mt7ebk6JfPvSomM9Ol3vDfemGRNjY+KWotO50dR0WtIab667aumYX/y8gYX1JSJRQUoZdwyGEycPl01rHU0R46U97nmSUpJYeErGI0dJCU9jEZje/Uwd27EkFtfhIR4sWhR7Jid2utLcLAnUVH2SyH5+7tb10ZptR7ExGygvb3YmjCRk1M/6IXU5eUtff77KBOfClDKuFRb287u3YWUljYP+Tn0eiMHDpT28xr7aGw8RXT0bXh42LZs9/Z2ZfFix66evL1d8fTUWdOxExICuOqqKHQ67ZDHPpqmTQvpc+zXXBNtXT8VGDgXX990ysreR69vorvbSEHB4K6IpJTk5fWfAahMXCpAKeNOdnYt+/eX0NExvE/WR49W0NZm/zm6uxspKXkLH58UwsKW9dq/alWyQ11tXV21LFwYy/LliaxencKNNyYxfXroiPZ1Gmlubi6kpQXb3efiomHlSnMHYXPCxNcwmbopKnoDKaXD2Xw9lZQ0093teIKFMnGoAKWMK93dRnJzh/+J2mAwsX+//asnKSVFRa8ipYH4+PsRwvbXJD09uM836MtNmRJsvdoQQuDm5txW7aMlLs6vzx5VSUmB1r8fD49wIiPX0dBwlLq6g1y4UDfojEuj0TToKy9lYhixACWEcBdCHBJCnBRCnBVC/Kdle6AQ4hMhRI7l+9hOYVLGlNLSZqfUbjt+vLLP7q719YdpbDxFVNTNuLuH2uzz8HBh5cpkh17Dx8dtwDbq45UQgtmzI/pMP7/++kRrwkRExAq8vZMoLn6NxsYqzpwZfPp4Xl4DdXXtwxqzMv44FKCEEG5CiLuFEP8mhPjpxa8BTusClkkpZwKZwEohxNXAk8AuKWUKsMvyWFEcUlw8/FbuVVWtfPFFod19en0LxcVv4OUVT1jY9b3233BDIt7erg69ztSpIeN6Km8gXl6uLFoUa7cLb0CAO1dfba4wIYSGxMSHkFJSUPB3duy4QGWl/T5bfTEaTRw8WEZ9vWrRMZk4egW1FbgZMABtPb76JM0u/i/UWb6k5Xletmx/GbhlcENWJquGhg5aWrqG9Rw1NW28+uppuyWNzFN7mzEaO0hIeKDX1F5SUoDDFR9CQryGtT5qvLhYBWP27IheldwXLIi1BnM3t2BiY++ipeUCpaUf8/bb5wadnWcOUqUqSE0ijgaoaCnlnVLKX0spf3vxa6CThBBaIcQJoBr4REp5EAiTUlYAWL6H9nHuRiHEESHEkZqawa+fUCae4V491dW18+qrp/t8Y6yr209DwzGiotb1ytrT6bTcdFOKQ68jhGDq1NHp5TRaoqJ8iYz0sdnm6qpl+fJLVTaCg6/F338WpaXvUVJymvfeOz/o1u8GgzlINTaqBbyTgaMBap8QImPgw2xJKY1SykwgGpgvhJg+iHNfkFLOlVLODQmZXL/sSm8Gg4mysqFXjKipaWPTplN93nfq7KymqOh1fHxSCQ9f0Wv/8uUJDq95mjo1xFqbbjKxVxEjIyPU2kJeCEFCwn24uQWTm/sXsrJy+PDD3EEnTRgMJg4fLqOzs+/CvsrE4GiAWggcFUJkCyFOCSFOCyFOOfoiUspGYDewEqgSQkQAWL6rglvKgMrLW4Zcb+/o0QpefPE4LS32g5PJZCA//yWE0JKQ8GCvqb2YGF/mzo1w6LUiI33GXFXyK8Xf371XyxEhBCtWJFmn/1xcvEhJ+TZSmsjJ+SOHDhXwl78cGbDa+eU6Ow0cPlymOvJOcI4GqFVACnAjsBZYY/neJyFEiBDC3/KzB3A9cB7YBtxvOex+zPe3FKVfRUWNgz6ns9PAO++cY8eOnH4rGFRUbKetrYD4+K/j5mbbA8nFRcPatakOJTt4e7uO2arkV0pCgn+vbRdr+V3k7h5GUtJGOjoqyct7kfr6dt544wxvvHFmUOudGhs7OXGi0gmjVsYqhwKUlLII8McclNYC/pZt/YkAPrdcaR3GfA/qn8DTwA1CiBzgBstjRelTQ0PHkO45/POfFwZcGFpff4Ty8h0EBV1DYODcXvsXLYolKMhzwNdycdEwb17UqLZpHwsiI33sLmC+9tpom9Ydfn5TiYu7i6amU5SVmT+j5uTU91vZw57y8hZycuqGN2hlzHI0zfy7wGbMCQ2hwKtCiMf7O0dKeUpKOUtKOUNKOV1K+XPL9jop5XIpZYrlu6pjovRrKNUHWlq6yM7u/42rsfE0+fkv4e2dRFzc3b32+/m5cfXV0QO+lkYjmDMn0uH084lMCEF8vL/d7TffPMXm7yg0dCnBwQupqPiQhoYTAOzbV9rnfcK+ZGfXqarnE5SjH/ceAq6SUv5USvlT4GrgmyM3LEUxq6lpG9ICzRMn+i8i29Jygdzc5/HwiCIl5dtotb2TGpYtS3DoiigzM3xSpJQ7Ki7Ov1fKOZjXTd12W5rNdGlc3Nfw9IyjoODvdHZWo9cb+eKLgSZnbEkpOX680ikLuJWxxdEAJYCek8NGyzZFGVHnzw/+6klKybFjFX3ub23N58KFP+LmFkRq6ndxcek9hRcZ6cO0aQNnj2ZkhPVZ3XuycnXVWjP3LhcX588DD8xk6tQQNBqBRqMjOfkRQENu7l8wGrs4frySmprBtVAxXzEP/v+KMrY5GqD+DhwUQjwlhHgKOAC8NGKjUhSgoqJlSPeecnPraW62v6C3sfEU2dm/Q6fzZsqUJ9DpfOwed8MNiQMmRqSmBtmdzlLMNQi1WvtvL9HRvtx+ezqPPz6fWbPCcXMLJinpITo6yiksfBWTycSuXQWDfs28vAYaGtQi3onEocqVUsrfCSF2Y043F8A3pJTHR3JgyuQmpRzS1ROY08rtqanZS2Hhq3h6RpOa+h10Ovuf8tPSgu2W7+nJ1VVLSkrQkMY3Gbi7u5CcHNjvVY2vrxs33ZRCfX0HRUXTiYpaR1nZVry8YhDiRgoKGu1mBfbl4lRfzzT/oCCPftvVK2Nbv1dQQghfy/dAoBB4FdgEFFm2KcqIKC1tHvTNcoCmpk671c4rKj6isPAVfH3TSUv7fp/BycVFY1P9oC/R0b5277MolyQnB/bZIv6ii8kTbm5aIiJWExAwm5KSd2lqOsvHH+cN+r5SW1s3p09XWb8GSpRRxraBpvhes3w/Chzp8XXxsaI4nV5vHFLmHpirlF9ePaep6RylpVsIDJxHSsq30Gr7rgixcmWyteFef+Li/Ic0vslEo3Gs5JOfnzsrViRbKk08gIdHFHl5f6W4OL/fe4mOqKpqRa9XvaTGq34DlJRyjeV7gpQyscdXgpQy8coMUZlssrJq6eoafBkbvd7I8eO2CzcNhlYKCv6Bu3s48fH3odH0PaudkRHKrFkDL7QNCvJUKeUOioz0cWgd2cyZYaSnB6PVupOS8hhCCHJy/syuXdnDakxpMknKy4deIksZXY6ug9rlyDZFGa6Gho4hVY0A2LOn2GZaUEpJYeGrGAwtJCY+ZDeV/KLgYE9Wr3asGOxE7fE0UjIyQh0K6KtXp+Du7oKbWzCJid+ks7OCwsIPB512frnS0uZhna+MnoHuQblb7jUFCyECLM0GA4UQ8UBkf+cqymBJKTl1qmpI59bVtXPwYOll2y5WJ78ZL6+4Ps/V6TSsX5+Oq6t2wNfR6bRERNjP/FPs8/FxY+nSeGbNisDTs+97Up6eOubPN7+t+PlNJSBgNhUVH7F//7lBp533VF/fMejWHsrYMNAV1COY7zelWb5f/NoK/Glkh6ZMNvn5DX2mhw/ko4/yMBov3XyyrU5+Y7/n3nBDEiEhji20jYlRyRFDIYQgOtqXZcsSerXl6Gn+/CjrB4Xo6NuR0kRx8Xvs3Jk3rNcvKRl+o0vlyhvoHtQfpJQJwPd73HtKkFLOlFL+8QqNUZkETCY56IrWF2Vl1ZKf39DjufTk5f2fpTr5N3pVJ+8pKSmAOXMcq1QOKjliuIQQTJsW2meFDg8PHfPmma+i3N1DCAu7nrq6/Zw5c2LIU7/AsFq1KKPH0WKxzwkhpgsh7hBC3Hfxa6QHp0weQ8220uuNfPyx7afr4uI3aG8vITHxQdzc+l6r5OHhwtq1qQ6/VmCgh0qOcAJ3d5d+15BdfXU0Op35rSkychUuLj4UF7/F558XDvk129q6VSfeccihhbpCiJ8BS4GpwA7M7Tf2Aq+M2MiUScXRG9lGo4m//e2EdSrQZJI2jetqaw9QU/Ml4eEr8fef0e9zrVyZPKhFnDExKjnCWRITAygubqKtrfdaN09PHbNnR3DwYBlarQfR0bdQWLiJkyc/o6Agzm5jREeUljY7tIRAGTscLXW0HlgOVEopvwHMBNTybMUp9Hoj1dWO3QQ/erSCyspW2tv1tLfrbYJTR0c5RUWv4uOTQnT0zf0+z9SpIUyfHurwGLVaTb/3TpTB0WhEv3//114bY50GDA5egIdHNCUl77JrV86QX7O8vEUVlB1nHA1QHVJKE2CwVJeoBtQ6KMUpHH3j6Ooy8OWXxXb3GY1d5Ob+HxqNG4mJ30SIvjPyEhL8WbPGsZTyi8LDvSd9rydnCw31IizM2+4+b29XZs823xsUQkNs7J10d9dx9Oi7diuFOEKvN1JRoe5FjSeO/sYdsXTH/SvmLL5jwKGRGpQyuTh6A3v//tI+04WLi1+ns7OSpKSHcXX17/M50tKCueuu6bi5OTS7bRUToyqWj4SpU0P6LMq7aFGstfmhr+8US9r5h3z00bEhv15JiVoTNZ44miTxmJSyUUr5POYuuPdbpvoUZVg6OvQO9Xtqbe3mwIEyu/tqa/dTW7uPyMjV+Pqm9/kcs2aFs359+qCvhNzdXQgOHrgagjJ43t6uRETYv4ry9NSxaFGs9XFMzHqkNHHkyCbOnq0e0uvV1rYPqzKFcmU5WkliqxDibiGEl5SyUEp5aqQHpkwOjl497dlTZDfLr6OjgqKizfj4pBIZubbP86dODWHNmtQBW2jYEx3tO6TzFMekpgb1+fc7b16kNbHBzS2Y8PAV1Ncf5M03PxrS4lsppaosMY44+lHyd5hbbZwTQrwthFgvhOi74qaiOMiRN4uamrZeNfYATKZu8vIu3nd6uM/1Tn5+5rYOQ6Wy90aWj49bn1dRWq2G66+/dLs7ImIFOp0/Fy68yocfnh/S66lpvvHD0Sm+L6SUj2FOjHgBuANzooSiDFlTUyctLf1XjjAaTbz/frbdJIqSknfo6CgnMfEbfd530mgEt92Wbr2XMVj+/u5q7dMVkJra97qoKVMuNYbUat2Jjb2L9vZiPvro/4aUMNHW1u3QtLIy+hyejBdCeAC3A48C84CXR2pQyuTgyJvL3r0lVFa29tre2HiS6urdhIXdgJ/f9D7PX7w4rs/24/a4uGiYOTPc+pWREebwucrQ+fi49ZvGb+5wbP45MHA2oaHLqKr6lL///fUhVb5XV1Hjg6MLdd8ErgI+wlyDb7cl7VxRhqSlpWvANggVFS189VXvtPLu7kYKCv6Bp2cM0dG3WLf7+7tzyy1TqKhopazMnLq+cGHMoMYVG+s3YDddZWSkpgbZVKPv7DTQ3W2+7xge7s306aGcPm2euImJuZ22tnzOnXuRt96azr33LhnUa5WXtzBtWgg63cAFgpXR4+i8x9+Bu6WUqvOX4hQXLvTf6dRoNLF16wWbArAAUpqYk/0sWw1txBjaqDr9E/4cdSs7g6/ihhsSiYnxG/I9I3PDvKFVKVCGz8fHjSVL4q2PS0ubOX78UsPC666L59y5WoxGExqNjqSkjZw9+19s3/5LAgJCWLNmqsOvZTSaOHy4nKuvjlbFf8cwR6f49gA/FkK8ACCESBFCrBm5YSkTmSNXT598km+3xcL83Bf4385y4jD/543oruffijZxr/YMaWnBwxpXRIR3v+0glCsrIsJ2cbSfn7u1kCyYs/oSEh6gvb2YLVt+26sm40Dq6to5cqRcVZcYwxwNUH8HuoFrLY9Lgf8akREpE95AV0/795dy+HB5r+11dYf4QeMxLm+M4WHq5hs5bw97XElJgcN+DsV5tFpNr95bCxfG2CS8BARkEhl5E7W1X7Ft2yY+/7xgUK9RVdXK8eMVSKmC1FjkaIBKklL+GtADSCk7AHVdrAzaQFdPZ85Us2tXfq/tra15FBT8g1g75wB41fdOQx+MoCBP/P3Vyomx5vIKHh4eOhYutP1fEBm5Fn//mRQXv8WOHZ/wySe9///0p7y8xXpvSxlbHA1Q3ZYsPgkghEgChtZZTpm0TCbJmTN9vxEUFDSybVs2l3+Y7eqqJSfnz7i6BlCps3+PqCPY8Z5O9iQmqntPY1FQkGevadeei3fBXKsvMfFB3N3DyMv7P7744gT//OeFQV0VFRU1kpc3tBp/yshxNED9DHMGX4wQYjOwC/jhiI1KmXAMBhMHD5ZSW2t//Ul7u5533jnXKynCYGjjwoXnkNJISsrj/CX6Njo0tuuSDG7uZN37xJDH5u3tSni4/YWiyui7fJmAi4uGO++cZjPVp9V6kJLyGFJKcnL+xJEjhWzZch6j0fFk46ysWrtLGpTR4+hC3U+A24AHgNeBuVLK3SM3LGUi0euNHDjQd3AC2L270KZ1BpgrlF+48BxdXTUkJ/8LHh7h7Ay+il/F3UuFayASQXtIJCe/9QvKl/Zd5qg/QghmzFBrncYye+vYgoM9Wb8+3SYDz909jKSkjXR0lJOf/xJnz1bxwQcXHH4dKSXHjlXQ1NTplHErw9dvmrkQYvZlmy7mfMYKIWKllEMvK6xMCkajiX37SqwNBu2pqWnj2DHbe0gmk4G8vBdoaysgKekRfH2nWPftDL6KhtW3sWJF0rDHl5QUQFCQKgQ7lnl5uRIY6NGrI25CQgCrViWzffulHlF+flOJjb2D4uI3KSvbhhC3EBvrZ23dMRCj0cShQ2UsW5aAVqvaq4y2gdZB/baffRJY5sSxKBNQfn5Dv8EJ4OOP823uF0gpKSzcRFPTaeLi7iEw0PZzUlJSADfcMPx2ZH5+7kyZMrzUdOXKiInxs9uyffbsCJqaOtm7t8S6LTR0Ge3tZVRU7MDDI5KdO83NJh2dxu3sNFBS0mwtr6SMnn4DlJTyuis1EGXi0euN5OU19HtMTk49+fm2x1RUfERd3X4iI9cQGmpbISA+3p/166cOe3GlVqthzpwItUhznIiM9OHMmWq795Suuy6B0FAv/vnPHLq7jQghiIu7m87OKgoKXsbLK45333Xj4YdnOdwHLD+/gbg4P1XFfpT1ew0rhPhhj583XLbvVyM1KGViyMtrsNsi4yKj0dQrJbix8TRlZe8TGDi/V/uMpKQA7rprGq6ugy9PExDgwcKFsWRmhpOcHEhmZjheXqoI7Hjh4qIhKqrvWn3TpoXy8MOzCA01r5LTaFxISvomGo2WoqLXqKtrZ9s2x+9HtbV1q4SJMWCgSda7evz848v2rezvRCFEjBDicyFElhDirBDiu5btgUKIT4QQOZbvKr93AurqMvS6MupJSsnOnXk2VaU7O6vJz38JD48o4uPvtfn0OmVKEHfeOW3ItdNiYnwJCPAgJsaP9PSQfguTKmNTXJx/v/uDgjx58MFM69opV1d/oqJuobk5i/r6w5w/X8uePUUOv95QW8srzjNQgBJ9/Gzv8eUMwP+TUqYDVwPfEkJMBZ4EdkkpUzCnqz85iPEq40Rubn2fKb5SSj744AJHj16qs2Y0dpKb+2dAkJLyGFqtm3VfQoI/t9+ePuSb1hqNUAFpAvD3d8fPr//F1DqdljvumEZAgPm40NCleHrGUVLyFgZDO3v2FJGVVevQ6zU2dqq2HKNsoN942cfP9h7b7pSy4mKWn5SyBcgCooCbudSq42XgFkcHq4wPHR16Cgsb7e4zmSTvv5/NyZNV1m1SSgoKXqajo4KkpG/i5nYpccHX143bbht6cAIIDfVSVasniLi4gQsBe3rqrOukhNAQH38Pen0LZWVbkRK2bj3v8PTdQPdQlZE10G/9TCFEsxCiBZhh+fni4wxHX0QIEQ/MAg4CYVLKCjAHMSC0j3M2CiGOCCGO1NTUOPpSyhiQnV1nU4BTrzdSVtbM0aMVvPba6V7VJKqqPqWh4SjR0bfi53epIrVWq2HDhqnDLuA6mH5QytgWFeXr0IeVkBAvbr/dvE7Kyyue0NClVFfvpq2tEL3exJtvnnWoZXxVVeuATTWVkdPvv7SUUiul9JVS+kgpXSw/X3zs0LuGEMIbeBf4npTS4S5hUsoXpJRzpZRzQ0JCHD1NGWWNjZ2UlDRZH+fk1PHrX+/jb387wY4dORQUNNoc39JygZKSd/H3n0V4+AqbfatWJQ17ak6n0xIWpqpETBQDJUv0lJgYYK3bFxV1MzqdD4WFryKlkebmLnbudKz6eXFx08AHKSNiRFeiCSF0mIPTZinlFsvmKiFEhGV/BKp1/ITS8+qora2bDz640Gc7g+7uRvLyXsDdPYTExAdskiJmzAhj1qzh1dcDc3qySiWfWAZKlujpmmui8fZ2xcXF09oqvqrqc8D8f9WRRIjS0mbVkmOUjFiAEuZ3m5eALCnl73rs2gbcb/n5fmDrSI1BubJKS5tpaLi0mHL79hza2uxPoxiNneTl/R9GYyfJyf+CVnup+KeHh4tTFuKCmt6biPz93QkI8Bj4QMDVVcvSpXEABATMwc9vOmVlW+nqMgemHTtyrF17+9LdbaS6undvMmXkjeQV1ALgXmCZEOKE5Ws18DRwgxAiB7jB8lgZ54xGE1lZl+4VHj9eSXa2/b5Pen0L58//ltbWAhISHsDDI9Jm/3XXJTilcaCnp86m6rUycUyfbvfWtV0zZ4YTEuJpXcALJoqLXwegqamL3bsLB3yOntPWypUzYgFKSrlXSimklDOklJmWrx1Syjop5XIpZYrlu1psMAHk5NRbi702NHT22d20q6uW8+d/TUdHOSkp/0Jg4Fyb/ZGRPsyeHe6UMan27ROXv787MTEDZ/SBeZnB8uXmK3I3t2AiI9fS2HiShobjABw6VEZZWf+3x6ur2wa80lKcT1VDVIatubnL2kunubmL118/bfeXua2tkKysX6PXtzBlyhP4+8+02S8ErFqV7JTyMmlpwarH0wSXnh7s8PKBlJRAEhL8AQgLux4PjyiKil7HYGhHSnj55ZNs2nSK/fvtV903mSSlpQ7neClOogKUMiwmk+T48QpMJkljYyevvHKSujrbop5Smqis/ISsrP9BCA1paT/Axye513NlZoY7ZUHt9OmhpKQEDft5lLHNzc2F1FTH/51XrEjC01OHRuNCQsL96PXNlJS8BYDRKCksbOTTT/N5/vkjdq+o1DTflacClDIsFy7U0dzcRX19By+/fJKGBtteOnp9Czk5f6Sk5G38/GYwbdpP8PSM6vU8aWnBXH/98BMjZs4MV1N7k0h8vD/e3o7VVAwJ8eK++2bg5aXDyyueiIiV1Nbuo7HxlM1xUpor7F+uublrwMr8inOpAKUMWWNjJ7m59bS0dLFp06lev7zd3Q1kZT1Nc/N54uLuJjn5UVxcvGyO8fBw4dZb09iwYapNh9ShmDIlmNhYx+5LKBODRiO4+upoMjLCiIryxcOj/+SakBAv7r9/Jj4+rkRG3oSHRxSFhZswGGyz9EpLm3stKAcoKGiwBqrm5i6Vfj7CRM8+PGPV3Llz5ZEjR0Z7GEoPRqOJPXuKrNN6ZWUtNvu7uxs5f/4Z9Ppmpkz5Ht7el66O3N1dCAvzIiLCx7pOZbgiI32YMydy4AOVCe/zzwtobe3u95iGhg7+/vcTVFfnkZX1KwID55GY+JDNMb6+bjz22Nx+73PNnh1BVJRayjBcQoijUsq5l28f3kdWZdLR640UFTVRUNBAZ6eB7dtzegUnvb6Z7Ozfodc3kZp6KTj5+7tzxx1TnV7Zwc/PncxM52T+KeNfQkIAp09X9XtMQIAH11+fyNateiIibqK8/AP8/DIICppvPaa5uYt9+0pYsiS+z+cpKGhUAWoEqSk+xSFGo4lz52r49NN8srJq6Ow0cOBAKadO2b4R6PUtZGf/ju7uelJTH8fHx9yWPSTEkwcemOn04OTu7sL8+VGqPbdiFR3ti4vLwP8fZswIIzbWj4iIVXh7J1FYuImOjnKbY/btK+33vlNDQwdNTZ197leGR/1WKwOqr+/giy+KyMurx2Awt9AoKGjg008LbI4zGNq4cOFZOjtrSEn5Nj4+qQBERflw330z8fFx6/Xcw6HRCObOjRz2vStlYnFx0Ti8Rmr16mRcXHQkJT2CRuNKbu7zGI2XAo7BYOKLL/rvIXV5fUnFeVSAUvokpeTs2Wr27Suhre3SnH5LSxdbtpyn5/1Lo7GTCxf+17oA19c3DYCMjFC+/vUZTqkMcbmMjDCHS94ok8vFNU8DCQnxYv78KFxd/UlK2khnZzUFBS/b/N8+daqK+vqOPp+jrKxZLeIdISpAKX3KyaknP7/B5pfVZJJs2XLeplWB0dhFTs5ztLUVkZS0ET+/6Xh7u3LHHdO45Za0IbVoH0hCQoDK2FP65OXlSkiI18AHAkuWxOHj44qv7xSio2+loeEoVVW7rPtNJsmXXxb3eb7JJFXF8xGiApRiV319Bxcu9K6l9/nnhTa/jCaTntzc52lpySUx8UECAmaRnBzII4/MYcqUkVksGxTkybRpqgWL0j9Hr6JcXbWsXGleOB4efiP+/pmUlr5Da+ultVCnT1f32123sLCR8ZARPd6oAKX0otcbOXasotcvXE5OHfv3l1gfS2kkP/9FmpvPEh9/L0FB8wkLMzeKG4kpPY1GkJwcyFVXRTmlHJIysYWGejn8/zAtLZjp00MRQpCQcD86XQB5eS9Y10dJKfu9F9XRoaeqSlU8dzYVoJReTp6soqPDtk1GY2MnW7dmczFmSWmioOAVGhqOExNzJyEhC/H01HHHHdNGZEovJMSLJUviSU8PURl7ikPM1cv9HT5+5cokS+8oL5KSHkGvbyI//+9IaU4MOneuhpqavoNQYWHjMEesXE79pis2cnLqqKiwXddkMJh4551zdHSYq5WbTAaKil6jrm4/UVE3Ex6+HI1GcPvt6fj7uw97DN7erixZEs/KlcnWr6uvds6CXmVyiYnxdfhq28NDx5o1KQB4e8cTE7OBpqZTVFZ+CphLIH32WWGf59fUtDnURl5xnApQCmC+0XviRCXnz9f22rdzZx4VFa3AxXYZz1BTs4fw8JVERKwG4MYbE4mP9x/2OMLDvVm0KA5fXzd0Oq31S1GGws3NXLXEUSkpQcyYEQZAaOh1BATMpqxsC+3t5qntCxfq7P6OXFRU1Dis8Sq2VIBS6OoysH9/id1qzSdPVnLsWAUA9fVHOXv2F3R2lpOUtJGYmNsQQnDNNdHMm9e7AOxgpaUFM29elEOLLBXFUY6uibpo5cok1q+fyurVKdx115PodN4UFLxiner78MNca++zy5WUqPbwzqTeCSa5zk4De/cW213nUV7ewo4duQBUVHxIXt7/4e4exrRpP7U2Gpw5M8wpVcgTEgJUiwxlRISFeeHm5vhibjc3F9LTg5k7N5JVq2Zwzz0/pL29yJp63trazaef9q52DuYPe5WVrU4Zt6IC1KRmMJg4dKjM7rx5Q0MHr79+BoPBREXFR5SWvkdg4HzS0n6Im1swYG4Ct2ZN6rDHERjowdSpKm1cGRlCCGJihl4v75Zbbicycg5lZVvp6jJP7x0/XtlnBQk1zec8KkBNUlJKjhwpt1tHrL1dz+uvn6G9XU9l5ceUlm6xVHv+BhqN+ZPolClB3H57OhrN8NK93dxcmDMnctjPoyj9Gc6ibiEE3/nOfwKCwsJXrcsvtm+/gF7fu4JEbW27TeUVZehUgJqkTp2qspsyq9cbefPNs9TVdVBZ+QklJe8QEDCHxMQHEUJr7d90xx3Thp28IISqpadcGV5ergQFeQ75/KlTk5k37z6am89RV7cfgIaGTvbutV9hoqhIVZZwBvXOMAmVljb3WZrl44/zKS1tpqLiQ0pL3yMgYDaJiQ8hhNY6pTfUdG83NxeuvTYGNzdzYBNCqIQI5YqJjfXrtxrEQDZufISzZz+nuPgtfH2n4urqz/79pWRkhBEcbBv8ioubiI/3H5EF65OJeneYZIxGE1lZNXb35ec3cPRoOWVl26z3nJKSvolG48KiRbHceee0IQcnrVbD/PlReHu7WlPHVXBSrqSoKB/8/Ia+Ti8kxJtVq/4Vk0lPUdFrSCkxGiUffpjb61i93sjBg6V2pwAVx6l3iEkmJ6febopsV5eBbduyKS19j/LyfxIcfC2JiQ+i0+m49dY0li6NH1Z5oVmzwp2yiFdRhkoIQUZG6LCe49ZbFxIffwuNjSeorzd3+S4sbOzVFw3M2X5Hj/YuGaY4TgWoSaSjQ09eXr3dfR9/nMe5c69RWfkRISGLiY+/Dzc3HffeO4Pp04f3S52eHkJEhM+wnkNRnCEgwGNYCRPe3q7cf/8jeHnFU1z8Onq9uerKJ5/k2/3gV1PTxpkz1UN+vclOBahJ5Ny5GruLCPPy6tm27Y9UVn5MaOh1xMXdgxAabrophehox9JzdTotvr5uhIV5Ex/vz7RpoVx1VTTLlyeSnBzo7D+KogxZenrIsBJ8Zs+OZsmS72I0dlBc/DpgznzduTPP7vGFhY2qHccQqSSJSaK+voPy8pZe20+fruLPf/4lVVWfEBq6jNjYOxFCMGtWeJ9XTjqdlunTQwkO9sTFRaPuJSnjiqurlvT0YLvTco66++5lnDmzluLi9wkMnE9AQCanTlWRmOhPRkZYr+PPnKkmKMgDLy9VT3Iw1DvLJKDXGzlxotJmW1VVKy+/fJLnnvtvyso+JixsuTU4hYZ6sWJFkt3nCg31YunSeKKjfXF3d1HBSRmXYmP9htWN2dfXjfvvfwwPjyiKil7DYDBnB+7YkWu3KovRaLLbwkbpn3p3meCklBw7VmGzcPDChTr++tfjHD78nuWe0xJiYu5ACIFOp+X229N7TYG4uGiYOTOcq66KVuuWlHFPCEFmZviwWrfMmxfL3LmPotc3UVq6BYDubiNbtmRhNJp6Hd/Y2Gm3CajSNxWgJrjs7Dqqqy8tyO3uNvLhh7nU1R2huPhN/P0ziYv7GkIIwsK8uPfejF5rOgIDPViyJF61WFcmFG9vV9LSgof1HMuWLSQs7HpqavbQ3JwNQEVFK59+WmD3+Jycehoael9hKfapADWBVVa2kpNj+4nt888LKSs7Q37+S3h7J5KU9DCuri4sX57AQw/NIirqUlKEi4uGtLRgrr02Ri04VCakhAR/AgOHPtWXkRFKYuKtuLkFU1i4CaOxC4BDh8rsXi1JKYd172uyUQFqAjIYTOTm1nP8eIXN9vLyFj7//Ctycv6Im1sQKSnfwt/fh40b53DttTHW6Y7AQA9mzgznhhuSSEkJUu3VlQnLnBAUMeSpPldXLbNmxRIffx9dXdWUlr5n3bdtWzbNzV29zmlu7qKqSlU8d4QKUBOIXm8kO7uWTz/NJyurBoPh0jy4ySTZvPljsrOfRav1JDX1e/j6BnD33dOtnyBdXDRcc00MCxbEEhvrpxIglEnB01M3rLV+c+ZE4OubRljYcqqrP6Op6SwAHR0GtmzJsru0IyfH/npExdaIvQMJIf4mhKgWQpzpsS1QCPGJECLH8j1gpF5/MjpzppoLF+rsllfZsuVz9u37FVqtO2lp/w8fn1C+9rXphISYu41erJN3+f0nRZkMYmP9SEgY2ttRSIgXsbF+REffirt7BAUFL2MwmO/7lpQ088UXRb3OaWjosFusWbE1kh+R/wGsvGzbk8AuKWUKsMvyWHGClpYuysp6r3Pq6jLw0ksf8dprP0CrdWXKlP+Hl1cod945jchIc3UHT08dCxbEDKtOmaKMd9OmhVg/sA3WvHmRaDSuJCY+iMHQbNOW46uvisnPb+h1jrqKGtiIBSgp5R7g8n+Bm4GXLT+/DNwyUq8/2Zw/X9trjUVlZSt/+MMOtm//KULoLMEpjA0bphIf7w+Y59AXLIhVCwiVSU8IwZw5EUP6XZgyJQhvb1e8vOKIjLyZhoaj1NUdAEBK2LIli8ZG295rdXXtdtdMKZdc6ZsMYVLKCgDL9z4nfoUQG4UQR4QQR2pq7FffVswaGjp6tZkuLW3mL3/5hIMHfwVIpkx5Ai+vcO64Y6pN6aHZsyPUuiZFsdDptMyfHzXoBpparYYVK5Lw83MjImIF3t4pFBW9Snt7KWC+H/X22+d6Tb+rdVH9G7N3waWUL0gp50op54aEqHbg/cnKqrV53NzcxSuvfMnZs89gMumZMuUJvL2jegWnlJSgIU9pKMpE5e3tOqT7UVOnhvDtb89nw4bpLFr0r2i1nuTm/sVaZaKyspUdO2xbc9TUtPX6cKlccqUDVJUQIgLA8l2V+R2mmpo2myZser2RTZv2c+LErzEa25gy5bsEBsZz113TbIJTUJAnU6YEjcaQFWXMS0kJHFJBWY1GkJ4ezKOPLmXhwu/T3V1Hfv7fkNKcUXvqVBWHD5fbnHPiRCUdHXqnjHuiudIBahtwv+Xn+4GtV/j1J5T2dj2nT9vG+C1bTvLll/9NV1c9KSmPExubzoMPZpKYeOkToZeXK3PmRKj1TYrSB53O3EF6qIQQPPTQOhITv0ZT0ynKy7db933ySR4lJZeqm+v1RlWnrw8jmWb+OrAfmCKEKBVCPAQ8DdwghMgBbrA8Voagvr6DL78sstbYMxpN/POf59i27T9pby8hOfkRMjPn8+CDswgKMqeOu7m5kJERxtKl8bi5qftOitKfhISAYVVQ8fV148EHNxIUdA3l5R9YGxwajZJ33smitfVSfcz6+g7On6/t66kmrRF7l5JSfq2PXctH6jUni9LSZk6erLQuAGxt7ebtt0/z+ee/oaUlm8TEh1i06HrWrZtivdmbmBjAlCnBavGtojhIoxGkpQVz7FjFwAf3ISMjjNWrn+C996rJz/8bOl0APj5JtLZ2884757j33hnWKha5ufUEBXkSGqruC1+k3q3GmZycOo4fr7AGp9LSZv785y/59NOf09h4gtjYu1i2bB0333wpOKWmBjFtWqgKTooySFFRvsNqywGwdu1UZs16AlfXQHJz/0Rnp3lavqSkmU8+ybc59ujRcrvlkSYr9Y41jmRl1dhMA2Rn1/HXv37O0aO/pLU1l4SEB1m58m7Wrk213l9KTQ1iypThVWxWlMls7tzIYS3FcHd3Yc2aTFJTHwckOTnPYTCYM/cOHy63+Z02GEwcPFiqkiYsVIAaJ06friI3t97m8csv7+TMmV/R3V1Paup3WbXqNm66KUUFJ0VxInd3F+bPjxpW76j09GDS06eQnPwturrqyMv7qzWzb/v2HJt+bZ2dBg4eLLNbsmyyUQFqHDhzpprCwkbr46NHK3j11X+SlfUbQENa2o+46aYbWbky2RqcwsK8VXBSFCfx83Nn1qzwYT3HypXJ+PunEBd3N83NWZSWvg+Ys3H/+c8cm2NbWro4enTo974mChWgxrjq6jYKCi7V8dq/v5TXXttCdvaz6HS+TJ36Q26+eRFLl8Zbj9HptMyYETYKo1WUiSsiwoepU4deNCA42JN586IICVlISMgSKis/smb2XbhQx/HjlTbH19S0UVvbbu+pJg0VoMYwvd7IyZOX/tN+8UURb775Brm5f8LdPYJp037E+vXXcPXV0TbnTZsWosoXKcoISEoKJCMjbMhrCBcvjsXb25XY2Dvx9k6ioOAf1nJIH3+c16teX89p/clIBagx7NSpKjo7DQDs2pXPu+9utnTCTWbOnB/z4IMLmDnTdtohNNSLmBjVml1RRkp8vD9z50YOul4fmNcirl2bire3B0lJj6DVepCb+2cMhla6u4289dZZuroM1uNratpoaurs5xknNhWgxqiysmbKy83tMz76KJf333+VwsJX8PWdypIl/84jj1zbKxC5uGjU1J6iXAHh4d5cc03MkJZuJCcH8u1vz2Pp0umkpj5Gd3cjubkvYDIZqKpq4623zmE0Xmo2OpnbcqgANQa1tHRx+nQ1Ukp27Mjhgw82UVT0Gv7+M1i37qc8+OA8fH3drMdrNIK4OH8WL47Dw2PoK98VRXFcYKDHkD8Qurm5sHx5Ij/84Z1MnfoNWlrOU1LyDgCFhY28/362tfRRZWWrTZbfZKIC1BjT3q7nwIFSursNluD0EiUlbxIQMJsNG55iw4YZ1k9tQgiSkwO5/vpEZswIUz2dFOUKi4ryHdaUekCAO48/vpHw8Ouprv6MmpovATh3rsa6iFdKOWnvRakANYZ0dRk4cMC8SG/79hy2bv0TZWXvExR0Ffff/wvWrp1qvTnr4aHj2mtjSE8PUXX1FGUUTZ8eOqwPh5GRPtx77/fx9Z1KUdFrNDWdAeDgwTIOHy4DzBVj2tsn3+JdFaDGCL3eyIEDpbS1dbN9ezbvvfcMlZU7CQlZwoMP/ifLliVZjw0L82bx4jgCA4dXgkVRlOFzcdEwZ07EkJImLlq4MI5ly36Ah0ckOTl/obk5C4CdO/PJyanHZJLWD6+TiQpQY0B7u56vviqhubmL7duzeOed/6KmZg/h4StZseI7LFoUbz02KSmQ+fOjcHUdfK8aRVFGhp+fOxkZQ09QEkKwYcNsMjN/gLt7CDk5f6KlJQcpJVu2ZFFVZb4PtW9fyaQKUipAjbKGhg727i2mpaWLbdtO8NprP6K+/gjR0beRmXkPN988xXpsWlrwsBYKKooycmJj/Yb1++nj48bXv341M2b8AFfXAC5c+F9aW/Po7jbyxhtnaWrqpL1dP6mClApQo6iiooV9+0ro6jKwZcsBNm/+V1pb80hMfIjY2JvYsGGq9f5SRkYYKSmqA66ijGVJSYGkpg799zQ62peHH17E7NlPotP5kZ39LM3N2TQ3d/HCC8c4fbqK9nY9+/eXTopafSpAjZLq6jaOHjW3zdi06SM2b/6etehrSMjVrFmTQkiIl2V+O5L4eP/RHrKiKA6YMiWY5OTAIVebCAvzZuPGpcyf/++4ugZy4cL/0tR0ls5OA++/n82772ZRU2N+/5joXXjFePgDzp07Vx45cmS0h+E0TU2dfPVVCQaDkeee+zu7dv0enc6XlJRvkZY2jVWrkgkN9cLf3505cyKH1dVTUZTRYTJJ2tv1tLV1U13dRlFR06ACSktLF5s2HeCLL35BZ2clSUkbCQjIBMzp6Q8+OItp00KZPj10hP4EV44Q4qiUcu7l21V+8hXW3q7n4MEyWlo6+Z//+SXHj7+Jt3cyM2Y8zpo1s6z/2ZKSAklLCx5WZpCiKKNHoxF4e7vi7e1KWJg3UVG+nDxZadPqvT8+Pm5885sL8ff/bz744Cfk5j5PfPw9hIQsoqGhk7ffPoebmxZfXzdiYydmeTMVoK6gzk4D+/eXsGvXOV566Snq6o4RHHwtCxc+xh13zMDb27yWYvr0UBISAkZ5tIqiOFNgoAdLlsSTnV3r8MJbnU7L3XfPIzT097z88n9QWLiJrq46oqJupri4iQ8+uIBOp6W9XU9SUgA63cTK7lUB6gqpqWlj585cXnjhQw4d+j3d3bXExt7JLbfcz7JlCWg0AiEEM2aETdhPQ4oy2Wk0gvR0c7eBM2eqHT7v+uvTCQl5juee+xkVFTvo7q4jPv4+Tp+uJjDQA5NJUljYSFJSAImJAcNqrjiWqHtQI0xKyYULdXz5ZTG/+90LZGf/Ha3WnalTH+PrX1/LlCnmjB8hBJmZ4URH+47yiBVFuRJKSpo4ebJqUPel8vLq+f3vf0Nx8Xt4eSWSnPworq7+LFuWwIIFMYD5Su2aa2LG1e0BdQ/qCpNSUlHRSl5ePSdPlvKrX/0HFRW78fZOYsmSH/C1r12Dr68bWq2G6Ghf4uP9bQrAKooyscXE+OHiouHEiUoMBlOfx0Xu/oD0Tb/Ho7aCjuAIpq95lO8ejOD8+Zc4d+6/SEp6lM8+g7a2bm64IZH6+g7Onq0e1sLhsUJdQY2AvLx6Cgoa6ejQc/ToGZ555l9paysmImIFd9/9XRYvTkCr1ZCWFkxsrN+EmzdWFMVxRqOJiopWioubqKuz7aAbufsDZv7pJ7h0XeoJZXBz56v7/4MfHIdTp/6X7u5aYmLuIDT0OjIywli3LhWtVsPMmeHj5naBuoK6QrKyasjNrUdKycsvb2Lr1t8BWubM+X9s3HgXERE+6HRa5s2LJCjIc7SHqyjKKLs4ixId7UtTUydHjpRbC8Omb/q9TXACcOnqZN57f+bRpz/gb38L4MyZ/6O4+A2am7MwGO6jpaWL9euncvp0Fb6+bvj7u4/GH8spVIByoqKiRnJz62lqauCpp35IXt6XeHsns2HDT1i3bh5arQZPTx1XXRVtzdhTFEW5yM/PncWL4zh+vJKqqlY8aivsHudRW0FIiBcPPHAVr7ziRmHhx5SWbuHs2V/Q0fEgTU1d3HHHVA4dKuOqq6Lw8xufQUoFKCeprm7j9OlqDhz4kt///kk6OxuYMeNrPPbY94iMNM81R0X5kpYWrAq9KorSJ51Oy/z5UeTl1dMZEoFHdXmvYzqCIwBz1Yl7781k+3YvfHxSyc//K9nZv6epaQUtLbdy663TMBhMzJkTQViY95X+owybClBOUFPTxoEDhfz2t79kz5438PYO5wc/eImFC82JEAkJAURF+UyY1E9FUUZeUlIgxmf+B9MjG9F0dFi3G9zcybr3Cevj8HBvHnpoFnV1aRw7Npd33/095eUf0dycRVvbw8yfP4O2tm7mzIkkMXF8ra9USRLD0NrazblzNXz11WF++csnqKsrZM6ctfzoR/+Jn58v6enBw+q2qSiKwubNyH/7NygpoTMkgnNff4LypWv7PFyvN/Lii5v5+ONnkdJITMztJCRcz+rVqSxaFEdKSuCYu//dV5KEClBDYDJJzp+v5cyZEv7yl9/yxRdvotN58eij/8mKFTeRkOBPSkqQtTW7oiiKM3R3G8nKqqG4uGnAY8+cyeF3v/sxtbVn8PKKJz7+XubPn8OqVclERvqQmhpESIjXFRj1wFSAcpKuLgOHDpXy6qubeeWVZ+jsbGTatBv58Y+fYt68VBIS/FXauKIoI6qxsZOysmaqqtpoa+u7tp9eb+S5515i796/YjC0ERZ2Hampt3DHHfOIi/MnJsaPadNCRv09SwUoJ2hs7ODZZ1/jz3/+b2pq8ggNTeXxx3/G+vU3kpoaNK5WbiuKMjG0tnaTn99AcXHf1dK/+OI8mzb9jurqL9FodISFLWP16vtZtWoGfn7uzJwZNqpXUypADZGUkpqaNrZs2cmvfvULSkpO4uMTxj33PM5NN61nzpxIIiJ8RmVsiqIoF128J15V1Wp3f0FBI2+8sZvs7C3U1x9Go3ElKmopd975EIsWZRAW5k1KShCBgR5XeOQqQA1ae7uerKwKXnjhZbZs+Qe1tfl4ePhxxx3/wrp1d+Pn58W8eVGqPJGiKGNKU1Mn1dVt1Na2U1/fgcl06T1eSklRURNffHGEPXs2UVNzGIDo6KtZv/4bXHfdQoKCPElKCiQszGvITRcHa0wFKCHESuAPgBZ4UUr5dH/HX8kAVVPTzPPPv82WLe9y7tyXdHe3EhISz6233seqVbcTFxdCRIQPoaFeakpPUZQxzWg00dDQSV1dO7W17TQ2dloDltFo4tSpC2zb9gonT36IwdCBl1c41157E7fdtp6EhGSio32JifEb8Q/iYyZACSG0wAXgBqAUOAx8TUp5rq9zRipAdXZ2kpNTyMGDx/nii/3s33+QoqLTGAwd6HQezJixiFtuuZslS5aSmBhARISPCkqKooxber2RiopWystbqK1tt96zam9vZdeu7Xz00RZKSk4BEi+vIJKSZjJ37tVMnz6VzMwpzJmThq+v8xf8jqUAdQ3wlJRyheXxjwGklP/d1znDDVA///nP2b37C1pb22lv76C1tY3a2ira2lp6jEtDQEAMSUkZrF17M9ddtwx/f2+io33HdS0rRVEUe/R6Iy0t3bS0dNHa2k1razft7Xry8wvZvv2fnD9/jKKi07S319mc5+bmiaenN76+PgQG+vO3v71IZmbmsMYylorFRgElPR6XAlddfpAQYiOwESA2NnZYL9je3k5HRweenh4EBgbg4eFBeHgY4eERhIWFkZ6exuzZs/HxGX+lQBRFUYZCp9MSGOjRKyniuusSuO++xUhpvmdVUlJCQUE+JSUllJaWUltbQ0tLCy0tLbS2tuDuPnIf4EfjCmoDsEJK+bDl8b3AfCnl432dM1bSzBVFURTn6+sKajRKHZQCMT0eRwO9qyEqiqIok9poBKjDQIoQIkEI4QrcBWwbhXEoiqIoY9gVvwclpTQIIb4N7MScZv43KeXZKz0ORVEUZWwblXYbUsodwI7ReG1FURRlfFDlthVFUZQxSQUoRVEUZUxSAUpRFEUZk1SAUhRFUcYkFaAURVGUMWlctNsQQtQARUM4NRiodfJwRoIap/ONl7GOl3HC+BmrGqfzjfRY46SUIZdvHBcBaqiEEEfslc8Ya9Q4nW+8jHW8jBPGz1jVOJ1vtMaqpvgURVGUMUkFKEVRFGVMmugB6oXRHoCD1Didb7yMdbyME8bPWNU4nW9Uxjqh70EpiqIo49dEv4JSFEVRxikVoBRFUZQxacIGKCHE94UQUggR3GPbj4UQuUKIbCHEitEcn2U8vxBCnBJCnBBCfCyEiOyxb8yMVQjxGyHEectY3xNC+PfYN5bGuUEIcVYIYRJCzL1s35gZ50VCiJWW8eQKIZ4c7fFcJIT4mxCiWghxpse2QCHEJ0KIHMv3gNEco2VMMUKIz4UQWZZ/9++O4bG6CyEOCSFOWsb6n2N1rABCCK0Q4rgQ4p+Wx6MzTinlhPvC3LF3J+bFvcGWbVOBk4AbkADkAdpRHqdvj5+/Azw/FscK3Ai4WH7+H+B/xug404EpwG5gbo/tY2qcljFpLeNIBFwt45s6mmPqMbbFwGzgTI9tvwaetPz85MX/A6M8zghgtuVnH+CC5d96LI5VAN6Wn3XAQeDqsThWy1j+FXgN+Odo/vtP1Cuo3wM/BHpmgNwMvCGl7JJSFgC5wPzRGNxFUsrmHg+9uDTeMTVWKeXHUkqD5eEBINry81gbZ5aUMtvOrjE1Tov5QK6UMl9K2Q28gXmco05KuQeov2zzzcDLlp9fBm65kmOyR0pZIaU8Zvm5BcgCohibY5VSylbLQ53lSzIGxyqEiAZuAl7ssXlUxjnhApQQYh1QJqU8edmuKKCkx+NSy7ZRJYT4pRCiBLgH+Kll85gcq8WDwIeWn8fyOHsai+Mci2PqT5iUsgLMgQEIHeXx2BBCxAOzMF+ZjMmxWqbNTgDVwCdSyrE61mcxf8A39dg2KuMclY66wyWE+BQIt7Pr34F/wzwl1es0O9tGPMe+v7FKKbdKKf8d+HchxI+BbwM/YxTGOtA4Lcf8O2AANl88zc7xoz5Oe6fZ2Tba6yvG4pjGJSGEN/Au8D0pZbMQ9v5qR5+U0ghkWu7hvieEmD7KQ+pFCLEGqJZSHhVCLB3l4YzPACWlvN7ediFEBuZ7DCct/0mjgWNCiPmYP6HG9Dg8Gigf4aH2OVY7XgO2Yw5QV3ysA41TCHE/sAZYLi0T0YzBcfZhVP7tBzAWx9SfKiFEhJSyQggRgfkqYNQJIXSYg9NmKeUWy+YxOdaLpJSNQojdwErG3lgXAOuEEKsBd8BXCPEqozTOCTXFJ6U8LaUMlVLGSynjMb8JzJZSVgLbgLuEEG5CiAQgBTg0isNFCJHS4+E64Lzl5zE1ViHESuBHwDopZXuPXWNqnP0Yi+M8DKQIIRKEEK7AXZjHOVZtA+63/Hw/0NfV6hUjzJ9CXwKypJS/67FrLI415GL2qxDCA7ge8+/7mBqrlPLHUspoy/vnXcBnUsqvM1rjHO1skZH8AgqxZPFZHv875sypbGDVGBjfu8AZ4BTwARA1FseKOamgBDhh+Xp+jI7zVswfSrqAKmDnWBxnjzGtxpx5lod5inLUx2QZ1+tABaC3/H0+BAQBu4Acy/fAMTDOhZinRU/1+L+5eoyOdQZw3DLWM8BPLdvH3Fh7jHkpl7L4RmWcqtSRoiiKMiZNqCk+RVEUZeJQAUpRFEUZk1SAUhRFUcYkFaAURVGUMUkFKEVRFGVMUgFKUYZBCGG0VKM/a6lU/a9CCI1l31whxP/2c268EOLuKzdaRRlfVJq5ogyDEKJVSult+TkUc0WQr6SUP3Pg3KXA96WUa0Z0kIoyTqkrKEVxEillNbAR+LYwW9qjn84Sy5XWCUufHR/gaWCRZdsTliuqL4UQxyxf11rOXSqE2C2EeEeY+3JttlRRQAgxTwixz3L1dkgI4WMpSvobIcRhYe7h9cho/Z0oynCMy1p8ijJWSSnzLVN8l1d7/j7wLSnlV5bipp2Y++pYr6CEEJ7ADVLKTksZrNeBi40XZwHTMNfr+wpYIIQ4BLwJ3CmlPCyE8AU6MFd+aJJSzhNCuAFfCSE+luZWI4oybqgApSjOZ6+c9lfA74QQm4EtUspSO1W3dcAfhRCZgBFI7bHvkJSyFMDSsiEeaAIqpJSH4VJ/MSHEjcAMIcR6y7l+mOsPqgCljCsqQCmKEwkhEjEHl2rMHX4BkFI+LYTYjrlW3AEhhL2q7E9griE4E/P0e2ePfV09fjZi/t0V2G/RIYDHpZQ7h/FHUZRRp+5BKYqTCCFCgOeBP8rLso+EEEnSXG3/f4AjQBrQgrlV+UV+mK+ITMC9mNvC9+c8ECmEmGd5DR8hhAuwE/gXSysKhBCpQgiv4f8JFeXKUldQijI8HpYpNx3mZo6bgN/ZOe57QojrMF/9nMPcldgEGIQQJ4F/AH8G3hVCbAA+B9r6e2EpZbcQ4k7gOUsLhw7MbRxexDwFeMySTFHDGGglriiDpdLMFUVRlDFJTfEpiqIoY5IKUIqiKMqYpAKUoiiKMiapAKUoiqKMSSpAKYqiKGOSClCKoijKmKQClKIoijIm/X9dHAPqph2K2AAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "source": [
    "Noticing that some points are out of range of cross-swath along longitudinal axis, but they are still projected to the swath profile. If it is not desired, user should delete all unwanted points before the analysis."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}