{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playing with seismic data (INTERACTIVE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proof of concept to show how to setup simple way to interactively scroll through 3D (seismic) cubes.\n",
    "\n",
    "**note May/2024** I will not bother with the ipywidgets anymore since I've found that streamlit makes it so much easier to make redistributable apps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import segyio\n",
    "import xarray as xr\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training images\n",
    "\n",
    "(from <http://www.trainingimages.org/training-images-library.html>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maules Creek 3D SGEMS dataset (3D grid representing the hydrofacies in an alluvial aquifer in the Maules Creek valley, Australia):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawdata = np.loadtxt('Maules_Creek_3D.SGEMS.bz2', skiprows=3)\n",
    "mauls = xr.DataArray(rawdata.reshape(80, 200, 340), dims=['Z','Y','X'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a function to plot seismic data (I'll show later on a quicker way to make plots):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_3_faces(cube,x,y,z):\n",
    "    opt = {'add_colorbar': False}\n",
    "    f, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 5))\n",
    "    cube.sel(X=x).plot(x='Y', y='Z', ax=ax[0], **opt)\n",
    "    cube.sel(Y=y).plot(x='X', y='Z', ax=ax[1], **opt)\n",
    "    cube.sel(Z=z).plot(x='X', y='Y', ax=ax[2], **opt)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_slice(cube,z):\n",
    "    nlev = np.size(np.unique(cube.data))\n",
    "    opt = {'add_colorbar': True, 'robust': True, 'levels': nlev}   \n",
    "    f, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))\n",
    "    cube.sel(Z=z).plot(x='X', y='Y', ax=ax, **opt)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XXnlFDz30kP7wD/+w+dr48eP17rvvaufOnX223bFjR2zr5PLly1Wr1Zr/XnvtNV/FBoBcUVcCgJk77rhDI0aM2G8Y+p/8yZ/orrvu0qOPPqrLL79cf/3Xf61Pf/rTkenQQAnAha4gCIKiC3HNNdfo7/7u7/Taa69pyJB9I95rtZrGjBmj73znO825OBs3btT06dOt5inW63VVq1XN0YI+gdRstc/BSBMwysV6haZ5FSGqpdB3K5evQFy2c5PizkFc8KA4NsF7bMpXxpbHqPK5vobqu/Zo1LTNqtVqqlQqztLNQx515VubpuqjR53obR9czWdLqk/Tfudcp5Elnyg2dZOrstoEUyurNHW0yzySFHkcw8rdG/RordaUtq7s6uraL5Baq+nTp+vDH/6wvv71r8em881vflOXX3653n77bQ0fPny/98Om4kyaNMn4vtLXvUmRc4PDArGmCUqchWmPsqs8XP92xeVVJi7PX1n30UTccTC9ryx8TvfevXt12223adGiRc2bSEmqVqu65JJLtGzZMo0ePVqVSkVXXnmlZs+eTWAgAAMOdSUAmPm///f/auPGjfrud7+buO2sWbPU29urX/ziFzrqqKP2e3/48OGhD+MAYKPwh+6HHnpIr776qi6++OL93vvqV7+qQYMGaeHCheru7ta8efN08803F1BKACgWdSUAmLn11lt10kknaebM5J61Z555RoMGDdLYsWMz5VnEcp9hozB99yaa9tTajgh1ffx8j350Le8e8CIijGe5PvtDRPTCH7rPPPNMRY1wP+CAA7Ry5UqtXLky51IBQLlQVwIY6N5++2299NJLzb+3bNmiZ555RqNHj9bkyZMl7Rv+fffdd+t//I//sd/n169fryeeeEKnnXaaRowYofXr12vp0qX6+Mc/rlGjRuW2HwAGnsIfugEAAIAkTz31lE477bTm38uWLZMkLVq0SLfffrsk6a677lIQBLrgggv2+/zw4cN111136ZprrlF3d7emTJmipUuXNtMBAF946AYAAEDpzZkzJ3LET8Nll12myy67LPS9E088UY8//rjzchURBC9smG2RgdXSDBU3HTLsY4mzqLKEveYzgFqWz9mWy3QfG+fTx3Xdeq2kSb/9M0kBMss0HJ2HbkPtJy1s7orPSiMusnMUmwsz7LO25W3/gjY+n1fUbBdRzFvTsE3HZnvTY2t67NJGPy4qWm5Y/q0Vf9j3zUVZ502cqd6gR9LmzGn1V+dOm6EhXW7TbD93tjcyadZQDbvGXEWvtllVwKW4+sD3jUWa9KN+J5N+X1x916Ouu6j3XchybeSx2gcAoBg8dAMAAAApdfpyeklMGv2z7GtU72cePdxh77k6b77Pf1iPtItl21o7zYpsnEwSVr7W/S9TL7ckDSq6AAAAAAAA9Ff0dAMAAACOue4pLKrnLmk/0u5n0jSLsCkhWaa6mCzLFTXvuWy9pg1x02iypJP3dJc0o0Xi5qgnpevSudNmyGTaIj3dAAAAAAB40hUkhYHscPV6XdVqVXO0QEO6hhq1Vpm0kqQJmpW2xShrK42r1kfXouagZA1SlKYl08UcGBM+Wg6j0jRpmW6VtgXYtIxR8mpJbZShN+jRWq1RrVZTpVLJJe9O0F5Xtst67pM+n/Y6M6kHTOozl9FabdNJU98W0Zofx0V5yjT/1eQcxm0TVb+aXotxecZ9prU30OR4xpWHujJcUl0Zpah7QBd83R/EBSV2GbW7PY+oNLN+Z5LyyJJvp0uafx3G5dxvF6LKU9+1R6OmbU6sK+npBgAAAADAE+Z0AwAAAJ5k7bW1GSnkmulojjQ9tibrRrvsBTaZH5yUr+kSwTbnrDXPvJbZzZvtUqGNbfvTqAF6ugEAAAAA8ISebgAAAMAD373cjW189Xb77JlMyrOI9bJ9bdsq6pi6GD3gk00MobTXY3uPuElMIJNRAy5kPR8D5qF79abnVRkxWFL2QF2N90zTyXKCTC8Y10F18v6SmwZVMxl2Y7LvtttkPR7tFYLL9OLet/2xlOKHAIUdk6QgdC6GYaXdvgwBaDpNa10ZxjZQU+sPX9QP8wNbn3W2zEl72kllavx/XBldlslke1d1mG2+WdJ2UR6bG22bQGe+blzbg5c1/j+qLElptdfbJsvhRL2f9L0w2W5fcKDY7AAAhgbMQzcAAACQlzx6ucsmTSNXmg6CIrlomDXdD19zvG1XAIp7L2luvk0Db2tDvGkHk+17abg49szpBgAAAADAEx66AQAAAADwhIduAAAAwMLqTc/vN+S0da583kPLfcUt8D3EPWmIcBmGk/tQ1NSBpGPq8ngnTTVoXF9hcU3643nnoRsAAAAAAE+6giAIii6ET/V6XdVqVW9tmhobkbedbQuLq2UDTKI+m0Rstf2MSyZRWtN+1jY927RtogibRtg1ydM2am/ctp3eOmgakThpmyi9QY/Wao1qtZoqlUqqMvZHaevKKEnfHZf1VFg9avvdc1WWNFx9Z4vsOXEtr33JsqyNr7rW176bBrhqbLcvevlm6so2jbpyjhbo4W0vpAp05XMpKJfXZZaVccq0yojrAGQul0WzkVe+nfj754vJsTCtK+npBgAAAADAEx66AQAAAEvtvXQmvXY+e7ldCxtF5LIHuyy9nD568F3lV0S+ZTkvRXP9PeWhGwAAAAAAT4YUXQAAAACgk6ze9Hwz/kWZenV9xhtozUPqnB7RuNEFjX3IawRC2mPWXr6wWAI28YFM8nOVVlIeZeJzf3nobpHl5Jt8GUzySROQIynNRgVsWr7W930EwmhPO+uXLqqibM/HVeCzqG3T7k/r9lHHPSzNvINh5FU5hh2DsCF8cecc+cvy3Wl/PemmMWyZnrDvUdpyNN5r/U53wnDQrDdJvm6y0gR9TPtb2Pis6WfSBqRsvz7a00obUNR0qZws5Y76fPs+9QY9kjYnlgUAkIyHbgAAACClpIZClw13JmmVpSfaZJ3mPBv0kzo04lYUcpG/L2liC9ikHXc9dUKDtCnf+8KcbgAAAAAAPOGhGwAAAMggLLK3iykbrXOObdPy3XPX2Ocs+eTVU5omHxc9xqZTRmzTlPI5dq15tJ7vqPNuur9Fj8Jol8ex5KEbAAAAAABPuoIgCIouhE/1el3ValVvbZrajDLZzlVri4u5KWkCxmQVFywobn5L2VqpGqLOQ9Kxtd2fTpnrI7lpZc+aTpr8WrnKu75rj0ZN26xaraZKpeIkzf6gUVfO0QIN6RoqyW5eYNbgXT6krZOzBF3MK8Bf+75l/X6kmcuYdzBHW77XwrUJfJmUdpZrJCxYapp02z9PXRnO5L7SRJp53j6C7UZxWae7vv+y5fq75yPIcFmlHSGQNtijqazPXHH1pi3TupKebgAAAAAAPCF6OQAAAJCjpN7XsPfzmKOdRpYewzKP9Itisr/9oZdbchtLwPXoXNMyRI3kynskFj3dAAAAAAB4wkM3AAAAAACeMLwcAAAAcKwxDNlmOHLWJbjC2ATFjBM11Dgpfdugg66YHMe0xyaPYGGdJGwIt8/g0jbpu5r6kPV7OWAeus+dNkNDuoY6q3jC5BH92jRarE1ZGhWHSWVoM4fCJF8TtlEibSp911Ers0Yj9SWqMrT5QfIp7ZqORUZGHghs65FWnXJu0pbTpl5ycfMRlnd7XWZ7Axj3e2j7GRfRfDslInB7/u0RpX1HlW7k03rOXTzM5Ln2LwAMNAPmoRsAAADIi2nAptZGElfLGMWVp72Bpb3TIqlzImzpMpNlZ1u3KUMvd/v2aXq7s6ZhqsxL9qbppJPSH/PW71WexyLrd5M53QAAAAAAeEJPNwAAAFCAuF5C30P9k6bj2Yhb4qy9J9+3oqZIuNq3PMrvan59ln1O+qzJdNGwEQC+lr7Lel7o6QYAAAAAwJOuIAiCogvhU71eV7Va1VubpqoyYrCzdPNszUoKPGMyjyZPRbXMlk3Wc+ta0tyXsPL6nFvWSN+E7byduDLXd+3RqGmbVavVVKlUjNPs7xp15Rwt0JCuoc7S9RHd1XRuW9pglT6uedP5bbbp5TU/Monr+i6vqMB5lTttejZ5ug7WR10ZLum+Mmy+c5SkQItF9Ni66OXuxHvQtPtdRJ0kmT0b2Kbt6jN5yvMeNSov07qSnm4AAAAAADxhTjcAAACQQZYe4qjRO6Yjb1yO0HERxbsoWY5BJ+93u7RLrJZtuUCTUW15xT9wkT493QAAAAAAeFL4Q/e///u/6+Mf/7gOOeQQHXjggZoxY4aeeuqp5vtBEOgLX/iCJkyYoAMPPFBz587Viy++WGCJASB/1JUAUB7nTpuheRNnNv/ZCvtsXG+eSaRnl/GG0vbsZflsWq15pj0nRfda2x43022LOB9xGuUJ+9f6vtT3mk67H1njx7g8foUOL3/rrbf0wQ9+UKeddpp+8IMfaMyYMXrxxRc1atSo5jZf+cpXdNNNN+mOO+7QlClT9PnPf17z5s3TCy+8oAMOOCDX8tqeOJPAElm/5CZB1PIOgmATRCRNmp0iasiL7fnJS1h5o4KrtUoa/mMa9MqkbLbbh+XdG/RI2pyqHEUpU10Zd736qOOSvh8+6hhXQY3SfNejPuNyaR+b8rTn4SLITx6fbxVXB5ucI5sgUSbHynXwJR+/j/MmzixlXfnYY4/phhtu0IYNG7Rt2zatXr1a55xzTvP9iy66SHfccUefz8ybN0/3339/8+8333xTV155pb7//e9r0KBBWrhwob72ta/p4IMPzms3AAxAhT50f/nLX9akSZN02223NV+bMmVK8/+DINCNN96oz33uc1qwYIEk6R//8R81btw43XPPPTr//PNzLzMA5I26EgCk3bt3a+bMmbr44ot13nnnhW5z1lln9akrhw8f3uf9Cy+8UNu2bdODDz6onp4effKTn9Rll12mO++807o8SQ0opg0ipmm0drRk7QE0kbaBKO+VWdrzzdpAGpdXWEOd78blMGWKeO+qDEmdPo3XTY5347tShuPRUOjw8n/5l3/R+9//fn30ox/V2LFj9b73vU/f+MY3mu9v2bJF27dv19y5c5uvVatVzZo1S+vXrw9Ns7u7W/V6vc8/AOhk1JUAIM2fP19f+tKXdO6550ZuM3z4cI0fP775r3VE0L/927/p/vvv1z/8wz9o1qxZOvXUU/X1r39dd911l7Zu3ZrHLgAYoAp96N68ebNWrVqlI488Ug888ICuuOIK/cmf/ElzaND27dslSePGjevzuXHjxjXfa7dixQpVq9Xmv0mTJvndCQDwjLoSAMysXbtWY8eO1VFHHaUrrrhCb7zxRvO99evXa+TIkXr/+9/ffG3u3LkaNGiQnnjiiSKKC2CAKHR4+d69e/X+979ff/3Xfy1Jet/73qef/vSnuuWWW7Ro0aJUaS5fvlzLli1r/l2v17mZBNDRqCsBINlZZ52l8847T1OmTNHLL7+sP//zP9f8+fO1fv16DR48WNu3b9fYsWP7fGbIkCEaPXp0ZANld3e3uru7m383RgWt3vS8pMHO9yFquHR7DJ+8hs1G5eMjzoQLPo+LyZDurPGfbD7nI4aSjSxxerIu75aUVpmGlTcU2tM9YcIEHXPMMX1eO/roo/Xqq69KksaPHy9J2rFjR59tduzY0Xyv3fDhw1WpVPr8A4BORl0JAMnOP/98/e7v/q5mzJihc845R/fee6+efPJJrV27NnWajAoC4EKhPd0f/OAHtXHjxj6vbdq0SYcffrikfYGCxo8fr4cfflgnnHCCpH0tjE888YSuuOIKq7zOnTZDQ7qGSnIzwd+G61a/NNGD4z6TJqKuqbStcD4j8ubdCmuyzEfeLdYmQVtMtnXxvk9Ft7i7kmddaar9unUVSCbs+ovLJ8t3JynoTntPQtz2JscjTQ+Iz2s4qX52EX3dhm2PWpY047T3LqYVd3x91/kuvif1XXs0apqrEhVj6tSpOvTQQ/XSSy/pjDPO0Pjx4/X666/32aa3t1dvvvlmZANl3qOCWuuSInq2TZTpt7X9eJkEU3O5tFonf96WTWDArM8dpsr0vUhS6EP30qVL9Ru/8Rv667/+a/3e7/2efvzjH+vv//7v9fd///eSpK6uLl111VX60pe+pCOPPLK5DM7EiRP7LBEBAP0ZdSUA2PvlL3+pN954QxMmTJAkzZ49Wzt37tSGDRt00kknSZIeeeQR7d27V7NmzQpNY/jw4ftFQAcAW4U+dJ988slavXq1li9fruuuu05TpkzRjTfeqAsvvLC5zac//Wnt3r1bl112mXbu3KlTTz1V999/f+5rdANAUagrAUB6++239dJLLzX/3rJli5555hmNHj1ao0eP1rXXXquFCxdq/Pjxevnll/XpT39av/7rv6558+ZJ2jct56yzztKll16qW265RT09PVqyZInOP/98TZw4sajd6qOTeu7KJG6JqDxHnObx+aKYlrtMIyHKpNCHbkn6nd/5Hf3O7/xO5PtdXV267rrrdN111+VYKgAoF+pKAAPdU089pdNOO635d2PY96JFi7Rq1So999xzuuOOO7Rz505NnDhRZ555pv7yL/+yT0/1t7/9bS1ZskRnnHGGBg0apIULF+qmm27KfV8ADCyFP3QDAAAASebMmaMgCCLff+CBBxLTGD16tO68805nZfIR2wJmTGP4uOyhdXmObMtXdu0xUPLar0ZeeeaZxoB86A4LeNN+klwG6yli+YKw7cq6D61cDEkxOZ+umATaMQnmERVwx2UFYnsMTLePC7iW13mAO6s3Pa/KiORlcFwF+YoKMBUWwCyMaf3dul37+7ZL3+S1VE6e3xeTvOLqIx9ltbkhdXVOTK+7Rrny+g22VeYbTwAYiAbkQzcAAADgkouI953QYOJ7ZQXTMrT/bdL4b5N+lhWB4soS1/DbCefflMsOzDhh0f59Svs9LXSdbgAAAAAA+jN6ugEAAICC+Z6OWNR0NZeSepmzlK21J7p9eottummPdaeMdkgjzx79LMcx7ly3Xw82edDTDQAAAACAJ11BXBjIfqBer6tarWqOFmhI19DM6fkKcmWLCJn7y2vuiG05JDcBxaIipIa1wqZtmc1SprD8fJyDqO+d6b72Bj1aqzWq1WqqVCqui9exGnXlW5umGgVSsxF1booItOdiLmKWwF6dKGmupI81cE2vGRsueqryrlPb83bZc5OUVn3XHo2atpm6sk3rfeXD216I3dY2IGknKKpui/q9cH3s4npjs9732xy7TromGspyved5niTz+0p6ugEAAAAA8IQ53QAAAIAlV72tPkdO9oc5wnFroZvsn80xMIlMbsLF3PI85B053ddoHtM8bD7velQHPd0AAAAAAHjCQzcAAAAAAJ4wvBwAAAAoSB6BpVzkk3cQNZNgm0kB1vIeXu8qUKfvcoeVM0swXF/Xhun0gQYXx8zXvgyYh+7Vm553EpHX9kTYXghJF5erC8H2IpbSR25PWuvQ1xckzZcwa9mizk/7MUhzjm1edxX5N+l6az/GriN9homb2xW2Tbt9EXmdFqnfM6l30s6Dy/smrv2a9BVNtsg1Wl0fU9O6J209a/N+luPj+ri6iISflEcjn/ZtXBzrpLL3Bj2SNhvlAwCIN2AeugEAAACXig5SlrTElUs+Gpri8mpl2lsf1qhaRK9xVr6WXI3LKyxIXVKZ0nRGRu1b2OtR28Y1BrsIpObjnDKnGwAAAAAAT+jpBgAAABxp9Lb57hmO6/21mXoWlUbeovYjbPpamnR99HjnOUUqy9xlk6krSddrnlNcfZehCPR0AwAAAADgSVcQBEHRhfCpXq+rWq3qrU1TYwOpZW39StPy4iqwWNpWHxdzM8LSynIsoti25mbJKyzNNBEc01xPpoHBXLX0uSyj63x82RdIbbNqtZoqlUrRxSmNRl05Rwv08LYXmq+7isQax8e8w7h0y3Q9xgmrS1xFIS4TV79hjd/xvIJ1FqV9H331pvYGPVqrNdSVbVrryiFdQ62D0oYpw3xjV4EQTcVdt2Wv33zWBzZzmLP+vpreW/qqY+ICNGfJz9X1Y1oG07qSnm4AAAAAADxhTjcAAABgybZHLeuymmG9ga579Uyjg2ft+SzT6BFTeZQ5aZnduG2Tto/6fLv2nm2fa3CblMdU2tGbeY2soKcbAAAAAABP6OkGAAAAPIjrRcvaU17k3GfX83zL2ktZVI+8bY+1y/NR9lEILmN35PkdGtAP3XksAm+Tf9EXTJZ9cznMKG6YjIuKJa6SjkrXJoCFj0B47WV2EbilfTvTcqcZxpQ0XCppuFyWZTJgz1c9ZxMQMMtyImW/YUhiMuQubVCdIkXVvWkDRZoel7wDpdr+vrR+zuQ70bo//eF6B4CBYEA/dAMAAAC2Vm96XlL0qjjwrwxR38ui08qbRad2xDCnGwAAAAAAT3joBgAAAADAEx66AQAAgH4mzZBjm888sPXZjhrea2ogDdVGtMb17eoa56EbAAAAAABPuoIgCIouhE/1el3ValVztEBDuoamjnwdFl3V9XIJUXn5NpBb9KKiaadJw5SL4+0qenlreu0RxtvTShth2CfTY99a1t6gR2u1RrVaTZVKxVfROk6jrnxr01RVRvgJDmQSvdlFOknyrGOjyhpXBpvj5GvJnDTljkvH1bnNWmeblMM0bZt7gCz1Z1FLIlFXhrO5ryy6FziPextX9wJFHyupHPc1tlzcx3aqIq6ZsONsWlfS0w0AAAAAgCcsGQYAAACkYNrLbbIGu0tZez5NR6lkHfXZ+LztqBiXOrGXuP04lXE0om++RnrFyTKygJ5uAAAAAAA8oacbAAAAcMim59ZlL6+LmBetaSSVrXVb217vRppxafvsyey03mCbY+Er7lRe2mOXlE1r+eq79mjUtOTPDLiH7jTDe8Iqn6TPZClLWAWWVDabcpTp4jWpFFxVuGH5uDgW7ecg7Jz4DPrj8nNS8nWZ9CNpkpYr7d+VKGkqR+TPZjhjUhom29qwTddlAKP2tEyvexum5TXJO6mutb1xTHo9bTCzrOKGFcedMxt5DJ2MyoO6EgDcGXAP3QAAAIArcR0INg0uaXt2s/QIR/Vsh+VhUg6bfF1sk0aenU9pz6ervMvU0WbKpFG1DNHubTGnGwAAAAAAT+jpBgAAAFJy2Zto02tt0kNtk0YWrntVffRwdkqvb9nnM/vS3yOw09MNAAAAAIAnXUEQBEUXwqd6va5qtaq3Nk1VZcTg1Om4bHEzDeYWtp1NS2JUi1FScDbf0rRcZS2rj9aypBY50zkpJmXzfa7KHOSnwdVcsX3BgTarVqupUqm4KFq/4KqubJc2UGJ7HWUbwCvPOsNl67xNkDiTzyQpqichrMx5r2OcxPbY+J4/mab3M8txo64M16gr52iBhnQN9ZKHz0CNZZEmGGva67mI41WWOcedeK0krYVdlmPbYFpX0tMNAAAAAIAnzOkGAAAASs71GtxFpSGZzV13OYqnLCNRBiLbY9+JvfMm6OkGAAAAAMCTQh+6r7nmGnV1dfX5N3369Ob777zzjhYvXqxDDjlEBx98sBYuXKgdO3YUWGIAyB91JQAAQOcqvKf72GOP1bZt25r/fvjDHzbfW7p0qb7//e/r7rvv1rp167R161add955BZYWAIpBXQlgoHvsscd09tlna+LEierq6tI999zTfK+np0ef+cxnNGPGDL3nPe/RxIkT9YlPfEJbt27tk8YRRxyxXyPm9ddfn/OeFOeBrc9aD4VufKbxufa/k7aJ+udbWJl85YH/EnY88jpGaYamz5s408nye0kKn9M9ZMgQjR8/fr/Xa7Wabr31Vt155506/fTTJUm33Xabjj76aD3++OP6wAc+4LVc7dE/XV0sSSeydY5LVJ6mUS2j8uq0yOVhn0vzg+FL3HFu5B2Xv0nkYx/nzNWcrAbf19RA/1HLq648d9oMo4i8JhHETURd23FzwFo/k7VuiIuSHsfX9Zj2hqEhbdTjvL9fZYtUHiYpgm473/MQW39T4qT5DoTpDXokbbZKy7fdu3dr5syZuvjii/drWPzVr36lp59+Wp///Oc1c+ZMvfXWW/rTP/1T/e7v/q6eeuqpPtted911uvTSS5t/jxgxIpfyAxi4Cn/ofvHFFzVx4kQdcMABmj17tlasWKHJkydrw4YN6unp0dy5c5vbTp8+XZMnT9b69esjbyS7u7vV3d3d/Lter3vfBwDwjboSwEA3f/58zZ8/P/S9arWqBx98sM9rf/u3f6tTTjlFr776qiZPntx8fcSIEaGNmGUQtqyrz2VP0y7rGPd+UUv8tfe8+1SWxsEkvgPIhYnqPHLBR/C9xjGK6u12da4LHV4+a9Ys3X777br//vu1atUqbdmyRb/5m7+pXbt2afv27Ro2bJhGjhzZ5zPjxo3T9u3bI9NcsWKFqtVq89+kSZM87wUA+EVdCQD2arWaurq69qsfr7/+eh1yyCF63/vepxtuuEG9vb3FFBDAgFFoT3dra+Xxxx+vWbNm6fDDD9c//dM/6cADD0yV5vLly7Vs2bLm3/V6nZtJAB2NuhIA7Lzzzjv6zGc+owsuuECVSqX5+p/8yZ/oxBNP1OjRo/Wv//qvWr58ubZt26a/+Zu/CU0n66igtMsl5dU7WURPqA/t+xHWO2m7nyZpdoKiz7HLvH1Nkc3jXBc+vLzVyJEjNW3aNL300kv68Ic/rHfffVc7d+7s00K5Y8eO2CFBw4cP1/Dhw3MoLQAUg7oSAKL19PTo937v9xQEgVatWtXnvdbGxuOPP17Dhg3T5ZdfrhUrVoTWiStWrNC1117rvcwA+reuIAiCogvR8Pbbb2vy5Mm65pprtGjRIo0ZM0bf+c53tHDhQknSxo0bNX369Nh5iu3q9bqq1are2jRVlRGDm6+nmS9j0+oR1lqSpoUtDRf5NI5PVKuPTfAi361rYYHIfLRQmVwzYa2JNq1nvoI45dliXlaN81fftUejpm1WrVbr0/vRSXzWlXO0IDaQWtL1nOZac9UjYaqoYGlpg76l/f5m+Q0p+3c5TJoym+5rHr9jScHlXOx3XBDCdr1Bj9ZqTWnryq6uLq1evVrnnHNOn9cbD9ybN2/WI488okMOOSQ2nZ/97Gc67rjj9POf/1xHHXXUfu+H9XRPmjQpsa4sisvAv2Hzy8vCNLBg2GeSlHF/XegPIxoaynSOTO8rC+3p/tSnPqWzzz5bhx9+uLZu3aovfvGLGjx4sC644AJVq1VdcsklWrZsmUaPHq1KpaIrr7xSs2fP9h65HADKhLoSAJI1HrhffPFFPfroo4kP3JL0zDPPaNCgQRo7dmzo+4wKAuBCoQ/dv/zlL3XBBRfojTfe0JgxY3Tqqafq8ccf15gxYyRJX/3qVzVo0CAtXLhQ3d3dmjdvnm6++eYiiwwAuaOuBIB9o3xeeuml5t9btmzRM888o9GjR2vChAn6b//tv+npp5/Wvffeqz179jSDSY4ePVrDhg3T+vXr9cQTT+i0007TiBEjtH79ei1dulQf//jHNWrUKOfldTESKE0+LtMsU4+itP8xNOmRtznuZdtfl4qe2+2Sz/nXvkZ3FPrQfdddd8W+f8ABB2jlypVauXJlTiUCgPKhrgQA6amnntJpp53W/LsxP3vRokW65ppr9C//8i+SpBNOOKHP5x599FHNmTNHw4cP11133aVrrrlG3d3dmjJlipYuXdpnnjcA+FCqQGoAAABAmDlz5iguFFFSmKITTzxRjz/+uOtihTKJG5Q25k9YHmFc9diVZV53XCydqF7cqHXETbfNU1LsoKLLZypuhEdSfIoyjErwle6Ae+iOWvjc9LOmJ6K1Is1juYEsw0WiglGEDeGxSSOvISxh+dhWVEnnNm4Jj/bPRVXwJmUzHRple01F/bC7DkCSJiiRaSA826Ap/WUIVd5Wb3q+T9DJJK3nxSSAZBGBBG3Tz5JHUr4mdVGaskTVg+11j2kdXcYbUim5zGl+g0wClLkOKBWWVly9l/YaisujPw01BYCyG3AP3QAAAIAvYY0dcQ1jtnO/bTuAsmptRC1D41tD67zzqGMW1jBc1kbFPHrgs46uCEvPplMkqZHTpDEw6/Eo6joelHuOAAAAAAAMEDx0AwAAAADgCcPLAQAAAAeihq0mDTFv/9sk1kDefCzNZJK+bfyJLNsVLa/zm2WYeXvcqqQyu4yJUZY00qCnGwAAAAAAT7qCpPUVOly9Xle1WtUcLdCQrqFGn3EZnTRLulGtnWkjhBcRWdxEXi1O7YEebI+d78AOcbJGXfZR9rg0oyImm+xH2vNj+pneoEdrtUa1Wk2VSsU4j/4ua10ZFzk6rpXb5NpMswKErzouTT1uk0YeAX58HJs0KyGkieTe/rm8f8vS9MS5+KxJOqZM86OuDBdXV7qqH8rWyx13/5SlTCb1ne0xSqMMQdSyiLvPcvGb6Xr1hjTlKPM5qu/ao1HTNifWlfR0AwAAAADgCXO6AQAAgJTS9sK1907m2aNoo1GusF7ULOUN67E26cHO0svt69jaLJvlmotlwOJGqrW/70tcL3cZvxe26OkGAAAAAMATeroBAACAFFxHU+603ry8yzvQ4xNJ+8/hjprjnqVMRfXYd+KcblMDJpDaW5umqjJicOR2aS7WsOEXWYKpmJSpv3EdwKtoeQW4MSmHr+A8WQKcRQXSch0UJQ7BgcKlCaTWKmswnbCAab6vYZPtbet006CFWa93m6B0aQLY+VLWG+Y04gIXlXHfbL+jpsGBBpr2urI/PAyY8BXkMUs9n2W5q07m+6G7yKHceQQTdc20rqSnGwAAALCwetPzsZ05/U3cahVZ003bSBW1ukV/7S0NazBOWg0jzT4XvRZ8f5i/HYY53QAAAAAAeMJDNwAAAAAjJmtpN6Kdu5h22d7L3pruA1ufbf6LSq/9/U5nE/G9jFNdkvSnc9WKh24AAAAAADwhkJrsAuWEzR2xSScpDx8tUjYtRlnm1TQ+nyYgV9KclE5i20JXtv1zfV274qpcBFILV2QgtTTymvOVd6C/stSFUeVwMVeyLHVKJ7OtD9N8VwikFs40QG9ZuVxPOiyIYNzrYZ+zrWvaJX2+03tNTepcl0Gci2b6bFCmPEzrSnq6AQAAAADwhOjlAAAAgIVzp83oyCXDfJY3agSSyZxj1+XqtPMSJmnEgG1aZT4mUXEBXJU56hrMo2e9gZ5uAAAAAAA84aEbAAAAAABPGF4OAAAApFD2YbtJ0g7vDhuua5tG3PZRQdnCypGUb1mCqpleKwMp2KSL82ubdlHXwYB96La9oKPmGrjkKvpg2Pp9phdY1gsxbWVSxgrG5Ze8Pcqk6Y9JEWzL5CpqsU3U0yxrbu6LMpnqowPC6k3Plzoib+v1kRQ5tz3qeNprtSzfUx/1hsub5Dz5npdaNNuI+UlR7zv9wRAAOt2AfegGAAAAiuQ7kFNU+mEdNEnbhAkrc95Lj6Ytu2/t5TLtkW+X9Lk8l7VMK+uSxK7yK7LxkTndAAAAAAB4Qk83AAAAYClrr1kevZONXlDT3lBX0xrCyhCXl+1SY2Fczw/OOi0j6/Q708+XqZc77JhlKV/acxA23SypTCwZBgAAAABAh+oKgiAouhA+1et1VatVzdECDekaWnRxnEgKmJLmc0np5TUHwrQ1rFMCwpSl9THP45UUCK19mzQtuVn2Z18gtc2q1WqqVCqp0+lvGnXlW5umGgdS81k/lOW7k1bauXth6US1yNv0Xpnm5ZuLspY9iFraKNA2PX1pe8Bs8ugNerRWa6gr26SpK13I2uvqq041vSc1qaui0sprREAcXz2jJt/RtGm1K+Leucjfp6R7FFfH3vS+kp5uAAAAAAA8YU43AAAAkJHriMkmEcazlCsr25GXSb3dUfuXdUSPSVTzNCMJXI0+cNUDbXJ8O2VEU1SaNuXP8j3xcZzo6QYAAAAAwBN6ugEAAAAL506bYRwryLSH0aaH0LRXznTequma3GkjoNvuW5r1wrNuF3eefKw97ro31UV0+k6IyG+Sd9Zo+Dbb9gY9kjYnbsdDd4dwEZTHZthE1vD+YfmY/OikqdDLGlQt74on70B57Z+1+RHLevORdC2F3WQ0XjOtHAeqc6fN0MPbXjDatnFck4YRur7ZTCPvpVeSbnRNr9+471Xc+7Y3rI3tXAUBc3k+fd4Epqmb4tKPO+5Jx8jkZjjqvJs+KJjm98DWZ3XGhGNiywMAMMdDNwAAAOCRacdD+2ei0rJJ26QxKakBPek911yu0d1+fOL21bRTpzUNlxHHi+5Ial0VoyyxA8LyiGrgdLHeuy/M6QYAAAAAwBMeugEAAAAA8ITh5QAAAIBneS3ZlCW/sCHu7fF2ih4CnUbW4dJp4iaYaD+2ZRhunhQzpAxMzk3Zyk9PNwAAAAAAnnQFQRAUXQif6vW6qtWq3to0VZURg41bPcoYPEDyu2RBlmUE0pTDZeCJtOnGReA2ifCeJkJwnLxbwMPyTYo47ftc+06/vmuPRk3brFqtpkqlYp1Xf9WoK+dogfEyOEUqY29RUhT3OL5a6rNG485T2Zaq8RnxPstvh8nKALYrPITpDXq0VmuoK9u03ld+9KgTrT8fFaQqbiWCNPeuYfc3We+7XH4nkvbL9N7UlIs61tfvjot72rL16hbB9T25KdO6kp5uAAAAAAA84aEbAAAAsHDutBmpPmfTy530XtS2jTzae4vnTZyZugfQppc3aamtxvuN/2/fPmx5r6ziRhK0/7NNI2u5XOQZlkbS/vQ3jeu7rL3+pXnovv7669XV1aWrrrqq+do777yjxYsX65BDDtHBBx+shQsXaseOHcUVEgBKgPoSAACgc5QievmTTz6pv/u7v9Pxxx/f5/WlS5fqvvvu0913361qtaolS5bovPPO049+9KOCSgoAxaK+BIDO5SM2T3t6SXNb84iinjVqepFM5pOnPYZR58OFpAjraeJHhClrT3LZFf7Q/fbbb+vCCy/UN77xDX3pS19qvl6r1XTrrbfqzjvv1Omnny5Juu2223T00Ufr8ccf1wc+8IFU+fn6UrtM1+RL7uqLk3a7RlmySLP8hEmeScstmFZ4URVq3NIOWeW1nEh7ucOuKx+B4kzKEsVVkKXeoEfS5kxpFSXv+rJIcd8938Fs4oY6+hoa6vv75eKYpfntiUvLVOs+FBW0NEra8mTZD5PfoKTy53ksXXrsscd0ww03aMOGDdq2bZtWr16tc845p/l+EAT64he/qG984xvauXOnPvjBD2rVqlU68sgjm9u8+eabuvLKK/X9739fgwYN0sKFC/W1r31NBx98cAF7BGCgKPyhe/HixfrIRz6iuXPn9rmJ3LBhg3p6ejR37tzma9OnT9fkyZO1fv36jryJBIAsqC8BdIqtW7dq4sSJTtPcvXu3Zs6cqYsvvljnnXfefu9/5Stf0U033aQ77rhDU6ZM0ec//3nNmzdPL7zwgg444ABJ0oUXXqht27bpwQcfVE9Pjz75yU/qsssu05133um0rGFcR+SOYtpBUabe5TzY7nPS3PQ0+afhu8HZNu2o6zipo2qgs5rTfcYZZ+h73/te5Pv/8R//oalTpxqnd9ddd+npp5/WihUr9ntv+/btGjZsmEaOHNnn9XHjxmn79u2RaXZ3d6ter/f5BwB5cl1XSu7rS+pKAD4de+yxzh9k58+fry996Us699xz93svCALdeOON+tznPqcFCxbo+OOP1z/+4z9q69atuueeeyRJ//Zv/6b7779f//AP/6BZs2bp1FNP1de//nXddddd2rp1q9OyAkArq57uRx99VOvWrdNf/MVf6Nprr93v/T179uiVV14xSuu1117Tn/7pn+rBBx9stj66sGLFitCyAUBeXNaVkp/6kroSgE9/9Vd/pcsvv1yrV6/W3/3d32n06NFe89uyZYu2b9/eZ8RPtVrVrFmztH79ep1//vlav369Ro4cqfe///3NbebOnatBgwbpiSeeCH2Y7+7uVnd3d/PvLA2U9PoVr4w9/EkjIBrTQWynZbZvH7eOe9xnTcTNJ09Ka6DMHbeOXr5q1SrdeOONOvfcc7V79+7UGW/YsEGvv/66TjzxRA0ZMkRDhgzRunXrdNNNN2nIkCEaN26c3n33Xe3cubPP53bs2KHx48dHprt8+XLVarXmv9deey11GQEgLVd1peSnvqSuBODTH//xH+u5557TG2+8oWOOOUbf//73vebXGNUzbty4Pq+3jvjZvn27xo4d2+f9IUOGaPTo0ZGjglasWKFqtdr8N2nSJA+lB9DfWc/pXrBggU499VQtWLBAH/jAB7RmzRrrYZLSvuGXzz//fJ/XPvnJT2r69On6zGc+o0mTJmno0KF6+OGHtXDhQknSxo0b9eqrr2r27NmR6Q4fPlzDhw+3Lo8Jm9axNEHC2rePy89HK11S0Ky44EImTIMNud43VwFrGqKCKKWZy5L0mbRzhtKUxWdEzbhjlFeLcyOf+q49GjXNf36u6krJT33ps65s5+ocuwqml/Xz/a31XUoXpNJFnjY9HLZB7FyULU2gxyKvj7ges8b+tK6RHMdFXTllyhQ98sgj+tu//Vudd955OvroozVkSN9bz6effjpbJp4tX75cy5Yta/5dr9c74sHb5Jo0va8tY+9wfxRV57ion9vPoev1yKPmd8elZRLwMW15XHNRx6cKpHb00UfrySef1AUXXKCTTz5Z3/3ud/sM5zExYsQIHXfccX1ee8973qNDDjmk+foll1yiZcuWafTo0apUKrryyis1e/ZsggIB6Agu6kqJ+hJA53rllVf0ve99T6NGjdKCBQv2e+h2pTGqZ8eOHZowYULz9R07duiEE05obvP666/3+Vxvb6/efPPNyFFBeTZQAui/Utd81WpV9913n5YvX67f/u3f1pe//GX9/u//vsuy6atf/WpzOYfu7m7NmzdPN998s9M8AMCnPOpKifoSQPl84xvf0NVXX625c+fqZz/7mcaMGeMtrylTpmj8+PF6+OGHmw/Z9XpdTzzxhK644gpJ0uzZs7Vz505t2LBBJ510kiTpkUce0d69ezVr1ixnZfE9R7W9RzRpBEhYD6qrKN6dKI9o4FF5Rr3XPgLFdHRi2qV+bUYNpVli0GXvdJEjicJGH6ctj9VDd1dX135/X3/99TrhhBP0h3/4h3rkkUdsktvP2rVr+/x9wAEHaOXKlVq5cmWmdAEgT77rSon6EkC5nXXWWfrxj3+sv/3bv9UnPvEJJ2m+/fbbeumll5p/b9myRc8884xGjx6tyZMn66qrrtKXvvQlHXnkkc0lwyZOnNhcy/voo4/WWWedpUsvvVS33HKLenp6tGTJEp1//vnOlzcDgFZWD91BEIS+fv7552v69OnNSg0ABjLqSgAD3Z49e/Tcc8/psMMOc5bmU089pdNOO635d2Ou9aJFi3T77bfr05/+tHbv3q3LLrtMO3fu1Kmnnqr777+/z6oP3/72t7VkyRKdccYZzdFBN910k7MyAkAY6yXDopZ8OOGEE7Rhwwbdd999TgoGAJ2KuhLAQPfggw86T3POnDmRjZrSvlFF1113na677rrIbUaPHu18/fBWeSx/lCZIa+sQ4bChxWUeQp5meLOJMu13+7kxHcKc9rjYfC7N8mG2wSht0m6V13DzuMDWpkEnrR66f+u3fiv2/UMOOcTZECLXzp02Q0O6hkrKFnU7TaTqtPNoovLwVUGkiW7dLq5saSNx20oTYT6tpPORNcK76ZydsM+5mANjm28Sk8jqLiO2h23XG/RI2mydh41OritXb3pelRGDU302y5xB31x838saSTWtNJG52z+fpRxp6iifvwlZ8mqtq13XmzbiVtxoj75fpu8nAPR3fkJIAgAAAP1cmiU6XTNtuI5r6CpTr2+esnYoZU27PwpbXtPXaAXfXH4vBjlJBQAAAAAA7IeebgAAAMCBPHrz4nrXTXq9k6ZDts7JRfSUjLRLcCXl1am9wnFspxGaToMxmfaVdbqlq+8CPd0AAAAAAHjSFcSFgewH6vW6qtWq5mhBM5BalCIXX2/Pv52LAEVRrUw+99VV8LS8WluzHos8gsWZtIC6mGOW1EJu01KZhs33IeqzYdv1Bj1aqzWq1WqqVCqpy9fftNeVrr5zNkHuorj8XqUNbmjynWqP2NpJvUU+5zVmzTdO0cfW5vouKuqulO447YvIu5m6so3NfaUvWa/7NPcQZeH7e5M1+nx/7KV2LY+A0K7r2/bnptb7zLs3Pm1UV9LTDQAAAACAJ8zpBgAAACy0Lq9YRLTyMvdG++R7pGbU6Ka4HuzW9/pDxG6ffF63ea0k0JrPvnW6zT5HTzcAAAAAAJ7w0A0AAAA4kGcPdNpIzD7SLcIDW5+NPN5FnofWcg3UEQlR0l6z7f9MP1MmDC9vUfTJCRvSYrKsg2m6Sa/5CMyRZgiU6wqqvQztARB85mWyfZo8TLdpHwKTRXu+Jvua5Vi074PNsSr6u9zJWodMZuVquF2aQJJR9WjawG4m9UbrNv3xhsvlkFZfAdTyHnZrE6zHJFhg2O9VXH42aZuUqz9drwBQJjx0AwAAABbOnTYjNHp5maPSp2m87oSGmLAG0bTnIW5/bdeEbn2tbJ0BRa6m4KKhL8v5tfmsy+uf4eUAAAAAAHjCQzcAAAAAAJ4wvBwAAACw1DpUNe8hxLaxeco2vNmHNDEPTD6fdjvb/IvUuH6LuI5N4gO5KFPRsVbo6QYAAAAAwJOuIAiCogvhU71eV7Va1VubpkZG5I1b7N5GHpGro9hGJ42KxOs7oIHLyLeuW3TDgkqUJVJvVu2thKb75SrYRF77bxIYpDfo0VqtUa1WU6VSyaVcnSCqrsz63Y677ooKZhIljzo5zcoRrlabSFunZSlz2HZ5R8sO2+8iA/n4kPXeI+zz9V17NGraZurKNo26co4WhAZSK1rSdy5tGp0s7e+Yy7qqTPWFVGwgtTB53kO6vr5N60p6ugEAAAAA8IQ53QAAAEAHiZrnWnSPZdmErXUfdYx8HbsynpP20UhFjdQtIq+wa8I2zzS95fR0AwAAAADgCT3dAAAAQMm1965F9dpmjfacd9yFPPnspU2KHxEl74jhWfmYD+4yjlKavNN+xqbMAy6QmosAW1nl8cUyKXNSOdIE97GRVyAgX8NEypx3UnDApKE1NmnGpVPUj0jc/hAcKJyr4EBF/mhGXd9S33J10s1NFNf1c97pZdUfzqGtuBtzH3U4dWW4IgOpmQZJc/kQ1+kP3Xkeh0586HZxfn0e47LX9Q9sfda4rqSnGwAAAOhAeTRwddqDt+3KGC4fGpMagMPk+WAZ9WCb5RwnHUPTh+n2crg+Lz7MmzhTvUGPpM2J2zKnGwAAAAAAT+jpBgAAAEqqiOHHrnq308x9zcrmeLk8rmE9xmnOXZZh1nE9xVHnwMW5se2hDjsuRU71zAM93QAAAAAAeDLgAqnFsQk+lTYImI0sLT5x8zPCWh1Ng6pFtUqZzgdx1aqVFCjJp6RW26KD9fkOcBf1PQh73VfrokkrcNR+EBwoXFxwoDy/22nkdZ016rk88/P5OdP0XLGd8+cij06LChyndV98HK/2NKkrw5kGUos7R3l8R5PuWbMGek0bXDXvezUX8ljDOo8AyHlyUdZOqbt7gx6t1ZrEupKebgAAAAAAPGFONwAAAGBh9abnQ5eiNenByzIarH30hq95ui5G87X3bJepJzZJ3nPQfW5fhPZI7g2N68dk1EN/W+qTnm4AAAAAADzhoRsAAAAAAE8YXg4AAACkkGWob5ph5u3bpg1OZpp31mG9tuVzEWwtS5mLGFZuMl0gzyB0rpgE5jXdp/4w1JyH7v8n7gSGvecygrVNBD9X0b7D1hGM2z4qImyeEdnD8m+VJbq8STT0qPxMfrjat2n/OylyvglXUXtNopPHvRaXhi9RZeu0H6gyab1+sl5HSfnYfiartNdF43NR362skbJt5z9mOT8m6bZytY6rSZ0R9/k4NnWUCy5X3zBN23bFENN8wn7Pe4MeSZsz5wEA4KEbAAAAKIzvZTbT5O2yLHnvl6vlF13LEvQuSyNfGTog4hpc0wSS68TebuZ0AwAAAADgCT3dAAAAQME6tQfPpbBezzx6azs9/bLl2+BzulJU+mVFTzcAAAAAAJ50BUEQFF0In+r1uqrVquZogYZ0DXXW4uOr5aY9j/ZWzzSBZKKkKa9JsDDbMoUFUvK9n0UHbPJ9nmz5CmJkm0Ye6rv2aNS0zarVaqpUKkUXpzTa68qBwFcgLB+/BSbp+g5smba3qZN6IkwUVY+lOb9ZglpSV4Zr1JVvbZqqyojBzdd9BBDNey5yUd/V9vtAF8FwbfL0IY/nhDJzESA4Kf2y9Hb3Bj1aqzWJdSU93QAAAAAAeMKcbgAAAMCRvEaPmaRjyscyiGn4XHM8Ly4jdXeiPHr5o5bXLMs1EIaebgAAAAAAPCm0p3vVqlVatWqVfvGLX0iSjj32WH3hC1/Q/PnzJUnvvPOOrr76at11113q7u7WvHnzdPPNN2vcuHEFlhoA8kVdCQDlZrs2tKseOR+9p1G9iK15lpFNz7zP3tis8RSyaI8HVUTPeiP/vPOOyrMs12uhD92HHXaYrr/+eh155JEKgkB33HGHFixYoJ/85Cc69thjtXTpUt133326++67Va1WtWTJEp133nn60Y9+lDpPVxehrwup/cKI+jvrUIr2L2TScWlcyCb7bVuesO2jKquw1/P6YQsrT1Je7X83Plu24UVZymNzfKOOR9b8TK6Fsh1zG3nXla6GFPr68TM9l7Z1g6shai5+Z5LqxVau65X2erb176QbybLc3BTJ9c1m2mMa93vUyfVhnCOOOEKvvPLKfq//8R//sVauXKk5c+Zo3bp1fd67/PLLdcstt+RVRAADVKEP3WeffXafv//qr/5Kq1at0uOPP67DDjtMt956q+68806dfvrpkqTbbrtNRx99tB5//HF94AMfKKLIAJA76koASPbkk09qz549zb9/+tOf6sMf/rA++tGPNl+79NJLdd111zX/Puigg7yUpT+vud0Jc2iLLqPPhi3TBuUiG9dcNjqbdviVvTGxNIHU9uzZo7vvvlu7d+/W7NmztWHDBvX09Gju3LnNbaZPn67Jkydr/fr1kTeS3d3d6u7ubv5dr9e9lx0A8kJdCQDhxowZ0+fv66+/Xu9973v1W7/1W83XDjroII0fPz7vogEY4AoPpPb888/r4IMP1vDhw/VHf/RHWr16tY455hht375dw4YN08iRI/tsP27cOG3fvj0yvRUrVqharTb/TZo0yfMeAIB/1JUAYO7dd9/Vt771LV188cXq6upqvv7tb39bhx56qI477jgtX75cv/rVrwosJYCBovCe7qOOOkrPPPOMarWa/vmf/1mLFi3ab76NjeXLl2vZsmXNv+v1OjeTADoedSUAmLvnnnu0c+dOXXTRRc3Xfv/3f1+HH364Jk6cqOeee06f+cxntHHjRn3ve9+LTCfLqCAXQ8zzHqZuO0Q3LGhY3sudxZWnNZ+yDoW3ZRJbKO/gba7yTLvcWtmHlksleOgeNmyYfv3Xf12SdNJJJ+nJJ5/U1772NX3sYx/Tu+++q507d/bpwdmxY0fssKDhw4dr+PDhvosNALmirgQAc7feeqvmz5+viRMnNl+77LLLmv8/Y8YMTZgwQWeccYZefvllvfe97w1NZ8WKFbr22mu9lxdA/9YVBEFQdCFanX766Zo8ebK+9rWvacyYMfrOd76jhQsXSpI2btyo6dOnx85TbFev11WtVjVHCzSka2joNr5bR+JagFy0gGYpk+u8imxFtDkW7UsqmKSd1MpWZMtunnyc46Rj4jLP3qBHa7VGtVpNlUrFWbp5K6KujBPXuxAnz4jzrurbpHRcReU3lddqGqZ5ttaVRSzhErZ0U5FBjVrlfW1kKUd91x6Nmra5I+vKV155RVOnTtX3vvc9LViwIHK73bt36+CDD9b999+vefPmhW4T1tM9adIkvbVpqiojBhuXKc9rPk9lq7tMfoeSVoJAsrTH0Nfzh+s8bZjeVxba0718+XLNnz9fkydP1q5du3TnnXdq7dq1euCBB1StVnXJJZdo2bJlGj16tCqViq688krNnj2baLwABhTqSgAwd9ttt2ns2LH6yEc+ErvdM888I0maMGFC5DaMCgLgQqEP3a+//ro+8YlPaNu2bapWqzr++OP1wAMP6MMf/rAk6atf/aoGDRqkhQsXqru7W/PmzdPNN99cZJEBIHfUlQBgZu/evbrtttu0aNEiDRnyX7e5L7/8su6880799m//tg455BA999xzWrp0qT70oQ/p+OOPL7DE0cJGbYQpcrmksi2N1loem2NCL7cZ1yN0fYwcC0ujDNdooQ/dt956a+z7BxxwgFauXKmVK1fmVCIAKB/qSgAw89BDD+nVV1/VxRdf3Of1YcOG6aGHHtKNN96o3bt3a9KkSVq4cKE+97nPFVRSAANJ4YHUAAAAABfOPPNMhYUrmjRpUqYVH7JKG/cibPus6eQpqpfRZ5k7tYe7iMjjNkx6i+PieJjuV9niubjCQ7fSBV1IEyjA9MtkGxAs7WdM8mvdtn1JiLg0i2BzLBrb2OxHWDA1k2OSdqhTVLmKqoyzBB5Juj7C9s/kmkpzLPYFB7L+2IDiKtiTyY+sq7xsvvdp66uw73GWuj4ujdbPm3x/sh7HsM+nrWtaPxeVhs/AOO3H33edaROoqcgATrb3Lb1Bj6TNHksEAAMHD90AAACAhXOnzUhcFae9Qcy0kdqmsXLexJmFzuluLU9cI2T7tmm43s8y9ShHdXK1SrNKT5byuFzfPKkB1kUPedkNKroAAAAAAAD0V/R0AwAAAI6E9Vr6ms9apl7AsJ5RXz3TaXpAyzhn2qYX2ce86KjpWu0jNGymAaY5N6ZTN9MoS/RyeroBAAAAAPCkKwgL8diP1Ot1VatVvbVpqiojBqdKIym4VtI2aUW17pisaRcX8MtHmYqWNGfI1ZwiX8G9sopqFUwzLyjtteIqIJbvvPcFUtusWq2mSqXiJP/+oFFXztECPbztBevP+7xu0qTtO/qpi4CbpnklpWe6vc1xTNtLknRckuqetD1XYbIEGI37fJl+77Pkk5R2b9CjtVpDXdmmta6MmtPdzkVAVZf60zzZTlSG37S4cuR9bTSux7zK47peNa0r6ekGAAAAAMATHroBAAAAAPCEQGoAAABACiZBmpICq4UN9zYdAp41aJVLA2HYuu00gTyn7CWxuaZ8lSGsPI18TKZtdTJ6ugEAAAAA8ISebgAAACCFtD1xSQEGswQCLKq32TZ4Yyf2itssW2Z7bfg+Hu3pJ43ScDFyIWpkR1h5wvg4Jkn77WuJMR66I5gebJ/DHrJW5D7KVpZIh63CytReUUT9EGSNbtuaRpHDqlorCJPzHlfWtMfEZv9dRO7Msg4qoq3e9Lwku5UestQ1UcMqfXDxvbcZ/pbXsLioVTTab+ptVttw8SAR937cQ4ZNRPK4qN9REcld/L67vinzteJIWD4AgPwxvBwAAAAoSFQjkYmohsAyNLAkNXDmUUafebQ3lJnm1WgEbf9XhDzyzaNB0YZNDAaXeOgGAAAAAMAThpcDAAAAOXLZwxjVk1iGaOIm84bDPuNCY/99TUGLmt7YruhpiEn5+prD7DvtNFyVpfV4njHhGKPP0NMNAAAAAIAnXUEQBEUXwqd6va5qtao5WqAhXUOt1qfLwibYTtnmOmTla23B1rSzRoRM07LqIwqnSfAcH9esi3NUZFA90+9SWFnqu/Zo1LTNqtVqqlQq3srYaRp15VubpqoyYl8gtbioo+1sz3sZ6royzDdMK2190591yvn0vb5vFq1lo64M13pf+fC2F0qxDnPr751NIMI8lS2St628znPekeh9R5g3OW55n+s012LcagOmdSU93QAAAAAAeMKcbgAAAMCS6+USs5ShrD3c7WXIq+fT5Txun8sDJuXjax54nktalmWUUZKwUbGN11ycA3q6AQAAAADwhJ5uAAAAIGcuejE7qSdRio555Lo3tww93K5iNqU9Pi56+12kEXcc8hyFYRuTxjSm0t0bnzbKf8A9dEddPD5CyLd/SWyGk7goQ9ykf5/5x+Wb5svWvvyF6edM0ot6PW3ANlsmP5Zpr1nfFVlRw9VMlh55YOuzpRhO1+nCrk8fxzXpexA35KudzXc2Kp0s3/u8AsqZ1u8DSVIgyjzqdVc3xr6WOAIAFGPAPXQDAAAARXK9TndSg7RJ50tRjTxlaVwqsqPFx5x8l519ptdQXL6t7yU1nuexproLD2x9VvVdZtsypxsAAAAAAE946AYAAAAAwBMeugEAAICc+IqNYfPevIkzm//aX0N27ce8dWh5UvwJk9eLYHJ9mAb2s71eTcqWRp4xr3joBgAAAADAk64gCIKiC+FTvV5XtVrVHC3QkK6hhZSh6FYq0xa1Ils3syyrkFe0YFNZj21UK2Ga1jibY5NHoJW4aNGuo/tHpVfftUejpm1WrVZTpVJJlXZ/1Kgr39o0VR896sQ+7+W19Irtd8f2+vbFZx1vWu6kMpSlfi+ai+NguuqG6x4U25VJsqKuDOfivjJuNRLT77Lpb6ZNvVqm72oRfK0mlCV/H+ckr9+DMl1PScc2y0otpnUlPd0AAAAAAHjCkmEAAABATrIs75XUA2eyfFiadPNislxU+zY2S0zFjbhzqWyjMIvgaumvrKN7ss4xd4WebgAAAAAAPKGnGwAAAChIe+901hgqJj3lLuK0+GBSnrDI4FnTN426nSWPMHn2cBfRm561t7u9zGniH5QFD93/T+uXzfXQhSyT87MwLVOjPEUOhWk99rbBZ0yHIrmQx7kLu17SDBVrfc90KFXrsYxK1+R7YvpdSsoji7BrfN7EmeoNeiRtzpx+f3XutBka0tX3taQAeCaBfUxuaEyuv/btG0yHXNqk71LYcMj28oUxDTJpU2+U7SY7D3kOL4xbFshFug1JdXBUsC7bobsAgOx46AYAAABSctFIEdYgaNpYZNtQQsNKuDzXbG7NM48VCcrW65vEtOHd9co3PjGnGwAAAAAAT+jpBgAAAErAdG5xe8943DrErVML6OVOx2Sqj+m5SzstcCCw6aHO0stdxPGlpxsAAAAAAE+6giAIii6ET/V6XdVqVW9tmqrKiMGh22Rp9cva2pUmeJmvoGH9rVUt7jikiVLp+hoxzTMqeE6W8rsMGmja+lu0Rtl6gx6t1RrVajVVKpWCS1UejbpyjhZoSNfQzOm5jARrk2eUuF6grJLqGh/lcV2v2OZfdmnOSV75+xD2u2CzbnEY6spwJnVl1O9i1oC1WerVMvdyl7VcPqTpyXVVXxcxz9lXkGbbXm6f11d91x6NmrY5sa6kpxsAAAAAAE946AYAAAAAwBMCqQEAAAAp2S73lXY4ddYgUzbTE/Mc7m0yTa11W5s168vGZoqAScC1RpplZXOtuxxaXsZjUmhP94oVK3TyySdrxIgRGjt2rM455xxt3LixzzbvvPOOFi9erEMOOUQHH3ywFi5cqB07dhRUYgDIH3UlAABA5yq0p3vdunVavHixTj75ZPX29urP//zPdeaZZ+qFF17Qe97zHknS0qVLdd999+nuu+9WtVrVkiVLdN555+lHP/pRkUUHgNxQVwJAsmuuuUbXXnttn9eOOuoo/fznP5e0r3Hy6quv1l133aXu7m7NmzdPN998s8aNG+ck/9betbCetrCe8PbtigqG255v3ktXJfVSxi2NZtND2h+X5HLR8+8j+GkjTdPe7qxlKPs5LfSh+/777+/z9+23366xY8dqw4YN+tCHPqRaraZbb71Vd955p04//XRJ0m233aajjz5ajz/+uD7wgQ+kzjvrhZX0+cYFZrJdQ9h6imF5hV28Lr4oWYdl+Io+GbW/SXm1Hr80w6zCymHzOVfnxGQtzqzlCcvHRbpR2xRRMTby3BdlMvfsMymyrrTV+iObt6zXrq/6y+XnGmXM8hvgow5pl+d3vGzlKuraf2Drs9YRquOiIndiXSlJxx57rB566KHm30OG/NetLo2TAIpSqjndtVpNkjR69GhJ0oYNG9TT06O5c+c2t5k+fbomT56s9evXh95Idnd3q7u7u/l3vV73XGoAyBd1JQCEGzJkiMaPH7/f63l05NguWxj3en/iYwndrOXw2ePdSec0bHSGq/KnOca2HZa26buSpqOxNNHL9+7dq6uuukof/OAHddxxx0mStm/frmHDhmnkyJF9th03bpy2b98ems6KFStUrVab/yZNmuS76ACQG+pKAIj24osvauLEiZo6daouvPBCvfrqq5KSGycBwKfS9HQvXrxYP/3pT/XDH/4wUzrLly/XsmXLmn/X63VuJgH0G9SVABBu1qxZuv3223XUUUdp27Ztuvbaa/Wbv/mb+ulPf5qqcVKyGxWUdjqclH1ud9xUgby177OLcviYcyy5iXjdKT3bpvvno8fbdW9367a+JPXUN97vDXokbU5MrxQP3UuWLNG9996rxx57TIcddljz9fHjx+vdd9/Vzp07+1SSO3bsCB06JEnDhw/X8OHDfRcZAHJHXQkA0ebPn9/8/+OPP16zZs3S4Ycfrn/6p3/SgQcemCrNFStW7BecDQBsdQVBEBSVeRAEuvLKK7V69WqtXbtWRx55ZJ/3a7WaxowZo+985ztauHChJGnjxo2aPn165DzFdvV6XdVqVW9tmqrKiMHGZYtqlbFt9fER8Kw13Txb1+KOh+uWJtv5Pz6DuMXla7K9Cy6uxaT0i2ypjYso6nruzr7gQJtVq9VUqVQypZWXPOvKOVqgIV1DC78mysJXHZ6UZ1w+/b03xrWodX3zXD82bXpF6g16tFZrOqquDHPyySdr7ty5+vCHP6wzzjhDb731Vp/GycMPP1xXXXWVli5dGvr5sJ7uSZMm7XdfadorFsbXdZM2YGzaz/vm+3tTxL2sb672Kc21l/YZoQwxAGz217SuLLSne/Hixbrzzju1Zs0ajRgxojm8p1qt6sADD1S1WtUll1yiZcuWafTo0apUKrryyis1e/bsXKPxAkCRqCsBwN7bb7+tl19+WX/wB3+gk046SUOHDtXDDz/cp3Hy1Vdf1ezZsyPTYFQQABcKfehetWqVJGnOnDl9Xr/tttt00UUXSZK++tWvatCgQVq4cGGfNRUBYKCgrgSAZJ/61Kd09tln6/DDD9fWrVv1xS9+UYMHD9YFF1zgvXHS9TKlUXnYLg9oO5fWhayjIIsexek67bx7vH3sU5prz1ae87iLGIVQ6EO3ycj2Aw44QCtXrtTKlStzKBEAlA91JQAk++Uvf6kLLrhAb7zxhsaMGaNTTz1Vjz/+uMaMGSOJxkkAxSlFIDUAAAAgi7vuuiv2fZeNk+dOm6EhXUP7vPbA1mczzWVNkibeTRHi5rWXoXxSvvPVXcVIySNSd1w+pufONmaGjTx7uV2PVBiwD90mBzDsArQNqtN60UVtm+UCyjOQQNy+xn1Z29+LC9SRZuhK40cuaVuTAGjt29geX19DqcLKb3MtJgV2KTrYh80PcdpgNLZLOwxUqzc93wwOlMdNiYt60ef1mybtrD/UJvVBVJ4m5TLNpwhprjnb4xX1e542/fZt05yLLGXohPMKAAPdgH3oBgAAALKImr/to/EjrEEvrme9bNHHy9LLnbdGY1yWRtyw9OLyS0rDJB/T7ZM612zS9LFyR6s0Df2urttBqT8JAAAAAABi8dANAAAAAIAnDC8HAAAALCXFsPGhdahyWHyCPMrggq8h+KZ8BfoKExdHwnY4dXssntZrISotV0u4mQRYc5GP6bZp8ok6TjbxONJet/R0AwAAAADgSVdgsgBsB6vX66pWq5qjBfst7WAra4tYUsuISfpRUWvTBE1Ik1cZuFqKwHbpDdv00p5PE67Ob1yUZdsWWd9sI/RGtfz3Bj1aqzWq1WqqVCpuC9nBGnXlW5umNqOXm/CxKkMWJitGtHO9LIjPHi7bfMtaj/vkermavHrEfOSTJShRfdcejZq2mbqyTVJdWVRPc9nq4iRlqZuKPD62dXrSOXZ1Xxv3rGF7X5xm+7QrdNjkkyX99s+b3lfS0w0AAAAAgCfM6QYAAAAcaO2py3OURNbPJJUzzznQUZJGdqY9Dq5Hh/oa6Ri1r7ZzodNon0veXp4w7T3wJsuc5TECwvXoNlP0dAMAAAAA4Ak93QAAAIADWSNFuxQ1DzdN9Gbfc2qj8mo/nqZlTNP7G3a8ks6nac9vex5ZFR39XTLf99ae5bQRx9MyPUZ5jOTgodtSmuEjrRVF3MmP+2KbfC4qXxsuv8C+gvq4GEaV5nMm+2N7zNMOQYr7EbVJs33/2ocruVoGIizvtJ9LWiIlaz7oy/YGpGit5fLxfSySi5uVrEMaXfPxPXU9tNc0DRdBjFx/v8p+TQPAQMFDNwAAAGDh3Gkz+qyKY9ITm5eonsWkaPamDVVZG7RsIlSnzSdtT3BYZ8O8iTMjG8RMy+fjuihqbnIYk06oNNdl2PZpyxTHJv20x5053QAAAAAAeEJPNwAAAGBh9abnQ9fpLkpUz6LNnFYX26Tlo4fdxbzn9s+7mLYS18trm3YZernDxE2VSTP3Ps1xN5mHH7dd1s+0o6cbAAAAAABPuoIgCIouhE/1el3ValVvbZrqvEWytdXFpAXGtjXKZWTCMswxciXqmGRdq9GE7TlJe9yzlt9HELs8okdG5WU698tEfdcejZq2WbVaTZVKJVUa/VGjrpyjBX3mKSYpS93ies5X3HfIZS/FQOYqgm8efP0W20Rxdlkek+NGXRnO531lFlmuifZ7m7h7HVffuTTBX7PknybQYdIc+CI1fvPK2tMdxeb+NM9jnHQPEfVbP2/iTPUGPVqrNYl1JT3dAAAAAAB4wkM3AAAAAACeEEgNAAAAyJmrddlb00kz3Lh9qLLJcO6w4bg203zSBK+Kyssk36Sh4zaB6PIe9tyed9h58sX11M2kayvpeos711nOle13J82xoKcbAAAAAABP6OkGAAAActTaU5Y1UFjrZ5KCoEW9Z9pLGFXu1tdc9ASbBhls7fXNGvDVNtChb1HH1zcfwXjT5Bu2XJttWXwEuk6Lh+6Uwk58WKWZ5US6vAhcRH4si6gfjTy+NLZ5REU7th0GZVuu9jxcDglKE2k0z8jnyGb1puf10aNONNo2rp6zHcrn6hrNOmTRZJu070Xp9Do5rTwjj7uMbJ+1LLbbu6jL2383TYZo9gY9kjanyg8A0BcP3QAAAEAJpF2G0FePXtZe8/bGWds531H7lceSukUuyVWWDgffS/A2jr/PZX9t54H7wpxuAAAAAAA8oacbAAAAyEl7T3Bcb7JNej56CNO8F7ddXI932HumU5Fsp5CYRD4vMnJ5f+9lT5qm6yu/Iqdz0dMNAAAAAIAnXUEQBEUXwqd6va5qtaq3Nk1VZcTgTGmlnWfTKVxEnIxrpXJ1vEwiWtq2ThY5d6YsAX7SMJ2D42Mf0wab6w16tFZrVKvVVKlUrMvVX7msK8O0rwObpCzRRssqqpfI5Bibzo+0mWOXtG0Rv5dlvEZMejXLUm7qynAu6kpfvW5luXZMufxNsBE3TzyqLivqnr/TzqkLrnv540Y1uMjHtK6kpxsAAAAAAE+Y0w0AAADkpNGTF9eT6nIJRJty+Uo7jOv1lm3yCksv7G9X646nYXN8ilpj3McoUt8R0/P+bAM93QAAAAAAeEJPNwAAAGDh3GkzNKRrqKTo3r64XtL2nrOwXrSo+Ah59L6WcS5xEb3OeeTnY+64bYwIH/GQfMepMO3dd5G/izQGzEN3a+UoxQd9Mb1ITE5A1qEweX7ZTY5JkrjhF1GBQ5KGV7W/blIW06UoyvCjYnqsXZQ1qYKyHcoWd0MQ9RlXAbLiAmKE5dF4rb5rj0ZNM8oCjoQtDZK1bjMNBlZmrm+eG+kkDYNLu9SPzbZFBgUr83WQdF4e2Pqs9+CarddHUUNSAWCgGTAP3QAAAIALqzc9r8qIwfvNzZb6NoDFSdM41d6I6TLSc39udCnTikMuGkXDPhfV4WCytnmaThibDrmwPNJK2+jOnG4AAAAAAPopHroBAAAAAPCEh24AAAAghahlp7Is92SjPw8JD1O2ZahM8m3/5zMvk9ek8Ou29Z8PLtJNM7TcRb4uzhsP3QAAAAAAeNIVBEFQdCF8qtfrqlarmqMFfaKXFyGq9TNrC0xS9Nss0XGzRhv20aLnqsWqqKAaWYI4xH3edn9s0/F5LsO+E7b5me7/vujlm1Wr1VSpVKzy6M8adeVbm6aqMmJw4vZZrweTVQRc1I2umEbdNy1zmXunfNb1aZefsa2XOvU8lCnYE3VlONu60pSvIFM+Aq4VrVPuA8t4vPNaci6Ky3uHuDSj9tPHOekNerRWaxLrSnq6AQAAAADwhCXDAAAAgAL56olu7enrL73dtiOLwrjs6e2kY9pJZTUVdS7LNHJIKrin+7HHHtPZZ5+tiRMnqqurS/fcc0+f94Mg0Be+8AVNmDBBBx54oObOnasXX3yxmMICQEGoKwEAADpXoQ/du3fv1syZM7Vy5crQ97/yla/opptu0i233KInnnhC73nPezRv3jy98847OZcUAIpDXQkA/Z9Jz1xSdOmkKNl5RKnOi0mvbRERw8us0895VkXuf2kCqXV1dWn16tU655xzJO3ruZk4caKuvvpqfepTn5Ik1Wo1jRs3TrfffrvOP/98o3TLFEgtjbBAU0nDg6LejwtclCYEf9LnXbIJPhc27MhXoCZTLoIM+WYbWKPsPzRR+9LpwYGKqCuz1A8+gphkvU6zBJb08R31+V3qT0sRuTr2eQbYS5uvr9+CpOCore+bBgcqkxUrVuh73/uefv7zn+vAAw/Ub/zGb+jLX/6yjjrqqOY2c+bM0bp16/p87vLLL9ctt9xilIevQGoNpssfmVxPJtdR2b7ntlwHubTVacevPwZSy7sM7UzrytLO6d6yZYu2b9+uuXPnNl+rVquaNWuW1q9fb3wjCQD9GXUlAOyzbt06LV68WCeffLJ6e3v153/+5zrzzDP1wgsv6D3veU9zu0svvVTXXXdd8++DDjqoiOKGcj3XOCm9Tp/nnfRQ3dg/lysH9Yfj5bsxIm8m++MrZsK+zpzkbUv70L19+3ZJ0rhx4/q8Pm7cuOZ7Ybq7u9Xd3d38u16v+ykgAJQAdSUA7HP//ff3+fv222/X2LFjtWHDBn3oQx9qvn7QQQdp/PjxeRcPwABW2ofutFasWKFrr7226GIAQKlRVwLo72q1miRp9OjRfV7/9re/rW9961saP368zj77bH3+85+P7O3upAbKsF7rqB7AqKkGndyLG6fIHt081opOq9PWK09Ky/exzXKsSrtOd6MFcseOHX1e37FjR2zr5PLly1Wr1Zr/XnvtNa/lBIAiUVcCwP727t2rq666Sh/84Ad13HHHNV///d//fX3rW9/So48+quXLl+t//s//qY9//OOR6axYsULVarX5b9KkSXkUH0A/U/pAap/61Kd09dVXS9rXujh27NhUwYFsAl6UqQUqbzYBOuI+H8fXnIq8AzS5FDcXJakVMs08paRAfFFlMg3wkqVsSXllnYfVicGBWvmuK+OCTpYl0F6a70kWeQV+yeO3pxN7tNrr97IFwiuar/PY6XXlFVdcoR/84Af64Q9/qMMOOyxyu0ceeURnnHGGXnrpJb33ve/d7/2wnu5JkyYVHkgt7rM290RZemGzBuF1Lc/vd1idlCbob9l0WrDQvMQdF9MAvYUOL3/77bf10ksvNf/esmWLnnnmGY0ePVqTJ0/WVVddpS996Us68sgjNWXKFH3+85/XxIkTmzebADAQUFcCgLklS5bo3nvv1WOPPRb7wC1Js2bNkqTIh+7hw4dr+PDhXsoJYOAo9KH7qaee0mmnndb8e9myZZKkRYsW6fbbb9enP/1p7d69W5dddpl27typU089Vffff78OOOCAoooMALmjrgSAZEEQ6Morr9Tq1au1du1aTZkyJfEzzzzzjCRpwoQJnksHYCAr9KF7zpw5ihvd3tXVpeuuu67Psg4AMNBQVwJAssWLF+vOO+/UmjVrNGLEiOYKDtVqVQceeKBefvll3Xnnnfrt3/5tHXLIIXruuee0dOlSfehDH9Lxxx9fcOn/awpE3HBck8Botmt4J+WbdephXlxMQzEVln6aYf1lP6YDka9rp99FLwcAAMDAs2rVKkn7Gipb3Xbbbbrooos0bNgwPfTQQ7rxxhu1e/duTZo0SQsXLtTnPve5AkoLYCDhoRsAAAAdLyk28KRJk7Ru3bqcSmPPNlhlmt7SLD20rflFldVVYNussgRyzcI2n7IcL8nfMSrTPibxeZ3w0B0i7+imPqI0x6Xr+jNRn89zXcL2fE2ic4eVqahKOir/1tdNow5nXXsz7pikiYweNoSt/e+0Nw+2Htj67P+LMukti34trx/NpLoj7Josc8RV03olj0jAed/4JJ0Xk/JkqZOy1mcmXEZFD7tWOuVmFQAQjYduAAAAoCRMG6HTdq5k7RTpxKX3ohq0yrAvZZjfndcc+DI3Ivo+BoO8pg4AAAAAwABGTzcAAABQoCJ6AH3l2dqLPJB7Nm0UdZzyPAZlvhbyQE83AAAAAACedAVJoR47XL1eV7Va1VubpqoyYnDstnmu79eeXxxaoeylCfhlmmZU0La4PLME/rEpmw8+gstlvc7SlmHexJnqDXq0VmtUq9VUqVQylaM/MakrW6/1pGCFpsEMy6Jsc+nKWBe7Pm+dvo8ug6CmrffTHkOTz1JXhrO5r0yjbN+LvK5nk3K4Srdsv0FlC3DpS9L9ZNHXftrjsi9A7+bEupKebgAAAAAAPGFONwAAAFACnTLyxSUfoxNt0ipbz3d/FTc61OUoBh/fFxeR7unpBgAAAADAE3q6AQAAgJIqco1jk7g1Ydub8JGmLXq57dleE62fS9vDbdqD7TMif1YD5qH73GkzNKRrqCSzyss2qFr7hRR3QdpeEHkFeMujMs8rCFfYNu3nJ21Z2j9nkk5UJRP22TRDY8KCu2UJNmaTl+v0bdIoww/2QNJ6vKOO/byJM42/o2UJ5mLC59I3YccijyGm3Gzuz6R+s6kj49KJS699yaWw689VQErqRwDwb8A8dAMAAACwZ9JQ6qoBJ8/VhIpsAG7Iez3ztD3VcemEdT4mNSbbrHJicnzCOj9tRF13SR15ppjTDQAAAACAJzx0AwAAAADgCcPLAQAAgAEgy1Bml/GKwj5TRCyL1jgkRQ4zLyJGTZr9Dsu/8VpremnOZVw5kmJQhA1lT3usTIapt77eG/RI2pyYLj3dAAAAAAB40hUEQVB0IXyq1+uqVquaowV9opf7bP1oz8NmYr/JtqaLy2eJLN3eomQTETtLS1bWtG25CCKRZskEF+WwDSqRNa0seeUdHTfuO94b9Git1qhWq6lSqeRarjJr1JVvbZqqyojB+73vK9iMbaRnk7SKDoqDZFnP1UCJuN1el0XVbT5+Q6grwyXVlbZs6tYir/uwa7FVp3wn++PvQ5Zj76MOjgqs5qoMpvcNWZ/1TD9vWlfS0w0AAAAAgCfM6QYAAAA8SxqlELdkUdE9tEWWwdWSWkUfQ1+yLJWV9ry6mGOd9nyYfi7tddM4JrY93kno6QYAAAAAwBN6ugEAAAAHXETxbn0tKhJ0mZR9PnfWmAdlP/5ZZN03373VWdn2doeVKy6NB7Y+qzMmHGOU9oB56F696fk+AS/STtp3ceJM8zJ5P27buGFKUWUKGw4S9V5Snknb2AQOaWzrOuBD1qEuRR4T2+2yDD9Km5ergD+2ZYirHOu79mjUNG9F6HjnTpvRDDrZyuV5M623bPPuLzdGpsP1XORjMuTPR3mKutFrZ1oXuqrr0+QV93dYnefq+0JdCQDuDJiHbgAAAMCHrGtfm3A9xzQrk8j6rdKWP2uHVxpRjY9RDZV5djJEzcMu4jiZKnJ1ERffm6hzvO+1HqM0mNMNAAAAAIAn9HQDAAAAKfjsdS7bWtitPaxh01OiehSjpkik2Z+ipq+177cJX727WXu5fQs7RmWY/pX0fUoqY9T7plNx6OkGAAAAAMCTriAIgqIL4VO9Xle1WtVbm6b2CaRmI2vrkYvWnTxasHy3QsXtg8/APWmPXdQamVEtY+0te1nnjtjI0lrsShnKYGJfi+Rm1Wo1VSqV3PMvK5u6Mqn1Pq8W9yLrLBNx9Vqa9UPL8J1t1diHouYGhpWnfTvbHiHXwTqzMP09sSmzTc8OdWW4Rl05RwtCg066Vpbe7qQIzu1sg/uZzge34eJ7Y9Nb7aNuMeXq9yRrnV6GNeVNuRqJYFpX0tMNAAAAAIAnPHQDAAAAAOAJgdQAAACAkslzaLnt8HGT9Hxub5Nm9FJP4fknHeeoIdRxx9D3sGtX10rWaUO+hq37kHUYvW0a9HQDAAAAAOAJPd0AAABAyeTdOxqXV5mWpDLReuxsAr0l9VQ33jdZKq1V2Xp547gOAP3A1mc7av9NNK6FeRNnqjfokbQ58TMD5qH73GkzmlEmXURRtGEbwTGMy4s1rkKxyc/0x8DkWJoeb5d52qaRNJQky5qE7WnbDltJG9Wz6ErQ1xqWcfmZVo74LyZ1RlpRN0NJ10Tjx85Vvj5uKOPSzJqf7TDQuBUYbPhae9V1nR13k+dzGGtaJsNfbT5rm2/RvwUA0N8NmIduAAAAoFPkscxUf21wSdov2+UE494vyygA23LYnn+TzrlGmmW4rnwsQ2eSbhTmdAMAAAAA4Ak93QAAAEDJtPdExkXHdpFHWXps8+ZyqpSrKZw+xc11N5V1WqRvvq7lLOnS0w0AAAAAgCddQRAERRfCp3q9rmq1qjla0Ayk1pDn+odh0gYQMuUjyJHN/IiwbW33zeWakWnziuKihTBLullbFdtbXduvPdugda5b4G2iqJoE3WroDXq0VmtUq9VUqVSyFbIfadSVb22aqsqIwX3ey3peXQY/jKuTfPQCDRRZe7rS1O1l6REpq6jfUN9rNTfypq4MF3df6UvR96smyvZ9Nr1Hcvn7lvZ3LCvTuiJt0F1brvLOwqTcrp5JTOtKeroBAAAAAPCEOd0AAABASWWdd206QiLLSIoyjV5p3Yc8RobY5hN3rMJ6iJPmg7taHtHVsSp6JEbeS4CaoqcbAAAAAABPOuKhe+XKlTriiCN0wAEHaNasWfrxj39cdJEAoJSoLwGg/8kSx8W0ly5rb17RPZwNvnvcTdehNt1u3sSZzX+Nz7W+F7VtllEJYf98yuPasN0Pk+1Ntlm96Xmj/Eo/vPy73/2uli1bpltuuUWzZs3SjTfeqHnz5mnjxo0aO3ZsprRtToyP4GamywrYLDPgIkBXHJs0fAavcB2gIU1Z0vwAmuSTNEQrrAI2LUfSNReWdlJwtfblTNKK2q/2dMP2weSHaiBwUV+eO22G8+BAJteo6TUftoROVDpZroGihkm6CsRjMtSzsU37TUVUkM+ktGzlEbzSRUBPH0yChLoaMpqUp+02/cXKlSt1ww03aPv27Zo5c6a+/vWv65RTTim6WAD6qdI/dP/N3/yNLr30Un3yk5+UJN1yyy2677779M1vflOf/exnCy4dAJQH9SUAJPPZoZOHtNHM82iY9t35UyTTBsmwxuC4joWoNE0aTjupocznvP+sHXm2jc2t29d3meVT6uHl7777rjZs2KC5c+c2Xxs0aJDmzp2r9evXh36mu7tb9Xq9zz8A6O9s60vqSgADVWsD5THHHKNbbrlFBx10kL75zW8WXTQA/VSpH7r/4z/+Q3v27NG4ceP6vD5u3Dht37499DMrVqxQtVpt/ps0aVIeRQWAQtnWl9SVAAaiNB06AJBVqR+601i+fLlqtVrz32uvvVZ0kQCgdKgrAQxEtg2UZRgVlBSQyzSoV1iapsG+XMhzKLTrfTJNL0sshsY2rgO1lYntNdAe08VHELgsQ8ttlHpO96GHHqrBgwdrx44dfV7fsWOHxo8fH/qZ4cOHa/jw4c2/gyCQJPWqRwrSl6W+a0/6D7foDXqs8239TFI54tJ3tQ+u2B6LOFn3LUtZbPI2zScuzag0TMqRZj9b0+0NerxdR6bXbtbterXv9Ubd0F/Y1pe+6so4aa+d9nNpmk5e32uXXNQRjXRMtzE5vi7r67TSnJOwcpfhtzBLPe46z8jt+2ldaWvFihW69tpr93vdZ13ZzsXve5b7R5fy/P7lcZ9p8/tkco7S3GeVoX62YXvf3Nje137a/ua1b19/e68kg7oyKLlTTjklWLJkSfPvPXv2BL/2a78WrFixwujzr732WqB91SL/+Mc//jX/vfbaa76qrcJkqS+pK/nHP/6F/etvdWV3d3cwePDgYPXq1X1e/8QnPhH87u/+7n7bv/POO0GtVmv+e+GFFwo/J/zjH//K9y+prix1T7ckLVu2TIsWLdL73/9+nXLKKbrxxhu1e/fuZnTeJBMnTtQLL7ygY445Rq+99poqlYrnEpdTvV7XpEmTOAYcgwF/DIIg0K5duzRx4sSii+JclvqSutIe3yd7HDM7RR6v/lpXDhs2TCeddJIefvhhnXPOOZKkvXv36uGHH9aSJUv22759VNDBBx9MXWmJ7709jpm9oo6ZaV1Z+ofuj33sY/r//r//T1/4whe0fft2nXDCCbr//vv3m4sTZdCgQfq1X/s1SVKlUhnwFy7HgGMgcQyq1WrRRfAiS31JXZkex8sex8xOUcerv9aVWRooqSvT43jZ45jZK+KYmdSVpX/olqQlS5aEtj4CAPqivgSAeFk7dADAVkc8dAMAAACu0EAJIE/9bsmwMMOHD9cXv/jFPnNyBhqOAcdA4hggHteHHY6XPY6ZHY5XOXFe7HC87HHM7JX9mHUFwQBfCwIAAAAAAE8GRE83AAAAAABF4KEbAAAAAABPeOgGAAAAAMCTfv/QvXLlSh1xxBE64IADNGvWLP34xz8uukjeXHPNNerq6urzb/r06c3333nnHS1evFiHHHKIDj74YC1cuFA7duwosMTZPfbYYzr77LM1ceJEdXV16Z577unzfhAE+sIXvqAJEybowAMP1Ny5c/Xiiy/22ebNN9/UhRdeqEqlopEjR+qSSy7R22+/neNeZJN0DC666KL9rouzzjqrzzadfgyQ3UCqK5NQr9hZsWKFTj75ZI0YMUJjx47VOeeco40bN/bZxuT359VXX9VHPvIRHXTQQRo7dqz+7M/+TL29vXnuSi5WrVql448/vrmW7OzZs/WDH/yg+T7HqtyoK/8LdaUd6kp7/am+7NcP3d/97ne1bNkyffGLX9TTTz+tmTNnat68eXr99deLLpo3xx57rLZt29b898Mf/rD53tKlS/X9739fd999t9atW6etW7fqvPPOK7C02e3evVszZ87UypUrQ9//yle+optuukm33HKLnnjiCb3nPe/RvHnz9M477zS3ufDCC/Wzn/1MDz74oO6991499thjuuyyy/LahcySjoEknXXWWX2ui+985zt93u/0Y4BsBmJdGYd6xc66deu0ePFiPf7443rwwQfV09OjM888U7t3725uk/T7s2fPHn3kIx/Ru+++q3/913/VHXfcodtvv11f+MIXitglrw477DBdf/312rBhg5566imdfvrpWrBggX72s59J4liVGXVlX9SVdqgr7fWr+jLox0455ZRg8eLFzb/37NkTTJw4MVixYkWBpfLni1/8YjBz5szQ93bu3BkMHTo0uPvuu5uv/du//VsgKVi/fn1OJfRLUrB69erm33v37g3Gjx8f3HDDDc3Xdu7cGQwfPjz4zne+EwRBELzwwguBpODJJ59sbvODH/wg6OrqCv793/89t7K70n4MgiAIFi1aFCxYsCDyM/3tGMDeQKsrbVCv2Hv99dcDScG6deuCIDD7/fnf//t/B4MGDQq2b9/e3GbVqlVBpVIJuru7892BAowaNSr4h3/4B45VyVFXRqOutEddmU6n1pf9tqf73Xff1YYNGzR37tzma4MGDdLcuXO1fv36Akvm14svvqiJEydq6tSpuvDCC/Xqq69KkjZs2KCenp4+x2P69OmaPHlyvz0eW7Zs0fbt2/vsc7Va1axZs5r7vH79eo0cOVLvf//7m9vMnTtXgwYN0hNPPJF7mX1Zu3atxo4dq6OOOkpXXHGF3njjjeZ7A+UYINxArSvTol5JVqvVJEmjR4+WZPb7s379es2YMUPjxo1rbjNv3jzV6/Vmj0Z/tGfPHt11113avXu3Zs+ezbEqMepKO9SVyagr7XR6fdlvH7r/4z/+Q3v27OlzkCVp3Lhx2r59e0Gl8mvWrFm6/fbbdf/992vVqlXasmWLfvM3f1O7du3S9u3bNWzYMI0cObLPZ/rz8WjsV9w1sH37do0dO7bP+0OGDNHo0aP7zXE566yz9I//+I96+OGH9eUvf1nr1q3T/PnztWfPHkkD4xgg2kCsK7OgXom3d+9eXXXVVfrgBz+o4447TpKMfn+2b98eekwb7/U3zz//vA4++GANHz5cf/RHf6TVq1frmGOO4ViVGHWlHerKeNSV5vpLfTkk19zg1fz585v/f/zxx2vWrFk6/PDD9U//9E868MADCywZinT++ec3/3/GjBk6/vjj9d73vldr167VGWecUWDJAPQ3ixcv1k9/+tM+8USwv6OOOkrPPPOMarWa/vmf/1mLFi3SunXrii4WgJxQV5rrL/Vlv+3pPvTQQzV48OD9Itjt2LFD48ePL6hU+Ro5cqSmTZuml156SePHj9e7776rnTt39tmmPx+Pxn7FXQPjx4/fLwBKb2+v3nzzzX57XKZOnapDDz1UL730kqSBeQzwX6gr7VCvRFuyZInuvfdePfroozrssMOar5v8/owfPz70mDbe62+GDRumX//1X9dJJ52kFStWaObMmfra177GsSox6ko71JXRqCvt9Jf6st8+dA8bNkwnnXSSHn744eZre/fu1cMPP6zZs2cXWLL8vP3223r55Zc1YcIEnXTSSRo6dGif47Fx40a9+uqr/fZ4TJkyRePHj++zz/V6XU888URzn2fPnq2dO3dqw4YNzW0eeeQR7d27V7Nmzcq9zHn45S9/qTfeeEMTJkyQNDCPAf4LdaUd6pX9BUGgJUuWaPXq1XrkkUc0ZcqUPu+b/P7Mnj1bzz//fJ8b8AcffFCVSkXHHHNMPjtSoL1796q7u5tjVWLUlXaoK/dHXelGx9aXuYZty9ldd90VDB8+PLj99tuDF154IbjsssuCkSNH9olg159cffXVwdq1a4MtW7YEP/rRj4K5c+cGhx56aPD6668HQRAEf/RHfxRMnjw5eOSRR4KnnnoqmD17djB79uyCS53Nrl27gp/85CfBT37yk0BS8Dd/8zfBT37yk+CVV14JgiAIrr/++mDkyJHBmjVrgueeey5YsGBBMGXKlOA///M/m2mcddZZwfve977giSeeCH74wx8GRx55ZHDBBRcUtUvW4o7Brl27gk996lPB+vXrgy1btgQPPfRQcOKJJwZHHnlk8M477zTT6PRjgGwGWl2ZhHrFzhVXXBFUq9Vg7dq1wbZt25r/fvWrXzW3Sfr96e3tDY477rjgzDPPDJ555png/vvvD8aMGRMsX768iF3y6rOf/Wywbt26YMuWLcFzzz0XfPaznw26urqC//N//k8QBByrMqOu7Iu60g51pb3+VF/264fuIAiCr3/968HkyZODYcOGBaecckrw+OOPF10kbz72sY8FEyZMCIYNGxb82q/9WvCxj30seOmll5rv/+d//mfwx3/8x8GoUaOCgw46KDj33HODbdu2FVji7B599NFA0n7/Fi1aFATBviUrPv/5zwfjxo0Lhg8fHpxxxhnBxo0b+6TxxhtvBBdccEFw8MEHB5VKJfjkJz8Z7Nq1q4C9SSfuGPzqV78KzjzzzGDMmDHB0KFDg8MPPzy49NJL97tB6PRjgOwGUl2ZhHrFTtixkhTcdtttzW1Mfn9+8YtfBPPnzw8OPPDA4NBDDw2uvvrqoKenJ+e98e/iiy8ODj/88GDYsGHBmDFjgjPOOKN5AxkEHKuyo678L9SVdqgr7fWn+rIrCILAb186AAAAAAADU7+d0w0AAAAAQNF46AYAAAAAwBMeugEAAAAA8ISHbgAAAAAAPOGhGwAAAAAAT3joBgAAAADAEx66AQAAAADwhIduAAAAAAA84aEbAAAAAABPeOhGvxUEgebOnat58+bt997NN9+skSNH6pe//GUBJQOA8tizZ49+4zd+Q+edd16f12u1miZNmqS/+Iu/KKhkAFAe1JXIoisIgqDoQgC+vPbaa5oxY4a+/OUv6/LLL5ckbdmyRTNmzNCqVav0B3/wBwWXEACKt2nTJp1wwgn6xje+oQsvvFCS9IlPfELPPvusnnzySQ0bNqzgEgJA8agrkRYP3ej37rjjDi1ZskTPPfecjjjiCJ1xxhkaOXKkvve97xVdNAAojZtuuknXXHONfvazn+nHP/6xPvrRj+rJJ5/UzJkziy4aAJQGdSXS4KEbA8I555yjWq2m8847T3/5l3+pn/3sZxozZkzRxQKA0giCQKeffroGDx6s559/XldeeaU+97nPFV0sACgV6kqkwUM3BoTXX39dxx57rN588039r//1v3TOOecUXSQAKJ2f//znOvroozVjxgw9/fTTGjJkSNFFAoDSoa6ELQKpYUAYO3asLr/8ch199NE8cANAhG9+85s66KCDtGXLFgJNAkAE6krY4qEbA8aQIUNoiQSACP/6r/+qr371q7r33nt1yimn6JJLLhGD4QCgL+pKpMFDNwAAA9yvfvUrXXTRRbriiit02mmn6dZbb9WPf/xj3XLLLUUXDQBKg7oSafHQDQDAALd8+XIFQaDrr79eknTEEUfov//3/65Pf/rT+sUvflFs4QCgJKgrkRaB1DBgXHPNNbrnnnv0zDPPFF0UACiNdevW6YwzztDatWt16qmn9nlv3rx56u3t1UMPPaSurq6CSggAxaOuRBY8dAMAAAAA4AnDywEAAAAA8ISHbgAAAAAAPOGhGwAAAAAAT3joBgAAAADAEx66AQAAAADwhIduAAAAAAA84aEbAAAAAABPeOgGAAAAAMATHroBAAAAAPCEh24AAAAAADzhoRsAAAAAAE946AYAAAAAwJP/H6Q48N1FLHoEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3_faces(mauls,50,50,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "748985eff1de43ada2544380d05d4557",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=169, description='x', max=339), IntSlider(value=99, description='y', max…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w=interact_manual(plot_3_faces,\n",
    "                  cube = fixed(mauls),\n",
    "                  x = (int(mauls.X.min()), int(mauls.X.max())),\n",
    "                  y = (int(mauls.Y.min()), int(mauls.Y.max())),\n",
    "                  z = (int(mauls.Z.min()), int(mauls.Z.max()))\n",
    "                 )"
   ]
  }
 ],
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