{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e95cb3d6-faab-4db6-84e0-3ff64cb9dfeb",
   "metadata": {},
   "source": [
    "# Generic PCM Photochemistry postprocessing and visualization demonstrator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b7b35c-fb19-4bda-81dd-ee03df1e4ef8",
   "metadata": {},
   "source": [
    "This Notebook will show you how to use the Generic PCM photochemistry postprocessing library and how to make interactive visualization with it. For it to work, you'll need to copy the *photochemistry_postprocessing.py* file along this notebook in the directory containing the output file (*diagfi.nc*) as well as the reaction network file chemnetwork/reactfile (to become *reaction.def*)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed55c2f3-5fa8-481c-b9d8-8e31f93f7993",
   "metadata": {},
   "source": [
    "## Loading simulation and calculating reaction rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd28bac5-65f6-464b-9c2b-6cba1bde9472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O2/3D/start_no_CO/diagfi61 loaded, simulations lasts 60.541668 sols\n"
     ]
    }
   ],
   "source": [
    "import photochem_postproc as pcpp\n",
    "\n",
    "sim_path        = 'H2O2/3D/start_no_CO'\n",
    "NetCDF_filename = 'diagfi61'\n",
    "\n",
    "# The simu class is just a wrapper for xr.Dataset\n",
    "my_sim = pcpp.GPCM_simu(sim_path,NetCDF_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703aaead-59d4-4a93-8bfd-25ad8478dee0",
   "metadata": {},
   "source": [
    "Now let's try to calculate automatically the rates of all reactions found in the reactfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80f1c90a-7bfc-4e95-b614-c6e8432c53b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reaction  no + hv -> n + o seems to be hard-coded. Add it manually if needed.\n",
      "reaction  co + oh -> co2 + h seems to be hard-coded. Add it manually if needed.\n",
      "['o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2o_vap', 'h', 'co2', 'co', 'ho2', 'h2']\n"
     ]
    }
   ],
   "source": [
    "my_sim = pcpp.compute_rates(my_sim)\n",
    "\n",
    "# We can see that species list have been added\n",
    "# to the simu object (as well as reactions dict)\n",
    "print(my_sim.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d2fe39-aedc-45b9-bfc8-d7670336a876",
   "metadata": {},
   "source": [
    "Some reactions' rates are hard-coded and need to be added manually (you should find their rates in *reaction_rate_lib.py*). To do that we first need to define a new reaction and call again **compute_rates** with the new reaction as second argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1ed253d-9186-4871-bf93-5ba0fc5f6a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First load the parametrization for its rate\n",
    "from reaction_rate_lib import k_JPL_2015\n",
    "\n",
    "# Then create the reaction objet (here for the reaction co + oh -> co2 + h):\n",
    "hard_coded_reaction = pcpp.reaction(['co','oh'],['co2','h'],k_JPL_2015)\n",
    "\n",
    "# Finally, add it to the reactions of my_sim\n",
    "my_sim = pcpp.compute_rates(my_sim,{'co + oh -> co2 + h':hard_coded_reaction})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042e4416-5ad3-4d1d-bbe7-896619253146",
   "metadata": {},
   "source": [
    "## Now let's do some visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e0e556-863a-4f39-b6be-ebe1402a5f95",
   "metadata": {},
   "source": [
    "### Static visualization\n",
    "Here we use the built-in visualization methods of the *simu* class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f34cbcfa-339b-46dd-b33e-4c699a118f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779d0619-582e-4628-8161-5c0d9d943261",
   "metadata": {},
   "source": [
    "#### Meridional slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f54ea828-ea01-4f8c-9349-e5f051ef7043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABRUAAAHHCAYAAAAhwb9EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB8XUlEQVR4nO3de3QU9f3/8VcSyAVCAuGSEAg3b9wkqSAxXgEjERBFUNGqDWjRVlAxokL9CqgIXoGq0agVUCsV8YIXWrykIKIgJRCqVajYIAgkgMgl4RJI5veHvywsue1sdndmdp+Pc3IOOzs7+97Znc+Lee/sTJhhGIYAAAAAAAAAwEPhVhcAAAAAAAAAwFloKgIAAAAAAAAwhaYiAAAAAAAAAFNoKgIAAAAAAAAwhaYiAAAAAAAAAFNoKgIAAAAAAAAwhaYiAAAAAAAAAFNoKgIAAAAAAAAwhaYiAAAAAAAAAFNoKgbAqFGj1KlTJ6vLsJVOnTpp1KhR9c4XFhamqVOn+r0eTyxbtkxhYWFatmxZQJ6vX79+6tevX0CeC4BzkTH2QK7Vj1wD4CmyzR7ItvoFW7YdOXLE6hLgMDQVvRQWFubRX6AGM1hj/vz5mj17ttVlAAgyZIy7l19+Wd26dVN0dLROO+00PfPMM1aXFLTINQD+Qra5I9sCJ5Szbe/evbrlllvUunVrNW3aVP3799fatWurzVdUVKT09HRFR0erV69e+ve//21BtXCiRlYX4FSvvfaa2+1XX31Vn3zySbXp3bp100svvaTKyspAlgc/uPDCC3Xo0CFFRka6ps2fP1/ffPONxo8fb11hAIIOGXPcCy+8oD/84Q8aMWKEcnJy9Pnnn+uOO+7QwYMHdd9991ldnqORawACiWw7jmzzH7LtuMrKSg0ZMkTr16/XPffco1atWum5555Tv379VFBQoNNOO8017y233KL27dvr/vvv1+LFi3Xttdfq22+/tbB6OAVNRS/dcMMNbrdXrVqlTz75pNp0BI/w8HBFR0dbXUZQMAxDhw8fVkxMjNWlALZExvzq0KFDuv/++zVkyBC99dZbkqQxY8aosrJSDz/8sG655Ra1aNHC4iqdi1zzHXINqB/Z9iuyzb/ItuPeeustffnll1q4cKGuuuoqSdI111yj008/XVOmTNH8+fNd865cuVLbtm1TfHy8Lr/8ciUkJOjnn39Wy5YtrSofDsHPnwPg5HOCbN68WWFhYXryySeVm5urLl26qEmTJho4cKC2bt0qwzD08MMPq3379oqJidEVV1yhPXv2VFvuP/7xD11wwQVq2rSpmjVrpiFDhug///mP13X++OOPuu2223TGGWcoJiZGLVu21NVXX63Nmze7zTdv3jyFhYXpiy++UE5OjutQ6iuvvFK7du1ym9cwDE2bNk3t27dXkyZN1L9//wbVKEnr1q3ToEGDFBcXp9jYWF188cVatWqV1zVWVlZq6tSpSk5OdtX47bffVjuHyMnn5+jXr58WL16sH3/80fVzjar3uer5T153tZ3j48UXX9Qpp5yimJgY9e3bV59//nmNr/3IkSOaMmWKTj31VEVFRSklJUX33nuvx+e+ePLJJ3XuueeqZcuWiomJUe/evV3/manSs2dP9e/fv9pjKysr1a5dO1cgVU2bPXu2evTooejoaCUmJurWW2/VL7/84vbYTp066bLLLtNHH32kPn36KCYmRi+88IIkae7cuRowYIDatGmjqKgode/eXc8//3yNz+/J+yT9epj/+PHjlZKSoqioKJ166ql67LHHgvobb4Qup2SMN5YuXaqff/5Zt912m9v0sWPHqqysTIsXL67z8eQauSaRa4ATkW21I9uCP9t85a233lJiYqKGDx/umta6dWtdc801eu+999zq6dKli5544gkVFRXpueeeU3h4uBISEgJaL5yJIxUt9Prrr6u8vFy333679uzZo8cff1zXXHONBgwYoGXLlum+++7Tpk2b9Mwzz2jChAmaM2eO67GvvfaasrOzlZWVpccee0wHDx7U888/r/PPP1/r1q3z6sTG//rXv/Tll1/q2muvVfv27bV582Y9//zz6tevn7799ls1adLEbf7bb79dLVq00JQpU7R582bNnj1b48aN04IFC1zzTJ48WdOmTdPgwYM1ePBgrV27VgMHDlR5eblX6+w///mPLrjgAsXFxenee+9V48aN9cILL6hfv3767LPPlJ6ebrrGSZMm6fHHH9fQoUOVlZWl9evXKysrS4cPH66zlvvvv1/79u3TTz/9pFmzZkmSYmNjTb+ml19+WbfeeqvOPfdcjR8/Xv/73/9c3w6lpKS45qusrNTll1+uFStW6JZbblG3bt309ddfa9asWfrvf/+rRYsW1ftcf/7zn3X55Zfr+uuvV3l5ud544w1dffXV+vDDDzVkyBBJ0siRIzV16lQVFxcrKSnJ9dgVK1Zo+/btuvbaa13Tbr31Vs2bN0+jR4/WHXfcoaKiIj377LNat26dvvjiCzVu3Ng178aNG3Xdddfp1ltv1ZgxY3TGGWdIkp5//nn16NFDl19+uRo1aqQPPvhAt912myorKzV27FjX4z19nw4ePKiLLrpI27Zt06233qoOHTroyy+/1KRJk7Rjx46QPZ8KQo9VGVNZWVnjjlxN4uPj3caJk61bt06S1KdPH7fpvXv3Vnh4uNatW1fnES7kGrkmkWtAMCHbyLZgz7aDBw/q4MGD9dYTERFR7xGt69at01lnnaXwcPdjyfr27asXX3xR//3vf3XmmWdK+jVPr7zySj3yyCNq0qSJ/vrXvyosLKzeOgAZ8ImxY8cata3O7Oxso2PHjq7bRUVFhiSjdevWxt69e13TJ02aZEgyUlNTjaNHj7qmX3fddUZkZKRx+PBhwzAM48CBA0bz5s2NMWPGuD1PcXGxER8fX226pw4ePFht2sqVKw1JxquvvuqaNnfuXEOSkZmZaVRWVrqm33XXXUZERITrNe3cudOIjIw0hgwZ4jbfn/70J0OSkZ2dXW9NkowpU6a4bg8bNsyIjIw0fvjhB9e07du3G82aNTMuvPBC0zUWFxcbjRo1MoYNG+b2vFOnTq1W49KlSw1JxtKlS13ThgwZ4vbenvz8RUVFbtNPXkZ5ebnRpk0bIy0tzThy5IhrvhdffNGQZFx00UWuaa+99poRHh5ufP75527LzMvLMyQZX3zxRbU6Tnbye1xeXm707NnTGDBggGvaxo0bDUnGM8884zbvbbfdZsTGxrqW8fnnnxuSjNdff91tviVLllSb3rFjR0OSsWTJknprMgzDyMrKMrp06eK6beZ9evjhh42mTZsa//3vf93mnThxohEREWFs2bKl2vMBduekjKl6fk/+ThxPa3vdERERNd7XunVr49prr63z8eTaceQauQbYDdlWHdlWc42hlG1Tpkzx6HNW0+s5WdOmTY2bbrqp2vTFixfXmGF79+41Vq1aZfz888/1Lhuows+fLXT11VcrPj7edbvqG5sbbrhBjRo1cpteXl6ubdu2SZI++eQT7d27V9ddd512797t+ouIiFB6erqWLl3qVT0nngfo6NGj+vnnn3XqqaeqefPmNV4h6pZbbnH79uKCCy5QRUWFfvzxR0nSp59+6vom8cT5vD1BbkVFhT7++GMNGzZMXbp0cU1v27atfvvb32rFihXav3+/qRrz8/N17Nixaj8/uP32272q0aw1a9Zo586d+sMf/uB2MuFRo0a5fTYkaeHCherWrZu6du3q9r4PGDBAkjx63098j3/55Rft27dPF1xwgdv7e/rppystLc3tm8GKigq99dZbGjp0qGsZCxcuVHx8vC655BK3enr37q3Y2Nhq9XTu3FlZWVl11rRv3z7t3r1bF110kf73v/9p3759ksy9TwsXLtQFF1ygFi1auNWVmZmpiooKLV++vN71BAQDqzImKSlJn3zyiUd/qampdS7r5BOtnyg6OlqHDh2q8/Hk2nHkGrkGBAOyjWw7UTBm2+9+9zuPPmevv/56vXUfOnRIUVFR1aZXnXPy5M9afHy80tPT+dkzTOHnzxbq0KGD2+2qAenEw6dPnF51Pp/vv/9eklwD08ni4uIk/TpIVP3ntcqJP/s52aFDhzRjxgzNnTtX27Ztk2EYrvtOXk5N9Vcdfl1VZ1UInHhVKenX8zh4c/LhXbt26eDBg66fF52oW7duqqys1NatW9WjRw/TNZ566qlu8yUkJATkBMm1raPGjRu7hbD06/v+3XffqXXr1jUua+fOnZKkPXv2uP1UISYmxvUZ+vDDDzVt2jQVFha6nUPj5EPbR44cqT/96U/atm2b2rVrp2XLlmnnzp0aOXKkWz379u1TmzZt6qynSufOnWuc74svvtCUKVO0cuXKaof679u3T/Hx8abep++//17//ve/611PQLDzd8bUJjo6WpmZmeYLrkFMTEytP73y5KIY5Npx5Bq5BgQDso1sO5GTs602Xbp0qbZMb8XExNR4Hseqn4xzcTH4Ak1FC0VERJiaXhUYVSflfu2112psElZ9S7dgwQKNHj26xmXU5Pbbb9fcuXM1fvx4ZWRkKD4+XmFhYbr22mtrPBF4fXXagVU11nb+iYqKCq+XWVlZqTPPPFMzZ86s8f6q/0wNHz5cn332mWt6dna25s2bp88//1yXX365LrzwQj333HNq27atGjdurLlz57pd+Uv6dedr0qRJWrhwocaPH68333xT8fHxuvTSS93qadOmTa3fkp0cpDWF1g8//KCLL75YXbt21cyZM5WSkqLIyEj9/e9/16xZs7w6AX1lZaUuueQS3XvvvTXef/rpp5teJuBE/s6Y2lRUVFQ7uXptEhISaj1aQ/r1qIaKigrt3LnTrdFTXl6un3/+WcnJyXUun1zzHXKNXAPsgGwj23zJymyrTWlpqUpLS+t9noiIiFobl1Xatm2rHTt2VJteNa2+zxrgCZqKDnTKKadIktq0aVPnN2ZZWVn65JNPPF7uW2+9pezsbD311FOuaYcPH9bevXu9qrNjx46Sfv225sRvW3bt2lXtKoqeaN26tZo0aaKNGzdWu2/Dhg0KDw+vd5CurcZNmza5HXHw888/e1RjbUFU9Y3Zyeuu6luuk5//+++/d/vm9OjRoyoqKnL7+cQpp5yi9evX6+KLL67zpLlPPfWUW+1VYfH2228rOjpaH330kdth8HPnzq22jM6dO6tv375asGCBxo0bp3feeUfDhg1ze9wpp5yiTz/9VOedd57X33J98MEHOnLkiN5//323byhP/lmAmffplFNOUWlpqc++TQZCjacZU5utW7fWegTXyZYuXap+/frVen9aWpqkX392NHjwYNf0NWvWqLKy0nV/bcg1cq0KuQaENrKtdmSbNdlWmyeffFIPPvhgvfN17Nix2lWrT5aWlqbPP/9clZWVbhdr+eqrr9SkSRO+lIJPcE5FB8rKylJcXJymT5+uo0ePVru/6lu0tm3bKjMz0+2vLhEREdW+DXrmmWe8/qYmMzNTjRs31jPPPOO2XG+vUhgREaGBAwfqvffecxtAS0pKNH/+fJ1//vn1/nThZBdffLEaNWqk559/3m36s88+69HjmzZtWuPPDKr+43LieY4qKir04osvus3Xp08ftW7dWnl5eW4/g5g3b161cLvmmmu0bds2vfTSS9We79ChQyorK5P065XjTnzPu3fvLunX9RcWFub2fm7evLnWK5CNHDlSq1at0pw5c7R79263n4hV1VNRUaGHH3642mOPHTvm0X9sqr6VPPlnGyfvEJp5n6655hqtXLlSH330UbX79u7dq2PHjtVbFxDKPM2Y2vjyvFMDBgxQQkJCtW3/+eefV5MmTVxX960NuXYcuUauAaGMbKsd2WZNttXGl+dUvOqqq1RSUqJ33nnHNW337t1auHChhg4dWuP5FgGzOFLRgeLi4vT888/rxhtv1FlnnaVrr71WrVu31pYtW7R48WKdd955Hg+wJ7rsssv02muvKT4+Xt27d9fKlSv16aefqmXLll7V2bp1a02YMEEzZszQZZddpsGDB2vdunX6xz/+oVatWnm1zGnTpumTTz7R+eefr9tuu02NGjXSCy+8oCNHjujxxx83vbzExETdeeedeuqpp3T55Zfr0ksv1fr161011vftUu/evbVgwQLl5OTo7LPPVmxsrIYOHaoePXronHPO0aRJk7Rnzx4lJCTojTfeqPYf/8aNG2vatGm69dZbNWDAAI0cOVJFRUWaO3dutXNp3HjjjXrzzTf1hz/8QUuXLtV5552niooKbdiwQW+++aY++ugj9enTp9ZahwwZopkzZ+rSSy/Vb3/7W+3cuVO5ubk69dRT9e9//7va/Ndcc40mTJigCRMmKCEhoVpT+qKLLtKtt96qGTNmqLCwUAMHDlTjxo31/fffa+HChfrzn/+sq666qs71N3DgQEVGRmro0KG69dZbVVpaqpdeeklt2rRxO1TfzPt0zz336P3339dll12mUaNGqXfv3iorK9PXX3+tt956S5s3b/b68weEgoZmjK/PO/Xwww9r7Nixuvrqq5WVlaXPP/9cf/3rX/XII4/UeyJxco1cOxG5BoQusq12ZJu9ss2X51S86qqrdM4552j06NH69ttv1apVKz333HOqqKjw6GhIwCMBvdZ0EBs7dqxR2+rMzs52u+R7UVGRIcl44okn3OarunT9woUL3aZXXer+X//6V7X5s7KyjPj4eCM6Oto45ZRTjFGjRhlr1qzx6jX88ssvxujRo41WrVoZsbGxRlZWlrFhwwajY8eORnZ2tkf1SDKWLl3qmlZRUWE8+OCDRtu2bY2YmBijX79+xjfffFNtmbWRZEyZMsVt2tq1a42srCwjNjbWaNKkidG/f3/jyy+/dJvHTI3Hjh0zHnjgASMpKcmIiYkxBgwYYHz33XdGy5YtjT/84Q91Pra0tNT47W9/azRv3tyQ5PY+//DDD0ZmZqYRFRVlJCYmGn/605+MTz75pNoyDMMwnnvuOaNz585GVFSU0adPH2P58uXGRRddZFx00UVu85WXlxuPPfaY0aNHDyMqKspo0aKF0bt3b+PBBx809u3bV+/6fPnll43TTjvNiIqKMrp27WrMnTvXmDJlSq2f3fPOO8+QZPz+97+vdZkvvvii0bt3byMmJsZo1qyZceaZZxr33nuvsX37dtc8HTt2NIYMGVLj499//32jV69eRnR0tNGpUyfjscceM+bMmWNIMoqKilzzefo+GYZhHDhwwJg0aZJx6qmnGpGRkUarVq2Mc88913jyySeN8vLyetcTYDfBkDEN8eKLLxpnnHGGERkZaZxyyinGrFmzjMrKynofR66Raycj1wD7INvINrLN//bs2WPcfPPNRsuWLY0mTZoYF110UbX1DTREmGHY6AytgE3s3btXLVq00LRp03T//fdbXQ5qwfsEAJ5hvHQG3icA8BxjJmA9zqmIkHfo0KFq06rOIVLXSZYRWLxPAOAZxktn4H0CAM8xZgL2xDkVEfIWLFigefPmafDgwYqNjdWKFSv0t7/9TQMHDtR5551ndXn4/3ifnG3r1q268cYbtXPnTjVq1EgPPPCArr76aqvLAoIS46Uz8D7ZA/kEOANjJkKJk7KJnz8j5K1du1b33nuvCgsLtX//fiUmJmrEiBGaNm2aYmNjrS4P/x/vk7Pt2LFDJSUlSktLU3FxsXr37q3//ve/atq0qdWlAUGH8dIZeJ/sgXwCnIExE6HESdlEUxEAEHCpqan68MMPlZKSYnUpAAC4kE8AALuxczZxTkUAgJYvX66hQ4cqOTlZYWFhWrRoUbV5cnNz1alTJ0VHRys9PV2rV6/26rkKCgpUUVFhy1AEANgL+QQAsBuy6TiaigAAlZWVKTU1Vbm5uTXev2DBAuXk5GjKlClau3atUlNTlZWVpZ07d7rmSUtLU8+ePav9bd++3TXPnj179Lvf/U4vvvii318TAMD5yCcAgN2QTccF/c+fKysrtX37djVr1kxhYWFWlwMgCBmGoQMHDig5OVnh4Q37rubw4cMqLy/3SU0nj3lRUVGKioqq97FhYWF69913NWzYMNe09PR0nX322Xr22Wcl/Tq2pqSk6Pbbb9fEiRM9qunIkSO65JJLNGbMGN14442ev5ggRDYBCARf5ZOvsqmqJvLJvsgnAP7GvlNwZVPQX/15+/bttj1MFEBw2bp1q9q3b+/14w8fPqwOHZtq187KBtcSGxur0tJSt2lTpkzR1KlTTS+rvLxcBQUFmjRpkmtaeHi4MjMztXLlSo+WYRiGRo0apQEDBtg6FAOFbAIQSA3JJ19mk0Q+2R35BCBQ2HeqnZOyKeibis2aNZMk9Uu4QY3CIy2uJjCOdW5rdQlASDlWcUQr1j7pGm+8VV5erl07K7V8dRvFxnp/dEBpqaEL++7U1q1bFRcX55ruyTdtNdm9e7cqKiqUmJjoNj0xMVEbNmzwaBlffPGFFixYoF69ernOOfLaa6/pzDPP9Komp7NrNpEfgHcaFe2wuoQaHass17I9f21QPvkqmyTyyQnsmE9kE+DOrpnjKV9kk8S+k10EfVOx6hDWRuGRtglGv2sUbXUFQEjy1c+EYmPDFNusIT8F+PXburi4OLdgtNL555+vykrfHOUSDGybTeQH4J3TOrv+2eiHbRYWUjNf5FPDs0kin+zPbvl07JR2wb/DCphkh23TF9h3qp2TsokLtQSZY6e0s7oEAEGmVatWioiIUElJidv0kpISJSUlWVQVfI38AHzj2Cnt2J4ChHwKbmxHAJwo1LKJpmIQIXgB+ENkZKR69+6t/Px817TKykrl5+crIyPDwsoAwL5oLvof+QQAsJtQyyaOJg8S/KcVQEOUlpZq06ZNrttFRUUqLCxUQkKCOnTooJycHGVnZ6tPnz7q27evZs+erbKyMo0ePdrCqgHA/qr+j2bHn0U7AfkUmti3AWp37JR2ZIrFyKbjaCoGAUIXQEOtWbNG/fv3d93OycmRJGVnZ2vevHkaOXKkdu3apcmTJ6u4uFhpaWlasmRJtRMQw5nIEcD/aC56h3wKPWQSALsjm46jqQgAUL9+/WQYRp3zjBs3TuPGjQtQRQgUdt6AwOIIE3PIJwCA3ZBNx3FORYdjZxAAAMBZON8iUDO2C8AzbCuwC5qKDsZAAgBoCHIEsBbbIHAc2wMAOA9NRYcidAEAAJyPoxYBAIBT0VR0IP7jCQBoKLIEsBe2SYQyPv8A4Ew0FR2Eb7IBAL5AlgD2xLaJUMTnHvAO2w7sgKaiQzBgAAAABD/+z4dQwucdAJyNpqIDELYAAF8hUwD749cpCAV8xoGGIy9gNZqKNscAAQAAEJr4fyCCFZ9twLfYpmAVmoo2xTcOAABfIlcAZ2K7RbDhMw34B9sWrEBT0WbY6QMA+Bq5Ajgb/z9EsOBzDPgX2xgCrZHVBeBXbPwAAH8gX4DgceyUdmr0wzarywBMI4uAwKna3sgLBAJNRYsRsAAAfyFjgODDziKchiwCrHHitkdmwF9oKlqAYAUA+BM5AwQ/mouwO7IIsI+atkfyA75AUzFACFUAgD+RM0BoorkIuyGPAGeobVslT2AGTUUfIkABAIFC5gA40cljAjuFCBTyCAgunmzTZAyqhExT8VjntlKjaKvLAADAhWwC4C/HTmmnY8cOS7utrgRORD4BqEtDvkwgm4JLuNUFAAAAAAAAAHAWmooAAAAAAAAATKGpCAAAAAAAAMAUmooAAAAAAAAATKGpCAAAAAAAAMAUmooAAAAAAAAATKGpCAAAAAAAAMAUmooAAAAAAAAATKGpCAAAAAAAAMAUmooAAAAAAAAATKGpCAAAAAAAAMAUmooAAL/bu3ev+vTpo7S0NPXs2VMvvfSS1SUBAEA+AQBsx0nZ1MjqAgAAwa9Zs2Zavny5mjRporKyMvXs2VPDhw9Xy5YtrS4NABDCyCcAgN04KZs4UhEA4HcRERFq0qSJJOnIkSMyDEOGYVhcFQAg1JFPAAC7cVI20VQEAGj58uUaOnSokpOTFRYWpkWLFlWbJzc3V506dVJ0dLTS09O1evVqU8+xd+9epaamqn379rrnnnvUqlUrH1UPAAhW5BMAwG7IpuNoKgIAVFZWptTUVOXm5tZ4/4IFC5STk6MpU6Zo7dq1Sk1NVVZWlnbu3Omap+qcHyf/bd++XZLUvHlzrV+/XkVFRZo/f75KSkoC8toAAM5FPgEA7IZsOo5zKgJAkNq/f7/b7aioKEVFRdU476BBgzRo0KBalzVz5kyNGTNGo0ePliTl5eVp8eLFmjNnjiZOnChJKiws9KiuxMREpaam6vPPP9dVV13l0WMAAMGDfAIA2A3Z5B2aigBgMx+XdVV0mPfD8+GyY5JKlJKS4jZ9ypQpmjp1qunllZeXq6CgQJMmTXJNCw8PV2ZmplauXOnRMkpKStSkSRM1a9ZM+/bt0/Lly/XHP/7RdC0AAGs0NJsk8gkA4HvsO1mLpiIABKmtW7cqLi7Odbu2b9rqs3v3blVUVCgxMdFtemJiojZs2ODRMn788UfdcsstrpMM33777TrzzDO9qgcA4GzkEwDAbsgm79BUBIAgFRcX5xaMVurbt6/Hh/gDAIIb+QQAsBuyyTtcqAUAUKdWrVopIiKi2smBS0pKlJSUZFFVAIBQRz4BAOwm1LLJ8qbitm3bdMMNN6hly5aKiYnRmWeeqTVr1rjuNwxDkydPVtu2bRUTE6PMzEx9//33FlYMAKElMjJSvXv3Vn5+vmtaZWWl8vPzlZGRYWFl/kM2AYD9kU/kEwDYTahlk6VNxV9++UXnnXeeGjdurH/84x/69ttv9dRTT6lFixaueR5//HE9/fTTysvL01dffaWmTZsqKytLhw8ftrByAAgupaWlKiwsdB1mX1RUpMLCQm3ZskWSlJOTo5deekmvvPKKvvvuO/3xj39UWVmZ64pmwYRsAgD7IJ+OI58AwB7IpuMsPafiY489ppSUFM2dO9c1rXPnzq5/G4ah2bNn6//+7/90xRVXSJJeffVVJSYmatGiRbr22msDXjMABKM1a9aof//+rts5OTmSpOzsbM2bN08jR47Url27NHnyZBUXFystLU1LliypdgLiYEA2AYB9kE/HkU8AYA9k03GWHqn4/vvvq0+fPrr66qvVpk0b/eY3v9FLL73kur+oqEjFxcXKzMx0TYuPj1d6enqtl+I+cuSI9u/f7/YHAKhbv379XFcXO/Fv3rx5rnnGjRunH3/8UUeOHNFXX32l9PR06wr2I7IJAOyDfDqOfAIAeyCbjrO0qfi///1Pzz//vE477TR99NFH+uMf/6g77rhDr7zyiiSpuLhYkmq8FHfVfSebMWOG4uPjXX8pKSn+fREAgKBCNgEA7Ih8AgDYjaVNxcrKSp111lmaPn26fvOb3+iWW27RmDFjlJeX5/UyJ02apH379rn+tm7d6sOKAQDBjmwCANgR+QQAsBtLm4pt27ZV9+7d3aZ169bNdXLLqsttm7kUd1RUlOLi4tz+AADwFNkEALAj8gkAYDeWNhXPO+88bdy40W3af//7X3Xs2FHSryceTkpKcrsU9/79+/XVV18F5aW4AQDWI5sAAHZEPgEA7MbSqz/fddddOvfcczV9+nRdc801Wr16tV588UW9+OKLkqSwsDCNHz9e06ZN02mnnabOnTvrgQceUHJysoYNG2Zl6QCAIEU2AQDsiHwCANiNpU3Fs88+W++++64mTZqkhx56SJ07d9bs2bN1/fXXu+a59957VVZWpltuuUV79+7V+eefryVLlig6OtrCygEAwYpsAgDYEfkEALAbS5uKknTZZZfpsssuq/X+sLAwPfTQQ3rooYcCWBUAIJSRTQAAOyKfAAB2Yuk5FQEAAAAAAAA4D01FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAACAIHWgc4wOdI6xugwEoUZWFwAAAAAAAADfO7GZWFtjsVnRoUCVgyDDkYoAAAAAAABBxMzRiRzFCG/RVAQAAAAAAAgC3v7UmcYivMHPnwEAAAAPeLrDxc/IAABWaGhj8EDnGDIMpnCkIgAgYA4ePKiOHTtqwoQJVpcCAB6pOuLDzI4aR3s4D/kEwOl8lT1kmH04IZtoKgIAAuaRRx7ROeecY3UZAFCvhl4pk50yZyGfADiZrzOHDLMHJ2QTTUUAQEB8//332rBhgwYNGmR1KQBQJ472CC3kEwAn81fWkGHWcko20VQEAGj58uUaOnSokpOTFRYWpkWLFlWbJzc3V506dVJ0dLTS09O1evVqU88xYcIEzZgxw0cVA4DvNfToxNqWCe+RTwBQO39nDBlWM7LpOJqKABztxHNdeXPeK/yqrKxMqampys3NrfH+BQsWKCcnR1OmTNHatWuVmpqqrKws7dy50zVPWlqaevbsWe1v+/bteu+993T66afr9NNPD9RLAgBT/Jkd5JL3yCcAqBnZYh2y6Tiu/gzAUTwNz6r5QvnqZfv373e7HRUVpaioqBrnHTRoUJ2H1s+cOVNjxozR6NGjJUl5eXlavHix5syZo4kTJ0qSCgsLa338qlWr9MYbb2jhwoUqLS3V0aNHFRcXp8mTJ5t8VQDge+yYBRb5BAANQ275HtnkHZqKABzB2+A80DnGcY3Fz34+XY0PR3r9+KNl5ZI+U0pKitv0KVOmaOrUqaaXV15eroKCAk2aNMk1LTw8XJmZmVq5cqVHy5gxY4br8P158+bpm2++sWUoAgg9gdoxc2Ienaih2SSRTwDgC4FuKNo9v9h3shZNRQC25ovQDNWjFrdu3aq4uDjX7dq+aavP7t27VVFRocTERLfpiYmJ2rBhQ4NqBAArcaSHNcgnAPAOueU/ZJN3aCoCsCV/BKbdv2Xztbi4OLdgtItRo0ZZXQIAsGNmIfIJAMwjt/yLbPIOF2oBYCv+vtAKF3Ixr1WrVoqIiFBJSYnb9JKSEiUlJVlUFQB4z6ocIH98i3wCECrID+cItWyiqQjAFgLd7COYPRcZGanevXsrPz/fNa2yslL5+fnKyMiwsDIAMIcvloIL+QQg2Nklt+xQg1OEWjbx82cAlrIyoELt59B1KS0t1aZNm1y3i4qKVFhYqISEBHXo0EE5OTnKzs5Wnz591LdvX82ePVtlZWWuK5oBgN2xQ+RM5BP85cQxgf8Pwm7ILHsjm46jqQjAEnYJShqLv1qzZo369+/vup2TkyNJys7O1rx58zRy5Ejt2rVLkydPVnFxsdLS0rRkyZJqJyAGADuyS+ZI5I5Z5BN8rabx4ORpbKOwip3yCrUjm46jqQggoOwYlKF6degT9evXT4Zh1DnPuHHjNG7cuABVhGBQ2/YeytsaAs+OuQPPkU/wFTNjAf83RCA5Jaf4Uuw4suk4mooA/M5JQSnxH0jAG97srFVhm4O/OCV/APhPQ8YBmijwB7IJwYSmIgC/cWpg0lwEPOOrbZxtDr5m9/yhUQH4HxkFu7F7NgHeoKkIwOeCJTA5mgqozp/bNztu8IVgySAA3vHXGMCXAfAWuYRgRlMRgE8Fc2jW9tqOHQ2T/hXgYoAACvR2zY4bvBXMGQSgdoHa9vnyC2YEYybxfzScjKYiAJ8IxtAEQp2V2zX/aYUZTswgPuNAw1m17bP9oi5OzCTAWzQVATQIoQkEFztt0+y0wRN2+swC8D+7bPNkFE5ml88mEEg0FQF4hdAEgotdt2l22lAXu35uAfiH3bZ5MgqS/T6XQCCFW10AAOchOIHgYvdt2u71IfAOdI7hcwGEGLtu83atC4ERiu9/KL5m1I6mIgCPsRMHBB+nbNNOqRP+F0yfhWB6LYA/2X1bsXt98A/ed8DipuLUqVMVFhbm9te1a1fX/YcPH9bYsWPVsmVLxcbGasSIESopKbGwYiA00UxEKAmlbHLadu20euF7fAYQykIpn07klO3eKXWi4dg3Ao6z/EjFHj16aMeOHa6/FStWuO6766679MEHH2jhwoX67LPPtH37dg0fPtzCaoHQQ2AiFIVCNrFtw2n4zAKhkU8nctp277R6YR7vMeDO8gu1NGrUSElJSdWm79u3Ty+//LLmz5+vAQMGSJLmzp2rbt26adWqVTrnnHMCXSoQcghNhKpgzyYnb9ucFD80OfkzWx8+0zAj2PPpRMG83cOZ+EweR3ahiuVHKn7//fdKTk5Wly5ddP3112vLli2SpIKCAh09elSZmZmuebt27aoOHTpo5cqVtS7vyJEj2r9/v9sfAHM4pB+hLpizKRi27WB4DfAMeQS4C+Z8ChaMWcGJ9xWomaVNxfT0dM2bN09LlizR888/r6KiIl1wwQU6cOCAiouLFRkZqebNm7s9JjExUcXFxbUuc8aMGYqPj3f9paSk+PlVAMGFwESoC+ZsYvuGk4TS5zWUXiu8F8z5dDKnbxNOrx/ueD+B2ln68+dBgwa5/t2rVy+lp6erY8eOevPNNxUT492GO2nSJOXk5Lhu79+/3zbhCNgZYQn8KlizKdi2cX52E9yC7fMK+EKw5tPJgmX7J6eCQ7B8Hv2BzzgkG/z8+UTNmzfX6aefrk2bNikpKUnl5eXau3ev2zwlJSU1nkekSlRUlOLi4tz+ANSNsARqFwzZxDYOJ+HzCngmGPLpZGz/sBM+j0D9bNVULC0t1Q8//KC2bduqd+/eaty4sfLz8133b9y4UVu2bFFGRoaFVQLBg3NVAfVzejYF8zYezK8tFIV6JoXya4d3nJ5PoYDt2rl47wDPWPrz5wkTJmjo0KHq2LGjtm/frilTpigiIkLXXXed4uPjdfPNNysnJ0cJCQmKi4vT7bffroyMDEdevQywCwISqBvZ5Cz89Mb5yKXj+DyjLsGeT8E6FlS9LrZt+wvWz6A/kVuwtKn4008/6brrrtPPP/+s1q1b6/zzz9eqVavUunVrSdKsWbMUHh6uESNG6MiRI8rKytJzzz1nZcmA4xCOgDnBlE2hsv2zw+Y8ofLZ9AY7aKhNMOXTyUJhTCCr7CkUPnuAP1naVHzjjTfqvD86Olq5ubnKzc0NUEXOZWYwJMiCG8EINEywZFMojgXssNlbKH4mvUVjETUJlnw6WaiNDWSVtULt8xYIZFZos7SpCM/5cvCrb1kMCM5AIAKoTaiPD/zn1nqh/hn0BT7HCHahPk7QXAyMUP+cAf5GU9GGrB74anp+ws46Vn8eADgLY8av2FkLHD5z/sPnGMGKceM4tnPf4/MVeHwRFrpoKlrMKQMejUb/ccpnAIC9MZbU7MT1Qm55h8+W9fgcI5gwptSM7dx7fKbsgc9waKKpGGDBNODRaKxdML3PAOyPMccz/Gf3OD4zzlXbexfqn2nYH+OO58irmvEZcg4+w6GDpqKfhdrAF8jzNYbaugWAmjAWeufk9ebP//DyHiEQavqcHTsaJv3LgmKA/4/xr+F8dSAH7wWscvJnj2wKLjQVfYiBun6sIwBoOMZS32OdAoBvMJ76H+sYgF3QVDSBwRsAYBUyCABgB+QRAKBKyDQVD3SKUaPG0VaXAQAhq1OnToqLi1N4eLhatGihpUuXWl2S5cgmALAe+VQd+QQA1nJKNoVMUxEAYL0vv/xSsbGxVpcBAIAb8gkAYDdOyKZwqwsAAAAAAAAA4Cw0FQEAWr58uYYOHark5GSFhYVp0aJF1ebJzc1Vp06dFB0drfT0dK1evdrUc4SFhemiiy7S2Wefrddff91HlQMAghn5BACwG7LpOH7+DABQWVmZUlNTddNNN2n48OHV7l+wYIFycnKUl5en9PR0zZ49W1lZWdq4caPatGkjSUpLS9OxY8eqPfbjjz9WcnKyVqxYoXbt2mnHjh3KzMzUmWeeqV69evn9tQEAnIt8AgDYDdl0HE1FAAhS+/fvd7sdFRWlqKioGucdNGiQBg0aVOuyZs6cqTFjxmj06NGSpLy8PC1evFhz5szRxIkTJUmFhYV11tOuXTtJUtu2bTV48GCtXbvWlsEIAPAv8gkAYDdkk3doKgKAzWwsaa2IJt5fcbHi4GFJUkpKitv0KVOmaOrUqaaXV15eroKCAk2aNMk1LTw8XJmZmVq5cqVHyygrK1NlZaWaNWum0tJS/fOf/9Q111xjuhYAgDUamk0S+QQA8D32naxFUxEAgtTWrVsVFxfnul3bN2312b17tyoqKpSYmOg2PTExURs2bPBoGSUlJbryyislSRUVFRozZozOPvtsr+oBADgb+QQAsBuyyTs0FQEgSMXFxbkFo5W6dOmi9evXW10GAMAGyCcAgN2QTd7h6s8AgDq1atVKERERKikpcZteUlKipKQki6oCAIQ68gkAYDehlk00FQEAdYqMjFTv3r2Vn5/vmlZZWan8/HxlZGRYWBkAIJSRTwAAuwm1bOLnzwAAlZaWatOmTa7bRUVFKiwsVEJCgjp06KCcnBxlZ2erT58+6tu3r2bPnq2ysjLXFc0AAPAH8gkAYDdk03E0FQEAWrNmjfr37++6nZOTI0nKzs7WvHnzNHLkSO3atUuTJ09WcXGx0tLStGTJkmonIAYAwJfIJwCA3ZBNx9FUBACoX79+MgyjznnGjRuncePGBagiAADIJwCA/ZBNx3FORQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYEojT2Z6//33TS/4kksuUUxMjOnHAQDgKfIJAGA3ZBMAIFR41FQcNmyYqYWGhYXp+++/V5cuXbypCQAAj5BPAAC7IZsAAKHC458/FxcXq7Ky0qO/Jk2a+LNmAABcyCcAgN2QTQCAUOBRUzE7O9vU4fg33HCD4uLivC4KAABPkE8AALshmwAAocKjnz/PnTvX1EKff/55r4oBAMAM8gkAYDdkEwAgVHD1ZwAAAAAAAACmeHSk4okOHz6sZ555RkuXLtXOnTtVWVnpdv/atWt9VhwAAJ4inwAAdkM2AQCCmemm4s0336yPP/5YV111lfr27auwsDB/1AUAgCnkEwDAbsgmAEAwM91U/PDDD/X3v/9d5513nj/qAQDAK+QTAMBuyCYAQDAzfU7Fdu3aqVmzZv6oBQAAr5FPAAC7IZsAAMHMdFPxqaee0n333acff/zRH/UAAOAV8gkAYDdkEwAgmJluKvbp00eHDx9Wly5d1KxZMyUkJLj9eevRRx9VWFiYxo8f75p2+PBhjR07Vi1btlRsbKxGjBihkpISr58DABC8yCcAgN2QTQCAYGb6nIrXXXedtm3bpunTpysxMdEnJxv+17/+pRdeeEG9evVym37XXXdp8eLFWrhwoeLj4zVu3DgNHz5cX3zxRYOfEwAQXMgnAIDdkE0AgGBmuqn45ZdfauXKlUpNTfVJAaWlpbr++uv10ksvadq0aa7p+/bt08svv6z58+drwIABkqS5c+eqW7duWrVqlc455xyfPD8AIDiQTwAAuyGbAADBzPTPn7t27apDhw75rICxY8dqyJAhyszMdJteUFCgo0ePuk3v2rWrOnTooJUrV9a6vCNHjmj//v1ufwCA4GfnfCKbACA02TmbJPIJANAwppuKjz76qO6++24tW7ZMP//8c4NC6I033tDatWs1Y8aMavcVFxcrMjJSzZs3d5uemJio4uLiWpc5Y8YMxcfHu/5SUlJM1QQAcCY75xPZBAChyc7ZJJFPAICGMf3z50svvVSSdPHFF7tNNwxDYWFhqqio8Gg5W7du1Z133qlPPvlE0dHRZsuo1aRJk5STk+O6vX//fsIRAEKAnfOJbAKA0GTnbJLIJwBAw5huKi5dutQnT1xQUKCdO3fqrLPOck2rqKjQ8uXL9eyzz+qjjz5SeXm59u7d6/aNW0lJiZKSkmpdblRUlKKionxSIwDAd4qKinTTTTeppKREERERWrVqlZo2beqz5ds5n8gmALAvf+aTnbNJIp8AwK78ve/kK6abiueee64aN25c4327d+/2eDkXX3yxvv76a7dpo0ePVteuXXXfffcpJSVFjRs3Vn5+vkaMGCFJ2rhxo7Zs2aKMjAyzZQMALDZq1ChNmzZNF1xwgfbs2ePznRjyCQDgDX/mE9kEAPCGv/edfMV0U/Haa6/VW2+9pbCwMLfpJSUluvjii/XNN994tJxmzZqpZ8+ebtOaNm2qli1buqbffPPNysnJUUJCguLi4nT77bcrIyODq5cBgMP85z//UePGjXXBBRdIkhISEny6/Pz8fN1www2KiYlRWFiYunXrpvHjxyszM5N8AgDUyp/5RDYBALwRiH2nWbNm6bvvvpMkt3wyy/SFWrZs2aLf//73btN27Nihfv36qWvXrqYLqMusWbN02WWXacSIEbrwwguVlJSkd955x6fPAQCQli9frqFDhyo5OVlhYWFatGhRtXlyc3PVqVMnRUdHKz09XatXr/Z4+d9//71iY2M1dOhQnXXWWZo+fbrPan/uued06aWXqry8XG3atNGdd96puLg4DR48WNOnTyefAMDBnJpPZBMABC+nZpN0PJ+aNWumO++80y2fcnNzTS/P9JGKf//733XhhRcqJydHM2fO1Pbt29W/f3+lpqbqjTfeMF3AiZYtW+Z2Ozo6Wrm5uV69MACA58rKypSamqqbbrpJw4cPr3b/ggULlJOTo7y8PKWnp2v27NnKysrSxo0b1aZNG0lSWlqajh07Vu2xH3/8sY4dO6bPP/9chYWFatOmjS699FKdffbZuuSSSxpc+/Tp0zVr1iyNHDlSF154oTZv3qz58+erR48emjx5soYPH04+AYBDOTWfyCYACF5OzSbpeD6NGzfONe2OO+7Qeeedp+nTp2vs2LGmlme6qdi6dWt9/PHHOv/88yVJH374oc466yy9/vrrCg83feAjAMBP9u/f73a7rpOxDxo0SIMGDap1WTNnztSYMWM0evRoSVJeXp4WL16sOXPmaOLEiZKkwsLCWh/frl079enTx3VFycGDB6uwsNAnwbh3715deuml1fLp3XfflST97W9/I58AwEZCIZ/IJgBwllDIJul4Pp1s4MCBuu+++0wvz6skS0lJ0SeffKLXX39dffv21d/+9jdFRER4sygAwEkqipvo2Hbv/yqKm0j6dayOj493/c2YMcOresrLy1VQUOB2jo3w8HBlZmZq5cqVHi3j7LPP1s6dO/XLL7+osrJSy5cvV7du3byq52SXX365ayftxHxq0aKFRowYQT4BgA80NJtCLZ/IJgAIDPadzDkxn0703nvv6bLLLjO9PI+OVGzRokW1C7NI0sGDB/XBBx+oZcuWrml79uwxXQQAwPe2bt2quLg4121vrxi2e/duVVRUKDEx0W16YmKiNmzY4NEyGjVqpOnTp+vCCy+UYRgaOHCgV6F1shYtWujIkSN64403dP/996tRo19j7ciRI9q5c6c2bNigpk2bSpJmzJihO+64o8HPCQBomGDPJ7IJAJwn2LOpSvfu3fXII49o2bJlysjIkCStWrVKX3zxhe6++249/fTTrnk9ySePmoqzZ8/2rloAgGXi4uLcgtFq9f1MwBuzZ8/WhAkTXDtnVU6+XTUvO24AYL1gzyeyCQCcJ9izqcrLL7+sFi1a6Ntvv9W3337rmt68eXO9/PLLrtthYWG+aypmZ2d7USoAIBi0atVKERERKikpcZteUlKipKQki6r6VXZ2NhkFACHKrvlENgFA6LJrNlUpKiry6fI8OqfiySesrM+BAwe8KgYAYD+RkZHq3bu38vPzXdMqKyuVn5/vOmTeKuQTAIQuu+YT2QQAocuu2eQvHp9TcceOHa5LX9enXbt2KiwsVJcuXRpUHAAgMEpLS7Vp0ybX7aKiIhUWFiohIUEdOnRQTk6OsrOz1adPH/Xt21ezZ89WWVmZ64pmVqnKp/Lycr3//vvasmWLysvL3eaZOXOm69/kEwA4ixPziWwCgODmxGw60U8//eRRPnnCo6aiYRj6y1/+otjYWI8WevToUVNFAACstWbNGvXv3991OycnR9KvP+GaN2+eRo4cqV27dmny5MkqLi5WWlqalixZUu0ExIFmGIYmTZqk1157Ta1atVJJSYnatm2rPXv2yDAMpaSkqFOnTq75yScAcBYn5hPZBADBzYnZVCU/P1+XX365unTpog0bNqhnz57avHmzDMPQWWedZXp5HjUVO3TooJdeesnjhSYlJalx48amiwEAWKNfv34yDKPOecaNG6dx48YFqCLPdOjQQX/961/VtGlTRUVFyTAMhYWFqU2bNtq1a5f27NmjWbNmueYnnwDAWZyYT2QTAAQ3J2ZTlUmTJmnChAl68MEH1axZM7399ttq06aNrr/+el166aWml+dRU3Hz5s2mFwwAgL9t3rxZzZo105o1a3TKKaeoRYsWWrJkiXr06KH169friiuu8PnJiAEAqAvZBACwq++++05/+9vfJEmNGjXSoUOHFBsbq4ceekhXXHGF/vjHP5pankcXagEAwK6aNm3qOhdI27Zt9cMPP7ju2717t1VlAQBCGNkEALAjX+eTR0cqAgBgV+ecc45WrFihbt26afDgwbr77rv19ddf65133tE555xjdXkAgBBENgEA7MjX+URTEQDgaDNnzlRpaakk6cEHH1RpaakWLFig0047zfTVywAA8AWyCQBgR77OJ5qKAABH69Kli+vfTZs2VV5enoXVAABANgEA7MnX+cQ5FQEAjvb73/9ey5Yts7oMAABcyCYAgB35Op+8aip+/vnnuuGGG5SRkaFt27ZJkl577TWtWLHCZ4UBAOCJXbt26dJLL1VKSoquu+46DRkyhHwCAFiKbAIA2NGJ+XTPPfdo/fr1DVqe6abi22+/raysLMXExGjdunU6cuSIJGnfvn2aPn16g4oBAMCs9957Tzt27NCQIUP05ptv6u9//7u++uorzZo1S5s3byafAAABRzYBAOyoKp8eeOAB/etf/9JZZ52lHj16aPr06dq8ebPp5ZluKk6bNk15eXl66aWX1LhxY9f08847T2vXrjVdAAAADdWiRQt99dVXmjt3rrZu3arGjRvr3Xff1amnnko+AQAsQTYBAOyoRYsWuuWWW7Rs2TL9+OOPGjVqlF577TWdeuqpppdluqm4ceNGXXjhhdWmx8fHa+/evaYLAADAFzZu3KiMjAytWbNGlZWV+umnn5SYmEg+AQAsQzYBAOzq6NGjWrNmjb766itt3rxZiYmJppdhuqmYlJSkTZs2VZu+YsUKt6vIAAAQKEuXLlWjRo101llnadSoUZKkv/zlL/rpp5/IJwCAJcgmAIAdLV26VGPGjFFiYqJGjRqluLg4ffjhh/rpp59ML8t0U3HMmDG688479dVXXyksLEzbt2/X66+/rgkTJuiPf/yj6QIAAGiIdu3aafDgwUpJSVHz5s31wQcfKCYmRp07d9b8+fPJJwBAwJFNAAA7qsqn3bt368UXX1RJSYnmzJmjiy++WGFhYaaX18jsAyZOnKjKykpdfPHFOnjwoC688EJFRUVpwoQJuv32200XAABAQ0ydOlVXX3214uPjNX36dA0aNKjGfPrpp5+UnJys8HDT36cBAGAK2QQAsKOqfGrevHmd83maT6abimFhYbr//vt1zz33aNOmTSotLVX37t0VGxtrdlEAADTYmDFjXP+uK5+6d++uwsJCfm4GAPA7sgkAYEcn5lNdPM0n003FKpGRkerevbu3DwcAwC9qyyfDMCyoBgAAsgkA4Cye5pNHTcXhw4d7/MTvvPOOx/MCANAQZvIJAIBAIJsAAKHCo5N3xMfHu/7i4uKUn5+vNWvWuO4vKChQfn6+4uPj/VYoAAAnI58AAHZDNgEAQoVHRyrOnTvX9e/77rtP11xzjfLy8hQRESFJqqio0G233aa4uDj/VAkAQA3IJwCA3ZBNAIBQYfqcinPmzNGKFStcoShJERERysnJ0bnnnqsnnnjCpwUCAOCJ+vIpLCzMwuoAAKGIbAIAOJGn+eTRz59PdOzYMW3YsKHa9A0bNqiystLs4gAA8In68omT4QMAAo1sAgA4kU8v1HKi0aNH6+abb9YPP/ygvn37SpK++uorPfrooxo9erTZxQEA4BOjR4/W6NGjNW7cOA0cOFCSez7dddddSk5OtrhKAEAoIZsAAHb1008/SZLat29f7b5vv/3Wo3wy3VR88sknlZSUpKeeeko7duyQJLVt21b33HOP7r77brOLAwCgQSorKzVt2jT95S9/UWlpqR5++GE9/PDDCgsLc8unE396BgCAP5FNAAA7qsqnp556SqWlpZKkZs2a6e6779b999+v8PBff9CckpLi0fJMNxXDw8N177336t5779X+/fsliZMMAwAsc//99+vll1/WY489pvPOO0+S9Mknn2jGjBkaNWqU7r33XosrBACEGrIJAGBHVfn06KOPuvJpxYoVmjp1qg4fPqxHHnnE1PJMNxVPRDMRAGC1V155RX/5y190+eWXu6b16tVLp556qm677TbTwQgAQEORTQAAO6otn9q1a+dVPpluKnbu3LnOq8D873//M7tIAAC8tmfPHnXt2rVaPh09elQ7duxQly5dJJFPAIDAIZsAAHZUlU8n69q1q/bs2WN6eaabiuPHj3e7ffToUa1bt05LlizRPffcY7oAAAAaIjU1Vc8++2y1fHrzzTd16NAh7du3j3wCAAQU2QQAsKOqfHr66afdpj/77LNKTU01vTzTTcU777yzxum5ublas2aN6QIAAGiIxx9/XEOGDFGHDh2UkZEhSVq5cqW2bt2qv//97/r3v/9NPgEAAopsAgDYUVU+ffrppzXmk1nhvips0KBBevvtt321OABAENm4caPS0tJcfzExMVq0aJFPln3RRRfpv//9r6688krt3btXe/fu1fDhw7Vx40ZdcMEF5BMAoFb+yieyCQDgLSv3ncxq0IVaTvTWW28pISHBV4sDAASRM844Q4WFhZKk0tJSderUSZdcconPlp+cnFzrSYXJJwBAbfyZT2QTAMAbVu47mWW6qfib3/zG7WTDhmGouLhYu3bt0nPPPeeTogAAwev999/XxRdfrKZNm/pkeZWVlQoPD68xn7Zt26Y9e/aQTwCAevkyn8gmAIAv+GvfqabpP/30kzp06GBqeaZ//nzFFVe4/Q0fPlxTpkzRN998o1tuucXs4gAANrB8+XINHTpUycnJCgsLq/Hw+tzcXHXq1EnR0dFKT0/X6tWrvXquN998UyNHjmxgxdL+/ft1zTXXqGnTpkpMTFRcXJyGDh3qyqeBAwdq9+7d5BMAOJjT8olsAoDg57Rskqrn0+TJk1VRUeG6f9euXercubPp5Zo+UnHq1KmmnwQAYG9lZWVKTU3VTTfdpOHDh1e7f8GCBcrJyVFeXp7S09M1e/ZsZWVlaePGjWrTpo0kKS0tTceOHav22I8//ljJycmSfg2zL7/8Um+88UaDa37ggQe0fv16vfbaa9q7d6+mTZumgoICvfPOO4qMjFRJSYmeeOIJde3atcHPBQCwhtPyiWwCgODntGySas6ntWvXuvJJ+vVoerNMNxUjIiK0Y8cO14qo8vPPP6tNmzZunU4AgHX279/vdjsqKkpRUVE1zjto0CANGjSo1mXNnDlTY8aM0ejRoyVJeXl5Wrx4sebMmaOJEydKkuu8H3V57733NHDgQEVHR3v4Kmq3aNEivfLKK+rXr58k6dZbb1VCQoKGDh2q999/X5IUFhZGPgGAzQRzPpFNAOBMwZxNUvV8GjZsmIYMGVItn8wy/fPn2jqXR44ccXU3PfX888+rV69eiouLU1xcnDIyMvSPf/zDdf/hw4c1duxYtWzZUrGxsRoxYoRKSkrMlgwAjhKzLVxNfvL+L2bbr0N7SkqK4uPjXX8zZszwqp7y8nIVFBQoMzPTNS08PFyZmZlauXKlqWX56vB96ddD9Dt27Oi6bRiG3nrrLR04cECDBw/WwYMHJZnPJ7IJAKpraDaFSj75K5sk8gkAasK+k2dOzqdWrVrp008/rZZPZnl8pOLTTz8t6dfO5V/+8hfFxsa67quoqNDy5ctNH8bfvn17PfroozrttNNkGIZeeeUVXXHFFVq3bp169Oihu+66S4sXL9bChQsVHx+vcePGafjw4friiy9MPQ8AhKKtW7cqLi7Odbu2b9rqs3v3blVUVCgxMdFtemJiojZs2ODxcvbt26fVq1fr7bff9qqOk3Xo0EHfffedPvjgA0m/5tMbb7yhK6+8Us8995zOP/98GYahsWPHmsonsgkA/CuY88lf2SSRTwDgT8GcTdLxfDrxvInNmjXTxx9/rIEDB+rKK6/0arkeNxVnzZol6ddv2/Ly8hQREeG6LzIyUp06dVJeXp6pJx86dKjb7UceeUTPP/+8Vq1apfbt2+vll1/W/PnzNWDAAEnS3Llz1a1bN61atUrnnHOOqecCgFBTdSSDXcTHx/v0iImBAwdq7ty5WrNmjST3fDIMQz///LMMw9DBgwdN5RPZBAD+Fcz55K9sksgnAPCnYM4m6Xg+DR482G16bGysPvroI11yySVeLdfjpmJRUZEkqX///nrnnXfUokULr56wNhUVFVq4cKHKysqUkZGhgoICHT161O2Q0a5du6pDhw5auXIlwQgAAdKqVStFRERUC7WSkhIlJSVZVJX04IMPavv27erRo4ek6vl04MABrV27VhdddJHXz0E2AYB92TGfApFNEvkEAHZlx2ySjudTTZo1a6ZPPvlEa9euNb1c0xdqWbp0qeknqcvXX3+tjIwMHT58WLGxsXr33XfVvXt3FRYWKjIyUs2bN3ebPzExUcXFxbUu78iRIzpy5Ijr9skn2wQAmBMZGanevXsrPz9fw4YNkyRVVlYqPz9f48aNs6yuFi1auHbS8vPzlZGRoXvuuUeVlZVu83mz40Y2AYD92TGf/JlNEvkEAHZnx2ySqudTfn6+du7c2eB88qipmJOTo4cfflhNmzZVTk5OnfPOnDnTVAFnnHGGCgsLtW/fPr311lvKzs7WZ599ZmoZJ5oxY4YefPBBrx8PAKGotLRUmzZtct0uKipSYWGhEhIS1KFDB+Xk5Cg7O1t9+vRR3759NXv2bJWVlbmuaGaVnJwcxcTE6NFHH1Xr1q3dzvd74jySuXwimwDAHpyYT/7KJol8AgA7cGI2VXnwwQf10EMPqU+fPmrbtq1XV3w+kUdNxXXr1uno0aOSpLVr1zb4SU8UGRmpU089VZLUu3dv/etf/9Kf//xnjRw5UuXl5dq7d6/bN271HTI6adIkt8bn/v37lZKS4rN6ASAYrVmzRv3793fdrhpHs7OzNW/ePI0cOVK7du3S5MmTVVxcrLS0NC1ZsqTaCYgDbd26dfr22281b948vfzyyzXm07p160wvl2wCAHtwYj75K5sk8gkA7MCJ2VQlLy9P8+bN04033uiT5XnUVDzxJ8/Lli3zyRPXprKyUkeOHFHv3r3VuHFj5efna8SIEZKkjRs3asuWLcrIyKj18VFRUV5fpQcAQlW/fv1kGEad84wbN87SQ/ZrsnTpUrVs2VLnnnuuz4KxJmQTAFjDifkUqGySyCcAsIITs6lKeXm5zj33XJ8tL9zsA2666SYdOHCg2vSysjLddNNNppY1adIkLV++XJs3b9bXX3+tSZMmadmyZbr++usVHx+vm2++WTk5OVq6dKkKCgo0evRoZWRkcKJhAIDL73//e82fP99n+UQ2AQAaytfZJJFPAICGq8onXzF9oZZXXnlFjz76qJo1a+Y2/dChQ3r11Vc1Z84cj5e1c+dO/e53v9OOHTsUHx+vXr16uV3KetasWQoPD9eIESN05MgRZWVl6bnnnjNbMgAgyJz4U63Kykq9+OKL2rt3r8LCwhQfH+8275/+9CdT+UQ2AQC84c9sksgnAIB3asqnTz/9VL169VLjxo3d5jV7rl+Pm4r79++XYRgyDEMHDhxQdHS0676Kigr9/e9/V5s2bUw9+csvv1zn/dHR0crNzVVubq6p5QIAgtuJ56I6duyYevbsqRUrVug///lPtWA0m09kEwDAG/7MJol8AgB45+Tz+KalpUmSvvnmG7fp3lw/xeOmYvPmzRUWFqawsDCdfvrp1e4PCwvjymEAgIA48Vy/4eHhCgsLU3h4uP71r39Vm3flypXkEwDA78gmAIAdnZhPvuZxU3Hp0qUyDEMDBgzQ22+/rYSEBNd9kZGR6tixo5KTk/1SJAAAtSGfAAB2QzYBAEKBx03Fiy66SJJUVFSkDh06eHVYJAAAvkY+AQDshmwCAIQCj5qK//73v91uf/3117XO26tXr4ZVBACAh8gnAIDdkE0AgFDhUVMxLS1NYWFhMgyjzvnCwsJUUVHhk8IAAKgP+QQAsBuyCQAQKjxqKhYVFfm7DgAATCOfAAB2QzYBAEKFR03Fjh07+rsOAABMI58AAHZDNgEAQoXHF2o52bfffqstW7aovLzcbfrll1/e4KIAAPAW+QQAsBuyCQAQjEw3Ff/3v//pyiuv1Ndff+12rpCqK5pxXhAAgBXIJwCA3ZBNAIBgFm72AXfeeac6d+6snTt3qkmTJvrPf/6j5cuXq0+fPlq2bJkfSgQAoH7kEwDAbsgmAEAwM32k4sqVK/XPf/5TrVq1Unh4uMLDw3X++edrxowZuuOOO7Ru3Tp/1AkAQJ3IJwCA3ZBNAIBgZvpIxYqKCjVr1kyS1KpVK23fvl3Sryck3rhxo2+rAwDAQ+QTAMBuyCYAQDAzfaRiz549tX79enXu3Fnp6el6/PHHFRkZqRdffFFdunTxR40AANSLfAIA2A3ZBAAIZqabiv/3f/+nsrIySdJDDz2kyy67TBdccIFatmypBQsW+LxAAAA8QT4BAOyGbAIABDPTTcWsrCzXv0899VRt2LBBe/bsUYsWLVxXMQMAINDIJwCA3ZBNAIBgZrqpWJOEhARfLAYAAJ8inwAAdkM2AQCChekLtQAAAAAAAAAIbTQVAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJjSyOoCAAAAAABAaCpt5/mxTrHbKv1YCQCzaCoCAAAAAICAMdNIrO1xNBgB6/HzZwAAAAAA4Hel7cK9bijWtCwA1mIrBAAExKxZs9SjRw91795dd9xxhwzDsLokAADIJyBA/NEEpLGIYOWUbGILBAD43a5du/Tss8+qoKBAX3/9tQoKCrRq1SqrywIAhDjyCQgMfzb/aCwi2DgpmzinIuBHdQUc5wBBqDl27JgOHz4sSTp69KjatGljcUVAaOKE+IA78glwvtJ24WQWgopTsomWPuBjVecJqW+nzZfnEwEaavny5Ro6dKiSk5MVFhamRYsWVZsnNzdXnTp1UnR0tNLT07V69WqPl9+6dWtNmDBBHTp0UHJysjIzM3XKKaf48BUA8ITZ3PE00wB/IZ8A5yNDEGzIpuPYugEf8Xani5012EFZWZlSU1OVm5tb4/0LFixQTk6OpkyZorVr1yo1NVVZWVnauXOna560tDT17Nmz2t/27dv1yy+/6MMPP9TmzZu1bds2ffnll1q+fHmgXh4ANXynjqyCFcgnwNkCmR3kFAKFbDqOnz8DDeTrq5dx2D58Zf/+/W63o6KiFBUVVeO8gwYN0qBBg2pd1syZMzVmzBiNHj1akpSXl6fFixdrzpw5mjhxoiSpsLCw1scvXLhQp556qhISEiRJQ4YM0apVq3ThhReaeUkAvERWwU7IJyA0WNHk42fQ8BbZ5B2aioCX/BWSBCGa7qhUo8befwaOHf31sSkpKW7Tp0yZoqlTp5peXnl5uQoKCjRp0iTXtPDwcGVmZmrlypUeLSMlJUVffvmlDh8+rMaNG2vZsmW65ZZbTNcCwDx/XW2TrAotDc0miXwCQglHDSJQ2HeyFk1FwKRABCQ7a/CFrVu3Ki4uznW7tm/a6rN7925VVFQoMTHRbXpiYqI2bNjg0TLOOeccDR48WL/5zW8UHh6uiy++WJdffrlX9QDwXCCutklewSzyCYA/sS8Fb5BN3qGpCHgo0N+2EYZoqLi4OLdgtNojjzyiRx55xOoygJARqNwir2AW+QQEN45ShBORTd5hawdsjECGHbRq1UoREREqKSlxm15SUqKkpCSLqgJQGysuAEZewQrkEwDAbkItm/gfIOABK3eW2FGD1SIjI9W7d2/l5+e7plVWVio/P18ZGRkWVgbgZFbnFZmFQCKfAPuxSw7YpQ6EnlDLJn7+DNTDDoHET8vgb6Wlpdq0aZPrdlFRkQoLC5WQkKAOHTooJydH2dnZ6tOnj/r27avZs2errKzMdUUzANazQ15JZBZ8i3wCANgN2XQcTUWgDnbZQZPYSYN/rVmzRv3793fdzsnJkSRlZ2dr3rx5GjlypHbt2qXJkyeruLhYaWlpWrJkSbUTEAOwhp3ySiKz4DvkE+AcZBFCBdl0HE1FoBZ2C0WJYIT/9OvXT4Zh1DnPuHHjNG7cuABVBMBTdswricyCb5BPgDPYNYsAfyCbjqOpiJDj9MCrqf5Q2Gnz9/sWCusQgPMEU2YxzgKAczk1j5z6BZdT17cT1zUahqYiHMWpg6u/Wb3TFgzvS0NeQ8UR579+AJ4LhjHPClXrjZwKHPIJQH1CdXz0JdbhcZ6sC7IpuNBUhC0xMHvPXw1G3hMAoYZxzz/8cdQI7xUAHMeY6I7cAfyHpiJsg4HZ97w9KoT3AkCoYdwLrIYetcj7BSBUMN7ZB+8FUJ2lW8WMGTN09tlnq1mzZmrTpo2GDRumjRs3us1z+PBhjR07Vi1btlRsbKxGjBihkpISiyqGL5W2C3f7g/94sq55L4BfkU2hg3HPembWP+8XQh355Hwn/5/ckz/4RkPXJe8FUDNLt4zPPvtMY8eO1apVq/TJJ5/o6NGjGjhwoMrKylzz3HXXXfrggw+0cOFCffbZZ9q+fbuGDx9uYdVoCALSHvgPC1A7sim4Me7ZkydfegGhjnxyHv6/bS/evge8d0DtLP3585IlS9xuz5s3T23atFFBQYEuvPBC7du3Ty+//LLmz5+vAQMGSJLmzp2rbt26adWqVTrnnHOsKNuv/DFgWX0FJgZhAE4Sitlk13HaF/ll19eGmvF+AbUL5XyqLQ/MjBkNyRTGpuBh9vyKvPdA3Wx1TsV9+/ZJkhISEiRJBQUFOnr0qDIzM13zdO3aVR06dNDKlStrDMYjR47oyJEjrtv79+/3c9UNE4hBytPnYOcNAKoLxmxyyljtlDoBwArBmE9VTh7/fZEHZs/lSgaFNt5/wDO2aSpWVlZq/PjxOu+889SzZ09JUnFxsSIjI9W8eXO3eRMTE1VcXFzjcmbMmKEHH3zQ3+U2iF0HKLvWBQBWcXI2MaYDQPBycj7Vx9/5deLyqxqMZGZoqe9oRT4PgOds01QcO3asvvnmG61YsaJBy5k0aZJycnJct/fv36+UlJSGlucTDE4A4CxWZxO5AQCoidX55C+Bzj1yNnTV1Fjk8wCYZ4um4rhx4/Thhx9q+fLlat++vWt6UlKSysvLtXfvXrdv3EpKSpSUlFTjsqKiohQVFeXvkj3GwAQAzmRFNpEZAID6OHHfqbZ840hBWOnExiKfQcA7ljYVDcPQ7bffrnfffVfLli1T586d3e7v3bu3GjdurPz8fI0YMUKStHHjRm3ZskUZGRlWlOwxBiUAcKZAZlNZ23BFRJEXAID6BXrfydv9GTNNGvaZYDU+g0DDWNpUHDt2rObPn6/33ntPzZo1c53rIz4+XjExMYqPj9fNN9+snJwcJSQkKC4uTrfffrsyMjJse/UyBiUAcLZgzCYAgPMFMp/K2oYrwss62R8CgNBhaVPx+eeflyT169fPbfrcuXM1atQoSdKsWbMUHh6uESNG6MiRI8rKytJzzz0X4ErrRnACQPAIlmwCAAQX8gkAYDeW//y5PtHR0crNzVVubm4AKqofDUQACG5OzCYAQPAjnwAAdmOLC7XYFQ1EAAAAAAAAoDqaiiegiQgAAAAAAADUL2SailxhEwAAAAAAAPANumwAAAAAAAAATAmZIxUBAAAAAIA5B9tXev3YJj9xHBMQzGgqIig1JPh8jSAFgOBmp8wxi4wCgNATyNwy+1zkEuAsNBXhKE7ccfOkZl+E58nP449lhgr+MwOgNsE2Ltb1epw+FjrtvXL6+gZgT04bC0+s167jYqD27zx9Lk/ZdX3C2WgqwtacFoLe8sfrDJV15w9m113lYdY1goe3Y0ew/kc1lMdSf3xZ1RDB/l40dCe1pseTT3Cymj7TVo9DThIMY6bdGoxm1qnZ2gPxfvnrOTx9b6qen2wKLjQVbaxqo7PDABoowRB+AGB3Tvkiw8r8I4+qa8jOHevTN1iPCAV1fc45t1/dgnmMsHLfuKHrta7ag+E9C4bXAO/RVLSQpxufrzZSuwYpgxAA+EawjadWnIcp2Nahv7CeAJjh6VGH/hxbGtKU8sURc56+Nr60qVsgm4u+Xrcn1h5q7xuCF03FALHDoBGIGswe+gwAoexQu0qFRzMe+kpt2WKXnx0BQKjx11GHDVFfU6q+usycqsCb1+hJA5PM8v8Xj4FocAPBgKain4TqQBGqrxsAYF9kEwCYEwpfejmhaUR++U5dR8uyngHv2fP3sA5zsH1ltT8AgLsnn3xSPXr0UM+ePfXXv/7V6nIAAJBEPgGhin132JlTsokjFU1gwAEA73z99deaP3++CgoKZBiG+vfvr8suu0zNmze3ujQAQAgjnwAAduOkbOJIxTpw9CEA+MZ3332njIwMRUdHKyYmRqmpqVqyZInVZQEAQhz5BACwGydlU8g0FQ+1q/4T5fr+ACBULF++XEOHDlVycrLCwsK0aNGiavPk5uaqU6dOio6OVnp6ulavXu3x8nv27Klly5Zp7969+uWXX7Rs2TJt27bNh68AABCMyCcAgN2QTcfx82cAgMrKypSamqqbbrpJw4cPr3b/ggULlJOTo7y8PKWnp2v27NnKysrSxo0b1aZNG0lSWlqajh07Vu2xH3/8sbp376477rhDAwYMUHx8vM455xxFRET4/XUBAJyNfAIA2A3ZdBxNRQAIUvv373e7HRUVpaioqBrnHTRokAYNGlTrsmbOnKkxY8Zo9OjRkqS8vDwtXrxYc+bM0cSJEyVJhYWFddZz66236tZbb5Uk/f73v9dpp53m6UsBAAQR8gkAYDdkk3doKgKAzTTbfEiNGhleP/7YscOSpJSUFLfpU6ZM0dSpU00vr7y8XAUFBZo0aZJrWnh4uDIzM7Vy5UqPl7Nz5061adNGGzdu1OrVq5WXl2e6FgCANRqaTRL5BADwPfadrEVTEQCC1NatWxUXF+e6Xds3bfXZvXu3KioqlJiY6DY9MTFRGzZs8Hg5V1xxhfbt26emTZtq7ty5atSICAKAUEQ+AQDshmzyjj2rAgA0WFxcnFswWs3MN3MAgOBFPgEA7IZs8g5NRQBAnVq1aqWIiAiVlJS4TS8pKVFSUpJFVQHO1Sj5YECe59j2JgF5HsAq5BPgvUBlUW3IKASrUMsmmopwNKvD8GT+DEe7vVYcV3HwsNUl+FVkZKR69+6t/Px8DRs2TJJUWVmp/Px8jRs3ztriAIs4YUw+uUYrduCcsJ6CGfkEJztx/AiWBlRtY6IvX59Txt2qOn393tb3+n3xfP54DiveNzN1evN/itpeE9kUXGgqWiwQg0cwhLDTwvFkZt8Dp7xeBI/S0lJt2rTJdbuoqEiFhYVKSEhQhw4dlJOTo+zsbPXp00d9+/bV7NmzVVZW5rqiGZwhFMeWUNxRq42/duBOXDbga+STc/lyXPDm/9ieNn7sMH75Yh/CDq/DG75qHnv6+muaryENsvrmrW3Zdni/GlKDHeq3Etl0HE1FP7DbBhboegIRBk4TrK8LwWPNmjXq37+/63ZOTo4kKTs7W/PmzdPIkSO1a9cuTZ48WcXFxUpLS9OSJUuqnYAY5kQkHVREk0qrywhqDTlaL1jHbl/swAXruoH9kE/WcEo+BXtTxNPx2gmvxRONkg9acjBGfV+6BfvnDOaRTcfRVKwDA4B3WG+A8/Tr10+GYdQ5z7hx44LykH2EFjLKnSdHL7LOYCXyCfhVTeN1MI7Pnh5V74/XfvJzB+P6hW+QTceFTFPRKd+2AQAABBo7TgDgDKEyXtd01GKgXnuorGPAF0KmqQgAAAAAAJyB5h5gf+FWFwAAAAAAAADAWWgqAgAAAAAAADCFpiIAAAAAAAAAU2gqAgAAAAAAADCFpiIAAAAAAAAAU2gqAgAAAAAAADCFpiIAAAAAAAAAU2gqAgAAAAAAADCFpiIAAAAAAAAAUxpZXQAA87ollVhdgmN9V5xodQkAEFKsyqyGjvc11U2GAIC91ZY5Thi/A5WXTlgXcA6aigga/hyErR54aSL6jj/W5dGycv3P50sF7IlGS3VObZr5g53yyh+12On1eYJ8gp2cuP3UN36dvK3Zcbyria/HCDOvuyHP7ZT1W5+61oE/mo0N/T+RVZlidZaRTcGFpqIFrN6IT+S0ALHTwOvvdWenzwkA5/PnmOKE8cqXY7ZdXq9ddrrtsj4ABFZDtn2zjw3Vcaa+fRBfrZduSSWO2y88mbfrwtNmt6fLD9XPKkIXTUUfcerg4a+6fRVKdl6v/tiZs/PrBeB7ZyTuUuOmkVaXERIaOmY7YXyuqjEQO4ZOWB8AvEc+2Ze/xl8nNxZ92VyVjucoWQfUj6biSRg4fCMU16OZn3TU9jgAQGAE89jrz+ZiMK83AAh1Tm4s+hJZB3jO0qs/L1++XEOHDlVycrLCwsK0aNEit/sNw9DkyZPVtm1bxcTEKDMzU99//71Xz3VG4i51Syqp9w/whZo+T3zmAOcIZD4B/uLLIzfILMB6ZBMCwczPfO2wf002AdaytKlYVlam1NRU5ebm1nj/448/rqefflp5eXn66quv1LRpU2VlZenw4cMBrhTwDjtigDORTwgWDc0f8guwD7IJgWKHZqEn7FIHEMos/fnzoEGDNGjQoBrvMwxDs2fP1v/93//piiuukCS9+uqrSkxM1KJFi3TttdcGslQAQAghnxBMvP05GztrgL2QTXAif/2kmowC7MHSIxXrUlRUpOLiYmVmZrqmxcfHKz09XStXrqz1cUeOHNH+/fvd/gAA8BVv8olsgtOwswY4C/tOAAAr2LapWFxcLElKTHT/ViMxMdF1X01mzJih+Ph4119KSopf6wQAhBZv8olsgtXMNAlpKALOw74TQgk5BdiHbZuK3po0aZL27dvn+tu6davVJQEAQhzZBDvwZCeMHTUgtJBPCASyBQhetm0qJiUlSZJKStwHoJKSEtd9NYmKilJcXJzbHwAAvuJNPpFNcAJ2+gDnYt8JoYKsAuzFtk3Fzp07KykpSfn5+a5p+/fv11dffaWMjAwLKwMAhDLyCU528s6Y3a7kCcA7ZBPsjpwBgpOlV38uLS3Vpk2bXLeLiopUWFiohIQEdejQQePHj9e0adN02mmnqXPnznrggQeUnJysYcOGWVc0ACDokU8IZuzYAc5ENiHUkV+A/VjaVFyzZo369+/vup2TkyNJys7O1rx583TvvfeqrKxMt9xyi/bu3avzzz9fS5YsUXR0tFUlAwBCAPkEALAbsgmhimYiYF+WNhX79esnwzBqvT8sLEwPPfSQHnrooQBWBQAIdeQTAMBuyCY4XbekEn1XnFj/jCfMD8DebHtORQDmDWi1odY/wGpXXnmlWrRooauuuqrafR9++KHOOOMMnXbaafrLX/5iQXUAgFBFPgH2Q0MRoc4p2WTpkYoAGsZMs9AXjcV/7u7a4GUgdN1555266aab9Morr7hNP3bsmHJycrR06VLFx8erd+/euvLKK9WyZUuLKgXga4H6coucgjfIJ8A+aCb6R1UOk5PO4ZRsoqkIOJBVRx466YhHAtN++vXrp2XLllWbvnr1avXo0UPt2rWTJA0aNEgff/yxrrvuugBXCPhWbWNmKI1Pgc4Ns8/nj/fixBpC6b12MvIJwcKTMdDqcam2n0D7oplY3+u3+rUHWk3rY0CrDQ1aDw19PDznlGyiqQg4iJOaelazw46lkyxfvlxPPPGECgoKtGPHDr377rvVrhaZm5urJ554QsXFxUpNTdUzzzyjvn37Nvi5t2/f7gpFSWrXrp22bdvW4OUCgebpuFPTfME2Bjklr/xdp6fLD7b335fIJ+dr6HbG9uEZbzLILus2UEcn+rohdvI6b+iyvakvkHlb9Vw0FsmmE9FURFAKxoaSU3bQnCrUd/zKysqUmpqqm266ScOHD692/4IFC5STk6O8vDylp6dr9uzZysrK0saNG9WmTRtJUlpamo4dO1btsR9//LGSk5P9/hpgjq/HlGDdNjzhi3UZLD9LIqu8w1GttSOfnMUfY0AgGxih1gC1ujlkxU+dG5K3/sq4E5dbX9PXV//nsKIBGkzIpuNoKtpAMDbAAq2hg6vdjxphJ80+anovDkcf0z8sqKU++/fvd7sdFRWlqKioGucdNGiQBg0aVOuyZs6cqTFjxmj06NGSpLy8PC1evFhz5szRxIkTJUmFhYVe1ZmcnOz27dq2bdt88i0ejgvEGOLv57DTmHwiX79uOx5B4imyyveCdaeNfHI+K05tUN+20JDx01evx6p9iobUb8U4449motl1YPZ1e7J8Xx9p6M/tzBevP9gyimzyDk3FAPHlgODtsoJpg6/i74G2ilXrjh200NSoaIcahUd6v4DKcklSSkqK2+QpU6Zo6tSpphdXXl6ugoICTZo0yTUtPDxcmZmZWrlypfd1/n99+/bVN998o23btik+Pl7/+Mc/9MADDzR4ucEolMcEu50nKZCNWifkdyh/Nv3NLjttDc4miXxyOKu389q2hdqaHVL942egv3Tz93lcYY6n46vZi2P6epn+4qRa68K+k7XZRFPRD+y60QXTEZFWnvjdKT+/ALZu3aq4uDjX7dq+aavP7t27VVFRocRE95NqJyYmasMGzz+nmZmZWr9+vcrKytS+fXstXLhQGRkZatSokZ566in1799flZWVuvfee0P6ypps+94J5NEhVmWQXXOZz6z/2aWx6Cvkk7PYaRs/cVvw9MgxO/H1eO7LIyydPMbY7UjNupZpx89kXa+/vnqd/tk5EdnkHZqKHrLbxh8Idb3mUD5yLxADpx1eJ5wvLi7OLRit9umnn9Z63+WXX67LL788gNXYC9u8f/ijGWfle2XH/7jz2Q0cO77/3iKfnMGu27dd6zIjmLbnYOCPJmBNy7TrZ9fMUcBmHu80ZJN3aCrKvhu3nVlxvhA7vU/+PGrETq8TkKRWrVopIiJCJSXu578pKSlRUlKSRVUFh4ta/lfRsUSxv/lizLbL2Gyn/7jbZZ2EEju9/3ZAPvnPRS3/K3YV/auh27M/zunrxPHF1+fDPHEd+OK8/f/c3dURednQX+Y59fPjD6GWTSGTFOy4+Z+vj2x02uB7MqtOEA34WmRkpHr37q38/HwNGzZMklRZWan8/HyNGzfO2uIAE04eZ516HiFvXoc/nx+BZYdzPtsF+QSnM7M9B+r8j6E+rvjzAmxO0ZCjM2sTSp+rUMsmumwICCcOpg0Viq8ZzlVaWqpNmza5bhcVFamwsFAJCQnq0KGDcnJylJ2drT59+qhv376aPXu2ysrKXFc0A5woWMbpQP0nPljWVzAJhQYj+YRQYZcx1kmNRbusM9TvxPfqcPQx/cPCWnyBbDqOpiIAQGvWrFH//v1dt3NyciRJ2dnZmjdvnkaOHKldu3Zp8uTJKi4uVlpampYsWVLtBMQA7MUXDUd22pyhpvcpGHbcyCcg8JzUWASsQDYdR1MRAKB+/frJMIw65xk3blxQHrIPhKqafkZNAxF2Qz4B1rC6sVjfl2LkFaxENh1HUxEAAADsoAEA3AS6sWjmasMA7IGmIgAAAAAAqKamqyL76zkAOA9NRQAAAAAAUCtfH7VIIxEIDjQVAQAAAABAnTw5apFmIRBaaCoCAAAAAACPnNg4rGow0kwEQhNNRQAAAAAAYBrNRCC0hVtdAAAAAAAAAABnoakIAAAAAAAAwBSaigAAAAAAAABMoakIAAAAAAAAwBSaigAAAAAAAKjT4NhvrS4BNkNTEQAAAAAAALWqaijSWMSJaCoCAAAAAACgRic3EmksokojqwsAAAAAAADO4E1D6e+l3f1QCepz8ntl9n2o670eHPst7ytoKiI0+fqblWAZTPnGyTulRqUmWl0EgKDDTlt1oZ5Twf7+AidiDLQPX4y9Jy6D96k6M+u4tvXnyTLMNAI9XV5dNSH40VRE0ArkjodTB9NQ3zkDULvaxgenjXNO4MuxuKFHJNgVefUrs+uBL73gVN5u8/4YKxragHHyOOyvsbem5Tp1PTW0WerNOm7o+1JfY9HfNZFNwYWmIhzNbjsZTjkE3G7rDYC1zIwJVu6wOZHV461TcqkmVq87AIFnx+3ejjXVp76afd1Q8gUnHMlY37oxc6CJ1Z+r2v5/YHVdcB6aihaycoO160BdF6cMcHY+atEp6xCA/9hxHPBFTYEec+24Hmtj51yqiZPWLQDfCdZtP1Bf7njzBeHfS7vbcr3b8ah7b9evt8sIhJM/m3arD85AU9GP7LxR+qs2Xw34dl53nrLTTlwwrE8gGA1sukGxseFWlxEU/PlTqmAZQ51w1GKwrGvA6erKJ1+PI6Gw3ftr/PXFz2CdwOqfS9vp5/i+5oQaYW80FWvBxuUd1lt1Vu7E8X4ACGVmz28VCmOmnb7wOlkorH8gGPjq3IGhts37Yp8g1NZZXQKRZ6xvoH4h01TkaBBYyYrGIiEIADVjfLTXUYu8H0BwYFuun9mxl3XqO6x7wD9CpqkIWM2O51MBAIQuOzQWySwAYCxsCF9nGe8FYA5NRSCA/L0DRwgCAMzgFB0AEFiMfb5XX5Z5us55bwDz+D0wEAQGx35LCAIAvBLo/CCzAABWqCt7yCXAOxypCASYr08qTAACABqKU3QAAILRyblDDgG+xZGKgEUaGmgc6QEA8CV/ZgqZBQDwJzIGsAZHKgIWMnvUImEJAPAnjqYHAAQLMgjwP45UBGyg6giO+v4AJ7vyyivVokULXXXVVabuAxB4Dc0dcgtOQj4BwYcMgtM5JZtoKgIAAuLOO+/Uq6++avo+ANbx9EsvvgSDk5FPAAC7cUo20VQEAAREv3791KxZM9P3AQDgT+QTEFz4cgvBwCnZRFMRAKDly5dr6NChSk5OVlhYmBYtWlRtntzcXHXq1EnR0dFKT0/X6tWrA18oACCkkE8AzKChiEAgm46jqQgAUFlZmVJTU5Wbm1vj/QsWLFBOTo6mTJmitWvXKjU1VVlZWdq5c6drnrS0NPXs2bPa3/bt2wP1MgAAQYZ8AuApGooIFLLpuKC/+rNhGJKk0tJKiysBEKyqxpeq8aahjhnlUgOGrGNGuSRp//79btOjoqIUFRVV42MGDRqkQYMG1brMmTNnasyYMRo9erQkKS8vT4sXL9acOXM0ceJESVJhYaH3RYcYsglAIPgynxqaTa5liHyyM/IJgL+x7xRc2RT0TcUDBw5Iki7su8viSgAEuwMHDig+Pt7rx0dGRiopKUnLiv/a4FpiY2OVkpLiNm3KlCmaOnWq6WWVl5eroKBAkyZNck0LDw9XZmamVq5c2dBSQxLZBCCQGpJPvswmiXyyO/IJQKCw7xQcgr6pmJycrK1bt6pZs2YKCwuzupyA2r9/v1JSUrR161bFxcVZXY5jsN68F6rrzjAMHThwQMnJyQ1aTnR0tIqKilReXu6Tmk4e82r7pq0+u3fvVkVFhRITE92mJyYmasOGDR4vJzMzU+vXr1dZWZnat2+vhQsXKiMjo977ghHZFHrjhC+w7rwTyuvNF/nky2yqqol8si/yKTTHioZgvXkvVNcd+06ecUo2BX1TMTw8XO3bt7e6DEvFxcWF1CDlK6w374XiumvIt2wnio6OVnR0tE+WZTeffvqpV/cFI7IpNMcJX2HdeSdU15sv8imYs0kin05EPoXuWNFQrDfvheK6Y9+pfk7JJi7UAgCoU6tWrRQREaGSkhK36SUlJUpKSrKoKgBAqCOfAAB2E2rZRFMRAFCnyMhI9e7dW/n5+a5plZWVys/PD+qfgAEA7I18AgDYTahlU9D//DmURUVFacqUKV6fCyBUsd68x7pzrtLSUm3atMl1u6ioSIWFhUpISFCHDh2Uk5Oj7Oxs9enTR3379tXs2bNVVlbmuqIZ4CnGCe+x7rzDenM28gmBwljhHdab91h3zkU2HRdm+Oo63gAAx1q2bJn69+9fbXp2drbmzZsnSXr22Wf1xBNPqLi4WGlpaXr66aeVnp4e4EoBAKGEfAIA2A3ZdBxNRQAAAAAAAACmcE5FAAAAAAAAAKbQVAQAAAAAAABgCk1FAAAAAAAAAKbQVAxSnTp1UlhYmNvfo48+6jbPv//9b11wwQWKjo5WSkqKHn/8cYuqtZfc3Fx16tRJ0dHRSk9P1+rVq60uyVamTp1a7bPVtWtX1/2HDx/W2LFj1bJlS8XGxmrEiBEqKSmxsGIAdkE2eY9sqhvZBKAhyCfvkU91I58Q7GgqBrGHHnpIO3bscP3dfvvtrvv279+vgQMHqmPHjiooKNATTzyhqVOn6sUXX7SwYustWLBAOTk5mjJlitauXavU1FRlZWVp586dVpdmKz169HD7bK1YscJ131133aUPPvhACxcu1Geffabt27dr+PDhFlYLwE7IJvPIJs+QTQAagnwyj3zyDPmEoGYgKHXs2NGYNWtWrfc/99xzRosWLYwjR464pt13333GGWecEYDq7Ktv377G2LFjXbcrKiqM5ORkY8aMGRZWZS9TpkwxUlNTa7xv7969RuPGjY2FCxe6pn333XeGJGPlypUBqhCAXZFN3iGb6kc2AWgI8sk75FP9yCcEO45UDGKPPvqoWrZsqd/85jd64okndOzYMdd9K1eu1IUXXqjIyEjXtKysLG3cuFG//PKLFeVarry8XAUFBcrMzHRNCw8PV2ZmplauXGlhZfbz/fffKzk5WV26dNH111+vLVu2SJIKCgp09OhRt3XYtWtXdejQgXUIQBLZZBbZ5DmyCUBDkE/mkE+eI58QzBpZXQD844477tBZZ52lhIQEffnll5o0aZJ27NihmTNnSpKKi4vVuXNnt8ckJia67mvRokXAa7ba7t27VVFR4VoPVRITE7VhwwaLqrKf9PR0zZs3T2eccYZ27NihBx98UBdccIG++eYbFRcXKzIyUs2bN3d7TGJiooqLi60pGIBtkE3mkU2eIZsANAT5ZB755BnyCcGOpqKDTJw4UY899lid83z33Xfq2rWrcnJyXNN69eqlyMhI3XrrrZoxY4aioqL8XSqC2KBBg1z/7tWrl9LT09WxY0e9+eabiomJsbAyAFYgm2AHZBOAk5FPsAPyCcGOpqKD3H333Ro1alSd83Tp0qXG6enp6Tp27Jg2b96sM844Q0lJSdWuKlV1OykpySf1Ok2rVq0UERFR43oJ1XXiiebNm+v000/Xpk2bdMkll6i8vFx79+51+8aNdQgEL7LJv8gm75BNAMgn/yKfvEM+IdhwTkUHad26tbp27Vrn34nn+ThRYWGhwsPD1aZNG0lSRkaGli9frqNHj7rm+eSTT3TGGWeE5OH7khQZGanevXsrPz/fNa2yslL5+fnKyMiwsDJ7Ky0t1Q8//KC2bduqd+/eaty4sds63Lhxo7Zs2cI6BIIU2eRfZJN3yCYA5JN/kU/eIZ8QdKy+Ugx878svvzRmzZplFBYWGj/88IPx17/+1WjdurXxu9/9zjXP3r17jcTEROPGG280vvnmG+ONN94wmjRpYrzwwgsWVm69N954w4iKijLmzZtnfPvtt8Ytt9xiNG/e3CguLra6NNu4++67jWXLlhlFRUXGF198YWRmZhqtWrUydu7caRiGYfzhD38wOnToYPzzn/801qxZY2RkZBgZGRkWVw3AamST98im+pFNALxFPnmPfKof+YRgR1MxCBUUFBjp6elGfHy8ER0dbXTr1s2YPn26cfjwYbf51q9fb5x//vlGVFSU0a5dO+PRRx+1qGJ7eeaZZ4wOHToYkZGRRt++fY1Vq1ZZXZKtjBw50mjbtq0RGRlptGvXzhg5cqSxadMm1/2HDh0ybrvtNqNFixZGkyZNjCuvvNLYsWOHhRUDsAOyqWHIprqRTQC8RT41DPlUN/IJwS7MMAzD6qMlAQAAAAAAADgH51QEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRQAAAAAAAACm0FQEAAAAAAAAYApNRViuX79+Gj9+vG2WU5NRo0Zp2LBhDVpGp06dFBYWprCwMO3du7fe+ZctW+aav6HPDQAwj3yqGfkEANYhm2pGNgHWoKkIx6kKjJPD5Z133tHDDz/sut2pUyfNnj07sMXV46GHHtKOHTsUHx/vmvbSSy+pY8eO+s1vfqOvvvrKNf3cc8/Vjh07dM0111hRKgDAJPIJAGA3ZBMAf2pkdQGAryQkJFhdQr2aNWumpKQk1+0tW7bo8ccf1xtvvKFt27Zp9OjR+vbbbyVJkZGRSkpKUkxMjI4cOWJVyQCABiKfAAB2QzYB8AWOVITtvPbaa+rTp48rRH77299q586dkqTNmzerf//+kqQWLVooLCxMo0aNkuR+CH+/fv30448/6q677nIdBi9JU6dOVVpamtvzzZ49W506dXLdrqioUE5Ojpo3b66WLVvq3nvvlWEYbo+prKzUjBkz1LlzZ8XExCg1NVVvvfWW6de6f/9+NW/eXL169VLv3r116NAh08sAAAQG+QQAsBuyCYCVaCrCdo4ePaqHH35Y69ev16JFi7R582ZX+KWkpOjtt9+WJG3cuFE7duzQn//852rLeOedd9S+fXvXIfM7duzw+PmfeuopzZs3T3PmzNGKFSu0Z88evfvuu27zzJgxQ6+++qry8vL0n//8R3fddZduuOEGffbZZ6Zea8+ePdWrVy/Fx8erR48emjZtmqnHAwACh3wCANgN2QTASvz8GbZz0003uf7dpUsXPf300zr77LNVWlqq2NhY16H6bdq0UfPmzWtcRkJCgiIiIqodMu+J2bNna9KkSRo+fLgkKS8vTx999JHr/iNHjmj69On69NNPlZGR4apzxYoVeuGFF3TRRReZer6XX35Zjz/+uJo0aaKYmBhTjwUABA75BACwG7IJgJVoKsJ2CgoKNHXqVK1fv16//PKLKisrJf16Do3u3bv79bn37dunHTt2KD093TWtUaNG6tOnj+sw/k2bNungwYO65JJL3B5bXl6u3/zmN149b8uWLb0vGgAQEOQTAMBuyCYAVqKpCFspKytTVlaWsrKy9Prrr6t169basmWLsrKyVF5e3uDlh4eHVzvHx9GjR00to7S0VJK0ePFitWvXzu2+qKiohhUIALAl8gkAYDdkEwCr0VSErWzYsEE///yzHn30UaWkpEiS1qxZ4zZPZGSkpF9PClyXyMjIavO0bt1axcXFMgzDdQLiwsJC1/3x8fFq27atvvrqK1144YWSpGPHjqmgoEBnnXWWJKl79+6KiorSli1bTB+uDwBwJvIJAGA3ZBMAq3GhFthKhw4dFBkZqWeeeUb/+9//9P777+vhhx92m6djx44KCwvThx9+qF27drm+/TpZp06dtHz5cm3btk27d++W9OuVzXbt2qXHH39cP/zwg3Jzc/WPf/zD7XF33nmnHn30US1atEgbNmzQbbfdpr1797rub9asmSZMmKC77rpLr7zyin744QetXbtWzzzzjF555RXfrhAAgC2QTwAAuyGbAFiNpiJspXXr1po3b54WLlyo7t2769FHH9WTTz7pNk+7du304IMPauLEiUpMTNS4ceNqXNZDDz2kzZs365RTTlHr1q0lSd26ddNzzz2n3NxcpaamavXq1ZowYYLb4+6++27deOONys7OVkZGhpo1a6Yrr7zSbZ6HH35YDzzwgGbMmKFu3brp0ksv1eLFi9W5c2cfrg0AgF2QTwAAuyGbAFgtzDj5JAkA/KJTp04aP368xo8fb+pxo0aN0t69e7Vo0SK/1AUACG3kEwDAbsgmwBk4UhEIoPvuu0+xsbHat29fvfN+/vnnio2N1euvvx6AygAAoYx8AgDYDdkE2B9HKgIB8uOPP7qultalSxeFh9fd0z906JC2bdsmSYqNjVVSUpLfawQAhB7yCQBgN2QT4Aw0FQEAAAAAAACYws+fAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJhCUxEAAAAAAACAKTQVAQAAAAAAAJjy/wCC3qUyYwQAfQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(131)\n",
    "my_sim.plot_meridional_slice('h2o_vap',logcb=True)\n",
    "plt.title('Time- and longitude-average')\n",
    "\n",
    "plt.subplot(132)\n",
    "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0)\n",
    "plt.title('Time = 0 and longitude-average')\n",
    "\n",
    "plt.subplot(133)\n",
    "my_sim.plot_meridional_slice('h2o_vap',logcb=True,t=0,lon=0)\n",
    "plt.title('Time = 0 and longitude = 0°')\n",
    "\n",
    "plt.subplots_adjust(right=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d691c4b-a690-4644-a00a-1ee77c85658a",
   "metadata": {},
   "source": [
    "#### Time evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2328745a-55e1-40d9-86c7-41f04bea872c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(131)\n",
    "my_sim.plot_time_evolution('h2o_vap',logcb=True)\n",
    "plt.title('latitude- and longitude-average')\n",
    "\n",
    "plt.subplot(132)\n",
    "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0)\n",
    "plt.title('Equatorial value, longitude-average')\n",
    "\n",
    "plt.subplot(133)\n",
    "my_sim.plot_time_evolution('h2o_vap',logcb=True,lat=0,lon=0)\n",
    "plt.title('Equatorial value longitude = 0°')\n",
    "\n",
    "plt.subplots_adjust(right=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f287c71d-83eb-4bad-a11e-dfb34e679adf",
   "metadata": {},
   "source": [
    "#### Vertical profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2aa2f5b-c527-44f8-98a5-ebde0e62f01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'kg/kg'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = my_sim.get_subset('h2o_vap',)\n",
    "profile.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f544843-012d-41a6-8d7a-c4b40f45a5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_sim.plot_profile('h2o_vap',logx=True,label='Time- and grid-averaged')\n",
    "my_sim.plot_profile('h2o_vap',t=0,logx=True,label='Time=0, grid-averaged')\n",
    "my_sim.plot_profile('h2o_vap',t=0,lat=0,logx=True,label='Time=0, equatorial value zonally-averaged')\n",
    "my_sim.plot_profile('h2o_vap',t=0,lat=0,lon=0,logx=True,label='Time=0, equatorial value, lon = 0°')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73804b8-98f3-4283-8c5d-153465db88e7",
   "metadata": {},
   "source": [
    "## Interactive visualization\n",
    "Now we go fancy!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f745af6-fe37-4075-b433-abb03ad3492e",
   "metadata": {},
   "source": [
    "#### Define widgets\n",
    "Let's define some widgets (see [here](https://ipywidgets.readthedocs.io/en/latest/) for documenttion on jupyter's widgets):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56b07967-3900-4454-ac0d-29cfae7cc1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "# Coordinates\n",
    "w_lat = widgets.FloatSlider(min=-90, max=90, step=1, description=\"latitude\")\n",
    "w_lon = widgets.FloatSlider(min=-180, max=180, step=1, description=\"longitude\")\n",
    "w_alt = widgets.FloatSlider(min=0, max=max(my_sim.data[\"altitude\"]), step=1, description=\"altitude\")\n",
    "w_time = widgets.FloatSlider(min=0, max=max(my_sim[\"Time\"]), step=1, description=\"time\")\n",
    "\n",
    "# Fields\n",
    "w_single_sp   = widgets.Select(options=my_sim.species, value=\"h2o_vap\", description=\"species\")\n",
    "w_multiple_sp = widgets.SelectMultiple(options=my_sim.species, value=[\"h2o_vap\"], description=\"species\")\n",
    "w_reactions   = widgets.SelectMultiple(options=my_sim.reactions.keys(), value=[\"co2 + hv -> co + o\"], description=\"reactions\")\n",
    "\n",
    "# Miscelaneous\n",
    "w_average = widgets.Checkbox(description='show average')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddeec0ba-12c2-4bd9-a38f-98a0e1949f3b",
   "metadata": {},
   "source": [
    "#### OH meridional slice at various longitudes\n",
    "OH is a photolysis product with a short lifetime, so it exist only on the dayside. Scrolling through the longitudes will exhibit this dichotomy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "56e81d82-fd55-464c-a746-66cf23822957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d5cd66506d34879ac2252b9a7c3e026",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(FloatSlider(value=0.0, description='longitude', max=180.0, min=-180.0, step=1.0), Output()))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slice plotting unction\n",
    "def make_slice(lon):\n",
    "    my_sim.plot_meridional_slice('oh',t=0,lon=lon,logcb=True)\n",
    "\n",
    "# Define interactive output\n",
    "out = widgets.interactive_output(make_slice,{'lon':w_lon})\n",
    "\n",
    "# Build the output frame\n",
    "widgets.VBox([w_lon,out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3379c31a-95c6-4ab3-b178-20b706eed1f3",
   "metadata": {},
   "source": [
    "#### Water vapor atlas\n",
    "We can have several action widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd7b4103-0436-4bda-bb39-96666c39f332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36798865efc74c7aba687cf67cb26d54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(FloatSlider(value=0.0, description='time', max=60.54166793823242, step=1.0), FloatSlider(value=…"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Atlas plotting function\n",
    "def make_atlas(t,alt):\n",
    "    my_sim.plot_atlas('h2o_vap',t=t,alt=alt)\n",
    "    plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n",
    "\n",
    "# Define interactive output\n",
    "out = widgets.interactive_output(make_atlas,{'t':w_time,'alt':w_alt})\n",
    "\n",
    "# Build the output frame\n",
    "widgets.VBox([w_time,w_alt,out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b6bb2f8-1aff-4872-9580-7c4bd9598c31",
   "metadata": {},
   "source": [
    "#### Temperature profile at various times and locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4691cae-637b-4555-ac87-d556521a4c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acdd52eb1fd8445c8b970c414fe2d10e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(FloatSlider(value=55.0, description='time', max=60.54166793823242, step=1.0), Fl…"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Profile plotting unction\n",
    "def make_prof(t,lon,lat,avg):\n",
    "    my_sim.plot_profile('temp',t=t,lon=lon,lat=lat,label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
    "    if avg:\n",
    "        my_sim.plot_profile('temp',t=t,label='average')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "\n",
    "# Define interactive output\n",
    "out = widgets.interactive_output(make_prof,{'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    "\n",
    "# Build the output frame\n",
    "widgets.HBox([widgets.VBox([w_time,w_lon,w_lat,w_average]),out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad5a23a-7ed6-4f56-abbf-3cf16daa087a",
   "metadata": {},
   "source": [
    "#### Extensive species visualizer\n",
    "Combining the above examples for arbitrary species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4db2d8b-6183-4fbe-8ca1-940ef15aaa28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6089ba159a1d4814b8807b78a65fa19e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Select(description='species', index=3, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_visualizer(sp,t,alt):\n",
    "    \n",
    "    plt.subplot(131) # zonal average\n",
    "    my_sim.plot_meridional_slice(sp,t=t,logcb=True)\n",
    "    plt.plot([-90,90],[alt,alt],ls='--',lw=3,c='white')\n",
    "\n",
    "    plt.subplot(132) # atlas\n",
    "    my_sim.plot_atlas(sp,t=t,alt=alt)\n",
    "    plt.title('t='+str(int(t))+' sol, altitude='+str(int(alt))+' km')\n",
    "\n",
    "    plt.subplot(133) # profile\n",
    "    my_sim.plot_profile('temp',t=t)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.subplots_adjust(right=2)\n",
    "\n",
    "out = widgets.interactive_output(make_visualizer,{'sp':w_single_sp,'t':w_time,'alt':w_alt})\n",
    "\n",
    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_alt]),out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d39b59-07f2-4988-8e5c-558221c825a1",
   "metadata": {},
   "source": [
    "#### Multi-species profiles\n",
    "Shift+click to select multiple species (Command+click on Mac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dd59a1c-58c4-41f0-9730-9e97d2607c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3d87cbaec0147bca8596046cdaad974",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(SelectMultiple(description='species', index=(6,), options=('o2', 'o', 'o1d', 'o3…"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"tab10\")\n",
    "def make_sp_prof(sps,t,lon,lat,avg):\n",
    "\n",
    "    for i,sp in enumerate(sps):\n",
    "        my_sim.plot_profile(sp,t=t,lon=lon,lat=lat,logx=True,label=sp,c=cmap(i))\n",
    "        if avg:\n",
    "            my_sim.plot_profile(sp,t=t,logx=True,c=cmap(i),ls='--')\n",
    "    if avg: # just for the legend\n",
    "        plt.plot([],[],c='k',label='lon='+str(int(lon))+'°, lat='+str(int(lat))+'°')\n",
    "        plt.plot([],[],ls='--',c='k',label='average')\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "\n",
    "out = widgets.interactive_output(make_sp_prof,{'sps':w_multiple_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    "\n",
    "widgets.HBox([widgets.VBox([w_multiple_sp,w_time,w_lon,w_lat,w_average]),out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb2c987-345d-4a74-b4bf-fb368eeaac2b",
   "metadata": {},
   "source": [
    "#### Species-specific reaction rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff21a3dc-d44a-4f0e-9aa4-211741bb592d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "525673a453824ea4bcdc5591d796dcb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_reaction_rate_viz(sp,t):\n",
    "\n",
    "    for r in my_sim.reactions:\n",
    "        if sp in my_sim.reactions[r].products:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
    "        elif sp in my_sim.reactions[r].reactants:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "\n",
    "out=widgets.interactive_output(make_reaction_rate_viz,{'sp':w_single_sp,'t':w_time})\n",
    "\n",
    "widgets.HBox([widgets.VBox([w_single_sp,w_time]),out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0b725d-58f5-4b4e-b170-125b4707df03",
   "metadata": {},
   "source": [
    "#### Profile with atlas locator\n",
    "Here the atlas shows the column mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5b73171-0101-4656-b2ce-7e398070ebbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ac76b3211434eb5b46298f1de86d554",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Select(description='species', index=2, options=('o2', 'o', 'o1d', 'o3', 'h2o2', …"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_sp_prof_atlas(sp,t,lon,lat,avg):\n",
    "\n",
    "    plt.subplot(121) # Vertical profile\n",
    "    for r in my_sim.reactions:\n",
    "        if sp in my_sim.reactions[r].products:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,label=r)\n",
    "        elif sp in my_sim.reactions[r].reactants:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,logx=True,ls='--',label=r)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "\n",
    "    plt.subplot(122) # Atlas\n",
    "    my_sim.plot_atlas(sp+'_col',t=t)\n",
    "    plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n",
    "\n",
    "    plt.subplots_adjust(right=2)\n",
    "\n",
    "out = widgets.interactive_output(make_sp_prof_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    "\n",
    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239adb94-d3b7-4a9a-81fc-297e72e63639",
   "metadata": {},
   "source": [
    "#### Species-specific reaction rates with atlas locator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c849fda1-3969-4709-bb17-fdcaff5cbf86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e6bc22e2111489c88cbae0c2b6eea97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Select(description='species', options=('o2', 'o', 'o1d', 'o3', 'h2o2', 'oh', 'h2…"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_sp_rate_atlas(sp,t,lon,lat,avg):\n",
    "\n",
    "    plt.subplot(121) # Vertical profile\n",
    "    for r in my_sim.reactions:\n",
    "        if sp in my_sim.reactions[r].products:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,label=r)\n",
    "        elif sp in my_sim.reactions[r].reactants:\n",
    "            my_sim.plot_profile('rate ('+r+')',t=t,lon=lon,lat=lat,logx=True,ls='--',label=r)\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "\n",
    "    plt.subplot(122) # Atlas\n",
    "    my_sim.plot_atlas(sp+'_col',t=t)\n",
    "    plt.scatter([lon],[lat],marker='o',s=[100],c=['tab:red'])\n",
    "\n",
    "    plt.subplots_adjust(right=2)\n",
    "\n",
    "out = widgets.interactive_output(make_sp_prof_atlas,{'sp':w_single_sp,'t':w_time,'lon':w_lon,'lat':w_lat,'avg':w_average})\n",
    "\n",
    "widgets.HBox([widgets.VBox([w_single_sp,w_time,w_lon,w_lat,w_average]),out])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}