{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow Through Trees\n",
    "\n",
    "The simulation of a turbulent flow over an arrangement of trees is used as validation for the points cloud implementation with immersed boundary method. The points cloud is distributed in an Cartesian arrangement with a distancing between points of 1 node of most refined lvl. \n",
    "The points area is the squared distance between points at lvl 0.\n",
    "For comparison, the results of [Qi and Ishihara, 2018](https://doi.org/10.1016/j.jweia.2018.04.007) and [Kang et al., 2020](https://doi.org/10.1016/j.scs.2020.102086) were used.\n",
    "\n",
    "It consists of a 2 x 74 x 6(m) tree arrangement positioned at 1m height after an atmospheric flow.\n",
    "The drag coefficient is estimated 1.6 and LAD=$1.16m^{-1}$ hence the `force_factor` will be 1.856 for $\\Delta x$=1m (and 0.468 for $\\Delta x$=0.25m), as illustrated below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![localimage](images/schematic.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numerical setup goal is to correctly capture both velocity and turbulent kinetic energy changes caused by the tree arrangement. The domain setup is shown below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![localimage](images/domain-config.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "from nassu.cfg.model import ConfigScheme\n",
    "\n",
    "filename = \"./tests/validation/cases/10_flow_through_trees.nassu.yaml\"\n",
    "\n",
    "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "H_num = 7\n",
    "scale = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load experimental data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = Path(\"./tests/validation/comparison/Tree_effect/qi2018/exp\")\n",
    "positions = [\"1H\", \"2H\", \"3H\", \"4H\", \"5H\"]\n",
    "\n",
    "df_exp = []\n",
    "\n",
    "vel = pd.read_csv(base_path / \"velocity/pitot.csv\")\n",
    "kin = pd.read_csv(base_path / \"kinetic_energy/pitot.csv\")\n",
    "df_csv = pd.concat([vel, kin], axis=1, join=\"inner\")\n",
    "df_exp.append(df_csv)\n",
    "\n",
    "for pos in positions:\n",
    "    vel = pd.read_csv(base_path / f\"velocity/{pos}.csv\")\n",
    "    kin = pd.read_csv(base_path / f\"kinetic_energy/{pos}.csv\")\n",
    "    df_csv = pd.concat([vel, kin], axis=1, join=\"inner\")\n",
    "    df_exp.append(df_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "base_path = Path(\"./tests/validation/comparison/Tree_effect/qi2018/num\")\n",
    "positions = [\"1H\", \"2H\", \"3H\", \"4H\", \"5H\"]\n",
    "\n",
    "df_num2 = []\n",
    "\n",
    "vel = pd.read_csv(base_path / \"velocity/pitot.csv\")\n",
    "kin = pd.read_csv(base_path / \"kinetic_energy/pitot.csv\")\n",
    "df_csv = pd.concat([vel, kin], axis=1, join=\"inner\")\n",
    "df_num2.append(df_csv)\n",
    "\n",
    "for pos in positions:\n",
    "    vel = pd.read_csv(base_path / f\"velocity/{pos}.csv\")\n",
    "    kin = pd.read_csv(base_path / f\"kinetic_energy/{pos}.csv\")\n",
    "    df_csv = pd.concat([vel, kin], axis=1, join=\"inner\")\n",
    "    df_num2.append(df_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1395056/3034823928.py:14: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  u_ref = ux_avg[1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float32(0.02552979)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_cfg = sim_cfgs[\"flowThroughTrees\", 0]\n",
    "\n",
    "point_ref = sim_cfg.output.series[\"velocities\"].points[\"velocity_probe\"]\n",
    "df_ref = point_ref.read_full_data(\"ux\")\n",
    "\n",
    "df_ref = df_ref[df_ref[\"time_step\"] > 10000]\n",
    "\n",
    "df_point = df_ref\n",
    "\n",
    "ux_avg = df_ref.mean()\n",
    "ux_rms = df_ref.std()\n",
    "Iu = ux_rms / ux_avg\n",
    "\n",
    "u_ref = ux_avg[1]\n",
    "u_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def line_stats(position: str, u_ref) -> pd.DataFrame:\n",
    "    line_ref = sim_cfg.output.series[\"velocities\"].lines[f\"{position}\"]\n",
    "    df_ux = line_ref.read_full_data(\"ux\")\n",
    "    df_uy = line_ref.read_full_data(\"uy\")\n",
    "    df_uz = line_ref.read_full_data(\"uz\")\n",
    "\n",
    "    df_ux = df_ux[df_ux[\"time_step\"] > 10000]\n",
    "    df_uy = df_uy[df_uy[\"time_step\"] > 10000]\n",
    "    df_uz = df_uz[df_uz[\"time_step\"] > 10000]\n",
    "\n",
    "    ux_avg = (df_ux.mean()) / u_ref\n",
    "    ux_rms = (df_ux.std()) ** 2\n",
    "\n",
    "    uy_rms = (df_uy.std()) ** 2\n",
    "    uz_rms = (df_uz.std()) ** 2\n",
    "\n",
    "    k = (ux_rms + uy_rms + uz_rms) / (2 * u_ref**2)\n",
    "\n",
    "    df = pd.DataFrame({\"ux_avg\": ux_avg, \"k\": k})\n",
    "    df = df.drop(df.index[0])\n",
    "    df[\"pos\"] = np.linspace(0, (2 * H_num) / H_num, num=len(df), endpoint=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "df_num = []\n",
    "df_csv = line_stats(\"velocity_profile\", u_ref)\n",
    "df_num.append(df_csv)\n",
    "\n",
    "for pos in positions:\n",
    "    df_csv = line_stats(f\"pos_{pos}\", u_ref)\n",
    "    df_num.append(df_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def plot_curves(position: str, df_num, df_num2, df_exp):\n",
    "    fig, ax = plt.subplots(1, 2)\n",
    "    fig.set_size_inches(12, 5)\n",
    "    fig.suptitle(f\"{position}\")\n",
    "\n",
    "    ax[0].plot(df_num[\"ux_avg\"], df_num[\"pos\"], \"-b\", label=\"AeroSim\")\n",
    "    ax[0].plot(df_num2[\"u\"], df_num2[\"z\"], \"--r\", label=\"Qi and Ishihara (2018)\")\n",
    "    ax[0].plot(df_exp[\"u\"], df_exp[\"z\"], marker=\"o\", linestyle=\"None\", color=\"g\", label=\"Exp\")\n",
    "    ax[0].set_ylabel(\"$z/H$\")\n",
    "    ax[0].set_xlabel(\"$u/u_{H}$\")\n",
    "    ax[0].set_xlim(0, 2.0)\n",
    "    ax[0].set_ylim(0, 1.2)\n",
    "    ax[0].legend(loc=\"best\")\n",
    "    ax[0].grid()\n",
    "\n",
    "    ax[1].plot(df_num[\"k\"], df_num[\"pos\"], \"-b\", label=\"AeroSim\")\n",
    "    ax[1].plot(df_num2[\"k\"], df_num2[\"h\"], \"--r\", label=\"Qi and Ishihara (2018)\")\n",
    "    ax[1].plot(df_exp[\"k\"], df_exp[\"h\"], marker=\"o\", linestyle=\"None\", color=\"g\", label=\"Exp\")\n",
    "    ax[1].set_ylabel(\"$z/H$\")\n",
    "    ax[1].set_xlabel(\"TKE/$u_{H}^{2}$\")\n",
    "    ax[1].set_xlim(0, 0.2)\n",
    "    ax[1].set_ylim(0, 1.2)\n",
    "    ax[1].legend(loc=\"best\")\n",
    "    ax[1].grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bBsDPP/9M7969mTRpEs2bN2fXrl08+uijAIwcOTLjHKNGjeK1115jwoQJBAUF8fXXXzNo0CAmTJhAq1at+O677+jTpw/ly5enZcuWREdHk5aWxtdff03Xrl0dbs1PSkpi0qRJzJ49m4SEBLp06cJdd91FsWLFWLBgAbt37+buu++mWbNmdO/e3fE/BBER8UknT8K8efDll7BokWlEAIiMhKefhsGDISLCBReaPx8eeQSAHXfdRdwDD7jgpCJS0JT0i7hRWhpMn26W2jt0CMBCtWonePfdSG691cX//KxW80i/alXz/u67zZOGBx6AMmVcey0/MG3aNO6//34A2rVrx+nTp1m+fDk333wzb7zxBs8++yy9evUCoFKlSrz00ks888wzWZL+++67jz59+mS879GjB71792bAgAEADBkyhF9++YWxY8fSsmVLGjduzHPPPcd9991Hv379aNiwIbfeeisPPvggUVFROcaampqa0YMAoGvXrnz00UccPnyYIkWKULNmTVq2bMnSpUuV9IuI+KmcEn2A66+H7t1hwAAzqb5LrF5tGgzS07E+8AB/delCnItOLSIFS0m/iBvYbGZs/jPPZM6ZV7kyvPxyGoUKraR58w6uveDGjaam37XLrJtbokTmRDsFKDw8c+3fghYe7vix27dvZ926dXz99dcABAUF0b17d6ZNm8bNN9/MH3/8wdq1axkzZkzGd9LT00lOTiYpKYnwCxdrcMlMi1u3bs3oEWDXrFkzJk6cmPH+lVdeYciQIfz000+sXbuWyZMnM2bMGFasWMENN9yQQ9nCMxJ+gKioKOLi4ihyUbeKqKiojMkARUTEP1wp0e/WDe65xw2T6P/xB9x+OyQnQ8eOpE+ebAIQEZ+kpF/ExTZuNF3rli4170uUgBEjoH9/sFhsLFjgwoudOgXDh8O775qW/ogI06X/tttceBHHWSzOdbH3lGnTppGWlkbZsmUz9tlsNkJDQ5k0aRJnz55l1KhR3H333Zd9t1ChQhmvC+exsCVLluSee+7hnnvuYcyYMdStW5exY8cya9asbI8PDg7O8t5isWS7z2q15ikeERHxHidOmOV6v/gCFi/OmujfcINJ8t2S6NtZrXDvveYeo0kTE8gldY6I+BYl/SIu8s8/8Pzz8Mkn5n1oqFkqZ9gwKFbM7HNggnfH2Gzw8cfw3/+atXjAVNBvvWWm6pUcpaWl8eGHH/LWW2/Rpk2bLJ/deeedfPbZZ9SqVYvt27dz7bXXOnXuGjVqsHr16oxhAWAm76tZs2aO3wkJCaFy5coZs/eLiMjVx+OJ/sUCAswSv089BZ9/brrSuewGRkQ8QUm/SD6dOgWvvgoTJ8L582Zfz55m0vxrrnHDBVNToU0bWLbMvK9eHd5+22Ot+77mu+++4+TJk/Tt25eiRYtm+ezuu+9mxowZDB06lHvvvZdrrrmGrl27EhAQwG+//cYff/zByy+/nOO5//vf/9KtWzfq1q1Lq1atmD9/PnPnzmXx4sUZ1549ezb33nsvVatWxWazMX/+fBYsWMCMGTPcWm4REfEuXpXoX6pWLXXnF/EjSvpF8iglxax9+9JLcPy42deyJbz5JtSv78YLBwfDtdfCunWma/+QIflci+fqMm3aNFq1anVZwg8m6X/jjTeIiori22+/5eWXX+b1118nODiY6tWr8/DDD+d67jvvvJOJEycyduxYBg0aRMWKFZkxYwYtWrQAoGbNmoSHh/PUU0+xf/9+QkNDqVKlClOnTuUBzYgsIuL3vDbRT02FBx808wM1b17AFxcRd1PSL+Ikmw2++gqGDjXz5gHUqGGS/Q4dzLh2l19wzhyzAK+9u/lrr8ELL7ipK4F/mz9/fo6fNWzYkPT0dM6cOUNkZCTt27fP8VibzZbt/v79+9O/f/9sP6tUqRIffPBBrvG1aNEiy7l79+5N7969sxwzatQoRo0alWXfzJkzcz2viIh4xokTmZPxZZfo2yfjq1bNQwFardCnD8yebVr39+xx0Xp/IuItlPSLOOHnn80kfWvWmPdRUfDii/DQQxDkjn9Nf/8NAweaSrhdO7MkgMUCJUuaHxEREfE6CQnBzJxpYe5cL0307f7+23RZ/OQTcyPz0UdK+EX8kJJ+EQfs2GEm5PvqK/M+PNzMoff003DRqmmuk5QEY8aY7gMpKWZWwEaNzNP4wEA3XFBERETyw96i/8UXgSxe3I709ICMz2rVyuy67/FEPz0dvvsO3nkn67j9GTMglx5uIuK7lPSL5OLYMdOS/9575il9QIBp1R892o2T5H/7LTzxhFkOAEwFPGlSZtd+ERER8QrZd903yf4NN9jo1s3iHYn+xc6dgwcegIQE03uwY0czP1DLlp6OTETcREm/SDasVpg6FZ591szOD2a8/uuvw/XXu/HCn30G991nXleoYJYEuPNON0wUICIiIs6yWmHDBli4EP73P1i71uyzq1ULunRJp3TpZTzyyM0Ee3p9e5sNfvnFzB746qvmfqJIEXjySUhOhn79oGJFz8YoIm6npF/kElu2mDrw55/N+zp1YOzYAloR7667oGZN6NTJzMxfuHABXFRERERycuQI/PijSfJ//NH0ArzYpV33U1OtLFiQ6Jlg7ZKS4NNPTRf+zZvNvo4dM2fmHz3aY6GJSMFT0i9yQVKS6cr/1lume16RIvDyy/DYY26apA/M3cP778Pnn5uLFCoEmzZpCT4REREPSUszLfj21vwNG7J+HhkJrVqZ+XXbtTMd87zGjh1mTOKMGZldFUND4d57oXRpj4YmIp6jpF8EMyn+Y4/B3r3m/V13mWH05cu76YKHDpnxc3PmmPfvv28CACX8IiIiBezAAfjhB5PkL1oEp09n/bxuXZPgt28PjRuDp3vtZ2vdOjPpr13FitC/v5mMSCv+iFzVlPTLVe3gQRg82EzAAxAbC2+/bXrXu0uZjRsJeuQROHrUzMT/+ONmQh0REREpECkpsHq1SfIXLjRD+y5WogS0aWOS/DZtIDraM3Hm6uhR+OsvuOUW875BA6hSxfwMGGCeUmjFHxFBSb9cpdLTYfJkeO45OHPG1ImDB8OoUW5agg8gJYWAoUNpMn68eV+rFnz4IdSu7aYLioiIiN3evZld9n/6CRIvGnZvsUDDhibJb9fO5M9emS/bbKZF/513zNDAokVh/37ThT8gwIzfDw/3dJQi4mUCrnyIiH/ZvBmaNIGBA03C37AhrF9vJutzW8IP8J//EHgh4U9/7DEzYFAJv9eLi4tjwoQJBX7dZcuWYbFYOGUfk5lPtWrVYuLEiTl+vnfvXiwWC5svTPjk6uu7yrRp02jTpo2nw8jWwoULqVOnDtaLp/IWEY85d8502R88GKpXz+zt/u23JuGPioIHHzQL5xw9aia5HznS9JD3uoT/3DmYPh1uvNGML/joI9Nd4ZprzNgEOyX8IpINJf1y1UhMhKeeMk/vf/3VTMTzzjtmlv46dQoggKFDscXGsva557COH28m7ROP2b9/Pw899BBly5YlJCSEa665hkGDBnH8+PEsx/366688+uijHooyd658IFGhQgXi4+O53q1rUuZPcnIyw4cPZ+TIkRn7pkyZQvPmzSlevDjFixenVatWrFu3Lsv3bDYbI0aMICYmhrCwMFq1asWOHTuyHPPKK6/QtGlTwsPDKVasWLbX//XXX7ntttsoVqwYxYsXp23btvz2228Zn7dr147g4GA++eQT1xVaRBxms8Hff5vVbtu3N13027Uz77dvN4l88+YwZgxs3GiG+M2aZea48+oh719/DeXKQd++ZlbB0FDo1cs0Hvz6K1Sq5OkIRcTLKemXq8K335qV8MaNM137u3WDbdvMkDe3Pc1PTDTr4tpVq0ba1q0catjQTRcUR+3evZsGDRqwY8cOPvvsM3bu3MnkyZNZsmQJzZo14+TJkxnHli5dmvCroOUkMDCQ6Ohogty2VIWRkpKS5+/OmTOHyMhImjVrlrFv+fLl9OjRg6VLl7JmzRoqVKhAmzZtOHBRy9cbb7zBpEmTmDx5MmvXrqVw4cK0bduW5OTkLHHdc8899O/fP9trJyYm0q5dO2JjY1m7di2rVq0iIiKCtm3bkpqamnFc7969mTRpUp7LKCLOSUw0dfyAAVC5slkyb/Bg040/OdlMyPvww/DVV3D8OKxYAcOGmYn5Arz1LthmM10R7apWhZMnIS4OXn8d/v0XZs40XRVFRBzgrf/dibjE/v1mJv7Onc3rihXNWL7PP4eYGDdeeNMmqF8funQxdxh2XjndrxucPZvzz0WJ1hWPPXfOsWOd9NhjjxESEsKPP/7ILbfcQmxsLO3bt2fx4sUcOHCAl156KePYK7Wm//rrr7Ru3ZpSpUpRtGhRbrnlFjZu3JjlGIvFwtSpU7nrrrsIDw+nSpUqfPvtt1mOWbBgAVWrViUsLIyWLVuy176UhINsNhujRo0iNjaW0NBQypYtyxNPPJHlmKSkJB566CEiIiKIjY3lgw8+yPjs0u79dhs2bKBBgwaEh4fTtGlTtm/fnvHZrl276Ny5M1FRURQpUoQbb7yRxYsXZ/l+XFwcL730Eg8++CCRkZEZvSaeffZZqlatSnh4OJUqVWL48OFZkufszJ49m06XzLL58ccfM2DAAOrUqUP16tWZOnUqVquVJUuWZPy5TJgwgRdeeIHOnTtTq1YtPvzwQw4ePMi8efMyzjN69GiefPJJbrjhhmyvvW3bNk6cOMGLL75ItWrVuO666xg5ciSHDx/mn3/+yTiuU6dOrF+/nl27duVaFhHJG5vNTLr35ptw222mNb9zZ7NK3Z49ZgGc224zQ/b++AP27YMpU0x1XLSop6O/goQE0wWxZk3zFMPuuutg1SrYuROeeQZKlfJcjCLik5T0i19KS4MJE0y9OW8eBAXB0KHmBqBdOzde2GYz/QgbNzZ9DMuW9cKBgQWgSJGcf+6+O+uxZcrkfGz79lmPjYvL/jgnnDhxgh9++IEBAwYQFhaW5bPo6Gjuu+8+vv76a2w2m0PnS0hIoFevXqxatYpffvmFKlWq0KFDBxISErIcN3r0aLp168bvv/9Ohw4d6NmzJydOnADMUIMuXbrQqVMnNm/ezMMPP8zQoUOdKtdXX33F+PHjef/999mxYwfz5s27LIEdN24cDRo0YNOmTQwYMID+/ftnSeKz8/zzz/PWW2+xfv16goKCeOihhzI+S0xMpEOHDixZsoRNmzbRrl07OnXqxL59+7KcY+zYsdSuXZtNmzYxfPhwACIiIpg5cyZ//fUXEydOZMqUKYy3T3KZg1WrVtGgQYNcj0lKSiI1NZUSJUoAsGfPHg4dOkSrVq0yjilatCiNGjVizZo1uZ7rYtWqVaNkyZJMmzaNlJQUzp07x7Rp06hRowZxcXEZx8XGxhIVFcXKlSsdPreI5O7UKbPC7cMPQ4UKZh7cZ54xk/Glppre7Y89BvPnm9b8xYvNcL7rrjMT9Hm9HTtg0CDTLWHgQNMVceFCSErKPKZZs6vzfkJEXEKz94vf+fNPMzGPvbG1aVN4/31w+1Dlo0ehTx/4/nvzvnNnmDbNywcKXn127NiBzWajRo0a2X5eo0YNTp06xdGjR4l2YI2mW2+9Ncv7Dz74gGLFirF8+XJuv/32jP29e/emR48eAIwZM4ZJkyaxbt062rVrx3vvvUflypV56623AJNgbtmyhddff93hcu3bt4/o6GhatWpFcHAwsbGxNLyk62f79u0ZcKH16Nlnn2X8+PEsXbqUatWq5XjeV155hVsuLAc1dOhQOnbsSHJyMoUKFaJ27drUvmgyypdeeomvv/6ab7/9loEDB2b5M3rqqaeynPeFF17IeB0XF8fTTz/N7NmzeeaZZ7KN49SpU5w+fZqyZcvm+ufw7LPPUrZs2Ywk/9ChQwBERUVlOS4qKirjM0dERESwbNky7rzzzoyeIFWqVOGHH364bEhE2bJls7T+i4jztm83D+2/+w7WrDFD8+zCwqBlS/MQv107uPZaH0nuL7Vsmemuv3Bh5r6qVU3i36uXJuUTEZdR0i9+5aOPoF8/83C8WDF44w0z743bx+0tXQo9e0J8vJlg5623TNc8n7wLcYGL10G61KUtFUeO5Hzspb84J7u85+ZKLfkhISEOnefw4cO88MILLFu2jCNHjpCenk5SUtJlrd21atXKeF24cGEiIyM5cqHsW7dupVGjRlmOb9KkiUPXt7vnnnuYMGEClSpVol27dnTo0IFOnTplSUgvjsFisRAdHZ0RQ04u/k7MhTExR44cITY2lsTEREaNGsX3339PfHw8aWlpnDt37rKyZ9c6//nnnzNp0iR27dpFYmIiaWlpREZG5hjHuQtDPQrlMgHma6+9xuzZs1m2bFmux+XFuXPn6Nu3L82aNeOzzz4jPT2dsWPH0rFjR3799dcsvUbCwsJIuriFTkSuyGo189LNm2d+tm3L+nmNGplJfvPmJvH3eevWmYTfYoEOHeDxx6F1ay+ebEBEfJWSfvELycmmZ5x9iHLr1uYBwCWNe+7z998m4a9eHWbP1lJ8hQt7/tgcXHvttVgsFrZu3cpdd9112edbt26lVKlSOc7gfqlevXpx/PhxJk6cyDXXXENoaChNmjS5bMK64Evmc7BYLC5d2q1ChQps376dxYsXs2jRIgYMGMCbb77J8uXLCbzwoCUvMVz8HcuFh1j27zz99NMsWrSIsWPHcu211xIWFkbXrl0vK3vhS35va9asoWfPnowePZq2bdtStGhRZs+endHTITslS5bEYrFkmWTxYmPHjuW1115j8eLFWR5U2HtrHD58OOOhhf19HSeW7fj000/Zu3cva9asIeDCDfmnn35K8eLF+eabb7j33nszjj1x4gSlS5d2+NwiV6uUFFi0yMLkybXo3z+I+PjMz4KD4dZbTae59u3N6C6f9tdf8Pbb0KqVmWAATKvE4cOZsxCKiLiJkn7xebt3Q9euZu48i8WssfvCCwUw9M1qzXwa/+ijZjz/Aw+4JDEV9ylZsiStW7fm3Xff5cknn8zSQnvo0CE+/fRT+vbt6/D5Vq9ezbvvvkuHDh0AMz7/2LFjTsVUo0aNyyb2++WXX5w6B5gW5k6dOtGpUycee+wxqlevzpYtW5xKbp2xevVqevfunfHwJDEx0aEJCH/++WeuueYann/++Yx9V+oOHxISQs2aNfnrr79o06ZNls/eeOMNXnnlFX744YfLehVUrFiR6OholixZkvHncObMGdauXZvjTP3ZSUpKIiAgIOPBB5Dx/uIHJ8nJyezatYu6des6fG6Rq8mZM2ZC3XnzYMECOHMmCKgIQESEafC+806T6Hv9xHtXkp5uCvl//wcXJhdl8+bMpL9kSdMzUETEzdR/SHzat99CvXom4S9Z0vSSGzmyABL+uXOhQQMzuxCYpw39+inh9xFvv/0258+fp23btqxYsYL9+/ezcOFCWrduTdWqVXMcV56dKlWq8NFHH7F161bWrl1Lz549L5sg8Er69evHjh07+O9//8v27dv59NNPmTlzplPnmDlzJtOmTeOPP/5g9+7dfPzxx4SFhXHNNdc4dR5nVKlShblz57J582Z+++037rvvPod6L1SpUoV9+/Yxe/Zsdu3axaRJk/j666+v+L22bduyatWqLPveeOMNhg8fzvTp04mLi+PQoUMcOnSIxAtDTCwWC4MHD+bll1/m22+/ZcuWLTz44IOULVuWO++8M+M8+/btY/Pmzezbt4/09HQ2b97M5s2bM87TunVrTp48yWOPPcbWrVv5888/6dOnD0FBQbRs2TLjPL/88ktGbw8RMQ4ehMmTTSJfqhTce6/pFHfmDMTE2GjXbg/ffZfG0aNm/733+njCf+IE1379NUE1apgnGEuWmEaCO++El182jQQiIgVISb/4pLQ0ePZZ0+3v9Glo0sQk/pc0ALreuXPQv7+ZgX7TJrMmkPicKlWq8Ouvv1KpUiW6devGNddcQ/v27alatSorV66kiBMrAkybNo2TJ09Sr149HnjgAZ544gnKlCnjVDyxsbF89dVXzJs3j9q1azN58mTGjBnj1DmKFSvGlClTaNasGbVq1WLx4sXMnz+fkm6cSHLcuHEUL16cpk2b0qlTJ9q2bUu9evWu+L077riDJ598koEDB1KnTh1+/vnnjFn9c9O3b18WLFjA6dOnM/ZNnjyZlJQUunbtSkxMTMbP2Iv+bT7zzDM8/vjjPProo9x4440kJiaycOHCLOP+R4wYQd26dRk5ciSJiYnUrVuXunXrsn79egCqV6/O/Pnz+f3332nSpAnNmzfn4MGDLFy4MMuwgc8++4yePXsSrgm45Cq3bRu89ppZzKZcOVN1LlxoZtuvXt2sqPPLL7BnTxr9+v1OmzY2QkM9HbVrBPbsyXWzZmHZu9esKfjMM6Zb4tdfmzELV+t8PyLiObar0OnTp22A7dixY54OxWVSUlJs8+bNs6WkpHg6FJfJqUwHDthszZvbbOZRuc02eLDNdv58AQT0xx822/XXZ1742WdtNif/vP3p93Tu3DnbX3/9ZTt79qzt5MmTtvT0dE+HlC8jRoywFSlSxLZ69Wq/KM/F0tPT/aZMXbt2tY0ZM8Yry3T06FFbiRIlbLt3787xGPu/m3Pnzl322bFjx2yA7fTp0+4M86rib/W9N9ch6ek225o1pmqsVi2zqrT/NG5ss732ms22dWvW73lzmRySkmKzffGFzXbkSMau1E8/tZ2Ki7Olvv++zZaU5MHgXMfnf0/ZUJm8n7+Vx2bzXF2vMf3iU5YuNd3+jhwxY/9mzLh82XeXs9lgyhQYPNi09EdFmVkCW7d284WlII0ePZq4uDjWrl1L9erVPR2O5ODNN99k/vz5ng4jW3v37uXdd9+lYsWKng5FpECcP2/q5Xnz4Jtv4OJVMIOD4bbbTI/2Tp3gCqtt+habzYzN//xz+PhjOHAAxoyBYcPMx126sCwsjA4dO5o/CBERD1PSLz7BajXdBIcPN69r1YI5c6BKlQK4+PjxYF9jvG1bmDWrAJcFkILUp08frFYrZ86c8XQokoO4uDgef/xxl6584CoNGjTIdnlCEX9y+rSZm+6bb8w2ISHzs4gI6NgxcyK+XFbh9D02G/zxh0n0v/gCduzI/Kx0abh4SE9AgLrwi4hXUdIvXu/4cbOqzYIF5n2fPmbVmwIbMturF7zzjhmQOGSI1s8VEZGryoEDZuLcefNMy35qauZnMTFmfp0774QWLfCbcfmXSU42EwidPWveFypknnB062b+APy24CLiD5T0i1f7++9iPPFEEPv2mfr1nXfgoYfcfFGr1dzddO5sntSXLAl//mkCEBER8XM2m5mIb94887NuXdbPq1c3Sf6dd8KNN/rhs/CdO02L/ubN8OWXZl9YGNxzD5w8aRL9Tp1M1wYRER+gpF+8ks0G770XwHPPNSctzcK115ru/LVru/nCqanmqcLHH8P06aZbASjhFxERv2azwdq1ZoL5efPg77+zft64sUnyO3c2Sb/f2bPHdNv//HOzOo/dH3/A9deb19Onq9u+iPgkJf3ilZ5/Hl59NRCAO++0MnNmgPvX7E1MhK5d4YcfIDDQzRcTERHxPHvntldfzdqiHxJiJuLr3BnuuMN04/dLCxfCiBHw66+Z+wIDTeG7d4cKFTL3K+EXER+lpF+8zsSJ5uYDoFevP/ngg6qEhLi57+CRI2Zs3vr1ZrKAOXPMLEQiIiJ+KC0NZs829e1ff5l9hQqZ1vy77oJ27fxsIj67gwfNeIToaPM+Lc0k/AEBZlKC7t3NH0Dp0h4NU0TElZT0i1eZPdusjAfw4ovp1Kq1E4ulqnsvunu3mZV/504oVQq+/x4aNnTvNUVERDwgOdksd/vGG7B3r9kXGQkDB8KgQVCmjEfDc4/Dh+Grr0zX/ZUr4dlnM1sX2rSBd981ib79QYCIiJ9R0i9eY9EiePBB8/rxx+HZZ638739uvujp09CsmVlcOC7OdO2v6uaHDCIiIgUsIQEmT4Zx40yVB6Yx+8knYcAA3D+ErqAdPWomJ/j8c7PkwMXLfP7zT+brkBCzOo+IiB9T0i9eYf166NLFzKPXrRtMmADp6QVw4aJFzTJ8n3wC//ufHw9a9G7p1nRW7ltJfEI8MRExNI9tTmCA++ZV6N27N7Nmzbpsf9u2bVlgXxtSRMQPHDsGkybB//0fnDpl9lWoAP/9r1kOt8CWvy1INhvUrWvWGrS78UbTdf+eeyA21nOxiYh4gJJ+8bgdO6BDBzOP3m23wYcfmqF1bk36U1LM032Ap582XQs0Q79HzN06l0ELB/HvmX8z9pWPLM/EdhPpUqOL267brl07ZsyYkWVfqNZZFhE/ceAAvPUWvP8+JCWZfdWqwdChcN99mVWgTzt1ynQTXLAANm40s+4HBJgJ99q0MUvudetmfipV8nS0IiIe428rq4qPiY83w+mPHoV69cxSQW7Pu8aPhyZN4MwZ895iUcLvIXO3zqXrF12zJPwAB84coOsXXZm7da7brh0aGkp0dHSWn+LFi7Ns2TLKlCnDypUrM4594403KFOmDIcPHwagRYsWDBw4kIEDB1K0aFFKlSrF8OHDsdlsbotXRMQRO3fCo49CxYqmuktKMo3eX34Jf/4JvXv7cMJvs5kl9F5/HW65xczD060bzJwJv/9uEn+7Dz4w74cOVcIvIlc9Jf3iMadPmwny9+yBa681vesjItx4QasVnnnGdOffuBE+/dSNF5MrSbemM2jhIGxcnijb9w1eOJh0a0GM88jUokUL+vXrR69evTh9+jSbNm1i+PDhTJ06laioqIzjZs2aRVBQEOvWrWPixImMGzeOqVOnFmisIiJ2v/8OPXqY1vwpU8xwuZtvNivSbdhgVqT1+dVoX34ZbrjBJPIrVpgugTVqwFNPwZIlUKtW5rFB6swqImKn/xHFI5KTzbJAv/0GUVFm/jy3zhicmgoPPQQff2zev/46/Oc/brygXMnKfSsva+G/mA0b+8/sZ+W+lbSIa+Hy63/33XcUKVIky77nnnuOoUOH8sILL7By5UoeffRR/vjjD3r16sUdd9yR5dgKFSowfvx4LBYL1apVY8uWLYwfP55HHnnE5bGKiORkzRoYMwa++y5zX4cOMGwY3HST5+LKM5vNjPtbsMD8PP206aoPpnW/UCG49VazzG779qZLg4iI5MrjLf0rVqygU6dOlC1bFovFwrx58674nWXLllGvXj1CQ0O59tprmTlzptvjFNdJT4eePWHZMtOyv3Chm3veJSZCp04m4Q8MhFmzTIu/xeLGi8qVxCfEu/Q4Z7Vs2ZLNmzdn+enXrx8AISEhfPTRR3z11VckJyczfvz4y77fuHFjLBf9HWrSpAk7duwgvUBmoBTxLarrXctmgx9/hJYtoWlTk/BbLKan+6ZNZuVZX0r4A1JSsPz4IzzxBFSpYrorPPmkGa8/f37mgc2awYkTpoADBijhFxFxkMdb+s+ePUvt2rV56KGH6NLlypN27dmzh44dO9KvXz8++eQTlixZwsMPP0xMTAxt27YtgIglP2w2eOwxmDvXjCn85huoU8eNFzxyxLQGrF9vpiieM8e0DIjHxUQ4tlKCo8c5q3Dhwlx77bWX7bdeWNbp559/BuDEiROcOHGCwoULuyUOkauB6nrXsFphzZoYXnwxMGP4enCwWe72mWd8dMXZAwdof//9BKWkZO4LDjZjEzp0MA/t7QIDISys4GMUEfFxHk/627dvT3snkrDJkydTsWJF3nrrLQBq1KjBqlWrGD9+fI43AufPn+f8+fMZ789cmMAtNTWV1NTUfETvPezl8PbyvPZaAO+/H4jFYmPWrHRuuslGTiG7pEwJCQQdPAilSpH+zTfYbryRHC9YAHzl9+SI1NRUbDZbxuR1NpstI2F2RLPyzSgfUZ4DCQeyHddvwUL5yPI0K9/MqfM6wh53due12Wzs2bOHp556ivfff58vvviCXr168eOPPxIQkNk5au3atVm+v2bNGqpUqYLFYnF5vPmV19+RN/PVMlmtVmw2G6mpqQReMsDaH/5fyElB1PXg3/X9mjUW/vOfQLZtawhAWJiNhx+2MniwlQoVzDFeXcS0NCxr1mC5sCyq9dVXAUgtXRpbsWIEBAZC+/ZY27fHduutWSf58eqCXc6f6no7lck3+FuZ/K084LmyeDzpd9aaNWto1apVln1t27Zl8ODBOX7n1VdfZfTo0ZftX7p0KeF+tkDtokWLPB1Cjg4eLMzo0bcC8MgjvxMWthdHlkTPb5kinn0Wa1AQZ48exaELFgBv/j05KigoiOjoaM6ePUtISAgJCQlOn2PMzWPo9X0vLFiyJP4WTLf5V5q/wtnEsy6L2S41NZWzZ8+yY8eOLPuDgoIoVqwY//nPf7j11lu5++67adasGc2aNWPMmDE88cQTAKSlpbFv3z4ef/xxevfuzW+//cbbb7/NSy+9lJFkeKO8/I68na+VKSUlhXPnzrFixQrS0tKyfJZkX1dN8lTXg//W90uWxPLee7VJSwsgPDyVjh13c/vtuylaNIUtW2DLFk9HmL3gM2eI2rSJqPXrKbNpE8GJiQCkhofzv6ZNsV148BX62mucL148c9jdRaun+DJ/qOsvpTL5Bn8rkz+Vx1N1vc8l/YcOHcoygzZAVFQUZ86c4dy5c4Rl0+1r2LBhDBkyJOP9mTNnqFChAi1btqRkyZJuj7kgpKamsmjRIlq3bk1wcLCnw8nWPfcEkp4eQLt2Vt5+uyZQM9fj81omy5IlkJiIrXPnfEbser7we3JUcnIy+/fvp3DhwqSmphIREZFljLsjetbrSVhYGE/+8CT/JmRO6lc+sjzj2oyjS40rdwPOi+DgYJYsWUL16tWz7K9WrRr33nsv+/fv57vvviMyMpLIyEjef/99evbsSadOnahduzZBQUE88MADpKen06pVKwIDA3niiSd44oknnP4zKAg2m42EhIQ8/Y68la+WKTk5mbCwMG6++WYKXbJU6PHjxz0UlffJS10P/lffp6XBsGEB/N//meS4c+c0unX7kTvvbElwsHcvQxfw2GMETJuG5aKeOLYSJbC1a4elfXvat20LISF+VS/aqUy+QWXyfv5WHvBcXe9zSX9ehIaGEprN4u/BwcF+8xfIzlvLtHy5Gb8fGAhvvRVAcLDjc0g6VabZs83gxoAAWL0a6tfPY8Tu5a2/J2ekp6djsVgyEi6LxZKl+7ujul7Xlbtq3MXKfSuJT4gnJiKG5rHNCQxw39pSs2bNYtasWdl+ZrVaGTx4MJGRkRnl6dq1K127ds1yXEhICBMmTGDy5Mlui9NV7N3f8/o78ka+WqaAgAAsFku2/wf4+v8J3sCf6vtTp+Dee83qNgAjRsBzz9lYuDDNu8qTnAxLl5rJ9V55BYoWNfvLlTOTENxwA9x+O9x+O5ZGjbAEBmY7i7RXlclFVCbfoDJ5P38qj6fK4XNJf3R0NIcPH86y7/Dhw0RGRub45F88y2oFe8PLo49Czdwb+PPurbfM0j5gpjC+/no3XUhcLTAg0C3L8omIb7ra6/q//4Y77oDt2828dbNmwT33eNHQ9gMHTJL/3XewZAnYu6u2aAH2B6T/+Q/06QOxsR4LU0REDJ9L+ps0acKCS8ZlL1q0iCZNmngoIrmSjz+GjRshMhJGjXLDBaxWk+zbl1V74gnz2oda/0REJNPVXNcvWmSeW586BeXLm15y9ep5OqoL1q6Ffv1g8+as+8uXNyvlVK6cuS86ukBDExGRnHk86U9MTGTnzp0Z7/fs2cPmzZspUaIEsbGxDBs2jAMHDvDhhx8C0K9fP95++22eeeYZHnroIX766Se++OILvv/+e08VQXKRlATPPWdeP/88lCnj4gucPw+9esHnn5v3b7xhHgD40Bhf8T3Lli3zdAgiPkV1/ZXZbPB//2d6xqWnQ5MmZnlbj+XOp0/Djz9CqVLQsqXZFxVlEn6LBRo3Nt32O3aEWrVU74qIeDGPJ/3r16+npb0ygYwJeHr16sXMmTOJj49n3759GZ9XrFiR77//nieffJKJEydSvnx5pk6delWv2+vN3nrL9AK85hrTAO9y06aZhD84GGbMgJ493XARERHJD9X1uUtJgcceg6lTzfteveD99yGb6Qncx2aDP/80if5335kZ9NPSTGJv/93FxcFXX0Hz5lC6dAEGJyIi+eHxpL9FixYZ6y1nZ+bMmdl+Z9OmTW6MSlzh4EF47TXz+vXX4ZKJql2jXz/YsAF69IBLlneSgpHbv18Ryepq/feiuj5nR4/C3XebHDsgwHRYGzKkABvObTYzAe4PP5hgLlatGtSpk3VfF/esqiIiIu7j8aRf/Nfw4aZ7f+PGZnyiy+zcaSYGCgkxd0jTprnw5OIo++yjSUlJfjOjqoi72dfn1b8ZAfj9dzNh3z//mHlvZs+G9u3ddDGbzcwMuGwZ7NoFb75p9lsssH+/SfjDw+Gmm6BDB9Nt/9pr3RSMiIgUJCX94habN5ve9mDm1HNZi8WqVdCpk7kh+egjTdbnQYGBgRQrVoyjR48SERFBcHAwgYHuW2avoFitVlJSUkhOTvappeByozJ5ns1mIykpiSNHjlCsWDG/+Lci+fP11/DAA3D2rMmtv/0WatRw4QVsNtixwyynt2yZ+Tl0KPPzoUOhZEnz+sUXzZq6N95oHqiLiIhfUdIvLmezwVNPme2995qWfpeYOxfuu89M3rd7t7lTiohw0cklL6Kjo0lPTyc+Pp6EhAQsfjCRk81m49y5c4SFhflFeUBl8ibFihUjWrOaX9VsNrOc/fDh5n2rVmZqmhIlXHyhxx+Hd97Jui80FJo2NUvrXTzc4uabXXxxERHxJkr6xeVWrYKffjL3Fq++6qKTvv8+9O9vblLuuAM++8x0QxSPslgsREVFsXHjRm699VaCgnz/v5TU1FRWrFjBzTff7DddsFUm7+AvvWEkf954IzPhf/xxGDcO3PJfZ/36ptW+cWMzEV/LltCokZsm2BEREW/m+3fo4nW++cZsu3c3E/3ml2XaNJPwAzz6qGm58IPk0p/YbDZCQ0N9JvnKTWBgIGlpaRQqVMgvygMqk4i3+Plns3wtmGT/ySddePLZs+HUKTPBLZhK+N57ISzMhRcRERFfpMxJXM6+jPLtt+f/XOWXLiVw0iTz5sknzRqAPtSVV0REBODECbPQTHq62Q4e7KITW63wwguma11gIDRoYH7UG05ERC5Q0i8utXs3bNtm7jtat87/+c4XK2bGCfTtq4RfRER8ks1mqrF9+6ByZZg82UXV2Zkz0LMnfPedef/UU1C3rgtOLCIi/kRJv7jU//5nts2aQbFi+T/f0bp1SVu7luDrr1fCLyIiPuntt2HePAgONpP2RUa64KQ7d5o5brZuNeP0p041DwBEREQu4f3rHIlPsXft79gxHydZuNCsJWxXo4aW5hMREZ+0cSM8/bR5PXasmV8vvyxLlkDDhibhL1sWVqxQwi8iIjlSS7+4TFKSWQ4YoEOHPJ7kxx+hc2coXtzcxIiIiPiohAQzn15KiqnaHn/cNee1bNkCJ0+amfnnzoWYGNecWERE/JKSfnGZZcsgORkqVIDrrsvDCZYuNXdFKSlmHeHy5U0rhoiIiI+x2czCMzt3mnpx+nTXjVKzDhpEYIkSpnVfS/CJiMgVqM+0uMzFXfudvrFZtcpM95+cbLazZ5vBjyIiIj5o5kz45BMzse1nn0GJEvk42aFD0Lu3mbgPTCXbt68SfhERcYha+sUlbDZYsMC8drpr/9q15ktJSdCmDXz5JYSEQGqqy+MUERFxt7/+goEDzesXXzST2+bZ+vVw551w4ACBKSlmvICIiIgT1NIvLrF1K+zda1bXu/VWJ75oH+iYkAAtW8LXX6vlQkREfNa5cyYvT0qCVq1g6NB8nGzOHGjeHA4cgOrVSX/hBZfFKSIiVw8l/eIS9lb+Fi2gcGEnvnjwoJmAqEwZ+PZbCA93R3giIiIF4umn4Y8/TLX20Uf5WHzmt9/MmP3kZDNu7pdfoEoVl8YqIiJXB3XvF5fYvNlsb7nFyS/GxcGmTaZJRAm/iIj4sD//hPfeM68//hiio/N4onPn4L77TG+422+HefPM5AAa9iYiInmgln5xiePHzTbPqwYp4RcRER83YoSZ4+auu6B163ycaOhQMzFAdLSZ9j8w0GUxiojI1UdJv7jEsWNmW7KkE19KTTUt/CIiIj7u119h7lwzsf5LL+XzZI88ArVqwaxZULq0S+ITEZGrl5J+cQl7S3+pUk586aefoHhxM2ZRRETEh9nn2HvgAbjuunye7PrrYeNGs6KNiIhIPinpF5fIU0v/kiVmvGJoqFtiEhERKQjLlsGPP0JwMIwalceTWK1mUgA7dekXEREXUdIv+ZaSYlbcAyeT/p9+MtvbbnN5TCIiIgXBZoPnnzevH3kEKlbM44n+7/+gdm0YO9ZlsYmIiICSfnEBe9f+gAAoVszBL504YbouArRs6Y6wRERE3G7BAvj5ZwgLy+zi77Tff4dnnoH0dHMiERERF1LSL/lmT/qLF3eiN+Ly5aZ5pEYNKFvWbbGJiIi4i9Wa2cr/+ON5XMHm4uX5OnaEAQNcGqOIiIiSfsm3PE3it2SJ2d56q8vjERERKQhz5sBvv0FkpGmoz5NnnjFj+aOizPJ8FotLYxQREVHSL/mW50n8QEm/iIj4pLQ0GD7cvH76aSfrQLvvv4e33zavZ86EMmVcFZ6IiEiGIE8HIL7P6ZZ+mw369TOJf4sW7gpLRETEbT78EP7+29R9gwfn4QTHj0OfPub1E09Au3auDE9ERCSDWvol3+xJf4kSDn7BYoFBg+Dbb534koiIiHew2WDMGPP6uecgIiIPJylRAkaMgIYN4fXXXRqfiIjIxZT0S76dP2+2mnBYRESuBuvWwa5dULgwPPpoHk9iscDAgbBmDRQq5NL4RERELqakX/ItJcVsg4MdONhmg08/hX/+cWtMIiIi7vLZZ2bbubNJ/J2yZw8kJGS+D9CtmIiIuJdqGsm31FSzdSjp370bevaEKlUgKcmtcYmIiLhaejp8/rl53aOHk19OToY77oA6dWDLFleHJiIiki0l/ZJv9qQ/JMSBg3/6yWwbN4bwcLfFJCIi4g7Ll8OhQ1C8OLRp4+SXhw6FP/6AxESzRJ+IiEgBUNIv+eZUS7+W6hMRER9m79rftauDD7vtFi6EiRPNay3PJyIiBUhJv+Sbw0m/zZbZ0n/bbW6NSURExNXOn4evvjKvneraf+QI9O5tXj/+OLRv7+rQREREcqSkX/LN4aT/jz/g6FHTrb9RI7fHJSIi4ko//AAnT0JMDNx8sxNfHDcODh+G66+HN95wW3wiIiLZUdIv+eZw0m9v5W/e3Mk+kSIiIp5n79rfvTsEBjr4pbQ0mDXLvH7xRS3PJyIiBU5Jv+Sbw0v22ZN+jecXEREfc/YsfPutee1U1/4TJ6BJEyhbFjp2dEtsIiIiuQnydADi+xyevX/6dFi2DOrVy/GQdGs6K/etJD4hntJhpUm3pbssThERkbz69luz0mzlynDjjU58sUwZmDvXLNfnol5u6dZ0Vu9dTXxCPDERMTSPbU5ggKNdD0RE5GqjpF/yzeHu/SVLwt135/jx3K1zGbRwEP+e+TfzK8Elebfyu3S7oZsLIhUREcmb2bPN9t57wWLJwwlc1K1/zak1PPbOYxxIOJCxr3xkeSa2m0iXGl1ccg0REfEv6t4v+Wa1mq3D4xuzMXfrXLp+0TVLwg9wPPU49869l7lb5+YjQhERkbxLT4elS83rLs7k1T//DDt3uiyOr7d9zet7X8+S8AMcOHOArl90VV0pIiLZUtIvLmOz5fLh00+bCYwOHLjso3RrOoMWDsJGzicYvHAw6VZ19RcRkYK3bRskJEDhwlCrloNfstmgXz+oUiVznb98SLemM2TRkOwvdaH+VF0pIiLZUdIv+RZw4W9Rrkn/++/DyJFw/PhlH63ct/KyFv6L2bCx/8x+Vu5bmc9IRUREnLd2rdk2aABBjg6M3LgRtmyB0FCXTGC7ct/Ky1r4L6a6UkREcqKkX/LNPrbR3s0/W5GRZmuf6v8i8QnxDl3H0eNERERcyZ70N2rkxJdmzDDbu+6C4sXzHYPqShERySsl/ZJv9qQ/15b+uDiz3bPnso9iImIcuo6jx4mIiLjSL7+YrcNJf3IyfPKJed2nj0tiUF0pIiJ5paRf8s2hpL9iRbPNJulvHtuc8pHlsZD9dMgWLFSIrEDz2Ob5jFRERMQ5iYnwxx/mtcNJ/zffwKlTUKEC3HabS+JoHtucchHlcvxcdaWIiORESb/km1NJ/969l30UGBDIxHYTzblySPwntJugNYhFRKTAbdhghq+VK2d+HGLv2t+rV/6WtrlIYEAg41qPAy6vK+3vVVeKiEh2lPRLvuW3ez9AlxpdmNNtDuUis95RlQouxewus7X2sIiIeITT4/kTEmDdOvO6d2+XxnJX9bt4Nu5ZykaUzbK/fGR55nSbo7pSRESy5egctCI5ym9Lv12XGl3oXK0zK/etJD4hntJhpTnzxxk6Ve/kslhFRESc4XTSHxFhlqddsQIqV3Z5PE2KNWHUvaP4Jf4X4hPiiYmIoXlsc7Xwi4hIjpT0S745lPQ3bgw7d5rxjbkIDAikRVwLAFJTU1nw5wLXBCkiIpIHeZq5PywM2rZ1SzyQta4UERG5EnXvl3xzKOkPDzctHiEhBRKTiIhIfv37r2m0DwiA+vUd+EJi4hUqQxERkYKnpF/yLeDC3yKr1bNxiIiIuNKGDWZ73XVQpIgDX3j8cahRAxYudGtcIiIizlDSL/kWdGGQSFraFQ6cMQMefBAWL3Z7TCIiIvm1fbvZXn+9AwcnJMAXX5gvRUa6NS4RERFnKOmXfLP32E9JucKBy5fDRx9lzmosIiLixf7+22yrVnXg4C+/hKQkqFYNmjRxa1wiIiLOUNIv+WZP+lNTr3DgFZbtExER8SZOJf0zZphtnz6Zk92IiIh4Aa9I+t955x3i4uIoVKgQjRo1Yt0VWoInTJhAtWrVCAsLo0KFCjz55JMkJycXULRyqeBgs71iS3+lSma7a5db4xEREe/ka/W9w0n/33/DqlVmkpsHHnB7XCIiIs7weNL/+eefM2TIEEaOHMnGjRupXbs2bdu25ciRI9ke/+mnnzJ06FBGjhzJ1q1bmTZtGp9//jnPPfdcAUcudg53769WzWztgyRFROSq4Wv1/enTcPiweV2lyhUOnjnTbNu3h7Jl3RmWiIiI04I8HcC4ceN45JFH6NOnDwCTJ0/m+++/Z/r06QwdOvSy43/++WeaNWvGfffdB0BcXBw9evRgrX0h3WycP3+e8+fPZ7w/c+YMYNaBT71in3TfYC+HJ8oTGBgABHL+fDqpqblM4V+pEsEABw+Sevz4FSc68mSZ3EVl8n7+Vh5QmXyFP5UlO75W32/dagGCiIqyER6elvMQtvR0gmbNwgKkPfAANjf+Hv35773K5N1UJt/gb2Xyt/KA58ri0aQ/JSWFDRs2MGzYsIx9AQEBtGrVijVr1mT7naZNm/Lxxx+zbt06GjZsyO7du1mwYAEP5NKd7tVXX2X06NGX7V+6dCnh4eH5L4gXWbRoUYFfc9++6kA1/v77HxYs2JLrsW2LF6fQyZP8PGMGp67YdGJ4okzupjJ5P38rD6hM3i4pKcnTIbiNL9b3y5eXAxpQsuRxFixYnfOBViul//Mfyq5axe+BgdgWLHDqOnnhT3/v7VQm36Ay+QZ/K5M/lcdTdb1Hk/5jx46Rnp5OVFRUlv1RUVFs27Yt2+/cd999HDt2jJtuugmbzUZaWhr9+vXLtbvfsGHDGDJkSMb7M2fOUKFCBVq2bEnJkiVdUxgPS01NZdGiRbRu3Zpg+yD7ArJxoxklUrbsNXToUCHXYwNr18a2ciXNYmOxdeiQ67GeLJO7qEzez9/KAyqTrzh+/LinQ3AbX6zv1683dVujRiXocIX6ittvB6CcU1dwnj/+vVeZfIPK5Bv8rUz+Vh7wXF3v8e79zlq2bBljxozh3XffpVGjRuzcuZNBgwbx0ksvMXz48Gy/ExoaSmho6GX7g4OD/eYvkJ0nyhQWZrbp6YEEBwfmfvDs2VCsGEHZ/D5yot+Tb/C3MvlbeUBl8nb+Ug5X8XR9v3u32VavHkBwsMenQMrCn/7e26lMvkFl8g3+ViZ/Ko+nyuHRpL9UqVIEBgZy2D5TzgWHDx8mOjo62+8MHz6cBx54gIcffhiAG264gbNnz/Loo4/y/PPPExDgXRXz1cDh2fsBLmnlERER/+eL9b3DM/dv3Wp+KlaEunXdGpOIiEheeDRDDgkJoX79+ixZsiRjn9VqZcmSJTRp0iTb7yQlJV1W0QcGmtZlm83mvmAlRw7P3i8iIlclX6vvbTYnkv4vvoC774YPPnBrTCIiInnl8e79Q4YMoVevXjRo0ICGDRsyYcIEzp49mzG774MPPki5cuV49dVXAejUqRPjxo2jbt26Gd39hg8fTqdOnTJuBqRgOZX0nz0LAwfCjh2wdGlmNwEREfFrvlTfnzoFFyb+p2JFt15KRETE7Tye9Hfv3p2jR48yYsQIDh06RJ06dVi4cGHGZD/79u3L8qT/hRdewGKx8MILL3DgwAFKly5Np06deOWVVzxVhKueffjkRask5SwszLSKJCXBnj0ONKGIiIg/8KX6/sABsy1RInPeGhEREV/l8aQfYODAgQwcODDbz5YtW5blfVBQECNHjmTkyJEFEJk4wqmkPyAAqlWDTZtg2zYl/SIiVxFfqe/tSX85d0/HLyIiUgA0653km1NJP0D16mabwzJNIiIinnTwoNmWLevZOERERFxBSb/km5J+ERHxJ2rpFxERf6KkX/LN6aS/Rg2zVdIvIiJeSEm/iIj4E68Y0y++zT57v9Mt/Vu3mnWRLBa3xCUiIpIXTiX9nTpB+fKZdZuIiIiXUdIv+eZ0S3+VKhAYCMWKmTWRihZ1V2giIiJOcyrpr1fP/IiIiHgpJf2Sb/akPyXFwS8UKgSJiWYrIiLiZexJvybyExERf6CkX/LN6ZZ+UMIvIiJeKTUVjhwxrx1q6d+50/xUqADXXefW2ERERPJCE/lJvuUp6RcREfFChw6Z6WaCg6F0aQe+8Mkn0L49vP2222MTERHJCyX9km/2pD852YkvrVwJN98MPXu6JSYREZG8iI832+hoCNBdkoiI+AF175d8Cwsz2/R0SEuDIEf/Vq1cCXFx7gpLRETEacePm23Jkp6NQ0RExFX0DFvy7eLh+efOOfgl+9JG//wDSUkuj0lERCQvTp402+LFPRuHiIiIqyjpl3zLU9JfurRpRrHZ4O+/3RKXiIiIs5T0i4iIv1HSL/kWEJDHcf321v5t21wek4iISF4o6RcREX+jpF9cwj6u3+GWflDSLyIiXkdJv4iI+BtN5CcuERYGp07lMen/8093hCQiIuI0p5P+du3MwTfc4LaYRERE8kNJv7hEnlr669XLHNsvIiLiBZxO+hs1Mj8iIiJeSkm/uIR9Mj+nkv4WLeDwYbBY3BGSiIiI09S9X0RE/I3G9ItL5KmlPyBACb+IiHgVp5P+Tz6BdesgNdVtMYmIiOSHWvrFJexJv1Oz99vZbJCQAJGRLo1JRETEWU4l/adOwf33Z36xWDE3RSUiIpJ3aukXl8hTSz/AqlVQvjzcdpvLYxIREXGWU0n/li1mGxurhF9ERLyWWvrFJfKc9MfGwsGDZmz/2bNQuLDLYxMREXGE1QqJiea1Q53Pfv/dbGvVcltMIiIi+aWWfnGJPE3kB1ChApQrB+np8OuvLo9LRETEURfXYQ49g1bSLyIiPkBJv7hEnlv6LRZo1sy8/vlnl8YkIiLijLNnM1+HhzvwBXv3/htucEs8IiIirqCkX1wiXxP5NW1qtqtXuyweERERZ9mT/rAws8BMrqzWzKRfLf0iIuLFlPSLS+S5pR8yW/rXrDE3USIiIh5gT/od6tq/d6+ZACAkBKpWdWdYIiIi+aKJ/MQl8pX0165tTnDyJGzfDjVquDQ2ERERRziV9EdHw+LFcOAABOl2SkREvJdqKXGJPE/kBxAcDH36QGho5olEREQKmFNJf3i4lpsVERGfoKRfXCJfLf0A77zjslhERETywqmkX0RExEdoTL+4RL6TfhEREQ9zKul/80345BNISHBrTCIiIvmlln5xiXzN3m939iysXQv160PRoi6JS0RExFEOJ/1JSTB0qJl8Nj4eIiLcHpuIiEheqaVfXMIlLf2NGpnxkWvWuCQmERERZyQlme0Vk/6//jIJf+nSEBXl9rhERETyQ0m/uIQ96bffMOWJvaXk/Pl8xyMiIuIse0t/ePgVDtyyxWxvuAEsFrfGJCIikl9K+sUl7K0i+Ur6bTaz1Q2UiIh4gH2Imv1Bdo5+/91sa9VyazwiIiKuoKRfXMKe9NtbSfJESb+IiHiQPem/4uqxSvpFRMSHKOkXl7B3hVTSLyIivsqhpN9my0z6b7jB7TGJiIjkl5J+cQm19IuIiK9zKOk/fBiOHYOAAKhZs0DiEhERyQ8t2ScucXHSb7PlMW9X0i8iIh7kUNIfFQV798KOHQ7M+CciIuJ5SvrFJexJv9VqJt+/4njI7PTuDa1aQeXKrgxNRETEIQ4l/RYLXHON+REREfEBSvrFJS5e0/js2Twm/QMHuiweERERZzk8kZ+IiIgP0Zh+cYmgIAgJMa/zNa5fRETEQ+xJf2hoLgc9/TSMHg3x8QUSk4iISH6ppV9cpnBhSEnJR9K/b585QdmyGicpIiIF7oot/VYrvP22Gcf2wAOXfZxuTWflvpXEJ8QTExFD89jmBAYEui9gERERByjpF5cpXBhOnjRJf55ufDp1Mssg/fgjtG5dMEGLiIhc4FDSf/68eV28eJaP5m6dy6CFg/j3zL8Z+8pHlmdiu4l0qdHFDdGKiIg4Rkm/uIx9XP+CPXO5a2Uebnw0e7+IiHiQQ937szF361y6ftEVG7Ys+w+cOUDXL7oyp9scJf4iIuIxGtMvLhMRAdSYy8i/umZJ+CHzxmfu1rk5n0BJv4iIeFBKitk6M5FfujWdQQsHXZbwAxn7Bi8cTLo13RUhioiIOE1Jv7hM4SLp0G4Q5PXGR0m/iIh4kD3pDw52/Dsr96287EH3xWzY2H9mPyv3rcxndCIiInmjpF9c5nzMSiiajxsfJf0iIuJBqalma1+NxhHxCY7N4u/ocSIiIq6mpF9cxhKRzxsfJf0iIuJBeWnpj4mIcelxIiIirqaJ/MRlSgTn88ZHSb+IiHjQFVv6AwNh2zbzOjISgOaxzSkfWZ4DZw5kO67fgoXykeVpHtvcDRGLiIhcmVNJf0REBHXr1qV+/frUq1ePevXqUbNmTSxK0gS4NqQ5nC4PkQfAkocbnwcegEOHoFw5N0cqIiI5uZrr+iu29FssUK1all2BAYFMbDeRrl90xYIlS+JvwfyZTWg34crL1oqIiLiJU0n/66+/zoYNG/jpp594++23sVqthIWFUatWrSw3B7Vr13ZXvOLFikYEwpcToVseb3yee64gwhQRkVxczXV9Xsb0A3Sp0YU53eYwaOHly9VOaDdBy/WJiIhHOZX0DxgwIOP1uXPnKFy4MI8//jgnTpzgl19+YerUqaSkpJCermVprkZFigBbu3DL4TnsqqobHxERX3Q11/VXbOlPT4dRo8zr556DsLCMj7rU6ELnap1ZuW8l8QnxxETE0Dy2uVr4RUTE4/I8pj/sQkXXo0cPatWqBUBaWhp//fWXayITnxMRYbYlDndhyTt5uPE5csTcUJUs6Xwzi4iIuNzVVNenp4PVal7nWAXZbPDyy+b1kCFZkn4wXf1bxLVwW4wiIiJ54dKJ/IKCgjJuCuTqU6SI2SYm5vHGp2lT2LULVq82r0VExOv4a11v79oPzs3eLyIi4u28Ysm+d955h7i4OAoVKkSjRo1Yt25drsefOnWKxx57jJiYGEJDQ6latSoLFiwooGglJxcn/Xmi2ftFRPyaN9f3Fyf96mwmIiL+xKmW/ocffpj69evToEGDjKf8+Z3N9/PPP2fIkCFMnjyZRo0aMWHCBNq2bcv27dspU6bMZcenpKTQunVrypQpw5w5cyhXrhz//PMPxYoVy1cckn/27v1nzuTxBEr6RUQ8zh11PXh/fX9x0h+kBY1FRMSPOFWt7dixgy+//JKEhASCLtSIo0ePpkWLFtSrV486deoQHh7uVADjxo3jkUceoU+fPgBMnjyZ77//nunTpzN06NDLjp8+fTonTpzg559/JvhC/7u4uDinrinuUbSo2SrpFxHxXe6o68H76/uL5yUM1Nx7IiLiR5xK+pcvXw7A33//zYYNG9i0aRMbN25kxIgRnDp1isDAQKpWrcqff/7p0PlSUlLYsGEDw4YNy9gXEBBAq1atWLNmTbbf+fbbb2nSpAmPPfYY33zzDaVLl+a+++7j2WefJTCHWvr8+fOcP38+4/2ZC1lpamoqqRc/2vdh9nJ4sjxmPqNgTp+2kZqa5vT3g2w2LEBaejq2i343/vI7Au/4Pbmav5XJ38oDKpOv8JayuLquB9+o75OTAczDhfT0VLJdnCAtDftw/9TU1KzdAwqAP/+9V5m8m8rkG/ytTP5WHvBcWZxK+keMGEHnzp2pX78+VatWpUePHhmf7dmzh/Xr17Np0yaHz3fs2DHS09OJiorKsj8qKopt27Zl+53du3fz008/0bNnTxYsWMDOnTsZMGAAqampjBw5MtvvvPrqq4wePfqy/UuXLs1Ta4U3W7RokceufepUKNCOhAT47rsFBDg5Y0TrpCTCgdU//8ypo0cz9nuyTO6iMnk/fysPqEzeLikpydMhAK6v68E36vvjxwsBbQkIsOY4b4AlPZ07LrxetGgRqfbJbAqYP/29t1OZfIPK5Bv8rUz+VB5P1fUWm83ep/rKHnroIb777jtCQkLo1KkTnTt35tZbbyUkjzPeHDx4kHLlyvHzzz/TpEmTjP3PPPMMy5cvZ+3atZd9p2rVqiQnJ7Nnz56MJ/3jxo3jzTffJD4+PtvrZPfkv0KFCsTHx1OyZMk8xe5tUlNTWbRoEa1bt87oBlnQkpMhMtJc++jR1Izu/o4KqlwZy/79pK1Zg61+fa8ok6upTN7P38oDKpOvOH78ODExMZw+fZrIyEiPxeHquh58o77ftw+uvTaYkBAbiYk59Faz2WDzZvP6hhsKfPC/P/69V5l8g8rkG/ytTP5WHvBcXe9UbTV9+nSsViurV69m/vz5DBo0iPj4eFq3bk3nzp25/fbbKVGihMPnK1WqFIGBgRw+fDjL/sOHDxMdHZ3td2JiYggODs7Sta9GjRocOnSIlJSUbG9KQkNDCQ0NvWx/cHCw3/wFsvNkmYKDzYzHKSmQlBRMqVJOnuDee+H4cYKio7Osl6Tfk2/wtzL5W3lAZfJ23lIOV9f14Bv1vb13WmCgJfdjGzbM9TwFwZ/+3tupTL5BZfIN/lYmfyqPp8rh9JJ9AQEBNG/enDfeeIPt27ezdu1aGjVqxPvvv0/ZsmW5+eabGTt2LAcOHLjiuUJCQqhfvz5LlizJ2Ge1WlmyZEmWloCLNWvWjJ07d2K1WjP2/f3338TExOSrFUJcI1+T+b3xBkybBpqYUUTEo1xZ14Nv1Pf2MfyaxE9ERPyN00k/wKRJkzh48CBgnro/88wzrF69mv3799O7d29WrlzJZ5995tC5hgwZwpQpU5g1axZbt26lf//+nD17NmN23wcffDDLxD/9+/fnxIkTDBo0iL///pvvv/+eMWPG8Nhjj+WlKOJi9qT/9GnPxiEiIvnjyroevL++dyjpt1rh1VfNz7lzbolDRETE1fI0GG3w4MFMnDiRZcuWUaFChYz9xYoVo3bt2jz00EMOn6t79+4cPXqUESNGcOjQIerUqcPChQszJvvZt28fARfNCFehQgV++OEHnnzySWrVqkW5cuUYNGgQzz77bF6KIi5mH5qSp6T/zBkzXrJIETW1iIh4mCvrevD++t7hpP+558zrfv3sy9aIiIh4tTzPQNOqVStuueUWli9fnnEzcOLECRo2bEh6tuvc5GzgwIEMHDgw28+WLVt22b4mTZrwyy+/OB2zuF++WvqrVYNDh8wkSbVruzIsERHJA1fW9eDd9b19FIGeOYuIiL/JU9JvsVh46aWXKFOmzGU3A04sBiB+KM9Jf3p65pcKF3ZpTCIi4ryrra7XmH4REfFX+Vpr5qWXXsJisWTcDISEhGCxWFwVm/igPCf9O3ea8ZFhYVCxosvjEhGRvLla6nol/SIi4q/ylPRf/IT/xRdfzLgZmD17tssCE9+U56Tfvu5xrVq64xIR8QJXW12vpF9ERPxVnpL+V155hcIXdcEePXo0AJ06dXJNVOKz8pz0//ab2Wosv4iIV7ja6nol/SIi4q/ylPRfvKSO3ejRowkODmbs2LH5Dkp8V75b+pX0i4h4hautrk9LM9ugfA18FBER8T4BVz7EcS+88AKnTp1y5SnFx+S7pb9OHVeGIyIiLuavdb1DLf2BgbBihfkpUqRA4hIREckvPc8WlypWzGydSvqtVhg0yCT+N9zgjrBERERy5VDSb7FA8+YFEo+IiIirKOkXl7K39DvVCBQQAM88445wREREHKIx/SIi4q+U9ItL5bl7v4iIiAfZx/TnmvRbrfD22+b1o49CoUJuj0tERCS/lPSLS+Up6V+71owLuPZaNbGIiIhHONTSbx+OBvDAA0r6RUTEJ7h0Ij8Re9J/5oy5N3LII49A9eqwYIHb4hIREcmNuveLiIi/UtIvLmWfyM9mg8REB75w/jxs3Wpea7k+ERHxEHvSryX7RETE3yjpF5cqVAhCQsxrhybz27rVDKQsXhwqVHBnaCIiIjlyaEy/iIiID1LSLy5nb+13KOnfvNlsa9c2SyGJiIh4gLr3i4iIv1LSLy5nT/odmswvPt5s4+LcFI2IiMiVpaaabXCwZ+MQERFxNSX94nJOtfTbm1RsNjdFIyIicmX27v1K+kVExN9ouhpxuTwl/fa7LREREQ+wt/TnOpFfQAAsXGheFyni9phERERcQUm/uJxTSf/NN8Prr8N117kxIhERkdw51L0/IADati2QeERERFxFSb+4nFNJ/403mh8REREPUvd+ERHxV0r6xeXsSf/Jkx4NQ0RExGEOtfRbrTBjhnl9//0QGur2uERERPJLSb+4nFMt/ceOwe7dULQoVKvmxqhERERyZm/pz3VMv9UKDz9sXnfpoqRfRER8gmbvF5crXtxsHUr658+HRo3gqafcGZKIiEiutGSfiIj4KyX94nKavV9ERHyNkn4REfFXSvrF5fKU9KenuykaERGRK3Ooe7+IiIgPUtIvLufURH72uysl/SIi4kFq6RcREX+lpF9cTt37RUTE19iT/pAQz8YhIiLiakr6xeXsE/mdPm0mOs6VuveLiIgXSEkxWyX9IiLibzRyTVyuaFGztdkgISHzfbbUvV9ERLyAQ0l/QAB89ZV5Xbiw22MSERFxBSX94nKFCpmf5GQzrj/XpL9aNRgxAipUKLD4RERELmVP+nMd0x8QAF26FEg8IiIirqKkX9yieHGIj3dgXH/VqjB6dEGEJCIikiN17xcREX+lMf3iFk5N5iciIuJhDiX9Vit89pn5sX9BRETEy6mlX9zCqWX7kpNh/nwoWRJuvdWdYYmIiGTLodn7rVa47z7z+sQJdQsQERGfoJZ+cQunWvrHj4du3WDMGDdGJCIikjN17xcREX+lpF/cwr5sn0NJv73V5Kef4J9/3BWSiIhIjpT0i4iIv1LSL27hVPf+a64x3fptNvjoI3eGJSIiki0l/SIi4q+U9ItbOD2RX+/eZjtzpkn+RURECpBDS/aJiIj4ICX94hb27v0OtfSDWfe4SBHYtQtWr3ZbXCIiItlR0i8iIv5KSb+4hdNJf+HCcM895vXMme4ISUREJEf22ftDQz0bh4iIiKsp6Re3KFHCbE+ccOJL9i7++/api7+IiBQoh1r6AwLgww/NT3h4gcQlIiKSX0GeDkD8kz3pd7ilH6B5c9i2DapVc0tMIiIiObG39Oc6kV9AADzwQIHEIyIi4ipq6Re3sHfvd6ql32JRwi8iIh6hMf0iIuKv1NIvbnFx936bzeTzIiIi3sqhJfusVvjuO/O6XTut7yciIj5BSb+4hb2lPy0Nzp41E/M7LCrKNLWsWOGW2ERERC7lUPd+qxU6dzavT5xQ0i8iIj5BSb+4RXi4uRdKSTH3RQ4n/SkpcOSIeV24sNviExERuZi694uIiL9S0i9uYbGY1v7Dh81kfrGxDn7RPvOfxQJFi7otPhERETubzcGWfgekW9NZuW8l8QnxxETE0Dy2OYEBgfkPUkREJI+U9IvL2W94AuvEw/YYjh5vDjh4w2Of+a9YMQjUTZKIiLhfenrmSrH5aemfu3UugxYO4t8z/2bsKx9ZnontJtKlRpd8RikiIpI3mr1fXGru1rnETYyj5ayWHGxyH/Ruyb2/xDF361zHTmBP+u0zAYqIiLiZvWs/5L2lf+7WuXT9omuWhB/gwJkDdP2iq+P1oIiIiIsp6ReXyemG53iqEzc89u79SvpFRKSA2Lv2Q95a+tOt6QxaOAgbtss+s+8bvHAw6db0vIYoIiKSZ0r6xSVyu+HBmRsee0u/ffp/ERERN8tv0r9y38rLHnhfzIaN/Wf2s3LfyjxEJyIikj8a0y8u4cwNT4u4FjmfqGhRaNYMbrjB9UGKiIhkw570BwSYnxwFBMDkyeZ1eHjG7viEeIeu4+hxIiIirqSkX1zCZTc8nTtnroF8cdOLiIiIm9irmyu28gcEwH/+c9numIgYh67j6HEiIiKupO794hK64REREV/lcNKfg+axzSkfWR4Llmw/t2ChQmQFmsc2z2OEIiIieaekX1xCNzwiIuKr0tLMNuhK/R+tVliyxPxc1BstMCCQie0mAlxWD9rfT2g3gcAALUUrIiIFz2uS/nfeeYe4uDgKFSpEo0aNWLdunUPfmz17NhaLhTvvvNO9AUqucrvhwebEDc9dd0G5cjBvnhuiFBERT/LWut7hln6rFVq1Mj+JiVk+6lKjC3O6zaFcZLks+8tHlmdOtzl0qdHFhRGLiIg4ziuS/s8//5whQ4YwcuRINm7cSO3atWnbti1HjhzJ9Xt79+7l6aefpnlztR57g5xueAKTnLjhOXAADh6EQLWGiIj4E2+u6x1O+i+eayabeqpLjS7sHbSXpb2W8mmXT1naayl7Bu1Rwi8iIh7lFUn/uHHjeOSRR+jTpw81a9Zk8uTJhIeHM3369By/k56eTs+ePRk9ejSVKlUqwGglNxff8LzZ+FOYuZSIaU7c8NiX7CtRwn1BiohIgfPmut7hpN9eRwUFQUREtocEBgTSIq4FPW7oQYu4FurSLyIiHufx2ftTUlLYsGEDw4YNy9gXEBBAq1atWLNmTY7fe/HFFylTpgx9+/Zl5crc1709f/4858+fz3h/5swZAFJTU0n1kxni7eXwlvI0K9eMyoHw373BnLbYOH8+NfdlkC4IOnkSC5AaEeF1ZXIFlcn7+Vt5QGXyFf5UlksVRF0Pea/vz52zAEEEB9tITU3L+QIHDxIM2EqVIi0tl+PcxJ//3qtM3k1l8g3+ViZ/Kw94riweT/qPHTtGeno6UVFRWfZHRUWxbdu2bL+zatUqpk2bxubNmx26xquvvsro0aMv27906VLCL1pn1x8sWrTI0yFkSE0NADphs1mYM2cRRYpc4S+51codJ08CsGTjRs7v2QN4V5lcRWXyfv5WHlCZvF1SUpKnQ3CbgqjrIe/1/R9/lARuIjk5kQULfsrxuNK//UZTICEkhKULFjgcl6v50997O5XJN6hMvsHfyuRP5fFUXe/xpN9ZCQkJPPDAA0yZMoVSpUo59J1hw4YxZMiQjPdnzpyhQoUKtGzZkpIlS7or1AKVmprKokWLaN26NcF5XXPIDcLDbSQlWWjQoDVX7Jl58iQWmw2A27p2JTUgwCvLlB/e+nvKD38rk7+VB1QmX3H8+HFPh+A18lLXQ97r+9BQM+FssWJF6NChQ47HWRISAChSqVKux7mLP/69V5l8g8rkG/ytTP5WHvBcXe/xpL9UqVIEBgZy+PDhLPsPHz5MdHT0Zcfv2rWLvXv30qlTp4x9VqsVgKCgILZv307lypWzfCc0NJTQ0NDLzhUcHOw3f4HsvK1MJUpAUhKcPh185bGSBw+abZEiBBcpkjHI0tvK5Aoqk/fzt/KAyuTt/KUc2SmIuh7yX98HBVlyP+5Cb7SA0qUJ8ODvy5/+3tupTL5BZfIN/lYmfyqPp8rh8Yn8QkJCqF+/PkuWLMnYZ7VaWbJkCU2aNLns+OrVq7NlyxY2b96c8XPHHXfQsmVLNm/eTIUKFQoyfLmCMmXM9uhRBw5OT4e2baFNG7fGJCIiBcvb6/oLzxOuvHBMixbwf/8HDz7o0uuLiIi4k8db+gGGDBlCr169aNCgAQ0bNmTChAmcPXuWPn36APDggw9Srlw5Xn31VQoVKsT111+f5fvFihUDuGy/eF7p0mbrUNJfvz4sXJh59yUiIn7Dm+t6h5P+G24wPyIiIj7EK5L+7t27c/ToUUaMGMGhQ4eoU6cOCxcuzJjwZ9++fQQ4MvW7eB2nkn47/a5FRPyON9f19qRf1Y+IiPgjr0j6AQYOHMjAgQOz/WzZsmW5fnfmzJmuD0hcwuGk/9dfoWxZKFfO7TGJiIhneGtd73DSv2qV2d5wAxQt6rZ4REREXEnPtMWt7GP6jxy5woGPPgrly8O337o9JhERkYs5nPT37w/Nm8O6dW6PSURExFWU9ItbOdTSv3MnbN4MQUFw000FEZaIiEiG9HSzvWLSf+yY2TqxjKCIiIinKekXt3Io6Z8zx2xvvdWs8SciIlKAHJrIz2bLTPrtlZuIiIgPUNIvbuVU0t+1q9vjERERuZRD3fvPnIG0NPO6ZEm3xyQiIuIqSvrFra44pn/PHtiwwdxp3XlnQYUlIiKSwaGk3/70unBhCAtze0wiIiKuoqRf3Mre0p+UZH4u89VXZtuihbpLioiIRziU9Gs8v4iI+Cgl/eJWEREQGmpeZ9vaP2+e2aprv4iIeIg96bdYcjlI4/lFRMRHBXk6APFvFgvExMDevRAfD3Fxlxzw3Xfmp00bD0QnIiLioOuug7ffhqJFPR2JiIiIU5T0i9uVLWuS/gMHsvmwWDG4//4CjkhERCSTvYXfZsvloIoV4bHHCiQeERERV1L3fnG7cuXM9uBBz8YhIiKSHYeSfhERER+lpF/crmxZs82S9B88CI0awZtv6i5LREQ8yqGkf+NGWLnyCmvQioiIeB8l/eJ29pb+LN37586Fdevg66+vMHOSiIhIwcg16R89Gm6+2dRbIiIiPkRJv7hdti39c+aY7T33FHg8IiIiF3Oopd/ewq8l+0RExMco6Re3uyzpP3wYVqwwr+++2yMxiYiI2DmU9NuX7FPSLyIiPkZJv7jdZRP5ff21ubNq2BBiYz0Wl4iICDiZ9Jcu7fZ4REREXElJv7hdTIzZnjkDiYnAr7+aHe3aeSwmERERuysm/ampcPKkea2WfhER8TFK+sXtIiLMD1xo7U9MNG904yQiIl7gikn/iROZB5YoUSAxiYiIuIqSfikQWWbwj46GqlUhKsqjMYmIiIADSb+9a3+JEhAYWCAxiYiIuEqQpwOQq8M118C2bbBrF7ScONHT4YiIiGQIDjbb1NQcDihTBt55B9LTCywmERERV1HSLwWiZk344Qf46y9PRyIiIpJVWJjZnjuXwwGlS8OAAQUWj4iIiCupe78UiBo1zHbrVs/GISIicqkrJv0iIiI+TEm/FIiaNc32r7+Axo2hVi34+2+PxiQiIgIQHm62SUm5HHTqFMyZA3PnFkRIIiIiLqPu/VIg7C39+/aB7cSfWBITIUDPnERExPMcaun//nu4/36oUwe6dCmIsERERFxCWZcUiBIlzGT9Fqxw9qzZWaSIZ4MSERHBwaS/VSuz3bwZjhxxd0giIiIuo6RfCkyNGhDGOSz2NZEiIjwbkIiICFmT/hyX7YuKMkPTAJYsKZC4REREXEFJvxSYmjUhggTzxmLJvMsSERHxIPuYfqsVUlJyObBNG7NdtMjtMYmIiLiKkn4pMDVqQBESzZvChTWmX0REvMLFz6Bz7eLfurXZLlqUS5cAERER76KsSwpMlqRfXftFRMRLhISYDmhwhaS/eXMIDYV//4Xt2wskNhERkfxS0i8FpmZNsGBjB9divaaip8MREREBTMLv0LJ9YWFw003m9Zo1bo9LRETEFbRknxSY6GjYW6wuVU/tYNN7UMfTAYmIiFxQuLBZXCYx8QoHjhsHxYpBbGxBhCUiIpJvaumXAmOxQL165vWGDZ6NRURE5GKlSpntsWNXOLBWLSX8IiLiU5T0S4Fq0MBs16/3bBwiIiIXK1PGbI8c8WwcIiIirqakXwpUl6SP2EQd6n0z0tOhiIiIZHAq6f/hB+jUCV5/3a0xiYiIuIKSfilQVUL3U4ffCDr0L+fPezoaERERw6mk/8AB+O47mDfPnSGJiIi4hJJ+KVDFKxYDIM62mz/+8GwsIiIidk4l/a1bm+26dXDqlLtCEhERcQkl/VKgLB07ANCclWxZfsLD0YiIiBhOJf0VKkC1amC1wtKlbo1LREQkv5T0S8GKiyO+TC2CSCft2wWejkZERATIw0R+9tb+RYvcEo+IiIirKOmXAnf65jsAuGbzNx6ORERExMhz0v/jj26JR0RExFWU9EuBK9arMwCNTy/k3CnN5iciIp7ndNLfogUEBsKuXbBnj7vCEhERybcgTwcgV5+oDvVZF9SUNWkNaLbuLA3ahHo6JBERucrZk/7EREhKgvDwK3whMhJuucWM6z992u3xiYiI5JWSfilwlgALo1qv5n//g4nboEEbT0ckIiJXu8hICAmBlBTT2h8X58CXFi2CAHWaFBER76aaSjzi1lvN9ssvPRuHiIgIgMWSmejv2OHgly5O+FeuhLNnXR2WiIhIvinpF4+47z4IsaQStGop/yzTWEgREfG8664z2z//dPKLhw5Bhw5QtSp89JHp8i8iIuIllPSLR5QtC99H92Upt/LPyOmeDkdERCTvSf/+/VC6NBw8CA8+CI0bw88/uzw+ERGRvFDSLx4TdodZ7qjML99gs3k4GBERuerlOem/8Ub46y947TUoUgR+/RWaNTPd2vbtc3mcIiIizlDSLx5T5/mOpBFI9ZQtbJijLv4iIuJZFyf9Tj+MLlQInn3WTAjQt6+ZJOCzz+CGGzS7v4iIeJRm7xePKVyhBNuibqL64eXsnvAtDe4Z5OmQRETkKla1KgQGwpkzcOAAlC+fh5NER8PUqfDYYzB4MNSvD0WLZn5us5kHAiI+6Pz586Snp3s6DJdITU0lKCiI5ORklclL+Wp5QkJCCPCylV2U9ItHBXbpDO8tJ2rdtyQnD6JQIU9HJCIiV6vQUKhSBbZtM639eUr67erWhWXLIDU1c9+mTeZhwLhxZty/iI9ISUkhKiqKffv2YfGTh1Y2m43o6Gj279+vMnkpXy1PQEAAFStWJCQkxNOhZFDSLx5V+ck74L0hNEtbzvezT9K5d3FPhyQiIlex667LTPrbts3nySwWCAkh3ZrOyn0riR/3JDHxm2netAmBPe4zcwBUqOCSuEXcxWazceTIEYoUKUJsbCxBQf6RPlitVhITEylSpIjXtcrmlb+VyRfLY7VaOXjwIPHx8cTGxnrNwwr/+FcrPiugSmUOl7qOqGN/8tfbP1C0RTTxCfHERMTQOEatICIiUrCuuw6++ioPk/nlYO7WuQxaOIh/z/wL1wLXQvnTMHHhp3Sp9jU884z5CQ93zQVFXCwtLY1z585RokQJwsPDfSb5uhKr1UpKSgqFChVSmbyUr5andOnSHDx4kLS0NIKDgz0dDqCkX7xA8msTqPTWJvbc/F+Y9W/G/nIR5bi/5P10oIMHoxMRkatJnmfwz8bcrXPp+kVXbGSdFfBAUejaDeZ8cY4uo0fDtGkwYQLcfXf+LyriYvax1P7Swi/ibvZu/enp6V6T9PvOIxPxWxuanmFPt2ch8t8s+w8mHOT1va/z9bavPRSZiIhcbexJ/19/5WEG/4ukW9MZtHDQZQk/YPZYLAzuWZL0uFj49184fDjvFxMREa/hLV36L6akXzzKflOExQaX/Puw3yg9tfgp0q2+M2OniIj4ripVzOp7CQmwfn3ez7Ny30rTpT8HNmzsTzvOyh+mwDvvwKOPZn64bh3s25f3i4uIiFzEa5L+d955h7i4OAoVKkSjRo1Yt25djsdOmTKF5s2bU7x4cYoXL06rVq1yPV6815VuigD+PfMvK/etLKCIRETEXXyhrg8JgS5dzOtp0/J+nviEeMeOO38cBgwAe9fp8+ehRw+oXBnuvx82bsx7ECIiInhJ0v/5558zZMgQRo4cycaNG6lduzZt27blyJEj2R6/bNkyevTowdKlS1mzZg0VKlSgTZs2HDhwoIAjl/xy+KbIweNERMQ7+VJd37ev2X72GZw9m7dzxETE5O24Y8fgmmsgLQ0++QTq14cWLeDbb8FqzVswIuJVli1bhsVi4dSpU54ORa4SXpH0jxs3jkceeYQ+ffpQs2ZNJk+eTHh4ONOnT8/2+E8++YQBAwZQp04dqlevztSpU7FarSxZsqSAI5f8yvNNkYiI+BRfqutbtIBKleDMGZgzJ2/naB7bnPKR5bFcOnbtAgsWKkRWoHls86wflCsHP/1kxhb07Gl6ACxfDp07E3T99ZTetClvAYlcpdasWUNgYCAdO3YssGv+9ttv3HHHHZQpU4ZChQoRFxdH9+7dMx5yNm3alPj4eIoWLVpgMcnVzePTcKakpLBhwwaGDRuWsS8gIIBWrVqxZs0ah86RlJREamoqJUqUyPbz8+fPc/78+Yz3Z86cASA1NZXU1NR8RO897OXwtfI0jmlMuYhyHEw4mO1kRxYblCtUhsYxjX2ubNnx1d9TbvytTP5WHlCZfIU/leVSBVHXg2vr+169Ahg5MpCpU63cd1/e5pV5q9Vb3Dv3XixYstRx9gcBY1uNxZpuxZqeTQt+rVowYwa89BIB775LwNSpWHbuJC0sLLMsNht44YRRzvDnf8v+UqbU1FRsF2a1tNlsWH2sx8nUqVMZOHAg06dP599//6Vs2bIAeSpTamrqFWdjP3r0KLfddhsdO3bkf//7H8WKFWPv3r3Mnz+fhIQESpUqRVBQEGXKlMFms2XE4Qq+/HvKjq+Wx2q1YrPZSE1NJTAwMMtnnvp/weNJ/7Fjx0hPTycqKirL/qioKLZt2+bQOZ599lnKli1Lq1atsv381VdfZfTo0ZftX7p0KeF+ti7uokWLPB2C0+4veT+vJ7x+2X7Lhf8Dn/i1Mj8s/KGAo3IvX/w9XYm/lcnfygMqk7dLSkrydAhuUxB1Pbi2vi9fvhABAW1YtSqAKVN+olw55/v5hxLKM3HPMPXAVI6nHs/YXzK4JH3L9SV0dygLdi+48oluuonA+vWJ/vVXTlavnvH3/roZMwg9fZpdd9zB6UqVnI7Pm/jTv2U7fylTUFAQ0dHRACQkJGCzgaf+uwoPd+45V2JiIl988QU//fQT+/fv5/333+epp57K+HzBggW8/vrrbN++nejoaHr06MFTTz2VsTxh8eLFGTt2LIsXL2bFihU8/vjjDB06lGnTpvH2229z4MABrrnmGp566inuvfdeABYvXszp06d56623Ms5TsmRJ6tevD5iHkatWraJTp07s3buXokWL8umnnzJs2DDef/99hg8fzoEDB2jdujXvvfce33zzDa+++ipnzpyhe/fujBkz5rJE8lIJCQnO/LF6PV8rT0pKCufOnWPFihWkpaVl+cxTdb3Hk/78eu2115g9ezbLli2jUKFC2R4zbNgwhgwZkvH+zJkzVKhQgZYtW1KyZMmCCtWtUlNTWbRoEa1bt/aa9SAd1YEO1NtWjyGLhnAgIXOsZtj5GHp+cwMzTn3GDxMiiI4GDh2CkiXBx8po58u/p5z4W5n8rTygMvmK48ePX/mgq5QjdT24vr7/6isbCxZY2L27JY88krdWpg50YJR1FKv2ryI+MZ6YIjHcVOEmAgNyv2nPTuodd3DA/vc+OZmgnj2xnD1LhWXLsN5yC9ZBg7B16AABXjF60yH++G/Z38qUnJzMvgurSURERJCUZKF8ec/8HTtzxkrhwo4fP2fOHKpXr079+vXp3bs3Q4YMYdSoUVgsFlasWEG/fv2YOHEizZs3Z9euXfTr14/Q0FBGjBiRcY433niDMWPG8H//938EBQWxZMkShg0bxvjx47ntttv4/vvvGThwIFWqVKFly5ZUrFiRtLQ0lixZQteuXbNdvs3+EDIiIoLIyEgKFSrEuXPnmDZtGrNnzyYhIYGuXbvSu3dvihUrxoIFC9i9ezf33HMPLVq0oHv37tmW12azkZCQQEREhFcuG+csXy1PcnIyYWFh3HzzzZfVWZ6q6z2e9JcqVYrAwEAOX7I+7eHDhzOeKuZk7NixvPbaayxevJhatWrleFxoaCihoaGX7Q8ODvaL/4wv5qtl6nZDN+6+7m5W7ltJfEI8MREx1AhvTIOPU/k3PoJ774WfltgIvf9+OHoUJkyANm08HXae+ervKTf+ViZ/Kw+oTN7OX8qRnYKo68H19f0jj8CCBfDxx4G8+mpgnp83BxNMq2tz7qHg9PmCgwkOD4elS2H8ePjiCwKWLydg+XKoWhUGD4YHH8Sp7MjD/Onfsp2/lCk9PT0j4bJYLAR48KFSQECAU8+0ZsyYwf33309AQAAdOnSgb9++rFy5khYtWvDyyy8zePBgevXqRUBAANdeey0vvfQSzzzzDKNGjco4x3333Udf++yeQM+ePenduzePPfYYANWrV2ft2rWMGzeO2267jaZNm/Lcc89x//33M2DAABo2bMitt97Kgw8+mNHbyf5naMpjflJTU5k8eTKVK1cGoGvXrnz00UccPnyYIkWKcP3119OyZUuWL19Ojx49si2vvQu8p39PruKr5QkICMBisWT7f4Cn/k/w+J9eSEgI9evXzzIxj32iniZNmuT4vTfeeIOXXnqJhQsX0qBBg4IIVdwsMCCQFnEt6HFDD1rEtaBE8UCGDVtL0aI2fv4ZRvb+B9tff8HWrdC2Ldx5J+ze7emwRUTkCny1ru/YEaKi4PBh+P77Ar/8ld14I3z6KezZA//9LxQtCn//bZYAfO01T0cnfio8HBITPfPjzCid7du3s27duowEOSgoiO7duzPtwlqcv/32G2+++SaRkZEUKVKEIkWK8MgjjxAfH5+lC/al//ds3bqVZs2aZdnXrFkztm7dmvH+lVde4dChQ0yePJnrrruOyZMnU716dbZs2ZLLn2t4RsIPZvhTXFwcRYoUybIvpxVPRHLj8aQfYMiQIUyZMoVZs2axdetW+vfvz9mzZ+nTpw8ADz74YJbJf15//XWGDx/O9OnTiYuL49ChQxw6dIjExERPFUHcpFy5s3z8cToBAfD653FMfWaHacEIDIRvvoGaNeH5501NICIiXssX6/rgYOjVy7y+kCd4pwoV4I03YP9+mDQJqlWD//wn8/PffoPNmz0WnvgXi8V0IvHEjzM9vKdNm0ZaWhply5YlKCiIoKAg3nvvPb766itOnz5NYmIiQ4cOZePGjWzevJnNmzezZcsWduzYkaVLduE89pgpWbIk99xzD2PHjmXr1q2ULVuWsWPH5nj8pS3A9pbiS/f50oR24j28Iunv3r07Y8eOZcSIEdSpU4fNmzezcOHCjC4w+/btIz4+c5329957j5SUFLp27UpMTEzGT27/kMR3tW1r4/UL8/z1H1aMnzqNh99/h1at4Px5GDMGqleH7ds9G6iIiOTIV+v6hx4y2wUL4MCB3I/1uIgIePxx0yOufPnM/UOHQt26cNttpsuCkgbxc2lpaXz44Ye89dZbGQn95s2b+e233yhbtiyfffYZ9erVY+fOnVx77bWX/eTWlbxGjRqsXr06y77Vq1dTs2bNHL8TEhJC5cqVOXvW+QlBRVzB42P67QYOHMjAgQOz/WzZsmVZ3u/du9f9AYlXeeop01Dx8cdmKP+dd9ak/7M/cuuAb7AMedI8/r2oS5SIiHgfX6zrq1WD5s1h5UrTmD5xoqcjcsDFzaFpaVC8uOkh99NP5qdaNXjySXjgAef6S4v4iO+++46TJ0/St29fihYtmuWzu+++m2nTpvHiiy9yxx13ULlyZe655x4CAgL47bff+OOPP3j55ZdzPPd///tfunXrRt26dWnVqhXz589n7ty5LF68OOPas2fP5t5776Vq1arYbDbmz5/PggULmDFjhlvLLZITr2jpF7kSiwU++ABuvx3S0+Grr6BVawvVh97JpH5/cWrmPLiwLAqnT8Ott8LkyaDZsEVEJJ+efdZsJ02Cr7/2bCxOCwoy4/5374ann4bISNMzrl8/iI01daWIn5k2bRqtWrW6LOEHk/SvX7+emJgYZs+ezaJFi7jxxhtp3Lgx48eP55prrsn13HfeeScTJ05k7NixXHfddbz//vvMmDGDFi1aAFCzZk3Cw8N56qmnqFOnDo0bN+aLL75g6tSpPPDAA+4orsgVeU1Lv8iVhIXB/Pnwxx/mHuXDD818RYOGhvFsoWvp3h3694eGf3yFZelSM6vxE09Ahw7Qs6d5YhAW5uliiIiIj+nYEYYMgXHjoHdvqFXLBzuXxcbCm2/CiBEwfbpZBWfv3swH5mAelKelmdkLRXzY/Pnzc/ysYcOG2Gw2rFYrcXFx3HXXXTl257fZbNnu79+/P/3798/2s0qVKvHBBx/kGl+LFi2ynLt379707t07yzGjRo3KsooAwMyZM3M9r0hO1NIvPuf66+Htt+HgQXj/fahTB5KTYdYsaNwY2k5oz5quY0mvVQdSU82Ef926QXQ09O0L//zj6SKIiIiPee01aNoUzpyBrl3h3DlPR5RHEREwaBDs3Gm6zfXsmfnZ9Ommrqxf30ySu2qVeQggIiI+TUm/+KwiReDRR2HjRlizxsywXKgQLPojhqZznqLE3k28cu8WjvYdamY2PnMGZswgy0LLmvVfREQcEBwMn38OpUqZifAHDfJ0RPkUGAhdumTtAbdrl9lu3GgmyW3e3BT4nnvMAwGffdIhInJ1U9IvPs9iMS38M2eamZXfeguqVDE5/guzr6fMtFe5OXYvi19YRtqrb0LZsplfvusuqF3bdHn891+PlUFERLxf+fJmeLzFAlOmmGFmfmXyZDh0yHSd69EDSpQw8+TMmWNWBbh4gsA9e0xvOhER8XpK+sWvlChhxl1u2waLFsHdd5vGjJWrA2j98i2Ufesphg419yqcPm26Lv7+OzzzjBnveOutpjXj9GlPF0VERLxQ69ZgH2bbrx9s2eLRcFwvKgoefNA83ThyxHSlGzkSBgww3ensOnaEkiVNb4EPPoD9+z0Xs4iI5EpJv/ilgABo1co0TuzbB6NHQ7lycPQovP66mYCpQ4+i/G/qAazvvW+6MNpsZvK/vn3NTc9LL3m6GCIi4oVeeMEsH3vunOn5npDg6YjcJDDQdKUbNcr0iLM7dQqOHTMF//pr+M9/zIPz6683KwRcsoa5iIh4lpJ+8Xtly5rJivfuNfcmbdqY/P5//4MO95cgbsyjvNxmBUfW7TVjGGvUgPPnsw4DOHbM3MTkMIuriIhcPQIC4OOPTXf/7dvh4YevsuqhWDEzDODXX+HFF80MhwEB8OefZozd9OmZx1qtpgIWERGPUdIvV42gILjzTvjhB9ixA/77X9Mzcf9+GD4cyjW9hns2DuOn//sT24aNpvnG7pNP4KabTBeBF16AlSs1oZGIyFWsdGn44gtTt3zxBbzzjqcjKmABAdCggalAV682XelmzzZrGt59d+ZxGzZAxYrmgfqQIbB4sXmwLiIiBUZJv1yVrr0W3njDzN330UemkSItzQwHuK2VhRr31WXC9EhOnrzwhcREs1zAnj3wyitw880QGQmNGsHgwWQeKCIiV4smTTJ7vQ8ZAmvXejYejypRArp3N6vkdOiQuX/LFjNMYNs2GD/eTIpQsqR5Cv/BB+ZhgYiIuJWSfrmqFSoE999vGil++w369ze5/fbt8OSTpod/9+7wQenn2bn6MLZPPzMtGNHR5inBunVmtuPChTNPOnUqTJpkuj1qZmMREb82aJCpFlJToVs3MxpMLvLQQ+YPZc4c8zomBs6ehW++gf/8B8u2bZnHnjqlelNExA2U9ItcUKsWvPsuHDwI771n3icnm26b//kPVKkdToX/3ssDYXOY/vJB9q/YY7r9jxkDISGZJ5o0ydwFNmwIRYvCLbfAsGEwf77uBkVE/IzFYoawV6liJo695x7lrZcpVsw8GZk2zaytu3EjvPwytG6NrXHjzONeeMGMm+jWzazDe/iwpyKWq0xcXBwTJkwo8OsuW7YMi8XCqVOnXHK+SpUq8d577+X4+d69e7FYLGzevNkt13eVadOm0aZNG0+Hka2FCxdSp04drFarp0NxipJ+kUtERJhlmDZvhl9+MSsV3XyzyesPHDCTN/V92ELszXHEPXcffbYM4aOPzFABbDa47z6zlFHx4mbc/4oV8NprcMcdBN18c9aL7d0L6ekeKKWIiLhKZKSZKLZIEVi2zIz6khxYLFC3Ljz/PPz4IwQHZ362dq1ZMvfLL6FPH9Or7sYbTUW8du1VNluiuML+/ft56KGHKFu2LCEhIVxzzTUMGjSI48ePZznu119/5dFHH/VQlLlz5QOJChUqEB8fz/XXX++S87lDcnIyw4cPZ+TIkRn7pkyZQvPmzSlevDjFixenVatWrFu3Lsv3bDYbI0aMICYmhrCwMFq1asWOHTuyHPPKK6/QtGlTwsPDKVasWLbX//XXX7ntttsoVqwYxYsXp23btvz2228Zn7dr147g4GA++eQT1xW6ACjpF8mBxWKG7I8aBcuXm2H7ixeb+5SmTc3kTf/8YxojHnwQKlSAKlUtPLp7KJ/1/I5DfxyDrVtNy0bfvlCjBrYmTTIvkJ5uuhOUKAFt25p1BRctgjNnPFVkERHJo+uuM0vbWyym19j773s6Ih/0yy+wZo2ZHLBePbNv/XqzQkCPHlmPTU4u+PjEp+zevZsGDRqwY8cOPvvsM3bu3MnkyZNZsmQJTZo04cSJExnHli5dmvDwcA9GWzACAwOJjo4mKCjIrddJSUnJ83fnzJlDZGQkzZo1y9i3fPlyevTowdKlS1mzZg0VKlSgTZs2HDhwIOOYN954g0mTJjF58mTWrl1L4cKFadu2LckX/V+RkpLCPffcQ//+/bO9dmJiIu3atSM2Npa1a9eyatUqIiIiaNu2LakXdeHq3bs3kyZNynMZPUFJv4iDwsPhtttMj8TVq81DgIUL4dlnTUNEQADs3AlTppjG/phyAdS4qzoD1j/El22ncnT5X6R/8EHmCf/5xyxldOaMae0YNcqsJ1isGNSuDW+/rVYNEREf0qmTqSMABg40Hb3ECYGB0LixSfI3bDDj7aZPh65dzQQ7Fos5LjXVrJd4001miN1vv6m+9ISzZ3P+ufShTG7HXroaUk7HOemxxx4jJCSEH3/8kVtuuYXY2Fjat2/P4sWLOXDgAC+88ELGsVdqTf/1119p3bo1pUqVomjRotxyyy1s3LgxyzEWi4WpU6dy1113ER4eTpUqVfj222+zHLNgwQKqVq1KWFgYLVu2ZK+Ty1nabDZGjRpFbGwsoaGhlC1blieeeCLLMUlJSfTt25eIiAhiY2P54KJ7z0u799tt2LCBBg0aEB4eTtOmTdm+fXvGZ7t27aJz585ERUVRpEgRbrzxRhYvXpzl+3Fxcbz00ks8+OCDREZGZvSaePbZZ6latSrh4eFUqlSJ4cOHZ0meszN79mw6deqUZd/HH3/MgAEDqFOnDtWrV2fq1KlYrVaWLFmS8ecyYcIEXnjhBTp37kytWrX48MMPOXjwIPPmzcs4z+jRo3nyySe54YYbsr32tm3bOHHiBC+++CLVqlXjuuuuY+TIkRw+fJh//vkn47hOnTqxfv16du3alWtZvImSfpE8KlLENNC/9pqZz+/ECTNsf8gQ03PRYjGTFb/3nhmeWKYM1G0QypQpNzBvnoUTxSqZSYs2bTJrPfXsaZY1stng99/NbIL2GxwREfEJw4aZ/DQtzQxj1xL1+RATY7r5f/klvPpq5v5Nm+D4cfME/vnnoU4d8xDgkUfMOIuEBI+FfFUpUiTnn4uXbQRzE5TTse3bZz02Li7745xw4sQJfvjhBwYMGEBYWFiWz6Kjo+nZsydffPEFNgcfFiUkJNCrVy9WrVrFL7/8QpUqVejQoQMJl/xdGz16NN26deP333+nQ4cO9OzZM6NHwf79++nSpQudOnVi8+bNPPzwwwwdOtSpcn311VeMHz+e999/nx07djBv3rzLEth33nmHBg0asGnTJgYMGED//v2zJPHZef7553nrrbdYv349QUFBPPTQQxmfJSYm0qFDB5YsWcKmTZto164dnTp1Yt++fVnOMXbsWGrXrs2mTZsYPnw4ABEREcycOZO//vqLiRMnMmXKFMaPH59rLKtWraJBgwa5HpOUlERqaiolSpQAYM+ePRw6dIhWrVplHFO0aFEaNWrEmjVrcj3XxapVq0bJkiWZNm0aKSkpnDt3jmnTplGjRg3i4uIyjouNjSUqKoqVK1c6fG5PU9Iv4iJFi8Ltt8Nbb5k5io4dM/ceTzwB9v+P//zTwvffV6JbtyBKlYJ6DYN46qM6fBc7gNPvfAy7d0N8vGnZeOstzxZIREScZp/Yr149Uw907mxWfRUXatjQPE157z3TvSL8/9u787ioqv4P4J9hR1YBFVBBRUQUcIcwzSUMpFKztMzcKrXS0p+PZeaWjz1q7q2aPS71lEuWW5qYS5QJahqYKKIihiXggqAIgsD5/XGagQsDsg2z+Hm/XvOCuXPu5Xw5MGe+955zbgM5KuC//wUGDwaWLSspW1TEUQAPoPPnz0MIAX9/f62v+/v74+bNm7hexQWW+/btixdeeAFt27aFv78/Vq9ejdzcXPz888+KcqNHj8awYcPQunVrzJ8/Hzk5OZq55ytXroSPjw+WLl0KPz8/DB8+HKNHj65WXKmpqXB3d0dYWBi8vLwQHByMsWPHKsr069cPr776Klq3bo1p06bBzc0NP/30U6XH/c9//oNevXqhXbt2ePvttxETE6MZFt+hQweMHz8eAQEB8PX1xbx58+Dj41NuFEPfvn3xr3/9Cz4+PvDx8QEAzJw5E927d0eLFi3w5JNPYurUqfjmm28qrEdWVhays7Ph6elZaX2nTZsGT09PTZKfnp4OAGjSpImiXJMmTTSvVYWDgwOio6Px1VdfwdbWFvb29oiKisKePXvKTYnw9PRUXP03dEz6iXTExUXehviDD+SF+6tXgY0bC9G/fwr8/ASEkBcrli2Tn1lcXOTnmGnL3RHlMQY5Bf/cEaCwUB5o2zZ9hkNERFXUoAGwfTvQpIl8/x89Ws7mojrk7S1X3d25U17137tX3jmndWvlleNt22TZ8ePlbQJ5Bqbu5ORU/PjuO2XZq1crLrtnj7LspUvay9XA/a7kW5W++1IlMjIyMHbsWPj6+sLJyQmOjo7Iyckpd7U7KChI872dnR0cHR1x9epVAEBiYiJCQkIU5UNLr/VUBUOGDEFeXh5atWqFsWPHYtu2bSgsLFSUad++veZ7lUoFd3d3TR0qUrreHh4eAKDZJycnB1OnToW/vz+cnZ1hb2+PxMTEcrFruzq/efNmPPzww3B3d4e9vT1mzpxZbr/S8v6Z6mFjY1NhmYULF2LTpk3Ytm1bpeVqIi8vDy+99BIefvhhHDlyBIcPH0ZAQAAef/xxTd3UbG1tkZubW6c/X5eY9BPVk0aNgKefFhg//g+cOlWIK1fkok9jx8rPKMXFwG+/AYsWyc8rDRvKBQN3PrFaflAZPFiOG+Vq/0REBq95c2DrVrk4/XffAfPm6btGJszGRq6Js2IFcP68XGhHbe9e4PJlYPVqeQLdxUUu0LN0KXDmDEcB1IadXcWPsslYZWXLDL+vsFw1tG7dGiqVComJiVpfT0xMRKNGjeDk5FSl440aNQrx8fH44IMPEBMTg/j4eLi6upZbsM6y9N0oIJPuury1W/PmzZGUlIRPP/0Utra2eO211/DII48o5snXpA6l91H9M7VUvc/UqVOxbds2zJ8/H4cOHUJ8fDwCAwPLxW5Xpo1iY2MxfPhwREZGYteuXYiLi8OMGTMqXeTP1dUVKpUKN2/e1Pr6kiVLsHDhQvz444+KExXu7u4A5MmZ0jIyMjSvVcWGDRtw6dIlrFu3Dt26dcNDDz2EDRs2ICUlBTt27FCUzczMRKNGjap8bH1j0k+kJx4ecjHi1avlZ5TLl4Evv5TTF1u0kBf4Y2OBp/eOxXJMljstXIhk3wgc3nEd+fn6rD0REd1P9+7AqlXy+3fflScBqB6UXg/ngw+A3bvlyoqtWslFAA8eBKZOlbdcuHxZf/UknXF1dUW/fv3w6aeflrtCm56ejq+//hqjRo2q8vEOHz6MN954A5GRkWjfvj2sra2rPDVAzd/fv9xt5o4cOVKtYwDyCvOTTz6JDz/8ENHR0YiNjcWpU6eqfZyqOnz4MEaPHo2nnnoKgYGBcHd3r9IChDExMfD29saMGTPQtWtX+Pr63nc4vJWVFdq1a4czZ86Ue23RokWYN28eoqKiyo0qaNmyJdzd3TUL+wHArVu3cPTo0WqNpsjNzYWZmZnmxAcAzfPSJ07u3r2L5ORkdOrUqcrH1jcm/UQGolkzYMQIORc0JUU+1q4Fho2wxLJmy/EcNuIOGsAnZT+aDeqCvo7HFXcTqMXdUYiISEdefFGOOgfke3yp2z1TfWjQAIiMBD76SN5iJylJjgh47DG56q6XV0nZMsOkybh9/PHHyM/PR3h4OH755RdcvnwZUVFR6NevH9q0aaNZbK4qfH198b///Q+JiYk4evQohg8fXm6BwPt55ZVXcP78ebz55ptISkrChg0bsH79+modY/369VizZg0SEhJw8eJFzdxzb2/vah2nOnx9fbF161bEx8fj5MmTeP7556s0esHX1xepqanYtGkTkpOT8eGHH2JbFaaqhoeH49dff1VsW7RoEWbNmoW1a9eiRYsWSE9PR3p6OnL+mfahUqkwefJkvPfee9i5cydOnTqFkSNHwtPTE4MGDdIcJzU1FfHx8UhNTUVRURHi4+MRHx+vOU6/fv1w8+ZNTJgwAYmJiTh9+jTGjBkDCwsL9OnTR3OcI0eOwNrautrTM/SJST+RgWrRQl71//JLIDUVmHfuOfww+yjSHHzhjVQcKOgBl4NbMGuWvGtRw4YldxM4d07ftSciIrUlS4CwMCA3Vy7sd+2avmv0gFKpgDZt5FmYvXuB48fl9qIiYM4ceQKg1H2/ybj5+vrit99+Q6tWrTB06FB4e3ujf//+aNOmDQ4fPgz7atwRYM2aNbh58yY6d+6MESNG4I033kDjxo2rVR8vLy9899132L59Ozp06IBVq1Zh/vz51TqGs7MzPv/8czz88MMICgrC/v378f3338PV1bVax6mOZcuWoWHDhujevTuefPJJhIeHo3Pnzvfdb8CAAfi///s/TJw4ER07dkRMTEyVTrS89NJL+OGHH5Cdna3ZtmrVKhQUFOCZZ56Bh4eH5rFkyRJNmbfeeguvv/46xo0bh27duiEnJwdRUVGKef+zZ89Gp06dMGfOHOTk5KBTp07o1KkTjv/zXtC2bVt8//33+OOPPxAaGoqePXviypUriIqK0qx1AAAbN27E8OHD0aBBgyr9Dg2CeABlZ2cLAOL69ev6rkqdKSgoENu3bxcFBQX6rkqdYUwVyMoSxQMGiCJbO7FhRoJ45hkh3NyEkBMT5cPOTojbt+uu3pUxtXYytXiEYEzG4vr16wKAyM7O1ndVTIYh9fc3bgjh4yPfo998s2bHMMW/e4OJqWdP2ThTp9b6UAYTUx3Jy8sTp0+fFhkZGaKoqEjf1amV2bNnC3t7exEbGyuKiorEzZs3jT6m0kwlpmeeeUbMnz/fIOO5du2acHFxERcvXqywTF5enjhz5ozIy8sr95q++npe6ScyNk5OUG3bBrPfjmLYe+2xZQuQkQGc+u0uPvgAcHQE7twBzp7Vd0WJiEjNxUXe1hXgeqwGado0+fWzz4CsLL1WhXRn7ty5+PDDD3HkyJE6XWCP6tbixYurNRKjPl26dAmffvopWrZsqe+qVAuTfiJjZGYmFyBSP435FQEDWuGNgINQL2Z6/rye6kZERFolJMivAQH6rQdpERkpG+b27ZLVF8kkjRkzBpMnT4aZGdMgQ9WiRQu8/vrr+q6GVl27dsWzzz6r72pUG//aiUzBwoVAWhrQrx9ev7sIgGDST0RkYNRJf6lztmQoVCrgzTfl9ytWAHfv6rU6RER1iUk/kSnYsgUYPRooLsbQ49OwBUNw+cxtfdeKiIj+cf26nIoFAO3a6bcuVIFhw4DmzWVDffmlvmtDRFRnmPQTmQJbW3l/v5UrUWxhiWfwHWbsCObEfiIiA3H6tPzasiVgoFNVydISmDJFfr9smVwbl4jIBDDpJzIVKhXwyiu4uO4X/IWmaHH3LNCtG+/fR0RkADif30i8/DIwYQKwY4fsV4mITICFvitARHXLc/BDaDniBDbhOXQPawxrX1/5wuXLwPr1gJubfLi6Kr+3ttZrvYmITBmTfiNhbw98/LG+a0FEVKeY9BOZmAYNAMumTdDv732InVyAbuorFWfPArNnV7zjkiXAv/4lv79wAXj33fInB9TPvbwAZ2ddh0JEZDK4iJ+RKioCzM31XQsiolph0k9kgnx9gb//tkDSZQt0U29s3BgYO1auJnX9OnDjRsnXoiJlEp+SAnz9dcU/4P33gbfe0mEERESmQwhe6Tc6KSnAjBnyFn7ff6/v2hAR1QqTfiIT5OsLREdDedu+Dh2A1avLFy4uBm7dkgsYqbVuLa/8lz45UPr7Jk10HQIRkcm4cgXIypIXjP389F0bqhIhgM2bZR958qTsQ6lWioqLcCj1ENJup8HDwQM9vXrC3Ex3oyhGjx6NL774otz28PBwREVF6eznEhkiJv1EJqC4GEhNBU6dkleTTpyQ2xVJf0XMzMoP1W/ZsmSof1n5+cDChUBaGuDhUZtqExGZvKIi4Kuv5Pe+voCNjX7rQ1XUqhUwZIhM/Bctqnz0G93X1sStmBQ1CX/d+kuzrZljM3wQ8QEG+w/W2c+NiIjAunXrFNusuYYRPYC4ej+Rkbl2DTh4EPjwQzlaPzQUcHKSefqAAcA77wC//y7LOjjooAJffSXn+/fsydsZERFVQAhg506gY0fg7bfltsce02uVqCqEAOLigEmTgAMH5Lbt2/VaJWO3NXErnvnmGUXCDwB/3/obz3zzDLYmbtXZz7a2toa7u7vi0bBhQ0RHR8PGxgYxMTGasosWLULjxo2RkZEBAOjduzcmTpyIiRMnwsnJCW5ubpg1axYEP/uQEeKVfiIDlZMj7+usvnqfkCC/v3pVe3lLS8DfHwgMlHNGAwN18AGzuBhYvFh+/9prvJ0REZEWP/8MTJ8OxMbK587OwLRpwOTJ+qwVVUlWFhASAty7J5+7u8vGpBopKi7CpKhJECifKAsIqKDC5KjJGOg3UKdD/cvq3bs3Jk2ahFdeeQUnT57EpUuXMGvWLGzZsgVNSk1h/OKLL/DSSy/h2LFjOH78OMaNGwcvLy+MHTu23upKVBeY9BPpWUEBcO5cSXKv/pqSor28SiVHHZZO7gMC5LDR0tPydWLXLiApSQ4tYIdHRKTw++9ytNXevfK5ra28YPzWW0DDhvqtG2lx965cpO/4cblALSAb6pln5LyMUaPk2XMLflyuqUOph8pd4S9NQODyrcs4lHoIvVv0rvOfv2vXLtjb2yu2vfPOO3jnnXcwb9487N27F+PHj8fp06cxatQoDBgwQFG2efPmWL58OVQqFfz8/HDq1CksX76cST8ZHb6LEdWT4mKZyB875o4//jDDmTMyuT97Figs1L6Ph4cysQ8IANq1A+zs6rfuGosWya+vvqqjuQNERMYnKQmYNQvYskU+t7AAxo0DZs7k0icGRwjgt9+A9euBTZuAmzfl9rFj5SK2gJy/z5FsdSLtdlqdlquuPn36YOXKlYptLi4uAAArKyusXr0aPXr0gLe3N5YvX15u/4ceegiqUn8LoaGhWLp0KYqKimDOWzmSEWHST6QDGRnKq/YJCXKofk6OJYCQcuUdHZXJfWCgvJezm1v9171CMTHA4cOAlRXwxhv6rg0Rkd799Rcwdy6wbp28MKxSAc8/L7f5+Oi7dqSQlgZ8+aVM9s+eLdnerBkwYoTybDoT/jrj4VC1s15VLVdddnZ2aK0+maPFsWPHAACZmZnIzMyEnd6uqhDpFpN+olq4fVs571799do17eWtrASaNs1GaKgjgoLMNEl+8+ZG8BlDPZd/xAheuiKiB9qNG8CCBcDHH8sbmgDAE08A//kPEBSk37pRBaKjS1ZUtLUFBg+Ww/f79pX3UiSd6OnVE80cm+HvW39rndevggrNHJuhp1fPeq9bcnIyZsyYgc8++wxbtmzBqFGjsH//fpiZlaxzfvToUcU+R44cga+vL6/yk9Fh0k9UBQUF8sJA6QX1EhKAS5e0l1ep5CjBslfvvb0L8eOPPyMyMhKWlkZ084ziYnlmws4OmDpV37UhItKLnBxg+XJgyRLg1i25rWdPeQLg4Yf1Wzf6hxByZNr69fIMzOuvy+2DBgEREXK+/pAhcogd6Zy5mTk+iPgAz3zzDFRQKRJ/FeTVjhURK3S2iF9+fj7S09MV2ywsLNCwYUOMHDkSffv2xZgxYxAZGYnAwEAsXboUb775pqZsamoqpkyZgvHjx+P333/HRx99hKVLl+qkrkS6xKSfqBT1vPvSif2pU3KhvYrm3Xt6lk/u/f2BBg3Kl1UvBmx0zMzkPQL/8x/O5SeiB05+PvDZZ8B775WM5OrQQSb7ERFGMFLrQfDnn3L4/pdfAhcuyG1+fsDEibKBbG2BPXv0W8cH1GD/wfh26LeYFDVJsahfM8dmWBGxAoP9B+vsZ0dFRcGjzOhEPz8/PP/88/jzzz/x9ddfAwA8PDywevVqDBs2DI899hg6dOgAABg5ciTy8vIQHBwMc3NzTJo0CePGjdNZfYl0hUk/PZCEKD/v/tQpOVQ/N1f7Pk5O5VfMDwgA/lkP5sHAhJ+IHiBFRcBXXwFz5sicEpBz9d97Dxg6VJ4PJT3bvFmekfnpp5Jtdnbyav6oUfqrFykM9h+MgX4DcSj1ENJup8HDwQM9vXrq9DZ969evx/r16yt8febMmbilHrIDYPDgwchXz9f5h6WlJVasWFFuMUAiY8Okn0zerVslw/JLJ/nXr2svb20tV8gve/W+adMH9GrOxo3yHoEh5RcgJCIyRUIA27fL1ffPnJHbPD2B2bOBF1+sh9ujkpSbK4ffpaQAFy/KR0qKXDlRfcb9++9LEv6+fWWiP3gwUOY2baR/5mbmOrktHxHdH5N+MhlZWXIY/rlzygQ/NVV7eTMzOe++7NV7H58H/Ja8t24B8fFAXJx8fPstcOeO/FDVu7e+a0dEpFOHDsmlS/5Z1BsNGwLTp8tR4ra2+q2bySkqAi5fhmtCAtCnT8nZlGXL5OKxZeZia1y8WJL0v/Ya0LatXGTW27t+6k1EZGQe5NSGjFBeHpCcXJLcl35UtGI+IK/Sl03u/f35AQ5FRSWrFkdHAy+/LH/BZQUEAI88Uq9VIyKqb1u3ymH7RUVyXZb/+z95AsDZWd81MwHx8cD+/cor9pcuwbKgAD0A3OvXDwgOlmVVqpKE38lJjjZr1Qpo2VJ+bdq05Ljdu8sHUR2Ljo7WdxWI6gyTfjI4hYVyVfzERBW+/74VoqLMcOGCTOwvX5bDLivi6Qn4+sp73Kvn3AcEyCs1D7TiYvkhS331Xv2YMaNkZWMXl5KEv3lzoFOnkkfv3py8SkQm7YcfgOeekwn/kCFy7VJ3d33XyogUFMiFcU6ckI+4OODzz+WZdgA4eBAotSq6mrCwwB03N9jk5JRsfPZZeaK5VSt24EREdYBJP+mFEMCVK8D58+Wv2F+8qF7l3gJAYLl9nZ3lgrxt2siHr2/JV07hK+PSJWDkSHmF5fbt8q/HxZV87+8P7Nsnk3xX1/qqIRGR3u3fL6eB37snr/R//fUDPs2rqo4fB9askV//+EMm/qWdO1eS9HftCgwbVnK1/p9HYePGOPDjj4jsWeo+7Z6e8kFEZIREZVco9YRdGulUZqb2xP78eTlNvCK2tkDr1gIODlfQs6c72rY11yT5rq4P6IJ62uTkACdPKufg9+0r50IC8pf166/yLIu1tfzwVfoKfmCpkyqWlkBYmF7CICLSl0OHgIED5W35Bg6Uq/Uz4S9FfQX/+HF5BX/kyJLh9H/9BaxaVVLW2Rno3Fkm+F26AD16lLz2yCPap4kZ7b1sHxzm/0wDLKzo3sVEpFDwzwlQ9f+OIWC3RrWWmwvN8Puyjxs3Kt7P3Fye6C99tV79aNoUKCoqxA8/HEdkZCQsLQ3nn6ZeFBXJhP72bfkwM5PDGwD5yXTUKJngnz9ffr6DnV3J9w4OciG+Nm3k/lxymohI49gx4PHHZT8WESHv/vbAv01evw5s21YyTL/sFfymTUuS/oceAt56Syb4XbrITp1n5U2OhYUFbG1tkZmZCUdHR1iYyFmx4uJiFBQU4O7duzAzkSmMphaTMcZTXFyMa9euoUGDBgb1v2I4NSGDdu+eHCmuLbH/66/K923WTHti37Jl5R+uiorqNATdKyqSCXmDBiXb9u8HsrM1ybvZzZtod/IkzPbskbcJKD2/sWtXOefh1q3ywyBCQ4GYGPm9tTXwyy9AWpp87ukpr9p37Ci/dumi3Hfw4DoPlYjI2MXFAeHh8u25Tx+5iJ+1tb5rVY8KCuRtbk6ckLey6dNHbk9PB8aNU5Z1di5J7NXlALnowfvv11uVST9UKhWaNGmCP/74A6mpqVCZyIkdIQTy8vJga2vLmAyUscZjZmYGLy8vg6ozk37SKC6WOae2xD4lRS6wVxFXV+2JfevWygvPBkkIeV+/27dlwl366+3bclE7deIsBDBokHy9bNncXKB/f7kalNqgQYoE3hyAr/pJ9+7KpD8trSSRV7OwkFfryy5WsGKFXNG4UyegceM6+TUQET0oTp8G+vWTt3p9+GFg504jvJuLELJ/UfdVZR9NmwK9esmyBQXynoPq19LT5T1t1VfwR44sSebbtpXDHoKCeAWfNCwtLZGRkYGAgACDunpZG/fu3cMvv/yCRx55BJYmMsTH1GIy1nisrKwMbmSCafzXUrXcuKE9sT9/Xt4SryINGpRP6tWJvkGv+xYVJZPx0l55BVi5Ui46sGkTMGFCxftHRpYk/SqVvHqfm6u97K1byucPPSSv/js4AA4OKLa3x8Xr19EyKAjmvr7Kstu3yyTf0VFTHjY22j9oDR1aachERKTduXPAo4/KvrBrV2D3bgNaBDYnB1iyRCbmpad4qUeLPfaY7FcAuc3JqeJjDRlSkvRbWMiV9MtSX8Hv3Llkm4UFsGdPnYVEpsXa2tqokq/KmJubo7CwEDY2NozJQJlaPPpkMEn/J598gsWLFyM9PR0dOnTARx99hGD1/Vq12LJlC2bNmoVLly7B19cX77//PiIjI+uxxobtzp2KF9DLzKx4PwuLknn2ZR+enkZ6ov+jj8pvu3xZfv3zz8oTfqDkQ9OFC/LKR9kzI/b2cojjiy8CU6YoX9u/X/G06N49nP7hB3hHRsK87JtXt273CYSIyLjpu69PSZEJf0YG0KEDsHdv5XlzvSssBObOrfBlVatWJUm/ehidmVnJiWL1w95e3q9WzcwMWLBAnkh2cJC3aO3QQc6zM8qOnYiIqsMgkv7NmzdjypQpWLVqFUJCQrBixQqEh4cjKSkJjbUMXY6JicGwYcOwYMECPPHEE9iwYQMGDRqE33//HQGlOzkTV1AgP8CcOwckJprhwIEOWL7cHBcuAH//Xfm+zZtrT+xbtDDBVYtXrwbeeEP+UszN5ZB79ZV7Ozu5XHNubsnjzp2S76dNk4sUAXK7tqEQOTnyhEBW1gM2IZSIqOr03ddfvixvbvLXXyV3KHVxqYvI6pC9vRyJZm9fPpF3cECRh4ecjgbI/uzOHTkvoSqJ+9tv67buRERksAwivVu2bBnGjh2LMWPGAABWrVqF3bt3Y+3atXhbSyf1wQcfICIiAm/+Mx963rx52LdvHz7++GOsKn3rGBNQXCw/oGi7Yp+SUnqxO3MALRT7urlpT+x9fJRrzZm8pk2B777T/lqbNnJYfVX4+8tfeukTBKUf/v51VmUiIlOjz74+PR146im5IG3r1sCBA0CjRrUOqe5ZWMipZxW5d68k6QcesM6ciIhqSu9Jf0FBAU6cOIHp06drtpmZmSEsLAyxsbFa94mNjcWUMsOow8PDsb2C5C0/Px/5+fma59nZ2QCAzMrGuRuAp54yx7FjKuTnV3wGv0EDAR8foFWrIpiZpaBvXy/4+ZmjVSsBZ2ft++TlVT5331Dcu3cPubm5uHHjhuHM41FfcalIZfcohIHGVEumFpOpxQMwJmOh7pNE2dtwmoD66OuBivv7gQPvIDnZEs2bC2zZUggrq/u+XRskU/y7Z0zGgTEZB1OLydTiAfTX1+s96b9+/TqKiorQpEkTxfYmTZrg7NmzWvdJT0/XWj49PV1r+QULFmCuljlybdq0qWGtDUdurlyA99Qp+XzbNv3Wh4iIaufGjRtwMqiJ5rVXH309UHF/n5zsDUAO8e/Uqbq1JyIiqlv13dfrPemvD9OnT1dcLcjKyoK3tzdSU1NN5oPVrVu30Lx5c1y+fBmOjo76rk6dYEzGwdRiMrV4AMZkLLKzs+Hl5QUXg5tobjxMvb83xb97xmQcGJNxMLWYTC0eQH99vd6Tfjc3N5ibmyMjI0OxPSMjA+7u7lr3cXd3r1Z5a2trWGtZYM3Jyclk/oDUHB0dGZMRYEyGz9TiARiTsTC0e/vWhfro64EHp783xb97xmQcGJNxMLWYTC0eoP77er1/srCyskKXLl1w4MABzbbi4mIcOHAAoaGhWvcJDQ1VlAeAffv2VVieiIiI9Id9PRERkf7o/Uo/AEyZMgWjRo1C165dERwcjBUrVuDOnTuaFX5HjhyJpk2bYsGCBQCASZMmoVevXli6dCkef/xxbNq0CcePH8fq1av1GQYRERFVgH09ERGRfhhE0v/ss8/i2rVrmD17NtLT09GxY0dERUVpFvBJTU1VDIHo3r07NmzYgJkzZ+Kdd96Br68vtm/fXuX79lpbW2POnDlahwAaK8ZkHBiT4TO1eADGZCxMMabS6ruvB0zvd2pq8QCMyVgwJuNgajGZWjyA/mJSCVO8NxARERERERER6X9OPxERERERERHpBpN+IiIiIiIiIhPFpJ+IiIiIiIjIRDHpJyIiIiIiIjJRTPqJiIiIiIiITJTJJP2ffPIJWrRoARsbG4SEhODYsWOVlt+yZQvatm0LGxsbBAYG4ocfflC8LoTA7Nmz4eHhAVtbW4SFheH8+fO6DKGc6sT0+eefo2fPnmjYsCEaNmyIsLCwcuVHjx4NlUqleEREROg6DIXqxLR+/fpy9bWxsVGU0Xc7VSee3r17l4tHpVLh8ccf15TRdxv98ssvePLJJ+Hp6QmVSoXt27ffd5/o6Gh07twZ1tbWaN26NdavX1+uTHX/P+tSdWPaunUr+vXrh0aNGsHR0RGhoaHYu3evosy7775brp3atm2rwyiUqhtTdHS01r+99PR0RTl9tVN149H2f6JSqdC+fXtNGX230YIFC9CtWzc4ODigcePGGDRoEJKSku67nzH0TfVJH317ZmYmhg8fDkdHRzg7O+Oll15CTk6OQcZ07949TJs2DYGBgbCzs4OnpydGjhyJK1euKI7RokWLcv8PCxcuNLh4gKr1g8bURgC0vl+pVCosXrxYU0aXbVTdmE6fPo2nn35aU6cVK1bU6Jh3797FhAkT4OrqCnt7ezz99NPIyMgw2Jiq8r6t7bPcK6+8YrAxVaUvNLZ20va/olKpMGHCBE0ZXbZTXedn9dYvCROwadMmYWVlJdauXStOnz4txo4dK5ydnUVGRobW8ocPHxbm5uZi0aJF4syZM2LmzJnC0tJSnDp1SlNm4cKFwsnJSWzfvl2cPHlSDBgwQLRs2VLk5eUZZEzPP/+8+OSTT0RcXJxITEwUo0ePFk5OTuKvv/7SlBk1apSIiIgQaWlpmkdmZma9xCNE9WNat26dcHR0VNQ3PT1dUUaf7VTdeG7cuKGIJSEhQZibm4t169Zpyui7jX744QcxY8YMsXXrVgFAbNu2rdLyFy9eFA0aNBBTpkwRZ86cER999JEwNzcXUVFRmjLV/T3VterGNGnSJPH++++LY8eOiXPnzonp06cLS0tL8fvvv2vKzJkzR7Rv317RTteuXdNxJCWqG9NPP/0kAIikpCRFnYuKijRl9NlO1Y0nKytLEcfly5eFi4uLmDNnjqaMvtsoPDxcrFu3TiQkJIj4+HgRGRkpvLy8RE5OToX7GEPfVJ/01bdHRESIDh06iCNHjohDhw6J1q1bi2HDhhlkTFlZWSIsLExs3rxZnD17VsTGxorg4GDRpUsXxXG8vb3Fv//9b8X/Q2V/i/qKR4iq9YPG1EZCCEUsaWlpYu3atUKlUonk5GRNGV21UU1iOnbsmJg6darYuHGjcHd3F8uXL6/RMV955RXRvHlzceDAAXH8+HHx0EMPie7duxtsTFV53+7Vq5cYO3asop2ys7MNNqaq9IXG1k5Xr15VxLNv3z4BQPz000+aMrpqJ13kZ/XVL5lE0h8cHCwmTJigeV5UVCQ8PT3FggULtJYfOnSoePzxxxXbQkJCxPjx44UQQhQXFwt3d3exePFizetZWVnC2tpabNy4UQcRlFfdmMoqLCwUDg4O4osvvtBsGzVqlBg4cGBdV7XKqhvTunXrhJOTU4XH03c71baNli9fLhwcHBSdib7bqLSqJF9vvfWWaN++vWLbs88+K8LDwzXPa/t7qktViUmbdu3aiblz52qez5kzR3To0KHuKlYL1Un6b968WWEZQ2mnmrTRtm3bhEqlEpcuXdJsM6Q2EkJ+SAEgfv755wrLGEPfVJ/00befOXNGABC//fabpsyePXuESqUSf//9t8HFpM2xY8cEAPHnn39qtnl7e2v98Fxbuojnfv2gKbTRwIEDRd++fRXbdNVGQtTu/b2iet3vmFlZWcLS0lJs2bJFUyYxMVEAELGxsbWIpmo/vzJV/V1re9/u1auXmDRpUk2qfF+6iOl+faEptNOkSZOEj4+PKC4u1mzTVTvVdX5Wn/2S0Q/vLygowIkTJxAWFqbZZmZmhrCwMMTGxmrdJzY2VlEeAMLDwzXlU1JSkJ6erijj5OSEkJCQCo9Zl2oSU1m5ubm4d+8eXFxcFNujo6PRuHFj+Pn54dVXX8WNGzfqtO4VqWlMOTk58Pb2RvPmzTFw4ECcPn1a85o+26ku2mjNmjV47rnnYGdnp9iurzaqifv9L9XF70nfiouLcfv27XL/S+fPn4enpydatWqF4cOHIzU1VU81rLqOHTvCw8MD/fr1w+HDhzXbjb2d1qxZg7CwMHh7eyu2G1IbZWdnA0C5v6PSDL1vqk/66ttjY2Ph7OyMrl27asqEhYXBzMwMR48eNbiYtMnOzoZKpYKzs7Ni+8KFC+Hq6opOnTph8eLFKCwsrHkw0G08lfWDxt5GGRkZ2L17N1566aVyr9V1GwG6eX+vyjFPnDiBe/fuKcq0bdsWXl5etX6/qq8+q6L37a+//hpubm4ICAjA9OnTkZubW+ufpcuYKusLjb2dCgoK8NVXX+HFF1+ESqVSvFbX7aSL/Kw++yWLKpc0UNevX0dRURGaNGmi2N6kSROcPXtW6z7p6elay6vnt6q/VlZGl2oSU1nTpk2Dp6en4o8oIiICgwcPRsuWLZGcnIx33nkH/fv3R2xsLMzNzes0hrJqEpOfnx/Wrl2LoKAgZGdnY8mSJejevTtOnz6NZs2a6bWdattGx44dQ0JCAtasWaPYrs82qomK/pdu3bqFvLw83Lx5s9Z/y/q2ZMkS5OTkYOjQoZptISEhWL9+Pfz8/JCWloa5c+eiZ8+eSEhIgIODgx5rq52HhwdWrVqFrl27Ij8/H//973/Ru3dvHD16FJ07d66T9xx9uXLlCvbs2YMNGzYothtSGxUXF2Py5Ml4+OGHERAQUGE5Q++b6pO++vb09HQ0btxY8bqFhQVcXFxq/TvWRUxl3b17F9OmTcOwYcPg6Oio2f7GG2+gc+fOcHFxQUxMDKZPn460tDQsW7bM4OK5Xz9o7G30xRdfwMHBAYMHD1Zs10UbAXXzmbImx0xPT4eVlVW5k0918X5VH31WRe/bzz//PLy9veHp6Yk//vgD06ZNQ1JSErZu3Vqrn6ermO7XFxp7O23fvh1ZWVkYPXq0Yrsu2kkX+Vl99ktGn/RTeQsXLsSmTZsQHR2tWPjuueee03wfGBiIoKAg+Pj4IDo6Go8++qg+qlqp0NBQhIaGap53794d/v7++OyzzzBv3jw91qz21qxZg8DAQAQHByu2G1sbmboNGzZg7ty52LFjh+INt3///prvg4KCEBISAm9vb3zzzTdar97om5+fH/z8/DTPu3fvjuTkZCxfvhz/+9//9Fiz2vviiy/g7OyMQYMGKbYbUhtNmDABCQkJ+PXXX+v159KD5d69exg6dCiEEFi5cqXitSlTpmi+DwoKgpWVFcaPH48FCxbA2tq6vqtaKVPvB9euXYvhw4eXW5jYmNroQVDR+/a4ceM03wcGBsLDwwOPPvookpOT4ePjU9/VvC9D6gt1Yc2aNejfvz88PT0V2w2xnSrKz+qL0Q/vd3Nzg7m5eblVJjMyMuDu7q51H3d390rLq79W55h1qSYxqS1ZsgQLFy7Ejz/+iKCgoErLtmrVCm5ubrhw4UKt63w/tYlJzdLSEp06ddLUV5/tVJt47ty5g02bNlXpzbY+26gmKvpfcnR0hK2tbZ20u75s2rQJL7/8Mr755ptyQzfLcnZ2Rps2bQy2nbQJDg7W1NdY20kIgbVr12LEiBGwsrKqtKy+2mjixInYtWsXfvrpJzRr1qzSsobeN9UnffXt7u7uuHr1quL1wsJCZGZm1vp3rIuY1NQJ/59//ol9+/YprvJrExISgsLCQly6dKn6gfxDl/GUVrYfNNY2AoBDhw4hKSkJL7/88n3rUhdtBOjm/b0qx3R3d0dBQQGysrLq7OdW5+fXRnXet0NCQgCg1n1LffXDZftCY26nP//8E/v376/y/xNQu3bSRX5Wn/2S0Sf9VlZW6NKlCw4cOKDZVlxcjAMHDiiuEpcWGhqqKA8A+/bt05Rv2bIl3N3dFWVu3bqFo0ePVnjMulSTmABg0aJFmDdvHqKiohTzPiry119/4caNG/Dw8KiTelempjGVVlRUhFOnTmnqq892qk08W7ZsQX5+Pl544YX7/pz6bKOauN//Ul20uz5s3LgRY8aMwcaNGxW3VKxITk4OkpOTDbadtImPj9fU11jb6eeff8aFCxeqdAKtvttICIGJEydi27ZtOHjwIFq2bHnffQy9b6pP+urbQ0NDkZWVhRMnTmjKHDx4EMXFxZoPjYYUE1CS8J8/fx779++Hq6vrfesSHx8PMzOzckNGq0NX8ZRVth80xjZSW7NmDbp06YIOHTrcty510UaAbt7fq3LMLl26wNLSUlEmKSkJqamptX6/0lWfVZP37fj4eACodd9SX/1w2b7QGNtJbd26dWjcuHGVPqfVRTvpIj+r136pykv+GbBNmzYJa2trsX79enHmzBkxbtw44ezsrLm924gRI8Tbb7+tKX/48GFhYWEhlixZIhITE8WcOXO03tbH2dlZ7NixQ/zxxx9i4MCB9X7LvurEtHDhQmFlZSW+/fZbxe0pbt++LYQQ4vbt22Lq1KkiNjZWpKSkiP3794vOnTsLX19fcffuXYOMae7cuWLv3r0iOTlZnDhxQjz33HPCxsZGnD59WhG3vtqpuvGo9ejRQzz77LPlthtCG92+fVvExcWJuLg4AUAsW7ZMxMXFaVaBfvvtt8WIESM05dW37HvzzTdFYmKi+OSTT7Tesq+y35OhxfT1118LCwsL8cknnyj+l7KysjRl/vWvf4no6GiRkpIiDh8+LMLCwoSbm5u4evWqQca0fPlysX37dnH+/Hlx6tQpMWnSJGFmZib279+vKaPPdqpuPGovvPCCCAkJ0XpMfbfRq6++KpycnER0dLTi7yg3N1dTxhj7pvqkr749IiJCdOrUSRw9elT8+uuvwtfXt05vB1eXMRUUFIgBAwaIZs2aifj4eMXfWn5+vhBCiJiYGLF8+XIRHx8vkpOTxVdffSUaNWokRo4caXDxVLUfNKY2UsvOzhYNGjQQK1euLPczddlGNYkpPz9f857s4eEhpk6dKuLi4sT58+erfEwh5K3gvLy8xMGDB8Xx48dFaGioCA0NNdiY7ve+feHCBfHvf/9bHD9+XKSkpIgdO3aIVq1aiUceecRgY6pKX2hs7SSEXDXfy8tLTJs2rdzP1GU71XV+pi5TH/2SSST9Qgjx0UcfCS8vL2FlZSWCg4PFkSNHNK/16tVLjBo1SlH+m2++EW3atBFWVlaiffv2Yvfu3YrXi4uLxaxZs0STJk2EtbW1ePTRR0VSUlJ9hKJRnZi8vb0FgHIP9X2rc3NzxWOPPSYaNWokLC0thbe3txg7dmy9JV41iWny5Mmask2aNBGRkZGKe6ULof92qu7f3dmzZwUA8eOPP5Y7liG0kfrWbmUf6jhGjRolevXqVW6fjh07CisrK9GqVSuxbt26cset7Peka9WNqVevXpWWF0LeltDDw0NYWVmJpk2bimeffVZcuHDBYGN6//33hY+Pj7CxsREuLi6id+/e4uDBg+WOq692qsnfXVZWlrC1tRWrV6/Wekx9t5G2eAAo/j+MtW+qT/ro22/cuCGGDRsm7O3thaOjoxgzZoziA5ohxZSSklLh35r6ntUnTpwQISEhwsnJSdjY2Ah/f38xf/78OjuZXJfxVLUfNKY2Uvvss8+Era2t4gSymq7bqLoxVfR3VfZ9+H59Rl5ennjttddEw4YNRYMGDcRTTz0l0tLSDDam+71vp6amikceeUS4uLgIa2tr0bp1a/Hmm2/Wyf3fdRVTVfpCY2snIYTYu3evAKC1/9N1O9VlfiZE/fVLKiGEqPq4ACIiIiIiIiIyFkY/p5+IiIiIiIiItGPST0RERERERGSimPQTERERERERmSgm/UREREREREQmikk/ERERERERkYli0k9ERERERERkopj0ExEREREREZkoJv1EREREREREJopJPxEREREREZGJYtJPRNU2ZswYzJw5U9/VICIiIiNx+fJl9O7dG+3atUNQUBC2bNmi7yoRPTBUQgih70oQkfEoKiqCu7s7du/ejeDgYH1Xh4iIiIxAWloaMjIy0LFjR6Snp6NLly44d+4c7Ozs9F01IpPHK/1EpBEVFQU7OzsUFxdrtiUkJEClUuH69esAgJiYGFhaWqJbt25V3oeIiIgebB4eHujYsSMAwN3dHW5ubsjMzNRvpYgeEEz6iUgjLi4OAQEBMDMreWuIj4+Hp6cn3NzcAAA7d+7Ek08+CZVKVeV9iIiISLdUKlWlj3fffRejR4/GoEGDFPt9++23sLGxwdKlSwEAo0eP1rp/REREuZ9Z0+l+J06cQFFREZo3b16jWImoeiz0XQEiMhzx8fHo0KGDYtvJkycV23bs2IHly5dXa59ly5YhMzMT7733HgCgsLAQrq6uuHHjBiws+DZERERUW2lpaZrvN2/ejNmzZyMpKUmzzd7eHhMnTlTs89///hcTJkzAqlWrMGbMGM32iIgIrFu3TlHW2tpa8byoqAi7du3C7t27q1XPzMxMjBw5Ep9//nm19iOimuOVfiLSiIuLQ1BQkGJb6aQ+MTERV65cwaOPPlrlfQA53F89pE99nBYtWjDhJyIiqiPu7u6ah5OTE1QqlWKbvb29ovyiRYvw+uuvY9OmTYqEH5AJful93d3d0bBhQ0WZmkz3y8/Px6BBg/D222+je/fuuvg1EJEWTPqJCABw584dJCcnK5L14uJixMXFabbt3LkT/fr1g42NTZX3AWSnX/p5fHw8AgMDdR0SERERaTFt2jTMmzcPu3btwlNPPVWjY1R3up8QAqNHj0bfvn0xYsSIOomDiKqGST8RAQBSUlJQXFyMtm3barbt3bsXN27c0CTsO3bswMCBA6u1jxACZ8+eRb9+/dCiRQu0aNECEydOREBAQD1FRkRERGp79uzBokWLsGPHDsXIvdJ27doFe3t7xWP+/PmKMjt27MCAAQM0z+833e/w4cPYvHkztm/fjo4dO6Jjx444depUHUdHRNpwbC0RAQBcXV2hUqnw22+/ITIyEkeOHMHEiRNhY2ODNm3a4OrVqzh+/Dh27txZ5X0AeWKgTZs2OH78uGa//v3780o/ERGRHgQFBeH69euYM2cOgoODyw37B4A+ffpg5cqVim0uLi6a7yua7vfGG28o9omPj0fXrl0BAD169FAM/Sei+sMr/UQEQN5KZ968eXjhhRfg7e2NVatWYciQIQgICIC5uTm+//57BAcHK1bkv98+gBza7+/vr/hZZ86c4ZV+IiIiPWjatCmio6Px999/IyIiArdv3y5Xxs7ODq1bt1Y8Sif9NZnut2zZMsVK/4WFhXByckJhYaGuQiWif/BKPxFpzJgxAzNmzND6WtlhfFXZB5BJf7t27TTP79y5g+zsbHh7e9e+wkRERFRt3t7e+Pnnn9GnTx9EREQgKioKDg4OVd5/x44dGDdunOZ5Vab7JSQkIDIyUvM6F/Ulqj+80k9EVdKjRw8MGzas2vuVvdJ/9uxZxUkAIiIiqn/NmzdHdHQ0rl69ivDwcNy6dUvzWn5+PtLT0xUP9Qr86ul+TzzxhKZ86el+ALRO9+OivkT6w1NrRFQlb731Vo3227Bhg+J5ly5dEBMTUxdVIiIiolpo1qwZoqOj0adPH4SHh2Pv3r0A5O33PDw8FGX9/Pxw9uzZ+073c3BwQJ8+fTBkyBAcOHAA5ubmikV91W7evInp06fXT6BEDziVEELouxJERERERGT4BgwYgB49elTrYsDFixcxdOjQcov6Tpw4EY8//rguqklEpXB4PxERERERVUlNpvtxUV8i/eLwfiIiIiIiqpKaTPfjor5E+sUr/UREREREpDNc1JdIvzinn4iIiIiIiMhE8Uo/ERERERERkYli0k9ERERERERkopj0ExEREREREZkoJv1EREREREREJopJPxEREREREZGJYtJPREREREREZKKY9BMRERERERGZKCb9RERERERERCaKST8RERERERGRiWLST0RERERERGSimPQTERERERERmSgm/UREREREREQm6v8BqiMnJ8WyaRUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Inflow plot\n",
    "plot_curves(\"Inflow\", df_num[0], df_num2[0], df_exp[0])\n",
    "\n",
    "## Post obstacles plots\n",
    "for i, pos in enumerate(positions):\n",
    "    plot_curves(pos, df_num[i + 1], df_num2[i + 1], df_exp[i + 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen a satisfactory match between inflow and experimental data despite the coarse resolution applied on the validation case.\n",
    "The qualitative aspects of the flow profile after the tree arrangement are well reproduced and quantitative aspects also have a decent representation.\n",
    "The difference compared to Qi and Ishihara results might be due our resolution of 0.25m against their 0.025m close to the ground."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: 1.6.33\n",
      "Commit hash: 577adbb9414c9ec214708ddaacd766cb53993ab4\n"
     ]
    }
   ],
   "source": [
    "sim_cfg = next(iter(sim_cfgs.values()))\n",
    "sim_info = sim_cfg.output.read_info()\n",
    "\n",
    "nassu_commit = sim_info[\"commit\"]\n",
    "nassu_version = sim_info[\"version\"]\n",
    "print(\"Version:\", nassu_version)\n",
    "print(\"Commit hash:\", nassu_commit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }\n",
       "td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       "span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }\n",
       ".output_html .hll { background-color: #ffffcc }\n",
       ".output_html { background: #f8f8f8; }\n",
       ".output_html .c { color: #3D7B7B; font-style: italic } /* Comment */\n",
       ".output_html .err { border: 1px solid #F00 } /* Error */\n",
       ".output_html .k { color: #008000; font-weight: bold } /* Keyword */\n",
       ".output_html .o { color: #666 } /* Operator */\n",
       ".output_html .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */\n",
       ".output_html .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */\n",
       ".output_html .cp { color: #9C6500 } /* Comment.Preproc */\n",
       ".output_html .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */\n",
       ".output_html .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */\n",
       ".output_html .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */\n",
       ".output_html .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".output_html .ge { font-style: italic } /* Generic.Emph */\n",
       ".output_html .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       ".output_html .gr { color: #E40000 } /* Generic.Error */\n",
       ".output_html .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".output_html .gi { color: #008400 } /* Generic.Inserted */\n",
       ".output_html .go { color: #717171 } /* Generic.Output */\n",
       ".output_html .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".output_html .gs { font-weight: bold } /* Generic.Strong */\n",
       ".output_html .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".output_html .gt { color: #04D } /* Generic.Traceback */\n",
       ".output_html .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".output_html .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".output_html .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".output_html .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".output_html .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".output_html .kt { color: #B00040 } /* Keyword.Type */\n",
       ".output_html .m { color: #666 } /* Literal.Number */\n",
       ".output_html .s { color: #BA2121 } /* Literal.String */\n",
       ".output_html .na { color: #687822 } /* Name.Attribute */\n",
       ".output_html .nb { color: #008000 } /* Name.Builtin */\n",
       ".output_html .nc { color: #00F; font-weight: bold } /* Name.Class */\n",
       ".output_html .no { color: #800 } /* Name.Constant */\n",
       ".output_html .nd { color: #A2F } /* Name.Decorator */\n",
       ".output_html .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       ".output_html .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       ".output_html .nf { color: #00F } /* Name.Function */\n",
       ".output_html .nl { color: #767600 } /* Name.Label */\n",
       ".output_html .nn { color: #00F; font-weight: bold } /* Name.Namespace */\n",
       ".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".output_html .nv { color: #19177C } /* Name.Variable */\n",
       ".output_html .ow { color: #A2F; font-weight: bold } /* Operator.Word */\n",
       ".output_html .w { color: #BBB } /* Text.Whitespace */\n",
       ".output_html .mb { color: #666 } /* Literal.Number.Bin */\n",
       ".output_html .mf { color: #666 } /* Literal.Number.Float */\n",
       ".output_html .mh { color: #666 } /* Literal.Number.Hex */\n",
       ".output_html .mi { color: #666 } /* Literal.Number.Integer */\n",
       ".output_html .mo { color: #666 } /* Literal.Number.Oct */\n",
       ".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".output_html .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".output_html .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".output_html .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".output_html .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       ".output_html .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".output_html .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       ".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
       ".output_html .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"nt\">variables</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1/1</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">plane_height</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">4.0</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">body_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">200</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">H_ref</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_025H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 0.25*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_05H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 0.5*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_1H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 1.0*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_2H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 2.0*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_3H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 3.0*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_4H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 4.0*${var.H_num}</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">pos_5H</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">${domain.body_pos} + 5.0*${var.H_num}</span>\n",
       "<span class=\"w\">  </span><span class=\"nt\">var</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">H_num</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">7*${domain.scale}</span>\n",
       "\n",
       "<span class=\"nt\">simulations</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">flowThroughTrees</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">save_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">./tests/validation/results/10_flow_through_trees</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">n_steps</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">40000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">report</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">500</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">600</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">144</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">64</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">block_size</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">8</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">point_clouds</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">tree</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">True</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">tree_cfg</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">interval_run</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">end_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">csv_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/point_clouds/cube_2x74x6.csv</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.scale}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.body_pos}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;1.0+${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">bodies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">full_plane</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">True</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">category_II</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">lnas_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/lnas/wind_tunnel/full_plane.lnas</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">small_triangles</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">add</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plates_obstacles</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">lnas_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/lnas/wind_tunnel/category_II/plates_Nx160Ny70_6x2_spacing16x32_offset19y.lnas</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">volumes_limits</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">body_transformed</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">10</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">20</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">120</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">124</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">64</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">small_triangles</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">add</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">1</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">4.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">4.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;2*32.0*${domain.scale}&quot;</span><span class=\"p p-Indicator\">]</span><span class=\"w\"> </span><span class=\"c1\">#2m</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">refinement</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">static</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">volumes_refine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\"># - start: [0, 56, 2]</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\">#   end: [256, 88, 12]</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\">#   lvl: 3</span>\n",
       "<span class=\"w\">              </span><span class=\"c1\">#   is_abs: true</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">34</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">288</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">110</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">24</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">352</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">144</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">40</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">data</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">divergence</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">monitors</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">fields</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">rho_max</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">stats</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">min</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">max</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">500</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">50</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">full_domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">},</span><span class=\"nt\"> macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">omega_LES</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">f_IBM</span><span class=\"p p-Indicator\">]</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">statistics</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">macrs_1st_order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">macrs_2nd_order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">exports</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">full_domain</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">probes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">historic_series</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">velocities</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;u&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">10</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">points</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">velocity_probe</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.body_pos}-50*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">7*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">lines</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">inlet_profile</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">velocity_profile</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.body_pos}-50*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.body_pos}-50*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_025H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_025H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_025H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_05H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_05H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_05H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_1H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_1H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_1H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_2H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_2H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_2H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_3H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_3H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_3H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_4H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_4H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_4H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">pos_5H</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">dist</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">start_pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"s\">&quot;${domain.pos_5H}&quot;</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end_pos</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                  </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.pos_5H}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"nv\">72.0</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                    </span><span class=\"s\">&quot;${domain.plane_height}</span><span class=\"nv\"> </span><span class=\"s\">+</span><span class=\"nv\"> </span><span class=\"s\">14*${domain.scale}&quot;</span><span class=\"p p-Indicator\">,</span>\n",
       "<span class=\"w\">                  </span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">precision</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">single</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">LBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">tau</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.500011</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">vel_set</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">D3Q27</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">coll_oper</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RRBGK</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">initialization</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">rho</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">u</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0.05</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">engine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">CUDA</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">BC</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">periodic_dims</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">false</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">BC_map</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RegularizedNeumannOutlet</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rho</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.0</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">E</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">F</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RegularizedHWBB</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">B</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">N</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "\n",
       "<span class=\"w\">          </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">pos</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">BC</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Neumann</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_normal</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">S</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">SEM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">eddies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">lengthscale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">20</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">20</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">20</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">domain_limits_yz</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">20</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">-20</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">124</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">40</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">profile</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">csv_profile_data</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;fixture/SEM/category_vprofile/profile_log_cat4_H150_Uh0.06.csv&quot;</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">z_offset</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"s\">&quot;${domain.plane_height}&quot;</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">K</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.0</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">LES</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">model</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Smagorinsky</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">sgs_cte</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.17</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">body_cfgs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">tree_cfg</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">n_iterations</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.6*1.16</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">kinetic_energy_correction</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">category_II</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">n_iterations</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">3</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.5</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_model</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">EqLog</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_ref</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">3.125</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_shell</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.125</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">5000</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">params</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">z0</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"s\">&quot;0.01*${domain.scale}&quot;</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_error</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1e-04</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_iters</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">10</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_n_div</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">51</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{}</span>\n",
       "</pre></div>\n"
      ],
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       "\\PY{+w}{            }\\PY{n+nt}{z\\PYZus{}offset}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!math}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{\\PYZdl{}\\PYZob{}domain.plane\\PYZus{}height\\PYZcb{}}\\PY{l+s}{\\PYZdq{}}\n",
       "\\PY{+w}{            }\\PY{n+nt}{K}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n",
       "\n",
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       "\n",
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       "\\PY{+w}{              }\\PY{n+nt}{start\\PYZus{}step}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{5000}\n",
       "\\PY{+w}{              }\\PY{n+nt}{params}\\PY{p}{:}\n",
       "\\PY{+w}{                }\\PY{n+nt}{z0}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!math}\\PY{+w}{ }\\PY{l+s}{\\PYZdq{}}\\PY{l+s}{0.01*\\PYZdl{}\\PYZob{}domain.scale\\PYZcb{}}\\PY{l+s}{\\PYZdq{}}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}max\\PYZus{}error}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1e\\PYZhy{}04}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}max\\PYZus{}iters}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{10}\n",
       "\\PY{+w}{                }\\PY{n+nt}{TDMA\\PYZus{}n\\PYZus{}div}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{51}\n",
       "\\PY{+w}{          }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "variables:\n",
       "  domain:\n",
       "    scale: !math 1/1\n",
       "    plane_height: 4.0\n",
       "    body_pos: 200\n",
       "    H_ref:\n",
       "    pos_025H: !math ${domain.body_pos} + 0.25*${var.H_num}\n",
       "    pos_05H: !math ${domain.body_pos} + 0.5*${var.H_num}\n",
       "    pos_1H: !math ${domain.body_pos} + 1.0*${var.H_num}\n",
       "    pos_2H: !math ${domain.body_pos} + 2.0*${var.H_num}\n",
       "    pos_3H: !math ${domain.body_pos} + 3.0*${var.H_num}\n",
       "    pos_4H: !math ${domain.body_pos} + 4.0*${var.H_num}\n",
       "    pos_5H: !math ${domain.body_pos} + 5.0*${var.H_num}\n",
       "  var:\n",
       "    H_num: !math 7*${domain.scale}\n",
       "\n",
       "simulations:\n",
       "  - name: flowThroughTrees\n",
       "    save_path: ./tests/validation/results/10_flow_through_trees\n",
       "\n",
       "    n_steps: 40000\n",
       "\n",
       "    report:\n",
       "      frequency: 500\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 600\n",
       "        y: 144\n",
       "        z: 64\n",
       "      block_size: 8\n",
       "      point_clouds:\n",
       "        tree:\n",
       "          IBM:\n",
       "            run: True\n",
       "            cfg_use: tree_cfg\n",
       "            order: 1\n",
       "            interval_run:\n",
       "              end_step: 0\n",
       "              start_step: 0\n",
       "          csv_path: fixture/point_clouds/cube_2x74x6.csv\n",
       "          transformation:\n",
       "            scale: !math [\"${domain.scale}\", \"${domain.scale}\", \"${domain.scale}\"]\n",
       "            translation: !math [\"${domain.body_pos}\", 72, \"1.0+${domain.plane_height}\"]\n",
       "      bodies:\n",
       "        full_plane:\n",
       "          IBM:\n",
       "            run: True\n",
       "            cfg_use: category_II\n",
       "            order: 0\n",
       "          lnas_path: fixture/lnas/wind_tunnel/full_plane.lnas\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            translation: !math [0, 0, \"${domain.plane_height}\"]\n",
       "        plates_obstacles:\n",
       "          IBM:\n",
       "            order: 1\n",
       "          lnas_path: fixture/lnas/wind_tunnel/category_II/plates_Nx160Ny70_6x2_spacing16x32_offset19y.lnas\n",
       "          volumes_limits:\n",
       "            body_transformed:\n",
       "              - start: !math [10, 20, 0]\n",
       "                end: !math [120, 124, 64]\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            translation: !math [0, 1, \"${domain.plane_height}\"]\n",
       "            scale: !math [4.0, 4.0, \"2*32.0*${domain.scale}\"] #2m\n",
       "\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            volumes_refine:\n",
       "              # - start: [0, 56, 2]\n",
       "              #   end: [256, 88, 12]\n",
       "              #   lvl: 3\n",
       "              #   is_abs: true\n",
       "              - start: [0, 34, 0]\n",
       "                end: [288, 110, 24]\n",
       "                lvl: 2\n",
       "                is_abs: true\n",
       "              - start: [0, 0, 0]\n",
       "                end: [352, 144, 40]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "\n",
       "    data:\n",
       "      divergence: { frequency: 10 }\n",
       "      monitors:\n",
       "        fields:\n",
       "          rho_max:\n",
       "            macrs: [rho]\n",
       "            stats: [min, max]\n",
       "            interval: { start_step: 500, frequency: 50 }\n",
       "      instantaneous:\n",
       "        full_domain:\n",
       "          { interval: { frequency: 10000 }, macrs: [rho, u, omega_LES, f_IBM] }\n",
       "      statistics:\n",
       "        interval: { frequency: 10, start_step: 10000 }\n",
       "        macrs_1st_order: [rho, u]\n",
       "        macrs_2nd_order: [u]\n",
       "        exports:\n",
       "          full_domain: { interval: { frequency: 10000 } }\n",
       "      probes:\n",
       "        historic_series:\n",
       "          velocities:\n",
       "            macrs: [\"u\"]\n",
       "            interval: { frequency: 10, lvl: 0 }\n",
       "            points:\n",
       "              velocity_probe:\n",
       "                pos:\n",
       "                  !math [\n",
       "                    \"${domain.body_pos}-50*${domain.scale}\",\n",
       "                    72,\n",
       "                    \"${domain.plane_height} + 7*${domain.scale}\",\n",
       "                  ]\n",
       "            lines:\n",
       "              inlet_profile:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [0, 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos: !math [0, 72.0, \"${domain.plane_height} + 14*${domain.scale}\"]\n",
       "              velocity_profile:\n",
       "                dist: 0.25\n",
       "                start_pos:\n",
       "                  !math [\n",
       "                    \"${domain.body_pos}-50*${domain.scale}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height}\",\n",
       "                  ]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.body_pos}-50*${domain.scale}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_025H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_025H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_025H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_05H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_05H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_05H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_1H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_1H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_1H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_2H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_2H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_2H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_3H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_3H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_3H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_4H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_4H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_4H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "              pos_5H:\n",
       "                dist: 0.25\n",
       "                start_pos: !math [\"${domain.pos_5H}\", 72.0, \"${domain.plane_height}\"]\n",
       "                end_pos:\n",
       "                  !math [\n",
       "                    \"${domain.pos_5H}\",\n",
       "                    72.0,\n",
       "                    \"${domain.plane_height} + 14*${domain.scale}\",\n",
       "                  ]\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.500011\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "      initialization:\n",
       "        rho: 1\n",
       "        u: { x: 0.05, y: 0, z: 0 }\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [false, false, false]\n",
       "        BC_map:\n",
       "          - pos: E\n",
       "            BC: RegularizedNeumannOutlet\n",
       "            rho: 1.0\n",
       "            wall_normal: E\n",
       "            order: 2\n",
       "\n",
       "          - pos: F\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 1\n",
       "\n",
       "          - pos: B\n",
       "            BC: RegularizedHWBB\n",
       "            wall_normal: B\n",
       "            order: 1\n",
       "\n",
       "          - pos: N\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 0\n",
       "\n",
       "          - pos: S\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 0\n",
       "        SEM:\n",
       "          eddies:\n",
       "            lengthscale: { x: 20, y: 20, z: 20 }\n",
       "            domain_limits_yz:\n",
       "              start: [20, -20]\n",
       "              end: [124, 40]\n",
       "          profile:\n",
       "            csv_profile_data: \"fixture/SEM/category_vprofile/profile_log_cat4_H150_Uh0.06.csv\"\n",
       "            z_offset: !math \"${domain.plane_height}\"\n",
       "            K: 1.0\n",
       "\n",
       "      LES:\n",
       "        model: Smagorinsky\n",
       "        sgs_cte: 0.17\n",
       "\n",
       "      IBM:\n",
       "        body_cfgs:\n",
       "          tree_cfg:\n",
       "            n_iterations: 1\n",
       "            forces_factor: !math 1.6*1.16\n",
       "            kinetic_energy_correction: true\n",
       "          category_II:\n",
       "            n_iterations: 3\n",
       "            forces_factor: 0.5\n",
       "            wall_model:\n",
       "              name: EqLog\n",
       "              dist_ref: 3.125\n",
       "              dist_shell: 0.125\n",
       "              start_step: 5000\n",
       "              params:\n",
       "                z0: !math \"0.01*${domain.scale}\"\n",
       "                TDMA_max_error: 1e-04\n",
       "                TDMA_max_iters: 10\n",
       "                TDMA_n_div: 51\n",
       "          default: {}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Code\n",
    "\n",
    "Code(filename=filename)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nassu",
   "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": "3.11.9"
  }
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 "nbformat": 4,
 "nbformat_minor": 2
}