{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soil Parameter Datasets\n",
    "\n",
    "*Martin Vonk (2025)*\n",
    "\n",
    "## Overview\n",
    "\n",
    "When measured data is unavailable, soil hydraulic parameters can be obtained from established parameter databases. These databases contain pre-determined parameter sets for common soils based on hundreds of laboratory measurements and professional judgment.\n",
    "\n",
    "`pedon` integrates soil parameter data from three major sources:\n",
    "1. **HYDRUS database** - Parameter sets from Carsel & Parrish (1988), widely used in HYDRUS and other simulators\n",
    "2. **VS2D database** - Additional parameter sets in both Brooks-Corey and van Genuchten formats\n",
    "3. **Staring series** - Dutch soil parameters based on BOFEK classification with many measured samples\n",
    "\n",
    "This notebook shows how to access these databases and understand their characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pedon as pe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sand',\n",
       " 'Loamy Sand',\n",
       " 'Sandy Loam',\n",
       " 'Loam',\n",
       " 'Silt',\n",
       " 'Silt Loam',\n",
       " 'Sandy Clay Loam',\n",
       " 'Clay Loam',\n",
       " 'Silty Clay Loam',\n",
       " 'Sandy Clay',\n",
       " 'Silty Clay',\n",
       " 'Clay',\n",
       " 'B01',\n",
       " 'B02',\n",
       " 'B03',\n",
       " 'B04',\n",
       " 'B05',\n",
       " 'B06',\n",
       " 'B07',\n",
       " 'B08',\n",
       " 'B09',\n",
       " 'B10',\n",
       " 'B11',\n",
       " 'B12',\n",
       " 'B13',\n",
       " 'B14',\n",
       " 'B15',\n",
       " 'B16',\n",
       " 'B17',\n",
       " 'B18',\n",
       " 'O01',\n",
       " 'O02',\n",
       " 'O03',\n",
       " 'O04',\n",
       " 'O05',\n",
       " 'O06',\n",
       " 'O07',\n",
       " 'O08',\n",
       " 'O09',\n",
       " 'O10',\n",
       " 'O11',\n",
       " 'O12',\n",
       " 'O13',\n",
       " 'O14',\n",
       " 'O15',\n",
       " 'O16',\n",
       " 'O17',\n",
       " 'O18',\n",
       " 'Medium Sand',\n",
       " 'Del Monte Sand',\n",
       " 'Fresno Medium Sand',\n",
       " 'Unconsolidated Sand',\n",
       " 'Fine Sand',\n",
       " 'Columbia Sandy Loam',\n",
       " 'Touchet Silt Loam',\n",
       " 'Hygiene Sandstone',\n",
       " 'Adelanto Loam',\n",
       " 'Limon Silt',\n",
       " 'Yolo Light Clay']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list all soil types for van genuchten\n",
    "pe.Soil.list_names(pe.Genuchten)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Available Datasets\n",
    "\n",
    "First, let's see what soil types are available. We can list soil names for specific soil models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Soil(name='Sand', model=Genuchten(k_s=712.8, theta_r=0.045, theta_s=0.43, alpha=0.145, n=2.68, l=0.5), sample=None, source='HYDRUS', description='Sand')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the HYDRUS sand\n",
    "soil = pe.Soil(\n",
    "    name=\"Sand\",\n",
    ").from_name(sm=pe.Genuchten, source=\"HYDRUS\")\n",
    "soil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Parameters from the HYDRUS Database\n",
    "\n",
    "The HYDRUS database contains parameter sets for standard soil textural classes. Once you select a soil name and model type, you can retrieve the full `Soil` object with its hydraulic model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Soil` class encapsulates a soil model along with metadata. Note that we need to specify both the soil model (`sm`) and optionally the source dataset. Some soil names may be available in multiple datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Parameters from the Dutch Staring Series\n",
    "\n",
    "The Staring series provides soil hydraulic parameters for Dutch soils. These are based on extensive laboratory measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Soil(name='O18', model=Genuchten(k_s=35.95, theta_r=0.01, theta_s=0.58, alpha=0.0127, n=1.32, l=-0.786), sample=SoilSample(sand_p=None, silt_p=np.float64(0.0), clay_p=np.float64(0.0), rho=np.float64(1.1), th33=None, th1500=None, om_p=np.float64(22.5), m50=np.float64(nan), d10=None, d20=None), source='Staring_2001', description='moerige tussenlaag')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get from the Staring series\n",
    "pe.Soil(\"O18\").from_staring(year=\"2001\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Parameters from the VS2D database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Limon Silt')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ghg0blrvOX6/cDB06lLNnz7JixQp27NjBTTfdxB133MGFC9c+u8OHD7Ns2TKWL1/O8uXL+fXXXys8xSmP2WwmPj6eDRs2sGDBAtLT05k+fXqp/jaFhYXMmDGDL7/8ktTUVI4fP86ECRMAmDBhAkOHDuXOO+/k9OnTnD59ml69emE0GhkyZAh+fn6sX7+eDRs24Ovry5133lnqdqO1a9dy+PBh1q5dy/z580lOTiY5ObnCen/77Td8fHxo3769dVnnzp3p3r07c+fOLbXuvHnz6NWrF+3atQMgNDSUX375hbNnK29++KucnBzAtslDxo8fj6IofPfdd9Zlnp6eREdHs379epv3WRNu0wnT7rQ68Am2XK0rOAP+FXey7NmqMcfOF7Lp8Hlub1e7ewT/6rLRRIfJP9lte9WR/locPp5V/3ocOnQIRVG44YYbSi0PDg7mypUrgOU/jrfffpvffvuNbdu28ccff3Ddddeh1WqZMWMGy5YtY8mSJYwePRqwhEtycrJ1MLJHH32UlJSUMqcXFfn555/ZunUrGRkZtG1rGQywVatWpdYxGo3Mnj2b1q1bA5Zbql577TUAfH19adCgAQaDodRQPl988QVms5k5c+ZYg27evHkEBgaybt06BgwYAECjRo348MMP0el0tGvXjrvuuouUlBRGjRpVbr3Hjh0jJCSkTMfIJ554ggkTJvD+++/j6+tLXl4eS5Ys4f3337euM3PmTIYMGUJoaCgdO3akV69e3HPPPRWOh2Y2m/nXv/5F79696dSpU5WfZVBQEE2aNOHo0aOlloeFhXHs2LEq318bbnPk5BDWdqcqGsWl3amMrVu3kpaWRseOHa1jvO/atYv8/Hxat26Nv78/vr6++Pr6kpmZyeHDh63vjYyMLDVKYtOmTa23Q9giLS2N5s2bW4OpPD4+PtZgsnUfu3fv5siRIwQEBFhrDwoK4sqVK6Xq79ixY6mjtKq2ffnyZby9y97+9NBDD2Eymfj6668By+QgWq2WYcOGWdfp0KEDe/fuZfPmzTz++OOcOXOGQYMG8eSTT5a7rwkTJrBv3z4WLVpU6c/6V4qilOm31KBBA4df6JAjp8pYr9hV0Sh+9YrdvlO5XCgoIqihfXp4N9DrSH8trsxys9lMXm4efv5+DrsNoYHetlsO2rRpg0ajKXP1p+RIpeRWBrAMp9O0aVO+//77Mvd3/fXSfplhkzWaUjdCa7XaMqedf+2J/Nd9VqS8fVR1Kpufn090dDQLFy4s87lfd911Ntf/d8HBwVy8WLYjr7+/P0OGDGHevHk8/vjjzJs3j6FDh+Lr61tqPa1WS/fu3enevTvPPfccCxYs4NFHH+Xll1+mZcuW1vXGjRvHTz/9RGpqKs2bl72ZvTznz5/n7NmzpbYDcOHChVLh7ghuE052v/EXwO/qIX1eVqWrNfH3pn1Ty3126w+e5Z7oZnbZvUajKffUymw2U+ypw8fTQ/V7vBo3bkz//v358MMPGTduXIXtTgA33XQTWVlZeHh40KZNmxrXft1113H69Gnr89zcXDIzM63Pb7zxRk6ePMkff/xR6dFTZTw9Pcv8LnXp0oXFixfTpEkTu/aT6tKlC1lZWVy8eJFGjRqVeu2JJ56gb9++LF++nI0bN1pnI6pMhw4dAKxX4xRFYdy4cSxbtozvv/++TNBU5r333kOr1ZbpVrF3716GDBli83Zqwm1O6+zeIA7gd7WdKe905esBfdpa/uf89Q/bGybdxUcffURxcTHdunVj8eLFZGRkcODAARYsWMD+/futpzj9+vWjZ8+eDB8+nNWrV3P06FE2btzIyy+/zPbt223e3+23386XX37J+vXr2bNnDwkJCaVOo/r06cNtt93GP/7xD9asWUNmZiY//vgjq1atsnkfkZGR7N69mwMHDnDu3DmMRiPDhw+ncePG3Hfffaxfv57MzEzWrVvHP//5T06ePGn7B/Y3Xbp0ITg4mA0bNpR57bbbbqNNmzaMGDGCdu3a0atXr1KvDxkyhHfffZctW7Zw7Ngx1q1bx5gxY2jbtq210XzMmDEsWLCABQsW4OvrS1ZWFllZWVy+fLnUtvLy8sjKyuLEiROkpqYyevRo3njjDd58803atGljXe/o0aP8+eef9OvXr8Y/sy3cJpwcogbhlPrHOcxm2650uYvWrVvz+++/069fPyZNmkRUVBTdunXjgw8+YMKECbz++uuA5Uhw+fLl9OrViyeeeIK2bdvy4IMPWhuEbTVp0iT69OnD3XffzV133cW9995b5hTjf//7H927d+ehhx6iQ4cOvPDCC9U6qh41ahQ33HAD3bp147rrrmPDhg34+PiwYsUKwsPDuf/++2nfvj1PPPEEV65cqXQyjarodDpGjhzJf//73zKvaTQaHn/8cS5evMjjjz9e5vW4uDh++OEHBg0aRNu2bUlISKBdu3asXr0aDw/LUffHH39MTk4Ot99+O+3ataNZs2Y0bdqUxYsXl9rW5MmTadq0KW3atOHRRx8lJyeHlJQUXnzxxVLrffXVVwwYMICIiIga/8w2qbSjQR1kax+KKvs5KYqipH9v6ef06e1V7tdgNCkd/u9HJeLF5cqek5eqW7aiKHW3n1N1SO3lO336tBIUFKQcPXrU7tsuYY/6DQaD0qJFC+W3336rdD3p5+RofmGW7zYcOXl6aOnVJhion6d2onZCQ0P5/PPPrWPtu6rjx4/z0ksv0bt3b4fvy20axB3irw3iZpOl71Ml+rS9jjXp2fz6x1nGxLapdF0h/s6Z9/LVVJs2bUq1PzmS2xw52X3IFADfEEsvccVUaS/xEiXtTjuOXSSnsOaDbAkh3CicHHK1TudxbVynvFNVrh4e5EPbEF9MZoW1B2zvNCiEKMttwslhSm5bya263QlgQAfLqeCa9Owq1hRCVEbCqSrV6E4A0L+D5ZL4ugNnuGKse3ObCeEqJJyqUs1w6twsgFB/bwqKTGw6LPfaCVFTEk5VqeZpnVarsR49rZZTOyFqTMKpKta+TlU3iJcoCaefM7LrXW9xIexFwqkqNt78+1c3t2qMn5cHZ/MM/H7ikmPqElaOGO87JSWF9u3b2/dGcgd48MEH+c9//qN2GQ4h4VQV/6tHTrm2Hzl5emi5vb2lC8KK3badDtZljz32mHVwfI1GQ+PGjbnzzjvZvXu32qXV2AsvvMArr7yCTqfjP//5D40aNbIOnvdXhYWF+Pv7WweA27VrF4MHD6ZJkyZ4e3sTGRnJsGHDrOM57dq1i4ceeojw8HAaNGhA+/btee+990ptMzk52fpZ6nQ6GjVqRExMDK+99pp1FMsSr7zyCm+++WaZ5e7AbcLJIZ0wAfyvDn9y5RIY8m1+26AbLaG2fPcpTPXg1O6vQ9qmpKTg4eHB3XffXel7ajMbrCP99ttvHD58mH/84x+AZSTOgoICvv322zLrLlmyhKKiIh555BHOnj3LHXfcQVBQED/99BMZGRnMmzePsLAw6/AlO3bsoEmTJixYsIB9+/bx8ssvM2nSJD788MNS2/X39+f06dOcPHmSjRs3Mnr0aL744guio6M5deraf5SdOnWidevWLFiwwIGfiEpqfAegi7Lrjb8l3gq33ACcnW5zHVeMxUrnKauUiBeXK5sOn7PpPXX1xt+SQf3/av369QqgnDlzRlEURcnMzFQAZeHChUqvXr0ULy8vZd68eYrJZFKmTp2qNGvWTPH09FSioqKUH3/8sdS2du/ercTGxire3t5KUFCQMmrUKCUvL6/C/W/dulUJDg5Wpk+friiKoqSlpSl9+/ZVfH19FT8/P+Wmm25Stm3bVuHPM2bMGGXIkCGllt1///3KHXfcUeZz79OnjzJs2DBFURRl6dKlioeHh2I0Gm3/8BRFefbZZ5XY2Fjr85IJGP4uOztbCQ4OVoYPH15q+dSpU5Vbbrmlyv048/dGbvx1loAWlu+XTtj8Fi8PHXd2srRX/bDL9lPCUhQFigrK/zIWVvyaPb5snOCgPPn5+SxYsIA2bdrQuHHjUq+99NJLPP300+zbt4+4uDjee+89/vOf/zBjxgx2795NXFwcgwcP5uDBg4BlwLS4uDgaNWrEtm3b+Oabb/j5558ZO3Zsufv+5Zdf6N+/P2+++aZ1qI/hw4fTvHlztm3bxo4dO5g4cWKZ0Sr/av369XTr1q3UsieeeIJffvml1I25R44cITU1lSeeeAKw3LxbXFzM0qVLbZ4gAiwTDtgy2UCTJk0YPnw433//fam2sB49erB161brcMjuQm78tUVAc8jeAzm2hxPA3TeG8fX2k/y4N4upgzvioavm/wXGQpgWVmaxFgis3paq76VT4FnxqJZ/t3z5cuvwsQUFBTRt2pTly5eXGe1y/PjxDBo0CH9/f+sEBy+++CIPPvggAG+//TZr165l1qxZJCUlsXDhQq5cucIXX3xhHWXzww8/ZNCgQbz99tulxoFaunQpI0aM4LPPPis1zvbx48d5/vnnrYOvXX/99ZX+LMeOHSMsrPTnHhcXR1hYGAsXLmTatGmApW0oPDycO+64A4Cbb76Zl156iYcffpinn36aHj16cPvttzNixIgKx6vauHEjixcvZsWKFZV/wFe1a9eOvLw8zp8/T5MmlnbNsLAwioqKyMrKcvwYS04kR062CAy3fK9mOPVq3Zighp5cKChio5t3yIyNjSUtLY20tDS2bt1KXFwc8fHxZWbo6Nq1q/Vxbm4up06dKjP8Ru/evcnIyAAgIyODqKioUsP/9u7dG7PZXGrc8i1btvDAAw/w5ZdflgomgMTERJ588kn69evH9OnTS01GUJ7yJhzQ6XSMGDGChQsXoigKZrOZ+fPnWyfCLPHmm2+SlZXF7Nmz6dixI7Nnz6Zdu3bs2bOnzH727t3LPffcw5QpU6wzt1Sl5IjsrxMOlIyZXhdnVq6MHDnZIuBqOFXjtA7AQ6dlYOdQFmw+zg+7TnFb2+uqftNf6X0sRzB/Yzabyc3Lw9/PcRMcoPep1uoNGzYsNZTGZ599RkBAAHPmzOGNN94otZ4jtG7dmsaNGzN37lzuuuuuUqdtr776Kg8//DArVqzgxx9/ZMqUKSxatIj77ruv3G1VNOHAyJEjmT59unWK7hMnTjBy5Mgy6zVu3JgHHniABx54gGnTptGlSxdmzJjB/Pnzreukp6dzxx13MHr0aF555RWbf86MjAz8/f1LnS6XzPn310kW3IEcOdmihkdOcO2q3Y97s7hcVM0+MxqN5dSqvC+9T8Wv2ePrb1MBVZdGo0Gr1ZYZp/qv/P39CQsLKzN29oYNG6yD9Ldv355du3aVmjp7w4YNaLXaUnPlBQcH88svv3Do0CGGDh1a5kpg27Zt+de//sXq1au5//77y53qu0SXLl1IT08vs7x169b07t2befPmMW/ePPr161flaZSnpyetW7cuVf++ffuIjY0lISHB5rn4AM6cOcPChQu59957S/2ntHfvXpo3b05wcLDN26oLJJxsUYMG8RLdI4No3qgB+YZiftpne0fOusZgMFgHzs/IyGDcuHHk5+czaNCgSt/3/PPP8/bbb7N48WIOHDjAxIkTSUtLY/z48YClMdvb25uEhAT27t3L2rVrGTduHI8++miZdpwmTZrwyy+/sH//fh566CGKi4u5fPkyY8eOZd26dRw7dowNGzawbdu2UrPr/l1cXBy//fZbua898sgjLF26lKVLl1obwkssX76cRx55hOXLl/PHH39w4MABZsyYwcqVK7nnnnsAS5DExsYyYMAAEhMTrZ/Z32fsVRSFrKwsTp8+TUZGBnPnzqVXr14EBASUmf14/fr1Np8W1imOuIyoJod0JcjNsnQlmBKgKMU2rP837645oES8uFwZPmdzpevV5a4EgPXLz89P6d69u7JkyRLrOiVdCXbs2FGqdpPJpLz66qtKs2bNFL1eb5euBKdOnVLatm2rDB06VLly5Yry4IMPKuHh4Yqnp6cSFhamjB07ttLP+Pz584q3t7eyf//+UstNJpNy6tQpJSAgQAkKClKuXLlS6vXDhw8ro0aNUtq2bas0aNBACQwMVLp3767MmzfPus6UKVNKfVYlXxEREdZ15s2bZ12u0WiUgIAApUePHsprr71W5vf68uXLSkBAgLJp06YKf56/1l+XuhJoFKUW14wdJDIy0no1p1GjRqxdu9bm9+bm5hIQEEBOTk6lM2IYjUZWrlzJwIEDK72sDIDZDG+GgskA43dBo0ib6wE4caGQW99Zi0YDv714O80Cy5/08cqVK2RmZtKyZctyZ4C9Vo6Z3Nxc62dUl9SV2p9//nlyc3P55JNPrMtcsfaPP/6YpUuXsnr16irXdWb9lf0u2/o36hqfcDk2btxIWlpatYLJYbRaS3cCqNGpXXiQDz1bNUZR4NsdNZ/fTDjPyy+/TERERKUz9boCvV7PBx98oHYZDuGy4eRyatEoDjCkqyXcluw8Wa0OekIdgYGBvPTSSy5zhFSRJ598stSFAXdi908+NTWVQYMGERYWhkajYdmyZWXWSUpKIjIyEm9vb2JiYti6dWup1zUaDX369KF79+7lTjSoihp2JygR3zkUXy8Pjp0vZEvmBTsWJoR7sns4FRQUEBUVRVJSUrmvL168mMTERKZMmcLOnTuJiooiLi7Oetc2WG683LFjB99//z3Tpk1zjbvbS8Ipp2bzivl4ejAoyjJw3cItrj03mRCuwO7hFB8fzxtvvFFhB7eZM2cyatQoRo4cSYcOHZg9ezY+Pj7MnTvXuk6zZpaRAJo2bcrAgQPZuXOnvcusvpJG8IvHKl2tMsNjLH1iftx7mnP57nUflBD25tQe4kVFRezYsYNJkyZZl2m1Wvr168emTZsAy5GX2WzGz8+P/Px8fvnlF4YOHVrhNg0GQ6kbHnNzcwHL1bjKhuQoec3WYTs0/uF4AMqFIxTXcKiPG5r4ENU8gF0nc/hq81Ge7tOq1OvFxcUoioLJZKq0IbakzUq5ehtFXSK1q8eZ9Zf8LhcXF5f5G7P1b86p4XTu3DlMJlOZznMhISHs378fgOzsbOtRl8lkYtSoUZWO0fTWW28xderUMstXr16Nj0/Vt2CsWbPGpto9jbnEA+SeYtXyZZi1nja97+86eWnYhY556w/SPH8/2r90xNZoNISEhHDq1Cn8/f1L3T9VnvPn6+79elK7ehxdf3FxMRcuXCA/P5+UlJQyr9t6D6DL3VvXqlUrdu3aZfP6kyZNIjEx0fo8NzeX8PBwBgwYUGU/pzVr1tC/f/+q+zkBKArKHy+iKSrgzps7QHBbm2v8q9uNJlb8O5ULl434tOnO7TeUvh+qoKCA06dPV/oLpCgKV65cwdvbu8oAczVSu3qcWX/Dhg1p1apVuX9bJWc3VXFqOAUHB6PT6cjOLj0rSXZ2NqGhoTXappeXF15eXiQlJZGUlGQd50av19sUOrauB0BQK8jagz73BDTtWKN69Xo9D3Rrzpz1mSzadpK4TqWH5ggMDMTPz6/KU9LU1FRuu+0222t3EVK7epxVv06nw8PDo8IAtHXfTg0nT09PunbtSkpKinVAerPZTEpKSoWDh9lqzJgxjBkzxtr71CEatYSsPXAxs1abGR4TwZz1maz74yyZ5wpoGVz6Tn2dTodOp6vw/TqdjuLiYry9vevcH4nUrp66Vr/dr9bl5+dbx/UByMzMJC0tzTqCYGJiInPmzGH+/PlkZGTwzDPPUFBQUO7QEy4nqKXl+4XahVNkcENub9cERYHPfztih8KEcD92P3Lavn07sbGx1ucl7UEJCQkkJyczbNgwzp49y+TJk8nKyiI6OppVq1ZVOFKgrf5+WucQjUrCqfaB8uStLfll/xmW7DjJ/+t/A40a1qyBXQh3Zfdw6tu3b5W3Z4wdO7bWp3F/55TTupIjp1qe1gH0bNWYTs382ftnLgs2H2PcHZUPHStEfePaNw65mqCr/ZIuHgNz7Y7QNBoNo261bG/+pmNcMbr25I1COJvbhJPD5q37K/9moNWD2Qi5f9Z6cwM7NyUswJtz+Qa+T6vhDC1CuCm3CacxY8aQnp7Otm3bHLcTrQ4aXR2WtZaN4gB6nZaRvS2ninPWH8FcDybfFMJWbhNOTmPHRnGAB3uE4+flwcEz+axOz676DULUE24TTk45rQNofHWGkfOH7LI5P289Cb0iAfjgl4My1pMQV7lNODnltA4g+OpVtXMH7bbJJ25piY+njn2ncll74EzVbxCiHnCbcHKaknvqzv1ht002aujJoz0tbVnvpxySoychkHCqvpIjp0vHwHjFbpsddWsrvPVa0k5c4rdD5+y2XSHqKrcJJ6e1OfmGgJc/KGa7NYoDBPt68XAPy9HTrJ+l7UkItwknp7U5aTR/aXey36kdwNN9LEdPO45dlLYnUe+5TTg5lbXdyX6N4gBN/L2tV+7+/dMf0u9J1GsSTjXhoCMngGf6tMbP24OM07n8sFt6jYv6S8KpJhxwxa5EoI8nT91muedu5po/MJrq3ljVQtiD24ST0xrE4Vo4nT8EDmi4Htm7JcG+nhw7X8jibTWbJ0+Ius5twslpDeJguYVFo4OifMg7bffNN/TyYGyspSf6eykHyTcU230fQrg6twknp/LwvDa2kwNO7QAejokgsrEPZ/MMfLzOPrfKCFGXSDjVVMmp3Zn9Dtm8p4eWlwa2B2DO+kxOXLBtOh0h3IWEU001sQQHZ/Y5bBf9O4TQq3VjiorNTF/lmBAUwlVJONVUkw6W72cyHLYLjUbD/93dAa0GVuw+zbajFxy2LyFcjduEk1Ov1gGEXJ237kwGOHBq5/ZN/RnWvQUAr/2QLh0zRb3hNuHk1Kt1YBnXSau3XLHLOe7QXf2/AW3x8/Jgz585fLXNsfsSwlW4TTg5nU4P191geZyd7tBdBft6kTjA0gD/9o/7OZ9vcOj+hHAFEk61YW13clyjeIlHb46gY5g/uVeKmb7KMd0XhHAlEk61EXI1nBx85ATgodPy5n2d0Whg2a7THMwpfx56IdyFhFNtNClpFHd8OAFEhwfySIxlzKevj2gxFMt9d8J9STjVRsmR07mDUOycdqAJcTcQ7OvJmSsa5qyv/fRUQrgqCafa8G8GXgGgmBx2G8vfBTTQ81K8pSH+o1+PcCArzyn7FcLZJJxqQ6NxartTibs7h9KpkRmjSWHCN7solmFVhBtym3ByeifMEqGdLd+zdjttlxqNhqGtzPh7W/o+fZJqv7HMhXAVbhNOTu+EWaJplOX76V1O3W2AJ/zfXe0AeO/ng/yRLad3wr24TTip5q/h5MDbWMpzT1RT7mjXhCKTWU7vhNuRcKqt69qBzhMMuXDpqFN3rdFomHZ/Z/y9Pdh9MoeP1x126v6FcCQJp9rS6a/dBOzkUzuAEH9vXh1s2f+slIP8fvyi02sQwhEknOxBpXanEvd1acagqDBMZoXxi9LIu2JUpQ4h7EnCyR5UDieNRsMb93aiWWADjl8oZMp3jr/XTwhHk3Cyh6bRlu+ndzlkNhZbBDTQ896D0Wg18O3vf7Ls9z9VqUMIe5FwsocmHUDrAYXnIVe9UOgWGcQ/77BM+PnKsr0y7rio0ySc7EHvDdddHVNcpVO7EmNj29AtohH5hmLGLtyJodikaj1C1JTLhlNhYSERERFMmDBB7VJsU9LudOp3Vcvw0GmZ9WA0AQ307DqZwxvLHTfGuRCO5LLh9Oabb3LzzTerXYbtmt1k+X5yu7p1AM0b+TDrwWg0Gvhy8zFpfxJ1kkuG08GDB9m/fz/x8fFql2K75lfv6ftzp9N7ipcn9oYmjLs6a/Ckb/fI7S2izrF7OKWmpjJo0CDCwsLQaDQsW7aszDpJSUlERkbi7e1NTEwMW7duLfX6hAkTeOutt+xdmmM16QB6HzDkwPmDalcDwPh+bbn1+mAuG008vWCH9H8SdYrdw6mgoICoqCiSkpLKfX3x4sUkJiYyZcoUdu7cSVRUFHFxcZw5cwaA7777jrZt29K2bVt7l+ZYOo9rXQpc4NQOQKfVMGtYNE0DvDlytoDEr3fJ1FKizvCw9wbj4+MrPR2bOXMmo0aNYuTIkQDMnj2bFStWMHfuXCZOnMjmzZtZtGgR33zzDfn5+RiNRvz9/Zk8eXK52zMYDBgM10ahzM3NBcBoNGI0VnykUPJaZetUlzbsJnTHN2I6vgVzp6F22+7fVad2fy8tHzwYxcOfb2NNejb//imDxH7XO6y2qjjic3eWulw7uE79tu5foyiO6zWo0WhYunQp9957LwBFRUX4+PiwZMkS6zKAhIQELl26xHfffVfq/cnJyezdu5cZM2ZUuI9XX32VqVOnllm+cOFCfHx87PJz2KrppW30yPyASw1a8Gu7N5y676psO6thwSEdAAnXm7gpWI6ghDoKCwt5+OGHycnJwd/fv8L17H7kVJlz585hMpkICQkptTwkJIT9+/fXaJuTJk0iMTHR+jw3N5fw8HAGDBhQ6Q9uNBpZs2YN/fv3R6/X12jfZeR2gQ8+IODKSQb26wOeDe2z3b+pSe0DgQY//cGc346yKFPPPXd0p3OzAIfUVxmHfO5OUpdrB9epv+TspipODafqeuyxx6pcx8vLCy8vL5KSkkhKSsJksnQ61Ov1Nv0D2LqeTRq3AL8wNHmn0J/dB5G97bPdClS39okDO3D4XCG/7D/Dswt38f3Y3jTx93ZghRWz6+fuZHW5dlC/flv37dSuBMHBweh0OrKzs0stz87OJjQ0tFbbVm0kzL9r3s3y/aTKdZRDp9Xw3oPRtGniS1buFR6fv40CQ7HaZQlRLqeGk6enJ127diUlJcW6zGw2k5KSQs+ePZ1ZiuOU9Hc6sbXy9VTi563n84RuNG7oyd4/cxm7cKeMoClckt3DKT8/n7S0NNLS0gDIzMwkLS2N48ePA5CYmMicOXOYP38+GRkZPPPMMxQUFFiv3tWUahMc/F2LqyF7fJNLdMYsT0TjhnyW0A1vvZa1B87yf9/txYHXRYSoEbuH0/bt2+nSpQtdunQBLGHUpUsXa1eAYcOGMWPGDCZPnkx0dDRpaWmsWrWqTCN5dbnMaV3TKPBoAJcvOG0uu5ro0qIR7z/YBa0Gvtp6gqS1h9QuSYhS7B5Offv2RVGUMl/JycnWdcaOHcuxY8cwGAxs2bKFmJgYe5ehHg/Pa+1OxzeqW0sVBnQMtQ7xO2P1H/xvx0mVKxLiGpe8t64mXOa0DiCil+X7sU3q1mGDET0jGX1bKwBe+N9uUjKyq3iHEM7hNuHkMqd1ULrdqQ6YeGc77uvSDJNZ4dn/7mTzkfNqlySE+4STS2neHTQ6yDkBl06oXU2VtFoN7wy5kX7tm2AoNvPk/O3sPnlJ7bJEPec24eRSp3VevhAWbXlcR46e9DotHz58Ez1bNSbfUEzC3K0clGFWhIrcJpxc6rQOrp3aHdugbh3V4K3XMSehG1HNA7hYaOSRz7fIOORCNW4TTi6nDjWK/5WvlwfJI3twfRNfsnMNPPjpZgkooQoJJ0cpOXI6dwDyz6hbSzU1aujJf5+MoVVwQ/68dFkCSqjCbcLJpdqcAHyCILSz5XFmqrq11EATf2++Gn2zNaAemrOZkxcloITzuE04uVybE0DLPpbvR9apWkZNhVwNqJbBDTl50XIEJQElnMVtwskltepr+X7kV9VmAq6tEH9vvhp1LaAemiOneMI5JJwcqUVPy0zAOcfhYqba1dRYaIAloCIb+3DiwmWGfrKJw2fz1S5LuDkJJ0fy8oXmPSyPj/yqbi21FBrgzaLRPWnTxJfTOVcYOnsT+07lqF2WcGNuE04u1yBeotXVdqfMuh1OYAmoxaNvpmOYP+cLinjw083sOHZB7bKEm3KbcHLJBnG41iiemeqy4ztVR2NfL74afTPdIxuRd6WYRz7bym8Hz6ldlnBDbhNOLqtZV9A3hMLzkL1X7Wrswt9bzxePx1gn7Hw8eRur9mapXZZwMxJOjubheW2ig8O/qFuLHTXw1PFZQjfu7BhKkcnMs//dwZebjqpdlnAjEk7O0Kaf5fuhn9Wtw868PHR8+HAXHuoRjlmB//tuH2+v2i9D/gq7kHByhpJwOr4Jrtg2Z1dd4aHTMu2+ziT2t0wf//G6w/y/r3dRVFz329eEuiScnKFxawhqDebiOttbvDIajYZ/3nE97wy5EZ1Ww7e//8njydvIu1I3p+0WrsFtwslluxKUuL6/5fuhNerW4UBDu4XzWUI3fDx1/HboHMM+2czpnMtqlyXqKLcJJ5ftSlCiJJwO/lxnb2WxRewNTVg0+maCfT1JP53LPR9uYNeJS2qXJeogtwknlxdxi2XKqLxTkL1P7Woc6sbmgSx9tjc3hPhxJs/A0E82sXz3KbXLEnWMhJOz6L2h5W2WxwdXq1uLE4QH+bDkmZ7c3s4yLvnYhb/z3s8H5UqesJmEkzNZ253cq0tBRfy89cwZ0Y1Rt7YE4N2f/+Bf3+yhyKRyYaJOkHByppJwOr4ZCuvHPWk6rYaX7+rA2//ojIdWw4o9WXyYruN0zhW1SxMuTsLJmRpFQpOOoJjgj5/UrsaphnVvwYInYwhsoOdYvob7Pt4s8+OJSkk4OVu7uyzf9y9Xtw4V3NyqMd8+E0MzH4XzBUUM/2wLc3/LlHYoUS4JJ2drf7fl+6EUKKp/I0qGN/LhuU4mBt/YFJNZ4bXl6fxrcRqXpSFK/I3bhJPLd8IsEXojBIRD8WW37C1uC08dzBjSicl3d0Cn1bAs7RT3f7yR4+frX1iLirlNOLl8J8wSGs1fTu1WqFuLijQaDY/f0pL/PhlD44aeZJzOZdCHv5GSka12acJFuE041Skl4XRgJZiK1a1FZTe3aszyf95CVHggOZeNPDF/O2+tzMBokhuH6zsJJzW06AXegXD5ApzYrHY1qmsa0ICvn7qZx3pFAvBJ6hEe/HQzpy7JfXn1mYSTGnQecEO85XE9PrX7Ky8PHa8O7sjsR27Cz9uDHccuctf761m7v27NlizsR8JJLe2uXrVL/94txha3lzs7NWXFuFvp3CyAi4VGRiZvY/qP++U0rx6ScFJLmzvA0xdyT8JJF2/Ed7IWjS335SX0jABg9q+HefBTmcyzvpFwUou+wbWG8X3fqluLC/Ly0DH1nk58NPwm/Lwsp3kD31vPst//VLs04SQSTmrqeL/l+75lYJZOiOUZ2LkpK8ffSreIRuQZinlucRrPLfqdXBll0+25XDhdunSJbt26ER0dTadOnZgzZ47aJTlO69vBOwDys+DYRrWrcVnhQT4sGn0zz/W7Hq0GlqWdYuB762VCTzfncuHk5+dHamoqaWlpbNmyhWnTpnH+vJveIOrhCe0HWR7LqV2lPHRanuvXlm+e7kl4UANOXrzM0E82M+vnPyiWxnK35HLhpNPp8PHxAcBgMKAoinvfGFpyapf+Xb3vkGmLrhFBrPznrdzXpRkms8Ksnw8yTBrL3ZLdwyk1NZVBgwYRFhaGRqNh2bJlZdZJSkoiMjISb29vYmJi2Lp1a6nXL126RFRUFM2bN+f5558nODjY3mW6jpZ9wKexZUbgzF/VrqZO8PPW8+6waN57MNraWB7/3noWbzvu3v+R1TN2D6eCggKioqJISkoq9/XFixeTmJjIlClT2LlzJ1FRUcTFxXHmzLXOdoGBgezatYvMzEwWLlxIdrYb32+l84AO91ge71mibi11zD3RzVg5/la6RzYi31DMi//bw+PJ28jOlYHs3IGHvTcYHx9PfHx8ha/PnDmTUaNGMXLkSABmz57NihUrmDt3LhMnTiy1bkhICFFRUaxfv54hQ4aUuz2DwYDBYLA+z821TFppNBoxGiu+olPyWmXrOIumw/14bJ+LkvEdxQPeAs+Gla7vSrVXl71rD/XT8+XIbiRvOsbMnw+x9sBZBrz7K1Pubs/dnUPRaDR22Q/U7c8dXKd+W/evURx4HKzRaFi6dCn33nsvAEVFRfj4+LBkyRLrMoCEhAQuXbrEd999R3Z2Nj4+Pvj5+ZGTk0Pv3r356quv6Ny5c7n7ePXVV5k6dWqZ5QsXLrS2Xbk8ReGO9OfxLTrDjoinOBnUW+2K6qSsQlhwSMeJAksgRQeZeaCVGV+9yoWJUgoLC3n44YfJycnB39+/wvXsfuRUmXPnzmEymQgJCSm1PCQkhP379wNw7NgxRo8ebW0IHzduXIXBBDBp0iQSExOtz3NzcwkPD2fAgAGV/uBGo5E1a9bQv39/9Hr1f3u1/umQ+jZdtPu5ceCbla7rarVXh6Nrf9RkZnZqJh+tO0LaBS0nDN68cU8H+rVvUutt1+XPHVyn/pKzm6o4NZxs0aNHD9LS0mxe38vLCy8vrzLL9Xq9Tf8Atq7ncF0ehtS30Wamoi3MhoDmVb7FZWqvAUfVrtdD4oB2DOjYlMSv0/gjO59nFqZx/03NmHJ3RwJ8ar/Puvy5g/r127pvp3YlCA4ORqfTlWngzs7OJjQ0tFbbrjMjYVakUSRE9AYU2P212tXUeZ2aBfDDuFt4qk8rNBr4duef3DHzV1buOS1X9OoIp4aTp6cnXbt2JSUlxbrMbDaTkpJCz549a7XtOjMSZmWiHrJ83/WVW09Z7ixeHjomxbdnydM9aX1dQ87lG3j2vzt56ssdckWvDrB7OOXn55OWlmY9NcvMzCQtLY3jx48DkJiYyJw5c5g/fz4ZGRk888wzFBQUWK/e1VSdP3ICS5cCjwZw7g/4c6fa1biNrhFBrPjnrfzz9jZ4aDWsTs+m339+ZeGW45jN8p+Aq7J7OG3fvp0uXbrQpUsXwBJGXbp0YfLkyQAMGzaMGTNmMHnyZKKjo0lLS2PVqlVlGsmryy2OnLz9r93OkvZfdWtxM956HYkDbrAOCZxnKOalpXt4aM5mMs8VqF2eKIfdw6lv377WK21//UpOTrauM3bsWI4dO4bBYGDLli3ExMTYu4y6q8twy/c930CR/NHYW7tQf759phf/d3cHGuh1bMm8QNysVD5ad0gGtHMxLndvXU25xWkdQORt0KglGHJhr9wM7Ag6rYYnbmnJ6n/dxq3XB1NUbOadVQe458MN7P0zR+3yxFVuE05ucVoHoNVC18csj3ckq1mJ2wsP8uGLx3vwnweiCPTRk346l3uSNvDWygyZ5NMFuE04uZXo4aDVw5/bIWuP2tW4NY1Gwz+6NmfNv/pw99VZiD9JPcKd76Wy8dA5tcur1yScXJHvddeG8JWjJ6e4zs+LDx++ic9GdCPU35tj5wt5+LMtvLhkNzmFdfNeurrObcLJbdqcSpSc2u3+WhrGnahfhxDWJN7GIze3AGDx9hP0e/dXftxzWuXK6h+3CSe3aXMq0bKPNIyrxM9bzxv3dubrp3rSKrghZ/MMPPPfnTy7MI2cIrWrqz/cJpzcjlYLXRMsj7d+Kj3GVdCjZRArx9/K2FhL5801GWd4K03H4u0n5RYYJ5BwcmU3JYCHN2TthhNb1K6mXvLW65gQdwPfj72Fzs38uWzS8Mp36Tw0ZzNHpfOmQ7lNOLldmxOATxDcONTyeMtsdWup5zqE+fP1qB7cG2HCW69l8xFL583Zvx6WCRYcxG3Cye3anEr0eMryPf17yJEJJdXkodMSG6awYmwvbmkTjKHYzPQf93NPknTedAS3CSe3FdoJIm8FxQTbPlO7GgG0CPLhyyd68O8hNxLQQM++U5bOm9N/3M8Vo3TetBcJp7og5urR045kMF5WtRRhodFoeKBbOGsSb+OuzpbOm7N/Pcyds1LZdNhN51l0MgmnuqBtPAS0gMsXZIYWF9PEz5uk4Tfx6aNdCfH34uj5Qh6as5lJ3+4m57J03qwNtwknt2wQL6HzgB5PWh5v/ki6FbigAR1DWZPYh4djLJ03v9p6ggHv/sqadDee1szB3Cac3LZBvMRNCeDpC2fS0RxOqXp94XT+3nqm3deZxaNvpmVwQ7JzDYz6YjtjF+7kfL6h6g2IUtwmnNxeg0DrLS3aTe+rWoqoXEyrxvw4/lae6tMKrQaW7z5Nv5m/8l3an9J5sxoknOqSm58FrQfa4xtpVHBY7WpEJbz1lvHLl43pTbtQPy4WGhm/KI0n5m/ndI5c1LCFhFNdEtAMOls6ZbY5s1LlYoQtbmweyA/jbuH/9W+Lp07LL/vP0H9mKv/dckzGL6+ChFNd0/ufADS9tB3OH1K5GGELvU7LuDuuZ8U/b6FLi0DyDcW8vHQvD38mt8BUxm3Cya2v1v1Vk/aY2wxAg4J2y0dqVyOq4foQP5Y83YvJV8cvL7kF5rP1R+QoqhxuE05uf7XuL8w9xwGg3b0Y8rJUrkZUh06r4fFbWvLTc7fRu01jDMVm3liRwUNzNnPiQqHa5bkUtwmn+kQJv5nzDa9HYzLAhvfULkfUQIvGPix4IoY37+uEj6dlFpg7Z6Xy1dbjckXvKgmnukij4UDovZbH2+dCnnT0q4s0Gg3DYyJYNf42ekQGUVBkYtK3e3g8eZvMSIyEU5111q8T5mbdoPgKbJR+T3VZi8Y+fDX6Zl4e2B5PDy1rD5xlwLup9b5flIRTXaXRYL71ecvjbZ9D/hl16xG1otNqGHVbK1aMu4XOzQLIuWzpFzX2q9/r7T16Ek51mNLqdmjWDYovS9uTm7g+xI9vn+3Fv/q1xUOrYcXu0wx8bz3bj15QuzSnk3CqyzQa6DvR8njb55B/Vt16hF3odVrG97ueJc/0okWQD39euszQTzbx3s8HMdWjLgcSTnVdm37QrKvl6GmjHD25k+jwQFb88xbu69IMswLv/vwHD326mT8v1Y/bX9wmnOpNJ8y/02igz9Wjp62fQa7Mr+ZO/Lz1vDssmneHRdHQU8fWoxeIn5VaL4ZicZtwqk+dMMu4vj+E32w5evr1bbWrEQ5wX5fmrBx/K1HhgeReKWbUF9t5Z9V+t55cwW3CqV7TaKDfq5bHO7+A8zJigTuKaNyQJU/3ZGTvSAA+WneYEXO3cs5Nx4qScHIXET3h+jjLRAi/vK52NcJB9DotUwZ15P2HuuDjqWPj4fMM+uA3dh6/qHZpdifh5E7umAxoYN9SOPW72tUIBxocFcayMb1pdV1DTudcYdgnm1iw+ZjaZdmVhJM7Ce10bRLOlNfUrUU4XNsQP74b05v4TqEYTQqvLNvLq9/vc5t2KAkndxP7Emj1cPgXOLxW7WqEg/l56/lo+E08H3cDAMkbj/L4/O3kXqn7vcolnNxNo0jo/oTl8epXwCyTPLo7jUbDmNg2fDz8Jrz1WlL/OMs/PtpY54dgkXByR31eBO9AyN4Lv3+pdjXCSeI7N+Wbp3oR4u/FwTP53P/xRtJP5apdVo25XDidOHGCvn370qFDB2688Ua++eYbtUuqe3yCrt3WkvI6XMlRtx7hNJ2bB/DdmFtoF+rH2TwDwz7ZxOYjdXMGYpcLJw8PD2bNmkV6ejqrV6/mueeeo6BAxlmutu5PQuProfAcrP+P2tUIJwoN8GbxUz3p0TKIPEMxI+Zu5ad9dW/EVJcLp6ZNmxIdHQ1AaGgowcHBXLhQ/+7IrjWdHga8YXm8+WO4kKluPcKpAhro+eLxHvTvEEJRsZlnFuzg+11169Ymu4dTamoqgwYNIiwsDI1Gw7Jly8qsk5SURGRkJN7e3sTExLB169Zyt7Vjxw5MJhPh4eH2LrN+aBsHrWLBVARrJqtdjXAyb72Oj4ffxANdm2NW4Pn/7WH7WY3aZdnM7uFUUFBAVFQUSUlJ5b6+ePFiEhMTmTJlCjt37iQqKoq4uDjOnCk9WNqFCxcYMWIEn376qb1LrD80GoibBhotZHwPR9apXZFwMg+dlrf/cSMPdg/HrMCCQ1p+2F03jqDsHk7x8fG88cYb3HfffeW+PnPmTEaNGsXIkSPp0KEDs2fPxsfHh7lz51rXMRgM3HvvvUycOJFevXrZu8T6JaSDpf0JYMUEKHbP+7BExbRaDdPu68wDXZuhoGHCkj2sqAMB5eHMnRUVFbFjxw4mTZpkXabVaunXrx+bNm0CQFEUHnvsMW6//XYeffTRKrdpMBgwGK79weXmWi6dGo1GjMaKO6KVvFbZOq6q2rXf+iIe+5ahOX8Q02/vY+79nOOKq0K9+txdzJSB13P8+Am2nNXy3OLf8ffScnOrIKfXYevn59RwOnfuHCaTiZCQkFLLQ0JC2L9/PwAbNmxg8eLF3Hjjjdb2qi+//JLOnTuXu8233nqLqVOnllm+evVqfHx8qqxpzZo11fwpXEd1am8efB9dCz5B+fUd1p4N4rJnsAMrq1p9+dxdzYOt4YoJdl3QMuqLbfyzo4lmDZ1bQ2GhbZ1DnRpOtrjlllswm22/N2jSpEkkJiZan+fm5hIeHs6AAQPw9/ev8H1Go5E1a9bQv39/9Hp9rWp2thrVrsRjXrAHj+Mb6Wdcg+ledTpn1rvP3YWU1J/8dF9GL9zDtqMXSc5syNeje9AssIHT6ig5u6mKU8MpODgYnU5HdnbpUfyys7MJDQ2t0Ta9vLzw8vIiKSmJpKQkTCbL7Rp6vd6mXyBb13NF1a797pkw+xa0f/yI9kgK3HCn44qrQr363F2MbwNvPkvoztDZmziQncczC3fx7TO9aOCpc8r+bf3snNrPydPTk65du5KSkmJdZjabSUlJoWfPnrXadr0eCdNWTdrDzc9aHv/4AhTV7XuvRM0FNNAzb2R3gn09yTidywv/2+1yc+TZPZzy8/NJS0sjLS0NgMzMTNLS0jh+/DgAiYmJzJkzh/nz55ORkcEzzzxDQUEBI0eOtHcpojx9XgT/ZnDpGKybpnY1QkVhgQ34aHhXPLQafth1ik9Tj6hdUil2D6ft27fTpUsXunTpAljCqEuXLkyebOkEOGzYMGbMmMHkyZOJjo4mLS2NVatWlWkkr656O8FBdXn5wt3vWh5vSoI/d6hbj1BVj5ZBTBnUAYC3V+1na6br3I1h93Dq27cviqKU+UpOTrauM3bsWI4dO4bBYGDLli3ExMTUer9yWlcNbeOg8wOgmOG7cVBcpHZFQkWP3BzB/TdZpp/61+I0lxkLyuXurRNOcud08GkMZ/bBhllqVyNUpNFoeO2eTtYJPKd8t0/tkgA3Cic5raumhsEQ/47l8a/vwJn96tYjVOXr5cG7w6LRaTUs/f1Plu8+pXZJ7hNOclpXA53+AW3vBLMRvh8no2bWc10jGjEmtg0Ar36fTs5ldU/v3CacRA1oNHDXTPD0g5NbYfNHalckVDYmtjWtrmvIuXwDM346oGotbhNOclpXQwHNIO7quE8pr0F2urr1CFV5eeh4495OACzYcozfVZwPz23CSU7rauGmBMuEnKYiWDpart7Vc71aB3P/Tc1QFHhjRYZqnTPdJpxELWg0MPgDaBAEWXvg1+lqVyRU9uKd7fDWa9lx7CI/Z5yp+g0OIOEkLPxCYNAsy+Pf3oXjW1QtR6grxN+bx3u3BOCdVfsxmZ1/9OQ24SRtTnbQ4R648UFL58ylT4EhX+2KhIqe6tOagAZ6Dp7J54ddzu9a4DbhJG1OdhL/Nvg3h4uZlkk5Rb0V0EDPqFstR0+zfz3s9LYntwknYScNAuHeq10KdsyD9O9ULUeo69GbI2noqWN/Vh7r/jjr1H1LOImyWvWB3uMtj78bBxePqVuPUE2Aj56HerQA4NNfnTtqgYSTKN/t/wfNu4MhB/73BJhc42ZQ4XyP39ISrQY2HTnPkbPOa4d0m3CSBnE70+nhH5+DdwCc3Aa/vK52RUIlYYENiL2hCQCLtp1w2n7dJpykQdwBGkXA4A8tjze8B4d+VrceoZqSU7slO05iKHbOPZhuE07CQToMvjbv3bdPQV6WuvUIVfS94Tqa+HlxoaCI3w6ec8o+JZxE1Qa8CSGdofAc/O9JMBWrXZFwMg+dloGdmwKwYo9zJuSUcBJV03vDA/PA0xeOroeUV9WuSKigJJzWpGc75dROwknYJvj6a/2fNn4A+5aqW49wum4RjWji50XelWI2HHL8qZ3bhJNcrXOCDvdAr39aHi8bA2fVHe9HOJdWq+HOTpb5JZ1xM7DbhJNcrXOSO6ZA5K1gLIBFw+GKbbO3CvfQp+11AE5pFHebcBJOovOAIfMsc9+dPwjLngEXm4xROE5Mq8Z4aDUcv1DI8fOOnZRVwklUn+91MPQL0HnC/uWQOkPtioST+Hp5cFOLRgCsP+TYe+0knETNNO8GA6+G0to3IP17desRTnPL9cEAbD7i2Ak4JZxEzXVNgJhnLI+XPgWnd6lbj3CKrhGWI6e0E44dX1zCSdTOgDeg9R1gLISvHoa8bLUrEg7WuXkAGg2cuHCZ8/kGh+1HwknUjs4DhsyFxtdD7klYPByMV9SuSjiQv7ee1tf5ArDr5CWH7UfCSdReg0B4eDF4B1pGMPhhvFzBc3NRzQMBSDuR47B9uE04SSdMlTVuDQ8kg0YHuxfBOpnBxZ1FhwcAsPdPCacqSSdMF9A6Fu6eaXn863TY+aW69QiHaRviB8DBM3kO24fbhJNwEV0fg1snWB7/MF7GgHJT118Np5MXL1NY5JhRKiSchP3d/grcOAwUE3ydAKd3q12RsLOghp4ENfREUeDI2QKH7EPCSdifRmMZQbPlbVCUD/99AC45b3hX4Rxtmliu2Dnq1E7CSTiGhycMWwBNOkB+Fvx3CBQ6tkexcK6ScJIjJ1H3eAfA8G/Arymc3Q8Lh1qOpIRbaN6oAQB/XrzskO1LOAnHCmgOjy6FBo3g5DZ0Sx5Da5ZpptxBs0BLOJ28JOEk6qom7WH4EtA3RJu5jq7HZoPZOTN4CMeRIyfhHpp3gwf/i6LzJOzSNnQrE6UXeR3XvJEPAFm5Vyg2me2+fZcMp/vuu49GjRoxZMgQtUsR9tQ6FtO9n6KgQbvrv7Dm/ySg6rDrfL3w1GkxmRWycu1/P6VLhtP48eP54osv1C5DOIDS7m7SWjxuebLxA/j1HXULEjWm1Wpo4u8FwJk8+49O4JLh1LdvX/z8/NQuQzjI8cZ9MPV7zfJk3TRY/x91CxI11tjXEk7n84vsvm27h1NqaiqDBg0iLCwMjUbDsmXLyqyTlJREZGQk3t7exMTEsHXrVnuXIVycOeZZy2QJACmvWaY7F3VOcENPAIeM62T3cCooKCAqKoqkpKRyX1+8eDGJiYlMmTKFnTt3EhUVRVxcHGfOOH6qGeFibk2E2Fcsj9dMhk3l/84I19XY92o4Fdj/yMnD3huMj48nPj6+wtdnzpzJqFGjGDlyJACzZ89mxYoVzJ07l4kTJ1Z7fwaDAYPhWmrn5lqmKjIajRiNFfenKXmtsnVclVvV3us5tMYr6H6bAT+9hEnRYO4+SsUKK1aXP3dwTP2BDSwRcib3ss3btXU9u4dTZYqKitixYweTJk2yLtNqtfTr149NmzbVaJtvvfUWU6dOLbN89erV+Pj4VPn+NWvW1Gi/rsBtalc60z5kEG2zf0C3ehJ70/dz9Lo71CuuCnX5cwf71n/mlAbQsffgUVauPGLTewoLbZtSyqnhdO7cOUwmEyEhIaWWh4SEsH//fuvzfv36sWvXLgoKCmjevDnffPMNPXv2LHebkyZNIjEx0fo8NzeX8PBwBgwYgL+/f4W1GI1G1qxZQ//+/dHr9bX8yZzLLWtXBmJa+xq6TR8QdXI+nTrcgLn7aPUKLUdd/tzBMfUb006x7NhevAKCGTiwm03vKTm7qYpTw8lWP/9s+xhAXl5eeHl5kZSURFJSEiaTpeexXq+36R/A1vVckdvVPuB1y4gGG99Ht/oldCaDpV3KxdTlzx3sW/91AZazk4uFxTZv09b1nNqVIDg4GJ1OR3Z26Rk6srOzCQ0NrdW2ZSRMN6DRQP/XoM/VtseUqfDLm9JR04X5eVuOb/IN9m+Hc2o4eXp60rVrV1JSUqzLzGYzKSkpFZ62iXpGo4HYSdDvVcvz1Hdg9SsSUC7K1+tqOF2x/2iYdj+ty8/P59ChQ9bnmZmZpKWlERQURIsWLUhMTCQhIYFu3brRo0cPZs2aRUFBgfXqXU39/bRO1HG3/Av0PvDjC7DpQzBetswwrHXJfsP1Vkk4FRjs/3dn93Davn07sbGx1ucljdUJCQkkJyczbNgwzp49y+TJk8nKyiI6OppVq1aVaSSvrjFjxjBmzBhyc3MJCAio1baEi4h5Cjy8LWORb//cElCDP7DMlSdcgu/V07oikxlDsQkvD53dtm33f+W+ffuiVHEIPnbsWMaOHWvvXQt31DUB9A1g6dOwayFcuWSZxFPfQO3KBNDQ81qE5F8pxsvXfuHkNsfIMm+dG7txKAz70nIUdWAlfHkfXL6odlUC0Gk1+HhaAsnep3ZuE05ytc7NtbvLMqKmVwAc3wTzBkLuKbWrElxrd8qz8xU7twknUQ9E9ILHfwTfUDiTDp8PgLN/qF1VvVfS7mTvK3ZuE05yWldPhHSEJ1ZD4zaQcwLmxsHJ7WpXVa9Zr9jZeXJNtwknOa2rRxpFwOM/QdhNcPkCzB8Ef/ykdlX1lpeHJUYMRvsO1es24STqmYbBkPADtL4DjIXw1YOw5RO1q6qXSroPFNl5HHEJJ1F3efnCw4uhy6OgmC0dNn98UWZ2cTJPOXKqnLQ51VM6vaVjZsntLltmw6LhYJDJO53FelonR07lkzanekyjsdzu8kCypS/UHz/CvHjpauAk146cpJ+TEOXreB8kLAefYMjaDXPugKw9alfl9kqOnKTNSYjKhHeHUSkQfAPknYLP4yDjB7WrcmvS5iSErRpFWvpCtewDxgJY/AisfQvM9p+VVsjVuipJg7gopUEgPPItxDxjef7rdPj6UTDkqVqWO5IjpypIg7goQ+cB8dPhno9A5wn7l8Nn/eH8YbUrcyvX2pykQVyI6ukyHEZevSfvbAbMiYVDKVW/T9hEjpyEqI3m3WD0OmjeHa7kwH+HwMYPZPhfO/DUXQ2nYgknIWrGvyk8tgK6PGLpUb76FfgmAa7YNlWRKJ/+ajiZzPYNegknUb94eMHgDyH+36DVQ/p38GlfyN6ndmV1llarASSchKg9jQZiRlvaofybw4XDlg6baQvVrqxO0mmuhpOdT5HdJpykK4GotvDu8FSqZWSD4suw7Bn4fhwYr6hdWZ1y9awOsxw5lU+6EogaadgYhi+B2JcBDez8Aj7vBxeOqF1ZnaGVIychHESrhT4vWMYo92lsuR/vk76Q/r3aldUJOmlzEsLBWsfCU+shPAYMOZYe5csTLfPliQpJOAnhDAHNLN0Neo+3PN/+OXwaC9np6tblwiSchHAWnR76v2Y5zfMNsfYq1+6YK502y1Fytc4sbU5COEnr2+HpDdCmPxRfQbfqBXpkvi8Tev6N9HMSQg2+18HDX0PcWyhaPU1zduAxpw9kpqpdmcu41s/JvtuVcBKiKlot9HyW4sdWke8ViibvlGU6qlUvSZ8orrU5ST+nCkgnTOFwTaNYd8NrmLqMsDzfnASf9oHTu9StS2VyWlcF6YQpnMGk88Y8cKblVK9hEzi7H+bcDqn/BpN9Z7ytK6RBXAhX0jYOnt0M7QeDuRh+ecMy40s9HMhOezVF5MhJCFfRsDEM/QLu+wS8/OHkVph9C2ydU6/GK5cbf4VwRRoNRD0Iz2yEyFstU6OvnGBpMK8n9+d56KRBXAjXFRgOI763jBOlbwjHfoOPesGmj9x+evSSG3+LJZyEcFFarWWcqGc3QsvbLMOw/DTJ0hZ17qDa1TmMdCUQoq5oFGk5irp7Fnj6wYkt8HFv+G2WW17RkyFThKhLNBroNhKe3WQZzM5kgJ+nwOf93W6K9KvZZHcuGU7Lly/nhhtu4Prrr+ezzz5Tuxwhai4wHB75n2XuPO8AOLUTPukDq/8PigrUrs6luVw4FRcXk5iYyC+//MLvv//Ov//9b86fP692WULUnEZjmTtvzFbocC8oJtj4Pnx0Mxxco3Z1Lsvlwmnr1q107NiRZs2a4evrS3x8PKtXr1a7LCFqzy8Uhs639C4PaAGXjlvmz/vmMcjLUrs6l2P3cEpNTWXQoEGEhYWh0WhYtmxZmXWSkpKIjIzE29ubmJgYtm7dan3t1KlTNGvWzPq8WbNm/Pnnn/YuUwj1tI2DMZuh51jQaGHfUviwB2z7vF513qyK3cOpoKCAqKgokpKSyn198eLFJCYmMmXKFHbu3ElUVBRxcXGcOXPG3qUI4bo8G0Lcm5ZZiMO6WIYFXpEIc+Pq/Y3EJTzsvcH4+Hji4+MrfH3mzJmMGjWKkSNHAjB79mxWrFjB3LlzmThxImFhYaWOlP7880969OhR4fYMBgMGg8H6PDfXMnur0WjEaDRW+L6S1ypbx1VJ7epwSO3BHSBhFdodn6Nd9yaak1tRPu2L+abHMPd5CRoE2m1Xjvrsi4st3SMUxbZt27p/jaI4btxRjUbD0qVLuffeewEoKirCx8eHJUuWWJcBJCQkcOnSJb777juKi4tp374969atIyAggK5du7Jx40YaN25c7j5effVVpk6dWmb5woUL8fHxccSPJYRDeBddoOOfi2h+aTMABg8/0sOGcjzoVsvpn4v6swDe2e2Bv17h9W5V94YvLCzk4YcfJicnB39//wrXs/uRU2XOnTuHyWQiJCSk1PKQkBD2799vKcjDg//85z/ExsZiNpt54YUXKgwmgEmTJpGYmGh9npubS3h4OAMGDKj0BzcajaxZs4b+/fuj1+tr+ZM5l9SuDufU/gjFR9ej+2kiXucO0OX450QVp2G6821oGl2rLTuq/ozTebyzexPe3t4MHNinyvVLzm6q4tRwstXgwYMZPHiwTet6eXnh5eVVZrler7fpH8DW9VyR1K4Oh9d+/e3QagNsmQ3rpqM9tQPt3P7Q9TG4YzL4BNVq8/au38PjWozY+jdnC6ceKwYHB6PT6cjOzi61PDs7m9DQ0FptW0bCFG5Fp4de42Dsduj8AKDAjnnwwU2WIVnc8DaYv3NqOHl6etK1a1dSUlKsy8xmMykpKfTs2bNW25aRMIVb8m8K//jMMpdekw6WmV9WToDZveHQz2pX51B2D6f8/HzS0tJIS0sDIDMzk7S0NI4fPw5AYmIic+bMYf78+WRkZPDMM89QUFBgvXpXU3LkJNxa5C2W2YgHzoAGQZbhgRf8AxYMgbMH1K7OIeze5rR9+3ZiY2Otz0saqxMSEkhOTmbYsGGcPXuWyZMnk5WVRXR0NKtWrSrTSF5dY8aMYcyYMeTm5hIQEFCrbQnhknQe0GMUdB4CqTMsbVKH1sDhX6D7k9B3Yq3bo2rD3pf97R5Offv2pareCWPHjmXs2LH23rUQ9UODRpYOnN0et9xAfGAFbP0Edi+2BFT3Jy1tVk5Sr0YlqAk5rRP1TuPW8NBCy9hRIZ3gyiVYNRGSYiD9uzo/dbrbhJM0iIt6q1UfeCoVBr0HDa+DC4fh6xHwWT84ukHt6mrMbcJJiHpNq7P0gxq3E257AfQ+8Od2SB4IC4fBmQy1K6w2CSch3Im3P9z+MvwzzdImpdHBH6vg417olo/Hu+iC2hXazG3CSdqchPgLvxC4+10YswXaDwLFjHbXf+mX/jzata/D5UtqV1gltwknaXMSohzB18OwBfDEGszhN6NTjOg2vgfvRcH6mXYdKtje7e9uE05CiEqE98D06A9safUcSnBby5W9lKmWkNr0ERiv1HjT0pVACFE7Gg1ZATdRPGq9ZQr1RpFQcNYyt977XWD7XCguUrtKK7cJJ2lzEsJGWp1lCvWx2y3dD/ybQd4pWP4v+LAbpH3lErMUu004SZuTENWk01/rfnDn29CwCVw6BsuetswMs/dbVcc0d5twEkLUkN4bbn4axqdBv6mW22PO/QFLRlpGP9j7rSpHUhJOQggLz4Zwy3Mwfjf0fQm8/OFMuiWkPuoJu7926jhSEk5CiNK8/aHvi/DcHktIeQfAuQPw7ShI6gFpCysIKfv2JXCbcJIGcSHsrEHg1ZDaC7f/n+V078JhWPYMfNgVdn4JJiMaHNOXwG3CSRrEhXAQb3+4bYLlSKrfVPAJhotH4fux8P5NBKQvQI/9T/fcJpyEEA7m5Wdpk3puNwx403J1L+c4oakTWeX5Il2UdLvuTsJJCFE9ng2h11hLSN35NsUNgmmtPc27prfseg+LhJMQomb0DeDmpzl2/3IAfLls181LOAkhakXxcMzM2m4TTnK1Tgj34jbhJFfrhFCJjEoghKhPJJyEEC5JwkkI4ZIknIQQLknCSQjhkiSchBAuScJJCFErDupJ4D7hJJ0whXAvbhNO0glTCPfiNuEkhHAvEk5CCJck4SSEcEkSTkIIlyThJISoFY2MSiCEqE8knIQQLsklw+m+++6jUaNGDBkyRO1ShBAqcclwGj9+PF988YXaZQghVOSS4dS3b1/8/PzULkMIoaJqh1NqaiqDBg0iLCwMjUbDsmXLyqyTlJREZGQk3t7exMTEsHXrVnvUKoSoR6odTgUFBURFRZGUlFTu64sXLyYxMZEpU6awc+dOoqKiiIuL48yZM9Z1oqOj6dSpU5mvU6dO1fwnEUKoxDF9CTyq+4b4+Hji4+MrfH3mzJmMGjWKkSNHAjB79mxWrFjB3LlzmThxIgBpaWk1q1YIUW9UO5wqU1RUxI4dO5g0aZJ1mVarpV+/fmzatMmeu7IyGAwYDAbr89zcXACMRiNGo7HC95W8Vtk6rkpqV0ddrh0cV7/JVFx6H1X0yrR1/3YNp3PnzmEymQgJCSm1PCQkhP3799u8nX79+rFr1y4KCgpo3rw533zzDT179ix33bfeeoupU6eWWb569Wp8fKqeiXTNmjU21+VqpHZ11OXawf71X8rL4/qrj1euXFllOBUWFtq0XbuGk738/PPPNq87adIkEhMTrc9zc3MJDw9nwIAB+Pv7V/g+o9HImjVr6N+/P3q9vlb1OpvUro66XDs4rv6jJ07AIcvjgQMHVhlOJWc3VbFrOAUHB6PT6cjOzi61PDs7m9DQUHvuysrLywsvLy+SkpJISkrCZDIBoNfrbfoHsHU9VyS1q6Mu1w72r1+nuxYjer2+ynCydd927efk6elJ165dSUlJsS4zm82kpKRUeFpmLzISphDupdpHTvn5+Rw6dMj6PDMzk7S0NIKCgmjRogWJiYkkJCTQrVs3evTowaxZsygoKLBevRNCuBdHjUpQ7XDavn07sbGx1ucl7T0JCQkkJyczbNgwzp49y+TJk8nKyiI6OppVq1aVaSS3t7+f1gkh6rZqh1Pfvn1RFKXSdcaOHcvYsWNrXFRNjBkzhjFjxpCbm0tAQIBT9y2EsD+XvLdOCCHcJpxk3joh3IvbhJNcrRPCvbhNOAkh3IvbhJOc1gmhDgf1JHCfcJLTOiHci9uEkxDCvUg4CSFcktuEk7Q5CeFe3CacpM1JCPfiNuEkhHAvEk5CiFpx1KgEEk5CCJfkNuEkDeJCuBe3CSdpEBfCvbhNOAkh3IuEkxDCJUk4CSFckoSTEKJWZFSCKsjVOiHci9uEk1ytE8K9uE04CSHci4STEMIlSTgJIVyShJMQwiVJOAkhakVGJRBC1CsSTkIIl+Q24SSdMIVwL24TTtIJUwj34jbhJIRwLxJOQgiXJOEkhKgVjYPGJZBwEkK4JAknIYRLknASQrgkCSchhEuScBJCuCSXC6cTJ07Qt29fOnTowI033sg333yjdklCCBV4qF3A33l4eDBr1iyio6PJysqia9euDBw4kIYNG6pdmhCiHI4alcDlwqlp06Y0bdoUgNDQUIKDg7lw4YKEkxD1TLVP61JTUxk0aBBhYWFoNBqWLVtWZp2kpCQiIyPx9vYmJiaGrVu31qi4HTt2YDKZCA8Pr9H7hRB1V7XDqaCggKioKJKSksp9ffHixSQmJjJlyhR27txJVFQUcXFxnDlzxrpOdHQ0nTp1KvN16tQp6zoXLlxgxIgRfPrppzX4sYQQdV21T+vi4+OJj4+v8PWZM2cyatQoRo4cCcDs2bNZsWIFc+fOZeLEiQCkpaVVug+DwcC9997LxIkT6dWrV5XrGgwG6/Pc3FwAjEYjRqOxwveVvFbZOq5KaldHXa4dHFe/sbi49D6qaISydf92bXMqKipix44dTJo0ybpMq9XSr18/Nm3aZNM2FEXhscce4/bbb+fRRx+tcv233nqLqVOnllm+evVqfHx8qnz/mjVrbKrLFUnt6qjLtYP968/PzyPy6uOVK1dWGU6FhYU2bdeu4XTu3DlMJhMhISGlloeEhLB//36btrFhwwYWL17MjTfeaG3P+vLLL+ncuXO560+aNInExETr89zcXMLDwxkwYAD+/v4V7sdoNLJmzRr69++PXq+3qTZXIbWroy7XDo6r/89TJ+Gg5fHAgQOrDKeSs5uquNzVultuuQWz2Wzz+l5eXnh5eZGUlERSUhImkwkAvV5v0z+Areu5IqldHXW5drB//XqPazGi1+urDCdb923XTpjBwcHodDqys7NLLc/OziY0NNSeuypDRsIUwr3YNZw8PT3p2rUrKSkp1mVms5mUlBR69uxpz10JIdxctU/r8vPzOXTokPV5ZmYmaWlpBAUF0aJFCxITE0lISKBbt2706NGDWbNmUVBQYL165yh/P60TQtRt1Q6n7du3Exsba31e0hidkJBAcnIyw4YN4+zZs0yePJmsrCyio6NZtWpVmUZyexszZgxjxowhNzeXgIAAh+5LCOF41Q6nvn37oihKpeuMHTuWsWPH1rgoIYRwuVEJakrmrRPCvbhNOMnVOiHU4ahRCdwmnIQQ7sVtwklO64RwL24TTnJaJ4R7cZtwEkK4FwknIYRLcptwkjYnIdyL24STtDkJoQ6Ng/oSuE04CSHci4STEMIlSTgJIVyS24STNIgL4V7cJpykQVwI9+I24SSEcC8STkKIWnHQoAQSTkII1yThJIRwSRJOQgiX5DbhJF0JhHAvbhNO0pVACPfiNuEkhHAvEk5CiNqRCQ6EEPWJhJMQwiVJOAkhXJKEkxDCJUk4CSFcktuEk3TCFMK9uE04SSdMIdShcVBfArcJJyGEe5FwEkK4JAknIYRLknASQrgkCSchhEuScBJCuCQJJyFErWjqy6gEly5dolu3bkRHR9OpUyfmzJmjdklCCBV4qF3A3/n5+ZGamoqPjw8FBQV06tSJ+++/n8aNG6tdmhDCiVzuyEmn0+Hj4wOAwWBAURQURVG5KiGEs1U7nFJTUxk0aBBhYWFoNBqWLVtWZp2kpCQiIyPx9vYmJiaGrVu3Vmsfly5dIioqiubNm/P8888THBxc3TKFEHVctcOpoKCAqKgokpKSyn198eLFJCYmMmXKFHbu3ElUVBRxcXGcOXPGuk5Je9Lfv06dOgVAYGAgu3btIjMzk4ULF5KdnV3DH08IUVdVu80pPj6e+Pj4Cl+fOXMmo0aNYuTIkQDMnj2bFStWMHfuXCZOnAhAWlqaTfsKCQkhKiqK9evXM2TIkHLXMRgMGAwG6/OcnBwALly4gNForHDbRqORwsJCzp8/j16vt6keVyG1q6Mu1w6Oq//ihYt4GyxNL8bz56u8fJeXlwdQdXONUguAsnTpUutzg8Gg6HS6UssURVFGjBihDB482KZtZmVlKbm5uYqiKMqlS5eUjh07Krt3765w/SlTpiiAfMmXfNWxrxMnTlSaBXa9Wnfu3DlMJhMhISGlloeEhLB//36btnHs2DFGjx5tbQgfN24cnTt3rnD9SZMmkZiYaH1uNpu5cOECjRs3RlNJgufm5hIeHs6JEyfw9/e3qTZXIbWroy7XDq5Tv6Io5OXlERYWVul6LteVoEePHjaf9gF4eXnh5eVVallgYKDN7/f396+Tv2ggtaulLtcOrlF/QEBAlevYtStBcHAwOp2uTAN2dnY2oaGh9tyVEMLN2TWcPD096dq1KykpKdZlZrOZlJQUevbsac9dCSHcXLVP6/Lz8zl06JD1eWZmJmlpaQQFBdGiRQsSExNJSEigW7du9OjRg1mzZlFQUGC9eucqvLy8mDJlSplTwrpAaldHXa4d6l79mqtX3Wy2bt06YmNjyyxPSEggOTkZgA8//JB///vfZGVlER0dzfvvv09MTIxdChZC1A/VDichhHAGl7u3TgghQMJJCOGiJJyEEC7JbcOpuiMjfPPNN7Rr1w5vb286d+7MypUrnVRp+apT/759+/jHP/5BZGQkGo2GWbNmOa/QclSn9jlz5nDrrbfSqFEjGjVqRL9+/ao9ioU9Vaf2b7/9lm7duhEYGEjDhg2Jjo7myy+/dGK1ZdV0RJBFixah0Wi49957HVtgddh0w1sds2jRIsXT01OZO3eusm/fPmXUqFFKYGCgkp2dXe76GzZsUHQ6nfLOO+8o6enpyiuvvKLo9Xplz549Tq7corr1b926VZkwYYLy1VdfKaGhocq7777r3IL/orq1P/zww0pSUpLy+++/KxkZGcpjjz2mBAQEKCdPnnRy5dWvfe3atcq3336rpKenK4cOHVJmzZql6HQ6ZdWqVU6u3KK69ZfIzMxUmjVrptx6663KPffc45xibeCW4dSjRw9lzJgx1ucmk0kJCwtT3nrrrXLXHzp0qHLXXXeVWhYTE6M89dRTDq2zItWt/68iIiJUDafa1K4oilJcXKz4+fkp8+fPd1SJFapt7YqiKF26dFFeeeUVR5RXpZrUX1xcrPTq1Uv57LPPlISEBJcKJ7c7rSsqKmLHjh3069fPukyr1dKvXz82bdpU7ns2bdpUan2AuLi4Ctd3pJrU7yrsUXthYSFGo5GgoCBHlVmu2tauKAopKSkcOHCA2267zZGllqum9b/22ms0adKEJ554whllVovL3fhbWzUZGSErK6vc9bOyshxWZ0XsMbKDWuxR+4svvkhYWFiZ/ywcraa15+Tk0KxZMwwGAzqdjo8++oj+/fs7utwyalL/b7/9xueff16tG+2dye3CSdRd06dPZ9GiRaxbtw5vb2+1y7GJn58faWlp5Ofnk5KSQmJiIq1ataJv375ql1apvLw8Hn30UebMmeOyw2C7XTjVZGSE0NBQlxlJoS6P7FCb2mfMmMH06dP5+eefufHGGx1ZZrlqWrtWq6VNmzaAZfjpjIwM3nrrLaeHU3XrP3z4MEePHmXQoEHWZWazGQAPDw8OHDhA69atHVt0FdyuzakmIyP07Nmz1PoAa9asUWUkhbo8skNNa3/nnXd4/fXXWbVqFd26dXNGqWXY63M3m82lho12lurW365dO/bs2UNaWpr1a/DgwcTGxpKWlkZ4eLgzyy+f2i3yjrBo0SLFy8tLSU5OVtLT05XRo0crgYGBSlZWlqIoivLoo48qEydOtK6/YcMGxcPDQ5kxY4aSkZGhTJkyRfWuBNWp32AwKL///rvy+++/K02bNlUmTJig/P7778rBgwddvvbp06crnp6eypIlS5TTp09bv/Ly8ly+9mnTpimrV69WDh8+rKSnpyszZsxQPDw8lDlz5ji99prU/3eudrXOLcNJURTlgw8+UFq0aKF4enoqPXr0UDZv3mx9rU+fPkpCQkKp9b/++mulbdu2iqenp9KxY0dlxYoVTq64tOrUn5mZWe4YzX369HF+4Ur1ao+IiCi39ilTpji/cKV6tb/88stKmzZtFG9vb6VRo0ZKz549lUWLFqlQ9TXV/b3/K1cLJxmVQAjhktyuzUkI4R4knIQQLknCSQjhkiSchBAuScJJCOGSJJyEEC5JwkkI4ZIknIQQLknCSQjhkiSchBAuScJJCOGSJJyEEC7p/wNbxy8wFjvb6QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 300x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get for both genuchten and brooks from VS2D dataset\n",
    "ls_gen = pe.Soil(\"Limon Silt\").from_name(sm=pe.Genuchten)\n",
    "ls_bro = pe.Soil(\"Limon Silt\").from_name(sm=pe.Brooks)\n",
    "\n",
    "ax = ls_gen.model.plot()\n",
    "ls_bro.model.plot(ax=ax)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels = [f\"{x.model.__class__.__name__} ({x.source})\" for x in [ls_gen, ls_bro]]\n",
    "ax.legend(handles, labels)\n",
    "ax.set_title(ls_gen.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Parameters Across Datasets\n",
    "\n",
    "When the same soil type is available in multiple databases, it's useful to compare their parameters. Different sources may give different values due to different measurement methodologies or soil samples used."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pedon (3.13.5)",
   "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": "3.13.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}