{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the `ws3` optimization modelling funtions (basic)\n",
    "\n",
    "The `ws3` package provides functions for formulating and solving optmization problems. In this notebook we will use these functions to \n",
    "formulate and solve a maximum even-flow harvest volume problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we (optionally) install the `ws3` package from the local source code in this repository using the `-e` flag to ensure that the package is installed in editable mode (i.e., any changes you make to the source code immediately affect `ws3` behaviour the next time you run the notebook). This is is not necessary if you have installed `ws3` using pip or another method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set auto-reload to reload modules when they are changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: ws3 1.1.0.dev0\n",
      "Uninstalling ws3-1.1.0.dev0:\n",
      "  Successfully uninstalled ws3-1.1.0.dev0\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Obtaining file:///home/gep/projects/ws3\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: dill in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (0.4.0)\n",
      "Requirement already satisfied: fiona in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.10.1)\n",
      "Requirement already satisfied: gurobipy in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (12.0.3)\n",
      "Requirement already satisfied: highspy in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.11.0)\n",
      "Requirement already satisfied: libcbm in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.8.1)\n",
      "Requirement already satisfied: matplotlib in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (3.10.5)\n",
      "Requirement already satisfied: numpy>=1.21 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.2.6)\n",
      "Requirement already satisfied: pandas>=1.3 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.3.1)\n",
      "Requirement already satisfied: profilehooks in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.13.0)\n",
      "Requirement already satisfied: pulp in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (3.2.2)\n",
      "Requirement already satisfied: rasterio in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.4.3)\n",
      "Requirement already satisfied: scipy>=1.7 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.16.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.3->ws3==1.1.0.dev0) (1.17.0)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (25.3.0)\n",
      "Requirement already satisfied: certifi in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (2025.8.3)\n",
      "Requirement already satisfied: click~=8.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (8.2.1)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (1.1.1.2)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (0.7.2)\n",
      "Requirement already satisfied: numexpr>=2.8.7 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (2.11.0)\n",
      "Requirement already satisfied: numba in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (0.61.2)\n",
      "Requirement already satisfied: pyyaml in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (6.0.2)\n",
      "Requirement already satisfied: mock in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (5.2.0)\n",
      "Requirement already satisfied: openpyxl in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (3.1.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (4.59.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (3.2.3)\n",
      "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from numba->libcbm->ws3==1.1.0.dev0) (0.44.0)\n",
      "Requirement already satisfied: et-xmlfile in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from openpyxl->libcbm->ws3==1.1.0.dev0) (2.0.0)\n",
      "Requirement already satisfied: affine in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from rasterio->ws3==1.1.0.dev0) (2.4.0)\n",
      "Building wheels for collected packages: ws3\n",
      "  Building editable for ws3 (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ws3: filename=ws3-1.1.0.dev0-py3-none-any.whl size=4140 sha256=234ec7265bfd9d1fa6baff1a4a2893b98fdda92b53bd338be7799e265eeb0626\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-_pvy0c7t/wheels/8a/d1/f0/2b533a60b366fa03a12ca91a1ad068761e66b9df68fa0cadb9\n",
      "Successfully built ws3\n",
      "Installing collected packages: ws3\n",
      "Successfully installed ws3-1.1.0.dev0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "clobber_ws3 = True\n",
    "if clobber_ws3:\n",
    "    %pip uninstall -y ws3\n",
    "    %pip install -e .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `pip` to install Python packages listed in `requirements.txt` (some extra packages needed for example notebooks to run correctly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 1)) (0.13.2)\n",
      "Requirement already satisfied: geopandas in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 2)) (1.1.1)\n",
      "Requirement already satisfied: ipywidgets in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 3)) (8.1.7)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.2.6)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.3.1)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (3.10.5)\n",
      "Requirement already satisfied: pyogrio>=0.7.2 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (0.11.1)\n",
      "Requirement already satisfied: packaging in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (25.0)\n",
      "Requirement already satisfied: pyproj>=3.5.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (3.7.1)\n",
      "Requirement already satisfied: shapely>=2.0.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (2.1.1)\n",
      "Requirement already satisfied: comm>=0.1.3 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (0.2.3)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (9.4.0)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (5.14.3)\n",
      "Requirement already satisfied: widgetsnbextension~=4.0.14 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (4.0.14)\n",
      "Requirement already satisfied: jupyterlab_widgets~=3.0.15 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (3.0.15)\n",
      "Requirement already satisfied: decorator in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (5.2.1)\n",
      "Requirement already satisfied: ipython-pygments-lexers in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (1.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.19.2)\n",
      "Requirement already satisfied: matplotlib-inline in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.1.7)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (4.9.0)\n",
      "Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (3.0.51)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (2.19.2)\n",
      "Requirement already satisfied: stack_data in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.6.3)\n",
      "Requirement already satisfied: wcwidth in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.2.13)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (4.59.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.4.8)\n",
      "Requirement already satisfied: pillow>=8 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn->-r requirements.txt (line 1)) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn->-r requirements.txt (line 1)) (2025.2)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.7.0)\n",
      "Requirement already satisfied: certifi in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from pyogrio>=0.7.2->geopandas->-r requirements.txt (line 2)) (2025.8.3)\n",
      "Requirement already satisfied: six>=1.5 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.17.0)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (2.2.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (3.0.0)\n",
      "Requirement already satisfied: pure-eval in /home/gep/projects/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.2.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `ws3` package, and import a function from the local `util` module to generate and run scenarios. We also import `matplotlib`, which is used for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import ws3\n",
    "import ws3.forest\n",
    "from util import run_scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up ``ForestModel`` instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set some reasonable model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_year = 2020\n",
    "horizon = 10\n",
    "period_length = 10\n",
    "max_age = 1000\n",
    "n_steps = 100\n",
    "tvy_name = \"totvol\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new `ForestModel` object and import a model dataset from the `data` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fm = ws3.forest.ForestModel(model_name=\"tsa24_clipped\",\n",
    "                            model_path=\"data/woodstock_model_files_tsa24_clipped\",\n",
    "                            base_year=base_year,\n",
    "                            horizon=horizon,\n",
    "                            period_length=period_length,\n",
    "                            max_age=max_age)\n",
    "fm.import_landscape_section()\n",
    "fm.import_areas_section(convert_periods_to_years=period_length)\n",
    "fm.import_yields_section(convert_periods_to_years=period_length)\n",
    "fm.import_actions_section(convert_periods_to_years=period_length)\n",
    "fm.import_transitions_section(convert_periods_to_years=period_length)\n",
    "fm.initialize_areas()\n",
    "fm.add_null_action()\n",
    "fm.reset_actions()\n",
    "\n",
    "fm.actions[\"harvest\"].is_harvest = True # set harvest action to be a harvest action (needed for `cmp_c_z` to work correctly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus growing stock constraints\n",
      "   period        oha           ohv            ogs\n",
      "0       1  92.742411  12945.008110  135863.963286\n",
      "1       2  97.379532  13592.258515  132839.040537\n",
      "2       3  88.105291  13592.258515  128034.433647\n",
      "3       4  90.176925  12297.757704  125301.751940\n",
      "4       5  90.366370  12297.757704  124421.651363\n",
      "5       6  91.904290  12297.757704  124170.013825\n",
      "6       7  91.433562  12297.757704  124057.295448\n",
      "7       8  90.561107  12297.757704  123503.944077\n",
      "8       9  89.863045  12297.757704  122048.775279\n",
      "9      10  97.379532  12297.757704  120000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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TTzxhuLi4mMcdW7ZsMSQZq1atMgzDMHbt2mVIMjp06GA0aNDAfF/btm2Ne++913z93XffGZJsflceeuihPMclWVlZRlBQkNG+fXuzbdKkSYYkY9myZWbbmTNnjLCwsDzbtGf16tVGsWLFjGLFihkRERHGoEGDjK+//trIzs7OE1uyZEmbfJQrOjracHd3N3777Tez7fDhw4a3t7fRpEkTs23EiBGGJOPzzz/Ps43cvJP7WSxZssQ4ceKE8dBDDxlly5Y1tm/ffsX9MAzD+OabbwxJxpdffplnXe7fxY0bN5ptX3/9tSHJ8PLysjn+fOedd/L97KKiosz85u7ubvTu3dvmb8al+y/JmDBhwlXHfTvg1HEnNmvWLCUmJuZZcmdFLnXpDEfuTNFDDz2k33//XRkZGTaxoaGhioqKsmm7++67VbduXS1atMhsu3Dhgj799FO1adPG3P6SJUvk6+ur//znP/rnn3/MJTw8XKVKlTK/ScydAVmxYoXdGaorOXr0qL799lt17NhRJ06cMPv4999/FRUVpX379pmnNK5atUoPPPCAzazBHXfcYZ7ydDWXfm5nz57VP//8Y17fsm3btjzxl99k4vPPP1dOTo46duxo83kEBQWpatWqNt+sXtrXqVOn9M8//+jBBx+UYRhXPVUn91T23Bmoy0VGRprfsEoXZ858fHz0+++/2+3/3Llz+vfff3XXXXfJz8/P7r7m9nWl05qA60V+K/z89uSTTyolJUW//fab2bZo0SJ5eHioXbt2ki6eAn/kyBH16dPH5lrJVq1aKSwsTCtXrrym/buaS6/1K1asmOrVqyfDMNSjRw+z3c/PT9WqVbPJX0uWLFH16tUVFhZm87PJvc75arNY//77b775U5J69OhhcwZTgwYN8owrd7yXjitXjRo1lJiYqGXLlmnQoEEqWbJknruO5ypdurTOnDmj06dPX3HMwLXKzMyUJLtPJmnatKnuuOMOc5k1a1aemBdffNHm9apVq1SsWDG9/PLLNu3//e9/ZRiGeQr4vffeq1KlSmndunWSLs5c554avW3bNp0+fVqGYWjDhg1q3LjxVfejVKlSevrpp83X7u7uuv/++21+9xISElS+fHlztly6eMPBgj6+7z//+Y+SkpLUtm1b7dy5UxMnTlRUVJTKly9v9zKey124cEGrV69WdHS07rzzTrO9XLlyeuqpp7Rhwwbz5/HZZ5+pTp065tlKl7r8zMmMjAw1b95cP//8s77//nvVrVv3qmO52jFijRo1FBERYb7OnWl/5JFHVLFixTzt9nLc+PHjtXr1ar3//vt64IEHlJ2drfPnz+eJ49jRFqeOO7H7779f9erVy9NeunTpPP+Af/jhB40cOVJJSUl5/nhnZGTI19fXfB0aGmq3vyeffFKvvvqqDh06pPLly+v777/XkSNH9OSTT5ox+/btU0ZGhs31K5fKvYHEQw89pPbt22v06NGaMmWKmjZtqujoaD311FPy8PC44n7/+uuvMgxDw4cP1/Dhw/Ptp3z58vrzzz/tnh5VrVq1K/aR6+jRoxo9erQWLlyY5+YXlx/AS3k/u3379skwDFWtWtXu9osXL27+/4EDBzRixAgtX748z/We9vqyx8jn2sBLE2Wu0qVL2/Rz5swZxcXFae7cuTp06JDNtuz1n7v+Zn9uLpwT+a3w81uHDh00YMAALVq0SK+++qoMw9CSJUvM6wklmddw29tmWFiY3Rt93YjLc5Wvr688PT3znDrp6+trc6+Mffv26aeffjKvfb9cQW5elF/+zG9ckmyuY81tvzx/S5KPj495/Xe7du20YMECtWvXTtu2bVOdOnXsjoPcCqt5e3tLkt0veXKfJJKenm5TxOZyc3NThQoVbNr+/PNPBQcHm9vNVb16dXO9dPFLqIiICK1fv17SxUK7cePGatSokS5cuKBNmzYpMDBQR48eLVChXaFChTy/H6VLl7Z5LNmff/6pKlWq5Im7lqcK1K9fX59//rmys7O1c+dOLV26VFOmTNETTzyhHTt2qEaNGvm+9++//9bp06ft5s7q1asrJydHBw8eVM2aNfXbb7+pffv2BRpTv379dPbsWW3fvl01a9Ys8L5IBT9GvFJ+k2Q3x11a8D/99NO677779Nxzz+V57jf5zRaF9i3gt99+U7NmzRQWFqbJkycrJCRE7u7uWrVqlaZMmZLnxjn5Xd/35JNPaujQoVqyZIn69eunxYsXy9fX1+aOgjk5OQoICND8+fPtbiP3IMjFxUWffvqpNm3apC+//FJff/21unfvrkmTJmnTpk1XfA507nhfeeWVPDNTuax6PEvHjh21ceNGDRw4UHXr1lWpUqWUk5OT57EsuS7/7HJycswbexQrVixPfO5+XrhwQf/5z3909OhRDR48WGFhYSpZsqQOHTqk55577qo3N8q9zvrYsWN5/hBKstu3ZJt0X3rpJc2dO1f9+vVTRESEfH195eLiok6dOtntPzfR2rt2CCgq5LfrFxwcrMaNG2vx4sV69dVXtWnTJh04cEATJkywZPv5HUhduHAh3/fYy1UFyV85OTmqXbu2Jk+ebDf28gPGy5UpU8buwePVxmCv/UoFe67HH39czzzzjBYuXJin0D527JhKlCjB3fBhOV9fX5UrV0579uzJsy73Szt7N0CVJA8Pj6veifxKGjVqZD5Def369Xrttdfk5+enWrVqaf369QoMDJSkAhXaBckJVnJ3d1f9+vVVv3593X333erWrZuWLFmikSNHFkp/V9KuXTstXLhQ48eP10cffVSgn8mlx4j2XEt+k67+Obu7u6tt27YaP368zpw5Y5PLOHa0RaF9C/jyyy+VlZWl5cuX23xrVdAbQuQKDQ3V/fffr0WLFik2Nlaff/65oqOjbWZoqlSpom+++UYNGzYs0EHCAw88oAceeECvv/66FixYoC5dumjhwoV6/vnn8z1Iyz0Fp3jx4le8y7Z08WYW+/bty9Oempp61bEdO3ZMa9as0ejRozVixAiz3d728lOlShUZhqHQ0FDdfffd+cbt3r1bv/zyiz788EObm+gkJiYWqJ+wsDBJ0v79+1W7du0Cj+9Sn376qbp27WpzN9+zZ8/avWtkbl9ly5bNdwYJKArkt+vLb7mefPJJ9enTR6mpqVq0aJFKlCihNm3a2PSRu83LHzeVmpqa783DpP93iuDlOaQwns9bpUoV7dy5U82aNbuumZKwsDB99tlnlo8rP1lZWcrJybF7ttD+/fvNGUHAaq1atdJ7772nzZs3270Z37WoVKmSvvnmG504ccJmVvvnn3821+dq3LixsrOz9cknn+jQoUNmQd2kSROz0L777rvNgvtGVapUST/++KMMw7DJCb/++usNbTf3TKv/+7//M9vs5Zw77rhDJUqUsJuPf/75Z7m6uppfAFapUsXulx/2REdHq3nz5nruuefk7e1t9wkbl7v0GLGonDlzRoZh6MSJEzZ/L3PHQI67iGu0bwG530hdfirw3Llzr3lbTz75pDZt2qQPPvhA//zzj81pldLFGeALFy5o7Nixed6be9dV6WIRe/k3YrmnneQ+iiX3LteXH6QFBASoadOmeuedd2wSXa6///7b/P9HH31UmzZt0ubNm23W5zcjdSl7n5skTZ069arvzfX444+rWLFiGj16dJ7tGIZhnvpory/DMDRt2rQC9RMeHi53d3dt3bq1wGO7XLFixfKMccaMGfnOPqWkpNhc0wM4Avnt+vJbrvbt26tYsWL65JNPtGTJErVu3drm2bj16tVTQECA4uPjbR6T9dVXX+mnn35Sq1at8t22j4+PypYta16Xmevtt98u8PgKqmPHjjp06JDefffdPOvOnDmjU6dOXfH9EREROnbsmN1rD2/E8ePH7V6n/95770mS3csjtm3bpgcffNDScQC5Bg0apBIlSqh79+5KT0/Ps/5aZoUfffRRXbhwQTNnzrRpnzJlilxcXMw7gEsXZ8yLFy+uCRMmyN/f3zztuXHjxtq0aZPWrl1boNnsgoqKitKhQ4dsrqc+e/as3Rxhz3fffWf3s1i1apUk28tpSpYsmSeXFytWTM2bN9cXX3xhc5ZAenq6FixYoEaNGpmX6LRv3948Nf1y9sbw7LPPavr06YqPj9fgwYOvui/ly5dXSEjIDR0j5sfeZTnHjx/XZ599ppCQkDyXWqWkpMjFxYXjx/8fM9q3gObNm8vd3V1t2rRR7969dfLkSb377rsKCAiweyB3JR07dtQrr7yiV155Rf7+/nlmXB566CH17t1bcXFx2rFjh5o3b67ixYtr3759WrJkiaZNm6YnnnhCH374od5++2099thjqlKlik6cOKF3331XPj4+evTRRyVdPMWzRo0aWrRoke6++275+/urVq1aqlWrlmbNmqVGjRqpdu3a6tmzp+68806lp6crKSlJf/31l3bu3Cnp4h+Ujz/+WC1atFDfvn3Nx99UqlTJ5loee3x8fNSkSRNNnDhR586dU/ny5bV69epr+kawSpUqGjdunIYOHao//vhD0dHR8vb21v79+7V06VL16tVLr7zyisLCwlSlShW98sorOnTokHx8fPTZZ59d8VTGS3l6eqp58+b65ptvNGbMmAKP71KtW7fWxx9/LF9fX9WoUUNJSUn65ptvzFOOLnXkyBHt2rUrz6OTgKJGfru+/JYrICBADz/8sCZPnqwTJ07k+XIh98C4W7dueuihh9S5c2fz8V6VK1dW//79r7j9559/XuPHj9fzzz+vevXqad26dfrll18K+iMpsGeeeUaLFy/WCy+8oO+++04NGzbUhQsX9PPPP2vx4sXms9Pz06pVK7m5uembb74xH9Fjhe+//14vv/yynnjiCVWtWlXZ2dlav369Pv/8c9WrVy/PtbApKSk6evSoeTM6wGpVq1bVggUL1LlzZ1WrVk1dunRRnTp1ZBiG9u/frwULFsjV1dXuZWiXa9OmjR5++GG99tpr+uOPP1SnTh2tXr1aX3zxhfr162dzE9YSJUooPDxcmzZtMp+hLV2c0T516pROnTplaaHdu3dvzZw5U507d1bfvn1Vrlw5zZ8/37yp49XOfHnppZd0+vRpPfbYYwoLC1N2drY2btyoRYsWqXLlyurWrZsZGx4erm+++UaTJ09WcHCwQkND1aBBA40bN06JiYlq1KiR+vTpIzc3N73zzjvKysqyee73wIED9emnn6pDhw7q3r27wsPDdfToUS1fvlzx8fF5Li+RpNjYWGVmZuq1116Tr6+v3UdpXapdu3ZaunRpnhn+G9WyZUtVqFBBDRo0UEBAgA4cOKC5c+fq8OHDNjcYzZWYmKiGDRvaPba8LRXiHc1xnXIfN5Lfo2MeeuihPI+/Wb58uXHPPfcYnp6eRuXKlY0JEyYYH3zwQZ5HwFSqVMlo1arVFftv2LChIcl4/vnn842ZM2eOER4ebnh5eRne3t5G7dq1jUGDBhmHDx82DMMwtm3bZnTu3NmoWLGi4eHhYQQEBBitW7c2tm7darOdjRs3GuHh4Ya7u3uex8H89ttvxrPPPmsEBQUZxYsXN8qXL2+0bt3a+PTTT222sWvXLuOhhx4yPD09jfLlyxtjx4413n///QI9/uavv/4yHnvsMcPPz8/w9fU1OnToYD6awN6jaf7++2+72/nss8+MRo0aGSVLljRKlixphIWFGTExMUZqaqoZ8+OPPxqRkZFGqVKljLJlyxo9e/Y0H8Nl7/E4l/v8888NFxcX48CBAzbtkoyYmJg88ZUqVbJ5HMWxY8eMbt26GWXLljVKlSplREVFGT///HOeOMMwjNmzZxslSpQwMjMzrzou4FqQ3y4qivyW69133zUkGd7e3nYfx2IYhrFo0SLj3nvvNTw8PAx/f3+jS5cuxl9//WUTc/njvQzj4qPXevToYfj6+hre3t5Gx44djSNHjhQ4h3bt2tUoWbJknvHY+3eQnZ1tTJgwwahZs6bh4eFhlC5d2ggPDzdGjx5tZGRkXPVzaNu2rdGsWTObtvz+PRZ0vL/++qvx7LPPGnfeeafh5eVleHp6GjVr1jRGjhxpnDx5Ms8YBg8ebFSsWNHmUWJAYfj111+NF1980bjrrrsMT09Pw8vLywgLCzNeeOEFY8eOHTax+f0eGoZhnDhxwujfv78RHBxsFC9e3Khatarx5ptv2v03PHDgQLuPdrrrrrsMSTaPwTKM/B/vdfnvfu4YK1WqZNP2+++/G61atTK8vLyMO+64w/jvf/9rfPbZZ4YkY9OmTVf6eIyvvvrK6N69uxEWFmaUKlXKcHd3N+666y7jpZdeyvNY059//tlo0qSJ4eXlZUiyOWbatm2bERUVZZQqVcooUaKE8fDDD9s8SivXv//+a8TGxhrly5c33N3djQoVKhhdu3Y1/vnnH5vPYsmSJTbvGzRokCHJmDlz5hX3Z9u2bYakPI9AzO/vor1jx9xHNr755ptm28yZM41GjRoZZcuWNdzc3Iw77rjDaNOmjc0jcHMdP37ccHd3N957770rjvV24mIYhXRnAQCWunDhgmrUqKGOHTvaPbXVSvfee6+aNm2qKVOmFGo/AFBU1q9fr6ZNm+rnn3/O90kRhSkrK0uVK1fWkCFD1Ldv3yLvH7gdTJ06Vf3799dff/2l8uXLO3o4RapZs2YKDg7Wxx9/7JD+p06dqokTJ+q3337jZo//Pwpt4CayaNEivfjiizpw4MAV72x8IxISEvTEE0/o999/z/cxRwBwM8o9DbKg13FaKT4+Xm+88Yb27dt31cfAAbi6y+94ffbsWd177726cOFCoVzC4uySk5PVuHFj7du374o3siwM586dU5UqVTRkyBD16dOnSPt2ZhTaAAAAAG4qLVu2VMWKFVW3bl1lZGTof//7n/bu3av58+frqaeecvTwAG6GBgAAAODmEhUVpffee0/z5883L69buHBhnps+Ao7CjDYAAAAAABbiOdoAAAAAAFiIQhsAAAAAAAvdlNdo5+Tk6PDhw/L29rb0oewAbh+GYejEiRMKDg6Wq+ut850j+RHAjbpV86NEjgRwY64lP96Uhfbhw4cVEhLi6GEAuAUcPHhQFSpUcPQwLEN+BGCVWy0/SuRIANYoSH68KQttb29vSRd30MfHx8GjAXAzyszMVEhIiJlPbhXkRwA36lbNjxI5EsCNuab8aNyEMjIyDElGRkaGo4cC4CZ1aR5Zu3at0bp1a6NcuXKGJGPp0qX5vq93796GJGPKlCk27f/++6/x1FNPGd7e3oavr6/RvXt348SJEzYxO3fuNBo1amR4eHgYFSpUMCZMmJBn+4sXLzaqVatmeHh4GLVq1TJWrlx53fsFAFdjL//ll0ccnf9ycnKM4cOHG0FBQYanp6fRrFkz45dffrmm/SVHArgR15JDrvnCm3Xr1qlNmzYKDg6Wi4uLli1bdnnhrhEjRqhcuXLy8vJSZGSk9u3bZxNz9OhRdenSRT4+PvLz81OPHj108uTJax0KAFji1KlTqlOnjmbNmnXFuKVLl2rTpk0KDg7Os65Lly7au3evEhMTtWLFCq1bt069evUy12dmZqp58+aqVKmSUlJS9Oabb2rUqFGaM2eOGbNx40Z17txZPXr00Pbt2xUdHa3o6Gjt2bPHup0FgEvcTPlv4sSJmj59uuLj45WcnKySJUsqKipKZ8+eteCTAACLXWsVv2rVKuO1114zPv/8c7szP+PHjzd8fX2NZcuWGTt37jTatm1rhIaGGmfOnDFjWrRoYdSpU8fYtGmTsX79euOuu+4yOnfuXOAx8G0kgBuVXx6xl9cMwzD++usvo3z58saePXuMSpUq2czo/Pjjj4YkY8uWLWbbV199Zbi4uBiHDh0yDMMw3n77baN06dJGVlaWGTN48GCjWrVq5uuOHTsarVq1sum3QYMGRu/evW94vwDgapTPjLYz5L+cnBwjKCjIePPNN831x48fNzw8PIxPPvmkwPtIjgRwIwp1Rrtly5YaN26cHnvsMXtFu6ZOnaphw4apXbt2uueee/TRRx/p8OHD5sz3Tz/9pISEBL333ntq0KCBGjVqpBkzZmjhwoU6fPjw9X1bAACFKCcnR88884wGDhyomjVr5lmflJQkPz8/1atXz2yLjIyUq6urkpOTzZgmTZrI3d3djImKilJqaqqOHTtmxkRGRtpsOyoqSklJSfmOLSsrS5mZmTYLAFjFWfLf/v37lZaWZhPj6+urBg0akCMBOCVLn9lQkCRYkIR8OZIkAEeaMGGC3Nzc9PLLL9tdn5aWpoCAAJs2Nzc3+fv7Ky0tzYwJDAy0icl9fbWY3PX2xMXFydfX11y4my4AKzlL/sv9LzkSwM3C0kK7IEmwIAn5ciRJAI6SkpKiadOmad68eU75zNWhQ4cqIyPDXA4ePOjoIQG4RWzfvt2p819BkCMBOIqlhXZhIUkCcJT169fryJEjqlixotzc3OTm5qY///xT//3vf1W5cmVJUlBQkI4cOWLzvvPnz+vo0aMKCgoyY9LT021icl9fLSZ3vT0eHh7y8fGxWQDACklJSU6T/3L/S44EcLOwtNAuSBIsSEK+HEkSgKM888wz2rVrl3bs2GEuwcHBGjhwoL7++mtJUkREhI4fP66UlBTzfd9++61ycnLUoEEDM2bdunU6d+6cGZOYmKhq1aqpdOnSZsyaNWts+k9MTFRERERh7yYA5NGpUyenyX+hoaEKCgqyicnMzFRycjI5EoBTcrNyY5cmwbp160r6f0nwxRdflGSbkMPDwyXlTcgAUJROnjypX3/91Xy9f/9+7dixQ/7+/qpYsaLKlCljE1+8eHEFBQWpWrVqkqTq1aurRYsW6tmzp+Lj43Xu3DnFxsaqU6dO5qNwnnrqKY0ePVo9evTQ4MGDtWfPHk2bNk1Tpkwxt9u3b1899NBDmjRpklq1aqWFCxdq69atNo/AAQAr2ct/u3btkiT5+/ubM9e5HJX/XFxc1K9fP40bN05Vq1ZVaGiohg8fruDgYEVHRxfiJwQA1+lab2l+4sQJY/v27cb27dsNScbkyZON7du3G3/++adhGBcf7+Xn52d88cUXxq5du4x27drZfbzXvffeayQnJxsbNmwwqlatyuO9ABSpS/PId999Z0jKs3Tt2tXuey9/vI1hGMa///5rdO7c2ShVqpTh4+NjdOvWzThx4oRNzM6dO41GjRoZHh4eRvny5Y3x48fn2fbixYuNu+++23B3dzdq1qxprFy58rr3CwCuJr/8l18ecWT+y8nJMYYPH24EBgYaHh4eRrNmzYzU1NRr2l9yJIAbcS05xMUwDONaCvPvv/9eDz/8cJ72rl27at68eTIMQyNHjtScOXN0/PhxNWrUSG+//bbuvvtuM/bo0aOKjY3Vl19+KVdXV7Vv317Tp09XqVKlCjSGzMxM+fr6KiMjg9PIAVyXWzWP3Kr7BaDo3Mp55FbeNwCF71pyyDUX2s6AJAngRt2qeeRW3S8ARedWziO38r4BKHzXkkNuiruOAwAAAABws7D0ZmiQKg9Zaen2/hjfytLtAc6C35XbT1H9zPm3BcBK5BQA14MZbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBC3HUcAAAAcCCr72wucXdzwNGY0QYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALDQbfUcbaufUcjzCQHcKsiPAAAA1mFGGwAAAAAAC91WM9q4PVk9UycxWwcAAAAgfxTaAAAAwG2Ay4SAokOhDViEP14AAAAAJK7RBgAAAADAUsxoAzcRZs0BAAAA50ehDYeicAQAAABwq6HQRr4oggEAAADg2lFoA8iDL1kAAMD14BgCuIiboQEAAAAAYCFmtAEAAADcNKyeNZfsz5wXxex8Ue0Lih4z2gAAAAAAWIgZbQAAAAC4hXHtfNGj0L4JcYoJADjWrXQ64c24L3xeN94HAKBwceo4AAAAAAAWotAGcNtbt26d2rRpo+DgYLm4uGjZsmXmunPnzmnw4MGqXbu2SpYsqeDgYD377LM6fPiwzTaOHj2qLl26yMfHR35+furRo4dOnjxpE7Nr1y41btxYnp6eCgkJ0cSJE/OMZcmSJQoLC5Onp6dq166tVatWFco+A4B0c+U/wzA0YsQIlStXTl5eXoqMjNS+ffus+zAA3JDKQ1ZautzsKLQB3PZOnTqlOnXqaNasWXnWnT59Wtu2bdPw4cO1bds2ff7550pNTVXbtm1t4rp06aK9e/cqMTFRK1as0Lp169SrVy9zfWZmppo3b65KlSopJSVFb775pkaNGqU5c+aYMRs3blTnzp3Vo0cPbd++XdHR0YqOjtaePXsKb+cB3NZupvw3ceJETZ8+XfHx8UpOTlbJkiUVFRWls2fPFsInAwA3hmu0Adz2WrZsqZYtW9pd5+vrq8TERJu2mTNn6v7779eBAwdUsWJF/fTTT0pISNCWLVtUr149SdKMGTP06KOP6q233lJwcLDmz5+v7OxsffDBB3J3d1fNmjW1Y8cOTZ482TwgnTZtmlq0aKGBAwdKksaOHavExETNnDlT8fHxhfgJALhd3Sz5zzAMTZ06VcOGDVO7du0kSR999JECAwO1bNkyderUqbA+IgC4LsxoA8A1ysjIkIuLi/z8/CRJSUlJ8vPzMw8yJSkyMlKurq5KTk42Y5o0aSJ3d3czJioqSqmpqTp27JgZExkZadNXVFSUkpKS8h1LVlaWMjMzbRYAKCyOyn/79+9XWlqaTYyvr68aNGhAjgTglJjRBoBrcPbsWQ0ePFidO3eWj4+PJCktLU0BAQE2cW5ubvL391daWpoZExoaahMTGBhoritdurTS0tLMtktjcrdhT1xcnEaPHn3D+wUAV+PI/Jf7X3IkcHu7mZ6+xIw2ABTQuXPn1LFjRxmGodmzZzt6OJKkoUOHKiMjw1wOHjzo6CEBuAU5Y/4rCHIkAEdhRhsACiD3IPPPP//Ut99+a87mSFJQUJCOHDliE3/+/HkdPXpUQUFBZkx6erpNTO7rq8XkrrfHw8NDHh4e179jAHAVzpD/cv+bnp6ucuXK2cTUrVs337GTIwE4CjPaAHAVuQeZ+/bt0zfffKMyZcrYrI+IiNDx48eVkpJitn377bfKyclRgwYNzJh169bp3LlzZkxiYqKqVaum0qVLmzFr1qyx2XZiYqIiIiIKa9cA4IqcJf+FhoYqKCjIJiYzM1PJycnkSABOiUIbwG3v5MmT2rFjh3bs2CHp4k13duzYoQMHDujcuXN64okntHXrVs2fP18XLlxQWlqa0tLSlJ2dLUmqXr26WrRooZ49e2rz5s364YcfFBsbq06dOik4OFiS9NRTT8nd3V09evTQ3r17tWjRIk2bNk0DBgwwx9G3b18lJCRo0qRJ+vnnnzVq1Cht3bpVsbGxRf6ZALg92Mt/u3btkiSnyn8uLi7q16+fxo0bp+XLl2v37t169tlnFRwcrOjo6KL7wACggDh1HMBtb+vWrXr44YfN17kHf127dtWoUaO0fPlyScpzeuJ3332npk2bSpLmz5+v2NhYNWvWTK6urmrfvr2mT59uxvr6+mr16tWKiYlReHi4ypYtqxEjRtg8a/bBBx/UggULNGzYML366quqWrWqli1bplq1ahXSngO43eWX/yTp8OHDTpX/Bg0apFOnTqlXr146fvy4GjVqpISEBHl6elr5kQCAJSi0Adz2mjZtKsMw8l1/pXW5/P39tWDBgivG3HPPPVq/fv0VYzp06KAOHTpctT8AsIK9/JeZmSlfX19VqlTJqfKfi4uLxowZozFjxlx1TADgaJw6DgAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCHLC+0LFy5o+PDhCg0NlZeXl6pUqaKxY8faPIfRMAyNGDFC5cqVk5eXlyIjI7Vv3z6rhwIAAAAAQJGzvNCeMGGCZs+erZkzZ+qnn37ShAkTNHHiRM2YMcOMmThxoqZPn674+HglJyerZMmSioqK0tmzZ60eDgAAAAAARcrN6g1u3LhR7dq1U6tWrSRJlStX1ieffKLNmzdLujibPXXqVA0bNkzt2rWTJH300UcKDAzUsmXL1KlTJ6uHBAAAAABAkbF8RvvBBx/UmjVr9Msvv0iSdu7cqQ0bNqhly5aSpP379ystLU2RkZHme3x9fdWgQQMlJSXZ3WZWVpYyMzNtFgAAAAAAnJHlM9pDhgxRZmamwsLCVKxYMV24cEGvv/66unTpIklKS0uTJAUGBtq8LzAw0Fx3ubi4OI0ePdrqoQIAAAAAYDnLZ7QXL16s+fPna8GCBdq2bZs+/PBDvfXWW/rwww+ve5tDhw5VRkaGuRw8eNDCEQMAAAAAYB3LZ7QHDhyoIUOGmNda165dW3/++afi4uLUtWtXBQUFSZLS09NVrlw5833p6emqW7eu3W16eHjIw8PD6qECAAAAAGA5y2e0T58+LVdX280WK1ZMOTk5kqTQ0FAFBQVpzZo15vrMzEwlJycrIiLC6uEAAAAAAFCkLJ/RbtOmjV5//XVVrFhRNWvW1Pbt2zV58mR1795dkuTi4qJ+/fpp3Lhxqlq1qkJDQzV8+HAFBwcrOjra6uEAAAAAAFCkLC+0Z8yYoeHDh6tPnz46cuSIgoOD1bt3b40YMcKMGTRokE6dOqVevXrp+PHjatSokRISEuTp6Wn1cAAAAAAAKFKWF9re3t6aOnWqpk6dmm+Mi4uLxowZozFjxljdPQAAAAAADmX5NdoAAAAAANzOKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoA7jtrVu3Tm3atFFwcLBcXFy0bNkym/WGYWjEiBEqV66cvLy8FBkZqX379tnEHD16VF26dJGPj4/8/PzUo0cPnTx50iZm165daty4sTw9PRUSEqKJEyfmGcuSJUsUFhYmT09P1a5dW6tWrbJ8fwEg182U/woyFgBwFhTaAG57p06dUp06dTRr1iy76ydOnKjp06crPj5eycnJKlmypKKionT27FkzpkuXLtq7d68SExO1YsUKrVu3Tr169TLXZ2Zmqnnz5qpUqZJSUlL05ptvatSoUZozZ44Zs3HjRnXu3Fk9evTQ9u3bFR0drejoaO3Zs6fwdh7Abe1myn8FGQsAOAs3Rw8AABytZcuWatmypd11hmFo6tSpGjZsmNq1aydJ+uijjxQYGKhly5apU6dO+umnn5SQkKAtW7aoXr16kqQZM2bo0Ucf1VtvvaXg4GDNnz9f2dnZ+uCDD+Tu7q6aNWtqx44dmjx5snlAOm3aNLVo0UIDBw6UJI0dO1aJiYmaOXOm4uPji+CTAHC7uVnyX0HGAgDOhBltALiC/fv3Ky0tTZGRkWabr6+vGjRooKSkJElSUlKS/Pz8zINMSYqMjJSrq6uSk5PNmCZNmsjd3d2MiYqKUmpqqo4dO2bGXNpPbkxuP/ZkZWUpMzPTZgEAK/zxxx9Ok/8KkovtIUcCcBQKbQC4grS0NElSYGCgTXtgYKC5Li0tTQEBATbr3dzc5O/vbxNjbxuX9pFfTO56e+Li4uTr62suISEh17qLAGDXkSNHJDlH/itILraHHAnAUSi0AeAmNnToUGVkZJjLwYMHHT0kAHAa5EgAjkKhDQBXEBQUJElKT0+3aU9PTzfXBQUFmTM/uc6fP6+jR4/axNjbxqV95BeTu94eDw8P+fj42CwAYIXcmWpnyH8FycX2kCMBOAqFNgBcQWhoqIKCgrRmzRqzLTMzU8nJyYqIiJAkRURE6Pjx40pJSTFjvv32W+Xk5KhBgwZmzLp163Tu3DkzJjExUdWqVVPp0qXNmEv7yY3J7QcAilLlypWdJv8VJBcDgDOh0AZw2zt58qR27NihHTt2SLp4050dO3bowIEDcnFxUb9+/TRu3DgtX75cu3fv1rPPPqvg4GBFR0dLkqpXr64WLVqoZ8+e2rx5s3744QfFxsaqU6dOCg4OliQ99dRTcnd3V48ePbR3714tWrRI06ZN04ABA8xx9O3bVwkJCZo0aZJ+/vlnjRo1Slu3blVsbGxRfyQAbhP28t+uXbskyanyX0HGAgDOhMd7Abjtbd26VQ8//LD5Ovfgr2vXrpo3b54GDRqkU6dOqVevXjp+/LgaNWqkhIQEeXp6mu+ZP3++YmNj1axZM7m6uqp9+/aaPn26ud7X11erV69WTEyMwsPDVbZsWY0YMcLmWbMPPvigFixYoGHDhunVV19V1apVtWzZMtWqVasIPgUAt6P88l8uZ8p/BRkLADgLCm0At72mTZvKMIx817u4uGjMmDEaM2ZMvjH+/v5asGDBFfu55557tH79+ivGdOjQQR06dLjygAHAIvbyX2Zmpnx9fSU5V/4ryFgAwFlw6jgAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALBQoRTahw4d0tNPP60yZcrIy8tLtWvX1tatW831hmFoxIgRKleunLy8vBQZGal9+/YVxlAAAAAAAChSlhfax44dU8OGDVW8eHF99dVX+vHHHzVp0iSVLl3ajJk4caKmT5+u+Ph4JScnq2TJkoqKitLZs2etHg4AAAAAAEXKzeoNTpgwQSEhIZo7d67ZFhoaav6/YRiaOnWqhg0bpnbt2kmSPvroIwUGBmrZsmXq1KmT1UMCAAAAAKDIWD6jvXz5ctWrV08dOnRQQECA7r33Xr377rvm+v379ystLU2RkZFmm6+vrxo0aKCkpCSrhwMAAAAAQJGyvND+/fffNXv2bFWtWlVff/21XnzxRb388sv68MMPJUlpaWmSpMDAQJv3BQYGmusul5WVpczMTJsFAAAAAABnZPmp4zk5OapXr57eeOMNSdK9996rPXv2KD4+Xl27dr2ubcbFxWn06NFWDhMAAAAAgEJh+Yx2uXLlVKNGDZu26tWr68CBA5KkoKAgSVJ6erpNTHp6urnuckOHDlVGRoa5HDx40OphAwAAAABgCcsL7YYNGyo1NdWm7ZdfflGlSpUkXbwxWlBQkNasWWOuz8zMVHJysiIiIuxu08PDQz4+PjYLAAAAAADOyPJCu3///tq0aZPeeOMN/frrr1qwYIHmzJmjmJgYSZKLi4v69euncePGafny5dq9e7eeffZZBQcHKzo62urhAMANu3DhgoYPH67Q0FB5eXmpSpUqGjt2rAzDMGMMw9CIESNUrlw5eXl5KTIyUvv27bPZztGjR9WlSxf5+PjIz89PPXr00MmTJ21idu3apcaNG8vT01MhISGaOHFikewjANjjbPlvyZIlCgsLk6enp2rXrq1Vq1YVzo4DwA2yvNCuX7++li5dqk8++US1atXS2LFjNXXqVHXp0sWMGTRokF566SX16tVL9evX18mTJ5WQkCBPT0+rhwMAN2zChAmaPXu2Zs6cqZ9++kkTJkzQxIkTNWPGDDNm4sSJmj59uuLj45WcnKySJUsqKipKZ8+eNWO6dOmivXv3KjExUStWrNC6devUq1cvc31mZqaaN2+uSpUqKSUlRW+++aZGjRqlOXPmFOn+AkAuZ8p/GzduVOfOndWjRw9t375d0dHRio6O1p49e4rmwwCAa2D5zdAkqXXr1mrdunW+611cXDRmzBiNGTOmMLoHAEtt3LhR7dq1U6tWrSRJlStX1ieffKLNmzdLujibM3XqVA0bNkzt2rWTJH300UcKDAzUsmXL1KlTJ/30009KSEjQli1bVK9ePUnSjBkz9Oijj+qtt95ScHCw5s+fr+zsbH3wwQdyd3dXzZo1tWPHDk2ePNnmgBQAiooz5b9p06apRYsWGjhwoCRp7NixSkxM1MyZMxUfH1/UHw0AXJHlM9oAcKt58MEHtWbNGv3yyy+SpJ07d2rDhg1q2bKlJGn//v1KS0tTZGSk+R5fX181aNBASUlJkqSkpCT5+fmZB5mSFBkZKVdXVyUnJ5sxTZo0kbu7uxkTFRWl1NRUHTt2rND3EwAu50z5Lykpyaaf3JjcfgDAmRTKjDYA3EqGDBmizMxMhYWFqVixYrpw4YJef/1185KYtLQ0SVJgYKDN+wIDA811aWlpCggIsFnv5uYmf39/m5jQ0NA828hdV7p06Txjy8rKUlZWlvk6MzPzRnYVAGw4U/5LS0u7Yj/2kCMBOAoz2gBwFYsXL9b8+fO1YMECbdu2TR9++KHeeustffjhh44emuLi4uTr62suISEhjh4SgFuIM+e/giBHAnAUCm0AuIqBAwdqyJAh6tSpk2rXrq1nnnlG/fv3V1xcnCQpKChIkpSenm7zvvT0dHNdUFCQjhw5YrP+/PnzOnr0qE2MvW1c2sflhg4dqoyMDHM5ePDgDe4tAPw/zpT/8ovJLz9K5EgAjkOhDQBXcfr0abm62qbLYsWKKScnR5IUGhqqoKAgrVmzxlyfmZmp5ORkRURESJIiIiJ0/PhxpaSkmDHffvutcnJy1KBBAzNm3bp1OnfunBmTmJioatWq2T1tXJI8PDzk4+NjswCAVZwp/0VERNj0kxuT24895EgAjkKhDQBX0aZNG73++utauXKl/vjjDy1dulSTJ0/WY489JunikxT69euncePGafny5dq9e7eeffZZBQcHKzo6WpJUvXp1tWjRQj179tTmzZv1ww8/KDY2Vp06dVJwcLAk6amnnpK7u7t69OihvXv3atGiRZo2bZoGDBjgqF0HcJtzpvzXt29fJSQkaNKkSfr55581atQobd26VbGxsUX+uQDA1XAzNAC4ihkzZmj48OHq06ePjhw5ouDgYPXu3VsjRowwYwYNGqRTp06pV69eOn78uBo1aqSEhAR5enqaMfPnz1dsbKyaNWsmV1dXtW/fXtOnTzfX+/r6avXq1YqJiVF4eLjKli2rESNG8GgvAA7jTPnvwQcf1IIFCzRs2DC9+uqrqlq1qpYtW6ZatWoVzYcBANeAQhsArsLb21tTp07V1KlT841xcXHRmDFjNGbMmHxj/P39tWDBgiv2dc8992j9+vXXO1QAsJSz5b8OHTqoQ4cOV4wBAGfAqeMAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAUACHDh3S008/rTJlysjLy0u1a9fW1q1bzfWGYWjEiBEqV66cvLy8FBkZqX379tls4+jRo+rSpYt8fHzk5+enHj166OTJkzYxu3btUuPGjeXp6amQkBBNnDixSPYPAPLjTPlvyZIlCgsLk6enp2rXrq1Vq1YVzk4DwA2i0AaAqzh27JgaNmyo4sWL66uvvtKPP/6oSZMmqXTp0mbMxIkTNX36dMXHxys5OVklS5ZUVFSUzp49a8Z06dJFe/fuVWJiolasWKF169apV69e5vrMzEw1b95clSpVUkpKit58802NGjVKc+bMKdL9BYBczpT/Nm7cqM6dO6tHjx7avn27oqOjFR0drT179hTNhwEA18DN0QMAAGc3YcIEhYSEaO7cuWZbaGio+f+GYWjq1KkaNmyY2rVrJ0n66KOPFBgYqGXLlqlTp0766aeflJCQoC1btqhevXqSpBkzZujRRx/VW2+9peDgYM2fP1/Z2dn64IMP5O7urpo1a2rHjh2aPHmyzQEpABQVZ8p/06ZNU4sWLTRw4EBJ0tixY5WYmKiZM2cqPj6+qD4SACgQZrQB4CqWL1+uevXqqUOHDgoICNC9996rd99911y/f/9+paWlKTIy0mzz9fVVgwYNlJSUJElKSkqSn5+feZApSZGRkXJ1dVVycrIZ06RJE7m7u5sxUVFRSk1N1bFjxwp7NwEgD2fKf0lJSTb95Mbk9gMAzoRCGwCu4vfff9fs2bNVtWpVff3113rxxRf18ssv68MPP5QkpaWlSZICAwNt3hcYGGiuS0tLU0BAgM16Nzc3+fv728TY28alfVwuKytLmZmZNgsAWMWZ8l9+MfnlR4kcCcBxOHUcAK4iJydH9erV0xtvvCFJuvfee7Vnzx7Fx8era9euDh1bXFycRo8e7dAxALh1OXP+KwhyJABHYUYbAK6iXLlyqlGjhk1b9erVdeDAAUlSUFCQJCk9Pd0mJj093VwXFBSkI0eO2Kw/f/68jh49ahNjbxuX9nG5oUOHKiMjw1wOHjx4PbsIAHY5U/7LLya//CiRIwE4DoU2AFxFw4YNlZqaatP2yy+/qFKlSpIu3hgoKChIa9asMddnZmYqOTlZERERkqSIiAgdP35cKSkpZsy3336rnJwcNWjQwIxZt26dzp07Z8YkJiaqWrVqNnf4vZSHh4d8fHxsFgCwijPlv4iICJt+cmNy+7GHHAnAUSi0AeAq+vfvr02bNumNN97Qr7/+qgULFmjOnDmKiYmRJLm4uKhfv34aN26cli9frt27d+vZZ59VcHCwoqOjJV2cAWrRooV69uypzZs364cfflBsbKw6deqk4OBgSdJTTz0ld3d39ejRQ3v37tWiRYs0bdo0DRgwwFG7DuA250z5r2/fvkpISNCkSZP0888/a9SoUdq6datiY2OL/HMBgKsp9EJ7/PjxZhLOdfbsWcXExKhMmTIqVaqU2rdvn+dUIABwFvXr19fSpUv1ySefqFatWho7dqymTp2qLl26mDGDBg3SSy+9pF69eql+/fo6efKkEhIS5OnpacbMnz9fYWFhatasmR599FE1atTI5hmxvr6+Wr16tfbv36/w8HD997//1YgRI3i0FwCHcab89+CDD5qFfp06dfTpp59q2bJlqlWrVtF8GABwDQr1ZmhbtmzRO++8o3vuucemvX///lq5cqWWLFkiX19fxcbG6vHHH9cPP/xQmMMBgOvWunVrtW7dOt/1Li4uGjNmjMaMGZNvjL+/vxYsWHDFfu655x6tX7/+uscJAFZzpvzXoUMHdejQ4coDBgAnUGgz2idPnlSXLl307rvv2lxbmJGRoffff1+TJ0/WI488ovDwcM2dO1cbN27Upk2bCms4AAAAAAAUiUIrtGNiYtSqVStFRkbatKekpOjcuXM27WFhYapYsaKSkpIKazgAAAAAABSJQjl1fOHChdq2bZu2bNmSZ11aWprc3d3l5+dn0x4YGKi0tDS728vKylJWVpb5OjMz09LxAgAAAABgFctntA8ePKi+fftq/vz5NjfBuBFxcXHy9fU1l5CQEEu2CwAAAACA1SwvtFNSUnTkyBHdd999cnNzk5ubm9auXavp06fLzc1NgYGBys7O1vHjx23el56erqCgILvbHDp0qDIyMszl4MGDVg8bAAAAAABLWH7qeLNmzbR7926btm7duiksLEyDBw9WSEiIihcvrjVr1qh9+/aSpNTUVB04cEARERF2t+nh4SEPDw+rhwoAAAAAgOUsL7S9vb3zPM+wZMmSKlOmjNneo0cPDRgwQP7+/vLx8dFLL72kiIgIPfDAA1YPBwAAAACAIlWoz9HOz5QpU+Tq6qr27dsrKytLUVFRevvttx0xFAAAAAAALFUkhfb3339v89rT01OzZs3SrFmziqJ7AAAAAACKTKE9RxsAAAAAgNsRhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAACAAhk/frxcXFzUr18/s+3s2bOKiYlRmTJlVKpUKbVv317p6ek27ztw4IBatWqlEiVKKCAgQAMHDtT58+dtYr7//nvdd9998vDw0F133aV58+bl6X/WrFmqXLmyPD091aBBA23evLkwdhMAbhiFNgBcI0cfaAKAI2zZskXvvPOO7rnnHpv2/v3768svv9SSJUu0du1aHT58WI8//ri5/sKFC2rVqpWys7O1ceNGffjhh5o3b55GjBhhxuzfv1+tWrXSww8/rB07dqhfv356/vnn9fXXX5sxixYt0oABAzRy5Eht27ZNderUUVRUlI4cOVL4Ow8A14hCGwCugaMPNAHAEU6ePKkuXbro3XffVenSpc32jIwMvf/++5o8ebIeeeQRhYeHa+7cudq4caM2bdokSVq9erV+/PFH/e9//1PdunXVsmVLjR07VrNmzVJ2drYkKT4+XqGhoZo0aZKqV6+u2NhYPfHEE5oyZYrZ1+TJk9WzZ09169ZNNWrUUHx8vEqUKKEPPvigaD8MACgACm0AKCBnONAEAEeIiYlRq1atFBkZadOekpKic+fO2bSHhYWpYsWKSkpKkiQlJSWpdu3aCgwMNGOioqKUmZmpvXv3mjGXbzsqKsrcRnZ2tlJSUmxiXF1dFRkZacYAgDOh0AaAAnL0gaY9WVlZyszMtFkAwEoLFy7Utm3bFBcXl2ddWlqa3N3d5efnZ9MeGBiotLQ0M+bS3Je7PnfdlWIyMzN15swZ/fPPP7pw4YLdmNxt2EOOBOAoFNoAUADOcKBpT1xcnHx9fc0lJCTkuvYPAOw5ePCg+vbtq/nz58vT09PRw7lm5EgAjkKhDQBX4cwHmkOHDlVGRoa5HDx40NFDAnALSUlJ0ZEjR3TffffJzc1Nbm5uWrt2raZPny43NzcFBgYqOztbx48ft3lfenq6goKCJElBQUF5bg6Z+/pqMT4+PvLy8lLZsmVVrFgxuzG527CHHAnAUSi0AeAqnOVA0x4PDw/5+PjYLABglWbNmmn37t3asWOHudSrV09dunQx/7948eJas2aN+Z7U1FQdOHBAERERkqSIiAjt3r3b5u7giYmJ8vHxUY0aNcyYS7eRG5O7DXd3d4WHh9vE5OTkaM2aNWaMPeRIAI7i5ugBAICzyz3QvFS3bt0UFhamwYMHKyQkxDzQbN++vST7B5qvv/66jhw5ooCAAEn2DzRXrVpl08+lB5oAUNS8vb1Vq1Ytm7aSJUuqTJkyZnuPHj00YMAA+fv7y8fHRy+99JIiIiL0wAMPSJKaN2+uGjVq6JlnntHEiROVlpamYcOGKSYmRh4eHpKkF154QTNnztSgQYPUvXt3ffvtt1q8eLFWrlxp9jtgwAB17dpV9erV0/3336+pU6fq1KlT6tatWxF9GgBQcBTaAHAVznSgCQDOZsqUKXJ1dVX79u2VlZWlqKgovf322+b6YsWKacWKFXrxxRcVERGhkiVLqmvXrhozZowZExoaqpUrV6p///6aNm2aKlSooPfee09RUVFmzJNPPqm///5bI0aMUFpamurWrauEhIQ897YAAGdAoQ0AFiiqA00AcLTvv//e5rWnp6dmzZqlWbNm5fueSpUq5Tlj53JNmzbV9u3brxgTGxur2NjYAo8VAByFQhsAroMjDzQBAADg3LgZGgAAAAAAFqLQBgAAAADAQhTaAAAAAABYiEIbAAAAAAALUWgDAAAAAGAhCm0AAAAAACxEoQ0AAAAAgIUotAEAAAAAsBCFNgAAAAAAFqLQBgAAAADAQhTaAAAAAABYiEIbAAAAAAALUWgDAAAAAGAhCm0AAAAAACxEoQ0AAAAAgIUsL7Tj4uJUv359eXt7KyAgQNHR0UpNTbWJOXv2rGJiYlSmTBmVKlVK7du3V3p6utVDAQAAAACgyFleaK9du1YxMTHatGmTEhMTde7cOTVv3lynTp0yY/r3768vv/xSS5Ys0dq1a3X48GE9/vjjVg8FAAAAAIAi52b1BhMSEmxez5s3TwEBAUpJSVGTJk2UkZGh999/XwsWLNAjjzwiSZo7d66qV6+uTZs26YEHHrB6SAAAAAAAFJlCv0Y7IyNDkuTv7y9JSklJ0blz5xQZGWnGhIWFqWLFikpKSrK7jaysLGVmZtosAAAAAAA4o0IttHNyctSvXz81bNhQtWrVkiSlpaXJ3d1dfn5+NrGBgYFKS0uzu524uDj5+vqaS0hISGEOGwAAAACA61aohXZMTIz27NmjhQsX3tB2hg4dqoyMDHM5ePCgRSMEAAAAAMBall+jnSs2NlYrVqzQunXrVKFCBbM9KChI2dnZOn78uM2sdnp6uoKCguxuy8PDQx4eHoU1VAAAAAAALGP5jLZhGIqNjdXSpUv17bffKjQ01GZ9eHi4ihcvrjVr1phtqampOnDggCIiIqweDgAAAAAARcryGe2YmBgtWLBAX3zxhby9vc3rrn19feXl5SVfX1/16NFDAwYMkL+/v3x8fPTSSy8pIiKCO44DAAAAAG56lhfas2fPliQ1bdrUpn3u3Ll67rnnJElTpkyRq6ur2rdvr6ysLEVFRentt9+2eigAAAAAABQ5ywttwzCuGuPp6alZs2Zp1qxZVncPAAAAAIBDFfpztAEAAAAAuJ1QaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGgAKIi4tT/fr15e3trYCAAEVHRys1NdUm5uzZs4qJiVGZMmVUqlQptW/fXunp6TYxBw4cUKtWrVSiRAkFBARo4MCBOn/+vE3M999/r/vuu08eHh666667NG/evMLePQCwy9ly36xZs1S5cmV5enqqQYMG2rx5s+X7DABWoNAGgAJYu3atYmJitGnTJiUmJurcuXNq3ry5Tp06Zcb0799fX375pZYsWaK1a9fq8OHDevzxx831Fy5cUKtWrZSdna2NGzfqww8/1Lx58zRixAgzZv/+/WrVqpUefvhh7dixQ/369dPzzz+vr7/+ukj3FwAk58p9ixYt0oABAzRy5Eht27ZNderUUVRUlI4cOVI0HwYAXAM3Rw8AAG4GCQkJNq/nzZungIAApaSkqEmTJsrIyND777+vBQsW6JFHHpEkzZ07V9WrV9emTZv0wAMPaPXq1frxxx/1zTffKDAwUHXr1tXYsWM1ePBgjRo1Su7u7oqPj1doaKgmTZokSapevbo2bNigKVOmKCoqqsj3G8DtzZly3+TJk9WzZ09169ZNkhQfH6+VK1fqgw8+0JAhQ4rwUwGAq2NGGwCuQ0ZGhiTJ399fkpSSkqJz584pMjLSjAkLC1PFihWVlJQkSUpKSlLt2rUVGBhoxkRFRSkzM1N79+41Yy7dRm5M7jYul5WVpczMTJsFAAqLo3Jfdna2UlJSbGJcXV0VGRmZb36UyJEAHIdCGwCuUU5Ojvr166eGDRuqVq1akqS0tDS5u7vLz8/PJjYwMFBpaWlmzKUHmrnrc9ddKSYzM1NnzpzJM5a4uDj5+vqaS0hIiCX7CACXc2Tu++eff3ThwgW7MbnbsIccCcBRKLQB4BrFxMRoz549WrhwoaOHoqFDhyojI8NcDh486OghAbhFOVPuKyhyJABH4RptALgGsbGxWrFihdatW6cKFSqY7UFBQcrOztbx48dtZnbS09MVFBRkxlx+h9zcO/NeGnP53XrT09Pl4+MjLy+vPOPx8PCQh4eHJfsGAPlxdO4rVqyYihUrZjcmdxv2kCMBOAoz2gBQAIZhKDY2VkuXLtW3336r0NBQm/Xh4eEqXry41qxZY7alpqbqwIEDioiIkCRFRERo9+7dNnfITUxMlI+Pj2rUqGHGXLqN3JjcbQBAUXKW3Ofu7q7w8HCbmJycHK1Zs4b8CMApMaMNAAUQExOjBQsW6IsvvpC3t7d5TaCvr6+8vLzk6+urHj16aMCAAfL395ePj49eeuklRURE6IEHHpAkNW/eXDVq1NAzzzyjiRMnKi0tTcOGDVNMTIw54/LCCy9o5syZGjRokLp3765vv/1Wixcv1sqVKx227wBuX86U+wYMGKCuXbuqXr16uv/++zV16lSdOnXKvAs5ADgTCm0AKIDZs2dLkpo2bWrTPnfuXD333HOSpClTpsjV1VXt27dXVlaWoqKi9Pbbb5uxxYoV04oVK/Tiiy8qIiJCJUuWVNeuXTVmzBgzJjQ0VCtXrlT//v01bdo0VahQQe+99x6P9gLgEM6U+5588kn9/fffGjFihNLS0lS3bl0lJCTkuUEaADgDCm0AKADDMK4a4+npqVmzZmnWrFn5xlSqVEmrVq264naaNm2q7du3X/MYAcBqzpb7YmNjFRsbe9UxAYCjcY02AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwEIU2AAAAAAAWotAGAAAAAMBCFNoAAAAAAFiIQhsAAAAAAAtRaAMAAAAAYCEKbQAAAAAALEShDQAAAACAhSi0AQAAAACwkEML7VmzZqly5cry9PRUgwYNtHnzZkcOBwCcBvkRAOwjPwK4GTis0F60aJEGDBigkSNHatu2bapTp46ioqJ05MgRRw0JAJwC+REA7CM/ArhZOKzQnjx5snr27Klu3bqpRo0aio+PV4kSJfTBBx84akgA4BTIjwBgH/kRwM3CzRGdZmdnKyUlRUOHDjXbXF1dFRkZqaSkpDzxWVlZysrKMl9nZGRIkjIzM6+p35ys09c5Yvvs9X8z9lFU/dwqfRRVP47qo6j6Kap9uVq8YRiWjuNGkR9vrI+i6udm7KOo+rlV+iiqfsiPBXet+VGyJkfejD/zournVumjqPphX5yvj/z6uVpsgfKj4QCHDh0yJBkbN260aR84cKBx//3354kfOXKkIYmFhYXF8uXgwYNFlfoKhPzIwsLiLMvNnh8NgxzJwsJSOEtB8qNDZrSv1dChQzVgwADzdU5Ojo4ePaoyZcrIxcXFgSO7PpmZmQoJCdHBgwfl4+Pj6OHcEPbF+dwq+yEV7r4YhqETJ04oODjY0u0WNfKj82JfnNOtsi/kx4IhRzqnW2U/JPbFWRXWvlxLfnRIoV22bFkVK1ZM6enpNu3p6ekKCgrKE+/h4SEPDw+bNj8/v8IcYpHw8fG56f8R52JfnM+tsh9S4e2Lr6+v5du8UeTHi/j365zYF+dDfsw/P0rkSGd3q+yHxL44q8LYl4LmR4fcDM3d3V3h4eFas2aN2ZaTk6M1a9YoIiLCEUMCAKdAfgQA+8iPAG4mDjt1fMCAAeratavq1aun+++/X1OnTtWpU6fUrVs3Rw0JAJwC+REA7CM/ArhZOKzQfvLJJ/X3339rxIgRSktLU926dZWQkKDAwEBHDanIeHh4aOTIkXlOZboZsS/O51bZD+nW2pdrQX68NX7m7ItzulX25VbZj2t1O+dH6db5ud8q+yGxL87KGfbFxTCc7NkNAAAAAADcxBxyjTYAAAAAALcqCm0AAAAAACxEoQ0AAAAAgIUotAEAAAAAsBCFdhGKi4tT/fr15e3trYCAAEVHRys1NdXRw7ph48ePl4uLi/r16+fooVyXQ4cO6emnn1aZMmXk5eWl2rVra+vWrY4e1jW7cOGChg8frtDQUHl5ealKlSoaO3asbob7Ha5bt05t2rRRcHCwXFxctGzZMpv1hmFoxIgRKleunLy8vBQZGal9+/Y5ZrAoFORH50R+dDzyI27V/CiRI50B+bHwUGgXobVr1yomJkabNm1SYmKizp07p+bNm+vUqVOOHtp127Jli9555x3dc889jh7KdTl27JgaNmyo4sWL66uvvtKPP/6oSZMmqXTp0o4e2jWbMGGCZs+erZkzZ+qnn37ShAkTNHHiRM2YMcPRQ7uqU6dOqU6dOpo1a5bd9RMnTtT06dMVHx+v5ORklSxZUlFRUTp79mwRjxSFhfzofMiPzoH8iFsxP0rkSGdBfixEBhzmyJEjhiRj7dq1jh7KdTlx4oRRtWpVIzEx0XjooYeMvn37OnpI12zw4MFGo0aNHD0MS7Rq1cro3r27Tdvjjz9udOnSxUEjuj6SjKVLl5qvc3JyjKCgIOPNN980244fP254eHgYn3zyiQNGiKJAfnQ88qPzIT/CMG7+/GgY5EhnQn4sPMxoO1BGRoYkyd/f38EjuT4xMTFq1aqVIiMjHT2U67Z8+XLVq1dPHTp0UEBAgO699169++67jh7WdXnwwQe1Zs0a/fLLL5KknTt3asOGDWrZsqWDR3Zj9u/fr7S0NJt/Z76+vmrQoIGSkpIcODIUJvKj45EfnR/58fZ0s+dHiRzpTMiPhcetSHpBHjk5OerXr58aNmyoWrVqOXo412zhwoXatm2btmzZ4uih3JDff/9ds2fP1oABA/Tqq69qy5Ytevnll+Xu7q6uXbs6enjXZMiQIcrMzFRYWJiKFSumCxcu6PXXX1eXLl0cPbQbkpaWJkkKDAy0aQ8MDDTX4dZCfnQO5EfnR368/dzs+VEiRzob8mPhodB2kJiYGO3Zs0cbNmxw9FCu2cGDB9W3b18lJibK09PT0cO5ITk5OapXr57eeOMNSdK9996rPXv2KD4+/qZKkpK0ePFizZ8/XwsWLFDNmjW1Y8cO9evXT8HBwTfdvuD2Rn50DuRHwPnczPlRIkc6I/Jj4eHUcQeIjY3VihUr9N1336lChQqOHs41S0lJ0ZEjR3TffffJzc1Nbm5uWrt2raZPny43NzdduHDB0UMssHLlyqlGjRo2bdWrV9eBAwccNKLrN3DgQA0ZMkSdOnVS7dq19cwzz6h///6Ki4tz9NBuSFBQkCQpPT3dpj09Pd1ch1sH+dF5kB+dH/nx9nKz50eJHOmMyI+Fh0K7CBmGodjYWC1dulTffvutQkNDHT2k69KsWTPt3r1bO3bsMJd69eqpS5cu2rFjh4oVK+boIRZYw4YN8zwi45dfflGlSpUcNKLrd/r0abm62v5KFytWTDk5OQ4akTVCQ0MVFBSkNWvWmG2ZmZlKTk5WRESEA0cGK5EfnQ/50fmRH28Pt0p+lMiRzoj8WHg4dbwIxcTEaMGCBfriiy/k7e1tXh/g6+srLy8vB4+u4Ly9vfNcF1SyZEmVKVPmprteqH///nrwwQf1xhtvqGPHjtq8ebPmzJmjOXPmOHpo16xNmzZ6/fXXVbFiRdWsWVPbt2/X5MmT1b17d0cP7apOnjypX3/91Xy9f/9+7dixQ/7+/qpYsaL69euncePGqWrVqgoNDdXw4cMVHBys6Ohoxw0aliI/Oh/yo3MgP+JWyY8SOdIZkR8LUZHc2xyGYVy87by9Ze7cuY4e2g27WR/NYBiG8eWXXxq1atUyPDw8jLCwMGPOnDmOHtJ1yczMNPr27WtUrFjR8PT0NO68807jtddeM7Kyshw9tKv67rvv7P5udO3a1TCMi49oGD58uBEYGGh4eHgYzZo1M1JTUx07aFiK/OicyI+OR37ErZwfDYMc6Wjkx8LjYhiGUejVPAAAAAAAtwmu0QYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgIQptAAAAAAAsRKENAAAAAICFKLQBAAAAALAQhTYAAAAAABai0AYAAAAAwEIU2gAAAAAAWIhCGwAAAAAAC1FoAwAAAABgof8PZ3WDir4vwSEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "problem = run_scenario(fm, scenario_name=\"base-cgen_gs\", print_df=True, workers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formulate and solve optimization models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `run_scenario` function (defined in the local `util` module) to formulate and solve various flavours of pre-defined scenarios. If only provided with an `fm` instance as an argument, the function will run the `base` scenario, which maximizes even-flow harvest volume. \n",
    "\n",
    "The default HiGHS solver bindings will be used unless otherwise specified. Solver output is turned off by default, but we can turn it on by passing `verbose=True` argument to the `run_scenario` function that gets passed to the `ws3.opt.Problem.solve` method. This will show what the standard console output from each solver looks like, and let us confirm that the LP problem definition (matrix shape and number of nonzero coefficients) is corectly sent to each solver backend, that the objective function value for the optimal solution is the same for all solver backends, and the time each solver takes to solve our problem (negligible, because this problem is very small). \n",
    "\n",
    "The different solver backends have pros and cons. \n",
    "\n",
    "The default HiGHS bindings work well, use a fully open-source codebase (so are free to use and free to distribute), and have built-in parallel dual simplex algorithms that can run on up to 8 cores. The `highspy` bindnings also allow `ws3` to pass the LP problem definition to the solver backend in an efficient way that keeps problem-building time relatively low, and provide full access to advanced HiGHS features for maximum flexibility in tweaking solver behaviour.\n",
    "\n",
    "The PuLP bindings run more slowly than the default HiGHS bindings (largely because PuLP is a middleware solution that provides a standardized interface to multiple solver backends, so `ws3` must first pass the LP problem to PuLP, and PuLP then has to build its own internal representation of the problem before passing it to a solver backend, and then the backend needs to build its own internal represenation of the problem before solving it). The PuLP bindings are interesting because they provide a single interface that is not specific to a particular solver backend (so it can be used with any solver that PuLP supports). The PuLP bindings are also fully open source (so free to use and free to distribute). `ws3` PuLP binding currently support two solver backends (the default `CBC` and `HiGHS`, which can be set via the `solver_backend` parameter. It is relatively easy to add support for more solvers, so eventually we plan to support more solvers through the PuLP bindings. \n",
    "\n",
    "The Gurobi solver is one of the fastest and most powerful solvers available, so this might be a good choice for larger more difficult LP problems. Gurobi is closed source commercial software (so not free to use or distribute). The `gurobipy` Python package allows solving of small problems without requiring an active softwarwe licence to be installed and detected, which is how we can demonstrate Gurobi use here. Gurobi is quite generous with providing an unlimited number of full no-cost licenses for academic use, so if you are a researcher you should be able to use Gurobi for full-sized problems in `ws3` (although licence acquisition and setup is a bit complicated and finicky)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario\n",
      "Running HiGHS 1.11.0 (git hash: 364c83a): Copyright (c) 2025 HiGHS under MIT licence terms\n",
      "LP   has 72 rows; 305 cols; 3425 nonzeros\n",
      "Coefficient ranges:\n",
      "  Matrix [3e-02, 4e+04]\n",
      "  Cost   [2e+01, 4e+04]\n",
      "  Bound  [1e+00, 1e+00]\n",
      "  RHS    [1e+00, 1e+00]\n",
      "Presolving model\n",
      "60 rows, 297 cols, 3201 nonzeros  0s\n",
      "Dependent equations search running on 24 equations with time limit of 1000.00s\n",
      "Dependent equations search removed 0 rows and 0 nonzeros in 0.00s (limit = 1000.00s)\n",
      "60 rows, 297 cols, 3201 nonzeros  0s\n",
      "Presolve : Reductions: rows 60(-12); columns 297(-8); elements 3201(-224)\n",
      "Solving the presolved LP\n",
      "Using EKK parallel dual simplex solver - SIP with concurrency of 8\n",
      "  Iteration        Objective     Infeasibilities num(sum)\n",
      "          0     0.0000000000e+00 Ph1: 0(0) 0s\n",
      "        115    -1.9380707345e+05 Pr: 0(0); Du: 0(1.02318e-12) 0s\n",
      "Solving the original LP from the solution after postsolve\n",
      "Model status        : Optimal\n",
      "Simplex   iterations: 115\n",
      "Objective value     : -1.9380707345e+05\n",
      "P-D objective error :  1.5016869536e-16\n",
      "HiGHS run time      :          0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, df, p = run_scenario(fm, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the same scenario using the CBC solver by way of PuLP bindings. The results should be identical to the he previous scenario (run using default HiGHS solver)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario\n",
      "Welcome to the CBC MILP Solver \n",
      "Version: 2.10.3 \n",
      "Build Date: Dec 15 2019 \n",
      "\n",
      "command line - /home/gep/projects/ws3/.venv/lib/python3.12/site-packages/pulp/apis/../solverdir/cbc/linux/i64/cbc /tmp/583d20d6781646168ae2a94d98328e95-pulp.mps -max -threads 0 -timeMode elapsed -branch -printingOptions all -solution /tmp/583d20d6781646168ae2a94d98328e95-pulp.sol (default strategy 1)\n",
      "At line 2 NAME          MODEL\n",
      "At line 3 ROWS\n",
      "At line 77 COLUMNS\n",
      "At line 3776 RHS\n",
      "At line 3849 BOUNDS\n",
      "At line 4155 ENDATA\n",
      "Problem MODEL has 72 rows, 305 columns and 3425 elements\n",
      "Coin0008I MODEL read with 0 errors\n",
      "Option for timeMode changed from cpu to elapsed\n",
      "Presolve 60 (-12) rows, 273 (-32) columns and 3177 (-248) elements\n",
      "0  Obj -0 Dual inf 2157186.5 (273)\n",
      "31  Obj 356474.73 Primal inf 656.98798 (31)\n",
      "62  Obj 209236.84 Primal inf 115.74605 (25)\n",
      "96  Obj 196675.93 Primal inf 32.967855 (17)\n",
      "127  Obj 193916.47 Primal inf 1.388767 (7)\n",
      "137  Obj 193807.07\n",
      "Optimal - objective value 193807.07\n",
      "After Postsolve, objective 193807.07, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Optimal objective 193807.0735 - 137 iterations time 0.012, Presolve 0.00\n",
      "Option for printingOptions changed from normal to all\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, df, p = run_scenario(fm, solver=ws3.opt.SOLVER_PULP, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the same scenario using the Gurobi solver bindings. The results should be identical to the he previous scenario (run using default HiGHS solver)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario\n",
      "Restricted license - for non-production use only - expires 2026-11-23\n",
      "Gurobi Optimizer version 12.0.3 build v12.0.3rc0 (linux64 - \"Ubuntu 24.04.3 LTS\")\n",
      "\n",
      "CPU model: Intel(R) Xeon(R) Gold 6254 CPU @ 3.10GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 36 physical cores, 72 logical processors, using up to 32 threads\n",
      "\n",
      "Optimize a model with 72 rows, 305 columns and 3425 nonzeros\n",
      "Model fingerprint: 0x4d7c1bc0\n",
      "Coefficient statistics:\n",
      "  Matrix range     [3e-02, 4e+04]\n",
      "  Objective range  [2e+01, 4e+04]\n",
      "  Bounds range     [0e+00, 0e+00]\n",
      "  RHS range        [1e+00, 1e+00]\n",
      "Presolve removed 12 rows and 32 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 60 rows, 273 columns, 3177 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    2.2455285e+05   1.160851e+04   0.000000e+00      0s\n",
      "      75    1.9380707e+05   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 75 iterations and 0.02 seconds (0.00 work units)\n",
      "Optimal objective  1.938070735e+05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, df, p = run_scenario(fm, solver=ws3.opt.SOLVER_GUROBI, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the ``base-cgen_ha`` scenario, which maximizes even-flow harvest volume plus adds a maximum (100 ha) harvest area constraint on period 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus harvest area constraints\n",
      "   period    oha           ohv            ogs\n",
      "0       1  100.0  14796.128110  133686.175050\n",
      "1       2  105.0  14104.788476  130186.412830\n",
      "2       3  105.0  14056.321705  125559.065143\n",
      "3       4  105.0  14056.321705  121504.669660\n",
      "4       5  105.0  14521.334872  118497.889467\n",
      "5       6  105.0  15535.934516  114355.356640\n",
      "6       7  105.0  15535.934516  108958.499863\n",
      "7       8   95.0  15535.934516  101915.256834\n",
      "8       9  105.0  15535.934516   94001.947263\n",
      "9      10  105.0  15535.934516   85148.810804\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, df, p = run_scenario(fm, scenario_name=\"base-cgen_ha\", print_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we run the ``basease-cgen_hv`` scenario, which maximizes even-flow harvest volume plus adds a maximum harvest volume (10 000 $m^3$ per 10-year period) constraint on period 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus harvest volume constraints\n",
      "   period        oha      ohv            ogs\n",
      "0       1  78.730132  10000.0  139328.678709\n",
      "1       2  74.793625  10500.0  140264.445013\n",
      "2       3  77.290264  10500.0  139665.672672\n",
      "3       4  81.534783  10500.0  138957.035504\n",
      "4       5  81.409579  10500.0  139777.840122\n",
      "5       6  78.426734  10500.0  140249.898543\n",
      "6       7  81.699726  10500.0  139167.789849\n",
      "7       8  77.691454  10500.0  137397.593486\n",
      "8       9  82.666639  10500.0  136223.388524\n",
      "9      10  82.666639  10500.0  133975.635313\n"
     ]
    },
    {
     "data": {
      "image/png": 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6l19+EQBI/p/07du3zO8lhYWFglKpFLy9vcW2FStWCACEuLg4se3evXuCo6NjmX2WZ9++fYKhoaFgaGgoqFQqYcaMGcLevXuFoqKiMrHm5uaSuUjHy8tLMDY2Fn777Tex7ebNm4KFhYXQp08fsS0kJEQAIPz4449l9qGbc3SfRWxsrPDPP/8Iffv2FRo3biycOnXqicchCIKwf/9+AYCwc+fOMn26n4nJycli2969ewUAgpmZmeT3zy+//PKxn51arRbnNmNjY2HSpEmSnxcPHz8AYfny5U/Nuy7g5dw1JCIiAgkJCWU23ZmMhz18VkJ3dqdv3774/fffkZeXJ4l1cHCAWq2WtL366qvo3Lkztm7dKraVlJTg+++/x9ChQ8X9x8bGQqFQ4PXXX8fff/8tbi4uLmjQoIH4DaDurMWuXbvKPav0JLdu3cKBAwcwcuRI/PPPP+IY//vf/6BWq3H58mXxEsM9e/age/fukm/6mzRpIl6G9DQPf24FBQX4+++/xftJTp48WSb+0QUbfvzxR5SWlmLkyJGSz0OpVKJNmzaSb0QfHis/Px9///03evToAUEQnnr5jO7yct1Zo0e5u7uL34wCD852yeVy/P777+WOX1xcjP/9739o3bo1LC0tyz1W3VhPutSI6Hlwjqv6Oe6dd95Beno6fvvtN7Ft69atMDExwbBhwwA8uCw9JycHkydPltyf6OnpCUdHR+zevbtCx/c0D99fZ2hoCFdXVwiCAD8/P7Hd0tISbdu2lcxhsbGxcHJygqOjo+TvRndf8dPOQP3vf/977BwKAH5+fpKrj9zc3Mrkpcv34bx02rVrh4SEBMTFxWHGjBkwNzcvszq3TsOGDXHv3j3cvXv3iTkTVYRWqwWAcp/g0a9fPzRp0kTcIiIiysR89NFHktd79uyBoaEhpk6dKmn/17/+BUEQxMuyu3TpggYNGiApKQnAgzPOusuVT548ibt370IQBBw5cgS9e/d+6nE0aNAA7733nvja2NgYr732muT/XXx8PJo1ayae5QYeLNyn7+PrXn/9daSkpODNN9/Er7/+irCwMKjVajRr1qzc22oeVVJSgn379sHLywuvvPKK2N60aVO8++67OHLkiPj38cMPP6BTp07iFUYPe/SKx7y8PHh4eODixYs4ePAgOnfu/NRcnvY7Yrt27aBSqcTXujPkAwYMQIsWLcq0lze/LVu2DPv27cPGjRvRvXt3FBUV4f79+2Xi+LujFC/nriGvvfYaXF1dy7Q3bNiwzD/Oo0ePYt68eUhJSSnzQzkvLw8KhUJ87eDgUO5477zzDj755BPcuHEDzZo1w8GDB5GTk4N33nlHjLl8+TLy8vIk94s8TLcYQ9++feHt7Y0FCxZg1apV6NevH7y8vPDuu+/CxMTkicd95coVCIKAuXPnYu7cuY8dp1mzZvjjjz/KvWSpbdu2TxxD59atW1iwYAG2bNlSZiGJR38xB8p+dpcvX4YgCGjTpk25+69Xr57456ysLISEhGDHjh1l7q0sb6zyCI+5D+/hSVCnYcOGknHu3buH0NBQREVF4caNG5J9lTe+rv9FfyYs1V6c46p+jhsxYgSCg4OxdetWfPLJJxAEAbGxseI9fADEe6bL26ejo2O5i2Y9j0fnK4VCAVNT0zKXNCoUCsn6FJcvX8aFCxfEe80fpc9iQI+bQx+XFwDJvaO69kfncACQy+Xi/dbDhg1DTEwMhg0bhpMnT6JTp07l5sH5lSqThYUFAJT75Y3uiRvZ2dmSAlXHyMgIzZs3l7T98ccfsLW1Ffer4+TkJPYDD75cUqlUOHz4MIAHRXTv3r3Rq1cvlJSUIDU1FTY2Nrh165ZeRXTz5s3L/N9o2LCh5NFcf/zxB1q1alUmriIr73fr1g0//vgjioqK8Ouvv2L79u1YtWoV3n77bWRkZKBdu3aPfe9ff/2Fu3fvljtvOjk5obS0FNevX0f79u3x22+/wdvbW6+cAgMDUVBQgFOnTqF9+/Z6Hwug/++IT5rbAJQ7vz1czL/33nvo2rUrPvjggzLPtebcJsUiupb77bffMHDgQDg6OmLlypWws7ODsbEx9uzZg1WrVpVZgOZx99K98847mD17NmJjYxEYGIht27ZBoVBIVt4rLS2FtbU1oqOjy92H7pcb3QPjU1NTsXPnTuzduxfjxo3DihUrkJqa+sTnHOvy/fjjj8ucTdKprMeTjBw5EsnJyZg+fTo6d+6MBg0aoLS0tMxjSXQe/exKS0vFRTIMDQ3LxOuOs6SkBK+//jpu3bqFmTNnwtHREebm5rhx4wY++OCDpy4SpLuv+fbt22V+yAEod2xAOqFOmTIFUVFRCAwMhEqlgkKhgEwmg4+PT7nj6ybR8u7VIapOnOOena2tLXr37o1t27bhk08+QWpqKrKysrB8+fJK2f/jflEqKSl57HvKm6/0mcNKS0vh7OyMlStXlhv76C+Ej2rUqFG5vxw+LYfy2p9UjOsMHz4c77//PrZs2VKmiL59+zbq16/PVeOpUikUCjRt2hRnz54t06f7Mq68xUQBwMTE5Kkrdj9Jr169xGcEHz58GJ9++iksLS3RoUMHHD58GDY2NgCgVxGtz3xQmYyNjdGtWzd069YNr776KsaOHYvY2FjMmzevSsZ7kmHDhmHLli1YtmwZvv32W73+Th7+HbE8FZnbgKd/zsbGxnjzzTexbNky3Lt3TzKP8XdHKRbRtdzOnTtRWFiIHTt2SL5t0ndxBR0HBwe89tpr2Lp1KwICAvDjjz/Cy8tLclalVatW2L9/P3r27KnXD//u3buje/fuWLJkCWJiYuDr64stW7Zg/Pjxj/3lS3dZTL169Z64GjXwYGGIy5cvl2nPzMx8am63b99GYmIiFixYgJCQELG9vP09TqtWrSAIAhwcHPDqq68+Nu7MmTO4dOkSNm/eLFmMJiEhQa9xHB0dAQBXr16Fs7Oz3vk97Pvvv8eYMWMkK94WFBSUu7qibqzGjRs/9qwPUXXhHPdsc5zOO++8g8mTJyMzMxNbt25F/fr1MXToUMkYun0++silzMzMxy7EBfz/S/cenUeq4hm0rVq1wq+//oqBAwc+01kOR0dH/PDDD5We1+MUFhaitLS03Ct9rl69Kp7NI6pMnp6e+Prrr3Hs2LFyF7WrCHt7e+zfvx///POP5Gz0xYsXxX6d3r17o6ioCN999x1u3LghFst9+vQRi+hXX31VLKafl729Pc6fPw9BECTzwZUrV55rv7qro/773/+KbeXNN02aNEH9+vXLnYsvXrwIAwMD8Yu9Vq1alfvFRnm8vLzg4eGBDz74ABYWFuU+heJRD/+OWF3u3bsHQRDwzz//SH5W6nLg/PYA74mu5XTfJD16eW5UVFSF9/XOO+8gNTUV33zzDf7++2/JZY7AgzO3JSUlWLRoUZn36lYmBR4UqI9+k6W7FET3KBLdatCP/vJlbW2Nfv364csvv5RMYjp//fWX+Oc33ngDqampOHbsmKT/cWeRHlbe5wYA4eHhT32vzvDhw2FoaIgFCxaU2Y8gCOKliOWNJQgCVq9erdc4Li4uMDY2xokTJ/TO7VGGhoZlcly7du1jzxilp6dL7qEhqimc455tjtPx9vaGoaEhvvvuO8TGxmLIkCGS57+6urrC2toakZGRkkdF/fzzz7hw4QI8PT0fu2+5XI7GjRuL90LqfPHFF3rnp6+RI0fixo0b+Oqrr8r03bt3D/n5+U98v0qlwu3bt8u93+955Obmlntf/Ndffw0A5d6ycPLkSfTo0aNS8yACgBkzZqB+/foYN24csrOzy/RX5GzuG2+8gZKSEqxbt07SvmrVKshkMnGlbODBme569eph+fLlsLKyEi9F7t27N1JTU3Ho0CG9zkLrS61W48aNG5L7lwsKCsqdH8rzyy+/lPtZ7NmzB4D09hZzc/My87ihoSE8PDzw008/Sc7uZ2dnIyYmBr169RJvmfH29hYvF39UeTmMHj0aa9asQWRkJGbOnPnUY2nWrBns7Oye63fExynvNpnc3Fz88MMPsLOzK3PrU3p6OmQyGX9//D88E13LeXh4wNjYGEOHDsWkSZNw584dfPXVV7C2ti73F7QnGTlyJD7++GN8/PHHsLKyKnOWpG/fvpg0aRJCQ0ORkZEBDw8P1KtXD5cvX0ZsbCxWr16Nt99+G5s3b8YXX3yBt956C61atcI///yDr776CnK5HG+88QaAB5dctmvXDlu3bsWrr74KKysrdOjQAR06dEBERAR69eoFZ2dnTJgwAa+88gqys7ORkpKCP//8E7/++iuABz8s/v3vf2PQoEGYNm2a+PgXe3t7yb0z5ZHL5ejTpw/CwsJQXFyMZs2aYd++fRX6Jq9Vq1ZYvHgxZs+ejWvXrsHLywsWFha4evUqtm/fjokTJ+Ljjz+Go6MjWrVqhY8//hg3btyAXC7HDz/88MRLCx9mamoKDw8P7N+/HwsXLtQ7v4cNGTIE//73v6FQKNCuXTukpKRg//794mVAD8vJycHp06fLPDqIqCZwjnu2OU7H2toa/fv3x8qVK/HPP/+U+eJA94vv2LFj0bdvX4waNUp8xFXLli0RFBT0xP2PHz8ey5Ytw/jx4+Hq6oqkpCRcunRJ378Svb3//vvYtm0bPvzwQ/zyyy/o2bMnSkpKcPHiRWzbtk18NvjjeHp6wsjICPv37xcfVVMZDh48iKlTp+Ltt99GmzZtUFRUhMOHD+PHH3+Eq6trmftP09PTcevWLXFhN6LK1KZNG8TExGDUqFFo27YtfH190alTJwiCgKtXryImJgYGBgbl3hr2qKFDh6J///749NNPce3aNXTq1An79u3DTz/9hMDAQMmCpvXr14eLiwtSU1PFZ0QDD85E5+fnIz8/v1KL6EmTJmHdunUYNWoUpk2bhqZNmyI6OlpcHPFpV6tMmTIFd+/exVtvvQVHR0cUFRUhOTkZW7duRcuWLTF27Fgx1sXFBfv378fKlStha2sLBwcHuLm5YfHixUhISECvXr0wefJkGBkZ4csvv0RhYaHkudbTp0/H999/jxEjRmDcuHFwcXHBrVu3sGPHDkRGRpa53QMAAgICoNVq8emnn0KhUJT7OKmHDRs2DNu3by9zZv55DR48GM2bN4ebmxusra2RlZWFqKgo3Lx5U7JQp05CQgJ69uxZ7u+WdVIVrvxN5dA9buNxj07p27dvmce/7NixQ+jYsaNgamoqtGzZUli+fLnwzTfflHkEir29veDp6fnE8Xv27CkAEMaPH//YmA0bNgguLi6CmZmZYGFhITg7OwszZswQbt68KQiCIJw8eVIYNWqU0KJFC8HExESwtrYWhgwZIpw4cUKyn+TkZMHFxUUwNjYu8ziU3377TRg9erSgVCqFevXqCc2aNROGDBkifP/995J9nD59Wujbt69gamoqNGvWTFi0aJGwceNGvR7/8ueffwpvvfWWYGlpKSgUCmHEiBHi8vzlPZrlr7/+Knc/P/zwg9CrVy/B3NxcMDc3FxwdHQV/f38hMzNTjDl//rzg7u4uNGjQQGjcuLEwYcIE8VFU5T0e5lE//vijIJPJhKysLEk7AMHf379MvL29veSRDLdv3xbGjh0rNG7cWGjQoIGgVquFixcvlokTBEFYv369UL9+fUGr1T41L6KK4hz3QHXMcTpfffWVAECwsLAo97EkgiAIW7duFbp06SKYmJgIVlZWgq+vr/Dnn39KYh59xJUgPHj8mJ+fn6BQKAQLCwth5MiRQk5Ojt7z6JgxYwRzc/My+ZT376CoqEhYvny50L59e8HExERo2LCh4OLiIixYsEDIy8t76ufw5ptvCgMHDpS0Pe7fo775XrlyRRg9erTwyiuvCGZmZoKpqanQvn17Yd68ecKdO3fK5DBz5kyhRYsWksdpEVW2K1euCB999JHQunVrwdTUVDAzMxMcHR2FDz/8UMjIyJDEPu7/oCAIwj///CMEBQUJtra2Qr169YQ2bdoIn332Wbn/fqdPn17u441at24tAJA8CkoQHv+Iq0f/3+tytLe3l7T9/vvvgqenp2BmZiY0adJE+Ne//iX88MMPAgAhNTX1SR+P8PPPPwvjxo0THB0dhQYNGgjGxsZC69athSlTppR5tOfFixeFPn36CGZmZgIAye9MJ0+eFNRqtdCgQQOhfv36Qv/+/SWPk9L53//+JwQEBAjNmjUTjI2NhebNmwtjxowR/v77b8lnERsbK3nfjBkzBADCunXrnng8J0+eFACUeQTg434mlve7o+6RhZ999pnYtm7dOqFXr15C48aNBSMjI6FJkybC0KFDJY+B1cnNzRWMjY2Fr7/++om51iUyQaiiO/mJSG8lJSVo164dRo4cWe6lppWpS5cu6NevH1atWlWl4xARVafDhw+jX79+uHjx4mOfqlCVCgsL0bJlS8yaNQvTpk2r9vGJXnbh4eEICgrCn3/+iWbNmtV0OtVq4MCBsLW1xb///e8aGT88PBxhYWH47bffuGji/2ERTVRLbN26FR999BGysrKeuPrv84iPj8fbb7+N33///bGP+SEielHpLk/U997JyhQZGYmlS5fi8uXLT30UGhE92aMrQxcUFKBLly4oKSmpkltKaru0tDT07t0bly9ffuKCkFWhuLgYrVq1wqxZszB58uRqHbs2YxFNRERERES1xuDBg9GiRQt07twZeXl5+M9//oNz584hOjoa7777bk2nR8SFxYiIiIiIqPZQq9X4+uuvER0dLd7ytmXLljKLJxLVFJ6JJiIiIiIiItITnxNNREREREREpCcW0URERERERER6qnX3RJeWluLmzZuwsLCo1AeKE1HdIQgC/vnnH9ja2sLA4OX6rpBzJBE9D86PRETlq8j8WOuK6Js3b8LOzq6m0yCil8D169fRvHnzmk6jUnGOJKLKwPmRiKh8+syPta6ItrCwAPAgeblcXsPZENGLSKvVws7OTpxPXiacI4noeXB+JCIqX0Xmx1pXROsuv5HL5ZwAiei5vIyX83GOJKLKwPmRiKh8+syPL9fNMERERERERERViEU0ERERERERkZ5YRBMRERERERHpiUU0ERERERERkZ5YRBMRERERERHpiUU0ERERERERkZ5YRBMRERERERHpiUU0ERERERERkZ5YRBMRERG9hJKSkjB06FDY2tpCJpMhLi7usbEffvghZDIZwsPDJe23bt2Cr68v5HI5LC0t4efnhzt37khiTp8+jd69e8PU1BR2dnYICwsrs//Y2Fg4OjrC1NQUzs7O2LNnj6RfEASEhISgadOmMDMzg7u7Oy5fvvzMx05EVJVYRBMRERG9hPLz89GpUydEREQ8MW779u1ITU2Fra1tmT5fX1+cO3cOCQkJ2LVrF5KSkjBx4kSxX6vVwsPDA/b29khPT8dnn32G+fPnY8OGDWJMcnIyRo0aBT8/P5w6dQpeXl7w8vLC2bNnxZiwsDCsWbMGkZGRSEtLg7m5OdRqNQoKCirhkyAiqlwyQRCEmk7iYVqtFgqFAnl5eZDL5TWdDhG9gF7meeRlPjYiqjoymQzbt2/HgAEDJHPIjRs34Obmhr1798LT0xOBgYEIDAwEAFy4cAHt2rXD8ePH4erqCgCIj4/HG2+8gT///BO2trZYv349Pv30U2g0GhgbGwMAZs2ahbi4OFy8eBEA8M477yA/Px+7du0S8+nevTs6d+6MyMhICIIAW1tb/Otf/8LHH38MAMjLy4ONjQ02bdoEHx8fvY6R8yMRPY+KzCE8E01ERERUB5WWluL999/H9OnT0b59+zL9KSkpsLS0FAtoAHB3d4eBgQHS0tLEmD59+ogFNACo1WpkZmbi9u3bYoy7u7tk32q1GikpKQCAq1evQqPRSGIUCgXc3NzEmPIUFhZCq9VKNiKi6mBU0wkQUfVqOWt3pe7v2jLPGhmDapfK/jsHau7fVnX9+31ZjoV/9zU7xuPG0cfy5cthZGSEqVOnltuv0WhgbW0taTMyMoKVlRU0Go0Y4+DgIImxsbER+xo2bAiNRiO2PRzz8D4efl95MeUJDQ3FggULnnaYtQJ/LtY+/Duh58EimoiIiKiOOXXqFFavXo2TJ09CJpPVdDrPZPbs2QgODhZfa7Va2NnZ1WBGNetlKgpfpmOhlxOLaCIiIqI6JiUlBTk5OWjRooXYVlJSgn/9618IDw/HtWvXoFQqkZOTI3nf/fv3cevWLSiVSgCAUqlEdna2JEb3+mkxD/fr2po2bSqJ6dy582OPwcTEBCYmJhU57DJq05UB9PLhv6+XF4toIj3wG1EiInqZ+Pj4YMiQIZI2tVqN999/H2PHjgUAqFQq5ObmIj09HS4uLgCAAwcOoLS0FG5ubmLMp59+iuLiYtSrVw8AkJCQgLZt26Jhw4ZiTGJiorhgmS5GpVIBABwcHKBUKpGYmCgWzVqtFmlpafjoo4+q7DOgimNRSPQAi+g6qq7d90VERFTX3LlzB1euXBFfX716FadPnwYAWFlZoWXLlpL4evXqQalUom3btgAAJycnDBo0CBMmTEBkZCSKi4sREBAAHx8f8XFY7777LhYsWAA/Pz/MnDkTZ8+exerVq7Fq1Spxv9OmTUPfvn2xYsUKeHp6YsuWLThx4oT4GCyZTIbAwEAsXrwYbdq0gYODA+bOnQtbW1t4eXlV4SdERPRsKrQ6d0lJCebOnQsHBweYmZmhVatWWLRoER5+SpYgCAgJCUHTpk1hZmYGd3d3XL58udITJyIiIqLHO3HiBLp06YIuXboAAIKDg9G7d+8K7SM6OhqOjo4YOHAg3njjDfTq1UvyDGiFQoF9+/bh6tWrcHFxwb/+9S+EhIRIniXdo0cPxMTEYMOGDejUqRO+//57xMXFoUOHDmLMjBkzMGXKFEycOBHdunXDnTt3EB8fD1NT0+f8FIiIKl+FzkQvX74c69evx+bNm9G+fXucOHECY8eOhUKhEFd2DAsLw5o1a7B582bxm0S1Wo3z589zIiQiIiKqJv369ZOc6AD+/3NQy3Pt2rUybVZWVoiJiXniOB07dsThw4efGDNixAiMGDHisf0ymQwLFy7EwoULn7gfIiqLtx1WvwoV0cnJyRg2bBg8PR98sC1btsR3332HY8eOAXhwFjo8PBxz5szBsGHDAADffvstbGxsEBcXBx8fn0pOn4iIiIiIiKj6VKiI7tGjBzZs2IBLly7h1Vdfxa+//oojR45g5cqVAB7ca6PRaODu7i6+R6FQwM3NDSkpKeUW0YWFhSgsLBRfa7XaZz0WIiIiIiIiqmRc60iqQvdEz5o1Cz4+PnB0dES9evXQpUsXBAYGwtfXFwCg0WgAADY2NpL32djYiH2PCg0NhUKhELe6/Hw/IqoaSUlJGDp0KGxtbSGTyRAXFyfp12cth1u3bsHX1xdyuRyWlpbw8/PDnTt3JDGnT59G7969YWpqCjs7O4SFhZXJJTY2Fo6OjjA1NYWzszP27NlT6cdLRERERFWnQkX0tm3bEB0djZiYGJw8eRKbN2/G559/js2bNz9zArNnz0ZeXp64Xb9+/Zn3RURUnvz8fHTq1AkRERHl9uvWcoiMjERaWhrMzc2hVqtRUFAgxvj6+uLcuXNISEjArl27kJSUJFk4R6vVwsPDA/b29khPT8dnn32G+fPnSxbgSU5OxqhRo+Dn54dTp07By8sLXl5eOHv2bNUdPBERERFVqgpdzj19+nTxbDQAODs7448//kBoaCjGjBkDpVIJAMjOzkbTpk3F92VnZ4vP/XuUiYkJTExMnjF9IqKnGzx4MAYPHlxunz5rOVy4cAHx8fE4fvw4XF1dAQBr167FG2+8gc8//xy2traIjo5GUVERvvnmGxgbG6N9+/bIyMjAypUrxWJ79erVGDRoEKZPnw4AWLRoERISErBu3TpERkZWwydBRERERM+rQmei7969CwMD6VsMDQ1RWloKAHBwcIBSqURiYqLYr9VqkZaWBpVKVQnpEhFVrqet5QAAKSkpsLS0FAtoAHB3d4eBgQHS0tLEmD59+sDY2FiMUavVyMzMxO3bt8WYh8fRxejGISIiIqLar0JnoocOHYolS5agRYsWaN++PU6dOoWVK1di3LhxAB48niAwMBCLFy9GmzZtxEdc2drawsvLqyryJyJ6Lvqs5aDRaGBtbS3pNzIygpWVlSTGwcGhzD50fQ0bNoRGo6nQmhEAF18kIiKiuuNFeVxXhYrotWvXYu7cuZg8eTJycnJga2uLSZMmISQkRIyZMWMG8vPzMXHiROTm5qJXr16Ij4+v8mdEvygfOBFRRYSGhmLBggXPtQ/Oj0RERESVp0JFtIWFBcLDwxEeHv7YGJlMhoULF2LhwoXPmxuRXlgg0PPQZy0HpVKJnJwcyfvu37+PW7duie9XKpXIzs6WxOhePy1G11+e2bNnIzg4WHyt1Wr5FAMiIiKiGlShe6KJiF42+qzloFKpkJubi/T0dDHmwIEDKC0thZubmxiTlJSE4uJiMSYhIQFt27ZFw4YNxZiHx9HFPGnNCBMTE8jlcslGRERERDWHRTQRvfTu3LmDjIwMZGRkAHiwmFhGRgaysrIkazns2LEDZ86cwejRoyVrOTg5OWHQoEGYMGECjh07hqNHjyIgIAA+Pj6wtbUFALz77rswNjaGn58fzp07h61bt2L16tWSs8jTpk1DfHw8VqxYgYsXL2L+/Pk4ceIEAgICqvsjISIiIqJnVKHLuYmo6lT2ZekAL03XOXHiBPr37y++1hW2Y8aMwaZNm/RayyE6OhoBAQEYOHAgDAwM4O3tjTVr1oj9CoUC+/btg7+/P1xcXNC4cWOEhIRIniXdo0cPxMTEYM6cOfjkk0/Qpk0bxMXFoUOHDtXwKRARERFRZWARXQvxHl+iytWvXz8IgvDYfn3WcrCyskJMTMwTx+nYsSMOHz78xJgRI0ZgxIgRT06YiIiIiGotXs5NREREREREpCcW0URERERERER6YhFNREREREREpCcW0URERERERER6YhFNREREREREpCcW0URERERERER6YhFNREREREREpCcW0URERERERER6MqrpBF4kLWftrtT9XVvmWan7IyIiIiIioqrFM9FEREREREREemIRTURERERERKQnFtFEREREREREemIRTURERERERKQnFtFEREREREREemIRTURERERERKQnFtFEREREREREemIRTURERERERKQnFtFEREREREREemIRTURERPQSSkpKwtChQ2FrawuZTIa4uDixr7i4GDNnzoSzszPMzc1ha2uL0aNH4+bNm5J93Lp1C76+vpDL5bC0tISfnx/u3LkjiTl9+jR69+4NU1NT2NnZISwsrEwusbGxcHR0hKmpKZydnbFnzx5JvyAICAkJQdOmTWFmZgZ3d3dcvny58j4MIqJKxCKaiIiI6CWUn5+PTp06ISIiokzf3bt3cfLkScydOxcnT57Ejz/+iMzMTLz55puSOF9fX5w7dw4JCQnYtWsXkpKSMHHiRLFfq9XCw8MD9vb2SE9Px2effYb58+djw4YNYkxycjJGjRoFPz8/nDp1Cl5eXvDy8sLZs2fFmLCwMKxZswaRkZFIS0uDubk51Go1CgoKquCTISJ6PhUqolu2bAmZTFZm8/f3BwAUFBTA398fjRo1QoMGDeDt7Y3s7OwqSZyIiIiIHm/w4MFYvHgx3nrrrTJ9CoUCCQkJGDlyJNq2bYvu3btj3bp1SE9PR1ZWFgDgwoULiI+Px9dffw03Nzf06tULa9euxZYtW8Qz1tHR0SgqKsI333yD9u3bw8fHB1OnTsXKlSvFsVavXo1BgwZh+vTpcHJywqJFi9C1a1esW7cOwIOz0OHh4ZgzZw6GDRuGjh074ttvv8XNmzclZ8+JiGqLChXRx48fx3//+19xS0hIAACMGDECABAUFISdO3ciNjYWhw4dws2bNzF8+PDKz5qIiIiIKlVeXh5kMhksLS0BACkpKbC0tISrq6sY4+7uDgMDA6SlpYkxffr0gbGxsRijVquRmZmJ27dvizHu7u6SsdRqNVJSUgAAV69ehUajkcQoFAq4ubmJMeUpLCyEVquVbERE1aFCRXSTJk2gVCrFbdeuXWjVqhX69u2LvLw8bNy4EStXrsSAAQPg4uKCqKgoJCcnIzU1taryJyIiIqLnVFBQgJkzZ2LUqFGQy+UAAI1GA2tra0mckZERrKysoNFoxBgbGxtJjO7102Ie7n/4feXFlCc0NBQKhULc7OzsKnTMRETP6pnviS4qKsJ//vMfjBs3DjKZDOnp6SguLpZ8i+jo6IgWLVo88VtEIiIiIqo5xcXFGDlyJARBwPr162s6Hb3Nnj0beXl54nb9+vWaTomI6gijZ31jXFwccnNz8cEHHwB48C2isbGxeAmQztO+RSwsLERhYaH4mpfiEBEREVUPXQH9xx9/4MCBA+JZaABQKpXIycmRxN+/fx+3bt2CUqkUYx5d/0b3+mkxD/fr2po2bSqJ6dy582NzNzExgYmJSUUOl4ioUjzzmeiNGzdi8ODBsLW1fa4EeCkOERERUfXTFdCXL1/G/v370ahRI0m/SqVCbm4u0tPTxbYDBw6gtLQUbm5uYkxSUhKKi4vFmISEBLRt2xYNGzYUYxITEyX7TkhIgEqlAgA4ODhAqVRKYrRaLdLS0sQYIqLa5JmK6D/++AP79+/H+PHjxTalUomioiLk5uZKYh/+prE8vBSHiIiIqPLduXMHGRkZyMjIAPBgAa/Tp08DeFBAv/322zhx4gSio6NRUlICjUYDjUaDoqIiAICTkxMGDRqECRMm4NixYzh69CgCAgLg4+MjnkR59913YWxsDD8/P5w7dw5bt27F6tWrERwcLOYxbdo0xMfHY8WKFbh48SLmz5+PEydOICAgAAAgk8kQGBiIxYsXY8eOHThz5gxGjx4NW1tbeHl5Vd8HRkSkp2e6nDsqKgrW1tbw9PQU21xcXFCvXj0kJibC29sbAJCZmYmsrKwnfovIS3GIiIiIKt+JEyfQv39/8fXDhe3NmzexY8cOAChzyfQvv/yCfv36AXjwCKuAgAAMHDgQBgYG8Pb2xpo1a8RYhUKBffv2wd/fHy4uLmjcuDFCQkIkz5Lu0aMHYmJiMGfOHHzyySdo06YN4uLi0KFDBzFmxowZyM/Px8SJE5Gbm4tevXohPj4epqamlfmREBFVigoX0aWlpYiKisKYMWNgZPT/365QKODn54fg4GBYWVlBLpdjypQpUKlU6N69e6UmTURERERP1q9fPwiCIGnTarVQKBSwt7cv01ceKysrxMTEPDGmY8eOOHz48BNjRowYIT4StTwymQwLFy7EwoULn5oTEVFNq3ARvX//fmRlZWHcuHFl+latWiV+S1lYWAi1Wo0vvviiUhIlIiIiIiIiqmkVLqI9PDwe+82lqakpIiIiEBER8dyJEREREREREdU2z7w6NxEREREREVFdwyKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomIiIiIiIj0xCKaiIiIiIiISE8soomozispKcHcuXPh4OAAMzMztGrVCosWLYIgCGKMIAgICQlB06ZNYWZmBnd3d1y+fFmyn1u3bsHX1xdyuRyWlpbw8/PDnTt3JDGnT59G7969YWpqCjs7O4SFhVXLMRIRERFR5WARTUR13vLly7F+/XqsW7cOFy5cwPLlyxEWFoa1a9eKMWFhYVizZg0iIyORlpYGc3NzqNVqFBQUiDG+vr44d+4cEhISsGvXLiQlJWHixIliv1arhYeHB+zt7ZGeno7PPvsM8+fPx4YNG6r1eImIiIjo2RnVdAJERDUtOTkZw4YNg6enJwCgZcuW+O6773Ds2DEAD85Ch4eHY86cORg2bBgA4Ntvv4WNjQ3i4uLg4+ODCxcuID4+HsePH4erqysAYO3atXjjjTfw+eefw9bWFtHR0SgqKsI333wDY2NjtG/fHhkZGVi5cqWk2CYiIiKi2otnoomozuvRowcSExNx6dIlAMCvv/6KI0eOYPDgwQCAq1evQqPRwN3dXXyPQqGAm5sbUlJSAAApKSmwtLQUC2gAcHd3h4GBAdLS0sSYPn36wNjYWIxRq9XIzMzE7du3q/w4iYiIiOj58Uw0EdV5s2bNglarhaOjIwwNDVFSUoIlS5bA19cXAKDRaAAANjY2kvfZ2NiIfRqNBtbW1pJ+IyMjWFlZSWIcHBzK7EPX17BhwzK5FRYWorCwUHyt1Wqf51CJiIiI6DnxTDQR1Xnbtm1DdHQ0YmJicPLkSWzevBmff/45Nm/eXNOpITQ0FAqFQtzs7OxqOiUiIiKiOo1FNBHVedOnT8esWbPg4+MDZ2dnvP/++wgKCkJoaCgAQKlUAgCys7Ml78vOzhb7lEolcnJyJP3379/HrVu3JDHl7ePhMR41e/Zs5OXlidv169ef82iJiIiI6HmwiCaiOu/u3bswMJBOh4aGhigtLQUAODg4QKlUIjExUezXarVIS0uDSqUCAKhUKuTm5iI9PV2MOXDgAEpLS+Hm5ibGJCUlobi4WIxJSEhA27Zty72UGwBMTEwgl8slGxERERHVHBbRRFTnDR06FEuWLMHu3btx7do1bN++HStXrsRbb70FAJDJZAgMDMTixYuxY8cOnDlzBqNHj4atrS28vLwAAE5OThg0aBAmTJiAY8eO4ejRowgICICPjw9sbW0BAO+++y6MjY3h5+eHc+fOYevWrVi9ejWCg4Nr6tCJiIiIqIK4sBgR1Xlr167F3LlzMXnyZOTk5MDW1haTJk1CSEiIGDNjxgzk5+dj4sSJyM3NRa9evRAfHw9TU1MxJjo6GgEBARg4cCAMDAzg7e2NNWvWiP0KhQL79u2Dv78/XFxc0LhxY4SEhPDxVkREREQvEBbRRFTnWVhYIDw8HOHh4Y+NkclkWLhwIRYuXPjYGCsrK8TExDxxrI4dO+Lw4cPPmioRERER1bAKX85948YNvPfee2jUqBHMzMzg7OyMEydOiP2CICAkJARNmzaFmZkZ3N3dcfny5UpNmoiIiIiIiKgmVKiIvn37Nnr27Il69erh559/xvnz57FixQrJgjhhYWFYs2YNIiMjkZaWBnNzc6jVahQUFFR68kRERERERETVqUKXcy9fvhx2dnaIiooS2xwcHMQ/C4KA8PBwzJkzB8OGDQMAfPvtt7CxsUFcXBx8fHwqKW0iIiIiIiKi6lehM9E7duyAq6srRowYAWtra3Tp0gVfffWV2H/16lVoNBq4u7uLbQqFAm5ubkhJSSl3n4WFhdBqtZKNiIiIiIiIqDaqUBH9+++/Y/369WjTpg327t2Ljz76CFOnTsXmzZsBABqNBgBgY2MjeZ+NjY3Y96jQ0FAoFApxs7Oze5bjICIiIqKHJCUlYejQobC1tYVMJkNcXJykX591bG7dugVfX1/I5XJYWlrCz88Pd+7ckcScPn0avXv3hqmpKezs7BAWFlYml9jYWDg6OsLU1BTOzs7Ys2dPhXMhIqotKlREl5aWomvXrli6dCm6dOmCiRMnYsKECYiMjHzmBGbPno28vDxxu379+jPvi4iIiIgeyM/PR6dOnRAREVFuvz7r2Pj6+uLcuXNISEjArl27kJSUJHksn1arhYeHB+zt7ZGeno7PPvsM8+fPx4YNG8SY5ORkjBo1Cn5+fjh16hS8vLzg5eWFs2fPVigXIqLaokJFdNOmTdGuXTtJm5OTE7KysgAASqUSAJCdnS2Jyc7OFvseZWJiArlcLtmIiIiI6PkMHjwYixcvxltvvVWm79F1bDp27Ihvv/0WN2/eFM9YX7hwAfHx8fj666/h5uaGXr16Ye3atdiyZQtu3rwJAIiOjkZRURG++eYbtG/fHj4+Ppg6dSpWrlwpjrV69WoMGjQI06dPh5OTExYtWoSuXbti3bp1eudCRFSbVKiI7tmzJzIzMyVtly5dgr29PYAHi4wplUokJiaK/VqtFmlpaVCpVJWQLhERERE9r2vXrj11HZuUlBRYWlrC1dVVjHF3d4eBgQHS0tLEmD59+sDY2FiMUavVyMzMxO3bt8WYh8fRxejGeZY1dQCuq0NENadCRXRQUBBSU1OxdOlSXLlyBTExMdiwYQP8/f0BADKZDIGBgVi8eDF27NiBM2fOYPTo0bC1tYWXl1dV5E9EREREFZSTkwPgyevYaDQaWFtbS/qNjIxgZWUliSlvH7q+J8U83P+0XMrDdXWIqKZUqIju1q0btm/fju+++w4dOnTAokWLEB4eDl9fXzFmxowZmDJlCiZOnIhu3brhzp07iI+Ph6mpaaUnT0RERER1E9fVIaKaUqHnRAPAkCFDMGTIkMf2y2QyLFy4EAsXLnyuxIiIiIioaujOMGdnZ6Np06Zie3Z2Njp37gzgwVo3ujPWOvfv38etW7fEtW6USmW5a+Ho+p4U83D/03Ipj4mJCUxMTPQ6XiKiylShM9FERERE9OJr2bLlU9exUalUyM3NRXp6uhhz4MABlJaWws3NTYxJSkpCcXGxGJOQkIC2bduiYcOGYszD4+hidONwTR0ietGwiCYiIiJ6Cd25cwcZGRnIyMgA8GABr9OnTwPQbx0bJycnDBo0CBMmTMCxY8dw9OhRBAQEwMfHB7a2tgCAd999F8bGxvDz88O5c+ewdetWrF69GsHBwWIe06ZNQ3x8PFasWIGLFy9i/vz5OHHiBAICAvTOhYioNqnw5dxEREREVPudOHEC/fv3F18/XNgCD9axyc/Px8SJE5Gbm4tevXqVWccmOjoaAQEBGDhwIAwMDODt7Y01a9aI/QqFAvv27YO/vz9cXFzQuHFjhISESJ4l3aNHD8TExGDOnDn45JNP0KZNG8TFxaFDhw4VyoWIqLZgEU1ERET0EurXrx8EQZC0abVaKBQKAPqtY2NlZYWYmJgnjtOxY0ccPnz4iTEjRozAiBEjHtvPNXWI6EXCy7mJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPFSqi58+fD5lMJtkcHR3F/oKCAvj7+6NRo0Zo0KABvL29kZ2dXelJExEREREREdWECp+Jbt++Pf773/+K25EjR8S+oKAg7Ny5E7GxsTh06BBu3ryJ4cOHV2rCRERERERERDXFqMJvMDKCUqks056Xl4eNGzciJiYGAwYMAABERUXByckJqamp6N69+/NnS0RERERERFSDKnwm+vLly7C1tcUrr7wCX19fZGVlAQDS09NRXFwMd3d3MdbR0REtWrRASkpK5WVMREREREREVEMqdCbazc0NmzZtQtu2bfHf//4XCxYsQO/evXH27FloNBoYGxvD0tJS8h4bGxtoNJrH7rOwsBCFhYXia61WW7EjICIiIiIiIqomFSqiBw8eLP65Y8eOcHNzg729PbZt2wYzM7NnSiA0NBQLFix4pvcSERERERERVafnesSVpaUlXn31VVy5cgVKpRJFRUXIzc2VxGRnZ5d7D7XO7NmzkZeXJ27Xr19/npSIiIiIiIiIqsxzFdF37tzBb7/9hqZNm8LFxQX16tVDYmKi2J+ZmYmsrCyoVKrH7sPExARyuVyyEREREREREdVGFbqc++OPP8bQoUNhb2+PmzdvYt68eTA0NMSoUaOgUCjg5+eH4OBgWFlZQS6XY8qUKVCpVFyZm4iIiIiIiF4KFSqi//zzT4waNQr/+9//0KRJE/Tq1Qupqalo0qQJAGDVqlUwMDCAt7c3CgsLoVar8cUXX1RJ4kRERERERETVrUKXc2/ZsgU3b95EYWEh/vzzT2zZsgWtWrUS+01NTREREYFbt24hPz8fP/744xPvhyYiqi1u3LiB9957D40aNYKZmRmcnZ1x4sQJsV8QBISEhKBp06YwMzODu7s7Ll++LNnHrVu34OvrC7lcDktLS/j5+eHOnTuSmNOnT6N3794wNTWFnZ0dwsLCquX4iIiIiKhyPNc90UREL4Pbt2+jZ8+eqFevHn7++WecP38eK1asQMOGDcWYsLAwrFmzBpGRkUhLS4O5uTnUajUKCgrEGF9fX5w7dw4JCQnYtWsXkpKSMHHiRLFfq9XCw8MD9vb2SE9Px2effYb58+djw4YN1Xq8REQAUFJSgrlz58LBwQFmZmZo1aoVFi1aBEEQxJjq/AIxNjYWjo6OMDU1hbOzM/bs2VM1B05E9JxYRBNRnbd8+XLY2dkhKioKr732GhwcHODh4SFeaSMIAsLDwzFnzhwMGzYMHTt2xLfffoubN28iLi4OAHDhwgXEx8fj66+/hpubG3r16oW1a9eKV/AAQHR0NIqKivDNN9+gffv28PHxwdSpU7Fy5cqaOnQiqsOWL1+O9evXY926dbhw4QKWL1+OsLAwrF27Voypri8Qk5OTMWrUKPj5+eHUqVPw8vKCl5cXzp49Wz0fBhFRBbCIJqI6b8eOHXB1dcWIESNgbW2NLl264KuvvhL7r169Co1GA3d3d7FNoVDAzc0NKSkpAICUlBRYWlrC1dVVjHF3d4eBgQHS0tLEmD59+sDY2FiMUavVyMzMxO3bt6v6MImIJJKTkzFs2DB4enqiZcuWePvtt+Hh4YFjx44BqN4vEFevXo1BgwZh+vTpcHJywqJFi9C1a1esW7eu2j8XIqKnYRFNRHXe77//jvXr16NNmzbYu3cvPvroI0ydOhWbN28GAGg0GgCAjY2N5H02NjZin0ajgbW1taTfyMgIVlZWkpjy9vHwGI8qLCyEVquVbERElaFHjx5ITEzEpUuXAAC//vorjhw5gsGDBwOo3i8QU1JSJOPoYnTjlIfzIxHVlAqtzk1E9DIqLS2Fq6srli5dCgDo0qULzp49i8jISIwZM6ZGcwsNDcWCBQtqNAciejnNmjULWq0Wjo6OMDQ0RElJCZYsWQJfX18AlfsFooODQ5l96PoaNmz42C8ZH/cFI8D5kYhqDs9EE1Gd17RpU7Rr107S5uTkhKysLAAQnzKQnZ0ticnOzhb7lEolcnJyJP3379/HrVu3JDHl7ePhMR41e/Zs5OXlidv169ef5RCJiMrYtm0boqOjERMTg5MnT2Lz5s34/PPPxatwajvOj0RUU1hEE1Gd17NnT2RmZkraLl26BHt7ewCAg4MDlEolEhMTxX6tVou0tDSoVCoAgEqlQm5uLtLT08WYAwcOoLS0FG5ubmJMUlISiouLxZiEhAS0bdtWshL4w0xMTCCXyyUbEVFlmD59OmbNmgUfHx84Ozvj/fffR1BQEEJDQwFU7xeIj4t50qNSOT8SUU1hEU1EdV5QUBBSU1OxdOlSXLlyBTExMdiwYQP8/f0BADKZDIGBgVi8eDF27NiBM2fOYPTo0bC1tYWXlxeAB2euBw0ahAkTJuDYsWM4evQoAgIC4OPjA1tbWwDAu+++C2NjY/j5+eHcuXPYunUrVq9ejeDg4Jo6dCKqw+7evQsDA+mvgoaGhigtLQVQvV8gqlQqyTi6GN04RES1Ce+JJqI6r1u3bti+fTtmz56NhQsXwsHBAeHh4eJ9gQAwY8YM5OfnY+LEicjNzUWvXr0QHx8PU1NTMSY6OhoBAQEYOHAgDAwM4O3tjTVr1oj9CoUC+/btg7+/P1xcXNC4cWOEhIRIHgVDRFRdhg4diiVLlqBFixZo3749Tp06hZUrV2LcuHEApF8gtmnTBg4ODpg7d+5jv0CMjIxEcXFxuV8gLliwAH5+fpg5cybOnj2L1atXY9WqVWIu06ZNQ9++fbFixQp4enpiy5YtOHHihOQxWEREtQWLaCIiAEOGDMGQIUMe2y+TybBw4UIsXLjwsTFWVlaIiYl54jgdO3bE4cOHnzlPIqLKsnbtWsydOxeTJ09GTk4ObG1tMWnSJISEhIgx1fUFYo8ePRATE4M5c+bgk08+QZs2bRAXF4cOHTpUz4dBRFQBLKKJiIiI6iALCwuEh4cjPDz8sTHV+QXiiBEjMGLEiCfGEBHVBrwnmoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9PRcRfSyZcsgk8kQGBgothUUFMDf3x+NGjVCgwYN4O3tjezs7OfNk4iIiIiIiKjGPXMRffz4cXz55Zfo2LGjpD0oKAg7d+5EbGwsDh06hJs3b2L48OHPnSgRERERERFRTXumIvrOnTvw9fXFV199hYYNG4rteXl52LhxI1auXIkBAwbAxcUFUVFRSE5ORmpqaqUlTURERERERFQTnqmI9vf3h6enJ9zd3SXt6enpKC4ulrQ7OjqiRYsWSElJKXdfhYWF0Gq1ko2IiIiIiIioNjKq6Bu2bNmCkydP4vjx42X6NBoNjI2NYWlpKWm3sbGBRqMpd3+hoaFYsGBBRdMgIiIiIiIiqnYVOhN9/fp1TJs2DdHR0TA1Na2UBGbPno28vDxxu379eqXsl4iIiIiIiKiyVaiITk9PR05ODrp27QojIyMYGRnh0KFDWLNmDYyMjGBjY4OioiLk5uZK3pednQ2lUlnuPk1MTCCXyyUbERERERERUW1Uocu5Bw4ciDNnzkjaxo4dC0dHR8ycORN2dnaoV68eEhMT4e3tDQDIzMxEVlYWVCpV5WVNREREREREVAMqVERbWFigQ4cOkjZzc3M0atRIbPfz80NwcDCsrKwgl8sxZcoUqFQqdO/evfKyJiIiIiIiIqoBFV5Y7GlWrVoFAwMDeHt7o7CwEGq1Gl988UVlD0NERERERERU7Z67iD548KDktampKSIiIhAREfG8uyYiIiIiIiKqVZ7pOdFEREREREREdRGLaCIiIiIiIiI9sYgmIiIiIiIi0hOLaCIiIiIiIiI9sYgmIiIiIiIi0hOLaCIiIqI66saNG3jvvffQqFEjmJmZwdnZGSdOnBD7BUFASEgImjZtCjMzM7i7u+Py5cuSfdy6dQu+vr6Qy+WwtLSEn58f7ty5I4k5ffo0evfuDVNTU9jZ2SEsLKxMLrGxsXB0dISpqSmcnZ2xZ8+eqjloIqLnxCKaiIiIqA66ffs2evbsiXr16uHnn3/G+fPnsWLFCjRs2FCMCQsLw5o1axAZGYm0tDSYm5tDrVajoKBAjPH19cW5c+eQkJCAXbt2ISkpCRMnThT7tVotPDw8YG9vj/T0dHz22WeYP38+NmzYIMYkJydj1KhR8PPzw6lTp+Dl5QUvLy+cPXu2ej4MIqIKeO7nRBMRERHRi2f58uWws7NDVFSU2Obg4CD+WRAEhIeHY86cORg2bBgA4Ntvv4WNjQ3i4uLg4+ODCxcuID4+HsePH4erqysAYO3atXjjjTfw+eefw9bWFtHR0SgqKsI333wDY2NjtG/fHhkZGVi5cqVYbK9evRqDBg3C9OnTAQCLFi1CQkIC1q1bh8jIyOr6SIiI9MIz0URERER10I4dO+Dq6ooRI0bA2toaXbp0wVdffSX2X716FRqNBu7u7mKbQqGAm5sbUlJSAAApKSmwtLQUC2gAcHd3h4GBAdLS0sSYPn36wNjYWIxRq9XIzMzE7du3xZiHx9HF6MYhIqpNWEQTERER1UG///471q9fjzZt2mDv3r346KOPMHXqVGzevBkAoNFoAAA2NjaS99nY2Ih9Go0G1tbWkn4jIyNYWVlJYsrbx8NjPC5G11+ewsJCaLVayUZEVB14OTcRERFRHVRaWgpXV1csXboUANClSxecPXsWkZGRGDNmTA1n93ShoaFYsGBBTadBRHUQz0QTERER1UFNmzZFu3btJG1OTk7IysoCACiVSgBAdna2JCY7O1vsUyqVyMnJkfTfv38ft27dksSUt4+Hx3hcjK6/PLNnz0ZeXp64Xb9+/ekHTURUCVhEExEREdVBPXv2RGZmpqTt0qVLsLe3B/BgkTGlUonExESxX6vVIi0tDSqVCgCgUqmQm5uL9PR0MebAgQMoLS2Fm5ubGJOUlITi4mIxJiEhAW3bthVXAlepVJJxdDG6ccpjYmICuVwu2YiIqgOLaCIiIqI6KCgoCKmpqVi6dCmuXLmCmJgYbNiwAf7+/gAAmUyGwMBALF68GDt27MCZM2cwevRo2NrawsvLC8CDM9eDBg3ChAkTcOzYMRw9ehQBAQHw8fGBra0tAODdd9+FsbEx/Pz8cO7cOWzduhWrV69GcHCwmMu0adMQHx+PFStW4OLFi5g/fz5OnDiBgICAav9ciIiehvdEExEREdVB3bp1w/bt2zF79mwsXLgQDg4OCA8Ph6+vrxgzY8YM5OfnY+LEicjNzUWvXr0QHx8PU1NTMSY6OhoBAQEYOHAgDAwM4O3tjTVr1oj9CoUC+/btg7+/P1xcXNC4cWOEhIRIniXdo0cPxMTEYM6cOfjkk0/Qpk0bxMXFoUOHDtXzYRARVQCLaCIiIqI6asiQIRgyZMhj+2UyGRYuXIiFCxc+NsbKygoxMTFPHKdjx444fPjwE2NGjBiBESNGPDlhIqJagJdzExEREREREemJRTQRERERERGRnlhEExEREREREemJRTQR0SOWLVsmrkqrU1BQAH9/fzRq1AgNGjSAt7d3mWeaZmVlwdPTE/Xr14e1tTWmT5+O+/fvS2IOHjyIrl27wsTEBK1bt8amTZuq4YiIiIiIqLKwiCYiesjx48fx5ZdfomPHjpL2oKAg7Ny5E7GxsTh06BBu3ryJ4cOHi/0lJSXw9PREUVERkpOTsXnzZmzatAkhISFizNWrV+Hp6Yn+/fsjIyMDgYGBGD9+PPbu3Vttx0dEREREz4dFNBHR/7lz5w58fX3x1VdfoWHDhmJ7Xl4eNm7ciJUrV2LAgAFwcXFBVFQUkpOTkZqaCgDYt28fzp8/j//85z/o3LkzBg8ejEWLFiEiIgJFRUUAgMjISDg4OGDFihVwcnJCQEAA3n77baxatapGjpeIiIiIKo5FNBHR//H394enpyfc3d0l7enp6SguLpa0Ozo6okWLFkhJSQEApKSkwNnZGTY2NmKMWq2GVqvFuXPnxJhH961Wq8V9EBEREVHtx+dEExEB2LJlC06ePInjx4+X6dNoNDA2NoalpaWk3cbGBhqNRox5uIDW9ev6nhSj1Wpx7949mJmZlRm7sLAQhYWF4mutVlvxgyMiIiKiSlOhM9Hr169Hx44dIZfLIZfLoVKp8PPPP4v9+iy8Q0RU21y/fh3Tpk1DdHQ0TE1NazodidDQUCgUCnGzs7Or6ZSIiIiI6rQKFdHNmzfHsmXLkJ6ejhMnTmDAgAEYNmyYeKni0xbeISKqjdLT05GTk4OuXbvCyMgIRkZGOHToENasWQMjIyPY2NigqKgIubm5kvdlZ2dDqVQCAJRKZZkvDXWvnxYjl8vLPQsNALNnz0ZeXp64Xb9+vTIOmYiIiIieUYUu5x46dKjk9ZIlS7B+/XqkpqaiefPm2LhxI2JiYjBgwAAAQFRUFJycnJCamoru3btXXtZERJVo4MCBOHPmjKRt7NixcHR0xMyZM2FnZ4d69eohMTER3t7eAIDMzExkZWVBpVIBAFQqFZYsWYKcnBxYW1sDABISEiCXy9GuXTsxZs+ePZJxEhISxH2Ux8TEBCYmJpV2rERERET0fJ75nuiSkhLExsYiPz8fKpXqqQvvsIgmotrKwsICHTp0kLSZm5ujUaNGYrufnx+Cg4NhZWUFuVyOKVOmQKVSiXObh4cH2rVrh/fffx9hYWHQaDSYM2cO/P39xSL4ww8/xLp16zBjxgyMGzcOBw4cwLZt27B79+7qPWAiIiIiemYVLqLPnDkDlUqFgoICNGjQANu3b0e7du2QkZHx1IV3ysNFc4joRbBq1SoYGBjA29sbhYWFUKvV+OKLL8R+Q0ND7Nq1Cx999BFUKhXMzc0xZswYLFy4UIxxcHDA7t27ERQUhNWrV6N58+b4+uuvoVara+KQiIiIiOgZVLiIbtu2LTIyMpCXl4fvv/8eY8aMwaFDh545gdDQUCxYsOCZ309EVBUOHjwoeW1qaoqIiAhEREQ89j329vZlLtd+VL9+/XDq1KnKSJGIiIiIakCFnxNtbGyM1q1bw8XFBaGhoejUqRNWr14NpVL51IV3ysNFc4iIiIiIiOhFUeEi+lGlpaUoLCyEi4uLuPCOzqML75THxMREfGSWbiMiIiIiIiKqjSp0Offs2bMxePBgtGjRAv/88w9iYmJw8OBB7N27FwqF4qkL7xARERERERG9yCpUROfk5GD06NH473//C4VCgY4dO2Lv3r14/fXXATx94R0iIiIiIiKiF1mFiuiNGzc+sV+fhXeIiIiIiIiIXlTPfU80ERERERERUV3BIpqIiIiIiIhITyyiiYiIiIiIiPTEIpqIiIiIiIhITyyiiYiIiIiIiPTEIpqIiIiIiIhITyyiiYiIiIiIiPTEIpqIiIiIiIhITyyiiYiIiIiIiPTEIpqIiIiIiIhITyyiiYiIiIiIiPTEIpqIiIiIiIhITyyiiYiIiAjLli2DTCZDYGCg2FZQUAB/f380atQIDRo0gLe3N7KzsyXvy8rKgqenJ+rXrw9ra2tMnz4d9+/fl8QcPHgQXbt2hYmJCVq3bo1NmzaVGT8iIgItW7aEqakp3NzccOzYsao4TCKi58YimoiIiKiOO378OL788kt07NhR0h4UFISdO3ciNjYWhw4dws2bNzF8+HCxv6SkBJ6enigqKkJycjI2b96MTZs2ISQkRIy5evUqPD090b9/f2RkZCAwMBDjx4/H3r17xZitW7ciODgY8+bNw8mTJ9GpUyeo1Wrk5ORU/cETEVUQi2giIiKiOuzOnTvw9fXFV199hYYNG4rteXl52LhxI1auXIkBAwbAxcUFUVFRSE5ORmpqKgBg3759OH/+PP7zn/+gc+fOGDx4MBYtWoSIiAgUFRUBACIjI+Hg4IAVK1bAyckJAQEBePvtt7Fq1SpxrJUrV2LChAkYO3Ys2rVrh8jISNSvXx/ffPNN9X4YRER6YBFNREREVIf5+/vD09MT7u7ukvb09HQUFxdL2h0dHdGiRQukpKQAAFJSUuDs7AwbGxsxRq1WQ6vV4ty5c2LMo/tWq9XiPoqKipCeni6JMTAwgLu7uxhDRFSbGNV0AkRERERUM7Zs2YKTJ0/i+PHjZfo0Gg2MjY1haWkpabexsYFGoxFjHi6gdf26vifFaLVa3Lt3D7dv30ZJSUm5MRcvXnxs7oWFhSgsLBRfa7XapxwtEVHl4JloIiIiojro+vXrmDZtGqKjo2FqalrT6VRYaGgoFAqFuNnZ2dV0SkRUR7CIJiIiIqqD0tPTkZOTg65du8LIyAhGRkY4dOgQ1qxZAyMjI9jY2KCoqAi5ubmS92VnZ0OpVAIAlEplmdW6da+fFiOXy2FmZobGjRvD0NCw3BjdPsoze/Zs5OXlidv169ef6XMgIqooFtFEREREddDAgQNx5swZZGRkiJurqyt8fX3FP9erVw+JiYniezIzM5GVlQWVSgUAUKlUOHPmjGQV7YSEBMjlcrRr106MeXgfuhjdPoyNjeHi4iKJKS0tRWJiohhTHhMTE8jlcslGRFQdeE80ERERUR1kYWGBDh06SNrMzc3RqFEjsd3Pzw/BwcGwsrKCXC7HlClToFKp0L17dwCAh4cH2rVrh/fffx9hYWHQaDSYM2cO/P39YWJiAgD48MMPsW7dOsyYMQPjxo3DgQMHsG3bNuzevVscNzg4GGPGjIGrqytee+01hIeHIz8/H2PHjq2mT4OISH8soomIiIioXKtWrYKBgQG8vb1RWFgItVqNL774Quw3NDTErl278NFHH0GlUsHc3BxjxozBwoULxRgHBwfs3r0bQUFBWL16NZo3b46vv/4aarVajHnnnXfw119/ISQkBBqNBp07d0Z8fHyZxcaIiGoDFtFEREREBAA4ePCg5LWpqSkiIiIQERHx2PfY29tjz549T9xvv379cOrUqSfGBAQEICAgQO9ciYhqCu+JJiIiIiIiItITi2giIiIiIiIiPVWoiA4NDUW3bt1gYWEBa2treHl5ITMzUxJTUFAAf39/NGrUCA0aNIC3t3eZRxYQERERERERvYgqVEQfOnQI/v7+SE1NRUJCAoqLi+Hh4YH8/HwxJigoCDt37kRsbCwOHTqEmzdvYvjw4ZWeOBEREREREVF1q9DCYvHx8ZLXmzZtgrW1NdLT09GnTx/k5eVh48aNiImJwYABAwAAUVFRcHJyQmpqqvg4BCIiIiIiIqIX0XPdE52XlwcAsLKyAgCkp6ejuLgY7u7uYoyjoyNatGiBlJSUcvdRWFgIrVYr2YiIiIiIiIhqo2cuoktLSxEYGIiePXuiQ4cOAACNRgNjY2NYWlpKYm1sbKDRaMrdT2hoKBQKhbjZ2dk9a0pEREREREREVeqZi2h/f3+cPXsWW7Zsea4EZs+ejby8PHG7fv36c+2PiIiIiIiIqKpU6J5onYCAAOzatQtJSUlo3ry52K5UKlFUVITc3FzJ2ejs7Gwolcpy92ViYgITE5NnSYOIiIiIiIioWlXoTLQgCAgICMD27dtx4MABODg4SPpdXFxQr149JCYmim2ZmZnIysqCSqWqnIyJiIiIiIiIakiFzkT7+/sjJiYGP/30EywsLMT7nBUKBczMzKBQKODn54fg4GBYWVlBLpdjypQpUKlUXJmbiIiIiIiIXngVKqLXr18PAOjXr5+kPSoqCh988AEAYNWqVTAwMIC3tzcKCwuhVqvxxRdfVEqyRERERERERDWpQkW0IAhPjTE1NUVERAQiIiKeOSkiIiIiIiKi2ui5nhNNREREREREVJewiCYiIiIiIiLSE4toIiIiIiIiIj2xiCYiIiIiIiLSE4toIqrzQkND0a1bN1hYWMDa2hpeXl7IzMyUxBQUFMDf3x+NGjVCgwYN4O3tjezsbElMVlYWPD09Ub9+fVhbW2P69Om4f/++JObgwYPo2rUrTExM0Lp1a2zatKmqD4+IiIiIKhGLaCKq8w4dOgR/f3+kpqYiISEBxcXF8PDwQH5+vhgTFBSEnTt3IjY2FocOHcLNmzcxfPhwsb+kpASenp4oKipCcnIyNm/ejE2bNiEkJESMuXr1Kjw9PdG/f39kZGQgMDAQ48ePx969e6v1eImIiIjo2VXoEVdERC+j+Ph4yetNmzbB2toa6enp6NOnD/Ly8rBx40bExMRgwIABAICoqCg4OTkhNTUV3bt3x759+3D+/Hns378fNjY26Ny5MxYtWoSZM2di/vz5MDY2RmRkJBwcHLBixQoAgJOTE44cOYJVq1ZBrVZX+3ETERERUcXxTDQR0SPy8vIAAFZWVgCA9PR0FBcXw93dXYxxdHREixYtkJKSAgBISUmBs7MzbGxsxBi1Wg2tVotz586JMQ/vQxej20d5CgsLodVqJRsRERER1RwW0UREDyktLUVgYCB69uyJDh06AAA0Gg2MjY1haWkpibWxsYFGoxFjHi6gdf26vifFaLVa3Lt3r9x8QkNDoVAoxM3Ozu65j5GIiIiInh2LaCKih/j7++Ps2bPYsmVLTacCAJg9ezby8vLE7fr16zWdEhEREVGdxnuiiYj+T0BAAHbt2oWkpCQ0b95cbFcqlSgqKkJubq7kbHR2djaUSqUYc+zYMcn+dKt3Pxzz6Ire2dnZkMvlMDMzKzcnExMTmJiYPPexEREREVHl4JloIqrzBEFAQEAAtm/fjgMHDsDBwUHS7+Lignr16iExMVFsy8zMRFZWFlQqFQBApVLhzJkzyMnJEWMSEhIgl8vRrl07MebhfehidPsgIiIiotqPZ6KJqM7z9/dHTEwMfvrpJ1hYWIj3MCsUCpiZmUGhUMDPzw/BwcGwsrKCXC7HlClToFKp0L17dwCAh4cH2rVrh/fffx9hYWHQaDSYM2cO/P39xTPJH374IdatW4cZM2Zg3LhxOHDgALZt24bdu3fX2LETERERUcXwTDQR1Xnr169HXl4e+vXrh6ZNm4rb1q1bxZhVq1ZhyJAh8Pb2Rp8+faBUKvHjjz+K/YaGhti1axcMDQ2hUqnw3nvvYfTo0Vi4cKEY4+DggN27dyMhIQGdOnXCihUr8PXXX/PxVkREREQvEJ6JJqI6TxCEp8aYmpoiIiICERERj42xt7fHnj17nriffv364dSpUxXOkYiIiIhqB56JJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItITi2giIiKiOig0NBTdunWDhYUFrK2t4eXlhczMTElMQUEB/P390ahRIzRo0ADe3t5lnneflZUFT09P1K9fH9bW1pg+fTru378viTl48CC6du0KExMTtG7dGps2bSqTT0REBFq2bAlTU1O4ubnh2LFjlX7MRESVgUU0ERERUR106NAh+Pv7IzU1FQkJCSguLoaHhwfy8/PFmKCgIOzcuROxsbE4dOgQbt68ieHDh4v9JSUl8PT0RFFREZKTk7F582Zs2rQJISEhYszVq1fh6emJ/v37IyMjA4GBgRg/fjz27t0rxmzduhXBwcGYN28eTp48iU6dOkGtViMnJ6d6Pgwiogrg6txEREREdVB8fLzk9aZNm2BtbY309HT06dMHeXl52LhxI2JiYjBgwAAAQFRUFJycnJCamoru3btj3759OH/+PPbv3w8bGxt07twZixYtwsyZMzF//nwYGxsjMjISDg4OWLFiBQDAyckJR44cwapVq8RH/K1cuRITJkzA2LFjAQCRkZHYvXs3vvnmG8yaNasaPxUioqfjmWgiIiIiQl5eHgDAysoKAJCeno7i4mK4u7uLMY6OjmjRogVSUlIAACkpKXB2doaNjY0Yo1arodVqce7cOTHm4X3oYnT7KCoqQnp6uiTGwMAA7u7uYkx5CgsLodVqJRsRUXVgEU1ERERUx5WWliIwMBA9e/ZEhw4dAAAajQbGxsawtLSUxNrY2ECj0YgxDxfQun5d35NitFot7t27h7///hslJSXlxuj2UZ7Q0FAoFApxs7Ozq/iBExE9AxbRRERERHWcv78/zp49iy1bttR0KnqbPXs28vLyxO369es1nRIR1REVLqKTkpIwdOhQ2NraQiaTIS4uTtIvCAJCQkLQtGlTmJmZwd3dHZcvX66sfImIiIioEgUEBGDXrl345Zdf0Lx5c7FdqVSiqKgIubm5kvjs7GwolUox5tHVunWvnxYjl8thZmaGxo0bw9DQsNwY3T7KY2JiArlcLtmIiKpDhYvo/Px8dOrUCREREeX2h4WFYc2aNYiMjERaWhrMzc2hVqtRUFDw3MkSERERUeUQBAEBAQHYvn07Dhw4AAcHB0m/i4sL6tWrh8TERLEtMzMTWVlZUKlUAACVSoUzZ85IVtFOSEiAXC5Hu3btxJiH96GL0e3D2NgYLi4ukpjS0lIkJiaKMUREtUmFV+cePHgwBg8eXG6fIAgIDw/HnDlzMGzYMADAt99+CxsbG8TFxcHHx+f5siUiIiKiSuHv74+YmBj89NNPsLCwEO8/VigUMDMzg0KhgJ+fH4KDg2FlZQW5XI4pU6ZApVKhe/fuAAAPDw+0a9cO77//PsLCwqDRaDBnzhz4+/vDxMQEAPDhhx9i3bp1mDFjBsaNG4cDBw5g27Zt2L17t5hLcHAwxowZA1dXV7z22msIDw9Hfn6+uFo3EVFtUqmPuLp69So0Go1kdUWFQgE3NzekpKSwiCYiIiKqJdavXw8A6Nevn6Q9KioKH3zwAQBg1apVMDAwgLe3NwoLC6FWq/HFF1+IsYaGhti1axc++ugjqFQqmJubY8yYMVi4cKEY4+DggN27dyMoKAirV69G8+bN8fXXX4uPtwKAd955B3/99RdCQkKg0WjQuXNnxMfHl1lsjIioNqjUIlr3DWZFVlcsLCxEYWGh+JqPJyAiIiKqeoIgPDXG1NQUERERj72NDwDs7e2xZ8+eJ+6nX79+OHXq1BNjAgICEBAQ8NSciIhqWo2vzs3HExAREREREdGLolKLaN0KihVZXZGPJyAiIiIiIqIXRaUW0Q4ODlAqlZLVFbVaLdLS0h67uiIfT0BEREREREQvigrfE33nzh1cuXJFfH316lVkZGTAysoKLVq0QGBgIBYvXow2bdrAwcEBc+fOha2tLby8vCozbyIiIiIiIqJqV+Ei+sSJE+jfv7/4Ojg4GAAwZswYbNq0CTNmzEB+fj4mTpyI3Nxc9OrVC/Hx8TA1Na28rImIiIiIiIhqQIWL6H79+j1xNUeZTIaFCxdKHm1ARERERERE9DKo8dW5iYiIiIiIiF4ULKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPLKKJiIiIiIiI9MQimoiIiIiIiEhPVVZER0REoGXLljA1NYWbmxuOHTtWVUMREb1QOD8SEZWP8yMRvQiqpIjeunUrgoODMW/ePJw8eRKdOnWCWq1GTk5OVQxHRPTC4PxIRFQ+zo9E9KKokiJ65cqVmDBhAsaOHYt27dohMjIS9evXxzfffFMVwxERvTA4PxIRlY/zIxG9KIwqe4dFRUVIT0/H7NmzxTYDAwO4u7sjJSWlTHxhYSEKCwvF13l5eQAArVZboXFLC+8+Y8blK2/86hijusZ5EceornFeljGqa5ya/L/ytHhBECo1j+dV0fkRqJw58kX8O6+ucTjX174xqmucl2WMx43ztFjOjw/w32/NjlFd47wsY1TXOC/7sTwtVq/5UahkN27cEAAIycnJkvbp06cLr732Wpn4efPmCQC4cePGrdK369evV/YU91wqOj8KAudIbty4Vc3G+ZEbN27cyt/0mR8r/Ux0Rc2ePRvBwcHi69LSUty6dQuNGjWCTCarwcyejVarhZ2dHa5fvw65XF7T6TwXHkvt9LIcS1UehyAI+Oeff2Bra1up+60JnCNrp5flOAAeS21VVcfC+bH24r/f2udlOQ6Ax6KPisyPlV5EN27cGIaGhsjOzpa0Z2dnQ6lUlok3MTGBiYmJpM3S0rKy06p2crn8hf8HqsNjqZ1elmOpquNQKBSVvs/nVdH5EeAcWdu9LMcB8Fhqq6o4Fs6PtRv//dY+L8txADyWp9F3fqz0hcWMjY3h4uKCxMREsa20tBSJiYlQqVSVPRwR0QuD8yMRUfk4PxLRi6RKLucODg7GmDFj4Orqitdeew3h4eHIz8/H2LFjq2I4IqIXBudHIqLycX4kohdFlRTR77zzDv766y+EhIRAo9Ggc+fOiI+Ph42NTVUMV6uYmJhg3rx5ZS4vehHxWGqnl+VYXpbjqKi6PD8CL8/f+8tyHACPpbZ6mY5FX5wfX56/85flWF6W4wB4LJVNJgi17BkHRERERERERLVUpd8TTURERERERPSyYhFNREREREREpCcW0URERERERER6YhFNREREREREpCcW0ZUkNDQU3bp1g4WFBaytreHl5YXMzMyaTuu5LVu2DDKZDIGBgTWdyjO5ceMG3nvvPTRq1AhmZmZwdnbGiRMnajqtCispKcHcuXPh4OAAMzMztGrVCosWLcKLsC5gUlIShg4dCltbW8hkMsTFxUn6BUFASEgImjZtCjMzM7i7u+Py5cs1kyxViZd1fgQ4R9YGnB/pRcb5sfZ6GeZHgHNkVWERXUkOHToEf39/pKamIiEhAcXFxfDw8EB+fn5Np/bMjh8/ji+//BIdO3as6VSeye3bt9GzZ0/Uq1cPP//8M86fP48VK1agYcOGNZ1ahS1fvhzr16/HunXrcOHCBSxfvhxhYWFYu3ZtTaf2VPn5+ejUqRMiIiLK7Q8LC8OaNWsQGRmJtLQ0mJubQ61Wo6CgoJozparyMs6PAOfI2oLzI73IOD/WTi/L/AhwjqwyAlWJnJwcAYBw6NChmk7lmfzzzz9CmzZthISEBKFv377CtGnTajqlCps5c6bQq1evmk6jUnh6egrjxo2TtA0fPlzw9fWtoYyeDQBh+/bt4uvS0lJBqVQKn332mdiWm5srmJiYCN99910NZEjV4UWfHwWBc2RtwvmRXiacH2uHl2V+FATOkVWFZ6KrSF5eHgDAysqqhjN5Nv7+/vD09IS7u3tNp/LMduzYAVdXV4wYMQLW1tbo0qULvvrqq5pO65n06NEDiYmJuHTpEgDg119/xZEjRzB48OAazuz5XL16FRqNRvLvTKFQwM3NDSkpKTWYGVWlF31+BDhH1iacH+llwvmxdnhZ5keAc2RVMaryEeqg0tJSBAYGomfPnujQoUNNp1NhW7ZswcmTJ3H8+PGaTuW5/P7771i/fj2Cg4PxySef4Pjx45g6dSqMjY0xZsyYmk6vQmbNmgWtVgtHR0cYGhqipKQES5Ysga+vb02n9lw0Gg0AwMbGRtJuY2Mj9tHL5UWfHwHOkbUN50d6WXB+rD1elvkR4BxZVVhEVwF/f3+cPXsWR44cqelUKuz69euYNm0aEhISYGpqWtPpPJfS0lK4urpi6dKlAIAuXbrg7NmziIyMfOEmwG3btiE6OhoxMTFo3749MjIyEBgYCFtb2xfuWKhue5HnR4BzZG3E+ZFeFpwfa4+XZX4EOEdWFV7OXckCAgKwa9cu/PLLL2jevHlNp1Nh6enpyMnJQdeuXWFkZAQjIyMcOnQIa9asgZGREUpKSmo6Rb01bdoU7dq1k7Q5OTkhKyurhjJ6dtOnT8esWbPg4+MDZ2dnvP/++wgKCkJoaGhNp/ZclEolACA7O1vSnp2dLfbRy+NFnx8BzpG1EedHehlwfqxdXpb5EeAcWVVYRFcSQRAQEBCA7du348CBA3BwcKjplJ7JwIEDcebMGWRkZIibq6srfH19kZGRAUNDw5pOUW89e/Ys85iIS5cuwd7evoYyenZ3796FgYH0v6uhoSFKS0trKKPK4eDgAKVSicTERLFNq9UiLS0NKpWqBjOjyvSyzI8A58jaiPMjvcg4P9ZOL8v8CHCOrCq8nLuS+Pv7IyYmBj/99BMsLCzEa/EVCgXMzMxqODv9WVhYlLkPx9zcHI0aNXrh7s8JCgpCjx49sHTpUowcORLHjh3Dhg0bsGHDhppOrcKGDh2KJUuWoEWLFmjfvj1OnTqFlStXYty4cTWd2lPduXMHV65cEV9fvXoVGRkZsLKyQosWLRAYGIjFixejTZs2cHBwwNy5c2FrawsvL6+aS5oq1csyPwKcI2sjzo/0IuP8WDu9LPMjwDmyylT5+t91BIByt6ioqJpO7bm9qI8nEARB2Llzp9ChQwfBxMREcHR0FDZs2FDTKT0TrVYrTJs2TWjRooVgamoqvPLKK8Knn34qFBYW1nRqT/XLL7+U+39jzJgxgiA8eETB3LlzBRsbG8HExEQYOHCgkJmZWbNJU6V6medHQeAcWdM4P9KLjPNj7fUyzI+CwDmyqsgEQRCqvFInIiIiIiIiegnwnmgiIiIiIiIiPbGIJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItITi2giIiIiIiIiPbGIJiIiIiIiItLT/wM3uxieDlSHTgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "problem = run_scenario(fm, scenario_name=\"base-cgen_hv\", print_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we run the ``basease-cgen_gs`` scenario, which maximizes even-flow harvest volume plus adds a minimum growning stock volume (120 000 $m^3$) constraint on period 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus growing stock constraints\n",
      "   period        oha           ohv            ogs\n",
      "0       1  92.742411  12945.008110  135863.963286\n",
      "1       2  97.379532  13592.258515  132839.040537\n",
      "2       3  88.105291  13592.258515  128034.433647\n",
      "3       4  90.176925  12297.757704  125301.751940\n",
      "4       5  90.366370  12297.757704  124421.651363\n",
      "5       6  91.904290  12297.757704  124170.013825\n",
      "6       7  91.433562  12297.757704  124057.295448\n",
      "7       8  90.561107  12297.757704  123503.944077\n",
      "8       9  89.863045  12297.757704  122048.775279\n",
      "9      10  97.379532  12297.757704  120000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "problem = run_scenario(fm, scenario_name=\"base-cgen_gs\", print_df=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ws3` optimization problem-building functions include a parallelized  function that runs the model in parallel. This is useful for problems with many decision variables or large numbers of periods to solve. The parallelization is controlledd by the `workers` argument to the `ForestModel.add_problem` method, which gets passed down through to several subordinate functions that handle various parts of the model-buildling process.\n",
    "\n",
    "`ws3` is configured to run in serial (single-core) mode by default, so we need to activate parallel model generation by setting the `workers` argument. The `run_scenario` function we defined in the local `util` module has a `workers` argument that we can use to activate parallel model building mode. We run the same `base-cgen_gs` scenario in parallel by setting the `workers` argument to a number greater than one. Note that the model output is identical to the serial version of the model (which is what we would want and expect). \n",
    "\n",
    "For a tiny model like the one we are using here for we will not see much of an advantage to running in parallel, but for larger models (with more development types, periods, or action options) we can expect to see a substantial speedup in the model-building step by running in parallel. The current implementation is tuned to run well for between 8 to 16 cores, but more cores could work well for larger models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus growing stock constraints\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   period        oha           ohv            ogs\n",
      "0       1  92.742411  12945.008110  135863.963286\n",
      "1       2  97.379532  13592.258515  132839.040537\n",
      "2       3  88.105291  13592.258515  128034.433647\n",
      "3       4  90.176925  12297.757704  125301.751940\n",
      "4       5  90.366370  12297.757704  124421.651363\n",
      "5       6  91.904290  12297.757704  124170.013825\n",
      "6       7  91.433562  12297.757704  124057.295448\n",
      "7       8  90.561107  12297.757704  123503.944077\n",
      "8       9  89.863045  12297.757704  122048.775279\n",
      "9      10  88.105291  12297.757704  120000.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "problem = run_scenario(fm, scenario_name=\"base-cgen_gs\", print_df=True, workers=4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}