examples/010_ws3_model_example-fromscratch.ipynb

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   "cell_type": "markdown",
   "id": "fe4de448-d218-420b-bdae-17c1e4b4181c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Building a `ws3` model from scratch\n",
    "\n",
    "This notebook has an example of building a new `ws3` model from scratch.\n",
    "\n",
    "> We _strongly recommend_ that you run this notebook in venv-sandboxed Python kernel (see `venv_python_kernel_setup` notebook for an example of how to do this). This will ensure that you are working from a fresh Python package environment, and not wasting time debugging random interactions between this notebook and whatever mishmash of packages you have installed on your system in various parts of your Python path. You have been warned. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec44080-352d-493a-9014-0892e4668c8f",
   "metadata": {},
   "source": [
    "Optionally, uninstall the `ws3` package and replace it with a pointer to _this local clone of the GitHub repository code_ (useful if you want ot tweak the source code for whatever reason). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8619cfa-a3d9-4da3-b943-b848918cd800",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: ws3 0.0.1.post3\n",
      "Uninstalling ws3-0.0.1.post3:\n",
      "  Successfully uninstalled ws3-0.0.1.post3\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Obtaining file:///media/data/project/gparadis/ws3\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: fiona in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.9.1)\n",
      "Requirement already satisfied: matplotlib in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (3.7.0)\n",
      "Requirement already satisfied: numpy in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.23.5)\n",
      "Requirement already satisfied: pandas in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.5.3)\n",
      "Requirement already satisfied: profilehooks in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.12.0)\n",
      "Requirement already satisfied: rasterio in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.3.6)\n",
      "Requirement already satisfied: scipy in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from ws3==0.0.1.post3) (1.10.1)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (1.1.1)\n",
      "Requirement already satisfied: munch>=2.3.2 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (2.5.0)\n",
      "Requirement already satisfied: certifi in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (2022.12.7)\n",
      "Requirement already satisfied: click~=8.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (8.1.3)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (22.2.0)\n",
      "Requirement already satisfied: setuptools in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (59.6.0)\n",
      "Requirement already satisfied: cligj>=0.5 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from fiona->ws3==0.0.1.post3) (0.7.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (9.4.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (4.38.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from matplotlib->ws3==0.0.1.post3) (1.4.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from pandas->ws3==0.0.1.post3) (2022.7.1)\n",
      "Requirement already satisfied: snuggs>=1.4.1 in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from rasterio->ws3==0.0.1.post3) (1.4.7)\n",
      "Requirement already satisfied: affine in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from rasterio->ws3==0.0.1.post3) (2.4.0)\n",
      "Requirement already satisfied: six in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (from munch>=2.3.2->fiona->ws3==0.0.1.post3) (1.16.0)\n",
      "Installing collected packages: ws3\n",
      "  Running setup.py develop for ws3\n",
      "Successfully installed ws3-0.0.1.post3\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",
   "id": "52e1114c-44ce-47b5-bde8-bd5b85ce9623",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79142af4-821c-45a9-9e9f-562cde0f98d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1271efba-f9c2-40a8-86c9-2d2e6ca4323f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import ws3.forest, ws3.core"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1090a31-8215-46dc-b6c9-61030fc01ff7",
   "metadata": {},
   "source": [
    "## Set up Python environment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2597ec84-062c-4d10-ae51-22d669185ba4",
   "metadata": {},
   "source": [
    "Define some basic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11a7ae85-6e7e-4537-b645-249a76208841",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_year = 2020\n",
    "horizon = 10\n",
    "period_length = 10\n",
    "max_age = 1000\n",
    "tvy_name = 'totvol'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ada77ae-106d-475b-b21f-f7095cbebd9e",
   "metadata": {},
   "source": [
    "Import sample inventory and yield curve data.\n",
    "\n",
    "The inventory data is imported a from vector data layer (stored in industry-standard ESRI Shapefile format). This is a small square of real forest data clipped from a larger dataset representing Timber Supply Area (TSA) 24 in British Columbia (BC), Canada. This data layer is derived from publicly-available BC Vegetation Resource Inventory (VRI) datasets (see [British Columbia Data Catalogue](https://catalogue.data.gov.bc.ca/)) that we pre-processed to include the data attributes we need (in the format we want) for this `ws3` model-building example. \n",
    "\n",
    "The yield curve data was generated from a complex process (the details of which are outside the scope of this example, contact [Gregory Paradis](mailto:gregory.paradis@ubc.ca) for details), using a methodology consistent with _de facto_ professional best-practices for the Timber Supply Review (TSR) modelling process in BC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "147a0b10-de95-45ea-9090-910f636ef418",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stands = gpd.read_file('data/shp/tsa24_clipped.shp/stands.shp')"
   ]
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   "id": "e2e354ee-96d1-474c-b92a-c2c106719bd2",
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     "metadata": {},
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   "source": [
    "stands"
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  {
   "cell_type": "markdown",
   "id": "be459641-89ec-4f7e-bad3-e20b01f76a31",
   "metadata": {},
   "source": [
    "Import data tables linking analysis units to yield curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "090ad90c-971a-4931-9a68-d50ee9a09e5d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "au_table = pd.read_csv('data/au_table.csv').set_index('au_id')\n",
    "curve_table = pd.read_csv('data/curve_table.csv')\n",
    "curve_points_table = pd.read_csv('data/curve_points_table.csv').set_index('curve_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "350f4224-c95e-4947-bd82-a5af0a9b4578",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "au_table['thlb'] = au_table.apply(lambda row: 0 if row.unmanaged_curve_id == row.managed_curve_id else 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf466615-38ba-4ede-b1a8-50b187b66129",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>tsa</th>\n",
       "      <th>stratum_code</th>\n",
       "      <th>si_level</th>\n",
       "      <th>canfi_species</th>\n",
       "      <th>unmanaged_curve_id</th>\n",
       "      <th>managed_curve_id</th>\n",
       "      <th>thlb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2401000</th>\n",
       "      <td>63</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX</td>\n",
       "      <td>L</td>\n",
       "      <td>100</td>\n",
       "      <td>2401000</td>\n",
       "      <td>2401000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402000</th>\n",
       "      <td>64</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX</td>\n",
       "      <td>M</td>\n",
       "      <td>100</td>\n",
       "      <td>2402000</td>\n",
       "      <td>2422000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403000</th>\n",
       "      <td>65</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX</td>\n",
       "      <td>H</td>\n",
       "      <td>100</td>\n",
       "      <td>2403000</td>\n",
       "      <td>2423000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401001</th>\n",
       "      <td>66</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_BL</td>\n",
       "      <td>L</td>\n",
       "      <td>304</td>\n",
       "      <td>2401001</td>\n",
       "      <td>2401001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402001</th>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_BL</td>\n",
       "      <td>M</td>\n",
       "      <td>304</td>\n",
       "      <td>2402001</td>\n",
       "      <td>2402001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403001</th>\n",
       "      <td>68</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_BL</td>\n",
       "      <td>H</td>\n",
       "      <td>304</td>\n",
       "      <td>2403001</td>\n",
       "      <td>2423001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401002</th>\n",
       "      <td>69</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_PLI</td>\n",
       "      <td>L</td>\n",
       "      <td>204</td>\n",
       "      <td>2401002</td>\n",
       "      <td>2421002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402002</th>\n",
       "      <td>70</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_PLI</td>\n",
       "      <td>M</td>\n",
       "      <td>204</td>\n",
       "      <td>2402002</td>\n",
       "      <td>2422002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403002</th>\n",
       "      <td>71</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_PLI</td>\n",
       "      <td>H</td>\n",
       "      <td>204</td>\n",
       "      <td>2403002</td>\n",
       "      <td>2423002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401003</th>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_BL</td>\n",
       "      <td>L</td>\n",
       "      <td>304</td>\n",
       "      <td>2401003</td>\n",
       "      <td>2401003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402003</th>\n",
       "      <td>73</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_BL</td>\n",
       "      <td>M</td>\n",
       "      <td>304</td>\n",
       "      <td>2402003</td>\n",
       "      <td>2422003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403003</th>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_BL</td>\n",
       "      <td>H</td>\n",
       "      <td>304</td>\n",
       "      <td>2403003</td>\n",
       "      <td>2423003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401004</th>\n",
       "      <td>75</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_SE</td>\n",
       "      <td>L</td>\n",
       "      <td>104</td>\n",
       "      <td>2401004</td>\n",
       "      <td>2401004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402004</th>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_SE</td>\n",
       "      <td>M</td>\n",
       "      <td>104</td>\n",
       "      <td>2402004</td>\n",
       "      <td>2422004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403004</th>\n",
       "      <td>77</td>\n",
       "      <td>24</td>\n",
       "      <td>ESSF_SE</td>\n",
       "      <td>H</td>\n",
       "      <td>104</td>\n",
       "      <td>2403004</td>\n",
       "      <td>2423004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401005</th>\n",
       "      <td>78</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT</td>\n",
       "      <td>L</td>\n",
       "      <td>1201</td>\n",
       "      <td>2401005</td>\n",
       "      <td>2401005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402005</th>\n",
       "      <td>79</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT</td>\n",
       "      <td>M</td>\n",
       "      <td>1201</td>\n",
       "      <td>2402005</td>\n",
       "      <td>2402005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403005</th>\n",
       "      <td>80</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT</td>\n",
       "      <td>H</td>\n",
       "      <td>1201</td>\n",
       "      <td>2403005</td>\n",
       "      <td>2403005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401006</th>\n",
       "      <td>81</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT+SX</td>\n",
       "      <td>L</td>\n",
       "      <td>1201</td>\n",
       "      <td>2401006</td>\n",
       "      <td>2401006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402006</th>\n",
       "      <td>82</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT+SX</td>\n",
       "      <td>M</td>\n",
       "      <td>1201</td>\n",
       "      <td>2402006</td>\n",
       "      <td>2402006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403006</th>\n",
       "      <td>83</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_AT+SX</td>\n",
       "      <td>H</td>\n",
       "      <td>1201</td>\n",
       "      <td>2403006</td>\n",
       "      <td>2403006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401007</th>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX+AT</td>\n",
       "      <td>L</td>\n",
       "      <td>100</td>\n",
       "      <td>2401007</td>\n",
       "      <td>2421007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402007</th>\n",
       "      <td>85</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX+AT</td>\n",
       "      <td>M</td>\n",
       "      <td>100</td>\n",
       "      <td>2402007</td>\n",
       "      <td>2422007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403007</th>\n",
       "      <td>86</td>\n",
       "      <td>24</td>\n",
       "      <td>SBS_SX+AT</td>\n",
       "      <td>H</td>\n",
       "      <td>100</td>\n",
       "      <td>2403007</td>\n",
       "      <td>2423007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         record_id  tsa stratum_code si_level  canfi_species  \\\n",
       "au_id                                                          \n",
       "2401000         63   24       SBS_SX        L            100   \n",
       "2402000         64   24       SBS_SX        M            100   \n",
       "2403000         65   24       SBS_SX        H            100   \n",
       "2401001         66   24      ESSF_BL        L            304   \n",
       "2402001         67   24      ESSF_BL        M            304   \n",
       "2403001         68   24      ESSF_BL        H            304   \n",
       "2401002         69   24      SBS_PLI        L            204   \n",
       "2402002         70   24      SBS_PLI        M            204   \n",
       "2403002         71   24      SBS_PLI        H            204   \n",
       "2401003         72   24       SBS_BL        L            304   \n",
       "2402003         73   24       SBS_BL        M            304   \n",
       "2403003         74   24       SBS_BL        H            304   \n",
       "2401004         75   24      ESSF_SE        L            104   \n",
       "2402004         76   24      ESSF_SE        M            104   \n",
       "2403004         77   24      ESSF_SE        H            104   \n",
       "2401005         78   24       SBS_AT        L           1201   \n",
       "2402005         79   24       SBS_AT        M           1201   \n",
       "2403005         80   24       SBS_AT        H           1201   \n",
       "2401006         81   24    SBS_AT+SX        L           1201   \n",
       "2402006         82   24    SBS_AT+SX        M           1201   \n",
       "2403006         83   24    SBS_AT+SX        H           1201   \n",
       "2401007         84   24    SBS_SX+AT        L            100   \n",
       "2402007         85   24    SBS_SX+AT        M            100   \n",
       "2403007         86   24    SBS_SX+AT        H            100   \n",
       "\n",
       "         unmanaged_curve_id  managed_curve_id  thlb  \n",
       "au_id                                                \n",
       "2401000             2401000           2401000     0  \n",
       "2402000             2402000           2422000     1  \n",
       "2403000             2403000           2423000     1  \n",
       "2401001             2401001           2401001     0  \n",
       "2402001             2402001           2402001     0  \n",
       "2403001             2403001           2423001     1  \n",
       "2401002             2401002           2421002     1  \n",
       "2402002             2402002           2422002     1  \n",
       "2403002             2403002           2423002     1  \n",
       "2401003             2401003           2401003     0  \n",
       "2402003             2402003           2422003     1  \n",
       "2403003             2403003           2423003     1  \n",
       "2401004             2401004           2401004     0  \n",
       "2402004             2402004           2422004     1  \n",
       "2403004             2403004           2423004     1  \n",
       "2401005             2401005           2401005     0  \n",
       "2402005             2402005           2402005     0  \n",
       "2403005             2403005           2403005     0  \n",
       "2401006             2401006           2401006     0  \n",
       "2402006             2402006           2402006     0  \n",
       "2403006             2403006           2403006     0  \n",
       "2401007             2401007           2421007     1  \n",
       "2402007             2402007           2422007     1  \n",
       "2403007             2403007           2423007     1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "au_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e2e9ac-b514-48d7-92e0-efa976cf056d",
   "metadata": {},
   "source": [
    "Need to rebuild the Timber Harvesting Land Base (THLB) attribute (i.e., `theme1`) in the stand inventory table. Current implementation is inconsistent with yield VDYP/TIPSY yield curve (and AU definition) modelling assumptions. \n",
    "\n",
    "In a nutshell, AUs either have only a VDYP yield curve or both VDYP and TISPY yield curves (if considered potentially operable). We need to set the THLB attribute to 0 for stands in AUs with have only a VDYP curve (i.e., where `au_table.unmanaged_curve_id == au_table.managed_curve_id`), and 1 otherwise. \n",
    "\n",
    "Otherwise, we would get weird cases where we can harvest a stand but there is not TIPSY curve defined for second-growth stand conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dbf4b93f-5060-429d-86aa-856bc3f23642",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stands.theme1 = stands.apply(lambda row: au_table.loc[row.theme2].thlb, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f99801-86ff-48d7-9a29-599ea60913dc",
   "metadata": {},
   "source": [
    "Copy `curve1` to `theme4` so we can track yield curve transitions independently from AU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02fde4ca-fe5c-424a-bfe7-efdba260df61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stands['theme4'] = stands.curve1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc02b7b-48d8-4ca8-8523-59dfd79a508c",
   "metadata": {},
   "source": [
    "Set up themes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40ab8116-4629-4363-ad32-18b6c1ecdd45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "theme_cols=['theme0', # TSA \n",
    "            'theme1', # THLB\n",
    "            'theme2', # AU\n",
    "            'theme3', # leading species code\n",
    "            'theme4'] # yield curve ID\n",
    "basecodes = [list(map(lambda x: str(x), stands[tc].unique())) for tc in theme_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecd94456-5e7e-4459-a6c5-17adbdbf95b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# also scrape au_table for AU and curve ID values that are not in inventory but might pop up later (hack?)\n",
    "basecodes[2] = list(set(basecodes[2] + list(au_table.index.astype(str))))\n",
    "basecodes[3] = list(set(basecodes[3] + list(au_table.canfi_species.astype(str))))\n",
    "basecodes[4] = list(set(basecodes[4] + list(au_table.unmanaged_curve_id.astype(str)) + list(au_table.managed_curve_id.astype(str))))               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d288b57d-18ec-4536-a09c-e609027d3c9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['tsa24_clipped'],\n",
       " ['1', '0'],\n",
       " ['2403007',\n",
       "  '2401003',\n",
       "  '2401006',\n",
       "  '2403000',\n",
       "  '2401007',\n",
       "  '2402003',\n",
       "  '2403002',\n",
       "  '2402004',\n",
       "  '2401005',\n",
       "  '2402006',\n",
       "  '2401002',\n",
       "  '2403003',\n",
       "  '2401001',\n",
       "  '2403006',\n",
       "  '2402005',\n",
       "  '2403001',\n",
       "  '2402001',\n",
       "  '2403004',\n",
       "  '2401000',\n",
       "  '2402000',\n",
       "  '2402007',\n",
       "  '2403005',\n",
       "  '2401004',\n",
       "  '2402002'],\n",
       " ['104', '1201', '204', '100', '304'],\n",
       " ['2403007',\n",
       "  '2401003',\n",
       "  '2422000',\n",
       "  '2401006',\n",
       "  '2403000',\n",
       "  '2401007',\n",
       "  '2402003',\n",
       "  '2403002',\n",
       "  '2402004',\n",
       "  '2422004',\n",
       "  '2401005',\n",
       "  '2402006',\n",
       "  '2401002',\n",
       "  '2403003',\n",
       "  '2401001',\n",
       "  '2423007',\n",
       "  '2403006',\n",
       "  '2423000',\n",
       "  '2423002',\n",
       "  '2421007',\n",
       "  '2402005',\n",
       "  '2403001',\n",
       "  '2402001',\n",
       "  '2423001',\n",
       "  '2403004',\n",
       "  '2401000',\n",
       "  '2422007',\n",
       "  '2402000',\n",
       "  '2402007',\n",
       "  '2422003',\n",
       "  '2421002',\n",
       "  '2423004',\n",
       "  '2403005',\n",
       "  '2423003',\n",
       "  '2401004',\n",
       "  '2402002',\n",
       "  '2422002']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basecodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e8b152-771b-44b9-a05f-5e4cddc0d788",
   "metadata": {},
   "source": [
    "Create a new blank `ForestModel` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f59fa17-7364-463f-b03b-19ec903249f3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm = ws3.forest.ForestModel(model_name='tsa24_clipped',\n",
    "                            model_path='data',\n",
    "                            base_year=base_year,\n",
    "                            horizon=horizon,\n",
    "                            period_length=period_length,\n",
    "                            max_age=max_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d97a6be-21ee-4920-984f-1b4b99a481d0",
   "metadata": {},
   "source": [
    "Now populate `fm` with data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3288ccb0-e93a-4bde-b81f-c2022f26aa85",
   "metadata": {},
   "source": [
    "Set up themes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3cf3d0c-086e-4c52-925d-e4566fead756",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for ti, t in enumerate(theme_cols):\n",
    "    fm.add_theme(t, basecodes[ti])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929d9ce2-9535-4edd-b199-66c0422dbd3e",
   "metadata": {},
   "source": [
    "Load areas from inventory data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d93ec79-1a15-4e36-85b7-1e9ff7925645",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gstands = stands.groupby(theme_cols+['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a275407-81e9-4c5c-8ab3-147189c1cb20",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name, group in gstands:\n",
    "    dtk, age, area = tuple(map(lambda x: str(x), name[:-1])), int(name[-1]), group['area'].sum()\n",
    "    if dtk not in fm.dtypes:\n",
    "        fm.dtypes[dtk] = ws3.forest.DevelopmentType(dtk, fm)\n",
    "    fm.dtypes[dtk].area(0, age, area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecf1dc0-d16a-434a-8bac-ececccf7240a",
   "metadata": {},
   "source": [
    "Inspect `dtypes` attribute of `ForestModel` instance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2be72d-7867-4a04-b5d8-283cbef09591",
   "metadata": {},
   "source": [
    "Directly inject yield curve data into the `fm` instance. \n",
    "\n",
    "This a bit obscure, and the exact process will differ from case to case depending on input data structure and desired model structure. This is _not_ the one and only way to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8cb25039-344f-47d7-a1be-d1b8dec7fb51",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for au_id, au_row in au_table.iterrows():\n",
    "    yname = 's%04d' % int(au_row.canfi_species)\n",
    "    for is_managed in (0, 1):\n",
    "        curve_id = au_row.unmanaged_curve_id if not is_managed else au_row.managed_curve_id\n",
    "        mask = ('?', '?', str(au_id), '?', str(curve_id))\n",
    "        points = [(r.x, r.y) for _, r in curve_points_table.loc[curve_id].iterrows() if not r.x % period_length and r.x <= max_age]\n",
    "        c = fm.register_curve(ws3.core.Curve(yname, points=points, type='a', is_volume=True, xmax=fm.max_age, period_length=period_length))\n",
    "        fm.yields.append((mask, 'a', [(yname, c)])) # only if not already present?\n",
    "        fm.ynames.add(yname) \n",
    "        for dtk in fm.unmask(mask): \n",
    "            fm.dtypes[dtk].add_ycomp('a', yname, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6179b1-b97f-4917-a29c-99c06032a622",
   "metadata": {},
   "source": [
    "Add total volume curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c869a2a-e2c5-48f4-8a78-ad76271e9292",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "expr = '_SUM(%s)' % ', '.join(fm.ynames)\n",
    "fm.yields.append((('?', '?', '?', '?', '?'), 'c', [(tvy_name, expr)]))\n",
    "fm.ynames.add(tvy_name)\n",
    "for dtk in fm.dtypes.keys(): fm.dtypes[dtk].add_ycomp('c', tvy_name, expr)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d0516f-657e-4072-bb31-d1892dbd6b06",
   "metadata": {},
   "source": [
    "Set up actions. This part is a bit ugly, because `ws3` was originally designed to import Woodstock code (not to directly code models like this). As with yield curves, there is more than one way to implement this, and the details will depend on your input data format and exactly what model you are trying to build.\n",
    "\n",
    "All of this gets automatically set up correctly if you use the built-in functions to import Woostock-style input files, but is your responsibility if you bypass and direct-code your model.\n",
    "\n",
    "There is a lot going on here. \n",
    "\n",
    "Generic transitions are configured at the `ForestModel` level. These are basically equivalent (conceptually, and functionally) to transitions defined in a Woodstock model. The source masks allow `ws3` to match the key of a new development type (whose runtime instantiation is triggered by application of an action) to one or more appropriate transitions, which then get compiled into the new development type. Etc. Makes more sense if you understand how Woodstock models work. Need to provide more information about this in the `ws3` documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2aa340e3-7a6a-431b-8d99-0349868af803",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "acode = 'harvest'\n",
    "oe = '_age >= 80 and _age <= 500' # operability expression\n",
    "fm.transitions[acode] = {}\n",
    "for au_id, au_row in au_table.iterrows():\n",
    "    if not au_row.thlb: continue\n",
    "    target_curve_id = au_row.managed_curve_id\n",
    "    smask = ('?', '?', str(au_id), '?', '?')\n",
    "    tmask = ('?', '?', '?', '?', str(target_curve_id))\n",
    "    target = [(tmask, 1.0, None, None, None, None, None)] # list of one (single target... not modelling \"divergent\" transitions)\n",
    "    fm.actions[acode] = ws3.forest.Action(acode, targetage=0, is_harvest=True)\n",
    "    fm.oper_expr[acode] = {smask:oe}        \n",
    "    fm.transitions[acode].update({smask:{'':target}}) # the '' is a blank source condition expression\n",
    "    for dtk in fm.unmask(smask):\n",
    "        dt = fm.dtypes[dtk]\n",
    "        dt.oper_expr[acode] = [oe]\n",
    "        for age in range(1, fm.max_age):\n",
    "            if not dt.is_operable(acode, 1, age): continue\n",
    "            #if dtk == ('tsa24_clipped', '1', '2402000', '100', '2402000'):\n",
    "            #    print('setting transition for', dtk, age, 'to', target)\n",
    "            fm.dtypes[dtk].transitions[acode, age] = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad390446-db3d-4a47-a5bc-0cdb57b4a20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.compile_actions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed414a3a-e46b-4202-9820-954a797c805c",
   "metadata": {},
   "source": [
    "## Poke around `ForestModel` instance data structures to see what we just did"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1340875e-11a8-4062-8931-cfa56457ce66",
   "metadata": {},
   "source": [
    "We can poke around the model data structures a bit to see the data we injected above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e315a7-a72e-4e39-8f0d-ae9a03bb3a06",
   "metadata": {},
   "source": [
    "Print list of yield component names we are using in this model. In our case, the `sXXX` curves are species-specific yield curves where `XXX` is a [CANFI](https://nfi.nfis.org/en/history) tree species code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1088edeb-e74e-4901-a144-50c55b5e0405",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'s0100', 's0104', 's0204', 's0304', 's1201', 'totvol'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.ynames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e030331-ff65-4329-8959-a95555c03ade",
   "metadata": {},
   "source": [
    "List yield components present in our example `DevelopmentType` instance from earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecd7bd8b-821a-4876-a4e8-424bae980125",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['s0204', 'totvol']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.ycomps()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5e8932-8d93-4580-b5fb-844a01129935",
   "metadata": {},
   "source": [
    "Get a reference to the total yield curve.\n",
    "\n",
    "The `ws3.core.Curve` class can do all sorts of neat things with yield curve (e.g., curves can resample themselves, interpolate between data points, extrapolate beyond data points, be operated on using standard Python arithmetic operators, CAI, MAI, YTP, etc.). See source code for details. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1b4be027-d64a-4e89-b9d3-b16808e6d1ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c = dt.ycomp('totvol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a705e982-159f-4499-aeeb-c948cc98db4f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.0),\n",
       " (30, 0.2),\n",
       " (40, 24.4),\n",
       " (50, 98.20000000000002),\n",
       " (60, 184.10000000000002),\n",
       " (70, 257.4),\n",
       " (80, 312.6),\n",
       " (90, 353.8),\n",
       " (100, 380.3),\n",
       " (110, 400.5),\n",
       " (120, 413.0),\n",
       " (130, 422.1),\n",
       " (140, 428.7),\n",
       " (150, 433.3),\n",
       " (160, 435.0),\n",
       " (180, 437.4),\n",
       " (190, 436.8),\n",
       " (200, 434.7),\n",
       " (210, 433.0),\n",
       " (230, 430.5),\n",
       " (250, 428.9),\n",
       " (1000, 426.9)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.points()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb04f08-7f28-47e0-a05a-eebdf256bc7b",
   "metadata": {
    "tags": []
   },
   "source": [
    "Plot total volume, CAI, MAI, MAI YTP, etc. Optimal rotation age in red.\n",
    "\n",
    "Note that all the stuff a forester would expect to see happening with these yield curves is seems to be happening:\n",
    "* A tangent to the volume curve passing through the origin touches the yield curve at the optimal rotation age.\n",
    "* The CAI curve is blocky because curve points are sparse (curves have been automatically resampled to 10-year intervals by default, so the yield curve is actually a piecewise linear assemblage).\n",
    "* The MAI curve intersects the CAI curve at the optimal rotation age.\n",
    "\n",
    "This is not meant to be an exhaustive demonstration of `ws3` curve manipulation and usage. Go have a look at the source code to see what else you can do. The `Curve` class implementation is intended to help avoid needing to so low-level math and logic on curve datasets for most typical model-building use cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "855b6693-e96c-43d0-a815-6ba222c6b0bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 300.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(8, 12), sharex=True)\n",
    "\n",
    "cvol = c\n",
    "ccai = c.cai()\n",
    "cmai = c.mai()\n",
    "cmaiytp = c.mai().ytp()\n",
    "x_cmai = cmaiytp.lookup(0) # optimal rotation age (i.e., culmination of MAI curve)\n",
    "labels = 'total volume', 'MAI (and CAI)', 'MAI YTP'\n",
    "\n",
    "ax[0].plot(*zip(*c.points()))\n",
    "ax[0].plot([0, x_cmai], [0., cvol[x_cmai]], linestyle='--', color='green')\n",
    "\n",
    "ax[1].plot(*zip(*c.mai().points()))\n",
    "ax[1].plot(*zip(*c.cai().points()), linestyle=':')\n",
    "\n",
    "ax[2].plot(*zip(*c.mai().ytp().points()))\n",
    "ax[2].axhline(0, color='black')\n",
    "\n",
    "for i in range(len(ax)):\n",
    "    ax[i].set_ylabel(labels[i])\n",
    "    ax[i].set_ylim(0, None)\n",
    "    ax[i].axvline(x_cmai, color='red')\n",
    "plt.xlim(0, 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89395e58-3954-440a-adb1-e14765d120f6",
   "metadata": {},
   "source": [
    "Note that when setting up a new `ForestModel` instance, you have to stash a copy of _all_ the yield curves you need, _including the curves for development types that are not present in the intial inventory_ (`ws3` grabs a copy of these when JIt-building new `DevelopmentType` objects on the fly, only if induced by applying an action to an development type and the specified transition induces a new development type). \n",
    "\n",
    "All of this gets automatically set up correctly if you use the built-in functions to import Woostock-style input files, but is your responsibility if you bypass and direct-code your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c2323544-b370-436c-8006-b390762accab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49,\n",
       " (('?', '?', '2401000', '?', '2401000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0ef3ac0>)]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fm.yields), fm.yields[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3094bd2-4a46-486f-99e9-67ff0b01e392",
   "metadata": {},
   "source": [
    "Have a look at the actions in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71b9999c-49a4-47c2-a069-f649ae4c17f0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'harvest': <ws3.forest.Action at 0x7fa7d0c4a980>}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3a683e85-c28e-4fea-a0af-59b064cfc349",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('?', '?', '2402000', '?', '?'),\n",
       "  {'': [(('?', '?', '?', '?', '2422000'),\n",
       "     1.0,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None)]}),\n",
       " (('?', '?', '2403000', '?', '?'),\n",
       "  {'': [(('?', '?', '?', '?', '2423000'),\n",
       "     1.0,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None)]}),\n",
       " (('?', '?', '2403001', '?', '?'),\n",
       "  {'': [(('?', '?', '?', '?', '2423001'),\n",
       "     1.0,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None,\n",
       "     None)]})]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(fm.transitions['harvest'].items())[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b3c9391f-cf41-45cc-b0ee-cd2f66dc3d3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list, {'harvest': ['_age >= 80 and _age <= 500']})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.oper_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fde6fb84-65ca-4133-bb5d-a994e07339e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'harvest': {1: (80, 500),\n",
       "  2: (80, 500),\n",
       "  3: (80, 500),\n",
       "  4: (80, 500),\n",
       "  5: (80, 500),\n",
       "  6: (80, 500),\n",
       "  7: (80, 500),\n",
       "  8: (80, 500),\n",
       "  9: (80, 500),\n",
       "  10: (80, 500)}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.operability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bb6e7ce8-7e80-4a05-9d9c-7583256682f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('harvest', 80),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)]),\n",
       " (('harvest', 81),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)]),\n",
       " (('harvest', 82),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)])]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dt.transitions.items())[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2a93cae4-96c5-4034-aec6-b1c8ba97fc43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('harvest', 498),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)]),\n",
       " (('harvest', 499),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)]),\n",
       " (('harvest', 500),\n",
       "  [(('?', '?', '?', '?', '2423002'), 1.0, None, None, None, None, None)])]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dt.transitions.items())[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f5b4801-63f7-4a73-b494-d3d2b3f999b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('tsa24_clipped',\n",
       "  '0',\n",
       "  '2401000',\n",
       "  '100',\n",
       "  '2401000'): <ws3.forest.DevelopmentType at 0x7fa7d0b6a7d0>,\n",
       " ('tsa24_clipped',\n",
       "  '0',\n",
       "  '2402005',\n",
       "  '1201',\n",
       "  '2402005'): <ws3.forest.DevelopmentType at 0x7fa7d0b6bbb0>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2401002',\n",
       "  '204',\n",
       "  '2401002'): <ws3.forest.DevelopmentType at 0x7fa7d0b6b8b0>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2401002',\n",
       "  '204',\n",
       "  '2421002'): <ws3.forest.DevelopmentType at 0x7fa7d0b6bc10>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2402000',\n",
       "  '100',\n",
       "  '2402000'): <ws3.forest.DevelopmentType at 0x7fa7d0b6ba60>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2402002',\n",
       "  '204',\n",
       "  '2402002'): <ws3.forest.DevelopmentType at 0x7fa7d0b6bfd0>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2403000',\n",
       "  '100',\n",
       "  '2403000'): <ws3.forest.DevelopmentType at 0x7fa7d0b6ace0>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2403002',\n",
       "  '204',\n",
       "  '2403002'): <ws3.forest.DevelopmentType at 0x7fa7d0b6b4c0>,\n",
       " ('tsa24_clipped',\n",
       "  '1',\n",
       "  '2403002',\n",
       "  '204',\n",
       "  '2423002'): <ws3.forest.DevelopmentType at 0x7fa7d0b68220>}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c476cdb8-9967-456f-b43b-4ec320bdcd49",
   "metadata": {},
   "source": [
    "Get a reference to one of the managed (i.e., THLB) `DevelopmentType` instances we now have in `fm` (specifically, the `DevelopmentType` with key `('tsa24_clipped', '1', '2403002', '204', '2423002')`. We do this by _unmasking_ the `DevelopmentType` list using a mask string in Woodstock format, where the `?` represents a wildcard. Refer to the `ws3` documentation for a more in-depth explanation of theme and masks and development types and such (this documentation is a WIP so this may not be clearly documented yet). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5b60b1cb-f7c5-4410-9eba-0c5cfb66d51b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('tsa24_clipped', '1', '2403002', '204', '2423002')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtk = fm.unmask('? 1 ? ? ?').pop()\n",
    "dtk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "90aee76f-10a4-4449-82b5-a84d394b317a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('tsa24_clipped', '1', '2403002', '204', '2423002')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = fm.dtypes[dtk]\n",
    "dt.key"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bca2dc2-70d9-4776-892f-89cb98522c78",
   "metadata": {},
   "source": [
    "Inspect the `_areas` private attribute of the `DevelopmentType` instance we grabbed. This data structure stores the _inventory_ for the parent `DevelopmentType` (i.e., area by period and ageclass). \n",
    "\n",
    "At this point in the modelling process, we have only defined the _initial inventory_ in period 0. The other periods get populated later when we initialize the period-1 inventory and simulated growth and actions (which we have not done yet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbd47c7d-0a3f-408c-8531-c1ad02b4340a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: defaultdict(float, {9: 59.81429119367274, 18: 32.366198551219505}),\n",
       " 1: defaultdict(float, {}),\n",
       " 2: defaultdict(float, {}),\n",
       " 3: defaultdict(float, {}),\n",
       " 4: defaultdict(float, {}),\n",
       " 5: defaultdict(float, {}),\n",
       " 6: defaultdict(float, {}),\n",
       " 7: defaultdict(float, {}),\n",
       " 8: defaultdict(float, {}),\n",
       " 9: defaultdict(float, {}),\n",
       " 10: defaultdict(float, {})}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt._areas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b0959e-e106-44e8-a12b-713d2b6ef599",
   "metadata": {},
   "source": [
    "You can see that initial inventory for this `DevelopementType` contains 59.8 hectares in ageclass 9 and 32.4 hectares in ageclass 18). \n",
    "\n",
    "Below, we should how you can access this same information using the public interfaces built into the `DevelopmentType` and `ForestModel` classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "71ebdf5a-9d8e-404b-8810-89c91f2226cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.18048974489224"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.area(period=0) # total inventory area in period 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d78c3229-626c-4416-a395-e806c5daa381",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{9: 59.81429119367274, 18: 32.366198551219505}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acd = fm.age_class_distribution(0, mask=dt.key, omit_null=True)\n",
    "acd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b3388762-813c-4af8-96f2-dc67e8d6ffd3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 59.81429119367274\n",
      "18 32.366198551219505\n"
     ]
    }
   ],
   "source": [
    "for age in acd:\n",
    "    print(age, dt.area(period=0, age=age)) # ageclass 9 inventory area in period 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce5cd85-f309-415b-849f-f4fd5691fb63",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Implement a priority queue harvest scheduling heuristic\n",
    "\n",
    "Below we define a function that implements a old-stand-first priority queue harvest scheduling heuristic. To make this demo as n00b friends as possible, we set this up to be self-parametrising, i.e., the model automatically figures out an appropriate \"periodic harvest area target\" parameter value, by analysing the shape of its own yield curves to estimate a landscape-level optimal rotation age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "15b44ea1-55fa-4c68-a907-a2ca82c5c742",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def schedule_harvest_areacontrol(fm, period=None, acode='harvest', util=0.85, \n",
    "                                 target_masks=None, target_areas=None,\n",
    "                                 target_scalefactors=None,\n",
    "                                 mask_area_thresh=0.,\n",
    "                                 verbose=0):\n",
    "    if not target_areas:\n",
    "        if not target_masks: # default to AU-wise THLB \n",
    "            au_vals = []\n",
    "            au_agg = []\n",
    "            for au in fm.theme_basecodes(2):\n",
    "                mask = '? 1 %s ? ?' % au\n",
    "                masked_area = fm.inventory(0, mask=mask)\n",
    "                if masked_area > mask_area_thresh:\n",
    "                    au_vals.append(au)\n",
    "                else:\n",
    "                    au_agg.append(au)\n",
    "                    if verbose > 0:\n",
    "                        print('adding to au_agg', mask, masked_area)\n",
    "            if au_agg:\n",
    "                fm._themes[2]['areacontrol_au_agg'] = au_agg \n",
    "                if fm.inventory(0, mask='? ? areacontrol_au_agg ? ?') > mask_area_thresh:\n",
    "                    au_vals.append('areacontrol_au_agg')\n",
    "            target_masks = ['? 1 %s ? ?' % au for au in au_vals]\n",
    "        target_areas = []\n",
    "        for i, mask in enumerate(target_masks): # compute area-weighted mean CMAI age for each masked DT set\n",
    "            masked_area = fm.inventory(0, mask=mask, verbose=verbose)\n",
    "            if not masked_area: continue\n",
    "            r = sum((fm.dtypes[dtk].ycomp('totvol').mai().ytp().lookup(0) * fm.dtypes[dtk].area(0)) for dtk in fm.unmask(mask))\n",
    "            r /= masked_area\n",
    "            asf = 1. if not target_scalefactors else target_scalefactors[i]  \n",
    "            ta = (1/r) * fm.period_length * masked_area * asf\n",
    "            #print(mask, masked_area, r, asf, ta)\n",
    "            target_areas.append(ta)\n",
    "    periods = fm.periods if not period else [period]\n",
    "    for period in periods:\n",
    "        for mask, target_area in zip(target_masks, target_areas):\n",
    "            if verbose > 0:\n",
    "                print('calling areaselector', period, acode, target_area, mask)\n",
    "            fm.areaselector.operate(period, acode, target_area, mask=mask, verbose=verbose)\n",
    "    sch = fm.compile_schedule()\n",
    "    return sch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd26c6b4-2c0f-4d3c-b915-2afd6e9671fb",
   "metadata": {},
   "source": [
    "Define some helper functions to compile and plot scenario results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4b76b658-b43c-43af-b884-36d5dd2297e8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def compile_scenario(fm):\n",
    "    oha = [fm.compile_product(period, '1.', acode='harvest') for period in fm.periods]\n",
    "    ohv = [fm.compile_product(period, 'totvol * 0.85', acode='harvest') for period in fm.periods]\n",
    "    ogs = [fm.inventory(period, 'totvol') for period in fm.periods]\n",
    "    data = {'period':fm.periods, \n",
    "            'oha':oha, \n",
    "            'ohv':ohv, \n",
    "            'ogs':ogs}\n",
    "    df = pd.DataFrame(data)\n",
    "    return df\n",
    "\n",
    "\n",
    "def plot_scenario(df):\n",
    "    fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
    "    ax[0].bar(df.period, df.oha)\n",
    "    ax[0].set_ylim(0, None)\n",
    "    ax[0].set_title('Harvested area (ha)')\n",
    "    ax[1].bar(df.period, df.ohv)\n",
    "    ax[1].set_ylim(0, None)\n",
    "    ax[1].set_title('Harvested volume (m3)')\n",
    "    ax[2].bar(df.period, df.ogs)\n",
    "    ax[2].set_ylim(0, None)\n",
    "    ax[2].set_title('Growing Stock (m3)')\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b1e30372-b8de-4498-89d0-25fdaa99e403",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f803e4ee-ef0e-4220-9242-939e26d12a2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sch = schedule_harvest_areacontrol(fm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a467f0a6-3052-4b53-8978-ea745e36740f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       " array([<Axes: title={'center': 'Harvested area (ha)'}>,\n",
       "        <Axes: title={'center': 'Harvested volume (m3)'}>,\n",
       "        <Axes: title={'center': 'Growing Stock (m3)'}>], dtype=object))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hQwIAyf+Vd955p8T3kvz8fEGtVguDBg0Sy5YuXSoAEKKjo8Wy+/fvC46OjiXa1Gf//v2CoaGhYGhoKLi5uQnTp08X9u3bJxQUFJSINTMzk8xHOt7e3oJSqRSuX78ult25c0eoXbu20K1bN7EsJCREACDs2LGjRBu6eUf3WWzfvl34559/hHfeeUeoV6+ecPbs2TLPQxAE4cCBAwIA4eeffy5Rp/u5mJCQIJbt27dPACCYmppKvn9+/fXXpX52np6e4vymVCqF8ePHS35mPH7+AIRFixY9ddyvA946Xo2tXbsWsbGxJQ7dVZHHPX6FQ3el6J133sFvv/2GnJwcSayDgwM8PT0lZW+99Rbatm2LrVu3imVFRUX44Ycf0K9fP7H97du3Q6VS4V//+hf++usv8XB2dkatWrXE3yTqroDs3r1b7xWqsmRlZeHgwYMYOnQo/vnnH7GPv//+G56enrh69ap4S+PevXvRsWNHyVWD+vXri7c8Pc3jn9uDBw/w119/ic+3nDlzpkT8k4tM7NixA8XFxRg6dKjk81Cr1WjatKnkN6uP95WXl4e//voLnTp1giAIT71VR3cru+4K1JPc3d3F37ACj66cmZub47ffftPbf2FhIf7++2+8+eabsLCw0Huuur7Kuq2J6Hlxfqv4+e39999HcnIyrl+/LpZt3boVxsbGGDBgAIBHt8BnZmbi448/ljwr6eXlBUdHR+zZs+eZzu9pHn/Wz9DQEC4uLhAEAX5+fmK5hYUFmjVrJpm/tm/fjubNm8PR0VHyd6N7zvlpV7H+/vvvUudPAPDz85PcweTq6lpiXLrxPj4unRYtWiA2NhbR0dGYPn06zMzMSqw6rlOnTh3cv38f9+7dK3PMRM9Kq9UCgN6dSbp374769euLx9q1a0vETJw4UfJ67969MDQ0xOTJkyXln3zyCQRBEG8Bb9euHWrVqoX4+HgAj65c626NPnPmDO7duwdBEHDs2DF07dr1qedRq1YtfPDBB+JrpVKJDh06SP7vxcTEoEGDBuLVcuDRgoPl3b7vX//6FxITE9G/f3/88ssvCA8Ph6enJxo0aKD3MZ4nFRUVYf/+/fD29sYbb7whltvY2GD48OE4duyY+Pfx448/ok2bNuLdSo978s7JnJwceHh44MqVKzh8+DDatm371LE87TtiixYt4ObmJr7WXWl/99130ahRoxLl+ua4hQsXYv/+/fjuu+/QsWNHFBQU4OHDhyXi+N1RireOV2MdOnSAi4tLifI6deqU+Ad8/PhxfPHFF0hMTCzxwzsnJwcqlUp87eDgoLe/999/H5999hlu376NBg0a4PDhw8jMzMT7778vxly9ehU5OTmS51cep1tA4p133sGgQYMwZ84cLF++HN27d4e3tzeGDx8OY2PjMs/72rVrEAQBs2fPxuzZs0vtp0GDBrh586be26OaNWtWZh86WVlZmDNnDrZs2VJi8Ysnv8ADJT+7q1evQhAENG3aVG/7NWrUEP+cnp6OkJAQ7Nq1q8Tznvr60kco5dnAxydKnTp16kj6uX//PsLCwrBx40bcvn1b0pa+/nX1L/u+uVQ9cX6r+PltyJAhCAoKwtatW/HZZ59BEARs375dfJ4QgPgMt742HR0d9S709SKenKtUKhVMTExK3DqpUqkka2VcvXoVly9fFp99f1J5Fi8qbf4sbVwAJM+x6sqfnL8BwNzcXHz+e8CAAYiKisKAAQNw5swZtGnTRu84OLeS3GrXrg0Aen/Jo9tJJCMjQ5LE6hgZGaFhw4aSsps3b8LW1lZsV6d58+ZiPfDol1Bubm44evQogEeJdteuXdGlSxcUFRXhxIkTsLa2RlZWVrkS7YYNG5b4/1GnTh3JtmQ3b95EkyZNSsQ9y64Cb7/9Nnbs2IGCggL88ssv2LlzJ5YvX47BgwcjJSUFLVq0KPW9f/75J+7du6d37mzevDmKi4tx69YttGzZEtevX8egQYPKNabAwEA8ePAAZ8+eRcuWLct9LkD5vyOWNb8B0DvHPZ7wf/DBB2jfvj0++uijEvt+c36TYqL9Crh+/Tp69uwJR0dHLFu2DHZ2dlAqldi7dy+WL19eYuGc0p7ve//99xEcHIzt27cjMDAQ27Ztg0qlkqwoWFxcDCsrK0RGRuptQ/clSKFQ4IcffsCJEyfw888/Y9++fRg9ejSWLl2KEydOlLkPtG68n376aYkrUzpybc8ydOhQJCQkYNq0aWjbti1q1aqF4uLiEtuy6Dz52RUXF4sLexgaGpaI151nUVER/vWvfyErKwszZsyAo6MjzMzMcPv2bXz00UdPXdxI95z13bt3S/wgBKC3b0A66U6aNAkbN25EYGAg3NzcoFKpoFAo4OPjo7d/3USr79khosrC+e352draomvXrti2bRs+++wznDhxAunp6Vi0aJEs7Zf2RaqoqKjU9+ibq8ozfxUXF8PJyQnLli3TG/vkF8Yn1a1bV++Xx6eNQV95WQm7zsCBA/Hhhx9iy5YtJRLtu3fvombNmlwNn2SnUqlgY2ODCxculKjT/dJO3wKoAGBsbPzUlcjL0qVLF3EP5aNHj+Lzzz+HhYUFWrVqhaNHj8La2hoAypVol2dOkJNSqcTbb7+Nt99+G2+99RZGjRqF7du344svvqiQ/soyYMAAbNmyBQsXLsT3339frr+Tx78j6vMs8xvw9M9ZqVSif//+WLhwIe7fvy+Zy/jdUYqJ9ivg559/Rn5+Pnbt2iX5rVV5F4TQcXBwQIcOHbB161YEBARgx44d8Pb2llyhadKkCQ4cOIDOnTuX60tCx44d0bFjR3z55ZeIioqCr68vtmzZgjFjxpT6JU13C06NGjXKXGUbeLSYxdWrV0uUp6amPnVsd+/eRVxcHObMmYOQkBCxXF97pWnSpAkEQYCDgwPeeuutUuPOnz+PX3/9FZs3b5YsohMbG1uufhwdHQEAaWlpcHJyKvf4HvfDDz9g5MiRktV8Hzx4oHfVSF1f9erVK/UKElFl4Pz2fPObzvvvv4+PP/4Yqamp2Lp1K2rWrIl+/fpJ+tC1+eR2U6mpqaUuHgb8/1sEn5xDKmJ/3iZNmuCXX35Bz549n+tKiaOjI3788UfZx1Wa/Px8FBcX671bKC0tTbwiSCQ3Ly8vfPvttzh58qTexfiehb29PQ4cOIB//vlHclX7ypUrYr1O165dUVBQgP/+97+4ffu2mFB369ZNTLTfeustMeF+Ufb29rh06RIEQZDMCdeuXXuhdnV3Wv3xxx9imb45p379+qhZs6be+fjKlSswMDAQfwHYpEkTvb/80Mfb2xseHh746KOPULt2bb07bDzp8e+IleX+/fsQBAH//POP5Oelbgyc4x7hM9qvAN1vpJ68FXjjxo3P3Nb777+PEydOYMOGDfjrr78kt1UCj64AFxUVYd68eSXeq1t1FXiUxD75GzHdbSe6rVh0q1w/+SXNysoK3bt3x9dffy2Z6HT+/PNP8c99+vTBiRMncPLkSUl9aVekHqfvcwOAFStWPPW9OgMHDoShoSHmzJlToh1BEMRbH/X1JQgCVq5cWa5+nJ2doVQqcfr06XKP7UmGhoYlxrh69epSrz4lJydLnukhqgqc355vftMZNGgQDA0N8d///hfbt29H3759JXvjuri4wMrKChEREZJtsv73v//h8uXL8PLyKrVtc3Nz1KtXT3wuU+err74q9/jKa+jQobh9+za++eabEnX3799HXl5eme93c3PD3bt39T57+CKys7P1Pqf/7bffAoDexyPOnDmDTp06yToOIp3p06ejZs2aGD16NDIyMkrUP8tV4T59+qCoqAhr1qyRlC9fvhwKhUJcARx4dMW8Ro0aWLRoESwtLcXbnrt27YoTJ07gyJEj5bqaXV6enp64ffu25HnqBw8e6J0j9Dl06JDez2Lv3r0ApI/TmJmZlZjLDQ0N4eHhgZ9++klyl0BGRgaioqLQpUsX8RGdQYMGibemP0nfGEaMGIFVq1YhIiICM2bMeOq5NGjQAHZ2di/0HbE0+h7Lyc7Oxo8//gg7O7sSj1olJydDoVDw++P/4RXtV4CHhweUSiX69euH8ePHIzc3F9988w2srKz0fpEry9ChQ/Hpp5/i008/haWlZYkrLu+88w7Gjx+PsLAwpKSkwMPDAzVq1MDVq1exfft2rFy5EoMHD8bmzZvx1Vdf4b333kOTJk3wzz//4JtvvoG5uTn69OkD4NEtni1atMDWrVvx1ltvwdLSEq1atUKrVq2wdu1adOnSBU5OThg7dizeeOMNZGRkIDExEb///jt++eUXAI9+oPz73/9Gr169MGXKFHH7G3t7e8mzPPqYm5ujW7duCA8PR2FhIRo0aID9+/c/028EmzRpgvnz5yM4OBg3btyAt7c3ateujbS0NOzcuRPjxo3Dp59+CkdHRzRp0gSffvopbt++DXNzc/z4449l3sr4OBMTE3h4eODAgQOYO3duucf3uL59++Lf//43VCoVWrRogcTERBw4cEC85ehxmZmZOHfuXImtk4gqG+e355vfdKysrNCjRw8sW7YM//zzT4lfLui+GI8aNQrvvPMOhg0bJm7v1bhxY0ydOrXM9seMGYOFCxdizJgxcHFxQXx8PH799dfy/pWU24cffoht27ZhwoQJOHToEDp37oyioiJcuXIF27ZtE/dOL42XlxeMjIxw4MABcYseORw+fBiTJ0/G4MGD0bRpUxQUFODo0aPYsWMHXFxcSjwLm5ycjKysLHExOiK5NW3aFFFRURg2bBiaNWsGX19ftGnTBoIgIC0tDVFRUTAwMND7GNqT+vXrhx49euDzzz/HjRs30KZNG+zfvx8//fQTAgMDJYuw1qxZE87Ozjhx4oS4hzbw6Ip2Xl4e8vLyZE20x48fjzVr1mDYsGGYMmUKbGxsEBkZKS7q+LQ7XyZNmoR79+7hvffeg6OjIwoKCpCQkICtW7eicePGGDVqlBjr7OyMAwcOYNmyZbC1tYWDgwNcXV0xf/58xMbGokuXLvj4449hZGSEr7/+Gvn5+ZJ9v6dNm4YffvgBQ4YMwejRo+Hs7IysrCzs2rULERERJR4vAYCAgABotVp8/vnnUKlUerfSetyAAQOwc+fOElf4X1Tv3r3RsGFDuLq6wsrKCunp6di4cSPu3LkjWWBUJzY2Fp07d9b73fK1VIErmtNz0m03UtrWMe+8806J7W927doltG7dWjAxMREaN24sLFq0SNiwYUOJLWDs7e0FLy+vMvvv3LmzAEAYM2ZMqTHr168XnJ2dBVNTU6F27dqCk5OTMH36dOHOnTuCIAjCmTNnhGHDhgmNGjUSjI2NBSsrK6Fv377C6dOnJe0kJCQIzs7OglKpLLEdzPXr14URI0YIarVaqFGjhtCgQQOhb9++wg8//CBp49y5c8I777wjmJiYCA0aNBDmzZsnfPfdd+Xa/ub3338X3nvvPcHCwkJQqVTCkCFDxK0J9G1N8+eff+pt58cffxS6dOkimJmZCWZmZoKjo6Pg7+8vpKamijGXLl0S3N3dhVq1agn16tUTxo4dK27DpW97nCft2LFDUCgUQnp6uqQcgODv718i3t7eXrIdxd27d4VRo0YJ9erVE2rVqiV4enoKV65cKREnCIKwbt06oWbNmoJWq33quIieBee3RypjftP55ptvBABC7dq19W7HIgiCsHXrVqFdu3aCsbGxYGlpKfj6+gq///67JObJ7b0E4dHWa35+foJKpRJq164tDB06VMjMzCz3HDpy5EjBzMysxHj0/TsoKCgQFi1aJLRs2VIwNjYW6tSpIzg7Owtz5swRcnJynvo59O/fX+jZs6ekrLR/j+Ud77Vr14QRI0YIb7zxhmBqaiqYmJgILVu2FL744gshNze3xBhmzJghNGrUSLKVGFFFuHbtmjBx4kThzTffFExMTARTU1PB0dFRmDBhgpCSkiKJLe3/oSAIwj///CNMnTpVsLW1FWrUqCE0bdpUWLx4sd5/w9OmTdO7tdObb74pAJBsgyUIpW/v9eT/fd0Y7e3tJWW//fab4OXlJZiamgr169cXPvnkE+HHH38UAAgnTpwo6+MR/ve//wmjR48WHB0dhVq1aglKpVJ48803hUmTJpXY1vTKlStCt27dBFNTUwGA5DvTmTNnBE9PT6FWrVpCzZo1hR49eki20tL5+++/hYCAAKFBgwaCUqkUGjZsKIwcOVL466+/JJ/F9u3bJe+bPn26AEBYs2ZNmedz5swZAUCJLRBL+7mo77ujbsvGxYsXi2Vr1qwRunTpItSrV08wMjIS6tevL/Tr10+yBa5Odna2oFQqhW+//bbMsb5OFIJQQSsLEJGsioqK0KJFCwwdOlTvra1yateuHbp3747ly5dXaD9ERJXl6NGj6N69O65cuVLqThEVKT8/H40bN8bMmTMxZcqUSu+f6HWwYsUKTJ06Fb///jsaNGhQ1cOpVD179oStrS3+/e9/V0n/K1asQHh4OK5fv87FHv8PE22il8jWrVsxceJEpKenl7my8YuIiYnB4MGD8dtvv5W6zRER0ctIdxtkeZ/jlFNERAQWLFiAq1evPnUbOCJ6uidXvH7w4AHatWuHoqKiCnmEpbpLSkpC165dcfXq1TIXsqwIhYWFaNKkCWbOnImPP/64UvuuzphoExERERHRS6V3795o1KgR2rZti5ycHPznP//BxYsXERkZieHDh1f18Ii4GBoREREREb1cPD098e233yIyMlJ8vG7Lli0lFn0kqiq8ok1EREREREQkI+6jTURERPSaio+PR79+/WBrawuFQoHo6OhSYydMmACFQoEVK1ZIyrOysuDr6wtzc3NYWFjAz88Pubm5kphz586ha9euMDExgZ2dnWT7I53t27fD0dERJiYmcHJyEvc01hEEASEhIbCxsYGpqSnc3d1x9erV5z53IqKKxESbiIiI6DWVl5eHNm3aYO3atWXG7dy5EydOnICtrW2JOl9fX1y8eBGxsbHYvXs34uPjJfuVa7VaeHh4wN7eHsnJyVi8eDFCQ0Oxfv16MSYhIQHDhg2Dn58fzp49C29vb3h7e+PChQtiTHh4OFatWoWIiAgkJSXBzMwMnp6eePDggQyfBBGRvF7KW8eLi4tx584d1K5dW9ZN2Yno9SEIAv755x/Y2tpi0aJF2LFjB65cuQJTU1N06tQJixYtQrNmzcT4Bw8e4JNPPsGWLVuQn58PT09PfPXVV7C2thZj0tPTMXHiRBw6dAi1atXCyJEjERYWBiOj/78cxuHDhxEUFISLFy/Czs4Os2bNwkcffSQZ29q1a7F48WJoNBq0adMGq1evRocOHcp1Xpwfieh5qVQqREZGwsvLS5wfDQwMcPv2bbi6umLfvn3w8vJCYGAgAgMDAQCXL19GixYtcOrUKbi4uAB4tHtFnz598Pvvv8PW1hbr1q3D559/Do1GA6VSCQCYOXMmoqOjceXKFQDA+++/j7y8POzevVscT8eOHdG2bVtERERAEATY2trik08+waeffgoAyMnJgbW1NTZt2gQfH59ynSPnSCJ6EY9/fzQweMo166rYvPtF3bp1SwDAgwcPHi983Lp1S/D09BQ2btwoXLhwQUhJSRH69OkjNGrUSMjNzRXnnQkTJgh2dnZCXFyccPr0aaFjx45Cp06dxPqHDx8KrVq1Etzd3YWzZ88Ke/fuFerVqycEBweLMb/99ptQs2ZNISgoSLh06ZKwevVqwdDQUIiJiRFjtmzZIiiVSmHDhg3CxYsXhbFjxwoWFhZCRkYG50cePHhU6nHr1i2hqKhI6NGjh7BixQpBEATB3t5eWL58uTjnfPfdd4KFhYVkHiosLBQMDQ2FHTt2CIIgCB9++KEwYMAASczBgwcFAEJWVpYgCIJgZ2cnaVcQBCEkJERo3bq1IAiCcP36dQGAcPbsWUlMt27dhMmTJ5drfuQcyYMHD7mOW7duPXW+eSlXHa9duzYA4NatWzA3N6/i0RDRy0ir1cLOzg61a9dGTEyMpG7Tpk2wsrJCcnIyunXrhpycHHz33XeIiorCu+++CwDYuHEjmjdvjhMnTqBjx47Yv38/Ll26hAMHDsDa2hpt27bFvHnzMGPGDISGhkKpVCIiIgIODg5YunQpAKB58+Y4duwYli9fDk9PTwDAsmXLMHbsWIwaNQrAo7139+zZgw0bNmDmzJlPPS/Oj0T0vHRXtLt16ybOj4sWLYKRkREmT56s9z0ajQZWVlaSMiMjI1haWkKj0YgxDg4Okhjd3UAajQZ16tSBRqOR3CGki3m8jcffpy9Gn/z8fOTn54uvhf+7kZNzJBE9j8e/Pz7NS5lo6271MTc35yRJRC9E362DOTk5AABLS0sAQHJyMgoLC+Hu7i7GODo6olGjRkhMTETHjh2RmJgIJycnyZdAT09PTJw4ERcvXkS7du2QmJgoaUMXo7sFs6CgAMnJyQgODhbrDQwM4O7ujsTExGc6H86PRPQ8atasKc4dKSkpWLlyJc6cOfPS3mYdFhaGOXPmlCjnHElEL6I8cyIXQyMiekxxcTECAwPRuXNntGrVCgDE5wotLCwksU9ebdF3pUVXV1aMVqvF/fv38ddff6GoqOiZrtjk5+dDq9VKDiIiOSQmJiIzMxONGjWCkZERjIyMcPPmTXzyySdo3LgxAECtViMzM1PyvocPHyIrKwtqtVqMycjIkMToXj8t5vH6x9+nL0af4OBg5OTkiMetW7ee5SMgInpuTLSJiB7j7++PCxcuYMuWLVU9lHIJCwuDSqUSDzs7u6oeEhG9Inx8fHDu3DmkpKSIh62tLaZNm4Z9+/YBANzc3JCdnY3k5GTxfQcPHkRxcTFcXV3FmPj4eBQWFooxsbGxaNasGerUqSPGxMXFSfqPjY2Fm5sbAMDBwQFqtVoSo9VqkZSUJMboY2xsLF695lVsIqpMTLSJiP5PQEAAdu/ejUOHDqFhw4ZiuVqtRkFBAbKzsyXxT15ted4rNubm5jA1NUW9evVgaGj4TFdseLWGiF5Ebm6umEQDQFpaGs6dOwfg0eMzrVq1khw1atSAWq0Wd2Vo3rw5evXqhbFjx+LkyZM4fvw4AgIC4OPjI24FNnz4cCiVSvj5+eHixYvYunUrVq5ciaCgIHEcU6ZMQUxMDJYuXYorV64gNDQUp0+fRkBAAIBHt2kGBgZi/vz52LVrF86fP48RI0bA1tYW3t7elfeBERGVExNtInrtCYKAgIAA7Ny5EwcPHiyxaI+zszNq1KghuZKSmpqK9PR08UqKm5sbzp8/L7mFMjY2Fubm5mjRooUYU9YVG6VSCWdnZ0lMcXEx4uLiSr1iw6s1RPQiTp8+jXbt2qFdu3YAgKCgIHTt2vWZ2oiMjISjoyN69uyJPn36oEuXLpI9slUqFfbv34+0tDQ4Ozvjk08+QUhIiGSv7U6dOiEqKgrr169HmzZt8MMPPyA6Olp8hAcApk+fjkmTJmHcuHF4++23kZubi5iYGJiYmLzgp0BEJL+Xch9trVYLlUqFnJwcfqkkoufy+Dwyc+ZMREVF4aeffpLsna1SqWBqagoAmDhxIvbu3YtNmzbB3NwckyZNAgAkJCQAAIqKitC2bVvY2toiPDwcGo0GH374IcaMGYMFCxYAeHSlqFWrVvD398fo0aNx8OBBTJ48GXv27BFXHd+6dStGjhyJr7/+Gh06dMCKFSuwbds2XLlypcSz2087L86PRPQ8XuV55FU+NyKqeM8yh7yUq44TEclp3bp1AIDu3btLyjdu3IiPPvoIALB8+XIYGBhg0KBByM/Ph6enJ7766isx1tDQELt378bEiRPh5uYGMzMzjBw5EnPnzhVjHBwcsGfPHkydOhUrV65Ew4YN8e2334pJNgC8//77+PPPPxESEgKNRoO2bdsiJiamXEk2EREREVUPvKJNRK+lV3UeeVXPi4gqz6s8j7zK50ZEFe9Z5hA+o01EREREREQkIybaRERERERERDJiok1EREREREQkIybaRERERERERDJiok1EREREREQkI27vRfQSaTxzj6zt3VjoJWt7RERVRe75EeAcSY/wZy8RPQ9e0SYiIiIiIiKS0TMn2vHx8ejXrx9sbW2hUCgQHR0t1hUWFmLGjBlwcnKCmZkZbG1tMWLECNy5c0fSRlZWFnx9fWFubg4LCwv4+fkhNzf3hU+GiIiIiIiIqKo9863jeXl5aNOmDUaPHo2BAwdK6u7du4czZ85g9uzZaNOmDe7evYspU6agf//+OH36tBjn6+uLP/74A7GxsSgsLMSoUaMwbtw4REVFvfgZEdEL421yRERERETP75kT7d69e6N3795661QqFWJjYyVla9asQYcOHZCeno5GjRrh8uXLiImJwalTp+Di4gIAWL16Nfr06YMlS5bA1tb2OU6DiIiIiIiIqHqo8Ge0c3JyoFAoYGFhAQBITEyEhYWFmGQDgLu7OwwMDJCUlFTRwyEiIiIiIiKqUBW66viDBw8wY8YMDBs2DObm5gAAjUYDKysr6SCMjGBpaQmNRqO3nfz8fOTn54uvtVptxQ2aiIiIiIiI6AVU2BXtwsJCDB06FIIgYN26dS/UVlhYGFQqlXjY2dnJNEoiIiIiIiIieVVIoq1Lsm/evInY2FjxajYAqNVqZGZmSuIfPnyIrKwsqNVqve0FBwcjJydHPG7dulURwyYiIiIiIiJ6YbLfOq5Lsq9evYpDhw6hbt26kno3NzdkZ2cjOTkZzs7OAICDBw+iuLgYrq6uets0NjaGsbGx3EMlIiIiIiIikt0zJ9q5ubm4du2a+DotLQ0pKSmwtLSEjY0NBg8ejDNnzmD37t0oKioSn7u2tLSEUqlE8+bN0atXL4wdOxYREREoLCxEQEAAfHx8uOI4ERERERERvfSeOdE+ffo0evToIb4OCgoCAIwcORKhoaHYtWsXAKBt27aS9x06dAjdu3cHAERGRiIgIAA9e/aEgYEBBg0ahFWrVj3nKRARERERvbwaz9wje5s3FnrJ3iYRld8zJ9rdu3eHIAil1pdVp2NpaYmoqKhn7ZqIiIiIiIio2qvwfbSJiIiIiIiIXidMtImIiIiIiIhkxESbiIiIiIiISEZMtImIiIiIiIhkxESbiIiIiIiISEZMtImIiIiIiIhkxESbiIiIiIiISEbPvI82la3xzD2ytndjoZes7REREdHz4895IiIqD17RJiIiIiIiIpIRE20iIiIiIiIiGTHRJqLXXnx8PPr16wdbW1soFApER0dL6hUKhd5j8eLFYkzjxo1L1C9cuFDSzrlz59C1a1eYmJjAzs4O4eHhJcayfft2ODo6wsTEBE5OTti7d2+FnDMRERERVRwm2kT02svLy0ObNm2wdu1avfV//PGH5NiwYQMUCgUGDRokiZs7d64kbtKkSWKdVquFh4cH7O3tkZycjMWLFyM0NBTr168XYxISEjBs2DD4+fnh7Nmz8Pb2hre3Ny5cuFAxJ05EREREFeK1WgztVVnARO7zAPSfS2V8Xi9jH5XVDxfIqTy9e/dG7969S61Xq9WS1z/99BN69OiBN954Q1Jeu3btErE6kZGRKCgowIYNG6BUKtGyZUukpKRg2bJlGDduHABg5cqV6NWrF6ZNmwYAmDdvHmJjY7FmzRpERES8yCkSERHxuwpRJeIVbSKiZ5CRkYE9e/bAz8+vRN3ChQtRt25dtGvXDosXL8bDhw/FusTERHTr1g1KpVIs8/T0RGpqKu7evSvGuLu7S9r09PREYmJiqePJz8+HVquVHERE5VXWozOFhYWYMWMGnJycYGZmBltbW4wYMQJ37tyRtJGVlQVfX1+Ym5vDwsICfn5+yM3NlcTI8eiMIAgICQmBjY0NTE1N4e7ujqtXr8r3YRARyYiJNhHRM9i8eTNq166NgQMHSsonT56MLVu24NChQxg/fjwWLFiA6dOni/UajQbW1taS9+heazSaMmN09fqEhYVBpVKJh52d3QudHxG9Xsp6dObevXs4c+YMZs+ejTNnzmDHjh1ITU1F//79JXG+vr64ePEiYmNjsXv3bsTHx4t36gDyPToTHh6OVatWISIiAklJSTAzM4OnpycePHhQAZ8MEdGLea1uHScielEbNmyAr68vTExMJOVBQUHin1u3bg2lUonx48cjLCwMxsbGFTae4OBgSd9arZbJNhGVW1mPzqhUKsTGxkrK1qxZgw4dOiA9PR2NGjXC5cuXERMTg1OnTsHFxQUAsHr1avTp0wdLliyBra2tLI/OCIKAFStWYNasWRgwYAAA4Pvvv4e1tTWio6Ph4+NTUR8REdFz4RVtIqJyOnr0KFJTUzFmzJinxrq6uuLhw4e4ceMGgEfPeWdkZEhidK91z3WXFlPac98AYGxsDHNzc8lBRFRRcnJyoFAoYGFhAeDRIy8WFhZikg0A7u7uMDAwQFJSkhjzoo/OpKWlQaPRSGJUKhVcXV35eA0RVUtMtImIyum7776Ds7Mz2rRp89TYlJQUGBgYwMrKCgDg5uaG+Ph4FBYWijGxsbFo1qwZ6tSpI8bExcVJ2omNjYWbm5uMZ0FE9HwePHiAGTNmYNiwYeIv9TQajTjP6RgZGcHS0vKpj8Xo6sqKebz+8ffpi9GHj9cQUVVhok1Er73c3FykpKQgJSUFwKMrJykpKUhPTxdjtFottm/frvdqdmJiIlasWIFffvkFv/32GyIjIzF16lR88MEHYhI9fPhwKJVK+Pn54eLFi9i6dStWrlwpue17ypQpiImJwdKlS3HlyhWEhobi9OnTCAgIqNgPgIjoKQoLCzF06FAIgoB169ZV9XDKLTg4GDk5OeJx69atqh4SEb0m+Iw2EVWJ6rTFyOnTp9GjRw/xtS75HTlyJDZt2gQA2LJlCwRBwLBhw0q839jYGFu2bEFoaCjy8/Ph4OCAqVOnSpJolUqF/fv3w9/fH87OzqhXrx5CQkIkCwZ16tQJUVFRmDVrFj777DM0bdoU0dHRaNWq1XOfGxHRi9Il2Tdv3sTBgwclj6io1WpkZmZK4h8+fIisrKynPhajqysr5vF6XZmNjY0kpm3btqWO3djYuELXySAiKg0TbSJ67XXv3h2CIJQZM27cOElS/Lj27dvjxIkTT+2ndevWOHr0aJkxQ4YMwZAhQ57aFhFRZdAl2VevXsWhQ4dQt25dSb2bmxuys7ORnJwMZ2dnAMDBgwdRXFwMV1dXMebzzz9HYWEhatSoAaD0R2cCAwPFth9/dMbBwQFqtRpxcXFiYq3VapGUlISJEydW5EdARPRceOs4ERER0WtK36Mz586dA/AoyR48eDBOnz6NyMhIFBUVQaPRQKPRoKCgAADQvHlz9OrVC2PHjsXJkydx/PhxBAQEwMfHB7a2tgDkeXRGoVAgMDAQ8+fPx65du3D+/HmMGDECtra28Pb2rrwPjIionHhFm4iIiOg1VdqjMwBw584d7Nq1CwBK3J596NAhdO/eHQAQGRmJgIAA9OzZEwYGBhg0aBBWrVolxsr16Mz06dORl5eHcePGITs7G126dEFMTEyJ7RaJiKoDJtpEREREryl9j85otVqoVCrY29s/9bEaALC0tERUVFSZMXI8OqNQKDB37lzMnTv3qWMiIqpqTLSJiIiIiEgW1WmxU6KqxGe0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTEfbSJiIiIiOilIfde3QD36yb58Yo2ERERERERkYyYaBMRERERERHJiIk2ERERERERkYyYaBMRERERERHJ6JkT7fj4ePTr1w+2trZQKBSIjo6W1AuCgJCQENjY2MDU1BTu7u64evWqJCYrKwu+vr4wNzeHhYUF/Pz8kJub+0InQkRERERERFQdPHOinZeXhzZt2mDt2rV668PDw7Fq1SpEREQgKSkJZmZm8PT0xIMHD8QYX19fXLx4EbGxsdi9ezfi4+Mxbty45z8LIiIiIiIiomrimbf36t27N3r37q23ThAErFixArNmzcKAAQMAAN9//z2sra0RHR0NHx8fXL58GTExMTh16hRcXFwAAKtXr0afPn2wZMkS2NravsDpEBFRdSX3dizcioWIiIiqK1n30U5LS4NGo4G7u7tYplKp4OrqisTERPj4+CAxMREWFhZikg0A7u7uMDAwQFJSEt57770S7ebn5yM/P198rdVq5Rw2ERERVSD+koWIiF43si6GptFoAADW1taScmtra7FOo9HAyspKUm9kZARLS0sx5klhYWFQqVTiYWdnJ+ewiYiIiIiIiGTzUqw6HhwcjJycHPG4detWVQ+JiIiIiIiISC9Zbx1Xq9UAgIyMDNjY2IjlGRkZaNu2rRiTmZkped/Dhw+RlZUlvv9JxsbGMDY2lnOoREREBN7WTUREVBFkTbQdHBygVqsRFxcnJtZarRZJSUmYOHEiAMDNzQ3Z2dlITk6Gs7MzAODgwYMoLi6Gq6urnMMhIiIiIiJ6LvxFJL2IZ060c3Nzce3aNfF1WloaUlJSYGlpiUaNGiEwMBDz589H06ZN4eDggNmzZ8PW1hbe3t4AgObNm6NXr14YO3YsIiIiUFhYiICAAPj4+HDFcSIiemH8YkRERERV7ZkT7dOnT6NHjx7i66CgIADAyJEjsWnTJkyfPh15eXkYN24csrOz0aVLF8TExMDExER8T2RkJAICAtCzZ08YGBhg0KBBWLVqlQynQ0RERERERFS1njnR7t69OwRBKLVeoVBg7ty5mDt3bqkxlpaWiIqKetauiYiIiIiIiKo9WZ/RJiJ6GcXHx2Px4sVITk7GH3/8gZ07d4qPuwDARx99hM2bN0ve4+npiZiYGPF1VlYWJk2ahJ9//lm8U2flypWoVauWGHPu3Dn4+/vj1KlTqF+/PiZNmoTp06dL2t2+fTtmz56NGzduoGnTpli0aBH69OlTMSf+GN5u/Wz4eREREVFZmGgT0WsvLy8Pbdq0wejRozFw4EC9Mb169cLGjRvF10/uhODr64s//vgDsbGxKCwsxKhRozBu3Djx7h2tVgsPDw+4u7sjIiIC58+fx+jRo2FhYYFx48YBABISEjBs2DCEhYWhb9++iIqKgre3N86cOYNWrVpV0NlTdSV3Mg8woSciIqosTLSJ6LXXu3dv9O7du8wYY2PjUrcgvHz5MmJiYnDq1Cm4uLgAAFavXo0+ffpgyZIlsLW1RWRkJAoKCrBhwwYolUq0bNkSKSkpWLZsmZhor1y5Er169cK0adMAAPPmzUNsbCzWrFmDiIgIGc+YiIiIiCqSQVUPgIjoZXD48GFYWVmhWbNmmDhxIv7++2+xLjExERYWFmKSDQDu7u4wMDBAUlKSGNOtWzcolUoxxtPTE6mpqbh7964Y4+7uLunX09MTiYmJpY4rPz8fWq1WchARERFR1WKiTUT0FL169cL333+PuLg4LFq0CEeOHEHv3r1RVFQEANBoNLCyspK8x8jICJaWltBoNGKMtbW1JEb3+mkxunp9wsLCoFKpxMPOzu7FTpaIiIiIXhhvHSciegofHx/xz05OTmjdujWaNGmCw4cPo2fPnlU4MiA4OFjcZhF49Cw4k20iIiKiqsUr2kREz+iNN95AvXr1cO3aNQCAWq1GZmamJObhw4fIysoSn+tWq9XIyMiQxOhePy2mtGfDgUfPjpubm0sOIiIiIqpaTLSJiJ7R77//jr///hs2NjYAADc3N2RnZyM5OVmMOXjwIIqLi+Hq6irGxMfHo7CwUIyJjY1Fs2bNUKdOHTEmLi5O0ldsbCzc3Nwq+pSIiIiISEZMtInotZebm4uUlBSkpKQAANLS0pCSkoL09HTk5uZi2rRpOHHiBG7cuIG4uDgMGDAAb775Jjw9PQEAzZs3R69evTB27FicPHkSx48fR0BAAHx8fGBrawsAGD58OJRKJfz8/HDx4kVs3boVK1eulNz2PWXKFMTExGDp0qW4cuUKQkNDcfr0aQQEBFT6Z0JEREREz4+JNhG99k6fPo127dqhXbt2AICgoCC0a9cOISEhMDQ0xLlz59C/f3+89dZb8PPzg7OzM44ePSrZSzsyMhKOjo7o2bMn+vTpgy5dumD9+vVivUqlwv79+5GWlgZnZ2d88sknCAkJEbf2AoBOnTohKioK69evR5s2bfDDDz8gOjqae2gTUYWJj49Hv379YGtrC4VCgejoaEm9IAgICQmBjY0NTE1N4e7ujqtXr0pisrKy4OvrC3Nzc1hYWMDPzw+5ubmSmHPnzqFr164wMTGBnZ0dwsPDS4xl+/btcHR0hImJCZycnLB3795nHgsRUXXBxdCI6LXXvXt3CIJQav2+ffue2oalpSWioqLKjGndujWOHj1aZsyQIUMwZMiQp/ZHRCSHvLw8tGnTBqNHj8bAgQNL1IeHh2PVqlXYvHkzHBwcMHv2bHh6euLSpUswMTEBAPj6+uKPP/5AbGwsCgsLMWrUKIwbN06cE7VaLTw8PODu7o6IiAicP38eo0ePhoWFhfjLxoSEBAwbNgxhYWHo27cvoqKi4O3tjTNnzoi/bCzPWIiIqgsm2kRERESvqd69e6N379566wRBwIoVKzBr1iwMGDAAAPD999/D2toa0dHR8PHxweXLlxETE4NTp07BxcUFALB69Wr06dMHS5Ysga2tLSIjI1FQUIANGzZAqVSiZcuWSElJwbJly8REe+XKlejVqxemTZsGAJg3bx5iY2OxZs0aRERElGssRC+jxjP3yN7mjYVesrdJz463jhMRERFRCTdu3IBGo4G7u7tYplKp4OrqisTERABAYmIiLCwsxCQbANzd3WFgYICkpCQxplu3blAqlWKMp6cnUlNTcffuXTHm8X50Mbp+0tLSnjoWffLz86HVaiUHEVFlYKJNRERERCXoti20traWlFtbW0Oj0QAANBoNrKysJPVGRkawtLSUxOhrQ1dXVszj9U8biz5hYWFQqVTiYWdn95SzJiKSBxNtIiIiInolBQcHIycnRzxu3bpV1UMiotcEE20iIiIiKkF3pTojI0NSnpGRAbVaDQBQq9XilW+dhw8fIisrSxKjrw1dXVkxj9c/bSz6GBsbw9zcXHIQEVUGJtpEREREVELjxo2hVqsRFxcnlmm1WiQlJcHNzQ0A4ObmhuzsbCQnJ4sxBw8eRHFxMVxdXcWY+Ph4FBYWijGxsbFo1qwZ6tSpI8Y83o8uRtePg4PDU8dCRFSdMNEmIiIiek3l5uYiJSUFKSkpAB4tOnbu3DkAgEKhQGBgIObPn49du3bh/PnzGDFiBGxtbeHt7Q0AaN68OXr16oWxY8fi5MmTOH78OAICAuDj4wNbW1sAwPDhw6FUKuHn54eLFy9i69atWLlyJYKCgsRxTJkyBTExMVi6dCmuXLmC0NBQnD59GgEBAeUeCxFRdcLtvYiIiIheU6dPn0aPHj3E148nvwAwffp05OXlYdy4ccjOzkaXLl0QExMj2bc6MjISAQEB6NmzJwwMDDBo0CCsWrVKrFepVNi/fz/8/f3h7OyMevXqISQkRNzaCwA6deqEqKgozJo1C5999hmaNm2K6OhocQ/t8o6FiKi6YKJNRERE9Jrq3r07BEGQlGm1WqhUKgCPriTPnTsXc+fOLbUNS0tLREVFldlP69atcfTo0TJjhgwZgiFDhpRaX56xEBFVF7x1nIiIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZMREm4iIiIiIiEhGTLSJiIiIiIiIZGRU1QMgIiIiIiKiitN45h5Z27ux0EvW9l5Fsl/RLioqwuzZs+Hg4ABTU1M0adIE8+bNgyAIYowgCAgJCYGNjQ1MTU3h7u6Oq1evyj0UIiIiIiIiokone6K9aNEirFu3DmvWrMHly5exaNEihIeHY/Xq1WJMeHg4Vq1ahYiICCQlJcHMzAyenp548OCB3MMhInqq+Ph49OvXD7a2tlAoFIiOjhbrCgsLMWPGDDg5OcHMzAy2trYYMWIE7ty5I2mjcePGUCgUkmPhwoWSmHPnzqFr164wMTGBnZ0dwsPDS4xl+/btcHR0hImJCZycnLB3794KOWciIiIiqjiyJ9oJCQkYMGAAvLy80LhxYwwePBgeHh44efIkgEdXs1esWIFZs2ZhwIABaN26Nb7//nvcuXNH8uWWiKiy5OXloU2bNli7dm2Junv37uHMmTOYPXs2zpw5gx07diA1NRX9+/cvETt37lz88ccf4jFp0iSxTqvVwsPDA/b29khOTsbixYsRGhqK9evXizEJCQkYNmwY/Pz8cPbsWXh7e8Pb2xsXLlyomBMnIiIiogoh+zPanTp1wvr16/Hrr7/irbfewi+//IJjx45h2bJlAIC0tDRoNBq4u7uL71GpVHB1dUViYiJ8fHxKtJmfn4/8/HzxtVarlXvYRPQa6927N3r37q23TqVSITY2VlK2Zs0adOjQAenp6WjUqJFYXrt2bajVar3tREZGoqCgABs2bIBSqUTLli2RkpKCZcuWYdy4cQCAlStXolevXpg2bRoAYN68eYiNjcWaNWsQEREhx6kSERERUSWQ/Yr2zJkz4ePjA0dHR9SoUQPt2rVDYGAgfH19AQAajQYAYG1tLXmftbW1WPeksLAwqFQq8bCzs5N72ERE5ZaTkwOFQgELCwtJ+cKFC1G3bl20a9cOixcvxsOHD8W6xMREdOvWDUqlUizz9PREamoq7t69K8Y8/ktIXUxiYmKpY8nPz4dWq5UcRERERFS1ZE+0t23bhsjISERFReHMmTPYvHkzlixZgs2bNz93m8HBwcjJyRGPW7duyThiIqLye/DgAWbMmIFhw4bB3NxcLJ88eTK2bNmCQ4cOYfz48ViwYAGmT58u1ms0Gr2/YNTVlRVT2i8hAf4ikoiIiKg6kv3W8WnTpolXtQHAyckJN2/eRFhYGEaOHCneVpmRkQEbGxvxfRkZGWjbtq3eNo2NjWFsbCz3UImInklhYSGGDh0KQRCwbt06SV1QUJD459atW0OpVGL8+PEICwur0PkrODhY0rdWq2WyTURERFTFZL+ife/ePRgYSJs1NDREcXExAMDBwQFqtRpxcXFivVarRVJSEtzc3OQeDhGRLHRJ9s2bNxEbGyu5mq2Pq6srHj58iBs3bgAA1Go1MjIyJDG617pfQJYWU9pz38CjX0Sam5tLDiIiIiKqWrIn2v369cOXX36JPXv24MaNG9i5cyeWLVuG9957DwCgUCgQGBiI+fPnY9euXTh//jxGjBgBW1tbeHt7yz0cIqIXpkuyr169igMHDqBu3bpPfU9KSgoMDAxgZWUFAHBzc0N8fDwKCwvFmNjYWDRr1gx16tQRYx7/JaQuhr+EJCIiInq5yH7r+OrVqzF79mx8/PHHyMzMhK2tLcaPH4+QkBAxZvr06cjLy8O4ceOQnZ2NLl26ICYmBiYmJnIPh4joqXJzc3Ht2jXxdVpaGlJSUmBpaQkbGxsMHjwYZ86cwe7du1FUVCQ+M21paQmlUonExEQkJSWhR48eqF27NhITEzF16lR88MEHYhI9fPhwzJkzB35+fpgxYwYuXLiAlStXYvny5WK/U6ZMwTvvvIOlS5fCy8sLW7ZswenTpyVbgBERERFR9Sd7ol27dm2sWLECK1asKDVGoVBg7ty5mDt3rtzdExE9s9OnT6NHjx7ia90zzyNHjkRoaCh27doFACXWkTh06BC6d+8OY2NjbNmyBaGhocjPz4eDgwOmTp0qeXZapVJh//798Pf3h7OzM+rVq4eQkBBxay/g0faIUVFRmDVrFj777DM0bdoU0dHRaNWqVQWePRERERHJTfZEm4joZdO9e3cIglBqfVl1ANC+fXucOHHiqf20bt0aR48eLTNmyJAhGDJkyFPbIiIiIqLqS/ZntImIiIiIiIheZ0y0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTERJuIiIiI9CoqKsLs2bPh4OAAU1NTNGnSBPPmzZMsEikIAkJCQmBjYwNTU1O4u7vj6tWrknaysrLg6+sLc3NzWFhYwM/PD7m5uZKYc+fOoWvXrjAxMYGdnR3Cw8NLjGf79u1wdHSEiYkJnJycsHfv3oo5cSKiF8REm4iIiIj0WrRoEdatW4c1a9bg8uXLWLRoEcLDw7F69WoxJjw8HKtWrUJERASSkpJgZmYGT09PPHjwQIzx9fXFxYsXERsbi927dyM+Pl6yvaFWq4WHhwfs7e2RnJyMxYsXIzQ0FOvXrxdjEhISMGzYMPj5+eHs2bPw9vaGt7c3Lly4UDkfBhHRM2CiTURERER6JSQkYMCAAfDy8kLjxo0xePBgeHh44OTJkwAeXc1esWIFZs2ahQEDBqB169b4/vvvcefOHURHRwMALl++jJiYGHz77bdwdXVFly5dsHr1amzZsgV37twBAERGRqKgoAAbNmxAy5Yt4ePjg8mTJ2PZsmXiWFauXIlevXph2rRpaN68OebNm4f27dtjzZo1lf65EBE9DRNtIiIiItKrU6dOiIuLw6+//goA+OWXX3Ds2DH07t0bAJCWlgaNRgN3d3fxPSqVCq6urkhMTAQAJCYmwsLCAi4uLmKMu7s7DAwMkJSUJMZ069YNSqVSjPH09ERqairu3r0rxjzejy5G148++fn50Gq1koOIqDIYVfUAiIiIiKh6mjlzJrRaLRwdHWFoaIiioiJ8+eWX8PX1BQBoNBoAgLW1teR91tbWYp1Go4GVlZWk3sjICJaWlpIYBweHEm3o6urUqQONRlNmP/qEhYVhzpw5z3raRPQcGs/cI2t7NxZ6ydpeZeMVbSIiIiLSa9u2bYiMjERUVBTOnDmDzZs3Y8mSJdi8eXNVD61cgoODkZOTIx63bt2q6iER0WuCV7SJiIiISK9p06Zh5syZ8PHxAQA4OTnh5s2bCAsLw8iRI6FWqwEAGRkZsLGxEd+XkZGBtm3bAgDUajUyMzMl7T58+BBZWVni+9VqNTIyMiQxutdPi9HV62NsbAxjY+NnPW0iohfGRJuIiIiI9Lp37x4MDKQ3QBoaGqK4uBgA4ODgALVajbi4ODGx1mq1SEpKwsSJEwEAbm5uyM7ORnJyMpydnQEABw8eRHFxMVxdXcWYzz//HIWFhahRowYAIDY2Fs2aNUOdOnXEmLi4OAQGBopjiY2NhZubW4WdPxFVL3Lfng5U3C3qvHWciIiIiPTq168fvvzyS+zZswc3btzAzp07sWzZMrz33nsAAIVCgcDAQMyfPx+7du3C+fPnMWLECNja2sLb2xsA0Lx5c/Tq1Qtjx47FyZMncfz4cQQEBMDHxwe2trYAgOHDh0OpVMLPzw8XL17E1q1bsXLlSgQFBYljmTJlCmJiYrB06VJcuXIFoaGhOH36NAICAir9cyEiehpe0SYiIiIivVavXo3Zs2fj448/RmZmJmxtbTF+/HiEhISIMdOnT0deXh7GjRuH7OxsdOnSBTExMTAxMRFjIiMjERAQgJ49e8LAwACDBg3CqlWrxHqVSoX9+/fD398fzs7OqFevHkJCQiR7bXfq1AlRUVGYNWsWPvvsMzRt2hTR0dFo1apV5XwYRETPgIk2ERERUTVSnVburV27NlasWIEVK1aUGqNQKDB37lzMnTu31BhLS0tERUWV2Vfr1q1x9OjRMmOGDBmCIUOGlBlDRFQd8NZxIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtInrtxcfHo1+/frC1tYVCoUB0dLSkXhAEhISEwMbGBqampnB3d8fVq1clMVlZWfD19YW5uTksLCzg5+eH3NxcScy5c+fQtWtXmJiYwM7ODuHh4SXGsn37djg6OsLExAROTk7Yu3ev7OdLRERERBWLiTYRvfby8vLQpk0brF27Vm99eHg4Vq1ahYiICCQlJcHMzAyenp548OCBGOPr64uLFy8iNjYWu3fvRnx8PMaNGyfWa7VaeHh4wN7eHsnJyVi8eDFCQ0Oxfv16MSYhIQHDhg2Dn58fzp49C29vb3h7e+PChQsVd/JEREREJDujqh4AEVFV6927N3r37q23ThAErFixArNmzcKAAQMAAN9//z2sra0RHR0NHx8fXL58GTExMTh16hRcXFwAAKtXr0afPn2wZMkS2NraIjIyEgUFBdiwYQOUSiVatmyJlJQULFu2TEzIV65ciV69emHatGkAgHnz5iE2NhZr1qxBREREJXwSRERERCQHXtEmIipDWloaNBoN3N3dxTKVSgVXV1ckJiYCABITE2FhYSEm2QDg7u4OAwMDJCUliTHdunWDUqkUYzw9PZGamoq7d++KMY/3o4vR9aNPfn4+tFqt5CAiIiKiqsVEm4ioDBqNBgBgbW0tKbe2thbrNBoNrKysJPVGRkawtLSUxOhr4/E+SovR1esTFhYGlUolHnZ2ds96ikREREQkMybaREQvseDgYOTk5IjHrVu3qnpIRERERK89JtpERGVQq9UAgIyMDEl5RkaGWKdWq5GZmSmpf/jwIbKysiQx+tp4vI/SYnT1+hgbG8Pc3FxyEBEREVHVYqJNRFQGBwcHqNVqxMXFiWVarRZJSUlwc3MDALi5uSE7OxvJyclizMGDB1FcXAxXV1cxJj4+HoWFhWJMbGwsmjVrhjp16ogxj/eji9H1Q0REREQvhwpJtG/fvo0PPvgAdevWhampKZycnHD69Gmxvjx70hIRVZbc3FykpKQgJSUFwKMF0FJSUpCeng6FQoHAwEDMnz8fu3btwvnz5zFixAjY2trC29sbANC8eXP06tULY8eOxcmTJ3H8+HEEBATAx8cHtra2AIDhw4dDqVTCz88PFy9exNatW7Fy5UoEBQWJ45gyZQpiYmKwdOlSXLlyBaGhoTh9+jQCAgIq+yMhIiIiohcge6J99+5ddO7cGTVq1MD//vc/XLp0CUuXLhWv2ADl25OWiKiynD59Gu3atUO7du0AAEFBQWjXrh1CQkIAANOnT8ekSZMwbtw4vP3228jNzUVMTAxMTEzENiIjI+Ho6IiePXuiT58+6NKli2SPbJVKhf379yMtLQ3Ozs745JNPEBISItlru1OnToiKisL69evRpk0b/PDDD4iOjkarVq0q6ZMgIiIiIjnIvo/2okWLYGdnh40bN4plDg4O4p/LsyctEVFl6t69OwRBKLVeoVBg7ty5mDt3bqkxlpaWiIqKKrOf1q1b4+jRo2XGDBkyBEOGDCl7wERERERUrcl+RXvXrl1wcXHBkCFDYGVlhXbt2uGbb74R68uzJy0RERERERHRy0r2RPu3337DunXr0LRpU+zbtw8TJ07E5MmTsXnzZgDl25P2Sfn5+dBqtZKDiIiIiIiIqDqS/dbx4uJiuLi4YMGCBQCAdu3a4cKFC4iIiMDIkSOfq82wsDDMmTNHzmESERERERERVQjZr2jb2NigRYsWkrLmzZsjPT0dQPn2pH1ScHAwcnJyxOPWrVtyD5uIiIiIiIhIFrIn2p07d0Zqaqqk7Ndff4W9vT2A8u1J+yRjY2OYm5tLDiIiIiIiIqLqSPZbx6dOnYpOnTphwYIFGDp0KE6ePIn169eL29w8vidt06ZN4eDggNmzZ0v2pCUiIiIiIiJ6WcmeaL/99tvYuXMngoODMXfuXDg4OGDFihXw9fUVY6ZPn468vDyMGzcO2dnZ6NKlS4k9aYmIiIiIiIheRrIn2gDQt29f9O3bt9T68uxJS0RERERERPQykv0ZbSIiIiIiIqLXGRNtIiIiIirV7du38cEHH6Bu3bowNTWFk5MTTp8+LdYLgoCQkBDY2NjA1NQU7u7uuHr1qqSNrKws+Pr6wtzcHBYWFvDz80Nubq4k5ty5c+jatStMTExgZ2eH8PDwEmPZvn07HB0dYWJiAicnJ+zdu7diTpqI6AUx0SYiIiIive7evYvOnTujRo0a+N///odLly5h6dKlqFOnjhgTHh6OVatWISIiAklJSTAzM4OnpycePHggxvj6+uLixYuIjY3F7t27ER8fj3Hjxon1Wq0WHh4esLe3R3JyMhYvXozQ0FBxMV0ASEhIwLBhw+Dn54ezZ8/C29sb3t7euHDhQuV8GEREz6BCntEmIiIiopffokWLYGdnh40bN4plDg4O4p8FQcCKFSswa9YsDBgwAADw/fffw9raGtHR0fDx8cHly5cRExODU6dOwcXFBQCwevVq9OnTB0uWLIGtrS0iIyNRUFCADRs2QKlUomXLlkhJScGyZcvEhHzlypXo1asXpk2bBgCYN28eYmNjsWbNGkRERFTWR0JEVC68ok1EREREeu3atQsuLi4YMmQIrKys0K5dO3zzzTdifVpaGjQaDdzd3cUylUoFV1dXJCYmAgASExNhYWEhJtkA4O7uDgMDAyQlJYkx3bp1g1KpFGM8PT2RmpqKu3fvijGP96OL0fVDRFSdMNEmIiIiIr1+++03rFu3Dk2bNsW+ffswceJETJ48GZs3bwYAaDQaAIC1tbXkfdbW1mKdRqOBlZWVpN7IyAiWlpaSGH1tPN5HaTG6en3y8/Oh1WolBxFRZeCt40RERESkV3FxMVxcXLBgwQIAQLt27XDhwgVERERg5MiRVTy6pwsLC8OcOXOqehhE9BriFW0iIiIi0svGxgYtWrSQlDVv3hzp6ekAALVaDQDIyMiQxGRkZIh1arUamZmZkvqHDx8iKytLEqOvjcf7KC1GV69PcHAwcnJyxOPWrVtPP2kiIhkw0SYiIiIivTp37ozU1FRJ2a+//gp7e3sAjxZGU6vViIuLE+u1Wi2SkpLg5uYGAHBzc0N2djaSk5PFmIMHD6K4uBiurq5iTHx8PAoLC8WY2NhYNGvWTFzh3M3NTdKPLkbXjz7GxsYwNzeXHERElYGJNhERERHpNXXqVJw4cQILFizAtWvXEBUVhfXr18Pf3x8AoFAoEBgYiPnz52PXrl04f/48RowYAVtbW3h7ewN4dAW8V69eGDt2LE6ePInjx48jICAAPj4+sLW1BQAMHz4cSqUSfn5+uHjxIrZu3YqVK1ciKChIHMuUKVMQExODpUuX4sqVKwgNDcXp06cREBBQ6Z8LEdHT8BltIiIiItLr7bffxs6dOxEcHIy5c+fCwcEBK1asgK+vrxgzffp05OXlYdy4ccjOzkaXLl0QExMDExMTMSYyMhIBAQHo2bMnDAwMMGjQIKxatUqsV6lU2L9/P/z9/eHs7Ix69eohJCREstd2p06dEBUVhVmzZuGzzz5D06ZNER0djVatWlXOh0FE9AyYaBMRERFRqfr27Yu+ffuWWq9QKDB37lzMnTu31BhLS0tERUWV2U/r1q1x9OjRMmOGDBmCIUOGlD1gIqJqgLeOExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRUTk0btwYCoWixOHv7w8A6N69e4m6CRMmSNpIT0+Hl5cXatasCSsrK0ybNg0PHz6UxBw+fBjt27eHsbEx3nzzTWzatKmyTpGIiIiIZGJU1QMgInoZnDp1CkVFReLrCxcu4F//+heGDBkilo0dOxZz584VX9esWVP8c1FREby8vKBWq5GQkIA//vgDI0aMQI0aNbBgwQIAQFpaGry8vDBhwgRERkYiLi4OY8aMgY2NDTw9PSvhLImIiIhIDky0iYjKoX79+pLXCxcuRJMmTfDOO++IZTVr1oRardb7/v379+PSpUs4cOAArK2t0bZtW8ybNw8zZsxAaGgolEolIiIi4ODggKVLlwIAmjdvjmPHjmH58uVMtImIiIheIrx1nIjoGRUUFOA///kPRo8eDYVCIZZHRkaiXr16aNWqFYKDg3Hv3j2xLjExEU5OTrC2thbLPD09odVqcfHiRTHG3d1d0penpycSExNLHUt+fj60Wq3kICIiIqKqxSvaRETPKDo6GtnZ2fjoo4/EsuHDh8Pe3h62trY4d+4cZsyYgdTUVOzYsQMAoNFoJEk2APG1RqMpM0ar1eL+/fswNTUtMZawsDDMmTNHztMjIiIiohfERJuI6Bl999136N27N2xtbcWycePGiX92cnKCjY0NevbsievXr6NJkyYVNpbg4GAEBQWJr7VaLezs7CqsPyIiIiJ6OibaRETP4ObNmzhw4IB4pbo0rq6uAIBr166hSZMmUKvVOHnypCQmIyMDAMTnutVqtVj2eIy5ubneq9kAYGxsDGNj4+c6FyIiIiKqGHxGm4joGWzcuBFWVlbw8vIqMy4lJQUAYGNjAwBwc3PD+fPnkZmZKcbExsbC3NwcLVq0EGPi4uIk7cTGxsLNzU3GMyAiIiKiisZEm4ionIqLi7Fx40aMHDkSRkb//4ag69evY968eUhOTsaNGzewa9cujBgxAt26dUPr1q0BAB4eHmjRogU+/PBD/PLLL9i3bx9mzZoFf39/8Yr0hAkT8Ntvv2H69Om4cuUKvvrqK2zbtg1Tp06tkvMlIiIioufDRJuIqJwOHDiA9PR0jB49WlKuVCpx4MABeHh4wNHREZ988gkGDRqEn3/+WYwxNDTE7t27YWhoCDc3N3zwwQcYMWKEZN9tBwcH7NmzB7GxsWjTpg2WLl2Kb7/9llt7EREREb1k+Iw2EVE5eXh4QBCEEuV2dnY4cuTIU99vb2+PvXv3lhnTvXt3nD179rnHSERERERVj1e0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGTERJuIiIiIiIhIRky0iYiIiIiIiGRU4Yn2woULoVAoEBgYKJY9ePAA/v7+qFu3LmrVqoVBgwYhIyOjoodCREREREREVOEqNNE+deoUvv76a7Ru3VpSPnXqVPz888/Yvn07jhw5gjt37mDgwIEVORQiIiIiIiKiSlFhiXZubi58fX3xzTffoE6dOmJ5Tk4OvvvuOyxbtgzvvvsunJ2dsXHjRiQkJODEiRMVNRwiIiIiIiKiSlFhiba/vz+8vLzg7u4uKU9OTkZhYaGk3NHREY0aNUJiYmJFDYeIiIiIiIioUhhVRKNbtmzBmTNncOrUqRJ1Go0GSqUSFhYWknJra2toNBq97eXn5yM/P198rdVqZR0vERERERERkVxkv6J969YtTJkyBZGRkTAxMZGlzbCwMKhUKvGws7OTpV0iIiIiIiIiucmeaCcnJyMzMxPt27eHkZERjIyMcOTIEaxatQpGRkawtrZGQUEBsrOzJe/LyMiAWq3W22ZwcDBycnLE49atW3IPm4iIiIiIiEgWst863rNnT5w/f15SNmrUKDg6OmLGjBmws7NDjRo1EBcXh0GDBgEAUlNTkZ6eDjc3N71tGhsbw9jYWO6hEhEREREREclO9ivatWvXRqtWrSSHmZkZ6tati1atWkGlUsHPzw9BQUE4dOgQkpOTMWrUKLi5uaFjx45yD4eIiIiIZLJw4UIoFAoEBgaKZQ8ePIC/vz/q1q2LWrVqYdCgQcjIyJC8Lz09HV5eXqhZsyasrKwwbdo0PHz4UBJz+PBhtG/fHsbGxnjzzTexadOmEv2vXbsWjRs3homJCVxdXXHy5MmKOE0iohdWoftol2b58uXo27cvBg0ahG7dukGtVmPHjh1VMRQiIiIiKodTp07h66+/RuvWrSXlU6dOxc8//4zt27fjyJEjuHPnDgYOHCjWFxUVwcvLCwUFBUhISMDmzZuxadMmhISEiDFpaWnw8vJCjx49kJKSgsDAQIwZMwb79u0TY7Zu3YqgoCB88cUXOHPmDNq0aQNPT09kZmZW/MkTET2jSkm0Dx8+jBUrVoivTUxMsHbtWmRlZSEvLw87duwo9flsIiIiIqpaubm58PX1xTfffIM6deqI5Tk5Ofjuu++wbNkyvPvuu3B2dsbGjRuRkJCAEydOAAD279+PS5cu4T//+Q/atm2L3r17Y968eVi7di0KCgoAABEREXBwcMDSpUvRvHlzBAQEYPDgwVi+fLnY17JlyzB27FiMGjUKLVq0QEREBGrWrIkNGzZU7odBRFQOVXJFm4iIiIheHv7+/vDy8oK7u7ukPDk5GYWFhZJyR0dHNGrUCImJiQCAxMREODk5wdraWozx9PSEVqvFxYsXxZgn2/b09BTbKCgoQHJysiTGwMAA7u7uYgwRUXVSIftoExEREdGrYcuWLThz5gxOnTpVok6j0UCpVMLCwkJSbm1tDY1GI8Y8nmTr6nV1ZcVotVrcv38fd+/eRVFRkd6YK1eulDr2/Px85Ofni6+1Wu1TzpaISB68ok1EREREet26dQtTpkxBZGQkTExMqno4zywsLAwqlUo87OzsqnpIRPSaYKJNRERERHolJycjMzMT7du3h5GREYyMjHDkyBGsWrUKRkZGsLa2RkFBAbKzsyXvy8jIENffUavVJVYh171+Woy5uTlMTU1Rr149GBoa6o0pa52f4OBg5OTkiMetW7ee63MgInpWTLSJiIiISK+ePXvi/PnzSElJEQ8XFxf4+vqKf65Rowbi4uLE96SmpiI9PR1ubm4AADc3N5w/f16yOnhsbCzMzc3RokULMebxNnQxujaUSiWcnZ0lMcXFxYiLixNj9DE2Noa5ubnkICKqDHxGm4iIiIj0ql27Nlq1aiUpMzMzQ926dcVyPz8/BAUFwdLSEubm5pg0aRLc3NzQsWNHAICHhwdatGiBDz/8EOHh4dBoNJg1axb8/f1hbGwMAJgwYQLWrFmD6dOnY/To0Th48CC2bduGPXv2iP0GBQVh5MiRcHFxQYcOHbBixQrk5eVh1KhRlfRpEBGVHxNtIiIiInpuy5cvh4GBAQYNGoT8/Hx4enriq6++EusNDQ2xe/duTJw4EW5ubjAzM8PIkSMxd+5cMcbBwQF79uzB1KlTsXLlSjRs2BDffvstPD09xZj3338ff/75J0JCQqDRaNC2bVvExMSUWCCNiKg6YKJNREREROV2+PBhyWsTExOsXbsWa9euLfU99vb22Lt3b5ntdu/eHWfPni0zJiAgAAEBAeUeKxFRVeEz2kREREREREQyYqJNRFQOoaGhUCgUksPR0VGsf/DgAfz9/VG3bl3UqlULgwYNKrE6bnp6Ory8vFCzZk1YWVlh2rRpePjwoSTm8OHDaN++PYyNjfHmm29i06ZNlXF6RERERCQjJtpEROXUsmVL/PHHH+Jx7NgxsW7q1Kn4+eefsX37dhw5cgR37tzBwIEDxfqioiJ4eXmhoKAACQkJ2Lx5MzZt2oSQkBAxJi0tDV5eXujRowdSUlIQGBiIMWPGYN++fZV6nkRERET0YviMNhFRORkZGendrzUnJwffffcdoqKi8O677wIANm7ciObNm+PEiRPo2LEj9u/fj0uXLuHAgQOwtrZG27ZtMW/ePMyYMQOhoaFQKpWIiIiAg4MDli5dCgBo3rw5jh07huXLl0sWBCIiIiKi6o1XtImIyunq1auwtbXFG2+8AV9fX6SnpwMAkpOTUVhYCHd3dzHW0dERjRo1QmJiIgAgMTERTk5OktVxPT09odVqcfHiRTHm8TZ0Mbo29MnPz4dWq5UcRERERFS1mGgTEZWDq6srNm3ahJiYGKxbtw5paWno2rUr/vnnH2g0GiiVSlhYWEjeY21tDY1GAwDQaDQltqDRvX5ajFarxf379/WOKywsDCqVSjzs7OzkOF0iIiIiegG8dZyIqBx69+4t/rl169ZwdXWFvb09tm3bBlNT0yobV3BwMIKCgsTXWq2WyTYRERFRFeMVbSKi52BhYYG33noL165dg1qtRkFBAbKzsyUxGRkZ4jPdarW6xCrkutdPizE3Ny81mTc2Noa5ubnkICIiIqKqxUSbiOg55Obm4vr167CxsYGzszNq1KiBuLg4sT41NRXp6elwc3MDALi5ueH8+fPIzMwUY2JjY2Fubo4WLVqIMY+3oYvRtUFERERELwcm2kRE5fDpp5/iyJEjuHHjBhISEvDee+/B0NAQw4YNg0qlgp+fH4KCgnDo0CEkJydj1KhRcHNzQ8eOHQEAHh4eaNGiBT788EP88ssv2LdvH2bNmgV/f38YGxsDACZMmIDffvsN06dPx5UrV/DVV19h27ZtmDp1alWeOhERERE9Iz6jTURUDr///juGDRuGv//+G/Xr10eXLl1w4sQJ1K9fHwCwfPlyGBgYYNCgQcjPz4enpye++uor8f2GhobYvXs3Jk6cCDc3N5iZmWHkyJGYO3euGOPg4IA9e/Zg6tSpWLlyJRo2bIhvv/2WW3sRERERvWSYaBMRlcOWLVvKrDcxMcHatWuxdu3aUmPs7e2xd+/eMtvp3r07zp49+1xjJCIiIqLqgbeOExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoExEREREREclI9kQ7LCwMb7/9NmrXrg0rKyt4e3sjNTVVEvPgwQP4+/ujbt26qFWrFgYNGoSMjAy5h0JERERERERU6WRPtI8cOQJ/f3+cOHECsbGxKCwshIeHB/Ly8sSYqVOn4ueff8b27dtx5MgR3LlzBwMHDpR7KERERERERESVzkjuBmNiYiSvN23aBCsrKyQnJ6Nbt27IycnBd999h6ioKLz77rsAgI0bN6J58+Y4ceIEOnbsKPeQiIiIiIiIiCpNhT+jnZOTAwCwtLQEACQnJ6OwsBDu7u5ijKOjIxo1aoTExES9beTn50Or1UoOIiIiIiIiouqoQhPt4uJiBAYGonPnzmjVqhUAQKPRQKlUwsLCQhJrbW0NjUajt52wsDCoVCrxsLOzq8hhExERERHkW3snPT0dXl5eqFmzJqysrDBt2jQ8fPhQEnP48GG0b98exsbGePPNN7Fp06YS41m7di0aN24MExMTuLq64uTJk7KfMxGRHCo00fb398eFCxewZcuWF2onODgYOTk54nHr1i2ZRkhEREREpZFj7Z2ioiJ4eXmhoKAACQkJ2Lx5MzZt2oSQkBAxJi0tDV5eXujRowdSUlIQGBiIMWPGYN++fWLM1q1bERQUhC+++AJnzpxBmzZt4OnpiczMzMr5MIiInoHsz2jrBAQEYPfu3YiPj0fDhg3FcrVajYKCAmRnZ0uuamdkZECtVutty9jYGMbGxhU1VCIiIiLSQ461d/bv349Lly7hwIEDsLa2Rtu2bTFv3jzMmDEDoaGhUCqViIiIgIODA5YuXQoAaN68OY4dO4bly5fD09MTALBs2TKMHTsWo0aNAgBERERgz5492LBhA2bOnFmJnwoR0dPJfkVbEAQEBARg586dOHjwIBwcHCT1zs7OqFGjBuLi4sSy1NRUpKenw83NTe7hEBEREZFMnmftncTERDg5OcHa2lqM8fT0hFarxcWLF8WYx9vQxejaKCgoQHJysiTGwMAA7u7upa7xA3CdHyKqOrJf0fb390dUVBR++ukn1K5dW3zuWqVSwdTUFCqVCn5+fggKCoKlpSXMzc0xadIkuLm5ccVxIiIiomrqedfe0Wg0kiRbV6+rKytGq9Xi/v37uHv3LoqKivTGXLlypdQxh4WFYc6cOc9+skREL0j2K9rr1q1DTk4OunfvDhsbG/HYunWrGLN8+XL07dsXgwYNQrdu3aBWq7Fjxw65h0JEREREMpFr7Z3KxHV+iKiqVMit4/qOjz76SIwxMTHB2rVrkZWVhby8POzYsaPU57OJiKqD8qy82717dygUCskxYcIESYxcK+8SEVUm3do7hw4dKnXtncc9vvaOWq0usQq57vXTYszNzWFqaop69erB0NBQb0xZ3yGNjY1hbm4uOYiIKkOF76NNRPQqKM/KuwAwduxY/PHHH+IRHh4u1sm18i4RUWWRY+0dNzc3nD9/XrI6eGxsLMzNzdGiRQsx5vE2dDG6NpRKJZydnSUxxcXFiIuL4xo/RFQtVdiq40REr5KnrbyrU7NmzVKvrsi18i4RUWWRY+0dDw8PtGjRAh9++CHCw8Oh0Wgwa9Ys+Pv7i7vKTJgwAWvWrMH06dMxevRoHDx4ENu2bcOePXvEsQQFBWHkyJFwcXFBhw4dsGLFCuTl5YmrkBMRVSe8ok1E9ByeXHlXJzIyEvXq1UOrVq0QHByMe/fuiXVyrLz7JK6oS0QVSY61dwwNDbF7924YGhrCzc0NH3zwAUaMGIG5c+eKMQ4ODtizZw9iY2PRpk0bLF26FN9++63kF4zvv/8+lixZgpCQELRt2xYpKSmIiYkpsUAaEVF1wCvaRETPSN/KuwAwfPhw2Nvbw9bWFufOncOMGTOQmpoqfuGUY+VdU1NTSR1X1CWiiiQIwlNjdGvvrF27ttQYe3t77N27t8x2unfvjrNnz5YZExAQgICAgKeOiYioqjHRJiJ6RrqVd48dOyYpHzdunPhnJycn2NjYoGfPnrh+/TqaNGlSIWMJDg5GUFCQ+Fqr1cLOzq5C+iIiIiKi8uGt40REz6C0lXf1cXV1BQBcu3YNgDwr7z6JK+oSERERVT9MtImIyuFpK+/qk5KSAgCwsbEBIM/Ku0RERERU/THRJiIqB39/f/znP/9BVFSUuPKuRqPB/fv3AQDXr1/HvHnzkJycjBs3bmDXrl0YMWIEunXrhtatWwOQrrz7yy+/YN++fXpX3v3tt98wffp0XLlyBV999RW2bduGqVOnVtm5ExEREdGzYaJNRFQOT1t5V6lU4sCBA/Dw8ICjoyM++eQTDBo0CD///LPYhlwr7xIRERFR9cbF0IiIyuFpK+/a2dnhyJEjT21HrpV3iYiIiKj64hVtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhkx0SYiIiIiIiKSERNtIiIiIiIiIhlVaaK9du1aNG7cGCYmJnB1dcXJkyercjhERNUG50ciIv04PxLRy6DKEu2tW7ciKCgIX3zxBc6cOYM2bdrA09MTmZmZVTUkIqJqgfMjEZF+nB+J6GVRZYn2smXLMHbsWIwaNQotWrRAREQEatasiQ0bNlTVkIiIqgXOj0RE+nF+JKKXhVFVdFpQUIDk5GQEBweLZQYGBnB3d0diYmKJ+Pz8fOTn54uvc3JyAABarfaZ+i3Ov/ecI9ZPX/8vYx+V1c+r0kdl9VNVfVRWP5V1Lk+LFwRB1nG8KM6PL9ZHZfXzMvZRWf28Kn1UVj+cH8vvWedHQJ458mX8O6+sfl6VPiqrH55L9eujtH6eFluu+VGoArdv3xYACAkJCZLyadOmCR06dCgR/8UXXwgAePDgwUP249atW5U19ZUL50cePHhUl+Nlnx8FgXMkDx48KuYoz/xYJVe0n1VwcDCCgoLE18XFxcjKykLdunWhUCiqcGTPR6vVws7ODrdu3YK5uXlVD+eF8Fyqn1flPICKPRdBEPDPP//A1tZW1nYrG+fH6ovnUj29KufC+bF8OEdWT6/KeQA8l+qqos7lWebHKkm069WrB0NDQ2RkZEjKMzIyoFarS8QbGxvD2NhYUmZhYVGRQ6wU5ubmL/0/Yh2eS/XzqpwHUHHnolKpZG/zRXF+fIT/fqsnnkv1w/mx9PkR4BxZ3b0q5wHwXKqrijiX8s6PVbIYmlKphLOzM+Li4sSy4uJixMXFwc3NrSqGRERULXB+JCLSj/MjEb1MquzW8aCgIIwcORIuLi7o0KEDVqxYgby8PIwaNaqqhkREVC1wfiQi0o/zIxG9LKos0X7//ffx559/IiQkBBqNBm3btkVMTAysra2rakiVxtjYGF988UWJW5leRjyX6udVOQ/g1TqXZ8H58dX4O+e5VE+vyrm8KufxrF7n+RF4df7eX5XzAHgu1VV1OBeFIFSzvRuIiIiIiIiIXmJV8ow2ERERERER0auKiTYRERERERGRjJhoExEREREREcmIiTYRERERERGRjJhoV6KwsDC8/fbbqF27NqysrODt7Y3U1NSqHtYLW7hwIRQKBQIDA6t6KM/l9u3b+OCDD1C3bl2YmprCyckJp0+fruphPbOioiLMnj0bDg4OMDU1RZMmTTBv3jy8DOsdxsfHo1+/frC1tYVCoUB0dLSkXhAEhISEwMbGBqampnB3d8fVq1erZrBUITg/Vk+cH6se50d6VedHgHNkdcD5seIw0a5ER44cgb+/P06cOIHY2FgUFhbCw8MDeXl5VT2053bq1Cl8/fXXaN26dVUP5bncvXsXnTt3Ro0aNfC///0Ply5dwtKlS1GnTp2qHtozW7RoEdatW4c1a9bg8uXLWLRoEcLDw7F69eqqHtpT5eXloU2bNli7dq3e+vDwcKxatQoRERFISkqCmZkZPD098eDBg0oeKVUUzo/VD+fH6oHzI72K8yPAObK64PxYgQSqMpmZmQIA4ciRI1U9lOfyzz//CE2bNhViY2OFd955R5gyZUpVD+mZzZgxQ+jSpUtVD0MWXl5ewujRoyVlAwcOFHx9fatoRM8HgLBz507xdXFxsaBWq4XFixeLZdnZ2YKxsbHw3//+twpGSJWB82PV4/xY/XB+JEF4+edHQeAcWZ1wfqw4vKJdhXJycgAAlpaWVTyS5+Pv7w8vLy+4u7tX9VCe265du+Di4oIhQ4bAysoK7dq1wzfffFPVw3ounTp1QlxcHH799VcAwC+//IJjx46hd+/eVTyyF5OWlgaNRiP5d6ZSqeDq6orExMQqHBlVJM6PVY/zY/XH+fH19LLPjwDnyOqE82PFMaqUXqiE4uJiBAYGonPnzmjVqlVVD+eZbdmyBWfOnMGpU6eqeigv5LfffsO6desQFBSEzz77DKdOncLkyZOhVCoxcuTIqh7eM5k5cya0Wi0cHR1haGiIoqIifPnll/D19a3qob0QjUYDALC2tpaUW1tbi3X0auH8WD1wfqz+OD++fl72+RHgHFndcH6sOEy0q4i/vz8uXLiAY8eOVfVQntmtW7cwZcoUxMbGwsTEpKqH80KKi4vh4uKCBQsWAADatWuHCxcuICIi4qWaJAFg27ZtiIyMRFRUFFq2bImUlBQEBgbC1tb2pTsXer1xfqweOD8SVT8v8/wIcI6sjjg/VhzeOl4FAgICsHv3bhw6dAgNGzas6uE8s+TkZGRmZqJ9+/YwMjKCkZERjhw5glWrVsHIyAhFRUVVPcRys7GxQYsWLSRlzZs3R3p6ehWN6PlNmzYNM2fOhI+PD5ycnPDhhx9i6tSpCAsLq+qhvRC1Wg0AyMjIkJRnZGSIdfTq4PxYfXB+rP44P75eXvb5EeAcWR1xfqw4TLQrkSAICAgIwM6dO3Hw4EE4ODhU9ZCeS8+ePXH+/HmkpKSIh4uLC3x9fZGSkgJDQ8OqHmK5de7cucQWGb/++ivs7e2raETP7969ezAwkP6XNjQ0RHFxcRWNSB4ODg5Qq9WIi4sTy7RaLZKSkuDm5laFIyM5cX6sfjg/Vn+cH18Pr8r8CHCOrI44P1Yc3jpeifz9/REVFYWffvoJtWvXFp8PUKlUMDU1reLRlV/t2rVLPBdkZmaGunXrvnTPC02dOhWdOnXCggULMHToUJw8eRLr16/H+vXrq3poz6xfv3748ssv0ahRI7Rs2RJnz57FsmXLMHr06Koe2lPl5ubi2rVr4uu0tDSkpKTA0tISjRo1QmBgIObPn4+mTZvCwcEBs2fPhq2tLby9vatu0CQrzo/VD+fH6oHzI70q8yPAObI64vxYgSplbXMSBOHRsvP6jo0bN1b10F7Yy7o1gyAIws8//yy0atVKMDY2FhwdHYX169dX9ZCei1arFaZMmSI0atRIMDExEd544w3h888/F/Lz86t6aE916NAhvf83Ro4cKQjCoy0aZs+eLVhbWwvGxsZCz549hdTU1KodNMmK82P1xPmx6nF+pFd5fhQEzpFVjfNjxVEIgiBUeDZPRERERERE9JrgM9pEREREREREMmKiTURERERERCQjJtpEREREREREMmKiTURERERERCQjJtpEREREREREMmKiTURERERERCQjJtpEREREREREMmKiTURERERERCQjJtpEREREREREMmKiTURERERERCQjJtpEREREREREMmKiTURERERERCSj/wed7mVtF0nyegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scenario(compile_scenario(fm))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b104cf8-75cd-4752-be4c-0c25e6c3a7ce",
   "metadata": {},
   "source": [
    "Not bad for a simplistic priority queue heuristic! Using the default `ws3.forest.GreedyAreaSelector` class to select harvest areas is definitely not the only way to simulate harvesting in `ws3`. \n",
    "\n",
    "You can always _manually prescribe_ any combination of treatments in any period, and `ws3` will its best to comply. By default `ws3` has several layers of fault protection turned on to avoid crashing with angry-looking error messages every time a user prescribes an invalid action. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2651a9c6-7f6e-412b-aab7-b000013936a5",
   "metadata": {},
   "source": [
    "## Implement some manually prescribed harvest actions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61902182-d363-4d64-afd1-09a7e95bf485",
   "metadata": {},
   "source": [
    "Below we reset the `fm` instance (which resets applied actions and inventory to starting positions), the manually prescribe a harvesting actions (using our sample `DevelopmentType` instance from earlier as the target, just as an example). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09fbbe86-62a4-4a32-9742-971c61ef9da4",
   "metadata": {},
   "source": [
    "First, we try to harvest 1.0 hectare of age 9 stands in period 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "df1df4d4-1a38-4dcf-b606-c92b7c753015",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not operable\n",
      "tsa24_clipped 1 2403002 204 2423002 harvest 1 9\n",
      "(80, 500)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4, None, None)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.reset()\n",
    "fm.apply_action(dtype_key=dt.key, \n",
    "                acode='harvest', \n",
    "                period=1,\n",
    "                age=9,\n",
    "                area=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a689ee-18cc-4b00-8368-d8a06087a785",
   "metadata": {},
   "source": [
    "No good. Not operable. Also not surprizing, given that we defined harvest operability window to start at age 40 for all development types.\n",
    "\n",
    "We can use the `operable_dtypes` method to find a suitable target for our harvesting action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3d8b63b7-2f82-4521-95ad-c3cb8bf04d99",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('tsa24_clipped', '1', '2401002', '204', '2401002'): [135,\n",
       "  105,\n",
       "  155,\n",
       "  80,\n",
       "  113,\n",
       "  145,\n",
       "  115,\n",
       "  85,\n",
       "  125,\n",
       "  153,\n",
       "  91,\n",
       "  93,\n",
       "  95],\n",
       " ('tsa24_clipped', '1', '2402000', '100', '2402000'): [165],\n",
       " ('tsa24_clipped', '1', '2402002', '204', '2402002'): [115, 93, 95],\n",
       " ('tsa24_clipped', '1', '2403000', '100', '2403000'): [93]}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.operable_dtypes(acode='harvest', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b708c948-b7a4-493f-b475-23ef5b04b849",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.operable_area(acode='harvest', period=5, age=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863f782f-d1ef-47cb-b11f-99745b7c670b",
   "metadata": {},
   "source": [
    "How much area is operable for development type `('tsa24_clipped', '1', '2401002', '204', '2401002')` age 135 in period 1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "61241bcf-e820-4b11-8d09-d04f823d093f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.24421919373785"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.operable_area(acode='harvest', period=1, age=135)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f2a2d6-3ab6-48b1-aca7-515042194a90",
   "metadata": {},
   "source": [
    "Harvest 50 ha of that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2dd7355a-c9d0-4860-a2df-72cdb28646aa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.0, [[('tsa24_clipped', '1', '2401002', '204', '2421002'), 1.0, 0]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.apply_action(dtype_key=('tsa24_clipped', '1', '2401002', '204', '2401002'), \n",
    "                acode='harvest', \n",
    "                period=1,\n",
    "                age=135,\n",
    "                area=50.,\n",
    "                compile_c_ycomps=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4900adf7-4181-4572-8019-468cf6d58f24",
   "metadata": {},
   "source": [
    "Note that the way we set this model up, the actionned area transitions to develoment type `('tsa24_clipped', '1', '2401002', '204', '2421002')`, which corresponds to second-growth yield curve (as opposed to the first-growth yield curve the area was originally tracking along). We will look for this second-growth development type key below and try to harvest it a second time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b05e83-05a4-433e-9f56-6a3b0a072e4a",
   "metadata": {},
   "source": [
    "Commit the action. Just roll with it. Normally `ws3` takes care of committing actions at appropriate moments, but you have to do this yourself when running in fully manual mode (like we are doing now)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "205dde37-b916-433f-b999-ace0c76eb531",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm.commit_actions(period=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2190a0c8-9bb5-4001-aebc-4375be70b7d1",
   "metadata": {},
   "source": [
    "Check the new operable area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2f7dadd0-ff66-4e59-9e05-cd7ab7058caa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.244219193737848"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.operable_area(acode='harvest', period=1, age=135)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ca99ed-8b1f-4882-8f65-d11f8ceba828",
   "metadata": {},
   "source": [
    "22.2 operable ha left. Makes sense."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60bdcbd-2e1a-479b-837b-93d7bdf27b88",
   "metadata": {},
   "source": [
    "So, we know that our previously harvested area transitions to development type `('tsa24_clipped', '1', '2401002', '204', '2421002')`. \n",
    "\n",
    "We can query the development type object about its period-wise operability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96a0508c-2c15-43be-9c19-e4595dead201",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'harvest': {1: (80, 500),\n",
       "  2: (80, 500),\n",
       "  3: (80, 500),\n",
       "  4: (80, 500),\n",
       "  5: (80, 500),\n",
       "  6: (80, 500),\n",
       "  7: (80, 500),\n",
       "  8: (80, 500),\n",
       "  9: (80, 500),\n",
       "  10: (80, 500)}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtk = ('tsa24_clipped', '1', '2401002', '204', '2421002')\n",
    "dt = fm.dtypes[dtk]\n",
    "dt.operability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a09cf9c-8c87-4b8a-a3db-5399a6a8476d",
   "metadata": {},
   "source": [
    "We can see that it become operable at age 80.\n",
    "\n",
    "If we inspect the private `_areas` attribute of the develoment type, we can see that there is 0.42 ha of age-20 area in the initial inventory, and a new 50.0 ha that gets created from our harvest action (which is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "349e2792-2296-42af-b5b2-9414d1355d38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: defaultdict(float, {20: 0.422054121206099}),\n",
       " 1: defaultdict(float, {20: 0.422054121206099, 0: 50.0}),\n",
       " 2: defaultdict(float, {30: 0.422054121206099, 10: 50.0}),\n",
       " 3: defaultdict(float, {40: 0.422054121206099, 20: 50.0}),\n",
       " 4: defaultdict(float, {50: 0.422054121206099, 30: 50.0}),\n",
       " 5: defaultdict(float, {60: 0.422054121206099, 40: 50.0}),\n",
       " 6: defaultdict(float, {70: 0.422054121206099, 50: 50.0}),\n",
       " 7: defaultdict(float, {80: 0.422054121206099, 60: 50.0}),\n",
       " 8: defaultdict(float, {90: 0.422054121206099, 70: 50.0}),\n",
       " 9: defaultdict(float, {100: 0.422054121206099, 80: 50.0}),\n",
       " 10: defaultdict(float, {110: 0.422054121206099, 90: 50.0})}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt._areas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241a1788-f956-4e8d-84b8-beeec9b38c89",
   "metadata": {},
   "source": [
    "We can use the `operable_ages` method to find the first period in which it become operable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "151e65bb-11e2-4ad5-8af5-6a65b9ff3867",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period: 1 operable ages: [] operable area: 0.0\n",
      "period: 2 operable ages: [] operable area: 0.0\n",
      "period: 3 operable ages: [] operable area: 0.0\n",
      "period: 4 operable ages: [] operable area: 0.0\n",
      "period: 5 operable ages: [] operable area: 0.0\n",
      "period: 6 operable ages: [] operable area: 0.0\n",
      "period: 7 operable ages: [80] operable area: 0.422054121206099\n",
      "period: 8 operable ages: [90] operable area: 0.422054121206099\n",
      "period: 9 operable ages: [80, 100] operable area: 50.4220541212061\n",
      "period: 10 operable ages: [90, 110] operable area: 50.4220541212061\n"
     ]
    }
   ],
   "source": [
    "for p in fm.periods: \n",
    "    print('period:', p, \n",
    "          'operable ages:', dt.operable_ages('harvest', p),\n",
    "          'operable area:', dt.operable_area('harvest', p))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a67c00-eb46-4562-97b4-063e3f67d09c",
   "metadata": {},
   "source": [
    "So, the 50 ha of area we previously harvested does not become operable again until period 9."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da09ea29-59e0-423a-b073-b7bfb14c0391",
   "metadata": {},
   "source": [
    "Try to harvest the same area (now tracking along a second-growth yield curve) a second time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "204bb45e-9c58-4437-b9b1-ef7e82cfec14",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.0, [[('tsa24_clipped', '1', '2401002', '204', '2421002'), 1.0, 0]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.apply_action(dtype_key=dtk, \n",
    "                acode='harvest', \n",
    "                period=9,\n",
    "                age=80,\n",
    "                area=50.,\n",
    "                compile_c_ycomps=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5bfcedc5-332e-4c4e-a352-9ad5cecb5f00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm.commit_actions(period=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53cc0bdf-db3e-495a-9581-5611bebac0ba",
   "metadata": {},
   "source": [
    "Plot results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9cce5ad3-c8a7-4d49-b3dc-3f6b73a5a73c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       " array([<Axes: title={'center': 'Harvested area (ha)'}>,\n",
       "        <Axes: title={'center': 'Harvested volume (m3)'}>,\n",
       "        <Axes: title={'center': 'Growing Stock (m3)'}>], dtype=object))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scenario(compile_scenario(fm))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4120fade-d91c-4214-83bc-c71b0aafd0de",
   "metadata": {},
   "source": [
    "Note that you would rarely (if ever) need to be poking around the guts of your `ws3` model in this way just to schedule some harvesting treatments. The point of the stuff above is to help you understand some of the basic action scheduling and action operability functions (and where the bit live in the `ws3` data model), which in turn are the building blocks of all the higher-level scheduling functions in ws3. \n",
    "\n",
    "If you like, take some time now to go have a closer look at the source code for the `GreedyAreaSelector` class in (defined in the `ws3.forest` module). If you look closely, you will see that it is basically just combining all the bit we used in our random-seeming example above (i.e., `operable_dtypes`, `operable_area`, `apply_action`, `commit_actions`), glued together with some `while` loops and `if` conditions and other stuff. Neat, right? \n",
    "\n",
    "If you wanted to, you could write your own `AreaSelector` class that uses different logic to select and operate on area in your model (as long as your class has an `__init__` method that sets a `parent` attribute that points back to your `ForestModel` instance and an `operate` method with appropriate args, it should work). You can monkey-patch the `ForestModel.areaselector` attribute at runtime to change the default behaviour of your model as well (several of the fault-recovery functions in the `ForestModel` class use the `areaselector` to find operable area when the things go a bit pear-shaped and you have set up the model to do its best to find operable area and keep going when part of the prescribed action schedule is infeasible). See `ForestModel.repair_actions`, `ForestModel.commit_actions`, `ForestModel.apply_action`, and `ForestModel.apply_schedule` for examples of where the `areaselector` gets used to smooth over bumps in the road."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19755940-60e6-46d2-ac78-d97f38644741",
   "metadata": {},
   "source": [
    "## Implement optimization-based action scheduling\n",
    "\n",
    "`ws3` also includes functions to automate the process of formulating and solving linear programming (LP) optimization problems to schedule actions in your model. Using an optimization approach, you formulate your forest-level management problem in terms of an _objective function_ and _constraints_.\n",
    "\n",
    "`ws3` currently includes functions to formulate and solve Model I type optimization problems, as first documented in Johson and Scheurman (1977).\n",
    "\n",
    ">Johnson, K.N. and H.L. Scheurman (1977). “Techniques for prescribing optimal\n",
    "timber harvest and investment under different objectives—discussion and\n",
    "synthesis”. In: Forest Science Monograph 23.suppl_1.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22273eb0-2152-4450-9b7d-587c95ec165f",
   "metadata": {},
   "source": [
    "The optimization problem can be formulated as follows\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "  \\text{max} \\quad &  \\sum_{i\\in I}\\sum_{j \\in J_{i}}c_{ij}x_{ij} & \\label{eq:z}\\\\\n",
    "  \\text{s.t.} \\quad & \\nonumber \\\\\n",
    "   &(1-\\varepsilon_{p})y_p \\leq \\sum_{i\\in I}\\sum_{j\\in J_{i}}\\mu_{ijpt}x_{ij}  \\leq  (1+\\varepsilon_{p})y_p, & \\forall p\\in O', t\\in T'_p \\label{eq:constr_evenflow}\\\\\n",
    "   &v^{-}_{ot}\\leq\\sum_{i\\in Z}\\sum_{j\\in J_{i}}\\mu_{ijot}x_{ij}  \\leq v^{+}_{ot}, &  \\forall o\\in O, t\\in T \\label{eq:constr_general} \\\\\n",
    "   &\\sum_{j\\in J_{i}}x_{ij}  = 1, \\forall i\\in Z \\label{eq:constr_coverage} \\\\\n",
    "   &0 \\leq x_{ij} \\leq 1, & \\forall i \\in Z, j \\in J_{i} \\label{eq:constr_bounds}\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align*}\n",
    "  I := & \\,\\, \\text{set of spatial zones}\\\\\n",
    "  J_{i} := & \\,\\, \\text{set of available prescriptions for zone $i \\in I$}\\\\\n",
    "  O := & \\,\\, \\text{set of forest outputs}\\\\\n",
    "  O' \\subseteq O := & \\,\\, \\text{set of targeted forest outputs}\\\\\n",
    "  T := & \\,\\, \\text{set of time periods in the planning horizon}\\\\\n",
    "  T'_p \\subseteq O := & \\,\\, \\text{subset of $T$ on which even-flow constraints for output $p \\in O^{\\prime}$ are applied}\\\\\n",
    "  \\varepsilon_{p} := & \\,\\, \\text{admissible level of variation on yield of targeted output $p \\in O^{\\prime}$} \\\\\n",
    "  \\mu_{ijot} := & \\,\\, \\text{quantity of output $o \\in O$ produced in period $t \\in T$ by prescription $j \\in J_{i}$}\\\\\n",
    "                     & \\,\\, \\text{in zone $i \\in I$}\\\\\n",
    "  \\mu_{ijpt} := & \\,\\, \\text{quantity of output $p \\in O'$ produced in period $t \\in T$ by prescription $j \\in J_{i}$}\\\\\n",
    "                     & \\,\\, \\text{in zone $i \\in I$}\\\\\n",
    "  v^{-}_{ot} := & \\,\\, \\text{lower bound on yield of output $o \\in O$ in period $t \\in T$} \\\\\n",
    "  v^{+}_{ot} := & \\,\\, \\text{upper bound on yield of output $o \\in O$ in period $t \\in T$} \\\\\n",
    "  c_{ij} := & \\,\\,  \\text{objective function contribution of prescription $j \\in J_{i}$ in zone $i \\in I$} \\\\\n",
    "  x_{ij} := & \\,\\, \\text{proportion of zone $i \\in Z$ on which prescription $j \\in J_{i}$ is applied} \\\\\n",
    "  y_p = &  \\sum_{i\\in I}\\sum_{j\\in J_i}\\mu_{ijpt^R_p}x_{ij}\\text{, i.e. total yield of targeted output $p \\in O'$ at reference period $t^R_p$} \n",
    "\\end{align*}\n",
    "\n",
    "The objective function maximizes the sum of $c_{ij}x_{ij}$ products, which represent yield of a user-defined output---the output can be anything, but common examples include maximizing harvest volume or minimizing harvest area. Other, more complex objectives functions can be defined (e.g., minimize a penalty-based weighted multi-objective goal programming objective function).\n",
    "\n",
    "The variables $x_{ij}$ are linear, with domain $\\{x_{ij} \\in \\mathbb{R}|0 \\leq x_{ij} \\leq 1\\}$. Coverage constraints require prescriptions to cover the entire zone---doing nothing for the entire planning horizon is considered a prescription that could generate some outputs. Variable bounds and coverage constraints are automatically set by the `ws3` optimization problem formulation functions.\n",
    "\n",
    "\n",
    "The set $O^{\\prime} \\subseteq O$ represents targeted outputs, for which we enforce even-flow constraints.\n",
    "Even-flow constraints are expressed in terms of $y_p$, which represents total yield of targeted output $p \\in O^{\\prime}$ in reference period $t^R_p$.\n",
    "Note that even-flow constraints are defined over time periods $T'_p$, which is any subset of $T$ (need not be contiguous)---$T'_p$ can be unique for each output  $p \\in O^{\\prime}$.\n",
    "\n",
    "General constraints set upper and lower bounds on periodic yield of any output $o \\in O$---we use these constraints to set minimum and maximum levels of any performance indicator defined in the model (or combinations thereof). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5589a4b9-f1b6-4d16-a79e-6b0630b88f71",
   "metadata": {},
   "source": [
    "Below we show an example of using the optimization functins in `ws3` to formulate and solve a Model I probleml."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f40bd9-1122-47bf-872c-91510e90fcd6",
   "metadata": {},
   "source": [
    "You will need to have `gurobipy` installed for this next step to work. At some point I will finish implementing bindings to the PuLP open-source solver library, but for now we are stuck with Gurobi as the only solver that works with ws3. \n",
    "\n",
    "You should be able to install `gurobipy` using the magic `%pip` command below, however this _only_ installs the Python module and minimal solver binaries. This _does not_ install the full Gurobi software stack, do the Gurobi licence managent tools (including the `grbgetkey` command) are _not_ installed automatically with the Python module. The `gurobipy` installation includes a limited software license that should allow you to solve _small_ problem instances, but will puke if you try to solve anything too large (which includes most `ws3` problems, unless you model is extremely small and simple). \n",
    "\n",
    "If you are eligible for an academic license, you should request and install a \"Named-User Acacdemic\" license from the Licenses tab in the [Gurobi User Portal](https://portal.gurobi.com/iam/licenses/request) (create a new Gurobi account if you do not have one yet, using your official academic institution email address). This license is per-user, per-machine, is valid for one year, should _only_ be used in an academic context (see [Gurobi Standard EULA](https://cdn.gurobi.com/wp-content/uploads/Gurobi_Standard_EULA_Nov2022.pdf) for details of license terms and restrictions).\n",
    "\n",
    "**Hint:** If you get a `grbgetkey: command not found` error, either the full Gurobi software stack is not installed at all on your system, or is installed but `grbgetkey` is not in your path. Depending on your situation, either [install the full Gurobi software stack](https://support.gurobi.com/hc/en-us/articles/4534161999889) or specify the full path to the `grbgetkey` command (Gurobi installation instructions recommend installing Gurobi into `/opt/gurobi` on linux platforms).   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8a4fc517-79ba-4582-a0f9-a68426175212",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gurobipy in /media/data/home/gparadis/.virtualenvs/foo/lib/python3.10/site-packages (10.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install gurobipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d1fe06ee-5911-4724-8804-4970cb0d40dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gurobipy as grb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a7a326-f965-4405-9364-1fef220e3f64",
   "metadata": {},
   "source": [
    "First we need to define a few utility functions that we will use to build the problems (e.g., objective function coefficient function, even flow constraint coefficient function, general constraint coefficient function).\n",
    "\n",
    "Note that the `spades_ws3` project contains several useful chunks of code that you can use as a starting point (on GitHub, see link below).\n",
    "\n",
    "https://github.com/gparadis/spades_ws3/blob/master/python/spadesws3.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "86b121d7-0383-4ff2-aaae-6ed9aae0e9a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cmp_c_z(fm, path, expr):\n",
    "    \"\"\"\n",
    "    Compile objective function coefficient (given ForestModel instance, \n",
    "    leaf-to-root-node path, and expression to evaluate).\n",
    "    \"\"\"\n",
    "    result = 0.\n",
    "    for t, n in enumerate(path, start=1):\n",
    "        d = n.data()\n",
    "        if fm.is_harvest(d['acode']):\n",
    "            result += fm.compile_product(t, expr, d['acode'], [d['dtk']], d['age'], coeff=False)\n",
    "    return result\n",
    "\n",
    "def cmp_c_cflw(fm, path, expr, mask=None): # product, all harvest actions\n",
    "    \"\"\"\n",
    "    Compile flow constraint coefficient for product indicator (given ForestModel \n",
    "    instance, leaf-to-root-node path, expression to evaluate, and optional mask).\n",
    "    \"\"\"\n",
    "    result = {}\n",
    "    for t, n in enumerate(path, start=1):\n",
    "        d = n.data()\n",
    "        if mask and not fm.match_mask(mask, d['dtk']): continue\n",
    "        if fm.is_harvest(d['acode']):\n",
    "            result[t] = fm.compile_product(t, expr, d['acode'], [d['dtk']], d['age'], coeff=False)\n",
    "    return result\n",
    "\n",
    "\n",
    "def cmp_c_caa(fm, path, expr, acodes, mask=None): # product, named actions\n",
    "    \"\"\"\n",
    "    Compile constraint coefficient for product indicator (given ForestModel \n",
    "    instance, leaf-to-root-node path, expression to evaluate, list of action codes, \n",
    "    and optional mask).\n",
    "    \"\"\"\n",
    "    result = {}\n",
    "    for t, n in enumerate(path, start=1):\n",
    "        d = n.data()\n",
    "        if mask and not fm.match_mask(mask, d['dtk']): continue\n",
    "        if d['acode'] in acodes:\n",
    "            result[t] = fm.compile_product(t, expr, d['acode'], [d['dtk']], d['age'], coeff=False)\n",
    "    return result\n",
    "\n",
    "\n",
    "def cmp_c_ci(fm, path, yname, mask=None): # product, named actions\n",
    "    \"\"\"\n",
    "    Compile constraint coefficient for inventory indicator (given ForestModel instance, \n",
    "    leaf-to-root-node path, expression to evaluate, and optional mask).\n",
    "    \"\"\"\n",
    "    result = {}\n",
    "    for t, n in enumerate(path, start=1):\n",
    "        d = n.data()\n",
    "        if mask and not fm.match_mask(mask, d['_dtk']): continue\n",
    "        result[t] = fm.inventory(t, yname=yname, age=d['_age'], dtype_keys=[d['_dtk']]) \n",
    "        #result[t] = fm.inventory(t, yname=yname, age=d['age'], dtype_keys=[d['dtk']]) \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f90cba3-c1f5-4847-8177-a8f0e01c3239",
   "metadata": {},
   "source": [
    "Define a generic base scenario function, and link it to a dispatch function keyed on scenario name string (e.g., `base`). \n",
    "\n",
    "Note how we use `functools.partial` to specialize the more general functions defined above for use in the `coeff_funcs` arg of `ForestModel.add_problem`. Otherwise we would have to define an entirely new function each time we defined a slightly different objective or constraint in one of our scenarios, which would get tedious and messy. The tedium and mess would be more evident if we had a large number of alternative scenarios defined in the same notebook (which we do not here, but use your imagination). \n",
    "\n",
    "Note also that the expected data structures for the various args to `ForestModel.add_problem` must be matched exactly or `ws3` will likely crash somewhere in one of the series of complicated private optimization model-building methods that get called from `ForestModel.add_problem`. You _should not_ have to unpack the exact logic of this model-building code to figure out why your model is crashing... it really is quite complicated and hard to follow. If you model is crashing there, you probably fed invalid (or incorrectly structured) args to `ForestModel.add_problem`. Carefully review the structure and values of your args to find the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb7864d-f55b-4362-8ab9-126ec41b0eae",
   "metadata": {},
   "source": [
    "`ForestModel.add_problem` arg specs are described below.\n",
    "\n",
    "`name`: String. Used as key to store `Problem` instances in a dict in the `ForestModel` instanace, so make sure it is unique within a given model or you will overwrite dict values (assuming you want to stuff multiple problems, and their solutions, into your model at the same time). \n",
    "    \n",
    "`coeff_funcs`: Dict of function references, keyed on _row name_ strings. These are the functions that generate the LP optimization problem matrix coefficients (for the objective function and constraint rows). This one gets complicated, and is a likely source of bugs. Make sure the row name key strings are all unique or you will make a mess. You can name the constraint rows anything you want, but the objective function row has to be named `z`. All coefficient functions must accept exactly two args, in this order: a `ws3.forest.ForestModel` instance and a `ws3.common.Path` instance. The `z` coefficient function is special in that it must return a single float value. All other (i.e., constraint) coefficient functions just return a dict of floats, keyed on period ints (can be sparse, i.e., not necessary to include key:value pairs in output dict if value is 0.0). It is useful (but not necessary) to use `functools.partial` to specialize a smaller number of more general function definitions (with more args, that get \"locked down\" and hidden by `partial`) as we have done in the example in this notebook.\n",
    "\n",
    "`cflw_e`: Dict of `(dict, int)` tuples, keyed on _row name_ strings (must match _row name_ key values used to define coefficient functions for flow constraints in `coeff_func` dict), where the int:float dict embedded in the tuple defines epsilon values keyed on periods (must include all periods, even if epsilon value is always the same). See example below. \n",
    "\n",
    "```\n",
    "{\n",
    "  'cflw_acut':({1:0.01, 2:0.01, ..., 10:0.01}, 1),\n",
    "  'cflw_vcut':({1:0.05, 2:0.05, ..., 10:0.05}, 1)\n",
    "}\n",
    "```\n",
    "\n",
    "`cgen_data`: Dict of dict of dicts. The outer-level dict is keyed on _row name_ strings (must match row names used in `coeff_funcs`. The middle second level of dicts always has keys `'lb'` and `'ub'`, and the inner level of dicts specifies lower- and upper-bound general constraint RHS (float) values, keyed on period (int).\n",
    "\n",
    "`acodes`: List of strings. Action codes to be included in optimization problem formulation (actions must defined in the `ForestModel` instance, but can be only a subset).\n",
    "\n",
    "`sense`: Must be one of `ws3.opt.SENSE_MAXIMIZE` or `ws3.opt.SENSE_MINIMIZE`.\n",
    "\n",
    "`mask`: Tuple of strings constituting a valid mask for your `ForestModel` instance. Can be `None` if you do not want to filter `DevelopmentType` instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "26e3d841-4a9c-40ee-998d-0d5e456d89f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def gen_scenario(fm, name='base', util=0.85, harvest_acode='harvest',\n",
    "                 cflw_ha={}, cflw_hv={}, \n",
    "                 cgen_ha={}, cgen_hv={}, cgen_gs={}, \n",
    "                 tvy_name='totvol', obj_mode='max_hv', mask=None):\n",
    "    from functools import partial\n",
    "    import numpy as np\n",
    "    coeff_funcs = {}\n",
    "    cflw_e = {}\n",
    "    cgen_data = {}\n",
    "    acodes = ['null', harvest_acode] # define list of action codes\n",
    "    vexpr = '%s * %0.2f' % (tvy_name, util) # define volume expression\n",
    "    if obj_mode == 'max_hv': # maximize harvest volume\n",
    "        sense = ws3.opt.SENSE_MAXIMIZE \n",
    "        zexpr = vexpr\n",
    "    elif obj_mode == 'min_ha': # minimize harvest area\n",
    "        sense = ws3.opt.SENSE_MINIMIZE \n",
    "        zexpr = '1.'\n",
    "    else:\n",
    "        raise ValueError('Invalid obj_mode: %s' % obj_mode)        \n",
    "    coeff_funcs['z'] = partial(cmp_c_z, expr=zexpr) # define objective function coefficient function  \n",
    "    T = fm.periods\n",
    "    if cflw_ha: # define even flow constraint (on harvest area)\n",
    "        cname = 'cflw_ha'\n",
    "        coeff_funcs[cname] = partial(cmp_c_caa, expr='1.', acodes=[harvest_acode], mask=None) \n",
    "        cflw_e[cname] = cflw_ha\n",
    "    if cflw_hv: # define even flow constraint (on harvest volume)\n",
    "        cname = 'cflw_hv'\n",
    "        coeff_funcs[cname] = partial(cmp_c_caa, expr=vexpr, acodes=[harvest_acode], mask=None) \n",
    "        cflw_e[cname] = cflw_hv         \n",
    "    if cgen_ha: # define general constraint (harvest area)\n",
    "        cname = 'cgen_ha'\n",
    "        coeff_funcs[cname] = partial(cmp_c_caa, expr='1.', acodes=[harvest_acode], mask=None) \n",
    "        cgen_data[cname] = cgen_ha\n",
    "    if cgen_hv: # define general constraint (harvest volume)\n",
    "        cname = 'cgen_hv'\n",
    "        coeff_funcs[cname] = partial(cmp_c_caa, expr=vexpr, acodes=[harvest_acode], mask=None) \n",
    "        cgen_data[cname] = cgen_hv\n",
    "    if cgen_gs: # define general constraint (growing stock)\n",
    "        cname = 'cgen_gs'\n",
    "        coeff_funcs[cname] = partial(cmp_c_ci, yname=tvy_name, mask=None)\n",
    "        cgen_data[cname] = cgen_gs\n",
    "    return fm.add_problem(name, coeff_funcs, cflw_e, cgen_data=cgen_data, acodes=acodes, sense=sense, mask=mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d74ea0-f88c-44b9-8195-f8ad6fc323b3",
   "metadata": {},
   "source": [
    "We need to add a \"null\" action to the model for the optimization functions to work correctly. This is basically a pass-through action that literally does nothing (i.e., just grow the forest for one time step, which `ws3` models as an explicit decision option in the dynamic programming state trees it builds when it generates the LP problem matrix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "80741c98-1b51-4bbb-9397-8cc7b426bf05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm.add_null_action()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ec1047-ed70-4aa9-b616-cfc738dddd46",
   "metadata": {},
   "source": [
    "We define some scenario options below. Specify which scenario by setting the `scenario_name` variable below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9acf3917-a6db-40bf-9052-7e14577c601e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_scenario(fm, scenario_name='base'):\n",
    "    cflw_ha = {}\n",
    "    cflw_hv = {}\n",
    "    cgen_ha = {}\n",
    "    cgen_hv = {}\n",
    "    cgen_gs = {}\n",
    "    \n",
    "    # define harvest area and harvest volume flow constraints\n",
    "    cflw_ha = ({p:0.05 for p in fm.periods}, 1)\n",
    "    cflw_hv = ({p:0.05 for p in fm.periods}, 1)\n",
    "\n",
    "    if scenario_name == 'base': \n",
    "        # Base scenario\n",
    "        print('running bsae scenario')\n",
    "    elif scenario_name == 'base-cgen_ha': \n",
    "        # Base scenario, plus harvest area general constraints\n",
    "        print('running base scenario plus harvest area constraints')\n",
    "        cgen_ha = {'lb':{1:100.}, 'ub':{1:101.}}    \n",
    "    elif scenario_name == 'base-cgen_hv': \n",
    "        # Base scenario, plus harvest volume general constraints\n",
    "        print('running base scenario plus harvest volume constraints')\n",
    "        cgen_hv = {'lb':{1:1000.}, 'ub':{1:1001.}}    \n",
    "    elif scenario_name == 'base-cgen_gs': \n",
    "        # Base scenario, plus growing stock general constraints\n",
    "        print('running base scenario plus growing stock constraints')\n",
    "        cgen_gs = {'lb':{10:100000.}, 'ub':{10:100001.}}\n",
    "    else:\n",
    "        assert False # bad scenario name\n",
    "\n",
    "    p = gen_scenario(fm=fm, \n",
    "                     name=scenario_name, \n",
    "                     cflw_ha=cflw_ha, \n",
    "                     cflw_hv=cflw_hv,\n",
    "                     cgen_ha=cgen_ha,\n",
    "                     cgen_hv=cgen_hv,\n",
    "                     cgen_gs=cgen_gs)\n",
    "\n",
    "    fm.reset()\n",
    "    m = p.solve()\n",
    "\n",
    "    if m.status != grb.GRB.OPTIMAL:\n",
    "        print('Model not optimal.')\n",
    "        sys.exit()\n",
    "    sch = fm.compile_schedule(p)\n",
    "    fm.apply_schedule(sch, \n",
    "                      force_integral_area=False, \n",
    "                      override_operability=False,\n",
    "                      fuzzy_age=False,\n",
    "                      recourse_enabled=False,\n",
    "                      verbose=False,\n",
    "                      compile_c_ycomps=True)\n",
    "    df = compile_scenario(fm)\n",
    "    fig, ax = plot_scenario(df)\n",
    "    return fig, df, p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552c8c7b-8c31-4d90-96b7-dcab85187a34",
   "metadata": {},
   "source": [
    "Note that the `Problem.solve` method return a reference to the lower-level `gurobi.Model` object in case we need or want to poke around it (can yield insight into how the optimization problem is formulated on the solver side of things, or help debug).\n",
    "\n",
    "Be vigilant for \"infeasible or unbounded model\" messages and such below, in case these are unexpected. Depending on how the rest of the model was set up, `ws3` may automatically attempt to resolve infeasible models using \"feasibility relaxation\" mode in Gurobi (which might not be what you want, depending on the situation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c5927ee2-4de5-4523-acee-40d1ea956f91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running bsae scenario\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2024-02-16\n",
      "Gurobi Optimizer version 10.0.1 build v10.0.1rc0 (linux64)\n",
      "\n",
      "CPU model: Intel Xeon Processor (Skylake, IBRS), instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
      "\n",
      "Optimize a model with 72 rows, 289 columns and 2961 nonzeros\n",
      "Model fingerprint: 0x59aa13fd\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, 257 columns, 2753 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    2.1443755e+05   1.043429e+04   0.000000e+00      0s\n",
      "      80    1.8837161e+05   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 80 iterations and 0.01 seconds (0.00 work units)\n",
      "Optimal objective  1.883716077e+05\n",
      "foo\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       "    period         oha           ohv            ogs\n",
       " 0       1  141.884536  18776.518275  129802.193269\n",
       " 1       2  134.790309  17837.692361  122458.829271\n",
       " 2       3  134.790309  17837.692361  113354.435725\n",
       " 3       4  134.790309  17837.692361  104521.374438\n",
       " 4       5  134.790309  18086.166885   96456.943325\n",
       " 5       6  134.790309  19715.344189   85374.110687\n",
       " 6       7  147.721253  19715.344189   72562.743824\n",
       " 7       8  135.688774  19715.344189   59660.980420\n",
       " 8       9  148.978762  19134.468737   49258.413311\n",
       " 9      10  148.978762  19715.344189   37323.465085,\n",
       " <ws3.opt.Problem at 0x7fa7ce7c0a90>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_scenario(fm, 'base')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "485e2eb9-529c-40f0-a4c1-47a4edb38fcd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus harvest area constraints\n",
      "Gurobi Optimizer version 10.0.1 build v10.0.1rc0 (linux64)\n",
      "\n",
      "CPU model: Intel Xeon Processor (Skylake, IBRS), instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
      "\n",
      "Optimize a model with 74 rows, 289 columns and 3049 nonzeros\n",
      "Model fingerprint: 0x96d58187\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+02]\n",
      "Presolve removed 13 rows and 32 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 61 rows, 258 columns, 2798 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    2.1443755e+05   1.080947e+04   0.000000e+00      0s\n",
      "     160    1.5092628e+05   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 160 iterations and 0.01 seconds (0.00 work units)\n",
      "Optimal objective  1.509262757e+05\n",
      "foo\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, df, problem = run_scenario(fm, 'base-cgen_ha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "82a9b0a4-9162-434f-93cc-110233624af6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus harvest volume constraints\n",
      "Gurobi Optimizer version 10.0.1 build v10.0.1rc0 (linux64)\n",
      "\n",
      "CPU model: Intel Xeon Processor (Skylake, IBRS), instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
      "\n",
      "Optimize a model with 74 rows, 289 columns and 3049 nonzeros\n",
      "Model fingerprint: 0xd332e1de\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+03]\n",
      "Presolve removed 13 rows and 32 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 61 rows, 258 columns, 2798 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    2.1443755e+05   1.247387e+04   0.000000e+00      0s\n",
      "     186    1.0460450e+04   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 186 iterations and 0.01 seconds (0.00 work units)\n",
      "Optimal objective  1.046045000e+04\n",
      "foo\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       "    period       oha      ohv            ogs\n",
       " 0       1  8.567822  1001.00  150714.567710\n",
       " 1       2  8.139430  1051.05  163609.637284\n",
       " 2       3  8.139430  1051.05  175243.425533\n",
       " 3       4  8.996213  1051.05  187669.286698\n",
       " 4       5  8.139430  1051.05  201607.662333\n",
       " 5       6  8.139430  1051.05  214454.748825\n",
       " 6       7  8.139430  1051.05  224467.572886\n",
       " 7       8  8.139430  1051.05  231509.940913\n",
       " 8       9  8.996213  1051.05  235988.640453\n",
       " 9      10  8.996213  1051.05  238140.632419,\n",
       " <ws3.opt.Problem at 0x7fa7ce7c1090>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_scenario(fm, 'base-cgen_hv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3fc85e87-6244-4c2e-9cfd-74e83f065fc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running base scenario plus growing stock constraints\n",
      "Gurobi Optimizer version 10.0.1 build v10.0.1rc0 (linux64)\n",
      "\n",
      "CPU model: Intel Xeon Processor (Skylake, IBRS), instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
      "\n",
      "Optimize a model with 74 rows, 289 columns and 3253 nonzeros\n",
      "Model fingerprint: 0xc3553ef4\n",
      "Coefficient statistics:\n",
      "  Matrix range     [3e-02, 5e+04]\n",
      "  Objective range  [2e+01, 4e+04]\n",
      "  Bounds range     [0e+00, 0e+00]\n",
      "  RHS range        [1e+00, 1e+05]\n",
      "Presolve removed 13 rows and 8 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 61 rows, 282 columns, 2916 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    2.1443755e+05   1.072442e+04   0.000000e+00      0s\n",
      "      73    1.4427678e+05   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 73 iterations and 0.01 seconds (0.00 work units)\n",
      "Optimal objective  1.442767844e+05\n",
      "foo\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       "    period         oha           ohv            ogs\n",
       " 0       1  105.352680  14648.349362  134658.862578\n",
       " 1       2  110.620314  15380.766830  129609.168767\n",
       " 2       3  100.085046  15371.310058  122559.290901\n",
       " 3       4  102.384809  13915.931894  117915.227866\n",
       " 4       5  104.299024  13915.931894  115172.497894\n",
       " 5       6  105.872157  13915.931894  112583.986634\n",
       " 6       7  104.250342  13915.931894  109980.433670\n",
       " 7       8  102.953620  13915.931894  107572.604247\n",
       " 8       9  101.162818  13915.931894  104839.327090\n",
       " 9      10  110.620314  15380.766830  100000.000000,\n",
       " <ws3.opt.Problem at 0x7fa7ce7c1bd0>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_scenario(fm, 'base-cgen_gs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07c4bfa-fd62-49a3-b9c7-9c2b61d451c9",
   "metadata": {},
   "source": [
    "Ta da!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b531d3d-e908-4612-86cf-41abe92e553f",
   "metadata": {},
   "source": [
    "# Instantiate `ForestModel` from Woodstock-format model input text files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709868de-4c7d-4e51-9f18-492fb8d13e51",
   "metadata": {},
   "source": [
    "Start by creating a new directory to hold the model definition files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83095b12-fce0-4bd4-8b88-15d12d918661",
   "metadata": {},
   "source": [
    "Woodstock models are defined in terms of a number of _sections_. The sections can be defined in a single text file, or in separate text files. We will use separate text files for each section in this example.\n",
    "\n",
    "Our model will include the following sections:\n",
    "\n",
    "LANDSCAPE\n",
    "AREAS\n",
    "YIELD\n",
    "ACTIONS\n",
    "TRANSITIONS\n",
    "\n",
    "There are other possible sections that one can include in a Woodstock model, which will not include here. This is not intended to be a comprehensive overview of Woodstock-format model definition. Refer to the Woodstock technical documentation for the complete story."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5eaed48-ed53-488e-a247-ace133a35591",
   "metadata": {},
   "source": [
    "> Note! Although we tried to make both the handed-coded model above and the Woodstock-format model below as similar as possible, a few differences have slipped in and the models are not totally equivalent (which explains why the same call to `schedule_harvest_areacontrol` produces somewhat different results. \n",
    "\n",
    "> This should not compromise the purpose of this notebook, but can be a bit distracting if you start comparing output from both parts of the example. We will try to fix this at some point to make output from both model match well enough that the differences will not be so evident to the naked eye."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6c738d45-ce65-4c0c-8a39-4afa6bd84234",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘data/_woodstock_model_files’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir data/_woodstock_model_files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8395ca5d-2511-49e3-827a-115ae0dee0c7",
   "metadata": {},
   "source": [
    "## `LANDSCAPE` section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc93d3f1-3f03-4367-9312-66734a09357e",
   "metadata": {},
   "source": [
    "The `LANDSCAPE` section defines themes (i.e., state variables), theme basecodes (i.e., valid state variable values), and theme aggregates (i.e., groups of state variable values within a given theme, which can include aggregates with no [documented] limit on recursion depth)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cdfe484-2efa-4742-99f7-efdd2eef629f",
   "metadata": {},
   "source": [
    "We can start by printing `fm._theme_basecodes`, to provide a \"cheat sheet\" for the values we need to code into the `LANDSCAPE` section of our Woodstock model definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5a44754-e10a-4ddf-9f30-5925b71c3aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['tsa24_clipped'],\n",
       " ['1', '0'],\n",
       " ['2403007',\n",
       "  '2401003',\n",
       "  '2401006',\n",
       "  '2403000',\n",
       "  '2401007',\n",
       "  '2402003',\n",
       "  '2403002',\n",
       "  '2402004',\n",
       "  '2401005',\n",
       "  '2402006',\n",
       "  '2401002',\n",
       "  '2403003',\n",
       "  '2401001',\n",
       "  '2403006',\n",
       "  '2402005',\n",
       "  '2403001',\n",
       "  '2402001',\n",
       "  '2403004',\n",
       "  '2401000',\n",
       "  '2402000',\n",
       "  '2402007',\n",
       "  '2403005',\n",
       "  '2401004',\n",
       "  '2402002'],\n",
       " ['104', '1201', '204', '100', '304'],\n",
       " ['2403007',\n",
       "  '2401003',\n",
       "  '2422000',\n",
       "  '2401006',\n",
       "  '2403000',\n",
       "  '2401007',\n",
       "  '2402003',\n",
       "  '2403002',\n",
       "  '2402004',\n",
       "  '2422004',\n",
       "  '2401005',\n",
       "  '2402006',\n",
       "  '2401002',\n",
       "  '2403003',\n",
       "  '2401001',\n",
       "  '2423007',\n",
       "  '2403006',\n",
       "  '2423000',\n",
       "  '2423002',\n",
       "  '2421007',\n",
       "  '2402005',\n",
       "  '2403001',\n",
       "  '2402001',\n",
       "  '2423001',\n",
       "  '2403004',\n",
       "  '2401000',\n",
       "  '2422007',\n",
       "  '2402000',\n",
       "  '2402007',\n",
       "  '2422003',\n",
       "  '2421002',\n",
       "  '2423004',\n",
       "  '2403005',\n",
       "  '2423003',\n",
       "  '2401004',\n",
       "  '2402002',\n",
       "  '2422002']]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm._theme_basecodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418fb41b-986a-418e-bbde-2ac1705113c4",
   "metadata": {},
   "source": [
    "Print the THEME values to a format that we can easily copy and past into our text file to speed up coding this up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "96ab4937-a238-46d3-a9ef-4137c1e88d78",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2403007\n",
      "2401003\n",
      "2401006\n",
      "2403000\n",
      "2401007\n",
      "2402003\n",
      "2403002\n",
      "2402004\n",
      "2401005\n",
      "2402006\n",
      "2401002\n",
      "2403003\n",
      "2401001\n",
      "2403006\n",
      "2402005\n",
      "2403001\n",
      "2402001\n",
      "2403004\n",
      "2401000\n",
      "2402000\n",
      "2402007\n",
      "2403005\n",
      "2401004\n",
      "2402002\n"
     ]
    }
   ],
   "source": [
    "for v in fm._theme_basecodes[2]: print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b8a9c4a6-1dd2-406e-ae44-401c0e640d8e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104\n",
      "1201\n",
      "204\n",
      "100\n",
      "304\n"
     ]
    }
   ],
   "source": [
    "for v in fm._theme_basecodes[3]: print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "82d92270-b21a-4799-915b-3b46a1b5d219",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2403007\n",
      "2401003\n",
      "2422000\n",
      "2401006\n",
      "2403000\n",
      "2401007\n",
      "2402003\n",
      "2403002\n",
      "2402004\n",
      "2422004\n",
      "2401005\n",
      "2402006\n",
      "2401002\n",
      "2403003\n",
      "2401001\n",
      "2423007\n",
      "2403006\n",
      "2423000\n",
      "2423002\n",
      "2421007\n",
      "2402005\n",
      "2403001\n",
      "2402001\n",
      "2423001\n",
      "2403004\n",
      "2401000\n",
      "2422007\n",
      "2402000\n",
      "2402007\n",
      "2422003\n",
      "2421002\n",
      "2423004\n",
      "2403005\n",
      "2423003\n",
      "2401004\n",
      "2402002\n",
      "2422002\n"
     ]
    }
   ],
   "source": [
    "for v in fm._theme_basecodes[4]: print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b3bd2911-bad8-47ae-a27d-a0a1cd710239",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "landscape_section = \\\n",
    "\"\"\"\n",
    "*THEME Timber Supply Area (TSA)\n",
    "tsa24_clipped\n",
    "\n",
    "*THEME Timber Harvesting Land Base (THLB)\n",
    "0\n",
    "1\n",
    "\n",
    "*THEME Analysis Unit (AU)\n",
    "2402002\n",
    "2401007\n",
    "2402001\n",
    "2401005\n",
    "2402000\n",
    "2403004\n",
    "2403006\n",
    "2401004\n",
    "2401006\n",
    "2402004\n",
    "2402007\n",
    "2403005\n",
    "2401002\n",
    "2403007\n",
    "2403003\n",
    "2401000\n",
    "2402005\n",
    "2402006\n",
    "2401003\n",
    "2403002\n",
    "2402003\n",
    "2403000\n",
    "2401001\n",
    "2403001\n",
    "\n",
    "*THEME Leading tree species (CANFI species code)\n",
    "304\n",
    "100\n",
    "1201\n",
    "204\n",
    "104\n",
    "\n",
    "*THEME Yield curve ID\n",
    "2402002\n",
    "2401007\n",
    "2422007\n",
    "2402001\n",
    "2401005\n",
    "2422003\n",
    "2402000\n",
    "2403004\n",
    "2423004\n",
    "2403006\n",
    "2421007\n",
    "2423003\n",
    "2401004\n",
    "2401006\n",
    "2402004\n",
    "2421002\n",
    "2422004\n",
    "2423002\n",
    "2422002\n",
    "2402007\n",
    "2403005\n",
    "2401002\n",
    "2403007\n",
    "2423007\n",
    "2403003\n",
    "2401000\n",
    "2402005\n",
    "2402006\n",
    "2401003\n",
    "2423001\n",
    "2403002\n",
    "2402003\n",
    "2422000\n",
    "2403000\n",
    "2401001\n",
    "2423000\n",
    "2403001\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6cb3f630-a475-43a8-b933-15b9cfc4670a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('data/_woodstock_model_files/tsa24_clipped.lan', 'w') as f: f.write(landscape_section)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e2538c-62e6-4d63-aa04-ef33a923ba19",
   "metadata": {},
   "source": [
    "## `AREAS` section\n",
    "\n",
    "The `AREAS` section defines the initial inventory, as area by development type and age. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087f1eb5-0e4a-45fc-8faa-5c81eac6b906",
   "metadata": {},
   "source": [
    "Rather can manually code this section (which would be long and also prone to user error), we can use a bit of code to print the required information in the correct format (which we can then copy and paste)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e2dc1ac7-cfd0-44c4-8ea8-db105506423d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*A tsa24_clipped 0 2401000 100 2401000 85 15.182274886309896\n",
      "*A tsa24_clipped 0 2401000 100 2401000 95 20.653788842921458\n",
      "*A tsa24_clipped 0 2401000 100 2401000 105 1.109374490200082\n",
      "*A tsa24_clipped 0 2401000 100 2401000 125 25.73174833461312\n",
      "*A tsa24_clipped 0 2401000 100 2401000 135 62.02382759721078\n",
      "*A tsa24_clipped 0 2401000 100 2401000 145 45.32228954967691\n",
      "*A tsa24_clipped 0 2401000 100 2401000 155 3.052804424896181\n",
      "*A tsa24_clipped 0 2402005 1201 2402005 85 1.812979326195168\n",
      "*A tsa24_clipped 1 2401002 204 2401002 78 103.76740323520823\n",
      "*A tsa24_clipped 1 2401002 204 2401002 80 4.173147018452507\n",
      "*A tsa24_clipped 1 2401002 204 2401002 85 282.1296355046733\n",
      "*A tsa24_clipped 1 2401002 204 2401002 91 73.1021561503533\n",
      "*A tsa24_clipped 1 2401002 204 2401002 93 28.37956666951611\n",
      "*A tsa24_clipped 1 2401002 204 2401002 95 94.94675966211176\n",
      "*A tsa24_clipped 1 2401002 204 2401002 105 32.175418531537815\n",
      "*A tsa24_clipped 1 2401002 204 2401002 113 4.184826329641321\n",
      "*A tsa24_clipped 1 2401002 204 2401002 115 50.030816858894816\n",
      "*A tsa24_clipped 1 2401002 204 2401002 125 78.16612132001225\n",
      "*A tsa24_clipped 1 2401002 204 2401002 135 72.24421919373785\n",
      "*A tsa24_clipped 1 2401002 204 2401002 145 96.38442685503642\n",
      "*A tsa24_clipped 1 2401002 204 2401002 153 9.591469412607397\n",
      "*A tsa24_clipped 1 2401002 204 2401002 155 34.32629241113743\n",
      "*A tsa24_clipped 1 2401002 204 2421002 20 0.422054121206099\n",
      "*A tsa24_clipped 1 2402000 100 2402000 165 0.638005468748551\n",
      "*A tsa24_clipped 1 2402002 204 2402002 78 32.64168183055375\n",
      "*A tsa24_clipped 1 2402002 204 2402002 93 48.21816452980633\n",
      "*A tsa24_clipped 1 2402002 204 2402002 95 33.89498244313859\n",
      "*A tsa24_clipped 1 2402002 204 2402002 115 3.195378954654358\n",
      "*A tsa24_clipped 1 2403000 100 2403000 93 14.811643286926836\n",
      "*A tsa24_clipped 1 2403002 204 2403002 73 2.243990590272984\n",
      "*A tsa24_clipped 1 2403002 204 2423002 9 59.81429119367274\n",
      "*A tsa24_clipped 1 2403002 204 2423002 18 32.366198551219505\n"
     ]
    }
   ],
   "source": [
    "for name, group in gstands:\n",
    "    dtk, age, area = tuple(map(lambda x: str(x), name[:-1])), int(name[-1]), group['area'].sum()\n",
    "    print('*A', ' '.join(v for v in dtk), age, area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "362774d0-9988-432b-8a4e-ad69fd74cb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_section = \\\n",
    "\"\"\"\n",
    "*A tsa24_clipped 0 2401000 100 2401000 85 15.182274886309896\n",
    "*A tsa24_clipped 0 2401000 100 2401000 95 20.653788842921458\n",
    "*A tsa24_clipped 0 2401000 100 2401000 105 1.109374490200082\n",
    "*A tsa24_clipped 0 2401000 100 2401000 125 25.73174833461312\n",
    "*A tsa24_clipped 0 2401000 100 2401000 135 62.02382759721078\n",
    "*A tsa24_clipped 0 2401000 100 2401000 145 45.32228954967691\n",
    "*A tsa24_clipped 0 2401000 100 2401000 155 3.052804424896181\n",
    "*A tsa24_clipped 0 2402005 1201 2402005 85 1.812979326195168\n",
    "*A tsa24_clipped 1 2401002 204 2401002 78 103.76740323520823\n",
    "*A tsa24_clipped 1 2401002 204 2401002 80 4.173147018452507\n",
    "*A tsa24_clipped 1 2401002 204 2401002 85 282.1296355046733\n",
    "*A tsa24_clipped 1 2401002 204 2401002 91 73.1021561503533\n",
    "*A tsa24_clipped 1 2401002 204 2401002 93 28.37956666951611\n",
    "*A tsa24_clipped 1 2401002 204 2401002 95 94.94675966211176\n",
    "*A tsa24_clipped 1 2401002 204 2401002 105 32.175418531537815\n",
    "*A tsa24_clipped 1 2401002 204 2401002 113 4.184826329641321\n",
    "*A tsa24_clipped 1 2401002 204 2401002 115 50.030816858894816\n",
    "*A tsa24_clipped 1 2401002 204 2401002 125 78.16612132001225\n",
    "*A tsa24_clipped 1 2401002 204 2401002 135 72.24421919373785\n",
    "*A tsa24_clipped 1 2401002 204 2401002 145 96.38442685503642\n",
    "*A tsa24_clipped 1 2401002 204 2401002 153 9.591469412607397\n",
    "*A tsa24_clipped 1 2401002 204 2401002 155 34.32629241113743\n",
    "*A tsa24_clipped 1 2401002 204 2421002 20 0.422054121206099\n",
    "*A tsa24_clipped 1 2402000 100 2402000 165 0.638005468748551\n",
    "*A tsa24_clipped 1 2402002 204 2402002 78 32.64168183055375\n",
    "*A tsa24_clipped 1 2402002 204 2402002 93 48.21816452980633\n",
    "*A tsa24_clipped 1 2402002 204 2402002 95 33.89498244313859\n",
    "*A tsa24_clipped 1 2402002 204 2402002 115 3.195378954654358\n",
    "*A tsa24_clipped 1 2403000 100 2403000 93 14.811643286926836\n",
    "*A tsa24_clipped 1 2403002 204 2403002 73 2.243990590272984\n",
    "*A tsa24_clipped 1 2403002 204 2423002 9 59.81429119367274\n",
    "*A tsa24_clipped 1 2403002 204 2423002 18 32.366198551219505\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d63d445a-b720-4633-8ff9-421fb132308e",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/_woodstock_model_files/tsa24_clipped.are', 'w') as f: f.write(areas_section)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "473cefbc-cc18-4e4d-b8e2-5c4274a06e70",
   "metadata": {
    "tags": []
   },
   "source": [
    "## `YIELDS` section\n",
    "\n",
    "The `YIELDS` section defines yield curves and links these to development types.\n",
    "\n",
    "We can use a bit of code to output the required data in the correct format. It would be possible to manually code this (like any other section) but would be long and error prone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3c0c760f-0a15-46e9-ac6b-0d82a748f6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('?', '?', '2401000', '?', '2401000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0ef3ac0>)]),\n",
       " (('?', '?', '2401000', '?', '2401000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0ef3ac0>)]),\n",
       " (('?', '?', '2402000', '?', '2402000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0b6a6e0>)]),\n",
       " (('?', '?', '2402000', '?', '2422000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0b6bd60>)]),\n",
       " (('?', '?', '2403000', '?', '2403000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0b6bc70>)]),\n",
       " (('?', '?', '2403000', '?', '2423000'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4a470>)]),\n",
       " (('?', '?', '2401001', '?', '2401001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c4a380>)]),\n",
       " (('?', '?', '2401001', '?', '2401001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c4a380>)]),\n",
       " (('?', '?', '2402001', '?', '2402001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c4a5c0>)]),\n",
       " (('?', '?', '2402001', '?', '2402001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c4a5c0>)]),\n",
       " (('?', '?', '2403001', '?', '2403001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c484f0>)]),\n",
       " (('?', '?', '2403001', '?', '2423001'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c48430>)]),\n",
       " (('?', '?', '2401002', '?', '2401002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c4a320>)]),\n",
       " (('?', '?', '2401002', '?', '2421002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c4a500>)]),\n",
       " (('?', '?', '2402002', '?', '2402002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c494b0>)]),\n",
       " (('?', '?', '2402002', '?', '2422002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c4a5f0>)]),\n",
       " (('?', '?', '2403002', '?', '2403002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c49450>)]),\n",
       " (('?', '?', '2403002', '?', '2423002'),\n",
       "  'a',\n",
       "  [('s0204', <ws3.core.Curve at 0x7fa7d0c4a620>)]),\n",
       " (('?', '?', '2401003', '?', '2401003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c48280>)]),\n",
       " (('?', '?', '2401003', '?', '2401003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c48280>)]),\n",
       " (('?', '?', '2402003', '?', '2402003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c484c0>)]),\n",
       " (('?', '?', '2402003', '?', '2422003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c49840>)]),\n",
       " (('?', '?', '2403003', '?', '2403003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c4a3b0>)]),\n",
       " (('?', '?', '2403003', '?', '2423003'),\n",
       "  'a',\n",
       "  [('s0304', <ws3.core.Curve at 0x7fa7d0c49750>)]),\n",
       " (('?', '?', '2401004', '?', '2401004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c499f0>)]),\n",
       " (('?', '?', '2401004', '?', '2401004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c499f0>)]),\n",
       " (('?', '?', '2402004', '?', '2402004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c49a80>)]),\n",
       " (('?', '?', '2402004', '?', '2422004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c498d0>)]),\n",
       " (('?', '?', '2403004', '?', '2403004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c49a50>)]),\n",
       " (('?', '?', '2403004', '?', '2423004'),\n",
       "  'a',\n",
       "  [('s0104', <ws3.core.Curve at 0x7fa7d0c49cf0>)]),\n",
       " (('?', '?', '2401005', '?', '2401005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c495d0>)]),\n",
       " (('?', '?', '2401005', '?', '2401005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c495d0>)]),\n",
       " (('?', '?', '2402005', '?', '2402005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49e40>)]),\n",
       " (('?', '?', '2402005', '?', '2402005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49e40>)]),\n",
       " (('?', '?', '2403005', '?', '2403005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49b70>)]),\n",
       " (('?', '?', '2403005', '?', '2403005'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49b70>)]),\n",
       " (('?', '?', '2401006', '?', '2401006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49c00>)]),\n",
       " (('?', '?', '2401006', '?', '2401006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c49c00>)]),\n",
       " (('?', '?', '2402006', '?', '2402006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c4a530>)]),\n",
       " (('?', '?', '2402006', '?', '2402006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c4a530>)]),\n",
       " (('?', '?', '2403006', '?', '2403006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c4a110>)]),\n",
       " (('?', '?', '2403006', '?', '2403006'),\n",
       "  'a',\n",
       "  [('s1201', <ws3.core.Curve at 0x7fa7d0c4a110>)]),\n",
       " (('?', '?', '2401007', '?', '2401007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4aa40>)]),\n",
       " (('?', '?', '2401007', '?', '2421007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4ab30>)]),\n",
       " (('?', '?', '2402007', '?', '2402007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4a9e0>)]),\n",
       " (('?', '?', '2402007', '?', '2422007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4a260>)]),\n",
       " (('?', '?', '2403007', '?', '2403007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4a1d0>)]),\n",
       " (('?', '?', '2403007', '?', '2423007'),\n",
       "  'a',\n",
       "  [('s0100', <ws3.core.Curve at 0x7fa7d0c4a920>)]),\n",
       " (('?', '?', '?', '?', '?'),\n",
       "  'c',\n",
       "  [('totvol', '_SUM(s0104, s0304, s0100, s0204, s1201)')])]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7fcb6ece-075d-4a9b-a3ba-0ec9ff0d4bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*Y ? ? 2401000 ? 2401000\n",
      "s0100 1 0 1 5 10 16 24 33 43 54 64 75 85 95 104 113 121 127 133 138 143 146 148 150 151 151 150 149 147 145 145\n",
      "*Y ? ? 2401000 ? 2401000\n",
      "s0100 1 0 1 5 10 16 24 33 43 54 64 75 85 95 104 113 121 127 133 138 143 146 148 150 151 151 150 149 147 145 145\n",
      "*Y ? ? 2402000 ? 2402000\n",
      "s0100 1 4 13 27 42 58 76 93 110 126 141 156 169 182 193 203 211 219 225 231 235 238 240 242 242 242 241 240 238 235 235\n",
      "*Y ? ? 2402000 ? 2422000\n",
      "s0100 1 0 0 0 0 3 18 46 79 112 144 171 195 215 234 248 261 272 282 290 298 305 311 316 320 325 328 330 333 335 336\n",
      "*Y ? ? 2403000 ? 2403000\n",
      "s0100 1 3 15 37 67 99 133 166 197 225 250 272 290 305 316 325 330 333 333 331 328 322 316 308 299 289 279 268 257 246 246\n",
      "*Y ? ? 2403000 ? 2423000\n",
      "s0100 1 0 0 0 5 38 88 142 190 231 265 293 315 334 350 365 377 388 396 403 410 416 420 424 427 429 431 433 433 433 433\n",
      "*Y ? ? 2401001 ? 2401001\n",
      "s0304 1 0 0 0 0 0 0 2 5 11 18 25 32 40 47 54 62 66 70 75 79 83 88 92 94 95 97 97 97 97 97\n",
      "*Y ? ? 2401001 ? 2401001\n",
      "s0304 1 0 0 0 0 0 0 2 5 11 18 25 32 40 47 54 62 66 70 75 79 83 88 92 94 95 97 97 97 97 97\n",
      "*Y ? ? 2402001 ? 2402001\n",
      "s0304 1 0 0 0 0 0 3 8 17 29 43 57 71 84 96 107 117 125 132 138 143 146 149 150 151 151 150 149 146 144 144\n",
      "*Y ? ? 2402001 ? 2402001\n",
      "s0304 1 0 0 0 0 0 3 8 17 29 43 57 71 84 96 107 117 125 132 138 143 146 149 150 151 151 150 149 146 144 144\n",
      "*Y ? ? 2403001 ? 2403001\n",
      "s0304 1 0 0 1 6 16 30 48 68 87 106 123 140 154 167 178 188 195 201 206 209 210 211 210 209 206 203 199 195 190 190\n",
      "*Y ? ? 2403001 ? 2423001\n",
      "s0304 1 0 0 0 0 0 0 6 21 43 70 97 126 152 176 199 218 236 252 265 277 288 297 306 314 321 326 331 336 339 340\n",
      "*Y ? ? 2401002 ? 2401002\n",
      "s0204 1 0 4 12 25 40 57 73 89 103 116 128 137 145 152 157 160 162 163 163 162 160 158 154 151 147 142 137 132 127 127\n",
      "*Y ? ? 2401002 ? 2421002\n",
      "s0204 1 0 0 0 0 1 7 23 47 71 94 114 132 146 157 166 173 179 183 187 190 191 192 193 194 195 195 195 196 196 196\n",
      "*Y ? ? 2402002 ? 2402002\n",
      "s0204 1 5 18 37 57 79 101 122 142 160 176 191 203 214 222 229 233 237 238 239 238 236 233 229 224 219 213 207 201 194 194\n",
      "*Y ? ? 2402002 ? 2422002\n",
      "s0204 1 0 0 0 1 16 55 100 143 180 208 231 247 261 271 278 282 286 288 289 290 291 292 292 293 293 293 293 293 293 293\n",
      "*Y ? ? 2403002 ? 2403002\n",
      "s0204 1 8 29 59 93 129 165 200 232 261 287 309 328 343 355 363 368 371 371 369 365 359 352 344 334 324 313 301 290 277 277\n",
      "*Y ? ? 2403002 ? 2423002\n",
      "s0204 1 0 0 0 24 98 184 257 312 353 380 400 413 422 428 433 435 436 437 436 434 433 431 430 429 428 428 428 428 428 428\n",
      "*Y ? ? 2401003 ? 2401003\n",
      "s0304 1 0 0 0 1 6 15 27 42 58 74 89 103 116 127 137 145 152 157 161 164 166 167 166 165 163 161 158 154 150 150\n",
      "*Y ? ? 2401003 ? 2401003\n",
      "s0304 1 0 0 0 1 6 15 27 42 58 74 89 103 116 127 137 145 152 157 161 164 166 167 166 165 163 161 158 154 150 150\n",
      "*Y ? ? 2402003 ? 2402003\n",
      "s0304 1 0 0 2 9 22 42 67 94 120 145 168 189 207 222 235 245 252 258 261 262 261 259 255 251 245 238 231 223 215 215\n",
      "*Y ? ? 2402003 ? 2422003\n",
      "s0304 1 0 0 0 0 0 6 25 52 84 117 150 180 206 228 247 263 277 290 301 311 319 327 333 338 342 347 350 353 357 358\n",
      "*Y ? ? 2403003 ? 2403003\n",
      "s0304 1 6 25 53 86 120 155 189 220 249 275 297 315 330 342 350 356 359 359 357 354 348 341 333 324 314 304 293 281 270 270\n",
      "*Y ? ? 2403003 ? 2423003\n",
      "s0304 1 0 0 0 1 17 60 114 170 221 264 299 327 352 372 390 404 416 426 435 443 450 456 461 466 469 471 473 474 475 476\n",
      "*Y ? ? 2401004 ? 2401004\n",
      "s0104 1 0 0 0 0 0 2 6 12 22 33 45 58 70 83 94 105 115 125 133 139 145 150 154 157 159 160 160 159 158 158\n",
      "*Y ? ? 2401004 ? 2401004\n",
      "s0104 1 0 0 0 0 0 2 6 12 22 33 45 58 70 83 94 105 115 125 133 139 145 150 154 157 159 160 160 159 158 158\n",
      "*Y ? ? 2402004 ? 2402004\n",
      "s0104 1 0 0 0 1 4 11 22 35 51 68 87 106 125 142 159 174 188 199 209 216 222 225 227 228 226 224 220 215 209 209\n",
      "*Y ? ? 2402004 ? 2422004\n",
      "s0104 1 0 0 0 0 0 0 5 18 37 60 85 111 134 156 176 194 210 224 236 247 257 265 273 280 286 291 296 300 303 304\n",
      "*Y ? ? 2403004 ? 2403004\n",
      "s0104 1 0 0 1 6 16 33 55 80 105 129 153 175 195 213 229 242 253 262 269 274 277 279 279 277 275 271 267 261 255 255\n",
      "*Y ? ? 2403004 ? 2423004\n",
      "s0104 1 0 0 0 0 1 10 33 63 98 133 165 195 222 245 266 284 301 316 329 341 351 360 368 374 380 384 387 390 393 394\n",
      "*Y ? ? 2401005 ? 2401005\n",
      "s1201 1 2 12 30 53 78 102 124 144 160 174 184 191 196 198 198 196 193 188 182 175 168 161 153 145 137 129 120 112 105 105\n",
      "*Y ? ? 2401005 ? 2401005\n",
      "s1201 1 2 12 30 53 78 102 124 144 160 174 184 191 196 198 198 196 193 188 182 175 168 161 153 145 137 129 120 112 105 105\n",
      "*Y ? ? 2402005 ? 2402005\n",
      "s1201 1 8 30 59 89 118 146 170 191 208 221 230 237 240 240 238 234 229 222 214 205 196 186 176 166 156 146 137 127 118 118\n",
      "*Y ? ? 2402005 ? 2402005\n",
      "s1201 1 8 30 59 89 118 146 170 191 208 221 230 237 240 240 238 234 229 222 214 205 196 186 176 166 156 146 137 127 118 118\n",
      "*Y ? ? 2403005 ? 2403005\n",
      "s1201 1 10 37 73 111 148 183 214 240 260 276 287 294 297 296 292 286 278 268 258 246 233 220 208 195 182 169 158 146 135 135\n",
      "*Y ? ? 2403005 ? 2403005\n",
      "s1201 1 10 37 73 111 148 183 214 240 260 276 287 294 297 296 292 286 278 268 258 246 233 220 208 195 182 169 158 146 135 135\n",
      "*Y ? ? 2401006 ? 2401006\n",
      "s1201 1 4 17 35 57 79 101 121 139 155 168 179 187 193 197 199 199 198 196 192 188 182 176 170 163 156 149 142 134 127 127\n",
      "*Y ? ? 2401006 ? 2401006\n",
      "s1201 1 4 17 35 57 79 101 121 139 155 168 179 187 193 197 199 199 198 196 192 188 182 176 170 163 156 149 142 134 127 127\n",
      "*Y ? ? 2402006 ? 2402006\n",
      "s1201 1 8 30 59 90 121 150 176 199 219 234 246 255 260 262 262 260 255 249 242 234 225 215 205 195 184 174 164 154 144 144\n",
      "*Y ? ? 2402006 ? 2402006\n",
      "s1201 1 8 30 59 90 121 150 176 199 219 234 246 255 260 262 262 260 255 249 242 234 225 215 205 195 184 174 164 154 144 144\n",
      "*Y ? ? 2403006 ? 2403006\n",
      "s1201 1 9 35 69 106 143 178 210 237 260 279 293 302 308 310 309 306 300 292 283 273 261 250 237 225 212 199 187 175 163 163\n",
      "*Y ? ? 2403006 ? 2403006\n",
      "s1201 1 9 35 69 106 143 178 210 237 260 279 293 302 308 310 309 306 300 292 283 273 261 250 237 225 212 199 187 175 163 163\n",
      "*Y ? ? 2401007 ? 2401007\n",
      "s0100 1 0 0 3 12 27 47 71 95 119 140 160 177 191 203 213 220 225 228 229 228 226 223 218 213 207 200 193 186 178 178\n",
      "*Y ? ? 2401007 ? 2421007\n",
      "s0100 1 0 0 0 0 3 19 49 83 118 151 180 205 226 245 261 275 286 296 305 313 320 327 332 337 341 344 347 350 352 353\n",
      "*Y ? ? 2402007 ? 2402007\n",
      "s0100 1 0 2 12 29 54 83 113 142 170 194 216 235 250 263 272 278 282 283 283 280 276 270 263 256 247 238 229 219 209 209\n",
      "*Y ? ? 2402007 ? 2422007\n",
      "s0100 1 0 0 0 2 24 65 114 161 201 235 264 288 307 323 338 351 362 371 379 385 391 396 401 405 407 410 412 413 414 415\n",
      "*Y ? ? 2403007 ? 2403007\n",
      "s0100 1 6 28 61 100 143 186 226 264 297 326 350 370 384 395 402 404 404 401 395 387 378 367 354 341 327 313 298 284 269 269\n",
      "*Y ? ? 2403007 ? 2423007\n",
      "s0100 1 0 0 0 30 99 177 244 300 343 378 407 432 452 468 483 495 505 513 519 523 526 529 531 534 535 535 535 535 536 535\n"
     ]
    }
   ],
   "source": [
    "for au_id, au_row in au_table.iterrows():\n",
    "    yname = 's%04d' % int(au_row.canfi_species)\n",
    "    for is_managed in (0, 1):\n",
    "        curve_id = au_row.unmanaged_curve_id if not is_managed else au_row.managed_curve_id\n",
    "        mask = ('?', '?', str(au_id), '?', str(curve_id))\n",
    "        points = [(r.x, r.y) for _, r in curve_points_table.loc[curve_id].iterrows() if not r.x % period_length and r.x <= max_age]\n",
    "        c = ws3.core.Curve(yname, points=points, type='a', is_volume=True, xmax=fm.max_age, period_length=period_length)\n",
    "        print('*Y', ' '.join(v for v in mask))\n",
    "        print(yname, '1', ' '.join(str(int(c[x])) for x in range(fm.period_length, 300+fm.period_length, fm.period_length)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d7842c82-546e-47bf-be8f-c1cf546c24d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "yields_section = \\\n",
    "\"\"\"\n",
    "*Y ? ? 2401000 ? 2401000\n",
    "s0100 1 0 1 5 10 16 24 33 43 54 64 75 85 95 104 113 121 127 133 138 143 146 148 150 151 151 150 149 147 145 145\n",
    "*Y ? ? 2401000 ? 2401000\n",
    "s0100 1 0 1 5 10 16 24 33 43 54 64 75 85 95 104 113 121 127 133 138 143 146 148 150 151 151 150 149 147 145 145\n",
    "*Y ? ? 2402000 ? 2402000\n",
    "s0100 1 4 13 27 42 58 76 93 110 126 141 156 169 182 193 203 211 219 225 231 235 238 240 242 242 242 241 240 238 235 235\n",
    "*Y ? ? 2402000 ? 2422000\n",
    "s0100 1 0 0 0 0 3 18 46 79 112 144 171 195 215 234 248 261 272 282 290 298 305 311 316 320 325 328 330 333 335 336\n",
    "*Y ? ? 2403000 ? 2403000\n",
    "s0100 1 3 15 37 67 99 133 166 197 225 250 272 290 305 316 325 330 333 333 331 328 322 316 308 299 289 279 268 257 246 246\n",
    "*Y ? ? 2403000 ? 2423000\n",
    "s0100 1 0 0 0 5 38 88 142 190 231 265 293 315 334 350 365 377 388 396 403 410 416 420 424 427 429 431 433 433 433 433\n",
    "*Y ? ? 2401001 ? 2401001\n",
    "s0304 1 0 0 0 0 0 0 2 5 11 18 25 32 40 47 54 62 66 70 75 79 83 88 92 94 95 97 97 97 97 97\n",
    "*Y ? ? 2401001 ? 2401001\n",
    "s0304 1 0 0 0 0 0 0 2 5 11 18 25 32 40 47 54 62 66 70 75 79 83 88 92 94 95 97 97 97 97 97\n",
    "*Y ? ? 2402001 ? 2402001\n",
    "s0304 1 0 0 0 0 0 3 8 17 29 43 57 71 84 96 107 117 125 132 138 143 146 149 150 151 151 150 149 146 144 144\n",
    "*Y ? ? 2402001 ? 2402001\n",
    "s0304 1 0 0 0 0 0 3 8 17 29 43 57 71 84 96 107 117 125 132 138 143 146 149 150 151 151 150 149 146 144 144\n",
    "*Y ? ? 2403001 ? 2403001\n",
    "s0304 1 0 0 1 6 16 30 48 68 87 106 123 140 154 167 178 188 195 201 206 209 210 211 210 209 206 203 199 195 190 190\n",
    "*Y ? ? 2403001 ? 2423001\n",
    "s0304 1 0 0 0 0 0 0 6 21 43 70 97 126 152 176 199 218 236 252 265 277 288 297 306 314 321 326 331 336 339 340\n",
    "*Y ? ? 2401002 ? 2401002\n",
    "s0204 1 0 4 12 25 40 57 73 89 103 116 128 137 145 152 157 160 162 163 163 162 160 158 154 151 147 142 137 132 127 127\n",
    "*Y ? ? 2401002 ? 2421002\n",
    "s0204 1 0 0 0 0 1 7 23 47 71 94 114 132 146 157 166 173 179 183 187 190 191 192 193 194 195 195 195 196 196 196\n",
    "*Y ? ? 2402002 ? 2402002\n",
    "s0204 1 5 18 37 57 79 101 122 142 160 176 191 203 214 222 229 233 237 238 239 238 236 233 229 224 219 213 207 201 194 194\n",
    "*Y ? ? 2402002 ? 2422002\n",
    "s0204 1 0 0 0 1 16 55 100 143 180 208 231 247 261 271 278 282 286 288 289 290 291 292 292 293 293 293 293 293 293 293\n",
    "*Y ? ? 2403002 ? 2403002\n",
    "s0204 1 8 29 59 93 129 165 200 232 261 287 309 328 343 355 363 368 371 371 369 365 359 352 344 334 324 313 301 290 277 277\n",
    "*Y ? ? 2403002 ? 2423002\n",
    "s0204 1 0 0 0 24 98 184 257 312 353 380 400 413 422 428 433 435 436 437 436 434 433 431 430 429 428 428 428 428 428 428\n",
    "*Y ? ? 2401003 ? 2401003\n",
    "s0304 1 0 0 0 1 6 15 27 42 58 74 89 103 116 127 137 145 152 157 161 164 166 167 166 165 163 161 158 154 150 150\n",
    "*Y ? ? 2401003 ? 2401003\n",
    "s0304 1 0 0 0 1 6 15 27 42 58 74 89 103 116 127 137 145 152 157 161 164 166 167 166 165 163 161 158 154 150 150\n",
    "*Y ? ? 2402003 ? 2402003\n",
    "s0304 1 0 0 2 9 22 42 67 94 120 145 168 189 207 222 235 245 252 258 261 262 261 259 255 251 245 238 231 223 215 215\n",
    "*Y ? ? 2402003 ? 2422003\n",
    "s0304 1 0 0 0 0 0 6 25 52 84 117 150 180 206 228 247 263 277 290 301 311 319 327 333 338 342 347 350 353 357 358\n",
    "*Y ? ? 2403003 ? 2403003\n",
    "s0304 1 6 25 53 86 120 155 189 220 249 275 297 315 330 342 350 356 359 359 357 354 348 341 333 324 314 304 293 281 270 270\n",
    "*Y ? ? 2403003 ? 2423003\n",
    "s0304 1 0 0 0 1 17 60 114 170 221 264 299 327 352 372 390 404 416 426 435 443 450 456 461 466 469 471 473 474 475 476\n",
    "*Y ? ? 2401004 ? 2401004\n",
    "s0104 1 0 0 0 0 0 2 6 12 22 33 45 58 70 83 94 105 115 125 133 139 145 150 154 157 159 160 160 159 158 158\n",
    "*Y ? ? 2401004 ? 2401004\n",
    "s0104 1 0 0 0 0 0 2 6 12 22 33 45 58 70 83 94 105 115 125 133 139 145 150 154 157 159 160 160 159 158 158\n",
    "*Y ? ? 2402004 ? 2402004\n",
    "s0104 1 0 0 0 1 4 11 22 35 51 68 87 106 125 142 159 174 188 199 209 216 222 225 227 228 226 224 220 215 209 209\n",
    "*Y ? ? 2402004 ? 2422004\n",
    "s0104 1 0 0 0 0 0 0 5 18 37 60 85 111 134 156 176 194 210 224 236 247 257 265 273 280 286 291 296 300 303 304\n",
    "*Y ? ? 2403004 ? 2403004\n",
    "s0104 1 0 0 1 6 16 33 55 80 105 129 153 175 195 213 229 242 253 262 269 274 277 279 279 277 275 271 267 261 255 255\n",
    "*Y ? ? 2403004 ? 2423004\n",
    "s0104 1 0 0 0 0 1 10 33 63 98 133 165 195 222 245 266 284 301 316 329 341 351 360 368 374 380 384 387 390 393 394\n",
    "*Y ? ? 2401005 ? 2401005\n",
    "s1201 1 2 12 30 53 78 102 124 144 160 174 184 191 196 198 198 196 193 188 182 175 168 161 153 145 137 129 120 112 105 105\n",
    "*Y ? ? 2401005 ? 2401005\n",
    "s1201 1 2 12 30 53 78 102 124 144 160 174 184 191 196 198 198 196 193 188 182 175 168 161 153 145 137 129 120 112 105 105\n",
    "*Y ? ? 2402005 ? 2402005\n",
    "s1201 1 8 30 59 89 118 146 170 191 208 221 230 237 240 240 238 234 229 222 214 205 196 186 176 166 156 146 137 127 118 118\n",
    "*Y ? ? 2402005 ? 2402005\n",
    "s1201 1 8 30 59 89 118 146 170 191 208 221 230 237 240 240 238 234 229 222 214 205 196 186 176 166 156 146 137 127 118 118\n",
    "*Y ? ? 2403005 ? 2403005\n",
    "s1201 1 10 37 73 111 148 183 214 240 260 276 287 294 297 296 292 286 278 268 258 246 233 220 208 195 182 169 158 146 135 135\n",
    "*Y ? ? 2403005 ? 2403005\n",
    "s1201 1 10 37 73 111 148 183 214 240 260 276 287 294 297 296 292 286 278 268 258 246 233 220 208 195 182 169 158 146 135 135\n",
    "*Y ? ? 2401006 ? 2401006\n",
    "s1201 1 4 17 35 57 79 101 121 139 155 168 179 187 193 197 199 199 198 196 192 188 182 176 170 163 156 149 142 134 127 127\n",
    "*Y ? ? 2401006 ? 2401006\n",
    "s1201 1 4 17 35 57 79 101 121 139 155 168 179 187 193 197 199 199 198 196 192 188 182 176 170 163 156 149 142 134 127 127\n",
    "*Y ? ? 2402006 ? 2402006\n",
    "s1201 1 8 30 59 90 121 150 176 199 219 234 246 255 260 262 262 260 255 249 242 234 225 215 205 195 184 174 164 154 144 144\n",
    "*Y ? ? 2402006 ? 2402006\n",
    "s1201 1 8 30 59 90 121 150 176 199 219 234 246 255 260 262 262 260 255 249 242 234 225 215 205 195 184 174 164 154 144 144\n",
    "*Y ? ? 2403006 ? 2403006\n",
    "s1201 1 9 35 69 106 143 178 210 237 260 279 293 302 308 310 309 306 300 292 283 273 261 250 237 225 212 199 187 175 163 163\n",
    "*Y ? ? 2403006 ? 2403006\n",
    "s1201 1 9 35 69 106 143 178 210 237 260 279 293 302 308 310 309 306 300 292 283 273 261 250 237 225 212 199 187 175 163 163\n",
    "*Y ? ? 2401007 ? 2401007\n",
    "s0100 1 0 0 3 12 27 47 71 95 119 140 160 177 191 203 213 220 225 228 229 228 226 223 218 213 207 200 193 186 178 178\n",
    "*Y ? ? 2401007 ? 2421007\n",
    "s0100 1 0 0 0 0 3 19 49 83 118 151 180 205 226 245 261 275 286 296 305 313 320 327 332 337 341 344 347 350 352 353\n",
    "*Y ? ? 2402007 ? 2402007\n",
    "s0100 1 0 2 12 29 54 83 113 142 170 194 216 235 250 263 272 278 282 283 283 280 276 270 263 256 247 238 229 219 209 209\n",
    "*Y ? ? 2402007 ? 2422007\n",
    "s0100 1 0 0 0 2 24 65 114 161 201 235 264 288 307 323 338 351 362 371 379 385 391 396 401 405 407 410 412 413 414 415\n",
    "*Y ? ? 2403007 ? 2403007\n",
    "s0100 1 6 28 61 100 143 186 226 264 297 326 350 370 384 395 402 404 404 401 395 387 378 367 354 341 327 313 298 284 269 269\n",
    "*Y ? ? 2403007 ? 2423007\n",
    "s0100 1 0 0 0 30 99 177 244 300 343 378 407 432 452 468 483 495 505 513 519 523 526 529 531 534 535 535 535 535 536 535\n",
    "*YC ? ? ? ? ?\n",
    "totvol _SUM(s0100, s0204, s0304, s1201, s0104)\n",
    "swdvol _SUM(s0100, s0204, s0304, s0104)\n",
    "hwdvol _SUM(s1201)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9e27d054-cd3b-4ccb-be94-bca3311b46a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/_woodstock_model_files/tsa24_clipped.yld', 'w') as f: f.write(yields_section)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e5ef58-3ce3-4130-83e4-d3aae3501ee2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## `ACTIONS` section\n",
    "\n",
    "The `ACTIONS` section defines actions (i.e., treatments). This part of our model is very simple, so it is easy to code this by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a554ec7b-8186-4d06-8703-a7556d111770",
   "metadata": {},
   "outputs": [],
   "source": [
    "actions_section = \\\n",
    "\"\"\"\n",
    "ACTIONS\n",
    "*ACTION harvest 0\n",
    "*OPERABLE harvest\n",
    "? 1 ? ? ? _AGE >= 80 AND _AGE <= 999\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ed4f7862-bcf0-45e6-8933-80f00670a2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/_woodstock_model_files/tsa24_clipped.act', 'w') as f: f.write(actions_section)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ea8f87-6f5d-4970-b5e2-724a813c31c1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## `TRANSITIONS` section\n",
    "\n",
    "The `TRANSITIONS` section defines transitions (i.e., change in theme-wise state variable values and age, induced by applying an action to an area of an age class within a development type).\n",
    "\n",
    "We have AU-wise transitions to specific TIPSY curves in this model, so easiest to use a bit of code to print out the correct data in the correcet format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "53903274-5bc7-49ae-b564-994fb344757d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*CASE harvest\n",
      "*SOURCE ? ? 2402000 ? ?\n",
      "*TARGET ? ? ? ? 2422000 100\n",
      "*SOURCE ? ? 2403000 ? ?\n",
      "*TARGET ? ? ? ? 2423000 100\n",
      "*SOURCE ? ? 2403001 ? ?\n",
      "*TARGET ? ? ? ? 2423001 100\n",
      "*SOURCE ? ? 2401002 ? ?\n",
      "*TARGET ? ? ? ? 2421002 100\n",
      "*SOURCE ? ? 2402002 ? ?\n",
      "*TARGET ? ? ? ? 2422002 100\n",
      "*SOURCE ? ? 2403002 ? ?\n",
      "*TARGET ? ? ? ? 2423002 100\n",
      "*SOURCE ? ? 2402003 ? ?\n",
      "*TARGET ? ? ? ? 2422003 100\n",
      "*SOURCE ? ? 2403003 ? ?\n",
      "*TARGET ? ? ? ? 2423003 100\n",
      "*SOURCE ? ? 2402004 ? ?\n",
      "*TARGET ? ? ? ? 2422004 100\n",
      "*SOURCE ? ? 2403004 ? ?\n",
      "*TARGET ? ? ? ? 2423004 100\n",
      "*SOURCE ? ? 2401007 ? ?\n",
      "*TARGET ? ? ? ? 2421007 100\n",
      "*SOURCE ? ? 2402007 ? ?\n",
      "*TARGET ? ? ? ? 2422007 100\n",
      "*SOURCE ? ? 2403007 ? ?\n",
      "*TARGET ? ? ? ? 2423007 100\n"
     ]
    }
   ],
   "source": [
    "# harvesting\n",
    "acode = 'harvest'\n",
    "print('*CASE', acode)\n",
    "for au_id, au_row in au_table.iterrows():\n",
    "    if not au_row.thlb: continue\n",
    "    target_curve_id = au_row.managed_curve_id\n",
    "    smask = ' '.join(('?', '?', str(au_id), '?', '?'))\n",
    "    tmask = ' '.join(('?', '?', '?', '?', str(target_curve_id)))\n",
    "    print('*SOURCE', smask)\n",
    "    print('*TARGET', tmask, '100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ed9af8b4-6fe3-49c1-86c0-58845ea6c187",
   "metadata": {},
   "outputs": [],
   "source": [
    "transitions_section = \\\n",
    "\"\"\"\n",
    "*CASE harvest\n",
    "*SOURCE ? ? 2402000 ? ?\n",
    "*TARGET ? ? ? ? 2422000 100\n",
    "*SOURCE ? ? 2403000 ? ?\n",
    "*TARGET ? ? ? ? 2423000 100\n",
    "*SOURCE ? ? 2403001 ? ?\n",
    "*TARGET ? ? ? ? 2423001 100\n",
    "*SOURCE ? ? 2401002 ? ?\n",
    "*TARGET ? ? ? ? 2421002 100\n",
    "*SOURCE ? ? 2402002 ? ?\n",
    "*TARGET ? ? ? ? 2422002 100\n",
    "*SOURCE ? ? 2403002 ? ?\n",
    "*TARGET ? ? ? ? 2423002 100\n",
    "*SOURCE ? ? 2402003 ? ?\n",
    "*TARGET ? ? ? ? 2422003 100\n",
    "*SOURCE ? ? 2403003 ? ?\n",
    "*TARGET ? ? ? ? 2423003 100\n",
    "*SOURCE ? ? 2402004 ? ?\n",
    "*TARGET ? ? ? ? 2422004 100\n",
    "*SOURCE ? ? 2403004 ? ?\n",
    "*TARGET ? ? ? ? 2423004 100\n",
    "*SOURCE ? ? 2401007 ? ?\n",
    "*TARGET ? ? ? ? 2421007 100\n",
    "*SOURCE ? ? 2402007 ? ?\n",
    "*TARGET ? ? ? ? 2422007 100\n",
    "*SOURCE ? ? 2403007 ? ?\n",
    "*TARGET ? ? ? ? 2423007 100\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "83253f93-9edd-4b1a-be49-609981f3e0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/_woodstock_model_files/tsa24_clipped.trn', 'w') as f: f.write(transitions_section)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5e79bf-78ff-4e2d-be35-061270bde2c8",
   "metadata": {},
   "source": [
    "## Create and run `ForestModel` instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "63caeb20-8ea2-4a9e-9bca-623b8e08e1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm = ws3.forest.ForestModel(model_name='tsa24_clipped',\n",
    "                            model_path='data/_woodstock_model_files',\n",
    "                            base_year=base_year,\n",
    "                            horizon=horizon,\n",
    "                            period_length=period_length,\n",
    "                            max_age=max_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2b17687f-0e31-4fd7-b0ac-be84a05571ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.import_landscape_section()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9b98c164-1697-44c5-8eb6-75480fb96414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm.import_areas_section()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "98118df3-ca57-4511-9b66-1cb1dca7ff7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.import_yields_section()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7c578f04-2274-4cdb-bd10-17764b7309da",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.import_actions_section()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4d3e528f-1de4-49de-a9f8-f87279004b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.import_transitions_section()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "21328d8c-a905-420c-afaa-927131d4681b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fm.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fdc3bf93-e461-465c-be5d-6d13dac2269f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sch = schedule_harvest_areacontrol(fm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "21689e1a-e3c2-4f0b-82c0-3969d9936ea6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1200x400 with 3 Axes>,\n",
       " array([<Axes: title={'center': 'Harvested area (ha)'}>,\n",
       "        <Axes: title={'center': 'Harvested volume (m3)'}>,\n",
       "        <Axes: title={'center': 'Growing Stock (m3)'}>], dtype=object))"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scenario(compile_scenario(fm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "757df121-3ca8-474d-a376-14e66e647bae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (venv:foo)",
   "language": "python",
   "name": "foo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}