{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bfb812b-cf02-4306-bc5f-6d97a6b53a10",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Running `ws3` and `libcbm` as a two-stage sequential pipeline (using built-in functions)\n",
    "\n",
    "We run `ws3` and `libcbm` in a two-stage sequential software pipeline, using the CBM linkage functions in `ws3`. See notebook `ws3_libcbm_sequential-fromscratch` for a more detailed discussion of these linkages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f47fea-d5a3-4194-bb69-4c42095bba2a",
   "metadata": {},
   "source": [
    "## Set up modelling environment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9862bb12-9260-46c8-bbb1-b486c40cc2c4",
   "metadata": {},
   "source": [
    "First, make sure we have the correct versions of `ws3` and `libcbm` installed. Both of these packages are relatively new and under active development, it is best we stick to known-working versions of each package from their respective GitHub repos. \n",
    "\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": "code",
   "execution_count": 1,
   "id": "973f32a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7abfa96",
   "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": "0c71796d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing installation: ws3 1.1.0.dev0\n",
      "Uninstalling ws3-1.1.0.dev0:\n",
      "  Successfully uninstalled ws3-1.1.0.dev0\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Obtaining file:///home/gep/tmp/ws3\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Checking if build backend supports build_editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build editable ... \u001b[?25ldone\n",
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: dill in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (0.4.0)\n",
      "Requirement already satisfied: fiona in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.10.1)\n",
      "Requirement already satisfied: gurobipy in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (12.0.3)\n",
      "Requirement already satisfied: highspy in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.11.0)\n",
      "Requirement already satisfied: libcbm in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.8.1)\n",
      "Requirement already satisfied: matplotlib in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (3.10.5)\n",
      "Requirement already satisfied: numpy>=1.21 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.2.6)\n",
      "Requirement already satisfied: pandas>=1.3 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (2.3.1)\n",
      "Requirement already satisfied: profilehooks in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.13.0)\n",
      "Requirement already satisfied: pulp in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (3.2.2)\n",
      "Requirement already satisfied: rasterio in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.4.3)\n",
      "Requirement already satisfied: scipy>=1.7 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ws3==1.1.0.dev0) (1.16.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.3->ws3==1.1.0.dev0) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.3->ws3==1.1.0.dev0) (1.17.0)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (25.3.0)\n",
      "Requirement already satisfied: certifi in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (2025.8.3)\n",
      "Requirement already satisfied: click~=8.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (8.2.1)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (1.1.1.2)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from fiona->ws3==1.1.0.dev0) (0.7.2)\n",
      "Requirement already satisfied: numexpr>=2.8.7 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (2.11.0)\n",
      "Requirement already satisfied: numba in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (0.61.2)\n",
      "Requirement already satisfied: pyyaml in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (6.0.2)\n",
      "Requirement already satisfied: mock in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (5.2.0)\n",
      "Requirement already satisfied: openpyxl in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from libcbm->ws3==1.1.0.dev0) (3.1.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (4.59.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib->ws3==1.1.0.dev0) (3.2.3)\n",
      "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from numba->libcbm->ws3==1.1.0.dev0) (0.44.0)\n",
      "Requirement already satisfied: et-xmlfile in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from openpyxl->libcbm->ws3==1.1.0.dev0) (2.0.0)\n",
      "Requirement already satisfied: affine in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from rasterio->ws3==1.1.0.dev0) (2.4.0)\n",
      "Building wheels for collected packages: ws3\n",
      "  Building editable for ws3 (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ws3: filename=ws3-1.1.0.dev0-py3-none-any.whl size=4136 sha256=5df49d1809d2f15004f3046010971c142390fb47fb3bfa7620b09e11903424a7\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-bww7qav3/wheels/fa/4b/10/3fe4b92a02fb87987a6fe53a10fad0a22a781bf98cd7b63f17\n",
      "Successfully built ws3\n",
      "Installing collected packages: ws3\n",
      "Successfully installed ws3-1.1.0.dev0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "clobber_ws3 = True\n",
    "if clobber_ws3:\n",
    "    %pip uninstall -y ws3\n",
    "    %pip install -e .."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09670791",
   "metadata": {},
   "source": [
    "Use `pip` to install Python packages listed in `requirements.txt` (some extra packages needed for example notebooks to run correctly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84854b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 1)) (0.13.2)\n",
      "Requirement already satisfied: geopandas in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 2)) (1.1.1)\n",
      "Requirement already satisfied: ipywidgets in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from -r requirements.txt (line 3)) (8.1.7)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.2.6)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (2.3.1)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from seaborn->-r requirements.txt (line 1)) (3.10.5)\n",
      "Requirement already satisfied: pyogrio>=0.7.2 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (0.11.1)\n",
      "Requirement already satisfied: packaging in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (25.0)\n",
      "Requirement already satisfied: pyproj>=3.5.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (3.7.1)\n",
      "Requirement already satisfied: shapely>=2.0.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from geopandas->-r requirements.txt (line 2)) (2.1.1)\n",
      "Requirement already satisfied: comm>=0.1.3 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (0.2.3)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (9.4.0)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (5.14.3)\n",
      "Requirement already satisfied: widgetsnbextension~=4.0.14 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (4.0.14)\n",
      "Requirement already satisfied: jupyterlab_widgets~=3.0.15 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipywidgets->-r requirements.txt (line 3)) (3.0.15)\n",
      "Requirement already satisfied: decorator in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (5.2.1)\n",
      "Requirement already satisfied: ipython-pygments-lexers in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (1.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.19.2)\n",
      "Requirement already satisfied: matplotlib-inline in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.1.7)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (4.9.0)\n",
      "Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (3.0.51)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (2.19.2)\n",
      "Requirement already satisfied: stack_data in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.6.3)\n",
      "Requirement already satisfied: wcwidth in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.2.13)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (4.59.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.4.8)\n",
      "Requirement already satisfied: pillow>=8 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn->-r requirements.txt (line 1)) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn->-r requirements.txt (line 1)) (2025.2)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.7.0)\n",
      "Requirement already satisfied: certifi in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from pyogrio>=0.7.2->geopandas->-r requirements.txt (line 2)) (2025.8.3)\n",
      "Requirement already satisfied: six>=1.5 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn->-r requirements.txt (line 1)) (1.17.0)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (2.2.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (3.0.0)\n",
      "Requirement already satisfied: pure-eval in /home/gep/tmp/ws3/.venv/lib/python3.12/site-packages (from stack_data->ipython>=6.1.0->ipywidgets->-r requirements.txt (line 3)) (0.2.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebf8946-bfca-49ce-ac5b-9602a25f0a0c",
   "metadata": {},
   "source": [
    "Create a `ForestModel` instance by loading Woodstock-formatted input files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "217801d4-7480-42e2-a3d4-900cb38762e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ws3.forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "958c0374-b240-4776-90b8-41a14156db94",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_year = 2020\n",
    "horizon = 10\n",
    "period_length = 10\n",
    "max_age = 1000\n",
    "tvy_name = \"totvol\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e63f1b9-91a0-4569-92dc-522590dfe3d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm = ws3.forest.ForestModel(model_name=\"tsa24_clipped\",\n",
    "                            model_path=\"data/woodstock_model_files_tsa24_clipped\",\n",
    "                            base_year=base_year,\n",
    "                            horizon=horizon,\n",
    "                            period_length=period_length,\n",
    "                            max_age=max_age)\n",
    "fm.import_landscape_section()\n",
    "fm.import_areas_section(convert_periods_to_years=period_length)\n",
    "fm.import_yields_section(convert_periods_to_years=period_length)\n",
    "fm.import_actions_section(convert_periods_to_years=period_length)\n",
    "fm.import_transitions_section(convert_periods_to_years=period_length)\n",
    "fm.initialize_areas()\n",
    "fm.add_null_action()\n",
    "fm.reset_actions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a33f9d-7812-479e-b04c-a944a02bbb84",
   "metadata": {},
   "source": [
    "Schedule some harvesting in our `ws3.ForestModel` instance using the self-parametrising priority queue heuristic defined in the local `util` module (just so we have something interesting to push through `libcbm`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f5b3fdf-0f6c-4431-af01-290441bd051e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from util import schedule_harvest_areacontrol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c4f8430-a7e8-45fc-9190-718287ea2fd4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sch = schedule_harvest_areacontrol(fm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e880c10-c27d-4edd-ade7-7fe830b552b3",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from util import compile_scenario, plot_scenario\n",
    "df = compile_scenario(fm)\n",
    "plot_scenario(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388df8ac-7b30-4960-a6da-520c37dea6d6",
   "metadata": {},
   "source": [
    "All of the stuff above is just to set up a working `ws3` model environment. The actual linkage with `libcbm` happens below and is quite simple."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db36fb62-6a7e-4bbc-81a1-a0bc9c559cb5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Hard-link `ForestModel` to `libcbm`\n",
    "\n",
    "Next, we use `ws3` built-in CBM linkage functions to compile a `sit_config` object (a JSON-like dict namespace) and a `sit_tables` object (a dict of `pandas.DataFrame` objects) the SIT-compatible format expected by `libcbm.input.sit.sit_cbm_factory`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93383fc-156b-4ac3-af8f-82d6597736dc",
   "metadata": {},
   "source": [
    "Before calling the `ForestModel.to_cbm_sit` method, we need to compile `disturbance_type_mapping` (a list of dict objects of mapping the action codes in our `ws3` model to one of the standard disturbance types defined in the CBM database) and also add a `last_pass_disturbance` attribute to each developement type in our `ws3` model (else will default to `fire`, which would still run but might make the DOM spin-up in CBM a bit wonky). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f0c61bf9-8aee-46bd-8799-e1f69fe8f4b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "disturbance_type_mapping = [{\"user_dist_type\": \"harvest\", \"default_dist_type\": \"Clearcut harvesting without salvage\"},\n",
    "                            {\"user_dist_type\": \"fire\", \"default_dist_type\": \"Wildfire\"}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51d17342-862e-4725-a5ea-ad3982ff8ac3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for dtype_key in fm.dtypes:\n",
    "    fm.dt(dtype_key).last_pass_disturbance = \"fire\" if dtype_key[2] == dtype_key[4] else \"harvest\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "acceef8c-ecf0-4ead-86a2-05cbee0f2625",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sit_config, sit_tables = fm.to_cbm_sit(softwood_volume_yname=\"swdvol\",\n",
    "                                       hardwood_volume_yname=\"hwdvol\",\n",
    "                                       admin_boundary=\"British Columbia\",\n",
    "                                       eco_boundary=\"Montane Cordillera\",\n",
    "                                       disturbance_type_mapping=disturbance_type_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59956c44-8893-4b93-b129-1bbd8989c1d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from util import run_cbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68523908-c2fc-479d-a01e-ba362ba88c43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#n_steps = fm.horizon * fm.period_length\n",
    "n_steps = 200\n",
    "cbm_output = run_cbm(sit_config, sit_tables, n_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5569eed",
   "metadata": {},
   "source": [
    "If you contrast this workflow to the workflow from notebook `030_ws3_libcbm_sequential-fromscratch`, this is a lot simpler and more compact."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}