{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# libcbm run of tutorial 2 from CBM-CFS3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Import the required packages from libcbm\n",
    " \n",
    "    - sit_cbm_factory: a module for initializing the CBM model from the CBM Standard import tool format\n",
    "    - cbm_simulator: simulates the sit dataset using the CBM model\n",
    "    - libcbm.resources: gets files for tutorial 2 that are bundled in libcbm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from libcbm.input.sit import sit_cbm_factory\n",
    "from libcbm.model.cbm import cbm_simulator\n",
    "from libcbm.model.cbm.cbm_output import CBMOutput\n",
    "from libcbm import resources\n",
    "from libcbm.storage.backends import BackendType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "Load the standard import tool configuration at the specified path.  This configuration encompasses the data source for the various sit inputs (sit_inventory, sit_classifiers etc.) and also the relationships of classifiers, and disturbance types to the default CBM parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_path = os.path.join(\n",
    "    resources.get_test_resources_dir(), \"cbm3_tutorial2\", \"sit_config.json\"\n",
    ")\n",
    "sit = sit_cbm_factory.load_sit(config_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize and validate the inventory in the sit dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers, inventory = sit_cbm_factory.initialize_inventory(sit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create storage for CBM simulation results.  This is a built in method, and it is not mandatory since it is possible to interact with the simulation dataframes directly as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbm_output = CBMOutput(\n",
    "    classifier_map=sit.classifier_value_names,\n",
    "    disturbance_type_map=sit.disturbance_name_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "The following line of code spins up the CBM inventory and runs it through 100 timesteps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sit_cbm_factory.initialize_cbm(sit) as cbm:\n",
    "    # Create a function to apply rule based disturbance events and transition rules based on the SIT input\n",
    "    rule_based_processor = sit_cbm_factory.create_sit_rule_based_processor(\n",
    "        sit, cbm\n",
    "    )\n",
    "    # The following line of code spins up the CBM inventory and runs it through 200 timesteps.\n",
    "    cbm_simulator.simulate(\n",
    "        cbm,\n",
    "        n_steps=200,\n",
    "        classifiers=classifiers,\n",
    "        inventory=inventory,\n",
    "        pre_dynamics_func=rule_based_processor.pre_dynamics_func,\n",
    "        reporting_func=cbm_output.append_simulation_result,\n",
    "        backend_type=BackendType.numpy,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pool Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi = cbm_output.classifiers.to_pandas().merge(\n",
    "    cbm_output.pools.to_pandas(),\n",
    "    left_on=[\"identifier\", \"timestep\"],\n",
    "    right_on=[\"identifier\", \"timestep\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>timestep</th>\n",
       "      <th>Working_Species_Or_Leading_Species</th>\n",
       "      <th>Site_Quality</th>\n",
       "      <th>Density_Class</th>\n",
       "      <th>Working_Status</th>\n",
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       "      <th>...</th>\n",
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       "      <th>SoftwoodStemSnag</th>\n",
       "      <th>SoftwoodBranchSnag</th>\n",
       "      <th>HardwoodStemSnag</th>\n",
       "      <th>HardwoodBranchSnag</th>\n",
       "      <th>CO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>Products</th>\n",
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       "  </thead>\n",
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       "      <td>0.030782</td>\n",
       "      <td>1.867345</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5278.708266</td>\n",
       "      <td>2553.024910</td>\n",
       "      <td>574.973378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345471.445778</td>\n",
       "      <td>315.629506</td>\n",
       "      <td>2840.630866</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>G</td>\n",
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       "      <td>0.117354</td>\n",
       "      <td>4.744316</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>100.0</td>\n",
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       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   identifier  timestep Working_Species_Or_Leading_Species Site_Quality  \\\n",
       "0           1         0                                  B            G   \n",
       "1           2         0                                  B            G   \n",
       "2           3         0                                  B            G   \n",
       "3           4         0                                  B            G   \n",
       "4           5         0                                  B            G   \n",
       "\n",
       "  Density_Class Working_Status  Input  SoftwoodMerch  SoftwoodFoliage  \\\n",
       "0             D              W  200.0       0.000000         0.000000   \n",
       "1             D              W  100.0       0.003124         0.376897   \n",
       "2             D              W  100.0       0.030782         1.867345   \n",
       "3             D              W  100.0       0.117354         4.744316   \n",
       "4             D              W  100.0       0.303283         9.168135   \n",
       "\n",
       "   SoftwoodOther  ...  BelowGroundSlowSoil  SoftwoodStemSnag  \\\n",
       "0            0.0  ...         10529.242115       5568.605479   \n",
       "1            0.0  ...          5273.531854       2666.157167   \n",
       "2            0.0  ...          5278.708266       2553.024910   \n",
       "3            0.0  ...          5281.125622       2444.693447   \n",
       "4            0.0  ...          5281.488695       2340.959458   \n",
       "\n",
       "   SoftwoodBranchSnag  HardwoodStemSnag  HardwoodBranchSnag            CO2  \\\n",
       "0         1544.809522               0.0                 0.0  690032.809697   \n",
       "1          666.417417               0.0                 0.0  345255.294728   \n",
       "2          574.973378               0.0                 0.0  345471.445778   \n",
       "3          496.077048               0.0                 0.0  345669.654559   \n",
       "4          428.006664               0.0                 0.0  345853.464520   \n",
       "\n",
       "          CH4           CO  NO2  Products  \n",
       "0  631.259013  5681.261733  0.0       0.0  \n",
       "1  315.629506  2840.630866  0.0       0.0  \n",
       "2  315.629506  2840.630866  0.0       0.0  \n",
       "3  315.629506  2840.630866  0.0       0.0  \n",
       "4  315.629506  2840.630866  0.0       0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "biomass_pools = [\n",
    "    \"SoftwoodMerch\",\n",
    "    \"SoftwoodFoliage\",\n",
    "    \"SoftwoodOther\",\n",
    "    \"SoftwoodCoarseRoots\",\n",
    "    \"SoftwoodFineRoots\",\n",
    "    \"HardwoodMerch\",\n",
    "    \"HardwoodFoliage\",\n",
    "    \"HardwoodOther\",\n",
    "    \"HardwoodCoarseRoots\",\n",
    "    \"HardwoodFineRoots\",\n",
    "]\n",
    "\n",
    "dom_pools = [\n",
    "    \"AboveGroundVeryFastSoil\",\n",
    "    \"BelowGroundVeryFastSoil\",\n",
    "    \"AboveGroundFastSoil\",\n",
    "    \"BelowGroundFastSoil\",\n",
    "    \"MediumSoil\",\n",
    "    \"AboveGroundSlowSoil\",\n",
    "    \"BelowGroundSlowSoil\",\n",
    "    \"SoftwoodStemSnag\",\n",
    "    \"SoftwoodBranchSnag\",\n",
    "    \"HardwoodStemSnag\",\n",
    "    \"HardwoodBranchSnag\",\n",
    "]\n",
    "\n",
    "biomass_result = pi[[\"timestep\"] + biomass_pools]\n",
    "dom_result = pi[[\"timestep\"] + dom_pools]\n",
    "total_eco_result = pi[[\"timestep\"] + biomass_pools + dom_pools]\n",
    "\n",
    "annual_carbon_stocks = pd.DataFrame(\n",
    "    {\n",
    "        \"Year\": pi[\"timestep\"],\n",
    "        \"Biomass\": pi[biomass_pools].sum(axis=1),\n",
    "        \"DOM\": pi[dom_pools].sum(axis=1),\n",
    "        \"Total Ecosystem\": pi[biomass_pools + dom_pools].sum(axis=1),\n",
    "    }\n",
    ")\n",
    "\n",
    "annual_carbon_stocks.groupby(\"Year\").sum().plot(\n",
    "    figsize=(10, 10), xlim=(0, 160), ylim=(0, 5.4e6)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State Variable Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "si = cbm_output.state.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>timestep</th>\n",
       "      <th>last_disturbance_type</th>\n",
       "      <th>time_since_last_disturbance</th>\n",
       "      <th>time_since_land_class_change</th>\n",
       "      <th>growth_enabled</th>\n",
       "      <th>enabled</th>\n",
       "      <th>land_class</th>\n",
       "      <th>age</th>\n",
       "      <th>growth_multiplier</th>\n",
       "      <th>regeneration_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   identifier  timestep last_disturbance_type  time_since_last_disturbance  \\\n",
       "0           1         0                     N                            0   \n",
       "1           2         0                     N                            1   \n",
       "2           3         0                     N                            2   \n",
       "3           4         0                     N                            3   \n",
       "4           5         0                     N                            4   \n",
       "\n",
       "   time_since_land_class_change  growth_enabled  enabled  land_class  age  \\\n",
       "0                            -1               1        1           0    0   \n",
       "1                            -1               1        1           0    1   \n",
       "2                            -1               1        1           0    2   \n",
       "3                            -1               1        1           0    3   \n",
       "4                            -1               1        1           0    4   \n",
       "\n",
       "   growth_multiplier  regeneration_delay  \n",
       "0                1.0                   0  \n",
       "1                1.0                   0  \n",
       "2                1.0                   0  \n",
       "3                1.0                   0  \n",
       "4                1.0                   0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestep'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_variables = [\n",
    "    \"timestep\",\n",
    "    \"time_since_last_disturbance\",\n",
    "    \"time_since_land_class_change\",\n",
    "    \"growth_enabled\",\n",
    "    \"enabled\",\n",
    "    \"land_class\",\n",
    "    \"age\",\n",
    "    \"growth_multiplier\",\n",
    "    \"regeneration_delay\",\n",
    "]\n",
    "si[state_variables].groupby(\"timestep\").mean().plot(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flux Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fi = cbm_output.flux.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>timestep</th>\n",
       "      <th>DisturbanceCO2Production</th>\n",
       "      <th>DisturbanceCH4Production</th>\n",
       "      <th>DisturbanceCOProduction</th>\n",
       "      <th>DisturbanceBioCO2Emission</th>\n",
       "      <th>DisturbanceBioCH4Emission</th>\n",
       "      <th>DisturbanceBioCOEmission</th>\n",
       "      <th>DecayDOMCO2Emission</th>\n",
       "      <th>DisturbanceSoftProduction</th>\n",
       "      <th>...</th>\n",
       "      <th>DisturbanceVFastBGToAir</th>\n",
       "      <th>DisturbanceFastAGToAir</th>\n",
       "      <th>DisturbanceFastBGToAir</th>\n",
       "      <th>DisturbanceMediumToAir</th>\n",
       "      <th>DisturbanceSlowAGToAir</th>\n",
       "      <th>DisturbanceSlowBGToAir</th>\n",
       "      <th>DisturbanceSWStemSnagToAir</th>\n",
       "      <th>DisturbanceSWBranchSnagToAir</th>\n",
       "      <th>DisturbanceHWStemSnagToAir</th>\n",
       "      <th>DisturbanceHWBranchSnagToAir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   identifier  timestep  DisturbanceCO2Production  DisturbanceCH4Production  \\\n",
       "0           1         0                       0.0                       0.0   \n",
       "1           2         0                       0.0                       0.0   \n",
       "2           3         0                       0.0                       0.0   \n",
       "3           4         0                       0.0                       0.0   \n",
       "4           5         0                       0.0                       0.0   \n",
       "\n",
       "   DisturbanceCOProduction  DisturbanceBioCO2Emission  \\\n",
       "0                      0.0                        0.0   \n",
       "1                      0.0                        0.0   \n",
       "2                      0.0                        0.0   \n",
       "3                      0.0                        0.0   \n",
       "4                      0.0                        0.0   \n",
       "\n",
       "   DisturbanceBioCH4Emission  DisturbanceBioCOEmission  DecayDOMCO2Emission  \\\n",
       "0                        0.0                       0.0                  0.0   \n",
       "1                        0.0                       0.0                  0.0   \n",
       "2                        0.0                       0.0                  0.0   \n",
       "3                        0.0                       0.0                  0.0   \n",
       "4                        0.0                       0.0                  0.0   \n",
       "\n",
       "   DisturbanceSoftProduction  ...  DisturbanceVFastBGToAir  \\\n",
       "0                        0.0  ...                      0.0   \n",
       "1                        0.0  ...                      0.0   \n",
       "2                        0.0  ...                      0.0   \n",
       "3                        0.0  ...                      0.0   \n",
       "4                        0.0  ...                      0.0   \n",
       "\n",
       "   DisturbanceFastAGToAir  DisturbanceFastBGToAir  DisturbanceMediumToAir  \\\n",
       "0                     0.0                     0.0                     0.0   \n",
       "1                     0.0                     0.0                     0.0   \n",
       "2                     0.0                     0.0                     0.0   \n",
       "3                     0.0                     0.0                     0.0   \n",
       "4                     0.0                     0.0                     0.0   \n",
       "\n",
       "   DisturbanceSlowAGToAir  DisturbanceSlowBGToAir  DisturbanceSWStemSnagToAir  \\\n",
       "0                     0.0                     0.0                         0.0   \n",
       "1                     0.0                     0.0                         0.0   \n",
       "2                     0.0                     0.0                         0.0   \n",
       "3                     0.0                     0.0                         0.0   \n",
       "4                     0.0                     0.0                         0.0   \n",
       "\n",
       "   DisturbanceSWBranchSnagToAir  DisturbanceHWStemSnagToAir  \\\n",
       "0                           0.0                         0.0   \n",
       "1                           0.0                         0.0   \n",
       "2                           0.0                         0.0   \n",
       "3                           0.0                         0.0   \n",
       "4                           0.0                         0.0   \n",
       "\n",
       "   DisturbanceHWBranchSnagToAir  \n",
       "0                           0.0  \n",
       "1                           0.0  \n",
       "2                           0.0  \n",
       "3                           0.0  \n",
       "4                           0.0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_process_fluxes = [\n",
    "    \"DecayDOMCO2Emission\",\n",
    "    \"DeltaBiomass_AG\",\n",
    "    \"DeltaBiomass_BG\",\n",
    "    \"TurnoverMerchLitterInput\",\n",
    "    \"TurnoverFolLitterInput\",\n",
    "    \"TurnoverOthLitterInput\",\n",
    "    \"TurnoverCoarseLitterInput\",\n",
    "    \"TurnoverFineLitterInput\",\n",
    "    \"DecayVFastAGToAir\",\n",
    "    \"DecayVFastBGToAir\",\n",
    "    \"DecayFastAGToAir\",\n",
    "    \"DecayFastBGToAir\",\n",
    "    \"DecayMediumToAir\",\n",
    "    \"DecaySlowAGToAir\",\n",
    "    \"DecaySlowBGToAir\",\n",
    "    \"DecaySWStemSnagToAir\",\n",
    "    \"DecaySWBranchSnagToAir\",\n",
    "    \"DecayHWStemSnagToAir\",\n",
    "    \"DecayHWBranchSnagToAir\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestep'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi[[\"timestep\"] + annual_process_fluxes].groupby(\"timestep\").sum().plot(\n",
    "    figsize=(15, 10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SIT source data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>class_size</th>\n",
       "      <th>start_year</th>\n",
       "      <th>end_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGEID0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGEID1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGEID2</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGEID3</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGEID4</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AGEID5</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGEID6</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGEID7</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AGEID8</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AGEID9</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AGEID10</td>\n",
       "      <td>10</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AGEID11</td>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AGEID12</td>\n",
       "      <td>10</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AGEID13</td>\n",
       "      <td>10</td>\n",
       "      <td>121</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AGEID14</td>\n",
       "      <td>10</td>\n",
       "      <td>131</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AGEID15</td>\n",
       "      <td>10</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AGEID16</td>\n",
       "      <td>10</td>\n",
       "      <td>151</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AGEID17</td>\n",
       "      <td>10</td>\n",
       "      <td>161</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AGEID18</td>\n",
       "      <td>10</td>\n",
       "      <td>171</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AGEID19</td>\n",
       "      <td>10</td>\n",
       "      <td>181</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AGEID20</td>\n",
       "      <td>10</td>\n",
       "      <td>191</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  class_size  start_year  end_year\n",
       "0    AGEID0           0           0         0\n",
       "1    AGEID1          10           1        10\n",
       "2    AGEID2          10          11        20\n",
       "3    AGEID3          10          21        30\n",
       "4    AGEID4          10          31        40\n",
       "5    AGEID5          10          41        50\n",
       "6    AGEID6          10          51        60\n",
       "7    AGEID7          10          61        70\n",
       "8    AGEID8          10          71        80\n",
       "9    AGEID9          10          81        90\n",
       "10  AGEID10          10          91       100\n",
       "11  AGEID11          10         101       110\n",
       "12  AGEID12          10         111       120\n",
       "13  AGEID13          10         121       130\n",
       "14  AGEID14          10         131       140\n",
       "15  AGEID15          10         141       150\n",
       "16  AGEID16          10         151       160\n",
       "17  AGEID17          10         161       170\n",
       "18  AGEID18          10         171       180\n",
       "19  AGEID19          10         181       190\n",
       "20  AGEID20          10         191       200"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit.sit_data.age_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
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      "text/plain": [
       "    Working_Species_Or_Leading_Species Site_Quality Density_Class  \\\n",
       "0                                   BF         GOOD            D1   \n",
       "1                                   BF         GOOD            D1   \n",
       "2                                   BF         GOOD            D1   \n",
       "3                                   BF         GOOD            D1   \n",
       "4                                   BF         GOOD            D1   \n",
       "..                                 ...          ...           ...   \n",
       "196                                 BF         GOOD            D1   \n",
       "197                                 BF         GOOD            D1   \n",
       "198                                 BF         GOOD            D1   \n",
       "199                                 BF         GOOD            D1   \n",
       "200                                 BF         GOOD            D1   \n",
       "\n",
       "    Working_Status  age  area  delay  land_class historical_disturbance_type  \\\n",
       "0                W    0   200      0           0                     DISTID1   \n",
       "1                W    1   100      0           0                     DISTID1   \n",
       "2                W    2   100      0           0                     DISTID1   \n",
       "3                W    3   100      0           0                     DISTID1   \n",
       "4                W    4   100      0           0                     DISTID1   \n",
       "..             ...  ...   ...    ...         ...                         ...   \n",
       "196              W  196   100      0           0                     DISTID1   \n",
       "197              W  197   100      0           0                     DISTID1   \n",
       "198              W  198   100      0           0                     DISTID1   \n",
       "199              W  199   100      0           0                     DISTID1   \n",
       "200              W  200   100      0           0                     DISTID1   \n",
       "\n",
       "    last_pass_disturbance_type  \n",
       "0                      DISTID1  \n",
       "1                      DISTID1  \n",
       "2                      DISTID1  \n",
       "3                      DISTID1  \n",
       "4                      DISTID1  \n",
       "..                         ...  \n",
       "196                    DISTID1  \n",
       "197                    DISTID1  \n",
       "198                    DISTID1  \n",
       "199                    DISTID1  \n",
       "200                    DISTID1  \n",
       "\n",
       "[201 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   id                                name\n",
       "0   1  Working_Species_Or_Leading_Species\n",
       "1   2                        Site_Quality\n",
       "2   3                       Density_Class\n",
       "3   4                      Working_Status"
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   "execution_count": 37,
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   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>NN</td>\n",
       "      <td>Null Value For The Non-forested Polyg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>W</td>\n",
       "      <td>Working forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>R</td>\n",
       "      <td>Reserve Forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    classifier_id    name                            description\n",
       "1               1      BF                             Balsam Fir\n",
       "2               1      BS                           Black Spruce\n",
       "3               1      JL                          Western Larch\n",
       "4               1      JP                              Jack Pine\n",
       "5               1      OS                           Other Spruce\n",
       "6               1      RP                               Red Pine\n",
       "7               1      SH                   Unspecified Softwood\n",
       "8               1      LT                                  Larch\n",
       "9               1      WS                           White Spruce\n",
       "11              2    GOOD                                   Good\n",
       "12              2  MEDIUM                                 Medium\n",
       "13              2    POOR                                   Poor\n",
       "15              3      D0                       No Crown Closure\n",
       "16              3      D1               75% - >    Crown Closure\n",
       "17              3      D2               51% - 74%  Crown Closure\n",
       "18              3      D3               26% - 50%  Crown Closure\n",
       "19              3      NN  Null Value For The Non-forested Polyg\n",
       "21              4       W                         Working forest\n",
       "22              4       R                         Reserve Forest"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit.sit_data.classifier_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sit_disturbance_type_id</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>DISTID1</td>\n",
       "      <td>Natural forest fire</td>\n",
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       "    <tr>\n",
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       "      <td>DISTID2</td>\n",
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       "      <td>DISTID3</td>\n",
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       "      <th>3</th>\n",
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       "      <td>DISTID4</td>\n",
       "      <td>ClearCut Harvesting</td>\n",
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      "text/plain": [
       "   sit_disturbance_type_id       id                              name\n",
       "0                        1  DISTID1               Natural forest fire\n",
       "1                        2  DISTID2                        Senescence\n",
       "2                        3  DISTID3  Mountain pine beetle infestation\n",
       "3                        4  DISTID4               ClearCut Harvesting"
      ]
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       "<p>160 rows × 33 columns</p>\n",
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      ],
      "text/plain": [
       "    Working_Species_Or_Leading_Species Site_Quality Density_Class  \\\n",
       "0                                   BF         GOOD            D1   \n",
       "1                                   BF         GOOD            D1   \n",
       "2                                   BF         GOOD            D1   \n",
       "3                                   BF         GOOD            D1   \n",
       "4                                   BF         GOOD            D1   \n",
       "..                                 ...          ...           ...   \n",
       "155                                 BF         GOOD            D1   \n",
       "156                                 BF         GOOD            D1   \n",
       "157                                 BF         GOOD            D1   \n",
       "158                                 BF         GOOD            D1   \n",
       "159                                 BF         GOOD            D1   \n",
       "\n",
       "    Working_Status  min_age  max_age  MinYearsSinceDist  MaxYearsSinceDist  \\\n",
       "0                W       80      200                 -1                 -1   \n",
       "1                W       80      200                 -1                 -1   \n",
       "2                W       80      200                 -1                 -1   \n",
       "3                W       80      200                 -1                 -1   \n",
       "4                W       80      200                 -1                 -1   \n",
       "..             ...      ...      ...                ...                ...   \n",
       "155              W       80      200                 -1                 -1   \n",
       "156              W       80      200                 -1                 -1   \n",
       "157              W       80      200                 -1                 -1   \n",
       "158              W       80      200                 -1                 -1   \n",
       "159              W       80      200                 -1                 -1   \n",
       "\n",
       "    LastDistTypeID  MinTotBiomassC  ...  MinSWMerchStemSnagC  \\\n",
       "0               -1              -1  ...                   -1   \n",
       "1               -1              -1  ...                   -1   \n",
       "2               -1              -1  ...                   -1   \n",
       "3               -1              -1  ...                   -1   \n",
       "4               -1              -1  ...                   -1   \n",
       "..             ...             ...  ...                  ...   \n",
       "155             -1              -1  ...                   -1   \n",
       "156             -1              -1  ...                   -1   \n",
       "157             -1              -1  ...                   -1   \n",
       "158             -1              -1  ...                   -1   \n",
       "159             -1              -1  ...                   -1   \n",
       "\n",
       "     MaxSWMerchStemSnagC  MinHWMerchStemSnagC  MaxHWMerchStemSnagC  \\\n",
       "0                     -1                   -1                   -1   \n",
       "1                     -1                   -1                   -1   \n",
       "2                     -1                   -1                   -1   \n",
       "3                     -1                   -1                   -1   \n",
       "4                     -1                   -1                   -1   \n",
       "..                   ...                  ...                  ...   \n",
       "155                   -1                   -1                   -1   \n",
       "156                   -1                   -1                   -1   \n",
       "157                   -1                   -1                   -1   \n",
       "158                   -1                   -1                   -1   \n",
       "159                   -1                   -1                   -1   \n",
       "\n",
       "     efficiency       sort_type  target_type  target  disturbance_type  \\\n",
       "0             1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "1             1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "2             1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "3             1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "4             1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "..          ...             ...          ...     ...               ...   \n",
       "155           1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "156           1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "157           1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "158           1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "159           1  SORT_BY_SW_AGE         Area     200           DISTID4   \n",
       "\n",
       "     time_step  \n",
       "0            1  \n",
       "1            2  \n",
       "2            3  \n",
       "3            4  \n",
       "4            5  \n",
       "..         ...  \n",
       "155        156  \n",
       "156        157  \n",
       "157        158  \n",
       "158        159  \n",
       "159        160  \n",
       "\n",
       "[160 rows x 33 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
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  },
  {
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   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "      <th>v18</th>\n",
       "      <th>v19</th>\n",
       "      <th>v20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BF</td>\n",
       "      <td>GOOD</td>\n",
       "      <td>D1</td>\n",
       "      <td>W</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Working_Species_Or_Leading_Species Site_Quality Density_Class  \\\n",
       "0                                 BF         GOOD            D1   \n",
       "\n",
       "  Working_Status  leading_species  v0  v1  v2  v3  v4  ...  v11  v12  v13  \\\n",
       "0              W               29   0   0   0   8  20  ...  112  113  113   \n",
       "\n",
       "   v14  v15  v16  v17  v18  v19  v20  \n",
       "0  113  113  113  113  113  113  113  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit.sit_data.yield_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"import_config\": {\n",
      "        \"age_classes\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"age_classes.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"classifiers\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"classifiers.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"disturbance_types\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"disturbance_types.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"events\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"disturbance_events.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"inventory\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"inventory.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"transitions\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"transition_rules.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        },\n",
      "        \"yield\": {\n",
      "            \"params\": {\n",
      "                \"path\": \"growth_and_yield.csv\"\n",
      "            },\n",
      "            \"type\": \"csv\"\n",
      "        }\n",
      "    },\n",
      "    \"mapping_config\": {\n",
      "        \"disturbance_types\": [\n",
      "            {\n",
      "                \"default_dist_type\": \"Clearcut harvesting without salvage\",\n",
      "                \"user_dist_type\": \"ClearCut Harvesting\"\n",
      "            },\n",
      "            {\n",
      "                \"default_dist_type\": \"Wildfire\",\n",
      "                \"user_dist_type\": \"Natural forest fire\"\n",
      "            },\n",
      "            {\n",
      "                \"default_dist_type\": \"Stand\\u2013replacing natural succession\",\n",
      "                \"user_dist_type\": \"Senescence\"\n",
      "            },\n",
      "            {\n",
      "                \"default_dist_type\": \"Insect disturbance\",\n",
      "                \"user_dist_type\": \"Mountain pine beetle infestation\"\n",
      "            }\n",
      "        ],\n",
      "        \"nonforest\": null,\n",
      "        \"spatial_units\": {\n",
      "            \"admin_boundary\": \"Ontario\",\n",
      "            \"eco_boundary\": \"Boreal Shield East\",\n",
      "            \"mapping_mode\": \"SingleDefaultSpatialUnit\"\n",
      "        },\n",
      "        \"species\": {\n",
      "            \"species_classifier\": \"Working Species Or Leading Species\",\n",
      "            \"species_mapping\": [\n",
      "                {\n",
      "                    \"default_species\": \"Balsam fir\",\n",
      "                    \"user_species\": \"Balsam Fir\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Black spruce\",\n",
      "                    \"user_species\": \"Black Spruce\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Western larch\",\n",
      "                    \"user_species\": \"Western Larch\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Jack pine\",\n",
      "                    \"user_species\": \"Jack Pine\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Other spruce\",\n",
      "                    \"user_species\": \"Other Spruce\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Red pine\",\n",
      "                    \"user_species\": \"Red Pine\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Unspecified softwood species\",\n",
      "                    \"user_species\": \"Unspecified Softwood\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"Tamarack/larch\",\n",
      "                    \"user_species\": \"Larch\"\n",
      "                },\n",
      "                {\n",
      "                    \"default_species\": \"White spruce\",\n",
      "                    \"user_species\": \"White Spruce\"\n",
      "                }\n",
      "            ]\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(sit.config, indent=4, sort_keys=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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