1_softwood-disturbance-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling carbon fluxes from wildfire\n",
    "\n",
    "This example was developed using overmature_decline_and_litterfall.ipynb from the cbm3_scripting_examples repository as a template.\n",
    "\n",
    "It shows how to initalize a stand with a wildfire disturbance using cbm-cfs3 using cbm3_python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "from dataclasses import dataclass, field\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\b_gra\\.conda\\envs\\cbm3\\lib\\site-packages\\cbm3_python\\cbm3data\\sit_helper.py:27: UserWarning: This method is deprecated, please acquire and install Operational-Scale CBM-CFS3 version 1.2.7606.313 or newer.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "import cbm3_python.simulation.projectsimulator\n",
    "from cbm3_python.cbm3data import cbm3_results\n",
    "from cbm3_python.cbm3data import sit_helper\n",
    "standard_import_tool_plugin_path=sit_helper.load_standard_import_tool_plugin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the basic configuration\n",
    "\n",
    "Here we need to specify the directories that we need to run our model including the location of CBM-CFS3 and teh directories for \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "toolbox_path = 'C:\\Program Files (x86)\\Operational-Scale CBM-CFS3'\n",
    "# This is the directory for the project we will create\n",
    "project_name = 'softwood_disturbance_example'\n",
    "# Let's store our project in the Projects folder of the CBM-CFS3 directory\n",
    "project_main_path = toolbox_path + '\\Projects' + '\\\\' + project_name\n",
    "project_path = project_main_path + '\\\\' + project_name + \".mdb\"\n",
    "results_db_path = project_main_path + '\\\\' + project_name + project_name + '_results.mdb'\n",
    "config_save_path =  project_main_path + '\\\\' + project_name + '\\config.json'\n",
    "\n",
    "sit_config = sit_helper.SITConfig(\n",
    "    imported_project_path=project_path,\n",
    "    initialize_mapping=True  # What is this for?\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's create an object to store the basic configuration requirements for a CBM-CFS3 stand model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sit_config.data_config(\n",
    "    age_class_size=10,\n",
    "    num_age_classes=27,\n",
    "    # The 'Scenario' classifier is used to specify unique scenario names.\n",
    "    classifiers=[\"Leading Species\", \"Scenario\"])\n",
    "\n",
    "sit_config.set_single_spatial_unit(42)\n",
    "sit_config.set_species_classifier(\"Leading Species\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we need to define a stand and it's disturbance events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we define a yield curve for our stand.  In this example, 10 year time steps is used.\n",
    "# Each value in the yield curve represents the yield (m3/ha) at 10 year time steps\n",
    "# starting at year 0.  The data source for this is not clear, but it comes from\n",
    "# overmature_decline_and_litterfall.ipynb in the cbm3_scripting_examples repo on Github\n",
    "yield_curve = [\n",
    "    0.0, 3.597241992, 9.485174636, 23.8405844, 53.78828427, 100, 146.2117157, 176.1594156,\n",
    "    190.5148254, 196.402758, 198.6614298, 199.5054754, 199.8177898, 199.93293, 199.9753211,\n",
    "    199.9909204, 199.9966597, 199.9987712, 199.9995479, 199.9998337, 199.9999388, 199.9999775,\n",
    "    199.9999917, 199.999997, 199.9999989, 199.9999996, 200\n",
    "  ]\n",
    "x_axis = [x*10 for x in range(len(yield_curve))]\n",
    "pd.DataFrame(\n",
    "    {\"age\": x_axis,\n",
    "     \"yield_curve [M^3/ha]\": yield_curve,\n",
    "     }) \\\n",
    "    .groupby(\"age\").sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a stand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# area=1 : a 1 hectare stand.\n",
    "\n",
    "# age=0 .  This looks like the starting point where results are shown.\n",
    "# It's not clear if whether it is valid to start at any year since\n",
    "# the model may need some spin-up time for the DOM pools to\n",
    "# equilibriate.  In Tutorial 1, for example, the model setup seems\n",
    "# to imply that most commont stand replace disturbance and most\n",
    "# recent stand replacing disturbance need to be specified. Defining\n",
    "# an arbitrary age may lead to some discontinuities.\n",
    "\n",
    "# classifier_set : this is used to specify the stand.  In this case,\n",
    "# we are setting the 'Spruce' stand with the 'fire disturbance'.\n",
    "# Note that additional scenarios can be added in the same way by\n",
    "# adding an new combination of 'Leading Species' and 'Scenario'.\n",
    "# Using the same classifier_set combination will overwrite previous\n",
    "# stands.\n",
    "\n",
    "# unfccc_land_class : ?\n",
    "sit_config.add_inventory(\n",
    "    classifier_set=[\"Spruce\", \"fire disturbance\"],\n",
    "    area=1, age=0, unfccc_land_class=0)\n",
    "sit_config.add_yield(\n",
    "    classifier_set=[\"Spruce\", \"fire disturbance\"],\n",
    "    leading_species_classifier_value=\"Spruce\",\n",
    "    values=yield_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a disturbance event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "classifier_set = [\"?\", \"?\"] means that the event is applied\n",
    "to all stands (stands with any value of \"Leading Species\" and\n",
    "\"Scenario\").\n",
    "\n",
    "disturbance_type : str\n",
    "    CBM-CFS3 disturbance type\n",
    "time_step : int\n",
    "    Year when disturbance occurs.\n",
    "target : int\n",
    "    Unclear what the target is.\n",
    "target_type : str\n",
    "sort : str\n",
    "\"\"\"\n",
    "\n",
    "sit_config.add_event(classifier_set=[\"?\", \"?\"], disturbance_type=\"Wildfire\", \n",
    "              time_step=0, target=30, target_type=\"Area\", sort=\"SORT_BY_SW_AGE\")\n",
    "\n",
    "# Dummy event to get the sit to run 250 years.  The simulation will only\n",
    "# run to the last disturbance event by default. \n",
    "sit_config.add_event(classifier_set=[\"?\", \"?\"], disturbance_type=\"Wildfire\", \n",
    "              time_step=255, target=30, target_type=\"Area\", sort=\"SORT_BY_SW_AGE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a CBM-CFS3 simulation using the project defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sit_config.import_project(standard_import_tool_plugin_path, config_save_path)\n",
    "\n",
    "results_db_path = cbm3_python.simulation.projectsimulator.run(\n",
    "    aidb_path=os.path.join(toolbox_path, \"admin\", \"dbs\", \"ArchiveIndex_Beta_Install.mdb\"), \n",
    "    project_path=os.path.abspath(project_path), \n",
    "    toolbox_installation_dir=toolbox_path,\n",
    "    cbm_exe_path=os.path.join(toolbox_path, \"admin\", \"executables\"),\n",
    "    results_database_path= results_db_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading results from the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_results = cbm3_results.load_pool_indicators(results_db_path,classifier_set_grouping=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TimeStep', 'UserDefdClassSetID', 'Aboveground Very Fast DOM',\n",
       "       'Belowground Very Fast DOM', 'Aboveground Fast DOM',\n",
       "       'Belowground Fast DOM', 'Medium DOM', 'Aboveground Slow DOM',\n",
       "       'Belowground Slow DOM', 'Softwood Stem Snag', 'Softwood Branch Snag',\n",
       "       'Hardwood Stem Snag', 'Hardwood Branch Snag', 'BlackCarbon', 'Peat',\n",
       "       'Softwood Merchantable', 'Softwood Foliage', 'Softwood Other',\n",
       "       'Softwood Coarse Roots', 'Softwood Fine Roots', 'Hardwood Merchantable',\n",
       "       'Hardwood Foliage', 'Hardwood Other', 'Hardwood Coarse Roots',\n",
       "       'Hardwood Fine Roots', 'Total Biomass', 'Aboveground Biomass',\n",
       "       'Belowground Biomass', 'Total Ecosystem', 'Dead Organic Matter',\n",
       "       'Aboveground DOM', 'Belowground DOM', 'Deadwood', 'Litter', 'Soil C',\n",
       "       'Leading Species', 'Scenario'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here are all the pools that we could inspect\n",
    "pool_results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total Biomass'}, xlabel='TimeStep'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pool_results[[\"TimeStep\",\"Scenario\", \"Total Biomass\"]] \\\n",
    "    .pivot(index=\"TimeStep\", columns=\"Scenario\", values=\"Total Biomass\") \\\n",
    "    .plot(title=\"Total Biomass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_change_results = cbm3_results.load_stock_changes(results_db_path, classifier_set_grouping=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TimeStep', 'UserDefdClassSetID', 'Delta Total Ecosystem',\n",
       "       'Delta Total Biomass', 'Delta Total DOM',\n",
       "       'Net Primary Productivity (NPP)', 'Net Ecosystem Productivity (NEP)',\n",
       "       'Net Growth', 'Net Litterfall', 'Total Litterfall',\n",
       "       'Decomposition Releases', 'NetCO2emissions_removals_CO2e',\n",
       "       'SumofCOProduction_CO2e', 'SumofCH4Production_CO2e', 'N2O_CO2e',\n",
       "       'ToFps_CO2e', 'Total Harvest (Biomass + Snags)',\n",
       "       'Total Harvest (Biomass)', 'Total Harvest (Snags)',\n",
       "       'Softwood Harvest (Biomass)', 'Hardwood Harvest (Biomass)', 'Deadwood',\n",
       "       'Litter', 'Soil C', 'Aboveground Very Fast DOM Emissions',\n",
       "       'Belowground Very Fast DOM Emissions', 'Aboveground Fast DOM Emissions',\n",
       "       'Belowground Fast DOM Emissions', 'Medium DOM Emissions',\n",
       "       'Aboveground Slow DOM Emissions', 'Belowground Slow DOM Emissions',\n",
       "       'Softwood Stem Snag Emissions', 'Softwood Branch Snag Emissions',\n",
       "       'Hardwood Stem Snag Emissions', 'Hardwood Branch Snag Emissions',\n",
       "       'Black Carbon Emissions', 'Peat Emissions', 'Biomass To DOM',\n",
       "       'Merchantable To DOM', 'Foliage To DOM', 'Other To DOM',\n",
       "       'Coarse Root To DOM', 'Fine Root To DOM', 'Total Emissions',\n",
       "       'Total Biomass Emissions', 'Total DOM Emissions', 'Total CO2 Emissions',\n",
       "       'Total CO Emissions', 'Total CH4 Emissions', 'Bio CO2 Emissions',\n",
       "       'Bio CO Emissions', 'Bio CH4 Emissions', 'DOM CO2 Emissions',\n",
       "       'DOM CO Emissions', 'DOM CH4 Emissions', 'Disturbance Losses',\n",
       "       'Bio To Soil from Disturbances', 'Net Biome Productivity (NBP)',\n",
       "       'Leading Species', 'Scenario'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And here are the stock change results we could explore\n",
    "stock_change_results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Net Ecosystem Productivity (NEP)'}, xlabel='TimeStep'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_change_results[[\"TimeStep\",\"Scenario\", 'Net Ecosystem Productivity (NEP)']] \\\n",
    "    .pivot(index=\"TimeStep\", columns=\"Scenario\", values=['Net Ecosystem Productivity (NEP)']) \\\n",
    "    .plot(title=\"Net Ecosystem Productivity (NEP)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}