examples/results/results_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cbm3_python.simulation import projectsimulator\n",
    "from cbm3_python.cbm3data import cbm3_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the bundled tutorial 3 project file for the purposes of this demonstration\n",
    "project_path = \"C:\\Program Files (x86)\\Operational-Scale CBM-CFS3\\Tutorials\\Tutorial 3\\Tutorial3.mdb\"\n",
    "results_path = \"C:\\Program Files (x86)\\Operational-Scale CBM-CFS3\\Tutorials\\Tutorial 3\\Tutorial3_results.mdb\"\n",
    "results_path = projectsimulator.run(\n",
    "    project_path=project_path,\n",
    "    results_database_path=results_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines load CBM3 results into pandas DataFrames from the loaded CBM3 results access database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_indicators = cbm3_results.load_pool_indicators(\n",
    "    results_path, spatial_unit_grouping=True,\n",
    "    classifier_set_grouping=True, land_class_grouping=True)\n",
    "stock_changes = cbm3_results.load_stock_changes(\n",
    "    results_path, spatial_unit_grouping=True,\n",
    "    classifier_set_grouping=True, land_class_grouping=True)\n",
    "disturbance_indicators = cbm3_results.load_disturbance_indicators(\n",
    "    results_path, spatial_unit_grouping=True, disturbance_type_grouping=True,\n",
    "    classifier_set_grouping=True, land_class_grouping=True)\n",
    "age_indicators = cbm3_results.load_age_indicators(\n",
    "    results_path, spatial_unit_grouping=True,\n",
    "    classifier_set_grouping=True, land_class_grouping=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells show a couple of examples of what can be done with the dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the results to a csv file named tutorial3_pool_indicators.csv\n",
    "pool_indicators.to_csv(\n",
    "    r\"C:\\Program Files (x86)\\Operational-Scale CBM-CFS3\\Tutorials\\Tutorial 3\\tutorial3_pool_indicators.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa2a57a700>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot some results\n",
    "pool_indicators[\n",
    "    ['TimeStep','Total Biomass', 'Dead Organic Matter', 'Total Ecosystem']\n",
    "].groupby('TimeStep').sum().plot(figsize=(10, 8))"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,md"
  },
  "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}