{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Usage\n",
    "\n",
    "Here is a demonstartion of the potential usage of ```GOBLIN lite```.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "Firstly, we start of by importing the relevant classes. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.display import Image\n",
    "\n",
    "from goblin_lite.goblin import ScenarioRunner\n",
    "from goblin_lite.resource_manager.goblin_data_manager import GoblinDataManager\n",
    "from goblin_lite.scenario_analysis.data_grapher import DataGrapher\n",
    "from goblin_lite.resource_manager.data_fetcher import DataFetcher\n",
    "import shutil\n",
    "import os\n",
    "\n",
    "\n",
    "import warnings\n",
    "\n",
    "# Filter out the RuntimeWarning related to unsupported linux distribution\n",
    "warnings.filterwarnings(\"ignore\", message=\"untested linux distribution:\", category=RuntimeWarning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```ScenarioRunner``` class is basically used to set to run the scenarios based on the configuration files for goblin and the CBM CFS3.\n",
    "\n",
    "The ```DataFetcher``` class is used to retrieve the various results as dataframes from the scenario run. Each of the methods in the ```DataFetcher``` class relates to specific table. \n",
    "\n",
    "The ```DataGrapher``` class is still being developed, but contains some basic plotting to assist the user in visualising the results tables. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Scenario Runner\n",
    "\n",
    "In the first example below, we will set the path to the configurations, define the emissions factor country and the base and target year. Finally, we will create a directory for our outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#configuration \n",
    "\n",
    "goblin_config = \"../tests/data/config.json\" #location of test goblin configuration \n",
    "\n",
    "cbm_config = \"../tests/data/cbm_factory.yaml\" #location of test cbm configuration\n",
    "\n",
    "ef_country = \"ireland\" #emissions factor country\n",
    "\n",
    "#years for the baseline, and the target year \n",
    "baseline_year = 2020\n",
    "target_year = 2050\n",
    "\n",
    "#Make directory to store exported file\n",
    "\n",
    "os.mkdir(\"jupyter_example\")\n",
    "\n",
    "data_path = \"./jupyter_example/\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define classes and Run Scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create goblin data manager\n",
    "goblin_data_manger = GoblinDataManager(\n",
    "    ef_country = ef_country, \n",
    "    calibration_year= baseline_year,\n",
    "    target_year= target_year,\n",
    "    configuration_path= goblin_config,\n",
    "    cbm_configuration_path= cbm_config,\n",
    ")\n",
    "\n",
    "# class instances\n",
    "runner_class = ScenarioRunner(goblin_data_manger)\n",
    "graph_class = DataGrapher()\n",
    "fetcher_class = DataFetcher()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No tables to clean.\n",
      "JSON file detected\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "... calibration year not present, 2015 default year used for Spread Manure Dictionary...\n",
      "Scenario Generation Complete ... \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Run Scenarios \n",
    "runner_class.run_scenarios()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the calibration year is not present for a specific dataset, the default year of 2015 is used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data Fetcher\n",
    "\n",
    "The Scenario Run is now complete, and we can use the ```DataFectcher``` class to retrieve our data. \n",
    "\n",
    "**Note:** Once a scenario run is complete, when the user again runs scenarios, table output from the previous run is completely deleted. \n",
    "\n",
    "Here we will retrieve data for: \n",
    "\n",
    "    - Livestock outputs \n",
    "    - Aggregated emissions\n",
    "    - Animal emissions by category \n",
    "    - Land use emissions by category \n",
    "    - Crop emissions by category \n",
    "    - Forest carbon flux \n",
    "    \n",
    "These are not the only potential returns, as there are several additional methods returning results out.\n",
    "Please consult the documentation for further details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           total_milk_kg  total_beef_kg\n",
      "Scenarios                              \n",
      "-1          8.251443e+09   6.528033e+08\n",
      " 0          5.339220e+09   2.596987e+08\n",
      " 1          5.339220e+09   2.596987e+08\n"
     ]
    }
   ],
   "source": [
    "#Livestock outputs \n",
    "\n",
    "animal_output_df = fetcher_class.get_livestock_output_summary()\n",
    "\n",
    "print(animal_output_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              CH4        N2O           CO2          CO2e\n",
      "index                                                   \n",
      "-1     531.729908  23.883325   5932.541486  27150.059976\n",
      " 0     232.712823  12.768333 -49587.580733 -39688.013426\n",
      " 1     232.713557  12.748906 -45684.640694 -35790.201118\n"
     ]
    }
   ],
   "source": [
    "#Aggregated emissions\n",
    "\n",
    "aggregated_emmissions_df = fetcher_class.get_climate_change_emission_totals()\n",
    "\n",
    "print(aggregated_emmissions_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       enteric_ch4  manure_management_N2O  manure_management_CH4  \\\n",
      "index                                                              \n",
      "-1      473.049267               2.721406              44.675498   \n",
      " 0      201.291244               1.151512              17.417980   \n",
      " 1      201.299522               1.151441              17.410440   \n",
      "\n",
      "       manure_applied_N  N_direct_PRP  N_indirect_PRP  N_direct_fertiliser  \\\n",
      "index                                                                        \n",
      "-1             1.040157      4.761255        0.784523             7.109264   \n",
      " 0             0.446434      2.014111        0.334084             2.135874   \n",
      " 1             0.446434      2.013622        0.334013             2.119852   \n",
      "\n",
      "       N_indirect_fertiliser   soils_CO2  soil_organic_N_direct  \\\n",
      "index                                                             \n",
      "-1                  1.239013  508.885373               5.801413   \n",
      " 0                  0.394315  436.522849               2.460546   \n",
      " 1                  0.391357  436.244992               2.460057   \n",
      "\n",
      "       soil_organic_N_indirect  soil_inorganic_N_direct  \\\n",
      "index                                                     \n",
      "-1                    0.784523                 7.109264   \n",
      " 0                    0.334084                 2.135874   \n",
      " 1                    0.334013                 2.119852   \n",
      "\n",
      "       soil_inorganic_N_indirect  soil_histosol_N_direct  crop_residue_direct  \\\n",
      "index                                                                           \n",
      "-1                      1.239013                2.125491             3.529468   \n",
      " 0                      0.394315                2.125491             3.529468   \n",
      " 1                      0.391357                2.125491             3.529468   \n",
      "\n",
      "       soil_N_direct  soil_N_indirect  soils_N2O  \n",
      "index                                             \n",
      "-1         18.565637         2.023536  20.589172  \n",
      " 0         10.251379         0.728399  10.979779  \n",
      " 1         10.234868         0.725370  10.960238  \n"
     ]
    }
   ],
   "source": [
    "#Animal emissions by category\n",
    "\n",
    "animal_emissions_df = fetcher_class.get_climate_change_animal_emissions_by_category()\n",
    "\n",
    "print(animal_emissions_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           land_use  year           CO2           CH4           N2O  \\\n",
      "scenario                                                              \n",
      "-1         cropland  2020      0.000000  1.542857e-07  4.000000e-09   \n",
      "-1        grassland  2020   6171.629406  8.996535e+00  2.144749e+00   \n",
      "-1           forest  2020   -951.470371  3.315404e+00  6.892956e-01   \n",
      "-1          wetland  2020   2233.286075  3.449994e+00  5.858179e-02   \n",
      "-1            total  2020  10112.301667  1.576193e+01  2.892627e+00   \n",
      " 0         cropland  2050      0.000000  1.542857e-07  4.000000e-09   \n",
      " 0        grassland  2050   6171.594074  8.996508e+00  2.143633e+00   \n",
      " 0           forest  2050   -526.083217  3.315869e+00  6.893213e-01   \n",
      " 0          wetland  2050   2047.184751  3.449994e+00  5.858179e-02   \n",
      " 0            total  2050  10112.421360  1.576237e+01  2.891536e+00   \n",
      " 1         cropland  2050      0.000000  1.542857e-07  4.000000e-09   \n",
      " 1        grassland  2050   6171.594074  8.996508e+00  2.143633e+00   \n",
      " 1           forest  2050   -526.083217  3.315869e+00  6.893213e-01   \n",
      " 1          wetland  2050   2047.184751  3.449994e+00  5.858179e-02   \n",
      " 1            total  2050  10112.421360  1.576237e+01  2.891536e+00   \n",
      "\n",
      "                  CO2e  \n",
      "scenario                \n",
      "-1            0.000005  \n",
      "-1         6991.890897  \n",
      "-1         -675.975718  \n",
      "-1         2345.410071  \n",
      "-1        11320.181813  \n",
      " 0            0.000005  \n",
      " 0         6991.558988  \n",
      " 0         -250.568754  \n",
      " 0         2159.308748  \n",
      " 0        11320.024738  \n",
      " 1            0.000005  \n",
      " 1         6991.558988  \n",
      " 1         -250.568754  \n",
      " 1         2159.308748  \n",
      " 1        11320.024738  \n"
     ]
    }
   ],
   "source": [
    "#Land use emissions by category \n",
    "\n",
    "land_emissions_df = fetcher_class.get_landuse_emissions_totals()\n",
    "\n",
    "print(land_emissions_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       crop_residue_direct  N_direct_fertiliser  N_indirect_fertiliser  \\\n",
      "index                                                                    \n",
      "-1                3.529468             0.491902               0.090813   \n",
      " 0                3.529468             0.491902               0.090813   \n",
      " 1                3.529468             0.491902               0.090813   \n",
      "\n",
      "       soils_CO2  soils_N2O  \n",
      "index                        \n",
      "-1      6.166289   4.112183  \n",
      " 0      6.166289   4.112183  \n",
      " 1      6.166289   4.112183  \n"
     ]
    }
   ],
   "source": [
    "# Crop emissions by category \n",
    "\n",
    "crop_emissions_df= fetcher_class.get_climate_change_crop_emissions_by_category()\n",
    "\n",
    "print(crop_emissions_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Year       Biomass            DOM  Total Ecosystem            HWP  \\\n",
      "index                                                                      \n",
      "0      1992 -9.133895e+05  526370.289705    -3.870192e+05  136372.854614   \n",
      "1      1993 -9.455125e+05  481174.091859    -4.643384e+05  119192.480962   \n",
      "2      1994 -1.010665e+06  420726.817546    -5.899379e+05  125846.064407   \n",
      "3      1995 -1.128195e+06  360518.333006    -7.676764e+05  133580.507173   \n",
      "4      1996 -1.302548e+06  297120.193722    -1.005428e+06  141585.362759   \n",
      "...     ...           ...            ...              ...            ...   \n",
      "262    2076 -1.201266e+06 -418583.734264    -1.619850e+06       0.000000   \n",
      "263    2077 -1.194714e+06 -423860.976407    -1.618575e+06       0.000000   \n",
      "264    2078 -1.186814e+06 -428942.613463    -1.615757e+06       0.000000   \n",
      "265    2079 -1.181875e+06 -433887.252177    -1.615762e+06       0.000000   \n",
      "266    2080 -1.175328e+06 -438791.446590    -1.614119e+06       0.000000   \n",
      "\n",
      "       Scenario  \n",
      "index            \n",
      "0            -1  \n",
      "1            -1  \n",
      "2            -1  \n",
      "3            -1  \n",
      "4            -1  \n",
      "...         ...  \n",
      "262           1  \n",
      "263           1  \n",
      "264           1  \n",
      "265           1  \n",
      "266           1  \n",
      "\n",
      "[267 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#Forest carbon flux \n",
    "\n",
    "forest_flux_df = fetcher_class.get_forest_flux()\n",
    "\n",
    "print(forest_flux_df)\n",
    "\n",
    "# baseline is index -1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are simple examples, additional information on inputs and outputs can be retrieved. Furhter impact categories related to eutrophication and air quality are also available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataGrapher \n",
    "\n",
    "The data grapher allows the user to plot some of the data from the generated tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save image\n",
    "graph_class.plot_animal_lca_emissions_by_category(data_path)\n",
    "\n",
    "#retrieve an display\n",
    "image_path = os.path.join(data_path,\"Livestock_LCA.png\")\n",
    "\n",
    "# display the image\n",
    "Image(filename=image_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Land use emissions\n",
    "\n",
    "graph_class.plot_land_use_emissions(data_path)\n",
    "\n",
    "#retrieve an display\n",
    "image_path = os.path.join(data_path,\"climate_change_land_use.png\")\n",
    "\n",
    "# display the image\n",
    "Image(filename=image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: ./jupyter_example/Livestock_LCA.png\n"
     ]
    }
   ],
   "source": [
    "# Remove Generated files \n",
    "\n",
    "for filename in os.listdir(data_path):\n",
    "    file_path = os.path.join(data_path, filename)\n",
    "    try:\n",
    "        if os.path.isfile(file_path):\n",
    "            os.remove(file_path)\n",
    "            print(f\"Deleted file: {file_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error deleting file: {file_path}. Reason: {e}\")\n",
    "        \n",
    "#Remove direcotry \n",
    "os.rmdir(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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