tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow to create a MESSAGE Scenario using d2ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from d2ix import Model\n",
    "from d2ix import PostProcess\n",
    "from d2ix import ModifyModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:38,281  INFO d2ix.core - Load data base configurations\n",
      "2018-10-19 13:04:38,395  INFO root - launching ixmp.Platform with local HSQLDB database at 'C:\\Users\\ga46gup\\.local\\ixmp\\localdb\\default'\n",
      "2018-10-19 13:04:42,646  INFO d2ix.core - Load model input data from: 'input/modell_data.xlsx'\n",
      "2018-10-19 13:04:42,707  INFO d2ix.core - Load model input data from: 'input/manual_input_parameter.xlsx'\n",
      "2018-10-19 13:04:42,794  INFO d2ix.core - Create parameters from: 'input/modell_data.xlsx'\n",
      "2018-10-19 13:04:43,640  INFO d2ix.core - Create demands from: 'input/modell_data.xlsx'\n",
      "2018-10-19 13:04:43,699  INFO d2ix.core - Create parameters from: 'input/manual_input_parameter.xlsx'\n",
      "2018-10-19 13:04:43,700  INFO d2ix.core - Create parameters from: 'input/modell_data.xlsx' - 'rel_and_flex'\n",
      "2018-10-19 13:04:43,738  INFO d2ix.core - Create parameters from: 'input/modell_data.xlsx' - 'renewable_potential'\n",
      "2018-10-19 13:04:43,742  INFO d2ix.core - Create sets from: 'input/modell_data.xlsx' and 'input/manual_input_parameter.xlsx'\n"
     ]
    }
   ],
   "source": [
    "model = Model(model='MESSAGE_Indonesia', scen='Indonesia baseline',\n",
    "              annotation='Indonesia model',\n",
    "              base_xls='input/modell_data.xlsx',\n",
    "              manual_parameter_xls='input/manual_input_parameter.xlsx',\n",
    "              historical_data=True, historical_range_year=1,\n",
    "              first_historical_year=2000, model_range_year=5,\n",
    "              first_model_year=2020, last_model_year=2030,\n",
    "              run_config='config/run_config.yml',\n",
    "              verbose=False, yaml_export=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_new_cap = model.get_parameter(par='historical_new_capacity')\n",
    "for par in ['input', 'output']:\n",
    "    df = model.get_parameter(par=par)\n",
    "    _model_input = df[df.year_vtg >= 2020].copy()\n",
    "    _hist_input = df[df.year_vtg < 2020].copy()\n",
    "    _hist_input = _hist_input.merge(hist_new_cap, how='inner',\n",
    "                                    on=['technology', 'node_loc',\n",
    "                                        'year_vtg'], suffixes=['', 'y'])\n",
    "\n",
    "    df = pd.concat([_model_input, _hist_input[_model_input.columns]]\n",
    "                   ).reset_index(drop=True)\n",
    "    model.set_parameter(par=df, name=par)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>node</th>\n",
       "      <th>commodity</th>\n",
       "      <th>level</th>\n",
       "      <th>year</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2020</td>\n",
       "      <td>year</td>\n",
       "      <td>50.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2025</td>\n",
       "      <td>year</td>\n",
       "      <td>80.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2030</td>\n",
       "      <td>year</td>\n",
       "      <td>100.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node    commodity   level  year  time  value unit\n",
       "0  Indonesia  electricity  useful  2020  year   50.0  GWa\n",
       "1  Indonesia  electricity  useful  2025  year   80.0  GWa\n",
       "2  Indonesia  electricity  useful  2030  year  100.0  GWa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example on how to access and edit parameters manually if neccessary\n",
    "demand = model.get_parameter(par='demand')\n",
    "list(set(demand.commodity))\n",
    "demand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>node</th>\n",
       "      <th>commodity</th>\n",
       "      <th>level</th>\n",
       "      <th>year</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2020</td>\n",
       "      <td>year</td>\n",
       "      <td>75.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2025</td>\n",
       "      <td>year</td>\n",
       "      <td>120.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>electricity</td>\n",
       "      <td>useful</td>\n",
       "      <td>2030</td>\n",
       "      <td>year</td>\n",
       "      <td>150.0</td>\n",
       "      <td>GWa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        node    commodity   level  year  time  value unit\n",
       "0  Indonesia  electricity  useful  2020  year   75.0  GWa\n",
       "1  Indonesia  electricity  useful  2025  year  120.0  GWa\n",
       "2  Indonesia  electricity  useful  2030  year  150.0  GWa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To test the slack technologies we now add a demand that we do not have yet added a technology\n",
    "demand.loc[:,'value'] = demand.loc[:,'value'].copy() *1.5\n",
    "demand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_parameter(par=demand, name='demand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:43,973  INFO d2ix.core - Prepare model input data\n",
      "2018-10-19 13:04:44,031  INFO d2ix.core - Unit check\n",
      "2018-10-19 13:04:44,043  INFO d2ix.core - Add sets to scenario\n",
      "2018-10-19 13:04:44,055  INFO d2ix.core - Add parameter to scenario\n"
     ]
    }
   ],
   "source": [
    "scenario = model.model2db()\n",
    "scenario.solve(model='MESSAGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Postprocessing Workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:46,760  INFO d2ix.postprocess.timeseries - Create timeseries\n"
     ]
    }
   ],
   "source": [
    "# Create timeseries from scenario results in preperation to post processing\n",
    "model.create_timeseries(scenario)\n",
    "model.close_db()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:47,582  INFO d2ix.core - Load data base configurations\n",
      "2018-10-19 13:04:47,583  INFO root - launching ixmp.Platform with local HSQLDB database at 'C:\\Users\\ga46gup\\.local\\ixmp\\localdb\\default'\n"
     ]
    }
   ],
   "source": [
    "# Crate an instance of the d2ix post process class:\n",
    "# Post process for a specific scenario: model, scen, version\n",
    "pp = PostProcess(run_config='config/run_config_notebook.yml', \n",
    "                 model='MESSAGE_Indonesia', scen='Indonesia baseline', version=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:49,444  INFO d2ix.core - Load results for model: 'MESSAGE_Indonesia', scenario: 'Indonesia baseline', version: 'None' \n"
     ]
    }
   ],
   "source": [
    "# Load results for\n",
    "results = pp.get_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pp.create_plotdata(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tecs = ['coal_ppl', 'bio_ppl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.barplot(df=df, filters={'technology': tecs, 'variable': ['ACT']},\n",
    "           title='ACT - heat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.barplot(df=df, filters={'technology': tecs, 'variable': ['CAP']},\n",
    "           title='CAP - heat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    pp.barplot(df=df, filters={'technology': tecs, 'variable': ['CAP_NEW']},\n",
    "               title='CAP_NEW - heat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow to get a scenario from data base and write all pars/sets to an excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:50,543  INFO d2ix.core - Load data base configurations\n",
      "2018-10-19 13:04:50,544  INFO root - launching ixmp.Platform with local HSQLDB database at 'C:\\Users\\ga46gup\\.local\\ixmp\\localdb\\default'\n"
     ]
    }
   ],
   "source": [
    "# create an isntance of the ModifyModel class \n",
    "mod_model = ModifyModel(run_config='config/run_config.yml', model='MESSAGE_Indonesia', scen='Indonesia baseline',\n",
    "                        xls_dir='input/scen2xls', file_name='data.xlsx', verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:50,580  INFO d2ix.core - Load results for model: 'MESSAGE_Indonesia', scenario: 'Indonesia baseline', version: 'None' \n",
      "2018-10-19 13:04:50,911  INFO d2ix.core - Write model to excel\n"
     ]
    }
   ],
   "source": [
    "# write the scenario to an excel workbook\n",
    "mod_model.scen2xls(version=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflow to get a scenario from an excel sheet to the data base\n",
    "The data in the excel sheet can now be mnodified and edited as required before beeing added to the database and solved in GAMS.  This can lead to **infeasibility** if non consistent changes are applied to the data no sanity checks are done to the data before beeing added to the data base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:51,749  INFO d2ix.core - Load data base configurations\n",
      "2018-10-19 13:04:51,750  INFO root - launching ixmp.Platform with local HSQLDB database at 'C:\\Users\\ga46gup\\.local\\ixmp\\localdb\\default'\n"
     ]
    }
   ],
   "source": [
    "# create an isntance of the ModifyModel class \n",
    "mod_model = ModifyModel(run_config='config/run_config.yml', model='MESSAGE_Indonesia', scen='Indonesia baseline',\n",
    "                        xls_dir='input/scen2xls',\n",
    "                        file_name='data.xlsx', verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-19 13:04:51,836  INFO d2ix.core - Create model from excel\n",
      "2018-10-19 13:04:52,119  INFO d2ix.core - Prepare model input data\n",
      "2018-10-19 13:04:52,203  INFO d2ix.core - Add sets to scenario\n",
      "2018-10-19 13:04:52,212  INFO d2ix.core - Add parameter to scenario\n",
      "2018-10-19 13:04:53,142  INFO d2ix.core - Write yaml output files\n"
     ]
    }
   ],
   "source": [
    "# write scenario from xlsx to data base and run the sceanrio in GAMS\n",
    "mod_model.xls2model(annotation=None)\n",
    "scenario = mod_model.model2db()\n",
    "scenario.solve(model='MESSAGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}