generation_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%Import of required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to read results csv files\n",
    "def read_csv(scen, param):\n",
    "    df = pd.read_csv('results/WP1 NetZero/results_csv/ProductionByTechnologyAnnual.csv'.format(scen,param))\n",
    "    df['pathway'] = scen\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to create dictionaries containing dictionaries for each scenario that contain the results as dataframes\n",
    "def build_dic(scens, params):\n",
    "    dic = {}\n",
    "    for scen in scens:\n",
    "        dic[scen] = {}\n",
    "    for scen in scens:\n",
    "        for param in params:\n",
    "            dic[scen][param] = read_csv(scen, param)\n",
    "    return dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to creat a df with the production by technology annual\n",
    "def build_PbTA_df(dic):\n",
    "    # dic = results_dic\n",
    "    df = pd.DataFrame(columns=['REGION','TECHNOLOGY','FUEL','YEAR','VALUE','pathway'])\n",
    "    for i in dic:\n",
    "        df_work = dic[i]['ProductionByTechnologyAnnual']\n",
    "        df = df.append(df_work)\n",
    "    df['region'] = df['TECHNOLOGY'].apply(lambda x: x[:2])\n",
    "    df['fuel'] = df['TECHNOLOGY'].apply(lambda x: x[2:4])\n",
    "    df['tech_type'] = df['TECHNOLOGY'].apply(lambda x: x[4:6])\n",
    "    df['tech_spec'] = df['TECHNOLOGY'].apply(lambda x: x[2:])\n",
    "    df = df[(df['fuel']!='OI')\n",
    "            &(df['tech_type']!='00')\n",
    "            &((df['YEAR']==2015)|(df['YEAR']==2020)|(df['YEAR']==2030)|(df['YEAR']==2040)|(df['YEAR']==2050))]\n",
    "    df['unit'] = 'PJ'\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to create dictionary with information\n",
    "def get_facts(df):\n",
    "    facts_dic = {}\n",
    "    facts_dic['pathways'] = df.loc[:,'pathway'].unique()\n",
    "    facts_dic['regions'] = df.loc[:,'region'].unique()\n",
    "    facts_dic['unit'] = df.loc[:, 'unit'].unique()\n",
    "    facts_dic['regions'] = np.append(facts_dic['regions'],'EU28')\n",
    "    return facts_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Dictionary of dictionaries with colour schemes\n",
    "colour_schemes = dict(\n",
    "    dES_colours = dict(\n",
    "        Coal = 'rgb(0, 0, 0)',\n",
    "        Oil = 'rgb(121, 43, 41)',\n",
    "        Gas = 'rgb(86, 108, 140)',\n",
    "        Nuclear = 'rgb(186, 28, 175)',\n",
    "        Waste = 'rgb(138, 171, 71)',\n",
    "        Biomass = 'rgb(172, 199, 119)',\n",
    "        Biofuel = 'rgb(79, 98, 40)',\n",
    "        Hydro = 'rgb(0, 139, 188)',\n",
    "        Wind = 'rgb(143, 119, 173)',\n",
    "        Solar = 'rgb(230, 175, 0)',\n",
    "        Geo = 'rgb(192, 80, 77)',\n",
    "        Ocean ='rgb(22, 54, 92)',\n",
    "        Imports = 'rgb(232, 133, 2)'),\n",
    "    TIMES_PanEU_colours = dict(\n",
    "        Coal = 'rgb(0, 0, 0)',\n",
    "        Oil = 'rgb(202, 171, 169)',\n",
    "        Gas = 'rgb(102, 77, 142)',\n",
    "        Nuclear = 'rgb(109, 109, 109)',\n",
    "        Waste = 'rgb(223, 134, 192)',\n",
    "        Biomass = 'rgb(80, 112, 45)',\n",
    "        Biofuel = 'rgb(178, 191, 225)',\n",
    "        Hydro = 'rgb(181, 192, 224)',\n",
    "        Wind = 'rgb(103, 154, 181)',\n",
    "        Solar = 'rgb(210, 136, 63)',\n",
    "        Geo = 'rgb(178, 191, 225)',\n",
    "        Ocean ='rgb(178, 191, 225)',\n",
    "        Imports = 'rgb(232, 133, 2)')\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% functions for returning positives and negatives\n",
    "def positives(value):\n",
    "    return max(value, 0)\n",
    "def negatives(value):\n",
    "    return min(value, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to create dfs with import and export of electricity for selected country\n",
    "def impex(data, paths, selected_country):\n",
    "    df_filtered = data[(data['fuel']=='EL')\n",
    "                       &((data['region']==selected_country)|(data['tech_type']==selected_country))\n",
    "                       &(data['tech_type']!='00')]\n",
    "    countries = []\n",
    "    countries = list(df_filtered['region'].unique())\n",
    "    countries.extend(df_filtered['tech_type'].unique())\n",
    "    countries = list(dict.fromkeys(countries))\n",
    "    df_filtered = df_filtered[df_filtered['FUEL'].str.contains('|'.join(countries))]\n",
    "    df_filtered = df_filtered[df_filtered['FUEL'].str.contains('E1')]\n",
    "    years = pd.Series(df_filtered['YEAR'].unique(),name='YEAR').sort_values()\n",
    "    #paths = list(path_names.keys())\n",
    "    neighbours = []\n",
    "    for i in countries:\n",
    "        if i != selected_country:\n",
    "            neighbours.append(i)\n",
    "    dict_path = {}\n",
    "    links = list(df_filtered['TECHNOLOGY'].unique())\n",
    "    label_imp = []\n",
    "    label_exp = []\n",
    "    for n in neighbours:\n",
    "        label_imp.append('Import from '+n)\n",
    "        label_exp.append('Export to '+n)\n",
    "    for j in paths:\n",
    "        i = 0\n",
    "        net_imp = pd.DataFrame(index=years)\n",
    "        for link in links:\n",
    "            imp = df_filtered[(df_filtered['pathway']==j)\n",
    "                              &(df_filtered['TECHNOLOGY']==link)\n",
    "                              &(df_filtered['FUEL']==(selected_country+'E1'))]\n",
    "            if len(imp.index)<5:\n",
    "                imp = imp.set_index('YEAR').reindex(years).reset_index().fillna(0)\n",
    "            imp = imp.set_index(years)\n",
    "            exp = df_filtered[(df_filtered['pathway']==j)\n",
    "                              &(df_filtered['TECHNOLOGY']==link)\n",
    "                              &(df_filtered['FUEL']==(neighbours[i]+'E1'))]\n",
    "            if len(exp.index)<5:\n",
    "                exp = exp.set_index('YEAR').reindex(years).reset_index().fillna(0)\n",
    "            exp = exp.set_index(years) \n",
    "            net_imp[link] = imp['VALUE'] - exp['VALUE']\n",
    "            i += 1\n",
    "        net_imp_pos = pd.DataFrame(index=years,columns=links)\n",
    "        net_imp_neg = pd.DataFrame(index=years,columns=links)\n",
    "        for link in links:\n",
    "            net_imp_pos[link] = net_imp[link].map(positives)\n",
    "            net_imp_neg[link] = net_imp[link].map(negatives)\n",
    "        net_imp_pos.columns = label_imp\n",
    "        net_imp_neg.columns = label_exp\n",
    "        dict_path[j] = {}\n",
    "        dict_path[j]['imports']=net_imp_pos\n",
    "        dict_path[j]['exports']=net_imp_neg\n",
    "    path_ind = []\n",
    "    year_ind = []\n",
    "    df_exports = pd.DataFrame(columns=label_exp)\n",
    "    df_imports = pd.DataFrame(columns=label_imp)\n",
    "    for year in years:\n",
    "        i=0\n",
    "        for j in paths:\n",
    "            df_exports = df_exports.append(dict_path[j]['exports'].loc[year])\n",
    "            df_imports = df_imports.append(dict_path[j]['imports'].loc[year])\n",
    "            path_ind.append(paths[i].upper())\n",
    "            i+=1\n",
    "    df_exports = df_exports.set_index([pd.Index(path_ind, name='paths')],append=True)\n",
    "    df_imports = df_imports.set_index([pd.Index(path_ind, name='paths')],append=True)\n",
    "    return df_exports, df_imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Function to create figure\n",
    "def create_fig(data, paths, country_sel, countries_mod, fuels, colours):\n",
    "    fig = go.Figure()\n",
    "    elexp, elimp = impex(data, paths, country_sel)\n",
    "    elexp = elexp.sum(axis=1)\n",
    "    elimp = elimp.sum(axis=1)\n",
    "    #paths = list(path_names.keys())\n",
    "    years = data['YEAR'].unique()\n",
    "    years.sort()\n",
    "    coms = fuels['fuel_name']\n",
    "    coms = coms[(coms!='EL')&(coms!='OI')]\n",
    "    info_dict = {}\n",
    "    info_dict['Unit'] = data.loc[:,'unit'].unique()\n",
    "    info_dict['Y-Axis'] = ['{}'.format(*info_dict['Unit'])]\n",
    "    countr_el1 = country_sel + 'E1'\n",
    "    countr_el2 = country_sel + 'E2'\n",
    "    dict_path = {}\n",
    "    for path in paths:\n",
    "        filtered_df = data[\n",
    "        (data['pathway'] == path) \n",
    "        & (data['region'] == country_sel) \n",
    "        & ((data['FUEL']==countr_el1)|(data['FUEL']==countr_el2)) \n",
    "        & (data['fuel']!='EL') \n",
    "        & (data['tech_type']!='00')]\n",
    "        filtered_df_p = filtered_df.pivot(index='YEAR', columns='tech_spec',  values='VALUE')\n",
    "        df_by_com = pd.DataFrame()\n",
    "        for com in coms:\n",
    "            com_selec = filtered_df_p.filter(regex=\"\\A\"+com, axis=1)\n",
    "            com_sum = com_selec.sum(axis=1)\n",
    "            df_by_com[com] = com_sum\n",
    "        dict_path[path] = df_by_com\n",
    "    df_fig = pd.DataFrame(columns=coms)\n",
    "    path_ind = []\n",
    "    year_ind = []\n",
    "    for y in years:\n",
    "        i = 0\n",
    "        for p in paths:\n",
    "            df_fig = df_fig.append(dict_path[p].loc[y])\n",
    "            path_ind.append(paths[i].upper())\n",
    "            year_ind.append(y)\n",
    "            i +=1\n",
    "    df_fig = df_fig.set_index([pd.Index(path_ind, name='paths')],append=True)\n",
    "    df_fig['EL'] = elimp\n",
    "    coms = coms.append(pd.Series('EL'))\n",
    "    for c in coms:\n",
    "        temp = fuels.loc[fuels['fuel_name']==c,'fuel_abr']\n",
    "        fuel_code = temp.iloc[0]\n",
    "        fig.add_trace(go.Bar(\n",
    "            y = df_fig.loc[:,c],\n",
    "            x = [year_ind,path_ind],\n",
    "            name = fuel_code,\n",
    "            hovertemplate = 'Power generation: %{y}PJ',\n",
    "            marker_color = colours[fuel_code]\n",
    "            ))\n",
    "    fig.add_trace(go.Bar(\n",
    "    y = elexp,\n",
    "    x = [year_ind,path_ind],\n",
    "    name = 'Exports',\n",
    "    hovertemplate = 'Exported electricity: %{y}PJ',\n",
    "    marker_color = colours['Imports'],\n",
    "    base=0\n",
    "    ))\n",
    "    fig.update_layout(\n",
    "        barmode = 'stack',\n",
    "        plot_bgcolor='rgba(0,0,0,0)',\n",
    "        title={\n",
    "            'text':'<b>Electricity generation in {}</b>'.format(countries_mod[country_sel]),\n",
    "            'y':0.95,\n",
    "            'x':0.5,\n",
    "            'xanchor': 'center',\n",
    "            'yanchor': 'top'},\n",
    "        xaxis = {'type': 'multicategory'},\n",
    "        yaxis = dict(title='Electricity in [{}]'.format(info_dict['Y-Axis'][0])),\n",
    "        font_family = \"Arial\",\n",
    "        font_color = \"black\",\n",
    "        title_font_size = 32,\n",
    "        legend_font_size = 26\n",
    "        )\n",
    "    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Black',title_font_size=26, tickfont_size=22)\n",
    "    fig.update_xaxes(tickfont_size=22)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% main function to execute the script\n",
    "def main(country,scenarios):\n",
    "\n",
    "    #scens = ['B1C0TxE0','B1C0T0E0','B1C0ToE0']\n",
    "    params = ['ProductionByTechnologyAnnual']\n",
    "    results_dic = build_dic(scenarios, params)\n",
    "    df_PbTA = build_PbTA_df(results_dic)\n",
    "    facts_dic = get_facts(df_PbTA)\n",
    "    #path_names = {'B1C0TxE0':'CBS','B1C0T0E0':'REF','B1C0ToE0':'OBS'}\n",
    "    countries_mod = {'AT':'Austria','BE':'Belgium','BG':'Bulgaria','CH':'Switzerland','CY':'Cyrpus','CZ':'Czech Republic','DE':'Germany','DK':'Denmark','EE':'Estonia','ES':'Spain','FI':'Finland','FR':'France','GR':'Greece','HR':'Croatia','HU':'Hungary','IE':'Ireland','IT':'Italy','LT':'Lithuania','LU':'Luxembourg','LV':'Latvia','MT':'Malta','NL':'Netherlands','NO':'Norway','PL':'Poland','PT':'Portugal','RO':'Romania','SE':'Sweden','SI':'Slovenia','SK':'Slovakia','UK':'United Kingdom','EU28':'EU28'}\n",
    "    fuels = pd.DataFrame({'fuel_name':['WI','HY','BF','CO','BM','WS','HF','NU','NG','OC','OI','GO','SO','EL'],'fuel_abr':['Wind','Hydro','Biofuel','Coal','Biomass','Waste','Oil','Nuclear','Gas','Ocean','Oil','Geo','Solar','Imports']}, columns = ['fuel_name','fuel_abr'])\n",
    "    fuels = fuels.sort_values(['fuel_name'])\n",
    "    for region in facts_dic['regions']:\n",
    "        print(region)\n",
    "    # selec_region = input('Please select a country from the above listed by typing here:')\n",
    "    #selec_region = 'DE'\n",
    "    print(list(colour_schemes.keys()))\n",
    "    # selec_scheme = input('Please select one of the above listed colour schemes by writing it here and confirming by enter:')\n",
    "    selec_scheme = 'dES_colours' \n",
    "    colours = colour_schemes[selec_scheme]\n",
    "    figure = create_fig(df_PbTA, scenarios, country, countries_mod, fuels, colours)\n",
    "    plot(figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s9/_fn8gz5x1tjcx320wd285d040000gn/T/ipykernel_3507/344075524.py:7: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AT\n",
      "BE\n",
      "BG\n",
      "CH\n",
      "CY\n",
      "CZ\n",
      "DE\n",
      "DK\n",
      "EE\n",
      "ES\n",
      "FI\n",
      "FR\n",
      "GR\n",
      "HR\n",
      "HU\n",
      "IE\n",
      "IT\n",
      "LT\n",
      "LU\n",
      "LV\n",
      "MT\n",
      "NL\n",
      "NO\n",
      "PL\n",
      "PT\n",
      "RO\n",
      "SE\n",
      "SI\n",
      "SK\n",
      "UK\n",
      "EU28\n",
      "['dES_colours', 'TIMES_PanEU_colours']\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "2015",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 2015",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [27], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m selec_region \u001b[39m=\u001b[39m sys\u001b[39m.\u001b[39margv[\u001b[39m1\u001b[39m]\n\u001b[1;32m      4\u001b[0m scens \u001b[39m=\u001b[39m sys\u001b[39m.\u001b[39margv[\u001b[39m2\u001b[39m:]\n\u001b[0;32m----> 5\u001b[0m main(selec_region,scens)\n",
      "Cell \u001b[0;32mIn [26], line 21\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(country, scenarios)\u001b[0m\n\u001b[1;32m     19\u001b[0m selec_scheme \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mdES_colours\u001b[39m\u001b[39m'\u001b[39m \n\u001b[1;32m     20\u001b[0m colours \u001b[39m=\u001b[39m colour_schemes[selec_scheme]\n\u001b[0;32m---> 21\u001b[0m figure \u001b[39m=\u001b[39m create_fig(df_PbTA, scenarios, country, countries_mod, fuels, colours)\n\u001b[1;32m     22\u001b[0m plot(figure)\n",
      "Cell \u001b[0;32mIn [25], line 38\u001b[0m, in \u001b[0;36mcreate_fig\u001b[0;34m(data, paths, country_sel, countries_mod, fuels, colours)\u001b[0m\n\u001b[1;32m     36\u001b[0m i \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m     37\u001b[0m \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m paths:\n\u001b[0;32m---> 38\u001b[0m     df_fig \u001b[39m=\u001b[39m df_fig\u001b[39m.\u001b[39mappend(dict_path[p]\u001b[39m.\u001b[39;49mloc[y])\n\u001b[1;32m     39\u001b[0m     path_ind\u001b[39m.\u001b[39mappend(paths[i]\u001b[39m.\u001b[39mupper())\n\u001b[1;32m     40\u001b[0m     year_ind\u001b[39m.\u001b[39mappend(y)\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39mor\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[1;32m   1072\u001b[0m maybe_callable \u001b[39m=\u001b[39m com\u001b[39m.\u001b[39mapply_if_callable(key, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj)\n\u001b[0;32m-> 1073\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_axis(maybe_callable, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/indexing.py:1312\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1310\u001b[0m \u001b[39m# fall thru to straight lookup\u001b[39;00m\n\u001b[1;32m   1311\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1312\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_label(key, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/indexing.py:1260\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1258\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_get_label\u001b[39m(\u001b[39mself\u001b[39m, label, axis: \u001b[39mint\u001b[39m):\n\u001b[1;32m   1259\u001b[0m     \u001b[39m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[0;32m-> 1260\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mobj\u001b[39m.\u001b[39;49mxs(label, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/generic.py:4056\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   4054\u001b[0m             new_index \u001b[39m=\u001b[39m index[loc]\n\u001b[1;32m   4055\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 4056\u001b[0m     loc \u001b[39m=\u001b[39m index\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   4058\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(loc, np\u001b[39m.\u001b[39mndarray):\n\u001b[1;32m   4059\u001b[0m         \u001b[39mif\u001b[39;00m loc\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mbool_:\n",
      "File \u001b[0;32m~/miniforge3/envs/camilo/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3803\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   3806\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   3807\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3810\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 2015"
     ]
    }
   ],
   "source": [
    "#%% If executed as script\n",
    "if __name__ == '__main__':\n",
    "    selec_region = sys.argv[1]\n",
    "    scens = sys.argv[2:]\n",
    "    main(selec_region,scens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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