BES RCP 85_5.2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) Importing required packages and getting connected to the database\n",
    "import os\n",
    "import ixmp as ix\n",
    "import message_ix\n",
    "import pyam\n",
    "import pandas as pd \n",
    "from ixmp.reporting import configure\n",
    "from message_ix.reporting import Reporter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from message_ix.util import make_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Loading a modelling platform (getting connected to the database)\n",
    "mp = ix.Platform(\"default\", jvmargs=[\"-Xms800m\", \"-Xmx8g\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the path to the folder you saved the model files\n",
    "current_path = r'C:\\Users\\Natalia\\GitKraken\\Brazil\\Notebooks'\n",
    "os.chdir(current_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Existing index sets of 'map_tec_storage' ['node', 'technology', 'mode', 'storage_tec', 'mode', 'level', 'commodity'] do not match ['node', 'technology', 'mode', 'technology', 'mode', 'level', 'commodity']\n",
      "Existing index names of 'map_tec_storage' ['node', 'technology', 'mode', 'storage_tec', 'mode_storage', 'level', 'commodity'] do not match ['node', 'technology', 'mode', 'storage_tec', 'storage_mode', 'level', 'commodity']\n"
     ]
    }
   ],
   "source": [
    "# Specifying input data\n",
    "# Defining the name of the model and scenario (you can give any name you like)\n",
    "model = \"MESSAGEix-BR\"\n",
    "sc_ref = \"storage_general\"\n",
    "version_ref = 57\n",
    "\n",
    "# Creating a new (empty) scenario\n",
    "my_scenario = message_ix.Scenario(mp, model, sc_ref, version_ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Existing index sets of 'map_tec_storage' ['node', 'technology', 'mode', 'storage_tec', 'mode', 'level', 'commodity'] do not match ['node', 'technology', 'mode', 'technology', 'mode', 'level', 'commodity']\n",
      "Existing index names of 'map_tec_storage' ['node', 'technology', 'mode', 'storage_tec', 'mode_storage', 'level', 'commodity'] do not match ['node', 'technology', 'mode', 'storage_tec', 'storage_mode', 'level', 'commodity']\n"
     ]
    }
   ],
   "source": [
    "scen = my_scenario.clone(model, 'scenario_rcp85_2',keep_solution=False)\n",
    "scen.check_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_df = scen.vintage_and_active_years()\n",
    "vintage_years, act_years = year_df['year_vtg'], year_df['year_act']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yearly_to_season(scen, parameter, data, filters=None):\n",
    "    if filters:        \n",
    "        old = scen.par(parameter, filters)\n",
    "    else:\n",
    "        old = scen.par(parameter)\n",
    "    scen.remove_par(parameter, old)\n",
    "    \n",
    "    # Finding \"time\" related indexes\n",
    "    time_idx = [x for x in scen.idx_names(parameter) if 'time' in x]\n",
    "    for h in data.keys():\n",
    "        new = old.copy()\n",
    "        for time in time_idx:\n",
    "            new[time] = h\n",
    "        new['value'] = data[h] \n",
    "        scen.add_par(parameter, new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(make_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### changing capacity factors of wind_ppl_off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#North \n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('n_85_new_cf_wind.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('ne_85_new_cf_wind.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Southeast \n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('se_85_new_cf_wind.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#South\n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('s_85_new_cf_wind.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'South'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'South'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing capacity factors of solar_pv_ppl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#North \n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('n_85_new_cf_sol.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'North'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'North'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Northeasteast \n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('ne_85_new_cf_sol.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Southeast \n",
    "#new_cf = make_df(rcp_45)\n",
    "df = pd.read_excel('se_85_new_cf_sol.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#South\n",
    "df = pd.read_excel('s_85_new_cf_sol.xls')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2030, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': df.iloc[0,[2]][0], \n",
    "           'autumn': df.iloc[1,[2]][0], \n",
    "           'winter': df.iloc[2,[2]][0], \n",
    "           'spring': df.iloc[3,[2]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2040, 'node_loc': 'South'}\n",
    "cf_data2 =  {'summer': df.iloc[0,[3]][0], \n",
    "           'autumn': df.iloc[1,[3]][0], \n",
    "           'winter': df.iloc[2,[3]][0], \n",
    "           'spring': df.iloc[3,[3]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2050, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': df.iloc[0,[4]][0], \n",
    "           'autumn': df.iloc[1,[4]][0], \n",
    "           'winter': df.iloc[2,[4]][0], \n",
    "           'spring': df.iloc[3,[4]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2060, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': df.iloc[0,[5]][0], \n",
    "           'autumn': df.iloc[1,[5]][0], \n",
    "           'winter': df.iloc[2,[5]][0], \n",
    "           'spring': df.iloc[3,[5]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2070, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': df.iloc[0,[6]][0], \n",
    "           'autumn': df.iloc[1,[6]][0], \n",
    "           'winter': df.iloc[2,[6]][0], \n",
    "           'spring': df.iloc[3,[6]][0],} \n",
    "yearly_to_season(scen, 'capacity_factor', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2080, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': df.iloc[0,[7]][0], \n",
    "           'autumn': df.iloc[1,[7]][0], \n",
    "           'winter': df.iloc[2,[7]][0], \n",
    "           'spring': df.iloc[3,[7]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2090, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': df.iloc[0,[8]][0], \n",
    "           'autumn': df.iloc[1,[8]][0], \n",
    "           'winter': df.iloc[2,[8]][0], \n",
    "           'spring': df.iloc[3,[8]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'year_act': 2100, 'node_loc': 'South'}\n",
    "cf_data8 = {'summer': df.iloc[0,[9]][0], \n",
    "           'autumn': df.iloc[1,[9]][0], \n",
    "           'winter': df.iloc[2,[9]][0], \n",
    "           'spring': df.iloc[3,[9]][0],}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': vintage_years, 'year_act': act_years, 'node_loc': 'South'}\n",
    "cf_data = {'summer': 0.01, \n",
    "           'autumn': 0.22, \n",
    "           'winter': 0.33, \n",
    "           'spring': 0.33,}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data, cf_filters)\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': vintage_years, 'year_act': act_years, 'node_loc': 'North'}\n",
    "cf_data = {'summer': 0.01, \n",
    "           'autumn': 0.22, \n",
    "           'winter': 0.33, \n",
    "           'spring': 0.33,}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data, cf_filters)\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': vintage_years, 'year_act': act_years, 'node_loc': 'Northeast'}\n",
    "cf_data = {'summer': 0.01, \n",
    "           'autumn': 0.22, \n",
    "           'winter': 0.33, \n",
    "           'spring': 0.33,}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data, cf_filters)\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': vintage_years, 'year_act': act_years, 'node_loc': 'Southeast'}\n",
    "cf_data = {'summer': 0.01, \n",
    "           'autumn': 0.22, \n",
    "           'winter': 0.33, \n",
    "           'spring': 0.33,}\n",
    "yearly_to_season(scen, 'capacity_factor', cf_data, cf_filters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#technical potencial wind \n",
    "\n",
    "cf_filters1= {'technology': 'wind_ppl_on', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data1= {'summer': 309., \n",
    "           'autumn':309., \n",
    "           'winter': 309., \n",
    "           'spring': 309.,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters2= {'technology': 'wind_ppl_on', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data2= {'summer': 103., \n",
    "           'autumn':103., \n",
    "           'winter': 103., \n",
    "           'spring': 103.,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters3= {'technology': 'wind_ppl_on', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data3= {'summer': 0, \n",
    "           'autumn': 0, \n",
    "           'winter': 0, \n",
    "           'spring': 0,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters4= {'technology': 'wind_ppl_on', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4= {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters5= {'technology': 'wind_ppl_off', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data5= {'summer': 500., \n",
    "           'autumn': 500., \n",
    "           'winter': 500., \n",
    "           'spring': 500.,}\n",
    "for tec, val in cf_data5.items():\n",
    "    df = make_df(cf_filters5, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters6= {'technology': 'wind_ppl_off', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data6= {'summer': 0., \n",
    "           'autumn': 0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data6.items():\n",
    "    df = make_df(cf_filters6, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters7= {'technology': 'wind_ppl_off', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data7= {'summer': 0., \n",
    "           'autumn': 0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data7.items():\n",
    "    df = make_df(cf_filters7, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters8= {'technology': 'wind_ppl_off', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data8= {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data8.items():\n",
    "    df = make_df(cf_filters8, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pv potential boundaries\n",
    "\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data1 = {'summer': 50./2, \n",
    "           'autumn':50./2, \n",
    "           'winter': 50./2, \n",
    "           'spring': 50./2,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data2 = {'summer': 500./5, \n",
    "           'autumn':500./5, \n",
    "           'winter': 500./5, \n",
    "           'spring': 500./5,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data3 = {'summer': 300./3, \n",
    "           'autumn':300./3, \n",
    "           'winter': 300./3, \n",
    "           'spring': 300./3,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data4 = {'summer': 150./2, \n",
    "           'autumn':150./2, \n",
    "           'winter': 150./2, \n",
    "           'spring': 150./2,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#oil ppl\n",
    "\n",
    "cf_filters1 = {'technology': 'oil_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data1 = {'summer': 20., \n",
    "           'autumn':20., \n",
    "           'winter': 20., \n",
    "           'spring': 20.,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters2 = {'technology': 'oil_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data2 = {'summer': 10., \n",
    "           'autumn':10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters3 = {'technology': 'oil_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data3 = {'summer': 20., \n",
    "           'autumn':20., \n",
    "           'winter': 20., \n",
    "           'spring': 20.,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters4 = {'technology': 'oil_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4 = {'summer': 20., \n",
    "           'autumn': 20., \n",
    "           'winter': 20., \n",
    "           'spring': 20.,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coal ppl \n",
    "\n",
    "cf_filters1 = {'technology': 'coal_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data1 = {'summer': 5., \n",
    "           'autumn':5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters2 = {'technology': 'coal_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data2 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters3 = {'technology': 'coal_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data3 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters4 = {'technology': 'coal_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gas ppl \n",
    "\n",
    "cf_filters1 = {'technology': 'gas_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data1 = {'summer': 40., \n",
    "           'autumn':40., \n",
    "           'winter': 40., \n",
    "           'spring': 40.,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters2 = {'technology': 'gas_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data2 = {'summer': 30., \n",
    "           'autumn':30., \n",
    "           'winter': 30., \n",
    "           'spring': 30.,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters3 = {'technology': 'gas_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data3 = {'summer': 30., \n",
    "           'autumn':30., \n",
    "           'winter': 30., \n",
    "           'spring': 30.,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters4 = {'technology': 'gas_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4 = {'summer': 60., \n",
    "           'autumn':60., \n",
    "           'winter': 60., \n",
    "           'spring': 60.,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters5 = {'technology': 'nuc_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data5 = {'summer': 30./2, \n",
    "           'autumn':30./2, \n",
    "           'winter': 30./2, \n",
    "           'spring': 30./2,}\n",
    "for tec, val in cf_data5.items():\n",
    "    df = make_df(cf_filters5, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)   \n",
    "    \n",
    "cf_filters6 = {'technology': 'nuc_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data6 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data6.items():\n",
    "    df = make_df(cf_filters6, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)  \n",
    "\n",
    "cf_filters7 = {'technology': 'nuc_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data7 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data7.items():\n",
    "    df = make_df(cf_filters7, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)  \n",
    "\n",
    "cf_filters8 = {'technology': 'nuc_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data8 = {'summer': 0., \n",
    "           'autumn':0., \n",
    "           'winter': 0., \n",
    "           'spring': 0.,}\n",
    "for tec, val in cf_data8.items():\n",
    "    df = make_df(cf_filters8, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bio ppl \n",
    "\n",
    "cf_filters1 = {'technology': 'bio_ppl', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data1 = {'summer': 10., \n",
    "           'autumn':10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters2 = {'technology': 'bio_ppl', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data2 = {'summer': 5., \n",
    "           'autumn': 5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters3 = {'technology': 'bio_ppl', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data3 = {'summer': 5., \n",
    "           'autumn': 5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters4 = {'technology': 'bio_ppl', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4 = {'summer': 40., \n",
    "           'autumn':40., \n",
    "           'winter': 40., \n",
    "           'spring': 40.,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## growth new capacity up\n",
    "\n",
    "cf_filters = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "\n",
    "cf_filters1 = {'technology': 'wind_ppl_off', 'year_vtg': vintage_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data1 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data1.items():\n",
    "    df = make_df(cf_filters1, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "    \n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': vintage_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data2 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data2.items():\n",
    "    df = make_df(cf_filters2, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data3 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data3.items():\n",
    "    df = make_df(cf_filters3, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data4 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data4.items():\n",
    "    df = make_df(cf_filters4, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data5 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data5.items():\n",
    "    df = make_df(cf_filters5, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)\n",
    "    \n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': vintage_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data6 = {'summer': 0.2, \n",
    "           'autumn':0.2, \n",
    "           'winter': 0.2, \n",
    "           'spring': 0.2,}\n",
    "for tec, val in cf_data6.items():\n",
    "    df = make_df(cf_filters6, time=tec, value=val) \n",
    "    scen.add_par('growth_new_capacity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_filters = {'technology': 'pump_sphs_1', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data = {'summer': 10., \n",
    "           'autumn':10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_2', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data = {'summer': 10., \n",
    "           'autumn':10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_3', 'year_act': act_years, 'node_loc': 'Northeast', 'time': 'season'}\n",
    "cf_data = {'summer': 10., \n",
    "           'autumn': 10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters = {'technology': 'pump_sphs_4', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data = {'summer': 5., \n",
    "           'autumn': 5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "    \n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_7', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data = {'summer': 5., \n",
    "           'autumn': 5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_8', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data = {'summer': 5., \n",
    "           'autumn': 5., \n",
    "           'winter': 5., \n",
    "           'spring': 5.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters = {'technology': 'pump_sphs_9', 'year_act': act_years, 'node_loc': 'North', 'time': 'season'}\n",
    "cf_data = {'summer': 3., \n",
    "           'autumn': 3., \n",
    "           'winter': 3., \n",
    "           'spring': 3.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_10', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data = {'summer': 15., \n",
    "           'autumn': 15., \n",
    "           'winter': 15., \n",
    "           'spring': 15.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "    \n",
    "cf_filters = {'technology': 'pump_sphs_11', 'year_act': act_years, 'node_loc': 'South', 'time': 'season'}\n",
    "cf_data = {'summer': 10., \n",
    "           'autumn':10., \n",
    "           'winter': 10., \n",
    "           'spring': 10.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)\n",
    "\n",
    "cf_filters = {'technology': 'pump_sphs_12', 'year_act': act_years, 'node_loc': 'Southeast', 'time': 'season'}\n",
    "cf_data = {'summer': 3., \n",
    "           'autumn': 3., \n",
    "           'winter': 3., \n",
    "           'spring': 3.,}\n",
    "for tec, val in cf_data.items():\n",
    "    df = make_df(cf_filters, time=tec, value=val) \n",
    "    scen.add_par('bound_total_capacity_up', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### changing bound activity of hydropower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_1\n",
    "df = pd.read_excel('ree1_rcp85.xlsx')\n",
    "#dx = pd.read_excel('min_inflow_1981_26.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river1', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river1', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river1', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river1', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river1', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river1', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river1', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river1', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_2\n",
    "df = pd.read_excel('ree2_rcp85.xlsx')\n",
    "#dx = pd.read_excel('min_inflow_1981_26.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river2', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river2', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river2', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river2', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river2', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river2', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river2', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river2', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#River_3\n",
    "df = pd.read_excel('ree3_rcp85.xlsx')\n",
    "#dx = pd.read_excel('min_inflow_1981_26.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river3', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river3', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river3', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river3', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river3', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river3', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river3', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river3', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_4\n",
    "df = pd.read_excel('ree4_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river4', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river4', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river4', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river4', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river4', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river4', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river4', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river4', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_5\n",
    "df = pd.read_excel('ree5_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river5', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river5', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river5', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river5', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river5', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river5', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river5', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river5', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_6\n",
    "df = pd.read_excel('ree6_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river6', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river6', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river6', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river6', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river6', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river6', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river6', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river6', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_7\n",
    "df = pd.read_excel('ree7_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river7', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river7', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river7', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river7', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river7', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river7', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river7', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river7', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_8\n",
    "df = pd.read_excel('ree8_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river8', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river8', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river8', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river8', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river8', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river8', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river8', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river8', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_9\n",
    "df = pd.read_excel('ree9_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river9', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river9', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river9', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river9', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river9', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river9', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river9', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river9', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_10\n",
    "df = pd.read_excel('ree10_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river10', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river10', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river10', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river10', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river10', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river10', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river10', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river10', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_11\n",
    "df = pd.read_excel('ree11_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river11', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river11', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river11', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river11', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river11', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river11', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river11', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river11', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_12\n",
    "df = pd.read_excel('ree12_rcp85.xlsx')\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'river12', 'year_act': 2030}\n",
    "cf_data1 = {'summer': df.iloc[2,[1]][0], \n",
    "           'autumn': df.iloc[0,[1]][0], \n",
    "           'winter': df.iloc[3,[1]][0], \n",
    "           'spring': df.iloc[1,[1]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'river12', 'year_act': 2040,}\n",
    "cf_data2 = {'summer': df.iloc[2,[2]][0], \n",
    "           'autumn': df.iloc[0,[2]][0], \n",
    "           'winter': df.iloc[3,[2]][0], \n",
    "           'spring': df.iloc[1,[2]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'river12', 'year_act': 2050}\n",
    "cf_data3 = {'summer': df.iloc[2,[3]][0], \n",
    "           'autumn': df.iloc[0,[3]][0], \n",
    "           'winter': df.iloc[3,[3]][0], \n",
    "           'spring': df.iloc[1,[3]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'river12', 'year_act': 2060}\n",
    "cf_data4 = {'summer': df.iloc[2,[4]][0], \n",
    "           'autumn': df.iloc[0,[4]][0], \n",
    "           'winter': df.iloc[3,[4]][0], \n",
    "           'spring': df.iloc[1,[4]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'river12', 'year_act': 2070}\n",
    "cf_data5 = {'summer': df.iloc[2,[5]][0], \n",
    "           'autumn': df.iloc[0,[5]][0], \n",
    "           'winter': df.iloc[3,[5]][0], \n",
    "           'spring': df.iloc[1,[5]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'river12', 'year_act': 2080}\n",
    "cf_data6 = {'summer': df.iloc[2,[6]][0], \n",
    "           'autumn': df.iloc[0,[6]][0], \n",
    "           'winter': df.iloc[3,[6]][0], \n",
    "           'spring': df.iloc[1,[6]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'river12', 'year_act': 2090}\n",
    "cf_data7 = {'summer': df.iloc[2,[7]][0], \n",
    "           'autumn': df.iloc[0,[7]][0], \n",
    "           'winter': df.iloc[3,[7]][0], \n",
    "           'spring': df.iloc[1,[7]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data7, cf_filters7)\n",
    "#2100\n",
    "cf_filters8 = {'technology': 'river12', 'year_act': 2100}\n",
    "cf_data8 = {'summer': df.iloc[2,[8]][0], \n",
    "           'autumn': df.iloc[0,[8]][0], \n",
    "           'winter': df.iloc[3,[8]][0], \n",
    "           'spring': df.iloc[1,[8]][0],}\n",
    "yearly_to_season(scen, 'bound_activity_up', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investment costs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': 2030, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': 1100.*1E6, \n",
    "           'autumn': 1100.*1E6, \n",
    "           'winter': 1100.*1E6, \n",
    "           'spring': 1100.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': 2040, 'node_loc': 'South'}\n",
    "cf_data2 = {'summer': 1050.*1E6, \n",
    "           'autumn': 1050.*1E6, \n",
    "           'winter': 1050.*1E6, \n",
    "           'spring': 1050.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': 2050, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': 850.*1E6, \n",
    "           'autumn': 850.*1E6, \n",
    "           'winter': 850.*1E6, \n",
    "           'spring': 850.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': 2060, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': 640.*1E6, \n",
    "           'autumn': 640.*1E6, \n",
    "           'winter': 640.*1E6, \n",
    "           'spring': 640.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': 2070, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': 435.*1E6, \n",
    "           'autumn': 435.*1E6, \n",
    "           'winter': 435.*1E6, \n",
    "           'spring': 435.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': 2080, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': 230.*1E6, \n",
    "           'autumn': 230.*1E6, \n",
    "           'winter': 230.*1E6, \n",
    "           'spring': 230.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': 2090, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': 30.*1E6, \n",
    "           'autumn': 30.*1E6, \n",
    "           'winter': 30.*1E6, \n",
    "           'spring': 30.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': 2100, 'node_loc': 'South'}\n",
    "cf_data8 = {'summer': 821.*1E5, \n",
    "           'autumn': 821.*1E5, \n",
    "           'winter': 821.*1E5, \n",
    "           'spring': 821.*1E5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': 1100.*1E6, \n",
    "           'autumn': 1100.*1E6, \n",
    "           'winter': 1100.*1E6, \n",
    "           'spring': 1100.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data2 = {'summer': 1050.*1E6, \n",
    "           'autumn': 1050.*1E6, \n",
    "           'winter': 1050.*1E6, \n",
    "           'spring': 1050.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': 850.*1E6, \n",
    "           'autumn': 850.*1E6, \n",
    "           'winter': 850.*1E6, \n",
    "           'spring': 850.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': 640.*1E6, \n",
    "           'autumn': 640.*1E6, \n",
    "           'winter': 640.*1E6, \n",
    "           'spring': 640.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': 435.*1E6, \n",
    "           'autumn': 435.*1E6, \n",
    "           'winter': 435.*1E6, \n",
    "           'spring': 435.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': 230.*1E6, \n",
    "           'autumn': 230.*1E6, \n",
    "           'winter': 230.*1E6, \n",
    "           'spring': 230.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': 30.*1E6, \n",
    "           'autumn': 30.*1E6, \n",
    "           'winter': 30.*1E6, \n",
    "           'spring': 30.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data8 = {'summer': 821.*1E5, \n",
    "           'autumn': 821.*1E5, \n",
    "           'winter': 821.*1E5, \n",
    "           'spring': 821.*1E5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': 1100.*1E6, \n",
    "           'autumn': 1100.*1E6, \n",
    "           'winter': 1100.*1E6, \n",
    "           'spring': 1100.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 = {'summer': 1050.*1E6, \n",
    "           'autumn': 1050.*1E6, \n",
    "           'winter': 1050.*1E6, \n",
    "           'spring': 1050.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': 850.*1E6, \n",
    "           'autumn': 850.*1E6, \n",
    "           'winter': 850.*1E6, \n",
    "           'spring': 850.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': 640.*1E6, \n",
    "           'autumn': 640.*1E6, \n",
    "           'winter': 640.*1E6, \n",
    "           'spring': 640.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': 435.*1E6, \n",
    "           'autumn': 435.*1E6, \n",
    "           'winter': 435.*1E6, \n",
    "           'spring': 435.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': 230.*1E6, \n",
    "           'autumn': 230.*1E6, \n",
    "           'winter': 230.*1E6, \n",
    "           'spring': 230.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': 30.*1E6, \n",
    "           'autumn': 30.*1E6, \n",
    "           'winter': 30.*1E6, \n",
    "           'spring': 30.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': 821.*1E5, \n",
    "           'autumn': 821.*1E5, \n",
    "           'winter': 821.*1E5, \n",
    "           'spring': 821.*1E5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'solar_pv_ppl', 'year_vtg': 2030, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': 1100.*1E6, \n",
    "           'autumn': 1100.*1E6, \n",
    "           'winter': 1100.*1E6, \n",
    "           'spring': 1100.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'solar_pv_ppl', 'year_vtg': 2040, 'node_loc': 'North'}\n",
    "cf_data2 = {'summer': 1050.*1E6, \n",
    "           'autumn': 1050.*1E6, \n",
    "           'winter': 1050.*1E6, \n",
    "           'spring': 1050.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'solar_pv_ppl', 'year_vtg': 2050, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': 850.*1E6, \n",
    "           'autumn': 850.*1E6, \n",
    "           'winter': 850.*1E6, \n",
    "           'spring': 850.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'solar_pv_ppl', 'year_vtg': 2060, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': 640.*1E6, \n",
    "           'autumn': 640.*1E6, \n",
    "           'winter': 640.*1E6, \n",
    "           'spring': 640.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'solar_pv_ppl', 'year_vtg': 2070, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': 435.*1E6, \n",
    "           'autumn': 435.*1E6, \n",
    "           'winter': 435.*1E6, \n",
    "           'spring': 435.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'solar_pv_ppl', 'year_vtg': 2080, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': 230.*1E6, \n",
    "           'autumn': 230.*1E6, \n",
    "           'winter': 230.*1E6, \n",
    "           'spring': 230.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'solar_pv_ppl', 'year_vtg': 2090, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': 30.*1E6, \n",
    "           'autumn': 30.*1E6, \n",
    "           'winter': 30.*1E6, \n",
    "           'spring': 30.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'solar_pv_ppl', 'year_vtg': 2100, 'node_loc': 'North'}\n",
    "cf_data8 = {'summer': 821.*1E5, \n",
    "           'autumn': 821.*1E5, \n",
    "           'winter': 821.*1E5, \n",
    "           'spring': 821.*1E5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 = {'summer': 1200.*1E6*0.9, \n",
    "           'autumn': 1200.*1E6*0.9, \n",
    "           'winter': 1200.*1E6*0.9, \n",
    "           'spring': 1200.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': 950.*1E6*0.9, \n",
    "           'autumn': 950.*1E6*0.9, \n",
    "           'winter': 950.*1E6*0.9, \n",
    "           'spring': 850.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': 872.*1E6*0.95, \n",
    "           'autumn': 872.*1E6*0.95, \n",
    "           'winter': 872.*1E6*0.95, \n",
    "           'spring': 872.*1E6*0.95,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': 747.*1E6, \n",
    "           'autumn': 747.*1E6, \n",
    "           'winter': 747.*1E6, \n",
    "           'spring': 747.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': 623.*1E6, \n",
    "           'autumn': 623.*1E6, \n",
    "           'winter': 623.*1E6, \n",
    "           'spring': 623.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': 500.*1E6, \n",
    "           'autumn': 500.*1E6, \n",
    "           'winter': 500.*1E6, \n",
    "           'spring': 500.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': 376.*1E6, \n",
    "           'autumn': 376.*1E6, \n",
    "           'winter': 376.*1E6, \n",
    "           'spring': 376.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': 2030, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': 2040, 'node_loc': 'North'}\n",
    "cf_data2 = {'summer': 1200.*1E6*0.9, \n",
    "           'autumn': 1200.*1E6*0.9, \n",
    "           'winter': 1200.*1E6*0.9, \n",
    "           'spring': 1200.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': 2050, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': 950.*1E6*0.9, \n",
    "           'autumn': 950.*1E6*0.9, \n",
    "           'winter': 950.*1E6*0.9, \n",
    "           'spring': 850.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': 2060, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': 872.*1E6*0.95, \n",
    "           'autumn': 872.*1E6*0.95, \n",
    "           'winter': 872.*1E6*0.95, \n",
    "           'spring': 872.*1E6*0.95,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': 2070, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': 747.*1E6, \n",
    "           'autumn': 747.*1E6, \n",
    "           'winter': 747.*1E6, \n",
    "           'spring': 747.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': 2080, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': 623.*1E6, \n",
    "           'autumn': 623.*1E6, \n",
    "           'winter': 623.*1E6, \n",
    "           'spring': 623.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': 2090, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': 500.*1E6, \n",
    "           'autumn': 500.*1E6, \n",
    "           'winter': 500.*1E6, \n",
    "           'spring': 500.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': 2100, 'node_loc': 'North'}\n",
    "cf_data8 = {'summer': 376.*1E6, \n",
    "           'autumn': 376.*1E6, \n",
    "           'winter': 376.*1E6, \n",
    "           'spring': 376.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters1 = {'technology': 'wind_ppl_on', 'year_vtg': 2030, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'wind_ppl_on', 'year_vtg': 2040, 'node_loc': 'South'}\n",
    "cf_data2 = {'summer': 1200.*1E6*0.9, \n",
    "           'autumn': 1200.*1E6*0.9, \n",
    "           'winter': 1200.*1E6*0.9, \n",
    "           'spring': 1200.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'wind_ppl_on', 'year_vtg': 2050, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': 950.*1E6*0.9, \n",
    "           'autumn': 950.*1E6*0.9, \n",
    "           'winter': 950.*1E6*0.9, \n",
    "           'spring': 850.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'wind_ppl_on', 'year_vtg': 2060, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': 872.*1E6*0.95, \n",
    "           'autumn': 872.*1E6*0.95, \n",
    "           'winter': 872.*1E6*0.95, \n",
    "           'spring': 872.*1E6*0.95,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'wind_ppl_on', 'year_vtg': 2070, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': 747.*1E6, \n",
    "           'autumn': 747.*1E6, \n",
    "           'winter': 747.*1E6, \n",
    "           'spring': 747.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'wind_ppl_on', 'year_vtg': 2080, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': 623.*1E6, \n",
    "           'autumn': 623.*1E6, \n",
    "           'winter': 623.*1E6, \n",
    "           'spring': 623.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'wind_ppl_on', 'year_vtg': 2090, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': 500.*1E6, \n",
    "           'autumn': 500.*1E6, \n",
    "           'winter': 500.*1E6, \n",
    "           'spring': 500.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'wind_ppl_on', 'year_vtg': 2100, 'node_loc': 'South'}\n",
    "cf_data8 = {'summer': 376.*1E6, \n",
    "           'autumn': 376.*1E6, \n",
    "           'winter': 376.*1E6, \n",
    "           'spring': 376.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2020\n",
    "cf_filters1 = {'technology': 'wind_ppl_off', 'year_vtg': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': 3100.*1E6, \n",
    "           'autumn': 3100.*1E6, \n",
    "           'winter': 3100.*1E6, \n",
    "           'spring': 3100.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2030\n",
    "cf_filters2 = {'technology': 'wind_ppl_off', 'year_vtg': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data2 = {'summer': 3100.*1E6*0.9, \n",
    "           'autumn': 3100.*1E6*0.9, \n",
    "           'winter': 3100.*1E6*0.9, \n",
    "           'spring': 3100.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2040\n",
    "cf_filters3 = {'technology': 'wind_ppl_off', 'year_vtg': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': 2500.*1E6*0.9, \n",
    "           'autumn': 2500.*1E6*0.9, \n",
    "           'winter': 2500.*1E6*0.9, \n",
    "           'spring': 2500.*1E6*0.9,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2050\n",
    "cf_filters4 = {'technology': 'wind_ppl_off', 'year_vtg': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': 1900.*1E6, \n",
    "           'autumn': 1900.*1E6, \n",
    "           'winter': 1900.*1E6, \n",
    "           'spring': 1900.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2060\n",
    "cf_filters5 = {'technology': 'wind_ppl_off', 'year_vtg': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': 1400.*1E6, \n",
    "           'autumn': 1400.*1E6, \n",
    "           'winter': 1400.*1E6, \n",
    "           'spring': 1400.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2070\n",
    "cf_filters6 = {'technology': 'wind_ppl_off', 'year_vtg': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': 860.*1E6, \n",
    "           'autumn': 860.*1E6, \n",
    "           'winter': 860.*1E6, \n",
    "           'spring': 860.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2080\n",
    "cf_filters7 = {'technology': 'wind_ppl_off', 'year_vtg': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': 320.*1E6, \n",
    "           'autumn': 320.*1E6, \n",
    "           'winter': 320.*1E6, \n",
    "           'spring': 320.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)\n",
    "#2090\n",
    "cf_filters8 = {'technology': 'wind_ppl_off', 'year_vtg': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data8 = {'summer': 880.*1E5, \n",
    "           'autumn': 880.*1E5, \n",
    "           'winter': 880.*1E5, \n",
    "           'spring': 800.*1E5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data8, cf_filters8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'nuc_ppl', 'year_vtg': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data = {'summer': 5000.*1E6*1.1, \n",
    "           'autumn': 5000.*1E6*1.1, \n",
    "           'winter': 5000.*1E6*1.1, \n",
    "           'spring': 5000.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'nuc_ppl', 'year_vtg': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': 5000.*1E6*1.1, \n",
    "           'autumn': 5000.*1E6*1.1, \n",
    "           'winter': 5000.*1E6*1.1, \n",
    "           'spring': 5000.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'nuc_ppl', 'year_vtg': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data2 = {'summer': 5000.*1E6*1.2, \n",
    "           'autumn': 5000.*1E6*1.2, \n",
    "           'winter': 5000.*1E6*1.2, \n",
    "           'spring': 5000.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'nuc_ppl', 'year_vtg': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': 5000.*1E6*1.3, \n",
    "           'autumn': 5000.*1E6*1.3, \n",
    "           'winter': 5000.*1E6*1.3, \n",
    "           'spring': 5000.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'nuc_ppl', 'year_vtg': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': 5000.*1E6*2, \n",
    "           'autumn': 5000.*1E6*2, \n",
    "           'winter': 5000.*1E6*2, \n",
    "           'spring': 5000.*1E6*2,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'nuc_ppl', 'year_vtg': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': 5000.*1E6*2.5, \n",
    "           'autumn': 5000.*1E6*2.5, \n",
    "           'winter': 5000.*1E6*2.5, \n",
    "           'spring': 5000.*1E6*2.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'nuc_ppl', 'year_vtg': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': 5000.*1E6*3, \n",
    "           'autumn': 5000.*1E6*3, \n",
    "           'winter': 5000.*1E6*3, \n",
    "           'spring': 5000.*1E6*3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'nuc_ppl', 'year_vtg': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': 5000.*1E6*3.7, \n",
    "           'autumn': 5000.*1E6*3.7, \n",
    "           'winter': 5000.*1E6*3.7, \n",
    "           'spring': 5000.*1E6*3.7,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'oil_ppl', 'year_vtg': 2030, 'node_loc': 'North'}\n",
    "cf_data = {'summer': 35.*1E6*1.2, \n",
    "           'autumn': 35.*1E6*1.2, \n",
    "           'winter': 35.*1E6*1.2, \n",
    "           'spring': 35.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'oil_ppl', 'year_vtg': 2040, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': 35.*1E6*1.5, \n",
    "           'autumn': 35.*1E6*1.5, \n",
    "           'winter': 35.*1E6*1.5, \n",
    "           'spring': 35.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'oil_ppl', 'year_vtg': 2050, 'node_loc': 'North'}\n",
    "cf_data2 = {'summer': 35.*1E6*2, \n",
    "           'autumn': 35.*1E6*2, \n",
    "           'winter': 35.*1E6*2, \n",
    "           'spring': 35.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'oil_ppl', 'year_vtg': 2060, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': 35.*1E6*3, \n",
    "           'autumn': 35.*1E6*3, \n",
    "           'winter': 35.*1E6*3, \n",
    "           'spring': 35.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'oil_ppl', 'year_vtg': 2070, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': 35.*1E6*4, \n",
    "           'autumn': 35.*1E6*4, \n",
    "           'winter': 35.*1E6*4, \n",
    "           'spring': 35.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'oil_ppl', 'year_vtg': 2080, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': 35.*1E6*5, \n",
    "           'autumn': 35.*1E6*5, \n",
    "           'winter': 35.*1E6*5, \n",
    "           'spring': 35.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'oil_ppl', 'year_vtg': 2090, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': 35.*1E6*6, \n",
    "           'autumn': 35.*1E6*6, \n",
    "           'winter': 35.*1E6*6, \n",
    "           'spring': 35.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'oil_ppl', 'year_vtg': 2100, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': 35.*1E6*7, \n",
    "           'autumn': 35.*1E6*7, \n",
    "           'winter': 35.*1E6*7, \n",
    "           'spring': 35.*1E6*7,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'oil_ppl', 'year_vtg': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data = {'summer': 35.*1E6*1.2, \n",
    "           'autumn': 35.*1E6*1.2, \n",
    "           'winter': 35.*1E6*1.2, \n",
    "           'spring': 35.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'oil_ppl', 'year_vtg': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': 35.*1E6*1.5, \n",
    "           'autumn': 35.*1E6*1.5, \n",
    "           'winter': 35.*1E6*1.5, \n",
    "           'spring': 35.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'oil_ppl', 'year_vtg': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data2 = {'summer': 35.*1E6*2, \n",
    "           'autumn': 35.*1E6*2, \n",
    "           'winter': 35.*1E6*2, \n",
    "           'spring': 35.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'oil_ppl', 'year_vtg': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': 35.*1E6*3, \n",
    "           'autumn': 35.*1E6*3, \n",
    "           'winter': 35.*1E6*3, \n",
    "           'spring': 35.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'oil_ppl', 'year_vtg': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': 35.*1E6*4, \n",
    "           'autumn': 35.*1E6*4, \n",
    "           'winter': 35.*1E6*4, \n",
    "           'spring': 35.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'oil_ppl', 'year_vtg': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': 35.*1E6*5, \n",
    "           'autumn': 35.*1E6*5, \n",
    "           'winter': 35.*1E6*5, \n",
    "           'spring': 35.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'oil_ppl', 'year_vtg': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': 35.*1E6*6, \n",
    "           'autumn': 35.*1E6*6, \n",
    "           'winter': 35.*1E6*6, \n",
    "           'spring': 35.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'oil_ppl', 'year_vtg': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': 35.*1E6*7, \n",
    "           'autumn': 35.*1E6*7, \n",
    "           'winter': 35.*1E6*7, \n",
    "           'spring': 35.*1E6*7,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'oil_ppl', 'year_vtg': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data = {'summer': 35.*1E6*1.2, \n",
    "           'autumn': 35.*1E6*1.2, \n",
    "           'winter': 35.*1E6*1.2, \n",
    "           'spring': 35.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'oil_ppl', 'year_vtg': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': 35.*1E6*1.5, \n",
    "           'autumn': 35.*1E6*1.5, \n",
    "           'winter': 35.*1E6*1.5, \n",
    "           'spring': 35.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'oil_ppl', 'year_vtg': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data2 = {'summer': 35.*1E6*2, \n",
    "           'autumn': 35.*1E6*2, \n",
    "           'winter': 35.*1E6*2, \n",
    "           'spring': 35.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'oil_ppl', 'year_vtg': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': 35.*1E6*3, \n",
    "           'autumn': 35.*1E6*3, \n",
    "           'winter': 35.*1E6*3, \n",
    "           'spring': 35.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'oil_ppl', 'year_vtg': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': 35.*1E6*4, \n",
    "           'autumn': 35.*1E6*4, \n",
    "           'winter': 35.*1E6*4, \n",
    "           'spring': 35.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'oil_ppl', 'year_vtg': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': 35.*1E6*5, \n",
    "           'autumn': 35.*1E6*5, \n",
    "           'winter': 35.*1E6*5, \n",
    "           'spring': 35.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'oil_ppl', 'year_vtg': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': 35.*1E6*6, \n",
    "           'autumn': 35.*1E6*6, \n",
    "           'winter': 35.*1E6*6, \n",
    "           'spring': 35.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'oil_ppl', 'year_vtg': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': 35.*1E6*7, \n",
    "           'autumn': 35.*1E6*7, \n",
    "           'winter': 35.*1E6*7, \n",
    "           'spring': 35.*1E6*7,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'oil_ppl', 'year_vtg': 2030, 'node_loc': 'South'}\n",
    "cf_data = {'summer': 35.*1E6*1.2, \n",
    "           'autumn': 35.*1E6*1.2, \n",
    "           'winter': 35.*1E6*1.2, \n",
    "           'spring': 35.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'oil_ppl', 'year_vtg': 2040, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': 35.*1E6*1.5, \n",
    "           'autumn': 35.*1E6*1.5, \n",
    "           'winter': 35.*1E6*1.5, \n",
    "           'spring': 35.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'oil_ppl', 'year_vtg': 2050, 'node_loc': 'South'}\n",
    "cf_data2 = {'summer': 35.*1E6*2, \n",
    "           'autumn': 35.*1E6*2, \n",
    "           'winter': 35.*1E6*2, \n",
    "           'spring': 35.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'oil_ppl', 'year_vtg': 2060, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': 35.*1E6*3, \n",
    "           'autumn': 35.*1E6*3, \n",
    "           'winter': 35.*1E6*3, \n",
    "           'spring': 35.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'oil_ppl', 'year_vtg': 2070, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': 35.*1E6*4, \n",
    "           'autumn': 35.*1E6*4, \n",
    "           'winter': 35.*1E6*4, \n",
    "           'spring': 35.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'oil_ppl', 'year_vtg': 2080, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': 35.*1E6*5, \n",
    "           'autumn': 35.*1E6*5, \n",
    "           'winter': 35.*1E6*5, \n",
    "           'spring': 35.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'oil_ppl', 'year_vtg': 2090, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': 35.*1E6*6, \n",
    "           'autumn': 35.*1E6*6, \n",
    "           'winter': 35.*1E6*6, \n",
    "           'spring': 35.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'oil_ppl', 'year_vtg': 2100, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': 35.*1E6*7, \n",
    "           'autumn': 35.*1E6*7, \n",
    "           'winter': 35.*1E6*7, \n",
    "           'spring': 35.*1E6*7,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2020\n",
    "cf_filters = {'technology': 'gas_ppl', 'year_vtg': 2030, 'node_loc': 'North'}\n",
    "cf_data = {'summer': 25.*1E6*1.1, \n",
    "           'autumn': 25.*1E6*1.1, \n",
    "           'winter': 25.*1E6*1.1, \n",
    "           'spring': 25.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'gas_ppl', 'year_vtg': 2040, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': 25.*1E6*1.2, \n",
    "           'autumn': 25.*1E6*1.2, \n",
    "           'winter': 25.*1E6*1.2, \n",
    "           'spring': 25.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'gas_ppl', 'year_vtg': 2050, 'node_loc': 'North'}\n",
    "cf_data2 = {'summer': 25.*1E6*1.3, \n",
    "           'autumn': 25.*1E6*1.3, \n",
    "           'winter': 25.*1E6*1.3, \n",
    "           'spring': 25.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'gas_ppl', 'year_vtg': 2060, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': 25.*1E6*1.5, \n",
    "           'autumn': 25.*1E6*1.5, \n",
    "           'winter': 25.*1E6*1.5, \n",
    "           'spring': 25.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'gas_ppl', 'year_vtg': 2070, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': 25.*1E6*2, \n",
    "           'autumn': 25.*1E6*2, \n",
    "           'winter': 25.*1E6*2, \n",
    "           'spring': 25.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'gas_ppl', 'year_vtg': 2080, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': 25.*1E6*3, \n",
    "           'autumn': 25.*1E6*3, \n",
    "           'winter': 25.*1E6*3, \n",
    "           'spring': 25.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'gas_ppl', 'year_vtg': 2090, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': 25.*1E6*4, \n",
    "           'autumn': 25.*1E6*4, \n",
    "           'winter': 25.*1E6*4, \n",
    "           'spring': 25.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'gas_ppl', 'year_vtg': 2100, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': 25.*1E6*5, \n",
    "           'autumn': 25.*1E6*5, \n",
    "           'winter': 25.*1E6*5, \n",
    "           'spring': 25.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2020\n",
    "cf_filters = {'technology': 'gas_ppl', 'year_vtg': 2030, 'node_loc': 'Northeast'}\n",
    "cf_data = {'summer': 25.*1E6*1.1, \n",
    "           'autumn': 25.*1E6*1.1, \n",
    "           'winter': 25.*1E6*1.1, \n",
    "           'spring': 25.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'gas_ppl', 'year_vtg': 2040, 'node_loc': 'Northeast'}\n",
    "cf_data1 = {'summer': 25.*1E6*1.2, \n",
    "           'autumn': 25.*1E6*1.2, \n",
    "           'winter': 25.*1E6*1.2, \n",
    "           'spring': 25.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'gas_ppl', 'year_vtg': 2050, 'node_loc': 'Northeast'}\n",
    "cf_data2 = {'summer': 25.*1E6*1.3, \n",
    "           'autumn': 25.*1E6*1.3, \n",
    "           'winter': 25.*1E6*1.3, \n",
    "           'spring': 25.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'gas_ppl', 'year_vtg': 2060, 'node_loc': 'Northeast'}\n",
    "cf_data3 = {'summer': 25.*1E6*2, \n",
    "           'autumn': 25.*1E6*2, \n",
    "           'winter': 25.*1E6*2, \n",
    "           'spring': 25.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'gas_ppl', 'year_vtg': 2070, 'node_loc': 'Northeast'}\n",
    "cf_data4 = {'summer': 25.*1E6*3, \n",
    "           'autumn': 25.*1E6*3, \n",
    "           'winter': 25.*1E6*3, \n",
    "           'spring': 25.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'gas_ppl', 'year_vtg': 2080, 'node_loc': 'Northeast'}\n",
    "cf_data5 = {'summer': 25.*1E6*4, \n",
    "           'autumn': 25.*1E6*4, \n",
    "           'winter': 25.*1E6*4, \n",
    "           'spring': 25.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'gas_ppl', 'year_vtg': 2090, 'node_loc': 'Northeast'}\n",
    "cf_data6 = {'summer': 25.*1E6*5, \n",
    "           'autumn': 25.*1E6*5, \n",
    "           'winter': 25.*1E6*5, \n",
    "           'spring': 25.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'gas_ppl', 'year_vtg': 2100, 'node_loc': 'Northeast'}\n",
    "cf_data7 = {'summer': 25.*1E6*6, \n",
    "           'autumn': 25.*1E6*6, \n",
    "           'winter': 25.*1E6*6, \n",
    "           'spring': 25.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2020\n",
    "cf_filters = {'technology': 'gas_ppl', 'year_vtg': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data = {'summer': 25.*1E6*1.1, \n",
    "           'autumn': 25.*1E6*1.1, \n",
    "           'winter': 25.*1E6*1.1, \n",
    "           'spring': 25.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'gas_ppl', 'year_vtg': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': 25.*1E6*1.2, \n",
    "           'autumn': 25.*1E6*1.2, \n",
    "           'winter': 25.*1E6*1.2, \n",
    "           'spring': 25.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'gas_ppl', 'year_vtg': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data2 = {'summer': 25.*1E6*1.3, \n",
    "           'autumn': 25.*1E6*1.3, \n",
    "           'winter': 25.*1E6*1.3, \n",
    "           'spring': 25.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'gas_ppl', 'year_vtg': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': 25.*1E6*1.5, \n",
    "           'autumn': 25.*1E6*1.5, \n",
    "           'winter': 25.*1E6*1.5, \n",
    "           'spring': 25.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'gas_ppl', 'year_vtg': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': 25.*1E6*2, \n",
    "           'autumn': 25.*1E6*2, \n",
    "           'winter': 25.*1E6*2, \n",
    "           'spring': 25.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'gas_ppl', 'year_vtg': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': 25.*1E6*3, \n",
    "           'autumn': 25.*1E6*3, \n",
    "           'winter': 25.*1E6*3, \n",
    "           'spring': 25.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'gas_ppl', 'year_vtg': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': 25.*1E6*4, \n",
    "           'autumn': 25.*1E6*4, \n",
    "           'winter': 25.*1E6*4, \n",
    "           'spring': 25.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'gas_ppl', 'year_vtg': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': 25.*1E6*5, \n",
    "           'autumn': 25.*1E6*5, \n",
    "           'winter': 25.*1E6*5, \n",
    "           'spring': 25.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2020\n",
    "cf_filters = {'technology': 'gas_ppl', 'year_vtg': 2030, 'node_loc': 'South'}\n",
    "cf_data = {'summer': 25.*1E6*1.1, \n",
    "           'autumn': 25.*1E6*1.1, \n",
    "           'winter': 25.*1E6*1.1, \n",
    "           'spring': 25.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'gas_ppl', 'year_vtg': 2040, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': 25.*1E6*1.2, \n",
    "           'autumn': 25.*1E6*1.2, \n",
    "           'winter': 25.*1E6*1.2, \n",
    "           'spring': 25.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'gas_ppl', 'year_vtg': 2050, 'node_loc': 'South'}\n",
    "cf_data2 = {'summer': 25.*1E6*1.3, \n",
    "           'autumn': 25.*1E6*1.3, \n",
    "           'winter': 25.*1E6*1.3, \n",
    "           'spring': 25.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'gas_ppl', 'year_vtg': 2060, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': 25.*1E6*2, \n",
    "           'autumn': 25.*1E6*2, \n",
    "           'winter': 25.*1E6*2, \n",
    "           'spring': 25.*1E6*2,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'gas_ppl', 'year_vtg': 2070, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': 25.*1E6*3, \n",
    "           'autumn': 25.*1E6*3, \n",
    "           'winter': 25.*1E6*3, \n",
    "           'spring': 25.*1E6*3,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'gas_ppl', 'year_vtg': 2080, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': 25.*1E6*4, \n",
    "           'autumn': 25.*1E6*4, \n",
    "           'winter': 25.*1E6*4, \n",
    "           'spring': 25.*1E6*4,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'gas_ppl', 'year_vtg': 2090, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': 25.*1E6*5, \n",
    "           'autumn': 25.*1E6*5, \n",
    "           'winter': 25.*1E6*5, \n",
    "           'spring': 25.*1E6*5,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'gas_ppl', 'year_vtg': 2100, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': 25.*1E6*6, \n",
    "           'autumn': 25.*1E6*6, \n",
    "           'winter': 25.*1E6*6, \n",
    "           'spring': 25.*1E6*6,}\n",
    "yearly_to_season(scen, 'var_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': 2030, 'node_loc': 'North'}\n",
    "cf_data = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'bio_ppl', 'year_vtg': 2040, 'node_loc': 'North'}\n",
    "cf_data1 = {'summer': 1200.*1E6*1.1, \n",
    "           'autumn': 1200.*1E6*1.1, \n",
    "           'winter': 1200.*1E6*1.1, \n",
    "           'spring': 1200.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'bio_ppl', 'year_vtg': 2050, 'node_loc': 'North'}\n",
    "cf_data2 = {'summer': 1200.*1E6*1.2, \n",
    "           'autumn': 1200.*1E6*1.2, \n",
    "           'winter': 1200.*1E6*1.2, \n",
    "           'spring': 1200.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'bio_ppl', 'year_vtg': 2060, 'node_loc': 'North'}\n",
    "cf_data3 = {'summer': 1200.*1E6*1.3, \n",
    "           'autumn': 1200.*1E6*1.3, \n",
    "           'winter': 1200.*1E6*1.3, \n",
    "           'spring': 1200.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'bio_ppl', 'year_vtg': 2070, 'node_loc': 'North'}\n",
    "cf_data4 = {'summer': 1200.*1E6*1.4, \n",
    "           'autumn': 1200.*1E6*1.4, \n",
    "           'winter': 1200.*1E6*1.4, \n",
    "           'spring': 1200.*1E6*1.4,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'bio_ppl', 'year_vtg': 2080, 'node_loc': 'North'}\n",
    "cf_data5 = {'summer': 1200.*1E6*1.5, \n",
    "           'autumn': 1200.*1E6*1.5, \n",
    "           'winter': 1200.*1E6*1.5, \n",
    "           'spring': 1200.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'bio_ppl', 'year_vtg': 2090, 'node_loc': 'North'}\n",
    "cf_data6 = {'summer': 1200.*1E6*1.6, \n",
    "           'autumn': 1200.*1E6*1.6, \n",
    "           'winter': 1200.*1E6*1.6, \n",
    "           'spring': 1200.*1E6*1.6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'bio_ppl', 'year_vtg': 2100, 'node_loc': 'North'}\n",
    "cf_data7 = {'summer': 1200.*1E6*1.7, \n",
    "           'autumn': 1200.*1E6*1.7, \n",
    "           'winter': 1200.*1E6*1.7, \n",
    "           'spring': 1200.*1E6*1.7,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': 2030, 'node_loc': 'Southeast'}\n",
    "cf_data = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'bio_ppl', 'year_vtg': 2040, 'node_loc': 'Southeast'}\n",
    "cf_data1 = {'summer': 1200.*1E6*1.1, \n",
    "           'autumn': 1200.*1E6*1.1, \n",
    "           'winter': 1200.*1E6*1.1, \n",
    "           'spring': 1200.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'bio_ppl', 'year_vtg': 2050, 'node_loc': 'Southeast'}\n",
    "cf_data2 = {'summer': 1200.*1E6*1.2, \n",
    "           'autumn': 1200.*1E6*1.2, \n",
    "           'winter': 1200.*1E6*1.2, \n",
    "           'spring': 1200.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'bio_ppl', 'year_vtg': 2060, 'node_loc': 'Southeast'}\n",
    "cf_data3 = {'summer': 1200.*1E6*1.3, \n",
    "           'autumn': 1200.*1E6*1.3, \n",
    "           'winter': 1200.*1E6*1.3, \n",
    "           'spring': 1200.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'bio_ppl', 'year_vtg': 2070, 'node_loc': 'Southeast'}\n",
    "cf_data4 = {'summer': 1200.*1E6*1.3, \n",
    "           'autumn': 1200.*1E6*1.3, \n",
    "           'winter': 1200.*1E6*1.3, \n",
    "           'spring': 1200.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'bio_ppl', 'year_vtg': 2080, 'node_loc': 'Southeast'}\n",
    "cf_data5 = {'summer': 1200.*1E6*1.5, \n",
    "           'autumn': 1200.*1E6*1.5, \n",
    "           'winter': 1200.*1E6*1.5, \n",
    "           'spring': 1200.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'bio_ppl', 'year_vtg': 2090, 'node_loc': 'Southeast'}\n",
    "cf_data6 = {'summer': 1200.*1E6*1.5, \n",
    "           'autumn': 1200.*1E6*1.5, \n",
    "           'winter': 1200.*1E6*1.5, \n",
    "           'spring': 1200.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'bio_ppl', 'year_vtg': 2100, 'node_loc': 'Southeast'}\n",
    "cf_data7 = {'summer': 1200.*1E6*1.5, \n",
    "           'autumn': 1200.*1E6*1.5, \n",
    "           'winter': 1200.*1E6*1.5, \n",
    "           'spring': 1200.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2030\n",
    "cf_filters = {'technology': 'bio_ppl', 'year_vtg': 2030, 'node_loc': 'South'}\n",
    "cf_data = {'summer': 1200.*1E6, \n",
    "           'autumn': 1200.*1E6, \n",
    "           'winter': 1200.*1E6, \n",
    "           'spring': 1200.*1E6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data, cf_filters)\n",
    "#2030\n",
    "cf_filters1 = {'technology': 'bio_ppl', 'year_vtg': 2040, 'node_loc': 'South'}\n",
    "cf_data1 = {'summer': 1200.*1E6*1.1, \n",
    "           'autumn': 1200.*1E6*1.1, \n",
    "           'winter': 1200.*1E6*1.1, \n",
    "           'spring': 1200.*1E6*1.1,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data1, cf_filters1)\n",
    "#2040\n",
    "cf_filters2 = {'technology': 'bio_ppl', 'year_vtg': 2050, 'node_loc': 'South'}\n",
    "cf_data2 = {'summer': 1200.*1E6*1.2, \n",
    "           'autumn': 1200.*1E6*1.2, \n",
    "           'winter': 1200.*1E6*1.2, \n",
    "           'spring': 1200.*1E6*1.2,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data2, cf_filters2)\n",
    "#2050\n",
    "cf_filters3 = {'technology': 'bio_ppl', 'year_vtg': 2060, 'node_loc': 'South'}\n",
    "cf_data3 = {'summer': 1200.*1E6*1.3, \n",
    "           'autumn': 1200.*1E6*1.3, \n",
    "           'winter': 1200.*1E6*1.3, \n",
    "           'spring': 1200.*1E6*1.3,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data3, cf_filters3)\n",
    "#2060\n",
    "cf_filters4 = {'technology': 'bio_ppl', 'year_vtg': 2070, 'node_loc': 'South'}\n",
    "cf_data4 = {'summer': 1200.*1E6*1.4, \n",
    "           'autumn': 1200.*1E6*1.4, \n",
    "           'winter': 1200.*1E6*1.4, \n",
    "           'spring': 1200.*1E6*1.4,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data4, cf_filters4)\n",
    "#2070\n",
    "cf_filters5 = {'technology': 'bio_ppl', 'year_vtg': 2080, 'node_loc': 'South'}\n",
    "cf_data5 = {'summer': 1200.*1E6*1.5, \n",
    "           'autumn': 1200.*1E6*1.5, \n",
    "           'winter': 1200.*1E6*1.5, \n",
    "           'spring': 1200.*1E6*1.5,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data5, cf_filters5)\n",
    "#2080\n",
    "cf_filters6 = {'technology': 'bio_ppl', 'year_vtg': 2090, 'node_loc': 'South'}\n",
    "cf_data6 = {'summer': 1200.*1E6*1.6, \n",
    "           'autumn': 1200.*1E6*1.6, \n",
    "           'winter': 1200.*1E6*1.6, \n",
    "           'spring': 1200.*1E6*1.6,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data6, cf_filters6)\n",
    "#2090\n",
    "cf_filters7 = {'technology': 'bio_ppl', 'year_vtg': 2100, 'node_loc': 'South'}\n",
    "cf_data7 = {'summer': 1200.*1E6*1.7, \n",
    "           'autumn': 1200.*1E6*1.7, \n",
    "           'winter': 1200.*1E6*1.7, \n",
    "           'spring': 1200.*1E6*1.7,}\n",
    "yearly_to_season(scen, 'inv_cost', cf_data7, cf_filters7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.commit(comment='introducing new values for rcp85')\n",
    "scen.set_as_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scen.check_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "311154507776.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scen.var('OBJ')['lvl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#scen.to_excel('BES_rcp85.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Postprocessing and reporting\n",
    "There are different ways to report MESSAGEix model results and visualize them. Here, we provide you with a ready-made postprocessing and plotting module. The results can be seen in three formats:\n",
    "- An Excel file of the results will be created in the same folder as input data\n",
    "- A PDF file of the results will be created in the same folder as input data\n",
    "- You can see the results here in Jupyter Notebook after running the cell below\n",
    "- You can see the results in tables via the variable \"results\" below.\n",
    "\n",
    "*Notice*: the model results are extensive and not all of them are visualized here. You can learn more about MESSAGEix reporting in [this tutorial](https://github.com/iiasa/message_ix/blob/master/tutorial/westeros/westeros_report.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyam.plotting import OUTSIDE_LEGEND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "output: mixed units ['-', 'm^3/a', 'GWa'] discarded\n",
      "output: mixed units ['-', 'm^3/a', 'GWa'] discarded\n",
      "output: mixed units ['-', 'm^3/a', 'GWa'] discarded\n",
      "pyam - INFO: Running in a notebook, setting up a basic logging at level INFO\n",
      "pyam.core - INFO: Reading file rcp85_bra.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import pyam\n",
    "from ixmp.reporting import configure\n",
    "from message_ix.reporting import Reporter\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "configure(units={'replace': {'-': 'GWa'}})\n",
    "\n",
    "rep = Reporter.from_scenario(scen)\n",
    "\n",
    "# plotting years\n",
    "plotyrs = [x for x in set(scen.set('year')) if x >= scen.firstmodelyear]\n",
    "\n",
    "rep.set_filters(c = 'electricity')\n",
    "elec = rep.full_key('out')\n",
    "elec = elec.drop('yv','m','nd', '')\n",
    "elec_gen = rep.get(elec)\n",
    "elec_gen\n",
    "elec_gen.to_csv('rcp85_bra.csv')\n",
    "\n",
    "rep.set_filters(c = 'electricity', hd = 'summer')\n",
    "elec = rep.full_key('out')\n",
    "elec = elec.drop('yv','m','nd','h')\n",
    "elec_gen = rep.get(elec)\n",
    "elec_gen\n",
    "\n",
    "\n",
    "def collapse_callback(df):\n",
    "    df['variable'] = 'Electricity Generation|' + df['l']+ '|'+df['t']\n",
    "    return df.drop(['t','l'], axis =1)\n",
    "\n",
    "    \n",
    "new_key = rep.convert_pyam(quantities=elec.drop('h', 'm', 'nd', 'yv'),\n",
    "                           rename=dict(nl=\"region\", ya=\"year\"),\n",
    "                           collapse=collapse_callback)\n",
    "    \n",
    "\n",
    "    \n",
    "df_elec = rep.get(new_key)\n",
    "df_elec.data.unit = 'GWa'\n",
    "df_elec.to_csv('rcp85_bra.csv')\n",
    "\n",
    "\n",
    "elec_gen = pd.read_csv(\"rcp85_bra.csv\")\n",
    "elec_gen.columns \n",
    "\n",
    "elec_gen = pyam.IamDataFrame(data='rcp85_bra.csv', encoding='ISO-8859-1')\n",
    "\n",
    "\n",
    "### North\n",
    "\n",
    "elec = elec_gen.filter(region=['North'], variable='Electricity Generation|secondary|*', year=plotyrs)\n",
    "elec.plot.bar(stacked=True)\n",
    "rename = {'bio_ppl': 'biomass',\n",
    "          'coal_ppl': 'coal',\n",
    "          'gas_ppl': 'natural gas',\n",
    "          'oil_ppl': 'diesel',\n",
    "          'solar_pv_ppl': 'solar',\n",
    "          'wind_ppl_on': 'wind_on',\n",
    "          'wind_ppl_off': 'wind_off',\n",
    "          'nuc_ppl': 'nuclear',\n",
    "          'pump_sphs_4': 'sphs_north',\n",
    "          'pump_sphs_8': 'sphs_belo_monte',\n",
    "          'pump_sphs_9': 'sphs_amazonas',\n",
    "          'hydro_4': 'hydro_north',\n",
    "          'hydro_8': 'hydro_belo_monte',\n",
    "          'hydro_9': 'hydro_amazonas',\n",
    "          'grid1': 'ext_n_ne'\n",
    "          }\n",
    "#elec.plot.bar(stacked=True)\n",
    "ax = plt.gca()\n",
    "h, lgs = ax.get_legend_handles_labels()\n",
    "ax.legend(labels=[rename[x.split(\"secondary|\")[1]] for x in lgs], bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"GWa\")\n",
    "plt.show()\n",
    "\n",
    "### Northeast\n",
    "elec = elec_gen.filter(region=['Northeast'], variable='Electricity Generation|secondary|*', year=plotyrs)\n",
    "elec.plot.bar(stacked=True)\n",
    "rename = {'bio_ppl': 'biomass',\n",
    "          'coal_ppl': 'coal',\n",
    "          'gas_ppl': 'natural gas',\n",
    "          'oil_ppl': 'diesel',\n",
    "          'solar_pv_ppl': 'solar',\n",
    "          'wind_ppl_on': 'wind_on',\n",
    "          'wind_ppl_off': 'wind_off',\n",
    "          'nuc_ppl': 'nuclear',\n",
    "          'hydro_3': 'hydro_northeast',\n",
    "          'pump_sphs_3': 'sphs_northeast',\n",
    "          'grid2': 'Northeast',\n",
    "          }\n",
    "#elec.plot.bar(stacked=True)\n",
    "ax = plt.gca()\n",
    "h, lgs = ax.get_legend_handles_labels()\n",
    "ax.legend(labels=[rename[x.split(\"secondary|\")[1]] for x in lgs], bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"GWa\")\n",
    "plt.show()\n",
    "\n",
    "### Southeast\n",
    "\n",
    "elec = elec_gen.filter(region=['Southeast'], variable='Electricity Generation|secondary|*', year=plotyrs)\n",
    "elec.plot.bar(stacked=True)\n",
    "rename = {'bio_ppl': 'biomass',\n",
    "          'coal_ppl': 'coal',\n",
    "          'gas_ppl': 'natural gas',\n",
    "          'oil_ppl': 'diesel',\n",
    "          'solar_pv_ppl': 'solar',\n",
    "          'wind_ppl_on': 'wind_on',\n",
    "          'wind_ppl_off': 'wind_off',\n",
    "          'nuc_ppl': 'nuclear',\n",
    "          'pump_sphs_1': 'sphs_southeast',\n",
    "          'pump_sphs_5': 'sphs_itaipu',\n",
    "          'pump_sphs_6': 'sphs_madeira',\n",
    "          'pump_sphs_7': 'sphs_teles_pires',\n",
    "          'pump_sphs_10': 'sphs_parana',\n",
    "          'pump_sphs_12': 'sphs_paranapanema',\n",
    "          'hydro_1': 'hydro_southeast',\n",
    "          'hydro_5': 'hydro_itaipu',\n",
    "          'hydro_6': 'hydro_madeira',\n",
    "          'hydro_7': 'hydro_teles_pires',\n",
    "          'hydro_10': 'hydro_parana',\n",
    "          'hydro_12': 'hydro_paranapanema',\n",
    "          'grid3': 'Southeast',\n",
    "          }\n",
    "#elec.plot.bar(stacked=True)\n",
    "ax = plt.gca()\n",
    "h, lgs = ax.get_legend_handles_labels()\n",
    "ax.legend(labels=[rename[x.split(\"secondary|\")[1]] for x in lgs], bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"GWa\")\n",
    "plt.show()\n",
    "\n",
    "### South\n",
    "\n",
    "elec = elec_gen.filter(region=['South'], variable='Electricity Generation|secondary|*', year=plotyrs)\n",
    "elec.plot.bar(stacked=True)\n",
    "rename = {'bio_ppl': 'biomass',\n",
    "          'coal_ppl': 'coal',\n",
    "          'gas_ppl': 'natural gas',\n",
    "          'oil_ppl': 'diesel',\n",
    "          'solar_pv_ppl': 'solar',\n",
    "           'wind_ppl_on': 'wind_on',\n",
    "          'wind_ppl_off': 'wind_off',\n",
    "          'nuc_ppl': 'nuclear',\n",
    "          'pump_sphs_2': 'sphs_south',\n",
    "          'pump_sphs_11': 'sphs_iguaçu',\n",
    "          'hydro_2': 'hydro_south',\n",
    "          'hydro_11': 'hydro_iguaçu',\n",
    "          'grid4': 'South',\n",
    "          }\n",
    "#elec.plot.bar(stacked=True)\n",
    "ax = plt.gca()\n",
    "h, lgs = ax.get_legend_handles_labels()\n",
    "ax.legend(labels=[rename[x.split(\"secondary|\")[1]] for x in lgs], bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"GWa\")\n",
    "plt.show()\n",
    "\n",
    "elec = elec_gen.filter(variable='Electricity Generation|final|*', year=plotyrs)\n",
    "elec.plot(legend=OUTSIDE_LEGEND['bottom'])\n",
    "rename = {'grid_n': 'N_in',\n",
    "          'grid_ne': 'NE_in',\n",
    "          'grid_s': 'S_in',\n",
    "          'grid_se': 'SE_in',\n",
    "           }\n",
    "#elec.plot.bar(stacked=True)\n",
    "ax = plt.gca()\n",
    "h, lgs = ax.get_legend_handles_labels()\n",
    "ax.legend(labels=[rename[x.split(\"final|\")[1]] for x in lgs], bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.ylabel(\"GWa\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep.set_filters(c = 'water_11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GWa')"
      ]
     },
     "execution_count": 68,
     "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": [
    "elec = elec_gen.filter(variable='Electricity Generation|secondary|hydro*', year=plotyrs)\n",
    "elec.plot(legend=OUTSIDE_LEGEND['bottom'])\n",
    "plt.ylabel(\"GWa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GWa')"
      ]
     },
     "execution_count": 69,
     "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": [
    "elec = elec_gen.filter(variable='Electricity Generation|secondary|pump_sphs*', year=plotyrs)\n",
    "elec.plot(legend=OUTSIDE_LEGEND['bottom'])\n",
    "plt.ylabel(\"GWa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GWa')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+8Ou9rqJ2BfB51PaQ7Zw7qYH5B8D89Uj34IuFLsGBfCH+TWpnpAZGsct2HNgPvx3vlnPuYefcsGAcDvh9xDzVN6+vRc1rlnNuYh397iIo6K/G3+CUF8xrGc2c16h9dncix1vfvNXaeSG9mWXhT+Gswp8GqnbOPRAUeV/ii6361nPtNBtz/fIN+Ms3vuOcK693Jvxdp7Xb70P19PYIcHqwbR2J31/2eFtr5PqLXHYp+DMIdd0ItgIYGbUdZTrnGrXdNsEK4MdR02nvnPtv0O3Aeoarbzk0tH3ubruqdxzOuXfwLSbfxBftU+sZdgl+e9/d8XhPlm9jho2chzPxp9pH4D8Q9Apetzr6rUv0MWw/6t5mdss5975z7jT86eTp+MuaoOH37BXAflb3jRZ1Zati14al+hSz6005O2uIPdgX/4W/DOMc4Enn3LYGpt0j4nn0zUEP44vHns65HGBK7bSdc5XOuRucc4X4Mx6nEHVjaD2i57USf4avvu5NWsctXQA+DpxsZscH1e2V+NOj/8WfQqsCLjGzdDMbi7+mqdY9wE+CKtrMrKP5iyob9Um/Abfjr296vSkDmb8o/0oz6xE874lvAXgneH6Z+Ytp25u/AP5c/PUa88xsX+B4/Eo+NPgbhC8Ealf61cAF5i9a7RaMswdwQHNm0syOxV9DNgFffN4R5GjMsF3wLZaLGujtFvxFx03+SgTzNw2dZmYd8dvDZnwLKMC9wM/MbEiw3nsHO/J7wCbzNxq0Dz4RD7Dga3h2oyN+Ry8Jpn8+dRTuDZgJFJjZmcG6/T6+sJ/RiHntY2bHmb/gehu+hTxyXn9rZgcH8zowWPYzgumdE+wb6WZ2uDXiZgz8NlcVzGuamV2Hv361sRraZ5viIWCEmX0vWGZdzOzQiO4nmb9JJAP4Lb4FegX+bn0LlnWKme2Fby3+AHZ+qu4TdOuC359fdc6VNRTGzK7Bv4mOcM6tb6jfxnD+Rql1+HX4vHOuNOi0p9taY9bfEDMbG7yZXoZfP+/UMa4pwO+C/Qczy7eor8JqIVOAa+yrmxJyzN9FC35b3js4PrYzs2z76iuw1gC9rGl3+tZ3fIhW37gfAO4EKiPPikQKWlWvBH5j/oaTvGBaB7Nrq9SeLN+mDpuNX8/r8ZcTRZ+dWUP9hTb4Dyy/DqbTFX8tdZO/Jsf8152dZWY5zrlK/GVNtcezht6z38MXTLcEr2ea2TER2S43swPMfxi8CXisntbCaI/jt7284P1yUkS35u6LD+Lf/86m4Yaix4HzzayfmXXAXz4TKRvY4JzbZmZH4I8/BFm+bWaHmFkqfhlW8tVybMjZZlYYTO9GfIFaHdH9WjPrEOyL5wOPNWKcO7VoAeicW4JfiHfgD5ajgFHOuR3OuR34T1jn4U8ZfB9/8WftsLPx19bciW9J+yTo92vM7JtmtrmRmTY4516u7/QPcLR9/buvDsdfG3ck8K6ZbcEfcBfiDxTgm43/hG+qXoe/HnCcc+4z/CeJ+c7fNby69g//5jXQzAYEB6Pj8NdG/s98c/Us/Hn/OxqRL3J5dMJvuD91zq0MWvnuA+4326XFtc75xrcalrDrzhTtWfx6ubCBfuqTAlyB/3SyAX+NyEQA59wT+JbLh/HLfDrQOdjIawvoz/nqzTdndxNzzn2EXzdv4w+Uh+Cv1WyUoGA4Bb+u1+OL9VOcc+tg5ynv5+oZvB2+WF6H3za64a+PA3+n4ePAC/iDwH34ayI3Ad8BzsAvo9X4DwvtGhH3efx28z/8KYBt7OaUY9S81rvPNnYcwXiW41vtrsSv4/ns+nVGDwO/CboNCaZJ0DI3Fn9z2MZguIX4a3jBv8nNwm8bC/Fvij9g927CfyL+JGK/+WVT5qkOD+NbZB6ufWFPtzUat/6ewh8vN+KPLWODN+Nof8G3QLxgZpvwx6yd3z9q/lKQr53ijrBPHceacdE9OeeK8Nvno+ZPTy7Et7QSbMsn4Lej1cBS4NvBoE8E/683s12+Mqs+9R0f6ui1vnFPxRcBDRY/zrnH8K2wZ/PVddSP428krB13g8t3N5o67AP47WEl8BFfL/jvAwrNn+qcXsfw/w9/PdsH+BvP5vLVPtVU5wDLgnX9E/zlKQ2+ZwfH71H4U6rL8ZctfD8Y3z/w6+V1/LF9Gw2/90S6Ab9cPscfR3e26jZ3Xww+iM7FF49fO0MW0d9z+Pfw/+DntXad1F5WcDH+zuJN+IL78YjB98Jfv1yOf799jfpbpCNNxZ95XI2/ROaSqO6vBVleBm51zjXpxwys/rpIRGTPmdk/8Rdq/zrsLJJczKw9/gaHw1zwdS7SMPO/JnK9c254uElix/xXpq2KPkaZ2TL8DXHL6himH/7DT7tGtl42NdOr+Bs5vvarTeZ/neRz/I2GzZ52In8RtIiIJLeJwPsq/qQ+QTE1Ft+qurt+xwSXNuThW8GfaY3iL1aS/puwReTrzGw//KmnuhQGp3xD1cBlICNdHTc7SXIJWm8M/x2Y0njL8KcdE56Z/RZ/+cnNzrnP6+hlMv7rgGr9GL9sqvGnX+u8MbIJ06/3GLYn42309HUKWERERCS56BSwiIiISJJRASgiIiKSZHQNoLS6rl27ul69eoUdQ0SkzZgzZ84651x+2DkkcakAlFbXq1cvZs+eHXYMEZE2w8yif45SpEXpFLCIiIhIklEBKCIiIpJkVACKiIiIJBkVgCIiIiJJRgWgiIiISJJRASgiIiKSZFQAioiIiCQZfQ+giMS18hdeYOUVV0JVVdhRRJqs70eLsBS1tUj8UQEoInFt49QHScvPJ3fMmLCjiIgkDBWAIhK3dqxYQcX775N/2aV0/clPwo4jIpIw1C4tInGrrGg6mJFz2mlhRxERSSgqAEUkLrmaGsqmT6fj0UeTvvfeYccREUkoKgBFJC5VvPc+latWkaNr/0REWpwKQBGJS2VF00jJyiL7hBFhRxERSTgqAEUk7lRv3kz58y/Q6aSTSMnMDDuOiEjCUQEoInFn06xZuG3byB2r078iIq1BBaCIxJ3SoulkHHAAmYMGhR1FRCQhqQAUkbiy44sv2DpnDjljxmBmYccREUlIKgBFJK6UFhVBSgo5p50adhQRkYSlAlBE4oarrqZs+lN0POYY0rt3DzuOiEjCUgEoInFjyzvvULV6tW7+EBFpZSoARSRulBVNJ6VTJ7KOOy7sKCIiCU0FoIjEhepNm9j04ot0OvkkUtq1CzuOiEhCUwEoInGhfOZzuO3byR07NuwoIiIJTwWgiMSFsqIiMnofROaAAWFHERFJeCoARSR02z/7nK3z55M7Zqy++09EJAZUAEqdzOwfZrbWzBZGvNbZzF40s6XB/3lhZpTEUVZUBKmpdBp1SthRRESSggpAqc8/gROjXvsF8LJz7mDg5eC5yB5x1dWUPfUUWcOGkd6tW9hxRESSggpAqZNz7nVgQ9TLpwH/Ch7/Cxgdy0ySmLb8979UrV1Ljm7+EBGJGRWA0hTdnXPFwePVgH6qQfZYWVERqTk5ZH17eNhRRESShgpAaRbnnANcfd3N7CIzm21ms0tKSmKYTNqS6rIyNr30Mp1GjSIlIyPsOCIiSUMFoDTFGjPbGyD4f219PTrn/u6cG+qcG5qfnx+zgNK2lM+ciduxg5wxo8OOIiKSVFQASlM8DZwbPD4XeCrELJIASoum066ggMzCwrCjiIgkFRWAUiczewR4G+hjZl+a2Q+BW4ATzGwpMCJ4LtIs2z/5hG0ffEDO2DH67j8RkRhLCzuAxCfn3A/q6XR8TINIwiotKoK0NHJGjQo7iohI0lELoIjEnKuqouzpp8k69ljSunQJO46ISNJRASgiMbf5zTepLllH7tgxYUcREUlKKgBFJObKiqaTmpdH1rHHhh1FRCQpqQAUkZiq2riRza+8Qs6pozB995+ISChUAIpITJU/OxNXWUnOGJ3+FREJiwpAEYmpsqIi2vXrR2bfvmFHERFJWioARSRmti35H9sWLSJXrX8iIqFSASgiMVNWVATp6XQadUrYUUREkpoKQBGJCVdZSdkzz5A9fDhpeXlhxxERSWoqAEUkJja/8QbV69fr5g8RkTigAlBEYqKsqIjULl3I+uawsKOIiCQ9FYAi0uqqNmxg039eJefUU7H09LDjiIgkPRWAItLqymfMgKoqcsaMDjuKiIigAlBEYqC0aDqZAwaQWVAQdhQREUEFoIi0sm0ff8z2jz9W65+ISBxRASgiraq0qAhLTyfn5JPDjiIiIgEVgCLSatyOHZQ/M4Os448nNTc37DgiIhJQASgirWbTa69RvXEjuTr9KyISV1QAikirKSuaTlp+Ph2POSbsKCIiEkEFoIi0iqp169j82mvknHYqlpYWdhwREYmgAlBEWkXZMzOgulo//SYiEodUAIpIi3POUTZtGpmDBtLuoIPCjiMiIlFUAIpIi9u26CO2L11Krlr/RETikgpAEWlxZUVFWEYGnUaODDuKiIjUQQWgiLSomh07KJ8xg+wRI0jNyQk7joiI1EEFoIi0qM2v/IfqsjLd/CEiEsdUAEqTmdnlZrbIzBaa2SNmlhl2JokfZUVFpHXvTsdvHB12FBERqYcKQGkSM9sXuAQY6pwbAKQCZ4SbSuJF5dq1bH7jDXJOOw1LTQ07joiI1EMFoDRHGtDezNKADsCqkPNInCh/5hmoqSFHP/0mIhLXVABKkzjnVgK3AsuBYqDMOfdCuKkkHjjnKJ1WRPvBg2l3wAFhxxERkQaoAJQmMbM84DTgAGAfoKOZnV1HfxeZ2Wwzm11SUhLrmBKCbR9+yI5PP1Xrn4hIG6ACUJpqBPC5c67EOVcJTAO+Ed2Tc+7vzrmhzrmh+fn5MQ8psVdaVIRlZuq7/0RE2gAVgNJUy4GjzKyDmRlwPPBxyJkkZDXbt1P+7EyyTziB1OzssOOIiMhuqACUJnHOvQs8CcwFPsRvQ38PNZSEbvPLL1NTXk6uTv+KiLQJaWEHkLbHOfcb4Ddh55D4UTqtiLS996bDUUeFHUVERBpBLYAiskcq16xhy3//S87o07AUHVJERNoCHa1FZI+UPfU01NSQq59+ExFpM1QAikizOecomzaN9kOHkLHffmHHERGRRlIBKCLNtnX+fHYsW6bWPxGRNkYFoIg0W9m0Iqx9e7K/e2LYUUREpAlUAIpIs9Rs3Ur5c8/R6TvfITWrY9hxRESkCVQAikizbHrpZWo2byZn7Niwo4iISBOpABSRZikrmkb6vvvS4fChYUcREZEmUgEoIk1WuWoVW95+h5zRo/XdfyIibZCO3CLSZGVPPQXOkaOffhMRaZNUAIpIkzjnKC2aTocjjiCjR4+w44iISDOoABSRJtk6dy6Vy5eTo+/+ExFps1QAikiTlE6bRkqHDnT67nfCjiIiIs2kAlBEGq2mooJNz80i+8QTSenQIew4IiLSTCoARaTRNr34IjUVFeSO1elfEZG2TAWgiDRa6bQi0nv2pP2QIWFHERGRPaACUEQaZceXK6l4911yxozGzMKOIyIie0AFoIg0Stn06WBG7ujRYUcREZE9pAJQRHbL1dRQNn06HY46kvR99gk7joiI7CEVgCKyWxWzZ1P55Zfkjh0bdhQREWkBKgBFZLfKphWR0rEj2SNGhB1FRERagApAEWlQzZYtlL/wAp1OGklK+/ZhxxERkRagAlBEGlQ+63lcRQU5Y3T6V0QkUagAFJEGlRUVkdGrF+0HHxp2FBERaSEqAEWkXjuWL6di9mxyxozRd/+JiCQQFYAiUq/a7/7LOe3UsKOIiEgLUgEoTWZmuWb2pJktNrOPzezosDNJy3M1NZROn07Hb3yD9L32CjuOiIi0oLSwA0hsmFkecDCQWfuac+71Zo7uL8As59zpZpYBdGiBiBJnKt59l6pVxXS78sqwo4iISAtTAZgEzOxHwKVAD2A+cBTwNnBcM8aVAxwLnAfgnNsB7GihqBJHSouKSMnOJvv448OOIiIiLUyngJPDpcDhwBfOuW8Dg4HSZo7rAKAEuN/M5pnZvWbWsWViSryo3ryZTS+8SKeTTyIlM3P3A4iISJuiAjA5bHPObQMws3bOucVAn2aOKw04DLjbOTcY2AL8IronM7vIzGab2eySkpLm5paQlD/3HG7bNnLHjAk7ioiItAIVgMnhSzPLBaYDL5rZU8AXzR0X8KVz7t3g+ZP4gnAXzrm/O+eGOueG5ufnN3NSEpayoulkHHggmQMHhh1FRERaga4BTGBmdiiwwDlX24xzvZn9B8gBZjVnnM651Wa2wsz6OOeWAMcDH7VIYIkLO5YtY+vcuXT72ZX67j8RkQSlAjCx3QscaGZzgP8CbwFvO+c27eF4JwEPBXcAfwacv4fjkzhSWjQdUlLoNErf/ScikqhUACYw59xQM+sAHAF8A7gEmGpmq4G3nHMXN3O884GhLRZU4oarrqbsqafoOOwY0rt3CzuOiIi0El0DmOCccxXOuVfx3913G/BXoCNwYpi5JD5tefsdqlavJnfs2LCjiIhIK1ILYAIzszPxLX+HAtuB94F3gWHOudUhRpM4VVZUREpODlnf/nbYUUREpBWpAExsfwOWAFOA151z/ws5j8Sx6vJyNr30ErnjxpHSrl3YcUREpBWpAExsucAgfCvg9WbWByjG/wrI2865V0LMJnGmfOZzuO3bydF3/4mIJDwVgAnMOVcNzA3+7jSz7sB44DLgRiA1vHQSb8qKimh3cG8yB/QPO4qIiLQyFYAJzMwG4lv/av8y8K1/d+C/EkYEgO2ffsrWBQvodvXV+u4/EZEkoAIwsf0TeBN4Dvi1c255uHEkXpVNnw6pqeSMOiXsKCIiEgP6GpjEdiPwP+fcI8655Wb2rpl9FvydHnY4iQ/+u/+eJuvYY0nTz/aJiCQFFYCJ7SrgqYjn7YDDgeHAxDACSfzZ8tZbVK1dS86Y0WFHERGRGNEp4MSW4ZxbEfH8TefcemC9mXUMK5TEl9KiIlJzc8kePjzsKCIiEiNqAUxseZFPnHM/jXiqc31CdWkpm196mU6jRmEZGWHHERGRGFEBmNjeNbMLo180sx8D74WQR+JM2cyZuMpKcnX6V0QkqegUcGK7HJge/CTc3OC1IfhrAUeHFUriR1nRdNr17UtmYWHYUUREJIZUACYw59xa4BtmdhxQ++2+z+oXQARg+9KlbPvwQ7pf84uwo4iISIypAEwCQcGnok92UVo0HdLS6DRqVNhRREQkxnQNoEgSclVVlD39NFnDv0Va585hxxERkRhTASiShDa/8QbV69aRO2ZM2FFERCQEKgBFklBZ0XRSO3cm69hjw44iIiIhUAEokmS2f/Y5m156iZzRo7H09LDjiIhICFQAiiSZkttvxzIz6fLDC8KOIiIiIVEBKJJEti5cxKZZs+hy3nmkdekSdhwREQmJCkCRJFJy222k5ubS+YLzw44iIiIhUgEokiS2vPMuW956iy4//jGpWVlhxxERkRCpABRJAs451t72Z9L22ou8M38QdhwREQmZCkCRJLD55ZfZtuAD8n/6f6S0axd2HBERCZkKQJEE56qrWTt5MhkHHEDO6NFhxxERkTigAlCaxcxSzWyemc0IO4s0rOyZZ9jxyafkX3oplqaf/xYRERWA0nyXAh+HHUIaVrNjB+tuv4PM/v3J/u53wo4jIiJxQgWgNJmZ9QBOBu4NO4s0rPSxx6lctYr8yy/HzMKOIyIicUIFoDTHZOBqoCbkHNKAmi1bWHf33XQ48kg6HvONsOOIiEgcUQEoTWJmpwBrnXNzdtPfRWY228xml5SUxCidRNrwwANUb9hAt8svU+ufiIjsQgWgNNUxwKlmtgx4FDjOzB6M7sk593fn3FDn3ND8/PxYZ0x6VRs3sv6+f5A14njaH3po2HFERCTOqACUJnHOXeOc6+Gc6wWcAbzinDs75FgSZf0991KzZQvdLr007CgiIhKHVACKJJjK1avZ+NBD5Jx2Gu0OPjjsOCIiEof0pWDSbM65V4FXQ44hUdb99S5cTQ1df/rTsKOIiEicUgugSALZ/vnnlE6bRt73v09Gj33DjiMiInFKBaBIAim5/XasXTu6/uTHYUcREZE4pgJQJEFsXbSITc/NovO5E0jr2jXsOCIiEsdUAIokiJLbJpOak0OXCy4IO4qIiMQ5FYAiCWDLe++x5c036XLRRaRmZ4cdR0RE4pwKQJE2zjlHyZ9vI617d/LOOjPsOCIi0gaoABRp4zb/5z9snT+frv93MSmZmWHHERGRNkAFoEgb5qqrKbltMhn770/u2LFhxxERkTZCBaBIG1Y+Ywbbly4l/7JLsTR9r7uIiDSOCkCRNsrt2EHJHXfSrrAf2d/9bthxRESkDVGTgUgbtfGJJ6j88kt63nMPlqLPciIi0nh61xBpg2oqKlh39xQ6HH44HYcdE3YcERFpY9QCKNIGbXhgKtXr1pF/x+2YWdhxRESkjVELoEgbU11ayvr77iPruOPoMHhw2HFERKQNUgEo0sasv/deajZvJv+yS8OOIiIibZQKQJE2pHLNGjZMfZCcU0eRWVAQdhwREWmjVACKtCHr7robV1ND10mTwo4iIiJtmApAkTZix7JllD75JHnf+x4ZPXqEHUdERNowFYAibUTJ7XdgGRl0nfiTsKOIiEgbpwJQpA3Y9vHHlM+cSedzJ5DWtWvYcUREpI1TASjSBqy97TZScnLocsEFYUcREZEEoAJQJM5VvP8+W15/g64XXUhqp05hxxERkQSgAlAkjjnnWPvn20jr1o28s84KO46IiCQIFYAicWzzq6+ydd48ul58MSmZmWHHERGRBKECUCROuZoaSm6bTPr++5E7bmzYcUREJIGoABSJU+XPPsv2//2P/EsuwdLTw44jIiIJRAWgSBxyO3ZQcvsdtOvXj04jR4YdR0REEowKQGkSM+tpZv8xs4/MbJGZXRp2pkS08cknqVyxgm6XX4alaDcVEZGWlRZ2AGlzqoArnXNzzSwbmGNmLzrnPgo7WKKoqahg3d1302HoUDp+85thxxERkQSkpgVpEudcsXNubvB4E/AxsG+4qRLLhqkPUl2yjvwrrsDMwo4jIiIJSAWgNJuZ9QIGA+/W0e0iM5ttZrNLSkpinq2tqi4rY/1995E1fDgdDhscdhwREUlQKgClWcwsC/g3cJlzrjy6u3Pu7865oc65ofn5+bEP2Eatv/c+ajZtIv/yy8KOIiIiCUwFoDSZmaXji7+HnHPTws6TKCrXrmXD1Kl0OuUUMvv0CTuOiIgkMBWA0iTmL0q7D/jYOffnsPMkknV3342rqiL/kklhRxERkQSnAlCa6hjgHOA4M5sf/J0Udqi2bsfy5ZQ+8SR53xtPRs+eYccREZEEp6+BkSZxzr0J6NbUFlZy+x1YejpdfvKTsKOIiEgSUAugSMi2LV5M+YwZdD7nHNK7dQs7joiIJAEVgCIhK7ltMimdOtHlhxeEHUVERJKECkCREFXMmcPm116jy4U/IjUnJ+w4IiKSJFQAioTEOcfaP99GWn4+nc8+O+w4IiKSRFQAioRky+uvs3XOHLr+38WktG8fdhwREUkiKgBFQuBqalh722TS99uP3HHjwo4jIiJJRgWgSAjKZz7H9sWLyb/kEiw9Pew4IiKSZFQAisSYq6yk5Pbbade3L51OGhl2HBERSUL6ImiRGCv997+pXL6cnn+bgqXoM5iIiMSe3n1EYqhm61bW/fUu2g8ZQsdjjw07joiIJCkVgCIxtOHBB6kqKaHbFZdjpl/UExGRcKgAFImR6rIy1t9zL1nf+hYdhgwJO46IiCQxFYAiMbL+vn9QU15O/uWXhR1FRESSnApAkRioXLuWDVOn0umUU8js2zfsOCIikuRUAIrEwPopU3CVleRfMinsKCIiIioARVrbjhUr2Pj4E+SOP52M/fYLO46IiIgKQJHWVnLHHVhaGl0nTgw7ioiICKACUKRVbVvyP8qfmUHnc84mvVu3sOOIiIgAKgBFWlXJ5MmkZGfT5Uc/CjuKiIjITioARVpJxdy5bP7Pf+jywx+SmpMTdhwREZGdVACKtALnHCV/vo3U/K50PufssOOIiIjsQgWgSCvY8uabVMyeTdeJE0np0CHsOCIiIrtQASjSwlxNDWv/fBvpPXuSd/rpYccRERH5GhWAIi1s06xZbP/4Y/IvmYRlZIQdR0RE5GtUAIq0IFdZydq//IV2BQV0OvnksOOIiIjUKS3sACKJpHRaEZVfLKfH3XdhKfp8JfHBOUf5tirKKirZWLGD0q2VlFbsoLSiks3bq8KO1yQdMlLJ65BBTod08jpkkNve/5+dmUZKioUdT6TNUAEoTWZmJwJ/AVKBe51zt4QcKS7UbNvGur/+lfaDB5M1fHjYcSQBOefYWlnNxopKNm7ZQdnWoKCr8AXdxorKnY9Lg25lFZWUbq2kusaFHb9VpRjkBMVgZHGY2yGDvA7p5Hbwj3NruwXPO2akYqbCUZKPCkBpEjNLBf4KnAB8CbxvZk875z4KN1n4Nj70EFVr17Lvn/+kNxTZre1V1UGxVlvE7QgeV1K6dQelW77eWldaUcmO6pp6x9khI/WroqdjOv326hQUOrVFT9Bi1jGdnPa+MMrKTMNoG9urw7Fle/XOYrds6w42Bstp12K4krWbtrFk9SZKK3awZUd1veNMT7Wdy6K2KMyLKBZzg261RWVt8ZiZnhrDORdpeSoApamOAD5xzn0GYGaPAqcBLV4A/vX64WzcurGlR9sqzMGJL+xg1QEp3LTgIlgQdiIJS207m3O+YPH/+9a7XR7vZjxmviwzg7yORpcswCAFw4yg266Po1VUQkUZrCprwRlsA9LTID/b/9WqXea7rJc6HpdthdKtULOu4TW0c/lDsA6CdRHxeP/0btxw1kzSUnU5iMQfFYDSVPsCKyKefwkcGd2TmV0EXASw3377NWtCg59bR15Z/Z/c4021wayjU6iobDuZpfXtUqgZpJgFBUL9hVzbaI9rW3Yu16/+aZSayMKxgSKyxjlcjX9ca8WmrTobIHFLBaC0Cufc34G/AwwdOrRZFx/l3/koVdu3t2iu1pTaMYuf7rV32DEkDnTMSCO3YzrZ7dJUACQZ5xxbdlSzccsOyrdVkqobUyROqQCUploJ9Ix43iN4rcUdfPiA1hitiEirMTOy2qWR1U5vrxLfdGGCNNX7wMFmdoCZZQBnAE+HnElERESaQB9RpEmcc1Vm9lPgefzXwPzDObco5FgiIiLSBCoApcmcczOBmWHnEBERkebRKWARERGRJKMCUERERCTJqAAUERERSTIqAEVERESSjDmX2D8QLuEzsxLgi2YO3hVY14JxWlNbygptK29bygptK29bygptK++eZN3fOZffkmFEIqkAlLhmZrOdc0PDztEYbSkrtK28bSkrtK28bSkrtK28bSmrJB+dAhYRERFJMioARURERJKMCkCJd38PO0ATtKWs0LbytqWs0LbytqWs0LbytqWskmR0DaCIiIhIklELoIiIiEiSUQEoIiIikmRUAEpMmVlPM/uPmX1kZovM7NLg9c5m9qKZLQ3+zwteP83MPjCz+WY228yGRYzr3KD/pWZ2bjzkjRjucDOrMrPTY5W3Gct2uJmVBct2vpldFzGuE81siZl9Yma/CDtrRN75Qf+vxSprc/Ka2VURy3WhmVWbWedY5G1G1hwze8bMFgT9nx8xrrjbx8wsz8yKguPCe2Y2IGJcYS3b8cHzGjMbGjXMNUGeJWb23VhlFdkt55z+9BezP2Bv4LDgcTbwP6AQ+APwi+D1XwC/Dx5n8dW1qgOBxcHjzsBnwf95weO8sPMGz1OBV4CZwOmxytuMZTscmFHHeFKBT4EDgQxgAVAYctZc4CNgv+B5t1hlbe52EDHsKOCVOF62v4x4nA9sCLLF5T4G/BH4TfC4L/ByHCzbfkAf4FVgaET/hUGOdsABQb7UWG23+tNfQ39qAZSYcs4VO+fmBo83AR8D+wKnAf8KevsXMDroZ7NzrvZOpY5A7ePvAi865zY45zYCLwInhp03MAn4N7A24rVWz9vMrHU5AvjEOfeZc24H8GgwjjCznglMc84tD4apXbatnrWZeSP9AHgkVnmbkdUB2WZm+A9cG4Aq4ncfK8R/wMI5txjoZWbdCXHZOuc+ds4tqWOQ04BHnXPbnXOfA58EOWOy3Yo0RAWghMbMegGDgXeB7s654qDTaqB7RH9jzGwx8CxwQfDyvsCKiNF9GbwWal4z2xcYA9wdNXhM8zZ22QJHB6f+njOz/nGctQDIM7NXzWyOmU0II2sT8tb22wFfNP07jLyNzHonvgVrFfAhcKlzribWWZuQdwEwNuj/CGB/oEes80ZlrU99mWK+bEWiqQCUUJhZFv5N8TLnXHlkt6DFz0U8L3LO9cW3APw2ljlrNSHvZODnwRtoKJqQdS7+90YHAXcA02OZE5qUNQ0YApyMb5m61swKYpkVmrbdBkYBbznnNsQo4k5NyPpdYD6wD3AocKeZdYpdUq8JeW8Bcs1sPr61fR5QHcOoDWYVaStUAErMmVk6/uD5kHNuWvDyGjPbO+i+N7uePgXAOfc6cKCZdQVWAj0jOvcIXgs771DgUTNbBpwO3GVmo2OVtylZnXPlzrnNweOZQHosl20Tl+uXwPPOuS3OuXXA68CgWGVtRt5aZ/DV6V9ilbeJWc/Hn153zrlPgM/x19bF5bINttvznXOHAhPw1y1+Fqu89WStT32ZYrZsReqjAlBiKrjO6D7gY+fcnyM6PQ3U3mV4LvBU0H/vYBjM7DD8xdTrgeeB7wR3BOYB3wleCzWvc+4A51wv51wv4EngYufc9Fjkbcay3Sti2R6BPx6sB94HDjazA8wsA1/EPB1m1uD/YWaWFpxWPRJ//VWrZ21mXswsB/hW5GuxyNuMrMuB44Nhu+NvZviMON3HzCw3WHYAPwJeD1rhwly29XkaOMPM2pnZAcDBwHuxyCqyW3XdGaI//bXWHzAMfyrnA/xpp/nASUAX4GVgKfAS0Dno/+fAoqC/t4FhEeO6AH9R9SfA+fGQN2rYfxLcBRyLvM1Ytj8Nlu0C4B3gGxHjOgl/h+OnwK/CzhoMcxX+TuCF+FNvMcm6B3nPw98AED2uuFq2+FO/L+Cv/1sInB3P+xhwdLD8lgDTiLgzOcRlOwbfSr0dWINvra4d5ldBniXAyFhut/rTX0N/+ik4ERERkSSjU8AiIiIiSUYFoIiIiEiSUQEoIiIikmRUAIqIiIgkGRWAIiIiIklGBaCIJBTz3jSzkRGvjTezWWHmEhGJJ/oaGBFJOGY2AHgC/1utafifCzvROfdpM8aV5pyrauGIIiKhUgEoIgnJzP4AbAE6Bv/vDwwA0oHrnXNPmVkvYGrQD8BPnXP/NbPh+N+d3gj0dc7F/HeHRURakwpAEUlIZtYRmAvsAGYAi5xzD5pZLv7nuAbjf9Whxjm3zcwOBh5xzg0NCsBngQHOuc/DyC8i0prSwg4gItIanHNbzOwxYDPwPWCUmf0s6JwJ7AesAu40s0OBaiCype89FX8ikqhUAIpIIqsJ/gwY55xbEtnRzK7H/3brIPxNcdsiOm+JUUYRkZjTXcAikgyeByaZmQGY2eDg9Ryg2DlXA5wDpIaUT0QkplQAikgy+C3+5o8PzGxR8BzgLuBcM1sA9EWtfiKSJHQTiIiIiEiSUQugiIiISJJRASgiIiKSZFQAioiIiCQZFYAiIiIiSUYFoIiIiEiSUQEoIiIikmRUAIqIiIgkGRWAIiIiIklGBaCIiIhIklEBKCIiIpJkVACKiIiIJBkVgCIiIiJJRgWgiIiISJJRASgiIiKSZFQAioiIiCQZFYAiIiIiSUYFoIiIiEiSUQEoIiIikmTSwg7QmubMmdMtLS3tXmAAKnZFklUNsLCqqupHQ4YMWRt2GBGReJDQBWBaWtq9e+21V7/8/PyNKSkpLuw8IhJ7NTU1VlJSUrh69ep7gVPDziMiEg8SvVVsQH5+frmKP5HklZKS4vLz88vwZwJERITELwBTVPyJSHAcSPTjnYhIo+mA2MrMbMiFF17Yo/b5dddd1/2KK67YpynjmDFjRvaLL77Ysfb5uHHjet1///15LZkz0f385z/fq3fv3v0LCgoK+/btW/jKK6903P1Qu0qm9dAWl9ftt9/eZdmyZemtNX4RkUSS0NcAxoOMjAw3c+bMvOLi4tV77713VVOHr6ys5JVXXsnOysqqPuGEE7a0RsZE99JLL3V8/vnncz/88MOP2rdv74qLi9O2b99uTR1PsqyHtrq8Hnzwwa6HHnro1l69elXGYnoiIm2ZWgBbWWpqqpswYULJTTfd1D2625IlSzKOOuqogoKCgsKjjz66YOnSpRngW0rOPPPM/QYOHNj35JNPPuiBBx7InzJlSve+ffsWzpo1Kwvgtddeyxo8eHDfHj16HJKorVAtZeXKlemdO3euat++vQPYe++9q3r16lX51FNPZffr16+woKCgcPz48b22bt1qAPvuu+8hxcXFaQCvv/56hyOOOKLPkiVLMpJlPYSxvK699truAwYM6FdQUFB4+eWX72whHzFixEH9+/fv17t37/633nprV4CqqirGjRvX6+CDD+5fUFBQeMMNN3S7//778xYuXNhhwoQJB/bt27dw8+bNTS5YRUSSSdK0AF715IKe/1u9qUNLjrNgr+yKP54+aMVup33VVWsPOeSQ/tdff/3qyNcnTpy431lnnbV+0qRJ6ydPntxl4sSJPV966aVPAYqLizPmzp27OC0tjSuuuGKfrKys6htvvHENwD333NN1zZo16bNnz148f/78zDFjxvQ+//zzN7bkvLWGa9+6tucnGz9p0XXQO693xW+P+W2D62D06NHlN9988z69evUaMGzYsPIf/OAHG7797W9v+fGPf3zACy+8sGTgwIHbx4wZ0+uPf/xj/nXXXVfn14T06dNnx4QJE0pivR5W/fJXPbcvXdqiy6zdwQdX7HPT7+pdZrFeXtOmTev0ySefZH7wwQcfO+cYMWJE7+eeey5r5MiRmx966KFl3bt3r968ebMNHjy48Oyzz964dOnSdsXFxelLly5dBLBu3brUrl27Vt99993dbr311hXHHntsRUsuLxGRRKQWwBjo3Llzzfjx49ffcsst3SJfnzdvXseLLrpoA8DEiRM3zJkzJ6u229ixYzempdVfn5966qmlqampDBkyZNv69et13VMDcnJyahYuXPjRnXfe+UV+fn7Vueeee9Cf/vSn/B49emwfOHDgdoDzzjtv/Ztvvpnd1HEn4nqI9fKaNWtWp9dff71TYWFhYf/+/Qs//fTTzMWLF2cC/P73v+/ep0+fwiFDhvRbvXp1+qJFizL79u27fcWKFe3OPffcnk8++WSnvLy86pZdAiIiiS9pWgAb01LXmq655po1hx12WOEZZ5yxrjH9Z2Vl1TTUPTMzc+fdzc61jRudd9dS15rS0tI45ZRTNp1yyimbBg4cuHXKlCn59fWbmprqamr84t+6dWuDH5Jaez001FLXmmK5vJxzXHbZZcVXXXXVLvvGjBkzsl977bXs2bNnL87Ozq454ogj+mzdujUlPz+/euHChR8VFRV1mjJlSv5jjz3W+Yknnli2B7MrIpJ01AIYI927d68eNWrUxocffrhr7WuDBw/ecu+99+YB/O1vf+s8dOjQzXUNm52dXb1p06bUWGVNNAsWLGj34Ycftqt9Pm/evPa9evXavnLlyoyFCxe2A3jggQe6fPOb39wE0KNHjx1vvfVWB4DHH39853VqybIeYr28Ro4cWT516tSuZWVlKQCff/55+sqVK9NKS0tTc3JyqrOzs2vmzZuXuWDBgo4AxcXFadXV1Zx33nmlN99888oPP/ywA0BWVlZ1WVlZwq8fEZGWoAIwhn71q1+tLi0t3dnqOmXKlOVTp07tWlBQUPjII490ueuuu+ps7Rk3blzps88+mxt5Mb00Xnl5eeqECRMOOOigg/oXFBQULl68uP3kyZNXTpkyZdn48eMPKigoKExJSeFnP/tZCcB111236uqrr95vwIAB/VJTU3e2WCXLeoj18ho7dmz5+PHjNxx++OF9CwoKCseMGXNQaWlp6rhx48qqqqrswAMP7H/VVVftO2jQoC0Ay5YtSx82bFifvn37Fp5zzjkH3njjjV8CTJgwYd2kSZP2100gIiK7Z23l9GFzLFiwYNmgQYMadcpVRBLbggULug4aNKhX2DlEROKBWgBFREREkowKQBEREZEkowJQREREJMmoABQRERFJMioARURERJKMCkARERGRJKMCsJWZ2ZALL7ywR+3z6667rvsVV1yxT0PDRJsxY0b2iy++2LH2+bhx43rdf//9eQ0Nsyduv/32LsuWLUuInzUTERGRr1MB2MoyMjLczJkz84qLi5v1s3uVlZW88sor2W+88UbMvnj4wQcf7Lp8+XIVgCIiIglKBWArS01NdRMmTCi56aabukd3W7JkScZRRx1VUFBQUHj00UcXLF26NAN8C9+ZZ56538CBA/uefPLJBz3wwAP5U6ZM6R75iwqvvfZa1uDBg/v26NHjkMjWwGuvvbb7gAED+hUUFBRefvnlO1saR4wYcVD//v379e7du/+tt97aFaCqqopx48b1Ovjgg/sXFBQU3nDDDd3uv//+vIULF3aYMGHCgfpFBRERkcTUrFapNmn6//Vk7UcdWnSc3QorGP3XOn++LdJVV1219pBDDul//fXXr458feLEifudddZZ6ydNmrR+8uTJXSZOnNjzpZde+hSguLg4Y+7cuYvT0tK44oor9snKyqq+8cYb1wDcc889XdesWZM+e/bsxfPnz88cM2ZM7/PPP3/jtGnTOn3yySeZH3zwwcfOOUaMGNH7ueeeyxo5cuTmhx56aFn37t2rN2/ebIMHDy48++yzNy5durRdcXFx+tKlSxcBrFu3LrVr167Vd999d7dbb711xbHHHlvRostLRERE4oJaAGOgc+fONePHj19/yy23dIt8fd68eR0vuuiiDQATJ07cMGfOnJ2neceOHbsxLa3++vzUU08tTU1NZciQIdvWr1+fDjBr1qxOr7/+eqfCwsLC/v37F3766aeZixcvzgT4/e9/371Pnz6FQ4YM6bd69er0RYsWZfbt23f7ihUr2p177rk9n3zyyU55eXnVrbIAREREJK4kTwtgI1rqWtM111yz5rDDDis844wzGvXbxFlZWTUNdc/MzNz5I861v+fsnOOyyy4rvuqqq3aZxowZM7Jfe+217NmzZy/Ozs6uOeKII/ps3bo1JT8/v3rhwoUfFRUVdZoyZUr+Y4891vmJJ55Y1vS5ExERkbZELYAx0r179+pRo0ZtfPjhh7vWvjZ48OAt9957bx7A3/72t85Dhw7dXNew2dnZ1Zs2bUrd3TRGjhxZPnXq1K5lZWUpAJ9//nn6ypUr00pLS1NzcnKqs7Oza+bNm5e5YMGCjgDFxcVp1dXVnHfeeaU333zzyg8//LADQFZWVnVZWdlupyciIiJtkwrAGPrVr361urS0dGer65QpU5ZPnTq1a0FBQeEjjzzS5a677qqzlXLcuHGlzz77bG7kTSB1GTt2bPn48eM3HH744X0LCgoKx4wZc1BpaWnquHHjyqqqquzAAw/sf9VVV+07aNCgLQDLli1LHzZsWJ++ffsWnnPOOQfeeOONXwJMmDBh3aRJk/bXTSAiIiKJyWpPHyaiBQsWLBs0aFCjTrmKSGJbsGBB10GDBvUKO4eISDxQC6CIiIhIklEBKCIiIpJkVACKiIiIJJlELwBrampqdBODSJILjgMNfrWSiEgySfQCcGFJSUmOikCR5FVTU2MlJSU5wMKws4iIxIuE/iLoqqqqH61evfre1atXDyDxi10RqVsNsLCqqupHYQcREYkXCf01MCIiIiLydWoVExEREUkyKgBFREREkowKQBEREZEkowJQREREJMmoABQRERFJMv8f6BYz+a5Y8uMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "elec = elec_gen.filter(variable='Electricity Generation|secondary|gas_ppl', year=plotyrs)\n",
    "elec.plot(legend=OUTSIDE_LEGEND['bottom'])\n",
    "plt.ylabel(\"GWa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GWa')"
      ]
     },
     "execution_count": 71,
     "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": [
    "elec = elec_gen.filter(variable='Electricity Generation|secondary|solar_pv_ppl', year=plotyrs)\n",
    "elec.plot(legend=OUTSIDE_LEGEND['bottom'])\n",
    "plt.ylabel(\"GWa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mp.close_db()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}