BES RCP 26_5.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_rcp26_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_26_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_26_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_26_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_26_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_26_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_26_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_26_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_26_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": [
    "# %% Introducing emissions\n",
    "\n",
    "# first we introduce the emission of CO2 and the emission category GHG\n",
    "scen.add_set('emission', 'CO2')\n",
    "scen.add_cat('emission', 'GHG', 'CO2')\n",
    "\n",
    "# we now add CO2 emissions to the coal powerplant\n",
    "base_emission_factor_s = {\n",
    "    'node_loc': 'South',\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'M1',\n",
    "    'unit': 'tCO2/GWa',\n",
    "}\n",
    "\n",
    "# adding new units to the model library (needed only once)\n",
    "mp.add_unit('tCO2/GWa')\n",
    "\n",
    "emiss_tech = {'coal_ppl': 0.814*10**6, #[tC/kWyr]\n",
    "             'gas_ppl': 0.482*10**6, #[tC/kWyr]\n",
    "              'oil_ppl': 0.57*10**6, #[tC/kWyr]\n",
    "                 }\n",
    "for tec, val in emiss_tech.items():\n",
    "    df = make_df(base_emission_factor_s, technology= tec , emission= 'CO2', value =val) #to convert tCO2/kWa into tCO2/GWa\n",
    "    scen.add_par('emission_factor', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_1\n",
    "df = pd.read_excel('ree1_rcp26.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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_2\n",
    "df = pd.read_excel('ree2_rcp26.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": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#River_3\n",
    "df = pd.read_excel('ree3_rcp26.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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_4\n",
    "df = pd.read_excel('ree4_rcp26.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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_5\n",
    "df = pd.read_excel('ree5_rcp26.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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_6\n",
    "df = pd.read_excel('ree6_rcp26.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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_7\n",
    "df = pd.read_excel('ree7_rcp26.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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_8\n",
    "df = pd.read_excel('ree8_rcp26.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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_9\n",
    "df = pd.read_excel('ree9_rcp26.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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_10\n",
    "df = pd.read_excel('ree10_rcp26.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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_11\n",
    "df = pd.read_excel('ree11_rcp26.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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#River_12\n",
    "df = pd.read_excel('ree12_rcp26.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": 41,
   "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": 42,
   "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": 43,
   "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": 44,
   "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": 45,
   "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": 46,
   "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": 47,
   "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": 48,
   "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": 49,
   "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": 50,
   "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": 51,
   "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": 52,
   "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": 53,
   "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": 54,
   "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": 55,
   "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": 56,
   "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": 57,
   "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": 58,
   "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": 59,
   "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": 60,
   "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.commit(comment='introducing new values for RCP26')\n",
    "scen.set_as_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scen.check_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "scen.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310984245248.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scen.var('OBJ')['lvl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#scen.to_excel('BES_rcp26.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mp.close_db()"
   ]
  }
 ],
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