tutorial/Austrian_energy_system/austria-add_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building an Energy Model\n",
    "\n",
    "In this notebook, we will build a model of the Austrian energy system from scratch. The process will involve defining our model's time horizon and spatial extent, and then populating the model with data associated with model parameters. Once we have a basline model, we will then move on to investigating policy scenarios.\n",
    "\n",
    "We will be populating different kinds of parameters including\n",
    "\n",
    "### Economic Parameters\n",
    "\n",
    "- `interestrate`\n",
    "- `demand`\n",
    "\n",
    "### Technology Parameters\n",
    "\n",
    "#### Engineering Parameters\n",
    "\n",
    "- `input`\n",
    "- `output`\n",
    "- `technical_lifetime`\n",
    "- `capacity_factor`\n",
    "\n",
    "\n",
    "#### Technoeconomic Parameters\n",
    "\n",
    "- `inv_cost`\n",
    "- `fix_cost`\n",
    "- `var_cost`\n",
    "\n",
    "### Dynamic Behavior Parameters\n",
    "\n",
    "- `bound_activity_up`\n",
    "- `bound_activity_lo`\n",
    "- `bound_new_capacity_up`\n",
    "- `initial_activity_up`\n",
    "- `growth_activity_up`\n",
    "\n",
    "### Emissions\n",
    "\n",
    "- `emission_factor`\n",
    "\n",
    "A full list of parameters can be found on the (internal) [MESSAGEix documentation website](http://ienecat.iiasa.ac.at:8787/message_ix_doc). This website can be built in the future from the repository that you are using. \n",
    "\n",
    "## The Final Product\n",
    "\n",
    "At the completion of this exercise, we will have developed an energy model is comprised of the below Reference Energy System (RES):\n",
    "\n",
    "![title](austria.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load required packages \n",
    "import itertools\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import ixmp as ix\n",
    "import message_ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "launching ixmp.Platform with local HSQLDB database at 'C:\\Users\\gu47zip\\.local\\ixmp\\localdb\\default'\n"
     ]
    }
   ],
   "source": [
    "# launch the IX modeling platform using the local default database                                                                                                                       \n",
    "mp = ix.Platform(dbtype='HSQLDB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = \"Austrian energy model\"\n",
    "scen = \"baseline\"\n",
    "annot = \"developing a stylized energy system model for illustration and testing\" \n",
    "\n",
    "scenario = mp.Scenario(model, scen, version='new', annotation=annot, scheme=\"MESSAGE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time and Spatial Detail\n",
    "\n",
    "The model includes the time periods 2010, 2020, 2030 and 2040."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = range(2010, 2041, 10)\n",
    "firstyear = horizon[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.add_set(\"year\", horizon)\n",
    "scenario.add_set(\"cat_year\", [\"firstmodelyear\", firstyear])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "country = \"Austria\"\n",
    "state = \"Vienna\"\n",
    "scenario.add_set(\"node\", [country, state, \"state\"])\n",
    "scenario.add_set(\"lvl_spatial\", [\"country\", \"state\"])\n",
    "scenario.add_set(\"map_spatial_hierarchy\", [\"state\", state, \"World\"])\n",
    "scenario.add_set(\"map_spatial_hierarchy\", [\"country\", country, \"state\"])\n",
    "scenario.add_set(\"mode\", \"standard\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.add_set(\"commodity\", [\"electricity\", \"light\", \"other_electricity\"])\n",
    "scenario.add_set(\"level\", [\"secondary\", \"final\", \"useful\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Economic Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rate = [0.05] * len(horizon)\n",
    "unit = ['%'] * len(horizon)\n",
    "scenario.add_par(\"interestrate\", key=horizon, val=rate, unit=unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 0.7 \n",
    "gdp = pd.Series([1., 1.21631, 1.4108, 1.63746], index=horizon)\n",
    "demand = gdp ** beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plants = [\n",
    "    \"coal_ppl\", \n",
    "    \"gas_ppl\", \n",
    "    \"oil_ppl\", \n",
    "    \"bio_ppl\", \n",
    "    \"hydro_ppl\",\n",
    "    \"wind_ppl\", \n",
    "    \"solar_pv_ppl\", # actually primary -> final\n",
    "]\n",
    "secondary_energy_techs = plants + ['import']\n",
    "\n",
    "final_energy_techs = ['electricity_grid']\n",
    "\n",
    "lights = [\n",
    "    \"bulb\", \n",
    "    \"cfl\", \n",
    "]\n",
    "useful_energy_techs = lights + ['appliances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "technologies = secondary_energy_techs + final_energy_techs + useful_energy_techs\n",
    "scenario.add_set(\"technology\", technologies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_per_year = 55209. / 8760 # from IEA statistics\n",
    "elec_demand = pd.DataFrame({\n",
    "        'node': country,\n",
    "        'commodity': 'other_electricity',\n",
    "        'level': 'useful',\n",
    "        'year': horizon,\n",
    "        'time': 'year',\n",
    "        'value': demand_per_year * demand,\n",
    "        'unit': 'GWa',\n",
    "    })\n",
    "scenario.add_par(\"demand\", elec_demand)\n",
    "\n",
    "demand_per_year = 6134. / 8760 # from IEA statistics\n",
    "light_demand = pd.DataFrame({\n",
    "        'node': country,\n",
    "        'commodity': 'light',\n",
    "        'level': 'useful',\n",
    "        'year': horizon,\n",
    "        'time': 'year',\n",
    "        'value': demand_per_year * demand,\n",
    "        'unit': 'GWa',\n",
    "    })\n",
    "scenario.add_par(\"demand\", light_demand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Engineering Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_pairs = [(y_v, y_a) for y_v, y_a in itertools.product(horizon, horizon) if y_v <= y_a]\n",
    "vintage_years, act_years = zip(*year_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_input = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'standard',\n",
    "    'node_origin': country,\n",
    "    'commodity': 'electricity',\n",
    "    'time': 'year',\n",
    "    'time_origin': 'year',\n",
    "}\n",
    "\n",
    "grid = pd.DataFrame(dict(\n",
    "        technology = 'electricity_grid',\n",
    "        level = 'secondary',\n",
    "        value = 1.0,\n",
    "        unit = '%',\n",
    "        **base_input\n",
    "        ))\n",
    "scenario.add_par(\"input\", grid)\n",
    "\n",
    "\n",
    "bulb = pd.DataFrame(dict(\n",
    "        technology = 'bulb',\n",
    "        level = 'final',\n",
    "        value = 1.0,\n",
    "        unit = '%',\n",
    "        **base_input\n",
    "        ))\n",
    "scenario.add_par(\"input\", bulb)\n",
    "\n",
    "cfl = pd.DataFrame(dict(\n",
    "        technology = 'cfl',\n",
    "        level = 'final',\n",
    "        value = 0.3, \n",
    "        unit = '%',\n",
    "        **base_input\n",
    "        ))\n",
    "scenario.add_par(\"input\", cfl)\n",
    "\n",
    "app = pd.DataFrame(dict(\n",
    "        technology = 'appliances',\n",
    "        level = 'final',\n",
    "        value = 1.0,\n",
    "        unit = '%',\n",
    "        **base_input\n",
    "        ))\n",
    "scenario.add_par(\"input\", app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_df(base, **kwargs):\n",
    "    base.update(kwargs)\n",
    "    return pd.DataFrame(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_output = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'standard',\n",
    "    'node_dest': country,\n",
    "    'time': 'year',\n",
    "    'time_dest': 'year', \n",
    "    'unit': '%',\n",
    "}\n",
    "\n",
    "imports = make_df(base_output, technology='import', commodity='electricity', \n",
    "                  level='secondary', value=1.)\n",
    "scenario.add_par('output', imports)\n",
    "\n",
    "grid = make_df(base_output, technology='electricity_grid', commodity='electricity', \n",
    "               level='final', value=0.873)\n",
    "scenario.add_par('output', grid)\n",
    "\n",
    "bulb = make_df(base_output, technology='bulb', commodity='light', \n",
    "               level='useful', value=1.)\n",
    "scenario.add_par('output', bulb)\n",
    "\n",
    "cfl = make_df(base_output, technology='cfl', commodity='light', \n",
    "              level='useful', value=1.)\n",
    "scenario.add_par('output', cfl)\n",
    "\n",
    "app = make_df(base_output, technology='appliances', commodity='other_electricity', \n",
    "              level='useful', value=1.)\n",
    "scenario.add_par('output', app)\n",
    "\n",
    "coal = make_df(base_output, technology='coal_ppl', commodity='electricity', \n",
    "               level='secondary', value=1.)\n",
    "scenario.add_par('output', coal)\n",
    "\n",
    "gas = make_df(base_output, technology='gas_ppl', commodity='electricity', \n",
    "              level='secondary', value=1.)\n",
    "scenario.add_par('output', gas)\n",
    "\n",
    "oil = make_df(base_output, technology='oil_ppl', commodity='electricity', \n",
    "              level='secondary', value=1.)\n",
    "scenario.add_par('output', oil)\n",
    "\n",
    "bio = make_df(base_output, technology='bio_ppl', commodity='electricity', \n",
    "              level='secondary', value=1.)\n",
    "scenario.add_par('output', bio)\n",
    "\n",
    "hydro = make_df(base_output, technology='hydro_ppl', commodity='electricity', \n",
    "                level='secondary', value=1.)\n",
    "scenario.add_par('output', hydro)\n",
    "\n",
    "wind = make_df(base_output, technology='wind_ppl', commodity='electricity', \n",
    "               level='secondary', value=1.)\n",
    "scenario.add_par('output', wind)\n",
    "\n",
    "solar_pv = make_df(base_output, technology='solar_pv_ppl', commodity='electricity', \n",
    "                   level='final', value=1.)\n",
    "scenario.add_par('output', solar_pv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_technical_lifetime = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': horizon,\n",
    "    'unit': 'y',\n",
    "}\n",
    "\n",
    "lifetimes = {\n",
    "    'coal_ppl': 40,\n",
    "    'gas_ppl': 30,\n",
    "    'oil_ppl': 30,\n",
    "    'bio_ppl': 30,\n",
    "    'hydro_ppl': 60,\n",
    "    'wind_ppl': 20,\n",
    "    'solar_pv_ppl': 20,\n",
    "    'bulb': 1,\n",
    "    'cfl': 10,\n",
    "}\n",
    "\n",
    "for tec, val in lifetimes.items():\n",
    "    df = make_df(base_technical_lifetime, technology=tec, value=val)\n",
    "    scenario.add_par('technical_lifetime', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_capacity_factor = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'time': 'year',\n",
    "    'unit': '%',\n",
    "}\n",
    "\n",
    "capacity_factor = {\n",
    "    'coal_ppl': 0.85,\n",
    "    'gas_ppl': 0.75,\n",
    "    'oil_ppl': 0.75,\n",
    "    'bio_ppl': 0.75,\n",
    "    'hydro_ppl': 0.5,\n",
    "    'wind_ppl': 0.2,\n",
    "    'solar_pv_ppl': 0.15,\n",
    "    'bulb': 0.1, \n",
    "    'cfl':  0.1, \n",
    "}\n",
    "\n",
    "for tec, val in capacity_factor.items():\n",
    "    df = make_df(base_capacity_factor, technology=tec, value=val)\n",
    "    scenario.add_par('capacity_factor', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Technoeconomic Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_inv_cost = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': horizon,\n",
    "    'unit': 'USD/GWa',\n",
    "}\n",
    "\n",
    "# in $ / kW\n",
    "costs = {\n",
    "    'coal_ppl': 1500,\n",
    "    'gas_ppl':  870,\n",
    "    'oil_ppl':  950,\n",
    "    'hydro_ppl': 3000,\n",
    "    'bio_ppl':  1600,\n",
    "    'wind_ppl': 1100,\n",
    "    'solar_pv_ppl': 4000,\n",
    "    'bulb': 5,\n",
    "    'cfl':  900, \n",
    "}\n",
    "\n",
    "for tec, val in costs.items():\n",
    "    df = make_df(base_inv_cost, technology=tec, value=val * 1e6)\n",
    "    scenario.add_par('inv_cost', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_fix_cost = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'unit': 'USD/GWa',\n",
    "}\n",
    "\n",
    "# in $ / kW\n",
    "costs = {\n",
    "    'coal_ppl': 40,\n",
    "    'gas_ppl':  25,\n",
    "    'oil_ppl':  25,\n",
    "    'hydro_ppl': 60,\n",
    "    'bio_ppl':  30,\n",
    "    'wind_ppl': 40,\n",
    "    'solar_pv_ppl': 25,\n",
    "}\n",
    "\n",
    "for tec, val in costs.items():\n",
    "    df = make_df(base_fix_cost, technology=tec, value=val * 1e6)\n",
    "    scenario.add_par('fix_cost', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_var_cost = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'standard',\n",
    "    'time': 'year',\n",
    "    'unit': 'USD/GWa',\n",
    "}\n",
    "\n",
    "# in $ / MWh\n",
    "costs = {\n",
    "    'coal_ppl': 24.4,\n",
    "    'gas_ppl':  42.4,\n",
    "    'oil_ppl':  77.8,\n",
    "    'bio_ppl':  48.2,\n",
    "    'electricity_grid': 47.8,\n",
    "}\n",
    "\n",
    "for tec, val in costs.items():\n",
    "    df = make_df(base_var_cost, technology=tec, value=val * 8760. * 1e3)\n",
    "    scenario.add_par('var_cost', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic Behavior Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_growth = {\n",
    "    'node_loc': country,\n",
    "    'year_act': horizon[1:],\n",
    "    'value': 0.05,\n",
    "    'time': 'year',\n",
    "    'unit': '%',\n",
    "}\n",
    "\n",
    "growth_technologies = [\n",
    "    \"coal_ppl\", \n",
    "    \"gas_ppl\", \n",
    "    \"oil_ppl\", \n",
    "    \"bio_ppl\", \n",
    "    \"hydro_ppl\",\n",
    "    \"wind_ppl\", \n",
    "    \"solar_pv_ppl\", \n",
    "    \"cfl\",\n",
    "    \"bulb\",\n",
    "]\n",
    "\n",
    "for tec in growth_technologies:\n",
    "    df = make_df(base_growth, technology=tec)\n",
    "    scenario.add_par('growth_activity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_initial = {\n",
    "    'node_loc': country,\n",
    "    'year_act': horizon[1:],\n",
    "    'time': 'year',\n",
    "    'unit': '%',\n",
    "}\n",
    "\n",
    "for tec in lights:\n",
    "    df = make_df(base_initial, technology=tec, value=0.01 * light_demand['value'].loc[horizon[1:]])\n",
    "    scenario.add_par('initial_activity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_activity = {\n",
    "    'node_loc': country,\n",
    "    'year_act': [2010],\n",
    "    'mode': 'standard',\n",
    "    'time': 'year',\n",
    "    'unit': 'GWa',\n",
    "}\n",
    "\n",
    "# in GWh - from IEA Electricity Output\n",
    "activity = {\n",
    "    'coal_ppl': 7184,\n",
    "    'gas_ppl':  14346,\n",
    "    'oil_ppl':  1275,\n",
    "    'hydro_ppl': 38406,\n",
    "    'bio_ppl':  4554,\n",
    "    'wind_ppl': 2064,\n",
    "    'solar_pv_ppl': 89,\n",
    "    'import': 2340,\n",
    "    'cfl': 0,\n",
    "}\n",
    "\n",
    "for tec, val in activity.items():\n",
    "    df = make_df(base_activity, technology=tec, value=val / 8760.)\n",
    "    scenario.add_par('bound_activity_up', df)\n",
    "    scenario.add_par('bound_activity_lo', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_capacity = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': [2010],\n",
    "    'unit': 'GWa',\n",
    "}\n",
    "\n",
    "cf = pd.Series(capacity_factor)\n",
    "act = pd.Series(activity)\n",
    "capacity = (act / 8760 / cf).dropna().to_dict()\n",
    "\n",
    "for tec, val in capacity.items():\n",
    "    df = make_df(base_capacity, technology=tec, value=val)\n",
    "    scenario.add_par('bound_new_capacity_up', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_activity = {\n",
    "    'node_loc': country,\n",
    "    'year_act': horizon[1:],\n",
    "    'mode': 'standard',\n",
    "    'time': 'year',\n",
    "    'unit': 'GWa',\n",
    "}\n",
    "\n",
    "# in GWh - base value from IEA Electricity Output\n",
    "keep_activity = {\n",
    "    'hydro_ppl': 38406,\n",
    "    'bio_ppl':  4554,\n",
    "    'import': 2340,\n",
    "}\n",
    "\n",
    "for tec, val in keep_activity.items():\n",
    "    df = make_df(base_activity, technology=tec, value=val / 8760.)\n",
    "    scenario.add_par('bound_activity_up', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.add_set(\"emission\", \"CO2\")\n",
    "scenario.add_cat('emission', 'GHGs', 'CO2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_emissions = {\n",
    "    'node_loc': country,\n",
    "    'year_vtg': vintage_years,\n",
    "    'year_act': act_years,\n",
    "    'mode': 'standard',\n",
    "    'unit': 'kg/kWa', # actually is tCO2/GWa\n",
    "}\n",
    "\n",
    "# units: tCO2/MWh\n",
    "emissions = {\n",
    "    'coal_ppl': ('CO2', 0.854),\n",
    "    'gas_ppl':  ('CO2', 0.339),\n",
    "    'oil_ppl':  ('CO2', 0.57),\n",
    "}\n",
    "\n",
    "for tec, (species, val) in emissions.items():\n",
    "    df = make_df(base_emissions, technology=tec, emission=species, value=val * 8760. * 1000)\n",
    "    scenario.add_par('emission_factor', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commit the datastructure and solve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "comment = 'initial commit for Austria model'\n",
    "scenario.commit(comment)\n",
    "scenario.set_as_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.solve('MESSAGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tools import Plots\n",
    "p = Plots(scenario, country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_new_capacity(baseyear=True, subset=plants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_new_capacity(baseyear=True, subset=lights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_capacity(baseyear=True, subset=plants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_capacity(baseyear=True, subset=lights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_demand(light_demand, elec_demand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bOwwYMKBrenr6MQsWLGhaF8dWU0rQIiISmNWrVzceMWLExqysrKWJiYklTz75ZErEuoYPPfTQETNmzFgxb9687CVLliTOmDGjRXll7d69O+7YY4/dNW/evJzevXvvuPfee3+8CWFhYWFcZmbmsrvuumvVmDFjjqrt44oFJWgREQlMSkpK0emnn74T4MILL9y8ePHipNJ1H3/8cWLPnj13tG3bdl/Dhg0ZPHhw/sKFC5PKKysuLo5LL700H+DSSy/d/Pnnn/+47dChQ/MBzjjjjIKdO3fG5efnx9feUcWGErSIiATGcZxyn7uuG7OyK9pPWClBi4hIYDZs2JCQlZWVCDBz5sxWvXr1Kihdd9JJJ+387LPPmq1fv77Bvn37eOONN1qdfPLJBeWVVVJSgrW2JcBLL73U+vjjj99Ruu7VV19tCTBv3rykpKSk4pYtWxbX3lHFRuCjuI0xfwSuBFzga+B31tpaHVovIiI/V9llUbWhY8eOu6dPn9567NixR3bo0GHPtddeu/G9995rAdC+ffu9N99889phw4b9ynVdp2/fvtuGDRu2tbyyGjduXLJs2bIm/fr1a5uUlFT8zDPPfFu6rnnz5sUDBgzounPnzvgHH3zwu7o4tpoKNEEbY44ARgNp1tpCY4wFLgGmBRmXiIjUvs6dOxd99NFH2dHLZ82atbz07xEjRuSPGDEiv6pl3nXXXXlAXvTywYMHb7nnnnvWRi4bNWrUZmBz9aKuO2Ho4m4ANDHGNACaUkbFioiIHGoCbUFba9caYx4CvgcKgUxrbWaQMYmISLideeaZXffu3btfA3PChAnf5ebmfl7W9pEt8vok6C7ulsD5wFHAVuA/xpjLrLXPRW13NXA1gLWW5OTkOo+1uho0aFAv4jxU6b2JLdVnbKk+K/buu+8uCzqGuhD0ILH/Ab6z1m4EMMa8ApwC7JegrbWTgcn+U3fTpk11GuSBSE5Opj7EeajSexNbqs/Yqo36TE1NrXwjCZWgE/T3wEnGmKZ4XdxnAouDDUlERCR4gQ4Ss9YuAmYAn+FdYhXHTy1lERGRQ1bQLWistbcDtwcZQ+oXX9dOuTEuL++4Y2NcooiIhFXgCVpERMLh/OeXxXQ+6NeGd631G5/UxpzOnTt3Pr68EeF1KQzXQYuIiEgUtaBFRCQwU6dObf3Pf/6zDUCXLl0Kx40bt/amm27qtHXr1gYtWrTYN3HixJVHHXVU0cyZM5s/9thj7fbt2xfXvHnzfZMmTfo2NTV1X2XlX3XVVZ0SEhJKcnNzm+Tn5zccO3bs6iFDhmybMmVK68zMzBZFRUVxeXl5jQYNGrT59ttv/6H2j7jq1IIWEZFAfPnll40nTZrUbsaMGSuysrKWPvDAA9//+c9/7jh06NDNWVlZSwcPHrz5tttu6wBw2mmnFbz99tvL3n///aUZGRn5jz76aNuq7icvL6/RrFmzlj/77LP/vf32248sLCx0AJYuXZo4efLkb+fOnZudmZnZauHChU1r61gPhFrQwL6kF4IOoYruDToAEZGYmTdv3mFnnXXWljZt2uwDSE5OLl6yZEni888/nwtw+eWX5z/yyCPtAb7//vuEK664ov3mzZsb7t27Ny41NXVPVfczaNCg/Pj4eLp167YnNTV1T3Z2dmOAPn36bE9JSSkGOPPMM7csWLAg6aSTTtoV+yM9MGpBi4hIIFzXxXGcCid9Lp23edy4cR0vv/zyDR9++OHSv//976uKioqqnL/Kmws67HNEK0GLiEgg+vfvv33OnDmtNm7cGA+wadOm+GOPPXbnCy+80BLg3//+d6sePXoUABQUFMQfccQRe8Gb67k6+5k9e3bL4uJiVqxY0SgvL69RWlraboBFixYdtmnTpvidO3c67733XouK5poOgrq4gXFvtgs6hCoZPTroCETkYFYXl0VF+vWvf7372muv/WHIkCFd4+Li3K5du+669957v7/ppps6TZkypW3pIDGA0aNH511//fWdU1JSinr06LEzLy+vUVX306lTpz0ZGRlH5+fnN7zjjjtWNWnSxPX3X3DNNdcctWbNmsaDBg3aHKbubVCCFhGRAF1xxRWbr7jiiv3mZH7jjTdWRG83bNiwrcOGDdsavbwqczr37t274OGHH14dvbx169b7yrp+OgzXQIO6uEVEREJJLWgREan37r777raZmZmtIpcNGDAg/+mnn15Z1vZVaXkHTQlaRETqvXHjxq0bN27cuqDjiCV1cYuIiISQErSIiEgIKUGLiIiEkM5Bi4gIAP/30taYTjd53sUt6vS66prIyMg4+m9/+9vqMF0LrRa0iIhICKkFLSIigbnrrrvazZo1q1WbNm2KWrRosa979+67mjVrVmytTdm7d6/ToUOHPZMnT/4uKSmpZPr06S3/8Y9/pMbFxblJSUnFs2fPXl5WmeVNJZmbm5swfPjwLt27d9+5fPnyph07dtz91FNPrUxKSiqp6+OuCrWgRUQkEAsXLmz69ttvt3z33XeX/utf/8rNyclJBBg2bNiWuXPn5mRlZS3t3Llz4ZQpU5IBHnvssXYvvvjiiqysrKXPP//8NxWVXd5UkqtXr248YsSIjVlZWUsTExNLnnzyyZTaP9IDowQtIiKBWLBgQVL//v23JiYmus2bNy857bTTtgJ89dVXTc4+++yj+/btm/bmm2+2XrFiRWPw7p39+9//vtOkSZOSi4uLKyy7dCrJxMREt3QqSYCUlJSi008/fSfAhRdeuHnx4sVJtXyYB0xd3CJyyDiqzcigQ5AIrlv2TJO33nrrUZMnT/6mZ8+ehVOmTGm9cOHCZgCPP/749/Pnz0+cM2dO87POOuuYt99+O7t0Pudo9XWKyUhqQYuISCBOOeWUgvfff795YWGhs3379risrKwWALt27YpLTU3dW1RU5Lz++us/3r5zxYoVjdLT03feeeedec2bN9+3atWqhPLKLm8qyQ0bNiRkZWUlAsycObNVr169QjXFZCS1oEVEBKj7y6JOPvnkXf369dvWr1+/Y9q2bbunW7duO5s1a1Z844035p177rnd2rZtW9SlS5ddO3fujAe4/fbb269evbqR67pOnz59tp9wwgmF5ZVd1lSSubm5CR07dtw9ffr01mPHjj2yQ4cOe6699tqNdXfE1aMELSIigbn55pvXjR8/Pq+goCDuvPPOO/r6669f36dPn1033HDDzxLniy++mFvVcsubSjIuLo7HH3/8Z8tnzZpV5ojwIFUrQRtjjgNOBZKBHzvurbX/L8ZxiYjIIWD06NFHfvvtt02KioqcwYMHb+7Tp09obhQStConaGPM1cCjQCZwDjAbGAC8VjuhiYjIwW7atGnfHehrZ82addj999/fPnJZamrqnunTp+dSxlSSnTt3Lpo/f372ge6vrlWnBX0rcLa1NssYs8VaO9QYcw5wSS3FJiIiUq6MjIztGRkZS4OOo7ZUZxT34dbaLP/vEmNMnLV2NnBeLcQlIiJySKtOC3qNMaaTtXYlsAI43xizCSiqSQDGmBbAM0B3wAWusNZ+VJMyRURE6rvqtKAfALr5f98JPAfMBe6oYQwTgLestV2BXwM5NSxPRESk3qtyC9paOy3i79nGmJZAgrX2gC/yNsYcBpwG/NYvt4gatshFREQOBpUmaGPME8AHwAfW2rzS5TFKpr8ANgJTjTG/Bj4FbrLW7qxhuSIiUk0TJ06M6XzQo0ePrvDGJ7m5uQkjR47sciAjq995551mkyZNajNjxowKJ82oDXU1d3RVWtANgPHAr4wx3wLv81PCPuDh8RFlnwD83lq7yBgzAbgN+FvkRv4lXlcDWGtJTk6u4W7rp0P1uGuD6jK26k99bg06gCqpP/UZPnv37qVhw4ZBhxETlSZoa+3VAMaYw/G6o/sCNwHPGGPWAe9bay87wP2vAdZYaxf5z2fgJejoGCYDk/2n7qZNmw5wd/XboXrctUF1GVuqz9iqjfpMTU2NeZmxUFJSwnXXXXfkl19+mZSSklJ05513rr7pppuOmjdvXg5ATk5Oo+uuu+4X8+bNy3njjTcOu/vuuzs0b958X7du3X5svY4fPz51w4YNDdeuXZvQsmXLfU8++eTK0aNHH5mTk9M0Pj6ecePGrT7rrLN2lLX/MM8dXeVBYtbaDdbaGdbaPwAj8Vq5DYFLD3Tn1tp1wGpjzNH+ojOBg/aaNhER2d+aNWsajxo1asOCBQuymzVrVvzFF180TUxMLF68eHETgOeeey556NChmwsLC51x48Z1mjp16jezZ89evmnTpv2ayUuXLm36wgsvfDNt2rTvHn/88cMBPvzww6WPP/74t2PGjOlUWFhY7rRVYZ07uirnoB28bujTgNOBE4HVQBZwPfBhDWP4PfC8MSYB+Bb4XQ3LExGReqJdu3Z7evXqVQjQvXv3XatXr2508cUXb3r++eeTjzvuuNVz5sxpOXv27Jzs7OzG7dq129OtW7c9ABdccMHmF1544ceEWTqvNMDixYuTrrjiig1+mbvbtm1blJOT07i8yTVK544GKJ07+vzzz98aPXf0lClTDgfW12qFRKjKOeitwFrgJeAfwPBYDuKy1n4B9IpVeSIiUn80bNjwx0mh4+Pj3d27d8dddNFFWx577LHU1157bUfXrl13paSkFK9atarCuZubNm36Y9dzefNMlyesc0dXpYvb4k2MMRIYAVxijPlVrUYlIiKHrCZNmrinnHLKtvHjx3e8+OKLNwEcc8wxu/Py8hJWrFjRCOC1115rVd7r+/TpU/Dyyy+3Ali6dGmj9evXJ6Slpe0ub/uwzh1dlUFiVwEYY1LwurlPBW4wxrQFFgBZ1toJtRqliIjUusoui6pLF154Yf7cuXNbnnPOOdvBS9p33333qpEjR/6yefPm+0444YSC//73v03Keu3111+/YfTo0Uf27ds3LT4+ngceeGBl48aNy21Wh3Xu6OrcqGQj8DLwsjGmO3Au8AdgKN7dwERERKosenapMWPG/Hh+96OPPko6//zzNzVo8FOaOvfcc7efe+65P7tmevz48XmRz5s0aeI+/fTTK6saR1jnjq7uILHSy6ySgE+AKXjXRYuIiMTEJZdc0nnt2rWNZsyYsSLoWIJU1UFiccAivBuU/C+w0Fq7pzYDExE4qs3IoEMQqXP+fM4xVR/njq5Kgh4IfObf2hNjTF/gRGNM6foF1tp9tRSfiIhIjdXHuaOrkqCPB27AG8ENkMlPvzaaArfidXWLiIhIjFTlMquRwEMRz/dYaztYazvg3fnrylqJTERE5BBWlQR9lLX2y4jnkV0EX+LNSCUiIiIxVJUEnWSMSSx9Yq1Nj1jXFEj8+UtERESkJqpyDnoJMACYWca6s4FAR7mJiEhsHP7NX2I6H/SGX957QDc+GTBgQNfMzMxlNZkvujx1NZdzLFQlQf8v8IQxxgVet9aWGGPigPOBx4CbazNAERE5tGRmZi4LOoYwqLSL21o7HW+Q2HPAbmNMHrAb+BfwiLX2xdoNUUREDlYPP/xwm/T09GPS09OPeeSRRw4H6Ny58/FVee2UKVNaX3zxxZ2HDh3a5cQTT+x+xx13tAPIzc1NOOmkk4658sorO5166qlpw4cP/0VBQUGVp1cOiyoFbK19GEgFzgPGAIOB9tbaB2sxNhEROYgtWrSo6cyZM1u/9dZbObNnz86ZMWNGyieffFLm/bXLE9a5nGOhOvfi3g7MqcVYRETkELJgwYKkM844Y2uzZs1KwJuLef78+c2qU0ZY53KOhXrX5BcRkYNDdedtLktY53KOBSVoEREJRN++fQvee++9FgUFBXE7duyImzt3bsv09PQd1SkjrHM5x0KVu7hFROTgdqCXRR2oPn367BoyZMjmgQMHdgMYNmzYxt69exdWp4ywzuUcC0rQIiISmFtuuWX9Lbfcst+54dzc3M+hajNKhXUu51hQF7eIiEgIqQUtIiKhVh/nco4FJWgREQm1+jiXcyyoi1tERCSElKBFRERCSAlaREQkhJSgRUREQkiDxEREBICXskfEdD7oi4/5d8xufFKf5nEuS+fOnY8vvb67qpSgJaaOajMy6BBERNi3bx8NGtTvFFe/oxcRkXprx44dcb/97W9/sWHDhoSSkhIKiu5tAAARzUlEQVTnhhtuyGvduvW+e+65p0NxcTFpaWm7Jk6cuKpx48b7zapx4403dszOzk7cs2dP3FlnnbXljjvuyAM44YQTjh0yZMim+fPnHzZy5MgNw4cP3xK9z4yMjKOPPvroXUuWLEncuXNn/IMPPvjdiSeeuKt3797HvvPOO0tbtWpVDNC7d+/ur7322rLU1NR90WVcddVVnRISEkpyc3Ob5OfnNxw7duzqIUOGbJsyZUrrzMzMFkVFRXF5eXmNBg0atPn222//4UDrJxQJ2hgTDywG1lprzw06HhERqX1vvvnmYYcffvjel19++RuALVu2xPfv3/+YF154YXlaWtqeUaNGdXriiSdSbr755g2Rrxs/fvza5OTk4n379nH++ecf/dlnnzU54YQTCgEaNWpUMmfOnApv51lYWBiXmZm5bO7cuUljxow5av78+dmnn3761ldeeaXFlVdeuXn+/PmJ7dq1KyorOZfKy8trNGvWrOUrVqxodMkllxw9cODAr8Gbn3ru3LnZTZs2LRkwYEDawIEDtx1ot3xYBondBOQEHYSIiNSdY489tvDjjz8+7LbbbjvivffeS/r2228T2rVrtyctLW0PgDFm8yeffPKz+aGtta369evXrX///mnfffdd45ycnMal6y666KKftZqjDR06NB/gjDPOKNi5c2dcfn5+/NChQ/NnzZrVCuCVV15pNWjQoPyKyhg0aFB+fHw83bp125OamronOzu7Mfw0P3ViYqJbOj919WrlJ4EnaGNMeyADeCboWEREpO6kpaXtmTNnztJu3boV3nfffUfMmjWrRWWv+eabbxKmTp3a5uWXX16RlZW1ND09fdvu3bt/zGWJiYkllZVR1lzRffv23blmzZpG69atazBv3rwWw4YNqzDR18U81IEnaOB/gVuBSitVREQOHqtXr26YmJhYcvnll+dfddVV6z///POkH374IWHZsmWNAGbMmNG6T58++80PvW3btvjGjRuXtGjRojgvL6/BggULmld3v6+++mpLgHnz5iUlJSUVt2zZsjguLo7+/ftvHTt2bIdOnToVpqSkFFdUxuzZs1sWFxezYsWKRnl5eY3S0tJ2Q/nzUx+IQM9BG2POBTZYaz81xvSrYLurgasBrLUkJyfXUYThUj+Oe2vQAVRJ/ahLUH3GmuqzIrG8LKoqvvrqqyb33Xdfe8dxaNCggXvPPfes2rZtW/w111zTuXSQ2HXXXbffPM49e/Ys7Nq1666+ffsek5qauqdHjx7VToDNmzcvHjBgQNfSQWKlyy+44IL8YcOGdbvrrrtWVlZGp06d9mRkZBydn5/f8I477ljVpEkTF8qen7q68ZUKepBYOjDYGDMIaAwcZox5zlp7WeRG1trJwGT/qbtp06Y6DjMcDtXjrg2qy9hSfcZWbdRnampqzMusqfImwRg4cODPlkXO4/z000+vLKu8zz777Ouq7Hfw4MFb7rnnnrXRy0866aRda9eurdKPlN69exc8/PDDq6OXlzc/dXWvgYaAE7S19i/AXwD8FvSfopOziIjIoSjoFrSIiEjMjR49uuMXX3yx3wjqkSNHro9siVfm7rvvbpuZmdkqctmAAQPyy2vBjxo1ajNlzE99oEKToK2184B5AYchIiIHgYkTJ/6sm7m6xo0bt27cuHHrYhHPgQjDKG4RERGJogQtIiISQkrQIiIiIRSac9AiIrXtmX2BnU6slvOo9IZacghQghYREQBSv/g6pvNB5x13bI1ufDJs2LBfPv3009+VzjBVmdzc3ISRI0d2mT9/fnZN9hspyHmolaBFRCSUSme5OlQpQYuISCDuv//+No0aNXL/8Ic/bLjllls6LF++vMkbb7yxYs6cOc2mT5+e/OWXXybNnj07p6CgIO6yyy7rctxxxxV8+eWXSSkpKUUvvPDCN4mJie6iRYua/ulPf+rUuHHjkuOPP77C236WN19zbm5uwvDhw7t079595/Lly5t27Nhx91NPPbUyKSkp0DkiNEhMREQCkZ6eXvDJJ58kAWRnZzctLCyMLyoqchYuXJgUPUnGmjVrGo8aNWrDggULsps1a1Y8Y8aMlgB/+tOfOo0fP/77t99+e1lV9rl06dLEyZMnfzt37tzszMzMVgsXLmwKsHr16sYjRozYmJWVtTQxMbHkySefTIn18VaXErSIiASiT58+u5YtW5a4bdu2uISEBLdHjx4FixYtavrpp582S09P36813K5duz29evUqBOjevfuu1atXN9qyZUv8jh074s8888wCgEsuuaTSu3iVN19zSkpK0emnn74T4MILL9y8ePHiA57HOVbUxS0iIoFISEhw27Vrt2fq1KnJxx13XEFaWlrhBx980Gzt2rWNunfvvjty24YNG7qlf8fHx7u7d++Oc1232vMt18U8zrGiFrSIiASmV69eBdOmTWtzyimn7DjttNN2/Oc//0np0qXLrri4ytNTq1atipOSkornzZuXBGCtbVXZa8qbr3nDhg0JWVlZiQAzZ85s1atXrwOexzlW1IIWERGg5pdFHYiTTz55xz//+c+26enpO5s1a1aSkJDg9u7du8rJ8aGHHlpZOkgsPT19e2XblzVfc25ubkLHjh13T58+vfXYsWOP7NChw55rr712Y2Vl1TYlaBERCczAgQN3fP/995+VPv/444+XlP5dOr9zmzZtiLy2ecyYMetL/z7xxBN3ZWVl/Th/9Pjx4/Mq2l958zXHxcXx+OOP/2x5dWa/ijV1cYuIiISQWtDAUW1GBh2CiIjEyKxZsw67//7720cuS01N3TN9+vRcypivuXPnzkWxvPtYrChBi4jIQSUjI2N7RkbG0sq3DDd1cYuIHLoCvVOW/KjM90EJWkTkEFVcXJyzd+9e5YEA7d27N664uDinrHXq4hYROUQVFRU9sH37duLj47uhBlsQSoqLi3OKiooeKGulErRIiGn+YqlNPXv2LAHuCzoOKZt+MYmIiISQErSIiEgIKUGLiIiEkBK0iIhICClBi4iIhJAStIiISAgpQYuIiISQErSIiEgIBXqjEmNMB+BfQFu8e5FOttZOCDImERGRMAi6Bb0PuMVa2w04CbjBGJMWcEwiIiKBCzRBW2t/sNZ+5v+9A8gBjggyJhERkTAIugX9I2NMJ+B4YFHAoYiIiAQuFJNlGGOSgJeBP1hrt5ex/mrgagBrLcnJyTGOYGuMy6sdsT/u2qC6PBSpPmNL9SkQggRtjGmIl5yft9a+UtY21trJwGT/qbtp06a6Ci9UDtXjrg2qy9hSfcZWbdRnampqzMuU2hVoF7cxxgGmADnW2keCjEVERCRMgm5BpwMjgK+NMV/4y8Zaa98MMCapAc1fLCISG4EmaGvth4ATZAwiIiJhFJpR3CIiIvITJWgREZEQUoIWEREJISVoERGREFKCFhERCSElaBERkRBSghYREQmhoG9UEgq6uYaIiISNWtAiIiIhpAQtIiISQkrQIiIiIaQELSIiEkJK0CIiIiGkBC0iIhJCStAiIiIhpAQtIiISQkrQIiIiIaQELSIiEkJK0CIiIiGkBC0iIhJCStAiIiIhpAQtIiISQkrQIiIiIaT5oIFX5t0adAhVM/z1oCOolOoytlSfIocutaBFRERCSAlaREQkhJSgRUREQkgJWkREJISUoEVEREIo8FHcxpizgQlAPPCMtfa+gEMSEREJXKAtaGNMPPA4cA6QBlxqjEkLMiYREZEwCLqLuw/wjbX2W2ttETAdOD/gmERERAIXdII+Algd8XyNv0xEROSQFvQ5aKeMZW70AmPM1cDVANZaUlNTYxvFrMWxLe9QprqMLdVnTH0yJsbfHSK1KOgW9BqgQ8Tz9kBe9EbW2snW2l7W2l54ST30D2PMp0HHcLA8VJeqzzA/6ll9Sj0SdAv6E6CLMeYoYC1wCfCbYEMSEREJXqAtaGvtPuBGYA6Q4y2y2UHGJCIiEgZBt6Cx1r4JvBl0HLVgctABHERUl7Gl+owt1afUCsd1fzYmS0RERAIW9CAxERERKYMStIiISAgpQYuIiIRQ4IPEDgbGmK54tyg9Au9GK3nA69banEADq6dUn7FljGkOnM3+9TnHWrs10MDqKdWn1BW1oGvIGPNnvHuIO8DHeNd2O8CLxpjbgoytPlJ9xpYxZiTwGdAPaAokAv2BT/11Ug2qT6lLakHX3CjgGGvt3siFxphHgGxA02dWj+oztv4K9Ixu3RljWgKLgH8FElX9pfqUOqMWdM2VAGXd4Ledv06qR/UZWw5l3N8ery5168fqU31KnVELuub+ALxrjPkvP83M1RH4Jd5d0qR6VJ+x9XfgM2NMJvvX51nAXYFFVX+pPqXO6EYlMWCMicOb2/oIvF/Ra4BPrLXFgQZWT6k+Y8vvfh3I/vU5x1q7JdDA6inVp9QVJehaZIxJstYWBB2HSCRjTCtrbX7QcRwsjDGDrbWvBx2HHHzUxV27luJ1f0kVGWN64N3b+AhgNvDn0paJMeZja22fIOOrb4wx6cAzeOdIrwDuBjobYxoCxlr7UZDx1TfGmAvKWPyEMaYBgLX2lToOSQ5iStA1ZIy5uZxVDpBUl7EcJJ4AxgMLgSuBD/0WSi7QMMjA6qlHAYP3WZwFDLHWfmiMOQH4B5AeZHD1kAXeAjbw06CwROA8vMFjStASM0rQNXcP8CCwr4x1GiVffUnW2rf8vx8yxnwKvGWMGUHZo2elYg2ttV8DGGM2Wms/BLDWfmaMaRJsaPXSyXiX+n0CTLLWusaYftba3wUclxyElKBr7jPgVWvtp9ErjDFXBhBPfecYY5pba7cBWGvfM8YMA14GWgUbWr0U+SPxL1HrEuoykIOBtfYTY8xZwO+Buf6NdfTDUWqFWng19zvg+3LW9arLQA4S9wPdIhdYa78CzkTdhwfib8aYpgDW2ldLFxpjOqObahwQa22JtXYCcBnwp6DjkYOXRnGLiIiEkLq4a8i/cf5fgCFAir94A/AacJ9uoF89qs/YUn3GlupT6pK6uGvOAluAftba1tba1ng3z98C/CfQyOon1WdsqT5jS/Updcd1XT1q8LjooouWH8g6PVSfqs/691B96lGXD3Vx19wqY8ytwLPW2vUAxpg2wG/56V69UnWqz9hSfcaW6lPqjBJ0zV0M3Aa87/9HdYH1wOt4N4iQ6lF9xpbqM7ZUn1JnNIo7BowxXYH2wMLIe28bY86OuOmGVJHqM7ZUn7Gl+pS6okFiNWSMGY03gvNGYIkx5vyI1fcEE1X9pfqMLdVnbKk+pS4pQdfcVUBPa+0QoB/ejSFu8tdpAvfqU33GluoztlSfUmeUoGsuvrSby1q7Eu8/7TnGmEfQf9gDofqMLdVnbKk+pc4oQdfcOmPMcaVP/P+85wLJwLGBRVV/qT5jS/UZW6pPqTNK0DU3ElgXucBau89aOxI4LZiQ6jXVZ2ypPmNL9Sl1RqO4RUREQkgtaBERkRBSghYREQkhJWgREZEQ0q0+RcpgjHke2GOtvSJi2enAK0B3a+0PgQUnIocEtaBFyjYaGGSMOQvAGNMYeBq4JZbJ2RgTH6uyROTgolHcIuUwxlwEPAB0B8YBx1lrzzHGxOFNmDAKaA68A1xnrd3ir7NAX6Ax8IW/Lscv8zlgG9AZOBXIsNbOq9MDE5F6QS1okXJYa/8DfAq8CFwNXOOvuhnIwLvutT2wE5gY8dI3gC5AW2AJ8O+oon8D3AE0Az6qpfBFpJ5TC1qkAv6UgrnAX621E/xl/wWutNa+7z/vAHwDNLHWlkS9PhnYCCRZa3f6LeiiyHPbIiJl0SAxkQpYa9cbYzYB2RGLOwL/Z4yJTMYucLgxZiNwL3Ah3u0fS7dJxmtpA6yu3ahF5GCgBC1SfWuA31hrF0WvMMb8DhgEnAGsAlrjtaAjJ1JQt5WIVErnoEWqbxJwjzGmI4Ax5nBjzGB/XTNgD7AZaAr8PZgQRaS+U4IWqb5HgLeAd40xO4AFQG9/3VQgz39k++tERKpNg8RERERCSC1oERGREFKCFhERCSElaBERkRBSghYREQkhJWgREZEQUoIWEREJISVoERGREFKCFhERCSElaBERkRD6/zxs1V6SWQ2+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_activity(baseyear=True, subset=plants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_activity(baseyear=True, subset=lights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot_prices(baseyear=False, subset=['light', 'other_electricity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mp.close_db()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      World\n",
       "1     global\n",
       "2    country\n",
       "3      state\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scenario.set(\"lvl_spatial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.check_out(timeseries_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACT</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>2020</td>\n",
       "      <td>???</td>\n",
       "      <td>0.873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  variable  region  year unit  value\n",
       "0      ACT  Vienna  2020  ???  0.873"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = {'variable':'ACT',\n",
    "       'region':state,\n",
    "        'year':2020,\n",
    "        'unit':'???',\n",
    "        'value':0.873}\n",
    "ts = pd.DataFrame.from_dict(base, orient='index').T\n",
    "\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.set('node')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "at.ac.iiasa.ixmp.exceptions.IxExceptionPyRaisable",
     "evalue": "at.ac.iiasa.ixmp.exceptions.IxException: The node 'Vienna' does not exist in the database!'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mat.ac.iiasa.ixmp.exceptions.IxExceptionPyRaisable\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-991cd1855e1c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscenario\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_timeseries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\message\\lib\\site-packages\\ixmp\\core.py\u001b[0m in \u001b[0;36madd_timeseries\u001b[1;34m(self, df, meta)\u001b[0m\n\u001b[0;32m    348\u001b[0m                           java.Double(float(df.value[i])))\n\u001b[0;32m    349\u001b[0m             \u001b[1;31m# add the final iteration of the loop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 350\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddTimeseries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;31m# if in 'IAMC-style' format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mat.ac.iiasa.ixmp.exceptions.IxExceptionPyRaisable\u001b[0m: at.ac.iiasa.ixmp.exceptions.IxException: The node 'Vienna' does not exist in the database!'"
     ]
    }
   ],
   "source": [
    "scenario.add_timeseries(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario.timeseries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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