analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "import pyam\n",
    "import pandas as pd\n",
    "import plotting\n",
    "import readdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select region here\n",
    "region = 'World'\n",
    "# region = 'Asian countries except Japan'\n",
    "# region = 'Countries of the Middle East and Africa'\n",
    "# region = 'Latin American countries'\n",
    "# region = 'OECD90 and EU (and EU candidate) countries'\n",
    "# region = 'Rest of the World (R5)'\n",
    "\n",
    "update_from_iiasadb = True # If False, then data is retreived from cache instead of databasa query\n",
    "\n",
    "df = readdata.read_iiasa(region=region, update=update_from_iiasadb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "variable = 'Final Energy|Residential and Commercial'\n",
    "\n",
    "scenario_type = ['act', 'tec'] # Scenario indicators: 'ref' / 'act' / 'ele' / 'tec' / 'all'\n",
    "scenario_target = ['15C', 'NPi'] # Climate target indicators: 'NPi' / '15C' / '20C'\n",
    "scenario_ctax = ['u'] # Include scenarios with global ('u') and/or differentiated ('d') carbon tax\n",
    "\n",
    "startyear = 1970 # Inital year for plotting\n",
    "endyear = 2050 # Final year for plotting\n",
    "\n",
    "color = 'scenario' # Color-coding by 'scenario' or 'model'\n",
    "use_markers = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting.generate_plots(df, variable, (scenario_target, scenario_type, scenario_ctax), region, startyear, endyear, color, use_markers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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