Start_Here.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11e7be6b",
   "metadata": {},
   "source": [
    "Welcome to the Drawdown Solutions library!\n",
    "\n",
    "This jupyter notebook is a stub.  It needs to be expanded to include additional useful samples.\n",
    "\n",
    "(A helpful tutorial for jupyter notebooks is [here](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb)---or [here](https://youtu.be/3C9E2yPBw7s?t=131) if you'd prefer a video introduction.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96fa055",
   "metadata": {},
   "source": [
    "# Working with Solutions/Scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70785e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules we'll use later\n",
    "\n",
    "from solution import factory\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2252191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The factory module has several ways of constructing solutions and scenarios.\n",
    "# The simplest is to get the most recent PDS scenario of a particular type for a particular solution\n",
    "\n",
    "bikeinfra_pds2 = factory.load_scenario(\"bikeinfrastructure\", \"PDS2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09f4ac17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running a scenario should be super quick. So what's happening behind the scenes? \n",
    "# Well, the line of code is calling on a specific script. The script is hidden at:\n",
    "# /solutions/solution/bikeinfrastructure/__init__.py \n",
    "# About halfway down the \"__init__.py\" is all of the subscripts that are being run to produce our results!\n",
    "\n",
    "# If you're interested in checking out each subscript you can go into the \n",
    "# /solutions/model/ directory to check each one out.\n",
    "\n",
    "# Not every subscript nickname is descriptive so here is a list of a few important ones:\n",
    "    # ac: Advanced Controls\n",
    "    # ad: Adoption Data\n",
    "    # ae: Agro-Ecological Zoning Allocation of the Land\n",
    "    # c2: CO2 Emissions Calculations & FaIR Climate Modeling\n",
    "    # c4: CH4 Emissions Calculations\n",
    "    # ef: Emissions Factors for converting to CO2eq\n",
    "    # fc: First Costs\n",
    "    # ht: Helper Tables\n",
    "    # n2o: N2O Emissions Calculations\n",
    "    # name: Naming Convention of the Solution\n",
    "    # oc: Operating Cost\n",
    "    # pds_ca: Custom Adoption Data for the Project Drawdown Scenario\n",
    "    # ref_ca: Custom Adoption Data for the Reference Scenario\n",
    "    # scenario: Naming Convention of the Scenario\n",
    "    # solution_category: LAND\n",
    "    # tla_per_region: Total Land Area for each Drawdown Region\n",
    "    # ua: Unit Adoption (a mix of functions to calculate annual adoption)\n",
    "    # units: Units for first cost, functional unit, implementation unit, operating cost\n",
    "    # vmas: Variable Meta Analysis Datasets\n",
    "    \n",
    "# The way to call on these is in the naming convention:\n",
    "# solutionname.subscriptname.variablename\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "523f5cdd",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>World</th>\n",
       "      <th>OECD90</th>\n",
       "      <th>Eastern Europe</th>\n",
       "      <th>Asia (Sans Japan)</th>\n",
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       "      <th>Latin America</th>\n",
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       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>2014</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>78.521223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>89.731723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>100.641071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>111.255350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>121.580472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>131.624606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>151.200284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>157.831089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>164.590307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>171.460410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>179.145789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>185.463192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>192.560849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>199.699347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>206.861193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>212.905981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>221.184782</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>228.311607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>235.391842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>242.407851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>250.928085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>256.178047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\n",
       "      <td>262.897327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>269.482775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>275.916966</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>281.215273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>288.261917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>294.137871</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>299.792955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>305.217274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>310.606903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           World  OECD90  Eastern Europe  Asia (Sans Japan)  Middle East and Africa  Latin America  China  India   EU  USA\n",
       "Year                                                                                                                      \n",
       "2014    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2015    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2016    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2017    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2018    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2019    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2020   78.521223     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2021   89.731723     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2022  100.641071     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2023  111.255350     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2024  121.580472     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2025  131.624606     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2026  151.200284     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2027  157.831089     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2028  164.590307     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2029  171.460410     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2030  179.145789     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2031  185.463192     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2032  192.560849     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2033  199.699347     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2034  206.861193     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2035  212.905981     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2036  221.184782     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2037  228.311607     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2038  235.391842     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2039  242.407851     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2040  250.928085     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2041  256.178047     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2042  262.897327     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2043  269.482775     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2044  275.916966     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2045  281.215273     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2046  288.261917     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2047  294.137871     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2048  299.792955     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2049  305.217274     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2050  310.606903     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2051    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2052    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2053    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2054    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2055    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2056    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2057    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2058    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2059    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0\n",
       "2060    0.000000     0.0             0.0                0.0                     0.0            0.0    0.0    0.0  0.0  0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's explore and practice calling the results!\n",
    "\n",
    "# All the work of calculating carbon impact, capital cost and operating cost for the scenario \n",
    "# was done by that constructure.  Here, for example, is the carbon impact.  (In this case, we\n",
    "# only have global data, and only modeled out to 2050)\n",
    "\n",
    "# Units: Megatons of CO2-eq emissions\n",
    "\n",
    "bi_co2e = bikeinfra_pds2.c2.co2eq_mmt_reduced()\n",
    "bi_co2e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd5f632f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The result is a pandas Dataframe, so you can do lots of things with it.\n",
    "# Lets graph the part of it that is interesting\n",
    "\n",
    "bi_co2e.loc[2020:2050, 'World'].plot(title=\"Bike Infrastructure:\\nCO2eq reduced per year, in metric tons\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f54a979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "World                     6477.004361\n",
       "OECD90                       0.000000\n",
       "Eastern Europe               0.000000\n",
       "Asia (Sans Japan)            0.000000\n",
       "Middle East and Africa       0.000000\n",
       "Latin America                0.000000\n",
       "China                        0.000000\n",
       "India                        0.000000\n",
       "EU                           0.000000\n",
       "USA                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's add up all the years to see our total cumulative CO2-eq emission reduction\n",
    "\n",
    "# Units: Megatons of CO2-eq emissions\n",
    "\n",
    "bi_co2eq_cumulative = bi_co2e.sum(axis=0)\n",
    "bi_co2eq_cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c46fd7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's a second example for the NET functional units adopted. That is to say the projected yearly increase\n",
    "# in adoption of the solution.\n",
    "\n",
    "# Units: Passenger Kilometers (PKm) per year\n",
    "\n",
    "bi_netfunits = bikeinfra_pds2.ua.soln_net_annual_funits_adopted()\n",
    "bi_netfunits.loc[2020:2050, 'World'].plot(title=\"Bike Infrastructure: \\nsolution adoption per year\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6c884c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "World                     6477.004361\n",
       "OECD90                       0.000000\n",
       "Eastern Europe               0.000000\n",
       "Asia (Sans Japan)            0.000000\n",
       "Middle East and Africa       0.000000\n",
       "Latin America                0.000000\n",
       "China                        0.000000\n",
       "India                        0.000000\n",
       "EU                           0.000000\n",
       "USA                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's add up all the years to see our total adoption of the solution\n",
    "\n",
    "# Units: Megatons of CO2-eq emissions\n",
    "\n",
    "ghgreduced_co2eq_cumulative = bi_co2e.sum(axis=0)\n",
    "ghgreduced_co2eq_cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21209a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1730008420054.4626"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To look at some economic results, we'll show the marginal operating cost.\n",
    "\n",
    "# Units: US$ (2014)\n",
    "\n",
    "bi_opcost = bikeinfra_pds2.oc.marginal_annual_operating_cost()\n",
    "\n",
    "# Print the cumulative margainal operating cost over the 2020-2050 period\n",
    "bi_opcost_cumulative = bi_opcost.sum(axis=0)\n",
    "bi_opcost_cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9e4215b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the annual marginal operating cost\n",
    "bi_opcost.loc[2020:2050].plot(title=\"Bike Infrastructure: \\noperating cost per year (US$)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037422ee",
   "metadata": {},
   "source": [
    "## Multi-gas emission results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81d37535",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's show some more results for a multi-gas solution in the agricultural sector!\n",
    "# We'll work with the Improved Rice solution and it's conservative scenario, PDS1\n",
    "\n",
    "rice_pds1 = factory.load_scenario(\"improvedrice\", \"PDS1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a7f1320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use the following function to get the annual greenhouse gas emission reductions of carbon dioxide (CO2),\n",
    "# methane (CH4), and nitrous oxide (N2O)\n",
    "\n",
    "emisreduction_annual = rice_pds1.c2.ghg_emissions_reductions_global_annual()\n",
    "\n",
    "# Plotting the results for a quick result:\n",
    "plt.rcParams['figure.figsize'] = (15, 8)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.plot(emisreduction_annual.index, emisreduction_annual.iloc[:,0], color='black')\n",
    "ax1.set_title('CO$_2$ emissions reduction (Gt-C)')\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.plot(emisreduction_annual.index, emisreduction_annual.iloc[:,1], color='black')\n",
    "ax2.set_title('CH$_4$ emissions reduction (Mt-CH$_4$)')\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.plot(emisreduction_annual.index, emisreduction_annual.iloc[:,2], color='black')\n",
    "ax3.set_title('N$_2$O emissions reduction (Mt-N$_2$O)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "295a3057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CO2 (Gt-C)      1.491021\n",
       "CH4 (Mt-CH4)    0.000159\n",
       "N2O (Mt-N2O)   -0.000005\n",
       "Name: ghg_emissions_reductions_global_cumulative, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about the cumulative emission reductions over the modeled time? We can use:\n",
    "\n",
    "emisreduction_cumulative = rice_pds1.c2.ghg_emissions_reductions_global_cumulative()\n",
    "emisreduction_cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8856b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "World                     3993.938029\n",
       "OECD90                       0.000000\n",
       "Eastern Europe               0.000000\n",
       "Asia (Sans Japan)            0.000000\n",
       "Middle East and Africa       0.000000\n",
       "Latin America                0.000000\n",
       "China                        0.000000\n",
       "India                        0.000000\n",
       "EU                           0.000000\n",
       "USA                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get our emission reduction results as a single number:\n",
    "\n",
    "# Units: Mt CO2-eq\n",
    "\n",
    "ghgreduced_co2eq = rice_pds1.c2.co2eq_mmt_reduced()\n",
    "ghgreduced_co2eq_cumulative = ghgreduced_co2eq.sum(axis=0)\n",
    "ghgreduced_co2eq_cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea59fea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO:\n",
    "# We need some utility functions to produce nice summary results, and then we should demonstrate them here,\n",
    "# also including some nice graphs."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "641170e26b916086f6ab8a8498639d254e86adadac2c3bbb49fa49caa88ce61e"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}