docs/Tutorial 1 - Running the model.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Scope of this tutorial\n",
    "\n",
    "Welcome to the first America's Water Model tutorial!  In this tutorial, you are going to learn how to (0) install the model, (1) run the model in simulation and optimization mode with different configurations, (2) look at the results, and (3) make requests of and get updates from the modeling team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some terminology\n",
    "\n",
    "We are going to use a bunch of terms in this tutorial, so it's important to clear up the confusion.\n",
    "\n",
    "* **The model**: A computer program that can provide information on how water is or could be supplied and consumed in the US.\n",
    "* **Julia**: The programming language used for the model.  <img style=\"float: right; position: relative; top: -20px; width: 150px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Julia_prog_language.svg/2000px-Julia_prog_language.svg.png\">  From the [Julia website](http://julialang.org/):\n",
    "\n",
    "    *Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.*\n",
    "    \n",
    "\n",
    "* **Jupyter Notebook**: A program to make documents, like this one, that combine Julia programming and text.\n",
    "* **Git**: A program that saves all previous versions of the model, and lets the modeling team collaborate.  All of these previous versions are stored on the website [Github](github.com/AmericasWater/operational-problem).\n",
    "* **Terminal**: A text interface to your computer, needed to use Git and Jupyter Notebook.\n",
    "* **Mimi**: The foundational code used by the model to provide its structure, developed by [David Anthoff](http://www.david-anthoff.com/).\n",
    "* **Simulation**: A way to run the model, where inputs (or *parameters*) are used to determine the value of outputs (or *variables*).\n",
    "* **Optimization**: Another way to run the model, where constraints on outputs (or *variables*) are used to determine inputs (or *parameters*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing the model\n",
    "\n",
    "If you are an advanced user, you can follow the more succinct [installation instructions](install.md) and then skip to \"What you just got\" below.\n",
    "\n",
    "## Setting up your environment\n",
    "\n",
    "You will need some basic software to use the America's Water Model.\n",
    "\n",
    "* **Julia (command line version)** from http://julialang.org/downloads/\n",
    "\n",
    "    Check your installation by running Julia and writing some arithmetic.\n",
    "    \n",
    "    \n",
    "* **Git** from https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\n",
    "\n",
    "    If you're on Windows, also download PowerShell (https://msdn.microsoft.com/en-us/powershell/)\n",
    "\n",
    "    Check that it's installed by opening a terminal/PowerShell and typing `git --version` and see a version number.\n",
    "    \n",
    "    \n",
    "* **Jupyter Notebook** from http://jupyter.readthedocs.io/en/latest/install.html\n",
    "\n",
    "    Check that it's installed by opening a terminal/PowerShell and typing `jupyter notebook` and see a browser window pop up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to download the model.  First, decide where you want to put the model, and open a terminal to this location.\n",
    "\n",
    "## Aside: How to navigate to the model in the terminal\n",
    "\n",
    "1. Opening a terminal.  Doing this is different by OS:\n",
    " - On Mac OS: Open the Terminal program.\n",
    " - On Windows: Open the PowerShell program.\n",
    " - On Linux: Open up a shell window.\n",
    "2. To change directory, type `cd <directory>`.  `<directory>` can be any of the following:\n",
    " - The name of a subdirectory.\n",
    " - Two periods (`..`) to go to the parent directory.\n",
    " - A single period (`.`) to not go anywhere.\n",
    " - Any combination of the above, separated by slashes (`/`).\n",
    " - A tilde (`~`) to go back to your home directory.\n",
    " - A slash (`/`) to go to the root of the server.\n",
    " - Either of the previous two, combined with any of the first four, using slashes.\n",
    " - Use the tab key to auto-complete the name you have started to type.\n",
    " - If there are spaces in your directory or filename, consider the life choices that led to this moment.\n",
    "3. The model will be created in a new folder, when you download it.  From then on, for anything from this tutorial, you will want to go into the `docs` subdirectory of that folder.\n",
    "\n",
    "## Downloading the model\n",
    "\n",
    "In the directory where you want the model folder, paste the following command:\n",
    "\n",
    "```\n",
    "git clone https://github.com/AmericasWater/awash.git```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What you just got\n",
    "\n",
    "The model is organized into different folders.  Here is what is in there:\n",
    "* `configs/`: Configuration files, that determine the basic operation of the model.\n",
    "* `data/`: Input data files.  There is also a `data/cache/` folder, with saved preparation files.  You can delete this to force the model to reinitialize.\n",
    "* `docs/`: Documentation, including tutorials like this one.\n",
    "* `prepare/`: Some source data files and the scripts needed to prepare them for use in the model.\n",
    "* `results/`: The standard location for output results, along with code for post-processing.\n",
    "* `src/`: The main source code for the model.  All the magic happens here.\n",
    "* `test/`: Automatic testing scripts, to make sure changes don't break how the model should work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opening the model\n",
    "\n",
    "For the purposes of this tutorial, the main interface of the model is Jupyter Notebooks, and the existing tutorials in the `docs/` subdirectory.  To start the model, do the following:\n",
    "\n",
    "1. Open a terminal to the `docs/` subdirectory of the model.\n",
    "2. Type/paste the following command: `jupyter notebook`.\n",
    "3. In the browser window that opens, find the item `start.ipynb` and click on it.\n",
    "4. Click the \"<span style=\"font-family: FontAwesome\" class=\"fa-step-forward fe\"></span>\" button, which will run the line below.\n",
    "\n",
    "If this is the first time you're running the model, it is going to install some needed libraries and data."
   ]
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     "text": [
      "\u001b[0;1;34;94m┌───────────────\u001b[0;34m────────────────\u001b[0;37m────────────────\u001b[0;1;30;90m───┐\u001b[0m\n",
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      "\u001b[0;34m│\u001b[0m   \u001b[0;34m████\u001b[0m   \u001b[0;37m██\u001b[0m      \u001b[0;37m██\u001b[0m   \u001b[0;1;30;90m████\u001b[0m    \u001b[0;1;30;90m▄█▀▀▀▀█\u001b[0m   \u001b[0;1;34;94m██\u001b[0m    \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m│\u001b[0m\n",
      "\u001b[0;37m│\u001b[0m   \u001b[0;37m████\u001b[0m   \u001b[0;37m▀█▄\u001b[0m \u001b[0;37m█\u001b[0;1;30;90m█\u001b[0m \u001b[0;1;30;90m▄█▀\u001b[0m   \u001b[0;1;30;90m████\u001b[0m    \u001b[0;1;34;94m██▄\u001b[0m       \u001b[0;1;34;94m██\u001b[0m    \u001b[0;34m██\u001b[0m \u001b[0;34m│\u001b[0m\n",
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      "\u001b[0;1;30;90m│\u001b[0m  \u001b[0;1;30;90m██████\u001b[0m   \u001b[0;1;30;90m███▀\u001b[0;1;34;94m▀███\u001b[0m   \u001b[0;1;34;94m██████\u001b[0m        \u001b[0;34m▀██\u001b[0m  \u001b[0;34m██\u001b[0m    \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n",
      "\u001b[0;1;30;90m│\u001b[0m \u001b[0;1;30;90m▄██\u001b[0m  \u001b[0;1;30;90m█\u001b[0;1;34;94m█▄\u001b[0m  \u001b[0;1;34;94m███\u001b[0m  \u001b[0;1;34;94m███\u001b[0m  \u001b[0;1;34;94m▄█\u001b[0;34m█\u001b[0m  \u001b[0;34m██▄\u001b[0m  \u001b[0;34m█▄▄▄▄▄█▀\u001b[0m  \u001b[0;37m██\u001b[0m    \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n",
      "\u001b[0;1;34;94m│\u001b[0m \u001b[0;1;34;94m▀▀\u001b[0m    \u001b[0;1;34;94m▀▀\u001b[0m  \u001b[0;1;34;94m▀▀▀\u001b[0m  \u001b[0;34m▀▀▀\u001b[0m  \u001b[0;34m▀▀\u001b[0m    \u001b[0;34m▀▀\u001b[0m   \u001b[0;37m▀▀▀▀▀\u001b[0m    \u001b[0;37m▀▀\u001b[0m    \u001b[0;1;30;90m▀▀\u001b[0m \u001b[0;1;30;90m│\u001b[0m\n",
      "\u001b[0;34m└───────\u001b[0;37m────────────────\u001b[0;1;30;90m────────────────\u001b[0;1;34;94m───────────┘\u001b[0m\n",
      "\n",
      "Welcome to AWASH, the America's Water Model, version 0.7.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "include(\"../src/nui.jl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuration parameters\n",
    "\n",
    "The configuration file determines the general bounds of the model.  The current configuration parameters are:\n",
    "\n",
    "* **`netset`**: The top-level decision of the world we are deciding.\n",
    "\n",
    "    options: `usa` (full-country), `three` (3-counties), `dummy` (5-counties)\n",
    "    \n",
    "    \n",
    "* **`filterstate`**: Only include counties within this state (give as 2 digit FIPS).\n",
    "\n",
    "    e.g., \"`10`\" for Delaware (3 counties), \"`08`\" for Colorado (64 counties)\n",
    "    \n",
    "    \n",
    "* **`startmonth`** and **`endmonth`**: First and last month of the simulation.\n",
    "\n",
    "    e.g., 10/2000 - 9/2010\n",
    "    \n",
    "    \n",
    "* **`startweather`**: The weather entry to match to `startmonth`.\n",
    "\n",
    "    e.g., 1 to use the first month in `VIC_WB`for the first month in the simulation.\n",
    "    \n",
    "    \n",
    "* **`timestep`**: The number of months for each timestep of the model.\n",
    "\n",
    "    The larger this number, the faster the model will run.  Currently agriculture is calculated separately for each timestep.\n",
    "    \n",
    "# Standard configuration sets\n",
    "\n",
    "There are three configuration sets to know about:\n",
    "\n",
    "* `complete.yml`: A monthly run from 1949 - 2010.  This is basically impossible on a laptop.\n",
    "* `standard.yml`: A 6-month timestep run from 2000 - 2010.  This is about the maximum practical for a laptop.\n",
    "* `standard-1year.yml`: A 6-month timestep run for 10/2009 - 9/2010 (two timesteps).  This is good for testing.\n",
    "* `single.yml`: A 12-month timestep run for 10/2009 - 9/2010 (one timestep).  We will use this one here.\n",
    "\n",
    "Here is what the `single.yml` configuration looks like:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Only include counties within this state (give as 2 digit FIPS)\n",
    "# \"10\" for Delaware (3 counties), \"08\" for Colorado (64 counties)\n",
    "filterstate: null\n",
    "\n",
    "# Current options: usa (full-country), dummy (5-counties)\n",
    "netset: usa\n",
    "\n",
    "# First and last month of the simulation\n",
    "startmonth: 10/2009\n",
    "endmonth: 9/2010\n",
    "\n",
    "# First entry in VIC_WB to include\n",
    "startweather: 720\n",
    "\n",
    "# Months per time step\n",
    "timestep: 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running a simulation\n",
    "\n",
    "There are three steps for running a simulation:\n",
    "1. Select a configuration: Create or select a file in `configs/`.\n",
    "2. Prepare the model: Call `prepsimulate(\"<config filename>\");`.\n",
    "3. Run the prepared model: Call `runmodel();`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from saved region network...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: imported binding for edges overwritten in module Main\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from saved water network...\n",
      "Loading from saved region network...\n",
      "Creating model...\n"
     ]
    }
   ],
   "source": [
    "prepsimulate(\"single.yml\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This loaded up the model geography of the model, put together the sectors, and prepared all of the inputs.  Now we can run the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "runmodel();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at the results of the model\n",
    "\n",
    "There are lots of ways to see the results of the model, but they all require that you know which variables you want to look at.  Start by identifying the component that you are interested, as a box in this image:\n",
    "\n",
    "![Operational Problem Model](https://raw.githubusercontent.com/AmericasWater/operational-problem/master/docs/ModelDiagram.png)\n",
    "\n",
    "Let's consider the Agriculture component.  Next, we need to know what variables are available within that component, which we do with the `getvariables` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "<table class=\"data-frame\"><tr><th></th><th>name</th><th>dims</th></tr><tr><th>1</th><td>rainfedareas</td><td>(3109,9,1)</td></tr><tr><th>2</th><td>deficit_coeff</td><td>(3109,9)</td></tr><tr><th>3</th><td>precipitation</td><td>(3109,1)</td></tr><tr><th>4</th><td>water_demand</td><td>(9,)</td></tr><tr><th>5</th><td>logirrigatedyield</td><td>(3109,9,1)</td></tr><tr><th>6</th><td>irrigatedareas</td><td>(3109,9,1)</td></tr><tr><th>7</th><td>allagarea</td><td>(3109,1)</td></tr><tr><th>8</th><td>totalirrigation</td><td>(3109,1)</td></tr><tr><th>9</th><td>production</td><td>(3109,9,1)</td></tr><tr><th>10</th><td>cultivationcost</td><td>(3109,9,1)</td></tr><tr><th>11</th><td>water_deficit</td><td>(3109,9,1)</td></tr><tr><th>12</th><td>lograinfedyield</td><td>(3109,9,1)</td></tr><tr><th>13</th><td>totalareas</td><td>(3109,9,1)</td></tr></table>"
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       "│ Row │ name              │ dims         │\n",
       "├─────┼───────────────────┼──────────────┤\n",
       "│ 1   │ rainfedareas      │ \"(3109,9,1)\" │\n",
       "│ 2   │ deficit_coeff     │ \"(3109,9)\"   │\n",
       "│ 3   │ precipitation     │ \"(3109,1)\"   │\n",
       "│ 4   │ water_demand      │ \"(9,)\"       │\n",
       "│ 5   │ logirrigatedyield │ \"(3109,9,1)\" │\n",
       "│ 6   │ irrigatedareas    │ \"(3109,9,1)\" │\n",
       "│ 7   │ allagarea         │ \"(3109,1)\"   │\n",
       "│ 8   │ totalirrigation   │ \"(3109,1)\"   │\n",
       "│ 9   │ production        │ \"(3109,9,1)\" │\n",
       "│ 10  │ cultivationcost   │ \"(3109,9,1)\" │\n",
       "│ 11  │ water_deficit     │ \"(3109,9,1)\" │\n",
       "│ 12  │ lograinfedyield   │ \"(3109,9,1)\" │\n",
       "│ 13  │ totalareas        │ \"(3109,9,1)\" │"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getvariables(:Agriculture)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `:` is important for both the component name and the variable names below.\n",
    "\n",
    "Then to see the results, call `getdata(<component>, <variable>)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "outputs": [
    {
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       "3109x9x1 Array{Float64,3}:\n",
       "[:, :, 1] =\n",
       "     0.0            0.0          …      0.0              0.0        \n",
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       "     0.0        32438.7                 0.0           8336.67       \n",
       "     0.0            0.0                 0.0              3.35729e5  \n",
       "     0.0            0.0          …      0.0              0.0        \n",
       "     0.0            9.73389e-23         0.0              0.0        \n",
       "     0.0            0.0                 0.0              0.0        \n",
       "     ⋮                           ⋱                                  \n",
       " 54033.4        42940.9                 0.0              0.0        \n",
       "     1.10014e5  15292.0                 0.0              0.0        \n",
       " 30610.1         5819.68                0.0              0.0        \n",
       " 78542.1        16184.3          …      0.0              0.0        \n",
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       " 42416.2        26897.1                 0.0              0.0        \n",
       "  5701.34       12085.1          …      0.0              0.0        \n",
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       " 10993.2         1075.5                 1.55547e-13      1.28253e-15"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getdata(:Agriculture, :production)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save the data to a file, call `savedata(<filename>, <component>, <variable>)`.  The `savedata` function is best used on 2-D data (or less) and since production is of dimension COUNTY x CROP x TIME, we would like to drop one.  We can do this with a fourth argument, which has the syntax for a Matlab index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "savedata(\"../results/agprod.csv\", :Agriculture, :production, (:, :, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot it.  This only works so far for results that describe nation-wide values, but it's a start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: RCall.jl Warning: package ‘ggplot2’ was built under R version 3.2.3\n",
      "Need help? Try the ggplot2 mailing list:\n",
      "http://groups.google.com/group/ggplot2.\n",
      "WARNING: RCall.jl \n",
      "-----------------------------------------------------------\n",
      "PBS Mapping 2.69.76 -- Copyright (C) 2003-2016 Fisheries and Oceans Canada\n",
      "\n",
      "PBS Mapping comes with ABSOLUTELY NO WARRANTY;\n",
      "for details see the file COPYING.\n",
      "This is free software, and you are welcome to redistribute\n",
      "it under certain conditions, as outlined in the above file.\n",
      "\n",
      "A complete user guide 'PBSmapping-UG.pdf' is located at \n",
      "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/PBSmapping/doc/PBSmapping-UG.pdf\n",
      "\n",
      "Packaged on 2015-04-23\n",
      "Pacific Biological Station, Nanaimo\n",
      "\n",
      "All available PBS packages can be found at\n",
      "http://code.google.com/p/pbs-software/\n",
      "\n",
      "To see demos, type '.PBSfigs()'.\n",
      "-----------------------------------------------------------\n",
      "\n",
      "\n",
      "WARNING: RCall.jl Loading required package: maptools\n",
      "Loading required package: sp\n",
      "Warning: package ‘sp’ was built under R version 3.2.5\n",
      "Checking rgeos availability: TRUE\n",
      "Loading required package: foreign\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.VecSxp}\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapdata(:Agriculture, :production, (:, 5, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using RCall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.RealSxp}\n",
       "  [1] -0.62122279  0.64831833 -0.48935885  0.92764611  1.02611765  1.59837396\n",
       "  [7] -0.96725780 -0.49321665 -1.10164360 -1.33944063  1.31890750  0.25991008\n",
       " [13] -0.36737477  0.08100751  0.01820744  0.57968764 -0.83115791 -0.30559390\n",
       " [19]  0.82804637  0.87722282 -0.36368721  0.29606504 -0.01717716  0.18193819\n",
       " [25]  0.98784232 -0.04729326 -0.76462815 -0.23869830  0.87341128 -0.56540542\n",
       " [31]  0.24752053 -0.93169866 -0.01312905  1.17846349  0.26131608  1.81919341\n",
       " [37]  1.72917614  0.14759056  0.88830378 -1.09978466  0.01376463 -0.23974337\n",
       " [43]  0.20255462 -1.12002824 -0.17669488  0.34420994 -0.46455188  1.14323986\n",
       " [49]  0.18923975 -0.17559747 -0.38363851 -1.08267915  1.09002894  0.94087581\n",
       " [55] -0.43349359 -0.44734339  3.37063110  1.36829580 -0.44395899 -1.16338533\n",
       " [61] -0.97981821  0.20694984 -0.06456482  0.47541359  0.52327356  1.63704958\n",
       " [67]  0.32423036  0.49562667 -0.33220034  0.54913155 -0.37544318  0.26960028\n",
       " [73]  0.55244578  0.89318511 -0.02618433 -1.09897372  0.95858630  0.87128071\n",
       " [79] -0.45338845 -0.44877374  1.78908111 -0.36408830 -0.88978697 -0.99465919\n",
       " [85]  0.33044888 -0.37553791 -1.42606459 -0.65207303  0.44036986  0.75407073\n",
       " [91] -0.75762170 -0.52891009  0.30297497 -2.11217177 -0.73390772  1.09533811\n",
       " [97]  1.34026477  0.26010075  0.97643020  0.18347344\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R\"x = rnorm(100)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.VecSxp}\n",
       "$breaks\n",
       " [1] -2.5 -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0  3.5\n",
       "\n",
       "$counts\n",
       " [1]  1  0  8 13 25 22 17  8  5  0  0  1\n",
       "\n",
       "$density\n",
       " [1] 0.02 0.00 0.16 0.26 0.50 0.44 0.34 0.16 0.10 0.00 0.00 0.02\n",
       "\n",
       "$mids\n",
       " [1] -2.25 -1.75 -1.25 -0.75 -0.25  0.25  0.75  1.25  1.75  2.25  2.75  3.25\n",
       "\n",
       "$xname\n",
       "[1] \"x\"\n",
       "\n",
       "$equidist\n",
       "[1] TRUE\n",
       "\n",
       "attr(,\"class\")\n",
       "[1] \"histogram\"\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R\"hist(x)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myjuliavar = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myjuliavar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.NilSxp}\n",
       "NULL\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R\"plot($myjuliavar)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.RealSxp}\n",
       "  [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n",
       "  [6] -0.625395697  0.788866194  0.503396481  0.806131966 -0.685962168\n",
       " [11]  1.409655399 -0.011001650  1.221350838  0.687594625  0.660120389\n",
       " [16]  0.323121302  1.121587552 -0.125611715  1.772632044 -1.576097599\n",
       " [21] -0.660744500 -0.429240447 -0.490280026  0.468150307  1.198544526\n",
       " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809  1.008367742\n",
       " [31]  1.042998949 -0.182158343 -0.198155519  0.064799689  1.055615930\n",
       " [36]  1.396022389  0.363787500 -0.265379634 -0.485115714  0.513091917\n",
       " [41] -0.340089246 -0.725946945  0.465066983  0.925172640 -0.499757044\n",
       " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050  1.549266436\n",
       " [51] -1.311331940  1.201508605 -0.271954598  2.534004825 -1.623282056\n",
       " [56]  0.001984485 -0.509082941  0.023470704 -0.710153768 -1.048219819\n",
       " [61]  0.950574020  0.223231617 -1.496654999 -0.660755363 -0.865973022\n",
       " [66] -1.125167386 -0.679917414 -1.014248154  1.170601061  1.133173229\n",
       " [71]  0.217915251 -0.863843341  0.858204953 -0.204352963 -0.521392645\n",
       " [76]  1.510171510  0.598071927  0.638948425  1.397193548 -0.639814179\n",
       " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727  1.295730250\n",
       " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995  0.781161860\n",
       " [91] -0.734598252  0.817520424 -1.157498298  0.815970762 -0.476418750\n",
       " [96]  0.162498461 -1.002618330  0.975990869 -0.457368921  0.461530679\n"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = R\"rnorm(100)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RCall.RObject{RCall.RealSxp}\n",
       "  [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n",
       "  [6] -0.625395697  0.788866194  0.503396481  0.806131966 -0.685962168\n",
       " [11]  1.409655399 -0.011001650  1.221350838  0.687594625  0.660120389\n",
       " [16]  0.323121302  1.121587552 -0.125611715  1.772632044 -1.576097599\n",
       " [21] -0.660744500 -0.429240447 -0.490280026  0.468150307  1.198544526\n",
       " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809  1.008367742\n",
       " [31]  1.042998949 -0.182158343 -0.198155519  0.064799689  1.055615930\n",
       " [36]  1.396022389  0.363787500 -0.265379634 -0.485115714  0.513091917\n",
       " [41] -0.340089246 -0.725946945  0.465066983  0.925172640 -0.499757044\n",
       " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050  1.549266436\n",
       " [51] -1.311331940  1.201508605 -0.271954598  2.534004825 -1.623282056\n",
       " [56]  0.001984485 -0.509082941  0.023470704 -0.710153768 -1.048219819\n",
       " [61]  0.950574020  0.223231617 -1.496654999 -0.660755363 -0.865973022\n",
       " [66] -1.125167386 -0.679917414 -1.014248154  1.170601061  1.133173229\n",
       " [71]  0.217915251 -0.863843341  0.858204953 -0.204352963 -0.521392645\n",
       " [76]  1.510171510  0.598071927  0.638948425  1.397193548 -0.639814179\n",
       " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727  1.295730250\n",
       " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995  0.781161860\n",
       " [91] -0.734598252  0.817520424 -1.157498298  0.815970762 -0.476418750\n",
       " [96]  0.162498461 -1.002618330  0.975990869 -0.457368921  0.461530679\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Nothing to be done\n",
      "INFO: METADATA is out-of-date — you may not have the latest version of YAML\n",
      "INFO: Use `Pkg.update()` to get the latest versions of your packages\n"
     ]
    }
   ],
   "source": [
    "Pkg.add(\"YAML\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>FIPS</th><th>County</th><th>ST</th><th>Neighboring</th><th>UrbanPop_2010</th><th>RuralPop_2010</th><th>MedianHouseholdInc_2008_12</th><th>Elevation_ft</th><th>TotalArea_sqmi</th><th>LandArea_sqmi</th><th>WaterArea_sqmi</th></tr><tr><th>1</th><td>1001</td><td>Autauga</td><td>AL</td><td>-1677436972259693513</td><td>31650</td><td>22921</td><td>53773</td><td>290.0</td><td>604.39</td><td>594.44</td><td>9.95</td></tr><tr><th>2</th><td>1003</td><td>Baldwin</td><td>AL</td><td>-7503806633868418079</td><td>105205</td><td>77060</td><td>50706</td><td>155.0</td><td>2027.31</td><td>1589.78</td><td>437.53</td></tr><tr><th>3</th><td>1005</td><td>Barbour</td><td>AL</td><td>-9162367971339582421</td><td>8844</td><td>18613</td><td>31889</td><td>220.0</td><td>904.52</td><td>884.88</td><td>19.64</td></tr><tr><th>4</th><td>1007</td><td>Bibb</td><td>AL</td><td>-3263215093836123959</td><td>7252</td><td>15663</td><td>36824</td><td>224.0</td><td>626.17</td><td>622.58</td><td>3.59</td></tr><tr><th>5</th><td>1009</td><td>Blount</td><td>AL</td><td>281814405218882823</td><td>5760</td><td>51562</td><td>45192</td><td>870.0</td><td>650.63</td><td>644.78</td><td>5.85</td></tr><tr><th>6</th><td>1011</td><td>Bullock</td><td>AL</td><td>-7528364842213094795</td><td>5307</td><td>5607</td><td>34500</td><td>455.0</td><td>625.14</td><td>622.8</td><td>2.34</td></tr><tr><th>7</th><td>1013</td><td>Butler</td><td>AL</td><td>7263807205192509739</td><td>6026</td><td>14921</td><td>30752</td><td>445.0</td><td>777.88</td><td>776.83</td><td>1.05</td></tr><tr><th>8</th><td>1015</td><td>Calhoun</td><td>AL</td><td>4089652745186409211</td><td>78617</td><td>39955</td><td>40093</td><td>565.0</td><td>612.29</td><td>605.87</td><td>6.42</td></tr><tr><th>9</th><td>1017</td><td>Chambers</td><td>AL</td><td>1546726499117356153</td><td>17399</td><td>16816</td><td>32181</td><td>745.0</td><td>603.11</td><td>596.53</td><td>6.58</td></tr><tr><th>10</th><td>1019</td><td>Cherokee</td><td>AL</td><td>3606844344126915727</td><td>3707</td><td>22282</td><td>36241</td><td>589.0</td><td>599.98</td><td>553.7</td><td>46.28</td></tr><tr><th>11</th><td>1021</td><td>Chilton</td><td>AL</td><td>2336210146209538225</td><td>5785</td><td>37858</td><td>40834</td><td>580.0</td><td>700.8</td><td>692.85</td><td>7.95</td></tr><tr><th>12</th><td>1023</td><td>Choctaw</td><td>AL</td><td>-6248320840704236021</td><td>0</td><td>13859</td><td>35123</td><td>350.0</td><td>920.86</td><td>913.5</td><td>7.36</td></tr><tr><th>13</th><td>1025</td><td>Clarke</td><td>AL</td><td>-2480994685536455671</td><td>6205</td><td>19628</td><td>30954</td><td>405.0</td><td>1252.57</td><td>1238.46</td><td>14.11</td></tr><tr><th>14</th><td>1027</td><td>Clay</td><td>AL</td><td>165275177167130605</td><td>0</td><td>13932</td><td>34556</td><td>745.0</td><td>606.0</td><td>603.96</td><td>2.04</td></tr><tr><th>15</th><td>1029</td><td>Cleburne</td><td>AL</td><td>-6945485711834679467</td><td>0</td><td>14972</td><td>37244</td><td>1175.0</td><td>561.01</td><td>560.1</td><td>0.91</td></tr><tr><th>16</th><td>1031</td><td>Coffee</td><td>AL</td><td>6132202507106331951</td><td>26375</td><td>23573</td><td>44626</td><td>195.0</td><td>680.49</td><td>678.97</td><td>1.52</td></tr><tr><th>17</th><td>1033</td><td>Colbert</td><td>AL</td><td>1077010790105928141</td><td>30537</td><td>23891</td><td>40158</td><td>536.0</td><td>622.13</td><td>592.62</td><td>29.51</td></tr><tr><th>18</th><td>1035</td><td>Conecuh</td><td>AL</td><td>1099010130103901053</td><td>2520</td><td>10708</td><td>27064</td><td>216.0</td><td>852.72</td><td>850.16</td><td>2.57</td></tr><tr><th>19</th><td>1037</td><td>Coosa</td><td>AL</td><td>6308886048509289181</td><td>0</td><td>11539</td><td>37425</td><td>660.0</td><td>666.35</td><td>650.93</td><td>15.42</td></tr><tr><th>20</th><td>1039</td><td>Covington</td><td>AL</td><td>-7456065813052386533</td><td>11461</td><td>26304</td><td>35321</td><td>260.0</td><td>1043.79</td><td>1030.46</td><td>13.33</td></tr><tr><th>21</th><td>1041</td><td>Crenshaw</td><td>AL</td><td>4065287195876681327</td><td>0</td><td>13906</td><td>37309</td><td>594.0</td><td>610.92</td><td>608.84</td><td>2.08</td></tr><tr><th>22</th><td>1043</td><td>Cullman</td><td>AL</td><td>6438249654864425607</td><td>21517</td><td>58889</td><td>39244</td><td>802.0</td><td>755.02</td><td>734.84</td><td>20.18</td></tr><tr><th>23</th><td>1045</td><td>Dale</td><td>AL</td><td>3596880842466902597</td><td>24679</td><td>25572</td><td>45247</td><td>470.0</td><td>562.7</td><td>561.15</td><td>1.55</td></tr><tr><th>24</th><td>1047</td><td>Dallas</td><td>AL</td><td>3774141952490839331</td><td>23821</td><td>19999</td><td>26178</td><td>147.0</td><td>993.8</td><td>978.69</td><td>15.11</td></tr><tr><th>25</th><td>1049</td><td>DeKalb</td><td>AL</td><td>-701081407554769413</td><td>7018</td><td>64091</td><td>36853</td><td>1040.0</td><td>778.69</td><td>777.09</td><td>1.59</td></tr><tr><th>26</th><td>1051</td><td>Elmore</td><td>AL</td><td>4377449246493266349</td><td>36330</td><td>42973</td><td>55514</td><td>290.0</td><td>657.04</td><td>618.48</td><td>38.56</td></tr><tr><th>27</th><td>1053</td><td>Escambia</td><td>AL</td><td>3957980972351392731</td><td>13982</td><td>24337</td><td>31075</td><td>85.0</td><td>953.14</td><td>945.08</td><td>8.06</td></tr><tr><th>28</th><td>1055</td><td>Etowah</td><td>AL</td><td>1525514402121635003</td><td>65286</td><td>39144</td><td>37660</td><td>565.0</td><td>548.63</td><td>534.99</td><td>13.64</td></tr><tr><th>29</th><td>1057</td><td>Fayette</td><td>AL</td><td>-6531450309154316941</td><td>3408</td><td>13833</td><td>33090</td><td>365.0</td><td>629.36</td><td>627.66</td><td>1.7</td></tr><tr><th>30</th><td>1059</td><td>Franklin</td><td>AL</td><td>7764312890763704205</td><td>9395</td><td>22309</td><td>37235</td><td>840.0</td><td>646.52</td><td>633.82</td><td>12.7</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>"
      ],
      "text/plain": [
       "3229×11 DataFrames.DataFrame\n",
       "│ Row  │ FIPS  │ County          │ ST   │ Neighboring          │ UrbanPop_2010 │\n",
       "├──────┼───────┼─────────────────┼──────┼──────────────────────┼───────────────┤\n",
       "│ 1    │ 1001  │ \"Autauga\"       │ \"AL\" │ -1677436972259693513 │ 31650         │\n",
       "│ 2    │ 1003  │ \"Baldwin\"       │ \"AL\" │ -7503806633868418079 │ 105205        │\n",
       "│ 3    │ 1005  │ \"Barbour\"       │ \"AL\" │ -9162367971339582421 │ 8844          │\n",
       "│ 4    │ 1007  │ \"Bibb\"          │ \"AL\" │ -3263215093836123959 │ 7252          │\n",
       "│ 5    │ 1009  │ \"Blount\"        │ \"AL\" │ 281814405218882823   │ 5760          │\n",
       "│ 6    │ 1011  │ \"Bullock\"       │ \"AL\" │ -7528364842213094795 │ 5307          │\n",
       "│ 7    │ 1013  │ \"Butler\"        │ \"AL\" │ 7263807205192509739  │ 6026          │\n",
       "│ 8    │ 1015  │ \"Calhoun\"       │ \"AL\" │ 4089652745186409211  │ 78617         │\n",
       "│ 9    │ 1017  │ \"Chambers\"      │ \"AL\" │ 1546726499117356153  │ 17399         │\n",
       "│ 10   │ 1019  │ \"Cherokee\"      │ \"AL\" │ 3606844344126915727  │ 3707          │\n",
       "│ 11   │ 1021  │ \"Chilton\"       │ \"AL\" │ 2336210146209538225  │ 5785          │\n",
       "⋮\n",
       "│ 3218 │ 72137 │ \"Toa Baja\"      │ \"PR\" │ NA                   │ 89609         │\n",
       "│ 3219 │ 72139 │ \"Trujillo Alto\" │ \"PR\" │ NA                   │ 74842         │\n",
       "│ 3220 │ 72141 │ \"Utuado\"        │ \"PR\" │ NA                   │ 17593         │\n",
       "│ 3221 │ 72143 │ \"Vega Alta\"     │ \"PR\" │ NA                   │ 39251         │\n",
       "│ 3222 │ 72145 │ \"Vega Baja\"     │ \"PR\" │ NA                   │ 57170         │\n",
       "│ 3223 │ 72147 │ \"Vieques\"       │ \"PR\" │ NA                   │ 8230          │\n",
       "│ 3224 │ 72149 │ \"Villalba\"      │ \"PR\" │ NA                   │ 22564         │\n",
       "│ 3225 │ 72151 │ \"Yabucoa\"       │ \"PR\" │ NA                   │ 32192         │\n",
       "│ 3226 │ 72153 │ \"Yauco\"         │ \"PR\" │ NA                   │ 34303         │\n",
       "│ 3227 │ 78010 │ \"St. Croix\"     │ \"VI\" │ NA                   │ 46601         │\n",
       "│ 3228 │ 78020 │ \"St. John\"      │ \"VI\" │ NA                   │ 3090          │\n",
       "│ 3229 │ 78030 │ \"St. Thomas\"    │ \"VI\" │ NA                   │ 50916         │\n",
       "\n",
       "│ Row  │ RuralPop_2010 │ MedianHouseholdInc_2008_12 │ Elevation_ft │\n",
       "├──────┼───────────────┼────────────────────────────┼──────────────┤\n",
       "│ 1    │ 22921         │ 53773                      │ 290.0        │\n",
       "│ 2    │ 77060         │ 50706                      │ 155.0        │\n",
       "│ 3    │ 18613         │ 31889                      │ 220.0        │\n",
       "│ 4    │ 15663         │ 36824                      │ 224.0        │\n",
       "│ 5    │ 51562         │ 45192                      │ 870.0        │\n",
       "│ 6    │ 5607          │ 34500                      │ 455.0        │\n",
       "│ 7    │ 14921         │ 30752                      │ 445.0        │\n",
       "│ 8    │ 39955         │ 40093                      │ 565.0        │\n",
       "│ 9    │ 16816         │ 32181                      │ 745.0        │\n",
       "│ 10   │ 22282         │ 36241                      │ 589.0        │\n",
       "│ 11   │ 37858         │ 40834                      │ 580.0        │\n",
       "⋮\n",
       "│ 3218 │ 0             │ 23572                      │ NA           │\n",
       "│ 3219 │ 0             │ 31354                      │ NA           │\n",
       "│ 3220 │ 15556         │ 14752                      │ NA           │\n",
       "│ 3221 │ 700           │ 17616                      │ NA           │\n",
       "│ 3222 │ 2492          │ 16462                      │ NA           │\n",
       "│ 3223 │ 1071          │ 16598                      │ NA           │\n",
       "│ 3224 │ 3509          │ 16014                      │ NA           │\n",
       "│ 3225 │ 5749          │ 17926                      │ NA           │\n",
       "│ 3226 │ 7740          │ 14728                      │ NA           │\n",
       "│ 3227 │ 4000          │ NA                         │ NA           │\n",
       "│ 3228 │ 1080          │ NA                         │ NA           │\n",
       "│ 3229 │ 718           │ NA                         │ NA           │\n",
       "\n",
       "│ Row  │ TotalArea_sqmi │ LandArea_sqmi │ WaterArea_sqmi │\n",
       "├──────┼────────────────┼───────────────┼────────────────┤\n",
       "│ 1    │ 604.39         │ 594.44        │ 9.95           │\n",
       "│ 2    │ 2027.31        │ 1589.78       │ 437.53         │\n",
       "│ 3    │ 904.52         │ 884.88        │ 19.64          │\n",
       "│ 4    │ 626.17         │ 622.58        │ 3.59           │\n",
       "│ 5    │ 650.63         │ 644.78        │ 5.85           │\n",
       "│ 6    │ 625.14         │ 622.8         │ 2.34           │\n",
       "│ 7    │ 777.88         │ 776.83        │ 1.05           │\n",
       "│ 8    │ 612.29         │ 605.87        │ 6.42           │\n",
       "│ 9    │ 603.11         │ 596.53        │ 6.58           │\n",
       "│ 10   │ 599.98         │ 553.7         │ 46.28          │\n",
       "│ 11   │ 700.8          │ 692.85        │ 7.95           │\n",
       "⋮\n",
       "│ 3218 │ 41.81          │ 23.24         │ 18.57          │\n",
       "│ 3219 │ 21.38          │ 20.76         │ 0.62           │\n",
       "│ 3220 │ 115.07         │ 113.53        │ 1.54           │\n",
       "│ 3221 │ 37.5           │ 27.73         │ 9.77           │\n",
       "│ 3222 │ 68.18          │ 45.86         │ 22.32          │\n",
       "│ 3223 │ 263.99         │ 50.77         │ 213.22         │\n",
       "│ 3224 │ 37.04          │ 35.64         │ 1.4            │\n",
       "│ 3225 │ 83.24          │ 55.21         │ 28.03          │\n",
       "│ 3226 │ 68.82          │ 68.19         │ 0.63           │\n",
       "│ 3227 │ 332.66         │ 83.32         │ 249.34         │\n",
       "│ 3228 │ 91.76          │ 19.69         │ 72.07          │\n",
       "│ 3229 │ 308.51         │ 31.31         │ 277.2          │"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    countyinfo = readtable(\"../data/county-info.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3229-element DataArrays.DataArray{Float64,1}:\n",
       "  594.44\n",
       " 1589.78\n",
       "  884.88\n",
       "  622.58\n",
       "  644.78\n",
       "  622.8 \n",
       "  776.83\n",
       "  605.87\n",
       "  596.53\n",
       "  553.7 \n",
       "  692.85\n",
       "  913.5 \n",
       " 1238.46\n",
       "    ⋮   \n",
       "   23.24\n",
       "   20.76\n",
       "  113.53\n",
       "   27.73\n",
       "   45.86\n",
       "   50.77\n",
       "   35.64\n",
       "   55.21\n",
       "   68.19\n",
       "   83.32\n",
       "   19.69\n",
       "   31.31"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areas = countyinfo[:, :LandArea_sqmi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: replacing module _mimi_implementation_Brewery\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "initbrewery (generic function with 1 method)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Brewery Component\n",
    "# This is how we make beer\n",
    "\n",
    "using Mimi\n",
    "\n",
    "@defcomp Brewery begin\n",
    "    region = Index()\n",
    "\n",
    "    production = Parameter(index=[region, time], unit=\"liter\")\n",
    "\n",
    "    hopsdemand = Variable(index=[region, time], unit=\"MT\")\n",
    "    waterdemand = Variable(index=[region, time], unit=\"1000 m^3\")\n",
    "end\n",
    "\n",
    "function run_timestep(c::Brewery, tt::Int64)\n",
    "    v, p, d = getvpd(c)\n",
    "\n",
    "    v.hopsdemand[:, tt] = .07 * p.production[:, tt]\n",
    "    v.waterdemand[:, tt] = 2 * p.production[:, tt] / 1e6\n",
    "end\n",
    "\n",
    "function initbrewery(m::Model)\n",
    "    brewery = addcomponent(m, Brewery)\n",
    "\n",
    "    production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n",
    "\n",
    "    brewery[:production] = repeat(production, outer=[1, numsteps])\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = Model()\n",
    "setindex(m, :time, [1]) # Single period\n",
    "setindex(m, :region, length(dropna(readtable(\"../data/county-info.csv\")[:LandArea_sqmi])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n  call{T}(::Type{T}, ::Any)\n  convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n  convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n  ...\nwhile loading In[31], in expression starting on line 1",
     "output_type": "error",
     "traceback": [
      "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n  call{T}(::Type{T}, ::Any)\n  convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n  convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n  ...\nwhile loading In[31], in expression starting on line 1",
      "",
      " in call at essentials.jl:57",
      " in initbrewery at In[18]:27"
     ]
    }
   ],
   "source": [
    "initbrewery(m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3229-element DataArrays.DataArray{Float64,1}:\n",
       "  594.44\n",
       " 1589.78\n",
       "  884.88\n",
       "  622.58\n",
       "  644.78\n",
       "  622.8 \n",
       "  776.83\n",
       "  605.87\n",
       "  596.53\n",
       "  553.7 \n",
       "  692.85\n",
       "  913.5 \n",
       " 1238.46\n",
       "    ⋮   \n",
       "   23.24\n",
       "   20.76\n",
       "  113.53\n",
       "   27.73\n",
       "   45.86\n",
       "   50.77\n",
       "   35.64\n",
       "   55.21\n",
       "   68.19\n",
       "   83.32\n",
       "   19.69\n",
       "   31.31"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    brewery = addcomponent(m, Brewery)\n",
    "\n",
    "    production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3225x1 Array{Float64,2}:\n",
       "  594.44\n",
       " 1589.78\n",
       "  884.88\n",
       "  622.58\n",
       "  644.78\n",
       "  622.8 \n",
       "  776.83\n",
       "  605.87\n",
       "  596.53\n",
       "  553.7 \n",
       "  692.85\n",
       "  913.5 \n",
       " 1238.46\n",
       "    ⋮   \n",
       "   23.24\n",
       "   20.76\n",
       "  113.53\n",
       "   27.73\n",
       "   45.86\n",
       "   50.77\n",
       "   35.64\n",
       "   55.21\n",
       "   68.19\n",
       "   83.32\n",
       "   19.69\n",
       "   31.31"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initbrewer (generic function with 1 method)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function initbrewer(m::Model)\n",
    "    brewery = addcomponent(m, Brewery)\n",
    "\n",
    "    production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n",
    "\n",
    "    brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "run(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3225x1 Array{Float64,2}:\n",
       "  41.6108\n",
       " 111.285 \n",
       "  61.9416\n",
       "  43.5806\n",
       "  45.1346\n",
       "  43.596 \n",
       "  54.3781\n",
       "  42.4109\n",
       "  41.7571\n",
       "  38.759 \n",
       "  48.4995\n",
       "  63.945 \n",
       "  86.6922\n",
       "   ⋮     \n",
       "   1.6268\n",
       "   1.4532\n",
       "   7.9471\n",
       "   1.9411\n",
       "   3.2102\n",
       "   3.5539\n",
       "   2.4948\n",
       "   3.8647\n",
       "   4.7733\n",
       "   5.8324\n",
       "   1.3783\n",
       "   2.1917"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:Brewery, :hopsdemand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3225x1 Array{Float64,2}:\n",
       " 0.00118888\n",
       " 0.00317956\n",
       " 0.00176976\n",
       " 0.00124516\n",
       " 0.00128956\n",
       " 0.0012456 \n",
       " 0.00155366\n",
       " 0.00121174\n",
       " 0.00119306\n",
       " 0.0011074 \n",
       " 0.0013857 \n",
       " 0.001827  \n",
       " 0.00247692\n",
       " ⋮         \n",
       " 4.648e-5  \n",
       " 4.152e-5  \n",
       " 0.00022706\n",
       " 5.546e-5  \n",
       " 9.172e-5  \n",
       " 0.00010154\n",
       " 7.128e-5  \n",
       " 0.00011042\n",
       " 0.00013638\n",
       " 0.00016664\n",
       " 3.938e-5  \n",
       " 6.262e-5  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:Brewery, :waterdemand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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