{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this post, we'll write a quick Sankey diagram tutorial using the matplotlib tools for that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are Sankey diagrams? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sankey diagrams](https://en.wikipedia.org/wiki/Sankey_diagram) are diagrams representing flows between different nodes by using arrows whose width scales according to the value of the flow.\n",
    "\n",
    "Two of the most famous Sankey diagrams (named after Irish captain Sankey) are Sankey's steam engine efficiency diagram and Minard's map of the French losses during the 1812 Russian campaign.\n",
    "\n",
    "![Sankey's steam machine](https://upload.wikimedia.org/wikipedia/commons/1/10/JIE_Sankey_V5_Fig1.png)\n",
    "\n",
    "![Minard's map of 1812](https://upload.wikimedia.org/wikipedia/commons/2/29/Minard.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see in the diagrams above, Sankey diagrams have the following components:\n",
    "\n",
    "- individual \"systems\" or boxes (think of the steam engine components)\n",
    "- that have inputs and outputs\n",
    "- these inputs and outputs can be represented as branches that align with the system (they can flow from left, right or parallel to the system)\n",
    "- the boxes connect to each other"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These diagrams can be quite complicated to draw. Luckily for us, the matplotlib package comes with ways to draw these diagrams easily. Unfortunately, the documentation is not so easy to understand. That's where this tutorial comes in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do Sankey diagrams work within matplotlib? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that matplotlib comes with a module named `matplotlib.sankey`. Its API documentation is [here](http://matplotlib.org/api/sankey_api.html).\n",
    "\n",
    "Also, the following examples are available : \n",
    "\n",
    "- a [basic example](http://matplotlib.org/examples/api/sankey_demo_basics.html), with a simple system and two connected systems\n",
    "- an [example with links between systems](http://matplotlib.org/examples/api/sankey_demo_links.html)\n",
    "- a [complex example drawing the Rankine cycle](http://matplotlib.org/examples/api/sankey_demo_rankine.html)\n",
    "\n",
    "Generally, a Sankey diagram ought to be built like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "sankey = Sankey()\n",
    "sankey.add() # 1\n",
    "sankey.add() # 2\n",
    "#...\n",
    "sankey.add() # n\n",
    "sankey.finish()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, one can do something like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Sankey().add().add...  .add().finish()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This being said, let's move to our first diagram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our first Sankey diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we first import the necessary matplotlib tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.sankey import Sankey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build a Sankey with an input flow and an output flow of 1. The flows are specified with the `flows` argument, while labels are provided using `labels`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 0.16954982  0.        ]\n",
       "  [ 0.6947228   0.        ]], texts=[<matplotlib.text.Text object at 0x10ed4dc50>, <matplotlib.text.Text object at 0x10ed57128>], patch=Poly((-0.25, 0.5) ...), text=Text(0,0,''))]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lj4PshIxGIwBg9+7daNOmDaKjo1G3bl3ExMTc/2kvPz8/xMXF4YUXXkBAQAAuXPjzMypi\nY2MfeK/r/93WmDFjsG/fPjRq1Ahz5sz5y/stV64cMranoejwGhRcOW3L/0X6X24V/GAIGoqwyE64\nfPnyE9/e7NmzUb9+fdSvXx8JCQnIzs5G/fr17//6zJkzMXHiRGzatAmZmZmIiYlBo0aNkJ+fb5PH\nlNZwkJ3ciRMnkJCQgO+//x4XL17EgQMH7v9a2bJlcebMGQwZMgTDhg37x9uZOnUqWrdujZMnT2L4\n8L+/dH3t2rXx9YZ1MKXNQnEO35plD141m0LXMBLtgkORm5v72LeTlZWFZcuW4ciRIzh8+DAWL16M\nnJy/vmJMdHQ0/P39sXr1apw8eRKenp4AbPOY0hIOspMLCAhA1apVodPp0KhRI2RnZ9//tR49etz/\nZ2l+y9uuXTtMj/8EpsRJsBSYSu126e8ZXuyIPwzVERXdDRbL451Y3b9/P6KiomAwGGA0GtG5c2fs\n27evRLdhq8eUVnCQnZy7u/v9f9fr9TCbzff/W5Kk//p3FxeX+2+lUhTlsa/v9vagQXi9c0fIqTOg\nWMwP/w30RCRJgvHVAcj6+TqGjRz9WLfxVx9edOfOnQfeWvewT52z5WNKCzjIGrZ+/fr7/2zevDmA\nP48t/9+P5m7duhXFxcUAAG9v7xJ/Ozxv7hw0qF4e8p4lpVhNf0fSu8IY9h5WrN2IRYsXl/j3BwYG\nYsuWLcjLy4Msy9i8eTNCQkJw48YN3L59G4WFhQ9cxeSvHhO2fkw5Ow6yhuXk5KBBgwb49NNP759U\neeutt7Bnzx40bNgQhw4dgsFgAAA0aNAALi4uaNiw4SOfgNHr9Uj8ZiO8c36CfJyXI7IHvac3vCPf\nx4jRY7Bnz54S/d4mTZogNjYWAQEBeOmll9CvXz80bdoUEyZMQEBAANq1a4e6deve//rY2FgMHDjw\n/kk9wPaPKWcnleQzVv39/ZXMzEwb5pC9+Pn5ITMzE76+vja/r0uXLqFJ05fg9uoQeP7rRZvfHwH5\n2SdRuCMBmUcOoVatWna5T3s+phyNJElZiqL4P+zr+AqZbK5GjRqY9+kc5O1eKDpFMzz9GkGp2RLD\nRsWJTqEScBEdQGL8/3db2FpOTg7Gvv8h3F/sbLf71Lqim9kwn9uLsXOS7Haf9nxMOSu+QtawPn36\noGLFig+88b+0FRcXIzQyCnmVGsDYsIPN7of+wyLfQW5iPBZ89ilatmxp8/uzx+NIKzjIGhYbG4v0\n9HSb3b6iKOg/8G2cu1UAQ2Csze6H/kMxF8GUMhUD+/RCr1497XKftn4caQkHWcMCAwNRrlw5m93+\nnIS5+Cb9Wxg7jISk09vsfuhPiqLAtHM+WrxQC9Om2O/6e7Z+HGkJjyGTTaSmpmLCx5Pg030adO5e\nonM0QT66CRUtN7FhzRbodHyt5Yg4yFTqzp49i+4xPWEMGwOXspVE52hC3rmDwPfbsPN45v33+ZLj\n4SBTqbp58ybadQiFe6tYeFStJzpHEwp/u4C8XQuwe+d2VKlSRXQOPQF+X0OlprCwEMFhkSh+tjkM\n/35VdI4mmHNvIzdpCpYuWgh//4f+3AGpHAdZw3r06IHmzZvj3LlzqFq1KpYsefzPnFAUBb1698Xl\nPBcYWsaUYiX9HWtxAeTkKRg19G107fqasI7SfBxpHX90mkrFpPgpmPnFcpSJjofOzUN0jtNTFCtM\nqTPxSr1nsHHdmgc+ZY3Uhz86TXazefNmTJ2VAGP4OI6xncgH16KqWz5WrVjGMXYiPKlHT+TEiRPo\n2bsvvDt+AJcy/FAZe5C/3w39xX3YfjwTHh58AnQmHGR6bNevX0dQaDg82wyAe+XaonM0ofDaDyjY\nuwQH9u5GpUp8S6Gz4SELeiz5+floHxIOpfarMNRtJTpHE8x3byA3ZRpWf7UcDRo0EJ1DNsBBphJT\nFAXd3+iF64oPDM26ic7RBGthHkxJ8fhgbBwiIyNF55CNcJCpxN6f8CH2Zn0PY/shPKFkB4rVAtO2\nOQhv2wpxo0aJziEb4iBTiaxZsxZzFyyGMXwsJBc30TmaIO//Cs/56LF08UI+ATo5ntSjR3bkyBH0\nHzQYZbp8DL3xKdE5miCf3g73a1lIPZ4JNzc+ATo7DjI9kitXriAkoiO82g+BW8UaonM0oeDKGRQe\nWo19h/ajfPnyonPIDjjI9FAmkwntOoRB90IYvGq9JDpHE4pzrsOUNhPfrF/zwJWeybnxGDL9I6vV\nii5de+C2ZxUY/KNE52iCtcAEU+IkTPnkIwQFBYnOITviINM/Gjk6DkfP/QLjqwN5QskOFKsFprSZ\n6NYpFO8MGSw6h+yMg0x/a8nSpViyaj2MYXGQ9K6iczRB3v0l6j9TFvM/mys6hQTgMWT6S3v37sXQ\nEaPhEz0Zeq+yonM0QT6RAuPtH5G4/ShcXPhXU4v4p07/5eLFi4iM6gJj8DC4+lYTnaMJ+ZdOoDhz\nIzKOHoaPj4/oHBKEg0wPuHv3LtoGhcDV/zV41mgiOkcTim//AtO2OUhL3IyaNWuKziGBeAyZ7jOb\nzYiIisa9cnVgaBwmOkcTLPn3kJs4GXNnzUBgYKDoHBKMg0z3DR76Lr67egfGNv1Ep2iCYimGKXka\n+sR0Rd++fUTnkApwkAkAMO/zz7FucwqMoaMg6fSic5yeoigwffsFmtauilkzpovOIZXgMWTCjh07\nMOb9D1G26xToPIyiczQhL2sryuVdxTcbD0Ov5xMg/YmDrHHnzp1DdLceMIaMgutTz4jO0YS8C0dg\nOZWIjKxjMBr5BEj/wUHWsNu3b6NtcAjcmsXAo/oLonM0oejGJeTtmIed21JRvXp10TmkMjyGrFFF\nRUUIjYxCQeUmMDTg5yXYg0XOQW5SPBbOn4dmzZqJziEV4iBrkKIo6Nt/IM7/UQxD616iczRBMRfB\nlDQVQ/r3QUzM66JzSKU4yBo0c9ZsJO3cB2OHEXxHhR0oigLTjnlo3bgOJn/ysegcUjEeQ9aY5ORk\nfBQ/FT7dpkPn7iU6RxPkIxvwNHKwbnUSdDq+BqK/x0HWkDNnzqBHzzfhHT4OLmUris7RBPnH/ZDO\nZWDn8Ux4efEJkP4ZB1kjbty4gfYhYfBo3QfuVXgFCnsovH4e+bsXYt+uDFSuXFl0DjkAfv+kAQUF\nBQgKjYC5RisY6rURnaMJ5txbyE2eghVLFqNx48aic8hBcJCdnKIoeOPN3vil0AOGFj1E52iCtagA\npqR4vDd8KLp06SI6hxwIB9nJfTxpMnYePglD0FBIEv+4bU1RrJC3JyC4VVO8P36c6BxyMPwb6sQ2\nbtyEGQnzYAwfC52rh+gcTZAPrMazXmZ8tWwJr0FIJcaTek4qKysLvd/qD+9OH8LFu7zoHE2Qz34L\n18uHsO14Jtzd3UXnkAPiIDuha9euITgsAl6vDIL707VE52hCwdUfULBvGQ7t34sKFSqIziEHxUF2\nMnl5eWgfEg48HwSvOi1E52iC+e7vMKVMw/rVK1G/fn3ROeTAeAzZiVitVnR9/Q38rvOFISBadI4m\nWAvzkJs4GRPfH4ewMF72ip4MB9mJjB3/AQ6cPg9ju8E8oWQHitUCOX02ooJfwcgRw0TnkBPgIDuJ\nlStXYf6Xy2EMGwPJxVV0jibIe5ejtq87Fi9cwCdAKhU8huwEDh06hIFDhqJs9CfQG3xE52iC6VQ6\nPH8/hZSsY3B15RMglQ4OsoO7fPkywiI7wRA0FG4V/ETnaELB5dMoPrIWBw8fRLly5UTnkBPhIDuw\n3NxctA0Ohb5RJLxqNhWdownFf1yDKW0mtmxaj9q1a4vOISfDY8gOymKxICq6G+4Yn4VXk46iczTB\nUmBCbuJkTJ8yCe3atROdQ06Ig+ygho0cjeMXf4PhlQE8oWQHisUMOXU6YqI74u1Bg0TnkJPiIDug\nRYsXY8XajTCExkHS86iTrSmKAnn3YjSs7ot5n84RnUNOjH+bHczu3bsxYvQYlH0tHnpPb9E5mpB3\nIhll7lzA1oyj0Ot5DUKyHQ6yA7lw4QI6do6GocMIuJavKjpHE/IvZsF8/BtkHDuCMmXKiM4hJ8dB\ndhB37txB2+BQuAV0h6dfI9E5mlB06wrk7Z8iPXkratSoITqHNIDHkB2A2WxGeKcukH3rwdAoRHSO\nJljy7sKUOBnzEmajVatWonNIIzjIDmDg20Pw/W8mGF7uIzpFExRzMUzJU/HWmzGIjX1TdA5pCAdZ\n5RI+/Qwbk7fDGDISko4nlGxNURTI3y7AS/X8MGPaFNE5pDE8hqxi6enpeP/Dj1C2+zTo3A2iczRB\nPvYNyhdex6Z1B6HT8fUK2RcHWaV++OEHdO0RA2NYHFx9nhadowl5Px0CzqYhI+sYjEaj6BzSIA6y\nCt26dQvtgkPh3vJNeFTjFSjsoej3i8j7dj527diGqlX5lkISg9+TqUxRURFCIjqhsFpTGOq3FZ2j\nCRZTDnKT4vHlFwvQtCk/pInE4SCriKIoiO37Fi7eAwyteorO0QRrcSFMyfEYNngAunfvJjqHNI6D\nrCJTpk1H6q6DMAQPgyTxj8bWFEWBvPMztPGvj48nfig6h4jHkNVi69atiJ8+Cz7dp0Pn5ik6RxPk\nw+tQRW/CmpWp/MQ8UgUOsgqcOnUKb8T2gXfEeLiUqSA6RxPkH/ZCd343th/PhKcnnwBJHTjIgv32\n229oHxIGj8B+cH+mjugcTSj89RwK9izG/j278PTTfEshqQcPVAqUn5+PoNAIWJ9rA8PzgaJzNMF8\n7yZMKdOwasUyNGzYUHQO0QM4yIIoioKYXrG4ZjbC0Ky76BxNsBblw5QUj7GjR6BjR172itSHgyzI\nhx99jF3HzsDQ/h2eULIDRbFC3paA0DbNMfa9ONE5RH+JgyzAunXrMWfeFzCEj4XO1V10jibI+1ei\nhreC5UsW8wmQVIsn9ezs2LFj6DdwEMpEfQQXYznROZogf5cBt1+OIv14Jtzc3ETnEP0tDrIdXb16\nFR3CI+H16ttwq/Qv0TmaUHD1LAoPrMCeA/vg6+srOofoH3GQ7USWZbTrEAbp3yHwqt1cdI4mFN/5\nDaaUGdi0djXq1asnOofooXgM2Q6sViuiu72Om66VYGjaWXSOJlgLZZgSJ+OTDz9Ahw4dROcQPRIO\nsh3EjRmHwz9kw9j2bZ5QsgPFaoEpbRaiw9pj2LvviM4hemQcZBtbvnwFFi1fBWPYe5BcXEXnaIK8\ndxmer2TAF/Pn8QmQHAqPIdvQ/v37MWTYCJSJngS9V1nROZogn0qH4cZ3SDl+DK6ufAIkx8JBtpFL\nly4holNnGILehZtvddE5mpCffRLFR9ch48gh+Pj4iM4hKjEOsg3cu3cPbYNDoW/cGZ7/elF0jiYU\n374KOX02krd8jVq1aonOIXosPIZcyiwWCyI7v4a7ZWvC0CRcdI4mWPJzkZs0GbOmT0GbNm1E5xA9\nNg5yKXvn3RE4ffkWjG3e4gklO1AsZsip0/Fmty4Y0L+/6ByiJ8JBLkULvvgCqzZtgSEsDpKeR4Ns\nTVEUyLsWocm/nkbC7Jmic4ieGFejlGRkZGD0mPEo23UK9B5G0TmakHc8ET6mbGzedRh6vV50DtET\n4yCXgp9++gmdX+sGY8gouJarIjpHE/J/PgbLya3IyDwKb29v0TlEpYKD/IQURUFYx84o1HvC9eIR\nmC4eEZ30ULePbhWdUGLVhm+Czs0DAFB0Mxum7XOxIy0Zzz77rOAyotLDQX5CkiRhxpRJuHLliuiU\nR7a0MBunTp0SnVEiRTcuwqNqPVjkO8hNjMcX8+aiRYsWorOIShUHuRR06tRJdEKJDB06VHRCibzw\nYgBuAlDMxTClTMOgvm+iZ883RGcRlTq+y4IchALTzs/RskEtTI2fJDqGyCb4CpkcgunoZlT3KMCG\nNVuh0/F1BDknDjI5BPc7l7DjRBa8vLxEpxDZDAeZVG/SxAmoXrUKqlThWwrJuXGQSfU6RvAzQUgb\neDCOiEglOMhERCrBQSYiUgkOMhGRSnCQiYhUgoNMRKQSHGQiIpXgIBMRqQQHmYhIJTjIREQqwUEm\nIlIJDjIRkUpwkImIVEJSFOXRv1iSbgK4bLscIiKn9KyiKBUe9kUlGmQiIrIdHrIgIlIJDjIRkUpw\nkImIVIIjtbtNAAAAJklEQVSDTESkEhxkIiKV4CATEakEB5mISCU4yEREKsFBJiJSif8BLIoolqaZ\nDCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cccbf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "sankey.add(flows=[1, -1],\n",
    "       labels=['input', 'output'])\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are other arguments that can be specified. For instance, one can change the orientation of the diagram using the `rotation` argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[-1.0, -1.0], flows=[ 1 -1], tips=[[  1.03819319e-17  -1.69549816e-01]\n",
       "  [  4.25395029e-17  -6.94722805e-01]], texts=[<matplotlib.text.Text object at 0x10f048b38>, <matplotlib.text.Text object at 0x10f048fd0>], patch=Poly((0.5, 0.25) ...), text=Text(0,0,''))]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GmNJW4eDe3RxjEoqDTEI9+eST0B07glBfb+RtnIoyQ47Vji2XGqHf9QGq3z2F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed0d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "sankey.add(flows=[1, -1],\n",
    "       labels=['input', 'output'],\n",
    "          rotation=-90)\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go a step further by providing a third flow to our diagram. To do this, we need to specify an orientations argument. In the previous diagram, we had only to flows, so by default the Sankey module assumes those to be input and output. Now, we need to specify how the flows align with the diagram using a list of orientations. Here, putting an orientation of 1 means it comes from the side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[-1.0, -1.0, 2.0], flows=[ 1.  -1.   0.5], tips=[[ -2.50000000e-01   5.80450184e-01]\n",
       "  [  4.25395029e-17  -6.94722805e-01]\n",
       "  [  7.90225092e-01   7.50000000e-01]], texts=[<matplotlib.text.Text object at 0x10f0f6f98>, <matplotlib.text.Text object at 0x10f0fe400>, <matplotlib.text.Text object at 0x10f0fe978>], patch=Poly((0.75, 0.25) ...), text=Text(0,0,''))]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OJaKu+iryDiyHTlMkdaQqU3wvFVm7ZuC9oUGI2P0DbGxspI5EJoqFTBVWu3Zt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\nIplgIRMRycT/AcLvI7NufmwVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed91c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "sankey.add(flows=[1, -1, 0.5],\n",
    "           orientations=[0, 0, 1],\n",
    "           labels=['input', 'output', 'third flow (input)'],\n",
    "           rotation=-90)\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing can be done for an output (a fourth flow), but on the other side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[-1.0, -1.0, 2.0, 2.0], flows=[ 1.  -1.   0.5 -0.5], tips=[[-0.25        0.58045018]\n",
       "  [ 0.25       -1.4447228 ]\n",
       "  [ 0.79022509  0.75      ]\n",
       "  [-1.2349479  -0.75      ]], texts=[<matplotlib.text.Text object at 0x10f1b06d8>, <matplotlib.text.Text object at 0x10f1b0b70>, <matplotlib.text.Text object at 0x10f1b5128>, <matplotlib.text.Text object at 0x10f1b50f0>], patch=Poly((0.75, 0.25) ...), text=Text(0,0,''))]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3BGlpdyEIAiwsLDBy5Ejs3LlTlHqoehjIJCkymRlsAsdB4zkUHTsHPhG+QUFB5V5GcvVX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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f06b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "sankey.add(flows=[1, -1, 0.5, -0.5],\n",
    "           orientations=[0, 0, 1, -1],\n",
    "           labels=['input', 'output', 'third flow (input)', 'fourth flow (output)'],\n",
    "           rotation=-90)\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is fun and all, but let's now see how we can connect two diagrams together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connecting diagrams together "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API documentation states that to connect sankey diagrams together, we need to provide the following arguments:\n",
    "\n",
    "- `prior` which is an index of the prior diagram to which this diagram should be connected\n",
    "- `connect` which is a `(prior, this)` tuple indexing the flow of the prior diagram and the flow of this diagram which should be connected\n",
    "\n",
    "Basically `prior` is the zero based index of the previous diagram element. `connect` however is more cryptic. Let's start again with a simple input output diagram and try to connect it to another one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 0.16954982  0.        ]\n",
       "  [ 0.6947228   0.        ]], texts=[<matplotlib.text.Text object at 0x10f381400>, <matplotlib.text.Text object at 0x10f381898>], patch=Poly((-0.25, 0.5) ...), text=Text(0,0,'')),\n",
       " Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 0.6947228   0.        ]\n",
       "  [ 1.21989579  0.        ]], texts=[<matplotlib.text.Text object at 0x10f387be0>, <matplotlib.text.Text object at 0x10f38d0b8>], patch=Poly((0.275173, 0.5) ...), text=Text(0.525173,0,''))]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DDNDwhkH3sdpltF0todu7fREeEaF2HHoAFrLgkpKS0CyoFdwbvANDSd4GUUny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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f175978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "\n",
    "# first diagram, indexed by prior=0\n",
    "sankey.add(flows=[1, -1],\n",
    "       labels=['input', 'output'])\n",
    "\n",
    "# second diagram indexed by prior=1\n",
    "sankey.add(flows=[1, -1],\n",
    "          labels=['input2', 'output2'],\n",
    "          prior=0,\n",
    "          connect=(1, 0))\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that we can now give a simpler explanation of the `connect` argument: it says which flows (indexed in the order they were defined) should be connected. So `connect` should really be described as \n",
    "\n",
    "> connect = (index_of_prior_flow, index_of_current_diagram_flow) that need to be connected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, the previous diagram does not look very nice. We can correct that by specifying the trunk lengths for each diagram (make them shorter or longer). According to the API:\n",
    "\n",
    "> trunklength: length between the bases of the input and output groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[-0.83045018  0.        ]\n",
       "  [ 1.6947228   0.        ]], texts=[<matplotlib.text.Text object at 0x10f56d390>, <matplotlib.text.Text object at 0x10f56d828>], patch=Poly((-1.25, 0.5) ...), text=Text(0,0,'')),\n",
       " Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 1.6947228   0.        ]\n",
       "  [ 2.21989579  0.        ]], texts=[<matplotlib.text.Text object at 0x10f573b00>, <matplotlib.text.Text object at 0x10f573f98>], patch=Poly((1.27517, 0.5) ...), text=Text(1.52517,0,''))]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sN77ik09kiASK/B3H9ybzdqwjrFYDANyx1Sk+ePxYasGXn4DveGHDLmqIK6Ya7273snGP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1d4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "# first diagram, indexed by prior=0\n",
    "sankey.add(flows=[1, -1],\n",
    "       labels=['input', 'output'],\n",
    "          trunklength=3)\n",
    "sankey.add(flows=[1, -1],\n",
    "          labels=['input2', 'output2'],\n",
    "           trunklength=1.0,\n",
    "          prior=0,\n",
    "          connect=(1, 0))\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another argument that can be used is the pathlength. These only work for top and bottom flows. Again, according to the API:\n",
    "\n",
    "> list of lengths of the arrows before break-in or after break-away If a single value is given, then it will be applied to the first (inside) paths on the top and bottom, and the length of all other arrows will be justified accordingly. The pathlengths are not applied to the horizontal inputs and outputs.\n",
    "\n",
    "We can make another example for them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0, 3, 1], flows=[ 1.  -1.   0.1  0.3], tips=[[-0.38045018  0.1       ]\n",
       "  [ 0.6947228   0.        ]\n",
       "  [-0.55        1.15804502]\n",
       "  [-0.65       -1.57413506]], texts=[<matplotlib.text.Text object at 0x10f623a90>, <matplotlib.text.Text object at 0x10f623eb8>, <matplotlib.text.Text object at 0x10f6284a8>, <matplotlib.text.Text object at 0x10f628438>], patch=Poly((-0.25, 0.7) ...), text=Text(0,0,'')),\n",
       " Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 0.6947228   0.        ]\n",
       "  [ 3.21989579  0.        ]], texts=[<matplotlib.text.Text object at 0x10f630cf8>, <matplotlib.text.Text object at 0x10f6341d0>], patch=Poly((0.275173, 0.5) ...), text=Text(1.52517,0,''))]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//vgD3iFtKd+3B+aKwW77Vuvkbz7E+/IR1j/pjadJyljo06YTdt7cYWL7zs2U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3adcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "# first diagram, indexed by prior=0\n",
    "sankey.add(flows=[1, -1, 0.1, 0.3],\n",
    "           orientations=[0, 0, 1, -1],\n",
    "       labels=['input', 'output', 'top', 'bottom'],\n",
    "          pathlengths=[0, 0, 0.5, 1.0])\n",
    "sankey.add(flows=[1, -1],\n",
    "           labels=['input2', 'output2'],\n",
    "           trunklength=3.0,\n",
    "          prior=0,\n",
    "          connect=(1, 0))\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we understand the basics, we can also build a diagram that introduces that outputs the second system to the bottom (by using an orientation of -1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0, 3, 1], flows=[ 1.  -1.   0.1  0.3], tips=[[-0.38045018  0.1       ]\n",
       "  [ 0.6947228   0.        ]\n",
       "  [-0.55        1.15804502]\n",
       "  [-0.65       -1.57413506]], texts=[<matplotlib.text.Text object at 0x10f6de7b8>, <matplotlib.text.Text object at 0x10f6dec50>, <matplotlib.text.Text object at 0x10f6e5240>, <matplotlib.text.Text object at 0x10f6e51d0>], patch=Poly((-0.25, 0.7) ...), text=Text(0,0,'')),\n",
       " Bunch(angles=[0, 3], flows=[ 1 -1], tips=[[ 0.6947228   0.        ]\n",
       "  [ 3.52517299 -1.1947228 ]], texts=[<matplotlib.text.Text object at 0x10f6ebac8>, <matplotlib.text.Text object at 0x10f6ebf60>], patch=Poly((0.275173, 0.5) ...), text=Text(1.52517,0,''))]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XideczDvw59fXHbfT+PM0wqal0fjzNF5bl8m51PyPQ/99/0IUlerlDGyKUZg05j2mTflU\n6zhFwuULOSkpiQYNGjBp0iQmTJgAwKBBg9i8eTOJx/4Pc8YfYLi+JJJ7xSAUg4Fzs4br8qRelafy\n95PfvXJ1vDqPpH+clV1nb5558ffC9PNSWB7txYHnvJn9iCeTdto4l5r/4Q4pZFGcqpUxsCFa4b/v\nvs5Xs2ZqHafQ6X4Jp7sJCgoiPj4ePz+/O25z/vx5GjSKhGZPY6nTshjT5d2p8b2p9vJirKf2c23r\nPIyevmRfPoV7lZr4dXsVRVE4M20Aljotyfw9AcXNHb/uI3Er58/llRNwZmXgeWYHw5q48Z/N2dSt\naCAtW+VqporNAR+2NzMiysy4bVmkZavUr2jg8cVWapVXsLgpbH/WQt0paTxe343Vx+x4mmDeY17U\nKm8gdmkm3YJN9K7nBoD3hymkveVL8y/S+e2yg+plDTwT7saIKPM93qUQBXfksoPWc1W+XbqKBx98\nUOs491RilnAqqKpVq/LDqhVkbvqMrPNHtY6Ta9kXf6dc+8H4D5yK/doFss7+eatCg9mC/7NT8GnU\njaQNM3K+bqn3IFkVQhjzczYebrBjoAWA8CpGapY33FKWiqJQ1gPmP+bF3iHeeLpdH4MvY1Y48Jw3\nw5u689Ia611zju5gplU1E3uHeEsZi2IT4mdkbnfo+1jPm07yuzqXLuTExMS7Hh3f0LBhQzq2b4ft\nyOZiSFU4zFWDMfn6oSgG3CvVwJ785zQ+r3rXjwgsdR8k6+zNk+bdgluimC1k2cHLDVpVM3L1Nuvz\n/ZHu5PX1VoLL3/pfIDrs+lFwdKgb28/IxSdCnzrUMPFGExs9O3cgLa1krCLk0oWcW1OmTmXjtni8\nmj+hdZRcU0xuf35sMMCdVkj538wSxWAEVUVBwT20Ew4VXt9wfWzX7gDnXzr5YprKwoN2vn7EM+eo\n+KZd3uZjk+HPfaiqSrb0tNCBF5oaifS+yNPRvXE6Xf8iKZcv5AEDBlCpUiVCQ0Nv+/i6det4/Z/v\n4N3jnxg8vIs5XdHIOLwFgPTDWzD7hwBgLFOJ7IvHMAfWz3l86u5s1h63Y3GHP9JVrmQ4uZjmZOru\nbNpXN9Gimgkfs0Jq1s1H0Av/N1tj4UEbUYFGAILKGkg4d72F447Ysf3v/76Pu0JqtsyBFtpQFIWp\nnRQuHtrGqHff1jpOgbl8IcfGxrJmzZrbPnbkyBF6PxGNd+dXcSvnX8zJio7Tmsa5WcNJjV9GufaD\nAPAJ74T11K9cWTURU9kqgEKWRwWSMq8fRJtNEPxpGk2/SCfbCZtPOoiYnsavFx0MXJ5JxPS0nCv+\nkjJVGkxLY9LObCZ08gBgUCM3fjrpIHx6GttPO7D87wC+QWUDJgOET09jwvYsLf46RClnNiks6aUy\n7dOJ7NixQ+s4BeLSsyxuSExMpFu3bvz66685X7ty5QrhjZuQVa87lgYPaZiucJ2ZNoCqz0zA6FUm\nV9tf27YAds9lz2AL1cvd++dv0MRU4gdb8PNy+Z/VopT59qCNd3+pyJ6DR/Dw8NA6zk1K9SyL7Oxs\nuvTohdW/UYkq4/wo+0Bf1KDmtJ+TSUqWDC2IkqtPPRP1vZN4959vaB0l30pcIauqyrODh3AsyY6l\n5dNaxyl0Ac/NyvXR8Q1ezaM5m+Lg96R7n/RIfMlHjo6FS1IUhakPqcyeNcNlhy5K3HfeuI/Hs3z9\nFiydRlyfeVDK2TNSSP92JG+28iCiivx9iJKtksXAJx1U+sc8gdV69zn0elSiCnnFihW8/+FovLu/\nhcHspXWcYnF51UROT47h3MyhtzzmdNpJnfs8narDu63dbvPsPw2Iy6TSR6mETi0Z8zlF6dWnnok6\nXkl8PHaM1lHyzOULOTo6mqioKI4cOUKPHj0whrTFVKaS1rGKjXdYByr1ef+2jyV/+xbV3ZOZ84j7\nTXfFu53YCDfWPFk6foiJkk1RFMa2cTJh/EdcuXJF6zh54vKFPH/+fPbt20eFylWp0O0Vyj34jNaR\nipVHYChGT59bvn513XQ8Lh9mbYwHHqa7lzFcX/m3vOe9txPCFdSuYKRPHYX/jnpP6yh54vKFbLVa\neahLd+zVW2Kp10brOLqQuu8HnL+uZN1TnlT2dvl/YiHy5e0WMGvWTE6dOqV1lFxz6e9WVVV58pn+\nnM7ywPJAtNZxdMF69jesGyYz/1EPGlSWk3ii9PL3MTCkoYH3/+U60+BcupBHffAfNuzYi+WhF1AU\nl34rhcKedpWMJW/xbhsz3UPufhJPiNLgtSgDy+KW8ttvv2kdJVdctsUWL17M2ImfYun2JgY3fV2V\nowVVVUmd+wI9gw2MjJIyFgKgrIfCsEYwefxHWkfJFZcs5ISEBGIHDsan+5uYfCpoHUdTl5aN5cKc\nV7FfOU1m6jVaBnDPGRW3E70kg6iZ6Ry54iRgfCozf8kugrRCFL/BjYwsWLiQlJQUraPck8vdy+Ls\n2bOEN26CIao/XiEPaJpFL5JWT8T7xEb2D/GiglxlJ8Qt+iyFNgM/YNjw5zV5/RJ5L4uMjAw6du4G\ndR+SMv6flIRlOA9vYMPTnlLGQtzB0HA7UyeNIy8HoFpwme9gp9NJn+gYLhr8sDTtrXUcXcg8uZ+s\nn2aw5HFP6vjJjAoh7qRNkBE1I4nNm/W9apDLFPKb/3ybbQeO4d1hWL7GSEsae8olMpa+w5gOHjxU\n06R1HCF0TVEUhobbmDYpfyu7FxeXKOQ5c75h6hdf4d31jZuWNiqtnPZsUue+QL9QI8Obyt+HELnx\nRH0Tq39Yr+ubDum+kLdv385zz7+IT4+3MFrKah1HF1LmjSCivJUpnd21jiKEy6hoMdDA34Mff/xR\n6yh3pOtCPnnyJF17PIJXx+dxrxikdRxdSFo+hrKZZ4h7wgOTQYZuhMiLHkFWli35VusYd6TbQk5N\nTaX9Q50xRvTAq2YTrePoQvKORXB8Kxue8qSsh5SxEHnVI9jI8uVxup1toctCdjgcPPLY41zzCcKr\nUU+t4+hC5vHdZG/7mmV9PalZXpf/bELoXoifEYvRzp49e7SOclu6/M5+6eVX+eX3C1ja/kNmVADZ\nV8+SvvwDJnX24MEgmVEhREF0r+Fk2dLvtI5xW7or5M8+/5zZCxbj3fV1FKOUjzM7g/T5IxjUyI2B\nDWVGhRAF1baak59/XKd1jNvSVSFv2rSJV157E58e/7rtTddLG6fTScrcl4iqYmdcByljIQpDY38j\nCft/1eU4sm4K+dixY/R8tA+Wh1/GrUKA1nF04drSD6jkuMjiPmaMMqNCiEJRxduAl5vCiRMntI5y\nC12MCaiqSpcevcgyeuKWmEBaYkKB9ufIzsB6/hjWC78D138KBry4AKOHdyGkLR5JW77BeHo364dY\n8HaXMhaiMDX2dychIYEaNWpoHeUmuihkRbm+9tXJkyfz9fzs7Gy+//57du3adeu+3b1Q3NxxpUpL\nP/Iz9t0L2fC0J9XK6OaXGCFKjMYVMknYtYM+ffpoHeUmuihkgMceeyzPzzlz5gxTpk1n+mefYywX\nQPmHX8DsH4xbhUAUg2vebCf70kkyV41hRncPmgfo5p9HiBKlcVWFiTu2ah3jFi75HX/16lWeG/4i\ny5cvw6tuGzwfeR93v2paxyowpzWN9IWv8GIzd/qFyUk8IYpKaCUjv204qnWMW7hcIS9dupQBg4dg\nqBGF38AvMJi9tI5UKJxOJ8nfvECbQJVRbeUeFUIUpao+Cn9cTcHpdGIw6GdY0GUKOSkpiYFDhrJu\n01a8Oo7AIzBU60iFKnnx2wQarzL/UU8McjGMEEXK3ahQxsudS5cuUblyZa3j5HCJQj5z5gyt2rQn\npVwwZZ+cqLtFTdMPb+Vy3Oh8P79sm/6knNhHldoW/vGDa459CxejgqfJSYPy2TzZwK1U3hulahkz\n58+fl0LOi8TERJq3bI09uAM+TfN+4q84uJXzxyOoYc7ntkuJONKTcv18a/wS/v3vfxMUFFQE6YS4\nlaqqZGRk8NO6Vbw9ZR0DG7rx3zaUqjsIVvVROHfuHBEREVpHyaHrQj5//jwtHmyLvW4XvBt31zrO\nHblXrkHlJ0YBkHXhGKlxo5g44wsGDOh/z/GphpFN+debr+drlokQBTVkyBAuXLhAbL/H6Ru3l4U9\n1VJzEZK/xcn58+e1jnET/Yxm/43NZqN9py5kBbXUdRn/Vda5I6Qu/TdzZs1g4MBnc3WyYE/8Lilj\noakqVaoQt3odFz1qMz3epnWcYlPZnMXFixe1jnET3Rby+6M+4KLNjKX5E1pHyRXb5dOkLPsPC76Z\nTa9evbSOI0SemM1m/vPRBKbsc9PlPR6KgofRSZbOlnPSZSHv27ePCZMm49nuOZe4/aYzK4O0lf9l\n0sdj6datm9ZxhMiXVq1aoXqUYfc5p9ZRioWbQcFuy9Y6xk10V8g2m43H+z2FZ8unMfn4aR3nnlRV\nJX3dZB7p3JGBzz6rdRwh8k1RFOoEB3M6uXQUsskAtuwsrWPcRHeFPP2zz7hkN2MJ7aB1lFzJSIij\nqimdz6dN0TqKEAXmbjZjLx19jEFBd8Mzupploaoq4yZ8gnuzAS4xVJGZuBfbnjhW7YnHbDZrHUcI\n4eJ0dYS8adMmkq12zC5wFV5m4l4y1o5n6ZJvqVbN9e+jIYTQnq6OkMdN/ARj/U66PzrOPB5PxvpP\nWLVsKa1atdI6jhCihNBNIV+6dImNGzdSceAXWke5I0dmCpkJcWQfXM8Pq1YQFRWldSQhRAmim0Le\nuXMnPgEhhXr3tuw/fif7wFqUa2dQbVkU5C71TrudzKSL9OnTh1FzE7j//vsLLacQQoCeCnnXbuzl\nqxfKvhzWNDLWjEe5dprhzw2hbZuReHl5FWgoxGQyERgYSIUKFQoloxBC/J1uCnnL9p0YKzUq8H6c\n2Zmkfvcu0T0eYvLEnzGZdPMWhRDirnQzy2L/3j2Yq9Qq8H7Sdy2mbdMGTJ08ScpYiFy6dOkS9YOr\ns3rdj/iWkhmcvmaFWbNm0iS8PtnZ+rhiTxeFnJaWRlpqCkbfSgXaj+qwYf11PR+Oel/3MzWE0BMv\nLy9CQkKobIFI/9JxT+4ONUwYHNk0adZMN6uG6CJFZmYmbmbPApeo9fRBqlevTt26dQspmRClg8Vi\nYXHcKvrEDqPZbJWDfzi0jlSktpy002KOg3c/GMPUz2fp5rdpXaSwWq0YTAVf1NOZkUyQzH4QIl8M\nBgMfjh1H3fphtH1xKF93s/NwLV1URKGavdfGyJ+MzJm/iE4PP6x1nJvo4m9bVVUUpeAH66rqxOSm\ni7ckhMt66plnqFGrFr17duXNpjaeb2IsEUOATlXlrU0qixPL8tO2Dbr8TVoXQxZCCH1p0aIF2+P3\n8vnxygxdq2Jz6OsmPHmVlq3y2HewPTuEHQn7dVnGIIUshLiDoKAgtu3eyymfSDp/C0mZrlnKp5Od\ntPpGpVxEV9b9tA0/P/3e1lcKWQhxR76+vixbs56wh2Jo/rWTo1dc62TfrrMOmn/tpN/QN5n59Tzc\n3d21jnRXMuAqhLgro9HIhMnTqFu/AS3ffJUFPey0ra7/6lj4q43h6w3MnD2PHj17ah0nV+QIWQiR\nK4OHPMf8Jcvpu9zIjF/0uxiqqqq8v9nBa9u8Wf/Tzy5TxiCFLITIg3bt2rFlRzzjfq3Ay+udOJz6\nGlfOtKn0Wwarr1Vn5y/7CQ8P1zpSnkghCyHyJDg4mB0J+9mnhNJzCaRk6aOUL6Q5aTsflFrt+fHn\nXVSpUkXrSHkmhSyEyLNy5cqxZuNmApo/Sos5KonXtF2Ib+8FB81mO+n69IvM/fY7PD09Nc2TX1LI\nQoh8cXNzY9oXXzLolfeJ+trJz6fsmuSIO2yj4wKVjybP5O33Rrn0RSxSyEKIfFMUhRdGvMysbxbR\n63sDc/YV38k+VVUZu83OsB89WbVuE4/37Vtsr11U9D93RQihe527dOHHrTvo/nB7fruaxgdtDBiK\n8Eg1y64yZC3sswayI2E9AQEBRfZaxUmOkIUQhaJ+/frs/OUAW6zB9Pke0rOL5mTf5QwnHRdAcpUH\n2LIjocSUMUghCyEKUcWKFVm/eTs+YZ1pPRfOphTuyb5Dlxw0m63S8tHBLI5bhcViKdT9a00KWQhR\nqMxmM1++9iw5AAAJuElEQVR+s4DH//EazWY7iT9XOJdbrzlmp81clXfHfMqHY8fp5qbyhankvSMh\nhOYUReH1t/7Fp198Q+dvYfGh/J/sU1WVybvs9F/jxncr1vJ0bGzhBdUZOaknhCgyj/Tqxf1BW+jZ\n5SEOX83kny0MeZqWZnOovLhOZXNSZbbt3kD16oWzMr1eyRGyEKJINWzYkJ2/7Gf5lSCeXA5We+5O\n9iVlqnRZBCd9Itm2e2+JL2OQQhZCFIOqVauyadtuHNXb0nYeXEy7+8m+o1ccRM1RCe0Yw7I16/H1\n9S2mpNqSQhZCFAtPT0/mL15Kp5jhNJutsv/i7U/2/XjCTstvVF5+ZywTJk/DaCwdq2CDjCELIYqR\noii8N+pD6tQNpf3QQczqbKN7yJ8LHM/4xca/tpqYv2Qp7dq10zCpNqSQhRDFrm+/flSvWZNHu3fm\nyNUsRjQzMnKjyspzFdiyYwPBwcFaR9SEDFkIIYrFV199xblz53I+b9asGTsS9vHN6fsInmZjnxLK\njoT9dyzjxMRE5s2bl/P5unXraNy4MWFhYTRu3JiNGzcW+XsoalLIQohi8fdCBggMDGTrrl/44JNZ\nrNm4mXLlyt3x+X8vZD8/P5YvX86BAweYPXs2Tz31VJFlLy5SyEKIfBs/fjyhoaGEhoYyceJEEhMT\nCQ0NzXl83LhxvPfeeyxevJj4+HhiYmKIiIggMzOToKAgXnvtNaKiopgwYQInT54EIDY2lsWLF+fs\nw9vbG4A33niDLVu2EBERwYQJE2jYsCH+/v7A9ftoWK1WsrKyivHdFz4pZCFEviQkJPDll1+yc+dO\nduzYwYwZM0hKSrrttr179yYyMpK5c+eyd+/enBvIlylThgMHDjB8+HBeeumlu77e6NGjadWqFXv3\n7mXEiBE3PbZkyRIaNmyI2WwunDenESlkIUS+bN26lV69emGxWPD29ubRRx9ly5YtedpHdHR0zp/b\nt2/PV46DBw/y+uuv89lnn+Xr+XoihSyEyBdVvfWKu2vXruF0/nnRh9Vqves+/noZ9Y2PTSZTzj5U\nVSU7O/uOzz9z5gy9evXi66+/pmbNmnnKr0dSyEKIfGndujVLly4lIyOD9PR0vv/+ezp37swff/zB\nlStXyMrKYsWKFTnb+/j4kJqaetM+Fi5cmPNnVFQUAEFBQSQkJAAQFxeHzWa77fOvXbtG165d+e9/\n/0uLFi2K9L0WF5mHLITIl0aNGhEbG0vTpk0BGDhwIE2aNOGdd96hadOm+Pv7U6dOnZztY2NjGTJk\nCJ6enjnDE0lJSTRo0ACz2cz8+fMBGDRoED179iQ8PJyHH344557HDRo0wGQyER4eTmxsLOnp6Rw7\ndoxRo0YxatQoAH744QcqVapUnH8NhUq53a8ddxIZGanGx8cXeohTp04R2qgZ5Qd+UaD9pB38kZYe\nZ1i6aEEhJRNCFJWgoCDi4+Px8/PTOkqRUxQlQVXVyHttJ0MWQgihEzJkIYTQRGJiotYRdEeOkIUQ\nQiekkIUQmhkwYACVKlW66eq+0kwKWQihmdjYWNasWaN1DN2QQhZCaKZ169aUL19e6xi6IYUshBA6\nIYUshBA6IYUshBA6IYUshBA6IYUshNBMdHQ0UVFRHDlyhICAAGbOnKl1JE3JlXpCCM3cuKGQuE6O\nkIUQQieKrZD/vtbWvaTsjsNp+/Pm1snbvy2KWEIIoRu6PUJOiY9DtV1fsFB1Ori2dS5XvxxC1oVj\nGicTQoiiUayFbLfbiYmJoW7duvTu3ZuMjAw2bNhA586dSb92mcurJqLabaTEL8ORdpWL89/iwtzX\nuThzCDgdlDUrXJr3GulHfiZl1/ecmzmUczOHkrI7DgBHRjIb1q4iNjaW4OBgYmJiWL9+PS1atKB2\n7drs2rWrON+uEELkSbEW8pEjRxg6dCi//fYbvr6+jB8/ntjYWKZMmYKlrB84HaTuWYVvZA+M3uUp\n3+UlzM4MYh7tjsVi4dSpU+zasR3b5hmk7FpC5Sc/pspTH5O2by3ZF48DkJ6WxiuvvMLhw4c5fPgw\n8+bNY+vWrYwbN44PP/ywON+uEELkSbEWcmBgYM7aV08++SQbNmygevXq1KhRAwBLaHusZ34FQHXY\nSF06ivdfG8EXn0/P2UejRo14+cXnKWfxIOPH6SgGI17BUVhPHwTAy2IhLCwMg8FA/fr1ad++PYqi\nEBYWJvdfFULoWrEW8l9XmL2b9IMbUTOSmfX5VF568flbnufr68vAAbG0qFGWlO/euenkn8FguOlj\ns9mc87Hdbi+EdyGEEEWjWAv51KlTOYsbzp8/nw4dOpCYmJhz5Jp+cCOKzYrbviXUqlUzZ/FEADc3\nt5zVZ1u3bs3KlSuZ9/VXDHisC+l7VmL0Llecb0UIIQpdsRZySEgIU6ZMoW7duly9epURI0bw5Zdf\n8txzz5GWdAnbmV8J8TOz/5d4nn/+eTp37kzbtm0BGDx4MA0aNCAmJiZntdvmzZuzdvVK+kX3JXPT\nDLJOHSjOtyOEEIVKF6tOnz59mmrVqtE35im+mjkjZ5ghL3bs2MHD3XrQoUMHFi+YV+gZhRAiv3K7\n6rQuLp0ODAzk6NGj1KxZM9fjzH/XvHlzfjuwDx8fn0JOJ4QQxUMXhQxQq1atAu+jatWqhZBECCG0\nodsr9YQQorTRZSGvWbOGkJAQatWqxejRo295fPr06YSFhREREUHLli05dOiQBimFEKJw6eKk3l85\nHA6Cg4NZt24dAQEBNGnShPnz51OvXr2cbVJSUvD19QVg2bJlTJ06VVauFULoVm5P6unuCHnXrl3U\nqlWLGjVq4O7uTt++fYmLi7tpmxtlDJCenp7vE4FCCKEnujmpd8PZs2cJDAzM+TwgIICdO3fest2U\nKVMYP3482dnZbNy4sTgjCiFEkdDdEfLthlBudwQ8bNgwjh8/zpgxY/jggw+KI5oQQhQp3RVyQEAA\np0+fzvn8zJkz+Pv733H7vn37snTp0uKIJoQQRUp3hdykSROOHj3KiRMnyM7OZsGCBfTo0eOmbY4e\nPZrz8cqVK6ldu3ZxxxRCiEKnuzFkk8nEp59+SqdOnXA4HAwYMID69evzzjvvEBkZSY8ePfj0009Z\nv349bm5ulCtXjtmzZ2sdWwghCkx3096EEKKkcdlpb0IIUVpJIQshhE5IIQshhE5IIQshhE5IIQsh\nhE5IIQshhE5IIQshhE5IIQshhE7k6cIQRVEuASeLLo4QQpRI96uqWvFeG+WpkIUQQhQdGbIQQgid\nkEIWQgidkEIWQgidkEIWQgidkEIWQgidkEIWQgidkEIWQgidkEIWQgidkEIWQgid+H9/j+qamU0i\nPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5eac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "# first diagram, indexed by prior=0\n",
    "sankey.add(flows=[1, -1, 0.1, 0.3],\n",
    "           orientations=[0, 0, 1, -1],\n",
    "       labels=['input', 'output', 'top', 'bottom'],\n",
    "          pathlengths=[0, 0, 0.5, 1.0])\n",
    "sankey.add(flows=[1, -1],\n",
    "           orientations=[0, -1],\n",
    "           labels=['input2', 'output2'],\n",
    "           trunklength=3.0,\n",
    "          prior=0,\n",
    "          connect=(1, 0))\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why not add a third branch to our system?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bunch(angles=[0, 0, 3, 1], flows=[ 1.  -1.   0.1  0.3], tips=[[-0.38045018  0.1       ]\n",
       "  [ 0.6947228   0.        ]\n",
       "  [-0.55        1.15804502]\n",
       "  [-0.65       -1.57413506]], texts=[<matplotlib.text.Text object at 0x10f8a3630>, <matplotlib.text.Text object at 0x10f8a3ac8>, <matplotlib.text.Text object at 0x10f8a90b8>, <matplotlib.text.Text object at 0x10f8a9048>], patch=Poly((-0.25, 0.7) ...), text=Text(0,0,'')),\n",
       " Bunch(angles=[0, 0], flows=[ 1 -1], tips=[[ 0.6947228   0.        ]\n",
       "  [ 3.21989579  0.        ]], texts=[<matplotlib.text.Text object at 0x10f8af940>, <matplotlib.text.Text object at 0x10f8afdd8>], patch=Poly((0.275173, 0.5) ...), text=Text(1.52517,0,'')),\n",
       " Bunch(angles=[0, 0], flows=[ 1.  -0.3], tips=[[ 3.21989579  0.        ]\n",
       "  [ 4.10138391  3.15      ]], texts=[<matplotlib.text.Text object at 0x10f8b4fd0>, <matplotlib.text.Text object at 0x10f8bc4e0>], patch=Poly((3.05035, 0.75) ...), text=Text(3.55035,1.75,''))]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T6yHLu8fPUlNTWbZsWeXjn3/+mfsfHEbobc/5PYw9FXHnCxzXNeIvC+243HVv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\njZBAFkIIjZBAFkIIjZBAFkIIjfh/GwEaqLIhIaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f64c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey()\n",
    "# first diagram, indexed by prior=0\n",
    "sankey.add(flows=[1, -1, 0.1, 0.3],\n",
    "           orientations=[0, 0, 1, -1],\n",
    "       labels=['input', 'output', 'top', 'bottom'],\n",
    "          pathlengths=[0, 0, 0.5, 1.0])\n",
    "sankey.add(flows=[1, -1],\n",
    "           orientations=[0, 0],\n",
    "           labels=['input2', 'output2'],\n",
    "           trunklength=3.0,\n",
    "          prior=0,\n",
    "          connect=(1, 0))\n",
    "sankey.add(flows=[1, -0.3],\n",
    "           orientations=[1, -1],\n",
    "          labels=['input3', 'output3'],\n",
    "          prior=1,\n",
    "           trunklength=2.5,\n",
    "          connect=(1, 0))\n",
    "sankey.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final project: why MOOCs are hard "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I'd like to finish this article by making a Sankey diagram with some stats from the MOOC I recently followed. It turns out that only a fraction (2%) of the learners that join a class actually get a statement of accomplishment. \n",
    "\n",
    "- Total learners joined: 14,460\n",
    "- Learners that visited the course: 9,720\n",
    "- Learners that watched a lecture: 7,047\n",
    "- Learners that browsed the forums: 3,059\n",
    "- Learners that submitted an exercise: 2,149\n",
    "- Learners that obtained a grade >70% (got an Statement of Accomplishment): 351\n",
    "\n",
    "I would like to represent this data with a Sankey diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-69.520770818713231, 5.1500000000000012)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bGLw+4Fiwnmk/R9fr2C0pm2Hrno6uMKcDaXnRWVgfz3qmItJPRFbKEZc6Z5LT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1AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8bcd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 12))\n",
    "ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[],\n",
    "                     title=\"Statistics from the 2nd edition of\\nfrom Audio Signal Processing for Music Applications by Stanford University\\nand Universitat Pompeu Fabra of Barcelona on Coursera (Jan. 2016)\")\n",
    "learners = [14460, 9720, 7047, 3059, 2149, 351]\n",
    "labels = [\"Total learners joined\", \"Learners that visited the course\", \"Learners that watched a lecture\",\n",
    "         \"Learners that browsed the forums\", \"Learners that submitted an exercise\", \n",
    "          \"Learners that obtained a grade >70%\\n(got a Statement of Accomplishment)\"]\n",
    "colors = [\"#FF0000\", \"#FF4000\", \"#FF8000\", \"#FFBF00\", \"#FFFF00\"]\n",
    "\n",
    "sankey = Sankey(ax=ax, scale=0.0015, offset=0.3)\n",
    "for input_learner, output_learner, label, prior, color in zip(learners[:-1], learners[1:], \n",
    "                                                              labels, [None, 0, 1, 2, 3],\n",
    "                                                             colors):\n",
    "    if prior != 3:\n",
    "        sankey.add(flows=[input_learner, -output_learner, output_learner - input_learner],\n",
    "               orientations=[0, 0, 1],\n",
    "               patchlabel=label,\n",
    "               labels=['', None, 'quit'],\n",
    "              prior=prior,\n",
    "              connect=(1, 0),\n",
    "               pathlengths=[0, 0, 2],\n",
    "              trunklength=10.,\n",
    "              rotation=-90,\n",
    "                  facecolor=color)\n",
    "    else:\n",
    "        sankey.add(flows=[input_learner, -output_learner, output_learner - input_learner],\n",
    "               orientations=[0, 0, 1],\n",
    "               patchlabel=label,\n",
    "               labels=['', labels[-1], 'quit'],\n",
    "              prior=prior,\n",
    "              connect=(1, 0),\n",
    "               pathlengths=[0, 0, 10],\n",
    "              trunklength=10.,\n",
    "              rotation=-90,\n",
    "                  facecolor=color)\n",
    "diagrams = sankey.finish()\n",
    "for diagram in diagrams:\n",
    "    diagram.text.set_fontweight('bold')\n",
    "    diagram.text.set_fontsize('10')\n",
    "    for text in diagram.texts:\n",
    "        text.set_fontsize('10')\n",
    "ylim = plt.ylim()\n",
    "plt.ylim(ylim[0]*1.05, ylim[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2017 addendum: google charts sankey diagrams "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following a question by a reader, I'll try to imitate the Sankey tutorial found on google charts: <https://developers.google.com/chart/interactive/docs/gallery/sankey>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd35f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sankey = Sankey(head_angle=180)\n",
    "# block A\n",
    "sankey.add(flows=[18, -5, -7, -6],\n",
    "           orientations=[0, 0, 0, 0],\n",
    "       labels=['inA', 'A->X', 'A->Y', 'A->Z'],\n",
    "          trunklength=15, alpha=0.1)\n",
    "# block B\n",
    "sankey.add(flows=[15, -2, -9, -4],\n",
    "           orientations=[0, 0, 0, 0],\n",
    "       labels=['inB', 'B->X', 'B->Y', 'B->Z'], \n",
    "          trunklength=15, alpha=0.1)\n",
    "# block X\n",
    "sankey.add(flows=[5, 2, -7],\n",
    "           orientations=[0, 0, 0],\n",
    "           prior=0, \n",
    "           connect=(1, 0), labels=['inA', 'inB', 'outX'],\n",
    "                    trunklength=5)\n",
    "# block Y\n",
    "sankey.add(flows=[7, 9, -16],\n",
    "           orientations=[0, 0, 0],\n",
    "           prior=0, \n",
    "           connect=(2, 0), labels=['inA', 'inB', 'outY'],\n",
    "                    trunklength=5)\n",
    "# block Z\n",
    "sankey.add(flows=[6, 4, -10],\n",
    "          orientations=[0, 0, 0],\n",
    "          prior=1,\n",
    "          connect=(3, 1), labels=['inA', 'inB', 'outZ'],\n",
    "                    trunklength=5)\n",
    "sankey.finish();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I believe the current matplotlib sankey API is not well suited to drawing diagrams like the one in Google charts."
   ]
  }
 ],
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