sankey.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating Sankey diagrams visualizing the paths people take when trying to spell a particular word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Proof of concept. Spellings of 'loogie' (made-up numbers)\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.sankey import Sankey\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(9,7))\n",
    "forms = {\n",
    "    'loogie': 75,\n",
    "    'loogy': 10,\n",
    "    'loagie': 10,\n",
    "    'loagy': 5,\n",
    "}\n",
    "\n",
    "flows_and_orients_labels = [\n",
    "    (100, 0, ''),  # input -> lo\n",
    "    (-15, 1, 'a'), # lo -> loa\n",
    "    (-85, 0, 'og'), # lo -> loog\n",
    "]\n",
    "\n",
    "flows, orients, labels = zip(*flows_and_orients_labels)\n",
    "    \n",
    "sankey = Sankey(scale=1/100.0,\n",
    "                ax=ax,\n",
    "    unit=None, # Don't show quantities\n",
    "    )\n",
    "sankey.add(flows=flows,\n",
    "    labels=labels,\n",
    "    orientations=orients,\n",
    "    patchlabel='lo',\n",
    ")\n",
    "\n",
    "sankey.add(\n",
    "    flows=[15, -10, -5],\n",
    "    labels=['', 'loagie', 'loagy'],\n",
    "    prior=0,\n",
    "    connect=(1, 0),\n",
    "    # XXX: why does passing None or 0 not work?\n",
    "    orientations=[-1, 1, 1],\n",
    ")\n",
    "\n",
    "sankey.add(\n",
    "    flows=[85, -10, -75],\n",
    "    labels=['', 'loogy', 'loogie'],\n",
    "    prior=0,\n",
    "    connect=(2, 0),\n",
    "    orientations=[0, -1, 0],\n",
    ")\n",
    "\n",
    "sankey.finish();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.cm\n",
    "import numpy as np\n",
    "\n",
    "[('gillenhall', 5),\n",
    " ('gylenhaal', 8),\n",
    " ('gylenhal', 9),\n",
    " ('gylenhall', 18),\n",
    " ('gyllenhaal', 29),\n",
    " ('gyllenhal', 41),\n",
    " ('gyllenhall', 41)]\n",
    "d = {'G': {\n",
    "    'i': {'llenhall': 5},\n",
    "    'yl' : {\n",
    "        'enha': {\n",
    "            'al': 8,\n",
    "            'l': 9,\n",
    "            'll': 18,\n",
    "        },\n",
    "        'lenha': {\n",
    "            'al': 29,\n",
    "            'l': 41,\n",
    "            'll': 41,\n",
    "        }\n",
    "    }\n",
    "}\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12,9))\n",
    "ax.axis('off')\n",
    "sankey = Sankey(\n",
    "    scale=0.01,\n",
    "                ax=ax,\n",
    "                gap=0.25,\n",
    "                #radius=0.25,\n",
    "    unit=None, # Don't show quantities\n",
    "    offset=0.3,\n",
    ")\n",
    "\n",
    "GIL = d['G']['i']['llenhall']\n",
    "gyle = d['G']['yl']['enha']\n",
    "GYLE = sum(gyle.values())\n",
    "gyll = d['G']['yl']['lenha']\n",
    "GYLL = sum(gyll.values())\n",
    "GYL = GYLE + GYLL\n",
    "tot = GIL + GYL\n",
    "\n",
    "cmap = matplotlib.cm.get_cmap('summer')\n",
    "cols = cmap(np.linspace(.1, .96, 5))\n",
    "\n",
    "# G -> {i, yl}\n",
    "sankey.add(\n",
    "    flows=[tot, -GIL, -tot+GIL],\n",
    "    labels=['G', '', ''],\n",
    "    orientations=[0, 1, 0],\n",
    "    facecolor=cols[0],\n",
    ")\n",
    "\n",
    "# Gil -> lenhall\n",
    "sankey.add(\n",
    "    flows=[GIL, -GIL],\n",
    "    labels=['I', 'Gillenhall'],\n",
    "    orientations=[0, -1],\n",
    "    prior=0,\n",
    "    connect=(1, 0),\n",
    "    facecolor=cmap(.76),\n",
    "    trunklength=.6,\n",
    "    pathlengths=[.0, 1.2],\n",
    ")\n",
    "\n",
    "# Gyl -> {l, e}\n",
    "remainder = tot - GIL # = GYL\n",
    "sankey.add(\n",
    "    flows=[remainder, -GYLE, -GYLL],\n",
    "    labels=['Y', '', ''],\n",
    "    orientations=[0, -1, 0],\n",
    "    trunklength=1.5,\n",
    "    prior=0,\n",
    "    connect=(2, 0),\n",
    "    patchlabel='L',\n",
    "    facecolor=cols[2],\n",
    ")\n",
    "\n",
    "# Gyle...\n",
    "suffs = ['al', 'l', 'll']\n",
    "flows = [GYLE] + [-1*gyle[s] for s in suffs]\n",
    "labels = ['E'] + [('Gylenha' + s) for s in suffs]\n",
    "fwd = 1\n",
    "if fwd:\n",
    "    ors = [1, -1, -1, -1] # ,0\n",
    "else:\n",
    "    ors = [0, -1, 0, 1]\n",
    "sankey.add(\n",
    "    flows=flows,\n",
    "    labels=labels,\n",
    "    orientations=ors,\n",
    "    prior=2,\n",
    "    connect=(1, 0),\n",
    "    patchlabel='N H A',\n",
    "    facecolor=cols[3],\n",
    "    trunklength=1.2,\n",
    ")\n",
    "\n",
    "terms = 1\n",
    "if terms:\n",
    "    # Label placement hack\n",
    "    for i, suff in enumerate(suffs):\n",
    "        f = -1 * flows[i+1]\n",
    "        sankey.add(\n",
    "            flows=[f, -f],\n",
    "            labels=[suff.upper(), ''],\n",
    "            orientations=[1, 1],\n",
    "            prior=3,\n",
    "            connect=(i+1, 0),\n",
    "            alpha=0,\n",
    "        )\n",
    "\n",
    "\n",
    "# Gyll..\n",
    "suffs = ['al', 'l', 'll']\n",
    "flows = [GYLL] + [-1*gyll[s] for s in suffs]\n",
    "labels = ['L'] + [('Gyllenha' + s) for s in suffs]\n",
    "sankey.add(\n",
    "    flows=flows,\n",
    "    labels=labels,\n",
    "    orientations=[0, 0, 1, -1],\n",
    "    prior=2,\n",
    "    connect=(2, 0),\n",
    "    patchlabel='E N H A',\n",
    "    #trunklength=1.5,\n",
    "    pathlengths=[1.1, .33, .25, .25],\n",
    "    facecolor=cols[4],\n",
    ")\n",
    "\n",
    "\n",
    "if terms:\n",
    "    # Label placement hack\n",
    "    for i, suff in enumerate(suffs):\n",
    "        f = -1 * flows[i+1]\n",
    "        sankey.add(\n",
    "            flows=[f, -f],\n",
    "            labels=[suff.upper(), ''],\n",
    "            orientations=[1, 1],\n",
    "            prior=4+3,\n",
    "            connect=(i+1, 0),\n",
    "            alpha=0,\n",
    "        )\n",
    "\n",
    "diags = sankey.finish();\n",
    "fprops = mpl.font_manager.FontProperties(\n",
    "    size=26,\n",
    "    weight='bold',\n",
    "    #stretch='ultra-expanded',\n",
    ")\n",
    "\n",
    "#t = diags[0].texts[0]\n",
    "for diag in diags:\n",
    "    for t in diag.texts:\n",
    "        txt = t.get_text()\n",
    "        if len(txt) <= 6:\n",
    "            t.set_font_properties(fprops)\n",
    "        else:\n",
    "            t.set_text('') # XXX\n",
    "            pass\n",
    "    diag.text.set_font_properties(fprops)\n",
    "    \n",
    "diags[2].text.set_x(1.48)\n",
    "diags[4+(3*terms)].text.set_x(3.3)\n",
    "diags[1].texts[0].set_x(.4)\n",
    "diags[1].texts[0].set_y(.9)\n",
    "\n",
    "for diag in diags[5+3:] + diags[4:7]:\n",
    "    for t in diag.texts:\n",
    "        t.set_fontsize(22)\n",
    "        \n",
    "for diag in diags[4:7]:\n",
    "    for t in diag.texts:\n",
    "        x, y = t.get_position()\n",
    "        t.set_y(y-.1)\n",
    "        t.set_fontsize(14)\n",
    "\n",
    "t = ax.text(.64, 1.5,\n",
    "            'LLENHALL',\n",
    "            fontsize=18,\n",
    "            fontweight='bold',\n",
    "           )\n",
    "\n",
    "if 1:\n",
    "    ax.annotate('(correct spelling)',\n",
    "                xy=(5.0, .15), xycoords='data',\n",
    "                xytext=(5.1, .12),\n",
    "                #arrowprops=dict(arrowstyle=\"->\"),\n",
    "               )\n",
    "ax.scatter(5.0, .15, marker='x');\n",
    "fig.set_facecolor('white')\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('gillenhall', 5),\n",
       " ('gylenhaal', 8),\n",
       " ('gylenhal', 9),\n",
       " ('gylenhall', 18),\n",
       " ('gyllenhaal', 29),\n",
       " ('gyllenhal', 41),\n",
       " ('gyllenhall', 41)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Real dat\n",
    "g = {'gylenhal': 9, 'gyllenhall': 41, 'gylenhaal': 8, 'gylenhall': 18, 'gyllenhaal': 29, 'gyllenhal': 41, \n",
    "     'gillenhall': 5}\n",
    "its = list(g.items())\n",
    "sorted(its, key=lambda tup:tup[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "c = {'comeraderie': 8, 'camaraderie': 6, 'comaraderie': 5, 'comraderie': 24, 'comradery': 54, \n",
    "     'cameraderie': 7, 'comradere': 8}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}