pycon_nx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img style=\"float: right;\", src='img/cover.jpg'/>\n",
    "\n",
    "# visualizing discussions on twitter with networkx\n",
    "\n",
    "kiran garimella, aalto university\n",
    "\n",
    "michael mathioudakis, aalto university\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## social media\n",
    "<img style='float: right; height:300px;' src='img/social_media.jpg'/>\n",
    "#### users generate digital content\n",
    "status updates, blog posts, pictures, videos, reviews, ...\n",
    "#### users interact\n",
    "comments, likes, ratings, re-posts\n",
    "#### digital traces\n",
    "we can observe human interactions at global scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img style='float:right; width: 250px' src='img/twitter_logo.png'/>\n",
    "## twitter\n",
    "\n",
    "#### microblogging platform\n",
    "users post short messages, '__tweets__'\n",
    "\n",
    "#### since 2006, 300m + active users\n",
    "\n",
    "#### tweets, retweets, replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "show examples of real tweets, retweets, and replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img style='float:left; width:250px;' src='img/trump_tweet.png'>\n",
    "<img style='float:left; width:250px;' src='img/greenwald_tweet.png'>\n",
    "<img style='float:left; width:250px;' src='img/leme_tweet.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "these are specific examples\n",
    "\n",
    "can we learn something from the stucture of people's interactions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "we'll do that by visualizing __graphs__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "graphs?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img style='float: right; width: 350px' src='img/generic_graph.png'/>\n",
    "\n",
    "## graphs!\n",
    "\n",
    "\n",
    "\n",
    "#### what is a graph?\n",
    "\n",
    "data structure\n",
    "\n",
    "two types of elements: nodes and edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "__todo__ include an example of a very simple social network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### what are graphs used for?\n",
    "represent social connections between people ...\n",
    "\n",
    "<img style='float: left; width: 350px' src='img/example_friends.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "... or represent networks\n",
    "e.g., road networks, computer networks\n",
    "\n",
    "<img style='float: left; width: 350px' src='img/road_network.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### terminology\n",
    "\n",
    "_graph_ vs _network_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## graphs with networkx\n",
    "\n",
    "python library\n",
    "\n",
    "create, process, visualize graphs\n",
    "\n",
    "development started in 2004\n",
    "\n",
    "mainly developed in 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## building a graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style='float: right; width: 400px' src='img/example_friends.png'/>\n",
    "\n",
    "let's build earlier example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style='float: right; height: 200px;' src='img/example_friends.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# initialize\n",
    "graph = nx.Graph()\n",
    "\n",
    "people = ['jere', 'ella', 'miika', 'anniina', 'mikko', 'olli', 'laura', 'maria']\n",
    "connections = [('jere', 'ella'), ('ella', 'anniina'), ('ella', 'miika'),\n",
    "               ('mikko', 'ella'), ('anniina', 'mikko'), ('laura', 'jere'),\n",
    "               ('olli', 'jere'), ('jere', 'maria'), ('miika', 'mikko'),\n",
    "               ('maria', 'laura'), ('olli', 'laura')]\n",
    "\n",
    "# add all nodes\n",
    "for node in people:\n",
    "    graph.add_node(node)\n",
    "\n",
    "# add all edges\n",
    "for node_a, node_b in connections:\n",
    "    graph.add_edge(node_a, node_b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## visualizing a graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def get_pyplot_ax(rows = 1, columns = 1, figsize = (7, 7)):\n",
    "    \"\"\" helper function \"\"\"\n",
    "    \n",
    "    import matplotlib.pyplot as plt\n",
    "    \n",
    "    fig, ax = plt.subplots(rows, columns, figsize = figsize)\n",
    "    \n",
    "    if rows == 1 and columns == 1:\n",
    "        ax.axis('off')\n",
    "    elif rows == 1 or columns == 1:\n",
    "        for subax in ax:\n",
    "            subax.axis('off')\n",
    "    elif rows > 1 or columns > 1:\n",
    "        for i in range(rows):\n",
    "            for j in range(columns):\n",
    "                ax[i][j].axis('off')\n",
    "        \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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eDYqiuMxms9doNAbMZrNXURRX7969G1RVrSSiu4loeGQSrRBxRUSpCD+He5yZ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\nR0REtvW/gv5B0Mi839EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb3e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = get_pyplot_ax()\n",
    "\n",
    "nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = 'white')\n",
    "\n",
    "fig.savefig('img/example_friends.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CpQZmNp0wG/NnTQZc9AjwjIY/syco5MbJzKaY2VWE6YKZtLNDCkLYzQT6zezKOGqszU8J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a648c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = get_pyplot_ax()\n",
    "\n",
    "nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = 'black', node_label = None)\n",
    "\n",
    "fig.savefig('img/generic_graph.png', dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## add color to nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9A3dPAdTHefLOicU3eeOeSdTPreORsOsQkYDuybUgM0z52T2nsVrzRcKF5qzb\nif/jeZ6eX+97h1VD5nSE9YERzT42B2YCk4E3M//7b3dv87R6MyuvqeDbVy+g12ZrZ7/uXPh2Pqxz\nOo2NCfq6+7yw6xER9eRaFC3jF7sNoVshBxzAhWOIVUbZwcxG5qM9C/Q3s5+Y2Z/N7GngB+BZgtMR\nvgUuAPq7+yB3P8Ldr3D3V9sTcADunkymuGrckxTNTv43PE8qVs59CjiRwqGe3DLMrKy2kq+f/y2r\n5eKol2wb+zDpCx/knvn1fmi2r21mq7J0D204UEbQM/tvL83dv81225n2+1bFmPbN1VT0qM5FC9mT\nSsMap1D//UK2c3fdkxMpEOrJLW+3tXpTVQwBB3DsDkTiSfYzsxVsWrVyZtbdzHY0s3PMbLyZTQOm\nAucANcDNwChgdXffy93/190fyVXAAbj7N7Eynrj0UZK5aiNbbn4ZTyT5TAEnUljUk1tGzxp78fIj\nGH309mFX0naHXkX9fZP5QyLpY9vyfDOrJLhv1ryXthbwDkt6aZOBT909y2dnt4+Z9a+O8eFrf6B2\nyFphVtK6mT/A4HNoWNTI1u7+dtj1iMgSCrlmzGyN6hjTvr+eyqpY2NW03cRPYI+xfDW/3peLATOL\nEhzTs3i4cQQwGPiYpQPtfXdP5K/qtisvs2PXX51x715ETbTA9uhxh13+Qv1rn3J5fZP/Lux6RGRp\nGq5c2sihA4nnMuC+mgPdjwt+OQLsORZufblz1xy5HjQmWMPMVjGzQWZ2hJldaWYTgXnAnQTn0r0H\nnAL0dvct3P0Ed7/B3d8u1IADSKW58eu5TLnwQQquxptewidP5euGOH8MuxYRWV6B/V0crvIyRmy7\nITndSGpAb1jwzyVfP3Zu569ZXgYbrI69P5MZwPcs6Z1NAKa4+4LOtxIed3czO3zsI7w/Yl2iYewf\n2pI3psIvb6ahrokx7h4Pux4RWZ56cs30qGL7Eet17ETqsG0zCAfGuvs67j7G3ce6+/PFHnCLufuM\nhji7HjyO+hc+CLsaeO8r+PFfaKhr4lDdhxMpXAq5ZhriDBk+cMnXFz8M658ZDC9uch5MeDP4/s0v\nwXZ/hHPuCA7nXO9X8MQ7S16345/g9/fCtn8IXrv7xfBDZu+OL2dD5IhgI9/Fz/3XC2277k0vwo/O\nCa65/pnw9+eWPLb1BkR71jA86z+UAuLuk+rj7LPXJdQ3/7nk2+SpsM0faKhr4nh3fzi8SkRkZRRy\nGWZWFU8RS7RpAAAgAElEQVRSO6DZRPz1V4dXLwiGFy84AI68NtjVAmDSZ7DRmjDnejhnLzjuhqWv\nd+dEuPkkmH0dNCXg0kebtbWCOt6Y2vp1V+8RDG8u+CfceAL86jZ4+4vgsQ37gsGGnfkZFAN3f64+\nzo8PvIKFVz1JOp3HuZ/ucPdrsOOFpBc0cGQy5Xfkr3UR6QiF3BLVsXKS1iyBDhwZBAvAmC2D0Htj\navD1On3g2B2CDZN/Nhq+mQffNTv45pjtYb3Vg7PgDh4Fb3/ZtiLWXnXp685qdt09Ng/aBdhuMOy6\nKbz88eLiIe1UduytFxd3n1gfZ/hvx/P+1n+gbloeTtb7bj7sexn1x/+D6XVNvAjsldnSTEQKmCae\nLFERLV/6NOpbXobLH4cvZgdf1zXB9wshYrBGs5PBF8/GXNQEi3dLbv54dQwWtXFzqmWv682u+/jb\n8McH4JNZwXBnQwKGDFhcPKSdIlr40Dnu/omZbfH2F5y9yXlccPFPqTh5FyJlWf6zzR3Gvw4n/JOG\nZJrr65v4DcG/m1eB04ErstuiiGSTQm6JeDK1pGc7/Xs44Z/w/G9hqw2C723xm8zU/xD+fo8n4aBx\ncNvJsN+w4ATsn1zOf8+iiSchYoU3xT6XMicWXGxmD/52PHf83wNseMYeVB6/A2V9unfu2gvq4ZZX\n8MsepW7OIr5b2Mhh7j5p8eNmti/wupl96O5Pdq41EckVhdwSDfHkkp9HXVPQY1u1W9BruvlleG9G\ndhrqyPL7eDL4WLVbEHCPvw1PvQubDlhcPESseDYzziZ3/wgYamYjLnqIM/94P/vvM5T00aOpHj4Q\nVuux0ksAMLcO3poGd71O4+2vQqyc5+fXcwnwgi+za4K7f2lmY4D7zGw7d/8k629MRDpNIbdEfVmE\npm/mUt63J2zUD87aE0ZdAGUROGpb2HYF0zqad+5WdqemI8+trYRxR8GYcUHY7TMU9hu65HlTgx0k\nv1jx1Uqbu08GfmpmvR54k+OefZ9D6hr5UW0l6WEDSW+zITV9uhOpjAY/18YE/FCHv/4ZdZOnwpxF\nRLtV8kldnAlNCa6rb/KvV9LeK2b2W+BhM9tSpw+IFB5t69XMqt3stZtOZNTeQ1f+3EJzxi0krnqK\n/02l/c9h11JIMpND1gWGxcoZXhll9bIINUAknWZRU5I5jQneIjhR4ZPFh7a2s40rgUHA3u5e8JtJ\ni3Ql6sk1s6iRl96cxsi9hxbfrNNXPqE+7UwOu45CkxlmnJr5GJ+jZs4CHgcuznwuIgWi6H6Z51JT\nkjde/phFYdfRXuk0vD+DKkDHvIQg03s7BNjXzI4OuRwRaUYht7TJk6cSTRTZgNN/pkN5GXPdfU7Y\ntXRV7v4DsA8w1sy2DrseEQko5Jpx9+llET6cMCXsStrn6mdoTKS4YeXPlFzKzPL8GXCPmQ0Iux4R\nUcgtZ149F1/6KAvDrqOtFtTD7a9CU4Jrw65FwN0fBy4HHjSznJ5oISIrp5Bb3oT3viL1QZbWxOXa\nra/isXKed1/xdHfJq8uAd4EbtfWXSLgUcstw93jauebSxwp/YXUyBZc8Qt38esaGXYsskZnReSIw\nANBp4SIhUsi1oDHB5Xe/RtPEAt/D4pJHSM6t4z3gxbBrkaW5eyNwAPBzMzsg7HpEuiotBm+FmR00\noDc3fXwpNVUFuO3xBzNgxP9QVx9nE3f/Iux6pGVmNgx4AtjF3UM8BU+ka1JPrhXufu/8ep47/27i\nYdeyrGQKDrmKuniScxRwhc3dpwCnAhPMbLWVPV9EskshtwILGjjuhudoeOa9sCtZ2gX3kZw+h3eT\naa4LuxZZOXe/G7gduNfMCnBcQKR0KeRWwN1n18fZ7yd/pX7xYalhu+opUlc+wewFDRyw7M74UtB+\nD/wAXK0ZlyL5o5BbCXd/cVETB+/yZxomfRZuLdc9Q/rXdzKvrolt3P2bcKuR9nD3NHAkMIpg+FJE\n8kATT9rIzPaqqWD8/WdQveuQ/LadTsOfHyR50cPMyQRcgfQrpb3MbCAwETjS3Z8Jux6RUqeQawcz\nG10dY8JR21F96WFU1FTmvs0vZsPhV1P37ld8sbCR3dx9Zu5blVwys+0JTkTY1t0/DbsekVKmkGsn\nM+vVrZLrayvZ885Tqd5+o9y0k07Ddc+SPudOmlJp/tSUYKzOKisdZnYicAYwyt3nh12PSKlSyHWQ\nme1THePmvbeg8qy9qBq5Xnaum0zBI/+GP01g0Sff8OXCRg529w+yc3UpJGb2N2AgsG9HDmsVkZVT\nyHWCmfWMlnFiRTm/GtCbqnP2ptsho6C6ov3XmjUP/v4cyXFPEk+lmTov2KrrLvXeSpeZRQkWik9x\n93PDrkekFCnkssDMyoDdVqnm3IY4W23Uj4ZtN6Rqy/WJDRsI6/SByiiYQSoN9U3w4dcwZRq89in1\nr31K8qs5xGLljF/YyOXu/nbY70nyw8x6A5OAP7r7LWHXI1JqFHJZZma9gKEGw1apYftkimH1cXql\n0pRHjHTasfIIydpKZrrz+vwGXgamAP9x94aQy5cQmNmPgBcIhi1fD7kckZKikMsTM4sA5UBCi7hl\nWWa2N3AdwUSUIjnoSaTwKeRECoSZnQeMAUa7e33Y9YiUAoWcSIHIbPd1C0GP/zD1+EU6T9t6iRSI\nTKj9HFgXOD/kckRKgnpyIgXGzNYkmHF5qrs/GHY9IsVMISdSgMxsBPAosLO7vxt2PSLFSsOVIgXI\n3ScTbPv1oJmtGnY9IsVKPTmRAmZmfwG2An7s7omw6xEpNgo5kQKWWV85AZjp7ieHXY9IsdFwpUgB\nyxy2egSwnZmdEnY9IsVGPTmRImBm6xIctnqYuz8Xdj0ixUI9OZEi4O6fAz8F7jCzLB3sJFL6FHIi\nRcLdnwf+CDxkZt3DrkekGGi4UqTImNm1QH9gfx22KrJi6smJFJ/TgFrgT2EXIlLoFHIiRSazXm4M\ncIiZHR52PSKFTMOVIkXKzDYBngP2yuyQIiLLUE9OpEi5+3vA8cADmU2dRWQZCjmRIubuDwHXABPM\nrCrsekQKjYYrRYpc5rDV24E0cKQOWxVZQj05kSKXCbXjgMHAuSGXI1JQ1JMTKRFm1o/gsNWT3P2R\nsOsRKQQKOZESYmZbAg8DO7r7+2HXIxI2DVeKlBB3nwScRXDYau+w6xEJm3pyIiXIzMYCw4Hd2nvY\namYiy9rAMCIMJ8qaGNVAGU4dKX4gydvAFOADd09m/Q2IZIlCTqQEmVkZ8BAwzd1PbcPzu2McRSVH\nkGATyjH6kmQtaqklQjlgQBJoBGayiJk4i6gkxlSSPEKSq939i5y+MZF2UsiJlCgz6wG8Blzp7te3\n8pxNiXEmaQ5hPdJsQQ39gG5tbKQRmAV8SJy3SFPG6zQyFngyc+CrSKgUciIlzMzWB14FDnb3F5t9\nfx0quJUIQxlJjGGU09nDe+LAe8CrLGQhC4hzZOZ4IJHQKORESpyZ7QzcBmwNfEmEk4hwCdsTY2vK\nKctygw58CjxAPSnuJM4Z7r4oy62ItIlCTqQLMLNfAr8gxgJW4UccRA2r5bjRBuAxGviIBSQ41N1f\nyHGLIstRyIl0AWY2hCiT2ZYo22JZ772tyCfAfdQT52RP+y15bFlEISdS6sxsOFGeY19q2RQLpYjZ\nwI3U08S5nvSrQ6lBuiSFnEgJM7ONiTKRA+nO4JCLmQv8nQYaOUk9OskXhZxIiTKzXkT5mH3ozZCQ\nenDL+g74J/U0sWfz2Z4iuaJtvURKVYwb2JxuBRNwAKsBB1BNlLvMrCbscqT0KeRESpCZ7UcFu/Nj\nKsKuZTmDgA3pQYy/hl2KlD6FnEiJyQxT3sSBVBMLu5pW7EUVEY40s+3DLkVKW3nYBYhIlsW4mCFU\nsU7YhaxANbA/VdzPrWa2TlfeAszMVgOGYQynku1JsyFOBU4UI4XRRIRviPMSKSYRbIz9uU6AbxuF\nnEgJMbPulHM4owtwmHJZg4AaehJnJ+CZsMvJJzPrQ4SfU86plNOL1WlkLarpR5RVgRhQBqSBBDCf\nfnzNUL5kEd9QTgK3mN1Jgit0buCKaXalSAkxs1PYkLEcRnFM6ngT52me9kbfLexS8sHMtqaCs0mx\nBz/C2ZIq1oR2Tw2aB0whyWQSwIc0cjHwQHuPVeoKFHIiJcLMjAqmcShrMzDsatqoCbiURhJs4O4z\nwi4nV8ysDzH+RZQd2YYqNidCdRYunAQ+Al5lEXP4ljhj3P3fWbhyydDEE5HSsQ0V9C7oe3HLqgA2\nwyjjpLBLyRUzG0OUzxjGrpxBDVtnKeAguOG0CXACtezJusR41aJ2oZkV6pSjvFPIiZQKYyc2obLd\nQ19XA1/koJ622ogKYuwVYgU5YWbdrNIeogc3chTd2Y0Y0Vw1BmyOcSpVrMUZxPjAzMLe46YgaLhS\npERYtT3LXuzEJmFX0k71wGU0kqKmVGZZmllvYrzIRqzH3lTmLNxa4sBbOE+wgAS7uPubeWy94Kgn\nJ1IqkmzOmnlqK5tRVA1UkgI2yOJVQ2NmvYgxiWFswP55DjgIenXDMA6kB1GeN7ORea6goGgJgUgJ\nMLPViVJDzw68+ApgX2Ag8ArwFsGEkIHA3kAVwWy+xc97EVgFOAb4CniK4JSBVYDdoUP3BNckzacM\nAz7uwKsLhpl1I8aLDGMAuxILdUO1wcBB1HIvT5vZ1l11qYF6ciKlYWN609ipX6qTCCLmWOAsgnB7\ndJnnfAmcChwJLADuALYHfg3sCtxNMPzYXv2pJcKQjhVeQCq4mcGsH3rALTYI2JtuRHmmq+4VqpAT\nKQ21VHbyCm8COwPdCBYibw98wJKhSQN2BKIEY0D/ATYE1s88vi6wJvBpB9quwCjvUD+0YJjZ/sTY\njb07MPknlzbDGEQPYlwedilh0HClSGmopLyTv1rnA3ex9MLkMqCu2dfdl3n++yw9wJiGDq3RKwcs\naxPr887MehPlRg4q0P1C96KKqRxuZne4+wthl5NPCjmR0pCisxOlewD7AQNaeGxeC9/rDmwG7NPJ\ndmFxbzGVhSuFI8Y/2Jwq1g67kFZUAftTzb3cZWbru/uisEvKFw1XipSGRpKdjLnhwLMsCbQ6gt00\nFlv26kMIenGfsWSPxS8I7tW1VxJIL9VnLBpmNpQydmWXAt8vdBCwHt2J8IuwS8kn9eRESsP3LOzk\ncOUogiC7FVgE1AAbE8zSg+X3V+wB/JRgduV9BH8y94MOLeteQJoEq5jZpgQ77BdP4MU4k62oKMhh\nymVtSxWf8yszu6RU1iSujEJOpDS8yzyqSdL+f9VOcO8NYKvMx7JWAS5o4fv9CJYSdNZ0ksB6BPMz\nB5rZPGAqQT9x6jKf/1Aox8yYWU/KOZCh//0JFrZ+QDeqmcNuwONhl5MPCjmREuDu9VZpM/mWtenX\njhfWEUz5XyVHhbWFA7NxYF93/87MIgTzNNcnCL71CO4WLv4aM1s2ABd//XVeeyjG0WxAitq8tdg5\nBmxDN57iXBRyIlJk3uCbdoTcTIKhyS0Jhh7DMheAOnf/DiATUjMyHy80f6qZGdCLpQNwO4L+5HrA\nKmY2jZYD8Et3j2e19hi/ZFSRHGu02CbA44wyszXcfVbY5eSaQk6kVDTxEtPZm+FUten5/QgWcYdt\nJlDO2215amaYck7mY9Kyj2cWPK/LkhDcmGCflvWBfmb2NS0H4OftnXFoZt2I0J/+7XlVO0wgmMG6\nE8GEnvuBMzOPXU1w73OdDlw3BqxBnK8YATzc6ToLnEJOpHQ8xIdcQhyKYhLEYlNYSAO3ZuNSmQkr\n72Y+lmJmUWBtlu4Fbpv5eqCZLaD1+4BzWrgPuDm9qacshH5wZ+dHrk0NMxmJQk5EioW7T7cqm8i7\n7MSwsKtpox+AGRjBhJOcypya/VnmYymZ+4B9CYJvcQju0+zzSAv3AbdgQKf3mQnHmpRRwfZhl5EP\nCjmRUtLIWCYykqHUFtTWUq2ZTALjn+7eEGYZmfuAMzMfLy37uJn1YukA3IYY+9A/C2vjZhPsETqL\nYHhyZ4I1bSuyeLPsdTvY5ppAsgT2Cm0DhZxIaXmahSxiBrUt7lxSSBLAFFIkuCrsUlbG3X8g6HdO\nXvw9q7YPWZXenbpwCrgTGEqw6fV0gq3Vft6pq65cDyBFjZnVFNWaxA7QjiciJcTd0yT5A49Sl9Uz\n33LhBRIYz7n71LBL6RDPwllxM4A4wZ3BMoJ9PzcE3uvkdVfGgDKSUKTDre2gkBMpNWn+zlw+4LUC\n3gtyJvAGjTRxXNildJgT6/RY2EKWX77Rg45tjdZeERyFnIgUG3dP08ShvEAT34ddTQuSwL3UkeCk\nol6nZSQ6/WdEN4LTHJqbz9KnPeRKGiPoR5Y0hZxICXL3z0lxPvdQV3D9uedIUM9EgrtRxctoItHJ\na/QnOJ/vFYL7c9OATwgWbOdamjIg1Ak/+aCQEylVaf7GXF7nXhoK5v7cm6SYzFyaOKpQ9p/shC8y\nu7V0XBlwGMFBs2OBx4CfAKt28rorEyx7T0JxnvzQHlb8/52JSGvMrIoYL/IjhrAvFaH+WfsfnIeZ\nR4KR7r7cWrViYxH7DSP5X/bo9PST/PsEeIDJXu8jwy4l19STEylh7t5AnJ35gHe4h4bQhi7fIM3D\nzCXBdqUQcAA4bzKd+rDL6JCvSRPn5bDLyAeFnEiJc/eFxNmBz3mF66ljdh4bbwDup4GnmUWCEe7+\nfh5bz7UpzKaqYIaC22M6i0gtv/dnKVLIiXQB7t5AE7vzPefyd+p4hVTOfzl/CoyjgY+4kwSD3f3z\nHLeYV+4+hwhz+TbsStopBcwkSrOF7aVMISfSRbh72lN+DQk24WXe5Hrq+JLgPLds+gG4jwbG00AD\nT3qTH+fuC7PcSmFI8Q/epDHsMtrlY8D41N2nhV1KPijkRLoYd/+CJrbmO87gdmYyjkVMxmnqxEXT\nBL88b2QR17CIj7iGBIOBUWZWupMbUlzHf6BTP7t8m8hCGrk47DLyRbMrRbqwzCGkO1LJOaTYgQ1J\nsRY19AXWoPUje1LA98DXwEya+IAUab7I/PIc7+6NmesfAZwFjHD3ZO7fUf5ZpT3BLuzKiCLYEvt7\n4HoWkGA1dy+maO4whZyIAGBm/YA9ibENEbYmzjrUEqWW+UQJ9jtMEPRa5lJNOd9jvEkjLwLPufu/\nW7imAU8Dj7n7X/P5fvLFzHZmFSZwGrUFPzb2CE28zZWe8PPCLiVfFHIi0iIzGwi8ARxKsMfh4h0y\nFgEfuPuyG1K1dp0NgNeAoe4+PUflhsbMjAomMZqhbENZ2PW0aiZwIwtJsoG7F9t0mQ7TUTsi0prV\ngWnu/mxnLuLun5rZOGAcsH9WKisg7u5mdigv8C4bUk2fsCtqQRK4hzqSnNiVAg408UREWjeA4DCY\nbLgYGGxmJRdy8N+9Qn/NfQV6xNHzxKnnZYLT6roUhZyItKY/8FU2LpSZ5HASMM7MumXjmgUnzXjm\nkObFrC/K6JwvgEk0EeeYEtgvtN0UciLSmv5kryeHu78APAf8IVvXLBRmNgx4gwQ3MpHZvFkgZz98\nDdxBA0kOLOpjjTpBIScircnmcOViZwOHm9nQLF83NGZ2FPAEcKa7n06CbXiSBbwV8sDl18DNNBDn\nCHd/OtRaQqSQE5HWZG24cjF3/x44H7jezAp3JmIbmFnUzK4E/gfYwd3vA3D3z0gwiseZwyskQxm8\nnAbcRANNHO7u94dQQcFQyIlIa3LRkwO4EagHTs7BtfPCzFYjWP+3ATBy2Y2n3f0TEgzjJaZyM3XL\nnf6dKwngCeLcwXzi7OPuD+Sp5YKldXIishwzKycIolp3j+fg+hsBLwGbufvX2b5+LmXuv90P3Ab8\n3t1bvf9mZlHK+S0RzmV3KtkCy9m+KF8B91JHI8/RxHHuns/zJgqWQk5ElpPZ/eRNd++bwzb+BAxy\n9zG5aiPbMvffLgNOWjw82cbXDSHGPazGmoymlvXJ3jjaLOA1GvmAJhIc7+73ZunKJUEhJyLLMbNR\nwFXuPiKHbVQB7wKnuftjuWonG8wsClwK7Ans35Fz8TLXOJIKzqOcfmxFFVsQoaYDBSWBDwg2W55D\nkjRXkeKqzD1PaUYhJyLLMbODgMPc/YAct/Nj4O/Axu5ekKdsZ+6/jScYvj3c3edm4ZojqOAskuxH\nL5oYQCX9qWBNYFWW3osqDcwDvgFmkmQ6dXxLJWW8ndkQ++FS3fw6GxRyIrIcM/sVMNDdT8tDW3cA\n093917luq73MbDhwH3ArcMGK7r918Pq1wBbAMCoZjTOCJvphQIQkaSI4ZUSZSznv0MQLpJkMTOlq\n23N1lEJORJZjZpcBs9z9kjy0tQbBsOVO7v5urttrq2b3307M5zT8zMkN5QSbYseBeFfcqSRbtEGz\niLSkP/BmPhpy91lm9juCtXPbunuoi6gz984uA/YgWP/W7vtvnZEJtETmQzpJ6+REpCW5WiPXmhsy\n/3t8HttcTrP1b+sRHPSa14CT7FPIiUhLsr7byYpkem8nAX8ys9Xz1W5zmftvk4FXgH3dfV4YdUh2\n6Z6ciCwls91WAzlaCL6StscC/dz98Dy3G8r9N8k9hZyILCUfC8FX0HYN8B5wQj42FV7m/luH1r9J\nYdNwpYgsK6tH7LSHu9cBpwLXmFllLtvS/beuQSEnIsvK96STpbj7o8A7wG9y1Ybuv3UdCjkRWVZe\nJ5204nTglMxGzlmVuf/2OPArd/9dthd4S2HROjkRWVaoPTkAd59pZn8ArjWzHbOxGDrs9W8SDoWc\niCwrbwvBV+Ia4CjgZ8BNrT0ps0NIP2BYtIwR3avY0qC7BzuGuBn1yRTzgQ2B7wh2Vgm7pyp5opAT\nkWWFNvGkOXdPmdmJwONm9kjzHfYzwTa6RzVnVcfYvixCbPO1iW87iJot1qGsRxVURMEdGuIwaz68\n/hmpiZ/S+5NvmNqj2mYnUtzdEOdv7v55eO9Sck1LCERkKWY2Hdje3aeFXQuAmV0B9HD3Y8ysR8Q4\nqraSs3vW0OvsvajZbxjWvxdYGw8jTabg/Rlw00vEb3iedLSMN+bVMxZ4QvfnSo9CTkT+K8yF4K0x\ns27AB2bcVVHOyT/eFM7cg5rtN2p7sLWmIQ53vwZjH2XhV3P4flEjB7t7IQzVSpYo5ETkvzILwae4\n+xph17KYmQ2oreTJfj0ZfM9p2KZrZb8Nd7hjIn7yv2hMpvlbQ5z/cfem7Lck+aYlBCLSXCEsH/iv\n8jI7pirGh+fsxQbvXpSbgIP/b+/Oo6Oq7z6Ov3/J7JOFQMoiRUGQ0mpRCaAo1PZxA6wVauvBI/pU\n+1iwwiMirYitVdGqLG6lUFsVLbhUqdQFqlUpKMoRjH1EW1A2EQoUkC2ZLZPJ7/ljEkUkZALJ3Jnr\n53VODgn33rnfgZN88ru/Ld0ivOR0zIfTCZ7Ri6uLAqw2xpzUOneTbFJLTkQ+Vb8j+CXW2uEO12FC\nPm5rW8S4hT8j1FrhdjDWwtw3sKMfIhqt4bvW2sXZu7u0NLXkRGR/jo+sNMaYsJ97u7Tjmsrbshtw\n6fvDpQMxL0wgXORngTHm3OxWIC1JISci+3P8cWXQx61HlfHjZbcQbl/qXB3fOR5emkgo5OcZY8xA\n5yqRI6GQE5H9ObraiTHmorIQ41+/iXBZ2KkqPnNaT5g/jlDQx8L6QTmSZxRyIrI/x1pyxpgOIR9/\nmD+eUAcHW3AHOqc3XDeUQEmQOfWT0CWPKOREZH+OtOSMMaYkyCM/PZtA/+7ZvnvTfjkcb4dS+hvD\nZU7XIs2jkBMR4NOJ4B2BLdm/Nxe1LWLQ5B/gy/a9M+HzwFNjCQe8zNBjy/yikBORBh2AXdmeBG2M\n8QW8/O7JMYQDORlxaSd1hWsG4y8JMs3pWiRzCjkRaeDUoJNh3+xC4Sk9HLhzM40fgremlmHGmLZO\n1yKZUciJSANHBp20CXH9hPMozvZ9D8dXSuD8PtR5CrnC6VokMwo5EWmQ9ZacMeZ44OvDKrJ51yNz\n7RBCQS/jjTH6+ZkH9J8kIg2yvtpJ2M+Yq8/G682jnS1P7QEd21AEfNvpWqRpCjkRaZD1x5XeQs4a\nelL2N2++6mG4/S+Hd60xMKyCYGEBZ7RsVdIa8uj3JxFpZVl9XGmM8XsKOeakY7J1x8/MOsIetX7d\n8ZSGFHL5QC05EWmQ7ZbcCZ3LiIf8WbwjUFd35K9R0Q3iSU488leS1qaWnIg0TATvRHYnglec2gNv\nYwe7jYOrz4Y5S2H9dhhxKtx+EfzoAVj6Qbpv7OlroDQEF90Pr38A8Ro48RiY+SP4xlfTr3P5AxD0\nwcad8NpqeHZ8+jW7tINbfwB7InDpLHhrHaTq4LTj4IEfw1FljRfe7SuAJWiM6Wit3day/yzSktSS\nExFITwTfnc2J4H4PP6zoRuBQ5zyzAl6dBB9Og+fegaFT4c4RsPMBSFm4/6X0eUNPhHV3w/ZZ0Kcr\nXDLz86/zxJvwy2FQ9RCc3vPzx+osXHEGbLofPr4PQj4Y88ihazcGenYiDvRq1puWrFNLTuRLxBjT\nDqgAugNBwA8kgfbAvvolq7bYVtxN2RjjBe4O+uhfdMiIg7HnQHn9DLpBX4MOpdC7fn+54X1h0T/T\nn/9ov96xm4bDvS9CVQyKg+m/u6ACTj0u/bn/gLZj2yIY3u+zYzd8D868o+n3UV97qOkzxUkKOREX\nM8Z0LDBc2ibEWTUp+gS8lBz/VWLf7IKvJEhhwEthIkldVRy7agsF721ibV0difJis3JfjEXJFHOt\ntWtbsJ4OwNPAXr+HRX4Pww51/v67EQR9X/y6Op7uY5v0FMxbDjurwJD+2Fn1Wch1adf4PWI1MG4O\nvLQS9kTTO4NXx9N/HmrPgaAPA4duiYrzFHIiLlO/Hcyg0hA/C/o4+6JTsOf2JlDRDXp0gIKCLyyC\nXNjwibV4Nn1CoHIDgxav4tTZS7i+LGwq90SZAiyw1qaOoK7+wDzgEeBmYE5tCwwCefxNeK4SFk2C\no6HxRoAAAAzzSURBVMthbxTKfgL7N0UPFVbTFsCabbBicnpFk3c3Qp8bmw65ZC2WdCtYcphCTsRF\njDGDS4LMahOifMJ5hC8bhCltxgM1Y9JBcXQ5DO+H964ReJ9+i9OnLmDu+u0kPIVmYqqOh5v7ONMY\nczkwBbjSWvsXgOKgicRrmvMqB1cdh4APysIQicMNf0q35JpzfdAHJUHYVQ03P5PZdbF07fHmVyzZ\npIEnIi5gjGlTEjSPdSjlz0+MoetH91E09tzmBdzBBHxw6SBYeSfFi26kvEcH7isJstgY89UM6/Ia\nY2YAE4FvNQQcQDTBxx/tbLwldGBQNdaqumwQHN0OOo+BEyamR0c2x7jBEE1A+Wg47eb0IJZMbN5F\nAaCRlTnOtGL/sohkgTFmSMjP3IsHELpnJIGGfqjWkKyF258lOfUFEolarknVMbuxVt3+/W/ASGvt\n3gOOD+53LE8un0wO7QOemX1RKB9NMpkiZK2tdboeaZxaciJ5LOgzvygvZt5z42n74JWtG3AAXg/c\nfCHeZbdQ1L099xcF+GP9HLvPqe9/WwEsBi44MODqVb6/mWA+/p79j41QHGCtAi73KeRE8pAxxhQF\nzPRObbjh3TsInXlCdu/f+2iovJ1w7y58vzjA/PppAQ21XQ4sAP7XWnuTtfagw0ustTsKDNXr/pOt\nqltO5QZI1PKG03VI0xRyInko6GPyUWWMWjGZ0KFW5mhNRQF4dRKhfsdyZlGAJ4wxvsb63xrj81C5\nbE3r19rS/v4vqiMJhVw+UMiJ5Bmfx4xqG+ba128i3M7hrUYDPnh+AqFenRji97IBOAbob61dlcn1\nuyM89NuXqWrdKlvWzip45X08wPNO1yJNU8iJ5BFjTHdvIXe/MolQhxwZrhHyw0sTCYV8tAemN9L/\n1piXV27Cu+rfrVVdy3vw76R8Hp611n7idC3SNIWcSJ4wxhSUBHnyVxfi73WU09V8XtsieHQ0nrCf\nJ4wx4abON8b0Msb8Blhbm+Kj+17Mj0nVqTq456/E98WY7nQtkhmFnEie8BYy5tj2fP26oXxhNGMu\nOL8PfPdkSov83H2w48YYjzFmmDHmFdKjLvcAvZMpzpmzlNS+aDarPTwL/w8StWy21q5wuhbJjEJO\nJA8YYzp5CrnjyTGEC3P4u3bm5QS9HkYaY/o1/J0xptwYMxFYB/wcmA0cY639pbV2s7V2U2EBz0x4\nnKztgHA4ogm46mGie6NMdLoWyVwOf7uISAO/h1EXD6Dgazn2mPJAbYvgxgsIlASZaIzpa4x5BFgD\n9AS+b609zVr72IFb+lTFufqxN4k07CqQiyY+SWJfjJczGTUquUMrnojkOGOMJ+Rn+5u/ouzEY5yu\npmm7qqHzGGw8yWZgBvBQJoM0jDFDO5by9Jq7CTW1BU+2Lf0Azr2TPdEajrPW7nS6HsmcWnIiue/8\nr3XEkw8BB5/uz5b0FPI7a+2UTEchWmsXRhI8N/ZRErn0u/feKFw8g2i0hisUcPlHISeS49qEuH7C\neTg8I655xg/B5/cwzhjTrJ8xVXFGz1vO5lvn58Zoy2gCzrqDyO4Ij1tr5ztdjzSfQk4khxljgtVx\nKob3O/R53cZ9tkt2Luh7LBQFCAK9mnOdtXZvdZyBU19gx13P4+i6kNEEDJ1KdPUW/hpJMMrJWuTw\nKeREclvvrl8hGjxwm9M80L87Fqho7nXW2m2RBKfcNp8tE58k6cSjyz0ROGMy0Xc2sKA6zsWNrb8p\nuU8hJ5LbKgYc58zmxqkj/LF+ek+KQj4GHM611trN1Qn6znyZ1WdMJvJxFnvCXn4Pel5HdPVWHqmK\nM0I7DeQ3hZxIDisJMnDAcWS89emKdemNP8uuTG8iOvZRqE2lj23cAQUjoW6/8PrObfDw4vTnj74G\nA2+B8XOhfBTc8gys3w5n/jr9dfurYOTM9F5qmejbDRPwMjDT2g9krd1RFafPivXc9Y2fE/v9Ilp1\nMPi+KFzxAPHh97BzRxXDq2L2arXg8p9CTiSHFRbQ96RmjKr0FMK9l8Ku38Oym9P9dDNf/ux4I5tr\nf+qtddCjA2yfBTdeANbCpO/Btpmwagps3gU3P5NZLSd3hao4PTOv/oustbWxGjs5kqD/hMf4YNCt\nRJasStfVUmI16YDvMZ7o08uZF0nQw1r7t5a7gzhJISeSw1J1lLQryvz8k7tC/+5gDBxdDj/5L1iy\nOvPrO5fBT8+GggLwe6F7BzjzhHR4tiuGawfDkoz2F4CyMCRT+Js7wvJgrLXvV8X55rI1XP+96Wzq\nNo7qGX/DHslSYGu3wbVzqGl/FfFxc3htRxXfrYrZS5u5wLTkOEee9YtIZurq8DVn0MmabenHjW9v\nSLdQalNQ0S3z67u0+/zX2/fCNXPg9dVQnUj307VtcvnlNGPAU0htbYoAcMQrU9b3jf3WGDNzX4wz\nfvEUP/v5E5w5oAeJQb0o6tuNgopu0Okg++vV1cGH2+Dt9bB8HTVLVhNfs5UCDA/GaphhrV13pPVJ\nblLIibjIVQ9Dn67wp7HpLXDuexH+vDx9LFy/iki0Jr3hKcC2A9osBz7OnPQUFBj45xQoDcGzb8PY\nP7bmO2iaTffMLQYWG2M6LvoXA5d+SP+iAN+KJjjBW0hB2E/K76WuzkIiScHeKH6fh92eQt7ZE2Gx\nhUrgDWttzNl3I61NISeSwwoKqInVZH5+dRxKgumAW70FZr0C7UvSx8qLoXNbmLs0/Rjzkddg3X8O\n/XpVMWgThuIA/HsXTF2QeS3WQm0KDxDP/KrmsdZuA+bVf2CMMfEkHaviFAFBIAXEgF3xpN3TWnVI\n7lKfnEgOKyxg3yfVTZ/X0AKbdgk89iaU/BhGPQQjDhjA/4f/gSkvQPloWLUFTm9iWMivvg+VG6DN\nlXD+dLiwf+a1746At5BENkco2rSt1to11tqV1tp/WmvXW6uA+7LSAs0iOaw0ZB6/cwQXX3WW05U0\n36vvw0X3894n1ba307XIl5daciI5bF+MpcvWHPmgDSe8vQEbT7LU6Trky00hJ5LbKpetcXYNx8P1\nxodUR2tY5nQd8uWmkBPJbSs/2kGoOYNPcsXydRjSoxhFHKOQE8lh1tpYUYDK+SucrqR53l4PkTgx\noBlT0UVankJOJMftiXLXtAVUOV1Hc9z7IrFELfdo7UdxmkJOJPc9/+E2ku9udLqMzOyqhj8vxyRT\nPOh0LSIKOZEcZ62trU3xm/tfar1J1S1p9hLqfB4WWmt3OF2LiObJieQBY0ynoI+179xOqNdRTlfT\nuF3V6dX8d0f4trU2z3oSxY3UkhPJA9barbUpbrh4BpEj3cy0Nf10NrFkLXMVcJIrFHIieSKZYsb6\n7ayavpCU07UczPPvwAv/YG91gvFO1yLSQI8rRfKIMebYkI/3KnPssWX9Y8rY7ghDrLVLnK5HpIFa\nciJ5xFq7Ppli/Fm/JrotR5Ycjibg3LuIJFM8qICTXKOQE8kzNbX2gV0R7vnWZCI7HZ49F6+B86cR\nXb2Fv1bHGedsNSJfpMeVInnIGGPCfqa1L2H06zcR6tw2+zVUxWDwXUTf28SrVXEutNYms1+FyKGp\nJSeSh6y1tjpur9u6h1+fNInoK+9n9/7vboSKXxBZuYl5VXGGK+AkV6klJ5LnjDGDw37mjhhA+J6R\nBIqDrXevZC3c9izJaS+QSNRyTaqO2VY/RCSHKeREXMAYU1ocYGbQx7DZowgNORGMafq65li+Di6b\nRWTrHt7eF2OktXZzy95BpOUp5ERcxBgzuDjArDZhyiecR/i/B2FKQ4f/evEaeOotmLqAqg3bScST\nXK/Wm+QThZyIyxhjDDCwNMSEmiTn/PAUOLc3gYpucFxHKDhET7y18PFOqPwIlqwiOXsJtYUFVO6J\nMgVYaK3NyYnoIo1RyIm4mDGmY4FhZJsQZ9ekODmZovT4zsR6H423KIAn6KMwkSRVHSf1wVZq3t2I\nv86SCPp4d1+MvydTzLXWrnX6fYgcLoWcyJeIMaYd0AfoAQTqP2qAGLAJqLTWbnGuQpGWpZATERHX\n0jw5ERFxLYWciIi4lkJORERcSyEnIiKupZATERHXUsiJiIhrKeRERMS1FHIiIuJaCjkREXEthZyI\niLiWQk5ERFxLISciIq6lkBMREddSyImIiGsp5ERExLUUciIi4loKORERcS2FnIiIuJZCTkREXEsh\nJyIirqWQExER11LIiYiIaynkRETEtRRyIiLiWgo5ERFxLYWciIi4lkJORERcSyEnIiKupZATERHX\nUsiJiIhrKeRERMS1FHIiIuJaCjkREXEthZyIiLiWQk5ERFxLISciIq6lkBMREddSyImIiGsp5ERE\nxLUUciIi4loKORERcS2FnIiIuJZCTkREXEshJyIirqWQExER11LIiYiIaynkRETEtRRyIiLiWgo5\nERFxLYWciIi4lkJORERc6/8BYSK9zhNaZKYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8db208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boy_color = 'green'\n",
    "girl_color = 'orange'\n",
    "\n",
    "color_by_gender = {\n",
    "    'jere': boy_color, 'miika': boy_color, 'mikko': boy_color, 'olli': boy_color,\n",
    "    'ella': girl_color, 'anniina': girl_color, 'laura': girl_color, 'maria': girl_color\n",
    "}\n",
    "\n",
    "# create a list of color per node\n",
    "colors = [color_by_gender[node] for node in graph.nodes_iter()]\n",
    "\n",
    "fig, ax = get_pyplot_ax()\n",
    "nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = colors);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## custom labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/Sg8enZw35rrewu+gufezex4dfVEAa1Kn4koF7Ixg9kxB78UxoBDqeAubjWz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8ee278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = get_pyplot_ax()\n",
    "\n",
    "labels = {}\n",
    "for person in people:\n",
    "    labels[person] = person.upper()\n",
    "nx.draw_networkx(graph, labels = labels, ax = ax, node_size = 2300, node_color = colors);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## fixed node positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MsUUPY9JgCXQaVPX1BHycZktPzzwF1MJyVfViUrQe0MYYT62PMmPGU1QUQiHZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111820710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.layout.spring_layout(graph)\n",
    "\n",
    "fig, ax = get_pyplot_ax(rows = 1, columns = 2, figsize = (12, 5))\n",
    "\n",
    "left_plot = ax[0]\n",
    "right_plot = ax[1]\n",
    "\n",
    "nx.draw_networkx(graph, pos = pos, ax = left_plot, node_size = 2300,)\n",
    "nx.draw_networkx(graph, pos = pos, ax = right_plot, node_size = 2300, node_color = colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## analysis of a graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anniina has 2 friends\n",
      "ella has 4 friends\n",
      "olli has 2 friends\n",
      "mikko has 3 friends\n",
      "maria has 2 friends\n",
      "jere has 4 friends\n",
      "laura has 3 friends\n",
      "miika has 2 friends\n"
     ]
    }
   ],
   "source": [
    "number_of_friends = graph.degree()\n",
    "\n",
    "for person in number_of_friends:\n",
    "    print(\"{} has {} friends\".format(person, number_of_friends[person]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style='float:left; width: 400px;' src='img/example_friends.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from clustering import spectral_clusters\n",
    "partition = spectral_clusters(graph, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "group_colors = []\n",
    "for pos, name in enumerate(graph.nodes_iter()):\n",
    "    side = partition[pos]\n",
    "    color = 'blue' if side == 1 else 'grey'\n",
    "    group_colors.append(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZrX79+skHH3zQdfXq1b8D/sfteprLZld6gIicDPxeVU9wu5bmEJEpwCJV/YPb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\nTkREAkshJyIigaWQExGRwFLIiYhIYP0/1my2MEV1944AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11162ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = get_pyplot_ax()\n",
    "nx.draw_networkx(graph, ax = ax, node_color = group_colors, node_size = 2300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# back to twitter\n",
    "\n",
    "* the twitter data\n",
    "* retweets and replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### dataset\n",
    "\n",
    "* tweets collected using the streaming api (1% sample)\n",
    "* can also be collected from the REST api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### a tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### a retweet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style='float:right; width:400px;' src='img/trump_tweet.png'>\n",
    "__original tweet author__ realDonaldTrump\n",
    "\n",
    "__retweet author__ terry_golfing\n",
    "\n",
    "__text__ RT @realDonaldTrump: REPEAL AND REPLACE OBAMACARE!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### a reply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style='float:right; width:400px;' src='img/trump_tweet.png'>\n",
    "__original tweet author__ realDonaldTrump\n",
    "\n",
    "__reply author__ tonyposnanski\n",
    "\n",
    "__text__ @realDonaldTrump have a better plan than the one you presented"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### building graph for retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "dataset = []\n",
    "tweet =  None\n",
    "def is_retweet(tweet):\n",
    "    pass\n",
    "def get_retweet_author(tweet):\n",
    "    pass\n",
    "def get_original_author(tweet):\n",
    "    pass\n",
    "def contains_topic(tweet, topic):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "topic = '#obamacare'\n",
    "for tweet in dataset:\n",
    "    if contains_topic(tweet, topic) and is_retweet(tweet):\n",
    "        from_who = get_retweet_author(tweet)\n",
    "        to_who = get_original_author(tweet)\n",
    "        graph.add_edge(from_who, to_who)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style='float:left; width: 400px;' src='img/germanwings_nocolor.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/nepal_nocolor.png'/>\n",
    "<p style=\"clear: both;\">\n",
    "<img style='float:left; width: 400px;' src='img/beefban_nocolor.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/russia_march_nocolor.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# what happens when people disagree?\n",
    "\n",
    "we notice a difference...\n",
    "\n",
    "retweet graphs for controversial topics contain two well-separated clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### clustering\n",
    "\n",
    "we cluster each graph like before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "partition = spectral_clusters(graph, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to find sides __if__ they exist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### visualize with clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "group_colors = []\n",
    "for pos, name in enumerate(graph.nodes_iter()):\n",
    "    side = partition[pos]\n",
    "    color = 'blue' if side == 1 else 'red'\n",
    "    group_colors.append(color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "nx.__draw_networkx__(graph, ax = ax, __node_color = group_colors__, node_size = 2300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style='float:left; width: 400px;' src='img/germanwings.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/nepal.png'/>\n",
    "<p style=\"clear: both;\">\n",
    "<img style='float:left; width: 400px;' src='img/beefban.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/russia_march.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# reply networks\n",
    "\n",
    "what about reply networks?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style='float:left; width: 400px;' src='img/men_germanwings.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/men_nepal.png'/>\n",
    "<p style=\"clear: both;\">\n",
    "<img style='float:left; width: 400px;' src='img/men_beefban.png'/>\n",
    "<img style='float:left; width: 400px;' src='img/men_russia_march.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# summary\n",
    "\n",
    "* __intro to networkx__ a python library to create, process, visualize graphs\n",
    "* __application__ twitter discussions\n",
    "    * controversial and non-controversial topics\n",
    "    * retweet and reply graphs\n",
    "    * retweet graphs are clearly separated for controversial topics\n",
    "    * replies also from one side to the other\n",
    "    * consistent pattern for many instances we've seen\n",
    "* graphs can offer __insights__ into human interactions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# more about networkx\n",
    "\n",
    "* directed graphs\n",
    "* more layout algorithms\n",
    "* centrality and clustering measures\n",
    "* more at http://networkx.github.io/\n",
    "\n",
    "slides at https://github.com/mmathioudakis/pycon2016polarization"
   ]
  }
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