Twitter Movement Derivation Summary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Twitter Movement Derivation & Vulnerable Locations\n",
    "=================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import psycopg2, psycopg2.extras, psycopg2.pool, pickle, json, os\n",
    "from multiprocessing import Pool\n",
    "from collections import OrderedDict\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "working_dir = 'working_data/zone_a_clustered/'\n",
    "file_names = os.listdir(working_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Heavy lifting: Load all the users in parallel\n",
    "def load_user(file_name):\n",
    "    return json.load(open(working_dir+file_name,'r'))\n",
    "\n",
    "pool = Pool(processes=24)\n",
    "res = pool.map_async(load_user, file_names)\n",
    "pool.close()\n",
    "pool.join()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "users = res.get()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweets  & Clusters Per User?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweet_count_per_user = [len(u['features']) for u in users]\n",
    "clusters_per_user    = [ len(np.unique([f['properties']['cluster'] for f in u['features'] if f['properties']['cluster'] != None])) for u in users]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FlI3Bz3zmM2ltbc0DDzyQJNm0aVM+/elPV3wwAAAAKqdsDG7dujXXX399jj/++CTJlVde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\nBAAAKCAxCAAAUED/D4mx7X77YPNRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd675008b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "fig.set_size_inches(15,8)\n",
    "ax.hist(tweet_count_per_user, bins=np.logspace(1,5,100))\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"Tweet Count\")\n",
    "ax.set_ylabel(\"Number of Users\")\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hfv/3fz/OOOOMkg8GAABA6RSMwXPOOaff2uuvvx633nprXHfddTF79uySDgYAAEDpFIzB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\nQILEIAAAQIL+H/rU98dIUn0fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd674daa400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "fig.set_size_inches(15,8)\n",
    "ax.hist(clusters_per_user, bins=np.logspace(1,2.5,100))\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"Cluster Count\")\n",
    "ax.set_ylabel(\"Number of Users\")\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22042"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "IPython (Python 3)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}