{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "from faculty_hiring.misc.util import *\n",
    "from faculty_hiring.misc.plotting import *  # Definitions for LABEL_SIZE and such\n",
    "from faculty_hiring.misc.gaussian_kde import gaussian_kde\n",
    "from faculty_hiring.parse import faculty_parser, institution_parser\n",
    "from faculty_hiring.parse import load\n",
    "from faculty_hiring.parse.nces import parse_phds_awarded\n",
    "from faculty_hiring.misc.subfield import topic_descriptions, longer_topic_descriptions, num_topics\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import csv\n",
    "import cPickle\n",
    "import random\n",
    "import copy\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# File locations\n",
    "faculty_file = '/Users/allisonmorgan/Documents/faculty_hiring/publication_data/current_data/faculty_cs_CURRENT.txt'\n",
    "inst_file = '/Users/allisonmorgan/Documents/faculty_hiring/publication_data/current_data/inst_cs_CURRENT.txt'\n",
    "\n",
    "# (Optional, these are for loading publication profiles)\n",
    "dblp_dir = '/Users/allisonmorgan/Documents/faculty_hiring/publication_data/profiles_DBLP_Nov16/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inst = institution_parser.parse_institution_records(open(inst_file, 'rU'))\n",
    "asst_faculty = load.load_assistant_profs(open(faculty_file), inst, ranking='pi')\n",
    "load.load_all_publications(asst_faculty, dblp_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_learning_keywords = [\"convolutional net\", \"convolutional neural net\", \"convolutional neural field\", \" rnn \", \"deep learning\", \"deep-learning\", \"recursive neural net\", \"lstm\", \"long short-term memory\", \"generative adversarial network\", \"theano\", \"neural network\", \"deep belief net\", \"boltzmann machine\", \"convnet\", \"deep reinforcement learning\", \"deep neural network\", \" dnn \", \" dnn-\", \"multilayer perceptron\", \"autoencoder\", \"auto-encoder\", \"activation function\", \"backprop\", \"back-prop\", \"ladder network\", \"bidirectional rnn\", \"bidirectional recurrent\", \"imagenet\", \"restricted boltzmann\", \"rmsprop\", \"convnet\", \"artificial neural network\", \"connectionist\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to find all faculty members who have ever worked at an institution. Returns an array of faculty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def faculty_at_institution(institution_name, asst_faculty):\n",
    "    people = []\n",
    "    for f in asst_faculty:\n",
    "        for job in f.faculty:\n",
    "            if job['rank'] != \"PostDoc\" and job['place'] == institution_name:\n",
    "                people.append(f)\n",
    "                break\n",
    "                \n",
    "    return people "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General trend in deep-learning research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distribution(asst_faculty, keywords):\n",
    "    yearly_rate = Counter()\n",
    "    for f in asst_faculty:\n",
    "        if f.__contains__(\"dblp_pubs\"):\n",
    "            for pub in f.dblp_pubs:\n",
    "                if any(pub['title'].lower().count(keyword) for keyword in keywords):\n",
    "                    yearly_rate[pub['year']] += 1\n",
    "                \n",
    "    return yearly_rate \n",
    "\n",
    "dist = distribution(asst_faculty, deep_learning_keywords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = dict(dist).keys(); y = [];\n",
    "for year, count in dict(dist).items():\n",
    "    y.append(count)\n",
    "    \n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(x, y, color = 'k')\n",
    "plt.xlim(1980, 2015)\n",
    "plt.title('Counts of \"deep learning\" publications over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walk-through an example university"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try this procedure on University of Colorado, Boulder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "place = 'University of Colorado, Boulder'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hires_and_publications(place, keywords, asst_faculty):\n",
    "    hires_and_publications = []\n",
    "    for f in faculty_at_institution(place, asst_faculty):\n",
    "        person = {\"facultyName\": f.facultyName, \"phdRank\": inst[f.phd()[0]]['pi'], \"currentRank\": inst[place]['pi']}\n",
    "\n",
    "        person[\"start\"] = 2020; person[\"end\"] = 0;\n",
    "        for job in f.faculty:\n",
    "            if job['place'] == place and not (job['start_year'] is None):\n",
    "                person[\"start\"] = min(job['start_year'], person[\"start\"])\n",
    "                person[\"end\"] = max(job['end_year'], person[\"end\"])\n",
    "    \n",
    "        if person[\"end\"] == 0:\n",
    "            person[\"end\"] = 2011\n",
    "        if person[\"start\"] == 2020:\n",
    "            continue\n",
    "    \n",
    "        relevant_pubs = []\n",
    "        if f.__contains__(\"dblp_pubs\"):\n",
    "            for pub in f.dblp_pubs:\n",
    "                if any((pub['title'].lower().count(keyword) > 0) for keyword in keywords): #or relevant_venue:\n",
    "                    #print(pub['title'])\n",
    "                    relevant_pubs.append(pub['year'])\n",
    "                        \n",
    "        person[\"pubs\"] = relevant_pubs\n",
    "        person[\"phd_location\"] = f.phd_location\n",
    "        person[\"faculty_record\"]= f\n",
    "    \n",
    "        hires_and_publications.append(person)\n",
    "    \n",
    "    return hires_and_publications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the above data so we can better see when hires and publications occurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_careers(place, keywords, asst_faculty):\n",
    "    i = 0\n",
    "    plt.figure(figsize=(20,10))\n",
    "    for person in get_hires_and_publications(place, keywords, asst_faculty):\n",
    "        #print person['facultyName']\n",
    "        plt.plot([person[\"start\"], person[\"end\"]], [i, i], linewidth= 4, color = 'k', alpha = .25)\n",
    "        plt.text(person[\"start\"], i, \"Faculty {0}\".format(i), fontsize=20)\n",
    "    \n",
    "        if len(person['pubs']) > 0:\n",
    "            plt.scatter(person['pubs'], [i]*len(person['pubs']), s = 24, color = [153.0/255.0, 0.0/255.0, 255.0/255.0])\n",
    "        i += 1\n",
    "\n",
    "    plt.xlim(1960, 2020)\n",
    "\n",
    "plot_careers(place, deep_learning_keywords, asst_faculty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did the first deep-learning publication coincide with a faculty hiring event? Is the date of publication within two years of the author's hire? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Was a \"deep-learning\" researcher hired who might have led to the research area? [({'faculty_record': <faculty_hiring.parse.faculty_parser.faculty_record instance at 0x60d3d99e0>, 'end': 2011, 'currentRank': 68.17, 'pubs': [1999, 1995, 1994, 1994, 1993, 1992, 1992, 1991, 1991, 1990], 'start': 1988, 'phd_location': 'UC San Diego', 'phdRank': 27.93, 'facultyName': 'Michael C. Mozer'}, [1999, 1995, 1994, 1994, 1993, 1992, 1992, 1991, 1991, 1990])]\n"
     ]
    }
   ],
   "source": [
    "def find_infected_hire(place, keywords, asst_faculty):\n",
    "    '''\n",
    "    Returns None if no infected hire found, otherwise the first hire\n",
    "    is returned as (person, year of infectious publication).\n",
    "    '''\n",
    "    candidates = []\n",
    "    for person in get_hires_and_publications(place, keywords, asst_faculty):\n",
    "        person_pubs = person[\"pubs\"]\n",
    "        # If we do not know the faculty member's start date, or they have no pubs\n",
    "        if not person[\"start\"] or len(person_pubs) == 0:\n",
    "            continue\n",
    "        \n",
    "        # Consider the publications when the entry was a professor at the institution\n",
    "        person_pubs = filter(lambda x: person[\"start\"] <= x <= person[\"end\"], person_pubs)\n",
    "        if person_pubs:\n",
    "            candidates.append((person, person_pubs))\n",
    "    \n",
    "    # If the university was never \"infected\"\n",
    "    if not candidates:\n",
    "        return None\n",
    "    else:\n",
    "        # return min(candidates, key=lambda xs: min(xs[1]))\n",
    "        return candidates\n",
    "        \n",
    "print \"\\nWas a \\\"deep-learning\\\" researcher hired who might have led to the research area? {0}\".format(find_infected_hire(place, deep_learning_keywords, asst_faculty))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_infected_hire(person, delta_t):\n",
    "    return any([(pub - person[0][\"start\"]) <= delta_t for pub in person[0][\"pubs\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was the person who infected CU Boulder (Mike Mozer), infected by hiring? True\n"
     ]
    }
   ],
   "source": [
    "print \"Was the person who infected CU Boulder (Mike Mozer), infected by hiring? {0}\".format(is_infected_hire(find_infected_hire(place, deep_learning_keywords, asst_faculty)[0], 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For all universities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_has_relevant_prior_pubs(delta_t, keywords, asst_faculty):\n",
    "    n_no_priors = 0\n",
    "    n_yes_priors = 0\n",
    "    total = 0\n",
    "    # Go through every institution\n",
    "    for place in inst.keys():\n",
    "        if place == \"UNKNOWN\" or place == \"All others\":\n",
    "            continue\n",
    "            \n",
    "        # Look for the faculty member which has infected the institution \n",
    "        infected_hires = find_infected_hire(place, keywords, asst_faculty)\n",
    "        if not infected_hires:\n",
    "            continue\n",
    "        \n",
    "        # Were they publishing on deep-learning before their hire date?\n",
    "        for i, (candidate, min_pub) in enumerate(copy.copy(infected_hires)):\n",
    "            if is_infected_hire([candidate], delta_t):\n",
    "                infected_hires[i] = (candidate, [candidate['start']])\n",
    "\n",
    "        # Check all the possible infections. Length of minima is greater than one\n",
    "        # if there are multiple possible infections\n",
    "        x = min(infected_hires, key=lambda xs: min(xs[1]))\n",
    "        minima = [candidate for (candidate, min_pub) in infected_hires if min_pub == x[1]]\n",
    "        \n",
    "        # If any of them could have spread the infection due to hiring\n",
    "        if any([not is_infected_hire([candidate], delta_t) for candidate in minima]):\n",
    "            n_no_priors += 1\n",
    "        else:\n",
    "            n_yes_priors += 1\n",
    "        total += 1\n",
    "    \n",
    "    print(\"Hires with no background in topic: {0}, Hires with background in topic: {1}, Total infected univesities: {2}\".format(n_no_priors, n_yes_priors, total))\n",
    "    return (float(n_no_priors)/total, float(n_yes_priors)/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Deep Learning (n_keywords: 34)\n",
      "Hires with no background in topic: 77, Hires with background in topic: 42, Total infected univesities: 119\n",
      "Fraction of infections due to hiring: 0.352941176471, not due to hiring: 0.647058823529\n",
      "\n",
      ">>> Topic Modeling (n_keywords: 7)\n",
      "Hires with no background in topic: 28, Hires with background in topic: 15, Total infected univesities: 43\n",
      "Fraction of infections due to hiring: 0.348837209302, not due to hiring: 0.651162790698\n",
      "\n",
      ">>> Incremental Computation (n_keywords: 23)\n",
      "Hires with no background in topic: 17, Hires with background in topic: 11, Total infected univesities: 28\n",
      "Fraction of infections due to hiring: 0.392857142857, not due to hiring: 0.607142857143\n",
      "\n",
      ">>> Quantum Computing (n_keywords: 56)\n",
      "Hires with no background in topic: 19, Hires with background in topic: 9, Total infected univesities: 28\n",
      "Fraction of infections due to hiring: 0.321428571429, not due to hiring: 0.678571428571\n",
      "\n",
      ">>> Mechanism Design (n_keywords: 7)\n",
      "Hires with no background in topic: 12, Hires with background in topic: 11, Total infected univesities: 23\n",
      "Fraction of infections due to hiring: 0.478260869565, not due to hiring: 0.521739130435\n"
     ]
    }
   ],
   "source": [
    "topic_modeling_keywords = [\"probabilistic latent semantic analysis\", \"plsa\", \"latent dirichlet allocation\",\"latent semantic analysis\", \"latent semantic indexing\", \"topic model\", \"probabilistic topic modeling\"]\n",
    "incremental_keywords = [\"incremental computation\", \"self-adjusting computation\", \"program derivative\",\"dbtoaster\", \"incremental view\", \"partial evaluation\", \"incremental computing\", \"incrementally compute\", \"frtime\", \"adaptive functional programming\", \"delta ml\", \"haskell adaptive\", \"cornell synthesizer generator\", \"icedust\", \"adapton\", \"one-way dataflow constraints\", \"reactive computation\", \"differential dataflow\", \"jane street incremental\", \"incremental datalog\", \"incremental prolog\", \"incremental type checking\", \"self-adjusting\"]\n",
    "deep_learning_keywords = [\"convolutional net\", \"convolutional neural net\", \"convolutional neural field\", \" rnn \", \"deep learning\", \"deep-learning\", \"recursive neural net\", \"lstm\", \"long short-term memory\", \"generative adversarial network\", \"theano\", \"neural network\", \"deep belief net\", \"boltzmann machine\", \"convnet\", \"deep reinforcement learning\", \"deep neural network\", \" dnn \", \" dnn-\", \"multilayer perceptron\", \"autoencoder\", \"auto-encoder\", \"activation function\", \"backprop\", \"back-prop\", \"ladder network\", \"bidirectional rnn\", \"bidirectional recurrent\", \"imagenet\", \"restricted boltzmann\", \"rmsprop\", \"convnet\", \"artificial neural network\", \"connectionist\"]\n",
    "quantum_computing_keywords = ['quantum information', 'non-locality', 'quantum comput', 'quantum teleport', 'quantum entangl', \"bell's inequality\", 'chsh inequality', \"tsirelson's inequality\", 'local hamiltonian', 'product state', 'mixed state', 'pure state', 'nonlocality', 'quantum tomography', 'unitary gate', 'quantum error correction', 'quantum algorithm', 'quantum complexity', 'quantum query complexity', 'quantum fourier transform', 'hidden subgroup problem', ' qma ', ' qcma ', ' bqp ', 'promisebqp', 'promise-bqp', 'quantum information', 'quantum communication', 'quantum channel', 'qubit', 'quantum code', 'quantum annealing', 'quantum supremacy', 'quantum ground state', 'linear optics', 'quantum lower bounds', 'quantum speed', 'boson sampling', 'classical simulation', 'povm', 'quantum advice', 'quantum cryptography', 'qip', 'quantum circuit', 'quantum walk', 'toffoli gate', 'hadamard gate', 'coset state', 'stabilizer state', 'quantum satisfiability', \"simon's problem\", \"shor's algorithm\", \"grover's algorithm\", 'fourier sampling', 'quantum gate', 'phase gate']\n",
    "mechanism_design_keywords = ['algorithmic game theory', 'algorithmic economics', 'computational social choice', 'fair division', 'mechanism design', 'fair allocation', 'cost allocation']\n",
    "\n",
    "print(\">>> Deep Learning (n_keywords: {0})\".format(len(deep_learning_keywords)))\n",
    "(prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, deep_learning_keywords, asst_faculty)\n",
    "print(\"Fraction of infections due to hiring: {0}, not due to hiring: {1}\\n\".format(prob_yes_priors, prob_no_priors))\n",
    "print(\">>> Topic Modeling (n_keywords: {0})\".format(len(topic_modeling_keywords)))\n",
    "(prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, topic_modeling_keywords, asst_faculty)\n",
    "print(\"Fraction of infections due to hiring: {0}, not due to hiring: {1}\\n\".format(prob_yes_priors, prob_no_priors))\n",
    "print(\">>> Incremental Computation (n_keywords: {0})\".format(len(incremental_keywords)))\n",
    "(prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, incremental_keywords, asst_faculty)\n",
    "print(\"Fraction of infections due to hiring: {0}, not due to hiring: {1}\\n\".format(prob_yes_priors, prob_no_priors))\n",
    "print(\">>> Quantum Computing (n_keywords: {0})\".format(len(quantum_computing_keywords)))\n",
    "(prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, quantum_computing_keywords, asst_faculty)\n",
    "print(\"Fraction of infections due to hiring: {0}, not due to hiring: {1}\\n\".format(prob_yes_priors, prob_no_priors))\n",
    "print(\">>> Mechanism Design (n_keywords: {0})\".format(len(mechanism_design_keywords)))\n",
    "(prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, mechanism_design_keywords, asst_faculty)\n",
    "print(\"Fraction of infections due to hiring: {0}, not due to hiring: {1}\".format(prob_yes_priors, prob_no_priors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_has_prestige(delta_t, keywords, asst_faculty):\n",
    "    n_came_from_prestige = 0\n",
    "    n_moved_to_prestige = 0\n",
    "    total = 0\n",
    "    # Go through every institution\n",
    "    for place in inst.keys():\n",
    "        if place == \"UNKNOWN\" or place == \"All others\":\n",
    "            continue\n",
    "            \n",
    "        # Look for the faculty member which has infected the institution \n",
    "        infected_hires = find_infected_hire(place, keywords, asst_faculty)\n",
    "        if not infected_hires:\n",
    "            continue\n",
    "            \n",
    "        # Were they publishing on deep-learning before their hire date?\n",
    "        for i, (candidate, min_pub) in enumerate(copy.copy(infected_hires)):\n",
    "            if is_infected_hire([candidate], delta_t):\n",
    "                infected_hires[i] = (candidate, [candidate['start']])\n",
    "\n",
    "        # Check all the possible infections. Length of minima is greater than one\n",
    "        # if there are two possible infections\n",
    "        x = min(infected_hires, key=lambda xs: min(xs[1]))\n",
    "        minima = [candidate for (candidate, min_pub) in infected_hires if min_pub == x[1]]\n",
    "        \n",
    "        # If the transmission was via hiring, did they come from a more prestigious \n",
    "        # university than they are currently employed?\n",
    "        if not any([not is_infected_hire([candidate], delta_t) for candidate in minima]):\n",
    "            if all([candidate['phdRank'] < candidate['currentRank'] for candidate in minima if is_infected_hire([candidate], delta_t)]):\n",
    "                n_came_from_prestige += 1\n",
    "            else:\n",
    "                n_moved_to_prestige += 1\n",
    "            total += 1\n",
    "    \n",
    "    print(\"Hires who came from more prestigious background: {0}, came from less prestigious background: {1}, total: {2}\".format(n_came_from_prestige, n_moved_to_prestige, total))\n",
    "    return (float(n_came_from_prestige)/total, float(n_moved_to_prestige)/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Deep Learning (n_keywords: 34)\n",
      "Hires who came from more prestigious background: 34, came from less prestigious background: 8, total: 42\n",
      "\n",
      ">>> Topic Modeling (n_keywords: 7)\n",
      "Hires who came from more prestigious background: 12, came from less prestigious background: 3, total: 15\n",
      "\n",
      ">>> Incremental Computation (n_keywords: 23)\n",
      "Hires who came from more prestigious background: 9, came from less prestigious background: 2, total: 11\n",
      "\n",
      ">>> Quantum Computing (n_keywords: 56)\n",
      "Hires who came from more prestigious background: 9, came from less prestigious background: 0, total: 9\n",
      "\n",
      ">>> Mechanism Design (n_keywords: 7)\n",
      "Hires who came from more prestigious background: 7, came from less prestigious background: 4, total: 11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\">>> Deep Learning (n_keywords: {0})\".format(len(deep_learning_keywords)))\n",
    "(prob_came_from_prestige, prob_moved_to_prestige) = percent_has_prestige(2, deep_learning_keywords, asst_faculty)\n",
    "print \"\"\n",
    "print(\">>> Topic Modeling (n_keywords: {0})\".format(len(topic_modeling_keywords)))\n",
    "(prob_came_from_prestige, prob_moved_to_prestige) = percent_has_prestige(2, topic_modeling_keywords, asst_faculty)\n",
    "print \"\"\n",
    "print(\">>> Incremental Computation (n_keywords: {0})\".format(len(incremental_keywords)))\n",
    "(prob_came_from_prestige, prob_moved_to_prestige) = percent_has_prestige(2, incremental_keywords, asst_faculty)\n",
    "print \"\"\n",
    "print(\">>> Quantum Computing (n_keywords: {0})\".format(len(quantum_computing_keywords)))\n",
    "(prob_came_from_prestige, prob_moved_to_prestige) = percent_has_prestige(2, quantum_computing_keywords, asst_faculty)\n",
    "print \"\"\n",
    "print(\">>> Mechanism Design (n_keywords: {0})\".format(len(mechanism_design_keywords)))\n",
    "(prob_came_from_prestige, prob_moved_to_prestige) = percent_has_prestige(2, mechanism_design_keywords, asst_faculty)\n",
    "print \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Null Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import random\n",
    "\n",
    "asst_faculty_shuffled = copy.deepcopy(asst_faculty)\n",
    "all_titles = []\n",
    "\n",
    "for f in asst_faculty:\n",
    "    if f.__contains__(\"dblp_pubs\"):\n",
    "        for pub in f.dblp_pubs:\n",
    "            all_titles.append(pub['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate our null model by shuffling the list of publications, and assigning them to faculty at random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hires with no background in topic: 62, Hires with background in topic: 13, Total infected univesities: 75\n",
      "(0) Fraction of infections due to hiring: 0.173333333333, not due to hiring: 0.826666666667\n",
      "Hires with no background in topic: 61, Hires with background in topic: 10, Total infected univesities: 71\n",
      "(1) Fraction of infections due to hiring: 0.140845070423, not due to hiring: 0.859154929577\n",
      "Hires with no background in topic: 64, Hires with background in topic: 19, Total infected univesities: 83\n",
      "(2) Fraction of infections due to hiring: 0.228915662651, not due to hiring: 0.771084337349\n",
      "Hires with no background in topic: 55, Hires with background in topic: 19, Total infected univesities: 74\n",
      "(3) Fraction of infections due to hiring: 0.256756756757, not due to hiring: 0.743243243243\n",
      "Hires with no background in topic: 64, Hires with background in topic: 14, Total infected univesities: 78\n",
      "(4) Fraction of infections due to hiring: 0.179487179487, not due to hiring: 0.820512820513\n",
      "Hires with no background in topic: 64, Hires with background in topic: 11, Total infected univesities: 75\n",
      "(5) Fraction of infections due to hiring: 0.146666666667, not due to hiring: 0.853333333333\n",
      "Hires with no background in topic: 63, Hires with background in topic: 18, Total infected univesities: 81\n",
      "(6) Fraction of infections due to hiring: 0.222222222222, not due to hiring: 0.777777777778\n",
      "Hires with no background in topic: 67, Hires with background in topic: 13, Total infected univesities: 80\n",
      "(7) Fraction of infections due to hiring: 0.1625, not due to hiring: 0.8375\n",
      "Hires with no background in topic: 66, Hires with background in topic: 15, Total infected univesities: 81\n",
      "(8) Fraction of infections due to hiring: 0.185185185185, not due to hiring: 0.814814814815\n",
      "Hires with no background in topic: 59, Hires with background in topic: 15, Total infected univesities: 74\n",
      "(9) Fraction of infections due to hiring: 0.202702702703, not due to hiring: 0.797297297297\n",
      "Hires with no background in topic: 71, Hires with background in topic: 9, Total infected univesities: 80\n",
      "(10) Fraction of infections due to hiring: 0.1125, not due to hiring: 0.8875\n",
      "Hires with no background in topic: 58, Hires with background in topic: 15, Total infected univesities: 73\n",
      "(11) Fraction of infections due to hiring: 0.205479452055, not due to hiring: 0.794520547945\n",
      "Hires with no background in topic: 62, Hires with background in topic: 20, Total infected univesities: 82\n",
      "(12) Fraction of infections due to hiring: 0.243902439024, not due to hiring: 0.756097560976\n",
      "Hires with no background in topic: 64, Hires with background in topic: 19, Total infected univesities: 83\n",
      "(13) Fraction of infections due to hiring: 0.228915662651, not due to hiring: 0.771084337349\n",
      "Hires with no background in topic: 53, Hires with background in topic: 15, Total infected univesities: 68\n",
      "(14) Fraction of infections due to hiring: 0.220588235294, not due to hiring: 0.779411764706\n",
      "Hires with no background in topic: 69, Hires with background in topic: 16, Total infected univesities: 85\n",
      "(15) Fraction of infections due to hiring: 0.188235294118, not due to hiring: 0.811764705882\n",
      "Hires with no background in topic: 60, Hires with background in topic: 13, Total infected univesities: 73\n",
      "(16) Fraction of infections due to hiring: 0.178082191781, not due to hiring: 0.821917808219\n",
      "Hires with no background in topic: 62, Hires with background in topic: 19, Total infected univesities: 81\n",
      "(17) Fraction of infections due to hiring: 0.234567901235, not due to hiring: 0.765432098765\n",
      "Hires with no background in topic: 53, Hires with background in topic: 15, Total infected univesities: 68\n",
      "(18) Fraction of infections due to hiring: 0.220588235294, not due to hiring: 0.779411764706\n",
      "Hires with no background in topic: 54, Hires with background in topic: 18, Total infected univesities: 72\n",
      "(19) Fraction of infections due to hiring: 0.25, not due to hiring: 0.75\n",
      "Hires with no background in topic: 57, Hires with background in topic: 13, Total infected univesities: 70\n",
      "(20) Fraction of infections due to hiring: 0.185714285714, not due to hiring: 0.814285714286\n",
      "Hires with no background in topic: 56, Hires with background in topic: 14, Total infected univesities: 70\n",
      "(21) Fraction of infections due to hiring: 0.2, not due to hiring: 0.8\n",
      "Hires with no background in topic: 67, Hires with background in topic: 16, Total infected univesities: 83\n",
      "(22) Fraction of infections due to hiring: 0.192771084337, not due to hiring: 0.807228915663\n",
      "Hires with no background in topic: 62, Hires with background in topic: 12, Total infected univesities: 74\n",
      "(23) Fraction of infections due to hiring: 0.162162162162, not due to hiring: 0.837837837838\n",
      "Hires with no background in topic: 65, Hires with background in topic: 12, Total infected univesities: 77\n",
      "(24) Fraction of infections due to hiring: 0.155844155844, not due to hiring: 0.844155844156\n",
      "Hires with no background in topic: 53, Hires with background in topic: 17, Total infected univesities: 70\n",
      "(25) Fraction of infections due to hiring: 0.242857142857, not due to hiring: 0.757142857143\n",
      "Hires with no background in topic: 58, Hires with background in topic: 21, Total infected univesities: 79\n",
      "(26) Fraction of infections due to hiring: 0.26582278481, not due to hiring: 0.73417721519\n",
      "Hires with no background in topic: 51, Hires with background in topic: 18, Total infected univesities: 69\n",
      "(27) Fraction of infections due to hiring: 0.260869565217, not due to hiring: 0.739130434783\n",
      "Hires with no background in topic: 61, Hires with background in topic: 19, Total infected univesities: 80\n",
      "(28) Fraction of infections due to hiring: 0.2375, not due to hiring: 0.7625\n",
      "Hires with no background in topic: 53, Hires with background in topic: 18, Total infected univesities: 71\n",
      "(29) Fraction of infections due to hiring: 0.253521126761, not due to hiring: 0.746478873239\n",
      "Hires with no background in topic: 57, Hires with background in topic: 16, Total infected univesities: 73\n",
      "(30) Fraction of infections due to hiring: 0.219178082192, not due to hiring: 0.780821917808\n",
      "Hires with no background in topic: 60, Hires with background in topic: 17, Total infected univesities: 77\n",
      "(31) Fraction of infections due to hiring: 0.220779220779, not due to hiring: 0.779220779221\n",
      "Hires with no background in topic: 60, Hires with background in topic: 12, Total infected univesities: 72\n",
      "(32) Fraction of infections due to hiring: 0.166666666667, not due to hiring: 0.833333333333\n",
      "Hires with no background in topic: 60, Hires with background in topic: 24, Total infected univesities: 84\n",
      "(33) Fraction of infections due to hiring: 0.285714285714, not due to hiring: 0.714285714286\n",
      "Hires with no background in topic: 57, Hires with background in topic: 15, Total infected univesities: 72\n",
      "(34) Fraction of infections due to hiring: 0.208333333333, not due to hiring: 0.791666666667\n",
      "Hires with no background in topic: 58, Hires with background in topic: 15, Total infected univesities: 73\n",
      "(35) Fraction of infections due to hiring: 0.205479452055, not due to hiring: 0.794520547945\n",
      "Hires with no background in topic: 54, Hires with background in topic: 22, Total infected univesities: 76\n",
      "(36) Fraction of infections due to hiring: 0.289473684211, not due to hiring: 0.710526315789\n",
      "Hires with no background in topic: 49, Hires with background in topic: 19, Total infected univesities: 68\n",
      "(37) Fraction of infections due to hiring: 0.279411764706, not due to hiring: 0.720588235294\n",
      "Hires with no background in topic: 67, Hires with background in topic: 17, Total infected univesities: 84\n",
      "(38) Fraction of infections due to hiring: 0.202380952381, not due to hiring: 0.797619047619\n",
      "Hires with no background in topic: 53, Hires with background in topic: 16, Total infected univesities: 69\n",
      "(39) Fraction of infections due to hiring: 0.231884057971, not due to hiring: 0.768115942029\n",
      "Hires with no background in topic: 56, Hires with background in topic: 20, Total infected univesities: 76\n",
      "(40) Fraction of infections due to hiring: 0.263157894737, not due to hiring: 0.736842105263\n",
      "Hires with no background in topic: 62, Hires with background in topic: 19, Total infected univesities: 81\n",
      "(41) Fraction of infections due to hiring: 0.234567901235, not due to hiring: 0.765432098765\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hires with no background in topic: 64, Hires with background in topic: 17, Total infected univesities: 81\n",
      "(42) Fraction of infections due to hiring: 0.20987654321, not due to hiring: 0.79012345679\n",
      "Hires with no background in topic: 58, Hires with background in topic: 15, Total infected univesities: 73\n",
      "(43) Fraction of infections due to hiring: 0.205479452055, not due to hiring: 0.794520547945\n",
      "Hires with no background in topic: 61, Hires with background in topic: 16, Total infected univesities: 77\n",
      "(44) Fraction of infections due to hiring: 0.207792207792, not due to hiring: 0.792207792208\n",
      "Hires with no background in topic: 64, Hires with background in topic: 13, Total infected univesities: 77\n",
      "(45) Fraction of infections due to hiring: 0.168831168831, not due to hiring: 0.831168831169\n",
      "Hires with no background in topic: 57, Hires with background in topic: 14, Total infected univesities: 71\n",
      "(46) Fraction of infections due to hiring: 0.197183098592, not due to hiring: 0.802816901408\n",
      "Hires with no background in topic: 64, Hires with background in topic: 10, Total infected univesities: 74\n",
      "(47) Fraction of infections due to hiring: 0.135135135135, not due to hiring: 0.864864864865\n",
      "Hires with no background in topic: 52, Hires with background in topic: 15, Total infected univesities: 67\n",
      "(48) Fraction of infections due to hiring: 0.223880597015, not due to hiring: 0.776119402985\n",
      "Hires with no background in topic: 64, Hires with background in topic: 10, Total infected univesities: 74\n",
      "(49) Fraction of infections due to hiring: 0.135135135135, not due to hiring: 0.864864864865\n",
      "Hires with no background in topic: 57, Hires with background in topic: 19, Total infected univesities: 76\n",
      "(50) Fraction of infections due to hiring: 0.25, not due to hiring: 0.75\n",
      "Hires with no background in topic: 44, Hires with background in topic: 23, Total infected univesities: 67\n",
      "(51) Fraction of infections due to hiring: 0.34328358209, not due to hiring: 0.65671641791\n",
      "Hires with no background in topic: 56, Hires with background in topic: 21, Total infected univesities: 77\n",
      "(52) Fraction of infections due to hiring: 0.272727272727, not due to hiring: 0.727272727273\n",
      "Hires with no background in topic: 52, Hires with background in topic: 18, Total infected univesities: 70\n",
      "(53) Fraction of infections due to hiring: 0.257142857143, not due to hiring: 0.742857142857\n",
      "Hires with no background in topic: 58, Hires with background in topic: 17, Total infected univesities: 75\n",
      "(54) Fraction of infections due to hiring: 0.226666666667, not due to hiring: 0.773333333333\n",
      "Hires with no background in topic: 46, Hires with background in topic: 23, Total infected univesities: 69\n",
      "(55) Fraction of infections due to hiring: 0.333333333333, not due to hiring: 0.666666666667\n",
      "Hires with no background in topic: 61, Hires with background in topic: 16, Total infected univesities: 77\n",
      "(56) Fraction of infections due to hiring: 0.207792207792, not due to hiring: 0.792207792208\n",
      "Hires with no background in topic: 63, Hires with background in topic: 14, Total infected univesities: 77\n",
      "(57) Fraction of infections due to hiring: 0.181818181818, not due to hiring: 0.818181818182\n",
      "Hires with no background in topic: 47, Hires with background in topic: 18, Total infected univesities: 65\n",
      "(58) Fraction of infections due to hiring: 0.276923076923, not due to hiring: 0.723076923077\n",
      "Hires with no background in topic: 53, Hires with background in topic: 21, Total infected univesities: 74\n",
      "(59) Fraction of infections due to hiring: 0.283783783784, not due to hiring: 0.716216216216\n",
      "Hires with no background in topic: 56, Hires with background in topic: 15, Total infected univesities: 71\n",
      "(60) Fraction of infections due to hiring: 0.211267605634, not due to hiring: 0.788732394366\n",
      "Hires with no background in topic: 62, Hires with background in topic: 17, Total infected univesities: 79\n",
      "(61) Fraction of infections due to hiring: 0.215189873418, not due to hiring: 0.784810126582\n",
      "Hires with no background in topic: 58, Hires with background in topic: 12, Total infected univesities: 70\n",
      "(62) Fraction of infections due to hiring: 0.171428571429, not due to hiring: 0.828571428571\n",
      "Hires with no background in topic: 61, Hires with background in topic: 14, Total infected univesities: 75\n",
      "(63) Fraction of infections due to hiring: 0.186666666667, not due to hiring: 0.813333333333\n",
      "Hires with no background in topic: 62, Hires with background in topic: 14, Total infected univesities: 76\n",
      "(64) Fraction of infections due to hiring: 0.184210526316, not due to hiring: 0.815789473684\n",
      "Hires with no background in topic: 53, Hires with background in topic: 19, Total infected univesities: 72\n",
      "(65) Fraction of infections due to hiring: 0.263888888889, not due to hiring: 0.736111111111\n",
      "Hires with no background in topic: 67, Hires with background in topic: 19, Total infected univesities: 86\n",
      "(66) Fraction of infections due to hiring: 0.220930232558, not due to hiring: 0.779069767442\n",
      "Hires with no background in topic: 62, Hires with background in topic: 10, Total infected univesities: 72\n",
      "(67) Fraction of infections due to hiring: 0.138888888889, not due to hiring: 0.861111111111\n",
      "Hires with no background in topic: 63, Hires with background in topic: 14, Total infected univesities: 77\n",
      "(68) Fraction of infections due to hiring: 0.181818181818, not due to hiring: 0.818181818182\n",
      "Hires with no background in topic: 58, Hires with background in topic: 14, Total infected univesities: 72\n",
      "(69) Fraction of infections due to hiring: 0.194444444444, not due to hiring: 0.805555555556\n",
      "Hires with no background in topic: 66, Hires with background in topic: 15, Total infected univesities: 81\n",
      "(70) Fraction of infections due to hiring: 0.185185185185, not due to hiring: 0.814814814815\n",
      "Hires with no background in topic: 58, Hires with background in topic: 21, Total infected univesities: 79\n",
      "(71) Fraction of infections due to hiring: 0.26582278481, not due to hiring: 0.73417721519\n",
      "Hires with no background in topic: 54, Hires with background in topic: 20, Total infected univesities: 74\n",
      "(72) Fraction of infections due to hiring: 0.27027027027, not due to hiring: 0.72972972973\n",
      "Hires with no background in topic: 56, Hires with background in topic: 15, Total infected univesities: 71\n",
      "(73) Fraction of infections due to hiring: 0.211267605634, not due to hiring: 0.788732394366\n",
      "Hires with no background in topic: 57, Hires with background in topic: 14, Total infected univesities: 71\n",
      "(74) Fraction of infections due to hiring: 0.197183098592, not due to hiring: 0.802816901408\n",
      "Hires with no background in topic: 62, Hires with background in topic: 14, Total infected univesities: 76\n",
      "(75) Fraction of infections due to hiring: 0.184210526316, not due to hiring: 0.815789473684\n",
      "Hires with no background in topic: 62, Hires with background in topic: 21, Total infected univesities: 83\n",
      "(76) Fraction of infections due to hiring: 0.253012048193, not due to hiring: 0.746987951807\n",
      "Hires with no background in topic: 57, Hires with background in topic: 9, Total infected univesities: 66\n",
      "(77) Fraction of infections due to hiring: 0.136363636364, not due to hiring: 0.863636363636\n",
      "Hires with no background in topic: 63, Hires with background in topic: 17, Total infected univesities: 80\n",
      "(78) Fraction of infections due to hiring: 0.2125, not due to hiring: 0.7875\n",
      "Hires with no background in topic: 65, Hires with background in topic: 9, Total infected univesities: 74\n",
      "(79) Fraction of infections due to hiring: 0.121621621622, not due to hiring: 0.878378378378\n",
      "Hires with no background in topic: 52, Hires with background in topic: 24, Total infected univesities: 76\n",
      "(80) Fraction of infections due to hiring: 0.315789473684, not due to hiring: 0.684210526316\n",
      "Hires with no background in topic: 63, Hires with background in topic: 14, Total infected univesities: 77\n",
      "(81) Fraction of infections due to hiring: 0.181818181818, not due to hiring: 0.818181818182\n",
      "Hires with no background in topic: 69, Hires with background in topic: 9, Total infected univesities: 78\n",
      "(82) Fraction of infections due to hiring: 0.115384615385, not due to hiring: 0.884615384615\n",
      "Hires with no background in topic: 64, Hires with background in topic: 20, Total infected univesities: 84\n",
      "(83) Fraction of infections due to hiring: 0.238095238095, not due to hiring: 0.761904761905\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hires with no background in topic: 56, Hires with background in topic: 22, Total infected univesities: 78\n",
      "(84) Fraction of infections due to hiring: 0.282051282051, not due to hiring: 0.717948717949\n",
      "Hires with no background in topic: 66, Hires with background in topic: 18, Total infected univesities: 84\n",
      "(85) Fraction of infections due to hiring: 0.214285714286, not due to hiring: 0.785714285714\n",
      "Hires with no background in topic: 60, Hires with background in topic: 16, Total infected univesities: 76\n",
      "(86) Fraction of infections due to hiring: 0.210526315789, not due to hiring: 0.789473684211\n",
      "Hires with no background in topic: 52, Hires with background in topic: 20, Total infected univesities: 72\n",
      "(87) Fraction of infections due to hiring: 0.277777777778, not due to hiring: 0.722222222222\n",
      "Hires with no background in topic: 67, Hires with background in topic: 7, Total infected univesities: 74\n",
      "(88) Fraction of infections due to hiring: 0.0945945945946, not due to hiring: 0.905405405405\n",
      "Hires with no background in topic: 60, Hires with background in topic: 12, Total infected univesities: 72\n",
      "(89) Fraction of infections due to hiring: 0.166666666667, not due to hiring: 0.833333333333\n",
      "Hires with no background in topic: 65, Hires with background in topic: 22, Total infected univesities: 87\n",
      "(90) Fraction of infections due to hiring: 0.252873563218, not due to hiring: 0.747126436782\n",
      "Hires with no background in topic: 66, Hires with background in topic: 12, Total infected univesities: 78\n",
      "(91) Fraction of infections due to hiring: 0.153846153846, not due to hiring: 0.846153846154\n",
      "Hires with no background in topic: 65, Hires with background in topic: 18, Total infected univesities: 83\n",
      "(92) Fraction of infections due to hiring: 0.21686746988, not due to hiring: 0.78313253012\n",
      "Hires with no background in topic: 56, Hires with background in topic: 15, Total infected univesities: 71\n",
      "(93) Fraction of infections due to hiring: 0.211267605634, not due to hiring: 0.788732394366\n",
      "Hires with no background in topic: 56, Hires with background in topic: 20, Total infected univesities: 76\n",
      "(94) Fraction of infections due to hiring: 0.263157894737, not due to hiring: 0.736842105263\n",
      "Hires with no background in topic: 64, Hires with background in topic: 19, Total infected univesities: 83\n",
      "(95) Fraction of infections due to hiring: 0.228915662651, not due to hiring: 0.771084337349\n",
      "Hires with no background in topic: 58, Hires with background in topic: 16, Total infected univesities: 74\n",
      "(96) Fraction of infections due to hiring: 0.216216216216, not due to hiring: 0.783783783784\n",
      "Hires with no background in topic: 55, Hires with background in topic: 19, Total infected univesities: 74\n",
      "(97) Fraction of infections due to hiring: 0.256756756757, not due to hiring: 0.743243243243\n",
      "Hires with no background in topic: 57, Hires with background in topic: 18, Total infected univesities: 75\n",
      "(98) Fraction of infections due to hiring: 0.24, not due to hiring: 0.76\n",
      "Hires with no background in topic: 58, Hires with background in topic: 9, Total infected univesities: 67\n",
      "(99) Fraction of infections due to hiring: 0.134328358209, not due to hiring: 0.865671641791\n"
     ]
    }
   ],
   "source": [
    "shuffled = copy.copy(all_titles)\n",
    "fraction_dist = []\n",
    "for i in range(100):\n",
    "    np.random.shuffle(shuffled)\n",
    "    for f in asst_faculty_shuffled:\n",
    "        if f.__contains__(\"dblp_pubs\"):\n",
    "            for pub in f.dblp_pubs:\n",
    "                pub['title'] = shuffled.pop()\n",
    "    \n",
    "    (prob_no_priors, prob_yes_priors) = percent_has_relevant_prior_pubs(2, mechanism_design_keywords, asst_faculty_shuffled)\n",
    "    \n",
    "    print(\"({0}) Fraction of infections due to hiring: {1}, not due to hiring: {2}\".format(len(fraction_dist), prob_yes_priors, prob_no_priors))\n",
    "    fraction_dist.append(prob_yes_priors)\n",
    "    shuffled = copy.copy(all_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.20632603, 2.81476075, 3.21686942, 6.03163017, 8.84639092,\n",
       "        7.2379562 , 6.03163017, 3.6189781 , 0.40210868, 0.80421736]),\n",
       " array([0.09459459, 0.11946349, 0.14433239, 0.16920129, 0.19407019,\n",
       "        0.21893909, 0.24380799, 0.26867689, 0.29354578, 0.31841468,\n",
       "        0.34328358]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(fraction_dist, density=True, color=[153.0/255.0, 0.0/255.0, 255.0/255.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.213397382929\n",
      "How many simulations resulting in a higher infection rate due to hiring? 0.00990099009901\n"
     ]
    }
   ],
   "source": [
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(fraction_dist)/len(fraction_dist)))\n",
    "print \"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([x for x in fraction_dist if x >= (0.478260869565)]) + 1)/(len(fraction_dist) + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import cPickle\n",
    "# with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/mechanism_design.p\", \"wb\") as fp:\n",
    "#     cPickle.dump(fraction_dist[:10000], fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Cached Simulations of Null Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above simulation was run 10,000 times to generate the five pickles in this folder: `deep_learning.p`, `incremental_computing.p`, `topic_modeling.p`, etc. \n",
    "\n",
    "Let's load them in here. First, checking \"incremental computation\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cPickle\n",
    "with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/incremental_computing.p\", \"rb\") as input_file:\n",
    "    x = cPickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many simulations have been run? 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"How many simulations have been run? {0}\".format(len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.19559785901\n",
      "How many simulations resulting in a higher infection rate due to hiring? 0.001099890011\n"
     ]
    }
   ],
   "source": [
    "real_infection_rate = 0.392857142857 # From \"For all universities\" section above\n",
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(x)/len(x)))\n",
    "print(\"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([i for i in x if i >= real_infection_rate]) + 1)/(len(x) + 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, checking \"topic modeling\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/topic_modeling.p\", \"rb\") as input_file:\n",
    "    x = cPickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many simulations have been run? 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"How many simulations have been run? {0}\".format(len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.228818623072\n",
      "How many simulations resulting in a higher infection rate due to hiring? 0.003599640036\n"
     ]
    }
   ],
   "source": [
    "real_infection_rate = 0.348837209302 # From \"For all universities\" section above\n",
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(x)/len(x)))\n",
    "print(\"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([i for i in x if i >= real_infection_rate]) + 1)/(len(x) + 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check \"deep learning\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/deep_learning.p\", \"rb\") as input_file:\n",
    "    x = cPickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many simulations have been run? 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"How many simulations have been run? {0}\".format(len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.338476302871\n",
      "How many simulations resulting in a higher infection rate due to hiring? 0.340865913409\n"
     ]
    }
   ],
   "source": [
    "real_infection_rate = 0.352941176471 # From \"For all universities\" section above\n",
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(x)/len(x)))\n",
    "print(\"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([i for i in x if i >= real_infection_rate]) + 1)/(len(x) + 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check \"quantum computing\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/quantum_computing.p\", \"rb\") as input_file:\n",
    "    x = cPickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many simulations have been run? 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"How many simulations have been run? {0}\".format(len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.221998671632\n",
      "How many simulations resulting in a higher infection rate due to hiring? 0.013898610139\n"
     ]
    }
   ],
   "source": [
    "real_infection_rate = 0.321428571429 # From \"For all universities\" section above\n",
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(x)/len(x)))\n",
    "print(\"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([i for i in x if i >= real_infection_rate]) + 1)/(len(x) + 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally check \"mechanism design\": "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/Users/allisonmorgan/Code/src/github.com/allisonmorgan/epistemic_inequality/publications/mechanism_design.p\", \"rb\") as input_file:\n",
    "    x = cPickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many simulations have been run? 10000\n"
     ]
    }
   ],
   "source": [
    "print(\"How many simulations have been run? {0}\".format(len(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the average infection due to hiring under model? 0.214939568762\n",
      "How many simulations resulting in a higher infection rate due to hiring? 9.99900009999e-05\n"
     ]
    }
   ],
   "source": [
    "real_infection_rate = 0.478260869565 # From \"For all universities\" section above\n",
    "print(\"What is the average infection due to hiring under model? {0}\".format(sum(x)/len(x)))\n",
    "print(\"How many simulations resulting in a higher infection rate due to hiring? {0}\".format((len([i for i in x if i >= real_infection_rate]) + 1)/(len(x) + 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Transmission diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patches as patches\n",
    "style = \"Simple,tail_width=0.5,head_width=7,head_length=8\"\n",
    "ACCENT_COLOR_1 = np.array([127, 37, 251]) / 255.\n",
    "kw = dict(arrowstyle=style)\n",
    "\n",
    "def get_infection_timeline(keywords):\n",
    "    delta_t = 2\n",
    "    infection_timeline = {}\n",
    "\n",
    "    for place in inst:\n",
    "        year = np.inf  # Initialize to inf (never infected)\n",
    "        hiring_inf = None\n",
    "        person = None\n",
    "\n",
    "        # Get person with first relevant publication \n",
    "        infected_hires = find_infected_hire(place, keywords, asst_faculty)\n",
    "        if infected_hires:\n",
    "            # Were they publishing on deep-learning before their hire date?\n",
    "            for i, (candidate, min_pub) in enumerate(copy.copy(infected_hires)):\n",
    "                if is_infected_hire([candidate], delta_t):\n",
    "                    infected_hires[i] = (candidate, [candidate['start']])\n",
    "\n",
    "            # Check all the possible infections. Length of minima is greater than one\n",
    "            # if there are multiple possible infections\n",
    "            x = min(infected_hires, key=lambda xs: min(xs[1]))\n",
    "            minima = [(candidate, min_pub) for (candidate, min_pub) in infected_hires if min_pub == x[1]]\n",
    "\n",
    "            # If an idea was not transmitted to an institution\n",
    "            if any([not is_infected_hire([candidate], delta_t) for (candidate, min_pub) in minima]):\n",
    "                 year = np.min([min_pub for (_, min_pub) in minima])\n",
    "            else:\n",
    "                # If any of them could have spread the infection due to hiring, choose one among\n",
    "                # the equally likely candidates \n",
    "                index = np.random.choice(len(minima), 1)[0]\n",
    "                year = minima[index][0][\"start\"]\n",
    "                hiring_inf = minima[index][0][\"phd_location\"]\n",
    "                person = minima[index][0]\n",
    "                \n",
    "        # Year of infection, infecting dept (if caused by hiring, else None), person\n",
    "        infection_timeline[place] = (year, hiring_inf, person)\n",
    "        \n",
    "    return infection_timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build timelines\n",
    "deep_learning_timeline = get_infection_timeline(deep_learning_keywords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_modeling_timeline = get_infection_timeline(topic_modeling_keywords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "incremental_timeline = get_infection_timeline(incremental_keywords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Examples: \n",
    "print(deep_learning_timeline[\"MIT\"])  # Year when MIT was infected and by whom.\n",
    "print(deep_learning_timeline[\"UCLA\"]) # UCLA was a spontaneous infection.\n",
    "print(deep_learning_timeline[\"University of Nebraska, Lincoln\"])  # UNL hasn't a deep learner. \n",
    "\n",
    "Outputs:\n",
    "(1988, 'UC San Diego', {'faculty_record': <faculty_hiring.parse.faculty_parser.faculty_record instance at 0x110e26dd0>, 'end': 1998, 'pubs': [2016, 2015, 2015, 2013, 2000, 1998, 1997, 1996, 1993, 1993, 1991, 1990], 'start': 1988, 'phd_location': 'UC San Diego', 'facultyName': 'Michael Jordan'})\n",
    "(1993, None, None)\n",
    "(inf, None, None)\n",
    "'''\n",
    "\n",
    "def save_timeline(filename, timeline):\n",
    "    with open(filename, 'w') as file:\n",
    "        writer = csv.writer(file, delimiter='\\t')\n",
    "        writer.writerow([\"target_inst\", \"target_inst_prestige\", \"target_inst_rank\", \"year_adopted\", \"source_inst\"])\n",
    "        ranks = sorted([inst[place]['pi'] for place in inst])\n",
    "        for place in inst:\n",
    "            x = timeline[place]\n",
    "            writer.writerow([place, inst[place]['pi'], ranks.index(inst[place]['pi']), x[0], x[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_timeline('deep_learning_timeline.tsv', deep_learning_timeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_timeline('topic_modeling_timeline.tsv', topic_modeling_timeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_timeline('incremental_timeline.tsv', incremental_timeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_year_snapshot(ax, timeline, limit=206, year=2000, prev_year=-np.inf, verbose=False, name=None):\n",
    "    ax.set_aspect(1)\n",
    "    sel_places = [p[1] for p in sorted([(inst[place]['pi'], place) for place in inst])][:limit]\n",
    "    deg_places = np.pi/2 - np.linspace(0, 2*np.pi, limit, endpoint=False)\n",
    "    r = 1\n",
    "    x = r*np.cos(deg_places)\n",
    "    y = r*np.sin(deg_places)\n",
    "    r2 = 0.955\n",
    "    x2 = r2*np.cos(deg_places)\n",
    "    y2 = r2*np.sin(deg_places)\n",
    "    ax.scatter(x, y, edgecolor='None', color=LIGHT_COLOR, s=5)\n",
    "    arrows = []\n",
    "    new_infections = 0\n",
    "    new_spotaneous = 0\n",
    "    \n",
    "    for i, place in enumerate(sel_places): \n",
    "        if timeline[place][0] < year:\n",
    "            if timeline[place][1] is None:\n",
    "                if timeline[place][0] >= prev_year:\n",
    "                    new_spotaneous += 1\n",
    "                edgecolor = DARK_COLOR\n",
    "                color = 'w'\n",
    "                s = 50\n",
    "            else:\n",
    "                color = ACCENT_COLOR_1\n",
    "                edgecolor = 'w'\n",
    "                s = 100\n",
    "                if timeline[place][1] in sel_places and timeline[place][0] >= prev_year:\n",
    "                    new_infections += 1\n",
    "                    if verbose: print(timeline[place][1])\n",
    "                    org = sel_places.index(timeline[place][1])\n",
    "                    \n",
    "                    #radius = .8 - (np.abs(deg_places[org]-deg_places[i]) / (np.pi))\n",
    "                    radius = -np.log((0.01+np.abs(deg_places[org]-deg_places[i]) / (np.pi)))\n",
    "                    p1 = (x[org],y[org])\n",
    "                    p2 = (x2[i],y2[i])\n",
    "                    \n",
    "                    if deg_places[i] > deg_places[org] and deg_places[i] - deg_places[org] < .9*np.pi: \n",
    "                        radius *= -1\n",
    "                        arrows.append(patches.FancyArrowPatch(p1, p2, zorder=-10, \n",
    "                            connectionstyle=\"arc3,rad=%f\" % (radius), color='k', **kw))\n",
    "                    # Check for really small radii\n",
    "                    elif np.abs(deg_places[i]-deg_places[org]) < .1:\n",
    "                        arrows.append(patches.FancyArrowPatch(p1, p2, zorder=-10,\n",
    "                            connectionstyle=\"arc3,rad=%f\" % (radius), color='k', **kw))\n",
    "                    else:\n",
    "                        arrows.append(patches.FancyArrowPatch(p1, p2, zorder=-10,\n",
    "                            connectionstyle=\"arc3,rad=%f\" % (radius), color='k', **kw))\n",
    "                     \n",
    "            ax.scatter(x[i], y[i], edgecolor=edgecolor, color=color, s=s)\n",
    "            \n",
    "    for a in arrows:\n",
    "        ax.add_patch(a)\n",
    "        \n",
    "    ax.axis('off')\n",
    "    \n",
    "    opts = {'y':0.975, 'fontsize':LABEL_SIZE+2}\n",
    "    \n",
    "    if prev_year > -np.inf:\n",
    "        ax.set_title('%d to %d' % (int(prev_year), int(year)-1), **opts)\n",
    "    else:\n",
    "        ax.set_title('Up to %d' % (int(year)), **opts)\n",
    "        \n",
    "    xnudge = .075\n",
    "    xloc=1.125\n",
    "    ax.scatter(xloc, -.75, edgecolor='w', color=ACCENT_COLOR_1, s=100)    \n",
    "    ax.text(xloc-xnudge, -.75, \"+%d\" % new_infections, color=ACCENT_COLOR_1, fontsize=LABEL_SIZE+1, ha='right', va='center')\n",
    "    \n",
    "    ax.scatter(xloc, -.925, color='w', edgecolor=DARK_COLOR, s=50)    \n",
    "    ax.text(xloc-xnudge, -.925, \"+%d\" % new_spotaneous, color=DARK_COLOR, fontsize=LABEL_SIZE+1, ha='right', va='center')\n",
    "        \n",
    "    if name is not None:\n",
    "        ax.text(-r*1.25, 0, name, rotation=90, fontsize=LABEL_SIZE+4, ha='center', va='center', rotation_mode='anchor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "\n",
    "# Constants\n",
    "SINGLE_FIG_SIZE = (6,4)\n",
    "BAR_WIDTH = 0.6\n",
    "TICK_SIZE = 15\n",
    "XLABEL_PAD = 10\n",
    "LABEL_SIZE = 14\n",
    "TITLE_SIZE = 16\n",
    "LEGEND_SIZE = 12\n",
    "LINE_WIDTH = 2\n",
    "LIGHT_COLOR = '0.8'\n",
    "LIGHT_COLOR_V = np.array([float(LIGHT_COLOR) for i in xrange(3)])\n",
    "DARK_COLOR = '0.4'\n",
    "DARK_COLOR_V = np.array([float(DARK_COLOR) for i in xrange(3)])\n",
    "ALMOST_BLACK = '0.125'\n",
    "ALMOST_BLACK_V = np.array([float(ALMOST_BLACK) for i in xrange(3)])\n",
    "# ACCENT_COLOR_1 = np.array([255., 145., 48.]) / 255.\n",
    "\n",
    "# Configuration\n",
    "#rcParams['text.usetex'] = True #Let TeX do the typsetting\n",
    "rcParams['pdf.use14corefonts'] = True\n",
    "rcParams['ps.useafm'] = True\n",
    "rcParams['text.latex.preamble'] = [r'\\usepackage{helvet}', \n",
    "#r'\\usepackage[scale=1.0]{tgheros}',\n",
    "r'\\usepackage[frenchmath]{newtxsf}',\n",
    "r'\\renewcommand{\\familydefault}{\\sfdefault}'\n",
    "r'\\usepackage{mathastext}'] #Force sans-serif math mode (for axes labels)\n",
    "#rcParams['font.family'] = 'sans-serif' # ... for regular text\n",
    "rcParams['font.sans-serif'] = ['Helvetica Neue', 'HelveticaNeue', 'Helvetica'] #, Avant Garde, Computer Modern Sans serif' # Choose a nice font here\n",
    "rcParams['pdf.fonttype'] = 42\n",
    "rcParams['ps.fonttype'] = 42\n",
    "rcParams['text.color'] = ALMOST_BLACK\n",
    "rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "rcParams['xtick.major.pad'] = '8'\n",
    "rcParams['axes.edgecolor']  = ALMOST_BLACK\n",
    "rcParams['axes.labelcolor'] = ALMOST_BLACK\n",
    "\n",
    "rcParams['lines.color']     = ALMOST_BLACK\n",
    "rcParams['xtick.color']     = ALMOST_BLACK\n",
    "rcParams['ytick.color']     = ALMOST_BLACK\n",
    "rcParams['text.color']      = ALMOST_BLACK\n",
    "rcParams['lines.solid_capstyle'] = 'butt'\n",
    "rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_grid = plt.subplots(3,3,figsize=(12,12))\n",
    "\n",
    "times = [(\"Topic Modeling\", topic_modeling_timeline), \n",
    "         (\"Incremental Computing\", incremental_timeline), \n",
    "         (\"Deep Learning\", deep_learning_timeline)]\n",
    "\n",
    "years = [[2000, 2005, 2012],\n",
    "         [1990, 2000, 2012],\n",
    "         [1990, 2000, 2012],]\n",
    "\n",
    "for j, axs in enumerate(ax_grid):\n",
    "    title, timeline = times[j]\n",
    "    for i, year in enumerate(years[j]):\n",
    "        if i == 0:\n",
    "            prev_year = -np.inf\n",
    "            label = title\n",
    "        else:\n",
    "            prev_year = years[j][i-1]\n",
    "            label = None\n",
    "            \n",
    "        make_year_snapshot(axs[i], timeline, year=year, prev_year=prev_year, name=label)\n",
    "        \n",
    "ax_grid[0][0].text(-0.3, -.5, 'Adoption\\nthrough hiring', ha='center', va='center', fontsize=LABEL_SIZE-1, color=ACCENT_COLOR_1)\n",
    "ax_grid[0][0].text(0.65, -.05, 'Non-hiring\\nadoption', ha='center', va='center', fontsize=LABEL_SIZE-1, color=DARK_COLOR)\n",
    "ax_grid[0][0].text(1.05, -1.1, '(New adoption counts)', ha='center', va='center', fontsize=LABEL_SIZE-1, color=DARK_COLOR)\n",
    "\n",
    "yn = 0.01\n",
    "ax_grid[0][0].add_patch(patches.FancyArrowPatch((0, 1.1-yn), (.95,.6-yn), zorder=-10,\n",
    "                            connectionstyle=\"arc3,rad=-.3\", color='0.6', **kw))\n",
    "ax_grid[0][0].text(1.2, .9, 'Institutions arranged\\nclockwise by\\nprestige', ha='center', va='center', fontsize=LABEL_SIZE-2, color=DARK_COLOR)\n",
    "\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig('../results/timelines.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}