{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/walrus/.virtualenvs/py3.6/lib/python3.6/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html5lib\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 193 of the file /usr/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n",
      "\n",
      " BeautifulSoup(YOUR_MARKUP})\n",
      "\n",
      "to this:\n",
      "\n",
      " BeautifulSoup(YOUR_MARKUP, \"html5lib\")\n",
      "\n",
      "  markup_type=markup_type))\n"
     ]
    }
   ],
   "source": [
    "b = BeautifulSoup(open('index.html', 'r').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = nltk.word_tokenize(b.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def word_positions(word):\n",
    "    pos = []\n",
    "    for i, w in enumerate(n):\n",
    "        if w.lower() == word:\n",
    "            pos += [i]\n",
    "    return pos\n",
    "\n",
    "def word_dists(pos):\n",
    "    return [pos[i + 1] - pos[i] for i in range(len(pos) - 1)]\n",
    "\n",
    "def words_stats(words):\n",
    "    word_stats = {}\n",
    "    for word in words:\n",
    "        pos = word_positions(word)\n",
    "        diffs = word_dists(pos)\n",
    "        word_stats[word] = {'diffs': diffs, 'pos': pos}\n",
    "\n",
    "    return word_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ws = words_stats(['the', 'cortex', 'brain', 'amygdala', 'neuron', 'female', 'hormone', 'maybe', 'dead'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def plot_words(words, stat, norm=True, log=True):\n",
    "    ws = words_stats(words)\n",
    "    fig,ax = plt.subplots(figsize=(20,10))\n",
    "    for word in words:\n",
    "        points = ws[word][stat]\n",
    "        if norm:\n",
    "            if log:\n",
    "                ax.loglog(np.linspace(0,1, len(points)), points, '.')\n",
    "            else:\n",
    "                ax.plot(np.linspace(0,1, len(points)), points, '.')\n",
    "        else:\n",
    "            if log:\n",
    "                ax.semilogx(points, '.')\n",
    "            else:\n",
    "                ax.plot(points, '.')\n",
    "    ax.legend(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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HI6/Pp/ScXBeqBACga4J5E90uIaHQ0dSO5l+GmjuaYvHLUFZWlt577z29/PLL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe27a10710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_words([ 'good', 'bad', 'evil', 'the', 'wait', 'system'], 'pos', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "whist = nltk.FreqDist(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efe27b0f3c8>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/7Ayje6emTT88c3VxVB1IWXk1W0rLefGDzVzz8GxWbw9uc//z/0S35lkbM7QB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\nuf8HMK8fappsjlIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efe27ac5128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([int(whist[str(i)]) for i in range(1000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whist['good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "evil = word_positions('good')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1445 => violence in the right context is different . We pay good money to watch it in a stadium , we\n",
      "\n",
      "1686 => , and states , and when these are bad or good things . It is a book about the ways\n",
      "\n",
      "3979 => The next quote : Normal psychic life depends upon the good functioning of brain synapses , and mental disorders appear\n",
      "\n",
      "6751 => pure altruism actually exist ? Can you ever separate doing good from the expectation of reciprocity , public acclaim ,\n",
      "\n",
      "7124 => , but another planet nonetheless . Crimes of passion and good acts of passion make the most sense to us\n",
      "\n",
      "7449 => expository tastes . Nor will they be labeled as “ good ” and “ evil ” —too hot-blooded and frothy\n",
      "\n",
      "7505 => and a behavior has happened . Perhaps it is a good act : you ’ ve empathically touched the arm\n",
      "\n",
      "7543 => , targeting an innocent person . Perhaps it is a good act : you ’ ve pulled a trigger ,\n",
      "\n",
      "8509 => which MacLean himself emphasized , this model will be a good organizing metaphor for us . THE LIMBIC SYSTEM To\n",
      "\n",
      "15745 => spent decades studying the hippocampus . It ’ s been good to me ; I ’ d like to think\n",
      "\n",
      "17235 => ; you ’ ll go to hell ; self-discipline is good ; you ’ re happier when you ’ re\n",
      "\n",
      "17375 => . Moreover , the frontal mantra of “ self-discipline is good ” when cookies beckon is also invoked when economizing\n",
      "\n",
      "17753 => . This transition to automaticity also happens when you get good at a sport , when metaphorically your body knows\n",
      "\n",
      "23604 => pain pathways at the news of the person ’ s good fortune predicted more dopaminergic activation after learning of their\n",
      "\n",
      "23769 => our world of habituation , where nothing is ever as good as that first time . Unfortunately , things have\n",
      "\n",
      "23985 => system is bidirectional.88 It responds with scale-free increases for unexpected good news and decreases for bad . Schultz demonstrated that\n",
      "\n",
      "24264 => scale-free manner over a wide range of experience for both good and bad surprises and is constantly habituating to yesterday\n",
      "\n",
      "26571 => insanely long times . No warthog restricts calories to look good in a bathing suit next summer . No gerbil\n",
      "\n",
      "26587 => summer . No gerbil works hard at school to get good SAT scores to get into a good college to\n",
      "\n",
      "26594 => school to get good SAT scores to get into a good college to get into a good grad school to\n",
      "\n",
      "26600 => to get into a good college to get into a good grad school to get a good job to get\n",
      "\n",
      "26606 => to get into a good grad school to get a good job to get into a good nursing home .\n",
      "\n",
      "26612 => school to get a good job to get into a good nursing home . We do something even beyond this\n",
      "\n",
      "27512 => ’ s simply not true ; for example , a good neuropsychologist can discern more of what ’ s happening\n",
      "\n",
      "36042 => Being fearless , overconfident , and delusionally optimistic sure feels good . No surprise , then , that testosterone can\n",
      "\n",
      "36099 => I don ’ t know why , but I feel good whenever I stand there. ” 8,9 The underlying neurobiology\n",
      "\n",
      "40895 => . Remarkably , oxytocin made subjects less likely to sacrifice good ol ’ Dirk or Peter , rather than Helmut\n",
      "\n",
      "44474 => And as stress becomes more severe and prolonged , those good effects disappear ( with , of course , dramatic\n",
      "\n",
      "46363 => differ in females . Showing that she can match the good old boys when it comes to snappy sound bites\n",
      "\n",
      "49024 => plasticity . Moderate , transient stress ( i.e. , the good , stimulatory stress ) promotes hippocampal LTP , while\n",
      "\n",
      "49707 => seems to determine whether the hippocampus interprets the glucocorticoids as good or bad stress.11 Spine number and branch length in\n",
      "\n",
      "50988 => the work was initially well received , being published in good journals , generating excitement . But within a few\n",
      "\n",
      "51242 => Nottebohm , a highly accomplished and esteemed neuroscientist , as good an old boy as you get , studied the\n",
      "\n",
      "52967 => to displace aggression . So what ’ s the subject good for in the realm of this book ? I\n",
      "\n",
      "55563 => tell them the actual likelihood . Such feedback can constitute good news ( i.e. , something good is actually more\n",
      "\n",
      "55569 => Such feedback can constitute good news ( i.e. , something good is actually more likely than the person estimated ,\n",
      "\n",
      "55625 => estimates . Adolescents update their estimates as adults do for good news , but feedback about bad news barely makes\n",
      "\n",
      "57692 => AND MORAL REASONING By adolescence , people are typically pretty good at perspective taking , seeing the world as someone\n",
      "\n",
      "58714 => that . My adult frontal cortex may enable whatever detached good I do . The trouble , of course ,\n",
      "\n",
      "59522 => copies of the genes of individuals , not for the good of the species ( stay tuned for chapter 10\n",
      "\n",
      "62454 => inequality because of merit or effort or for a greater good ( “ She should play more than him ;\n",
      "\n",
      "62485 => ) . Some kids even manage self-sacrifice for the greater good ( “ She should play more than me ;\n",
      "\n",
      "63092 => s nice to think of others ; it ’ s good to be well regarded . Stage 4 . It\n",
      "\n",
      "63391 => to live with yourself afterward . It recognizes that being good and being law-abiding aren ’ t synonymous . As\n",
      "\n",
      "63415 => “ Pretty Boy Floyd , ” “ I love a good man outside the law , just as much as\n",
      "\n",
      "63512 => it can also be insufferable , premised on “ being good ” and “ being law abiding ” as opposites\n",
      "\n",
      "64415 => kids describe strategies like “ I kept thinking about how good that second marshmallow would taste. ” The problem ,\n",
      "\n",
      "67746 => produce ; you ’ re less likely to attend a good school or have parents with time to read to\n",
      "\n",
      "70021 => . . . ) this book , this produces a good adult outcome—happy , emotionally and socially mature and fulfilled\n",
      "\n",
      "76622 => the entire chapter . Stimulating environments , harsh parents , good neighborhoods , uninspiring teachers , optimal diets—all alter genes\n",
      "\n",
      "76841 => it hints at how bad outcomes might be reversed and good outcomes reinforced . There is another use . In\n",
      "\n",
      "76910 => thus that it profoundly matters to provide childhoods filled with good health and safety , love and nurturance and opportunity\n",
      "\n",
      "80271 => , that boring DNA sequence you inherited isn ’ t good enough . Remarkably , transpositional events occur in neurons\n",
      "\n",
      "85679 => increase of 701 . Those are pretty similar numbers ; good . And in Alberta ? More than 5,000 .\n",
      "\n",
      "94166 => successes to intrinsic attributes ( “ I ’ m really good at X ” ) rather than to situational ones\n",
      "\n",
      "100833 => with other people ? ” ) is at least as good a predictor of health as is objective SES .\n",
      "\n",
      "101335 => more secession of the wealthy from contributing to the public good . Kaplan has shown , for example , that\n",
      "\n",
      "103378 => showed that the clarity of borders matters as well . Good , clear-cut fences—e.g. , mountain ranges or rivers between\n",
      "\n",
      "103390 => clear-cut fences—e.g. , mountain ranges or rivers between groups—make for good neighbors . “ Peace does not depend on integrated\n",
      "\n",
      "104277 => the consensus is that global warming won ’ t do good things to global conflict . For starters , warmer\n",
      "\n",
      "104735 => war and valor in battle as an entrée to a good afterlife . Agriculturalists invent gods who alter the weather\n",
      "\n",
      "108119 => to this point . A behavior has occurred that is good , bad , or ambiguous . How have cultural\n",
      "\n",
      "108620 => for some , forgotten matriarchies for others . It is good to dream , but a sober , waking rationality\n",
      "\n",
      "112707 => on of copies of genes through any other route—e.g. , good health , foraging skills , predator avoidance . The\n",
      "\n",
      "113351 => answer with patrician authority : because animals behave For the Good of the Species . Yes , behavior evolves by\n",
      "\n",
      "113367 => , behavior evolves by “ group selection ” for the good of the species . This idea was championed in\n",
      "\n",
      "113404 => Lamarck . *5 Animals don ’ t behave for the good of the species . But what about that wildebeest\n",
      "\n",
      "113443 => day ? Because he was old and weak . “ Good of the species ” my keister . They pushed\n",
      "\n",
      "113615 => group selection . Animals don ’ t behave for the good of the species . They behave to maximize the\n",
      "\n",
      "114568 => toward extinction . This isn ’ t behaving for the good of the species . KIN SELECTION To understand the\n",
      "\n",
      "117290 => in via that transporter . Strength : it ’ s good at getting food . Weakness : vulnerability to the\n",
      "\n",
      "121713 => from a male except genes , so they should be good ones . This helps explain the flamboyant secondary sexual\n",
      "\n",
      "121777 => species A females look for stable , affiliative behavior and good parenting skills in males . This is seen in\n",
      "\n",
      "122026 => and life span Low High Females select for Parenting skill Good genes Rates of cuckoldry High Low Primates that pair-bond\n",
      "\n",
      "124005 => is a world away from “ animals behave for the good of the species. ” Instead , this is the\n",
      "\n",
      "127539 => ’ s caveat that this assumes that contemporary hunter-gatherers are good stand-ins for ancestral ones ) . So humans excel\n",
      "\n",
      "130267 => result that may not be the most adaptive but is good enough , given the starting materials . Squid are\n",
      "\n",
      "130298 => miles per hour ) . But they ’ re damn good for something whose great-great-grandparents were mollusks . Meanwhile ,\n",
      "\n",
      "130917 => if nature has produced something , it must be a good thing . That furthermore , “ good ” in\n",
      "\n",
      "130924 => must be a good thing . That furthermore , “ good ” in the sense of , say , solving\n",
      "\n",
      "131165 => these things are in our nature and probably evolved for good reasons . The critics used the “ is versus\n",
      "\n",
      "132490 => sacrificing Them ( but not Us ) for the greater good . Oxytocin exaggerates Us/Them-ing . This is hugely interesting\n",
      "\n",
      "132881 => opinion of Them . I guess that ’ s meager good news—at least we resist thinking that people who came\n",
      "\n",
      "133686 => ill intent . When a kindergarten teacher says , “ Good morning , boys and girls , ” the kids\n",
      "\n",
      "133712 => world that way is more meaningful than saying , “ Good morning , those of you who have lost a\n",
      "\n",
      "134753 => an out-group member , Them showing fear might even be good news—if it scares Them , bring it on .\n",
      "\n",
      "138536 => people who are/aren ’ t knowledgeable about caviar is a good stand-in for a dichotomy about socioeconomic status . As\n",
      "\n",
      "138591 => American women , built around the stereotypes of Asians being good at math , and women not . Half the\n",
      "\n",
      "140384 => naturally . And Americans typically view this group as containing good Christians , African American professionals , and the middle\n",
      "\n",
      "141383 => I would delight in discussing aeronautics with you over some good wine. ” “ Baron , it is an honor\n",
      "\n",
      "142745 => all races and ethnicities peering in microscopes plus , for good measure , a cheerleader type fawning over a nerdy\n",
      "\n",
      "144594 => being on the right side and doing both well and good . There are even Us/Thems that I—eggheady , meek\n",
      "\n",
      "144964 => , ” attempting to maximize this thing called the common good . Furthermore , individuals vie for leadership with differing\n",
      "\n",
      "145257 => i.e. , the one who gets first dibs on anything good ) was in some manner a “ leader ”\n",
      "\n",
      "146918 => Specialization of Some Ranking Systems A high-ranking chimp is generally good at related things . But humans can dwell in\n",
      "\n",
      "147842 => species , attaining high rank is about sharp teeth and good fighting skills . But maintaining the high rank is\n",
      "\n",
      "148870 => a stressor ; ( b ) lower levels of “ good ” HDL cholesterol ; ( c ) subtle immune\n",
      "\n",
      "149620 => ) ; ( d ) doesn ’ t differentiate between good and bad news ( e.g. , distinguishing behaviorally between\n",
      "\n",
      "150088 => to directly supervise lots of subordinates did not predict those good outcomes . This gives credence to executives ’ bellyaching\n",
      "\n",
      "150757 => leaders , anchored in the unique notion of the common good . What counts as the common good , and\n",
      "\n",
      "150764 => of the common good . What counts as the common good , and the leader ’ s role in fostering\n",
      "\n",
      "151712 => struggle among the powerful with differing visions of the common good . Forget liberals accusing conservatives of waging war on\n",
      "\n",
      "154084 => spies working in the State Department ” is a pretty good demonstration of imagined threat . * The difference in\n",
      "\n",
      "154950 => conservatives are more likely to think that disgust is a good metric for deciding whether something is moral . Which\n",
      "\n",
      "159074 => . As he states : “ Any deed , for good or evil , that any human being has ever\n",
      "\n",
      "159184 => perpetually cited in this literature , “ The line dividing good and evil cuts through the heart of every human\n",
      "\n",
      "163057 => instead actually lead , actually attempt to facilitate the common good . We ’ ve even developed bottom-up mechanisms for\n",
      "\n",
      "163134 => differing visions of how best to bring about the common good . We tend to come as internally consistent packages\n",
      "\n",
      "165431 => punishing bad acts . A kid watches puppets , one good , one bad ( sharing versus not ) .\n",
      "\n",
      "165474 => The bad puppet . Who should gain one ? The good puppet . Remarkably , toddlers even assess secondary punishment\n",
      "\n",
      "165486 => . Remarkably , toddlers even assess secondary punishment . The good and bad puppets then interact with two additional puppets\n",
      "\n",
      "169841 => British and Swiss economists . Subjects played a “ public good ” economic game where players begin with a certain\n",
      "\n",
      "172784 => address the cutting of old-growth forests . Amid the potential good that can come from such shaming , Jacquet also\n",
      "\n",
      "173602 => the emphasis on ends over means requires that you be good at predicting what the actual ends will be ,\n",
      "\n",
      "173740 => where it is clear that it ’ s not a good guide for everyday moral decision making . Greene and\n",
      "\n",
      "174101 => do X in order to accomplish Y seems like a good trade-off in the short run . But in the\n",
      "\n",
      "174151 => done to me , and there ’ s also a good chance that W would happen , and that ’\n",
      "\n",
      "175244 => way . Say I decide that it would be a good thing to have pictures here demonstrating cultural relativism ,\n",
      "\n",
      "175302 => me , most readers will likely resonate with dogs. ” Good plan . On to Google Images , and the\n",
      "\n",
      "175791 => them relate a time when intuition led them to a good decision or where careful reasoning did the opposite )\n",
      "\n",
      "175857 => “ Yes , we all agree that cooperation is a good thing . . . but here is why I\n",
      "\n",
      "175992 => moral dilemmas of resisting selfishness , our rapid intuitions are good , honed by evolutionary selection for cooperation in a\n",
      "\n",
      "176165 => ” It was a bald-faced lie . Is this a good thing or bad thing ? Well , it depends\n",
      "\n",
      "176582 => things to a whole other level.37 If there is a good piece of food and a higher-ranking animal nearby ,\n",
      "\n",
      "177566 => frontocortical . These are people who habitually lie and are good at it ( and typically have high verbal IQs\n",
      "\n",
      "178316 => . This book ’ s focus is not really how good a liar someone is . It ’ s whether\n",
      "\n",
      "180448 => . For example , say some baboons flush out something good to eat—say , a young gazelle . The gazelle\n",
      "\n",
      "182494 => ACC cares about is the meaning of the pain . Good news or bad , and of what nature ?\n",
      "\n",
      "183022 => ? 17 “ Ouch , that hurt ” is a good way to learn not to repeat whatever you just\n",
      "\n",
      "184679 => 12—wealthier people are more likely to endorse greed as being good , to view the class system as fair and\n",
      "\n",
      "187942 => feel bad , but that it can make us feel good , which can in turn encourage us to think\n",
      "\n",
      "188630 => toward one individual because of a globalized sense of wishing good things for the world . * A handful of\n",
      "\n",
      "189404 => the presses ; science has proven that it can feel good to do good , complete with activation of the\n",
      "\n",
      "189407 => science has proven that it can feel good to do good , complete with activation of the mesolimbic dopamine system\n",
      "\n",
      "189556 => five.53 The question , of course , is why doing good can feel good , which raises the classic question\n",
      "\n",
      "189559 => , of course , is why doing good can feel good , which raises the classic question : is there\n",
      "\n",
      "189582 => act that contains no element of self-interest ? Does doing good feel good because there ’ s something in it\n",
      "\n",
      "189584 => contains no element of self-interest ? Does doing good feel good because there ’ s something in it for you\n",
      "\n",
      "189867 => purely internal reward of altruism—the warm glow of having done good , the lessened sting of guilt , the increased\n",
      "\n",
      "190167 => perhaps the rarest form as well . Intuitively , if good acts must be motivated by self-interest , the reputational\n",
      "\n",
      "190208 => contrast , the motivation to think of yourself as a good person seems a pretty benign one . After all\n",
      "\n",
      "190343 => other words , the exact same amount of public “ good ” was accomplished in each case , but the\n",
      "\n",
      "190599 => there is typically dopaminergic activation in the one with the good luck when some of the reward is transferred afterward\n",
      "\n",
      "190749 => cared for , feeling the warm glow of doing a good thing . And that it is rare to be\n",
      "\n",
      "191090 => no reason why we should expect ourselves to have particularly good intuitions when aiming to heal this far-flung , heterogeneous\n",
      "\n",
      "191185 => “ reciprocity ” being evolutionarily inseparable . Better that our good acts be self-serving and self-aggrandizing than that they don\n",
      "\n",
      "191494 => t represent choosing a “ cognitive ” approach to doing good over an “ affective ” one . The detachment\n",
      "\n",
      "191552 => problem to worry about . The key is neither a good ( limbic ) heart nor a frontal cortex that\n",
      "\n",
      "193234 => metaphorically inside something when we are “ in ” a good mood , “ in ” cahoots with someone ,\n",
      "\n",
      "193790 => of some unlikely disease . Or maybe it ’ s good news : you ’ re going to get your\n",
      "\n",
      "194658 => posits that being viscerally disgusted by something is a pretty good indicator that it is morally wrong ; implicitly evoking\n",
      "\n",
      "195220 => disorderly = bad , metaphorically being clean and orderly = good . *17 Just consider the use of the word\n",
      "\n",
      "195604 => be a moral state as well—cleanliness was not just a good way to avoid uncontrolled diarrhea , dehydration , and\n",
      "\n",
      "196246 => Many of our moments of prosociality , of altruism and Good Samaritanism , are acts of restitution , attempts to\n",
      "\n",
      "199048 => . A GLIMMER A goal might be to use the good side of a double-edged sword to cut loose the\n",
      "\n",
      "199902 => end to the violence of the Troubles and the 1998 Good Friday Agreement laid the groundwork for Republicans and Unionists\n",
      "\n",
      "211412 => sociality . And most important , because it can feel good to punish , to be part of a righteous\n",
      "\n",
      "211549 => of dopaminergic reward systems . Punishment that feels just feels good . It makes sense that we ’ ve evolved\n",
      "\n",
      "212807 => feel up a pineapple to tell if it ’ s good . The sheer luck of the socioeconomic trajectory of\n",
      "\n",
      "215053 => killed in a hundred days . * This suggests both good and bad news . Compared with the past ,\n",
      "\n",
      "215201 => . But that doesn ’ t mean they ’ re good . Thus we now consider insights provided by this\n",
      "\n",
      "215558 => nations , long-distance trade that makes them interdependent is a good deterrent . Cultural diffusion in general ( which includes\n",
      "\n",
      "217992 => as sustained online relationships—can work a bit as well.20 All good news . Contact theory has prompted an experimental approach\n",
      "\n",
      "221411 => a colobus monkey and was eating , getting to the good part , when this guy comes by , starts\n",
      "\n",
      "221548 => , we work well together on patrols—it ’ s probably good if we sort things out . “ So I\n",
      "\n",
      "223388 => and consume . Forgiveness seems to be at least somewhat good for your health—victims who show spontaneous forgiveness , or\n",
      "\n",
      "223654 => able to explain why . Thus it ’ s a good idea to recognize the systematic features to our irrationality\n",
      "\n",
      "228257 => finally shook America ’ s self-perception as a force of good , was the My Lai Massacre . On March\n",
      "\n",
      "230047 => “ I can use the profits from the investments for good works , ” “ Those people really are so\n",
      "\n",
      "230591 => a stunning scale . It must be admitted that some good came of World War I—thanks to the subsequent collapse\n",
      "\n",
      "231260 => ; it was a holiday that stood for peace and good will toward all men . Thus we have the\n",
      "\n",
      "231494 => spot . What are you communicating ? “ Look how good our guy is . He could have aimed at\n",
      "\n",
      "234513 => than Wilson , in that I ’ ve had the good fortune of brilliant friends , ones who have been\n",
      "\n",
      "236234 => charges ? * No , never ! That ’ s good enough for some cell in your spleen or your\n",
      "\n",
      "246644 => stretch of genes on a particular chromosome . Definitely not good . Finally , there are insertion mutations . During\n",
      "\n",
      "248403 => wonderfully written—by the anthropologist/physician Mel Konner . To my vast good fortune , Konner was my academic adviser and mentor\n",
      "\n",
      "248483 => York : Putnam , 2009 ) . 2 . For good reviews of these distinctions , see K. Miczek et\n",
      "\n",
      "251092 => : 363 ; K. Krendl et al. , “ The Good , the Bad , and the Ugly : An\n",
      "\n",
      "257838 => Nsci 15 ( 2012 ) : 940 ; for a good review of the entire subject , see : D.\n",
      "\n",
      "257910 => JPSP 85 ( 2003 ) : 616 . For a good review of how implicit attitudes are studied , see\n",
      "\n",
      "258996 => “ Images of Eyes Enhance Investments in a Real-Life Public Good , ” PLoS ONE 7 ( 2012 ) :\n",
      "\n",
      "265596 => Behav 63 ( 2013 ) : 97 . For a good review see C. Bodo and E. Rissman , “\n",
      "\n",
      "276260 => ) : 1367 ; R. Sullivan et al. , “ Good Memories of Bad Events , ” Nat 407 (\n",
      "\n",
      "277779 => 2013 ) : 576 . 41 . For a particularly good review , see L. Huesmann and L. Taylor ,\n",
      "\n",
      "277980 => 2007 ) : 470 ; C. Ferguson , “ The Good , the Bad and the Ugly : A Meta-analytic\n",
      "\n",
      "288813 => ( 2013 ) : 469 . 3 . For a good discussion of this , see A. Norenzayan , “\n",
      "\n",
      "291415 => ) : 1480 ; Footnote : B. Maheer , “ Good Gaming , ” Nat 531 ( 2016 ) :\n",
      "\n",
      "291652 => ) : 1540 ; A. Rutherford et al. , “ Good Fences : The Importance of Setting Boundaries for Peaceful\n",
      "\n",
      "299331 => 814 ; J. Haidt , The Righteous Mind : Why Good People Are Divided by Politics and Religion ( New\n",
      "\n",
      "301355 => 751 . 72 . A. Rutherford et al. , “ Good Fences : The Importance of Setting Boundaries for Peaceful\n",
      "\n",
      "303579 => K. Dion et al. , “ What Is Beautiful Is Good , ” JPSP 24 ( 1972 ) : 285\n",
      "\n",
      "304413 => et al. , “ The Political Left Rolls with the Good and the Political Right Confronts the Bad : Connecting\n",
      "\n",
      "305145 => 47 . J. Haidt , The Righteous Mind : Why Good People Are Divided by Politics and Religion ( New\n",
      "\n",
      "306757 => . P. Zimbardo , The Lucifer Effect : Understanding How Good People Turn Evil ( New York : Random House\n",
      "\n",
      "307969 => excellent P. Bloom , Just Babies : The Origins of Good and Evil ( Portland , OR : Broadway Books\n",
      "\n",
      "308903 => ) . 28 . M. Shermer , The Science of Good and Evil ( New York : Holt , 2004\n",
      "\n",
      "313110 => “ Do Mirror Neurons Give Us Empathy ? ” Greater Good Newsletter , March 29 , 2012 ; V. Ramachandran\n",
      "\n",
      "314404 => M. Hsu et al. , “ The Right and the Good : Distributive Justice and Neural Encoding of Equity and\n",
      "\n",
      "317498 => ; S. Georgianna , “ Is a Religious Neighbor a Good Neighbor ? ” Humboldt J Soc Relations 11 (\n",
      "\n",
      "324176 => 372 , 393 , 557 , 633 , 634 public good , 495–96 third-party punishment , 497 Tit for Tat\n",
      "\n",
      "337206 => in a later chapter . * A reminder—as with all good studies of individuals with damage to particular brain regions\n",
      "\n",
      "337447 => cortex , where there is no such thing as a good decision , where each choice is worse than the\n",
      "\n",
      "341892 => words , the contrast between “ bad ” and “ good ” stress ) . * As one additional ,\n",
      "\n",
      "343163 => have to buy a new one. ” * “ Greater good ” for kids , as at any age ,\n",
      "\n",
      "343232 => of the divide were willing to undergo sacrifice for the good of their ideological group . * I once received\n",
      "\n",
      "343695 => . And nothing about the science says that the same good effects of attachment can ’ t be provided by\n",
      "\n",
      "344494 => don ’ t communicate ; I ’ d be pretty good at just asking the person to be considerate and\n",
      "\n",
      "348310 => youth , being told that StarKist wants tuna that taste good , not tuna with good taste . * !\n",
      "\n",
      "348315 => StarKist wants tuna that taste good , not tuna with good taste . * ! Kung speak a click language\n",
      "\n",
      "348636 => was only then that we started to hear about the good stuff , via her becoming friends with some of\n",
      "\n",
      "349477 => equally biologically responsible for the children . * There is good evidence that inbreeding was responsible for the demise of\n",
      "\n",
      "351394 => to birdshit. ” * Pääbo , who is a stunningly good scientist , pioneered the sequencing of ancient DNA ,\n",
      "\n",
      "352622 => notion that anything that is bad for Them is automatically good for Us : So God appears to all the\n",
      "\n",
      "352660 => president assembles his cabinet and says , “ I have good news and bad news . God exists , but\n",
      "\n",
      "352736 => minister of Israel tells his cabinet , “ I have good news and great news . God exists , and\n",
      "\n",
      "352942 => Arab American writer Amer Zahr . * Examples of “ good alien ” movies include The Day the Earth Stood\n",
      "\n",
      "353678 => once every few years that the Packers are having a good or bad season , my mood is briefly influenced\n",
      "\n",
      "357723 => along the lines of “ This is how I do good , ” rather than , say , “ I\n",
      "\n",
      "357942 => , have been the perfect anonymous interaction . While the Good Samaritan who carried my message and I were anonymous\n",
      "\n",
      "358028 => Lest we get carried away with ourselves , there is good evidence that some of the most impressive cave paintings\n",
      "\n",
      "358969 => some Zulu , Sesotho , and English thrown in for good measure . While its existence is intensely moving ,\n",
      "\n",
      "360132 => religiosity declines . Probably more important , this is a good demonstration that religion sure isn ’ t the only\n",
      "\n",
      "360921 => In his Nobel address , Cajal managed to muster the good manners to praise Golgi . Golgi , in his\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epos in evil:\n",
    "    print(str(epos) + ' => ' + ' '.join((n[epos-10:epos+10])) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f801c3f55f8>]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J+aab0jjvaUjSMnwv5o080xiQYTtSGbb2aLit5+3F92LeyDONARi2I5Vha4+G23rfXpp4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f801c089128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md = ws['maybe']['diffs']\n",
    "\n",
    "plt.plot(np.linspace(0, 1, len(md)), np.array(md), '.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 25.003248324 38.3931494419 162667.368901 102807.28804\n",
      "cortex 812.739819005 2184.65374369 141499.354402 118505.508343\n",
      "brain 528.903367496 1126.43115256 167967.349415 113795.47728\n",
      "amygdala 998.246418338 2786.06354809 133315.045714 119965.164196\n"
     ]
    }
   ],
   "source": [
    "for w in ws.keys():\n",
    "    print(w, mean(ws[w]['diffs']),std(ws[w]['diffs']), mean(ws[w]['pos']), std(ws[w]['pos']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8021e25a90>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8HEn0uj4Hv1BI64ef+bI2wQzV3hOKExbxuSKeI2ZjVgr6aw7Gp6/tC0WpqwrE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\nCRUOiqIoShIqHBRFUZQkVDgoiqIoSfx/gPMBhUyd+rUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f802ce2ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(difs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "812.73981900452486"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(difs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18816"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(diffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(diffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}