Bechdel_test.ipynb

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      "The Need for Openness in Data Journalism"
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      "[Brian Keegan, Ph.D.](http://www.brianckeegan.com) \\([@bkeegan](https://twitter.com/bkeegan)\\) College of Humanities and Social Sciences, Northeastern University\n",
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      "Do films that pass the Bechdel Test make more money for their producers? I've replicated Walt Hickey's [recent article](http://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/) in FiveThirtyEight to find out. My results confirm his own in part, but also find notable differences that point the need for clarification at a minimum. While I am far from the first to make this argument, this case is illustrative of a larger need for journalism and other data-driven enterprises to borrow from hard-won scientific practices of sharing data and code as well as supporting the review and revision of findings. This admittedly lengthy post is a critique of not only this particular case but also an attempt to work through what open data journalism could look like."
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     "source": [
      "The Angle: Data Journalism should emulate the openness of science"
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      "New data-driven journalists such as FiveThirtyEight have faced criticism from many quarters and the critiques, particularly around the na\u00efvet\u00e9 of assuming credentialed experts can be bowled over by quantitative analysis so easily as the terrifyingly innumerate pundits who infest our political media \\[[1](http://krugman.blogs.nytimes.com/2014/03/18/sergeant-friday-was-not-a-fox/),[2](http://pressthink.org/2014/03/review-and-comment-on-the-launch-of-nate-silvers-fivethirtyeight-com-for-espn/),[3](http://gigaom.com/2014/03/17/nate-silvers-new-fivethirtyeight-site-launches-but-can-his-data-driven-journalism-find-a-market/),[4](http://www.vanityfair.com/online/daily/2014/03/nate-silver-vs-the-experts-five-thirty-eight)\\]. While I find these critiques persuasive, I depart from them here to instead argue that I have found this \"new\" brand of data journalism disappointing foremost because *it wants to perform science without abiding by scientific norms.*\n",
      "\n",
      "The questions of demarcating what is or is not science are fraught, so let's instead label my gripe a \"failure to be open.\" By openness, I don't mean users commenting on articles or publishing whistleblowers' documents. I mean \"openness\" more in the sense of \"open source software\" where the code is made freely available to everyone to inspect, copy, modify, and redistribute. But the principles of open-source software trace their roots more directly back norms in the scientific community that [Robert Merton](https://en.wikipedia.org/wiki/Robert_K._Merton#Sociology_of_science) identified and came to known as \"[CUDOS](https://en.wikipedia.org/wiki/Mertonian_norms)\" norms. It's worth reviewing two of these norms because Punk-ass Data Journalism is very much on the lawn of Old Man Science and therein lie exciting possibilities for [exciting adventures](http://viewhdwallpaper.com/wp-content/uploads/2014/02/Pixar%E2%80%99s-UP-Movie-HD-Wallpaper.jpg).\n",
      "\n",
      "The first and last elements of Merton's \"CUDOS\" norms merit special attention for our discussion of openness. **Communalism** is the norm that scientific results are shared and become part of a commons that others can build upon --- this is the bit about \"standing upon the shoulders of giants.\" **Skepticism** is the norm that claims must be subject to organized scrutiny by community --- which typically manifests as peer review. Both of these strongly motivated philosophies in the open source movement, and while they are practiced imperfectly in my experience within the social and information sciences (see my colleagues' recent work on the \"[Parable of Google Flu](http://bit.ly/NHIgqM)\"), I nevertheless think data journalists should strive to make them their own practice as well.\n",
      "\n",
      "1. *Data journalists should be open in making their data and analysis available to all comers*. This flies in the face of traditions and professional anxieties surrounding autonomy, scooping, and the protection of sources. But how can claims be evaluated as true unless they can be inspected? If I ask a data journalist for her data or code, is she bound by the same norms as a scientist to share it? Where and how should journalists share and document these code and data?\n",
      "\n",
      "2. *Data journalists should be open in soliciting and publishing feedback*. Sure, journalists are used to clearing their story with an editor, but have they solicited an expert's evaluation of their claims? How willing are they to publish critiques of, commentary on, or revisions to their findings? If not, what are the venues for these discussions? How should a reporter or editor manage such a system?\n",
      "\n",
      "*The Guardian*'s [DataBlog](http://www.theguardian.com/news/datablog) and [ProPublica](http://www.propublica.org/tools/) have each been doing exemplary work in posting their datasets, code, and other tools for several years. Other organizations like the [Sunlight Foundation](https://sunlightfoundation.com/tools/) develop outstanding tools to aid reporters and activists, the [Knight Foundation](http://www.knightfoundation.org/what-we-fund/innovating-media) has been funding exciting projects around journalism innovation for years, and the [Data Journalism Handbook](http://datajournalismhandbook.org/1.0/en/) reviews other excellent cases as well. My former colleague, Professor [Richard Gordon](http://www.medill.northwestern.edu/experience/people/faculty/rich-gordon.html) at Medill reminded me ideas around \"[computer assisted reporting](https://en.wikipedia.org/wiki/Computer_assisted_reporting)\" have been in circulation in the outer orbits of journalism for decades. For example, [Philip Meyer](https://en.wikipedia.org/wiki/Philip_Meyer) has been (what we would now call) evangelizing since the 1970s for \"[precision journalism](http://www.unc.edu/~pmeyer/book/)\" in which journalists adopt the tools and methods of the social and behavioral sciences as well as its norms of sharing data and replicating research. Actually, if you stopped reading now and promised to read his [2011 Hedy Lamarr Lecture](http://www.nieman.harvard.edu/reports/article-online-exclusive/100044/Precision-Journalism-and-Narrative-Journalism-Toward-a-Unified-Field-Theory.aspx), I won't even be mad.\n",
      "\n",
      "The remainder of this post is an attempt demonstrate some ideas of what an \"open collaboration\" model for data journalism might look like. To that end, this article tries to do many things for many audiences which admittedly makes it hard for any single person to read. Let me try to sketch some of these out now and send you off in the right path. \n",
      "\n",
      "* First, I use an article Walt Hickey of FiveThirtyEight published on the relationship between the financial performance of films that the extent to which they grant their female characters substantive roles as a case to illustrate some pitfalls in both the practice and interpretation of statistical data. This is a story about having good questions, ambiguous models, wrong inferences, and exciting opportunities for investigation going forward. If you don't care for code or statistics, you can start reading at \"The Hook\" below and stop after \"The Clip\" below.\n",
      "* Second, for those readers who are willing to pay what one might call the \"[Iron Price](http://gameofthrones.wikia.com/wiki/Iron_price) of Data Journalism\", I go \"[soup to nuts](https://en.wikipedia.org/wiki/Soup_to_nuts)\" and attempt to replicate Hickey's findings. I document all the steps I took to crawl and analyze this data to illustrate the need for better documentation of analyses and methods. This level of documentation may be excessive or it may yet be insufficient for others to replicate my own findings. But providing this code and data may expose flaws in my technical style (almost certainly), shortcomings in my interpretations (likely), and errors in my data and modeling (hopefully not). I actively invite this feedback via email, tweets, comments, or pull requests and hope to learn from it. I wish new data journalism enterprises adopted the same openness and tentativeness in their empirical claims. You should start reading at \"Start Your Kernels...\"\n",
      "* Third, I want to experiment with styles for analyzing and narrating findings that make both available in the same document. The hope is that motivated users can find the detail and skimmers can learn something new or relevant while being confident they can come back and dive in deeper if they wish. Does it make sense to have the story up front and the analysis \"below the fold\" or to mix narrative with analysis? How much background should I presume or provide about different analytical techniques? How much time do I need to spend on tweaking a visualization? Are there better libraries or platforms for serving the needs of mixed audiences? This is a meta point as we're in it now, but it'll crop up in the conclusion.\n",
      "* Fourth, I want to experiment with technologies for supporting collaboration in data journalism by adopting best practices from open collaborations in free software, Wikipedia, and others. For example, this blog post is not written in a traditional content-management system like WordPress, but is an interactive \"[notebook](http://ipython.org/notebook.html)\" that you can download and execute the code to verify that it works. Furthermore, I'm also \"hosting\" this data on [GitHub](https://github.com/brianckeegan/Bechdel) so that others can easily access the writeup, code, and data, to see how it's changed over time (and has it ever...), and to suggest changes that I should incorporate. These can be frustrating tools with demoralizing learning curves, but these are *incredibly* powerful once apprenticed. Moreover, there are amazing resources and communities who exist to support newcomers and new tools are being released to flatten these learning curves. If data journalists joined data scientists and data analysts in sharing their work, it would contribute to an incredible knowledge commons of examples and cases that is lowering the bars for others who want to learn. This is also a meta point since it exists outside of this story, but I'll also come back to it in the conclusion.\n",
      "\n",
      "In this outro to a very unusual introduction, I want to thank Professor Gordon from above, Professor [Deen Freelon](http://www.american.edu/soc/faculty/freelon.cfm), [Nathan Matias](http://natematias.com/), and [Alex Leavitt](http://alexleavitt.com/) for their invaluable feedback on earlier drafts of this... post? article? piece? notebook?"
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     "source": [
      "The Hook: The Bechdel Test article in FiveThirtyEight"
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      "Walk Hickey published an article on April 1 on FiveThirtyEight, titled [The Dollar-And-Cents Case Against Hollywood\u2019s Exclusion of Women](http://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/). The article examines the relationship between movies' finances and their portrayals of women using a well-known heuristic call the [Bechdel test](https://en.wikipedia.org/wiki/Bechdel_test). The test has 3 simple requirements: a movie passes the Bechdel test if there are (1) two women in it, (2) who talk to each other, (3) about something besides a man.\n",
      "\n",
      "Let me say at the outset, *I like this article*: It identifies a troubling problem, asks important questions, identifies appropriate data, and brings in relevant voices to speak to these issues. I should also include the disclaimer that I am not an expert in the area of empirical film studies like [Dean Keith Simonton](http://psychology.ucdavis.edu/faculty_sites/simonton/) or [Nick Redfern](http://nickredfern.wordpress.com/). I've invested a good amount time in criticizing the methods and findings of this article, but to Hickey's credit, I also haven't come across any scholarship that has attempted to quantify this relationship before: **this is new knowledge about the world**. Crucially, it speaks to empirical scholarship that has exposed how films with award-winning female roles are significantly less likely to win awards themselves [\\[5\\]](http://link.springer.com/article/10.1023/B:SERS.0000029097.98802.2c), older women are less likely to win awards [\\[6\\]](http://www.amsciepub.com/doi/abs/10.2466/pr0.2000.86.1.175?journalCode=pr0), actresses' earnings peak 17 years earlier than actors' earnings [\\[7\\]](http://jmi.sagepub.com/content/early/2014/01/27/1056492613519861.abstract), and differences in how male and female critics rate films [\\[8\\]](http://baywood.metapress.com/app/home/contribution.asp?referrer=parent&backto=issue,5,8;journal,6,63;linkingpublicationresults,1:300310,1). I have qualms about the methods and others may be justified in complaining it overlooks related scholarship like those I cited above, but this article is in the best traditions of journalism that focuses our attention on problems we should address as a society.\n",
      "\n",
      "Hickey's article makes two central claims:\n",
      "\n",
      "> 1. We found that the median budget of movies that passed the test...was substantially lower than the median budget of all films in the sample.\n",
      "\n",
      "> 2. We found evidence that films that feature meaningful interactions between women may in fact have a better return on investment, overall, than films that don\u2019t.\n",
      "\n",
      "I call Claim 1 the \"Budgets Differ\" finding and Claim 2 the \"Earnings Differ\" finding. The results, as they're summarized here are relatively straightforward to test whether there's an effect of Bechdel scores on earnings and budget controlling for other explanatory variables.\n",
      "\n",
      "But before I even get to running the numbers, I want to examine the claims Hickey made in the article. The interpretations he makes about the return on investment are particularly problematic interpretations of basic statistics. Hickey reports the following findings from his models (emphasis added).\n",
      "> We did a statistical analysis of films to test two claims: first, that films that pass the Bechdel test \u2014 featuring women in stronger roles \u2014 see a lower return on investment, and second, that they see lower gross profits. We found no evidence to support either claim.\n",
      "\n",
      "> On the first test, we ran a regression to find out if passing the Bechdel test corresponded to lower return on investment. Controlling for the movie\u2019s budget, which has a negative and significant relationship to a film\u2019s return on investment, **passing the Bechdel test had no effect on the film\u2019s return on investment.** In other words, adding women to a film\u2019s cast didn\u2019t hurt its investors\u2019 returns, contrary to what Hollywood investors seem to believe.\n",
      "\n",
      "> The total median gross return on investment for a film that passed the Bechdel test was \\$2.68 for each dollar spent. The total median gross return on investment for films that failed was only \\$2.45 for each dollar spent.\n",
      "\n",
      "> ...On the second test, we ran a regression to find out if passing the Bechdel test corresponded to having lower gross profits \u2014 domestic and international. Also controlling for the movie\u2019s budget, which has a positive and significant relationship to a film\u2019s gross profits, once again **passing the Bechdel test did not have any effect on a film\u2019s gross profits.**\n",
      "\n",
      "Both models (whatever their faults, and there are some as we will explore in the next section) apparently produce an estimate that the Bechdel test has no effect on a film's financial performance. That is to say, the statistical test could not determine with a greater than 95% confidence that the correlation between these two variables was greater or less than 0. *Because we cannot confidently rule out the possibility of there being zero effect, we cannot make any claims about its direction.* \n",
      "\n",
      "Hickey argues that passing the test \"didn't hurt its investors' returns\", which is to say there was no significant negative relationship, but neither was there a significant positive relationship: *The model provides no evidence of a positive correlation between Bechdel scores and financial performance.* However, Hickey switches gears an in the conclusions, writes:\n",
      "\n",
      "> ...our data demonstrates that films containing meaningful interactions between women **do better at the box office than movies that don\u2019t**...\n",
      "\n",
      "I don't know what analysis supports this interpretation. The analysis Hickey just performed, again taking the findings at their face, concluded that \"passing the Bechdel test did not have any effect on a film\u2019s gross profits\" ***not*** \"passing the Bechdel test increased the film's profits.\" While Bayesians will cavil about frequentist assumptions --- as they are wont to do --- and the absence of evidence is not evidence of absence, the \"Results Differ finding\" is not empirically supported in any appropriate interpretation of the analysis. The appropriate conclusion from Hickey's analysis is \"there no relationship between the Bechdel test and financial performance,\" which he makes... then ignores.\n",
      "\n",
      "What to make of this analysis? In the next section, I summarize the findings of my own analysis of the same data. In the subsequent sections, I attempt to replicate the findings of this article, and in so doing, highlight the perils of reporting statistical findings without adhering to scientific norms."
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      "The Clip: Look in here for what to tweet"
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      "I tried to retrieve and re-analyze the data that Hickey described in his article, but came to some conclusions that were the same, others that were very different, and still others that I hope are new. \n",
      "\n",
      "In the absence of knowing the precise methods used but making reasnable assumptions of what was done, I was able to replicate some of his findings, but not others because specific decisions had to be made about the data or modeling that dramatically change the results of the statistical models. However, the article provides no specifics so we're left to wonder when and where these findings hold, which points to the need for openness in sharing data and code. Specifically, while Hickey found that women's representation in movies had no significant relationship on revenue, I found a positive and significant relationship. \n",
      "\n",
      "But the questions and hypotheses Hickey posed about systematic biases in Hollywood were also the right ones. With a reanalysis using different methods as well as adding in new data, I found statistically significant differences in popular ratings also exist. These differences persist after controlling for each other and in the face of other potential explanations about differences arising because of genres, MPAA ratings, time, and other effects.\n",
      "\n",
      "In the image below, we see that movies that have non-trivial women's roles get 24% lower budgets, make 55% more revenue, get better reviews from critics, and face harsher criticism from IMDB users. Bars that are faded out mean my models are less confident about these findings being non-random (higher p-values) while bars that are darker mean my models are more confident that this is a significant finding (lower p-values).\n",
      "\n",
      "Movies passing the Bechdel test (the red bars):\n",
      "\n",
      "* ...receive budgets that are 24% *smaller*\n",
      "\n",
      "* ...make 55% *more* revenue \n",
      "\n",
      "* ...are awarded 1.8 *more* Metacritic points by professional reviewers\n",
      "\n",
      "* ...are awarded 0.12 *fewer* stars by IMDB's amateur reviewers\n"
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49tup2LZtF6KiLuC772bi888HIT39HQDgjz9isW/fQZw7dwnR0ZG4f/8ejhwJ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\nTkSuQmCC\n",
       "prompt_number": 165,
       "text": [
        "<IPython.core.display.Image at 0x16ff79b0>"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Start your kernels..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the sections below, I provide all the code I used to scrape data from the websites Hickey mentioned as sources as well as some others I use to extend his analysis to include other control measures commonly used in statstical models of movie performance (for example, Nagle & Riedl's [work on movie ratings](http://www.hbs.edu/faculty/Publication%20Files/13-091_83a8b1fc-5b05-4941-84cc-86f5fe323ade.pdf)). The goal here is to attempt to replicate Hickey's analyses using the same data and same methods he described. I want to emphasize three points ahead of time: \n",
      "\n",
      "1. **What are the data?** Through this process of obtaining the data, it becomes clear that the analyst quickly has to make choices about the types of data to be obtained. Does the data come from Table X or Table Y? Does budget data include marketing expenditures or not? Are these inflation-corrected real dollars or nominal dollars? They *should* be the same, but there are things missing in one that aren't missing in the other. These decisions are not documented in the article nor are these data made available, both of which makes it hard to determine what exactly is being measured.\n",
      "\n",
      "2. **What are the variables?** The article also creates variables such as \"return on investment\" and \"gross profits\" from other variables in the data. These data can be highly-skewed or contain missing data which can break some statistical assumptions -- how were these dealt with? The article doesn't actually say how these variables are constructed or where they come from, so I could be wrong but the data and tradition definitions of these variables present obvious candidates involving different relationships between the same two variables Budget (Expenses) and Income (Revenue). In creating a new variable that combines two other variables, the behavior of this new variable is intrinsically related to the other variables. This can become problematic very quickly, as we will see in the next step.\n",
      "\n",
      "3. **What are the models?** The article then performs analyses on these new and old variables using methods that assume they should be independent. As the previous bullet emphasizes, \"return on investment\" and \"gross profits\" are financial performance outcomes that already capture a relationship between Budget and Income. The model *could* measure these outcomes as a function of the Bechdel score alone. The model could *also* estimate basic Income as a function of Bechdel plus throw in Budget as a control. The model might still also could try to estimate the combined financial performance as a function of Bechdel controlling for Budget again, *even though is already in the outcome.* The write-up makes it seem like the last model is the one used, which is problematic."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. What are the data?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import seaborn as sb\n",
      "import json,urllib2,string\n",
      "from itertools import product\n",
      "from matplotlib.collections import LineCollection\n",
      "import statsmodels.formula.api as smf\n",
      "from bs4 import BeautifulSoup\n",
      "from scipy.stats import chisquare\n",
      "from IPython.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Revenue data from The-Numbers.com"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Revenue data taken from scraping http://www.the-numbers.com/movies/#tab=letter\n",
      "\n",
      "Hickey's article uses data about international receipts, which doesn't appear to be available publicly from this website, so I cannot replicate these analyses. Indeed, if The-Numbers.com provided Hickey with the data, it may very well be different in extent and detail than what is possible to access from their website."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This may take a bit of time\n",
      "revenue_df_list = list()\n",
      "\n",
      "for character in string.printable[1:36]:\n",
      "    soup = BeautifulSoup(urllib2.urlopen('http://www.the-numbers.com/movies/letter/{0}'.format(character)).read())\n",
      "    archive = pd.read_html(soup.findAll('table')[0].encode('latin1'),header=0,flavor='bs4',infer_types=False)[0]\n",
      "    archive.columns = ['Released','Movie','Genre','Budget','Revenue','Trailer']\n",
      "    archive['Released'] = pd.to_datetime(archive['Released'],format=u\"%b\\xa0%d,\\xa0%Y\",coerce=True)\n",
      "    archive = archive.replace([u'\\xa0',u'nan'],[np.nan,np.nan])\n",
      "    archive['Budget'] = archive['Budget'].dropna().str.replace('$','').apply(lambda x:int(x.replace(',','')))\n",
      "    archive['Revenue'] = archive['Revenue'].dropna().str.replace('$','').apply(lambda x:int(x.replace(',','')))\n",
      "    archive['Year'] = archive['Released'].apply(lambda x:x.year)\n",
      "    archive.dropna(subset=['Movie'],inplace=True)\n",
      "    revenue_df_list.append(archive)\n",
      "    \n",
      "numbers_df = pd.concat(revenue_df_list)\n",
      "numbers_df.reset_index(inplace=True,drop=True)\n",
      "numbers_df.to_csv('revenue.csv',encoding='utf8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numbers_df = pd.read_csv('revenue.csv',encoding='utf8',index_col=0)\n",
      "numbers_df['Released'] = pd.to_datetime(numbers_df['Released'],unit='D')\n",
      "numbers_df.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Released</th>\n",
        "      <th>Movie</th>\n",
        "      <th>Genre</th>\n",
        "      <th>Budget</th>\n",
        "      <th>Revenue</th>\n",
        "      <th>Trailer</th>\n",
        "      <th>Year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>19967</th>\n",
        "      <td>1979-12-14</td>\n",
        "      <td>  Zulu Dawn</td>\n",
        "      <td>               NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       0</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19968</th>\n",
        "      <td>2003-02-07</td>\n",
        "      <td>   Zus &amp; Zo</td>\n",
        "      <td>               NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   49468</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19969</th>\n",
        "      <td>2007-04-06</td>\n",
        "      <td>  Zwartboek</td>\n",
        "      <td> Thriller/Suspense</td>\n",
        "      <td> 22000000</td>\n",
        "      <td> 4398532</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2007</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19970</th>\n",
        "      <td>2014-02-28</td>\n",
        "      <td> Zwei Leben</td>\n",
        "      <td>             Drama</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   39673</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19971</th>\n",
        "      <td>2006-02-24</td>\n",
        "      <td> Zyzzyx Rd.</td>\n",
        "      <td> Thriller/Suspense</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      20</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2006</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "        Released       Movie              Genre    Budget  Revenue Trailer  \\\n",
        "19967 1979-12-14   Zulu Dawn                NaN       NaN        0     NaN   \n",
        "19968 2003-02-07    Zus & Zo                NaN       NaN    49468     NaN   \n",
        "19969 2007-04-06   Zwartboek  Thriller/Suspense  22000000  4398532     NaN   \n",
        "19970 2014-02-28  Zwei Leben              Drama       NaN    39673     NaN   \n",
        "19971 2006-02-24  Zyzzyx Rd.  Thriller/Suspense       NaN       20     NaN   \n",
        "\n",
        "       Year  \n",
        "19967  1979  \n",
        "19968  2003  \n",
        "19969  2007  \n",
        "19970  2014  \n",
        "19971  2006  \n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Inflation data from BLS"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here I tried to write some code to get historical CPI data from the [BLS API](http://www.bls.gov/developers/home.htm), but I couldn't get it to work. Instead, I downloaded the January monthly data from 1913 through 2014 for Series ID `CUUR0000SA0` which is the \"CPI - All Urban Consumers\" and saved it as a CSV. The `inflator` dictionary provides a mapping that lets me to adjust the dollars reported in previous years for inflation: a dollar in 1913 bought 23.8 times more goods and services than a dollar in 2014."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpi = pd.read_csv('cpi.csv',index_col='Year')\n",
      "inflator = dict(cpi.ix[2014,'Annual']/cpi['Annual'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bechdel Test data from BechdelTest.com's API"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Documentation here: http://bechdeltest.com/api/v1/doc#getMovieByImdbId\n",
      "\n",
      "First, download a list of all movie IDs using the `getAllMovieIds` method. Inspect the first 5. Create a list of IMDB ids out of this list of dictionaries to pass to the API."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movie_ids = json.loads(urllib2.urlopen('http://bechdeltest.com/api/v1/getAllMovieIds').read())\n",
      "imdb_ids = [movie[u'imdbid'] for movie in movie_ids]\n",
      "print u\"There are {0} movies in the Bechdel test corpus\".format(len(imdb_ids))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 5062 movies in the Bechdel test corpus\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pull an example `getMovieByImdbId` from the API."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json.loads(urllib2.urlopen('http://bechdeltest.com/api/v1/getMovieByImdbId?imdbid=0000091').read())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "{u'date': u'2013-12-25 14:31:21',\n",
        " u'dubious': u'0',\n",
        " u'id': u'4982',\n",
        " u'imdbid': u'0000091',\n",
        " u'rating': u'0',\n",
        " u'submitterid': u'9025',\n",
        " u'title': u'House of the Devil, The',\n",
        " u'visible': u'1',\n",
        " u'year': u'1896'}"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pull all of the data we can down. ***This will take a very long time.*** We don't want to have to repeat that crawl again, so let's write the data to disk as `bechdel.json`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings = dict()\n",
      "exceptions = list()\n",
      "for num,imdb_id in enumerate(imdb_ids):\n",
      "    try:\n",
      "        if num in range(0,len(imdb_ids),int(round(len(imdb_ids)/10,0))):\n",
      "            print imdb_id\n",
      "        ratings[imdb_id] = json.loads(urllib2.urlopen('http://bechdeltest.com/api/v1/getMovieByImdbId?imdbid={0}'.format(imdb_id)).read())\n",
      "    except:\n",
      "        exceptions.append(imdb_id)\n",
      "        pass\n",
      "\n",
      "with open('bechdel.json', 'wb') as f:\n",
      "  json.dump(ratings.values(), f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read the data back from disk and convert to `ratings_df`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_df = pd.read_json('bechdel.json')\n",
      "ratings_df['imdbid'] = ratings_df['imdbid'].dropna().apply(int)\n",
      "ratings_df = ratings_df.set_index('imdbid')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_df.dropna(subset=['title'],inplace=True)\n",
      "ratings_df2 = ratings_df[['rating','title']]\n",
      "ratings_df2.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>rating</th>\n",
        "      <th>title</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>imdbid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>435528 </th>\n",
        "      <td> 2</td>\n",
        "      <td>            Whisper</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1456472</th>\n",
        "      <td> 1</td>\n",
        "      <td>     We Have a Pope</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>435680 </th>\n",
        "      <td> 3</td>\n",
        "      <td>         Kidulthood</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>460721 </th>\n",
        "      <td> 3</td>\n",
        "      <td>  Big Bad Swim, The</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1571222</th>\n",
        "      <td> 3</td>\n",
        "      <td> A Dangerous Method</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "         rating               title\n",
        "imdbid                             \n",
        "435528        2             Whisper\n",
        "1456472       1      We Have a Pope\n",
        "435680        3          Kidulthood\n",
        "460721        3   Big Bad Swim, The\n",
        "1571222       3  A Dangerous Method\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The field we want is `rating` which is defined as: \n",
      "\n",
      "    \"The actual score. Number from 0 to 3.\n",
      "    0.0 means no two women,\n",
      "    1.0 means no talking, \n",
      "    2.0 means talking about a man, \n",
      "    3.0 means it passes the test.\""
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Additional data from OMDBAPI"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pull an example from http://www.omdbapi.com/."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json.loads(urllib2.urlopen('http://www.omdbapi.com/?i=tt0000091').read())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "{u'Actors': u\"Jeanne d'Alcy, Georges M\\xe9li\\xe8s\",\n",
        " u'Awards': u'N/A',\n",
        " u'Country': u'France',\n",
        " u'Director': u'Georges M\\xe9li\\xe8s',\n",
        " u'Genre': u'Short, Horror',\n",
        " u'Language': u'N/A',\n",
        " u'Metascore': u'N/A',\n",
        " u'Plot': u'A bat flies into an ancient castle and transforms itself into Mephistopheles himself. Producing a cauldron, Mephistopheles conjures up a young girl and various supernatural creatures, one ...',\n",
        " u'Poster': u'N/A',\n",
        " u'Rated': u'N/A',\n",
        " u'Released': u'N/A',\n",
        " u'Response': u'True',\n",
        " u'Runtime': u'3 min',\n",
        " u'Title': u'The House of the Devil',\n",
        " u'Type': u'movie',\n",
        " u'Writer': u'Georges M\\xe9li\\xe8s',\n",
        " u'Year': u'1896',\n",
        " u'imdbID': u'tt0000091',\n",
        " u'imdbRating': u'6.8',\n",
        " u'imdbVotes': u'768'}"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Crawl the data. ***This will take a while.*** Write the data to disk when you're done as `imdb_data.json`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imdb_data = dict()\n",
      "exceptions = list()\n",
      "for num,imdb_id in enumerate(imdb_ids):\n",
      "    try:\n",
      "        if num in range(0,len(imdb_ids),int(round(len(imdb_ids)/10,0))):\n",
      "            print imdb_id\n",
      "        imdb_data[imdb_id] = json.loads(urllib2.urlopen('http://www.omdbapi.com/?i=tt{0}'.format(imdb_id)).read())\n",
      "    except:\n",
      "        exceptions.append(imdb_id)\n",
      "        pass\n",
      "\n",
      "with open('imdb_data.json', 'wb') as f:\n",
      "  json.dump(imdb_data.values(), f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read the data back from disk and convert to `imdb_df`. Print out an example row, but transpose it because there are a lot of columns."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read the data in\n",
      "imdb_df = pd.read_json('imdb_data.json')\n",
      "\n",
      "# Drop non-movies\n",
      "imdb_df = imdb_df[imdb_df['Type'] == 'movie']\n",
      "\n",
      "# Convert to datetime objects\n",
      "imdb_df['Released'] = pd.to_datetime(imdb_df['Released'], format=\"%d %b %Y\", unit='D', coerce=True)\n",
      "\n",
      "# Drop errant identifying characters in the ID field\n",
      "imdb_df['imdbID'] = imdb_df['imdbID'].str.slice(start=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "More cleanup happens below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Remove the \" min\" at the end of Runtime entries so we can convert to ints\n",
      "imdb_df['Runtime'] = imdb_df['Runtime'].str.slice(stop=-4).replace('',np.nan)\n",
      "\n",
      "# Some errant runtimes have \"h\" in them. Commented-out code below identifies them.\n",
      "#s = imdb_df['Runtime'].dropna()\n",
      "#s[s.str.contains('h')]\n",
      "\n",
      "# Manually recode these h-containing Runtimes to minutes\n",
      "imdb_df.ix[946,'Runtime'] = '169'\n",
      "imdb_df.ix[1192,'Runtime'] = '96'\n",
      "imdb_df.ix[1652,'Runtime'] = '80'\n",
      "imdb_df.ix[2337,'Runtime'] = '87'\n",
      "imdb_df.ix[3335,'Runtime'] = '62'\n",
      "\n",
      "# Blank out non-MPAA or minor ratings (NC-17, X)\n",
      "imdb_df['Rated'] = imdb_df['Rated'].replace(to_replace=['N/A','Not Rated','Approved','Unrated','TV-PG','TV-G','TV-14','TV-MA','NC-17','X'],value=np.nan)\n",
      "\n",
      "# Convert Release dateimte into new columns for year, month, and week\n",
      "imdb_df['Year'] = imdb_df['Released'].apply(lambda x:x.year)\n",
      "imdb_df['Month'] = imdb_df['Released'].apply(lambda x:x.month)\n",
      "imdb_df['Week'] = imdb_df['Released'].apply(lambda x:x.week)\n",
      "\n",
      "# Convert the series to float\n",
      "imdb_df['Runtime'] = imdb_df['Runtime'].apply(float)\n",
      "\n",
      "# Take the imdbVotes formatted as string containing \"N/A\" and comma-delimited thousands, convert to float\n",
      "imdb_df['imdbVotes'] = imdb_df['imdbVotes'].dropna().replace('N/A',np.nan).dropna().apply(lambda x:float(x.replace(',','')))\n",
      "\n",
      "# Take the Metascore formatted as string containing \"N/A\", convert to float\n",
      "# Also divide by 10 to make effect sizes more comparable\n",
      "imdb_df['Metascore'] = imdb_df['Metascore'].dropna().replace('N/A',np.nan).dropna().apply(float)/10.\n",
      "\n",
      "# Take the imdbRating formatted as string containing \"N/A\", convert to float\n",
      "imdb_df['imdbRating'] = imdb_df['imdbRating'].dropna().replace('N/A',np.nan).dropna().apply(float)\n",
      "\n",
      "# Create a dummy variable for English language\n",
      "imdb_df['English'] = (imdb_df['Language']  == u'English').astype(int)\n",
      "imdb_df['USA'] = (imdb_df['Country']  == u'USA').astype(int)\n",
      "\n",
      "# Convert imdb_ID to int, set it as the index\n",
      "imdb_df['imdbID'] = imdb_df['imdbID'].dropna().apply(int)\n",
      "imdb_df = imdb_df.set_index('imdbID')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Join datasets together"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = imdb_df.join(ratings_df2,how='inner').reset_index()\n",
      "df = pd.merge(df,numbers_df,left_on=['Title','Year'],right_on=['Movie','Year'])\n",
      "df['Year'] = df['Released_x'].apply(lambda x:x.year)\n",
      "df['Adj_Revenue'] = df.apply(lambda x:x['Revenue']*inflator[x['Year']],axis=1)\n",
      "df['Adj_Budget'] = df.apply(lambda x:x['Budget']*inflator[x['Year']],axis=1)\n",
      "\n",
      "df.tail(2).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>2624</th>\n",
        "      <th>2625</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>imdbID</th>\n",
        "      <td>                                           1531663</td>\n",
        "      <td>                                            282771</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Actors</th>\n",
        "      <td> Will Ferrell, Christopher Jordan Wallace, Rebe...</td>\n",
        "      <td> Om Puri, Aasif Mandvi, Ayesha Dharker, Jimi Mi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Awards</th>\n",
        "      <td>                                               N/A</td>\n",
        "      <td>                                            1 win.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <td>                                               USA</td>\n",
        "      <td>                                    UK, India, USA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Director</th>\n",
        "      <td>                                          Dan Rush</td>\n",
        "      <td>                                   Ismail Merchant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Error</th>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Genre_x</th>\n",
        "      <td>                                     Comedy, Drama</td>\n",
        "      <td>                                     Comedy, Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Language</th>\n",
        "      <td>                                           English</td>\n",
        "      <td>                                           English</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Metascore</th>\n",
        "      <td>                                               6.5</td>\n",
        "      <td>                                               6.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Plot</th>\n",
        "      <td> When an alcoholic relapses, causing him to los...</td>\n",
        "      <td> In 1950s Trinidad, a frustrated writer support...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Poster</th>\n",
        "      <td> http://ia.media-imdb.com/images/M/MV5BMjEwMjI0...</td>\n",
        "      <td> http://ia.media-imdb.com/images/M/MV5BMjEzNDIy...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rated</th>\n",
        "      <td>                                                 R</td>\n",
        "      <td>                                                PG</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Released_x</th>\n",
        "      <td>                               2011-10-14 00:00:00</td>\n",
        "      <td>                               2002-03-29 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Response</th>\n",
        "      <td>                                              True</td>\n",
        "      <td>                                              True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runtime</th>\n",
        "      <td>                                                97</td>\n",
        "      <td>                                               117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Title</th>\n",
        "      <td>                                Everything Must Go</td>\n",
        "      <td>                                The Mystic Masseur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Type</th>\n",
        "      <td>                                             movie</td>\n",
        "      <td>                                             movie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Writer</th>\n",
        "      <td> Dan Rush, Raymond Carver (short story \"Why Don...</td>\n",
        "      <td> V.S. Naipaul (novel), Caryl Phillips (screenplay)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Year</th>\n",
        "      <td>                                              2011</td>\n",
        "      <td>                                              2002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>imdbRating</th>\n",
        "      <td>                                               6.5</td>\n",
        "      <td>                                               5.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>imdbVotes</th>\n",
        "      <td>                                             31814</td>\n",
        "      <td>                                               393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Month</th>\n",
        "      <td>                                                10</td>\n",
        "      <td>                                                 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Week</th>\n",
        "      <td>                                                41</td>\n",
        "      <td>                                                13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>English</th>\n",
        "      <td>                                                 1</td>\n",
        "      <td>                                                 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USA</th>\n",
        "      <td>                                                 1</td>\n",
        "      <td>                                                 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rating</th>\n",
        "      <td>                                                 1</td>\n",
        "      <td>                                                 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <td>                                Everything Must Go</td>\n",
        "      <td>                               Mystic Masseur, The</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Released_y</th>\n",
        "      <td>                               2011-05-13 00:00:00</td>\n",
        "      <td>                               2002-05-03 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Movie</th>\n",
        "      <td>                                Everything Must Go</td>\n",
        "      <td>                                The Mystic Masseur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Genre_y</th>\n",
        "      <td>                                             Drama</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Budget</th>\n",
        "      <td>                                           5000000</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Revenue</th>\n",
        "      <td>                                           2712131</td>\n",
        "      <td>                                            398610</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Trailer</th>\n",
        "      <td>                                              Play</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adj_Revenue</th>\n",
        "      <td>                                           2880766</td>\n",
        "      <td>                                          526489.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adj_Budget</th>\n",
        "      <td>                                           5310889</td>\n",
        "      <td>                                               NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                                                          2624  \\\n",
        "imdbID                                                 1531663   \n",
        "Actors       Will Ferrell, Christopher Jordan Wallace, Rebe...   \n",
        "Awards                                                     N/A   \n",
        "Country                                                    USA   \n",
        "Director                                              Dan Rush   \n",
        "Error                                                      NaN   \n",
        "Genre_x                                          Comedy, Drama   \n",
        "Language                                               English   \n",
        "Metascore                                                  6.5   \n",
        "Plot         When an alcoholic relapses, causing him to los...   \n",
        "Poster       http://ia.media-imdb.com/images/M/MV5BMjEwMjI0...   \n",
        "Rated                                                        R   \n",
        "Released_x                                 2011-10-14 00:00:00   \n",
        "Response                                                  True   \n",
        "Runtime                                                     97   \n",
        "Title                                       Everything Must Go   \n",
        "Type                                                     movie   \n",
        "Writer       Dan Rush, Raymond Carver (short story \"Why Don...   \n",
        "Year                                                      2011   \n",
        "imdbRating                                                 6.5   \n",
        "imdbVotes                                                31814   \n",
        "Month                                                       10   \n",
        "Week                                                        41   \n",
        "English                                                      1   \n",
        "USA                                                          1   \n",
        "rating                                                       1   \n",
        "title                                       Everything Must Go   \n",
        "Released_y                                 2011-05-13 00:00:00   \n",
        "Movie                                       Everything Must Go   \n",
        "Genre_y                                                  Drama   \n",
        "Budget                                                 5000000   \n",
        "Revenue                                                2712131   \n",
        "Trailer                                                   Play   \n",
        "Adj_Revenue                                            2880766   \n",
        "Adj_Budget                                             5310889   \n",
        "\n",
        "                                                          2625  \n",
        "imdbID                                                  282771  \n",
        "Actors       Om Puri, Aasif Mandvi, Ayesha Dharker, Jimi Mi...  \n",
        "Awards                                                  1 win.  \n",
        "Country                                         UK, India, USA  \n",
        "Director                                       Ismail Merchant  \n",
        "Error                                                      NaN  \n",
        "Genre_x                                          Comedy, Drama  \n",
        "Language                                               English  \n",
        "Metascore                                                  6.4  \n",
        "Plot         In 1950s Trinidad, a frustrated writer support...  \n",
        "Poster       http://ia.media-imdb.com/images/M/MV5BMjEzNDIy...  \n",
        "Rated                                                       PG  \n",
        "Released_x                                 2002-03-29 00:00:00  \n",
        "Response                                                  True  \n",
        "Runtime                                                    117  \n",
        "Title                                       The Mystic Masseur  \n",
        "Type                                                     movie  \n",
        "Writer       V.S. Naipaul (novel), Caryl Phillips (screenplay)  \n",
        "Year                                                      2002  \n",
        "imdbRating                                                 5.9  \n",
        "imdbVotes                                                  393  \n",
        "Month                                                        3  \n",
        "Week                                                        13  \n",
        "English                                                      1  \n",
        "USA                                                          0  \n",
        "rating                                                       2  \n",
        "title                                      Mystic Masseur, The  \n",
        "Released_y                                 2002-05-03 00:00:00  \n",
        "Movie                                       The Mystic Masseur  \n",
        "Genre_y                                                    NaN  \n",
        "Budget                                                     NaN  \n",
        "Revenue                                                 398610  \n",
        "Trailer                                                    NaN  \n",
        "Adj_Revenue                                           526489.3  \n",
        "Adj_Budget                                                 NaN  \n",
        "\n",
        "[35 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One question that both Hickey and others have posed is whether the Bechdel site data is biased in some way. We can do a quick check to see if the distributions of the movies in the joined dataset (where movies are only included if they appear across datasets) and the raw Bechdel data. There's clearly a bias towards the Bechdel community covering movies that pass the test and potentially omitting data about movies that do not. This is problematic, but unless we want to watch these movies and code them ourselves, there's not much to be done. But in terms of the data we use for our analysis, it doesn't appear to be the case that using the ..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "representative_df = pd.DataFrame()\n",
      "\n",
      "# Group data by scores\n",
      "representative_df['All Bechdel'] = ratings_df2.groupby('rating')['title'].agg(len)\n",
      "representative_df['Analysis Subset'] = df.groupby('rating')['title'].agg(len)\n",
      "\n",
      "# Convert values of data to fractions of total\n",
      "representative_df2 = representative_df.apply(lambda x: x/x.sum(),axis=0)\n",
      "\n",
      "# Make the plot\n",
      "representative_df2.plot(kind='barh')\n",
      "plt.legend(fontsize=15,loc='lower right')\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=15)\n",
      "plt.xticks(fontsize=15)\n",
      "plt.ylabel('')\n",
      "plt.xlabel('Fraction of movies',fontsize=18)\n",
      "plt.title('Does the analyzed data differ from the original data?',fontsize=18)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x10f448cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DAwM8e/YMAHDv3j1kZWXh+++/x/fff19sbDdv3iyxbYVCgcuXL2Pjxo1ISkrClStX5DWS\nxd21Xjh3hfT19VUe51Le9gp5e3vDzc0NO3fuxJgxY5CZmYn9+/dj6tSpRcreu3cPvXr1gqWlJbZv\n3y63q85c3LhxA+np6cjOzoabm1uRMh4eHvjxxx9LjLM8dSs6F4XS0tIwf/58nD17FleuXMH169ch\nhIAkSaU+QufatWvo3bt3ke3FHefllZycDABYsWIFVqxYUeT9wjlu2bIlAMDS0rJS/b1c/9q1awDU\n/5m1tLRUWb9Z+MfDyz9TOjo6FX4skaWlZZH9aWBggLS0NADqzVlJP6MVGW9pMjIycP/+fTlxf5G6\nx0dxnwEAkJ+fL2/Lzc3FzJkzcfLkSSQnJ+Pq1avy++WZ52vXrhVZ019SrJXtc8GCBfjjjz+QnJyM\n5ORk+TO2uLpCCHz88ceoW7cu9uzZU+HP9leJCSvRP0hWVhauXLmC7t27Ayj9TtEXF9IXPhQ6LCys\nxMfDFH6ADho0CF26dMGuXbuwd+9e/Pzzz4iJicHq1avx559/vvKF+YUfyP3798eIESOKLVPao2nC\nw8MxZcoUeHh4oG3btujfvz+8vb2xYsUKbN26tdzxVKa9QYMG4bPPPkNKSgr279+PZ8+eqZxdBp7f\nxd6/f3+kpKTg8OHDsLKykt9TZy5cXV3l5KK4h32X9YutPHUrMxfffvstAgMD4ejoiA4dOqBbt27w\n8vLC/v37sWDBglLrKhSKCo1NHYVzPHbs2CJnKQs1aNBA/nfhDVQV9XJ9dX9mC714dvtFr/IRYmU9\nlL+8c/ai8o63rPkuTMKKS7CUSmWpdQuVNd7Y2Fh07twZpqam8PPzg6+vL5o0aYLLly9jzJgxavVR\nSJIktY7lyvR54cIFtG7dGs+ePUPnzp0RGBiIRo0aoaCgoMT9dfHiRVy+fBmrVq2CiYlJucb0ujBh\nJfoHKbzc3LNnTwCAi4sLAOD8+fNyElvowoULAIDatWvLH/JGRkYqdwcDz+8Wv3//PgwMDJCTk4NT\np07B09MTISEhCAkJwbNnzzBlyhQsX74cMTExePfdd1/pmKytrWFoaIinT58Wie327ds4depUic+c\nzc3NxaxZs9ChQwfExMSo/CJKTU0t9y/1yrYXGBiIsLAw7NmzB3v37sXbb7+N+vXrq5QZP348Dh8+\njIiICDRv3lzlPXXmwtDQEJaWljA1NcXFixeLxHDlypVS41S3bmXnYtq0aXjjjTcQFxcHAwMDefuW\nLVtKrQc8T8pLiq+yCn9mdHR0iszxX3/9hatXr8LQ0LDS/ZTV//nz54u89+LPrDapzJy96vHa2NjA\n2NhYrvuiS5cuqd1OaWbNmgUjIyMkJSWpnPEtadlDaVxdXREbG4v8/HyVZPzlY7kyfS5atAgPHjzA\nhQsXVK6clPaUlbt370KSpFdy1eJV4RpWon+IO3fu4D//+Q8cHR0RFBQEAPDy8oK9vT1Wr16Nhw8f\nymWzsrKwevVqODg4oGnTpmjatCns7e2xYsUKlbWHjx49wsCBAxEcHAw9PT0kJSWhbdu22LBhg1xG\nT09PXktY0tke4H9nRl68rAaUfCaocLuuri78/f2xd+9enD17VqXMhAkT5HW3xcnJyUFOTg7q1aun\nklCdPn1a/kawF89klBVLZdvz8PBA48aNsWvXLhw6dKjI2dUNGzZg9erVCA0NxQcffFAkDnXnQpIk\n9O7dG/v371d5zM61a9ewd+/eYsf4Yszq1C3PXBTu+xfnJiMjA87OzirJ6s2bNxEVFQVJkpCXl1di\njH369EFSUpLK49syMzOxZcuWSp9ZtLe3h5eXFzZt2qSy5jMvLw/vv/8++vbtW+QYfpXs7Ozg5eWF\nyMhIlcd7PX36FEuXLoVSqVRZi/gqlfQzWpbKzNmrGm/hflcoFOjRowf27dsnLzcAnj+Ob9u2ba/k\nzHN6ejpsbGxUEsfMzExs2rQJAEo9dl/Wt29fZGZmyg/qB56fJV67dm2F+3x5SU16ejqMjIxUvmzm\n6dOniIiIKDHeFi1a4M6dO2jbtq3aY3ndeIaVqBr6/vvv5Q+unJwc/PXXX/j666/x5MkT7N+/X74c\npqurixUrVmDgwIHw8vLCsGHDIITA+vXrkZKSIp+R1dPTk8s1adIE77//PgwMDLBhwwZcu3YNW7du\nhUKhgJeXF9q3b49PPvkEN27cwFtvvYWbN2/iiy++QP369fHOO++UGHPhmro1a9YgJSVFTtZKuiT4\n4vaFCxfi0KFDaNu2LcaMGQNnZ2fs27cPP/zwA0aOHFnkLGWhmjVrwtvbGxs2bICJiQnq1auHxMRE\nfPXVV6hXrx6SkpKQlZUl3/hTViyvor3AwEBMnjwZCoVCJWGNj4/H6NGjYWtri06dOuGbb75R+aVj\nYmKCnj17qj0X8+bNw969e+Hj44OPP/4YOjo6WLFiBUxNTYt89ebL1KlbnrkoXBO4ZcsWFBQUYOjQ\noejatSt27NiBUaNGwcvLC1euXMH69evh4OCAjIwMZGVllRjfxIkTsXXrVvTp0wfjx4+HtbU1vvzy\ny1L3YXkUfiVl06ZNMWrUKFhZWWH79u04evQoFi5ciJo1a8plK9tfcfUL+2/WrBlGjx4NY2NjREZG\nIj4+Xt4PFem/rLKFP6NLlixB165dy1xa9OL28szZ6xjvi9vnzp2LvXv3okWLFvjwww9Ro0YNRERE\n4P79+wAqv1zC398fixYtwsCBA+Hn54eUlBT55wBAqcfuy9577z2sW7cOY8eOxblz5+Du7o7IyEj5\nhraK9GljY4NffvkF69evR+fOneHv748ff/wR3bp1Q79+/ZCZmSk/p7WkeG/evIk//vgDrVu31p5v\nA6u6BxIQUWUFBwcLSZJU/tPX1xdubm5i2LBh4tKlS8XWO3jwoPD19RVGRkbC3NxcdOnSRfz+++/F\nluvQoYMwMTERZmZmok2bNiqPzBFCiPv374vx48cLV1dXoVQqhYODg/jggw9EampqqbE/e/ZMDBw4\nUBgaGgpLS0uRm5srgoODi33EUnHbL1++LAIDA4W1tbUwMDAQb775pvj8889VHsNVnJs3b4p+/foJ\nKysrYWRkJHx8fERMTIz8DNHC55uqG0tl27t9+7bQ0dEp8oihTZs2CUmShEKhKLKPJUkSderUKfdc\nXLx4UfTs2VOYmZkJW1tbMXPmTDFlypQyH2ulbl1150KI54/1MjU1FaampuLKlSvi/v374v333xd2\ndnZCqVSKZs2aiW3btokLFy4ISZLE0qVLS40vJSVFDBkyRFhaWoqaNWuKUaNGiWXLlhV5bJFCoSjz\nsVYvlxFCiFOnTonu3bsLc3NzYWRkJJo2bSq+/vprlTK+vr4q+6U0xfVTWv1Tp06Jd999V5iZmcnP\nVX3xuckl1f/ll1+EQqFQeYSYurE+ePBA+Pn5CaVSKRo0aFBqveK2qzNnJanoeIUo/mftzJkzws/P\nTxgZGQlra2sxceJEMWXKFCFJkvxZ9fI+KelYeHl7bm6umDRpkqhdu7bQ19cXb775pli5cqV4+PCh\n0NXVFR9++GGp7b3s0aNHYty4ccLOzk4YGxuLgIAAsXXr1gr1KYQQmzdvFvb29sLAwEBERkYKIYRY\nsGCBcHV1Ffr6+sLd3V3MmzdP5ObmCnt7e9GjR48iMRU+kuvl40iTJCH4HWxERFXl77//hpOTE1at\nWlXijVNEVHF3794t8pQE4Pk3OUVERCA3N7fSN8tR1eMaViKiKrR27VoolUoEBARoOhSif6QBAwbA\n09NTZZnA48eP8eOPP6Jx48ZMVqsprmElIqoC06dPR2JiIvbt24exY8fCzMxM0yER/SMFBwcjNDQU\n3bp1Q48ePZCbm4stW7bg77//xrp16zQdHlUQE1YioiqQnZ2NX375Bb179y7zOaNEVHHBwcEwNDTE\n0qVLMXXqVCgUCjRr1gwHDx7UqrveqXy4hpWIiIiItBrPsBJVsadP85CZWfSbTeh/zMyeP5uT81Q2\nzpV6OE/q4Typj3OlHjMzA9SoUfl0kzddEVWxefPmIiXlTtkFiYiICAATVqIqFxY2D6mpKZoOg4iI\nqNpgwkpEREREWo0JKxERERFpNSasRERERKTVmLASERERkVZjwkpUxWbO/BS2tnaaDoOIiKja4HNY\niarYp5/+h8/tIyIiKgeeYSUiIiIircaElYiIiIi0GhNWIiIiItJqTFiJiIiISKsxYSWqYvPmzUVK\nyh1Nh0FERFRtMGElqmJhYfOQmpqi6TCIiIiqDSasRERERKTVmLASERERkVbjFwcQacCtWzdhbGys\n6TC0lrGxEgDw6FGuhiPRfpwr9XCe1MN5Up+2zZWLiyt0dHQ0HcZrw4SVSAPWnv0aZvctNB0GERH9\nA2SnPcTiHnPh5uau6VBeGyasRFXMrZMnLN1tYWBhpOlQiIiIqgWuYSWqYu5d3mKySkREVA5MWImI\niIhIqzFhJSIiIiKtxoSViIiIiLQaE1YiIiIi0mpMWImq2KX9CcjJyNZ0GERERNUGE1aiKpYck4Tc\nB481HQYREVG1wYSViIiIiLQaE1YiIiIi0mpMWImIiIhIqzFhJSIiIiKtxoSVqIq5dfKE0txQ02EQ\nERFVG0xYiaqYe5e3YGBhpOkwiIiIqg0mrERERESk1ZiwEhEREZFWY8JKRERERFqNCSsRERERaTUm\nrERV7NL+BORkZGs6DCIiomqDCWs5ubi4QKFQyP/p6uqiZs2a8Pf3x9mzZzUdXoUEBwerjElPTw/O\nzs6YNm0anj179sr6cXFxwfz588tVZ9iwYWjfvr3a5X19fTF8+PAi269du6YyxuL++/rrr8sV28uO\nHj2KP/74o8xyyTFJyH3wuFJ9ERER/ZswYS0nSZIwbdo0pKSkICUlBbdu3cKhQ4eQlZUFPz8/PHr0\nSNMhVki7du3kMV29ehURERH4+uuv8cknn7yyPiRJgiRJFapX2T6cnJzk8d25cwcDBgxQGXNKSgoG\nDBhQ7the1K5dO1y+fLlSbRAREVFRupoOoDoyNjaGjY2N/NrOzg7h4eFo1aoVfvnlF3Tv3l2D0VWM\nnp6eypgcHR3x4Ycf4vPPP8fixYs1GBkghKh0GwqFQmV8SqWyyJhfhVcRKxEREaniGdZXREdHBwCg\nr68PADhz5gz8/f1Rs2ZN6Ovrw8PDA1u2bJHLHzt2DK1bt4axsTGsrKwwZMgQ3L9/HwCQn5+PSZMm\nwdHREUqlEm+//Ta+++47uW5BQQE+++wzuLi4wNjYGM2bN8e+ffvk97OzsxESEgI7OzsYGBigZcuW\n+OWXX0qNv7izkoaGhlAo/neIlNUvABw/fhzt27eHsbExatWqhalTpyI/P19+/9atW+jRoweMjIzg\n4OCABQsWqNRftWoV6tSpAyMjIwwZMgSPH6teOr958yb69esHMzMz2NnZYdCgQbhz506pY1PX/fv3\nERoaCisrK1haWqJbt264ePGi/P6FCxfQqVMnmJmZwdzcHL1798b169cBPF/ukJ+fj5CQEHTo0OGV\nxENERETPMWGtgJfPol25cgXTpk2Dg4MDWrVqhezsbHTq1AmOjo44fvw4EhIS0K5dOwwfPhxpaWnI\nz89Hjx494Ofnh3PnzuGnn37CiRMnMHnyZADA6tWrsWvXLkRFReHixYvo378/Bg0aJCdH06dPx+bN\nm7Fu3TqcPXsWQ4cORZ8+fXD48GEAwH/+8x+cP38eMTExOH/+PBo3bozevXsjJydH7TFdunQJERER\nGDZsmLytrH6vXr2K9u3bo169eoiLi0NkZCS2bNmCWbNmyX1s2LAB7777Ls6dO4ePPvoIn3zyCWJj\nYwEAW7ZswcSJEzFz5kycPn0atWrVwvbt2+VkOjs7G76+vjAyMsLRo0cRExODp0+fokOHDpVea1tQ\nUAB/f3+kpKQgJiYGR44cgbOzM9q0aYOMjAwAQGBgIOrUqYP4+HjExsbi3r17CA0NBQDExcVBR0cH\ny5cvR1RUVKViISIiIlVcElBOQgjMmzcPCxcuBAA8e/YMz549Q5MmTRAVFQVjY2OkpaVh0qRJGDdu\nHJRKJYDnyd769etx8eJF1K9fH+np6bC1tYWTkxOcnJzw/fffy0lXcnIyDA0N4ezsDFtbW8ycORPe\n3t6oWbM25RK+AAAgAElEQVQmHj16hBUrViAqKgp+fn4AgDFjxuD06dNYsGABfHx8kJycDBMTE7i4\nuMDU1BTh4eHo16+fytnSl/36668wMTEBAOTl5eHJkyeoW7cuRo8eDQBq9bt27Vo4ODggIiICkiTB\nw8MD69atw40bN+R+BgwYgA8++AAAMHXqVCxcuBAnT55E27ZtsXLlSrz33nt4//33AQALFizAoUOH\n5Lrbtm3D48ePsXHjRnks33zzDaytrfF///d/CAgIqPAl+UOHDiEuLg4ZGRnyPKxevRoHDx7E2rVr\nMW3aNCQnJ6Nz585wdnaGjo4OIiMjkZqaCgCwsrICAPnsa2ncOnlCaW5YoTiJiIj+jZiwlpMkSRg7\ndqycyOnq6sLS0hJGRv/7bnhra2uMGDECmzZtQnx8PC5fvozTp08DeH6538LCAhMnTsSYMWMwa9Ys\n+Pn5oXv37ujXrx8AYPTo0YiKikKtWrXg5eWFLl26ICgoCKampjhx4gSePHlSJAF99uwZ7OzsAACT\nJ09Gjx49YG1tjVatWqFLly4YPHiwvFyhOC1atMDmzZvlGG/cuIGwsDA0b94cp0+fRnJycpn9JiQk\noGnTpirLC7p166Yyd/Xq1VPp19zcXD7zm5SUhJCQkCJxJSQkAADi4+ORlpYGMzMzlTI5OTn466+/\nShybOuLj45Gfnw8HBweV7U+ePJHbnjdvHiZOnIjVq1ejQ4cOePfddxEQEFDuvhyauiAv9xke/v2g\nUjETEREBQHbaQ9y7lwJjY2W56rm5uclLGl8XXd1X0z4T1gqwsLCAq6trie///fffaNmyJWrXro3u\n3bujR48esLe3h5eXl1xm8eLFGDt2LPbs2YOYmBiEhIRg3bp1OHjwIOrVq4crV67g4MGDiImJwbZt\n2/DZZ59h//79sLS0BAB8//33qFu3rtyeEEI+6Fq3bo1bt24hOjoaMTExWLVqFcLCwvDnn3+iQYMG\nxcasVCpVxuTu7o769evD0dERO3bsQOvWrcvst0aNGmWe4SzuB6OwjiRJRerr6enJ/65RowY8PT3x\n/fffF6lfeFazIk8hKGzbwsICx48fL9K2sbExAGDcuHEYOHAg9uzZgwMHDuDjjz9GeHg4zpw5oxJn\nWR7E1cETE4sKxUlERFSc8L+vQFJcU7v848y72DBnQJETSdqKCetrsG3bNjx69AixsbFyAhUdHQ3g\neQJ09epVLFq0CMuXL8fo0aMxevRofPvttwgICEBaWhp27twJS0tLDBw4EJ07d0Z4eDgaNmyIqKgo\nLFmyBHp6erh58yY6deok9zl37lzk5+djzpw5mDdvHlq2bIlevXqhV69eWLZsGRwcHPDTTz+VmLAW\nl+gVFBTI/3d3dy+z3/r162Pnzp0QQsjtrV27FmvXrkVcXFyZ89aoUSMcOXIEo0aNkrfFxcXJZ3Q9\nPT2xYcMGWFhYyAnqw4cPERQUhIkTJ8LHx6fMPkri6emJjIwMCCHg5uYmjzswMBB9+/aFn58fZs6c\nienTpyM0NBShoaE4ceIEvL29cebMGXh5eamdLFs61odxzVoVjpWIiOhVePQoF5mZJd/f8iqYmRmg\nRo3Kp5u86aqc1Fkj6eTkhKysLHz33Xe4fv06fvjhB4wbNw6SJOHJkyfymsvRo0fjwoULOH/+PHbs\n2IG6devCysoKWVlZGDduHPbu3Yvr169j165duHr1Kry9vWFgYIAJEyZg+vTp+Pbbb3HlyhWsWLEC\nc+fOlROt27dvY/To0Th8+DCuX7+OLVu2IDMzE97e3iXG/OTJE6SmpsrPJD116hSGDx8OY2Nj9OnT\nR61+x4wZgzt37mDcuHH466+/cODAAcyZMwfvvvtuiXP34rZJkyZhx44dWLFiBS5evIiwsDAcOXJE\nfn/w4MGwsrLCgAEDcPLkSSQmJmLQoEE4fvw4PD095fYqso71nXfeQYsWLTBgwADExsbi4sWLGD58\nOPbs2YO33noL5ubmOHjwIEaMGIGEhARcvnwZGzduRM2aNfHGG28AAExMTJCUlIS0tLRy909EREQl\n4xnWclLnLFr//v1x/PhxjBs3DllZWWjVqhUiIyMREhKCEydOoFOnTti3bx8mT54Mb29vFBQUwMfH\nBz/99BMkScKUKVPw6NEjjBkzBikpKahduzbmzp2L9957DwAQFhaGGjVqYPLkyUhNTYWbmxvWrl2L\nIUOGAACWLl2KSZMmITAwEOnp6XB3d8emTZvQtm3bEscUGxsLe3t7+bWZmRmaN2+OAwcOyNvL6tfB\nwQH79+/HlClT0LhxY1hbW2PYsGHyUwKKm7sXt/Xs2RNfffUV5s2bh6lTp8LPzw/Dhw+X15AqlUoc\nOHAAEydORIcOHSBJElq1aoVDhw7JNz2p++UExZXbtWsXJk2ahF69euHJkydo0qQJoqOj4eHhAQD4\n8ccf8fHHH8PX1xe5ublo3rw5oqOj5Zu0pk2bhrlz5+Lnn3/GyZMny4yBiIiI1CMJPumcqEq5NO4G\n9+b9oDTmOlYiItKMR/dvY8EHLeDm5v5a++GSAKJq6vrpn5CbnaHpMIiIiKoNJqxEREREpNWYsBIR\nERGRVmPCSkRERERajQkrEREREWk1JqxEVcy5kT+URnxCABERkbqYsBJVsTqNu/GRVkREROXAhJWI\niIiItBoTViIiIiLSakxYiYiIiEirMWElIiIiIq3GhJWoil2N34vcR/xqViIiInUxYSWqYtdP/4Tc\nbCasRERE6mLCSkRERERajQkrEREREWk1JqxEREREpNWYsBIRERGRVmPCSlTFnBv5Q2nEr2YlIiJS\nFxNWoipWp3E3KI2ZsBIREamLCSsRERERaTUmrERERESk1XQ1HQDRv83jzLuaDoGIiP7lqtvvIkkI\nITQdBNG/SWLiOTx6lKvpMLSasbESADhPauBcqYfzpB7Ok/r+CXPl4uIKHR2d19qHmZkBatSo/PlR\nnmElqmI7dmzHgAGDYWdnr+lQtJaZmQEAIDMzR8ORaD/OlXo4T+rhPKmPc1W1uIaVqIqFhc1DamqK\npsMgIiKqNpiwEhEREZFWY8JKRERERFqNCSsRERERaTUmrERERESk1ZiwElWxmTM/ha2tnabDICIi\nqjb4WCuiKvbpp//hY1CIiIjKgWdYiYiIiEirMWElIiIiIq3GhJWIiIiItBoTViIiIiLSakxYiarY\nvHlzkZJyR9NhEBERVRtMWImqWFjYPKSmpmg6DCIiomqDCSsRERERaTUmrERERESk1fjFAUQacOvW\nTRgbG2s6DK1lbKwEADx6lFtqORcXV+jo6FRFSEREpEFMWIk0YO3Zr2F230LTYVRr2WkPsbjHXLi5\nuWs6FCIies2YsBJVMbdOnrB0t4WBhZGmQyEiIqoWuIaVqIq5d3mLySoREVE5MGElIiIiIq3GhJWI\niIiItBoTViIiIiLSakxYiYiIiEirMWElqmKX9icgJyNb02EQERFVG0xYiapYckwSch881nQYRERE\n1QYTViIiIiLSakxYiYiIiEirMWElIiIiIq3GhJWIiIiItBoTVqIq5tbJE0pzQ02HQUREVG0wYSWq\nYu5d3oKBhZGmwyAiIqo2mLASERERkVZjwkpEREREWo0JKxERERFpNSasRERERKTVmLASVbFL+xOQ\nk5Gt6TCIiIiqDSasRFUsOSYJuQ8eazoMIiKiakMjCWvv3r3h6+tbZLuTkxMUCgX+/vtvle0ff/wx\nPDw8qig67eDr64vhw4dXqG5GRgY2btyodvlNmzZBT09Pfq1QKPDNN99UqO+XXbt2DQqFAn/88ccr\naa8kR48efe19EBERkWZoJGHt2LEj4uLiUFBQIG87f/487ty5A3t7e8TExKiUj42NhZ+fX1WHqVGS\nJEGSpArVnTp1Kr7++utXHJF2a9euHS5fvqzpMIiIiOg10EjC2r59ezx+/BinTp2St8XExKBJkybo\n3LkzoqOj5e0PHz7EmTNn/nUJa2UIITQdgkb8W8dNRET0T6eRhNXT0xO2trYql3BjYmLQqVMn+Pn5\n4eeff5a3Hz16FMDzJBcAbty4gYCAANjY2MDU1BS9e/fG1atX5fIuLi4IDw9H165dYWhoCDc3N+zZ\nswdRUVFwd3eHiYkJunXrhvT0dLlOYmIiOnfuDCMjI9SuXRsjR45EZmamSptLly5F9+7dYWRkBHt7\ne8ydO7fUMf7+++/w8fGBqakp7Ozs8NFHHyEnJwfA/y6TR0VFoUmTJlAqlfDw8MDu3buLtJOXlwdr\na2uEh4erbP/Pf/6Dxo0bFyk/e/ZsfPXVVzh8+DAUCgVu3LiB3NxcTJgwAS4uLtDX14eNjQ3ef/99\nOZ7S3LhxAy4uLggICEB+fn6xZSIiIvDmm2/CwMAApqam6Ny5M5KTk1XK/Pbbb/D09ISBgQHatGmj\n8sdKXl4eFi9eDHd3dxgYGKBhw4bYuXOnypjc3d2LjLNwm4uLC/Lz8xESEoIOHToUG6O2HBdERERU\nfhq76ap9+/Zywvr06VP89ttv8PPzQ8eOHZGRkYG4uDgAz5cDNGvWDCYmJsjKykLr1q3x4MEDxMTE\n4Ndff0VmZiZ8fHyQlZUltz1nzhwEBQUhMTERDRs2RFBQEMLDw7Fjxw78+OOPOHbsGJYsWQIAuH37\nNnx8fNCoUSOcPn0a3333Hc6dO4c+ffqoxPvpp5+iZ8+eSEpKwoQJEzB79mwcOXKk2LH9+eef6NCh\nA7y9vREXF4dNmzZh9+7dGDhwoEq5KVOmYOHChUhKSkKjRo0wdOjQIkmkrq4ugoKCEBkZKW8TQiAy\nMhIhISFF+p48eTICAwPRqlUrpKSkwNHREZMmTcLevXvxzTff4NKlS1i5ciW2bduGtWvXlrqP7ty5\ng44dO6Jly5b45ptvoKOjU6TMd999hwkTJmDWrFm4ePEi9uzZg+vXr2PSpEkq5ZYuXYrPPvsMp06d\ngr29Pfz9/fH48fMbjyZMmIDw8HAsWrQICQkJGDRoEAICAhAVFSXXL215RFxcHHR0dLB8+XKVOi/T\n9HFRyK2TJ5TmhqWWISIiov/R1VTHHTt2xJw5cwBA/gXfqlUr6OrqomHDhti/fz+8vLxU1q9GRkbi\nwYMH2L59O8zNzQEAO3fuhLOzM7Zu3YpRo0YBAHr16oXBgwcDAIYNG4bdu3djwYIFaNKkCQDAz88P\nSUlJAIA1a9bAzc0NixYtkmPbtm0bateujT///BPe3t4AgB49emDYsGEAnieFCxYswLFjx9C6desi\nY/vvf/+L5s2bY/HixQCAevXqISIiAv7+/jh//jwMDAzkdjp16gQAmDFjBr799lucO3cOTZs2Vbm8\nPXToUKxYsQJJSUnw9PTEkSNHcPv2bQQFBRXp28jICEqlEnp6erCxsQEAtGzZEkFBQWjZsiWA5ze3\nrVq1ComJiSXun7t376Jjx45o1qwZtm7dWmLCaGNjg40bN6J///4AgNq1a2PgwIHYunWrSrmwsDD0\n7NkTALBx40bUqlUL27dvR79+/RAREYHVq1fLyeD06dNx5swZLFy4UN5W2uV+KysrAICZmZl8XLxM\nkiSNHxeFHJq6IC/3GR7+/aDEMlS27LSHMDZWwszMQNOhaJSu7vM/JP/t81AWzpN6OE/q41ypp3Ce\nKt3OK2mlAtq3b4/hw4fj5s2biImJga+vL3R1n4fj5+eHX3/9FVOnTsXx48cxb948AM8v0davX18l\nKbG0tESDBg3k5EuSJNStW1d+38jICADg5uYmb1MqlcjIyAAAxMfHIz4+HiYmJirxSZKE8+fPw9vb\nG5IkoV69eirvm5mZ4enTp8WOLSkpCd26dVPZ1qZNG3kMzZo1AwCVNk1NTQGg2DYbN26Mhg0bYsuW\nLVi4cCG2bNkCf39/WFpaFtv/y4KCghATE4MpU6bg0qVLSEpKQnJyssqcvGz69Ol49uwZunXrVurZ\nzXbt2iExMRFz5szBhQsXcOHCBSQkJMDR0VGlXKtWreR/Gxsbw8PDA4mJiXjzzTeRl5en8j4AtG3b\nFj/88INa41OXpo+LQg/i6uCJiUWlxvJvk/MwA5992BUuLi4q20s7homI6J9DYwmrq6srnJ2dcezY\nMRw8eBDvvfee/J6fnx9WrVqFY8eOQUdHRz4zaGBgUOyZtry8PJXHMr3470IKRfGrH2rUqIHOnTtj\nxYoVKtuFELC2tpZf6+vrF6lb0lm/4uIsfCLCi7GVp83g4GAsW7YMc+fOxbfffotNmzYVW644w4YN\nww8//IDg4GD07dsX8+fPx9ixY0ut4+/vj969eyMkJAQDBgyQk+yXbdmyBcOGDcOQIUPg4+ODDz/8\nED/99BO2bNmiUu7l5QT5+fnQ19eHoWHxl8bz8/OL3Y+F8vLySo2/OJo+LgpZOtaHcc1a6oRM/9+j\n+7dhZWUHW9vaqtsflf7Hwb9B4dmdzMyy16T/m3Ge1MN5Uh/nSj1mZgaoUaPy6aZGvzigQ4cOOHTo\nEOLj4+VL48Dzs5EFBQXYuHEjfHx85DOvnp6e+Ouvv3D//n257L1793Dx4kU0aNCgQjG8+eabOHfu\nHJycnODq6gpXV1coFAp89NFHuHXrVoXabNCgQZFngv7+++8AgPr166vVxstnNQMDA3Hnzh2Eh4dD\nV1e3yBnckuqmp6fjq6++wtq1a7F48WIMHjwY9erVw+XLl0tNrPr27YvBgwfDz88PoaGhePbsWbHl\nFi9ejFGjRmHdunUYMWIEWrRogUuXLhUp9+JNVhkZGbhw4QI8PT1Rt25d1KhRQ56fQr///js8PT0B\nPE8eHz58qPL+pUuXVMZZ0UeAleR1HBdERERUMRpPWCMjI+Hg4IA33nhD3l54J/nOnTvxzjvvyNuD\ngoJgY2ODgIAAxMfH49SpUwgICICFhQUCAgIAqP9oo8JyY8eOxf379zF06FAkJiYiLi4OAQEBSE5O\nli/3Ftdmaf1MnToVJ06cwOTJk3HhwgVER0djzJgx6Natm8o4y4rvxT5sbGzg7++P+fPnIygoSE7i\ni2Nqaorbt2/j2rVrMDU1hampKXbt2oXk5GTEx8cjMDAQd+/eRW5ubplxrFmzBlevXsX8+fOLfd/J\nyQmxsbE4e/YsLl26hDlz5mD37t1F2p48eTL27duHhIQEDB48GPb29ggICIBSqcSECRMwc+ZM/N//\n/R8uXbqEhQsXIioqChMnTgTwfDnB3bt3sXz5cly7dg1r1qzB/v37VebHxMQESUlJSEtLK3E+1fE6\njwsiIiKqGI0nrNnZ2cU+Y9XPzw+PHz9WSVj19fURHR0NfX19tGvXDh07dkTNmjURGxsrrwEt7kzb\ny9tefCi/ra0tfv75Z6SmpsLb2xtdunSBs7MzDhw4ICeF6rT5Ik9PT+zZsweHDx/G22+/jdDQUPTt\n21flUU1ltVncFwe89957yMnJwdChQ0vsGwBCQkKQn5+PBg0a4OzZs/j2229x8uRJvPnmmxg4cCBa\nt26NOXPm4OTJk2WOx9nZGbNnz8bChQuLvUnriy++gLm5OVq1aoU2bdogPT0dP/zwA9LS0uQzkZIk\nYdasWfjoo4/QvHlzFBQUYP/+/fL8zp07FyNGjMD48ePlR1rt2LEDffv2BfD8W7/mzJmDhQsXwtPT\nE4cOHcKcOXNUYp42bRpWr16NLl26FDsObTguCl2N34vcRxllliMiIqLnJMFTQtXGypUrsWHDBsTH\nx2s6FKoESZLQJigc5rZ1yy5MAJ6vYV3wQQu4ubmXXfhfhuvo1MN5Ug/nSX2cK/W8qjWsGrvpitR3\n6tQpnDt3Dp999pn8xAQiIiKifwuNLgkg9Rw5cgQjRoxA586d8f7772s6HCIiIqIqxYS1Ghg3bhyy\ns7OxceNGTYdCREREVOWYsBIRERGRVmPCSlTFnBv5Q2nEb7oiIiJSFxNWoipWp3E3KI2ZsBIREamL\nCSsRERERaTUmrERERESk1ZiwEhEREZFWY8JKRERERFqNCStRFbsavxe5jzI0HQYREVG1wYSVqIpd\nP/0TcrOZsBIREamLCSsRERERaTUmrERERESk1ZiwEhEREZFWY8JKRERERFqNCStRFXNu5A+lEb+a\nlYiISF1MWImqWJ3G3aA0ZsJKRESkLiasRERERKTVmLASERERkVbT1XQARP82jzPvajqEaodzRkT0\n78aElaiKbZgzAI8e5Wo6DK1mbKwEAJV5cnFx1VQ4RESkYUxYiarYjh3bMWDAYNjZ2Ws6FK1lZmYA\nAMjMzNFwJEREpA24hpWoioWFzUNqaoqmwyAiIqo2mLASERERkVZjwkpEREREWo0JKxERERFpNSas\nRERERKTVmLASVbGZMz+Fra2dpsMgIiKqNvhYK6Iq9umn/+HjmoiIiMqBZ1iJiIiISKsxYSUiIiIi\nrcaElYiIiIi0GhNWIiIiItJqTFiJqti8eXORknJH02EQERFVG0xYiapYWNg8pKamaDoMIiKiaoMJ\nKxERERFpNSasRERERKTV+MUBRBpw69ZNGBsbazoMrWVsrAQAPHqUq+FItF91nSsXF1fo6OhoOgwi\nqiaYsBJpwNqzX8PsvoWmwyDSiOy0h1jcYy7c3Nw1HQoRVRNMWImqmFsnT1i628LAwkjToRAREVUL\nXMNKVMXcu7zFZJWIiKgcmLASERERkVZjwkpEREREWo0JKxERERFpNSasRERERKTVmLASVbFL+xOQ\nk5Gt6TCIiIiqDSasRFUsOSYJuQ8eazoMIiKiaoMJKxERERFpNSasRERERKTVmLASERERkVZjwkpE\nREREWo0JK1EVc+vkCaW5oabDICIiqjaYsBJVMfcub8HAwkjTYRAREVUbTFiJiIiISKsxYSUiIiIi\nrcaElYiIiIi0GhNWIiIiItJqTFiJqtil/QnIycjWdBhERETVBhNWoiqWHJOE3AePNR0GERFRtVFq\nwtq7d2/4+voW2e7k5ASFQoG///5bZfvHH38MDw+PVxpgVQsLC0OdOnVeWXu+vr5o3749ACAvLw/L\nli0rtfzevXtx/vx5tdt3cXHB/PnzAQCzZ8+Gu7u72nVv3ryJHTt2qF2+qv3666/FHmeV8fI+2LRp\nE/T09F5Z+0RERPTqlZqwduzYEXFxcSgoKJC3nT9/Hnfu3IG9vT1iYmJUysfGxsLPz+/1RFpNKRQK\nKBTPp3nHjh2YOHFiiWVv376N7t27Iy0tTe32JUmCJEkqr9UVGhqK6Ohotcv/E5S1D4iIiEj7lJqw\ntm/fHo8fP8apU6fkbTExMWjSpAk6d+6skuw8fPgQZ86cYcL6Ejs7O9jb2wMAhBClli18v6xy6rSh\nbtnK9FUd/dvGS0RE9E9QasLq6ekJW1tb/PHHH/K2mJgYdOrUCX5+fvj555/l7UePHgUA+fL3jRs3\nEBAQABsbG5iamqJ37964evWqXN7FxQXh4eHo2rUrDA0N4ebmhj179iAqKgru7u4wMTFBt27dkJ6e\nLtdJTExE586dYWRkhNq1a2PkyJHIzMxUaXPp0qXo3r07jIyMYG9vj7lz55Y6Ad9++y3q168PQ0ND\n+Pv74969eyrvp6enY8SIEXB0dISRkRHeeecdnD59Wn7f19cXM2bMwHvvvQczMzNYWlpi3LhxyM/P\nBwA0aNAADRo0wOHDhzFkyJDnk65Q4Ouvvy4Si5OTkzyHoaGhAJ5fFvfx8YGJiQmUSiUaN26s9lnR\nOXPmwNTUVGX/FQoODsahQ4ewefNm6OjoAHh+uXzx4sVwd3eHgYEBGjZsiJ07d5baR2xsLFq1aiXv\nwxkzZuDJkyfy+2fOnIG/vz9q1qwJfX19eHh4YMuWLSptLFu2DO7u7jAyMkLjxo2xb98+lfd37dqF\n+vXrQ6lUwsvLC8ePHy8xntLG8Ouvv6rsg82bN8v11q9fDxcXFxgaGsLX1xeXLl2S37t//z5CQ0Nh\nZWUFS0tLdOvWDRcvXlSZy4EDB6J9+/YwNzfHl19+WeqcERERUfmUedNV+/bt5YTn6dOn+O233+Dn\n54eOHTsiIyMDcXFxAJ4nLs2aNYOJiQmysrLQunVrPHjwADExMfj111+RmZkJHx8fZGVlyW3PmTMH\nQUFBSExMRMOGDREUFITw8HDs2LEDP/74I44dO4YlS5YAeH653MfHB40aNcLp06fx3Xff4dy5c+jT\np49KvJ9++il69uyJpKQkTJgwAbNnz8aRI0eKHdtvv/2GQYMGISQkBGfPnkWnTp2watUq+bJ6fn4+\n/Pz8cPLkSezcuRN//vknrKys4OPjg+vXr8vtLF26FPXr18fp06exfPlyrFmzBtu3bwcAzJw5EzNm\nzECrVq2wcuVKAEBKSgoGDBhQJJ7CM9lRUVFYvnw5bt68CX9/f/j4+CAhIQFxcXFwcnLCkCFDkJeX\nV+p+W7JkCZYuXYro6Gi0atWqyPsrVqxA27ZtMXDgQNy5cwcAMGHCBISHh2PRokVISEjAoEGDEBAQ\ngKioqGL7OH36NLp06YJ+/fohMTER69evx48//ohRo0YBALKzs9GpUyc4Ojri+PHjSEhIQLt27TB8\n+HB52cOiRYswe/ZszJo1C4mJiejfvz969+6Nc+fOyf1ERETgq6++Qnx8PIyMjBAYGFjiuEsbQ+vW\nrVX2wcCBAwE8389bt27Frl27cOTIEdy9excjR44EABQUFMDf3x8pKSmIiYnBkSNH4OzsjDZt2iAj\nI0Pud+fOnejbty+OHz+OXr16lbpv3Dp5QmluWGoZIiIi+h/dsgp07NgRc+bMAQA58WvVqhV0dXXR\nsGFD7N+/H15eXirrVyMjI/HgwQNs374d5ubmAJ7/Qnd2dsbWrVvlhKZXr14YPHgwAGDYsGHYvXs3\nFixYgCZNmgAA/Pz8kJSUBABYs2YN3NzcsGjRIjm2bdu2oXbt2vjzzz/h7e0NAOjRoweGDRsGAJg8\neTIWLFiAY8eOoXXr1kXGtmrVKnTo0AFTpkwBAIwfPx5Hjx7FiRMnAADR0dE4ffo0Ll68iLp16wIA\ntlUbDqwAACAASURBVGzZgrp162LVqlVYvHgxAKBx48aYMWMGAKBOnTpYunQpjh07hqCgILkvPT09\nmJqaAgBsbGyKnWsrKysAgIWFBUxMTJCWloawsDBMmDBBLjN+/Hh07NgRqampqFWrVrHtrFy5EmFh\nYYiOjkaLFi2KLWNqago9PT0YGBjAxsYGWVlZiIiIwOrVq+U/AqZPn44zZ85g4cKFRf4wAIDw8HB0\n69ZNjs/V1RURERFo27YtFixYAIVCgUmTJmHcuHFQKpVym+vXr8fFixdhZWWF5cuXY+LEifJxMGPG\nDOTl5eHRo0dyP8uWLUPLli0BPL+xr0+fPnjw4IF8bBVSZwwl7YP169fDzc0NAPDBBx9g1qxZAIBD\nhw4hLi4OGRkZMDExAQCsXr0aBw8exNq1azFt2jQAgL29PcaOHVvsXL/MoakL8nKf4eHfD9QqT/RP\nk532EMbGSpiZGVRZn7q6z68kVWWf1RHnSX2cK/UUzlOl2ymrQPv27TF8+HDcvHkTMTEx8PX1ha7u\n82p+fn749ddfMXXqVBw/fhzz5s0D8PzSff369VUSCktLSzRo0ACJiYkAnt8cVJgEAoCRkREAyEkD\nACiVSvksVnx8POLj4+WkoZAkSTh//jy8vb0hSRLq1aun8r6ZmRmePn1a7NiSkpLw7rvvqmzz9vaW\nLzknJibC0tJSJU49PT14e3vLiTSAcvVZHq6urhg8eDCWLVuGxMREXLp0CfHx8ZAkSV5y8LKbN2/i\n448/homJibzEoCQv3qD1119/IS8vr8jZ2LZt2+KHH34otn58fDwuX76ssk+EEPI+8fX1xf9r777D\norjaNoDfS1+qgoCiwgISBcXeg4oasWEJFohGRWPLByhW9FXsvcWQaBR8sUajohhLrBFjiV1jDYrB\nXsCKohSB8/3By8SVuqi7q9y/69pL9uyZOc88LPIwe+bMoEGDsGLFCqlvznSKzMxMPH78GA8ePED9\n+vWV9jthwgQA2R/hA8r5zXlPpaSk5CpYi3MMOXl4831XqlQppKSkSMeYmZkJOzs7pW3S0tIQGxsr\nPXdycsp3/297dsoRaWaWRe5PpG4pL55gxpC2UCgUH2yMN3/miIgKU2jB6uTkBAcHBxw7dgy///47\nevXqJb3WqlUrLFq0CMeOHYOurq50Fkwul+d5cUtGRobSEkJ5LSeUc0X92wwMDNC6dWuEhYUptQsh\nYG1tLT03NDTMtW1+F9rIZLJcrxkYGEhfy+V5/9X09nGoMqYqLl68iCZNmqBx48Zo2bIl/Pz8kJ6e\njg4dOuS7jY6ODnbv3o2goCAMHjy4wELtTfkda2ZmZr7LPhkaGsLf3x8hISFK7UIIlCtXDvfu3UOj\nRo1QsWJFdOjQAR07dkS5cuVQt25dAHl///OSM8f27THexzEAeb/ncvZvYGAAS0vLXPNmhRAwNTWV\nnuecQS4KqwquMC2d99lxIm2Q/PQuypQpC1vbih9ujOR3/6NeFTlnwZKSUtQ67seGeSo65qpoLCzk\nMDAotNwsVJFuHNCiRQvs378fZ8+ehZeXl9Tu4eGBrKwsLF++HM2aNZPOvFatWhWxsbF4+vSp1PfR\no0e4evUq3NzcihVotWrVcPnyZdjb28PJyQlOTk7Q0dHB0KFDcefOnWLts2bNmrnmt546dUo68+jm\n5obHjx8rXWCTnp6OkydPFus4Clty6u3Xly5dCoVCgR07dmD48OFo1aqVdKz5FcTly5dHs2bNEB4e\njh07dmDdunVFiq1SpUowMDDA4cOHldoPHz6MqlWr5rlN1apVcfnyZen74eTkhMTERIwcORIvXrzA\nunXrkJycjEOHDiEkJATt27eX5q4KIWBhYYFy5cpJUzByNG/eHPPmzStS3KoegyrLfuUc45MnTyCE\nkI5RoVBg3LhxOHjwoNRP1f0SERFR0RW5YF2zZg3s7OxQuXJlqV0ul8PDwwMbN27EF198IbX37NkT\nNjY28PPzw9mzZ3HmzBn4+fnB0tISfn5+AIp+BjKnX2BgIJ4+fYo+ffrg4sWLOHXqFPz8/PDPP/9I\nHxnntc+CxgkODsbx48cxfvx4XL16FUuXLlVaSL9ly5Zo1KgRevTogT///BMXL16Ev78/nj9/joED\nB+Y7Rn7LReV8dH7q1Cm8fJn71pw5r587dw6PHz+Gvb09rl+/jn379uHmzZtYs2aNtOpBzpX4+R1f\nw4YNMXjwYAwdOjTXygc5zM3NER8fj1u3bkEul2P48OEYP348Nm3ahLi4OMyaNQubN2/Od93SkJAQ\nHD9+HCNGjEBsbCwOHjyI3r17IykpCba2trC3t8fz588RFRWFmzdvYuvWrQgKCoJMJkNqaioAYPTo\n0ViwYAHWr1+Pf/75B9OmTcOJEyfQvn37PMcsSFGOobDvwdu++OILNGzYEN27d8ehQ4dw9epVDBgw\nANu3b4e7u7vUj8tlERERfThFLlhfvnyZ5xqrrVq1wqtXr5QKVkNDQ+zevRuGhoZo2rQpWrZsidKl\nS+PQoUPSRS95nZF6u+3NRfFtbW2xb98+JCQkoEGDBmjTpg0cHBywd+9e6cxuUfb5pjp16mDr1q3Y\nvn07atSogZ9//jlXcRYdHY0qVaqgffv2aNSoEZ4+fYpDhw5Jc7veXrg/vzYgO4/NmjXD559/joiI\niFyvm5ubIygoCCEhIRg4cCCGDBmCL7/8Er6+vqhWrRo2bdqEvXv3wszMTDor+fZNA958PnPmTBgY\nGGDIkCF5Hn9AQACuXLkCNzc3JCYmYsqUKRg0aBCCg4Ol5aDWr1+PLl265Ll9tWrVsGPHDhw5cgS1\natVC9+7d4enpiejoaABAt27dEBwcjKCgILi6uiIsLAxr1qxBlSpVpNUlhgwZgtGjR2P06NFwd3fH\ntm3bsG3bNri6uuY6vjePMz+FHcPb34P8vldvtm3ZsgVVq1ZF586dUbt2bcTFxWH37t3SXd3y20d+\nrp/dgdTkJ4V3JCIiIgCATPDUEJFayWQyePSch1K2lQrvTKQByU/vYubAhnB2LvqtnrUd5xsWDfNU\ndMxV0ah1DisRERERkaawYCUiIiIircaClYiIiIi0GgtWIiIiItJqLFiJ1MyhZjsYmfBOV0REREXF\ngpVIzRxrtYeRKQtWIiKiomLBSkRERERajQUrEREREWk1FqxEREREpNVYsBIRERGRVmPBSqRm18/u\nQGryE02HQURE9NFgwUqkZjf/+g2pL1mwEhERFRULViIiIiLSaixYiYiIiEirsWAlIiIiIq3GgpWI\niIiItBoLViI1c6jZDkYmvDUrERFRUbFgJVIzx1rtYWTKgpWIiKioWLASERERkVZjwUpEREREWo0F\nKxERERFpNT1NB0BU0rxKStR0CEQF4nuUiLQNC1YiNatj+xBt2zaCtbW1pkPRWqamRgCA5ORUDUei\n/T5UrhQKp/e6PyKidyETQghNB0FUkshkMuzd+wdq1Kil6VC0loWFHACQlJSi4Ui0H3NVNMxT0TBP\nRcdcFY2FhRwGBu9+fpRzWImIiIhIq7FgJSIiIiKtxoKViIiIiLQaC1YiIiIi0mosWInUbPz4UNja\nltV0GERERB8NLmtFpGahoRN4VSkREZEKeIaViIiIiLQaC1YiIiIi0mosWImIiIhIq7FgJSIiIiKt\nxoKVSM2mTp2CBw/uazoMIiKijwYLViI1mzZtKhISHmg6DCIioo8GC1YiIiIi0mosWImIiIhIq7Fg\nJSIiIiKtxjtdEWnAnTu3YWpqqukwPjiFwgm6urqaDoOIiD5yLFiJ1MzewwUrb2yE0VO5pkP5oF4+\nfIE5HafA2dlF06EQEdFHjgUrkZq5+dSBmV0pTYdBRET00eAcViIiIiLSaixYiYiIiEirsWAlIiIi\nIq3GgpWIiIiItBoLViI1i9t1ASlPXmo6DCIioo8GC1YiNftnzyWkPnul6TCIiIg+GixYiYiIiEir\nsWAlIiIiIq3GgpWIiIiItBoLViIiIiLSaixYidTM2asqjEoZazoMIiKijwYLViI1c2njDrmliabD\nICIi+miwYCUiIiIircaClYiIiIi0GgtWIiIiItJqLFiJiIiISKuxYCVSs7hdF5Dy5KWmwyAiIvpo\nsGAlUrN/9lxC6rNXmg6DiIjoo1EiC1ZPT0/o6Ojk+Vi8eLGmw8vX0aNH8eeff0rPdXR0sHbtWrXG\ncPv2baxfv16tYxIREVHJViILVplMhp49e+LBgwe5Hn379tV0ePlq2rQprl27ptEY+vXrh927d2s0\nBiIiIipZ9DQdgKbI5XLY2NhoOgyVCSE0Pr6mYyAiosJlZmbixo14lbYxNTUCACQnp77T2AqFE3R1\ndd9pH0RvKpFnWIsiLS0NI0aMgJ2dHSwsLODp6Ynjx48DALZu3QoDAwMkJydL/R0cHODl5SU9P3ny\nJPT19fHkyRMAQEREBCpXrgxjY2NUr14dq1atkvoeOHAARkZGmD59OqysrNCyZctc8SgUCmRmZqJv\n375o0aKF1H7p0iV4enpCLpfD0dERy5cvl15LTU3F8OHDoVAoYGhoCBsbG3zzzTdISUkBAKxYsQKu\nrq4IDw+HQqGAkZERmjZtitjY2Dxz4u/vj/3792PlypXQ0dHBiBEj0KBBA+n1+Ph46OjoYMaMGVLb\n3LlzUbNmTQDAq1evMGbMGDg6OkIul6Nhw4bYv39/nmMVJ8eRkZGoVq0ajI2N4eLigkWLFkl9c451\n8eLFsLe3h4mJCXx9fXHv3j306NEDpqamsLe3V/q+ZGVlYcaMGVAoFDA1NUX9+vWxc+fOXPssav6I\niNTpxo14DJ27FWPDjxX5EbTgAIIWHFBpm7cfQ+duVblQfltCQgLs7CzRpEn9XK/Z2lpg06YNAICg\noMHo2rVTvvuxtbVQeigU5eDl1Qx79+56p/jedOTIIdjaWuDBg/sqbVeuXGmsX1+0aX23bt2Era0F\nTpw4XpwQPwkltmAt7Cxh7969cfjwYWzcuBGnT59GixYt4Onpibi4OHzxxRfQ09NDTEwMACAuLg53\n7tzB0aNHkZmZCQDYuXMnGjduDEtLS/z0008YP348Zs6ciUuXLiEkJARDhw5VKo7S09Nx4MABnDx5\nEmFhYbniOXXqFHR1dfH9999j8+bNUvvixYsRGBiIv//+Gx07dsSAAQNw8+ZNAMDIkSOxY8cOrF27\nFnFxcfjxxx+xbt06hIeHS9vHx8dj3bp1iI6OxrFjx/DkyRMEBQXlmZOwsDA0adIEvr6+ePDgAby9\nvXH69GkkJSUBAH7//XfIZDIcOHBA2mbnzp3o1Cn7PxM/Pz9ERUUhPDwc586dQ8OGDdGmTRucOHEi\n11itWrVSKccLFixAUFAQhg8fjgsXLmDUqFEYNWoUFixYoHSsW7duxa5du7Bp0yZER0ejevXqaNSo\nEc6ePYs2bdpg0KBB0vGMHTsWK1euREREBM6fP48+ffrAx8cHf/zxR7Hyl8PZqyqMShkX2IeI6H0w\ntrCBaenyan0YW7z7p5dRUevh4KDA1atXcOzY0QL7ymQF72vWrPm4ePEaLlyIw/79h+Dl1Rb+/j1x\n6dLFd47zXcgKC5yUlMiCVQiBlStXwszMTOkxcOBAAMC1a9ewceNGLF++HJ9//jkqVaqECRMmwMPD\nA/Pnz4exsTGaN2+OvXv3AgD27duHVq1aISsrCydPngSQXUx17NgRADB9+nRMmjQJPj4+cHR0RM+e\nPTFy5EilM5EAMHr0aDg5OaFq1aq5Yi5TpgwAwMLCAqVKlZLaAwMD0bVrVygUCkyePBlZWVk4e/Ys\nAKBRo0ZYsWIFGjduDHt7e3Tv3h316tXDxYv//pC+fv0aS5YsQa1atVCzZk0MHDgQR4/m/Z+Dubk5\n9PX1pekUHh4eMDMzk86S7tu3Dx07dsSff/6JzMxMvHjxAkeOHEHHjh1x+fJlbN++HUuWLEGrVq3w\n2WefYeHChahTpw7mzZuXayy5XK5SjufMmYNhw4ahX79+cHZ2xsCBAzFkyBDMmTNH6VgXLVoENzc3\ntGnTBjVr1oS7uzuCgoLg4uKCYcOGIS0tDdeuXUNycjLCwsKwcOFCtGrVCk5OTggICMDXX3+NmTNn\nFit/OVzauENuaVJgHyKikmzDhrXo3LkL3N1rYPXq5QX2LWyWmrm5OaytrWFjYwMnp0oYOXIMHBwU\n2Lx543uMmD60EjmHVSaTwcfHJ1fBaGZmBgBSwffmx91A9jSB169fAwC8vb3xww8/AMg+s+jl5YW0\ntDTExMTAxcUFJ0+exKpVq/Dw4UPcu3cPI0aMwOjRo6V9ZWRkIDMzExkZGVKbk5OTysfy2WefSV/n\nFLI5H/n37NkTe/bswejRoxEXF4dLly7hn3/+gbOzs1IuXFxcpOfm5uZIT0/Pd7w3/yLU19eHl5cX\n9u7diy+//BIHDhxAdHQ0du/ejRMnTuDBgwewtrZGnTp1sGFD9sc3jRs3Vtqfh4cHduzYkedYRc1x\nYmIiEhMTc+27SZMmmDNnDh4+fCi1vXnsJiYmSs/lcjmA7O/z33//jbS0NHTt2hU6Ov/+Xff69WuU\nLVu22PkDgJcPXxT4+qfi5cMXePTogTQnThWPH2fPfcvIyISzszPnwhVATy87NxYWcg1Hot1KYp6K\n87P3Pscubq5Pnz6F2Ni/sWjRIlhYmGLmzBn48ccflE7WGBsbwMJCDgMDXejp6RQ4Vk5f5fhMIJf/\n23779m2MGjUSv/++D3K5HM2aeWLOnLkoV66ctM333y/E0qVLcf/+Pbi4uGD69Blo27YtTEwMAQAx\nMbuxePFi3LhxHVWrVkVY2A+oVy97SsOTJ08QHDwUu3bthImJCaZMmZortl9//RVTp05GXFwcHBwU\n6Nu3H4KDgyGTyWBmZvS/uHMfi7bL+dl75/28l718hMzNzfMtEA0MDAAAx44dk4oYIPvMrKFh9huz\nffv2CAgIwO3bt3HgwAGEhobi5cuXOHDgABwcHFCpUiW4uLhIHy//+OOP8PT0VNoXAKVfxG+OVVR5\n/SLP2Xf//v2xdetW+Pv7o0uXLpg+fToCAwOV+uYs55XX9kXh7e2NKVOm4Pz588jIyECjRo3QqFEj\nHDhwANevX0eHDh0A5H9smZmZUr7fVtQcv3iRdwGYM3VAX18fQN65evvYc+TEFB0djUqVKkntQgil\n/RQnf89OOSLNzLLAPp+KeffiIdO5UeztXyUl4r+Tuyv9YUZEn7ZVq1ahbNmy+PxzD9jY2GLixAlY\ns2Y1AgMLnm6Vnzf/T87IyMCmTVG4cuUKVqzInpb38uVLtGr1BT7/vDEOHjyEjIwMTJs2Da1be+H0\n6TPQ19fHvHlzMXv2LHz/fRgaNWqMDRvWo0sXH5w6dVrad0REOMLDw2FhUQpBQQHo3bs3/v47+5qG\nr77yw+PHj7B9+w7o6upiyJAg6XcUkP2JYd++fbBw4fdo0qQpLl26hODgIXj16iXGjRtfrOP+1JTY\ngrUgOR/JP3jwQOkCqICAALi5uSEgIAD29vZwd3fHvHnzIJPJUKNGDSQnJ2P27NmwsLCQ5m1aWFig\nfPnyuH79Ovr16yfta8mSJTh37hx++umnIselynyXx48fIzIyEps3b0bnzp0BZP+gXrt2DQ4ODkXe\nT2Hatm2Lvn37IjIyEp6enpDJZGjZsiX279+P2NhYab6sm5sbAODw4cNKF04dOXJEeu1tRc2xmZkZ\nKlSogMOHD6Ndu3bS9ocPH0a5cuWU/iovKhcXF+jr6+P27dtK8U6ZMgWZmZmYPHmyyvvMYVXBFaal\nyxd7+5ImOTkVSUkpmg5Da+WcbWGOClYS8/SuV/q/69jFyXV6ejrWr/8FPj7dkJSUAhubCqhevSYi\nIiLQq1d/qd+rV+lISkpBenomMjKyChxr8OBBCAgIAACkpaUiMzMT/fsPQrlyDkhKSsGaNavx8uVL\nzJv3o3QC4ocfwuHq6oSff/4FnTt3QVhYGAYPDkT79j7/2+dQZGRkICkpCS9fpgEAJk2aAVfX7IuM\nv/nmW/Tt2xO3bt1HQkIC/vjjALZu3Y3KlasDABYu/AlNmtSXjmPGjBno23cAOnXqDgBo0qQsxo6d\ngJEjhyIwcARevEj9X17TP7r3cPaZ8HcvN0tkwVrY0kyVKlWCr68vBg4ciEWLFsHFxQWRkZFYunSp\nNKcSyD67OH/+fHh7ewMA6tevD5lMhs2bNytdmDN+/HgMHz4c9vb2aNGiBY4fP46RI0cqTREoCjMz\nM1y6dAkPHz6EtbV1gX0tLCxgbm6OLVu2wN3dHc+fP8fMmTORmJiI1NTi/ydmbm6O+Ph43Lp1C/b2\n9ihTpgwaNGiApUuXYu7cuQCAli1bIjQ0FCYmJlLB7+zsDD8/P3z77bdYsmQJKlasiPDwcJw9ezbP\ni8xyqJLjYcOGwdnZGc2aNUNMTAx+/PFHTJ06Nd99F/Q+MDY2xvDhwzF27FiYmZmhbt262L59O6ZM\nmYLIyEiV80ZERIXbvfs3PHv2DB06dJbaOnb8EtOmTcSJE8dRv36DArbO23/+MwFt2rQHkL16zl9/\nncGECWORkZGB2bMX4MKFc3j8+BGcnSsobZeamoK4uKt48uQJEhMTULt2HaXXc858/vbbHgCAs/O/\nn8ZZWFhI48XGXgYA1KhRU3r9s88qw9TUTHp+8eJ5nDt3FsuXL5PahMhCamoqbt26qfIxf4pKZMEq\nk8kKPVu5bNkyjBkzBn379kVSUhLc3NwQHR2N5s2bS328vb0xa9YsaZkpfX19NGvWDCdPnlSaTzlo\n0CCkpaVh7ty5CAoKQoUKFRAaGoqQkBClmAozZswYTJkyBfv27cPp06cL7Kunp4cNGzZgxIgRqFat\nGipWrIiAgADUqVMH//3vfwsct6BYAgIC0LNnT7i5uSE+Ph42Njbw9vbGsWPHpDzUrVsXZmZmaNmy\npdLH/RERERg9ejS+/vprJCcno3bt2tizZ0+uucJvKmqOBw4ciJSUFMycORMBAQFwdnbGd999h0GD\nBuV7XHm9D958Pm3aNBgYGGDUqFFISEiAs7MzwsPD0bt372LnDwCun90Bl/pdYWRaMqYFEBEV1S+/\n/AwA6Nq1o9SWc2Jh1arIYhWs1tY2UCgcpedVqrjiwYP7mDVrGkJDp8DAwACVK7tixYqflbYTQsDC\nwgJ6ekUrlfKbopfzO+HtEyQGBvpvfG2AQYMC0KVL91zblytnh/v37xUphk+ZTHAVeCK1kslk8Og5\nD6VsKxXeuYRLfnoXMwc2hLOzS+GdS6iS+FF3cZTEPP3zTxzGhh9T+xSk4v7cJiQkoFYtV/Tp0w/+\n/v9+/C+EwKRJ43Ds2J84dy4Wn33mgMWLI9ClS3cEBQ3Ggwf3sXHjr3nu09bWQur7pgUL5mDu3JmI\ni7uFLVs2Y8KE/+Ds2UuwsMieQpac/ALfftsf334bhMaNPVC9emX06dMPI0b8e6KpW7cOaNOmHapU\nqQYfH2+cOxeLsmWzL9I6cuSQ1Pbq1Us0alQHGzZsQbNm2Se9bt26iXr1qiMs7Cf4+vZAhw6tUbGi\nPRYvjpD2/9tv27FlSxQWLYrA/fv3UK9edWzbtqdYRbsmva8pASVyWSsiIiLSLlFR6yGEQGBgMCpX\nriI9qlRxRWBgMFJSUrBhw7pc2xV22i0pKQkJCQlISEjA/fv38Ntv2xEevhitW7eDqakZunb1haWl\nFfr374Nz587i778vY9Cgfjhz5jQqV3YFAAQGDsWSJYuwZcsmXL8ejwUL5uDkyZNo27Ztocfl5FQJ\nbdq0R0jIcBw9egQXLpxHYOAgpQt2hw0bhejoKISFfYf4+GvYt283Ro4cCrncWLpwuKQrkVMCiIiI\nSoJXSYkfzZgbNqyDl1dblC9fIddrHh5NUbWqO9asWak07Sp7alfB+x07diTGjh0JIHu6XNmy5fDl\nl10xbtxEAICRkRE2btyCiRPHwcenA2QyGerVq4/Nm7fDysoKADBgwLdISUnBlCkT8PjxI7i6umHz\n5i1wdXXF9eu3C50etnhxBEJDx6BPn6+gp6eHgIBg3L59S3q9RYsvsGhROMLCvsPcuTNgZVUGvr49\n8J//TMhzfyURpwQQqRmnBBQdpwQUriR+1F0cJTFPmZmZKt8iNWft1nddYUChcPrk108uie+p4uAq\nAURERJQvXV1dlf/YYxFG2opzWInUzKFmOxiZcIUAIiKiomLBSqRmjrXac0krIiIiFbBgJSIiIiKt\nxoKViIiIiLQaC1YiIiIi0mosWImIiIhIq7FgJVKz62d3IDX5iabDICIi+miwYCVSs5t//YbUlyxY\niYiIioo3DiAiIvoE8U5X9ClhwUpERPQJunEjHqO3ToCJtZlax3358AXmdJzyTrdUTkhIQK1arnB2\nroRDh068x+iAX375GcOHB+HevXf/pMvIyACLF0egS5fuxdp+/fq1iIwMx5UrV6CjowM3t6oYMGAw\nOnXyKfI+goIG4/79+4iK+rVYMRQ1zpYtvVCmTJkPNkZhWLASERF9okyszWBmV0rTYagsKmo9HBwU\nuHr1Co4dO4qGDRtpOqQ83bp1B4BBsbZdtWo5Jk8OxYwZc9CgQSO8fv0aO3ZsxaBB/ZCamgpf3x5F\n2o9MJoNMVqwQiuT48WMYMuRbnD598cMNUgScw0pERERaZcOGtejcuQvc3Wtg9erlmg4nXzY2NjA0\nNCzWtqtWLUevXv7w9e0BhcIRLi6fITh4JLp188OyZUuLvB8hRLHGV3X/H3qcwrBgJVIzh5rtYGTC\nW7MSEeXlr7/OIDb2bzRr1gLe3h2xffuvSEp6Jr1ua2uBdevWoGPHNrC3t0Ht2lWxevUK6fXU1FSE\nho5FnTrVUKFCGbi5OSE4OAApKSm5xho/PgRNmzZQart+PR62tha4dOkiEhMT4e/fE5UrO0ChKIeu\nXTvh4sULUl8jIwNs2rQBAHDtWhy6desEZ+cKqFSpIvr06YHbt2/le5x6ero4fvwoXrx4rtQ+xvl9\nUAAAHO9JREFUadJ0LF++Rul4c8bIr+3169cYOTIYTk7lUbVqJUyfPhlZWVkAsucyT5w4DjVqVEHF\nitbw9GyMbdu2SNtmZWVh4cJ5qFOnGhSKcmjd2hO//74HAHDr1k106tQGAFC3rjvmzZuV7/F8aCxY\nidTMsVZ7GJmyYCUiyssvv/wMGxtbNGzYCB07dkZqaio2bFin1Gfq1AkYMGAwDh8+ifbtO2D06GG4\ne/cOAGDSpHHYt283fvrpvzh27CxmzpyH6OioPM/U+vr2xJUrsbhw4bzUFhW1HtWqVUfVqtUQEjIc\nWVmZ2LFjH37//SBMTU3Rr9/XecY9ePA3sLdX4PffD2Hr1l148uQxgoMD8j3OgIChOHv2NNzdK6N3\nbz8sXvwDLl68ACsrK1SoUFGlnB09egSpqSnYvTsGs2bNw/Lly7BkySIAwPLlEdi5czuWL1+Do0fP\noGPHzhg0qJ9UTE+bNgnr16/F/PlhOHDgT3Tv3gN9+36NP/88jAoVKmLVql8AAHv2HMC33wapFNf7\nxDmsREREpBXS09MRHR0FH59uAAAnp0qoXr0mVq9egQEDvpX69ejRGx06dAYAjB79H4SH/4QzZ06j\nfPkKqFu3Prp06Y569bLPnFaoUBGRkRGIjf0713ju7tXh5lYNUVHr4e5eHUB2wdq//yAAwI0b1+Hm\nVhUVK9rD0NAQ8+Z9j7i4K3l+PH7jxnU0b94SFSvaQ1dXF4sXR+Dhw8R8j7VDh87Ytm03wsN/woED\n+7F7987/xVQDixaFo3LlKkXOW/nyFbBw4SLo6enBxeUzXLkSi/Dwxfi//wvC9evxkMuNUaGCPWxs\nbDB8+GjUrl0XpUqVQnJyMpYtW4Lly9fA07MFAOCbbwbi0qUL+P77+Wjc2AMWFtlzoK2sysDExKTI\nMb1vPMNKREREWmH37t/w7NkzqRgFgI4dv8SVK7E4ceK41ObsXEn62szMHADw+nU6AKBrV18kJydj\n8uRQ9OnTAw0b1sLx40eRmZmZ55h+fj0QHR0FIQROnjyOO3duw8cn+6r/4cNHY8eOrahc2QFffdUF\nW7ZEoUoVV8jyuMppzJhxWLw4DJUrK+Dv3xOHDv2BKlXcCjzeevUaICJiBa5evYldu/Zj2LBRuHnz\nBr76qgsyMjKKmDWgZs3a0NPTe+N5Ldy/fw8vXjxHv34D8Px5EmrUqIy2bVtgzpwZsLe3h5mZOeLi\nriAtLQ3ffNMbjo520mPjxl9w7VpckcdXBxasREREpBV++eVnAEDXrh1hZ2cJOztLzJgxGQCwalWk\n1M/AIPeV+TlnPYcNC0RAwAAAgLd3R6xcuQ6NG3vkO6aPT3c8fvwIhw8fRFTUenzxRWtYWVkBADp0\n6ITz569gwYIfYGNjiwUL5qBJkwZ49OhRrv307z8Yf/0ViylTZsDQ0AChoWPh5dUM6enpufrevXsH\no0cPw7NnTwFkX+lfq1YdjBkzHhERK3D37h1cvpz3Vfl5FbI6OsrlXFZWFmQyGfT1DeDs7IKTJ8/j\n5583ol69hoiOjkKTJg1w6NAf0NfPzuPy5T8jJuaI9Dh48Diio3fkmzNN4JQAIjV7lZT/R0SkjLki\nKjkSEhIQE/M7+vUbAH///lK7EAKTJo3D9u2/Yvr02QXu48mTx1i7djWWL/8Z7dp5A8gu8K5fj893\nXqi1tTVatvTC9u2/YvfunZgxYy6A7KJv4sRx8PXtAR+fbvDx6YZHjx6halVnHD16GM7OX0n7SEp6\nhpkzp2Lo0BHo0aMXevTohbNnT6NNmxa4fPkiatasrTSmkZEca9euRrVq1dG7d1+l18zMzCCTyVCm\njDUAQF9fH8+f/3thVnz8P7mO4eLF80rPT5w4Dnt7BxgZGSEyMgKWlpbo3LkLWrT4ApMnT4enZyPs\n2LEVEydOg76+Pu7du4vmzVtK28+bNwuZmZkICRmX59lkTWDBSqRmdWwfom3bRrC2ttZ0KFrrzbvt\nKBROGo6G6OP18uGLj2bMqKj1EEIgMDAY5ctXUHotMDAYMTG/57r46m3m5hYwMzPHzp3b4erqhuTk\nF/j++wV49Ogh0tLyv3uXn19PfPvtNzAxMYGXV/ZV8To6Orh6NRajRg3F9OlzYGVVBps2bYCBgQHc\n3WsobW9hUQqHDv2B27dvYdy4STAyMsK6dWtQqlQpVKqU+wYKVlZWCAwcitDQMXj06CHatvWGoaEB\nLl++jFmzpsLPryfs7MoDAOrWrY/Vq1egfv2GyMzMQGjo2FxLad28eQMjRgzFwIHf4ty5s1i2bClm\nzswuvJOTX2DevJkwMTFBlSpuOH/+HG7evImAgKGQy+UYPDgQ06dPgqmpKWrUqIW9e3dh/vzZWLgw\n+6ItU1NTAMD58+dgYWEhTcFQNxasRGr200+L4ePj+053gfnUWVjIAQBJSbmXoSGiolEonDCn4xSV\ntnmft2ZV1YYN6+Dl1TZXsQoAHh5NUbWqO9asWVngGT89PT1ERKzApEnj0KxZQ9jZlUe/fgNQvXpN\nrF27Sur39j68vNpALpfjyy+7Ks0F/fHHcISGjsHXX/siOfkFXF3dsHLlOigUjrnGXr16PSZMGIsv\nv2yHtLQ01KpVB+vXR8PUNO87jY0ZEwqFwglr1qzE4sU/IC0tFY6OTvjqq14YPPjf1QXmzPkOo0cP\nQ9u2LVC2bDmEhIzDgwf3lY6lffuOSE9PQ+vWnihd2hJjxozDV19lr2YQGBiMly+TMWbMSCQmJsDO\nrjxCQsahe/fsM8Rjx4ZCX18fkyeH4uHDRCgUjpg/P0y6cUGVKq7w9u6EQYP6wt+/P6ZOnZlv/j8k\nmdD0SrBEJYxMJsPevX+gRo1amg5Fa7FgLTrmqmiYp6IpqXl68uQxatSogt9++11aLaAwJTVXqrKw\nkMPA4N3Pj/IMKxEREZVIT58+weHDh7Bx4zrUrFm7yMUqqR8LViIiIiqR0tNfY/jwIJQrV07p7lKk\nfViwEhERUYlka2uLuLj8b59K2oPrsBKp2fjxobC1LavpMIiIiD4aPMNKpGahoRM4SZ+IiEgFPMNK\nRERERFqNBSsRERERaTUWrERERESk1ViwEhEREZFWY8FKpGZTp05Ruq0eERERFYwFK5GaTZs2FQkJ\nDzQdBhER0UeDBSsRERERaTUWrERERESk1ViwEhEREZFWY8FKRERERFpNJoQQmg6CiIiIiCg/PMNK\nRERERFqNBSsRERERaTUWrERERESk1ViwEhEREZFWY8FKRERERFqNBSsRERERaTUWrETvUWZmJsaO\nHQs7OzuYmZmhW7duSExMzLf/qVOn8Pnnn8PExASfffYZVq9ercZoNUvVXOX4559/YGpqinv37qkh\nSs1TNU/r169HzZo1YWpqChcXF8yePRtZWVlqjFgzVM1TZGQkXF1dIZfLUbVqVaxYsUJ9wWpYcX/2\nAMDb2xvNmzf/wBFqB1Xz1L17d+jo6Cg9vLy81BixZqiapzt37qBr164wNzeHra0tAgICkJKSUvhA\ngojem/Hjxws7Ozuxb98+cebMGdGwYUPh4eGRZ9/ExERhaWkphgwZIq5cuSJ++OEHoa+vL/bs2aPm\nqDVDlVzluHLlinBychI6Ojri7t27aopUs1TJ02+//Sb09PTEokWLRHx8vIiKihKlS5cWU6dOVXPU\n6qdKnqKiooShoaFYuXKluHHjhli2bJnQ09MTW7duVXPUmlGcnz0hhFiyZImQyWSiefPmaohS81TN\nk6urq5gzZ45ISEiQHs+ePVNjxJqhSp5SU1NFlSpVRMuWLcWFCxdETEyMsLe3FwEBAYWOw4KV6D1J\nS0sT5ubmYuXKlVLbjRs3hEwmE3/++Weu/jNmzBDOzs5KbX379hVeXl4fPFZNUzVXQgixcOFCYW5u\nLurUqSNkMlmJKFhVzVOnTp2En5+fUtvUqVOFk5PTB49Vk1TN09KlS8Xs2bOV2mrVqiWCg4M/eKya\nVpyfPSGEiIuLE1ZWVqJx48bC09NTHaFqlKp5Sk1NFfr6+uLAgQPqDFPjVM1TZGSkKFWqlFIhHxkZ\nKerVq1foWJwSQPSe/PXXX3jx4gU8PT2lNgcHBygUChw6dChX/0OHDqFp06ZKbc2aNcORI0c+dKga\np2quAGDr1q2IiIjA/Pnz1RSl5qmap/Hjx2PixIlKbTKZDE+fPv3QoWqUqnkaOHAgRo8eDQDIyMjA\nxo0b8ffff6NVq1bqClljivOzl5mZid69e2PMmDFwc3NTU6SapWqeYmNjkZGRgSpVqqgxSs1TNU+7\nd++Gl5cXLCwspLa+ffvixIkThY7FgpXoPblz5w4AoHz58krtdnZ20mtvunv3bp59X716hSdPnny4\nQLWAqrkCgN9//x3du3eHKEF3k1Y1T3Xr1lX6hfn8+XP89NNPaNu27YcNVMOK834CsueQGxkZwdfX\nF7169UK7du0+aJzaoDi5mjlzJnR1dTFixIgS8/Onap4uXrwIAwMDTJw4EQ4ODqhSpQpCQ0ORlpam\nlng1RdU8xcXFwd7eHqGhoXBycoKzszNGjRpVpDzpvZ+QiejVq1fQ0dGBrq6uUruhoSFSU1Pz7G9k\nZJSrL4A8+39KVM1VSfUueXr16hU6d+6MtLQ0zJo160OGqXHFzZOTkxPOnDmDM2fOYOjQobCxscG0\nadM+dLgapWquTp8+jQULFuDUqVOQyWQAIP37KVM1T5cvXwYAuLq6IigoCOfPn8fw4cNx+/btT/qC\nPlXzlJSUhP/+979o164doqKicOfOHQQGBiIxMRErV64scCwWrETviVwuR1ZWFrKysqCj8++HF2lp\naTAxMcmz/9t/VeY8z6v/p0TVXJVUxc3To0eP0LFjR8TGxmLv3r2oWLGiOsLVmOLmydLSEpaWlqhe\nvToSExMxefJkTJ069ZMuyFTJVWpqKnr16oVp06bByclJai8JZ1lVfU9NmzYNISEhMDc3BwBUrVoV\nurq68PPzw3fffYfSpUurLXZ1UjVP+vr6sLKywurVqyGTyVC7dm28fv0a3bp1w8KFCwvME6cEEL0n\nOUXB/fv3ldrz+ug/p//bSzPdu3cPpqamSvN7PkWq5qqkKk6ebty4gcaNG+PmzZs4ePAg6tSp88Hj\n1DRV8/THH3/g3LlzSm3VqlVDSkrKJz8dR5VcHT9+HLGxsQgJCYGZmRnMzMywatUqHDp0CGZmZgVO\nt/jYqfqekslkUrGao1q1agCA27dvf6AoNU/VPFWoUAGurq5KfxS6uroCyP6/qyAsWInekxo1asDM\nzAwHDhyQ2m7cuIGbN2/murgKADw8PHDw4EGltpiYGHh4eHzoUDVO1VyVVKrmKTExUVoj888//5R+\nYX7qVM3T7NmzMX78eKW2EydOwNbWFlZWVh86XI1SJVcNGjTAtWvXcO7cOZw7dw5//fUXvvzyS9Sr\nVw/nzp1DuXLl1By9+qj6nurWrRt8fHyU2k6dOgVDQ0NUqlTpQ4erMarmqUmTJjh79iwyMjKktosX\nL0JXVxcKhaLgwd7DqgZE9D9jxowRZcuWFbt27RKnT58WDRo0kNYsTE9PF/fv3xfp6elCCCESEhJE\nqVKlxKBBg8Tly5dFWFiYMDAwEDExMRo8AvVRJVdviomJKTHLWgmhWp66du0qzMzMxMmTJ8X9+/el\nx4MHDzR5CGqhSp727NkjdHR0xLx580RcXJxYtmyZMDY2FkuXLtXkIahNcX/2hBDim2++KRHLWgmh\nWp6ioqKEjo6OWLBggbh27ZrYuHGjsLGxEaGhoZo8BLVQ9fdemTJlxFdffSViY2PF3r17hYODg/jm\nm28KHYcFK9F7lJGRIUaMGCHKlCkjLCwshJ+fn3j8+LEQ4t9C648//pD6Hzt2TNSvX18YGRmJKlWq\niPXr12sqdLVTNVc5YmJiStSNA4qap1evXgldXV2ho6MjZDKZ0kNfX1/DR/Hhqfp+2rx5s6hRo4aQ\ny+WicuXKIjIyUlOhq11xf/aEEKJ///4l5sYBqubp559/FtWrVxdyuVw4OjqKGTNmaCp0tVI1T5cv\nXxatW7cWxsbGwsbGRowYMSLfP5DeJBOiBMyeJiIiIqKPFuewEhEREZFWY8FKRERERFqNBSsRERER\naTUWrERERESk1ViwEhEREZFWY8FKRERERFqNBSsRERERaTUWrEREn4hJkyZBR0enwMf58+fVGlNi\nYiJevXolPff394eOjvb+6lm7di0cHR0hl8vx9ddfazqcAml7LoneJz1NB0BERO/XuHHj4Orqmudr\n9vb2aotj586d6NmzJ/766y9p3MGDB8PLy0ttMaji8ePH6Nu3L5ydnfHDDz/AxcVF0yEVSJtzSfS+\nsWAlIvrEtGrVCk2bNtV0GDh+/DiePXum1NawYUM0bNhQQxEV7MqVK3j9+jUCAgLQv39/TYdTKG3O\nJdH7xs8SiIjog/pY7gCenp4OADA1NdVwJET0NhasREQlkEKhwMCBA/HNN99ALpejYsWKePLkCQBg\nyZIlqF+/PszNzSGXy+Hq6oo5c+bk2sfx48fRrl07lC5dGmXKlIG3tzcuXrwIIHt+5ZQpUwAAjo6O\naNGihdT+9rzLmzdvolevXrC2toZcLkfNmjWxbNkypT7+/v5wdXXFyZMn0axZM5iYmKBs2bIYOnQo\nUlNTCz3ewsbw9/eXYuzbty90dHRw69atPPfl7+8Pd3d3HDlyBI0aNYKxsTGcnZ2xatUqvH79GmPH\njoWtrS0sLS3h5+cn5TXHhQsX0LlzZ5QuXRrGxsZo1KgRfv31V+n12bNnQ0dHB2fPns01tqOjI1q2\nbJlvLu/cuYPevXtLx1m7dm2sXbtWqY8QAlOmTEHlypUhl8tRtmxZ9O7dG3fu3Ck0j0QaI4iI6JMw\nceJEIZPJxK+//ioePnyY6/H69Wupr0KhEKampqJ+/foiPDxcTJkyRQghxLhx44RMJhN9+/YVy5Yt\nEwsWLBA1a9YUMplMLF68WNr+4MGDwsDAQDg6OoqZM2eKhQsXCoVCIaysrMSNGzfE0aNHhY+Pj5DJ\nZOL7778X+/btE0II0adPH6GjoyPtJz4+XlhbWwsLCwsxbtw48eOPP4rmzZsLmUwmRo8eLfXz9/cX\nlpaWonTp0mLw4MFi2bJl0v7f7JeXooxx9OhR6dgHDx4sfv75Z/Hy5cs89+fv7y+srKxE6dKlRUhI\niFi8eLH47LPPhK6urvDy8hINGzYUixcvFoGBgVIuc5w4cUIYGxsLOzs7MW3aNLFw4UJRu3ZtIZPJ\nxKJFi4QQQty6dUvo6OiIMWPGKI177NgxIZPJRGRkZJ65vHv3rrCzsxPly5cX06ZNE4sWLRKtWrUS\nMplMzJ07V+o3bdo0oaurK4KDg0VkZKQYP368MDMzE66uriIzM7PAXBJpCgtWIqJPRE7Bmt/jjz/+\nkPo6ODgIPT09cf/+faktPT1dWFhYiB49eijt9/nz58LIyEh06tRJaqtfv74oX768ePLkidR29epV\noaurK0JCQpTiuXnzptSnT58+QiaTSc99fX2Fnp6eOHv2rNSWlZUlOnToIHR0dMSlS5eUtvvxxx+V\nYnNzcxPly5cvMC9FHSMmJkbIZDKxcuXKAveXE0tOgSmEEL/99puQyWTC0dFRpKenS+0eHh5K8TVo\n0ECYm5uLu3fvSm2pqamiTp06wtjYWDx+/FgIIUSzZs2Es7Oz0rjDhg0TcrlcJCUlKcXxZlxlypQR\nDx48UNquR48ewsjISDx8+FAIIYSrq6vo0KGDUp+lS5eKWrVqifj4+AKPnUhTOCWAiOgTM3/+fOzb\nty/Xo3r16kr9KlWqhLJly0rP9fX1kZiYiKVLlyr1e/jwIczMzJCcnAwge6mqkydPokePHihdurTU\nz8XFBadPn0ZISEiR4szMzMSOHTvQunVr1KxZU2qXyWQYN24chBDYtm2b0jbdu3dXel69enU8ePDg\nncbYunVrkeJ925dffil9nbOiQNu2baGvry+1KxQK3L9/HwCQkJCAEydOoFevXrCzs5P6GBoaYtSo\nUUhJScHevXsBAD179kR8fDzOnDkDIPtj/A0bNqB9+/YwNzfPFUtWVha2bNmCpk2bQk9PD48ePZIe\nPj4+SEtLk/ZdsWJF7N+/H2FhYUhISAAADBw4EGfOnIGjo2OxckH0oXGVACKiT0ydOnWKtEqAjY1N\nrjY9PT1s27YNv/76K65cuYJr167h6dOnALKLIiB7PiiAPJd9qlGjRpHjfPToEV6+fInKlSvneq1K\nlSpKY+WwtrZWem5oaCjFVdwx8purWhhbW1vpaz297F+nb+dUV1dX+vrGjRsAUKTj7dq1K4KCgrBx\n40bUrl0bhw8fxr1799CjR488Y3n06BGeP3+O6OhoREdH53pdJpNJxzlv3jx06NABwcHBGDZsGOrU\nqYOOHTtiwIABSsdEpE1YsBIRlVBvFlNA9lm8zp07Y/v27WjSpAk8PDzw7bffokmTJtIFSUD2WUsg\nuwh6F6KA1QNyilADAwOtHSOvRfsLyokqsZQuXRpt2rTBxo0bMXPmTKxfvx6lSpWCt7d3ntvnfE+6\ndeuGQYMG5dkn5+ypu7s74uLisGvXLmzbtg27du3ChAkTMH/+fBw7dizPgppI01iwEhERAODQoUPY\nvn07JkyYgEmTJkntGRkZePToEZydnQH8e/OBa9eu5dpHSEgILC0tizQtwNraGiYmJvj7779zvXbl\nyhUA2R9fvwt1jFGYnEJVoVAAQJFj6dmzJ3x9fXHu3Dls2rQJXbp0UZpu8CZra2sYGxsjPT1d6Y8L\nALh79y7OnDkDExMTZGVl4fz58zAzM0OHDh3QoUMHAMDGjRvh6+uLiIgIzJs3752Pmeh94xxWIiIC\nkH2nJwC57pIVERGBlJQUZGRkAADs7OxQo0YNrFu3Di9evJD6xcfH4/vvv0diYiKAf8/g5pz9y5Fz\nFlJXVxdt27bFnj17lJZwEkJISzu1b98+13ZvK+ispqpjFFVxzi6XLVsWdevWxZo1a3D37l2pPT09\nHQsWLICRkRFatWoltXfo0AFmZmYIDQ1FQkICevbsmW8cenp6aNeuHXbs2JHr9rvDhw9Hp06d8Pjx\nY2RmZqJ58+YIDg5W6lO/fn1pP0TaiO9MIiICAHz++ecwNzfHsGHDcPPmTZQqVQoxMTHYsWMHHBwc\n8Pz5c6nvd999h9atW6NevXro378/ZDIZfvjhB6WzqznzOefOnYu2bdtKZ/Pe/Gh81qxZ2L9/Pzw9\nPREUFISyZcsiOjoaMTExGDFihDS38+3t3lTQR+2qjlFUhY2Zn7CwMLRo0QL16tXD//3f/8HU1BRr\n1qzB2bNnERYWpnRBlZGREXx8fLBy5UqUL18enp6eBcaRc5xNmjRBQEAAHBwcsHPnTmzduhWDBw+W\n/hAZNmwYJk2aBB8fH7Ru3RqvXr1CeHg4TExM0K9fv2IdF9EHp4GVCYiI6AOYNGmS0NHRUVq+Kj8K\nhUI0b948V/vhw4fF559/LszMzISlpaXw9fUVV69eFWPHjhX6+voiMTFR6nvkyBHRsmVLYWpqKqyt\nrUXXrl2VlkV69uyZaNWqlTAyMhJubm5CiOw1TN9cO1QIIa5duyb8/PyElZWVMDY2FnXr1hXLly9X\n6pPXdgW1v60oY8TExAgdHZ1Cl7XKa8zr168LmUwmJk+eXGjfM2fOCG9vb2FhYSFMTU1FkyZNxNat\nW/Mca+/evUImk4mRI0cWKY5r166JHj16CGtrayGXy0W1atXEwoULRVZWltQnKytLLFy4ULi7uwtT\nU1NRqlQp4e3tLc6cOVPgcRNpkkyIj+SeeURERERUInEOKxERERFpNRasRERERKTVWLASERERkVZj\nwUpEREREWo0FKxERERFpNRasRERERKTVWLASERERkVZjwUpEREREWo0FKxERERFpNRasRERERKTV\n/h97Bns4co5E7QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f321c10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Performing a $\\chi^2$-test to see if these differences in these frequencies are significant, we find they are (p-values reported below). This suggests that the distribution of movies across Bechdel categories in the combined data we analyze are significantly different than the raw data from the site -- or there's a risk that the day we're using in the full analysis isn't representative of the data on the Bechdel site itself. Alternatively, the Bechdel site's data may be biased away from the expected distribution. $\\chi^2(3,n=4) = 27.78, p < 0.01$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The observed data are the counts of the number of movies in each of the different categories for the Analysis subset.\n",
      "# The expected data are the fractions of the number of movies in the original Bechdel data,\n",
      "# multiplied by the number of movies in the Analysis subset.\n",
      "chisquare(f_obs=representative_df['Analysis Subset'].as_matrix(),\n",
      "          f_exp=representative_df2['All Bechdel'].as_matrix()*representative_df['Analysis Subset'].sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "(27.784302084004732, 4.0310508706887412e-06)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2 . What are the variables?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have data in hand about movies, their financial performance, and their performance on the Bechdel test from the data sources that Hickey descibed in the article, we can attempt to replicate the specific variables used. Because the article does not make clear which data or definitions were used for making these constructs, we're left to infer what exactly Hickey did.\n",
      "\n",
      "The article claims to use two specific outcomes: **return on investment** and **gross profits** which are different ways of accounting for the relationship between income and costs. I don't claim to be an accounting or business expert, but \"gross profit\" is traditionally defined as \"income minus costs\" ($P = I - C$) while \"return on investment\" (RoI) is traditionally defined as profits divided by assets ($ROI = P/A$). Thus we need at least three variables: income, costs, and assets. \n",
      "\n",
      "However, only two of these are available in the movie financial data we obtained from [The-Numbers.com](http://www.the-numbers.com): income and costs. We can calculate profits easily, but it's unclear what a movie's assets are here unless we use costs again. Again, Hickey may have done something different for make this variable, but we don't know so we can't replicate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Profit'] = df['Adj_Revenue'] - df['Adj_Budget']\n",
      "df['ROI'] = df['Profit']/df['Adj_Budget']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. What are the models?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bechdel Test over time"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can do some exploratory analysis, First, we might want to plot the change in the average Bechdel score over time. Of course, the number of movies that have been released has also increased in recent years. So we'll change the size of the line by the (logged) number of movies released that year. We observe a general upward trend but a lot of noise in the early years owing the sparsity of the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Do some fancy re-indexing and groupby operations to get average scores and number of movies\n",
      "avg_bechdel = df.set_index('Released_x').groupby(lambda x:x.year)['rating'].agg(np.mean)\n",
      "num_movies = df.set_index('Released_x').groupby(lambda x:x.year)['rating'].agg(lambda x:np.log(len(x)+1))\n",
      "\n",
      "# Changing line widths from: http://stackoverflow.com/questions/19862011/matplotlib-change-linewidth-on-line-segments-using-list\n",
      "coords = sorted(dict(avg_bechdel).items(),key=lambda x:x[0])\n",
      "lines = [(start, end) for start, end in zip(coords[:-1], coords[1:])]\n",
      "lines = LineCollection(lines, linewidths=dict(num_movies).values())\n",
      "\n",
      "# Plot the figure\n",
      "fig, ax = plt.subplots()\n",
      "ax.add_collection(lines)\n",
      "plt.autoscale()\n",
      "plt.xlim((1912,2020))\n",
      "plt.xlabel('Time',fontsize=18)\n",
      "plt.ylabel('Avg. Bechdel score',fontsize=18)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.text.Text at 0x10f7696d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7qYBNTcpgweycZJVGbi4HxeYa8bheWbLKRmluBm6+2Yfx4wPYs0dASwuwbFnm1rKLF7vx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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f75e310>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also try to replicate the stacked bar chart that Hickey used to show the distributions of different classes of movies by decade. I find this harder to read and interpret, but I reproduce it below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_rating_ct = pd.crosstab(df['Year'],df['rating'])\n",
      "\n",
      "# Bakes in a lot here and is bad Pythonic form -- but I'm lazy\n",
      "# http://stackoverflow.com/questions/17764619/pandas-dataframe-group-year-index-by-decade\n",
      "# http://stackoverflow.com/questions/21247203/how-to-make-a-pandas-crosstab-with-percentages\n",
      "# http://stackoverflow.com/questions/9938130/plotting-stacked-barplots-on-a-panda-data-frame\n",
      "\n",
      "df_rating_ct.groupby((df_rating_ct.index//10)*10).sum().apply(lambda x: x/x.sum(),axis=1).ix[1930:].plot(kind='bar',stacked=True)\n",
      "\n",
      "# Fix up the plot\n",
      "plt.ylabel('Percentage of movies',fontsize=18)\n",
      "plt.yticks(np.arange(0,1.1,.10),np.arange(0,110,10),fontsize=15)\n",
      "plt.xlabel('Decade',fontsize=18)\n",
      "plt.xticks(rotation=0,fontsize=15)\n",
      "plt.legend(loc='center',bbox_to_anchor=(1.1,.5),fontsize=15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.legend.Legend at 0x10f7d72d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kiJKHiIiIqDo1NlNGRkaIi4tT+d3Y2LjaM0WV48bGxjoLZmJiAhsbG+WPqakpIiIiEBER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\ntPA/198O7kiZ7CcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f7d70d0>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also estimate a statistical model to forecast future changes in the average Bechdel score over time. We observe a general upward trend in movies passing more of the Bechdel test and can try to extrapolate this going forward."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg_bechdel = df.set_index('Released_x').groupby(lambda x:x.year)['rating'].agg(np.mean)\n",
      "avg_bechdel.index.name = 'date'\n",
      "avg_bechdel.name = 'bechdel'\n",
      "avg_bechdel = avg_bechdel.reset_index()\n",
      "\n",
      "l_model = smf.ols(formula='bechdel ~ date', data=avg_bechdel).fit()\n",
      "#print l_model.summary()\n",
      "\n",
      "# Create a DataFrame \"X\" that we can extrapolate into\n",
      "X = pd.DataFrame({\"date\": np.linspace(start=1912.,stop=2100.,num=100)})\n",
      "\n",
      "# Plot the observed data\n",
      "plt.plot(avg_bechdel['date'],avg_bechdel['bechdel'],c='b',label='Observed')\n",
      "\n",
      "# Plot the predictions from the model\n",
      "plt.plot(X,l_model.predict(X),c='r',label='Linear model',lw=4)\n",
      "plt.legend(loc='lower right')\n",
      "plt.ylim((0,3))\n",
      "plt.xlim((1912,2100))\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Avg. Bechdel Test')\n",
      "\n",
      "plt.xlabel('Year',fontsize=18)\n",
      "plt.ylabel('Avg. Bechdel Test',fontsize=18)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.text.Text at 0x10bef8e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fCJ8PePVVM267zQ+DAdi1S10ISBKJBZg4MVSd9usn4quvWAEhBiPRMPmdJMLr\nCLS9a0A9RiAtTX6uhEaPx+4akJ/HYxEIdw0oLQKVlVzw+qj5tqNlDVDrQUaG1CaVBZXtkls7RqCq\nisO5c6QTX6wxAkrXALXaq8V3jB1L/FAhGSRuNyxvr4Xxp+NgHno1Hgy8AKevUvU4Ykoq3IvuRs3H\nn6P2ky9g+p/F+O7HzKBff/NmI86c4XH//V6MGBHA/v28au+JsjIO9fVchEWgf38RlZU8qqtjOm0G\ng9FKxCwEVq1ahePHj0fdfvDgQTz77LOtMqlLgfA6Am2VNUAXUHWLgDJYMHTRiUUIKBdg5ftjsQhE\nCgE6X3k/dIEDIgPnJEk2+4eb/+lC7XS2jUUg1PXQuvum8QE33BCA283FJDTodfJ65UyDqqpIi0Dn\nzhIGDAjgiy+MMBzYj5RHH0LWkAFIX/oz8F9+GXX/+1NH4m/DX0HV3sNo+MtL8A8dDgAYP94Pm03C\nBx8YUVbGYdkyC8aO9ePqq0UMHx6A389h377IfzHU/E9TBylUGBw+HN2lwGAwWp+YhUBBQQG++iq6\nrfDzzz/HsmXLWmVSlwJawYJtkTUQHo0PkDt3u12C0Ri5YFZWcjAYpGDKmBLZIhBZWRBQr+//6aeG\nkEWBCgfqZrDbqUVAfg/1fSvPQ+144YslXagzM9vKNaC0CFy8xeHbb3ls3Ei8dPv28bDbJQwbRj4w\nteY/4VRUcDCZyPWjVoHqag7Z2WFfpIYGPNJpJX777hhk/uQG2Fe+At5Vq7rPGjjxw6T7UP3ZV7ij\n53bsuLoASE0NGWO3EzGwcaMRd91lg88HvPQSUYGDBokwmyVV98CRIzyMRgm9e0daBCZP9qFTJ52W\na2QwOihRYwSOHz+OxYsXAwCk5pXp6aefxsqVKyPGiqKI3bt3o0uXLm00zY5HeEGhtnINUDN5NIuA\n1UoWdjXXQEaGFIzqVqJVWRBQtwj84Q8WXHediOXLhZD3KisLkvlGswhEzl3t2IAsRJxOCSdPtr73\nS2kRuNhgwbVrjfjtb63w+Tikpzdh3z4DrrxShMNBrkddHfkctCgv59C3r4iDBw2oqOCRlUXuxqlF\nwPjfXbAWF8Gyfh3yG7QDD7ZhDHIeWYQRzy7E8lnAzKv8qK+XY0nCmTrVj1/8wgazWcLGjU3BdECL\nBRg8mAYMhn75Dh/m0bu3GNFPyGIBioraKM2DwWBEJaoQ6N27N/r164cPP/ww+FpFRQUaVZy1BoMB\nV155JZ6a/71yAAAgAElEQVR66qm2mWUHJNHpg+F3roEAec1iIf+Aw10DVVUqd5TNRKssSPoFcBEW\ngUAAOHmS+L0p0V0D8vvKy3kYDBICgchaAqEiJNw1QB6dTqlN0gdDLQIt38/zz5vx/PMWLFzoxZkz\nPJYutcJiASZM8AfTOkkK4YWEAI8RIwI4eNCAykoOVVUc0uHCdTsL4Xz5DZj2/lfz/WJmJqS8RdiY\ncw/mPDYIx5bUQ3jWhoYGcvHq6jhVyxBAihVddVUAS5d6ce21oV/c4cMD2Lo18l+MWsYAg8FIHppZ\nA6+88krwOc/zePHFF7Fw4cI2n9SlQGTWQGTzodYgWh0BekdtNpNCLmoWAbXUQUCZNRC6INrtErze\nSIvA2bNcMC+dEu4aMJsBkym0umBFBYcePSScPMlFuAaUi380i0BGhtQm6YOtkZ4oScBf/2rG3Xd7\n8eyzHlRUcBg3zo6zZ3lcfbUYXHhjCRgsL+cwcKAIDiJMO79BtzWv4xzeRcr/0y7o4B3zEwh5+fBM\nngZHJyeOvsTBZpOQmko+y/p6Ms+6OkS1CKSnA599pn6c4cMDeO01M2pqgIwM+fXDh3ksWNBGTRoY\nDEbcxJw+KOq1zVo7JXEWAfIYLgTo4mm1UotA6Ha1YDMKzwNGoxRRWdBoJPsLtwgcP843H5MLGx/q\nekhJiQwW7NZNRFkZp+kaCBcxdKFuq2DB1sgaqK7m4PFwGD06AI4DOnWSsGKFgEWLbBg1KqCwCGjv\np7ERMDfWYNKh17HQUIgr/7Zfc3ygU2dsvXwRHj6yBB+s6xJy/Wk1QgBITZXQ0MChsZGI1GhCQIsR\nI8iXe9cuA8aPJ89dLmLBYBYBBkM/xOVAPX78ONavXx/8fe3atbj22mtx/fXXY/Xq1a0+uY5MuCVA\nGSDYFsGCkRYB8rrFQiwCaq4BtYwBitkcmT5oNpNuhuEWgRMn+OZjyq95vbJbgEL6Dci/V1RwyMmR\nkJoa2YeAHjs1VYpwe7jdJHguNZWY8Vu7o11oHYGW7ePcOTLnLl3kBXHs2ACOHm3AgAFimGtABUmC\nafuXSLtvCc6hG8a8+xCuDKiLAInj4PnpLXC9sRrVuw6g7uEn8b2rH/bvD/3zr6qSs0RSU8mx5T4D\n8V/EPn0kpKdLIYWFDh0iz8MzBhgMRvKI2SKwfft2TJgwAT179sSsWbOwZ88e5OXlISMjAw6HA3l5\neTCZTJg3b15bzrfDkOjKguGLJV3MLJbowYJaQsBkCjfPczAaAZst0jUgWwTk13w+LiJYLLzxEDV5\n2+2RJnIqZFJTI+/6m5o42Gxyi2WvF6pdFFuK0uKh1vAoFs6fJ+/r2jX0Ghub/yJpzER4dUGuogLl\nz6/FZR8VIe3cEc1jBLp1h7AgD8KCPIg9Lgu+ft11ATidEtasMeHZZ+WLV1EhV5JMSyOijLYgjhYj\noAXPA1dfHQjperhvH8kYGDiQCQEGQy/EbBF48skn0bVrV6xbtw4A8Nprr0EURWzbtg2HDx/GxIkT\n8cILL7TZRDsa4YWE6KPRKCWksiBdSK1W8qP0ddP0M20hIIXs0+8n8QbEIhDuGpDz3OXjyxkDFOIa\nkH8vL+fRqRO1CIQeP9QiELqtqQmw2aRgB8TWdg8IApl7+DWIh9JSHhwnqVf4A1lEU1MlYhEQRZg+\n24q0e/KRNWwgrip8LKoI8MOA//aejrty3kP1d/vQ9LtHQ0QAQMTaPfd4sXq1KSQzQ2kFose+GIsA\nAFx9tRhsogQAe/YYMHCg2KrCjMFgXBwxC4EdO3Zg6dKlGDx4MABgy5YtGDJkCK688krwPI/bb78d\ne/fubbOJdjSixQgYjW0TLBheR4AujrJrQN7W0EAW7WgxAkCka8DrRVSLAHUNKF+nrgQlSouAxwPU\n1nLo1EkM+qvV5p+WptZrgFgE6GLT0uqCa9ca8dBDFpSVqe+fWEVatGuUlpKsjPBroKRfyllc/8lz\nyBw5DM55t8P63gZwUZSH//JeWH/NMoy/4jieG/Uu9lw2BTBEL8xzzz1e8Dzw6quyWaaiQnYNpKWR\na05jFFoqBIYMCeDMGT5YKnrPHh5DhrA2wwyGnohZCIiiiNTmgiKHDh3C8ePHMXny5OB2j8cD64Wa\n0DOCRHMNGAyJqSyo5Rqg/7S1hED4Iuj3c8EYAWUwnSQRIWAwhIoNr1fNNSAH4tG6+Tk5ElJSIusI\n0MVfzTXgdhOLgMVCGypFPQ1N1q83oajIjNGjU1BYKE+W7t9svjjXQLhbAAAQCMD88b+Qvmg+dpT1\nwpTtT8Jw6oTqPkSjCbv7z8Fc54eo2bEbOyb8Dgdqu18wvgMAMjOB/HwfXn/dDJeLvKaMEaDXnLoG\naF2DeKEpo3v38hAE4IcfSFYEg8HQDzELgQEDBuD9998HAKxYsQIAcPvttwMAGhsbUVRUhEGDBrXB\nFDsm4ZaAtrII0IU1vNeA7BqQIuoI0PLC0eoIAMQ0row78HqJu8BqDb3zLysjdQV69xYjsgbCXQN2\nu+wCoCZr2TUQnj5IHlNSIs3zbjcHux0K10DLFuuqKg7TpvkwYYIfv/udNWgZcLs5WK3kfFseLMiH\nCAH+zGnY//Q0Mq8ZDMfCebD8630YoX7nfAgD8Bv8Bc8/cBQvjFqNQz1+CvA8srMlVFdzKC/XtuZQ\n7rvPC68XWLnSDL8fqKkJjRGgrgGOk4IxC/FyxRUi7HYJe/YYcOgQD7+fYxYBBkNnxBws+PDDD+OO\nO+6A0+lEXV0dbr75ZowaNQo7d+7EjBkzUF5ejs2bN7flXDsUdLEP7zVgNEqtnDVAHiNjBMgjsQhI\nIdHpLbMIkNeIa0DeF3ULDBggYudO2VRNYgRC90lcA2S8UgikpJBaBErosdPS1HoNEFFBLQItLSpU\nXc1hwgQREyb4sWGDCVVVpGsftQj4/bH1AlDj/HkON1wrwPz+ZtiK34Dp00/AaXzwksUCz/TbsX3Q\n3fjpU7egSxcJ1x0OwOcj1wggwk2SOPz4I48bb7zwYtuli4SCAh+WLzdj/HgRkhQaI0BcAxzS0qBa\nYTIWDAZg8GASMOh0GsDzEq66ilkEGAw9EbMQmD17Nj755BO89dZbuOyyy7B06VIAQGZmJkaMGIHf\n/OY3GDduXJtNtKORKNeAbBEIfZ0u1rSyYFWVvC1WIRAeI2AyEQuDsh0uDRTs31/E9u3y183n41SE\nABQWAbLyZGdHCxaUXQNqFgFljEBLXAPKgEmHg7xGA+cEgQvGQrTENcAfO4olx97EPacKkVZcpjn2\nmH0Qch7Lh2fOHZCcGfj2dRMMBmD6dD8++MCIzp2lYCoeteA0NsZmEQCARx/14LPPDMjNJddbjhGg\nroGWxwdQhgwR8cknRjgcEvr1E1tsXWAwGG1DzEIAAMaOHYuxY8eGvNanTx9s2bKlVSd1KSAHC9Ls\nAfJoNLZeHYFAILoQoIuj1Rrp666uJuWCbbbo+w5PH6TpgMQ1oBQCPLp0IbXzw+sI0EY5FGWwIDFv\nk3r0KSnRgwVTUyPN8243WdBo+mBLXAONjeQ8MjMlOJ1kP9SXTi0CjY1xBAt6PLD8cwusxYUwf/E5\nfgUAUQSKZLdDuH02nqtagvfO34CP7pFLGZ48yaN7dwkjRwbw//6fGbW1Em68kXy4OTmygszOjk1N\n2u3AK68IuPVWOwBEFBQi5YUvVggEsHKlGTxvxPDhzC3AYOiNuA1+7733Hu69915MnjwZu3btwuHD\nh/H3v/8dQpz2V5/Ph7y8vKCLIdytsHnzZowcORKjR49WbXTU3olmEWjNGAFaYc9ojCy6E+4aCA8W\nvFCwGYkRkH/3+WiMgBRSgvfECR69eokR1QvVsgbsdjnLoaKCC5q8U1Mj6wjQfaWmRi70rWERULZh\npgthbS21CBAXCBFQ2vsxHDmMlMcfRdbQAUi/9y6Yv/g86ljfkGGof/4l0u73pb+hrNco1DeE/ome\nPMmhZ08xWLWvsZELcQ1QYrUIAKRT4F/+Qnz5XbuSL19aGikyVVl58UKABgcePcoyBhgMPRKzRcDn\n82H27NnYsmULDAYDAoEAHnroIfz444+4//778frrr+Ojjz5ChrKouAarV69GTk4OiouLUVNTg2HD\nhmH69OnBY/3617/Gzp07YbfbceONN2LGjBno1KlTy85Sh0SrI9CargG6qDockqprgOelYFlg5WJ5\noRoCQGSMABECkTECx4/zuOoqEWYzCVgMBMg5alUWlCRiEcjJke9O6etc866pNcJuV68jQGIE5HON\nF+oeIZYFInyoa6CpiSyOZjOn3obY7YZl80bYigth+iZ6624AEFPT4Jk9D0JePvxDhoVsIwF7oeNP\nnOBxzTUB9OghISdHREUFHxQCaWkIVom80OcXzr33Sli0KBD8HlDz/blzfPBzaCn9+4uwWEjfB2Xj\nKQaDoQ9itgj87//+Lz744AO88sorOH78ePD1mTNn4m9/+xv27NkTV/fBuXPnYtmyZQBIaqLRKGuS\ngwcPom/fvnA4HDCZTLjpppuwbdu2mPfdHohuEZBarbIg9as7HOrBghYLWVjDSwxr9RmgkDoCka4B\nm00uMSxJRAj07i0GMwToQkPSByNdA4EAaU6kFAIpKSQwL9y1QGogkH0q3SnhlQUvxiKQmSmB44iY\nohYBuY5AqCXFsH8fUh/5LbKGDED60p9pioCvMQrlz/4NVXt+QMPzL0aIAECO3KdIEnEN9OxJ5jRi\nBPnS0OvEcbJVIF4hAMhZFgARXwAphXyxFgGTCcEAwcGDmUWAwdAbMVsESkpKcNddd2HJkiWorKyU\nd2A04r777sPBgwfx3nvv4aWXXoppfynNtxz19fWYO3cunn766eC2uro6OGiEFoC0tDS4qIM2Cg6H\nukNbkoA1azjMmydF5K0nE5OJ/IM3GIxwOPjgHZjJxIPn5fPZt4+Y+K+7Tn7vxo0c9u4NFQs2m4Sl\nS6WQf+b07jkri3TvU14jnicpcA6HDenpPHw+ebvLZUDv3pLqNTUaSeQ/aTksz1MUeaSmcnA6eQgC\n2VdVFQmwu+oqY3AuVqsNDgcZn5IS+rl16kQGLV+egmPHONxwA5kDfd1gsAUD93ietFB2Ok2QJA6p\nqbZgeV6Ph0NGhhGdO/PNY81wOOL78GlsRZ8+VtjtQEYGB0EwweEwwOfjkZ7enKLob0DGu2vBv/4a\n+G+/1dyn5HRCXLAQrxvuwa9eH4qqXwWC10WNTp245lRIG0wmoLycWHkGDTLC4TBg9GgOH34I9Olj\nDl6Xzp05nDsH9O5theJP6ILQz5V+Hl27ktfLyzlkZxui/n3FyujRHDweCZdddnH7YcRP+GfL6DjQ\nz/ai9xPrwDNnzuA65WoUxqBBg/Dqq6/GdfDTp09j1qxZuP/++3HnnXcGX3c4HKhX2ETr6+tjdjmE\nc/YscPfdBuTkBDBxYit3n7kIYo0R+POfeZw7x+Hf/5bvpJYuJcVZmus7we8HKip4XHttAD/5iXyO\ntAiP0ynfzVIEgQuKBmq2pVRWhgoPNSwWoKYmvJsg7T5IXjt1ijz26iWhtFSuGEjGcxF1BK68UkKv\nXhLWrCGL/I03ysVtANKJLzubPKeuBepe8HjkOv20xLDJBHBcaMxCrFRWkn3YSQwdnE6gtrZ5/40S\nBtTvxKzDK/GT0rUwvtcQfUcAxDFjIN69GOLMWYDNhv/+mkf37tAUAQCCC3l9PSkARA1xvXuT6zJj\nhoSPPpLQq5f8npwc4u6JRwSokZZGHiWJa3ExISXLlokRmR8MBkMfxCwEunXrhoMHD0bdvmPHDnSl\ntxExUFZWhokTJ2LFihURaYcDBw7EkSNHUFNTg5SUFGzbtg0PPfSQ5v5cLq3/9mk4c8YLl8uvMSax\nNDaaARggCH64XB7U1fEgH4cIn08+n8ZGK2pr+ZDzq69PxWOPeXDvvcTef+4ch2HDUlFR4YHLJQuG\nigoDACNSUgLweAwh+3C5zDCZTHC53BBFEwTBEtxeUZGK1FQfXK7ISDh6V8HzEhoa5HkJgh1AAIAI\nt9uK2lo3jh0jx7fbBQQCPAA7KisF2GwS3G47OC4Al0u2rXftCuzYEXo8lwvgOB5ACkpLPcjMJCqp\nro7MPxDwArChosIdjINoakoFz/tQV+eD1ZqK2lofXK74Ev7PnTMjK8sUPL/UVBsaz9XC8+IqvHui\nGIOP7NZ8v5iVBeGOhRBy8xHo24+86AXgdePkSSs6d+Yu8J0FeN4AwI6zZwUYDBL27TMCMCI72w2X\nC+jeHdiwgYgiGq/hdFqRmWlAXV186ke2BtH3cQCI0rRYvHFfPzWsVjnzgpE4Ij9bRkfB4bDBbI4r\n+U+VmPewcOFCvPDCC5gyZQpGjBgRsm3FihUoLCzEb37zm5gP/Mwzz8DlcmHZsmXBWIElS5agsbER\nS5YswQsvvIBJkyZBFEUsXrw4LpGhJDUVMBgi74iTDfVpq1kElP78QAAhVfX8fnI3T+9UAXLnCkTW\n1NcKFvR4uKAPXRnR7/eT6PgLxQiEZwfI3QdlvzwtCpSTI+HECToOwe2xumqo5UN5R0nrFtB9kBgH\nEjjo95P0R3puLUkfDMZJSBKM3+7AH34sxugz62D9xI3BGu/7GBPQ8495yLhramQ0ZDOlpTz69Llw\n0FxoK2IJJ0/yyMiQNDsB3nCDtrshVmiMANCyzoMMBqP9EFUInDp1CtnZ2bA3rziPPfYYvv76a0ya\nNAk5OTkAgF/84heorKxEdXU1rrvuOjz++OMxH3j58uVYvnx51O3Tpk3DtGnTYt5fNDhO3TSebCLr\nCJDfDYbQ4DZR5EIWwKYm8piSIv+jpvn+4SZw+j6nUy1rQE6vU0b0U3P/hYLNwusFyN0H5f2Xl/Nw\nOqVgiiJAF2VJNX0wGvRclSmEXi8Hi0WuHkgFBr0+9JpYLFKLKgt6S6txb9NqZNy8EsYfDmG8xthA\n5y4Q5ufixtd+jv/WX4Hl6W7MN0e3PpWWcrjxxliEAHmkAYMkUFD7fQsX+rBw4cXfvVPxBVx8QSEG\ng6FvomYN9OrVCxs3bgz+brVa8eGHH+L111/HyJEjMXDgQEiShGuuuQYvv/wyvvjii2BTIr1B/Lt6\nEwKhXQHlgkKhbYjDLQL0uVIIkOh/SdUiYDSSwkBqFgEqBJTNeZRpc1rYbJEFgkiMAC3rS2red+ok\nBudIx5HHyKZD0ZAtAqG9DZQxAtSKQq8BtUwQi0Bsx4EkwfTlNqT9/G6s2toL9//4Wxh/OKQ6VOR4\n7O52K37d+11U7zqApkcfxyFvHwDAN99ED+AJBEj/BdWGQ2HQBZiGy5w4waFXr8Sk3xkMCFpVmBBg\nMDo2cTkXDAYDCgoKUFBQ0EbTaRucTikksE0PhLsE5F4DoalwRAjIOfT0Ll9ZppXjyB1wpEWAQ0oK\ndTdEFhRSugYAsrgq0+a0CG8uRCwCodaJigo5BZAu2NSK4PPJAuRCUNET7howm2XXALU0hFsErFbp\ngq4BrqIC1jdXw7q6CMZjRzXH+rt2xx9Ll6DXkwuw7XhP7NhhwCPGJgQCZA42m4QdO6ILgcpKDoEA\nhy5dLnzuoa4BYhEYNeri7/ZjJTVVCtZMYDAYHZcWthJpX2Rk6M81EE0IhBcUCgRI5DZd5NUsAgC5\ne4u0CJBxJtOFXQMAWchitQhYrVKIa4DWBaCuAbebWgSo2Ag14ccTI2CxEEtJuGvAbI50DahZBFRd\nA6II06efIP3uPGQNHYDUPz4eVQRIBgN+HDwdU/A+jny0H8vwBMTuPWCxyJYIeozrrw/gxx8NIf0W\nlNDsiW7dLnxnL2dLcGhqIrEFPXsmblGmlhgWI8BgdGwuCSHQHiwC0dIH6XMqAGQhELq/6BYBkkan\n7hqIXKSrqkjbWVpfPxo0TZAKGLn7oNzxr7ycVwgBBI8BqDcdigbH0YZEka4BOViQPFKLAA2mDA8W\n5EvPwf7Cn5E5ciicd8yEZcsmcOEXp5nazN5o+MMTqN59EJ8/+Bb+iSkorzI2n7/U3IZYLjIEAGPH\nkn19+626VeDYMfIn1737hRd0nid35fX1wK5dZH+JLNFLLRLMIsBgdGw0XQO/+tWv8Nhjj8W1w2PH\njl3UhNoCp1PCnj3JnkUocrAgeQytLMhHjGtsBHJylK6B0H/OJCVPzSIguwaUJXo9HoCWZqALMnUN\nZGZKMFygToXNRlrekgp/yu6DZDuNEQh3DdBFmfYmiBXSBEf+nQoBWcSELshUkFitEnxuP8wf/hPW\nkkKYP/4QnEYNZ8lkQt2EGZj1z/uQ++frMW0GGUtz6c+fp/un1RXRfFzyOGCAiM6dRXzzjQGTJ0cK\njK1bjRgwIBAUSBeCVhf8+msDUlMlDBqUuBK9NHOACQEGo2OjKQQkSYIYR+F7rjXyltqA9mQRUHMN\nALTSnRSseBcpBLQtAnRfyup7dBENdw3E0rBGmR1ArBi0+6DcoKe+XhksGGrCV+s1oAXpNxCZNaAU\nMYB8Dex2gD99Cj87uxa3flsIx+azmvv39+0HIbcAwrz5OFTVCVv/mYIHcpqC26kQKCuThYay3wJ1\nk9jtwMiRAWzfbmjuUigfIxAAPvnEgPnzY/fzp6cTIXDkCI+RIwMXFGitSVqaFAw2ZTAYHRdNIfDi\niy9i4cKFiZpLm0FjBJR3xMkm3BIQzTVAswuoJYA+KusIAGRhoiKB0tREggUNBrKI0ep/AI0RUHcN\nxCYE5OwA+n5ljMCpU2Qu9M5XWQFQFEm6YjxCICUlPH2QNtmRfwcAod6PWXgXfe7/O+xfbkWuRk9n\nyWqFZ/rtEPIK4Bt1Q/DLUX0kMk6C3hWfP883nz/tPkgtEfJ1ufnmAB56yIQrrkjFsGEiXnpJQP/+\nInbt4lFVxWPixNjN+6mpJKXz228NePDBWHsetw4pKeS89fI3w2Aw2gZNIaDXO/x4cTpJG97GxtD8\n6GQiFxTiQn43GEKzBtRiBMzmyL4JytK+FOJOCLUIUGjTISDUIhBL50F6PIAck4oSZYzAqVNkwQx3\nDXi9XHDRjsc1QDsTUuT0wWYxc/oYUv74Oha8sRr3oBz4Ivq+/FcOgjsvH545d0ByRpaupoF+SkFE\nYyaoa8Bul0JaMVMRZrMBixb5MGxYAN9+a8CKFWY88YQFa9e68fHHRjidEq69NnYhkJYm4auvDGhq\n4nD99Ylt2JOWJgVrGTAYjI7LxdcmbAdkZMjmamXFtGQSWUeAPEZaBMijUgiEBwoCtOtfZB2BXr3E\noBBQViykpnUgtEtfVRWHyy67sDtIGQvg88mLvWwRIEKAWgR4Hs3BdfI84rMIRGYN2A1uZPxrHT5B\nCcY/+qnm+yV7CoSZsyHk5sM/4lpN01B1NQmYpN8bQI6cp0LAaiXCh9YooCLMZiN30EOHihg6VERG\nhoT77rPhm28M+PhjI8aP98MYx19dWpqEc+d4WCwShg9PrBBYtMiHm29m3QIZjI7OJSEE6N1cbS2H\nHj30IQS02xDL45TBgvQxPD4AIHeiNTWhrymDBQFaS0CO6g+3CCiDBS+E7BqQaxQYjWRxNBolnDpF\nFlOldcFsJuOpOT2+GAG52JHhh0N48FQJZh8sQeqb1ZpV/05kjcAbpiX45X+mQ0qLLQ+uqopDRkZo\nwKTRSOIUysqIwLHZSMVEUSQVGakIC/enz5zpx1//GsDvf2/BgQMG3H9/fOZ9Grk/YkQg+HklisGD\nRQwenLjgRAaDkRyiCoFjx46hU6dOiZxLm6EUAnohWtZAeLCgmmtATQiQOgKh2aB0rNFIxiuz5JSV\nBS/GNRBqEZCC206d4pGVJYXc/VqtUohrILz7oBaZ1kb0O7sBzmmvwrTja+RrjK3n0mDMnwchrwB/\nWjcSH31kxNK02FvfRRNDDocUkjVAXRteb6hFQAnPA7//vRcFBTbwvITx4+NrfEVN84l2CzAYjEuH\nqEKgl7K3aTvH6SSPesociLQIyHfV6kKAPkZzDWhXFgTChYCysiB5dLlI+l28FgHZ5y9vq6zkI8rh\nkuC6yPFaGPbuga2kEC++vQ52rws4F32s79qReCv9Hjx95E5s+3PzXDZLsZcYbqaqSl0MpadLOHyY\nB8fR/gnNx/XJaYtUICmZPNmPESMCsNkkxNtNm1oERo1iQoDBYLQNl5xrQC9oFRQKLzEMKC0C0VwD\noXUEJElZWZC8RoWAJNFof/I7XdDOnSMWhezsCwsBagJXugbocei28Fx5s5lYHS4UI8A11MOy4V1Y\nSwph2vW95jxEpxN/b1wELF6Eecv6Y+vvLeBKDQBI6l9cvQaaiZY54XRKCARIZ0OOCy1v7HYTC4Ga\nuOE44M03mxBHJm6QzExi0Rk5kgkBBoPRNlwSQsBoJHdWerIIhLsGopcYDq0oGM01EG4R8HpJip5S\nCNAYAXpHLqcPkt9p+dt40wepa4Cayuk2mjFAsVikZosAjRFQbJckGHd/D2txIazr3wHXpG3K/9I4\nFmen3IXxL0/B48Oy8LMMHwAv3O7QFs2k+2B8n3t1NYehQyNXbVpLINySQi0CatYACrVKxcu8eT4M\nGRLQTbYLg8HoeFwSQgCgtQSSPQuZcJdAaLCgvHDJBYXkx65dI/cXXkdA2ZxIWUcAkO+Q6cLF8+S4\nNEc+3oJCdL+ya4A8qlsEoIgRADhXLSzvvA1bSRGM+/dqHrMcObD/fD68i/Jx6y3D8dvhHoy3+oL7\nBYgYogs1nUtrWQRo5gC1eCjLG5PiQa0fiJqeDowcyQL2GAxG23HJCAGnU4LLpT+LQEt6DaSkRC4M\n1CJAiyZRUWC3R9YRoHfIyih0sxk4dy62hkN0PG19HC4E6IJIqwpSSCliDl6PhBvxJQb/5e/I2rYB\nXHhwgwKJ4+AbOw6f91+Mqa/OwYHfeOBw0O6F8lzoHGprQ7vlkcZAJLI/1qp80YIFqYuJnp+yNoLb\nzbEKfAwGo10SVQjcddddLSoo9Prrr1/UhNoKh0NfrgFJCrUEXKjEsDJYMLyqIBBZ+1/ZnEhOHySP\n9CoPxuMAACAASURBVA5Z2QbYapVQWsrBapVU9x8ObX2sFiMQzSKQzVXilv3FGHPfa5iKQ8CH0fcf\n6NwFwoJcCAsWQezZCxUfG+B71YyGBi/S06XmbodkrNksN/8hKaLyBVTWSKDndfYshwkT7Hj//Sb0\n6RM6x6am6AGTVGDQ86OuDWoRsNv1kZrKYDAY8RBVCBQVFbVoh3oVAhkZEqqr9SMEojcdulBBoeh1\nBACyIBEhQH4PDRYk+wh3DQDk7vb8eR7duokxl5SlrYhliwC9Yya/5+RIpN3v9i9hLX4D7323GWYp\neh69xPPw/vQWCHl3wTthIpS5h9RH3tgoxyQoeyXQc6qp4UIKASldGFQIfPedAVVVPA4dMqBPn9B0\nPvodUQuYlC0CCB4XIAJLENQzBhgMBkPvRBUC8TQbag84nRKOHtVP1+VYswbo69TUr1VZECB3s06n\npLAISMH90QVbdg2EFvsBYnMLUKzW0BgBug+rVUInlGHEx68i87dvwHDiuOZ+Aj0ug7AgD8L8XIjd\ne6iOoeKnoSEy/VDpGqiu5kJS9Og5kq6H5PnBg+R7QGsCKKFCQNsiQIMjEdw3cQ0wiwCDwWh/tChG\nQBRFVFRUwOl0wpLocmcthDYe0gvRswbCKwvKlgBlSmA4SosAHQ8Q14AgkOeRrgH5/XTBjCVQkEL6\nG3BBs7yRC8C09RM88l0xRmALTK9EL57jgxHCLVMQWJwP39jxF3Tg04j92lou2MqYCg+zWYLHQ9wi\nDQ2hFgF6jvQaALIQoJ0EldDqhcp9yHMgj9SyQF0DJGsgsqogg8FgtAfiukU+cuQIZs2ahbS0NHTr\n1g3/+c9/8Nlnn2HUqFH44guNLi86QG+tiOliLzcfIo9qrgGOI3f4gkCyDKLVEQBCLQcAItoQAwgu\npOHBgkB8FgGLhRTrMVecxWP4I/pPGQLnnbMx6sxGmKAuAk5brsDXM/+Iy3AaFf8ogW/8LTFF8VFT\nfWWl7IpQpj/6fHKdCDUhQM8ZAA4dIsejWRJKqEVA7TpECxb0+ch1ZxYBBoPRHonZInDkyBGMGjUK\nHMdh8uTJWL9+PQDAYDDghx9+wMSJE7F161bccMMNbTbZi8HpJP+sade6ZEMXezlGQL2yYCBAysw2\nNoYGAIYTaRGQS+HS1EMa1KcWLEgXzJiFgN+PiZ7NyPtoJYYX/hM8ROCs+lDJbIZnyjT837p7sb56\nPArG+VG2wQaTqT62Y4Gcs80mobKSC86fChza/IcKPaVVQxksCJBrcfw4GRfNNWAySap5++HBgsr0\nQUEAOkhFbgaDcYkRs0XgkUcegc1mw4EDB/CPf/wj+PqYMWNw8OBBdO3aFU899VSbTLI10Ft1wXAB\nQBd/no9MH0xPJxYBZQBgOMoYAQBwuUinRYMBEb0G1FwD1Mx9QdfAiRMwPPkEMkcMwkvHZuKas++D\nl9TjSfz9+qPhqWdQ9d8fUP9qIX68fBwELx8UJPEKsuxsCVVVSouAPHevlwsKAfpZK8fQuIgjR3hI\nEoerrw5EFQKZmZJqwGSkRYBmDbAYAQaD0X6JWQhs3boV9913Hzp37hyxrWvXrvjFL36BnTt3turk\nWhNlK2I9EGkRIC4Ag4GkFlKXAbEIEB94XZ1s7g+H+q2pRcDlkheu8DbE1EyuLLyj6RrwemHevAmO\nO2bCNLA/DM8+A8P5UtXzkqxWCHPvRO17/0LNl9/Cfd9SSFlZAGgdAfJjNErg44zdzMoiPQzkGIFQ\n14CaRUAOFiS/0/iAsWP9qjECWt0XaZxCeNaAnD4Y3/kwGAyGHojZNeDxeJCZmRl1u8FggFujMEyy\noYuiXuIEaBCgMmuA5xFcHGlhoEBANklXVNAiQZH7C7cI1NZywYUrvOkQDZxTWgSouVspBAzHfoS1\nZBWsb64GX1mheT57MAS9n82DZ/Y8SE71zjrKoL6WuGeysohFQFmZEFC6BsjvSosAPS9ZCBhw+eUi\n+vYVUV3Nw+MJvQ5aQoBWaaQCKjR9kAsRVgwGg9FeiPmebOjQodi0aZPqNr/fjzVr1mDIkCGtNrHW\nRrYIJHkizYQHCUpSqBCQLQZcsLRteXl0i4CyCRBAXAPhFgG5joBasCAZm53qhmX9OjhmTkXm9SNg\nf/mlqCLAbUjBpk53Y+WSbbjBtgvC4p9FFQH0eB4PiVWIpfNgONQ1IPcqoPuV4PORFsp2uxSWDUEe\nqWvg4EEeAweK6NKFnG+4VUBLCHAc0Lu3iMsuCxVYtOkQyxpgMBjtkZgtAo8++ihuu+02LFy4ELfd\ndhsA4Pjx49i0aROef/55fPfdd3j77bfbbKIXi15jBJSuASIEpODvVAzQVrTl5UQlqAULmkzkbpVm\nDSgtAjQoX5k+aLGE+sH7CAfwAgoxeckqmOqqNecuXnMNGucvwq92LMR3h52Y1tkPo+nC1zVYYtgb\n1nAoRrKyJHz1ldIiIAsdj4ecc/giHh4sePAgjzvv9KFzZ/L6+fMcLr9cfk9VFYc+faLX0Pj006ag\niOE4KkLAYgQYDEa7JWYhMG3aNLz22mt44IEHsHbtWgDAkiVLAABWqxUvvPAC5syZ0zazbAVsNvJP\nWy9CQK2gEM8juDiLoiwSwl0DahYBjqN5/eR3l4tD9+5icJvRKClcA80tiJuaYHlvA2zFhVj+7Tdk\nY12U+aalwzNnHoz3/QzS0GEQXG7goCVYUCiWhZ0E9aHFroHsbDEkWFC2CJB9hlcVpNvoOVdXA2Vl\n4RYBHoC88NfUcJqZE+FlM6gIIemD8Z8Tg8FgJJu4CgoVFBRg1qxZ+Pjjj3H06FEEAgH06tULt9xy\nC7Kzs9tqjq2GnmoJqFkEOC7SNQAoLQIcOE6KuuDYbFJYjIC8zWSSYwSyTu3GS95CZF29Bnx9lJW/\nGd91o+DOK4Bn+u1ASgocDvngtKCQzxdSDTgqcvfBlrsGGhu5YPMoZUEhr5e4BpTxAQCZl9FI6h3Q\n+gFXXikiM1OCySSFZA5IkrZrQP2cJDQ0RAZfMhgMRnsh7sqC6enpmD17dlvMpc3RU3VBGiOgjBUI\njxGg25QxAnY7okbb2+2hFgHloug01GPIN8Vwrn8ND+zeRV4UVHYCQMzIgDBvPoSF+QgMvDLqOZBe\nA9QicKEzJnfTgQApjNQS1wAtKkS7JCpdA9QikJMTuV+LBdi+3YBduwwwGiX07Uv6KXTpEioEGhuJ\nv1+tqmA0TCYEhQnLGmAwGO2RVus+KEkSOI7TbdMhgFgE9CIEZIsAzR7gIrIGqBBQWgTU3AIUahEQ\nRZI+6EgXYfzuW1hLinC44V2kvt+oOaf/Zo5F76fz4Jk6I6YOOqT7IAnei80iQPsFcC3OGgCA0lK+\neX/kdWX6YP/+kf79AQNEbN5MTBBjxviD7+vcWQqpLqhVVTAaFossBFiMAIPBaI+0uPtgp06dEAgE\nUFVVBQCw2WxIp7euOkVProHwGAG1rAHZIkBjBHjNu1WbjVgEms7V4j6xCItffhUZZ/dqzyM7B8Kd\nCyHkLkK3Pn3hieMcrFZicvf7Y7vDp/71+vqWpw8CQGlpqGvAZCJpiTU16j0C/vWvJtX9dekihlgE\ntBoORcNkQrC+A+s+yGAw2iNR0wdFUQz52bNnD5xOJ5544glUVVXh/PnzqKioQG1tLZ5++mkYDIZg\nEKFeyciQ796SDbUEhGYNSCFCgI6xWORAx6gWAUnCKN8XWPzFYvS8oT9exi+RHUUEiOCwPX0iXK8V\no2r3QTQ+vgyBPn3jPgeLhVoEEJPPny7cDQ1csGVxPFAhcO4cD56XglYIrWBBLbp0kULSB1siBMxm\nCS4Xec4sAgwGoz0Sc4zAPffcgxkzZuCJJ54IeT09PR2PPPIITp06hQcffBC7d+9u9Um2Fg6HfiwC\ngQAJYguPEZCzBrigSOB5king8ZA8eSVcdRWsb6+FtaQI/zj8g+Yxa1O6wfSzhfjN/iXYU9cbm6Zf\nXAEoGhxHFvYLj6dV/hoaSPnjeElJAex2CaWlXETDJFpbIF4hoOYaiE8IyBYBljXAYDDaIzEXFNqz\nZw9GjRoVdfugQYPwww/aC1Gy0VuwoMmknTVA3QYGgxSsHZCSQjaatn2GtHsLkDVkAFIffxTGKCJA\n4nl4Jk3GvV024tEFR9D08P/BaUOviDS4lkAXvrq62O7w6TEbGlre+Ck7mwT4KYWH0i0RjxDo3FlE\nXZ3cw6G6moPZLKnWaYiGMliQWQQYDEZ7JGYhcNlll2HLli2q2wKBANatW4f+/fu32sTaAhIsGJqa\nlyyIRUCtoJD8O91mMJA74c44j/zS55B5/XA458yAdeN6cLS6ThgncTnKlz6G6u/3o674LWxzTINX\nJAYgr7d1yuHSfdTXx+oaUFoQWnb8rCwJosiFLP5KURGvRQCQqwtqNRyKBnENMIsAg8Fov8TsGvif\n//kf/PKXv8Ts2bNx77334oorroDb7cbhw4fx0ksv4T//+Q9Wr17dlnO9aDIyJEgSh7o60pY4mYgi\nCXKj7YLDKwtKEhECPALovvcjrDhfhDHYAtMhf9R9+jkjPkubjro778LcV6fg9CNN4JoXaKMxtPtg\nPHe90aDBcfX1XHBR1UIOFmxZ1gAgpxAq3698Ho9ZX1lUqE+fAKqq4qshABABJLd8ZhYBBoPR/ohZ\nCNx///2oqKjAc889hw0bNoRsS01NxYoVKzB//vxWn2Bromw8FF54JtEEAmQREUUumCqotAgYSs+i\n8zvFOIZi9HzulPa+evWGOzcfT524C5u+6Y75XXyw2vmQu3RlQSFSWbA1LQJcsP6+FnTBbmyMzYKg\nBg0YDBUCinoJcQi8Ll2IaYhmDlyoqqAaShcLswgwGIz2SFwFhZ588kksXboUn376KU6cOAGO49Cn\nTx/ccsstSEtLa6s5thrUbExMuckVAjRGgD4XRcDE+dHj+/fxHlbhyskfgNPwYUhmMzzTZkDILYBv\n9E0Az8P3tBluNxdRTAggFgGfjzYdQqsIgXhjBOiCLUktFyJ0oVa+n4oCjpPiEnjp6eQungqBeKsK\nAgieN89LLbZyMBgMRjKJu7JgdnY25s6d2xZzaXNoEx49ZA4EAhyMxuZF5/gJTP7PajxcWowu//cc\neS2KBijLGoiUBxZBmDsfUlZWyDZaR0DZcIii7DXg8bROzrvsGoi9siClpRaB7Gwx4v302A6H3GAp\nFmh1QVqgqKqKQ9++8QWQ0GNbrYgrtoDBYDD0QlxCYNeuXdi4cSPKysrgjRKkpufKgnIr4uT/xzYE\nvJgmbMJ0rESn0f/GJCn6nWjAbMP2HnPw8LGfYfIDI/Czn6vHCdDKgmoWgbZ0DUhSbJUFw1P+WoJs\nEVDui7zWEnfPwIEB7NhB1ENLLAL0PMLTOhkMBqO9ELMQWL9+PebNmwfxAiH3ehYCaWnEhJtMi4Dh\n6BFYS1bhQP0a5NRVkBejrCH1V1yNh4/+DLcV344Nn+Zg+z/MmJMapUEAiEWgqYlDTY2aRSC8DfHF\nn4vSqhBbZUF5TEstArSXgNIVQRfjeBdxAJg61Y+lS204e5Y0LYo3RoCeN4sPYDAY7ZWYhcBTTz2F\n7t27o6SkBNdeey1s7fA/H88nqd+AIMCyZROsJUUwb/8SABCtP43Pmoo3hPkY/UYuynqMwIpbUjHD\n0Ri849S686RR6+XlHIYOVbMI0BgBrlVcA8oo+Vi7D1IuNkZALWsgntRByqRJfphMEt56ywSfL77K\nhIAsaFjnQQaD0V6JWQgcPnwYzz77/9u78/CmyvRv4N+TNt3bNJQtVQGxsg0wIGUpXbTIIFhEKG5l\nE0SkwkgH0BEYfygiyODCMI4CCrYIWFHZZUQ2ZQdZfHFj2GFAgQFa0o1uyXn/OJxsTULanpN0+X6u\ny6vtSXrytMdy7tzP/dzPm0hMTFRzPKqLjPTe1IDfsV8RtDwLQV98Bs2NG26fe7ZRVywWnkXc/IEY\nm9YYe1sXwJQvjdPPDzYNhVzfcOTd7y5f1iApyWQ/Fj/rFsVKFQvaZwRu/3zb51R31YBtRkP+Waoy\nNaDTAYmJJixfLg2oqlMDtTAuJiICUImGQtHR0SiTc8sKOnDgAJKTkyscnzdvHtq3b4/k5GQkJyfj\nxIkTirye6hmBwkIEZi9H5MO90eD+Hgj5aKHLICDfT4f3MQ7n1+3B7Ed3Y5V+NMTQMADOWwwD7tf/\ny+/QnS2PlLsYiiJQXKzM1IC/vxRgyJ/fjiBYU+nVrRGwnRqQg4qqTA0AQP/+5bh4UWN3fk9ZpwaY\nESCi2snjjMALL7yAefPmYejQoYiOjlbkxefOnYvly5cjLCyswmNHjhzBsmXL0LlzZ0VeSyZ3F1Sa\n/09HEbQsC4GrvoAmP8/tc8u6x2HM92NxsdsgbNsXgQdbFTjZa8C66ZCUEZADAfe7D8qcLx+UCgbN\nZmWKBQEpK1BY6FmNACDvC4AqdxYMCZGmR5wVHlZlagAA+vYtx4svSh0Lq9JQCGBGgIhqL5eBwKhR\noyDYrIcSRRHXr19HmzZtkJCQgMaNG0OjqZhQqEyxYExMDFavXo3hw4dXeOzw4cOYPXs2Ll++jJSU\nFEyZMsXtuXQ6z/4lbtxYgwsXBI+f71ZeHjQrP4Pm4yXQHDni9qliVBTMQ4fB9MwzQNt2WBroj96h\nUuFlaGgQ/P018PcXEBERcOtYIOSFGZGRgWjcWLoWTZoEQqdz/hqNGlk/Nxi00Omslzc0VHNr/b70\nc+v1AdDpKp+f9/eXKuzl319IiBQIhIdrodPdfu1eUJC014BOZz++ymjcGAgN9bOMQc6cGAz+Ho3B\nkU4HJCWJ+O47AXffHVSprosREdJ1CQ/XKPP/lI84XleqO3ht6y752lb7PK4eWLp0qctv2rRpk8vH\nKhMIpKam4ty5c04fS0tLw/jx4xEeHo5BgwZh48aNSElJ8fjcruj1wNGj1TiBKEI4+L108//8cwjy\njjUumB9Ihnn0aJgfHWjJx8srBeV3k/IGQ457DZjN0k1GowHuvVdE48YiDAbXr2WfEbB/TM4IFN9a\ndKBEsaDta3r6Dl9+J1+d5jv33Sfi3nutrxceDtx1l4j27aue5Rg5UsT586KlzsJT1uWDVX5pIiKf\nchkI3G6ZoNoyMjIQEREBAEhJScEPP/zgNhAwGj3bUjckJADXr2s9fr5MuJGLwC9XInjZUvgf+8Xt\nc80NG6E4bRiKhw6HqWWMdLDYDBRLrymt5w+HIJgAaHDjRjFKSgIgin4oKioB4I+8vJJb29uGoKio\nGC1bivj5Z/lndf660qoAaZpFqy2G0Wi9hmZzIIqL/XDtWjGAMJhMJTAaTc5P5Ib8rkL+/QUEhADw\ng8lUBqPx9jUkWm0oAAHl5aUwGl3vm+DOwoW4NQbrscOHKx6rjIcfBvr1A/Lcz+pUYDZrAfjB378c\nRmNJ1V68BnC8rlR38NrWXTpdMAICqpZZtVWpMxiNRmRnZ2PYsGGWef0lS5agqKgIzz77rGJLCo1G\nIzp27Ihff/0VISEh2L59O0aPHq3IufV6abc4UfSgE5woQrt/rzT3/9U6CMWu1/CLgoCyB3rh5rCR\nKH2on9u3vHKMdbuMgO3ug56wfVfqmBGQiwXlH0GJYkHAmlnwdBWAXJtQE9vxVqUzIGsEiKi28zgQ\nOH/+PB588EGcOXMGXbt2RZcuXQAAe/bsQVZWFhYtWoRvv/0WjWwnqj0k1yJkZ2ejoKAAY8aMwZw5\nc5CcnIzAwED07t0bffv2rfR5ndHpRJSUCLh503U6V7h2DUGfZyNoeRb8T510ez6TIVp69z9kOMzN\nmns0BvkGL1fam0zSNEDFqQHpc08DAdvKdecNhQSUlEi/ayWLBQHPAwE5AKjq8sGaRv49so8AEdVW\nHgcCU6ZMgdFoxJYtWyxBACDVBIwePRoDBw7E1KlTsXjx4koNoEWLFti7dy8A2O1emJaWpspuhrZt\nhu2a85jN0O7eiaDlWQjcuAGCm6WSokaD0j89hOJhI1H64J88Wztnw1VGQBAcVw1In3seCFg/r7hq\nQNproORW9lq5GgF5OZ9nz5cDAaUCEV9jRoCIajuP72DfffcdJk+ejAcffLDCY/Hx8cjIyMBCefK2\nBrPdijg6WoTmymUEfrYCwcuXwu/8Obffa2rWHMVDhqM4bRjMhqovobQGAqLla+dTA9ZiQU9otdIN\nPyCgYupdq5WLBeWMQJWHb8eaEfC0WLBygUNNZ20oVDcCGyKqfzwOBAoLCxHo5u4RHh6OnJwcRQal\nJr0e0MCEwG3fIGJuJgI2fw3B5LpoTvT3R2m//rg57GmU3Z/s+V3ZDcepAbNZuBUIiDaBgFDpjAAg\nvTMNC6t4U/L3h11GQLmpgcrN+cvPq4k1AlXBjAAR1XYeBwKdO3fG0qVLMW7cuAoBQVlZGVasWIE/\n/vGPig9QSZrfLiLm0+U4i2Vo9sYFt88tb3kPioeNRPGTQyBWoe7BHcepAbnjn21DIVG0rRHw/KYd\nHCw6bbUr7zWg9NRA5YsF5efXjXfQckDFjAAR1VaVqhFISUlBjx49MGbMGNx7770QBAGnTp1CZmYm\nDh8+jHXr1qk51qopK0PAlm8QtDwLAdu3QnCzLFIMDERJygAUDx+Jsp4Jqm0wL6f8rRkBayBQnVUD\ngPTO1LFQUD5HWRlUKBas2PLXHbkDoVJTE77GjAAR1XYeBwL9+vXDp59+ikmTJuHPf/6z3WONGjXC\nJ598gv79+ys+wKrSnD+HoBWfICh7OfyuXHb73PLWbVA8fCSKH3sSYoMo1cfmWCMgrRqQAwFr3YDt\nXgOeCglxlRGQigV9v3ywcs+v6ayBADMCRFQ7Varc/amnnsITTzyBw4cP49y5czCZTGjWrBliY2MR\nUBMmfUtLEfj1VwhathQBO791/1T/YJgfS8XNYSNR3rWbau/+nZEDAduMgBKrBgBp05ymTV1NDdhm\nBKo6envWjIBnz7f2EagbN07r8kEfD4SIqIoq3ZJIo9GgS5cuaNasGSIjI90WEHpT6GuvIGjlCmiu\nX3f7vPI/dMCcnOdwMekJzPynb/K5njYUqmwfAQBYsKDY6Zp2qVhQQHGxtLKgMud0p773EZB7UYSH\n143Ahojqn0qVwJ88eRKpqakIDw9HdHQ09uzZg++++w7du3fHrl271BqjR0I++KfLIMAcGoabw0ch\nd/N3yN2+G5tj0vF7kd7LI7Ry3lDI9fLByty0mzQRnW5KJL9WYaGg6Px8ZZcPWvsIKDcGX2rVyozs\n7CLcd59vW3ITEVWVxxmBkydPonv37hAEAf369cPq1asBAH5+fjh+/Dj69OmD7du3Iy4uTrXBVlZZ\nl1ip8v/RVMBmq2NpK2LvTQU4ctZHwLFYUBSrViPgir+/9FqFhcp2watsQ6G61kdAEIAHH6z8ng1E\nRDWFx7eYqVOnIjg4GL/++qtd46DExEQcO3YMBoMBM2bMUGWQlWHWRaLo2bHI+W4fbny9HcVDR9gF\nAUDNCQTkd8dSH4GKLYZNJkAQREXKF+Qbr3oZAc+eb+0jwFQ6EVFN4HFGYPv27Zg0aRKaNGmCa9eu\n2T1mMBgwbtw4zJkzR/EBeqpofAbK2/0BJf0fve1arpoSCDgWC/r7ixVqBJSay5dv1AUFSgcClSv+\nq2urBoiIajuPA4GSkhI0aNDA5eN+fn64edN321wWvjrT4+dGRkothn1Fnvt3XD5ov2pA6iyoVCAg\nBx0FBcpODcgZAU+3W6jJuw8SEdVHHk8N/PGPf3TZMKi8vByffvopOnbsqNjA1KTXiygoEOBmXyFV\nVWwx7HobYuUCAekGrHRGoHlzM0JCRMtmTrdz110ioqPNitQ9EBFR9Xn8z/G0adOwdetWDB06FNu3\nbwcAnD17FuvWrcMDDzyAw4cPY/LkyaoNVElywx1fTQ84azHsatWAUjdM66oBZXf+69DBjDNnCpyu\nVHDm4YfLceRIoWKvT0RE1ePx1ED//v2xZMkSZGRkIDs7GwAwZswYAEBQUBDeffddPPbYY+qMUmHy\nu1ejEVB4GwGPONYIiKLtqgHRckytGoGGDZUt1KtMsGI7/UFERL5XqYZCI0eORGpqKrZs2YLTp0/D\nZDKhRYsW6NOnD6Ki1G/NqxS5F79UJ+D96nX3LYatz5GmBpQZn7VGQMCdd7Jin4iIJJXuLBgREYHB\ngwerMRavkTMCvpoakGsErFMDgqVY0DEQUG5qwNpHQMmpASIiqt1ue5vJzc3FwYMHcfXqVcux3377\nDePGjUPnzp3RpUsXTJ48GZcuXVJ1oEqSawR8tXLA1fJB222IlS4WVKuPABER1W4uAwGz2YyXXnoJ\nBoMB3bt3R3R0NCZMmIBr166hZ8+eWLhwIc6fP48zZ85g3rx56NSpE86ePevNsVdZSIi07t13GQF5\n+aD0tRQIVGwopGSNgBx0FBcLzAgQEZGFy0DgH//4B9555x0MHDgQH3zwAf76178iKysLSUlJKCgo\nwNatW5GTk4Pc3Fxs3LgRRUVFmDnT87X8viQIUlbAVxkB8dZ9WE7Xe2P5oG0DH2YEiIhI5rJGYOnS\npXjyySctKwQAoHXr1hg5ciRmzpyJXr16WY7369cPzz77LNauXavuaBWk14swGn1bIyA31TGZ5FUD\nosNeA0ouH7RmARgIEBGRzOVt5vTp00hKSrI7dv/99wOQmgs56tSpE37//XeFh6cenc53GQF3NQLW\njIA6nQUBZTsLEhFR7eYyECgqKkJERITdsdDQUABAiLwJuw0/Pz+U+apVXxXo9TVp1QCcrhqQagSU\nuWlzaoCIiJxxm3gW6nDnF19uPOTYR0BuHqTmqgHbjACLBYmISFapGWh3gUFtCxp8WSzoODUg9xGQ\nAwFBEFXrIwBYNwoiIiJy21Bo4sSJeOWVVyxfm2/dwYYNG4Ygh7tJQUFBrQoGpGJB37y249SA2Wxt\nMQxIH9Xahhjg1AAREVm5DASaNWsGwHrzv93xkJAQSw1BbSBnBOR34t5kNksBk3yTt20xDEgfIOkR\nzAAAIABJREFUpVUDnBogIiJ1uQwEzp0758VheF9kpAizWUBBAeBQE6k6OSOg0UjFgM4CAXn3QTUy\nApwaICIiWb3dFd6XbYblhkJ+fqLl3b/ZLFgKBa2BgHLZCtvzMCNARESyehsI+HLjIfuMQMWpAUFQ\nfvmgIFhXKbBGgIiIZPU2EPBlRkAur/Dzsy8M1GikMdlmBJSaGgCs0wMMBIiISFZvAwE5I+CLNsNy\nRkBuIGRtMSwdlwIB5QsZ5YJBTg0QEZGs3gYCERHSen1fZwT8/Kw3fdsaAaVXDQDWXgLMCBARkaze\nBgIaDaDT+aZGQA4E5L0FbPcakI6LivcRAKwZAe41QEREMsUCgaKiIvz3v/9V6nRe4avugiaTtY+A\nn5/1pl9x1YByywcB1ggQEVFFigUCq1evxt13363U6bxCr/fNfgO2GQFBcL1qQFo+qNy7d2uNgGKn\nJCKiWk6xQOCee+7B8OHDlTqdV0gZAe+/rskk1ScIglwjoH5nQYBTA0REVJHbvQYqIy4uDnFxcUqd\nziv0ehGXLvmmoZB8g5enAdTeawBgHwEiIqqo3hYLAoBO55upAduOgdaGQoJXMgIajWi37wAREdVv\nHt8SZsyYcdttiAMDA9G4cWN069YN7dq1U2SAatLrfbd8UL7B23YRrLjXgLJ9BLRaaZ+BWrRJJBER\nqczjQGDmzJkQRRGi6Nn88vDhw5GVlVWjtyaOjPRdRkD+tUgZAfs+AtZiQUGxFsOAlBHgtAAREdny\n+P3m4cOHodPpMGjQIOzfvx+5ubm4efMmfvzxR4wdOxZBQUFYt24dDh48iMmTJ2P58uV4++231Rx7\ntUVGiiguFnDzpndf12wWKtQIVGwxLKjQR0BkV0EiIrLjcUYgIyMDPXr0wKpVq+yOt2/fHgsWLMCV\nK1cwf/58bN26FV26dEFOTg6ysrLw0ksvKT5opdi2GQ4O9t4N0nYaQO4j4KxYUKoRYEaAiIjU43FG\n4ODBg3jkkUdcPv6nP/0Je/bssXzdo0cPnD179rbnPXDgAJKTkysc37BhA7p164aePXti8eLFng6z\nUiIjpY/erhOw3VXQXWdBdWoEmBEgIiIrj28zUVFROHjwoMvHDx06hEj5zgogJycHer3e7Tnnzp2L\nMWPGoKSkxO54WVkZJk2ahC1btmDHjh348MMP8b///c/ToXpM3oHQ23UCtjd4edMhb/QR0GqBgADl\nzkdERLWfx4HA008/jaysLEyfPh03btywHM/Pz8fcuXORmZmJtLQ0AMC+ffvw/vvvIykpye05Y2Ji\nsHr16goFiMeOHUNMTAx0Oh20Wi0SEhKwc+fOyvxcHvHVVsSOywddrRpQukbAz0/k1AAREdnxuEZg\n+vTpOH78ON544w288cYbiIqKQmBgIC5fvgyz2YxHHnkEs2bNQnFxMRISEqDX6/H666+7PWdqairO\nnTtX4XheXh50Op3l6/DwcBiNRrfn0umCPf1RLIKCpI8lJQHQ6bSV/v6qCgjQwN9fgE4XDK1W+txs\nBkJCtNDp/OHvLx0TRQHBwQJ0OmXmB+64QwOttmq/K5m/vxSZVOccVPPwutZdvLZ1l3xtq30eT5+o\n1Wrx+eefY9euXVizZg1OnjyJsrIyDBgwAKmpqejduzcA6Sa+ZMkSPPLII4iKiqrSoHQ6HfLz8y1f\n5+fn33aaoSoCA4HQUBE5OYqf2i3blL81IyC4KBZU7nXnzjVb9jkgIiICKhEI7N69GwkJCUhMTERi\nYqLL50VERGDkyJHVGlSbNm1w8uRJ5ObmIjQ0FDt37rzt6gOjsWprACMjQ3HlSjmMxtIqfX9V3LwZ\nAEHQwmi8CbM5BEVFZgAalJSUwWgsgyiGoLjYhLIyf5SXlyk+ttskV9yS31VU9fdNNROva93Fa1t3\n6XTBCAiofqtYj3POSUlJuPvuuzFt2jT88ssv1X5hW3LToezsbHz00UfQarV499138dBDD6Fnz54Y\nPXo0DAaDoq8p88VWxCaTYNNQSER5ufS5Y7Gg0jUCREREjjwOJT7++GOsXLkSb731FubMmYMOHTpg\n6NChGDJkCO68884qD6BFixbYu3cvAFiKDQGgf//+6N+/f5XP6ylfdBe0vcFrNLAJBOyXFCq9fJCI\niMiRx7eZkSNH4uuvv8bly5excOFCNGzYEH/729/QvHlzPPDAA/joo4/sVhPUFr7ICNh2EfTzA8rK\npOP2NQICAwEiIlJdpW8zUVFReO6557Bt2zZcvHgR7733HgRBwNixY9G0aVM1xqgqvd73GQGTSXp9\nx70GODVARERqq/L7zfz8fGzevBnbtm3D4cOHAQCxsbGKDcxbfDE14NhQqGJGQO4sKDAQICIiVVWq\n3DAvLw/r16/H559/js2bN6O0tBRt27bFlClTMGTIELRo0UKlYaonMtL7nQUdmwc5KxZUY/kgERGR\nI48DgUcffRSbN29GSUkJ7rjjDkyYMAFDhgxBp06d1Byf6iIjReTlCSgvlzbl8Qb7TYcAucOysxbD\nci0BERGRGjy+9e3cuRPDhg3D0KFDkZSUBI1NFdulS5ewbNkyLF26VPGlhWqz3YEwKso7N13HhkKu\nMgKsESAiIrV5HAhcvnwZgTaN6ktLS7Fu3TpkZWVh8+bNMJlMdsFBbWHdeAioYiPESjObrXP/giCi\nvFz6vcm/PrlYkFMDRESkNo8DATkIOHToEDIzM/HZZ58hNzcXANC0aVM888wzeO6559QZpYrsNx7y\nXkbA2lDImhFwXDXAQICIiNTmUSBw5coVl6n/1157DdOmTYO/tybYFWY7NeAtUsrf2jzIeR8BadVA\nLUyyEBFRLeLy7l1WVob169cjKysL33zzDcrLyxEYGIiUlBSkpqaiY8eO6Nq1Kzp16lRrgwDAN1sR\nOxYLOussKPcWYEaAiIjU5PIObjAYkJOTA51Oh9TUVAwaNAj9+vVDREQEADjdPrg2Cg0F/P2920ug\nYothwfK5/FHOEjAQICIiNbkMBHJychAaGoohQ4agV69eSEpKsgQBdYkgeL/NsG2NgKs+AvKSQnkK\ngYiISA0uZ6C3bduGJ598EtnZ2Xj88cdhMBiQmJiIefPm4fz585YdA+sCb7cZdr3pkPyx4o6ERERE\nanB5m0lOTsbixYtx6dIlrFq1CgMHDsShQ4cwefJktGzZEn379gUgtRqu7SIjfVsjIE8D2K4aKCtj\njQAREanvtu83AwMDMWjQIHz55Ze4cuUKlixZgl69euHEiRMAgBEjRqB3797Izs5GiZzPrmX0etHL\nqwYEm4yAaCkMdNZ2mIEAERGpqVKJ54iICIwaNQpbtmzBxYsX8c477+C+++7D9u3bMXToUBgMBrXG\nqSpf1Ai433SIxYJEROQdVZ6BNhgMmDhxIg4ePIj//Oc/mD59Oho1aqTk2LxG2oHQe69nu4eAtFQQ\nls9dHSMiIlKDIreZVq1a4bXXXsPx48eVOJ3XeXsrYttiQdsaAfuMgFwjwFUDRESkHr7fhFQjkJsr\nQPTSPbdiQyHppm9fLGh9nIiISC0MBCBlBEwmAYWF3nk9x+WDMtvpAhYLEhGRNzAQgHW/AW8VDDo2\nFJK5KyAkIiJSA28zAHQ6eSti7wQC9ssHrcftlw+yjwAREamPgQC8nxGwrxGwFibYdhZkjQAREXkD\nAwFYdyD0XkbAfhtimbOGQpwaICIiNfE2A0Cnkz56s0bA9qYvc75qgMsHiYhIPQwEIKXfdTrvtRm2\nnRpwlRGQ2w5zaoCIiNTEQOAWb7YZts0I2N7ona0kYCBARERqYiBwizfbDLvuI+D6GBERkRp4m7nF\nmxkBxxbDMmeBADMCRESkJgYCt+j13ttvwGQSbKYBbJcPVlxJwECAiIjUxEDgFl9lBJxNAwg2w+Cq\nASIiUhMDgVv0em+vGpBu8LcrFmSNABERqYm3mVu8uRXx7YsFrVkATg0QEZGaGAjcEhkpoqhIQHGx\n+q/lqqEQiwWJiMjbGAjcEhkpffTG9IAnDYVkDASIiEhNDARu8ebGQ64aCrGPABEReRtvM7d4c+Mh\nUazMqgHVh0NERPUYA4Fb5IyAN7oLmkyCTUMha2Gg8xbDXD5IRETqYSBwi07nvYyAyWS/06CMNQJE\nRORtDARuCQoCgoO901RIWj5YsY+As86CrBEgIiI18TZjw1u9BGxXDbBYkIiIfIm3GRveajNcuRbD\nqg+HiIjqMQYCNrzVZvj2ywfZWZCIiLzDZ4GA2WxGeno6evbsieTkZJw+fdru8Xnz5qF9+/ZITk5G\ncnIyTpw4ofqYvJkRcPbu3/mqAdWHQ0RE9Zi/r1547dq1KC0txd69e3HgwAFMnjwZa9eutTx+5MgR\nLFu2DJ07d/bamCIjRfz+u7qxkSgCZrPgUY2AIIh2gQIREZHSfJYR2LNnD/r27QsA6N69Ow4dOmT3\n+OHDhzF79mwkJiZizpw5XhlTZKT6nQXFW1l/a42AdRrAMRBgNoCIiNTms4xAXl4eIiIiLF/7+fnB\nbDZDc+sumJaWhvHjxyM8PByDBg3Cxo0bkZKS4vJ8Ol1wtcdkMAgwGgVFzuVKWZn0MTRUC53OH+Hh\n1sBDrw+GVguEhEjH/PyU+bmU5O8vRSc1bVxUPbyudRevbd0lX9vq8llGICIiAvn5+ZavbYMAAMjI\nyECDBg2g1WqRkpKCH374QfUx6fVSQyGTSb3XMJulj+63IbZ/DhERkVp8lhGIj4/Hhg0b8Pjjj2P/\n/v3o2LGj5TGj0YiOHTvi119/RUhICLZv347Ro0e7PZ/ReLPaYwoK8gcQjAsXbkKvr/bpnCoqAoBw\nlJSUwmgsR3GxH4AQAEB+/k0IAlBSogXgB41GmZ9LSfK7ipo2LqoeXte6i9e27tLpghEQUP3buM8C\ngUGDBmHLli2Ij48HAGRmZiI7OxsFBQUYM2YM5syZg+TkZAQGBqJ3796WegI1yRsP5eYKlr0HlCZn\nBJw1D3JcNcCMABERqc1ngYAgCFiwYIHdsVatWlk+T0tLQ1pamlfHZL8DobqBgHXTIemjs6JBbjhE\nRERqY0MhG97YiliuP3DMCLirFSAiIlILbzU25OkANZcQms3Sud0HAhU3JCIiIlIDAwEbYWFSOt6b\nGQE5/e+sVoCBABERqY2BgA1BkLICagYC1oZC9gGAs6kBBgJERKQ2BgIO1N6KuGJGQPpo20qYNQJE\nROQtvNU40OnUrhGQPjo2FGJGgIiIfIGBgAO1pwYqs2qAyweJiEhtDAQcqL0VsauGQswIEBGRLzAQ\ncKDXizAa1Tu/Jw2FHDsMEhERqYW3GgdqZwRMJsc+AlIA4KxYkBkBIiJSGwMBB3KNgKjS9DynBoiI\nqCZhIOBApxNRViagsFCd81uLBd31EWBnQSIi8g4GAg7kNsNqrRxwXSNgfQ5rBIiIyFt4q3FguxWx\nGhynBpwFAlw+SERE3sJAwIGcETAavZMRYI0AERH5EgMBB5GR0ke1MgJyjYBj+t921QCnBoiIyFt4\nq3Gg03m3RoAZASKiqjl//hxGjRqGQYNS0L9/H7z88iQUFOTjhRfScfToDz4d2//7f0cwYcLzPh2D\np/x9PYCaxt8fCA9Xr5eA2Syd11osWHEbYgYCRFTbnD0rIC9PuX83IyJE3H236zqpmzdvYsSINPzj\nH/9C585dAAArV36KsWOfQVRUQ8XGUR8wEHBC6iWgzrld9xGw/g/PQICIapNr1wTExYVa3ugoQaMR\n8fPPhWjY0HkwsHXrN4iPT7AEAQDw5JNDkJW1BA0aRGH+/HdhNN6AKIp4551/wmCIxpgxTyM/Px83\nbxZh6tTpeOCBXli/fg0WLnwffn5+6N49Dq+88hrmzp2NgwcPoKioCAMHpsJoNOLFF6egpKQEvXrF\n47vv9mHp0iVYvfpLCIKAQYMG49ln03Hq1ElkZIxDUFAwGjRogODgYMV+H2piIOBEZKSoWrGgXCMg\nZwLcb0PMVQNEVPM1bChi375CxTMCroIAADh//jyaN29R4XizZs2wb98eZGRMxvDhI7Ft22a8/vp0\nTJnyCnJycrBy5WpcvXoVZ86cQm5uDt56601s2bITQUFBGD/+OezY8S0EQUCbNm0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WAAAD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\nz2SVlxZKlgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f784a10>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Re-arrange the $y=Bx+I$ equation to solve for $y=3$: $x=(y-I)/B$, or in other words, find the time in the future when the average movie will pass the Bechdel test.\n",
      "\n",
      "Then convert the float into a proper date and return the day of the week as well (Mondays are 0)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime",
      "year_est = (3 - l_model.params['Intercept'])/l_model.params['date']\n",
      "year_int = int(year_est)\n",
      "d = datetime.timedelta(days=(year_est - year_int)*365.25)\n",
      "day_one = datetime.datetime(year_int,1,1)\n",
      "date = d + day_one\n",
      "print date, date.weekday()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2089-08-30 04:54:43.147324 1\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extrapolating this linear model forward in time, on **Tuesday, August 30, 2089**, the ***average*** movie will finally pass the Bechdel test. Just 75 years to go even the average summer blockbuster will have minimally-developed female characters! Hooray!\n",
      "\n",
      "But there could also be other things going on in the model. Maybe the rate at which movies passing the Bechdel test is accelerating or decelerating instead of changing constantly. We estimate a model with a quadratic term on time below. This model returns a [concave function](https://en.wikipedia.org/wiki/Concave_function) which suggests we've apparently passed \"Peak Bechdel\" back in the 1990s and we're well on our way regressing towards a misogynist cultural dystopia in the future. \n",
      "\n",
      "But we shouldn't put too much stock in such simple models -- especially when they make such divergent predictions about the 2090s. However, the lack of progress and perhaps the presence of even a slowdown in the improvement in Bechdel scores over time should be a cause for concern."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q_model = smf.ols(formula='bechdel ~ date + I(date**2)', data=avg_bechdel).fit()\n",
      "\n",
      "# Create a DataFrame \"X\" that we can extrapolate into\n",
      "X = pd.DataFrame({\"date\": np.linspace(start=1912.,stop=2112.,num=201)})\n",
      "\n",
      "# Plot the observed data\n",
      "plt.plot(avg_bechdel['date'],avg_bechdel['bechdel'],c='b',label='Observed')\n",
      "\n",
      "# Plot the predictions from the model\n",
      "plt.plot(X,q_model.predict(X),c='r',label='Quadratic model',lw=4)\n",
      "plt.legend(loc='upper right')\n",
      "plt.ylim((0,3))\n",
      "plt.xlim((1912,2112))\n",
      "plt.xlabel('Year',fontsize=18)\n",
      "plt.ylabel('Avg. Bechdel Test',fontsize=18)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.text.Text at 0x10bf4ddd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d9AUFwJIlOmRmcr5mMJLAF/9a+TWRC3x8vFJIdTriXt+9Wz1GnplJPleLRVRY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\ndrtd9A4cPXoUKSkpedrnzp2LGjVqICUlBSkpKTh37lyRz8V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8OSiVnN9CN1IEeJqiJ4QE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bf245d0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Budgets differ"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As I discussed above, the claim that budgets for Bechdel-passing movies is relatively uncontroversial and given the description of the data in the article, should be easy to reproduce. Visualizing the distribution of the data, there are no strong differences that jump out but if you squint hard enough, you can make out a negative trend: movies passing the Bechdel test have lower budgets.\n",
      "\n",
      "You'll notice each cloud of points around 0, 1, 2, and 3. These are simply \"jittered\" by adding a bit of normally-distributed errors to the plots (but not the underlying data we're estimating) to show the frequency of datapoints without them all sitting on top of each other."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = df['rating'].apply(lambda x:float(x)+np.random.normal(0, 0.05))\n",
      "y = df['Adj_Budget']\n",
      "\n",
      "# Plot with an alpha so the overlaps reveal something about relative density\n",
      "plt.scatter(x,y,alpha=.2,label='Data',c='g')\n",
      "plt.ylabel('Budgets (2014 $)',fontsize=18)\n",
      "plt.xlabel('Bechdel dimensions',fontsize=18)\n",
      "plt.xticks(np.arange(0,4))\n",
      "plt.yscale('log')\n",
      "plt.grid(False,which='minor')\n",
      "#plt.ylim((2e0,1e10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+C2qEMtFZzi/zvzz7aRr9A+IkoVqpvefCoWa/ye5oh0ROSJPi8vxV8Sz2D8D3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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bf24b90>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also visualize the median budgets with a bar chart to see that movies featuring two women who don't talk to each other appear to have much larger budgets than the rest. Movies that pass the Bechdel test also appear to have slightly smaller budgets than movies that don't pass."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Adj_Budget'].groupby(df['rating']).agg(np.median).plot(kind='barh')\n",
      "plt.xticks(arange(0e7,7e7,1e7),arange(0,70,10),fontsize=15)\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Budget (2014 $millions)',fontsize=18)\n",
      "plt.title('Median Budget for Films',fontsize=24)\n",
      "plt.ylabel('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.text.Text at 0x10c9fa490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dGkOHDkXXrl0hl8u1fs5OTk744IMPEBUVBR8fn9c2TpuIiOhtw7/5vmHvv/8+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\nEREREZEe/g/dpzPixOplKwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f7ee990>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> In order to get a consistent look at budget data, we\u2019re going to focus on films released from 1990 to 2013, since the data has significantly more depth since then... We rely on median, as opposed to average, budgets to minimize the effect of outliers, or in this case, huge blockbusters whose budgets are orders of magnitude larger than the typical movie\u2019s budget. We ran a statistical test analyzing the inflation-adjusted median budgets of films, and found that films passing the Bechdet Test had a median budget that was 16 percent lower than the median budget of all films in the set.\n",
      "\n",
      "With skewed data choosing medians doesn't always help, so I'll log-transform the data instead to enforce more normal distributions. We can quantitatively test this hypothesis by regressing budget against these categories. Indeed, the `-0.2504` estimate for `C(rating)[T.3.0]` confirms the finding that movies passing all 3 Bechdel dimensions have significantly lower budgets than movies that pass none of the Bechdel dimensions. The model predicts that movies that fail every Bechdel dimension have budgets of \\$13.6 million on average while movies that pass every Bechdel dimension have budgets of \\$10.6 million on average --- a $3.0 million dollar or 22% difference. \n",
      "\n",
      "But this simple model does a poor job explaining the distribution of data -- it explains 2.3% of the variance. Furthermore, we can also chalk this difference up to a number of other explanations as well: the Bechdel test may be standing in for differences in other variables such as genre, year, time of year, and composition of the cast and crew, among many possibilities. We'll explore some of these later."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bd_m = smf.ols(formula='log(Adj_Budget+1) ~ C(rating)', data=df).fit()\n",
      "print bd_m.summary()\n",
      "\n",
      "print \"\\n\\nMovies passing every Bechdel dimension have budgets of ${:,} on average.\".format(round(np.exp(bd_m.params['Intercept']+bd_m.params['C(rating)[T.3.0]']-1),2))\n",
      "print \"Movies failing every Bechdel dimension have budgets of ${:,} on average.\".format(round(np.exp(bd_m.params['Intercept']-1),2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                             OLS Regression Results                            \n",
        "===============================================================================\n",
        "Dep. Variable:     log(Adj_Budget + 1)   R-squared:                       0.023\n",
        "Model:                             OLS   Adj. R-squared:                  0.021\n",
        "Method:                  Least Squares   F-statistic:                     12.35\n",
        "Date:                 Mon, 07 Apr 2014   Prob (F-statistic):           5.52e-08\n",
        "Time:                         09:19:18   Log-Likelihood:                -2632.4\n",
        "No. Observations:                 1583   AIC:                             5273.\n",
        "Df Residuals:                     1579   BIC:                             5294.\n",
        "Df Model:                            3                                         \n",
        "====================================================================================\n",
        "                       coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------------\n",
        "Intercept           17.4318      0.115    151.286      0.000        17.206    17.658\n",
        "C(rating)[T.1.0]     0.1693      0.130      1.304      0.192        -0.085     0.424\n",
        "C(rating)[T.2.0]    -0.3081      0.148     -2.076      0.038        -0.599    -0.017\n",
        "C(rating)[T.3.0]    -0.2504      0.124     -2.026      0.043        -0.493    -0.008\n",
        "==============================================================================\n",
        "Omnibus:                      420.224   Durbin-Watson:                   2.067\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1253.612\n",
        "Skew:                          -1.339   Prob(JB):                    6.05e-273\n",
        "Kurtosis:                       6.440   Cond. No.                         8.76\n",
        "==============================================================================\n",
        "\n",
        "\n",
        "Movies passing every Bechdel dimension have budgets of $10,653,341.87 on average.\n",
        "Movies failing every Bechdel dimension have budgets of $13,684,932.09 on average.\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Earnings differ"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we turn our attention to the second claim the article makes that earnings differ. Let's plot the data first."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['ROI'].groupby(df['rating']).agg(np.median).plot(kind='barh')\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Return on Investment',fontsize=18)\n",
      "plt.title('Median ROI for Films',fontsize=24)\n",
      "plt.ylabel('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<matplotlib.text.Text at 0x10ca04110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NmzZFQEAA3Nzc1ILo119/ja+++go7d+7Enj17pCu+S5YsQVxcHAwMDNCiRQv8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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c9fa990>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It looks like movies satisfying 2 or more of the Bechdel provide much better ROI, but we'll need to run the model to see if these differences are significant. \n",
      "\n",
      "> ...we ran a regression to find out if passing the Bechdel test corresponded to lower return on investment. Controlling for the movie\u2019s budget, which has a negative and significant relationship to a film\u2019s return on investment, passing the Bechdel test had no effect on the film\u2019s return on investment.\n",
      "\n",
      "Our model replicates the same findings described in the article: negative and significant relationship with budget and no significant relationship with ratings. Thus, what appears to be higher ROIs actually isn't a significant difference.\n",
      "\n",
      "However, it's important to note that we could have estimated this model differently by coding the rating as a continuous variable or not logging the budget. The article isn't clear whether it does this, and while estimating such a \"bad\" model (`ROI ~ rating  + Budget`) doesn't change the significance or direction of the findings, it does produce a much poorer model (explaining only 2% of the variance, versus the 12% below). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Estimate the model\n",
      "ed_m1 = smf.ols(formula='ROI ~ C(rating)  + log(Adj_Budget+1)', data=df).fit()\n",
      "print ed_m1.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                    ROI   R-squared:                       0.117\n",
        "Model:                            OLS   Adj. R-squared:                  0.114\n",
        "Method:                 Least Squares   F-statistic:                     52.12\n",
        "Date:                Mon, 07 Apr 2014   Prob (F-statistic):           2.83e-41\n",
        "Time:                        09:19:19   Log-Likelihood:                -6221.7\n",
        "No. Observations:                1583   AIC:                         1.245e+04\n",
        "Df Residuals:                    1578   BIC:                         1.248e+04\n",
        "Df Model:                           4                                         \n",
        "=======================================================================================\n",
        "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------\n",
        "Intercept              61.2394      4.380     13.980      0.000        52.647    69.832\n",
        "C(rating)[T.1.0]        1.3111      1.254      1.045      0.296        -1.149     3.771\n",
        "C(rating)[T.2.0]        2.4859      1.435      1.733      0.083        -0.328     5.300\n",
        "C(rating)[T.3.0]        0.7976      1.195      0.667      0.505        -1.547     3.142\n",
        "log(Adj_Budget + 1)    -3.4338      0.243    -14.128      0.000        -3.911    -2.957\n",
        "==============================================================================\n",
        "Omnibus:                     2613.532   Durbin-Watson:                   2.029\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1650679.978\n",
        "Skew:                          10.671   Prob(JB):                         0.00\n",
        "Kurtosis:                     159.750   Cond. No.                         248.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The article ran a second regression model using gross profits as an outcome.\n",
      "\n",
      "> we ran a regression to find out if passing the Bechdel test corresponded to having lower gross profits \u2014 domestic and international. Also controlling for the movie\u2019s budget, which has a positive and significant relationship to a film\u2019s gross profits, once again passing the Bechdel test did not have any effect on a film\u2019s gross profits.\n",
      "\n",
      "It's unclear why Hickey expected this outcome to differ given that it's another relationship between the original Budget and Revenue data. The article also mentions international receipts when the data available from The-Numbers.com only reports domestic receipts, so we can't replicate this finding. \n",
      "\n",
      "We find, unsurprisingly, that the same results as above hold that Bechdel scores don't significantly influence Profit and Budget has a negative relationship with Profit."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Profit'].groupby(df['rating']).agg(np.median).plot(kind='barh')\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Gross Profit',fontsize=18)\n",
      "plt.title('Median ROI for Films',fontsize=24)\n",
      "plt.ylabel('')\n",
      "\n",
      "# Estimate the model\n",
      "ed_m2 = smf.ols(formula='Profit ~ C(rating)  + log(Budget+1)', data=df).fit()\n",
      "print ed_m2.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                 Profit   R-squared:                       0.012\n",
        "Model:                            OLS   Adj. R-squared:                  0.009\n",
        "Method:                 Least Squares   F-statistic:                     4.622\n",
        "Date:                Mon, 07 Apr 2014   Prob (F-statistic):            0.00103\n",
        "Time:                        09:19:19   Log-Likelihood:                -31704.\n",
        "No. Observations:                1583   AIC:                         6.342e+04\n",
        "Df Residuals:                    1578   BIC:                         6.344e+04\n",
        "Df Model:                           4                                         \n",
        "====================================================================================\n",
        "                       coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------------\n",
        "Intercept         1.957e+08   3.84e+07      5.099      0.000       1.2e+08  2.71e+08\n",
        "C(rating)[T.1.0]  6.223e+06   1.23e+07      0.506      0.613     -1.79e+07  3.03e+07\n",
        "C(rating)[T.2.0]  9.217e+06   1.41e+07      0.656      0.512     -1.83e+07  3.68e+07\n",
        "C(rating)[T.3.0] -6.948e+05   1.17e+07     -0.059      0.953     -2.36e+07  2.22e+07\n",
        "log(Budget + 1)  -8.921e+06   2.17e+06     -4.115      0.000     -1.32e+07 -4.67e+06\n",
        "==============================================================================\n",
        "Omnibus:                     1394.494   Durbin-Watson:                   1.955\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            48699.636\n",
        "Skew:                           4.044   Prob(JB):                         0.00\n",
        "Kurtosis:                      28.941   Cond. No.                         219.\n",
        "==============================================================================\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rvnHjBgRB0Pi684yMDGzcuBEAkJOTU+R9vi12dnaYMmUKAODQoUNYs2aNynoLCwvMnTsX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\noKurK1y4cEGcVlpaKgwYMECQy+XClStXBEEQhPj4eEEmkwlbt24Vl5s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       "text": [
        "<matplotlib.figure.Figure at 0x10f32b390>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A different model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model above inexplicably use \"Budget\" on both sides of the equation, which is a big no-no. Remember, we constructed ROI as $(Revenue - Budget)/Budget$ and Profit as $Revenue - Budget$ so in these models budget ends up being a function of itself.\n",
      "\n",
      "What happens if we just leave Budget on the right side of the equation and simply estimate Revenue as a function of Bechdel rating and controlling for Budget?\n",
      "\n",
      "We get *very* different findings. Now there is a significant and *positive* relationship between Budget and Revenue, as we'd expect. Furthermore, there is also a significant and positive relationship between Bechdel criteria and Revenue. Better roles for women translates into better revenue, even controlling for the fact that bigger budgets also create more revenue. This model also explains approximately 24% of the variance, versus 15% in the article's model suggesting it's doing a better job modeling the relationship of Bechdel test and financial performance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make the bar-plot\n",
      "df['Adj_Revenue'].groupby(df['rating']).agg(np.median).plot(kind='barh')\n",
      "\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Return on Investment',fontsize=18)\n",
      "plt.title('Revenue for Films',fontsize=24)\n",
      "\n",
      "plt.ylabel('')\n",
      "\n",
      "# Estimate the model\n",
      "ed_m3 = smf.ols(formula='log(Adj_Revenue+1) ~ C(rating)  + log(Adj_Budget+1)', data=df).fit()\n",
      "print ed_m3.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                             OLS Regression Results                             \n",
        "================================================================================\n",
        "Dep. Variable:     log(Adj_Revenue + 1)   R-squared:                       0.244\n",
        "Model:                              OLS   Adj. R-squared:                  0.242\n",
        "Method:                   Least Squares   F-statistic:                     127.2\n",
        "Date:                  Mon, 07 Apr 2014   Prob (F-statistic):           3.12e-94\n",
        "Time:                          09:19:20   Log-Likelihood:                -3338.6\n",
        "No. Observations:                  1583   AIC:                             6687.\n",
        "Df Residuals:                      1578   BIC:                             6714.\n",
        "Df Model:                             4                                         \n",
        "=======================================================================================\n",
        "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------\n",
        "Intercept               1.8864      0.709      2.661      0.008         0.496     3.277\n",
        "C(rating)[T.1.0]        0.2741      0.203      1.351      0.177        -0.124     0.672\n",
        "C(rating)[T.2.0]        0.6096      0.232      2.626      0.009         0.154     1.065\n",
        "C(rating)[T.3.0]        0.4483      0.193      2.318      0.021         0.069     0.828\n",
        "log(Adj_Budget + 1)     0.8825      0.039     22.438      0.000         0.805     0.960\n",
        "==============================================================================\n",
        "Omnibus:                     1650.678   Durbin-Watson:                   1.998\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           111167.694\n",
        "Skew:                          -5.042   Prob(JB):                         0.00\n",
        "Kurtosis:                      42.796   Cond. No.                         248.\n",
        "==============================================================================\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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69OiBw4cPAwD8/PyQl5eHVatWydotX74c27ZtQ8WKFRXX0KVLF6SlpWH16tXS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\nuFUBMg9lAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca0ec10>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, this effect is pretty weak and the Budget variable does most of the heavy lifting in terms of explaining why this model does well. In the figure below, we plot the relationships between Revenue and Bechdel scores after controlling for Budget. The clouds of green dots are the residual variance in the data that's not explained by the Budget. If we estimate a model that tries to explain this residual variance using the Bechdel scores now, we see the small but positive trend in red. This is the effect of Bechdel-passing movies making more money."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Estimate model without the Bechdel test IV and containing only the IVs to get residuals\n",
      "ed_m3_bad_res = smf.ols(formula='log(Adj_Revenue+1) ~ log(Adj_Budget+1)', data=df[df['Adj_Revenue']>0]).fit()\n",
      "\n",
      "# Put these residual estimates in a temporary DataFrame\n",
      "df_temp = df\n",
      "df_temp['residuals'] = ed_m3_bad_res.resid\n",
      "\n",
      "# Estimate a bad model with a continuous Bechdel rating against the residuals\n",
      "ed_m3_bad = smf.ols(formula='residuals ~ rating', data=df_temp).fit()\n",
      "ed_m3_bad_x = pd.DataFrame({'rating':np.linspace(min(df['rating']),max(df['rating']),100)})\n",
      "\n",
      "# Add a jitter so the points aren't overlapping\n",
      "x = df['rating'].apply(lambda x:float(x)+np.random.normal(0, 0.05))\n",
      "y = df_temp['residuals']\n",
      "\n",
      "plt.plot(ed_m3_bad_x['rating'],ed_m3_bad.predict(ed_m3_bad_x),c='r',lw=2,label='Model')\n",
      "\n",
      "# Plot with an alpha so the overlaps reveal something about relative density\n",
      "plt.scatter(x,y,alpha=.2,label='Data',c='g')\n",
      "plt.xticks(arange(0,4))\n",
      "plt.autoscale()\n",
      "plt.xlabel('Bechdel score',fontsize=18)\n",
      "plt.ylabel('Residual Adj. Revenue',fontsize=18)\n",
      "plt.ylim((-5,5))\n",
      "plt.legend(loc='lower left')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.legend.Legend at 0x10ca1b4d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tvEUw09upbaG2gF8NcCRwjE6lTUNtcXDoEF7XYzp5+IrPf5AnPG67otwf/MEf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EEQODN3jtXKKfZaO6AHqKJCXp9UvYuiyPn5h7BtG1mW7/idTq/38VE0ylxqXUJSRSI3JGflSCXT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\nCCHGCEnqQgghxBghSV0IIYQYI/4fvKt5CfaLztgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f77d4d0>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary of replication results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I summarize the findings of my replication attempts, alternative statistical analyses, as well as other exploratory findings:\n",
      "\n",
      "* Hickey found that the number of movies passing the Bechdel test has been increasing over time. I found the same, but the effect had slowed in recent years. Simplistic extrapolations suggest it may take until the end of the century until the average movie passes the Bechdel test or that we run the risk of regressing towards more backwards portrayals of women in film.\n",
      "* Hickey found that Bechdel movies had lower median budgets. I also found that passing the Bechdel test was correlated with significantly lower budgets.\n",
      "* Hickey found that Bechdel movies had no effect on the film's ROI, controlling for budget. I also found that there was no significant relationship between ROI and movies passing the Bechdel test.\n",
      "* Hickey found that Bechdel movies had no effect on the film's gross profits, controlling for budget. I also found that there was no significant relationship between Profit and movies passing the Bechdel test.\n",
      "* I noted that ROI and Profit are problematic variables for the modeling Hickey described, so I used a simpler model of Revenue alone but still controlling for budget. This model provided a better fit to the data and also found that Bechdel movies had significantly higher revenue."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "BONUS! Relationship between Bechdel scores and ratings"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you've made it this far, I want to reward you with some new analysis rather than trying to replicate and criticizing others' models. Here I want to extend the bigger --- and very important --- question that Hickey was posing of how gender biases manifest in the movie business. In particular, I want to ask: ***are movies that pass the Bechdel test are systematically rated lower by fans and critics?***\n",
      "\n",
      "Here I draw on data from the OMDB that captures Metacritic scores (professional critics) as well as the ratings of IMDB users (crowd critics). We'll examine each of these in turn, starting with the Metacritic.\n",
      "\n",
      "As before, we can plot the distribution of median Metacritic scores across the four Bechdel dimensions. This suggests that movies passing the Bechdel test are more strongly criticized and received lower ratings that movies that fail it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make the bar-plot\n",
      "df['Metascore'].groupby(df['rating']).agg(np.mean).plot(kind='barh')\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Rating',fontsize=18)\n",
      "plt.title('Mean Metascore',fontsize=24)\n",
      "plt.xlim((5,6))\n",
      "plt.ylabel('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.text.Text at 0x10d3cb210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/xbZt29CpUyccPXoUtWvXRt++fdG3b18IIbBw4UJ8+umnWLduHYYNG4YKFSog\nJydHL6xfuXIFhw8fhoWFBQoKCnD8+HFUrFgR3bt3R/fu3QEA69evR9++fbF06VIsWLBA0T1rA6s2\n8NrY2ODQoUN69eTl5SEqKgp16tQBANSrVw87duzA/fv3YWFhIbc7fPgwvvnmG0yZMqXUNdjZ2eHP\nP/9Efn4+jIz+9xvq+fPnddqVtrayYGNjA6DwrxhPXu///u//kJaWJs+zL+59Xrt2rd5UGCIiKp2s\nrIe4c0d/sOVF0Y5Il2cN/3UajTlMTJTFYs6ZfgllZGQAABo3bqyzfenSpXjw4IG8fN5ff/0FT09P\nLF++XG5jbGwMZ2dnAIVTRW7duoVWrVphzpw5chtJkuT5wkZGRjAyMkKXLl2wefNmHD9+XOeaQUFB\n8PHxQUZGBvLz8+Ht7Y1x48bptHFzc5Ovp9SaNWsAAG3btgUA+Pj44NatW3qjv0uXLkXfvn2xc+dO\nAEDXrl1RUFCApUuX6rQLCwvD+vXrUatWrVLX4Ofnhzt37mDZsmXyttzcXPz444867UpbW1moVasW\nXFxcEB4ejmvXrsnb8/LyMHjwYPj5+SE/P7/E91n73mh/SXhytJ2IiIiU4cj0S+itt95CpUqVMH78\neFy8eBFWVlZISEjA5s2bUb9+fdy9exdA4RfovL29MXnyZPz9999o1qwZLl26hO+++w6NGzdG+/bt\noVar0b9/f4SGhuL+/ftwd3dHRkYGvv/+e9SsWVNeVu3rr79GfHw8PD09MXLkSNSvXx9bt25FdHQ0\nhg0bJgf78ePHY/r06ejVqxc6deqE7Oxs/Pjjj7CwsMCgQYNKvLdjx45h9erV8uvs7GxERUUhLi4O\n/v7+8hMBAwICsHLlSowePRqHDh2Cm5sb/vrrL/zwww9o2bIlBg4cCADo0aMHOnbsiI8//hh//fUX\nXFxcsGfPHqxatQrTpk2DlZVVqfv9ww8/xNKlSzFq1CicOnUKDg4OWL16tfxFS63S1lZWFi1ahHbt\n2qFly5YYPnw4qlWrhrVr12Lv3r34+uuv5WklpXmftfO8V61ahYKCAvTv319nFJ6IiIieDsP0CyJJ\nUqkf4WxtbY3Nmzfjs88+w1dffQVjY2N06NABhw4dwooVK7BgwQL5C3ARERH48ssvER0djR9//BFV\nqlTBu+++i5kzZ8qjkWFhYahXrx7Wrl2LtWvXwsLCAu3bt8esWbNQpUoVAIVTHJKTkzF16lQsW7YM\nWVlZsLedU5p/AAAgAElEQVS3R3BwMMaMGSPX9sUXX0Cj0WD58uXYvn071Go1PDw88Ouvv6Jhw4bF\n3j8AbNiwQedLjhYWFvJDVh4f8TYxMcHOnTsxY8YMrF+/Hr/88gtee+01DB8+HNOmTYOZmZl83o0b\nN2LGjBn45ZdfsHr1ajRo0AChoaE687+L6vvHt6tUKsTFxWHixIn47bffkJWVhW7dumHMmDH48MMP\nn7q24q77NFq1aoXdu3dj2rRpWLhwIXJzc9GoUSOsXLlSp67SvM+NGjXC6NGjER4ejoMHD8Lb27tU\nq7AQERGRYZLgc4WJyIDuH28s7xKIiMpd1u0rmDO0FeztHcqtBs6Zfv44Z5qIiIiIqBwwTBMRERER\nKcQwTURERESkEMM0EREREZFCDNNERERERAoxTBMRERERKcQwTURERESkEMM0EREREZFCDNNERERE\nRAoxTBMRERERKcQwTURERESkEMM0EREREZFCDNNERERERAoxTBMRERERKcQwTURERESkEMM0ERER\nEZFCDNNERERERAoxTBMRERERKcQwTURERESkEMM0EREREZFC6vIugIheTlm3r5R3CURE5S77zo3y\nLoFecpIQQpR3EUT08jl58hSysh6Wdxn/WZaWZgDAPn6O2McvxqvQzzY2djAyMiq362s05gCAO3ce\nlFsN/3UajTlMTJSNMXNkmogMatiwIf/D/Rzxf47PH/v4xWA/06uOc6aJiIiIiBRimCYiIiIiUohh\nmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRi\nmCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIXV5F0BEL6ez\nZ88iK+theZfxn2VpaQYA7OPniH38YrCfi2djYwcjI6PyLoOeI4ZpIjJo9MLE8i6BiOhfLfvODXz7\naQ/Y2zuUdyn0HDFME5FBlpVrl3cJRERELz3OmSYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiI\niBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIi\nIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUuilCdN9\n+/aFSqXC7du39fb1798fKpUKvr6+evuysrKgVqvh7+//Isr8VwoPD4dKpUJSUlKZnC8tLe2pj0lM\nTIRKpcLKlSsBABcuXIBKpcKXX35ZJjU96XmfvyTnz58vl+sSERHRi/XShOm2bdsCAJKTk/X2JSQk\nwNjYGElJSSgoKNDZl5ycjIKCArRr1+5FlPnK69SpE2bMmKH4eEmSin1d1p73+Q356quv0KlTpxd+\nXSIiInrxXpow3aZNGwDA/v37dbanpKTg8uXL8Pf3R2ZmJg4dOqSzf8+ePQD+F8bp+dq+fXu5BNR/\nkx07diA/P7+8yyAiIqIX4KUJ002aNEHVqlWxb98+ne3x8fFQqVSYPHkyJEnCzp07dfbv2bMHtWvX\nRoMGDV5kua80IUR5l/DSYx8RERG9Gl6aMC1JEjw9PfVGpuPj4+Hs7IwGDRrAyckJ8fHx8j4hBJKT\nk3VGpfPz8zF//nw4OjrCzMwMtWvXxogRI5CRkSG30c7f3blzJwICAlClShVYWVlh0KBByM7OxpYt\nW+Ds7AwLCws0b94cCQkJOjU9fPgQU6ZMga2tLUxNTWFvb49p06YhNzdXbqOdp3z8+HH4+/ujSpUq\nqFixInr27ImLFy+W2B/Z2dmYOHEibGxsYGpqCltbW0ycOBEPHjx4pmvcuXMH5ubm6Nu3r96+JUuW\nQKVS4fTp03r7tHOQAWDlypU6c7D/+ecfjBw5EnZ2djAzM4OVlRXefvtt+a8GpZWUlARzc3O0adMG\n2dnZRba7d+8eJk6ciEaNGsHc3BwVK1aEu7s7Nm3apNf20aNHCAoKQrVq1VCpUiX07NkT586d02u3\nfPlyODs7w9zcHNbW1vjggw90+rCoOdja7dqpLzY2NkhKSsLFixdLnLOtUqkwf/58fP3116hXrx4s\nLCzQrl07pKam4uzZs+jUqRMsLS1hZ2eH7777Tu/48PBwNG/eXK554MCB+Oeff/RqW716NaZMmYI6\nderA3NwcrVq1QmJiYpF1ERERUem9NGEaKJzqcevWLTnsCCGQmJgIb29vAIC3tzd2794th9bTp08j\nMzNT3g8A/fr1w2effQYnJyeEhISgd+/eWLZsGd566y3cuXNH53oDBgzA5cuXMXfuXHTp0gXh4eHw\n8fHBRx99BD8/P8yZMwf//PMPevfuLR+bn5+Pbt26YeHChfD19cV3332Hdu3aYdasWfDz89O7px49\neuDOnTuYM2cOhg0bhpiYGPTp06fYfsjJyUGHDh0wb948dOjQAYsWLULbtm0xd+5cdOzYEXl5eYqv\nodFo0LVrV2zevFknmAPAmjVr8MYbb6Bx48Z6x1lbW2PVqlUACt+n1atXo1GjRnjw4AE8PT0RERGB\nQYMGISwsDMOGDcPBgwfRqVMnpKenF3uvWkeOHEH37t3h5OSELVu2oEKFCgbbCSHQtWtXLF68GH5+\nfggNDcUnn3yCCxcuoGfPnjh58qRO+0WLFmHDhg2YOHEigoKCEB8fDw8PD9y4cUNu8+mnn2LIkCGw\ntrbGggULEBAQgI0bN8LNzU3vl5KSprh8++23aNSoEapVq4bVq1cb/Ew8Wd/KlSsxYcIEjB8/Hrt2\n7YKfnx/efvtt2NvbIzg4GNWqVcPYsWN1vkD65ZdfYtCgQWjYsCFCQkIwdOhQREVFwd3dXecXRwCY\nMmUKNmzYgE8//RQzZsxAWloaunbtilu3bhVbGxEREZVMXd4FPE47wrx//340aNAAJ0+eRHp6uvzl\nQm9vb4SEhGD37t1o27at3nzp2NhYREREYNy4cVi4cKF8Xk9PT/Tp0wezZ8/G3Llz5e21a9dGbGws\nACAgIAAJCQnYuXMnYmNj0bFjRwCAhYUFhgwZgoMHD+Ltt9/GqlWrEB8fj7i4OHTo0AEAMHToULi5\nuSEwMBDR0dHo0aOHfA1XV1esX79efn3//n0sWbIEqampsLe3N9gPP/30E/bu3YuQkBCMGTMGABAY\nGIimTZtiwoQJWLp0KYYPH674Gu+//z4iIyMRExODd999FwBw9epV7N69G3PmzDFYU4UKFfD+++/j\nww8/hJ2dnbx6yrp163D+/HnExsbK/QEAdnZ2GDZsGHbv3m1wFZbHpaSk4J133oGdnR3i4uJgaWlZ\nZNv9+/dj165d+OGHHzBkyBB5u7u7O9555x3s2LEDr7/+urxdrVZj3759sLa2BgC0a9cObdu2xbx5\n87BgwQKcOnUK33zzDXr16oXff/9dPs7X1xfu7u6YMGEC1q1bV2z9j/Px8UFwcDAePnxYqhVmMjMz\ncfjwYVSvXl3ui/Xr1+Pzzz/H7Nmz5ZodHBywfft2tGnTBufPn8eMGTMwceJEzJo1Sz7Xe++9hxYt\nWmDWrFk6n38AOHDgAMzNzQEA9evXR79+/RAZGYmAgIBS3xsRERHpe6nCtJOTEzQaDZKTk+Hv74/4\n+HgYGRnB09MTQOGIqJGREf744w85TNetWxd2dnYAgOjoaADAxIkTdc7bu3dvODo6YuPGjTph2sfH\nR/63JEmwt7fHvXv35CANFP7ZHgCuXbsGAIiIiED16tXRokUL3Lx5U27XuXNnGBkZISYmRidMPzlC\n/MYbbwAonBpRVJiOjo6GRqPByJEjdbaPHTsWX331FaKjo3XC9NNeo0uXLtBoNPjtt9/kML1u3ToI\nIdCvXz+DNRWlb9++aN++PapWrSpvy8nJkecMZ2VlFXv85cuX0aFDB6hUKmzfvh1WVlbFtn/zzTeR\nmZkpB0Og8K8F2tH6J6/34YcfykEaKPwMOTk5YfPmzViwYAFiYmIAAJ9//rnOcW5ubujYsSM2b96s\nt4JMWWrdurUcpAHAwcEBANCzZ09525OfwaioKAgh0L17d53PYI0aNeDs7IyYmBidMN21a1ed/tJ+\nPq5fv172N0RERDosLc2g0ZiX3LAYarURADzzeaho2j5WdGwZ1vHMVCoVPDw85C8hxsfHw9XVVR6p\n1Gg0aN68OXbt2gUA2Lt3r84Uj7S0NFSuXFknnGg1atQIcXFxOttq1Kih81qtVusda2RU2LnaQJWa\nmor09HSD1wCAS5cu6bx+sp2pqSkAFLvaQ1paGuzs7ORraxkbG8PW1lZv6sHTXsPU1BR+fn5Ys2YN\nHjx4AHNzc6xduxZvvfUW6tatW2RdxZkzZw727NmD1NRUpKamylNxSgqiy5Ytg0qlghACZ8+eRbVq\n1Uq8lpGREcLCwpCYmIhz584hNTVVnrLy5PUaNWqkd7ydnZ38FwntmtmOjo567bSfmccDa1kz9BkE\noPMLgKHPIFAYxA3Rvv9aSj6DREREVDovVZgGCqdkTJ06FY8ePUJSUpLe6Gzbtm0RFhaGjIwMnD17\nFp999pm8r7gVFAoKCmBiYqKzTRtcHlfSnNj8/Hw0bNgQoaGhBvdXrlxZ57X2S3tP42nvQ8k1/P39\n8dNPP2HTpk1wdXXFgQMHsHjx4qc+z5kzZ/DWW28hNzcXnTp1gr+/P5ydnVFQUFDi9A4AqFu3Ln7/\n/Xd07twZgYGBOHLkiMH3RSs9PR1vvvkmrl27ho4dO8LX1xdvvPEG6tWrhzfffFOvvaH3UwghB9SS\n+hoATExMivxC5LMG0qLutbjPofaamzZt0hlxLoqSzwcREZWNrKyHuHPnQckNi6EdkX7W81DRNBpz\nmJgoi8UvXZj28vJCTk4O1q9fjzt37uiMPAPA22+/jQULFmDNmjUQQujst7GxwbZt23Djxg2dkT2g\nMPQpHXV9nI2NDQ4dOqT3kJi8vDxERUWhTp06ZXKNffv2IS8vTyds5eTkIC0tDV5eXs98DW9vb9Sq\nVQvR0dG4du0a1Gp1iV+MNGTu3LnIzMzEmTNndKaU/Prrr6U6fvDgwXB1dcWsWbMwfPhwLFiwQG/K\nxePCwsJw4cIFxMfH66ziUtTKIYae1nj27Fm5Vu0UitOnT8PNzU2n3ZkzZ2BpaQkrKyvcu3cPQOHq\nII97fPWMF0Vbc506deQpG1pxcXHQaDQvvCYiIqJX1Us3ZNWyZUtYWFggLCwMpqam8PDw0Nn/1ltv\nQa1WIzw8HDY2Nqhfv768TztX+ckv0W3YsAFnz55Ft27dnrk+Hx8f3Lp1S29keunSpejbt6/eOthK\n9OjRA3fv3tUbKQ4NDUVWVlaZ3IckSejXrx/i4uKwefNmvXnPxR33+FSKjIwMWFhYoF69evK2nJwc\nLFmyBAD0Vh4pytChQ+Hi4oKZM2cW+7hy7UoVj684IoSQl4578nrr1q3TmUe9detWnD59Wh41135m\nHp9LDwCHDx/G9u3b0bVrVwBA1apVoVarceTIEb3zP8nIyOi5zrMu6nN+4sQJdO3aFcHBwc/t2kRE\nRKTrpRuZVqvVaN26tbxywZPzPy0tLeHq6oq9e/diwIABOvu6dOkCHx8ffPvtt7h8+TK8vb1x9uxZ\nhIWFwd7eXu+LiYaU9LCNgIAArFy5EqNHj8ahQ4fg5uaGv/76Cz/88ANatmyJgQMHPvU9F3WNoKAg\nnDhxAi1btsTBgwcRHh4Od3f3MluBwd/fH8HBwdixYwd+/vnnUh1jbW2NhIQELFu2DJ06dUKXLl2w\nadMmdO3aVV5CULsONQDcvXu3VOeVJAmLFy+Gu7s7RowYga1btxps16VLF3z33Xfo1q0bBg0ahJyc\nHKxbtw7p6ekwMzPTu152djY8PDwwdOhQXL58GSEhIXBwcMAnn3wCoPBhQWPGjMGiRYvQoUMH+Pj4\n4Nq1a/juu+9QtWpVfP311wAKVzPx8fFBREQEhgwZgjfffBMJCQnYs2eP3rQba2trJCUlYeHChfDw\n8NAb8X5WTZs2lWu+efMmfH19kZmZie+++w4ajQYzZ84s0+sRERFR0V66kWmgcKqHJEl6Uym0vL29\nIUmSwUeIr1+/HjNnzsSxY8cQFBSEqKgoDBs2DAcOHEClSpXkdobmpEqSVOR2LRMTE+zcuRMff/wx\n4uPjMXbsWMTExGD48OHYtm0bzMzMir1GcdufvEZQUBC2b9+O8ePHIykpCZMnT5ZXOHnaaxhq17Jl\nSzg4OMDc3Fxn9YjizJ07F7m5uRgzZgySkpIQGBiI2bNnIzU1FWPGjMHSpUvRr18/HDhwADVr1tR5\n4E1J9+3q6oqAgABs27YNv/32m8E2nTp1wrJly3D//n0EBQVh4cKFaN26NQ4ePAhnZ2e9602fPh2t\nW7fG5MmTERYWht69e+PPP//UWX4vJCQEixcvxvXr1/HJJ59gxYoV8PPzw6FDh3T+8vHDDz+gf//+\niIyMRFBQEB48eIA//vgDxsbGOjVOmDABDRs2xMSJE7FixYpS9evjNZfmce0hISEIDQ3FzZs38emn\nn2Lx4sXw9PTErl270LBhw6e6JhERESknCT73+JXWqFEjNG/eHGvWrCnvUugl0/3jjeVdAhHRv1rW\n7SuYM7QV7O0dnuk8/ALi8/csX0B8KUem6cVITEzE2bNny2RqChEREdGr6KWbM03P388//4yYmBhs\n27YNzs7OOg+pISIiIqLS48j0K8jY2BixsbFwcHB4qkdlExEREZEujky/gt577z2899575V0GERER\n0b8eR6aJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiI\niIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYi\nIiIiUohhmoiIiIhIIXV5F0BEL6es21fKuwQion+17Ds3yrsEegEkIYQo7yKI6OVz8uQpZGU9LO8y\n/rMsLc0AgH38HLGPXwz2c/FsbOxgZGT0TOfQaMwBAHfuPCiLksgAjcYcJibKxpg5Mk1EBjVs2JD/\n4X6O+D/H5499/GKwn+lVxznTREREREQKMUwTERERESnEME1EREREpBDDNBERERGRQgzTREREREQK\nMUwTERERESnEME1EREREpBDDNBERERGRQgzTREREREQKMUwTERERESnEME1EREREpBDDNBERERGR\nQgzTREREREQKMUwTERERESnEME1EREREpJC6vAsgopfT2bNnkZX1sLzL+M+ytDQDAPbxc8Q+fjHY\nz89fWfSxjY0djIyMyqokegzDNBEZNHphYnmXQEREZSD7zg18+2kP2Ns7lHcp/0kM00RkkGXl2uVd\nAhER0UuPc6aJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohh\nmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRimCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBRi\nmCYiIiIiUohhmoiIiIhIIYZpIiIiIiKFGKaJiIiIiBQqNkz37dsXKpUKt2/f1tvXv39/qFQq+Pr6\n6u3LysqCWq2Gv79/2VX6kgoPD4dKpUJSUtJzOX/btm2hUum+TefPny/VsTdu3EB2draia9ra2sqv\nBwwYoFdDaeXk5ODq1auKjn0ZTJ8+HSqVCn///fdzvU5BQQEuXrwov37enysiIiIqG8UmpLZt2wIA\nkpOT9fYlJCTA2NgYSUlJKCgo0NmXnJyMgoICtGvXruwqfYVJkiT/e8WKFXj99ddLPGbr1q1o1KgR\nbt68+czXNPS6NC5evIhmzZph+/btimp4Vdy9exetWrVCeHh4eZdCRERET6nYMN2mTRsAwP79+3W2\np6Sk4PLly/D390dmZiYOHTqks3/Pnj0A/hfGSTkzMzOYmZnJr//44w88evSoxOOSk5ORmZlZZnUI\nIZ76mLS0NKSkpCgK4q+SW7du4eDBg+wnIiKif6Fiw3STJk1QtWpV7Nu3T2d7fHw8VCoVJk+eDEmS\nsHPnTp39e/bsQe3atdGgQYOyr/gVY29vDwcHB51tTxNslYTgsvYy1PBvwH4iIiL69yk2TEuSBE9P\nT72R6fj4eDg7O6NBgwZwcnJCfHy8vE8IgeTkZJ1R6fz8fMyfPx+Ojo4wMzND7dq1MWLECGRkZMht\nEhMToVKpsHPnTgQEBKBKlSqwsrLCoEGDkJ2djS1btsDZ2RkWFhZo3rw5EhISdGp6+PAhpkyZAltb\nW5iamsLe3h7Tpk1Dbm6u3EY7D/X48ePw9/dHlSpVULFiRfTs2VNnvmpRbty4gYEDB6J69eqwsrLC\niBEjDI4SZ2dnY+LEibCxsYGpqSlsbW0xceJEPHjw4KlrcXR0hKOjI4DCkf6ff/4ZAKBSqTBw4ECD\ndQ4YMAAzZswAANja2sLb21vet379enh5ecHKygqmpqaws7PDZ599hpycnBLvXysvLw89evSAsbEx\nIiMjDbYJDw+Xp/kMHDhQZ851RkYGRowYgdq1a8PMzAyNGjXC3Llz9aYLGXL79m2MHj1aPrZJkyZY\ntGiRXrvDhw/Dz88PNWvWhImJCWrUqIH3338fV65c0Wl39+5djB8/HvXq1YOFhQWcnJywfPlyvfOl\npKSge/fuqFixIqpWrYqBAwca/C7Bk0q618TERNjZ2QEAvvzyS6hUKp33/59//sEHH3yAypUrQ6PR\noFevXrh06ZLONZ7msx8ZGQlbW1tYWFjgyy+/LLF+IiIiKp66pAZt2rTBhg0bcO7cOTRo0ABCCCQm\nJuKjjz4CAHh7e+OHH35Abm4ujI2Ncfr0aWRmZuoEuH79+iEiIgJ+fn4YP348Tp8+jbCwMMTHxyM5\nORkajUZuO2DAADRt2hRz585FQkICwsPDcenSJRw5cgRjx46FRqPBnDlz0Lt3b5w/fx4ajQb5+fno\n1q0b9uzZg8DAQDRu3BgHDhzArFmzcOTIEURHR+vcU48ePdC0aVPMmTMH586dQ0hICK5evWpwbrjW\nw4cP4eXlhYsXL2Ls2LGoWbMmwsPD8euvv+q0y8nJQYcOHbBv3z4MGjQILi4u2LdvH+bOnYtdu3Yh\nISEBavX/ur2kWsaMGYMxY8YAAKZMmYKZM2fizz//xOrVq2Fvb2+w1mHDhuHevXuIiopCSEgImjZt\nCgBYtmwZhg4dCh8fH8ybNw85OTmIiIjA/PnzAQBz584t/sOAwl+WBg8ejC1btuDnn39Gr169DLbz\n8vLCpEmTMHv2bAQGBsLT0xNAYRhu3bo1Ll68iOHDh8PR0RFxcXGYOHEijhw5grVr1xZ57fv376NN\nmza4cuUKRowYgbp162Lnzp0YN24czp49i++//x4AcOLECXh4eMDR0RGTJk1ChQoVsGvXLqxatQrn\nzp2T+zYnJwdt2rTBX3/9hcDAQLzxxhvYvHkzhgwZguzsbIwePVq+to+PD3x9fREcHIxdu3Zh5cqV\nyMzMRFRUVJH1luZemzRpguDgYIwfPx69evVCr169UL16dfkcgwYNgpeXF+bNm4eTJ08iNDQUaWlp\nOHLkCAA89Wd/8ODBGDNmDCpVqgR3d/fi3moiIiIqhRLDtHaEef/+/WjQoAFOnjyJ9PR0edTR29sb\nISEh2L17N9q2bas3Xzo2NhYREREYN24cFi5cKJ/X09MTffr0wezZs3VCXO3atREbGwsACAgIQEJC\nAnbu3InY2Fh07NgRAGBhYYEhQ4bg4MGDePvtt7Fq1SrEx8cjLi4OHTp0AAAMHToUbm5uCAwMRHR0\nNHr06CFfw9XVFevXr5df379/H0uWLEFqamqRAXXZsmU4c+YMNmzYIJ9ryJAhcHNzw6lTp+R2P/30\nE/bu3YuQkBA5BAcGBqJp06aYMGECli5diuHDhyuqpX379li9ejX+/PPPYldKadWqFZo1a4aoqCj4\n+vqiXr16AICFCxeidevWOgFw+PDhsLW1RVxcXLFhWjuf9+OPP8bq1avx448/FluDra0t2rdvj9mz\nZ8Pd3V1uO3fuXKSkpOj047BhwzBq1CiEhoaif//+6Ny5s8Fzzp8/HykpKTh06JD8C0JgYCAmT56M\nOXPmYOjQoXByckJoaCiMjIyQkJAAKysrAIWfpZycHKxduxaZmZmwsrLC8uXLcfz4cfz666/o168f\ngML31MvLC19//TVGjRolX3vIkCEIDg6Wz3Xp0iVs2bJF/iXSkNLeq4+PD8aPHw8nJye9Pu3YsaPO\n6H9WVhZWrFiBCxcuwMbG5qk/+/7+/hyRJiIiKkMlrnfm5OQEjUYjj+bFx8fDyMhIHmls06YNjIyM\n8McffwAonC9dt25d+U/X2pGxiRMn6py3d+/ecHR0xMaNG3W2+/j4yP+WJAn29vaoUKGCHKQBwMbG\nBgBw7do1AEBERASqV6+OFi1a4ObNm/JP586dYWRkhJiYGJ1r9OnTR+f1G2+8AaDwT+pF2bp1K2rW\nrKkTTCpUqICAgACddtHR0dBoNBg5cqTO9rFjx6JSpUp6I4VKalHqxIkT2Lx5s86269evw8rKCllZ\nWcUeK4TArFmzEBISgunTp2Pw4MGKaoiOjkaTJk10+hEAvvjiC3l/USIiItCsWTPUrFlT533Wfma0\n73NYWBguXLggB2mgcDqHqakpAMj3GhMTA2trazlIa61atQp//vmnzhcC33vvPZ02Li4uyM3N1Zmq\nVJb3qvVkbS4uLgD+9/l42s++9kvFRET0arG0NINGY86fIn7UaiPFfVviyLRKpYKHh4f8JcT4+Hi4\nurrC0tISAKDRaNC8eXPs2rULALB3716dKR5paWmoXLmyzp+utRo1aoS4uDidbTVq1NAtUK3WO9bI\nqPCGtfNOU1NTkZ6ebvAaAPTmmD7ZThuy8vPzDR4PABcuXJB/QXicdj6zVlpaGuzs7OQatYyNjWFr\nayOQJC0AACAASURBVKs3N1tJLUoZGRnhwIEDWLNmDf7v//4PqampuHHjBoD//YJSnC+++AIqlUp+\nr5VIS0tDly5d9LbXqFEDGo2m2LnrqampePjwocH3WZIknfc5PT0ds2bNwvHjx3H+/HlcvHgRQghI\nkiR/bi5cuGDwLxHakfzHWVtb67w2NzcHgGLnmj/LvZb2uk/72X/yfERERPRsSgzTQOGUjKlTp+LR\no0dISkrSG3Vt27YtwsLCkJGRgbNnz+Kzzz6T9xW3QkFBQQFMTEx0C1Lrl1TSkmH5+flo2LAhQkND\nDe6vXLmyzmslDyCRJEnnC4RaT35p7mnvV+nDUJQYPXo0Fi9ejBYtWsDd3R39+/dH69atMXLkSL3Q\nZcjkyZOhUqkwc+ZMrFmzRm+09lkZ6p8n93t6emLatGkG97/22msAgN9++w3+/v6oU6cO2rVrh65d\nu8LF5f+1d+fhNd75/8dfJwnZVErsa+xLq7UkRpQsluoJQZvORNtB0EGK2sZ20UEUraX2pWVspWkt\nE1OllhJi1CimiLaofY/asggicn5/+OV8HYlIbokjPB/XleuS+77Pfb/vd06bVz7ncz7HW+vXr9f4\n8eOtx9+9ezfby9Hl9s/pUfea3evm9Ln/4B95AIDnQ1LSLcXHZ8wxuMfDw1UFC2YrFmeQrUf5+/sr\nJSVFK1asUHx8vM3IsyQ1b95ckyZNUmRkpCwWi81+Ly8vbdy4UZcuXcowKnb48GGVL1/eUOH38/Ly\n0t69ezN8SExqaqqioqJUrly5x75G5cqVtX37dt29e9cmkDz4aYReXl7673//q9TUVJs/DFJSUnTi\nxAn5+/s/di1GnDp1SrNmzVKnTp0yfDhI+nSZRxkzZoxu3bqlL7/8UgMGDFBQUJDNm0ezw8vLS4cO\nHcqw/eLFi0pMTMzy+eDl5aWEhIQMP+f4+Hht2bLFuhTj0KFDVaNGDe3Zs8c6kivdm75xvwoVKig2\nNjbDdb7//nt98803mjBhQo7uLbN6jd5rTq6R1899AADwcNkabmvQoIHc3d01Z84cOTs7q0mTJjb7\nX3vtNTk5OWnRokXy8vJSxYoVrfvS54vePyIoSatXr9aRI0fUpk2bx70HtWvXTlevXs0wOjdv3jyF\nhoZmWAfbiJCQEMXHx2v+/PnWbXfu3NEXX3xhc1zbtm2VkJCgWbNm2WyfPXu2kpKSHvt+04P8o9Yk\nTj8ufbrI1atXJUm1atWyOW7dunU6evSoUlNTszxf+giui4uLpk2bpri4OJtXILKq4f7R++DgYP32\n228Z5sp/8sknkpRlf9q2bav9+/dr3bp1NtvHjRunkJAQ/fLLL5Lu3WvFihVtgvSZM2f0r3/9SyaT\nyXqvrVu3VlxcnFavXm1zvilTpmjdunUqVqxYlvf3KNm918z6lF1P4rkPAAAeLlsj005OTmrcuLE2\nbdokPz8/67zedIUKFZKPj4927typsLAwm31BQUFq166dpk2bprNnzyowMFBHjhzRnDlzVKVKlQxv\nTMzMo4Lj+++/r8WLF6tPnz7au3evGjZsqF9++UWff/65GjRo8ND1mHOiY8eOmjdvnnr37q1ff/1V\n1apV09KlSxUXF5dpLQMGDFBsbKwaNGigPXv2aNGiRfL19c3whsWcSh/dHzlypAIDAzO8SvDgcRMn\nTpTZbFarVq1UoUIFjRs3Trdu3VLZsmX1008/admyZapRo0aG0ekHe37/98HBwWrdurXmzZunsLAw\nNWrUKNMa0ufxfvnll0pLS1Pnzp01bNgwrVq1SqGhoQoPD1e1atW0efNmRUVFKSQkRK1atXrovac/\n9q233lLPnj1Vu3Zt7dy5U4sXL1ZQUJB1FRCz2axvvvlG4eHh8vb21vHjxzV//nyVKVNGV69eVUJC\ngqR7K4EsWLBAHTp0UK9evVS9enWtXbtWP/zwgxYuXPjYUzuye6+enp5ycHDQ6tWrVb58eYWEhGT7\nGk/iuQ8AAB4u22nB399fJpMpw8vJ6QIDA2UymTL9CPEVK1ZozJgx2r9/vwYMGKCoqCj17NlTu3fv\nVuHCha3HZTZ/1WQyPXR7uoIFC2rz5s0aOHCgtmzZor59++q7775TeHi4Nm7caPNx3A+bI/uoubMO\nDg7asGGDwsPDtXz5cg0bNkyVKlXS1KlTM61lwIAB2rRpk/r376+YmBgNHz7cuhLK49QSHh4uHx8f\nTZgwwbo+dGY6dOigFi1aaOHChRo6dKgKFiyodevWydfXV1OnTtXAgQN17tw5bdu2Tf3791diYqJ1\n7eIHe57Zz2D69OlydnZWjx49HjqqXbNmTfXp00d79uxR//79dfr0aRUpUkQ7d+5Up06d9PXXX2vg\nwIE6fPiwJk2apOXLlz/0fiRZHxsWFqYVK1aob9++2rlzp/7xj39o5cqV1uPmzJmjrl27avXq1erd\nu7c2bdqkadOmWY9J/8AfFxcXbd26Vd26dVNkZKQGDBigCxcuaMWKFercufND7z2r7ZnV+6h7dXNz\n09ixY3X27Fn17dtXBw4cyPL8uf3cBwAAxpksfIYxgEwED/z3ow8CADz1kq6d0/jujVSlSjV7l/LU\nepw3ID65pSQAAACAZwxhGgAAADCIMA0AAAAYRJgGAAAADCJMAwAAAAYRpgEAAACDCNMAAACAQYRp\nAAAAwCDCNAAAAGAQYRoAAAAwiDANAAAAGESYBgAAAAwiTAMAAAAGEaYBAAAAgwjTAAAAgEGEaQAA\nAMAgwjQAAABgEGEaAAAAMIgwDQAAABhEmAYAAAAMcrJ3AQCeTknXztm7BABALkiOv2TvEp5pJovF\nYrF3EQCePgcP/qqkpFv2LuOZVaiQiyTR4zxEj58M+pz3cqPHXl6V5ejomFslPXM8PFxVsKCxMWbC\nNIBMpaSkKj7+pr3LeGZ5eLhKEj3OQ/T4yaDPeY8e573HCdPMmQYAAAAMIkwDAAAABhGmAQAAAIMI\n0wAAAIBBhGkAAADAIMI0AAAAYBBhGgAAADCIMA0AAAAYRJgGAAAADCJMAwAAAAYRpgEAAACDCNMA\nAACAQYRpAAAAwCDCNAAAAGAQYRoAAAAwyMneBQB4Oh05ckRJSbfsXcYzq1AhF0mix3mIHj8Z9Dnv\n2bPHXl6V5ejo+MSvm58QpgFkqs9nW+1dAgDAjpLjL2naoLaqUqWavUt5qhGmAWSqUJGy9i4BAICn\nHnOmAQAAAIMI0wAAAIBBhGkAAADAIMI0AAAAYBBhGgAAADCIMA0AAAAYRJgGAAAADCJMAwAAAAYR\npgEAAACDCNMAAACAQYRpAAAAwCDCNAAAAGAQYRoAAAAwiDANAAAAGESYBgAAAAwiTAMAAAAGEaYf\nYdSoUXJwcMjy68CBA/Yu07BLly4pOTnZ+n1AQIAqVapkx4psJSYm6vLly/YuAwAAIFNO9i4gvxg+\nfLhq1aqV6b4KFSo84Wpyx/fff6/33ntP+/bts7kHk8lkx6r+z969e9W2bVtFRkbKz8/P3uUAAABk\nQJjOppYtWz5zgW7Xrl26fv26vct4qNjYWF24cMHeZQAAADwU0zwgi8Vi7xKy9LTXBwAAnl+E6Vy2\nc+dOtWzZUoULF1bhwoXVqlUr7d6927q/Xr16qlevns1jZs6cKQcHB02ZMsVme926ddW6detsn1uS\nvLy81L17d3Xr1k2urq4qX768rl69mqHOsLAwRURESJIqVaqkZs2aWfdZLBZt3LhR3t7ecnV1VcWK\nFTV27NgMoXbFihXy9/fXiy++KGdnZ1WuXFlDhgxRSkqK9ZiAgACZzWatX7/eer4KFSpo9OjRWYbk\nUaNGqWvXrpKkwMBAVapUSQMGDJCjo6OuXbtmPe7gwYNycHBQu3btbB7fr18/vfjii7p7964k6dSp\nU+rYsaOKFy8uV1dX1a1bV/Pnz3/o9SWpf//+eXq9sLAw1alTRzt27JCvr6/c3NxUpUoVLVmyRHfu\n3NGwYcNUsmRJFS1aVB06dMjwc/z111/15ptvqkiRInJ3d1eTJk20ceNGm2OM9h8AAGQPYTqbrl+/\nrsuXL2f4Sk1NtR6zadMm+fv7KzExUR9//LFGjBih06dPy8/PT//5z38kSUFBQTpw4IBNMIqOjpYk\nbd++3brt4sWLio2NVZs2bbJ9bunefOfIyEgdPHhQ06dPV/fu3VW0aNEM99OzZ0+9+eabkqSpU6dq\n+PDhNtd+++231aJFC02bNk0VK1bURx99pOnTp1uPmT9/vkJDQ1W0aFFNmDBBkydPVsWKFTVx4kR9\n9NFHNvXExsYqNDRUzZo104wZM1SlShWNHj1ac+fOfWi/Q0JC1L17d0n35qtPmzZNZrNZFotFW7du\nzdC7H3/80ebxGzZsUKtWreTo6KgTJ07Ix8dHa9asUY8ePTRp0iQVLVpU3bt315AhQx5aQ1BQUJ5e\nz2Qy6cKFCwoODpa/v78mT54sJycnde3aVW3atNHWrVs1atQovffee1q+fLn+/ve/Wx8bGxsrX19f\nHTp0SMOHD9fYsWN1584dBQUFafny5Y/dfwAAkD0mC8NTWRo1apR1BDczW7dulZ+fn9LS0lStWjWV\nLVtW27Zts76JLzk5WXXr1lWhQoX0v//9T9u3b5e/v79WrFihkJAQWSwW6+jl7du3denSJUnSkiVL\nFBYWppMnT6pcuXLZOrd0b2T63LlzOnPmjEqVKpWtezt58qT1DYgBAQGKiYlRVFSUdfQ1KSlJ5cqV\n06uvvqpt27ZJkmrXrq2iRYvaBPm7d++qUqVKKlq0qPbt22dzvjVr1lhH2W/fvq0yZcqoVq1aNo9/\n0KJFi9S1a1drj2/fvi1PT0916dJFM2bMkCS99dZb2r17t86dO6cDBw7o5Zdf1unTp+Xl5aVFixap\nU6dO6tChg1atWqXdu3erbt26ku6Nvrdr105r165VbGysateuneH6eX29sLAwLVmyRDNnztQHH3wg\n6d6bQlu3bi0vLy8dPnxYBQoUkCQ1bdpUJ06c0NmzZ619PX/+vPbv3y9XV1dr/5s1a6YjR47ozJkz\ncnJyeqz+Bw/890P3AQCefUnXzml890aqUqWavUvJcx4eripY0NhbCRmZzqbJkyfrhx9+yPD1yiuv\nSJJ+/vlnnThxQu3atdOVK1esI9fJyclq06aN9u3bpwsXLsjX11ceHh7asmWLJFlHqfv166fLly/r\n0KFDkqT169frpZdeUoUKFbJ97nRVq1Z9ZJDOiru7u9q2bWv9vlChQqpRo4bi4uKs22JjY7V27Vqb\nx8XFxenFF19UUlJShvPdP13F2dlZ1atXtzlfdjg7OyswMNDaO4vFopiYGH344YdycHCwjuyvX79e\nJpNJZrNZd+/e1dq1a9WqVStrsJXujdgOHz5cFotF33777RO93po1a2yuk/4KgSRVq3bvf1hms9ka\npKV7fySl/4yvXLmimJgYBQUF6caNG9bnw7Vr19S+fXvFxcXZTP/Jrf4DAICMWM0jmxo0aJDlah7H\njh2TJA0aNEiDBg3KsN9kMun06dMqXbq0WrZsaQ1o0dHRKlWqlLp06aLBgwcrJiZG1atX16ZNm6xz\nhnNybkkqUaLEY92rp6dnhuXxXF1d9ccff1i/d3R01O7duxUZGalDhw7p2LFj1lF1Ly+vDOd7kLOz\ns3V+cU6YzWb17t1bly5d0vnz53X16lUFBwfrq6++UkxMjMLDw7VhwwZ5e3urePHiiouL040bN1Sj\nRo0M56pZs6Yk6fTp00/0eqdOnbLZXrJkSeu/nZzu/Sf54M/Q0dHR+u/058P06dNtpt6kS38++Pr6\nSsrd/gMAni+FCrnIw8PV3mXkOScnx0cf9LDH5mIdz7X0YPLxxx+rUaNGmR6THrDMZrNWrlypCxcu\nKDo6Wn5+fipatKjq1KmjmJgY1a9fX1euXLGOJubk3JJt8DLCweHRL1j06dNHs2bNUv369eXr66vO\nnTurcePG6tWrl86cOZPj82WX2WyWJG3evFkXL15UyZIlVbNmTfn5+WnVqlW6e/euNm/erAEDBkjK\neiWQtLQ0SVLBggXter3M+pPVWt/pz4fevXurffv2mR5z/7SV3Ow/AACwRZjOJemjse7u7jYrY0j3\nPnzk2rVr1rmtb7zxhiTphx9+0I4dOzRmzBhJkr+/v6KiolSrVi15eHioSZMmOT73k3Dq1CnNmjVL\nnTp10qJFi2z25fW60JUqVVKNGjW0ZcsWXblyxfpqgb+/v2bMmKFly5YpISHB+odI8eLF5e7urt9+\n+y3DuQ4fPixJKl++/FNzvaykB/X054Ojo2OG58OhQ4d04sQJubm5GboGAAD3S0q6pfj4m/YuI88x\nZ/op4OPjo9KlS2v69Om6ceOGdXtSUpJCQ0MVFhZmnQNbunRp1a1bVzNnztTVq1fl7+8v6d6bys6e\nPauFCxeqVatW1hHFnJw7J9JHsHP6cn/6SiQPfiLkunXrdPToUZsVTh5Hen3pI7rpzGazNm/erB07\ndlh75+/vL5PJpIiICJUqVUoNGjSwnsNsNmvjxo36+eefreewWCz69NNP5eDgYDOfODN5eT0jnzZZ\nunRpeXt7a9GiRTZ/vKSmpqpbt24KCQlhCgcAAE8II9O5xMnJSdOnT1doaKjq169vXef5n//8p06e\nPKlly5bZvNxuNps1fvx4FStWzPqSfPqo5/HjxzVy5EjD586u9Hm5EydOlNlsVnBwsKSHT1VI3167\ndm1VqFBB48aN061bt1S2bFn99NNPWrZsmWrUqJFhdPpR53tUfbNnz9aFCxf0zjvvSLrXu6lTp0qS\nNdx6enrqpZde0sGDB9WlSxeb83zyySfasmWLAgIC1KdPH5UqVUpRUVGKjo7WwIEDrXOZHyYvr2d0\nMZ3p06erWbNmatCggcLDw1WsWDF9/fXX2rlzpz755BMVKVLkkddgIR8AAB4fI9OPYDKZsj16GBIS\noo0bN6pcuXL6+OOP9dFHH+mFF17Qt99+q9DQUJtj0+fiNm3a1LotPaA5ODhY9+f03DkZ6ezQoYNa\ntGihhQsXaujQoVne7/3bnZ2dtW7dOvn6+mrq1KkaOHCgzp07p23btql///5KTEy0jspm53wP07x5\nc/3lL3/R2rVr1adPH+uHwfj7+8vNzc3ar3Tpo8VBQUE256lcubJ27dqloKAgzZ07V0OGDFFCQoIW\nLFigiRMnPrJPeXW9nDy3Hjy2UaNG2rFjh7y9vfXZZ59p0KBBunHjhhYvXqzBgwc/8ho5uTYAAHg4\n1pkGkCnWmQaA5xvrTGcPI9MAAACAQYRpAAAAwCDCNAAAAGAQYRoAAAAwiDANAAAAGESYBgAAAAwi\nTAMAAAAGEaYBAAAAgwjTAAAAgEGEaQAAAMAgwjQAAABgEGEaAAAAMIgwDQAAABhEmAYAAAAMIkwD\nAAAABhGmAQAAAIMI0wAAAIBBhGkAAADAIMI0AAAAYBBhGgAAADDIyd4FAHg6JV07Z+8SAAB2lBx/\nyd4l5Asmi8VisXcRAJ4+Bw/+qqSkW/Yu45lVqJCLJNHjPESPnwz6nPfs2WMvr8pydHR84td90jw8\nXFWwoLExZsI0gEylpKQqPv6mvct4Znl4uEoSPc5D9PjJoM95jx7nvccJ08yZBgAAAAwiTAMAAAAG\nEaYBAAAAgwjTAAAAgEGEaQAAAMAgwjQAAABgEGEaAAAAMIgwDQAAABhEmAYAAAAMIkwDAAAABhGm\nAQAAAIMI0wAAAIBBhGkAAADAIMI0AAAAYBBhGgAAADCIMA0AAAAYZLJYLBZ7FwEAAADkR4xMAwAA\nAAYRpgEAAACDCNMAAACAQYRpAAAAwCDCNAAAAGAQYRoAAAAwiDANPOfq16+vwMBABQYGqlu3bjb7\n1qxZo4YNG6px48aaP3++nSp8NmTV58jISDVq1EhNmjRReHi4WLHUmKx6nK579+4aNmzYE67s2ZFV\nj3fv3i0/Pz81bdpUHTp0UEpKip2qzP+y6nNUVJR8fHzUsGFDzZ07104V5n/jx49X48aN5ePjo8WL\nF9vsy/HvPguA59bNmzct9erVy3RfSkqKpWrVqpbr169bUlJSLD4+Ppa4uLgnXOGzIas+JycnW6pU\nqWK5efOmxWKxWN555x3Lt99++yTLeyZk1eN0c+fOtfj6+lqGDRv2hKp6tmTV47S0NEvdunUtx44d\ns1gsFssXX3xhOXTo0JMs75nxqOeyl5eX5dq1azb/j0bOREdHW4KDgy0Wi8WSlJRk+cc//mHdZ+R3\nHyPTwHNs//79Sk5OVqtWrdS8eXPt2rXLuu+3335T1apV5eHhoQIFCqhJkyaKiYmxY7X5V1Z9dnFx\n0c6dO+Xi4iJJSk1Nlaurq71Kzbey6rEk/fjjj/rpp5/Uo0cPRv4NyqrHR44ckaenpz777DMFBATo\n+vXrqlGjhh2rzb8e9VwuUKCArl+/rps3b8pischkMtmp0vxr48aNqlOnjtq3b6/g4GC1bdvWus/I\n7z6nvC4YwNPL3d1dgwYNUrdu3fT777/LbDbryJEjcnBwUEJCgjw8PKzHvvDCC4qPj7djtflXVn02\nmUwqXry4JGnGjBm6ceOGWrRoYeeK85+senzhwgVFREQoKipK33zzjb1Lzbey6vHly5f1448/atas\nWapSpYratGkjb29vBQYG2rvsfCerPkvSwIED1aBBA7m7uyskJESFCxe2c8X5zx9//KEzZ87ou+++\n0/Hjx9W2bVsdOnRIkgz97iNMA8+x6tWrq2rVqpKkatWqydPTUxcuXFDZsmXl4eGhxMRE67GJiYkq\nUqSIvUrN17LqsySlpaVp8ODBOnr0qFatWmXPUvOtrHq8cuVKXb58WUFBQbp48aKSk5NVq1YtderU\nyc5V5y9Z9djT01NVq1a1jka/8cYb2rNnD2HagKz6fPr0ac2cOVOnTp2Sm5ub/vrXv2rlypV6++23\n7Vx1/lKsWDHVqlVLTk5Oql69ulxcXHT58mUVK1bM0O8+pnkAz7GFCxdq4MCBkqTz588rISFBpUqV\nkiTVrFlTv//+u65du6aUlBTFxMTI19fXnuXmW1n1WZJ69Oih27dvKyoqyjrdAzmTVY/79OmjPXv2\nKDo6WkOHDtW7775LkDYgqx5XrlxZSUlJOnbsmCRp+/btevnll+1Wa36WVZ9v3bolR0dHOTs7y8HB\nQSVKlND169ftWW6+1KRJE61fv17SvR7fuHFDRYsWlWTsd5/JwuQx4LmVmpqqLl266NSpU5KkCRMm\n6MSJE0pKStLf/vY3fffdd4qIiFBaWpq6deum8PBwO1ecP2XVZ29vb3l7e8vPz896fN++fdW+fXt7\nlZsvPeq5nG7x4sU6fPiwxo0bZ69S861H9Tj9jxWLxaLXXntNU6ZMsXPF+dOj+jxlyhR99dVXcnFx\nUdWqVTVv3jw5OTHRIKeGDBmi6OhopaWlafz48bp8+bLh332EaQAAAMAgpnkAAAAABhGmAQAAAIMI\n0wAAAIBBhGkAAADAIMI0AAAAYBBhGgAAADCIMA0AQDaFhYXJwcEhw5erq6u8vLz0/vvv69KlS4bO\nffz4cZvvAwICVKlSpdwoG0AeYpVvAAByaOrUqSpWrJj1+4SEBG3atEkLFizQnj17tHv3bhUoUCDb\n51u4cKF69eql5ORk67YRI0bYfA/g6USYBgAgh9q3b68KFSrYbOvZs6d69eqlOXPmaPXq1frzn/+c\n7fNt27ZNt2/fttnWokWLXKkVQN5imgcAALmkc+fOkqRdu3bl+LF8IDGQPxGmAQDIJW5ubpL+Lxjf\nuXNH48eP16uvvip3d3e5ubmpbt26WrhwofUxAQEBWrJkiSTJwcFBXbt2tW6/f850QECAzGaz1q9f\nL29vb7m6uqpChQoaPXp0hiC+a9cuNWvWTIULF1a5cuU0evRoRUREyMGBX/tAbmOaBwAAuWT9+vWS\npHr16kmSunTpouXLl+uDDz5Q37599ccff2jevHnq1q2bSpUqJbPZrBEjRmjMmDHavn27li5dqipV\nqljPZzKZbP4dGxur0NBQ9ejRQz179tSyZcs0evRolShRQuHh4ZKkvXv3KjAwUGXKlNHIkSOVlJSk\nadOmycHBweZ8AHIHYRoAgBy6evWqdRRakuLj47VhwwaNGjVKtWvX1jvvvKOLFy8qMjJSQ4cO1dix\nY63Hvvnmm6pZs6Y2bNggs9msFi1aaOnSpdq+fbvefffdh17TYrHo/PnzWrNmjVq3bi1J6tixo8qU\nKaNly5ZZw/TgwYPl5uamXbt2ydPTU5LUrl07eXt750UrgOceYRoAgByqX79+hm1ubm5q3769ZsyY\nIUdHR5UqVUqJiYk2o8EWi0UpKSmSpKSkpBxf193d3RqkJcnZ2VnVq1dXXFycJOnatWvatm2bevfu\nbQ3SklS3bl21bNlSGzduzPE1AWSNMA0AQA4tW7ZMJUuW1J07d7Ru3TrNmjVLoaGhmj17tpydna3H\nFShQQEuXLtWGDRt05MgRHTt2TImJiZKktLS0HF/3/oCcztnZWXfv3pV0b63qtLQ0VatWLcNx6aPh\nAHIXYRoAgBx67bXXrEvjtWrVStWqVdOHH36oK1euaPXq1ZKkW7duqWnTptq3b5+aNWum119/Xa+8\n8or8/PwyLKuXXY96A+GdO3ckySbQp3NxcTF0TQBZI0wDAPCYevfurc2bN+vf//63pk6dqn79+mn5\n8uXau3evFixYoLCwMOux58+fz7M6KleuLEk6fPhwhn2///47b0AE8gBr5AAAkAs+//xzFSlSRCNG\njNDJkyd15coVSVKtWrVsjps2bZokKTU11brN0dFR0uOvNV2iRAk1btxYkZGRun79unX7iRMn9P33\n3z/WuQFkjjANAEAuKFGihD799FMlJyerZ8+eev311+Xk5KSOHTtq+vTpmj17tsxmsxYvXqzixYsr\nISHB5rGSNHLkSEVHR1u3PxiuHxa2798+adIkXb58WT4+Ppo8ebLGjh2rRo0a8aEwQB4hTAMA/2A0\nTQAAATNJREFUkE0mkynLqRLvv/++mjRpok2bNunnn3/WqlWrVKhQIQ0bNkwREREqV66c9u3bp+bN\nm2vHjh3WNw6Gh4fLx8dHEyZM0MSJEzO91sOu/eD2Ro0aaf369SpevLhGjBihuXPnql+/fgoJCVHB\nggVzqxUA/j+ThT9VAQB4ZsTFxalkyZIZtgcHBys2NlYnT5588kUBzzBGpgEAeIb86U9/ktlsttkW\nFxen6OhoNWzY0E5VAc8uVvMAAOAZEhYWpoiICL333nsKCAjQ9evX9cUXX0i6NycbQO5imgcAAM8Q\ni8WiuXPn6vPPP9fRo0fl6uqqJk2aKCIiQnXq1LF3ecAzhzANAAAAGMScaQAAAMAgwjQAAABgEGEa\nAAAAMIgwDQAAABhEmAYAAAAMIkwDAAAABv0/BOc6TlzSiXMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca09290>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We run an analogous statistical model to those above to estimate the relationship between Metascores and Bechdel ratings controlling for budget. There is a significant and negative relationship between budget and Metascore suggesting that blockbuster budgets, while they do translate into money for studios, don't translate into glowing reviews from critics. However, we can put away the pitchforks because although there is a negative relationship between Bechdel scores and MetaCritic scores, it is not statistically significant. In other words the differences we saw above can be chalked up to differences in the budgets of movies and other variability rather than systematic biases by critics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m2 = smf.ols(formula='Metascore ~ C(rating) + log(Adj_Budget+1)', data=df).fit()\n",
      "print m2.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:              Metascore   R-squared:                       0.022\n",
        "Model:                            OLS   Adj. R-squared:                  0.019\n",
        "Method:                 Least Squares   F-statistic:                     7.607\n",
        "Date:                Mon, 07 Apr 2014   Prob (F-statistic):           4.63e-06\n",
        "Time:                        09:19:23   Log-Likelihood:                -2672.0\n",
        "No. Observations:                1373   AIC:                             5354.\n",
        "Df Residuals:                    1368   BIC:                             5380.\n",
        "Df Model:                           4                                         \n",
        "=======================================================================================\n",
        "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------\n",
        "Intercept               9.2635      0.655     14.138      0.000         7.978    10.549\n",
        "C(rating)[T.1.0]       -0.0015      0.187     -0.008      0.994        -0.368     0.365\n",
        "C(rating)[T.2.0]       -0.2650      0.213     -1.242      0.214        -0.683     0.153\n",
        "C(rating)[T.3.0]       -0.1679      0.178     -0.944      0.345        -0.517     0.181\n",
        "log(Adj_Budget + 1)    -0.1947      0.036     -5.388      0.000        -0.266    -0.124\n",
        "==============================================================================\n",
        "Omnibus:                       15.070   Durbin-Watson:                   1.927\n",
        "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               10.743\n",
        "Skew:                          -0.095   Prob(JB):                      0.00465\n",
        "Kurtosis:                       2.611   Cond. No.                         253.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next I examine whether there are systematic biases among the ratings of IMDB users in their assessments of movies that pass or fail the Bechdel test. As before, it appears that users are more critical of movies passing the Bechdel test than movies that fail it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make the bar-plot\n",
      "df['imdbRating'].groupby(df['rating']).agg(np.mean).plot(kind='barh')\n",
      "plt.yticks(plt.yticks()[0],[\"Fewer than two women\",\n",
      "                             \"Women don't talk to each other\",\n",
      "                             'Women only talk about men',\n",
      "                             'Passes Bechdel Test'\n",
      "                             ],fontsize=18)\n",
      "plt.xlabel('Rating',fontsize=18)\n",
      "plt.title('Mean IMDB rating',fontsize=24)\n",
      "plt.xlim((6,7))\n",
      "plt.ylabel('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.text.Text at 0x10d3eae90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EY6GP3PtlZ2eHFStWwNbWFnfu3MHVq1eRmpqqcz1FjV1R+wcMGICEhAR07twZeXl5OHfu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\ngNfeGX1PxgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d3d06d0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we run a model that predicts the IMDB rating as a combination of Bechdel rating, Budget, and now we add the number of votes received as well. This new variable might control for the fact that popular movies may receive more glowing reviews while less popular movies are subject to the whims of receiving votes from cinephiles who don't like anything but early 20th century European avant-garde experimental films.\n",
      "\n",
      "The results show a significant and negative relationship between Bechdel score and popular rating: IMDB users appear to rate \"feminist\" films significantly lower than movies that have negligible female presence  Keep in mind we've controlled for other possible explanations like Budget (more budget, significantly lower scores) and the number of votes a movie received (more votes, significantly higher scores) and the model explains approximately 30% of the variance in the data. Put another way, for two movies with the same budget and same number of votes, ***the movie that passes the Bechdel test will receive an IMDB score 0.25 below the movie that passes none of the Bechdel criteria.***"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3 = smf.ols(formula='imdbRating ~ C(rating) + log(Adj_Budget+1) + log(imdbVotes+1)', data=df).fit()\n",
      "print m3.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:             imdbRating   R-squared:                       0.300\n",
        "Model:                            OLS   Adj. R-squared:                  0.298\n",
        "Method:                 Least Squares   F-statistic:                     134.9\n",
        "Date:                Mon, 07 Apr 2014   Prob (F-statistic):          3.05e-119\n",
        "Time:                        09:19:24   Log-Likelihood:                -1927.4\n",
        "No. Observations:                1582   AIC:                             3867.\n",
        "Df Residuals:                    1576   BIC:                             3899.\n",
        "Df Model:                           5                                         \n",
        "=======================================================================================\n",
        "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------\n",
        "Intercept               5.1252      0.308     16.624      0.000         4.520     5.730\n",
        "C(rating)[T.1.0]       -0.1312      0.083     -1.575      0.116        -0.295     0.032\n",
        "C(rating)[T.2.0]       -0.1067      0.095     -1.119      0.263        -0.294     0.080\n",
        "C(rating)[T.3.0]       -0.2477      0.080     -3.113      0.002        -0.404    -0.092\n",
        "log(Adj_Budget + 1)    -0.2154      0.018    -12.230      0.000        -0.250    -0.181\n",
        "log(imdbVotes + 1)      0.4978      0.020     25.044      0.000         0.459     0.537\n",
        "==============================================================================\n",
        "Omnibus:                      102.934   Durbin-Watson:                   2.017\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              153.629\n",
        "Skew:                          -0.530   Prob(JB):                     4.36e-34\n",
        "Kurtosis:                       4.098   Cond. No.                         312.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "DOUBLE SECRET BONUS! The kitchen sink model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url='http://i1.ytimg.com/vi/EYiZeszLosE/maxresdefault.jpg',height=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://i1.ytimg.com/vi/EYiZeszLosE/maxresdefault.jpg\" height=\"200\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<IPython.core.display.Image at 0x10d924a10>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At this point I've found some evidence that there are systematic differences how movies that pass or fail the Bechdel test have different budgets, different earnings, and different reviews. But I've also included only a handful of relevant variables to explain the relationship when there may be others that explain it instead. You may be thinking to yourself that X, Y, Z probably explains the patterns we're observing and these models just aren't capturing it. So let's throw everything and the kitchen sink into the model and see what happens. \n",
      "\n",
      "The significant differences seen above in the IMDB scores seem especially problematic, so let's try to see if adding other variables makes this finding change in direction or significance. In addition to adding Revenue, Budget, Metascore, and number of IMDB votes, we add several new variables as controls that we hadn't included before. The result is a model that has the potential to be [overfitted](https://en.wikipedia.org/wiki/Overfitting), but we're not interested in the combined model for any predictive purposes --- only when the estimate for the Bechdel dimensions has changed. \n",
      "\n",
      "* **MPAA Rating**. People dislike G-rated movies that happen to pass the Bechdel test more, perhaps.\n",
      "* **Runtime**. Instead of people hating \"feminist\" movies, maybe movies passing the Bechdel test are just longer and people don't like 2-hour marathons. \n",
      "* **Genre**. Maybe some genres like romantic comedies or dramas have an easier time passing the Bechdel test.\n",
      "* **Year**. There may be a nostalgia effect of movies in the past that pass the test being rated differently than movies released more recently that pass the test.\n",
      "* **Week**. Summer and holiday blockbusters are different animals than awards vehicles that are released in the fall and winter.\n",
      "* **English language**. \"Seriously, who likes strong female leads **and** subtitles? Get me a Bud Light Lime and let's fire up Michael Bay's magnum opus *Transformers*!\"\n",
      "* **USA**. As bad as it may be here, other countries may have it worse.\n",
      "\n",
      "There are certainly others that also may explain patterns we see, some of which might be relevant such as the reputation of the cast or crewmembers or others that may not be relevant such as the fraction of seats Republicans hold in the House. Neither the relevant nor unlikely variables are present in the data above, but you're welcome to collect them and integrate it into the analysis below -- that's what's so great about making the models, data, and findings open for others!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m4 = smf.ols(formula='imdbRating ~ C(rating) + log(Adj_Revenue+1) + log(Adj_Budget+1) + Metascore + log(imdbVotes+1) + Runtime + C(Genre_y) + Year + C(Week) + C(English) + C(USA) + C(Rated)', data=df).fit()\n",
      "print m4.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:             imdbRating   R-squared:                       0.994\n",
        "Model:                            OLS   Adj. R-squared:                  0.994\n",
        "Method:                 Least Squares   F-statistic:                     2827.\n",
        "Date:                Mon, 07 Apr 2014   Prob (F-statistic):               0.00\n",
        "Time:                        09:19:27   Log-Likelihood:                -1018.5\n",
        "No. Observations:                1335   AIC:                             2189.\n",
        "Df Residuals:                    1259   BIC:                             2584.\n",
        "Df Model:                          76                                         \n",
        "=====================================================================================================\n",
        "                                        coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-----------------------------------------------------------------------------------------------------\n",
        "Intercept                            19.0301      4.094      4.648      0.000        10.999    27.062\n",
        "C(rating)[T.1.0]                     -0.0272      0.062     -0.440      0.660        -0.149     0.094\n",
        "C(rating)[T.2.0]                     -0.0204      0.072     -0.284      0.777        -0.162     0.121\n",
        "C(rating)[T.3.0]                     -0.1245      0.061     -2.034      0.042        -0.245    -0.004\n",
        "C(Genre_y)[T.Adventure]               0.1224      0.063      1.944      0.052        -0.001     0.246\n",
        "C(Genre_y)[T.Black Comedy]            0.1418      0.131      1.081      0.280        -0.116     0.399\n",
        "C(Genre_y)[T.Comedy]                  0.0472      0.055      0.863      0.388        -0.060     0.155\n",
        "C(Genre_y)[T.Concert/Performance]  7.664e-14   1.64e-14      4.675      0.000      4.45e-14  1.09e-13\n",
        "C(Genre_y)[T.Documentary]             0.5056      0.391      1.293      0.196        -0.261     1.273\n",
        "C(Genre_y)[T.Drama]                   0.2554      0.058      4.436      0.000         0.142     0.368\n",
        "C(Genre_y)[T.Horror]                 -0.1923      0.070     -2.732      0.006        -0.330    -0.054\n",
        "C(Genre_y)[T.Musical]                 0.0938      0.133      0.706      0.480        -0.167     0.354\n",
        "C(Genre_y)[T.Romantic Comedy]         0.1290      0.079      1.627      0.104        -0.027     0.285\n",
        "C(Genre_y)[T.Thriller/Suspense]       0.0999      0.060      1.671      0.095        -0.017     0.217\n",
        "C(Genre_y)[T.Western]                 0.2121      0.177      1.197      0.232        -0.136     0.560\n",
        "C(Week)[T.2]                          0.5191      0.237      2.191      0.029         0.054     0.984\n",
        "C(Week)[T.3]                          0.4848      0.242      2.004      0.045         0.010     0.959\n",
        "C(Week)[T.4]                          0.6735      0.236      2.852      0.004         0.210     1.137\n",
        "C(Week)[T.5]                          0.6091      0.224      2.721      0.007         0.170     1.048\n",
        "C(Week)[T.6]                          0.4566      0.219      2.083      0.037         0.027     0.887\n",
        "C(Week)[T.7]                          0.5184      0.215      2.407      0.016         0.096     0.941\n",
        "C(Week)[T.8]                          0.6153      0.231      2.661      0.008         0.162     1.069\n",
        "C(Week)[T.9]                          0.5156      0.224      2.302      0.021         0.076     0.955\n",
        "C(Week)[T.10]                         0.6411      0.225      2.847      0.004         0.199     1.083\n",
        "C(Week)[T.11]                         0.6942      0.221      3.134      0.002         0.260     1.129\n",
        "C(Week)[T.12]                         0.5089      0.218      2.329      0.020         0.080     0.938\n",
        "C(Week)[T.13]                         0.4294      0.222      1.931      0.054        -0.007     0.866\n",
        "C(Week)[T.14]                         0.4885      0.219      2.235      0.026         0.060     0.917\n",
        "C(Week)[T.15]                         0.4384      0.226      1.937      0.053        -0.006     0.882\n",
        "C(Week)[T.16]                         0.6169      0.232      2.656      0.008         0.161     1.073\n",
        "C(Week)[T.17]                         0.5584      0.236      2.362      0.018         0.095     1.022\n",
        "C(Week)[T.18]                         0.6164      0.238      2.593      0.010         0.150     1.083\n",
        "C(Week)[T.19]                         0.4574      0.225      2.032      0.042         0.016     0.899\n",
        "C(Week)[T.20]                         0.3126      0.236      1.323      0.186        -0.151     0.776\n",
        "C(Week)[T.21]                         0.4790      0.213      2.245      0.025         0.060     0.898\n",
        "C(Week)[T.22]                         0.3992      0.220      1.812      0.070        -0.033     0.832\n",
        "C(Week)[T.23]                         0.3518      0.221      1.590      0.112        -0.082     0.786\n",
        "C(Week)[T.24]                         0.4425      0.222      1.996      0.046         0.008     0.877\n",
        "C(Week)[T.25]                         0.4382      0.215      2.043      0.041         0.017     0.859\n",
        "C(Week)[T.26]                         0.2977      0.215      1.383      0.167        -0.125     0.720\n",
        "C(Week)[T.27]                         0.4669      0.214      2.180      0.029         0.047     0.887\n",
        "C(Week)[T.28]                         0.4005      0.225      1.778      0.076        -0.041     0.842\n",
        "C(Week)[T.29]                         0.6816      0.215      3.164      0.002         0.259     1.104\n",
        "C(Week)[T.30]                         0.1955      0.214      0.912      0.362        -0.225     0.616\n",
        "C(Week)[T.31]                         0.4792      0.219      2.192      0.029         0.050     0.908\n",
        "C(Week)[T.32]                         0.5554      0.218      2.547      0.011         0.128     0.983\n",
        "C(Week)[T.33]                         0.4002      0.216      1.853      0.064        -0.024     0.824\n",
        "C(Week)[T.34]                         0.4123      0.226      1.826      0.068        -0.031     0.855\n",
        "C(Week)[T.35]                         0.4659      0.231      2.018      0.044         0.013     0.919\n",
        "C(Week)[T.36]                         0.5719      0.247      2.315      0.021         0.087     1.057\n",
        "C(Week)[T.37]                         0.5011      0.222      2.261      0.024         0.066     0.936\n",
        "C(Week)[T.38]                         0.5194      0.215      2.412      0.016         0.097     0.942\n",
        "C(Week)[T.39]                         0.7210      0.211      3.412      0.001         0.306     1.136\n",
        "C(Week)[T.40]                         0.6133      0.219      2.806      0.005         0.184     1.042\n",
        "C(Week)[T.41]                         0.5021      0.217      2.317      0.021         0.077     0.927\n",
        "C(Week)[T.42]                         0.5390      0.211      2.554      0.011         0.125     0.953\n",
        "C(Week)[T.43]                         0.3747      0.222      1.691      0.091        -0.060     0.809\n",
        "C(Week)[T.44]                         0.5368      0.212      2.532      0.011         0.121     0.953\n",
        "C(Week)[T.45]                         0.4429      0.217      2.039      0.042         0.017     0.869\n",
        "C(Week)[T.46]                         0.5039      0.227      2.220      0.027         0.059     0.949\n",
        "C(Week)[T.47]                         0.5634      0.211      2.667      0.008         0.149     0.978\n",
        "C(Week)[T.48]                         0.4132      0.236      1.754      0.080        -0.049     0.875\n",
        "C(Week)[T.49]                         0.4696      0.221      2.123      0.034         0.036     0.904\n",
        "C(Week)[T.50]                         0.3296      0.215      1.529      0.126        -0.093     0.752\n",
        "C(Week)[T.51]                         0.5439      0.210      2.587      0.010         0.131     0.956\n",
        "C(Week)[T.52]                         0.6576      0.217      3.034      0.002         0.232     1.083\n",
        "C(Week)[T.53]                     -5.274e-17   7.33e-17     -0.719      0.472     -1.97e-16  9.11e-17\n",
        "C(English)[T.1]                      -0.0113      0.032     -0.352      0.725        -0.074     0.052\n",
        "C(USA)[T.1]                          -0.0094      0.032     -0.290      0.772        -0.073     0.054\n",
        "C(Rated)[T.PG]                       -0.0466      0.109     -0.430      0.668        -0.260     0.166\n",
        "C(Rated)[T.PG-13]                    -0.1838      0.110     -1.665      0.096        -0.400     0.033\n",
        "C(Rated)[T.R]                        -0.1111      0.113     -0.978      0.328        -0.334     0.112\n",
        "log(Adj_Revenue + 1)                 -0.0557      0.014     -3.974      0.000        -0.083    -0.028\n",
        "log(Adj_Budget + 1)                  -0.0810      0.019     -4.184      0.000        -0.119    -0.043\n",
        "Metascore                             0.2885      0.011     25.339      0.000         0.266     0.311\n",
        "log(imdbVotes + 1)                    0.3533      0.021     17.205      0.000         0.313     0.394\n",
        "Runtime                               0.0069      0.001      6.804      0.000         0.005     0.009\n",
        "Year                                 -0.0083      0.002     -4.100      0.000        -0.012    -0.004\n",
        "==============================================================================\n",
        "Omnibus:                      135.731   Durbin-Watson:                   1.999\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              400.944\n",
        "Skew:                          -0.518   Prob(JB):                     8.63e-88\n",
        "Kurtosis:                       5.477   Cond. No.                          nan\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The smallest eigenvalue is -3.98e-14. This might indicate that there are\n",
        "strong multicollinearity problems or that the design matrix is singular.\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remarkably, even after controlling for all these other potential explanations for why IMDB voters might dislike movies that pass the Bechdel, the model still has a significant and negative estimate for the Bechdel test variable (`C(rating)[T.3.0]`): ***movies that pass the Bechdel test receive IMDB scores 0.12 below similar movies that pass none of the Bechdel criteria.*** The significance of this finding also means that the chances of this finding be attributable to randomness is less than 5%. Given everything that we did to try to erase this pattern of IMDB users systematically awarding lower scores to movies passing the Bechdel test, the persistence of this significant and negative finding suggests that there's something to the argument that the IMDB audience is biased against movies with \"non-trivial\" female roles.\n",
      "\n",
      "But we can run the same kitchen sink model on the relationship between revenue and the Bechdel test. Indeed, the significant and positive finding remains here: movies that pass the Bechdel movie make 43% more money controlling for all the other explanatory factors than movies that do not pass the Bechdel test. Indeed, even for movies that don't pass all of its dimensions, a movie passing 2 makes 34% more and a movie passing 1 makes 21% more than movies passing none."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5 = smf.ols(formula='Metascore ~ C(rating) + log(Adj_Revenue+1) + log(Adj_Budget+1) + imdbRating + log(imdbVotes+1) + Runtime + C(Genre_y) + Year + C(Week) + C(English) + C(USA) + C(Rated)', data=df).fit()\n",
      "m6 = smf.ols(formula='log(Adj_Revenue+1) ~ C(rating) + Metascore + log(Adj_Budget+1) + imdbRating + log(imdbVotes+1) + Runtime + C(Genre_y) + Year + C(Week) + C(English) + C(USA) + C(Rated)', data=df).fit()\n",
      "m7 = smf.ols(formula='log(Adj_Budget+1) ~ C(rating) + Metascore + log(Adj_Revenue+1) + imdbRating + log(imdbVotes+1) + Runtime + C(Genre_y) + Year + C(Week) + C(English) + C(USA) + C(Rated)', data=df).fit()\n",
      "\n",
      "print \"Difference in IMDB scores between movies that pass all and fail all requirements of the Bechdel test: {0}.\".format(round(m4.params['C(rating)[T.3.0]'],2))\n",
      "print \"Difference in Metascores between movies that pass all and fail all requirements of the Bechdel test: {0}.\".format(round(m5.params['C(rating)[T.3.0]']*10,2))\n",
      "print \"Difference in revenue between movies that pass all and fail all requirements of the Bechdel test: {0}%.\".format(round(100*(np.exp(m6.params['C(rating)[T.3.0]']) - 1),2))\n",
      "print \"Difference in budget between movies that pass all and fail all requirements of the Bechdel test: {0}%.\".format(round(100*(np.exp(m7.params['C(rating)[T.3.0]']) - 1),2))\n",
      "\n",
      "results_df = pd.DataFrame({'m4_params':m4.params[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm4_pvalues':m4.pvalues[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm5_params':m5.params[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm5_pvalues':m5.pvalues[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm6_params':m6.params[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm6_pvalues':m6.pvalues[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm7_params':m7.params[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']],\n",
      "                           'm7_pvalues':m7.pvalues[['C(rating)[T.1.0]','C(rating)[T.2.0]','C(rating)[T.3.0]']]\n",
      "                           })\n",
      "results_df.index = [\"Women don't talk to each other\",\n",
      "                    'Women only talk about men',\n",
      "                    'Passes Bechdel Test']\n",
      "\n",
      "ax = results_df[['m4_params','m5_params','m6_params','m7_params']].T.plot(kind='barh')\n",
      "plt.legend(bbox_to_anchor=(1,1),fontsize=12)\n",
      "\n",
      "# Change the alpha channel to correspond with the p-values\n",
      "# More transparent bars are less significant results\n",
      "# http://stackoverflow.com/questions/22888607/changing-alpha-values-in-pandas-barplot-to-match-variable/\n",
      "for i, (j, k) in enumerate(product(range(3), range(3))):\n",
      "    patch = ax.patches[i]\n",
      "    alpha = 1 - results_df[['m4_pvalues','m5_pvalues','m6_pvalues','m7_pvalues']].T.iloc[j, k]\n",
      "    patch.set_alpha(max(alpha, .2))\n",
      "    \n",
      "plt.yticks(plt.yticks()[0],['IMDB Ratings','Metascore','Revenue','Budget'],fontsize=18)\n",
      "plt.xlabel('Effect size compared to movies failing\\nall elements of the Bechdel test',fontsize=18)\n",
      "plt.title(\"Effects of women's roles in movies\",fontsize=24)\n",
      "plt.xticks(fontsize=15)\n",
      "plt.autoscale()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Difference in IMDB scores between movies that pass all and fail all requirements of the Bechdel test: -0.12.\n",
        "Difference in Metascores between movies that pass all and fail all requirements of the Bechdel test: 1.87.\n",
        "Difference in revenue between movies that pass all and fail all requirements of the Bechdel test: 54.49%.\n",
        "Difference in budget between movies that pass all and fail all requirements of the Bechdel test: -24.0%.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fT8XWrTsRHX0e33wzE59+OggZGW8BAH/8EYe9ew/g7NmLiImJwr17d3H4cCh+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       "text": [
        "<matplotlib.figure.Figure at 0x1121094d0>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Conclusions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The four main findings from this analysis of the effects of women's roles in movies are summarized in the chart above. Even after controlling for everything in the \"kitchen sink\" model, compared to similar movies that pass none of the requirements, movies passing the Bechdel test (the red bars): \n",
      "\n",
      "* Receive budgets that are 24% *smaller*\n",
      "\n",
      "* Make 55% *more* revenue \n",
      "\n",
      "* Are awarded 1.8 *more* Metacritic points by professional reviewers\n",
      "\n",
      "* Are awarded 0.12 *fewer* stars by IMDB's amateur reviewers\n",
      "\n",
      "These four points point to a paradox in which movies that pass an embarrassingly  low bar for female character development make more money and are rated more highly by critics, but have to deal with lower budgets and more critical community responses. Is this definitive evidence of active discrimination in the film industry and culture? No, but it suggests systemic prejudices are contributing to producers irrationally ignoring significant evidence that \"feminist\" films make them more money and earn higher praise.\n",
      "\n",
      "The data that I used here was scraped from public-facing websites, but there may be reasons to think that these data are inaccurate by those who are more familiar with how they're generated. Similarly, the models I used here are simple Stats 101 ordinary least squares regression models with some minor changes to account for categorical variables and skewed data. There are no Bayesian models, no cross-validation or bootstrapping, and no exotic machine learning methods here. But in making the data available (or at least the process for replicating how I obtained my own data), others are welcome to perform and share the results such analyses --- and this is ultimately my goal of asking data journalism to adopt the norms of open collaboration. When other people take their methodological hammers or other datasets and still can't break the finding, we have greater confidence that the finding is \"real\".\n",
      "\n",
      "But the length and technical complexity of this post also raise the question of, who is the audience for this kind of work? Journalistic norms emphasize quick summaries turned around rapidly with opaque discussions of methods and analysis and making definitive claims. Scientific norms emphasize more deliberative and transparent processes that prize abstruse discussions and tentative claims about their \"truth\". I am certainly not saying that Hickey should have the output of regression models in his article --- 99% of people won't care to see that. But in the absence of soliciting peer reviews of this research, how are we as analysts, scientists, and journalists to evaluate the validity of the claims unless the code and data are made available for others to inspect? Even this is a higher bar than many scientific publications hold their authors to (and I'm certainly guilty of not doing more to make my own code and data available), but it *should* be the standard, especially for a genre of research like data journalism where the claims reach such large audiences.\n",
      "\n",
      "However, there are exciting technologies for supporting this kind of open documentation and collaboration. I used an \"open notebook\" technology called IPython Notebook to write this post in such a way that the text, code, and figures I generated are all stitched together into one file. You're likely reading this post on a website that lets you view any such Notebook on the web where other developers and researchers share code about how to do [all manner of data analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks). Unfortunately, this was intended as a word processing or blogging tool, so the the lack of features such as more dynamic layout options or spell-checking will frustrate many journalists (apologies for the typos!). However, there are tools for customizing the CSS so that it plays well (see [here](http://danielfrg.com/blog/2013/02/16/blogging-pelican-ipython-notebook/) and [here](http://jakevdp.github.io/blog/2012/10/04/blogging-with-ipython/)). The code and data are [hosted on GitHub](https://github.com/brianckeegan/Bechdel), which is traditionally used for software collaboration, but its features for others to discuss problems in my analysis (issue tracker) or propose changes to my code (pull requests) promote critique, deliberation, and improvement. I have no idea how these will work in the context of a journalistic project, and to be honest, I've never used them before, but I'd love to try and see what breaks.\n",
      "\n",
      "Realistically, practices only change if there are incentives to do so. Academic scientists aren't awarded tenure on the basis of writing well-trafficed blogs or high-quality Wikipedia articles, they are promoted for publishing rigorous research in competitive, peer-reviewed outlets. Likewise, journalists aren't promoted for providing meticulously-documented supplemental material or replicating other analyses instead of contributing to coverage of a major news event. Amidst contemporary anxieties about information overload as well as the weaponization of [fear, uncertainty, and doubt](https://en.wikipedia.org/wiki/Fear,_uncertainty_and_doubt) tactics, data-driven journalism could serve a crucial role in empirically grounding our discussions of policies, economic trends, and social changes. But unless the new leaders set and enforce standards that emulate the scientific community's norms, this data-driven journalism risks falling into traps that can undermine the public's and scientific community's trust.\n",
      "\n",
      "This suggests several models going forward:\n",
      "\n",
      "* **Open data**. Data-driven journalists could share their code and data on open source repositories like GitHub for others to inspect, replicate, and extend. But as any data librarian will rush to tell you, there are non-trivial standards for ensuring the documentation, completeness, formatting, and non-proprietaryness of data.\n",
      "* **Open collaboration**. Journalists could collaborate with scientists and analysts to pose questions that they jointly analyze and then write up as articles or features as well as submitting for academic peer review. But peer review takes time and publishing results in advance of this review, even working with credentialed experts, doesn't imply their reliability.\n",
      "* **Open deliberation**. Organizations that practice data-driven journalism (to the extent this is different from other flavors of journalism) should invite and provide empirical critiques of their analyses and findings. Making well-documented data available or finding the right experts to collaborate with are extremely time-intensive, but if you're going to publish original empirical research, you should accept and respond to legitimate critiques.\n",
      "* **Data omsbudsmen**. Data-driven news organizations might consider appointing independent advocates to represent public interests and promote scientific norms of communalism, skepticism, and empirical rigor. Such a position would serve as a check against authors making sloppy claims, using improper methods, analyzing proprietary data, or acting for their personal benefit.\n",
      "\n",
      "I have very much enjoyed thinking through many of these larger issues and confronting the challenges of the critiques I've raised. I look forward to your feedback and I very hope this drives conversations about what kind of science data-driven journalism hopes to become."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}